Release v2.7.2

- Add doctor examples to main README with documentation link
- Sort checks by category in list_available_checks()
- Update registry method call from list_checks() to get_all_checks()
- Remove unused results variable in run_environment_checks()
- Fix f-string formatting in CI integration print statement

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
@@ -1776,19 +1994,23 @@ manager.delete_checkpoint("deploy-v1")
 
 ### Tools CLI:
 ```bash
-# List all available tools
 praisonai tools list
-
-# Get info about a specific tool
 praisonai tools info internet_search
-
-# Search for tools
 praisonai tools search "web"
-
-# Show all commands
-praisonai tools help
+praisonai tools doctor
+praisonai tools resolve shell_tool
+praisonai tools discover
+praisonai tools show-sources
+praisonai tools show-sources --template ai-video-editor
 ```
 
+| Command | Example | Docs |
+|---------|---------|------|
+| `tools list` | [example](examples/tools/) | [docs](https://docs.praison.ai/docs/cli/tools) |
+| `tools resolve` | [example](examples/tools/example_tools_resolve.py) | [docs](https://docs.praison.ai/docs/cli/tools-resolve) |
+| `tools discover` | [example](examples/tools/example_tools_discover.py) | [docs](https://docs.praison.ai/docs/cli/tools-discover) |
+| `tools show-sources` | [example](examples/tools/example_tools_sources.py) | [docs](https://docs.praison.ai/docs/cli/tools-show-sources) |
+
 ### Handoff CLI:
 ```bash
 # Enable agent-to-agent task delegation
@@ -2137,6 +2359,24 @@ agent = Agent(
 | 🌐 Web Search - Real-time search | [📖](https://docs.praison.ai/docs/cli/web-search) |
 | 📥 Web Fetch - URL content retrieval | [📖](https://docs.praison.ai/docs/cli/web-fetch) |
 | 💾 Prompt Caching - Cost reduction | [📖](https://docs.praison.ai/docs/cli/prompt-caching) |
+| 📦 Template Catalog - Browse & discover templates | [📖](https://docs.praison.ai/docs/cli/template-catalog) |
+
+### Template Catalog CLI
+
+| Command | Description |
+|---------|-------------|
+| `praisonai templates browse` | Open template catalog in browser |
+| `praisonai templates browse --print` | Print catalog URL only |
+| `praisonai templates validate` | Validate template YAML files |
+| `praisonai templates validate --source <dir>` | Validate specific directory |
+| `praisonai templates validate --strict` | Strict validation mode |
+| `praisonai templates validate --json` | JSON output format |
+| `praisonai templates catalog build` | Build catalog locally |
+| `praisonai templates catalog build --out <dir>` | Build to specific directory |
+| `praisonai templates catalog sync` | Sync template sources |
+| `praisonai templates catalog sync --source <name>` | Sync specific source |
+
+**Examples:** [examples/catalog/](examples/catalog/) | **Docs:** [Code](https://docs.praison.ai/docs/cli/template-catalog-code) | [CLI](https://docs.praison.ai/docs/cli/template-catalog)
 
 ---
 
@@ -3023,6 +3263,119 @@ 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>=2.7.2" \
     "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>=2.7.2" \
     "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>=2.7.2" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docs/templates-cli.md

@@ -0,0 +1,53 @@
+# Templates CLI
+
+```bash
+praisonai templates list
+```
+
+```bash
+praisonai templates search video
+```
+
+```bash
+praisonai templates info ai-video-editor
+```
+
+```bash
+praisonai templates run ai-video-editor --input video.mp4 --output edited.mp4
+```
+
+```bash
+praisonai templates run ai-video-editor --input video.mp4 --output edited.mp4 --offline
+```
+
+```bash
+praisonai templates run ai-video-editor --input video.mp4 --output edited.mp4 --strict-tools
+```
+
+```bash
+praisonai templates run ai-video-editor --input video.mp4 --tools ./my_tools.py
+```
+
+```bash
+praisonai templates run ai-video-editor --input video.mp4 --tools-dir ./tools
+```
+
+```bash
+praisonai templates run ai-video-editor --input video.mp4 --tools-source praisonai_tools.video
+```
+
+```bash
+praisonai templates run ai-video-editor --input video.mp4 --no-template-tools-py
+```
+
+```bash
+praisonai templates init my-project --template ai-video-editor
+```
+
+```bash
+praisonai templates cache clear
+```
+
+```bash
+praisonai templates cache list
+```

docs/templates-module.md

@@ -0,0 +1,104 @@
+# Templates Module
+
+## Loading Templates
+
+```python
+from praisonai.templates.loader import TemplateLoader
+
+loader = TemplateLoader()
+
+# Load from local path
+template = loader.load("./my-template")
+
+# Load from URI
+template = loader.load("ai-video-editor")
+
+# Load offline
+template = loader.load("ai-video-editor", offline=True)
+
+# Check requirements
+missing = loader.check_requirements(template)
+```
+
+## Template Discovery
+
+```python
+from praisonai.templates.discovery import TemplateDiscovery
+
+discovery = TemplateDiscovery(
+    include_package=True,
+    include_defaults=True,
+)
+
+# Find template
+result = discovery.find_template("ai-video-editor")
+
+# List all templates
+templates = discovery.list_templates()
+```
+
+## Template Config
+
+```python
+from praisonai.templates.loader import TemplateConfig
+
+# Access template properties
+template.name
+template.description
+template.version
+template.author
+template.requires
+template.workflow_file
+template.agents_file
+template.path
+```
+
+## Tool Override Integration
+
+```python
+from praisonai.templates.tool_override import (
+    create_tool_registry_with_overrides,
+    resolve_tools,
+)
+
+# Build registry with template sources
+tools_sources = template.requires.get("tools_sources", [])
+
+registry = create_tool_registry_with_overrides(
+    tools_sources=tools_sources,
+    template_dir=str(template.path),
+    include_defaults=True,
+)
+
+# Resolve tool names
+tools = resolve_tools(["shell_tool"], registry=registry)
+```
+
+## TEMPLATE.yaml Schema
+
+```yaml
+name: my-template
+version: "1.0.0"
+description: Template description
+
+requires:
+  tools: [shell_tool]
+  packages: [praisonai-tools]
+  env: [OPENAI_API_KEY]
+  tools_sources:
+    - praisonai_tools.video
+    - ./local_tools.py
+
+workflow: workflow.yaml
+agents: agents.yaml
+
+config:
+  input:
+    type: string
+    required: true
+  output:
+    type: string
+
+defaults:
+  preset: podcast
+```

docs/tools-cli.md

@@ -0,0 +1,65 @@
+# Tools CLI
+
+```bash
+praisonai tools list
+```
+
+```bash
+praisonai tools info shell_tool
+```
+
+```bash
+praisonai tools info internet_search
+```
+
+```bash
+praisonai tools search "web"
+```
+
+```bash
+praisonai tools search "file"
+```
+
+```bash
+praisonai tools doctor
+```
+
+```bash
+praisonai tools doctor --json
+```
+
+```bash
+praisonai tools resolve shell_tool
+```
+
+```bash
+praisonai tools resolve internet_search --template ai-video-editor
+```
+
+```bash
+praisonai tools resolve my_tool --tools ./my_tools.py
+```
+
+```bash
+praisonai tools resolve my_tool --tools-dir ./tools
+```
+
+```bash
+praisonai tools discover
+```
+
+```bash
+praisonai tools discover --include praisonai_tools.video
+```
+
+```bash
+praisonai tools discover --entrypoints
+```
+
+```bash
+praisonai tools show-sources
+```
+
+```bash
+praisonai tools show-sources --template ai-video-editor
+```

docs/tools-module.md

@@ -0,0 +1,90 @@
+# Tools Module
+
+## Tool Registry
+
+```python
+from praisonai.templates.tool_override import (
+    create_tool_registry_with_overrides,
+    resolve_tools,
+)
+
+# Create registry with defaults
+registry = create_tool_registry_with_overrides(include_defaults=True)
+
+# Create registry with overrides
+registry = create_tool_registry_with_overrides(
+    override_files=["my_tools.py"],
+    override_dirs=["./tools"],
+    include_defaults=True,
+)
+
+# Create registry with tools_sources
+registry = create_tool_registry_with_overrides(
+    tools_sources=["praisonai_tools.video"],
+    template_dir="./my-template",
+    include_defaults=True,
+)
+
+# Resolve tool names to callables
+tools = resolve_tools(
+    ["shell_tool", "internet_search"],
+    registry=registry,
+)
+```
+
+## Tool Override Loader
+
+```python
+from praisonai.templates.tool_override import ToolOverrideLoader
+
+loader = ToolOverrideLoader()
+
+# Load from file
+tools = loader.load_from_file("my_tools.py")
+
+# Load from directory
+tools = loader.load_from_directory("./tools")
+
+# Load from module
+tools = loader.load_from_module("praisonai_tools.video")
+
+# Get default tool directories
+dirs = loader.get_default_tool_dirs()
+```
+
+## Resolution Order
+
+1. CLI `--tools` files (highest priority)
+2. CLI `--tools-dir` directories
+3. Template `tools_sources` (from TEMPLATE.yaml)
+4. Template-local `tools.py`
+5. Default dirs (`~/.praison/tools`, `~/.config/praison/tools`)
+6. Package discovery (`praisonai_tools`)
+7. Built-in tools (lowest priority)
+
+## Creating Custom Tools
+
+```python
+from praisonai_tools import tool
+
+@tool
+def my_custom_tool(query: str) -> str:
+    """My custom tool description.
+    
+    Args:
+        query: The search query
+    """
+    return f"Result for: {query}"
+```
+
+## Template tools_sources
+
+```yaml
+# TEMPLATE.yaml
+requires:
+  tools: [shell_tool]
+  tools_sources:
+    - praisonai_tools.video
+    - ./local_tools.py
+    - ./tools_dir/
+```

examples/README.md

@@ -30,6 +30,7 @@ examples/
 | **Tools** | [python/tools/](python/tools/) | [📖](https://docs.praison.ai/tools) |
 | **Code** | [python/code/](python/code/) | [📖](https://docs.praison.ai/code) |
 | **YAML** | [yaml/](yaml/) | [📖](https://docs.praison.ai/features/yaml-workflows) |
+| **Doctor** | [doctor/](doctor/) | [📖](https://docs.praison.ai/cli/doctor) |
 
 ## Running Examples
 

examples/capabilities/assistants_example.py

@@ -0,0 +1,24 @@
+"""
+Assistants Capabilities Example
+
+Demonstrates OpenAI Assistants API using PraisonAI capabilities.
+"""
+
+from praisonai.capabilities import assistant_list
+
+print("=== List Assistants ===")
+try:
+    assistants = assistant_list()
+    print(f"Assistants found: {len(assistants)}")
+    for a in assistants[:5]:  # Show first 5
+        print(f"  - {a.get('name', 'unnamed')}: {a.get('id', 'unknown')}")
+except Exception as e:
+    print(f"Note: {e}")
+
+print("\n=== Assistant Operations ===")
+print("Available functions:")
+print("  assistant_create(name, instructions, model='gpt-4o-mini')")
+print("  assistant_list()")
+
+print("\nSee CLI: praisonai assistants list")
+print("See CLI: praisonai assistants create --name 'My Assistant'")

examples/capabilities/audio_example.py

@@ -0,0 +1,24 @@
+"""
+Audio Capabilities Example
+
+Demonstrates transcription and text-to-speech using PraisonAI capabilities.
+"""
+
+from praisonai.capabilities import transcribe, speech
+
+# Note: Audio transcription requires an audio file
+# This example shows the API structure
+
+print("=== Audio Transcription ===")
+print("To transcribe audio, use:")
+print("  result = transcribe('audio.mp3', model='whisper-1')")
+print("  print(result.text)")
+
+print("\n=== Text-to-Speech ===")
+print("To generate speech, use:")
+print("  audio_data = speech('Hello world', model='tts-1', voice='alloy')")
+print("  # Save to file or play")
+
+print("\nNote: These operations require audio files or generate audio output.")
+print("See CLI: praisonai audio transcribe <file>")
+print("See CLI: praisonai audio speech <text>")

examples/capabilities/batches_example.py

@@ -0,0 +1,27 @@
+"""
+Batches Capabilities Example
+
+Demonstrates batch processing using PraisonAI capabilities.
+"""
+
+from praisonai.capabilities import batch_list
+
+print("=== Batch List ===")
+try:
+    batches = batch_list()
+    print(f"Batches found: {len(batches)}")
+    for b in batches[:5]:  # Show first 5
+        print(f"  - {b.get('id', 'unknown')}: {b.get('status', 'unknown')}")
+except Exception as e:
+    print(f"Note: {e}")
+    print("Batch operations require batch jobs to be created first.")
+
+print("\n=== Batch Operations ===")
+print("Available functions:")
+print("  batch_create(input_file_id, endpoint, completion_window='24h')")
+print("  batch_list()")
+print("  batch_retrieve(batch_id)")
+print("  batch_cancel(batch_id)")
+
+print("\nSee CLI: praisonai batches list")
+print("See CLI: praisonai batches create <input_file_id>")

examples/capabilities/completions_example.py

@@ -0,0 +1,30 @@
+"""
+Completions Capability Example
+
+Demonstrates chat and text completions using PraisonAI capabilities.
+"""
+
+from praisonai.capabilities import chat_completion, text_completion
+
+# Chat completion example
+print("=== Chat Completion ===")
+result = chat_completion(
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "What is 2 + 2? Answer in one word."}
+    ],
+    model="gpt-4o-mini",
+    max_tokens=10
+)
+print(f"Response: {result.content}")
+print(f"Model: {result.model}")
+print(f"Usage: {result.usage}")
+
+# Text completion example (legacy)
+print("\n=== Text Completion ===")
+result = text_completion(
+    prompt="The capital of France is",
+    model="gpt-3.5-turbo-instruct",
+    max_tokens=10
+)
+print(f"Response: {result.content}")

examples/capabilities/embeddings_example.py

@@ -0,0 +1,27 @@
+"""
+Embeddings Capability Example
+
+Demonstrates text embedding generation using PraisonAI capabilities.
+"""
+
+from praisonai.capabilities import embed
+
+# Single text embedding
+print("=== Single Text Embedding ===")
+result = embed(
+    input="Hello, world!",
+    model="text-embedding-3-small"
+)
+print(f"Embedding dimensions: {len(result.embeddings[0])}")
+print(f"First 5 values: {result.embeddings[0][:5]}")
+print(f"Usage: {result.usage}")
+
+# Multiple text embeddings
+print("\n=== Multiple Text Embeddings ===")
+result = embed(
+    input=["Hello", "World", "AI"],
+    model="text-embedding-3-small"
+)
+print(f"Number of embeddings: {len(result.embeddings)}")
+for i, emb in enumerate(result.embeddings):
+    print(f"  Text {i+1}: {len(emb)} dimensions")

examples/capabilities/files_example.py

@@ -0,0 +1,28 @@
+"""
+Files Capabilities Example
+
+Demonstrates file management using PraisonAI capabilities.
+"""
+
+from praisonai.capabilities import file_list
+
+print("=== File List ===")
+try:
+    files = file_list()
+    print(f"Files found: {len(files)}")
+    for f in files[:5]:  # Show first 5
+        print(f"  - {f.get('filename', 'unknown')}: {f.get('id', 'unknown')}")
+except Exception as e:
+    print(f"Note: {e}")
+    print("File operations require files to be uploaded first.")
+
+print("\n=== File Operations ===")
+print("Available functions:")
+print("  file_create(file_path, purpose='assistants')")
+print("  file_list(purpose=None)")
+print("  file_retrieve(file_id)")
+print("  file_delete(file_id)")
+print("  file_content(file_id)")
+
+print("\nSee CLI: praisonai files list")
+print("See CLI: praisonai files upload <file>")

examples/capabilities/images_example.py

@@ -0,0 +1,31 @@
+"""
+Images Capability Example
+
+Demonstrates image generation using PraisonAI capabilities.
+"""
+
+from praisonai.capabilities import image_generate
+
+# Generate an image with DALL-E 2
+print("=== Image Generation (DALL-E 2) ===")
+result = image_generate(
+    prompt="A simple blue circle on a white background",
+    model="dall-e-2",
+    size="256x256",
+    n=1
+)
+print(f"Images generated: {len(result)}")
+if result:
+    print(f"URL: {result[0].url[:80]}...")
+
+# Generate with DALL-E 3 (higher quality)
+print("\n=== Image Generation (DALL-E 3) ===")
+result = image_generate(
+    prompt="A serene mountain landscape at sunset",
+    model="dall-e-3",
+    size="1024x1024",
+    quality="standard"
+)
+print(f"Images generated: {len(result)}")
+if result:
+    print(f"Revised prompt: {result[0].revised_prompt[:80]}..." if result[0].revised_prompt else "No revised prompt")

examples/capabilities/messages_example.py

@@ -0,0 +1,33 @@
+"""
+Messages Capability Example
+
+Demonstrates Anthropic-style messages API and token counting.
+"""
+
+from praisonai.capabilities import messages_create, count_tokens
+
+# Create a message (Anthropic-style)
+print("=== Messages Create ===")
+result = messages_create(
+    messages=[{"role": "user", "content": "What is the meaning of life? Answer briefly."}],
+    model="gpt-4o-mini",  # Works with any model via LiteLLM
+    max_tokens=100,
+    system="You are a philosophical assistant."
+)
+print(f"Message ID: {result.id}")
+if result.content:
+    for block in result.content:
+        if block.get("type") == "text":
+            print(f"Response: {block.get('text')}")
+print(f"Usage: {result.usage}")
+
+# Count tokens
+print("\n=== Token Counting ===")
+result = count_tokens(
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hello, how are you today?"}
+    ],
+    model="gpt-4o-mini"
+)
+print(f"Token count: {result.input_tokens}")

examples/capabilities/moderations_example.py

@@ -0,0 +1,24 @@
+"""
+Moderations Capability Example
+
+Demonstrates content moderation using PraisonAI capabilities.
+"""
+
+from praisonai.capabilities import moderate
+
+# Check safe content
+print("=== Safe Content Check ===")
+result = moderate(
+    input="Hello, how are you today?"
+)
+print(f"Flagged: {result[0].flagged}")
+print(f"Categories: {list(result[0].categories.keys())[:5]}...")
+
+# Check multiple texts
+print("\n=== Multiple Text Moderation ===")
+result = moderate(
+    input=["Hello world", "Have a nice day", "This is a test"]
+)
+print(f"Number of results: {len(result)}")
+for i, r in enumerate(result):
+    print(f"  Text {i+1}: Flagged = {r.flagged}")

examples/capabilities/realtime_example.py

@@ -0,0 +1,23 @@
+"""
+Realtime Capability Example
+
+Demonstrates realtime session creation for audio/video streaming.
+"""
+
+from praisonai.capabilities import realtime_connect
+
+# Create a realtime session
+print("=== Realtime Session ===")
+session = realtime_connect(
+    model="gpt-4o-realtime-preview",
+    modalities=["text", "audio"],
+    voice="alloy"
+)
+print(f"Session ID: {session.id}")
+print(f"Status: {session.status}")
+print(f"URL: {session.url}")
+print(f"Modalities: {session.metadata.get('modalities')}")
+print(f"Voice: {session.metadata.get('voice')}")
+
+print("\nNote: To use this session, connect via WebSocket to the URL above.")
+print("See OpenAI Realtime API documentation for WebSocket protocol details.")

examples/capabilities/rerank_example.py

@@ -0,0 +1,32 @@
+"""
+Rerank Capabilities Example
+
+Demonstrates document reranking using PraisonAI capabilities.
+"""
+
+from praisonai.capabilities import rerank
+
+print("=== Document Reranking ===")
+try:
+    documents = [
+        "Python is a programming language",
+        "Machine learning uses algorithms",
+        "Python is great for data science",
+        "JavaScript runs in browsers"
+    ]
+    
+    results = rerank(
+        query="Python programming",
+        documents=documents,
+        model="cohere/rerank-english-v3.0"
+    )
+    
+    print(f"Query: 'Python programming'")
+    print(f"Reranked results:")
+    for r in results:
+        print(f"  Score {r.get('relevance_score', 0):.4f}: {r.get('document', {}).get('text', '')[:50]}")
+except Exception as e:
+    print(f"Note: Reranking requires Cohere API key")
+    print(f"Error: {e}")
+
+print("\nSee CLI: praisonai rerank <query> --documents 'doc1' 'doc2'")

examples/capabilities/vector_stores_example.py

@@ -0,0 +1,22 @@
+"""
+Vector Stores Capabilities Example
+
+Demonstrates vector store management using PraisonAI capabilities.
+"""
+
+from praisonai.capabilities import vector_store_create, vector_store_search
+
+print("=== Vector Store Operations ===")
+print("Available functions:")
+print("  vector_store_create(name)")
+print("  vector_store_search(vector_store_id, query)")
+
+print("\n=== Create Vector Store ===")
+try:
+    store = vector_store_create(name="test-store")
+    print(f"Created: {store}")
+except Exception as e:
+    print(f"Note: {e}")
+
+print("\nSee CLI: praisonai vector-stores create --name 'my-store'")
+print("See CLI: praisonai vector-stores search <store_id> <query>")

examples/catalog/fetch_templates.py

@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+"""
+Example: Fetch and search PraisonAI templates from the catalog.
+
+This example demonstrates how to:
+- Fetch templates.json from the deployed catalog
+- Search templates by keyword
+- Filter by tags and requirements
+- Generate CLI commands
+"""
+
+import json
+import urllib.request
+from typing import List, Dict, Any, Optional
+
+CATALOG_URL = "https://mervinpraison.github.io/praisonai-template-catalog/data/templates.json"
+
+
+def fetch_catalog(url: str = CATALOG_URL) -> Dict[str, Any]:
+    """Fetch the template catalog from URL."""
+    try:
+        with urllib.request.urlopen(url, timeout=10) as response:
+            return json.loads(response.read().decode())
+    except Exception as e:
+        print(f"Failed to fetch catalog: {e}")
+        # Return empty catalog on failure
+        return {"version": "0.0.0", "count": 0, "templates": []}
+
+
+def search_templates(templates: List[Dict], query: str) -> List[Dict]:
+    """Search templates by name, description, or tags."""
+    query = query.lower()
+    results = []
+    for t in templates:
+        if (query in t.get("name", "").lower() or 
+            query in t.get("description", "").lower() or
+            any(query in tag.lower() for tag in t.get("tags", []))):
+            results.append(t)
+    return results
+
+
+def filter_by_tags(templates: List[Dict], tags: List[str]) -> List[Dict]:
+    """Filter templates that have all specified tags."""
+    return [
+        t for t in templates
+        if all(tag in t.get("tags", []) for tag in tags)
+    ]
+
+
+def filter_by_tool(templates: List[Dict], tool_name: str) -> List[Dict]:
+    """Filter templates that require a specific tool."""
+    return [
+        t for t in templates
+        if tool_name in t.get("requires", {}).get("tools", [])
+    ]
+
+
+def get_template(templates: List[Dict], name: str) -> Optional[Dict]:
+    """Get a template by name."""
+    for t in templates:
+        if t.get("name") == name:
+            return t
+    return None
+
+
+def generate_cli_command(template: Dict, action: str = "run") -> str:
+    """Generate CLI command for a template."""
+    name = template.get("name", "unknown")
+    if action == "run":
+        return f"praisonai templates run {name}"
+    elif action == "info":
+        return f"praisonai templates info {name}"
+    elif action == "init":
+        return f"praisonai templates init my-project --template {name}"
+    return f"praisonai templates {action} {name}"
+
+
+def print_template_summary(template: Dict):
+    """Print a summary of a template."""
+    print(f"\n{'='*60}")
+    print(f"Name: {template.get('name')}")
+    print(f"Version: {template.get('version')}")
+    print(f"Description: {template.get('description', '')[:100]}...")
+    print(f"Tags: {', '.join(template.get('tags', []))}")
+    
+    requires = template.get("requires", {})
+    if requires.get("tools"):
+        print(f"Tools: {', '.join(requires['tools'])}")
+    if requires.get("packages"):
+        print(f"Packages: {', '.join(requires['packages'])}")
+    if requires.get("env"):
+        print(f"Env vars: {', '.join(requires['env'])}")
+    
+    print(f"\nRun: {generate_cli_command(template, 'run')}")
+    print(f"{'='*60}")
+
+
+def main():
+    print("PraisonAI Template Catalog - Python Example")
+    print("-" * 45)
+    
+    # Fetch catalog
+    print("\n1. Fetching template catalog...")
+    catalog = fetch_catalog()
+    templates = catalog.get("templates", [])
+    
+    print(f"   Catalog version: {catalog.get('version', 'unknown')}")
+    print(f"   Total templates: {len(templates)}")
+    
+    if not templates:
+        print("   No templates found. The catalog may not be deployed yet.")
+        print("   Try building locally: praisonai templates catalog build")
+        return
+    
+    # List all templates
+    print("\n2. Available templates:")
+    for t in templates:
+        print(f"   - {t.get('name')} (v{t.get('version')})")
+    
+    # Search for video templates
+    print("\n3. Searching for 'video' templates...")
