Release v3.1.9

## 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

api_server.py

@@ -0,0 +1,71 @@
+"""
+Auto-generated API server for PraisonAI agents.
+"""
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from praisonai import PraisonAI
+import os
+
+app = Flask(__name__)
+
+# CORS configuration
+CORS(app)
+
+# Authentication
+AUTH_ENABLED = False
+AUTH_TOKEN = None
+
+def check_auth():
+    """Check authentication if enabled."""
+    if not AUTH_ENABLED:
+        return True
+    
+    token = request.headers.get('Authorization', '').replace('Bearer ', '')
+    return token == AUTH_TOKEN
+
+@app.route('/health', methods=['GET'])
+def health():
+    """Health check endpoint."""
+    return jsonify({"status": "ok", "service": "praisonai-api"})
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    """Chat endpoint for agent interaction."""
+    if not check_auth():
+        return jsonify({"error": "Unauthorized"}), 401
+    
+    data = request.get_json()
+    if not data or 'message' not in data:
+        return jsonify({"error": "Message required"}), 400
+    
+    try:
+        praisonai = PraisonAI(agent_file="agents.yaml")
+        result = praisonai.run()
+        
+        return jsonify({
+            "response": result,
+            "status": "success"
+        })
+    except Exception as e:
+        return jsonify({
+            "error": str(e),
+            "status": "error"
+        }), 500
+
+@app.route('/agents', methods=['GET'])
+def list_agents():
+    """List available agents."""
+    if not check_auth():
+        return jsonify({"error": "Unauthorized"}), 401
+    
+    return jsonify({
+        "agents": ["default"],
+        "agent_file": "agents.yaml"
+    })
+
+if __name__ == '__main__':
+    app.run(
+        host='0.0.0.0',
+        port=8080,
+        debug=False
+    )

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>=2.7.9" \
+    "praisonai>=3.1.9" \
     "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>=2.7.9" \
+    "praisonai>=3.1.9" \
     "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>=2.7.9" \
+    "praisonai>=3.1.9" \
     "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/acp/README.md

@@ -0,0 +1,137 @@
+# ACP (Agent Client Protocol) Examples
+
+This directory contains examples for using PraisonAI with the Agent Client Protocol (ACP).
+
+## What is ACP?
+
+The Agent Client Protocol (ACP) is a standardized JSON-RPC 2.0 protocol that allows code editors and IDEs to communicate with AI coding agents. It enables seamless integration between PraisonAI and editors like:
+
+- **Zed**
+- **JetBrains IDEs** (IntelliJ, PyCharm, WebStorm, etc.)
+- **VSCode** (via extensions)
+- **Toad**
+
+## Quick Start
+
+### CLI Usage
+
+```bash
+# Start ACP server with defaults
+praisonai acp
+
+# Enable debug logging
+praisonai acp --debug
+
+# Allow file writes
+praisonai acp --allow-write
+
+# Use a specific model
+praisonai acp -m gpt-4o
+
+# Resume last session
+praisonai acp --resume --last
+```
+
+### Python API
+
+```python
+from praisonai.acp import serve
+
+serve(
+    workspace=".",
+    debug=True,
+    allow_write=True,
+)
+```
+
+## Editor Configuration
+
+### Zed
+
+Add to `~/.config/zed/settings.json`:
+
+```json
+{
+  "agent_servers": {
+    "PraisonAI": {
+      "command": "praisonai",
+      "args": ["acp"],
+      "env": {}
+    }
+  }
+}
+```
+
+### JetBrains
+
+Add to `~/.jetbrains/acp.json`:
+
+```json
+{
+  "agent_servers": {
+    "PraisonAI": {
+      "command": "praisonai",
+      "args": ["acp"],
+      "env": {}
+    }
+  }
+}
+```
+
+### Toad
+
+```bash
+toad acp "praisonai acp"
+```
+
+## Examples
+
+- `basic_acp_server.py` - Simple ACP server with default settings
+- `custom_agent_acp.py` - ACP server with a custom agent
+
+## CLI Options
+
+| Option | Description |
+|--------|-------------|
+| `-w, --workspace` | Workspace root directory |
+| `-a, --agent` | Agent name or config file |
+| `-m, --model` | LLM model to use |
+| `-r, --resume` | Resume session by ID |
+| `--last` | Resume the last session |
+| `--approve` | Approval mode: manual, auto, scoped |
+| `--read-only` | Read-only mode (default) |
+| `--allow-write` | Allow file writes |
+| `--allow-shell` | Allow shell commands |
+| `--debug` | Enable debug logging |
+
+## Installation
+
+```bash
+pip install praisonai[acp]
+```
+
+## Protocol Details
+
+ACP uses JSON-RPC 2.0 over stdio:
+- **stdin**: Receives JSON-RPC requests from the client
+- **stdout**: Sends JSON-RPC responses to the client
+- **stderr**: Debug/log output (never pollutes stdout)
+
+### Supported Methods
+
+**Agent Methods (client → agent):**
+- `initialize` - Negotiate protocol version and capabilities
+- `authenticate` - Optional authentication
+- `session/new` - Create a new session
+- `session/load` - Load an existing session
+- `session/prompt` - Send user message
+- `session/cancel` - Cancel ongoing operations
+- `session/set_mode` - Change operating mode
+
+**Client Methods (agent → client):**
+- `session/update` - Send progress updates
+- `session/request_permission` - Request user approval
+- `fs/read_text_file` - Read file contents
+- `fs/write_text_file` - Write file contents
+- `terminal/create` - Create terminal
+- `terminal/output` - Get terminal output

examples/acp/basic_acp_server.py

@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+"""
+Basic ACP Server Example
+
+This example demonstrates how to run PraisonAI as an ACP server
+that can be connected to by IDEs like Zed, JetBrains, VSCode, or Toad.
+
+Usage:
+    # Run from command line
+    praisonai acp
+    
+    # Or run this script directly
+    python basic_acp_server.py
+"""
+
+from praisonai.acp import serve
+
+if __name__ == "__main__":
+    # Start ACP server with default settings
+    # This will listen on stdin/stdout for JSON-RPC messages
+    serve(
+        workspace=".",           # Current directory as workspace
+        agent="default",         # Use default agent
+        debug=False,             # Set to True for debug logging to stderr
+        read_only=True,          # Safe by default
+        approval_mode="manual",  # Require approval for actions
+    )

examples/acp/custom_agent_acp.py

@@ -0,0 +1,45 @@
+#!/usr/bin/env python3
+"""
+Custom Agent ACP Server Example
+
+This example shows how to run a custom PraisonAI agent as an ACP server.
+
+Usage:
+    python custom_agent_acp.py
+"""
+
+from praisonai.acp import serve, ACPServer, ACPConfig
+from praisonaiagents import Agent
+
+# Create a custom agent
+agent = Agent(
+    name="CodeAssistant",
+    instructions="""You are an expert coding assistant. You help users:
+    - Write clean, efficient code
+    - Debug issues
+    - Explain complex concepts
+    - Review code for best practices
+    
+    Always provide clear explanations with your code suggestions.""",
+    model="gpt-4o-mini",
+)
+
+if __name__ == "__main__":
+    # Create config
+    config = ACPConfig(
+        workspace=".",
+        debug=True,
+        allow_write=True,  # Allow file modifications
+        approval_mode="manual",
+    )
+    
+    # Create server with custom agent
+    server = ACPServer(config=config, agent=agent)
+    
+    # Run the server
+    serve(
+        workspace=".",
+        debug=True,
+        allow_write=True,
+        approval_mode="manual",
+    )

examples/agent_tools/agent_centric_example.py

@@ -0,0 +1,135 @@
+"""
+Agent-Centric Tools Example
+
+This example demonstrates how to use LSP/ACP-powered tools that make
+the Agent the central orchestrator for file operations.
+
+How to run:
+    export OPENAI_API_KEY=your_key
+    python examples/agent_tools/agent_centric_example.py
+
+Prerequisites:
+    pip install praisonai praisonaiagents
+"""
+
+import asyncio
+import os
+import sys
+import tempfile
+import shutil
+
+# 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'))
+
+
+async def main():
+    print("=" * 60)
+    print("AGENT-CENTRIC TOOLS EXAMPLE")
+    print("=" * 60)
+    print()
+    
+    # Create temporary workspace
+    workspace = tempfile.mkdtemp(prefix="praisonai_example_")
+    print(f"Workspace: {workspace}")
+    print()
+    
+    try:
+        # Import after path setup
+        from praisonai.cli.features import (
+            create_agent_centric_tools,
+            InteractiveRuntime,
+            RuntimeConfig
+        )
+        from praisonaiagents import Agent
+        
+        # 1. Create runtime with ACP enabled
+        print("1. Creating InteractiveRuntime with ACP...")
+        config = RuntimeConfig(
+            workspace=workspace,
+            lsp_enabled=False,  # Skip LSP for this example
+            acp_enabled=True,
+            approval_mode="auto"  # Auto-approve for demo
+        )
+        runtime = InteractiveRuntime(config)
+        status = await runtime.start()
+        
+        print(f"   ACP ready: {runtime.acp_ready}")
+        print(f"   Read-only: {runtime.read_only}")
+        print()
+        
+        # 2. Create agent-centric tools
+        print("2. Creating agent-centric tools...")
+        tools = create_agent_centric_tools(runtime)
+        print(f"   Tools created: {len(tools)}")
+        for tool in tools:
+            print(f"     - {tool.__name__}")
+        print()
+        
+        # 3. Create Agent with ACP-powered tools
+        print("3. Creating Agent with ACP-powered tools...")
+        agent = Agent(
+            name="FileAgent",
+            instructions="""You help create and manage files. 
+            Use acp_create_file to create files.
+            Use read_file to read files.
+            Use list_files to list directory contents.""",
+            tools=tools,
+            verbose=True
+        )
+        print()
+        
+        # 4. Ask agent to create a file
+        print("4. Asking agent to create a Python file...")
+        print("-" * 40)
+        result = agent.start(
+            "Create a Python file called calculator.py with add and subtract functions"
+        )
+        print("-" * 40)
+        print(f"Agent response: {result[:200]}..." if len(str(result)) > 200 else f"Agent response: {result}")
+        print()
+        
+        # 5. Verify file was created
+        print("5. Verifying file creation...")
+        calc_path = os.path.join(workspace, "calculator.py")
+        if os.path.exists(calc_path):
+            print(f"   ✓ File created at {calc_path}")
+            with open(calc_path) as f:
+                content = f.read()
+                print(f"   Content preview:")
+                for line in content.split('\n')[:10]:
+                    print(f"     {line}")
+        else:
+            # Check what files exist
+            files = os.listdir(workspace)
+            print(f"   Files in workspace: {files}")
+            for f in files:
+                fp = os.path.join(workspace, f)
+                if os.path.isfile(fp):
+                    print(f"   ✓ Found: {f}")
+        print()
+        
+        # 6. Ask agent to read the file
+        print("6. Asking agent to read the file...")
+        print("-" * 40)
+        result2 = agent.start("Read the calculator.py file and tell me what functions it has")
+        print("-" * 40)
+        print(f"Agent response: {result2[:300]}..." if len(str(result2)) > 300 else f"Agent response: {result2}")
+        print()
+        
+        # Cleanup
+        await runtime.stop()
+        
+    finally:
+        # Clean up workspace
+        shutil.rmtree(workspace, ignore_errors=True)
+        print(f"Cleaned up workspace: {workspace}")
+    
+    print()
+    print("=" * 60)
+    print("EXAMPLE COMPLETE")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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/debug/debug_interactive_example.py

