Release v2.4.2

- Check for @pytest.mark.integration marker
- Check if test file is in integration/ directory
- This prevents conftest from overriding real API keys with 'test-key'

README.md

@@ -171,12 +171,13 @@ npm install praisonai
 
 | Feature | Code | Docs |
 |---------|:----:|:----:|
-| 🚀 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) |
+| **🤖 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) |
@@ -188,10 +189,7 @@ npm install praisonai
 | ↳ 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) |
-| 📚 Add Custom Knowledge | [Example](examples/python/concepts/knowledge-agents.py) | [📖](https://docs.praison.ai/features/knowledge) |
-| 🧠 Memory (Short & Long Term) | [Example](examples/python/general/memory_example.py) | [📖](https://docs.praison.ai/concepts/memory) |
-| 📄 Chat with PDF Agents | [Example](examples/python/concepts/chat-with-pdf.py) | [📖](https://docs.praison.ai/features/chat-with-pdf) |
-| **💻 Code** | | |
+| **💻 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) |
@@ -199,65 +197,84 @@ npm install praisonai
 | ↳ 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) |
-| 📚 RAG Agents | [Example](examples/python/concepts/rag-agents.py) | [📖](https://docs.praison.ai/features/rag) |
-| 🤔 Async & Parallel Processing | [Example](examples/python/general/async_example.py) | [📖](https://docs.praison.ai/features/async) |
-| 🔢 Math Agents | [Example](examples/python/agents/math-agent.py) | [📖](https://docs.praison.ai/features/mathagent) |
-| 🎯 Structured Output Agents | [Example](examples/python/general/structured_agents_example.py) | [📖](https://docs.praison.ai/features/structured) |
-| 🔗 LangChain Integrated Agents | [Example](examples/python/general/langchain_example.py) | [📖](https://docs.praison.ai/features/langchain) |
-| 📞 Callback Agents | [Example](examples/python/general/advanced-callback-systems.py) | [📖](https://docs.praison.ai/features/callbacks) |
-| 🛠️ 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) |
-| 🔬 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) |
-| 📥 Web Fetch (Anthropic) | [Example](#web-search-web-fetch--prompt-caching) | [📖](https://docs.praison.ai/features/model-capabilities) |
-| 💾 Prompt Caching | [Example](#web-search-web-fetch--prompt-caching) | [📖](https://docs.praison.ai/features/model-capabilities) |
-| 🧠 Claude Memory Tool | [Example](#claude-memory-tool-cli) | [📖](https://docs.praison.ai/features/claude-memory-tool) |
-| 💾 File-Based Memory | [Example](examples/python/general/memory_example.py) | [📖](https://docs.praison.ai/concepts/memory) |
-| 🔍 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) |
-| 📋 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) |
-| 🔌 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) |
-| ⚡ Fast Context | [Example](examples/context/00_agent_fast_context_basic.py) | [📖](https://docs.praison.ai/features/fast-context) |
-| 🖼️ 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) |
-| 📊 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) |
-| 📝 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) |
-| 🔀 Model Router | [Example](examples/python/agents/router-agent-cost-optimization.py) | [📖](https://docs.praison.ai/features/model-router) |
-| ⛓️ 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) |
-| ⚡ 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) |
-| 🛡️ Guardrails | [Example](examples/python/guardrails/comprehensive-guardrails-example.py) | [📖](https://docs.praison.ai/features/guardrails) |
-| 💬 Sessions Management | [Example](examples/python/sessions/comprehensive-session-management.py) | [📖](https://docs.praison.ai/features/sessions) |
-| ✅ Human Approval | [Example](examples/python/general/human_approval_example.py) | [📖](https://docs.praison.ai/features/approval) |
-| 🔄 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) |
-| 📜 Rules & Instructions | [Example](#6-rules--instructions) | [📖](https://docs.praison.ai/features/rules) |
-| 🪝 Hooks | [Example](#9-hooks) | [📖](https://docs.praison.ai/features/hooks) |
-| 📈 Telemetry | [Example](examples/python/telemetry/production-telemetry-example.py) | [📖](https://docs.praison.ai/features/telemetry) |
-| 📹 Camera Integration | [Example](examples/python/camera/) | [📖](https://docs.praison.ai/features/camera-integration) |
-| 📄 Project Docs (.praison/docs/) | [Example](#docs-cli) | [📖](https://docs.praison.ai/docs/cli/docs) |
-| 🔌 MCP Config Management | [Example](#mcp-config-cli) | [📖](https://docs.praison.ai/docs/cli/mcp) |
-| 💬 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) |
-| 💾 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) |
+| **🧠 Memory & Knowledge** | | |
+| ↳ Memory (Short & Long Term) | [Example](examples/python/general/memory_example.py) | [📖](https://docs.praison.ai/concepts/memory) |
+| ↳ File-Based Memory | [Example](examples/python/general/memory_example.py) | [📖](https://docs.praison.ai/concepts/memory) |
+| ↳ Claude Memory Tool | [Example](#claude-memory-tool-cli) | [📖](https://docs.praison.ai/features/claude-memory-tool) |
+| ↳ Add Custom Knowledge | [Example](examples/python/concepts/knowledge-agents.py) | [📖](https://docs.praison.ai/features/knowledge) |
+| ↳ RAG Agents | [Example](examples/python/concepts/rag-agents.py) | [📖](https://docs.praison.ai/features/rag) |
+| ↳ Chat with PDF Agents | [Example](examples/python/concepts/chat-with-pdf.py) | [📖](https://docs.praison.ai/features/chat-with-pdf) |
+| **🔬 Research & Intelligence** | | |
+| ↳ Deep Research Agents | [Example](examples/python/agents/research-agent.py) | [📖](https://docs.praison.ai/agents/deep-research) |
+| ↳ Query Rewriter Agent | [Example](#5-query-rewriter-agent) | [📖](https://docs.praison.ai/agents/query-rewriter) |
+| ↳ Native Web Search | [Example](examples/python/agents/websearch-agent.py) | [📖](https://docs.praison.ai/agents/websearch) |
+| ↳ Built-in Search Tools | [Example](examples/python/agents/websearch-agent.py) | [📖](https://docs.praison.ai/tools/tavily) |
+| ↳ Unified Web Search | [Example](src/praisonai-agents/examples/web_search_example.py) | [📖](https://docs.praison.ai/tools/web-search) |
+| ↳ Web Fetch (Anthropic) | [Example](#web-search-web-fetch--prompt-caching) | [📖](https://docs.praison.ai/features/model-capabilities) |
+| **📋 Planning & Execution** | | |
+| ↳ Planning Mode | [Example](examples/python/agents/planning-agent.py) | [📖](https://docs.praison.ai/features/planning-mode) |
+| ↳ Planning Tools | [Example](#3-agent-with-planning-mode) | [📖](https://docs.praison.ai/features/planning-mode) |
+| ↳ Planning Reasoning | [Example](#3-agent-with-planning-mode) | [📖](https://docs.praison.ai/features/planning-mode) |
+| ↳ Prompt Chaining | [Example](examples/python/general/prompt_chaining.py) | [📖](https://docs.praison.ai/features/promptchaining) |
+| ↳ Evaluator Optimiser | [Example](examples/python/general/evaluator-optimiser.py) | [📖](https://docs.praison.ai/features/evaluator-optimiser) |
+| ↳ Orchestrator Workers | [Example](examples/python/general/orchestrator-workers.py) | [📖](https://docs.praison.ai/features/orchestrator-worker) |