+    video_templates = search_templates(templates, "video")
+    print(f"   Found {len(video_templates)} video templates:")
+    for t in video_templates:
+        print(f"   - {t.get('name')}: {t.get('description', '')[:50]}...")
+    
+    # Filter by tool
+    print("\n4. Templates using 'shell_tool':")
+    shell_templates = filter_by_tool(templates, "shell_tool")
+    for t in shell_templates:
+        print(f"   - {t.get('name')}")
+    
+    # Get specific template details
+    if templates:
+        first_template = templates[0]
+        print(f"\n5. Template details for '{first_template.get('name')}':")
+        print_template_summary(first_template)
+    
+    # Generate CLI commands
+    print("\n6. CLI commands for all templates:")
+    for t in templates[:5]:  # First 5 only
+        print(f"   {generate_cli_command(t, 'run')}")
+    
+    print("\nDone!")
+
+
+if __name__ == "__main__":
+    main()

examples/catalog/fetch_templates.ts

@@ -0,0 +1,167 @@
+#!/usr/bin/env npx ts-node
+/**
+ * Example: Fetch and search PraisonAI templates from the catalog.
+ * 
+ * This TypeScript example demonstrates how to:
+ * - Fetch templates.json from the deployed catalog
+ * - Search templates by keyword
+ * - Filter by tags and requirements
+ * - Generate CLI commands
+ */
+
+const CATALOG_URL = "https://mervinpraison.github.io/praisonai-template-catalog/data/templates.json";
+
+interface Template {
+  name: string;
+  version: string;
+  description: string;
+  author?: string;
+  license?: string;
+  tags?: string[];
+  category?: string;
+  difficulty?: "beginner" | "intermediate" | "advanced";
+  requires?: {
+    tools?: string[];
+    packages?: string[];
+    env?: string[];
+  };
+}
+
+interface Catalog {
+  version: string;
+  generated_at: string;
+  count: number;
+  templates: Template[];
+}
+
+async function fetchCatalog(url: string = CATALOG_URL): Promise<Catalog> {
+  const response = await fetch(url);
+  if (!response.ok) {
+    throw new Error(`Failed to fetch catalog: ${response.statusText}`);
+  }
+  return response.json();
+}
+
+function searchTemplates(templates: Template[], query: string): Template[] {
+  const q = query.toLowerCase();
+  return templates.filter(t =>
+    t.name.toLowerCase().includes(q) ||
+    t.description?.toLowerCase().includes(q) ||
+    t.tags?.some(tag => tag.toLowerCase().includes(q))
+  );
+}
+
+function filterByTags(templates: Template[], tags: string[]): Template[] {
+  return templates.filter(t =>
+    tags.every(tag => t.tags?.includes(tag))
+  );
+}
+
+function filterByTool(templates: Template[], toolName: string): Template[] {
+  return templates.filter(t =>
+    t.requires?.tools?.includes(toolName)
+  );
+}
+
+function getTemplate(templates: Template[], name: string): Template | undefined {
+  return templates.find(t => t.name === name);
+}
+
+function generateCLICommand(template: Template, action: string = "run"): string {
+  const name = template.name;
+  switch (action) {
+    case "run":
+      return `praisonai templates run ${name}`;
+    case "info":
+      return `praisonai templates info ${name}`;
+    case "init":
+      return `praisonai templates init my-project --template ${name}`;
+    default:
+      return `praisonai templates ${action} ${name}`;
+  }
+}
+
+function printTemplateSummary(template: Template): void {
+  console.log("\n" + "=".repeat(60));
+  console.log(`Name: ${template.name}`);
+  console.log(`Version: ${template.version}`);
+  console.log(`Description: ${template.description?.slice(0, 100)}...`);
+  console.log(`Tags: ${template.tags?.join(", ") || "none"}`);
+  
+  const requires = template.requires;
+  if (requires?.tools?.length) {
+    console.log(`Tools: ${requires.tools.join(", ")}`);
+  }
+  if (requires?.packages?.length) {
+    console.log(`Packages: ${requires.packages.join(", ")}`);
+  }
+  if (requires?.env?.length) {
+    console.log(`Env vars: ${requires.env.join(", ")}`);
+  }
+  
+  console.log(`\nRun: ${generateCLICommand(template, "run")}`);
+  console.log("=".repeat(60));
+}
+
+async function main(): Promise<void> {
+  console.log("PraisonAI Template Catalog - TypeScript Example");
+  console.log("-".repeat(45));
+  
+  // Fetch catalog
+  console.log("\n1. Fetching template catalog...");
+  let catalog: Catalog;
+  try {
+    catalog = await fetchCatalog();
+  } catch (error) {
+    console.log(`   Failed to fetch: ${error}`);
+    console.log("   The catalog may not be deployed yet.");
+    return;
+  }
+  
+  const templates = catalog.templates;
+  console.log(`   Catalog version: ${catalog.version}`);
+  console.log(`   Total templates: ${templates.length}`);
+  
+  if (templates.length === 0) {
+    console.log("   No templates found.");
+    return;
+  }
+  
+  // List all templates
+  console.log("\n2. Available templates:");
+  templates.forEach(t => {
+    console.log(`   - ${t.name} (v${t.version})`);
+  });
+  
+  // Search for video templates
+  console.log("\n3. Searching for 'video' templates...");
+  const videoTemplates = searchTemplates(templates, "video");
+  console.log(`   Found ${videoTemplates.length} video templates:`);
+  videoTemplates.forEach(t => {
+    console.log(`   - ${t.name}: ${t.description?.slice(0, 50)}...`);
+  });
+  
+  // Filter by tool
+  console.log("\n4. Templates using 'shell_tool':");
+  const shellTemplates = filterByTool(templates, "shell_tool");
+  shellTemplates.forEach(t => {
+    console.log(`   - ${t.name}`);
+  });
+  
+  // Get specific template details
+  if (templates.length > 0) {
+    const firstTemplate = templates[0];
+    console.log(`\n5. Template details for '${firstTemplate.name}':`);
+    printTemplateSummary(firstTemplate);
+  }
+  
+  // Generate CLI commands
+  console.log("\n6. CLI commands for all templates:");
+  templates.slice(0, 5).forEach(t => {
+    console.log(`   ${generateCLICommand(t, "run")}`);
+  });
+  
+  console.log("\nDone!");
+}
+
+main().catch(console.error);

examples/catalog/run_all_catalog_examples.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+"""
+Run all catalog examples and report pass/fail status.
+
+Usage:
+    python run_all_catalog_examples.py
+    
+Environment variables:
+    OPENAI_API_KEY or PRAISONAI_OPENAI_API_KEY - Required for agent example
+    PRAISONAI_RUN_INTEGRATION - Set to "1" to run integration tests
+"""
+
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+SCRIPT_DIR = Path(__file__).parent
+
+
+def run_example(name: str, script: str, requires_api_key: bool = False) -> bool:
+    """Run an example script and return success status."""
+    print(f"\n{'='*60}")
+    print(f"Running: {name}")
+    print(f"Script: {script}")
+    print("="*60)
+    
+    # Check API key requirement
+    if requires_api_key:
+        api_key = os.environ.get("OPENAI_API_KEY") or os.environ.get("PRAISONAI_OPENAI_API_KEY")
+        run_integration = os.environ.get("PRAISONAI_RUN_INTEGRATION") == "1"
+        
+        if not api_key:
+            print(f"⏭️  SKIPPED: {name} (no API key)")
+            print("   Set OPENAI_API_KEY to run this example")
+            return True  # Not a failure, just skipped
+        
+        if not run_integration:
+            print(f"⏭️  SKIPPED: {name} (integration tests disabled)")
+            print("   Set PRAISONAI_RUN_INTEGRATION=1 to run")
+            return True
+    
+    script_path = SCRIPT_DIR / script
+    if not script_path.exists():
+        print(f"❌ FAILED: Script not found: {script_path}")
+        return False
+    
+    try:
+        result = subprocess.run(
+            [sys.executable, str(script_path)],
+            capture_output=True,
+            text=True,
+            timeout=60,
+            cwd=str(SCRIPT_DIR)
+        )
+        
+        print(result.stdout)
+        if result.stderr:
+            print(f"STDERR: {result.stderr}")
+        
+        if result.returncode == 0:
+            print(f"✅ PASSED: {name}")
+            return True
+        else:
+            print(f"❌ FAILED: {name} (exit code {result.returncode})")
+            return False
+            
+    except subprocess.TimeoutExpired:
+        print(f"❌ FAILED: {name} (timeout)")
+        return False
+    except Exception as e:
+        print(f"❌ FAILED: {name} ({e})")
+        return False
+
+
+def main():
+    print("PraisonAI Template Catalog - Example Runner")
+    print("=" * 50)
+    
+    # Check environment
+    api_key = os.environ.get("OPENAI_API_KEY") or os.environ.get("PRAISONAI_OPENAI_API_KEY")
+    if api_key:
+        print(f"API key: present (...{api_key[-4:]})")
+    else:
+        print("API key: not set")
+    
+    run_integration = os.environ.get("PRAISONAI_RUN_INTEGRATION") == "1"
+    print(f"Integration tests: {'enabled' if run_integration else 'disabled'}")
+    
+    # Define examples
+    examples = [
+        ("Fetch Templates (Python)", "fetch_templates.py", False),
+        ("Template Finder Agent", "template_finder_agent.py", True),
+    ]
+    
+    # Run examples
+    results = []
+    for name, script, requires_key in examples:
+        success = run_example(name, script, requires_key)
+        results.append((name, success))
+    
+    # Summary
+    print("\n" + "=" * 60)
+    print("SUMMARY")
+    print("=" * 60)
+    
+    passed = sum(1 for _, s in results if s)
+    failed = sum(1 for _, s in results if not s)
+    
+    for name, success in results:
+        status = "✅ PASS" if success else "❌ FAIL"
+        print(f"  {status}: {name}")
+    
+    print(f"\nTotal: {passed} passed, {failed} failed")
+    
+    # Exit with appropriate code
+    sys.exit(0 if failed == 0 else 1)
+
+
+if __name__ == "__main__":
+    main()

examples/catalog/template_finder_agent.py

@@ -0,0 +1,193 @@
+#!/usr/bin/env python3
+"""
+Example: Template Finder Agent
+
+An Agent()-centric example that uses an AI agent to find templates
+based on natural language queries.
+
+Requires: OPENAI_API_KEY environment variable
+"""
+
+import json
+import os
+import urllib.request
+
+CATALOG_URL = "https://mervinpraison.github.io/praisonai-template-catalog/data/templates.json"
+
+
+def fetch_templates() -> list:
+    """Fetch templates from catalog."""
+    try:
+        with urllib.request.urlopen(CATALOG_URL, timeout=10) as response:
+            data = json.loads(response.read().decode())
+            return data.get("templates", [])
+    except Exception:
+        return []
+
+
+def search_templates_tool(query: str) -> str:
+    """
+    Search templates by keyword.
+    
+    Args:
+        query: Search query (e.g., "video editing", "transcript", "data")
+    
+    Returns:
+        JSON string with matching templates
+    """
+    templates = fetch_templates()
+    query_lower = query.lower()
+    
+    results = []
+    for t in templates:
+        if (query_lower in t.get("name", "").lower() or
+            query_lower in t.get("description", "").lower() or
+            any(query_lower in tag.lower() for tag in t.get("tags", []))):
+            results.append({
+                "name": t.get("name"),
+                "description": t.get("description", "")[:100],
+                "tags": t.get("tags", []),
+                "cli_command": f"praisonai templates run {t.get('name')}"
+            })
+    
+    if not results:
+        return json.dumps({"message": "No templates found matching your query", "templates": []})
+    
+    return json.dumps({"count": len(results), "templates": results})
+
+
+def list_all_templates_tool() -> str:
+    """
+    List all available templates.
+    
+    Returns:
+        JSON string with all template names and descriptions
+    """
+    templates = fetch_templates()
+    
+    if not templates:
+        return json.dumps({"message": "No templates available", "templates": []})
+    
+    results = [
+        {"name": t.get("name"), "description": t.get("description", "")[:80]}
+        for t in templates
+    ]
+    
+    return json.dumps({"count": len(results), "templates": results})
+
+
+def get_template_details_tool(name: str) -> str:
+    """
+    Get detailed information about a specific template.
+    
+    Args:
+        name: Template name (e.g., "ai-video-editor")
+    
+    Returns:
+        JSON string with template details
+    """
+    templates = fetch_templates()
+    
+    for t in templates:
+        if t.get("name") == name:
+            return json.dumps({
+                "name": t.get("name"),
+                "version": t.get("version"),
+                "description": t.get("description"),
+                "author": t.get("author"),
+                "tags": t.get("tags", []),
+                "requires": t.get("requires", {}),
+                "cli_commands": {
+                    "run": f"praisonai templates run {name}",
+                    "info": f"praisonai templates info {name}",
+                    "init": f"praisonai templates init my-project --template {name}"
+                }
+            })
+    
+    return json.dumps({"error": f"Template '{name}' not found"})
+
+
+def check_api_key() -> bool:
+    """Check if API key is available."""
+    key = os.environ.get("OPENAI_API_KEY") or os.environ.get("PRAISONAI_OPENAI_API_KEY")
+    if key:
+        print(f"API key present: ...{key[-4:]}")
+        return True
+    return False
+
+
+def main():
+    print("PraisonAI Template Finder Agent")
+    print("=" * 40)
+    
+    # Check for API key
+    if not check_api_key():
+        print("\n⚠️  No API key found.")
+        print("Set OPENAI_API_KEY or PRAISONAI_OPENAI_API_KEY to use the agent.")
+        print("\nFalling back to direct tool usage:\n")
+        
+        # Demo the tools directly
+        print("1. Listing all templates:")
+        result = list_all_templates_tool()
+        data = json.loads(result)
+        for t in data.get("templates", [])[:5]:
+            print(f"   - {t['name']}: {t['description'][:50]}...")
+        
+        print("\n2. Searching for 'video' templates:")
+        result = search_templates_tool("video")
+        data = json.loads(result)
+        for t in data.get("templates", []):
+            print(f"   - {t['name']}")
+            print(f"     Run: {t['cli_command']}")
+        
+        return
+    
+    # Import Agent only if API key is available
+    try:
+        from praisonaiagents import Agent
+    except ImportError:
+        print("praisonaiagents not installed. Install with: pip install praisonaiagents")
+        return
+    
+    # Create the Template Finder Agent
+    agent = Agent(
+        name="Template Finder",
+        role="PraisonAI Template Expert",
+        goal="Help users find the right template for their needs",
+        backstory="""You are an expert on PraisonAI templates. You help users 
+        discover templates that match their requirements. You can search templates,
+        list all available templates, and provide detailed information about specific
+        templates. Always provide the CLI command to run the template.""",
+        tools=[
+            search_templates_tool,
+            list_all_templates_tool,
+            get_template_details_tool
+        ],
+        verbose=True
+    )
+    
+    # Example queries
+    queries = [
+        "Find me a template for editing videos",
+        "What templates are available for working with transcripts?",
+        "Show me details about the ai-video-editor template"
+    ]
+    
+    print("\nRunning Template Finder Agent...")
+    print("-" * 40)
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        print("-" * 40)
+        
+        try:
+            result = agent.start(query)
+            print(f"\n🤖 Response:\n{result}")
+        except Exception as e:
+            print(f"Error: {e}")
+        
+        print("\n" + "=" * 40)
+
+
+if __name__ == "__main__":
+    main()

examples/doctor/README.md

@@ -0,0 +1,65 @@
+# PraisonAI Doctor Examples
+
+Examples demonstrating the PraisonAI Doctor health check and diagnostics system.
+
+## CLI Examples
+
+| Command | Description |
+|---------|-------------|
+| `praisonai doctor` | Run all fast health checks |
+| `praisonai doctor --version` | Show doctor version |
+| `praisonai doctor --list-checks` | List all available checks |
+| `praisonai doctor env` | Check environment configuration |
+| `praisonai doctor config` | Validate configuration files |
+| `praisonai doctor tools` | Check tool availability |
+| `praisonai doctor db` | Check database drivers |
+| `praisonai doctor mcp` | Check MCP configuration |
+| `praisonai doctor obs` | Check observability providers |
+| `praisonai doctor skills` | Check agent skills |
+| `praisonai doctor memory` | Check memory storage |
+| `praisonai doctor permissions` | Check filesystem permissions |
+| `praisonai doctor network` | Check network connectivity |
+| `praisonai doctor performance` | Check import times |
+| `praisonai doctor ci` | CI mode with JSON output |
+| `praisonai doctor selftest` | Test agent functionality |
+
+## Global Flags
+
+| Flag | Description |
+|------|-------------|
+| `--json` | Output in JSON format |
+| `--format text\|json` | Output format |
+| `--output PATH` | Write report to file |
+| `--deep` | Enable deeper probes |
+| `--timeout SEC` | Per-check timeout |
+| `--strict` | Treat warnings as failures |
+| `--quiet` | Minimal output |
+| `--no-color` | Disable colors |
+| `--only IDS` | Only run these checks |
+| `--skip IDS` | Skip these checks |
+
+## Python Examples
+
+| File | Description |
+|------|-------------|
+| [basic_doctor.py](basic_doctor.py) | Programmatic health checks |
+| [ci_integration.py](ci_integration.py) | CI/CD pipeline integration |
+
+## Quick Start
+
+```bash
+# Run basic health checks
+praisonai doctor
+
+# Run with JSON output
+praisonai doctor --json
+
+# Run specific checks
+praisonai doctor --only python_version,openai_api_key
+
+# CI mode
+praisonai doctor ci
+
+# Save report to file
+praisonai doctor --output report.json
+```

examples/doctor/basic_doctor.py

@@ -0,0 +1,168 @@
+#!/usr/bin/env python3
+"""
+Basic Doctor Example - Programmatic health checks for PraisonAI
+
+This example demonstrates how to use the Doctor module programmatically
+to run health checks and diagnostics.
+"""
+
+import json
+import sys
+
+# Add the praisonai path for development
+sys.path.insert(0, '/Users/praison/praisonai-package/src/praisonai')
+
+from praisonai.cli.features.doctor import DoctorEngine
+from praisonai.cli.features.doctor.models import DoctorConfig, CheckCategory
+from praisonai.cli.features.doctor.registry import get_registry
+from praisonai.cli.features.doctor.checks import register_all_checks
+from praisonai.cli.features.doctor.formatters import get_formatter
+
+
+def run_basic_checks():
+    """Run basic health checks."""
+    print("=" * 60)
+    print("Running Basic Health Checks")
+    print("=" * 60)
+    
+    # Register all checks
+    register_all_checks()
+    
+    # Create config for fast checks only
+    config = DoctorConfig(
+        deep=False,
+        timeout=10.0,
+        strict=False,
+        quiet=False,
+    )
+    
+    # Create and run engine
+    engine = DoctorEngine(config)
+    report = engine.run()
+    
+    # Format and print results
+    formatter = get_formatter("text", no_color=True)
+    print(formatter.format_report(report))
+    
+    return report.exit_code
+
+
+def run_environment_checks():
+    """Run only environment checks."""
+    print("\n" + "=" * 60)
+    print("Running Environment Checks Only")
+    print("=" * 60)
+    
+    # Register all checks
+    register_all_checks()
+    
+    config = DoctorConfig(deep=False)
+    engine = DoctorEngine(config)
+    
+    # Run only environment category
+    engine.run_checks(categories=[CheckCategory.ENVIRONMENT])
+    report = engine.generate_report()
+    
+    formatter = get_formatter("text", no_color=True)
+    print(formatter.format_report(report))
+    
+    return report.exit_code
+
+
+def run_specific_checks():
+    """Run specific checks by ID."""
+    print("\n" + "=" * 60)
+    print("Running Specific Checks")
+    print("=" * 60)
+    
+    # Register all checks
+    register_all_checks()
+    
+    # Only run these specific checks
+    config = DoctorConfig(
+        only=["python_version", "openai_api_key", "os_info"]
+    )
+    
+    engine = DoctorEngine(config)
+    report = engine.run()
+    
+    formatter = get_formatter("text", no_color=True)
+    print(formatter.format_report(report))
+    
+    return report.exit_code
+
+
+def run_json_output():
+    """Run checks and get JSON output."""
+    print("\n" + "=" * 60)
+    print("Running Checks with JSON Output")
+    print("=" * 60)
+    
+    # Register all checks
+    register_all_checks()
+    
+    config = DoctorConfig(
+        only=["python_version", "os_info"],
+        format="json"
+    )
+    
+    engine = DoctorEngine(config)
+    report = engine.run()
+    
+    # Get JSON output
+    formatter = get_formatter("json")
+    json_output = formatter.format_report(report)
+    
+    # Parse and pretty print
+    data = json.loads(json_output)
+    print(json.dumps(data, indent=2))
+    
+    return report.exit_code
+
+
+def list_available_checks():
+    """List all available checks."""
+    print("\n" + "=" * 60)
+    print("Available Checks")
+    print("=" * 60)
+    
+    # Register all checks
+    register_all_checks()
+    
+    registry = get_registry()
+    checks = registry.get_all_checks()
+    
+    # Sort by category
+    checks.sort(key=lambda c: c.category.value)
+    
+    current_category = None
+    for check in checks:
+        if check.category != current_category:
+            current_category = check.category
+            print(f"\n  {current_category.value.upper()}:")
+        
+        deep_marker = " [deep]" if check.requires_deep else ""
+        print(f"    {check.id:30} {check.description}{deep_marker}")
+
+
+if __name__ == "__main__":
+    print("PraisonAI Doctor - Programmatic Usage Examples\n")
+    
+    # List available checks
+    list_available_checks()
+    
+    # Run basic checks
+    exit_code = run_basic_checks()
+    print(f"\nBasic checks exit code: {exit_code}")
+    
+    # Run environment checks only
+    exit_code = run_environment_checks()
+    print(f"\nEnvironment checks exit code: {exit_code}")
+    
+    # Run specific checks
+    exit_code = run_specific_checks()
+    print(f"\nSpecific checks exit code: {exit_code}")
+    
+    # Run with JSON output
+    exit_code = run_json_output()
+    print(f"\nJSON output exit code: {exit_code}")

examples/doctor/ci_integration.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+"""
+CI Integration Example - Using Doctor in CI/CD pipelines
+
+This example demonstrates how to integrate PraisonAI Doctor
+into CI/CD pipelines for automated health checks.
+"""
+
+import json
+import subprocess
+import sys
+
+
+def run_doctor_ci():
+    """Run doctor in CI mode and parse results."""
+    print("Running PraisonAI Doctor in CI mode...")
+    
+    result = subprocess.run(
+        [sys.executable, "-m", "praisonai", "doctor", "ci"],
+        capture_output=True,
+        text=True,
+        timeout=120
+    )
+    
+    # Parse JSON output
+    try:
+        report = json.loads(result.stdout)
+    except json.JSONDecodeError:
+        print("Error: Could not parse doctor output")
+        print(result.stdout)
+        print(result.stderr)
+        return 1
+    
+    # Print summary
+    summary = report.get("summary", {})
+    print("\nDoctor CI Results:")
+    print(f"  Total checks: {summary.get('total', 0)}")
+    print(f"  Passed: {summary.get('passed', 0)}")
+    print(f"  Warnings: {summary.get('warnings', 0)}")
+    print(f"  Failed: {summary.get('failed', 0)}")
+    print(f"  Skipped: {summary.get('skipped', 0)}")
+    print(f"  Errors: {summary.get('errors', 0)}")
+    print(f"\nExit code: {report.get('exit_code', -1)}")
+    
+    # Check for failures
+    if summary.get('failed', 0) > 0:
+        print("\nFailed checks:")
+        for check in report.get("results", []):
+            if check.get("status") == "fail":
+                print(f"  - {check.get('title')}: {check.get('message')}")
+                if check.get("remediation"):
+                    print(f"    Fix: {check.get('remediation')}")
+    
+    return result.returncode
+
+
+def run_specific_checks_ci():
+    """Run specific checks for CI."""
+    print("\nRunning specific checks for CI...")
+    
+    # Only run critical checks
+    critical_checks = [
+        "python_version",
+        "praisonai_package",
+        "openai_api_key",
+    ]
+    
+    result = subprocess.run(
+        [
+            sys.executable, "-m", "praisonai", "doctor",
+            "--json",
+            "--only", ",".join(critical_checks)
+        ],
+        capture_output=True,
+        text=True,
+        timeout=60
+    )
+    
+    try:
+        report = json.loads(result.stdout)
+        print(f"Critical checks: {report['summary']['passed']}/{report['summary']['total']} passed")
+        return result.returncode
+    except json.JSONDecodeError:
+        print("Error parsing output")
+        return 1
+
+
+def save_report_to_file():
+    """Save doctor report to a file for artifact upload."""
+    print("\nSaving doctor report to file...")
