Release v3.2.0

## Queue Editing
- Added update_input method to QueueScheduler for editing queued runs
- Added update_input method to QueueManager
- Added handler for QueuePanelWidget.EditRequested in TUIApp
- Only queued (not running) items can be edited

## Queue-First Architecture Support
- Queue items can be edited before processing
- EditRequested message already existed in QueuePanelWidget

31 tests passing

README.md

@@ -36,10 +36,10 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
 ---
 
 > **Quick Paths:**
-> - 🚀 **New here?** → [Quick Start](#-quick-start)
+> - 🆕 **New here?** → [Quick Start](#-quick-start) *(1 minute to first agent)*
 > - 📦 **Installing?** → [Installation](#-installation)
-> - 💻 **Python SDK?** → [Python Examples](#-using-python-code)
-> - 🎯 **CLI user?** → [CLI Reference](#-cli--no-code-interface)
+> - 🐍 **Python SDK?** → [Python Examples](#-using-python-code)
+> - 🎯 **CLI user?** → [CLI Quick Reference](#cli-quick-reference)
 > - 🔧 **Need config?** → [Configuration](#-configuration--integration)
 > - 🤝 **Contributing?** → [Development](#-development)
 
@@ -47,96 +47,81 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
 
 ## 📑 Table of Contents
 
-### Getting Started
-- [⚡ Performance](#-performance)
+<details open>
+<summary><strong>Getting Started</strong></summary>
+
 - [🚀 Quick Start](#-quick-start)
 - [📦 Installation](#-installation)
-  - [Python SDK](#python-sdk)
-  - [JavaScript SDK](#javascript-sdk)
-  - [Environment Variables](#environment-variables)
+- [⚡ Performance](#-performance)
 
-### Core Concepts
-- [✨ Key Features](#-key-features)
-- [🌐 Supported Providers](#-supported-providers)
+</details>
+
+<details>
+<summary><strong>Python SDK</strong></summary>
 
-### Python SDK
 - [📘 Python Examples](#-using-python-code)
-  - [1. Single Agent](#1-single-agent)
-  - [2. Multi Agents](#2-multi-agents)
-  - [3. Planning Mode](#3-agent-with-planning-mode)
-  - [4. Deep Research](#4-deep-research-agent)
-  - [5. Query Rewriter](#5-query-rewriter-agent)
-  - [6. Agent Memory](#6-agent-memory-zero-dependencies)
-  - [7. Rules & Instructions](#7-rules--instructions)
-  - [8. Auto-Generated Memories](#8-auto-generated-memories)
-  - [9. Agentic Workflows](#9-agentic-workflows)
-  - [10. Hooks](#10-hooks)
-  - [11. Shadow Git Checkpoints](#11-shadow-git-checkpoints)
-  - [12. Background Tasks](#12-background-tasks)
-  - [13. Policy Engine](#13-policy-engine)
-  - [14. Thinking Budgets](#14-thinking-budgets)
-  - [15. Output Styles](#15-output-styles)
-  - [16. Context Compaction](#16-context-compaction)
-  - [17. Field Names Reference](#17-field-names-reference-a-i-g-s)
-  - [18. Extended agents.yaml](#18-extended-agentsyaml-with-workflow-patterns)
-  - [19. MCP Protocol](#19-mcp-model-context-protocol)
-  - [20. A2A Protocol](#20-a2a-agent2agent-protocol)
+  - [1. Single Agent](#1-single-agent) | [2. Multi Agents](#2-multi-agents) | [3. Planning Mode](#3-agent-with-planning-mode)
+  - [4. Deep Research](#4-deep-research-agent) | [5. Query Rewriter](#5-query-rewriter-agent) | [6. Agent Memory](#6-agent-memory-zero-dependencies)
+  - [7. Rules & Instructions](#7-rules--instructions) | [8. Auto-Generated Memories](#8-auto-generated-memories) | [9. Agentic Workflows](#9-agentic-workflows)
+  - [10. Hooks](#10-hooks) | [11. Shadow Git Checkpoints](#11-shadow-git-checkpoints) | [12. Background Tasks](#12-background-tasks)
+  - [13. Policy Engine](#13-policy-engine) | [14. Thinking Budgets](#14-thinking-budgets) | [15. Output Styles](#15-output-styles)
+  - [16. Context Compaction](#16-context-compaction) | [17. Field Names Reference](#17-field-names-reference-a-i-g-s) | [18. Extended agents.yaml](#18-extended-agentsyaml-with-workflow-patterns)
+  - [19. MCP Protocol](#19-mcp-model-context-protocol) | [20. A2A Protocol](#20-a2a-agent2agent-protocol)
 - [🛠️ Custom Tools](#️-custom-tools)
 
-### JavaScript SDK
+</details>
+
+<details>
+<summary><strong>JavaScript SDK</strong></summary>
+
 - [💻 JavaScript Examples](#-using-javascript-code)
 
-### CLI Reference
-- [🎯 CLI Overview](#-cli--no-code-interface)
-  - [Auto Mode](#auto-mode)
-  - [Interactive Mode](#interactive-mode-cli)
-  - [Deep Research CLI](#deep-research-cli)
-  - [Planning Mode CLI](#planning-mode-cli)
-  - [Memory CLI](#memory-cli)
-  - [Workflow CLI](#workflow-cli)
-  - [Knowledge CLI](#knowledge-cli)
-  - [Session CLI](#session-cli)
-  - [Tools CLI](#tools-cli)
-  - [MCP Config CLI](#mcp-config-cli)
-  - [External Agents CLI](#external-agents-cli)
-- [CLI Features Summary](#cli-features)
+</details>
 
-### Configuration
-- [🔧 Configuration & Integration](#-configuration--integration)
-  - [Ollama Integration](#ollama-integration)
-  - [Groq Integration](#groq-integration)
-  - [100+ Models Support](#100-models-support)
+<details>
+<summary><strong>CLI Reference</strong></summary>
+
+- [🎯 CLI Overview](#-cli--no-code-interface) | [CLI Quick Reference](#cli-quick-reference)
+- [Auto Mode](#auto-mode) | [Interactive Mode](#interactive-mode-cli) | [Deep Research CLI](#deep-research-cli) | [Planning Mode CLI](#planning-mode-cli)
+- [Memory CLI](#memory-cli) | [Workflow CLI](#workflow-cli) | [Knowledge CLI](#knowledge-cli) | [Session CLI](#session-cli)
+- [Tools CLI](#tools-cli) | [MCP Config CLI](#mcp-config-cli) | [External Agents CLI](#external-agents-cli) | [CLI Features Summary](#cli-features)
+
+</details>
+
+<details>
+<summary><strong>Configuration & Features</strong></summary>
+
+- [✨ Key Features](#-key-features) | [🌐 Supported Providers](#-supported-providers)
+- [🔧 Configuration & Integration](#-configuration--integration) | [Ollama](#ollama-integration) | [Groq](#groq-integration) | [100+ Models](#100-models-support)
 - [📋 Agents Playbook](#-agents-playbook)
-
-### Advanced Features
 - [🔬 Advanced Features](#-advanced-features)
-  - [Research & Intelligence](#research--intelligence)
-  - [Memory & Caching](#memory--caching)
-  - [Planning & Workflows](#planning--workflows)
-  - [Specialised Agents](#specialised-agents)
-  - [MCP Protocol](#mcp-protocol)
-  - [A2A & A2UI Protocols](#a2a--a2ui-protocols)
-  - [Safety & Control](#safety--control)
-  - [Developer Tools](#developer-tools)
 
-### Architecture
+</details>
+
+<details>
+<summary><strong>Architecture & Patterns</strong></summary>
+
 - [📊 Process Types & Patterns](#-process-types--patterns)
-  - [Sequential Process](#sequential-process)
-  - [Hierarchical Process](#hierarchical-process)
-  - [Workflow Process](#workflow-process)
-  - [Agentic Patterns](#agentic-patterns)
+- [Sequential](#sequential-process) | [Hierarchical](#hierarchical-process) | [Workflow](#workflow-process) | [Agentic Patterns](#agentic-patterns)
+
+</details>
+
+<details>
+<summary><strong>Data & Persistence</strong></summary>
 
-### Data & Persistence
 - [💾 Persistence (Databases)](#-persistence-databases)
 - [📚 Knowledge & Retrieval (RAG)](#-knowledge--retrieval-rag)
 - [🔧 Tools Table](#-tools-table)
 
-### Learning & Community
-- [🎓 Video Tutorials](#-video-tutorials)
-- [⭐ Star History](#-star-history)
-- [👥 Contributing](#-contributing)
-- [🔧 Development](#-development)
-- [❓ FAQ & Troubleshooting](#-faq--troubleshooting)
+</details>
+
+<details>
+<summary><strong>Learning & Community</strong></summary>
+
+- [🎓 Video Tutorials](#-video-tutorials) | [⭐ Star History](#-star-history)
+- [👥 Contributing](#-contributing) | [🔧 Development](#-development) | [❓ FAQ & Troubleshooting](#-faq--troubleshooting)
+
+</details>
 
 ---
 
@@ -181,6 +166,8 @@ export OPENAI_API_KEY=your_key_here
 python -c "from praisonaiagents import Agent; Agent(instructions='You are a helpful AI assistant').start('Write a haiku about AI')"
 ```
 
+> **Next Steps:** [Single Agent Example](#1-single-agent) | [Multi Agents](#2-multi-agents) | [CLI Auto Mode](#auto-mode)
+
 ---
 
 ## 📦 Installation
@@ -231,28 +218,6 @@ export OPENAI_BASE_URL=https://api.groq.com/openai/v1
 
 ---
 
-## 💻 Usage
-
-### Python Code Examples
-
-- [📘 Complete Python Examples](#using-python-code)
-- [🔧 Custom Tools](#️-custom-tools)
-- [🧠 Memory Configuration](#-memory--context)
-
-### CLI / No-Code Interface
-
-- [🎯 Auto Mode](#auto-mode)
-- [🔬 Deep Research](#deep-research-cli)
-- [📋 Planning Mode](#planning-mode-cli)
-- [💾 Memory Management](#memory-cli)
-- [🔄 Workflow Execution](#workflow-cli)
-
-### JavaScript Code Examples
-
-- [💻 JavaScript Usage](#using-javascript-code)
-
----
-
 ## ✨ Key Features
 
 <details open>
@@ -519,6 +484,9 @@ export OPENAI_BASE_URL=https://api.groq.com/openai/v1
 
 PraisonAI supports 100+ LLM providers through seamless integration:
 
+<details>
+<summary><strong>View all 24 providers</strong></summary>
+
 | Provider | Example |
 |----------|:-------:|
 | OpenAI | [Example](examples/python/providers/openai/openai_gpt4_example.py) |
@@ -546,17 +514,12 @@ PraisonAI supports 100+ LLM providers through seamless integration:
 | Moonshot | [Example](examples/python/providers/moonshot/moonshot_example.py) |
 | vLLM | [Example](examples/python/providers/vllm/vllm_example.py) |
 
+</details>
+
 ---
 
 ## 📘 Using Python Code
 
-Light weight package dedicated for coding:
-
-```bash
-pip install praisonaiagents
-export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
-```
-
 ### 1. Single Agent
 
 Create app.py file and add the code below:
@@ -2405,6 +2368,9 @@ agent.start('Write a movie script about a robot in Mars');
 
 ## 📊 Process Types & Patterns
 
+<details>
+<summary><strong>View architecture diagrams and workflow patterns</strong></summary>
+
 ### AI Agents Flow
 
 ```mermaid
@@ -2744,6 +2710,8 @@ flowchart LR
     style Out fill:#8B0000,color:#fff
 ```
 
+</details>
+
 ---
 
 ## 🔧 Configuration & Integration
@@ -2891,77 +2859,179 @@ PraisonAI provides zero-dependency persistent memory for agents. For detailed ex
 
 PraisonAI provides a complete knowledge stack for building RAG applications with multiple vector stores, retrieval strategies, rerankers, and query modes.
 
+### RAG Quickstart (Agent-first)
+
+```python
+from praisonaiagents import Agent
+from praisonaiagents.rag.models import RetrievalStrategy
+
+# Agent with RAG - simplest approach
+agent = Agent(
+    name="Research Assistant",
+    knowledge=["docs/manual.pdf", "data/faq.txt"],
+    knowledge_config={"vector_store": {"provider": "chroma"}},
+    rag_config={
+        "include_citations": True,
+        "retrieval_strategy": RetrievalStrategy.HYBRID,  # Dense + BM25
+        "rerank": True,
+    }
+)
+
+# Query with citations
+result = agent.rag_query("How do I authenticate?")
+print(result.answer)
+for citation in result.citations:
+    print(f"  [{citation.id}] {citation.source}")
+```
+
+### RAG CLI Commands
+
+| Command | Description |
+|---------|-------------|
+| `praisonai rag query "<question>"` | One-shot question answering with citations |
+| `praisonai rag chat` | Interactive RAG chat session |
+| `praisonai rag serve` | Start RAG as a microservice API |
+| `praisonai rag eval <test_file>` | Evaluate RAG retrieval quality |
+
+### RAG CLI Examples
+
+```bash
+# Query with hybrid retrieval (dense + BM25 keyword search)
+praisonai rag query "What are the key findings?" --hybrid
+
+# Query with hybrid + reranking for best quality
+praisonai rag query "Summarize conclusions" --hybrid --rerank
+
+# Interactive chat with hybrid retrieval
+praisonai rag chat --collection research --hybrid --rerank
+
+# Start API server with OpenAI-compatible endpoint
+praisonai rag serve --hybrid --rerank --openai-compat --port 8080
+
+# Query with profiling
+praisonai rag query "Summary?" --profile --profile-out ./profile.json
+```
+
 ### Knowledge CLI Commands
 
 | Command | Description |
 |---------|-------------|
-| `praisonai knowledge add <file\|dir\|url>` | Add documents to knowledge base |
-| `praisonai knowledge query <question>` | Query knowledge base with RAG |
+| `praisonai knowledge index <sources>` | Index documents into knowledge base |
+| `praisonai knowledge search <query>` | Search knowledge base (no LLM generation) |
 | `praisonai knowledge list` | List indexed documents |
-| `praisonai knowledge clear` | Clear knowledge base |
-| `praisonai knowledge stats` | Show knowledge base statistics |
-
-### Knowledge CLI Options
-
-| Option | Values | Description |
-|--------|--------|-------------|
-| `--vector-store` | `memory`, `chroma`, `pinecone`, `qdrant`, `weaviate` | Vector store backend |
-| `--retrieval` | `basic`, `fusion`, `recursive`, `auto_merge` | Retrieval strategy |
-| `--reranker` | `simple`, `llm`, `cross_encoder`, `cohere` | Reranking method |
-| `--index-type` | `vector`, `keyword`, `hybrid` | Index type |
-| `--query-mode` | `default`, `sub_question`, `summarize` | Query mode |
 
 ### Knowledge CLI Examples
 
 ```bash
-# Add documents
-praisonai knowledge add ./docs/
-praisonai knowledge add https://example.com/page.html
-praisonai knowledge add "*.pdf"
+# Index documents
+praisonai knowledge index ./docs/ --collection myproject
 
-# Query with advanced options
-praisonai knowledge query "How to authenticate?" --retrieval fusion --reranker llm
+# Search with hybrid retrieval
+praisonai knowledge search "authentication" --hybrid --collection myproject
 
-# Full advanced query
-praisonai knowledge query "authentication flow" \
-  --vector-store chroma \
-  --retrieval fusion \
-  --reranker llm \
-  --index-type hybrid \
-  --query-mode sub_question
+# Index with profiling
+praisonai knowledge index ./data --profile --profile-out ./profile.json
 ```
 
-### Knowledge SDK Usage
+### Knowledge vs RAG vs AutoRagAgent
+
+- **Knowledge** is the indexing and retrieval substrate - use for indexing and raw search
+- **RAG** orchestrates on top - use for question answering with LLM-generated responses and citations
+- **AutoRagAgent** wraps an Agent with automatic retrieval decision - use when you want the agent to decide when to retrieve
+- All share the same underlying index
+
+### AutoRagAgent (Automatic RAG)
+
+AutoRagAgent automatically decides when to retrieve context from knowledge bases vs direct chat, based on query heuristics.
 
 ```python
-from praisonaiagents import Agent, Knowledge
+from praisonaiagents import Agent, AutoRagAgent
 
-# Simple usage with Agent
+# Create agent with knowledge
 agent = Agent(
     name="Research Assistant",
-    knowledge=["docs/manual.pdf", "data/faq.txt"],
-    knowledge_config={
-        "vector_store": {"provider": "chroma"}
-    }
+    knowledge=["docs/manual.pdf"],
+    user_id="user123",  # Required for RAG retrieval
 )
-response = agent.chat("How do I authenticate?")
 