+| **👥 Specialized Agents** | | |
+| ↳ Data Analyst Agent | [Example](examples/python/agents/data-analyst-agent.py) | [📖](https://docs.praison.ai/agents/data-analyst) |
+| ↳ Finance Agent | [Example](examples/python/agents/finance-agent.py) | [📖](https://docs.praison.ai/agents/finance) |
+| ↳ Shopping Agent | [Example](examples/python/agents/shopping-agent.py) | [📖](https://docs.praison.ai/agents/shopping) |
+| ↳ Recommendation Agent | [Example](examples/python/agents/recommendation-agent.py) | [📖](https://docs.praison.ai/agents/recommendation) |
+| ↳ Wikipedia Agent | [Example](examples/python/agents/wikipedia-agent.py) | [📖](https://docs.praison.ai/agents/wikipedia) |
+| ↳ Programming Agent | [Example](examples/python/agents/programming-agent.py) | [📖](https://docs.praison.ai/agents/programming) |
+| ↳ Math Agents | [Example](examples/python/agents/math-agent.py) | [📖](https://docs.praison.ai/features/mathagent) |
+| ↳ Markdown Agent | [Example](examples/python/agents/markdown-agent.py) | [📖](https://docs.praison.ai/agents/markdown) |
+| ↳ Prompt Expander Agent | [Example](#prompt-expansion) | [📖](https://docs.praison.ai/agents/prompt-expander) |
+| **🎨 Media & Multimodal** | | |
+| ↳ Image Generation Agent | [Example](examples/python/image/image-agent.py) | [📖](https://docs.praison.ai/features/image-generation) |
+| ↳ Image to Text Agent | [Example](examples/python/agents/image-to-text-agent.py) | [📖](https://docs.praison.ai/agents/image-to-text) |
+| ↳ Video Agent | [Example](examples/python/agents/video-agent.py) | [📖](https://docs.praison.ai/agents/video) |
+| ↳ Camera Integration | [Example](examples/python/camera/) | [📖](https://docs.praison.ai/features/camera-integration) |
+| **🔌 Protocols & Integration** | | |
+| ↳ MCP Transports | [Example](examples/python/mcp/mcp-transports-overview.py) | [📖](https://docs.praison.ai/mcp/transports) |
+| ↳ WebSocket MCP | [Example](examples/python/mcp/websocket-mcp.py) | [📖](https://docs.praison.ai/mcp/sse-transport) |
+| ↳ MCP Security | [Example](examples/python/mcp/mcp-security.py) | [📖](https://docs.praison.ai/mcp/transports) |
+| ↳ MCP Resumability | [Example](examples/python/mcp/mcp-resumability.py) | [📖](https://docs.praison.ai/mcp/sse-transport) |
+| ↳ MCP Config Management | [Example](#mcp-config-cli) | [📖](https://docs.praison.ai/docs/cli/mcp) |
+| ↳ LangChain Integrated Agents | [Example](examples/python/general/langchain_example.py) | [📖](https://docs.praison.ai/features/langchain) |
+| **🛡️ Safety & Control** | | |
+| ↳ Guardrails | [Example](examples/python/guardrails/comprehensive-guardrails-example.py) | [📖](https://docs.praison.ai/features/guardrails) |
+| ↳ Human Approval | [Example](examples/python/general/human_approval_example.py) | [📖](https://docs.praison.ai/features/approval) |
+| ↳ Rules & Instructions | [Example](#6-rules--instructions) | [📖](https://docs.praison.ai/features/rules) |
+| **⚙️ Advanced Features** | | |
+| ↳ Async & Parallel Processing | [Example](examples/python/general/async_example.py) | [📖](https://docs.praison.ai/features/async) |
+| ↳ Parallelisation | [Example](examples/python/general/parallelisation.py) | [📖](https://docs.praison.ai/features/parallelisation) |
+| ↳ Repetitive Agents | [Example](examples/python/concepts/repetitive-agents.py) | [📖](https://docs.praison.ai/features/repetitive) |
+| ↳ Agent Handoffs | [Example](examples/python/handoff/handoff_basic.py) | [📖](https://docs.praison.ai/features/handoffs) |
+| ↳ Stateful Agents | [Example](examples/python/stateful/workflow-state-example.py) | [📖](https://docs.praison.ai/features/stateful-agents) |
+| ↳ Autonomous Workflow | [Example](examples/python/general/autonomous-agent.py) | [📖](https://docs.praison.ai/features/autonomous-workflow) |
+| ↳ Structured Output Agents | [Example](examples/python/general/structured_agents_example.py) | [📖](https://docs.praison.ai/features/structured) |
+| ↳ Model Router | [Example](examples/python/agents/router-agent-cost-optimization.py) | [📖](https://docs.praison.ai/features/model-router) |
+| ↳ Prompt Caching | [Example](#web-search-web-fetch--prompt-caching) | [📖](https://docs.praison.ai/features/model-capabilities) |
+| ↳ Fast Context | [Example](examples/context/00_agent_fast_context_basic.py) | [📖](https://docs.praison.ai/features/fast-context) |
+| **🛠️ Tools & Configuration** | | |
+| ↳ 100+ Custom Tools | [Example](examples/python/general/tools_example.py) | [📖](https://docs.praison.ai/tools/tools) |
+| ↳ YAML Configuration | [Example](examples/cookbooks/yaml/secondary_market_research_agents.yaml) | [📖](https://docs.praison.ai/developers/agents-playbook) |
+| ↳ 100+ LLM Support | [Example](examples/python/providers/openai/openai_gpt4_example.py) | [📖](https://docs.praison.ai/models) |
+| ↳ Callback Agents | [Example](examples/python/general/advanced-callback-systems.py) | [📖](https://docs.praison.ai/features/callbacks) |
+| ↳ Hooks | [Example](#9-hooks) | [📖](https://docs.praison.ai/features/hooks) |
+| ↳ 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) |
@@ -266,6 +283,23 @@ npm install praisonai
 | ↳ 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) |
 
 ---
 
@@ -835,21 +869,258 @@ callbacks:
 
 ### 9. Hooks
 
-Configure in `.praison/hooks.json`:
+Intercept and modify agent behavior at various lifecycle points:
 
 ```python
-from praisonaiagents.memory import HooksManager
+from praisonaiagents.hooks import (
+    HookRegistry, HookRunner, HookEvent, HookResult,
+    BeforeToolInput
+)
 
-hooks = HooksManager()
+# Create a hook registry
+registry = HookRegistry()
 
-# Register Python hooks
-hooks.register("pre_write_code", lambda ctx: print(f"Writing {ctx['file']}"))
+# Log all tool calls
+@registry.on(HookEvent.BEFORE_TOOL)
+def log_tools(event_data: BeforeToolInput) -> HookResult:
+    print(f"Tool: {event_data.tool_name}")
+    return HookResult.allow()
+
+# Block dangerous operations
+@registry.on(HookEvent.BEFORE_TOOL)
+def security_check(event_data: BeforeToolInput) -> HookResult:
+    if "delete" in event_data.tool_name.lower():
+        return HookResult.deny("Delete operations blocked")
+    return HookResult.allow()
 
 # Execute hooks
-result = hooks.execute("pre_write_code", {"file": "main.py"})
+runner = HookRunner(registry)
 ```
 
-### 10. Field Names Reference (A-I-G-S)
+**CLI Commands:**
+```bash
+praisonai hooks list                    # List registered hooks
+praisonai hooks test before_tool        # Test hooks for an event
+praisonai hooks run "echo test"         # Run a command hook
+praisonai hooks validate hooks.json     # Validate configuration
+```
+
+
+### 10. Shadow Git Checkpoints
+
+File-level undo/restore using shadow git:
+
+```python
+from praisonaiagents.checkpoints import CheckpointService
+
+service = CheckpointService(workspace_dir="./my_project")
+await service.initialize()
+
+# Save checkpoint before changes
+result = await service.save("Before refactoring")
+
+# Make changes...