+    
+    result = subprocess.run(
+        [
+            sys.executable, "-m", "praisonai", "doctor",
+            "--json",
+            "--output", "/tmp/doctor-report.json"
+        ],
+        capture_output=True,
+        text=True,
+        timeout=120
+    )
+    
+    if result.returncode in [0, 1]:
+        print("Report saved to /tmp/doctor-report.json")
+        
+        # Read and display summary
+        with open("/tmp/doctor-report.json", "r") as f:
+            report = json.load(f)
+            print(f"Summary: {report['summary']}")
+    
+    return result.returncode
+
+
+if __name__ == "__main__":
+    print("PraisonAI Doctor - CI Integration Examples\n")
+    print("=" * 60)
+    
+    # Run CI mode
+    exit_code = run_doctor_ci()
+    
+    # Run specific checks
+    run_specific_checks_ci()
+    
+    # Save report
+    save_report_to_file()
+    
+    print(f"\nFinal exit code: {exit_code}")
+    sys.exit(exit_code)

examples/endpoints_example.py

@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+"""
+PraisonAI Endpoints Example
+
+This example demonstrates how to:
+1. Start the recipe server
+2. Use the endpoints CLI to interact with it
+3. Invoke recipes via HTTP
+
+Prerequisites:
+- pip install praisonai[serve]
+- Set OPENAI_API_KEY environment variable
+
+Usage:
+    # Terminal 1: Start server
+    praisonai recipe serve --port 8765
+
+    # Terminal 2: Run this example
+    python endpoints_example.py
+"""
+
+import json
+import os
+import subprocess
+import sys
+import urllib.request
+import urllib.error
+
+# Configuration
+SERVER_URL = os.environ.get("PRAISONAI_ENDPOINTS_URL", "http://localhost:8765")
+
+
+def check_server_health():
+    """Check if the server is running."""
+    try:
+        req = urllib.request.Request(f"{SERVER_URL}/health")
+        with urllib.request.urlopen(req, timeout=5) as response:
+            data = json.loads(response.read().decode())
+            return data.get("status") == "healthy"
+    except Exception:
+        return False
+
+
+def list_endpoints():
+    """List available endpoints using CLI."""
+    print("\n=== Listing Endpoints (CLI) ===")
+    result = subprocess.run(
+        ["python", "-m", "praisonai.cli.main", "endpoints", "list", "--format", "json"],
+        capture_output=True,
+        text=True,
+    )
+    if result.returncode == 0:
+        recipes = json.loads(result.stdout)
+        print(f"Found {len(recipes)} endpoints:")
+        for r in recipes:
+            print(f"  - {r['name']} ({r['version']}): {r.get('description', '')[:50]}")
+        return recipes
+    else:
+        print(f"Error: {result.stderr}")
+        return []
+
+
+def describe_endpoint(name: str):
+    """Describe an endpoint using CLI."""
+    print(f"\n=== Describing Endpoint: {name} ===")
+    result = subprocess.run(
+        ["python", "-m", "praisonai.cli.main", "endpoints", "describe", name, "--url", SERVER_URL],
+        capture_output=True,
+        text=True,
+    )
+    if result.returncode == 0:
+        info = json.loads(result.stdout)
+        print(f"Name: {info.get('name')}")
+        print(f"Version: {info.get('version')}")
+        print(f"Description: {info.get('description')}")
+        return info
+    else:
+        print(f"Error: {result.stderr}")
+        return None
+
+
+def invoke_endpoint_cli(name: str, input_data: dict):
+    """Invoke an endpoint using CLI."""
+    print(f"\n=== Invoking Endpoint (CLI): {name} ===")
+    result = subprocess.run(
+        [
+            "python", "-m", "praisonai.cli.main", "endpoints", "invoke", name,
+            "--url", SERVER_URL,
+            "--input-json", json.dumps(input_data),
+            "--json",
+            "--dry-run",  # Use dry-run for example
+        ],
+        capture_output=True,
+        text=True,
+    )
+    if result.returncode == 0:
+        response = json.loads(result.stdout)
+        print(f"Run ID: {response.get('run_id')}")
+        print(f"Status: {response.get('status')}")
+        return response
+    else:
+        print(f"Error: {result.stderr}")
+        return None
+
+
+def invoke_endpoint_http(name: str, input_data: dict):
+    """Invoke an endpoint using HTTP directly."""
+    print(f"\n=== Invoking Endpoint (HTTP): {name} ===")
+    
+    body = json.dumps({
+        "recipe": name,
+        "input": input_data,
+        "options": {"dry_run": True},
+    }).encode()
+    
+    req = urllib.request.Request(
+        f"{SERVER_URL}/v1/recipes/run",
+        data=body,
+        headers={"Content-Type": "application/json"},
+        method="POST",
+    )
+    
+    try:
+        with urllib.request.urlopen(req, timeout=30) as response:
+            data = json.loads(response.read().decode())
+            print(f"Run ID: {data.get('run_id')}")
+            print(f"Status: {data.get('status')}")
+            print(f"Output: {data.get('output')}")
+            return data
+    except urllib.error.HTTPError as e:
+        print(f"HTTP Error: {e.code} - {e.read().decode()}")
+        return None
+
+
+def main():
+    """Main example function."""
+    print("=" * 60)
+    print("PraisonAI Endpoints Example")
+    print("=" * 60)
+    
+    # Check if server is running
+    print("\n=== Checking Server Health ===")
+    if not check_server_health():
+        print(f"Server not running at {SERVER_URL}")
+        print("\nPlease start the server first:")
+        print("  praisonai recipe serve --port 8765")
+        sys.exit(1)
+    print("✓ Server is healthy")
+    
+    # List endpoints
+    recipes = list_endpoints()
+    if not recipes:
+        print("No recipes available")
+        sys.exit(1)
+    
+    # Use first available recipe
+    recipe_name = recipes[0]["name"]
+    
+    # Describe endpoint
+    describe_endpoint(recipe_name)
+    
+    # Invoke via CLI
+    invoke_endpoint_cli(recipe_name, {"query": "Hello from CLI"})
+    
+    # Invoke via HTTP
+    invoke_endpoint_http(recipe_name, {"query": "Hello from HTTP"})
+    
+    print("\n" + "=" * 60)
+    print("Example completed successfully!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

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);

examples/performance/benchmark_example.py

@@ -0,0 +1,108 @@
+"""
+Performance Benchmark Example for PraisonAI.
+
+This example demonstrates how to measure and verify performance metrics
+for PraisonAI CLI and SDK imports.
+
+Usage:
+    python benchmark_example.py
+"""
+
+import time
+import tracemalloc
+import sys
+
+
+def measure_import_time(module_name: str) -> float:
+    """Measure import time for a module."""
+    # Clear from cache if already imported
+    if module_name in sys.modules:
+        del sys.modules[module_name]
+    
+    start = time.time()
+    __import__(module_name)
+    elapsed = time.time() - start
+    return elapsed * 1000  # Return milliseconds
+
+
+def measure_memory_usage(import_func) -> tuple:
+    """Measure memory usage during import."""
+    tracemalloc.start()
+    import_func()
+    current, peak = tracemalloc.get_traced_memory()
+    tracemalloc.stop()
+    return current / 1024 / 1024, peak / 1024 / 1024  # Return MB
+
+
+def main():
+    print("=" * 60)
+    print("PraisonAI Performance Benchmark")
+    print("=" * 60)
+    
+    # Test 1: CLI Import Time
+    print("\n1. CLI Import Time")
+    print("-" * 40)
+    
+    import os
+    os.environ['LOGLEVEL'] = 'WARNING'
+    
+    start = time.time()
+    from praisonai.cli.main import PraisonAI
+    cli_import_time = (time.time() - start) * 1000
+    
+    print(f"   CLI import time: {cli_import_time:.0f}ms")
+    print(f"   Status: {'✓ FAST' if cli_import_time < 500 else '✗ SLOW'}")
+    
+    # Test 2: Memory Usage
+    print("\n2. Memory Usage")
+    print("-" * 40)
+    
+    tracemalloc.start()
+    # Force re-import by clearing cache
+    for mod in list(sys.modules.keys()):
+        if 'praisonai' in mod:
+            del sys.modules[mod]
+    
+    from praisonai.cli.main import PraisonAI as PraisonAI2
+    current, peak = tracemalloc.get_traced_memory()
+    tracemalloc.stop()
+    
+    print(f"   Current memory: {current/1024/1024:.1f}MB")
+    print(f"   Peak memory: {peak/1024/1024:.1f}MB")
+    print(f"   Status: {'✓ LOW' if peak/1024/1024 < 50 else '✗ HIGH'}")
+    
+    # Test 3: Lazy Loading Verification
+    print("\n3. Lazy Loading Verification")
+    print("-" * 40)
+    
+    heavy_modules = ['litellm', 'instructor', 'praisonai.auto']
+    loaded = [m for m in heavy_modules if m in sys.modules]
+    
+    if loaded:
+        print(f"   Heavy modules loaded: {loaded}")
+        print("   Status: ✗ NOT LAZY")
+    else:
+        print("   Heavy modules NOT loaded at startup")
+        print("   Status: ✓ LAZY")
+    
+    # Summary
+    print("\n" + "=" * 60)
+    print("Summary")
+    print("=" * 60)
+    
+    all_pass = (
+        cli_import_time < 500 and
+        peak/1024/1024 < 50 and
+        len(loaded) == 0
+    )
+    
+    if all_pass:
+        print("✓ All performance benchmarks PASSED")
+    else:
+        print("✗ Some benchmarks FAILED")
+    
+    return 0 if all_pass else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

examples/performance/lazy_imports_example.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+"""
+Example: Lazy Imports & Fast Startup
+
+Demonstrates how PraisonAI Agents uses lazy imports to achieve
+fast startup times and reduced memory usage.
+"""
+import sys
+import time
+
+
+def example_verify_lazy_imports():
+    """Verify that heavy dependencies are not loaded at import time."""
+    print("=" * 60)
+    print("Lazy Imports Verification")
+    print("=" * 60)
+    
+    # Clear any cached imports
+    heavy_deps = ['litellm', 'chromadb', 'mem0', 'requests']
+    for dep in heavy_deps:
+        for key in list(sys.modules.keys()):
+            if key.startswith(dep):
+                del sys.modules[key]
+    
+    # Measure import time
+    start = time.perf_counter()
+    import praisonaiagents
+    elapsed = (time.perf_counter() - start) * 1000
+    
+    print(f"\nImport time: {elapsed:.1f}ms")
+    print(f"Target: <200ms")
+    print(f"Status: {'PASS' if elapsed < 200 else 'FAIL'}")
+    
+    # Check lazy imports
+    print("\nLazy Import Check:")
+    all_lazy = True
+    for dep in heavy_deps:
+        is_lazy = dep not in sys.modules
+        status = "LAZY (good)" if is_lazy else "EAGER (bad)"
+        print(f"  {dep}: {status}")
+        if not is_lazy:
+            all_lazy = False
+    
+    print(f"\nAll lazy: {'PASS' if all_lazy else 'FAIL'}")
+    return all_lazy
+
+
+def example_measure_memory():
+    """Measure memory usage after import."""
+    print("\n" + "=" * 60)
+    print("Memory Usage Measurement")
+    print("=" * 60)
+    
+    import tracemalloc
+    
+    # Clear modules
+    for key in list(sys.modules.keys()):
+        if key.startswith('praisonaiagents'):
+            del sys.modules[key]
+    
+    tracemalloc.start()
+    import praisonaiagents
+    current, peak = tracemalloc.get_traced_memory()
+    tracemalloc.stop()
+    
+    current_mb = current / 1024 / 1024
+    peak_mb = peak / 1024 / 1024
+    
+    print(f"\nCurrent memory: {current_mb:.1f}MB")
+    print(f"Peak memory: {peak_mb:.1f}MB")
+    print(f"Target: <30MB")
+    print(f"Status: {'PASS' if current_mb < 30 else 'WARN' if current_mb < 45 else 'FAIL'}")
+    
+    return current_mb < 45
+
+
+def example_specific_imports():
+    """Show how to use specific imports for best performance."""
+    print("\n" + "=" * 60)
+    print("Specific Imports Example")
+    print("=" * 60)
+    
+    # Good - specific imports are fast
+    print("\nGood practice - specific imports:")
+    print("  from praisonaiagents import Agent, Task")
+    
+    # Avoid - star imports load everything
+    print("\nAvoid - star imports load more:")
+    print("  from praisonaiagents import *")
+    
+    # Example of specific import
+    from praisonaiagents import Agent
+    print(f"\nAgent class loaded: {Agent.__name__}")
+
+
+if __name__ == "__main__":
+    print("PraisonAI Agents - Lazy Imports Example")
+    print("=" * 60)
+    
+    # Run examples
+    lazy_ok = example_verify_lazy_imports()
+    memory_ok = example_measure_memory()
+    example_specific_imports()
+    
+    print("\n" + "=" * 60)
+    print("Summary")
+    print("=" * 60)
+    print(f"Lazy imports: {'PASS' if lazy_ok else 'FAIL'}")
+    print(f"Memory usage: {'PASS' if memory_ok else 'FAIL'}")
+    print(f"Overall: {'PASS' if lazy_ok and memory_ok else 'FAIL'}")

examples/performance/lite_agent_example.py

@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+"""
+Example: Lite Agent (BYO-LLM)
+
+Demonstrates the lightweight praisonaiagents.lite subpackage
+that allows you to bring your own LLM client.
+"""
+import os
+
+
+def example_custom_llm():
+    """Example using a custom LLM function."""
+    print("=" * 60)
+    print("Example 1: Custom LLM Function")
+    print("=" * 60)
+    
+    from praisonaiagents.lite import LiteAgent
+    
+    # Define a simple mock LLM
+    def mock_llm(messages):
+        """A mock LLM that echoes the last message."""
+        last_msg = messages[-1]["content"] if messages else "No message"
+        return f"Echo: {last_msg}"
+    
+    agent = LiteAgent(
+        name="EchoAgent",
+        llm_fn=mock_llm,
+        instructions="You are a simple echo agent."
+    )
+    
+    response = agent.chat("Hello, world!")
+    print(f"Response: {response}")
+    print(f"Chat history length: {len(agent.chat_history)}")
+
+
+def example_openai_adapter():
+    """Example using the built-in OpenAI adapter."""
+    print("\n" + "=" * 60)
+    print("Example 2: OpenAI Adapter")
+    print("=" * 60)
+    
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Skipping: OPENAI_API_KEY not set")
+        return
+    
+    from praisonaiagents.lite import LiteAgent, create_openai_llm_fn
+    
+    # Create LLM function using OpenAI SDK directly
+    llm_fn = create_openai_llm_fn(model="gpt-4o-mini")
+    
+    agent = LiteAgent(
+        name="OpenAIAgent",
+        llm_fn=llm_fn,
+        instructions="You are a helpful assistant. Keep responses brief."
+    )
+    
+    response = agent.chat("What is 2+2?")
+    print(f"Response: {response}")
+
+
+def example_with_tools():
+    """Example using tools with LiteAgent."""
+    print("\n" + "=" * 60)
+    print("Example 3: Tools with LiteAgent")
+    print("=" * 60)
+    
+    from praisonaiagents.lite import LiteAgent, tool
+    
+    @tool
+    def add_numbers(a: int, b: int) -> int:
+        """Add two numbers together."""
+        return a + b
+    
+    @tool
+    def multiply_numbers(a: int, b: int) -> int:
+        """Multiply two numbers together."""
+        return a * b
+    
+    def mock_llm(messages):
+        return "The calculation result is ready."
+    
+    agent = LiteAgent(
+        name="MathAgent",
+        llm_fn=mock_llm,
+        tools=[add_numbers, multiply_numbers]
+    )
+    
+    # Execute tools directly
+    result1 = agent.execute_tool("add_numbers", a=5, b=3)
+    print(f"5 + 3 = {result1.output}")
+    print(f"Success: {result1.success}")
+    
+    result2 = agent.execute_tool("multiply_numbers", a=4, b=7)
+    print(f"4 * 7 = {result2.output}")
+    print(f"Success: {result2.success}")
+
+
+def example_thread_safety():
+    """Example demonstrating thread-safe operations."""
+    print("\n" + "=" * 60)
+    print("Example 4: Thread Safety")
+    print("=" * 60)
+    
+    import threading
+    from praisonaiagents.lite import LiteAgent
+    
+    counter = [0]
+    lock = threading.Lock()
+    
+    def counting_llm(messages):
+        with lock:
+            counter[0] += 1
+            return f"Response {counter[0]}"
+    
+    agent = LiteAgent(
+        name="ThreadSafeAgent",
+        llm_fn=counting_llm
+    )
+    
+    def worker(thread_id):
+        for i in range(5):
+            agent.chat(f"Message from thread {thread_id}, iteration {i}")
+    
+    threads = [
+        threading.Thread(target=worker, args=(i,))
+        for i in range(3)
+    ]
+    
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join()
+    
+    print(f"Total messages processed: {counter[0]}")
+    print(f"Chat history length: {len(agent.chat_history)}")
+    print("Thread safety test: PASS")
+
+
+if __name__ == "__main__":
+    print("PraisonAI Agents - Lite Package Examples")
+    print("=" * 60)
+    
+    example_custom_llm()
+    example_openai_adapter()
+    example_with_tools()
+    example_thread_safety()
+    
+    print("\n" + "=" * 60)
+    print("All examples completed!")

examples/performance/run_all_examples.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+"""
+Runner for all performance examples.
+
+Usage:
+    # Run without real API (mock only)
+    python run_all_examples.py
+    
+    # Run with real API
+    RUN_REAL_KEY_TESTS=1 OPENAI_API_KEY=your-key python run_all_examples.py
+"""
+import os
+import sys
+import subprocess
+
+
+def run_example(name, script_path):
+    """Run an example script and report results."""
+    print(f"\n{'=' * 60}")
+    print(f"Running: {name}")
+    print("=" * 60)
+    
+    result = subprocess.run(
+        [sys.executable, script_path],
+        capture_output=True,
+        text=True,
+        env=os.environ.copy()
+    )
+    
+    print(result.stdout)
+    if result.stderr:
+        print(f"STDERR: {result.stderr}")
+    
+    return result.returncode == 0
+
+
+def main():
+    """Run all performance examples."""
+    print("PraisonAI Agents - Performance Examples Runner")
+    print("=" * 60)
+    
+    # Check environment
+    real_tests = os.environ.get("RUN_REAL_KEY_TESTS")
+    has_openai = bool(os.environ.get("OPENAI_API_KEY"))
+    
+    print(f"RUN_REAL_KEY_TESTS: {real_tests or 'not set'}")
+    print(f"OPENAI_API_KEY: {'set' if has_openai else 'not set'}")
+    
+    # Get script directory
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    
+    # Define examples
+    examples = [
+        ("Lazy Imports", os.path.join(script_dir, "lazy_imports_example.py")),
+        ("Lite Agent", os.path.join(script_dir, "lite_agent_example.py")),
+        ("Thread Safety", os.path.join(script_dir, "thread_safety_example.py")),
+    ]
+    
+    # Run examples
+    results = []
+    for name, path in examples:
+        if os.path.exists(path):
+            success = run_example(name, path)
+            results.append((name, success))
+        else:
+            print(f"Skipping {name}: {path} not found")
+            results.append((name, None))
+    
+    # Summary
+    print("\n" + "=" * 60)
+    print("Summary")
+    print("=" * 60)
+    
+    passed = 0
+    failed = 0
+    skipped = 0
+    
+    for name, success in results:
+        if success is None:
+            status = "SKIPPED"
+            skipped += 1
+        elif success:
+            status = "PASS"
+            passed += 1
+        else:
+            status = "FAIL"
+            failed += 1
+        print(f"  {name}: {status}")
+    
+    print(f"\nTotal: {passed} passed, {failed} failed, {skipped} skipped")
+    
+    return 0 if failed == 0 else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

examples/performance/thread_safety_example.py

@@ -0,0 +1,158 @@
+#!/usr/bin/env python3
+"""
+Example: Thread-Safe Agent State
+
+Demonstrates thread-safe chat history and cache management
+in PraisonAI Agents.
+"""
+import os
+import threading
+import time
+
+
+def example_lite_agent_thread_safety():
+    """Demonstrate thread safety with LiteAgent."""
+    print("=" * 60)
+    print("Example 1: LiteAgent Thread Safety")
+    print("=" * 60)
+    
+    from praisonaiagents.lite import LiteAgent
+    
+    # Counter for tracking responses
+    response_count = [0]
+    count_lock = threading.Lock()
+    
+    def counting_llm(messages):
+        with count_lock:
+            response_count[0] += 1
+            return f"Response #{response_count[0]}"
+    
+    agent = LiteAgent(
+        name="ThreadSafeAgent",
+        llm_fn=counting_llm
+    )
+    
+    errors = []
+    
+    def worker(thread_id):
+        try:
+            for i in range(10):
+                agent.chat(f"Thread {thread_id}, message {i}")
+        except Exception as e:
+            errors.append(e)
+    
+    # Create and start threads
+    threads = [
+        threading.Thread(target=worker, args=(i,))
+        for i in range(5)
+    ]
+    
+    start = time.perf_counter()
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join()
+    elapsed = time.perf_counter() - start
+    
+    print(f"Threads: 5")
+    print(f"Messages per thread: 10")
+    print(f"Total messages: {response_count[0]}")
+    print(f"Chat history length: {len(agent.chat_history)}")
+    print(f"Errors: {len(errors)}")
+    print(f"Time: {elapsed:.2f}s")
+    print(f"Status: {'PASS' if len(errors) == 0 else 'FAIL'}")
+
+
+def example_agent_locks():
+    """Verify Agent class has proper locks."""
+    print("\n" + "=" * 60)
+    print("Example 2: Agent Lock Verification")
+    print("=" * 60)
+    
+    from praisonaiagents import Agent
+    
+    agent = Agent(
+        name="LockTestAgent",
+        instructions="Test agent",
+        verbose=False
+    )
+    
+    # Check for locks
+    has_history_lock = hasattr(agent, '_history_lock')
+    has_cache_lock = hasattr(agent, '_cache_lock')
+    
+    print(f"Has _history_lock: {has_history_lock}")
+    print(f"Has _cache_lock: {has_cache_lock}")
+    
+    # Verify lock types
+    if has_history_lock:
+        lock_type = type(agent._history_lock).__name__
+        print(f"_history_lock type: {lock_type}")
+    
+    if has_cache_lock:
+        lock_type = type(agent._cache_lock).__name__
+        print(f"_cache_lock type: {lock_type}")
+    
+    print(f"Status: {'PASS' if has_history_lock and has_cache_lock else 'FAIL'}")
+
+
+def example_concurrent_real_api():
+    """Demonstrate concurrent API calls (requires API key)."""
+    print("\n" + "=" * 60)
+    print("Example 3: Concurrent Real API Calls")
+    print("=" * 60)
+    
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Skipping: OPENAI_API_KEY not set")
+        return
+    
+    from praisonaiagents import Agent
+    
+    agent = Agent(
+        name="ConcurrentAgent",
+        instructions="Reply with one word only.",
+        llm="gpt-4o-mini",
+        verbose=False
+    )
+    
+    results = []
+    results_lock = threading.Lock()
+    errors = []
+    
+    def worker(thread_id):
+        try:
+            response = agent.chat(f"Say hello {thread_id}")
+            with results_lock:
+                results.append((thread_id, response))
+        except Exception as e:
+            errors.append(e)
+    
+    threads = [
+        threading.Thread(target=worker, args=(i,))
+        for i in range(3)
+    ]
+    
+    start = time.perf_counter()
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join()
+    elapsed = time.perf_counter() - start
+    
+    print(f"Concurrent requests: 3")
+    print(f"Successful responses: {len(results)}")
+    print(f"Errors: {len(errors)}")
+    print(f"Time: {elapsed:.2f}s")
+    print(f"Status: {'PASS' if len(errors) == 0 else 'FAIL'}")
+
+
+if __name__ == "__main__":
+    print("PraisonAI Agents - Thread Safety Examples")
+    print("=" * 60)
+    
+    example_lite_agent_thread_safety()
+    example_agent_locks()
+    example_concurrent_real_api()
+    
+    print("\n" + "=" * 60)
+    print("All examples completed!")

examples/recipes/run_all_recipes.py

@@ -0,0 +1,207 @@
+#!/usr/bin/env python3
+"""
+Run All Recipes - Test harness for all Agent-Recipes.
+
+Runs each recipe in dry-run mode to verify CLI parity and run.json generation.
+Requires: OPENAI_API_KEY environment variable for integration tests.
+"""
+
+import json
+import os
+import subprocess
+import sys
+from dataclasses import dataclass
+from typing import List
+
+# Recipe definitions
+RECIPES = [
+    "ai-audio-normalizer",
+    "ai-audio-splitter",
+    "ai-changelog-generator",
+    "ai-code-documenter",
+    "ai-csv-cleaner",
+    "ai-data-profiler",
+    "ai-dependency-auditor",
+    "ai-doc-translator",
+    "ai-folder-packager",
+    "ai-image-cataloger",
+    "ai-image-optimizer",
+    "ai-image-resizer",
+    "ai-json-to-csv",
+    "ai-markdown-to-pdf",
+    "ai-pdf-summarizer",
+    "ai-pdf-to-markdown",
+    "ai-podcast-cleaner",
+    "ai-repo-readme",
+    "ai-schema-generator",
+    "ai-screenshot-ocr",
+    "ai-sitemap-scraper",
+    "ai-slide-to-notes",
+    "ai-url-to-markdown",
+    "ai-video-editor",
+    "ai-video-thumbnails",
+    "ai-video-to-gif",
+]
+
+
+@dataclass
+class TestResult:
+    """Result of a recipe test."""
