Release v2.5.7

- 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

README.md

@@ -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
@@ -169,147 +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) |
-| ↳ 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) |
-| **🔬 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>
 
 ---
 
@@ -543,7 +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:
 
@@ -592,7 +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
@@ -607,7 +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:
 
@@ -877,7 +1079,7 @@ callbacks:
   on_workflow_complete: log_complete
 ```
 
-### 9. Hooks
+### 10. Hooks
 
 Intercept and modify agent behavior at various lifecycle points:
 
@@ -916,7 +1118,7 @@ praisonai hooks validate hooks.json     # Validate configuration
 ```
 
 
-### 10. Shadow Git Checkpoints
+### 11. Shadow Git Checkpoints
 
 File-level undo/restore using shadow git:
 
@@ -953,7 +1155,7 @@ praisonai checkpoint restore abc123         # Restore to checkpoint
 
 ---
 
-### 11. Background Tasks
+### 12. Background Tasks
 
 Run agent tasks asynchronously without blocking:
 
@@ -991,7 +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:
 
@@ -1032,7 +1234,7 @@ praisonai policy init                  # Create template
 
 ---
 
-### 13. Thinking Budgets
+### 14. Thinking Budgets
 
 Configure token budgets for extended thinking:
 
@@ -1065,7 +1267,7 @@ praisonai thinking stats       # Show usage statistics
 
 ---
 
-### 14. Output Styles
+### 15. Output Styles
 
 Configure how agents format their responses:
 
@@ -1095,7 +1297,7 @@ praisonai output set concise   # Set output style
 
 ---
 
-### 15. Context Compaction
+### 16. Context Compaction
 
 Automatically manage context window size:
 
@@ -1130,7 +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:
 
@@ -1164,7 +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)
@@ -1240,7 +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.
 
@@ -1316,7 +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.
 
@@ -1364,6 +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
@@ -3023,6 +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>

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.5.2" \
+    "praisonai>=2.5.7" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.5.2" \
+    "praisonai>=2.5.7" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.5.2" \
+    "praisonai>=2.5.7" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/js/agents/basic-agent.ts

@@ -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);

examples/js/agents/context-agent.ts

@@ -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);

examples/js/agents/image-agent.ts

@@ -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);

examples/js/agents/multi-agent.ts

@@ -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);

examples/js/agents/research-agent.ts

@@ -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);

examples/js/agents/router-agent.ts

@@ -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);

examples/js/auto-agents/auto-generate.ts

@@ -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);

examples/js/databases/redis-example.ts

@@ -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);

examples/js/evaluation/eval-suite.ts

@@ -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);

examples/js/guardrails/content-guardrails.ts

@@ -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);

examples/js/knowledge/rag-example.ts

@@ -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);

examples/js/memory/conversation-memory.ts

@@ -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);

examples/js/observability/tracing.ts

@@ -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);

examples/js/planning/task-planning.ts

@@ -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);

examples/js/skills/skill-manager.ts

@@ -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);

examples/js/tools/basic-tools.ts

@@ -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);

examples/js/tools/custom-tool.ts

@@ -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);

examples/js/vector-stores/pinecone-example.ts

@@ -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);

examples/js/voice/tts-example.ts

@@ -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);

examples/js/workflows/basic-workflow.ts

@@ -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);

examples/js/workflows/conditional-workflow.ts

@@ -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);

examples/js/workflows/parallel-workflow.ts

@@ -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);

src/praisonai-agents/praisonaiagents/eval/accuracy.py

@@ -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,

src/praisonai-agents/praisonaiagents/eval/criteria.py

@@ -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,

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

@@ -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

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.3.2"
+version = "0.3.4"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/tests/test_workflow_patterns.py

@@ -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."""

src/praisonai-agents/tests/unit/eval/test_evaluators.py

@@ -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"

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.3.2"
+version = "0.3.4"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai-ts/FEATURE_PARITY_TRACKER.json

@@ -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"
+      ]
+    }
+  }
+}

src/praisonai-ts/jest.config.ts

@@ -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: [

src/praisonai-ts/package.json

@@ -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": {

src/praisonai-ts/specs/cli-spec.v1.yaml

@@ -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"

src/praisonai-ts/src/agent/context.ts

@@ -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);
+}

src/praisonai-ts/src/agent/enhanced.ts

@@ -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();
+  }
+}

src/praisonai-ts/src/agent/handoff.ts

@@ -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));
+    };
+  },
+};

src/praisonai-ts/src/agent/image.ts

@@ -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);
+}

src/praisonai-ts/src/agent/prompt-expander.ts

@@ -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);
+}

src/praisonai-ts/src/agent/query-rewriter.ts

@@ -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);
+}

src/praisonai-ts/src/agent/research.ts

@@ -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);
+}

src/praisonai-ts/src/agent/router.ts

@@ -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);
+}

src/praisonai-ts/src/auto/index.ts

@@ -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);
+}

src/praisonai-ts/src/cache/index.ts

@@ -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);
+}

src/praisonai-ts/src/cli/commands/chat.ts

@@ -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);
+  }
+}

src/praisonai-ts/src/cli/config/index.ts

@@ -0,0 +1,7 @@
+/**
+ * Config utilities index
+ */
+
+export * from './schema';
+export * from './load';
+export * from './resolve';

src/praisonai-ts/src/cli/config/load.ts

@@ -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;
+}

src/praisonai-ts/src/cli/config/resolve.ts

@@ -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 || {});
+}

src/praisonai-ts/src/cli/config/schema.ts

@@ -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;
+}

src/praisonai-ts/src/cli/index.ts

@@ -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();
+}

src/praisonai-ts/src/cli/runtime/env.ts

@@ -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;
+  }
+}

src/praisonai-ts/src/cli/runtime/exit.ts

@@ -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);
+}

src/praisonai-ts/src/cli/runtime/index.ts

@@ -0,0 +1,7 @@
+/**
+ * Runtime utilities index
+ */
+
+export * from './env';
+export * from './exit';
+export * from './lazy';

src/praisonai-ts/src/cli/runtime/lazy.ts

@@ -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;
+  }
+}

src/praisonai-ts/src/cli/spec/cli-spec.ts

@@ -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];
+}

src/praisonai-ts/src/eval/index.ts

@@ -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)}%`);
+    }
+  }
+}

src/praisonai-ts/src/events/index.ts

@@ -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();
+}

src/praisonai-ts/src/guardrails/index.ts

@@ -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 },
+          };
+        }
+      },
+    });
+  },
+};

src/praisonai-ts/src/guardrails/llm-guardrail.ts

@@ -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);
+}

src/praisonai-ts/src/index.ts

@@ -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';

src/praisonai-ts/src/integrations/index.ts

@@ -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';

src/praisonai-ts/src/integrations/observability/base.ts

@@ -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();
+}

src/praisonai-ts/src/integrations/observability/index.ts

@@ -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';

src/praisonai-ts/src/integrations/observability/langfuse.ts

@@ -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);
+}

src/praisonai-ts/src/integrations/vector/base.ts

@@ -0,0 +1,270 @@
+/**
+ * Base Vector Store - Abstract class for vector database integrations
+ * Matches mastra's MastraVector pattern
+ */
+
+export interface VectorDocument {
+  id: string;
+  vector: number[];
+  metadata?: Record<string, any>;
+  content?: string;
+}
+
+export interface QueryResult {
+  id: string;
+  score: number;
+  metadata?: Record<string, any>;
+  content?: string;
+  vector?: number[];
+}
+
+export interface IndexStats {
+  dimension: number;
+  count: number;
+  metric: 'cosine' | 'euclidean' | 'dotProduct';
+}
+
+export interface CreateIndexParams {
+  indexName: string;
+  dimension: number;
+  metric?: 'cosine' | 'euclidean' | 'dotProduct';
+}
+
+export interface UpsertParams {
+  indexName: string;
+  vectors: VectorDocument[];
+}
+
+export interface QueryParams {
+  indexName: string;
+  vector: number[];
+  topK?: number;
+  filter?: Record<string, any>;
+  includeMetadata?: boolean;
+  includeVectors?: boolean;
+}
+
+export interface DeleteParams {
+  indexName: string;
+  ids?: string[];
+  filter?: Record<string, any>;
+}
+
+/**
+ * Abstract base class for vector store implementations
+ */
+export abstract class BaseVectorStore {
+  readonly id: string;
+  readonly name: string;
+
+  constructor(config: { id: string; name?: string }) {
+    this.id = config.id;
+    this.name = config.name || 'VectorStore';
+  }
+
+  /**
+   * Create a new index
+   */
+  abstract createIndex(params: CreateIndexParams): Promise<void>;
+
+  /**
+   * List all indexes
+   */
+  abstract listIndexes(): Promise<string[]>;
+
+  /**
+   * Get index statistics
+   */
+  abstract describeIndex(indexName: string): Promise<IndexStats>;
+
+  /**
+   * Delete an index
+   */
+  abstract deleteIndex(indexName: string): Promise<void>;
+
+  /**
+   * Upsert vectors into an index
+   */
+  abstract upsert(params: UpsertParams): Promise<string[]>;
+
+  /**
+   * Query vectors by similarity
+   */
+  abstract query(params: QueryParams): Promise<QueryResult[]>;
+
+  /**
+   * Delete vectors by ID or filter
+   */
+  abstract delete(params: DeleteParams): Promise<void>;
+
+  /**
+   * Update vector metadata
+   */
+  abstract update(indexName: string, id: string, metadata: Record<string, any>): Promise<void>;
+}
+
+/**
+ * In-memory vector store for testing and development
+ */
+export class MemoryVectorStore extends BaseVectorStore {
+  private indexes: Map<string, {
+    dimension: number;
+    metric: 'cosine' | 'euclidean' | 'dotProduct';
+    vectors: Map<string, VectorDocument>;
+  }> = new Map();
+
+  constructor(config: { id: string } = { id: 'memory' }) {
+    super({ ...config, name: 'MemoryVectorStore' });
+  }
+
+  async createIndex(params: CreateIndexParams): Promise<void> {
+    if (this.indexes.has(params.indexName)) {
+      return; // Index already exists
+    }
+    this.indexes.set(params.indexName, {
+      dimension: params.dimension,
+      metric: params.metric || 'cosine',
+      vectors: new Map()
+    });
+  }
+
+  async listIndexes(): Promise<string[]> {
+    return Array.from(this.indexes.keys());
+  }
+
+  async describeIndex(indexName: string): Promise<IndexStats> {
+    const index = this.indexes.get(indexName);
+    if (!index) {
+      throw new Error(`Index ${indexName} not found`);
+    }
+    return {
+      dimension: index.dimension,
+      count: index.vectors.size,
+      metric: index.metric
+    };
+  }
+
+  async deleteIndex(indexName: string): Promise<void> {
+    this.indexes.delete(indexName);
+  }
+
+  async upsert(params: UpsertParams): Promise<string[]> {
+    const index = this.indexes.get(params.indexName);
+    if (!index) {
+      throw new Error(`Index ${params.indexName} not found`);
+    }
+
+    const ids: string[] = [];
+    for (const doc of params.vectors) {
+      index.vectors.set(doc.id, doc);
+      ids.push(doc.id);
+    }
+    return ids;
+  }
+
+  async query(params: QueryParams): Promise<QueryResult[]> {
+    const index = this.indexes.get(params.indexName);
+    if (!index) {
+      throw new Error(`Index ${params.indexName} not found`);
+    }
+
+    const results: QueryResult[] = [];
+    
+    for (const [id, doc] of index.vectors) {
+      // Apply filter if provided
+      if (params.filter && !this.matchesFilter(doc.metadata || {}, params.filter)) {
+        continue;
+      }
+
+      const score = this.calculateSimilarity(params.vector, doc.vector, index.metric);
+      results.push({
+        id,
+        score,
+        metadata: params.includeMetadata ? doc.metadata : undefined,
+        content: doc.content,
+        vector: params.includeVectors ? doc.vector : undefined
+      });
+    }
+
+    // Sort by score descending and limit
+    results.sort((a, b) => b.score - a.score);
+    return results.slice(0, params.topK || 10);
+  }
+
+  async delete(params: DeleteParams): Promise<void> {
+    const index = this.indexes.get(params.indexName);
+    if (!index) {
+      throw new Error(`Index ${params.indexName} not found`);
+    }
+
+    if (params.ids) {
+      for (const id of params.ids) {
+        index.vectors.delete(id);
+      }
+    } else if (params.filter) {
+      for (const [id, doc] of index.vectors) {
+        if (this.matchesFilter(doc.metadata || {}, params.filter)) {
+          index.vectors.delete(id);
+        }
+      }
+    }
+  }
+
+  async update(indexName: string, id: string, metadata: Record<string, any>): Promise<void> {
+    const index = this.indexes.get(indexName);
+    if (!index) {
+      throw new Error(`Index ${indexName} not found`);
+    }
+
+    const doc = index.vectors.get(id);
+    if (doc) {
+      doc.metadata = { ...doc.metadata, ...metadata };
+    }
+  }
+
+  private calculateSimilarity(a: number[], b: number[], metric: string): number {
+    if (a.length !== b.length) {
+      throw new Error('Vector dimensions must match');
+    }
+
+    switch (metric) {
+      case 'cosine':
+        return this.cosineSimilarity(a, b);
+      case 'euclidean':
+        return 1 / (1 + this.euclideanDistance(a, b));
+      case 'dotProduct':
+        return this.dotProduct(a, b);
+      default:
+        return this.cosineSimilarity(a, b);
+    }
+  }
+
+  private cosineSimilarity(a: number[], b: number[]): number {
+    const dot = this.dotProduct(a, b);
+    const normA = Math.sqrt(a.reduce((sum, x) => sum + x * x, 0));
+    const normB = Math.sqrt(b.reduce((sum, x) => sum + x * x, 0));
+    return dot / (normA * normB);
+  }
+
+  private euclideanDistance(a: number[], b: number[]): number {
+    return Math.sqrt(a.reduce((sum, x, i) => sum + Math.pow(x - b[i], 2), 0));
+  }
+
+  private dotProduct(a: number[], b: number[]): number {
+    return a.reduce((sum, x, i) => sum + x * b[i], 0);
+  }
+
+  private matchesFilter(metadata: Record<string, any>, filter: Record<string, any>): boolean {
+    for (const [key, value] of Object.entries(filter)) {
+      if (metadata[key] !== value) {
+        return false;
+      }
+    }
+    return true;
+  }
+}
+
+// Factory function
+export function createMemoryVectorStore(id?: string): MemoryVectorStore {
+  return new MemoryVectorStore({ id: id || 'memory' });
+}

src/praisonai-ts/src/integrations/vector/chroma.ts

@@ -0,0 +1,167 @@
+/**
+ * Chroma Vector Store Integration
+ */
+
+import { BaseVectorStore, CreateIndexParams, UpsertParams, QueryParams, DeleteParams, QueryResult, IndexStats } from './base';
+
+export interface ChromaConfig {
+  host?: string;
+  port?: number;
+  path?: string;
+}
+
+export class ChromaVectorStore extends BaseVectorStore {
+  private baseUrl: string;
+
+  constructor(config: ChromaConfig & { id?: string } = {}) {
+    super({ id: config.id || 'chroma', name: 'ChromaVectorStore' });
+    const host = config.host || 'localhost';
+    const port = config.port || 8000;
+    this.baseUrl = config.path || `http://${host}:${port}`;
+  }
+
+  private async request(path: string, options: RequestInit = {}): Promise<any> {
+    const response = await fetch(`${this.baseUrl}${path}`, {
+      ...options,
+      headers: {
+        'Content-Type': 'application/json',
+        ...options.headers as Record<string, string>
+      }
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`Chroma API error: ${response.status} - ${error}`);
+    }
+
+    const text = await response.text();
+    return text ? JSON.parse(text) : {};
+  }
+
+  async createIndex(params: CreateIndexParams): Promise<void> {
+    const metadataMap: Record<string, string> = {
+      cosine: 'cosine',
+      euclidean: 'l2',
+      dotProduct: 'ip'
+    };
+
+    await this.request('/api/v1/collections', {
+      method: 'POST',
+      body: JSON.stringify({
+        name: params.indexName,
+        metadata: {
+          'hnsw:space': metadataMap[params.metric || 'cosine'],
+          dimension: params.dimension
+        }
+      })
+    });
+  }
+
+  async listIndexes(): Promise<string[]> {
+    const response = await this.request('/api/v1/collections');
+    return (response || []).map((c: any) => c.name);
+  }
+
+  async describeIndex(indexName: string): Promise<IndexStats> {
+    const response = await this.request(`/api/v1/collections/${indexName}`);
+    
+    const spaceMap: Record<string, 'cosine' | 'euclidean' | 'dotProduct'> = {
+      'cosine': 'cosine',
+      'l2': 'euclidean',
+      'ip': 'dotProduct'
+    };
+
+    return {
+      dimension: response.metadata?.dimension || 0,
+      count: response.count || 0,
+      metric: spaceMap[response.metadata?.['hnsw:space']] || 'cosine'
+    };
+  }
+
+  async deleteIndex(indexName: string): Promise<void> {
+    await this.request(`/api/v1/collections/${indexName}`, { method: 'DELETE' });
+  }
+
+  async upsert(params: UpsertParams): Promise<string[]> {
+    const ids = params.vectors.map(v => v.id);
+    const embeddings = params.vectors.map(v => v.vector);
+    const metadatas = params.vectors.map(v => v.metadata || {});
+    const documents = params.vectors.map(v => v.content || '');
+
+    await this.request(`/api/v1/collections/${params.indexName}/upsert`, {
+      method: 'POST',
+      body: JSON.stringify({
+        ids,
+        embeddings,
+        metadatas,
+        documents
+      })
+    });
+
+    return ids;
+  }
+
+  async query(params: QueryParams): Promise<QueryResult[]> {
+    let where: any = undefined;
+    if (params.filter) {
+      where = params.filter;
+    }
+
+    const response = await this.request(`/api/v1/collections/${params.indexName}/query`, {
+      method: 'POST',
+      body: JSON.stringify({
+        query_embeddings: [params.vector],
+        n_results: params.topK || 10,
+        where,
+        include: [
+          'documents',
+          'metadatas',
+          'distances',
+          ...(params.includeVectors ? ['embeddings'] : [])
+        ]
+      })
+    });
+
+    const ids = response.ids?.[0] || [];
+    const distances = response.distances?.[0] || [];
+    const metadatas = response.metadatas?.[0] || [];
+    const documents = response.documents?.[0] || [];
+    const embeddings = response.embeddings?.[0] || [];
+
+    return ids.map((id: string, i: number) => ({
+      id,
+      score: 1 - (distances[i] || 0), // Convert distance to similarity
+      metadata: metadatas[i],
+      content: documents[i],
+      vector: embeddings[i]
+    }));
+  }
+
+  async delete(params: DeleteParams): Promise<void> {
+    if (params.ids) {
+      await this.request(`/api/v1/collections/${params.indexName}/delete`, {
+        method: 'POST',
+        body: JSON.stringify({ ids: params.ids })
+      });
+    } else if (params.filter) {
+      await this.request(`/api/v1/collections/${params.indexName}/delete`, {
+        method: 'POST',
+        body: JSON.stringify({ where: params.filter })
+      });
+    }
+  }
+
+  async update(indexName: string, id: string, metadata: Record<string, any>): Promise<void> {
+    await this.request(`/api/v1/collections/${indexName}/update`, {
+      method: 'POST',
+      body: JSON.stringify({
+        ids: [id],
+        metadatas: [metadata]
+      })
+    });
+  }
+}
+
+export function createChromaStore(config?: ChromaConfig): ChromaVectorStore {
+  return new ChromaVectorStore(config);
+}

src/praisonai-ts/src/integrations/vector/index.ts

@@ -0,0 +1,17 @@
+/**
+ * Vector Store Integrations
+ * Provides adapters for various vector databases
+ */
+
+export * from './base';
+export * from './pinecone';
+export * from './weaviate';
+export * from './qdrant';
+export * from './chroma';
+
+// Re-export factory functions for convenience
+export { createMemoryVectorStore } from './base';
+export { createPineconeStore } from './pinecone';
+export { createWeaviateStore } from './weaviate';
+export { createQdrantStore } from './qdrant';
+export { createChromaStore } from './chroma';

src/praisonai-ts/src/integrations/vector/pinecone.ts

@@ -0,0 +1,200 @@
+/**
+ * Pinecone Vector Store Integration
+ */
+
+import { BaseVectorStore, CreateIndexParams, UpsertParams, QueryParams, DeleteParams, QueryResult, IndexStats } from './base';
+
+export interface PineconeConfig {
+  apiKey: string;
+  environment?: string;
+  projectId?: string;
+}
+
+export class PineconeVectorStore extends BaseVectorStore {
+  private apiKey: string;
+  private baseUrl: string;
+  private indexHosts: Map<string, string> = new Map();
+
+  constructor(config: PineconeConfig & { id?: string }) {
+    super({ id: config.id || 'pinecone', name: 'PineconeVectorStore' });
+    this.apiKey = config.apiKey;
+    this.baseUrl = 'https://api.pinecone.io';
+  }
+
+  private async request(path: string, options: RequestInit = {}): Promise<any> {
+    const response = await fetch(`${this.baseUrl}${path}`, {
+      ...options,
+      headers: {
+        'Api-Key': this.apiKey,
+        'Content-Type': 'application/json',
+        ...options.headers
+      }
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`Pinecone API error: ${response.status} - ${error}`);
+    }
+
+    const text = await response.text();
+    return text ? JSON.parse(text) : {};
+  }
+
+  private async indexRequest(indexName: string, path: string, options: RequestInit = {}): Promise<any> {
+    let host = this.indexHosts.get(indexName);
+    if (!host) {
+      const info = await this.request(`/indexes/${indexName}`);
+      host = info.host as string;
+      this.indexHosts.set(indexName, host);
+    }
+
+    const response = await fetch(`https://${host}${path}`, {
+      ...options,
+      headers: {
+        'Api-Key': this.apiKey,
+        'Content-Type': 'application/json',
+        ...options.headers
+      }
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`Pinecone Index API error: ${response.status} - ${error}`);
+    }
+
+    const text = await response.text();
+    return text ? JSON.parse(text) : {};
+  }
+
+  async createIndex(params: CreateIndexParams): Promise<void> {
+    const metricMap: Record<string, string> = {
+      cosine: 'cosine',
+      euclidean: 'euclidean',
+      dotProduct: 'dotproduct'
+    };
+
+    await this.request('/indexes', {
+      method: 'POST',
+      body: JSON.stringify({
+        name: params.indexName,
+        dimension: params.dimension,
+        metric: metricMap[params.metric || 'cosine'],
+        spec: {
+          serverless: {
+            cloud: 'aws',
+            region: 'us-east-1'
+          }
+        }
+      })
+    });
+
+    // Wait for index to be ready
+    let ready = false;
+    let attempts = 0;
+    while (!ready && attempts < 60) {
+      await new Promise(r => setTimeout(r, 2000));
+      try {
+        const info = await this.request(`/indexes/${params.indexName}`);
+        ready = info.status?.ready === true;
+      } catch {
+        // Index not ready yet
+      }
+      attempts++;
+    }
+  }
+
+  async listIndexes(): Promise<string[]> {
+    const response = await this.request('/indexes');
+    return (response.indexes || []).map((idx: any) => idx.name);
+  }
+
+  async describeIndex(indexName: string): Promise<IndexStats> {
+    const info = await this.request(`/indexes/${indexName}`);
+    const stats = await this.indexRequest(indexName, '/describe_index_stats', { method: 'POST', body: '{}' });
+    
+    const metricMap: Record<string, 'cosine' | 'euclidean' | 'dotProduct'> = {
+      cosine: 'cosine',
+      euclidean: 'euclidean',
+      dotproduct: 'dotProduct'
+    };
+
+    return {
+      dimension: info.dimension,
+      count: stats.totalVectorCount || 0,
+      metric: metricMap[info.metric] || 'cosine'
+    };
+  }
+
+  async deleteIndex(indexName: string): Promise<void> {
+    await this.request(`/indexes/${indexName}`, { method: 'DELETE' });
+    this.indexHosts.delete(indexName);
+  }
+
+  async upsert(params: UpsertParams): Promise<string[]> {
+    const vectors = params.vectors.map(v => ({
+      id: v.id,
+      values: v.vector,
+      metadata: v.metadata
+    }));
+
+    // Batch upsert in chunks of 100
+    const batchSize = 100;
+    const ids: string[] = [];
+
+    for (let i = 0; i < vectors.length; i += batchSize) {
+      const batch = vectors.slice(i, i + batchSize);
+      await this.indexRequest(params.indexName, '/vectors/upsert', {
+        method: 'POST',
+        body: JSON.stringify({ vectors: batch })
+      });
+      ids.push(...batch.map(v => v.id));
+    }
+
+    return ids;
+  }
+
+  async query(params: QueryParams): Promise<QueryResult[]> {
+    const response = await this.indexRequest(params.indexName, '/query', {
+      method: 'POST',
+      body: JSON.stringify({
+        vector: params.vector,
+        topK: params.topK || 10,
+        filter: params.filter,
+        includeMetadata: params.includeMetadata !== false,
+        includeValues: params.includeVectors
+      })
+    });
+
+    return (response.matches || []).map((match: any) => ({
+      id: match.id,
+      score: match.score,
+      metadata: match.metadata,
+      vector: match.values
+    }));
+  }
+
+  async delete(params: DeleteParams): Promise<void> {
+    if (params.ids) {
+      await this.indexRequest(params.indexName, '/vectors/delete', {
+        method: 'POST',
+        body: JSON.stringify({ ids: params.ids })
+      });
+    } else if (params.filter) {
+      await this.indexRequest(params.indexName, '/vectors/delete', {
+        method: 'POST',
+        body: JSON.stringify({ filter: params.filter })
+      });
+    }
+  }
+
+  async update(indexName: string, id: string, metadata: Record<string, any>): Promise<void> {
+    await this.indexRequest(indexName, '/vectors/update', {
+      method: 'POST',
+      body: JSON.stringify({ id, setMetadata: metadata })
+    });
+  }
+}
+
+export function createPineconeStore(config: PineconeConfig): PineconeVectorStore {
+  return new PineconeVectorStore(config);
+}