+
+# Restore if needed
+await service.restore(result.checkpoint.id)
+
+# View diff
+diff = await service.diff()
+```
+
+**CLI Commands:**
+```bash
+praisonai checkpoint save "Before changes"  # Save checkpoint
+praisonai checkpoint list                   # List checkpoints
+praisonai checkpoint diff                   # Show changes
+praisonai checkpoint restore abc123         # Restore to checkpoint
+```
+
+**Links:**
+- [📖 Coding Docs](https://docs.praison.ai/features/checkpoints)
+- [📖 CLI Docs](https://docs.praison.ai/docs/cli/checkpoint)
+- [💻 Example](examples/checkpoints/basic_checkpoints.py)
+
+---
+
+### 11. Background Tasks
+
+Run agent tasks asynchronously without blocking:
+
+```python
+import asyncio
+from praisonaiagents.background import BackgroundRunner, BackgroundConfig
+
+async def main():
+    config = BackgroundConfig(max_concurrent_tasks=3)
+    runner = BackgroundRunner(config=config)
+    
+    async def my_task(name: str) -> str:
+        await asyncio.sleep(2)
+        return f"Task {name} completed"
+    
+    task = await runner.submit(my_task, args=("example",), name="my_task")
+    await task.wait(timeout=10.0)
+    print(task.result)
+
+asyncio.run(main())
+```
+
+**CLI Commands:**
+```bash
+praisonai background list          # List running tasks
+praisonai background status <id>   # Check task status
+praisonai background cancel <id>   # Cancel a task
+praisonai background clear         # Clear completed tasks
+```
+
+**Links:**
+- [📖 Coding Docs](https://docs.praison.ai/features/background-tasks)
+- [📖 CLI Docs](https://docs.praison.ai/docs/cli/background)
+- [💻 Example](examples/background/basic_background.py)
+
+---
+
+### 12. Policy Engine
+
+Control what agents can and cannot do with policy-based execution:
+
+```python
+from praisonaiagents.policy import (
+    PolicyEngine, Policy, PolicyRule, PolicyAction
+)
+
+engine = PolicyEngine()
+
+policy = Policy(
+    name="no_delete",
+    rules=[
+        PolicyRule(
+            action=PolicyAction.DENY,
+            resource="tool:delete_*",
+            reason="Delete operations blocked"
+        )
+    ]
+)
+engine.add_policy(policy)
+
+result = engine.check("tool:delete_file", {})
+print(f"Allowed: {result.allowed}")
+```
+
+**CLI Commands:**
+```bash
+praisonai policy list                  # List policies
+praisonai policy check "tool:name"     # Check if allowed
+praisonai policy init                  # Create template
+```
+
+**Links:**
+- [📖 Coding Docs](https://docs.praison.ai/features/policy-engine)
+- [📖 CLI Docs](https://docs.praison.ai/docs/cli/policy)
+- [💻 Example](examples/policy/basic_policy.py)
+
+---
+
+### 13. Thinking Budgets
+
+Configure token budgets for extended thinking:
+
+```python
+from praisonaiagents.thinking import ThinkingBudget, ThinkingTracker
+
+# Use predefined levels
+budget = ThinkingBudget.high()  # 16,000 tokens
+
+# Track usage
+tracker = ThinkingTracker()
+session = tracker.start_session(budget_tokens=16000)
+tracker.end_session(session, tokens_used=12000)
+
+summary = tracker.get_summary()
+print(f"Utilization: {summary['average_utilization']:.1%}")
+```
+
+**CLI Commands:**
+```bash
+praisonai thinking status      # Show current budget
+praisonai thinking set high    # Set budget level
+praisonai thinking stats       # Show usage statistics
+```
+
+**Links:**
+- [📖 Coding Docs](https://docs.praison.ai/features/thinking-budgets)
+- [📖 CLI Docs](https://docs.praison.ai/docs/cli/thinking)
+- [💻 Example](examples/thinking/basic_thinking.py)
+
+---
+
+### 14. Output Styles
+
+Configure how agents format their responses:
+
+```python
+from praisonaiagents.output import OutputStyle, OutputFormatter
+
+# Use preset styles
+style = OutputStyle.concise()
+formatter = OutputFormatter(style)
+
+# Format output
+text = "# Hello\n\nThis is **bold** text."
+plain = formatter.format(text)
+print(plain)
+```
+
+**CLI Commands:**
+```bash
+praisonai output status        # Show current style
+praisonai output set concise   # Set output style
+```
+
+**Links:**
+- [📖 Coding Docs](https://docs.praison.ai/features/output-styles)
+- [📖 CLI Docs](https://docs.praison.ai/docs/cli/output-style)
+- [💻 Example](examples/output/basic_output.py)
+
+---
+
+### 15. Context Compaction
+
+Automatically manage context window size:
+
+```python
+from praisonaiagents.compaction import (
+    ContextCompactor, CompactionStrategy
+)
+
+compactor = ContextCompactor(
+    max_tokens=4000,
+    strategy=CompactionStrategy.SLIDING,
+    preserve_recent=3
+)
+
+messages = [...]  # Your conversation history
+compacted, result = compactor.compact(messages)
+
+print(f"Compression: {result.compression_ratio:.1%}")
+```
+
+**CLI Commands:**
+```bash
+praisonai compaction status        # Show settings
+praisonai compaction set sliding   # Set strategy
+praisonai compaction stats         # Show statistics
+```
+
+**Links:**
+- [📖 Coding Docs](https://docs.praison.ai/features/context-compaction)
+- [📖 CLI Docs](https://docs.praison.ai/docs/cli/compaction)
+- [💻 Example](examples/compaction/basic_compaction.py)
+
+---
+
+### 16. 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:
 
@@ -883,7 +1154,7 @@ 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.
 
-### 11. Extended agents.yaml with Workflow Patterns
+### 12. 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)
@@ -959,6 +1230,124 @@ Run with the same simple command:
 praisonai agents.yaml
 ```
 
+### 12. MCP (Model Context Protocol)
+
+PraisonAI supports MCP Protocol Revision 2025-11-25 with multiple transports.
+
+#### MCP Client (Consume MCP Servers)
+```python
+from praisonaiagents import Agent, MCP
+
+# stdio - Local NPX/Python servers
+agent = Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))
+
+# Streamable HTTP - Production servers
+agent = Agent(tools=MCP("https://api.example.com/mcp"))
+
+# WebSocket - Real-time bidirectional
+agent = Agent(tools=MCP("wss://api.example.com/mcp", auth_token="token"))
+
+# SSE (Legacy) - Backward compatibility
+agent = Agent(tools=MCP("http://localhost:8080/sse"))
+
+# With environment variables
+agent = Agent(
+    tools=MCP(
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-brave-search"],
+        env={"BRAVE_API_KEY": "your-key"}
+    )
+)
+
+# Multiple MCP servers + regular functions
+def my_custom_tool(query: str) -> str:
+    """Custom tool function."""
+    return f"Result: {query}"
+
+agent = Agent(
+    name="MultiToolAgent",
+    instructions="Agent with multiple MCP servers",
+    tools=[
+        MCP("uvx mcp-server-time"),                    # Time tools
+        MCP("npx @modelcontextprotocol/server-memory"), # Memory tools
+        my_custom_tool                                  # Regular function
+    ]
+)
+```
+
+#### MCP Server (Expose Tools as MCP Server)
+
+Expose your Python functions as MCP tools for Claude Desktop, Cursor, and other MCP clients:
+
+```python
+from praisonaiagents.mcp import ToolsMCPServer
+
+def search_web(query: str, max_results: int = 5) -> dict:
+    """Search the web for information."""
+    return {"results": [f"Result for {query}"]}
+
+def calculate(expression: str) -> dict:
+    """Evaluate a mathematical expression."""
+    return {"result": eval(expression)}
+
+# Create and run MCP server
+server = ToolsMCPServer(name="my-tools")
+server.register_tools([search_web, calculate])
+server.run()  # stdio for Claude Desktop
+# server.run_sse(host="0.0.0.0", port=8080)  # SSE for web clients
+```
+
+#### MCP Features
+| Feature | Description |
+|---------|-------------|
+| Session Management | Automatic Mcp-Session-Id handling |
+| Protocol Versioning | Mcp-Protocol-Version header |
+| Resumability | SSE stream recovery via Last-Event-ID |
+| Security | Origin validation, DNS rebinding prevention |
+| WebSocket | Auto-reconnect with exponential backoff |
+
+### 13. 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.
+
+#### A2A Server (Expose Agent as A2A Server)
+```python
+from praisonaiagents import Agent, A2A
+from fastapi import FastAPI
+
+# Create an agent with tools
+def search_web(query: str) -> str:
+    """Search the web for information."""
+    return f"Results for: {query}"
+
+agent = Agent(
+    name="Research Assistant",
+    role="Research Analyst",
+    goal="Help users research topics",
+    tools=[search_web]
+)
+
+# Expose as A2A Server
+a2a = A2A(agent=agent, url="http://localhost:8000/a2a")
+
+app = FastAPI()
+app.include_router(a2a.get_router())
+
+# Run: uvicorn app:app --reload
+# Agent Card: GET /.well-known/agent.json
+# Status: GET /status
+```
+
+#### A2A Features
+| Feature | Description |
+|---------|-------------|
+| Agent Card | JSON metadata for agent discovery |
+| Skills Extraction | Auto-generate skills from tools |
+| Task Management | Stateful task lifecycle |
+| Streaming | SSE streaming for real-time updates |
+
+> **Documentation**: [docs.praison.ai/a2a](https://docs.praison.ai/a2a) | **Examples**: [examples/python/a2a](https://github.com/MervinPraison/PraisonAI/tree/main/examples/python/a2a)
+
 ---
 
 ## 🎯 CLI / No-Code Interface
@@ -1058,6 +1447,20 @@ praisonai "Complex analysis task" --planning --planning-reasoning
 praisonai "Task" --planning --auto-approve-plan
 ```
 
+### Tool Approval CLI:
+```bash
+# Auto-approve ALL tool executions (use with caution!)