@@ -0,0 +1,228 @@
+"""
+Debug Interactive Mode Example
+
+This example demonstrates how to use the debug CLI commands to test
+the Interactive Coding Assistant without entering TUI mode.
+
+Features demonstrated:
+- LSP symbol listing
+- LSP definition lookup
+- ACP action planning
+- ACP action execution
+- Trace capture and JSON output
+"""
+
+import asyncio
+import json
+import sys
+from pathlib import Path
+
+# Add the src directory to path for development
+sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src" / "praisonai"))
+
+from praisonai.cli.features.interactive_runtime import create_runtime
+from praisonai.cli.features.code_intelligence import CodeIntelligenceRouter
+from praisonai.cli.features.action_orchestrator import ActionOrchestrator
+
+
+async def demo_lsp_queries(workspace: str):
+    """Demonstrate LSP-based code queries."""
+    print("\n" + "=" * 60)
+    print("LSP CODE QUERIES DEMO")
+    print("=" * 60)
+    
+    runtime = create_runtime(
+        workspace=workspace,
+        lsp=True,
+        acp=False,
+        trace=True
+    )
+    
+    try:
+        await runtime.start()
+        router = CodeIntelligenceRouter(runtime)
+        
+        # Query 1: List symbols
+        print("\n[Query 1] List all symbols in app.py")
+        result = await router.handle_query("list all functions in app.py", "app.py")
+        print(f"  Success: {result.success}")
+        print(f"  LSP Used: {result.lsp_used}")
+        print(f"  Fallback Used: {result.fallback_used}")
+        if result.data:
+            print(f"  Symbols found: {len(result.data)}")
+            for symbol in result.data[:5]:
+                print(f"    - {symbol['name']} ({symbol['kind']}) at line {symbol['line']}")
+        
+        # Query 2: Find definition
+        print("\n[Query 2] Find definition of Calculator")
+        result = await router.handle_query("where is Calculator defined", "app.py")
+        print(f"  Success: {result.success}")
+        if result.citations:
+            print(f"  Found at: {result.citations[0]}")
+        
+        # Get trace
+        trace = runtime.get_trace()
+        print(f"\n[Trace] {len(trace.entries)} entries captured")
+        
+    finally:
+        await runtime.stop()
+
+
+async def demo_acp_actions(workspace: str):
+    """Demonstrate ACP-based code modifications."""
+    print("\n" + "=" * 60)
+    print("ACP CODE MODIFICATIONS DEMO")
+    print("=" * 60)
+    
+    runtime = create_runtime(
+        workspace=workspace,
+        lsp=False,
+        acp=True,
+        approval="auto",
+        trace=True
+    )
+    
+    try:
+        await runtime.start()
+        print(f"\n  ACP Ready: {runtime.acp_ready}")
+        print(f"  Read Only: {runtime.read_only}")
+        
+        orchestrator = ActionOrchestrator(runtime)
+        
+        # Action 1: Create a plan
+        print("\n[Action 1] Create plan for new file")
+        result = await orchestrator.create_plan("create a new file called demo_output.py")
+        print(f"  Success: {result.success}")
+        if result.plan:
+            print(f"  Plan ID: {result.plan.id}")
+            print(f"  Steps: {len(result.plan.steps)}")
+            for step in result.plan.steps:
+                print(f"    - {step.action_type.value}: {step.target}")
+        
+        # Action 2: Execute with auto-approve
+        print("\n[Action 2] Execute file creation")
+        exec_result = await orchestrator.execute(
+            "create a new file called demo_output.py with a hello function",
+            auto_approve=True
+        )
+        print(f"  Success: {exec_result.success}")
+        print(f"  Applied Steps: {exec_result.applied_steps}")
+        
+        # Verify file was created
+        output_file = Path(workspace) / "demo_output.py"
+        if output_file.exists():
+            print(f"  File created: {output_file}")
+            print(f"  Content preview: {output_file.read_text()[:100]}...")
+        
+        # Get trace
+        trace = runtime.get_trace()
+        print(f"\n[Trace] {len(trace.entries)} entries captured")
+        for entry in trace.entries:
+            print(f"    [{entry.category}] {entry.action}")
+        
+    finally:
+        await runtime.stop()
+
+
+async def demo_full_interactive_turn(workspace: str):
+    """Demonstrate full interactive turn simulation."""
+    print("\n" + "=" * 60)
+    print("FULL INTERACTIVE TURN SIMULATION")
+    print("=" * 60)
+    
+    runtime = create_runtime(
+        workspace=workspace,
+        lsp=True,
+        acp=True,
+        approval="auto",
+        trace=True
+    )
+    
+    try:
+        status = await runtime.start()
+        print("\n  Runtime Status:")
+        print(f"    LSP: {'ready' if status['lsp']['ready'] else 'fallback'}")
+        print(f"    ACP: {'ready' if status['acp']['ready'] else 'unavailable'}")
+        
+        router = CodeIntelligenceRouter(runtime)
+        orchestrator = ActionOrchestrator(runtime)
+        
+        # Simulate user prompt: code query
+        prompt1 = "What functions are in app.py?"
+        print(f"\n[Turn 1] User: {prompt1}")
+        
+        # Classify and route
+        result = await router.handle_query(prompt1, "app.py")
+        print(f"  Intent: {result.intent.value}")
+        print(f"  Response: Found {len(result.data) if result.data else 0} symbols")
+        
+        # Simulate user prompt: code modification
+        prompt2 = "Create a new test file"
+        print(f"\n[Turn 2] User: {prompt2}")
+        
+        exec_result = await orchestrator.execute(
+            "create a new file called test_demo.py",
+            auto_approve=True
+        )
+        print("  Action: file_create")
+        print(f"  Result: {'success' if exec_result.success else 'failed'}")
+        
+        # Export trace as JSON
+        trace = runtime.get_trace()
+        trace_json = trace.to_dict()
+        print("[Trace Export]")
+        print(json.dumps(trace_json, indent=2, default=str)[:500] + "...")
+        
+    finally:
+        await runtime.stop()
+
+
+def main():
+    """Run the debug demo."""
+    # Create a temporary workspace
+    workspace = Path(__file__).parent / "_demo_workspace"
+    workspace.mkdir(exist_ok=True)
+    
+    # Create a sample app.py
+    app_file = workspace / "app.py"
+    app_file.write_text('''"""Sample application."""
+
+def greet(name: str) -> str:
+    """Greet someone."""
+    return f"Hello, {name}!"
+
+def add(a: int, b: int) -> int:
+    """Add two numbers."""
+    return a + b
+
+class Calculator:
+    """Simple calculator."""
+    
+    def __init__(self):
+        self.value = 0
+    
+    def add(self, x):
+        self.value += x
+        return self.value
+
+if __name__ == "__main__":
+    print(greet("World"))
+''')
+    
+    print("=" * 60)
+    print("PRAISONAI DEBUG MODE DEMO")
+    print("=" * 60)
+    print(f"Workspace: {workspace}")
+    
+    # Run demos
+    asyncio.run(demo_lsp_queries(str(workspace)))
+    asyncio.run(demo_acp_actions(str(workspace)))
+    asyncio.run(demo_full_interactive_turn(str(workspace)))
+    
+    print("\n" + "=" * 60)
+    print("DEMO 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/js/ai-sdk/README.md

@@ -0,0 +1,41 @@
+# AI SDK Examples
+
+Examples demonstrating the AI SDK integration in PraisonAI TypeScript.
+
+## Prerequisites
+
+```bash
+npm install praisonai ai @ai-sdk/openai @ai-sdk/anthropic @ai-sdk/google zod
+```
+
+## Environment Variables
+
+```bash
+export OPENAI_API_KEY=sk-...
+export ANTHROPIC_API_KEY=sk-ant-...
+export GOOGLE_API_KEY=AIza...
+```
+
+## Examples
+
+| File | Description |
+|------|-------------|
+| `basic-chat.ts` | Simple text generation |
+| `streaming.ts` | Streaming text output |
+| `tool-calling.ts` | Function/tool calling |
+| `multi-provider.ts` | Using multiple providers |
+| `structured-output.ts` | JSON schema output |
+| `multi-agent.ts` | Multi-agent attribution |
+
+## Running Examples
+
+```bash
+npx ts-node basic-chat.ts
+npx ts-node streaming.ts
+npx ts-node tool-calling.ts
+```
+
+## More Information
+
+- [AI SDK Documentation](https://docs.praisonai.com/docs/js/ai-sdk)
+- [AI SDK CLI](https://docs.praisonai.com/docs/js/ai-sdk-cli)

examples/js/ai-sdk/basic-chat.ts

@@ -0,0 +1,39 @@
+/**
+ * AI SDK Basic Chat Example
+ * 
+ * Demonstrates simple text generation using the AI SDK backend.
+ * 
+ * Usage:
+ *   npx ts-node basic-chat.ts
+ * 
+ * Environment:
+ *   OPENAI_API_KEY - Your OpenAI API key
+ */
+
+import { createAISDKBackend } from 'praisonai';
+
+async function main() {
+  // Create backend with model string
+  const backend = createAISDKBackend('openai/gpt-4o-mini');
+
+  console.log('Sending message to OpenAI...\n');
+
+  // Generate text
+  const result = await backend.generateText({
+    messages: [
+      { role: 'system', content: 'You are a helpful assistant.' },
+      { role: 'user', content: 'What are the three primary colors?' }
+    ],
+    maxTokens: 200,
+    temperature: 0.7,
+  });
+
+  console.log('Response:', result.text);
+  console.log('\nFinish reason:', result.finishReason);
+  
+  if (result.usage) {
+    console.log('Token usage:', result.usage);
+  }
+}
+
+main().catch(console.error);

examples/js/ai-sdk/generate-text.ts

@@ -0,0 +1,32 @@
+/**
+ * Generate Text Example
+ * 
+ * Demonstrates basic text generation using AI SDK v6 via praisonai-ts.
+ * 
+ * Run: npx ts-node generate-text.ts
+ * Required: OPENAI_API_KEY
+ */
+
+// Import from local praisonai-ts package
+// In production: import { Agent } from 'praisonai';
+import { Agent } from '../../../src/praisonai-ts/src';
+
+async function main() {
+  // Simple text generation with Agent
+  const agent = new Agent({
+    instructions: 'You are a helpful assistant that provides concise answers.',
+    llm: 'openai/gpt-4o-mini',
+  });
+
+  console.log('=== Generate Text Example ===\n');
+
+  // Basic chat
+  const response = await agent.chat('What is the capital of France?');
+  console.log('Response:', response);
+
+  // With context
+  const response2 = await agent.chat('What is its population?');
+  console.log('\nFollow-up:', response2);
+}
+
+main().catch(console.error);

examples/js/ai-sdk/mcp-client.ts

@@ -0,0 +1,91 @@
+/**
+ * MCP Client Example (AI SDK v6 Parity)
+ * 
+ * Demonstrates Model Context Protocol client with HTTP/SSE transports.
+ * 
+ * Run: npx ts-node mcp-client.ts
+ * Note: Requires an MCP server running
+ */
+
+import { 
+  createMCP,
+  closeMCPClient,
+  type MCPConfig,
+} from '../../../src/praisonai-ts/src';
+
+async function main() {
+  console.log('=== MCP Client Example ===\n');
+
+  // Example configurations for different transports
+  const configs: Record<string, MCPConfig> = {
+    // HTTP transport (recommended for production)
+    http: {
+      transport: {
+        type: 'http',
+        url: 'https://mcp-server.example.com/mcp',
+        headers: { Authorization: 'Bearer token' },
+      },
+      name: 'http-client',
+    },
+    // SSE transport
+    sse: {
+      transport: {
+        type: 'sse',
+        url: 'https://mcp-server.example.com/sse',
+      },
+      name: 'sse-client',
+    },
+    // Stdio transport (for local servers)
+    stdio: {
+      transport: {
+        type: 'stdio',
+        command: 'npx',
+        args: ['-y', '@modelcontextprotocol/server-everything'],
+      },
+      name: 'stdio-client',
+    },
+  };
+
+  console.log('MCP Transport Configurations:');
+  console.log('');
+  console.log('1. HTTP Transport (recommended):');
+  console.log(`   ${JSON.stringify(configs.http.transport, null, 2)}`);
+  console.log('');
+  console.log('2. SSE Transport:');
+  console.log(`   ${JSON.stringify(configs.sse.transport, null, 2)}`);
+  console.log('');
+  console.log('3. Stdio Transport (local):');
+  console.log(`   ${JSON.stringify(configs.stdio.transport, null, 2)}`);
+
+  console.log('\n--- Example Usage ---\n');
+  console.log(`
+// Create MCP client
+const client = await createMCP({
+  transport: {
+    type: 'http',
+    url: 'https://your-mcp-server.com/mcp',
+    headers: { Authorization: 'Bearer your-token' },
+  }
+});
+
+// Get available tools
+const tools = await client.tools();
+console.log('Available tools:', Object.keys(tools));
+
+// List resources
+const resources = await client.listResources();
+console.log('Resources:', resources);
+
+// List prompts
+const prompts = await client.listPrompts();
+console.log('Prompts:', prompts);
+
+// Close when done
+await closeMCPClient('default');
+`);
+
+  console.log('\n✅ MCP Client demo completed!');
+  console.log('Note: To run with a real MCP server, uncomment the connection code.');
+}
+
+main().catch(console.error);

examples/js/ai-sdk/multi-agent.ts

@@ -0,0 +1,96 @@
+/**
+ * AI SDK Multi-Agent Attribution Example
+ * 
+ * Demonstrates how to propagate attribution context (agentId, runId, traceId)
+ * to LLM calls for multi-agent observability and safety.
+ * 
+ * Usage:
+ *   npx ts-node multi-agent.ts
+ * 
+ * Environment:
+ *   OPENAI_API_KEY - Your OpenAI API key
+ */
+
+import { createAISDKBackend } from 'praisonai';
+
+// Simulate a multi-agent system with attribution
+interface AgentContext {
+  agentId: string;
+  runId: string;
+  traceId: string;
+  sessionId?: string;
+}
+
+async function runAgent(name: string, task: string, context: AgentContext) {
+  console.log(`\n[${name}] Starting task: ${task}`);
+  console.log(`  Attribution: agentId=${context.agentId}, runId=${context.runId}`);
+
+  const backend = createAISDKBackend('openai/gpt-4o-mini', {
+    attribution: context,
+    timeout: 30000,
+  });
+
+  const result = await backend.generateText({
+    messages: [
+      { role: 'system', content: `You are ${name}, a specialized AI agent.` },
+      { role: 'user', content: task }
+    ],
+    maxTokens: 100,
+  });
+
+  console.log(`  Response: ${result.text.slice(0, 100)}...`);
+  return result.text;
+}
+
+async function main() {
+  // Create a shared trace ID for the entire workflow
+  const traceId = `trace-${Date.now().toString(36)}`;
+  const sessionId = 'session-user123';
+
+  console.log('Multi-Agent Workflow with Attribution');
+  console.log('=====================================');
+  console.log(`Trace ID: ${traceId}`);
+  console.log(`Session ID: ${sessionId}`);
+
+  // Agent 1: Research Agent
+  const researchResult = await runAgent(
+    'ResearchAgent',
+    'What are the key benefits of TypeScript?',
+    {
+      agentId: 'agent-research',
+      runId: `run-${Date.now().toString(36)}-1`,
+      traceId,
+      sessionId,
+    }
+  );
+
+  // Agent 2: Summary Agent
+  const summaryResult = await runAgent(
+    'SummaryAgent',
+    `Summarize this in one sentence: ${researchResult}`,
+    {
+      agentId: 'agent-summary',
+      runId: `run-${Date.now().toString(36)}-2`,
+      traceId,
+      sessionId,
+    }
+  );
+
+  // Agent 3: Review Agent
+  await runAgent(
+    'ReviewAgent',
+    `Review this summary for accuracy: ${summaryResult}`,
+    {
+      agentId: 'agent-review',
+      runId: `run-${Date.now().toString(36)}-3`,
+      traceId,
+      sessionId,
+    }
+  );
+
+  console.log('\n\nWorkflow complete!');
+  console.log('All requests were tagged with the same trace ID for observability.');
+  console.log('Check your LLM provider dashboard to see the correlated requests.');
+}
+
+main().catch(console.error);