src/praisonai-ts/src/integrations/vector/qdrant.ts

@@ -0,0 +1,171 @@
+/**
+ * Qdrant Vector Store Integration
+ */
+
+import { BaseVectorStore, CreateIndexParams, UpsertParams, QueryParams, DeleteParams, QueryResult, IndexStats } from './base';
+
+export interface QdrantConfig {
+  url: string;
+  apiKey?: string;
+}
+
+export class QdrantVectorStore extends BaseVectorStore {
+  private url: string;
+  private apiKey?: string;
+
+  constructor(config: QdrantConfig & { id?: string }) {
+    super({ id: config.id || 'qdrant', name: 'QdrantVectorStore' });
+    this.url = config.url.replace(/\/$/, '');
+    this.apiKey = config.apiKey;
+  }
+
+  private async request(path: string, options: RequestInit = {}): Promise<any> {
+    const headers: Record<string, string> = {
+      'Content-Type': 'application/json'
+    };
+    
+    if (this.apiKey) {
+      headers['api-key'] = this.apiKey;
+    }
+
+    const response = await fetch(`${this.url}${path}`, {
+      ...options,
+      headers: { ...headers, ...options.headers as Record<string, string> }
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`Qdrant API error: ${response.status} - ${error}`);
+    }
+
+    const text = await response.text();
+    return text ? JSON.parse(text) : {};
+  }
+
+  async createIndex(params: CreateIndexParams): Promise<void> {
+    const distanceMap: Record<string, string> = {
+      cosine: 'Cosine',
+      euclidean: 'Euclid',
+      dotProduct: 'Dot'
+    };
+
+    await this.request(`/collections/${params.indexName}`, {
+      method: 'PUT',
+      body: JSON.stringify({
+        vectors: {
+          size: params.dimension,
+          distance: distanceMap[params.metric || 'cosine']
+        }
+      })
+    });
+  }
+
+  async listIndexes(): Promise<string[]> {
+    const response = await this.request('/collections');
+    return (response.result?.collections || []).map((c: any) => c.name);
+  }
+
+  async describeIndex(indexName: string): Promise<IndexStats> {
+    const response = await this.request(`/collections/${indexName}`);
+    const config = response.result?.config?.params?.vectors;
+    
+    const distanceMap: Record<string, 'cosine' | 'euclidean' | 'dotProduct'> = {
+      'Cosine': 'cosine',
+      'Euclid': 'euclidean',
+      'Dot': 'dotProduct'
+    };
+
+    return {
+      dimension: config?.size || 0,
+      count: response.result?.points_count || 0,
+      metric: distanceMap[config?.distance] || 'cosine'
+    };
+  }
+
+  async deleteIndex(indexName: string): Promise<void> {
+    await this.request(`/collections/${indexName}`, { method: 'DELETE' });
+  }
+
+  async upsert(params: UpsertParams): Promise<string[]> {
+    const points = params.vectors.map(v => ({
+      id: v.id,
+      vector: v.vector,
+      payload: {
+        ...v.metadata,
+        _content: v.content
+      }
+    }));
+
+    await this.request(`/collections/${params.indexName}/points`, {
+      method: 'PUT',
+      body: JSON.stringify({ points })
+    });
+
+    return params.vectors.map(v => v.id);
+  }
+
+  async query(params: QueryParams): Promise<QueryResult[]> {
+    let filter: any = undefined;
+    if (params.filter) {
+      filter = {
+        must: Object.entries(params.filter).map(([key, value]) => ({
+          key,
+          match: { value }
+        }))
+      };
+    }
+
+    const response = await this.request(`/collections/${params.indexName}/points/search`, {
+      method: 'POST',
+      body: JSON.stringify({
+        vector: params.vector,
+        limit: params.topK || 10,
+        filter,
+        with_payload: params.includeMetadata !== false,
+        with_vector: params.includeVectors
+      })
+    });
+
+    return (response.result || []).map((r: any) => ({
+      id: r.id,
+      score: r.score,
+      metadata: r.payload ? { ...r.payload, _content: undefined } : undefined,
+      content: r.payload?._content,
+      vector: r.vector
+    }));
+  }
+
+  async delete(params: DeleteParams): Promise<void> {
+    if (params.ids) {
+      await this.request(`/collections/${params.indexName}/points/delete`, {
+        method: 'POST',
+        body: JSON.stringify({ points: params.ids })
+      });
+    } else if (params.filter) {
+      const filter = {
+        must: Object.entries(params.filter).map(([key, value]) => ({
+          key,
+          match: { value }
+        }))
+      };
+      await this.request(`/collections/${params.indexName}/points/delete`, {
+        method: 'POST',
+        body: JSON.stringify({ filter })
+      });
+    }
+  }
+
+  async update(indexName: string, id: string, metadata: Record<string, any>): Promise<void> {
+    await this.request(`/collections/${indexName}/points/payload`, {
+      method: 'POST',
+      body: JSON.stringify({
+        points: [id],
+        payload: metadata
+      })
+    });
+  }
+}
+
+export function createQdrantStore(config: QdrantConfig): QdrantVectorStore {
+  return new QdrantVectorStore(config);
+}

src/praisonai-ts/src/integrations/vector/weaviate.ts

@@ -0,0 +1,243 @@
+/**
+ * Weaviate Vector Store Integration
+ */
+
+import { BaseVectorStore, CreateIndexParams, UpsertParams, QueryParams, DeleteParams, QueryResult, IndexStats } from './base';
+
+export interface WeaviateConfig {
+  host: string;
+  apiKey?: string;
+  scheme?: 'http' | 'https';
+}
+
+export class WeaviateVectorStore extends BaseVectorStore {
+  private host: string;
+  private apiKey?: string;
+  private scheme: string;
+
+  constructor(config: WeaviateConfig & { id?: string }) {
+    super({ id: config.id || 'weaviate', name: 'WeaviateVectorStore' });
+    this.host = config.host;
+    this.apiKey = config.apiKey;
+    this.scheme = config.scheme || 'https';
+  }
+
+  private get baseUrl(): string {
+    return `${this.scheme}://${this.host}`;
+  }
+
+  private async request(path: string, options: RequestInit = {}): Promise<any> {
+    const headers: Record<string, string> = {
+      'Content-Type': 'application/json'
+    };
+    
+    if (this.apiKey) {
+      headers['Authorization'] = `Bearer ${this.apiKey}`;
+    }
+
+    const response = await fetch(`${this.baseUrl}${path}`, {
+      ...options,
+      headers: { ...headers, ...options.headers as Record<string, string> }
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`Weaviate API error: ${response.status} - ${error}`);
+    }
+
+    const text = await response.text();
+    return text ? JSON.parse(text) : {};
+  }
+
+  private async graphql(query: string, variables?: Record<string, any>): Promise<any> {
+    const response = await this.request('/v1/graphql', {
+      method: 'POST',
+      body: JSON.stringify({ query, variables })
+    });
+    return response;
+  }
+
+  private toClassName(indexName: string): string {
+    // Weaviate class names must start with uppercase
+    return indexName.charAt(0).toUpperCase() + indexName.slice(1);
+  }
+
+  async createIndex(params: CreateIndexParams): Promise<void> {
+    const className = this.toClassName(params.indexName);
+    
+    const vectorIndexConfig: Record<string, any> = {
+      distance: params.metric === 'euclidean' ? 'l2-squared' : 
+                params.metric === 'dotProduct' ? 'dot' : 'cosine'
+    };
+
+    await this.request('/v1/schema', {
+      method: 'POST',
+      body: JSON.stringify({
+        class: className,
+        vectorIndexConfig,
+        properties: [
+          {
+            name: 'content',
+            dataType: ['text']
+          },
+          {
+            name: 'metadata',
+            dataType: ['object']
+          }
+        ]
+      })
+    });
+  }
+
+  async listIndexes(): Promise<string[]> {
+    const response = await this.request('/v1/schema');
+    return (response.classes || []).map((c: any) => c.class.toLowerCase());
+  }
+
+  async describeIndex(indexName: string): Promise<IndexStats> {
+    const className = this.toClassName(indexName);
+    const response = await this.request(`/v1/schema/${className}`);
+    
+    // Get count via aggregate
+    const countQuery = `{
+      Aggregate {
+        ${className} {
+          meta { count }
+        }
+      }
+    }`;
+    const countResponse = await this.graphql(countQuery);
+    const count = countResponse.data?.Aggregate?.[className]?.[0]?.meta?.count || 0;
+
+    const distanceMap: Record<string, 'cosine' | 'euclidean' | 'dotProduct'> = {
+      'cosine': 'cosine',
+      'l2-squared': 'euclidean',
+      'dot': 'dotProduct'
+    };
+
+    return {
+      dimension: response.vectorIndexConfig?.dimensions || 0,
+      count,
+      metric: distanceMap[response.vectorIndexConfig?.distance] || 'cosine'
+    };
+  }
+
+  async deleteIndex(indexName: string): Promise<void> {
+    const className = this.toClassName(indexName);
+    await this.request(`/v1/schema/${className}`, { method: 'DELETE' });
+  }
+
+  async upsert(params: UpsertParams): Promise<string[]> {
+    const className = this.toClassName(params.indexName);
+    const ids: string[] = [];
+
+    // Batch upsert
+    const objects = params.vectors.map(v => ({
+      class: className,
+      id: v.id,
+      vector: v.vector,
+      properties: {
+        content: v.content || '',
+        metadata: v.metadata || {}
+      }
+    }));
+
+    // Batch in chunks of 100
+    const batchSize = 100;
+    for (let i = 0; i < objects.length; i += batchSize) {
+      const batch = objects.slice(i, i + batchSize);
+      await this.request('/v1/batch/objects', {
+        method: 'POST',
+        body: JSON.stringify({ objects: batch })
+      });
+      ids.push(...batch.map(o => o.id));
+    }
+
+    return ids;
+  }
+
+  async query(params: QueryParams): Promise<QueryResult[]> {
+    const className = this.toClassName(params.indexName);
+    const topK = params.topK || 10;
+
+    let whereClause = '';
+    if (params.filter) {
+      const conditions = Object.entries(params.filter).map(([key, value]) => {
+        return `{ path: ["metadata", "${key}"], operator: Equal, valueString: "${value}" }`;
+      });
+      if (conditions.length > 0) {
+        whereClause = `where: { operator: And, operands: [${conditions.join(', ')}] }`;
+      }
+    }
+
+    const query = `{
+      Get {
+        ${className}(
+          nearVector: { vector: [${params.vector.join(',')}] }
+          limit: ${topK}
+          ${whereClause}
+        ) {
+          _additional {
+            id
+            distance
+            ${params.includeVectors ? 'vector' : ''}
+          }
+          content
+          metadata
+        }
+      }
+    }`;
+
+    const response = await this.graphql(query);
+    const results = response.data?.Get?.[className] || [];
+
+    return results.map((r: any) => ({
+      id: r._additional.id,
+      score: 1 - (r._additional.distance || 0), // Convert distance to similarity
+      metadata: r.metadata,
+      content: r.content,
+      vector: r._additional.vector
+    }));
+  }
+
+  async delete(params: DeleteParams): Promise<void> {
+    const className = this.toClassName(params.indexName);
+
+    if (params.ids) {
+      for (const id of params.ids) {
+        await this.request(`/v1/objects/${className}/${id}`, { method: 'DELETE' });
+      }
+    } else if (params.filter) {
+      // Delete by filter using batch delete
+      const conditions = Object.entries(params.filter).map(([key, value]) => ({
+        path: ['metadata', key],
+        operator: 'Equal',
+        valueString: String(value)
+      }));
+
+      await this.request('/v1/batch/objects', {
+        method: 'DELETE',
+        body: JSON.stringify({
+          match: {
+            class: className,
+            where: { operator: 'And', operands: conditions }
+          }
+        })
+      });
+    }
+  }
+
+  async update(indexName: string, id: string, metadata: Record<string, any>): Promise<void> {
+    const className = this.toClassName(indexName);
+    await this.request(`/v1/objects/${className}/${id}`, {
+      method: 'PATCH',
+      body: JSON.stringify({
+        properties: { metadata }
+      })
+    });
+  }
+}
+
+export function createWeaviateStore(config: WeaviateConfig): WeaviateVectorStore {
+  return new WeaviateVectorStore(config);
+}

src/praisonai-ts/src/integrations/voice/base.ts

@@ -0,0 +1,242 @@
+/**
+ * Base Voice - Abstract class for voice/TTS integrations
+ * Matches mastra's MastraVoice pattern
+ */
+
+export interface VoiceConfig {
+  apiKey?: string;
+  model?: string;
+  voice?: string;
+}
+
+export interface SpeakOptions {
+  voice?: string;
+  speed?: number;
+  pitch?: number;
+  format?: 'mp3' | 'wav' | 'ogg' | 'opus';
+}
+
+export interface ListenOptions {
+  language?: string;
+  model?: string;
+}
+
+export interface Speaker {
+  id: string;
+  name: string;
+  language?: string;
+  gender?: 'male' | 'female' | 'neutral';
+}
+
+/**
+ * Abstract base class for voice providers
+ */
+export abstract class BaseVoiceProvider {
+  readonly name: string;
+  protected apiKey?: string;
+  protected defaultVoice?: string;
+
+  constructor(config: VoiceConfig & { name?: string }) {
+    this.name = config.name || 'VoiceProvider';
+    this.apiKey = config.apiKey;
+    this.defaultVoice = config.voice;
+  }
+
+  /**
+   * Convert text to speech
+   */
+  abstract speak(text: string, options?: SpeakOptions): Promise<Buffer | ReadableStream>;
+
+  /**
+   * Convert speech to text
+   */
+  abstract listen(audio: Buffer | ReadableStream, options?: ListenOptions): Promise<string>;
+
+  /**
+   * Get available voices/speakers
+   */
+  abstract getSpeakers(): Promise<Speaker[]>;
+
+  /**
+   * Check if provider is available
+   */
+  abstract isAvailable(): Promise<boolean>;
+}
+
+/**
+ * OpenAI Voice Provider (TTS and Whisper)
+ */
+export class OpenAIVoiceProvider extends BaseVoiceProvider {
+  private baseUrl: string;
+
+  constructor(config: VoiceConfig = {}) {
+    super({ ...config, name: 'OpenAIVoice' });
+    this.apiKey = config.apiKey || process.env.OPENAI_API_KEY;
+    this.defaultVoice = config.voice || 'alloy';
+    this.baseUrl = 'https://api.openai.com/v1';
+  }
+
+  async speak(text: string, options?: SpeakOptions): Promise<Buffer> {
+    if (!this.apiKey) {
+      throw new Error('OpenAI API key required for TTS');
+    }
+
+    const response = await fetch(`${this.baseUrl}/audio/speech`, {
+      method: 'POST',
+      headers: {
+        'Authorization': `Bearer ${this.apiKey}`,
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        model: 'tts-1',
+        input: text,
+        voice: options?.voice || this.defaultVoice,
+        speed: options?.speed || 1.0,
+        response_format: options?.format || 'mp3'
+      })
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`OpenAI TTS error: ${response.status} - ${error}`);
+    }
+
+    const arrayBuffer = await response.arrayBuffer();
+    return Buffer.from(arrayBuffer);
+  }
+
+  async listen(audio: Buffer | ReadableStream, options?: ListenOptions): Promise<string> {
+    if (!this.apiKey) {
+      throw new Error('OpenAI API key required for Whisper');
+    }
+
+    const formData = new FormData();
+    
+    // Convert Buffer to Blob
+    const audioBlob = audio instanceof Buffer 
+      ? new Blob([new Uint8Array(audio)], { type: 'audio/mp3' })
+      : audio;
+    
+    formData.append('file', audioBlob as Blob, 'audio.mp3');
+    formData.append('model', options?.model || 'whisper-1');
+    if (options?.language) {
+      formData.append('language', options.language);
+    }
+
+    const response = await fetch(`${this.baseUrl}/audio/transcriptions`, {
+      method: 'POST',
+      headers: {
+        'Authorization': `Bearer ${this.apiKey}`
+      },
+      body: formData
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`OpenAI Whisper error: ${response.status} - ${error}`);
+    }
+
+    const result = await response.json();
+    return result.text;
+  }
+
+  async getSpeakers(): Promise<Speaker[]> {
+    return [
+      { id: 'alloy', name: 'Alloy', gender: 'neutral' },
+      { id: 'echo', name: 'Echo', gender: 'male' },
+      { id: 'fable', name: 'Fable', gender: 'neutral' },
+      { id: 'onyx', name: 'Onyx', gender: 'male' },
+      { id: 'nova', name: 'Nova', gender: 'female' },
+      { id: 'shimmer', name: 'Shimmer', gender: 'female' }
+    ];
+  }
+
+  async isAvailable(): Promise<boolean> {
+    return !!this.apiKey;
+  }
+}
+
+/**
+ * ElevenLabs Voice Provider
+ */
+export class ElevenLabsVoiceProvider extends BaseVoiceProvider {
+  private baseUrl: string;
+
+  constructor(config: VoiceConfig = {}) {
+    super({ ...config, name: 'ElevenLabsVoice' });
+    this.apiKey = config.apiKey || process.env.ELEVENLABS_API_KEY;
+    this.baseUrl = 'https://api.elevenlabs.io/v1';
+  }
+
+  async speak(text: string, options?: SpeakOptions): Promise<Buffer> {
+    if (!this.apiKey) {
+      throw new Error('ElevenLabs API key required');
+    }
+
+    const voiceId = options?.voice || this.defaultVoice || '21m00Tcm4TlvDq8ikWAM'; // Rachel
+
+    const response = await fetch(`${this.baseUrl}/text-to-speech/${voiceId}`, {
+      method: 'POST',
+      headers: {
+        'xi-api-key': this.apiKey,
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        text,
+        model_id: 'eleven_monolingual_v1',
+        voice_settings: {
+          stability: 0.5,
+          similarity_boost: 0.5
+        }
+      })
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`ElevenLabs error: ${response.status} - ${error}`);
+    }
+
+    const arrayBuffer = await response.arrayBuffer();
+    return Buffer.from(arrayBuffer);
+  }
+
+  async listen(_audio: Buffer | ReadableStream, _options?: ListenOptions): Promise<string> {
+    throw new Error('ElevenLabs does not support speech-to-text');
+  }
+
+  async getSpeakers(): Promise<Speaker[]> {
+    if (!this.apiKey) {
+      return [];
+    }
+
+    const response = await fetch(`${this.baseUrl}/voices`, {
+      headers: {
+        'xi-api-key': this.apiKey
+      }
+    });
+
+    if (!response.ok) {
+      return [];
+    }
+
+    const data = await response.json();
+    return (data.voices || []).map((v: any) => ({
+      id: v.voice_id,
+      name: v.name,
+      gender: v.labels?.gender
+    }));
+  }
+
+  async isAvailable(): Promise<boolean> {
+    return !!this.apiKey;
+  }
+}
+
+// Factory functions
+export function createOpenAIVoice(config?: VoiceConfig): OpenAIVoiceProvider {
+  return new OpenAIVoiceProvider(config);
+}
+
+export function createElevenLabsVoice(config?: VoiceConfig): ElevenLabsVoiceProvider {
+  return new ElevenLabsVoiceProvider(config);
+}

src/praisonai-ts/src/integrations/voice/index.ts

@@ -0,0 +1,9 @@
+/**
+ * Voice Integrations
+ * Provides adapters for TTS and STT services
+ */
+
+export * from './base';
+
+// Re-export factory functions
+export { createOpenAIVoice, createElevenLabsVoice } from './base';