+praisonai "run ls command" --trust
+
+# Auto-approve tools up to a risk level (prompt for higher)
+# Levels: low, medium, high, critical
+praisonai "write to file" --approve-level high  # Prompts for critical tools only
+praisonai "task" --approve-level medium         # Prompts for high and critical
+
+# Default behavior (no flags): prompts for all dangerous tools
+praisonai "run shell command"  # Will prompt for approval
+```
+
 ### Memory CLI:
 ```bash
 # Enable memory for agent (persists across sessions)
@@ -1228,6 +1631,21 @@ praisonai "Analyze this data" --metrics
 praisonai "Complex task" --metrics --planning
 ```
 
+### Scheduler CLI:
+
+```bash
+praisonai schedule start <name> "task" --interval hourly
+praisonai schedule list
+praisonai schedule logs <name> [--follow]
+praisonai schedule stop <name>
+praisonai schedule restart <name>
+praisonai schedule delete <name>
+praisonai schedule describe <name>
+praisonai schedule save <name> [file.yaml]
+praisonai schedule "task" --interval hourly  # foreground mode
+praisonai schedule agents.yaml  # foreground mode
+```
+
 ### Image Processing CLI:
 ```bash
 # Process images with vision-based tasks
@@ -1679,124 +2097,6 @@ agent = Agent(
 | Web Fetch | Anthropic |
 | Prompt Caching | OpenAI (auto), Anthropic, Bedrock, Deepseek |
 
-## MCP (Model Context Protocol)
-
-PraisonAI supports MCP Protocol Revision 2025-11-25 with multiple transports.
-
-### MCP Client (Consume MCP Servers)
-```python
-from praisonaiagents import Agent, MCP
-
-# stdio - Local NPX/Python servers
-agent = Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))
-
-# Streamable HTTP - Production servers
-agent = Agent(tools=MCP("https://api.example.com/mcp"))
-
-# WebSocket - Real-time bidirectional
-agent = Agent(tools=MCP("wss://api.example.com/mcp", auth_token="token"))
-
-# SSE (Legacy) - Backward compatibility
-agent = Agent(tools=MCP("http://localhost:8080/sse"))
-
-# With environment variables
-agent = Agent(
-    tools=MCP(
-        command="npx",
-        args=["-y", "@modelcontextprotocol/server-brave-search"],
-        env={"BRAVE_API_KEY": "your-key"}
-    )
-)
-
-# Multiple MCP servers + regular functions
-def my_custom_tool(query: str) -> str:
-    """Custom tool function."""
-    return f"Result: {query}"
-
-agent = Agent(
-    name="MultiToolAgent",
-    instructions="Agent with multiple MCP servers",
-    tools=[
-        MCP("uvx mcp-server-time"),                    # Time tools
-        MCP("npx @modelcontextprotocol/server-memory"), # Memory tools
-        my_custom_tool                                  # Regular function
-    ]
-)
-```
-
-### MCP Server (Expose Tools as MCP Server)
-
-Expose your Python functions as MCP tools for Claude Desktop, Cursor, and other MCP clients:
-
-```python
-from praisonaiagents.mcp import ToolsMCPServer
-
-def search_web(query: str, max_results: int = 5) -> dict:
-    """Search the web for information."""
-    return {"results": [f"Result for {query}"]}
-
-def calculate(expression: str) -> dict:
-    """Evaluate a mathematical expression."""
-    return {"result": eval(expression)}
-
-# Create and run MCP server
-server = ToolsMCPServer(name="my-tools")
-server.register_tools([search_web, calculate])
-server.run()  # stdio for Claude Desktop
-# server.run_sse(host="0.0.0.0", port=8080)  # SSE for web clients
-```
-
-### MCP Features
-| Feature | Description |
-|---------|-------------|
-| Session Management | Automatic Mcp-Session-Id handling |
-| Protocol Versioning | Mcp-Protocol-Version header |
-| Resumability | SSE stream recovery via Last-Event-ID |
-| Security | Origin validation, DNS rebinding prevention |
-| WebSocket | Auto-reconnect with exponential backoff |
-
-## 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.
-
-### A2A Server (Expose Agent as A2A Server)
-```python
-from praisonaiagents import Agent, A2A
-from fastapi import FastAPI
-
-# Create an agent with tools
-def search_web(query: str) -> str:
-    """Search the web for information."""
-    return f"Results for: {query}"
-
-agent = Agent(
-    name="Research Assistant",
-    role="Research Analyst",
-    goal="Help users research topics",
-    tools=[search_web]
-)
-
-# Expose as A2A Server
-a2a = A2A(agent=agent, url="http://localhost:8000/a2a")
-
-app = FastAPI()
-app.include_router(a2a.get_router())
-
-# Run: uvicorn app:app --reload
-# Agent Card: GET /.well-known/agent.json
-# Status: GET /status
-```
-
-### A2A Features
-| Feature | Description |
-|---------|-------------|
-| Agent Card | JSON metadata for agent discovery |
-| Skills Extraction | Auto-generate skills from tools |
-| Task Management | Stateful task lifecycle |
-| Streaming | SSE streaming for real-time updates |
-
-> **Documentation**: [docs.praison.ai/a2a](https://docs.praison.ai/a2a) | **Examples**: [examples/python/a2a](https://github.com/MervinPraison/PraisonAI/tree/main/examples/python/a2a)
-
 ## CLI Features
 
 | Feature | Docs |
@@ -2399,6 +2699,7 @@ PraisonAI provides zero-dependency persistent memory for agents. For detailed ex
 - 🤝 **Agent Handoffs** - Transfer context between specialised agents
 - 🛡️ **Guardrails** - Input/output validation and safety checks
 - ✅ **Human Approval** - Require human confirmation for critical actions
+- 🔐 **Tool Approval CLI** - `--trust` (auto-approve all) and `--approve-level` (risk-based approval)
 - 💬 **Sessions Management** - Isolated conversation contexts
 - 🔄 **Stateful Agents** - Maintain state across interactions
 
@@ -2423,6 +2724,103 @@ PraisonAI provides zero-dependency persistent memory for agents. For detailed ex
 
 ---
 
+## 💾 Persistence (Databases)
+
+Enable automatic conversation persistence with 2 lines of code:
+
+```python
+from praisonaiagents import Agent, db
+
+agent = Agent(
+    name="Assistant",
+    db=db(database_url="postgresql://localhost/mydb"),  # db(...) shortcut
+    session_id="my-session"  # Optional: defaults to per-hour ID (YYYYMMDDHH)
+)
+agent.chat("Hello!")  # Auto-persists messages, runs, traces
+```
+
+### Persistence CLI Commands
+
+| Command | Description |
+|---------|-------------|
+| `praisonai persistence doctor` | Validate DB connectivity |
+| `praisonai persistence run` | Run agent with persistence |
+| `praisonai persistence resume` | Resume existing session |
+| `praisonai persistence export` | Export session to JSONL |
+| `praisonai persistence import` | Import session from JSONL |
+| `praisonai persistence migrate` | Apply schema migrations |
+| `praisonai persistence status` | Show schema status |
+
+### Databases Table
+
+| Database | Store Type | Install | Example | Docs |
+|----------|------------|---------|---------|------|
+| PostgreSQL | Conversation | `pip install "praisonai[tools]"` | [simple_db_agent.py](examples/persistence/simple_db_agent.py) | [docs](https://docs.praison.ai/docs/databases/postgres) |
+| MySQL | Conversation | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| SQLite | Conversation | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| SingleStore | Conversation | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| Supabase | Conversation | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| SurrealDB | Conversation | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| Qdrant | Knowledge | `pip install "praisonai[tools]"` | [knowledge_qdrant.py](examples/persistence/knowledge_qdrant.py) | [docs](https://docs.praison.ai/docs/databases/qdrant) |
+| ChromaDB | Knowledge | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| Pinecone | Knowledge | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| Weaviate | Knowledge | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| LanceDB | Knowledge | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| Milvus | Knowledge | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| PGVector | Knowledge | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| Redis Vector | Knowledge | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| Cassandra | Knowledge | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| ClickHouse | Knowledge | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| Redis | State | `pip install "praisonai[tools]"` | [state_redis.py](examples/persistence/state_redis.py) | [docs](https://docs.praison.ai/docs/databases/redis) |
