Release v2.5.7

Release v2.5.6
Release v1.2.1 - refactor TypeScript tools to use generic BaseTool
2026-11-10 17:29:39 -10:00 · 2025-12-26 13:31:45 +00:00 · 2025-12-26 13:11:09 +00:00 · 2025-12-26 11:47:17 +00:00 · 2025-12-26 08:39:22 +00:00 · 2025-12-26 08:16:54 +00:00
235 changed files with 35053 additions and 1130 deletions
@@ -35,46 +35,108 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,

 ---

+> **Quick Paths:**
+> - 🚀 **New here?** → [Quick Start](#-quick-start)
+> - 📦 **Installing?** → [Installation](#-installation)
+> - 💻 **Python SDK?** → [Python Examples](#-using-python-code)
+> - 🎯 **CLI user?** → [CLI Reference](#-cli--no-code-interface)
+> - 🔧 **Need config?** → [Configuration](#-configuration--integration)
+> - 🤝 **Contributing?** → [Development](#-development)
+
+---
+
 ## 📑 Table of Contents

+### Getting Started
 - [⚡ Performance](#-performance)
 - [🚀 Quick Start](#-quick-start)
 - [📦 Installation](#-installation)
  - [Python SDK](#python-sdk)
  - [JavaScript SDK](#javascript-sdk)
- [💻 Usage](#-usage)
-  - [Python Code Examples](#python-code-examples)
-  - [CLI / No-Code Interface](#cli--no-code-interface)
-  - [JavaScript Code Examples](#javascript-code-examples)
+  - [Environment Variables](#environment-variables)
+
+### Core Concepts
 - [✨ Key Features](#-key-features)
 - [🌐 Supported Providers](#-supported-providers)
+
+### 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)
 - [🛠️ Custom Tools](#️-custom-tools)
+
+### JavaScript SDK
+- [💻 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)
+
+### Configuration
 - [🔧 Configuration & Integration](#-configuration--integration)
  - [Ollama Integration](#ollama-integration)
  - [Groq Integration](#groq-integration)
  - [100+ Models Support](#100-models-support)
- [📋 Workflows](#-workflows)
-  - [Workflow Patterns](#workflow-patterns)
-  - [YAML Configuration](#yaml-configuration)
-  - [Agents Playbook](#agents-playbook)
- [🧠 Memory & Context](#-memory--context)
+- [📋 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
 - [📊 Process Types & Patterns](#-process-types--patterns)
  - [Sequential Process](#sequential-process)
  - [Hierarchical Process](#hierarchical-process)
  - [Workflow Process](#workflow-process)
  - [Agentic Patterns](#agentic-patterns)
+
+### 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)

 ---

@@ -143,6 +205,30 @@ pip install praisonai
 npm install praisonai
 ```

+### Environment Variables
+
+| Variable | Required | Description |
+|----------|----------|-------------|
+| `OPENAI_API_KEY` | Yes* | OpenAI API key |
+| `ANTHROPIC_API_KEY` | No | Anthropic Claude API key |
+| `GOOGLE_API_KEY` | No | Google Gemini API key |
+| `GROQ_API_KEY` | No | Groq API key |
+| `OPENAI_BASE_URL` | No | Custom API endpoint (for Ollama, Groq, etc.) |
+
+> *At least one LLM provider API key is required.
+
+```bash
+# Set your API key
+export OPENAI_API_KEY=your_key_here
+
+# For Ollama (local models)
+export OPENAI_BASE_URL=http://localhost:11434/v1
+
+# For Groq
+export OPENAI_API_KEY=your_groq_key
+export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+```
+
 ---

 ## 💻 Usage
@@ -169,141 +255,263 @@ npm install praisonai

 ## ✨ Key Features

+<details open>
+<summary><strong>🤖 Core Agents</strong></summary>
+
 | Feature | Code | Docs |
 |---------|:----:|:----:|
-| **🤖 Core Agents** | | |
-| ↳ Single Agent | [Example](examples/python/agents/single-agent.py) | [📖](https://docs.praison.ai/agents/single) |
-| ↳ Multi Agents | [Example](examples/python/general/mini_agents_example.py) | [📖](https://docs.praison.ai/concepts/agents) |
-| ↳ Auto Agents | [Example](examples/python/general/auto_agents_example.py) | [📖](https://docs.praison.ai/features/autoagents) |
-| ↳ Self Reflection AI Agents | [Example](examples/python/concepts/self-reflection-details.py) | [📖](https://docs.praison.ai/features/selfreflection) |
-| ↳ Reasoning AI Agents | [Example](examples/python/concepts/reasoning-extraction.py) | [📖](https://docs.praison.ai/features/reasoning) |
-| ↳ Multi Modal AI Agents | [Example](examples/python/general/multimodal.py) | [📖](https://docs.praison.ai/features/multimodal) |
-| **🔄 Workflows** | | |
-| ↳ Simple Workflow | [Example](examples/python/workflows/simple_workflow.py) | [📖](https://docs.praison.ai/features/workflows) |
-| ↳ Workflow with Agents | [Example](examples/python/workflows/workflow_with_agents.py) | [📖](https://docs.praison.ai/features/workflows) |
-| ↳ Agentic Routing (`route()`) | [Example](examples/python/workflows/workflow_routing.py) | [📖](https://docs.praison.ai/features/routing) |
-| ↳ Parallel Execution (`parallel()`) | [Example](examples/python/workflows/workflow_parallel.py) | [📖](https://docs.praison.ai/features/parallelisation) |
-| ↳ Loop over List/CSV (`loop()`) | [Example](examples/python/workflows/workflow_loop_csv.py) | [📖](https://docs.praison.ai/features/repetitive) |
-| ↳ Evaluator-Optimizer (`repeat()`) | [Example](examples/python/workflows/workflow_repeat.py) | [📖](https://docs.praison.ai/features/evaluator-optimiser) |
-| ↳ Conditional Steps | [Example](examples/python/workflows/workflow_conditional.py) | [📖](https://docs.praison.ai/features/workflows) |
-| ↳ Workflow Branching | [Example](examples/python/workflows/workflow_branching.py) | [📖](https://docs.praison.ai/features/workflows) |
-| ↳ Workflow Early Stop | [Example](examples/python/workflows/workflow_early_stop.py) | [📖](https://docs.praison.ai/features/workflows) |
-| ↳ Workflow Checkpoints | [Example](examples/python/workflows/workflow_checkpoints.py) | [📖](https://docs.praison.ai/features/workflows) |
-| **💻 Code & Development** | | |
-| ↳ Code Interpreter Agents | [Example](examples/python/agents/code-agent.py) | [📖](https://docs.praison.ai/features/codeagent) |
-| ↳ AI Code Editing Tools | [Example](examples/python/code/code_editing_example.py) | [📖](https://docs.praison.ai/code/editing) |
-| ↳ External Agents (All) | [Example](examples/python/code/external_agents_example.py) | [📖](https://docs.praison.ai/code/external-agents) |
-| ↳ Claude Code CLI | [Example](examples/python/code/claude_code_example.py) | [📖](https://docs.praison.ai/code/claude-code) |
-| ↳ Gemini CLI | [Example](examples/python/code/gemini_cli_example.py) | [📖](https://docs.praison.ai/code/gemini-cli) |
-| ↳ Codex CLI | [Example](examples/python/code/codex_cli_example.py) | [📖](https://docs.praison.ai/code/codex-cli) |
-| ↳ Cursor CLI | [Example](examples/python/code/cursor_cli_example.py) | [📖](https://docs.praison.ai/code/cursor-cli) |
-| **🧠 Memory & Knowledge** | | |
-| ↳ Memory (Short & Long Term) | [Example](examples/python/general/memory_example.py) | [📖](https://docs.praison.ai/concepts/memory) |
-| ↳ File-Based Memory | [Example](examples/python/general/memory_example.py) | [📖](https://docs.praison.ai/concepts/memory) |
-| ↳ Claude Memory Tool | [Example](#claude-memory-tool-cli) | [📖](https://docs.praison.ai/features/claude-memory-tool) |
-| ↳ Add Custom Knowledge | [Example](examples/python/concepts/knowledge-agents.py) | [📖](https://docs.praison.ai/features/knowledge) |
-| ↳ RAG Agents | [Example](examples/python/concepts/rag-agents.py) | [📖](https://docs.praison.ai/features/rag) |
-| ↳ Chat with PDF Agents | [Example](examples/python/concepts/chat-with-pdf.py) | [📖](https://docs.praison.ai/features/chat-with-pdf) |
-| **🔬 Research & Intelligence** | | |
-| ↳ Deep Research Agents | [Example](examples/python/agents/research-agent.py) | [📖](https://docs.praison.ai/agents/deep-research) |
-| ↳ Query Rewriter Agent | [Example](#5-query-rewriter-agent) | [📖](https://docs.praison.ai/agents/query-rewriter) |
-| ↳ Native Web Search | [Example](examples/python/agents/websearch-agent.py) | [📖](https://docs.praison.ai/agents/websearch) |
-| ↳ Built-in Search Tools | [Example](examples/python/agents/websearch-agent.py) | [📖](https://docs.praison.ai/tools/tavily) |
-| ↳ Unified Web Search | [Example](src/praisonai-agents/examples/web_search_example.py) | [📖](https://docs.praison.ai/tools/web-search) |
-| ↳ Web Fetch (Anthropic) | [Example](#web-search-web-fetch--prompt-caching) | [📖](https://docs.praison.ai/features/model-capabilities) |
-| **📋 Planning & Execution** | | |
-| ↳ Planning Mode | [Example](examples/python/agents/planning-agent.py) | [📖](https://docs.praison.ai/features/planning-mode) |
-| ↳ Planning Tools | [Example](#3-agent-with-planning-mode) | [📖](https://docs.praison.ai/features/planning-mode) |
-| ↳ Planning Reasoning | [Example](#3-agent-with-planning-mode) | [📖](https://docs.praison.ai/features/planning-mode) |
-| ↳ Prompt Chaining | [Example](examples/python/general/prompt_chaining.py) | [📖](https://docs.praison.ai/features/promptchaining) |
-| ↳ Evaluator Optimiser | [Example](examples/python/general/evaluator-optimiser.py) | [📖](https://docs.praison.ai/features/evaluator-optimiser) |
-| ↳ Orchestrator Workers | [Example](examples/python/general/orchestrator-workers.py) | [📖](https://docs.praison.ai/features/orchestrator-worker) |
-| **👥 Specialized Agents** | | |
-| ↳ Data Analyst Agent | [Example](examples/python/agents/data-analyst-agent.py) | [📖](https://docs.praison.ai/agents/data-analyst) |
-| ↳ Finance Agent | [Example](examples/python/agents/finance-agent.py) | [📖](https://docs.praison.ai/agents/finance) |
-| ↳ Shopping Agent | [Example](examples/python/agents/shopping-agent.py) | [📖](https://docs.praison.ai/agents/shopping) |
-| ↳ Recommendation Agent | [Example](examples/python/agents/recommendation-agent.py) | [📖](https://docs.praison.ai/agents/recommendation) |
-| ↳ Wikipedia Agent | [Example](examples/python/agents/wikipedia-agent.py) | [📖](https://docs.praison.ai/agents/wikipedia) |
-| ↳ Programming Agent | [Example](examples/python/agents/programming-agent.py) | [📖](https://docs.praison.ai/agents/programming) |
-| ↳ Math Agents | [Example](examples/python/agents/math-agent.py) | [📖](https://docs.praison.ai/features/mathagent) |
-| ↳ Markdown Agent | [Example](examples/python/agents/markdown-agent.py) | [📖](https://docs.praison.ai/agents/markdown) |
-| ↳ Prompt Expander Agent | [Example](#prompt-expansion) | [📖](https://docs.praison.ai/agents/prompt-expander) |
-| **🎨 Media & Multimodal** | | |
-| ↳ Image Generation Agent | [Example](examples/python/image/image-agent.py) | [📖](https://docs.praison.ai/features/image-generation) |
-| ↳ Image to Text Agent | [Example](examples/python/agents/image-to-text-agent.py) | [📖](https://docs.praison.ai/agents/image-to-text) |
-| ↳ Video Agent | [Example](examples/python/agents/video-agent.py) | [📖](https://docs.praison.ai/agents/video) |
-| ↳ Camera Integration | [Example](examples/python/camera/) | [📖](https://docs.praison.ai/features/camera-integration) |
-| **🔌 Protocols & Integration** | | |
-| ↳ MCP Transports | [Example](examples/python/mcp/mcp-transports-overview.py) | [📖](https://docs.praison.ai/mcp/transports) |
-| ↳ WebSocket MCP | [Example](examples/python/mcp/websocket-mcp.py) | [📖](https://docs.praison.ai/mcp/sse-transport) |
-| ↳ MCP Security | [Example](examples/python/mcp/mcp-security.py) | [📖](https://docs.praison.ai/mcp/transports) |
-| ↳ MCP Resumability | [Example](examples/python/mcp/mcp-resumability.py) | [📖](https://docs.praison.ai/mcp/sse-transport) |
-| ↳ MCP Config Management | [Example](#mcp-config-cli) | [📖](https://docs.praison.ai/docs/cli/mcp) |
-| ↳ LangChain Integrated Agents | [Example](examples/python/general/langchain_example.py) | [📖](https://docs.praison.ai/features/langchain) |
-| **🛡️ Safety & Control** | | |
-| ↳ Guardrails | [Example](examples/python/guardrails/comprehensive-guardrails-example.py) | [📖](https://docs.praison.ai/features/guardrails) |
-| ↳ Human Approval | [Example](examples/python/general/human_approval_example.py) | [📖](https://docs.praison.ai/features/approval) |
-| ↳ Rules & Instructions | [Example](#6-rules--instructions) | [📖](https://docs.praison.ai/features/rules) |
-| **⚙️ Advanced Features** | | |
-| ↳ Async & Parallel Processing | [Example](examples/python/general/async_example.py) | [📖](https://docs.praison.ai/features/async) |
-| ↳ Parallelisation | [Example](examples/python/general/parallelisation.py) | [📖](https://docs.praison.ai/features/parallelisation) |
-| ↳ Repetitive Agents | [Example](examples/python/concepts/repetitive-agents.py) | [📖](https://docs.praison.ai/features/repetitive) |
-| ↳ Agent Handoffs | [Example](examples/python/handoff/handoff_basic.py) | [📖](https://docs.praison.ai/features/handoffs) |
-| ↳ Stateful Agents | [Example](examples/python/stateful/workflow-state-example.py) | [📖](https://docs.praison.ai/features/stateful-agents) |
-| ↳ Autonomous Workflow | [Example](examples/python/general/autonomous-agent.py) | [📖](https://docs.praison.ai/features/autonomous-workflow) |
-| ↳ Structured Output Agents | [Example](examples/python/general/structured_agents_example.py) | [📖](https://docs.praison.ai/features/structured) |
-| ↳ Model Router | [Example](examples/python/agents/router-agent-cost-optimization.py) | [📖](https://docs.praison.ai/features/model-router) |
-| ↳ Prompt Caching | [Example](#web-search-web-fetch--prompt-caching) | [📖](https://docs.praison.ai/features/model-capabilities) |
-| ↳ Fast Context | [Example](examples/context/00_agent_fast_context_basic.py) | [📖](https://docs.praison.ai/features/fast-context) |
-| **🛠️ Tools & Configuration** | | |
-| ↳ 100+ Custom Tools | [Example](examples/python/general/tools_example.py) | [📖](https://docs.praison.ai/tools/tools) |
-| ↳ YAML Configuration | [Example](examples/cookbooks/yaml/secondary_market_research_agents.yaml) | [📖](https://docs.praison.ai/developers/agents-playbook) |
-| ↳ 100+ LLM Support | [Example](examples/python/providers/openai/openai_gpt4_example.py) | [📖](https://docs.praison.ai/models) |
-| ↳ Callback Agents | [Example](examples/python/general/advanced-callback-systems.py) | [📖](https://docs.praison.ai/features/callbacks) |
-| ↳ Hooks | [Example](#9-hooks) | [📖](https://docs.praison.ai/features/hooks) |
-| ↳ Middleware System | [Example](examples/middleware/basic_middleware.py) | [📖](https://docs.praison.ai/features/middleware) |
-| ↳ Configurable Model | [Example](examples/middleware/configurable_model.py) | [📖](https://docs.praison.ai/features/configurable-model) |
-| ↳ Rate Limiter | [Example](examples/middleware/rate_limiter.py) | [📖](https://docs.praison.ai/features/rate-limiter) |
-| ↳ Injected Tool State | [Example](examples/middleware/injected_state.py) | [📖](https://docs.praison.ai/features/injected-state) |
-| ↳ Shadow Git Checkpoints | [Example](#10-shadow-git-checkpoints) | [📖](https://docs.praison.ai/features/checkpoints) |
-| ↳ Background Tasks | [Example](examples/background/basic_background.py) | [📖](https://docs.praison.ai/features/background-tasks) |
-| ↳ Policy Engine | [Example](examples/policy/basic_policy.py) | [📖](https://docs.praison.ai/features/policy-engine) |
-| ↳ Thinking Budgets | [Example](examples/thinking/basic_thinking.py) | [📖](https://docs.praison.ai/features/thinking-budgets) |
-| ↳ Output Styles | [Example](examples/output/basic_output.py) | [📖](https://docs.praison.ai/features/output-styles) |
-| ↳ Context Compaction | [Example](examples/compaction/basic_compaction.py) | [📖](https://docs.praison.ai/features/context-compaction) |
-| **📊 Monitoring & Management** | | |
-| ↳ Sessions Management | [Example](examples/python/sessions/comprehensive-session-management.py) | [📖](https://docs.praison.ai/features/sessions) |
-| ↳ Auto-Save Sessions | [Example](#session-management-python) | [📖](https://docs.praison.ai/docs/cli/session) |
-| ↳ History in Context | [Example](#session-management-python) | [📖](https://docs.praison.ai/docs/cli/session) |
-| ↳ Telemetry | [Example](examples/python/telemetry/production-telemetry-example.py) | [📖](https://docs.praison.ai/features/telemetry) |
-| ↳ Project Docs (.praison/docs/) | [Example](#docs-cli) | [📖](https://docs.praison.ai/docs/cli/docs) |
-| ↳ AI Commit Messages | [Example](#ai-commit-cli) | [📖](https://docs.praison.ai/docs/cli/commit) |
-| ↳ @Mentions in Prompts | [Example](#mentions-in-prompts) | [📖](https://docs.praison.ai/docs/cli/mentions) |
-| **🖥️ CLI Features** | | |
-| ↳ Slash Commands | [Example](examples/python/cli/slash_commands_example.py) | [📖](https://docs.praison.ai/docs/cli/slash-commands) |
-| ↳ Autonomy Modes | [Example](examples/python/cli/autonomy_modes_example.py) | [📖](https://docs.praison.ai/docs/cli/autonomy-modes) |
-| ↳ Cost Tracking | [Example](examples/python/cli/cost_tracking_example.py) | [📖](https://docs.praison.ai/docs/cli/cost-tracking) |
-| ↳ Repository Map | [Example](examples/python/cli/repo_map_example.py) | [📖](https://docs.praison.ai/docs/cli/repo-map) |
-| ↳ Interactive TUI | [Example](examples/python/cli/interactive_tui_example.py) | [📖](https://docs.praison.ai/docs/cli/interactive-tui) |
-| ↳ Git Integration | [Example](examples/python/cli/git_integration_example.py) | [📖](https://docs.praison.ai/docs/cli/git-integration) |
-| ↳ Sandbox Execution | [Example](examples/python/cli/sandbox_execution_example.py) | [📖](https://docs.praison.ai/docs/cli/sandbox-execution) |
-| ↳ CLI Compare | [Example](examples/compare/cli_compare_basic.py) | [📖](https://docs.praison.ai/docs/cli/compare) |
-| ↳ Profile/Benchmark | [Example](#profile-benchmark) | [📖](https://docs.praison.ai/docs/cli/profile) |
-| ↳ Auto Mode | [Example](#auto-mode) | [📖](https://docs.praison.ai/docs/cli/auto) |
-| ↳ Init | [Example](#init) | [📖](https://docs.praison.ai/docs/cli/init) |
-| ↳ File Input | [Example](#file-input) | [📖](https://docs.praison.ai/docs/cli/file-input) |
-| ↳ Final Agent | [Example](#final-agent) | [📖](https://docs.praison.ai/docs/cli/final-agent) |
-| ↳ Max Tokens | [Example](#max-tokens) | [📖](https://docs.praison.ai/docs/cli/max-tokens) |
-| **🧪 Evaluation** | | |
-| ↳ Accuracy Evaluation | [Example](examples/eval/accuracy_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
-| ↳ Performance Evaluation | [Example](examples/eval/performance_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
-| ↳ Reliability Evaluation | [Example](examples/eval/reliability_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
-| ↳ Criteria Evaluation | [Example](examples/eval/criteria_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
-| **🎯 Agent Skills** | | |
-| ↳ Skills Management | [Example](examples/skills/basic_skill_usage.py) | [📖](https://docs.praison.ai/features/skills) |
-| ↳ Custom Skills | [Example](examples/skills/custom_skill_example.py) | [📖](https://docs.praison.ai/features/skills) |
-| **⏰ 24/7 Scheduling** | | |
-| ↳ Agent Scheduler | [Example](examples/python/scheduled_agents/news_checker_live.py) | [📖](https://docs.praison.ai/docs/cli/scheduler) |
+| Single Agent | [Example](examples/python/agents/single-agent.py) | [📖](https://docs.praison.ai/agents/single) |
+| Multi Agents | [Example](examples/python/general/mini_agents_example.py) | [📖](https://docs.praison.ai/concepts/agents) |
+| Auto Agents | [Example](examples/python/general/auto_agents_example.py) | [📖](https://docs.praison.ai/features/autoagents) |
+| Self Reflection AI Agents | [Example](examples/python/concepts/self-reflection-details.py) | [📖](https://docs.praison.ai/features/selfreflection) |
+| Reasoning AI Agents | [Example](examples/python/concepts/reasoning-extraction.py) | [📖](https://docs.praison.ai/features/reasoning) |
+| Multi Modal AI Agents | [Example](examples/python/general/multimodal.py) | [📖](https://docs.praison.ai/features/multimodal) |
+
+</details>
+
+<details>
+<summary><strong>🔄 Workflows</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Simple Workflow | [Example](examples/python/workflows/simple_workflow.py) | [📖](https://docs.praison.ai/features/workflows) |
+| Workflow with Agents | [Example](examples/python/workflows/workflow_with_agents.py) | [📖](https://docs.praison.ai/features/workflows) |
+| Agentic Routing (`route()`) | [Example](examples/python/workflows/workflow_routing.py) | [📖](https://docs.praison.ai/features/routing) |
+| Parallel Execution (`parallel()`) | [Example](examples/python/workflows/workflow_parallel.py) | [📖](https://docs.praison.ai/features/parallelisation) |
+| Loop over List/CSV (`loop()`) | [Example](examples/python/workflows/workflow_loop_csv.py) | [📖](https://docs.praison.ai/features/repetitive) |
+| Evaluator-Optimizer (`repeat()`) | [Example](examples/python/workflows/workflow_repeat.py) | [📖](https://docs.praison.ai/features/evaluator-optimiser) |
+| Conditional Steps | [Example](examples/python/workflows/workflow_conditional.py) | [📖](https://docs.praison.ai/features/workflows) |
+| Workflow Branching | [Example](examples/python/workflows/workflow_branching.py) | [📖](https://docs.praison.ai/features/workflows) |
+| Workflow Early Stop | [Example](examples/python/workflows/workflow_early_stop.py) | [📖](https://docs.praison.ai/features/workflows) |
+| Workflow Checkpoints | [Example](examples/python/workflows/workflow_checkpoints.py) | [📖](https://docs.praison.ai/features/workflows) |
+
+</details>
+
+<details>
+<summary><strong>💻 Code & Development</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Code Interpreter Agents | [Example](examples/python/agents/code-agent.py) | [📖](https://docs.praison.ai/features/codeagent) |
+| AI Code Editing Tools | [Example](examples/python/code/code_editing_example.py) | [📖](https://docs.praison.ai/code/editing) |
+| External Agents (All) | [Example](examples/python/code/external_agents_example.py) | [📖](https://docs.praison.ai/code/external-agents) |
+| Claude Code CLI | [Example](examples/python/code/claude_code_example.py) | [📖](https://docs.praison.ai/code/claude-code) |
+| Gemini CLI | [Example](examples/python/code/gemini_cli_example.py) | [📖](https://docs.praison.ai/code/gemini-cli) |
+| Codex CLI | [Example](examples/python/code/codex_cli_example.py) | [📖](https://docs.praison.ai/code/codex-cli) |
+| Cursor CLI | [Example](examples/python/code/cursor_cli_example.py) | [📖](https://docs.praison.ai/code/cursor-cli) |
+
+</details>
+
+<details>
+<summary><strong>🧠 Memory & Knowledge</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Memory (Short & Long Term) | [Example](examples/python/general/memory_example.py) | [📖](https://docs.praison.ai/concepts/memory) |
+| File-Based Memory | [Example](examples/python/general/memory_example.py) | [📖](https://docs.praison.ai/concepts/memory) |
+| Claude Memory Tool | [Example](#claude-memory-tool-cli) | [📖](https://docs.praison.ai/features/claude-memory-tool) |
+| Add Custom Knowledge | [Example](examples/python/concepts/knowledge-agents.py) | [📖](https://docs.praison.ai/features/knowledge) |
+| RAG Agents | [Example](examples/python/concepts/rag-agents.py) | [📖](https://docs.praison.ai/features/rag) |
+| Chat with PDF Agents | [Example](examples/python/concepts/chat-with-pdf.py) | [📖](https://docs.praison.ai/features/chat-with-pdf) |
+| Data Readers (PDF, DOCX, etc.) | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-readers-api) |
+| Vector Store Selection | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-vector-store-api) |
+| Retrieval Strategies | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-retrieval-api) |
+| Rerankers | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-reranker-api) |
+| Index Types (Vector/Keyword/Hybrid) | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-index-api) |
+| Query Engines (Sub-Question, etc.) | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-query-engine-api) |
+
+</details>
+
+<details>
+<summary><strong>🔬 Research & Intelligence</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Deep Research Agents | [Example](examples/python/agents/research-agent.py) | [📖](https://docs.praison.ai/agents/deep-research) |
+| Query Rewriter Agent | [Example](#5-query-rewriter-agent) | [📖](https://docs.praison.ai/agents/query-rewriter) |
+| Native Web Search | [Example](examples/python/agents/websearch-agent.py) | [📖](https://docs.praison.ai/agents/websearch) |
+| Built-in Search Tools | [Example](examples/python/agents/websearch-agent.py) | [📖](https://docs.praison.ai/tools/tavily) |
+| Unified Web Search | [Example](src/praisonai-agents/examples/web_search_example.py) | [📖](https://docs.praison.ai/tools/web-search) |
+| Web Fetch (Anthropic) | [Example](#web-search-web-fetch--prompt-caching) | [📖](https://docs.praison.ai/features/model-capabilities) |
+
+</details>
+
+<details>
+<summary><strong>📋 Planning & Execution</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Planning Mode | [Example](examples/python/agents/planning-agent.py) | [📖](https://docs.praison.ai/features/planning-mode) |
+| Planning Tools | [Example](#3-agent-with-planning-mode) | [📖](https://docs.praison.ai/features/planning-mode) |
+| Planning Reasoning | [Example](#3-agent-with-planning-mode) | [📖](https://docs.praison.ai/features/planning-mode) |
+| Prompt Chaining | [Example](examples/python/general/prompt_chaining.py) | [📖](https://docs.praison.ai/features/promptchaining) |
+| Evaluator Optimiser | [Example](examples/python/general/evaluator-optimiser.py) | [📖](https://docs.praison.ai/features/evaluator-optimiser) |
+| Orchestrator Workers | [Example](examples/python/general/orchestrator-workers.py) | [📖](https://docs.praison.ai/features/orchestrator-worker) |
+
+</details>
+
+<details>
+<summary><strong>👥 Specialized Agents</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Data Analyst Agent | [Example](examples/python/agents/data-analyst-agent.py) | [📖](https://docs.praison.ai/agents/data-analyst) |
+| Finance Agent | [Example](examples/python/agents/finance-agent.py) | [📖](https://docs.praison.ai/agents/finance) |
+| Shopping Agent | [Example](examples/python/agents/shopping-agent.py) | [📖](https://docs.praison.ai/agents/shopping) |
+| Recommendation Agent | [Example](examples/python/agents/recommendation-agent.py) | [📖](https://docs.praison.ai/agents/recommendation) |
+| Wikipedia Agent | [Example](examples/python/agents/wikipedia-agent.py) | [📖](https://docs.praison.ai/agents/wikipedia) |
+| Programming Agent | [Example](examples/python/agents/programming-agent.py) | [📖](https://docs.praison.ai/agents/programming) |
+| Math Agents | [Example](examples/python/agents/math-agent.py) | [📖](https://docs.praison.ai/features/mathagent) |
+| Markdown Agent | [Example](examples/python/agents/markdown-agent.py) | [📖](https://docs.praison.ai/agents/markdown) |
+| Prompt Expander Agent | [Example](#prompt-expansion) | [📖](https://docs.praison.ai/agents/prompt-expander) |
+
+</details>
+
+<details>
+<summary><strong>🎨 Media & Multimodal</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Image Generation Agent | [Example](examples/python/image/image-agent.py) | [📖](https://docs.praison.ai/features/image-generation) |
+| Image to Text Agent | [Example](examples/python/agents/image-to-text-agent.py) | [📖](https://docs.praison.ai/agents/image-to-text) |
+| Video Agent | [Example](examples/python/agents/video-agent.py) | [📖](https://docs.praison.ai/agents/video) |
+| Camera Integration | [Example](examples/python/camera/) | [📖](https://docs.praison.ai/features/camera-integration) |
+
+</details>
+
+<details>
+<summary><strong>🔌 Protocols & Integration</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| MCP Transports | [Example](examples/python/mcp/mcp-transports-overview.py) | [📖](https://docs.praison.ai/mcp/transports) |
+| WebSocket MCP | [Example](examples/python/mcp/websocket-mcp.py) | [📖](https://docs.praison.ai/mcp/sse-transport) |
+| MCP Security | [Example](examples/python/mcp/mcp-security.py) | [📖](https://docs.praison.ai/mcp/transports) |
+| MCP Resumability | [Example](examples/python/mcp/mcp-resumability.py) | [📖](https://docs.praison.ai/mcp/sse-transport) |
+| MCP Config Management | [Example](#mcp-config-cli) | [📖](https://docs.praison.ai/docs/cli/mcp) |
+| LangChain Integrated Agents | [Example](examples/python/general/langchain_example.py) | [📖](https://docs.praison.ai/features/langchain) |
+
+</details>
+
+<details>
+<summary><strong>🛡️ Safety & Control</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Guardrails | [Example](examples/python/guardrails/comprehensive-guardrails-example.py) | [📖](https://docs.praison.ai/features/guardrails) |
+| Human Approval | [Example](examples/python/general/human_approval_example.py) | [📖](https://docs.praison.ai/features/approval) |
+| Rules & Instructions | [Example](#7-rules--instructions) | [📖](https://docs.praison.ai/features/rules) |
+
+</details>
+
+<details>
+<summary><strong>⚙️ Advanced Features</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Async & Parallel Processing | [Example](examples/python/general/async_example.py) | [📖](https://docs.praison.ai/features/async) |
+| Parallelisation | [Example](examples/python/general/parallelisation.py) | [📖](https://docs.praison.ai/features/parallelisation) |
+| Repetitive Agents | [Example](examples/python/concepts/repetitive-agents.py) | [📖](https://docs.praison.ai/features/repetitive) |
+| Agent Handoffs | [Example](examples/python/handoff/handoff_basic.py) | [📖](https://docs.praison.ai/features/handoffs) |
+| Stateful Agents | [Example](examples/python/stateful/workflow-state-example.py) | [📖](https://docs.praison.ai/features/stateful-agents) |
+| Autonomous Workflow | [Example](examples/python/general/autonomous-agent.py) | [📖](https://docs.praison.ai/features/autonomous-workflow) |
+| Structured Output Agents | [Example](examples/python/general/structured_agents_example.py) | [📖](https://docs.praison.ai/features/structured) |
+| Model Router | [Example](examples/python/agents/router-agent-cost-optimization.py) | [📖](https://docs.praison.ai/features/model-router) |
+| Prompt Caching | [Example](#web-search-web-fetch--prompt-caching) | [📖](https://docs.praison.ai/features/model-capabilities) |
+| Fast Context | [Example](examples/context/00_agent_fast_context_basic.py) | [📖](https://docs.praison.ai/features/fast-context) |
+
+</details>
+
+<details>
+<summary><strong>🛠️ Tools & Configuration</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| 100+ Custom Tools | [Example](examples/python/general/tools_example.py) | [📖](https://docs.praison.ai/tools/tools) |
+| YAML Configuration | [Example](examples/cookbooks/yaml/secondary_market_research_agents.yaml) | [📖](https://docs.praison.ai/developers/agents-playbook) |
+| 100+ LLM Support | [Example](examples/python/providers/openai/openai_gpt4_example.py) | [📖](https://docs.praison.ai/models) |
+| Callback Agents | [Example](examples/python/general/advanced-callback-systems.py) | [📖](https://docs.praison.ai/features/callbacks) |
+| Hooks | [Example](#10-hooks) | [📖](https://docs.praison.ai/features/hooks) |
+| Middleware System | [Example](examples/middleware/basic_middleware.py) | [📖](https://docs.praison.ai/features/middleware) |
+| Configurable Model | [Example](examples/middleware/configurable_model.py) | [📖](https://docs.praison.ai/features/configurable-model) |
+| Rate Limiter | [Example](examples/middleware/rate_limiter.py) | [📖](https://docs.praison.ai/features/rate-limiter) |
+| Injected Tool State | [Example](examples/middleware/injected_state.py) | [📖](https://docs.praison.ai/features/injected-state) |
+| Shadow Git Checkpoints | [Example](#11-shadow-git-checkpoints) | [📖](https://docs.praison.ai/features/checkpoints) |
+| Background Tasks | [Example](examples/background/basic_background.py) | [📖](https://docs.praison.ai/features/background-tasks) |
+| Policy Engine | [Example](examples/policy/basic_policy.py) | [📖](https://docs.praison.ai/features/policy-engine) |
+| Thinking Budgets | [Example](examples/thinking/basic_thinking.py) | [📖](https://docs.praison.ai/features/thinking-budgets) |
+| Output Styles | [Example](examples/output/basic_output.py) | [📖](https://docs.praison.ai/features/output-styles) |
+| Context Compaction | [Example](examples/compaction/basic_compaction.py) | [📖](https://docs.praison.ai/features/context-compaction) |
+
+</details>
+
+<details>
+<summary><strong>📊 Monitoring & Management</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Sessions Management | [Example](examples/python/sessions/comprehensive-session-management.py) | [📖](https://docs.praison.ai/features/sessions) |
+| Auto-Save Sessions | [Example](#session-management-python) | [📖](https://docs.praison.ai/docs/cli/session) |
+| History in Context | [Example](#session-management-python) | [📖](https://docs.praison.ai/docs/cli/session) |
+| Telemetry | [Example](examples/python/telemetry/production-telemetry-example.py) | [📖](https://docs.praison.ai/features/telemetry) |
+| Project Docs (.praison/docs/) | [Example](#docs-cli) | [📖](https://docs.praison.ai/docs/cli/docs) |
+| AI Commit Messages | [Example](#ai-commit-cli) | [📖](https://docs.praison.ai/docs/cli/commit) |
+| @Mentions in Prompts | [Example](#mentions-in-prompts) | [📖](https://docs.praison.ai/docs/cli/mentions) |
+
+</details>
+
+<details>
+<summary><strong>🖥️ CLI Features</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Slash Commands | [Example](examples/python/cli/slash_commands_example.py) | [📖](https://docs.praison.ai/docs/cli/slash-commands) |
+| Autonomy Modes | [Example](examples/python/cli/autonomy_modes_example.py) | [📖](https://docs.praison.ai/docs/cli/autonomy-modes) |
+| Cost Tracking | [Example](examples/python/cli/cost_tracking_example.py) | [📖](https://docs.praison.ai/docs/cli/cost-tracking) |
+| Repository Map | [Example](examples/python/cli/repo_map_example.py) | [📖](https://docs.praison.ai/docs/cli/repo-map) |
+| Interactive TUI | [Example](examples/python/cli/interactive_tui_example.py) | [📖](https://docs.praison.ai/docs/cli/interactive-tui) |
+| Git Integration | [Example](examples/python/cli/git_integration_example.py) | [📖](https://docs.praison.ai/docs/cli/git-integration) |
+| Sandbox Execution | [Example](examples/python/cli/sandbox_execution_example.py) | [📖](https://docs.praison.ai/docs/cli/sandbox-execution) |
+| CLI Compare | [Example](examples/compare/cli_compare_basic.py) | [📖](https://docs.praison.ai/docs/cli/compare) |
+| Profile/Benchmark | [Example](#profile-benchmark) | [📖](https://docs.praison.ai/docs/cli/profile) |
+| Auto Mode | [Example](#auto-mode) | [📖](https://docs.praison.ai/docs/cli/auto) |
+| Init | [Example](#init) | [📖](https://docs.praison.ai/docs/cli/init) |
+| File Input | [Example](#file-input) | [📖](https://docs.praison.ai/docs/cli/file-input) |
+| Final Agent | [Example](#final-agent) | [📖](https://docs.praison.ai/docs/cli/final-agent) |
+| Max Tokens | [Example](#max-tokens) | [📖](https://docs.praison.ai/docs/cli/max-tokens) |
+
+</details>
+
+<details>
+<summary><strong>🧪 Evaluation</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Accuracy Evaluation | [Example](examples/eval/accuracy_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
+| Performance Evaluation | [Example](examples/eval/performance_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
+| Reliability Evaluation | [Example](examples/eval/reliability_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
+| Criteria Evaluation | [Example](examples/eval/criteria_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
+
+</details>
+
+<details>
+<summary><strong>🎯 Agent Skills</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Skills Management | [Example](examples/skills/basic_skill_usage.py) | [📖](https://docs.praison.ai/features/skills) |
+| Custom Skills | [Example](examples/skills/custom_skill_example.py) | [📖](https://docs.praison.ai/features/skills) |
+
+</details>
+
+<details>
+<summary><strong>⏰ 24/7 Scheduling</strong></summary>
+
+| Feature | Code | Docs |
+|---------|:----:|:----:|
+| Agent Scheduler | [Example](examples/python/scheduled_agents/news_checker_live.py) | [📖](https://docs.praison.ai/docs/cli/scheduler) |
+
+</details>

 ---

@@ -537,7 +745,7 @@ memory.handle_command("/memory save my_session")
 | `memory="sqlite"` | Built-in | SQLite with indexing |
 | `memory="chromadb"` | chromadb | Vector/semantic search |

-### 6. Rules & Instructions
+### 7. Rules & Instructions

 PraisonAI auto-discovers instruction files from your project root and git root:

@@ -586,7 +794,7 @@ activation: always  # always, glob, manual, ai_decision
 - Follow PEP 8
 ```

-### 7. Auto-Generated Memories
+### 8. Auto-Generated Memories

 ```python
 from praisonaiagents.memory import FileMemory, AutoMemory
@@ -601,7 +809,7 @@ memories = auto.process_interaction(
 # Extracts: name="John", preference="Python for backend"
 ```

-### 8. Agentic Workflows
+### 9. Agentic Workflows

 Create powerful multi-agent workflows with the `Workflow` class:

@@ -871,7 +1079,7 @@ callbacks:
  on_workflow_complete: log_complete
 ```

-### 9. Hooks
+### 10. Hooks

 Intercept and modify agent behavior at various lifecycle points:

@@ -910,7 +1118,7 @@ praisonai hooks validate hooks.json     # Validate configuration
 ```


-### 10. Shadow Git Checkpoints
+### 11. Shadow Git Checkpoints

 File-level undo/restore using shadow git:

@@ -947,7 +1155,7 @@ praisonai checkpoint restore abc123         # Restore to checkpoint

 ---

-### 11. Background Tasks
+### 12. Background Tasks

 Run agent tasks asynchronously without blocking:

@@ -985,7 +1193,7 @@ praisonai background clear         # Clear completed tasks

 ---

-### 12. Policy Engine
+### 13. Policy Engine

 Control what agents can and cannot do with policy-based execution:

@@ -1026,7 +1234,7 @@ praisonai policy init                  # Create template

 ---

-### 13. Thinking Budgets
+### 14. Thinking Budgets

 Configure token budgets for extended thinking:

@@ -1059,7 +1267,7 @@ praisonai thinking stats       # Show usage statistics

 ---

-### 14. Output Styles
+### 15. Output Styles

 Configure how agents format their responses:

@@ -1089,7 +1297,7 @@ praisonai output set concise   # Set output style

 ---

-### 15. Context Compaction
+### 16. Context Compaction

 Automatically manage context window size:

@@ -1124,7 +1332,7 @@ praisonai compaction stats         # Show statistics

 ---

-### 16. Field Names Reference (A-I-G-S)
+### 17. Field Names Reference (A-I-G-S)

 PraisonAI accepts both old (agents.yaml) and new (workflow.yaml) field names. Use the **canonical names** for new projects:

@@ -1158,7 +1366,7 @@ steps:                         # Define steps (not 'tasks')

 > **Note:** The parser accepts both old and new names. Run `praisonai workflow validate <file.yaml>` to see suggestions for canonical names.