examples/js/ai-sdk/multi-provider.ts

@@ -0,0 +1,59 @@
+/**
+ * AI SDK Multi-Provider Example
+ * 
+ * Demonstrates using multiple LLM providers with the same interface.
+ * 
+ * Usage:
+ *   npx ts-node multi-provider.ts
+ * 
+ * Environment:
+ *   OPENAI_API_KEY - Your OpenAI API key
+ *   ANTHROPIC_API_KEY - Your Anthropic API key
+ *   GOOGLE_API_KEY - Your Google API key
+ */
+
+import { createAISDKBackend } from 'praisonai';
+
+const PROMPT = 'Explain what an API is in one sentence.';
+
+async function testProvider(modelString: string) {
+  try {
+    const backend = createAISDKBackend(modelString, {
+      timeout: 30000,
+    });
+
+    const startTime = Date.now();
+    const result = await backend.generateText({
+      messages: [{ role: 'user', content: PROMPT }],
+      maxTokens: 100,
+    });
+    const duration = Date.now() - startTime;
+
+    console.log(`\n${modelString}:`);
+    console.log(`  Response: ${result.text}`);
+    console.log(`  Duration: ${duration}ms`);
+    return true;
+  } catch (error: any) {
+    console.log(`\n${modelString}:`);
+    console.log(`  Error: ${error.message}`);
+    return false;
+  }
+}
+
+async function main() {
+  console.log('Testing multiple providers with the same prompt...');
+  console.log(`Prompt: "${PROMPT}"`);
+
+  const providers = [
+    'openai/gpt-4o-mini',
+    'anthropic/claude-3-haiku-20240307',
+    'google/gemini-1.5-flash',
+  ];
+
+  const results = await Promise.all(providers.map(testProvider));
+  
+  const successful = results.filter(Boolean).length;
+  console.log(`\n\nSummary: ${successful}/${providers.length} providers succeeded`);
+}
+
+main().catch(console.error);

examples/js/ai-sdk/speech-transcription.ts

@@ -0,0 +1,87 @@
+/**
+ * Speech & Transcription Example (AI SDK v6 Parity)
+ * 
+ * Demonstrates speech generation and transcription wrappers.
+ * 
+ * Run: npx ts-node speech-transcription.ts
+ * Required: OPENAI_API_KEY or ELEVENLABS_API_KEY
+ */
+
+import { 
+  generateSpeech,
+  transcribe,
+  SPEECH_MODELS,
+  TRANSCRIPTION_MODELS,
+} from '../../../src/praisonai-ts/src';
+
+async function main() {
+  console.log('=== Speech & Transcription Example ===\n');
+
+  // Show available models
+  console.log('1. Available Speech Models:');
+  Object.entries(SPEECH_MODELS).forEach(([key, value]) => {
+    console.log(`   ${key}: ${value}`);
+  });
+
+  console.log('\n2. Available Transcription Models:');
+  Object.entries(TRANSCRIPTION_MODELS).forEach(([key, value]) => {
+    console.log(`   ${key}: ${value}`);
+  });
+
+  // Example: Generate speech (requires API key)
+  console.log('\n3. Speech Generation Example:');
+  console.log(`
+// Generate speech from text
+const result = await generateSpeech({
+  text: 'Hello, this is a test of speech synthesis.',
+  model: 'openai/tts-1',
+  voice: 'alloy',
+});
+
+// Save to file
+await fs.writeFile('output.mp3', result.audio);
+console.log('Audio saved to output.mp3');
+`);
+
+  // Example: Transcribe audio (requires API key)
+  console.log('4. Transcription Example:');
+  console.log(`
+// Transcribe audio file
+const result = await transcribe({
+  audio: await fs.readFile('audio.mp3'),
+  model: 'openai/whisper-1',
+});
+
+console.log('Transcription:', result.text);
+console.log('Language:', result.language);
+console.log('Duration:', result.durationInSeconds, 'seconds');
+
+// With segments
+if (result.segments) {
+  result.segments.forEach(seg => {
+    console.log(\`[\${seg.start}s - \${seg.end}s]: \${seg.text}\`);
+  });
+}
+`);
+
+  // Live test if API key is available
+  if (process.env.OPENAI_API_KEY) {
+    console.log('\n5. Live Speech Generation Test:');
+    try {
+      const result = await generateSpeech({
+        text: 'Hello from PraisonAI!',
+        model: 'openai/tts-1',
+        voice: 'alloy',
+      });
+      console.log(`   ✅ Generated ${result.audio.length} bytes of audio`);
+    } catch (error: any) {
+      console.log(`   ⚠️ Speech generation failed: ${error.message}`);
+    }
+  } else {
+    console.log('\n5. Live Test: Skipped (OPENAI_API_KEY not set)');
+  }
+
+  console.log('\n✅ Speech & Transcription demo completed!');
+}
+
+main().catch(console.error);

examples/js/ai-sdk/stream-text.ts

@@ -0,0 +1,27 @@
+/**
+ * Stream Text Example
+ * 
+ * Demonstrates streaming text generation using AI SDK v6 via praisonai-ts.
+ * 
+ * Run: npx ts-node stream-text.ts
+ * Required: OPENAI_API_KEY
+ */
+
+import { Agent } from '../../../src/praisonai-ts/src';
+
+async function main() {
+  const agent = new Agent({
+    instructions: 'You are a helpful assistant that provides detailed answers.',
+    llm: 'openai/gpt-4o-mini',
+    stream: true,
+  });
+
+  console.log('=== Stream Text Example ===\n');
+  console.log('Streaming response:\n');
+
+  // Stream the response
+  const response = await agent.chat('Explain quantum computing in 3 paragraphs.');
+  console.log('\n\nFinal response:', response);
+}
+
+main().catch(console.error);

examples/js/ai-sdk/streaming.ts

@@ -0,0 +1,39 @@
+/**
+ * AI SDK Streaming Example
+ * 
+ * Demonstrates streaming text generation with real-time output.
+ * 
+ * Usage:
+ *   npx ts-node streaming.ts
+ * 
+ * Environment:
+ *   OPENAI_API_KEY - Your OpenAI API key
+ */
+
+import { createAISDKBackend } from 'praisonai';
+
+async function main() {
+  const backend = createAISDKBackend('openai/gpt-4o-mini');
+
+  console.log('Streaming response from OpenAI...\n');
+
+  const stream = await backend.streamText({
+    messages: [
+      { role: 'user', content: 'Write a short poem about coding.' }
+    ],
+    maxTokens: 200,
+  });
+
+  // Stream chunks as they arrive
+  for await (const chunk of stream) {
+    if (chunk.text) {
+      process.stdout.write(chunk.text);
+    }
+    
+    if (chunk.finishReason) {
+      console.log('\n\nFinish reason:', chunk.finishReason);
+    }
+  }
+}
+
+main().catch(console.error);

examples/js/ai-sdk/structured-output.ts

@@ -0,0 +1,72 @@
+/**
+ * AI SDK Structured Output Example
+ * 
+ * Demonstrates generating typed JSON objects with Zod schemas.
+ * 
+ * Usage:
+ *   npx ts-node structured-output.ts
+ * 
+ * Environment:
+ *   OPENAI_API_KEY - Your OpenAI API key
+ */
+
+import { generateObject } from 'ai';
+import { openai } from '@ai-sdk/openai';
+import { z } from 'zod';
+
+// Define schema for a person profile
+const PersonSchema = z.object({
+  name: z.string().describe('Full name'),
+  age: z.number().describe('Age in years'),
+  occupation: z.string().describe('Job title'),
+  city: z.string().describe('City of residence'),
+  interests: z.array(z.string()).describe('List of hobbies/interests'),
+  bio: z.string().describe('Short biography'),
+});
+
+// Define schema for a recipe
+const RecipeSchema = z.object({
+  name: z.string().describe('Recipe name'),
+  cuisine: z.string().describe('Type of cuisine'),
+  prepTime: z.number().describe('Preparation time in minutes'),
+  cookTime: z.number().describe('Cooking time in minutes'),
+  servings: z.number().describe('Number of servings'),
+  ingredients: z.array(z.object({
+    item: z.string(),
+    amount: z.string(),
+  })).describe('List of ingredients'),
+  instructions: z.array(z.string()).describe('Step-by-step instructions'),
+});
+
+async function main() {
+  console.log('Generating structured output with Zod schemas...\n');
+
+  // Generate a person profile
+  console.log('1. Generating a fictional person profile...');
+  const personResult = await generateObject({
+    model: openai('gpt-4o-mini'),
+    schema: PersonSchema,
+    prompt: 'Generate a fictional software engineer profile.',
+  });
+
+  console.log('Person:', JSON.stringify(personResult.object, null, 2));
+
+  // Generate a recipe
+  console.log('\n2. Generating a recipe...');
+  const recipeResult = await generateObject({
+    model: openai('gpt-4o-mini'),
+    schema: RecipeSchema,
+    prompt: 'Generate a simple pasta recipe.',
+  });
+
+  console.log('Recipe:', JSON.stringify(recipeResult.object, null, 2));
+
+  // Type-safe access
+  console.log('\n3. Type-safe access:');
+  console.log(`  Person name: ${personResult.object.name}`);
+  console.log(`  Person age: ${personResult.object.age}`);
+  console.log(`  Recipe name: ${recipeResult.object.name}`);
+  console.log(`  Total time: ${recipeResult.object.prepTime + recipeResult.object.cookTime} minutes`);
+}
+
+main().catch(console.error);

examples/js/ai-sdk/telemetry.ts

@@ -0,0 +1,78 @@
+/**
+ * Telemetry Example (AI SDK v6 Parity)
+ * 
+ * Demonstrates telemetry and tracing with optional OpenTelemetry integration.
+ * 
+ * Run: npx ts-node telemetry.ts
+ * Required: OPENAI_API_KEY
+ */
+
+import { 
+  configureTelemetry,
+  isTelemetryEnabled,
+  createAISpan,
+  recordEvent,
+  getEvents,
+  clearEvents,
+  Agent,
+} from '../../../src/praisonai-ts/src';
+
+async function main() {
+  console.log('=== Telemetry Example ===\n');
+
+  // Configure telemetry
+  configureTelemetry({
+    isEnabled: true,
+    functionId: 'telemetry-demo',
+    metadata: {
+      version: '1.0.0',
+      environment: 'development',
+    },
+  });
+
+  console.log('1. Telemetry Status:');
+  console.log(`   Enabled: ${isTelemetryEnabled()}`);
+
+  // Create a span for an operation
+  console.log('\n2. Creating AI Span:');
+  const span = createAISpan('generateText', {
+    model: 'gpt-4o-mini',
+    provider: 'openai',
+  });
+
+  try {
+    // Simulate AI operation
+    console.log('   Performing AI operation...');
+    await new Promise(resolve => setTimeout(resolve, 100));
+    
+    span.setAttribute('tokens', 150);
+    span.setStatus({ code: 'ok' });
+    console.log('   Span completed successfully');
+  } catch (error: any) {
+    span.recordException(error);
+    span.setStatus({ code: 'error', message: error.message });
+  } finally {
+    span.end();
+  }
+
+  // Record custom events
+  console.log('\n3. Recording Events:');
+  recordEvent('agent.started', { agentName: 'demo-agent' });
+  recordEvent('tool.called', { toolName: 'search', duration: 250 });
+  recordEvent('agent.completed', { success: true });
+
+  const events = getEvents();
+  console.log(`   Recorded ${events.length} events:`);
+  events.forEach(e => {
+    console.log(`   - ${e.name}: ${JSON.stringify(e.attributes)}`);
+  });
+
+  // Clear events
+  console.log('\n4. Clearing Events:');
+  clearEvents();
+  console.log(`   Events after clear: ${getEvents().length}`);
+
+  console.log('\n✅ Telemetry demo completed!');
+}
+
+main().catch(console.error);

examples/js/ai-sdk/tool-approval.ts

@@ -0,0 +1,71 @@
+/**
+ * Tool Approval Example (AI SDK v6 Parity)
+ * 
+ * Demonstrates human-in-the-loop tool approval using needsApproval.
+ * 
+ * Run: npx ts-node tool-approval.ts
+ * Required: OPENAI_API_KEY
+ */
+
+import { 
+  Agent,
+  withApproval,
+  getApprovalManager,
+  createDangerousPatternChecker,
+} from '../../../src/praisonai-ts/src';
+
+async function main() {
+  console.log('=== Tool Approval Example ===\n');
+
+  // Set up approval handler
+  const manager = getApprovalManager();
+  manager.onApprovalRequest(async (request) => {
+    console.log(`\n🔐 APPROVAL REQUESTED`);
+    console.log(`   Tool: ${request.toolName}`);
+    console.log(`   Input: ${JSON.stringify(request.input)}`);
+    console.log(`   Reason: ${request.reason || 'No reason provided'}`);
+    
+    // Auto-approve for demo (in production, prompt user)
+    console.log(`   ✅ Auto-approved for demo\n`);
+    return true;
+  });
+
+  // Create a tool that always requires approval
+  const deleteFile = withApproval({
+    name: 'deleteFile',
+    description: 'Delete a file from the filesystem',
+    needsApproval: true, // Always require approval
+    execute: async (input: { path: string }) => {
+      console.log(`   Executing: Would delete ${input.path}`);
+      return { success: true, deleted: input.path };
+    },
+  });
+
+  // Create a tool with conditional approval (dangerous patterns only)
+  const runCommand = withApproval({
+    name: 'runCommand',
+    description: 'Run a shell command',
+    needsApproval: createDangerousPatternChecker(),
+    execute: async (input: { command: string }) => {
+      console.log(`   Executing: ${input.command}`);
+      return { output: 'command executed' };
+    },
+  });
+
+  // Test the tools
+  console.log('1. Testing deleteFile (always requires approval):');
+  const result1 = await deleteFile({ path: '/tmp/test.txt' });
+  console.log('   Result:', result1);
+
+  console.log('\n2. Testing runCommand with safe command (no approval needed):');
+  const result2 = await runCommand({ command: 'ls -la' });
+  console.log('   Result:', result2);
+
+  console.log('\n3. Testing runCommand with dangerous command (requires approval):');
+  const result3 = await runCommand({ command: 'rm -rf /tmp/test' });
+  console.log('   Result:', result3);
+
+  console.log('\n✅ Tool approval demo completed!');
+}
+
+main().catch(console.error);