+| MongoDB | State | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| DynamoDB | State | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| Firestore | State | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| Upstash | State | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| Memory | State | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+
+---
+
+## 🔧 Tools Table
+
+Install all tools with: `pip install "praisonai[tools]"`
+
+| Tool | Category | Import | Docs |
+|------|----------|--------|------|
+| Tavily | Web Search | `from praisonai_tools import TavilyTool` | [docs](https://docs.praison.ai/docs/tools/external/tavily) |
+| DuckDuckGo | Web Search | `from praisonai_tools import DuckDuckGoTool` | [docs](https://docs.praison.ai/docs/tools/external/duckduckgo) |
+| Exa | Web Search | `from praisonai_tools import ExaTool` | [docs](https://docs.praison.ai/docs/tools/external/exa) |
+| Serper | Web Search | `from praisonai_tools import SerperTool` | [docs](https://docs.praison.ai/docs/tools/external/serper) |
+| Jina | Web Reader | `from praisonai_tools import JinaTool` | [docs](https://docs.praison.ai/docs/tools/external/jina) |
+| Firecrawl | Web Scraping | `from praisonai_tools import FirecrawlTool` | [docs](https://docs.praison.ai/docs/tools/external/firecrawl) |
+| Crawl4AI | Web Scraping | `from praisonai_tools import Crawl4AITool` | [docs](https://docs.praison.ai/docs/tools/external/crawl4ai) |
+| Wikipedia | Knowledge | `from praisonai_tools import WikipediaTool` | [docs](https://docs.praison.ai/docs/tools/external/wikipedia) |
+| ArXiv | Research | `from praisonai_tools import ArxivTool` | [docs](https://docs.praison.ai/docs/tools/external/arxiv) |
+| HackerNews | News | `from praisonai_tools import HackerNewsTool` | [docs](https://docs.praison.ai/docs/tools/external/hackernews) |
+| YouTube | Media | `from praisonai_tools import YouTubeTool` | [docs](https://docs.praison.ai/docs/tools/external/youtube) |
+| Weather | Data | `from praisonai_tools import WeatherTool` | [docs](https://docs.praison.ai/docs/tools/external/weather) |
+| PostgreSQL | Database | `from praisonai_tools import PostgresTool` | [docs](https://docs.praison.ai/docs/tools/external/postgres) |
+| MySQL | Database | `from praisonai_tools import MySQLTool` | [docs](https://docs.praison.ai/docs/tools/external/mysql) |
+| SQLite | Database | `from praisonai_tools import SQLiteTool` | [docs](https://docs.praison.ai/docs/tools/external/sqlite) |
+| MongoDB | Database | `from praisonai_tools import MongoDBTool` | [docs](https://docs.praison.ai/docs/tools/external/mongodb) |
+| Redis | Database | `from praisonai_tools import RedisTool` | [docs](https://docs.praison.ai/docs/tools/external/redis) |
+| Qdrant | Vector DB | `from praisonai_tools import QdrantTool` | [docs](https://docs.praison.ai/docs/tools/external/qdrant) |
+| GitHub | DevOps | `from praisonai_tools import GitHubTool` | [docs](https://docs.praison.ai/docs/tools/external/github) |
+| Slack | Communication | `from praisonai_tools import SlackTool` | [docs](https://docs.praison.ai/docs/tools/external/slack) |
+| Discord | Communication | `from praisonai_tools import DiscordTool` | [docs](https://docs.praison.ai/docs/tools/external/discord) |
+| Telegram | Communication | `from praisonai_tools import TelegramTool` | [docs](https://docs.praison.ai/docs/tools/external/telegram) |
+| Email | Communication | `from praisonai_tools import EmailTool` | [docs](https://docs.praison.ai/docs/tools/external/email) |
+| Notion | Productivity | `from praisonai_tools import NotionTool` | [docs](https://docs.praison.ai/docs/tools/external/notion) |
+| File | File System | `from praisonai_tools import FileTool` | [docs](https://docs.praison.ai/docs/tools/external/file) |
+| Shell | System | `from praisonai_tools import ShellTool` | [docs](https://docs.praison.ai/docs/tools/external/shell) |
+| Python | Code | `from praisonai_tools import PythonTool` | [docs](https://docs.praison.ai/docs/tools/external/python) |
+| JSON | Data | `from praisonai_tools import JSONTool` | [docs](https://docs.praison.ai/docs/tools/external/json) |
+| CSV | Data | `from praisonai_tools import CSVTool` | [docs](https://docs.praison.ai/docs/tools/external/csv) |
+| Calculator | Math | `from praisonai_tools import CalculatorTool` | [docs](https://docs.praison.ai/docs/tools/external/calculator) |
+
+> See [full tools documentation](https://docs.praison.ai/docs/tools/tools) for all 100+ available tools.
+
+---
+
 ## 🎓 Video Tutorials
 
 Learn PraisonAI through our comprehensive video series:

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>=2.3.52" \
+    "praisonai>=2.4.2" \
     "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>=2.3.52" \
+    "praisonai>=2.4.2" \
     "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>=2.3.52" \
+    "praisonai>=2.4.2" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/background/basic_background.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+"""
+Basic Background Tasks Example for PraisonAI Agents.
+
+This example demonstrates how to use the background task system to:
+1. Run tasks asynchronously
+2. Track task progress
+3. Cancel running tasks
+4. Handle task completion
+
+Usage:
+    python basic_background.py
+"""
+
+import asyncio
+import time
+
+from praisonaiagents.background import BackgroundRunner, BackgroundConfig, TaskStatus
+
+
+async def main():
+    print("=" * 60)
+    print("Background Tasks Demo")
+    print("=" * 60)
+    
+    # Create a background runner with config
+    config = BackgroundConfig(max_concurrent_tasks=3)
+    runner = BackgroundRunner(config=config)
+    
+    # Define some example tasks
+    def slow_task(duration: float, name: str) -> str:
+        """A slow task that simulates work."""
+        print(f"  🔄 Task '{name}' starting (will take {duration}s)")
+        time.sleep(duration)
+        return f"Task '{name}' completed after {duration}s"
+    
+    async def async_task(duration: float, name: str) -> str:
+        """An async task that simulates work."""
+        print(f"  🔄 Async task '{name}' starting (will take {duration}s)")
+        await asyncio.sleep(duration)
+        return f"Async task '{name}' completed after {duration}s"
+    
+    # Submit tasks
+    print("\n--- Submitting Tasks ---")
+    
+    task1 = await runner.submit(slow_task, args=(1.0, "quick-job"), name="task1")
+    print(f"✅ Submitted task 1: {task1.id[:8]}")
+    
+    task2 = await runner.submit(slow_task, args=(2.0, "medium-job"), name="task2")
+    print(f"✅ Submitted task 2: {task2.id[:8]}")
+    
+    task3 = await runner.submit(async_task, args=(1.5, "async-job"), name="task3")
+    print(f"✅ Submitted async task 3: {task3.id[:8]}")
+    
+    # List all tasks
+    print("\n--- Task Status ---")
+    for task in runner.tasks:
+        print(f"  [{task.id[:8]}] {task.status.value}")
+    
+    # Wait for task 1 to complete
+    print("\n--- Waiting for Task 1 ---")
+    await task1.wait(timeout=5.0)
+    print(f"✅ Task 1 result: {task1.result}")
+    
+    # Check status of all tasks
+    print("\n--- Updated Task Status ---")
+    for task in [task1, task2, task3]:
+        status = "✅" if task.status == TaskStatus.COMPLETED else "🔄"
+        print(f"  {status} [{task.id[:8]}] {task.status.value}")
+    
+    # Wait for all remaining tasks
+    print("\n--- Waiting for All Tasks ---")
+    await task2.wait(timeout=10.0)
+    await task3.wait(timeout=10.0)
+    
+    # Final status
+    print("\n--- Final Results ---")
+    for task in [task1, task2, task3]:
+        if task.result:
+            print(f"  ✅ {task.result}")
+    
+    # Cleanup
+    runner.clear_completed()
+    print("\n✅ Cleared completed tasks")
+    
+    print("\n" + "=" * 60)
+    print("Demo Complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/checkpoints/basic_checkpoints.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+"""
+Basic Checkpoints Example for PraisonAI Agents.