-### 12. Extended agents.yaml with Workflow Patterns
+### 18. Extended agents.yaml with Workflow Patterns

 **Feature Parity:** Both `agents.yaml` and `workflow.yaml` now support the same features:
 - All workflow patterns (route, parallel, loop, repeat)
@@ -1234,7 +1442,7 @@ Run with the same simple command:
 praisonai agents.yaml
 ```

-### 12. MCP (Model Context Protocol)
+### 19. MCP (Model Context Protocol)

 PraisonAI supports MCP Protocol Revision 2025-11-25 with multiple transports.

@@ -1310,7 +1518,7 @@ server.run()  # stdio for Claude Desktop
 | Security | Origin validation, DNS rebinding prevention |
 | WebSocket | Auto-reconnect with exponential backoff |

-### 13. A2A (Agent2Agent Protocol)
+### 20. A2A (Agent2Agent Protocol)

 PraisonAI supports the [A2A Protocol](https://a2a-protocol.org) for agent-to-agent communication, enabling your agents to be discovered and collaborate with other AI agents.

@@ -1358,6 +1566,22 @@ app.include_router(a2a.get_router())

 PraisonAI provides a powerful CLI for no-code automation and quick prototyping.

+### CLI Quick Reference
+
+| Category | Commands |
+|----------|----------|
+| **Execution** | `praisonai`, `--auto`, `--interactive`, `--chat` |
+| **Research** | `research`, `--query-rewrite`, `--deep-research` |
+| **Planning** | `--planning`, `--planning-tools`, `--planning-reasoning` |
+| **Workflows** | `workflow run`, `workflow list`, `workflow auto` |
+| **Memory** | `memory show`, `memory add`, `memory search`, `memory clear` |
+| **Knowledge** | `knowledge add`, `knowledge query`, `knowledge list` |
+| **Sessions** | `session list`, `session resume`, `session delete` |
+| **Tools** | `tools list`, `tools info`, `tools search` |
+| **MCP** | `mcp list`, `mcp create`, `mcp enable` |
+| **Development** | `commit`, `docs`, `checkpoint`, `hooks` |
+| **Scheduling** | `schedule start`, `schedule list`, `schedule stop` |
+
 ### Auto Mode
 ```bash
 pip install praisonai
@@ -2641,6 +2865,84 @@ PraisonAI provides zero-dependency persistent memory for agents. For detailed ex

 ---

+## 📚 Knowledge & Retrieval (RAG)
+
+PraisonAI provides a complete knowledge stack for building RAG applications with multiple vector stores, retrieval strategies, rerankers, and query modes.
+
+### 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 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"
+
+# Query with advanced options
+praisonai knowledge query "How to authenticate?" --retrieval fusion --reranker llm
+
+# Full advanced query
+praisonai knowledge query "authentication flow" \
+  --vector-store chroma \
+  --retrieval fusion \
+  --reranker llm \
+  --index-type hybrid \
+  --query-mode sub_question
+```
+
+### Knowledge SDK Usage
+
+```python
+from praisonaiagents import Agent, Knowledge
+
+# Simple usage with Agent
+agent = Agent(
+    name="Research Assistant",
+    knowledge=["docs/manual.pdf", "data/faq.txt"],
+    knowledge_config={
+        "vector_store": {"provider": "chroma"}
+    }
+)
+response = agent.chat("How do I authenticate?")
+
+# Direct Knowledge usage
+knowledge = Knowledge()
+knowledge.add("document.pdf")
+results = knowledge.search("authentication", limit=5)
+```
+
+### 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) |
+| **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) |
+
+---
+
 ## 🔬 Advanced Features

 ### Research & Intelligence
@@ -2755,6 +3057,44 @@ 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
+```
+
 ### Databases Table

 | Database | Store Type | Install | Example | Docs |
@@ -2901,6 +3241,119 @@ cd src/praisonai && uv publish

 ---

+## ❓ FAQ & Troubleshooting
+
+<details>
+<summary><strong>ModuleNotFoundError: No module named 'praisonaiagents'</strong></summary>
+
+Install the package:
+```bash
+pip install praisonaiagents
+```
+
+</details>
+
+<details>
+<summary><strong>API key not found / Authentication error</strong></summary>
+
+Ensure your API key is set:
+```bash
+export OPENAI_API_KEY=your_key_here
+```
+
+For other providers, see [Environment Variables](#environment-variables).
+
+</details>
+
+<details>
+<summary><strong>How do I use a local model (Ollama)?</strong></summary>
+
+```bash
+# Start Ollama server first
+ollama serve
+
+# Set environment variable
+export OPENAI_BASE_URL=http://localhost:11434/v1
+```
+
+See [Ollama Integration](#ollama-integration) for more details.
+
+</details>
+
+<details>
+<summary><strong>How do I persist conversations to a database?</strong></summary>
+
+Use the `db` parameter:
+```python
+from praisonaiagents import Agent, db
+
+agent = Agent(
+    name="Assistant",
+    db=db(database_url="postgresql://localhost/mydb"),
+    session_id="my-session"
+)
+```
+
+See [Persistence (Databases)](#-persistence-databases) for supported databases.
+
+</details>
+
+<details>
+<summary><strong>How do I enable agent memory?</strong></summary>
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    name="Assistant",
+    memory=True,  # Enables file-based memory (no extra deps!)
+    user_id="user123"
+)
+```
+
+See [Agent Memory](#6-agent-memory-zero-dependencies) for more options.
+
+</details>
+
+<details>
+<summary><strong>How do I run multiple agents together?</strong></summary>
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+
+agent1 = Agent(instructions="Research topics")
+agent2 = Agent(instructions="Summarize findings")
+agents = PraisonAIAgents(agents=[agent1, agent2])
+agents.start()
+```
+
+See [Multi Agents](#2-multi-agents) for more examples.
+
+</details>
+
+<details>
+<summary><strong>How do I use MCP tools?</strong></summary>
+
+```python
+from praisonaiagents import Agent, MCP
+
+agent = Agent(
+    tools=MCP("npx @modelcontextprotocol/server-memory")
+)
+```
+
+See [MCP Protocol](#19-mcp-model-context-protocol) for all transport options.
+
+</details>
+
+### Getting Help
+
+- 📚 [Full Documentation](https://docs.praison.ai)
+- 🐛 [Report Issues](https://github.com/MervinPraison/PraisonAI/issues)
+- 💬 [Discussions](https://github.com/MervinPraison/PraisonAI/discussions)
+
+---
+
 <div align="center">
  <p><strong>Made with ❤️ by the PraisonAI Team</strong></p>
  <p>
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.5.0" \
+    "praisonai>=2.5.7" \
    "praisonai[chat]" \
    "embedchain[github,youtube]"

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.5.0" \
+    "praisonai>=2.5.7" \
    "praisonai[ui]" \
    "praisonai[chat]" \
    "praisonai[realtime]" \
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.5.0" \
+    "praisonai>=2.5.7" \
    "praisonai[ui]" \
    "praisonai[crewai]"

@@ -0,0 +1,35 @@
+"""
+Async Runs Examples
+===================
+
+This directory contains examples for the PraisonAI Async Jobs API.
+
+Examples:
+---------
+1. sdk_submit_poll_result.py - Python SDK example for submit/poll/result
+2. cli_submit_poll_result.sh - Bash/curl example for submit/poll/result
+3. idempotency.sh - Demonstrates idempotency key usage
+4. cancel.sh - Demonstrates job cancellation
+5. sse_stream.sh - Demonstrates SSE streaming
+
+Prerequisites:
+--------------
+1. Set OPENAI_API_KEY environment variable
+2. Start the jobs server:
+   
+   cd /path/to/praisonai-package/src/praisonai
+   python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory
+
+3. Run examples:
+   
+   python sdk_submit_poll_result.py
+   bash cli_submit_poll_result.sh
+   bash idempotency.sh
+   bash cancel.sh
+   bash sse_stream.sh
+
+Environment Variables:
+----------------------
+- OPENAI_API_KEY: Required for agent execution
+- PRAISONAI_BASE_URL: API server URL (default: http://127.0.0.1:8005)
+"""
@@ -0,0 +1,54 @@
+#!/bin/bash
+# CLI Example: Cancel a Running Job
+#
+# This example demonstrates how to cancel a running job.
+#
+# Requirements:
+# - Jobs server running on http://127.0.0.1:8005
+#
+# Usage:
+#     bash cancel.sh
+
+set -e
+
+API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
+
+echo "============================================================"
+echo "PraisonAI Job Cancellation Example"
+echo "============================================================"
+
+# Submit a long-running job
+echo ""
+echo "1. Submitting a job..."
+RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
+    -H "Content-Type: application/json" \
+    -d '{"prompt": "Write a detailed essay about the history of computing."}')
+
+JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
+echo "   Job ID: $JOB_ID"
+
+# Wait briefly
+echo ""
+echo "2. Waiting 1 second..."
+sleep 1
+
+# Cancel the job
+echo ""
+echo "3. Cancelling job..."
+CANCEL_RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs/$JOB_ID/cancel")
+STATUS=$(echo "$CANCEL_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
+echo "   Status after cancel: $STATUS"
+
+# Verify cancellation
+echo ""
+echo "4. Verification:"
+if [ "$STATUS" = "cancelled" ]; then
+    echo "   ✓ SUCCESS: Job was cancelled"
+else
+    echo "   ℹ Job status: $STATUS (may have completed before cancel)"
+fi
+
+echo ""
+echo "============================================================"
+echo "Cancellation example completed!"
+echo "============================================================"
@@ -0,0 +1,75 @@
+#!/bin/bash
+# CLI Example: Submit, Poll, and Get Result for Async Jobs
+#
+# This example demonstrates how to use the PraisonAI CLI to:
+# 1. Submit an async job
+# 2. Check job status
+# 3. Get the result
+#
+# Requirements:
+# - OPENAI_API_KEY environment variable set
+# - Jobs server running on http://127.0.0.1:8005
+#
+# Usage:
+#     bash cli_submit_poll_result.sh
+
+set -e
+
+API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
+
+echo "============================================================"
+echo "PraisonAI Async Jobs CLI Example"
+echo "============================================================"
+
+# Check if server is running
+echo ""
+echo "Checking server health..."
+if ! curl -s "$API_URL/health" > /dev/null 2>&1; then
+    echo "✗ Server not available at $API_URL"
+    echo "  Start the server with: python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory"
+    exit 1
+fi
+echo "✓ Server is healthy"
+
+# 1. Submit a job
+echo ""
+echo "1. Submitting job..."
+RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
+    -H "Content-Type: application/json" \
+    -d '{"prompt": "What is 2+2? Answer with just the number."}')
+
+JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
+echo "   Job ID: $JOB_ID"
+
+# 2. Poll for status
+echo ""
+echo "2. Polling for status..."
+while true; do
+    STATUS_RESPONSE=$(curl -s "$API_URL/api/v1/runs/$JOB_ID")
+    STATUS=$(echo "$STATUS_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
+    PROGRESS=$(echo "$STATUS_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['progress']['percentage'])")
+    
+    echo "   Status: $STATUS | Progress: ${PROGRESS}%"
+    
+    if [ "$STATUS" = "succeeded" ] || [ "$STATUS" = "failed" ] || [ "$STATUS" = "cancelled" ]; then
+        break
+    fi
+    
+    sleep 2
+done
+
+# 3. Get result
+echo ""
+echo "3. Getting result..."
+if [ "$STATUS" = "succeeded" ]; then
+    RESULT=$(curl -s "$API_URL/api/v1/runs/$JOB_ID/result")
+    echo "   Result: $(echo "$RESULT" | python3 -c "import sys,json; print(json.load(sys.stdin)['result'])")"
+    echo "   Duration: $(echo "$RESULT" | python3 -c "import sys,json; print(f\"{json.load(sys.stdin)['duration_seconds']:.2f}s\")")"
+else
+    echo "   Job did not succeed: $STATUS"
+fi
+
+echo ""
+echo "============================================================"
+echo "Example completed!"
+echo "============================================================"
@@ -0,0 +1,57 @@
+#!/bin/bash
+# CLI Example: Idempotency Key Usage
+#
+# This example demonstrates how idempotency keys prevent duplicate job submissions.
+# Submitting the same request with the same idempotency key returns the existing job.
+#
+# Requirements:
+# - Jobs server running on http://127.0.0.1:8005
+#
+# Usage:
+#     bash idempotency.sh
+
+set -e
+
+API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
+IDEM_KEY="example-idem-key-$(date +%s)"
+
+echo "============================================================"
+echo "PraisonAI Idempotency Key Example"
+echo "============================================================"
+
+# First submission
+echo ""
+echo "1. First submission with Idempotency-Key: $IDEM_KEY"
+RESPONSE1=$(curl -s -X POST "$API_URL/api/v1/runs" \
+    -H "Content-Type: application/json" \
+    -H "Idempotency-Key: $IDEM_KEY" \
+    -d '{"prompt": "What is 1+1?"}')
+
+JOB_ID1=$(echo "$RESPONSE1" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
+echo "   Job ID: $JOB_ID1"
+
+# Second submission with same key
+echo ""
+echo "2. Second submission with SAME Idempotency-Key: $IDEM_KEY"
+RESPONSE2=$(curl -s -X POST "$API_URL/api/v1/runs" \
+    -H "Content-Type: application/json" \
+    -H "Idempotency-Key: $IDEM_KEY" \
+    -d '{"prompt": "Different prompt but same key"}')
+
+JOB_ID2=$(echo "$RESPONSE2" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
+echo "   Job ID: $JOB_ID2"
+
+# Verify same job ID
+echo ""
+echo "3. Verification:"
+if [ "$JOB_ID1" = "$JOB_ID2" ]; then
+    echo "   ✓ SUCCESS: Same job ID returned (no duplicate created)"
+else
+    echo "   ✗ FAILURE: Different job IDs returned"
+    exit 1
+fi
+
+echo ""
+echo "============================================================"
+echo "Idempotency example completed!"
+echo "============================================================"
@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+"""
+SDK Example: Submit, Poll, and Get Result for Async Jobs
+
+This example demonstrates how to use the PraisonAI Jobs SDK to:
+1. Submit an async job
+2. Poll for status
+3. Get the result
+
+Requirements:
+- OPENAI_API_KEY environment variable set
+- Jobs server running on http://127.0.0.1:8005
+
+Usage:
+    python sdk_submit_poll_result.py
+"""
+
+import os
+import time
+import httpx
+
+# Configuration
+API_URL = os.getenv("PRAISONAI_BASE_URL", "http://127.0.0.1:8005")
+POLL_INTERVAL = 2  # seconds
+
+
+def submit_job(prompt: str, idempotency_key: str = None) -> dict:
+    """Submit a job and return the response."""
+    headers = {}
+    if idempotency_key:
+        headers["Idempotency-Key"] = idempotency_key
+    
+    with httpx.Client(timeout=30.0) as client:
+        response = client.post(
+            f"{API_URL}/api/v1/runs",
+            json={"prompt": prompt},
+            headers=headers if headers else None
+        )
+        response.raise_for_status()
+        return response.json()
+
+
+def get_status(job_id: str) -> dict:
+    """Get job status."""
+    with httpx.Client(timeout=30.0) as client:
+        response = client.get(f"{API_URL}/api/v1/runs/{job_id}")
+        response.raise_for_status()
+        return response.json()
+
+
+def get_result(job_id: str) -> dict:
+    """Get job result."""
+    with httpx.Client(timeout=30.0) as client:
+        response = client.get(f"{API_URL}/api/v1/runs/{job_id}/result")
+        response.raise_for_status()
+        return response.json()
+
+
+def wait_for_completion(job_id: str, max_wait: int = 120) -> dict:
+    """Poll until job completes."""
+    start = time.time()
+    while time.time() - start < max_wait:
+        status = get_status(job_id)
+        print(f"  Status: {status['status']} | Progress: {status['progress']['percentage']:.0f}%")
+        
+        if status["status"] in ("succeeded", "failed", "cancelled"):
+            return status
+        
+        # Honor retry_after if present
+        wait_time = status.get("retry_after") or POLL_INTERVAL
+        time.sleep(wait_time)
+    
+    raise TimeoutError(f"Job {job_id} did not complete within {max_wait}s")
+
+
+def main():
+    print("=" * 60)
+    print("PraisonAI Async Jobs SDK Example")
+    print("=" * 60)
+    
+    # Check if server is running
+    try:
+        with httpx.Client(timeout=5.0) as client:
+            health = client.get(f"{API_URL}/health")
+            health.raise_for_status()
+            print(f"✓ Server healthy: {health.json()['status']}")
+    except Exception as e:
+        print(f"✗ Server not available at {API_URL}: {e}")
+        print("  Start the server with: python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory")
+        return
+    
+    # 1. Submit a job
+    print("\n1. Submitting job...")
+    prompt = "What is the capital of France? Answer in one word."
+    result = submit_job(prompt)
+    job_id = result["job_id"]
+    print(f"   Job ID: {job_id}")
+    print(f"   Status: {result['status']}")
+    print(f"   Poll URL: {result['poll_url']}")
+    
+    # 2. Wait for completion
+    print("\n2. Waiting for completion...")
+    final_status = wait_for_completion(job_id)
+    print(f"   Final status: {final_status['status']}")
+    
+    # 3. Get result
+    if final_status["status"] == "succeeded":
+        print("\n3. Getting result...")
+        result = get_result(job_id)
+        print(f"   Result: {result['result']}")
+        print(f"   Duration: {result['duration_seconds']:.2f}s")
+    else:
+        print(f"\n3. Job did not succeed: {final_status.get('error', 'Unknown error')}")
+    
+    print("\n" + "=" * 60)
+    print("Example completed successfully!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,51 @@
+#!/bin/bash
+# CLI Example: SSE Streaming
+#
+# This example demonstrates how to stream job progress via Server-Sent Events.
+#
+# Requirements:
+# - Jobs server running on http://127.0.0.1:8005
+#
+# Usage:
+#     bash sse_stream.sh
+
+set -e
+
+API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
+
+echo "============================================================"
+echo "PraisonAI SSE Streaming Example"
+echo "============================================================"
+
+# Submit a job
+echo ""
+echo "1. Submitting job..."
+RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
+    -H "Content-Type: application/json" \
+    -d '{"prompt": "Count from 1 to 5."}')
+
+JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
+echo "   Job ID: $JOB_ID"
+
+# Stream progress
+echo ""
+echo "2. Streaming progress (Ctrl+C to stop)..."
+echo "   Connecting to: $API_URL/api/v1/runs/$JOB_ID/stream"
+echo ""
+
+# Use curl to stream SSE events (timeout after 60s)
+curl -s -N --max-time 60 "$API_URL/api/v1/runs/$JOB_ID/stream" | while read -r line; do
+    if [[ "$line" == data:* ]]; then
+        data="${line#data: }"
+        if [ "$data" = "[DONE]" ]; then
+            echo "   Stream completed"
+            break
+        fi
+        echo "   Event: $data"
+    fi
+done
+
+echo ""
+echo "============================================================"
+echo "Streaming example completed!"
+echo "============================================================"
@@ -0,0 +1,19 @@
+/**
+ * Basic Agent Example
+ * Demonstrates simple agent usage with OpenAI
+ */
+
+import { Agent } from 'praisonai';
+
+async function main() {
+  const agent = new Agent({
+    name: 'Assistant',
+    instructions: 'You are a helpful assistant. Be concise and informative.',
+    llm: 'openai/gpt-4o-mini'
+  });
+
+  const response = await agent.chat('What is TypeScript?');
+  console.log('Response:', response.text);
+}
+
+main().catch(console.error);
@@ -0,0 +1,47 @@
+/**
+ * Context Agent Example
+ * Demonstrates RAG-enabled agent with knowledge base
+ */
+
+import { ContextAgent, createContextAgent, KnowledgeBase } from 'praisonai';
+
+async function main() {
+  // Create knowledge base
+  const kb = new KnowledgeBase();
+  
+  // Add documents
+  await kb.add({
+    id: 'doc1',
+    content: 'TypeScript is a strongly typed programming language that builds on JavaScript.',
+    metadata: { topic: 'typescript' }
+  });
+  
+  await kb.add({
+    id: 'doc2',
+    content: 'TypeScript adds optional static typing and class-based object-oriented programming.',
+    metadata: { topic: 'typescript' }
+  });
+
+  await kb.add({
+    id: 'doc3',
+    content: 'PraisonAI is a framework for building AI agents in TypeScript and Python.',
+    metadata: { topic: 'praisonai' }
+  });
+
+  // Create context agent with knowledge base
+  const agent = createContextAgent({
+    name: 'KnowledgeAgent',
+    llm: 'openai/gpt-4o-mini',
+    knowledgeBase: kb,
+    maxContextDocs: 3
+  });
+
+  // Query with context
+  const response = await agent.chat('What is TypeScript?');
+  console.log('Response:', response.text);
+
+  // Check conversation history
+  console.log('\nConversation history:', agent.getHistory().length, 'messages');
+}
+
+main().catch(console.error);
@@ -0,0 +1,29 @@
+/**
+ * Image Agent Example
+ * Demonstrates image analysis capabilities
+ */
+
+import { ImageAgent, createImageAgent } from 'praisonai';
+
+async function main() {
+  const agent = createImageAgent({
+    name: 'VisionAgent',
+    llm: 'openai/gpt-4o-mini',
+    verbose: true
+  });
+
+  console.log('Image Agent created:', agent.name);
+
+  // Note: For actual image analysis, provide a real image URL
+  // const analysis = await agent.analyze({
+  //   imageUrl: 'https://example.com/image.jpg',
+  //   prompt: 'Describe this image in detail'
+  // });
+  // console.log('Analysis:', analysis);
+
+  // Chat without image
+  const response = await agent.chat('What types of images can you analyze?');
+  console.log('Response:', response);
+}
+
+main().catch(console.error);
@@ -0,0 +1,48 @@
+/**
+ * Multi-Agent Example
+ * Demonstrates multiple agents working together
+ */
+
+import { Agent, PraisonAIAgents, Task } from 'praisonai';
+
+async function main() {
+  // Create specialized agents
+  const researcher = new Agent({
+    name: 'Researcher',
+    role: 'Research Specialist',
+    goal: 'Find accurate information on topics',
+    instructions: 'You research topics thoroughly and provide factual information.'
+  });
+
+  const writer = new Agent({
+    name: 'Writer',
+    role: 'Content Writer',
+    goal: 'Create engaging content from research',
+    instructions: 'You write clear, engaging content based on provided research.'
+  });
+
+  // Create tasks
+  const researchTask = new Task({
+    description: 'Research the benefits of TypeScript over JavaScript',
+    expectedOutput: 'A list of key benefits with explanations',
+    agent: researcher
+  });
+
+  const writeTask = new Task({
+    description: 'Write a short article about TypeScript benefits',
+    expectedOutput: 'A 200-word article',
+    agent: writer
+  });
+
+  // Run agents
+  const agents = new PraisonAIAgents({
+    agents: [researcher, writer],
+    tasks: [researchTask, writeTask],
+    process: 'sequential'
+  });
+
+  const result = await agents.start();
+  console.log('Result:', result);
+}
+
+main().catch(console.error);
@@ -0,0 +1,33 @@
+/**
+ * Deep Research Agent Example
+ * Demonstrates comprehensive research with citations
+ */
+
+import { DeepResearchAgent, createDeepResearchAgent } from 'praisonai';
+
+async function main() {
+  const agent = createDeepResearchAgent({
+    name: 'Scholar',
+    llm: 'openai/gpt-4o-mini',
+    maxIterations: 5,
+    verbose: true
+  });
+
+  console.log('Research Agent:', agent.name);
+
+  // Conduct research
+  const result = await agent.research('What are the key benefits of TypeScript?');
+
+  console.log('\n=== Research Results ===');
+  console.log('Answer:', result.answer.substring(0, 500) + '...');
+  console.log('\nConfidence:', result.confidence);
+  console.log('Reasoning steps:', result.reasoning.length);
+
+  // Show reasoning
+  console.log('\n=== Reasoning Process ===');
+  result.reasoning.forEach(step => {
+    console.log(`Step ${step.step}: ${step.thought}`);
+  });
+}
+
+main().catch(console.error);
@@ -0,0 +1,57 @@
+/**
+ * Router Agent Example
+ * Demonstrates routing requests to specialized agents
+ */
+
+import { RouterAgent, createRouter, routeConditions, Agent } from 'praisonai';
+
+async function main() {
+  // Create specialized agents
+  const mathAgent = {
+    name: 'MathAgent',
+    chat: async (input: string) => ({ text: `Math answer for: ${input}` })
+  } as any;
+
+  const codeAgent = {
+    name: 'CodeAgent', 
+    chat: async (input: string) => ({ text: `Code solution for: ${input}` })
+  } as any;
+
+  const generalAgent = {
+    name: 'GeneralAgent',
+    chat: async (input: string) => ({ text: `General response: ${input}` })
+  } as any;
+
+  // Create router with conditions
+  const router = createRouter({
+    name: 'SmartRouter',
+    routes: [
+      {
+        agent: mathAgent,
+        condition: routeConditions.keywords(['math', 'calculate', 'number', 'equation']),
+        priority: 10
+      },
+      {
+        agent: codeAgent,
+        condition: routeConditions.keywords(['code', 'program', 'function', 'javascript']),
+        priority: 10
+      }
+    ],
+    defaultAgent: generalAgent
+  });
+
+  // Test routing
+  const queries = [
+    'Calculate 2 + 2',
+    'Write a function to sort an array',
+    'What is the weather today?'
+  ];
+
+  for (const query of queries) {
+    const result = await router.route(query);
+    console.log(`Query: "${query}"`);
+    console.log(`Routed to: ${result?.agent.name || 'default'}\n`);
+  }
+}
+
+main().catch(console.error);
@@ -0,0 +1,59 @@
+/**
+ * AutoAgents Example
+ * Demonstrates automatic agent generation from task descriptions
+ */
+
+import { AutoAgents, createAutoAgents } from 'praisonai';
+
+async function main() {
+  const auto = createAutoAgents({
+    pattern: 'sequential',
+    verbose: true
+  });
+
+  // Analyze task complexity
+  console.log('=== Complexity Analysis ===');
+  const tasks = [
+    'Write a hello world function',
+    'Build a web scraper that extracts product data',
+    'Create a multi-step data pipeline with validation, transformation, and multiple output formats'
+  ];
+
+  tasks.forEach(task => {
+    const complexity = auto.analyzeComplexity(task);
+    console.log(`"${task.substring(0, 40)}..." -> ${complexity}`);
+  });
+
+  // Recommend patterns
+  console.log('\n=== Pattern Recommendations ===');
+  const patternTasks = [
+    'Process items in parallel',
+    'Route requests to different handlers',
+    'Orchestrate multiple workers',
+    'Evaluate and optimize results'
+  ];
+
+  patternTasks.forEach(task => {
+    const pattern = auto.recommendPattern(task);
+    console.log(`"${task}" -> ${pattern}`);
+  });
+
+  // Generate agent configuration
+  console.log('\n=== Generate Agent Config ===');
+  console.log('Generating agents for: "Build a web scraper"');
+  
+  const team = await auto.generate('Build a web scraper that extracts product prices from e-commerce sites');
+  
+  console.log('\nGenerated Team:');
+  console.log('Pattern:', team.pattern);
+  console.log('Agents:', team.agents.length);
+  team.agents.forEach(agent => {
+    console.log(`  - ${agent.name}: ${agent.role}`);
+  });
+  console.log('Tasks:', team.tasks.length);
+  team.tasks.forEach(task => {
+    console.log(`  - ${task.description.substring(0, 50)}...`);
+  });
+}
+
+main().catch(console.error);
@@ -0,0 +1,47 @@
+/**
+ * Redis Database Example
+ */
+
+import { createMemoryRedis } from '../../../src/praisonai-ts/src';
+
+async function main() {
+  // Use in-memory Redis for this example
+  const redis = createMemoryRedis();
+  await redis.connect();
+
+  console.log('Connected to Redis');
+
+  // Basic get/set
+  await redis.set('user:123', { name: 'John', role: 'admin' });
+  const user = await redis.get('user:123');
+  console.log('User:', user);
+
+  // Set with TTL
+  await redis.set('session:abc', { token: 'xyz' }, 60);
+  const ttl = await redis.ttl('session:abc');
+  console.log('Session TTL:', ttl, 'seconds');
+
+  // Hash operations
+  await redis.hset('profile:123', 'email', '[email protected]');
+  await redis.hset('profile:123', 'phone', '555-1234');
+  const profile = await redis.hgetall('profile:123');
+  console.log('Profile:', profile);
+
+  // List operations
+  await redis.rpush('queue:tasks', { id: 1, type: 'process' });
+  await redis.rpush('queue:tasks', { id: 2, type: 'notify' });
+  const tasks = await redis.lrange('queue:tasks', 0, -1);
+  console.log('Tasks:', tasks);
+
+  // Pub/Sub (memory only)
+  await redis.subscribe('events', (message) => {
+    console.log('Received event:', message);
+  });
+  await redis.publish('events', JSON.stringify({ type: 'user_joined' }));
+
+  // Cleanup
+  await redis.disconnect();
+  console.log('Done!');
+}
+
+main().catch(console.error);
@@ -0,0 +1,64 @@
+/**
+ * Evaluation Framework Example
+ * Demonstrates accuracy, performance, and reliability evaluation
+ */
+
+import { accuracyEval, performanceEval, reliabilityEval, EvalSuite } from 'praisonai';
+
+async function main() {
+  console.log('=== Accuracy Evaluation ===');
+  const accuracyResult = await accuracyEval({
+    input: 'What is 2 + 2?',
+    expectedOutput: '4',
+    actualOutput: 'The answer is 4'
+  });
+  console.log('Passed:', accuracyResult.passed);
+  console.log('Score:', accuracyResult.score);
+  console.log('Duration:', accuracyResult.duration, 'ms');
+
+  console.log('\n=== Performance Evaluation ===');
+  const perfResult = await performanceEval({
+    func: async () => {
+      // Simulate some work
+      await new Promise(r => setTimeout(r, 100));
+      return 'done';
+    },
+    iterations: 5,
+    warmupRuns: 1
+  });
+  console.log('Iterations:', perfResult.times.length);
+  console.log('Avg time:', perfResult.avgTime.toFixed(2), 'ms');
+  console.log('Min time:', perfResult.minTime.toFixed(2), 'ms');
+  console.log('Max time:', perfResult.maxTime.toFixed(2), 'ms');
+
+  console.log('\n=== Reliability Evaluation ===');
+  const reliabilityResult = await reliabilityEval({
+    expectedToolCalls: ['search', 'calculate'],
+    actualToolCalls: ['search', 'calculate']
+  });
+  console.log('Passed:', reliabilityResult.passed);
+  console.log('Score:', reliabilityResult.score);
+
+  console.log('\n=== Eval Suite ===');
+  const suite = new EvalSuite();
+
+  await suite.runAccuracy('test1', {
+    input: 'Hello',
+    expectedOutput: 'Hi',
+    actualOutput: 'Hi there!'
+  });
+
+  await suite.runAccuracy('test2', {
+    input: 'Goodbye',
+    expectedOutput: 'Bye',
+    actualOutput: 'Bye!'
+  });
+
+  const summary = suite.getSummary();
+  console.log('Total tests:', summary.total);
+  console.log('Passed:', summary.passed);
+  console.log('Failed:', summary.failed);
+  console.log('Pass rate:', (summary.passRate * 100).toFixed(1) + '%');
+}
+
+main().catch(console.error);
@@ -0,0 +1,67 @@
+/**
+ * Content Guardrails Example
+ * Demonstrates input/output validation
+ */
+
+import { guardrail, GuardrailManager, builtinGuardrails, LLMGuardrail } from 'praisonai';
+
+async function main() {
+  // Create custom guardrail
+  const noSpamGuardrail = guardrail({
+    name: 'no_spam',
+    check: (content) => {
+      const spamWords = ['buy now', 'free money', 'click here'];
+      const hasSpam = spamWords.some(word => 
+        content.toLowerCase().includes(word)
+      );
+      return {
+        status: hasSpam ? 'failed' : 'passed',
+        message: hasSpam ? 'Content contains spam' : undefined
+      };
+    }
+  });
+
+  // Use built-in guardrails
+  const maxLength = builtinGuardrails.maxLength(500);
+  const minLength = builtinGuardrails.minLength(10);
+  const blockedWords = builtinGuardrails.blockedWords(['badword1', 'badword2']);
+
+  // Create manager
+  const manager = new GuardrailManager();
+  manager.add(noSpamGuardrail);
+  manager.add(maxLength);
+  manager.add(minLength);
+  manager.add(blockedWords);
+
+  // Test content
+  const testCases = [
+    'This is a normal, helpful response about programming.',
+    'BUY NOW! Free money if you click here!',
+    'Short',
+    'This is a valid response that provides useful information about TypeScript and its benefits for developers.'
+  ];
+
+  console.log('=== Guardrail Tests ===\n');
+
+  for (const content of testCases) {
+    console.log(`Content: "${content.substring(0, 40)}..."`);
+    const result = await manager.runAll(content);
+    console.log(`  Passed: ${result.passed}`);
+    if (!result.passed) {
+      result.results
+        .filter(r => r.status === 'failed')
+        .forEach(r => console.log(`  Failed: ${r.message}`));
+    }
+    console.log();
+  }
+
+  // LLM Guardrail example
+  console.log('=== LLM Guardrail ===');
+  const llmGuard = new LLMGuardrail({
+    name: 'quality_check',
+    criteria: 'Content must be helpful and professional'
+  });
+  console.log('LLM Guardrail created:', llmGuard.name);
+}
+
+main().catch(console.error);
@@ -0,0 +1,62 @@
+/**
+ * Knowledge Base (RAG) Example
+ * Demonstrates retrieval-augmented generation
+ */
+
+import { KnowledgeBase, createKnowledgeBase, Chunking } from 'praisonai';
+
+async function main() {
+  // Create knowledge base
+  const kb = createKnowledgeBase();
+
+  // Add documents
+  const documents = [
+    {
+      id: 'ts-intro',
+      content: 'TypeScript is a strongly typed programming language that builds on JavaScript, giving you better tooling at any scale.',
+      metadata: { category: 'introduction', language: 'typescript' }
+    },
+    {
+      id: 'ts-types',
+      content: 'TypeScript adds optional static typing and class-based object-oriented programming to the language.',
+      metadata: { category: 'features', language: 'typescript' }
+    },
+    {
+      id: 'ts-benefits',
+      content: 'Benefits of TypeScript include catching errors early, better IDE support, and improved code maintainability.',
+      metadata: { category: 'benefits', language: 'typescript' }
+    },
+    {
+      id: 'js-intro',
+      content: 'JavaScript is a lightweight, interpreted programming language with first-class functions.',
+      metadata: { category: 'introduction', language: 'javascript' }
+    }
+  ];
+
+  for (const doc of documents) {
+    await kb.add(doc);
+  }
+
+  console.log('Documents added:', kb.size);
+
+  // Search
+  console.log('\n=== Search: "TypeScript benefits" ===');
+  const results = await kb.search('TypeScript benefits', 3);
+  results.forEach(r => {
+    console.log(`Score: ${r.score.toFixed(2)} - ${r.document.content.substring(0, 50)}...`);
+  });
+
+  // Build context
+  console.log('\n=== Context for LLM ===');
+  const context = kb.buildContext(results);
+  console.log(context);
+
+  // Chunking example
+  console.log('\n=== Chunking Example ===');
+  const chunker = new Chunking({ chunkSize: 100, overlap: 20 });
+  const longText = 'This is a long document. '.repeat(20);
+  const chunks = chunker.chunk(longText);
+  console.log(`Chunked into ${chunks.length} pieces`);
+}
+
+main().catch(console.error);
@@ -0,0 +1,42 @@
+/**
+ * Conversation Memory Example
+ * Demonstrates memory management for conversations
+ */
+
+import { Memory, createMemory } from 'praisonai';
+
+async function main() {
+  const memory = createMemory({
+    maxEntries: 100,
+    maxTokens: 10000
+  });
+
+  // Simulate a conversation
+  await memory.add('What is TypeScript?', 'user');
+  await memory.add('TypeScript is a strongly typed programming language that builds on JavaScript.', 'assistant');
+  await memory.add('What are its main benefits?', 'user');
+  await memory.add('Key benefits include type safety, better IDE support, and easier refactoring.', 'assistant');
+  await memory.add('Can you give an example?', 'user');
+
+  console.log('Memory size:', memory.size, 'entries\n');
+
+  // Get recent messages
+  console.log('=== Recent Messages ===');
+  const recent = memory.getRecent(3);
+  recent.forEach(entry => {
+    console.log(`[${entry.role}]: ${entry.content.substring(0, 50)}...`);
+  });
+
+  // Search memory
+  console.log('\n=== Search Results for "TypeScript" ===');
+  const results = await memory.search('TypeScript');
+  results.forEach(r => {
+    console.log(`Score: ${r.score.toFixed(2)} - ${r.entry.content.substring(0, 40)}...`);
+  });
+
+  // Build context
+  console.log('\n=== Full Context ===');
+  console.log(memory.buildContext());
+}
+
+main().catch(console.error);
@@ -0,0 +1,60 @@
+/**
+ * Observability Example
+ * Demonstrates tracing and monitoring
+ */
+
+import { MemoryObservabilityAdapter, ConsoleObservabilityAdapter } from 'praisonai';
+
+async function main() {
+  // Memory adapter - stores traces in memory
+  const memoryAdapter = new MemoryObservabilityAdapter();
+
+  // Start a trace
+  const trace = memoryAdapter.startTrace('agent-execution');
+  console.log('Trace started:', trace.traceId);
+
+  // Create spans for different operations
+  const llmSpan = trace.startSpan('llm-call', 'llm');
+  llmSpan.setAttribute('model', 'gpt-4o-mini');
+  llmSpan.setAttribute('tokens', 150);
+  await new Promise(r => setTimeout(r, 100)); // Simulate work
+  llmSpan.end();
+
+  const toolSpan = trace.startSpan('tool-execution', 'tool');
+  toolSpan.setAttribute('tool', 'calculator');
+  toolSpan.addEvent('tool-started', { input: '2+2' });
+  await new Promise(r => setTimeout(r, 50)); // Simulate work
+  toolSpan.addEvent('tool-completed', { output: '4' });
+  toolSpan.end();
+
+  // End trace
+  trace.end();
+
+  // Retrieve trace data
+  const traceData = memoryAdapter.getTrace(trace.traceId);
+  console.log('\n=== Trace Data ===');
+  console.log('Trace ID:', traceData?.traceId);
+  console.log('Spans:', traceData?.spans.length);
+  console.log('Duration:', traceData?.endTime! - traceData?.startTime!, 'ms');
+
+  // Show spans
+  console.log('\n=== Spans ===');
+  traceData?.spans.forEach(span => {
+    console.log(`- ${span.name} (${span.kind}): ${span.endTime! - span.startTime}ms`);
+    if (span.attributes) {
+      Object.entries(span.attributes).forEach(([k, v]) => {
+        console.log(`    ${k}: ${v}`);
+      });
+    }
+  });
+
+  // Console adapter example
+  console.log('\n=== Console Adapter ===');
+  const consoleAdapter = new ConsoleObservabilityAdapter();
+  const trace2 = consoleAdapter.startTrace('demo-trace');
+  const span = trace2.startSpan('demo-span', 'custom');
+  span.end();
+  trace2.end();
+}
+
+main().catch(console.error);
@@ -0,0 +1,71 @@
+/**
+ * Planning System Example
+ * Demonstrates plans, steps, and todo lists
+ */
+
+import { Plan, PlanStep, TodoList, TodoItem, PlanStorage, createPlan, createTodoList } from 'praisonai';
+
+async function main() {
+  // Create a project plan
+  console.log('=== Project Plan ===');
+  const plan = createPlan({ 
+    name: 'Build AI Agent',
+    description: 'Create a fully functional AI agent'
+  });
+
+  plan.addStep(new PlanStep({ description: 'Design agent architecture' }));
+  plan.addStep(new PlanStep({ description: 'Implement core functionality' }));
+  plan.addStep(new PlanStep({ description: 'Add tool support' }));
+  plan.addStep(new PlanStep({ description: 'Write tests' }));
+  plan.addStep(new PlanStep({ description: 'Deploy to production' }));
+
+  console.log('Plan:', plan.name);
+  console.log('Steps:', plan.steps.length);
+
+  // Execute steps
+  plan.start();
+  plan.steps[0].start();
+  plan.steps[0].complete();
+  plan.steps[1].start();
+  plan.steps[1].complete();
+
+  const progress = plan.getProgress();
+  console.log(`Progress: ${progress.completed}/${progress.total} (${progress.percentage}%)`);
+
+  // Create todo list
+  console.log('\n=== Todo List ===');
+  const todos = createTodoList('Sprint Tasks');
+
+  todos.add(new TodoItem({ content: 'Review PR #123', priority: 'high' }));
+  todos.add(new TodoItem({ content: 'Fix bug in router', priority: 'high' }));
+  todos.add(new TodoItem({ content: 'Update documentation', priority: 'medium' }));
+  todos.add(new TodoItem({ content: 'Refactor tests', priority: 'low' }));
+
+  console.log('Total items:', todos.items.length);
+  console.log('High priority:', todos.getByPriority('high').length);
+
+  // Complete some items
+  todos.items[0].complete();
+  todos.items[1].start();
+
+  console.log('Pending:', todos.getPending().length);
+  console.log('Completed:', todos.getCompleted().length);
+
+  const todoProgress = todos.getProgress();
+  console.log(`Progress: ${todoProgress.completed}/${todoProgress.total} (${todoProgress.percentage}%)`);
+
+  // Use storage
+  console.log('\n=== Plan Storage ===');
+  const storage = new PlanStorage();
+  storage.savePlan(plan);
+  storage.saveTodoList(todos);
+
+  console.log('Saved plans:', storage.listPlans().length);
+  console.log('Saved todo lists:', storage.listTodoLists().length);
+
+  // Retrieve
+  const retrieved = storage.getPlan(plan.id);
+  console.log('Retrieved plan:', retrieved?.name);
+}
+
+main().catch(console.error);
@@ -0,0 +1,74 @@
+/**
+ * Skills System Example
+ * Demonstrates skill management and parsing
+ */
+
+import { SkillManager, parseSkillFile, createSkillManager } from 'praisonai';
+
+async function main() {
+  // Parse a skill file
+  const skillContent = `---
+name: code-review
+description: Reviews code for best practices and potential issues
+license: MIT
+metadata:
+  author: praisonai
+  version: "1.0"
+allowed-tools: Read Grep
+---
+
+# Code Review Skill
+
+When reviewing code:
+1. Check for syntax errors
+2. Look for security vulnerabilities
+3. Suggest performance improvements
+4. Ensure proper error handling
+`;
+
+  console.log('=== Parse Skill File ===');
+  const skill = parseSkillFile(skillContent);
+  console.log('Name:', skill.metadata.name);
+  console.log('Description:', skill.metadata.description);
+  console.log('License:', skill.metadata.license);
+  console.log('Instructions preview:', skill.instructions.substring(0, 100) + '...');
+
+  // Create skill manager
+  console.log('\n=== Skill Manager ===');
+  const manager = createSkillManager({ paths: [] });
+
+  // Register skills manually
+  manager.register({
+    metadata: {
+      name: 'typescript-expert',
+      description: 'Expert knowledge in TypeScript development'
+    },
+    instructions: 'You are a TypeScript expert. Help with type definitions, generics, and best practices.'
+  });
+
+  manager.register({
+    metadata: {
+      name: 'api-designer',
+      description: 'Designs RESTful APIs following best practices'
+    },
+    instructions: 'You design clean, RESTful APIs with proper versioning and documentation.'
+  });
+
+  // List skills
+  console.log('Registered skills:');
+  manager.list().forEach(s => {
+    console.log(`- ${s.metadata.name}: ${s.metadata.description}`);
+  });
+
+  // Get specific skill
+  const tsSkill = manager.get('typescript-expert');
+  console.log('\n=== TypeScript Expert Skill ===');
+  console.log('Instructions:', tsSkill?.instructions);
+
+  // Generate prompt
+  console.log('\n=== Generated Prompt ===');
+  const prompt = manager.generatePrompt(['typescript-expert']);
+  console.log(prompt.substring(0, 200) + '...');
+}
+
+main().catch(console.error);
@@ -0,0 +1,61 @@
+/**
+ * Basic Tools Example
+ * Demonstrates tool creation and usage
+ */
+
+import { tool, ToolRegistry, getRegistry } from 'praisonai';
+
+async function main() {
+  // Create tools
+  const calculator = tool({
+    name: 'calculator',
+    description: 'Perform basic math calculations',
+    parameters: {
+      type: 'object',
+      properties: {
+        expression: { type: 'string', description: 'Math expression to evaluate' }
+      },
+      required: ['expression']
+    },
+    execute: async ({ expression }) => {
+      try {
+        const result = eval(expression);
+        return `Result: ${result}`;
+      } catch (e) {
+        return 'Error: Invalid expression';
+      }
+    }
+  });
+
+  const greeter = tool({
+    name: 'greeter',
+    description: 'Generate a greeting',
+    parameters: {
+      type: 'object',
+      properties: {
+        name: { type: 'string', description: 'Name to greet' }
+      },
+      required: ['name']
+    },
+    execute: async ({ name }) => `Hello, ${name}! Welcome to PraisonAI.`
+  });
+
+  // Create registry and register tools
+  const registry = new ToolRegistry();
+  registry.register(calculator);
+  registry.register(greeter);
+
+  console.log('Registered tools:', registry.list().map(t => t.name));
+
+  // Execute tools
+  const calcResult = await calculator.execute({ expression: '10 * 5 + 2' });
+  console.log('\nCalculator:', calcResult);
+
+  const greetResult = await greeter.execute({ name: 'Developer' });
+  console.log('Greeter:', greetResult);
+
+  // Get OpenAI format
+  console.log('\nOpenAI tool format:', JSON.stringify(calculator.toOpenAITool(), null, 2));
+}
+
+main().catch(console.error);
@@ -0,0 +1,113 @@
+/**
+ * Custom Tool Example
+ * Demonstrates creating custom tools using BaseTool class (plugin pattern)
+ */
+
+import { BaseTool, createTool, tool, ToolRegistry } from 'praisonai';
+
+// Method 1: Extend BaseTool class (recommended for complex tools)
+class WeatherTool extends BaseTool<{ location: string; units?: string }, { temp: number; condition: string }> {
+  name = 'get_weather';
+  description = 'Get current weather for a location';
+  parameters = {
+    type: 'object' as const,
+    properties: {
+      location: { type: 'string', description: 'City name' },
+      units: { type: 'string', description: 'celsius or fahrenheit', default: 'celsius' }
+    },
+    required: ['location']
+  };
+
+  async run(params: { location: string; units?: string }): Promise<{ temp: number; condition: string }> {
+    // Simulate API call
+    console.log(`Getting weather for ${params.location} in ${params.units || 'celsius'}`);
+    return { temp: 22, condition: 'sunny' };
+  }
+}
+
+// Method 2: Use createTool function (quick inline tools)
+const calculatorTool = createTool({
+  name: 'calculator',
+  description: 'Evaluate a math expression',
+  parameters: {
+    type: 'object',
+    properties: {
+      expression: { type: 'string', description: 'Math expression to evaluate' }
+    },
+    required: ['expression']
+  },
+  run: (params: { expression: string }) => {
+    try {
+      return eval(params.expression);
+    } catch {
+      return 'Error: Invalid expression';
+    }
+  }
+});
+
+// Method 3: Use tool() function with config object
+const greeterTool = tool({
+  name: 'greeter',
+  description: 'Generate a personalized greeting',
+  parameters: {
+    type: 'object',
+    properties: {
+      name: { type: 'string', description: 'Name to greet' },
+      style: { type: 'string', description: 'Greeting style: formal or casual' }
+    },
+    required: ['name']
+  },
+  execute: async (params: { name: string; style?: string }) => {
+    if (params.style === 'formal') {
+      return `Good day, ${params.name}. How may I assist you?`;
+    }
+    return `Hey ${params.name}! What's up?`;
+  }
+});
+
+async function main() {
+  console.log('=== Custom Tool Examples ===\n');
+
+  // Test WeatherTool (class-based)
+  console.log('--- WeatherTool (class-based) ---');
+  const weather = new WeatherTool();
+  console.log('Name:', weather.name);
+  console.log('Description:', weather.description);
+  
+  const weatherResult = await weather.run({ location: 'New York' });
+  console.log('Result:', weatherResult);
+  
+  // Safe execution with error handling
+  const safeResult = await weather.safeRun({ location: 'London' });
+  console.log('Safe result:', safeResult);
+
+  // Get OpenAI schema
+  console.log('OpenAI Schema:', JSON.stringify(weather.getSchema(), null, 2));
+
+  // Test calculatorTool (createTool)
+  console.log('\n--- Calculator Tool (createTool) ---');
+  const calcResult = await calculatorTool.run({ expression: '10 * 5 + 2' });
+  console.log('10 * 5 + 2 =', calcResult);
+
+  // Test greeterTool (tool function)
+  console.log('\n--- Greeter Tool (tool function) ---');
+  const greeting1 = await greeterTool.execute({ name: 'Alice' });
+  console.log('Casual:', greeting1);
+  
+  const greeting2 = await greeterTool.execute({ name: 'Mr. Smith', style: 'formal' });
+  console.log('Formal:', greeting2);
+
+  // Register tools in registry
+  console.log('\n--- Tool Registry ---');
+  const registry = new ToolRegistry();
+  registry.register(greeterTool);
+  
+  console.log('Registered tools:', registry.list().map(t => t.name));
+  console.log('OpenAI tools format:', registry.toOpenAITools().length, 'tools');
+
+  // Validate tool
+  console.log('\n--- Validation ---');
+  console.log('WeatherTool valid:', weather.validate());
+}
+
+main().catch(console.error);
@@ -0,0 +1,57 @@
+/**
+ * Pinecone Vector Store Example
+ */
+
+import { createPineconeStore } from 'praisonai';
+
+async function main() {
+  const pinecone = createPineconeStore({
+    apiKey: process.env.PINECONE_API_KEY!
+  });
+
+  const indexName = 'test-index';
+
+  // Create index
+  console.log('Creating index...');
+  await pinecone.createIndex({
+    indexName,
+    dimension: 3,
+    metric: 'cosine'
+  });
+
+  // List indexes
+  const indexes = await pinecone.listIndexes();
+  console.log('Indexes:', indexes);
+
+  // Upsert vectors
+  console.log('Upserting vectors...');
+  await pinecone.upsert({
+    indexName,
+    vectors: [
+      { id: 'vec-1', vector: [0.1, 0.2, 0.3], metadata: { category: 'A' } },
+      { id: 'vec-2', vector: [0.4, 0.5, 0.6], metadata: { category: 'B' } },
+      { id: 'vec-3', vector: [0.7, 0.8, 0.9], metadata: { category: 'A' } }
+    ]
+  });
+
+  // Query
+  console.log('Querying...');
+  const results = await pinecone.query({
+    indexName,
+    vector: [0.1, 0.2, 0.3],
+    topK: 2,
+    includeMetadata: true
+  });
+
+  console.log('Results:');
+  for (const r of results) {
+    console.log(`  ${r.id}: score=${r.score.toFixed(4)}, metadata=${JSON.stringify(r.metadata)}`);
+  }
+
+  // Cleanup
+  console.log('Deleting index...');
+  await pinecone.deleteIndex(indexName);
+  console.log('Done!');
+}
+
+main().catch(console.error);
@@ -0,0 +1,30 @@
+/**
+ * Voice/TTS Example
+ */
+
+import { createOpenAIVoice } from '../../../src/praisonai-ts/src';
+import { writeFileSync } from 'fs';
+
+async function main() {
+  const voice = createOpenAIVoice({
+    apiKey: process.env.OPENAI_API_KEY
+  });
+
+  // Check available voices
+  const speakers = await voice.getSpeakers();
+  console.log('Available voices:', speakers.map(s => s.name).join(', '));
+
+  // Generate speech
+  console.log('Generating speech...');
+  const audio = await voice.speak('Hello! Welcome to PraisonAI TypeScript SDK.', {
+    voice: 'nova',
+    speed: 1.0,
+    format: 'mp3'
+  });
+
+  // Save to file
+  writeFileSync('output.mp3', audio);
+  console.log('Audio saved to output.mp3');
+}
+
+main().catch(console.error);
@@ -0,0 +1,29 @@
+/**
+ * Basic Workflow Example
+ * Demonstrates sequential workflow execution
+ */
+
+import { Workflow, parallel, route, loop } from 'praisonai';
+
+async function main() {
+  // Create a simple workflow
+  const workflow = new Workflow<string, string>('DataProcessor')
+    .step('validate', async (input) => {
+      console.log('Step 1: Validating input...');
+      return input.trim();
+    })
+    .step('transform', async (input) => {
+      console.log('Step 2: Transforming...');
+      return input.toUpperCase();
+    })
+    .step('format', async (input) => {
+      console.log('Step 3: Formatting...');
+      return `[PROCESSED] ${input}`;
+    });
+
+  const result = await workflow.run('  hello world  ');
+  console.log('\nFinal output:', result.output);
+  console.log('Steps executed:', result.steps.length);
+}
+
+main().catch(console.error);
@@ -0,0 +1,48 @@
+/**
+ * Conditional Workflow Example
+ * Demonstrates routing based on conditions
+ */
+
+import { route } from 'praisonai';
+
+async function main() {
+  const processRequest = async (type: string) => {
+    console.log(`Processing request type: ${type}`);
+
+    const result = await route([
+      {
+        condition: () => type === 'urgent',
+        execute: async () => {
+          console.log('  -> Urgent handler activated');
+          return 'Processed urgently';
+        }
+      },
+      {
+        condition: () => type === 'normal',
+        execute: async () => {
+          console.log('  -> Normal handler activated');
+          return 'Processed normally';
+        }
+      },
+      {
+        condition: () => true, // Default
+        execute: async () => {
+          console.log('  -> Default handler activated');
+          return 'Processed with default handler';
+        }
+      }
+    ]);
+
+    return result;
+  };
+
+  // Test different request types
+  const types = ['urgent', 'normal', 'unknown'];
+  
+  for (const type of types) {
+    const result = await processRequest(type);
+    console.log(`Result: ${result}\n`);
+  }
+}
+
+main().catch(console.error);
@@ -0,0 +1,36 @@
+/**
+ * Parallel Workflow Example
+ * Demonstrates parallel execution of tasks
+ */
+
+import { parallel } from 'praisonai';
+
+async function main() {
+  console.log('Starting parallel tasks...\n');
+
+  const results = await parallel([
+    async () => {
+      console.log('Task 1: Starting...');
+      await new Promise(r => setTimeout(r, 1000));
+      console.log('Task 1: Complete');
+      return 'Result 1';
+    },
+    async () => {
+      console.log('Task 2: Starting...');
+      await new Promise(r => setTimeout(r, 500));
+      console.log('Task 2: Complete');
+      return 'Result 2';
+    },
+    async () => {
+      console.log('Task 3: Starting...');
+      await new Promise(r => setTimeout(r, 750));
+      console.log('Task 3: Complete');
+      return 'Result 3';
+    }
+  ]);
+
+  console.log('\nAll tasks complete!');
+  console.log('Results:', results);
+}
+
+main().catch(console.error);
@@ -1,7 +1,7 @@
 """
-Qdrant Knowledge Store Example for PraisonAI.
+Qdrant Knowledge Store - Agent-First Example

-Demonstrates vector insert + search through the knowledge store.
+Demonstrates using Qdrant for knowledge-based RAG with an Agent.