examples/js/ai-sdk/tool-calling.ts

@@ -0,0 +1,75 @@
+/**
+ * AI SDK Tool Calling Example
+ * 
+ * Demonstrates function/tool calling with Zod schemas.
+ * 
+ * Usage:
+ *   npx ts-node tool-calling.ts
+ * 
+ * Environment:
+ *   OPENAI_API_KEY - Your OpenAI API key
+ */
+
+import { generateText, tool } from 'ai';
+import { openai } from '@ai-sdk/openai';
+import { z } from 'zod';
+
+// Define tools with Zod schemas
+const tools = {
+  get_weather: tool({
+    description: 'Get the current weather for a city',
+    inputSchema: z.object({
+      city: z.string().describe('The city name'),
+      unit: z.enum(['celsius', 'fahrenheit']).optional().describe('Temperature unit'),
+    }),
+    execute: async ({ city, unit = 'celsius' }) => {
+      // Mock weather data
+      const weather = {
+        city,
+        temperature: unit === 'celsius' ? 22 : 72,
+        unit,
+        condition: 'sunny',
+        humidity: 45,
+      };
+      return weather;
+    },
+  }),
+  
+  get_time: tool({
+    description: 'Get the current time in a timezone',
+    inputSchema: z.object({
+      timezone: z.string().describe('Timezone like "America/New_York"'),
+    }),
+    execute: async ({ timezone }) => {
+      const time = new Date().toLocaleString('en-US', { timeZone: timezone });
+      return { timezone, time };
+    },
+  }),
+};
+
+async function main() {
+  console.log('Asking about weather with tool calling...\n');
+
+  const result = await generateText({
+    model: openai('gpt-4o-mini'),
+    messages: [
+      { role: 'user', content: 'What is the weather in Paris and what time is it in New York?' }
+    ],
+    tools,
+    maxSteps: 5, // Allow multiple tool calls
+  });
+
+  console.log('Tool Calls:');
+  for (const toolCall of result.toolCalls) {
+    console.log(`  - ${toolCall.toolName}:`, toolCall.args);
+  }
+
+  console.log('\nTool Results:');
+  for (const toolResult of result.toolResults) {
+    console.log(`  - ${toolResult.toolName}:`, toolResult.result);
+  }
+
+  console.log('\nFinal Response:', result.text);
+}
+
+main().catch(console.error);

examples/js/ai-sdk/ui-message.ts

@@ -0,0 +1,63 @@
+/**
+ * UI Message Example (AI SDK v6 Parity)
+ * 
+ * Demonstrates UIMessage protocol for Next.js/React integration.
+ * 
+ * Run: npx ts-node ui-message.ts
+ * Required: OPENAI_API_KEY
+ */
+
+import { 
+  createTextMessage,
+  createSystemMessage,
+  convertToModelMessages,
+  convertToUIMessages,
+  validateUIMessages,
+  hasPendingApprovals,
+  type UIMessage,
+} from '../../../src/praisonai-ts/src';
+
+async function main() {
+  console.log('=== UI Message Example ===\n');
+
+  // Create UI messages
+  const messages: UIMessage[] = [
+    createSystemMessage('You are a helpful assistant.'),
+    createTextMessage('user', 'What is the capital of France?'),
+    createTextMessage('assistant', 'The capital of France is Paris.'),
+    createTextMessage('user', 'What is its population?'),
+  ];
+
+  console.log('1. Created UI Messages:');
+  messages.forEach((msg, i) => {
+    const text = msg.parts.find(p => p.type === 'text');
+    console.log(`   [${i}] ${msg.role}: ${text ? (text as any).text.substring(0, 50) : '...'}`);
+  });
+
+  // Validate messages
+  console.log('\n2. Validating messages:');
+  const isValid = validateUIMessages(messages);
+  console.log(`   Valid: ${isValid}`);
+
+  // Convert to model messages (for AI SDK)
+  console.log('\n3. Converting to Model Messages:');
+  const modelMessages = await convertToModelMessages(messages);
+  modelMessages.forEach((msg, i) => {
+    const content = typeof msg.content === 'string' ? msg.content : JSON.stringify(msg.content);
+    console.log(`   [${i}] ${msg.role}: ${content.substring(0, 50)}...`);
+  });
+
+  // Check for pending approvals
+  console.log('\n4. Checking for pending approvals:');
+  const hasPending = messages.some(m => hasPendingApprovals(m));
+  console.log(`   Has pending approvals: ${hasPending}`);
+
+  // Convert model messages back to UI messages
+  console.log('\n5. Round-trip conversion:');
+  const uiMessages = convertToUIMessages(modelMessages);
+  console.log(`   Converted ${uiMessages.length} messages back to UI format`);
+
+  console.log('\n✅ UI Message demo completed!');
+}
+
+main().catch(console.error);

examples/js/embeddings/embed-basic.ts

@@ -0,0 +1,65 @@
+/**
+ * Basic Embedding Example
+ * 
+ * Demonstrates using Agent.embed() for text embeddings
+ * 
+ * Run: npx ts-node examples/js/embeddings/embed-basic.ts
+ */
+
+import { Agent } from '../../../src/praisonai-ts/dist/agent/simple';
+import { embed, embedMany, cosineSimilarity } from '../../../src/praisonai-ts/dist/llm/embeddings';
+
+async function main() {
+  console.log('=== Basic Embedding Example ===\n');
+
+  // Method 1: Using Agent.embed()
+  console.log('1. Agent.embed() - Single text');
+  const agent = new Agent({
+    instructions: 'You are a helpful assistant',
+    llm: 'openai/gpt-4o-mini',
+    verbose: false
+  });
+
+  const singleEmbedding = await agent.embed('Hello world');
+  console.log(`   Dimensions: ${(singleEmbedding as number[]).length}`);
+  console.log(`   First 5 values: [${(singleEmbedding as number[]).slice(0, 5).map(v => v.toFixed(4)).join(', ')}...]`);
+
+  // Method 2: Using Agent.embed() with multiple texts
+  console.log('\n2. Agent.embed() - Multiple texts');
+  const multiEmbeddings = await agent.embed(['Hello', 'World', 'How are you?']);
+  console.log(`   Count: ${(multiEmbeddings as number[][]).length}`);
+  console.log(`   Each has ${(multiEmbeddings as number[][])[0].length} dimensions`);
+
+  // Method 3: Direct embed function
+  console.log('\n3. Direct embed() function');
+  const result = await embed('PraisonAI is an AI agent framework');
+  console.log(`   Dimensions: ${result.embedding.length}`);
+  console.log(`   Tokens used: ${result.usage?.tokens || 'N/A'}`);
+
+  // Method 4: Batch embeddings
+  console.log('\n4. Batch embedMany() function');
+  const texts = [
+    'Machine learning is a subset of AI',
+    'Deep learning uses neural networks',
+    'Natural language processing handles text'
+  ];
+  const batchResult = await embedMany(texts);
+  console.log(`   Embedded ${batchResult.embeddings.length} texts`);
+  console.log(`   Total tokens: ${batchResult.usage?.tokens || 'N/A'}`);
+
+  // Method 5: Similarity comparison
+  console.log('\n5. Cosine Similarity');
+  const emb1 = await embed('I love programming');
+  const emb2 = await embed('Coding is my passion');
+  const emb3 = await embed('The weather is nice today');
+
+  const sim12 = cosineSimilarity(emb1.embedding, emb2.embedding);
+  const sim13 = cosineSimilarity(emb1.embedding, emb3.embedding);
+
+  console.log(`   "I love programming" vs "Coding is my passion": ${(sim12 * 100).toFixed(1)}%`);
+  console.log(`   "I love programming" vs "The weather is nice": ${(sim13 * 100).toFixed(1)}%`);
+
+  console.log('\n=== Complete ===');
+}
+
+main().catch(console.error);

examples/js/embeddings/embed-docs.ts

@@ -0,0 +1,119 @@
+/**
+ * Document Embedding Example
+ * 
+ * Demonstrates chunking and embedding documents for retrieval
+ * 
+ * Run: npx ts-node examples/js/embeddings/embed-docs.ts
+ */
+
+import { embedMany, cosineSimilarity } from '../../../src/praisonai-ts/dist/llm/embeddings';
+
+// Sample documents
+const documents = [
+  {
+    id: 'doc1',
+    title: 'Introduction to AI',
+    content: 'Artificial Intelligence (AI) is the simulation of human intelligence by machines. It includes machine learning, natural language processing, and computer vision.'
+  },
+  {
+    id: 'doc2',
+    title: 'Machine Learning Basics',
+    content: 'Machine learning is a subset of AI that enables systems to learn from data. It includes supervised learning, unsupervised learning, and reinforcement learning.'
+  },
+  {
+    id: 'doc3',
+    title: 'Deep Learning',
+    content: 'Deep learning uses neural networks with multiple layers. It excels at tasks like image recognition, speech processing, and natural language understanding.'
+  },
+  {
+    id: 'doc4',
+    title: 'Natural Language Processing',
+    content: 'NLP enables computers to understand human language. Applications include chatbots, translation, sentiment analysis, and text summarization.'
+  },
+  {
+    id: 'doc5',
+    title: 'Computer Vision',
+    content: 'Computer vision allows machines to interpret visual information. It powers applications like facial recognition, autonomous vehicles, and medical imaging.'
+  }
+];
+
+interface EmbeddedDocument {
+  id: string;
+  title: string;
+  content: string;
+  embedding: number[];
+}
+
+async function main() {
+  console.log('=== Document Embedding Example ===\n');
+
+  // Step 1: Embed all documents
+  console.log('1. Embedding documents...');
+  const contents = documents.map(d => d.content);
+  const { embeddings } = await embedMany(contents);
+  
+  const embeddedDocs: EmbeddedDocument[] = documents.map((doc, i) => ({
+    ...doc,
+    embedding: embeddings[i]
+  }));
+  
+  console.log(`   Embedded ${embeddedDocs.length} documents`);
+  console.log(`   Embedding dimensions: ${embeddings[0].length}`);
+
+  // Step 2: Search function
+  async function search(query: string, topK: number = 3): Promise<Array<{ doc: EmbeddedDocument; score: number }>> {
+    const { embedding: queryEmbedding } = await (await import('../../../src/praisonai-ts/dist/llm/embeddings')).embed(query);
+    
+    const results = embeddedDocs.map(doc => ({
+      doc,
+      score: cosineSimilarity(queryEmbedding, doc.embedding)
+    }));
+    
+    return results
+      .sort((a, b) => b.score - a.score)
+      .slice(0, topK);
+  }
+
+  // Step 3: Test queries
+  console.log('\n2. Testing search queries...\n');
+
+  const queries = [
+    'How do machines learn from data?',
+    'What is image recognition?',
+    'Tell me about chatbots and translation'
+  ];
+
+  for (const query of queries) {
+    console.log(`Query: "${query}"`);
+    const results = await search(query);
+    
+    console.log('Top results:');
+    results.forEach((r, i) => {
+      console.log(`  ${i + 1}. [${(r.score * 100).toFixed(1)}%] ${r.doc.title}`);
+    });
+    console.log('');
+  }
+
+  // Step 4: Show document similarity matrix
+  console.log('3. Document Similarity Matrix');
+  console.log('');
+  
+  // Header
+  process.stdout.write('         ');
+  documents.forEach((_, i) => process.stdout.write(`Doc${i + 1}  `));
+  console.log('');
+  
+  // Matrix
+  for (let i = 0; i < embeddedDocs.length; i++) {
+    process.stdout.write(`Doc${i + 1}     `);
+    for (let j = 0; j < embeddedDocs.length; j++) {
+      const sim = cosineSimilarity(embeddedDocs[i].embedding, embeddedDocs[j].embedding);
+      process.stdout.write(`${(sim * 100).toFixed(0).padStart(3)}%  `);
+    }
+    console.log('');
+  }
+
+  console.log('\n=== Complete ===');
+}
+
+main().catch(console.error);

examples/js/embeddings/retrieval.ts

@@ -0,0 +1,131 @@
+/**
+ * Retrieval Example - Simple Vector Search
+ * 
+ * Demonstrates building a simple retrieval system with embeddings
+ * 
+ * Run: npx ts-node examples/js/embeddings/retrieval.ts
+ */
+
+import { embed, embedMany, cosineSimilarity } from '../../../src/praisonai-ts/dist/llm/embeddings';
+
+interface Document {
+  id: string;
+  content: string;
+  embedding?: number[];
+}
+
+interface SearchResult {
+  document: Document;
+  score: number;
+}
+
+// Simple in-memory vector store
+class SimpleVectorStore {
+  private documents: Document[] = [];
+
+  async add(doc: Document): Promise<void> {
+    const { embedding } = await embed(doc.content);
+    this.documents.push({ ...doc, embedding });
+  }
+
+  async addBatch(docs: Document[]): Promise<void> {
+    const contents = docs.map(d => d.content);
+    const { embeddings } = await embedMany(contents);
+    docs.forEach((doc, i) => {
+      this.documents.push({ ...doc, embedding: embeddings[i] });
+    });
+  }
+
+  async search(query: string, topK: number = 3): Promise<SearchResult[]> {
+    const { embedding: queryEmb } = await embed(query);
+    
+    const results = this.documents
+      .filter(doc => doc.embedding)
+      .map(doc => ({
+        document: doc,
+        score: cosineSimilarity(queryEmb, doc.embedding!)
+      }))
+      .sort((a, b) => b.score - a.score)
+      .slice(0, topK);
+    
+    return results;
+  }
+
+  get(id: string): Document | undefined {
+    return this.documents.find(d => d.id === id);
+  }
+
+  delete(id: string): boolean {
+    const idx = this.documents.findIndex(d => d.id === id);
+    if (idx >= 0) {
+      this.documents.splice(idx, 1);
+      return true;
+    }
+    return false;
+  }
+}
+
+async function main() {
+  console.log('=== Simple Vector Retrieval ===\n');
+
+  // Create vector store
+  console.log('1. Creating vector store...');
+  const store = new SimpleVectorStore();
+
+  // Add documents
+  console.log('2. Adding documents...');
+  const docs: Document[] = [
+    { id: '1', content: 'PraisonAI is an AI agent framework for building intelligent applications.' },
+    { id: '2', content: 'Agents can use tools to interact with external systems and APIs.' },
+    { id: '3', content: 'Workflows allow orchestrating multiple agents in sequence or parallel.' },
+    { id: '4', content: 'Memory systems help agents maintain context across conversations.' },
+    { id: '5', content: 'The AI SDK provides a unified interface for multiple LLM providers.' },
+    { id: '6', content: 'Embeddings enable semantic search and similarity matching.' },
+    { id: '7', content: 'Guardrails ensure agent outputs meet safety and quality standards.' },
+    { id: '8', content: 'Sessions persist conversation state for multi-turn interactions.' }
+  ];
+
+  await store.addBatch(docs);
+  console.log(`   Added ${docs.length} documents`);
+
+  // Search queries
+  console.log('\n3. Running search queries...\n');
+
+  const queries = [
+    'How do I build an AI application?',
+    'Can agents call external APIs?',
+    'How to maintain conversation context?',
+    'What providers are supported?'
+  ];
+
+  for (const query of queries) {
+    console.log(`Query: "${query}"`);
+    const results = await store.search(query);
+    
+    if (results.length > 0) {
+      console.log('Results:');
+      results.forEach((r, i) => {
+        console.log(`  ${i + 1}. [${(r.score * 100).toFixed(1)}%] ${r.document.content.substring(0, 60)}...`);
+      });
+    } else {
+      console.log('  No results found');
+    }
+    console.log('');
+  }
+
+  // Get specific document
+  console.log('4. Get document by ID...');
+  const doc = store.get('5');
+  if (doc) {
+    console.log(`   Found: ${doc.content}`);
+  }
+
+  // Delete document
+  console.log('\n5. Delete document...');
+  const deleted = store.delete('8');
+  console.log(`   Deleted doc 8: ${deleted}`);
+
+  console.log('\n=== Complete ===');
+}
+
+main().catch(console.error);