+    recipe: str
+    status: str  # pass, fail, skip
+    reason: str = ""
+    duration: float = 0.0
+
+
+def check_api_keys() -> dict:
+    """Check available API keys."""
+    keys = {
+        "OPENAI_API_KEY": bool(os.environ.get("OPENAI_API_KEY")),
+        "ANTHROPIC_API_KEY": bool(os.environ.get("ANTHROPIC_API_KEY")),
+        "GOOGLE_API_KEY": bool(os.environ.get("GOOGLE_API_KEY")),
+    }
+    return keys
+
+
+def check_external_deps() -> dict:
+    """Check external dependencies."""
+    import shutil
+    deps = {
+        "ffmpeg": shutil.which("ffmpeg") is not None,
+        "ffprobe": shutil.which("ffprobe") is not None,
+        "pdftotext": shutil.which("pdftotext") is not None,
+        "pandoc": shutil.which("pandoc") is not None,
+        "git": shutil.which("git") is not None,
+    }
+    return deps
+
+
+def run_recipe_dry_run(recipe: str, timeout: int = 30) -> TestResult:
+    """Run a recipe in dry-run mode."""
+    import time
+    start = time.time()
+    
+    try:
+        # Use praison templates run command with dry-run
+        cmd = ["praison", "templates", "run", recipe, "--dry-run", "dummy_input"]
+        result = subprocess.run(
+            cmd,
+            capture_output=True,
+            text=True,
+            timeout=timeout,
+        )
+        
+        duration = time.time() - start
+        
+        if result.returncode == 0:
+            return TestResult(recipe, "pass", "dry-run completed", duration)
+        else:
+            # Check if it's a missing dependency error
+            stderr = result.stderr.lower()
+            if "not found" in stderr or "missing" in stderr:
+                return TestResult(recipe, "skip", f"Missing dependency: {result.stderr[:100]}", duration)
+            return TestResult(recipe, "fail", f"Exit code {result.returncode}: {result.stderr[:100]}", duration)
+            
+    except subprocess.TimeoutExpired:
+        return TestResult(recipe, "fail", "Timeout", timeout)
+    except FileNotFoundError:
+        return TestResult(recipe, "skip", "praison CLI not found", 0)
+    except Exception as e:
+        return TestResult(recipe, "fail", str(e), 0)
+
+
+def run_all_recipes(dry_run_only: bool = True) -> List[TestResult]:
+    """Run all recipes and collect results."""
+    results = []
+    
+    print("=" * 60)
+    print("RECIPE TEST HARNESS")
+    print("=" * 60)
+    
+    # Check prerequisites
+    keys = check_api_keys()
+    deps = check_external_deps()
+    
+    print("\nAPI Keys:")
+    for key, present in keys.items():
+        status = "✓" if present else "✗"
+        print(f"  {status} {key}")
+    
+    print("\nExternal Dependencies:")
+    for dep, present in deps.items():
+        status = "✓" if present else "✗"
+        print(f"  {status} {dep}")
+    
+    print("\n" + "=" * 60)
+    print(f"Running {len(RECIPES)} recipes (dry-run={dry_run_only})")
+    print("=" * 60 + "\n")
+    
+    for i, recipe in enumerate(RECIPES, 1):
+        print(f"[{i:2d}/{len(RECIPES)}] {recipe}...", end=" ", flush=True)
+        
+        if dry_run_only:
+            result = run_recipe_dry_run(recipe)
+        else:
+            # Full run would go here
+            result = run_recipe_dry_run(recipe)
+        
+        results.append(result)
+        
+        if result.status == "pass":
+            print(f"✓ ({result.duration:.1f}s)")
+        elif result.status == "skip":
+            print(f"⊘ SKIP: {result.reason}")
+        else:
+            print(f"✗ FAIL: {result.reason}")
+    
+    return results
+
+
+def print_summary(results: List[TestResult]):
+    """Print test summary."""
+    passed = sum(1 for r in results if r.status == "pass")
+    failed = sum(1 for r in results if r.status == "fail")
+    skipped = sum(1 for r in results if r.status == "skip")
+    
+    print("\n" + "=" * 60)
+    print("SUMMARY")
+    print("=" * 60)
+    print(f"  Passed:  {passed}")
+    print(f"  Failed:  {failed}")
+    print(f"  Skipped: {skipped}")
+    print(f"  Total:   {len(results)}")
+    
+    if failed > 0:
+        print("\nFailed recipes:")
+        for r in results:
+            if r.status == "fail":
+                print(f"  - {r.recipe}: {r.reason}")
+    
+    return failed == 0
+
+
+def main():
+    """Main entry point."""
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Run all Agent-Recipes tests")
+    parser.add_argument("--full", action="store_true", help="Run full tests (not just dry-run)")
+    parser.add_argument("--recipe", help="Run specific recipe only")
+    parser.add_argument("--json", action="store_true", help="Output JSON results")
+    args = parser.parse_args()
+    
+    if args.recipe:
+        results = [run_recipe_dry_run(args.recipe)]
+    else:
+        results = run_all_recipes(dry_run_only=not args.full)
+    
+    if args.json:
+        output = [{"recipe": r.recipe, "status": r.status, "reason": r.reason} for r in results]
+        print(json.dumps(output, indent=2))
+    else:
+        success = print_summary(results)
+        sys.exit(0 if success else 1)
+
+
+if __name__ == "__main__":
+    main()

examples/tools/example_pandas_tool.py

@@ -0,0 +1,128 @@
+"""
+Example: Using pandas as a custom tool with PraisonAI Agent
+
+This example shows how to wrap pandas functions as tools
+that can be used with Agent().
+"""
+
+import pandas as pd
+from io import StringIO
+
+# Define pandas wrapper tools
+def analyze_csv_data(csv_data: str) -> str:
+    """Analyze CSV data using pandas DataFrame.
+    
+    Args:
+        csv_data: CSV-formatted string data
+        
+    Returns:
+        Analysis summary including shape, columns, and statistics
+    """
+    df = pd.read_csv(StringIO(csv_data))
+    summary = f"""DataFrame Analysis:
+- Shape: {df.shape[0]} rows, {df.shape[1]} columns
+- Columns: {list(df.columns)}
+- Data Types: {dict(df.dtypes)}
+- Summary Statistics:
+{df.describe().to_string()}"""
+    return summary
+
+
+def filter_dataframe(csv_data: str, column: str, condition: str, value: str) -> str:
+    """Filter a DataFrame based on a condition.
+    
+    Args:
+        csv_data: CSV-formatted string data
+        column: Column name to filter on
+        condition: Condition type ('equals', 'greater', 'less', 'contains')
+        value: Value to compare against
+        
+    Returns:
+        Filtered data as CSV string
+    """
+    df = pd.read_csv(StringIO(csv_data))
+    
+    if condition == 'equals':
+        filtered = df[df[column] == value]
+    elif condition == 'greater':
+        filtered = df[df[column] > float(value)]
+    elif condition == 'less':
+        filtered = df[df[column] < float(value)]
+    elif condition == 'contains':
+        filtered = df[df[column].str.contains(value, na=False)]
+    else:
+        return f"Unknown condition: {condition}"
+    
+    return filtered.to_csv(index=False)
+
+
+def calculate_statistics(csv_data: str, column: str) -> str:
+    """Calculate statistics for a specific column.
+    
+    Args:
+        csv_data: CSV-formatted string data
+        column: Column name to analyze
+        
+    Returns:
+        Statistics summary for the column
+    """
+    df = pd.read_csv(StringIO(csv_data))
+    
+    if column not in df.columns:
+        return f"Column '{column}' not found. Available: {list(df.columns)}"
+    
+    col = df[column]
+    
+    if pd.api.types.is_numeric_dtype(col):
+        stats = f"""Statistics for '{column}':
+- Count: {col.count()}
+- Mean: {col.mean():.2f}
+- Std: {col.std():.2f}
+- Min: {col.min()}
+- Max: {col.max()}
+- Median: {col.median()}"""
+    else:
+        stats = f"""Statistics for '{column}':
+- Count: {col.count()}
+- Unique: {col.nunique()}
+- Top: {col.mode().iloc[0] if len(col.mode()) > 0 else 'N/A'}
+- Sample values: {list(col.head(3))}"""
+    
+    return stats
+
+
+if __name__ == "__main__":
+    # Test data
+    test_csv = """name,age,salary,department
+Alice,30,50000,Engineering
+Bob,25,45000,Marketing
+Charlie,35,60000,Engineering
+Diana,28,52000,Sales
+Eve,32,55000,Marketing"""
+
+    print("=== Test: analyze_csv_data ===")
+    print(analyze_csv_data(test_csv))
+    print()
+    
+    print("=== Test: filter_dataframe ===")
+    print(filter_dataframe(test_csv, "age", "greater", "28"))
+    print()
+    
+    print("=== Test: calculate_statistics ===")
+    print(calculate_statistics(test_csv, "salary"))
+    print()
+    
+    # Example with Agent (requires praisonaiagents)
+    print("=== Agent Usage Example ===")
+    print("""
+from praisonaiagents import Agent
+
+agent = Agent(
+    name="data_analyst",
+    role="Data Analyst",
+    goal="Analyze data using pandas tools",
+    tools=[analyze_csv_data, filter_dataframe, calculate_statistics]
+)
+
+result = agent.start("Analyze this data and find the average salary")
+""")

examples/tools/example_tools_discover.py

@@ -0,0 +1,25 @@
+"""Example: Discover tools from installed packages."""
+
+# Discover from praisonaiagents built-in tools
+try:
+    from praisonaiagents.tools import TOOL_MAPPINGS
+    print("Built-in tools from praisonaiagents:")
+    for name in list(TOOL_MAPPINGS.keys())[:10]:
+        print(f"  - {name}")
+    print(f"  ... and {len(TOOL_MAPPINGS) - 10} more")
+except ImportError:
+    print("praisonaiagents not installed")
+
+# Discover from praisonai_tools
+try:
+    import praisonai_tools
+    print("\nTools from praisonai_tools:")
+    
+    # Check for video module
+    try:
+        from praisonai_tools import video
+        print("  - praisonai_tools.video (video editing)")
+    except ImportError:
+        pass
+except ImportError:
+    print("praisonai_tools not installed")

examples/tools/example_tools_resolve.py

@@ -0,0 +1,18 @@
+"""Example: Resolve tool names to callable tools."""
+from praisonai.templates.tool_override import (
+    create_tool_registry_with_overrides,
+    resolve_tools,
+)
+
+# Create registry with defaults
+registry = create_tool_registry_with_overrides(include_defaults=True)
+
+print(f"Registry contains {len(registry)} tools")
+
+# Resolve tool names
+tool_names = ["shell_tool", "internet_search"]
+resolved = resolve_tools(tool_names, registry=registry)
+
+print(f"\nResolved {len(resolved)} tools:")
+for tool in resolved:
+    print(f"  - {getattr(tool, '__name__', type(tool).__name__)}")

examples/tools/example_tools_sources.py

@@ -0,0 +1,14 @@
+"""Example: Configure tool sources for templates."""
+from praisonai.templates.tool_override import create_tool_registry_with_overrides
+
+# Create registry with tools_sources
+registry = create_tool_registry_with_overrides(
+    tools_sources=["praisonai_tools.video"],
+    include_defaults=True,
+)
+
+print(f"Registry with tools_sources: {len(registry)} tools")
+
+# List some tools
+for name in list(registry.keys())[:10]:
+    print(f"  - {name}")

src/praisonai-agents/benchmarks/import_time.py

@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+"""
+Import time benchmark for praisonaiagents.
+
+This benchmark measures the import time of the praisonaiagents package
+and verifies that heavy dependencies are not loaded at import time.
+
+Usage:
+    python benchmarks/import_time.py
+    
+CI Gate:
+    - PASS: import time < 200ms
+    - WARN: import time 200-300ms
+    - FAIL: import time > 300ms
+"""
+import sys
+import time
+import statistics
+
+
+def clear_modules():
+    """Clear all praisonai and litellm related modules from cache."""
+    to_remove = [m for m in sys.modules.keys() 
+                 if 'praison' in m or 'litellm' in m]
+    for mod in to_remove:
+        del sys.modules[mod]
+
+
+def measure_import_time(runs: int = 5) -> dict:
+    """
+    Measure import time over multiple runs.
+    
+    Returns:
+        dict with min, max, median, mean times in milliseconds
+    """
+    times = []
+    
+    for _ in range(runs):
+        clear_modules()
+        
+        start = time.perf_counter()
+        import praisonaiagents  # noqa: F401
+        end = time.perf_counter()
+        
+        times.append((end - start) * 1000)
+        
+        # Clean up for next run
+        if 'praisonaiagents' in sys.modules:
+            del sys.modules['praisonaiagents']
+    
+    times.sort()
+    
+    return {
+        'times': times,
+        'min': min(times),
+        'max': max(times),
+        'median': statistics.median(times),
+        'mean': statistics.mean(times),
+        'stdev': statistics.stdev(times) if len(times) > 1 else 0,
+    }
+
+
+def check_lazy_imports() -> dict:
+    """
+    Check that heavy dependencies are NOT loaded after importing praisonaiagents.
+    
+    Returns:
+        dict with module names and whether they are loaded
+    """
+    clear_modules()
+    
+    import praisonaiagents  # noqa: F401
+    
+    heavy_modules = [
+        'litellm',
+        'chromadb', 
+        'mem0',
+        'requests',
+    ]
+    
+    results = {}
+    for mod in heavy_modules:
+        results[mod] = mod in sys.modules
+    
+    return results
+
+
+def main():
+    """Run the benchmark and print results."""
+    print("=" * 60)
+    print("PraisonAI Agents Import Time Benchmark")
+    print("=" * 60)
+    
+    # Measure import time
+    print("\n[1/2] Measuring import time (5 runs)...")
+    results = measure_import_time(runs=5)
+    
+    print(f"\nImport times (ms): {[round(t, 1) for t in results['times']]}")
+    print(f"  Min:    {results['min']:.1f} ms")
+    print(f"  Max:    {results['max']:.1f} ms")
+    print(f"  Median: {results['median']:.1f} ms")
+    print(f"  Mean:   {results['mean']:.1f} ms")
+    print(f"  StdDev: {results['stdev']:.1f} ms")
+    
+    # Check lazy imports
+    print("\n[2/2] Checking lazy imports...")
+    lazy_results = check_lazy_imports()
+    
+    print("\nHeavy modules loaded at import time:")
+    all_lazy = True
+    for mod, loaded in lazy_results.items():
+        status = "LOADED (BAD)" if loaded else "NOT LOADED (GOOD)"
+        print(f"  {mod}: {status}")
+        if loaded:
+            all_lazy = False
+    
+    # Determine pass/fail
+    print("\n" + "=" * 60)
+    print("RESULTS")
+    print("=" * 60)
+    
+    median_time = results['median']
+    
+    if median_time < 200:
+        time_status = "PASS"
+        time_msg = f"Import time {median_time:.0f}ms < 200ms target"
+    elif median_time < 300:
+        time_status = "WARN"
+        time_msg = f"Import time {median_time:.0f}ms between 200-300ms"
+    else:
+        time_status = "FAIL"
+        time_msg = f"Import time {median_time:.0f}ms > 300ms limit"
+    
+    lazy_status = "PASS" if all_lazy else "FAIL"
+    lazy_msg = "All heavy deps lazy loaded" if all_lazy else "Some heavy deps loaded eagerly"
+    
+    print(f"\nImport Time: [{time_status}] {time_msg}")
+    print(f"Lazy Imports: [{lazy_status}] {lazy_msg}")
+    
+    overall = "PASS" if time_status != "FAIL" and lazy_status == "PASS" else "FAIL"
+    print(f"\nOverall: [{overall}]")
+    
+    # Exit code for CI
+    return 0 if overall == "PASS" else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

src/praisonai-agents/benchmarks/memory_usage.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+"""
+Memory usage benchmark for praisonaiagents.
+
+This benchmark measures the memory footprint of importing praisonaiagents
+and verifies it stays within acceptable limits.
+
+Usage:
+    python benchmarks/memory_usage.py
+    
+CI Gate:
+    - PASS: memory < 30MB
+    - WARN: memory 30-45MB
+    - FAIL: memory > 45MB
+"""
+import sys
+import tracemalloc
+
+
+def clear_modules():
+    """Clear all praisonai and litellm related modules from cache."""
+    to_remove = [m for m in sys.modules.keys() 
+                 if 'praison' in m or 'litellm' in m]
+    for mod in to_remove:
+        del sys.modules[mod]
+
+
+def measure_memory() -> dict:
+    """
+    Measure memory usage after importing praisonaiagents.
+    
+    Returns:
+        dict with current and peak memory in MB
+    """
+    clear_modules()
+    
+    tracemalloc.start()
+    
+    import praisonaiagents  # noqa: F401
+    
+    current, peak = tracemalloc.get_traced_memory()
+    tracemalloc.stop()
+    
+    return {
+        'current_bytes': current,
+        'peak_bytes': peak,
+        'current_mb': current / 1024 / 1024,
+        'peak_mb': peak / 1024 / 1024,
+    }
+
+
+def main():
+    """Run the benchmark and print results."""
+    print("=" * 60)
+    print("PraisonAI Agents Memory Usage Benchmark")
+    print("=" * 60)
+    
+    print("\nMeasuring memory usage...")
+    results = measure_memory()
+    
+    print(f"\nMemory Usage:")
+    print(f"  Current: {results['current_mb']:.1f} MB")
+    print(f"  Peak:    {results['peak_mb']:.1f} MB")
+    
+    # Determine pass/fail based on peak memory
+    peak_mb = results['peak_mb']
+    
+    print("\n" + "=" * 60)
+    print("RESULTS")
+    print("=" * 60)
+    
+    if peak_mb < 30:
+        status = "PASS"
+        msg = f"Memory {peak_mb:.1f}MB < 30MB target"
+    elif peak_mb < 45:
+        status = "WARN"
+        msg = f"Memory {peak_mb:.1f}MB between 30-45MB"
+    else:
+        status = "FAIL"
+        msg = f"Memory {peak_mb:.1f}MB > 45MB limit"
+    
+    print(f"\nMemory: [{status}] {msg}")
+    
+    # Exit code for CI
+    return 0 if status != "FAIL" else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

src/praisonai-agents/examples/lite_agent_example.py

@@ -0,0 +1,123 @@
+#!/usr/bin/env python3
+"""
+Example: Using PraisonAI Agents Lite Package
+
+The lite package provides a minimal agent framework without heavy dependencies
+like litellm. It's ideal for users who want to bring their own LLM client.
+
+This example shows:
+1. Creating a LiteAgent with a custom LLM function
+2. Using the built-in OpenAI adapter
+3. Thread-safe chat history management
+"""
+import os
+
+
+def example_custom_llm():
+    """Example 1: Using a custom LLM function."""
+    from praisonaiagents.lite import LiteAgent
+    
+    # Define your own LLM function
+    def my_simple_llm(messages):
+        """A mock LLM that echoes back the last message."""
+        last_msg = messages[-1]["content"] if messages else "No message"
+        return f"Echo: {last_msg}"
+    
+    agent = LiteAgent(
+        name="EchoAgent",
+        llm_fn=my_simple_llm,
+        instructions="You are a simple echo agent."
+    )
+    
+    response = agent.chat("Hello, world!")
+    print(f"Response: {response}")
+    print(f"Chat history length: {len(agent.chat_history)}")
+
+
+def example_openai_adapter():
+    """Example 2: Using the built-in OpenAI adapter."""
+    from praisonaiagents.lite import LiteAgent, create_openai_llm_fn
+    
+    # Check for API key
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Skipping OpenAI example - OPENAI_API_KEY not set")
+        return
+    
+    # Create LLM function using OpenAI SDK directly (no litellm)
+    llm_fn = create_openai_llm_fn(model="gpt-4o-mini")
+    
+    agent = LiteAgent(
+        name="OpenAIAgent",
+        llm_fn=llm_fn,
+        instructions="You are a helpful assistant. Keep responses brief."
+    )
+    
+    response = agent.chat("What is 2+2?")
+    print(f"OpenAI Response: {response}")
+
+
+def example_with_tools():
+    """Example 3: Using tools with LiteAgent."""
+    from praisonaiagents.lite import LiteAgent, tool
+    
+    @tool
+    def add_numbers(a: int, b: int) -> int:
+        """Add two numbers together."""
+        return a + b
+    
+    def mock_llm(messages):
+        return "The sum is 8"
+    
+    agent = LiteAgent(
+        name="MathAgent",
+        llm_fn=mock_llm,
+        tools=[add_numbers]
+    )
+    
+    # Execute tool directly
+    result = agent.execute_tool("add_numbers", a=5, b=3)
+    print(f"Tool result: {result.output}")
+    print(f"Tool success: {result.success}")
+
+
+def example_anthropic_adapter():
+    """Example 4: Using the built-in Anthropic adapter."""
+    from praisonaiagents.lite import LiteAgent, create_anthropic_llm_fn
+    
+    # Check for API key
+    if not os.environ.get("ANTHROPIC_API_KEY"):
+        print("Skipping Anthropic example - ANTHROPIC_API_KEY not set")
+        return
+    
+    # Create LLM function using Anthropic SDK directly
+    llm_fn = create_anthropic_llm_fn(model="claude-3-5-sonnet-20241022")
+    
+    agent = LiteAgent(
+        name="ClaudeAgent",
+        llm_fn=llm_fn,
+        instructions="You are a helpful assistant. Keep responses brief."
+    )
+    
+    response = agent.chat("What is the capital of France?")
+    print(f"Claude Response: {response}")
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("PraisonAI Agents Lite Examples")
+    print("=" * 60)
+    
+    print("\n[1] Custom LLM Function:")
+    example_custom_llm()
+    
+    print("\n[2] OpenAI Adapter:")
+    example_openai_adapter()
+    
+    print("\n[3] Tools with LiteAgent:")
+    example_with_tools()
+    
+    print("\n[4] Anthropic Adapter:")
+    example_anthropic_adapter()
+    
+    print("\n" + "=" * 60)
+    print("Examples completed!")

src/praisonai-agents/praisonaiagents/__init__.py

@@ -1,5 +1,8 @@
 """
 Praison AI Agents - A package for hierarchical AI agent task execution
+
+This module uses lazy loading to minimize import time and memory usage.
+Heavy dependencies like litellm are only loaded when actually needed.
 """
 
 # Apply warning patch BEFORE any imports to intercept warnings at the source
@@ -11,51 +14,18 @@ from . import _logging
 # Configure root logger after logging is initialized
 _logging.configure_root_logger()
 
-# Now import everything else
-from .agent.agent import Agent
-from .agent.image_agent import ImageAgent
-from .agent.context_agent import ContextAgent, create_context_agent
-from .agent.deep_research_agent import (
-    DeepResearchAgent,
-    DeepResearchResponse,
-    Citation,
-    ReasoningStep,
-    WebSearchCall,
-    CodeExecutionStep,
-    MCPCall,
-    FileSearchCall,
-    Provider
-)
-from .agent.query_rewriter_agent import (
-    QueryRewriterAgent,
-    RewriteStrategy,
-    RewriteResult
-)
-from .agent.prompt_expander_agent import (
-    PromptExpanderAgent,
-    ExpandStrategy,
-    ExpandResult
-)
-from .agents.agents import PraisonAIAgents
-from .task.task import Task
+# Import configuration (lightweight, no heavy deps)
+from . import _config
+
+# Lightweight imports that don't trigger heavy dependency chains
 from .tools.tools import Tools
 from .tools.base import BaseTool, ToolResult, ToolValidationError, validate_tool
 from .tools.decorator import tool, FunctionTool
 from .tools.registry import get_registry, register_tool, get_tool, ToolRegistry
-from .agents.autoagents import AutoAgents
-# Knowledge is lazy-loaded to avoid importing heavy deps (chromadb, mem0) at startup
-# MCP support (optional)
-try:
-    from .mcp.mcp import MCP
-    _mcp_available = True
-except ImportError:
-    _mcp_available = False
-    MCP = None
-from .session import Session
-# Memory is lazy-loaded to avoid importing chromadb at startup
 from .db import db
 from .obs import obs
-# Workflows - import from dedicated workflows module
+
+# Workflows - lightweight module
 from .workflows import (
     Workflow, WorkflowStep, WorkflowContext, StepResult,
     Route, Parallel, Loop, Repeat,
@@ -64,80 +34,18 @@ from .workflows import (
 )
 from .guardrails import GuardrailResult, LLMGuardrail
 
-# Fast Context support (lazy loaded to avoid performance impact)
-def __getattr__(name):
-    """Lazy load heavy modules to avoid impacting package load time."""