 Requirements:
    pip install "praisonai[tools]"
@@ -13,67 +13,59 @@ Run:
    python knowledge_qdrant.py

 Expected Output:
-    === Qdrant Knowledge Store Demo ===
-    Created collection: demo_knowledge
-    Inserted 3 documents
-    Search results: 2 documents found
+    Agent responds using knowledge from documents
 """

-from praisonai.persistence.factory import create_knowledge_store
-from praisonai.persistence.knowledge.base import KnowledgeDocument
-import random
+from praisonaiagents import Agent
+import tempfile
+import os
+import shutil

-# Create Qdrant knowledge store
-store = create_knowledge_store("qdrant", url="http://localhost:6333")
+# Create sample knowledge document
+sample_doc = """
+# AI and Programming Guide

-collection_name = "demo_knowledge"
-dimension = 384  # Embedding dimension
+## Python Programming
+Python is a versatile programming language used for web development, data science, and AI.
+It has a simple syntax and extensive libraries.

-# Create collection
-print("=== Qdrant Knowledge Store Demo ===")
-if store.collection_exists(collection_name):
-    store.delete_collection(collection_name)
+## Machine Learning
+Machine learning enables computers to learn patterns from data without explicit programming.
+Common frameworks include TensorFlow, PyTorch, and scikit-learn.

-store.create_collection(collection_name, dimension=dimension)
-print(f"Created collection: {collection_name}")
+## Databases
+PostgreSQL is a powerful open-source relational database system.
+It supports both SQL and JSON data types.
+"""

-# Create sample documents with embeddings
-random.seed(42)
-documents = [
-    KnowledgeDocument(
-        id="doc-1",
-        content="Python is a versatile programming language used for web development, data science, and AI.",
-        embedding=[random.random() for _ in range(dimension)],
-        metadata={"category": "programming", "language": "python"}
-    ),
-    KnowledgeDocument(
-        id="doc-2", 
-        content="Machine learning enables computers to learn patterns from data without explicit programming.",
-        embedding=[random.random() for _ in range(dimension)],
-        metadata={"category": "ai", "topic": "ml"}
-    ),
-    KnowledgeDocument(
-        id="doc-3",
-        content="PostgreSQL is a powerful open-source relational database system.",
-        embedding=[random.random() for _ in range(dimension)],
-        metadata={"category": "database", "type": "sql"}
-    ),
-]
+# Save to temp file
+temp_dir = tempfile.mkdtemp()
+doc_path = os.path.join(temp_dir, "guide.txt")
+with open(doc_path, "w") as f:
+    f.write(sample_doc)

-# Insert documents
-ids = store.insert(collection_name, documents)
-print(f"Inserted {len(ids)} documents")
+print("=== Qdrant Knowledge Store Demo (Agent-First) ===")

-# Search for similar documents
-query_embedding = [random.random() for _ in range(dimension)]
-results = store.search(collection_name, query_embedding, limit=2)
-print(f"Search results: {len(results)} documents found")
+# Agent-first approach: use knowledge parameter with Qdrant
+agent = Agent(
+    name="KnowledgeAssistant",
+    instructions="You are a helpful assistant with access to technical documentation.",
+    knowledge=[doc_path],
+    knowledge_config={
+        "vector_store": "qdrant",
+        "url": "http://localhost:6333"
+    }
+)

-for doc in results:
-    print(f"  - {doc.id}: {doc.content[:50]}...")
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What programming language is good for AI?")
+print(f"Response: {response}")

 # Cleanup
-store.delete_collection(collection_name)
-store.close()
+shutil.rmtree(temp_dir)

 print("\n=== Demo Complete ===")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence.factory import create_knowledge_store
+# store = create_knowledge_store("qdrant", url="http://localhost:6333")
@@ -1,83 +1,46 @@
 """
-Redis State Store
+Redis State Store - Agent-First Example

-Use Redis for state management, tracing, and caching.
+Use Redis for state management with an Agent.
+
+Docker Setup:
+    docker run -d --name redis -p 6379:6379 redis:7

 Run:
    python redis_state.py

 Expected output:
-    - State operations (set/get/delete)
-    - Tracing operations
+    Agent responds with state persisted to Redis
 """

-from praisonaiagents import db
+from praisonaiagents import Agent, db

-# Create Redis-only database (for state/tracing)
-redis_db = db.RedisDB(host="localhost", port=6379)
+print("=== Redis State Store (Agent-First) ===")

-print("=== Redis State Store ===")
-
-# Test state operations
-print("\n1. State Operations:")
-redis_db._init_stores()  # Initialize stores
-
-# Set a value
-redis_db._state_store.set("user:123:preferences", {
-    "theme": "dark",
-    "language": "en"
-})
-print("   Set user preferences")
-
-# Get the value
-prefs = redis_db._state_store.get("user:123:preferences")
-print(f"   Got preferences: {prefs}")
-
-# Set with TTL (expires in 60 seconds)
-redis_db._state_store.set("temp:token", "abc123", ttl=60)
-print("   Set temporary token (60s TTL)")
-
-# Delete
-redis_db._state_store.delete("user:123:preferences")
-print("   Deleted preferences")
-
-# Test tracing
-print("\n2. Tracing Operations:")
-
-# Start a trace
-trace_id = "trace-example-001"
-redis_db.on_trace_start(
-    trace_id=trace_id,
-    session_id="example-session",
-    agent_name="ExampleAgent",
-    metadata={"purpose": "demo"}
+# Agent-first approach: use db parameter with Redis state store
+my_db = db(
+    database_url="sqlite:///conversations.db",  # Conversations
+    state_url="redis://localhost:6379"          # State/tracing
 )
-print(f"   Started trace: {trace_id}")

-# Add spans
-span_id = "span-llm-001"
-redis_db.on_span_start(
-    span_id=span_id,
-    trace_id=trace_id,
-    name="llm_call",
-    attributes={"model": "gpt-4", "temperature": 0.7}
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant.",
+    db=my_db,
+    session_id="redis-state-session"
 )
-print(f"   Started span: {span_id}")

-redis_db.on_span_end(
-    span_id=span_id,
-    status="ok",
-    attributes={"tokens": 150, "latency_ms": 450}
-)
-print(f"   Ended span: {span_id}")
+# Chat - state is automatically managed via Redis
+response = agent.chat("Hello! Remember my name is Alice.")
+print(f"Response: {response}")

-# End trace
-redis_db.on_trace_end(
-    trace_id=trace_id,
-    status="ok",
-    metadata={"total_spans": 1}
-)
-print(f"   Ended trace: {trace_id}")
+# Second message to test state
+response2 = agent.chat("What is my name?")
+print(f"Response: {response2}")

-redis_db.close()
+my_db.close()
 print("\n✅ Done")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonaiagents import db
+# redis_db = db.RedisDB(host="localhost", port=6379)
@@ -1,10 +1,7 @@
 """
-Redis State Store Example for PraisonAI.
+Redis State Store - Agent-First Example

-Demonstrates set/get/delete + TTL operations.
-
-Requirements:
-    pip install "praisonai[tools]"
+Use Redis for state management with an Agent.

 Docker Setup:
    docker run -d --name praison-redis -p 6379:6379 redis:7
@@ -13,55 +10,37 @@ Run:
    python state_redis.py

 Expected Output:
-    === Redis State Store Demo ===
-    Set key: session:user123:preferences
-    Get value: {'theme': 'dark', 'language': 'en'}
-    TTL remaining: 3600 seconds
-    Hash operations: {'name': 'Alice', 'role': 'admin'}
+    Agent responds with state persisted to Redis
 """

-from praisonai.persistence.factory import create_state_store
+from praisonaiagents import Agent, db

-# Create Redis state store
-store = create_state_store("redis", url="redis://localhost:6379")
+print("=== Redis State Store (Agent-First) ===")

-print("=== Redis State Store Demo ===")
+# Agent-first approach: use db parameter with Redis state store
+my_db = db(
+    database_url="sqlite:///conversations.db",  # Conversations
+    state_url="redis://localhost:6379"          # State/tracing
+)

-# Basic set/get
-key = "session:user123:preferences"
-value = {"theme": "dark", "language": "en", "notifications": True}
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant.",
+    db=my_db,
+    session_id="redis-state-example"
+)

-store.set(key, value)
-print(f"Set key: {key}")
+# Chat - state is automatically managed via Redis
+response = agent.chat("Hello! Remember my favorite color is blue.")
+print(f"Response: {response}")

-retrieved = store.get(key)
-print(f"Get value: {retrieved}")
-
-# Set with TTL (expires in 1 hour)
-ttl_key = "session:user123:token"
-store.set(ttl_key, "abc123xyz", ttl=3600)
-remaining = store.ttl(ttl_key)
-print(f"TTL remaining: {remaining} seconds")
-
-# Hash operations (for structured data)
-hash_key = "user:123:profile"
-store.hset(hash_key, "name", "Alice")
-store.hset(hash_key, "role", "admin")
-store.hset(hash_key, "last_login", "2024-12-24")
-
-profile = store.hgetall(hash_key)
-print(f"Hash operations: {profile}")
-
-# Check existence
-exists = store.exists(key)
-print(f"Key exists: {exists}")
-
-# Delete keys
-store.delete(key)
-store.delete(ttl_key)
-store.delete(hash_key)
-print("Cleaned up test keys")
-
-store.close()
+# Second message to test state
+response2 = agent.chat("What is my favorite color?")
+print(f"Response: {response2}")

+my_db.close()
 print("\n=== Demo Complete ===")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence.factory import create_state_store
+# store = create_state_store("redis", url="redis://localhost:6379")
@@ -15,6 +15,7 @@ Features demonstrated:
 from praisonaiagents import Agent
 import tempfile
 import os
+import shutil

 # Create a sample knowledge document for demonstration
 sample_document = """
@@ -91,38 +92,15 @@ doc_path = os.path.join(temp_dir, "ai_healthcare_guide.txt")
 with open(doc_path, "w") as f:
    f.write(sample_document)

-# Create an agent with advanced knowledge management configuration
+# Create an agent with knowledge management
 knowledge_agent = Agent(
    name="AdvancedKnowledgeAgent",
    role="Healthcare AI Knowledge Expert", 
    goal="Provide comprehensive answers about AI in healthcare using advanced knowledge retrieval",
    backstory="You are an expert in healthcare AI with access to comprehensive knowledge bases and advanced retrieval capabilities.",
    
-    # Advanced knowledge configuration
+    # Knowledge configuration - simple and working
    knowledge=[doc_path],
-    knowledge_config={
-        "vector_store": {
-            "provider": "chroma",
-            "collection_name": "healthcare_ai_knowledge"
-        },
-        "chunking": {
-            "strategy": "semantic",  # Advanced semantic chunking
-            "chunk_size": 500,       # Optimal chunk size for retrieval
-            "chunk_overlap": 50,     # Overlap for context preservation
-            "separators": ["\n\n", "\n", ".", "!", "?"]  # Smart separators
-        },
-        "retrieval": {
-            "search_type": "similarity",
-            "k": 5,  # Retrieve top 5 most relevant chunks
-            "score_threshold": 0.7,  # Minimum relevance score
-            "rerank": True  # Enable reranking for better results
-        },
-        "embedding": {
-            "provider": "openai",
-            "model": "text-embedding-3-small",  # Efficient embedding model
-            "dimensions": 1536
-        }
-    },
    
    instructions="""You are an expert in healthcare AI. Use the knowledge base to provide 
    comprehensive, accurate answers. Always cite specific information from the documents 
@@ -180,7 +158,6 @@ relationship_result = knowledge_agent.start(
 print(f"Relationship analysis result:\n{relationship_result}\n")

 # Clean up temporary files
-import shutil
 shutil.rmtree(temp_dir)

 print("="*80)
@@ -1,21 +1,23 @@
-"""ChromaDB Vector Store - Basic Test"""
-import sys
-sys.path.insert(0, 'src/praisonai')
-from praisonai.persistence import create_knowledge_store
-from praisonai.persistence.knowledge.base import KnowledgeDocument
+"""ChromaDB Vector Store - Agent-First Example"""
+from praisonaiagents import Agent

-store = create_knowledge_store("chroma", path="/tmp/chroma_demo")
-try:
-    store.delete_collection("demo")
-except Exception:
-    pass
-store.create_collection("demo", dimension=1536)
-docs = [
-    KnowledgeDocument(id="1", content="Python is a programming language", embedding=[0.1]*1536),
-    KnowledgeDocument(id="2", content="JavaScript runs in browsers", embedding=[0.2]*1536)
-]
-store.insert("demo", docs)
-results = store.search("demo", query_embedding=[0.1]*1536, limit=1)
-print(f"Found: {len(results)} results")
-assert len(results) >= 1
-print("PASSED: ChromaDB vector store")
+# Agent-first approach: use knowledge parameter with ChromaDB
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "chroma",
+        "path": "/tmp/chroma_demo"
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: ChromaDB with Agent")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence import create_knowledge_store
+# store = create_knowledge_store("chroma", path="/tmp/chroma_demo")
@@ -1,31 +1,25 @@
-"""LanceDB Vector Store - Local Test"""
-import sys
-import os
-import shutil
+"""LanceDB Vector Store - Agent-First Example"""
+from praisonaiagents import Agent

-try:
-    import lancedb
-except ImportError:
-    print("SKIPPED: LanceDB - lancedb not installed")
-    sys.exit(0)
+# Agent-first approach: use knowledge parameter with LanceDB
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "lancedb",
+        "path": "/tmp/lancedb_test"
+    }
+)

-# Clean up previous test
-db_path = "/tmp/lancedb_test"
-if os.path.exists(db_path):
-    shutil.rmtree(db_path)
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")

-db = lancedb.connect(db_path)
+print("PASSED: LanceDB with Agent")

-# Create table with vectors
-data = [
-    {"id": "1", "text": "Machine learning is AI", "vector": [0.1] * 128},
-    {"id": "2", "text": "Deep learning uses neural nets", "vector": [0.2] * 128},
-]
-table = db.create_table("demo", data)
-
-# Search
-results = table.search([0.1] * 128).limit(1).to_list()
-print(f"Found: {len(results)} results")
-print(f"Top result: {results[0]['text'][:30]}...")
-assert len(results) >= 1
-print("PASSED: LanceDB vector store")
+# --- Advanced: Direct LanceDB Usage ---
+# import lancedb
+# db = lancedb.connect("/tmp/lancedb_test")
+# data = [{"id": "1", "text": "ML is AI", "vector": [0.1] * 128}]
+# table = db.create_table("demo", data)
@@ -1,20 +1,23 @@
-"""LanceDB Vector Store - Basic Test"""
-import sys
-sys.path.insert(0, 'src/praisonai')
-try:
-    from praisonai.persistence import create_knowledge_store
-    from praisonai.persistence.knowledge.base import KnowledgeDocument
-    
-    store = create_knowledge_store("lancedb", path="/tmp/lancedb_demo")
-    store.create_collection("demo", dimension=1536)
-    docs = [
-        KnowledgeDocument(id="doc1", content="The Eiffel Tower is in Paris", embedding=[0.1]*1536),
-        KnowledgeDocument(id="doc2", content="The Statue of Liberty is in New York", embedding=[0.2]*1536)
-    ]
-    store.insert("demo", docs)
-    results = store.search("demo", query_embedding=[0.1]*1536, limit=1)
-    print(f"Found: {len(results)} results")
-    assert len(results) >= 1
-    print("PASSED: LanceDB vector store")
-except ImportError as e:
-    print(f"SKIPPED: LanceDB - {e}")
+"""LanceDB Vector Store - Agent-First Example"""
+from praisonaiagents import Agent
+
+# Agent-first approach: use knowledge parameter with LanceDB
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "lancedb",
+        "path": "/tmp/lancedb_demo"
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: LanceDB with Agent")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence import create_knowledge_store
+# store = create_knowledge_store("lancedb", path="/tmp/lancedb_demo")
@@ -1,59 +1,31 @@
-"""PGVector Vector Store - Docker Test"""
-import sys
+"""PGVector Vector Store - Agent-First Example
+
+Docker: docker run -d --name pgvector -e POSTGRES_PASSWORD=postgres -p 5433:5432 pgvector/pgvector:pg16
+"""
 import os
+from praisonaiagents import Agent

-# Docker: docker run -d --name pgvector -e POSTGRES_PASSWORD=postgres -p 5433:5432 pgvector/pgvector:pg16
 url = os.getenv("PGVECTOR_URL", "postgresql://postgres:postgres@localhost:5433/postgres")

-try:
-    import psycopg2
-except ImportError:
-    print("SKIPPED: PGVector - psycopg2 not installed")
-    sys.exit(0)
+# Agent-first approach: use knowledge parameter with PGVector
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "pgvector",
+        "url": url
+    }
+)

-try:
-    conn = psycopg2.connect(url)
-    cur = conn.cursor()
-    
-    # Ensure extension
-    cur.execute("CREATE EXTENSION IF NOT EXISTS vector")
-    conn.commit()
-    
-    # Create test table
-    cur.execute("DROP TABLE IF EXISTS test_vectors")
-    cur.execute("""
-        CREATE TABLE test_vectors (
-            id SERIAL PRIMARY KEY,
-            content TEXT,
-            embedding vector(128)
-        )
-    """)
-    conn.commit()
-    
-    # Insert vectors
-    cur.execute("""
-        INSERT INTO test_vectors (content, embedding) VALUES
-        ('Machine learning is AI', %s),
-        ('Deep learning uses neural nets', %s)
-    """, ([0.1] * 128, [0.2] * 128))
-    conn.commit()
-    
-    # Search (cosine similarity)
-    cur.execute("""
-        SELECT content, embedding <=> %s::vector AS distance
-        FROM test_vectors
-        ORDER BY distance
-        LIMIT 1
-    """, ([0.1] * 128,))
-    
-    row = cur.fetchone()
-    print(f"Top result: {row[0][:30]}... (distance: {row[1]:.4f})")
-    
-    # Cleanup
-    cur.execute("DROP TABLE test_vectors")
-    conn.commit()
-    conn.close()
-    
-    print("PASSED: PGVector vector store")
-except Exception as e:
-    print(f"SKIPPED: PGVector - {e}")
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: PGVector with Agent")
+
+# --- Advanced: Direct PGVector Usage ---
+# import psycopg2
+# conn = psycopg2.connect(url)
+# cur = conn.cursor()
+# cur.execute("CREATE EXTENSION IF NOT EXISTS vector")
@@ -1,23 +1,29 @@
-"""PGVector Knowledge Store - Basic Test (requires Docker)"""
-import sys
+"""PGVector Knowledge Store - Agent-First Example (requires Docker)
+
+Docker: docker run -d --name pgvector -e POSTGRES_PASSWORD=postgres -p 5433:5432 pgvector/pgvector:pg16
+"""
 import os
-sys.path.insert(0, 'src/praisonai')
-from praisonai.persistence import create_knowledge_store
-from praisonai.persistence.knowledge.base import KnowledgeDocument
+from praisonaiagents import Agent

-# Docker: docker run -d --name pgvector -e POSTGRES_PASSWORD=postgres -p 5433:5432 pgvector/pgvector:pg16
 url = os.getenv("PGVECTOR_URL", "postgresql://postgres:postgres@localhost:5433/postgres")
-try:
-    store = create_knowledge_store("pgvector", url=url)
-    store.create_collection("demo", dimension=1536)
-    docs = [
-        KnowledgeDocument(id="1", content="Kubernetes orchestrates containers", embedding=[0.1]*1536),
-        KnowledgeDocument(id="2", content="Docker packages applications", embedding=[0.2]*1536)
-    ]
-    store.insert("demo", docs)
-    results = store.search("demo", query_embedding=[0.1]*1536, limit=1)
-    print(f"Found: {len(results)} results")
-    assert len(results) >= 1
-    print("PASSED: PGVector vector store")
-except Exception as e:
-    print(f"SKIPPED: PGVector - {e}")
+
+# Agent-first approach: use knowledge parameter with PGVector
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "pgvector",
+        "url": url
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: PGVector with Agent")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence import create_knowledge_store
+# store = create_knowledge_store("pgvector", url=url)
@@ -1,27 +1,34 @@
-"""Pinecone Vector Store - Real API Test"""
+"""Pinecone Vector Store - Agent-First Example
+
+Requires: export PINECONE_API_KEY=...
+"""
 import os
 import sys
-sys.path.insert(0, 'src/praisonai')
+from praisonaiagents import Agent

-# Requires: export PINECONE_API_KEY=...
 api_key = os.getenv("PINECONE_API_KEY")
 if not api_key:
    print("SKIPPED: Pinecone - PINECONE_API_KEY not set")
    sys.exit(0)

-from pinecone import Pinecone
+# Agent-first approach: use knowledge parameter with Pinecone
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "pinecone",
+        "api_key": api_key,
+        "environment": "us-east-1"
+    }
+)

-pc = Pinecone(api_key=api_key)
-indexes = pc.list_indexes()
-print(f"Pinecone connected! Found {len(list(indexes))} indexes")
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")

-# Use existing 'test' index or create one
-index_name = "test"
-if index_name not in [idx.name for idx in pc.list_indexes()]:
-    print(f"SKIPPED: Pinecone - index '{index_name}' not found")
-    sys.exit(0)
+print("PASSED: Pinecone with Agent")

-index = pc.Index(index_name)
-stats = index.describe_index_stats()
-print(f"Index '{index_name}' has {stats.total_vector_count} vectors")
-print("PASSED: Pinecone vector store")
+# --- Advanced: Direct Store Usage ---
+# from pinecone import Pinecone
+# pc = Pinecone(api_key=api_key)
@@ -1,23 +1,29 @@
-"""Qdrant Vector Store - Basic Test (requires Docker)"""
-import sys, os
-sys.path.insert(0, 'src/praisonai')
-from praisonai.persistence import create_knowledge_store
-from praisonai.persistence.knowledge.base import KnowledgeDocument
+"""Qdrant Vector Store - Agent-First Example (requires Docker)

-# Docker: docker run -d --name qdrant -p 6333:6333 qdrant/qdrant
+Docker: docker run -d --name qdrant -p 6333:6333 qdrant/qdrant
+"""
+import os
+from praisonaiagents import Agent
+
+# Agent-first approach: use knowledge parameter with Qdrant
 url = os.getenv("QDRANT_URL", "http://localhost:6333")
-store = create_knowledge_store("qdrant", url=url)
-try:
-    store.delete_collection("demo")
-except Exception:
-    pass
-store.create_collection("demo", dimension=1536)
-docs = [
-    KnowledgeDocument(id="1", content="Machine learning is AI", embedding=[0.1]*1536),
-    KnowledgeDocument(id="2", content="Deep learning uses neural nets", embedding=[0.2]*1536)
-]
-store.insert("demo", docs)
-results = store.search("demo", query_embedding=[0.1]*1536, limit=1)
-print(f"Found: {len(results)} results")
-assert len(results) >= 1
-print("PASSED: Qdrant vector store")
+
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "qdrant",
+        "url": url
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: Qdrant with Agent")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence import create_knowledge_store
+# store = create_knowledge_store("qdrant", url=url)
@@ -1,24 +1,36 @@
-"""Weaviate Cloud Vector Store - Real API Test"""
+"""Weaviate Cloud Vector Store - Agent-First Example
+
+Requires: export WEAVIATE_URL=... and WEAVIATE_API_KEY=...
+"""
 import os
 import sys
+from praisonaiagents import Agent

-# Requires: export WEAVIATE_URL=... and WEAVIATE_API_KEY=...
-weaviate_url = os.getenv("WEAVIATE_URL", "https://thhoyczshsawyt16ot3uw.c0.europe-west3.gcp.weaviate.cloud")
+weaviate_url = os.getenv("WEAVIATE_URL", "https://your-cluster.weaviate.cloud")
 weaviate_key = os.getenv("WEAVIATE_API_KEY")
 if not weaviate_key:
    print("SKIPPED: Weaviate - WEAVIATE_API_KEY not set")
    sys.exit(0)

-import weaviate
-from weaviate.classes.init import Auth
-
-client = weaviate.connect_to_weaviate_cloud(
-    cluster_url=weaviate_url,
-    auth_credentials=Auth.api_key(weaviate_key)
+# Agent-first approach: use knowledge parameter with Weaviate
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "weaviate",
+        "url": weaviate_url,
+        "api_key": weaviate_key
+    }
 )

-print(f"Weaviate connected: {client.is_ready()}")
-collections = client.collections.list_all()
-print(f"Found {len(collections)} collections")
-client.close()
-print("PASSED: Weaviate cloud vector store")
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: Weaviate with Agent")
+
+# --- Advanced: Direct Store Usage ---
+# import weaviate
+# from weaviate.classes.init import Auth
+# client = weaviate.connect_to_weaviate_cloud(cluster_url=weaviate_url, auth_credentials=Auth.api_key(weaviate_key))
@@ -62,6 +62,8 @@ docs/generated/
 *.log
 *.sqlite
 *.db
+# But keep the db module directory
+!praisonaiagents/db/

 # OS generated files
 .DS_Store
@@ -43,8 +43,7 @@ from .tools.base import BaseTool, ToolResult, ToolValidationError, validate_tool
 from .tools.decorator import tool, FunctionTool
 from .tools.registry import get_registry, register_tool, get_tool, ToolRegistry
 from .agents.autoagents import AutoAgents
-from .knowledge.knowledge import Knowledge
-from .knowledge.chunking import Chunking
+# Knowledge is lazy-loaded to avoid importing heavy deps (chromadb, mem0) at startup
 # MCP support (optional)
 try:
    from .mcp.mcp import MCP
@@ -53,7 +52,7 @@ except ImportError:
    _mcp_available = False
    MCP = None
 from .session import Session
-from .memory.memory import Memory
+# Memory is lazy-loaded to avoid importing chromadb at startup
 from .db import db
 from .obs import obs
 # Workflows - import from dedicated workflows module
@@ -67,8 +66,16 @@ from .guardrails import GuardrailResult, LLMGuardrail

 # Fast Context support (lazy loaded to avoid performance impact)
 def __getattr__(name):
-    """Lazy load FastContext to avoid impacting package load time."""
-    if name == "FastContext":
+    """Lazy load heavy modules to avoid impacting package load time."""
+    # Knowledge module (imports chromadb, mem0)
+    if name == "Knowledge":
+        from praisonaiagents.knowledge.knowledge import Knowledge
+        return Knowledge
+    elif name == "Chunking":
+        from praisonaiagents.knowledge.chunking import Chunking
+        return Chunking
+    # FastContext support
+    elif name == "FastContext":
        from praisonaiagents.context.fast import FastContext
        return FastContext
    elif name == "FastContextResult":
@@ -93,6 +100,10 @@ def __getattr__(name):
    elif name == "SkillLoader":
        from praisonaiagents.skills import SkillLoader
        return SkillLoader
+    # Memory module (imports chromadb)
+    elif name == "Memory":
+        from praisonaiagents.memory.memory import Memory
+        return Memory
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

 # Planning mode support
@@ -0,0 +1,85 @@
+"""
+Database adapter interface for PraisonAI Agents.
+
+This module provides the protocol and types for database persistence.
+Implementations are provided by the wrapper layer (praisonai.db).
+
+Usage (simplest - recommended):
+    from praisonaiagents import Agent, db
+    
+    agent = Agent(
+        name="Assistant",
+        db=db(database_url="postgresql://localhost/mydb"),  # db(...) shortcut
+        session_id="my-session"  # optional: defaults to per-hour ID
+    )
+    agent.chat("Hello!")  # auto-persists messages, runs, traces, tool calls
+
+Alternative (explicit backend):
+    db=db.PraisonDB(database_url="...")  # Auto-detect backend
+    db=db.PostgresDB(host="localhost")   # PostgreSQL
+    db=db.SQLiteDB(path="data.db")       # SQLite
+    db=db.RedisDB(host="localhost")      # Redis (state only)
+"""
+
+from .protocol import (
+    DbAdapter,
+    AsyncDbAdapter,
+    DbMessage,
+    DbToolCall,
+    DbRun,
+    DbSpan,
+    DbTrace,
+)
+
+
+class _LazyDbModule:
+    """
+    Lazy proxy for database backends.
+    
+    Allows `from praisonaiagents import db` without importing heavy deps.
+    Actual backend classes are loaded only when accessed.
+    """
+    
+    _BACKENDS = {
+        "PraisonDB": "praisonai.db.adapter",
+        "PostgresDB": "praisonai.db.adapter",
+        "SQLiteDB": "praisonai.db.adapter",
+        "RedisDB": "praisonai.db.adapter",
+    }
+    
+    def __getattr__(self, name: str):
+        if name in self._BACKENDS:
+            try:
+                import importlib
+                module = importlib.import_module(self._BACKENDS[name])
+                return getattr(module, name)
+            except ImportError as e:
+                raise ImportError(
+                    f"Database backend '{name}' requires the praisonai package. "
+                    f"Install with: pip install praisonai\n"
+                    f"Original error: {e}"
+                ) from e
+        raise AttributeError(f"module 'db' has no attribute {name!r}")
+    
+    def __repr__(self):
+        return "<module 'praisonaiagents.db' (lazy database backends)>"
+    
+    def __call__(self, **kwargs):
+        """Shortcut: db(...) is equivalent to db.PraisonDB(...)"""
+        return self.PraisonDB(**kwargs)
+
+
+# Singleton lazy module instance
+db = _LazyDbModule()
+
+
+__all__ = [
+    "DbAdapter",
+    "AsyncDbAdapter",
+    "DbMessage",
+    "DbToolCall",
+    "DbRun",
+    "DbSpan",
+    "DbTrace",
+    "db",
+]
@@ -0,0 +1,464 @@
+"""
+Database adapter protocol for PraisonAI Agents.
+
+This module uses only stdlib (typing, dataclasses) to define the interface.
+No external dependencies are imported here.
+
+Implementations are provided by the wrapper layer (praisonai.db).
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
+import time
+
+
+@dataclass
+class DbToolCall:
+    """A tool call to be persisted."""
+    tool_name: str
+    args: Dict[str, Any]
+    result: Any = None
+    call_id: Optional[str] = None
+    timestamp: float = field(default_factory=time.time)
+    duration_ms: Optional[float] = None
+    error: Optional[str] = None
+
+
+@dataclass
+class DbMessage:
+    """A message to be persisted."""
+    role: str  # "user", "assistant", "system", "tool"
+    content: str
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    timestamp: float = field(default_factory=time.time)
+    id: Optional[str] = None
+    tool_calls: Optional[List["DbToolCall"]] = None  # For assistant messages with tool calls
+    run_id: Optional[str] = None  # Group messages by run
+
+
+@dataclass
+class DbRun:
+    """A single agent run (turn) to be persisted."""
+    run_id: str
+    session_id: str
+    agent_name: str
+    started_at: float = field(default_factory=time.time)
+    ended_at: Optional[float] = None
+    status: str = "running"  # running, completed, error, cancelled
+    input_content: Optional[str] = None
+    output_content: Optional[str] = None
+    messages: List[DbMessage] = field(default_factory=list)
+    tool_calls: List[DbToolCall] = field(default_factory=list)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    metrics: Dict[str, Any] = field(default_factory=dict)  # tokens, latency, etc.
+
+
+@dataclass
+class DbSpan:
+    """A span for tracing/observability."""
+    span_id: str
+    trace_id: str
+    name: str
+    started_at: float = field(default_factory=time.time)
+    ended_at: Optional[float] = None
+    parent_span_id: Optional[str] = None
+    status: str = "running"  # running, ok, error
+    attributes: Dict[str, Any] = field(default_factory=dict)
+    events: List[Dict[str, Any]] = field(default_factory=list)
+
+
+@dataclass
+class DbTrace:
+    """A trace for observability (groups spans)."""
+    trace_id: str
+    session_id: Optional[str] = None
+    run_id: Optional[str] = None
+    agent_name: Optional[str] = None
+    user_id: Optional[str] = None
+    started_at: float = field(default_factory=time.time)
+    ended_at: Optional[float] = None
+    status: str = "running"  # running, ok, error
+    spans: List[DbSpan] = field(default_factory=list)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@runtime_checkable
+class DbAdapter(Protocol):
+    """
+    Protocol for database adapters that provide persistence for agents.
+    
+    Implementations must provide these methods. The agent will call them
+    at appropriate lifecycle points if a db adapter is provided.
+    
+    All methods are synchronous. Async support can be added via wrapper.
+    """
+    
+    def on_agent_start(
+        self,
+        agent_name: str,
+        session_id: str,
+        user_id: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> List[DbMessage]:
+        """
+        Called when agent starts a session.
+        
+        Args:
+            agent_name: Name of the agent
+            session_id: Session identifier
+            user_id: Optional user identifier
+            metadata: Optional session metadata
+            
+        Returns:
+            List of previous messages for this session (for resume)
+        """
+        ...
+    
+    def on_user_message(
+        self,
+        session_id: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when user sends a message.
+        
+        Args:
+            session_id: Session identifier
+            content: Message content
+            metadata: Optional message metadata
+        """
+        ...
+    
+    def on_agent_message(
+        self,
+        session_id: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when agent produces a response.
+        
+        Args:
+            session_id: Session identifier
+            content: Response content
+            metadata: Optional message metadata
+        """
+        ...
+    
+    def on_tool_call(
+        self,
+        session_id: str,
+        tool_name: str,
+        args: Dict[str, Any],
+        result: Any,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a tool is executed.
+        
+        Args:
+            session_id: Session identifier
+            tool_name: Name of the tool
+            args: Tool arguments
+            result: Tool result
+            metadata: Optional call metadata
+        """
+        ...
+    
+    def on_agent_end(
+        self,
+        session_id: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when agent session ends.
+        
+        Args:
+            session_id: Session identifier
+            metadata: Optional end metadata
+        """
+        ...
+    
+    def on_run_start(
+        self,
+        session_id: str,
+        run_id: str,
+        input_content: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a new run (turn) starts.
+        
+        Args:
+            session_id: Session identifier
+            run_id: Run identifier
+            input_content: User input for this run
+            metadata: Optional run metadata
+        """
+        ...
+    
+    def on_run_end(
+        self,
+        session_id: str,
+        run_id: str,
+        output_content: Optional[str] = None,
+        status: str = "completed",
+        metrics: Optional[Dict[str, Any]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a run (turn) ends.
+        
+        Args:
+            session_id: Session identifier
+            run_id: Run identifier
+            output_content: Agent output for this run
+            status: Run status (completed, error, cancelled)
+            metrics: Optional metrics (tokens, latency)
+            metadata: Optional run metadata
+        """
+        ...
+    
+    def get_runs(
+        self,
+        session_id: str,
+        limit: Optional[int] = None,
+    ) -> List["DbRun"]:
+        """
+        Get runs for a session.
+        
+        Args:
+            session_id: Session identifier
+            limit: Maximum number of runs to return
+            
+        Returns:
+            List of runs for this session
+        """
+        ...
+    
+    def export_session(
+        self,
+        session_id: str,
+    ) -> Dict[str, Any]:
+        """
+        Export a session as a dictionary (for JSONL export).
+        
+        Args:
+            session_id: Session identifier
+            
+        Returns:
+            Dictionary with session data, messages, runs
+        """
+        ...
+    
+    def import_session(
+        self,
+        data: Dict[str, Any],
+    ) -> str:
+        """
+        Import a session from a dictionary.
+        
+        Args:
+            data: Session data dictionary
+            
+        Returns:
+            Session ID of imported session
+        """
+        ...
+    
+    # --- Tracing/Observability ---
+    
+    def on_trace_start(
+        self,
+        trace_id: str,
+        session_id: Optional[str] = None,
+        run_id: Optional[str] = None,
+        agent_name: Optional[str] = None,
+        user_id: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a new trace starts.
+        
+        Args:
+            trace_id: Unique trace identifier
+            session_id: Optional session identifier
+            run_id: Optional run identifier
+            agent_name: Optional agent name
+            user_id: Optional user identifier
+            metadata: Optional trace metadata
+        """
+        ...
+    
+    def on_trace_end(
+        self,
+        trace_id: str,
+        status: str = "ok",
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a trace ends.
+        
+        Args:
+            trace_id: Trace identifier
+            status: Trace status (ok, error)
+            metadata: Optional end metadata
+        """
+        ...
+    
+    def on_span_start(
+        self,
+        span_id: str,
+        trace_id: str,
+        name: str,
+        parent_span_id: Optional[str] = None,
+        attributes: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a new span starts.
+        
+        Args:
+            span_id: Unique span identifier
+            trace_id: Parent trace identifier
+            name: Span name (e.g., "llm_call", "tool_execution")
+            parent_span_id: Optional parent span for nesting
+            attributes: Optional span attributes
+        """
+        ...
+    
+    def on_span_end(
+        self,
+        span_id: str,
+        status: str = "ok",
+        attributes: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a span ends.
+        
+        Args:
+            span_id: Span identifier
+            status: Span status (ok, error)
+            attributes: Optional end attributes
+        """
+        ...
+    
+    def get_traces(
+        self,
+        session_id: Optional[str] = None,
+        user_id: Optional[str] = None,
+        limit: Optional[int] = None,
+    ) -> List["DbTrace"]:
+        """
+        Get traces with optional filters.
+        
+        Args:
+            session_id: Filter by session
+            user_id: Filter by user
+            limit: Maximum number of traces
+            
+        Returns:
+            List of traces
+        """
+        ...
+    
+    def close(self) -> None:
+        """Close the database connection."""
+        ...
+
+
+@runtime_checkable
+class AsyncDbAdapter(Protocol):
+    """
+    Async protocol for database adapters.
+    
+    Provides async variants of all DbAdapter methods for use with
+    async/await patterns and async backends like asyncpg.
+    """
+    
+    async def on_agent_start(
+        self,
+        agent_name: str,
+        session_id: str,
+        user_id: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> List["DbMessage"]:
+        """Async version of on_agent_start."""
+        ...
+    
+    async def on_user_message(
+        self,
+        session_id: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_user_message."""
+        ...
+    
+    async def on_agent_message(
+        self,
+        session_id: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_agent_message."""
+        ...
+    
+    async def on_tool_call(
+        self,
+        session_id: str,
+        tool_name: str,
+        args: Dict[str, Any],
+        result: Any,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_tool_call."""
+        ...
+    
+    async def on_agent_end(
+        self,
+        session_id: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_agent_end."""
+        ...
+    
+    async def on_run_start(
+        self,
+        session_id: str,
+        run_id: str,
+        input_content: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_run_start."""
+        ...
+    
+    async def on_run_end(
+        self,
+        session_id: str,
+        run_id: str,
+        output_content: Optional[str] = None,
+        status: str = "completed",
+        metrics: Optional[Dict[str, Any]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_run_end."""
+        ...
+    
+    async def export_session(
+        self,
+        session_id: str,
+    ) -> Dict[str, Any]:
+        """Async version of export_session."""
+        ...
+    
+    async def import_session(
+        self,
+        data: Dict[str, Any],
+    ) -> str:
+        """Async version of import_session."""
+        ...
+    
+    async def close(self) -> None:
+        """Async close the database connection."""
+        ...
@@ -0,0 +1 @@
+"""TDD tests for db integration."""
@@ -0,0 +1,246 @@
+"""
+TDD tests for PraisonAI Agents DB integration.
+
+Tests:
+1. Lazy import verification - no heavy deps on import
+2. Agent(db=MockDb) calls hook lifecycle in correct order
+3. Session resume injects history
+4. db=None behavior unchanged
+"""
+
+import sys
+import time
+
+
+def test_lazy_import_no_heavy_deps():
+    """Test that importing praisonaiagents.db does not import DB libs."""
+    print("=== Test: Lazy Import Verification ===")
+    
+    # Record modules before import
+    before_modules = set(sys.modules.keys())
+    
+    # Import only the db module (not full praisonaiagents which loads Knowledge)
+    from praisonaiagents.db import DbAdapter, DbMessage
+    
+    # Check that heavy DB libs are NOT loaded by db module itself
+    # Note: praisonaiagents main package loads qdrant/chroma via Knowledge
+    # but the db module should not add any additional heavy deps
+    heavy_libs = ['psycopg2', 'pymysql', 'redis']  # DB-specific libs
+    loaded_heavy = [lib for lib in heavy_libs if lib in sys.modules]
+    
+    if loaded_heavy:
+        print(f"  ❌ Heavy DB libs loaded by db module: {loaded_heavy}")
+        return False
+    
+    print("  ✅ No heavy DB libs loaded by praisonaiagents.db module")
+    return True
+
+
+def test_agent_accepts_db_parameter():
+    """Test that Agent accepts db and session_id parameters."""
+    print("\n=== Test: Agent Accepts db Parameter ===")
+    
+    import inspect
+    from praisonaiagents import Agent
+    
+    sig = inspect.signature(Agent.__init__)
+    params = list(sig.parameters.keys())
+    
+    if 'db' not in params:
+        print("  ❌ Agent does not accept 'db' parameter")
+        return False
+    
+    if 'session_id' not in params:
+        print("  ❌ Agent does not accept 'session_id' parameter")
+        return False
+    
+    print("  ✅ Agent accepts db and session_id parameters")
+    return True
+
+
+class MockDbAdapter:
+    """Mock DB adapter for testing hook lifecycle."""
+    
+    def __init__(self):
+        self.calls = []
+        self.history_to_return = []
+    
+    def on_agent_start(self, agent_name, session_id, user_id=None, metadata=None):
+        self.calls.append(('on_agent_start', agent_name, session_id))
+        return self.history_to_return
+    
+    def on_user_message(self, session_id, content, metadata=None):
+        self.calls.append(('on_user_message', session_id, content))
+    
+    def on_agent_message(self, session_id, content, metadata=None):
+        self.calls.append(('on_agent_message', session_id, content))
+    
+    def on_tool_call(self, session_id, tool_name, args, result, metadata=None):
+        self.calls.append(('on_tool_call', session_id, tool_name))
+    
+    def on_agent_end(self, session_id, metadata=None):
+        self.calls.append(('on_agent_end', session_id))
+    
+    def close(self):
+        self.calls.append(('close',))
+
+
+def test_db_hook_lifecycle():
+    """Test that Agent calls db hooks in correct order."""
+    print("\n=== Test: DB Hook Lifecycle ===")
+    
+    from praisonaiagents import Agent
+    
+    mock_db = MockDbAdapter()
+    
+    # Create agent with mock db
+    agent = Agent(
+        name="TestAgent",
+        instructions="Test agent",
+        db=mock_db,
+        session_id="test-session-001",
+        verbose=False
+    )
+    
+    # Verify db is stored
+    if agent._db is not mock_db:
+        print("  ❌ Agent did not store db adapter")
+        return False
+    
+    if agent._session_id != "test-session-001":
+        print("  ❌ Agent did not store session_id")
+        return False
+    
+    print("  ✅ Agent stores db and session_id correctly")
+    return True
+
+
+def test_db_none_unchanged_behavior():
+    """Test that db=None behavior is unchanged."""
+    print("\n=== Test: db=None Unchanged Behavior ===")
+    
+    from praisonaiagents import Agent
+    
+    # Create agent without db
+    agent = Agent(
+        name="TestAgent",
+        instructions="Test agent",
+        verbose=False
+    )
+    
+    # Verify db is None
+    if agent._db is not None:
+        print("  ❌ Agent._db should be None when not provided")
+        return False
+    
+    # Verify _init_db_session does nothing when db is None
+    agent._init_db_session()
+    
+    if agent._db_initialized:
+        print("  ❌ _db_initialized should remain False when db is None")
+        return False
+    
+    print("  ✅ db=None behavior unchanged")
+    return True
+
+
+def test_session_id_auto_generation():
+    """Test that session_id is auto-generated if not provided."""
+    print("\n=== Test: Session ID Auto-Generation ===")
+    
+    from praisonaiagents import Agent
+    
+    mock_db = MockDbAdapter()
+    
+    agent = Agent(
+        name="TestAgent",
+        instructions="Test agent",
+        db=mock_db,
+        # No session_id provided
+        verbose=False
+    )
+    
+    # Trigger db initialization
+    agent._init_db_session()
+    
+    if agent._session_id is None:
+        print("  ❌ session_id should be auto-generated")
+        return False
+    
+    if not agent._session_id.startswith("session-"):
+        print(f"  ❌ session_id format unexpected: {agent._session_id}")
+        return False
+    
+    print(f"  ✅ session_id auto-generated: {agent._session_id}")
+    return True
+
+
+def test_protocol_compliance():
+    """Test that DbAdapter protocol is properly defined."""
+    print("\n=== Test: DbAdapter Protocol Compliance ===")
+    
+    from praisonaiagents.db.protocol import DbAdapter, DbMessage
+    
+    # Check DbMessage fields
+    msg = DbMessage(role="user", content="test")
+    if msg.role != "user" or msg.content != "test":
+        print("  ❌ DbMessage fields not working")
+        return False
+    
+    # Check protocol methods
+    required_methods = [
+        'on_agent_start',
+        'on_user_message', 
+        'on_agent_message',
+        'on_tool_call',
+        'on_agent_end',
+        'close'
+    ]
+    
+    mock = MockDbAdapter()
+    for method in required_methods:
+        if not hasattr(mock, method):
+            print(f"  ❌ MockDbAdapter missing {method}")
+            return False
+    
+    # Check protocol isinstance
+    if not isinstance(mock, DbAdapter):
+        print("  ❌ MockDbAdapter should implement DbAdapter protocol")
+        return False
+    
+    print("  ✅ DbAdapter protocol properly defined")
+    return True
+
+
+def run_all_tests():
+    """Run all TDD tests."""
+    print("=" * 60)
+    print("PraisonAI Agents DB Integration - TDD Tests")
+    print("=" * 60)
+    
+    results = []
+    
+    results.append(("Lazy Import", test_lazy_import_no_heavy_deps()))
+    results.append(("Agent db Parameter", test_agent_accepts_db_parameter()))
+    results.append(("DB Hook Lifecycle", test_db_hook_lifecycle()))
+    results.append(("db=None Unchanged", test_db_none_unchanged_behavior()))
+    results.append(("Session ID Auto-Gen", test_session_id_auto_generation()))
+    results.append(("Protocol Compliance", test_protocol_compliance()))
+    
+    print("\n" + "=" * 60)
+    passed = sum(1 for _, r in results if r)
+    total = len(results)
+    print(f"Results: {passed}/{total} tests passed")
+    
+    for name, result in results:
+        status = "✅" if result else "❌"
+        print(f"  {status} {name}")
+    
+    print("=" * 60)
+    
+    return passed == total
+
+
+if __name__ == "__main__":
+    success = run_all_tests()
+    sys.exit(0 if success else 1)
@@ -89,11 +89,9 @@ class AccuracyEvaluator(BaseEvaluator):
            EvaluationScore with score and reasoning
        """
        try:
-            from openai import OpenAI
+            import litellm
        except ImportError:
-            raise ImportError("OpenAI package required for accuracy evaluation. Install with: pip install openai")
-        
-        client = OpenAI()
+            raise ImportError("litellm package required for accuracy evaluation. Install with: pip install litellm")
        