+
+This example demonstrates how to use the shadow git checkpointing system to:
+1. Save checkpoints before making changes
+2. List available checkpoints
+3. View diffs between checkpoints
+4. Restore to a previous checkpoint
+
+Usage:
+    python basic_checkpoints.py
+"""
+
+import asyncio
+import tempfile
+from pathlib import Path
+
+from praisonaiagents.checkpoints import CheckpointService
+
+
+async def main():
+    # Create a temporary workspace for demonstration
+    with tempfile.TemporaryDirectory() as workspace:
+        print("=" * 60)
+        print("Shadow Git Checkpointing Demo")
+        print("=" * 60)
+        print(f"\nWorkspace: {workspace}")
+        
+        # Create some initial files
+        test_file = Path(workspace) / "example.py"
+        test_file.write_text("# Initial content\nprint('Hello, World!')\n")
+        print(f"\n✅ Created initial file: {test_file.name}")
+        
+        # Initialize the checkpoint service
+        service = CheckpointService(workspace_dir=workspace)
+        
+        try:
+            # Initialize the shadow git repository
+            print("\n--- Initializing Checkpoint Service ---")
+            await service.initialize()
+            print("✅ Shadow git repository initialized")
+            
+            # Save first checkpoint
+            print("\n--- Saving Checkpoint 1: Initial State ---")
+            result1 = await service.save("Initial state")
+            print(f"✅ Checkpoint saved: {result1.checkpoint.id[:8]}")
+            print(f"   Message: {result1.checkpoint.message}")
+            
+            # Make some changes
+            print("\n--- Making Changes ---")
+            test_file.write_text("# Modified content\nprint('Hello, PraisonAI!')\nprint('Checkpoints are awesome!')\n")
+            print("✅ Modified example.py")
+            
+            # Create a new file
+            new_file = Path(workspace) / "utils.py"
+            new_file.write_text("def helper():\n    return 'I help!'\n")
+            print("✅ Created utils.py")
+            
+            # Save second checkpoint
+            print("\n--- Saving Checkpoint 2: After Changes ---")
+            result2 = await service.save("Added features")
+            print(f"✅ Checkpoint saved: {result2.checkpoint.id[:8]}")
+            
+            # List all checkpoints
+            print("\n--- Listing All Checkpoints ---")
+            checkpoints = await service.list_checkpoints()
+            for i, cp in enumerate(checkpoints, 1):
+                print(f"  {i}. [{cp.id[:8]}] {cp.message} ({cp.timestamp})")
+            
+            # Show diff
+            print("\n--- Current Diff (uncommitted changes) ---")
+            diff = await service.diff()
+            if diff.files:
+                for f in diff.files:
+                    print(f"  {f.status}: {f.path}")
+            else:
+                print("  No uncommitted changes")
+            
+            # Make more changes to show diff
+            test_file.write_text("# Even more changes\nprint('This will be reverted')\n")
+            print("\n✅ Made more changes to example.py")
+            
+            diff = await service.diff()
+            print("\n--- New Diff ---")
+            if diff.files:
+                for f in diff.files:
+                    print(f"  {f.status}: {f.path}")
+            
+            # Restore to checkpoint 2
+            print(f"\n--- Restoring to Checkpoint: {result2.checkpoint.id[:8]} ---")
+            await service.restore(result2.checkpoint.id)
+            print("✅ Restored successfully!")
+            
+            # Verify restoration
+            content = test_file.read_text()
+            print(f"\n--- Verified Content After Restore ---")
+            print(content)
+            
+        except Exception as e:
+            print(f"\n❌ Error: {e}")
+            raise
+        
+        print("\n" + "=" * 60)
+        print("Demo Complete!")
+        print("=" * 60)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/cli/persistence_commands.md

@@ -0,0 +1,93 @@
+# Persistence CLI Commands
+
+## Quick Reference
+
+```bash
+# Check database connectivity
+praisonai persistence doctor \
+  --conversation-url postgresql://localhost:5432/praisonai \
+  --state-url redis://localhost:6379
+
+# Run agent with persistence
+praisonai persistence run \
+  --conversation-url postgresql://localhost:5432/praisonai \
+  --session-id my-session
+
+# Resume a session
+praisonai persistence resume \
+  --conversation-url postgresql://localhost:5432/praisonai \
+  --session-id my-session
+
+# Export session to file
+praisonai persistence export \
+  --conversation-url postgresql://localhost:5432/praisonai \
+  --session-id my-session \
+  --output session_backup.jsonl
+
+# Import session from file
+praisonai persistence import \
+  --conversation-url postgresql://localhost:5432/praisonai \
+  --file session_backup.jsonl
+
+# Check schema status
+praisonai persistence status \
+  --conversation-url postgresql://localhost:5432/praisonai \
+  --state-url redis://localhost:6379
+
+# Apply migrations
+praisonai persistence migrate \
+  --conversation-url postgresql://localhost:5432/praisonai \
+  --state-url redis://localhost:6379
+```
+
+## Environment Variables
+
+Instead of CLI flags, you can use environment variables:
+
+```bash
+export PRAISON_CONVERSATION_URL=postgresql://localhost:5432/praisonai
+export PRAISON_STATE_URL=redis://localhost:6379
+export PRAISON_KNOWLEDGE_URL=http://localhost:6333
+
+# Then run without URL flags
+praisonai persistence doctor
+praisonai persistence status
+```
+
+## Docker Setup
+
+```bash
+# Start all services
+docker run -d --name praison-postgres \
+  -e POSTGRES_PASSWORD=praison123 \
+  -e POSTGRES_DB=praisonai \
+  -p 5432:5432 postgres:16
+
+docker run -d --name praison-redis \
+  -p 6379:6379 redis:7
+
+docker run -d --name praison-qdrant \
+  -p 6333:6333 -p 6334:6334 qdrant/qdrant
+
+# Verify
+docker ps
+```
+
+## Example Workflow
+
+```bash
+# 1. Start services
+docker-compose up -d
+
+# 2. Check connectivity
+praisonai persistence doctor
+
+# 3. Run agent
+praisonai persistence run --session-id demo-session
+
+# 4. Export for backup
+praisonai persistence export --session-id demo-session --output backup.jsonl
+
+# 5. Import to another environment
+praisonai persistence import --file backup.jsonl
+```

examples/cli/persistence_end_to_end.md

@@ -0,0 +1,124 @@
+# PraisonAI Persistence CLI - End-to-End Guide
+
+This guide demonstrates the CLI commands for database persistence.
+
+## Prerequisites
+
+```bash
+pip install "praisonai[tools]"
+
+# Start Docker containers
+docker run -d --name praison-postgres -p 5432:5432 \
+    -e POSTGRES_PASSWORD=praison123 \
+    -e POSTGRES_DB=praisonai \
+    postgres:16
+
+docker run -d --name praison-qdrant -p 6333:6333 qdrant/qdrant
+docker run -d --name praison-redis -p 6379:6379 redis:7
+```
+
+## 1. Doctor Command - Validate Connectivity
+
+```bash
+# Check all stores
+praisonai persistence doctor \
+    --conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
+    --knowledge-url "http://localhost:6333" \
+    --state-url "redis://localhost:6379"
+```
+
+**Expected Output:**
+```
+==================================================
+PraisonAI Persistence Doctor
+==================================================
+
+[Conversation Store] Testing: postgresql://postgres:****@localhost:5432/praisonai
+  ✅ Connected (postgres)
+
+[Knowledge Store] Testing: http://localhost:6333
+  ✅ Connected (qdrant)
+
+[State Store] Testing: redis://localhost:6379
+  ✅ Connected (redis)
+
+==================================================
+Results: 3/3 stores connected successfully
+==================================================
+```
+
+## 2. Run Command - Execute Agent with Persistence
+
+```bash
+# Run agent with persistence (dry-run first)
+praisonai persistence run \
+    --session-id "cli-demo-001" \
+    --conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
+    --dry-run \
+    "Hello, my name is Bob"
+
+# Actually run it
+praisonai persistence run \
+    --session-id "cli-demo-001" \
+    --conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
+    "Hello, my name is Bob"
+```
+
+**Expected Output:**
+```
+[Session: cli-demo-001]
+Agent: Hello Bob! Nice to meet you. How can I help you today?