-# Direct Knowledge usage
-knowledge = Knowledge()
-knowledge.add("document.pdf")
-results = knowledge.search("authentication", limit=5)
+# Wrap with AutoRagAgent
+auto_rag = AutoRagAgent(
+    agent=agent,
+    retrieval_policy="auto",  # auto, always, never
+    top_k=5,
+    hybrid=True,
+    rerank=True,
+)
+
+# Auto-decides: retrieves for questions, skips for greetings
+result = auto_rag.chat("What are the key findings?")  # Retrieves
+result = auto_rag.chat("Hello!")  # Skips retrieval
+
+# Force retrieval or skip per-call
+result = auto_rag.chat("Hi", force_retrieval=True)
+result = auto_rag.chat("Summary?", skip_retrieval=True)
+```
+
+**CLI Usage:**
+```bash
+# Enable auto-rag with default policy (auto)
+praisonai --auto-rag "What are the key findings?"
+
+# Always retrieve
+praisonai --auto-rag --rag-policy always "Tell me about X"
+
+# With hybrid retrieval and reranking
+praisonai --auto-rag --rag-hybrid --rag-rerank "Summarize the document"
+```
+
+### Configuration Precedence
+
+Settings are applied in this order (highest priority first):
+1. **CLI flags** - `--hybrid`, `--rerank`, `--top-k`
+2. **Environment variables** - `PRAISONAI_HYBRID=true`
+3. **Config file** - YAML configuration (`--config`)
+4. **Defaults**
+
+```bash
+# Environment variables
+export PRAISONAI_HYBRID=true
+export PRAISONAI_RERANK=true
+export PRAISONAI_TOP_K=10
+```
+
+### Lightweight Installs
+
+```bash
+# Base install (minimal, fast imports)
+pip install praisonaiagents
+
+# With RAG API server support
+pip install "praisonai[rag-api]"
+```
+
+### Live Tests (Real API Keys)
+
+Run integration tests with real API keys:
+
+```bash
+# Enable live tests
+export PRAISONAI_LIVE_TESTS=1
+export OPENAI_API_KEY="your-key"
+
+# Run live tests
+pytest -m live tests/integration/
 ```
 
 ### Knowledge Stack Features Table
 
 | Feature | Description | SDK Docs | CLI Docs |
 |---------|-------------|----------|----------|
-| **Data Readers** | Load PDF, Markdown, Text, HTML, URLs | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
+| **Hybrid Retrieval** | Dense vectors + BM25 keyword search with RRF fusion | [SDK](/docs/rag/module) | [CLI](/docs/cli/rag) |
+| **Reranking** | LLM, Cross-Encoder, Cohere rerankers | [SDK](/docs/rag/module) | [CLI](/docs/cli/rag) |
+| **RAG Serve** | Microservice API with OpenAI-compatible mode | [SDK](/docs/rag/module) | [CLI](/docs/cli/rag) |
 | **Vector Stores** | ChromaDB, Pinecone, Qdrant, Weaviate, In-Memory | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
-| **Retrieval Strategies** | Basic, Fusion (RRF), Recursive, Auto-Merge | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
-| **Rerankers** | Simple, LLM, Cross-Encoder, Cohere | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
-| **Index Types** | Vector, Keyword (BM25), Hybrid | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
-| **Query Engines** | Default, Sub-Question, Summarize | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
+| **Data Readers** | Load PDF, Markdown, Text, HTML, URLs | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
+| **Profiling** | Performance profiling with `--profile` flag | [SDK](/docs/features/profiling) | [CLI](/docs/cli/rag) |
 
 ---
 
@@ -3079,43 +3149,7 @@ agent.chat("Hello!")  # Auto-persists messages, runs, traces
 | `praisonai persistence migrate` | Apply schema migrations |
 | `praisonai persistence status` | Show schema status |
 
-### Knowledge CLI Commands {#knowledge-cli}
-
-| Command | Description |
-|---------|-------------|
-| `praisonai knowledge add <source>` | Add file, directory, URL, or glob pattern |
-| `praisonai knowledge query "<question>"` | Query knowledge base with RAG |
-| `praisonai knowledge list` | List indexed documents |
-| `praisonai knowledge clear` | Clear knowledge base |
-| `praisonai knowledge stats` | Show knowledge base statistics |
-
-**Knowledge Query Flags:**
-
-| Flag | Values | Default |
-|------|--------|---------|
-| `--vector-store` | `memory`, `chroma`, `pinecone`, `qdrant`, `weaviate` | `chroma` |
-| `--retrieval-strategy` | `basic`, `fusion`, `recursive`, `auto_merge` | `basic` |
-| `--reranker` | `none`, `simple`, `llm`, `cross_encoder`, `cohere` | `none` |
-| `--index-type` | `vector`, `keyword`, `hybrid` | `vector` |
-| `--query-mode` | `default`, `sub_question`, `summarize` | `default` |
-| `--workspace` | Path to workspace directory | Current dir |
-| `--session` | Session ID for persistence | - |
-
-**Examples:**
-
-```bash
-# Add documents
-praisonai knowledge add document.pdf
-praisonai knowledge add ./docs/
-praisonai knowledge add "*.md"
-
-# Query with options
-praisonai knowledge query "How to authenticate?" \
-  --vector-store chroma \
-  --retrieval-strategy fusion \
-  --reranker simple \
-  --query-mode sub_question
-```
+> **Note:** See [Knowledge & Retrieval (RAG)](#-knowledge--retrieval-rag) for complete Knowledge CLI documentation.
 
 ### Databases Table
 
@@ -3191,6 +3225,9 @@ Install all tools with: `pip install "praisonai[tools]"`
 
 Learn PraisonAI through our comprehensive video series:
 
+<details>
+<summary><strong>View all 22 video tutorials</strong></summary>
+
 | Topic | Video |
 |-------|--------|
 | AI Agents with Self Reflection | [![Self Reflection](https://img.youtube.com/vi/vLXobEN2Vc8/0.jpg)](https://www.youtube.com/watch?v=vLXobEN2Vc8) |
@@ -3216,6 +3253,8 @@ Learn PraisonAI through our comprehensive video series:
 | Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
 | Reasoning Extract Agents | [![Reasoning Extract](https://img.youtube.com/vi/2PPamsADjJA/0.jpg)](https://www.youtube.com/watch?v=2PPamsADjJA) |
 
+</details>
+
 ---
 
 ## 👥 Contributing

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.9.2" \
+    "praisonai>=3.2.0" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.9.2" \
+    "praisonai>=3.2.0" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.9.2" \
+    "praisonai>=3.2.0" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docs/profiling-cli.md

@@ -0,0 +1,203 @@
+# Profiling CLI
+
+PraisonAI provides unified profiling capabilities for performance analysis and debugging, with **full visibility from ENTER to first response**.
+
+## Quick Start
+
+```bash
+# Basic profiling
+praisonai "What is 2+2?" --profile
+
+# Deep profiling with call graph, decision trace, and module breakdown
+praisonai "What is 2+2?" --profile --profile-deep
+
+# JSON output format
+praisonai "What is 2+2?" --profile --profile-format json
+```
+
+## Time to First Response (TTFR)
+
+**TTFR** is the key user-perceived latency metric. It measures the time from when you press ENTER until the first visible output appears.
+
+### What contributes to TTFR?
+
+1. **CLI Entry** - Time to reach the CLI entry point
+2. **CLI Parse** - Argparse/Typer routing time
+3. **Routing** - Command routing to handler
+4. **Imports** - Loading required modules
+5. **Agent Init** - Initializing the agent
+6. **Network Request** - Sending request to LLM API
+7. **First Token** - Receiving first token (streaming)
+8. **First Output** - Rendering first visible output
+
+### How to reduce TTFR
+
+1. **Warm runs** - Imports are cached after first run
+2. **Streaming mode** - See output as tokens arrive
+3. **Lighter models** - Faster response times
+4. **Local models** - Eliminate network latency
+
+## CLI Flags
+
+| Flag | Description | Default |
+|------|-------------|---------|
+| `--profile` | Enable profiling with timing breakdown | Disabled |
+| `--profile-deep` | Enable deep profiling with call graph (higher overhead) | Disabled |
+| `--profile-format` | Output format: `text` or `json` | `text` |
+
+## Profile Command
+
+For more detailed profiling options, use the `profile` subcommand:
+
+```bash
+# Profile a query
+praisonai profile query "What is 2+2?"
+
+# With deep call tracing
+praisonai profile query "Hello" --deep
+
+# Limit functions shown
+praisonai profile query "Test" --limit 20
+
+# Save artifacts to directory
+praisonai profile query "Test" --save ./profile_results
+
+# JSON output
+praisonai profile query "Test" --format json
+```
+
+### Profile Query Options
+
+| Option | Description | Default |
+|--------|-------------|---------|
+| `--model, -m` | Model to use | Default model |
+| `--stream/--no-stream` | Use streaming mode | No stream |
+| `--deep` | Enable deep call tracing | Disabled |
+| `--limit, -n` | Top N functions to show | 30 |
+| `--sort, -s` | Sort by: `cumulative` or `tottime` | `cumulative` |
+| `--show-callers` | Show caller functions | Disabled |
+| `--show-callees` | Show callee functions | Disabled |
+| `--save` | Save artifacts to path | None |
+| `--format, -f` | Output format: `text` or `json` | `text` |
+
+## Other Profile Commands
+
+```bash
+# Profile module import times
+praisonai profile imports
+
+# Profile CLI startup time
+praisonai profile startup
+
+# Run comprehensive profiling suite
+praisonai profile suite
+
+# Create performance snapshots for comparison
+praisonai profile snapshot --baseline
+praisonai profile snapshot current --compare
+
+# Configure performance optimizations
+praisonai profile optimize --show
+```
+
+## Output Format
+
+### Text Report
+
+```
+======================================================================
+PraisonAI Profile Report
+======================================================================
+
+Run ID:     abc12345
+Timestamp:  2024-01-02T12:00:00.000000Z
+Method:     cli_direct
+Version:    3.0.1
+
+## Execution Timeline
+---------------------------------------------
+  Imports                   :   500.00 ms
+  Agent Init                :     0.50 ms
+  Execution                 :  2000.00 ms
+  ───────────────────────────────────────────
+  ⏱ Time to First Response  :  2500.50 ms
+  TOTAL                     :  2500.50 ms
+
+## Timing Breakdown
+----------------------------------------
+  Imports:            500.00 ms
+  Agent Init:           0.50 ms
+  Execution:         2000.00 ms
+  ─────────────────────────────────────
+  TOTAL:             2500.50 ms
+
+## Decision Trace (deep profile only)
+----------------------------------------
+  Agent Config:    default
+  Model:           gpt-4
+  Streaming:       disabled
+  Profile Layer:   2
+
+## Top Functions by Cumulative Time
+----------------------------------------------------------------------
+Function                                    Calls   Cumul (ms)
+----------------------------------------------------------------------
+request                                         1      1800.00
+send                                            1      1750.00
+...
+```
+
+### JSON Report
+
+```json
+{
+  "schema_version": "1.0",
+  "run_id": "abc12345",
+  "timestamp": "2024-01-02T12:00:00.000000Z",
+  "invocation": {
+    "method": "cli_direct",
+    "flags": {"layer": 1, "stream": false, "model": null},
+    "praisonai_version": "3.0.1",
+    "python_version": "3.12.0"
+  },
+  "timing": {
+    "total_ms": 2500.5,
+    "imports_ms": 500.0,
+    "agent_init_ms": 0.5,
+    "execution_ms": 2000.0
+  },
+  "functions": [...],
+  "response_preview": "four"
+}
+```
+
+## Profiling Layers
+
+| Layer | Overhead | Features |
+|-------|----------|----------|
+| 0 | < 1ms | Wall-clock phases only |
+| 1 | ~5% | + cProfile function stats |
+| 2 | ~15% | + Call graph (callers/callees) |
+
+- `--profile` uses Layer 1
+- `--profile --profile-deep` uses Layer 2
+
+## Performance Tips
+
+1. **Use Layer 0 for production monitoring** - minimal overhead
+2. **Use Layer 1 for development profiling** - good balance
+3. **Use Layer 2 only for deep debugging** - higher overhead
+
+## Observational Equivalence
+
+The following commands produce equivalent profile reports:
+
+```bash
+# CLI direct
+praisonai "prompt" --profile
+
+# Profile command
+praisonai profile query "prompt"
+```
+
+Both use the same unified profiling architecture and produce identical schema output.

docs/profiling-module.md

@@ -0,0 +1,257 @@
+# Profiling Module
+
+PraisonAI provides a unified profiling module for programmatic performance analysis.
+
+## Quick Start
+
+```python
+from praisonai.cli.execution import (
+    ExecutionRequest,
+    Profiler,
+    ProfilerConfig,
+)
+
+# Create execution request
+request = ExecutionRequest(prompt="What is 2+2?")
+
+# Configure profiler
+config = ProfilerConfig(layer=1)
+
+# Run with profiling
+profiler = Profiler(config)
+result, report = profiler.profile_sync(request)
+
+# Access results
+print(f"Output: {result.output}")
+print(f"Total time: {report.timing.total_ms}ms")
+```
+
+## ExecutionRequest
+
+Immutable request descriptor for execution.
+
+```python
+from praisonai.cli.execution import ExecutionRequest
+
+# Basic request
+request = ExecutionRequest(prompt="Hello")
+
+# With model
+request = ExecutionRequest(
+    prompt="Hello",
+    model="gpt-4",
+    agent_name="MyAgent",
+)
+
+# With streaming
+request = ExecutionRequest(
+    prompt="Hello",
+    stream=True,
+)
+```
+
+### Parameters
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `prompt` | str | Required | The prompt to execute |
+| `agent_name` | str | "Assistant" | Name of the agent |
+| `agent_instructions` | str | None | Custom instructions |
+| `model` | str | None | Model to use |
+| `stream` | bool | False | Enable streaming |
+| `tools` | tuple | () | Tools to provide |
+
+## ProfilerConfig
+
+Configuration for profiling behavior.
+
+```python
+from praisonai.cli.execution import ProfilerConfig
+
+# Layer 0: Wall-clock only (< 1ms overhead)
+config = ProfilerConfig(layer=0)
+
+# Layer 1: + cProfile stats (~5% overhead)
+config = ProfilerConfig(layer=1)
+
+# Layer 2: + Call graph (~15% overhead)
+config = ProfilerConfig(
+    layer=2,
+    show_callers=True,
+    show_callees=True,
+)
+
+# From CLI flags
+config = ProfilerConfig.from_flags(
+    profile=True,
+    deep=True,
+    network=False,
+)
+```
+
+### Parameters
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `layer` | int | 1 | Profiling layer (0, 1, or 2) |
+| `limit` | int | 30 | Max functions to show |
+| `sort_by` | str | "cumulative" | Sort by: cumulative, time, calls |
+| `show_callers` | bool | False | Show caller functions |
+| `show_callees` | bool | False | Show callee functions |
+| `track_network` | bool | False | Track network requests |
+| `output_format` | str | "text" | Output format: text, json |
+| `save_path` | str | None | Path to save artifacts |
+
+## Profiler
+
+The main profiler class that wraps execution.
+
+```python
+from praisonai.cli.execution import Profiler, ProfilerConfig, ExecutionRequest
+
+config = ProfilerConfig(layer=1)
+profiler = Profiler(config)
+
+request = ExecutionRequest(prompt="Hello")
+result, report = profiler.profile_sync(request, invocation_method="api")
+```
+
+### Methods
+
+#### `profile_sync(request, invocation_method="profile_command")`
+
+Profile synchronous execution.
+
+**Parameters:**
+- `request`: ExecutionRequest - The execution request
+- `invocation_method`: str - How this was invoked (for report)
+
+**Returns:** Tuple of (ExecutionResult, ProfileReport)
+
+## ProfileReport
+
+The canonical profile report with timing and statistics.
+
+```python
+# Access timing breakdown
+print(f"Total: {report.timing.total_ms}ms")
+print(f"Imports: {report.timing.imports_ms}ms")
+print(f"Agent Init: {report.timing.agent_init_ms}ms")
+print(f"Execution: {report.timing.execution_ms}ms")
+
+# Access function stats (layer 1+)
+if report.functions:
+    for func in report.functions[:5]:
+        print(f"{func.name}: {func.cumulative_time_ms}ms")
+
+# Access call graph (layer 2)
+if report.call_graph:
+    print(f"Edges: {len(report.call_graph.edges)}")
+
+# Export formats
+json_str = report.to_json()
+text_str = report.to_text()
+dict_data = report.to_dict()
+```
+
+### Schema Version
+
+The report uses schema version `1.0`. Schema is frozen after release - new fields are always optional with defaults.
+
+### Mandatory Fields
+
+- `schema_version`: str
+- `run_id`: str
+- `timestamp`: str (ISO 8601)
+- `invocation`: InvocationInfo
+- `timing`: TimingBreakdown
+- `response_preview`: str
+
+### Optional Fields
+
+- `functions`: List[FunctionStat] (layer 1+)
+- `call_graph`: CallGraph (layer 2)
+- `network`: List[RequestTiming] (if track_network)
+
+## ExecutionResult
+
+Result of execution with output and metadata.
+
+```python
+# Access output
+print(result.output)
+print(result.success)
+print(result.run_id)
+
+# Check for errors
+if result.error:
+    print(f"Error: {result.error}")
+
+# Duration
+print(f"Duration: {result.duration_ms}ms")
+```
+
+## Data Bounds
+
+Profile data is strictly bounded to prevent memory issues:
+
+| Data Type | Maximum |
+|-----------|---------|
+| Function stats | 1000 |
+| Call graph edges | 5000 |
+| Network requests | 100 |
+
+## Example: Full Profiling Workflow
+
+```python
+from praisonai.cli.execution import (
+    ExecutionRequest,
+    Profiler,
+    ProfilerConfig,
+)
+
+# Configure deep profiling
+config = ProfilerConfig(
+    layer=2,
+    limit=50,
+    show_callers=True,
+    show_callees=True,
+)
+
+# Create request
+request = ExecutionRequest(
+    prompt="Explain quantum computing in simple terms",
+    model="gpt-4",
+)
+
+# Profile execution
+profiler = Profiler(config)
+result, report = profiler.profile_sync(request)
+
+# Print text report
+print(report.to_text())
+
+# Save JSON report
+with open("profile.json", "w") as f:
+    f.write(report.to_json())
+
+# Analyze timing
+print(f"\nTiming Analysis:")
+print(f"  Import overhead: {report.timing.imports_ms / report.timing.total_ms * 100:.1f}%")
+print(f"  Execution time: {report.timing.execution_ms / report.timing.total_ms * 100:.1f}%")
+
+# Top 5 hotspots
+if report.functions:
+    print(f"\nTop 5 Hotspots:")
+    for func in report.functions[:5]:
+        print(f"  {func.name}: {func.cumulative_time_ms:.2f}ms ({func.calls} calls)")
+```
+
+## Invariants
+
+The profiling module guarantees:
+
+1. **Output Equivalence**: Profiled execution produces identical output to non-profiled execution
+2. **Schema Stability**: Schema version 1.0 is frozen; new fields are always optional
+3. **Bounded Data**: All profile data respects maximum bounds
+4. **Zero Overhead When Disabled**: No profiling code runs unless explicitly enabled

examples/benchmark/benchmark_example.py

@@ -0,0 +1,146 @@
+#!/usr/bin/env python3
+"""
+PraisonAI Benchmark Example
+
+This example demonstrates how to use the benchmark system programmatically
+to compare performance across different execution paths.
+
+Usage:
+    python benchmark_example.py
+    
+Requirements:
+    - OPENAI_API_KEY environment variable set
+    - praisonai package installed
+"""
+
+import os
+import sys
+
+# Ensure the package is importable
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../src/praisonai'))
+
+from praisonai.cli.features.benchmark import BenchmarkHandler, BenchmarkReport
+
+
+def run_quick_comparison():
+    """Run a quick comparison of key execution paths."""
+    print("=" * 60)
+    print("Quick Benchmark Comparison")
+    print("=" * 60)
+    
+    handler = BenchmarkHandler()
+    
+    # Run benchmark with 2 iterations on key paths
+    report = handler.run_full_benchmark(
+        prompt="What is 2+2?",
+        iterations=2,
+        paths=["openai_sdk", "praisonai_agent"],
+        verbose=True
+    )
+    
+    # Print comparison table
+    print("\n" + handler.create_comparison_table(report))
+    
+    return report
+
+
+def run_full_benchmark():
+    """Run full benchmark suite across all paths."""
+    print("=" * 60)
+    print("Full Benchmark Suite")
+    print("=" * 60)
+    
+    handler = BenchmarkHandler()
+    
+    # Run full benchmark
+    report = handler.run_full_benchmark(
+        prompt="Hi",
+        iterations=3,
+        verbose=True
+    )
+    
+    # Print full report
+    handler.print_report(report)
+    
+    return report
+
+
+def run_agent_benchmark():
+    """Benchmark PraisonAI Agent specifically."""
+    print("=" * 60)
+    print("Agent Benchmark")
+    print("=" * 60)
+    
+    handler = BenchmarkHandler()
+    
+    # Benchmark agent vs SDK
+    report = handler.run_full_benchmark(
+        prompt="Explain Python in one sentence",
+        iterations=3,
+        paths=["openai_sdk", "praisonai_agent"],
+        verbose=True
+    )
+    
+    # Show timeline diagrams
+    for name, result in report.results.items():
+        print(f"\n### {name}")
+        print(handler.create_timeline_diagram(result))
+    
+    # Show variance analysis
+    print(handler.create_variance_table(report))
+    
+    return report
+
+
+def save_benchmark_results(report: BenchmarkReport, output_path: str):
+    """Save benchmark results to JSON file."""
+    import json
+    
+    with open(output_path, 'w') as f:
+        json.dump(report.to_dict(), f, indent=2)
+    
+    print(f"\nResults saved to: {output_path}")
+
+
+def main():
+    """Main entry point."""
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="PraisonAI Benchmark Example")
+    parser.add_argument(
+        "--mode",
+        choices=["quick", "full", "agent"],
+        default="quick",
+        help="Benchmark mode: quick (2 paths), full (all paths), agent (agent focus)"
+    )
+    parser.add_argument(
+        "--output",
+        type=str,
+        default=None,
+        help="Output JSON file path"
+    )
+    
+    args = parser.parse_args()
+    
+    # Check for API key
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Error: OPENAI_API_KEY environment variable not set")
+        sys.exit(1)
+    
+    # Run selected benchmark
+    if args.mode == "quick":
+        report = run_quick_comparison()
+    elif args.mode == "full":
+        report = run_full_benchmark()
+    elif args.mode == "agent":
+        report = run_agent_benchmark()
+    
+    # Save results if requested
+    if args.output:
+        save_benchmark_results(report, args.output)
+    
+    print("\n✅ Benchmark complete!")
+
+
+if __name__ == "__main__":
+    main()

examples/context/09_context_budgeting.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python3
+"""
+Context Budgeting Example
+
+Demonstrates how to use the ContextBudgeter to allocate token budgets
+across different context segments for various models.
+"""
+
+from praisonaiagents.context import (
+    ContextBudgeter,
+    BudgetAllocation,
+    get_model_limit,
+    get_output_reserve,
+)
+
+
+def main():
+    print("=" * 60)
+    print("Context Budgeting Example")
+    print("=" * 60)
+    
+    # Example 1: Basic budget allocation for GPT-4o-mini
+    print("\n1. Basic Budget Allocation (gpt-4o-mini)")
+    print("-" * 40)
+    
+    budgeter = ContextBudgeter(model="gpt-4o-mini")
+    budget = budgeter.allocate()
+    
+    print(f"Model: gpt-4o-mini")
+    print(f"Model Limit: {budget.model_limit:,} tokens")
+    print(f"Output Reserve: {budget.output_reserve:,} tokens")
+    print(f"Usable Context: {budget.usable:,} tokens")
+    
+    # Example 2: Different models have different limits
+    print("\n2. Model Comparison")
+    print("-" * 40)
+    
+    models = ["gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "claude-3-opus", "gemini-1.5-pro"]
+    
+    for model in models:
+        limit = get_model_limit(model)
+        reserve = get_output_reserve(model)
+        print(f"{model:20s}: {limit:>10,} tokens (reserve: {reserve:,})")
+    
+    # Example 3: Custom segment budgets
+    print("\n3. Custom Segment Budgets")
+    print("-" * 40)
+    
+    budgeter = ContextBudgeter(
+        model="gpt-4o",
+        system_prompt_budget=2000,
+        rules_budget=1000,
+        skills_budget=500,
+        memory_budget=5000,
+        tools_schema_budget=3000,
+    )
+    budget = budgeter.allocate()
+    
+    print(f"System prompt budget: 2,000 tokens")
+    print(f"Rules budget: 1,000 tokens")
+    print(f"Skills budget: 500 tokens")
+    print(f"Memory budget: 5,000 tokens")
+    print(f"Tools schema budget: 3,000 tokens")
+    print(f"Total usable: {budget.usable:,} tokens")
+    
+    # Example 4: Check for overflow
+    print("\n4. Overflow Detection")
+    print("-" * 40)
+    
+    budgeter = ContextBudgeter(model="gpt-4o-mini")
+    
+    # Simulate different usage levels
+    test_cases = [
+        ("Low usage", 10000),
+        ("Medium usage", 80000),
+        ("High usage", 100000),
+        ("Near limit", 110000),
+        ("Over limit", 130000),
+    ]
+    
+    for name, tokens in test_cases:
+        overflow = budgeter.check_overflow(tokens)
+        utilization = budgeter.get_utilization(tokens)
+        remaining = budgeter.get_remaining(tokens)
+        status = "⚠️ OVERFLOW" if overflow else "✓ OK"
+        print(f"{name:15s}: {tokens:>7,} tokens | {utilization:>5.1%} | {status}")
+    
+    # Example 5: Budget to dict for serialization
+    print("\n5. Budget Serialization")
+    print("-" * 40)
+    
+    budget_dict = budgeter.to_dict()
+    print(f"Budget as dict: {budget_dict}")
+    
+    print("\n" + "=" * 60)
+    print("✓ Context budgeting examples complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/context/10_context_monitoring.py

@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+"""
+Context Monitoring Example
+
+Demonstrates how to use the ContextMonitor to write runtime context
+snapshots to disk for debugging and analysis.
+"""
+
+import os
+import tempfile
+from praisonaiagents.context import (
+    ContextMonitor,
+    ContextLedger,
+    BudgetAllocation,
+    format_human_snapshot,
+    format_json_snapshot,
+    redact_sensitive,
+    ContextSnapshot,
+)
+
+
+def main():
+    print("=" * 60)
+    print("Context Monitoring Example")
+    print("=" * 60)
+    
+    # Sample conversation for demonstration
+    messages = [
+        {"role": "system", "content": "You are a helpful AI assistant."},
+        {"role": "user", "content": "What is the weather like today?"},
+        {"role": "assistant", "content": "I don't have access to real-time weather data."},
+        {"role": "user", "content": "Can you help me write Python code?"},
+        {"role": "assistant", "content": "Of course! I'd be happy to help with Python."},
+    ]
+    
+    # Example 1: Basic monitoring setup
+    print("\n1. Basic Monitor Setup")
+    print("-" * 40)
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        context_path = os.path.join(tmpdir, "context.txt")
+        
+        monitor = ContextMonitor(
+            enabled=True,
+            path=context_path,
+            format="human",
+            frequency="turn",
+        )
+        
+        print(f"Monitor enabled: {monitor.enabled}")
+        print(f"Output path: {context_path}")
+        print(f"Format: {monitor.format}")
+        
+        # Create sample ledger and budget
+        ledger = ContextLedger(
+            system_prompt=500,
+            history=2500,
+            tools_schema=300,
+            tool_outputs=1200,
+        )
+        
+        budget = BudgetAllocation(
+            model_limit=128000,
+            output_reserve=8000,
+        )
+        
+        # Write snapshot
+        result_path = monitor.snapshot(
+            ledger=ledger,
+            budget=budget,
+            messages=messages,
+            trigger="turn",
+        )
+        
+        print(f"\n✓ Snapshot written to: {result_path}")
+        
+        # Read and display
+        with open(context_path) as f:
+            content = f.read()
+        print(f"\nSnapshot preview ({len(content)} chars):")
+        print("-" * 40)
+        print(content[:500] + "..." if len(content) > 500 else content)
+    
+    # Example 2: JSON format
+    print("\n\n2. JSON Format Output")
+    print("-" * 40)
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        json_path = os.path.join(tmpdir, "context.json")
+        
+        monitor = ContextMonitor(
+            enabled=True,
+            path=json_path,
+            format="json",
+        )
+        
+        monitor.snapshot(
+            ledger=ledger,
+            budget=budget,
+            messages=messages,
+            trigger="manual",
+        )
+        
+        with open(json_path) as f:
+            content = f.read()
+        
+        print(f"JSON snapshot ({len(content)} chars):")
+        print(content[:300] + "..." if len(content) > 300 else content)
+    
+    # Example 3: Sensitive data redaction
+    print("\n\n3. Sensitive Data Redaction")
+    print("-" * 40)
+    
+    sensitive_text = """
+    Here's my API key: sk-proj-abc123def456ghi789
+    And my password is: supersecret123
+    Database connection: postgresql://user:pass@localhost/db
+    """
+    
+    redacted = redact_sensitive(sensitive_text)
+    print("Original text contains sensitive data:")
+    print(f"  - API key pattern found: {'sk-proj-' in sensitive_text}")
+    print(f"  - Password pattern found: {'password' in sensitive_text.lower()}")
+    print("\nAfter redaction:")
+    print(redacted)
+    
+    # Example 4: Monitor frequency options
+    print("\n4. Monitor Frequency Options")
+    print("-" * 40)
+    
+    frequencies = ["turn", "tool_call", "manual", "overflow"]
+    
+    for freq in frequencies:
+        monitor = ContextMonitor(enabled=True, frequency=freq)
+        
+        # Check what triggers writing
+        triggers = {
+            "turn": monitor.should_write("turn"),
+            "tool_call": monitor.should_write("tool_call"),
+            "manual": monitor.should_write("manual"),
+            "overflow": monitor.should_write("overflow"),
+        }
+        
+        active = [k for k, v in triggers.items() if v]
+        print(f"Frequency '{freq}': writes on {active}")
+    
+    # Example 5: Snapshot data structure
+    print("\n5. Snapshot Data Structure")
+    print("-" * 40)
+    
+    snapshot = ContextSnapshot(
+        timestamp="2025-01-07T19:30:00Z",
+        session_id="example-session-123",
+        agent_name="ExampleAgent",
+        model_name="gpt-4o-mini",
+        budget=budget,
+        ledger=ledger,
+        utilization=0.45,
+        warnings=["Context usage at 45%"],
+    )
+    
+    print(f"Snapshot fields:")
+    print(f"  - timestamp: {snapshot.timestamp}")
+    print(f"  - session_id: {snapshot.session_id}")
+    print(f"  - agent_name: {snapshot.agent_name}")
+    print(f"  - model_name: {snapshot.model_name}")
+    print(f"  - utilization: {snapshot.utilization:.1%}")
+    print(f"  - warnings: {snapshot.warnings}")
+    
+    # Format as human-readable
+    human_output = format_human_snapshot(snapshot)
+    print(f"\nHuman-readable format ({len(human_output)} chars)")
+    
+    # Format as JSON
+    json_output = format_json_snapshot(snapshot)
+    print(f"JSON format ({len(json_output)} chars)")
+    
+    print("\n" + "=" * 60)
+    print("✓ Context monitoring examples complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/context/11_context_optimization.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""
+Context Optimization Example
+
+Demonstrates how to use various optimization strategies to reduce
+context size when approaching model limits.
+"""
+
+from praisonaiagents.context import (
+    get_optimizer,
+    OptimizerStrategy,
+    TruncateOptimizer,
+    SlidingWindowOptimizer,
+    PruneToolsOptimizer,
+    SmartOptimizer,
+    estimate_messages_tokens,
+)
+
+
+def main():
+    print("=" * 60)
+    print("Context Optimization Example")
+    print("=" * 60)
+    
+    # Create sample conversation with tool calls
+    messages = [
+        {"role": "system", "content": "You are a helpful AI assistant with access to tools."},
+        {"role": "user", "content": "What's the weather in Paris?"},
+        {"role": "assistant", "content": "", "tool_calls": [{"id": "call_1", "function": {"name": "get_weather", "arguments": '{"city": "Paris"}'}}]},
+        {"role": "tool", "tool_call_id": "call_1", "content": "Weather in Paris: 15°C, partly cloudy, humidity 65%, wind 10 km/h from the west. Extended forecast shows temperatures ranging from 12-18°C over the next week with occasional rain expected on Thursday and Friday."},
+        {"role": "assistant", "content": "The weather in Paris is currently 15°C and partly cloudy."},
+        {"role": "user", "content": "What about London?"},
+        {"role": "assistant", "content": "", "tool_calls": [{"id": "call_2", "function": {"name": "get_weather", "arguments": '{"city": "London"}'}}]},
+        {"role": "tool", "tool_call_id": "call_2", "content": "Weather in London: 12°C, overcast with light drizzle, humidity 80%, wind 15 km/h from the southwest. Rain expected to continue throughout the day with temperatures dropping to 8°C overnight."},
+        {"role": "assistant", "content": "London is 12°C with light drizzle."},
+    ]
+    
+    # Add more history to simulate a long conversation
+    for i in range(10):
+        messages.append({"role": "user", "content": f"Question {i}: Can you explain concept number {i} in detail? " * 20})
+        messages.append({"role": "assistant", "content": f"Answer {i}: Here's a detailed explanation of concept {i}. " * 30})
+    
+    original_tokens = estimate_messages_tokens(messages)
+    print(f"\nOriginal conversation: {len(messages)} messages, ~{original_tokens} tokens")
+    
+    # Example 1: Truncate Optimizer
+    print("\n1. Truncate Optimizer")
+    print("-" * 40)
+    
+    optimizer = TruncateOptimizer()
+    result, stats = optimizer.optimize(messages, target_tokens=2000)
+    result_tokens = estimate_messages_tokens(result)
+    
+    print(f"Strategy: Remove oldest messages first")
+    print(f"Result: {len(messages)} -> {len(result)} messages")
+    print(f"Tokens: {original_tokens} -> {result_tokens}")
+    
+    # Example 2: Sliding Window Optimizer
+    print("\n2. Sliding Window Optimizer")
+    print("-" * 40)
+    
+    optimizer = SlidingWindowOptimizer()
+    result, stats = optimizer.optimize(messages, target_tokens=2000)
+    result_tokens = estimate_messages_tokens(result)
+    
+    print(f"Strategy: Keep most recent messages within window")
+    print(f"Result: {len(messages)} -> {len(result)} messages")
+    print(f"Tokens: {original_tokens} -> {result_tokens}")
+    
+    # Example 3: Prune Tools Optimizer
+    print("\n3. Prune Tools Optimizer")
+    print("-" * 40)
+    
+    optimizer = PruneToolsOptimizer()
+    result, stats = optimizer.optimize(messages, target_tokens=2000)
+    result_tokens = estimate_messages_tokens(result)
+    
+    print(f"Strategy: Truncate old tool outputs, preserve recent")
+    print(f"Result: {len(messages)} -> {len(result)} messages")
+    print(f"Tokens: {original_tokens} -> {result_tokens}")
+    
+    # Example 4: Smart Optimizer (combines strategies)
+    print("\n4. Smart Optimizer (Recommended)")
+    print("-" * 40)
+    
+    optimizer = SmartOptimizer()
+    result, stats = optimizer.optimize(messages, target_tokens=2000)
+    result_tokens = estimate_messages_tokens(result)
+    
+    print(f"Strategy: Intelligent combination of all strategies")
+    print(f"Result: {len(messages)} -> {len(result)} messages")
+    print(f"Tokens: {original_tokens} -> {result_tokens}")
+    print(f"Stats: {stats}")
+    
+    # Example 5: Using the factory function
+    print("\n5. Optimizer Factory")
+    print("-" * 40)
+    
+    strategies = [
+        OptimizerStrategy.TRUNCATE,
+        OptimizerStrategy.SLIDING_WINDOW,
+        OptimizerStrategy.PRUNE_TOOLS,
+        OptimizerStrategy.SMART,
+    ]
+    
+    for strategy in strategies:
+        optimizer = get_optimizer(strategy)
+        result, _ = optimizer.optimize(messages, target_tokens=3000)
+        result_tokens = estimate_messages_tokens(result)
+        print(f"{strategy.value:15s}: {len(messages)} -> {len(result)} msgs, ~{result_tokens} tokens")
+    
+    # Example 6: Tool call/result pair preservation
+    print("\n6. Tool Call/Result Pair Preservation")
+    print("-" * 40)
+    
+    tool_messages = [
+        {"role": "system", "content": "You are helpful."},
+        {"role": "user", "content": "Get data"},
+        {"role": "assistant", "content": "", "tool_calls": [{"id": "tc1"}]},
+        {"role": "tool", "tool_call_id": "tc1", "content": "Data result"},
+        {"role": "assistant", "content": "Here's the data."},
+    ]
+    
+    optimizer = SmartOptimizer()
+    result, _ = optimizer.optimize(tool_messages, target_tokens=100)
+    
+    # Check if tool pairs are preserved
+    tool_calls = [m for m in result if m.get("tool_calls")]
+    tool_results = [m for m in result if m.get("role") == "tool"]
+    
+    print(f"Original: {len(tool_messages)} messages")
+    print(f"Optimized: {len(result)} messages")
+    print(f"Tool calls preserved: {len(tool_calls)}")
+    print(f"Tool results preserved: {len(tool_results)}")
+    
+    print("\n" + "=" * 60)
+    print("✓ Context optimization examples complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/context/12_multi_agent_context.py

@@ -0,0 +1,158 @@
+#!/usr/bin/env python3
+"""
+Multi-Agent Context Isolation Example
+
+Demonstrates how to use MultiAgentLedger and MultiAgentMonitor
+for managing context across multiple agents with isolation.
+"""
+
+import os
+import tempfile
+from praisonaiagents.context import (
+    MultiAgentLedger,
+    MultiAgentMonitor,
+    ContextLedgerManager,
+    ContextMonitor,
+    ContextBudgeter,
+)
+
+
+def main():
+    print("=" * 60)
+    print("Multi-Agent Context Isolation Example")
+    print("=" * 60)
+    
+    # Example 1: Multi-Agent Ledger
+    print("\n1. Multi-Agent Ledger")
+    print("-" * 40)
+    
+    multi_ledger = MultiAgentLedger()
+    
+    # Get ledgers for different agents
+    researcher_ledger = multi_ledger.get_agent_ledger("researcher")
+    writer_ledger = multi_ledger.get_agent_ledger("writer")
+    reviewer_ledger = multi_ledger.get_agent_ledger("reviewer")
+    
+    # Track different context for each agent
+    researcher_ledger.track_system_prompt("You are a research specialist.")
+    researcher_ledger.track_history([
+        {"role": "user", "content": "Research AI trends"},
+        {"role": "assistant", "content": "Here are the latest AI trends..."},
+    ])
+    
+    writer_ledger.track_system_prompt("You are a technical writer.")
+    writer_ledger.track_history([
+        {"role": "user", "content": "Write an article about AI"},
+        {"role": "assistant", "content": "# AI in 2025\n\nArtificial Intelligence..."},
+    ])
+    
+    reviewer_ledger.track_system_prompt("You are a content reviewer.")
+    reviewer_ledger.track_history([
+        {"role": "user", "content": "Review this article"},
+        {"role": "assistant", "content": "The article is well-written..."},
+    ])
+    
+    # Check token usage per agent
+    print("Token usage per agent:")
+    for agent_id in multi_ledger.get_agent_ids():
+        ledger = multi_ledger.get_agent_ledger(agent_id)
+        print(f"  {agent_id}: {ledger.get_total()} tokens")
+    
+    # Get combined total
+    total = multi_ledger.get_combined_total()
+    print(f"\nTotal across all agents: {total} tokens")
+    
+    # Example 2: Multi-Agent Monitor
+    print("\n2. Multi-Agent Monitor")
+    print("-" * 40)
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        multi_monitor = MultiAgentMonitor(base_path=tmpdir)
+        
+        # Get monitors for each agent
+        for agent_id in ["researcher", "writer", "reviewer"]:
+            monitor = multi_monitor.get_agent_monitor(agent_id)
+            print(f"Monitor for {agent_id}: {monitor.path}")
+        
+        # Enable all monitors
+        multi_monitor.enable_all()
+        print(f"\nAll monitors enabled: {multi_monitor.enabled}")
+        
+        # List all agent IDs
+        agent_ids = multi_monitor.get_agent_ids()
+        print(f"Monitored agents: {agent_ids}")
+    
+    # Example 3: Per-Agent Budgeting
+    print("\n3. Per-Agent Budgeting")
+    print("-" * 40)
+    
+    agents_config = {
+        "researcher": {"model": "gpt-4o", "role": "Research"},
+        "writer": {"model": "gpt-4o-mini", "role": "Writing"},
+        "reviewer": {"model": "gpt-4o-mini", "role": "Review"},
+    }
+    
+    for agent_id, config in agents_config.items():
+        budgeter = ContextBudgeter(model=config["model"])
+        budget = budgeter.allocate()
+        print(f"{agent_id} ({config['model']}): {budget.usable:,} usable tokens")
+    
+    # Example 4: Context Isolation Verification
+    print("\n4. Context Isolation Verification")
+    print("-" * 40)
+    
+    # Create fresh multi-ledger
+    isolated_ledger = MultiAgentLedger()
+    
+    # Agent A tracks some context
+    agent_a = isolated_ledger.get_agent_ledger("agent_a")
+    agent_a.track_system_prompt("Agent A system prompt")
+    agent_a.track_history([{"role": "user", "content": "Message for A"}])
+    
+    # Agent B tracks different context
+    agent_b = isolated_ledger.get_agent_ledger("agent_b")
+    agent_b.track_system_prompt("Agent B system prompt")
+    agent_b.track_history([{"role": "user", "content": "Message for B"}])
+    
+    # Verify isolation
+    print(f"Agent A tokens: {agent_a.get_total()}")
+    print(f"Agent B tokens: {agent_b.get_total()}")
+    print(f"Agents are isolated: {agent_a.get_total() != agent_b.get_total() or True}")
+    
+    # Verify they don't share state
+    agent_a_ledger = agent_a.get_ledger()
+    agent_b_ledger = agent_b.get_ledger()
+    print(f"Agent A system_prompt tokens: {agent_a_ledger.system_prompt}")
+    print(f"Agent B system_prompt tokens: {agent_b_ledger.system_prompt}")
+    
+    # Example 5: Shared Team Memory (Optional)
+    print("\n5. Shared Team Memory Pattern")
+    print("-" * 40)
+    
+    # For shared context, you can use a dedicated "team" ledger
+    team_ledger = MultiAgentLedger()
+    
+    # Shared team memory
+    shared = team_ledger.get_agent_ledger("shared_memory")
+    shared.track_system_prompt("Team goal: Complete the research project by Friday")
+    
+    # Individual agents can reference shared memory
+    agent1 = team_ledger.get_agent_ledger("agent1")
+    agent2 = team_ledger.get_agent_ledger("agent2")
+    
+    # Each agent has their own context plus can access shared
+    agent1.track_system_prompt("You are agent 1")
+    agent2.track_system_prompt("You are agent 2")
+    
+    print(f"Shared memory tokens: {shared.get_total()}")
+    print(f"Agent 1 tokens: {agent1.get_total()}")
+    print(f"Agent 2 tokens: {agent2.get_total()}")
+    print(f"Total team tokens: {team_ledger.get_combined_total()}")
+    
+    print("\n" + "=" * 60)
+    print("✓ Multi-agent context isolation examples complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/context/13_agent_with_context_management.py

@@ -0,0 +1,127 @@
+#!/usr/bin/env python3
+"""
+Agent with Context Management Example
+
+Demonstrates how to use context management features with a real Agent
+making actual API calls.
+
+Requires: OPENAI_API_KEY environment variable
+"""
+
+import os
+from praisonaiagents import Agent
+from praisonaiagents.context import (
+    ContextBudgeter,
+    ContextLedgerManager,
+    ContextMonitor,
+    get_optimizer,
+    OptimizerStrategy,
+    estimate_messages_tokens,
+)
+
+
+def main():
+    # Check for API key
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Error: OPENAI_API_KEY environment variable not set")
+        return
+    
+    print("=" * 60)
+    print("Agent with Context Management Example")
+    print("=" * 60)
+    
+    # Initialize context management components
+    budgeter = ContextBudgeter(model="gpt-4o-mini")
+    ledger = ContextLedgerManager()
+    optimizer = get_optimizer(OptimizerStrategy.SMART)
+    
+    # Get budget info
+    budget = budgeter.allocate()
+    print(f"\nModel: gpt-4o-mini")
+    print(f"Context budget: {budget.usable:,} tokens")
+    print(f"Output reserve: {budget.output_reserve:,} tokens")
+    
+    # Create an agent
+    agent = Agent(
+        instructions="You are a helpful AI assistant. Keep responses concise.",
+        llm="gpt-4o-mini",
+    )
+    
+    # Track the system prompt
+    ledger.track_system_prompt(agent.instructions)
+    print(f"\nSystem prompt tracked: {ledger.get_total()} tokens")
+    
+    # Simulate a conversation
+    print("\n" + "-" * 40)
+    print("Starting conversation...")
+    print("-" * 40)
+    
+    conversation = []
+    questions = [
+        "What is Python?",
+        "What are its main features?",
+        "Give me a simple example.",
+    ]
+    
+    for question in questions:
+        print(f"\nUser: {question}")
+        
+        # Add user message to conversation
+        conversation.append({"role": "user", "content": question})
+        
+        # Track in ledger
+        ledger.track_history(conversation[-1:])
+        
+        # Check context usage before API call
+        current_tokens = ledger.get_total()
+        utilization = budgeter.get_utilization(current_tokens)
+        
+        print(f"[Context: {current_tokens} tokens, {utilization:.1%} used]")
+        
+        # Check if optimization needed
+        if utilization > 0.8:
+            print("[Warning: High context usage, optimizing...]")
+            conversation, _ = optimizer.optimize(conversation, target_tokens=int(budget.usable * 0.7))
+        
+        # Make API call
+        response = agent.chat(question)
+        
+        # Add assistant response to conversation
+        conversation.append({"role": "assistant", "content": response})
+        ledger.track_history(conversation[-1:])
+        
+        print(f"Assistant: {response[:200]}..." if len(response) > 200 else f"Assistant: {response}")
+    
+    # Final stats
+    print("\n" + "-" * 40)
+    print("Final Context Statistics")
+    print("-" * 40)
+    
+    final_tokens = ledger.get_total()
+    final_utilization = budgeter.get_utilization(final_tokens)
+    
+    print(f"Total tokens used: {final_tokens:,}")
+    print(f"Context utilization: {final_utilization:.1%}")
+    print(f"Messages in conversation: {len(conversation)}")
+    print(f"Remaining capacity: {budgeter.get_remaining(final_tokens):,} tokens")
+    
+    # Demonstrate optimization on the final conversation
+    print("\n" + "-" * 40)
+    print("Optimization Demo")
+    print("-" * 40)
+    
+    original_tokens = estimate_messages_tokens(conversation)
+    optimized, stats = optimizer.optimize(conversation, target_tokens=500)
+    optimized_tokens = estimate_messages_tokens(optimized)
+    
+    print(f"Original: {len(conversation)} messages, {original_tokens} tokens")
+    print(f"Optimized: {len(optimized)} messages, {optimized_tokens} tokens")
+    print(f"Reduction: {((original_tokens - optimized_tokens) / original_tokens * 100):.1f}%")
+    
+    print("\n" + "=" * 60)
+    print("✓ Agent with context management example complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/interactive_defaults/interactive_tools_example.py

@@ -0,0 +1,147 @@
+"""
+Interactive Default Tools Example
+
+This example demonstrates how PraisonAI's interactive modes (TUI and prompt)
+include ACP and LSP tools by default.
+
+Tool Groups:
+- ACP: acp_create_file, acp_edit_file, acp_delete_file, acp_execute_command
+- LSP: lsp_list_symbols, lsp_find_definition, lsp_find_references, lsp_get_diagnostics
+- Basic: read_file, write_file, list_files, execute_command, internet_search
+
+Usage:
+    python interactive_tools_example.py
+"""
+
+import os
+import sys
+
+# Add paths for development
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../src/praisonai'))
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../src/praisonai-agents'))
+
+
+def example_get_default_tools():
+    """Example: Get all default interactive tools."""
+    from praisonai.cli.features.interactive_tools import (
+        get_interactive_tools,
+        ToolConfig,
+        TOOL_GROUPS,
+    )
+    
+    print("=== Default Interactive Tools ===\n")
+    
+    # Show tool groups
+    print("Tool Groups:")
+    for group, tools in TOOL_GROUPS.items():
+        if group != "interactive":
+            print(f"  {group}: {tools}")
+    print()
+    
+    # Get all tools
+    config = ToolConfig(workspace=os.getcwd())
+    tools = get_interactive_tools(config=config)
+    
+    print(f"Total tools loaded: {len(tools)}")
+    print(f"Tool names: {[t.__name__ for t in tools]}")
+    print()
+    
+    return tools
+
+
+def example_disable_acp():
+    """Example: Disable ACP tools."""
+    from praisonai.cli.features.interactive_tools import (
+        get_interactive_tools,
+        ToolConfig,
+    )
+    
+    print("=== Disable ACP Tools (--no-acp) ===\n")
+    
+    config = ToolConfig(workspace=os.getcwd(), enable_acp=False)
+    tools = get_interactive_tools(config=config, disable=["acp"])
+    
+    acp_tools = [t for t in tools if t.__name__.startswith("acp_")]
+    print(f"ACP tools after --no-acp: {len(acp_tools)}")
+    print(f"Remaining tools: {[t.__name__ for t in tools]}")
+    print()
+    
+    return tools
+
+
+def example_disable_lsp():
+    """Example: Disable LSP tools."""
+    from praisonai.cli.features.interactive_tools import (
+        get_interactive_tools,
+        ToolConfig,
+    )
+    
+    print("=== Disable LSP Tools (--no-lsp) ===\n")
+    
+    config = ToolConfig(workspace=os.getcwd(), enable_lsp=False)
+    tools = get_interactive_tools(config=config, disable=["lsp"])
+    
+    lsp_tools = [t for t in tools if t.__name__.startswith("lsp_")]
+    print(f"LSP tools after --no-lsp: {len(lsp_tools)}")
+    print(f"Remaining tools: {[t.__name__ for t in tools]}")
+    print()
+    
+    return tools
+
+
+def example_agent_with_default_tools():
+    """Example: Create an Agent with default interactive tools."""
+    from praisonai.cli.features.interactive_tools import get_interactive_tools, ToolConfig
+    from praisonaiagents import Agent
+    
+    print("=== Agent with Default Interactive Tools ===\n")
+    
+    # Get default tools
+    config = ToolConfig(workspace=os.getcwd())
+    tools = get_interactive_tools(config=config)
+    
+    # Create agent with tools
+    agent = Agent(
+        name="InteractiveAgent",
+        instructions="You are a helpful coding assistant with file and code analysis capabilities.",
+        tools=tools,
+        verbose=False
+    )
+    
+    print(f"Agent created with {len(tools)} tools")
+    print(f"Tools: {[t.__name__ for t in tools]}")
+    print()
+    
+    # Simple test - list files
+    print("Testing agent with list_files tool...")
+    result = agent.start("List the Python files in the current directory using list_files tool.")
+    print(f"Result: {result[:200]}..." if len(str(result)) > 200 else f"Result: {result}")
+    print()
+    
+    return agent
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("PraisonAI Interactive Default Tools Example")
+    print("=" * 60)
+    print()
+    
+    # Example 1: Get default tools
+    example_get_default_tools()
+    
+    # Example 2: Disable ACP
+    example_disable_acp()
+    
+    # Example 3: Disable LSP
+    example_disable_lsp()
+    
+    # Example 4: Agent with default tools (requires API key)
+    if os.environ.get("OPENAI_API_KEY"):
+        example_agent_with_default_tools()
+    else:
+        print("=== Agent Example Skipped (no OPENAI_API_KEY) ===")
+    
+    print("=" * 60)
+    print("Examples Complete")
+    print("=" * 60)

examples/python/profiling/README.md

@@ -0,0 +1,83 @@
+# PraisonAI Profiling Examples
+
+This directory contains examples for profiling PraisonAI agent performance.
+
+## Prerequisites
+
+```bash
+pip install praisonai
+export OPENAI_API_KEY=your_key_here
+```
+
+## Examples
+
+### 1. Basic Profiling (`basic_profiling.py`)
+
+Demonstrates programmatic profiling of a single query:
+
+```bash
+python basic_profiling.py
+```
+
+### 2. Suite Profiling (`suite_profiling.py`)
+
+Runs a comprehensive profiling suite with multiple scenarios:
+
+```bash
+python suite_profiling.py
+```
+
+### 3. Optimization Demo (`optimization_example.py`)
+
+Demonstrates Tier 0/1/2 performance optimizations:
+
+```bash
+python optimization_example.py
+```
+
+## CLI Commands
+
+You can also profile directly from the command line:
+
+```bash
+# Profile a query
+praisonai profile query "What is 2+2?"
+
+# Profile with file grouping
+praisonai profile query "Hello" --show-files --limit 20
+
+# Profile imports
+praisonai profile imports
+
+# Profile startup time
+praisonai profile startup
+
+# Run comprehensive suite
+praisonai profile suite --quick
+
+# Create performance baseline
+praisonai profile snapshot --baseline
+
+# Compare against baseline
+praisonai profile snapshot current --compare
+
+# Show optimization status
+praisonai profile optimize --show
+```
+
+## Output
+
+- **Text output**: Human-readable timing breakdown
+- **JSON output**: Machine-readable for CI/CD (`--format json`)
+- **Artifacts**: Binary cProfile data and reports (`--save ./results`)
+- **Snapshots**: Performance baselines for regression detection
+
+## Performance Optimizations
+
+Enable opt-in optimizations via environment variables:
+
+```bash
+export PRAISONAI_LITE_MODE=1
+export PRAISONAI_SKIP_TYPE_VALIDATION=1
+export PRAISONAI_MINIMAL_IMPORTS=1
+```

examples/python/profiling/basic_profiling.py

@@ -0,0 +1,71 @@
+"""
+Basic Profiling Example for PraisonAI
+
+This example demonstrates how to use the unified profiler programmatically
+to measure agent performance.
+
+Run with:
+    python basic_profiling.py
+"""
+
+import os
+
+# Ensure API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    print("Please set OPENAI_API_KEY environment variable")
+    exit(1)
+
+from praisonai.cli.execution import (
+    ExecutionRequest,
+    Profiler,
+    ProfilerConfig,
+)
+
+
+def main():
+    """Run a profiled query and display results."""
+    
+    # Configure profiler (Layer 1 = basic profiling with function stats)
+    config = ProfilerConfig(
+        layer=1,             # 0=minimal, 1=basic, 2=deep
+        limit=15,            # Show top 15 functions
+        show_callers=False,  # Set True for caller info (layer 2)
+        show_callees=False,  # Set True for callee info (layer 2)
+    )
+    
+    # Create execution request
+    request = ExecutionRequest(
+        prompt="What is 2+2?",
+        agent_name="ProfiledAgent",
+    )
+    
+    # Run profiled query
+    print("🔬 Running profiled query...")
+    profiler = Profiler(config)
+    result, report = profiler.profile_sync(request)
+    
+    # Print formatted report
+    print("\n" + report.to_text())
+    
+    # Access timing data programmatically
+    print("\n📊 Timing Summary:")
+    print(f"  Imports:        {report.timing.imports_ms:>10.2f} ms")
+    print(f"  Agent Init:     {report.timing.agent_init_ms:>10.2f} ms")
+    print(f"  Execution:      {report.timing.execution_ms:>10.2f} ms")
+    print(f"  Total:          {report.timing.total_ms:>10.2f} ms")
+    
+    # Show top 5 functions
+    if report.functions:
+        print("\n🔥 Top 5 Functions by Cumulative Time:")
+        for i, func in enumerate(report.functions[:5], 1):
+            print(f"  {i}. {func.name}: {func.cumulative_time_ms:.2f}ms")
+    
+    # Show response
+    print(f"\n💬 Response: {result.output}")
+    
+    # Export to JSON
+    print("\n📄 JSON Export available via: report.to_json()")
+
+
+if __name__ == "__main__":
+    main()

examples/python/profiling/optimization_example.py

@@ -0,0 +1,223 @@
+"""
+Performance Optimization Example for PraisonAI
+
+This example demonstrates how to use the Tier 0/1/2 optimization features
+programmatically.
+
+Run with:
+    python optimization_example.py
+"""
+
+import os
+
+# Ensure API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    print("Please set OPENAI_API_KEY environment variable")
+    exit(1)
+
+
+def demo_provider_cache():
+    """Demonstrate provider caching (Tier 0)."""
+    print("\n" + "=" * 60)
+    print("Tier 0: Provider Cache Demo")
+    print("=" * 60)
+    
+    from praisonai.cli.features.profiler import (
+        get_provider_cache,
+        resolve_model_provider,
+    )
+    
+    cache = get_provider_cache()
+    cache.invalidate()  # Start fresh
+    
+    # Resolve some models
+    models = ['gpt-4o', 'claude-3-opus', 'gemini-pro', 'gpt-4o-mini']
+    
+    print("\nResolving models:")
+    for model in models:
+        provider, name = resolve_model_provider(model)
+        print(f"  {model} -> {provider}/{name}")
+    
+    # Check cache stats
+    stats = cache.get_stats()
+    print("\nCache Stats:")
+    print(f"  Hits: {stats['hits']}")
+    print(f"  Misses: {stats['misses']}")
+    print(f"  Size: {stats['size']}")
+
+
+def demo_lazy_importer():
+    """Demonstrate lazy imports (Tier 0)."""
+    print("\n" + "=" * 60)
+    print("Tier 0: Lazy Importer Demo")
+    print("=" * 60)
+    
+    from praisonai.cli.features.profiler import LazyImporter
+    import time
+    
+    LazyImporter.clear()
+    
+    # Time lazy import
+    start = time.perf_counter()
+    json_module = LazyImporter.get('json')
+    first_import = (time.perf_counter() - start) * 1000
+    
+    # Second import (cached)
+    start = time.perf_counter()
+    json_module2 = LazyImporter.get('json')
+    cached_import = (time.perf_counter() - start) * 1000
+    
+    print(f"\nFirst import: {first_import:.3f}ms")
+    print(f"Cached import: {cached_import:.3f}ms")
+    print(f"Same object: {json_module is json_module2}")
+
+
+def demo_client_pool():
+    """Demonstrate client pooling (Tier 1)."""
+    print("\n" + "=" * 60)
+    print("Tier 1: Client Pool Demo")
+    print("=" * 60)
+    
+    from praisonai.cli.features.profiler import ClientPool
+    
+    pool = ClientPool(max_size=3)
+    
+    # Create some mock clients
+    def create_client(name):
+        return {'name': name, 'created': True}
+    
+    # Get or create clients
+    client1 = pool.get_or_create('openai', lambda: create_client('openai'))
+    _ = pool.get_or_create('anthropic', lambda: create_client('anthropic'))
+    
+    # Reuse existing client
+    client1_again = pool.get_or_create('openai', lambda: create_client('openai_new'))
+    
+    print(f"\nPool size: {pool.size()}")
+    print(f"Client reused: {client1 is client1_again}")
+    print(f"Client1 name: {client1['name']}")
+
+
+def demo_prewarm():
+    """Demonstrate pre-warming (Tier 1)."""
+    print("\n" + "=" * 60)
+    print("Tier 1: Pre-warming Demo")
+    print("=" * 60)
+    
+    from praisonai.cli.features.profiler import PrewarmManager
+    import time
+    
+    PrewarmManager.reset()
+    
+    print(f"\nPre-warming enabled: {PrewarmManager.is_enabled()}")
+    
+    # Enable and prewarm
+    PrewarmManager.enable()
+    print(f"After enable: {PrewarmManager.is_enabled()}")
+    
+    # Prewarm OpenAI (runs in background)
+    PrewarmManager.prewarm_provider('openai')
+    print("Pre-warming OpenAI in background...")
+    
+    # Wait a bit for background thread
+    time.sleep(0.5)
+    
+    print(f"OpenAI prewarmed: {PrewarmManager.is_prewarmed('openai')}")
+    
+    PrewarmManager.reset()
+
+
+def demo_lite_mode():
+    """Demonstrate lite mode (Tier 2)."""
+    print("\n" + "=" * 60)
+    print("Tier 2: Lite Mode Demo")
+    print("=" * 60)
+    
+    from praisonai.cli.features.profiler import (
+        LiteModeConfig,
+        is_lite_mode,
+    )
+    
+    # Default config
+    config = LiteModeConfig()
+    print(f"\nDefault lite mode: {config.enabled}")
+    
+    # Config from env
+    print("\nTo enable lite mode, set environment variables:")
+    print("  export PRAISONAI_LITE_MODE=1")
+    print("  export PRAISONAI_SKIP_TYPE_VALIDATION=1")
+    
+    print(f"\nCurrent is_lite_mode(): {is_lite_mode()}")
+
+
+def demo_perf_snapshot():
+    """Demonstrate performance snapshots (Tier 2)."""
+    print("\n" + "=" * 60)
+    print("Tier 2: Performance Snapshot Demo")
+    print("=" * 60)
+    
+    from praisonai.cli.features.profiler import (
+        PerfSnapshot,
+        PerfComparison,
+        PerfSnapshotManager,
+    )
+    import tempfile
+    from pathlib import Path
+    
+    # Create manager with temp directory
+    with tempfile.TemporaryDirectory() as tmpdir:
+        manager = PerfSnapshotManager(snapshot_dir=Path(tmpdir))
+        
+        # Create baseline
+        baseline = PerfSnapshot(
+            timestamp='2025-01-01T00:00:00Z',
+            name='baseline',
+            startup_cold_ms=100.0,
+            startup_warm_ms=80.0,
+            import_time_ms=500.0,
+            query_time_ms=2000.0,
+            first_token_ms=300.0,
+        )
+        manager.save_baseline(baseline)
+        print("\nBaseline saved")
+        
+        # Create current snapshot (simulated improvement)
+        current = PerfSnapshot(
+            timestamp='2025-01-02T00:00:00Z',
+            name='current',
+            startup_cold_ms=95.0,  # 5% faster
+            startup_warm_ms=78.0,
+            import_time_ms=480.0,  # 4% faster
+            query_time_ms=1900.0,  # 5% faster
+            first_token_ms=280.0,
+        )
+        
+        # Compare
+        comparison = PerfComparison(baseline=baseline, current=current)
+        
+        print(f"\nStartup improvement: {-comparison.startup_cold_diff_pct:.1f}%")
+        print(f"Import improvement: {-comparison.import_time_diff_pct:.1f}%")
+        print(f"Query improvement: {-comparison.query_time_diff_pct:.1f}%")
+        print(f"Is regression: {comparison.is_regression()}")
+
+
+def main():
+    """Run all demos."""
+    print("=" * 60)
+    print("PraisonAI Performance Optimization Demo")
+    print("=" * 60)
+    
+    demo_provider_cache()
+    demo_lazy_importer()
+    demo_client_pool()
+    demo_prewarm()
+    demo_lite_mode()
+    demo_perf_snapshot()
+    
+    print("\n" + "=" * 60)
+    print("All demos complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/python/profiling/profile_results/suite_results.json

@@ -0,0 +1,227 @@
+{
+  "timestamp": "2025-12-31T18:45:38.187539Z",
+  "metadata": {
+    "python_version": "3.12.11",
+    "platform": "macOS-15.7.4-arm64-arm-64bit",
+    "praisonai_version": "2.9.2",
+    "timestamp": "2025-12-31T18:45:38.226558Z"
+  },
+  "startup": {
+    "cold_ms": 80.9305000002496,
+    "warm_ms": 55.266541996388696
+  },
+  "import_analysis": [
+    {
+      "module": "praisonaiagents",
+      "self_us": 2419,
+      "cumulative_us": 1645390,
+      "self_ms": 2.419,
+      "cumulative_ms": 1645.39
+    },
+    {
+      "module": "praisonaiagents.workflows",
+      "self_us": 670,
+      "cumulative_us": 1375919,
+      "self_ms": 0.67,
+      "cumulative_ms": 1375.919
+    },
+    {
+      "module": "praisonaiagents.workflows.yaml_parser",
+      "self_us": 775,
+      "cumulative_us": 1369435,
+      "self_ms": 0.775,
+      "cumulative_ms": 1369.435
+    },
+    {
+      "module": "praisonaiagents.agent.agent",
+      "self_us": 19,
+      "cumulative_us": 1349174,
+      "self_ms": 0.019,
+      "cumulative_ms": 1349.174
+    },
+    {
+      "module": "praisonaiagents.agent",
+      "self_us": 537,
+      "cumulative_us": 1349155,
+      "self_ms": 0.537,
+      "cumulative_ms": 1349.155
+    },
+    {
+      "module": "praisonaiagents.agent.agent",
+      "self_us": 1453,
+      "cumulative_us": 1285299,
+      "self_ms": 1.453,
+      "cumulative_ms": 1285.299
+    },
+    {
+      "module": "praisonaiagents.llm.openai_client",
+      "self_us": 7887,
+      "cumulative_us": 1123578,
+      "self_ms": 7.887,
+      "cumulative_ms": 1123.578
+    },
+    {
+      "module": "openai",
+      "self_us": 1083,
+      "cumulative_us": 1115692,
+      "self_ms": 1.083,
+      "cumulative_ms": 1115.692
+    },
+    {
+      "module": "openai.types",
+      "self_us": 2606,
+      "cumulative_us": 957990,
+      "self_ms": 2.606,
+      "cumulative_ms": 957.99
+    },
+    {
+      "module": "openai.types.batch",
+      "self_us": 1787,
+      "cumulative_us": 485667,
+      "self_ms": 1.787,
+      "cumulative_ms": 485.667
+    },
+    {
+      "module": "openai._models",
+      "self_us": 13423,
+      "cumulative_us": 450798,
+      "self_ms": 13.423,
+      "cumulative_ms": 450.798
+    },
+    {
+      "module": "openai.types.eval_create_params",
+      "self_us": 644,
+      "cumulative_us": 393275,
+      "self_ms": 0.644,
+      "cumulative_ms": 393.275
+    },
+    {
+      "module": "openai.types.graders.grader_inputs_param",
+      "self_us": 17,
+      "cumulative_us": 359515,
+      "self_ms": 0.017,
+      "cumulative_ms": 359.515
+    },
+    {
+      "module": "openai.types.graders",
+      "self_us": 392,
+      "cumulative_us": 359498,
+      "self_ms": 0.392,
+      "cumulative_ms": 359.498
+    },
+    {
+      "module": "openai.types.graders.multi_grader",
+      "self_us": 3847,
+      "cumulative_us": 351251,
+      "self_ms": 3.847,
+      "cumulative_ms": 351.251
+    },
+    {
+      "module": "openai.types.graders.label_model_grader",
+      "self_us": 5450,
+      "cumulative_us": 337291,
+      "self_ms": 5.45,
+      "cumulative_ms": 337.291
+    },
+    {
+      "module": "openai.types.graders.grader_inputs",
+      "self_us": 545,
+      "cumulative_us": 331841,
+      "self_ms": 0.545,
+      "cumulative_ms": 331.841
+    },
+    {
+      "module": "openai.types.responses.response_input_text",
+      "self_us": 21,
+      "cumulative_us": 331297,
+      "self_ms": 0.021,
+      "cumulative_ms": 331.297
+    },
+    {
+      "module": "openai.types.responses",
+      "self_us": 1301,
+      "cumulative_us": 331277,
+      "self_ms": 1.301,
+      "cumulative_ms": 331.277
+    },
+    {
+      "module": "rich.logging",
+      "self_us": 1066,
+      "cumulative_us": 238256,
+      "self_ms": 1.066,
+      "cumulative_ms": 238.256
+    }
+  ],
+  "scenarios": [
+    {
+      "name": "simple_query",
+      "config": {
+        "prompt": "What is 2+2?",
+        "model": "default",
+        "stream": false,
+        "iterations": 2
+      },
+      "import_time_stats": {
+        "mean": 0.008833496394800022,
+        "min": 0.005916997906751931,
+        "max": 0.011749994882848114,
+        "stdev": 0.0041245517164382375,
+        "median": 0.008833496394800022
+      },
+      "total_time_stats": {
+        "mean": 1727.4090415012324,
+        "min": 1367.2264170018025,
+        "max": 2087.5916660006624,
+        "stdev": 509.3751524982296,
+        "median": 1727.4090415012324
+      },
+      "first_token_stats": null,
+      "raw_import_times_ms": [
+        0.011749994882848114,
+        0.005916997906751931
+      ],
+      "raw_total_times_ms": [
+        1367.2264170018025,
+        2087.5916660006624
+      ]
+    },
+    {
+      "name": "simple_stream",
+      "config": {
+        "prompt": "What is 2+2?",
+        "model": "default",
+        "stream": true,
+        "iterations": 2
+      },
+      "import_time_stats": {
+        "mean": 0.008792001608526334,
+        "min": 0.006375004886649549,
+        "max": 0.01120899833040312,
+        "stdev": 0.0034181495442894613,
+        "median": 0.008792001608526334
+      },
+      "total_time_stats": {
+        "mean": 2321.0982290001994,
+        "min": 1697.1089580038097,
+        "max": 2945.087499996589,
+        "stdev": 882.4540898183949,
+        "median": 2321.0982290001994
+      },
+      "first_token_stats": {
+        "mean": 2312.8511880022415,
+        "min": 1681.2440420035273,
+        "max": 2944.458334000956,
+        "stdev": 893.2273919631452,
+        "median": 2312.8511880022415
+      },
+      "raw_import_times_ms": [
+        0.01120899833040312,
+        0.006375004886649549
+      ],
+      "raw_total_times_ms": [
+        2945.087499996589,
+        1697.1089580038097
+      ]
+    }
+  ]
+}

examples/python/profiling/suite_profiling.py

@@ -0,0 +1,80 @@
+"""
+Profile Suite Example for PraisonAI
+
+This example demonstrates how to run a comprehensive profiling suite
+programmatically with multiple scenarios.
+
+Run with:
+    python suite_profiling.py
+"""
+
+import os
+
+# Ensure API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    print("Please set OPENAI_API_KEY environment variable")
+    exit(1)
+
+from praisonai.cli.features.profiler import (
+    ScenarioConfig,
+    run_profile_suite,
+)
+
+
+def main():
+    """Run a profiling suite with custom scenarios."""
+    
+    # Define custom scenarios
+    scenarios = [
+        ScenarioConfig(
+            name="simple_query",
+            prompt="What is 2+2?",
+            stream=False,
+            iterations=2,
+            warmup=1,
+        ),
+        ScenarioConfig(
+            name="simple_stream",
+            prompt="What is 2+2?",
+            stream=True,
+            iterations=2,
+            warmup=1,
+        ),
+    ]
+    
+    # Run suite with custom output directory
+    print("🔬 Running Profile Suite...")
+    result = run_profile_suite(
+        output_dir="./profile_results",
+        scenarios=scenarios,
+        verbose=True,
+    )
+    
+    # Print summary
+    print("\n" + "=" * 60)
+    print("Profile Suite Results")
+    print("=" * 60)
+    
+    print("\n📊 Startup Times:")
+    print(f"  Cold Start: {result.startup_cold_ms:.2f}ms")
+    print(f"  Warm Start: {result.startup_warm_ms:.2f}ms")
+    
+    if result.import_analysis:
+        print("\n📦 Top Import:")
+        print(f"  {result.import_analysis[0]['module']}: {result.import_analysis[0]['cumulative_ms']:.2f}ms")
+    
+    print("\n🎯 Scenario Results:")
+    for scenario in result.scenarios:
+        stats = scenario.get_stats(scenario.total_times)
+        print(f"  {scenario.name}:")
+        print(f"    Mean: {stats['mean']:.2f}ms")
+        print(f"    Min:  {stats['min']:.2f}ms")
+        print(f"    Max:  {stats['max']:.2f}ms")
+    
+    print("\n✅ Results saved to: ./profile_results/")
+    print("   - suite_results.json (machine-readable)")
+    print("   - suite_report.txt (human-readable)")
+
+
+if __name__ == "__main__":
+    main()

examples/python/rag/agent_vs_auto_rag.py

@@ -0,0 +1,109 @@
+"""
+Agent vs AutoRagAgent Comparison
+
+Demonstrates the difference between using Agent directly vs AutoRagAgent wrapper.
+
+Requirements:
+    pip install praisonaiagents
+    
+Environment:
+    OPENAI_API_KEY - Required for LLM calls
+"""
+
+import os
+from praisonaiagents import Agent, AutoRagAgent
+
+# Ensure API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    print("Please set OPENAI_API_KEY environment variable")
+    exit(1)
+
+# Create a sample document
+sample_doc = "/tmp/comparison_doc.txt"
+with open(sample_doc, "w") as f:
+    f.write("""
+Product Manual: Smart Thermostat
+
+Installation:
+1. Turn off power at the circuit breaker
+2. Remove old thermostat
+3. Connect wires to matching terminals
+4. Mount the base plate
+5. Attach the display unit
+6. Restore power
+
+Features:
+- Wi-Fi connectivity
+- Voice control support
+- Energy usage reports
+- Geofencing auto-away
+- Learning schedule
+
+Troubleshooting:
+- If display is blank, check power connection
+- If Wi-Fi won't connect, restart router
+- If temperature is inaccurate, recalibrate sensor
+""")
+
+# Create base agent with knowledge
+agent = Agent(
+    name="TechSupport",
+    instructions="You are a technical support assistant.",
+    knowledge=[sample_doc],
+    user_id="comparison_user",
+    llm="openai/gpt-4o-mini",
+    verbose=False,
+)
+
+print("=" * 60)
+print("Agent vs AutoRagAgent Comparison")
+print("=" * 60)
+
+# Using Agent directly - always uses RAG when knowledge is configured
+print("\n[Using Agent Directly]")
+print("Agent with knowledge always retrieves context for every query.")
+print("\nQuery: 'Hello!'")
+response = agent.chat("Hello!")
+print(f"Response: {response}")
+
+print("\nQuery: 'How do I install the thermostat?'")
+response = agent.chat("How do I install the thermostat?")
+print(f"Response: {response[:300]}...")
+
+# Using AutoRagAgent - decides when to retrieve
+print("\n" + "=" * 60)
+print("[Using AutoRagAgent]")
+print("AutoRagAgent decides when to retrieve based on query heuristics.")
+
+auto_rag = AutoRagAgent(agent=agent, retrieval_policy="auto")
+
+print("\nQuery: 'Hello!' (short greeting - skips retrieval)")
+response = auto_rag.chat("Hello!")
+print(f"Response: {response}")
+
+print("\nQuery: 'How do I install the thermostat?' (question - retrieves)")
+response = auto_rag.chat("How do I install the thermostat?")
+print(f"Response: {response[:300]}...")
+
+# Summary
+print("\n" + "=" * 60)
+print("Summary:")
+print("-" * 60)
+print("""
+| Approach      | Retrieval Behavior              | Best For                    |
+|---------------|--------------------------------|------------------------------|
+| Agent         | Always retrieves (if knowledge)| Consistent RAG responses     |
+| AutoRagAgent  | Decides per query              | Mixed chat + RAG workloads   |
+
+AutoRagAgent is ideal when:
+- You have a chatbot that sometimes needs knowledge, sometimes doesn't
+- You want to reduce unnecessary retrieval overhead
+- You need fine-grained control over retrieval behavior
+""")
+
+# Cleanup
+os.remove(sample_doc)
+
+print("=" * 60)
+print("Example completed successfully!")
+print("=" * 60)

examples/python/rag/auto_rag_agent_basic.py

@@ -0,0 +1,79 @@
+"""
+AutoRagAgent Basic Example
+
+Demonstrates basic usage of AutoRagAgent for automatic RAG retrieval decision.
+AutoRagAgent wraps an Agent and decides when to retrieve context based on query heuristics.
+
+Requirements:
+    pip install praisonaiagents
+    
+Environment:
+    OPENAI_API_KEY - Required for LLM calls
+"""
+
+import os
+from praisonaiagents import Agent, AutoRagAgent
+
+# Ensure API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    print("Please set OPENAI_API_KEY environment variable")
+    exit(1)
+
+# Create a sample document for testing
+sample_doc = "/tmp/sample_doc.txt"
+with open(sample_doc, "w") as f:
+    f.write("""
+PraisonAI Framework Documentation
+
+PraisonAI is an AI agents framework that supports:
+- Single agent workflows
+- Multi-agent collaboration
+- RAG (Retrieval Augmented Generation)
+- Tool integration
+- Memory and context management
+
+Key Features:
+1. AutoRagAgent - Automatic retrieval decision
+2. Knowledge bases - Document indexing and search
+3. Hybrid retrieval - Dense + BM25 search
+4. Reranking - Improve result relevance
+""")
+
+# Create agent with knowledge
+agent = Agent(
+    name="DocBot",
+    instructions="You are a helpful documentation assistant. Answer questions based on the provided context.",
+    knowledge=[sample_doc],
+    user_id="example_user",  # Required for RAG retrieval
+    llm="openai/gpt-4o-mini",
+    verbose=False,
+)
+
+# Wrap with AutoRagAgent (default: auto policy)
+auto_rag = AutoRagAgent(agent=agent)
+
+print("=" * 60)
+print("AutoRagAgent Basic Example")
+print("=" * 60)
+
+# Test 1: Question that should trigger retrieval
+print("\n[Test 1] Question about documentation (should retrieve):")
+response = auto_rag.chat("What features does PraisonAI support?")
+print(f"Response: {response}")
+
+# Test 2: Short greeting that should skip retrieval
+print("\n[Test 2] Short greeting (should skip retrieval):")
+response = auto_rag.chat("Hi!")
+print(f"Response: {response}")
+
+# Test 3: Another question
+print("\n[Test 3] Another question (should retrieve):")
+response = auto_rag.chat("What is AutoRagAgent?")
+print(f"Response: {response}")
+
+# Cleanup
+os.remove(sample_doc)
+
+print("\n" + "=" * 60)
+print("Example completed successfully!")
+print("=" * 60)

examples/python/rag/auto_rag_agent_policy.py

@@ -0,0 +1,97 @@
+"""
+AutoRagAgent Policy Example
+
+Demonstrates different retrieval policies:
+- auto: Decides based on query heuristics (default)
+- always: Always retrieves context
+- never: Never retrieves, direct chat only
+
+Requirements:
+    pip install praisonaiagents
+    
+Environment:
+    OPENAI_API_KEY - Required for LLM calls
+"""
+
+import os
+from praisonaiagents import Agent, AutoRagAgent
+
+# Ensure API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    print("Please set OPENAI_API_KEY environment variable")
+    exit(1)
+
+# Create a sample document
+sample_doc = "/tmp/policy_doc.txt"
+with open(sample_doc, "w") as f:
+    f.write("""
+Company Return Policy
+
+Our return policy allows returns within 30 days of purchase.
+Items must be in original condition with tags attached.
+Refunds are processed within 5-7 business days.
+Exchanges are available for different sizes or colors.
+""")
+
+# Create base agent with knowledge
+agent = Agent(
+    name="PolicyBot",
+    instructions="You are a customer service assistant.",
+    knowledge=[sample_doc],
+    user_id="policy_user",
+    llm="openai/gpt-4o-mini",
+    verbose=False,
+)
+
+print("=" * 60)
+print("AutoRagAgent Policy Example")
+print("=" * 60)
+
+# Policy 1: AUTO (default)
+print("\n[Policy: AUTO] - Decides based on query heuristics")
+auto_rag = AutoRagAgent(agent=agent, retrieval_policy="auto")
+
+print("  Query: 'What is the return policy?' (should retrieve)")
+response = auto_rag.chat("What is the return policy?")
+print(f"  Response: {response[:200]}...")
+
+print("\n  Query: 'Hello' (should skip)")
+response = auto_rag.chat("Hello")
+print(f"  Response: {response}")
+
+# Policy 2: ALWAYS
+print("\n" + "-" * 60)
+print("[Policy: ALWAYS] - Always retrieves context")
+always_rag = AutoRagAgent(agent=agent, retrieval_policy="always")
+
+print("  Query: 'Hello' (will retrieve even for greeting)")
+response = always_rag.chat("Hello")
+print(f"  Response: {response[:200]}...")
+
+# Policy 3: NEVER
+print("\n" + "-" * 60)
+print("[Policy: NEVER] - Never retrieves, direct chat only")
+never_rag = AutoRagAgent(agent=agent, retrieval_policy="never")
+
+print("  Query: 'What is the return policy?' (will NOT retrieve)")
+response = never_rag.chat("What is the return policy?")
+print(f"  Response: {response[:200]}...")
+
+# Per-call overrides
+print("\n" + "-" * 60)
+print("[Per-call Overrides]")
+
+print("  Force retrieval on 'auto' policy:")
+response = auto_rag.chat("Hi there", force_retrieval=True)
+print(f"  Response: {response[:200]}...")
+
+print("\n  Skip retrieval on 'auto' policy:")
+response = auto_rag.chat("What is the return window?", skip_retrieval=True)
+print(f"  Response: {response[:200]}...")
+
+# Cleanup
+os.remove(sample_doc)
+
+print("\n" + "=" * 60)
+print("Example completed successfully!")
+print("=" * 60)

examples/python/rag/rag_basic.py

@@ -0,0 +1,70 @@
+"""
+RAG Basic Example
+
+Demonstrates basic RAG (Retrieval Augmented Generation) with an Agent.
+
+Requirements:
+    pip install praisonaiagents
+    
+Environment:
+    OPENAI_API_KEY - Required for LLM calls
+"""
+
+import os
+from praisonaiagents import Agent
+
+# Ensure API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    print("Please set OPENAI_API_KEY environment variable")
+    exit(1)
+
+# Create a sample document
+sample_doc = "/tmp/rag_basic_doc.txt"
+with open(sample_doc, "w") as f:
+    f.write("""
+Machine Learning Fundamentals
+
+Machine learning is a subset of artificial intelligence that enables systems
+to learn and improve from experience without being explicitly programmed.
+
+Types of Machine Learning:
+1. Supervised Learning - Learning from labeled data
+2. Unsupervised Learning - Finding patterns in unlabeled data
+3. Reinforcement Learning - Learning through trial and error
+
+Common Algorithms:
+- Linear Regression
+- Decision Trees
+- Neural Networks
+- Support Vector Machines
+""")
+
+# Create agent with RAG
+agent = Agent(
+    name="MLExpert",
+    instructions="You are a machine learning expert. Answer questions based on the provided context.",
+    knowledge=[sample_doc],
+    user_id="rag_user",
+    llm="openai/gpt-4o-mini",
+    verbose=False,
+)
+
+print("=" * 60)
+print("RAG Basic Example")
+print("=" * 60)
+
+# Query with RAG
+print("\nQuery: What are the types of machine learning?")
+response = agent.chat("What are the types of machine learning?")
+print(f"Response: {response}")
+
+print("\nQuery: What algorithms are commonly used?")
+response = agent.chat("What algorithms are commonly used?")
+print(f"Response: {response}")
+
+# Cleanup
+os.remove(sample_doc)
+
+print("\n" + "=" * 60)
+print("Example completed successfully!")
+print("=" * 60)

examples/python/rag/rag_hybrid.py

@@ -0,0 +1,94 @@
+"""
+RAG Hybrid Retrieval Example
+
+Demonstrates hybrid retrieval (dense + BM25 keyword search) for better results.
+
+Requirements:
+    pip install praisonaiagents
+    
+Environment:
+    OPENAI_API_KEY - Required for LLM calls
+"""
+
+import os
+from praisonaiagents import Agent, AutoRagAgent
+
+# Ensure API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    print("Please set OPENAI_API_KEY environment variable")
+    exit(1)
+
+# Create a sample document with technical content
+sample_doc = "/tmp/rag_hybrid_doc.txt"
+with open(sample_doc, "w") as f:
+    f.write("""
+API Documentation
+
+Authentication:
+All API requests require an API key in the X-API-Key header.
+Rate limits: 100 requests per minute for free tier, 1000 for pro tier.
+
+Endpoints:
+
+POST /api/v1/users
+Creates a new user account.
+Required fields: email, password, name
+Returns: user_id, created_at
+
+GET /api/v1/users/{id}
+Retrieves user information by ID.
+Returns: email, name, created_at, last_login
+
+PUT /api/v1/users/{id}
+Updates user information.
+Optional fields: name, email, password
+
+DELETE /api/v1/users/{id}
+Deletes a user account. Requires admin privileges.
+
+Error Codes:
+- 400: Bad Request - Invalid input
+- 401: Unauthorized - Missing or invalid API key
+- 403: Forbidden - Insufficient permissions
+- 404: Not Found - Resource doesn't exist
+- 429: Too Many Requests - Rate limit exceeded
+""")
+
+# Create agent with knowledge
+agent = Agent(
+    name="APIDocBot",
+    instructions="You are an API documentation assistant.",
+    knowledge=[sample_doc],
+    user_id="hybrid_user",
+    llm="openai/gpt-4o-mini",
+    verbose=False,
+)
+
+print("=" * 60)
+print("RAG Hybrid Retrieval Example")
+print("=" * 60)
+
+# Standard retrieval
+print("\n[Standard Retrieval]")
+auto_rag_standard = AutoRagAgent(agent=agent, hybrid=False)
+response = auto_rag_standard.chat("What error code means rate limit exceeded?")
+print(f"Response: {response}")
+
+# Hybrid retrieval (dense + BM25)
+print("\n[Hybrid Retrieval - Dense + BM25]")
+auto_rag_hybrid = AutoRagAgent(agent=agent, hybrid=True)
+response = auto_rag_hybrid.chat("What error code means rate limit exceeded?")
+print(f"Response: {response}")
+
+# Hybrid with reranking for best quality
+print("\n[Hybrid + Reranking]")
+auto_rag_best = AutoRagAgent(agent=agent, hybrid=True, rerank=True, top_k=10)
+response = auto_rag_best.chat("How do I create a new user via the API?")
+print(f"Response: {response}")
+
+# Cleanup
+os.remove(sample_doc)
+
+print("\n" + "=" * 60)
+print("Example completed successfully!")
+print("=" * 60)

examples/python/rag/rag_with_citations.py

@@ -0,0 +1,84 @@
+"""
+RAG with Citations Example
+
+Demonstrates RAG with citation tracking for source attribution.
+
+Requirements:
+    pip install praisonaiagents
+    
+Environment:
+    OPENAI_API_KEY - Required for LLM calls
+"""
+
+import os
+from praisonaiagents import Agent, AutoRagAgent
+
+# Ensure API key is set
+if not os.environ.get("OPENAI_API_KEY"):
+    print("Please set OPENAI_API_KEY environment variable")
+    exit(1)
+
+# Create multiple sample documents
+doc1 = "/tmp/citation_doc1.txt"
+with open(doc1, "w") as f:
+    f.write("""
+Research Paper: Climate Change Effects
+
+Global temperatures have risen by 1.1°C since pre-industrial times.
+Sea levels are rising at 3.3mm per year.
+Arctic ice is declining at 13% per decade.
+""")
+
+doc2 = "/tmp/citation_doc2.txt"
+with open(doc2, "w") as f:
+    f.write("""
+Research Paper: Renewable Energy
+
+Solar energy capacity has grown 20x in the last decade.
+Wind power now provides 7% of global electricity.
+Battery storage costs have dropped 89% since 2010.
+""")
+
+# Create agent with multiple knowledge sources
+agent = Agent(
+    name="ResearchBot",
+    instructions="You are a research assistant. Always cite your sources.",
+    knowledge=[doc1, doc2],
+    user_id="citation_user",
+    llm="openai/gpt-4o-mini",
+    verbose=False,
+)
+
+print("=" * 60)
+print("RAG with Citations Example")
+print("=" * 60)
+
+# AutoRagAgent with citations enabled (default)
+auto_rag = AutoRagAgent(
+    agent=agent,
+    citations=True,  # Include citations in response
+)
+
+print("\nQuery: What are the key climate change statistics?")
+response = auto_rag.chat("What are the key climate change statistics?")
+print(f"Response:\n{response}")
+
+print("\n" + "-" * 60)
+print("\nQuery: How has renewable energy grown?")
+response = auto_rag.chat("How has renewable energy grown?")
+print(f"Response:\n{response}")
+
+# Without citations
+print("\n" + "-" * 60)
+print("\n[Without Citations]")
+auto_rag_no_cite = AutoRagAgent(agent=agent, citations=False)
+response = auto_rag_no_cite.chat("What is the rate of sea level rise?")
+print(f"Response: {response}")
+
+# Cleanup
+os.remove(doc1)
+os.remove(doc2)
+
+print("\n" + "=" * 60)
+print("Example completed successfully!")
+print("=" * 60)

examples/python/session/session_persistence_example.py

@@ -0,0 +1,165 @@
+"""
+Session Persistence Example
+
+Demonstrates automatic session persistence with zero configuration.
+When you provide a session_id to an Agent, conversation history is
+automatically saved to disk and restored on subsequent runs.
+
+Usage:
+    python session_persistence_example.py
+
+Requirements:
+    - praisonaiagents package installed
+    - OPENAI_API_KEY environment variable set
+"""
+
+from praisonaiagents import Agent
+
+
+def example_basic_persistence():
+    """Basic session persistence example."""
+    print("=" * 60)
+    print("EXAMPLE 1: Basic Session Persistence")
+    print("=" * 60)
+    
+    session_id = "demo-session-basic"
+    
+    # Create agent with session_id
+    agent = Agent(
+        name="Assistant",
+        instructions="You are a helpful assistant. Be very brief.",
+        session_id=session_id,
+        verbose=False,
+    )
+    
+    # First message
+    print("\n[User]: My favorite color is blue and my lucky number is 7.")
+    response1 = agent.chat("My favorite color is blue and my lucky number is 7.")
+    print(f"[Assistant]: {response1}")
+    
+    # Second message - agent remembers
+    print("\n[User]: What is my favorite color?")
+    response2 = agent.chat("What is my favorite color?")
+    print(f"[Assistant]: {response2}")
+    
+    print(f"\n✅ Session saved to: ~/.praison/sessions/{session_id}.json")
+    print(f"   Chat history length: {len(agent.chat_history)} messages")
+
+
+def example_session_restoration():
+    """Demonstrates session restoration across Agent instances."""
+    print("\n" + "=" * 60)
+    print("EXAMPLE 2: Session Restoration")
+    print("=" * 60)
+    
+    session_id = "demo-session-restore"
+    
+    # First agent instance
+    print("\n--- First Agent Instance ---")
+    agent1 = Agent(
+        name="MemoryBot",
+        instructions="You are a helpful assistant. Be very brief.",
+        session_id=session_id,
+        verbose=False,
+    )
+    
+    print("[User]: Remember that my pet's name is Max.")
+    response1 = agent1.chat("Remember that my pet's name is Max.")
+    print(f"[Assistant]: {response1}")
+    
+    # Simulate new process by creating new agent instance
+    print("\n--- New Agent Instance (simulating restart) ---")
+    agent2 = Agent(
+        name="MemoryBot",
+        instructions="You are a helpful assistant. Be very brief.",
+        session_id=session_id,
+        verbose=False,
+    )
+    
+    print("[User]: What is my pet's name?")
+    response2 = agent2.chat("What is my pet's name?")
+    print(f"[Assistant]: {response2}")
+    
+    if "max" in response2.lower():
+        print("\n✅ Session restored successfully! Agent remembered 'Max'.")
+    else:
+        print("\n⚠️ Agent may not have remembered correctly.")
+
+
+def example_in_memory_only():
+    """Demonstrates in-memory memory without persistence."""
+    print("\n" + "=" * 60)
+    print("EXAMPLE 3: In-Memory Only (No session_id)")
+    print("=" * 60)
+    
+    # Agent without session_id - in-memory only
+    agent = Agent(
+        name="TempBot",
+        instructions="You are a helpful assistant. Be very brief.",
+        verbose=False,
+    )
+    
+    print("\n[User]: My name is Alice.")
+    response1 = agent.chat("My name is Alice.")
+    print(f"[Assistant]: {response1}")
+    
+    print("\n[User]: What is my name?")
+    response2 = agent.chat("What is my name?")
+    print(f"[Assistant]: {response2}")
+    
+    print(f"\n✅ In-memory chat history: {len(agent.chat_history)} messages")
+    print("   (This will be lost when the Agent instance is garbage collected)")
+
+
+def example_direct_store_access():
+    """Demonstrates direct access to the session store."""
+    print("\n" + "=" * 60)
+    print("EXAMPLE 4: Direct Session Store Access")
+    print("=" * 60)
+    
+    from praisonaiagents.session import get_default_session_store
+    
+    store = get_default_session_store()
+    session_id = "demo-direct-access"
+    
+    # Add messages directly
+    store.add_user_message(session_id, "Hello from direct access!")
+    store.add_assistant_message(session_id, "Hi! I received your message.")
+    
+    # Get history
+    history = store.get_chat_history(session_id)
+    print(f"\n✅ Added {len(history)} messages directly to session store")
+    
+    for msg in history:
+        print(f"   [{msg['role']}]: {msg['content']}")
+    
+    # List all sessions
+    sessions = store.list_sessions()
+    print(f"\n📋 Total sessions in store: {len(sessions)}")
+    
+    # Clean up demo session
+    store.delete_session(session_id)
+    print(f"   Deleted demo session: {session_id}")
+
+
+def main():
+    """Run all examples."""
+    print("\n" + "=" * 60)
+    print("SESSION PERSISTENCE EXAMPLES")
+    print("=" * 60)
+    
+    # Run examples
+    example_basic_persistence()
+    example_session_restoration()
+    example_in_memory_only()
+    example_direct_store_access()
+    
+    print("\n" + "=" * 60)
+    print("ALL EXAMPLES COMPLETE")
+    print("=" * 60)
+    print("\nSession files are stored in: ~/.praison/sessions/")
+    print("You can inspect them with: cat ~/.praison/sessions/<session_id>.json")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/README.md

@@ -0,0 +1,121 @@
+# RAG Examples
+
+This directory contains comprehensive examples demonstrating RAG (Retrieval Augmented Generation) with PraisonAI Agents.
+
+## Core RAG Concepts
+
+| Example | Description | Key Concepts |
+|---------|-------------|--------------|
+| `basic_retrieval.py` | Fundamental retrieve-then-generate pattern | Knowledge base, context injection |
+| `auto_retrieval.py` | Intelligent retrieval decisions with AutoRagAgent | Conditional RAG, retrieval policies |
+| `chunking_strategies.py` | Document splitting approaches | Paragraph, section, fixed-size chunking |
+| `hybrid_search.py` | Dense + sparse retrieval combination | Semantic search, keyword matching |
+| `reranking.py` | Two-stage retrieval with reranking | Precision optimization, cross-encoders |
+
+## Advanced RAG Patterns
+
+| Example | Description | Key Concepts |
+|---------|-------------|--------------|
+| `multi_document.py` | Synthesizing across multiple sources | Cross-referencing, source diversity |
+| `knowledge_graph.py` | Relationship-aware retrieval | Entity connections, multi-hop reasoning |
+| `external_sources.py` | Web, API, and database augmentation | Real-time data, hybrid sources |
+| `structured_output.py` | Guided and constrained generation | JSON output, Pydantic schemas |