src/praisonai-ts/src/knowledge/chunking.ts

@@ -0,0 +1,193 @@
+/**
+ * Chunking - Text chunking utilities for RAG
+ */
+
+export type ChunkStrategy = 'size' | 'sentence' | 'paragraph' | 'semantic';
+
+export interface ChunkingConfig {
+  chunkSize?: number;
+  overlap?: number;
+  strategy?: ChunkStrategy;
+  separator?: string;
+}
+
+export interface Chunk {
+  content: string;
+  index: number;
+  startOffset: number;
+  endOffset: number;
+  metadata?: Record<string, any>;
+}
+
+/**
+ * Chunking class for splitting text into chunks
+ */
+export class Chunking {
+  private chunkSize: number;
+  private overlap: number;
+  private strategy: ChunkStrategy;
+  private separator: string;
+
+  constructor(config: ChunkingConfig = {}) {
+    this.chunkSize = config.chunkSize ?? 500;
+    this.overlap = config.overlap ?? 50;
+    this.strategy = config.strategy ?? 'size';
+    this.separator = config.separator ?? ' ';
+  }
+
+  /**
+   * Chunk text based on configured strategy
+   */
+  chunk(text: string): Chunk[] {
+    switch (this.strategy) {
+      case 'sentence':
+        return this.chunkBySentence(text);
+      case 'paragraph':
+        return this.chunkByParagraph(text);
+      case 'semantic':
+        return this.chunkBySemantic(text);
+      default:
+        return this.chunkBySize(text);
+    }
+  }
+
+  /**
+   * Chunk by fixed size with overlap
+   */
+  chunkBySize(text: string): Chunk[] {
+    const chunks: Chunk[] = [];
+    let startOffset = 0;
+    let index = 0;
+
+    while (startOffset < text.length) {
+      const endOffset = Math.min(startOffset + this.chunkSize, text.length);
+      const content = text.slice(startOffset, endOffset);
+
+      chunks.push({
+        content,
+        index,
+        startOffset,
+        endOffset
+      });
+
+      startOffset = endOffset - this.overlap;
+      if (startOffset >= text.length - this.overlap) break;
+      index++;
+    }
+
+    return chunks;
+  }
+
+  /**
+   * Chunk by sentences
+   */
+  chunkBySentence(text: string): Chunk[] {
+    const sentences = text.match(/[^.!?]+[.!?]+/g) || [text];
+    return sentences.map((content, index) => {
+      const startOffset = text.indexOf(content);
+      return {
+        content: content.trim(),
+        index,
+        startOffset,
+        endOffset: startOffset + content.length
+      };
+    });
+  }
+
+  /**
+   * Chunk by paragraphs
+   */
+  chunkByParagraph(text: string): Chunk[] {
+    const paragraphs = text.split(/\n\n+/).filter(p => p.trim().length > 0);
+    let offset = 0;
+    
+    return paragraphs.map((content, index) => {
+      const startOffset = text.indexOf(content, offset);
+      offset = startOffset + content.length;
+      return {
+        content: content.trim(),
+        index,
+        startOffset,
+        endOffset: offset
+      };
+    });
+  }
+
+  /**
+   * Chunk by semantic boundaries (simplified)
+   */
+  chunkBySemantic(text: string): Chunk[] {
+    // Simplified semantic chunking - split on headers, lists, etc.
+    const patterns = [
+      /^#{1,6}\s+.+$/gm,  // Headers
+      /^\s*[-*]\s+/gm,    // List items
+      /^\d+\.\s+/gm       // Numbered lists
+    ];
+
+    let chunks: Chunk[] = [];
+    let lastEnd = 0;
+    let index = 0;
+
+    // Find semantic boundaries
+    const boundaries: number[] = [0];
+    for (const pattern of patterns) {
+      let match;
+      while ((match = pattern.exec(text)) !== null) {
+        boundaries.push(match.index);
+      }
+    }
+    boundaries.push(text.length);
+    boundaries.sort((a, b) => a - b);
+
+    // Create chunks from boundaries
+    for (let i = 0; i < boundaries.length - 1; i++) {
+      const start = boundaries[i];
+      const end = boundaries[i + 1];
+      const content = text.slice(start, end).trim();
+      
+      if (content.length > 0) {
+        chunks.push({
+          content,
+          index: index++,
+          startOffset: start,
+          endOffset: end
+        });
+      }
+    }
+
+    return chunks.length > 0 ? chunks : this.chunkBySize(text);
+  }
+
+  /**
+   * Merge small chunks
+   */
+  mergeSmallChunks(chunks: Chunk[], minSize: number = 100): Chunk[] {
+    const merged: Chunk[] = [];
+    let current: Chunk | null = null;
+
+    for (const chunk of chunks) {
+      if (!current) {
+        current = { ...chunk };
+      } else if (current.content.length < minSize) {
+        current.content += '\n' + chunk.content;
+        current.endOffset = chunk.endOffset;
+      } else {
+        merged.push(current);
+        current = { ...chunk };
+      }
+    }
+
+    if (current) {
+      merged.push(current);
+    }
+
+    return merged.map((c, i) => ({ ...c, index: i }));
+  }
+}
+
+/**
+ * Create a Chunking instance
+ */
+export function createChunking(config?: ChunkingConfig): Chunking {
+  return new Chunking(config);
+}

src/praisonai-ts/src/knowledge/graph-rag.ts

@@ -0,0 +1,366 @@
+/**
+ * Graph RAG - Graph-based Retrieval Augmented Generation
+ * Inspired by mastra's graph-rag module
+ */
+
+export interface GraphNode {
+  id: string;
+  type: string;
+  content: string;
+  metadata?: Record<string, any>;
+  embedding?: number[];
+}
+
+export interface GraphEdge {
+  id: string;
+  source: string;
+  target: string;
+  type: string;
+  weight?: number;
+  metadata?: Record<string, any>;
+}
+
+export interface GraphQueryResult {
+  nodes: GraphNode[];
+  edges: GraphEdge[];
+  paths: GraphNode[][];
+  score: number;
+}
+
+export interface GraphRAGConfig {
+  maxDepth?: number;
+  maxNodes?: number;
+  minScore?: number;
+  includeEdges?: boolean;
+}
+
+/**
+ * In-memory Graph Store for Graph RAG
+ */
+export class GraphStore {
+  private nodes: Map<string, GraphNode> = new Map();
+  private edges: Map<string, GraphEdge> = new Map();
+  private adjacencyList: Map<string, Set<string>> = new Map();
+  private reverseAdjacencyList: Map<string, Set<string>> = new Map();
+
+  /**
+   * Add a node to the graph
+   */
+  addNode(node: GraphNode): void {
+    this.nodes.set(node.id, node);
+    if (!this.adjacencyList.has(node.id)) {
+      this.adjacencyList.set(node.id, new Set());
+    }
+    if (!this.reverseAdjacencyList.has(node.id)) {
+      this.reverseAdjacencyList.set(node.id, new Set());
+    }
+  }
+
+  /**
+   * Add an edge to the graph
+   */
+  addEdge(edge: GraphEdge): void {
+    this.edges.set(edge.id, edge);
+    
+    // Update adjacency lists
+    if (!this.adjacencyList.has(edge.source)) {
+      this.adjacencyList.set(edge.source, new Set());
+    }
+    this.adjacencyList.get(edge.source)!.add(edge.target);
+
+    if (!this.reverseAdjacencyList.has(edge.target)) {
+      this.reverseAdjacencyList.set(edge.target, new Set());
+    }
+    this.reverseAdjacencyList.get(edge.target)!.add(edge.source);
+  }
+
+  /**
+   * Get a node by ID
+   */
+  getNode(id: string): GraphNode | undefined {
+    return this.nodes.get(id);
+  }
+
+  /**
+   * Get all nodes
+   */
+  getAllNodes(): GraphNode[] {
+    return Array.from(this.nodes.values());
+  }
+
+  /**
+   * Get neighbors of a node
+   */
+  getNeighbors(nodeId: string, direction: 'outgoing' | 'incoming' | 'both' = 'both'): GraphNode[] {
+    const neighborIds = new Set<string>();
+
+    if (direction === 'outgoing' || direction === 'both') {
+      const outgoing = this.adjacencyList.get(nodeId);
+      if (outgoing) {
+        outgoing.forEach(id => neighborIds.add(id));
+      }
+    }
+
+    if (direction === 'incoming' || direction === 'both') {
+      const incoming = this.reverseAdjacencyList.get(nodeId);
+      if (incoming) {
+        incoming.forEach(id => neighborIds.add(id));
+      }
+    }
+
+    return Array.from(neighborIds)
+      .map(id => this.nodes.get(id))
+      .filter((n): n is GraphNode => n !== undefined);
+  }
+
+  /**
+   * Get edges between two nodes
+   */
+  getEdgesBetween(sourceId: string, targetId: string): GraphEdge[] {
+    return Array.from(this.edges.values()).filter(
+      e => (e.source === sourceId && e.target === targetId) ||
+           (e.source === targetId && e.target === sourceId)
+    );
+  }
+
+  /**
+   * Find paths between two nodes using BFS
+   */
+  findPaths(startId: string, endId: string, maxDepth: number = 3): GraphNode[][] {
+    const paths: GraphNode[][] = [];
+    const queue: Array<{ path: string[]; visited: Set<string> }> = [
+      { path: [startId], visited: new Set([startId]) }
+    ];
+
+    while (queue.length > 0) {
+      const { path, visited } = queue.shift()!;
+      const currentId = path[path.length - 1];
+
+      if (currentId === endId) {
+        paths.push(path.map(id => this.nodes.get(id)!).filter(n => n));
+        continue;
+      }
+
+      if (path.length >= maxDepth) {
+        continue;
+      }
+
+      const neighbors = this.adjacencyList.get(currentId) || new Set();
+      for (const neighborId of neighbors) {
+        if (!visited.has(neighborId)) {
+          const newVisited = new Set(visited);
+          newVisited.add(neighborId);
+          queue.push({ path: [...path, neighborId], visited: newVisited });
+        }
+      }
+    }
+
+    return paths;
+  }
+
+  /**
+   * Get subgraph around a node
+   */
+  getSubgraph(nodeId: string, depth: number = 2): { nodes: GraphNode[]; edges: GraphEdge[] } {
+    const visitedNodes = new Set<string>();
+    const resultEdges: GraphEdge[] = [];
+    const queue: Array<{ id: string; currentDepth: number }> = [{ id: nodeId, currentDepth: 0 }];
+
+    while (queue.length > 0) {
+      const { id, currentDepth } = queue.shift()!;
+      
+      if (visitedNodes.has(id) || currentDepth > depth) {
+        continue;
+      }
+      
+      visitedNodes.add(id);
+
+      if (currentDepth < depth) {
+        const neighbors = this.getNeighbors(id);
+        for (const neighbor of neighbors) {
+          if (!visitedNodes.has(neighbor.id)) {
+            queue.push({ id: neighbor.id, currentDepth: currentDepth + 1 });
+            
+            // Add edges
+            const edges = this.getEdgesBetween(id, neighbor.id);
+            resultEdges.push(...edges);
+          }
+        }
+      }
+    }
+
+    const resultNodes = Array.from(visitedNodes)
+      .map(id => this.nodes.get(id))
+      .filter((n): n is GraphNode => n !== undefined);
+
+    return { nodes: resultNodes, edges: resultEdges };
+  }
+
+  /**
+   * Clear the graph
+   */
+  clear(): void {
+    this.nodes.clear();
+    this.edges.clear();
+    this.adjacencyList.clear();
+    this.reverseAdjacencyList.clear();
+  }
+
+  /**
+   * Get graph statistics
+   */
+  getStats(): { nodeCount: number; edgeCount: number } {
+    return {
+      nodeCount: this.nodes.size,
+      edgeCount: this.edges.size
+    };
+  }
+}
+
+/**
+ * Graph RAG - Combines graph traversal with vector similarity
+ */
+export class GraphRAG {
+  private graphStore: GraphStore;
+  private embedFn?: (text: string) => Promise<number[]>;
+
+  constructor(config: { embedFn?: (text: string) => Promise<number[]> } = {}) {
+    this.graphStore = new GraphStore();
+    this.embedFn = config.embedFn;
+  }
+
+  /**
+   * Add a document as a node
+   */
+  async addDocument(doc: { id: string; content: string; type?: string; metadata?: Record<string, any> }): Promise<void> {
+    const embedding = this.embedFn ? await this.embedFn(doc.content) : undefined;
+    
+    this.graphStore.addNode({
+      id: doc.id,
+      type: doc.type || 'document',
+      content: doc.content,
+      metadata: doc.metadata,
+      embedding
+    });
+  }
+
+  /**
+   * Add a relationship between documents
+   */
+  addRelationship(sourceId: string, targetId: string, type: string, weight?: number): void {
+    this.graphStore.addEdge({
+      id: `${sourceId}-${type}-${targetId}`,
+      source: sourceId,
+      target: targetId,
+      type,
+      weight
+    });
+  }
+
+  /**
+   * Query the graph with a natural language query
+   */
+  async query(query: string, config: GraphRAGConfig = {}): Promise<GraphQueryResult> {
+    const { maxDepth = 2, maxNodes = 10, minScore = 0, includeEdges = true } = config;
+
+    // Get all nodes and score them
+    const allNodes = this.graphStore.getAllNodes();
+    
+    let scoredNodes: Array<{ node: GraphNode; score: number }>;
+
+    if (this.embedFn) {
+      // Use embedding similarity
+      const queryEmbedding = await this.embedFn(query);
+      scoredNodes = allNodes
+        .filter(n => n.embedding)
+        .map(node => ({
+          node,
+          score: this.cosineSimilarity(queryEmbedding, node.embedding!)
+        }));
+    } else {
+      // Use keyword matching
+      const queryTerms = query.toLowerCase().split(/\s+/);
+      scoredNodes = allNodes.map(node => {
+        const content = node.content.toLowerCase();
+        let score = 0;
+        for (const term of queryTerms) {
+          if (content.includes(term)) score += 1;
+        }
+        return { node, score: score / queryTerms.length };
+      });
+    }
+
+    // Filter and sort by score
+    scoredNodes = scoredNodes
+      .filter(s => s.score >= minScore)
+      .sort((a, b) => b.score - a.score);
+
+    // Get top nodes and expand graph
+    const topNodes = scoredNodes.slice(0, Math.min(3, maxNodes));
+    const resultNodes: GraphNode[] = [];
+    const resultEdges: GraphEdge[] = [];
+    const seenNodeIds = new Set<string>();
+
+    for (const { node } of topNodes) {
+      const subgraph = this.graphStore.getSubgraph(node.id, maxDepth);
+      
+      for (const n of subgraph.nodes) {
+        if (!seenNodeIds.has(n.id) && resultNodes.length < maxNodes) {
+          seenNodeIds.add(n.id);
+          resultNodes.push(n);
+        }
+      }
+
+      if (includeEdges) {
+        resultEdges.push(...subgraph.edges);
+      }
+    }
+
+    // Find paths between top nodes
+    const paths: GraphNode[][] = [];
+    if (topNodes.length >= 2) {
+      for (let i = 0; i < topNodes.length - 1; i++) {
+        const foundPaths = this.graphStore.findPaths(
+          topNodes[i].node.id,
+          topNodes[i + 1].node.id,
+          maxDepth
+        );
+        paths.push(...foundPaths.slice(0, 3));
+      }
+    }
+
+    return {
+      nodes: resultNodes,
+      edges: resultEdges,
+      paths,
+      score: topNodes.length > 0 ? topNodes[0].score : 0
+    };
+  }
+
+  /**
+   * Get context string from query results
+   */
+  getContext(result: GraphQueryResult): string {
+    const nodeContents = result.nodes.map(n => n.content);
+    return nodeContents.join('\n\n---\n\n');
+  }
+
+  /**
+   * Get the underlying graph store
+   */
+  getGraphStore(): GraphStore {
+    return this.graphStore;
+  }
+
+  private cosineSimilarity(a: number[], b: number[]): number {
+    const dot = a.reduce((sum, x, i) => sum + x * b[i], 0);
+    const normA = Math.sqrt(a.reduce((sum, x) => sum + x * x, 0));
+    const normB = Math.sqrt(b.reduce((sum, x) => sum + x * x, 0));
+    return dot / (normA * normB);
+  }
+}
+
+// Factory function
+export function createGraphRAG(config?: { embedFn?: (text: string) => Promise<number[]> }): GraphRAG {
+  return new GraphRAG(config);
+}

src/praisonai-ts/src/knowledge/index.ts

@@ -47,3 +47,9 @@ export class BaseKnowledgeBase implements KnowledgeBase {
     return this.knowledge.delete(id);
   }
 }
+
+// Export Reranker
+export * from './reranker';
+
+// Export Graph RAG
+export * from './graph-rag';

src/praisonai-ts/src/knowledge/rag.ts

@@ -0,0 +1,193 @@
+/**
+ * Knowledge Base (RAG) - Retrieval Augmented Generation
+ */
+
+export interface Document {
+  id: string;
+  content: string;
+  metadata?: Record<string, any>;
+  embedding?: number[];
+}
+
+export interface SearchResult {
+  document: Document;
+  score: number;
+}
+
+export interface EmbeddingProvider {
+  embed(text: string): Promise<number[]>;
+  embedBatch(texts: string[]): Promise<number[][]>;
+}
+
+export interface KnowledgeBaseConfig {
+  embeddingProvider?: EmbeddingProvider;
+  similarityThreshold?: number;
+  maxResults?: number;
+}
+
+/**
+ * Simple in-memory vector store for RAG
+ */
+export class KnowledgeBase {
+  private documents: Map<string, Document> = new Map();
+  private embeddingProvider?: EmbeddingProvider;
+  private similarityThreshold: number;
+  private maxResults: number;
+
+  constructor(config: KnowledgeBaseConfig = {}) {
+    this.embeddingProvider = config.embeddingProvider;
+    this.similarityThreshold = config.similarityThreshold ?? 0.7;
+    this.maxResults = config.maxResults ?? 5;
+  }
+
+  /**
+   * Add a document to the knowledge base
+   */
+  async add(doc: Omit<Document, 'embedding'>): Promise<Document> {
+    const document: Document = { ...doc };
+    
+    if (this.embeddingProvider) {
+      document.embedding = await this.embeddingProvider.embed(doc.content);
+    }
+    
+    this.documents.set(doc.id, document);
+    return document;
+  }
+
+  /**
+   * Add multiple documents
+   */
+  async addBatch(docs: Array<Omit<Document, 'embedding'>>): Promise<Document[]> {
+    if (this.embeddingProvider && docs.length > 0) {
+      const embeddings = await this.embeddingProvider.embedBatch(docs.map(d => d.content));
+      return Promise.all(docs.map(async (doc, i) => {
+        const document: Document = { ...doc, embedding: embeddings[i] };
+        this.documents.set(doc.id, document);
+        return document;
+      }));
+    }
+    
+    return Promise.all(docs.map(doc => this.add(doc)));
+  }
+
+  /**
+   * Get a document by ID
+   */
+  get(id: string): Document | undefined {
+    return this.documents.get(id);
+  }
+
+  /**
+   * Delete a document
+   */
+  delete(id: string): boolean {
+    return this.documents.delete(id);
+  }
+
+  /**
+   * Search for similar documents
+   */
+  async search(query: string, limit?: number): Promise<SearchResult[]> {
+    const maxResults = limit ?? this.maxResults;
+    
+    if (!this.embeddingProvider) {
+      // Fallback to simple text matching
+      return this.textSearch(query, maxResults);
+    }
+
+    const queryEmbedding = await this.embeddingProvider.embed(query);
+    const results: SearchResult[] = [];
+
+    for (const doc of this.documents.values()) {
+      if (doc.embedding) {
+        const score = this.cosineSimilarity(queryEmbedding, doc.embedding);
+        if (score >= this.similarityThreshold) {
+          results.push({ document: doc, score });
+        }
+      }
+    }
+
+    return results
+      .sort((a, b) => b.score - a.score)
+      .slice(0, maxResults);
+  }
+
+  /**
+   * Simple text-based search fallback
+   */
+  private textSearch(query: string, limit: number): SearchResult[] {
+    const queryLower = query.toLowerCase();
+    const results: SearchResult[] = [];
+
+    for (const doc of this.documents.values()) {
+      const contentLower = doc.content.toLowerCase();
+      if (contentLower.includes(queryLower)) {
+        const score = queryLower.length / contentLower.length;
+        results.push({ document: doc, score: Math.min(score * 10, 1) });
+      }
+    }
+
+    return results
+      .sort((a, b) => b.score - a.score)
+      .slice(0, limit);
+  }
+
+  /**
+   * Calculate cosine similarity between two vectors
+   */
+  private cosineSimilarity(a: number[], b: number[]): number {
+    if (a.length !== b.length) return 0;
+    
+    let dotProduct = 0;
+    let normA = 0;
+    let normB = 0;
+    
+    for (let i = 0; i < a.length; i++) {
+      dotProduct += a[i] * b[i];
+      normA += a[i] * a[i];
+      normB += b[i] * b[i];
+    }
+    
+    const denominator = Math.sqrt(normA) * Math.sqrt(normB);
+    return denominator === 0 ? 0 : dotProduct / denominator;
+  }
+
+  /**
+   * Get all documents
+   */
+  list(): Document[] {
+    return Array.from(this.documents.values());
+  }
+
+  /**
+   * Clear all documents
+   */
+  clear(): void {
+    this.documents.clear();
+  }
+
+  /**
+   * Get document count
+   */
+  get size(): number {
+    return this.documents.size;
+  }
+
+  /**
+   * Build context from search results for RAG
+   */
+  buildContext(results: SearchResult[]): string {
+    if (results.length === 0) return '';
+    
+    return results
+      .map((r, i) => `[${i + 1}] ${r.document.content}`)
+      .join('\n\n');
+  }
+}
+
+/**
+ * Create a knowledge base
+ */
+export function createKnowledgeBase(config?: KnowledgeBaseConfig): KnowledgeBase {
+  return new KnowledgeBase(config);
+}