+```
+
+## 3. Resume Command - Continue a Session
+
+```bash
+# Show history
+praisonai persistence resume \
+    --session-id "cli-demo-001" \
+    --conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
+    --show-history
+
+# Continue conversation
+praisonai persistence resume \
+    --session-id "cli-demo-001" \
+    --conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
+    --continue "What's my name?"
+```
+
+**Expected Output:**
+```
+[Session: cli-demo-001]
+Messages in history: 2
+
+--- Conversation History ---
+[USER] Hello, my name is Bob
+[ASSISTANT] Hello Bob! Nice to meet you...
+--- End History ---
+
+Agent: Your name is Bob!
+```
+
+## Environment Variables
+
+Instead of passing URLs on command line, use environment variables:
+
+```bash
+export PRAISON_CONVERSATION_URL="postgresql://postgres:praison123@localhost:5432/praisonai"
+export PRAISON_KNOWLEDGE_URL="http://localhost:6333"
+export PRAISON_STATE_URL="redis://localhost:6379"
+export OPENAI_API_KEY="your-key"
+
+# Now commands are simpler
+praisonai persistence doctor --all
+praisonai persistence run --session-id "my-session" "Hello!"
+```
+
+## Full CLI Reference
+
+```
+praisonai persistence --help
+praisonai persistence doctor --help
+praisonai persistence run --help
+praisonai persistence resume --help
+```

examples/compaction/basic_compaction.py

@@ -0,0 +1,146 @@
+#!/usr/bin/env python3
+"""
+Basic Context Compaction Example for PraisonAI Agents.
+
+This example demonstrates how to use context compaction to:
+1. Manage conversation context length
+2. Use different compaction strategies
+3. Track compaction results
+4. Preserve important messages
+
+Usage:
+    python basic_compaction.py
+"""
+
+from praisonaiagents.compaction import (
+    ContextCompactor, CompactionConfig, CompactionStrategy, CompactionResult
+)
+
+
+def main():
+    print("=" * 60)
+    print("Context Compaction Demo")
+    print("=" * 60)
+    
+    # ==========================================================================
+    # Sample Messages
+    # ==========================================================================
+    messages = [
+        {"role": "system", "content": "You are a helpful AI assistant."},
+        {"role": "user", "content": "Hello! Can you help me with Python?"},
+        {"role": "assistant", "content": "Of course! I'd be happy to help you with Python. What would you like to know?"},
+        {"role": "user", "content": "How do I read a file in Python?"},
+        {"role": "assistant", "content": "To read a file in Python, you can use the built-in open() function. Here's an example:\n\nwith open('filename.txt', 'r') as f:\n    content = f.read()\n\nThis opens the file, reads its content, and automatically closes it."},
+        {"role": "user", "content": "What about writing to a file?"},
+        {"role": "assistant", "content": "To write to a file, use the 'w' mode:\n\nwith open('filename.txt', 'w') as f:\n    f.write('Hello, World!')\n\nUse 'a' mode to append instead of overwriting."},
+        {"role": "user", "content": "Can you explain list comprehensions?"},
+        {"role": "assistant", "content": "List comprehensions are a concise way to create lists. Instead of:\n\nresult = []\nfor x in range(10):\n    result.append(x * 2)\n\nYou can write:\n\nresult = [x * 2 for x in range(10)]"},
+    ]
+    
+    print(f"\n--- Original Messages ---")
+    print(f"  Message count: {len(messages)}")
+    
+    # ==========================================================================
+    # Basic Compaction
+    # ==========================================================================
+    print("\n--- Basic Compaction (Truncate Strategy) ---")
+    
+    compactor = ContextCompactor(
+        max_tokens=50,  # Low limit to trigger compaction
+        strategy=CompactionStrategy.TRUNCATE,
+        preserve_system=True,
+        preserve_recent=2
+    )
+    
+    # Check stats before
+    stats = compactor.get_stats(messages)
+    print(f"  Total tokens: {stats['total_tokens']}")
+    print(f"  Max tokens: {stats['max_tokens']}")
+    print(f"  Needs compaction: {stats['needs_compaction']}")
+    
+    # Compact
+    compacted, result = compactor.compact(messages)
+    
+    print(f"\n  After compaction:")
+    print(f"    Messages: {len(messages)} -> {len(compacted)}")
+    print(f"    Tokens: {result.original_tokens} -> {result.compacted_tokens}")
+    print(f"    Saved: {result.tokens_saved} tokens")
+    print(f"    Compression: {result.compression_ratio:.1%}")
+    
+    # ==========================================================================
+    # Sliding Window Strategy
+    # ==========================================================================
+    print("\n--- Sliding Window Strategy ---")
+    
+    compactor = ContextCompactor(
+        max_tokens=50,
+        strategy=CompactionStrategy.SLIDING,
+        preserve_system=True,
+        preserve_recent=3
+    )
+    
+    compacted, result = compactor.compact(messages)
+    
+    print(f"  Messages kept: {result.messages_kept}")
+    print(f"  Messages removed: {result.messages_removed}")
+    print(f"  Strategy: {result.strategy_used.value}")
+    
+    # ==========================================================================
+    # Summarize Strategy
+    # ==========================================================================
+    print("\n--- Summarize Strategy ---")
+    
+    compactor = ContextCompactor(
+        max_tokens=50,
+        strategy=CompactionStrategy.SUMMARIZE,
+        preserve_system=True,
+        preserve_recent=2
+    )
+    
+    compacted, result = compactor.compact(messages)
+    
+    print(f"  Messages kept: {result.messages_kept}")
+    print(f"  Strategy: {result.strategy_used.value}")
+    
+    # Show what messages remain
+    print(f"\n  Remaining message roles:")
+    for msg in compacted:
+        role = msg.get("role", "unknown")
+        content_preview = msg.get("content", "")[:50]
+        print(f"    [{role}] {content_preview}...")
+    
+    # ==========================================================================
+    # No Compaction Needed
+    # ==========================================================================
+    print("\n--- No Compaction Needed ---")
+    
+    compactor = ContextCompactor(
+        max_tokens=10000,  # High limit
+        strategy=CompactionStrategy.TRUNCATE
+    )
+    
+    compacted, result = compactor.compact(messages)
+    
+    print(f"  Was compacted: {result.was_compacted}")
+    print(f"  Messages unchanged: {len(compacted) == len(messages)}")
+    
+    # ==========================================================================
+    # Result Serialization
+    # ==========================================================================
+    print("\n--- Result Serialization ---")
+    
+    compactor = ContextCompactor(max_tokens=50)
+    _, result = compactor.compact(messages)
+    
+    data = result.to_dict()
+    print(f"  Original tokens: {data['original_tokens']}")
+    print(f"  Compacted tokens: {data['compacted_tokens']}")
+    print(f"  Compression ratio: {data['compression_ratio']:.2f}")
+    
+    print("\n" + "=" * 60)
+    print("Demo Complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/compare/cli_compare_advanced.py

@@ -0,0 +1,85 @@
+"""
+CLI Compare Example - Advanced Usage
+
+This example demonstrates advanced comparison features including
+custom mode configurations and result analysis.