examples/js/mcp/filesystem-mcp.ts

@@ -0,0 +1,49 @@
+/**
+ * MCP Filesystem Server Example
+ * 
+ * Demonstrates using MCP to connect to a filesystem server.
+ * 
+ * Prerequisites:
+ *   npm install praisonai-ts
+ *   export OPENAI_API_KEY=your-api-key
+ * 
+ * Run:
+ *   npx ts-node filesystem-mcp.ts
+ */
+
+import { Agent, createMCP } from '../../../src/praisonai-ts/src';
+
+async function main() {
+  console.log('=== MCP Filesystem Server Example ===\n');
+
+  // Connect to MCP filesystem server
+  const mcp = await createMCP({
+    transport: {
+      type: 'stdio',
+      command: 'npx',
+      args: ['-y', '@modelcontextprotocol/server-filesystem', '.'],
+    },
+  });
+
+  console.log('Connected to MCP filesystem server\n');
+
+  // Get tools from MCP server
+  const tools = await mcp.tools();
+  console.log('Available tools:', Object.keys(tools));
+
+  // Create agent with MCP tools
+  const agent = new Agent({
+    name: 'FileAgent',
+    instructions: 'You help manage files using the filesystem tools.',
+    tools: Object.values(tools),
+  });
+
+  // Ask the agent to list files
+  const response = await agent.chat('List all TypeScript files in the current directory');
+  console.log('\nResponse:', response);
+
+  // Cleanup
+  await mcp.close();
+}
+
+main().catch(console.error);

examples/js/multimodal/image-analysis.ts

@@ -0,0 +1,41 @@
+/**
+ * Multimodal Image Analysis Example
+ * 
+ * Demonstrates analyzing images with a vision-capable model.
+ * 
+ * Prerequisites:
+ *   npm install praisonai-ts
+ *   export OPENAI_API_KEY=your-api-key
+ * 
+ * Run:
+ *   npx ts-node image-analysis.ts
+ */
+
+import { Agent } from '../../../src/praisonai-ts/src';
+
+async function main() {
+  console.log('=== Multimodal Image Analysis Example ===\n');
+
+  // Create an agent with a vision-capable model
+  const agent = new Agent({
+    name: 'VisionAgent',
+    llm: 'gpt-4o', // Vision-capable model
+    instructions: 'You analyze images and describe what you see in detail.',
+  });
+
+  // For multimodal, we describe the image in the prompt
+  // The agent will use its vision capabilities
+  const imageUrl = 'https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/300px-PNG_transparency_demonstration_1.png';
+  
+  console.log('Analyzing image...\n');
+  console.log('Image URL:', imageUrl, '\n');
+  
+  // Ask the agent to analyze (in production, use multimodal message format)
+  const response = await agent.chat(
+    `Please analyze this image: ${imageUrl}\nDescribe what you see in detail.`
+  );
+  
+  console.log('Analysis:', response);
+}
+
+main().catch(console.error);

examples/js/observability/README.md

@@ -0,0 +1,50 @@
+# Observability Examples
+
+Examples demonstrating how to use observability integrations with PraisonAI TypeScript.
+
+## Setup
+
+```bash
+# Install dependencies
+cd /path/to/praisonai-ts
+pnpm install
+
+# Set API keys for observability tools
+export LANGFUSE_SECRET_KEY=sk-...
+export LANGFUSE_PUBLIC_KEY=pk-...
+# Or use built-in tools (no API key needed)
+```
+
+## Examples
+
+| File | Description |
+|------|-------------|
+| `basic-tracing.ts` | Basic tracing with memory adapter |
+| `langfuse-integration.ts` | Langfuse observability integration |
+| `multi-agent-attribution.ts` | Track agent_id/run_id across agents |
+| `switchable-tools.ts` | Switch between observability tools via env |
+
+## Running Examples
+
+```bash
+# Run with ts-node or tsx
+npx tsx examples/js/observability/basic-tracing.ts
+npx tsx examples/js/observability/multi-agent-attribution.ts
+
+# With Langfuse (requires API keys)
+LANGFUSE_SECRET_KEY=sk-... npx tsx examples/js/observability/langfuse-integration.ts
+```
+
+## Supported Tools
+
+| Tool | Env Variable | Package |
+|------|--------------|---------|
+| Langfuse | `LANGFUSE_SECRET_KEY` | `langfuse` |
+| LangSmith | `LANGCHAIN_API_KEY` | `langsmith` |
+| LangWatch | `LANGWATCH_API_KEY` | `langwatch` |
+| Arize | `ARIZE_API_KEY` | `arize-phoenix` |
+| Axiom | `AXIOM_TOKEN` | `@axiomhq/js` |
+| Braintrust | `BRAINTRUST_API_KEY` | `braintrust` |
+| Helicone | `HELICONE_API_KEY` | `@helicone/helicone` |
+| Console | (none) | built-in |
+| Memory | (none) | built-in |

examples/js/observability/basic-tracing.ts

@@ -0,0 +1,45 @@
+/**
+ * Basic Tracing Example
+ * 
+ * Demonstrates basic observability with the built-in memory adapter.
+ * No external API keys required.
+ */
+
+import { Agent, MemoryObservabilityAdapter, setObservabilityAdapter } from 'praisonai';
+
+async function main() {
+  // Create memory adapter (stores traces in memory)
+  const obs = new MemoryObservabilityAdapter();
+  setObservabilityAdapter(obs);
+
+  // Create agent with observability enabled
+  const agent = new Agent({
+    name: 'TracedAgent',
+    instructions: 'You are a helpful assistant. Answer questions concisely.'
+  });
+
+  console.log('Running agent with tracing...\n');
+
+  // Run some interactions
+  await agent.chat('What is 2 + 2?');
+  await agent.chat('What is the capital of France?');
+
+  // View traces
+  const traces = obs.getAllTraces();
+  
+  console.log('\n=== Trace Summary ===');
+  console.log(`Total traces: ${traces.length}`);
+  
+  for (const trace of traces) {
+    console.log(`\nTrace: ${trace.name}`);
+    console.log(`  Status: ${trace.status}`);
+    console.log(`  Spans: ${trace.spans.length}`);
+    
+    for (const span of trace.spans) {
+      const duration = span.endTime ? span.endTime - span.startTime : 'running';
+      console.log(`    - ${span.kind}: ${span.name} (${duration}ms)`);
+    }
+  }
+}
+
+main().catch(console.error);

examples/js/observability/multi-agent-attribution.ts

@@ -0,0 +1,60 @@
+/**
+ * Multi-Agent Attribution Example
+ * 
+ * Demonstrates tracking agent_id, run_id, and trace_id across
+ * multi-agent workflows for proper attribution.
+ */
+
+import { Agent, Agents, MemoryObservabilityAdapter, setObservabilityAdapter } from 'praisonai';
+
+async function main() {
+  // Create memory adapter
+  const obs = new MemoryObservabilityAdapter();
+  setObservabilityAdapter(obs);
+
+  // Create agents
+  const researcher = new Agent({
+    name: 'Researcher',
+    instructions: 'Research topics and gather information.'
+  });
+
+  const writer = new Agent({
+    name: 'Writer',
+    instructions: 'Write clear summaries based on research.'
+  });
+
+  // Create multi-agent workflow
+  const agents = new Agents({
+    agents: [researcher, writer],
+    tasks: [
+      { agent: researcher, description: 'Research: {topic}' },
+      { agent: writer, description: 'Summarize the research findings' }
+    ]
+  });
+
+  console.log('Running multi-agent workflow with attribution...\n');
+
+  await agents.start({ topic: 'TypeScript best practices' });
+
+  // View traces with attribution
+  const traces = obs.getAllTraces();
+  
+  console.log('\n=== Attribution Report ===');
+  for (const trace of traces) {
+    console.log(`\nTrace ID: ${trace.id}`);
+    console.log(`  Name: ${trace.name}`);
+    
+    if (trace.attribution) {
+      console.log(`  Agent ID: ${trace.attribution.agentId || 'N/A'}`);
+      console.log(`  Run ID: ${trace.attribution.runId || 'N/A'}`);
+      console.log(`  Session ID: ${trace.attribution.sessionId || 'N/A'}`);
+    }
+    
+    console.log(`  Spans: ${trace.spans.length}`);
+    for (const span of trace.spans) {
+      console.log(`    - [${span.kind}] ${span.name}`);
+    }
+  }
+}
+
+main().catch(console.error);

examples/js/observability/switchable-tools.ts

@@ -0,0 +1,40 @@
+/**
+ * Switchable Observability Tools Example
+ * 
+ * Demonstrates switching between observability tools via environment variable.
+ * Set OBS_TOOL env var to: langfuse, langsmith, console, memory, noop
+ * 
+ * Usage:
+ *   OBS_TOOL=console npx tsx switchable-tools.ts
+ *   OBS_TOOL=langfuse LANGFUSE_SECRET_KEY=sk-... npx tsx switchable-tools.ts
+ */
+
+import { Agent, createObservabilityAdapter, setObservabilityAdapter } from 'praisonai';
+
+async function main() {
+  // Get tool from environment (default to memory)
+  const toolName = process.env.OBS_TOOL || 'memory';
+  
+  console.log(`Using observability tool: ${toolName}\n`);
+
+  // Create adapter dynamically
+  const obs = await createObservabilityAdapter(toolName as any);
+  setObservabilityAdapter(obs);
+
+  // Create agent
+  const agent = new Agent({
+    name: 'ObservedAgent',
+    instructions: 'You are a helpful assistant.'
+  });
+
+  // Run interaction
+  console.log('Running agent...\n');
+  const response = await agent.chat('Hello! What is 2 + 2?');
+  console.log('Response:', response);
+
+  // Flush traces to backend
+  await obs.flush();
+  console.log('\nTraces flushed to', toolName);
+}
+
+main().catch(console.error);

examples/js/providers/README.md

@@ -0,0 +1,47 @@
+# Provider Examples
+
+Examples demonstrating how to use different LLM providers with PraisonAI TypeScript.
+
+## Setup
+
+```bash
+# Install dependencies
+cd /path/to/praisonai-ts
+pnpm install
+
+# Set API keys
+export OPENAI_API_KEY=sk-...
+export ANTHROPIC_API_KEY=sk-ant-...
+export GOOGLE_API_KEY=AIza...
+```
+
+## Examples
+
+| File | Description |
+|------|-------------|
+| `multi-provider.ts` | Use multiple providers in one workflow |
+| `openai-compatible.ts` | Use OpenAI-compatible APIs (LM Studio, etc.) |
+| `local-ollama.ts` | Use local Ollama models |
+| `provider-selection.ts` | Dynamic provider selection based on task |
+
+## Running Examples
+
+```bash
+# Run with ts-node or tsx
+npx tsx examples/js/providers/multi-provider.ts
+npx tsx examples/js/providers/openai-compatible.ts
+npx tsx examples/js/providers/local-ollama.ts
+```
+
+## Environment Variables
+
+| Provider | Variable | Example |
+|----------|----------|---------|
+| OpenAI | `OPENAI_API_KEY` | `sk-...` |
+| Anthropic | `ANTHROPIC_API_KEY` | `sk-ant-...` |
+| Google | `GOOGLE_API_KEY` | `AIza...` |
+| xAI | `XAI_API_KEY` | `xai-...` |
+| Groq | `GROQ_API_KEY` | `gsk_...` |
+| Mistral | `MISTRAL_API_KEY` | `...` |
+| DeepSeek | `DEEPSEEK_API_KEY` | `...` |
+| Ollama | `OLLAMA_BASE_URL` | `http://localhost:11434` |

examples/js/providers/local-ollama.ts

@@ -0,0 +1,40 @@
+/**
+ * Local Ollama Provider Example
+ * 
+ * Demonstrates using local Ollama models with PraisonAI.
+ * 
+ * Setup:
+ * 1. Install Ollama: https://ollama.ai
+ * 2. Pull a model: ollama pull llama3.2
+ * 3. Run this example
+ * 
+ * Optional env var:
+ * - OLLAMA_BASE_URL (default: http://localhost:11434)
+ */
+
+import { Agent } from 'praisonai';
+
+async function main() {
+  // Ollama with llama3.2
+  const ollamaAgent = new Agent({
+    name: 'LocalLlama',
+    instructions: 'You are a helpful local AI assistant running on Ollama.',
+    llm: 'ollama/llama3.2',
+    llmConfig: {
+      baseUrl: process.env.OLLAMA_BASE_URL || 'http://localhost:11434'
+    }
+  });
+
+  console.log('Testing Ollama local model...\n');
+
+  try {
+    const response = await ollamaAgent.chat('Hello! What model are you running?');
+    console.log('Response:', response);
+  } catch (error: any) {
+    console.log('Note: Ollama not running or model not pulled.');
+    console.log('Start Ollama and run: ollama pull llama3.2');
+    console.log('Error:', error.message);
+  }
+}
+
+main().catch(console.error);