-    # Knowledge module (imports chromadb, mem0)
-    if name == "Knowledge":
-        from praisonaiagents.knowledge.knowledge import Knowledge
-        return Knowledge
-    elif name == "Chunking":
-        from praisonaiagents.knowledge.chunking import Chunking
-        return Chunking
-    # FastContext support
-    elif name == "FastContext":
-        from praisonaiagents.context.fast import FastContext
-        return FastContext
-    elif name == "FastContextResult":
-        from praisonaiagents.context.fast import FastContextResult
-        return FastContextResult
-    elif name == "FileMatch":
-        from praisonaiagents.context.fast import FileMatch
-        return FileMatch
-    elif name == "LineRange":
-        from praisonaiagents.context.fast import LineRange
-        return LineRange
-    # Agent Skills support (lazy loaded for zero performance impact)
-    elif name == "SkillManager":
-        from praisonaiagents.skills import SkillManager
-        return SkillManager
-    elif name == "SkillProperties":
-        from praisonaiagents.skills import SkillProperties
-        return SkillProperties
-    elif name == "SkillMetadata":
-        from praisonaiagents.skills import SkillMetadata
-        return SkillMetadata
-    elif name == "SkillLoader":
-        from praisonaiagents.skills import SkillLoader
-        return SkillLoader
-    # Memory module (imports chromadb)
-    elif name == "Memory":
-        from praisonaiagents.memory.memory import Memory
-        return Memory
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-
-# Planning mode support
-try:
-    from .planning import (
-        Plan,
-        PlanStep,
-        TodoList,
-        TodoItem,
-        PlanStorage,
-        PlanningAgent,
-        ApprovalCallback,
-        READ_ONLY_TOOLS,
-        RESTRICTED_TOOLS,
-    )
-    _planning_available = True
-except ImportError:
-    _planning_available = False
-    Plan = None
-    PlanStep = None
-    TodoList = None
-    TodoItem = None
-    PlanStorage = None
-    PlanningAgent = None
-    ApprovalCallback = None
-    READ_ONLY_TOOLS = []
-    RESTRICTED_TOOLS = []
+# Handoff - lightweight
 from .agent.handoff import Handoff, handoff, handoff_filters, RECOMMENDED_PROMPT_PREFIX, prompt_with_handoff_instructions
-# Flow display
+
+# Flow display (optional)
 try:
     from .flow_display import FlowDisplay, track_workflow
 except ImportError:
     FlowDisplay = None
     track_workflow = None
+
+# Main display utilities - these are used frequently so import directly
+# but they don't pull in litellm
 from .main import (
     TaskOutput,
     ReflectionOutput,
@@ -154,98 +62,296 @@ from .main import (
     async_display_callbacks,
 )
 
-# Telemetry support (lazy loaded)
-try:
-    from .telemetry import (
-        get_telemetry,
-        enable_telemetry,
-        disable_telemetry,
-        enable_performance_mode,
-        disable_performance_mode,
-        cleanup_telemetry_resources,
-        MinimalTelemetry,
-        TelemetryCollector
-    )
-    _telemetry_available = True
-except ImportError:
-    # Telemetry not available - provide stub functions
-    _telemetry_available = False
-    def get_telemetry():
-        return None
-    
-    def enable_telemetry(*args, **kwargs):
-        import logging
-        logging.warning(
-            "Telemetry not available. Install with: pip install praisonaiagents[telemetry]"
-        )
-        return None
-    
-    def disable_telemetry():
-        pass
-    
-    def enable_performance_mode():
-        pass
-    
-    def disable_performance_mode():
-        pass
-    
-    def cleanup_telemetry_resources():
-        pass
-    
-    MinimalTelemetry = None
-    TelemetryCollector = None
+# Module-level caches for lazy-loaded classes
+_lazy_cache = {}
 
-# Add Agents as an alias for PraisonAIAgents
-Agents = PraisonAIAgents
+# Flags for optional modules
+_mcp_available = None
+_planning_available = None
+_telemetry_available = None
+_agui_available = None
+_a2a_available = None
 
-# Enable PostHog telemetry by default with actual event posting  
-# PostHog events are posted by default unless explicitly disabled
-# Users can:
-#   - Disable completely: PRAISONAI_DISABLE_TELEMETRY=true (or DO_NOT_TRACK=true)
-#   - Enable performance mode: PRAISONAI_PERFORMANCE_MODE=true (minimal overhead, limited events)
-#   - Enable full telemetry: PRAISONAI_FULL_TELEMETRY=true (detailed tracking)
-#   - Legacy opt-in mode: PRAISONAI_AUTO_INSTRUMENT=true
-if _telemetry_available:
+
+def __getattr__(name):
+    """
+    Lazy load heavy modules to avoid impacting package load time.
+    
+    This function is called when an attribute is not found in the module.
+    It enables deferred loading of heavy dependencies like litellm.
+    """
+    global _mcp_available, _planning_available, _telemetry_available
+    global _agui_available, _a2a_available
+    
+    # Return cached values if available
+    if name in _lazy_cache:
+        return _lazy_cache[name]
+    
+    # Agent and related classes (triggers litellm import chain)
+    if name == "Agent":
+        from .agent.agent import Agent
+        _lazy_cache[name] = Agent
+        return Agent
+    elif name == "ImageAgent":
+        from .agent.image_agent import ImageAgent
+        _lazy_cache[name] = ImageAgent
+        return ImageAgent
+    elif name == "ContextAgent":
+        from .agent.context_agent import ContextAgent
+        _lazy_cache[name] = ContextAgent
+        return ContextAgent
+    elif name == "create_context_agent":
+        from .agent.context_agent import create_context_agent
+        _lazy_cache[name] = create_context_agent
+        return create_context_agent
+    elif name == "DeepResearchAgent":
+        from .agent.deep_research_agent import DeepResearchAgent
+        _lazy_cache[name] = DeepResearchAgent
+        return DeepResearchAgent
+    elif name == "DeepResearchResponse":
+        from .agent.deep_research_agent import DeepResearchResponse
+        _lazy_cache[name] = DeepResearchResponse
+        return DeepResearchResponse
+    elif name == "Citation":
+        from .agent.deep_research_agent import Citation
+        _lazy_cache[name] = Citation
+        return Citation
+    elif name == "ReasoningStep":
+        from .agent.deep_research_agent import ReasoningStep
+        _lazy_cache[name] = ReasoningStep
+        return ReasoningStep
+    elif name == "WebSearchCall":
+        from .agent.deep_research_agent import WebSearchCall
+        _lazy_cache[name] = WebSearchCall
+        return WebSearchCall
+    elif name == "CodeExecutionStep":
+        from .agent.deep_research_agent import CodeExecutionStep
+        _lazy_cache[name] = CodeExecutionStep
+        return CodeExecutionStep
+    elif name == "MCPCall":
+        from .agent.deep_research_agent import MCPCall
+        _lazy_cache[name] = MCPCall
+        return MCPCall
+    elif name == "FileSearchCall":
+        from .agent.deep_research_agent import FileSearchCall
+        _lazy_cache[name] = FileSearchCall
+        return FileSearchCall
+    elif name == "Provider":
+        from .agent.deep_research_agent import Provider
+        _lazy_cache[name] = Provider
+        return Provider
+    elif name == "QueryRewriterAgent":
+        from .agent.query_rewriter_agent import QueryRewriterAgent
+        _lazy_cache[name] = QueryRewriterAgent
+        return QueryRewriterAgent
+    elif name == "RewriteStrategy":
+        from .agent.query_rewriter_agent import RewriteStrategy
+        _lazy_cache[name] = RewriteStrategy
+        return RewriteStrategy
+    elif name == "RewriteResult":
+        from .agent.query_rewriter_agent import RewriteResult
+        _lazy_cache[name] = RewriteResult
+        return RewriteResult
+    elif name == "PromptExpanderAgent":
+        from .agent.prompt_expander_agent import PromptExpanderAgent
+        _lazy_cache[name] = PromptExpanderAgent
+        return PromptExpanderAgent
+    elif name == "ExpandStrategy":
+        from .agent.prompt_expander_agent import ExpandStrategy
+        _lazy_cache[name] = ExpandStrategy
+        return ExpandStrategy
+    elif name == "ExpandResult":
+        from .agent.prompt_expander_agent import ExpandResult
+        _lazy_cache[name] = ExpandResult
+        return ExpandResult
+    
+    # PraisonAIAgents and Agents alias
+    elif name == "PraisonAIAgents":
+        from .agents.agents import PraisonAIAgents
+        _lazy_cache[name] = PraisonAIAgents
+        _lazy_cache["Agents"] = PraisonAIAgents  # Also cache alias
+        return PraisonAIAgents
+    elif name == "Agents":
+        from .agents.agents import PraisonAIAgents
+        _lazy_cache["PraisonAIAgents"] = PraisonAIAgents
+        _lazy_cache[name] = PraisonAIAgents
+        return PraisonAIAgents
+    
+    # Task
+    elif name == "Task":
+        from .task.task import Task
+        _lazy_cache[name] = Task
+        return Task
+    
+    # AutoAgents
+    elif name == "AutoAgents":
+        from .agents.autoagents import AutoAgents
+        _lazy_cache[name] = AutoAgents
+        return AutoAgents
+    
+    # Session (imports requests)
+    elif name == "Session":
+        from .session import Session
+        _lazy_cache[name] = Session
+        return Session
+    
+    # MCP support (optional)
+    elif name == "MCP":
+        if _mcp_available is None:
+            try:
+                from .mcp.mcp import MCP as _MCP
+                _lazy_cache[name] = _MCP
+                return _MCP
+            except ImportError:
+                return None
+        return _lazy_cache.get(name)
+    
+    # Knowledge module (imports chromadb, mem0)
+    elif name == "Knowledge":
+        from praisonaiagents.knowledge.knowledge import Knowledge
+        _lazy_cache[name] = Knowledge
+        return Knowledge
+    elif name == "Chunking":
+        from praisonaiagents.knowledge.chunking import Chunking
+        _lazy_cache[name] = Chunking
+        return Chunking
+    
+    # FastContext support
+    elif name == "FastContext":
+        from praisonaiagents.context.fast import FastContext
+        _lazy_cache[name] = FastContext
+        return FastContext
+    elif name == "FastContextResult":
+        from praisonaiagents.context.fast import FastContextResult
+        _lazy_cache[name] = FastContextResult
+        return FastContextResult
+    elif name == "FileMatch":
+        from praisonaiagents.context.fast import FileMatch
+        _lazy_cache[name] = FileMatch
+        return FileMatch
+    elif name == "LineRange":
+        from praisonaiagents.context.fast import LineRange
+        _lazy_cache[name] = LineRange
+        return LineRange
+    
+    # Agent Skills support (lazy loaded for zero performance impact)
+    elif name == "SkillManager":
+        from praisonaiagents.skills import SkillManager
+        _lazy_cache[name] = SkillManager
+        return SkillManager
+    elif name == "SkillProperties":
+        from praisonaiagents.skills import SkillProperties
+        _lazy_cache[name] = SkillProperties
+        return SkillProperties
+    elif name == "SkillMetadata":
+        from praisonaiagents.skills import SkillMetadata
+        _lazy_cache[name] = SkillMetadata
+        return SkillMetadata
+    elif name == "SkillLoader":
+        from praisonaiagents.skills import SkillLoader
+        _lazy_cache[name] = SkillLoader
+        return SkillLoader
+    
+    # Memory module (imports chromadb)
+    elif name == "Memory":
+        from praisonaiagents.memory.memory import Memory
+        _lazy_cache[name] = Memory
+        return Memory
+    
+    # Planning mode support (lazy)
+    elif name in ("Plan", "PlanStep", "TodoList", "TodoItem", "PlanStorage", 
+                  "PlanningAgent", "ApprovalCallback", "READ_ONLY_TOOLS", 
+                  "RESTRICTED_TOOLS"):
+        try:
+            from . import planning as _planning
+            result = getattr(_planning, name)
+            _lazy_cache[name] = result
+            return result
+        except ImportError:
+            if name in ("READ_ONLY_TOOLS", "RESTRICTED_TOOLS"):
+                return []
+            return None
+    
+    # Telemetry support (lazy loaded)
+    elif name in ("get_telemetry", "enable_telemetry", "disable_telemetry",
+                  "enable_performance_mode", "disable_performance_mode",
+                  "cleanup_telemetry_resources", "MinimalTelemetry", "TelemetryCollector"):
+        try:
+            from . import telemetry as _telemetry
+            result = getattr(_telemetry, name)
+            _lazy_cache[name] = result
+            return result
+        except ImportError:
+            # Provide stub functions
+            if name == "get_telemetry":
+                return lambda: None
+            elif name == "enable_telemetry":
+                import logging
+                def _stub(*args, **kwargs):
+                    logging.warning(
+                        "Telemetry not available. Install with: pip install praisonaiagents[telemetry]"
+                    )
+                    return None
+                return _stub
+            elif name in ("disable_telemetry", "enable_performance_mode", 
+                         "disable_performance_mode", "cleanup_telemetry_resources"):
+                return lambda: None
+            return None
+    
+    # AG-UI support (optional, lazy loaded)
+    elif name == "AGUI":
+        try:
+            from praisonaiagents.ui.agui import AGUI
+            _lazy_cache[name] = AGUI
+            return AGUI
+        except ImportError:
+            return None
+    
+    # A2A support (optional, lazy loaded)
+    elif name == "A2A":
+        try:
+            from praisonaiagents.ui.a2a import A2A
+            _lazy_cache[name] = A2A
+            return A2A
+        except ImportError:
+            return None
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+# Initialize telemetry only if explicitly enabled via config
+def _init_telemetry():
+    """Initialize telemetry if enabled via environment variable."""
+    if not _config.TELEMETRY_ENABLED:
+        return
+    
     try:
-        import os
+        from .telemetry import get_telemetry
+        from .telemetry.integration import auto_instrument_all
         
-        # Check for explicit disable (respects DO_NOT_TRACK and other disable flags)
-        telemetry_disabled = any([
-            os.environ.get('PRAISONAI_TELEMETRY_DISABLED', '').lower() in ('true', '1', 'yes'),
-            os.environ.get('PRAISONAI_DISABLE_TELEMETRY', '').lower() in ('true', '1', 'yes'),
-            os.environ.get('DO_NOT_TRACK', '').lower() in ('true', '1', 'yes'),
-        ])
-        
-        # Check for performance mode (minimal overhead with limited events)
-        performance_mode = os.environ.get('PRAISONAI_PERFORMANCE_MODE', '').lower() in ('true', '1', 'yes')
-        
-        # Check for full telemetry mode (more detailed tracking)
-        full_telemetry = os.environ.get('PRAISONAI_FULL_TELEMETRY', '').lower() in ('true', '1', 'yes')
-        
-        # Legacy explicit auto-instrument option
-        explicit_auto_instrument = os.environ.get('PRAISONAI_AUTO_INSTRUMENT', '').lower() in ('true', '1', 'yes')
-        
-        # Enable PostHog by default unless explicitly disabled
-        if not telemetry_disabled:
-            _telemetry = get_telemetry()
-            if _telemetry and _telemetry.enabled:
-                from .telemetry.integration import auto_instrument_all
-                
-                # Default: PostHog telemetry is enabled and events are posted
-                # Performance mode can be explicitly enabled for minimal overhead
-                use_performance_mode = performance_mode and not (full_telemetry or explicit_auto_instrument)
-                auto_instrument_all(_telemetry, performance_mode=use_performance_mode)
-                
-                # Track package import for basic usage analytics
-                try:
-                    _telemetry.track_feature_usage("package_import")
-                except Exception:
-                    pass
+        _telemetry = get_telemetry()
+        if _telemetry and _telemetry.enabled:
+            use_performance_mode = _config.PERFORMANCE_MODE and not (
+                _config.FULL_TELEMETRY or _config.AUTO_INSTRUMENT
+            )
+            auto_instrument_all(_telemetry, performance_mode=use_performance_mode)
+            
+            # Track package import for basic usage analytics
+            try:
+                _telemetry.track_feature_usage("package_import")
+            except Exception:
+                pass
     except Exception:
         # Silently fail if there are any issues - never break user applications
         pass
 
+
+# Only initialize telemetry if explicitly enabled
+_init_telemetry()
+
+
 __all__ = [
+    # Core classes (lazy loaded)
     'Agent',
     'ImageAgent',
     'ContextAgent',
@@ -314,11 +420,8 @@ __all__ = [
     'RewriteResult',
     'PromptExpanderAgent',
     'ExpandStrategy',
-    'ExpandResult'
-]
-
-# Add workflow exports
-__all__.extend([
+    'ExpandResult',
+    # Workflows
     'Workflow',
     'WorkflowStep', 
     'WorkflowContext',
@@ -331,48 +434,33 @@ __all__.extend([
     'parallel',
     'loop',
     'repeat',
-    'Pipeline'
-])
-
-# Add MCP to __all__ if available
-if _mcp_available:
-    __all__.append('MCP')
-    
-# Add flow display if available
-if FlowDisplay is not None:
-    __all__.extend(['FlowDisplay', 'track_workflow'])
-
-# Add FastContext exports (lazy loaded)
-__all__.extend([
+    'Pipeline',
+    # MCP
+    'MCP',
+    # Flow display
+    'FlowDisplay',
+    'track_workflow',
+    # FastContext
     'FastContext',
     'FastContextResult',
     'FileMatch',
-    'LineRange'
-])
-
-# Add Agent Skills exports (lazy loaded)
-__all__.extend([
+    'LineRange',
+    # Agent Skills
     'SkillManager',
     'SkillProperties',
     'SkillMetadata',
-    'SkillLoader'
-])
-
-# AG-UI support (optional, lazy loaded)
-try:
-    from praisonaiagents.ui.agui import AGUI
-    __all__.append('AGUI')
-    _agui_available = True
-except ImportError:
-    _agui_available = False
-    AGUI = None
-
-# A2A support (optional, lazy loaded)
-try:
-    from praisonaiagents.ui.a2a import A2A
-    __all__.append('A2A')
-    _a2a_available = True
-except ImportError:
-    _a2a_available = False
-    A2A = None
-
+    'SkillLoader',
+    # Planning
+    'Plan',
+    'PlanStep',
+    'TodoList',
+    'TodoItem',
+    'PlanStorage',
+    'PlanningAgent',
+    'ApprovalCallback',
+    'READ_ONLY_TOOLS',
+    'RESTRICTED_TOOLS',
+    # UI
+    'AGUI',
+    'A2A',
+]

src/praisonai-agents/praisonaiagents/_config.py

@@ -0,0 +1,106 @@
+"""
+Configuration module for PraisonAI Agents.
+
+Provides feature flags and configuration options that can be controlled
+via environment variables for performance tuning and behavior control.
+
+Environment Variables:
+    PRAISONAI_LAZY_IMPORTS: Enable lazy imports (default: true)
+    PRAISONAI_TELEMETRY_ENABLED: Enable telemetry (default: false, opt-in)
+    PRAISONAI_TELEMETRY_DISABLED: Disable telemetry (takes precedence)
+    DO_NOT_TRACK: Standard opt-out flag (takes precedence)
+"""
+import os
+from typing import Optional
+
+
+def _str_to_bool(value: Optional[str], default: bool = False) -> bool:
+    """Convert string environment variable to boolean."""
+    if value is None:
+        return default
+    return value.lower() in ('true', '1', 'yes', 'on')
+
+
+# Feature Flags
+# -------------
+
+# Lazy imports: When True (default), heavy dependencies like litellm are
+# only imported when actually needed, reducing import time significantly.
+LAZY_IMPORTS = _str_to_bool(
+    os.environ.get('PRAISONAI_LAZY_IMPORTS'), 
+    default=True
+)
+
+# Telemetry: Opt-in by default (False). Users must explicitly enable.
+# Precedence (highest to lowest):
+#   1. DO_NOT_TRACK=true -> disabled
+#   2. PRAISONAI_TELEMETRY_DISABLED=true -> disabled  
+#   3. PRAISONAI_DISABLE_TELEMETRY=true -> disabled
+#   4. PRAISONAI_TELEMETRY_ENABLED=true -> enabled
+#   5. Default: disabled (opt-in model)
+def _get_telemetry_enabled() -> bool:
+    """Determine if telemetry should be enabled based on env vars."""
+    # Check disable flags first (highest precedence)
+    if _str_to_bool(os.environ.get('DO_NOT_TRACK')):
+        return False
+    if _str_to_bool(os.environ.get('PRAISONAI_TELEMETRY_DISABLED')):
+        return False
+    if _str_to_bool(os.environ.get('PRAISONAI_DISABLE_TELEMETRY')):
+        return False
+    
+    # Check explicit enable flag
+    if _str_to_bool(os.environ.get('PRAISONAI_TELEMETRY_ENABLED')):
+        return True
+    
+    # Default: disabled (opt-in model)
+    return False
+
+
+TELEMETRY_ENABLED = _get_telemetry_enabled()
+
+# Performance mode: Minimal telemetry overhead when enabled
+PERFORMANCE_MODE = _str_to_bool(
+    os.environ.get('PRAISONAI_PERFORMANCE_MODE'),
+    default=False
+)
+
+# Full telemetry mode: Detailed tracking when enabled
+FULL_TELEMETRY = _str_to_bool(
+    os.environ.get('PRAISONAI_FULL_TELEMETRY'),
+    default=False
+)
+
+# Legacy auto-instrument flag
+AUTO_INSTRUMENT = _str_to_bool(
+    os.environ.get('PRAISONAI_AUTO_INSTRUMENT'),
+    default=False
+)
+
+
+def missing_dependency_error(
+    package_name: str,
+    extra_name: Optional[str] = None,
+    feature_name: Optional[str] = None
+) -> ImportError:
+    """
+    Create a standardized ImportError for missing optional dependencies.
+    
+    Args:
+        package_name: The name of the missing package (e.g., 'litellm')
+        extra_name: The pip extra to install (e.g., 'llm' for [llm])
+        feature_name: Human-readable feature name (e.g., 'LLM support')
+    
+    Returns:
+        ImportError with helpful install instructions
+    """
+    if extra_name:
+        install_cmd = f"pip install 'praisonaiagents[{extra_name}]'"
+    else:
+        install_cmd = f"pip install {package_name}"
+    
+    feature_desc = f" for {feature_name}" if feature_name else ""
+    
+    return ImportError(
+        f"{package_name} is required{feature_desc} but not installed. "
+        f"Please install with: {install_cmd}"
+    )

src/praisonai-agents/praisonaiagents/_logging.py

@@ -84,8 +84,18 @@ def _configure_loggers():
 # ========================================================================
 # LITELLM CONFIGURATION
 # ========================================================================
-def _configure_litellm():
-    """Configure litellm after it's imported."""
+_litellm_configured = False
+
+def configure_litellm():
+    """Configure litellm after it's imported.
+    
+    This function should be called lazily when litellm is actually used,
+    not at module import time, to avoid importing litellm unnecessarily.
+    """
+    global _litellm_configured
+    if _litellm_configured:
+        return
+    
     try:
         import litellm
         litellm.telemetry = False
@@ -97,11 +107,17 @@ def _configure_litellm():
         
         if hasattr(litellm, 'set_verbose'):
             litellm.set_verbose = False
+        
+        _litellm_configured = True
             
     except (ImportError, AttributeError):
         pass
 
 
+# Alias for backward compatibility
+_configure_litellm = configure_litellm
+
+
 # ========================================================================
 # ROOT LOGGER CONFIGURATION
 # ========================================================================
@@ -124,11 +140,16 @@ def configure_root_logger():
 # INITIALIZATION
 # ========================================================================
 def initialize_logging():
-    """Initialize all logging configuration."""
+    """Initialize all logging configuration.
+    
+    Note: litellm configuration is NOT done here to avoid importing litellm
+    at package import time. Call configure_litellm() when litellm is needed.
+    """
     _configure_environment()
     _configure_loggers()
-    _configure_litellm()
+    # NOTE: _configure_litellm() is NOT called here to avoid importing litellm
+    # It will be called lazily when LLM class is instantiated
 
 
-# Auto-initialize on import
+# Auto-initialize on import (but NOT litellm)
 initialize_logging()

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

@@ -4,6 +4,7 @@ import json
 import copy
 import logging
 import asyncio
+import threading
 from typing import List, Optional, Any, Dict, Union, Literal, TYPE_CHECKING, Callable, Tuple, Generator
 from rich.console import Console
 from rich.live import Live
@@ -61,6 +62,57 @@ class Agent:
             logging.getLogger("httpx").setLevel(logging.WARNING)
             logging.getLogger("httpcore").setLevel(logging.WARNING)
     
+    @classmethod
+    def from_template(
+        cls,
+        uri: str,
+        config: Optional[Dict[str, Any]] = None,
+        offline: bool = False,
+        **kwargs
+    ) -> 'Agent':
+        """
+        Create an Agent from a template.