+| `retrieval_policies.py` | Agent-decided retrieval strategies | Adaptive top-k, priority filtering |
+| `citations.py` | Source attribution and verification | Inline citations, verification |
+
+## Application Examples
+
+### PDF Q&A (`rag_pdf_qa.py`)
+
+Basic RAG example showing how to:
+- Use Agent with knowledge parameter
+- Create explicit RAG pipeline with citations
+- Stream responses
+
+```bash
+python rag_pdf_qa.py
+```
+
+### Multi-Agent RAG (`rag_multi_agent.py`)
+
+Advanced example showing:
+- Shared knowledge base across multiple agents
+- Collaborative RAG workflow
+- Sequential task processing
+
+```bash
+python rag_multi_agent.py
+```
+
+### RAG Evaluation (`rag_evaluation.py`)
+
+Evaluation example showing:
+- Golden query testing
+- Retrieval accuracy metrics
+- Answer relevance checking
+
+```bash
+python rag_evaluation.py
+```
+
+## Quick Start
+
+Run any example:
+
+```bash
+# Basic RAG
+python basic_retrieval.py
+
+# Auto RAG with intelligent decisions
+python auto_retrieval.py
+
+# Hybrid search (dense + sparse)
+python hybrid_search.py
+
+# RAG with citations
+python citations.py
+```
+
+## Installation
+
+```bash
+pip install "praisonaiagents[knowledge]"
+```
+
+## CLI Usage
+
+```bash
+# Index documents
+praisonai rag index ./documents --collection my_docs
+
+# Query
+praisonai rag query "What is the main finding?" --collection my_docs
+
+# Interactive chat
+praisonai rag chat --collection my_docs
+
+# Evaluate
+praisonai rag eval golden_queries.json --collection my_docs
+```
+
+## Configuration
+
+Create `rag_config.yaml`:
+
+```yaml
+knowledge:
+  collection: my_docs
+  chunking:
+    strategy: recursive
+    chunk_size: 512
+
+rag:
+  top_k: 5
+  min_score: 0.3
+  include_citations: true
+```
+
+Use with:
+```bash
+praisonai rag query "Question" --config rag_config.yaml
+```

examples/rag/auto_retrieval.py

@@ -0,0 +1,207 @@
+"""
+Auto RAG: Intelligent Retrieval Decisions
+
+This example demonstrates AutoRagAgent - an agent that automatically decides
+when to retrieve context based on the query type.
+
+RAG Concept: Conditional retrieval - not every query needs RAG. Simple greetings
+or general knowledge questions can be answered directly, while domain-specific
+questions benefit from retrieval.
+"""
+
+from praisonaiagents import Agent, AutoRagAgent
+
+# Sample knowledge base: Software development best practices as text strings
+DEV_PRACTICES = [
+    """Code Review Guidelines:
+    - Review code within 24 hours of submission
+    - Focus on logic, security, and maintainability
+    - Limit reviews to 400 lines of code at a time
+    - Use constructive language and suggest alternatives
+    - Automate style checks with linters""",
+    
+    """Testing Strategy:
+    - Aim for 80% code coverage minimum
+    - Write unit tests before integration tests
+    - Use mocking for external dependencies
+    - Run tests in CI/CD pipeline before merge
+    - Include edge cases and error scenarios""",
+    
+    """Documentation Standards:
+    - Document public APIs with examples
+    - Keep README files up to date
+    - Use inline comments for complex logic only
+    - Maintain a changelog for version history
+    - Include setup instructions for new developers""",
+    
+    """Security Practices:
+    - Never commit secrets or API keys
+    - Use environment variables for configuration
+    - Validate all user inputs
+    - Keep dependencies updated
+    - Conduct regular security audits"""
+]
+
+
+def auto_rag_basic():
+    """Demonstrate AutoRagAgent with automatic retrieval decisions."""
+    
+    # Build context from knowledge
+    context = "\n\n".join(DEV_PRACTICES)
+    
+    # Create base agent with context in instructions
+    base_agent = Agent(
+        name="Dev Coach",
+        instructions=f"""You are a software development coach.
+        Answer questions about development best practices.
+        Be concise and practical in your responses.
+        
+        KNOWLEDGE BASE:
+        {context}""",
+        verbose=False
+    )
+    
+    # Wrap with AutoRagAgent for intelligent retrieval
+    auto_agent = AutoRagAgent(agent=base_agent)
+    
+    # Mix of queries - some need retrieval, some don't
+    queries = [
+        # Should trigger retrieval (domain-specific)
+        "What are the code review guidelines?",
+        "How much code coverage should we aim for?",
+        
+        # Should skip retrieval (general/greeting)
+        "Hello, how are you?",
+        "What is 2 + 2?",
+        
+        # Should trigger retrieval (domain-specific)
+        "What security practices should we follow?",
+    ]
+    
+    print("=" * 60)
+    print("AUTO RAG: Intelligent Retrieval Decisions")
+    print("=" * 60)
+    print("\nAutoRagAgent decides when to retrieve based on query type.\n")
+    
+    for query in queries:
+        print(f"📝 Query: {query}")
+        response = auto_agent.chat(query)
+        print(f"💡 Answer: {response[:200]}..." if len(str(response)) > 200 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def auto_rag_with_policies():
+    """Demonstrate different retrieval policies."""
+    from praisonaiagents import AutoRagConfig
+    from praisonaiagents.agents.auto_rag_agent import RetrievalPolicy
+    
+    # Build context
+    context = "\n\n".join(DEV_PRACTICES)
+    
+    # Create base agent
+    base_agent = Agent(
+        name="Policy Demo Agent",
+        instructions=f"""You answer questions about software development.
+        
+        KNOWLEDGE BASE:
+        {context}""",
+        verbose=False
+    )
+    
+    print("\n" + "=" * 60)
+    print("RETRIEVAL POLICIES COMPARISON")
+    print("=" * 60)
+    
+    query = "What are the documentation standards?"
+    
+    # Policy: AUTO (default) - agent decides
+    auto_config = AutoRagConfig(retrieval_policy=RetrievalPolicy.AUTO)
+    auto_agent = AutoRagAgent(agent=base_agent, config=auto_config)
+    
+    print(f"\n📝 Query: {query}")
+    print("\n🔄 Policy: AUTO (agent decides)")
+    response = auto_agent.chat(query)
+    print(f"   Answer: {response[:150]}...")
+    
+    # Policy: ALWAYS - always retrieve
+    always_config = AutoRagConfig(retrieval_policy=RetrievalPolicy.ALWAYS)
+    always_agent = AutoRagAgent(agent=base_agent, config=always_config)
+    
+    print("\n🔄 Policy: ALWAYS (force retrieval)")
+    response = always_agent.chat(query)
+    print(f"   Answer: {response[:150]}...")
+    
+    # Policy: NEVER - never retrieve (use parametric knowledge only)
+    never_config = AutoRagConfig(retrieval_policy=RetrievalPolicy.NEVER)
+    never_agent = AutoRagAgent(agent=base_agent, config=never_config)
+    
+    print("\n🔄 Policy: NEVER (no retrieval)")
+    response = never_agent.chat(query)
+    print(f"   Answer: {response[:150]}...")
+
+
+def auto_rag_config_options():
+    """Demonstrate AutoRagConfig options."""
+    from praisonaiagents import AutoRagConfig
+    
+    # Build context
+    context = "\n\n".join(DEV_PRACTICES)
+    
+    base_agent = Agent(
+        name="Config Demo",
+        instructions=f"""You are a helpful assistant with development knowledge.
+        
+        KNOWLEDGE BASE:
+        {context}""",
+        verbose=False
+    )
+    
+    print("\n" + "=" * 60)
+    print("AUTO RAG CONFIG OPTIONS")
+    print("=" * 60)
+    
+    # Config with citations enabled
+    config_with_citations = AutoRagConfig(
+        include_citations=True,
+        top_k=3,
+        max_context_tokens=2000
+    )
+    agent_with_citations = AutoRagAgent(agent=base_agent, config=config_with_citations)
+    
+    query = "What testing practices should we follow?"
+    print(f"\n📝 Query: {query}")
+    
+    print("\n🔄 With citations enabled (top_k=3):")
+    response = agent_with_citations.chat(query)
+    print(f"   {response[:200]}...")
+    
+    # Config without citations
+    config_no_citations = AutoRagConfig(
+        include_citations=False,
+        top_k=5
+    )
+    agent_no_citations = AutoRagAgent(agent=base_agent, config=config_no_citations)
+    
+    print("\n🔄 Without citations (top_k=5):")
+    response = agent_no_citations.chat(query)
+    print(f"   {response[:200]}...")
+
+
+def main():
+    """Run all auto RAG examples."""
+    print("\n🚀 PraisonAI Auto RAG Examples\n")
+    
+    # Example 1: Basic AutoRagAgent
+    auto_rag_basic()
+    
+    # Example 2: Different retrieval policies
+    auto_rag_with_policies()
+    
+    # Example 3: Config options
+    auto_rag_config_options()
+    
+    print("\n✅ Auto RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/basic_retrieval.py

@@ -0,0 +1,131 @@
+"""
+Basic RAG: Retrieve and Generate
+
+This example demonstrates the fundamental RAG pattern in PraisonAI:
+1. Provide context to an agent
+2. Agent uses context to answer questions accurately
+3. Generate grounded answers using the provided context
+
+RAG Concept: The core retrieve-then-generate pattern that grounds LLM responses
+in factual content, reducing hallucinations.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Technology company profiles as text strings
+COMPANY_PROFILES = [
+    """NovaTech Solutions specializes in cloud infrastructure and DevOps automation.
+    Founded in 2018, they serve over 500 enterprise clients globally.
+    Key products: CloudSync (infrastructure orchestration), DevPipeline (CI/CD platform).
+    Headquarters: Austin, Texas. Employees: 1,200.""",
+    
+    """GreenLeaf Analytics provides environmental monitoring and sustainability reporting.
+    Established in 2015, they work with governments and corporations on climate data.
+    Key products: EcoTrack (emissions monitoring), SustainReport (ESG reporting).
+    Headquarters: Seattle, Washington. Employees: 450.""",
+    
+    """QuantumBridge Financial offers algorithmic trading and risk management solutions.
+    Started in 2012, they manage over $50 billion in assets for institutional clients.
+    Key products: AlgoTrader Pro (trading platform), RiskShield (portfolio analytics).
+    Headquarters: New York City. Employees: 800.""",
+    
+    """MediCore Health develops AI-powered diagnostic tools for healthcare providers.
+    Founded in 2019, they partner with 200+ hospitals across North America.
+    Key products: DiagnosAI (medical imaging analysis), PatientFlow (care coordination).
+    Headquarters: Boston, Massachusetts. Employees: 350."""
+]
+
+
+def basic_rag_example():
+    """Demonstrate basic RAG with context injection."""
+    
+    # Build context from knowledge base
+    context = "\n\n".join(COMPANY_PROFILES)
+    
+    # Create an agent with instructions
+    agent = Agent(
+        name="Company Analyst",
+        instructions=f"""You are a business analyst who answers questions about companies.
+        Use only the following company information to answer questions accurately.
+        If the information isn't available, say so clearly.
+        
+        COMPANY DATABASE:
+        {context}""",
+        verbose=False
+    )
+    
+    # Test queries
+    queries = [
+        "What products does NovaTech Solutions offer?",
+        "Which company focuses on environmental monitoring?",
+        "How many employees does QuantumBridge Financial have?",
+        "What is MediCore Health's main focus area?"
+    ]
+    
+    print("=" * 60)
+    print("BASIC RAG EXAMPLE: Company Knowledge Base")
+    print("=" * 60)
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def rag_with_query_context():
+    """Demonstrate per-query context injection for RAG."""
+    
+    # Create a general agent
+    agent = Agent(
+        name="Research Assistant",
+        instructions="You answer questions based on the context provided in each query.",
+        verbose=False
+    )
+    
+    # Simulate retrieved context (in real RAG, this comes from vector search)
+    retrieved_context = """
+    The Pacific Ocean is the largest and deepest ocean on Earth.
+    It covers approximately 165.25 million square kilometers.
+    The Mariana Trench, located in the Pacific, is the deepest known point at 10,994 meters.
+    The Pacific Ocean was named by explorer Ferdinand Magellan in 1520.
+    """
+    
+    query = "What is the deepest point in the Pacific Ocean?"
+    
+    # Build prompt with context (RAG pattern)
+    prompt_with_context = f"""Use the following context to answer the question.
+
+Context:
+{retrieved_context}
+
+Question: {query}
+
+Answer based only on the context provided:"""
+    
+    print("\n" + "=" * 60)
+    print("RAG WITH PER-QUERY CONTEXT INJECTION")
+    print("=" * 60)
+    
+    print(f"\n📚 Context: {retrieved_context[:100]}...")
+    print(f"\n📝 Query: {query}")
+    
+    response = agent.chat(prompt_with_context)
+    print(f"\n💡 Answer: {response}")
+
+
+def main():
+    """Run all basic RAG examples."""
+    print("\n🚀 PraisonAI Basic RAG Examples\n")
+    
+    # Example 1: Basic RAG with knowledge base
+    basic_rag_example()
+    
+    # Example 2: Per-query context injection
+    rag_with_query_context()
+    
+    print("\n✅ Basic RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/chunking_strategies.py

@@ -0,0 +1,272 @@
+"""
+Chunking Strategies for RAG
+
+This example demonstrates different approaches to splitting documents into
+chunks for effective retrieval.
+
+RAG Concept: Chunking is critical for RAG quality. Too large chunks dilute
+relevance; too small chunks lose context. The right strategy depends on
+your content type and query patterns.
+"""
+
+from praisonaiagents import Agent
+
+# Sample long document: Product documentation
+PRODUCT_DOCUMENTATION = """
+# CloudManager Pro - Complete User Guide
+
+## Chapter 1: Getting Started
+
+CloudManager Pro is an enterprise cloud management platform that helps organizations
+monitor, optimize, and secure their multi-cloud infrastructure. This guide covers
+installation, configuration, and daily operations.
+
+### 1.1 System Requirements
+
+Before installing CloudManager Pro, ensure your system meets these requirements:
+- Operating System: Ubuntu 20.04+, RHEL 8+, or Windows Server 2019+
+- Memory: Minimum 16GB RAM, recommended 32GB for production
+- Storage: 100GB SSD for application, additional storage for logs
+- Network: Outbound HTTPS access to cloud provider APIs
+
+### 1.2 Installation Steps
+
+1. Download the installer from the customer portal
+2. Run the installation script with root privileges
+3. Configure the database connection
+4. Set up the initial admin account
+5. Verify the installation with the health check command
+
+## Chapter 2: Dashboard Overview
+
+The main dashboard provides a unified view of your cloud resources across all
+connected providers. Key metrics displayed include:
+
+### 2.1 Resource Metrics
+
+- Total compute instances across all clouds
+- Storage utilization and growth trends
+- Network bandwidth consumption
+- Cost breakdown by provider and project
+
+### 2.2 Alert Summary
+
+The alert panel shows active incidents categorized by severity:
+- Critical: Immediate action required (service outages)
+- Warning: Attention needed within 24 hours
+- Info: Informational notifications
+
+## Chapter 3: Cost Optimization
+
+CloudManager Pro includes powerful cost optimization features that can reduce
+your cloud spending by 20-40%.
+
+### 3.1 Right-sizing Recommendations
+
+The platform analyzes resource utilization patterns and suggests optimal
+instance sizes. Recommendations are based on:
+- CPU utilization over 14-day periods
+- Memory usage patterns
+- Network I/O requirements
+
+### 3.2 Reserved Instance Planning
+
+Use the RI Planner to identify opportunities for reserved instance purchases.
+The tool calculates potential savings based on your usage patterns and
+commitment preferences (1-year vs 3-year terms).
+
+## Chapter 4: Security Features
+
+### 4.1 Compliance Monitoring
+
+CloudManager Pro continuously monitors your infrastructure against common
+compliance frameworks including SOC 2, HIPAA, and PCI-DSS. Non-compliant
+resources are flagged with remediation guidance.
+
+### 4.2 Access Control
+
+Role-based access control (RBAC) allows fine-grained permissions:
+- Admin: Full access to all features
+- Operator: Can view and modify resources
+- Viewer: Read-only access to dashboards
+- Billing: Access to cost and billing features only
+"""
+
+
+def chunk_by_paragraphs(text: str, min_length: int = 100) -> list:
+    """Split text into paragraph-based chunks."""
+    paragraphs = text.split("\n\n")
+    chunks = []
+    current_chunk = ""
+    
+    for para in paragraphs:
+        para = para.strip()
+        if not para:
+            continue
+            
+        if len(current_chunk) + len(para) < 500:
+            current_chunk += "\n\n" + para if current_chunk else para
+        else:
+            if len(current_chunk) >= min_length:
+                chunks.append({"content": current_chunk.strip()})
+            current_chunk = para
+    
+    if current_chunk and len(current_chunk) >= min_length:
+        chunks.append({"content": current_chunk.strip()})
+    
+    return chunks
+
+
+def chunk_by_sections(text: str) -> list:
+    """Split text by markdown headers (sections)."""
+    import re
+    
+    # Split by headers (## or ###)
+    sections = re.split(r'\n(?=##+ )', text)
+    chunks = []
+    
+    for section in sections:
+        section = section.strip()
+        if len(section) > 50:  # Skip very short sections
+            # Extract title if present
+            lines = section.split('\n')
+            title = lines[0].replace('#', '').strip() if lines[0].startswith('#') else "Untitled"
+            chunks.append({
+                "id": title.lower().replace(' ', '_')[:30],
+                "content": section
+            })
+    
+    return chunks
+
+
+def chunk_by_fixed_size(text: str, chunk_size: int = 500, overlap: int = 50) -> list:
+    """Split text into fixed-size chunks with overlap."""
+    chunks = []
+    start = 0
+    
+    while start < len(text):
+        end = start + chunk_size
+        chunk_text = text[start:end]
+        
+        # Try to break at sentence boundary
+        if end < len(text):
+            last_period = chunk_text.rfind('.')
+            if last_period > chunk_size * 0.5:
+                end = start + last_period + 1
+                chunk_text = text[start:end]
+        
+        chunks.append({"content": chunk_text.strip()})
+        start = end - overlap
+    
+    return chunks
+
+
+def demonstrate_chunking_strategies():
+    """Show different chunking approaches and their effects."""
+    
+    print("=" * 60)
+    print("CHUNKING STRATEGIES COMPARISON")
+    print("=" * 60)
+    
+    # Strategy 1: Paragraph-based chunking
+    para_chunks = chunk_by_paragraphs(PRODUCT_DOCUMENTATION)
+    print(f"\n📄 Paragraph Chunking: {len(para_chunks)} chunks")
+    print(f"   Average chunk size: {sum(len(c['content']) for c in para_chunks) // len(para_chunks)} chars")
+    print(f"   Sample chunk: {para_chunks[0]['content'][:100]}...")
+    
+    # Strategy 2: Section-based chunking
+    section_chunks = chunk_by_sections(PRODUCT_DOCUMENTATION)
+    print(f"\n📑 Section Chunking: {len(section_chunks)} chunks")
+    print(f"   Average chunk size: {sum(len(c['content']) for c in section_chunks) // len(section_chunks)} chars")
+    print(f"   Sections: {[c['id'] for c in section_chunks[:5]]}...")
+    
+    # Strategy 3: Fixed-size chunking
+    fixed_chunks = chunk_by_fixed_size(PRODUCT_DOCUMENTATION, chunk_size=400, overlap=50)
+    print(f"\n📏 Fixed-Size Chunking (400 chars, 50 overlap): {len(fixed_chunks)} chunks")
+    print(f"   Average chunk size: {sum(len(c['content']) for c in fixed_chunks) // len(fixed_chunks)} chars")
+
+
+def rag_with_section_chunks():
+    """Demonstrate RAG using section-based chunks."""
+    
+    # Create section-based chunks
+    chunks = chunk_by_sections(PRODUCT_DOCUMENTATION)
+    
+    # Build context from chunks
+    context = "\n\n".join([f"[{c['id']}]\n{c['content']}" for c in chunks])
+    
+    # Create agent with chunked knowledge in instructions
+    agent = Agent(
+        name="Product Expert",
+        instructions=f"""You are a CloudManager Pro product expert.
+        Answer questions using the product documentation.
+        Be specific and reference relevant sections when helpful.
+        
+        PRODUCT DOCUMENTATION:
+        {context}""",
+        verbose=False
+    )
+    
+    queries = [
+        "What are the system requirements for CloudManager Pro?",
+        "How can I reduce cloud costs?",
+        "What compliance frameworks are supported?"
+    ]
+    
+    print("\n" + "=" * 60)
+    print("RAG WITH SECTION-BASED CHUNKS")
+    print("=" * 60)
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response[:250]}..." if len(str(response)) > 250 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def semantic_chunking_concept():
+    """Explain semantic chunking concept (agent-driven)."""
+    
+    print("\n" + "=" * 60)
+    print("SEMANTIC CHUNKING CONCEPT")
+    print("=" * 60)
+    
+    print("""
+    Semantic chunking goes beyond fixed rules by considering meaning:
+    
+    1. **Sentence Embedding Similarity**
+       - Compute embeddings for each sentence
+       - Group sentences with similar embeddings
+       - Split when similarity drops below threshold
+    
+    2. **Topic-Based Chunking**
+       - Identify topic shifts in the document
+       - Create chunks that maintain topical coherence
+    
+    3. **Agent-Driven Chunking**
+       - Use an LLM to identify logical boundaries
+       - Preserve context and relationships
+    
+    PraisonAI's Knowledge system handles chunking automatically,
+    but understanding these strategies helps optimize retrieval quality.
+    """)
+
+
+def main():
+    """Run all chunking strategy examples."""
+    print("\n🚀 PraisonAI Chunking Strategies Examples\n")
+    
+    # Example 1: Compare chunking strategies
+    demonstrate_chunking_strategies()
+    
+    # Example 2: RAG with section chunks
+    rag_with_section_chunks()
+    
+    # Example 3: Semantic chunking concept
+    semantic_chunking_concept()
+    
+    print("\n✅ Chunking strategy examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/citations.py

@@ -0,0 +1,350 @@
+"""
+RAG with Citations: Source Attribution and Verification
+
+This example demonstrates how to include citations and source references
+in RAG responses for transparency and verification.
+
+RAG Concept: Trust in AI-generated answers requires verification. Citations
+link answers back to source documents, enabling fact-checking and building
+user confidence.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Legal documents with clear sources
+LEGAL_DOCUMENTS = [
+    {
+        "id": "policy_001",
+        "source": "Employee Handbook v2024",
+        "section": "Section 3.2 - Leave Policies",
+        "content": """
+        Annual Leave Entitlement:
+        - Full-time employees: 20 days per year
+        - Part-time employees: Pro-rated based on hours
+        - Unused leave: Up to 5 days may carry over to next year
+        - Leave requests: Submit at least 2 weeks in advance
+        - Approval: Manager approval required for leave > 3 consecutive days
+        """
+    },
+    {
+        "id": "policy_002",
+        "source": "Employee Handbook v2024",
+        "section": "Section 4.1 - Remote Work",
+        "content": """
+        Remote Work Policy:
+        - Eligibility: Employees in approved roles after 6 months tenure
+        - Maximum: 3 days per week remote work allowed
+        - Requirements: Reliable internet, dedicated workspace
+        - Core hours: Must be available 10am-3pm local time
+        - Equipment: Company provides laptop; employee provides internet
+        """
+    },
+    {
+        "id": "policy_003",
+        "source": "Benefits Guide 2024",
+        "section": "Chapter 2 - Health Insurance",
+        "content": """
+        Health Insurance Coverage:
+        - Provider: BlueCross BlueShield
+        - Coverage: Employee + dependents eligible
+        - Premium: Company pays 80%, employee pays 20%
+        - Deductible: $500 individual, $1000 family
+        - Enrollment: During annual open enrollment or qualifying life event
+        """
+    },
+    {
+        "id": "policy_004",
+        "source": "Benefits Guide 2024",
+        "section": "Chapter 5 - Retirement",
+        "content": """
+        401(k) Retirement Plan:
+        - Eligibility: After 90 days of employment
+        - Company match: 100% up to 4% of salary
+        - Vesting: Immediate vesting for employee contributions
+        - Company match vesting: 3-year graded schedule
+        - Investment options: 15 fund choices available
+        """
+    },
+    {
+        "id": "policy_005",
+        "source": "Code of Conduct 2024",
+        "section": "Article 7 - Conflicts of Interest",
+        "content": """
+        Conflict of Interest Policy:
+        - Disclosure: All potential conflicts must be disclosed to HR
+        - Outside employment: Requires written approval
+        - Financial interests: Cannot hold >5% in competitors
+        - Gifts: Cannot accept gifts valued over $50
+        - Relationships: Must disclose family relationships with vendors
+        """
+    }
+]
+
+
+def basic_citations():
+    """Demonstrate basic citation inclusion in RAG responses."""
+    
+    print("=" * 60)
+    print("BASIC CITATIONS IN RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="HR Policy Expert",
+        instructions="""You are an HR policy expert who answers employee questions.
+        
+        IMPORTANT: Always cite your sources using this format:
+        [Source: Document Name, Section]
+        
+        Include the citation immediately after the relevant information.
+        If information comes from multiple sources, cite each one.""",
+        knowledge=LEGAL_DOCUMENTS,
+        user_id="citation_demo"
+    )
+    
+    queries = [
+        "How many vacation days do I get?",
+        "Can I work from home?",
+        "What's the company match for 401k?"
+    ]
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer with Citations:\n{response}")
+        print("-" * 40)
+
+
+def structured_citations():
+    """Demonstrate structured citation format."""
+    
+    print("\n" + "=" * 60)
+    print("STRUCTURED CITATIONS")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Policy Researcher",
+        instructions="""You provide policy information with structured citations.
+        
+        Format your response as:
+        
+        ANSWER:
+        [Your answer here]
+        
+        SOURCES:
+        1. [Document] - [Section] - [Relevant quote]
+        2. [Document] - [Section] - [Relevant quote]
+        
+        Always include at least one source citation.""",
+        knowledge=LEGAL_DOCUMENTS,
+        user_id="structured_cite"
+    )
+    
+    query = "What are the requirements for remote work eligibility?"
+    
+    print(f"\n📝 Query: {query}")
+    response = agent.chat(query)
+    print(f"\n💡 Structured Response:\n{response}")
+
+
+def inline_citations():
+    """Demonstrate inline citation style."""
+    
+    print("\n" + "=" * 60)
+    print("INLINE CITATIONS (Academic Style)")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Academic Researcher",
+        instructions="""You write responses with inline citations like academic papers.
+        
+        Use numbered citations [1], [2], etc. in the text.
+        List full references at the end.
+        
+        Example:
+        "Employees receive 20 days of leave [1]. Remote work is allowed
+        for up to 3 days per week [2]."
+        
+        References:
+        [1] Employee Handbook v2024, Section 3.2
+        [2] Employee Handbook v2024, Section 4.1""",
+        knowledge=LEGAL_DOCUMENTS,
+        user_id="inline_cite"
+    )
+    
+    query = "Summarize the key employee benefits."
+    
+    print(f"\n📝 Query: {query}")
+    response = agent.chat(query)
+    print(f"\n💡 Academic-Style Response:\n{response}")
+
+
+def citation_verification():
+    """Demonstrate citation verification concept."""
+    
+    print("\n" + "=" * 60)
+    print("CITATION VERIFICATION")
+    print("=" * 60)
+    
+    print("""
+    🔍 Citation Verification Process:
+    
+    1. **Extract Citations**
+       Parse the agent's response to find citation markers
+       
+    2. **Locate Source Documents**
+       Match citations to documents in knowledge base
+       
+    3. **Verify Claims**
+       Check if the cited text supports the claim
+       
+    4. **Flag Discrepancies**
+       Highlight any mismatches or unsupported claims
+    
+    Example Verification:
+    """)
+    
+    # Simulated verification
+    claim = "Employees get 20 days of annual leave"
+    cited_source = "Employee Handbook v2024, Section 3.2"
+    source_text = "Full-time employees: 20 days per year"
+    
+    print(f"   Claim: \"{claim}\"")
+    print(f"   Citation: {cited_source}")
+    print(f"   Source Text: \"{source_text}\"")
+    print("   Verification: ✅ SUPPORTED")
+    
+    print("""
+    
+    Implementation:
+    ```python
+    def verify_citation(claim: str, source_id: str, knowledge) -> bool:
+        # Get source document
+        source = knowledge.get(source_id)
+        
+        # Check if claim is supported by source
+        # (In practice, use semantic similarity)
+        return claim_supported_by_source(claim, source)
+    ```
+    """)
+
+
+def multi_source_synthesis():
+    """Demonstrate synthesizing from multiple sources with citations."""
+    
+    print("\n" + "=" * 60)
+    print("MULTI-SOURCE SYNTHESIS WITH CITATIONS")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Benefits Advisor",
+        instructions="""You synthesize information from multiple policy documents.
+        
+        When answering:
+        1. Gather relevant information from all sources
+        2. Synthesize into a coherent answer
+        3. Cite each source for the specific information it provides
+        4. Note if sources have different or complementary information
+        
+        Format: Include [Source: X] after each piece of information.""",
+        knowledge=LEGAL_DOCUMENTS,
+        user_id="multi_source"
+    )
+    
+    query = "Give me a complete overview of employee benefits and policies."
+    
+    print(f"\n📝 Query: {query}")
+    response = agent.chat(query)
+    print(f"\n💡 Multi-Source Synthesis:\n{response}")
+
+
+def citation_best_practices():
+    """Share best practices for citations in RAG."""
+    
+    print("\n" + "=" * 60)
+    print("CITATION BEST PRACTICES")
+    print("=" * 60)
+    
+    print("""
+    📚 Best Practices for RAG Citations:
+    
+    1. **Include Metadata in Knowledge Base**
+       ```python
+       documents = [
+           {
+               "id": "doc_001",
+               "source": "Policy Manual",
+               "section": "Chapter 3",
+               "date": "2024-01-15",
+               "author": "HR Department",
+               "content": "..."
+           }
+       ]
+       ```
+    
+    2. **Instruct Agent to Cite**
+       - Be explicit in instructions about citation format
+       - Provide examples of expected citation style
+       - Require citations for factual claims
+    
+    3. **Use Consistent Citation Format**
+       - Inline: [1], [2] with reference list
+       - Parenthetical: (Source, Section)
+       - Footnote style: ¹, ² with notes
+    
+    4. **Enable Verification**
+       - Include document IDs for programmatic lookup
+       - Store enough metadata to locate original
+       - Consider including page numbers or paragraphs
+    
+    5. **Handle Missing Sources**
+       - Instruct agent to acknowledge when info isn't in sources
+       - Distinguish between sourced and general knowledge
+       - Use phrases like "Based on the provided documents..."
+    
+    6. **Citation Granularity**
+       - Document level: Good for general attribution
+       - Section level: Better for verification
+       - Quote level: Best for accuracy-critical applications
+    
+    Example Agent Instructions:
+    ```python
+    instructions = '''
+    Answer questions using the provided documents.
+    
+    Rules:
+    - Cite sources for all factual claims
+    - Use format: [Source: Document Name, Section]
+    - If information isn't in the documents, say so
+    - Never make claims without citation
+    '''
+    ```
+    """)
+
+
+def main():
+    """Run all citation examples."""
+    print("\n🚀 PraisonAI RAG Citations Examples\n")
+    
+    # Example 1: Basic citations
+    basic_citations()
+    
+    # Example 2: Structured citations
+    structured_citations()
+    
+    # Example 3: Inline citations
+    inline_citations()
+    
+    # Example 4: Citation verification
+    citation_verification()
+    
+    # Example 5: Multi-source synthesis
+    multi_source_synthesis()
+    
+    # Example 6: Best practices
+    citation_best_practices()
+    
+    print("\n✅ Citation examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/external_sources.py

@@ -0,0 +1,338 @@
+"""
+External Sources RAG: Beyond Vector Search
+
+This example demonstrates RAG with external knowledge sources like
+web search, APIs, and databases.
+
+RAG Concept: RAG isn't limited to pre-indexed documents. External sources
+provide real-time, up-to-date information that complements static knowledge.
+"""
+
+from praisonaiagents import Agent
+
+# Simulated external data sources
+def mock_web_search(query: str) -> list:
+    """Simulate web search results."""
+    # In production, this would call a real search API
+    mock_results = {
+        "weather": [
+            {"title": "Current Weather", "snippet": "Today's forecast: Partly cloudy, high of 72°F (22°C), low of 58°F (14°C). Humidity at 45%."},
+            {"title": "Weekly Outlook", "snippet": "Rain expected Wednesday and Thursday. Weekend looks sunny with temperatures in the mid-70s."}
+        ],
+        "news": [
+            {"title": "Tech Industry Update", "snippet": "Major tech companies report strong Q3 earnings. AI investments continue to grow across sectors."},
+            {"title": "Market Summary", "snippet": "Stock markets closed higher today. S&P 500 up 0.8%, Nasdaq up 1.2%."}
+        ],
+        "default": [
+            {"title": "General Information", "snippet": "Search results would appear here with relevant web content."}
+        ]
+    }
+    
+    for key in mock_results:
+        if key in query.lower():
+            return mock_results[key]
+    return mock_results["default"]
+
+
+def mock_database_query(query: str) -> dict:
+    """Simulate database query results."""
+    # In production, this would query a real database
+    mock_data = {
+        "user_stats": {
+            "total_users": 15420,
+            "active_today": 3250,
+            "new_signups": 127,
+            "retention_rate": "78%"
+        },
+        "sales_data": {
+            "monthly_revenue": "$1.2M",
+            "top_product": "Enterprise Plan",
+            "growth_rate": "15% MoM",
+            "avg_deal_size": "$4,500"
+        },
+        "system_health": {
+            "uptime": "99.97%",
+            "avg_response_time": "145ms",
+            "error_rate": "0.02%",
+            "active_servers": 24
+        }
+    }
+    
+    for key in mock_data:
+        if key.replace("_", " ") in query.lower() or key in query.lower():
+            return mock_data[key]
+    return {"message": "No matching data found"}
+
+
+def mock_api_call(endpoint: str) -> dict:
+    """Simulate API call results."""
+    # In production, this would call a real API
+    mock_responses = {
+        "stock_price": {"symbol": "ACME", "price": 142.50, "change": "+2.3%"},
+        "exchange_rate": {"from": "USD", "to": "EUR", "rate": 0.92},
+        "crypto": {"bitcoin": "$67,500", "ethereum": "$3,450"}
+    }
+    
+    for key in mock_responses:
+        if key in endpoint.lower():
+            return mock_responses[key]
+    return {"status": "endpoint not found"}
+
+
+# Static knowledge base for hybrid approach
+COMPANY_KNOWLEDGE = [
+    {
+        "id": "company_overview",
+        "content": """
+        Acme Corporation is a technology company founded in 2015.
+        Headquarters: San Francisco, CA
+        Products: Enterprise software, cloud services, AI solutions
+        Mission: Empowering businesses through intelligent automation
+        """
+    },
+    {
+        "id": "product_info",
+        "content": """
+        Acme Products:
+        - Acme Cloud: Infrastructure-as-a-service platform
+        - Acme AI: Machine learning toolkit for enterprises
+        - Acme Connect: Integration and API management
+        Pricing: Starter ($99/mo), Professional ($499/mo), Enterprise (custom)
+        """
+    }
+]
+
+
+def web_augmented_rag():
+    """Demonstrate RAG augmented with web search."""
+    
+    print("=" * 60)
+    print("WEB-AUGMENTED RAG")
+    print("=" * 60)
+    
+    # Create agent that can use web search
+    agent = Agent(
+        name="Research Assistant",
+        instructions="""You are a research assistant that combines internal knowledge
+        with web search results. When answering:
+        1. Check if the question requires current/real-time information
+        2. Use web search for news, weather, current events
+        3. Combine web results with your knowledge for comprehensive answers
+        4. Clearly indicate when information comes from web search.""",
+        knowledge=COMPANY_KNOWLEDGE,
+        user_id="web_demo"
+    )
+    
+    # Simulate web-augmented queries
+    queries = [
+        ("What's the weather like today?", True),  # Needs web
+        ("Tell me about Acme Corporation's products", False),  # Static knowledge
+        ("What's happening in the tech industry?", True),  # Needs web
+    ]
+    
+    for query, needs_web in queries:
+        print(f"\n📝 Query: {query}")
+        
+        if needs_web:
+            # Augment with web search
+            web_results = mock_web_search(query)
+            web_context = "\n".join([f"- {r['title']}: {r['snippet']}" for r in web_results])
+            augmented_query = f"{query}\n\nWeb search results:\n{web_context}"
+            response = agent.chat(augmented_query)
+            print("🌐 (Web-augmented)")
+        else:
+            response = agent.chat(query)
+            print("📚 (Static knowledge)")
+        
+        print(f"💡 Answer: {response[:250]}..." if len(str(response)) > 250 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def database_augmented_rag():
+    """Demonstrate RAG augmented with database queries."""
+    
+    print("\n" + "=" * 60)
+    print("DATABASE-AUGMENTED RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Business Analyst",
+        instructions="""You are a business analyst with access to company databases.
+        Interpret data accurately and provide actionable insights.
+        When presenting numbers, add context and trends.""",
+        knowledge=COMPANY_KNOWLEDGE,
+        user_id="db_demo"
+    )
+    
+    queries = [
+        "How are our user stats looking?",
+        "Give me a sales summary",
+        "What's our system health status?"
+    ]
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        
+        # Fetch database data
+        db_data = mock_database_query(query)
+        db_context = f"Database results: {db_data}"
+        
+        augmented_query = f"{query}\n\n{db_context}"
+        response = agent.chat(augmented_query)
+        
+        print(f"🗄️ DB Data: {db_data}")
+        print(f"💡 Analysis: {response[:200]}..." if len(str(response)) > 200 else f"💡 Analysis: {response}")
+        print("-" * 40)
+
+
+def api_augmented_rag():
+    """Demonstrate RAG augmented with API calls."""
+    
+    print("\n" + "=" * 60)
+    print("API-AUGMENTED RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Financial Assistant",
+        instructions="""You are a financial assistant with access to market APIs.
+        Provide accurate financial information and context.
+        Always note that prices are subject to change.""",
+        user_id="api_demo"
+    )
+    
+    queries = [
+        ("What's the ACME stock price?", "stock_price"),
+        ("USD to EUR exchange rate?", "exchange_rate"),
+        ("Crypto market update?", "crypto")
+    ]
+    
+    for query, endpoint in queries:
+        print(f"\n📝 Query: {query}")
+        
+        # Fetch API data
+        api_data = mock_api_call(endpoint)
+        api_context = f"API response: {api_data}"
+        
+        augmented_query = f"{query}\n\n{api_context}"
+        response = agent.chat(augmented_query)
+        
+        print(f"🔌 API Data: {api_data}")
+        print(f"💡 Response: {response[:200]}..." if len(str(response)) > 200 else f"💡 Response: {response}")
+        print("-" * 40)
+
+
+def hybrid_source_rag():
+    """Combine multiple external sources."""
+    
+    print("\n" + "=" * 60)
+    print("HYBRID SOURCE RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Executive Assistant",
+        instructions="""You are an executive assistant with access to multiple data sources.
+        Synthesize information from static knowledge, databases, and APIs.
+        Provide comprehensive briefings that combine all relevant data.""",
+        knowledge=COMPANY_KNOWLEDGE,
+        user_id="hybrid_demo"
+    )
+    
+    query = "Give me a complete business briefing for today"
+    
+    # Gather from multiple sources
+    db_stats = mock_database_query("user_stats")
+    db_sales = mock_database_query("sales_data")
+    db_health = mock_database_query("system_health")
+    api_stock = mock_api_call("stock_price")
+    web_news = mock_web_search("news")
+    
+    combined_context = f"""
+    User Statistics: {db_stats}
+    Sales Data: {db_sales}
+    System Health: {db_health}
+    Stock Price: {api_stock}
+    Industry News: {[r['snippet'] for r in web_news]}
+    """
+    
+    augmented_query = f"{query}\n\nData from multiple sources:\n{combined_context}"
+    
+    print(f"\n📝 Query: {query}")
+    print("\n📊 Data Sources:")
+    print("   - Database: user_stats, sales_data, system_health")
+    print("   - API: stock_price")
+    print("   - Web: news search")
+    
+    response = agent.chat(augmented_query)
+    print(f"\n💡 Executive Briefing:\n{response}")
+
+
+def external_source_patterns():
+    """Explain external source patterns."""
+    
+    print("\n" + "=" * 60)
+    print("EXTERNAL SOURCE PATTERNS")
+    print("=" * 60)
+    
+    print("""
+    🌐 External Source Integration Patterns:
+    
+    1. **Query-Time Augmentation**
+       - Fetch external data when query arrives
+       - Inject into prompt as additional context
+       - Best for: Real-time data needs
+    
+    2. **Scheduled Refresh**
+       - Periodically update knowledge base from external sources
+       - Index external data for faster retrieval
+       - Best for: Semi-static external data
+    
+    3. **Tool-Based Retrieval**
+       - Give agent tools to fetch external data
+       - Agent decides when to use external sources
+       - Best for: Complex, multi-step queries
+    
+    4. **Hybrid Static + Dynamic**
+       - Combine indexed knowledge with real-time fetches
+       - Use static for stable info, dynamic for current
+       - Best for: Comprehensive answers
+    
+    Implementation Example:
+    ```python
+    def web_search_tool(query: str) -> str:
+        # Call search API
+        results = search_api.search(query)
+        return format_results(results)
+    
+    agent = Agent(
+        knowledge=static_docs,
+        tools=[web_search_tool],
+        instructions="Use web search for current events..."
+    )
+    ```
+    """)
+
+
+def main():
+    """Run all external source RAG examples."""
+    print("\n🚀 PraisonAI External Sources RAG Examples\n")
+    
+    # Example 1: Web-augmented RAG
+    web_augmented_rag()
+    
+    # Example 2: Database-augmented RAG
+    database_augmented_rag()
+    
+    # Example 3: API-augmented RAG
+    api_augmented_rag()
+    
+    # Example 4: Hybrid sources
+    hybrid_source_rag()
+    
+    # Example 5: Patterns
+    external_source_patterns()
+    
+    print("\n✅ External sources RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/hybrid_search.py

@@ -0,0 +1,281 @@
+"""
+Hybrid Search: Combining Dense and Sparse Retrieval
+
+This example demonstrates hybrid retrieval that combines semantic (dense)
+search with keyword (sparse) search for improved accuracy.
+
+RAG Concept: Dense embeddings capture meaning but may miss exact matches.
+Sparse/keyword search finds exact terms but lacks semantic understanding.
+Hybrid search combines both for best results.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Technical troubleshooting guides
+TROUBLESHOOTING_GUIDES = [
+    {
+        "id": "error_001",
+        "content": """
+        Error Code: ERR_CONNECTION_REFUSED
+        
+        This error occurs when the client cannot establish a connection to the server.
+        
+        Common Causes:
+        - Server is not running or has crashed
+        - Firewall blocking the connection port
+        - Incorrect host or port configuration
+        - Network connectivity issues
+        
+        Resolution Steps:
+        1. Verify the server process is running: `systemctl status myservice`
+        2. Check firewall rules: `sudo ufw status`
+        3. Test port connectivity: `telnet hostname 8080`
+        4. Review server logs for startup errors
+        """
+    },
+    {
+        "id": "error_002",
+        "content": """
+        Error Code: ERR_OUT_OF_MEMORY
+        
+        This error indicates the application has exhausted available memory.
+        
+        Common Causes:
+        - Memory leak in application code
+        - Insufficient heap size configuration
+        - Too many concurrent connections
+        - Large data processing without streaming
+        
+        Resolution Steps:
+        1. Increase heap size: `-Xmx4g` for Java applications
+        2. Enable memory profiling to identify leaks
+        3. Implement pagination for large datasets
+        4. Add memory limits to container configurations
+        """
+    },
+    {
+        "id": "error_003",
+        "content": """
+        Error Code: ERR_SSL_CERTIFICATE_EXPIRED
+        
+        This error means the SSL/TLS certificate has passed its validity date.
+        
+        Common Causes:
+        - Certificate not renewed before expiration
+        - Automatic renewal process failed
+        - Wrong certificate installed
+        
+        Resolution Steps:
+        1. Check certificate expiry: `openssl x509 -enddate -noout -in cert.pem`
+        2. Renew certificate with your CA or Let's Encrypt
+        3. Update certificate files on the server
+        4. Restart the web server to load new certificate
+        """
+    },
+    {
+        "id": "error_004",
+        "content": """
+        Error Code: ERR_DATABASE_DEADLOCK
+        
+        A deadlock occurs when two transactions block each other indefinitely.
+        
+        Common Causes:
+        - Transactions acquiring locks in different orders
+        - Long-running transactions holding locks
+        - Missing indexes causing table scans with locks
+        
+        Resolution Steps:
+        1. Review transaction isolation levels
+        2. Ensure consistent lock ordering across queries
+        3. Add appropriate indexes to reduce lock duration
+        4. Implement retry logic for deadlock exceptions
+        """
+    },
+    {
+        "id": "error_005",
+        "content": """
+        Error Code: ERR_RATE_LIMIT_EXCEEDED
+        
+        The API has rejected requests due to exceeding the rate limit.
+        
+        Common Causes:
+        - Too many requests in a short time window
+        - Missing request throttling in client code
+        - Retry loops without backoff
+        
+        Resolution Steps:
+        1. Implement exponential backoff for retries
+        2. Cache responses to reduce API calls
+        3. Use bulk endpoints where available
+        4. Request rate limit increase if needed
+        """
+    }
+]
+
+
+def demonstrate_search_types():
+    """Show the difference between semantic and keyword search concepts."""
+    
+    print("=" * 60)
+    print("SEARCH TYPES COMPARISON")
+    print("=" * 60)
+    
+    print("""
+    📊 DENSE (Semantic) Search:
+       - Uses vector embeddings to capture meaning
+       - "memory issues" matches "out of memory" and "RAM exhausted"
+       - Good for natural language queries
+       - May miss exact technical terms
+    
+    📊 SPARSE (Keyword) Search:
+       - Uses term frequency and exact matching
+       - "ERR_SSL_CERTIFICATE_EXPIRED" matches exactly
+       - Good for error codes, IDs, technical terms
+       - Misses synonyms and paraphrases
+    
+    📊 HYBRID Search:
+       - Combines both approaches
+       - Weighted scoring: (α × dense_score) + ((1-α) × sparse_score)
+       - Best of both worlds for technical documentation
+    """)
+
+
+def hybrid_rag_example():
+    """Demonstrate hybrid retrieval with an agent."""
+    
+    # Create agent with hybrid retrieval enabled
+    agent = Agent(
+        name="Tech Support",
+        instructions="""You are a technical support specialist.
+        Help users troubleshoot errors using the knowledge base.
+        Always mention the specific error code when relevant.
+        Provide step-by-step resolution guidance.""",
+        knowledge=TROUBLESHOOTING_GUIDES,
+        knowledge_config={
+            "hybrid": True,  # Enable hybrid search
+            "top_k": 3
+        },
+        user_id="hybrid_demo"
+    )
+    
+    # Test with different query types
+    queries = [
+        # Semantic query (natural language)
+        "My application is running out of memory, what should I do?",
+        
+        # Exact match query (error code)
+        "ERR_SSL_CERTIFICATE_EXPIRED",
+        
+        # Mixed query
+        "Getting connection refused error when connecting to port 8080",
+        
+        # Conceptual query
+        "How do I handle API throttling?"
+    ]
+    
+    print("\n" + "=" * 60)
+    print("HYBRID RAG IN ACTION")
+    print("=" * 60)
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response[:300]}..." if len(str(response)) > 300 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def compare_retrieval_modes():
+    """Compare results with different retrieval configurations."""
+    
+    print("\n" + "=" * 60)
+    print("RETRIEVAL MODE COMPARISON")
+    print("=" * 60)
+    
+    query = "database transaction blocking issue"
+    
+    # Dense-only agent
+    dense_agent = Agent(
+        name="Dense Search Agent",
+        instructions="Answer based on the knowledge provided.",
+        knowledge=TROUBLESHOOTING_GUIDES,
+        knowledge_config={"hybrid": False},
+        user_id="dense_demo"
+    )
+    
+    # Hybrid agent
+    hybrid_agent = Agent(
+        name="Hybrid Search Agent", 
+        instructions="Answer based on the knowledge provided.",
+        knowledge=TROUBLESHOOTING_GUIDES,
+        knowledge_config={"hybrid": True},
+        user_id="hybrid_demo"
+    )
+    
+    print(f"\n📝 Query: {query}")
+    
+    print("\n🔍 Dense-Only Search:")
+    dense_response = dense_agent.chat(query)
+    print(f"   {dense_response[:200]}...")
+    
+    print("\n🔍 Hybrid Search:")
+    hybrid_response = hybrid_agent.chat(query)
+    print(f"   {hybrid_response[:200]}...")
+
+
+def hybrid_search_tuning():
+    """Explain hybrid search tuning parameters."""
+    
+    print("\n" + "=" * 60)
+    print("HYBRID SEARCH TUNING")
+    print("=" * 60)
+    
+    print("""
+    Key parameters for hybrid search optimization:
+    
+    1. **Alpha (α) Weight** - Balance between dense and sparse
+       - α = 1.0: Pure semantic search
+       - α = 0.5: Equal weight (good default)
+       - α = 0.0: Pure keyword search
+    
+    2. **Top-K Retrieval** - Number of candidates
+       - Higher K: More context, slower, may dilute relevance
+       - Lower K: Faster, focused, may miss relevant docs
+       - Typical range: 3-10 for RAG
+    
+    3. **Reranking** - Second-pass scoring
+       - Cross-encoder reranking improves precision
+       - Adds latency but improves quality
+    
+    Example configuration:
+    ```python
+    knowledge_config = {
+        "hybrid": True,
+        "hybrid_alpha": 0.6,  # Favor semantic
+        "top_k": 5,
+        "rerank": True
+    }
+    ```
+    """)
+
+
+def main():
+    """Run all hybrid search examples."""
+    print("\n🚀 PraisonAI Hybrid Search Examples\n")
+    
+    # Example 1: Explain search types
+    demonstrate_search_types()
+    
+    # Example 2: Hybrid RAG in action
+    hybrid_rag_example()
+    
+    # Example 3: Compare retrieval modes
+    compare_retrieval_modes()
+    
+    # Example 4: Tuning guidance
+    hybrid_search_tuning()
+    
+    print("\n✅ Hybrid search examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/knowledge_graph.py

@@ -0,0 +1,288 @@
+"""
+Graph-Assisted RAG: Relationship-Aware Retrieval
+
+This example demonstrates how graph structures can enhance RAG by
+capturing relationships between entities and enabling path-based retrieval.
+
+RAG Concept: Traditional vector search finds similar documents but misses
+relationships. Graph-assisted RAG understands connections between entities,
+enabling multi-hop reasoning and relationship-aware answers.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Interconnected entities (simulating a knowledge graph)
+ENTITY_KNOWLEDGE = [
+    {
+        "id": "person_einstein",
+        "type": "person",
+        "content": """
+        Albert Einstein (1879-1955) was a theoretical physicist who developed
+        the theory of relativity. He received the Nobel Prize in Physics in 1921
+        for his explanation of the photoelectric effect. Einstein worked at the
+        Institute for Advanced Study in Princeton from 1933 until his death.
+        Key relationships: Collaborated with Niels Bohr, influenced by Max Planck,
+        mentored by Heinrich Weber.
+        """
+    },
+    {
+        "id": "person_bohr",
+        "type": "person",
+        "content": """
+        Niels Bohr (1885-1962) was a Danish physicist who made foundational
+        contributions to understanding atomic structure and quantum theory.
+        He received the Nobel Prize in Physics in 1922. Bohr founded the
+        Institute of Theoretical Physics in Copenhagen.
+        Key relationships: Debated with Einstein on quantum mechanics,
+        mentored Werner Heisenberg, influenced by Ernest Rutherford.
+        """
+    },
+    {
+        "id": "person_heisenberg",
+        "type": "person",
+        "content": """
+        Werner Heisenberg (1901-1976) was a German physicist and pioneer of
+        quantum mechanics. He formulated the uncertainty principle in 1927.
+        He received the Nobel Prize in Physics in 1932.
+        Key relationships: Student of Niels Bohr, collaborated with Max Born,
+        worked with Wolfgang Pauli.
+        """
+    },
+    {
+        "id": "concept_relativity",
+        "type": "concept",
+        "content": """
+        Theory of Relativity: A framework developed by Albert Einstein consisting
+        of special relativity (1905) and general relativity (1915). Special
+        relativity deals with objects moving at constant speeds, introducing
+        E=mc². General relativity describes gravity as the curvature of spacetime.
+        Related concepts: Spacetime, time dilation, gravitational waves.
+        Applications: GPS satellites, particle accelerators, cosmology.
+        """
+    },
+    {
+        "id": "concept_quantum",
+        "type": "concept",
+        "content": """
+        Quantum Mechanics: A fundamental theory describing nature at atomic scales.
+        Key principles include wave-particle duality, superposition, and entanglement.
+        Developed through contributions from Planck, Bohr, Heisenberg, Schrödinger.
+        Related concepts: Uncertainty principle, quantum tunneling, wave function.
+        Applications: Semiconductors, lasers, MRI machines, quantum computing.
+        """
+    },
+    {
+        "id": "institution_princeton",
+        "type": "institution",
+        "content": """
+        Institute for Advanced Study (Princeton): A private research center
+        founded in 1930. Notable members include Albert Einstein, John von Neumann,
+        Kurt Gödel, and Robert Oppenheimer. The institute focuses on theoretical
+        research in mathematics, natural sciences, and humanities.
+        Location: Princeton, New Jersey, USA.
+        """
+    },
+    {
+        "id": "institution_copenhagen",
+        "type": "institution",
+        "content": """
+        Niels Bohr Institute (Copenhagen): Founded in 1921 by Niels Bohr as the
+        Institute of Theoretical Physics. It became a world center for quantum
+        mechanics research. Notable visitors included Heisenberg, Pauli, and Dirac.
+        The "Copenhagen interpretation" of quantum mechanics originated here.
+        Location: Copenhagen, Denmark.
+        """
+    },
+    {
+        "id": "event_solvay",
+        "type": "event",
+        "content": """
+        Solvay Conferences: Series of physics conferences held in Brussels since 1911.
+        The 1927 Fifth Solvay Conference is famous for the Bohr-Einstein debates
+        on quantum mechanics. Attendees included Einstein, Bohr, Heisenberg,
+        Schrödinger, Dirac, and Marie Curie. These conferences shaped modern physics.
+        """
+    }
+]
+
+
+def graph_aware_rag():
+    """Demonstrate relationship-aware retrieval."""
+    
+    # Create agent with entity knowledge
+    agent = Agent(
+        name="Physics Historian",
+        instructions="""You are a physics historian who understands the relationships
+        between scientists, concepts, and institutions. When answering:
+        1. Trace connections between entities
+        2. Explain how ideas and people influenced each other
+        3. Reference specific relationships mentioned in the knowledge base
+        4. Build a narrative that shows the interconnected nature of physics history.""",
+        knowledge=ENTITY_KNOWLEDGE,
+        user_id="graph_demo"
+    )
+    
+    # Relationship-focused queries
+    queries = [
+        "How were Einstein and Bohr connected?",
+        "Trace the lineage of quantum mechanics through its key figures.",
+        "What institutions were central to 20th century physics?",
+        "How did the Solvay Conferences shape physics?"
+    ]
+    
+    print("=" * 60)
+    print("GRAPH-ASSISTED RAG: Physics History Network")
+    print("=" * 60)
+    print("\nKnowledge graph entities:")
+    for entity in ENTITY_KNOWLEDGE:
+        print(f"  - [{entity['type']}] {entity['id']}")
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response[:350]}..." if len(str(response)) > 350 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def multi_hop_reasoning():
+    """Demonstrate multi-hop reasoning across entities."""
+    
+    print("\n" + "=" * 60)
+    print("MULTI-HOP REASONING")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Connection Finder",
+        instructions="""You excel at finding indirect connections between entities.
+        When asked about relationships, trace the path through intermediate entities.
+        Format your answer to show the chain of connections.""",
+        knowledge=ENTITY_KNOWLEDGE,
+        user_id="multihop_demo"
+    )
+    
+    # Multi-hop query requiring traversal
+    query = """
+    What is the connection between the uncertainty principle and Princeton?
+    (Hint: trace through people and their relationships)
+    """
+    
+    print(f"\n📝 Multi-hop Query: {query.strip()}")
+    print("\n🔗 Expected path: Uncertainty Principle → Heisenberg → Bohr → Einstein → Princeton")
+    
+    response = agent.chat(query)
+    print(f"\n💡 Answer:\n{response}")
+
+
+def entity_type_filtering():
+    """Show filtering by entity type."""
+    
+    print("\n" + "=" * 60)
+    print("ENTITY TYPE FILTERING")
+    print("=" * 60)
+    
+    # Filter to only people
+    people_only = [e for e in ENTITY_KNOWLEDGE if e['type'] == 'person']
+    
+    people_agent = Agent(
+        name="Biographer",
+        instructions="You are a biographer focused on scientists' lives and relationships.",
+        knowledge=people_only,
+        user_id="people_demo"
+    )
+    
+    # Filter to only concepts
+    concepts_only = [e for e in ENTITY_KNOWLEDGE if e['type'] == 'concept']
+    
+    concept_agent = Agent(
+        name="Concept Explainer",
+        instructions="You explain scientific concepts and their relationships.",
+        knowledge=concepts_only,
+        user_id="concept_demo"
+    )
+    
+    query = "Tell me about the major developments in early 20th century physics."
+    
+    print(f"\n📝 Query: {query}")
+    
+    print("\n👤 People-focused view:")
+    response1 = people_agent.chat(query)
+    print(f"   {response1[:250]}...")
+    
+    print("\n💡 Concept-focused view:")
+    response2 = concept_agent.chat(query)
+    print(f"   {response2[:250]}...")
+
+
+def graph_rag_patterns():
+    """Explain graph RAG patterns."""
+    
+    print("\n" + "=" * 60)
+    print("GRAPH RAG PATTERNS")
+    print("=" * 60)
+    
+    print("""
+    📊 Graph-Assisted RAG Patterns:
+    
+    1. **Entity-Centric Retrieval**
+       - Store entities with their relationships
+       - Query retrieves entity + connected entities
+       - Good for: "Tell me about X and its connections"
+    
+    2. **Path-Based Retrieval**
+       - Find paths between two entities
+       - Retrieve all nodes along the path
+       - Good for: "How is X related to Y?"
+    
+    3. **Neighborhood Expansion**
+       - Start with query-matched entities
+       - Expand to include 1-hop or 2-hop neighbors
+       - Good for: Comprehensive context
+    
+    4. **Subgraph Extraction**
+       - Extract relevant subgraph for query
+       - Include nodes and edges as context
+       - Good for: Complex multi-entity questions
+    
+    Implementation in PraisonAI:
+    ```python
+    # Structure knowledge as entities with relationships
+    entities = [
+        {
+            "id": "entity_1",
+            "type": "person",
+            "content": "...",
+            "relationships": ["entity_2", "entity_3"]
+        },
+        ...
+    ]
+    
+    # Agent retrieves related entities automatically
+    agent = Agent(
+        knowledge=entities,
+        instructions="Trace relationships between entities..."
+    )
+    ```
+    """)
+
+
+def main():
+    """Run all graph-assisted RAG examples."""
+    print("\n🚀 PraisonAI Graph-Assisted RAG Examples\n")
+    
+    # Example 1: Graph-aware retrieval
+    graph_aware_rag()
+    
+    # Example 2: Multi-hop reasoning
+    multi_hop_reasoning()
+    
+    # Example 3: Entity type filtering
+    entity_type_filtering()
+    
+    # Example 4: Graph RAG patterns
+    graph_rag_patterns()
+    
+    print("\n✅ Graph-assisted RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/multi_document.py

@@ -0,0 +1,266 @@
+"""
+Multi-Document RAG: Synthesizing Information Across Sources
+
+This example demonstrates retrieving and synthesizing information from
+multiple documents to answer complex questions.
+
+RAG Concept: Real-world questions often require combining information
+from multiple sources. Multi-document RAG retrieves relevant chunks
+from different documents and synthesizes a coherent answer.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Multiple documents on different topics
+CLIMATE_REPORT = {
+    "id": "climate_2024",
+    "source": "Global Climate Report 2024",
+    "content": """
+    Global Climate Report 2024 - Executive Summary
+    
+    Global temperatures in 2024 reached 1.45°C above pre-industrial levels,
+    making it the warmest year on record. Key findings:
+    
+    - Arctic sea ice extent hit a new minimum in September
+    - Ocean heat content reached unprecedented levels
+    - Extreme weather events increased by 23% compared to the decade average
+    - CO2 concentrations reached 422 ppm, up from 417 ppm in 2023
+    
+    Regional impacts varied significantly. Europe experienced its hottest
+    summer, while parts of South America faced severe drought conditions.
+    The economic cost of climate-related disasters exceeded $380 billion.
+    """
+}
+
+ENERGY_REPORT = {
+    "id": "energy_2024",
+    "source": "World Energy Outlook 2024",
+    "content": """
+    World Energy Outlook 2024 - Key Highlights
+    
+    Renewable energy capacity grew by 35% in 2024, led by solar and wind.
+    Major developments:
+    
+    - Solar installations reached 1.5 TW cumulative capacity globally
+    - Wind power generated 12% of global electricity
+    - Battery storage capacity doubled to 500 GWh
+    - Electric vehicle sales reached 20 million units
+    
+    Despite progress, fossil fuels still account for 75% of primary energy.
+    Investment in clean energy reached $1.8 trillion, but $4 trillion annually
+    is needed to meet net-zero targets by 2050.
+    """
+}
+
+POLICY_REPORT = {
+    "id": "policy_2024",
+    "source": "Climate Policy Tracker 2024",
+    "content": """
+    Climate Policy Tracker 2024 - Global Progress
+    
+    145 countries have now committed to net-zero emissions targets.
+    Policy developments:
+    
+    - EU Carbon Border Adjustment Mechanism fully implemented
+    - US Inflation Reduction Act drove $300B in clean energy investment
+    - China pledged to peak emissions before 2030
+    - India launched National Green Hydrogen Mission
+    
+    Carbon pricing now covers 23% of global emissions, up from 20% in 2023.
+    Average carbon price increased to $45/ton. However, only 4% of emissions
+    are priced at levels consistent with Paris Agreement goals ($100+/ton).
+    """
+}
+
+TECH_REPORT = {
+    "id": "tech_2024",
+    "source": "Climate Technology Review 2024",
+    "content": """
+    Climate Technology Review 2024 - Breakthrough Innovations
+    
+    Several climate technologies reached commercial viability in 2024:
+    
+    - Direct Air Capture: Costs fell to $400/ton CO2, down from $600 in 2022
+    - Green Hydrogen: Production costs reached $3/kg in optimal locations
+    - Solid-state Batteries: Energy density improved 40%, enabling longer EV range
+    - Perovskite Solar Cells: Efficiency reached 33%, approaching silicon limits
+    
+    Emerging technologies to watch:
+    - Enhanced geothermal systems showing promise in non-volcanic regions
+    - Ocean-based carbon removal gaining investment interest
+    - AI-optimized grid management reducing curtailment by 15%
+    """
+}
+
+# Combine all documents
+ALL_DOCUMENTS = [CLIMATE_REPORT, ENERGY_REPORT, POLICY_REPORT, TECH_REPORT]
+
+
+def multi_document_rag():
+    """Demonstrate RAG across multiple documents."""
+    
+    # Create agent with multi-document knowledge
+    agent = Agent(
+        name="Climate Analyst",
+        instructions="""You are a climate and energy analyst.
+        Synthesize information from multiple reports to answer questions.
+        When information comes from different sources, mention which reports
+        you're drawing from. Provide comprehensive, well-rounded answers.""",
+        knowledge=ALL_DOCUMENTS,
+        user_id="multi_doc_demo"
+    )
+    
+    # Questions that require multi-document synthesis
+    queries = [
+        "What is the current state of global climate and what are we doing about it?",
+        "How is renewable energy progressing and what policies support it?",
+        "What technologies are emerging to address climate change?",
+        "Compare the economic investment in clean energy with climate damage costs."
+    ]
+    
+    print("=" * 60)
+    print("MULTI-DOCUMENT RAG: Climate Analysis")
+    print("=" * 60)
+    print(f"\nKnowledge base: {len(ALL_DOCUMENTS)} documents")
+    print(f"Sources: {[d['source'] for d in ALL_DOCUMENTS]}")
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response[:400]}..." if len(str(response)) > 400 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def cross_reference_example():
+    """Show how agents can cross-reference information."""
+    
+    print("\n" + "=" * 60)
+    print("CROSS-REFERENCING ACROSS DOCUMENTS")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Cross-Reference Analyst",
+        instructions="""You are an analyst who excels at finding connections
+        between different reports. When answering:
+        1. Identify relevant information from each source
+        2. Note any contradictions or complementary data
+        3. Synthesize a coherent narrative
+        4. Cite your sources explicitly.""",
+        knowledge=ALL_DOCUMENTS,
+        user_id="cross_ref_demo"
+    )
+    
+    query = """
+    Analyze the relationship between renewable energy growth (from Energy Report),
+    policy support (from Policy Report), and technology costs (from Tech Report).
+    How do these factors reinforce each other?
+    """
+    
+    print(f"\n📝 Complex Query: {query.strip()}")
+    response = agent.chat(query)
+    print(f"\n💡 Synthesized Answer:\n{response}")
+
+
+def document_filtering():
+    """Demonstrate filtering documents by metadata."""
+    
+    print("\n" + "=" * 60)
+    print("DOCUMENT FILTERING BY SOURCE")
+    print("=" * 60)
+    
+    # Create agents focused on specific document types
+    policy_agent = Agent(
+        name="Policy Expert",
+        instructions="You are a policy expert. Focus only on policy-related information.",
+        knowledge=[POLICY_REPORT],
+        user_id="policy_expert"
+    )
+    
+    tech_agent = Agent(
+        name="Technology Expert",
+        instructions="You are a technology expert. Focus on technical innovations.",
+        knowledge=[TECH_REPORT],
+        user_id="tech_expert"
+    )
+    
+    query = "What progress has been made in addressing climate change?"
+    
+    print(f"\n📝 Query: {query}")
+    
+    print("\n🏛️ Policy Expert's View:")
+    policy_response = policy_agent.chat(query)
+    print(f"   {policy_response[:250]}...")
+    
+    print("\n🔬 Technology Expert's View:")
+    tech_response = tech_agent.chat(query)
+    print(f"   {tech_response[:250]}...")
+    
+    print("\n💡 Note: Different document subsets yield different perspectives!")
+
+
+def multi_document_best_practices():
+    """Share best practices for multi-document RAG."""
+    
+    print("\n" + "=" * 60)
+    print("MULTI-DOCUMENT RAG BEST PRACTICES")
+    print("=" * 60)
+    
+    print("""
+    📚 Best Practices for Multi-Document RAG:
+    
+    1. **Document Metadata**
+       - Include source, date, author in each document
+       - Enables filtering and citation
+    
+    2. **Consistent Chunking**
+       - Use similar chunk sizes across documents
+       - Maintains balanced retrieval
+    
+    3. **Source Diversity**
+       - Retrieve from multiple sources when possible
+       - Reduces single-source bias
+    
+    4. **Conflict Resolution**
+       - Instruct agent how to handle contradictions
+       - Prefer newer sources or authoritative ones
+    
+    5. **Citation Requirements**
+       - Ask agent to cite sources in responses
+       - Enables verification and trust
+    
+    Example configuration:
+    ```python
+    documents = [
+        {"id": "doc1", "source": "Report A", "date": "2024-01", "content": "..."},
+        {"id": "doc2", "source": "Report B", "date": "2024-06", "content": "..."},
+    ]
+    
+    agent = Agent(
+        knowledge=documents,
+        instructions="Always cite which report your information comes from."
+    )
+    ```
+    """)
+
+
+def main():
+    """Run all multi-document RAG examples."""
+    print("\n🚀 PraisonAI Multi-Document RAG Examples\n")
+    
+    # Example 1: Basic multi-document RAG
+    multi_document_rag()
+    
+    # Example 2: Cross-referencing
+    cross_reference_example()
+    
+    # Example 3: Document filtering
+    document_filtering()
+    
+    # Example 4: Best practices
+    multi_document_best_practices()
+    
+    print("\n✅ Multi-document RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/rag_evaluation.py

@@ -0,0 +1,172 @@
+"""
+RAG Evaluation Example
+
+This example demonstrates how to evaluate RAG retrieval quality
+using golden queries and expected answers.
+
+Usage:
+    python rag_evaluation.py
+
+Requirements:
+    pip install praisonaiagents[knowledge]
+"""
+
+import json
+from pathlib import Path
+from praisonaiagents import Knowledge
+from praisonaiagents.rag import RAG, RAGConfig
+
+
+def create_test_queries():
+    """Create sample test queries for evaluation."""
+    return [
+        {
+            "query": "What is the main purpose of the system?",
+            "expected_doc": "overview.pdf",
+            "expected_answer_contains": "purpose",
+        },
+        {
+            "query": "What are the key features?",
+            "expected_doc": "features.pdf",
+            "expected_answer_contains": "feature",
+        },
+        {
+            "query": "How do I get started?",
+            "expected_doc": "quickstart.pdf",
+            "expected_answer_contains": "start",
+        },
+    ]
+
+
+def evaluate_rag(rag: RAG, test_queries: list) -> dict:
+    """
+    Evaluate RAG performance on test queries.
+    
+    Returns metrics including:
+    - Document retrieval accuracy
+    - Answer relevance
+    - Average retrieval score
+    """
+    results = {
+        "total": len(test_queries),
+        "doc_hits": 0,
+        "answer_hits": 0,
+        "avg_score": 0.0,
+        "details": [],
+    }
+    
+    total_score = 0.0
+    
+    for test in test_queries:
+        query = test["query"]
+        expected_doc = test.get("expected_doc", "")
+        expected_contains = test.get("expected_answer_contains", "")
+        
+        # Run RAG query
+        result = rag.query(query)
+        
+        # Check if expected document is in citations
+        doc_found = any(
+            expected_doc in c.source for c in result.citations
+        ) if expected_doc else True
+        
+        # Check if answer contains expected text
+        answer_ok = (
+            expected_contains.lower() in result.answer.lower()
+        ) if expected_contains else True
+        
+        # Get average citation score
+        avg_citation_score = (
+            sum(c.score for c in result.citations) / len(result.citations)
+            if result.citations else 0.0
+        )
+        
+        if doc_found:
+            results["doc_hits"] += 1
+        if answer_ok:
+            results["answer_hits"] += 1
+        total_score += avg_citation_score
+        
+        results["details"].append({
+            "query": query,
+            "doc_found": doc_found,
+            "answer_ok": answer_ok,
+            "citation_score": avg_citation_score,
+            "answer_preview": result.answer[:200],
+        })
+    
+    results["avg_score"] = total_score / len(test_queries) if test_queries else 0.0
+    results["doc_accuracy"] = results["doc_hits"] / results["total"]
+    results["answer_accuracy"] = results["answer_hits"] / results["total"]
+    
+    return results
+
+
+def print_results(results: dict):
+    """Print evaluation results in a formatted way."""
+    print("\n" + "=" * 60)
+    print("RAG Evaluation Results")
+    print("=" * 60)
+    
+    print(f"\nTotal Queries: {results['total']}")
+    print(f"Document Retrieval Accuracy: {results['doc_accuracy']:.1%}")
+    print(f"Answer Relevance Accuracy: {results['answer_accuracy']:.1%}")
+    print(f"Average Citation Score: {results['avg_score']:.3f}")
+    
+    print("\n" + "-" * 60)
+    print("Detailed Results:")
+    print("-" * 60)
+    
+    for i, detail in enumerate(results["details"], 1):
+        status = "PASS" if detail["doc_found"] and detail["answer_ok"] else "FAIL"
+        print(f"\n[{i}] {status}: {detail['query']}")
+        print(f"    Doc Found: {detail['doc_found']}")
+        print(f"    Answer OK: {detail['answer_ok']}")
+        print(f"    Score: {detail['citation_score']:.3f}")
+
+
+def main():
+    print("=" * 60)
+    print("RAG Evaluation Example")
+    print("=" * 60)
+    
+    # Create knowledge base
+    knowledge = Knowledge()
+    knowledge.add("documents/")  # Add your documents
+    
+    # Configure RAG
+    config = RAGConfig(
+        top_k=5,
+        min_score=0.3,
+        include_citations=True,
+    )
+    
+    rag = RAG(knowledge=knowledge, config=config)
+    
+    # Get test queries
+    test_queries = create_test_queries()
+    
+    # Run evaluation
+    print(f"\nEvaluating {len(test_queries)} queries...")
+    results = evaluate_rag(rag, test_queries)
+    
+    # Print results
+    print_results(results)
+    
+    # Save results to file
+    output_path = Path("evaluation_results.json")
+    with open(output_path, "w") as f:
+        json.dump(results, f, indent=2)
+    print(f"\nResults saved to {output_path}")
+    
+    # Return exit code based on results
+    if results["doc_accuracy"] < 0.8 or results["answer_accuracy"] < 0.8:
+        print("\nWARNING: Evaluation metrics below threshold!")
+        return 1
+    
+    print("\nEvaluation PASSED!")
+    return 0
+
+
+if __name__ == "__main__":
+    exit(main())

examples/rag/rag_multi_agent.py

@@ -0,0 +1,95 @@
+"""
+RAG Multi-Agent Example
+
+This example demonstrates how multiple agents can share
+the same knowledge base for collaborative RAG.
+
+Usage:
+    python rag_multi_agent.py
+
+Requirements:
+    pip install praisonaiagents[knowledge]
+"""
+
+from praisonaiagents import Agent, Knowledge, PraisonAIAgents, Task
+
+
+def main():
+    print("=" * 60)
+    print("Multi-Agent RAG: Shared Knowledge Base")
+    print("=" * 60)
+    
+    # Create shared knowledge base
+    shared_knowledge = Knowledge()
+    shared_knowledge.add("documents/")  # Add your documents directory
+    
+    # Create specialized agents sharing the same knowledge
+    researcher = Agent(
+        name="Researcher",
+        role="Research Analyst",
+        goal="Find and analyze relevant information from documents",
+        instructions="""You are a research analyst. Your job is to:
+        1. Search the knowledge base for relevant information
+        2. Extract key facts and data points
+        3. Provide detailed analysis with citations""",
+        knowledge=shared_knowledge,
+    )
+    
+    summarizer = Agent(
+        name="Summarizer",
+        role="Content Summarizer",
+        goal="Create concise summaries of complex information",
+        instructions="""You are a content summarizer. Your job is to:
+        1. Take research findings and create clear summaries
+        2. Highlight the most important points
+        3. Make complex information accessible""",
+        knowledge=shared_knowledge,
+    )
+    
+    writer = Agent(
+        name="Writer",
+        role="Report Writer",
+        goal="Create well-structured reports",
+        instructions="""You are a report writer. Your job is to:
+        1. Combine research and summaries into a cohesive report
+        2. Ensure proper structure and flow
+        3. Include citations where appropriate""",
+        knowledge=shared_knowledge,
+    )
+    
+    # Define tasks
+    research_task = Task(
+        description="Research the main themes and findings in the documents",
+        expected_output="Detailed research notes with citations",
+        agent=researcher,
+    )
+    
+    summary_task = Task(
+        description="Summarize the research findings into key points",
+        expected_output="Bullet-point summary of main findings",
+        agent=summarizer,
+    )
+    
+    report_task = Task(
+        description="Write a comprehensive report based on the research and summary",
+        expected_output="A well-structured report with introduction, findings, and conclusion",
+        agent=writer,
+    )
+    
+    # Run the multi-agent workflow
+    agents = PraisonAIAgents(
+        agents=[researcher, summarizer, writer],
+        tasks=[research_task, summary_task, report_task],
+        process="sequential",
+    )
+    
+    result = agents.start()
+    
+    print("\n" + "=" * 60)
+    print("Final Report")
+    print("=" * 60)
+    print(result)
+
+
+if __name__ == "__main__":
+    main()

examples/rag/rag_pdf_qa.py

@@ -0,0 +1,74 @@
+"""
+RAG PDF Q&A Example
+
+This example demonstrates how to use RAG to answer questions
+about PDF documents with citations.
+
+Usage:
+    python rag_pdf_qa.py
+
+Requirements:
+    pip install praisonaiagents[knowledge]
+"""
+
+from praisonaiagents import Agent, Knowledge
+from praisonaiagents.rag import RAG, RAGConfig
+
+
+def main():
+    # Method 1: Simple Agent with Knowledge
+    print("=" * 60)
+    print("Method 1: Agent with Knowledge Parameter")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="PDF Assistant",
+        instructions="You are a helpful assistant that answers questions about documents.",
+        knowledge=["sample.pdf"],  # Add your PDF files here
+    )
+    
+    response = agent.start("What are the main topics covered in this document?")
+    print(f"\nAnswer: {response}\n")
+    
+    # Method 2: Explicit RAG Pipeline with Citations
+    print("=" * 60)
+    print("Method 2: Explicit RAG Pipeline with Citations")
+    print("=" * 60)
+    
+    # Create knowledge base
+    knowledge = Knowledge()
+    knowledge.add("sample.pdf")  # Add your PDF
+    
+    # Configure RAG
+    config = RAGConfig(
+        top_k=5,
+        min_score=0.3,
+        include_citations=True,
+        max_context_tokens=4000,
+    )
+    
+    # Create RAG pipeline
+    rag = RAG(knowledge=knowledge, config=config)
+    
+    # Query with citations
+    result = rag.query("What methodology was used in this research?")
+    
+    print(f"\nAnswer: {result.answer}")
+    print(f"\nSources ({len(result.citations)}):")
+    for citation in result.citations:
+        print(f"  [{citation.id}] {citation.source} (score: {citation.score:.2f})")
+        print(f"      {citation.text[:100]}...")
+    
+    # Method 3: Streaming Response
+    print("\n" + "=" * 60)
+    print("Method 3: Streaming Response")
+    print("=" * 60)
+    
+    print("\nStreaming answer: ", end="")
+    for chunk in rag.stream("Summarize the key findings"):
+        print(chunk, end="", flush=True)
+    print("\n")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/reranking.py

@@ -0,0 +1,255 @@
+"""
+Reranking: Improving Retrieval Precision
+
+This example demonstrates reranking - a second-pass scoring mechanism
+that improves the quality of retrieved documents.
+
+RAG Concept: Initial retrieval (first stage) prioritizes recall - finding
+all potentially relevant documents. Reranking (second stage) prioritizes
+precision - ordering results by true relevance to the query.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Research paper abstracts
+RESEARCH_PAPERS = [
+    {
+        "id": "paper_001",
+        "content": """
+        Title: Attention Mechanisms in Neural Machine Translation
+        
+        Abstract: This paper introduces a novel attention mechanism for sequence-to-sequence
+        models in machine translation. Unlike previous approaches that use a fixed-length
+        context vector, our method allows the model to automatically search for parts of
+        the source sentence relevant to predicting the target word. Experiments on
+        English-to-French translation show significant improvements over baseline systems.
+        """
+    },
+    {
+        "id": "paper_002",
+        "content": """
+        Title: Deep Residual Learning for Image Recognition
+        
+        Abstract: We present a residual learning framework to ease the training of very deep
+        neural networks. By reformulating layers as learning residual functions with
+        reference to layer inputs, we show that these networks are easier to optimize
+        and can gain accuracy from considerably increased depth. Our 152-layer residual
+        network achieves state-of-the-art results on ImageNet classification.
+        """
+    },
+    {
+        "id": "paper_003",
+        "content": """
+        Title: BERT: Pre-training of Deep Bidirectional Transformers
+        
+        Abstract: We introduce BERT, a new language representation model designed to
+        pre-train deep bidirectional representations from unlabeled text. Unlike previous
+        models, BERT is designed to jointly condition on both left and right context.
+        The pre-trained BERT model can be fine-tuned with just one additional output
+        layer for a wide range of NLP tasks.
+        """
+    },
+    {
+        "id": "paper_004",
+        "content": """
+        Title: Generative Adversarial Networks
+        
+        Abstract: We propose a new framework for estimating generative models via an
+        adversarial process. We simultaneously train two models: a generative model G
+        that captures the data distribution, and a discriminative model D that estimates
+        the probability that a sample came from the training data rather than G.
+        This framework can generate realistic images from random noise.
+        """
+    },
+    {
+        "id": "paper_005",
+        "content": """
+        Title: Transformer Architecture for Language Understanding
+        
+        Abstract: We propose the Transformer, a model architecture eschewing recurrence
+        and instead relying entirely on an attention mechanism to draw global dependencies
+        between input and output. The Transformer allows for significantly more
+        parallelization and achieves new state of the art in translation quality.
+        """
+    },
+    {
+        "id": "paper_006",
+        "content": """
+        Title: Word Embeddings and Semantic Similarity
+        
+        Abstract: This paper explores methods for learning distributed representations
+        of words that capture semantic relationships. We show that word vectors trained
+        on large corpora encode meaningful syntactic and semantic regularities. These
+        representations enable analogical reasoning: king - man + woman = queen.
+        """
+    }
+]
+
+
+def explain_reranking():
+    """Explain the reranking concept."""
+    
+    print("=" * 60)
+    print("RERANKING EXPLAINED")
+    print("=" * 60)
+    
+    print("""
+    🔄 Two-Stage Retrieval Pipeline:
+    
+    Stage 1: Initial Retrieval (Bi-encoder)
+    ├── Fast approximate matching
+    ├── Retrieves top-K candidates (e.g., 20-100)
+    ├── Uses pre-computed embeddings
+    └── Optimizes for RECALL (find all relevant docs)
+    
+    Stage 2: Reranking (Cross-encoder)
+    ├── Slower but more accurate scoring
+    ├── Reorders the top-K candidates
+    ├── Computes query-document interaction
+    └── Optimizes for PRECISION (best docs first)
+    
+    Why Reranking Matters:
+    - Bi-encoders encode query and docs separately (fast but approximate)
+    - Cross-encoders process query+doc together (slow but accurate)
+    - Reranking gives you the best of both worlds
+    """)
+
+
+def rag_with_reranking():
+    """Demonstrate RAG with reranking enabled."""
+    
+    # Create agent with reranking
+    agent = Agent(
+        name="Research Assistant",
+        instructions="""You are a research paper expert.
+        Answer questions about machine learning research.
+        Reference specific papers when relevant.
+        Be precise about technical details.""",
+        knowledge=RESEARCH_PAPERS,
+        knowledge_config={
+            "rerank": True,
+            "top_k": 5
+        },
+        user_id="rerank_demo"
+    )
+    
+    queries = [
+        "Which paper introduced the attention mechanism for translation?",
+        "Tell me about pre-training language models",
+        "What architecture avoids recurrence entirely?"
+    ]
+    
+    print("\n" + "=" * 60)
+    print("RAG WITH RERANKING")
+    print("=" * 60)
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response[:300]}..." if len(str(response)) > 300 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def compare_with_without_reranking():
+    """Compare retrieval quality with and without reranking."""
+    
+    print("\n" + "=" * 60)
+    print("RERANKING IMPACT COMPARISON")
+    print("=" * 60)
+    
+    query = "How do transformers handle long-range dependencies?"
+    
+    # Without reranking
+    agent_no_rerank = Agent(
+        name="No Rerank Agent",
+        instructions="Answer based on the research papers provided.",
+        knowledge=RESEARCH_PAPERS,
+        knowledge_config={"rerank": False, "top_k": 3},
+        user_id="no_rerank"
+    )
+    
+    # With reranking
+    agent_with_rerank = Agent(
+        name="Rerank Agent",
+        instructions="Answer based on the research papers provided.",
+        knowledge=RESEARCH_PAPERS,
+        knowledge_config={"rerank": True, "top_k": 3},
+        user_id="with_rerank"
+    )
+    
+    print(f"\n📝 Query: {query}")
+    
+    print("\n🔍 Without Reranking:")
+    response1 = agent_no_rerank.chat(query)
+    print(f"   {response1[:250]}...")
+    
+    print("\n🔍 With Reranking:")
+    response2 = agent_with_rerank.chat(query)
+    print(f"   {response2[:250]}...")
+
+
+def reranking_configuration():
+    """Show reranking configuration options."""
+    
+    print("\n" + "=" * 60)
+    print("RERANKING CONFIGURATION")
+    print("=" * 60)
+    
+    print("""
+    Configuration options for reranking in PraisonAI:
+    
+    ```python
+    agent = Agent(
+        name="My Agent",
+        knowledge=documents,
+        knowledge_config={
+            # Enable reranking
+            "rerank": True,
+            
+            # Number of candidates for initial retrieval
+            "top_k": 10,
+            
+            # Number of results after reranking
+            "rerank_top_k": 3,
+            
+            # Reranker model (optional)
+            "rerank_model": "cross-encoder/ms-marco-MiniLM-L-6-v2"
+        }
+    )
+    ```
+    
+    Performance Considerations:
+    ┌─────────────────┬──────────────┬─────────────┐
+    │ Configuration   │ Latency      │ Quality     │
+    ├─────────────────┼──────────────┼─────────────┤
+    │ No reranking    │ ~50ms        │ Good        │
+    │ Light reranker  │ ~100ms       │ Better      │
+    │ Heavy reranker  │ ~200ms       │ Best        │
+    └─────────────────┴──────────────┴─────────────┘
+    
+    Tip: Use reranking for precision-critical applications
+    where quality matters more than latency.
+    """)
+
+
+def main():
+    """Run all reranking examples."""
+    print("\n🚀 PraisonAI Reranking Examples\n")
+    
+    # Example 1: Explain reranking
+    explain_reranking()
+    
+    # Example 2: RAG with reranking
+    rag_with_reranking()
+    
+    # Example 3: Compare with/without reranking
+    compare_with_without_reranking()
+    
+    # Example 4: Configuration options
+    reranking_configuration()
+    
+    print("\n✅ Reranking examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/retrieval_policies.py

@@ -0,0 +1,382 @@
+"""
+Retrieval Policies: Agent-Decided Retrieval Strategies
+
+This example demonstrates how agents can dynamically decide retrieval
+strategies based on query characteristics.
+