examples/js/providers/multi-provider.ts

@@ -0,0 +1,57 @@
+/**
+ * Multi-Provider Example
+ * 
+ * Demonstrates using multiple LLM providers in a single workflow.
+ * Each agent uses a different provider optimized for its task.
+ * 
+ * Required env vars:
+ * - OPENAI_API_KEY
+ * - ANTHROPIC_API_KEY
+ * - GOOGLE_API_KEY
+ */
+
+import { Agent, Agents } from 'praisonai';
+
+async function main() {
+  // Fast model for quick categorization
+  const triageAgent = new Agent({
+    name: 'Triage',
+    instructions: 'Quickly categorize the user request into: technical, creative, or general.',
+    llm: 'openai/gpt-4o-mini'  // Fast and cheap
+  });
+
+  // Powerful model for complex reasoning
+  const analysisAgent = new Agent({
+    name: 'Analyst',
+    instructions: 'Analyze the request in depth and provide detailed insights.',
+    llm: 'anthropic/claude-3-5-sonnet'  // Best reasoning
+  });
+
+  // Creative model for content generation
+  const writerAgent = new Agent({
+    name: 'Writer',
+    instructions: 'Write a clear, engaging response based on the analysis.',
+    llm: 'google/gemini-2.0-flash'  // Good for creative tasks
+  });
+
+  // Create multi-agent workflow
+  const agents = new Agents({
+    agents: [triageAgent, analysisAgent, writerAgent],
+    tasks: [
+      { agent: triageAgent, description: 'Categorize: {input}' },
+      { agent: analysisAgent, description: 'Analyze the categorized request' },
+      { agent: writerAgent, description: 'Write the final response' }
+    ]
+  });
+
+  console.log('Starting multi-provider workflow...\n');
+  
+  const result = await agents.start({
+    input: 'Explain how neural networks learn'
+  });
+
+  console.log('\n=== Final Result ===');
+  console.log(result);
+}
+
+main().catch(console.error);

examples/js/providers/openai-compatible.ts

@@ -0,0 +1,48 @@
+/**
+ * OpenAI-Compatible Provider Example
+ * 
+ * Demonstrates using any OpenAI-compatible API endpoint.
+ * Works with LM Studio, vLLM, LocalAI, and other compatible servers.
+ * 
+ * Required env vars:
+ * - OPENAI_COMPATIBLE_BASE_URL (e.g., http://localhost:1234/v1)
+ * - OPENAI_COMPATIBLE_API_KEY (optional, use 'not-needed' for local)
+ */
+
+import { Agent } from 'praisonai';
+
+async function main() {
+  // LM Studio example (local)
+  const lmStudioAgent = new Agent({
+    name: 'LocalAssistant',
+    instructions: 'You are a helpful local AI assistant.',
+    llm: 'openai-compatible/local-model',
+    llmConfig: {
+      baseUrl: process.env.LM_STUDIO_BASE_URL || 'http://localhost:1234/v1',
+      apiKey: 'not-needed'
+    }
+  });
+
+  // Custom OpenAI-compatible endpoint
+  const customAgent = new Agent({
+    name: 'CustomAssistant',
+    instructions: 'You are a helpful assistant.',
+    llm: 'openai-compatible/my-model',
+    llmConfig: {
+      baseUrl: process.env.OPENAI_COMPATIBLE_BASE_URL || 'http://localhost:8000/v1',
+      apiKey: process.env.OPENAI_COMPATIBLE_API_KEY || 'not-needed'
+    }
+  });
+
+  console.log('Testing OpenAI-compatible endpoint...\n');
+
+  try {
+    const response = await lmStudioAgent.chat('Hello! What model are you?');
+    console.log('Response:', response);
+  } catch (error: any) {
+    console.log('Note: LM Studio not running. Start it with a model loaded.');
+    console.log('Error:', error.message);
+  }
+}
+
+main().catch(console.error);

examples/js/rag/basic-rag.ts

@@ -0,0 +1,57 @@
+/**
+ * Basic RAG Agent Example
+ * 
+ * Demonstrates building a RAG agent with context injection.
+ * 
+ * Prerequisites:
+ *   npm install praisonai-ts
+ *   export OPENAI_API_KEY=your-api-key
+ * 
+ * Run:
+ *   npx ts-node basic-rag.ts
+ */
+
+import { Agent } from '../../../src/praisonai-ts/src';
+
+// Simulated knowledge base (in production, use a vector store)
+const knowledgeBase = [
+  { id: '1', content: 'PraisonAI is an AI agent framework for building intelligent applications.' },
+  { id: '2', content: 'Agents can use tools to accomplish tasks like web search and calculations.' },
+  { id: '3', content: 'RAG combines retrieval with generation for accurate, grounded responses.' },
+];
+
+// Simple search function (in production, use vector similarity)
+function searchKnowledge(query: string): string[] {
+  const queryLower = query.toLowerCase();
+  return knowledgeBase
+    .filter(doc => doc.content.toLowerCase().includes(queryLower.split(' ')[0]))
+    .map(doc => doc.content);
+}
+
+async function main() {
+  console.log('=== Basic RAG Agent Example ===\n');
+
+  // Create an agent with RAG-style instructions
+  const agent = new Agent({
+    name: 'RAGAgent',
+    instructions: `You are a helpful assistant that answers questions using provided context.
+Always base your answers on the context provided.
+If the context doesn't contain relevant information, say so.`,
+  });
+
+  // Query with context injection
+  const query = 'What is PraisonAI?';
+  console.log(`Q: ${query}`);
+  
+  // Retrieve relevant context
+  const context = searchKnowledge(query).join('\n\n');
+  
+  // Ask the agent with context
+  const response = await agent.chat(
+    `Context:\n${context}\n\nQuestion: ${query}`
+  );
+  
+  console.log(`A: ${response}\n`);
+}
+
+main().catch(console.error);

examples/js/server-adapters/express-server.ts

@@ -0,0 +1,77 @@
+/**
+ * Express Server Adapter Example
+ * 
+ * Demonstrates deploying an AI agent as an Express HTTP server.
+ * 
+ * Prerequisites:
+ *   npm install praisonai-ts express
+ *   export OPENAI_API_KEY=your-api-key
+ * 
+ * Run:
+ *   npx ts-node express-server.ts
+ * 
+ * Test:
+ *   curl -X POST http://localhost:3000/api/chat \
+ *     -H "Content-Type: application/json" \
+ *     -d '{"message": "Hello!"}'
+ */
+
+import { Agent } from '../../../src/praisonai-ts/src';
+
+// Simple HTTP server without Express dependency for this example
+import http from 'http';
+
+async function main() {
+  console.log('=== Express Server Adapter Example ===\n');
+
+  // Create an agent
+  const agent = new Agent({
+    name: 'APIAgent',
+    instructions: 'You are a helpful API assistant. Keep responses concise.',
+  });
+
+  // Create HTTP server
+  const server = http.createServer(async (req, res) => {
+    // CORS headers
+    res.setHeader('Access-Control-Allow-Origin', '*');
+    res.setHeader('Access-Control-Allow-Methods', 'POST, OPTIONS');
+    res.setHeader('Access-Control-Allow-Headers', 'Content-Type');
+
+    if (req.method === 'OPTIONS') {
+      res.writeHead(200);
+      res.end();
+      return;
+    }
+
+    if (req.method === 'POST' && req.url === '/api/chat') {
+      let body = '';
+      req.on('data', chunk => body += chunk);
+      req.on('end', async () => {
+        try {
+          const { message } = JSON.parse(body);
+          const response = await agent.chat(message);
+          
+          res.writeHead(200, { 'Content-Type': 'application/json' });
+          res.end(JSON.stringify({ response }));
+        } catch (error: any) {
+          res.writeHead(500, { 'Content-Type': 'application/json' });
+          res.end(JSON.stringify({ error: error.message }));
+        }
+      });
+    } else {
+      res.writeHead(404);
+      res.end('Not Found');
+    }
+  });
+
+  const PORT = process.env.PORT || 3000;
+  server.listen(PORT, () => {
+    console.log(`Server running on http://localhost:${PORT}`);
+    console.log('\nTest with:');
+    console.log(`  curl -X POST http://localhost:${PORT}/api/chat \\`);
+    console.log('    -H "Content-Type: application/json" \\');
+    console.log('    -d \'{"message": "Hello!"}\'');
+  });
+}
+
+main().catch(console.error);

examples/lsp/lsp_code_analysis_example.py

@@ -0,0 +1,130 @@
+"""
+LSP Code Intelligence Example
+
+This example demonstrates how to use LSP-powered tools through an Agent
+for semantic code analysis (listing symbols, finding definitions, etc.).
+
+How to run:
+    export OPENAI_API_KEY=your_key
+    python examples/lsp/lsp_code_analysis_example.py
+
+Prerequisites:
+    pip install praisonai praisonaiagents
+    pip install python-lsp-server  # Optional, for full LSP support
+"""
+
+import asyncio
+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'))
+
+
+async def main():
+    print("=" * 60)
+    print("LSP CODE INTELLIGENCE EXAMPLE")
+    print("=" * 60)
+    print()
+    
+    # Import after path setup
+    from praisonai.cli.features import (
+        create_agent_centric_tools,
+        InteractiveRuntime,
+        RuntimeConfig
+    )
+    from praisonaiagents import Agent
+    
+    # Use the praisonaiagents source as workspace for analysis
+    workspace = os.path.join(
+        os.path.dirname(__file__), 
+        '../../src/praisonai-agents/praisonaiagents'
+    )
+    workspace = os.path.abspath(workspace)
+    
+    print(f"Workspace: {workspace}")
+    print()
+    
+    # 1. Create runtime with LSP enabled
+    print("1. Creating InteractiveRuntime with LSP...")
+    config = RuntimeConfig(
+        workspace=workspace,
+        lsp_enabled=True,  # Enable LSP for code intelligence
+        acp_enabled=False,  # Disable ACP for this example
+        approval_mode="auto"
+    )
+    runtime = InteractiveRuntime(config)
+    status = await runtime.start()
+    
+    print(f"   LSP enabled: {status['lsp']['enabled']}")
+    print(f"   LSP ready: {runtime.lsp_ready}")
+    print(f"   LSP status: {status['lsp']['status']}")
+    if not runtime.lsp_ready:
+        print("   Note: LSP not available, will use regex fallback")
+    print()
+    
+    # 2. Create agent-centric tools
+    print("2. Creating agent-centric tools...")
+    tools = create_agent_centric_tools(runtime)
+    
+    # Filter to show only LSP tools
+    lsp_tools = [t for t in tools if t.__name__.startswith('lsp_')]
+    print(f"   LSP tools available:")
+    for tool in lsp_tools:
+        print(f"     - {tool.__name__}")
+    print()
+    
+    # 3. Create Agent with LSP-powered tools
+    print("3. Creating Agent with LSP tools...")
+    agent = Agent(
+        name="CodeAnalyzer",
+        instructions="""You are a code analysis assistant.
+        Use lsp_list_symbols to list functions and classes in files.
+        Use lsp_find_definition to find where symbols are defined.
+        Use lsp_find_references to find where symbols are used.
+        Provide clear, organized summaries of code structure.""",
+        tools=tools,
+        verbose=True
+    )
+    print()
+    
+    # 4. Test 1: List symbols in a file
+    print("4. TEST 1: List symbols in agent/agent.py")
+    print("-" * 50)
+    result1 = agent.start("List all the main classes and public methods in agent/agent.py. Give me a brief summary.")
+    print("-" * 50)
+    print(f"Summary: {str(result1)[:300]}...")
+    print()
+    
+    # 5. Test 2: Find definition of a symbol
+    print("5. TEST 2: Find where 'Agent' class is defined")
+    print("-" * 50)
+    result2 = agent.start("Where is the Agent class defined? Use lsp_find_definition to find it.")
+    print("-" * 50)
+    print(f"Result: {str(result2)[:300]}...")
+    print()
+    
+    # 6. Test 3: Analyze main.py
+    print("6. TEST 3: Analyze main.py structure")
+    print("-" * 50)
+    result3 = agent.start("List all the functions in main.py and briefly describe what the file does.")
+    print("-" * 50)
+    print(f"Analysis: {str(result3)[:300]}...")
+    print()
+    
+    # Cleanup
+    await runtime.stop()
+    
+    print("=" * 60)
+    print("EXAMPLE COMPLETE")
+    print("=" * 60)
+    print()
+    print("Key takeaways:")
+    print("- LSP tools work through the Agent (agentic LSP)")
+    print("- Falls back to regex if LSP server not available")
+    print("- Returns structured data with citations")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/output_ux/basic_output_example.py

@@ -0,0 +1,133 @@
+"""
+PraisonAI Output UX Examples
+
+This example demonstrates the various output modes and configurations
+available in PraisonAI agents.
+
+Features demonstrated:
+- verbose=True: Full output with agent info, task, and response panels
+- verbose=False: Clean output with just the result
+- Tool call display with consolidated output
+- Multi-agent output with clear attribution
+"""
+
+import sys
+sys.path.insert(0, '/Users/praison/praisonai-package/src/praisonai-agents')
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+
+def get_weather(city: str) -> str:
+    """Get the current weather for a city.
+    
+    Args:
+        city: The name of the city to get weather for
+    
+    Returns:
+        Weather information string
+    """
+    return f"The weather in {city} is sunny and 72°F"
+
+
+def example_verbose_true():
+    """Example with verbose=True (default) - shows full output panels."""
+    print("\n" + "=" * 60)
+    print("EXAMPLE 1: verbose=True (Full Output)")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Assistant",
+        instructions="You are a helpful assistant. Be concise.",
+        verbose=True  # Default - shows Agent Info, Task, and Response panels
+    )
+    result = agent.start("What is the capital of France? Answer in one sentence.")
+    print(f"\nReturned value: {result}")
+
+
+def example_verbose_false():
+    """Example with verbose=False - clean output, just the result."""
+    print("\n" + "=" * 60)
+    print("EXAMPLE 2: verbose=False (Clean Output)")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Assistant",
+        instructions="You are a helpful assistant. Be concise.",
+        verbose=False  # No panels, just returns the result
+    )
+    result = agent.start("What is 2+2? Answer in one word.")
+    print(f"Result: {result}")
+
+
+def example_with_tools():
+    """Example showing tool call output."""
+    print("\n" + "=" * 60)
+    print("EXAMPLE 3: Agent with Tools")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Weather Bot",
+        instructions="You help users check the weather. Use the get_weather tool.",
+        tools=[get_weather],
+        verbose=True
+    )
+    result = agent.start("What's the weather in Tokyo?")
+    print(f"\nReturned value: {result}")
+
+
+def example_multi_agent():
+    """Example showing multi-agent output with clear attribution."""
+    print("\n" + "=" * 60)
+    print("EXAMPLE 4: Multi-Agent Output")
+    print("=" * 60)
+    
+    researcher = Agent(
+        name="Researcher",
+        role="Research Analyst",
+        instructions="You research topics and provide factual information.",
+        verbose=True
+    )
+    
+    writer = Agent(
+        name="Writer",
+        role="Content Writer",
+        instructions="You write clear summaries based on research.",
+        verbose=True
+    )
+    
+    task1 = Task(
+        description="Research what AI is in one sentence",
+        expected_output="A brief factual description",
+        agent=researcher
+    )
+    
+    task2 = Task(
+        description="Write a one-sentence summary about AI",
+        expected_output="A clear summary",
+        agent=writer
+    )
+    
+    agents = PraisonAIAgents(
+        agents=[researcher, writer],
+        tasks=[task1, task2],
+        process="sequential",
+        verbose=1
+    )
+    
+    result = agents.start()
+    print(f"\nFinal result: {result}")
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("PRAISONAI OUTPUT UX EXAMPLES")
+    print("=" * 60)
+    
+    # Run examples
+    example_verbose_true()
+    example_verbose_false()
+    example_with_tools()
+    example_multi_agent()
+    
+    print("\n" + "=" * 60)
+    print("ALL EXAMPLES COMPLETE")
+    print("=" * 60)