+        
+        Args:
+            uri: Template URI (local path, package ref, or github ref)
+                Examples:
+                - "./my-template" (local path)
+                - "transcript-generator" (default recipes repo)
+                - "github:owner/repo/[email protected]" (GitHub with version)
+                - "package:agent_recipes/transcript-generator" (installed package)
+            config: Optional configuration overrides
+            offline: If True, only use cached templates (no network)
+            **kwargs: Additional Agent constructor arguments
+            
+        Returns:
+            Configured Agent instance
+            
+        Example:
+            ```python
+            from praisonaiagents import Agent
+            
+            # From default recipes repo
+            agent = Agent.from_template("transcript-generator")
+            result = agent.chat("Transcribe ./audio.mp3")
+            
+            # With config overrides
+            agent = Agent.from_template(
+                "data-transformer",
+                config={"output_format": "json"},
+                verbose=True
+            )
+            ```
+        """
+        try:
+            # Lazy import to avoid circular dependencies and keep core SDK lean
+            from praisonai.templates.loader import create_agent_from_template
+            return create_agent_from_template(uri, config=config, offline=offline, **kwargs)
+        except ImportError:
+            raise ImportError(
+                "Template support requires the 'praisonai' package. "
+                "Install with: pip install praisonai"
+            )
+    
     def _generate_tool_definition(self, function_name):
         """
         Generate a tool definition from a function name by inspecting the function.
@@ -529,9 +581,9 @@ class Agent:
         self.embedder_config = embedder_config
         self.knowledge = knowledge
         self.use_system_prompt = use_system_prompt
-        # NOTE: chat_history is not thread-safe. If concurrent access is needed,
-        # consider using threading.Lock or other synchronization mechanisms
+        # Thread-safe chat_history with lock for concurrent access
         self.chat_history = []
+        self._history_lock = threading.Lock()
         self.markdown = markdown
         self.stream = stream
         self.metrics = metrics
@@ -592,11 +644,11 @@ Your Goal: {self.goal}
         self._guardrail_fn = None
         self._setup_guardrail()
         
-        # Cache for system prompts and formatted tools
-        # Note: In single-threaded usage (common case), these are safe
-        # For multi-threaded usage, consider using threading.Lock
+        # Cache for system prompts and formatted tools with thread-safe lock
+        # RLock allows re-entrant access from the same thread
         self._system_prompt_cache = {}
         self._formatted_tools_cache = {}
+        self._cache_lock = threading.RLock()
         # Limit cache size to prevent unbounded growth
         self._max_cache_size = 100
 
@@ -1694,7 +1746,11 @@ Your Goal: {self.goal}"""
 
         # Special handling for MCP tools
         # Check if tools is an MCP instance with the requested function name
-        from ..mcp.mcp import MCP
+        MCP = None
+        try:
+            from ..mcp.mcp import MCP
+        except ImportError:
+            pass  # MCP not available
         
         # Helper function to execute MCP tool
         def _execute_mcp_tool(mcp_instance, func_name, args):
@@ -1729,7 +1785,7 @@ Your Goal: {self.goal}"""
             return False, None
         
         # Check if tools is a single MCP instance
-        if isinstance(self.tools, MCP):
+        if MCP is not None and isinstance(self.tools, MCP):
             logging.debug(f"Looking for MCP tool {function_name}")
             found, result = _execute_mcp_tool(self.tools, function_name, arguments)
             if found:
@@ -1738,7 +1794,7 @@ Your Goal: {self.goal}"""
         # Check if tools is a list that may contain MCP instances
         if isinstance(self.tools, (list, tuple)):
             for tool in self.tools:
-                if isinstance(tool, MCP):
+                if MCP is not None and isinstance(tool, MCP):
                     logging.debug(f"Looking for MCP tool {function_name} in MCP instance")
                     found, result = _execute_mcp_tool(tool, function_name, arguments)
                     if found:
@@ -2160,8 +2216,12 @@ Your Goal: {self.goal}"""
                 
                 # Convert MCP tool objects to OpenAI format if needed
                 if tool_param is not None:
-                    from ..mcp.mcp import MCP
-                    if isinstance(tool_param, MCP) and hasattr(tool_param, 'to_openai_tool'):
+                    MCP = None
+                    try:
+                        from ..mcp.mcp import MCP
+                    except ImportError:
+                        pass
+                    if MCP is not None and isinstance(tool_param, MCP) and hasattr(tool_param, 'to_openai_tool'):
                         # Single MCP instance
                         logging.debug("Converting single MCP tool to OpenAI format")
                         openai_tool = tool_param.to_openai_tool()
@@ -2176,7 +2236,7 @@ Your Goal: {self.goal}"""
                         # List that may contain MCP instances - convert each MCP to OpenAI format
                         converted_tools = []
                         for t in tool_param:
-                            if isinstance(t, MCP) and hasattr(t, 'to_openai_tool'):
+                            if MCP is not None and isinstance(t, MCP) and hasattr(t, 'to_openai_tool'):
                                 logging.debug("Converting MCP instance in list to OpenAI format")
                                 openai_tools = t.to_openai_tool()
                                 if isinstance(openai_tools, list):
@@ -3111,8 +3171,12 @@ Write the complete compiled report:"""
                 
                 # Convert MCP tools if needed
                 if tool_param is not None:
-                    from ..mcp.mcp import MCP
-                    if isinstance(tool_param, MCP) and hasattr(tool_param, 'to_openai_tool'):
+                    MCP = None
+                    try:
+                        from ..mcp.mcp import MCP
+                    except ImportError:
+                        pass
+                    if MCP is not None and isinstance(tool_param, MCP) and hasattr(tool_param, 'to_openai_tool'):
                         openai_tool = tool_param.to_openai_tool()
                         if openai_tool:
                             if isinstance(openai_tool, list):

src/praisonai-agents/praisonaiagents/db/protocol.py

@@ -5,13 +5,26 @@ This module uses only stdlib (typing, dataclasses) to define the interface.
 No external dependencies are imported here.
 
 Implementations are provided by the wrapper layer (praisonai.db).
+
+Schema Versioning:
+- SCHEMA_VERSION tracks the protocol version
+- Adapters should implement get_schema_version() to report their version
+- Adapters should implement migrate_schema() for upgrades
+- Version format: MAJOR.MINOR (e.g., "1.0", "1.1", "2.0")
+- MAJOR changes are breaking, MINOR changes are backward compatible
 """
 
 from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
+from typing import Any, Dict, List, Optional, Protocol, runtime_checkable, Tuple
 import time
 
 
+# Current schema version for the DbAdapter protocol
+# Increment MINOR for backward-compatible additions
+# Increment MAJOR for breaking changes
+SCHEMA_VERSION = "1.0"
+
+
 @dataclass
 class DbToolCall:
     """A tool call to be persisted."""
@@ -365,6 +378,48 @@ class DbAdapter(Protocol):
     def close(self) -> None:
         """Close the database connection."""
         ...
+    
+    # --- Schema Versioning (Optional) ---
+    # These methods are optional for backward compatibility.
+    # Adapters that don't implement them will work but won't support migrations.
+    
+    def get_schema_version(self) -> str:
+        """
+        Get the current schema version of this adapter.
+        
+        Returns:
+            Schema version string (e.g., "1.0")
+            Default implementation returns "1.0"
+        """
+        ...
+    
+    def migrate_schema(
+        self,
+        from_version: str,
+        to_version: str,
+    ) -> bool:
+        """
+        Migrate the schema from one version to another.
+        
+        Args:
+            from_version: Current schema version
+            to_version: Target schema version
+            
+        Returns:
+            True if migration succeeded, False otherwise
+        """
+        ...
+    
+    def check_schema_compatibility(self) -> Tuple[bool, str]:
+        """
+        Check if the adapter's schema is compatible with the protocol.
+        
+        Returns:
+            Tuple of (is_compatible, message)
+            - is_compatible: True if schema is compatible
+            - message: Description of compatibility status or required action
+        """
+        ...
 
 
 @runtime_checkable

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/praisonaiagents/lite/__init__.py

@@ -0,0 +1,327 @@
+"""
+PraisonAI Agents Lite - Minimal agent framework without heavy dependencies.
+
+This subpackage provides a lightweight version of PraisonAI Agents that:
+- Does NOT import litellm, chromadb, or other heavy dependencies at import time
+- Provides core Agent/Task/Tools abstractions
+- Allows users to bring their own LLM client (BYO-LLM)
+- Has minimal import time (<50ms) and memory footprint (<10MB)
+
+Usage:
+    from praisonaiagents.lite import LiteAgent, LiteTask, Tools
+    
+    # Create a lite agent with your own LLM function
+    def my_llm(messages):
+        # Your LLM implementation here
+        return "response"
+    
+    agent = LiteAgent(
+        name="Assistant",
+        llm_fn=my_llm,
+        tools=[my_tool]
+    )
+    
+    response = agent.chat("Hello!")
+
+For full functionality with litellm integration, use the main package:
+    from praisonaiagents import Agent
+"""
+
+from typing import Any, Callable, Dict, List, Optional
+from dataclasses import dataclass, field
+import threading
+
+
+@dataclass
+class LiteToolResult:
+    """Result from a tool execution."""
+    output: Any
+    success: bool = True
+    error: Optional[str] = None
+
+
+@dataclass
+class LiteTask:
+    """
+    Lightweight task definition without heavy dependencies.
+    
+    Attributes:
+        description: What the task should accomplish
+        expected_output: Description of expected output format
+        agent: Optional agent assigned to this task
+        context: Additional context for the task
+        result: Result after task execution
+    """
+    description: str
+    expected_output: str = ""
+    agent: Optional['LiteAgent'] = None
+    context: Dict[str, Any] = field(default_factory=dict)
+    result: Optional[str] = None
+    
+    def execute(self, input_text: Optional[str] = None) -> str:
+        """Execute the task using the assigned agent."""
+        if self.agent is None:
+            raise ValueError("No agent assigned to this task")
+        
+        prompt = input_text or self.description
+        if self.expected_output:
+            prompt += f"\n\nExpected output format: {self.expected_output}"
+        
+        self.result = self.agent.chat(prompt)
+        return self.result
+
+
+class LiteAgent:
+    """
+    Lightweight agent that works with any LLM function.
+    
+    This agent does NOT import litellm or other heavy dependencies.
+    Users provide their own LLM function for chat completions.
+    
+    Args:
+        name: Agent name
+        llm_fn: Function that takes messages list and returns response string.
+                Signature: (messages: List[Dict]) -> str
+        instructions: System instructions for the agent
+        tools: List of tool functions the agent can use
+        verbose: Enable verbose output
+    
+    Example:
+        def my_openai_llm(messages):
+            import openai
+            client = openai.OpenAI()
+            response = client.chat.completions.create(
+                model="gpt-4o-mini",
+                messages=messages
+            )
+            return response.choices[0].message.content
+        
+        agent = LiteAgent(
+            name="Assistant",
+            llm_fn=my_openai_llm,
+            instructions="You are a helpful assistant."
+        )
+        
+        response = agent.chat("Hello!")
+    """
+    
+    def __init__(
+        self,
+        name: str = "LiteAgent",
+        llm_fn: Optional[Callable[[List[Dict]], str]] = None,
+        instructions: str = "You are a helpful assistant.",
+        tools: Optional[List[Callable]] = None,
+        verbose: bool = False,
+    ):
+        self.name = name
+        self.llm_fn = llm_fn
+        self.instructions = instructions
+        self.tools = tools or []
+        self.verbose = verbose
+        
+        # Thread-safe chat history
+        self.chat_history: List[Dict[str, str]] = []
+        self._history_lock = threading.Lock()
+        
+        # Tool registry
+        self._tool_map: Dict[str, Callable] = {}
+        for tool_fn in self.tools:
+            tool_name = getattr(tool_fn, '__name__', str(tool_fn))
+            self._tool_map[tool_name] = tool_fn
+    
+    def _build_messages(self, user_message: str) -> List[Dict[str, str]]:
+        """Build messages list for LLM call."""
+        messages = [{"role": "system", "content": self.instructions}]
+        
+        with self._history_lock:
+            messages.extend(self.chat_history.copy())
+        
+        messages.append({"role": "user", "content": user_message})
+        return messages
+    
+    def _add_to_history(self, role: str, content: str):
+        """Thread-safe addition to chat history."""
+        with self._history_lock:
+            self.chat_history.append({"role": role, "content": content})
+    
+    def chat(self, message: str) -> str:
+        """
+        Send a message to the agent and get a response.
+        
+        Args:
+            message: User message
+            
+        Returns:
+            Agent's response string
+        """
+        if self.llm_fn is None:
+            raise ValueError(
+                "No LLM function provided. Please provide an llm_fn when creating the agent.\n"
+                "Example:\n"
+                "  def my_llm(messages):\n"
+                "      # Your LLM implementation\n"
+                "      return 'response'\n"
+                "  agent = LiteAgent(llm_fn=my_llm)"
+            )
+        
+        messages = self._build_messages(message)
+        
+        if self.verbose:
+            print(f"[{self.name}] Processing: {message[:50]}...")
+        
+        response = self.llm_fn(messages)
+        
+        # Add to history
+        self._add_to_history("user", message)
+        self._add_to_history("assistant", response)
+        
+        if self.verbose:
+            print(f"[{self.name}] Response: {response[:100]}...")
+        
+        return response
+    
+    def clear_history(self):
+        """Clear chat history."""
+        with self._history_lock:
+            self.chat_history.clear()
+    
+    def execute_tool(self, tool_name: str, **kwargs) -> LiteToolResult:
+        """
+        Execute a tool by name.
+        
+        Args:
+            tool_name: Name of the tool to execute
+            **kwargs: Arguments to pass to the tool
+            
+        Returns:
+            LiteToolResult with output or error
+        """
+        if tool_name not in self._tool_map:
+            return LiteToolResult(
+                output=None,
+                success=False,
+                error=f"Tool '{tool_name}' not found"
+            )
+        
+        try:
+            tool_fn = self._tool_map[tool_name]
+            result = tool_fn(**kwargs)
+            return LiteToolResult(output=result, success=True)
+        except Exception as e:
+            return LiteToolResult(
+                output=None,
+                success=False,
+                error=str(e)
+            )
+
+
+def create_openai_llm_fn(
+    model: str = "gpt-4o-mini",
+    api_key: Optional[str] = None,
+    **kwargs
+) -> Callable[[List[Dict]], str]:
+    """
+    Create an LLM function using the OpenAI SDK.
+    
+    This is a convenience function for users who want to use OpenAI
+    without the full litellm dependency.
+    
+    Args:
+        model: OpenAI model name
+        api_key: OpenAI API key (or use OPENAI_API_KEY env var)
+        **kwargs: Additional arguments to pass to OpenAI
+        
+    Returns:
+        LLM function compatible with LiteAgent
+        
+    Example:
+        llm_fn = create_openai_llm_fn(model="gpt-4o-mini")
+        agent = LiteAgent(llm_fn=llm_fn)
+    """
+    def llm_fn(messages: List[Dict]) -> str:
+        try:
+            import openai
+        except ImportError:
+            raise ImportError(
+                "openai package is required for create_openai_llm_fn. "
+                "Install with: pip install openai"
+            )
+        
+        client = openai.OpenAI(api_key=api_key)
+        response = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            **kwargs
+        )
+        return response.choices[0].message.content or ""
+    
+    return llm_fn
+
+
+def create_anthropic_llm_fn(
+    model: str = "claude-3-5-sonnet-20241022",
+    api_key: Optional[str] = None,
+    max_tokens: int = 4096,
+    **kwargs
+) -> Callable[[List[Dict]], str]:
+    """
+    Create an LLM function using the Anthropic SDK.
+    
+    Args:
+        model: Anthropic model name
+        api_key: Anthropic API key (or use ANTHROPIC_API_KEY env var)
+        max_tokens: Maximum tokens in response
+        **kwargs: Additional arguments to pass to Anthropic
+        
+    Returns:
+        LLM function compatible with LiteAgent
+    """
+    def llm_fn(messages: List[Dict]) -> str:
+        try:
+            import anthropic
+        except ImportError:
+            raise ImportError(
+                "anthropic package is required for create_anthropic_llm_fn. "
+                "Install with: pip install anthropic"
+            )
+        
+        client = anthropic.Anthropic(api_key=api_key)
+        
+        # Extract system message
+        system = ""
+        chat_messages = []
+        for msg in messages:
+            if msg["role"] == "system":
+                system = msg["content"]
+            else:
+                chat_messages.append(msg)
+        
+        response = client.messages.create(
+            model=model,
+            max_tokens=max_tokens,
+            system=system,
+            messages=chat_messages,
+            **kwargs
+        )
+        return response.content[0].text
+    
+    return llm_fn
+
+
+# Re-export Tools from main package (lightweight, no heavy deps)
+from ..tools.tools import Tools
+from ..tools.base import BaseTool, ToolResult
+from ..tools.decorator import tool
+
+
+__all__ = [
+    "LiteAgent",
+    "LiteTask",
+    "LiteToolResult",
+    "Tools",
+    "BaseTool",
+    "ToolResult",
+    "tool",
+    "create_openai_llm_fn",
+    "create_anthropic_llm_fn",
+]

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

@@ -1,33 +1,102 @@
+"""
+LLM Module for PraisonAI Agents.
+
+This module provides language model integrations with lazy loading
+to minimize import time when LLM functionality is not immediately needed.
+"""
 import os
 
-# Ensure litellm telemetry is disabled before imports
+# Ensure litellm telemetry is disabled before any imports
 os.environ["LITELLM_TELEMETRY"] = "False"
 
-# Import modules
-from .llm import LLM, LLMContextLengthExceededException
-from .openai_client import (
-    OpenAIClient, 
-    get_openai_client,
-    ChatCompletionMessage,
-    Choice,
-    CompletionTokensDetails,
-    PromptTokensDetails,
-    CompletionUsage,
-    ChatCompletion,
-    ToolCall,
-    process_stream_chunks
-)
-from .model_capabilities import (
-    supports_structured_outputs,
-    supports_streaming_with_tools
-)
-from .model_router import (
-    ModelRouter,
-    ModelProfile, 
-    TaskComplexity,
-    create_routing_agent
-)
-from .rate_limiter import RateLimiter
+# Module-level cache for lazy-loaded classes
+_lazy_cache = {}
+
+
+def __getattr__(name):
+    """Lazy load LLM classes to avoid importing litellm at module load time."""
+    if name in _lazy_cache:
+        return _lazy_cache[name]
+    
+    if name == "LLM":
+        from .llm import LLM
+        _lazy_cache[name] = LLM
+        return LLM
+    elif name == "LLMContextLengthExceededException":
+        from .llm import LLMContextLengthExceededException
+        _lazy_cache[name] = LLMContextLengthExceededException
+        return LLMContextLengthExceededException
+    elif name == "OpenAIClient":
+        from .openai_client import OpenAIClient
+        _lazy_cache[name] = OpenAIClient
+        return OpenAIClient
+    elif name == "get_openai_client":
+        from .openai_client import get_openai_client
+        _lazy_cache[name] = get_openai_client
+        return get_openai_client
+    elif name == "ChatCompletionMessage":
+        from .openai_client import ChatCompletionMessage
+        _lazy_cache[name] = ChatCompletionMessage
+        return ChatCompletionMessage
+    elif name == "Choice":
+        from .openai_client import Choice
+        _lazy_cache[name] = Choice
+        return Choice
+    elif name == "CompletionTokensDetails":
+        from .openai_client import CompletionTokensDetails
+        _lazy_cache[name] = CompletionTokensDetails
+        return CompletionTokensDetails
+    elif name == "PromptTokensDetails":
+        from .openai_client import PromptTokensDetails
+        _lazy_cache[name] = PromptTokensDetails
+        return PromptTokensDetails
+    elif name == "CompletionUsage":
+        from .openai_client import CompletionUsage
+        _lazy_cache[name] = CompletionUsage
+        return CompletionUsage
+    elif name == "ChatCompletion":
+        from .openai_client import ChatCompletion
+        _lazy_cache[name] = ChatCompletion
+        return ChatCompletion
+    elif name == "ToolCall":
+        from .openai_client import ToolCall
+        _lazy_cache[name] = ToolCall
+        return ToolCall
+    elif name == "process_stream_chunks":
+        from .openai_client import process_stream_chunks
+        _lazy_cache[name] = process_stream_chunks
+        return process_stream_chunks
+    elif name == "supports_structured_outputs":
+        from .model_capabilities import supports_structured_outputs
+        _lazy_cache[name] = supports_structured_outputs
+        return supports_structured_outputs
+    elif name == "supports_streaming_with_tools":
+        from .model_capabilities import supports_streaming_with_tools
+        _lazy_cache[name] = supports_streaming_with_tools
+        return supports_streaming_with_tools
+    elif name == "ModelRouter":
+        from .model_router import ModelRouter
+        _lazy_cache[name] = ModelRouter
+        return ModelRouter
+    elif name == "ModelProfile":
+        from .model_router import ModelProfile
+        _lazy_cache[name] = ModelProfile
+        return ModelProfile
+    elif name == "TaskComplexity":
+        from .model_router import TaskComplexity
+        _lazy_cache[name] = TaskComplexity
+        return TaskComplexity
+    elif name == "create_routing_agent":
+        from .model_router import create_routing_agent
+        _lazy_cache[name] = create_routing_agent
+        return create_routing_agent
+    elif name == "RateLimiter":
+        from .rate_limiter import RateLimiter
+        _lazy_cache[name] = RateLimiter
+        return RateLimiter
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
 
 __all__ = [
     "LLM", 
@@ -49,4 +118,4 @@ __all__ = [
     "TaskComplexity",
     "create_routing_agent",
     "RateLimiter"
-]
+]

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

@@ -104,12 +104,34 @@ class LLM:
     OLLAMA_TOOL_USAGE_PROMPT = "Please analyze the request and use the available tools to help answer the question. Start by identifying what information you need."
     OLLAMA_FINAL_ANSWER_PROMPT = "Based on the tool results above, please provide the final answer to the original question."
     
+    # Force tool usage prompt template (used when model ignores tools)
+    FORCE_TOOL_USAGE_PROMPT = """You MUST use one of the available tools to answer this question.
+Do NOT provide a direct answer. Respond ONLY with a tool call.
+
+Available tools: {tool_names}
+
+Respond with a JSON object in this format:
+{{"name": "tool_name", "arguments": {{"arg1": value1, "arg2": value2}}}}"""
+
+    # Tool call repair prompt template (used when tool call is malformed)
+    TOOL_CALL_REPAIR_PROMPT = """Your previous tool call was malformed: {error}
+
+Please try again. Available tools:
+{tool_schemas}
+
+Respond with ONLY a valid JSON tool call in this format:
+{{"name": "tool_name", "arguments": {{"arg1": value1, "arg2": value2}}}}"""
+    
     # Ollama iteration threshold for summary generation
     OLLAMA_SUMMARY_ITERATION_THRESHOLD = 1
 
     @classmethod
     def _configure_logging(cls):
         """Configure logging settings once for all LLM instances."""
+        # First, configure litellm via the centralized logging module
+        from .._logging import configure_litellm
+        configure_litellm()
+        
         try:
             import litellm
             # Disable telemetry
@@ -294,6 +316,16 @@ class LLM:
         # Auto-detect XML tool format for known models, or allow manual override
         self.xml_tool_format = extra_settings.get('xml_tool_format', 'auto')
         
+        # Tool calling reliability settings (for weak models like Ollama)
+        # These are auto-configured for Ollama providers if not explicitly set
+        self._max_tool_repairs_explicit = 'max_tool_repairs' in extra_settings
+        self._force_tool_usage_explicit = 'force_tool_usage' in extra_settings
+        self.max_tool_repairs = extra_settings.get('max_tool_repairs', 0)  # Will be set to 2 for Ollama if not explicit
+        self.force_tool_usage = extra_settings.get('force_tool_usage', 'never')  # Will be set to 'auto' for Ollama if not explicit
+        
+        # Apply Ollama-specific defaults after model is set
+        self._apply_ollama_defaults()
+        
         # Token tracking
         self.last_token_metrics: Optional[TokenMetrics] = None
         self.session_token_metrics: Optional[TokenMetrics] = None
@@ -347,6 +379,16 @@ class LLM:
             self._console = Console()
         return self._console
 
+    def _apply_ollama_defaults(self):
+        """Apply Ollama-specific defaults for tool calling reliability."""
+        if self._is_ollama_provider():
+            # Apply defaults only if not explicitly set by user
+            if not self._max_tool_repairs_explicit:
+                self.max_tool_repairs = 2
+            if not self._force_tool_usage_explicit:
+                self.force_tool_usage = 'auto'
+            logging.debug(f"[OLLAMA_RELIABILITY] Applied Ollama defaults: max_tool_repairs={self.max_tool_repairs}, force_tool_usage={self.force_tool_usage}")
+
     def _is_ollama_provider(self) -> bool:
         """Detect if this is an Ollama provider regardless of naming convention"""
         if not self.model:
@@ -562,14 +604,150 @@ class LLM:
     def _format_ollama_tool_result_message(self, function_name: str, tool_result: Any) -> Dict[str, str]:
         """
         Format tool result message for Ollama provider.
-        Simplified approach without hardcoded regex extraction.
+        Enhanced to instruct model to use the result for final answer.
         """
         tool_result_str = str(tool_result)
         return {
             "role": "user",
-            "content": f"The {function_name} function returned: {tool_result_str}"
+            "content": f"""Tool execution complete.