src/praisonai-ts/src/knowledge/reranker.ts

@@ -0,0 +1,265 @@
+/**
+ * Reranker - Rerank search results for improved relevance
+ * Inspired by mastra's rerank module
+ */
+
+export interface RerankResult {
+  id: string;
+  score: number;
+  content: string;
+  metadata?: Record<string, any>;
+  originalRank: number;
+  newRank: number;
+}
+
+export interface RerankConfig {
+  model?: string;
+  topK?: number;
+  threshold?: number;
+}
+
+/**
+ * Abstract base class for rerankers
+ */
+export abstract class BaseReranker {
+  readonly name: string;
+
+  constructor(name: string) {
+    this.name = name;
+  }
+
+  abstract rerank(
+    query: string,
+    documents: Array<{ id: string; content: string; metadata?: Record<string, any> }>,
+    config?: RerankConfig
+  ): Promise<RerankResult[]>;
+}
+
+/**
+ * Cohere Reranker - Uses Cohere's rerank API
+ */
+export class CohereReranker extends BaseReranker {
+  private apiKey: string;
+  private model: string;
+
+  constructor(config: { apiKey?: string; model?: string } = {}) {
+    super('CohereReranker');
+    this.apiKey = config.apiKey || process.env.COHERE_API_KEY || '';
+    this.model = config.model || 'rerank-english-v3.0';
+  }
+
+  async rerank(
+    query: string,
+    documents: Array<{ id: string; content: string; metadata?: Record<string, any> }>,
+    config?: RerankConfig
+  ): Promise<RerankResult[]> {
+    if (!this.apiKey) {
+      throw new Error('Cohere API key required for reranking');
+    }
+
+    const response = await fetch('https://api.cohere.ai/v1/rerank', {
+      method: 'POST',
+      headers: {
+        'Authorization': `Bearer ${this.apiKey}`,
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        model: config?.model || this.model,
+        query,
+        documents: documents.map(d => d.content),
+        top_n: config?.topK || documents.length,
+        return_documents: true
+      })
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`Cohere rerank error: ${response.status} - ${error}`);
+    }
+
+    const data = await response.json();
+    const threshold = config?.threshold || 0;
+
+    return (data.results || [])
+      .filter((r: any) => r.relevance_score >= threshold)
+      .map((r: any, newRank: number) => ({
+        id: documents[r.index].id,
+        score: r.relevance_score,
+        content: documents[r.index].content,
+        metadata: documents[r.index].metadata,
+        originalRank: r.index,
+        newRank
+      }));
+  }
+}
+
+/**
+ * Cross-Encoder Reranker - Uses sentence similarity for reranking
+ * This is a simple implementation using cosine similarity
+ */
+export class CrossEncoderReranker extends BaseReranker {
+  private embedFn?: (texts: string[]) => Promise<number[][]>;
+
+  constructor(config: { embedFn?: (texts: string[]) => Promise<number[][]> } = {}) {
+    super('CrossEncoderReranker');
+    this.embedFn = config.embedFn;
+  }
+
+  async rerank(
+    query: string,
+    documents: Array<{ id: string; content: string; metadata?: Record<string, any> }>,
+    config?: RerankConfig
+  ): Promise<RerankResult[]> {
+    if (!this.embedFn) {
+      // Fallback to simple keyword matching
+      return this.keywordRerank(query, documents, config);
+    }
+
+    // Get embeddings for query and all documents
+    const texts = [query, ...documents.map(d => d.content)];
+    const embeddings = await this.embedFn(texts);
+    
+    const queryEmbedding = embeddings[0];
+    const docEmbeddings = embeddings.slice(1);
+
+    // Calculate similarity scores
+    const scores = docEmbeddings.map((docEmb, i) => ({
+      index: i,
+      score: this.cosineSimilarity(queryEmbedding, docEmb)
+    }));
+
+    // Sort by score descending
+    scores.sort((a, b) => b.score - a.score);
+
+    const threshold = config?.threshold || 0;
+    const topK = config?.topK || documents.length;
+
+    return scores
+      .filter(s => s.score >= threshold)
+      .slice(0, topK)
+      .map((s, newRank) => ({
+        id: documents[s.index].id,
+        score: s.score,
+        content: documents[s.index].content,
+        metadata: documents[s.index].metadata,
+        originalRank: s.index,
+        newRank
+      }));
+  }
+
+  private keywordRerank(
+    query: string,
+    documents: Array<{ id: string; content: string; metadata?: Record<string, any> }>,
+    config?: RerankConfig
+  ): RerankResult[] {
+    const queryTerms = query.toLowerCase().split(/\s+/);
+    
+    const scores = documents.map((doc, i) => {
+      const content = doc.content.toLowerCase();
+      let score = 0;
+      
+      for (const term of queryTerms) {
+        if (content.includes(term)) {
+          score += 1;
+        }
+      }
+      
+      // Normalize by query length
+      score = score / queryTerms.length;
+      
+      return { index: i, score };
+    });
+
+    scores.sort((a, b) => b.score - a.score);
+
+    const threshold = config?.threshold || 0;
+    const topK = config?.topK || documents.length;
+
+    return scores
+      .filter(s => s.score >= threshold)
+      .slice(0, topK)
+      .map((s, newRank) => ({
+        id: documents[s.index].id,
+        score: s.score,
+        content: documents[s.index].content,
+        metadata: documents[s.index].metadata,
+        originalRank: s.index,
+        newRank
+      }));
+  }
+
+  private cosineSimilarity(a: number[], b: number[]): number {
+    const dot = a.reduce((sum, x, i) => sum + x * b[i], 0);
+    const normA = Math.sqrt(a.reduce((sum, x) => sum + x * x, 0));
+    const normB = Math.sqrt(b.reduce((sum, x) => sum + x * x, 0));
+    return dot / (normA * normB);
+  }
+}
+
+/**
+ * LLM Reranker - Uses an LLM to score relevance
+ */
+export class LLMReranker extends BaseReranker {
+  private generateFn: (prompt: string) => Promise<string>;
+
+  constructor(config: { generateFn: (prompt: string) => Promise<string> }) {
+    super('LLMReranker');
+    this.generateFn = config.generateFn;
+  }
+
+  async rerank(
+    query: string,
+    documents: Array<{ id: string; content: string; metadata?: Record<string, any> }>,
+    config?: RerankConfig
+  ): Promise<RerankResult[]> {
+    const scores: Array<{ index: number; score: number }> = [];
+
+    // Score each document
+    for (let i = 0; i < documents.length; i++) {
+      const doc = documents[i];
+      const prompt = `Rate the relevance of the following document to the query on a scale of 0 to 10.
+Query: ${query}
+Document: ${doc.content.slice(0, 500)}
+
+Respond with only a number between 0 and 10.`;
+
+      try {
+        const response = await this.generateFn(prompt);
+        const score = parseFloat(response.trim()) / 10;
+        scores.push({ index: i, score: isNaN(score) ? 0 : Math.min(1, Math.max(0, score)) });
+      } catch {
+        scores.push({ index: i, score: 0 });
+      }
+    }
+
+    scores.sort((a, b) => b.score - a.score);
+
+    const threshold = config?.threshold || 0;
+    const topK = config?.topK || documents.length;
+
+    return scores
+      .filter(s => s.score >= threshold)
+      .slice(0, topK)
+      .map((s, newRank) => ({
+        id: documents[s.index].id,
+        score: s.score,
+        content: documents[s.index].content,
+        metadata: documents[s.index].metadata,
+        originalRank: s.index,
+        newRank
+      }));
+  }
+}
+
+// Factory functions
+export function createCohereReranker(config?: { apiKey?: string; model?: string }): CohereReranker {
+  return new CohereReranker(config);
+}
+
+export function createCrossEncoderReranker(config?: { embedFn?: (texts: string[]) => Promise<number[][]> }): CrossEncoderReranker {
+  return new CrossEncoderReranker(config);
+}
+
+export function createLLMReranker(config: { generateFn: (prompt: string) => Promise<string> }): LLMReranker {
+  return new LLMReranker(config);
+}

src/praisonai-ts/src/llm/providers/anthropic.ts

@@ -0,0 +1,334 @@
+/**
+ * Anthropic Provider - Implementation for Anthropic Claude API
+ */
+
+import { BaseProvider } from './base';
+import type {
+  ProviderConfig,
+  GenerateTextOptions,
+  GenerateTextResult,
+  StreamTextOptions,
+  StreamChunk,
+  GenerateObjectOptions,
+  GenerateObjectResult,
+  Message,
+  ToolDefinition,
+  ToolCall,
+} from './types';
+
+interface AnthropicMessage {
+  role: 'user' | 'assistant';
+  content: string | Array<{ type: string; text?: string; tool_use_id?: string; content?: string }>;
+}
+
+interface AnthropicTool {
+  name: string;
+  description: string;
+  input_schema: Record<string, any>;
+}
+
+export class AnthropicProvider extends BaseProvider {
+  readonly providerId = 'anthropic';
+  private apiKey: string;
+  private baseUrl: string;
+
+  constructor(modelId: string, config: ProviderConfig = {}) {
+    super(modelId, config);
+    this.apiKey = config.apiKey || process.env.ANTHROPIC_API_KEY || '';
+    this.baseUrl = config.baseUrl || 'https://api.anthropic.com';
+    
+    if (!this.apiKey) {
+      throw new Error('ANTHROPIC_API_KEY is required');
+    }
+  }
+
+  async generateText(options: GenerateTextOptions): Promise<GenerateTextResult> {
+    return this.withRetry(async () => {
+      const { systemPrompt, messages } = this.extractSystemPrompt(options.messages);
+      
+      const response = await fetch(`${this.baseUrl}/v1/messages`, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          'x-api-key': this.apiKey,
+          'anthropic-version': '2023-06-01',
+        },
+        body: JSON.stringify({
+          model: this.modelId,
+          max_tokens: options.maxTokens || 4096,
+          system: systemPrompt,
+          messages: this.formatMessages(messages),
+          temperature: options.temperature ?? 0.7,
+          tools: options.tools ? this.formatTools(options.tools) : undefined,
+          stop_sequences: options.stop,
+          top_p: options.topP,
+        }),
+      });
+
+      if (!response.ok) {
+        const error = await response.json().catch(() => ({}));
+        throw Object.assign(new Error(error.error?.message || `Anthropic API error: ${response.status}`), {
+          status: response.status,
+        });
+      }
+
+      const data = await response.json();
+      
+      let text = '';
+      const toolCalls: ToolCall[] = [];
+      
+      for (const block of data.content || []) {
+        if (block.type === 'text') {
+          text += block.text;
+        } else if (block.type === 'tool_use') {
+          toolCalls.push({
+            id: block.id,
+            type: 'function',
+            function: {
+              name: block.name,
+              arguments: JSON.stringify(block.input),
+            },
+          });
+        }
+      }
+
+      return {
+        text,
+        toolCalls: toolCalls.length > 0 ? toolCalls : undefined,
+        usage: {
+          promptTokens: data.usage?.input_tokens || 0,
+          completionTokens: data.usage?.output_tokens || 0,
+          totalTokens: (data.usage?.input_tokens || 0) + (data.usage?.output_tokens || 0),
+        },
+        finishReason: this.mapStopReason(data.stop_reason),
+        raw: data,
+      };
+    });
+  }
+
+  async streamText(options: StreamTextOptions): Promise<AsyncIterable<StreamChunk>> {
+    const self = this;
+    const { systemPrompt, messages } = this.extractSystemPrompt(options.messages);
+    
+    return {
+      async *[Symbol.asyncIterator]() {
+        const response = await fetch(`${self.baseUrl}/v1/messages`, {
+          method: 'POST',
+          headers: {
+            'Content-Type': 'application/json',
+            'x-api-key': self.apiKey,
+            'anthropic-version': '2023-06-01',
+          },
+          body: JSON.stringify({
+            model: self.modelId,
+            max_tokens: options.maxTokens || 4096,
+            system: systemPrompt,
+            messages: self.formatMessages(messages),
+            temperature: options.temperature ?? 0.7,
+            tools: options.tools ? self.formatTools(options.tools) : undefined,
+            stream: true,
+          }),
+        });
+
+        if (!response.ok) {
+          const error = await response.json().catch(() => ({}));
+          throw new Error(error.error?.message || `Anthropic API error: ${response.status}`);
+        }
+
+        const reader = response.body?.getReader();
+        if (!reader) throw new Error('No response body');
+
+        const decoder = new TextDecoder();
+        let buffer = '';
+        const toolCalls: ToolCall[] = [];
+        let currentToolCall: Partial<ToolCall> | null = null;
+
+        while (true) {
+          const { done, value } = await reader.read();
+          if (done) break;
+
+          buffer += decoder.decode(value, { stream: true });
+          const lines = buffer.split('\n');
+          buffer = lines.pop() || '';
+
+          for (const line of lines) {
+            if (!line.startsWith('data: ')) continue;
+            const data = line.slice(6);
+            if (data === '[DONE]') continue;
+
+            try {
+              const event = JSON.parse(data);
+              
+              if (event.type === 'content_block_delta') {
+                if (event.delta?.type === 'text_delta') {
+                  const text = event.delta.text;
+                  if (options.onToken) options.onToken(text);
+                  yield { text };
+                } else if (event.delta?.type === 'input_json_delta') {
+                  if (currentToolCall) {
+                    currentToolCall.function!.arguments += event.delta.partial_json;
+                  }
+                }
+              } else if (event.type === 'content_block_start') {
+                if (event.content_block?.type === 'tool_use') {
+                  currentToolCall = {
+                    id: event.content_block.id,
+                    type: 'function',
+                    function: {
+                      name: event.content_block.name,
+                      arguments: '',
+                    },
+                  };
+                }
+              } else if (event.type === 'content_block_stop') {
+                if (currentToolCall) {
+                  toolCalls.push(currentToolCall as ToolCall);
+                  currentToolCall = null;
+                }
+              } else if (event.type === 'message_delta') {
+                yield {
+                  finishReason: self.mapStopReason(event.delta?.stop_reason),
+                  toolCalls: toolCalls.length > 0 ? toolCalls : undefined,
+                  usage: event.usage ? {
+                    promptTokens: 0,
+                    completionTokens: event.usage.output_tokens,
+                    totalTokens: event.usage.output_tokens,
+                  } : undefined,
+                };
+              }
+            } catch (e) {
+              // Skip malformed JSON
+            }
+          }
+        }
+      },
+    };
+  }
+
+  async generateObject<T = any>(options: GenerateObjectOptions<T>): Promise<GenerateObjectResult<T>> {
+    const { systemPrompt, messages } = this.extractSystemPrompt(options.messages);
+    
+    // Add JSON instruction to system prompt
+    const jsonSystemPrompt = `${systemPrompt}\n\nYou must respond with valid JSON matching this schema:\n${JSON.stringify(options.schema, null, 2)}`;
+    
+    return this.withRetry(async () => {
+      const response = await fetch(`${this.baseUrl}/v1/messages`, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          'x-api-key': this.apiKey,
+          'anthropic-version': '2023-06-01',
+        },
+        body: JSON.stringify({
+          model: this.modelId,
+          max_tokens: options.maxTokens || 4096,
+          system: jsonSystemPrompt,
+          messages: this.formatMessages(messages),
+          temperature: options.temperature ?? 0.7,
+        }),
+      });
+
+      if (!response.ok) {
+        const error = await response.json().catch(() => ({}));
+        throw new Error(error.error?.message || `Anthropic API error: ${response.status}`);
+      }
+
+      const data = await response.json();
+      let text = '';
+      
+      for (const block of data.content || []) {
+        if (block.type === 'text') {
+          text += block.text;
+        }
+      }
+
+      // Extract JSON from response
+      const jsonMatch = text.match(/\{[\s\S]*\}/);
+      if (!jsonMatch) {
+        throw new Error(`No JSON found in response: ${text}`);
+      }
+
+      let parsed: T;
+      try {
+        parsed = JSON.parse(jsonMatch[0]);
+      } catch (e) {
+        throw new Error(`Failed to parse JSON: ${jsonMatch[0]}`);
+      }
+
+      return {
+        object: parsed,
+        usage: {
+          promptTokens: data.usage?.input_tokens || 0,
+          completionTokens: data.usage?.output_tokens || 0,
+          totalTokens: (data.usage?.input_tokens || 0) + (data.usage?.output_tokens || 0),
+        },
+        raw: data,
+      };
+    });
+  }
+
+  private extractSystemPrompt(messages: Message[]): { systemPrompt: string; messages: Message[] } {
+    const systemMessages = messages.filter(m => m.role === 'system');
+    const otherMessages = messages.filter(m => m.role !== 'system');
+    const systemPrompt = systemMessages.map(m => m.content).join('\n');
+    return { systemPrompt, messages: otherMessages };
+  }
+
+  protected formatMessages(messages: Message[]): AnthropicMessage[] {
+    const result: AnthropicMessage[] = [];
+    
+    for (const msg of messages) {
+      if (msg.role === 'system') continue; // Handled separately
+      
+      if (msg.role === 'tool') {
+        // Tool results need to be part of user message in Anthropic
+        result.push({
+          role: 'user',
+          content: [{
+            type: 'tool_result',
+            tool_use_id: msg.tool_call_id || '',
+            content: msg.content || '',
+          }],
+        });
+      } else if (msg.role === 'assistant' && msg.tool_calls) {
+        const content: any[] = [];
+        if (msg.content) {
+          content.push({ type: 'text', text: msg.content });
+        }
+        for (const tc of msg.tool_calls) {
+          content.push({
+            type: 'tool_use',
+            id: tc.id,
+            name: tc.function.name,
+            input: JSON.parse(tc.function.arguments),
+          });
+        }
+        result.push({ role: 'assistant', content });
+      } else {
+        result.push({
+          role: msg.role as 'user' | 'assistant',
+          content: msg.content || '',
+        });
+      }
+    }
+    
+    return result;
+  }
+
+  protected formatTools(tools: ToolDefinition[]): AnthropicTool[] {
+    return tools.map(tool => ({
+      name: tool.name,
+      description: tool.description || `Function ${tool.name}`,
+      input_schema: tool.parameters || { type: 'object', properties: {} },
+    }));
+  }
+
+  private mapStopReason(reason: string | null): GenerateTextResult['finishReason'] {
+    switch (reason) {
+      case 'end_turn': return 'stop';
+      case 'max_tokens': return 'length';
+      case 'tool_use': return 'tool_calls';
+      default: return 'stop';
+    }
+  }
+}

src/praisonai-ts/src/llm/providers/base.ts

@@ -0,0 +1,77 @@
+/**
+ * Base Provider - Abstract base class for LLM providers
+ */
+
+import type {
+  LLMProvider,
+  ProviderConfig,
+  GenerateTextOptions,
+  GenerateTextResult,
+  StreamTextOptions,
+  StreamChunk,
+  GenerateObjectOptions,
+  GenerateObjectResult,
+  Message,
+  ToolDefinition,
+} from './types';
+
+export abstract class BaseProvider implements LLMProvider {
+  abstract readonly providerId: string;
+  readonly modelId: string;
+  protected config: ProviderConfig;
+
+  constructor(modelId: string, config: ProviderConfig = {}) {
+    this.modelId = modelId;
+    this.config = {
+      maxRetries: 3,
+      timeout: 60000,
+      ...config,
+    };
+  }
+
+  abstract generateText(options: GenerateTextOptions): Promise<GenerateTextResult>;
+  abstract streamText(options: StreamTextOptions): Promise<AsyncIterable<StreamChunk>>;
+  abstract generateObject<T = any>(options: GenerateObjectOptions<T>): Promise<GenerateObjectResult<T>>;
+
+  /**
+   * Convert tool definitions to provider-specific format
+   */
+  protected abstract formatTools(tools: ToolDefinition[]): any[];
+
+  /**
+   * Convert messages to provider-specific format
+   */
+  protected abstract formatMessages(messages: Message[]): any[];
+
+  /**
+   * Retry logic with exponential backoff
+   */
+  protected async withRetry<T>(
+    fn: () => Promise<T>,
+    maxRetries: number = this.config.maxRetries || 3
+  ): Promise<T> {
+    let lastError: Error | undefined;
+    for (let attempt = 0; attempt <= maxRetries; attempt++) {
+      try {
+        return await fn();
+      } catch (error: any) {
+        lastError = error;
+        // Check if error is retryable (rate limit, server error)
+        const isRetryable = 
+          error.status === 429 || 
+          error.status >= 500 ||
+          error.code === 'ECONNRESET' ||
+          error.code === 'ETIMEDOUT';
+        
+        if (!isRetryable || attempt === maxRetries) {
+          throw error;
+        }
+        
+        // Exponential backoff
+        const delay = Math.min(1000 * Math.pow(2, attempt), 30000);
+        await new Promise(resolve => setTimeout(resolve, delay));
+      }
+    }
+    throw lastError;
+  }
+}