examples/provider-registry/isolated_registry_example.py

@@ -0,0 +1,197 @@
+"""
+Isolated Registry Example - Multi-Agent Safety
+
+This example demonstrates how to use isolated registries to prevent
+provider registration collisions in multi-agent scenarios.
+
+Key concepts:
+- Each agent/context can have its own registry
+- No global state mutation
+- Safe for concurrent/parallel runs
+- Different agents can use different provider implementations
+
+Requirements:
+- pip install praisonai
+"""
+
+import sys
+sys.path.insert(0, '../../src/praisonai')
+
+from praisonai.llm import (
+    LLMProviderRegistry,
+    create_llm_provider,
+    register_llm_provider,
+    get_default_llm_registry,
+    _reset_default_registry
+)
+
+
+# Define two different provider implementations with the same name
+class Agent1Provider:
+    """Provider implementation for Agent 1."""
+    provider_id = "custom"
+    
+    def __init__(self, model_id, config=None):
+        self.model_id = model_id
+        self.config = config or {}
+        self.agent_name = "Agent1"
+    
+    def generate(self, prompt):
+        return f"[{self.agent_name}] Response to: {prompt}"
+
+
+class Agent2Provider:
+    """Provider implementation for Agent 2 - different behavior."""
+    provider_id = "custom"
+    
+    def __init__(self, model_id, config=None):
+        self.model_id = model_id
+        self.config = config or {}
+        self.agent_name = "Agent2"
+    
+    def generate(self, prompt):
+        return f"[{self.agent_name}] Different response to: {prompt}"
+
+
+def demonstrate_collision_problem():
+    """Show what happens with global registry collisions."""
+    print("=== Problem: Global Registry Collision ===\n")
+    
+    # Reset to clean state
+    _reset_default_registry()
+    
+    # Agent 1 registers "custom" provider
+    register_llm_provider("custom", Agent1Provider)
+    print("Agent 1 registered 'custom' provider")
+    
+    # Agent 2 tries to register same name - ERROR!
+    try:
+        register_llm_provider("custom", Agent2Provider)
+        print("Agent 2 registered 'custom' provider")
+    except ValueError as e:
+        print(f"Agent 2 FAILED: {e}")
+    
+    print()
+    print("Problem: Both agents want to use 'custom' but with different implementations.")
+    print("Solution: Use isolated registries.\n")
+
+
+def demonstrate_isolated_solution():
+    """Show how isolated registries solve the collision problem."""
+    print("=== Solution: Isolated Registries ===\n")
+    
+    # Create isolated registries for each agent
+    agent1_registry = LLMProviderRegistry()
+    agent2_registry = LLMProviderRegistry()
+    
+    # Each agent registers its own "custom" provider
+    agent1_registry.register("custom", Agent1Provider)
+    agent2_registry.register("custom", Agent2Provider)
+    
+    print("Agent 1 registry providers:", agent1_registry.list())
+    print("Agent 2 registry providers:", agent2_registry.list())
+    print()
+    
+    # Create providers from each registry
+    provider1 = create_llm_provider("custom/model-v1", registry=agent1_registry)
+    provider2 = create_llm_provider("custom/model-v1", registry=agent2_registry)
+    
+    # They have different implementations!
+    print("Provider 1 response:", provider1.generate("Hello"))
+    print("Provider 2 response:", provider2.generate("Hello"))
+    print()
+    
+    # Verify isolation
+    print("Verification:")
+    print(f"  provider1.agent_name = {provider1.agent_name}")
+    print(f"  provider2.agent_name = {provider2.agent_name}")
+    print(f"  Same class? {type(provider1) == type(provider2)}")
+    print()
+
+
+def demonstrate_parallel_agents():
+    """Simulate parallel agent execution with isolated registries."""
+    print("=== Parallel Agent Simulation ===\n")
+    
+    def run_agent(agent_id, registry):
+        """Simulate an agent run with its own registry."""
+        provider = create_llm_provider("custom/model", registry=registry)
+        result = provider.generate(f"Query from agent {agent_id}")
+        return result
+    
+    # Setup registries
+    registries = {}
+    for i in range(3):
+        reg = LLMProviderRegistry()
+        # Each agent gets a slightly different provider
+        class DynamicProvider:
+            provider_id = "custom"
+            def __init__(self, model_id, config=None):
+                self.model_id = model_id
+                self.agent_id = i  # Capture agent ID
+            def generate(self, prompt):
+                return f"Agent-{self.agent_id} processed: {prompt}"
+        
+        reg.register("custom", DynamicProvider)
+        registries[i] = reg
+    
+    # Simulate parallel execution
+    print("Simulating parallel agent runs:")
+    for agent_id, registry in registries.items():
+        result = run_agent(agent_id, registry)
+        print(f"  {result}")
+    
+    print()
+    print("Each agent used its own isolated registry - no collisions!")
+    print()
+
+
+def demonstrate_shared_vs_isolated():
+    """Compare shared (global) vs isolated registry patterns."""
+    print("=== Shared vs Isolated Patterns ===\n")
+    
+    # Reset global registry
+    _reset_default_registry()
+    
+    # Pattern 1: Shared (global) registry
+    print("Pattern 1: Shared Registry (use for app-wide providers)")
+    register_llm_provider("shared-provider", Agent1Provider)
+    
+    # All code sees the same provider
+    global_reg = get_default_llm_registry()
+    print(f"  Global registry has 'shared-provider': {global_reg.has('shared-provider')}")
+    
+    # Pattern 2: Isolated registry
+    print("\nPattern 2: Isolated Registry (use for per-agent/per-run providers)")
+    isolated = LLMProviderRegistry()
+    isolated.register("isolated-provider", Agent2Provider)
+    
+    # Only code with reference to this registry sees it
+    print(f"  Isolated registry has 'isolated-provider': {isolated.has('isolated-provider')}")
+    print(f"  Global registry has 'isolated-provider': {global_reg.has('isolated-provider')}")
+    
+    print()
+    print("Recommendation:")
+    print("  - Use global registry for app-wide, stable providers")
+    print("  - Use isolated registries for per-agent or per-run customization")
+    print()
+
+
+def main():
+    print("=" * 60)
+    print("Isolated Registry Example - Multi-Agent Safety")
+    print("=" * 60)
+    print()
+    
+    demonstrate_collision_problem()
+    demonstrate_isolated_solution()
+    demonstrate_parallel_agents()
+    demonstrate_shared_vs_isolated()
+    
+    print("=" * 60)
+    print("Example Complete")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

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 +1,82 @@
+# RAG Examples
+
+This directory contains examples demonstrating RAG (Retrieval Augmented Generation) with PraisonAI.
+
+## Examples
+
+### 1. 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
+```
+
+### 2. 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
+```
+
+### 3. RAG Evaluation (`rag_evaluation.py`)
+
+Evaluation example showing:
+- Golden query testing
+- Retrieval accuracy metrics
+- Answer relevance checking
+
+```bash
+python rag_evaluation.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/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/tui/basic_usage.py

@@ -0,0 +1,30 @@
+"""
+Basic TUI Usage Example for PraisonAI.
+
+This example demonstrates how to launch the interactive TUI.
+"""
+
+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 main():
+    """Launch the TUI."""
+    try:
+        from praisonai.cli.features.tui import run_tui
+        
+        # Launch with default settings
+        run_tui()
+        
+    except ImportError:
+        print("Textual is required for TUI. Install with:")
+        print("  pip install praisonai[tui]")
+        exit(1)
+
+
+if __name__ == "__main__":
+    main()

examples/tui/headless_simulation.py

@@ -0,0 +1,134 @@
+"""
+Headless TUI Simulation Example for PraisonAI.
+
+This example demonstrates how to run TUI simulations without
+launching the interactive UI - useful for testing and CI.
+"""
+
+import asyncio
+from praisonai.cli.features.tui import (
+    TuiOrchestrator,
+    SimulationRunner,
+    MockProvider,
+)
+from praisonai.cli.features.tui.orchestrator import OutputMode
+from praisonai.cli.features.queue import QueueConfig
+
+
+async def run_simulation():
+    """Run a headless simulation."""
+    
+    # Create orchestrator with mock provider (no real API calls)
+    config = QueueConfig(enable_persistence=False)
+    orchestrator = TuiOrchestrator(
+        queue_config=config,
+        output_mode=OutputMode.PRETTY,
+        debug=True,
+    )
+    
+    # Create simulation runner
+    runner = SimulationRunner(orchestrator, assert_mode=True)
+    
+    # Define simulation script
+    script = {
+        "session_id": "sim-example",
+        "model": "gpt-4o-mini",
+        "steps": [
+            # Navigate to different screens
+            {"action": "navigate", "args": {"screen": "main"}},
+            {"action": "focus", "args": {"widget": "composer"}},
+            
+            # Change model
+            {
+                "action": "model",
+                "args": {"model": "gpt-4"},
+                "expected": {"model": "gpt-4"}
+            },
+            
+            # Navigate to queue screen
+            {"action": "navigate", "args": {"screen": "queue"}},
+            
+            # Take a snapshot
+            {"action": "snapshot"},
+            
+            # Navigate back
+            {"action": "navigate", "args": {"screen": "main"}},
+            
+            # Small delay
+            {"action": "sleep", "args": {"seconds": 0.5}},
+        ]
+    }
+    
+    # Run the simulation
+    print("Running headless TUI simulation...")
+    print("=" * 50)
+    
+    success = await runner.run_script(script)
+    
+    # Print results
+    print("=" * 50)
+    summary = runner.get_summary()
+    
+    print(f"\nSimulation {'PASSED' if success else 'FAILED'}")
+    print(f"  Assertions passed: {summary['assertions_passed']}")
+    print(f"  Assertions failed: {summary['assertions_failed']}")
+    
+    if summary['errors']:
+        print("\nErrors:")
+        for error in summary['errors']:
+            print(f"  - {error}")
+    
+    return success
+
+
+async def run_with_events():
+    """Run simulation and capture all events."""
+    
+    config = QueueConfig(enable_persistence=False)
+    orchestrator = TuiOrchestrator(
+        queue_config=config,
+        output_mode=OutputMode.SILENT,
+    )
+    
+    # Capture events
+    events = []
+    orchestrator.add_event_callback(lambda e: events.append(e))
+    
+    await orchestrator.start(session_id="event-capture")
+    
+    # Perform some actions
+    orchestrator.set_model("gpt-4")
+    orchestrator.navigate_screen("queue")
+    orchestrator.set_focus("queue-panel")
+    orchestrator.navigate_screen("main")
+    
+    await orchestrator.stop()
+    
+    # Print captured events
+    print("\nCaptured Events:")
+    print("-" * 40)
+    for event in events:
+        print(f"  {event.event_type.value}")
+    
+    return events
+
+
+def main():
+    """Main entry point."""
+    print("PraisonAI TUI Headless Simulation Example")
+    print("=" * 50)
+    
+    # Run simulation
+    success = asyncio.run(run_simulation())
+    
+    # Run event capture
+    print("\n" + "=" * 50)
+    events = asyncio.run(run_with_events())
+    
+    print(f"\nTotal events captured: {len(events)}")
+    
+    return 0 if success else 1
+
+
+if __name__ == "__main__":
+    exit(main())

examples/tui/queue_management.py

@@ -0,0 +1,122 @@
+"""
+Queue Management Example for PraisonAI.
+
+This example demonstrates how to use the queue system programmatically.
+"""
+
+import asyncio
+from praisonai.cli.features.queue import (
+    QueueManager,
+    QueueConfig,
+    QueuedRun,
+    RunState,
+    RunPriority,
+)
+
+
+async def main():
+    """Demonstrate queue management."""
+    
+    print("PraisonAI Queue Management Example")
+    print("=" * 50)
+    
+    # Create queue configuration
+    config = QueueConfig(
+        max_concurrent_global=2,
+        max_concurrent_per_agent=1,
+        max_queue_size=10,
+        enable_persistence=False,  # Use in-memory for demo
+    )
+    
+    # Track outputs
+    outputs = {}
+    
+    async def on_output(run_id: str, chunk: str):
+        if run_id not in outputs:
+            outputs[run_id] = ""
+        outputs[run_id] += chunk
+        print(f"  [{run_id[:8]}] {chunk}", end="", flush=True)
+    
+    async def on_complete(run_id: str, run: QueuedRun):
+        print(f"\n  [{run_id[:8]}] Completed: {run.state.value}")
+    
+    async def on_error(run_id: str, error: Exception):
+        print(f"\n  [{run_id[:8]}] Error: {error}")
+    
+    # Create queue manager
+    manager = QueueManager(
+        config=config,
+        on_output=on_output,
+        on_complete=on_complete,
+        on_error=on_error,
+    )
+    
+    # Start the manager
+    await manager.start(recover=False)
+    print("Queue manager started")
+    
+    # Submit some runs with different priorities
+    print("\nSubmitting runs...")
+    
+    run1 = await manager.submit(
+        input_content="Low priority task",
+        agent_name="Agent1",
+        priority=RunPriority.LOW,
+    )
+    print(f"  Submitted run1: {run1} (LOW priority)")
+    
+    run2 = await manager.submit(
+        input_content="High priority task",
+        agent_name="Agent2",
+        priority=RunPriority.HIGH,
+    )
+    print(f"  Submitted run2: {run2} (HIGH priority)")
+    
+    run3 = await manager.submit(
+        input_content="Normal priority task",
+        agent_name="Agent1",
+        priority=RunPriority.NORMAL,
+    )
+    print(f"  Submitted run3: {run3} (NORMAL priority)")
+    
+    # Check queue status
+    print(f"\nQueue status:")
+    print(f"  Queued: {manager.queued_count}")
+    print(f"  Running: {manager.running_count}")
+    
+    # List runs
+    runs = manager.list_runs()
+    print(f"\nAll runs ({len(runs)}):")
+    for run in runs:
+        print(f"  {run.run_id[:8]}: {run.agent_name} - {run.state.value} ({run.priority.name})")
+    
+    # Wait a bit for processing
+    print("\nWaiting for processing...")
+    await asyncio.sleep(2)
+    
+    # Cancel a run
+    if runs:
+        cancel_id = runs[-1].run_id
+        print(f"\nCancelling run: {cancel_id[:8]}")
+        await manager.cancel(cancel_id)
+    
+    # Get stats
+    stats = manager.get_stats()
+    print(f"\nQueue statistics:")
+    print(f"  Total runs: {stats.total_runs}")
+    print(f"  Queued: {stats.queued_count}")
+    print(f"  Running: {stats.running_count}")
+    print(f"  Succeeded: {stats.succeeded_count}")
+    print(f"  Failed: {stats.failed_count}")
+    print(f"  Cancelled: {stats.cancelled_count}")
+    
+    # Stop the manager
+    await manager.stop()
+    print("\nQueue manager stopped")
+    
+    print("\n" + "=" * 50)
+    print("Example complete!")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/typescript/tools/exa-search.ts