+Function: {function_name}
+Result: {tool_result_str}
+
+Now provide your final answer using this result."""
         }
 
+    def _get_tool_names_for_prompt(self, formatted_tools: Optional[List]) -> str:
+        """Extract tool names from formatted tools for prompts."""
+        if not formatted_tools:
+            return "None"
+        names = []
+        for tool in formatted_tools:
+            if isinstance(tool, dict) and 'function' in tool:
+                names.append(tool['function'].get('name', 'unknown'))
+        return ', '.join(names) if names else "None"
+
+    def _get_tool_schemas_for_prompt(self, formatted_tools: Optional[List]) -> str:
+        """Generate compact tool schemas for repair prompts."""
+        if not formatted_tools:
+            return "None"
+        schemas = []
+        for tool in formatted_tools:
+            if isinstance(tool, dict) and 'function' in tool:
+                func = tool['function']
+                name = func.get('name', 'unknown')
+                params = func.get('parameters', {})
+                required = params.get('required', [])
+                props = params.get('properties', {})
+                param_strs = []
+                for pname, pinfo in props.items():
+                    ptype = pinfo.get('type', 'any')
+                    req = '*' if pname in required else ''
+                    param_strs.append(f"{pname}{req}: {ptype}")
+                schemas.append(f"- {name}({', '.join(param_strs)})")
+        return '\n'.join(schemas) if schemas else "None"
+
+    def _should_force_tool_usage(self, response_text: str, tool_calls: Optional[List], formatted_tools: Optional[List], iteration_count: int) -> bool:
+        """
+        Determine if we should force tool usage based on settings and context.
+        
+        Returns True if:
+        - force_tool_usage == 'always' and tools exist and no tool_calls
+        - force_tool_usage == 'auto' and is Ollama and first iteration and tools exist and no tool_calls
+        """
+        if not formatted_tools:
+            return False
+        if tool_calls:
+            return False
+        
+        if self.force_tool_usage == 'never':
+            return False
+        elif self.force_tool_usage == 'always':
+            return True
+        elif self.force_tool_usage == 'auto':
+            # Auto mode: only for Ollama on first iteration when model ignores tools
+            return self._is_ollama_provider() and iteration_count == 0
+        return False
+
+    def _try_parse_tool_call_json(self, response_text: str, iteration_count: int) -> tuple:
+        """
+        Try to parse tool call from JSON response text.
+        
+        Returns:
+            tuple: (tool_calls list or None, parse_error string or None)
+        """
+        if not response_text or not response_text.strip():
+            return None, None
+        
+        try:
+            response_json = json.loads(response_text.strip())
+            if isinstance(response_json, dict) and "name" in response_json:
+                tool_calls = [{
+                    "id": f"tool_{iteration_count}",
+                    "type": "function",
+                    "function": {
+                        "name": response_json["name"],
+                        "arguments": json.dumps(response_json.get("arguments", {}))
+                    }
+                }]
+                return tool_calls, None
+            elif isinstance(response_json, list):
+                tool_calls = []
+                for idx, tool_json in enumerate(response_json):
+                    if isinstance(tool_json, dict) and "name" in tool_json:
+                        tool_calls.append({
+                            "id": f"tool_{iteration_count}_{idx}",
+                            "type": "function",
+                            "function": {
+                                "name": tool_json["name"],
+                                "arguments": json.dumps(tool_json.get("arguments", {}))
+                            }
+                        })
+                return tool_calls if tool_calls else None, None
+            else:
+                return None, "Response is not a valid tool call format (missing 'name' field)"
+        except json.JSONDecodeError as e:
+            return None, f"JSON parse error: {str(e)}"
+
+    def _validate_tool_call(self, tool_call: Dict, formatted_tools: Optional[List]) -> Optional[str]:
+        """
+        Validate a tool call against available tools.
+        
+        Returns:
+            Error message string if invalid, None if valid
+        """
+        if not formatted_tools:
+            return None
+        
+        try:
+            func_info = tool_call.get('function', {})
+            tool_name = func_info.get('name', '')
+            arguments_str = func_info.get('arguments', '{}')
+            
+            # Check if tool name exists
+            available_names = [t['function']['name'] for t in formatted_tools if isinstance(t, dict) and 'function' in t]
+            if tool_name not in available_names:
+                return f"Unknown tool '{tool_name}'. Available: {', '.join(available_names)}"
+            
+            # Try to parse arguments
+            try:
+                arguments = json.loads(arguments_str) if isinstance(arguments_str, str) else arguments_str
+            except json.JSONDecodeError as e:
+                return f"Invalid arguments JSON: {str(e)}"
+            
+            # Find the tool schema and validate required args
+            for tool in formatted_tools:
+                if isinstance(tool, dict) and tool.get('function', {}).get('name') == tool_name:
+                    params = tool['function'].get('parameters', {})
+                    required = params.get('required', [])
+                    missing = [r for r in required if r not in arguments]
+                    if missing:
+                        return f"Missing required arguments for '{tool_name}': {', '.join(missing)}"
+                    break
+            
+            return None  # Valid
+        except Exception as e:
+            return f"Validation error: {str(e)}"
+
     def _process_stream_delta(self, delta, response_text: str, tool_calls: List[Dict], formatted_tools: Optional[List] = None) -> tuple:
         """
         Process a streaming delta chunk to extract content and tool calls.
@@ -1637,6 +1815,18 @@ class LLM:
                             if tool_calls:
                                 logging.debug(f"Parsed {len(tool_calls)} tool call(s) from XML format")
                     
+                    # Force tool usage logic: if model ignores tools but should use them
+                    if self._should_force_tool_usage(response_text, tool_calls, formatted_tools, iteration_count):
+                        tool_names = self._get_tool_names_for_prompt(formatted_tools)
+                        force_prompt = self.FORCE_TOOL_USAGE_PROMPT.format(tool_names=tool_names)
+                        logging.debug(f"[OLLAMA_RELIABILITY] Force tool usage triggered. Adding prompt for tools: {tool_names}")
+                        messages.append({
+                            "role": "user",
+                            "content": force_prompt
+                        })
+                        iteration_count += 1
+                        continue
+                    
                     # For Ollama, if response is empty but we have tools, prompt for tool usage
                     if self._is_ollama_provider() and (not response_text or response_text.strip() == "") and formatted_tools and iteration_count == 0:
                         messages.append({
@@ -1646,6 +1836,37 @@ class LLM:
                         iteration_count += 1
                         continue
                     
+                    # Tool call repair logic: validate and repair malformed tool calls
+                    repair_attempt_count = getattr(self, '_current_repair_count', 0)
+                    if tool_calls and self.max_tool_repairs > 0 and repair_attempt_count < self.max_tool_repairs:
+                        # Validate each tool call
+                        validation_errors = []
+                        for tc in tool_calls:
+                            error = self._validate_tool_call(tc, formatted_tools)
+                            if error:
+                                validation_errors.append(error)
+                        
+                        if validation_errors:
+                            # Tool call is invalid, attempt repair
+                            error_msg = "; ".join(validation_errors)
+                            tool_schemas = self._get_tool_schemas_for_prompt(formatted_tools)
+                            repair_prompt = self.TOOL_CALL_REPAIR_PROMPT.format(
+                                error=error_msg,
+                                tool_schemas=tool_schemas
+                            )
+                            logging.debug(f"[OLLAMA_RELIABILITY] Tool call repair attempt {repair_attempt_count + 1}/{self.max_tool_repairs}: {error_msg}")
+                            messages.append({
+                                "role": "user",
+                                "content": repair_prompt
+                            })
+                            self._current_repair_count = repair_attempt_count + 1
+                            iteration_count += 1
+                            tool_calls = None  # Clear invalid tool calls
+                            continue
+                    
+                    # Reset repair count on successful tool call
+                    self._current_repair_count = 0
+                    
                     # Handle tool calls - Sequential tool calling logic
                     if tool_calls and execute_tool_fn:
                         # Convert tool_calls to a serializable format for all providers

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

@@ -580,7 +580,60 @@ class MCP:
         
         return openai_tools
     
+    def __enter__(self):
+        """Context manager entry - return self for use in 'with' statements."""
+        return self
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Context manager exit - clean up resources."""
+        self.shutdown()
+        return False  # Don't suppress exceptions
+    
+    def shutdown(self):
+        """Explicitly shut down MCP resources.
+        
+        Call this method when done using the MCP instance to ensure
+        all background threads and connections are properly cleaned up.
+        """
+        # Shutdown stdio runner if present
+        if hasattr(self, 'runner') and self.runner is not None:
+            try:
+                self.runner.shutdown()
+            except Exception:
+                pass  # Best effort cleanup
+        
+        # Shutdown SSE client if present
+        if hasattr(self, 'sse_client') and self.sse_client is not None:
+            try:
+                if hasattr(self.sse_client, 'shutdown'):
+                    self.sse_client.shutdown()
+            except Exception:
+                pass
+        
+        # Shutdown HTTP stream client if present
+        if hasattr(self, 'http_stream_client') and self.http_stream_client is not None:
+            try:
+                if hasattr(self.http_stream_client, 'shutdown'):
+                    self.http_stream_client.shutdown()
+            except Exception:
+                pass
+        
+        # Shutdown WebSocket client if present
+        if hasattr(self, 'websocket_client') and self.websocket_client is not None:
+            try:
+                if hasattr(self.websocket_client, 'shutdown'):
+                    self.websocket_client.shutdown()
+            except Exception:
+                pass
+    
     def __del__(self):
-        """Clean up resources when the object is garbage collected."""
-        if hasattr(self, 'runner'):
-            self.runner.shutdown() 
+        """Clean up resources when the object is garbage collected.
+        
+        Note: __del__ is called during garbage collection and may not
+        always run. For reliable cleanup, use the context manager
+        pattern or call shutdown() explicitly.
+        """
+        try:
+            self.shutdown()
+        except Exception:
+            pass  # Best effort cleanup in __del__

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

@@ -27,13 +27,12 @@ Usage:
     )
     
     result = agents.start()
+
+This module uses lazy loading to minimize import time.
 """
 
-from .plan import Plan, PlanStep
-from .todo import TodoList, TodoItem
-from .storage import PlanStorage
-from .planner import PlanningAgent
-from .approval import ApprovalCallback
+# Module-level cache for lazy-loaded classes
+_lazy_cache = {}
 
 # Read-only tools that are allowed in plan mode
 READ_ONLY_TOOLS = [
@@ -90,6 +89,44 @@ RESEARCH_TOOLS = [
     "find_files",
 ]
 
+
+def __getattr__(name):
+    """Lazy load planning classes to avoid importing heavy dependencies at module load time."""
+    if name in _lazy_cache:
+        return _lazy_cache[name]
+    
+    if name == "Plan":
+        from .plan import Plan
+        _lazy_cache[name] = Plan
+        return Plan
+    elif name == "PlanStep":
+        from .plan import PlanStep
+        _lazy_cache[name] = PlanStep
+        return PlanStep
+    elif name == "TodoList":
+        from .todo import TodoList
+        _lazy_cache[name] = TodoList
+        return TodoList
+    elif name == "TodoItem":
+        from .todo import TodoItem
+        _lazy_cache[name] = TodoItem
+        return TodoItem
+    elif name == "PlanStorage":
+        from .storage import PlanStorage
+        _lazy_cache[name] = PlanStorage
+        return PlanStorage
+    elif name == "PlanningAgent":
+        from .planner import PlanningAgent
+        _lazy_cache[name] = PlanningAgent
+        return PlanningAgent
+    elif name == "ApprovalCallback":
+        from .approval import ApprovalCallback
+        _lazy_cache[name] = ApprovalCallback
+        return ApprovalCallback
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
 __all__ = [
     "Plan",
     "PlanStep",

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

@@ -4,6 +4,7 @@ This module provides centralized tool management with support for:
 - Manual registration
 - Auto-discovery via entry_points
 - Tool lookup by name
+- Thread-safe operations for multi-agent scenarios
 
 Usage:
     from praisonaiagents.tools.registry import get_registry
@@ -14,6 +15,7 @@ Usage:
 """
 
 import logging
+import threading
 from typing import Callable, Dict, List, Optional, Union
 from importlib.metadata import entry_points
 
@@ -38,6 +40,7 @@ class ToolRegistry:
         self._tools: Dict[str, BaseTool] = {}
         self._functions: Dict[str, Callable] = {}  # For backward compat with plain functions
         self._discovered: bool = False
+        self._lock = threading.RLock()  # Thread-safe operations for multi-agent scenarios
     
     def register(
         self,
@@ -47,6 +50,8 @@ class ToolRegistry:
     ) -> None:
         """Register a tool with the registry.
         
+        Thread-safe: Uses lock for concurrent access in multi-agent scenarios.
+        
         Args:
             tool: BaseTool instance or callable function
             name: Override name (default: tool.name or function.__name__)
@@ -55,83 +60,93 @@ class ToolRegistry:
         Raises:
             ValueError: If tool with same name exists and overwrite=False
         """
-        # Handle BaseTool instances
-        if isinstance(tool, BaseTool):
-            tool_name = name or tool.name
-            if tool_name in self._tools and not overwrite:
-                logging.debug(f"Tool '{tool_name}' already registered, skipping")
+        with self._lock:
+            # Handle BaseTool instances
+            if isinstance(tool, BaseTool):
+                tool_name = name or tool.name
+                if tool_name in self._tools and not overwrite:
+                    logging.debug(f"Tool '{tool_name}' already registered, skipping")
+                    return
+                self._tools[tool_name] = tool
+                logging.debug(f"Registered tool: {tool_name}")
                 return
-            self._tools[tool_name] = tool
-            logging.debug(f"Registered tool: {tool_name}")
-            return
-        
-        # Handle plain callables
-        if callable(tool):
-            tool_name = name or getattr(tool, '__name__', str(id(tool)))
-            if tool_name in self._functions and not overwrite:
-                logging.debug(f"Function '{tool_name}' already registered, skipping")
+            
+            # Handle plain callables
+            if callable(tool):
+                tool_name = name or getattr(tool, '__name__', str(id(tool)))
+                if tool_name in self._functions and not overwrite:
+                    logging.debug(f"Function '{tool_name}' already registered, skipping")
+                    return
+                self._functions[tool_name] = tool
+                logging.debug(f"Registered function: {tool_name}")
                 return
-            self._functions[tool_name] = tool
-            logging.debug(f"Registered function: {tool_name}")
-            return
-        
-        raise TypeError(f"Cannot register {type(tool)}, expected BaseTool or callable")
+            
+            raise TypeError(f"Cannot register {type(tool)}, expected BaseTool or callable")
     
     def unregister(self, name: str) -> bool:
         """Remove a tool from the registry.
         
+        Thread-safe: Uses lock for concurrent access.
+        
         Args:
             name: Tool name to remove
             
         Returns:
             True if tool was removed, False if not found
         """
-        if name in self._tools:
-            del self._tools[name]
-            return True
-        if name in self._functions:
-            del self._functions[name]
-            return True
-        return False
+        with self._lock:
+            if name in self._tools:
+                del self._tools[name]
+                return True
+            if name in self._functions:
+                del self._functions[name]
+                return True
+            return False
     
     def get(self, name: str) -> Optional[Union[BaseTool, Callable]]:
         """Get a tool by name.
         
+        Thread-safe: Uses lock for concurrent access.
+        
         Args:
             name: Tool name
             
         Returns:
             BaseTool instance, callable, or None if not found
         """
-        # Check BaseTool registry first
-        if name in self._tools:
-            return self._tools[name]
-        
-        # Check functions registry
-        if name in self._functions:
-            return self._functions[name]
-        
-        # Try auto-discovery if not found
-        if not self._discovered:
-            self.discover_plugins()
+        with self._lock:
+            # Check BaseTool registry first
             if name in self._tools:
                 return self._tools[name]
-        
-        return None
+            
+            # Check functions registry
+            if name in self._functions:
+                return self._functions[name]
+            
+            # Try auto-discovery if not found
+            if not self._discovered:
+                self.discover_plugins()
+                if name in self._tools:
+                    return self._tools[name]
+            
+            return None
     
     def list_tools(self) -> List[str]:
-        """List all registered tool names."""
-        return list(self._tools.keys()) + list(self._functions.keys())
+        """List all registered tool names. Thread-safe."""
+        with self._lock:
+            return list(self._tools.keys()) + list(self._functions.keys())
     
     def list_base_tools(self) -> List[BaseTool]:
-        """List all registered BaseTool instances."""
-        return list(self._tools.values())
+        """List all registered BaseTool instances. Thread-safe."""
+        with self._lock:
+            return list(self._tools.values())
     
     def get_all(self) -> Dict[str, Union[BaseTool, Callable]]:
-        """Get all registered tools as a dict."""
-        result = dict(self._tools)
-        result.update(self._functions)
-        return result
+        """Get all registered tools as a dict. Thread-safe."""
+        with self._lock:
+            result = dict(self._tools)
+            result.update(self._functions)
+            return result
     
     def discover_plugins(self) -> int:
         """Discover and register tools from entry_points.
@@ -185,16 +200,19 @@ class ToolRegistry:
         return count
     
     def clear(self) -> None:
-        """Clear all registered tools."""
-        self._tools.clear()
-        self._functions.clear()
-        self._discovered = False
+        """Clear all registered tools. Thread-safe."""
+        with self._lock:
+            self._tools.clear()
+            self._functions.clear()
+            self._discovered = False
     
     def __contains__(self, name: str) -> bool:
-        return name in self._tools or name in self._functions
+        with self._lock:
+            return name in self._tools or name in self._functions
     
     def __len__(self) -> int:
-        return len(self._tools) + len(self._functions)
+        with self._lock:
+            return len(self._tools) + len(self._functions)
     
     def __repr__(self) -> str:
         return f"ToolRegistry(tools={len(self._tools)}, functions={len(self._functions)})"

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

@@ -6,8 +6,35 @@ via various frontend protocols.
 
 Available integrations:
 - agui: AG-UI (Agent-User Interface) protocol for CopilotKit and compatible frontends
+- a2a: Agent-to-Agent protocol
+- a2ui: Agent-to-UI protocol
+
+This module uses lazy loading to minimize import time.
 """
 
-from praisonaiagents.ui.agui import AGUI
+# Module-level cache for lazy-loaded classes
+_lazy_cache = {}
 
-__all__ = ["AGUI"]
+
+def __getattr__(name):
+    """Lazy load UI classes to avoid importing heavy dependencies at module load time."""
+    if name in _lazy_cache:
+        return _lazy_cache[name]
+    
+    if name == "AGUI":
+        from praisonaiagents.ui.agui import AGUI
+        _lazy_cache[name] = AGUI
+        return AGUI
+    elif name == "A2A":
+        from praisonaiagents.ui.a2a import A2A
+        _lazy_cache[name] = A2A
+        return A2A
+    elif name == "A2UI":
+        from praisonaiagents.ui.a2ui import A2UI
+        _lazy_cache[name] = A2UI
+        return A2UI
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+__all__ = ["AGUI", "A2A", "A2UI"]

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

@@ -313,6 +313,56 @@ class Workflow:
             "planning_llm": self.planning_llm
         }
     
+    @classmethod
+    def from_template(
+        cls,
+        uri: str,
+        config: Optional[Dict[str, Any]] = None,
+        offline: bool = False,
+        **kwargs
+    ) -> 'Workflow':
+        """
+        Create a Workflow from a template.
+        
+        Args:
+            uri: Template URI (local path, package ref, or github ref)
+                Examples:
+                - "./my-template" (local path)
+                - "transcript-generator" (default recipes repo)
+                - "github:owner/repo/[email protected]" (GitHub with version)
+                - "package:agent_recipes/transcript-generator" (installed package)
+            config: Optional configuration overrides
+            offline: If True, only use cached templates (no network)
+            **kwargs: Additional Workflow constructor arguments
+            
+        Returns:
+            Configured Workflow instance
+            
+        Example:
+            ```python
+            from praisonaiagents import Workflow
+            
+            # From default recipes repo
+            workflow = Workflow.from_template("transcript-generator")
+            result = workflow.run("./audio.mp3")
+            
+            # With config overrides
+            workflow = Workflow.from_template(
+                "data-transformer",
+                config={"output_format": "json"}
+            )
+            ```
+        """
+        try:
+            # Lazy import to avoid circular dependencies and keep core SDK lean
+            from praisonai.templates.loader import create_workflow_from_template
+            return create_workflow_from_template(uri, config=config, offline=offline, **kwargs)
+        except ImportError:
+            raise ImportError(
+                "Template support requires the 'praisonai' package. "
+                "Install with: pip install praisonai"
+            )
+    
     def run(
         self,
         input: str = "",

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.3.2"
+version = "0.5.1"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/tests/integration/test_real_api.py

@@ -0,0 +1,243 @@
+"""
+Real API key integration tests for praisonaiagents.
+
+These tests are gated by the RUN_REAL_KEY_TESTS environment variable.
+They require valid API keys to be set in the environment.
+
+Usage:
+    RUN_REAL_KEY_TESTS=1 pytest tests/integration/test_real_api.py -v
+
+Required environment variables:
+    - OPENAI_API_KEY: OpenAI API key
+    
+Optional:
+    - ANTHROPIC_API_KEY: Anthropic API key (for Claude tests)
+    - GOOGLE_API_KEY: Google API key (for Gemini tests)
+"""
+import os
+import sys
+import pytest
+
+# Gate all tests in this module
+pytestmark = pytest.mark.skipif(
+    os.environ.get('RUN_REAL_KEY_TESTS', '').lower() not in ('1', 'true', 'yes'),
+    reason="Real API key tests disabled. Set RUN_REAL_KEY_TESTS=1 to enable."
+)
+
+
+def clear_modules():
+    """Clear all praisonai and litellm related modules from cache."""
+    to_remove = [m for m in list(sys.modules.keys()) 
+                 if 'praison' in m or 'litellm' in m]
+    for mod in to_remove:
+        del sys.modules[mod]
+
+
+class TestAgentRealAPI:
+    """Test Agent with real API calls."""
+    
+    @pytest.fixture(autouse=True)
+    def setup(self):
+        """Set up test fixtures."""
+        clear_modules()
+        
+        # Verify API key is set (without printing it)
+        api_key = os.environ.get('OPENAI_API_KEY')
+        if not api_key:
+            pytest.skip("OPENAI_API_KEY not set")
+        
+        yield
+        clear_modules()
+    
+    def test_agent_simple_chat(self):
+        """Test basic Agent.chat() with real API."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="TestAgent",
+            instructions="You are a helpful assistant. Keep responses very brief.",
+            llm="gpt-4o-mini",
+            verbose=False
+        )
+        
+        response = agent.chat("Say 'hello' and nothing else.")
+        
+        assert response is not None
+        assert len(response) > 0
+        assert 'hello' in response.lower()
+        
+        print(f"[PASS] Agent response received (length: {len(response)})")
+    
+    def test_agent_with_tool(self):
+        """Test Agent with a simple tool."""
+        from praisonaiagents import Agent
+        
+        # Define a simple deterministic tool
+        def add_numbers(a: int, b: int) -> int:
+            """Add two numbers together."""
+            return a + b
+        
+        agent = Agent(
+            name="MathAgent",
+            instructions="You are a math assistant. Use the add_numbers tool when asked to add.",
+            llm="gpt-4o-mini",
+            tools=[add_numbers],
+            verbose=False
+        )
+        
+        response = agent.chat("What is 5 + 3? Use the add_numbers tool.")
+        
+        assert response is not None
+        assert '8' in response
+        
+        print(f"[PASS] Tool call successful, response contains '8'")
+    
+    def test_agent_chat_history(self):
+        """Test that chat history is maintained."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="HistoryAgent",
+            instructions="You are a helpful assistant. Remember what the user tells you.",
+            llm="gpt-4o-mini",
+            verbose=False
+        )
+        
+        # First message
+        agent.chat("My favorite color is blue.")
+        
+        # Second message referencing first
+        response = agent.chat("What is my favorite color?")
+        
+        assert response is not None
+        assert 'blue' in response.lower()
+        
+        print(f"[PASS] Chat history maintained correctly")
+
+
+class TestLiteAgentRealAPI:
+    """Test LiteAgent with real API using OpenAI SDK directly."""
+    
+    @pytest.fixture(autouse=True)
+    def setup(self):
+        """Set up test fixtures."""
+        clear_modules()
+        
+        api_key = os.environ.get('OPENAI_API_KEY')
+        if not api_key:
+            pytest.skip("OPENAI_API_KEY not set")
+        
+        yield
+        clear_modules()
+    
+    def test_lite_agent_with_openai(self):
+        """Test LiteAgent with OpenAI SDK."""
+        from praisonaiagents.lite import LiteAgent, create_openai_llm_fn
+        
+        # Create LLM function using OpenAI
+        llm_fn = create_openai_llm_fn(model="gpt-4o-mini")
+        
+        agent = LiteAgent(
+            name="LiteTestAgent",
+            llm_fn=llm_fn,
+            instructions="You are a helpful assistant. Keep responses very brief."
+        )
+        
+        response = agent.chat("Say 'hello' and nothing else.")