src/praisonai-ts/src/llm/providers/google.ts

@@ -0,0 +1,322 @@
+/**
+ * Google Provider - Implementation for Google Gemini API
+ */
+
+import { BaseProvider } from './base';
+import type {
+  ProviderConfig,
+  GenerateTextOptions,
+  GenerateTextResult,
+  StreamTextOptions,
+  StreamChunk,
+  GenerateObjectOptions,
+  GenerateObjectResult,
+  Message,
+  ToolDefinition,
+  ToolCall,
+} from './types';
+
+interface GeminiContent {
+  role: 'user' | 'model';
+  parts: Array<{ text?: string; functionCall?: any; functionResponse?: any }>;
+}
+
+export class GoogleProvider extends BaseProvider {
+  readonly providerId = 'google';
+  private apiKey: string;
+  private baseUrl: string;
+
+  constructor(modelId: string, config: ProviderConfig = {}) {
+    super(modelId, config);
+    this.apiKey = config.apiKey || process.env.GOOGLE_API_KEY || '';
+    this.baseUrl = config.baseUrl || 'https://generativelanguage.googleapis.com/v1beta';
+    
+    if (!this.apiKey) {
+      throw new Error('GOOGLE_API_KEY is required');
+    }
+  }
+
+  async generateText(options: GenerateTextOptions): Promise<GenerateTextResult> {
+    return this.withRetry(async () => {
+      const { systemInstruction, contents } = this.formatRequest(options.messages);
+      
+      const response = await fetch(
+        `${this.baseUrl}/models/${this.modelId}:generateContent?key=${this.apiKey}`,
+        {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({
+            systemInstruction: systemInstruction ? { parts: [{ text: systemInstruction }] } : undefined,
+            contents,
+            generationConfig: {
+              temperature: options.temperature ?? 0.7,
+              maxOutputTokens: options.maxTokens,
+              topP: options.topP,
+              stopSequences: options.stop,
+            },
+            tools: options.tools ? this.formatTools(options.tools) : undefined,
+          }),
+        }
+      );
+
+      if (!response.ok) {
+        const error = await response.json().catch(() => ({}));
+        throw Object.assign(new Error(error.error?.message || `Google API error: ${response.status}`), {
+          status: response.status,
+        });
+      }
+
+      const data = await response.json();
+      const candidate = data.candidates?.[0];
+      
+      let text = '';
+      const toolCalls: ToolCall[] = [];
+      
+      for (const part of candidate?.content?.parts || []) {
+        if (part.text) {
+          text += part.text;
+        } else if (part.functionCall) {
+          toolCalls.push({
+            id: `call_${Math.random().toString(36).substr(2, 9)}`,
+            type: 'function',
+            function: {
+              name: part.functionCall.name,
+              arguments: JSON.stringify(part.functionCall.args || {}),
+            },
+          });
+        }
+      }
+
+      return {
+        text,
+        toolCalls: toolCalls.length > 0 ? toolCalls : undefined,
+        usage: {
+          promptTokens: data.usageMetadata?.promptTokenCount || 0,
+          completionTokens: data.usageMetadata?.candidatesTokenCount || 0,
+          totalTokens: data.usageMetadata?.totalTokenCount || 0,
+        },
+        finishReason: this.mapFinishReason(candidate?.finishReason),
+        raw: data,
+      };
+    });
+  }
+
+  async streamText(options: StreamTextOptions): Promise<AsyncIterable<StreamChunk>> {
+    const self = this;
+    const { systemInstruction, contents } = this.formatRequest(options.messages);
+    
+    return {
+      async *[Symbol.asyncIterator]() {
+        const response = await fetch(
+          `${self.baseUrl}/models/${self.modelId}:streamGenerateContent?key=${self.apiKey}&alt=sse`,
+          {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({
+              systemInstruction: systemInstruction ? { parts: [{ text: systemInstruction }] } : undefined,
+              contents,
+              generationConfig: {
+                temperature: options.temperature ?? 0.7,
+                maxOutputTokens: options.maxTokens,
+              },
+              tools: options.tools ? self.formatTools(options.tools) : undefined,
+            }),
+          }
+        );
+
+        if (!response.ok) {
+          const error = await response.json().catch(() => ({}));
+          throw new Error(error.error?.message || `Google API error: ${response.status}`);
+        }
+
+        const reader = response.body?.getReader();
+        if (!reader) throw new Error('No response body');
+
+        const decoder = new TextDecoder();
+        let buffer = '';
+        const toolCalls: ToolCall[] = [];
+
+        while (true) {
+          const { done, value } = await reader.read();
+          if (done) break;
+
+          buffer += decoder.decode(value, { stream: true });
+          const lines = buffer.split('\n');
+          buffer = lines.pop() || '';
+
+          for (const line of lines) {
+            if (!line.startsWith('data: ')) continue;
+            const data = line.slice(6);
+            if (data === '[DONE]') continue;
+
+            try {
+              const event = JSON.parse(data);
+              const candidate = event.candidates?.[0];
+              
+              for (const part of candidate?.content?.parts || []) {
+                if (part.text) {
+                  if (options.onToken) options.onToken(part.text);
+                  yield { text: part.text };
+                } else if (part.functionCall) {
+                  toolCalls.push({
+                    id: `call_${Math.random().toString(36).substr(2, 9)}`,
+                    type: 'function',
+                    function: {
+                      name: part.functionCall.name,
+                      arguments: JSON.stringify(part.functionCall.args || {}),
+                    },
+                  });
+                }
+              }
+              
+              if (candidate?.finishReason) {
+                yield {
+                  finishReason: self.mapFinishReason(candidate.finishReason),
+                  toolCalls: toolCalls.length > 0 ? toolCalls : undefined,
+                  usage: event.usageMetadata ? {
+                    promptTokens: event.usageMetadata.promptTokenCount || 0,
+                    completionTokens: event.usageMetadata.candidatesTokenCount || 0,
+                    totalTokens: event.usageMetadata.totalTokenCount || 0,
+                  } : undefined,
+                };
+              }
+            } catch (e) {
+              // Skip malformed JSON
+            }
+          }
+        }
+      },
+    };
+  }
+
+  async generateObject<T = any>(options: GenerateObjectOptions<T>): Promise<GenerateObjectResult<T>> {
+    const { systemInstruction, contents } = this.formatRequest(options.messages);
+    
+    return this.withRetry(async () => {
+      const response = await fetch(
+        `${this.baseUrl}/models/${this.modelId}:generateContent?key=${this.apiKey}`,
+        {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({
+            systemInstruction: systemInstruction 
+              ? { parts: [{ text: `${systemInstruction}\n\nRespond with valid JSON matching this schema:\n${JSON.stringify(options.schema, null, 2)}` }] }
+              : { parts: [{ text: `Respond with valid JSON matching this schema:\n${JSON.stringify(options.schema, null, 2)}` }] },
+            contents,
+            generationConfig: {
+              temperature: options.temperature ?? 0.7,
+              maxOutputTokens: options.maxTokens,
+              responseMimeType: 'application/json',
+            },
+          }),
+        }
+      );
+
+      if (!response.ok) {
+        const error = await response.json().catch(() => ({}));
+        throw new Error(error.error?.message || `Google API error: ${response.status}`);
+      }
+
+      const data = await response.json();
+      const candidate = data.candidates?.[0];
+      let text = '';
+      
+      for (const part of candidate?.content?.parts || []) {
+        if (part.text) text += part.text;
+      }
+
+      let parsed: T;
+      try {
+        parsed = JSON.parse(text);
+      } catch (e) {
+        // Try to extract JSON from text
+        const jsonMatch = text.match(/\{[\s\S]*\}/);
+        if (!jsonMatch) throw new Error(`No JSON found: ${text}`);
+        parsed = JSON.parse(jsonMatch[0]);
+      }
+
+      return {
+        object: parsed,
+        usage: {
+          promptTokens: data.usageMetadata?.promptTokenCount || 0,
+          completionTokens: data.usageMetadata?.candidatesTokenCount || 0,
+          totalTokens: data.usageMetadata?.totalTokenCount || 0,
+        },
+        raw: data,
+      };
+    });
+  }
+
+  private formatRequest(messages: Message[]): { systemInstruction: string | null; contents: GeminiContent[] } {
+    const systemMessages = messages.filter(m => m.role === 'system');
+    const otherMessages = messages.filter(m => m.role !== 'system');
+    const systemInstruction = systemMessages.length > 0 
+      ? systemMessages.map(m => m.content).join('\n')
+      : null;
+    
+    return {
+      systemInstruction,
+      contents: this.formatMessages(otherMessages),
+    };
+  }
+
+  protected formatMessages(messages: Message[]): GeminiContent[] {
+    const result: GeminiContent[] = [];
+    
+    for (const msg of messages) {
+      if (msg.role === 'system') continue;
+      
+      const role = msg.role === 'assistant' ? 'model' : 'user';
+      
+      if (msg.role === 'tool') {
+        result.push({
+          role: 'user',
+          parts: [{
+            functionResponse: {
+              name: msg.name || 'function',
+              response: { result: msg.content },
+            },
+          }],
+        });
+      } else if (msg.role === 'assistant' && msg.tool_calls) {
+        const parts: any[] = [];
+        if (msg.content) parts.push({ text: msg.content });
+        for (const tc of msg.tool_calls) {
+          parts.push({
+            functionCall: {
+              name: tc.function.name,
+              args: JSON.parse(tc.function.arguments),
+            },
+          });
+        }
+        result.push({ role: 'model', parts });
+      } else {
+        result.push({
+          role,
+          parts: [{ text: msg.content || '' }],
+        });
+      }
+    }
+    
+    return result;
+  }
+
+  protected formatTools(tools: ToolDefinition[]): any[] {
+    return [{
+      functionDeclarations: tools.map(tool => ({
+        name: tool.name,
+        description: tool.description || `Function ${tool.name}`,
+        parameters: tool.parameters || { type: 'object', properties: {} },
+      })),
+    }];
+  }
+
+  private mapFinishReason(reason: string | null): GenerateTextResult['finishReason'] {
+    switch (reason) {
+      case 'STOP': return 'stop';
+      case 'MAX_TOKENS': return 'length';
+      case 'SAFETY': return 'content_filter';
+      default: return 'stop';
+    }
+  }
+}

src/praisonai-ts/src/llm/providers/index.ts

@@ -0,0 +1,104 @@
+/**
+ * LLM Providers - Factory and exports for multi-provider support
+ */
+
+export * from './types';
+export * from './base';
+export { OpenAIProvider } from './openai';
+export { AnthropicProvider } from './anthropic';
+export { GoogleProvider } from './google';
+
+import type { LLMProvider, ProviderConfig } from './types';
+import { OpenAIProvider } from './openai';
+import { AnthropicProvider } from './anthropic';
+import { GoogleProvider } from './google';
+
+/**
+ * Provider registry for dynamic provider loading
+ */
+const PROVIDER_MAP: Record<string, new (modelId: string, config?: ProviderConfig) => LLMProvider> = {
+  openai: OpenAIProvider,
+  anthropic: AnthropicProvider,
+  google: GoogleProvider,
+  gemini: GoogleProvider, // Alias
+};
+
+/**
+ * Parse model string into provider and model ID
+ * Supports formats:
+ * - "provider/model" (e.g., "openai/gpt-4o")
+ * - "model" (defaults to OpenAI, e.g., "gpt-4o-mini")
+ */
+export function parseModelString(model: string): { providerId: string; modelId: string } {
+  if (model.includes('/')) {
+    const [providerId, ...rest] = model.split('/');
+    return { providerId: providerId.toLowerCase(), modelId: rest.join('/') };
+  }
+  
+  // Default to OpenAI for common model prefixes
+  if (model.startsWith('gpt-') || model.startsWith('o1') || model.startsWith('o3')) {
+    return { providerId: 'openai', modelId: model };
+  }
+  if (model.startsWith('claude-')) {
+    return { providerId: 'anthropic', modelId: model };
+  }
+  if (model.startsWith('gemini-')) {
+    return { providerId: 'google', modelId: model };
+  }
+  
+  // Default to OpenAI
+  return { providerId: 'openai', modelId: model };
+}
+
+/**
+ * Create a provider instance from a model string
+ * 
+ * @example
+ * ```typescript
+ * const provider = createProvider('openai/gpt-4o');
+ * const provider = createProvider('anthropic/claude-3-5-sonnet-latest');
+ * const provider = createProvider('google/gemini-2.0-flash');
+ * const provider = createProvider('gpt-4o-mini'); // Defaults to OpenAI
+ * ```
+ */
+export function createProvider(model: string, config?: ProviderConfig): LLMProvider {
+  const { providerId, modelId } = parseModelString(model);
+  
+  const ProviderClass = PROVIDER_MAP[providerId];
+  if (!ProviderClass) {
+    throw new Error(`Unknown provider: ${providerId}. Available providers: ${Object.keys(PROVIDER_MAP).join(', ')}`);
+  }
+  
+  return new ProviderClass(modelId, config);
+}
+
+/**
+ * Get the default provider (OpenAI with gpt-4o-mini)
+ */
+export function getDefaultProvider(config?: ProviderConfig): LLMProvider {
+  return createProvider('openai/gpt-4o-mini', config);
+}
+
+/**
+ * Check if a provider is available (has required API key)
+ */
+export function isProviderAvailable(providerId: string): boolean {
+  switch (providerId.toLowerCase()) {
+    case 'openai':
+      return !!process.env.OPENAI_API_KEY;
+    case 'anthropic':
+      return !!process.env.ANTHROPIC_API_KEY;
+    case 'google':
+    case 'gemini':
+      return !!process.env.GOOGLE_API_KEY;
+    default:
+      return false;
+  }
+}
+
+/**
+ * Get list of available providers
+ */
+export function getAvailableProviders(): string[] {
+  return Object.keys(PROVIDER_MAP).filter(isProviderAvailable);
+}

src/praisonai-ts/src/llm/providers/openai.ts

@@ -0,0 +1,227 @@
+/**
+ * OpenAI Provider - Implementation for OpenAI API
+ */
+
+import OpenAI from 'openai';
+import type { ChatCompletionMessageParam, ChatCompletionTool } from 'openai/resources/chat/completions';
+import { BaseProvider } from './base';
+import type {
+  ProviderConfig,
+  GenerateTextOptions,
+  GenerateTextResult,
+  StreamTextOptions,
+  StreamChunk,
+  GenerateObjectOptions,
+  GenerateObjectResult,
+  Message,
+  ToolDefinition,
+  ToolCall,
+} from './types';
+
+export class OpenAIProvider extends BaseProvider {
+  readonly providerId = 'openai';
+  private client: OpenAI;
+
+  constructor(modelId: string, config: ProviderConfig = {}) {
+    super(modelId, config);
+    this.client = new OpenAI({
+      apiKey: config.apiKey || process.env.OPENAI_API_KEY,
+      baseURL: config.baseUrl,
+      timeout: config.timeout || 60000,
+      maxRetries: 0, // We handle retries ourselves
+    });
+  }
+
+  async generateText(options: GenerateTextOptions): Promise<GenerateTextResult> {
+    return this.withRetry(async () => {
+      const response = await this.client.chat.completions.create({
+        model: this.modelId,
+        messages: this.formatMessages(options.messages),
+        temperature: options.temperature ?? 0.7,
+        max_tokens: options.maxTokens,
+        tools: options.tools ? this.formatTools(options.tools) : undefined,
+        tool_choice: options.toolChoice as any,
+        stop: options.stop,
+        top_p: options.topP,
+        frequency_penalty: options.frequencyPenalty,
+        presence_penalty: options.presencePenalty,
+      });
+
+      const choice = response.choices[0];
+      const message = choice.message;
+
+      return {
+        text: message.content || '',
+        toolCalls: message.tool_calls?.map(tc => ({
+          id: tc.id,
+          type: 'function' as const,
+          function: {
+            name: tc.function.name,
+            arguments: tc.function.arguments,
+          },
+        })),
+        usage: {
+          promptTokens: response.usage?.prompt_tokens || 0,
+          completionTokens: response.usage?.completion_tokens || 0,
+          totalTokens: response.usage?.total_tokens || 0,
+        },
+        finishReason: this.mapFinishReason(choice.finish_reason),
+        raw: response,
+      };
+    });
+  }
+
+  async streamText(options: StreamTextOptions): Promise<AsyncIterable<StreamChunk>> {
+    const self = this;
+    
+    return {
+      async *[Symbol.asyncIterator]() {
+        const stream = await self.client.chat.completions.create({
+          model: self.modelId,
+          messages: self.formatMessages(options.messages),
+          temperature: options.temperature ?? 0.7,
+          max_tokens: options.maxTokens,
+          tools: options.tools ? self.formatTools(options.tools) : undefined,
+          tool_choice: options.toolChoice as any,
+          stop: options.stop,
+          stream: true,
+        });
+
+        let toolCalls: ToolCall[] = [];
+        
+        for await (const chunk of stream) {
+          const delta = chunk.choices[0]?.delta;
+          
+          if (delta?.content) {
+            if (options.onToken) {
+              options.onToken(delta.content);
+            }
+            yield { text: delta.content };
+          }
+          
+          if (delta?.tool_calls) {
+            for (const tc of delta.tool_calls) {
+              if (tc.index !== undefined) {
+                if (!toolCalls[tc.index]) {
+                  toolCalls[tc.index] = {
+                    id: tc.id || '',
+                    type: 'function',
+                    function: { name: '', arguments: '' },
+                  };
+                }
+                if (tc.id) toolCalls[tc.index].id = tc.id;
+                if (tc.function?.name) toolCalls[tc.index].function.name = tc.function.name;
+                if (tc.function?.arguments) toolCalls[tc.index].function.arguments += tc.function.arguments;
+              }
+            }
+          }
+          
+          if (chunk.choices[0]?.finish_reason) {
+            yield {
+              finishReason: chunk.choices[0].finish_reason,
+              toolCalls: toolCalls.length > 0 ? toolCalls : undefined,
+              usage: chunk.usage ? {
+                promptTokens: chunk.usage.prompt_tokens,
+                completionTokens: chunk.usage.completion_tokens,
+                totalTokens: chunk.usage.total_tokens,
+              } : undefined,
+            };
+          }
+        }
+      },
+    };
+  }
+
+  async generateObject<T = any>(options: GenerateObjectOptions<T>): Promise<GenerateObjectResult<T>> {
+    return this.withRetry(async () => {
+      const response = await this.client.chat.completions.create({
+        model: this.modelId,
+        messages: this.formatMessages(options.messages),
+        temperature: options.temperature ?? 0.7,
+        max_tokens: options.maxTokens,
+        response_format: {
+          type: 'json_schema',
+          json_schema: {
+            name: 'response',
+            schema: this.normalizeSchema(options.schema),
+            strict: true,
+          },
+        },
+      });
+
+      const choice = response.choices[0];
+      const content = choice.message.content || '{}';
+      
+      let parsed: T;
+      try {
+        parsed = JSON.parse(content);
+      } catch (e) {
+        throw new Error(`Failed to parse JSON response: ${content}`);
+      }
+
+      return {
+        object: parsed,
+        usage: {
+          promptTokens: response.usage?.prompt_tokens || 0,
+          completionTokens: response.usage?.completion_tokens || 0,
+          totalTokens: response.usage?.total_tokens || 0,
+        },
+        raw: response,
+      };
+    });
+  }
+
+  protected formatMessages(messages: Message[]): ChatCompletionMessageParam[] {
+    return messages.map(msg => {
+      if (msg.role === 'tool') {
+        return {
+          role: 'tool' as const,
+          content: msg.content || '',
+          tool_call_id: msg.tool_call_id || '',
+        };
+      }
+      if (msg.role === 'assistant' && msg.tool_calls) {
+        return {
+          role: 'assistant' as const,
+          content: msg.content,
+          tool_calls: msg.tool_calls,
+        };
+      }
+      return {
+        role: msg.role as 'system' | 'user' | 'assistant',
+        content: msg.content || '',
+      };
+    });
+  }
+
+  protected formatTools(tools: ToolDefinition[]): ChatCompletionTool[] {
+    return tools.map(tool => ({
+      type: 'function' as const,
+      function: {
+        name: tool.name,
+        description: tool.description || `Function ${tool.name}`,
+        parameters: tool.parameters || { type: 'object', properties: {} },
+      },
+    }));
+  }
+
+  private mapFinishReason(reason: string | null): GenerateTextResult['finishReason'] {
+    switch (reason) {
+      case 'stop': return 'stop';
+      case 'length': return 'length';
+      case 'tool_calls': return 'tool_calls';
+      case 'content_filter': return 'content_filter';
+      default: return 'stop';
+    }
+  }
+
+  private normalizeSchema(schema: any): any {
+    // If it's a Zod schema, convert to JSON schema
+    if (schema && typeof schema.parse === 'function' && typeof schema._def === 'object') {
+      // This is a Zod schema - we need zod-to-json-schema
+      // For now, throw an error suggesting to use JSON schema directly
+      throw new Error('Zod schemas require zod-to-json-schema. Please use JSON schema directly or install zod-to-json-schema.');
+    }
+    return schema;
+  }
+}

src/praisonai-ts/src/llm/providers/types.ts

@@ -0,0 +1,103 @@
+/**
+ * LLM Provider Types - Core type definitions for the provider abstraction layer
+ */
+
+export interface Message {
+  role: 'system' | 'user' | 'assistant' | 'tool';
+  content: string | null;
+  name?: string;
+  tool_call_id?: string;
+  tool_calls?: ToolCall[];
+}
+
+export interface ToolCall {
+  id: string;
+  type: 'function';
+  function: {
+    name: string;
+    arguments: string;
+  };
+}
+
+export interface ToolDefinition {
+  name: string;
+  description?: string;
+  parameters?: Record<string, any>;
+}
+
+export interface TokenUsage {
+  promptTokens: number;
+  completionTokens: number;
+  totalTokens: number;
+}
+
+export interface GenerateTextOptions {
+  messages: Message[];
+  temperature?: number;
+  maxTokens?: number;
+  tools?: ToolDefinition[];
+  toolChoice?: 'auto' | 'none' | 'required' | { type: 'function'; function: { name: string } };
+  stop?: string[];
+  topP?: number;
+  frequencyPenalty?: number;
+  presencePenalty?: number;
+}
+
+export interface GenerateTextResult {
+  text: string;
+  toolCalls?: ToolCall[];
+  usage: TokenUsage;
+  finishReason: 'stop' | 'length' | 'tool_calls' | 'content_filter' | 'error';
+  raw?: any;
+}
+
+export interface StreamTextOptions extends GenerateTextOptions {
+  onToken?: (token: string) => void;
+  onToolCall?: (toolCall: ToolCall) => void;
+}
+
+export interface StreamChunk {
+  text?: string;
+  toolCalls?: ToolCall[];
+  usage?: Partial<TokenUsage>;
+  finishReason?: string;
+}
+
+export interface GenerateObjectOptions<T = any> {
+  messages: Message[];
+  schema: Record<string, any> | any; // JSON Schema or Zod schema
+  temperature?: number;
+  maxTokens?: number;
+  maxRetries?: number;
+}
+
+export interface GenerateObjectResult<T = any> {
+  object: T;
+  usage: TokenUsage;
+  raw?: any;
+}
+
+export interface ProviderConfig {
+  apiKey?: string;
+  baseUrl?: string;
+  maxRetries?: number;
+  timeout?: number;
+  defaultModel?: string;
+}
+
+/**
+ * Base interface for all LLM providers
+ */
+export interface LLMProvider {
+  readonly providerId: string;
+  readonly modelId: string;
+
+  generateText(options: GenerateTextOptions): Promise<GenerateTextResult>;
+  streamText(options: StreamTextOptions): Promise<AsyncIterable<StreamChunk>>;
+  generateObject<T = any>(options: GenerateObjectOptions<T>): Promise<GenerateObjectResult<T>>;
+}
+
+/**
+ * Provider factory function type
+ */
+export type ProviderFactory = (modelId: string, config?: ProviderConfig) => LLMProvider;

src/praisonai-ts/src/memory/memory.ts

@@ -0,0 +1,221 @@
+/**
+ * Memory System - Conversation and context memory management
+ */
+
+export interface MemoryEntry {
+  id: string;
+  content: string;
+  role: 'user' | 'assistant' | 'system';
+  timestamp: number;
+  metadata?: Record<string, any>;
+  embedding?: number[];
+}
+
+export interface MemoryConfig {
+  maxEntries?: number;
+  maxTokens?: number;
+  embeddingProvider?: EmbeddingProvider;
+}
+
+export interface EmbeddingProvider {
+  embed(text: string): Promise<number[]>;
+  embedBatch(texts: string[]): Promise<number[][]>;
+}
+
+export interface SearchResult {
+  entry: MemoryEntry;
+  score: number;
+}
+
+/**
+ * Memory class for managing conversation history and context
+ */
+export class Memory {
+  private entries: Map<string, MemoryEntry> = new Map();
+  private maxEntries: number;
+  private maxTokens: number;
+  private embeddingProvider?: EmbeddingProvider;
+
+  constructor(config: MemoryConfig = {}) {
+    this.maxEntries = config.maxEntries ?? 1000;
+    this.maxTokens = config.maxTokens ?? 100000;
+    this.embeddingProvider = config.embeddingProvider;
+  }
+
+  /**
+   * Add a memory entry
+   */
+  async add(content: string, role: 'user' | 'assistant' | 'system', metadata?: Record<string, any>): Promise<MemoryEntry> {
+    const id = this.generateId();
+    const entry: MemoryEntry = {
+      id,
+      content,
+      role,
+      timestamp: Date.now(),
+      metadata
+    };
+
+    if (this.embeddingProvider) {
+      entry.embedding = await this.embeddingProvider.embed(content);
+    }
+
+    this.entries.set(id, entry);
+    this.enforceLimit();
+
+    return entry;
+  }
+
+  /**
+   * Get a memory entry by ID
+   */
+  get(id: string): MemoryEntry | undefined {
+    return this.entries.get(id);
+  }
+
+  /**
+   * Get all entries
+   */
+  getAll(): MemoryEntry[] {
+    return Array.from(this.entries.values())
+      .sort((a, b) => a.timestamp - b.timestamp);
+  }
+
+  /**
+   * Get recent entries
+   */
+  getRecent(count: number): MemoryEntry[] {
+    return this.getAll().slice(-count);
+  }
+
+  /**
+   * Search memory by text similarity
+   */
+  async search(query: string, limit: number = 5): Promise<SearchResult[]> {
+    if (!this.embeddingProvider) {
+      // Fallback to simple text matching
+      return this.textSearch(query, limit);
+    }
+
+    const queryEmbedding = await this.embeddingProvider.embed(query);
+    const results: SearchResult[] = [];
+
+    for (const entry of this.entries.values()) {
+      if (entry.embedding) {
+        const score = this.cosineSimilarity(queryEmbedding, entry.embedding);
+        results.push({ entry, score });
+      }
+    }
+
+    return results
+      .sort((a, b) => b.score - a.score)
+      .slice(0, limit);
+  }
+
+  /**
+   * Simple text search fallback
+   */
+  private textSearch(query: string, limit: number): SearchResult[] {
+    const queryLower = query.toLowerCase();
+    const results: SearchResult[] = [];
+
+    for (const entry of this.entries.values()) {
+      const contentLower = entry.content.toLowerCase();
+      if (contentLower.includes(queryLower)) {
+        const score = queryLower.length / contentLower.length;
+        results.push({ entry, score: Math.min(score * 10, 1) });
+      }
+    }
+
+    return results
+      .sort((a, b) => b.score - a.score)
+      .slice(0, limit);
+  }
+
+  /**
+   * Calculate cosine similarity
+   */
+  private cosineSimilarity(a: number[], b: number[]): number {
+    if (a.length !== b.length) return 0;
+    
+    let dotProduct = 0;
+    let normA = 0;
+    let normB = 0;
+    
+    for (let i = 0; i < a.length; i++) {
+      dotProduct += a[i] * b[i];
+      normA += a[i] * a[i];
+      normB += b[i] * b[i];
+    }
+    
+    const denominator = Math.sqrt(normA) * Math.sqrt(normB);
+    return denominator === 0 ? 0 : dotProduct / denominator;
+  }
+
+  /**
+   * Delete a memory entry
+   */
+  delete(id: string): boolean {
+    return this.entries.delete(id);
+  }
+
+  /**
+   * Clear all memory
+   */
+  clear(): void {
+    this.entries.clear();
+  }
+
+  /**
+   * Get memory size
+   */
+  get size(): number {
+    return this.entries.size;
+  }
+
+  /**
+   * Build context string from recent memory
+   */
+  buildContext(count?: number): string {
+    const entries = count ? this.getRecent(count) : this.getAll();
+    return entries
+      .map(e => `${e.role}: ${e.content}`)
+      .join('\n');
+  }
+
+  /**
+   * Export memory to JSON
+   */
+  toJSON(): MemoryEntry[] {
+    return this.getAll();
+  }
+
+  /**
+   * Import memory from JSON
+   */
+  fromJSON(entries: MemoryEntry[]): void {
+    this.entries.clear();
+    for (const entry of entries) {
+      this.entries.set(entry.id, entry);
+    }
+  }
+
+  private generateId(): string {
+    return `mem_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
+  }
+
+  private enforceLimit(): void {
+    while (this.entries.size > this.maxEntries) {
+      const oldest = this.getAll()[0];
+      if (oldest) {
+        this.entries.delete(oldest.id);
+      }
+    }
+  }
+}
+
+/**
+ * Create a memory instance
+ */
+export function createMemory(config?: MemoryConfig): Memory {
+  return new Memory(config);
+}