@@ -0,0 +1,84 @@
+/**
+ * Exa Web Search Tool Example
+ * 
+ * Demonstrates semantic web search with Exa AI.
+ * 
+ * Prerequisites:
+ *   npm install praisonai-ts @exalabs/ai-sdk
+ *   export EXA_API_KEY=your-api-key
+ *   export OPENAI_API_KEY=your-api-key
+ */
+
+// For local development, use relative imports
+// For production, use: import { Agent, exaSearch } from 'praisonai-ts'
+import { Agent } from '../../../src/praisonai-ts/src';
+import { exaSearch } from '../../../src/praisonai-ts/src/tools/builtins';
+
+async function main() {
+  console.log('=== Exa Web Search Tool Example ===\n');
+
+  // Example 1: Basic Semantic Search
+  console.log('1. Basic Semantic Search');
+  console.log('------------------------');
+  
+  const searchAgent = new Agent({
+    name: 'SemanticSearcher',
+    instructions: 'You search the web using semantic search and provide detailed summaries.',
+    tools: [exaSearch({ numResults: 5, type: 'auto' })],
+  });
+
+  const searchResult = await searchAgent.chat('Find the latest AI research breakthroughs');
+  console.log('Search Result:', searchResult);
+  console.log();
+
+  // Example 2: Company Research
+  console.log('2. Company Research');
+  console.log('-------------------');
+  
+  const companyAgent = new Agent({
+    name: 'CompanyResearcher',
+    instructions: 'You research companies and provide analysis.',
+    tools: [
+      exaSearch({
+        type: 'auto',
+        numResults: 5,
+        category: 'company',
+        contents: {
+          text: { maxCharacters: 1000 },
+          summary: true,
+          livecrawl: 'preferred',
+        },
+      }),
+    ],
+  });
+
+  const companyResult = await companyAgent.chat(
+    'Find AI startups in Europe founded after 2020'
+  );
+  console.log('Company Result:', companyResult);
+  console.log();
+
+  // Example 3: Academic Paper Search
+  console.log('3. Academic Paper Search');
+  console.log('------------------------');
+  
+  const academicAgent = new Agent({
+    name: 'AcademicResearcher',
+    instructions: 'You search for academic papers and summarize findings.',
+    tools: [
+      exaSearch({
+        type: 'neural',
+        category: 'research paper',
+        numResults: 5,
+        includeDomains: ['arxiv.org', 'nature.com', 'science.org'],
+      }),
+    ],
+  });
+
+  const academicResult = await academicAgent.chat(
+    'Find recent papers on transformer architectures and attention mechanisms'
+  );
+  console.log('Academic Result:', academicResult);
+}
+
+main().catch(console.error);

examples/typescript/tools/tavily-search.ts

@@ -0,0 +1,86 @@
+/**
+ * Tavily Search Tool Example
+ * 
+ * Demonstrates web search, content extraction, and crawling with Tavily.
+ * 
+ * Prerequisites:
+ *   npm install praisonai @tavily/ai-sdk
+ *   export TAVILY_API_KEY=your-api-key
+ *   export OPENAI_API_KEY=your-api-key
+ */
+
+// For local development, use relative imports
+// For production, use: import { Agent, tavilySearch } from 'praisonai-ts'
+import { Agent } from '../../../src/praisonai-ts/src';
+import { tavilySearch, tavilyExtract, tavilyCrawl } from '../../../src/praisonai-ts/src/tools/builtins';
+
+async function main() {
+  console.log('=== Tavily Search Tool Example ===\n');
+
+  // Example 1: Basic Web Search
+  console.log('1. Basic Web Search');
+  console.log('-------------------');
+  
+  const searchAgent = new Agent({
+    name: 'WebSearcher',
+    instructions: 'You search the web for information and provide concise summaries with sources.',
+    tools: [tavilySearch({ maxResults: 5, includeAnswer: true })],
+  });
+
+  const searchResult = await searchAgent.chat('What are the latest developments in AI agents?');
+  console.log('Search Result:', searchResult);
+  console.log();
+
+  // Example 2: News Search with Time Filter
+  console.log('2. News Search with Time Filter');
+  console.log('-------------------------------');
+  
+  const newsAgent = new Agent({
+    name: 'NewsSearcher',
+    instructions: 'You search for recent news and summarize key points.',
+    tools: [
+      tavilySearch({
+        topic: 'news',
+        timeRange: 'week',
+        maxResults: 5,
+      }),
+    ],
+  });
+
+  const newsResult = await newsAgent.chat('Latest news about OpenAI');
+  console.log('News Result:', newsResult);
+  console.log();
+
+  // Example 3: Content Extraction
+  console.log('3. Content Extraction');
+  console.log('--------------------');
+  
+  const extractAgent = new Agent({
+    name: 'ContentExtractor',
+    instructions: 'You extract and summarize content from web pages.',
+    tools: [tavilyExtract()],
+  });
+
+  const extractResult = await extractAgent.chat(
+    'Extract the main content from https://docs.tavily.com and summarize it'
+  );
+  console.log('Extract Result:', extractResult);
+  console.log();
+
+  // Example 4: Website Crawling
+  console.log('4. Website Crawling');
+  console.log('------------------');
+  
+  const crawlAgent = new Agent({
+    name: 'WebCrawler',
+    instructions: 'You crawl websites and analyze their structure and content.',
+    tools: [tavilyCrawl({ maxDepth: 2 })],
+  });
+
+  const crawlResult = await crawlAgent.chat(
+    'Crawl https://docs.tavily.com and list the main documentation sections'
+  );
+  console.log('Crawl Result:', crawlResult);
+}
+
+main().catch(console.error);

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/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/main.py

@@ -150,7 +150,10 @@ def _clean_display_content(content: str, max_length: int = 20000) -> str:
     return content.strip()
 
 def display_interaction(message, response, markdown=True, generation_time=None, console=None, agent_name=None, agent_role=None, agent_tools=None, task_name=None, task_description=None, task_id=None):
-    """Synchronous version of display_interaction."""
+    """Synchronous version of display_interaction.
+    
+    Displays the task/message and response in clean panels with response time in title.
+    """
     if console is None:
         console = Console()
     
@@ -179,16 +182,18 @@ def display_interaction(message, response, markdown=True, generation_time=None,
         task_description=task_description,
         task_id=task_id
     )
-    # Rest of the display logic...
+    
+    # Build response title with time (Agno-style: "Response (1.2s)")
+    response_title = "Response"
     if generation_time:
-        console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
+        response_title = f"Response ({generation_time:.1f}s)"
 
     if markdown:
         console.print(Panel.fit(Markdown(message), title="Task", border_style="cyan"))
-        console.print(Panel.fit(Markdown(response), title="Response", border_style="cyan"))
+        console.print(Panel.fit(Markdown(response), title=response_title, border_style="cyan"))
     else:
         console.print(Panel.fit(Text(message, style="bold green"), title="Task", border_style="cyan"))
-        console.print(Panel.fit(Text(response, style="bold blue"), title="Response", border_style="cyan"))
+        console.print(Panel.fit(Text(response, style="bold blue"), title=response_title, border_style="cyan"))
 
 def display_self_reflection(message: str, console=None):
     if not message or not message.strip():
@@ -226,7 +231,16 @@ def display_instruction(message: str, console=None, agent_name: str = None, agen
     if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
         console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
 
-def display_tool_call(message: str, console=None):
+def display_tool_call(message: str, console=None, tool_name: str = None, tool_input: dict = None, tool_output: str = None):
+    """Display tool call information in a consolidated panel.
+    
+    Args:
+        message: The tool call message (legacy format)
+        console: Rich console for output
+        tool_name: Name of the tool being called
+        tool_input: Input arguments to the tool
+        tool_output: Output from the tool (if available)
+    """
     logging.debug(f"display_tool_call called with message: {repr(message)}")
     if not message or not message.strip():
         logging.debug("Empty message in display_tool_call, returning early")
@@ -235,11 +249,21 @@ def display_tool_call(message: str, console=None):
     logging.debug(f"Cleaned message in display_tool_call: {repr(message)}")
     
     # Execute synchronous callbacks (always, even when console is None)
-    execute_sync_callback('tool_call', message=message)
+    execute_sync_callback('tool_call', message=message, tool_name=tool_name, tool_input=tool_input, tool_output=tool_output)
     
     # Only print panel if console is provided (verbose mode)
     if console is not None:
-        console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))
+        # Build consolidated tool call display (Agno-style)
+        if tool_name and tool_input is not None:
+            # Format: tool_name(arg1=val1, arg2=val2)
+            args_str = ", ".join(f"{k}={v}" for k, v in tool_input.items()) if tool_input else ""
+            tool_display = f"• {tool_name}({args_str})"
+            if tool_output:
+                tool_display += f"\n  → {tool_output}"
+            console.print(Panel.fit(Text(tool_display, style="bold cyan"), title="Tool Call", border_style="green"))
+        else:
+            # Legacy format
+            console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))
 
 def display_error(message: str, console=None):
     if not message or not message.strip():

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

@@ -11,8 +11,15 @@ import platform
 from typing import Any, List, Optional, Callable, Iterable, Union
 from functools import wraps, partial
 
-from mcp import ClientSession, StdioServerParameters
-from mcp.client.stdio import stdio_client
+try:
+    from mcp import ClientSession, StdioServerParameters
+    from mcp.client.stdio import stdio_client
+    MCP_AVAILABLE = True
+except ImportError:
+    MCP_AVAILABLE = False
+    ClientSession = None
+    StdioServerParameters = None
+    stdio_client = None
 
 class MCPToolRunner(threading.Thread):
     """A dedicated thread for running MCP operations."""
@@ -196,10 +203,17 @@ class MCP:
             debug: Enable debug logging for MCP operations (default: False)
             **kwargs: Additional parameters for StdioServerParameters
         """
+        # Check if MCP is available
+        if not MCP_AVAILABLE:
+            raise ImportError(
+                "MCP (Model Context Protocol) package is not installed. "
+                "Install it with: pip install praisonaiagents[mcp]"
+            )
+
         # Handle backward compatibility with named parameter 'command'
         if command_or_string is None and command is not None:
             command_or_string = command
-        
+
         # Set up logging - default to WARNING level to hide INFO messages
         if debug:
             logging.getLogger("mcp-wrapper").setLevel(logging.DEBUG)

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

@@ -16,11 +16,17 @@ import weakref
 from typing import List, Dict, Any, Optional, Callable, Iterable, Union
 from urllib.parse import urlparse, urljoin
 
-from mcp import ClientSession
+try:
+    from mcp import ClientSession
+    MCP_AVAILABLE = True
+except ImportError:
+    MCP_AVAILABLE = False
+    ClientSession = None
+
 try:
     import aiohttp
 except ImportError:
-    raise ImportError("aiohttp is required for HTTP Stream transport. Install with: pip install praisonaiagents[mcp]")
+    aiohttp = None
 
 logger = logging.getLogger("mcp-http-stream")
 
@@ -507,9 +513,23 @@ class HTTPStreamMCPClient:
             timeout: Timeout in seconds for operations (default: 60)
             options: Additional configuration options for the transport
         """
+        # Check if MCP is available
+        if not MCP_AVAILABLE:
+            raise ImportError(
+                "MCP (Model Context Protocol) package is not installed. "
+                "Install it with: pip install praisonaiagents[mcp]"
+            )
+
+        # Check if aiohttp is available
+        if aiohttp is None:
+            raise ImportError(
+                "aiohttp is required for HTTP Stream transport. "
+                "Install it with: pip install praisonaiagents[mcp]"
+            )
+
         # Parse URL to extract base URL and endpoint
         parsed = urlparse(server_url)
-        
+
         # If the URL already has a path, use it; otherwise use default /mcp endpoint
         if parsed.path and parsed.path != '/':
             self.base_url = server_url

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

@@ -11,8 +11,14 @@ import inspect
 import json
 from typing import List, Dict, Any, Optional, Callable, Iterable
 
-from mcp import ClientSession
-from mcp.client.sse import sse_client
+try:
+    from mcp import ClientSession
+    from mcp.client.sse import sse_client
+    MCP_AVAILABLE = True
+except ImportError:
+    MCP_AVAILABLE = False
+    ClientSession = None
+    sse_client = None
 
 logger = logging.getLogger("mcp-sse")
 
@@ -169,12 +175,19 @@ class SSEMCPClient:
             debug: Whether to enable debug logging
             timeout: Timeout in seconds for operations (default: 60)
         """
+        # Check if MCP is available
+        if not MCP_AVAILABLE:
+            raise ImportError(
+                "MCP (Model Context Protocol) package is not installed. "
+                "Install it with: pip install praisonaiagents[mcp]"
+            )
+
         self.server_url = server_url
         self.debug = debug
         self.timeout = timeout
         self.session = None
         self.tools = []
-        
+
         # Set up logging
         if debug:
             logger.setLevel(logging.DEBUG)

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
+        """
+        ...