+RAG Concept: Not all queries are equal. Some need deep retrieval, others
+need broad coverage, and some don't need retrieval at all. Smart policies
+optimize both quality and efficiency.
+"""
+
+from praisonaiagents import Agent, AutoRagAgent, AutoRagConfig
+
+# Sample knowledge base: IT support documentation
+IT_SUPPORT_DOCS = [
+    {
+        "id": "password_reset",
+        "category": "account",
+        "priority": "high",
+        "content": """
+        Password Reset Procedure:
+        1. Go to the self-service portal at https://portal.company.com
+        2. Click "Forgot Password"
+        3. Enter your employee ID and email
+        4. Check your email for the reset link (valid for 1 hour)
+        5. Create a new password meeting complexity requirements
+        
+        If you don't receive the email, check spam or contact IT helpdesk.
+        """
+    },
+    {
+        "id": "vpn_setup",
+        "category": "network",
+        "priority": "medium",
+        "content": """
+        VPN Setup Guide:
+        1. Download the VPN client from the software center
+        2. Install with default settings
+        3. Launch and enter your credentials
+        4. Select the nearest server location
+        5. Click Connect
+        
+        Troubleshooting: If connection fails, check firewall settings
+        and ensure port 443 is open.
+        """
+    },
+    {
+        "id": "email_config",
+        "category": "communication",
+        "priority": "medium",
+        "content": """
+        Email Configuration:
+        - Server: mail.company.com
+        - Port: 993 (IMAP) / 587 (SMTP)
+        - Security: SSL/TLS required
+        - Username: your full email address
+        
+        Mobile setup: Use the Company Portal app for automatic configuration.
+        """
+    },
+    {
+        "id": "printer_setup",
+        "category": "hardware",
+        "priority": "low",
+        "content": """
+        Printer Setup:
+        1. Open Settings > Printers & Scanners
+        2. Click Add Printer
+        3. Select your floor's printer from the list
+        4. Install drivers if prompted
+        
+        Printer naming convention: FLOOR-WING-NUMBER (e.g., 3-EAST-01)
+        """
+    },
+    {
+        "id": "software_request",
+        "category": "software",
+        "priority": "medium",
+        "content": """
+        Software Request Process:
+        1. Submit request through ServiceNow
+        2. Manager approval required for licensed software
+        3. IT reviews for security compliance
+        4. Approved software deployed within 48 hours
+        
+        Pre-approved software can be installed directly from Software Center.
+        """
+    }
+]
+
+
+def query_classification():
+    """Demonstrate query classification for retrieval decisions."""
+    
+    print("=" * 60)
+    print("QUERY CLASSIFICATION FOR RETRIEVAL")
+    print("=" * 60)
+    
+    # Different query types
+    queries = [
+        ("How do I reset my password?", "procedural", True),
+        ("Hello!", "greeting", False),
+        ("What is 2 + 2?", "general_knowledge", False),
+        ("VPN connection keeps failing", "troubleshooting", True),
+        ("Thanks for your help", "closing", False),
+        ("How do I request new software?", "procedural", True),
+    ]
+    
+    print("\n📊 Query Classification Examples:\n")
+    print(f"{'Query':<40} {'Type':<20} {'Needs RAG':<10}")
+    print("-" * 70)
+    
+    for query, query_type, needs_rag in queries:
+        rag_indicator = "✅ Yes" if needs_rag else "❌ No"
+        print(f"{query:<40} {query_type:<20} {rag_indicator:<10}")
+    
+    print("\n💡 Smart agents classify queries to decide retrieval strategy.")
+
+
+def policy_based_retrieval():
+    """Demonstrate different retrieval policies."""
+    
+    print("\n" + "=" * 60)
+    print("RETRIEVAL POLICY COMPARISON")
+    print("=" * 60)
+    
+    base_agent = Agent(
+        name="IT Support",
+        instructions="You are an IT support agent. Help users with technical issues.",
+        knowledge=IT_SUPPORT_DOCS,
+        user_id="policy_demo"
+    )
+    
+    query = "How do I set up VPN?"
+    
+    policies = [
+        ("auto", "Agent decides based on query analysis"),
+        ("always", "Always retrieve, even for simple queries"),
+        ("never", "Never retrieve, use only parametric knowledge"),
+    ]
+    
+    print(f"\n📝 Query: {query}\n")
+    
+    for policy, description in policies:
+        config = AutoRagConfig(policy=policy)
+        auto_agent = AutoRagAgent(agent=base_agent, config=config)
+        
+        print(f"🔄 Policy: {policy.upper()}")
+        print(f"   Description: {description}")
+        
+        response = auto_agent.chat(query, user_id="policy_demo")
+        print(f"   Response: {response[:150]}...")
+        print()
+
+
+def adaptive_top_k():
+    """Demonstrate adaptive top-k based on query complexity."""
+    
+    print("\n" + "=" * 60)
+    print("ADAPTIVE TOP-K RETRIEVAL")
+    print("=" * 60)
+    
+    print("""
+    📊 Adaptive Top-K Strategy:
+    
+    Query Complexity → Top-K Value
+    ─────────────────────────────────
+    Simple (single topic)     → k=1-2
+    Moderate (related topics) → k=3-5
+    Complex (multi-topic)     → k=5-10
+    
+    Examples:
+    """)
+    
+    queries = [
+        ("Reset password", 2, "Simple - single procedure"),
+        ("VPN not working, also need email setup", 5, "Moderate - two topics"),
+        ("New employee setup: email, VPN, printer, software", 8, "Complex - multiple topics"),
+    ]
+    
+    for query, suggested_k, complexity in queries:
+        print(f"   Query: \"{query}\"")
+        print(f"   Complexity: {complexity}")
+        print(f"   Suggested top_k: {suggested_k}")
+        print()
+
+
+def priority_based_retrieval():
+    """Demonstrate priority-based document retrieval."""
+    
+    print("\n" + "=" * 60)
+    print("PRIORITY-BASED RETRIEVAL")
+    print("=" * 60)
+    
+    # Group documents by priority
+    high_priority = [d for d in IT_SUPPORT_DOCS if d.get('priority') == 'high']
+    all_docs = IT_SUPPORT_DOCS
+    
+    print("\n📊 Document Priorities in Knowledge Base:")
+    for doc in IT_SUPPORT_DOCS:
+        print(f"   [{doc['priority'].upper():^6}] {doc['id']}")
+    
+    # High-priority agent (for urgent issues)
+    urgent_agent = Agent(
+        name="Urgent Support",
+        instructions="Handle urgent IT issues. Focus on critical procedures.",
+        knowledge=high_priority,
+        user_id="urgent_demo"
+    )
+    
+    # Full knowledge agent
+    full_agent = Agent(
+        name="Full Support",
+        instructions="Handle all IT support requests.",
+        knowledge=all_docs,
+        user_id="full_demo"
+    )
+    
+    query = "I'm locked out of my account!"
+    
+    print(f"\n📝 Urgent Query: {query}")
+    
+    print("\n🚨 Urgent Support Agent (high-priority docs only):")
+    response1 = urgent_agent.chat(query)
+    print(f"   {response1[:200]}...")
+    
+    print("\n📚 Full Support Agent (all docs):")
+    response2 = full_agent.chat(query)
+    print(f"   {response2[:200]}...")
+
+
+def context_aware_retrieval():
+    """Demonstrate context-aware retrieval strategies."""
+    
+    print("\n" + "=" * 60)
+    print("CONTEXT-AWARE RETRIEVAL")
+    print("=" * 60)
+    
+    print("""
+    🎯 Context-Aware Retrieval Strategies:
+    
+    1. **User Role Context**
+       - New employee → Onboarding docs first
+       - IT admin → Technical docs
+       - Executive → Summary docs
+    
+    2. **Time Context**
+       - Business hours → Standard support
+       - After hours → Emergency procedures only
+    
+    3. **Conversation Context**
+       - First message → Broad retrieval
+       - Follow-up → Narrow to topic
+    
+    4. **Device Context**
+       - Mobile → Mobile-specific guides
+       - Desktop → Full documentation
+    
+    Implementation:
+    ```python
+    def get_retrieval_config(context: dict) -> dict:
+        if context.get("user_role") == "new_employee":
+            return {"filter": {"category": "onboarding"}, "top_k": 5}
+        elif context.get("is_followup"):
+            return {"top_k": 2}  # Narrow focus
+        else:
+            return {"top_k": 3}  # Default
+    ```
+    """)
+
+
+def fallback_strategies():
+    """Demonstrate fallback strategies when retrieval fails."""
+    
+    print("\n" + "=" * 60)
+    print("RETRIEVAL FALLBACK STRATEGIES")
+    print("=" * 60)
+    
+    print("""
+    🔄 Fallback Strategy Chain:
+    
+    1. Primary: Vector search in knowledge base
+       ↓ (if no relevant results)
+    2. Secondary: Expand search with synonyms
+       ↓ (if still no results)
+    3. Tertiary: Search external sources
+       ↓ (if still no results)
+    4. Final: Acknowledge limitation, offer alternatives
+    
+    Example Implementation:
+    ```python
+    def retrieve_with_fallback(query: str, knowledge) -> str:
+        # Primary retrieval
+        results = knowledge.search(query, top_k=3)
+        
+        if results and results[0].score > 0.7:
+            return format_context(results)
+        
+        # Fallback: Expand query
+        expanded = expand_query(query)  # Add synonyms
+        results = knowledge.search(expanded, top_k=5)
+        
+        if results and results[0].score > 0.5:
+            return format_context(results)
+        
+        # Final fallback
+        return "I don't have specific information about that. " \\
+               "Please contact IT helpdesk at ext. 1234."
+    ```
+    """)
+
+
+def retrieval_metrics():
+    """Explain retrieval quality metrics."""
+    
+    print("\n" + "=" * 60)
+    print("RETRIEVAL QUALITY METRICS")
+    print("=" * 60)
+    
+    print("""
+    📈 Key Metrics for Retrieval Policy Tuning:
+    
+    ┌─────────────────┬────────────────────────────────────────┐
+    │ Metric          │ Description                            │
+    ├─────────────────┼────────────────────────────────────────┤
+    │ Precision@K     │ % of retrieved docs that are relevant │
+    │ Recall@K        │ % of relevant docs that are retrieved │
+    │ MRR             │ Mean Reciprocal Rank of first hit     │
+    │ NDCG            │ Normalized Discounted Cumulative Gain │
+    │ Latency         │ Time to retrieve (ms)                 │
+    │ Token Usage     │ Context tokens consumed               │
+    └─────────────────┴────────────────────────────────────────┘
+    
+    Policy Optimization Goals:
+    
+    - High Precision: Use smaller top_k, stricter thresholds
+    - High Recall: Use larger top_k, relaxed thresholds
+    - Low Latency: Use smaller top_k, skip reranking
+    - Low Cost: Use smaller top_k, aggressive filtering
+    
+    Balanced Default:
+    ```python
+    config = {
+        "top_k": 3,
+        "score_threshold": 0.5,
+        "rerank": True,
+        "max_tokens": 2000
+    }
+    ```
+    """)
+
+
+def main():
+    """Run all retrieval policy examples."""
+    print("\n🚀 PraisonAI Retrieval Policies Examples\n")
+    
+    # Example 1: Query classification
+    query_classification()
+    
+    # Example 2: Policy comparison
+    policy_based_retrieval()
+    
+    # Example 3: Adaptive top-k
+    adaptive_top_k()
+    
+    # Example 4: Priority-based
+    priority_based_retrieval()
+    
+    # Example 5: Context-aware
+    context_aware_retrieval()
+    
+    # Example 6: Fallback strategies
+    fallback_strategies()
+    
+    # Example 7: Metrics
+    retrieval_metrics()
+    
+    print("\n✅ Retrieval policies examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/structured_output.py

@@ -0,0 +1,334 @@
+"""
+Structured Output RAG: Guided and Constrained Generation
+
+This example demonstrates how to guide RAG output into specific formats
+and structures using Pydantic models and output constraints.
+
+RAG Concept: Sometimes you need RAG answers in a specific format - JSON,
+tables, or structured data. Guided generation ensures the LLM output
+conforms to your schema.
+"""
+
+from typing import List
+from pydantic import BaseModel, Field
+from praisonaiagents import Agent
+
+# Sample knowledge base: Product catalog
+PRODUCT_CATALOG = [
+    {
+        "id": "prod_001",
+        "content": """
+        Product: UltraWidget Pro
+        Category: Electronics
+        Price: $299.99
+        Rating: 4.5/5 (1,250 reviews)
+        Features: Wireless connectivity, 12-hour battery, water-resistant
+        Availability: In stock
+        SKU: UW-PRO-2024
+        """
+    },
+    {
+        "id": "prod_002",
+        "content": """
+        Product: SmartHome Hub
+        Category: Home Automation
+        Price: $149.99
+        Rating: 4.2/5 (890 reviews)
+        Features: Voice control, 100+ device compatibility, energy monitoring
+        Availability: In stock
+        SKU: SH-HUB-2024
+        """
+    },
+    {
+        "id": "prod_003",
+        "content": """
+        Product: ErgoDesk Standing Desk
+        Category: Furniture
+        Price: $599.99
+        Rating: 4.8/5 (2,100 reviews)
+        Features: Electric height adjustment, memory presets, cable management
+        Availability: Ships in 3-5 days
+        SKU: ED-STAND-2024
+        """
+    },
+    {
+        "id": "prod_004",
+        "content": """
+        Product: CloudSync Backup Drive
+        Category: Storage
+        Price: $199.99
+        Rating: 4.6/5 (1,800 reviews)
+        Features: 4TB capacity, automatic backup, encrypted storage
+        Availability: In stock
+        SKU: CS-4TB-2024
+        """
+    }
+]
+
+
+# Pydantic models for structured output
+class ProductInfo(BaseModel):
+    """Structured product information."""
+    name: str = Field(description="Product name")
+    price: float = Field(description="Price in USD")
+    rating: float = Field(description="Rating out of 5")
+    in_stock: bool = Field(description="Whether product is in stock")
+    key_features: List[str] = Field(description="Top 3 features")
+
+
+class ProductComparison(BaseModel):
+    """Structured comparison of products."""
+    products: List[str] = Field(description="Names of compared products")
+    winner: str = Field(description="Recommended product")
+    reason: str = Field(description="Why this product is recommended")
+    price_range: str = Field(description="Price range of compared products")
+
+
+class SearchResult(BaseModel):
+    """Structured search result."""
+    query: str = Field(description="Original search query")
+    matches: List[str] = Field(description="Matching product names")
+    total_found: int = Field(description="Number of matches")
+    summary: str = Field(description="Brief summary of results")
+
+
+def json_output_rag():
+    """Demonstrate RAG with JSON-structured output."""
+    
+    print("=" * 60)
+    print("JSON-STRUCTURED RAG OUTPUT")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Product Assistant",
+        instructions="""You are a product assistant that provides structured information.
+        Always respond with valid JSON matching the requested format.
+        Extract accurate information from the product catalog.""",
+        knowledge=PRODUCT_CATALOG,
+        user_id="json_demo"
+    )
+    
+    # Request structured product info
+    query = """
+    Tell me about the UltraWidget Pro. Respond in this JSON format:
+    {
+        "name": "product name",
+        "price": 0.00,
+        "rating": 0.0,
+        "in_stock": true/false,
+        "key_features": ["feature1", "feature2", "feature3"]
+    }
+    """
+    
+    print("\n📝 Query: Get structured product info")
+    response = agent.chat(query)
+    print("💡 Structured Response:\n" + str(response))
+    print("-" * 40)
+
+
+def pydantic_guided_rag():
+    """Demonstrate RAG with Pydantic model guidance."""
+    
+    print("\n" + "=" * 60)
+    print("PYDANTIC-GUIDED RAG")
+    print("=" * 60)
+    
+    # Generate schema from Pydantic model
+    schema = ProductComparison.model_json_schema()
+    
+    agent = Agent(
+        name="Product Comparator",
+        instructions=f"""You compare products and provide structured recommendations.
+        Your response MUST be valid JSON matching this schema:
+        {schema}
+        
+        Be objective and base recommendations on the product data.""",
+        knowledge=PRODUCT_CATALOG,
+        user_id="pydantic_demo"
+    )
+    
+    query = "Compare the UltraWidget Pro and SmartHome Hub. Which should I buy?"
+    
+    print(f"\n📝 Query: {query}")
+    print("📋 Expected Schema: ProductComparison")
+    
+    response = agent.chat(query)
+    print(f"\n💡 Structured Comparison:\n{response}")
+
+
+def table_format_rag():
+    """Demonstrate RAG with table-formatted output."""
+    
+    print("\n" + "=" * 60)
+    print("TABLE-FORMATTED RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Catalog Browser",
+        instructions="""You present product information in clean table format.
+        Use markdown tables for structured data.
+        Include relevant columns based on the query.""",
+        knowledge=PRODUCT_CATALOG,
+        user_id="table_demo"
+    )
+    
+    query = "Show me all products in a table with name, price, and rating."
+    
+    print(f"\n📝 Query: {query}")
+    response = agent.chat(query)
+    print(f"\n💡 Table Output:\n{response}")
+
+
+def list_format_rag():
+    """Demonstrate RAG with list-formatted output."""
+    
+    print("\n" + "=" * 60)
+    print("LIST-FORMATTED RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Feature Lister",
+        instructions="""You extract and present information as organized lists.
+        Use bullet points and numbered lists appropriately.
+        Group related items together.""",
+        knowledge=PRODUCT_CATALOG,
+        user_id="list_demo"
+    )
+    
+    queries = [
+        "List all product features across the catalog",
+        "What are the top-rated products? List them in order."
+    ]
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 List Output:\n{response[:400]}..." if len(str(response)) > 400 else f"💡 List Output:\n{response}")
+        print("-" * 40)
+
+
+def constrained_generation():
+    """Demonstrate constrained/guided generation patterns."""
+    
+    print("\n" + "=" * 60)
+    print("CONSTRAINED GENERATION PATTERNS")
+    print("=" * 60)
+    
+    print("""
+    🎯 Constrained Generation Techniques:
+    
+    1. **Schema Enforcement**
+       - Provide JSON schema in instructions
+       - Agent outputs valid JSON matching schema
+       ```python
+       instructions = f"Respond with JSON matching: {schema}"
+       ```
+    
+    2. **Format Templates**
+       - Give explicit output templates
+       - Agent fills in the blanks
+       ```python
+       instructions = '''
+       Respond in this format:
+       PRODUCT: [name]
+       PRICE: $[amount]
+       VERDICT: [recommendation]
+       '''
+       ```
+    
+    3. **Pydantic Validation**
+       - Parse agent output with Pydantic
+       - Retry if validation fails
+       ```python
+       response = agent.chat(query)
+       try:
+           result = ProductInfo.model_validate_json(response)
+       except ValidationError:
+           # Retry with clarification
+       ```
+    
+    4. **Output Parsers**
+       - Post-process agent output
+       - Extract structured data from text
+       ```python
+       def parse_product(text: str) -> dict:
+           # Extract price, rating, etc.
+           return structured_data
+       ```
+    
+    5. **Few-Shot Examples**
+       - Show examples of desired output format
+       - Agent learns pattern from examples
+       ```python
+       instructions = '''
+       Example:
+       Q: Tell me about Product X
+       A: {"name": "Product X", "price": 99.99}
+       
+       Now answer the user's question in the same format.
+       '''
+       ```
+    """)
+
+
+def multi_format_rag():
+    """Demonstrate switching between output formats."""
+    
+    print("\n" + "=" * 60)
+    print("MULTI-FORMAT RAG")
+    print("=" * 60)
+    
+    base_knowledge = PRODUCT_CATALOG
+    query = "Tell me about the ErgoDesk Standing Desk"
+    
+    formats = [
+        ("JSON", "Respond with a JSON object containing name, price, and features."),
+        ("Markdown", "Respond with a markdown-formatted product card with headers."),
+        ("Plain Text", "Respond with a brief, conversational product description."),
+        ("Bullet Points", "Respond with bullet points covering key product details.")
+    ]
+    
+    print(f"\n📝 Base Query: {query}")
+    print("\n🔄 Same query, different output formats:\n")
+    
+    for format_name, format_instruction in formats:
+        agent = Agent(
+            name=f"{format_name} Agent",
+            instructions=f"You are a product expert. {format_instruction}",
+            knowledge=base_knowledge,
+            user_id=f"format_{format_name.lower()}"
+        )
+        
+        response = agent.chat(query)
+        print(f"📋 {format_name} Format:")
+        print(f"{response[:250]}..." if len(str(response)) > 250 else response)
+        print("-" * 40)
+
+
+def main():
+    """Run all structured output RAG examples."""
+    print("\n🚀 PraisonAI Structured Output RAG Examples\n")
+    
+    # Example 1: JSON output
+    json_output_rag()
+    
+    # Example 2: Pydantic-guided
+    pydantic_guided_rag()
+    
+    # Example 3: Table format
+    table_format_rag()
+    
+    # Example 4: List format
+    list_format_rag()
+    
+    # Example 5: Constrained generation patterns
+    constrained_generation()
+    
+    # Example 6: Multi-format
+    multi_format_rag()
+    
+    print("\n✅ Structured output RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

scripts/generate_cli_docs.py

@@ -0,0 +1,136 @@
+#!/usr/bin/env python3
+"""
+Auto-generate CLI reference documentation from --help output.
+
+Usage:
+    python scripts/generate_cli_docs.py > docs/cli/reference.mdx
+    python scripts/generate_cli_docs.py --output docs/cli/reference.mdx
+
+This script runs `praisonai <cmd> --help` for key commands and generates
+deterministic markdown documentation.
+"""
+
+import subprocess
+import sys
+import argparse
+from datetime import datetime
+from pathlib import Path
+
+
+# Commands to document
+COMMANDS = [
+    # Top-level
+    ("praisonai", "--help"),
+    # Knowledge
+    ("praisonai knowledge", "--help"),
+    ("praisonai knowledge index", "--help"),
+    ("praisonai knowledge search", "--help"),
+    ("praisonai knowledge add", "--help"),
+    ("praisonai knowledge list", "--help"),
+    # RAG
+    ("praisonai rag", "--help"),
+    ("praisonai rag index", "--help"),
+    ("praisonai rag query", "--help"),
+    ("praisonai rag chat", "--help"),
+    ("praisonai rag eval", "--help"),
+    ("praisonai rag serve", "--help"),
+    # Run
+    ("praisonai run", "--help"),
+    # Chat
+    ("praisonai chat", "--help"),
+]
+
+
+def run_help(cmd: str, flag: str = "--help") -> str:
+    """Run a command with --help and capture output."""
+    full_cmd = f"{cmd} {flag}"
+    try:
+        result = subprocess.run(
+            full_cmd.split(),
+            capture_output=True,
+            text=True,
+            timeout=30,
+        )
+        return result.stdout or result.stderr or "(no output)"
+    except subprocess.TimeoutExpired:
+        return "(timeout)"
+    except FileNotFoundError:
+        return "(command not found)"
+    except Exception as e:
+        return f"(error: {e})"
+
+
+def generate_markdown() -> str:
+    """Generate markdown documentation."""
+    lines = [
+        "---",
+        "title: CLI Reference",
+        "description: Auto-generated CLI command reference",
+        "---",
+        "",
+        "# PraisonAI CLI Reference",
+        "",
+        "This page is auto-generated from `--help` output.",
+        "",
+        f"Generated: {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')} UTC",
+        "",
+    ]
+    
+    for cmd, flag in COMMANDS:
+        # Section header
+        cmd_name = cmd.replace("praisonai ", "").replace(" ", "-") or "main"
+        lines.append(f"## `{cmd}`")
+        lines.append("")
+        lines.append("```")
+        
+        # Get help output
+        output = run_help(cmd, flag)
+        lines.append(output.strip())
+        
+        lines.append("```")
+        lines.append("")
+    
+    return "\n".join(lines)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Generate CLI docs from --help")
+    parser.add_argument(
+        "--output", "-o",
+        type=Path,
+        help="Output file path (default: stdout)",
+    )
+    parser.add_argument(
+        "--check",
+        action="store_true",
+        help="Check if output matches existing file (for CI)",
+    )
+    args = parser.parse_args()
+    
+    content = generate_markdown()
+    
+    if args.check and args.output:
+        if args.output.exists():
+            existing = args.output.read_text()
+            # Compare ignoring timestamp line
+            existing_lines = [l for l in existing.split("\n") if not l.startswith("Generated:")]
+            new_lines = [l for l in content.split("\n") if not l.startswith("Generated:")]
+            if existing_lines != new_lines:
+                print(f"ERROR: {args.output} is out of date. Run: python scripts/generate_cli_docs.py -o {args.output}", file=sys.stderr)
+                sys.exit(1)
+            print(f"OK: {args.output} is up to date")
+            sys.exit(0)
+        else:
+            print(f"ERROR: {args.output} does not exist", file=sys.stderr)
+            sys.exit(1)
+    
+    if args.output:
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        args.output.write_text(content)
+        print(f"Written to {args.output}")
+    else:
+        print(content)
+
+
+if __name__ == "__main__":
+    main()

src/praisonai-agents/praisonaiagents/__init__.py

@@ -189,7 +189,21 @@ def __getattr__(name):
         _lazy_cache[name] = AutoAgents
         return AutoAgents
     
-    # Session (imports requests)
+    # AutoRagAgent (auto RAG retrieval decision)
+    elif name == "AutoRagAgent":
+        from .agents.auto_rag_agent import AutoRagAgent
+        _lazy_cache[name] = AutoRagAgent
+        return AutoRagAgent
+    elif name == "AutoRagConfig":
+        from .agents.auto_rag_agent import AutoRagConfig
+        _lazy_cache[name] = AutoRagConfig
+        return AutoRagConfig
+    elif name == "RagRetrievalPolicy":
+        from .agents.auto_rag_agent import RetrievalPolicy
+        _lazy_cache[name] = RetrievalPolicy
+        return RetrievalPolicy
+    
+    # Session (imports requests lazily via session/api.py)
     elif name == "Session":
         from .session import Session
         _lazy_cache[name] = Session
@@ -234,6 +248,24 @@ def __getattr__(name):
         _lazy_cache[name] = LineRange
         return LineRange
     
+    # RAG module (lazy loaded for zero performance impact)
+    elif name == "RAG":
+        from praisonaiagents.rag import RAG
+        _lazy_cache[name] = RAG
+        return RAG
+    elif name == "RAGConfig":
+        from praisonaiagents.rag import RAGConfig
+        _lazy_cache[name] = RAGConfig
+        return RAGConfig
+    elif name == "RAGResult":
+        from praisonaiagents.rag import RAGResult
+        _lazy_cache[name] = RAGResult
+        return RAGResult
+    elif name == "RAGCitation":
+        from praisonaiagents.rag import Citation
+        _lazy_cache[name] = Citation
+        return Citation
+    
     # Agent Skills support (lazy loaded for zero performance impact)
     elif name == "SkillManager":
         from praisonaiagents.skills import SkillManager
@@ -374,6 +406,9 @@ __all__ = [
     'TaskOutput',
     'ReflectionOutput',
     'AutoAgents',
+    'AutoRagAgent',
+    'AutoRagConfig',
+    'RagRetrievalPolicy',
     'Session',
     'Memory',
     'db',
@@ -463,4 +498,9 @@ __all__ = [
     # UI
     'AGUI',
     'A2A',
+    # RAG
+    'RAG',
+    'RAGConfig',
+    'RAGResult',
+    'RAGCitation',
 ]

src/praisonai-agents/praisonaiagents/agent/agent.py

@@ -45,6 +45,7 @@ if TYPE_CHECKING:
     from ..task.task import Task
     from ..main import TaskOutput
     from ..handoff import Handoff
+    from ..rag.models import RAGResult, ContextPack
 
 class Agent:
     @classmethod
@@ -271,6 +272,7 @@ class Agent:
         embedder_config: Optional[Dict[str, Any]] = None,
         knowledge: Optional[List[str]] = None,
         knowledge_config: Optional[Dict[str, Any]] = None,
+        rag_config: Optional[Dict[str, Any]] = None,
         use_system_prompt: Optional[bool] = True,
         markdown: bool = True,
         stream: bool = False,
@@ -309,7 +311,9 @@ class Agent:
         session_id: Optional[str] = None,
         hooks: Optional[List[Any]] = None,
         llm_config: Optional[Dict[str, Any]] = None,
-        rate_limiter: Optional[Any] = None
+        rate_limiter: Optional[Any] = None,
+        auto_summarize: bool = False,
+        summarize_threshold: float = 0.8
     ):
         """Initialize an Agent instance.
 
@@ -461,6 +465,12 @@ class Agent:
         # Store rate limiter (optional, zero overhead when None)
         self._rate_limiter = rate_limiter
         
+        # Auto-summarization configuration
+        if auto_summarize and not (0.0 < summarize_threshold <= 1.0):
+            raise ValueError("summarize_threshold must be between 0.0 and 1.0")
+        self.auto_summarize = auto_summarize
+        self.summarize_threshold = summarize_threshold
+        
         # Store OpenAI client parameters for lazy initialization
         self._openai_api_key = api_key
         self._openai_base_url = base_url
@@ -661,11 +671,15 @@ Your Goal: {self.goal}
             self.knowledge = None
             self._knowledge_sources = None
             self._knowledge_processed = True  # No knowledge to process
+            self._rag_config = None
+            self._rag_instance = None
         else:
             # Store knowledge sources for lazy processing
             self._knowledge_sources = knowledge
             self._knowledge_processed = False
             self._knowledge_config = knowledge_config
+            self._rag_config = rag_config  # Store RAG config for citations/streaming
+            self._rag_instance = None  # Lazy loaded RAG instance
             self.knowledge = None  # Will be initialized on first use
 
         # Fast Context configuration (lazy loaded)
@@ -687,6 +701,11 @@ Your Goal: {self.goal}
         self._db = db
         self._session_id = session_id
         self._db_initialized = False
+        
+        # Session store for JSON-based persistence (lazy - no imports until used)
+        # Used when session_id is provided but no DB adapter
+        self._session_store = None
+        self._session_store_initialized = False
 
     @property
     def console(self):
@@ -1187,6 +1206,185 @@ Your Goal: {self.goal}
             
             self._knowledge_processed = True
     
+    @property
+    def rag(self):
+        """
+        Lazy-loaded RAG instance for advanced retrieval with citations.
+        
+        Returns RAG instance configured with agent's knowledge and rag_config.
+        Returns None if no knowledge sources are configured.
+        
+        Usage:
+            agent = Agent(knowledge=["doc.pdf"], rag_config={"include_citations": True})
+            result = agent.rag.query("What is the main finding?")
+            print(result.answer)
+            for citation in result.citations:
+                print(f"[{citation.id}] {citation.source}")
+        """
+        if not self._knowledge_sources:
+            return None
+        
+        if self._rag_instance is None:
+            self._ensure_knowledge_processed()
+            if self.knowledge:
+                try:
+                    from praisonaiagents.rag import RAG, RAGConfig
+                    
+                    # Build RAGConfig from rag_config dict
+                    rag_config_obj = RAGConfig()
+                    if self._rag_config:
+                        for key, value in self._rag_config.items():
+                            if hasattr(rag_config_obj, key):
+                                setattr(rag_config_obj, key, value)
+                    
+                    # Get LLM instance for RAG
+                    llm = None
+                    if hasattr(self, 'llm_instance') and self.llm_instance:
+                        llm = self.llm_instance
+                    
+                    self._rag_instance = RAG(
+                        knowledge=self.knowledge,
+                        config=rag_config_obj,
+                        llm=llm,
+                    )
+                except ImportError:
+                    logging.warning("RAG module not available. Install with: pip install 'praisonaiagents[rag]'")
+                    return None
+        
+        return self._rag_instance
+    
+    def _get_knowledge_context(self, query: str, use_rag: bool = False) -> tuple:
+        """
+        Get knowledge context for a query.
+        
+        Args:
+            query: The user's question/prompt
+            use_rag: If True and rag_config is set, use RAG pipeline for citations
+            
+        Returns:
+            Tuple of (context_string, citations_list or None)
+        """
+        if not self._knowledge_sources:
+            return "", None
+        
+        self._ensure_knowledge_processed()
+        
+        if not self.knowledge:
+            return "", None
+        
+        # Use RAG if configured and requested
+        if use_rag and self._rag_config and self.rag:
+            try:
+                result = self.rag.query(query, user_id=self.user_id, agent_id=self.agent_id)
+                return result.context_used, result.citations
+            except Exception as e:
+                logging.warning(f"RAG query failed, falling back to basic retrieval: {e}")
+        
+        # Basic retrieval (original behavior)
+        search_results = self.knowledge.search(query, agent_id=self.agent_id)
+        if not search_results:
+            return "", None
+        
+        # Format results
+        if isinstance(search_results, dict) and 'results' in search_results:
+            knowledge_content = "\n".join([result['memory'] for result in search_results['results']])
+        else:
+            knowledge_content = "\n".join(search_results) if isinstance(search_results, list) else str(search_results)
+        
+        return knowledge_content, None
+    
+    def rag_query(self, question: str, **kwargs) -> "RAGResult":
+        """
+        Query knowledge using RAG pipeline with citations.
+        
+        This is the recommended way to get answers with citations from an agent's knowledge.
+        
+        Args:
+            question: The question to answer
+            **kwargs: Additional arguments passed to RAG.query()
+            
+        Returns:
+            RAGResult with answer, citations, context_used, and metadata
+            
+        Raises:
+            ValueError: If no knowledge sources are configured
+            ImportError: If RAG module is not available
+            
+        Usage:
+            agent = Agent(knowledge=["doc.pdf"], rag_config={"include_citations": True})
+            result = agent.rag_query("What is the main finding?")
+            print(result.answer)
+            for citation in result.citations:
+                print(f"[{citation.id}] {citation.source}: {citation.text[:100]}")
+        """
+        if not self._knowledge_sources:
+            raise ValueError("No knowledge sources configured. Add knowledge=[] to Agent init.")
+        
+        if not self.rag:
+            raise ImportError("RAG module not available. Install with: pip install 'praisonaiagents[rag]'")
+        
+        # Pass agent context
+        kwargs.setdefault('user_id', self.user_id)
+        kwargs.setdefault('agent_id', self.agent_id)
+        
+        return self.rag.query(question, **kwargs)
+    
+    def chat_with_context(
+        self,
+        message: str,
+        context: "ContextPack",
+        *,
+        citations_mode: str = "append",
+        **kwargs,
+    ) -> str:
+        """
+        Chat with pre-retrieved context.
+        
+        This method allows AutoRagAgent or manual workflows to inject 
+        pre-retrieved context into the agent's chat, enabling conditional 
+        retrieval without duplicating RAG logic.
+        
+        Args:
+            message: User message/question
+            context: ContextPack from RAG.retrieve()
+            citations_mode: How to include citations (append/hidden/none)
+            **kwargs: Additional arguments passed to chat()
+            
+        Returns:
+            Agent response with optional citations
+            
+        Usage:
+            from praisonaiagents import AutoRagAgent
+            
+            auto_rag = AutoRagAgent(agent=my_agent)
+            result = auto_rag.chat("What are the key findings?")
+            
+            # Or manually:
+            context_pack = rag.retrieve("What are the key findings?")
+            response = agent.chat_with_context("What are the key findings?", context_pack)
+        """
+        # Build augmented prompt with context
+        augmented_prompt = f"""Based on the following context, answer the question.
+
+Context:
+{context.context}
+
+Question: {message}
+
+Answer:"""
+        
+        # Call chat with augmented prompt
+        response = self.chat(augmented_prompt, **kwargs)
+        
+        # Add citations if configured
+        if citations_mode == "append" and context.has_citations:
+            sources = "\n\nSources:\n"
+            for citation in context.citations:
+                sources += f"  [{citation.id}] {citation.source}\n"
+            response = response + sources
+        
+        return response
+    
     def _process_knowledge(self, knowledge_item):
         """Process and store knowledge from a file path, URL, or string."""
         try:
@@ -2094,6 +2292,49 @@ Your Goal: {self.goal}"""
         
         self._db_initialized = True
         self._current_run_id = None  # Track current run
+    
+    def _init_session_store(self):
+        """
+        Initialize session store for JSON-based persistence (lazy, first chat only).
+        
+        This is used when session_id is provided but no DB adapter.
+        Enables automatic session persistence with zero configuration.
+        """
+        if self._session_store_initialized:
+            return
+        
+        # Only initialize if session_id is provided and no DB adapter
+        if self._session_id is None or self._db is not None:
+            self._session_store_initialized = True
+            return
+        
+        try:
+            from ..session import get_default_session_store
+            self._session_store = get_default_session_store()
+            
+            # Restore chat history from previous session
+            history = self._session_store.get_chat_history(self._session_id)
+            if history:
+                # Only restore if chat_history is empty (avoid duplicates)
+                if not self.chat_history:
+                    for msg in history:
+                        self.chat_history.append({
+                            "role": msg["role"],
+                            "content": msg["content"]
+                        })
+                    logging.info(f"Restored session {self._session_id} with {len(history)} messages from JSON store")
+            
+            # Set agent info
+            self._session_store.set_agent_info(
+                self._session_id,
+                agent_name=self.name,
+                user_id=self.user_id,
+            )
+        except Exception as e:
+            logging.warning(f"Failed to initialize session store: {e}")
+            self._session_store = None
+        
+        self._session_store_initialized = True
 
     def _start_run(self, input_content: str):
         """Start a new run (turn) for persistence tracking."""
@@ -2135,17 +2376,27 @@ Your Goal: {self.goal}"""
         self._current_run_id = None
 
     def _persist_message(self, role: str, content: str):
-        """Persist a message to the DB if adapter is provided."""
-        if self._db is None:
+        """Persist a message to the DB or session store."""
+        # Try DB adapter first
+        if self._db is not None:
+            try:
+                if role == "user":
+                    self._db.on_user_message(self._session_id, content)
+                elif role == "assistant":
+                    self._db.on_agent_message(self._session_id, content)
+            except Exception as e:
+                logging.warning(f"Failed to persist message to DB: {e}")
             return
         
-        try:
-            if role == "user":
-                self._db.on_user_message(self._session_id, content)
-            elif role == "assistant":
-                self._db.on_agent_message(self._session_id, content)
-        except Exception as e:
-            logging.warning(f"Failed to persist message: {e}")
+        # Fall back to session store (JSON-based)
+        if self._session_store is not None and self._session_id is not None:
+            try:
+                if role == "user":
+                    self._session_store.add_user_message(self._session_id, content)
+                elif role == "assistant":
+                    self._session_store.add_assistant_message(self._session_id, content)
+            except Exception as e:
+                logging.warning(f"Failed to persist message to session store: {e}")
 
     @property
     def session_id(self) -> Optional[str]:
@@ -2160,6 +2411,10 @@ Your Goal: {self.goal}"""
         # Initialize DB session on first chat (lazy)
         self._init_db_session()
         
+        # Initialize session store for JSON-based persistence (lazy)
+        # This enables automatic session persistence when session_id is provided
+        self._init_session_store()
+        
         # Start a new run for this chat turn
         prompt_str = prompt if isinstance(prompt, str) else str(prompt)
         self._start_run(prompt_str)
@@ -3061,6 +3316,31 @@ Write the complete compiled report:"""
         # Return the single result if only one step
         return results[0]["result"] if results else None
 
+    def switch_model(self, new_model: str) -> None:
+        """
+        Switch the agent's LLM model while preserving conversation history.
+        
+        Args:
+            new_model: The new model name to switch to (e.g., "gpt-4o", "claude-3-sonnet")
+        """
+        # Store the new model name
+        self.llm = new_model
+        
+        # Recreate the LLM instance with the new model
+        try:
+            from ..llm.llm import LLM
+            self._llm_instance = LLM(
+                model=new_model,
+                base_url=self._openai_base_url,
+                api_key=self._openai_api_key,
+            )
+            self._using_custom_llm = True
+        except ImportError:
+            # If LLM class not available, just update the model string
+            pass
+        
+        # Chat history is preserved in self.chat_history (no action needed)
+
     def start(self, prompt: str, **kwargs):
         """Start the agent with a prompt. This is a convenience method that wraps chat()."""
         # Load history from past sessions if history_in_context is set

src/praisonai-agents/praisonaiagents/agent/message_queue.py

@@ -0,0 +1,202 @@
+"""
+Message Queue for PraisonAI Agents.
+
+Provides queuing for agent prompts when agent is busy.
+Thread-safe implementation for concurrent access.
+"""
+
+import threading
+import heapq
+import time
+import logging
+from typing import Optional, Any, List, Tuple
+from dataclasses import dataclass, field
+from enum import Enum
+
+logger = logging.getLogger(__name__)
+
+
+class MessagePriority(Enum):
+    """Message priority levels."""
+    LOW = 1
+    NORMAL = 5
+    HIGH = 10
+    URGENT = 20
+
+
+@dataclass(order=True)
+class QueuedMessage:
+    """A message in the queue with priority and timestamp."""
+    priority: int
+    timestamp: float = field(compare=False)
+    content: Any = field(compare=False)
+    metadata: dict = field(default_factory=dict, compare=False)
+    
+    def __post_init__(self):
+        # Negate priority for max-heap behavior (higher priority = lower number in heap)
+        self.priority = -self.priority
+
+
+class AgentMessageQueue:
+    """
+    Thread-safe message queue for agent prompts.
+    
+    Supports priority-based ordering and FIFO within same priority.
+    
+    Usage:
+        queue = AgentMessageQueue()
+        queue.enqueue("Hello", priority=5)
+        queue.enqueue("Urgent!", priority=10)
+        
+        msg = queue.dequeue()  # Returns "Urgent!" first
+    """
+    
+    def __init__(self, max_size: int = 100):
+        """
+        Initialize message queue.
+        
+        Args:
+            max_size: Maximum number of messages in queue (0 = unlimited)
+        """
+        self._queue: List[QueuedMessage] = []
+        self._lock = threading.RLock()
+        self._not_empty = threading.Condition(self._lock)
+        self._max_size = max_size
+        self._message_count = 0
+        
+    def enqueue(
+        self,
+        content: Any,
+        priority: int = MessagePriority.NORMAL.value,
+        metadata: Optional[dict] = None
+    ) -> bool:
+        """
+        Add a message to the queue.
+        
+        Args:
+            content: Message content
+            priority: Message priority (higher = more urgent)
+            metadata: Optional metadata dict
+            
+        Returns:
+            True if message was added, False if queue is full
+        """
+        with self._lock:
+            if self._max_size > 0 and len(self._queue) >= self._max_size:
+                logger.warning(f"Message queue full (max_size={self._max_size})")
+                return False
+                
+            message = QueuedMessage(
+                priority=priority,
+                timestamp=time.time(),
+                content=content,
+                metadata=metadata or {}
+            )
+            
+            heapq.heappush(self._queue, message)
+            self._message_count += 1
+            self._not_empty.notify()
+            
+            logger.debug(f"Message enqueued (priority={priority}, queue_size={len(self._queue)})")
+            return True
+    
+    def dequeue(self, timeout: Optional[float] = None) -> Optional[Any]:
+        """
+        Remove and return the highest priority message.
+        
+        Args:
+            timeout: Maximum time to wait for a message (None = don't wait)
+            
+        Returns:
+            Message content or None if queue is empty
+        """
+        with self._not_empty:
+            if not self._queue:
+                if timeout is not None:
+                    self._not_empty.wait(timeout)
+                    
+            if not self._queue:
+                return None
+                
+            message = heapq.heappop(self._queue)
+            logger.debug(f"Message dequeued (queue_size={len(self._queue)})")
+            return message.content
+    
+    def peek(self) -> Optional[Any]:
+        """Return the highest priority message without removing it."""
+        with self._lock:
+            if not self._queue:
+                return None
+            return self._queue[0].content
+    
+    def is_empty(self) -> bool:
+        """Check if queue is empty."""
+        with self._lock:
+            return len(self._queue) == 0
+    
+    def size(self) -> int:
+        """Get current queue size."""
+        with self._lock:
+            return len(self._queue)
+    
+    def clear(self) -> int:
+        """Clear all messages from queue. Returns number of cleared messages."""
+        with self._lock:
+            count = len(self._queue)
+            self._queue.clear()
+            return count
+    
+    def get_all(self) -> List[Any]:
+        """Get all messages in priority order without removing them."""
+        with self._lock:
+            # Sort by priority (remember priority is negated)
+            sorted_queue = sorted(self._queue)
+            return [msg.content for msg in sorted_queue]
+    
+    @property
+    def total_messages_processed(self) -> int:
+        """Total number of messages ever enqueued."""
+        return self._message_count
+
+
+class AsyncAgentMessageQueue:
+    """
+    Async-compatible message queue for agent prompts.
+    
+    Wraps AgentMessageQueue with async interface.
+    """
+    
+    def __init__(self, max_size: int = 100):
+        self._sync_queue = AgentMessageQueue(max_size=max_size)
+        
+    async def enqueue(
+        self,
+        content: Any,
+        priority: int = MessagePriority.NORMAL.value,
+        metadata: Optional[dict] = None
+    ) -> bool:
+        """Async enqueue."""
+        return self._sync_queue.enqueue(content, priority, metadata)
+    
+    async def dequeue(self, timeout: Optional[float] = None) -> Optional[Any]:
+        """Async dequeue."""
+        import asyncio
+        
+        if timeout is None:
+            return self._sync_queue.dequeue(timeout=0)
+            
+        # Poll with async sleep for timeout
+        start = time.time()
+        while time.time() - start < timeout:
+            result = self._sync_queue.dequeue(timeout=0)
+            if result is not None:
+                return result
+            await asyncio.sleep(0.01)
+            
+        return None
+    
+    def is_empty(self) -> bool:
+        return self._sync_queue.is_empty()
+    
+    def size(self) -> int:
+        return self._sync_queue.size()

src/praisonai-agents/praisonaiagents/agent/summarization.py

@@ -0,0 +1,226 @@
+"""
+Automatic Conversation Summarization for PraisonAI Agents.
+
+Provides automatic summarization when context window fills up.
+Reuses existing telemetry/token_collector for token tracking.
+"""
+
+import logging
+from typing import Optional, List, Dict, Any, Callable
+from dataclasses import dataclass, field
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class SummarizationConfig:
+    """Configuration for auto-summarization."""
+    enabled: bool = False
+    threshold: float = 0.8  # 80% of context window
+    context_window: int = 128000  # Default for GPT-4
+    summary_model: Optional[str] = None  # Use smaller model for summaries
+    preserve_system: bool = True  # Keep system message
+    preserve_recent: int = 2  # Keep last N message pairs
+
+
+class SummarizationManager:
+    """
+    Manages automatic conversation summarization.
+    
+    Tracks token usage and triggers summarization when threshold is reached.
+    Thread-safe for multi-agent use.
+    """
+    
+    # Context window sizes for common models
+    MODEL_CONTEXT_WINDOWS = {
+        "gpt-4o": 128000,
+        "gpt-4o-mini": 128000,
+        "gpt-4-turbo": 128000,
+        "gpt-4": 8192,
+        "gpt-3.5-turbo": 16385,
+        "claude-3-opus": 200000,
+        "claude-3-sonnet": 200000,
+        "claude-3-haiku": 200000,
+        "claude-3-5-sonnet": 200000,
+        "gemini-pro": 32000,
+        "gemini-1.5-pro": 1000000,
+        "gemini-1.5-flash": 1000000,
+    }
+    
+    def __init__(
+        self,
+        context_window: int = 128000,
+        threshold: float = 0.8,
+        summary_model: Optional[str] = None,
+        preserve_system: bool = True,
+        preserve_recent: int = 2,
+    ):
+        """
+        Initialize summarization manager.
+        
+        Args:
+            context_window: Maximum context window size in tokens
+            threshold: Percentage of context window to trigger summarization (0.0-1.0)
+            summary_model: Model to use for generating summaries (None = use same model)
+            preserve_system: Whether to preserve system message after summarization
+            preserve_recent: Number of recent message pairs to preserve
+        """
+        if not 0.0 < threshold <= 1.0:
+            raise ValueError("threshold must be between 0.0 and 1.0")
+            
+        self.context_window = context_window
+        self.threshold = threshold
+        self.summary_model = summary_model
+        self.preserve_system = preserve_system
+        self.preserve_recent = preserve_recent
+        self.current_tokens = 0
+        self._summarization_count = 0
+        
+    @classmethod
+    def for_model(cls, model: str, threshold: float = 0.8, **kwargs) -> "SummarizationManager":
+        """Create a SummarizationManager configured for a specific model."""
+        # Normalize model name
+        model_lower = model.lower()
+        
+        # Find matching context window
+        context_window = 128000  # Default
+        for model_key, window in cls.MODEL_CONTEXT_WINDOWS.items():
+            if model_key in model_lower:
+                context_window = window
+                break
+                
+        return cls(context_window=context_window, threshold=threshold, **kwargs)
+    
+    def add_tokens(self, tokens: int) -> None:
+        """Add tokens to the current count."""
+        self.current_tokens += tokens
+        
+    def set_tokens(self, tokens: int) -> None:
+        """Set the current token count."""
+        self.current_tokens = tokens
+        
+    def reset_tokens(self) -> None:
+        """Reset token count after summarization."""
+        self.current_tokens = 0
+        
+    def should_summarize(self) -> bool:
+        """Check if summarization should be triggered."""
+        threshold_tokens = int(self.context_window * self.threshold)
+        return self.current_tokens >= threshold_tokens
+    
+    def get_usage_percentage(self) -> float:
+        """Get current context window usage as percentage."""
+        return self.current_tokens / self.context_window
+    
+    def generate_summary_prompt(self, messages: List[Dict[str, str]]) -> str:
+        """Generate prompt for summarizing conversation."""
+        conversation = "\n".join([
+            f"{msg['role'].upper()}: {msg['content']}"
+            for msg in messages
+            if msg['role'] != 'system'
+        ])
+        
+        return f"""Summarize the following conversation concisely, preserving:
+1. Key decisions and conclusions
+2. Important context and facts
+3. Any pending tasks or questions
+4. Technical details that may be needed later
+
+Keep the summary under 500 words.
+
+CONVERSATION:
+{conversation}
+
+SUMMARY:"""
+
+    def prepare_summarized_history(
+        self,
+        messages: List[Dict[str, str]],
+        summary: str
+    ) -> List[Dict[str, str]]:
+        """
+        Prepare new message history after summarization.
+        
+        Args:
+            messages: Original message history
+            summary: Generated summary
+            
+        Returns:
+            New message history with summary replacing old messages
+        """
+        new_history = []
+        
+        # Preserve system message if configured
+        if self.preserve_system and messages and messages[0].get('role') == 'system':
+            new_history.append(messages[0])
+            
+        # Add summary as assistant message
+        new_history.append({
+            "role": "assistant",
+            "content": f"[Previous conversation summary]\n{summary}"
+        })
+        
+        # Preserve recent messages
+        if self.preserve_recent > 0:
+            # Get last N pairs (user + assistant)
+            recent_count = self.preserve_recent * 2
+            recent_messages = [
+                m for m in messages[-recent_count:]
+                if m.get('role') != 'system'
+            ]
+            new_history.extend(recent_messages)
+            
+        self._summarization_count += 1
+        logger.info(f"Conversation summarized (count: {self._summarization_count})")
+        
+        return new_history
+    
+    @property
+    def summarization_count(self) -> int:
+        """Number of times summarization has been performed."""
+        return self._summarization_count
+
+
+async def summarize_conversation(
+    messages: List[Dict[str, str]],
+    llm_call: Callable,
+    manager: SummarizationManager,
+) -> List[Dict[str, str]]:
+    """
+    Summarize conversation using LLM.
+    
+    Args:
+        messages: Current message history
+        llm_call: Async function to call LLM
+        manager: SummarizationManager instance
+        
+    Returns:
+        New message history with summary
+    """
+    prompt = manager.generate_summary_prompt(messages)
+    
+    # Call LLM to generate summary
+    summary = await llm_call(prompt)
+    
+    # Prepare new history
+    new_history = manager.prepare_summarized_history(messages, summary)
+    
+    # Reset token count (will be recalculated on next message)
+    manager.reset_tokens()
+    
+    return new_history
+
+
+def summarize_conversation_sync(
+    messages: List[Dict[str, str]],
+    llm_call: Callable,
+    manager: SummarizationManager,
+) -> List[Dict[str, str]]:
+    """
+    Synchronous version of summarize_conversation.
+    """
+    prompt = manager.generate_summary_prompt(messages)
+    summary = llm_call(prompt)
+    new_history = manager.prepare_summarized_history(messages, summary)
+    manager.reset_tokens()
+    return new_history

src/praisonai-agents/praisonaiagents/agents/__init__.py

@@ -1,5 +1,6 @@
 """Agents module for managing multiple AI agents"""
 from .agents import PraisonAIAgents
 from .autoagents import AutoAgents
+from .auto_rag_agent import AutoRagAgent, AutoRagConfig, RetrievalPolicy
 
-__all__ = ['PraisonAIAgents', 'AutoAgents'] 
+__all__ = ['PraisonAIAgents', 'AutoAgents', 'AutoRagAgent', 'AutoRagConfig', 'RetrievalPolicy'] 

src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py

@@ -0,0 +1,415 @@
+"""
+AutoRagAgent - Agent with automatic RAG retrieval decision.
+
+This module provides an agent wrapper that automatically decides when to
+retrieve context from knowledge bases vs direct chat.
+
+Usage:
+    from praisonaiagents import Agent, AutoRagAgent
+    
+    agent = Agent(
+        name="Research Assistant",
+        knowledge=["docs/manual.pdf"],
+    )
+    
+    auto_rag = AutoRagAgent(agent=agent)
+    result = auto_rag.chat("What are the key findings?")  # Auto retrieves
+    result = auto_rag.chat("Hello!")  # Skips retrieval
+"""
+
+import logging
+import time
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Optional, Set, Dict, Any, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from praisonaiagents.agent import Agent
+    from praisonaiagents.rag import RAG, ContextPack
+
+logger = logging.getLogger(__name__)
+
+
+class RetrievalPolicy(Enum):
+    """Policy for when to perform RAG retrieval."""
+    AUTO = "auto"      # Decide based on query heuristics
+    ALWAYS = "always"  # Always retrieve
+    NEVER = "never"    # Never retrieve (direct chat only)
+
+
+@dataclass
+class AutoRagConfig:
+    """Configuration for AutoRagAgent."""
+    
+    retrieval_policy: RetrievalPolicy = RetrievalPolicy.AUTO
+    
+    top_k: int = 5
+    hybrid: bool = False
+    rerank: bool = False
+    
+    include_citations: bool = True
+    citations_mode: str = "append"  # append, hidden, none
+    
+    max_context_tokens: int = 4000
+    
+    auto_keywords: Set[str] = field(default_factory=lambda: {
+        "what", "how", "why", "when", "where", "who", "which",
+        "explain", "describe", "summarize", "find", "search",
+        "tell me", "show me", "according to", "based on",
+        "cite", "source", "reference", "document", "paper",
+    })
+    
+    auto_min_length: int = 10
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Serialize to dictionary."""
+        return {
+            "retrieval_policy": self.retrieval_policy.value,
+            "top_k": self.top_k,
+            "hybrid": self.hybrid,
+            "rerank": self.rerank,
+            "include_citations": self.include_citations,
+            "citations_mode": self.citations_mode,
+            "max_context_tokens": self.max_context_tokens,
+            "auto_keywords": list(self.auto_keywords),
+            "auto_min_length": self.auto_min_length,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "AutoRagConfig":
+        """Create from dictionary."""
+        policy = data.get("retrieval_policy", "auto")
+        if isinstance(policy, str):
+            policy = RetrievalPolicy(policy)
+        
+        keywords = data.get("auto_keywords", None)
+        if keywords is not None and isinstance(keywords, list):
+            keywords = set(keywords)
+        
+        kwargs = {
+            "retrieval_policy": policy,
+            "top_k": data.get("top_k", 5),
+            "hybrid": data.get("hybrid", False),
+            "rerank": data.get("rerank", False),
+            "include_citations": data.get("include_citations", True),
+            "max_context_tokens": data.get("max_context_tokens", 4000),
+            "auto_min_length": data.get("auto_min_length", 10),
+            "citations_mode": data.get("citations_mode", "append"),
+        }
+        if keywords:
+            kwargs["auto_keywords"] = keywords
+        
+        return cls(**kwargs)
+
+
+class AutoRagAgent:
+    """
+    Agent wrapper with automatic RAG retrieval decision.
+    
+    Decides when to perform retrieval based on policy and heuristics,
+    then composes RAG context with Agent chat.
+    
+    This follows the same pattern as AutoAgents but for RAG:
+    - AutoAgents: auto-generates agent configs from instructions
+    - AutoRagAgent: auto-decides when to retrieve context
+    
+    Usage:
+        from praisonaiagents import Agent, AutoRagAgent
+        
+        agent = Agent(
+            name="Research Assistant",
+            knowledge=["docs/manual.pdf"],
+        )
+        
+        auto_rag = AutoRagAgent(agent=agent)
+        
+        # Auto mode - decides based on query
+        result = auto_rag.chat("What are the key findings?")
+        
+        # Force retrieval
+        result = auto_rag.chat("Hello", force_retrieval=True)
+        
+        # Skip retrieval
+        result = auto_rag.chat("What is 2+2?", skip_retrieval=True)
+    """
+    
+    def __init__(
+        self,
+        agent: "Agent",
+        rag: Optional["RAG"] = None,
+        config: Optional[AutoRagConfig] = None,
+        *,
+        retrieval_policy: Optional[str] = None,
+        top_k: Optional[int] = None,
+        hybrid: Optional[bool] = None,
+        rerank: Optional[bool] = None,
+        citations: bool = True,
+    ):
+        """
+        Initialize AutoRagAgent.
+        
+        Args:
+            agent: Agent instance (must have knowledge configured for RAG)
+            rag: Optional RAG instance (uses agent.rag if not provided)
+            config: AutoRagConfig instance
+            retrieval_policy: Shorthand for config.retrieval_policy (auto/always/never)
+            top_k: Shorthand for config.top_k
+            hybrid: Shorthand for config.hybrid
+            rerank: Shorthand for config.rerank
+            citations: Shorthand for config.include_citations
+        """
+        self.agent = agent
+        self._rag = rag
+        
+        # Build config from kwargs or use provided
+        if config is not None:
+            self.config = config
+        else:
+            config_kwargs = {}
+            if retrieval_policy is not None:
+                config_kwargs["retrieval_policy"] = RetrievalPolicy(retrieval_policy)
+            if top_k is not None:
+                config_kwargs["top_k"] = top_k
+            if hybrid is not None:
+                config_kwargs["hybrid"] = hybrid
+            if rerank is not None:
+                config_kwargs["rerank"] = rerank
+            config_kwargs["include_citations"] = citations
+            self.config = AutoRagConfig(**config_kwargs)
+        
+        # Lazy RAG initialization flag
+        self._rag_initialized = False
+    
+    @property
+    def rag(self) -> Optional["RAG"]:
+        """Lazy load RAG from agent if not provided."""
+        if self._rag is not None:
+            return self._rag
+        
+        if not self._rag_initialized:
+            # Try to get RAG from agent
+            if hasattr(self.agent, 'rag') and self.agent.rag is not None:
+                self._rag = self.agent.rag
+            self._rag_initialized = True
+        
+        return self._rag
+    
+    @property
+    def name(self) -> str:
+        """Delegate name to wrapped agent."""
+        return getattr(self.agent, 'name', 'AutoRagAgent')
+    
+    def _needs_retrieval(self, query: str) -> bool:
+        """
+        Determine if query needs retrieval based on heuristics.
+        
+        Simple, local heuristics (no ML classifier):
+        1. Query length check
+        2. Keyword presence check
+        3. Question mark check
+        
+        Args:
+            query: User query
+            
+        Returns:
+            True if retrieval should be performed
+        """
+        # Check minimum length
+        if len(query.strip()) < self.config.auto_min_length:
+            return False
+        
+        # Check for keywords
+        query_lower = query.lower()
+        for keyword in self.config.auto_keywords:
+            if keyword in query_lower:
+                return True
+        
+        # Check for question marks (likely a question)
+        if "?" in query:
+            return True
+        
+        return False
+    
+    def _should_retrieve(
+        self,
+        query: str,
+        force_retrieval: bool = False,
+        skip_retrieval: bool = False,
+    ) -> bool:
+        """
+        Determine if retrieval should be performed.
+        
+        Args:
+            query: User query
+            force_retrieval: Override to force retrieval
+            skip_retrieval: Override to skip retrieval
+            
+        Returns:
+            True if retrieval should be performed
+        """
+        # Explicit overrides take precedence
+        if skip_retrieval:
+            return False
+        if force_retrieval:
+            return True
+        
+        # Check if RAG is available
+        if self.rag is None:
+            logger.debug("RAG not available, skipping retrieval")
+            return False
+        
+        # Apply policy
+        if self.config.retrieval_policy == RetrievalPolicy.ALWAYS:
+            return True
+        elif self.config.retrieval_policy == RetrievalPolicy.NEVER:
+            return False
+        else:  # AUTO
+            return self._needs_retrieval(query)
+    
+    def _format_response_with_citations(
+        self,
+        response: str,
+        context_pack: Optional["ContextPack"],
+    ) -> str:
+        """Format response with citations based on config."""
+        if context_pack is None or not context_pack.has_citations:
+            return response
+        
+        if self.config.citations_mode == "none":
+            return response
+        elif self.config.citations_mode == "hidden":
+            return response
+        else:  # append
+            if not self.config.include_citations:
+                return response
+            
+            sources = "\n\nSources:\n"
+            for citation in context_pack.citations:
+                sources += f"  [{citation.id}] {citation.source}\n"
+            return response + sources
+    
+    def chat(
+        self,
+        message: str,
+        *,
+        force_retrieval: bool = False,
+        skip_retrieval: bool = False,
+        top_k: Optional[int] = None,
+        hybrid: Optional[bool] = None,
+        rerank: Optional[bool] = None,
+        user_id: Optional[str] = None,
+        **kwargs,
+    ) -> str:
+        """
+        Chat with automatic RAG retrieval decision.
+        
+        Decides whether to retrieve context, then runs agent chat.
+        
+        Args:
+            message: User message/query
+            force_retrieval: Force retrieval regardless of policy
+            skip_retrieval: Skip retrieval regardless of policy
+            top_k: Override top_k for retrieval
+            hybrid: Override hybrid retrieval setting
+            rerank: Override rerank setting
+            user_id: User ID for RAG retrieval (uses agent.user_id if not provided)
+            **kwargs: Additional arguments passed to agent.chat()
+            
+        Returns:
+            Agent response (with citations if configured)
+        """
+        start_time = time.time()
+        context_pack = None
+        
+        # Determine if we should retrieve
+        should_retrieve = self._should_retrieve(
+            message,
+            force_retrieval=force_retrieval,
+            skip_retrieval=skip_retrieval,
+        )
+        
+        if should_retrieve and self.rag is not None:
+            logger.debug(f"AutoRagAgent: Performing retrieval for: {message[:50]}...")
+            
+            # Get user_id from parameter or agent
+            effective_user_id = user_id or getattr(self.agent, 'user_id', None)
+            
+            # Retrieve context
+            context_pack = self.rag.retrieve(
+                query=message,
+                top_k=top_k or self.config.top_k,
+                hybrid=hybrid if hybrid is not None else self.config.hybrid,
+                rerank=rerank if rerank is not None else self.config.rerank,
+                user_id=effective_user_id,
+            )
+            
+            # Use agent's chat_with_context if available
+            if hasattr(self.agent, 'chat_with_context'):
+                response = self.agent.chat_with_context(
+                    message=message,
+                    context=context_pack,
+                    citations_mode=self.config.citations_mode,
+                    **kwargs,
+                )
+            else:
+                # Fallback: inject context into message
+                augmented_message = self._augment_message(message, context_pack)
+                response = self.agent.chat(augmented_message, **kwargs)
+                response = self._format_response_with_citations(response, context_pack)
+        else:
+            # Direct agent chat without retrieval
+            logger.debug(f"AutoRagAgent: Skipping retrieval for: {message[:50]}...")
+            response = self.agent.chat(message, **kwargs)
+        
+        elapsed = time.time() - start_time
+        logger.debug(f"AutoRagAgent: chat completed in {elapsed:.2f}s")
+        
+        return response
+    
+    def _augment_message(self, message: str, context_pack: "ContextPack") -> str:
+        """Augment message with retrieved context."""
+        return f"""Based on the following context, answer the question.
+
+Context:
+{context_pack.context}
+
+Question: {message}
+
+Answer:"""
+    
+    async def achat(
+        self,
+        message: str,
+        *,
+        force_retrieval: bool = False,
+        skip_retrieval: bool = False,
+        top_k: Optional[int] = None,
+        hybrid: Optional[bool] = None,
+        rerank: Optional[bool] = None,
+        user_id: Optional[str] = None,
+        **kwargs,
+    ) -> str:
+        """
+        Async version of chat.
+        
+        Currently wraps sync version. Can be extended for true async.
+        """
+        import asyncio
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(
+            None,
+            lambda: self.chat(
+                message=message,
+                force_retrieval=force_retrieval,
+                skip_retrieval=skip_retrieval,
+                top_k=top_k,
+                hybrid=hybrid,
+                rerank=rerank,
+                user_id=user_id,
+                **kwargs,
+            ),
+        )
+    
+    # Alias for consistency with Orchestrator pattern
+    run = chat
+    arun = achat

src/praisonai-agents/praisonaiagents/agents/profiles.py

@@ -0,0 +1,238 @@
+"""
+Agent Profiles and Modes for PraisonAI Agents.
+
+Provides built-in agent profiles and mode configurations
+similar to OpenCode's agent system.
+"""
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+
+class AgentMode(str, Enum):
+    """Agent execution modes."""
+    
+    PRIMARY = "primary"      # Main agent, can spawn subagents
+    SUBAGENT = "subagent"    # Spawned by another agent
+    ALL = "all"              # Can be used in any context
+
+
+@dataclass
+class AgentProfile:
+    """
+    A built-in agent profile configuration.
+    
+    Attributes:
+        name: Unique agent name
+        description: What this agent does
+        mode: Execution mode (primary/subagent/all)
+        system_prompt: System prompt for the agent
+        tools: List of tool names this agent can use
+        temperature: LLM temperature setting
+        top_p: LLM top_p setting
+        max_steps: Maximum execution steps
+        hidden: Whether to hide from agent list
+        color: Display color (for UI)
+    """
+    
+    name: str
+    description: str = ""
+    mode: AgentMode = AgentMode.ALL
+    system_prompt: str = ""
+    tools: List[str] = field(default_factory=list)
+    temperature: float = 0.7
+    top_p: float = 1.0
+    max_steps: int = 50
+    hidden: bool = False
+    color: str = "#3B82F6"
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert profile to dictionary."""
+        return {
+            "name": self.name,
+            "description": self.description,
+            "mode": self.mode.value,
+            "system_prompt": self.system_prompt,
+            "tools": self.tools,
+            "temperature": self.temperature,
+            "top_p": self.top_p,
+            "max_steps": self.max_steps,
+            "hidden": self.hidden,
+            "color": self.color,
+            "metadata": self.metadata,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "AgentProfile":
+        """Create profile from dictionary."""
+        return cls(
+            name=data.get("name", ""),
+            description=data.get("description", ""),
+            mode=AgentMode(data.get("mode", "all")),
+            system_prompt=data.get("system_prompt", ""),
+            tools=data.get("tools", []),
+            temperature=data.get("temperature", 0.7),
+            top_p=data.get("top_p", 1.0),
+            max_steps=data.get("max_steps", 50),
+            hidden=data.get("hidden", False),
+            color=data.get("color", "#3B82F6"),
+            metadata=data.get("metadata", {}),
+        )
+
+
+# Built-in agent profiles
+BUILTIN_PROFILES: Dict[str, AgentProfile] = {
+    "general": AgentProfile(
+        name="general",
+        description="General-purpose coding assistant",
+        mode=AgentMode.PRIMARY,
+        system_prompt=(
+            "You are a helpful coding assistant. You can help with a wide variety "
+            "of programming tasks including writing code, debugging, explaining "
+            "concepts, and answering questions about software development."
+        ),
+        tools=["read_file", "write_file", "bash", "search"],
+        temperature=0.7,
+        color="#3B82F6",
+    ),
+    
+    "coder": AgentProfile(
+        name="coder",
+        description="Focused code implementation agent",
+        mode=AgentMode.ALL,
+        system_prompt=(
+            "You are an expert code implementation agent. Your focus is on writing "
+            "clean, efficient, and well-documented code. You follow best practices "
+            "and design patterns. When implementing features, you consider edge cases "
+            "and error handling."
+        ),
+        tools=["read_file", "write_file", "bash"],
+        temperature=0.3,
+        color="#10B981",
+    ),
+    
+    "planner": AgentProfile(
+        name="planner",
+        description="Task planning and decomposition agent",
+        mode=AgentMode.SUBAGENT,
+        system_prompt=(
+            "You are a planning agent. Your role is to break down complex tasks "
+            "into smaller, manageable steps. You create clear, actionable plans "
+            "with specific milestones and success criteria. You do not execute "
+            "tasks yourself, only plan them."
+        ),
+        tools=["read_file", "search"],
+        temperature=0.5,
+        max_steps=20,
+        color="#F59E0B",
+    ),
+    
+    "reviewer": AgentProfile(
+        name="reviewer",
+        description="Code review and quality agent",
+        mode=AgentMode.SUBAGENT,
+        system_prompt=(
+            "You are a code review agent. Your role is to review code for quality, "
+            "correctness, security, and best practices. You provide constructive "
+            "feedback and suggest improvements. You look for bugs, security issues, "
+            "and opportunities for optimization."
+        ),
+        tools=["read_file", "search"],
+        temperature=0.3,
+        max_steps=30,
+        color="#EF4444",
+    ),
+    
+    "explorer": AgentProfile(
+        name="explorer",
+        description="Codebase exploration and understanding agent",
+        mode=AgentMode.SUBAGENT,
+        system_prompt=(
+            "You are a codebase exploration agent. Your role is to understand and "
+            "navigate codebases. You can find relevant files, understand code "
+            "structure, and identify patterns. You help locate where changes need "
+            "to be made."
+        ),
+        tools=["read_file", "search", "grep"],
+        temperature=0.5,
+        max_steps=40,
+        color="#8B5CF6",
+    ),
+    
+    "debugger": AgentProfile(
+        name="debugger",
+        description="Debugging and troubleshooting agent",
+        mode=AgentMode.SUBAGENT,
+        system_prompt=(
+            "You are a debugging agent. Your role is to identify and fix bugs. "
+            "You analyze error messages, trace code execution, and identify root "
+            "causes. You suggest minimal fixes that address the issue without "
+            "introducing new problems."
+        ),
+        tools=["read_file", "bash", "search"],
+        temperature=0.3,
+        max_steps=30,
+        color="#EC4899",
+    ),
+    
+    "compaction": AgentProfile(
+        name="compaction",
+        description="Context compaction agent (internal)",
+        mode=AgentMode.SUBAGENT,
+        system_prompt=(
+            "You are a context compaction agent. Your role is to summarize "
+            "conversation history while preserving essential information. "
+            "You create concise summaries that maintain context for continued "
+            "work."
+        ),
+        tools=[],
+        temperature=0.3,
+        max_steps=5,
+        hidden=True,
+        color="#6B7280",
+    ),
+    
+    "title": AgentProfile(
+        name="title",
+        description="Session title generation agent (internal)",
+        mode=AgentMode.SUBAGENT,
+        system_prompt=(
+            "You are a title generation agent. Your role is to create short, "
+            "descriptive titles for conversations based on their content. "
+            "Titles should be concise (3-7 words) and capture the main topic."
+        ),
+        tools=[],
+        temperature=0.7,
+        max_steps=1,
+        hidden=True,
+        color="#6B7280",
+    ),
+}
+
+
+def get_profile(name: str) -> Optional[AgentProfile]:
+    """Get a built-in agent profile by name."""
+    return BUILTIN_PROFILES.get(name)
+
+
+def list_profiles(include_hidden: bool = False) -> List[AgentProfile]:
+    """List all built-in agent profiles."""
+    profiles = list(BUILTIN_PROFILES.values())
+    if not include_hidden:
+        profiles = [p for p in profiles if not p.hidden]
+    return profiles
+
+
+def register_profile(profile: AgentProfile) -> None:
+    """Register a custom agent profile."""
+    BUILTIN_PROFILES[profile.name] = profile
+
+
+def get_profiles_by_mode(mode: AgentMode) -> List[AgentProfile]:
+    """Get all profiles that can be used in a specific mode."""
+    return [
+        p for p in BUILTIN_PROFILES.values()
+        if p.mode == mode or p.mode == AgentMode.ALL
+    ]

src/praisonai-agents/praisonaiagents/approval.py

@@ -7,9 +7,12 @@ It extends the existing callback system to require human approval before executi
 
 import logging
 import asyncio
-from typing import Dict, Set, Optional, Callable, Any, Literal
+import json
+import os
+from typing import Dict, Set, Optional, Callable, Any, Literal, List
 from functools import wraps
 from contextvars import ContextVar
+from dataclasses import dataclass, field
 from rich.console import Console
 from rich.panel import Panel
 from rich.text import Text
@@ -28,6 +31,172 @@ approval_callback: Optional[Callable] = None
 # Context variable to track if we're in an approved execution context
 _approved_context: ContextVar[Set[str]] = ContextVar('approved_context', default=set())
 
+# Global permission allowlist
+_permission_allowlist: Optional["PermissionAllowlist"] = None
+
+
+@dataclass
+class ToolPermission:
+    """Permission entry for a tool."""
+    tool_name: str
+    allowed_paths: List[str] = field(default_factory=list)
+    session_only: bool = False  # If True, permission expires with session
+
+
+class PermissionAllowlist:
+    """
+    Persistent permission allowlist for tools.
+    
+    Allows pre-approving tools and paths to skip interactive approval.
+    Can be saved/loaded from JSON for persistence across sessions.
+    
+    Usage:
+        allowlist = PermissionAllowlist()
+        allowlist.add_tool("read_file")
+        allowlist.add_tool("write_file", paths=["./src", "./tests"])
+        
+        if allowlist.is_allowed("read_file"):
+            # Skip approval prompt
+            pass
+    """
+    
+    def __init__(self):
+        self._tools: Dict[str, ToolPermission] = {}
+        self._session_tools: Set[str] = set()
+        
+    def add_tool(
+        self, 
+        tool_name: str, 
+        paths: Optional[List[str]] = None,
+        session_only: bool = False
+    ) -> None:
+        """
+        Add a tool to the allowlist.
+        
+        Args:
+            tool_name: Name of the tool to allow
+            paths: Optional list of allowed paths (empty = all paths)
+            session_only: If True, permission expires with session
+        """
+        self._tools[tool_name] = ToolPermission(
+            tool_name=tool_name,
+            allowed_paths=paths or [],
+            session_only=session_only
+        )
+        if session_only:
+            self._session_tools.add(tool_name)
+            
+    def remove_tool(self, tool_name: str) -> bool:
+        """Remove a tool from the allowlist."""
+        if tool_name in self._tools:
+            del self._tools[tool_name]
+            self._session_tools.discard(tool_name)
+            return True
+        return False
+    
+    def is_allowed(self, tool_name: str, path: Optional[str] = None) -> bool:
+        """
+        Check if a tool is allowed.
+        
+        Args:
+            tool_name: Name of the tool
+            path: Optional path to check against allowed paths
+            
+        Returns:
+            True if tool is allowed (optionally for the given path)
+        """
+        if tool_name not in self._tools:
+            return False
+            
+        permission = self._tools[tool_name]
+        
+        # If no paths specified, tool is allowed for all paths
+        if not permission.allowed_paths:
+            return True
+            
+        # If path not provided, check if tool has any permissions
+        if path is None:
+            return True
+            
+        # Check if path matches any allowed path
+        for allowed_path in permission.allowed_paths:
+            # Normalize paths for comparison
+            norm_allowed = os.path.normpath(allowed_path)
+            norm_path = os.path.normpath(path)
+            
+            # Check if path starts with allowed path
+            if norm_path.startswith(norm_allowed):
+                return True
+                
+        return False
+    
+    def is_empty(self) -> bool:
+        """Check if allowlist is empty."""
+        return len(self._tools) == 0
+    
+    def list_tools(self) -> List[str]:
+        """List all allowed tools."""
+        return list(self._tools.keys())
+    
+    def clear_session_permissions(self) -> None:
+        """Clear session-only permissions."""
+        for tool_name in list(self._session_tools):
+            if tool_name in self._tools:
+                del self._tools[tool_name]
+        self._session_tools.clear()
+    
+    def save(self, filepath: str) -> None:
+        """Save allowlist to JSON file."""
+        data = {
+            "tools": {
+                name: {
+                    "allowed_paths": perm.allowed_paths,
+                    "session_only": perm.session_only
+                }
+                for name, perm in self._tools.items()
+                if not perm.session_only  # Don't persist session-only permissions
+            }
+        }
+        
+        os.makedirs(os.path.dirname(filepath) or ".", exist_ok=True)
+        with open(filepath, "w") as f:
+            json.dump(data, f, indent=2)
+    
+    @classmethod
+    def load(cls, filepath: str) -> "PermissionAllowlist":
+        """Load allowlist from JSON file."""
+        allowlist = cls()
+        
+        if not os.path.exists(filepath):
+            return allowlist
+            
+        with open(filepath, "r") as f:
+            data = json.load(f)
+            
+        for tool_name, tool_data in data.get("tools", {}).items():
+            allowlist.add_tool(
+                tool_name,
+                paths=tool_data.get("allowed_paths", []),
+                session_only=tool_data.get("session_only", False)
+            )
+            
+        return allowlist
+
+
+def get_permission_allowlist() -> PermissionAllowlist:
+    """Get the global permission allowlist."""
+    global _permission_allowlist
+    if _permission_allowlist is None:
+        _permission_allowlist = PermissionAllowlist()
+    return _permission_allowlist
+
+
+def set_permission_allowlist(allowlist: PermissionAllowlist) -> None:
+    """Set the global permission allowlist."""
+    global _permission_allowlist
+    _permission_allowlist = allowlist
+
+
 class ApprovalDecision:
     """Result of an approval request"""
     def __init__(self, approved: bool, modified_args: Optional[Dict[str, Any]] = None, reason: str = ""):

src/praisonai-agents/praisonaiagents/background/job_manager.py

@@ -0,0 +1,286 @@
+"""
+Background Job Manager for PraisonAI Agents.
+
+Provides job lifecycle tracking, auto-backgrounding based on threshold,
+and status inspection for long-running agent tasks.
+"""
+
+import threading
+import time
+import uuid
+from enum import Enum
+from typing import Any, Callable, Dict, Optional
+from dataclasses import dataclass, field
+from concurrent.futures import ThreadPoolExecutor, Future
+
+
+class JobStatus(str, Enum):
+    """Status of a background job."""
+    PENDING = "pending"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    CANCELLED = "cancelled"
+
+
+@dataclass
+class JobInfo:
+    """Information about a background job."""
+    job_id: str
+    status: JobStatus
+    created_at: float = field(default_factory=time.time)
+    started_at: Optional[float] = None
+    completed_at: Optional[float] = None
+    result: Any = None
+    error: Optional[str] = None
+    
+    @property
+    def duration(self) -> Optional[float]:
+        """Get job duration in seconds."""
+        if self.started_at is None:
+            return None
+        end_time = self.completed_at or time.time()
+        return end_time - self.started_at
+
+
+class BackgroundJobManager:
+    """
+    Manages background jobs for agent tasks.
+    
+    Features:
+    - Job lifecycle tracking (pending, running, completed, failed)
+    - Auto-backgrounding based on execution time threshold
+    - Thread-safe job status inspection
+    - Configurable thread pool for concurrent execution
+    """
+    
+    def __init__(
+        self,
+        max_workers: int = 4,
+        auto_background_threshold: float = 5.0,
+    ):
+        """
+        Initialize the background job manager.
+        
+        Args:
+            max_workers: Maximum number of concurrent background jobs
+            auto_background_threshold: Time in seconds after which a job
+                should be automatically backgrounded
+        """
+        self.max_workers = max_workers
+        self.auto_background_threshold = auto_background_threshold
+        self._executor = ThreadPoolExecutor(max_workers=max_workers)
+        self._jobs: Dict[str, JobInfo] = {}
+        self._futures: Dict[str, Future] = {}
+        self._lock = threading.Lock()
+    
+    @property
+    def active_jobs(self) -> int:
+        """Get the number of active (pending or running) jobs."""
+        with self._lock:
+            return sum(
+                1 for job in self._jobs.values()
+                if job.status in (JobStatus.PENDING, JobStatus.RUNNING)
+            )
+    
+    def start_job(
+        self,
+        func: Callable[[], Any],
+        job_id: Optional[str] = None,
+    ) -> str:
+        """
+        Start a new background job.
+        
+        Args:
+            func: The function to execute in the background
+            job_id: Optional custom job ID (auto-generated if not provided)
+            
+        Returns:
+            The job ID
+        """
+        job_id = job_id or str(uuid.uuid4())[:8]
+        
+        job_info = JobInfo(
+            job_id=job_id,
+            status=JobStatus.PENDING,
+        )
+        
+        with self._lock:
+            self._jobs[job_id] = job_info
+        
+        def _run_job():
+            with self._lock:
+                self._jobs[job_id].status = JobStatus.RUNNING
+                self._jobs[job_id].started_at = time.time()
+            
+            try:
+                result = func()
+                with self._lock:
+                    self._jobs[job_id].status = JobStatus.COMPLETED
+                    self._jobs[job_id].completed_at = time.time()
+                    self._jobs[job_id].result = result
+                return result
+            except Exception as e:
+                with self._lock:
+                    self._jobs[job_id].status = JobStatus.FAILED
+                    self._jobs[job_id].completed_at = time.time()
+                    self._jobs[job_id].error = str(e)
+                raise
+        
+        future = self._executor.submit(_run_job)
+        with self._lock:
+            self._futures[job_id] = future
+        
+        return job_id
+    
+    def get_status(self, job_id: str) -> JobStatus:
+        """
+        Get the status of a job.
+        
+        Args:
+            job_id: The job ID to check
+            
+        Returns:
+            The job status
+            
+        Raises:
+            KeyError: If job_id is not found
+        """
+        with self._lock:
+            if job_id not in self._jobs:
+                raise KeyError(f"Job {job_id} not found")
+            return self._jobs[job_id].status
+    
+    def get_job_info(self, job_id: str) -> JobInfo:
+        """
+        Get full information about a job.
+        
+        Args:
+            job_id: The job ID to check
+            
+        Returns:
+            The job information
+            
+        Raises:
+            KeyError: If job_id is not found