src/praisonai-ts/src/observability/index.ts

@@ -0,0 +1,238 @@
+/**
+ * Observability - Tracing, logging, and metrics hooks
+ */
+
+export type SpanKind = 'llm' | 'tool' | 'agent' | 'workflow' | 'custom';
+export type SpanStatus = 'pending' | 'running' | 'completed' | 'failed';
+
+export interface SpanData {
+  id: string;
+  traceId: string;
+  parentId?: string;
+  name: string;
+  kind: SpanKind;
+  status: SpanStatus;
+  startTime: number;
+  endTime?: number;
+  attributes: Record<string, any>;
+  events: SpanEvent[];
+}
+
+export interface SpanEvent {
+  name: string;
+  timestamp: number;
+  attributes?: Record<string, any>;
+}
+
+export interface TraceData {
+  id: string;
+  name: string;
+  startTime: number;
+  endTime?: number;
+  status: SpanStatus;
+  spans: SpanData[];
+  metadata: Record<string, any>;
+}
+
+/**
+ * Observability Adapter Protocol
+ */
+export interface ObservabilityAdapter {
+  startTrace(name: string, metadata?: Record<string, any>): TraceContext;
+  endTrace(traceId: string, status?: SpanStatus): void;
+  startSpan(traceId: string, name: string, kind: SpanKind, parentId?: string): SpanContext;
+  endSpan(spanId: string, status?: SpanStatus, attributes?: Record<string, any>): void;
+  addEvent(spanId: string, name: string, attributes?: Record<string, any>): void;
+  flush(): Promise<void>;
+}
+
+export interface TraceContext {
+  traceId: string;
+  startSpan(name: string, kind: SpanKind): SpanContext;
+  end(status?: SpanStatus): void;
+}
+
+export interface SpanContext {
+  spanId: string;
+  traceId: string;
+  addEvent(name: string, attributes?: Record<string, any>): void;
+  setAttributes(attributes: Record<string, any>): void;
+  end(status?: SpanStatus): void;
+}
+
+/**
+ * In-memory observability adapter for development
+ */
+export class MemoryObservabilityAdapter implements ObservabilityAdapter {
+  private traces: Map<string, TraceData> = new Map();
+  private spans: Map<string, SpanData> = new Map();
+
+  startTrace(name: string, metadata: Record<string, any> = {}): TraceContext {
+    const traceId = this.generateId();
+    const trace: TraceData = {
+      id: traceId,
+      name,
+      startTime: Date.now(),
+      status: 'running',
+      spans: [],
+      metadata
+    };
+    this.traces.set(traceId, trace);
+
+    const self = this;
+    return {
+      traceId,
+      startSpan(spanName: string, kind: SpanKind): SpanContext {
+        return self.startSpan(traceId, spanName, kind);
+      },
+      end(status: SpanStatus = 'completed'): void {
+        self.endTrace(traceId, status);
+      }
+    };
+  }
+
+  endTrace(traceId: string, status: SpanStatus = 'completed'): void {
+    const trace = this.traces.get(traceId);
+    if (trace) {
+      trace.endTime = Date.now();
+      trace.status = status;
+    }
+  }
+
+  startSpan(traceId: string, name: string, kind: SpanKind, parentId?: string): SpanContext {
+    const spanId = this.generateId();
+    const span: SpanData = {
+      id: spanId,
+      traceId,
+      parentId,
+      name,
+      kind,
+      status: 'running',
+      startTime: Date.now(),
+      attributes: {},
+      events: []
+    };
+    this.spans.set(spanId, span);
+
+    const trace = this.traces.get(traceId);
+    if (trace) {
+      trace.spans.push(span);
+    }
+
+    const self = this;
+    return {
+      spanId,
+      traceId,
+      addEvent(eventName: string, attributes?: Record<string, any>): void {
+        self.addEvent(spanId, eventName, attributes);
+      },
+      setAttributes(attributes: Record<string, any>): void {
+        const s = self.spans.get(spanId);
+        if (s) {
+          s.attributes = { ...s.attributes, ...attributes };
+        }
+      },
+      end(status: SpanStatus = 'completed'): void {
+        self.endSpan(spanId, status);
+      }
+    };
+  }
+
+  endSpan(spanId: string, status: SpanStatus = 'completed', attributes?: Record<string, any>): void {
+    const span = this.spans.get(spanId);
+    if (span) {
+      span.endTime = Date.now();
+      span.status = status;
+      if (attributes) {
+        span.attributes = { ...span.attributes, ...attributes };
+      }
+    }
+  }
+
+  addEvent(spanId: string, name: string, attributes?: Record<string, any>): void {
+    const span = this.spans.get(spanId);
+    if (span) {
+      span.events.push({
+        name,
+        timestamp: Date.now(),
+        attributes
+      });
+    }
+  }
+
+  async flush(): Promise<void> {
+    // No-op for memory adapter
+  }
+
+  // Utility methods
+  getTrace(traceId: string): TraceData | undefined {
+    return this.traces.get(traceId);
+  }
+
+  getSpan(spanId: string): SpanData | undefined {
+    return this.spans.get(spanId);
+  }
+
+  getAllTraces(): TraceData[] {
+    return Array.from(this.traces.values());
+  }
+
+  clear(): void {
+    this.traces.clear();
+    this.spans.clear();
+  }
+
+  private generateId(): string {
+    return Math.random().toString(36).substring(2, 15);
+  }
+}
+
+/**
+ * Console observability adapter for debugging
+ */
+export class ConsoleObservabilityAdapter implements ObservabilityAdapter {
+  private memory = new MemoryObservabilityAdapter();
+
+  startTrace(name: string, metadata?: Record<string, any>): TraceContext {
+    console.log(`[TRACE START] ${name}`, metadata || '');
+    return this.memory.startTrace(name, metadata);
+  }
+
+  endTrace(traceId: string, status?: SpanStatus): void {
+    console.log(`[TRACE END] ${traceId} - ${status || 'completed'}`);
+    this.memory.endTrace(traceId, status);
+  }
+
+  startSpan(traceId: string, name: string, kind: SpanKind, parentId?: string): SpanContext {
+    console.log(`  [SPAN START] ${name} (${kind})`);
+    return this.memory.startSpan(traceId, name, kind, parentId);
+  }
+
+  endSpan(spanId: string, status?: SpanStatus, attributes?: Record<string, any>): void {
+    console.log(`  [SPAN END] ${spanId} - ${status || 'completed'}`, attributes || '');
+    this.memory.endSpan(spanId, status, attributes);
+  }
+
+  addEvent(spanId: string, name: string, attributes?: Record<string, any>): void {
+    console.log(`    [EVENT] ${name}`, attributes || '');
+    this.memory.addEvent(spanId, name, attributes);
+  }
+
+  async flush(): Promise<void> {
+    await this.memory.flush();
+  }
+}
+
+// Global observability instance
+let globalAdapter: ObservabilityAdapter | null = null;
+
+export function setObservabilityAdapter(adapter: ObservabilityAdapter): void {
+  globalAdapter = adapter;
+}
+
+export function getObservabilityAdapter(): ObservabilityAdapter {
+  if (!globalAdapter) {
+    globalAdapter = new MemoryObservabilityAdapter();
+  }
+  return globalAdapter;
+}

src/praisonai-ts/src/planning/index.ts

@@ -0,0 +1,307 @@
+/**
+ * Planning System - Plans, Steps, and TodoLists
+ */
+
+export type PlanStatus = 'pending' | 'in_progress' | 'completed' | 'failed' | 'cancelled';
+export type TodoStatus = 'pending' | 'in_progress' | 'completed';
+
+export interface PlanConfig {
+  name: string;
+  description?: string;
+  metadata?: Record<string, any>;
+}
+
+export interface PlanStepConfig {
+  description: string;
+  status?: PlanStatus;
+  order?: number;
+  dependencies?: string[];
+  metadata?: Record<string, any>;
+}
+
+export interface TodoItemConfig {
+  content: string;
+  priority?: 'low' | 'medium' | 'high';
+  status?: TodoStatus;
+  dueDate?: Date;
+  metadata?: Record<string, any>;
+}
+
+/**
+ * PlanStep - A single step in a plan
+ */
+export class PlanStep {
+  readonly id: string;
+  description: string;
+  status: PlanStatus;
+  order: number;
+  dependencies: string[];
+  metadata: Record<string, any>;
+  startedAt?: number;
+  completedAt?: number;
+
+  constructor(config: PlanStepConfig) {
+    this.id = `step_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
+    this.description = config.description;
+    this.status = config.status ?? 'pending';
+    this.order = config.order ?? 0;
+    this.dependencies = config.dependencies ?? [];
+    this.metadata = config.metadata ?? {};
+  }
+
+  start(): this {
+    this.status = 'in_progress';
+    this.startedAt = Date.now();
+    return this;
+  }
+
+  complete(): this {
+    this.status = 'completed';
+    this.completedAt = Date.now();
+    return this;
+  }
+
+  fail(): this {
+    this.status = 'failed';
+    this.completedAt = Date.now();
+    return this;
+  }
+
+  cancel(): this {
+    this.status = 'cancelled';
+    return this;
+  }
+}
+
+/**
+ * Plan - A collection of steps to accomplish a goal
+ */
+export class Plan {
+  readonly id: string;
+  name: string;
+  description?: string;
+  steps: PlanStep[] = [];
+  status: PlanStatus = 'pending';
+  metadata: Record<string, any>;
+  createdAt: number;
+  updatedAt: number;
+
+  constructor(config: PlanConfig) {
+    this.id = `plan_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
+    this.name = config.name;
+    this.description = config.description;
+    this.metadata = config.metadata ?? {};
+    this.createdAt = Date.now();
+    this.updatedAt = Date.now();
+  }
+
+  addStep(step: PlanStep): this {
+    step.order = this.steps.length;
+    this.steps.push(step);
+    this.updatedAt = Date.now();
+    return this;
+  }
+
+  removeStep(stepId: string): boolean {
+    const index = this.steps.findIndex(s => s.id === stepId);
+    if (index >= 0) {
+      this.steps.splice(index, 1);
+      this.updatedAt = Date.now();
+      return true;
+    }
+    return false;
+  }
+
+  getStep(stepId: string): PlanStep | undefined {
+    return this.steps.find(s => s.id === stepId);
+  }
+
+  getNextStep(): PlanStep | undefined {
+    return this.steps.find(s => s.status === 'pending');
+  }
+
+  getProgress(): { completed: number; total: number; percentage: number } {
+    const completed = this.steps.filter(s => s.status === 'completed').length;
+    const total = this.steps.length;
+    return {
+      completed,
+      total,
+      percentage: total > 0 ? Math.round((completed / total) * 100) : 0
+    };
+  }
+
+  start(): this {
+    this.status = 'in_progress';
+    this.updatedAt = Date.now();
+    return this;
+  }
+
+  complete(): this {
+    this.status = 'completed';
+    this.updatedAt = Date.now();
+    return this;
+  }
+}
+
+/**
+ * TodoItem - A single todo item
+ */
+export class TodoItem {
+  readonly id: string;
+  content: string;
+  priority: 'low' | 'medium' | 'high';
+  status: TodoStatus;
+  dueDate?: Date;
+  metadata: Record<string, any>;
+  createdAt: number;
+  completedAt?: number;
+
+  constructor(config: TodoItemConfig) {
+    this.id = `todo_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
+    this.content = config.content;
+    this.priority = config.priority ?? 'medium';
+    this.status = config.status ?? 'pending';
+    this.dueDate = config.dueDate;
+    this.metadata = config.metadata ?? {};
+    this.createdAt = Date.now();
+  }
+
+  start(): this {
+    this.status = 'in_progress';
+    return this;
+  }
+
+  complete(): this {
+    this.status = 'completed';
+    this.completedAt = Date.now();
+    return this;
+  }
+
+  reset(): this {
+    this.status = 'pending';
+    this.completedAt = undefined;
+    return this;
+  }
+}
+
+/**
+ * TodoList - A collection of todo items
+ */
+export class TodoList {
+  readonly id: string;
+  name: string;
+  items: TodoItem[] = [];
+  createdAt: number;
+
+  constructor(name: string = 'Todo List') {
+    this.id = `todolist_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
+    this.name = name;
+    this.createdAt = Date.now();
+  }
+
+  add(item: TodoItem): this {
+    this.items.push(item);
+    return this;
+  }
+
+  remove(itemId: string): boolean {
+    const index = this.items.findIndex(i => i.id === itemId);
+    if (index >= 0) {
+      this.items.splice(index, 1);
+      return true;
+    }
+    return false;
+  }
+
+  get(itemId: string): TodoItem | undefined {
+    return this.items.find(i => i.id === itemId);
+  }
+
+  getPending(): TodoItem[] {
+    return this.items.filter(i => i.status === 'pending');
+  }
+
+  getCompleted(): TodoItem[] {
+    return this.items.filter(i => i.status === 'completed');
+  }
+
+  getByPriority(priority: 'low' | 'medium' | 'high'): TodoItem[] {
+    return this.items.filter(i => i.priority === priority);
+  }
+
+  getProgress(): { completed: number; total: number; percentage: number } {
+    const completed = this.items.filter(i => i.status === 'completed').length;
+    const total = this.items.length;
+    return {
+      completed,
+      total,
+      percentage: total > 0 ? Math.round((completed / total) * 100) : 0
+    };
+  }
+
+  clear(): this {
+    this.items = [];
+    return this;
+  }
+}
+
+/**
+ * PlanStorage - Persist plans (in-memory implementation)
+ */
+export class PlanStorage {
+  private plans: Map<string, Plan> = new Map();
+  private todoLists: Map<string, TodoList> = new Map();
+
+  savePlan(plan: Plan): void {
+    this.plans.set(plan.id, plan);
+  }
+
+  getPlan(planId: string): Plan | undefined {
+    return this.plans.get(planId);
+  }
+
+  deletePlan(planId: string): boolean {
+    return this.plans.delete(planId);
+  }
+
+  listPlans(): Plan[] {
+    return Array.from(this.plans.values());
+  }
+
+  saveTodoList(list: TodoList): void {
+    this.todoLists.set(list.id, list);
+  }
+
+  getTodoList(listId: string): TodoList | undefined {
+    return this.todoLists.get(listId);
+  }
+
+  deleteTodoList(listId: string): boolean {
+    return this.todoLists.delete(listId);
+  }
+
+  listTodoLists(): TodoList[] {
+    return Array.from(this.todoLists.values());
+  }
+}
+
+/**
+ * Create a Plan
+ */
+export function createPlan(config: PlanConfig): Plan {
+  return new Plan(config);
+}
+
+/**
+ * Create a TodoList
+ */
+export function createTodoList(name?: string): TodoList {
+  return new TodoList(name);
+}
+
+/**
+ * Create a PlanStorage
+ */
+export function createPlanStorage(): PlanStorage {
+  return new PlanStorage();
+}

src/praisonai-ts/src/session/index.ts

@@ -0,0 +1,321 @@
+/**
+ * Session Management - Session, Run, and Trace tracking
+ */
+
+import { randomUUID } from 'crypto';
+
+export interface SessionConfig {
+  id?: string;
+  metadata?: Record<string, any>;
+}
+
+export interface RunConfig {
+  id?: string;
+  metadata?: Record<string, any>;
+}
+
+export interface TraceConfig {
+  id?: string;
+  name: string;
+  attributes?: Record<string, any>;
+}
+
+export type RunStatus = 'pending' | 'running' | 'completed' | 'failed';
+export type TraceStatus = 'pending' | 'running' | 'completed' | 'failed';
+
+export interface Message {
+  id: string;
+  role: 'system' | 'user' | 'assistant' | 'tool';
+  content: string | null;
+  name?: string;
+  tool_call_id?: string;
+  tool_calls?: any[];
+  timestamp: number;
+  runId?: string;
+}
+
+/**
+ * Trace represents a span within a run (e.g., LLM call, tool call)
+ */
+export class Trace {
+  readonly id: string;
+  readonly runId: string;
+  readonly name: string;
+  readonly parentId: string | null;
+  readonly startTime: number;
+  endTime: number | null = null;
+  status: TraceStatus = 'pending';
+  attributes: Record<string, any>;
+  private children: Trace[] = [];
+
+  constructor(runId: string, config: TraceConfig, parentId: string | null = null) {
+    this.id = config.id || randomUUID();
+    this.runId = runId;
+    this.name = config.name;
+    this.parentId = parentId;
+    this.startTime = Date.now();
+    this.attributes = config.attributes || {};
+  }
+
+  start(): this {
+    this.status = 'running';
+    return this;
+  }
+
+  complete(attributes?: Record<string, any>): this {
+    this.status = 'completed';
+    this.endTime = Date.now();
+    if (attributes) {
+      this.attributes = { ...this.attributes, ...attributes };
+    }
+    return this;
+  }
+
+  fail(error?: Error): this {
+    this.status = 'failed';
+    this.endTime = Date.now();
+    if (error) {
+      this.attributes.error = error.message;
+      this.attributes.errorStack = error.stack;
+    }
+    return this;
+  }
+
+  get duration(): number | null {
+    if (this.endTime === null) return null;
+    return this.endTime - this.startTime;
+  }
+
+  createChild(config: TraceConfig): Trace {
+    const child = new Trace(this.runId, config, this.id);
+    this.children.push(child);
+    return child;
+  }
+
+  getChildren(): Trace[] {
+    return [...this.children];
+  }
+
+  toJSON(): Record<string, any> {
+    return {
+      id: this.id,
+      runId: this.runId,
+      name: this.name,
+      parentId: this.parentId,
+      startTime: this.startTime,
+      endTime: this.endTime,
+      duration: this.duration,
+      status: this.status,
+      attributes: this.attributes,
+      children: this.children.map(c => c.toJSON()),
+    };
+  }
+}
+
+/**
+ * Run represents a single execution within a session (e.g., one chat call)
+ */
+export class Run {
+  readonly id: string;
+  readonly sessionId: string;
+  readonly startTime: number;
+  endTime: number | null = null;
+  status: RunStatus = 'pending';
+  error: Error | null = null;
+  metadata: Record<string, any>;
+  private traces: Trace[] = [];
+  private messages: Message[] = [];
+
+  constructor(sessionId: string, config: RunConfig = {}) {
+    this.id = config.id || randomUUID();
+    this.sessionId = sessionId;
+    this.startTime = Date.now();
+    this.metadata = config.metadata || {};
+  }
+
+  start(): this {
+    this.status = 'running';
+    return this;
+  }
+
+  complete(metadata?: Record<string, any>): this {
+    this.status = 'completed';
+    this.endTime = Date.now();
+    if (metadata) {
+      this.metadata = { ...this.metadata, ...metadata };
+    }
+    return this;
+  }
+
+  fail(error: Error): this {
+    this.status = 'failed';
+    this.endTime = Date.now();
+    this.error = error;
+    return this;
+  }
+
+  get duration(): number | null {
+    if (this.endTime === null) return null;
+    return this.endTime - this.startTime;
+  }
+
+  createTrace(config: TraceConfig): Trace {
+    const trace = new Trace(this.id, config);
+    this.traces.push(trace);
+    return trace;
+  }
+
+  addMessage(message: Omit<Message, 'id' | 'timestamp' | 'runId'>): Message {
+    const msg: Message = {
+      ...message,
+      id: randomUUID(),
+      timestamp: Date.now(),
+      runId: this.id,
+    };
+    this.messages.push(msg);
+    return msg;
+  }
+
+  getTraces(): Trace[] {
+    return [...this.traces];
+  }
+
+  getMessages(): Message[] {
+    return [...this.messages];
+  }
+
+  toJSON(): Record<string, any> {
+    return {
+      id: this.id,
+      sessionId: this.sessionId,
+      startTime: this.startTime,
+      endTime: this.endTime,
+      duration: this.duration,
+      status: this.status,
+      error: this.error?.message,
+      metadata: this.metadata,
+      traces: this.traces.map(t => t.toJSON()),
+      messageCount: this.messages.length,
+    };
+  }
+}
+
+/**
+ * Session represents a conversation session with message history
+ */
+export class Session {
+  readonly id: string;
+  readonly createdAt: number;
+  metadata: Record<string, any>;
+  private _runs: Run[] = [];
+  private _messages: Message[] = [];
+
+  constructor(config: SessionConfig = {}) {
+    this.id = config.id || randomUUID();
+    this.createdAt = Date.now();
+    this.metadata = config.metadata || {};
+  }
+
+  createRun(config: RunConfig = {}): Run {
+    const run = new Run(this.id, config);
+    this._runs.push(run);
+    return run;
+  }
+
+  get runs(): Run[] {
+    return [...this._runs];
+  }
+
+  get messages(): Message[] {
+    return [...this._messages];
+  }
+
+  addMessage(message: Omit<Message, 'id' | 'timestamp'>): Message {
+    const msg: Message = {
+      ...message,
+      id: randomUUID(),
+      timestamp: Date.now(),
+    };
+    this._messages.push(msg);
+    return msg;
+  }
+
+  getMessagesForLLM(): Array<{ role: string; content: string | null; tool_call_id?: string; tool_calls?: any[] }> {
+    return this._messages.map(m => ({
+      role: m.role,
+      content: m.content,
+      ...(m.tool_call_id ? { tool_call_id: m.tool_call_id } : {}),
+      ...(m.tool_calls ? { tool_calls: m.tool_calls } : {}),
+    }));
+  }
+
+  clearMessages(): void {
+    this._messages = [];
+  }
+
+  toJSON(): Record<string, any> {
+    return {
+      id: this.id,
+      createdAt: this.createdAt,
+      metadata: this.metadata,
+      runCount: this._runs.length,
+      messageCount: this._messages.length,
+      runs: this._runs.map(r => r.toJSON()),
+    };
+  }
+}
+
+/**
+ * SessionManager for managing multiple sessions
+ */
+export class SessionManager {
+  private sessions: Map<string, Session> = new Map();
+
+  create(config: SessionConfig = {}): Session {
+    const session = new Session(config);
+    this.sessions.set(session.id, session);
+    return session;
+  }
+
+  get(id: string): Session | undefined {
+    return this.sessions.get(id);
+  }
+
+  getOrCreate(id: string, config: Omit<SessionConfig, 'id'> = {}): Session {
+    let session = this.sessions.get(id);
+    if (!session) {
+      session = new Session({ ...config, id });
+      this.sessions.set(id, session);
+    }
+    return session;
+  }
+
+  list(): Session[] {
+    return Array.from(this.sessions.values());
+  }
+
+  delete(id: string): boolean {
+    return this.sessions.delete(id);
+  }
+
+  clear(): void {
+    this.sessions.clear();
+  }
+
+  toJSON(): Record<string, any> {
+    return {
+      sessionCount: this.sessions.size,
+      sessions: this.list().map(s => s.toJSON()),
+    };
+  }
+}
+
+// Default session manager instance
+let defaultManager: SessionManager | null = null;
+
+export function getSessionManager(): SessionManager {
+  if (!defaultManager) {
+    defaultManager = new SessionManager();
+  }
+  return defaultManager;
+}