        prompt = f"""You are an expert evaluator. Compare the actual output against the expected output and provide a score from 1-10.

@@ -115,7 +113,8 @@ Respond in this exact format:
 SCORE: [number 1-10]
 REASONING: [brief explanation]"""

-        response = client.chat.completions.create(
+        # Use litellm for unified multi-provider support
+        response = litellm.completion(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.1,
@@ -99,11 +99,9 @@ class CriteriaEvaluator(BaseEvaluator):
            CriteriaScore with score, passed status, and reasoning
        """
        try:
-            from openai import OpenAI
+            import litellm
        except ImportError:
-            raise ImportError("OpenAI package required for criteria evaluation. Install with: pip install openai")
-        
-        client = OpenAI()
+            raise ImportError("litellm package required for criteria evaluation. Install with: pip install litellm")
        
        if self.scoring_type == "binary":
            prompt = f"""You are an expert evaluator. Evaluate the following output against the given criteria.
@@ -138,7 +136,8 @@ Respond in this exact format:
 SCORE: [number 1-10]
 REASONING: [brief explanation]"""

-        response = client.chat.completions.create(
+        # Use litellm for unified multi-provider support
+        response = litellm.completion(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.1,
@@ -1,8 +1,130 @@
 """
-PraisonAI Knowledge - Advanced knowledge management system with configurable features
+PraisonAI Knowledge - Advanced knowledge management system with configurable features.
+
+This module provides:
+- Document readers (ReaderProtocol, ReaderRegistry)
+- Vector stores (VectorStoreProtocol, VectorStoreRegistry)
+- Retrieval strategies (RetrieverProtocol, RetrievalStrategy)
+- Rerankers (RerankerProtocol, RerankerRegistry)
+- Index types (IndexProtocol, IndexType)
+- Query engines (QueryEngineProtocol, QueryMode)
 """

+# Core Knowledge class (always available)
 from praisonaiagents.knowledge.knowledge import Knowledge
 from praisonaiagents.knowledge.chunking import Chunking

-__all__ = ["Knowledge", "Chunking"] 
+# Lazy loading for protocols and registries to avoid import overhead
+_LAZY_IMPORTS = {
+    # Readers
+    "Document": ("praisonaiagents.knowledge.readers", "Document"),
+    "ReaderProtocol": ("praisonaiagents.knowledge.readers", "ReaderProtocol"),
+    "ReaderRegistry": ("praisonaiagents.knowledge.readers", "ReaderRegistry"),
+    "get_reader_registry": ("praisonaiagents.knowledge.readers", "get_reader_registry"),
+    "detect_source_kind": ("praisonaiagents.knowledge.readers", "detect_source_kind"),
+    
+    # Vector stores
+    "VectorRecord": ("praisonaiagents.knowledge.vector_store", "VectorRecord"),
+    "VectorStoreProtocol": ("praisonaiagents.knowledge.vector_store", "VectorStoreProtocol"),
+    "VectorStoreRegistry": ("praisonaiagents.knowledge.vector_store", "VectorStoreRegistry"),
+    "get_vector_store_registry": ("praisonaiagents.knowledge.vector_store", "get_vector_store_registry"),
+    "InMemoryVectorStore": ("praisonaiagents.knowledge.vector_store", "InMemoryVectorStore"),
+    
+    # Retrieval
+    "RetrievalResult": ("praisonaiagents.knowledge.retrieval", "RetrievalResult"),
+    "RetrievalStrategy": ("praisonaiagents.knowledge.retrieval", "RetrievalStrategy"),
+    "RetrieverProtocol": ("praisonaiagents.knowledge.retrieval", "RetrieverProtocol"),
+    "RetrieverRegistry": ("praisonaiagents.knowledge.retrieval", "RetrieverRegistry"),
+    "get_retriever_registry": ("praisonaiagents.knowledge.retrieval", "get_retriever_registry"),
+    "reciprocal_rank_fusion": ("praisonaiagents.knowledge.retrieval", "reciprocal_rank_fusion"),
+    "merge_adjacent_chunks": ("praisonaiagents.knowledge.retrieval", "merge_adjacent_chunks"),
+    
+    # Rerankers
+    "RerankResult": ("praisonaiagents.knowledge.rerankers", "RerankResult"),
+    "RerankerProtocol": ("praisonaiagents.knowledge.rerankers", "RerankerProtocol"),
+    "RerankerRegistry": ("praisonaiagents.knowledge.rerankers", "RerankerRegistry"),
+    "get_reranker_registry": ("praisonaiagents.knowledge.rerankers", "get_reranker_registry"),
+    "SimpleReranker": ("praisonaiagents.knowledge.rerankers", "SimpleReranker"),
+    
+    # Index
+    "IndexType": ("praisonaiagents.knowledge.index", "IndexType"),
+    "IndexStats": ("praisonaiagents.knowledge.index", "IndexStats"),
+    "IndexProtocol": ("praisonaiagents.knowledge.index", "IndexProtocol"),
+    "IndexRegistry": ("praisonaiagents.knowledge.index", "IndexRegistry"),
+    "get_index_registry": ("praisonaiagents.knowledge.index", "get_index_registry"),
+    "KeywordIndex": ("praisonaiagents.knowledge.index", "KeywordIndex"),
+    
+    # Query engine
+    "QueryMode": ("praisonaiagents.knowledge.query_engine", "QueryMode"),
+    "QueryResult": ("praisonaiagents.knowledge.query_engine", "QueryResult"),
+    "QueryEngineProtocol": ("praisonaiagents.knowledge.query_engine", "QueryEngineProtocol"),
+    "QueryEngineRegistry": ("praisonaiagents.knowledge.query_engine", "QueryEngineRegistry"),
+    "get_query_engine_registry": ("praisonaiagents.knowledge.query_engine", "get_query_engine_registry"),
+    "decompose_question": ("praisonaiagents.knowledge.query_engine", "decompose_question"),
+    "SimpleQueryEngine": ("praisonaiagents.knowledge.query_engine", "SimpleQueryEngine"),
+    "SubQuestionEngine": ("praisonaiagents.knowledge.query_engine", "SubQuestionEngine"),
+}
+
+
+def __getattr__(name: str):
+    """Lazy load protocols and registries."""
+    if name in _LAZY_IMPORTS:
+        module_path, attr_name = _LAZY_IMPORTS[name]
+        import importlib
+        module = importlib.import_module(module_path)
+        return getattr(module, attr_name)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+def __dir__():
+    """List available attributes."""
+    return list(_LAZY_IMPORTS.keys()) + ["Knowledge", "Chunking"]
+
+
+__all__ = [
+    # Core
+    "Knowledge",
+    "Chunking",
+    # Readers
+    "Document",
+    "ReaderProtocol",
+    "ReaderRegistry",
+    "get_reader_registry",
+    "detect_source_kind",
+    # Vector stores
+    "VectorRecord",
+    "VectorStoreProtocol",
+    "VectorStoreRegistry",
+    "get_vector_store_registry",
+    "InMemoryVectorStore",
+    # Retrieval
+    "RetrievalResult",
+    "RetrievalStrategy",
+    "RetrieverProtocol",
+    "RetrieverRegistry",
+    "get_retriever_registry",
+    "reciprocal_rank_fusion",
+    "merge_adjacent_chunks",
+    # Rerankers
+    "RerankResult",
+    "RerankerProtocol",
+    "RerankerRegistry",
+    "get_reranker_registry",
+    "SimpleReranker",
+    # Index
+    "IndexType",
+    "IndexStats",
+    "IndexProtocol",
+    "IndexRegistry",
+    "get_index_registry",
+    "KeywordIndex",
+    # Query engine
+    "QueryMode",
+    "QueryResult",
+    "QueryEngineProtocol",
+    "QueryEngineRegistry",
+    "get_query_engine_registry",
+    "decompose_question",
+    "SimpleQueryEngine",
+    "SubQuestionEngine",
+] 
@@ -0,0 +1,332 @@
+"""
+Index Type Selection for PraisonAI Agents.
+
+This module defines index types and protocols.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by the wrapper layer.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+from enum import Enum
+import logging
+import math
+
+logger = logging.getLogger(__name__)
+
+
+class IndexType(str, Enum):
+    """Available index types."""
+    VECTOR = "vector"
+    KEYWORD = "keyword"
+    HYBRID = "hybrid"
+    GRAPH = "graph"
+
+
+@dataclass
+class IndexStats:
+    """Statistics about an index."""
+    index_type: IndexType
+    document_count: int
+    chunk_count: int
+    total_tokens: int = 0
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@runtime_checkable
+class IndexProtocol(Protocol):
+    """
+    Protocol for document indices.
+    
+    Implementations must provide methods for indexing and querying.
+    """
+    
+    name: str
+    index_type: IndexType
+    
+    def add_documents(
+        self,
+        texts: List[str],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        ids: Optional[List[str]] = None,
+        embeddings: Optional[List[List[float]]] = None
+    ) -> List[str]:
+        """
+        Add documents to the index.
+        
+        Args:
+            texts: List of text content
+            metadatas: Optional list of metadata dicts
+            ids: Optional list of IDs
+            embeddings: Optional pre-computed embeddings (for vector index)
+            
+        Returns:
+            List of document IDs
+        """
+        ...
+    
+    def query(
+        self,
+        query: str,
+        top_k: int = 10,
+        filter: Optional[Dict[str, Any]] = None,
+        query_embedding: Optional[List[float]] = None
+    ) -> List[Dict[str, Any]]:
+        """
+        Query the index.
+        
+        Args:
+            query: Search query
+            top_k: Number of results
+            filter: Optional metadata filter
+            query_embedding: Optional pre-computed query embedding
+            
+        Returns:
+            List of results with text, score, metadata
+        """
+        ...
+    
+    def delete(
+        self,
+        ids: Optional[List[str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
+        delete_all: bool = False
+    ) -> int:
+        """Delete documents from the index."""
+        ...
+    
+    def stats(self) -> IndexStats:
+        """Get index statistics."""
+        ...
+
+
+class IndexRegistry:
+    """Registry for index types."""
+    
+    _instance: Optional["IndexRegistry"] = None
+    
+    def __new__(cls) -> "IndexRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._indices: Dict[str, Callable[..., IndexProtocol]] = {}
+        return cls._instance
+    
+    def register(self, name: str, factory: Callable[..., IndexProtocol]) -> None:
+        """Register an index factory."""
+        self._indices[name] = factory
+    
+    def get(self, name: str, **kwargs) -> Optional[IndexProtocol]:
+        """Get an index by name."""
+        if name in self._indices:
+            try:
+                return self._indices[name](**kwargs)
+            except Exception as e:
+                logger.warning(f"Failed to initialize index '{name}': {e}")
+                return None
+        return None
+    
+    def list_indices(self) -> List[str]:
+        """List all registered index names."""
+        return list(self._indices.keys())
+    
+    def clear(self) -> None:
+        """Clear all registered indices."""
+        self._indices.clear()
+
+
+def get_index_registry() -> IndexRegistry:
+    """Get the global index registry instance."""
+    return IndexRegistry()
+
+
+class KeywordIndex:
+    """
+    Simple BM25-like keyword index.
+    
+    Pure Python implementation with no external dependencies.
+    """
+    
+    name: str = "keyword"
+    index_type: IndexType = IndexType.KEYWORD
+    
+    def __init__(self, k1: float = 1.5, b: float = 0.75, **kwargs):
+        self.k1 = k1
+        self.b = b
+        self._documents: Dict[str, Dict[str, Any]] = {}  # id -> {text, metadata, terms}
+        self._term_doc_freq: Dict[str, int] = {}  # term -> doc count
+        self._avg_doc_len: float = 0.0
+        self._total_docs: int = 0
+    
+    def add_documents(
+        self,
+        texts: List[str],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        ids: Optional[List[str]] = None,
+        embeddings: Optional[List[List[float]]] = None
+    ) -> List[str]:
+        """Add documents to the keyword index."""
+        import uuid
+        
+        metadatas = metadatas or [{} for _ in texts]
+        ids = ids or [str(uuid.uuid4()) for _ in texts]
+        
+        total_len = sum(len(doc["text"].split()) for doc in self._documents.values())
+        
+        for text, metadata, doc_id in zip(texts, metadatas, ids):
+            terms = self._tokenize(text)
+            term_freq = {}
+            for term in terms:
+                term_freq[term] = term_freq.get(term, 0) + 1
+            
+            self._documents[doc_id] = {
+                "text": text,
+                "metadata": metadata,
+                "terms": term_freq,
+                "length": len(terms)
+            }
+            
+            # Update document frequency
+            for term in set(terms):
+                self._term_doc_freq[term] = self._term_doc_freq.get(term, 0) + 1
+            
+            total_len += len(terms)
+        
+        self._total_docs = len(self._documents)
+        self._avg_doc_len = total_len / self._total_docs if self._total_docs > 0 else 0
+        
+        return ids
+    
+    def query(
+        self,
+        query: str,
+        top_k: int = 10,
+        filter: Optional[Dict[str, Any]] = None,
+        query_embedding: Optional[List[float]] = None
+    ) -> List[Dict[str, Any]]:
+        """Query using BM25 scoring."""
+        query_terms = self._tokenize(query)
+        
+        scores = []
+        for doc_id, doc in self._documents.items():
+            # Apply filter
+            if filter:
+                match = True
+                for key, value in filter.items():
+                    if doc["metadata"].get(key) != value:
+                        match = False
+                        break
+                if not match:
+                    continue
+            
+            score = self._bm25_score(query_terms, doc)
+            scores.append({
+                "id": doc_id,
+                "text": doc["text"],
+                "score": score,
+                "metadata": doc["metadata"]
+            })
+        
+        # Sort by score
+        scores.sort(key=lambda x: x["score"], reverse=True)
+        return scores[:top_k]
+    
+    def delete(
+        self,
+        ids: Optional[List[str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
+        delete_all: bool = False
+    ) -> int:
+        """Delete documents from the index."""
+        if delete_all:
+            count = len(self._documents)
+            self._documents.clear()
+            self._term_doc_freq.clear()
+            self._total_docs = 0
+            self._avg_doc_len = 0
+            return count
+        
+        deleted = 0
+        to_delete = []
+        
+        if ids:
+            to_delete = [id_ for id_ in ids if id_ in self._documents]
+        elif filter:
+            for doc_id, doc in self._documents.items():
+                match = True
+                for key, value in filter.items():
+                    if doc["metadata"].get(key) != value:
+                        match = False
+                        break
+                if match:
+                    to_delete.append(doc_id)
+        
+        for doc_id in to_delete:
+            doc = self._documents[doc_id]
+            # Update term frequencies
+            for term in doc["terms"]:
+                if term in self._term_doc_freq:
+                    self._term_doc_freq[term] -= 1
+                    if self._term_doc_freq[term] <= 0:
+                        del self._term_doc_freq[term]
+            del self._documents[doc_id]
+            deleted += 1
+        
+        self._total_docs = len(self._documents)
+        if self._total_docs > 0:
+            self._avg_doc_len = sum(d["length"] for d in self._documents.values()) / self._total_docs
+        else:
+            self._avg_doc_len = 0
+        
+        return deleted
+    
+    def stats(self) -> IndexStats:
+        """Get index statistics."""
+        return IndexStats(
+            index_type=self.index_type,
+            document_count=self._total_docs,
+            chunk_count=self._total_docs,
+            total_tokens=sum(d["length"] for d in self._documents.values()),
+            metadata={"unique_terms": len(self._term_doc_freq)}
+        )
+    
+    def _tokenize(self, text: str) -> List[str]:
+        """Simple tokenization."""
+        import re
+        # Lowercase and split on non-alphanumeric
+        tokens = re.findall(r'\b\w+\b', text.lower())
+        # Remove very short tokens
+        return [t for t in tokens if len(t) > 1]
+    
+    def _bm25_score(self, query_terms: List[str], doc: Dict[str, Any]) -> float:
+        """Calculate BM25 score."""
+        score = 0.0
+        doc_len = doc["length"]
+        term_freq = doc["terms"]
+        
+        for term in query_terms:
+            if term not in term_freq:
+                continue
+            
+            tf = term_freq[term]
+            df = self._term_doc_freq.get(term, 0)
+            
+            # IDF component
+            idf = math.log((self._total_docs - df + 0.5) / (df + 0.5) + 1)
+            
+            # TF component with length normalization
+            tf_norm = (tf * (self.k1 + 1)) / (tf + self.k1 * (1 - self.b + self.b * doc_len / self._avg_doc_len))
+            
+            score += idf * tf_norm
+        
+        return score
+
+
+# Register the keyword index by default
+def _register_default_indices():
+    """Register default indices."""
+    registry = get_index_registry()
+    registry.register("keyword", KeywordIndex)
+
+
+_register_default_indices()
@@ -91,19 +91,20 @@ class MongoDBMemory:
            raise Exception(f"Failed to initialize MongoDB: {e}")
    
    def _init_embedding_model(self):
-        """Initialize embedding model from config."""
+        """Initialize embedding model from config using litellm for unified provider support."""
        try:
            # Set up embedding model based on config
            embedder_config = self.config.get("embedder", {})
-            if embedder_config.get("provider") == "openai":
-                import openai
-                self.embedding_model = openai.OpenAI()
-                self.embedding_model_name = embedder_config.get("config", {}).get("model", "text-embedding-3-small")
-            else:
-                # Default to OpenAI
-                import openai
-                self.embedding_model = openai.OpenAI()
-                self.embedding_model_name = "text-embedding-3-small"
+            provider = embedder_config.get("provider", "openai")
+            model_name = embedder_config.get("config", {}).get("model", "text-embedding-3-small")
+            
+            # Store model name for later use
+            self.embedding_model_name = model_name
+            
+            # Use litellm for embeddings - it handles all providers uniformly
+            # We'll use litellm.embedding() in _get_embeddings() instead of storing a client
+            self.embedding_provider = provider
+            self.embedding_model = None  # Will use litellm.embedding() directly
        except Exception as e:
            raise Exception(f"Failed to initialize embedding model: {e}")
    
@@ -172,13 +173,16 @@ class MongoDBMemory:
            logging.warning(f"Could not create vector search index: {e}")
    
    def _get_embedding(self, text):
-        """Get embedding for text."""
+        """Get embedding for text using litellm for unified provider support."""
        try:
-            response = self.embedding_model.embeddings.create(
-                input=text,
-                model=self.embedding_model_name
+            import litellm
+            
+            # Use litellm.embedding() for unified multi-provider support
+            response = litellm.embedding(
+                model=self.embedding_model_name,
+                input=text
            )
-            return response.data[0].embedding
+            return response.data[0]["embedding"]
        except Exception as e:
            logging.error(f"Error getting embedding: {e}")
            return None
@@ -500,6 +504,33 @@ class Knowledge:
                base_config["graph_store"] = self._config["graph_store"]
        return base_config

+    def _prepare_mem0_config(self, config):
+        """Prepare config for mem0 by removing PraisonAI-specific fields.
+        
+        mem0's RerankerConfig only accepts 'provider' and 'config' fields.
+        PraisonAI adds 'enabled' and 'default_rerank' for internal use.
+        """
+        mem0_config = config.copy()
+        
+        # Strip PraisonAI-specific reranker fields that mem0 doesn't accept
+        if "reranker" in mem0_config:
+            reranker = mem0_config["reranker"]
+            if isinstance(reranker, dict):
+                # Keep only mem0-compatible fields: provider, config
+                mem0_reranker = {}
+                if "provider" in reranker:
+                    mem0_reranker["provider"] = reranker["provider"]
+                if "config" in reranker:
+                    mem0_reranker["config"] = reranker["config"]
+                
+                # If no valid mem0 fields, remove reranker entirely
+                if mem0_reranker:
+                    mem0_config["reranker"] = mem0_reranker
+                else:
+                    del mem0_config["reranker"]
+        
+        return mem0_config
+
    @cached_property
    def memory(self):
        # Check if MongoDB provider is specified
@@ -511,19 +542,22 @@ class Knowledge:
                # Fall back to default memory
                pass
        
+        # Prepare config for mem0 (strip PraisonAI-specific fields)
+        mem0_config = self._prepare_mem0_config(self.config)
+        
        # Default Mem0 memory
        try:
-            return CustomMemory.from_config(self.config)
+            return CustomMemory.from_config(mem0_config)
        except (NotImplementedError, ValueError) as e:
            if "list_collections" in str(e) or "Extra fields not allowed" in str(e):
                # Keep only allowed fields
                vector_store_config = {
-                    "collection_name": self.config["vector_store"]["config"]["collection_name"],
-                    "path": self.config["vector_store"]["config"]["path"]
+                    "collection_name": mem0_config["vector_store"]["config"]["collection_name"],
+                    "path": mem0_config["vector_store"]["config"]["path"]
                }
-                self.config["vector_store"]["config"] = vector_store_config
+                mem0_config["vector_store"]["config"] = vector_store_config
                from mem0 import Memory
-                return Memory.from_config(self.config)
+                return Memory.from_config(mem0_config)
            raise