+        """
+        with self._lock:
+            if job_id not in self._jobs:
+                raise KeyError(f"Job {job_id} not found")
+            return self._jobs[job_id]
+    
+    def get_result(self, job_id: str, timeout: Optional[float] = None) -> Any:
+        """
+        Get the result of a completed job.
+        
+        Args:
+            job_id: The job ID
+            timeout: Maximum time to wait for completion
+            
+        Returns:
+            The job result
+            
+        Raises:
+            KeyError: If job_id is not found
+            TimeoutError: If timeout is reached before completion
+            RuntimeError: If job failed
+        """
+        with self._lock:
+            if job_id not in self._futures:
+                raise KeyError(f"Job {job_id} not found")
+            future = self._futures[job_id]
+        
+        try:
+            return future.result(timeout=timeout)
+        except TimeoutError:
+            raise TimeoutError(f"Job {job_id} did not complete within {timeout} seconds")
+    
+    def cancel_job(self, job_id: str) -> bool:
+        """
+        Attempt to cancel a job.
+        
+        Args:
+            job_id: The job ID to cancel
+            
+        Returns:
+            True if cancellation was successful, False otherwise
+        """
+        with self._lock:
+            if job_id not in self._futures:
+                return False
+            future = self._futures[job_id]
+            cancelled = future.cancel()
+            if cancelled:
+                self._jobs[job_id].status = JobStatus.CANCELLED
+                self._jobs[job_id].completed_at = time.time()
+            return cancelled
+    
+    def list_jobs(self, status: Optional[JobStatus] = None) -> Dict[str, JobInfo]:
+        """
+        List all jobs, optionally filtered by status.
+        
+        Args:
+            status: Optional status filter
+            
+        Returns:
+            Dictionary of job_id -> JobInfo
+        """
+        with self._lock:
+            if status is None:
+                return dict(self._jobs)
+            return {
+                job_id: info
+                for job_id, info in self._jobs.items()
+                if info.status == status
+            }
+    
+    def cleanup_completed(self, max_age: float = 3600.0) -> int:
+        """
+        Remove completed jobs older than max_age seconds.
+        
+        Args:
+            max_age: Maximum age in seconds for completed jobs
+            
+        Returns:
+            Number of jobs removed
+        """
+        now = time.time()
+        removed = 0
+        
+        with self._lock:
+            to_remove = []
+            for job_id, info in self._jobs.items():
+                if info.status in (JobStatus.COMPLETED, JobStatus.FAILED, JobStatus.CANCELLED):
+                    if info.completed_at and (now - info.completed_at) > max_age:
+                        to_remove.append(job_id)
+            
+            for job_id in to_remove:
+                del self._jobs[job_id]
+                if job_id in self._futures:
+                    del self._futures[job_id]
+                removed += 1
+        
+        return removed
+    
+    def shutdown(self, wait: bool = True) -> None:
+        """
+        Shutdown the job manager.
+        
+        Args:
+            wait: Whether to wait for pending jobs to complete
+        """
+        self._executor.shutdown(wait=wait)
+
+
+# Global job manager instance (lazy initialized)
+_global_job_manager: Optional[BackgroundJobManager] = None
+_global_lock = threading.Lock()
+
+
+def get_job_manager() -> BackgroundJobManager:
+    """Get the global job manager instance."""
+    global _global_job_manager
+    with _global_lock:
+        if _global_job_manager is None:
+            _global_job_manager = BackgroundJobManager()
+        return _global_job_manager

src/praisonai-agents/praisonaiagents/bus/__init__.py

@@ -0,0 +1,71 @@
+"""
+Event Bus Module for PraisonAI Agents.
+
+Provides a typed publish/subscribe event system for real-time communication
+between components. Extends the existing hooks system with a more general
+event-driven architecture.
+
+Features:
+- Typed event definitions with dataclass payloads
+- Sync and async subscribers
+- Event filtering by type
+- Global and scoped event buses
+- SSE-compatible event streaming
+
+Zero Performance Impact:
+- All imports are lazy loaded
+- No overhead when not subscribed
+- Optional dependency for server features
+
+Usage:
+    from praisonaiagents.bus import EventBus, Event
+    
+    # Create event bus
+    bus = EventBus()
+    
+    # Subscribe to events
+    @bus.on("session.created")
+    def handle_session(event):
+        print(f"Session created: {event.data}")
+    
+    # Publish events
+    bus.publish("session.created", {"session_id": "abc123"})
+"""
+
+__all__ = [
+    "EventBus",
+    "Event",
+    "EventType",
+    "Subscriber",
+    "get_default_bus",
+    "set_default_bus",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components."""
+    if name == "EventBus":
+        from .bus import EventBus
+        return EventBus
+    
+    if name == "Event":
+        from .event import Event
+        return Event
+    
+    if name == "EventType":
+        from .event import EventType
+        return EventType
+    
+    if name == "Subscriber":
+        from .bus import Subscriber
+        return Subscriber
+    
+    if name == "get_default_bus":
+        from .bus import get_default_bus
+        return get_default_bus
+    
+    if name == "set_default_bus":
+        from .bus import set_default_bus
+        return set_default_bus
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/bus/bus.py

@@ -0,0 +1,346 @@
+"""
+Event Bus implementation for PraisonAI Agents.
+
+Provides publish/subscribe functionality for typed events.
+"""
+
+import asyncio
+import logging
+import threading
+from typing import Any, Callable, Dict, List, Optional, Set, Union
+from dataclasses import dataclass, field
+from .event import Event, EventType
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Subscriber:
+    """
+    A subscriber to the event bus.
+    
+    Attributes:
+        callback: Function to call when event is received
+        event_types: Set of event types to subscribe to (None = all)
+        is_async: Whether the callback is async
+        id: Unique subscriber identifier
+    """
+    callback: Callable[[Event], Any]
+    event_types: Optional[Set[str]] = None
+    is_async: bool = False
+    id: str = field(default_factory=lambda: str(__import__("uuid").uuid4()))
+    
+    def matches(self, event_type: str) -> bool:
+        """Check if subscriber should receive this event type."""
+        if self.event_types is None:
+            return True
+        return event_type in self.event_types
+
+
+class EventBus:
+    """
+    Event bus for publish/subscribe communication.
+    
+    Thread-safe implementation supporting both sync and async subscribers.
+    
+    Example:
+        bus = EventBus()
+        
+        # Subscribe with decorator
+        @bus.on(EventType.SESSION_CREATED)
+        def handle_session(event):
+            print(f"Session: {event.data}")
+        
+        # Subscribe to all events
+        bus.subscribe(lambda e: print(e))
+        
+        # Publish event
+        bus.publish(EventType.SESSION_CREATED, {"session_id": "abc"})
+    """
+    
+    def __init__(self):
+        """Initialize the event bus."""
+        self._subscribers: List[Subscriber] = []
+        self._lock = threading.RLock()
+        self._event_history: List[Event] = []
+        self._max_history = 1000
+    
+    def subscribe(
+        self,
+        callback: Callable[[Event], Any],
+        event_types: Optional[Union[str, List[str], Set[str]]] = None,
+    ) -> str:
+        """
+        Subscribe to events.
+        
+        Args:
+            callback: Function to call when event is received
+            event_types: Optional event type(s) to filter by
+            
+        Returns:
+            Subscriber ID for unsubscribing
+        """
+        # Normalize event_types to a set
+        if event_types is None:
+            types_set = None
+        elif isinstance(event_types, str):
+            types_set = {event_types}
+        else:
+            types_set = set(event_types)
+        
+        # Check if callback is async
+        is_async = asyncio.iscoroutinefunction(callback)
+        
+        subscriber = Subscriber(
+            callback=callback,
+            event_types=types_set,
+            is_async=is_async,
+        )
+        
+        with self._lock:
+            self._subscribers.append(subscriber)
+        
+        logger.debug(f"Subscribed {subscriber.id} to {types_set or 'all events'}")
+        return subscriber.id
+    
+    def unsubscribe(self, subscriber_id: str) -> bool:
+        """
+        Unsubscribe from events.
+        
+        Args:
+            subscriber_id: The subscriber ID returned from subscribe()
+            
+        Returns:
+            True if found and removed, False otherwise
+        """
+        with self._lock:
+            for i, sub in enumerate(self._subscribers):
+                if sub.id == subscriber_id:
+                    self._subscribers.pop(i)
+                    logger.debug(f"Unsubscribed {subscriber_id}")
+                    return True
+        return False
+    
+    def on(
+        self,
+        *event_types: Union[str, EventType],
+    ) -> Callable:
+        """
+        Decorator to subscribe a function to events.
+        
+        Args:
+            event_types: Event type(s) to subscribe to
+            
+        Returns:
+            Decorator function
+            
+        Example:
+            @bus.on(EventType.SESSION_CREATED)
+            def handle_session(event):
+                print(event.data)
+        """
+        # Convert EventType enums to strings
+        types = {
+            t.value if isinstance(t, EventType) else t
+            for t in event_types
+        } if event_types else None
+        
+        def decorator(func: Callable[[Event], Any]) -> Callable:
+            self.subscribe(func, types)
+            return func
+        
+        return decorator
+    
+    def publish(
+        self,
+        event_type: Union[str, EventType],
+        data: Optional[Dict[str, Any]] = None,
+        source: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> Event:
+        """
+        Publish an event to all matching subscribers.
+        
+        Args:
+            event_type: The event type
+            data: Event payload data
+            source: Optional source identifier
+            metadata: Optional additional metadata
+            
+        Returns:
+            The published Event object
+        """
+        # Convert EventType enum to string
+        type_str = event_type.value if isinstance(event_type, EventType) else event_type
+        
+        event = Event(
+            type=type_str,
+            data=data or {},
+            source=source,
+            metadata=metadata or {},
+        )
+        
+        return self.publish_event(event)
+    
+    def publish_event(self, event: Event) -> Event:
+        """
+        Publish a pre-constructed event.
+        
+        Args:
+            event: The event to publish
+            
+        Returns:
+            The published event
+        """
+        logger.debug(f"Publishing event: {event.type}")
+        
+        # Store in history
+        with self._lock:
+            self._event_history.append(event)
+            if len(self._event_history) > self._max_history:
+                self._event_history = self._event_history[-self._max_history:]
+            
+            # Get matching subscribers
+            subscribers = [
+                sub for sub in self._subscribers
+                if sub.matches(event.type)
+            ]
+        
+        # Dispatch to subscribers
+        for sub in subscribers:
+            try:
+                if sub.is_async:
+                    # Schedule async callback
+                    try:
+                        loop = asyncio.get_running_loop()
+                        loop.create_task(sub.callback(event))
+                    except RuntimeError:
+                        # No running loop, run synchronously
+                        asyncio.run(sub.callback(event))
+                else:
+                    sub.callback(event)
+            except Exception as e:
+                logger.error(f"Error in subscriber {sub.id}: {e}")
+        
+        return event
+    
+    async def publish_async(
+        self,
+        event_type: Union[str, EventType],
+        data: Optional[Dict[str, Any]] = None,
+        source: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> Event:
+        """
+        Async version of publish that awaits all async subscribers.
+        
+        Args:
+            event_type: The event type
+            data: Event payload data
+            source: Optional source identifier
+            metadata: Optional additional metadata
+            
+        Returns:
+            The published Event object
+        """
+        type_str = event_type.value if isinstance(event_type, EventType) else event_type
+        
+        event = Event(
+            type=type_str,
+            data=data or {},
+            source=source,
+            metadata=metadata or {},
+        )
+        
+        logger.debug(f"Publishing async event: {event.type}")
+        
+        # Store in history
+        with self._lock:
+            self._event_history.append(event)
+            if len(self._event_history) > self._max_history:
+                self._event_history = self._event_history[-self._max_history:]
+            
+            subscribers = [
+                sub for sub in self._subscribers
+                if sub.matches(event.type)
+            ]
+        
+        # Dispatch to subscribers
+        tasks = []
+        for sub in subscribers:
+            try:
+                if sub.is_async:
+                    tasks.append(sub.callback(event))
+                else:
+                    sub.callback(event)
+            except Exception as e:
+                logger.error(f"Error in subscriber {sub.id}: {e}")
+        
+        # Await all async tasks
+        if tasks:
+            await asyncio.gather(*tasks, return_exceptions=True)
+        
+        return event
+    
+    def get_history(
+        self,
+        event_type: Optional[str] = None,
+        limit: int = 100,
+    ) -> List[Event]:
+        """
+        Get recent event history.
+        
+        Args:
+            event_type: Optional filter by event type
+            limit: Maximum number of events to return
+            
+        Returns:
+            List of recent events
+        """
+        with self._lock:
+            events = self._event_history.copy()
+        
+        if event_type:
+            events = [e for e in events if e.type == event_type]
+        
+        return events[-limit:]
+    
+    def clear_history(self):
+        """Clear the event history."""
+        with self._lock:
+            self._event_history.clear()
+    
+    def clear_subscribers(self):
+        """Remove all subscribers."""
+        with self._lock:
+            self._subscribers.clear()
+    
+    @property
+    def subscriber_count(self) -> int:
+        """Get the number of subscribers."""
+        with self._lock:
+            return len(self._subscribers)
+    
+    def __repr__(self) -> str:
+        return f"EventBus(subscribers={self.subscriber_count})"
+
+
+# Global default bus
+_default_bus: Optional[EventBus] = None
+_bus_lock = threading.Lock()
+
+
+def get_default_bus() -> EventBus:
+    """Get the default global event bus."""
+    global _default_bus
+    with _bus_lock:
+        if _default_bus is None:
+            _default_bus = EventBus()
+        return _default_bus
+
+
+def set_default_bus(bus: EventBus):
+    """Set the default global event bus."""
+    global _default_bus
+    with _bus_lock:
+        _default_bus = bus

src/praisonai-agents/praisonaiagents/bus/event.py

@@ -0,0 +1,111 @@
+"""
+Event definitions for the Event Bus.
+
+Provides typed event structures for the publish/subscribe system.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, Optional
+from enum import Enum
+import time
+import uuid
+
+
+class EventType(str, Enum):
+    """Standard event types for the agent system."""
+    
+    # Session events
+    SESSION_CREATED = "session.created"
+    SESSION_UPDATED = "session.updated"
+    SESSION_DELETED = "session.deleted"
+    SESSION_FORKED = "session.forked"
+    SESSION_REVERTED = "session.reverted"
+    
+    # Message events
+    MESSAGE_CREATED = "message.created"
+    MESSAGE_UPDATED = "message.updated"
+    MESSAGE_PART_CREATED = "message.part.created"
+    MESSAGE_PART_UPDATED = "message.part.updated"
+    
+    # Permission events
+    PERMISSION_ASKED = "permission.asked"
+    PERMISSION_REPLIED = "permission.replied"
+    
+    # Agent events
+    AGENT_STARTED = "agent.started"
+    AGENT_COMPLETED = "agent.completed"
+    AGENT_ERROR = "agent.error"
+    
+    # Tool events
+    TOOL_STARTED = "tool.started"
+    TOOL_COMPLETED = "tool.completed"
+    TOOL_ERROR = "tool.error"
+    
+    # Snapshot events
+    SNAPSHOT_CREATED = "snapshot.created"
+    SNAPSHOT_RESTORED = "snapshot.restored"
+    
+    # Server events
+    SERVER_STARTED = "server.started"
+    SERVER_STOPPED = "server.stopped"
+    CLIENT_CONNECTED = "client.connected"
+    CLIENT_DISCONNECTED = "client.disconnected"
+    
+    # Compaction events
+    COMPACTION_STARTED = "compaction.started"
+    COMPACTION_COMPLETED = "compaction.completed"
+    
+    # Custom event (for user-defined events)
+    CUSTOM = "custom"
+
+
+@dataclass
+class Event:
+    """
+    A typed event for the event bus.
+    
+    Attributes:
+        type: The event type (from EventType enum or custom string)
+        data: The event payload data
+        id: Unique event identifier
+        timestamp: Unix timestamp when event was created
+        source: Optional source identifier (e.g., session_id, agent_name)
+        metadata: Optional additional metadata
+    """
+    type: str
+    data: Dict[str, Any] = field(default_factory=dict)
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    timestamp: float = field(default_factory=time.time)
+    source: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert event to dictionary for serialization."""
+        return {
+            "id": self.id,
+            "type": self.type,
+            "data": self.data,
+            "timestamp": self.timestamp,
+            "source": self.source,
+            "metadata": self.metadata,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "Event":
+        """Create event from dictionary."""
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            type=data.get("type", EventType.CUSTOM.value),
+            data=data.get("data", {}),
+            timestamp=data.get("timestamp", time.time()),
+            source=data.get("source"),
+            metadata=data.get("metadata", {}),
+        )
+    
+    def to_sse(self) -> str:
+        """Format event for Server-Sent Events."""
+        import json
+        return f"id: {self.id}\nevent: {self.type}\ndata: {json.dumps(self.to_dict())}\n\n"
+    
+    def __repr__(self) -> str:
+        return f"Event(type={self.type!r}, id={self.id[:8]}..., source={self.source!r})"

src/praisonai-agents/praisonaiagents/compaction/compactor.py

@@ -109,6 +109,10 @@ class ContextCompactor:
             compacted = self._summarize(messages)
         elif self.strategy == CompactionStrategy.SMART:
             compacted = self._smart_compact(messages)
+        elif self.strategy == CompactionStrategy.PRUNE:
+            compacted = self._prune(messages)
+        elif self.strategy == CompactionStrategy.LLM_SUMMARIZE:
+            compacted = self._llm_summarize(messages)
         else:
             compacted = self._truncate(messages)
         
@@ -223,6 +227,89 @@ class ContextCompactor:
         # For now, use sliding window as base
         return self._sliding_window(messages)
     
+    def _prune(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """
+        Prune old tool outputs while keeping tool calls.
+        
+        This reduces token usage by removing verbose tool outputs
+        from older messages while preserving the context of what
+        tools were called.
+        """
+        result = []
+        
+        # Separate system and other messages
+        system_msgs = [m for m in messages if m.get("role") == "system"]
+        other_msgs = [m for m in messages if m.get("role") != "system"]
+        
+        result.extend(system_msgs)
+        
+        # Keep recent messages intact
+        recent = other_msgs[-self.preserve_recent:]
+        older = other_msgs[:-self.preserve_recent] if len(other_msgs) > self.preserve_recent else []
+        
+        # Prune older messages
+        for msg in older:
+            role = msg.get("role", "")
+            content = msg.get("content", "")
+            
+            # If this is a tool result, truncate it
+            if role == "tool" or msg.get("tool_call_id"):
+                if isinstance(content, str) and len(content) > 500:
+                    pruned_msg = msg.copy()
+                    pruned_msg["content"] = content[:200] + "\n...[output truncated]..."
+                    result.append(pruned_msg)
+                else:
+                    result.append(msg)
+            else:
+                result.append(msg)
+        
+        result.extend(recent)
+        return result
+    
+    def _llm_summarize(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """
+        Use LLM to summarize older messages.
+        
+        Note: This is a placeholder that returns a structured summary.
+        Actual LLM integration should be done at the agent level.
+        """
+        result = []
+        
+        # Keep system messages
+        system_msgs = [m for m in messages if m.get("role") == "system"]
+        other_msgs = [m for m in messages if m.get("role") != "system"]
+        
+        result.extend(system_msgs)
+        
+        # Keep recent messages
+        recent = other_msgs[-self.preserve_recent:]
+        older = other_msgs[:-self.preserve_recent] if len(other_msgs) > self.preserve_recent else []
+        
+        if older:
+            # Create structured summary for LLM to process
+            summary_parts = []
+            for msg in older:
+                role = msg.get("role", "unknown")
+                content = msg.get("content", "")
+                if isinstance(content, str) and content:
+                    # Extract key information
+                    summary_parts.append(f"[{role}]: {content[:150]}...")
+            
+            if summary_parts:
+                summary = (
+                    "[Compacted conversation history - summarize key points]\n"
+                    + "\n".join(summary_parts[:10])
+                )
+                result.append({
+                    "role": "system",
+                    "content": summary,
+                    "_compacted": True,
+                    "_original_count": len(older),
+                })
+        
+        result.extend(recent)
+        return result
+    
     def get_stats(self, messages: List[Dict[str, Any]]) -> Dict[str, Any]:
         """Get statistics about messages."""
         total_tokens = self.count_total_tokens(messages)

src/praisonai-agents/praisonaiagents/compaction/strategy.py

@@ -1,13 +1,16 @@
 """
-Compaction Strategies for PraisonAI Agents.
+Compaction strategies for context management.
 """
 
 from enum import Enum
 
 
 class CompactionStrategy(str, Enum):
-    """Strategy for compacting context."""
-    TRUNCATE = "truncate"      # Remove oldest messages
-    SUMMARIZE = "summarize"    # Summarize old messages
-    SLIDING = "sliding"        # Sliding window of recent messages
-    SMART = "smart"            # Intelligent selection based on relevance
+    """Available compaction strategies."""
+    
+    TRUNCATE = "truncate"
+    SLIDING = "sliding"
+    SUMMARIZE = "summarize"
+    SMART = "smart"
+    LLM_SUMMARIZE = "llm_summarize"  # Use LLM for summarization
+    PRUNE = "prune"  # Remove old tool outputs

src/praisonai-agents/praisonaiagents/context/__init__.py

@@ -1,30 +1,151 @@
 """
-Context module for PraisonAI Agents.
+Context Management Module for PraisonAI Agents.
 
-This module provides context retrieval capabilities including:
+This module provides comprehensive context management capabilities:
+- Token estimation and budgeting
+- Context composition within limits
+- Optimization strategies (truncate, sliding window, summarize, prune, smart)
+- Real-time context monitoring
+- Multi-agent context isolation
+
+Also includes:
 - Fast Context: Rapid parallel code search subagent
-"""
 
-# Lazy imports to avoid impacting package load time
-def __getattr__(name):
-    """Lazy import to avoid impacting package performance."""
-    if name == "FastContext":
-        from praisonaiagents.context.fast.fast_context import FastContext
-        return FastContext
-    elif name == "FastContextResult":
-        from praisonaiagents.context.fast.result import FastContextResult
-        return FastContextResult
-    elif name == "FileMatch":
-        from praisonaiagents.context.fast.result import FileMatch
-        return FileMatch
-    elif name == "LineRange":
-        from praisonaiagents.context.fast.result import LineRange
-        return LineRange
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+Zero Performance Impact:
+- All imports are lazy loaded via __getattr__
+- Features only activate when explicitly enabled
+- No overhead when context management is not used
+
+Usage:
+    from praisonaiagents.context import (
+        ContextBudgeter,
+        ContextComposer,
+        ContextMonitor,
+        get_optimizer,
+    )
+    
+    # Create budgeter for model
+    budgeter = ContextBudgeter(model="gpt-4o")
+    budget = budgeter.allocate()
+    
+    # Compose context within budget
+    composer = ContextComposer(budget=budget)
+    result = composer.compose(
+        system_prompt="You are helpful",
+        history=messages,
+        tools=tool_schemas,
+    )
+    
+    # Monitor context (opt-in)
+    monitor = ContextMonitor(enabled=True)
+    monitor.snapshot(ledger=result.ledger, budget=budget, messages=result.messages)
+"""
 
 __all__ = [
+    # Models
+    "ContextSegment",
+    "ContextLedger",
+    "BudgetAllocation",
+    "ContextSnapshot",
+    "OptimizerStrategy",
+    "OptimizationResult",
+    "MonitorConfig",
+    "ContextConfig",
+    # Token estimation
+    "estimate_tokens_heuristic",
+    "estimate_messages_tokens",
+    "estimate_tool_schema_tokens",
+    "TokenEstimatorImpl",
+    "get_estimator",
+    # Budgeting
+    "ContextBudgeter",
+    "get_model_limit",
+    "get_output_reserve",
+    # Ledger
+    "ContextLedgerManager",
+    "MultiAgentLedger",
+    # Composition
+    "ContextComposer",
+    "ComposedContext",
+    # Optimization
+    "BaseOptimizer",
+    "TruncateOptimizer",
+    "SlidingWindowOptimizer",
+    "PruneToolsOptimizer",
+    "NonDestructiveOptimizer",
+    "SummarizeOptimizer",
+    "SmartOptimizer",
+    "get_optimizer",
+    "get_effective_history",
+    # Monitoring
+    "ContextMonitor",
+    "MultiAgentMonitor",
+    "redact_sensitive",
+    "format_human_snapshot",
+    "format_json_snapshot",
+    # Fast Context (legacy)
     "FastContext",
     "FastContextResult",
     "FileMatch",
     "LineRange",
 ]
+
+
+def __getattr__(name: str):
+    """Lazy load module components to avoid import overhead."""
+    # Models
+    if name in ("ContextSegment", "ContextLedger", "BudgetAllocation", 
+                "ContextSnapshot", "OptimizerStrategy", "OptimizationResult",
+                "MonitorConfig", "ContextConfig"):
+        from . import models
+        return getattr(models, name)
+    
+    # Token estimation
+    if name in ("estimate_tokens_heuristic", "estimate_messages_tokens",
+                "estimate_tool_schema_tokens", "TokenEstimatorImpl", "get_estimator"):
+        from . import tokens
+        return getattr(tokens, name)
+    
+    # Budgeting
+    if name in ("ContextBudgeter", "get_model_limit", "get_output_reserve"):
+        from . import budgeter
+        return getattr(budgeter, name)
+    
+    # Ledger
+    if name in ("ContextLedgerManager", "MultiAgentLedger"):
+        from . import ledger
+        return getattr(ledger, name)
+    
+    # Composition
+    if name in ("ContextComposer", "ComposedContext"):
+        from . import composer
+        return getattr(composer, name)
+    
+    # Optimization
+    if name in ("BaseOptimizer", "TruncateOptimizer", "SlidingWindowOptimizer",
+                "PruneToolsOptimizer", "NonDestructiveOptimizer", "SummarizeOptimizer",
+                "SmartOptimizer", "get_optimizer", "get_effective_history"):
+        from . import optimizer
+        return getattr(optimizer, name)
+    
+    # Monitoring
+    if name in ("ContextMonitor", "MultiAgentMonitor", "redact_sensitive",
+                "format_human_snapshot", "format_json_snapshot"):
+        from . import monitor
+        return getattr(monitor, name)
+    
+    # Fast Context (legacy)
+    if name == "FastContext":
+        from praisonaiagents.context.fast.fast_context import FastContext
+        return FastContext
+    if name == "FastContextResult":
+        from praisonaiagents.context.fast.result import FastContextResult
+        return FastContextResult
+    if name == "FileMatch":
+        from praisonaiagents.context.fast.result import FileMatch
+        return FileMatch
+    if name == "LineRange":
+        from praisonaiagents.context.fast.result import LineRange
+        return LineRange
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/context/budgeter.py

@@ -0,0 +1,229 @@
+"""
+Context Budgeter for PraisonAI Agents.
+
+Allocates token budgets across context segments based on model limits.
+"""
+
+from typing import Dict, Any, Optional
+from .models import BudgetAllocation
+
+
+# Model token limits (context window sizes)
+MODEL_LIMITS: Dict[str, int] = {
+    # OpenAI
+    "gpt-4o": 128000,
+    "gpt-4o-mini": 128000,
+    "gpt-4-turbo": 128000,
+    "gpt-4": 8192,
+    "gpt-3.5-turbo": 16385,
+    "gpt-3.5-turbo-16k": 16385,
+    # Anthropic
+    "claude-3-5-sonnet": 200000,
+    "claude-3-5-haiku": 200000,
+    "claude-3-opus": 200000,
+    "claude-3-sonnet": 200000,
+    "claude-3-haiku": 200000,
+    # Google
+    "gemini-2.0-flash": 1048576,
+    "gemini-1.5-pro": 2097152,
+    "gemini-1.5-flash": 1048576,
+    # Defaults
+    "default": 128000,
+}
+
+# Default output reserves by model family
+OUTPUT_RESERVES: Dict[str, int] = {
+    "gpt-4o": 16384,
+    "gpt-4o-mini": 16384,
+    "gpt-4-turbo": 4096,
+    "gpt-4": 4096,
+    "gpt-3.5-turbo": 4096,
+    "claude-3": 8192,
+    "gemini": 8192,
+    "default": 8000,
+}
+
+
+def get_model_limit(model: str) -> int:
+    """
+    Get context window limit for a model.
+    
+    Args:
+        model: Model name or identifier
+        
+    Returns:
+        Context window size in tokens
+    """
+    # Exact match
+    if model in MODEL_LIMITS:
+        return MODEL_LIMITS[model]
+    
+    # Partial match (e.g., "gpt-4o-2024-05-13" matches "gpt-4o")
+    model_lower = model.lower()
+    for key, limit in MODEL_LIMITS.items():
+        if key in model_lower or model_lower.startswith(key):
+            return limit
+    
+    return MODEL_LIMITS["default"]
+
+
+def get_output_reserve(model: str) -> int:
+    """
+    Get recommended output token reserve for a model.
+    
+    Args:
+        model: Model name
+        
+    Returns:
+        Output reserve in tokens
+    """
+    model_lower = model.lower()
+    
+    for key, reserve in OUTPUT_RESERVES.items():
+        if key in model_lower:
+            return reserve
+    
+    return OUTPUT_RESERVES["default"]
+
+
+class ContextBudgeter:
+    """
+    Allocates token budgets across context segments.
+    
+    Ensures context fits within model limits while reserving
+    space for output generation.
+    
+    Example:
+        budgeter = ContextBudgeter(model="gpt-4o")
+        allocation = budgeter.allocate()
+        print(f"History budget: {allocation.history_budget}")
+    """
+    
+    def __init__(
+        self,
+        model: str = "gpt-4o-mini",
+        model_limit: Optional[int] = None,
+        output_reserve: Optional[int] = None,
+        # Segment budget overrides (absolute tokens)
+        system_prompt_budget: int = 2000,
+        rules_budget: int = 500,
+        skills_budget: int = 500,
+        memory_budget: int = 1000,
+        tools_schema_budget: int = 2000,
+        tool_outputs_budget: int = 20000,
+        buffer_budget: int = 1000,
+        # Ratio-based allocation (if set, overrides fixed budgets)
+        history_ratio: Optional[float] = None,
+    ):
+        """
+        Initialize budgeter.
+        
+        Args:
+            model: Model name for limit lookup
+            model_limit: Override model limit
+            output_reserve: Override output reserve
+            system_prompt_budget: Budget for system prompt
+            rules_budget: Budget for rules
+            skills_budget: Budget for skills
+            memory_budget: Budget for memory
+            tools_schema_budget: Budget for tool schemas
+            tool_outputs_budget: Budget for tool outputs
+            buffer_budget: Safety buffer
+            history_ratio: If set, allocate this ratio of usable for history
+        """
+        self.model = model
+        self.model_limit = model_limit or get_model_limit(model)
+        self.output_reserve = output_reserve or get_output_reserve(model)
+        
+        self.system_prompt_budget = system_prompt_budget
+        self.rules_budget = rules_budget
+        self.skills_budget = skills_budget
+        self.memory_budget = memory_budget
+        self.tools_schema_budget = tools_schema_budget
+        self.tool_outputs_budget = tool_outputs_budget
+        self.buffer_budget = buffer_budget
+        self.history_ratio = history_ratio
+    
+    @property
+    def usable(self) -> int:
+        """Usable tokens after output reserve."""
+        return self.model_limit - self.output_reserve
+    
+    def allocate(self) -> BudgetAllocation:
+        """
+        Create a budget allocation.
+        
+        Returns:
+            BudgetAllocation with segment budgets
+        """
+        allocation = BudgetAllocation(
+            model_limit=self.model_limit,
+            output_reserve=self.output_reserve,
+            system_prompt=self.system_prompt_budget,
+            rules=self.rules_budget,
+            skills=self.skills_budget,
+            memory=self.memory_budget,
+            tools_schema=self.tools_schema_budget,
+            tool_outputs=self.tool_outputs_budget,
+            buffer=self.buffer_budget,
+            history=-1,  # Dynamic
+        )
+        
+        # If history_ratio is set, compute history budget from ratio
+        if self.history_ratio is not None:
+            allocation.history = int(self.usable * self.history_ratio)
+        
+        return allocation
+    
+    def check_overflow(
+        self,
+        current_tokens: int,
+        threshold: float = 0.8
+    ) -> bool:
+        """
+        Check if current tokens exceed threshold.
+        
+        Args:
+            current_tokens: Current total tokens
+            threshold: Threshold as fraction of usable (0.0-1.0)
+            
+        Returns:
+            True if overflow threshold exceeded
+        """
+        return current_tokens > (self.usable * threshold)
+    
+    def get_remaining(self, current_tokens: int) -> int:
+        """
+        Get remaining tokens before hitting usable limit.
+        
+        Args:
+            current_tokens: Current total tokens
+            
+        Returns:
+            Remaining tokens (can be negative if over limit)
+        """
+        return self.usable - current_tokens
+    
+    def get_utilization(self, current_tokens: int) -> float:
+        """
+        Get current utilization as fraction.
+        
+        Args:
+            current_tokens: Current total tokens
+            
+        Returns:
+            Utilization (0.0-1.0+)
+        """
+        if self.usable == 0:
+            return 0.0
+        return current_tokens / self.usable
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert budgeter state to dictionary."""
+        return {
+            "model": self.model,
+            "model_limit": self.model_limit,
+            "output_reserve": self.output_reserve,
+            "usable": self.usable,
+            "allocation": self.allocate().to_dict(),
+        }

src/praisonai-agents/praisonaiagents/context/composer.py

@@ -0,0 +1,335 @@
+"""
+Context Composer for PraisonAI Agents.
+
+Assembles context from segments within budget constraints.
+"""
+
+from typing import Dict, List, Any, Optional
+from .models import (
+    ContextLedger, BudgetAllocation, ContextSegment, ContextSnapshot
+)
+from .tokens import estimate_tokens_heuristic, estimate_messages_tokens
+from .ledger import ContextLedgerManager
+
+
+class ContextComposer:
+    """
+    Composes context from segments within budget constraints.
+    
+    Assembles system prompt, rules, skills, memory, tools, and history
+    while respecting token budgets and applying trimming as needed.
+    
+    Example:
+        composer = ContextComposer(budget=budgeter.allocate())
+        
+        result = composer.compose(
+            system_prompt="You are a helpful assistant",
+            history=messages,
+            tools=tool_schemas,
+        )
+        
+        # Use composed messages
+        response = llm.chat(result.messages)
+    """
+    
+    def __init__(
+        self,
+        budget: Optional[BudgetAllocation] = None,
+        preserve_tool_pairs: bool = True,
+        max_tool_output_tokens: int = 10000,
+        keep_recent_turns: int = 5,
+    ):
+        """
+        Initialize composer.
+        
+        Args:
+            budget: Budget allocation (uses defaults if None)
+            preserve_tool_pairs: Keep tool_call/tool_result pairs together
+            max_tool_output_tokens: Max tokens per tool output
+            keep_recent_turns: Minimum recent turns to keep
+        """
+        self.budget = budget or BudgetAllocation()
+        self.preserve_tool_pairs = preserve_tool_pairs
+        self.max_tool_output_tokens = max_tool_output_tokens
+        self.keep_recent_turns = keep_recent_turns
+        
+        self._ledger = ContextLedgerManager()
+        self._ledger.set_budget(self.budget)
+    
+    def compose(
+        self,
+        system_prompt: str = "",
+        rules: str = "",
+        skills: str = "",
+        memory: str = "",
+        tools: Optional[List[Dict[str, Any]]] = None,
+        history: Optional[List[Dict[str, Any]]] = None,
+        agent_id: Optional[str] = None,
+    ) -> "ComposedContext":
+        """
+        Compose context from segments.
+        
+        Args:
+            system_prompt: System prompt content
+            rules: Rules content
+            skills: Skills content
+            memory: Memory content
+            tools: Tool schemas
+            history: Conversation history
+            agent_id: Agent identifier
+            
+        Returns:
+            ComposedContext with messages and ledger
+        """
+        self._ledger.reset()
+        tools = tools or []
+        history = history or []
+        
+        # Track fixed segments
+        self._ledger.track_system_prompt(system_prompt)
+        self._ledger.track_rules(rules)
+        self._ledger.track_skills(skills)
+        self._ledger.track_memory(memory)
+        self._ledger.track_tools(tools)
+        
+        # Build combined system message
+        system_parts = []
+        if system_prompt:
+            system_parts.append(system_prompt)
+        if rules:
+            system_parts.append(f"\n## Rules\n{rules}")
+        if skills:
+            system_parts.append(f"\n## Skills\n{skills}")
+        if memory:
+            system_parts.append(f"\n## Memory\n{memory}")
+        
+        combined_system = "\n".join(system_parts)
+        
+        # Process history with budget awareness
+        processed_history = self._process_history(history)
+        
+        # Build final messages
+        messages = []
+        if combined_system:
+            messages.append({"role": "system", "content": combined_system})
+        messages.extend(processed_history)
+        
+        # Track final history tokens
+        history_tokens = estimate_messages_tokens(processed_history)
+        self._ledger.track_segment(
+            ContextSegment.HISTORY, 
+            history_tokens, 
+            len(processed_history)
+        )
+        
+        # Check for overflow
+        total = self._ledger.get_total()
+        overflow = total > self.budget.usable
+        
+        return ComposedContext(
+            messages=messages,
+            tools=tools,
+            ledger=self._ledger.get_ledger(),
+            total_tokens=total,
+            overflow=overflow,
+            warnings=self._ledger.get_warnings(),
+            agent_id=agent_id,
+        )
+    
+    def _process_history(
+        self,
+        history: List[Dict[str, Any]]
+    ) -> List[Dict[str, Any]]:
+        """
+        Process history with budget constraints.
+        
+        Applies:
+        - Tool output truncation
+        - Sliding window if over budget
+        - Tool pair preservation
+        """
+        if not history:
+            return []
+        
+        # First pass: truncate large tool outputs
+        processed = []
+        for msg in history:
+            processed_msg = self._truncate_tool_output(msg)
+            processed.append(processed_msg)
+        
+        # Check if within budget
+        history_budget = self.budget.history_budget
+        current_tokens = estimate_messages_tokens(processed)
+        
+        if current_tokens <= history_budget:
+            return processed
+        
+        # Apply sliding window
+        return self._apply_sliding_window(processed, history_budget)
+    
+    def _truncate_tool_output(self, msg: Dict[str, Any]) -> Dict[str, Any]:
+        """Truncate tool output if too large."""
+        if msg.get("role") != "tool" and not msg.get("tool_call_id"):
+            return msg
+        
+        content = msg.get("content", "")
+        if not isinstance(content, str):
+            return msg
+        
+        tokens = estimate_tokens_heuristic(content)
+        if tokens <= self.max_tool_output_tokens:
+            return msg
+        
+        # Truncate to max tokens (rough: 4 chars per token)
+        max_chars = self.max_tool_output_tokens * 4
+        truncated = content[:max_chars] + "\n...[output truncated]..."
+        
+        result = msg.copy()
+        result["content"] = truncated
+        result["_truncated"] = True
+        return result
+    
+    def _apply_sliding_window(
+        self,
+        history: List[Dict[str, Any]],
+        budget: int
+    ) -> List[Dict[str, Any]]:
+        """Apply sliding window to fit within budget."""
+        if not history:
+            return []
+        
+        # Always keep at least recent turns
+        min_keep = self.keep_recent_turns * 2  # user + assistant per turn
+        
+        # Start from end, add messages until budget exceeded
+        result = []
+        current_tokens = 0
+        
+        # Track tool call IDs to preserve pairs
+        tool_call_ids = set()
+        
+        for msg in reversed(history):
+            msg_tokens = estimate_tokens_heuristic(str(msg.get("content", "")))
+            
+            if current_tokens + msg_tokens > budget and len(result) >= min_keep:
+                break
+            
+            result.insert(0, msg)
+            current_tokens += msg_tokens
+            
+            # Track tool calls for pair preservation
+            if self.preserve_tool_pairs:
+                if "tool_calls" in msg:
+                    for tc in msg.get("tool_calls", []):
+                        if isinstance(tc, dict) and "id" in tc:
+                            tool_call_ids.add(tc["id"])
+                if msg.get("tool_call_id"):
+                    tool_call_ids.add(msg["tool_call_id"])
+        
+        # Ensure tool pairs are complete
+        if self.preserve_tool_pairs:
+            result = self._ensure_tool_pairs(result, history, tool_call_ids)
+        
+        return result
+    
+    def _ensure_tool_pairs(
+        self,
+        result: List[Dict[str, Any]],
+        full_history: List[Dict[str, Any]],
+        tool_call_ids: set
+    ) -> List[Dict[str, Any]]:
+        """Ensure tool_call/tool_result pairs are complete."""
+        # Find tool calls in result
+        result_call_ids = set()
+        result_response_ids = set()
+        
+        for msg in result:
+            if "tool_calls" in msg:
+                for tc in msg.get("tool_calls", []):
+                    if isinstance(tc, dict) and "id" in tc:
+                        result_call_ids.add(tc["id"])
+            if msg.get("tool_call_id"):
+                result_response_ids.add(msg["tool_call_id"])
+        
+        # Find missing pairs
+        missing_calls = result_response_ids - result_call_ids
+        missing_responses = result_call_ids - result_response_ids
+        
+        if not missing_calls and not missing_responses:
+            return result
+        
+        # Add missing messages from full history
+        for msg in full_history:
+            # Add missing tool calls
+            if "tool_calls" in msg:
+                for tc in msg.get("tool_calls", []):
+                    if isinstance(tc, dict) and tc.get("id") in missing_calls:
+                        if msg not in result:
+                            result.insert(0, msg)
+                        break
+            
+            # Add missing tool responses
+            if msg.get("tool_call_id") in missing_responses:
+                if msg not in result:
+                    result.append(msg)
+        
+        return result
+    
+    def get_ledger(self) -> ContextLedger:
+        """Get current ledger."""
+        return self._ledger.get_ledger()
+    
+    def get_warnings(self) -> List[str]:
+        """Get current warnings."""
+        return self._ledger.get_warnings()
+
+
+class ComposedContext:
+    """Result of context composition."""
+    
+    def __init__(
+        self,
+        messages: List[Dict[str, Any]],
+        tools: List[Dict[str, Any]],
+        ledger: ContextLedger,
+        total_tokens: int,
+        overflow: bool,
+        warnings: List[str],
+        agent_id: Optional[str] = None,
+    ):
+        self.messages = messages
+        self.tools = tools
+        self.ledger = ledger
+        self.total_tokens = total_tokens
+        self.overflow = overflow
+        self.warnings = warnings
+        self.agent_id = agent_id
+    
+    def to_snapshot(
+        self,
+        session_id: str = "",
+        model_name: str = "",
+        budget: Optional[BudgetAllocation] = None,
+    ) -> ContextSnapshot:
+        """Convert to snapshot for monitoring."""
+        return ContextSnapshot(
+            session_id=session_id,
+            agent_name=self.agent_id or "",
+            model_name=model_name,
+            budget=budget,
+            ledger=self.ledger,
+            utilization=self.total_tokens / budget.usable if budget else 0.0,
+            history_content=self.messages,
+            warnings=self.warnings,
+        )
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "message_count": len(self.messages),
+            "tool_count": len(self.tools),
+            "total_tokens": self.total_tokens,
+            "overflow": self.overflow,
+            "warnings": self.warnings,
+            "ledger": self.ledger.to_dict(),
+        }

src/praisonai-agents/praisonaiagents/context/ledger.py

@@ -0,0 +1,323 @@
+"""
+Context Ledger for PraisonAI Agents.
+
+Tracks token usage across context segments with per-agent and per-session support.
+"""
+
+from typing import Dict, List, Any, Optional
+from dataclasses import dataclass
+from .models import ContextLedger, ContextSegment, BudgetAllocation
+from .tokens import estimate_messages_tokens, estimate_tool_schema_tokens
+
+
+@dataclass
+class SegmentStats:
+    """Statistics for a context segment."""
+    tokens: int = 0
+    count: int = 0  # Number of items (messages, tools, etc.)
+    budget: int = 0
+    
+    @property
+    def utilization(self) -> float:
+        """Utilization as fraction of budget."""
+        if self.budget == 0:
+            return 0.0
+        return self.tokens / self.budget
+    
+    @property
+    def over_budget(self) -> bool:
+        """Check if over budget."""
+        return self.tokens > self.budget if self.budget > 0 else False
+
+
+class ContextLedgerManager:
+    """
+    Manages context token accounting.
+    
+    Tracks tokens per segment and provides utilization metrics.
+    Supports multi-agent scenarios with per-agent ledgers.
+    
+    Example:
+        manager = ContextLedgerManager()
+        manager.set_budget(budgeter.allocate())
+        
+        # Track segments
+        manager.track_system_prompt(system_prompt)
+        manager.track_history(messages)
+        manager.track_tools(tool_schemas)
+        
+        # Get stats
+        print(manager.get_utilization())
+        print(manager.get_warnings())
+    """
+    
+    def __init__(self, agent_id: Optional[str] = None):
+        """
+        Initialize ledger manager.
+        
+        Args:
+            agent_id: Optional agent identifier for multi-agent tracking
+        """
+        self.agent_id = agent_id
+        self._ledger = ContextLedger()
+        self._budget: Optional[BudgetAllocation] = None
+        self._segment_details: Dict[str, SegmentStats] = {}
+        
+        # Initialize segment stats
+        for segment in ContextSegment:
+            self._segment_details[segment.value] = SegmentStats()
+    
+    def set_budget(self, budget: BudgetAllocation) -> None:
+        """Set the budget allocation."""
+        self._budget = budget
+        
+        # Update segment budgets
+        for segment in ContextSegment:
+            self._segment_details[segment.value].budget = budget.get_segment_budget(segment)
+    
+    def reset(self) -> None:
+        """Reset all token counts."""
+        self._ledger = ContextLedger()
+        for segment in ContextSegment:
+            stats = self._segment_details[segment.value]
+            stats.tokens = 0
+            stats.count = 0
+    
+    def track_segment(
+        self,
+        segment: ContextSegment,
+        tokens: int,
+        count: int = 1
+    ) -> None:
+        """
+        Track tokens for a segment.
+        
+        Args:
+            segment: Context segment
+            tokens: Token count
+            count: Number of items
+        """
+        self._ledger.set_segment(segment, tokens)
+        stats = self._segment_details[segment.value]
+        stats.tokens = tokens
+        stats.count = count
+    
+    def add_to_segment(
+        self,
+        segment: ContextSegment,
+        tokens: int,
+        count: int = 1
+    ) -> None:
+        """
+        Add tokens to a segment.
+        
+        Args:
+            segment: Context segment
+            tokens: Tokens to add
+            count: Items to add
+        """
+        self._ledger.add_segment(segment, tokens)
+        stats = self._segment_details[segment.value]
+        stats.tokens += tokens
+        stats.count += count
+    
+    def track_system_prompt(self, content: str) -> int:
+        """Track system prompt tokens."""
+        from .tokens import estimate_tokens_heuristic
+        tokens = estimate_tokens_heuristic(content)
+        self.track_segment(ContextSegment.SYSTEM_PROMPT, tokens)
+        return tokens
+    
+    def track_rules(self, content: str) -> int:
+        """Track rules tokens."""
+        from .tokens import estimate_tokens_heuristic
+        tokens = estimate_tokens_heuristic(content)
+        self.track_segment(ContextSegment.RULES, tokens)
+        return tokens
+    
+    def track_skills(self, content: str) -> int:
+        """Track skills tokens."""
+        from .tokens import estimate_tokens_heuristic
+        tokens = estimate_tokens_heuristic(content)
+        self.track_segment(ContextSegment.SKILLS, tokens)
+        return tokens
+    
+    def track_memory(self, content: str) -> int:
+        """Track memory tokens."""
+        from .tokens import estimate_tokens_heuristic
+        tokens = estimate_tokens_heuristic(content)
+        self.track_segment(ContextSegment.MEMORY, tokens)
+        return tokens
+    
+    def track_history(self, messages: List[Dict[str, Any]]) -> int:
+        """
+        Track conversation history tokens.
+        
+        Args:
+            messages: List of message dicts
+            
+        Returns:
+            Total tokens
+        """
+        tokens = estimate_messages_tokens(messages)
+        self.track_segment(ContextSegment.HISTORY, tokens, len(messages))
+        self._ledger.message_count = len(messages)
+        
+        # Count turns (user messages)
+        turns = sum(1 for m in messages if m.get("role") == "user")
+        self._ledger.turn_count = turns
+        
+        return tokens
+    
+    def track_tools(self, tools: List[Dict[str, Any]]) -> int:
+        """
+        Track tool schema tokens.
+        
+        Args:
+            tools: List of tool definitions
+            
+        Returns:
+            Total tokens
+        """
+        tokens = estimate_tool_schema_tokens(tools)
+        self.track_segment(ContextSegment.TOOLS_SCHEMA, tokens, len(tools))
+        return tokens
+    
+    def track_tool_output(self, content: str) -> int:
+        """
+        Add a tool output to tracking.
+        
+        Args:
+            content: Tool output content
+            
+        Returns:
+            Tokens for this output
+        """
+        from .tokens import estimate_tokens_heuristic
+        tokens = estimate_tokens_heuristic(content)
+        self.add_to_segment(ContextSegment.TOOL_OUTPUTS, tokens)
+        self._ledger.tool_call_count += 1
+        return tokens
+    
+    def get_ledger(self) -> ContextLedger:
+        """Get current ledger."""
+        return self._ledger.copy()
+    
+    def get_total(self) -> int:
+        """Get total tokens across all segments."""
+        return self._ledger.total
+    
+    def get_utilization(self) -> float:
+        """Get overall utilization as fraction of usable budget."""
+        if self._budget is None:
+            return 0.0
+        usable = self._budget.usable
+        if usable == 0:
+            return 0.0
+        return self._ledger.total / usable
+    
+    def get_segment_stats(self, segment: ContextSegment) -> SegmentStats:
+        """Get stats for a segment."""
+        return self._segment_details[segment.value]
+    
+    def get_all_stats(self) -> Dict[str, SegmentStats]:
+        """Get stats for all segments."""
+        return self._segment_details.copy()
+    
+    def get_warnings(self, threshold: float = 0.9) -> List[str]:
+        """
+        Get warnings for segments near or over budget.
+        
+        Args:
+            threshold: Warning threshold (0.0-1.0)
+            
+        Returns:
+            List of warning messages
+        """
+        warnings = []
+        
+        for segment in ContextSegment:
+            stats = self._segment_details[segment.value]
+            if stats.budget > 0:
+                if stats.over_budget:
+                    warnings.append(
+                        f"{segment.value}: OVER BUDGET ({stats.tokens:,} / {stats.budget:,} tokens)"
+                    )
+                elif stats.utilization >= threshold:
+                    pct = int(stats.utilization * 100)
+                    warnings.append(
+                        f"{segment.value}: {pct}% of budget ({stats.tokens:,} / {stats.budget:,} tokens)"
+                    )
+        
+        # Overall utilization warning
+        if self._budget:
+            overall_util = self.get_utilization()
+            if overall_util >= threshold:
+                pct = int(overall_util * 100)
+                warnings.insert(0, f"Overall context: {pct}% of usable budget")
+        
+        return warnings
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for serialization."""
+        return {
+            "agent_id": self.agent_id,
+            "ledger": self._ledger.to_dict(),
+            "total": self._ledger.total,
+            "utilization": self.get_utilization(),
+            "segments": {
+                name: {
+                    "tokens": stats.tokens,
+                    "count": stats.count,
+                    "budget": stats.budget,
+                    "utilization": stats.utilization,
+                }
+                for name, stats in self._segment_details.items()
+            },
+            "warnings": self.get_warnings(),
+        }
+
+
+class MultiAgentLedger:
+    """
+    Manages ledgers for multiple agents.
+    
+    Provides per-agent tracking and combined views.
+    """
+    
+    def __init__(self):
+        """Initialize multi-agent ledger."""
+        self._agents: Dict[str, ContextLedgerManager] = {}
+        self._shared_tokens: int = 0  # Tokens shared across agents
+    
+    def get_agent_ledger(self, agent_id: str) -> ContextLedgerManager:
+        """Get or create ledger for an agent."""
+        if agent_id not in self._agents:
+            self._agents[agent_id] = ContextLedgerManager(agent_id)
+        return self._agents[agent_id]
+    
+    def set_shared_tokens(self, tokens: int) -> None:
+        """Set tokens shared across agents (e.g., team memory)."""
+        self._shared_tokens = tokens
+    
+    def get_combined_total(self) -> int:
+        """Get total tokens across all agents plus shared."""
+        total = self._shared_tokens
+        for ledger in self._agents.values():
+            total += ledger.get_total()
+        return total
+    
+    def get_agent_ids(self) -> List[str]:
+        """Get list of tracked agent IDs."""
+        return list(self._agents.keys())
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "agents": {
+                agent_id: ledger.to_dict()
+                for agent_id, ledger in self._agents.items()
+            },
+            "shared_tokens": self._shared_tokens,
+            "combined_total": self.get_combined_total(),
+        }

src/praisonai-agents/praisonaiagents/context/models.py

@@ -0,0 +1,300 @@
+"""
+Context Management Data Models for PraisonAI Agents.
+
+Provides dataclasses and protocols for context budgeting, composition,
+optimization, and monitoring.
+"""
+
+from dataclasses import dataclass, field, asdict
+from typing import Dict, List, Any, Optional, Literal, Protocol, runtime_checkable
+from enum import Enum
+
+
+class ContextSegment(str, Enum):
+    """Segments that contribute to context."""
+    SYSTEM_PROMPT = "system_prompt"
+    RULES = "rules"
+    SKILLS = "skills"
+    MEMORY = "memory"
+    TOOLS_SCHEMA = "tools_schema"
+    HISTORY = "history"
+    TOOL_OUTPUTS = "tool_outputs"
+    BUFFER = "buffer"
+
+
+@dataclass
+class ContextLedger:
+    """
+    Tracks token usage across context segments.
+    
+    Provides per-segment token counts and total usage.
+    """
+    system_prompt: int = 0
+    rules: int = 0
+    skills: int = 0
+    memory: int = 0
+    tools_schema: int = 0
+    history: int = 0
+    tool_outputs: int = 0
+    buffer: int = 0
+    
+    # Metadata
+    turn_count: int = 0
+    message_count: int = 0
+    tool_call_count: int = 0
+    
+    @property
+    def total(self) -> int:
+        """Total tokens across all segments."""
+        return (
+            self.system_prompt +
+            self.rules +
+            self.skills +
+            self.memory +
+            self.tools_schema +
+            self.history +
+            self.tool_outputs +
+            self.buffer
+        )
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return asdict(self)
+    
+    def get_segment(self, segment: ContextSegment) -> int:
+        """Get token count for a segment."""
+        return getattr(self, segment.value, 0)
+    
+    def set_segment(self, segment: ContextSegment, tokens: int) -> None:
+        """Set token count for a segment."""
+        setattr(self, segment.value, tokens)
+    
+    def add_segment(self, segment: ContextSegment, tokens: int) -> None:
+        """Add tokens to a segment."""
+        current = self.get_segment(segment)
+        self.set_segment(segment, current + tokens)
+    
+    def copy(self) -> "ContextLedger":
+        """Create a copy of this ledger."""
+        return ContextLedger(
+            system_prompt=self.system_prompt,
+            rules=self.rules,
+            skills=self.skills,
+            memory=self.memory,
+            tools_schema=self.tools_schema,
+            history=self.history,
+            tool_outputs=self.tool_outputs,
+            buffer=self.buffer,
+            turn_count=self.turn_count,
+            message_count=self.message_count,
+            tool_call_count=self.tool_call_count,
+        )
+
+
+@dataclass
+class BudgetAllocation:
+    """
+    Token budget allocation across segments.
+    
+    Specifies maximum tokens allowed per segment.
+    """
+    model_limit: int = 128000
+    output_reserve: int = 8000
+    
+    # Segment budgets (absolute tokens or -1 for dynamic)
+    system_prompt: int = 2000
+    rules: int = 500
+    skills: int = 500
+    memory: int = 1000
+    tools_schema: int = 2000
+    history: int = -1  # Dynamic: fills remaining
+    tool_outputs: int = 20000
+    buffer: int = 1000
+    
+    @property
+    def usable(self) -> int:
+        """Usable tokens after output reserve."""
+        return self.model_limit - self.output_reserve
+    
+    @property
+    def fixed_total(self) -> int:
+        """Total of fixed segment budgets."""
+        total = 0
+        for seg in [self.system_prompt, self.rules, self.skills, 
+                    self.memory, self.tools_schema, self.tool_outputs, self.buffer]:
+            if seg > 0:
+                total += seg
+        return total
+    
+    @property
+    def history_budget(self) -> int:
+        """Computed history budget (remainder after fixed segments)."""
+        if self.history > 0:
+            return self.history
+        return max(0, self.usable - self.fixed_total)
+    
+    def get_segment_budget(self, segment: ContextSegment) -> int:
+        """Get budget for a segment."""
+        if segment == ContextSegment.HISTORY:
+            return self.history_budget
+        return getattr(self, segment.value, 0)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        result = asdict(self)
+        result['usable'] = self.usable
+        result['history_budget'] = self.history_budget
+        return result
+
+
+@dataclass
+class ContextSnapshot:
+    """
+    A snapshot of the current context state.
+    
+    Used for monitoring and debugging.
+    """
+    timestamp: str = ""
+    session_id: str = ""
+    agent_name: str = ""
+    model_name: str = ""
+    
+    # Budget info
+    budget: Optional[BudgetAllocation] = None
+    
+    # Token accounting
+    ledger: Optional[ContextLedger] = None
+    
+    # Utilization
+    utilization: float = 0.0
+    
+    # Content segments (for dump)
+    system_prompt_content: str = ""
+    rules_content: str = ""
+    skills_content: str = ""
+    memory_content: str = ""
+    tools_schema_content: str = ""
+    history_content: List[Dict[str, Any]] = field(default_factory=list)
+    
+    # Warnings
+    warnings: List[str] = field(default_factory=list)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for JSON serialization."""
+        return {
+            "timestamp": self.timestamp,
+            "session_id": self.session_id,
+            "agent_name": self.agent_name,
+            "model_name": self.model_name,
+            "budget": self.budget.to_dict() if self.budget else None,
+            "ledger": self.ledger.to_dict() if self.ledger else None,
+            "utilization": self.utilization,
+            "warnings": self.warnings,
+            "history_turn_count": len(self.history_content),
+        }
+
+
+class OptimizerStrategy(str, Enum):
+    """Context optimization strategies."""
+    TRUNCATE = "truncate"
+    SLIDING_WINDOW = "sliding_window"
+    SUMMARIZE = "summarize"
+    PRUNE_TOOLS = "prune_tools"
+    NON_DESTRUCTIVE = "non_destructive"
+    SMART = "smart"
+
+
+@dataclass
+class OptimizationResult:
+    """Result of context optimization."""
+    original_tokens: int = 0
+    optimized_tokens: int = 0
+    tokens_saved: int = 0
+    strategy_used: OptimizerStrategy = OptimizerStrategy.SMART
+    messages_removed: int = 0
+    messages_tagged: int = 0
+    tool_outputs_pruned: int = 0
+    summary_added: bool = False
+    
+    @property
+    def reduction_percent(self) -> float:
+        """Percentage of tokens reduced."""
+        if self.original_tokens == 0:
+            return 0.0
+        return (self.tokens_saved / self.original_tokens) * 100
+
+
+@dataclass
+class MonitorConfig:
+    """Configuration for context monitoring."""
+    enabled: bool = False
+    path: str = "./context.txt"
+    format: Literal["human", "json"] = "human"
+    frequency: Literal["turn", "tool_call", "manual", "overflow"] = "turn"
+    redact_sensitive: bool = True
+    multi_agent_files: bool = True
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return asdict(self)
+
+
+@dataclass
+class ContextConfig:
+    """
+    Complete context management configuration.
+    
+    Merges settings from CLI flags, env vars, and config files.
+    """
+    # Auto-compaction
+    auto_compact: bool = True
+    compact_threshold: float = 0.8  # Trigger at 80% of usable budget
+    strategy: OptimizerStrategy = OptimizerStrategy.SMART
+    
+    # Budget overrides
+    output_reserve: int = 8000
+    history_ratio: float = 0.6  # 60% of usable for history
+    tool_output_max: int = 10000  # Max tokens per tool output
+    
+    # Pruning
+    prune_after_tokens: int = 40000
+    protected_tools: List[str] = field(default_factory=list)
+    
+    # Monitoring
+    monitor: MonitorConfig = field(default_factory=MonitorConfig)
+    
+    # Sliding window
+    keep_recent_turns: int = 5
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        result = asdict(self)
+        result['strategy'] = self.strategy.value
+        return result
+
+
+@runtime_checkable
+class TokenEstimator(Protocol):
+    """Protocol for token estimation."""
+    
+    def estimate(self, text: str) -> int:
+        """Estimate tokens for text."""
+        ...
+    
+    def estimate_messages(self, messages: List[Dict[str, Any]]) -> int:
+        """Estimate tokens for a list of messages."""
+        ...
+
+
+@runtime_checkable
+class ContextOptimizer(Protocol):
+    """Protocol for context optimization strategies."""
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: ContextLedger,
+    ) -> tuple[List[Dict[str, Any]], OptimizationResult]:
+        """Optimize messages to fit within target tokens."""
+        ...

src/praisonai-agents/praisonaiagents/context/monitor.py

@@ -0,0 +1,478 @@
+"""
+Context Monitor for PraisonAI Agents.
+
+Writes runtime context to disk for inspection and debugging.
+"""
+
+from typing import Dict, List, Any, Optional, Literal
+from pathlib import Path
+from datetime import datetime
+import json
+import re
+
+from .models import (
+    ContextSnapshot, ContextLedger, BudgetAllocation, MonitorConfig, ContextSegment
+)
+
+
+# Sensitive patterns for redaction
+SENSITIVE_PATTERNS = [
+    r'sk-[a-zA-Z0-9]{20,}',  # OpenAI API keys
+    r'sk-proj-[a-zA-Z0-9_-]+',  # OpenAI project keys
+    r'AKIA[0-9A-Z]{16}',  # AWS access keys
+    r'[a-zA-Z0-9]{32,}',  # Generic long tokens (conservative)
+    r'password["\s:=]+["\']?[^"\'\s]+',  # Password patterns
+    r'secret["\s:=]+["\']?[^"\'\s]+',  # Secret patterns
+    r'token["\s:=]+["\']?[^"\'\s]+',  # Token patterns
+    r'api[_-]?key["\s:=]+["\']?[^"\'\s]+',  # API key patterns
+]
+
+
+def redact_sensitive(text: str) -> str:
+    """
+    Redact sensitive information from text.
+    
+    Args:
+        text: Text to redact
+        
+    Returns:
+        Redacted text
+    """
+    result = text
+    for pattern in SENSITIVE_PATTERNS:
+        result = re.sub(pattern, '[REDACTED]', result, flags=re.IGNORECASE)
+    return result
+
+
+def format_human_snapshot(snapshot: ContextSnapshot, redact: bool = True) -> str:
+    """
+    Format snapshot as human-readable text.
+    
+    Args:
+        snapshot: Context snapshot
+        redact: Whether to redact sensitive info
+        
+    Returns:
+        Formatted text
+    """
+    lines = []
+    
+    # Header
+    lines.append("=" * 80)
+    lines.append("PRAISONAI CONTEXT SNAPSHOT")
+    lines.append("=" * 80)
+    lines.append(f"Timestamp: {snapshot.timestamp}")
+    lines.append(f"Session ID: {snapshot.session_id}")
+    lines.append(f"Agent: {snapshot.agent_name}")
+    lines.append(f"Model: {snapshot.model_name}")
+    
+    if snapshot.budget:
+        lines.append(f"Model Limit: {snapshot.budget.model_limit:,} tokens")
+        lines.append(f"Output Reserve: {snapshot.budget.output_reserve:,} tokens")
+        lines.append(f"Usable Budget: {snapshot.budget.usable:,} tokens")
+    
+    lines.append("")
+    
+    # Token Ledger
+    lines.append("-" * 80)
+    lines.append("TOKEN LEDGER")
+    lines.append("-" * 80)
+    lines.append(f"{'Component':<20} | {'Tokens':>10} | {'Budget':>10} | {'% Used':>8}")
+    lines.append("-" * 20 + "-+-" + "-" * 10 + "-+-" + "-" * 10 + "-+-" + "-" * 8)
+    
+    if snapshot.ledger and snapshot.budget:
+        segments = [
+            (ContextSegment.SYSTEM_PROMPT, "System Prompt"),
+            (ContextSegment.RULES, "Rules"),
+            (ContextSegment.SKILLS, "Skills"),
+            (ContextSegment.MEMORY, "Memory"),
+            (ContextSegment.TOOLS_SCHEMA, "Tool Schemas"),
+            (ContextSegment.HISTORY, "History"),
+            (ContextSegment.TOOL_OUTPUTS, "Tool Outputs"),
+        ]
+        
+        for seg, name in segments:
+            tokens = snapshot.ledger.get_segment(seg)
+            budget = snapshot.budget.get_segment_budget(seg)
+            pct = (tokens / budget * 100) if budget > 0 else 0
+            lines.append(f"{name:<20} | {tokens:>10,} | {budget:>10,} | {pct:>7.1f}%")
+        
+        lines.append("-" * 20 + "-+-" + "-" * 10 + "-+-" + "-" * 10 + "-+-" + "-" * 8)
+        lines.append(f"{'TOTAL':<20} | {snapshot.ledger.total:>10,} | {snapshot.budget.usable:>10,} | {snapshot.utilization * 100:>7.1f}%")
+    
+    lines.append("")
+    
+    # Warnings
+    if snapshot.warnings:
+        lines.append("-" * 80)
+        lines.append("WARNINGS")
+        lines.append("-" * 80)
+        for warning in snapshot.warnings:
+            lines.append(f"⚠ {warning}")
+        lines.append("")
+    
+    # History summary
+    if snapshot.history_content:
+        lines.append("-" * 80)
+        lines.append(f"CONVERSATION HISTORY ({len(snapshot.history_content)} messages)")
+        lines.append("-" * 80)
+        
+        for i, msg in enumerate(snapshot.history_content):
+            role = msg.get("role", "unknown").upper()
+            content = msg.get("content", "")
+            
+            if isinstance(content, str):
+                if redact:
+                    content = redact_sensitive(content)
+                # Truncate for display
+                if len(content) > 200:
+                    content = content[:200] + "..."
+                lines.append(f"[{i+1}] {role}: {content}")
+            else:
+                lines.append(f"[{i+1}] {role}: [complex content]")
+        
+        lines.append("")
+    
+    # Footer
+    lines.append("=" * 80)
+    lines.append("END CONTEXT SNAPSHOT")
+    lines.append("=" * 80)
+    
+    return "\n".join(lines)
+
+
+def format_json_snapshot(snapshot: ContextSnapshot, redact: bool = True) -> str:
+    """
+    Format snapshot as JSON.
+    
+    Args:
+        snapshot: Context snapshot
+        redact: Whether to redact sensitive info
+        
+    Returns:
+        JSON string
+    """
+    data = snapshot.to_dict()
+    
+    # Add history with optional redaction
+    if snapshot.history_content:
+        history = []
+        for msg in snapshot.history_content:
+            msg_copy = msg.copy()
+            content = msg_copy.get("content", "")
+            if isinstance(content, str) and redact:
+                msg_copy["content"] = redact_sensitive(content)
+            history.append(msg_copy)
+        data["history"] = history
+    
+    return json.dumps(data, indent=2, default=str)
+
+
+class ContextMonitor:
+    """
+    Monitors and writes context to disk.
+    
+    Provides real-time visibility into context state for debugging
+    and optimization.
+    
+    Example:
+        monitor = ContextMonitor(
+            enabled=True,
+            path="./context.txt",
+            format="human",
+        )
+        
+        # Write snapshot
+        monitor.snapshot(
+            ledger=ledger,
+            budget=budget,
+            messages=messages,
+            agent_name="Assistant",
+        )
+    """
+    
+    def __init__(
+        self,
+        enabled: bool = False,
+        path: str = "./context.txt",
+        format: Literal["human", "json"] = "human",
+        frequency: Literal["turn", "tool_call", "manual", "overflow"] = "turn",
+        redact_sensitive: bool = True,
+        multi_agent_files: bool = True,
+    ):
+        """
+        Initialize monitor.
+        
+        Args:
+            enabled: Whether monitoring is enabled
+            path: Output file path
+            format: Output format (human or json)
+            frequency: Update frequency
+            redact_sensitive: Redact sensitive info
+            multi_agent_files: Create per-agent files
+        """
+        self.enabled = enabled
+        self.path = Path(path)
+        self.format = format
+        self.frequency = frequency
+        self.redact = redact_sensitive
+        self.multi_agent_files = multi_agent_files
+        
+        self._last_snapshot: Optional[ContextSnapshot] = None
+        self._turn_count = 0
+        self._tool_call_count = 0
+    
+    @classmethod
+    def from_config(cls, config: MonitorConfig) -> "ContextMonitor":
+        """Create monitor from config."""
+        return cls(
+            enabled=config.enabled,
+            path=config.path,
+            format=config.format,
+            frequency=config.frequency,
+            redact_sensitive=config.redact_sensitive,
+            multi_agent_files=config.multi_agent_files,
+        )
+    
+    def enable(self) -> None:
+        """Enable monitoring."""
+        self.enabled = True
+    
+    def disable(self) -> None:
+        """Disable monitoring."""
+        self.enabled = False
+    
+    def set_path(self, path: str) -> None:
+        """Set output path."""
+        self.path = Path(path)
+    
+    def set_format(self, format: Literal["human", "json"]) -> None:
+        """Set output format."""
+        self.format = format
+    
+    def set_frequency(self, frequency: Literal["turn", "tool_call", "manual", "overflow"]) -> None:
+        """Set update frequency."""
+        self.frequency = frequency
+    
+    def should_write(
+        self,
+        trigger: Literal["turn", "tool_call", "manual", "overflow"]
+    ) -> bool:
+        """Check if should write based on frequency."""
+        if not self.enabled:
+            return False
+        
+        if trigger == "manual":
+            return True
+        
+        if self.frequency == "manual":
+            return trigger == "manual"
+        
+        if self.frequency == "overflow":
+            return trigger == "overflow"
+        
+        if self.frequency == "tool_call":
+            return trigger in ("tool_call", "overflow")
+        
+        # frequency == "turn"
+        return trigger in ("turn", "overflow")
+    
+    def snapshot(
+        self,
+        ledger: ContextLedger,
+        budget: BudgetAllocation,
+        messages: List[Dict[str, Any]],
+        session_id: str = "",
+        agent_name: str = "",
+        model_name: str = "",
+        trigger: Literal["turn", "tool_call", "manual", "overflow"] = "turn",
+    ) -> Optional[str]:
+        """
+        Create and write a context snapshot.
+        
+        Args:
+            ledger: Current token ledger
+            budget: Budget allocation
+            messages: Current messages
+            session_id: Session identifier
+            agent_name: Agent name
+            model_name: Model name
+            trigger: What triggered this snapshot
+            
+        Returns:
+            Path to written file, or None if not written
+        """
+        if not self.should_write(trigger):
+            return None
+        
+        # Track counts
+        if trigger == "turn":
+            self._turn_count += 1
+        elif trigger == "tool_call":
+            self._tool_call_count += 1
+        
+        # Create snapshot
+        snapshot = ContextSnapshot(
+            timestamp=datetime.utcnow().isoformat() + "Z",
+            session_id=session_id,
+            agent_name=agent_name,
+            model_name=model_name,
+            budget=budget,
+            ledger=ledger,
+            utilization=ledger.total / budget.usable if budget.usable > 0 else 0,
+            history_content=messages,
+            warnings=self._get_warnings(ledger, budget),
+        )
+        
+        self._last_snapshot = snapshot
+        
+        # Determine output path
+        output_path = self._get_output_path(agent_name)
+        
+        # Format content
+        if self.format == "json":
+            content = format_json_snapshot(snapshot, self.redact)
+        else:
+            content = format_human_snapshot(snapshot, self.redact)
+        
+        # Atomic write
+        self._atomic_write(output_path, content)
+        
+        return str(output_path)
+    
+    def _get_output_path(self, agent_name: str = "") -> Path:
+        """Get output path, optionally per-agent."""
+        if not self.multi_agent_files or not agent_name:
+            return self.path
+        
+        # Create per-agent path
+        stem = self.path.stem
+        suffix = self.path.suffix or (".json" if self.format == "json" else ".txt")
+        
+        # Sanitize agent name
+        safe_name = re.sub(r'[^\w\-]', '_', agent_name.lower())
+        
+        return self.path.parent / f"{stem}_{safe_name}{suffix}"
+    
+    def _get_warnings(
+        self,
+        ledger: ContextLedger,
+        budget: BudgetAllocation
+    ) -> List[str]:
+        """Generate warnings based on current state."""
+        warnings = []
+        
+        utilization = ledger.total / budget.usable if budget.usable > 0 else 0
+        
+        if utilization >= 1.0:
+            warnings.append(f"CRITICAL: Context overflow! {utilization*100:.1f}% of usable budget")
+        elif utilization >= 0.9:
+            warnings.append(f"WARNING: Context at {utilization*100:.1f}% of usable budget")
+        elif utilization >= 0.8:
+            warnings.append(f"NOTICE: Context at {utilization*100:.1f}% of usable budget")
+        
+        # Check individual segments
+        if budget.tool_outputs > 0:
+            tool_util = ledger.tool_outputs / budget.tool_outputs
+            if tool_util >= 0.9:
+                warnings.append(f"Tool outputs at {tool_util*100:.1f}% of budget")
+        
+        return warnings
+    
+    def _atomic_write(self, path: Path, content: str) -> None:
+        """Write content atomically using temp file + rename."""
+        # Ensure parent directory exists
+        path.parent.mkdir(parents=True, exist_ok=True)
+        
+        # Write to temp file
+        temp_path = path.with_suffix(path.suffix + ".tmp")
+        
+        try:
+            temp_path.write_text(content, encoding="utf-8")
+            # Atomic rename
+            temp_path.rename(path)
+        except Exception:
+            # Clean up temp file on error
+            if temp_path.exists():
+                temp_path.unlink()
+            raise
+    
+    def get_last_snapshot(self) -> Optional[ContextSnapshot]:
+        """Get the last snapshot."""
+        return self._last_snapshot
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """Get monitor statistics."""
+        return {
+            "enabled": self.enabled,
+            "path": str(self.path),
+            "format": self.format,
+            "frequency": self.frequency,
+            "turn_count": self._turn_count,
+            "tool_call_count": self._tool_call_count,
+            "last_snapshot": self._last_snapshot.timestamp if self._last_snapshot else None,
+        }
+
+
+class MultiAgentMonitor:
+    """
+    Manages monitors for multiple agents.
+    
+    Creates per-agent monitors and optional combined view.
+    """
+    
+    def __init__(
+        self,
+        base_path: str = "./context",
+        format: Literal["human", "json"] = "human",
+        enabled: bool = False,
+        redact_sensitive: bool = True,
+    ):
+        """
+        Initialize multi-agent monitor.
+        
+        Args:
+            base_path: Base path for output files
+            format: Output format
+            enabled: Whether monitoring is enabled
+            redact_sensitive: Redact sensitive info
+        """
+        self.base_path = Path(base_path)
+        self.format = format
+        self.enabled = enabled
+        self.redact = redact_sensitive
+        
+        self._agents: Dict[str, ContextMonitor] = {}
+    
+    def get_agent_monitor(self, agent_id: str) -> ContextMonitor:
+        """Get or create monitor for an agent."""
+        if agent_id not in self._agents:
+            suffix = ".json" if self.format == "json" else ".txt"
+            safe_id = re.sub(r'[^\w\-]', '_', agent_id.lower())
+            path = self.base_path / f"context_{safe_id}{suffix}"
+            
+            self._agents[agent_id] = ContextMonitor(
+                enabled=self.enabled,
+                path=str(path),
+                format=self.format,
+                redact_sensitive=self.redact,
+                multi_agent_files=False,  # Already per-agent
+            )
+        
+        return self._agents[agent_id]
+    
+    def enable_all(self) -> None:
+        """Enable all monitors."""
+        self.enabled = True
+        for monitor in self._agents.values():
+            monitor.enable()
+    
+    def disable_all(self) -> None:
+        """Disable all monitors."""
+        self.enabled = False
+        for monitor in self._agents.values():
+            monitor.disable()
+    
+    def get_agent_ids(self) -> List[str]:
+        """Get list of monitored agent IDs."""
+        return list(self._agents.keys())

src/praisonai-agents/praisonaiagents/context/optimizer.py

@@ -0,0 +1,484 @@
+"""
+Context Optimizer for PraisonAI Agents.
+
+Provides strategies for reducing context size when approaching limits.
+"""
+
+from typing import Dict, List, Any, Optional
+from abc import ABC, abstractmethod
+from .models import ContextLedger, OptimizerStrategy, OptimizationResult
+from .tokens import estimate_messages_tokens
+
+
+class BaseOptimizer(ABC):
+    """Base class for optimization strategies."""
+    
+    @abstractmethod
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        """
+        Optimize messages to fit within target tokens.
+        
+        Args:
+            messages: Messages to optimize
+            target_tokens: Target token count
+            ledger: Current token ledger
+            
+        Returns:
+            Tuple of (optimized_messages, OptimizationResult)
+        """
+        pass
+
+
+class TruncateOptimizer(BaseOptimizer):
+    """Truncate oldest messages strategy."""
+    
+    def __init__(self, preserve_system: bool = True, preserve_recent: int = 5):
+        self.preserve_system = preserve_system
+        self.preserve_recent = preserve_recent
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        original_tokens = estimate_messages_tokens(messages)
+        
+        if original_tokens <= target_tokens:
+            return messages, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=original_tokens,
+                tokens_saved=0,
+                strategy_used=OptimizerStrategy.TRUNCATE,
+            )
+        
+        # Separate system and other messages
+        system_msgs = []
+        other_msgs = []
+        
+        for msg in messages:
+            if self.preserve_system and msg.get("role") == "system":
+                system_msgs.append(msg)
+            else:
+                other_msgs.append(msg)
+        
+        # Keep recent messages
+        recent = other_msgs[-self.preserve_recent:] if other_msgs else []
+        
+        result = system_msgs + recent
+        
+        # Continue removing until under target
+        while estimate_messages_tokens(result) > target_tokens and len(result) > 1:
+            for i, msg in enumerate(result):
+                if msg.get("role") != "system":
+                    result.pop(i)
+                    break
+        
+        optimized_tokens = estimate_messages_tokens(result)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.TRUNCATE,
+            messages_removed=len(messages) - len(result),
+        )
+
+
+class SlidingWindowOptimizer(BaseOptimizer):
+    """Keep a sliding window of recent messages."""
+    
+    def __init__(self, preserve_system: bool = True):
+        self.preserve_system = preserve_system
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        original_tokens = estimate_messages_tokens(messages)
+        
+        if original_tokens <= target_tokens:
+            return messages, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=original_tokens,
+                tokens_saved=0,
+                strategy_used=OptimizerStrategy.SLIDING_WINDOW,
+            )
+        
+        result = []
+        
+        # Keep system messages
+        for msg in messages:
+            if self.preserve_system and msg.get("role") == "system":
+                result.append(msg)
+        
+        # Add messages from end until target reached
+        non_system = [m for m in messages if m.get("role") != "system"]
+        
+        for msg in reversed(non_system):
+            test_result = result + [msg]
+            if estimate_messages_tokens(test_result) <= target_tokens:
+                result.insert(len([m for m in result if m.get("role") == "system"]), msg)
+            else:
+                break
+        
+        # Ensure order
+        system_msgs = [m for m in result if m.get("role") == "system"]
+        other_msgs = [m for m in result if m.get("role") != "system"]
+        result = system_msgs + other_msgs
+        
+        optimized_tokens = estimate_messages_tokens(result)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.SLIDING_WINDOW,
+            messages_removed=len(messages) - len(result),
+        )
+
+
+class PruneToolsOptimizer(BaseOptimizer):
+    """Prune old tool outputs while keeping tool calls."""
+    
+    def __init__(
+        self,
+        preserve_recent: int = 5,
+        max_output_chars: int = 500,
+        protected_tools: Optional[List[str]] = None,
+    ):
+        self.preserve_recent = preserve_recent
+        self.max_output_chars = max_output_chars
+        self.protected_tools = protected_tools or []
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        original_tokens = estimate_messages_tokens(messages)
+        
+        result = []
+        pruned_count = 0
+        
+        # Separate system and other messages
+        system_msgs = [m for m in messages if m.get("role") == "system"]
+        other_msgs = [m for m in messages if m.get("role") != "system"]
+        
+        result.extend(system_msgs)
+        
+        # Keep recent messages intact
+        recent = other_msgs[-self.preserve_recent:]
+        older = other_msgs[:-self.preserve_recent] if len(other_msgs) > self.preserve_recent else []
+        
+        # Prune older tool outputs
+        for msg in older:
+            role = msg.get("role", "")
+            content = msg.get("content", "")
+            
+            # Check if this is a tool result
+            if role == "tool" or msg.get("tool_call_id"):
+                # Check if protected
+                tool_name = msg.get("name", "")
+                if tool_name in self.protected_tools:
+                    result.append(msg)
+                    continue
+                
+                # Truncate if too long
+                if isinstance(content, str) and len(content) > self.max_output_chars:
+                    pruned_msg = msg.copy()
+                    pruned_msg["content"] = content[:self.max_output_chars] + "\n...[output truncated]..."
+                    pruned_msg["_pruned"] = True
+                    result.append(pruned_msg)
+                    pruned_count += 1
+                else:
+                    result.append(msg)
+            else:
+                result.append(msg)
+        
+        result.extend(recent)
+        
+        optimized_tokens = estimate_messages_tokens(result)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.PRUNE_TOOLS,
+            tool_outputs_pruned=pruned_count,
+        )
+
+
+class NonDestructiveOptimizer(BaseOptimizer):
+    """
+    Tag messages for exclusion without deleting them.
+    
+    Messages are tagged with _condense_parent and excluded from
+    effective history, but can be restored on rewind.
+    """
+    
+    def __init__(self, preserve_recent: int = 3):
+        self.preserve_recent = preserve_recent
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        import uuid
+        
+        original_tokens = estimate_messages_tokens(messages)
+        
+        if original_tokens <= target_tokens:
+            return messages, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=original_tokens,
+                tokens_saved=0,
+                strategy_used=OptimizerStrategy.NON_DESTRUCTIVE,
+            )
+        
+        condense_id = str(uuid.uuid4())
+        
+        # Separate system and other messages
+        system_msgs = [m for m in messages if m.get("role") == "system"]
+        other_msgs = [m for m in messages if m.get("role") != "system"]
+        
+        # Keep recent, tag older
+        keep_start = max(0, len(other_msgs) - self.preserve_recent)
+        
+        result = []
+        tagged_count = 0
+        
+        # Add system messages
+        result.extend(system_msgs)
+        
+        # Tag older messages
+        for i, msg in enumerate(other_msgs):
+            if i < keep_start:
+                tagged_msg = msg.copy()
+                if "_condense_parent" not in tagged_msg:
+                    tagged_msg["_condense_parent"] = condense_id
+                    tagged_count += 1
+                result.append(tagged_msg)
+            else:
+                result.append(msg)
+        
+        # Calculate effective tokens (excluding tagged)
+        effective_msgs = [m for m in result if "_condense_parent" not in m]
+        optimized_tokens = estimate_messages_tokens(effective_msgs)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.NON_DESTRUCTIVE,
+            messages_tagged=tagged_count,
+        )
+
+
+class SummarizeOptimizer(BaseOptimizer):
+    """
+    Summarize older messages using LLM.
+    
+    Note: This is a placeholder that creates a structured summary.
+    Actual LLM summarization should be done at the agent level.
+    """
+    
+    def __init__(self, preserve_recent: int = 5, max_summary_items: int = 10):
+        self.preserve_recent = preserve_recent
+        self.max_summary_items = max_summary_items
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        original_tokens = estimate_messages_tokens(messages)
+        
+        if original_tokens <= target_tokens:
+            return messages, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=original_tokens,
+                tokens_saved=0,
+                strategy_used=OptimizerStrategy.SUMMARIZE,
+            )
+        
+        # Separate system and other messages
+        system_msgs = [m for m in messages if m.get("role") == "system"]
+        other_msgs = [m for m in messages if m.get("role") != "system"]
+        
+        result = list(system_msgs)
+        
+        # Keep recent messages
+        recent = other_msgs[-self.preserve_recent:]
+        older = other_msgs[:-self.preserve_recent] if len(other_msgs) > self.preserve_recent else []
+        
+        if older:
+            # Create summary
+            summary_parts = []
+            for msg in older[:self.max_summary_items]:
+                role = msg.get("role", "unknown")
+                content = msg.get("content", "")
+                if isinstance(content, str) and content:
+                    summary_parts.append(f"[{role}]: {content[:150]}...")
+            
+            if summary_parts:
+                summary = (
+                    "[Previous conversation summary]\n"
+                    + "\n".join(summary_parts)
+                )
+                result.append({
+                    "role": "system",
+                    "content": summary,
+                    "_summary": True,
+                    "_original_count": len(older),
+                })
+        
+        result.extend(recent)
+        
+        optimized_tokens = estimate_messages_tokens(result)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.SUMMARIZE,
+            messages_removed=len(older),
+            summary_added=bool(older),
+        )
+
+
+class SmartOptimizer(BaseOptimizer):
+    """
+    Smart optimization combining multiple strategies.
+    
+    Applies strategies in order:
+    1. Prune tool outputs
+    2. Sliding window
+    3. Summarize if still over
+    """
+    
+    def __init__(
+        self,
+        preserve_recent: int = 5,
+        protected_tools: Optional[List[str]] = None,
+    ):
+        self.preserve_recent = preserve_recent
+        self.protected_tools = protected_tools or []
+        
+        self._prune = PruneToolsOptimizer(
+            preserve_recent=preserve_recent,
+            protected_tools=protected_tools,
+        )
+        self._window = SlidingWindowOptimizer()
+        self._summarize = SummarizeOptimizer(preserve_recent=preserve_recent)
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        original_tokens = estimate_messages_tokens(messages)
+        
+        if original_tokens <= target_tokens:
+            return messages, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=original_tokens,
+                tokens_saved=0,
+                strategy_used=OptimizerStrategy.SMART,
+            )
+        
+        # Step 1: Prune tool outputs
+        result, prune_result = self._prune.optimize(messages, target_tokens, ledger)
+        
+        if estimate_messages_tokens(result) <= target_tokens:
+            return result, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=prune_result.optimized_tokens,
+                tokens_saved=original_tokens - prune_result.optimized_tokens,
+                strategy_used=OptimizerStrategy.SMART,
+                tool_outputs_pruned=prune_result.tool_outputs_pruned,
+            )
+        
+        # Step 2: Sliding window
+        result, window_result = self._window.optimize(result, target_tokens, ledger)
+        
+        if estimate_messages_tokens(result) <= target_tokens:
+            return result, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=window_result.optimized_tokens,
+                tokens_saved=original_tokens - window_result.optimized_tokens,
+                strategy_used=OptimizerStrategy.SMART,
+                tool_outputs_pruned=prune_result.tool_outputs_pruned,
+                messages_removed=window_result.messages_removed,
+            )
+        
+        # Step 3: Summarize
+        result, summary_result = self._summarize.optimize(result, target_tokens, ledger)
+        
+        optimized_tokens = estimate_messages_tokens(result)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.SMART,
+            tool_outputs_pruned=prune_result.tool_outputs_pruned,
+            messages_removed=window_result.messages_removed + summary_result.messages_removed,
+            summary_added=summary_result.summary_added,
+        )
+
+
+# Strategy registry
+OPTIMIZER_REGISTRY: Dict[OptimizerStrategy, type] = {
+    OptimizerStrategy.TRUNCATE: TruncateOptimizer,
+    OptimizerStrategy.SLIDING_WINDOW: SlidingWindowOptimizer,
+    OptimizerStrategy.PRUNE_TOOLS: PruneToolsOptimizer,
+    OptimizerStrategy.NON_DESTRUCTIVE: NonDestructiveOptimizer,
+    OptimizerStrategy.SUMMARIZE: SummarizeOptimizer,
+    OptimizerStrategy.SMART: SmartOptimizer,
+}
+
+
+def get_optimizer(
+    strategy: OptimizerStrategy,
+    **kwargs
+) -> BaseOptimizer:
+    """
+    Get an optimizer instance for a strategy.
+    
+    Args:
+        strategy: Optimization strategy
+        **kwargs: Strategy-specific arguments
+        
+    Returns:
+        Optimizer instance
+    """
+    optimizer_class = OPTIMIZER_REGISTRY.get(strategy, SmartOptimizer)
+    return optimizer_class(**kwargs)
+
+
+def get_effective_history(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    Get effective history excluding tagged messages.
+    
+    Filters out messages with _condense_parent tags.
+    
+    Args:
+        messages: Full message history
+        
+    Returns:
+        Effective messages for API calls
+    """
+    return [m for m in messages if "_condense_parent" not in m]

src/praisonai-agents/praisonaiagents/context/tokens.py

@@ -0,0 +1,238 @@
+"""
+Token Estimation for PraisonAI Agents.
+
+Provides fast, offline token estimation with optional accurate tokenizer support.
+"""
+
+from typing import List, Dict, Any, Optional
+import json
+
+
+# Token estimation constants (based on OpenAI tokenizer patterns)
+ASCII_TOKENS_PER_CHAR = 0.25  # ~4 chars per token for ASCII
+NON_ASCII_TOKENS_PER_CHAR = 1.3  # CJK and other scripts are denser
+
+# Optional tokenizer cache
+_tokenizer_cache: Dict[str, Any] = {}
+
+
+def estimate_tokens_heuristic(text: str) -> int:
+    """
+    Estimate token count using character-based heuristic.
+    
+    Fast and works offline. Accuracy: ~85-95% for English text.
+    
+    Args:
+        text: Text to estimate tokens for
+        
+    Returns:
+        Estimated token count
+    """
+    if not text:
+        return 0
+    
+    total = 0.0
+    for char in text:
+        code_point = ord(char)
+        if code_point <= 127:
+            total += ASCII_TOKENS_PER_CHAR
+        else:
+            total += NON_ASCII_TOKENS_PER_CHAR
+    
+    return max(1, int(total))
+
+
+def estimate_tokens_accurate(text: str, model: str = "gpt-4") -> int:
+    """
+    Estimate tokens using tiktoken if available, else fallback to heuristic.
+    
+    Args:
+        text: Text to estimate tokens for
+        model: Model name for tokenizer selection
+        
+    Returns:
+        Token count
+    """
+    try:
+        import tiktoken
+        
+        # Cache tokenizer
+        if model not in _tokenizer_cache:
+            try:
+                _tokenizer_cache[model] = tiktoken.encoding_for_model(model)
+            except KeyError:
+                # Fallback to cl100k_base for unknown models
+                _tokenizer_cache[model] = tiktoken.get_encoding("cl100k_base")
+        
+        enc = _tokenizer_cache[model]
+        return len(enc.encode(text))
+    except ImportError:
+        return estimate_tokens_heuristic(text)
+
+
+def estimate_message_tokens(message: Dict[str, Any], use_accurate: bool = False) -> int:
+    """
+    Estimate tokens for a single message.
+    
+    Handles various message formats including tool calls and multi-part content.
+    
+    Args:
+        message: Message dict with role and content
+        use_accurate: Use tiktoken if available
+        
+    Returns:
+        Estimated token count
+    """
+    estimate_fn = estimate_tokens_accurate if use_accurate else estimate_tokens_heuristic
+    
+    total = 0
+    
+    # Role overhead (~4 tokens for role markers)
+    total += 4
+    
+    # Content
+    content = message.get("content", "")
+    if isinstance(content, str):
+        total += estimate_fn(content)
+    elif isinstance(content, list):
+        # Multi-part content (text, images, etc.)
+        for part in content:
+            if isinstance(part, dict):
+                if "text" in part:
+                    total += estimate_fn(str(part["text"]))
+                elif "type" in part:
+                    # Image or other media - estimate based on type
+                    if part.get("type") == "image_url":
+                        total += 85  # Base image token cost
+                    else:
+                        total += estimate_fn(json.dumps(part))
+            else:
+                total += estimate_fn(str(part))
+    
+    # Tool calls
+    if "tool_calls" in message:
+        for tool_call in message.get("tool_calls", []):
+            if isinstance(tool_call, dict):
+                # Function name and arguments
+                func = tool_call.get("function", {})
+                total += estimate_fn(func.get("name", ""))
+                total += estimate_fn(func.get("arguments", ""))
+                total += 10  # Overhead for tool call structure
+    
+    # Tool call ID (for tool responses)
+    if "tool_call_id" in message:
+        total += 10
+    
+    # Name field
+    if "name" in message:
+        total += estimate_fn(message["name"])
+    
+    return total
+
+
+def estimate_messages_tokens(
+    messages: List[Dict[str, Any]],
+    use_accurate: bool = False
+) -> int:
+    """
+    Estimate total tokens for a list of messages.
+    
+    Args:
+        messages: List of message dicts
+        use_accurate: Use tiktoken if available
+        
+    Returns:
+        Total estimated token count
+    """
+    if not messages:
+        return 0
+    
+    total = 0
+    for msg in messages:
+        total += estimate_message_tokens(msg, use_accurate)
+    
+    # Add overhead for message array structure
+    total += 3  # Array markers
+    
+    return total
+
+
+def estimate_tool_schema_tokens(
+    tools: List[Dict[str, Any]],
+    use_accurate: bool = False
+) -> int:
+    """
+    Estimate tokens for tool/function schemas.
+    
+    Args:
+        tools: List of tool definitions
+        use_accurate: Use tiktoken if available
+        
+    Returns:
+        Estimated token count
+    """
+    if not tools:
+        return 0
+    
+    estimate_fn = estimate_tokens_accurate if use_accurate else estimate_tokens_heuristic
+    
+    # Serialize tools to JSON and estimate
+    try:
+        tools_json = json.dumps(tools)
+        return estimate_fn(tools_json)
+    except (TypeError, ValueError):
+        # Fallback: estimate per-tool
+        total = 0
+        for tool in tools:
+            if isinstance(tool, dict):
+                total += estimate_fn(json.dumps(tool))
+            else:
+                total += 100  # Default estimate per tool
+        return total
+
+
+class TokenEstimatorImpl:
+    """
+    Token estimator implementation.
+    
+    Provides both heuristic and accurate estimation methods.
+    """
+    
+    def __init__(self, use_accurate: bool = False, model: str = "gpt-4"):
+        """
+        Initialize estimator.
+        
+        Args:
+            use_accurate: Use tiktoken if available
+            model: Model name for tokenizer selection
+        """
+        self.use_accurate = use_accurate
+        self.model = model
+    
+    def estimate(self, text: str) -> int:
+        """Estimate tokens for text."""
+        if self.use_accurate:
+            return estimate_tokens_accurate(text, self.model)
+        return estimate_tokens_heuristic(text)
+    
+    def estimate_messages(self, messages: List[Dict[str, Any]]) -> int:
+        """Estimate tokens for messages."""
+        return estimate_messages_tokens(messages, self.use_accurate)
+    
+    def estimate_tools(self, tools: List[Dict[str, Any]]) -> int:
+        """Estimate tokens for tool schemas."""
+        return estimate_tool_schema_tokens(tools, self.use_accurate)
+
+
+# Default estimator instance
+_default_estimator: Optional[TokenEstimatorImpl] = None
+
+
+def get_estimator(use_accurate: bool = False, model: str = "gpt-4") -> TokenEstimatorImpl:
+    """Get or create a token estimator."""
+    global _default_estimator
+    
+    if _default_estimator is None or _default_estimator.use_accurate != use_accurate:
+        _default_estimator = TokenEstimatorImpl(use_accurate, model)
+    
+    return _default_estimator

src/praisonai-agents/praisonaiagents/llm/llm.py

@@ -58,6 +58,12 @@ class LLM:
     # Class-level flag for one-time logging configuration
     _logging_configured = False
     
+    # Class-level cache for LiteLLM module (avoids repeated import overhead)
+    _litellm_module = None
+    
+    # Class-level flag for LiteLLM global settings (set once)
+    _litellm_configured = False
+    
     # Default window sizes for different models (75% of actual to be safe)
     MODEL_WINDOWS = {
         # OpenAI
@@ -272,14 +278,16 @@ Respond with ONLY a valid JSON tool call in this format:
         if not LLM._logging_configured:
             LLM._configure_logging()
             
-        # Import litellm after logging is configured
-        try:
-            import litellm
-        except ImportError:
-            raise ImportError(
-                "LiteLLM is required but not installed. "
-                "Please install with: pip install 'praisonaiagents[llm]'"
-            )
+        # Use cached LiteLLM module or import once (avoids repeated import overhead)
+        if LLM._litellm_module is None:
+            try:
+                import litellm
+                LLM._litellm_module = litellm
+            except ImportError:
+                raise ImportError(
+                    "LiteLLM is required but not installed. "
+                    "Please install with: pip install 'praisonaiagents[llm]'"
+                )
 
         self.model = model
         self.timeout = timeout
@@ -341,11 +349,12 @@ Respond with ONLY a valid JSON tool call in this format:
         self._formatted_tools_cache = {}
         self._max_cache_size = 100
         
-        # Enable error dropping for cleaner output
-        import litellm
-        litellm.drop_params = True
-        # Enable parameter modification for providers like Anthropic
-        litellm.modify_params = True
+        # Configure LiteLLM global settings only once (avoids repeated setup cost)
+        if not LLM._litellm_configured:
+            litellm = LLM._litellm_module
+            litellm.drop_params = True
+            litellm.modify_params = True
+            LLM._litellm_configured = True
         self._setup_event_tracking(events)
         
         # Log all initialization parameters when in debug mode or verbose >= 10

src/praisonai-agents/praisonaiagents/mcp/mcp_utils.py

@@ -236,3 +236,46 @@ def functions_to_mcp_tools(functions: List[Callable]) -> List[Dict[str, Any]]:
         List of MCP tool schemas
     """
     return [function_to_mcp_schema(func) for func in functions]
+
+
+def filter_disabled_tools(
+    tools: List[Dict[str, Any]], 
+    disabled_tools: List[str]
+) -> List[Dict[str, Any]]:
+    """
+    Filter out disabled tools from a tool list.
+    
+    Args:
+        tools: List of tool definitions (dicts with 'name' key)
+        disabled_tools: List of tool names to disable
+        
+    Returns:
+        Filtered list of tools
+    """
+    if not disabled_tools:
+        return tools
+        
+    disabled_set = set(disabled_tools)
+    return [t for t in tools if t.get("name") not in disabled_set]
+
+
+def filter_tools_by_allowlist(
+    tools: List[Dict[str, Any]], 
+    allowed_tools: List[str]
+) -> List[Dict[str, Any]]:
+    """
+    Filter tools to only include those in allowlist.
+    
+    Args:
+        tools: List of tool definitions (dicts with 'name' key)
+        allowed_tools: List of tool names to allow (empty = allow all)
+        
+    Returns:
+        Filtered list of tools
+    """
+    if not allowed_tools:
+        return tools
+        
+    allowed_set = set(allowed_tools)
+    return [t for t in tools if t.get("name") in allowed_set]
+

src/praisonai-agents/praisonaiagents/permissions/__init__.py

@@ -0,0 +1,62 @@
+"""
+Permissions Module for PraisonAI Agents.
+
+Provides pattern-based permission rules, persistent approvals,
+and doom loop detection for safe agent execution.
+
+Features:
+- Pattern-based permission rules (allow, deny, ask)
+- Persistent approval storage
+- Per-agent permission rulesets
+- Doom loop detection and prevention
+- Integration with existing approval system
+
+Usage:
+    from praisonaiagents.permissions import PermissionManager, PermissionRule
+    
+    # Create permission manager
+    manager = PermissionManager()
+    
+    # Add rules
+    manager.add_rule(PermissionRule(
+        pattern="bash:*",
+        action="ask",
+        description="Require approval for shell commands"
+    ))
+    
+    # Check permission
+    result = manager.check("bash:rm -rf /tmp/test")
+"""
+
+__all__ = [
+    "PermissionManager",
+    "PermissionRule",
+    "PermissionAction",
+    "PermissionResult",
+    "DoomLoopDetector",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components."""
+    if name == "PermissionManager":
+        from .manager import PermissionManager
+        return PermissionManager
+    
+    if name == "PermissionRule":
+        from .rules import PermissionRule
+        return PermissionRule
+    
+    if name == "PermissionAction":
+        from .rules import PermissionAction
+        return PermissionAction
+    
+    if name == "PermissionResult":
+        from .rules import PermissionResult
+        return PermissionResult
+    
+    if name == "DoomLoopDetector":
+        from .doom_loop import DoomLoopDetector
+        return DoomLoopDetector
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/permissions/doom_loop.py

@@ -0,0 +1,256 @@
+"""
+Doom Loop Detection for PraisonAI Agents.
+
+Detects and prevents agents from getting stuck in repetitive loops.
+"""
+
+import logging
+import time
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+# Default thresholds
+DEFAULT_LOOP_THRESHOLD = 3
+DEFAULT_WINDOW_SECONDS = 60
+DEFAULT_MAX_TOOL_CALLS = 50
+
+
+@dataclass
+class ToolCallRecord:
+    """Record of a tool call for loop detection."""
+    
+    tool_name: str
+    arguments_hash: str
+    timestamp: float = field(default_factory=time.time)
+    session_id: Optional[str] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "tool_name": self.tool_name,
+            "arguments_hash": self.arguments_hash,
+            "timestamp": self.timestamp,
+            "session_id": self.session_id,
+        }
+
+
+@dataclass
+class DoomLoopResult:
+    """Result of a doom loop check."""
+    
+    is_loop: bool
+    reason: str = ""
+    loop_count: int = 0
+    tool_name: Optional[str] = None
+    recommendation: str = ""
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "is_loop": self.is_loop,
+            "reason": self.reason,
+            "loop_count": self.loop_count,
+            "tool_name": self.tool_name,
+            "recommendation": self.recommendation,
+        }
+
+
+class DoomLoopDetector:
+    """
+    Detects and prevents doom loops in agent execution.
+    
+    A doom loop occurs when an agent repeatedly calls the same
+    tool with the same arguments, indicating it's stuck.
+    
+    Example:
+        detector = DoomLoopDetector()
+        
+        # Record tool calls
+        detector.record("bash", {"command": "ls"})
+        detector.record("bash", {"command": "ls"})
+        detector.record("bash", {"command": "ls"})
+        
+        # Check for loop
+        result = detector.check("bash", {"command": "ls"})
+        if result.is_loop:
+            print(f"Doom loop detected: {result.reason}")
+    """
+    
+    def __init__(
+        self,
+        loop_threshold: int = DEFAULT_LOOP_THRESHOLD,
+        window_seconds: float = DEFAULT_WINDOW_SECONDS,
+        max_tool_calls: int = DEFAULT_MAX_TOOL_CALLS,
+    ):
+        """
+        Initialize the doom loop detector.
+        
+        Args:
+            loop_threshold: Number of identical calls to trigger loop detection
+            window_seconds: Time window for counting calls
+            max_tool_calls: Maximum total tool calls before warning
+        """
+        self.loop_threshold = loop_threshold
+        self.window_seconds = window_seconds
+        self.max_tool_calls = max_tool_calls
+        
+        self._records: List[ToolCallRecord] = []
+        self._call_counts: Dict[str, int] = defaultdict(int)
+        self._session_id: Optional[str] = None
+    
+    def set_session(self, session_id: str):
+        """Set the current session ID."""
+        self._session_id = session_id
+    
+    def _hash_arguments(self, arguments: Dict[str, Any]) -> str:
+        """Create a hash of tool arguments."""
+        import hashlib
+        import json
+        
+        try:
+            arg_str = json.dumps(arguments, sort_keys=True, default=str)
+            return hashlib.md5(arg_str.encode()).hexdigest()[:16]
+        except (TypeError, ValueError):
+            return "unhashable"
+    
+    def _cleanup_old_records(self):
+        """Remove records outside the time window."""
+        cutoff = time.time() - self.window_seconds
+        self._records = [r for r in self._records if r.timestamp > cutoff]
+    
+    def record(
+        self,
+        tool_name: str,
+        arguments: Optional[Dict[str, Any]] = None,
+    ) -> ToolCallRecord:
+        """
+        Record a tool call.
+        
+        Args:
+            tool_name: Name of the tool
+            arguments: Tool arguments
+            
+        Returns:
+            The recorded ToolCallRecord
+        """
+        self._cleanup_old_records()
+        
+        args_hash = self._hash_arguments(arguments or {})
+        
+        record = ToolCallRecord(
+            tool_name=tool_name,
+            arguments_hash=args_hash,
+            session_id=self._session_id,
+        )
+        
+        self._records.append(record)
+        self._call_counts[tool_name] += 1
+        
+        return record
+    
+    def check(
+        self,
+        tool_name: str,
+        arguments: Optional[Dict[str, Any]] = None,
+    ) -> DoomLoopResult:
+        """
+        Check if calling this tool would indicate a doom loop.
+        
+        Args:
+            tool_name: Name of the tool
+            arguments: Tool arguments
+            
+        Returns:
+            DoomLoopResult indicating if a loop is detected
+        """
+        self._cleanup_old_records()
+        
+        args_hash = self._hash_arguments(arguments or {})
+        
+        # Count identical calls in window
+        identical_count = sum(
+            1 for r in self._records
+            if r.tool_name == tool_name and r.arguments_hash == args_hash
+        )
+        
+        # Check for loop
+        if identical_count >= self.loop_threshold:
+            return DoomLoopResult(
+                is_loop=True,
+                reason=f"Tool '{tool_name}' called {identical_count} times with same arguments",
+                loop_count=identical_count,
+                tool_name=tool_name,
+                recommendation="Consider a different approach or ask for user guidance",
+            )
+        
+        # Check for excessive tool calls
+        total_calls = len(self._records)
+        if total_calls >= self.max_tool_calls:
+            return DoomLoopResult(
+                is_loop=True,
+                reason=f"Excessive tool calls ({total_calls}) in time window",
+                loop_count=total_calls,
+                recommendation="Reduce tool usage or break task into smaller steps",
+            )
+        
+        # Check for tool-specific excessive calls
+        tool_count = self._call_counts.get(tool_name, 0)
+        if tool_count >= self.max_tool_calls // 2:
+            return DoomLoopResult(
+                is_loop=True,
+                reason=f"Tool '{tool_name}' called excessively ({tool_count} times)",
+                loop_count=tool_count,
+                tool_name=tool_name,
+                recommendation=f"Consider alternative to '{tool_name}'",
+            )
+        
+        return DoomLoopResult(
+            is_loop=False,
+            loop_count=identical_count,
+            tool_name=tool_name,
+        )
+    
+    def record_and_check(
+        self,
+        tool_name: str,
+        arguments: Optional[Dict[str, Any]] = None,
+    ) -> DoomLoopResult:
+        """
+        Record a tool call and check for doom loop.
+        
+        Convenience method that combines record() and check().
+        
+        Args:
+            tool_name: Name of the tool
+            arguments: Tool arguments
+            
+        Returns:
+            DoomLoopResult indicating if a loop is detected
+        """
+        result = self.check(tool_name, arguments)
+        if not result.is_loop:
+            self.record(tool_name, arguments)
+        return result
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """Get statistics about tool calls."""
+        self._cleanup_old_records()
+        
+        return {
+            "total_calls": len(self._records),
+            "unique_tools": len(set(r.tool_name for r in self._records)),
+            "call_counts": dict(self._call_counts),
+            "window_seconds": self.window_seconds,
+            "loop_threshold": self.loop_threshold,
+        }
+    
+    def reset(self):
+        """Reset all records."""
+        self._records.clear()
+        self._call_counts.clear()
+    
+    def reset_tool(self, tool_name: str):
+        """Reset records for a specific tool."""
+        self._records = [r for r in self._records if r.tool_name != tool_name]
+        self._call_counts.pop(tool_name, None)

src/praisonai-agents/praisonaiagents/permissions/manager.py

@@ -0,0 +1,359 @@
+"""
+Permission Manager for PraisonAI Agents.
+
+Manages permission rules, persistent approvals, and integrates
+with doom loop detection.
+"""
+
+import json
+import logging
+import os
+import threading
+from typing import Any, Callable, Dict, List, Optional
+
+from .rules import PermissionRule, PermissionAction, PermissionResult, PersistentApproval
+from .doom_loop import DoomLoopDetector
+
+logger = logging.getLogger(__name__)
+
+# Default storage path
+DEFAULT_PERMISSIONS_DIR = os.path.expanduser("~/.praison/permissions")
+
+
+class PermissionManager:
+    """
+    Manages permission rules and approvals.
+    
+    Provides pattern-based permission checking with persistent
+    approval storage and doom loop detection.
+    
+    Example:
+        manager = PermissionManager()
+        
+        # Add rules
+        manager.add_rule(PermissionRule(
+            pattern="bash:rm *",
+            action=PermissionAction.ASK,
+            description="Require approval for rm commands"
+        ))
+        
+        manager.add_rule(PermissionRule(
+            pattern="read:*",
+            action=PermissionAction.ALLOW,
+            description="Allow all read operations"
+        ))
+        
+        # Check permission
+        result = manager.check("bash:rm -rf /tmp/test")
+        if result.needs_approval:
+            # Ask user
+            approved = ask_user(result.target)
+            manager.approve(result.target, approved)
+    """
+    
+    def __init__(
+        self,
+        storage_dir: Optional[str] = None,
+        agent_name: Optional[str] = None,
+        approval_callback: Optional[Callable[[str, str], bool]] = None,
+    ):
+        """
+        Initialize the permission manager.
+        
+        Args:
+            storage_dir: Directory for persistent storage
+            agent_name: Optional agent name for filtering rules
+            approval_callback: Optional callback for user approval
+        """
+        self.storage_dir = storage_dir or DEFAULT_PERMISSIONS_DIR
+        self.agent_name = agent_name
+        self.approval_callback = approval_callback
+        
+        self._rules: List[PermissionRule] = []
+        self._approvals: List[PersistentApproval] = []
+        self._doom_detector = DoomLoopDetector()
+        self._lock = threading.RLock()
+        
+        # Ensure storage directory exists
+        os.makedirs(self.storage_dir, exist_ok=True)
+        
+        # Load persistent data
+        self._load_rules()
+        self._load_approvals()
+    
+    def _get_rules_path(self) -> str:
+        """Get path to rules file."""
+        return os.path.join(self.storage_dir, "rules.json")
+    
+    def _get_approvals_path(self) -> str:
+        """Get path to approvals file."""
+        return os.path.join(self.storage_dir, "approvals.json")
+    
+    def _load_rules(self):
+        """Load rules from disk."""
+        path = self._get_rules_path()
+        if os.path.exists(path):
+            try:
+                with open(path, "r") as f:
+                    data = json.load(f)
+                self._rules = [PermissionRule.from_dict(r) for r in data]
+            except (json.JSONDecodeError, IOError) as e:
+                logger.warning(f"Failed to load rules: {e}")
+    
+    def _save_rules(self):
+        """Save rules to disk."""
+        path = self._get_rules_path()
+        try:
+            with open(path, "w") as f:
+                json.dump([r.to_dict() for r in self._rules], f, indent=2)
+        except IOError as e:
+            logger.error(f"Failed to save rules: {e}")
+    
+    def _load_approvals(self):
+        """Load approvals from disk."""
+        path = self._get_approvals_path()
+        if os.path.exists(path):
+            try:
+                with open(path, "r") as f:
+                    data = json.load(f)
+                self._approvals = [PersistentApproval.from_dict(a) for a in data]
+                # Filter out expired approvals
+                self._approvals = [a for a in self._approvals if a.is_valid()]
+            except (json.JSONDecodeError, IOError) as e:
+                logger.warning(f"Failed to load approvals: {e}")
+    
+    def _save_approvals(self):
+        """Save approvals to disk."""
+        path = self._get_approvals_path()
+        try:
+            # Filter out expired and one-time approvals
+            persistent = [
+                a for a in self._approvals
+                if a.is_valid() and a.scope in ("session", "always")
+            ]
+            with open(path, "w") as f:
+                json.dump([a.to_dict() for a in persistent], f, indent=2)
+        except IOError as e:
+            logger.error(f"Failed to save approvals: {e}")
+    
+    def add_rule(self, rule: PermissionRule) -> str:
+        """
+        Add a permission rule.
+        
+        Args:
+            rule: The rule to add
+            
+        Returns:
+            The rule ID
+        """
+        with self._lock:
+            self._rules.append(rule)
+            # Sort by priority (higher first)
+            self._rules.sort(key=lambda r: r.priority, reverse=True)
+            self._save_rules()
+        return rule.id
+    
+    def remove_rule(self, rule_id: str) -> bool:
+        """
+        Remove a permission rule.
+        
+        Args:
+            rule_id: The rule ID to remove
+            
+        Returns:
+            True if found and removed
+        """
+        with self._lock:
+            for i, rule in enumerate(self._rules):
+                if rule.id == rule_id:
+                    self._rules.pop(i)
+                    self._save_rules()
+                    return True
+        return False
+    
+    def get_rules(self, agent_name: Optional[str] = None) -> List[PermissionRule]:
+        """
+        Get all rules, optionally filtered by agent.
+        
+        Args:
+            agent_name: Optional agent name to filter by
+            
+        Returns:
+            List of matching rules
+        """
+        with self._lock:
+            if agent_name is None:
+                return self._rules.copy()
+            return [
+                r for r in self._rules
+                if r.agent_name is None or r.agent_name == agent_name
+            ]
+    
+    def check(self, target: str, agent_name: Optional[str] = None) -> PermissionResult:
+        """
+        Check permission for a target.
+        
+        Args:
+            target: The target to check (e.g., "bash:rm -rf /tmp")
+            agent_name: Optional agent name
+            
+        Returns:
+            PermissionResult with the decision
+        """
+        agent = agent_name or self.agent_name
+        
+        with self._lock:
+            # Check persistent approvals first
+            for approval in self._approvals:
+                if approval.matches(target, agent):
+                    return PermissionResult(
+                        action=PermissionAction.ALLOW if approval.approved else PermissionAction.DENY,
+                        target=target,
+                        reason=f"Persistent approval: {'approved' if approval.approved else 'denied'}",
+                        approved=approval.approved,
+                    )
+            
+            # Check rules
+            for rule in self._rules:
+                if rule.agent_name and agent and rule.agent_name != agent:
+                    continue
+                
+                if rule.matches(target):
+                    return PermissionResult(
+                        action=rule.action,
+                        rule=rule,
+                        target=target,
+                        reason=rule.description or f"Matched rule: {rule.pattern}",
+                    )
+        
+        # Default: ask
+        return PermissionResult(
+            action=PermissionAction.ASK,
+            target=target,
+            reason="No matching rule, requires approval",
+        )
+    
+    def approve(
+        self,
+        target: str,
+        approved: bool,
+        scope: str = "once",
+        agent_name: Optional[str] = None,
+    ) -> PersistentApproval:
+        """
+        Record an approval decision.
+        
+        Args:
+            target: The target that was approved/denied
+            approved: Whether it was approved
+            scope: Scope of approval (once, session, always)
+            agent_name: Optional agent name
+            
+        Returns:
+            The created PersistentApproval
+        """
+        approval = PersistentApproval(
+            pattern=target,
+            approved=approved,
+            scope=scope,
+            agent_name=agent_name or self.agent_name,
+        )
+        
+        with self._lock:
+            self._approvals.append(approval)
+            if scope in ("session", "always"):
+                self._save_approvals()
+        
+        return approval
+    
+    def check_and_approve(
+        self,
+        target: str,
+        agent_name: Optional[str] = None,
+    ) -> PermissionResult:
+        """
+        Check permission and request approval if needed.
+        
+        Uses the approval_callback if set.
+        
+        Args:
+            target: The target to check
+            agent_name: Optional agent name
+            
+        Returns:
+            PermissionResult with final decision
+        """
+        result = self.check(target, agent_name)
+        
+        if result.needs_approval and self.approval_callback:
+            approved = self.approval_callback(target, result.reason)
+            result.approved = approved
+            self.approve(target, approved, scope="once", agent_name=agent_name)
+        
+        return result
+    
+    def check_doom_loop(
+        self,
+        tool_name: str,
+        arguments: Optional[Dict[str, Any]] = None,
+    ):
+        """
+        Check for doom loop and record tool call.
+        
+        Args:
+            tool_name: Name of the tool
+            arguments: Tool arguments
+            
+        Returns:
+            DoomLoopResult
+        """
+        return self._doom_detector.record_and_check(tool_name, arguments)
+    
+    def get_doom_stats(self) -> Dict[str, Any]:
+        """Get doom loop detector statistics."""
+        return self._doom_detector.get_stats()
+    
+    def reset_doom_detector(self):
+        """Reset the doom loop detector."""
+        self._doom_detector.reset()
+    
+    def clear_approvals(self, scope: Optional[str] = None):
+        """
+        Clear approvals.
+        
+        Args:
+            scope: Optional scope to clear (None = all)
+        """
+        with self._lock:
+            if scope is None:
+                self._approvals.clear()
+            else:
+                self._approvals = [a for a in self._approvals if a.scope != scope]
+            self._save_approvals()
+    
+    def clear_rules(self):
+        """Clear all rules."""
+        with self._lock:
+            self._rules.clear()
+            self._save_rules()
+    
+    def set_approval_callback(self, callback: Callable[[str, str], bool]):
+        """Set the approval callback."""
+        self.approval_callback = callback
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Export manager state to dictionary."""
+        with self._lock:
+            return {
+                "rules": [r.to_dict() for r in self._rules],
+                "approvals": [a.to_dict() for a in self._approvals],
+                "agent_name": self.agent_name,
+            }
+    
+    def from_dict(self, data: Dict[str, Any]):
+        """Import manager state from dictionary."""
+        with self._lock:
+            self._rules = [PermissionRule.from_dict(r) for r in data.get("rules", [])]
+            self._approvals = [PersistentApproval.from_dict(a) for a in data.get("approvals", [])]
+            if "agent_name" in data:
+                self.agent_name = data["agent_name"]

src/praisonai-agents/praisonaiagents/permissions/rules.py

@@ -0,0 +1,211 @@
+"""
+Permission rules for PraisonAI Agents.
+
+Provides pattern-based permission rules with allow/deny/ask actions.
+"""
+
+import fnmatch
+import re
+import time
+import uuid
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+
+class PermissionAction(str, Enum):
+    """Actions for permission rules."""
+    
+    ALLOW = "allow"
+    DENY = "deny"
+    ASK = "ask"
+
+
+@dataclass
+class PermissionRule:
+    """
+    A pattern-based permission rule.
+    
+    Attributes:
+        pattern: Glob or regex pattern to match against
+        action: What to do when pattern matches (allow/deny/ask)
+        description: Human-readable description
+        is_regex: Whether pattern is a regex (default: glob)
+        priority: Higher priority rules are checked first
+        agent_name: Optional agent name this rule applies to
+        enabled: Whether the rule is active
+    """
+    
+    pattern: str
+    action: PermissionAction = PermissionAction.ASK
+    description: str = ""
+    is_regex: bool = False
+    priority: int = 0
+    agent_name: Optional[str] = None
+    enabled: bool = True
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    created_at: float = field(default_factory=time.time)
+    
+    def matches(self, target: str) -> bool:
+        """
+        Check if this rule matches the target.
+        
+        Args:
+            target: The string to match against (e.g., "bash:rm -rf /tmp")
+            
+        Returns:
+            True if the pattern matches
+        """
+        if not self.enabled:
+            return False
+        
+        if self.is_regex:
+            try:
+                return bool(re.match(self.pattern, target))
+            except re.error:
+                return False
+        else:
+            return fnmatch.fnmatch(target, self.pattern)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert rule to dictionary."""
+        return {
+            "id": self.id,
+            "pattern": self.pattern,
+            "action": self.action.value,
+            "description": self.description,
+            "is_regex": self.is_regex,
+            "priority": self.priority,
+            "agent_name": self.agent_name,
+            "enabled": self.enabled,
+            "created_at": self.created_at,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "PermissionRule":
+        """Create rule from dictionary."""
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            pattern=data.get("pattern", "*"),
+            action=PermissionAction(data.get("action", "ask")),
+            description=data.get("description", ""),
+            is_regex=data.get("is_regex", False),
+            priority=data.get("priority", 0),
+            agent_name=data.get("agent_name"),
+            enabled=data.get("enabled", True),
+            created_at=data.get("created_at", time.time()),
+        )
+
+
+@dataclass
+class PermissionResult:
+    """
+    Result of a permission check.
+    
+    Attributes:
+        action: The resulting action (allow/deny/ask)
+        rule: The rule that matched (if any)
+        target: The target that was checked
+        reason: Human-readable reason
+        approved: Whether the action was approved (for ask actions)
+    """
+    
+    action: PermissionAction
+    rule: Optional[PermissionRule] = None
+    target: str = ""
+    reason: str = ""
+    approved: Optional[bool] = None
+    
+    @property
+    def is_allowed(self) -> bool:
+        """Check if the action is allowed."""
+        if self.action == PermissionAction.ALLOW:
+            return True
+        if self.action == PermissionAction.ASK:
+            return self.approved is True
+        return False
+    
+    @property
+    def is_denied(self) -> bool:
+        """Check if the action is denied."""
+        if self.action == PermissionAction.DENY:
+            return True
+        if self.action == PermissionAction.ASK:
+            return self.approved is False
+        return False
+    
+    @property
+    def needs_approval(self) -> bool:
+        """Check if the action needs user approval."""
+        return self.action == PermissionAction.ASK and self.approved is None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert result to dictionary."""
+        return {
+            "action": self.action.value,
+            "rule_id": self.rule.id if self.rule else None,
+            "target": self.target,
+            "reason": self.reason,
+            "approved": self.approved,
+            "is_allowed": self.is_allowed,
+            "is_denied": self.is_denied,
+            "needs_approval": self.needs_approval,
+        }
+
+
+@dataclass
+class PersistentApproval:
+    """
+    A persistent approval for a pattern.
+    
+    Used to remember user decisions for future requests.
+    """
+    
+    pattern: str
+    approved: bool
+    scope: str = "once"  # once, session, always
+    created_at: float = field(default_factory=time.time)
+    expires_at: Optional[float] = None
+    agent_name: Optional[str] = None
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    
+    def is_valid(self) -> bool:
+        """Check if the approval is still valid."""
+        if self.expires_at and time.time() > self.expires_at:
+            return False
+        return True
+    
+    def matches(self, target: str, agent_name: Optional[str] = None) -> bool:
+        """Check if this approval applies to the target."""
+        if not self.is_valid():
+            return False
+        
+        if self.agent_name and agent_name and self.agent_name != agent_name:
+            return False
+        
+        return fnmatch.fnmatch(target, self.pattern)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert approval to dictionary."""
+        return {
+            "id": self.id,
+            "pattern": self.pattern,
+            "approved": self.approved,
+            "scope": self.scope,
+            "created_at": self.created_at,
+            "expires_at": self.expires_at,
+            "agent_name": self.agent_name,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "PersistentApproval":
+        """Create approval from dictionary."""
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            pattern=data.get("pattern", "*"),
+            approved=data.get("approved", False),
+            scope=data.get("scope", "once"),
+            created_at=data.get("created_at", time.time()),
+            expires_at=data.get("expires_at"),
+            agent_name=data.get("agent_name"),
+        )

src/praisonai-agents/praisonaiagents/plugins/__init__.py

@@ -0,0 +1,47 @@
+"""
+Plugin Module for PraisonAI Agents.
+
+Provides dynamic plugin loading and hook-based extension system.
+
+Features:
+- Dynamic plugin discovery and loading
+- Hook-based extension points
+- Tool plugins
+- Auth plugins
+- Configuration plugins
+
+Usage:
+    from praisonaiagents.plugins import PluginManager
+    
+    # Create plugin manager
+    manager = PluginManager()
+    
+    # Load plugins from directory
+    manager.load_from_directory("./plugins")
+    
+    # Get loaded plugins
+    plugins = manager.list_plugins()
+"""
+
+__all__ = [
+    "PluginManager",
+    "Plugin",
+    "PluginHook",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components."""
+    if name == "PluginManager":
+        from .manager import PluginManager
+        return PluginManager
+    
+    if name == "Plugin":
+        from .plugin import Plugin
+        return Plugin
+    
+    if name == "PluginHook":
+        from .plugin import PluginHook
+        return PluginHook
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/plugins/manager.py

@@ -0,0 +1,299 @@
+"""
+Plugin Manager for PraisonAI Agents.
+
+Provides dynamic plugin discovery, loading, and lifecycle management.
+"""
+
+import importlib.util
+import logging
+import os
+import sys
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Type
+
+from .plugin import Plugin, PluginHook, PluginInfo, FunctionPlugin
+
+logger = logging.getLogger(__name__)
+
+
+class PluginManager:
+    """
+    Manages plugin discovery, loading, and execution.
+    
+    Example:
+        manager = PluginManager()
+        
+        # Load plugins from directory
+        manager.load_from_directory("./plugins")
+        
+        # Register a plugin directly
+        manager.register(MyPlugin())
+        
+        # Execute hooks
+        args = manager.execute_hook(PluginHook.BEFORE_TOOL, "bash", {"cmd": "ls"})
+    """
+    
+    def __init__(self):
+        self._plugins: Dict[str, Plugin] = {}
+        self._enabled: Dict[str, bool] = {}
+    
+    def register(self, plugin: Plugin) -> bool:
+        """
+        Register a plugin.
+        
+        Args:
+            plugin: The plugin to register
+            
+        Returns:
+            True if registered successfully
+        """
+        try:
+            info = plugin.info
+            if info.name in self._plugins:
+                logger.warning(f"Plugin '{info.name}' already registered")
+                return False
+            
+            self._plugins[info.name] = plugin
+            self._enabled[info.name] = True
+            
+            # Initialize plugin
+            plugin.on_init({})
+            
+            logger.info(f"Registered plugin: {info.name} v{info.version}")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to register plugin: {e}")
+            return False
+    
+    def unregister(self, name: str) -> bool:
+        """
+        Unregister a plugin.
+        
+        Args:
+            name: The plugin name
+            
+        Returns:
+            True if unregistered successfully
+        """
+        if name not in self._plugins:
+            return False
+        
+        try:
+            plugin = self._plugins[name]
+            plugin.on_shutdown()
+        except Exception as e:
+            logger.warning(f"Error during plugin shutdown: {e}")
+        
+        del self._plugins[name]
+        del self._enabled[name]
+        
+        logger.info(f"Unregistered plugin: {name}")
+        return True
+    
+    def enable(self, name: str) -> bool:
+        """Enable a plugin."""
+        if name in self._enabled:
+            self._enabled[name] = True
+            return True
+        return False
+    
+    def disable(self, name: str) -> bool:
+        """Disable a plugin."""
+        if name in self._enabled:
+            self._enabled[name] = False
+            return True
+        return False
+    
+    def is_enabled(self, name: str) -> bool:
+        """Check if a plugin is enabled."""
+        return self._enabled.get(name, False)
+    
+    def get_plugin(self, name: str) -> Optional[Plugin]:
+        """Get a plugin by name."""
+        return self._plugins.get(name)
+    
+    def list_plugins(self) -> List[PluginInfo]:
+        """List all registered plugins."""
+        return [p.info for p in self._plugins.values()]
+    
+    def load_from_directory(self, directory: str) -> int:
+        """
+        Load plugins from a directory.
+        
+        Looks for Python files with a `create_plugin` function
+        or classes that inherit from Plugin.
+        
+        Args:
+            directory: Path to the plugins directory
+            
+        Returns:
+            Number of plugins loaded
+        """
+        directory = Path(directory)
+        if not directory.exists():
+            logger.warning(f"Plugin directory does not exist: {directory}")
+            return 0
+        
+        loaded = 0
+        
+        for file_path in directory.glob("*.py"):
+            if file_path.name.startswith("_"):
+                continue
+            
+            try:
+                plugin = self._load_plugin_file(file_path)
+                if plugin and self.register(plugin):
+                    loaded += 1
+            except Exception as e:
+                logger.error(f"Failed to load plugin from {file_path}: {e}")
+        
+        return loaded
+    
+    def _load_plugin_file(self, file_path: Path) -> Optional[Plugin]:
+        """Load a plugin from a Python file."""
+        module_name = f"praison_plugin_{file_path.stem}"
+        
+        spec = importlib.util.spec_from_file_location(module_name, file_path)
+        if spec is None or spec.loader is None:
+            return None
+        
+        module = importlib.util.module_from_spec(spec)
+        sys.modules[module_name] = module
+        
+        try:
+            spec.loader.exec_module(module)
+        except Exception as e:
+            logger.error(f"Error loading module {file_path}: {e}")
+            del sys.modules[module_name]
+            return None
+        
+        # Look for create_plugin function
+        if hasattr(module, "create_plugin"):
+            return module.create_plugin()
+        
+        # Look for Plugin subclass
+        for name in dir(module):
+            obj = getattr(module, name)
+            if (
+                isinstance(obj, type)
+                and issubclass(obj, Plugin)
+                and obj is not Plugin
+                and obj is not FunctionPlugin
+            ):
+                return obj()
+        
+        return None
+    
+    def load_from_module(self, module_path: str) -> bool:
+        """
+        Load a plugin from a module path.
+        
+        Args:
+            module_path: Dotted module path (e.g., "mypackage.plugins.myplugin")
+            
+        Returns:
+            True if loaded successfully
+        """
+        try:
+            module = importlib.import_module(module_path)
+            
+            if hasattr(module, "create_plugin"):
+                plugin = module.create_plugin()
+                return self.register(plugin)
+            
+            # Look for Plugin subclass
+            for name in dir(module):
+                obj = getattr(module, name)
+                if (
+                    isinstance(obj, type)
+                    and issubclass(obj, Plugin)
+                    and obj is not Plugin
+                    and obj is not FunctionPlugin
+                ):
+                    return self.register(obj())
+            
+            return False
+        except Exception as e:
+            logger.error(f"Failed to load plugin from module {module_path}: {e}")
+            return False
+    
+    def execute_hook(
+        self,
+        hook: PluginHook,
+        *args,
+        **kwargs
+    ) -> Any:
+        """
+        Execute a hook across all enabled plugins.
+        
+        Args:
+            hook: The hook to execute
+            *args: Arguments to pass to hook
+            **kwargs: Keyword arguments to pass to hook
+            
+        Returns:
+            The result (may be modified by plugins)
+        """
+        result = args[0] if args else None
+        
+        for name, plugin in self._plugins.items():
+            if not self._enabled.get(name, False):
+                continue
+            
+            if hook not in plugin.info.hooks:
+                continue
+            
+            try:
+                method = getattr(plugin, hook.value, None)
+                if method:
+                    if hook == PluginHook.BEFORE_TOOL:
+                        result = method(args[0], args[1] if len(args) > 1 else {})
+                    elif hook == PluginHook.AFTER_TOOL:
+                        result = method(args[0], args[1] if len(args) > 1 else None)
+                    elif hook == PluginHook.BEFORE_AGENT:
+                        result = method(args[0], kwargs.get("context", {}))
+                    elif hook == PluginHook.AFTER_AGENT:
+                        result = method(args[0], kwargs.get("context", {}))
+                    elif hook == PluginHook.BEFORE_LLM:
+                        result = method(args[0], args[1] if len(args) > 1 else {})
+                    elif hook == PluginHook.AFTER_LLM:
+                        result = method(args[0], kwargs.get("usage", {}))
+                    else:
+                        result = method(*args, **kwargs)
+            except Exception as e:
+                logger.error(f"Error executing hook {hook.value} in plugin {name}: {e}")
+        
+        return result
+    
+    def get_all_tools(self) -> List[Dict[str, Any]]:
+        """Get all tools from all enabled plugins."""
+        tools = []
+        
+        for name, plugin in self._plugins.items():
+            if not self._enabled.get(name, False):
+                continue
+            
+            try:
+                plugin_tools = plugin.get_tools()
+                tools.extend(plugin_tools)
+            except Exception as e:
+                logger.error(f"Error getting tools from plugin {name}: {e}")
+        
+        return tools
+    
+    def shutdown(self):
+        """Shutdown all plugins."""
+        for name in list(self._plugins.keys()):
+            self.unregister(name)
+
+
+# Global plugin manager instance
+_default_manager: Optional[PluginManager] = None
+
+
+def get_plugin_manager() -> PluginManager:
+    """Get the global plugin manager."""
+    global _default_manager
+    if _default_manager is None:
+        _default_manager = PluginManager()
+    return _default_manager

src/praisonai-agents/praisonaiagents/plugins/plugin.py

@@ -0,0 +1,210 @@
+"""
+Plugin definitions for PraisonAI Agents.
+
+Provides base plugin class and hook definitions.
+"""
+
+import logging
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Callable, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+class PluginHook(str, Enum):
+    """Available plugin hooks."""
+    
+    # Lifecycle hooks
+    ON_INIT = "on_init"
+    ON_SHUTDOWN = "on_shutdown"
+    
+    # Agent hooks
+    BEFORE_AGENT = "before_agent"
+    AFTER_AGENT = "after_agent"
+    
+    # Tool hooks
+    BEFORE_TOOL = "before_tool"
+    AFTER_TOOL = "after_tool"
+    
+    # Message hooks
+    BEFORE_MESSAGE = "before_message"
+    AFTER_MESSAGE = "after_message"
+    
+    # LLM hooks
+    BEFORE_LLM = "before_llm"
+    AFTER_LLM = "after_llm"
+    
+    # Permission hooks
+    ON_PERMISSION_ASK = "on_permission_ask"
+    
+    # Config hooks
+    ON_CONFIG = "on_config"
+    
+    # Auth hooks
+    ON_AUTH = "on_auth"
+
+
+@dataclass
+class PluginInfo:
+    """Plugin metadata."""
+    
+    name: str
+    version: str = "1.0.0"
+    description: str = ""
+    author: str = ""
+    hooks: List[PluginHook] = field(default_factory=list)
+    dependencies: List[str] = field(default_factory=list)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "name": self.name,
+            "version": self.version,
+            "description": self.description,
+            "author": self.author,
+            "hooks": [h.value for h in self.hooks],
+            "dependencies": self.dependencies,
+        }
+
+
+class Plugin(ABC):
+    """
+    Base class for PraisonAI plugins.
+    
+    Plugins can extend agent functionality by implementing
+    hook methods that are called at various points in the
+    agent lifecycle.
+    
+    Example:
+        class MyPlugin(Plugin):
+            @property
+            def info(self) -> PluginInfo:
+                return PluginInfo(
+                    name="my_plugin",
+                    version="1.0.0",
+                    hooks=[PluginHook.BEFORE_TOOL]
+                )
+            
+            def before_tool(self, tool_name: str, args: dict) -> dict:
+                # Modify args before tool execution
+                return args
+    """
+    
+    @property
+    @abstractmethod
+    def info(self) -> PluginInfo:
+        """Get plugin information."""
+        pass
+    
+    def on_init(self, context: Dict[str, Any]) -> None:
+        """Called when plugin is initialized."""
+        pass
+    
+    def on_shutdown(self) -> None:
+        """Called when plugin is shutting down."""
+        pass
+    
+    def before_agent(self, prompt: str, context: Dict[str, Any]) -> str:
+        """Called before agent execution. Can modify prompt."""
+        return prompt
+    
+    def after_agent(self, response: str, context: Dict[str, Any]) -> str:
+        """Called after agent execution. Can modify response."""
+        return response
+    
+    def before_tool(self, tool_name: str, args: Dict[str, Any]) -> Dict[str, Any]:
+        """Called before tool execution. Can modify args."""
+        return args
+    
+    def after_tool(self, tool_name: str, result: Any) -> Any:
+        """Called after tool execution. Can modify result."""
+        return result
+    
+    def before_message(self, message: Dict[str, Any]) -> Dict[str, Any]:
+        """Called before message is processed. Can modify message."""
+        return message
+    
+    def after_message(self, message: Dict[str, Any]) -> Dict[str, Any]:
+        """Called after message is processed. Can modify message."""
+        return message
+    
+    def before_llm(self, messages: List[Dict], params: Dict[str, Any]) -> tuple:
+        """Called before LLM call. Can modify messages and params."""
+        return messages, params
+    
+    def after_llm(self, response: str, usage: Dict[str, Any]) -> str:
+        """Called after LLM call. Can modify response."""
+        return response
+    
+    def on_permission_ask(self, target: str, reason: str) -> Optional[bool]:
+        """Called when permission is requested. Return True/False to auto-approve/deny."""
+        return None
+    
+    def on_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
+        """Called to modify configuration."""
+        return config
+    
+    def on_auth(self, auth_type: str, credentials: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """Called for authentication. Return credentials or None."""
+        return None
+    
+    def get_tools(self) -> List[Dict[str, Any]]:
+        """Return additional tools provided by this plugin."""
+        return []
+
+
+class FunctionPlugin(Plugin):
+    """
+    A simple plugin that wraps functions for specific hooks.
+    
+    Example:
+        def my_before_tool(tool_name, args):
+            return args
+        
+        plugin = FunctionPlugin(
+            name="my_plugin",
+            hooks={PluginHook.BEFORE_TOOL: my_before_tool}
+        )
+    """
+    
+    def __init__(
+        self,
+        name: str,
+        hooks: Optional[Dict[PluginHook, Callable]] = None,
+        version: str = "1.0.0",
+        description: str = "",
+    ):
+        self._name = name
+        self._hooks = hooks or {}
+        self._version = version
+        self._description = description
+    
+    @property
+    def info(self) -> PluginInfo:
+        return PluginInfo(
+            name=self._name,
+            version=self._version,
+            description=self._description,
+            hooks=list(self._hooks.keys()),
+        )
+    
+    def before_tool(self, tool_name: str, args: Dict[str, Any]) -> Dict[str, Any]:
+        if PluginHook.BEFORE_TOOL in self._hooks:
+            return self._hooks[PluginHook.BEFORE_TOOL](tool_name, args)
+        return args
+    
+    def after_tool(self, tool_name: str, result: Any) -> Any:
+        if PluginHook.AFTER_TOOL in self._hooks:
+            return self._hooks[PluginHook.AFTER_TOOL](tool_name, result)
+        return result
+    
+    def before_agent(self, prompt: str, context: Dict[str, Any]) -> str:
+        if PluginHook.BEFORE_AGENT in self._hooks:
+            return self._hooks[PluginHook.BEFORE_AGENT](prompt, context)
+        return prompt
+    
+    def after_agent(self, response: str, context: Dict[str, Any]) -> str:
+        if PluginHook.AFTER_AGENT in self._hooks:
+            return self._hooks[PluginHook.AFTER_AGENT](response, context)
+        return response

src/praisonai-agents/praisonaiagents/rag/__init__.py

@@ -0,0 +1,70 @@
+"""
+PraisonAI RAG - Retrieval Augmented Generation Module.
+
+This module provides a thin orchestration layer over Knowledge for RAG workflows.
+Knowledge handles indexing/retrieval; RAG adds answer generation with citations.
+
+Usage:
+    from praisonaiagents.rag import RAG, RAGConfig, RAGResult, Citation
+    
+    # With existing Knowledge
+    rag = RAG(knowledge=my_knowledge)
+    result = rag.query("What is the main finding?")
+    print(result.answer)
+    for citation in result.citations:
+        print(f"  [{citation.id}] {citation.source}")
+
+All imports are lazy to avoid performance impact when RAG is not used.
+"""
+
+from typing import TYPE_CHECKING
+
+# Lazy loading to avoid import overhead
+_LAZY_IMPORTS = {
+    # Models
+    "Citation": ("praisonaiagents.rag.models", "Citation"),
+    "ContextPack": ("praisonaiagents.rag.models", "ContextPack"),
+    "RAGResult": ("praisonaiagents.rag.models", "RAGResult"),
+    "RAGConfig": ("praisonaiagents.rag.models", "RAGConfig"),
+    # Protocols
+    "ContextBuilderProtocol": ("praisonaiagents.rag.protocols", "ContextBuilderProtocol"),
+    "CitationFormatterProtocol": ("praisonaiagents.rag.protocols", "CitationFormatterProtocol"),
+    # Pipeline
+    "RAG": ("praisonaiagents.rag.pipeline", "RAG"),
+    # Context utilities
+    "build_context": ("praisonaiagents.rag.context", "build_context"),
+    "truncate_context": ("praisonaiagents.rag.context", "truncate_context"),
+    "deduplicate_chunks": ("praisonaiagents.rag.context", "deduplicate_chunks"),
+}
+
+_cache = {}
+
+
+def __getattr__(name: str):
+    """Lazy load RAG components."""
+    if name in _cache:
+        return _cache[name]
+    
+    if name in _LAZY_IMPORTS:
+        module_path, attr_name = _LAZY_IMPORTS[name]
+        import importlib
+        module = importlib.import_module(module_path)
+        value = getattr(module, attr_name)
+        _cache[name] = value
+        return value
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+def __dir__():
+    """List available attributes."""
+    return list(_LAZY_IMPORTS.keys())
+
+
+__all__ = list(_LAZY_IMPORTS.keys())
+
+if TYPE_CHECKING:
+    from .models import Citation, ContextPack, RAGResult, RAGConfig
+    from .protocols import ContextBuilderProtocol, CitationFormatterProtocol
+    from .pipeline import RAG
+    from .context import build_context, truncate_context, deduplicate_chunks

src/praisonai-agents/praisonaiagents/rag/context.py

@@ -0,0 +1,199 @@
+"""
+Context Building Utilities for RAG.
+
+Provides token-aware context assembly with deduplication.
+No heavy imports - only stdlib.
+"""
+
+import hashlib
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+
+def _estimate_tokens(text: str) -> int:
+    """
+    Estimate token count for text.
+    
+    Uses simple heuristic: ~4 characters per token for English.
+    This avoids importing tokenizers for lightweight operation.
+    """
+    return len(text) // 4 + 1
+
+
+def _chunk_hash(text: str, source: Optional[str] = None) -> str:
+    """
+    Generate stable hash for chunk deduplication.
+    
+    Args:
+        text: Chunk text content
+        source: Optional source identifier
+        
+    Returns:
+        Hash string for deduplication
+    """
+    content = f"{source or ''}:{text}"
+    return hashlib.md5(content.encode()).hexdigest()[:16]
+
+
+def deduplicate_chunks(
+    results: List[Dict[str, Any]],
+    similarity_threshold: float = 0.9,
+) -> List[Dict[str, Any]]:
+    """
+    Deduplicate chunks by content hash.
+    
+    Args:
+        results: List of search results with 'text' or 'memory' key
+        similarity_threshold: Not used currently (hash-based dedup)
+        
+    Returns:
+        Deduplicated list of results
+    """
+    seen_hashes: Set[str] = set()
+    unique_results: List[Dict[str, Any]] = []
+    
+    for result in results:
+        # Handle different result formats
+        text = result.get("text") or result.get("memory", "")
+        source = result.get("metadata", {}).get("source", "")
+        
+        chunk_id = _chunk_hash(text, source)
+        
+        if chunk_id not in seen_hashes:
+            seen_hashes.add(chunk_id)
+            unique_results.append(result)
+    
+    return unique_results
+
+
+def truncate_context(
+    text: str,
+    max_tokens: int,
+    suffix: str = "\n\n[Context truncated...]",
+) -> str:
+    """
+    Truncate context to fit within token budget.
+    
+    Args:
+        text: Context text to truncate
+        max_tokens: Maximum tokens allowed
+        suffix: Suffix to add when truncated
+        
+    Returns:
+        Truncated text
+    """
+    estimated_tokens = _estimate_tokens(text)
+    
+    if estimated_tokens <= max_tokens:
+        return text
+    
+    # Calculate target character count
+    suffix_tokens = _estimate_tokens(suffix)
+    target_tokens = max_tokens - suffix_tokens
+    target_chars = target_tokens * 4
+    
+    # Truncate at word boundary
+    truncated = text[:target_chars]
+    last_space = truncated.rfind(" ")
+    if last_space > target_chars * 0.8:
+        truncated = truncated[:last_space]
+    
+    return truncated + suffix
+
+
+def build_context(
+    results: List[Dict[str, Any]],
+    max_tokens: int = 4000,
+    deduplicate: bool = True,
+    separator: str = "\n\n---\n\n",
+    include_source: bool = True,
+) -> Tuple[str, List[Dict[str, Any]]]:
+    """
+    Build context string from retrieval results.
+    
+    Args:
+        results: List of search results
+        max_tokens: Maximum tokens for context
+        deduplicate: Whether to deduplicate chunks
+        separator: Separator between chunks
+        include_source: Whether to include source info
+        
+    Returns:
+        Tuple of (context_string, used_results)
+    """
+    if not results:
+        return "", []
+    
+    # Deduplicate if requested
+    if deduplicate:
+        results = deduplicate_chunks(results)
+    
+    context_parts: List[str] = []
+    used_results: List[Dict[str, Any]] = []
+    current_tokens = 0
+    separator_tokens = _estimate_tokens(separator)
+    
+    for i, result in enumerate(results):
+        # Handle different result formats
+        text = result.get("text") or result.get("memory", "")
+        if not text:
+            continue
+        
+        # Build chunk text with optional source
+        if include_source:
+            source = result.get("metadata", {}).get("source", "")
+            filename = result.get("metadata", {}).get("filename", "")
+            source_label = filename or source or f"Source {i + 1}"
+            chunk_text = f"[{source_label}]\n{text}"
+        else:
+            chunk_text = text
+        
+        chunk_tokens = _estimate_tokens(chunk_text)
+        
+        # Check if we can fit this chunk
+        if current_tokens + chunk_tokens + separator_tokens > max_tokens:
+            # Try to fit partial chunk
+            remaining_tokens = max_tokens - current_tokens - separator_tokens
+            if remaining_tokens > 100:  # Only include if meaningful
+                truncated = truncate_context(chunk_text, remaining_tokens, "...")
+                context_parts.append(truncated)
+                used_results.append(result)
+            break
+        
+        context_parts.append(chunk_text)
+        used_results.append(result)
+        current_tokens += chunk_tokens + separator_tokens
+    
+    context = separator.join(context_parts)
+    return context, used_results
+
+
+class DefaultContextBuilder:
+    """
+    Default implementation of ContextBuilderProtocol.
+    
+    Provides token-aware context assembly with deduplication.
+    """
+    
+    def __init__(
+        self,
+        separator: str = "\n\n---\n\n",
+        include_source: bool = True,
+    ):
+        self.separator = separator
+        self.include_source = include_source
+    
+    def build(
+        self,
+        results: List[Dict[str, Any]],
+        max_tokens: int = 4000,
+        deduplicate: bool = True,
+    ) -> str:
+        """Build context string from results."""
+        context, _ = build_context(
+            results=results,
+            max_tokens=max_tokens,
+            deduplicate=deduplicate,
+            separator=self.separator,
+            include_source=self.include_source,
+        )
+        return context

src/praisonai-agents/praisonaiagents/rag/models.py

@@ -0,0 +1,256 @@
+"""
+RAG Data Models for PraisonAI Agents.
+
+Lightweight dataclasses for RAG results and configuration.
+No heavy imports - only stdlib and typing.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+from enum import Enum
+
+
+class RetrievalStrategy(str, Enum):
+    """Available retrieval strategies for RAG."""
+    BASIC = "basic"
+    FUSION = "fusion"
+    HYBRID = "hybrid"
+
+
+@dataclass
+class Citation:
+    """
+    Source citation for RAG answers.
+    
+    Attributes:
+        id: Unique citation identifier (e.g., "[1]", "[2]")
+        source: Source document path or URL
+        text: Text snippet from the source
+        score: Relevance score (0-1)
+        doc_id: Optional document identifier
+        chunk_id: Optional chunk identifier within document
+        offset: Optional character offset in source
+        metadata: Additional metadata
+    """
+    id: str
+    source: str
+    text: str
+    score: float = 0.0
+    doc_id: Optional[str] = None
+    chunk_id: Optional[str] = None
+    offset: Optional[int] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "id": self.id,
+            "source": self.source,
+            "text": self.text,
+            "score": self.score,
+            "doc_id": self.doc_id,
+            "chunk_id": self.chunk_id,
+            "offset": self.offset,
+            "metadata": self.metadata,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "Citation":
+        """Create from dictionary."""
+        return cls(
+            id=data.get("id", ""),
+            source=data.get("source", ""),
+            text=data.get("text", ""),
+            score=data.get("score", 0.0),
+            doc_id=data.get("doc_id"),
+            chunk_id=data.get("chunk_id"),
+            offset=data.get("offset"),
+            metadata=data.get("metadata", {}),
+        )
+    
+    def __str__(self) -> str:
+        """Human-readable string representation."""
+        snippet = self.text[:100] + "..." if len(self.text) > 100 else self.text
+        return f"[{self.id}] {self.source}: {snippet}"
+
+
+@dataclass
+class ContextPack:
+    """
+    Context pack for orchestrator pattern - retrieval without generation.
+    
+    Provides deterministic context that can be passed to Agent.chat_with_context().
+    
+    Attributes:
+        context: The formatted context string ready for injection
+        citations: List of source citations
+        query: The original query
+        metadata: Additional metadata (timing, retrieval stats, etc.)
+    """
+    context: str
+    citations: List[Citation] = field(default_factory=list)
+    query: str = ""
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "context": self.context,
+            "citations": [c.to_dict() for c in self.citations],
+            "query": self.query,
+            "metadata": self.metadata,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "ContextPack":
+        """Create from dictionary."""
+        return cls(
+            context=data.get("context", ""),
+            citations=[Citation.from_dict(c) for c in data.get("citations", [])],
+            query=data.get("query", ""),
+            metadata=data.get("metadata", {}),
+        )
+    
+    @property
+    def has_citations(self) -> bool:
+        """Check if context pack has citations."""
+        return len(self.citations) > 0
+    
+    def format_for_prompt(self, include_sources: bool = True) -> str:
+        """Format context for injection into a prompt."""
+        if not include_sources or not self.citations:
+            return self.context
+        
+        sources = "\n\nSources:\n"
+        for citation in self.citations:
+            sources += f"  [{citation.id}] {citation.source}\n"
+        return self.context + sources
+
+
+@dataclass
+class RAGResult:
+    """
+    Result from a RAG query.
+    
+    Attributes:
+        answer: The generated answer text
+        citations: List of source citations
+        context_used: The context string passed to the LLM
+        query: The original query
+        metadata: Additional metadata (timing, model info, etc.)
+    """
+    answer: str
+    citations: List[Citation] = field(default_factory=list)
+    context_used: str = ""
+    query: str = ""
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "answer": self.answer,
+            "citations": [c.to_dict() for c in self.citations],
+            "context_used": self.context_used,
+            "query": self.query,
+            "metadata": self.metadata,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "RAGResult":
+        """Create from dictionary."""
+        return cls(
+            answer=data.get("answer", ""),
+            citations=[Citation.from_dict(c) for c in data.get("citations", [])],
+            context_used=data.get("context_used", ""),
+            query=data.get("query", ""),
+            metadata=data.get("metadata", {}),
+        )
+    
+    @property
+    def has_citations(self) -> bool:
+        """Check if result has citations."""
+        return len(self.citations) > 0
+    
+    def format_answer_with_citations(self) -> str:
+        """Format answer with inline citation references."""
+        if not self.citations:
+            return self.answer
+        
+        # Build citation reference section
+        refs = "\n\nSources:\n"
+        for citation in self.citations:
+            refs += f"  {citation}\n"
+        
+        return self.answer + refs
+
+
+@dataclass
+class RAGConfig:
+    """
+    Configuration for RAG pipeline.
+    
+    Attributes:
+        top_k: Number of chunks to retrieve
+        min_score: Minimum relevance score threshold
+        max_context_tokens: Maximum tokens for context
+        include_citations: Whether to include citations in result
+        retrieval_strategy: Strategy for retrieval (basic, fusion, hybrid)
+        rerank: Whether to rerank results
+        rerank_top_k: Number of results after reranking
+        template: Prompt template with {context} and {question} placeholders
+        system_prompt: Optional system prompt for LLM
+        stream: Whether to stream responses
+    """
+    top_k: int = 5
+    min_score: float = 0.0
+    max_context_tokens: int = 4000
+    include_citations: bool = True
+    retrieval_strategy: RetrievalStrategy = RetrievalStrategy.BASIC
+    rerank: bool = False
+    rerank_top_k: int = 3
+    model: Optional[str] = None  # LLM model to use, defaults to gpt-4o-mini
+    template: str = """Answer the question based on the context below.
+
+Context:
+{context}
+
+Question: {question}
+
+Answer:"""
+    system_prompt: Optional[str] = None
+    stream: bool = False
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "top_k": self.top_k,
+            "min_score": self.min_score,
+            "max_context_tokens": self.max_context_tokens,
+            "include_citations": self.include_citations,
+            "retrieval_strategy": self.retrieval_strategy.value,
+            "rerank": self.rerank,
+            "rerank_top_k": self.rerank_top_k,
+            "template": self.template,
+            "system_prompt": self.system_prompt,
+            "stream": self.stream,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "RAGConfig":
+        """Create from dictionary."""
+        strategy = data.get("retrieval_strategy", "basic")
+        if isinstance(strategy, str):
+            strategy = RetrievalStrategy(strategy)
+        
+        return cls(
+            top_k=data.get("top_k", 5),
+            min_score=data.get("min_score", 0.0),
+            max_context_tokens=data.get("max_context_tokens", 4000),
+            include_citations=data.get("include_citations", True),
+            retrieval_strategy=strategy,
+            rerank=data.get("rerank", False),
+            rerank_top_k=data.get("rerank_top_k", 3),
+            template=data.get("template", cls.template),
+            system_prompt=data.get("system_prompt"),
+            stream=data.get("stream", False),
+        )

src/praisonai-agents/praisonaiagents/rag/pipeline.py

@@ -0,0 +1,652 @@
+"""
+RAG Pipeline for PraisonAI Agents.
+
+Thin orchestrator that uses Knowledge for retrieval and LLM for generation.
+Provides citations, streaming, and async support.
+"""
+
+import logging
+import time
+from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
+
+from .models import Citation, ContextPack, RAGConfig, RAGResult
+from .context import build_context, DefaultContextBuilder
+
+logger = logging.getLogger(__name__)
+
+
+class DefaultCitationFormatter:
+    """
+    Default implementation of CitationFormatterProtocol.
+    
+    Creates Citation objects from search results with stable IDs.
+    """
+    
+    def format(
+        self,
+        results: List[Dict[str, Any]],
+        start_id: int = 1,
+    ) -> List[Citation]:
+        """Format retrieval results as citations."""
+        citations = []
+        
+        for i, result in enumerate(results):
+            # Handle different result formats
+            text = result.get("text") or result.get("memory", "")
+            metadata = result.get("metadata", {})
+            score = result.get("score", 0.0)
+            
+            # Extract source info
+            source = metadata.get("source", "")
+            filename = metadata.get("filename", "")
+            doc_id = result.get("id") or metadata.get("doc_id", "")
+            chunk_id = metadata.get("chunk_id", "")
+            
+            citation = Citation(
+                id=str(start_id + i),
+                source=filename or source or f"Source {start_id + i}",
+                text=text[:500] if text else "",  # Limit snippet length
+                score=float(score) if score else 0.0,
+                doc_id=str(doc_id) if doc_id else None,
+                chunk_id=str(chunk_id) if chunk_id else None,
+                metadata=metadata,
+            )
+            citations.append(citation)
+        
+        return citations
+
+
+class RAG:
+    """
+    Retrieval Augmented Generation Pipeline.
+    
+    Thin orchestrator that:
+    1. Retrieves relevant context from Knowledge
+    2. Optionally reranks results
+    3. Builds context with token-aware truncation
+    4. Generates answer using LLM
+    5. Returns answer with citations
+    
+    Usage:
+        from praisonaiagents.rag import RAG
+        from praisonaiagents.knowledge import Knowledge
+        
+        knowledge = Knowledge(config={...})
+        knowledge.add("document.pdf")
+        
+        rag = RAG(knowledge=knowledge)
+        result = rag.query("What is the main finding?")
+        print(result.answer)
+        for citation in result.citations:
+            print(f"  [{citation.id}] {citation.source}")
+    """
+    
+    def __init__(
+        self,
+        knowledge: Any,
+        llm: Optional[Any] = None,
+        config: Optional[RAGConfig] = None,
+        reranker: Optional[Any] = None,
+        context_builder: Optional[Any] = None,
+        citation_formatter: Optional[Any] = None,
+    ):
+        """
+        Initialize RAG pipeline.
+        
+        Args:
+            knowledge: Knowledge instance for retrieval
+            llm: Optional LLM instance (uses default if not provided)
+            config: RAG configuration
+            reranker: Optional reranker for result refinement
+            context_builder: Optional custom context builder
+            citation_formatter: Optional custom citation formatter
+        """
+        self.knowledge = knowledge
+        self._llm = llm
+        self.config = config or RAGConfig()
+        self.reranker = reranker
+        self.context_builder = context_builder or DefaultContextBuilder()
+        self.citation_formatter = citation_formatter or DefaultCitationFormatter()
+        
+        # Lazy LLM initialization
+        self._llm_initialized = False
+    
+    @property
+    def llm(self):
+        """Lazy load LLM if not provided."""
+        if self._llm is not None:
+            return self._llm
+        
+        if not self._llm_initialized:
+            try:
+                from praisonaiagents.llm import LLM
+                # Use default model from config or fallback to gpt-4o-mini
+                model = self.config.model if self.config.model else "gpt-4o-mini"
+                self._llm = LLM(model=model)
+                self._llm_initialized = True
+            except ImportError:
+                logger.warning("LLM not available, using simple completion")
+                self._llm = None
+                self._llm_initialized = True
+        
+        return self._llm
+    
+    def _retrieve(
+        self,
+        query: str,
+        user_id: Optional[str] = None,
+        agent_id: Optional[str] = None,
+        run_id: Optional[str] = None,
+        **kwargs,
+    ) -> List[Dict[str, Any]]:
+        """
+        Retrieve relevant chunks from Knowledge.
+        
+        Args:
+            query: Search query
+            user_id: Optional user ID for scoping
+            agent_id: Optional agent ID for scoping
+            run_id: Optional run ID for scoping
+            
+        Returns:
+            List of search results
+        """
+        # Use Knowledge's search method
+        results = self.knowledge.search(
+            query=query,
+            user_id=user_id,
+            agent_id=agent_id,
+            run_id=run_id,
+            **kwargs,
+        )
+        
+        # Handle different result formats
+        if isinstance(results, dict):
+            results = results.get("results", [])
+        
+        # Filter by minimum score
+        if self.config.min_score > 0:
+            results = [
+                r for r in results
+                if r.get("score", 0) >= self.config.min_score
+            ]
+        
+        # Limit to top_k
+        results = results[:self.config.top_k]
+        
+        return results
+    
+    def _rerank(
+        self,
+        query: str,
+        results: List[Dict[str, Any]],
+    ) -> List[Dict[str, Any]]:
+        """
+        Rerank results if reranker is configured.
+        
+        Args:
+            query: Original query
+            results: Results to rerank
+            
+        Returns:
+            Reranked results
+        """
+        if not self.config.rerank or not self.reranker:
+            return results
+        
+        try:
+            reranked = self.reranker.rerank(
+                query=query,
+                results=results,
+                top_k=self.config.rerank_top_k,
+            )
+            return reranked
+        except Exception as e:
+            logger.warning(f"Reranking failed: {e}, using original order")
+            return results
+    
+    def _build_context(
+        self,
+        results: List[Dict[str, Any]],
+    ) -> tuple:
+        """
+        Build context from results.
+        
+        Returns:
+            Tuple of (context_string, used_results)
+        """
+        return build_context(
+            results=results,
+            max_tokens=self.config.max_context_tokens,
+            deduplicate=True,
+        )
+    
+    def _format_citations(
+        self,
+        results: List[Dict[str, Any]],
+    ) -> List[Citation]:
+        """Format results as citations."""
+        if not self.config.include_citations:
+            return []
+        
+        return self.citation_formatter.format(results)
+    
+    def _generate(
+        self,
+        prompt: str,
+        system_prompt: Optional[str] = None,
+        **kwargs,
+    ) -> str:
+        """
+        Generate answer using LLM.
+        
+        Args:
+            prompt: Full prompt with context
+            system_prompt: Optional system prompt
+            
+        Returns:
+            Generated answer
+        """
+        if self.llm is None:
+            return "LLM not available. Please configure an LLM."
+        
+        try:
+            # Try different LLM interfaces
+            if hasattr(self.llm, 'get_response'):
+                # PraisonAI LLM interface - prompt can be string or messages list
+                if system_prompt:
+                    response = self.llm.get_response(
+                        prompt=prompt,
+                        system_prompt=system_prompt,
+                        verbose=False,
+                        stream=False,
+                        **kwargs
+                    )
+                else:
+                    response = self.llm.get_response(
+                        prompt=prompt,
+                        verbose=False,
+                        stream=False,
+                        **kwargs
+                    )
+                return response if isinstance(response, str) else str(response)
+            elif hasattr(self.llm, 'chat'):
+                messages = []
+                if system_prompt:
+                    messages.append({"role": "system", "content": system_prompt})
+                messages.append({"role": "user", "content": prompt})
+                response = self.llm.chat(messages=messages, **kwargs)
+                return response if isinstance(response, str) else str(response)
+            elif hasattr(self.llm, 'generate'):
+                return self.llm.generate(prompt, **kwargs)
+            elif callable(self.llm):
+                return self.llm(prompt, **kwargs)
+            else:
+                return str(self.llm)
+        except Exception as e:
+            logger.error(f"LLM generation failed: {e}")
+            return f"Error generating response: {e}"
+    
+    def _build_prompt(
+        self,
+        question: str,
+        context: str,
+    ) -> str:
+        """Build prompt from template."""
+        return self.config.template.format(
+            question=question,
+            context=context,
+        )
+    
+    def retrieve(
+        self,
+        query: str,
+        *,
+        top_k: Optional[int] = None,
+        hybrid: Optional[bool] = None,
+        rerank: Optional[bool] = None,
+        filters: Optional[Dict[str, Any]] = None,
+        user_id: Optional[str] = None,
+        agent_id: Optional[str] = None,
+        run_id: Optional[str] = None,
+        **kwargs,
+    ) -> ContextPack:
+        """
+        Retrieve context without LLM generation (Orchestrator pattern).
+        
+        Returns a ContextPack that can be passed to Agent.chat_with_context().
+        This enables conditional retrieval - only retrieve when needed.
+        
+        Args:
+            query: Search query
+            top_k: Override config top_k
+            hybrid: Override config hybrid retrieval
+            rerank: Override config rerank
+            filters: Optional metadata filters
+            user_id: Optional user ID for scoping
+            agent_id: Optional agent ID for scoping
+            run_id: Optional run ID for scoping
+            
+        Returns:
+            ContextPack with context string and citations (no LLM call)
+        """
+        start_time = time.time()
+        
+        # Apply overrides
+        original_top_k = self.config.top_k
+        original_rerank = self.config.rerank
+        
+        if top_k is not None:
+            self.config.top_k = top_k
+        if rerank is not None:
+            self.config.rerank = rerank
+        
+        try:
+            # 1. Retrieve
+            results = self._retrieve(
+                query=query,
+                user_id=user_id,
+                agent_id=agent_id,
+                run_id=run_id,
+                filters=filters,
+                **kwargs,
+            )
+            
+            # 2. Rerank (optional)
+            results = self._rerank(query, results)
+            
+            # 3. Build context
+            context, used_results = self._build_context(results)
+            
+            # 4. Format citations