src/praisonai-ts/src/skills/index.ts

@@ -0,0 +1,246 @@
+/**
+ * Skills System - Agent Skills standard implementation
+ */
+
+import * as fs from 'fs';
+import * as path from 'path';
+
+export interface SkillMetadata {
+  name: string;
+  description: string;
+  license?: string;
+  compatibility?: string;
+  allowedTools?: string[];
+  metadata?: Record<string, string>;
+}
+
+export interface Skill {
+  metadata: SkillMetadata;
+  instructions: string;
+  path?: string;
+}
+
+export interface SkillDiscoveryOptions {
+  paths?: string[];
+  includeSystem?: boolean;
+  includeUser?: boolean;
+  includeProject?: boolean;
+}
+
+/**
+ * Parse SKILL.md file
+ */
+export function parseSkillFile(content: string): Skill {
+  const frontmatterMatch = content.match(/^---\n([\s\S]*?)\n---\n([\s\S]*)$/);
+  
+  if (!frontmatterMatch) {
+    throw new Error('Invalid SKILL.md format: missing frontmatter');
+  }
+
+  const [, frontmatter, instructions] = frontmatterMatch;
+  const metadata = parseYamlFrontmatter(frontmatter);
+
+  if (!metadata.name || !metadata.description) {
+    throw new Error('Invalid SKILL.md: name and description are required');
+  }
+
+  return {
+    metadata: {
+      name: metadata.name,
+      description: metadata.description,
+      license: metadata.license,
+      compatibility: metadata.compatibility,
+      allowedTools: metadata['allowed-tools']?.split(/\s+/),
+      metadata: metadata.metadata
+    },
+    instructions: instructions.trim()
+  };
+}
+
+/**
+ * Simple YAML frontmatter parser
+ */
+function parseYamlFrontmatter(yaml: string): Record<string, any> {
+  const result: Record<string, any> = {};
+  const lines = yaml.split('\n');
+  let currentKey = '';
+  let inMetadata = false;
+  const metadataObj: Record<string, string> = {};
+
+  for (const line of lines) {
+    if (line.startsWith('metadata:')) {
+      inMetadata = true;
+      continue;
+    }
+
+    if (inMetadata && line.startsWith('  ')) {
+      const match = line.match(/^\s+(\w+):\s*(.*)$/);
+      if (match) {
+        metadataObj[match[1]] = match[2].replace(/^["']|["']$/g, '');
+      }
+      continue;
+    } else if (inMetadata && !line.startsWith('  ')) {
+      inMetadata = false;
+      result.metadata = metadataObj;
+    }
+
+    const match = line.match(/^(\S+):\s*(.*)$/);
+    if (match) {
+      currentKey = match[1];
+      result[currentKey] = match[2].replace(/^["']|["']$/g, '');
+    }
+  }
+
+  if (inMetadata) {
+    result.metadata = metadataObj;
+  }
+
+  return result;
+}
+
+/**
+ * Skill Manager - Load and manage skills
+ */
+export class SkillManager {
+  private skills: Map<string, Skill> = new Map();
+  private discoveryPaths: string[] = [];
+
+  constructor(options: SkillDiscoveryOptions = {}) {
+    this.setupDiscoveryPaths(options);
+  }
+
+  private setupDiscoveryPaths(options: SkillDiscoveryOptions): void {
+    if (options.paths) {
+      this.discoveryPaths.push(...options.paths);
+    }
+
+    if (options.includeProject !== false) {
+      this.discoveryPaths.push('./.praison/skills');
+      this.discoveryPaths.push('./.claude/skills');
+    }
+
+    if (options.includeUser !== false) {
+      const home = process.env.HOME || process.env.USERPROFILE || '';
+      if (home) {
+        this.discoveryPaths.push(path.join(home, '.praison/skills'));
+      }
+    }
+
+    if (options.includeSystem) {
+      this.discoveryPaths.push('/etc/praison/skills');
+    }
+  }
+
+  /**
+   * Discover and load skills from configured paths
+   */
+  async discover(): Promise<Skill[]> {
+    const discovered: Skill[] = [];
+
+    for (const basePath of this.discoveryPaths) {
+      if (!fs.existsSync(basePath)) continue;
+
+      const entries = fs.readdirSync(basePath, { withFileTypes: true });
+      for (const entry of entries) {
+        if (entry.isDirectory()) {
+          const skillPath = path.join(basePath, entry.name, 'SKILL.md');
+          if (fs.existsSync(skillPath)) {
+            try {
+              const skill = await this.loadSkill(skillPath);
+              skill.path = path.join(basePath, entry.name);
+              discovered.push(skill);
+              this.skills.set(skill.metadata.name, skill);
+            } catch (e) {
+              // Skip invalid skills
+            }
+          }
+        }
+      }
+    }
+
+    return discovered;
+  }
+
+  /**
+   * Load a skill from a SKILL.md file
+   */
+  async loadSkill(skillPath: string): Promise<Skill> {
+    const content = fs.readFileSync(skillPath, 'utf-8');
+    return parseSkillFile(content);
+  }
+
+  /**
+   * Register a skill manually
+   */
+  register(skill: Skill): void {
+    this.skills.set(skill.metadata.name, skill);
+  }
+
+  /**
+   * Get a skill by name
+   */
+  get(name: string): Skill | undefined {
+    return this.skills.get(name);
+  }
+
+  /**
+   * List all loaded skills
+   */
+  list(): Skill[] {
+    return Array.from(this.skills.values());
+  }
+
+  /**
+   * Generate XML prompt for skills
+   */
+  generatePrompt(skillNames?: string[]): string {
+    const skillsToInclude = skillNames
+      ? skillNames.map(n => this.skills.get(n)).filter(Boolean) as Skill[]
+      : this.list();
+
+    if (skillsToInclude.length === 0) return '';
+
+    const parts = ['<skills>'];
+    for (const skill of skillsToInclude) {
+      parts.push(`  <skill name="${skill.metadata.name}">`);
+      parts.push(`    <description>${skill.metadata.description}</description>`);
+      parts.push(`    <instructions>${skill.instructions}</instructions>`);
+      parts.push('  </skill>');
+    }
+    parts.push('</skills>');
+
+    return parts.join('\n');
+  }
+
+  /**
+   * Validate a skill
+   */
+  validate(skill: Skill): { valid: boolean; errors: string[] } {
+    const errors: string[] = [];
+
+    if (!skill.metadata.name) {
+      errors.push('Missing required field: name');
+    } else if (!/^[a-z0-9-]{1,64}$/.test(skill.metadata.name)) {
+      errors.push('Invalid name: must be 1-64 lowercase chars, numbers, or hyphens');
+    }
+
+    if (!skill.metadata.description) {
+      errors.push('Missing required field: description');
+    } else if (skill.metadata.description.length > 1024) {
+      errors.push('Description too long: max 1024 characters');
+    }
+
+    if (skill.instructions.length > 5000) {
+      errors.push('Instructions too long: max 5000 characters');
+    }
+
+    return { valid: errors.length === 0, errors };
+  }
+}
+
+/**
+ * Create a skill manager
+ */
+export function createSkillManager(options?: SkillDiscoveryOptions): SkillManager {
+  return new SkillManager(options);
+}

src/praisonai-ts/src/telemetry/index.ts

@@ -0,0 +1,227 @@
+/**
+ * Telemetry - Usage tracking and analytics
+ */
+
+export interface TelemetryEvent {
+  name: string;
+  timestamp: number;
+  properties?: Record<string, any>;
+  userId?: string;
+  sessionId?: string;
+}
+
+export interface TelemetryConfig {
+  enabled?: boolean;
+  endpoint?: string;
+  batchSize?: number;
+  flushInterval?: number;
+}
+
+/**
+ * Telemetry collector for tracking usage
+ */
+export class TelemetryCollector {
+  private events: TelemetryEvent[] = [];
+  private enabled: boolean;
+  private endpoint?: string;
+  private batchSize: number;
+  private flushInterval: number;
+  private flushTimer?: NodeJS.Timeout;
+  private userId?: string;
+  private sessionId: string;
+
+  constructor(config: TelemetryConfig = {}) {
+    this.enabled = config.enabled ?? this.checkEnabled();
+    this.endpoint = config.endpoint;
+    this.batchSize = config.batchSize ?? 100;
+    this.flushInterval = config.flushInterval ?? 60000;
+    this.sessionId = this.generateSessionId();
+
+    if (this.enabled && this.flushInterval > 0) {
+      this.startFlushTimer();
+    }
+  }
+
+  private checkEnabled(): boolean {
+    const disabled = process.env.PRAISONAI_TELEMETRY_DISABLED === 'true' ||
+                     process.env.PRAISONAI_DISABLE_TELEMETRY === 'true' ||
+                     process.env.DO_NOT_TRACK === 'true';
+    return !disabled;
+  }
+
+  /**
+   * Track an event
+   */
+  track(name: string, properties?: Record<string, any>): void {
+    if (!this.enabled) return;
+
+    const event: TelemetryEvent = {
+      name,
+      timestamp: Date.now(),
+      properties,
+      userId: this.userId,
+      sessionId: this.sessionId
+    };
+
+    this.events.push(event);
+
+    if (this.events.length >= this.batchSize) {
+      this.flush();
+    }
+  }
+
+  /**
+   * Track feature usage
+   */
+  trackFeatureUsage(feature: string, metadata?: Record<string, any>): void {
+    this.track('feature_usage', { feature, ...metadata });
+  }
+
+  /**
+   * Track agent execution
+   */
+  trackAgentExecution(agentName: string, duration: number, success: boolean): void {
+    this.track('agent_execution', { agentName, duration, success });
+  }
+
+  /**
+   * Track tool call
+   */
+  trackToolCall(toolName: string, duration: number, success: boolean): void {
+    this.track('tool_call', { toolName, duration, success });
+  }
+
+  /**
+   * Track LLM call
+   */
+  trackLLMCall(provider: string, model: string, tokens: number, duration: number): void {
+    this.track('llm_call', { provider, model, tokens, duration });
+  }
+
+  /**
+   * Track error
+   */
+  trackError(error: string, context?: Record<string, any>): void {
+    this.track('error', { error, ...context });
+  }
+
+  /**
+   * Set user ID
+   */
+  setUserId(userId: string): void {
+    this.userId = userId;
+  }
+
+  /**
+   * Flush events
+   */
+  async flush(): Promise<void> {
+    if (this.events.length === 0) return;
+
+    const eventsToSend = [...this.events];
+    this.events = [];
+
+    if (this.endpoint) {
+      try {
+        await fetch(this.endpoint, {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({ events: eventsToSend })
+        });
+      } catch (error) {
+        // Silently fail - never break user applications
+      }
+    }
+  }
+
+  /**
+   * Enable telemetry
+   */
+  enable(): void {
+    this.enabled = true;
+    this.startFlushTimer();
+  }
+
+  /**
+   * Disable telemetry
+   */
+  disable(): void {
+    this.enabled = false;
+    this.stopFlushTimer();
+    this.events = [];
+  }
+
+  /**
+   * Check if telemetry is enabled
+   */
+  isEnabled(): boolean {
+    return this.enabled;
+  }
+
+  /**
+   * Get pending events count
+   */
+  getPendingCount(): number {
+    return this.events.length;
+  }
+
+  /**
+   * Cleanup resources
+   */
+  cleanup(): void {
+    this.stopFlushTimer();
+    this.flush();
+  }
+
+  private startFlushTimer(): void {
+    if (this.flushTimer) return;
+    this.flushTimer = setInterval(() => this.flush(), this.flushInterval);
+  }
+
+  private stopFlushTimer(): void {
+    if (this.flushTimer) {
+      clearInterval(this.flushTimer);
+      this.flushTimer = undefined;
+    }
+  }
+
+  private generateSessionId(): string {
+    return `session_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
+  }
+}
+
+// Global telemetry instance
+let globalTelemetry: TelemetryCollector | null = null;
+
+/**
+ * Get global telemetry instance
+ */
+export function getTelemetry(): TelemetryCollector {
+  if (!globalTelemetry) {
+    globalTelemetry = new TelemetryCollector();
+  }
+  return globalTelemetry;
+}
+
+/**
+ * Enable telemetry
+ */
+export function enableTelemetry(): void {
+  getTelemetry().enable();
+}
+
+/**
+ * Disable telemetry
+ */
+export function disableTelemetry(): void {
+  getTelemetry().disable();
+}
+
+/**
+ * Cleanup telemetry resources
+ */
+export function cleanupTelemetry(): void {
+  if (globalTelemetry) {
+    globalTelemetry.cleanup();
+  }
+}

src/praisonai-ts/src/tools/arxivTools.ts

@@ -1,6 +1,6 @@
 import axios from 'axios';
-import { Tool, BaseTool } from './index';
 import { XMLParser } from 'fast-xml-parser';
+import { BaseTool } from './base';
 
 export interface ArxivPaper {
   id: string;
@@ -12,21 +12,21 @@ export interface ArxivPaper {
   link: string;
 }
 
-export class ArxivSearchTool extends BaseTool {
+export class ArxivSearchTool extends BaseTool<{ query: string; maxResults?: number }, ArxivPaper[]> {
+  name = 'arxiv-search';
+  description = 'Search for academic papers on arXiv';
   private parser: XMLParser;
 
   constructor() {
-    super(
-      'arxiv-search',
-      'Search for academic papers on arXiv'
-    );
+    super();
     this.parser = new XMLParser({
       ignoreAttributes: false,
       attributeNamePrefix: '@_'
     });
   }
 
-  async execute(query: string, maxResults: number = 10): Promise<ArxivPaper[]> {
+  async run(params: { query: string; maxResults?: number }): Promise<ArxivPaper[]> {
+    const { query, maxResults = 10 } = params;
     try {
       const response = await axios.get(`http://export.arxiv.org/api/query`, {
         params: {
@@ -67,15 +67,12 @@ export class ArxivSearchTool extends BaseTool {
   }
 }
 
-export class ArxivDownloadTool extends BaseTool {
-  constructor() {
-    super(
-      'arxiv-download',
-      'Download PDF of an arXiv paper'
-    );
-  }
+export class ArxivDownloadTool extends BaseTool<{ paperId: string }, Buffer> {
+  name = 'arxiv-download';
+  description = 'Download PDF of an arXiv paper';
 
-  async execute(paperId: string): Promise<Buffer> {
+  async run(params: { paperId: string }): Promise<Buffer> {
+    const { paperId } = params;
     try {
       const response = await axios.get(`https://arxiv.org/pdf/${paperId}.pdf`, {
         responseType: 'arraybuffer'

src/praisonai-ts/src/tools/base.ts

@@ -0,0 +1,189 @@
+/**
+ * BaseTool - Abstract base class for creating custom tools (plugins)
+ * 
+ * This provides the same extensibility pattern as Python's BaseTool.
+ * External developers can create plugins by extending BaseTool.
+ * 
+ * Usage:
+ *   import { BaseTool } from 'praisonai';
+ * 
+ *   class MyTool extends BaseTool {
+ *     name = 'my_tool';
+ *     description = 'Does something useful';
+ *     
+ *     async run(params: { query: string }): Promise<string> {
+ *       return `Result for ${params.query}`;
+ *     }
+ *   }
+ */
+
+export interface ToolResult<T = any> {
+  output: T;
+  success: boolean;
+  error?: string;
+  metadata?: Record<string, any>;
+}
+
+export class ToolValidationError extends Error {
+  constructor(message: string) {
+    super(message);
+    this.name = 'ToolValidationError';
+  }
+}
+
+export interface ToolParameters {
+  type: 'object';
+  properties: Record<string, {
+    type: string;
+    description?: string;
+    enum?: string[];
+    default?: any;
+  }>;
+  required?: string[];
+}
+
+/**
+ * Abstract base class for all PraisonAI tools.
+ * 
+ * Subclass this to create custom tools that can be:
+ * - Used directly by agents
+ * - Distributed as npm packages (plugins)
+ * - Auto-discovered via package.json
+ */
+export abstract class BaseTool<TParams = any, TResult = any> {
+  /** Unique identifier for the tool */
+  abstract name: string;
+  
+  /** Human-readable description (used by LLM) */
+  abstract description: string;
+  
+  /** Tool version string */
+  version: string = '1.0.0';
+  
+  /** JSON Schema for parameters */
+  parameters?: ToolParameters;
+
+  /**
+   * Execute the tool with given arguments.
+   * This method must be implemented by subclasses.
+   */
+  abstract run(params: TParams): Promise<TResult> | TResult;
+
+  /**
+   * Allow tool to be called directly like a function.
+   */
+  async execute(params: TParams): Promise<TResult> {
+    return this.run(params);
+  }
+
+  /**
+   * Execute tool with error handling, returning ToolResult.
+   */
+  async safeRun(params: TParams): Promise<ToolResult<TResult>> {
+    try {
+      const output = await this.run(params);
+      return { output, success: true };
+    } catch (error: any) {
+      return {
+        output: null as any,
+        success: false,
+        error: error.message
+      };
+    }
+  }
+
+  /**
+   * Get OpenAI-compatible function schema for this tool.
+   */
+  getSchema(): { type: 'function'; function: { name: string; description: string; parameters: any } } {
+    return {
+      type: 'function',
+      function: {
+        name: this.name,
+        description: this.description,
+        parameters: this.parameters || { type: 'object', properties: {}, required: [] }
+      }
+    };
+  }
+
+  /**
+   * Validate the tool configuration.
+   */
+  validate(): boolean {
+    const errors: string[] = [];
+
+    if (!this.name || typeof this.name !== 'string') {
+      errors.push("Tool must have a non-empty string 'name'");
+    }
+
+    if (!this.description || typeof this.description !== 'string') {
+      errors.push("Tool must have a non-empty string 'description'");
+    }
+
+    if (errors.length > 0) {
+      throw new ToolValidationError(`Tool '${this.name}' validation failed: ${errors.join('; ')}`);
+    }
+
+    return true;
+  }
+
+  toString(): string {
+    return `${this.constructor.name}(name='${this.name}')`;
+  }
+}
+
+/**
+ * Validate any tool-like object.
+ */
+export function validateTool(tool: any): boolean {
+  if (tool instanceof BaseTool) {
+    return tool.validate();
+  }
+
+  if (typeof tool === 'function' || (tool && typeof tool.run === 'function')) {
+    const name = tool.name || tool.__name__;
+    if (!name) {
+      throw new ToolValidationError('Tool must have a name');
+    }
+    return true;
+  }
+
+  throw new ToolValidationError(`Invalid tool type: ${typeof tool}`);
+}
+
+/**
+ * Create a simple tool from a function (alternative to class-based approach)
+ */
+export function createTool<TParams = any, TResult = any>(config: {
+  name: string;
+  description: string;
+  parameters?: ToolParameters;
+  run: (params: TParams) => Promise<TResult> | TResult;
+}): BaseTool<TParams, TResult> {
+  return {
+    name: config.name,
+    description: config.description,
+    version: '1.0.0',
+    parameters: config.parameters,
+    run: config.run,
+    execute: async (params: TParams) => config.run(params),
+    safeRun: async (params: TParams) => {
+      try {
+        const output = await config.run(params);
+        return { output, success: true };
+      } catch (error: any) {
+        return { output: null as any, success: false, error: error.message };
+      }
+    },
+    getSchema: () => ({
+      type: 'function' as const,
+      function: {
+        name: config.name,
+        description: config.description,
+        parameters: config.parameters || { type: 'object', properties: {}, required: [] }
+      }
+    }),
+    validate: () => true,
+    toString: () => `Tool(name='${config.name}')`
+  } as BaseTool<TParams, TResult>;
+}