    @cached_property
@@ -0,0 +1,351 @@
+"""
+Query Engine Patterns for PraisonAI Agents.
+
+This module defines query modes and protocols.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by the wrapper layer.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+from enum import Enum
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class QueryMode(str, Enum):
+    """Available query modes."""
+    DEFAULT = "default"
+    SUB_QUESTION = "sub_question"
+    SQL = "sql"
+    ROUTER = "router"
+    SUMMARIZE = "summarize"
+
+
+@dataclass
+class QueryResult:
+    """
+    Result from a query operation.
+    
+    Attributes:
+        answer: The synthesized answer
+        sources: List of source documents/chunks used
+        sub_questions: Optional list of sub-questions (for sub_question mode)
+        metadata: Additional metadata
+    """
+    answer: str
+    sources: List[Dict[str, Any]] = field(default_factory=list)
+    sub_questions: Optional[List[str]] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "answer": self.answer,
+            "sources": self.sources,
+            "sub_questions": self.sub_questions,
+            "metadata": self.metadata
+        }
+
+
+@runtime_checkable
+class QueryEngineProtocol(Protocol):
+    """
+    Protocol for query engines.
+    
+    Implementations must provide a query method that takes a question
+    and returns an answer with sources.
+    """
+    
+    name: str
+    mode: QueryMode
+    
+    def query(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        **kwargs
+    ) -> QueryResult:
+        """
+        Query the knowledge base.
+        
+        Args:
+            question: The user's question
+            context: Optional pre-retrieved context
+            **kwargs: Additional engine-specific options
+            
+        Returns:
+            QueryResult with answer and sources
+        """
+        ...
+    
+    async def aquery(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        **kwargs
+    ) -> QueryResult:
+        """Async version of query."""
+        ...
+
+
+class QueryEngineRegistry:
+    """Registry for query engines."""
+    
+    _instance: Optional["QueryEngineRegistry"] = None
+    
+    def __new__(cls) -> "QueryEngineRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._engines: Dict[str, Callable[..., QueryEngineProtocol]] = {}
+        return cls._instance
+    
+    def register(self, name: str, factory: Callable[..., QueryEngineProtocol]) -> None:
+        """Register a query engine factory."""
+        self._engines[name] = factory
+    
+    def get(self, name: str, **kwargs) -> Optional[QueryEngineProtocol]:
+        """Get a query engine by name."""
+        if name in self._engines:
+            try:
+                return self._engines[name](**kwargs)
+            except Exception as e:
+                logger.warning(f"Failed to initialize query engine '{name}': {e}")
+                return None
+        return None
+    
+    def list_engines(self) -> List[str]:
+        """List all registered engine names."""
+        return list(self._engines.keys())
+    
+    def clear(self) -> None:
+        """Clear all registered engines."""
+        self._engines.clear()
+
+
+def get_query_engine_registry() -> QueryEngineRegistry:
+    """Get the global query engine registry instance."""
+    return QueryEngineRegistry()
+
+
+def decompose_question(question: str) -> List[str]:
+    """
+    Simple deterministic question decomposition.
+    
+    Splits complex questions into sub-questions without LLM.
+    Used as fallback when LLM is not available.
+    
+    Args:
+        question: The complex question
+        
+    Returns:
+        List of sub-questions
+    """
+    sub_questions = []
+    
+    # Split on common conjunctions
+    conjunctions = [" and ", " also ", " additionally ", " furthermore ", " moreover "]
+    parts = [question]
+    
+    for conj in conjunctions:
+        new_parts = []
+        for part in parts:
+            new_parts.extend(part.split(conj))
+        parts = new_parts
+    
+    # Clean up parts
+    for part in parts:
+        part = part.strip()
+        if part:
+            # Ensure it ends with a question mark
+            if not part.endswith("?"):
+                part = part + "?"
+            # Capitalize first letter
+            part = part[0].upper() + part[1:] if len(part) > 1 else part.upper()
+            sub_questions.append(part)
+    
+    # If no decomposition happened, return original
+    if len(sub_questions) <= 1:
+        return [question]
+    
+    return sub_questions
+
+
+def synthesize_answer(
+    question: str,
+    contexts: List[str],
+    max_context_length: int = 4000
+) -> str:
+    """
+    Simple answer synthesis without LLM.
+    
+    Concatenates relevant context with the question.
+    Used as fallback when LLM is not available.
+    
+    Args:
+        question: The original question
+        contexts: List of context strings
+        max_context_length: Maximum total context length
+        
+    Returns:
+        Synthesized answer string
+    """
+    if not contexts:
+        return "No relevant information found."
+    
+    # Truncate contexts to fit
+    total_length = 0
+    selected_contexts = []
+    
+    for ctx in contexts:
+        if total_length + len(ctx) > max_context_length:
+            remaining = max_context_length - total_length
+            if remaining > 100:
+                selected_contexts.append(ctx[:remaining] + "...")
+            break
+        selected_contexts.append(ctx)
+        total_length += len(ctx)
+    
+    # Format as a simple answer
+    context_text = "\n\n".join(selected_contexts)
+    
+    return f"Based on the available information:\n\n{context_text}"
+
+
+class SimpleQueryEngine:
+    """
+    Simple query engine for testing and fallback.
+    
+    Uses basic context concatenation without LLM synthesis.
+    """
+    
+    name: str = "simple"
+    mode: QueryMode = QueryMode.DEFAULT
+    
+    def __init__(self, **kwargs):
+        pass
+    
+    def query(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        **kwargs
+    ) -> QueryResult:
+        """Simple query that returns context as answer."""
+        contexts = context or []
+        answer = synthesize_answer(question, contexts)
+        
+        return QueryResult(
+            answer=answer,
+            sources=[{"text": ctx} for ctx in contexts],
+            metadata={"mode": self.mode.value}
+        )
+    
+    async def aquery(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        **kwargs
+    ) -> QueryResult:
+        """Async version (just calls sync)."""
+        return self.query(question, context, **kwargs)
+
+
+class SubQuestionEngine:
+    """
+    Sub-question decomposition query engine.
+    
+    Decomposes complex questions into simpler sub-questions.
+    Uses deterministic fallback when LLM is not available.
+    """
+    
+    name: str = "sub_question"
+    mode: QueryMode = QueryMode.SUB_QUESTION
+    
+    def __init__(self, llm: Optional[Any] = None, **kwargs):
+        self.llm = llm
+    
+    def query(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        retriever: Optional[Any] = None,
+        **kwargs
+    ) -> QueryResult:
+        """Query with sub-question decomposition."""
+        # Decompose question
+        if self.llm:
+            sub_questions = self._decompose_with_llm(question)
+        else:
+            sub_questions = decompose_question(question)
+        
+        # Gather context for each sub-question
+        all_contexts = list(context or [])
+        sub_answers = []
+        
+        for sub_q in sub_questions:
+            if retriever:
+                # Retrieve for each sub-question
+                try:
+                    results = retriever.retrieve(sub_q, top_k=3)
+                    for r in results:
+                        if hasattr(r, 'text'):
+                            all_contexts.append(r.text)
+                        elif isinstance(r, dict):
+                            all_contexts.append(r.get('text', str(r)))
+                except Exception as e:
+                    logger.warning(f"Retrieval failed for sub-question: {e}")
+            
+            # Simple sub-answer (would use LLM in full implementation)
+            sub_answers.append(f"Sub-question: {sub_q}")
+        
+        # Synthesize final answer
+        answer = synthesize_answer(question, all_contexts)
+        
+        return QueryResult(
+            answer=answer,
+            sources=[{"text": ctx} for ctx in all_contexts[:10]],
+            sub_questions=sub_questions,
+            metadata={"mode": self.mode.value, "sub_question_count": len(sub_questions)}
+        )
+    
+    async def aquery(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        **kwargs
+    ) -> QueryResult:
+        """Async version (just calls sync)."""
+        return self.query(question, context, **kwargs)
+    
+    def _decompose_with_llm(self, question: str) -> List[str]:
+        """Decompose question using LLM."""
+        try:
+            prompt = f"""Break down this complex question into simpler sub-questions that can be answered independently.
+            
+Question: {question}
+
+Return only the sub-questions, one per line, without numbering or bullets."""
+            
+            response = self.llm.chat(prompt)
+            if response:
+                lines = response.strip().split("\n")
+                return [line.strip() for line in lines if line.strip()]
+        except Exception as e:
+            logger.warning(f"LLM decomposition failed: {e}")
+        
+        return decompose_question(question)
+
+
+# Register default engines
+def _register_default_engines():
+    """Register default query engines."""
+    registry = get_query_engine_registry()
+    registry.register("default", SimpleQueryEngine)
+    registry.register("simple", SimpleQueryEngine)
+    registry.register("sub_question", SubQuestionEngine)
+
+
+_register_default_engines()
@@ -0,0 +1,286 @@
+"""
+Data Reader Adapter Protocol for PraisonAI Agents.
+
+This module defines the protocol for document readers and a registry system.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by the wrapper layer (praisonai.adapters.readers).
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+import os
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Document:
+    """
+    Lightweight document representation.
+    
+    Attributes:
+        content: The text content of the document
+        metadata: Optional metadata dict (source, filename, page, etc.)
+        doc_id: Optional unique identifier
+        embedding: Optional pre-computed embedding
+    """
+    content: str
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    doc_id: Optional[str] = None
+    embedding: Optional[List[float]] = None
+    
+    def __post_init__(self):
+        if self.doc_id is None:
+            import uuid
+            self.doc_id = str(uuid.uuid4())
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "content": self.content,
+            "metadata": self.metadata,
+            "doc_id": self.doc_id,
+            "embedding": self.embedding
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "Document":
+        """Create from dictionary."""
+        return cls(
+            content=data.get("content", ""),
+            metadata=data.get("metadata", {}),
+            doc_id=data.get("doc_id"),
+            embedding=data.get("embedding")
+        )
+
+
+@runtime_checkable
+class ReaderProtocol(Protocol):
+    """
+    Protocol for document readers.
+    
+    Implementations must provide:
+    - name: Identifier for the reader
+    - supported_extensions: List of file extensions this reader handles
+    - load(): Method to load documents from a source
+    """
+    
+    name: str
+    supported_extensions: List[str]
+    
+    def load(
+        self, 
+        source: str, 
+        *, 
+        metadata: Optional[Dict[str, Any]] = None
+    ) -> List[Document]:
+        """
+        Load documents from a source.
+        
+        Args:
+            source: File path, URL, or other source identifier
+            metadata: Optional metadata to attach to all documents
+            
+        Returns:
+            List of Document objects
+        """
+        ...
+    
+    def can_handle(self, source: str) -> bool:
+        """
+        Check if this reader can handle the given source.
+        
+        Args:
+            source: File path, URL, or other source identifier
+            
+        Returns:
+            True if this reader can handle the source
+        """
+        ...
+
+
+class ReaderRegistry:
+    """
+    Registry for document readers.
+    
+    Provides lazy loading and automatic reader selection based on source type.
+    """
+    
+    _instance: Optional["ReaderRegistry"] = None
+    
+    def __new__(cls) -> "ReaderRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._readers: Dict[str, Callable[[], ReaderProtocol]] = {}
+            cls._instance._extension_map: Dict[str, str] = {}
+            cls._instance._initialized_readers: Dict[str, ReaderProtocol] = {}
+        return cls._instance
+    
+    def register(
+        self, 
+        name: str, 
+        factory: Callable[[], ReaderProtocol],
+        extensions: Optional[List[str]] = None
+    ) -> None:
+        """
+        Register a reader factory.
+        
+        Args:
+            name: Unique name for the reader
+            factory: Callable that returns a ReaderProtocol instance
+            extensions: List of file extensions this reader handles
+        """
+        self._readers[name] = factory
+        if extensions:
+            for ext in extensions:
+                ext_lower = ext.lower().lstrip(".")
+                self._extension_map[ext_lower] = name
+    
+    def get(self, name: str) -> Optional[ReaderProtocol]:
+        """
+        Get a reader by name (lazy initialization).
+        
+        Args:
+            name: Reader name
+            
+        Returns:
+            ReaderProtocol instance or None
+        """
+        if name in self._initialized_readers:
+            return self._initialized_readers[name]
+        
+        if name in self._readers:
+            try:
+                reader = self._readers[name]()
+                self._initialized_readers[name] = reader
+                return reader
+            except Exception as e:
+                logger.warning(f"Failed to initialize reader '{name}': {e}")
+                return None
+        return None
+    
+    def get_for_source(self, source: str) -> Optional[ReaderProtocol]:
+        """
+        Get the best reader for a given source.
+        
+        Args:
+            source: File path, URL, or other source identifier
+            
+        Returns:
+            Best matching ReaderProtocol or None
+        """
+        source_kind = detect_source_kind(source)
+        
+        if source_kind == "file":
+            ext = os.path.splitext(source)[1].lower().lstrip(".")
+            if ext in self._extension_map:
+                return self.get(self._extension_map[ext])
+        elif source_kind == "url":
+            # Try URL reader
+            if "url" in self._readers:
+                return self.get("url")
+        elif source_kind == "directory":
+            if "directory" in self._readers:
+                return self.get("directory")
+        
+        # Fallback to auto reader if available
+        if "auto" in self._readers:
+            return self.get("auto")
+        
+        return None
+    
+    def list_readers(self) -> List[str]:
+        """List all registered reader names."""
+        return list(self._readers.keys())
+    
+    def list_extensions(self) -> Dict[str, str]:
+        """List all registered extensions and their readers."""
+        return dict(self._extension_map)
+    
+    def clear(self) -> None:
+        """Clear all registered readers (mainly for testing)."""
+        self._readers.clear()
+        self._extension_map.clear()
+        self._initialized_readers.clear()
+
+
+def get_reader_registry() -> ReaderRegistry:
+    """Get the global reader registry instance."""
+    return ReaderRegistry()
+
+
+def detect_source_kind(source: str) -> str:
+    """
+    Detect the kind of source without importing heavy libraries.
+    
+    Args:
+        source: File path, URL, directory, or glob pattern
+        
+    Returns:
+        One of: "file", "url", "directory", "glob", "unknown"
+    """
+    if not isinstance(source, str):
+        return "unknown"
+    
+    source = source.strip()
+    
+    # Check for URL
+    if source.startswith(("http://", "https://", "ftp://", "s3://", "gs://")):
+        return "url"
+    
+    # Check for glob pattern
+    if "*" in source or "?" in source or "[" in source:
+        return "glob"
+    
+    # Check if it's a directory
+    if os.path.isdir(source):
+        return "directory"
+    
+    # Check if it's a file
+    if os.path.isfile(source):
+        return "file"
+    
+    # Check if it looks like a file path (has extension)
+    if os.path.splitext(source)[1]:
+        return "file"
+    
+    return "unknown"
+
+
+def get_file_extension(source: str) -> str:
+    """
+    Get the file extension from a source path.
+    
+    Args:
+        source: File path or URL
+        
+    Returns:
+        Lowercase extension without dot, or empty string
+    """
+    # Handle URLs by extracting path
+    if source.startswith(("http://", "https://")):
+        from urllib.parse import urlparse
+        path = urlparse(source).path
+        return os.path.splitext(path)[1].lower().lstrip(".")
+    
+    return os.path.splitext(source)[1].lower().lstrip(".")
+
+
+# Default extension mappings (reader name -> extensions)
+DEFAULT_EXTENSION_MAPPINGS = {
+    "text": ["txt", "text"],
+    "markdown": ["md", "markdown"],
+    "json": ["json", "jsonl"],
+    "csv": ["csv", "tsv"],
+    "html": ["html", "htm"],
+    "xml": ["xml"],
+    "pdf": ["pdf"],
+    "docx": ["docx", "doc"],
+    "xlsx": ["xlsx", "xls"],
+    "pptx": ["pptx", "ppt"],
+    "image": ["jpg", "jpeg", "png", "gif", "bmp", "tiff", "webp"],
+    "audio": ["mp3", "wav", "ogg", "m4a", "flac"],
+    "video": ["mp4", "avi", "mov", "mkv", "webm"],
+}
@@ -0,0 +1,196 @@
+"""
+Reranker Protocol for PraisonAI Agents.
+
+This module defines the protocol for rerankers and a registry system.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by praisonai-tools or wrapper layer.
+"""
+
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RerankResult:
+    """
+    Result from a reranking operation.
+    
+    Attributes:
+        text: The text content
+        score: Reranking score (higher is more relevant)
+        original_index: Original position before reranking
+        metadata: Optional metadata
+    """
+    text: str
+    score: float
+    original_index: int
+    metadata: Dict[str, Any] = None
+    
+    def __post_init__(self):
+        if self.metadata is None:
+            self.metadata = {}
+
+
+@runtime_checkable
+class RerankerProtocol(Protocol):
+    """
+    Protocol for rerankers.
+    
+    Implementations must provide a rerank method that takes a query
+    and a list of documents and returns reranked results.
+    """
+    
+    name: str
+    
+    def rerank(
+        self,
+        query: str,
+        documents: List[str],
+        top_k: Optional[int] = None,
+        **kwargs
+    ) -> List[RerankResult]:
+        """
+        Rerank documents based on relevance to query.
+        
+        Args:
+            query: The search query
+            documents: List of document texts to rerank
+            top_k: Optional limit on results (None = return all)
+            **kwargs: Additional reranker-specific options
+            
+        Returns:
+            List of RerankResult objects sorted by relevance
+        """
+        ...
+    
+    async def arerank(
+        self,
+        query: str,
+        documents: List[str],
+        top_k: Optional[int] = None,
+        **kwargs
+    ) -> List[RerankResult]:
+        """Async version of rerank."""
+        ...
+
+
+class RerankerRegistry:
+    """
+    Registry for rerankers.
+    """
+    
+    _instance: Optional["RerankerRegistry"] = None
+    
+    def __new__(cls) -> "RerankerRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._rerankers: Dict[str, Callable[..., RerankerProtocol]] = {}
+        return cls._instance
+    
+    def register(
+        self, 
+        name: str, 
+        factory: Callable[..., RerankerProtocol]
+    ) -> None:
+        """Register a reranker factory."""
+        self._rerankers[name] = factory
+    
+    def get(
+        self, 
+        name: str, 
+        **kwargs
+    ) -> Optional[RerankerProtocol]:
+        """Get a reranker by name."""
+        if name in self._rerankers:
+            try:
+                return self._rerankers[name](**kwargs)
+            except Exception as e:
+                logger.warning(f"Failed to initialize reranker '{name}': {e}")
+                return None
+        return None
+    
+    def list_rerankers(self) -> List[str]:
+        """List all registered reranker names."""
+        return list(self._rerankers.keys())
+    
+    def clear(self) -> None:
+        """Clear all registered rerankers."""
+        self._rerankers.clear()
+
+
+def get_reranker_registry() -> RerankerRegistry:
+    """Get the global reranker registry instance."""
+    return RerankerRegistry()
+
+
+class SimpleReranker:
+    """
+    Simple keyword-based reranker for testing and fallback.
+    
+    Uses term frequency overlap for scoring. No external dependencies.
+    """
+    
+    name: str = "simple"
+    
+    def __init__(self, **kwargs):
+        pass
+    
+    def rerank(
+        self,
+        query: str,
+        documents: List[str],
+        top_k: Optional[int] = None,
+        **kwargs
+    ) -> List[RerankResult]:
+        """Rerank using simple term overlap."""
+        query_terms = set(query.lower().split())
+        
+        results = []
+        for i, doc in enumerate(documents):
+            doc_terms = set(doc.lower().split())
+            
+            # Calculate Jaccard similarity
+            if not query_terms or not doc_terms:
+                score = 0.0
+            else:
+                intersection = len(query_terms & doc_terms)
+                union = len(query_terms | doc_terms)
+                score = intersection / union if union > 0 else 0.0
+            
+            results.append(RerankResult(
+                text=doc,
+                score=score,
+                original_index=i
+            ))
+        
+        # Sort by score descending
+        results.sort(key=lambda x: x.score, reverse=True)
+        
+        if top_k is not None:
+            results = results[:top_k]
+        
+        return results
+    
+    async def arerank(
+        self,
+        query: str,
+        documents: List[str],
+        top_k: Optional[int] = None,
+        **kwargs
+    ) -> List[RerankResult]:
+        """Async version (just calls sync)."""
+        return self.rerank(query, documents, top_k, **kwargs)
+
+
+# Register the simple reranker by default
+def _register_default_rerankers():
+    """Register default rerankers."""
+    registry = get_reranker_registry()
+    registry.register("simple", SimpleReranker)
+
+
+_register_default_rerankers()
@@ -0,0 +1,285 @@
+"""
+Retrieval Strategy Patterns for PraisonAI Agents.
+
+This module defines protocols and strategies for document retrieval.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by the wrapper layer.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+from enum import Enum
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class RetrievalStrategy(str, Enum):
+    """Available retrieval strategies."""
+    BASIC = "basic"
+    FUSION = "fusion"
+    RECURSIVE = "recursive"
+    AUTO_MERGE = "auto_merge"
+    HYBRID = "hybrid"
+
+
+@dataclass
+class RetrievalResult:
+    """
+    Result from a retrieval operation.
+    
+    Attributes:
+        text: The retrieved text content
+        score: Relevance score (0-1)
+        metadata: Source metadata
+        doc_id: Document identifier
+        chunk_index: Index of chunk within document
+    """
+    text: str
+    score: float
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    doc_id: Optional[str] = None
+    chunk_index: Optional[int] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "text": self.text,
+            "score": self.score,
+            "metadata": self.metadata,
+            "doc_id": self.doc_id,
+            "chunk_index": self.chunk_index
+        }
+
+
+@runtime_checkable
+class RetrieverProtocol(Protocol):
+    """
+    Protocol for document retrievers.
+    
+    Implementations must provide a retrieve method that takes a query
+    and returns relevant documents.
+    """
+    
+    name: str
+    strategy: RetrievalStrategy
+    
+    def retrieve(
+        self,
+        query: str,
+        top_k: int = 10,
+        filter: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ) -> List[RetrievalResult]:
+        """
+        Retrieve relevant documents for a query.
+        
+        Args:
+            query: The search query
+            top_k: Maximum number of results
+            filter: Optional metadata filter
+            **kwargs: Additional retriever-specific options
+            
+        Returns:
+            List of RetrievalResult objects sorted by relevance
+        """
+        ...
+    
+    async def aretrieve(
+        self,
+        query: str,
+        top_k: int = 10,
+        filter: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ) -> List[RetrievalResult]:
+        """Async version of retrieve."""
+        ...
+
+
+class RetrieverRegistry:
+    """
+    Registry for retrieval strategies.
+    """
+    
+    _instance: Optional["RetrieverRegistry"] = None
+    
+    def __new__(cls) -> "RetrieverRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._retrievers: Dict[str, Callable[..., RetrieverProtocol]] = {}
+        return cls._instance
+    
+    def register(
+        self, 
+        name: str, 
+        factory: Callable[..., RetrieverProtocol]
+    ) -> None:
+        """Register a retriever factory."""
+        self._retrievers[name] = factory
+    
+    def get(
+        self, 
+        name: str, 
+        **kwargs
+    ) -> Optional[RetrieverProtocol]:
+        """Get a retriever by name."""
+        if name in self._retrievers:
+            try:
+                return self._retrievers[name](**kwargs)
+            except Exception as e:
+                logger.warning(f"Failed to initialize retriever '{name}': {e}")
+                return None
+        return None
+    
+    def list_retrievers(self) -> List[str]:
+        """List all registered retriever names."""
+        return list(self._retrievers.keys())
+    
+    def clear(self) -> None:
+        """Clear all registered retrievers."""
+        self._retrievers.clear()
+
+
+def get_retriever_registry() -> RetrieverRegistry:
+    """Get the global retriever registry instance."""
+    return RetrieverRegistry()
+
+
+def reciprocal_rank_fusion(
+    result_lists: List[List[RetrievalResult]],
+    k: int = 60
+) -> List[RetrievalResult]:
+    """
+    Reciprocal Rank Fusion (RRF) algorithm for combining multiple result lists.
+    
+    Pure Python implementation with no external dependencies.
+    
+    Args:
+        result_lists: List of result lists from different retrievers
+        k: RRF constant (default 60)
+        
+    Returns:
+        Fused and re-ranked list of results
+    """
+    # Track scores by document ID
+    fused_scores: Dict[str, float] = {}
+    doc_map: Dict[str, RetrievalResult] = {}
+    
+    for results in result_lists:
+        for rank, result in enumerate(results):
+            doc_key = result.doc_id or result.text[:100]  # Use text prefix as fallback key
+            
+            # RRF score: 1 / (k + rank)
+            rrf_score = 1.0 / (k + rank + 1)
+            
+            if doc_key in fused_scores:
+                fused_scores[doc_key] += rrf_score
+            else:
+                fused_scores[doc_key] = rrf_score
+                doc_map[doc_key] = result
+    
+    # Sort by fused score
+    sorted_keys = sorted(fused_scores.keys(), key=lambda x: fused_scores[x], reverse=True)
+    
+    # Build result list with updated scores
+    fused_results = []
+    for key in sorted_keys:
+        result = doc_map[key]
+        fused_results.append(RetrievalResult(
+            text=result.text,
+            score=fused_scores[key],
+            metadata=result.metadata,
+            doc_id=result.doc_id,
+            chunk_index=result.chunk_index
+        ))
+    
+    return fused_results
+
+
+def merge_adjacent_chunks(
+    results: List[RetrievalResult],
+    max_gap: int = 1
+) -> List[RetrievalResult]:
+    """
+    Merge adjacent chunks from the same document.
+    
+    Args:
+        results: List of retrieval results
+        max_gap: Maximum gap between chunk indices to merge
+        
+    Returns:
+        Merged results
+    """
+    if not results:
+        return results
+    
+    # Group by document ID
+    doc_groups: Dict[str, List[RetrievalResult]] = {}
+    for result in results:
+        doc_id = result.doc_id or "unknown"
+        if doc_id not in doc_groups:
+            doc_groups[doc_id] = []
+        doc_groups[doc_id].append(result)
+    
+    merged_results = []
+    
+    for doc_id, chunks in doc_groups.items():
+        # Sort by chunk index
+        chunks_with_index = [c for c in chunks if c.chunk_index is not None]
+        chunks_without_index = [c for c in chunks if c.chunk_index is None]
+        
+        if not chunks_with_index:
+            merged_results.extend(chunks_without_index)
+            continue
+        
+        chunks_with_index.sort(key=lambda x: x.chunk_index or 0)
+        
+        # Merge adjacent chunks
+        current_group = [chunks_with_index[0]]
+        
+        for chunk in chunks_with_index[1:]:
+            last_chunk = current_group[-1]
+            last_idx = last_chunk.chunk_index or 0
+            curr_idx = chunk.chunk_index or 0
+            
+            if curr_idx - last_idx <= max_gap:
+                current_group.append(chunk)
+            else:
+                # Merge current group and start new one
+                merged_results.append(_merge_chunk_group(current_group))
+                current_group = [chunk]
+        
+        # Don't forget the last group
+        if current_group:
+            merged_results.append(_merge_chunk_group(current_group))
+        
+        merged_results.extend(chunks_without_index)
+    
+    # Sort by score
+    merged_results.sort(key=lambda x: x.score, reverse=True)
+    return merged_results
+
+
+def _merge_chunk_group(chunks: List[RetrievalResult]) -> RetrievalResult:
+    """Merge a group of chunks into a single result."""
+    if len(chunks) == 1:
+        return chunks[0]
+    
+    # Combine text
+    merged_text = "\n".join(c.text for c in chunks)
+    
+    # Average score
+    avg_score = sum(c.score for c in chunks) / len(chunks)
+    
+    # Use first chunk's metadata
+    metadata = chunks[0].metadata.copy()
+    metadata["merged_chunks"] = len(chunks)
+    
+    return RetrievalResult(
+        text=merged_text,
+        score=avg_score,
+        metadata=metadata,
+        doc_id=chunks[0].doc_id,
+        chunk_index=chunks[0].chunk_index
+    )
@@ -0,0 +1,422 @@
+"""
+Vector Store Adapter Protocol for PraisonAI Agents.
+
+This module defines the protocol for vector stores and a registry system.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by praisonai-tools or wrapper layer.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class VectorRecord:
+    """
+    A record in a vector store.
+    
+    Attributes:
+        id: Unique identifier
+        text: The text content
+        embedding: The vector embedding
+        metadata: Optional metadata dict
+        score: Optional similarity score (for query results)
+    """
+    id: str
+    text: str
+    embedding: List[float]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    score: Optional[float] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "id": self.id,
+            "text": self.text,
+            "embedding": self.embedding,
+            "metadata": self.metadata,
+            "score": self.score
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "VectorRecord":
+        """Create from dictionary."""
+        return cls(
+            id=data["id"],
+            text=data["text"],
+            embedding=data["embedding"],
+            metadata=data.get("metadata", {}),
+            score=data.get("score")
+        )
+
+
+@runtime_checkable
+class VectorStoreProtocol(Protocol):
+    """
+    Protocol for vector stores.
+    
+    Implementations must provide methods for:
+    - Adding vectors
+    - Querying by vector similarity
+    - Deleting vectors
+    - Getting count
+    """
+    
+    name: str
+    
+    def add(
+        self,
+        texts: List[str],
+        embeddings: List[List[float]],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        ids: Optional[List[str]] = None,
+        namespace: Optional[str] = None
+    ) -> List[str]:
+        """
+        Add vectors to the store.
+        
+        Args:
+            texts: List of text content
+            embeddings: List of embedding vectors
+            metadatas: Optional list of metadata dicts
+            ids: Optional list of IDs (auto-generated if not provided)
+            namespace: Optional namespace for multi-tenant isolation
+            
+        Returns:
+            List of IDs for the added vectors
+        """
+        ...
+    
+    def query(
+        self,
+        embedding: List[float],
+        top_k: int = 10,
+        namespace: Optional[str] = None,
+        filter: Optional[Dict[str, Any]] = None
+    ) -> List[VectorRecord]:
+        """
+        Query vectors by similarity.
+        
+        Args:
+            embedding: Query embedding vector
+            top_k: Number of results to return
+            namespace: Optional namespace filter
+            filter: Optional metadata filter
+            
+        Returns:
+            List of VectorRecord objects sorted by similarity
+        """
+        ...
+    
+    def delete(
+        self,
+        ids: Optional[List[str]] = None,
+        namespace: Optional[str] = None,
+        filter: Optional[Dict[str, Any]] = None,
+        delete_all: bool = False
+    ) -> int:
+        """
+        Delete vectors from the store.
+        
+        Args:
+            ids: Optional list of IDs to delete
+            namespace: Optional namespace filter
+            filter: Optional metadata filter
+            delete_all: If True, delete all vectors
+            
+        Returns:
+            Number of vectors deleted
+        """
+        ...
+    
+    def count(self, namespace: Optional[str] = None) -> int:
+        """
+        Get the number of vectors in the store.
+        
+        Args:
+            namespace: Optional namespace filter
+            
+        Returns:
+            Number of vectors
+        """
+        ...
+    
+    def get(
+        self,
+        ids: List[str],
+        namespace: Optional[str] = None
+    ) -> List[VectorRecord]:
+        """
+        Get vectors by ID.
+        
+        Args:
+            ids: List of IDs to retrieve
+            namespace: Optional namespace filter
+            
+        Returns:
+            List of VectorRecord objects
+        """
+        ...
+
+
+class VectorStoreRegistry:
+    """
+    Registry for vector stores.
+    
+    Provides lazy loading and configuration-based initialization.
+    """
+    
+    _instance: Optional["VectorStoreRegistry"] = None
+    
+    def __new__(cls) -> "VectorStoreRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._stores: Dict[str, Callable[..., VectorStoreProtocol]] = {}
+            cls._instance._initialized_stores: Dict[str, VectorStoreProtocol] = {}
+        return cls._instance
+    
+    def register(
+        self, 
+        name: str, 
+        factory: Callable[..., VectorStoreProtocol]
+    ) -> None:
+        """
+        Register a vector store factory.
+        
+        Args:
+            name: Unique name for the store (e.g., "chroma", "pinecone")
+            factory: Callable that returns a VectorStoreProtocol instance
+        """
+        self._stores[name] = factory
+    
+    def get(
+        self, 
+        name: str, 
+        config: Optional[Dict[str, Any]] = None,
+        namespace: Optional[str] = None
+    ) -> Optional[VectorStoreProtocol]:
+        """
+        Get a vector store by name (lazy initialization).
+        
+        Args:
+            name: Store name
+            config: Optional configuration dict
+            namespace: Optional namespace for multi-tenant isolation
+            
+        Returns:
+            VectorStoreProtocol instance or None
+        """
+        cache_key = f"{name}:{namespace or 'default'}"
+        
+        if cache_key in self._initialized_stores:
+            return self._initialized_stores[cache_key]
+        
+        if name in self._stores:
+            try:
+                store = self._stores[name](config=config, namespace=namespace)
+                self._initialized_stores[cache_key] = store
+                return store
+            except Exception as e:
+                logger.warning(f"Failed to initialize vector store '{name}': {e}")
+                return None
+        return None
+    
+    def list_stores(self) -> List[str]:
+        """List all registered store names."""
+        return list(self._stores.keys())
+    
+    def clear(self) -> None:
+        """Clear all registered stores (mainly for testing)."""
+        self._stores.clear()
+        self._initialized_stores.clear()
+
+
+def get_vector_store_registry() -> VectorStoreRegistry:
+    """Get the global vector store registry instance."""
+    return VectorStoreRegistry()
+
+
+class InMemoryVectorStore:
+    """
+    Pure Python in-memory vector store for testing and default usage.
+    
+    Uses cosine similarity for queries. No external dependencies.
+    """
+    
+    name: str = "memory"
+    
+    def __init__(
+        self, 
+        config: Optional[Dict[str, Any]] = None,
+        namespace: Optional[str] = None
+    ):
+        self.config = config or {}
+        self.default_namespace = namespace or "default"
+        self._data: Dict[str, Dict[str, VectorRecord]] = {}  # namespace -> id -> record
+    
+    def _get_namespace_data(self, namespace: Optional[str] = None) -> Dict[str, VectorRecord]:
+        """Get or create namespace data."""
+        ns = namespace or self.default_namespace
+        if ns not in self._data:
+            self._data[ns] = {}
+        return self._data[ns]
+    
+    def add(
+        self,
+        texts: List[str],
+        embeddings: List[List[float]],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        ids: Optional[List[str]] = None,
+        namespace: Optional[str] = None
+    ) -> List[str]:
+        """Add vectors to the store."""
+        import uuid
+        
+        ns_data = self._get_namespace_data(namespace)
+        metadatas = metadatas or [{} for _ in texts]
+        ids = ids or [str(uuid.uuid4()) for _ in texts]
+        
+        result_ids = []
+        for i, (text, embedding, metadata, id_) in enumerate(zip(texts, embeddings, metadatas, ids)):
+            record = VectorRecord(
+                id=id_,
+                text=text,
+                embedding=embedding,
+                metadata=metadata
+            )
+            ns_data[id_] = record
+            result_ids.append(id_)
+        
+        return result_ids
+    
+    def query(
+        self,
+        embedding: List[float],
+        top_k: int = 10,
+        namespace: Optional[str] = None,
+        filter: Optional[Dict[str, Any]] = None
+    ) -> List[VectorRecord]:
+        """Query vectors by cosine similarity."""
+        ns_data = self._get_namespace_data(namespace)
+        
+        if not ns_data:
+            return []
+        
+        # Calculate cosine similarity for all records
+        results = []
+        for record in ns_data.values():
+            # Apply metadata filter if provided
+            if filter:
+                match = True
+                for key, value in filter.items():
+                    if record.metadata.get(key) != value:
+                        match = False
+                        break
+                if not match:
+                    continue
+            
+            score = self._cosine_similarity(embedding, record.embedding)
+            results.append(VectorRecord(
+                id=record.id,
+                text=record.text,
+                embedding=record.embedding,
+                metadata=record.metadata,
+                score=score
+            ))
+        
+        # Sort by score descending and return top_k
+        results.sort(key=lambda x: x.score or 0, reverse=True)
+        return results[:top_k]
+    
+    def delete(
+        self,
+        ids: Optional[List[str]] = None,
+        namespace: Optional[str] = None,
+        filter: Optional[Dict[str, Any]] = None,
+        delete_all: bool = False
+    ) -> int:
+        """Delete vectors from the store."""
+        ns = namespace or self.default_namespace
+        
+        if delete_all:
+            if ns in self._data:
+                count = len(self._data[ns])
+                self._data[ns] = {}
+                return count
+            return 0
+        
+        if ns not in self._data:
+            return 0
+        
+        ns_data = self._data[ns]
+        deleted = 0
+        
+        if ids:
+            for id_ in ids:
+                if id_ in ns_data:
+                    del ns_data[id_]
+                    deleted += 1
+        elif filter:
+            to_delete = []
+            for id_, record in ns_data.items():
+                match = True
+                for key, value in filter.items():
+                    if record.metadata.get(key) != value:
+                        match = False
+                        break
+                if match:
+                    to_delete.append(id_)
+            for id_ in to_delete:
+                del ns_data[id_]
+                deleted += 1
+        
+        return deleted
+    
+    def count(self, namespace: Optional[str] = None) -> int:
+        """Get the number of vectors in the store."""
+        ns = namespace or self.default_namespace
+        if ns in self._data:
+            return len(self._data[ns])
+        return 0
+    
+    def get(
+        self,
+        ids: List[str],
+        namespace: Optional[str] = None
+    ) -> List[VectorRecord]:
+        """Get vectors by ID."""
+        ns_data = self._get_namespace_data(namespace)
+        results = []
+        for id_ in ids:
+            if id_ in ns_data:
+                results.append(ns_data[id_])
+        return results
+    
+    @staticmethod
+    def _cosine_similarity(a: List[float], b: List[float]) -> float:
+        """Calculate cosine similarity between two vectors."""
+        if len(a) != len(b):
+            return 0.0
+        
+        dot_product = sum(x * y for x, y in zip(a, b))
+        norm_a = sum(x * x for x in a) ** 0.5
+        norm_b = sum(x * x for x in b) ** 0.5
+        
+        if norm_a == 0 or norm_b == 0:
+            return 0.0
+        
+        return dot_product / (norm_a * norm_b)
+
+
+# Register the in-memory store by default
+def _register_default_stores():
+    """Register default vector stores."""
+    registry = get_vector_store_registry()
+    registry.register("memory", InMemoryVectorStore)
+
+
+_register_default_stores()
@@ -4,16 +4,20 @@ Session Management for PraisonAI Agents
 A simple wrapper around existing stateful capabilities to provide a unified
 session API for developers building stateful agent applications.
 """
+from __future__ import annotations

 import os
 import uuid
 import requests
 import json
 import time
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from .memory.memory import Memory
+    from .knowledge.knowledge import Knowledge
+
 from .agent import Agent
-from .memory import Memory
-from .knowledge import Knowledge


 class Session:
@@ -112,6 +116,7 @@ class Session:
        if self.is_remote:
            raise ValueError("Memory operations are not available for remote agent sessions")
        if self._memory is None:
+            from .memory.memory import Memory
            self._memory = Memory(config=self.memory_config)
        return self._memory

@@ -121,6 +126,7 @@ class Session:
        if self.is_remote:
            raise ValueError("Knowledge operations are not available for remote agent sessions")
        if self._knowledge is None:
+            from .knowledge.knowledge import Knowledge
            self._knowledge = Knowledge(config=self.knowledge_config)
        return self._knowledge

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "praisonaiagents"
-version = "0.3.0"
+version = "0.3.4"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"
@@ -672,16 +672,16 @@ class TestPlanningAgent:
        
        planner = PlanningAgent()
        
-        assert planner.llm_model == "gpt-4o-mini"
+        assert planner.llm_model in ["gpt-4o-mini", "gpt-5-nano"]
        assert planner.read_only is True
        
    def test_planning_agent_custom_llm(self):
        """Test PlanningAgent with custom LLM."""
        from praisonaiagents.planning import PlanningAgent
        
-        planner = PlanningAgent(llm="gpt-4o")
+        planner = PlanningAgent(llm="gpt-5-nano")
        
-        assert planner.llm_model == "gpt-4o"
+        assert planner.llm_model == "gpt-5-nano"
        
    def test_planning_agent_allowed_tools(self):
        """Test PlanningAgent has only read-only tools."""
@@ -840,10 +840,10 @@ class TestPraisonAIAgentsPlanningIntegration:
            agents=[agent],
            tasks=[task],
            planning=True,
-            planning_llm="gpt-4o"
+            planning_llm="gpt-5-nano"
        )
        
-        assert agents.planning_llm == "gpt-4o"
+        assert agents.planning_llm == "gpt-5-nano"
        
    def test_agents_auto_approve_plan(self):
        """Test auto-approve plan option."""
@@ -867,7 +867,7 @@ class TestPraisonAIAgentsPlanningIntegration:
        from praisonaiagents import Agent, Task, PraisonAIAgents
        from praisonaiagents.planning import Plan, PlanStep
        
-        agent = Agent(name="Test", role="Tester", llm="gpt-4o-mini")
+        agent = Agent(name="Test", role="Tester", llm="gpt-5-nano")
        task = Task(description="Test task", agent=agent)
        
        agents = PraisonAIAgents(
@@ -880,7 +880,7 @@ class TestPraisonAIAgentsPlanningIntegration:
        # Test that planning is enabled and configured correctly
        assert agents.planning is True
        assert agents.auto_approve_plan is True
-        assert agents.planning_llm == "gpt-4o-mini"
+        assert agents.planning_llm == "gpt-5-nano"
        assert agents._current_plan is None  # No plan created yet
        assert agents._todo_list is None
                
@@ -1189,16 +1189,16 @@ class TestWorkflowConfiguration:
        """Test that workflow planning setting is stored."""
        def step1(ctx): return StepResult(output="Done")
        
-        workflow = Workflow(steps=[step1], planning=True, planning_llm="gpt-4o")
+        workflow = Workflow(steps=[step1], planning=True, planning_llm="gpt-5-nano")
        assert workflow.planning == True
-        assert workflow.planning_llm == "gpt-4o"
+        assert workflow.planning_llm == "gpt-5-nano"
    
    def test_workflow_default_llm(self):
        """Test default LLM setting."""
        def step1(ctx): return StepResult(output="Done")
        
-        workflow = Workflow(steps=[step1], default_llm="gpt-4o-mini")
-        assert workflow.default_llm == "gpt-4o-mini"
+        workflow = Workflow(steps=[step1], default_llm="gpt-5-nano")
+        assert workflow.default_llm == "gpt-5-nano"
    
    def test_workflow_default_agent_config(self):
        """Test default agent config."""
@@ -1393,10 +1393,10 @@ class TestPlanningAndReasoning:
        workflow = Workflow(
            steps=[lambda ctx: StepResult(output="test")],
            planning=True,
-            planning_llm="gpt-4o"
+            planning_llm="gpt-5-nano"
        )
        assert workflow.planning == True
-        assert workflow.planning_llm == "gpt-4o"
+        assert workflow.planning_llm == "gpt-5-nano"
    
    def test_workflow_reasoning_parameter_exists(self):
        """Test that Workflow accepts reasoning parameter."""
@@ -91,14 +91,11 @@ class TestAccuracyEvaluator:
    
    def test_judge_output(self):
        """Test LLM judging of output."""
-        with patch('openai.OpenAI') as mock_openai_class:
-            mock_client = MagicMock()
-            mock_openai_class.return_value = mock_client
-            
+        with patch('litellm.completion') as mock_completion:
            mock_response = MagicMock()
            mock_response.choices = [MagicMock()]
            mock_response.choices[0].message.content = "SCORE: 8\nREASONING: Good match"
-            mock_client.chat.completions.create.return_value = mock_response
+            mock_completion.return_value = mock_response
            
            def test_func(x):
                return "4"
@@ -115,14 +112,11 @@ class TestAccuracyEvaluator:
    
    def test_run_returns_accuracy_result(self):
        """Test that run returns AccuracyResult."""
-        with patch('openai.OpenAI') as mock_openai_class:
-            mock_client = MagicMock()
-            mock_openai_class.return_value = mock_client
-            
+        with patch('litellm.completion') as mock_completion:
            mock_response = MagicMock()
            mock_response.choices = [MagicMock()]
            mock_response.choices[0].message.content = "SCORE: 9\nREASONING: Excellent"
-            mock_client.chat.completions.create.return_value = mock_response
+            mock_completion.return_value = mock_response
            
            def test_func(x):
                return "4"
@@ -140,14 +134,11 @@ class TestAccuracyEvaluator:
    
    def test_evaluate_output(self):
        """Test evaluate_output method for pre-generated outputs."""
-        with patch('openai.OpenAI') as mock_openai_class:
-            mock_client = MagicMock()
-            mock_openai_class.return_value = mock_client
-            
+        with patch('litellm.completion') as mock_completion:
            mock_response = MagicMock()
            mock_response.choices = [MagicMock()]
            mock_response.choices[0].message.content = "SCORE: 10\nREASONING: Perfect"
-            mock_client.chat.completions.create.return_value = mock_response
+            mock_completion.return_value = mock_response
            
            def test_func(x):
                return "unused"
@@ -325,14 +316,11 @@ class TestCriteriaEvaluator:
    
    def test_judge_output_numeric(self):
        """Test numeric scoring."""
-        with patch('openai.OpenAI') as mock_openai_class:
-            mock_client = MagicMock()
-            mock_openai_class.return_value = mock_client
-            
+        with patch('litellm.completion') as mock_completion:
            mock_response = MagicMock()
            mock_response.choices = [MagicMock()]
            mock_response.choices[0].message.content = "SCORE: 8\nREASONING: Good"
-            mock_client.chat.completions.create.return_value = mock_response
+            mock_completion.return_value = mock_response
            
            def test_func(x):
                return "test"
@@ -350,14 +338,11 @@ class TestCriteriaEvaluator:
    
    def test_judge_output_binary(self):
        """Test binary scoring."""
-        with patch('openai.OpenAI') as mock_openai_class:
-            mock_client = MagicMock()
-            mock_openai_class.return_value = mock_client
-            
+        with patch('litellm.completion') as mock_completion:
            mock_response = MagicMock()
            mock_response.choices = [MagicMock()]
            mock_response.choices[0].message.content = "RESULT: PASS\nREASONING: Meets criteria"
-            mock_client.chat.completions.create.return_value = mock_response
+            mock_completion.return_value = mock_response
            
            def test_func(x):
                return "test"
@@ -374,14 +359,11 @@ class TestCriteriaEvaluator:
    
    def test_on_fail_callback(self):
        """Test on_fail callback is called."""
-        with patch('openai.OpenAI') as mock_openai_class:
-            mock_client = MagicMock()
-            mock_openai_class.return_value = mock_client
-            
+        with patch('litellm.completion') as mock_completion:
            mock_response = MagicMock()
            mock_response.choices = [MagicMock()]
            mock_response.choices[0].message.content = "SCORE: 3\nREASONING: Poor"
-            mock_client.chat.completions.create.return_value = mock_response
+            mock_completion.return_value = mock_response
            
            on_fail_called = []
            def on_fail(score):
@@ -403,14 +385,11 @@ class TestCriteriaEvaluator:
    
    def test_evaluate_output_criteria(self):
        """Test evaluate_output method."""
-        with patch('openai.OpenAI') as mock_openai_class:
-            mock_client = MagicMock()
-            mock_openai_class.return_value = mock_client
-            
+        with patch('litellm.completion') as mock_completion:
            mock_response = MagicMock()
            mock_response.choices = [MagicMock()]
            mock_response.choices[0].message.content = "SCORE: 9\nREASONING: Great"
-            mock_client.chat.completions.create.return_value = mock_response
+            mock_completion.return_value = mock_response
            
            def test_func(x):
                return "unused"
@@ -2422,7 +2422,7 @@ wheels = [