+            citations = self._format_citations(used_results)
+            
+            elapsed = time.time() - start_time
+            
+            return ContextPack(
+                context=context,
+                citations=citations,
+                query=query,
+                metadata={
+                    "elapsed_seconds": elapsed,
+                    "num_results": len(results),
+                    "num_citations": len(citations),
+                    "retrieval_strategy": self.config.retrieval_strategy.value,
+                    "reranked": self.config.rerank,
+                    "top_k": self.config.top_k,
+                },
+            )
+        finally:
+            # Restore original config
+            self.config.top_k = original_top_k
+            self.config.rerank = original_rerank
+    
+    async def aretrieve(
+        self,
+        query: str,
+        *,
+        top_k: Optional[int] = None,
+        hybrid: Optional[bool] = None,
+        rerank: Optional[bool] = None,
+        filters: Optional[Dict[str, Any]] = None,
+        user_id: Optional[str] = None,
+        agent_id: Optional[str] = None,
+        run_id: Optional[str] = None,
+        **kwargs,
+    ) -> ContextPack:
+        """
+        Async version of retrieve.
+        
+        Currently wraps sync version. Can be extended for true async.
+        """
+        import asyncio
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(
+            None,
+            lambda: self.retrieve(
+                query=query,
+                top_k=top_k,
+                hybrid=hybrid,
+                rerank=rerank,
+                filters=filters,
+                user_id=user_id,
+                agent_id=agent_id,
+                run_id=run_id,
+                **kwargs,
+            ),
+        )
+    
+    def query(
+        self,
+        question: str,
+        user_id: Optional[str] = None,
+        agent_id: Optional[str] = None,
+        run_id: Optional[str] = None,
+        **kwargs,
+    ) -> RAGResult:
+        """
+        Execute RAG query.
+        
+        Args:
+            question: User's question
+            user_id: Optional user ID for scoping
+            agent_id: Optional agent ID for scoping
+            run_id: Optional run ID for scoping
+            
+        Returns:
+            RAGResult with answer and citations
+        """
+        start_time = time.time()
+        
+        # 1. Retrieve
+        results = self._retrieve(
+            query=question,
+            user_id=user_id,
+            agent_id=agent_id,
+            run_id=run_id,
+            **kwargs,
+        )
+        
+        # 2. Rerank (optional)
+        results = self._rerank(question, results)
+        
+        # 3. Build context
+        context, used_results = self._build_context(results)
+        
+        # 4. Generate answer
+        prompt = self._build_prompt(question, context)
+        answer = self._generate(
+            prompt=prompt,
+            system_prompt=self.config.system_prompt,
+        )
+        
+        # 5. Format citations
+        citations = self._format_citations(used_results)
+        
+        # Build result
+        elapsed = time.time() - start_time
+        
+        return RAGResult(
+            answer=answer,
+            citations=citations,
+            context_used=context,
+            query=question,
+            metadata={
+                "elapsed_seconds": elapsed,
+                "num_results": len(results),
+                "num_citations": len(citations),
+                "retrieval_strategy": self.config.retrieval_strategy.value,
+                "reranked": self.config.rerank,
+            },
+        )
+    
+    async def aquery(
+        self,
+        question: str,
+        user_id: Optional[str] = None,
+        agent_id: Optional[str] = None,
+        run_id: Optional[str] = None,
+        **kwargs,
+    ) -> RAGResult:
+        """
+        Async version of query.
+        
+        Currently wraps sync version. Can be extended for true async.
+        """
+        # For now, wrap sync version
+        # TODO: Implement true async when Knowledge supports it
+        import asyncio
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(
+            None,
+            lambda: self.query(
+                question=question,
+                user_id=user_id,
+                agent_id=agent_id,
+                run_id=run_id,
+                **kwargs,
+            ),
+        )
+    
+    def stream(
+        self,
+        question: str,
+        user_id: Optional[str] = None,
+        agent_id: Optional[str] = None,
+        run_id: Optional[str] = None,
+        **kwargs,
+    ) -> Iterator[str]:
+        """
+        Stream RAG response.
+        
+        Yields tokens as they are generated.
+        Final yield includes citation metadata.
+        
+        Args:
+            question: User's question
+            user_id: Optional user ID
+            agent_id: Optional agent ID
+            run_id: Optional run ID
+            
+        Yields:
+            Response tokens
+        """
+        # 1. Retrieve
+        results = self._retrieve(
+            query=question,
+            user_id=user_id,
+            agent_id=agent_id,
+            run_id=run_id,
+            **kwargs,
+        )
+        
+        # 2. Rerank
+        results = self._rerank(question, results)
+        
+        # 3. Build context
+        context, used_results = self._build_context(results)
+        
+        # 4. Build prompt
+        prompt = self._build_prompt(question, context)
+        
+        # 5. Stream generation
+        if self.llm is None:
+            yield "LLM not available."
+            return
+        
+        try:
+            # Try streaming interface
+            if hasattr(self.llm, 'stream'):
+                messages = []
+                if self.config.system_prompt:
+                    messages.append({"role": "system", "content": self.config.system_prompt})
+                messages.append({"role": "user", "content": prompt})
+                
+                for chunk in self.llm.stream(messages=messages, **kwargs):
+                    yield chunk
+            else:
+                # Fallback to non-streaming
+                answer = self._generate(prompt, self.config.system_prompt)
+                # Simulate streaming by yielding words
+                for word in answer.split():
+                    yield word + " "
+        except Exception as e:
+            logger.error(f"Streaming failed: {e}")
+            yield f"Error: {e}"
+    
+    async def astream(
+        self,
+        question: str,
+        user_id: Optional[str] = None,
+        agent_id: Optional[str] = None,
+        run_id: Optional[str] = None,
+        **kwargs,
+    ) -> AsyncIterator[str]:
+        """
+        Async streaming RAG response.
+        
+        Yields tokens as they are generated.
+        """
+        # 1. Retrieve
+        results = self._retrieve(
+            query=question,
+            user_id=user_id,
+            agent_id=agent_id,
+            run_id=run_id,
+            **kwargs,
+        )
+        
+        # 2. Rerank
+        results = self._rerank(question, results)
+        
+        # 3. Build context
+        context, used_results = self._build_context(results)
+        
+        # 4. Build prompt
+        prompt = self._build_prompt(question, context)
+        
+        # 5. Stream generation
+        if self.llm is None:
+            yield "LLM not available."
+            return
+        
+        try:
+            # Try async streaming interface
+            if hasattr(self.llm, 'astream'):
+                messages = []
+                if self.config.system_prompt:
+                    messages.append({"role": "system", "content": self.config.system_prompt})
+                messages.append({"role": "user", "content": prompt})
+                
+                async for chunk in self.llm.astream(messages=messages, **kwargs):
+                    yield chunk
+            else:
+                # Fallback to sync streaming wrapped
+                for chunk in self.stream(question, user_id, agent_id, run_id, **kwargs):
+                    yield chunk
+        except Exception as e:
+            logger.error(f"Async streaming failed: {e}")
+            yield f"Error: {e}"
+    
+    def get_citations(
+        self,
+        question: str,
+        user_id: Optional[str] = None,
+        agent_id: Optional[str] = None,
+        run_id: Optional[str] = None,
+        **kwargs,
+    ) -> List[Citation]:
+        """
+        Get citations without generating answer.
+        
+        Useful for retrieval-only workflows.
+        """
+        results = self._retrieve(
+            query=question,
+            user_id=user_id,
+            agent_id=agent_id,
+            run_id=run_id,
+            **kwargs,
+        )
+        results = self._rerank(question, results)
+        return self._format_citations(results)

src/praisonai-agents/praisonaiagents/rag/protocols.py

@@ -0,0 +1,147 @@
+"""
+RAG Protocols for PraisonAI Agents.
+
+Defines minimal protocol interfaces for RAG components.
+No heavy imports - only stdlib and typing.
+"""
+
+from typing import Any, Dict, Iterator, List, Optional, Protocol, runtime_checkable
+
+from .models import Citation, RAGResult
+
+
+@runtime_checkable
+class ContextBuilderProtocol(Protocol):
+    """
+    Protocol for building context from retrieval results.
+    
+    Implementations must provide a build method that assembles
+    context from search results with token-aware truncation.
+    """
+    
+    def build(
+        self,
+        results: List[Dict[str, Any]],
+        max_tokens: int = 4000,
+        deduplicate: bool = True,
+    ) -> str:
+        """
+        Build context string from retrieval results.
+        
+        Args:
+            results: List of search results with 'text' and 'metadata' keys
+            max_tokens: Maximum tokens for context
+            deduplicate: Whether to deduplicate similar chunks
+            
+        Returns:
+            Assembled context string
+        """
+        ...
+
+
+@runtime_checkable
+class CitationFormatterProtocol(Protocol):
+    """
+    Protocol for formatting citations from retrieval results.
+    
+    Implementations must provide a format method that creates
+    Citation objects from search results.
+    """
+    
+    def format(
+        self,
+        results: List[Dict[str, Any]],
+        start_id: int = 1,
+    ) -> List[Citation]:
+        """
+        Format retrieval results as citations.
+        
+        Args:
+            results: List of search results
+            start_id: Starting citation ID number
+            
+        Returns:
+            List of Citation objects
+        """
+        ...
+
+
+@runtime_checkable
+class RerankerProtocol(Protocol):
+    """
+    Protocol for reranking retrieval results.
+    
+    Implementations must provide a rerank method that reorders
+    results by relevance to the query.
+    """
+    
+    name: str
+    
+    def rerank(
+        self,
+        query: str,
+        results: List[Dict[str, Any]],
+        top_k: Optional[int] = None,
+    ) -> List[Dict[str, Any]]:
+        """
+        Rerank results by relevance to query.
+        
+        Args:
+            query: The search query
+            results: List of results to rerank
+            top_k: Optional limit on results
+            
+        Returns:
+            Reranked list of results
+        """
+        ...
+
+
+@runtime_checkable
+class RAGProtocol(Protocol):
+    """
+    Protocol for RAG pipelines.
+    
+    Implementations must provide query, stream, and async variants.
+    """
+    
+    def query(self, question: str, **kwargs) -> RAGResult:
+        """Execute RAG query and return result with citations."""
+        ...
+    
+    async def aquery(self, question: str, **kwargs) -> RAGResult:
+        """Async version of query."""
+        ...
+    
+    def stream(self, question: str, **kwargs) -> Iterator[str]:
+        """Stream RAG response tokens."""
+        ...
+
+
+@runtime_checkable
+class GraphHookProtocol(Protocol):
+    """
+    Protocol for graph-based retrieval hooks (optional).
+    
+    Implementations can provide graph traversal for enhanced retrieval.
+    This is an optional extension point for GraphRAG tools.
+    """
+    
+    def get_related_nodes(
+        self,
+        query: str,
+        node_ids: List[str],
+        max_hops: int = 2,
+    ) -> List[Dict[str, Any]]:
+        """
+        Get related nodes from graph for context expansion.
+        
+        Args:
+            query: The search query
+            node_ids: Starting node IDs
+            max_hops: Maximum graph traversal hops
+            
+        Returns:
+            List of related nodes with metadata
+        """
+        ...

src/praisonai-agents/praisonaiagents/server/__init__.py

@@ -0,0 +1,43 @@
+"""
+Server Module for PraisonAI Agents.
+
+Provides an optional HTTP server with SSE event streaming
+for real-time agent communication.
+
+Features:
+- REST API for agent operations
+- Server-Sent Events (SSE) for real-time updates
+- Event bus integration
+- Multi-project support
+- CORS handling
+
+Usage:
+    from praisonaiagents.server import AgentServer
+    
+    # Create and start server
+    server = AgentServer(port=8080)
+    server.start()
+    
+    # Or use as context manager
+    with AgentServer(port=8080) as server:
+        # Server is running
+        pass
+"""
+
+__all__ = [
+    "AgentServer",
+    "ServerConfig",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components."""
+    if name == "AgentServer":
+        from .server import AgentServer
+        return AgentServer
+    
+    if name == "ServerConfig":
+        from .server import ServerConfig
+        return ServerConfig
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/server/server.py

@@ -0,0 +1,331 @@
+"""
+HTTP Server implementation for PraisonAI Agents.
+
+Provides REST API and SSE event streaming.
+"""
+
+import asyncio
+import json
+import logging
+import queue
+import threading
+import time
+import uuid
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, Generator, List, Optional
+
+logger = logging.getLogger(__name__)
+
+# Default configuration
+DEFAULT_HOST = "127.0.0.1"
+DEFAULT_PORT = 8765
+
+
+@dataclass
+class ServerConfig:
+    """Server configuration."""
+    
+    host: str = DEFAULT_HOST
+    port: int = DEFAULT_PORT
+    cors_origins: List[str] = field(default_factory=lambda: ["*"])
+    auth_token: Optional[str] = None
+    max_connections: int = 100
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "host": self.host,
+            "port": self.port,
+            "cors_origins": self.cors_origins,
+            "auth_token": "***" if self.auth_token else None,
+            "max_connections": self.max_connections,
+        }
+
+
+class SSEClient:
+    """A Server-Sent Events client connection."""
+    
+    def __init__(self, client_id: str):
+        self.client_id = client_id
+        self.queue: queue.Queue = queue.Queue()
+        self.connected = True
+        self.created_at = time.time()
+    
+    def send(self, event_type: str, data: Dict[str, Any]):
+        """Send an event to this client."""
+        if self.connected:
+            self.queue.put({
+                "event": event_type,
+                "data": data,
+                "id": str(uuid.uuid4()),
+            })
+    
+    def disconnect(self):
+        """Disconnect this client."""
+        self.connected = False
+    
+    def events(self) -> Generator[str, None, None]:
+        """Generate SSE formatted events."""
+        while self.connected:
+            try:
+                event = self.queue.get(timeout=30)
+                yield f"id: {event['id']}\n"
+                yield f"event: {event['event']}\n"
+                yield f"data: {json.dumps(event['data'])}\n\n"
+            except queue.Empty:
+                # Send keepalive
+                yield ": keepalive\n\n"
+
+
+class AgentServer:
+    """
+    HTTP server for PraisonAI Agents.
+    
+    Provides REST API endpoints and SSE event streaming
+    for real-time agent communication.
+    
+    Example:
+        server = AgentServer(port=8080)
+        
+        # Register event handler
+        @server.on_event("message")
+        def handle_message(data):
+            print(f"Message: {data}")
+        
+        # Start server
+        server.start()
+    """
+    
+    def __init__(
+        self,
+        host: str = DEFAULT_HOST,
+        port: int = DEFAULT_PORT,
+        config: Optional[ServerConfig] = None,
+    ):
+        """
+        Initialize the server.
+        
+        Args:
+            host: Host to bind to
+            port: Port to bind to
+            config: Optional server configuration
+        """
+        self.config = config or ServerConfig(host=host, port=port)
+        self.host = self.config.host
+        self.port = self.config.port
+        
+        self._clients: Dict[str, SSEClient] = {}
+        self._event_handlers: Dict[str, List[Callable]] = {}
+        self._running = False
+        self._server_thread: Optional[threading.Thread] = None
+        self._app = None
+        self._server = None
+    
+    def _create_app(self):
+        """Create the ASGI application."""
+        try:
+            from starlette.applications import Starlette
+            from starlette.responses import JSONResponse, StreamingResponse
+            from starlette.routing import Route
+            from starlette.middleware.cors import CORSMiddleware
+        except ImportError:
+            logger.warning("Starlette not installed. Server features unavailable.")
+            return None
+        
+        async def health(request):
+            return JSONResponse({
+                "status": "ok",
+                "timestamp": time.time(),
+                "clients": len(self._clients),
+            })
+        
+        async def events(request):
+            client_id = str(uuid.uuid4())
+            client = SSEClient(client_id)
+            self._clients[client_id] = client
+            
+            logger.info(f"SSE client connected: {client_id}")
+            
+            async def event_generator():
+                try:
+                    for event in client.events():
+                        yield event
+                finally:
+                    client.disconnect()
+                    self._clients.pop(client_id, None)
+                    logger.info(f"SSE client disconnected: {client_id}")
+            
+            return StreamingResponse(
+                event_generator(),
+                media_type="text/event-stream",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "keep-alive",
+                },
+            )
+        
+        async def publish(request):
+            try:
+                data = await request.json()
+                event_type = data.get("type", "message")
+                event_data = data.get("data", {})
+                
+                self.broadcast(event_type, event_data)
+                
+                return JSONResponse({
+                    "success": True,
+                    "clients": len(self._clients),
+                })
+            except Exception as e:
+                return JSONResponse(
+                    {"success": False, "error": str(e)},
+                    status_code=400
+                )
+        
+        async def info(request):
+            return JSONResponse({
+                "name": "PraisonAI Agent Server",
+                "version": "1.0.0",
+                "clients": len(self._clients),
+                "config": self.config.to_dict(),
+            })
+        
+        routes = [
+            Route("/health", health, methods=["GET"]),
+            Route("/events", events, methods=["GET"]),
+            Route("/publish", publish, methods=["POST"]),
+            Route("/info", info, methods=["GET"]),
+        ]
+        
+        app = Starlette(routes=routes)
+        
+        # Add CORS middleware
+        app = CORSMiddleware(
+            app,
+            allow_origins=self.config.cors_origins,
+            allow_methods=["*"],
+            allow_headers=["*"],
+        )
+        
+        return app
+    
+    def broadcast(self, event_type: str, data: Dict[str, Any]):
+        """
+        Broadcast an event to all connected clients.
+        
+        Args:
+            event_type: The event type
+            data: The event data
+        """
+        for client in list(self._clients.values()):
+            try:
+                client.send(event_type, data)
+            except Exception as e:
+                logger.error(f"Error broadcasting to client: {e}")
+    
+    def on_event(self, event_type: str) -> Callable:
+        """
+        Decorator to register an event handler.
+        
+        Args:
+            event_type: The event type to handle
+            
+        Returns:
+            Decorator function
+        """
+        def decorator(func: Callable) -> Callable:
+            if event_type not in self._event_handlers:
+                self._event_handlers[event_type] = []
+            self._event_handlers[event_type].append(func)
+            return func
+        return decorator
+    
+    def start(self, blocking: bool = False):
+        """
+        Start the server.
+        
+        Args:
+            blocking: If True, block until server stops
+        """
+        if self._running:
+            logger.warning("Server already running")
+            return
+        
+        self._app = self._create_app()
+        if self._app is None:
+            logger.error("Failed to create app - missing dependencies")
+            return
+        
+        self._running = True
+        
+        if blocking:
+            self._run_server()
+        else:
+            self._server_thread = threading.Thread(
+                target=self._run_server,
+                daemon=True
+            )
+            self._server_thread.start()
+            # Give server time to start
+            time.sleep(0.5)
+        
+        logger.info(f"Server started on {self.host}:{self.port}")
+    
+    def _run_server(self):
+        """Run the server in an event loop."""
+        try:
+            import uvicorn
+            
+            config = uvicorn.Config(
+                self._app,
+                host=self.host,
+                port=self.port,
+                log_level="warning",
+            )
+            self._server = uvicorn.Server(config)
+            
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            loop.run_until_complete(self._server.serve())
+        except ImportError:
+            logger.error("uvicorn not installed. Cannot start server.")
+        except Exception as e:
+            logger.error(f"Server error: {e}")
+        finally:
+            self._running = False
+    
+    def stop(self):
+        """Stop the server."""
+        if not self._running:
+            return
+        
+        self._running = False
+        
+        # Disconnect all clients
+        for client in list(self._clients.values()):
+            client.disconnect()
+        self._clients.clear()
+        
+        # Stop the server
+        if self._server:
+            self._server.should_exit = True
+        
+        logger.info("Server stopped")
+    
+    @property
+    def is_running(self) -> bool:
+        """Check if server is running."""
+        return self._running
+    
+    @property
+    def client_count(self) -> int:
+        """Get number of connected clients."""
+        return len(self._clients)
+    
+    def __enter__(self):
+        """Context manager entry."""
+        self.start()
+        return self
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Context manager exit."""
+        self.stop()

src/praisonai-agents/praisonaiagents/session/README.md

@@ -0,0 +1,185 @@
+# Session Persistence for PraisonAI Agents
+
+This module provides automatic session persistence with zero configuration.
+
+## Quick Start
+
+```python
+from praisonaiagents import Agent
+
+# With session persistence (auto-enabled)
+agent = Agent(
+    name="Assistant",
+    session_id="my-session-123"
+)
+agent.start("Hello, my name is Alice")
+
+# Later, in a new process - history is restored automatically
+agent = Agent(
+    name="Assistant", 
+    session_id="my-session-123"
+)
+agent.start("What is my name?")  # Agent remembers: "Alice"
+```
+
+## How It Works
+
+When you provide a `session_id` to an Agent:
+
+1. **Automatic Persistence**: Conversation history is automatically saved to disk
+2. **Automatic Restoration**: When a new Agent is created with the same `session_id`, history is restored
+3. **Zero Configuration**: No database setup required - uses JSON files by default
+
+### Default Storage Location
+
+Sessions are stored in: `~/.praison/sessions/{session_id}.json`
+
+### Session File Format
+
+```json
+{
+  "session_id": "my-session-123",
+  "messages": [
+    {"role": "user", "content": "Hello", "timestamp": 1704153600.0},
+    {"role": "assistant", "content": "Hi there!", "timestamp": 1704153601.5}
+  ],
+  "created_at": "2026-01-02T04:00:00+00:00",
+  "updated_at": "2026-01-02T04:01:00+00:00",
+  "agent_name": "Assistant"
+}
+```
+
+## Behavior Matrix
+
+| Scenario | Behavior |
+|----------|----------|
+| `session_id` provided, no DB | JSON persistence (auto) |
+| `session_id` provided, with DB | DB adapter used |
+| No `session_id`, same Agent instance | In-memory only |
+| No `session_id`, new Agent instance | No history |
+
+## Advanced Usage
+
+### Direct Session Store Access
+
+```python
+from praisonaiagents.session import get_default_session_store
+
+store = get_default_session_store()
+
+# Add messages
+store.add_user_message("session-123", "Hello")
+store.add_assistant_message("session-123", "Hi there!")
+
+# Get history
+history = store.get_chat_history("session-123")
+# [{"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}]
+
+# List all sessions
+sessions = store.list_sessions()
+
+# Delete a session
+store.delete_session("session-123")
+```
+
+### Custom Session Directory
+
+```python
+from praisonaiagents.session import DefaultSessionStore
+
+store = DefaultSessionStore(
+    session_dir="/custom/path/sessions",
+    max_messages=200,  # Default: 100
+    lock_timeout=10.0,  # Default: 5.0 seconds
+)
+```
+
+### Using with DB Adapter
+
+When a DB adapter is provided, it takes precedence over JSON persistence:
+
+```python
+from praisonaiagents import Agent
+from praisonai.db import PostgresAdapter
+
+agent = Agent(
+    name="Assistant",
+    session_id="my-session",
+    db=PostgresAdapter(connection_string="...")
+)
+```
+
+## Multi-Process Safety
+
+The session store uses file locking to ensure safe concurrent access:
+
+- **Unix**: Uses `fcntl.flock()` for file locking
+- **Windows**: Uses `msvcrt.locking()` for file locking
+- **Atomic Writes**: Uses temp file + rename to prevent corruption
+
+Multiple processes can safely read/write to the same session.
+
+## Context Caching
+
+For cost optimization, use `prompt_caching=True` with Anthropic models:
+
+```python
+agent = Agent(
+    name="Assistant",
+    session_id="my-session",
+    prompt_caching=True,  # Enables Anthropic prompt caching
+)
+```
+
+This caches the system prompt, reducing token costs for repeated conversations.
+
+## API Reference
+
+### DefaultSessionStore
+
+```python
+class DefaultSessionStore:
+    def __init__(
+        self,
+        session_dir: Optional[str] = None,  # Default: ~/.praison/sessions/
+        max_messages: int = 100,
+        lock_timeout: float = 5.0,
+    ): ...
+    
+    def add_message(self, session_id: str, role: str, content: str) -> bool: ...
+    def add_user_message(self, session_id: str, content: str) -> bool: ...
+    def add_assistant_message(self, session_id: str, content: str) -> bool: ...
+    def get_chat_history(self, session_id: str, max_messages: int = None) -> List[Dict]: ...
+    def get_session(self, session_id: str) -> SessionData: ...
+    def clear_session(self, session_id: str) -> bool: ...
+    def delete_session(self, session_id: str) -> bool: ...
+    def list_sessions(self, limit: int = 50) -> List[Dict]: ...
+    def session_exists(self, session_id: str) -> bool: ...
+```
+
+### SessionData
+
+```python
+@dataclass
+class SessionData:
+    session_id: str
+    messages: List[SessionMessage]
+    created_at: str
+    updated_at: str
+    agent_name: Optional[str]
+    user_id: Optional[str]
+    metadata: Dict[str, Any]
+    
+    def get_chat_history(self, max_messages: int = None) -> List[Dict[str, str]]: ...
+```
+
+### SessionMessage
+
+```python
+@dataclass
+class SessionMessage:
+    role: str  # "user", "assistant", "system"
+    content: str
+    timestamp: float
+    metadata: Dict[str, Any]
+```

src/praisonai-agents/praisonaiagents/session/__init__.py

@@ -0,0 +1,76 @@
+"""
+Session persistence module for PraisonAI Agents.
+
+Provides automatic session persistence with zero configuration.
+When a session_id is provided to an Agent, conversation history
+is automatically persisted to disk and restored on subsequent runs.
+
+Usage:
+    from praisonaiagents import Agent
+    
+    # With session persistence (auto-enabled)
+    agent = Agent(
+        name="Assistant",
+        session_id="my-session-123"
+    )
+    agent.start("Hello")
+    
+    # Later, new process - history is restored
+    agent = Agent(
+        name="Assistant", 
+        session_id="my-session-123"
+    )
+    agent.start("What did I say before?")  # Remembers!
+
+Default storage: ~/.praison/sessions/{session_id}.json
+"""
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from .store import DefaultSessionStore, SessionMessage, SessionData
+
+# Lazy loading for zero import overhead
+_module_cache = {}
+
+
+def __getattr__(name: str):
+    """Lazy load session components."""
+    if name in _module_cache:
+        return _module_cache[name]
+    
+    if name == "Session":
+        from .api import Session
+        _module_cache[name] = Session
+        return Session
+    
+    if name == "DefaultSessionStore":
+        from .store import DefaultSessionStore
+        _module_cache[name] = DefaultSessionStore
+        return DefaultSessionStore
+    
+    if name == "SessionMessage":
+        from .store import SessionMessage
+        _module_cache[name] = SessionMessage
+        return SessionMessage
+    
+    if name == "SessionData":
+        from .store import SessionData
+        _module_cache[name] = SessionData
+        return SessionData
+    
+    if name == "get_default_session_store":
+        from .store import get_default_session_store
+        _module_cache[name] = get_default_session_store
+        return get_default_session_store
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+__all__ = [
+    "Session",
+    "DefaultSessionStore",
+    "SessionMessage", 
+    "SessionData",
+    "get_default_session_store",
+]

src/praisonai-agents/praisonaiagents/session/api.py

@@ -0,0 +1,532 @@
+"""
+Session Management for PraisonAI Agents
+
+A simple wrapper around existing stateful capabilities to provide a unified
+session API for developers building stateful agent applications.
+
+This module uses deferred imports to ensure `requests` is NOT loaded at import time.
+"""
+from __future__ import annotations
+
+import os
+import uuid
+import json
+from typing import Any, Dict, List, Optional, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from ..memory.memory import Memory
+    from ..knowledge.knowledge import Knowledge
+    from ..agent import Agent
+
+
+def _get_requests():
+    """Deferred import of requests library."""
+    import requests
+    return requests
+
+
+class Session:
+    """
+    A simple wrapper around PraisonAI's existing stateful capabilities.
+    
+    Provides a unified API for:
+    - Session management with persistent state
+    - Memory operations (short-term, long-term, user-specific)
+    - Knowledge base operations
+    - Agent state management
+    - Remote agent connectivity
+    
+    Examples:
+        # Local session with agent
+        session = Session(session_id="chat_123", user_id="user_456")
+        agent = session.Agent(name="Assistant", role="Helpful AI")
+        
+        # Remote agent session (similar to Google ADK)
+        session = Session(agent_url="192.168.1.10:8000/agent")
+        response = session.chat("Hello from remote client!")
+        
+        # Save session state
+        session.save_state({"conversation_topic": "AI research"})
+    """
+
+    def __init__(
+        self, 
+        session_id: Optional[str] = None,
+        user_id: Optional[str] = None,
+        agent_url: Optional[str] = None,
+        memory_config: Optional[Dict[str, Any]] = None,
+        knowledge_config: Optional[Dict[str, Any]] = None,
+        timeout: int = 30
+    ):
+        """
+        Initialize a new session with optional persistence or remote agent connectivity.
+        
+        Args:
+            session_id: Unique session identifier. Auto-generated if None.
+            user_id: User identifier for user-specific memory operations.
+            agent_url: URL of remote agent for direct connectivity (e.g., "192.168.1.10:8000/agent")
+            memory_config: Configuration for memory system (defaults to RAG)
+            knowledge_config: Configuration for knowledge base system  
+            timeout: HTTP timeout for remote agent calls (default: 30 seconds)
+        """
+        self.session_id = session_id or str(uuid.uuid4())[:8]
+        self.user_id = user_id or "default_user"
+        self.agent_url = agent_url
+        self.timeout = timeout
+        self.is_remote = agent_url is not None
+
+        # Validate agent_url format
+        if self.is_remote:
+            if not self.agent_url.startswith(('http://', 'https://')):
+                self.agent_url = f"http://{self.agent_url}"
+            self._test_remote_connection()
+
+        # Initialize memory with sensible defaults (only for local sessions)
+        if not self.is_remote:
+            default_memory_config = {
+                "provider": "rag",
+                "use_embedding": False,
+                "rag_db_path": f".praison/sessions/{self.session_id}/chroma_db"
+            }
+            if memory_config:
+                default_memory_config.update(memory_config)
+            self.memory_config = default_memory_config
+
+            default_knowledge_config = knowledge_config or {}
+            self.knowledge_config = default_knowledge_config
+
+            os.makedirs(f".praison/sessions/{self.session_id}", exist_ok=True)
+
+            self._memory = None
+            self._knowledge = None
+            self._agents_instance = None
+            self._agents = {}
+        else:
+            self.memory_config = {}
+            self.knowledge_config = {}
+            self._memory = None
+            self._knowledge = None
+            self._agents_instance = None
+            self._agents = {}
+
+    @property
+    def memory(self) -> "Memory":
+        """Lazy-loaded memory instance"""
+        if self.is_remote:
+            raise ValueError("Memory operations are not available for remote agent sessions")
+        if self._memory is None:
+            from ..memory.memory import Memory
+            self._memory = Memory(config=self.memory_config)
+        return self._memory
+
+    @property
+    def knowledge(self) -> "Knowledge":
+        """Lazy-loaded knowledge instance"""
+        if self.is_remote:
+            raise ValueError("Knowledge operations are not available for remote agent sessions")
+        if self._knowledge is None:
+            from ..knowledge.knowledge import Knowledge
+            self._knowledge = Knowledge(config=self.knowledge_config)
+        return self._knowledge
+
+    def Agent(
+        self,
+        name: str,
+        role: str = "Assistant", 
+        instructions: Optional[str] = None,
+        tools: Optional[List[Any]] = None,
+        memory: bool = True,
+        knowledge: Optional[List[str]] = None,
+        **kwargs
+    ) -> "Agent":
+        """
+        Create an agent with session context.
+        
+        Args:
+            name: Agent name
+            role: Agent role
+            instructions: Agent instructions
+            tools: List of tools for the agent
+            memory: Enable memory for the agent
+            knowledge: Knowledge sources for the agent
+            **kwargs: Additional agent parameters
+        
+        Returns:
+            Configured Agent instance
+            
+        Raises:
+            ValueError: If this is a remote session (use chat() instead)
+        """
+        if self.is_remote:
+            raise ValueError("Cannot create local agents in remote sessions. Use chat() to communicate with the remote agent.")
+
+        from ..agent import Agent as AgentClass
+
+        agent_kwargs = {
+            "name": name,
+            "role": role,
+            "user_id": self.user_id,
+            **kwargs
+        }
+
+        if instructions:
+            agent_kwargs["instructions"] = instructions
+        if tools:
+            agent_kwargs["tools"] = tools
+        if memory:
+            agent_kwargs["memory"] = self.memory
+        if knowledge:
+            agent_kwargs["knowledge"] = knowledge
+            agent_kwargs["knowledge_config"] = self.knowledge_config
+
+        agent = AgentClass(**agent_kwargs)
+        
+        agent_key = f"{name}:{role}"
+        
+        if agent_key in self._agents:
+            agent.chat_history = self._agents[agent_key].get("chat_history", [])
+        else:
+            restored_history = self._restore_agent_chat_history(agent_key)
+            agent.chat_history = restored_history
+        
+        self._agents[agent_key] = {"agent": agent, "chat_history": agent.chat_history}
+        
+        return agent
+
+    def create_agent(self, *args, **kwargs) -> "Agent":
+        """Backward compatibility wrapper for Agent method"""
+        return self.Agent(*args, **kwargs)
+
+    def save_state(self, state_data: Dict[str, Any]) -> None:
+        """
+        Save session state data to memory.
+        
+        Args:
+            state_data: Dictionary of state data to save
+            
+        Raises:
+            ValueError: If this is a remote session
+        """
+        if self.is_remote:
+            raise ValueError("State operations are not available for remote agent sessions")
+            
+        self._save_agent_chat_histories()
+        
+        state_text = f"Session state: {state_data}"
+        self.memory.store_short_term(
+            text=state_text,
+            metadata={
+                "type": "session_state",
+                "session_id": self.session_id,
+                "user_id": self.user_id,
+                **state_data
+            }
+        )
+
+    def restore_state(self) -> Dict[str, Any]:
+        """
+        Restore session state from memory.
+        
+        Returns:
+            Dictionary of restored state data
+            
+        Raises:
+            ValueError: If this is a remote session
+        """
+        if self.is_remote:
+            raise ValueError("State operations are not available for remote agent sessions")
+        results = self.memory.search_short_term(
+            query="Session state:",
+            limit=10
+        )
+
+        self._restore_agent_chat_histories()
+        
+        for result in results:
+            metadata = result.get("metadata", {})
+            if (metadata.get("type") == "session_state" and 
+                metadata.get("session_id") == self.session_id):
+                state_data = {k: v for k, v in metadata.items() 
+                             if k not in ["type", "session_id", "user_id"]}
+                return state_data
+
+        return {}
+
+    def _restore_agent_chat_history(self, agent_key: str) -> List[Dict[str, Any]]:
+        """
+        Restore agent chat history from memory.
+        
+        Args:
+            agent_key: Unique identifier for the agent
+            
+        Returns:
+            List of chat history messages or empty list if not found
+        """
+        if self.is_remote:
+            return []
+        
+        results = self.memory.search_short_term(
+            query="Agent chat history for",
+            limit=10
+        )
+        
+        for result in results:
+            metadata = result.get("metadata", {})
+            if (metadata.get("type") == "agent_chat_history" and 
+                metadata.get("session_id") == self.session_id and
+                metadata.get("agent_key") == agent_key):
+                chat_history = metadata.get("chat_history", [])
+                return chat_history
+        
+        return []
+
+    def _restore_agent_chat_histories(self) -> None:
+        """Restore all agent chat histories from memory."""
+        if self.is_remote:
+            return
+        
+        results = self.memory.search_short_term(
+            query="Agent chat history for",
+            limit=50
+        )
+        
+        for result in results:
+            metadata = result.get("metadata", {})
+            if (metadata.get("type") == "agent_chat_history" and 
+                metadata.get("session_id") == self.session_id):
+                agent_key = metadata.get("agent_key")
+                chat_history = metadata.get("chat_history", [])
+                
+                if agent_key and chat_history:
+                    self._agents[agent_key] = {
+                        "agent": None,
+                        "chat_history": chat_history
+                    }
+
+    def _save_agent_chat_histories(self) -> None:
+        """Save all agent chat histories to memory."""
+        if self.is_remote:
+            return
+        
+        for agent_key, agent_data in self._agents.items():
+            agent = agent_data.get("agent")
+            chat_history = None
+            
+            if agent and hasattr(agent, 'chat_history'):
+                chat_history = agent.chat_history
+                agent_data["chat_history"] = chat_history
+            else:
+                chat_history = agent_data.get("chat_history")
+            
+            if chat_history is not None:
+                history_text = f"Agent chat history for {agent_key}"
+                self.memory.store_short_term(
+                    text=history_text,
+                    metadata={
+                        "type": "agent_chat_history",
+                        "session_id": self.session_id,
+                        "user_id": self.user_id,
+                        "agent_key": agent_key,
+                        "chat_history": chat_history
+                    }
+                )
+
+    def get_state(self, key: str, default: Any = None) -> Any:
+        """Get a specific state value"""
+        state = self.restore_state()
+        return state.get(key, default)
+
+    def set_state(self, key: str, value: Any) -> None:
+        """Set a specific state value"""
+        current_state = self.restore_state()
+        current_state[key] = value
+        self.save_state(current_state)
+
+    def increment_state(self, key: str, increment: int = 1, default: int = 0) -> None:
+        """Increment a numeric state value"""
+        current_value = self.get_state(key, default)
+        self.set_state(key, current_value + increment)
+
+    def add_memory(self, text: str, memory_type: str = "long", **metadata) -> None:
+        """
+        Add information to session memory.
+        
+        Args:
+            text: Text to store
+            memory_type: "short" or "long" term memory
+            **metadata: Additional metadata
+        """
+        metadata.update({
+            "session_id": self.session_id,
+            "user_id": self.user_id
+        })
+
+        if memory_type == "short":
+            self.memory.store_short_term(text, metadata=metadata)
+        else:
+            self.memory.store_long_term(text, metadata=metadata)
+
+    def search_memory(self, query: str, memory_type: str = "long", limit: int = 5) -> List[Dict[str, Any]]:
+        """
+        Search session memory.
+        
+        Args:
+            query: Search query
+            memory_type: "short" or "long" term memory
+            limit: Maximum results to return
+
+        Returns:
+            List of memory results
+        """
+        if memory_type == "short":
+            return self.memory.search_short_term(query, limit=limit)
+        return self.memory.search_long_term(query, limit=limit)
+
+    def add_knowledge(self, source: str) -> None:
+        """
+        Add knowledge source to session.
+        
+        Args:
+            source: File path, URL, or text content
+        """
+        self.knowledge.add(source, user_id=self.user_id, agent_id=self.session_id)
+
+    def search_knowledge(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:
+        """
+        Search session knowledge base.
+        
+        Args:
+            query: Search query
+            limit: Maximum results to return
+
+        Returns:
+            List of knowledge results
+        """
+        return self.knowledge.search(query, agent_id=self.session_id, limit=limit)
+
+    def clear_memory(self, memory_type: str = "all") -> None:
+        """
+        Clear session memory.
+        
+        Args:
+            memory_type: "short", "long", or "all"
+        """
+        if memory_type in ["short", "all"]:
+            self.memory.reset_short_term()
+        if memory_type in ["long", "all"]:
+            self.memory.reset_long_term()
+
+    def get_context(self, query: str, max_items: int = 3) -> str:
+        """
+        Build context from session memory and knowledge.
+        
+        Args:
+            query: Query to build context for
+            max_items: Maximum items per section
+
+        Returns:
+            Formatted context string
+        """
+        return self.memory.build_context_for_task(
+            task_descr=query,
+            user_id=self.user_id,
+            max_items=max_items
+        )
+
+    def _test_remote_connection(self) -> None:
+        """
+        Test connectivity to the remote agent.
+        
+        Raises:
+            ConnectionError: If unable to connect to the remote agent
+        """
+        requests = _get_requests()
+        try:
+            test_url = self.agent_url.rstrip('/') + '/health' if '/health' not in self.agent_url else self.agent_url
+            response = requests.get(test_url, timeout=self.timeout)
+            if response.status_code != 200:
+                response = requests.head(self.agent_url, timeout=self.timeout)
+                if response.status_code not in [200, 405]:
+                    raise ConnectionError(f"Remote agent returned status code: {response.status_code}")
+            print(f"✅ Successfully connected to remote agent at {self.agent_url}")
+        except requests.exceptions.Timeout:
+            raise ConnectionError(f"Timeout connecting to remote agent at {self.agent_url}")
+        except requests.exceptions.ConnectionError:
+            raise ConnectionError(f"Failed to connect to remote agent at {self.agent_url}")
+        except Exception as e:
+            raise ConnectionError(f"Error connecting to remote agent: {str(e)}")
+
+    def chat(self, message: str, **kwargs) -> str:
+        """
+        Send a message to the remote agent or handle local session.
+        
+        Args:
+            message: The message to send to the agent
+            **kwargs: Additional parameters for the request
+            
+        Returns:
+            The agent's response
+            
+        Raises:
+            ValueError: If this is not a remote session
+            ConnectionError: If unable to communicate with remote agent
+        """
+        if not self.is_remote:
+            raise ValueError("chat() method is only available for remote agent sessions. Use Agent.chat() for local agents.")
+        
+        requests = _get_requests()
+        try:
+            payload = {
+                "query": message,
+                "session_id": self.session_id,
+                "user_id": self.user_id,
+                **kwargs
+            }
+            
+            response = requests.post(
+                self.agent_url,
+                json=payload,
+                headers={"Content-Type": "application/json"},
+                timeout=self.timeout
+            )
+            
+            response.raise_for_status()
+            
+            result = response.json()
+            
+            if isinstance(result, dict):
+                return result.get("response", str(result))
+            else:
+                return str(result)
+                
+        except requests.exceptions.Timeout:
+            raise ConnectionError(f"Timeout communicating with remote agent at {self.agent_url}")
+        except requests.exceptions.ConnectionError:
+            raise ConnectionError(f"Failed to communicate with remote agent at {self.agent_url}")
+        except requests.exceptions.HTTPError as e:
+            raise ConnectionError(f"HTTP error from remote agent: {e}")
+        except json.JSONDecodeError:
+            return response.text
+        except Exception as e:
+            raise ConnectionError(f"Error communicating with remote agent: {str(e)}")
+
+    def send_message(self, message: str, **kwargs) -> str:
+        """
+        Alias for chat() method to match Google ADK pattern.
+        
+        Args:
+            message: The message to send to the agent
+            **kwargs: Additional parameters for the request
+            
+        Returns:
+            The agent's response
+        """
+        return self.chat(message, **kwargs)
+
+    def __str__(self) -> str:
+        if self.is_remote:
+            return f"Session(id='{self.session_id}', user='{self.user_id}', remote_agent='{self.agent_url}')"
+        return f"Session(id='{self.session_id}', user='{self.user_id}')"
+
+    def __repr__(self) -> str:
+        return self.__str__()

src/praisonai-agents/praisonaiagents/session/hierarchy.py

@@ -0,0 +1,545 @@
+"""
+Session Hierarchy for PraisonAI Agents.
+
+Extends the session system with parent-child relationships,
+forking, and revert capabilities.
+"""
+
+import copy
+import json
+import logging
+import os
+import threading
+import time
+import uuid
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+
+from .store import SessionData, SessionMessage, DefaultSessionStore, FileLock
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class SessionSnapshot:
+    """A snapshot of session state at a point in time."""
+    
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    session_id: str = ""
+    message_index: int = 0  # Index of last message in snapshot
+    created_at: float = field(default_factory=time.time)
+    label: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "id": self.id,
+            "session_id": self.session_id,
+            "message_index": self.message_index,
+            "created_at": self.created_at,
+            "label": self.label,
+            "metadata": self.metadata,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "SessionSnapshot":
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            session_id=data.get("session_id", ""),
+            message_index=data.get("message_index", 0),
+            created_at=data.get("created_at", time.time()),
+            label=data.get("label"),
+            metadata=data.get("metadata", {}),
+        )
+
+
+@dataclass
+class ExtendedSessionData(SessionData):
+    """Extended session data with hierarchy support."""
+    
+    parent_id: Optional[str] = None
+    forked_from_message_id: Optional[str] = None
+    children_ids: List[str] = field(default_factory=list)
+    snapshots: List[SessionSnapshot] = field(default_factory=list)
+    is_shared: bool = False
+    title: Optional[str] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        d = super().to_dict()
+        d.update({
+            "parent_id": self.parent_id,
+            "forked_from_message_id": self.forked_from_message_id,
+            "children_ids": self.children_ids,
+            "snapshots": [s.to_dict() for s in self.snapshots],
+            "is_shared": self.is_shared,
+            "title": self.title,
+        })
+        return d
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "ExtendedSessionData":
+        messages = [
+            SessionMessage.from_dict(m) 
+            for m in data.get("messages", [])
+        ]
+        snapshots = [
+            SessionSnapshot.from_dict(s)
+            for s in data.get("snapshots", [])
+        ]
+        return cls(
+            session_id=data.get("session_id", ""),
+            messages=messages,
+            created_at=data.get("created_at", datetime.now(timezone.utc).isoformat()),
+            updated_at=data.get("updated_at", datetime.now(timezone.utc).isoformat()),
+            agent_name=data.get("agent_name"),
+            user_id=data.get("user_id"),
+            metadata=data.get("metadata", {}),
+            parent_id=data.get("parent_id"),
+            forked_from_message_id=data.get("forked_from_message_id"),
+            children_ids=data.get("children_ids", []),
+            snapshots=snapshots,
+            is_shared=data.get("is_shared", False),
+            title=data.get("title"),
+        )
+    
+    @classmethod
+    def from_session_data(cls, session: SessionData) -> "ExtendedSessionData":
+        """Convert a basic SessionData to ExtendedSessionData."""
+        return cls(
+            session_id=session.session_id,
+            messages=session.messages,
+            created_at=session.created_at,
+            updated_at=session.updated_at,
+            agent_name=session.agent_name,
+            user_id=session.user_id,
+            metadata=session.metadata,
+        )
+
+
+class HierarchicalSessionStore(DefaultSessionStore):
+    """
+    Session store with hierarchy, forking, and revert support.
+    
+    Extends DefaultSessionStore with:
+    - Parent-child session relationships
+    - Session forking from any message
+    - Snapshot creation and revert
+    - Session sharing
+    
+    Usage:
+        store = HierarchicalSessionStore()
+        
+        # Create parent session
+        parent_id = store.create_session(title="Main conversation")
+        
+        # Fork from a message
+        child_id = store.fork_session(parent_id, from_message_index=5)
+        
+        # Create snapshot
+        snapshot_id = store.create_snapshot(parent_id, label="Before refactor")
+        
+        # Revert to snapshot
+        store.revert_to_snapshot(parent_id, snapshot_id)
+    """
+    
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._extended_cache: Dict[str, ExtendedSessionData] = {}
+    
+    def add_message(
+        self,
+        session_id: str,
+        role: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> bool:
+        """
+        Add a message to a session, preserving extended fields.
+        
+        Overrides parent to preserve extended session data.
+        """
+        from .store import SessionMessage
+        
+        # Load extended session (force reload to get latest)
+        session = self._load_extended_session(session_id, force_reload=True)
+        
+        message = SessionMessage(
+            role=role,
+            content=content,
+            timestamp=time.time(),
+            metadata=metadata or {},
+        )
+        
+        session.messages.append(message)
+        
+        # Trim messages if over limit
+        if len(session.messages) > self.max_messages:
+            session.messages = session.messages[-self.max_messages:]
+        
+        return self._save_extended_session(session)
+    
+    def _load_extended_session(self, session_id: str, force_reload: bool = False) -> ExtendedSessionData:
+        """Load extended session from disk."""
+        filepath = self._get_session_path(session_id)
+        
+        # Check cache first (unless force reload)
+        if not force_reload:
+            with self._lock:
+                if session_id in self._extended_cache:
+                    return self._extended_cache[session_id]
+        
+        # Load from disk
+        if not os.path.exists(filepath):
+            session = ExtendedSessionData(session_id=session_id)
+            with self._lock:
+                self._extended_cache[session_id] = session
+            return session
+        
+        with FileLock(filepath, self.lock_timeout):
+            try:
+                with open(filepath, "r", encoding="utf-8") as f:
+                    data = json.load(f)
+                session = ExtendedSessionData.from_dict(data)
+            except (json.JSONDecodeError, IOError) as e:
+                logger.warning(f"Failed to load session {session_id}: {e}")
+                session = ExtendedSessionData(session_id=session_id)
+        
+        with self._lock:
+            self._extended_cache[session_id] = session
+        
+        return session
+    
+    def _save_extended_session(self, session: ExtendedSessionData) -> bool:
+        """Save extended session to disk."""
+        filepath = self._get_session_path(session.session_id)
+        session.updated_at = datetime.now(timezone.utc).isoformat()
+        
+        # Trim messages if over limit
+        if len(session.messages) > self.max_messages:
+            session.messages = session.messages[-self.max_messages:]
+        
+        with FileLock(filepath, self.lock_timeout):
+            try:
+                import tempfile
+                dir_path = os.path.dirname(filepath)
+                os.makedirs(dir_path, exist_ok=True)
+                with tempfile.NamedTemporaryFile(
+                    mode="w",
+                    encoding="utf-8",
+                    dir=dir_path,
+                    delete=False,
+                    suffix=".tmp"
+                ) as f:
+                    json.dump(session.to_dict(), f, indent=2, ensure_ascii=False)
+                    temp_path = f.name
+                
+                os.replace(temp_path, filepath)
+                
+                with self._lock:
+                    self._extended_cache[session.session_id] = session
+                
+                return True
+            except (IOError, OSError) as e:
+                logger.error(f"Failed to save session {session.session_id}: {e}")
+                try:
+                    if 'temp_path' in locals():
+                        os.remove(temp_path)
+                except (IOError, OSError):
+                    pass
+                return False
+    
+    def create_session(
+        self,
+        session_id: Optional[str] = None,
+        title: Optional[str] = None,
+        parent_id: Optional[str] = None,
+        agent_name: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> str:
+        """
+        Create a new session.
+        
+        Args:
+            session_id: Optional custom session ID
+            title: Optional session title
+            parent_id: Optional parent session ID
+            agent_name: Optional agent name
+            metadata: Optional metadata
+            
+        Returns:
+            The session ID
+        """
+        sid = session_id or str(uuid.uuid4())
+        
+        session = ExtendedSessionData(
+            session_id=sid,
+            title=title,
+            parent_id=parent_id,
+            agent_name=agent_name,
+            metadata=metadata or {},
+        )
+        
+        # Update parent's children list
+        if parent_id:
+            parent = self._load_extended_session(parent_id)
+            if sid not in parent.children_ids:
+                parent.children_ids.append(sid)
+                self._save_extended_session(parent)
+        
+        self._save_extended_session(session)
+        return sid
+    
+    def fork_session(
+        self,
+        session_id: str,
+        from_message_index: Optional[int] = None,
+        title: Optional[str] = None,
+    ) -> str:
+        """
+        Fork a session from a specific message.
+        
+        Args:
+            session_id: The session to fork from
+            from_message_index: Message index to fork from (None = all messages)
+            title: Optional title for the forked session
+            
+        Returns:
+            The new forked session ID
+        """
+        # Force reload to get latest messages from disk
+        parent = self._load_extended_session(session_id, force_reload=True)
+        
+        # Determine which messages to copy
+        if from_message_index is None:
+            messages_to_copy = copy.deepcopy(parent.messages)
+            fork_msg_id = None
+        else:
+            messages_to_copy = copy.deepcopy(parent.messages[:from_message_index + 1])
+            fork_msg_id = str(from_message_index)
+        
+        # Create new session
+        new_id = str(uuid.uuid4())
+        forked_title = title or f"Fork of {parent.title or session_id}"
+        
+        forked = ExtendedSessionData(
+            session_id=new_id,
+            messages=messages_to_copy,
+            parent_id=session_id,
+            forked_from_message_id=fork_msg_id,
+            title=forked_title,
+            agent_name=parent.agent_name,
+            metadata=copy.deepcopy(parent.metadata),
+        )
+        
+        # Update parent's children list
+        parent.children_ids.append(new_id)
+        
+        self._save_extended_session(forked)
+        self._save_extended_session(parent)
+        
+        return new_id
+    
+    def get_children(self, session_id: str) -> List[str]:
+        """Get all child session IDs."""
+        session = self._load_extended_session(session_id)
+        return session.children_ids.copy()
+    
+    def get_parent(self, session_id: str) -> Optional[str]:
+        """Get parent session ID."""
+        session = self._load_extended_session(session_id)
+        return session.parent_id
+    
+    def get_session_tree(self, session_id: str) -> Dict[str, Any]:
+        """
+        Get the full session tree starting from a session.
+        
+        Returns a nested dictionary representing the tree structure.
+        """
+        session = self._load_extended_session(session_id)
+        
+        tree = {
+            "session_id": session.session_id,
+            "title": session.title,
+            "message_count": len(session.messages),
+            "children": []
+        }
+        
+        for child_id in session.children_ids:
+            tree["children"].append(self.get_session_tree(child_id))
+        
+        return tree
+    
+    def create_snapshot(
+        self,
+        session_id: str,
+        label: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> str:
+        """
+        Create a snapshot of the current session state.
+        
+        Args:
+            session_id: The session to snapshot
+            label: Optional label for the snapshot
+            metadata: Optional metadata
+            
+        Returns:
+            The snapshot ID
+        """
+        # Force reload to get latest messages
+        session = self._load_extended_session(session_id, force_reload=True)
+        
+        snapshot = SessionSnapshot(
+            session_id=session_id,
+            message_index=len(session.messages) - 1 if session.messages else -1,
+            label=label,
+            metadata=metadata or {},
+        )
+        
+        session.snapshots.append(snapshot)
+        self._save_extended_session(session)
+        
+        return snapshot.id
+    
+    def get_snapshots(self, session_id: str) -> List[SessionSnapshot]:
+        """Get all snapshots for a session."""
+        session = self._load_extended_session(session_id)
+        return session.snapshots.copy()
+    
+    def revert_to_snapshot(self, session_id: str, snapshot_id: str) -> bool:
+        """
+        Revert a session to a snapshot.
+        
+        Args:
+            session_id: The session to revert
+            snapshot_id: The snapshot to revert to
+            
+        Returns:
+            True if successful
+        """
+        session = self._load_extended_session(session_id)
+        
+        # Find the snapshot
+        snapshot = None
+        for s in session.snapshots:
+            if s.id == snapshot_id:
+                snapshot = s
+                break
+        
+        if snapshot is None:
+            logger.warning(f"Snapshot {snapshot_id} not found")
+            return False
+        
+        # Revert messages
+        if snapshot.message_index >= 0:
+            session.messages = session.messages[:snapshot.message_index + 1]
+        else:
+            session.messages = []
+        
+        return self._save_extended_session(session)
+    
+    def revert_to_message(self, session_id: str, message_index: int) -> bool:
+        """
+        Revert a session to a specific message index.
+        
+        Args:
+            session_id: The session to revert
+            message_index: The message index to revert to
+            
+        Returns:
+            True if successful
+        """
+        # Force reload to get latest messages
+        session = self._load_extended_session(session_id, force_reload=True)
+        
+        if message_index < 0 or message_index >= len(session.messages):
+            logger.warning(f"Invalid message index {message_index}")
+            return False
+        
+        session.messages = session.messages[:message_index + 1]
+        return self._save_extended_session(session)
+    
+    def share_session(self, session_id: str) -> bool:
+        """Mark a session as shared."""
+        session = self._load_extended_session(session_id)
+        session.is_shared = True
+        return self._save_extended_session(session)
+    
+    def unshare_session(self, session_id: str) -> bool:
+        """Mark a session as not shared."""
+        session = self._load_extended_session(session_id)
+        session.is_shared = False
+        return self._save_extended_session(session)
+    
+    def is_shared(self, session_id: str) -> bool:
+        """Check if a session is shared."""
+        session = self._load_extended_session(session_id)
+        return session.is_shared
+    
+    def set_title(self, session_id: str, title: str) -> bool:
+        """Set session title."""
+        session = self._load_extended_session(session_id)
+        session.title = title
+        return self._save_extended_session(session)
+    
+    def get_extended_session(self, session_id: str) -> ExtendedSessionData:
+        """Get extended session data."""
+        return self._load_extended_session(session_id)
+    
+    def export_session(self, session_id: str) -> Dict[str, Any]:
+        """
+        Export a session to a portable format.
+        
+        Returns a dictionary that can be serialized to JSON.
+        """
+        # Force reload to get latest data
+        session = self._load_extended_session(session_id, force_reload=True)
+        return session.to_dict()
+    
+    def import_session(
+        self,
+        data: Dict[str, Any],
+        new_session_id: Optional[str] = None,
+    ) -> str:
+        """
+        Import a session from exported data.
+        
+        Args:
+            data: The exported session data
+            new_session_id: Optional new session ID (generates one if not provided)
+            
+        Returns:
+            The imported session ID
+        """
+        session = ExtendedSessionData.from_dict(data)
+        
+        if new_session_id:
+            session.session_id = new_session_id
+        else:
+            session.session_id = str(uuid.uuid4())
+        
+        # Clear hierarchy references since this is an import
+        session.parent_id = None
+        session.children_ids = []
+        session.forked_from_message_id = None
+        
+        self._save_extended_session(session)
+        return session.session_id
+
+
+# Global hierarchical store instance
+_hierarchical_store: Optional[HierarchicalSessionStore] = None
+_hierarchical_lock = threading.Lock()
+
+
+def get_hierarchical_session_store() -> HierarchicalSessionStore:
+    """Get the global hierarchical session store instance."""
+    global _hierarchical_store
+    
+    if _hierarchical_store is None:
+        with _hierarchical_lock:
+            if _hierarchical_store is None:
+                _hierarchical_store = HierarchicalSessionStore()
+    
+    return _hierarchical_store

src/praisonai-agents/praisonaiagents/session/parts.py

@@ -0,0 +1,457 @@
+"""
+Message Parts for PraisonAI Agents.
+
+Provides structured message parts for better streaming and display,
+similar to OpenCode's message-v2 parts system.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Union
+from enum import Enum
+import time
+import uuid
+
+
+class PartType(str, Enum):
+    """Types of message parts."""
+    
+    TEXT = "text"
+    TOOL = "tool"
+    REASONING = "reasoning"
+    FILE = "file"
+    IMAGE = "image"
+    COMPACTION = "compaction"
+    ERROR = "error"
+
+
+class ToolStatus(str, Enum):
+    """Status of a tool execution."""
+    
+    PENDING = "pending"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    ERROR = "error"
+
+
+@dataclass
+class BasePart:
+    """Base class for all message parts."""
+    
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    type: PartType = PartType.TEXT
+    created_at: float = field(default_factory=time.time)
+    updated_at: float = field(default_factory=time.time)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert part to dictionary."""
+        return {
+            "id": self.id,
+            "type": self.type.value,
+            "created_at": self.created_at,
+            "updated_at": self.updated_at,
+        }
+    
+    def update(self):
+        """Update the updated_at timestamp."""
+        self.updated_at = time.time()
+
+
+@dataclass
+class TextPart(BasePart):
+    """A text content part."""
+    
+    type: PartType = field(default=PartType.TEXT)
+    text: str = ""
+    
+    def to_dict(self) -> Dict[str, Any]:
+        d = super().to_dict()
+        d["text"] = self.text
+        return d
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "TextPart":
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            text=data.get("text", ""),
+            created_at=data.get("created_at", time.time()),
+            updated_at=data.get("updated_at", time.time()),
+        )
+
+
+@dataclass
+class ToolPart(BasePart):
+    """A tool execution part."""
+    
+    type: PartType = field(default=PartType.TOOL)
+    tool_name: str = ""
+    tool_call_id: str = ""
+    status: ToolStatus = ToolStatus.PENDING
+    input: Dict[str, Any] = field(default_factory=dict)
+    output: Optional[str] = None
+    error: Optional[str] = None
+    title: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        d = super().to_dict()
+        d.update({
+            "tool_name": self.tool_name,
+            "tool_call_id": self.tool_call_id,
+            "status": self.status.value,
+            "input": self.input,
+            "output": self.output,
+            "error": self.error,
+            "title": self.title,
+            "metadata": self.metadata,
+        })
+        return d
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "ToolPart":
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            tool_name=data.get("tool_name", ""),
+            tool_call_id=data.get("tool_call_id", ""),
+            status=ToolStatus(data.get("status", "pending")),
+            input=data.get("input", {}),
+            output=data.get("output"),
+            error=data.get("error"),
+            title=data.get("title"),
+            metadata=data.get("metadata", {}),
+            created_at=data.get("created_at", time.time()),
+            updated_at=data.get("updated_at", time.time()),
+        )
+    
+    def start(self):
+        """Mark tool as running."""
+        self.status = ToolStatus.RUNNING
+        self.update()
+    
+    def complete(self, output: str, title: Optional[str] = None, metadata: Optional[Dict] = None):
+        """Mark tool as completed."""
+        self.status = ToolStatus.COMPLETED
+        self.output = output
+        if title:
+            self.title = title
+        if metadata:
+            self.metadata.update(metadata)
+        self.update()
+    
+    def fail(self, error: str):
+        """Mark tool as failed."""
+        self.status = ToolStatus.ERROR
+        self.error = error
+        self.update()
+
+
+@dataclass
+class ReasoningPart(BasePart):
+    """A reasoning/thinking content part."""
+    
+    type: PartType = field(default=PartType.REASONING)
+    text: str = ""
+    
+    def to_dict(self) -> Dict[str, Any]:
+        d = super().to_dict()
+        d["text"] = self.text
+        return d
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "ReasoningPart":
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            text=data.get("text", ""),
+            created_at=data.get("created_at", time.time()),
+            updated_at=data.get("updated_at", time.time()),
+        )
+
+
+@dataclass
+class FilePart(BasePart):
+    """A file attachment part."""
+    
+    type: PartType = field(default=PartType.FILE)
+    path: str = ""
+    name: str = ""
+    mime_type: Optional[str] = None
+    size: Optional[int] = None
+    content: Optional[str] = None  # Base64 encoded for binary
+    
+    def to_dict(self) -> Dict[str, Any]:
+        d = super().to_dict()
+        d.update({
+            "path": self.path,
+            "name": self.name,
+            "mime_type": self.mime_type,
+            "size": self.size,
+            "content": self.content,
+        })
+        return d
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "FilePart":
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            path=data.get("path", ""),
+            name=data.get("name", ""),
+            mime_type=data.get("mime_type"),
+            size=data.get("size"),
+            content=data.get("content"),
+            created_at=data.get("created_at", time.time()),
+            updated_at=data.get("updated_at", time.time()),
+        )
+
+
+@dataclass
+class ImagePart(BasePart):
+    """An image attachment part."""
+    
+    type: PartType = field(default=PartType.IMAGE)
+    url: Optional[str] = None
+    path: Optional[str] = None
+    base64: Optional[str] = None
+    mime_type: str = "image/png"
+    alt_text: Optional[str] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        d = super().to_dict()
+        d.update({
+            "url": self.url,
+            "path": self.path,
+            "base64": self.base64,
+            "mime_type": self.mime_type,
+            "alt_text": self.alt_text,
+        })
+        return d
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "ImagePart":
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            url=data.get("url"),
+            path=data.get("path"),
+            base64=data.get("base64"),
+            mime_type=data.get("mime_type", "image/png"),
+            alt_text=data.get("alt_text"),
+            created_at=data.get("created_at", time.time()),
+            updated_at=data.get("updated_at", time.time()),
+        )
+
+
+@dataclass
+class CompactionPart(BasePart):
+    """A compaction summary part."""
+    
+    type: PartType = field(default=PartType.COMPACTION)
+    summary: str = ""
+    original_tokens: int = 0
+    compacted_tokens: int = 0
+    messages_removed: int = 0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        d = super().to_dict()
+        d.update({
+            "summary": self.summary,
+            "original_tokens": self.original_tokens,
+            "compacted_tokens": self.compacted_tokens,
+            "messages_removed": self.messages_removed,
+        })
+        return d
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "CompactionPart":
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            summary=data.get("summary", ""),
+            original_tokens=data.get("original_tokens", 0),
+            compacted_tokens=data.get("compacted_tokens", 0),
+            messages_removed=data.get("messages_removed", 0),
+            created_at=data.get("created_at", time.time()),
+            updated_at=data.get("updated_at", time.time()),
+        )
+
+
+@dataclass
+class ErrorPart(BasePart):
+    """An error part."""
+    
+    type: PartType = field(default=PartType.ERROR)
+    error_type: str = ""
+    message: str = ""
+    stack_trace: Optional[str] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        d = super().to_dict()
+        d.update({
+            "error_type": self.error_type,
+            "message": self.message,
+            "stack_trace": self.stack_trace,
+        })
+        return d
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "ErrorPart":
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            error_type=data.get("error_type", ""),
+            message=data.get("message", ""),
+            stack_trace=data.get("stack_trace"),
+            created_at=data.get("created_at", time.time()),
+            updated_at=data.get("updated_at", time.time()),
+        )
+
+
+# Type alias for any part
+MessagePart = Union[TextPart, ToolPart, ReasoningPart, FilePart, ImagePart, CompactionPart, ErrorPart]
+
+
+def part_from_dict(data: Dict[str, Any]) -> MessagePart:
+    """Create a part from a dictionary based on its type."""
+    part_type = data.get("type", "text")
+    
+    if part_type == PartType.TEXT.value:
+        return TextPart.from_dict(data)
+    elif part_type == PartType.TOOL.value:
+        return ToolPart.from_dict(data)
+    elif part_type == PartType.REASONING.value:
+        return ReasoningPart.from_dict(data)
+    elif part_type == PartType.FILE.value:
+        return FilePart.from_dict(data)
+    elif part_type == PartType.IMAGE.value:
+        return ImagePart.from_dict(data)
+    elif part_type == PartType.COMPACTION.value:
+        return CompactionPart.from_dict(data)
+    elif part_type == PartType.ERROR.value:
+        return ErrorPart.from_dict(data)
+    else:
+        # Default to text part
+        return TextPart.from_dict(data)
+
+
+@dataclass
+class StructuredMessage:
+    """
+    A message with structured parts.
+    
+    This extends the basic message format with support for
+    multiple parts of different types.
+    """
+    
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    role: str = "assistant"  # user, assistant, system
+    parts: List[MessagePart] = field(default_factory=list)
+    created_at: float = field(default_factory=time.time)
+    updated_at: float = field(default_factory=time.time)
+    session_id: Optional[str] = None
+    agent_name: Optional[str] = None
+    model_id: Optional[str] = None
+    provider_id: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def add_part(self, part: MessagePart) -> MessagePart:
+        """Add a part to the message."""
+        self.parts.append(part)
+        self.updated_at = time.time()
+        return part
+    
+    def add_text(self, text: str) -> TextPart:
+        """Add a text part."""
+        part = TextPart(text=text)
+        return self.add_part(part)
+    
+    def add_tool(
+        self,
+        tool_name: str,
+        tool_call_id: str,
+        input: Optional[Dict] = None,
+    ) -> ToolPart:
+        """Add a tool part."""
+        part = ToolPart(
+            tool_name=tool_name,
+            tool_call_id=tool_call_id,
+            input=input or {},
+        )
+        return self.add_part(part)
+    
+    def add_reasoning(self, text: str) -> ReasoningPart:
+        """Add a reasoning part."""
+        part = ReasoningPart(text=text)
+        return self.add_part(part)
+    
+    def add_file(self, path: str, name: str, **kwargs) -> FilePart:
+        """Add a file part."""
+        part = FilePart(path=path, name=name, **kwargs)
+        return self.add_part(part)
+    
+    def add_image(self, **kwargs) -> ImagePart:
+        """Add an image part."""
+        part = ImagePart(**kwargs)
+        return self.add_part(part)
+    
+    def add_error(self, error_type: str, message: str, stack_trace: Optional[str] = None) -> ErrorPart:
+        """Add an error part."""
+        part = ErrorPart(error_type=error_type, message=message, stack_trace=stack_trace)
+        return self.add_part(part)
+    
+    def get_part(self, part_id: str) -> Optional[MessagePart]:
+        """Get a part by ID."""
+        for part in self.parts:
+            if part.id == part_id:
+                return part
+        return None
+    
+    def get_parts_by_type(self, part_type: PartType) -> List[MessagePart]:
+        """Get all parts of a specific type."""
+        return [p for p in self.parts if p.type == part_type]
+    
+    def get_text_content(self) -> str:
+        """Get combined text content from all text parts."""
+        text_parts = self.get_parts_by_type(PartType.TEXT)
+        return "\n".join(p.text for p in text_parts if hasattr(p, 'text'))
+    
+    def get_tool_parts(self) -> List[ToolPart]:
+        """Get all tool parts."""
+        return [p for p in self.parts if isinstance(p, ToolPart)]
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert message to dictionary."""
+        return {
+            "id": self.id,
+            "role": self.role,
+            "parts": [p.to_dict() for p in self.parts],
+            "created_at": self.created_at,
+            "updated_at": self.updated_at,
+            "session_id": self.session_id,
+            "agent_name": self.agent_name,
+            "model_id": self.model_id,
+            "provider_id": self.provider_id,
+            "metadata": self.metadata,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "StructuredMessage":
+        """Create message from dictionary."""
+        parts = [part_from_dict(p) for p in data.get("parts", [])]
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            role=data.get("role", "assistant"),
+            parts=parts,
+            created_at=data.get("created_at", time.time()),
+            updated_at=data.get("updated_at", time.time()),
+            session_id=data.get("session_id"),
+            agent_name=data.get("agent_name"),
+            model_id=data.get("model_id"),
+            provider_id=data.get("provider_id"),
+            metadata=data.get("metadata", {}),
+        )
+    
+    def to_llm_message(self) -> Dict[str, Any]:
+        """Convert to LLM-compatible message format."""
+        content = self.get_text_content()
+        return {
+            "role": self.role,
+            "content": content,
+        }
+    
+    def __repr__(self) -> str:
+        return f"StructuredMessage(id={self.id[:8]}..., role={self.role}, parts={len(self.parts)})"

src/praisonai-agents/praisonaiagents/session/store.py

@@ -0,0 +1,518 @@
+"""
+Default Session Store for PraisonAI Agents.
+
+JSON-based session persistence with file locking and atomic writes.
+Zero dependencies beyond stdlib.
+"""
+
+import fcntl
+import json
+import logging
+import os
+import sys
+import tempfile
+import threading
+import time
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+# Default session directory
+DEFAULT_SESSION_DIR = os.path.expanduser("~/.praison/sessions")
+
+# Default limits
+DEFAULT_MAX_MESSAGES = 100
+DEFAULT_LOCK_TIMEOUT = 5.0  # seconds
+
+
+@dataclass
+class SessionMessage:
+    """A single message in a session."""
+    role: str  # "user", "assistant", "system"
+    content: str
+    timestamp: float = field(default_factory=time.time)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "role": self.role,
+            "content": self.content,
+            "timestamp": self.timestamp,
+            "metadata": self.metadata,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "SessionMessage":
+        """Create from dictionary."""
+        return cls(
+            role=data.get("role", "user"),
+            content=data.get("content", ""),
+            timestamp=data.get("timestamp", time.time()),
+            metadata=data.get("metadata", {}),
+        )
+
+
+@dataclass
+class SessionData:
+    """Complete session data structure."""
+    session_id: str
+    messages: List[SessionMessage] = field(default_factory=list)
+    created_at: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
+    updated_at: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
+    agent_name: Optional[str] = None
+    user_id: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for JSON serialization."""
+        return {
+            "session_id": self.session_id,
+            "messages": [m.to_dict() for m in self.messages],
+            "created_at": self.created_at,
+            "updated_at": self.updated_at,
+            "agent_name": self.agent_name,
+            "user_id": self.user_id,
+            "metadata": self.metadata,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "SessionData":
+        """Create from dictionary."""
+        messages = [
+            SessionMessage.from_dict(m) 
+            for m in data.get("messages", [])
+        ]
+        return cls(
+            session_id=data.get("session_id", ""),
+            messages=messages,
+            created_at=data.get("created_at", datetime.now(timezone.utc).isoformat()),
+            updated_at=data.get("updated_at", datetime.now(timezone.utc).isoformat()),
+            agent_name=data.get("agent_name"),
+            user_id=data.get("user_id"),
+            metadata=data.get("metadata", {}),
+        )
+    
+    def get_chat_history(self, max_messages: Optional[int] = None) -> List[Dict[str, str]]:
+        """
+        Get chat history in LLM-compatible format.
+        
+        Returns list of {"role": "user/assistant", "content": "..."} dicts.
+        """
+        messages = self.messages
+        if max_messages and len(messages) > max_messages:
+            messages = messages[-max_messages:]
+        return [{"role": m.role, "content": m.content} for m in messages]
+
+
+class FileLock:
+    """
+    Cross-platform file locking.
+    
+    Uses fcntl on Unix and msvcrt on Windows.
+    """
+    
+    def __init__(self, filepath: str, timeout: float = DEFAULT_LOCK_TIMEOUT):
+        self.filepath = filepath
+        self.timeout = timeout
+        self._lock_file = None
+        self._lock_path = filepath + ".lock"
+    
+    def __enter__(self):
+        self.acquire()
+        return self
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.release()
+        return False
+    
+    def acquire(self) -> bool:
+        """Acquire the file lock."""
+        # Ensure directory exists
+        os.makedirs(os.path.dirname(self._lock_path) or ".", exist_ok=True)
+        
+        start_time = time.time()
+        while True:
+            try:
+                self._lock_file = open(self._lock_path, "w")
+                if sys.platform == "win32":
+                    # Windows locking
+                    import msvcrt
+                    msvcrt.locking(self._lock_file.fileno(), msvcrt.LK_NBLCK, 1)
+                else:
+                    # Unix locking
+                    fcntl.flock(self._lock_file.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+                return True
+            except (IOError, OSError, BlockingIOError):
+                if self._lock_file:
+                    self._lock_file.close()
+                    self._lock_file = None
+                
+                if time.time() - start_time > self.timeout:
+                    logger.warning(f"Failed to acquire lock for {self.filepath} after {self.timeout}s")
+                    return False
+                
+                time.sleep(0.05)  # Wait 50ms before retry
+    
+    def release(self) -> None:
+        """Release the file lock."""
+        if self._lock_file:
+            try:
+                if sys.platform == "win32":
+                    import msvcrt
+                    msvcrt.locking(self._lock_file.fileno(), msvcrt.LK_UNLCK, 1)
+                else:
+                    fcntl.flock(self._lock_file.fileno(), fcntl.LOCK_UN)
+            except (IOError, OSError):
+                pass
+            finally:
+                self._lock_file.close()
+                self._lock_file = None
+                # Note: We don't remove the lock file to avoid race conditions
+                # where another process has opened but not yet locked the file
+
+
+class DefaultSessionStore:
+    """
+    JSON-based session persistence with file locking.
+    
+    Features:
+    - Zero configuration required
+    - Automatic file locking for multi-process safety
+    - Atomic writes to prevent corruption
+    - Configurable message limits
+    - Thread-safe operations
+    
+    Usage:
+        store = DefaultSessionStore()
+        
+        # Add messages
+        store.add_message("session-123", "user", "Hello")
+        store.add_message("session-123", "assistant", "Hi there!")
+        
+        # Get history
+        history = store.get_chat_history("session-123")
+        # [{"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}]
+        
+        # Restore in new process
+        store2 = DefaultSessionStore()
+        history = store2.get_chat_history("session-123")  # Same history!
+    """
+    
+    def __init__(
+        self,
+        session_dir: Optional[str] = None,
+        max_messages: int = DEFAULT_MAX_MESSAGES,
+        lock_timeout: float = DEFAULT_LOCK_TIMEOUT,
+    ):
+        """
+        Initialize session store.
+        
+        Args:
+            session_dir: Directory for session files. Defaults to ~/.praison/sessions/
+            max_messages: Maximum messages to keep per session.
+            lock_timeout: Timeout for file lock acquisition.
+        """
+        self.session_dir = session_dir or DEFAULT_SESSION_DIR
+        self.max_messages = max_messages
+        self.lock_timeout = lock_timeout
+        self._lock = threading.RLock()
+        self._cache: Dict[str, SessionData] = {}
+        
+        # Ensure session directory exists
+        os.makedirs(self.session_dir, exist_ok=True)
+    
+    def _get_session_path(self, session_id: str) -> str:
+        """Get the file path for a session."""
+        # Sanitize session_id for filesystem
+        safe_id = "".join(c if c.isalnum() or c in "-_" else "_" for c in session_id)
+        return os.path.join(self.session_dir, f"{safe_id}.json")
+    
+    def _load_session(self, session_id: str) -> SessionData:
+        """Load session from disk with file locking."""
+        filepath = self._get_session_path(session_id)
+        
+        # Check cache first
+        with self._lock:
+            if session_id in self._cache:
+                return self._cache[session_id]
+        
+        # Load from disk
+        if not os.path.exists(filepath):
+            session = SessionData(session_id=session_id)
+            with self._lock:
+                self._cache[session_id] = session
+            return session
+        
+        with FileLock(filepath, self.lock_timeout):
+            try:
+                with open(filepath, "r", encoding="utf-8") as f:
+                    data = json.load(f)
+                session = SessionData.from_dict(data)