src/praisonai-ts/src/tools/decorator.ts

@@ -0,0 +1,225 @@
+/**
+ * Tool Decorator and Registry - Type-safe tool creation and management
+ */
+
+export interface ToolParameters {
+  type: 'object';
+  properties: Record<string, {
+    type: string;
+    description?: string;
+    enum?: string[];
+    default?: any;
+  }>;
+  required?: string[];
+}
+
+export interface ToolConfig<TParams = any, TResult = any> {
+  name: string;
+  description?: string;
+  parameters?: ToolParameters | any; // Support Zod schemas
+  category?: string;
+  execute: (params: TParams, context?: ToolContext) => Promise<TResult> | TResult;
+}
+
+export interface ToolContext {
+  agentName?: string;
+  sessionId?: string;
+  runId?: string;
+  signal?: AbortSignal;
+}
+
+export interface ToolDefinition {
+  name: string;
+  description: string;
+  parameters: ToolParameters;
+  category?: string;
+}
+
+/**
+ * Tool class - Represents an executable tool
+ */
+export class FunctionTool<TParams = any, TResult = any> {
+  readonly name: string;
+  readonly description: string;
+  readonly parameters: ToolParameters;
+  readonly category?: string;
+  private readonly executeFn: (params: TParams, context?: ToolContext) => Promise<TResult> | TResult;
+
+  constructor(config: ToolConfig<TParams, TResult>) {
+    this.name = config.name;
+    this.description = config.description || `Function ${config.name}`;
+    this.parameters = this.normalizeParameters(config.parameters);
+    this.category = config.category;
+    this.executeFn = config.execute;
+  }
+
+  private normalizeParameters(params: any): ToolParameters {
+    if (!params) {
+      return { type: 'object', properties: {}, required: [] };
+    }
+    
+    // Check if it's a Zod schema
+    if (params && typeof params.parse === 'function' && typeof params._def === 'object') {
+      return this.zodToJsonSchema(params);
+    }
+    
+    // Already a JSON schema
+    return params;
+  }
+
+  private zodToJsonSchema(zodSchema: any): ToolParameters {
+    // Basic Zod to JSON Schema conversion
+    // For full support, use zod-to-json-schema package
+    const def = zodSchema._def;
+    
+    if (def.typeName === 'ZodObject') {
+      const properties: Record<string, any> = {};
+      const required: string[] = [];
+      
+      for (const [key, value] of Object.entries(def.shape())) {
+        const fieldDef = (value as any)._def;
+        properties[key] = this.zodFieldToJsonSchema(fieldDef);
+        
+        // Check if field is required (not optional)
+        if (fieldDef.typeName !== 'ZodOptional' && fieldDef.typeName !== 'ZodDefault') {
+          required.push(key);
+        }
+      }
+      
+      return { type: 'object', properties, required };
+    }
+    
+    return { type: 'object', properties: {}, required: [] };
+  }
+
+  private zodFieldToJsonSchema(def: any): any {
+    const typeName = def.typeName;
+    
+    switch (typeName) {
+      case 'ZodString':
+        return { type: 'string', description: def.description };
+      case 'ZodNumber':
+        return { type: 'number', description: def.description };
+      case 'ZodBoolean':
+        return { type: 'boolean', description: def.description };
+      case 'ZodArray':
+        return { type: 'array', items: this.zodFieldToJsonSchema(def.type._def) };
+      case 'ZodEnum':
+        return { type: 'string', enum: def.values };
+      case 'ZodOptional':
+        return this.zodFieldToJsonSchema(def.innerType._def);
+      case 'ZodDefault':
+        const inner = this.zodFieldToJsonSchema(def.innerType._def);
+        inner.default = def.defaultValue();
+        return inner;
+      default:
+        return { type: 'string' };
+    }
+  }
+
+  async execute(params: TParams, context?: ToolContext): Promise<TResult> {
+    // Validate parameters if we have a schema
+    // For now, just execute - validation can be added later
+    return this.executeFn(params, context);
+  }
+
+  /**
+   * Get the tool definition for LLM
+   */
+  getDefinition(): ToolDefinition {
+    return {
+      name: this.name,
+      description: this.description,
+      parameters: this.parameters,
+      category: this.category,
+    };
+  }
+
+  /**
+   * Get OpenAI-compatible tool format
+   */
+  toOpenAITool(): { type: 'function'; function: { name: string; description: string; parameters: any } } {
+    return {
+      type: 'function',
+      function: {
+        name: this.name,
+        description: this.description,
+        parameters: this.parameters,
+      },
+    };
+  }
+}
+
+/**
+ * Create a tool from a configuration object
+ */
+export function tool<TParams = any, TResult = any>(
+  config: ToolConfig<TParams, TResult>
+): FunctionTool<TParams, TResult> {
+  return new FunctionTool(config);
+}
+
+/**
+ * Tool Registry - Manages tool registration and lookup
+ */
+export class ToolRegistry {
+  private tools: Map<string, FunctionTool> = new Map();
+
+  register(tool: FunctionTool, options?: { overwrite?: boolean }): this {
+    if (this.tools.has(tool.name) && !options?.overwrite) {
+      throw new Error(`Tool '${tool.name}' is already registered. Use { overwrite: true } to replace.`);
+    }
+    this.tools.set(tool.name, tool);
+    return this;
+  }
+
+  get(name: string): FunctionTool | undefined {
+    return this.tools.get(name);
+  }
+
+  has(name: string): boolean {
+    return this.tools.has(name);
+  }
+
+  list(): FunctionTool[] {
+    return Array.from(this.tools.values());
+  }
+
+  getByCategory(category: string): FunctionTool[] {
+    return this.list().filter(t => t.category === category);
+  }
+
+  getDefinitions(): ToolDefinition[] {
+    return this.list().map(t => t.getDefinition());
+  }
+
+  toOpenAITools(): Array<{ type: 'function'; function: any }> {
+    return this.list().map(t => t.toOpenAITool());
+  }
+
+  delete(name: string): boolean {
+    return this.tools.delete(name);
+  }
+
+  clear(): void {
+    this.tools.clear();
+  }
+}
+
+// Global registry instance
+let globalRegistry: ToolRegistry | null = null;
+
+export function getRegistry(): ToolRegistry {
+  if (!globalRegistry) {
+    globalRegistry = new ToolRegistry();
+  }
+  return globalRegistry;
+}
+
+export function registerTool(tool: FunctionTool, options?: { overwrite?: boolean }): void {
+  getRegistry().register(tool, options);
+}
+
+export function getTool(name: string): FunctionTool | undefined {
+  return getRegistry().get(name);
+}

src/praisonai-ts/src/tools/index.ts

@@ -1,23 +1,8 @@
-// Export base tool interfaces
-export interface Tool {
-  name: string;
-  description: string;
-  execute(...args: any[]): Promise<any>;
-}
+// Export base tool interfaces and classes
+export { BaseTool, ToolResult, ToolValidationError, validateTool, createTool, type ToolParameters } from './base';
 
-export class BaseTool implements Tool {
-  name: string;
-  description: string;
-
-  constructor(name: string, description: string) {
-    this.name = name;
-    this.description = description;
-  }
-
-  async execute(...args: any[]): Promise<any> {
-    throw new Error('Method not implemented.');
-  }
-}
+// Export decorator and registry
+export * from './decorator';
 
 // Export all tool modules
 export * from './arxivTools';

src/praisonai-ts/src/tools/mcpSse.ts

@@ -1,6 +1,6 @@
 import { Client } from '@modelcontextprotocol/sdk/client/index.js';
 import { SSEClientTransport } from '@modelcontextprotocol/sdk/client/sse.js';
-import { BaseTool } from './index';
+import { BaseTool } from './base';
 
 export interface MCPToolInfo {
   name: string;
@@ -8,12 +8,16 @@ export interface MCPToolInfo {
   inputSchema?: any;
 }
 
-export class MCPTool extends BaseTool {
+export class MCPTool extends BaseTool<any, any> {
+  name: string;
+  description: string;
   private client: Client;
   private inputSchema: any;
 
   constructor(info: MCPToolInfo, client: Client) {
-    super(info.name, info.description || `Call the ${info.name} tool`);
+    super();
+    this.name = info.name;
+    this.description = info.description || `Call the ${info.name} tool`;
     this.client = client;
     this.inputSchema = info.inputSchema || { type: 'object', properties: {}, required: [] };
   }
@@ -22,7 +26,7 @@ export class MCPTool extends BaseTool {
     return this.inputSchema?.properties;
   }
 
-  async execute(args: any = {}): Promise<any> {
+  async run(args: any = {}): Promise<any> {
     try {
       const result: any = await this.client.callTool({ name: this.name, arguments: args });
       if (result.structuredContent) {

src/praisonai-ts/src/workflows/index.ts

@@ -0,0 +1,300 @@
+/**
+ * Workflows - Pipeline and orchestration patterns
+ */
+
+import { randomUUID } from 'crypto';
+
+export type StepStatus = 'pending' | 'running' | 'completed' | 'failed' | 'skipped';
+
+export interface StepResult<T = any> {
+  stepId: string;
+  stepName: string;
+  status: StepStatus;
+  output?: T;
+  error?: Error;
+  duration: number;
+  startedAt: number;
+  completedAt?: number;
+}
+
+export interface WorkflowContext {
+  workflowId: string;
+  stepResults: Map<string, StepResult>;
+  metadata: Record<string, any>;
+  get<T = any>(stepName: string): T | undefined;
+  set(key: string, value: any): void;
+}
+
+export type StepFunction<TInput = any, TOutput = any> = (
+  input: TInput,
+  context: WorkflowContext
+) => Promise<TOutput> | TOutput;
+
+export interface WorkflowStepConfig<TInput = any, TOutput = any> {
+  name: string;
+  execute: StepFunction<TInput, TOutput>;
+  condition?: (context: WorkflowContext) => boolean;
+  onError?: 'fail' | 'skip' | 'retry';
+  maxRetries?: number;
+  timeout?: number;
+}
+
+/**
+ * Workflow Step
+ */
+export class WorkflowStep<TInput = any, TOutput = any> {
+  readonly id: string;
+  readonly name: string;
+  readonly execute: StepFunction<TInput, TOutput>;
+  readonly condition?: (context: WorkflowContext) => boolean;
+  readonly onError: 'fail' | 'skip' | 'retry';
+  readonly maxRetries: number;
+  readonly timeout?: number;
+
+  constructor(config: WorkflowStepConfig<TInput, TOutput>) {
+    this.id = randomUUID();
+    this.name = config.name;
+    this.execute = config.execute;
+    this.condition = config.condition;
+    this.onError = config.onError || 'fail';
+    this.maxRetries = config.maxRetries || 0;
+    this.timeout = config.timeout;
+  }
+
+  async run(input: TInput, context: WorkflowContext): Promise<StepResult<TOutput>> {
+    const startedAt = Date.now();
+    
+    // Check condition
+    if (this.condition && !this.condition(context)) {
+      return {
+        stepId: this.id,
+        stepName: this.name,
+        status: 'skipped',
+        duration: 0,
+        startedAt,
+        completedAt: Date.now(),
+      };
+    }
+
+    let lastError: Error | undefined;
+    let attempts = 0;
+
+    while (attempts <= this.maxRetries) {
+      attempts++;
+      try {
+        const output = await this.executeWithTimeout(input, context);
+        const completedAt = Date.now();
+        
+        return {
+          stepId: this.id,
+          stepName: this.name,
+          status: 'completed',
+          output,
+          duration: completedAt - startedAt,
+          startedAt,
+          completedAt,
+        };
+      } catch (error: any) {
+        lastError = error;
+        
+        if (this.onError === 'skip') {
+          return {
+            stepId: this.id,
+            stepName: this.name,
+            status: 'skipped',
+            error: lastError,
+            duration: Date.now() - startedAt,
+            startedAt,
+            completedAt: Date.now(),
+          };
+        }
+        
+        if (this.onError !== 'retry' || attempts > this.maxRetries) {
+          break;
+        }
+      }
+    }
+
+    return {
+      stepId: this.id,
+      stepName: this.name,
+      status: 'failed',
+      error: lastError,
+      duration: Date.now() - startedAt,
+      startedAt,
+      completedAt: Date.now(),
+    };
+  }
+
+  private async executeWithTimeout(input: TInput, context: WorkflowContext): Promise<TOutput> {
+    if (!this.timeout) {
+      return this.execute(input, context);
+    }
+
+    return Promise.race([
+      this.execute(input, context),
+      new Promise<never>((_, reject) => {
+        setTimeout(() => reject(new Error(`Step ${this.name} timed out after ${this.timeout}ms`)), this.timeout);
+      }),
+    ]);
+  }
+}
+
+/**
+ * Create a workflow context
+ */
+function createContext(workflowId: string): WorkflowContext {
+  const stepResults = new Map<string, StepResult>();
+  const metadata: Record<string, any> = {};
+
+  return {
+    workflowId,
+    stepResults,
+    metadata,
+    get<T = any>(stepName: string): T | undefined {
+      const result = stepResults.get(stepName);
+      return result?.output as T | undefined;
+    },
+    set(key: string, value: any): void {
+      metadata[key] = value;
+    },
+  };
+}
+
+/**
+ * Workflow - Sequential pipeline execution
+ */
+export class Workflow<TInput = any, TOutput = any> {
+  readonly id: string;
+  readonly name: string;
+  private steps: WorkflowStep[] = [];
+
+  constructor(name: string) {
+    this.id = randomUUID();
+    this.name = name;
+  }
+
+  /**
+   * Add a step to the workflow
+   */
+  addStep<TStepInput = any, TStepOutput = any>(
+    config: WorkflowStepConfig<TStepInput, TStepOutput>
+  ): this {
+    this.steps.push(new WorkflowStep(config));
+    return this;
+  }
+
+  /**
+   * Add a step using a simpler syntax
+   */
+  step<TStepInput = any, TStepOutput = any>(
+    name: string,
+    execute: StepFunction<TStepInput, TStepOutput>
+  ): this {
+    return this.addStep({ name, execute });
+  }
+
+  /**
+   * Run the workflow
+   */
+  async run(input: TInput): Promise<{ output: TOutput | undefined; results: StepResult[]; context: WorkflowContext }> {
+    const context = createContext(this.id);
+    const results: StepResult[] = [];
+    let currentInput: any = input;
+
+    for (const step of this.steps) {
+      const result = await step.run(currentInput, context);
+      results.push(result);
+      context.stepResults.set(step.name, result);
+
+      if (result.status === 'failed') {
+        return { output: undefined, results, context };
+      }
+
+      if (result.status === 'completed') {
+        currentInput = result.output;
+      }
+    }
+
+    const lastResult = results[results.length - 1];
+    return {
+      output: lastResult?.output as TOutput | undefined,
+      results,
+      context,
+    };
+  }
+
+  /**
+   * Get step count
+   */
+  get stepCount(): number {
+    return this.steps.length;
+  }
+}
+
+/**
+ * Pipeline - Alias for Workflow
+ */
+export const Pipeline = Workflow;
+
+/**
+ * Parallel execution helper
+ */
+export async function parallel<T>(
+  tasks: Array<() => Promise<T>>
+): Promise<T[]> {
+  return Promise.all(tasks.map(task => task()));
+}
+
+/**
+ * Route helper - Execute based on condition
+ */
+export async function route<T>(
+  conditions: Array<{ condition: () => boolean; execute: () => Promise<T> }>,
+  defaultExecute?: () => Promise<T>
+): Promise<T | undefined> {
+  for (const { condition, execute } of conditions) {
+    if (condition()) {
+      return execute();
+    }
+  }
+  return defaultExecute?.();
+}
+
+/**
+ * Loop helper - Repeat until condition
+ */
+export async function loop<T>(
+  execute: (iteration: number) => Promise<T>,
+  shouldContinue: (result: T, iteration: number) => boolean,
+  maxIterations: number = 100
+): Promise<T[]> {
+  const results: T[] = [];
+  let iteration = 0;
+
+  while (iteration < maxIterations) {
+    const result = await execute(iteration);
+    results.push(result);
+    
+    if (!shouldContinue(result, iteration)) {
+      break;
+    }
+    iteration++;
+  }
+
+  return results;
+}
+
+/**
+ * Repeat helper - Execute N times
+ */
+export async function repeat<T>(
+  execute: (iteration: number) => Promise<T>,
+  times: number
+): Promise<T[]> {
+  const results: T[] = [];
+  for (let i = 0; i < times; i++) {
+    results.push(await execute(i));
+  }
+  return results;
+}

src/praisonai-ts/src/workflows/yaml-parser.ts

@@ -0,0 +1,315 @@
+/**
+ * YAML Workflow Parser
+ * Parse YAML workflow definitions into executable workflows
+ */
+
+import { Workflow, WorkflowStep, WorkflowStepConfig } from './index';
+
+export interface YAMLWorkflowDefinition {
+  name: string;
+  description?: string;
+  version?: string;
+  steps: YAMLStepDefinition[];
+  metadata?: Record<string, any>;
+}
+
+export interface YAMLStepDefinition {
+  name: string;
+  type: 'agent' | 'tool' | 'condition' | 'parallel' | 'loop';
+  agent?: string;
+  tool?: string;
+  input?: string | Record<string, any>;
+  output?: string;
+  condition?: string;
+  onError?: 'fail' | 'skip' | 'retry';
+  maxRetries?: number;
+  timeout?: number;
+  steps?: YAMLStepDefinition[]; // For parallel/loop
+  loopCondition?: string;
+  maxIterations?: number;
+}
+
+export interface ParsedWorkflow {
+  workflow: Workflow;
+  definition: YAMLWorkflowDefinition;
+  errors: string[];
+}
+
+/**
+ * Parse YAML string into workflow definition
+ */
+export function parseYAMLWorkflow(yamlContent: string): YAMLWorkflowDefinition {
+  // Simple YAML parser for workflow definitions
+  // For production, use js-yaml package
+  const lines = yamlContent.split('\n');
+  const result: YAMLWorkflowDefinition = {
+    name: '',
+    steps: []
+  };
+
+  let currentStep: Partial<YAMLStepDefinition> | null = null;
+  let indent = 0;
+
+  for (const line of lines) {
+    const trimmed = line.trim();
+    if (!trimmed || trimmed.startsWith('#')) continue;
+
+    const currentIndent = line.search(/\S/);
+    
+    // Parse key-value pairs
+    const colonIndex = trimmed.indexOf(':');
+    if (colonIndex === -1) continue;
+
+    const key = trimmed.substring(0, colonIndex).trim();
+    const value = trimmed.substring(colonIndex + 1).trim();
+
+    if (currentIndent === 0) {
+      // Top-level keys
+      if (key === 'name') result.name = value;
+      else if (key === 'description') result.description = value;
+      else if (key === 'version') result.version = value;
+      else if (key === 'steps') {
+        // Steps array starts
+        indent = currentIndent;
+      }
+    } else if (trimmed.startsWith('- name:')) {
+      // New step
+      if (currentStep && currentStep.name) {
+        result.steps.push(currentStep as YAMLStepDefinition);
+      }
+      currentStep = {
+        name: value,
+        type: 'agent'
+      };
+    } else if (currentStep) {
+      // Step properties
+      if (key === 'type') currentStep.type = value as any;
+      else if (key === 'agent') currentStep.agent = value;
+      else if (key === 'tool') currentStep.tool = value;
+      else if (key === 'input') currentStep.input = value;
+      else if (key === 'output') currentStep.output = value;
+      else if (key === 'condition') currentStep.condition = value;
+      else if (key === 'onError') currentStep.onError = value as any;
+      else if (key === 'maxRetries') currentStep.maxRetries = parseInt(value);
+      else if (key === 'timeout') currentStep.timeout = parseInt(value);
+      else if (key === 'loopCondition') currentStep.loopCondition = value;
+      else if (key === 'maxIterations') currentStep.maxIterations = parseInt(value);
+    }
+  }
+
+  // Add last step
+  if (currentStep && currentStep.name) {
+    result.steps.push(currentStep as YAMLStepDefinition);
+  }
+
+  return result;
+}
+
+/**
+ * Create executable workflow from YAML definition
+ */
+export function createWorkflowFromYAML(
+  definition: YAMLWorkflowDefinition,
+  agents: Record<string, any> = {},
+  tools: Record<string, any> = {}
+): ParsedWorkflow {
+  const errors: string[] = [];
+  const workflow = new Workflow(definition.name);
+
+  for (const stepDef of definition.steps) {
+    try {
+      const stepConfig = createStepConfig(stepDef, agents, tools, errors);
+      if (stepConfig) {
+        workflow.addStep(stepConfig);
+      }
+    } catch (error: any) {
+      errors.push(`Error creating step ${stepDef.name}: ${error.message}`);
+    }
+  }
+
+  return { workflow, definition, errors };
+}
+
+function createStepConfig(
+  stepDef: YAMLStepDefinition,
+  agents: Record<string, any>,
+  tools: Record<string, any>,
+  errors: string[]
+): WorkflowStepConfig | null {
+  const { name, type, agent, tool, onError, maxRetries, timeout, condition } = stepDef;
+
+  let execute: any;
+
+  switch (type) {
+    case 'agent':
+      if (!agent) {
+        errors.push(`Step ${name}: agent type requires 'agent' field`);
+        return null;
+      }
+      const agentInstance = agents[agent];
+      if (!agentInstance) {
+        errors.push(`Step ${name}: agent '${agent}' not found`);
+        return null;
+      }
+      execute = async (input: any) => {
+        if (typeof agentInstance.chat === 'function') {
+          return agentInstance.chat(typeof input === 'string' ? input : JSON.stringify(input));
+        }
+        return agentInstance(input);
+      };
+      break;
+
+    case 'tool':
+      if (!tool) {
+        errors.push(`Step ${name}: tool type requires 'tool' field`);
+        return null;
+      }
+      const toolInstance = tools[tool];
+      if (!toolInstance) {
+        errors.push(`Step ${name}: tool '${tool}' not found`);
+        return null;
+      }
+      execute = async (input: any) => {
+        if (typeof toolInstance.execute === 'function') {
+          return toolInstance.execute(input);
+        }
+        if (typeof toolInstance.run === 'function') {
+          return toolInstance.run(input);
+        }
+        return toolInstance(input);
+      };
+      break;
+
+    case 'condition':
+      execute = async (input: any, context: any) => {
+        // Evaluate condition and return input or skip
+        if (condition) {
+          // Simple condition evaluation
+          const result = evaluateCondition(condition, input, context);
+          return result ? input : null;
+        }
+        return input;
+      };
+      break;
+
+    default:
+      execute = async (input: any) => input;
+  }
+
+  return {
+    name,
+    execute,
+    onError,
+    maxRetries,
+    timeout,
+    condition: condition ? (context) => evaluateCondition(condition, null, context) : undefined
+  };
+}
+
+function evaluateCondition(condition: string, input: any, context: any): boolean {
+  // Simple condition evaluation
+  // Supports: "result.success", "input.length > 0", etc.
+  try {
+    // Create a safe evaluation context
+    const evalContext = {
+      input,
+      context,
+      result: context?.get?.('lastResult'),
+      ...context?.metadata
+    };
+
+    // Simple expression evaluation
+    if (condition.includes('===')) {
+      const [left, right] = condition.split('===').map(s => s.trim());
+      return getNestedValue(evalContext, left) === parseValue(right);
+    }
+    if (condition.includes('!==')) {
+      const [left, right] = condition.split('!==').map(s => s.trim());
+      return getNestedValue(evalContext, left) !== parseValue(right);
+    }
+    if (condition.includes('>')) {
+      const [left, right] = condition.split('>').map(s => s.trim());
+      return getNestedValue(evalContext, left) > parseValue(right);
+    }
+    if (condition.includes('<')) {
+      const [left, right] = condition.split('<').map(s => s.trim());
+      return getNestedValue(evalContext, left) < parseValue(right);
+    }
+
+    // Boolean check
+    return !!getNestedValue(evalContext, condition);
+  } catch {
+    return false;
+  }
+}
+
+function getNestedValue(obj: any, path: string): any {
+  return path.split('.').reduce((current, key) => current?.[key], obj);
+}
+
+function parseValue(value: string): any {
+  value = value.trim();
+  if (value === 'true') return true;
+  if (value === 'false') return false;
+  if (value === 'null') return null;
+  if (value.startsWith('"') && value.endsWith('"')) return value.slice(1, -1);
+  if (value.startsWith("'") && value.endsWith("'")) return value.slice(1, -1);
+  const num = Number(value);
+  if (!isNaN(num)) return num;
+  return value;
+}
+
+/**
+ * Load workflow from YAML file
+ */
+export async function loadWorkflowFromFile(
+  filePath: string,
+  agents: Record<string, any> = {},
+  tools: Record<string, any> = {}
+): Promise<ParsedWorkflow> {
+  const fs = await import('fs/promises');
+  const content = await fs.readFile(filePath, 'utf-8');
+  const definition = parseYAMLWorkflow(content);
+  return createWorkflowFromYAML(definition, agents, tools);
+}
+
+/**
+ * Validate YAML workflow definition
+ */
+export function validateWorkflowDefinition(definition: YAMLWorkflowDefinition): string[] {
+  const errors: string[] = [];
+
+  if (!definition.name) {
+    errors.push('Workflow must have a name');
+  }
+
+  if (!definition.steps || definition.steps.length === 0) {
+    errors.push('Workflow must have at least one step');
+  }
+
+  const stepNames = new Set<string>();
+  for (const step of definition.steps) {
+    if (!step.name) {
+      errors.push('Each step must have a name');
+    } else if (stepNames.has(step.name)) {
+      errors.push(`Duplicate step name: ${step.name}`);
+    } else {
+      stepNames.add(step.name);
+    }
+
+    if (!step.type) {
+      errors.push(`Step ${step.name}: must have a type`);
+    }
+
+    if (step.type === 'agent' && !step.agent) {
+      errors.push(`Step ${step.name}: agent type requires 'agent' field`);
+    }
+
+    if (step.type === 'tool' && !step.tool) {
+      errors.push(`Step ${step.name}: tool type requires 'tool' field`);
+    }
+  }
+
+  return errors;
+}
+