 [[package]]
 name = "praisonaiagents"
-version = "0.3.0"
+version = "0.3.4"
 source = { editable = "." }
 dependencies = [
    { name = "aiohttp" },
@@ -0,0 +1,229 @@
+{
+  "version": "1.2.1",
+  "lastUpdated": "2024-12-26",
+  "implementationStatus": "Phase 4 Complete - Full Feature Parity",
+  "summary": {
+    "pythonCoreFeatures": 85,
+    "pythonWrapperFeatures": 45,
+    "typescriptFeatures": 75,
+    "gapCount": 10,
+    "priorityP0": 0,
+    "priorityP1": 3,
+    "priorityP2": 5,
+    "priorityP3": 2
+  },
+  "pythonCoreSDK": {
+    "path": "/Users/praison/praisonai-package/src/praisonai-agents/praisonaiagents",
+    "exports": [
+      "Agent", "ImageAgent", "ContextAgent", "DeepResearchAgent", "QueryRewriterAgent", "PromptExpanderAgent",
+      "PraisonAIAgents", "Agents", "Task", "Tools", "BaseTool", "ToolResult", "tool", "FunctionTool",
+      "ToolRegistry", "get_registry", "register_tool", "get_tool", "AutoAgents", "Session", "Memory",
+      "Knowledge", "Chunking", "db", "obs", "MCP", "Workflow", "Pipeline", "WorkflowStep", "WorkflowContext",
+      "StepResult", "Route", "Parallel", "Loop", "Repeat", "GuardrailResult", "LLMGuardrail",
+      "Handoff", "handoff", "FlowDisplay", "FastContext", "SkillManager", "SkillProperties",
+      "get_telemetry", "enable_telemetry", "MinimalTelemetry", "TelemetryCollector", "AGUI", "A2A"
+    ],
+    "modules": {
+      "agent": ["Agent", "ImageAgent", "ContextAgent", "DeepResearchAgent", "QueryRewriterAgent", "PromptExpanderAgent", "RouterAgent", "Handoff"],
+      "agents": ["PraisonAIAgents", "AutoAgents"],
+      "task": ["Task"],
+      "tools": ["Tools", "BaseTool", "ToolResult", "tool", "FunctionTool", "ToolRegistry", "40+ built-in tools"],
+      "llm": ["LLM", "OpenAIClient", "ModelRouter", "RateLimiter", "ModelCapabilities"],
+      "memory": ["Memory", "AutoMemory", "FileMemory", "RulesManager", "DocsManager", "Hooks"],
+      "knowledge": ["Knowledge", "Chunking", "VectorStore", "QueryEngine", "Readers", "Rerankers"],
+      "db": ["DbAdapter", "AsyncDbAdapter", "DbMessage", "DbToolCall", "DbRun", "DbSpan", "DbTrace"],
+      "obs": ["ObservabilityAdapter", "Span", "Trace", "16 provider integrations"],
+      "mcp": ["MCP", "MCPServer", "MCPSession", "MCPTransport", "MCPWebSocket", "MCPSSE", "MCPHTTPStream"],
+      "workflows": ["Workflow", "Pipeline", "WorkflowStep", "WorkflowContext", "StepResult", "Route", "Parallel", "Loop", "Repeat", "YAMLWorkflowParser"],
+      "guardrails": ["GuardrailResult", "LLMGuardrail"],
+      "eval": ["AccuracyEval", "PerformanceEval", "ReliabilityEval", "CriteriaEval"],
+      "skills": ["SkillManager", "SkillProperties", "SkillLoader", "SkillDiscovery", "SkillValidator"],
+      "telemetry": ["MinimalTelemetry", "TelemetryCollector", "PerformanceMonitor", "TokenCollector"],
+      "context": ["FastContext", "FastContextResult"],
+      "session": ["Session"],
+      "ui": ["AGUI", "A2A"]
+    }
+  },
+  "pythonWrapper": {
+    "path": "/Users/praison/praisonai-package/src/praisonai/praisonai",
+    "exports": ["PraisonAI"],
+    "cliFeatures": [
+      "slash_commands", "autonomy_mode", "cost_tracker", "repo_map", "interactive_tui",
+      "git_integration", "sandbox_executor", "agent_scheduler", "agents", "at_mentions",
+      "auto_memory", "background", "checkpoints", "compare", "eval", "external_agents",
+      "fast_context", "flow_display", "guardrail", "handoff", "hooks", "image", "jobs",
+      "knowledge", "mcp", "message_queue", "metrics", "n8n", "observability", "persistence",
+      "profile", "profiling", "router", "session", "skills", "telemetry", "todo", "tools", "workflow"
+    ]
+  },
+  "typescriptSDK": {
+    "path": "/Users/praison/praisonai-package/src/praisonai-ts/src",
+    "exports": [
+      "Agent", "ImageAgent", "ContextAgent", "DeepResearchAgent", "QueryRewriterAgent", "PromptExpanderAgent",
+      "RouterAgent", "Handoff", "AutoAgents", "Task", "BaseTool", "ToolResult", "tool", "FunctionTool",
+      "ToolRegistry", "createTool", "validateTool", "Session", "SessionManager", "Memory", "KnowledgeBase",
+      "Chunking", "MemoryAdapter", "createDbFactory", "Workflow", "parallel", "route", "Guardrail",
+      "GuardrailManager", "LLMGuardrail", "accuracyEval", "performanceEval", "reliabilityEval", "EvalSuite",
+      "MemoryObservabilityAdapter", "ConsoleObservabilityAdapter", "SkillManager", "TelemetryCollector",
+      "Plan", "PlanStep", "TodoList", "TodoItem", "PlanStorage",
+      "OpenAIProvider", "AnthropicProvider", "GoogleProvider", "createProvider"
+    ],
+    "modules": {
+      "agent": ["Agent", "ImageAgent", "ContextAgent", "DeepResearchAgent", "QueryRewriterAgent", "PromptExpanderAgent", "RouterAgent", "Handoff"],
+      "auto": ["AutoAgents", "createAutoAgents"],
+      "task": ["Task"],
+      "tools": ["BaseTool", "ToolResult", "tool", "FunctionTool", "ToolRegistry", "createTool", "validateTool", "arxivTools", "mcpSse"],
+      "llm": ["OpenAIProvider", "AnthropicProvider", "GoogleProvider", "createProvider", "BaseProvider"],
+      "memory": ["Memory", "createMemory", "MemoryEntry"],
+      "knowledge": ["KnowledgeBase", "Chunking", "GraphRAG", "GraphStore", "BaseReranker", "CohereReranker", "CrossEncoderReranker", "LLMReranker"],
+      "db": ["MemoryAdapter", "createDbFactory", "SQLiteAdapter", "UpstashRedisAdapter", "MemoryRedisAdapter", "NeonPostgresAdapter", "MemoryPostgresAdapter", "PostgresSessionStorage"],
+      "session": ["Session", "SessionManager"],
+      "workflows": ["Workflow", "parallel", "route", "loop", "repeat", "WorkflowStep", "WorkflowContext", "parseYAMLWorkflow", "createWorkflowFromYAML"],
+      "guardrails": ["Guardrail", "GuardrailManager", "LLMGuardrail", "builtInGuardrails"],
+      "eval": ["accuracyEval", "performanceEval", "reliabilityEval", "EvalSuite"],
+      "observability": ["MemoryObservabilityAdapter", "ConsoleObservabilityProvider", "LangfuseObservabilityProvider", "BaseObservabilityProvider"],
+      "skills": ["SkillManager", "parseSkillFile", "createSkillManager"],
+      "telemetry": ["TelemetryCollector", "getTelemetry", "enableTelemetry", "disableTelemetry"],
+      "planning": ["Plan", "PlanStep", "TodoList", "TodoItem", "PlanStorage"],
+      "cache": ["MemoryCache", "FileCache", "BaseCache", "createMemoryCache", "createFileCache"],
+      "events": ["EventEmitterPubSub", "AgentEventBus", "PubSub", "createEventBus", "createPubSub", "AgentEvents"],
+      "vector": ["BaseVectorStore", "MemoryVectorStore", "PineconeVectorStore", "WeaviateVectorStore", "QdrantVectorStore", "ChromaVectorStore"],
+      "voice": ["BaseVoiceProvider", "OpenAIVoiceProvider", "ElevenLabsVoiceProvider"],
+      "cli": ["chat", "listProviders", "version", "help"]
+    }
+  },
+  "gapMatrix": {
+    "P0_CoreParity": [
+      {"feature": "Multi-provider LLM support (OpenAI, Anthropic, Google)", "python": true, "typescript": true, "priority": "P0", "effort": "high", "status": "DONE"},
+      {"feature": "Streaming responses", "python": true, "typescript": true, "priority": "P0", "effort": "medium", "status": "DONE"},
+      {"feature": "Structured outputs (JSON schema)", "python": true, "typescript": true, "priority": "P0", "effort": "medium", "status": "DONE"},
+      {"feature": "Tool/function calling with validation", "python": true, "typescript": true, "priority": "P0", "effort": "medium", "status": "DONE"},
+      {"feature": "Session management (session_id, run_id)", "python": true, "typescript": true, "priority": "P0", "effort": "medium", "status": "DONE"},
+      {"feature": "Multi-agent orchestration patterns", "python": true, "typescript": true, "priority": "P0", "effort": "high", "status": "DONE"},
+      {"feature": "Agent handoff", "python": true, "typescript": true, "priority": "P0", "effort": "medium", "status": "DONE"},
+      {"feature": "Tool decorator (@tool)", "python": true, "typescript": true, "priority": "P0", "effort": "low", "status": "DONE"},
+      {"feature": "Tool registry", "python": true, "typescript": true, "priority": "P0", "effort": "medium", "status": "DONE"},
+      {"feature": "Async agent execution", "python": true, "typescript": true, "priority": "P0", "effort": "low", "status": "DONE"},
+      {"feature": "Error handling and retries", "python": true, "typescript": true, "priority": "P0", "effort": "medium", "status": "DONE"},
+      {"feature": "Verbose/debug logging", "python": true, "typescript": true, "priority": "P0", "effort": "low", "status": "DONE"}
+    ],
+    "P1_Persistence": [
+      {"feature": "Database adapter protocol", "python": true, "typescript": true, "priority": "P1", "effort": "high", "status": "DONE"},
+      {"feature": "SQLite adapter", "python": true, "typescript": false, "priority": "P1", "effort": "medium", "status": "TODO"},
+      {"feature": "PostgreSQL adapter", "python": true, "typescript": false, "priority": "P1", "effort": "medium", "status": "TODO"},
+      {"feature": "Redis adapter", "python": true, "typescript": false, "priority": "P1", "effort": "medium", "status": "TODO"},
+      {"feature": "Message persistence", "python": true, "typescript": true, "priority": "P1", "effort": "medium", "status": "DONE"},
+      {"feature": "Run/trace persistence", "python": true, "typescript": true, "priority": "P1", "effort": "medium", "status": "DONE"},
+      {"feature": "Memory persistence (vector)", "python": true, "typescript": true, "priority": "P1", "effort": "high", "status": "DONE"},
+      {"feature": "Knowledge base (RAG)", "python": true, "typescript": true, "priority": "P1", "effort": "high", "status": "DONE"},
+      {"feature": "Chunking strategies", "python": true, "typescript": true, "priority": "P1", "effort": "medium", "status": "DONE"},
+      {"feature": "Vector store integration", "python": true, "typescript": true, "priority": "P1", "effort": "high", "status": "DONE"},
+      {"feature": "Semantic search/recall", "python": true, "typescript": true, "priority": "P1", "effort": "high", "status": "DONE"},
+      {"feature": "Working memory", "python": true, "typescript": true, "priority": "P1", "effort": "medium", "status": "DONE"},
+      {"feature": "File-based memory", "python": true, "typescript": false, "priority": "P1", "effort": "medium", "status": "TODO"},
+      {"feature": "Auto-memory", "python": true, "typescript": false, "priority": "P1", "effort": "medium", "status": "TODO"},
+      {"feature": "Session export/import", "python": true, "typescript": true, "priority": "P1", "effort": "medium", "status": "DONE"}
+    ],
+    "P2_CLI": [
+      {"feature": "CLI entrypoint", "python": true, "typescript": true, "priority": "P2", "effort": "medium", "status": "DONE"},
+      {"feature": "Slash commands", "python": true, "typescript": false, "priority": "P2", "effort": "medium", "status": "TODO"},
+      {"feature": "Interactive TUI", "python": true, "typescript": false, "priority": "P2", "effort": "high", "status": "TODO"},
+      {"feature": "Cost tracking", "python": true, "typescript": false, "priority": "P2", "effort": "medium", "status": "TODO"},
+      {"feature": "Repo map", "python": true, "typescript": false, "priority": "P2", "effort": "high", "status": "TODO"},
+      {"feature": "Git integration", "python": true, "typescript": false, "priority": "P2", "effort": "medium", "status": "TODO"},
+      {"feature": "Sandbox executor", "python": true, "typescript": false, "priority": "P2", "effort": "medium", "status": "TODO"},
+      {"feature": "Autonomy modes", "python": true, "typescript": false, "priority": "P2", "effort": "medium", "status": "TODO"},
+      {"feature": "Agent scheduler", "python": true, "typescript": false, "priority": "P2", "effort": "high", "status": "TODO"},
+      {"feature": "Background jobs", "python": true, "typescript": false, "priority": "P2", "effort": "medium", "status": "TODO"},
+      {"feature": "Checkpoints", "python": true, "typescript": false, "priority": "P2", "effort": "medium", "status": "TODO"},
+      {"feature": "Flow display", "python": true, "typescript": false, "priority": "P2", "effort": "medium", "status": "TODO"},
+      {"feature": "Profiling", "python": true, "typescript": true, "priority": "P2", "effort": "medium", "status": "DONE"},
+      {"feature": "Metrics", "python": true, "typescript": true, "priority": "P2", "effort": "medium", "status": "DONE"},
+      {"feature": "External agent integrations", "python": true, "typescript": false, "priority": "P2", "effort": "high", "status": "TODO"},
+      {"feature": "N8N integration", "python": true, "typescript": false, "priority": "P2", "effort": "high", "status": "TODO"},
+      {"feature": "MCP CLI support", "python": true, "typescript": true, "priority": "P2", "effort": "medium", "status": "DONE"},
+      {"feature": "Skills CLI", "python": true, "typescript": true, "priority": "P2", "effort": "medium", "status": "DONE"},
+      {"feature": "Eval CLI", "python": true, "typescript": true, "priority": "P2", "effort": "medium", "status": "DONE"},
+      {"feature": "Workflow CLI", "python": true, "typescript": true, "priority": "P2", "effort": "medium", "status": "DONE"}
+    ],
+    "P3_Advanced": [
+      {"feature": "Workflows/Pipelines", "python": true, "typescript": true, "priority": "P3", "effort": "high", "status": "DONE"},
+      {"feature": "Route pattern", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"},
+      {"feature": "Parallel pattern", "python": true, "typescript": true, "priority": "P3", "effort": "low", "status": "DONE"},
+      {"feature": "Loop pattern", "python": true, "typescript": false, "priority": "P3", "effort": "medium", "status": "TODO"},
+      {"feature": "Repeat pattern", "python": true, "typescript": false, "priority": "P3", "effort": "medium", "status": "TODO"},
+      {"feature": "YAML workflow parser", "python": true, "typescript": false, "priority": "P3", "effort": "medium", "status": "TODO"},
+      {"feature": "Guardrails", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"},
+      {"feature": "LLM Guardrail", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"},
+      {"feature": "Evaluation framework", "python": true, "typescript": true, "priority": "P3", "effort": "high", "status": "DONE"},
+      {"feature": "Accuracy eval", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"},
+      {"feature": "Performance eval", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"},
+      {"feature": "Reliability eval", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"},
+      {"feature": "Criteria eval", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"},
+      {"feature": "Observability protocol", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"},
+      {"feature": "Telemetry", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"},
+      {"feature": "Performance monitoring", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"},
+      {"feature": "Token tracking", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"},
+      {"feature": "Skills system", "python": true, "typescript": true, "priority": "P3", "effort": "high", "status": "DONE"},
+      {"feature": "Fast context", "python": true, "typescript": false, "priority": "P3", "effort": "medium", "status": "TODO"},
+      {"feature": "Image agent", "python": true, "typescript": true, "priority": "P3", "effort": "medium", "status": "DONE"}
+    ]
+  },
+  "mastraCapabilities": {
+    "implemented": [
+      "Agent core", "Tool calling", "Streaming (partial)", "Multi-agent (partial)"
+    ],
+    "missing": [
+      "Memory with semantic recall", "Working memory", "Thread management", "Resource scoping",
+      "Storage abstraction", "Vector store integration", "Embeddings", "Evaluation scorers",
+      "Workflow engine", "Step execution", "Branching logic", "Event-driven workflows",
+      "MCP integration", "Voice/TTS", "Observability hooks", "Request context",
+      "Tool validation", "Tool suspend/resume", "Provider options per tool"
+    ]
+  },
+  "implementationPlan": {
+    "phase1_P0": {
+      "description": "Core agent parity - streaming, structured outputs, sessions, tool system",
+      "tasks": [
+        "Implement AI SDK provider abstraction (OpenAI, Anthropic, Google)",
+        "Add streaming response support with proper typing",
+        "Implement structured output with JSON schema/Zod",
+        "Add session management (session_id, run_id, trace_id)",
+        "Implement tool decorator and registry",
+        "Add agent handoff support",
+        "Improve error handling and retries"
+      ]
+    },
+    "phase2_P1": {
+      "description": "Persistence layer - db adapters, memory, knowledge",
+      "tasks": [
+        "Implement DbAdapter protocol",
+        "Add SQLite adapter",
+        "Add message/run/trace persistence",
+        "Implement Memory with persistence",
+        "Add Knowledge base (RAG) support",
+        "Implement vector store integration"
+      ]
+    },
+    "phase3_P2": {
+      "description": "CLI parity",
+      "tasks": [
+        "Create CLI entrypoint",
+        "Implement slash commands",
+        "Add cost tracking",
+        "Implement interactive mode"
+      ]
+    },
+    "phase4_P3": {
+      "description": "Advanced features",
+      "tasks": [
+        "Implement Workflow engine",
+        "Add workflow patterns (Route, Parallel, Loop, Repeat)",
+        "Implement Guardrails",
+        "Add Evaluation framework",
+        "Implement Observability hooks",
+        "Add Skills system"
+      ]
+    }
+  }
+}
@@ -4,7 +4,7 @@ const config: Config.InitialOptions = {
  preset: 'ts-jest',
  testEnvironment: 'node',
  roots: ['<rootDir>/tests'],
-  testMatch: ['**/*.main.test.ts', '**/*.spec.ts'],
+  testMatch: ['**/*.test.ts', '**/*.spec.ts'],
  moduleFileExtensions: ['ts', 'js', 'json'],
  coverageDirectory: 'coverage',
  collectCoverageFrom: [
@@ -1,9 +1,12 @@
 {
  "name": "praisonai",
-  "version": "1.0.19",
+  "version": "1.2.2",
  "description": "PraisonAI TypeScript AI Agents Framework - Node.js, npm, and Javascript AI Agents Framework",
  "main": "dist/index.js",
  "types": "dist/index.d.ts",
+  "bin": {
+    "praisonai-ts": "./dist/cli/index.js"
+  },
  "scripts": {
    "build": "tsc",
    "test": "jest",
@@ -62,7 +65,6 @@
    "fast-xml-parser": "^4.5.1",
    "node-fetch": "^2.6.9",
    "openai": "^4.81.0",
-    "praisonai": "^1.0.19",
    "@modelcontextprotocol/sdk": "^1.12.1"
  },
  "optionalDependencies": {
@@ -0,0 +1,266 @@
+# PraisonAI CLI Specification v1.0.0
+# This is the canonical contract for CLI behavior across all runtimes (Python, TypeScript, etc.)
+# Any CLI implementation MUST conform to this spec for parity.
+
+version: "1.0.0"
+spec_name: "praisonai-cli"
+
+# Binary names per ecosystem (NO COLLISION)
+binaries:
+  python: praisonai
+  typescript: praisonai-ts
+
+# Commands (all implementations MUST support)
+commands:
+  chat:
+    description: "Chat with an AI agent"
+    args:
+      - name: prompt
+        type: string
+        required: true
+        position: 0
+    flags:
+      - name: model
+        short: m
+        type: string
+        description: "Model to use (e.g., openai/gpt-4o-mini)"
+      - name: stream
+        short: s
+        type: boolean
+        default: false
+        description: "Enable streaming output"
+      - name: session
+        type: string
+        description: "Session ID for conversation continuity"
+
+  run:
+    description: "Run an agent with a task"
+    args:
+      - name: task
+        type: string
+        required: true
+        position: 0
+    flags:
+      - name: agent
+        short: a
+        type: string
+        description: "Agent configuration file or name"
+      - name: tools
+        short: t
+        type: string
+        description: "Comma-separated list of tools"
+
+  workflow:
+    description: "Execute a multi-agent workflow"
+    args:
+      - name: file
+        type: string
+        required: true
+        position: 0
+        description: "Workflow YAML file path"
+    flags:
+      - name: parallel
+        type: boolean
+        default: false
+        description: "Run workflow steps in parallel where possible"
+
+  eval:
+    description: "Evaluate agent performance"
+    subcommands:
+      accuracy:
+        description: "Run accuracy evaluation"
+        flags:
+          - name: input
+            type: string
+            required: true
+          - name: expected
+            type: string
+            required: true
+          - name: iterations
+            type: integer
+            default: 1
+      performance:
+        description: "Run performance benchmark"
+        flags:
+          - name: iterations
+            type: integer
+            default: 10
+          - name: warmup
+            type: integer
+            default: 2
+      reliability:
+        description: "Run reliability check"
+        flags:
+          - name: expected-tools
+            type: string
+            description: "Comma-separated expected tool calls"
+
+  providers:
+    description: "List available LLM providers"
+    flags: []
+
+  tools:
+    description: "List or manage tools"
+    subcommands:
+      list:
+        description: "List available tools"
+      info:
+        description: "Show tool information"
+        args:
+          - name: name
+            type: string
+            required: true
+            position: 0
+
+  version:
+    description: "Show CLI version"
+    flags: []
+
+  help:
+    description: "Show help information"
+    args:
+      - name: command
+        type: string
+        required: false
+        position: 0
+
+# Global flags (apply to all commands)
+global_flags:
+  - name: verbose
+    short: v
+    type: boolean
+    default: false
+    description: "Enable verbose output"
+  - name: config
+    short: c
+    type: string
+    description: "Path to config file"
+  - name: profile
+    short: p
+    type: string
+    description: "Profile name to use"
+  - name: output
+    short: o
+    type: string
+    enum: [json, text, pretty]
+    default: pretty
+    description: "Output format"
+  - name: json
+    type: boolean
+    default: false
+    description: "Shorthand for --output json"
+
+# Exit codes (MUST be identical across implementations)
+exit_codes:
+  success: 0
+  runtime_error: 1
+  invalid_arguments: 2
+  config_error: 3
+  network_error: 4
+  auth_error: 5
+
+# Output schema for JSON mode (MUST be byte-compatible)
+output_schema:
+  success:
+    type: object
+    properties:
+      success:
+        type: boolean
+        value: true
+      data:
+        type: object
+      meta:
+        type: object
+        properties:
+          duration_ms:
+            type: number
+          model:
+            type: string
+          tokens:
+            type: object
+            properties:
+              input:
+                type: integer
+              output:
+                type: integer
+  error:
+    type: object
+    properties:
+      success:
+        type: boolean
+        value: false
+      error:
+        type: object
+        properties:
+          code:
+            type: string
+          message:
+            type: string
+          details:
+            type: object
+
+# Environment variables (MUST be respected)
+environment:
+  PRAISONAI_MODEL:
+    description: "Default model to use"
+    example: "openai/gpt-4o-mini"
+  PRAISONAI_PROFILE:
+    description: "Default profile name"
+  PRAISONAI_VERBOSE:
+    description: "Enable verbose mode (true/false)"
+  PRAISONAI_CONFIG:
+    description: "Path to config file"
+  OPENAI_API_KEY:
+    description: "OpenAI API key"
+  ANTHROPIC_API_KEY:
+    description: "Anthropic API key"
+  GOOGLE_API_KEY:
+    description: "Google API key"
+
+# Config file schema reference
+config:
+  files:
+    - .praisonai.yaml
+    - .praisonai.json
+  resolution_order:
+    - cli_flags
+    - environment_variables
+    - project_config
+    - user_config
+    - defaults
+  schema:
+    model:
+      type: string
+      default: "openai/gpt-4o-mini"
+    verbose:
+      type: boolean
+      default: false
+    stream:
+      type: boolean
+      default: false
+    profiles:
+      type: object
+      additionalProperties:
+        type: object
+        properties:
+          model:
+            type: string
+          verbose:
+            type: boolean
+          stream:
+            type: boolean
+
+# Tool interface contract
+tool_schema:
+  name:
+    type: string
+    required: true
+  description:
+    type: string
+    required: true
+  inputs:
+    type: object
+    description: "JSON Schema for tool inputs"
+  outputs:
+    type: object
+    description: "JSON Schema for tool outputs"
@@ -0,0 +1,159 @@
+/**
+ * Context Agent - Agent with enhanced context management
+ */
+
+import { createProvider, type LLMProvider, type Message } from '../llm/providers';
+import { Session } from '../session';
+import { KnowledgeBase, type SearchResult } from '../knowledge/rag';
+
+export interface ContextAgentConfig {
+  name?: string;
+  instructions: string;
+  llm?: string;
+  knowledgeBase?: KnowledgeBase;
+  contextWindow?: number;
+  maxContextTokens?: number;
+  verbose?: boolean;
+}
+
+export interface ContextMessage {
+  role: 'system' | 'user' | 'assistant';
+  content: string;
+  timestamp: number;
+  metadata?: Record<string, any>;
+}
+
+/**
+ * Context Agent - Agent with RAG and context management
+ */
+export class ContextAgent {
+  readonly name: string;
+  readonly instructions: string;
+  
+  private provider: LLMProvider;
+  private knowledgeBase?: KnowledgeBase;
+  private contextWindow: number;
+  private maxContextTokens: number;
+  private messages: ContextMessage[] = [];
+  private verbose: boolean;
+
+  constructor(config: ContextAgentConfig) {
+    this.name = config.name || `ContextAgent_${Math.random().toString(36).substr(2, 9)}`;
+    this.instructions = config.instructions;
+    this.provider = createProvider(config.llm || 'gpt-4o-mini');
+    this.knowledgeBase = config.knowledgeBase;
+    this.contextWindow = config.contextWindow ?? 10;
+    this.maxContextTokens = config.maxContextTokens ?? 4000;
+    this.verbose = config.verbose ?? false;
+  }
+
+  /**
+   * Chat with context awareness
+   */
+  async chat(prompt: string): Promise<{ text: string; context?: SearchResult[] }> {
+    // Add user message
+    this.messages.push({
+      role: 'user',
+      content: prompt,
+      timestamp: Date.now()
+    });
+
+    // Build context from knowledge base
+    let ragContext: SearchResult[] = [];
+    let contextString = '';
+    
+    if (this.knowledgeBase) {
+      ragContext = await this.knowledgeBase.search(prompt);
+      if (ragContext.length > 0) {
+        contextString = this.knowledgeBase.buildContext(ragContext);
+        if (this.verbose) {
+          console.log(`[ContextAgent] Found ${ragContext.length} relevant documents`);
+        }
+      }
+    }
+
+    // Build messages array with context
+    const systemPrompt = contextString
+      ? `${this.instructions}\n\nRelevant context:\n${contextString}`
+      : this.instructions;
+
+    const messages: Message[] = [
+      { role: 'system', content: systemPrompt },
+      ...this.getRecentMessages()
+    ];
+
+    // Generate response
+    const result = await this.provider.generateText({ messages });
+
+    // Add assistant message
+    this.messages.push({
+      role: 'assistant',
+      content: result.text,
+      timestamp: Date.now()
+    });
+
+    return {
+      text: result.text,
+      context: ragContext.length > 0 ? ragContext : undefined
+    };
+  }
+
+  /**
+   * Get recent messages within context window
+   */
+  private getRecentMessages(): Message[] {
+    const recent = this.messages.slice(-this.contextWindow);
+    return recent.map(m => ({
+      role: m.role,
+      content: m.content
+    }));
+  }
+
+  /**
+   * Add document to knowledge base
+   */
+  async addDocument(id: string, content: string, metadata?: Record<string, any>): Promise<void> {
+    if (!this.knowledgeBase) {
+      throw new Error('No knowledge base configured');
+    }
+    await this.knowledgeBase.add({ id, content, metadata });
+  }
+
+  /**
+   * Search knowledge base
+   */
+  async searchKnowledge(query: string, limit?: number): Promise<SearchResult[]> {
+    if (!this.knowledgeBase) {
+      return [];
+    }
+    return this.knowledgeBase.search(query, limit);
+  }
+
+  /**
+   * Clear conversation history
+   */
+  clearHistory(): void {
+    this.messages = [];
+  }
+
+  /**
+   * Get conversation history
+   */
+  getHistory(): ContextMessage[] {
+    return [...this.messages];
+  }
+
+  /**
+   * Set knowledge base
+   */
+  setKnowledgeBase(kb: KnowledgeBase): void {
+    this.knowledgeBase = kb;
+  }
+}
+
+/**
+ * Create a context agent
+ */
+export function createContextAgent(config: ContextAgentConfig): ContextAgent {
+  return new ContextAgent(config);
+}
@@ -0,0 +1,337 @@
+/**
+ * Enhanced Agent - Agent with session management, provider abstraction, and tool support
+ */
+
+import { createProvider, type LLMProvider, type Message, type ToolCall, type GenerateTextResult } from '../llm/providers';
+import { Session, Run, Trace, getSessionManager } from '../session';
+import { FunctionTool, ToolRegistry, tool } from '../tools/decorator';
+import { Logger } from '../utils/logger';
+
+export interface EnhancedAgentConfig {
+  name?: string;
+  instructions: string;
+  llm?: string;
+  tools?: Array<FunctionTool | Function | { name: string; description?: string; parameters?: any; execute: Function }>;
+  session?: Session;
+  sessionId?: string;
+  verbose?: boolean;
+  stream?: boolean;
+  maxToolCalls?: number;
+  temperature?: number;
+  maxTokens?: number;
+  outputSchema?: any;
+}
+
+export interface ChatOptions {
+  stream?: boolean;
+  temperature?: number;
+  maxTokens?: number;
+  outputSchema?: any;
+  onToken?: (token: string) => void;
+}
+
+export interface ChatResult {
+  text: string;
+  structured?: any;
+  toolCalls?: ToolCall[];
+  usage: {
+    promptTokens: number;
+    completionTokens: number;
+    totalTokens: number;
+  };
+  runId: string;
+  sessionId: string;
+}
+
+/**
+ * Enhanced Agent with full feature support
+ */
+export class EnhancedAgent {
+  readonly name: string;
+  readonly instructions: string;
+  readonly sessionId: string;
+  
+  private provider: LLMProvider;
+  private session: Session;
+  private toolRegistry: ToolRegistry;
+  private verbose: boolean;
+  private stream: boolean;
+  private maxToolCalls: number;
+  private temperature: number;
+  private maxTokens?: number;
+  private outputSchema?: any;
+
+  constructor(config: EnhancedAgentConfig) {
+    this.name = config.name || `Agent_${Math.random().toString(36).substr(2, 9)}`;
+    this.instructions = config.instructions;
+    this.verbose = config.verbose ?? true;
+    this.stream = config.stream ?? true;
+    this.maxToolCalls = config.maxToolCalls ?? 10;
+    this.temperature = config.temperature ?? 0.7;
+    this.maxTokens = config.maxTokens;
+    this.outputSchema = config.outputSchema;
+
+    // Initialize provider
+    this.provider = createProvider(config.llm || 'gpt-4o-mini');
+
+    // Initialize session
+    if (config.session) {
+      this.session = config.session;
+    } else if (config.sessionId) {
+      this.session = getSessionManager().getOrCreate(config.sessionId);
+    } else {
+      this.session = getSessionManager().create();
+    }
+    this.sessionId = this.session.id;
+
+    // Initialize tool registry
+    this.toolRegistry = new ToolRegistry();
+    if (config.tools) {
+      this.registerTools(config.tools);
+    }
+
+    Logger.setVerbose(this.verbose);
+  }
+
+  private registerTools(tools: EnhancedAgentConfig['tools']): void {
+    if (!tools) return;
+
+    for (const t of tools) {
+      if (t instanceof FunctionTool) {
+        this.toolRegistry.register(t);
+      } else if (typeof t === 'function') {
+        // Convert function to tool
+        const funcName = t.name || `function_${Math.random().toString(36).substr(2, 9)}`;
+        this.toolRegistry.register(tool({
+          name: funcName,
+          description: `Function ${funcName}`,
+          execute: t as any,
+        }));
+      } else if (typeof t === 'object' && 'execute' in t) {
+        // Tool config object
+        this.toolRegistry.register(tool({
+          name: t.name,
+          description: t.description,
+          parameters: t.parameters,
+          execute: t.execute as any,
+        }));
+      }
+    }
+  }
+
+  /**
+   * Add a tool to the agent
+   */
+  addTool(t: FunctionTool | { name: string; description?: string; parameters?: any; execute: Function }): this {
+    if (t instanceof FunctionTool) {
+      this.toolRegistry.register(t);
+    } else {
+      this.toolRegistry.register(tool({
+        name: t.name,
+        description: t.description,
+        parameters: t.parameters,
+        execute: t.execute as any,
+      }));
+    }
+    return this;
+  }
+
+  /**
+   * Get all registered tools
+   */
+  getTools(): FunctionTool[] {
+    return this.toolRegistry.list();
+  }
+
+  /**
+   * Chat with the agent
+   */
+  async chat(prompt: string, options: ChatOptions = {}): Promise<ChatResult> {
+    const run = this.session.createRun().start();
+    const trace = run.createTrace({ name: 'chat' }).start();
+
+    try {
+      // Add user message to session
+      this.session.addMessage({ role: 'user', content: prompt });
+
+      // Build messages array
+      const messages: Message[] = [
+        { role: 'system', content: this.instructions },
+        ...this.session.getMessagesForLLM() as Message[],
+      ];
+
+      // Get tool definitions
+      const tools = this.toolRegistry.list().length > 0
+        ? this.toolRegistry.getDefinitions()
+        : undefined;
+
+      let result: GenerateTextResult;
+      let fullText = '';
+      let allToolCalls: ToolCall[] = [];
+      let iterations = 0;
+
+      // Tool calling loop
+      while (iterations < this.maxToolCalls) {
+        iterations++;
+
+        if (options.stream && !tools && options.onToken) {
+          // Streaming without tools
+          const stream = await this.provider.streamText({
+            messages,
+            temperature: options.temperature ?? this.temperature,
+            maxTokens: options.maxTokens ?? this.maxTokens,
+            onToken: options.onToken,
+          });
+
+          let usage = { promptTokens: 0, completionTokens: 0, totalTokens: 0 };
+          for await (const chunk of stream) {
+            if (chunk.text) fullText += chunk.text;
+            if (chunk.usage) usage = { ...usage, ...chunk.usage };
+          }
+
+          result = {
+            text: fullText,
+            usage,
+            finishReason: 'stop',
+          };
+          break;
+        } else if (options.outputSchema) {
+          // Structured output
+          const objResult = await this.provider.generateObject({
+            messages,
+            schema: options.outputSchema ?? this.outputSchema,
+            temperature: options.temperature ?? this.temperature,
+            maxTokens: options.maxTokens ?? this.maxTokens,
+          });
+
+          result = {
+            text: JSON.stringify(objResult.object),
+            usage: objResult.usage,
+            finishReason: 'stop',
+          };
+          fullText = result.text;
+          break;
+        } else {
+          // Regular generation with potential tool calls
+          result = await this.provider.generateText({
+            messages,
+            temperature: options.temperature ?? this.temperature,
+            maxTokens: options.maxTokens ?? this.maxTokens,
+            tools,
+          });
+
+          if (result.toolCalls && result.toolCalls.length > 0) {
+            allToolCalls.push(...result.toolCalls);
+
+            // Add assistant message with tool calls
+            messages.push({
+              role: 'assistant',
+              content: result.text || null,
+              tool_calls: result.toolCalls,
+            });
+
+            // Execute tool calls
+            for (const tc of result.toolCalls) {
+              const toolTrace = trace.createChild({ name: `tool:${tc.function.name}` }).start();
+              
+              try {
+                const toolFn = this.toolRegistry.get(tc.function.name);
+                if (!toolFn) {
+                  throw new Error(`Tool '${tc.function.name}' not found`);
+                }
+
+                const args = JSON.parse(tc.function.arguments);
+                const toolResult = await toolFn.execute(args, {
+                  agentName: this.name,
+                  sessionId: this.sessionId,
+                  runId: run.id,
+                });
+
+                const resultStr = typeof toolResult === 'string' 
+                  ? toolResult 
+                  : JSON.stringify(toolResult);
+
+                messages.push({
+                  role: 'tool',
+                  content: resultStr,
+                  tool_call_id: tc.id,
+                });
+
+                toolTrace.complete({ result: resultStr });
+              } catch (error: any) {
+                messages.push({
+                  role: 'tool',
+                  content: `Error: ${error.message}`,
+                  tool_call_id: tc.id,
+                });
+                toolTrace.fail(error);
+              }
+            }
+
+            // Continue loop to get final response
+            continue;
+          }
+
+          // No tool calls, we have our final response
+          fullText = result.text;
+          break;
+        }
+      }
+
+      // Add assistant message to session
+      this.session.addMessage({
+        role: 'assistant',
+        content: fullText,
+        tool_calls: allToolCalls.length > 0 ? allToolCalls : undefined,
+      });
+
+      trace.complete({ responseLength: fullText.length });
+      run.complete();
+
+      // Parse structured output if schema was provided
+      let structured: any;
+      if (options.outputSchema) {
+        try {
+          structured = JSON.parse(fullText);
+        } catch {
+          // Not valid JSON
+        }
+      }
+
+      return {
+        text: fullText,
+        structured,
+        toolCalls: allToolCalls.length > 0 ? allToolCalls : undefined,
+        usage: result!.usage,
+        runId: run.id,
+        sessionId: this.sessionId,
+      };
+    } catch (error: any) {
+      trace.fail(error);
+      run.fail(error);
+      throw error;
+    }
+  }
+
+  /**
+   * Start method for compatibility
+   */
+  async start(prompt: string, options?: ChatOptions): Promise<string> {
+    const result = await this.chat(prompt, options);
+    return result.text;
+  }
+
+  /**
+   * Get the session
+   */
+  getSession(): Session {
+    return this.session;
+  }
+
+  /**
+   * Clear conversation history
+   */
+  clearHistory(): void {
+    this.session.clearMessages();
+  }
+}
@@ -0,0 +1,162 @@
+/**
+ * Agent Handoff - Transfer conversations between agents
+ */
+
+import type { EnhancedAgent } from './enhanced';
+
+export interface HandoffConfig {
+  agent: EnhancedAgent;
+  name?: string;
+  description?: string;
+  condition?: (context: HandoffContext) => boolean;
+  transformContext?: (messages: any[]) => any[];
+}
+
+export interface HandoffContext {
+  messages: any[];
+  lastMessage: string;
+  topic?: string;
+  metadata?: Record<string, any>;
+}
+
+export interface HandoffResult {
+  handedOffTo: string;
+  response: string;
+  context: HandoffContext;
+}
+
+/**
+ * Handoff class - Represents a handoff target
+ */
+export class Handoff {
+  readonly targetAgent: EnhancedAgent;
+  readonly name: string;
+  readonly description: string;
+  readonly condition?: (context: HandoffContext) => boolean;
+  readonly transformContext?: (messages: any[]) => any[];
+
+  constructor(config: HandoffConfig) {
+    this.targetAgent = config.agent;
+    this.name = config.name || `handoff_to_${config.agent.name}`;
+    this.description = config.description || `Transfer conversation to ${config.agent.name}`;
+    this.condition = config.condition;
+    this.transformContext = config.transformContext;
+  }
+
+  /**
+   * Check if handoff should be triggered
+   */
+  shouldTrigger(context: HandoffContext): boolean {
+    if (this.condition) {
+      return this.condition(context);
+    }
+    return true;
+  }
+
+  /**
+   * Execute the handoff
+   */
+  async execute(context: HandoffContext): Promise<HandoffResult> {
+    let messages = context.messages;
+    
+    if (this.transformContext) {
+      messages = this.transformContext(messages);
+    }
+
+    // Transfer context to target agent
+    const response = await this.targetAgent.chat(context.lastMessage);
+
+    return {
+      handedOffTo: this.targetAgent.name,
+      response: response.text,
+      context: {
+        ...context,
+        messages,
+      },
+    };
+  }
+
+  /**
+   * Get tool definition for LLM
+   */
+  getToolDefinition(): { name: string; description: string; parameters: any } {
+    return {
+      name: this.name,
+      description: this.description,
+      parameters: {
+        type: 'object',
+        properties: {
+          reason: {
+            type: 'string',
+            description: 'Reason for the handoff',
+          },
+        },
+        required: [],
+      },
+    };
+  }
+}
+
+/**
+ * Create a handoff configuration
+ */
+export function handoff(config: HandoffConfig): Handoff {
+  return new Handoff(config);
+}
+
+/**
+ * Handoff filters - Common condition functions
+ */
+export const handoffFilters = {
+  /**
+   * Trigger handoff based on topic keywords
+   */
+  topic: (keywords: string | string[]) => {
+    const keywordList = Array.isArray(keywords) ? keywords : [keywords];
+    return (context: HandoffContext): boolean => {
+      const lastMessage = context.lastMessage.toLowerCase();
+      return keywordList.some(kw => lastMessage.includes(kw.toLowerCase()));
+    };
+  },
+
+  /**
+   * Trigger handoff based on metadata
+   */
+  metadata: (key: string, value: any) => {
+    return (context: HandoffContext): boolean => {
+      return context.metadata?.[key] === value;
+    };
+  },
+
+  /**
+   * Always trigger
+   */
+  always: () => {
+    return (): boolean => true;
+  },
+
+  /**
+   * Never trigger (manual only)
+   */
+  never: () => {
+    return (): boolean => false;
+  },
+
+  /**
+   * Combine multiple conditions with AND
+   */
+  and: (...conditions: Array<(context: HandoffContext) => boolean>) => {
+    return (context: HandoffContext): boolean => {
+      return conditions.every(cond => cond(context));
+    };
+  },
+
+  /**
+   * Combine multiple conditions with OR
+   */
+  or: (...conditions: Array<(context: HandoffContext) => boolean>) => {
+    return (context: HandoffContext): boolean => {
+      return conditions.some(cond => cond(context));
+    };
+  },
+};
@@ -0,0 +1,130 @@
+/**
+ * ImageAgent - Agent for image generation and analysis
+ */
+
+import { createProvider, type LLMProvider, type Message } from '../llm/providers';
+
+export interface ImageGenerationConfig {
+  prompt: string;
+  size?: '256x256' | '512x512' | '1024x1024' | '1792x1024' | '1024x1792';
+  quality?: 'standard' | 'hd';
+  style?: 'vivid' | 'natural';
+  n?: number;
+}
+
+export interface ImageAnalysisConfig {
+  imageUrl: string;
+  prompt?: string;
+  detail?: 'low' | 'high' | 'auto';
+}
+
+export interface ImageAgentConfig {
+  name?: string;
+  llm?: string;
+  imageModel?: string;
+  verbose?: boolean;
+}
+
+/**
+ * ImageAgent - Agent for image generation and analysis
+ */
+export class ImageAgent {
+  readonly name: string;
+  private provider: LLMProvider;
+  private imageModel: string;
+  private verbose: boolean;
+
+  constructor(config: ImageAgentConfig = {}) {
+    this.name = config.name || `ImageAgent_${Math.random().toString(36).substr(2, 9)}`;
+    this.provider = createProvider(config.llm || 'openai/gpt-4o-mini');
+    this.imageModel = config.imageModel || 'dall-e-3';
+    this.verbose = config.verbose ?? false;
+  }
+
+  /**
+   * Analyze an image
+   */
+  async analyze(config: ImageAnalysisConfig): Promise<string> {
+    const prompt = config.prompt || 'Describe this image in detail.';
+    
+    const messages: Message[] = [
+      {
+        role: 'user',
+        content: [
+          { type: 'text', text: prompt },
+          { 
+            type: 'image_url', 
+            image_url: { 
+              url: config.imageUrl,
+              detail: config.detail || 'auto'
+            } 
+          }
+        ] as any
+      }
+    ];
+
+    const result = await this.provider.generateText({ messages });
+    
+    if (this.verbose) {
+      console.log(`[ImageAgent] Analysis: ${result.text.substring(0, 100)}...`);
+    }
+
+    return result.text;
+  }
+
+  /**
+   * Generate an image (requires OpenAI DALL-E)
+   */
+  async generate(config: ImageGenerationConfig): Promise<string[]> {
+    // This would require direct OpenAI API call for image generation
+    // For now, return a placeholder
+    if (this.verbose) {
+      console.log(`[ImageAgent] Generating image: ${config.prompt}`);
+    }
+
+    throw new Error('Image generation requires direct OpenAI API integration. Use the OpenAI SDK directly for DALL-E.');
+  }
+
+  /**
+   * Chat with image context
+   */
+  async chat(prompt: string, imageUrl?: string): Promise<string> {
+    if (imageUrl) {
+      return this.analyze({ imageUrl, prompt });
+    }
+
+    const result = await this.provider.generateText({
+      messages: [{ role: 'user', content: prompt }]
+    });
+
+    return result.text;
+  }
+
+  /**
+   * Compare two images
+   */
+  async compare(imageUrl1: string, imageUrl2: string, prompt?: string): Promise<string> {
+    const comparePrompt = prompt || 'Compare these two images and describe the differences and similarities.';
+    
+    const messages: Message[] = [
+      {
+        role: 'user',
+        content: [
+          { type: 'text', text: comparePrompt },
+          { type: 'image_url', image_url: { url: imageUrl1 } },
+          { type: 'image_url', image_url: { url: imageUrl2 } }
+        ] as any
+      }
+    ];
+
+    const result = await this.provider.generateText({ messages });
+    return result.text;
+  }
+}
+
+/**
+ * Create an ImageAgent
+ */
+export function createImageAgent(config?: ImageAgentConfig): ImageAgent {
+  return new ImageAgent(config);
+}
@@ -0,0 +1,112 @@
+/**
+ * PromptExpanderAgent - Expand and enhance prompts
+ */
+
+import { createProvider, type LLMProvider } from '../llm/providers';
+
+export type ExpandStrategy = 'detail' | 'context' | 'examples' | 'constraints' | 'auto';
+
+export interface ExpandResult {
+  original: string;
+  expanded: string;
+  strategy: ExpandStrategy;
+  additions: string[];
+}
+
+export interface PromptExpanderConfig {
+  name?: string;
+  llm?: string;
+  defaultStrategy?: ExpandStrategy;
+  verbose?: boolean;
+}
+
+/**
+ * PromptExpanderAgent - Expand prompts with more detail and context
+ */
+export class PromptExpanderAgent {
+  readonly name: string;
+  private provider: LLMProvider;
+  private defaultStrategy: ExpandStrategy;
+  private verbose: boolean;
+
+  constructor(config: PromptExpanderConfig = {}) {
+    this.name = config.name || `PromptExpander_${Math.random().toString(36).substr(2, 9)}`;
+    this.provider = createProvider(config.llm || 'openai/gpt-4o-mini');
+    this.defaultStrategy = config.defaultStrategy || 'auto';
+    this.verbose = config.verbose ?? false;
+  }
+
+  /**
+   * Expand a prompt
+   */
+  async expand(prompt: string, strategy?: ExpandStrategy): Promise<ExpandResult> {
+    const useStrategy = strategy || this.defaultStrategy;
+    const actualStrategy = useStrategy === 'auto' ? this.detectStrategy(prompt) : useStrategy;
+
+    const systemPrompt = this.getSystemPrompt(actualStrategy);
+    
+    const result = await this.provider.generateText({
+      messages: [
+        { role: 'system', content: systemPrompt },
+        { role: 'user', content: `Expand this prompt: ${prompt}` }
+      ]
+    });
+
+    const expanded = result.text;
+    const additions = this.extractAdditions(prompt, expanded);
+
+    if (this.verbose) {
+      console.log(`[PromptExpander] Strategy: ${actualStrategy}, Added ${additions.length} elements`);
+    }
+
+    return {
+      original: prompt,
+      expanded,
+      strategy: actualStrategy,
+      additions
+    };
+  }
+
+  /**
+   * Detect the best expansion strategy
+   */
+  detectStrategy(prompt: string): ExpandStrategy {
+    const words = prompt.split(/\s+/).length;
+    const lower = prompt.toLowerCase();
+    
+    if (words < 5) return 'detail';
+    if (lower.includes('why') || lower.includes('explain') || lower.includes('context')) return 'context';
+    if (lower.includes('example') || lower.includes('show') || lower.includes('demonstrate')) return 'examples';
+    if (lower.includes('must') || lower.includes('should') || lower.includes('require')) return 'constraints';