+        
+        assert response is not None
+        assert len(response) > 0
+        
+        print(f"[PASS] LiteAgent with OpenAI response received")
+    
+    def test_lite_agent_no_litellm_loaded(self):
+        """Verify LiteAgent doesn't load litellm."""
+        from praisonaiagents.lite import LiteAgent, create_openai_llm_fn
+        
+        llm_fn = create_openai_llm_fn(model="gpt-4o-mini")
+        agent = LiteAgent(llm_fn=llm_fn)
+        
+        response = agent.chat("Hi")
+        
+        assert response is not None
+        assert 'litellm' not in sys.modules, \
+            "LiteAgent should not load litellm"
+        
+        print(f"[PASS] LiteAgent works without litellm")
+
+
+class TestAnthropicAPI:
+    """Test with Anthropic API (optional)."""
+    
+    @pytest.fixture(autouse=True)
+    def setup(self):
+        """Set up test fixtures."""
+        clear_modules()
+        
+        api_key = os.environ.get('ANTHROPIC_API_KEY')
+        if not api_key:
+            pytest.skip("ANTHROPIC_API_KEY not set")
+        
+        yield
+        clear_modules()
+    
+    def test_agent_with_claude(self):
+        """Test Agent with Claude model."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="ClaudeAgent",
+            instructions="You are a helpful assistant. Keep responses very brief.",
+            llm="claude-3-5-sonnet-20241022",
+            verbose=False
+        )
+        
+        response = agent.chat("Say 'hello' and nothing else.")
+        
+        assert response is not None
+        assert len(response) > 0
+        
+        print(f"[PASS] Claude response received")
+
+
+class TestGoogleAPI:
+    """Test with Google API (optional)."""
+    
+    @pytest.fixture(autouse=True)
+    def setup(self):
+        """Set up test fixtures."""
+        clear_modules()
+        
+        api_key = os.environ.get('GOOGLE_API_KEY')
+        if not api_key:
+            pytest.skip("GOOGLE_API_KEY not set")
+        
+        yield
+        clear_modules()
+    
+    def test_agent_with_gemini(self):
+        """Test Agent with Gemini model."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="GeminiAgent",
+            instructions="You are a helpful assistant. Keep responses very brief.",
+            llm="gemini/gemini-2.0-flash",
+            verbose=False
+        )
+        
+        response = agent.chat("Say 'hello' and nothing else.")
+        
+        assert response is not None
+        assert len(response) > 0
+        
+        print(f"[PASS] Gemini response received")
+
+
+if __name__ == "__main__":
+    # Run with real API keys
+    if os.environ.get('RUN_REAL_KEY_TESTS', '').lower() not in ('1', 'true', 'yes'):
+        print("Set RUN_REAL_KEY_TESTS=1 to run these tests")
+        sys.exit(1)
+    
+    pytest.main([__file__, "-v", "-s"])

src/praisonai-agents/tests/test_http_stream_basic.py

@@ -5,11 +5,16 @@ Basic test to verify HTTP Stream implementation is working correctly.
 
 import sys
 import os
+import pytest
 
 # Add parent directory to path for imports
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
-from praisonaiagents.mcp import MCP
+# Skip entire module if mcp package is not installed
+try:
+    from praisonaiagents.mcp import MCP
+except ImportError:
+    pytest.skip("MCP package not installed", allow_module_level=True)
 
 def test_transport_selection():
     """Test that URLs are correctly routed to appropriate transports."""

src/praisonai-agents/tests/test_multiple_mcp_tools.py

@@ -18,8 +18,12 @@ from unittest.mock import Mock
 # Add the package to path for testing
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
 
-from praisonaiagents.mcp.mcp import MCP
-from praisonaiagents.agent.agent import Agent
+# Skip entire module if mcp package is not installed
+try:
+    from praisonaiagents.mcp.mcp import MCP
+    from praisonaiagents.agent.agent import Agent
+except ImportError:
+    pytest.skip("MCP package not installed", allow_module_level=True)
 
 
 # ============================================================================

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/tests/unit/test_lazy_imports.py

@@ -0,0 +1,217 @@
+"""
+Tests for lazy import behavior in praisonaiagents.
+
+These tests verify that heavy dependencies like litellm are NOT loaded
+at package import time, ensuring fast startup.
+"""
+import sys
+import pytest
+
+
+def clear_modules():
+    """Clear all praisonai and litellm related modules from cache."""
+    to_remove = [m for m in list(sys.modules.keys()) 
+                 if 'praison' in m or 'litellm' in m]
+    for mod in to_remove:
+        del sys.modules[mod]
+
+
+class TestLazyImports:
+    """Test that heavy dependencies are lazily loaded."""
+    
+    def setup_method(self):
+        """Clear module cache before each test."""
+        clear_modules()
+    
+    def teardown_method(self):
+        """Clean up after each test."""
+        clear_modules()
+    
+    def test_litellm_not_loaded_on_import(self):
+        """litellm should NOT be loaded when importing praisonaiagents."""
+        import praisonaiagents  # noqa: F401
+        
+        assert 'litellm' not in sys.modules, \
+            "litellm should not be loaded at import time"
+    
+    def test_requests_not_loaded_on_import(self):
+        """requests should NOT be loaded when importing praisonaiagents."""
+        import praisonaiagents  # noqa: F401
+        
+        assert 'requests' not in sys.modules, \
+            "requests should not be loaded at import time"
+    
+    def test_chromadb_not_loaded_on_import(self):
+        """chromadb should NOT be loaded when importing praisonaiagents."""
+        import praisonaiagents  # noqa: F401
+        
+        assert 'chromadb' not in sys.modules, \
+            "chromadb should not be loaded at import time"
+    
+    def test_agent_available_via_getattr(self):
+        """Agent should be available via lazy loading."""
+        import praisonaiagents
+        
+        # Access Agent - this triggers lazy loading
+        Agent = praisonaiagents.Agent
+        
+        assert Agent is not None
+        assert hasattr(Agent, 'chat')
+    
+    def test_session_available_via_getattr(self):
+        """Session should be available via lazy loading."""
+        import praisonaiagents
+        
+        # Access Session - this triggers lazy loading
+        Session = praisonaiagents.Session
+        
+        assert Session is not None
+    
+    def test_knowledge_available_via_getattr(self):
+        """Knowledge should be available via lazy loading."""
+        import praisonaiagents
+        
+        # Access Knowledge - this triggers lazy loading
+        Knowledge = praisonaiagents.Knowledge
+        
+        assert Knowledge is not None
+    
+    def test_memory_available_via_getattr(self):
+        """Memory should be available via lazy loading."""
+        import praisonaiagents
+        
+        # Access Memory - this triggers lazy loading
+        Memory = praisonaiagents.Memory
+        
+        assert Memory is not None
+    
+    def test_tools_loaded_eagerly(self):
+        """Tools should be available immediately (lightweight)."""
+        import praisonaiagents
+        
+        # Tools is lightweight and should be available
+        assert hasattr(praisonaiagents, 'Tools')
+        assert praisonaiagents.Tools is not None
+    
+    def test_all_exports_accessible(self):
+        """All items in __all__ should be accessible."""
+        import praisonaiagents
+        
+        for name in praisonaiagents.__all__:
+            # Some items may be None if optional deps not installed
+            try:
+                attr = getattr(praisonaiagents, name)
+                # Just verify it's accessible, may be None
+            except AttributeError:
+                pytest.fail(f"'{name}' in __all__ but not accessible")
+
+
+class TestLitePackage:
+    """Test the lite subpackage."""
+    
+    def setup_method(self):
+        """Clear module cache before each test."""
+        clear_modules()
+    
+    def test_lite_import_fast(self):
+        """Lite package should import quickly without heavy deps."""
+        import time
+        
+        start = time.perf_counter()
+        from praisonaiagents.lite import LiteAgent  # noqa: F401
+        end = time.perf_counter()
+        
+        import_time_ms = (end - start) * 1000
+        
+        # Lite should import in under 200ms (allowing for system variance)
+        assert import_time_ms < 200, \
+            f"Lite import took {import_time_ms:.0f}ms, expected < 200ms"
+    
+    def test_lite_agent_no_litellm(self):
+        """LiteAgent should not require litellm."""
+        from praisonaiagents.lite import LiteAgent
+        
+        # Create agent without LLM function
+        agent = LiteAgent(name="Test")
+        
+        assert agent.name == "Test"
+        assert 'litellm' not in sys.modules
+    
+    def test_lite_agent_with_custom_llm(self):
+        """LiteAgent should work with custom LLM function."""
+        from praisonaiagents.lite import LiteAgent
+        
+        # Create a mock LLM function
+        def mock_llm(messages):
+            return "Hello from mock LLM!"
+        
+        agent = LiteAgent(
+            name="TestAgent",
+            llm_fn=mock_llm,
+            instructions="You are a test agent."
+        )
+        
+        response = agent.chat("Hi")
+        
+        assert response == "Hello from mock LLM!"
+        assert len(agent.chat_history) == 2  # user + assistant
+
+
+class TestTelemetryConfig:
+    """Test telemetry configuration."""
+    
+    def setup_method(self):
+        """Clear module cache and env vars before each test."""
+        clear_modules()
+        import os
+        # Save original env vars
+        self._orig_env = {}
+        for key in ['PRAISONAI_TELEMETRY_ENABLED', 'PRAISONAI_TELEMETRY_DISABLED', 
+                    'DO_NOT_TRACK', 'PRAISONAI_DISABLE_TELEMETRY']:
+            self._orig_env[key] = os.environ.get(key)
+            if key in os.environ:
+                del os.environ[key]
+    
+    def teardown_method(self):
+        """Restore env vars after each test."""
+        import os
+        for key, value in self._orig_env.items():
+            if value is None:
+                if key in os.environ:
+                    del os.environ[key]
+            else:
+                os.environ[key] = value
+        clear_modules()
+    
+    def test_telemetry_disabled_by_default(self):
+        """Telemetry should be disabled by default (opt-in model)."""
+        from praisonaiagents._config import TELEMETRY_ENABLED
+        
+        assert TELEMETRY_ENABLED is False, \
+            "Telemetry should be disabled by default"
+    
+    def test_telemetry_enabled_via_env(self):
+        """Telemetry can be enabled via environment variable."""
+        import os
+        os.environ['PRAISONAI_TELEMETRY_ENABLED'] = 'true'
+        
+        # Re-import to pick up new env var
+        clear_modules()
+        from praisonaiagents._config import _get_telemetry_enabled
+        
+        assert _get_telemetry_enabled() is True
+    
+    def test_do_not_track_takes_precedence(self):
+        """DO_NOT_TRACK should disable telemetry even if enabled."""
+        import os
+        os.environ['PRAISONAI_TELEMETRY_ENABLED'] = 'true'
+        os.environ['DO_NOT_TRACK'] = 'true'
+        
+        clear_modules()
+        from praisonaiagents._config import _get_telemetry_enabled
+        
+        assert _get_telemetry_enabled() is False
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

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

@@ -0,0 +1,180 @@
+"""
+Tests for thread safety in praisonaiagents.
+
+These tests verify that Agent chat_history and caches are thread-safe
+when accessed concurrently.
+"""
+import sys
+import threading
+import time
+import pytest
+
+
+def clear_modules():
+    """Clear all praisonai and litellm related modules from cache."""
+    to_remove = [m for m in list(sys.modules.keys()) 
+                 if 'praison' in m or 'litellm' in m]
+    for mod in to_remove:
+        del sys.modules[mod]
+
+
+class TestLiteAgentThreadSafety:
+    """Test thread safety of LiteAgent (no external deps)."""
+    
+    def test_concurrent_chat_history_access(self):
+        """Multiple threads should safely access chat_history."""
+        from praisonaiagents.lite import LiteAgent
+        
+        # Create agent with mock LLM
+        call_count = [0]
+        lock = threading.Lock()
+        
+        def mock_llm(messages):
+            with lock:
+                call_count[0] += 1
+            time.sleep(0.01)  # Simulate some work
+            return f"Response {call_count[0]}"
+        
+        agent = LiteAgent(llm_fn=mock_llm)
+        
+        errors = []
+        
+        def worker(thread_id):
+            try:
+                for i in range(5):
+                    response = agent.chat(f"Message from thread {thread_id}, iteration {i}")
+                    assert response is not None
+            except Exception as e:
+                errors.append(f"Thread {thread_id}: {e}")
+        
+        # Start multiple threads
+        threads = []
+        for i in range(4):
+            t = threading.Thread(target=worker, args=(i,))
+            threads.append(t)
+            t.start()
+        
+        # Wait for all threads
+        for t in threads:
+            t.join()
+        
+        # Check for errors
+        assert len(errors) == 0, f"Thread errors: {errors}"
+        
+        # Verify history was updated (should have entries from all threads)
+        assert len(agent.chat_history) > 0
+    
+    def test_concurrent_clear_history(self):
+        """Clearing history while other threads are chatting should be safe."""
+        from praisonaiagents.lite import LiteAgent
+        
+        def mock_llm(messages):
+            return "Response"
+        
+        agent = LiteAgent(llm_fn=mock_llm)
+        
+        errors = []
+        stop_flag = threading.Event()
+        
+        def chat_worker():
+            try:
+                while not stop_flag.is_set():
+                    agent.chat("Hello")
+                    time.sleep(0.001)
+            except Exception as e:
+                errors.append(f"Chat error: {e}")
+        
+        def clear_worker():
+            try:
+                for _ in range(10):
+                    time.sleep(0.01)
+                    agent.clear_history()
+            except Exception as e:
+                errors.append(f"Clear error: {e}")
+        
+        # Start chat threads
+        chat_threads = []
+        for _ in range(2):
+            t = threading.Thread(target=chat_worker)
+            chat_threads.append(t)
+            t.start()
+        
+        # Start clear thread
+        clear_thread = threading.Thread(target=clear_worker)
+        clear_thread.start()
+        
+        # Wait for clear thread to finish
+        clear_thread.join()
+        
+        # Stop chat threads
+        stop_flag.set()
+        for t in chat_threads:
+            t.join()
+        
+        assert len(errors) == 0, f"Thread errors: {errors}"
+
+
+class TestAgentThreadSafety:
+    """Test thread safety of main Agent class.
+    
+    Note: These tests use a mock LLM to avoid network calls.
+    """
+    
+    def setup_method(self):
+        """Set up test fixtures."""
+        clear_modules()
+    
+    def test_agent_has_history_lock(self):
+        """Agent should have a history lock for thread safety."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="Test",
+            instructions="Test agent",
+            llm="gpt-4o-mini"  # Won't actually call
+        )
+        
+        assert hasattr(agent, '_history_lock')
+        assert isinstance(agent._history_lock, type(threading.Lock()))
+    
+    def test_agent_has_cache_lock(self):
+        """Agent should have a cache lock for thread safety."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="Test",
+            instructions="Test agent",
+            llm="gpt-4o-mini"
+        )
+        
+        assert hasattr(agent, '_cache_lock')
+        # RLock is a different type than Lock
+        assert agent._cache_lock is not None
+
+
+class TestMCPCleanup:
+    """Test MCP cleanup and context manager support."""
+    
+    def test_mcp_has_context_manager(self):
+        """MCP should support context manager protocol."""
+        try:
+            from praisonaiagents.mcp.mcp import MCP
+        except ImportError:
+            pytest.skip("MCP package not installed")
+        
+        assert hasattr(MCP, '__enter__')
+        assert hasattr(MCP, '__exit__')
+        assert hasattr(MCP, 'shutdown')
+    
+    def test_mcp_shutdown_method(self):
+        """MCP should have a shutdown method."""
+        try:
+            from praisonaiagents.mcp.mcp import MCP
+        except ImportError:
+            pytest.skip("MCP package not installed")
+        
+        assert callable(getattr(MCP, 'shutdown', None))
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.3.2"
+version = "0.5.1"
 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/examples/features/evaluation.ts

@@ -0,0 +1,52 @@
+/**
+ * Evaluation Example - Testing agent accuracy and performance
+ * 
+ * Run: npx ts-node examples/features/evaluation.ts
+ */
+
+import { Agent, accuracyEval, performanceEval } from '../../src';
+
+async function main() {
+  // Create an agent to evaluate
+  const agent = new Agent({
+    instructions: "You are a math tutor. Answer math questions accurately.",
+    llm: "gpt-4o-mini",
+    verbose: false
+  });
+
+  console.log("=== Accuracy Evaluation ===");
+  
+  // Test accuracy with expected outputs
+  const testCases = [
+    { input: "What is 2 + 2?", expected: "4" },
+    { input: "What is 10 * 5?", expected: "50" },
+    { input: "What is 100 / 4?", expected: "25" }
+  ];
+
+  for (const test of testCases) {
+    const response = await agent.chat(test.input);
+    const passed = response.includes(test.expected);
+    console.log(`  ${passed ? '✅' : '❌'} "${test.input}" → Expected: ${test.expected}`);
+  }
+
+  console.log("\n=== Performance Evaluation ===");
+  
+  // Measure response time
+  const iterations = 3;
+  const times: number[] = [];
+  
+  for (let i = 0; i < iterations; i++) {
+    const start = Date.now();
+    await agent.chat("What is 5 + 5?");
+    const duration = Date.now() - start;
+    times.push(duration);
+    console.log(`  Run ${i + 1}: ${duration}ms`);
+  }
+
+  const avgTime = times.reduce((a, b) => a + b, 0) / times.length;
+  console.log(`\n  Average: ${avgTime.toFixed(0)}ms`);
+  console.log(`  Min: ${Math.min(...times)}ms`);
+  console.log(`  Max: ${Math.max(...times)}ms`);
+}
+
+main().catch(console.error);

src/praisonai-ts/examples/features/guardrails.ts

@@ -0,0 +1,49 @@
+/**
+ * Guardrails Example - Input/output validation for agents
+ * 
+ * Run: npx ts-node examples/features/guardrails.ts
+ */
+
+import { Agent, LLMGuardrail } from '../../src';
+
+async function main() {
+  // Create an LLM-based guardrail
+  const contentGuardrail = new LLMGuardrail({
+    name: "ContentSafety",
+    criteria: "Content must be professional, non-offensive, and factually accurate",
+    llm: "gpt-4o-mini",
+    threshold: 0.7,
+    verbose: true
+  });
+
+  // Create agent
+  const agent = new Agent({
+    instructions: "You are a helpful assistant that provides accurate information.",
+    verbose: true
+  });
+
+  // Example: Check content before responding
+  const userInput = "Tell me about artificial intelligence";
+  
+  // Validate input using check()
+  const inputCheck = await contentGuardrail.check(userInput);
+  console.log("Input validation:", inputCheck);
+
+  if (inputCheck.status === 'passed') {
+    const response = await agent.chat(userInput);
+    
+    // Validate output
+    const outputCheck = await contentGuardrail.check(response);
+    console.log("Output validation:", outputCheck);
+    
+    if (outputCheck.status === 'passed') {
+      console.log("\n✅ Response passed guardrails");
+    } else {
+      console.log("\n❌ Response failed guardrails:", outputCheck.reasoning);
+    }
+  } else {
+    console.log("❌ Input failed guardrails:", inputCheck.reasoning);
+  }
+}
+
+main().catch(console.error);

src/praisonai-ts/examples/features/knowledge.ts

@@ -0,0 +1,64 @@
+/**
+ * Knowledge Base Example - RAG with agents
+ * 
+ * Run: npx ts-node examples/features/knowledge.ts
+ */
+
+import { Agent, BaseKnowledgeBase, type Knowledge } from '../../src';
+
+async function main() {
+  // Create knowledge base
+  const kb = new BaseKnowledgeBase();
+
+  // Add knowledge entries
+  kb.addKnowledge({
+    id: "overview",
+    type: "documentation",
+    content: `
+      PraisonAI is a TypeScript SDK for building AI agents.
+      It supports multiple LLM providers including OpenAI, Anthropic, and Google.
+      Key features include:
+      - Simple Agent API with instructions
+      - Multi-agent orchestration with Agents class
+      - Workflow-based execution
+      - Tool integration with auto-schema generation
+      - Session persistence with db() factory
+    `,
+    metadata: { source: "docs", topic: "overview" }
+  });
+
+  kb.addKnowledge({
+    id: "agent-usage",
+    type: "documentation",
+    content: `
+      The Agent class is the primary entry point.
+      Create an agent with: new Agent({ instructions: "..." })
+      Use agent.chat("message") to interact.
+      Tools can be plain functions passed in the tools array.
+    `,
+    metadata: { source: "docs", topic: "agent" }
+  });
+
+  // Search the knowledge base
+  console.log("=== Searching Knowledge Base ===");
+  const results = kb.searchKnowledge("agent");
+  console.log("Found", results.length, "relevant entries");
+  results.forEach((r: Knowledge, i: number) => {
+    console.log(`\n[${i + 1}] ID: ${r.id}`);
+    console.log(String(r.content).substring(0, 100) + "...");
+  });
+
+  // Create agent that uses knowledge
+  const knowledgeContext = results.map((r: Knowledge) => r.content).join('\n\n');
+  const agent = new Agent({
+    instructions: `You are a helpful assistant. Use the following knowledge to answer questions:
+    
+${knowledgeContext}`,
+    verbose: true
+  });
+
+  console.log("\n=== Agent Response ===");
+  await agent.chat("How do I create an agent in PraisonAI?");
+}
+
+main().catch(console.error);

src/praisonai-ts/examples/features/memory.ts

@@ -0,0 +1,37 @@
+/**
+ * Memory Example - Using memory for agent context
+ * 
+ * Run: npx ts-node examples/features/memory.ts
+ */
+
+import { Agent, Memory } from '../../src';
+
+async function main() {
+  // Create a memory instance
+  const memory = new Memory({
+    maxEntries: 100,
+    maxTokens: 50000
+  });
+
+  // Add some context to memory
+  await memory.add("User prefers concise answers", "system", { priority: "high" });
+  await memory.add("User is working on a TypeScript project", "system", { topic: "programming" });
+
+  // Search memory
+  const results = await memory.search("TypeScript");
+  console.log("Memory search results:", results);
+
+  // Get recent entries
+  const recent = memory.getRecent(5);
+  console.log("Recent entries:", recent);
+
+  // Create agent
+  const agent = new Agent({
+    instructions: "You are a helpful assistant. Use context from memory.",
+    verbose: true
+  });
+
+  await agent.chat("Help me with my project");
+}
+
+main().catch(console.error);

src/praisonai-ts/examples/features/observability.ts

@@ -0,0 +1,31 @@
+/**
+ * Observability Example - Tracing and monitoring agents
+ * 
+ * Run: npx ts-node examples/features/observability.ts
+ */
+
+import { Agent, setObservabilityAdapter, ConsoleObservabilityAdapter } from '../../src';
+
+async function main() {
+  // Set up console observability (logs all traces)
+  const adapter = new ConsoleObservabilityAdapter();
+  setObservabilityAdapter(adapter);
+
+  console.log("=== Observability Enabled ===");
+  console.log("All agent operations will be traced to console.\n");
+
+  // Create agent
+  const agent = new Agent({
+    instructions: "You are a helpful assistant.",
+    verbose: true
+  });
+
+  // Run some operations - they will be traced
+  console.log("=== Running Agent ===");
+  await agent.chat("Hello! Tell me a joke.");
+
+  console.log("\n=== Trace Complete ===");
+  console.log("Check console output above for trace information.");
+}
+
+main().catch(console.error);

src/praisonai-ts/examples/features/planning.ts

@@ -0,0 +1,50 @@
+/**
+ * Planning Example - Using planning mode for complex tasks
+ * 
+ * Run: npx ts-node examples/features/planning.ts
+ */
+
+import { Agent, Plan, PlanStep, TodoList, TodoItem } from '../../src';
+
+async function main() {
+  // Create a plan for a complex task
+  console.log("=== Creating a Plan ===");
+  const plan = new Plan({
+    name: "Build a Web App",
+    description: "Plan to build a simple web application"
+  });
+
+  // Add steps to the plan
+  plan.addStep(new PlanStep({ description: "Initialize project structure" }));
+  plan.addStep(new PlanStep({ description: "Build API endpoints" }));
+  plan.addStep(new PlanStep({ description: "Create UI components" }));
+  plan.addStep(new PlanStep({ description: "Write and run tests" }));
+  plan.addStep(new PlanStep({ description: "Deploy to production" }));
+
+  console.log("Plan created with", plan.steps.length, "steps");
+
+  // Create a todo list
+  console.log("\n=== Creating a Todo List ===");
+  const todos = new TodoList();
+  
+  todos.add(new TodoItem({ content: "Research frameworks", priority: "high" }));
+  todos.add(new TodoItem({ content: "Design database schema", priority: "medium" }));
+  todos.add(new TodoItem({ content: "Write documentation", priority: "low" }));
+
+  console.log("Todo list has", todos.items.length, "items");
+
+  // Create agent with planning context
+  const agent = new Agent({
+    instructions: `You are a project planning assistant.
+    
+Current plan: ${plan.name}
+Steps: ${plan.steps.map((s: PlanStep) => s.description).join(', ')}
+
+Help the user execute this plan.`,
+    verbose: true
+  });
+
+  await agent.chat("What should I do first?");
+}
+
+main().catch(console.error);