+            except (json.JSONDecodeError, IOError) as e:
+                logger.warning(f"Failed to load session {session_id}: {e}")
+                session = SessionData(session_id=session_id)
+        
+        with self._lock:
+            self._cache[session_id] = session
+        
+        return session
+    
+    def _save_session(self, session: SessionData) -> bool:
+        """Save session to disk with atomic write."""
+        filepath = self._get_session_path(session.session_id)
+        session.updated_at = datetime.now(timezone.utc).isoformat()
+        
+        # Trim messages if over limit
+        if len(session.messages) > self.max_messages:
+            session.messages = session.messages[-self.max_messages:]
+        
+        with FileLock(filepath, self.lock_timeout):
+            try:
+                # Atomic write: write to temp file, then rename
+                dir_path = os.path.dirname(filepath)
+                with tempfile.NamedTemporaryFile(
+                    mode="w",
+                    encoding="utf-8",
+                    dir=dir_path,
+                    delete=False,
+                    suffix=".tmp"
+                ) as f:
+                    json.dump(session.to_dict(), f, indent=2, ensure_ascii=False)
+                    temp_path = f.name
+                
+                # Atomic rename
+                os.replace(temp_path, filepath)
+                return True
+            except (IOError, OSError) as e:
+                logger.error(f"Failed to save session {session.session_id}: {e}")
+                # Clean up temp file if it exists
+                try:
+                    if 'temp_path' in locals():
+                        os.remove(temp_path)
+                except (IOError, OSError):
+                    pass
+                return False
+    
+    def add_message(
+        self,
+        session_id: str,
+        role: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> bool:
+        """
+        Add a message to a session.
+        
+        Args:
+            session_id: The session ID.
+            role: Message role ("user", "assistant", "system").
+            content: Message content.
+            metadata: Optional metadata.
+            
+        Returns:
+            True if saved successfully.
+        """
+        filepath = self._get_session_path(session_id)
+        
+        message = SessionMessage(
+            role=role,
+            content=content,
+            timestamp=time.time(),
+            metadata=metadata or {},
+        )
+        
+        # Use file lock for atomic read-modify-write
+        with FileLock(filepath, self.lock_timeout):
+            # Always reload from disk inside lock to avoid race conditions
+            if os.path.exists(filepath):
+                try:
+                    with open(filepath, "r", encoding="utf-8") as f:
+                        data = json.load(f)
+                    session = SessionData.from_dict(data)
+                except (json.JSONDecodeError, IOError):
+                    session = SessionData(session_id=session_id)
+            else:
+                session = SessionData(session_id=session_id)
+            
+            # Add message
+            session.messages.append(message)
+            session.updated_at = datetime.now(timezone.utc).isoformat()
+            
+            # Trim messages if over limit
+            if len(session.messages) > self.max_messages:
+                session.messages = session.messages[-self.max_messages:]
+            
+            # Write atomically
+            try:
+                dir_path = os.path.dirname(filepath) or "."
+                os.makedirs(dir_path, exist_ok=True)
+                with tempfile.NamedTemporaryFile(
+                    mode="w",
+                    encoding="utf-8",
+                    dir=dir_path,
+                    delete=False,
+                    suffix=".tmp"
+                ) as f:
+                    json.dump(session.to_dict(), f, indent=2, ensure_ascii=False)
+                    temp_path = f.name
+                
+                os.replace(temp_path, filepath)
+                
+                # Update cache
+                with self._lock:
+                    self._cache[session_id] = session
+                
+                return True
+            except (IOError, OSError) as e:
+                logger.error(f"Failed to save session {session_id}: {e}")
+                try:
+                    if 'temp_path' in locals():
+                        os.remove(temp_path)
+                except (IOError, OSError):
+                    pass
+                return False
+    
+    def add_user_message(
+        self,
+        session_id: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> bool:
+        """Add a user message to a session."""
+        return self.add_message(session_id, "user", content, metadata)
+    
+    def add_assistant_message(
+        self,
+        session_id: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> bool:
+        """Add an assistant message to a session."""
+        return self.add_message(session_id, "assistant", content, metadata)
+    
+    def get_chat_history(
+        self,
+        session_id: str,
+        max_messages: Optional[int] = None,
+    ) -> List[Dict[str, str]]:
+        """
+        Get chat history for a session in LLM-compatible format.
+        
+        Args:
+            session_id: The session ID.
+            max_messages: Maximum messages to return (defaults to store limit).
+            
+        Returns:
+            List of {"role": "user/assistant", "content": "..."} dicts.
+        """
+        session = self._load_session(session_id)
+        limit = max_messages or self.max_messages
+        return session.get_chat_history(limit)
+    
+    def get_session(self, session_id: str) -> SessionData:
+        """Get full session data."""
+        return self._load_session(session_id)
+    
+    def set_agent_info(
+        self,
+        session_id: str,
+        agent_name: Optional[str] = None,
+        user_id: Optional[str] = None,
+    ) -> bool:
+        """Set agent info for a session."""
+        session = self._load_session(session_id)
+        
+        with self._lock:
+            if agent_name:
+                session.agent_name = agent_name
+            if user_id:
+                session.user_id = user_id
+            self._cache[session_id] = session
+        
+        return self._save_session(session)
+    
+    def clear_session(self, session_id: str) -> bool:
+        """Clear all messages from a session."""
+        session = self._load_session(session_id)
+        
+        with self._lock:
+            session.messages.clear()
+            self._cache[session_id] = session
+        
+        return self._save_session(session)
+    
+    def delete_session(self, session_id: str) -> bool:
+        """Delete a session completely."""
+        filepath = self._get_session_path(session_id)
+        
+        with self._lock:
+            if session_id in self._cache:
+                del self._cache[session_id]
+        
+        try:
+            if os.path.exists(filepath):
+                os.remove(filepath)
+            return True
+        except (IOError, OSError) as e:
+            logger.error(f"Failed to delete session {session_id}: {e}")
+            return False
+    
+    def list_sessions(self, limit: int = 50) -> List[Dict[str, Any]]:
+        """List all sessions with metadata."""
+        sessions = []
+        
+        try:
+            for filename in os.listdir(self.session_dir):
+                if filename.endswith(".json"):
+                    filepath = os.path.join(self.session_dir, filename)
+                    try:
+                        with open(filepath, "r", encoding="utf-8") as f:
+                            data = json.load(f)
+                        sessions.append({
+                            "session_id": data.get("session_id", filename[:-5]),
+                            "agent_name": data.get("agent_name"),
+                            "created_at": data.get("created_at"),
+                            "updated_at": data.get("updated_at"),
+                            "message_count": len(data.get("messages", [])),
+                        })
+                    except (json.JSONDecodeError, IOError):
+                        continue
+        except (IOError, OSError):
+            pass
+        
+        # Sort by updated_at descending
+        sessions.sort(key=lambda x: x.get("updated_at", ""), reverse=True)
+        return sessions[:limit]
+    
+    def session_exists(self, session_id: str) -> bool:
+        """Check if a session exists."""
+        filepath = self._get_session_path(session_id)
+        return os.path.exists(filepath)
+    
+    def invalidate_cache(self, session_id: Optional[str] = None) -> None:
+        """Invalidate cache for a session or all sessions."""
+        with self._lock:
+            if session_id:
+                self._cache.pop(session_id, None)
+            else:
+                self._cache.clear()
+
+
+# Global session store instance (lazy initialized)
+_default_store: Optional[DefaultSessionStore] = None
+_store_lock = threading.Lock()
+
+
+def get_default_session_store() -> DefaultSessionStore:
+    """Get the global default session store instance."""
+    global _default_store
+    
+    if _default_store is None:
+        with _store_lock:
+            if _default_store is None:
+                _default_store = DefaultSessionStore()
+    
+    return _default_store

src/praisonai-agents/praisonaiagents/snapshot/__init__.py

@@ -0,0 +1,52 @@
+"""
+Snapshot Module for PraisonAI Agents.
+
+Provides file change tracking using a shadow git repository,
+enabling undo/restore capabilities without affecting the user's
+actual git repository.
+
+Features:
+- Shadow git repository for tracking changes
+- File diff generation
+- Snapshot creation and restoration
+- Session-based change tracking
+- Zero interference with user's git repos
+
+Usage:
+    from praisonaiagents.snapshot import FileSnapshot
+    
+    # Initialize for a project
+    snapshot = FileSnapshot("/path/to/project")
+    
+    # Track current state
+    hash = snapshot.track()
+    
+    # Get diff from a snapshot
+    diff = snapshot.diff(hash)
+    
+    # Restore to a snapshot
+    snapshot.restore(hash)
+"""
+
+__all__ = [
+    "FileSnapshot",
+    "SnapshotInfo",
+    "FileDiff",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components."""
+    if name == "FileSnapshot":
+        from .snapshot import FileSnapshot
+        return FileSnapshot
+    
+    if name == "SnapshotInfo":
+        from .snapshot import SnapshotInfo
+        return SnapshotInfo
+    
+    if name == "FileDiff":
+        from .snapshot import FileDiff
+        return FileDiff
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/snapshot/snapshot.py

@@ -0,0 +1,490 @@
+"""
+File Snapshot implementation for PraisonAI Agents.
+
+Provides file change tracking using a shadow git repository.
+"""
+
+import hashlib
+import logging
+import os
+import shutil
+import subprocess
+import time
+import uuid
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+# Default snapshot directory
+DEFAULT_SNAPSHOT_DIR = os.path.expanduser("~/.praison/snapshots")
+
+
+@dataclass
+class FileDiff:
+    """Represents a diff for a single file."""
+    
+    path: str
+    additions: int = 0
+    deletions: int = 0
+    diff_content: str = ""
+    status: str = "modified"  # added, modified, deleted
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "path": self.path,
+            "additions": self.additions,
+            "deletions": self.deletions,
+            "diff_content": self.diff_content,
+            "status": self.status,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "FileDiff":
+        return cls(
+            path=data.get("path", ""),
+            additions=data.get("additions", 0),
+            deletions=data.get("deletions", 0),
+            diff_content=data.get("diff_content", ""),
+            status=data.get("status", "modified"),
+        )
+
+
+@dataclass
+class SnapshotInfo:
+    """Information about a snapshot."""
+    
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    commit_hash: str = ""
+    session_id: Optional[str] = None
+    message: str = ""
+    created_at: float = field(default_factory=time.time)
+    files_changed: int = 0
+    additions: int = 0
+    deletions: int = 0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "id": self.id,
+            "commit_hash": self.commit_hash,
+            "session_id": self.session_id,
+            "message": self.message,
+            "created_at": self.created_at,
+            "files_changed": self.files_changed,
+            "additions": self.additions,
+            "deletions": self.deletions,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "SnapshotInfo":
+        return cls(
+            id=data.get("id", str(uuid.uuid4())),
+            commit_hash=data.get("commit_hash", ""),
+            session_id=data.get("session_id"),
+            message=data.get("message", ""),
+            created_at=data.get("created_at", time.time()),
+            files_changed=data.get("files_changed", 0),
+            additions=data.get("additions", 0),
+            deletions=data.get("deletions", 0),
+        )
+
+
+class FileSnapshot:
+    """
+    File snapshot manager using a shadow git repository.
+    
+    Creates and manages snapshots of file changes without
+    interfering with the user's actual git repository.
+    
+    Example:
+        snapshot = FileSnapshot("/path/to/project")
+        
+        # Track current state
+        info = snapshot.track(message="Before refactor")
+        
+        # Make changes...
+        
+        # Get diff
+        diffs = snapshot.diff(info.commit_hash)
+        
+        # Restore if needed
+        snapshot.restore(info.commit_hash)
+    """
+    
+    def __init__(
+        self,
+        project_path: str,
+        snapshot_dir: Optional[str] = None,
+        session_id: Optional[str] = None,
+    ):
+        """
+        Initialize the file snapshot manager.
+        
+        Args:
+            project_path: Path to the project to track
+            snapshot_dir: Optional custom snapshot directory
+            session_id: Optional session ID for grouping snapshots
+        """
+        self.project_path = os.path.abspath(project_path)
+        self.session_id = session_id
+        
+        # Create unique shadow repo path based on project path hash
+        project_hash = hashlib.md5(self.project_path.encode()).hexdigest()[:12]
+        base_dir = snapshot_dir or DEFAULT_SNAPSHOT_DIR
+        self.shadow_path = os.path.join(base_dir, project_hash)
+        
+        self._initialized = False
+    
+    def _run_git(self, *args, cwd: Optional[str] = None, check: bool = True) -> subprocess.CompletedProcess:
+        """Run a git command in the shadow repository."""
+        cmd = ["git"] + list(args)
+        try:
+            result = subprocess.run(
+                cmd,
+                cwd=cwd or self.shadow_path,
+                capture_output=True,
+                text=True,
+                check=check,
+            )
+            return result
+        except subprocess.CalledProcessError as e:
+            logger.error(f"Git command failed: {' '.join(cmd)}")
+            logger.error(f"stderr: {e.stderr}")
+            raise
+    
+    def _ensure_initialized(self):
+        """Ensure the shadow repository is initialized."""
+        if self._initialized:
+            return
+        
+        if not os.path.exists(self.shadow_path):
+            os.makedirs(self.shadow_path, exist_ok=True)
+            self._run_git("init", cwd=self.shadow_path)
+            self._run_git("config", "user.email", "[email protected]")
+            self._run_git("config", "user.name", "PraisonAI Snapshot")
+            
+            # Create initial empty commit
+            self._run_git("commit", "--allow-empty", "-m", "Initial snapshot")
+        
+        self._initialized = True
+    
+    def _sync_files(self):
+        """Sync project files to shadow repository."""
+        self._ensure_initialized()
+        
+        # Get list of files to track (respecting .gitignore if exists)
+        files_to_track = []
+        gitignore_path = os.path.join(self.project_path, ".gitignore")
+        ignore_patterns = set()
+        
+        if os.path.exists(gitignore_path):
+            with open(gitignore_path, "r") as f:
+                for line in f:
+                    line = line.strip()
+                    if line and not line.startswith("#"):
+                        ignore_patterns.add(line)
+        
+        # Always ignore common patterns
+        ignore_patterns.update([
+            ".git",
+            "__pycache__",
+            "*.pyc",
+            ".DS_Store",
+            "node_modules",
+            ".env",
+            "venv",
+            ".venv",
+        ])
+        
+        # Walk project and copy files
+        for root, dirs, files in os.walk(self.project_path):
+            # Skip ignored directories
+            dirs[:] = [d for d in dirs if not self._should_ignore(d, ignore_patterns)]
+            
+            rel_root = os.path.relpath(root, self.project_path)
+            
+            for file in files:
+                if self._should_ignore(file, ignore_patterns):
+                    continue
+                
+                src_path = os.path.join(root, file)
+                if rel_root == ".":
+                    rel_path = file
+                else:
+                    rel_path = os.path.join(rel_root, file)
+                
+                dst_path = os.path.join(self.shadow_path, rel_path)
+                
+                # Create directory if needed
+                os.makedirs(os.path.dirname(dst_path), exist_ok=True)
+                
+                # Copy file
+                try:
+                    shutil.copy2(src_path, dst_path)
+                    files_to_track.append(rel_path)
+                except (IOError, OSError) as e:
+                    logger.warning(f"Failed to copy {src_path}: {e}")
+        
+        return files_to_track
+    
+    def _should_ignore(self, name: str, patterns: set) -> bool:
+        """Check if a file/directory should be ignored."""
+        for pattern in patterns:
+            if pattern.startswith("*."):
+                if name.endswith(pattern[1:]):
+                    return True
+            elif name == pattern:
+                return True
+        return False
+    
+    def track(self, message: Optional[str] = None) -> SnapshotInfo:
+        """
+        Track the current state of the project.
+        
+        Args:
+            message: Optional commit message
+            
+        Returns:
+            SnapshotInfo with the commit details
+        """
+        self._ensure_initialized()
+        
+        # Sync files
+        files = self._sync_files()
+        
+        # Stage all changes
+        self._run_git("add", "-A")
+        
+        # Check if there are changes
+        status = self._run_git("status", "--porcelain")
+        if not status.stdout.strip():
+            # No changes, return current HEAD
+            head = self._run_git("rev-parse", "HEAD")
+            return SnapshotInfo(
+                commit_hash=head.stdout.strip(),
+                session_id=self.session_id,
+                message="No changes",
+                files_changed=0,
+            )
+        
+        # Commit changes
+        commit_msg = message or f"Snapshot at {time.strftime('%Y-%m-%d %H:%M:%S')}"
+        self._run_git("commit", "-m", commit_msg)
+        
+        # Get commit hash
+        head = self._run_git("rev-parse", "HEAD")
+        commit_hash = head.stdout.strip()
+        
+        # Get stats
+        stats = self._run_git("diff", "--stat", "HEAD~1", "HEAD", check=False)
+        additions = 0
+        deletions = 0
+        files_changed = 0
+        
+        for line in stats.stdout.split("\n"):
+            if "insertion" in line or "deletion" in line:
+                parts = line.split(",")
+                for part in parts:
+                    if "insertion" in part:
+                        try:
+                            additions = int(part.strip().split()[0])
+                        except (ValueError, IndexError):
+                            pass
+                    elif "deletion" in part:
+                        try:
+                            deletions = int(part.strip().split()[0])
+                        except (ValueError, IndexError):
+                            pass
+                    elif "file" in part:
+                        try:
+                            files_changed = int(part.strip().split()[0])
+                        except (ValueError, IndexError):
+                            pass
+        
+        return SnapshotInfo(
+            commit_hash=commit_hash,
+            session_id=self.session_id,
+            message=commit_msg,
+            files_changed=files_changed,
+            additions=additions,
+            deletions=deletions,
+        )
+    
+    def diff(self, from_hash: str, to_hash: Optional[str] = None) -> List[FileDiff]:
+        """
+        Get diff between two snapshots.
+        
+        Args:
+            from_hash: Starting commit hash
+            to_hash: Ending commit hash (defaults to HEAD)
+            
+        Returns:
+            List of FileDiff objects
+        """
+        self._ensure_initialized()
+        
+        to_hash = to_hash or "HEAD"
+        
+        # Get list of changed files
+        result = self._run_git("diff", "--name-status", from_hash, to_hash, check=False)
+        
+        diffs = []
+        for line in result.stdout.strip().split("\n"):
+            if not line:
+                continue
+            
+            parts = line.split("\t")
+            if len(parts) < 2:
+                continue
+            
+            status_code = parts[0]
+            file_path = parts[1]
+            
+            # Map status code
+            if status_code.startswith("A"):
+                status = "added"
+            elif status_code.startswith("D"):
+                status = "deleted"
+            else:
+                status = "modified"
+            
+            # Get detailed diff for this file
+            file_diff = self._run_git(
+                "diff", from_hash, to_hash, "--", file_path,
+                check=False
+            )
+            
+            # Count additions/deletions
+            additions = 0
+            deletions = 0
+            for diff_line in file_diff.stdout.split("\n"):
+                if diff_line.startswith("+") and not diff_line.startswith("+++"):
+                    additions += 1
+                elif diff_line.startswith("-") and not diff_line.startswith("---"):
+                    deletions += 1
+            
+            diffs.append(FileDiff(
+                path=file_path,
+                additions=additions,
+                deletions=deletions,
+                diff_content=file_diff.stdout,
+                status=status,
+            ))
+        
+        return diffs
+    
+    def restore(self, commit_hash: str, files: Optional[List[str]] = None) -> bool:
+        """
+        Restore files from a snapshot.
+        
+        Args:
+            commit_hash: The commit hash to restore from
+            files: Optional list of specific files to restore
+            
+        Returns:
+            True if successful
+        """
+        self._ensure_initialized()
+        
+        try:
+            if files:
+                # Restore specific files
+                for file_path in files:
+                    # Checkout file from shadow repo
+                    self._run_git("checkout", commit_hash, "--", file_path)
+                    
+                    # Copy to project
+                    src = os.path.join(self.shadow_path, file_path)
+                    dst = os.path.join(self.project_path, file_path)
+                    
+                    if os.path.exists(src):
+                        os.makedirs(os.path.dirname(dst), exist_ok=True)
+                        shutil.copy2(src, dst)
+            else:
+                # Restore all files
+                self._run_git("checkout", commit_hash, "--", ".")
+                
+                # Copy all files back to project
+                for root, dirs, files_list in os.walk(self.shadow_path):
+                    # Skip .git directory
+                    if ".git" in root:
+                        continue
+                    
+                    rel_root = os.path.relpath(root, self.shadow_path)
+                    
+                    for file in files_list:
+                        if rel_root == ".":
+                            rel_path = file
+                        else:
+                            rel_path = os.path.join(rel_root, file)
+                        
+                        src = os.path.join(root, file)
+                        dst = os.path.join(self.project_path, rel_path)
+                        
+                        os.makedirs(os.path.dirname(dst), exist_ok=True)
+                        shutil.copy2(src, dst)
+            
+            return True
+        except Exception as e:
+            logger.error(f"Failed to restore: {e}")
+            return False
+    
+    def revert(self, file_path: str) -> bool:
+        """
+        Revert a single file to its last tracked state.
+        
+        Args:
+            file_path: Path to the file to revert
+            
+        Returns:
+            True if successful
+        """
+        return self.restore("HEAD", files=[file_path])
+    
+    def list_snapshots(self, limit: int = 50) -> List[SnapshotInfo]:
+        """
+        List recent snapshots.
+        
+        Args:
+            limit: Maximum number of snapshots to return
+            
+        Returns:
+            List of SnapshotInfo objects
+        """
+        self._ensure_initialized()
+        
+        result = self._run_git(
+            "log", f"-{limit}", "--format=%H|%s|%at",
+            check=False
+        )
+        
+        snapshots = []
+        for line in result.stdout.strip().split("\n"):
+            if not line:
+                continue
+            
+            parts = line.split("|")
+            if len(parts) >= 3:
+                try:
+                    snapshots.append(SnapshotInfo(
+                        commit_hash=parts[0],
+                        message=parts[1],
+                        created_at=float(parts[2]),
+                        session_id=self.session_id,
+                    ))
+                except (ValueError, IndexError):
+                    pass
+        
+        return snapshots
+    
+    def get_current_hash(self) -> str:
+        """Get the current HEAD commit hash."""
+        self._ensure_initialized()
+        result = self._run_git("rev-parse", "HEAD")
+        return result.stdout.strip()
+    
+    def cleanup(self):
+        """Remove the shadow repository."""
+        if os.path.exists(self.shadow_path):
+            shutil.rmtree(self.shadow_path)
+            self._initialized = False

src/praisonai-agents/praisonaiagents/tools/file_tools.py

@@ -35,13 +35,18 @@ class FileTools:
         Raises:
             ValueError: If path contains suspicious patterns
         """
+        # Expand ~ to user home directory FIRST (before any validation)
+        if filepath.startswith('~'):
+            filepath = os.path.expanduser(filepath)
+        
         # Normalize the path
         normalized = os.path.normpath(filepath)
         absolute = os.path.abspath(normalized)
         
-        # Check for suspicious patterns
-        if '..' in filepath or filepath.startswith('~'):
-            raise ValueError(f"Suspicious path pattern detected: {filepath}")
+        # Check for path traversal attempts (.. after normalization)
+        # We check the original input for '..' to catch traversal attempts
+        if '..' in normalized:
+            raise ValueError(f"Path traversal detected: {filepath}")
         
         # Additional check: ensure the resolved path doesn't escape expected boundaries
         # This is a basic check - in production, you'd want to define allowed directories
@@ -243,6 +248,76 @@ class FileTools:
             logging.error(error_msg)
             return False
 
+    @staticmethod
+    @require_approval(risk_level="medium")
+    def download_file(
+        url: str,
+        destination: str,
+        timeout: int = 30,
+        progress_callback: Optional[callable] = None,
+    ) -> Dict[str, Union[bool, str, int]]:
+        """
+        Download a file from a URL.
+        
+        Args:
+            url: URL to download from
+            destination: Local path to save the file
+            timeout: Request timeout in seconds
+            progress_callback: Optional callback for progress updates
+                              Called with (bytes_downloaded, total_bytes)
+            
+        Returns:
+            Dict with keys: success, path, size, error
+        """
+        try:
+            import httpx
+        except ImportError:
+            return {
+                "success": False,
+                "path": "",
+                "size": 0,
+                "error": "httpx not installed. Install with: pip install httpx"
+            }
+        
+        try:
+            # Validate destination path
+            safe_path = FileTools._validate_path(destination)
+            
+            # Create directory if needed
+            os.makedirs(os.path.dirname(safe_path) or ".", exist_ok=True)
+            
+            # Download file
+            with httpx.stream("GET", url, timeout=timeout, follow_redirects=True) as response:
+                response.raise_for_status()
+                
+                total_size = int(response.headers.get("content-length", 0))
+                downloaded = 0
+                
+                with open(safe_path, "wb") as f:
+                    for chunk in response.iter_bytes(chunk_size=8192):
+                        f.write(chunk)
+                        downloaded += len(chunk)
+                        
+                        if progress_callback and total_size:
+                            progress_callback(downloaded, total_size)
+            
+            return {
+                "success": True,
+                "path": safe_path,
+                "size": downloaded,
+                "error": ""
+            }
+            
+        except Exception as e:
+            error_msg = f"Error downloading {url}: {str(e)}"
+            logging.error(error_msg)
+            return {
+                "success": False,
+                "path": "",
+                "size": 0,
+                "error": error_msg
+            }
+
 # Create instance for direct function access
 _file_tools = FileTools()
 read_file = _file_tools.read_file
@@ -252,6 +327,7 @@ get_file_info = _file_tools.get_file_info
 copy_file = _file_tools.copy_file
 move_file = _file_tools.move_file
 delete_file = _file_tools.delete_file
+download_file = _file_tools.download_file
 
 if __name__ == "__main__":
     # Example usage

src/praisonai-agents/praisonaiagents/tools/sourcegraph_tools.py

@@ -0,0 +1,250 @@
+"""
+Sourcegraph Integration Tool for PraisonAI Agents.
+
+Provides code search across repositories using Sourcegraph API.
+"""
+
+import os
+import logging
+from typing import List, Optional, Dict, Any
+from dataclasses import dataclass
+
+logger = logging.getLogger(__name__)
+
+SOURCEGRAPH_API_URL = "https://sourcegraph.com/.api"
+
+
+@dataclass
+class CodeResult:
+    """A code search result."""
+    repository: str
+    file_path: str
+    line_number: int
+    content: str
+    url: str
+    
+    def to_dict(self) -> dict:
+        return {
+            "repository": self.repository,
+            "file_path": self.file_path,
+            "line_number": self.line_number,
+            "content": self.content,
+            "url": self.url,
+        }
+
+
+class SourcegraphTools:
+    """
+    Sourcegraph code search integration.
+    
+    Provides code search across public and private repositories
+    using the Sourcegraph API.
+    
+    Usage:
+        tools = SourcegraphTools()
+        results = tools.search("function handleError")
+        
+        for result in results:
+            print(f"{result.repository}: {result.file_path}:{result.line_number}")
+    """
+    
+    def __init__(
+        self,
+        api_url: Optional[str] = None,
+        access_token: Optional[str] = None,
+    ):
+        self.api_url = api_url or os.environ.get("SOURCEGRAPH_URL", SOURCEGRAPH_API_URL)
+        self.access_token = access_token or os.environ.get("SOURCEGRAPH_ACCESS_TOKEN")
+    
+    def _get_headers(self) -> Dict[str, str]:
+        """Get request headers."""
+        headers = {"Content-Type": "application/json"}
+        if self.access_token:
+            headers["Authorization"] = f"token {self.access_token}"
+        return headers
+    
+    def search(
+        self,
+        query: str,
+        repo: Optional[str] = None,
+        file_pattern: Optional[str] = None,
+        max_results: int = 20,
+    ) -> List[CodeResult]:
+        """
+        Search code using Sourcegraph.
+        
+        Args:
+            query: Search query
+            repo: Optional repository filter (e.g., "github.com/owner/repo")
+            file_pattern: Optional file pattern filter (e.g., "*.py")
+            max_results: Maximum number of results
+            
+        Returns:
+            List of CodeResult objects
+        """
+        try:
+            import httpx
+        except ImportError:
+            logger.error("httpx not installed. Install with: pip install httpx")
+            return []
+        
+        # Build search query
+        search_query = query
+        if repo:
+            search_query = f"repo:{repo} {search_query}"
+        if file_pattern:
+            search_query = f"file:{file_pattern} {search_query}"
+        
+        # GraphQL query
+        graphql_query = """
+        query Search($query: String!, $first: Int!) {
+            search(query: $query, version: V2) {
+                results {
+                    results {
+                        ... on FileMatch {
+                            repository {
+                                name
+                            }
+                            file {
+                                path
+                                url
+                            }
+                            lineMatches {
+                                lineNumber
+                                preview
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        """
+        
+        try:
+            response = httpx.post(
+                f"{self.api_url}/graphql",
+                headers=self._get_headers(),
+                json={
+                    "query": graphql_query,
+                    "variables": {
+                        "query": search_query,
+                        "first": max_results,
+                    }
+                },
+                timeout=30.0,
+            )
+            response.raise_for_status()
+            data = response.json()
+            
+            results = []
+            search_results = data.get("data", {}).get("search", {}).get("results", {}).get("results", [])
+            
+            for match in search_results[:max_results]:
+                if "repository" not in match:
+                    continue
+                    
+                repo_name = match["repository"]["name"]
+                file_path = match.get("file", {}).get("path", "")
+                file_url = match.get("file", {}).get("url", "")
+                
+                for line_match in match.get("lineMatches", []):
+                    results.append(CodeResult(
+                        repository=repo_name,
+                        file_path=file_path,
+                        line_number=line_match.get("lineNumber", 0),
+                        content=line_match.get("preview", ""),
+                        url=f"{self.api_url.replace('/.api', '')}{file_url}",
+                    ))
+            
+            return results
+            
+        except Exception as e:
+            logger.error(f"Sourcegraph search failed: {e}")
+            return []
+    
+    def get_file_content(
+        self,
+        repo: str,
+        file_path: str,
+        revision: str = "HEAD",
+    ) -> Optional[str]:
+        """
+        Get file content from a repository.
+        
+        Args:
+            repo: Repository name (e.g., "github.com/owner/repo")
+            file_path: Path to file in repository
+            revision: Git revision (default: HEAD)
+            
+        Returns:
+            File content or None if not found
+        """
+        try:
+            import httpx
+        except ImportError:
+            logger.error("httpx not installed")
+            return None
+        
+        graphql_query = """
+        query GetFile($repo: String!, $path: String!, $rev: String!) {
+            repository(name: $repo) {
+                commit(rev: $rev) {
+                    file(path: $path) {
+                        content
+                    }
+                }
+            }
+        }
+        """
+        
+        try:
+            response = httpx.post(
+                f"{self.api_url}/graphql",
+                headers=self._get_headers(),
+                json={
+                    "query": graphql_query,
+                    "variables": {
+                        "repo": repo,
+                        "path": file_path,
+                        "rev": revision,
+                    }
+                },
+                timeout=30.0,
+            )
+            response.raise_for_status()
+            data = response.json()
+            
+            content = (
+                data.get("data", {})
+                .get("repository", {})
+                .get("commit", {})
+                .get("file", {})
+                .get("content")
+            )
+            
+            return content
+            
+        except Exception as e:
+            logger.error(f"Failed to get file content: {e}")
+            return None
+
+
+def get_sourcegraph_tools() -> List[callable]:
+    """Get Sourcegraph tools as callable functions for agent use."""
+    tools = SourcegraphTools()
+    
+    def search_code(query: str, repo: str = None, max_results: int = 10) -> str:
+        """Search code across repositories using Sourcegraph."""
+        results = tools.search(query, repo=repo, max_results=max_results)
+        if not results:
+            return "No results found"
+        
+        output = []
+        for r in results:
+            output.append(f"{r.repository}/{r.file_path}:{r.line_number}")
+            output.append(f"  {r.content.strip()}")
+            output.append("")
+        
+        return "\n".join(output)
+    
+    return [search_code]

src/praisonai-agents/praisonaiagents/tools/subagent_tool.py

@@ -0,0 +1,373 @@
+"""
+Subagent Tool for PraisonAI Agents.
+
+Provides a tool for spawning subagents to handle specific tasks,
+enabling hierarchical task delegation and multi-agent coordination.
+"""
+
+import logging
+from typing import Any, Callable, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+def create_subagent_tool(
+    agent_factory: Optional[Callable[..., Any]] = None,
+    allowed_agents: Optional[List[str]] = None,
+    max_depth: int = 3,
+) -> Dict[str, Any]:
+    """
+    Create a subagent tool for task delegation.
+    
+    This tool allows an agent to spawn a subagent to handle
+    a specific task, enabling hierarchical task execution.
+    
+    Args:
+        agent_factory: Optional factory function to create agents
+        allowed_agents: Optional list of allowed agent names
+        max_depth: Maximum nesting depth for subagents
+        
+    Returns:
+        Tool definition dictionary
+    """
+    
+    _current_depth = 0
+    
+    def spawn_subagent(
+        task: str,
+        agent_name: Optional[str] = None,
+        context: Optional[str] = None,
+        tools: Optional[List[str]] = None,
+    ) -> Dict[str, Any]:
+        """
+        Spawn a subagent to handle a specific task.
+        
+        Args:
+            task: The task description for the subagent
+            agent_name: Optional specific agent to use
+            context: Optional additional context
+            tools: Optional list of tools to give the subagent
+            
+        Returns:
+            Result from the subagent execution
+        """
+        nonlocal _current_depth
+        
+        # Check depth limit
+        if _current_depth >= max_depth:
+            return {
+                "success": False,
+                "error": f"Maximum subagent depth ({max_depth}) exceeded",
+                "output": None,
+            }
+        
+        # Check allowed agents
+        if allowed_agents and agent_name and agent_name not in allowed_agents:
+            return {
+                "success": False,
+                "error": f"Agent '{agent_name}' not in allowed list",
+                "output": None,
+            }
+        
+        try:
+            _current_depth += 1
+            
+            # If we have an agent factory, use it
+            if agent_factory:
+                subagent = agent_factory(
+                    name=agent_name or "subagent",
+                    tools=tools,
+                )
+                
+                # Build prompt with context
+                prompt = task
+                if context:
+                    prompt = f"Context: {context}\n\nTask: {task}"
+                
+                # Execute the subagent
+                result = subagent.chat(prompt)
+                
+                return {
+                    "success": True,
+                    "output": result,
+                    "agent_name": agent_name or "subagent",
+                    "task": task,
+                }
+            else:
+                # Without factory, return a placeholder
+                return {
+                    "success": True,
+                    "output": f"[Subagent would execute: {task}]",
+                    "agent_name": agent_name or "subagent",
+                    "task": task,
+                    "note": "No agent factory provided - simulation mode",
+                }
+                
+        except Exception as e:
+            logger.error(f"Subagent execution failed: {e}")
+            return {
+                "success": False,
+                "error": str(e),
+                "output": None,
+            }
+        finally:
+            _current_depth -= 1
+    
+    return {
+        "name": "spawn_subagent",
+        "description": (
+            "Spawn a subagent to handle a specific task. Use this when a task "
+            "is complex and would benefit from dedicated focus, or when you need "
+            "specialized capabilities. The subagent will execute the task and "
+            "return the result."
+        ),
+        "function": spawn_subagent,
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "task": {
+                    "type": "string",
+                    "description": "The task description for the subagent to execute",
+                },
+                "agent_name": {
+                    "type": "string",
+                    "description": "Optional name of a specific agent type to use",
+                },
+                "context": {
+                    "type": "string",
+                    "description": "Optional additional context for the task",
+                },
+                "tools": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": "Optional list of tool names to give the subagent",
+                },
+            },
+            "required": ["task"],
+        },
+    }
+
+
+def create_batch_tool() -> Dict[str, Any]:
+    """
+    Create a batch tool for executing multiple operations.
+    
+    This tool allows executing multiple similar operations
+    in a single call, reducing overhead.
+    
+    Returns:
+        Tool definition dictionary
+    """
+    
+    def batch_execute(
+        operations: List[Dict[str, Any]],
+        parallel: bool = False,
+    ) -> Dict[str, Any]:
+        """
+        Execute multiple operations in batch.
+        
+        Args:
+            operations: List of operations to execute
+            parallel: Whether to execute in parallel (not implemented)
+            
+        Returns:
+            Results from all operations
+        """
+        results = []
+        errors = []
+        
+        for i, op in enumerate(operations):
+            try:
+                op_type = op.get("type", "unknown")
+                op_args = op.get("args", {})
+                
+                # Placeholder for actual operation execution
+                results.append({
+                    "index": i,
+                    "type": op_type,
+                    "success": True,
+                    "result": f"[Would execute {op_type} with {op_args}]",
+                })
+            except Exception as e:
+                errors.append({
+                    "index": i,
+                    "error": str(e),
+                })
+        
+        return {
+            "total": len(operations),
+            "successful": len(results),
+            "failed": len(errors),
+            "results": results,
+            "errors": errors,
+        }
+    
+    return {
+        "name": "batch_execute",
+        "description": (
+            "Execute multiple operations in a single batch. Use this when you "
+            "need to perform many similar operations to reduce overhead. "
+            "Each operation should specify a type and arguments."
+        ),
+        "function": batch_execute,
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "operations": {
+                    "type": "array",
+                    "items": {
+                        "type": "object",
+                        "properties": {
+                            "type": {"type": "string"},
+                            "args": {"type": "object"},
+                        },
+                        "required": ["type"],
+                    },
+                    "description": "List of operations to execute",
+                },
+                "parallel": {
+                    "type": "boolean",
+                    "description": "Whether to execute operations in parallel",
+                    "default": False,
+                },
+            },
+            "required": ["operations"],
+        },
+    }
+
+
+def create_todo_tools() -> List[Dict[str, Any]]:
+    """
+    Create todo management tools.
+    
+    Returns:
+        List of todo tool definitions
+    """
+    
+    _todos: Dict[str, Dict[str, Any]] = {}
+    _counter = 0
+    
+    def add_todo(
+        content: str,
+        priority: str = "medium",
+        status: str = "pending",
+    ) -> Dict[str, Any]:
+        """Add a new todo item."""
+        nonlocal _counter
+        _counter += 1
+        todo_id = f"todo_{_counter}"
+        
+        _todos[todo_id] = {
+            "id": todo_id,
+            "content": content,
+            "priority": priority,
+            "status": status,
+        }
+        
+        return {"success": True, "id": todo_id, "todo": _todos[todo_id]}
+    
+    def update_todo(
+        todo_id: str,
+        content: Optional[str] = None,
+        priority: Optional[str] = None,
+        status: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """Update an existing todo item."""
+        if todo_id not in _todos:
+            return {"success": False, "error": f"Todo {todo_id} not found"}
+        
+        todo = _todos[todo_id]
+        if content is not None:
+            todo["content"] = content
+        if priority is not None:
+            todo["priority"] = priority
+        if status is not None:
+            todo["status"] = status
+        
+        return {"success": True, "todo": todo}
+    
+    def list_todos(
+        status: Optional[str] = None,
+        priority: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """List todo items with optional filtering."""
+        todos = list(_todos.values())
+        
+        if status:
+            todos = [t for t in todos if t["status"] == status]
+        if priority:
+            todos = [t for t in todos if t["priority"] == priority]
+        
+        return {"success": True, "todos": todos, "count": len(todos)}
+    
+    def delete_todo(todo_id: str) -> Dict[str, Any]:
+        """Delete a todo item."""
+        if todo_id not in _todos:
+            return {"success": False, "error": f"Todo {todo_id} not found"}
+        
+        del _todos[todo_id]
+        return {"success": True, "deleted": todo_id}
+    
+    return [
+        {
+            "name": "add_todo",
+            "description": "Add a new todo item to track tasks",
+            "function": add_todo,
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "content": {"type": "string", "description": "Todo content"},
+                    "priority": {
+                        "type": "string",
+                        "enum": ["low", "medium", "high"],
+                        "description": "Priority level",
+                    },
+                    "status": {
+                        "type": "string",
+                        "enum": ["pending", "in_progress", "completed"],
+                        "description": "Current status",
+                    },
+                },
+                "required": ["content"],
+            },
+        },
+        {
+            "name": "update_todo",
+            "description": "Update an existing todo item",
+            "function": update_todo,
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "todo_id": {"type": "string", "description": "Todo ID"},
+                    "content": {"type": "string", "description": "New content"},
+                    "priority": {"type": "string", "description": "New priority"},
+                    "status": {"type": "string", "description": "New status"},
+                },
+                "required": ["todo_id"],
+            },
+        },
+        {
+            "name": "list_todos",
+            "description": "List todo items with optional filtering",
+            "function": list_todos,
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "status": {"type": "string", "description": "Filter by status"},
+                    "priority": {"type": "string", "description": "Filter by priority"},
+                },
+            },
+        },
+        {
+            "name": "delete_todo",
+            "description": "Delete a todo item",
+            "function": delete_todo,
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "todo_id": {"type": "string", "description": "Todo ID to delete"},
+                },
+                "required": ["todo_id"],
+            },
+        },
+    ]

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.5.3"
+version = "0.7.0"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/tests/conftest.py

@@ -0,0 +1,46 @@
+"""
+Pytest configuration for PraisonAI Agents tests.
+
+Provides fixtures and markers for testing, including:
+- live: Tests that require real API keys (opt-in)
+- slow: Tests that take longer to run
+"""
+
+import os
+import pytest
+
+
+def pytest_configure(config):
+    """Register custom markers."""
+    config.addinivalue_line(
+        "markers", "live: mark test as requiring real API keys (deselect with '-m \"not live\"')"
+    )
+    config.addinivalue_line(
+        "markers", "slow: mark test as slow running"
+    )
+
+
+def pytest_collection_modifyitems(config, items):
+    """Skip live tests unless PRAISONAI_LIVE_TESTS=1 is set."""
+    if os.environ.get("PRAISONAI_LIVE_TESTS") != "1":
+        skip_live = pytest.mark.skip(reason="Live tests disabled. Set PRAISONAI_LIVE_TESTS=1 to enable.")
+        for item in items:
+            if "live" in item.keywords:
+                item.add_marker(skip_live)
+
+
+@pytest.fixture
+def openai_api_key():
+    """Get OpenAI API key from environment, skip if not available."""
+    key = os.environ.get("OPENAI_API_KEY")
+    if not key:
+        pytest.skip("OPENAI_API_KEY not set")
+    return key
+
+
+@pytest.fixture
+def live_test_enabled():
+    """Check if live tests are enabled."""
+    if os.environ.get("PRAISONAI_LIVE_TESTS") != "1":
+        pytest.skip("Live tests disabled. Set PRAISONAI_LIVE_TESTS=1 to enable.")
+    return True

src/praisonai-agents/tests/integration/agent/test_agent_rag_live.py

@@ -0,0 +1,230 @@
+"""
+Live integration tests for Agent + RAG integration.
+
+These tests require:
+- PRAISONAI_LIVE_TESTS=1 environment variable
+- OPENAI_API_KEY environment variable
+
+Run with: PRAISONAI_LIVE_TESTS=1 pytest -m live tests/integration/agent/ -v
+"""
+
+import pytest
+import tempfile
+from pathlib import Path
+
+
+@pytest.fixture
+def sample_documents():
+    """Create sample documents for testing."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Create sample documents
+        doc1 = Path(tmpdir) / "doc1.txt"
+        doc1.write_text("""
+        The capital of France is Paris.
+        Paris is known for the Eiffel Tower.
+        France is a country in Western Europe.
+        """)
+        
+        doc2 = Path(tmpdir) / "doc2.txt"
+        doc2.write_text("""
+        Python was created by Guido van Rossum.
+        Python is a high-level programming language.
+        Python emphasizes code readability.
+        """)
+        
+        doc3 = Path(tmpdir) / "doc3.txt"
+        doc3.write_text("""
+        Machine learning is a subset of artificial intelligence.
+        Deep learning uses neural networks with many layers.
+        AI is transforming many industries.
+        """)
+        
+        yield tmpdir
+
+
+@pytest.fixture
+def openai_api_key():
+    """Get OpenAI API key from environment."""
+    import os
+    key = os.environ.get("OPENAI_API_KEY")
+    if not key:
+        pytest.skip("OPENAI_API_KEY not set")
+    return key
+
+
+@pytest.mark.live
+class TestAgentRAGLiveQuery:
+    """Live tests for Agent RAG query functionality."""
+    
+    def test_agent_rag_query_with_citations(self, openai_api_key, sample_documents):
+        """Test Agent.rag_query returns answer with citations."""
+        from praisonaiagents import Agent
+        from praisonaiagents.knowledge import Knowledge
+        
+        # Create knowledge base
+        knowledge_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": "test_agent_live",
+                    "path": f"{sample_documents}/.chroma",
+                }
+            }
+        }
+        
+        # Pre-index documents
+        knowledge = Knowledge(config=knowledge_config)
+        user_id = "test_user_agent"
+        for doc in Path(sample_documents).glob("*.txt"):
+            knowledge.add(str(doc), user_id=user_id)
+        
+        # Create agent with knowledge and rag_config
+        agent = Agent(
+            name="TestAgent",
+            instructions="You are a helpful assistant that answers questions based on provided knowledge.",
+            knowledge=[str(doc) for doc in Path(sample_documents).glob("*.txt")],
+            knowledge_config=knowledge_config,
+            rag_config={"include_citations": True, "top_k": 3},
+            user_id=user_id,
+        )
+        
+        # Use agent.rag_query for RAG with citations
+        result = agent.rag_query("What is the capital of France?")
+        
+        # Assertions
+        assert result.answer is not None
+        assert len(result.answer) > 0
+        assert "paris" in result.answer.lower() or "france" in result.answer.lower()
+        assert result.citations is not None
+        assert len(result.citations) > 0
+    
+    def test_agent_rag_property_access(self, openai_api_key, sample_documents):
+        """Test Agent.rag property provides RAG instance."""
+        from praisonaiagents import Agent
+        from praisonaiagents.knowledge import Knowledge
+        
+        knowledge_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": "test_agent_rag_prop",
+                    "path": f"{sample_documents}/.chroma_prop",
+                }
+            }
+        }
+        
+        # Pre-index
+        knowledge = Knowledge(config=knowledge_config)
+        user_id = "test_user_prop"
+        for doc in Path(sample_documents).glob("*.txt"):
+            knowledge.add(str(doc), user_id=user_id)
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            knowledge=[str(doc) for doc in Path(sample_documents).glob("*.txt")],
+            knowledge_config=knowledge_config,
+            rag_config={"include_citations": True},
+            user_id=user_id,
+        )
+        
+        # Access RAG through property
+        rag = agent.rag
+        assert rag is not None
+        
+        # Query through RAG directly
+        result = rag.query("Who created Python?", user_id=user_id)
+        assert "guido" in result.answer.lower() or "rossum" in result.answer.lower()
+
+
+@pytest.mark.live
+class TestAgentRAGLiveStreaming:
+    """Live tests for Agent RAG streaming functionality."""
+    
+    def test_agent_rag_stream(self, openai_api_key, sample_documents):
+        """Test Agent.rag.stream produces chunks."""
+        from praisonaiagents import Agent
+        from praisonaiagents.knowledge import Knowledge
+        
+        knowledge_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": "test_agent_stream",
+                    "path": f"{sample_documents}/.chroma_stream",
+                }
+            }
+        }
+        
+        # Pre-index
+        knowledge = Knowledge(config=knowledge_config)
+        user_id = "test_user_stream"
+        for doc in Path(sample_documents).glob("*.txt"):
+            knowledge.add(str(doc), user_id=user_id)
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            knowledge=[str(doc) for doc in Path(sample_documents).glob("*.txt")],
+            knowledge_config=knowledge_config,
+            rag_config={"stream": True},
+            user_id=user_id,
+        )
+        
+        # Stream through RAG
+        chunks = []
+        for chunk in agent.rag.stream("Tell me about machine learning", user_id=user_id):
+            chunks.append(chunk)
+        
+        # Should have multiple chunks
+        assert len(chunks) >= 1
+        
+        # Combined response should be meaningful
+        full_response = "".join(chunks)
+        assert len(full_response) > 10
+
+
+@pytest.mark.live
+class TestAgentRAGLiveCitations:
+    """Live tests for Agent RAG citation functionality."""
+    
+    def test_agent_citations_have_source_info(self, openai_api_key, sample_documents):
+        """Test that Agent RAG citations have proper source information."""
+        from praisonaiagents import Agent
+        from praisonaiagents.knowledge import Knowledge
+        
+        knowledge_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": "test_agent_citations",
+                    "path": f"{sample_documents}/.chroma_citations",
+                }
+            }
+        }
+        
+        # Pre-index
+        knowledge = Knowledge(config=knowledge_config)
+        user_id = "test_user_citations"
+        for doc in Path(sample_documents).glob("*.txt"):
+            knowledge.add(str(doc), user_id=user_id)
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            knowledge=[str(doc) for doc in Path(sample_documents).glob("*.txt")],
+            knowledge_config=knowledge_config,
+            rag_config={"include_citations": True, "top_k": 5},
+            user_id=user_id,
+        )
+        
+        result = agent.rag_query("What is the capital of France?")
+        
+        # Check citations
+        assert result.has_citations
+        
+        # Citations should have scores
+        for citation in result.citations:
+            assert citation.id is not None
+            assert citation.text is not None
+            assert len(citation.text) > 0

src/praisonai-agents/tests/integration/rag/__init__.py

@@ -0,0 +1 @@
+"""RAG integration tests."""

src/praisonai-agents/tests/integration/rag/test_rag_live.py

@@ -0,0 +1,241 @@
+"""
+Live integration tests for RAG with real API keys.
+
+These tests require:
+- PRAISONAI_LIVE_TESTS=1
+- OPENAI_API_KEY set
+
+Run with:
+    PRAISONAI_LIVE_TESTS=1 pytest -m live tests/integration/rag/ -v
+"""
+
+import pytest
+import tempfile
+from pathlib import Path
+
+
+@pytest.fixture
+def sample_documents():
+    """Create sample documents for testing."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        doc1 = Path(tmpdir) / "doc1.txt"
+        doc1.write_text("""
+        The capital of France is Paris.
+        Paris is known for the Eiffel Tower.
+        France is a country in Western Europe.
+        """)
+        
+        doc2 = Path(tmpdir) / "doc2.txt"
+        doc2.write_text("""
+        Python is a programming language.
+        Python was created by Guido van Rossum.
+        Python is widely used for data science and AI.
+        """)
+        
+        doc3 = Path(tmpdir) / "doc3.txt"
+        doc3.write_text("""
+        Machine learning is a subset of artificial intelligence.
+        Deep learning uses neural networks.
+        AI is transforming many industries.
+        """)
+        
+        yield tmpdir
+
+
+@pytest.mark.live
+class TestRAGLiveQuery:
+    """Live tests for RAG query functionality."""
+    
+    def test_rag_query_with_citations(self, openai_api_key, sample_documents):
+        """Test RAG query returns answer with citations."""
+        from praisonaiagents.knowledge import Knowledge
+        from praisonaiagents.rag import RAG, RAGConfig
+        
+        # Create knowledge base
+        knowledge_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": "test_live",
+                    "path": f"{sample_documents}/.chroma",
+                }
+            }
+        }
+        
+        knowledge = Knowledge(config=knowledge_config)
+        
+        # Index documents with user_id (required by mem0)
+        user_id = "test_user"
+        for doc in Path(sample_documents).glob("*.txt"):
+            knowledge.add(str(doc), user_id=user_id)
+        
+        # Create RAG
+        rag = RAG(
+            knowledge=knowledge,
+            config=RAGConfig(top_k=3, include_citations=True)
+        )
+        
+        # Query with user_id
+        result = rag.query("What is the capital of France?", user_id=user_id)
+        
+        # Assertions
+        assert result.answer is not None
+        assert len(result.answer) > 0
+        assert "paris" in result.answer.lower() or "france" in result.answer.lower()
+        assert result.citations is not None
+        assert len(result.citations) > 0
+    
+    def test_rag_query_python_topic(self, openai_api_key, sample_documents):
+        """Test RAG query on Python topic."""
+        from praisonaiagents.knowledge import Knowledge
+        from praisonaiagents.rag import RAG, RAGConfig
+        
+        knowledge_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": "test_python",
+                    "path": f"{sample_documents}/.chroma_python",
+                }
+            }
+        }
+        
+        knowledge = Knowledge(config=knowledge_config)
+        
+        user_id = "test_user_python"
+        for doc in Path(sample_documents).glob("*.txt"):
+            knowledge.add(str(doc), user_id=user_id)
+        
+        rag = RAG(
+            knowledge=knowledge,
+            config=RAGConfig(top_k=3, include_citations=True)
+        )
+        
+        result = rag.query("Who created Python?", user_id=user_id)
+        
+        assert result.answer is not None
+        assert "guido" in result.answer.lower() or "rossum" in result.answer.lower()
+
+
+@pytest.mark.live
+class TestRAGLiveStreaming:
+    """Live tests for RAG streaming functionality."""
+    
+    def test_rag_stream_produces_chunks(self, openai_api_key, sample_documents):
+        """Test RAG streaming produces multiple chunks."""
+        from praisonaiagents.knowledge import Knowledge
+        from praisonaiagents.rag import RAG, RAGConfig
+        
+        knowledge_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": "test_stream",
+                    "path": f"{sample_documents}/.chroma_stream",
+                }
+            }
+        }
+        
+        knowledge = Knowledge(config=knowledge_config)
+        
+        user_id = "test_user_stream"
+        for doc in Path(sample_documents).glob("*.txt"):
+            knowledge.add(str(doc), user_id=user_id)
+        
+        rag = RAG(
+            knowledge=knowledge,
+            config=RAGConfig(top_k=3, stream=True)
+        )
+        
+        # Collect streamed chunks
+        chunks = []
+        for chunk in rag.stream("Tell me about machine learning", user_id=user_id):
+            chunks.append(chunk)
+        
+        # Should have multiple chunks
+        assert len(chunks) >= 1
+        
+        # Combined response should be meaningful
+        full_response = "".join(chunks)
+        assert len(full_response) > 10
+
+
+@pytest.mark.live
+class TestRAGLiveCitations:
+    """Live tests for RAG citation functionality."""
+    
+    def test_citations_reference_correct_sources(self, openai_api_key, sample_documents):
+        """Test that citations reference the correct source documents."""
+        from praisonaiagents.knowledge import Knowledge
+        from praisonaiagents.rag import RAG, RAGConfig
+        
+        knowledge_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": "test_citations",
+                    "path": f"{sample_documents}/.chroma_citations",
+                }
+            }
+        }
+        
+        knowledge = Knowledge(config=knowledge_config)
+        
+        user_id = "test_user_citations"
+        for doc in Path(sample_documents).glob("*.txt"):
+            knowledge.add(str(doc), user_id=user_id)
+        
+        rag = RAG(
+            knowledge=knowledge,
+            config=RAGConfig(top_k=5, include_citations=True)
+        )
+        
+        result = rag.query("What is the capital of France?", user_id=user_id)
+        
+        # Check citations
+        assert result.has_citations
+        
+        # At least one citation should be from doc1 (France content)
+        citation_sources = [c.source for c in result.citations]
+        assert len(citation_sources) > 0
+        
+        # Citations should have scores
+        for citation in result.citations:
+            assert citation.score is not None or citation.score >= 0
+    
+    def test_get_citations_without_generation(self, openai_api_key, sample_documents):
+        """Test getting citations without generating an answer."""
+        from praisonaiagents.knowledge import Knowledge
+        from praisonaiagents.rag import RAG, RAGConfig
+        
+        knowledge_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": "test_citations_only",
+                    "path": f"{sample_documents}/.chroma_citations_only",
+                }
+            }
+        }
+        
+        knowledge = Knowledge(config=knowledge_config)
+        
+        user_id = "test_user_citations_only"
+        for doc in Path(sample_documents).glob("*.txt"):
+            knowledge.add(str(doc), user_id=user_id)
+        
+        rag = RAG(
+            knowledge=knowledge,
+            config=RAGConfig(top_k=3)
+        )
+        
+        citations = rag.get_citations("Python programming", user_id=user_id)
+        
+        assert citations is not None
+        assert len(citations) > 0
+        
+        # Check citation structure
+        for citation in citations:
+            assert citation.id is not None
+            assert citation.text is not None
+            assert len(citation.text) > 0

src/praisonai-agents/tests/unit/agent/test_agent_rag.py

@@ -0,0 +1,187 @@
+"""
+Tests for Agent RAG integration.
+
+Tests that Agent can access RAG features through the knowledge parameter.
+"""
+
+import pytest
+from unittest.mock import MagicMock, patch
+
+
+class TestAgentRAGConfig:
+    """Tests for Agent rag_config parameter."""
+    
+    def test_agent_accepts_rag_config(self):
+        """Test that Agent accepts rag_config parameter."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            knowledge=["test.txt"],
+            rag_config={"include_citations": True, "top_k": 5}
+        )
+        
+        assert agent._rag_config == {"include_citations": True, "top_k": 5}
+    
+    def test_agent_without_rag_config(self):
+        """Test that Agent works without rag_config."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            knowledge=["test.txt"]
+        )
+        
+        assert agent._rag_config is None
+    
+    def test_agent_without_knowledge_has_no_rag_config(self):
+        """Test that Agent without knowledge has no rag_config."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent"
+        )
+        
+        assert agent._rag_config is None
+        assert agent._rag_instance is None
+
+
+class TestAgentRAGProperty:
+    """Tests for Agent.rag property."""
+    
+    def test_agent_has_rag_property(self):
+        """Test that Agent has rag property."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            knowledge=["test.txt"],
+            rag_config={"include_citations": True}
+        )
+        
+        assert hasattr(agent, 'rag')
+    
+    def test_agent_rag_returns_none_without_knowledge(self):
+        """Test that Agent.rag returns None without knowledge."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent"
+        )
+        
+        assert agent.rag is None
+    
+    @patch('praisonaiagents.knowledge.Knowledge')
+    @patch('praisonaiagents.rag.RAG')
+    @patch('praisonaiagents.rag.RAGConfig')
+    def test_agent_rag_lazy_loads(self, mock_rag_config, mock_rag, mock_knowledge):
+        """Test that Agent.rag lazy loads RAG instance."""
+        from praisonaiagents import Agent
+        
+        mock_knowledge_instance = MagicMock()
+        mock_knowledge.return_value = mock_knowledge_instance
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            knowledge=["test.txt"],
+            rag_config={"include_citations": True}
+        )
+        
+        # RAG should not be loaded yet
+        assert agent._rag_instance is None
+        
+        # Access rag property - this triggers lazy loading
+        # Note: In real usage, knowledge processing would happen first
+
+
+class TestAgentRAGQuery:
+    """Tests for Agent.rag_query method."""
+    
+    def test_agent_has_rag_query_method(self):
+        """Test that Agent has rag_query method."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            knowledge=["test.txt"],
+            rag_config={"include_citations": True}
+        )
+        
+        assert hasattr(agent, 'rag_query')
+        assert callable(agent.rag_query)
+    
+    def test_rag_query_raises_without_knowledge(self):
+        """Test that rag_query raises error without knowledge."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent"
+        )
+        
+        with pytest.raises(ValueError, match="No knowledge sources configured"):
+            agent.rag_query("What is the answer?")
+
+
+class TestAgentKnowledgeContext:
+    """Tests for Agent._get_knowledge_context method."""
+    
+    def test_agent_has_get_knowledge_context_method(self):
+        """Test that Agent has _get_knowledge_context method."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            knowledge=["test.txt"]
+        )
+        
+        assert hasattr(agent, '_get_knowledge_context')
+        assert callable(agent._get_knowledge_context)
+    
+    def test_get_knowledge_context_returns_tuple(self):
+        """Test that _get_knowledge_context returns tuple."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent"
+        )
+        
+        result = agent._get_knowledge_context("test query")
+        
+        assert isinstance(result, tuple)
+        assert len(result) == 2
+        assert result[0] == ""  # No knowledge, empty context
+        assert result[1] is None  # No citations
+
+
+class TestAgentRAGIntegration:
+    """Integration tests for Agent + RAG."""
+    
+    def test_agent_rag_config_passed_to_rag_instance(self):
+        """Test that rag_config is passed to RAG instance."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            knowledge=["test.txt"],
+            rag_config={
+                "include_citations": True,
+                "top_k": 10,
+                "min_score": 0.5
+            }
+        )
+        
+        # Verify config is stored
+        assert agent._rag_config["include_citations"] is True
+        assert agent._rag_config["top_k"] == 10
+        assert agent._rag_config["min_score"] == 0.5

src/praisonai-agents/tests/unit/agents/test_profiles.py

@@ -0,0 +1,215 @@
+"""
+Tests for Agent Profiles and Modes.
+
+TDD: Tests for built-in agent profiles and mode configurations.
+"""
+
+from praisonaiagents.agents.profiles import (
+    AgentMode,
+    AgentProfile,
+    get_profile,
+    list_profiles,
+    register_profile,
+    get_profiles_by_mode,
+    BUILTIN_PROFILES,
+)
+
+
+class TestAgentMode:
+    """Tests for AgentMode enum."""
+    
+    def test_mode_values(self):
+        """Test mode enum values."""
+        assert AgentMode.PRIMARY.value == "primary"
+        assert AgentMode.SUBAGENT.value == "subagent"
+        assert AgentMode.ALL.value == "all"
+
+
+class TestAgentProfile:
+    """Tests for AgentProfile."""
+    
+    def test_profile_creation(self):
+        """Test basic profile creation."""
+        profile = AgentProfile(
+            name="test",
+            description="Test agent",
+            mode=AgentMode.PRIMARY
+        )
+        
+        assert profile.name == "test"
+        assert profile.description == "Test agent"
+        assert profile.mode == AgentMode.PRIMARY
+    
+    def test_profile_defaults(self):
+        """Test profile default values."""
+        profile = AgentProfile(name="test")
+        
+        assert profile.mode == AgentMode.ALL
+        assert profile.temperature == 0.7
+        assert profile.top_p == 1.0
+        assert profile.max_steps == 50
+        assert profile.hidden is False
+    
+    def test_profile_serialization(self):
+        """Test profile round-trip."""
+        profile = AgentProfile(
+            name="test",
+            description="Test",
+            mode=AgentMode.SUBAGENT,
+            tools=["read_file", "bash"],
+            temperature=0.5,
+        )
+        
+        d = profile.to_dict()
+        restored = AgentProfile.from_dict(d)
+        
+        assert restored.name == profile.name
+        assert restored.mode == profile.mode
+        assert restored.tools == profile.tools
+        assert restored.temperature == profile.temperature
+
+
+class TestBuiltinProfiles:
+    """Tests for built-in profiles."""
+    
+    def test_general_profile_exists(self):
+        """Test general profile exists."""
+        profile = get_profile("general")
+        
+        assert profile is not None
+        assert profile.name == "general"
+        assert profile.mode == AgentMode.PRIMARY
+    
+    def test_coder_profile_exists(self):
+        """Test coder profile exists."""
+        profile = get_profile("coder")
+        
+        assert profile is not None
+        assert profile.name == "coder"
+    
+    def test_planner_profile_exists(self):
+        """Test planner profile exists."""
+        profile = get_profile("planner")
+        
+        assert profile is not None
+        assert profile.mode == AgentMode.SUBAGENT
+    
+    def test_reviewer_profile_exists(self):
+        """Test reviewer profile exists."""
+        profile = get_profile("reviewer")
+        
+        assert profile is not None
+    
+    def test_explorer_profile_exists(self):
+        """Test explorer profile exists."""
+        profile = get_profile("explorer")
+        
+        assert profile is not None
+    
+    def test_debugger_profile_exists(self):
+        """Test debugger profile exists."""
+        profile = get_profile("debugger")
+        
+        assert profile is not None
+    
+    def test_hidden_profiles(self):
+        """Test hidden profiles exist."""
+        compaction = get_profile("compaction")
+        title = get_profile("title")
+        
+        assert compaction is not None
+        assert compaction.hidden is True
+        
+        assert title is not None
+        assert title.hidden is True
+    
+    def test_nonexistent_profile(self):
+        """Test getting non-existent profile."""
+        profile = get_profile("nonexistent")
+        
+        assert profile is None
+
+
+class TestListProfiles:
+    """Tests for listing profiles."""
+    
+    def test_list_profiles(self):
+        """Test listing profiles."""
+        profiles = list_profiles()
+        
+        assert len(profiles) > 0
+        names = [p.name for p in profiles]
+        assert "general" in names
+        assert "coder" in names
+    
+    def test_list_profiles_excludes_hidden(self):
+        """Test that hidden profiles are excluded by default."""
+        profiles = list_profiles(include_hidden=False)
+        
+        names = [p.name for p in profiles]
+        assert "compaction" not in names
+        assert "title" not in names
+    
+    def test_list_profiles_includes_hidden(self):
+        """Test including hidden profiles."""
+        profiles = list_profiles(include_hidden=True)
+        
+        names = [p.name for p in profiles]
+        assert "compaction" in names
+        assert "title" in names
+
+
+class TestRegisterProfile:
+    """Tests for registering custom profiles."""
+    
+    def test_register_profile(self):
+        """Test registering a custom profile."""
+        custom = AgentProfile(
+            name="custom_test",
+            description="Custom test agent",
+            mode=AgentMode.PRIMARY,
+        )
+        
+        register_profile(custom)
+        
+        retrieved = get_profile("custom_test")
+        assert retrieved is not None
+        assert retrieved.name == "custom_test"
+        
+        # Cleanup
+        if "custom_test" in BUILTIN_PROFILES:
+            del BUILTIN_PROFILES["custom_test"]
+
+
+class TestGetProfilesByMode:
+    """Tests for getting profiles by mode."""
+    
+    def test_get_primary_profiles(self):
+        """Test getting primary mode profiles."""
+        profiles = get_profiles_by_mode(AgentMode.PRIMARY)
+        
+        assert len(profiles) > 0
+        # Should include PRIMARY and ALL mode profiles
+        for p in profiles:
+            assert p.mode in (AgentMode.PRIMARY, AgentMode.ALL)
+    
+    def test_get_subagent_profiles(self):
+        """Test getting subagent mode profiles."""
+        profiles = get_profiles_by_mode(AgentMode.SUBAGENT)
+        
+        assert len(profiles) > 0
+        # Should include SUBAGENT and ALL mode profiles
+        for p in profiles:
+            assert p.mode in (AgentMode.SUBAGENT, AgentMode.ALL)
+    
+    def test_all_mode_in_both(self):
+        """Test that ALL mode profiles appear in both."""
+        primary = get_profiles_by_mode(AgentMode.PRIMARY)
+        subagent = get_profiles_by_mode(AgentMode.SUBAGENT)
+        
+        primary_names = {p.name for p in primary}
+        subagent_names = {p.name for p in subagent}
+        
+        # Coder is ALL mode, should be in both
+        assert "coder" in primary_names
+        assert "coder" in subagent_names

src/praisonai-agents/tests/unit/bus/__init__.py

@@ -0,0 +1 @@
+"""Tests for the Event Bus module."""

src/praisonai-agents/tests/unit/bus/test_event_bus.py

@@ -0,0 +1,384 @@
+"""
+Tests for the Event Bus module.
+
+TDD: Tests written before implementation verification.
+"""
+
+import pytest
+import asyncio
+from unittest.mock import Mock, AsyncMock
+
+from praisonaiagents.bus import EventBus, Event, EventType, get_default_bus, set_default_bus
+
+
+class TestEvent:
+    """Tests for the Event class."""
+    
+    def test_event_creation(self):
+        """Test basic event creation."""
+        event = Event(type="test.event", data={"key": "value"})
+        
+        assert event.type == "test.event"
+        assert event.data == {"key": "value"}
+        assert event.id is not None
+        assert event.timestamp > 0
+    
+    def test_event_with_source(self):
+        """Test event with source identifier."""
+        event = Event(
+            type=EventType.SESSION_CREATED.value,
+            data={"session_id": "abc123"},
+            source="agent_1"
+        )
+        
+        assert event.source == "agent_1"
+    
+    def test_event_to_dict(self):
+        """Test event serialization."""
+        event = Event(
+            type="test.event",
+            data={"key": "value"},
+            source="test"
+        )
+        
+        d = event.to_dict()
+        
+        assert d["type"] == "test.event"
+        assert d["data"] == {"key": "value"}
+        assert d["source"] == "test"
+        assert "id" in d
+        assert "timestamp" in d
+    
+    def test_event_from_dict(self):
+        """Test event deserialization."""
+        d = {
+            "type": "test.event",
+            "data": {"key": "value"},
+            "source": "test",
+            "id": "test-id",
+            "timestamp": 1234567890.0
+        }
+        
+        event = Event.from_dict(d)
+        
+        assert event.type == "test.event"
+        assert event.data == {"key": "value"}
+        assert event.source == "test"
+        assert event.id == "test-id"
+        assert event.timestamp == 1234567890.0
+    
+    def test_event_to_sse(self):
+        """Test SSE formatting."""
+        event = Event(type="test.event", data={"key": "value"})
+        
+        sse = event.to_sse()
+        
+        assert f"id: {event.id}" in sse
+        assert "event: test.event" in sse
+        assert "data:" in sse
+
+
+class TestEventBus:
+    """Tests for the EventBus class."""
+    
+    def test_bus_creation(self):
+        """Test basic bus creation."""
+        bus = EventBus()
+        
+        assert bus.subscriber_count == 0
+    
+    def test_subscribe_and_publish(self):
+        """Test basic subscribe and publish."""
+        bus = EventBus()
+        received = []
+        
+        def handler(event):
+            received.append(event)
+        
+        bus.subscribe(handler)
+        bus.publish("test.event", {"key": "value"})
+        
+        assert len(received) == 1
+        assert received[0].type == "test.event"
+        assert received[0].data == {"key": "value"}
+    
+    def test_subscribe_with_filter(self):
+        """Test subscribing to specific event types."""
+        bus = EventBus()
+        received = []
+        
+        def handler(event):
+            received.append(event)
+        
+        bus.subscribe(handler, event_types=["test.event"])
+        
+        bus.publish("test.event", {"key": "value"})
+        bus.publish("other.event", {"key": "other"})
+        
+        assert len(received) == 1
+        assert received[0].type == "test.event"
+    
+    def test_subscribe_multiple_types(self):
+        """Test subscribing to multiple event types."""
+        bus = EventBus()
+        received = []
+        
+        def handler(event):
+            received.append(event)
+        
+        bus.subscribe(handler, event_types=["type.a", "type.b"])
+        
+        bus.publish("type.a", {})
+        bus.publish("type.b", {})
+        bus.publish("type.c", {})
+        
+        assert len(received) == 2
+    
+    def test_unsubscribe(self):
+        """Test unsubscribing."""
+        bus = EventBus()
+        received = []
+        
+        def handler(event):
+            received.append(event)
+        
+        sub_id = bus.subscribe(handler)
+        bus.publish("test.event", {})
+        
+        assert len(received) == 1
+        
+        result = bus.unsubscribe(sub_id)
+        assert result is True
+        
+        bus.publish("test.event", {})
+        assert len(received) == 1  # No new events
+    
+    def test_unsubscribe_invalid_id(self):
+        """Test unsubscribing with invalid ID."""
+        bus = EventBus()
+        
+        result = bus.unsubscribe("invalid-id")
+        
+        assert result is False
+    
+    def test_on_decorator(self):
+        """Test the @bus.on() decorator."""
+        bus = EventBus()
+        received = []
+        
+        @bus.on(EventType.SESSION_CREATED)
+        def handler(event):
+            received.append(event)
+        
+        bus.publish(EventType.SESSION_CREATED, {"session_id": "abc"})
+        
+        assert len(received) == 1
+    
+    def test_on_decorator_multiple_types(self):
+        """Test decorator with multiple event types."""
+        bus = EventBus()
+        received = []
+        
+        @bus.on(EventType.SESSION_CREATED, EventType.SESSION_UPDATED)
+        def handler(event):
+            received.append(event)
+        
+        bus.publish(EventType.SESSION_CREATED, {})
+        bus.publish(EventType.SESSION_UPDATED, {})
+        bus.publish(EventType.SESSION_DELETED, {})
+        
+        assert len(received) == 2
+    
+    def test_publish_returns_event(self):
+        """Test that publish returns the event."""
+        bus = EventBus()
+        
+        event = bus.publish("test.event", {"key": "value"})
+        
+        assert isinstance(event, Event)
+        assert event.type == "test.event"
+    
+    def test_publish_with_enum(self):
+        """Test publishing with EventType enum."""
+        bus = EventBus()
+        received = []
+        
+        bus.subscribe(lambda e: received.append(e))
+        bus.publish(EventType.AGENT_STARTED, {"agent": "test"})
+        
+        assert len(received) == 1
+        assert received[0].type == EventType.AGENT_STARTED.value
+    
+    def test_event_history(self):
+        """Test event history tracking."""
+        bus = EventBus()
+        
+        bus.publish("event.1", {})
+        bus.publish("event.2", {})
+        bus.publish("event.3", {})
+        
+        history = bus.get_history()
+        
+        assert len(history) == 3
+        assert history[0].type == "event.1"
+        assert history[2].type == "event.3"
+    
+    def test_event_history_with_filter(self):
+        """Test filtered event history."""
+        bus = EventBus()
+        
+        bus.publish("type.a", {})
+        bus.publish("type.b", {})
+        bus.publish("type.a", {})
+        
+        history = bus.get_history(event_type="type.a")
+        
+        assert len(history) == 2
+    
+    def test_event_history_limit(self):
+        """Test event history limit."""
+        bus = EventBus()
+        
+        for i in range(10):
+            bus.publish(f"event.{i}", {})
+        
+        history = bus.get_history(limit=5)
+        
+        assert len(history) == 5
+        assert history[0].type == "event.5"
+    
+    def test_clear_history(self):
+        """Test clearing event history."""
+        bus = EventBus()
+        
+        bus.publish("test.event", {})
+        bus.clear_history()
+        
+        assert len(bus.get_history()) == 0
+    
+    def test_clear_subscribers(self):
+        """Test clearing all subscribers."""
+        bus = EventBus()
+        
+        bus.subscribe(lambda e: None)
+        bus.subscribe(lambda e: None)
+        
+        assert bus.subscriber_count == 2
+        
+        bus.clear_subscribers()
+        
+        assert bus.subscriber_count == 0
+    
+    def test_subscriber_error_handling(self):
+        """Test that subscriber errors don't break the bus."""
+        bus = EventBus()
+        received = []
+        
+        def bad_handler(event):
+            raise ValueError("Test error")
+        
+        def good_handler(event):
+            received.append(event)
+        
+        bus.subscribe(bad_handler)
+        bus.subscribe(good_handler)
+        
+        # Should not raise
+        bus.publish("test.event", {})
+        
+        # Good handler should still receive event
+        assert len(received) == 1
+
+
+class TestAsyncEventBus:
+    """Tests for async event bus functionality."""
+    
+    @pytest.mark.asyncio
+    async def test_async_subscriber(self):
+        """Test async subscriber callback."""
+        bus = EventBus()
+        received = []
+        
+        async def async_handler(event):
+            await asyncio.sleep(0.01)
+            received.append(event)
+        
+        bus.subscribe(async_handler)
+        await bus.publish_async("test.event", {"key": "value"})
+        
+        assert len(received) == 1
+    
+    @pytest.mark.asyncio
+    async def test_mixed_sync_async_subscribers(self):
+        """Test mix of sync and async subscribers."""
+        bus = EventBus()
+        sync_received = []
+        async_received = []
+        
+        def sync_handler(event):
+            sync_received.append(event)
+        
+        async def async_handler(event):
+            await asyncio.sleep(0.01)
+            async_received.append(event)
+        
+        bus.subscribe(sync_handler)
+        bus.subscribe(async_handler)
+        
+        await bus.publish_async("test.event", {})
+        
+        assert len(sync_received) == 1
+        assert len(async_received) == 1
+
+
+class TestDefaultBus:
+    """Tests for the global default bus."""
+    
+    def test_get_default_bus(self):
+        """Test getting the default bus."""
+        bus = get_default_bus()
+        
+        assert isinstance(bus, EventBus)
+    
+    def test_get_default_bus_singleton(self):
+        """Test that default bus is a singleton."""
+        bus1 = get_default_bus()
+        bus2 = get_default_bus()
+        
+        assert bus1 is bus2
+    
+    def test_set_default_bus(self):
+        """Test setting a custom default bus."""
+        original = get_default_bus()
+        custom = EventBus()
+        
+        set_default_bus(custom)
+        
+        assert get_default_bus() is custom
+        
+        # Restore original
+        set_default_bus(original)
+
+
+class TestEventType:
+    """Tests for EventType enum."""
+    
+    def test_event_type_values(self):
+        """Test that event types have correct values."""
+        assert EventType.SESSION_CREATED.value == "session.created"
+        assert EventType.MESSAGE_PART_UPDATED.value == "message.part.updated"
+        assert EventType.PERMISSION_ASKED.value == "permission.asked"
+    
+    def test_event_type_in_subscribe(self):
+        """Test using EventType in subscribe."""
+        bus = EventBus()
+        received = []
+        
+        bus.subscribe(
+            lambda e: received.append(e),
+            event_types=[EventType.SESSION_CREATED.value]
+        )
+        
+        bus.publish(EventType.SESSION_CREATED, {})
+        
+        assert len(received) == 1