+    
+    return 'detail';
+  }
+
+  private getSystemPrompt(strategy: ExpandStrategy): string {
+    const prompts: Record<ExpandStrategy, string> = {
+      detail: 'Expand this prompt by adding specific details, requirements, and clarifications. Make it more precise and actionable.',
+      context: 'Expand this prompt by adding relevant background context, explaining the purpose, and providing necessary information.',
+      examples: 'Expand this prompt by adding concrete examples, sample inputs/outputs, and demonstrations of expected behavior.',
+      constraints: 'Expand this prompt by adding explicit constraints, requirements, limitations, and success criteria.',
+      auto: 'Expand this prompt to make it clearer, more specific, and more actionable. Add relevant details and context.'
+    };
+    return prompts[strategy];
+  }
+
+  private extractAdditions(original: string, expanded: string): string[] {
+    const originalWords = new Set(original.toLowerCase().split(/\s+/));
+    const expandedWords = expanded.toLowerCase().split(/\s+/);
+    
+    const newWords = expandedWords.filter(w => !originalWords.has(w) && w.length > 3);
+    const uniqueNew = [...new Set(newWords)];
+    
+    return uniqueNew.slice(0, 10);
+  }
+}
+
+/**
+ * Create a PromptExpanderAgent
+ */
+export function createPromptExpanderAgent(config?: PromptExpanderConfig): PromptExpanderAgent {
+  return new PromptExpanderAgent(config);
+}
@@ -0,0 +1,106 @@
+/**
+ * QueryRewriterAgent - Rewrite and optimize queries
+ */
+
+import { createProvider, type LLMProvider } from '../llm/providers';
+
+export type RewriteStrategy = 'expand' | 'simplify' | 'decompose' | 'rephrase' | 'auto';
+
+export interface RewriteResult {
+  original: string;
+  rewritten: string[];
+  strategy: RewriteStrategy;
+  confidence: number;
+}
+
+export interface QueryRewriterConfig {
+  name?: string;
+  llm?: string;
+  defaultStrategy?: RewriteStrategy;
+  verbose?: boolean;
+}
+
+/**
+ * QueryRewriterAgent - Optimize and rewrite queries
+ */
+export class QueryRewriterAgent {
+  readonly name: string;
+  private provider: LLMProvider;
+  private defaultStrategy: RewriteStrategy;
+  private verbose: boolean;
+
+  constructor(config: QueryRewriterConfig = {}) {
+    this.name = config.name || `QueryRewriter_${Math.random().toString(36).substr(2, 9)}`;
+    this.provider = createProvider(config.llm || 'openai/gpt-4o-mini');
+    this.defaultStrategy = config.defaultStrategy || 'auto';
+    this.verbose = config.verbose ?? false;
+  }
+
+  /**
+   * Rewrite a query
+   */
+  async rewrite(query: string, strategy?: RewriteStrategy): Promise<RewriteResult> {
+    const useStrategy = strategy || this.defaultStrategy;
+    const actualStrategy = useStrategy === 'auto' ? this.detectStrategy(query) : useStrategy;
+
+    const prompt = this.buildPrompt(query, actualStrategy);
+    
+    const result = await this.provider.generateText({
+      messages: [
+        { role: 'system', content: this.getSystemPrompt(actualStrategy) },
+        { role: 'user', content: prompt }
+      ]
+    });
+
+    const rewritten = this.parseRewrittenQueries(result.text);
+
+    if (this.verbose) {
+      console.log(`[QueryRewriter] Strategy: ${actualStrategy}, Rewrites: ${rewritten.length}`);
+    }
+
+    return {
+      original: query,
+      rewritten,
+      strategy: actualStrategy,
+      confidence: rewritten.length > 0 ? 0.85 : 0.5
+    };
+  }
+
+  private detectStrategy(query: string): RewriteStrategy {
+    const words = query.split(/\s+/).length;
+    
+    if (words < 3) return 'expand';
+    if (words > 20) return 'simplify';
+    if (query.includes(' and ') || query.includes(' or ')) return 'decompose';
+    return 'rephrase';
+  }
+
+  private getSystemPrompt(strategy: RewriteStrategy): string {
+    const prompts: Record<RewriteStrategy, string> = {
+      expand: 'Expand this query with more context and detail. Generate 3 expanded versions.',
+      simplify: 'Simplify this query to its core intent. Generate 3 simpler versions.',
+      decompose: 'Break this query into smaller, focused sub-queries. Generate 3-5 sub-queries.',
+      rephrase: 'Rephrase this query in different ways while keeping the same meaning. Generate 3 versions.',
+      auto: 'Optimize this query for better search results. Generate 3 improved versions.'
+    };
+    return prompts[strategy] + ' Output one query per line.';
+  }
+
+  private buildPrompt(query: string, strategy: RewriteStrategy): string {
+    return `Query: ${query}\n\nRewrite using ${strategy} strategy:`;
+  }
+
+  private parseRewrittenQueries(response: string): string[] {
+    return response
+      .split('\n')
+      .map(line => line.replace(/^\d+[\.\)]\s*/, '').trim())
+      .filter(line => line.length > 0 && line.length < 500);
+  }
+}
+
+/**
+ * Create a QueryRewriterAgent
+ */
+export function createQueryRewriterAgent(config?: QueryRewriterConfig): QueryRewriterAgent {
+  return new QueryRewriterAgent(config);
+}
@@ -0,0 +1,162 @@
+/**
+ * DeepResearchAgent - Agent for comprehensive research tasks
+ */
+
+import { createProvider, type LLMProvider, type Message } from '../llm/providers';
+
+export interface Citation {
+  title: string;
+  url: string;
+  snippet?: string;
+}
+
+export interface ReasoningStep {
+  step: number;
+  thought: string;
+  action?: string;
+  result?: string;
+}
+
+export interface ResearchResponse {
+  answer: string;
+  citations: Citation[];
+  reasoning: ReasoningStep[];
+  confidence: number;
+}
+
+export interface DeepResearchConfig {
+  name?: string;
+  llm?: string;
+  maxIterations?: number;
+  searchTool?: (query: string) => Promise<Citation[]>;
+  verbose?: boolean;
+}
+
+/**
+ * DeepResearchAgent - Comprehensive research with citations
+ */
+export class DeepResearchAgent {
+  readonly name: string;
+  private provider: LLMProvider;
+  private maxIterations: number;
+  private searchTool?: (query: string) => Promise<Citation[]>;
+  private verbose: boolean;
+
+  constructor(config: DeepResearchConfig = {}) {
+    this.name = config.name || `ResearchAgent_${Math.random().toString(36).substr(2, 9)}`;
+    this.provider = createProvider(config.llm || 'openai/gpt-4o-mini');
+    this.maxIterations = config.maxIterations ?? 5;
+    this.searchTool = config.searchTool;
+    this.verbose = config.verbose ?? false;
+  }
+
+  /**
+   * Conduct deep research on a topic
+   */
+  async research(query: string): Promise<ResearchResponse> {
+    const reasoning: ReasoningStep[] = [];
+    const citations: Citation[] = [];
+    let iteration = 0;
+
+    // Step 1: Analyze the query
+    reasoning.push({
+      step: ++iteration,
+      thought: `Analyzing research query: "${query}"`,
+      action: 'query_analysis'
+    });
+
+    // Step 2: Generate search queries
+    const searchQueries = await this.generateSearchQueries(query);
+    reasoning.push({
+      step: ++iteration,
+      thought: `Generated ${searchQueries.length} search queries`,
+      action: 'generate_queries',
+      result: searchQueries.join(', ')
+    });
+
+    // Step 3: Execute searches if tool available
+    if (this.searchTool) {
+      for (const sq of searchQueries.slice(0, 3)) {
+        try {
+          const results = await this.searchTool(sq);
+          citations.push(...results);
+          reasoning.push({
+            step: ++iteration,
+            thought: `Searched for: "${sq}"`,
+            action: 'web_search',
+            result: `Found ${results.length} results`
+          });
+        } catch (error) {
+          if (this.verbose) {
+            console.log(`[Research] Search failed for: ${sq}`);
+          }
+        }
+      }
+    }
+
+    // Step 4: Synthesize answer
+    const answer = await this.synthesizeAnswer(query, citations);
+    reasoning.push({
+      step: ++iteration,
+      thought: 'Synthesizing final answer from gathered information',
+      action: 'synthesize',
+      result: 'Answer generated'
+    });
+
+    // Calculate confidence based on citations
+    const confidence = Math.min(0.5 + (citations.length * 0.1), 0.95);
+
+    return {
+      answer,
+      citations,
+      reasoning,
+      confidence
+    };
+  }
+
+  private async generateSearchQueries(query: string): Promise<string[]> {
+    const result = await this.provider.generateText({
+      messages: [
+        {
+          role: 'system',
+          content: 'Generate 3 search queries to research this topic. Output only the queries, one per line.'
+        },
+        { role: 'user', content: query }
+      ]
+    });
+
+    return result.text.split('\n').filter(q => q.trim().length > 0).slice(0, 3);
+  }
+
+  private async synthesizeAnswer(query: string, citations: Citation[]): Promise<string> {
+    const context = citations.length > 0
+      ? `\n\nRelevant sources:\n${citations.map(c => `- ${c.title}: ${c.snippet || ''}`).join('\n')}`
+      : '';
+
+    const result = await this.provider.generateText({
+      messages: [
+        {
+          role: 'system',
+          content: 'You are a research assistant. Provide a comprehensive, well-structured answer based on the available information. Cite sources when available.'
+        },
+        { role: 'user', content: `${query}${context}` }
+      ]
+    });
+
+    return result.text;
+  }
+
+  /**
+   * Set search tool
+   */
+  setSearchTool(tool: (query: string) => Promise<Citation[]>): void {
+    this.searchTool = tool;
+  }
+}
+
+/**
+ * Create a DeepResearchAgent
+ */
+export function createDeepResearchAgent(config?: DeepResearchConfig): DeepResearchAgent {
+  return new DeepResearchAgent(config);
+}
@@ -0,0 +1,146 @@
+/**
+ * Router Agent - Route requests to specialized agents
+ */
+
+import type { EnhancedAgent } from './enhanced';
+
+export interface RouteConfig {
+  agent: EnhancedAgent;
+  condition: (input: string, context?: RouteContext) => boolean | Promise<boolean>;
+  priority?: number;
+}
+
+export interface RouteContext {
+  history?: string[];
+  metadata?: Record<string, any>;
+}
+
+export interface RouterConfig {
+  name?: string;
+  routes: RouteConfig[];
+  defaultAgent?: EnhancedAgent;
+  verbose?: boolean;
+}
+
+/**
+ * Router Agent - Routes requests to the most appropriate agent
+ */
+export class RouterAgent {
+  readonly name: string;
+  private routes: RouteConfig[];
+  private defaultAgent?: EnhancedAgent;
+  private verbose: boolean;
+
+  constructor(config: RouterConfig) {
+    this.name = config.name || 'Router';
+    this.routes = config.routes.sort((a, b) => (b.priority || 0) - (a.priority || 0));
+    this.defaultAgent = config.defaultAgent;
+    this.verbose = config.verbose ?? false;
+  }
+
+  /**
+   * Route a request to the appropriate agent
+   */
+  async route(input: string, context?: RouteContext): Promise<{ agent: EnhancedAgent; response: string } | null> {
+    for (const route of this.routes) {
+      const matches = await route.condition(input, context);
+      if (matches) {
+        if (this.verbose) {
+          console.log(`[Router] Routing to: ${route.agent.name}`);
+        }
+        const response = await route.agent.chat(input);
+        return { agent: route.agent, response: response.text };
+      }
+    }
+
+    if (this.defaultAgent) {
+      if (this.verbose) {
+        console.log(`[Router] Using default agent: ${this.defaultAgent.name}`);
+      }
+      const response = await this.defaultAgent.chat(input);
+      return { agent: this.defaultAgent, response: response.text };
+    }
+
+    return null;
+  }
+
+  /**
+   * Add a route
+   */
+  addRoute(config: RouteConfig): this {
+    this.routes.push(config);
+    this.routes.sort((a, b) => (b.priority || 0) - (a.priority || 0));
+    return this;
+  }
+
+  /**
+   * Get all routes
+   */
+  getRoutes(): RouteConfig[] {
+    return [...this.routes];
+  }
+}
+
+/**
+ * Route condition helpers
+ */
+export const routeConditions = {
+  /**
+   * Match by keywords
+   */
+  keywords: (keywords: string | string[]) => {
+    const keywordList = Array.isArray(keywords) ? keywords : [keywords];
+    return (input: string): boolean => {
+      const lower = input.toLowerCase();
+      return keywordList.some(kw => lower.includes(kw.toLowerCase()));
+    };
+  },
+
+  /**
+   * Match by regex
+   */
+  pattern: (regex: RegExp) => {
+    return (input: string): boolean => regex.test(input);
+  },
+
+  /**
+   * Match by metadata
+   */
+  metadata: (key: string, value: any) => {
+    return (_input: string, context?: RouteContext): boolean => {
+      return context?.metadata?.[key] === value;
+    };
+  },
+
+  /**
+   * Always match (for default routes)
+   */
+  always: () => {
+    return (): boolean => true;
+  },
+
+  /**
+   * Combine conditions with AND
+   */
+  and: (...conditions: Array<(input: string, context?: RouteContext) => boolean>) => {
+    return (input: string, context?: RouteContext): boolean => {
+      return conditions.every(c => c(input, context));
+    };
+  },
+
+  /**
+   * Combine conditions with OR
+   */
+  or: (...conditions: Array<(input: string, context?: RouteContext) => boolean>) => {
+    return (input: string, context?: RouteContext): boolean => {
+      return conditions.some(c => c(input, context));
+    };
+  }
+};
+
+/**
+ * Create a router agent
+ */
+export function createRouter(config: RouterConfig): RouterAgent {
+  return new RouterAgent(config);
+}
@@ -0,0 +1,185 @@
+/**
+ * AutoAgents - Automatic agent generation from task descriptions
+ */
+
+import { createProvider, type LLMProvider, type Message } from '../llm/providers';
+
+export interface AgentConfig {
+  name: string;
+  role: string;
+  goal: string;
+  backstory?: string;
+  instructions?: string;
+  tools?: string[];
+}
+
+export interface TaskConfig {
+  description: string;
+  expectedOutput?: string;
+  agent?: string;
+}
+
+export interface TeamStructure {
+  agents: AgentConfig[];
+  tasks: TaskConfig[];
+  pattern: 'sequential' | 'parallel' | 'hierarchical';
+}
+
+export interface AutoAgentsConfig {
+  llm?: string;
+  pattern?: 'sequential' | 'parallel' | 'routing' | 'orchestrator-workers' | 'evaluator-optimizer';
+  singleAgent?: boolean;
+  verbose?: boolean;
+}
+
+/**
+ * AutoAgents - Generate agent configurations from task descriptions
+ */
+export class AutoAgents {
+  private provider: LLMProvider;
+  private pattern: string;
+  private singleAgent: boolean;
+  private verbose: boolean;
+
+  constructor(config: AutoAgentsConfig = {}) {
+    this.provider = createProvider(config.llm || 'openai/gpt-4o-mini');
+    this.pattern = config.pattern || 'sequential';
+    this.singleAgent = config.singleAgent ?? false;
+    this.verbose = config.verbose ?? false;
+  }
+
+  /**
+   * Generate agent configuration from task description
+   */
+  async generate(taskDescription: string): Promise<TeamStructure> {
+    const prompt = this.buildPrompt(taskDescription);
+    
+    const result = await this.provider.generateText({
+      messages: [
+        { role: 'system', content: this.getSystemPrompt() },
+        { role: 'user', content: prompt }
+      ]
+    });
+
+    return this.parseResponse(result.text);
+  }
+
+  /**
+   * Recommend a pattern for the task
+   */
+  recommendPattern(taskDescription: string): string {
+    const lower = taskDescription.toLowerCase();
+    
+    if (lower.includes('parallel') || lower.includes('concurrent') || lower.includes('simultaneously')) {
+      return 'parallel';
+    }
+    if (lower.includes('route') || lower.includes('classify') || lower.includes('categorize')) {
+      return 'routing';
+    }
+    if (lower.includes('orchestrat') || lower.includes('coordinat') || lower.includes('manage')) {
+      return 'orchestrator-workers';
+    }
+    if (lower.includes('evaluat') || lower.includes('optimi') || lower.includes('improv')) {
+      return 'evaluator-optimizer';
+    }
+    
+    return 'sequential';
+  }
+
+  /**
+   * Analyze task complexity
+   */
+  analyzeComplexity(taskDescription: string): 'simple' | 'moderate' | 'complex' {
+    const words = taskDescription.split(/\s+/).length;
+    const hasMultipleSteps = /step|then|after|before|first|second|third|finally/i.test(taskDescription);
+    const hasMultipleAgents = /team|multiple|several|different|various/i.test(taskDescription);
+    
+    if (words < 20 && !hasMultipleSteps && !hasMultipleAgents) {
+      return 'simple';
+    }
+    if (words > 100 || (hasMultipleSteps && hasMultipleAgents)) {
+      return 'complex';
+    }
+    return 'moderate';
+  }
+
+  private getSystemPrompt(): string {
+    return `You are an AI agent architect. Your job is to analyze task descriptions and generate optimal agent configurations.
+
+Output a JSON object with the following structure:
+{
+  "agents": [
+    {
+      "name": "AgentName",
+      "role": "Role description",
+      "goal": "Agent's goal",
+      "backstory": "Optional backstory",
+      "instructions": "Specific instructions",
+      "tools": ["tool1", "tool2"]
+    }
+  ],
+  "tasks": [
+    {
+      "description": "Task description",
+      "expectedOutput": "What the task should produce",
+      "agent": "AgentName"
+    }
+  ],
+  "pattern": "sequential|parallel|hierarchical"
+}
+
+Guidelines:
+- For simple tasks, use 1 agent
+- For moderate tasks, use 2-3 agents
+- For complex tasks, use 3-5 agents
+- Match the pattern to the task requirements
+- Be specific about roles and goals`;
+  }
+
+  private buildPrompt(taskDescription: string): string {
+    const complexity = this.analyzeComplexity(taskDescription);
+    const recommendedPattern = this.recommendPattern(taskDescription);
+    
+    return `Task Description: ${taskDescription}
+
+Complexity: ${complexity}
+Recommended Pattern: ${this.pattern || recommendedPattern}
+Single Agent Mode: ${this.singleAgent}
+
+Generate an optimal agent configuration for this task.`;
+  }
+
+  private parseResponse(response: string): TeamStructure {
+    try {
+      // Extract JSON from response
+      const jsonMatch = response.match(/\{[\s\S]*\}/);
+      if (jsonMatch) {
+        return JSON.parse(jsonMatch[0]);
+      }
+    } catch (error) {
+      // Fallback to default structure
+    }
+
+    // Default fallback
+    return {
+      agents: [{
+        name: 'GeneralAgent',
+        role: 'General purpose agent',
+        goal: 'Complete the assigned task',
+        instructions: 'Follow the task description carefully'
+      }],
+      tasks: [{
+        description: 'Complete the task',
+        agent: 'GeneralAgent'
+      }],
+      pattern: 'sequential'
+    };
+  }
+}
+
+/**
+ * Create an AutoAgents instance
+ */
+export function createAutoAgents(config?: AutoAgentsConfig): AutoAgents {
+  return new AutoAgents(config);
+}
@@ -0,0 +1,253 @@
+/**
+ * Cache System - In-memory and persistent caching for agents
+ * Inspired by mastra's cache module
+ */
+
+export interface CacheConfig {
+  name?: string;
+  ttl?: number; // Time to live in milliseconds
+  maxSize?: number; // Maximum number of entries
+}
+
+export interface CacheEntry<T = any> {
+  value: T;
+  createdAt: number;
+  expiresAt?: number;
+}
+
+/**
+ * Abstract base class for cache implementations
+ */
+export abstract class BaseCache {
+  readonly name: string;
+  
+  constructor(config: CacheConfig = {}) {
+    this.name = config.name || 'cache';
+  }
+
+  abstract get<T = any>(key: string): Promise<T | undefined>;
+  abstract set<T = any>(key: string, value: T, ttl?: number): Promise<void>;
+  abstract delete(key: string): Promise<boolean>;
+  abstract clear(): Promise<void>;
+  abstract has(key: string): Promise<boolean>;
+  abstract keys(): Promise<string[]>;
+  abstract size(): Promise<number>;
+  
+  // List operations
+  abstract listPush<T = any>(key: string, value: T): Promise<void>;
+  abstract listLength(key: string): Promise<number>;
+  abstract listRange<T = any>(key: string, start: number, end?: number): Promise<T[]>;
+}
+
+/**
+ * In-memory cache implementation
+ */
+export class MemoryCache extends BaseCache {
+  private cache: Map<string, CacheEntry> = new Map();
+  private lists: Map<string, any[]> = new Map();
+  private ttl?: number;
+  private maxSize?: number;
+
+  constructor(config: CacheConfig = {}) {
+    super(config);
+    this.ttl = config.ttl;
+    this.maxSize = config.maxSize;
+  }
+
+  async get<T = any>(key: string): Promise<T | undefined> {
+    const entry = this.cache.get(key);
+    if (!entry) return undefined;
+    
+    // Check expiration
+    if (entry.expiresAt && Date.now() > entry.expiresAt) {
+      this.cache.delete(key);
+      return undefined;
+    }
+    
+    return entry.value as T;
+  }
+
+  async set<T = any>(key: string, value: T, ttl?: number): Promise<void> {
+    // Enforce max size
+    if (this.maxSize && this.cache.size >= this.maxSize && !this.cache.has(key)) {
+      // Remove oldest entry
+      const firstKey = this.cache.keys().next().value;
+      if (firstKey) this.cache.delete(firstKey);
+    }
+
+    const effectiveTtl = ttl ?? this.ttl;
+    const entry: CacheEntry<T> = {
+      value,
+      createdAt: Date.now(),
+      expiresAt: effectiveTtl ? Date.now() + effectiveTtl : undefined
+    };
+    
+    this.cache.set(key, entry);
+  }
+
+  async delete(key: string): Promise<boolean> {
+    const existed = this.cache.has(key);
+    this.cache.delete(key);
+    this.lists.delete(key);
+    return existed;
+  }
+
+  async clear(): Promise<void> {
+    this.cache.clear();
+    this.lists.clear();
+  }
+
+  async has(key: string): Promise<boolean> {
+    const entry = this.cache.get(key);
+    if (!entry) return false;
+    if (entry.expiresAt && Date.now() > entry.expiresAt) {
+      this.cache.delete(key);
+      return false;
+    }
+    return true;
+  }
+
+  async keys(): Promise<string[]> {
+    return Array.from(this.cache.keys());
+  }
+
+  async size(): Promise<number> {
+    return this.cache.size;
+  }
+
+  async listPush<T = any>(key: string, value: T): Promise<void> {
+    if (!this.lists.has(key)) {
+      this.lists.set(key, []);
+    }
+    this.lists.get(key)!.push(value);
+  }
+
+  async listLength(key: string): Promise<number> {
+    return this.lists.get(key)?.length ?? 0;
+  }
+
+  async listRange<T = any>(key: string, start: number, end?: number): Promise<T[]> {
+    const list = this.lists.get(key) ?? [];
+    return list.slice(start, end) as T[];
+  }
+}
+
+/**
+ * File-based cache implementation
+ */
+export class FileCache extends BaseCache {
+  private cacheDir: string;
+  private fs: any;
+  private path: any;
+
+  constructor(config: CacheConfig & { cacheDir?: string } = {}) {
+    super(config);
+    this.cacheDir = config.cacheDir || '.cache';
+  }
+
+  private async ensureDir(): Promise<void> {
+    if (!this.fs) {
+      this.fs = await import('fs/promises');
+      this.path = await import('path');
+    }
+    try {
+      await this.fs.mkdir(this.cacheDir, { recursive: true });
+    } catch {}
+  }
+
+  private getFilePath(key: string): string {
+    const safeKey = key.replace(/[^a-zA-Z0-9-_]/g, '_');
+    return this.path.join(this.cacheDir, `${safeKey}.json`);
+  }
+
+  async get<T = any>(key: string): Promise<T | undefined> {
+    await this.ensureDir();
+    try {
+      const data = await this.fs.readFile(this.getFilePath(key), 'utf-8');
+      const entry: CacheEntry<T> = JSON.parse(data);
+      if (entry.expiresAt && Date.now() > entry.expiresAt) {
+        await this.delete(key);
+        return undefined;
+      }
+      return entry.value;
+    } catch {
+      return undefined;
+    }
+  }
+
+  async set<T = any>(key: string, value: T, ttl?: number): Promise<void> {
+    await this.ensureDir();
+    const entry: CacheEntry<T> = {
+      value,
+      createdAt: Date.now(),
+      expiresAt: ttl ? Date.now() + ttl : undefined
+    };
+    await this.fs.writeFile(this.getFilePath(key), JSON.stringify(entry));
+  }
+
+  async delete(key: string): Promise<boolean> {
+    await this.ensureDir();
+    try {
+      await this.fs.unlink(this.getFilePath(key));
+      return true;
+    } catch {
+      return false;
+    }
+  }
+
+  async clear(): Promise<void> {
+    await this.ensureDir();
+    try {
+      const files = await this.fs.readdir(this.cacheDir);
+      await Promise.all(
+        files.filter((f: string) => f.endsWith('.json'))
+          .map((f: string) => this.fs.unlink(this.path.join(this.cacheDir, f)))
+      );
+    } catch {}
+  }
+
+  async has(key: string): Promise<boolean> {
+    return (await this.get(key)) !== undefined;
+  }
+
+  async keys(): Promise<string[]> {
+    await this.ensureDir();
+    try {
+      const files = await this.fs.readdir(this.cacheDir);
+      return files
+        .filter((f: string) => f.endsWith('.json'))
+        .map((f: string) => f.replace('.json', ''));
+    } catch {
+      return [];
+    }
+  }
+
+  async size(): Promise<number> {
+    return (await this.keys()).length;
+  }
+
+  async listPush<T = any>(key: string, value: T): Promise<void> {
+    const list = (await this.get<T[]>(`list_${key}`)) || [];
+    list.push(value);
+    await this.set(`list_${key}`, list);
+  }
+
+  async listLength(key: string): Promise<number> {
+    const list = (await this.get<any[]>(`list_${key}`)) || [];
+    return list.length;
+  }
+
+  async listRange<T = any>(key: string, start: number, end?: number): Promise<T[]> {
+    const list = (await this.get<T[]>(`list_${key}`)) || [];
+    return list.slice(start, end);
+  }
+}
+
+// Factory functions
+export function createMemoryCache(config?: CacheConfig): MemoryCache {
+  return new MemoryCache(config);
+}
+
+export function createFileCache(config?: CacheConfig & { cacheDir?: string }): FileCache {
+  return new FileCache(config);
+}
@@ -0,0 +1,114 @@
+/**
+ * Chat command - Chat with an AI agent
+ */
+
+import { createProvider } from '../../llm/providers';
+import { Session } from '../../session';
+import { resolveConfig } from '../config/resolve';
+import { printSuccess, printError, outputJson, formatSuccess, formatError } from '../output/json';
+import * as pretty from '../output/pretty';
+import { EXIT_CODES } from '../spec/cli-spec';
+import { ERROR_CODES, normalizeError } from '../output/errors';
+
+export interface ChatOptions {
+  model?: string;
+  stream?: boolean;
+  session?: string;
+  verbose?: boolean;
+  profile?: string;
+  config?: string;
+  output?: 'json' | 'text' | 'pretty';
+  json?: boolean;
+}
+
+export async function execute(args: string[], options: ChatOptions): Promise<void> {
+  const prompt = args[0];
+  
+  if (!prompt) {
+    if (options.json || options.output === 'json') {
+      printError(ERROR_CODES.MISSING_ARG, 'Please provide a prompt');
+    } else {
+      await pretty.error('Please provide a prompt');
+    }
+    process.exit(EXIT_CODES.INVALID_ARGUMENTS);
+  }
+
+  // Resolve config with precedence
+  const config = resolveConfig({
+    configPath: options.config,
+    profile: options.profile,
+    model: options.model,
+    verbose: options.verbose,
+    stream: options.stream
+  });
+
+  const startTime = Date.now();
+  const outputFormat = options.json ? 'json' : (options.output || 'pretty');
+
+  try {
+    const provider = createProvider(config.model);
+    const session = new Session({ id: options.session });
+    session.addMessage({ role: 'user', content: prompt });
+
+    let responseText = '';
+    let inputTokens = 0;
+    let outputTokens = 0;
+
+    if (config.stream && outputFormat !== 'json') {
+      // Streaming mode (only for non-JSON output)
+      const stream = await provider.streamText({
+        messages: [{ role: 'user', content: prompt }],
+        onToken: (token: string) => {
+          process.stdout.write(token);
+          responseText += token;
+        }
+      });
+
+      for await (const chunk of stream) {
+        // Stream is consumed by onToken
+      }
+      console.log(); // New line after streaming
+    } else {
+      // Non-streaming mode
+      const result = await provider.generateText({
+        messages: [{ role: 'user', content: prompt }]
+      });
+      responseText = result.text;
+      inputTokens = result.usage?.promptTokens || 0;
+      outputTokens = result.usage?.completionTokens || 0;
+
+      if (outputFormat === 'json') {
+        const duration = Date.now() - startTime;
+        outputJson(formatSuccess(
+          { response: responseText },
+          {
+            duration_ms: duration,
+            model: config.model,
+            tokens: {
+              input: inputTokens,
+              output: outputTokens
+            }
+          }
+        ));
+      } else {
+        console.log(responseText);
+      }
+    }
+
+    session.addMessage({ role: 'assistant', content: responseText });
+
+  } catch (error) {
+    const cliError = normalizeError(error);
+    
+    if (outputFormat === 'json') {
+      outputJson(formatError(cliError.code, cliError.message, cliError.details));
+    } else {
+      await pretty.error(cliError.message);
+      if (config.verbose && error instanceof Error && error.stack) {
+        await pretty.dim(error.stack);
+      }
+    }
+    
+    process.exit(cliError.exitCode);
+  }
+}
@@ -0,0 +1,7 @@
+/**
+ * Config utilities index
+ */
+
+export * from './schema';
+export * from './load';
+export * from './resolve';
@@ -0,0 +1,213 @@
+/**
+ * Config file loading
+ */
+
+import * as fs from 'fs';
+import * as path from 'path';
+import { CONFIG_FILES } from '../spec/cli-spec';
+import { FullConfig, validateConfig, DEFAULT_CONFIG } from './schema';
+
+export interface LoadConfigOptions {
+  configPath?: string;
+  cwd?: string;
+}
+
+/**
+ * Find config file in directory hierarchy
+ */
+function findConfigFile(startDir: string): string | null {
+  let currentDir = startDir;
+  
+  while (true) {
+    for (const filename of CONFIG_FILES) {
+      const filePath = path.join(currentDir, filename);
+      if (fs.existsSync(filePath)) {
+        return filePath;
+      }
+    }
+    
+    const parentDir = path.dirname(currentDir);
+    if (parentDir === currentDir) {
+      break; // Reached root
+    }
+    currentDir = parentDir;
+  }
+  
+  return null;
+}
+
+/**
+ * Find user config file
+ */
+function findUserConfigFile(): string | null {
+  const homeDir = process.env.HOME || process.env.USERPROFILE;
+  if (!homeDir) return null;
+  
+  for (const filename of CONFIG_FILES) {
+    const filePath = path.join(homeDir, filename);
+    if (fs.existsSync(filePath)) {
+      return filePath;
+    }
+  }
+  
+  return null;
+}
+
+/**
+ * Parse config file content
+ */
+function parseConfigFile(filePath: string): unknown {
+  const content = fs.readFileSync(filePath, 'utf-8');
+  const ext = path.extname(filePath).toLowerCase();
+  
+  if (ext === '.json') {
+    return JSON.parse(content);
+  }
+  
+  if (ext === '.yaml' || ext === '.yml') {
+    // Simple YAML parsing for basic config
+    // For complex YAML, we'd need a proper parser
+    return parseSimpleYaml(content);
+  }
+  
+  throw new Error(`Unsupported config file format: ${ext}`);
+}
+
+/**
+ * Simple YAML parser for basic key-value configs
+ * Handles: strings, booleans, numbers, simple objects
+ */
+function parseSimpleYaml(content: string): Record<string, unknown> {
+  const result: Record<string, unknown> = {};
+  const lines = content.split('\n');
+  let currentObject: Record<string, unknown> | null = null;
+  let currentKey: string | null = null;
+  let baseIndent = 0;
+  
+  for (const line of lines) {
+    // Skip empty lines and comments
+    if (!line.trim() || line.trim().startsWith('#')) {
+      continue;
+    }
+    
+    const indent = line.search(/\S/);
+    const trimmed = line.trim();
+    
+    // Check for key: value pattern
+    const colonIndex = trimmed.indexOf(':');
+    if (colonIndex === -1) continue;
+    
+    const key = trimmed.slice(0, colonIndex).trim();
+    const value = trimmed.slice(colonIndex + 1).trim();
+    
+    if (indent === 0) {
+      // Top-level key
+      if (value === '' || value === '{}') {
+        // Start of nested object
+        currentObject = {};
+        currentKey = key;
+        baseIndent = indent;
+        result[key] = currentObject;
+      } else {
+        result[key] = parseYamlValue(value);
+        currentObject = null;
+        currentKey = null;
+      }
+    } else if (currentObject !== null && indent > baseIndent) {
+      // Nested key
+      if (value === '' || value === '{}') {
+        // Nested object within nested object
+        const nestedObj: Record<string, unknown> = {};
+        currentObject[key] = nestedObj;
+        // For simplicity, we only support one level of nesting in profiles
+      } else {
+        currentObject[key] = parseYamlValue(value);
+      }
+    }
+  }
+  
+  return result;
+}
+
+function parseYamlValue(value: string): string | number | boolean {
+  // Remove quotes
+  if ((value.startsWith('"') && value.endsWith('"')) ||
+      (value.startsWith("'") && value.endsWith("'"))) {
+    return value.slice(1, -1);
+  }
+  
+  // Boolean
+  if (value === 'true') return true;
+  if (value === 'false') return false;
+  
+  // Number
+  const num = Number(value);
+  if (!isNaN(num) && value !== '') return num;
+  
+  return value;
+}
+
+/**
+ * Load config from file
+ */
+export function loadConfigFile(filePath: string): FullConfig {
+  try {
+    const parsed = parseConfigFile(filePath);
+    if (!validateConfig(parsed)) {
+      throw new Error('Invalid config format');
+    }
+    return parsed;
+  } catch (error) {
+    throw new Error(`Failed to load config from ${filePath}: ${error instanceof Error ? error.message : String(error)}`);
+  }
+}
+
+/**
+ * Load config with resolution order:
+ * 1. Explicit config path
+ * 2. Project config (current dir and parents)
+ * 3. User config (~/.praisonai.yaml)
+ * 4. Defaults
+ */
+export function loadConfig(options: LoadConfigOptions = {}): FullConfig {
+  const configs: Partial<FullConfig>[] = [DEFAULT_CONFIG];
+  
+  // User config
+  const userConfigPath = findUserConfigFile();
+  if (userConfigPath) {
+    try {
+      configs.push(loadConfigFile(userConfigPath));
+    } catch {
+      // Ignore user config errors
+    }
+  }
+  
+  // Project config
+  const cwd = options.cwd || process.cwd();
+  const projectConfigPath = findConfigFile(cwd);
+  if (projectConfigPath) {
+    try {
+      configs.push(loadConfigFile(projectConfigPath));
+    } catch {
+      // Ignore project config errors
+    }
+  }
+  
+  // Explicit config path
+  if (options.configPath) {
+    configs.push(loadConfigFile(options.configPath));
+  }
+  
+  // Merge all configs
+  const result: FullConfig = { ...DEFAULT_CONFIG };
+  for (const config of configs) {
+    if (config.model !== undefined) result.model = config.model;
+    if (config.verbose !== undefined) result.verbose = config.verbose;
+    if (config.stream !== undefined) result.stream = config.stream;
+    if (config.profiles !== undefined) {
+      result.profiles = { ...result.profiles, ...config.profiles };
+    }
+  }
+  
+  return result;
+}
@@ -0,0 +1,80 @@
+/**
+ * Config resolution with profiles and CLI overrides
+ */
+
+import { loadConfig } from './load';
+import { loadEnvConfig } from '../runtime/env';
+import { FullConfig, ProfileConfig, DEFAULT_CONFIG } from './schema';
+
+export interface ResolvedConfig {
+  model: string;
+  verbose: boolean;
+  stream: boolean;
+}
+
+export interface ResolveOptions {
+  configPath?: string;
+  profile?: string;
+  model?: string;
+  verbose?: boolean;
+  stream?: boolean;
+}
+
+/**
+ * Resolve final config with precedence:
+ * 1. CLI flags (highest)
+ * 2. Environment variables
+ * 3. Profile settings
+ * 4. Config file
+ * 5. Defaults (lowest)
+ */
+export function resolveConfig(options: ResolveOptions = {}): ResolvedConfig {
+  // Load base config from files
+  const fileConfig = loadConfig({ configPath: options.configPath });
+  
+  // Load env config
+  const envConfig = loadEnvConfig();
+  
+  // Get profile config if specified
+  let profileConfig: ProfileConfig = {};
+  const profileName = options.profile || envConfig.profile;
+  if (profileName && fileConfig.profiles?.[profileName]) {
+    profileConfig = fileConfig.profiles[profileName];
+  }
+  
+  // Resolve with precedence
+  const resolved: ResolvedConfig = {
+    model: DEFAULT_CONFIG.model!,
+    verbose: DEFAULT_CONFIG.verbose!,
+    stream: DEFAULT_CONFIG.stream!
+  };
+  
+  // Apply file config
+  if (fileConfig.model !== undefined) resolved.model = fileConfig.model;
+  if (fileConfig.verbose !== undefined) resolved.verbose = fileConfig.verbose;
+  if (fileConfig.stream !== undefined) resolved.stream = fileConfig.stream;
+  
+  // Apply profile config
+  if (profileConfig.model !== undefined) resolved.model = profileConfig.model;
+  if (profileConfig.verbose !== undefined) resolved.verbose = profileConfig.verbose;
+  if (profileConfig.stream !== undefined) resolved.stream = profileConfig.stream;
+  
+  // Apply env config
+  if (envConfig.model !== undefined) resolved.model = envConfig.model;
+  if (envConfig.verbose !== undefined) resolved.verbose = envConfig.verbose;
+  
+  // Apply CLI flags (highest precedence)
+  if (options.model !== undefined) resolved.model = options.model;
+  if (options.verbose !== undefined) resolved.verbose = options.verbose;
+  if (options.stream !== undefined) resolved.stream = options.stream;
+  
+  return resolved;
+}
+
+/**
+ * Get available profile names
+ */
+export function getAvailableProfiles(configPath?: string): string[] {
+  const config = loadConfig({ configPath });
+  return Object.keys(config.profiles || {});
+}
@@ -0,0 +1,80 @@
+/**
+ * Config schema definitions
+ */
+
+import { ConfigSchema } from '../spec/cli-spec';
+
+export interface ProfileConfig {
+  model?: string;
+  verbose?: boolean;
+  stream?: boolean;
+}
+
+export interface FullConfig extends ConfigSchema {
+  profiles?: Record<string, ProfileConfig>;
+}
+
+export const DEFAULT_CONFIG: FullConfig = {
+  model: 'openai/gpt-4o-mini',
+  verbose: false,
+  stream: false,
+  profiles: {}
+};
+
+export function validateConfig(config: unknown): config is FullConfig {
+  if (typeof config !== 'object' || config === null) {
+    return false;
+  }
+
+  const c = config as Record<string, unknown>;
+
+  if (c.model !== undefined && typeof c.model !== 'string') {
+    return false;
+  }
+
+  if (c.verbose !== undefined && typeof c.verbose !== 'boolean') {
+    return false;
+  }
+
+  if (c.stream !== undefined && typeof c.stream !== 'boolean') {
+    return false;
+  }
+
+  if (c.profiles !== undefined) {
+    if (typeof c.profiles !== 'object' || c.profiles === null) {
+      return false;
+    }
+    for (const profile of Object.values(c.profiles as Record<string, unknown>)) {
+      if (typeof profile !== 'object' || profile === null) {
+        return false;
+      }
+      const p = profile as Record<string, unknown>;
+      if (p.model !== undefined && typeof p.model !== 'string') {
+        return false;
+      }
+      if (p.verbose !== undefined && typeof p.verbose !== 'boolean') {
+        return false;
+      }
+      if (p.stream !== undefined && typeof p.stream !== 'boolean') {
+        return false;
+      }
+    }
+  }
+
+  return true;
+}
+
+export function mergeConfigs(...configs: Partial<FullConfig>[]): FullConfig {
+  const result: FullConfig = { ...DEFAULT_CONFIG };
+
+  for (const config of configs) {
+    if (config.model !== undefined) result.model = config.model;
+    if (config.verbose !== undefined) result.verbose = config.verbose;
+    if (config.stream !== undefined) result.stream = config.stream;
+    if (config.profiles !== undefined) {
+      result.profiles = { ...result.profiles, ...config.profiles };
+    }
+  }
+
+  return result;
+}
@@ -0,0 +1,163 @@
+#!/usr/bin/env node
+/**
+ * PraisonAI TypeScript CLI
+ */
+
+import { createProvider, isProviderAvailable, getAvailableProviders } from '../llm/providers';
+import { Session } from '../session';
+import { tool, ToolRegistry } from '../tools/decorator';
+
+interface CLIOptions {
+  model?: string;
+  stream?: boolean;
+  verbose?: boolean;
+  sessionId?: string;
+}
+
+async function chat(prompt: string, options: CLIOptions = {}): Promise<string> {
+  const model = options.model || process.env.PRAISONAI_MODEL || 'openai/gpt-4o-mini';
+  const provider = createProvider(model);
+  
+  const session = new Session({ id: options.sessionId });
+  session.addMessage({ role: 'user', content: prompt });
+
+  if (options.stream) {
+    let fullText = '';
+    const stream = await provider.streamText({
+      messages: [{ role: 'user', content: prompt }],
+      onToken: (token) => {
+        process.stdout.write(token);
+        fullText += token;
+      }
+    });
+    
+    for await (const chunk of stream) {
+      // Stream is consumed by onToken
+    }
+    console.log(); // New line after streaming
+    return fullText;
+  } else {
+    const result = await provider.generateText({
+      messages: [{ role: 'user', content: prompt }]
+    });
+    console.log(result.text);
+    return result.text;
+  }
+}
+
+async function listProviders(): Promise<void> {
+  console.log('Available Providers:');
+  const available = getAvailableProviders();
+  
+  const providers = ['openai', 'anthropic', 'google'];
+  for (const p of providers) {
+    const status = available.includes(p) ? '✅' : '❌';
+    console.log(`  ${status} ${p}`);
+  }
+}
+
+async function version(): Promise<void> {
+  const pkg = require('../../package.json');
+  console.log(`praisonai v${pkg.version}`);
+}
+
+async function help(): Promise<void> {
+  console.log(`
+PraisonAI TypeScript CLI
+
+Usage:
+  praisonai-ts chat <prompt>     Chat with an AI agent
+  praisonai-ts providers         List available providers
+  praisonai-ts version           Show version
+  praisonai-ts help              Show this help
+
+Options:
+  --model, -m <model>     Model to use (e.g., openai/gpt-4o-mini)
+  --stream, -s            Enable streaming output
+  --verbose, -v           Verbose output
+  --session <id>          Session ID for conversation continuity
+
+Environment Variables:
+  OPENAI_API_KEY          OpenAI API key
+  ANTHROPIC_API_KEY       Anthropic API key
+  GOOGLE_API_KEY          Google API key
+  PRAISONAI_MODEL         Default model
+
+Examples:
+  praisonai-ts chat "Hello, how are you?"
+  praisonai-ts chat "Write a poem" --stream
+  praisonai-ts chat "Explain AI" -m anthropic/claude-sonnet-4-20250514
+  praisonai-ts providers
+`);
+}
+
+function parseArgs(args: string[]): { command: string; prompt: string; options: CLIOptions } {
+  const options: CLIOptions = {};
+  let command = 'help';
+  let prompt = '';
+  
+  let i = 0;
+  while (i < args.length) {
+    const arg = args[i];
+    
+    if (arg === '--model' || arg === '-m') {
+      options.model = args[++i];
+    } else if (arg === '--stream' || arg === '-s') {
+      options.stream = true;
+    } else if (arg === '--verbose' || arg === '-v') {
+      options.verbose = true;
+    } else if (arg === '--session') {
+      options.sessionId = args[++i];
+    } else if (!arg.startsWith('-')) {
+      if (!command || command === 'help') {
+        command = arg;
+      } else {
+        prompt = arg;
+      }
+    }
+    i++;
+  }
+  
+  return { command, prompt, options };
+}
+
+async function main(): Promise<void> {
+  const args = process.argv.slice(2);
+  const { command, prompt, options } = parseArgs(args);
+  
+  try {
+    switch (command) {
+      case 'chat':
+        if (!prompt) {
+          console.error('Error: Please provide a prompt');
+          process.exit(1);
+        }
+        await chat(prompt, options);
+        break;
+      case 'providers':
+        await listProviders();
+        break;
+      case 'version':
+        await version();
+        break;
+      case 'help':
+      default:
+        await help();
+        break;
+    }
+  } catch (error: any) {
+    console.error('Error:', error.message);
+    if (options.verbose) {
+      console.error(error.stack);
+    }
+    process.exit(1);
+  }
+}
+
+// Export for programmatic use
+export { chat, listProviders, version, help, parseArgs };
+
+// Run CLI if executed directly
+if (require.main === module) {
+  main();
+}
@@ -0,0 +1,56 @@
+/**
+ * Environment variable handling for CLI
+ */
+
+import { ENV_VARS } from '../spec/cli-spec';
+
+export interface EnvConfig {
+  model?: string;
+  profile?: string;
+  verbose?: boolean;
+  configPath?: string;
+}
+
+export function loadEnvConfig(): EnvConfig {
+  const config: EnvConfig = {};
+
+  const model = process.env[ENV_VARS.PRAISONAI_MODEL];
+  if (model) config.model = model;
+
+  const profile = process.env[ENV_VARS.PRAISONAI_PROFILE];
+  if (profile) config.profile = profile;
+
+  const verbose = process.env[ENV_VARS.PRAISONAI_VERBOSE];
+  if (verbose) config.verbose = verbose.toLowerCase() === 'true';
+
+  const configPath = process.env[ENV_VARS.PRAISONAI_CONFIG];
+  if (configPath) config.configPath = configPath;
+
+  return config;
+}
+
+export function hasApiKey(provider: 'openai' | 'anthropic' | 'google'): boolean {
+  switch (provider) {
+    case 'openai':
+      return !!process.env[ENV_VARS.OPENAI_API_KEY];
+    case 'anthropic':
+      return !!process.env[ENV_VARS.ANTHROPIC_API_KEY];
+    case 'google':
+      return !!process.env[ENV_VARS.GOOGLE_API_KEY];
+    default:
+      return false;
+  }
+}
+
+export function getApiKey(provider: 'openai' | 'anthropic' | 'google'): string | undefined {
+  switch (provider) {
+    case 'openai':
+      return process.env[ENV_VARS.OPENAI_API_KEY];
+    case 'anthropic':
+      return process.env[ENV_VARS.ANTHROPIC_API_KEY];
+    case 'google':
+      return process.env[ENV_VARS.GOOGLE_API_KEY];
+    default:
+      return undefined;
+  }
+}
@@ -0,0 +1,48 @@
+/**
+ * Exit code handling for CLI
+ */
+
+import { EXIT_CODES, ExitCode } from '../spec/cli-spec';
+
+export function exit(code: ExitCode): never {
+  process.exit(code);
+}
+
+export function exitSuccess(): never {
+  exit(EXIT_CODES.SUCCESS);
+}
+
+export function exitError(message?: string): never {
+  if (message) {
+    console.error(message);
+  }
+  exit(EXIT_CODES.RUNTIME_ERROR);
+}
+
+export function exitInvalidArgs(message?: string): never {
+  if (message) {
+    console.error(`Error: ${message}`);
+  }
+  exit(EXIT_CODES.INVALID_ARGUMENTS);
+}
+
+export function exitConfigError(message?: string): never {
+  if (message) {
+    console.error(`Config Error: ${message}`);
+  }
+  exit(EXIT_CODES.CONFIG_ERROR);
+}
+
+export function exitNetworkError(message?: string): never {
+  if (message) {
+    console.error(`Network Error: ${message}`);
+  }
+  exit(EXIT_CODES.NETWORK_ERROR);
+}
+
+export function exitAuthError(message?: string): never {
+  if (message) {
+    console.error(`Auth Error: ${message}`);
+  }
+  exit(EXIT_CODES.AUTH_ERROR);
+}
@@ -0,0 +1,7 @@
+/**
+ * Runtime utilities index
+ */
+
+export * from './env';
+export * from './exit';
+export * from './lazy';
@@ -0,0 +1,56 @@
+/**
+ * Lazy loading utilities for CLI commands
+ * Ensures startup time stays <100ms by only loading command code when invoked
+ */
+
+import { validateCommand } from '../spec/cli-spec';
+
+export type CommandModule = {
+  execute: (args: string[], options: Record<string, unknown>) => Promise<void>;
+};
+
+const commandCache = new Map<string, CommandModule>();
+
+export async function loadCommand(command: string): Promise<CommandModule> {
+  if (!validateCommand(command)) {
+    throw new Error(`Unknown command: ${command}`);
+  }
+
+  // Check cache first
+  if (commandCache.has(command)) {
+    return commandCache.get(command)!;
+  }
+
+  // Lazy import the command module
+  const module = await import(`../commands/${command}`);
+  commandCache.set(command, module);
+  return module;
+}
+
+export function clearCommandCache(): void {
+  commandCache.clear();
+}
+
+/**
+ * Lazy load optional dependencies
+ * Returns undefined if not available
+ */
+export async function tryLoadOptional<T>(moduleName: string): Promise<T | undefined> {
+  try {
+    return await import(moduleName);
+  } catch {
+    return undefined;
+  }
+}
+
+/**
+ * Check if an optional dependency is available
+ */
+export async function hasOptionalDep(moduleName: string): Promise<boolean> {
+  try {
+    await import(moduleName);
+    return true;
+  } catch {
+    return false;
+  }
+}
@@ -0,0 +1,204 @@
+/**
+ * PraisonAI CLI Specification - TypeScript Runtime
+ * Parsed and validated spec from cli-spec.v1.yaml
+ */
+
+export const CLI_SPEC_VERSION = '1.0.0';
+
+export interface CommandArg {
+  name: string;
+  type: 'string' | 'integer' | 'boolean';
+  required: boolean;
+  position?: number;
+  description?: string;
+}
+
+export interface CommandFlag {
+  name: string;
+  short?: string;
+  type: 'string' | 'integer' | 'boolean';
+  default?: string | number | boolean;
+  enum?: string[];
+  description?: string;
+  required?: boolean;
+}
+
+export interface Subcommand {
+  description: string;
+  args?: CommandArg[];
+  flags?: CommandFlag[];
+}
+
+export interface Command {
+  description: string;
+  args?: CommandArg[];
+  flags?: CommandFlag[];
+  subcommands?: Record<string, Subcommand>;
+}
+
+export const COMMANDS: Record<string, Command> = {
+  chat: {
+    description: 'Chat with an AI agent',
+    args: [
+      { name: 'prompt', type: 'string', required: true, position: 0 }
+    ],
+    flags: [
+      { name: 'model', short: 'm', type: 'string', description: 'Model to use (e.g., openai/gpt-4o-mini)' },
+      { name: 'stream', short: 's', type: 'boolean', default: false, description: 'Enable streaming output' },
+      { name: 'session', type: 'string', description: 'Session ID for conversation continuity' }
+    ]
+  },
+  run: {
+    description: 'Run an agent with a task',
+    args: [
+      { name: 'task', type: 'string', required: true, position: 0 }
+    ],
+    flags: [
+      { name: 'agent', short: 'a', type: 'string', description: 'Agent configuration file or name' },
+      { name: 'tools', short: 't', type: 'string', description: 'Comma-separated list of tools' }
+    ]
+  },
+  workflow: {
+    description: 'Execute a multi-agent workflow',
+    args: [
+      { name: 'file', type: 'string', required: true, position: 0, description: 'Workflow YAML file path' }
+    ],
+    flags: [
+      { name: 'parallel', type: 'boolean', default: false, description: 'Run workflow steps in parallel where possible' }
+    ]
+  },
+  eval: {
+    description: 'Evaluate agent performance',
+    subcommands: {
+      accuracy: {
+        description: 'Run accuracy evaluation',
+        flags: [
+          { name: 'input', type: 'string', required: true },
+          { name: 'expected', type: 'string', required: true },
+          { name: 'iterations', type: 'integer', default: 1 }
+        ]
+      },
+      performance: {
+        description: 'Run performance benchmark',
+        flags: [
+          { name: 'iterations', type: 'integer', default: 10 },
+          { name: 'warmup', type: 'integer', default: 2 }
+        ]
+      },
+      reliability: {
+        description: 'Run reliability check',
+        flags: [
+          { name: 'expected-tools', type: 'string', description: 'Comma-separated expected tool calls' }
+        ]
+      }
+    }
+  },
+  providers: {
+    description: 'List available LLM providers',
+    flags: []
+  },
+  tools: {
+    description: 'List or manage tools',
+    subcommands: {
+      list: {
+        description: 'List available tools'
+      },
+      info: {
+        description: 'Show tool information',
+        args: [
+          { name: 'name', type: 'string', required: true, position: 0 }
+        ]
+      }
+    }
+  },
+  version: {
+    description: 'Show CLI version',
+    flags: []
+  },
+  help: {
+    description: 'Show help information',
+    args: [
+      { name: 'command', type: 'string', required: false, position: 0 }
+    ]
+  }
+};
+
+export const GLOBAL_FLAGS: CommandFlag[] = [
+  { name: 'verbose', short: 'v', type: 'boolean', default: false, description: 'Enable verbose output' },
+  { name: 'config', short: 'c', type: 'string', description: 'Path to config file' },
+  { name: 'profile', short: 'p', type: 'string', description: 'Profile name to use' },
+  { name: 'output', short: 'o', type: 'string', enum: ['json', 'text', 'pretty'], default: 'pretty', description: 'Output format' },
+  { name: 'json', type: 'boolean', default: false, description: 'Shorthand for --output json' }
+];
+
+export const EXIT_CODES = {
+  SUCCESS: 0,
+  RUNTIME_ERROR: 1,
+  INVALID_ARGUMENTS: 2,
+  CONFIG_ERROR: 3,
+  NETWORK_ERROR: 4,
+  AUTH_ERROR: 5
+} as const;
+
+export type ExitCode = typeof EXIT_CODES[keyof typeof EXIT_CODES];
+
+export interface SuccessOutput<T = unknown> {
+  success: true;
+  data: T;
+  meta?: {
+    duration_ms?: number;
+    model?: string;
+    tokens?: {
+      input?: number;
+      output?: number;
+    };
+  };
+}
+
+export interface ErrorOutput {
+  success: false;
+  error: {
+    code: string;
+    message: string;
+    details?: Record<string, unknown>;
+  };
+}
+
+export type CLIOutput<T = unknown> = SuccessOutput<T> | ErrorOutput;
+
+export const ENV_VARS = {
+  PRAISONAI_MODEL: 'PRAISONAI_MODEL',
+  PRAISONAI_PROFILE: 'PRAISONAI_PROFILE',
+  PRAISONAI_VERBOSE: 'PRAISONAI_VERBOSE',
+  PRAISONAI_CONFIG: 'PRAISONAI_CONFIG',
+  OPENAI_API_KEY: 'OPENAI_API_KEY',
+  ANTHROPIC_API_KEY: 'ANTHROPIC_API_KEY',
+  GOOGLE_API_KEY: 'GOOGLE_API_KEY'
+} as const;
+
+export const CONFIG_FILES = ['.praisonai.yaml', '.praisonai.json'] as const;
+
+export interface ConfigSchema {
+  model?: string;
+  verbose?: boolean;
+  stream?: boolean;
+  profiles?: Record<string, {
+    model?: string;
+    verbose?: boolean;
+    stream?: boolean;
+  }>;
+}
+
+export function validateCommand(command: string): command is keyof typeof COMMANDS {
+  return command in COMMANDS;
+}
+
+export function getCommandSpec(command: string): Command | undefined {
+  return COMMANDS[command];
+}
+
+export function getAllFlags(command: string): CommandFlag[] {
+  const cmdSpec = COMMANDS[command];
+  const cmdFlags = cmdSpec?.flags || [];
+  return [...GLOBAL_FLAGS, ...cmdFlags];
+}
@@ -0,0 +1,207 @@
+/**
+ * Evaluation Framework - Accuracy, Performance, and Reliability evaluation
+ */
+
+export interface EvalResult {
+  passed: boolean;
+  score: number;
+  message?: string;
+  details?: Record<string, any>;
+  duration: number;
+}
+
+export interface AccuracyEvalConfig {
+  input: string;
+  expectedOutput: string;
+  actualOutput: string;
+  threshold?: number;
+}
+
+export interface PerformanceEvalConfig {
+  func: () => Promise<any>;
+  iterations?: number;
+  warmupRuns?: number;
+}
+
+export interface PerformanceResult extends EvalResult {
+  avgTime: number;
+  minTime: number;
+  maxTime: number;
+  p95Time: number;
+  times: number[];
+}
+
+export interface ReliabilityEvalConfig {
+  expectedToolCalls: string[];
+  actualToolCalls: string[];
+}
+
+/**
+ * Accuracy Evaluation - Compare actual output to expected
+ */
+export async function accuracyEval(config: AccuracyEvalConfig): Promise<EvalResult> {
+  const start = Date.now();
+  const threshold = config.threshold ?? 0.8;
+  
+  const similarity = calculateSimilarity(config.expectedOutput, config.actualOutput);
+  const passed = similarity >= threshold;
+  
+  return {
+    passed,
+    score: similarity,
+    message: passed ? 'Output matches expected' : 'Output does not match expected',
+    details: {
+      expected: config.expectedOutput,
+      actual: config.actualOutput,
+      threshold
+    },
+    duration: Date.now() - start
+  };
+}
+
+/**
+ * Performance Evaluation - Measure execution time
+ */
+export async function performanceEval(config: PerformanceEvalConfig): Promise<PerformanceResult> {
+  const iterations = config.iterations ?? 10;
+  const warmupRuns = config.warmupRuns ?? 2;
+  const times: number[] = [];
+  
+  // Warmup runs
+  for (let i = 0; i < warmupRuns; i++) {
+    await config.func();
+  }
+  
+  // Actual runs
+  const start = Date.now();
+  for (let i = 0; i < iterations; i++) {
+    const runStart = Date.now();
+    await config.func();
+    times.push(Date.now() - runStart);
+  }
+  
+  const sortedTimes = [...times].sort((a, b) => a - b);
+  const avgTime = times.reduce((a, b) => a + b, 0) / times.length;
+  const minTime = sortedTimes[0];
+  const maxTime = sortedTimes[sortedTimes.length - 1];
+  const p95Index = Math.floor(sortedTimes.length * 0.95);
+  const p95Time = sortedTimes[p95Index] || maxTime;
+  
+  return {
+    passed: true,
+    score: 1,
+    avgTime,
+    minTime,
+    maxTime,
+    p95Time,
+    times,
+    duration: Date.now() - start,
+    details: {
+      iterations,
+      warmupRuns
+    }
+  };
+}
+
+/**
+ * Reliability Evaluation - Check tool call accuracy
+ */
+export async function reliabilityEval(config: ReliabilityEvalConfig): Promise<EvalResult> {
+  const start = Date.now();
+  
+  const expected = new Set(config.expectedToolCalls);
+  const actual = new Set(config.actualToolCalls);
+  
+  const matched = config.expectedToolCalls.filter(t => actual.has(t));
+  const missing = config.expectedToolCalls.filter(t => !actual.has(t));
+  const extra = config.actualToolCalls.filter(t => !expected.has(t));
+  
+  const score = expected.size > 0 ? matched.length / expected.size : 1;
+  const passed = missing.length === 0;
+  
+  return {
+    passed,
+    score,
+    message: passed ? 'All expected tool calls made' : `Missing tool calls: ${missing.join(', ')}`,
+    details: {
+      matched,
+      missing,
+      extra,
+      expected: config.expectedToolCalls,
+      actual: config.actualToolCalls
+    },
+    duration: Date.now() - start
+  };
+}
+
+/**
+ * Calculate text similarity (simple Jaccard similarity)
+ */
+function calculateSimilarity(a: string, b: string): number {
+  const wordsA = new Set(a.toLowerCase().split(/\s+/));
+  const wordsB = new Set(b.toLowerCase().split(/\s+/));
+  
+  const intersection = new Set([...wordsA].filter(x => wordsB.has(x)));
+  const union = new Set([...wordsA, ...wordsB]);
+  
+  return union.size > 0 ? intersection.size / union.size : 0;
+}
+
+/**
+ * Eval Suite - Run multiple evaluations
+ */
+export class EvalSuite {
+  private results: Map<string, EvalResult> = new Map();
+
+  async runAccuracy(name: string, config: AccuracyEvalConfig): Promise<EvalResult> {
+    const result = await accuracyEval(config);
+    this.results.set(name, result);
+    return result;
+  }
+
+  async runPerformance(name: string, config: PerformanceEvalConfig): Promise<PerformanceResult> {
+    const result = await performanceEval(config);
+    this.results.set(name, result);
+    return result;
+  }
+
+  async runReliability(name: string, config: ReliabilityEvalConfig): Promise<EvalResult> {
+    const result = await reliabilityEval(config);
+    this.results.set(name, result);
+    return result;
+  }
+
+  getResults(): Map<string, EvalResult> {
+    return new Map(this.results);
+  }
+
+  getSummary(): { total: number; passed: number; failed: number; avgScore: number } {
+    const results = Array.from(this.results.values());
+    const passed = results.filter(r => r.passed).length;
+    const avgScore = results.length > 0 
+      ? results.reduce((a, b) => a + b.score, 0) / results.length 
+      : 0;
+    
+    return {
+      total: results.length,
+      passed,
+      failed: results.length - passed,
+      avgScore
+    };
+  }
+
+  printSummary(): void {
+    const summary = this.getSummary();
+    console.log('\n=== Evaluation Summary ===');
+    console.log(`Total: ${summary.total}`);
+    console.log(`Passed: ${summary.passed}`);
+    console.log(`Failed: ${summary.failed}`);
+    console.log(`Avg Score: ${(summary.avgScore * 100).toFixed(1)}%`);
+    
+    console.log('\nResults:');
+    for (const [name, result] of this.results) {
+      const status = result.passed ? '✅' : '❌';
+      console.log(`  ${status} ${name}: ${(result.score * 100).toFixed(1)}%`);
+    }
+  }
+}
@@ -0,0 +1,184 @@
+/**
+ * Event System - PubSub and Event Emitter for agent communication
+ * Inspired by mastra's events module
+ */
+
+import { EventEmitter } from 'events';
+
+export interface Event {
+  id: string;
+  topic: string;
+  data: any;
+  createdAt: Date;
+  metadata?: Record<string, any>;
+}
+
+export type EventHandler = (event: Event, ack?: () => Promise<void>) => void | Promise<void>;
+
+/**
+ * Abstract PubSub base class
+ */
+export abstract class PubSub {
+  abstract publish(topic: string, data: any, metadata?: Record<string, any>): Promise<void>;
+  abstract subscribe(topic: string, handler: EventHandler): Promise<void>;
+  abstract unsubscribe(topic: string, handler: EventHandler): Promise<void>;
+  abstract close(): Promise<void>;
+}
+
+/**
+ * In-memory EventEmitter-based PubSub implementation
+ */
+export class EventEmitterPubSub extends PubSub {
+  private emitter: EventEmitter;
+  private handlers: Map<string, Set<EventHandler>> = new Map();
+
+  constructor(existingEmitter?: EventEmitter) {
+    super();
+    this.emitter = existingEmitter ?? new EventEmitter();
+    this.emitter.setMaxListeners(100); // Allow many listeners
+  }
+
+  async publish(topic: string, data: any, metadata?: Record<string, any>): Promise<void> {
+    const event: Event = {
+      id: crypto.randomUUID(),
+      topic,
+      data,
+      createdAt: new Date(),
+      metadata
+    };
+    this.emitter.emit(topic, event);
+  }
+
+  async subscribe(topic: string, handler: EventHandler): Promise<void> {
+    if (!this.handlers.has(topic)) {
+      this.handlers.set(topic, new Set());
+    }
+    this.handlers.get(topic)!.add(handler);
+    this.emitter.on(topic, handler);
+  }
+
+  async unsubscribe(topic: string, handler: EventHandler): Promise<void> {
+    this.handlers.get(topic)?.delete(handler);
+    this.emitter.off(topic, handler);
+  }
+
+  async close(): Promise<void> {
+    this.emitter.removeAllListeners();
+    this.handlers.clear();
+  }
+
+  /**
+   * Wait for a specific event with optional timeout
+   */
+  async waitFor(topic: string, timeout?: number): Promise<Event> {
+    return new Promise((resolve, reject) => {
+      const timer = timeout ? setTimeout(() => {
+        this.emitter.off(topic, handler);
+        reject(new Error(`Timeout waiting for event: ${topic}`));
+      }, timeout) : null;
+
+      const handler = (event: Event) => {
+        if (timer) clearTimeout(timer);
+        this.emitter.off(topic, handler);
+        resolve(event);
+      };
+
+      this.emitter.once(topic, handler);
+    });
+  }
+
+  /**
+   * Get the underlying EventEmitter
+   */
+  getEmitter(): EventEmitter {
+    return this.emitter;
+  }
+}
+
+/**
+ * Agent Event Bus - Specialized event system for agent communication
+ */
+export class AgentEventBus {
+  private pubsub: PubSub;
+  private agentId: string;
+
+  constructor(agentId: string, pubsub?: PubSub) {
+    this.agentId = agentId;
+    this.pubsub = pubsub ?? new EventEmitterPubSub();
+  }
+
+  /**
+   * Emit an agent event
+   */
+  async emit(eventType: string, data: any): Promise<void> {
+    await this.pubsub.publish(`agent:${this.agentId}:${eventType}`, data, {
+      agentId: this.agentId,
+      eventType
+    });
+  }
+
+  /**
+   * Listen for agent events
+   */
+  async on(eventType: string, handler: (data: any) => void | Promise<void>): Promise<void> {
+    await this.pubsub.subscribe(`agent:${this.agentId}:${eventType}`, (event) => {
+      handler(event.data);
+    });
+  }
+
+  /**
+   * Broadcast to all agents
+   */
+  async broadcast(eventType: string, data: any): Promise<void> {
+    await this.pubsub.publish(`broadcast:${eventType}`, data, {
+      sourceAgentId: this.agentId,
+      eventType
+    });
+  }
+
+  /**
+   * Listen for broadcast events
+   */
+  async onBroadcast(eventType: string, handler: (data: any, sourceAgentId: string) => void | Promise<void>): Promise<void> {
+    await this.pubsub.subscribe(`broadcast:${eventType}`, (event) => {
+      handler(event.data, event.metadata?.sourceAgentId);
+    });
+  }
+
+  /**
+   * Send message to specific agent
+   */
+  async sendTo(targetAgentId: string, eventType: string, data: any): Promise<void> {
+    await this.pubsub.publish(`agent:${targetAgentId}:${eventType}`, data, {
+      sourceAgentId: this.agentId,
+      targetAgentId,
+      eventType
+    });
+  }
+
+  async close(): Promise<void> {
+    await this.pubsub.close();
+  }
+}
+
+// Standard event types
+export const AgentEvents = {
+  STARTED: 'started',
+  COMPLETED: 'completed',
+  ERROR: 'error',
+  TOOL_CALLED: 'tool_called',
+  TOOL_RESULT: 'tool_result',
+  MESSAGE_RECEIVED: 'message_received',
+  MESSAGE_SENT: 'message_sent',
+  HANDOFF_INITIATED: 'handoff_initiated',
+  HANDOFF_COMPLETED: 'handoff_completed'
+} as const;
+
+// Factory functions
+export function createEventBus(agentId: string): AgentEventBus {
+  return new AgentEventBus(agentId);
+}
+
+export function createPubSub(): EventEmitterPubSub {
+  return new EventEmitterPubSub();
+}
@@ -0,0 +1,244 @@
+/**
+ * Guardrails - Input/output validation and safety checks
+ */
+
+export type GuardrailStatus = 'passed' | 'failed' | 'warning';
+
+export interface GuardrailResult {
+  status: GuardrailStatus;
+  message?: string;
+  details?: Record<string, any>;
+  modifiedContent?: string;
+}
+
+export type GuardrailFunction = (
+  content: string,
+  context?: GuardrailContext
+) => Promise<GuardrailResult> | GuardrailResult;
+
+export interface GuardrailContext {
+  role: 'input' | 'output';
+  agentName?: string;
+  sessionId?: string;
+  metadata?: Record<string, any>;
+}
+
+export interface GuardrailConfig {
+  name: string;
+  description?: string;
+  check: GuardrailFunction;
+  onFail?: 'block' | 'warn' | 'modify';
+}
+
+/**
+ * Guardrail class
+ */
+export class Guardrail {
+  readonly name: string;
+  readonly description: string;
+  readonly check: GuardrailFunction;
+  readonly onFail: 'block' | 'warn' | 'modify';
+
+  constructor(config: GuardrailConfig) {
+    this.name = config.name;
+    this.description = config.description || `Guardrail: ${config.name}`;
+    this.check = config.check;
+    this.onFail = config.onFail || 'block';
+  }
+
+  async run(content: string, context?: GuardrailContext): Promise<GuardrailResult> {
+    try {
+      return await this.check(content, context);
+    } catch (error: any) {
+      return {
+        status: 'failed',
+        message: `Guardrail error: ${error.message}`,
+        details: { error: error.message },
+      };
+    }
+  }
+}
+
+/**
+ * Create a guardrail
+ */
+export function guardrail(config: GuardrailConfig): Guardrail {
+  return new Guardrail(config);
+}
+
+/**
+ * Guardrail Manager - Run multiple guardrails
+ */
+export class GuardrailManager {
+  private guardrails: Guardrail[] = [];
+
+  add(g: Guardrail): this {
+    this.guardrails.push(g);
+    return this;
+  }
+
+  async runAll(
+    content: string,
+    context?: GuardrailContext
+  ): Promise<{ passed: boolean; results: Array<{ name: string; result: GuardrailResult }> }> {
+    const results: Array<{ name: string; result: GuardrailResult }> = [];
+    let passed = true;
+
+    for (const g of this.guardrails) {
+      const result = await g.run(content, context);
+      results.push({ name: g.name, result });
+
+      if (result.status === 'failed') {
+        passed = false;
+        if (g.onFail === 'block') {
+          break;
+        }
+      }
+    }
+
+    return { passed, results };
+  }
+
+  get count(): number {
+    return this.guardrails.length;
+  }
+}
+
+/**
+ * Built-in guardrails
+ */
+export const builtinGuardrails = {
+  /**
+   * Check for maximum length
+   */
+  maxLength: (maxChars: number): Guardrail => {
+    return guardrail({
+      name: 'max_length',
+      description: `Ensure content is under ${maxChars} characters`,
+      check: (content) => {
+        if (content.length > maxChars) {
+          return {
+            status: 'failed',
+            message: `Content exceeds maximum length of ${maxChars} characters`,
+            details: { length: content.length, max: maxChars },
+          };
+        }
+        return { status: 'passed' };
+      },
+    });
+  },
+
+  /**
+   * Check for minimum length
+   */
+  minLength: (minChars: number): Guardrail => {
+    return guardrail({
+      name: 'min_length',
+      description: `Ensure content is at least ${minChars} characters`,
+      check: (content) => {
+        if (content.length < minChars) {
+          return {
+            status: 'failed',
+            message: `Content is below minimum length of ${minChars} characters`,
+            details: { length: content.length, min: minChars },
+          };
+        }
+        return { status: 'passed' };
+      },
+    });
+  },
+
+  /**
+   * Check for blocked words
+   */
+  blockedWords: (words: string[]): Guardrail => {
+    return guardrail({
+      name: 'blocked_words',
+      description: 'Check for blocked words',
+      check: (content) => {
+        const lowerContent = content.toLowerCase();
+        const found = words.filter(w => lowerContent.includes(w.toLowerCase()));
+        if (found.length > 0) {
+          return {
+            status: 'failed',
+            message: `Content contains blocked words`,
+            details: { blockedWords: found },
+          };
+        }
+        return { status: 'passed' };
+      },
+    });
+  },
+
+  /**
+   * Check for required words
+   */
+  requiredWords: (words: string[]): Guardrail => {
+    return guardrail({
+      name: 'required_words',
+      description: 'Check for required words',
+      check: (content) => {
+        const lowerContent = content.toLowerCase();
+        const missing = words.filter(w => !lowerContent.includes(w.toLowerCase()));
+        if (missing.length > 0) {
+          return {
+            status: 'failed',
+            message: `Content missing required words`,
+            details: { missingWords: missing },
+          };
+        }
+        return { status: 'passed' };
+      },
+    });
+  },
+
+  /**
+   * Regex pattern check
+   */
+  pattern: (regex: RegExp, mustMatch: boolean = true): Guardrail => {
+    return guardrail({
+      name: 'pattern',
+      description: `Check content against pattern: ${regex}`,
+      check: (content) => {
+        const matches = regex.test(content);
+        if (mustMatch && !matches) {
+          return {
+            status: 'failed',
+            message: `Content does not match required pattern`,
+            details: { pattern: regex.toString() },
+          };
+        }
+        if (!mustMatch && matches) {
+          return {
+            status: 'failed',
+            message: `Content matches forbidden pattern`,
+            details: { pattern: regex.toString() },
+          };
+        }
+        return { status: 'passed' };
+      },
+    });
+  },
+
+  /**
+   * JSON validity check
+   */
+  validJson: (): Guardrail => {
+    return guardrail({
+      name: 'valid_json',
+      description: 'Ensure content is valid JSON',
+      check: (content) => {
+        try {
+          JSON.parse(content);
+          return { status: 'passed' };
+        } catch (e: any) {
+          return {
+            status: 'failed',
+            message: 'Content is not valid JSON',
+            details: { error: e.message },
+          };
+        }
+      },
+    });
+  },
+};
@@ -0,0 +1,116 @@
+/**
+ * LLMGuardrail - LLM-based content validation
+ */
+
+import { createProvider, type LLMProvider } from '../llm/providers';
+
+export interface LLMGuardrailConfig {
+  name: string;
+  criteria: string;
+  llm?: string;
+  threshold?: number;
+  verbose?: boolean;
+}
+
+export interface LLMGuardrailResult {
+  status: 'passed' | 'failed' | 'warning';
+  score: number;
+  message?: string;
+  reasoning?: string;
+}
+
+/**
+ * LLMGuardrail - Use LLM to validate content against criteria
+ */
+export class LLMGuardrail {
+  readonly name: string;
+  readonly criteria: string;
+  private provider: LLMProvider;
+  private threshold: number;
+  private verbose: boolean;
+
+  constructor(config: LLMGuardrailConfig) {
+    this.name = config.name;
+    this.criteria = config.criteria;
+    this.provider = createProvider(config.llm || 'openai/gpt-4o-mini');
+    this.threshold = config.threshold ?? 0.7;
+    this.verbose = config.verbose ?? false;
+  }
+
+  /**
+   * Check content against criteria
+   */
+  async check(content: string): Promise<LLMGuardrailResult> {
+    const prompt = `Evaluate the following content against this criteria:
+
+Criteria: ${this.criteria}
+
+Content: ${content}
+
+Respond with a JSON object containing:
+- score: number from 0 to 1 (1 = fully meets criteria)
+- passed: boolean
+- reasoning: brief explanation
+
+JSON response:`;
+
+    try {
+      const result = await this.provider.generateText({
+        messages: [{ role: 'user', content: prompt }]
+      });
+
+      const parsed = this.parseResponse(result.text);
+      
+      if (this.verbose) {
+        console.log(`[LLMGuardrail:${this.name}] Score: ${parsed.score}, Passed: ${parsed.status}`);
+      }
+
+      return parsed;
+    } catch (error: any) {
+      return {
+        status: 'warning',
+        score: 0.5,
+        message: `Guardrail check failed: ${error.message}`
+      };
+    }
+  }
+
+  /**
+   * Run guardrail (alias for check)
+   */
+  async run(content: string): Promise<LLMGuardrailResult> {
+    return this.check(content);
+  }
+
+  private parseResponse(response: string): LLMGuardrailResult {
+    try {
+      const jsonMatch = response.match(/\{[\s\S]*\}/);
+      if (jsonMatch) {
+        const parsed = JSON.parse(jsonMatch[0]);
+        const score = typeof parsed.score === 'number' ? parsed.score : 0.5;
+        return {
+          status: score >= this.threshold ? 'passed' : 'failed',
+          score,
+          reasoning: parsed.reasoning
+        };
+      }
+    } catch (e) {
+      // Parse failed
+    }
+
+    // Fallback parsing
+    const hasPositive = /pass|good|valid|accept|meets/i.test(response);
+    return {
+      status: hasPositive ? 'passed' : 'failed',
+      score: hasPositive ? 0.8 : 0.3,
+      reasoning: response.substring(0, 200)
+    };
+  }
+}
+
+/**
+ * Create an LLMGuardrail
+ */
+export function createLLMGuardrail(config: LLMGuardrailConfig): LLMGuardrail {
+  return new LLMGuardrail(config);
+}
@@ -4,4 +4,168 @@ export * from './knowledge';
 export * from './llm';
 export * from './memory';
 export * from './process';
-export * from './tools';
+
+// Export tools (excluding conflicting types)
+export { 
+  BaseTool, ToolResult, ToolValidationError, validateTool, createTool,
+  FunctionTool, tool, ToolRegistry, getRegistry, registerTool, getTool,
+  type ToolConfig, type ToolContext, type ToolParameters
+} from './tools';
+export * from './tools/arxivTools';
+export * from './tools/mcpSse';
+
+// Export session management
+export * from './session';
+
+// Export database adapters
+export * from './db';
+
+// Export workflows
+export * from './workflows';
+
+// Export guardrails
+export * from './guardrails';
+
+// Export handoff
+export { Handoff, handoff, handoffFilters, type HandoffConfig, type HandoffContext, type HandoffResult } from './agent/handoff';
+
+// Export router agent
+export { RouterAgent, createRouter, routeConditions, type RouterConfig, type RouteConfig, type RouteContext } from './agent/router';
+
+// Export context agent
+export { ContextAgent, createContextAgent, type ContextAgentConfig, type ContextMessage } from './agent/context';
+
+// Export knowledge base (RAG)
+export { KnowledgeBase, createKnowledgeBase, type Document, type SearchResult, type EmbeddingProvider, type KnowledgeBaseConfig } from './knowledge/rag';
+
+// Export evaluation framework
+export { accuracyEval, performanceEval, reliabilityEval, EvalSuite, type EvalResult, type PerformanceResult, type AccuracyEvalConfig, type PerformanceEvalConfig, type ReliabilityEvalConfig } from './eval';
+
+// Export observability
+export { MemoryObservabilityAdapter, ConsoleObservabilityAdapter, setObservabilityAdapter, getObservabilityAdapter, type ObservabilityAdapter, type TraceContext, type SpanContext, type SpanData, type TraceData } from './observability';
+
+// Export skills
+export { SkillManager, createSkillManager, parseSkillFile, type Skill, type SkillMetadata, type SkillDiscoveryOptions } from './skills';
+
+// Export CLI
+export { chat, listProviders, version, help } from './cli';
+
+// Export Memory
+export { Memory, createMemory, type MemoryEntry, type MemoryConfig, type SearchResult as MemorySearchResult } from './memory/memory';
+
+// Export Telemetry
+export { TelemetryCollector, getTelemetry, enableTelemetry, disableTelemetry, cleanupTelemetry, type TelemetryEvent, type TelemetryConfig } from './telemetry';
+
+// Export AutoAgents
+export { AutoAgents, createAutoAgents, type AgentConfig, type TaskConfig, type TeamStructure, type AutoAgentsConfig } from './auto';
+
+// Export ImageAgent
+export { ImageAgent, createImageAgent, type ImageAgentConfig, type ImageGenerationConfig, type ImageAnalysisConfig } from './agent/image';
+
+// Export DeepResearchAgent
+export { DeepResearchAgent, createDeepResearchAgent, type DeepResearchConfig, type ResearchResponse, type Citation, type ReasoningStep } from './agent/research';
+
+// Export QueryRewriterAgent
+export { QueryRewriterAgent, createQueryRewriterAgent, type QueryRewriterConfig, type RewriteResult, type RewriteStrategy } from './agent/query-rewriter';
+
+// Export PromptExpanderAgent
+export { PromptExpanderAgent, createPromptExpanderAgent, type PromptExpanderConfig, type ExpandResult, type ExpandStrategy } from './agent/prompt-expander';
+
+// Export Chunking
+export { Chunking, createChunking, type ChunkingConfig, type Chunk, type ChunkStrategy } from './knowledge/chunking';
+
+// Export LLMGuardrail
+export { LLMGuardrail, createLLMGuardrail, type LLMGuardrailConfig, type LLMGuardrailResult } from './guardrails/llm-guardrail';
+
+// Export Planning
+export { Plan, PlanStep, TodoList, TodoItem, PlanStorage, createPlan, createTodoList, createPlanStorage, type PlanConfig, type PlanStepConfig, type TodoItemConfig, type PlanStatus, type TodoStatus } from './planning';
+
+// Export Cache
+export { BaseCache, MemoryCache, FileCache, createMemoryCache, createFileCache, type CacheConfig, type CacheEntry } from './cache';
+
+// Export Events
+export { PubSub, EventEmitterPubSub, AgentEventBus, AgentEvents, createEventBus, createPubSub, type Event, type EventHandler } from './events';
+
+// Export YAML Workflow Parser
+export { parseYAMLWorkflow, createWorkflowFromYAML, loadWorkflowFromFile, validateWorkflowDefinition, type YAMLWorkflowDefinition, type YAMLStepDefinition, type ParsedWorkflow } from './workflows/yaml-parser';
+
+// Export SQLite Adapter
+export { SQLiteAdapter, createSQLiteAdapter, type SQLiteConfig, type DbMessage, type DbRun, type DbTrace } from './db/sqlite';
+
+// Export Redis Adapter
+export { 
+  UpstashRedisAdapter, MemoryRedisAdapter,
+  createUpstashRedis, createMemoryRedis,
+  type RedisConfig, type RedisAdapter
+} from './db/redis';
+
+// Export PostgreSQL Adapter
+export {
+  NeonPostgresAdapter, MemoryPostgresAdapter, PostgresSessionStorage,
+  createNeonPostgres, createMemoryPostgres, createPostgresSessionStorage,
+  type PostgresConfig, type PostgresAdapter
+} from './db/postgres';
+
+// Export Integrations - Vector Stores
+export { 
+  BaseVectorStore, MemoryVectorStore, createMemoryVectorStore,
+  PineconeVectorStore, createPineconeStore,
+  WeaviateVectorStore, createWeaviateStore,
+  QdrantVectorStore, createQdrantStore,
+  ChromaVectorStore, createChromaStore,
+  type VectorDocument, type QueryResult as VectorQueryResult, type IndexStats
+} from './integrations/vector';
+
+// Export Integrations - Observability
+export {
+  BaseObservabilityProvider, ConsoleObservabilityProvider, MemoryObservabilityProvider,
+  LangfuseObservabilityProvider,
+  createConsoleObservability, createMemoryObservability, createLangfuseObservability,
+  type Span, type TraceContext as ObservabilityTraceContext, type LogEntry, type Metric
+} from './integrations/observability';
+
+// Export Integrations - Voice
+export {
+  BaseVoiceProvider, OpenAIVoiceProvider, ElevenLabsVoiceProvider,
+  createOpenAIVoice, createElevenLabsVoice,
+  type VoiceConfig, type SpeakOptions, type ListenOptions, type Speaker
+} from './integrations/voice';
+
+// Export Reranker
+export {
+  BaseReranker, CohereReranker, CrossEncoderReranker, LLMReranker,
+  createCohereReranker, createCrossEncoderReranker, createLLMReranker,
+  type RerankResult, type RerankConfig
+} from './knowledge/reranker';
+
+// Export Graph RAG
+export {
+  GraphStore, GraphRAG, createGraphRAG,
+  type GraphNode, type GraphEdge, type GraphQueryResult, type GraphRAGConfig
+} from './knowledge/graph-rag';
+
+// Export providers with explicit names to avoid conflicts
+export {
+  createProvider,
+  getDefaultProvider,
+  parseModelString,
+  isProviderAvailable,
+  getAvailableProviders,
+  OpenAIProvider,
+  AnthropicProvider,
+  GoogleProvider,
+  BaseProvider,
+  type LLMProvider,
+  type ProviderConfig,
+  type ProviderFactory,
+  type GenerateTextOptions,
+  type GenerateTextResult,
+  type StreamTextOptions,
+  type StreamChunk,
+  type GenerateObjectOptions,
+  type GenerateObjectResult,
+  type TokenUsage,
+  type Message as ProviderMessage,
+  type ToolCall,
+  type ToolDefinition as ProviderToolDefinition,
+} from './llm/providers';
@@ -0,0 +1,13 @@
+/**
+ * PraisonAI Integrations
+ * Extensions for vector stores, observability, and voice
+ */
+
+// Vector Store Integrations
+export * from './vector';
+
+// Observability Integrations
+export * from './observability';
+
+// Voice Integrations
+export * from './voice';
@@ -0,0 +1,290 @@
+/**
+ * Base Observability - Abstract class for observability integrations
+ * Supports tracing, logging, and metrics
+ */
+
+export interface Span {
+  id: string;
+  traceId: string;
+  parentId?: string;
+  name: string;
+  startTime: number;
+  endTime?: number;
+  status: 'ok' | 'error' | 'unset';
+  attributes: Record<string, any>;
+  events: SpanEvent[];
+}
+
+export interface SpanEvent {
+  name: string;
+  timestamp: number;
+  attributes?: Record<string, any>;
+}
+
+export interface TraceContext {
+  traceId: string;
+  spanId: string;
+  parentSpanId?: string;
+}
+
+export interface LogEntry {
+  level: 'debug' | 'info' | 'warn' | 'error';
+  message: string;
+  timestamp: number;
+  traceId?: string;
+  spanId?: string;
+  attributes?: Record<string, any>;
+}
+
+export interface Metric {
+  name: string;
+  value: number;
+  type: 'counter' | 'gauge' | 'histogram';
+  timestamp: number;
+  tags?: Record<string, string>;
+}
+
+/**
+ * Abstract base class for observability providers
+ */
+export abstract class BaseObservabilityProvider {
+  readonly name: string;
+
+  constructor(name: string) {
+    this.name = name;
+  }
+
+  /**
+   * Start a new span
+   */
+  abstract startSpan(name: string, attributes?: Record<string, any>, parentContext?: TraceContext): Span;
+
+  /**
+   * End a span
+   */
+  abstract endSpan(span: Span, status?: 'ok' | 'error', error?: Error): void;
+
+  /**
+   * Add event to span
+   */
+  abstract addSpanEvent(span: Span, name: string, attributes?: Record<string, any>): void;
+
+  /**
+   * Log a message
+   */
+  abstract log(entry: LogEntry): void;
+
+  /**
+   * Record a metric
+   */
+  abstract recordMetric(metric: Metric): void;
+
+  /**
+   * Flush all pending data
+   */
+  abstract flush(): Promise<void>;
+
+  /**
+   * Shutdown the provider
+   */
+  abstract shutdown(): Promise<void>;
+
+  /**
+   * Helper to create trace context
+   */
+  protected createTraceContext(span: Span): TraceContext {
+    return {
+      traceId: span.traceId,
+      spanId: span.id,
+      parentSpanId: span.parentId
+    };
+  }
+
+  /**
+   * Generate unique ID
+   */
+  protected generateId(): string {
+    return crypto.randomUUID().replace(/-/g, '');
+  }
+}
+
+/**
+ * Console-based observability provider for development
+ */
+export class ConsoleObservabilityProvider extends BaseObservabilityProvider {
+  private spans: Map<string, Span> = new Map();
+  private verbose: boolean;
+
+  constructor(config: { verbose?: boolean } = {}) {
+    super('ConsoleObservability');
+    this.verbose = config.verbose ?? false;
+  }
+
+  startSpan(name: string, attributes?: Record<string, any>, parentContext?: TraceContext): Span {
+    const span: Span = {
+      id: this.generateId(),
+      traceId: parentContext?.traceId || this.generateId(),
+      parentId: parentContext?.spanId,
+      name,
+      startTime: Date.now(),
+      status: 'unset',
+      attributes: attributes || {},
+      events: []
+    };
+
+    this.spans.set(span.id, span);
+
+    if (this.verbose) {
+      console.log(`[SPAN START] ${name}`, { traceId: span.traceId, spanId: span.id });
+    }
+
+    return span;
+  }
+
+  endSpan(span: Span, status: 'ok' | 'error' = 'ok', error?: Error): void {
+    span.endTime = Date.now();
+    span.status = status;
+
+    if (error) {
+      span.attributes.error = error.message;
+      span.attributes.errorStack = error.stack;
+    }
+
+    const duration = span.endTime - span.startTime;
+
+    if (this.verbose) {
+      console.log(`[SPAN END] ${span.name}`, {
+        duration: `${duration}ms`,
+        status,
+        ...(error && { error: error.message })
+      });
+    }
+
+    this.spans.delete(span.id);
+  }
+
+  addSpanEvent(span: Span, name: string, attributes?: Record<string, any>): void {
+    span.events.push({
+      name,
+      timestamp: Date.now(),
+      attributes
+    });
+
+    if (this.verbose) {
+      console.log(`[SPAN EVENT] ${span.name} -> ${name}`, attributes);
+    }
+  }
+
+  log(entry: LogEntry): void {
+    const prefix = `[${entry.level.toUpperCase()}]`;
+    const context = entry.traceId ? ` [trace:${entry.traceId.slice(0, 8)}]` : '';
+    
+    switch (entry.level) {
+      case 'debug':
+        console.debug(`${prefix}${context} ${entry.message}`, entry.attributes);
+        break;
+      case 'info':
+        console.info(`${prefix}${context} ${entry.message}`, entry.attributes);
+        break;
+      case 'warn':
+        console.warn(`${prefix}${context} ${entry.message}`, entry.attributes);
+        break;
+      case 'error':
+        console.error(`${prefix}${context} ${entry.message}`, entry.attributes);
+        break;
+    }
+  }
+
+  recordMetric(metric: Metric): void {
+    if (this.verbose) {
+      console.log(`[METRIC] ${metric.name}: ${metric.value} (${metric.type})`, metric.tags);
+    }
+  }
+
+  async flush(): Promise<void> {
+    // Console provider doesn't need flushing
+  }
+
+  async shutdown(): Promise<void> {
+    this.spans.clear();
+  }
+}
+
+/**
+ * Memory-based observability provider for testing
+ */
+export class MemoryObservabilityProvider extends BaseObservabilityProvider {
+  spans: Span[] = [];
+  logs: LogEntry[] = [];
+  metrics: Metric[] = [];
+
+  constructor() {
+    super('MemoryObservability');
+  }
+
+  startSpan(name: string, attributes?: Record<string, any>, parentContext?: TraceContext): Span {
+    const span: Span = {
+      id: this.generateId(),
+      traceId: parentContext?.traceId || this.generateId(),
+      parentId: parentContext?.spanId,
+      name,
+      startTime: Date.now(),
+      status: 'unset',
+      attributes: attributes || {},
+      events: []
+    };
+
+    this.spans.push(span);
+    return span;
+  }
+
+  endSpan(span: Span, status: 'ok' | 'error' = 'ok', error?: Error): void {
+    span.endTime = Date.now();
+    span.status = status;
+    if (error) {
+      span.attributes.error = error.message;
+    }
+  }
+
+  addSpanEvent(span: Span, name: string, attributes?: Record<string, any>): void {
+    span.events.push({ name, timestamp: Date.now(), attributes });
+  }
+
+  log(entry: LogEntry): void {
+    this.logs.push(entry);
+  }
+
+  recordMetric(metric: Metric): void {
+    this.metrics.push(metric);
+  }
+
+  async flush(): Promise<void> {}
+
+  async shutdown(): Promise<void> {
+    this.spans = [];
+    this.logs = [];
+    this.metrics = [];
+  }
+
+  // Helper methods for testing
+  getSpansByName(name: string): Span[] {
+    return this.spans.filter(s => s.name === name);
+  }
+
+  getLogsByLevel(level: LogEntry['level']): LogEntry[] {
+    return this.logs.filter(l => l.level === level);
+  }
+
+  getMetricsByName(name: string): Metric[] {
+    return this.metrics.filter(m => m.name === name);
+  }
+}
+
+// Factory functions
+export function createConsoleObservability(config?: { verbose?: boolean }): ConsoleObservabilityProvider {
+  return new ConsoleObservabilityProvider(config);
+}
+
+export function createMemoryObservability(): MemoryObservabilityProvider {
+  return new MemoryObservabilityProvider();
+}
@@ -0,0 +1,11 @@
+/**
+ * Observability Integrations
+ * Provides adapters for various observability platforms
+ */
+
+export * from './base';
+export * from './langfuse';
+
+// Re-export factory functions
+export { createConsoleObservability, createMemoryObservability } from './base';
+export { createLangfuseObservability } from './langfuse';
@@ -0,0 +1,208 @@
+/**
+ * Langfuse Observability Integration
+ */
+
+import { BaseObservabilityProvider, Span, TraceContext, LogEntry, Metric } from './base';
+
+export interface LangfuseConfig {
+  publicKey: string;
+  secretKey: string;
+  baseUrl?: string;
+  flushAt?: number;
+  flushInterval?: number;
+}
+
+export class LangfuseObservabilityProvider extends BaseObservabilityProvider {
+  private publicKey: string;
+  private secretKey: string;
+  private baseUrl: string;
+  private queue: any[] = [];
+  private flushAt: number;
+  private flushInterval: number;
+  private flushTimer?: NodeJS.Timeout;
+
+  constructor(config: LangfuseConfig) {
+    super('LangfuseObservability');
+    this.publicKey = config.publicKey;
+    this.secretKey = config.secretKey;
+    this.baseUrl = config.baseUrl || 'https://cloud.langfuse.com';
+    this.flushAt = config.flushAt || 20;
+    this.flushInterval = config.flushInterval || 10000;
+
+    this.startFlushTimer();
+  }
+
+  private startFlushTimer(): void {
+    this.flushTimer = setInterval(() => {
+      if (this.queue.length > 0) {
+        this.flush();
+      }
+    }, this.flushInterval);
+  }
+
+  private async request(path: string, body: any): Promise<any> {
+    const auth = Buffer.from(`${this.publicKey}:${this.secretKey}`).toString('base64');
+    
+    const response = await fetch(`${this.baseUrl}${path}`, {
+      method: 'POST',
+      headers: {
+        'Authorization': `Basic ${auth}`,
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify(body)
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`Langfuse API error: ${response.status} - ${error}`);
+    }
+
+    return response.json();
+  }
+
+  private enqueue(event: any): void {
+    this.queue.push(event);
+    if (this.queue.length >= this.flushAt) {
+      this.flush();
+    }
+  }
+
+  startSpan(name: string, attributes?: Record<string, any>, parentContext?: TraceContext): Span {
+    const span: Span = {
+      id: this.generateId(),
+      traceId: parentContext?.traceId || this.generateId(),
+      parentId: parentContext?.spanId,
+      name,
+      startTime: Date.now(),
+      status: 'unset',
+      attributes: attributes || {},
+      events: []
+    };
+
+    // Create trace if this is root span
+    if (!parentContext) {
+      this.enqueue({
+        type: 'trace-create',
+        body: {
+          id: span.traceId,
+          name,
+          metadata: attributes,
+          timestamp: new Date(span.startTime).toISOString()
+        }
+      });
+    }
+
+    // Create span/generation
+    this.enqueue({
+      type: 'span-create',
+      body: {
+        id: span.id,
+        traceId: span.traceId,
+        parentObservationId: span.parentId,
+        name,
+        startTime: new Date(span.startTime).toISOString(),
+        metadata: attributes
+      }
+    });
+
+    return span;
+  }
+
+  endSpan(span: Span, status: 'ok' | 'error' = 'ok', error?: Error): void {
+    span.endTime = Date.now();
+    span.status = status;
+
+    const updateBody: any = {
+      spanId: span.id,
+      endTime: new Date(span.endTime).toISOString(),
+      level: status === 'error' ? 'ERROR' : 'DEFAULT'
+    };
+
+    if (error) {
+      updateBody.statusMessage = error.message;
+    }
+
+    this.enqueue({
+      type: 'span-update',
+      body: updateBody
+    });
+  }
+
+  addSpanEvent(span: Span, name: string, attributes?: Record<string, any>): void {
+    span.events.push({
+      name,
+      timestamp: Date.now(),
+      attributes
+    });
+
+    this.enqueue({
+      type: 'event-create',
+      body: {
+        traceId: span.traceId,
+        observationId: span.id,
+        name,
+        metadata: attributes,
+        timestamp: new Date().toISOString()
+      }
+    });
+  }
+
+  log(entry: LogEntry): void {
+    // Langfuse doesn't have direct log support, use events instead
+    if (entry.traceId) {
+      this.enqueue({
+        type: 'event-create',
+        body: {
+          traceId: entry.traceId,
+          observationId: entry.spanId,
+          name: `log:${entry.level}`,
+          metadata: {
+            message: entry.message,
+            ...entry.attributes
+          },
+          timestamp: new Date(entry.timestamp).toISOString()
+        }
+      });
+    }
+  }
+
+  recordMetric(metric: Metric): void {
+    // Langfuse uses scores for metrics
+    this.enqueue({
+      type: 'score-create',
+      body: {
+        name: metric.name,
+        value: metric.value,
+        timestamp: new Date(metric.timestamp).toISOString(),
+        comment: JSON.stringify(metric.tags)
+      }
+    });
+  }
+
+  async flush(): Promise<void> {
+    if (this.queue.length === 0) return;
+
+    const batch = this.queue.splice(0, this.queue.length);
+    
+    try {
+      await this.request('/api/public/ingestion', {
+        batch
+      });
+    } catch (error) {
+      // Re-add failed items to queue
+      this.queue.unshift(...batch);
+      console.error('Langfuse flush error:', error);
+    }
+  }
+
+  async shutdown(): Promise<void> {
+    if (this.flushTimer) {
+      clearInterval(this.flushTimer);
+    }
+    await this.flush();
+  }
+}
+
+export function createLangfuseObservability(config: LangfuseConfig): LangfuseObservabilityProvider {
+  return new LangfuseObservabilityProvider(config);
+}
Author	SHA1	Message	Date
Mervin Praison	f0f0f653fe	Release v2.5.7	2025-12-26 13:31:45 +00:00
Mervin Praison	98a4ff8450	Release v2.5.6	2025-12-26 13:11:09 +00:00
Mervin Praison	d6333aaf33	Release v1.2.1 - refactor TypeScript tools to use generic BaseTool - Bump praisonai-ts version from 1.2.0 to 1.2.1 - Refactor BaseTool to use generics for type-safe input/output parameters - Update ArxivSearchTool and ArxivDownloadTool to extend generic BaseTool with typed params - Update MCPTool to extend generic BaseTool - Change tool method from execute() to run() for consistency - Export ToolResult, ToolValidationError, validateTool, createTool from base module - Remove Tool interface export	2025-12-26 11:47:17 +00:00
Mervin Praison	8005094267	update	2025-12-26 08:39:22 +00:00
Mervin Praison	08b21dabe3	Release v2.5.5	2025-12-26 08:16:54 +00:00
Mervin Praison	8c70d840df	docs: add knowledge export/import CLI commands to README - Add knowledge export/import row to features table with CLI link - Document `praisonai knowledge export <file.json>` command - Document `praisonai knowledge import <file.json>` command	2025-12-26 06:53:20 +00:00
Mervin Praison	d8e944c1d3	Release v2.5.4	2025-12-26 06:49:33 +00:00
Mervin Praison	79b2b88f72	Release v2.5.3	2025-12-26 05:54:09 +00:00
Mervin Praison	f5dfbd5670	fix: resolve Core Tests failures in knowledge/reranker integration - Fix #1: MemoryConfig Pydantic validation - add _prepare_mem0_config() to strip PraisonAI-specific reranker fields (enabled, default_rerank) before passing to mem0, which only accepts provider and config fields - Fix #2: LLM Reranker 'Chat' object not callable - check if client.chat is callable, not just if it exists. OpenAI client has chat as namespace object. Also handle max_completion_tokens vs max_tokens for different model versions. - Fix #3: Chroma vector store metadata validation - ensure non-empty metadata dicts by adding placeholder key when metadata is empty or None - Fix test file issues: add user_id parameter (required by mem0 API), handle dict return type from knowledge.search, use gpt-4o-mini for reranker test (gpt-5-nano returns empty responses with max_completion_tokens=10)	2025-12-26 05:52:46 +00:00
Mervin Praison	aa6aa5933d	fix: update test files to use gpt-5-nano for cheaper CI runs - Update test_workflow_patterns.py to use gpt-5-nano instead of gpt-4o/gpt-4o-mini - Also set OPENAI_MODEL_NAME=gpt-5-nano as CI secret	2025-12-26 05:26:40 +00:00
Mervin Praison	ff3a4f2016	Release v2.5.2	2025-12-26 05:25:08 +00:00
Mervin Praison	0d5444fc23	fix: add missing praisonai.db module to git - Add praisonai/db/ module that was being ignored by *.db pattern - This fixes ModuleNotFoundError: No module named 'praisonai.db' in CI tests	2025-12-26 04:54:05 +00:00
Mervin Praison	d7d5dab17b	fix: add missing db module to git and update .gitignore - Add praisonaiagents/db/ module that was being ignored by *.db pattern - Add exception in .gitignore for the db module directory - This fixes ModuleNotFoundError: No module named 'praisonaiagents.db' in CI	2025-12-26 04:49:31 +00:00
Mervin Praison	05678394e6	fix: remove sys.exit() at module level in test files causing pytest INTERNALERROR - test_mini_agents_fix.py: Convert module-level sys.exit(1) to pytest.mark.skipif pattern - test_xai_fix.py: Convert to proper pytest test function with cleanup These sys.exit() calls at module level caused pytest to crash during test collection with INTERNALERROR, breaking all CI pipelines.	2025-12-26 04:46:58 +00:00
Mervin Praison	54cddc09b4	Release v2.5.1	2025-12-26 04:23:51 +00:00