+"""
+
+from praisonai.cli.features.compare import (
+    CompareHandler,
+    CompareResult,
+    ModeResult,
+    get_mode_config,
+    save_compare_result,
+    format_comparison_table,
+)
+
+# Create custom mode configurations
+custom_modes = {
+    "fast": {"llm": "gpt-4o-mini"},
+    "accurate": {"llm": "gpt-4o", "planning": True},
+    "research": {"research": True, "web_search": True},
+}
+
+# Create handler with verbose output
+handler = CompareHandler(verbose=True)
+
+# Example: Manually create comparison results for analysis
+# (useful for testing without API calls)
+mock_result = CompareResult(
+    query="What are the latest AI trends?",
+    comparisons=[
+        ModeResult(
+            mode="basic",
+            output="AI trends include LLMs, multimodal models, and agents.",
+            execution_time_ms=1234.5,
+            model_used="gpt-4o-mini",
+            tools_used=[]
+        ),
+        ModeResult(
+            mode="tools",
+            output="Based on recent search results, AI trends in 2024 include...",
+            execution_time_ms=2567.8,
+            model_used="gpt-4o-mini",
+            tools_used=["internet_search"]
+        ),
+        ModeResult(
+            mode="research",
+            output="Comprehensive research shows that AI trends...",
+            execution_time_ms=5432.1,
+            model_used="gpt-4o-mini",
+            tools_used=["internet_search", "web_scraper"]
+        ),
+    ]
+)
+
+# Get summary statistics
+summary = mock_result.get_summary()
+print("Comparison Summary:")
+print(f"  Fastest mode: {summary['fastest']} ({summary['fastest_time_ms']:.1f}ms)")
+print(f"  Slowest mode: {summary['slowest']} ({summary['slowest_time_ms']:.1f}ms)")
+
+# Format as table
+print("\n" + format_comparison_table(mock_result))
+
+# Convert to JSON for storage
+print("\nJSON Output:")
+print(mock_result.to_json())
+
+# Save to file
+# save_compare_result(mock_result, "advanced_comparison.json")
+
+print("\n--- CLI Usage Examples ---")
+print("""
+# Basic comparison
+praisonai "What is AI?" --compare "basic,tools"
+
+# Compare with specific model
+praisonai "Explain quantum computing" --compare "basic,planning,research" --model gpt-4o
+
+# Save results to file
+praisonai "Latest tech news" --compare "basic,tools,web_search" --compare-output results.json
+
+# Verbose output
+praisonai "Write a poem" --compare "basic,planning" --verbose
+""")

examples/compare/cli_compare_basic.py

@@ -0,0 +1,45 @@
+"""
+CLI Compare Example - Basic Usage
+
+This example demonstrates how to compare different CLI modes programmatically.
+"""
+
+from praisonai.cli.features.compare import (
+    CompareHandler,
+    get_mode_config,
+    list_available_modes,
+    parse_modes,
+)
+
+# List all available comparison modes
+print("Available modes:")
+for mode in list_available_modes():
+    config = get_mode_config(mode)
+    print(f"  - {mode}: {config}")
+
+# Parse modes from comma-separated string
+modes = parse_modes("basic,tools,planning")
+print(f"\nParsed modes: {modes}")
+
+# Create compare handler
+handler = CompareHandler(verbose=True)
+
+# Compare modes (this will run actual agent calls)
+# Uncomment to run:
+# result = handler.compare(
+#     query="What is artificial intelligence?",
+#     modes=["basic", "tools"],
+#     model="gpt-4o-mini"
+# )
+# handler.print_result(result)
+
+# Save results to file
+# result = handler.execute(
+#     query="Explain machine learning",
+#     modes_str="basic,planning",
+#     output_path="comparison_results.json"
+# )
+
+print("\nTo run comparison via CLI:")
+print('  praisonai "What is AI?" --compare "basic,tools,planning"')
+print('  praisonai "What is AI?" --compare "basic,research" --compare-output results.json')

examples/cookbooks/yaml/data_collector_scheduled.yaml

@@ -0,0 +1,23 @@
+# Example: Data Collection Agent with 30-minute Schedule
+# Collects and analyzes data every 30 minutes
+
+framework: praisonai
+
+agents:
+  - name: "Data Collector"
+    role: "Data Analysis Specialist"
+    goal: "Collect and analyze market data at regular intervals"
+    instructions: |
+      You are a data analysis specialist focused on collecting and analyzing
+      market trends and metrics. Provide actionable insights from the data.
+    tools:
+      - search_tool
+    verbose: false
+
+task: "Collect latest market data and identify key trends"
+
+# Schedule: Every 30 minutes with immediate execution
+schedule:
+  interval: "*/30m"
+  max_retries: 5
+  run_immediately: true

examples/cookbooks/yaml/news_monitor_scheduled.yaml

@@ -0,0 +1,53 @@
+# Example: 24/7 AI News Monitor with Scheduler
+# This agent runs continuously at regular intervals to monitor AI news
+
+framework: praisonai
+
+agents:
+  - name: "AI News Monitor"
+    role: "Technology News Analyst"
+    goal: "Monitor and summarize the latest AI developments"
+    instructions: |
+      You are an expert technology news analyst specializing in AI and machine learning.
+      Your job is to search for the latest AI news, identify the most important developments,
+      and provide clear, concise summaries with sources.
+      
+      Focus on:
+      - Major AI product launches
+      - Research breakthroughs
+      - Industry trends
+      - Policy and regulation updates
+    tools:
+      - search_tool
+    verbose: true
+
+# Task to execute on each scheduled run
+task: |
+  Search for the latest AI news from the past 24 hours.
+  Provide the top 3 most important stories with:
+  1. Headline
+  2. Brief summary (2-3 sentences)
+  3. Source URL
+  4. Why it's significant
+
+# Schedule configuration
+schedule:
+  # Interval options:
+  # - "hourly" - Every hour
+  # - "daily" - Every 24 hours
+  # - "*/30m" - Every 30 minutes
+  # - "*/6h" - Every 6 hours
+  # - "3600" - Custom seconds
+  interval: "hourly"
+  
+  # Maximum retry attempts if execution fails
+  max_retries: 3
+  
+  # Run immediately when scheduler starts (before first scheduled interval)
+  run_immediately: true
+
+# Optional: Variables for customization
+variables:
+  news_category: "AI and Machine Learning"
+  region: "Global"
+  time_range: "24 hours"

examples/eval/accuracy_example.py

@@ -0,0 +1,69 @@
+"""
+Accuracy Evaluation Example
+
+This example demonstrates how to evaluate agent output accuracy
+by comparing against expected outputs using LLM-as-judge.
+"""
+
+import os
+from praisonaiagents import Agent
+from praisonaiagents.eval import AccuracyEvaluator
+
+# Check if we have an API key
+has_api_key = os.getenv("OPENAI_API_KEY") is not None
+
+if has_api_key:
+    # Create a simple math tutor agent
+    agent = Agent(
+        instructions="You are a math tutor. Answer math questions concisely with just the number."
+    )
+
+    # Create accuracy evaluator
+    evaluator = AccuracyEvaluator(
+        agent=agent,
+        input_text="What is 2 + 2?",
+        expected_output="4",
+        num_iterations=3,  # Run 3 times for statistical significance
+        verbose=True
+    )
+
+    # Run evaluation
+    result = evaluator.run(print_summary=True)
+
+    # Check results
+    print(f"\nAverage Score: {result.avg_score}/10")
+    print(f"Passed: {result.passed}")
+else:
+    print("⚠️  No OPENAI_API_KEY found. Using mock evaluation...")
+    
+    # Create evaluator without agent for mock testing
+    evaluator = AccuracyEvaluator(
+        func=lambda x: "4",  # Mock function
+        input_text="What is 2 + 2?",
+        expected_output="4",
+        num_iterations=1,
+        verbose=True
+    )
+    
+    # Test with mock output
+    try:
+        result = evaluator.run(print_summary=True)
+        print(f"\nMock Average Score: {result.avg_score}/10")
+        print(f"Mock Passed: {result.passed}")
+    except Exception as e:
+        print(f"Mock evaluation failed: {e}")
+        print("This is expected without an API key")
+
+# You can also evaluate pre-generated outputs
+print("\n--- Testing pre-generated output evaluation ---")
+output = "The answer is 4"
+
+if has_api_key:
+    try:
+        result2 = evaluator.evaluate_output(output, print_summary=True)
+        print(f"Pre-generated output score: {result2.avg_score}/10")
+    except Exception as e:
+        print(f"Pre-generated evaluation failed: {e}")
+else:
+    print("⚠️  Skipping pre-generated evaluation (no API key)")
+    print("To run full evaluation, set OPENAI_API_KEY environment variable")

examples/eval/batch_example.py

@@ -0,0 +1,105 @@
+"""
+Batch Evaluation Example
+
+This example demonstrates how to run batch evaluations
+from a JSON test file.
+"""
+
+import os
+import json
+import tempfile
+from praisonaiagents.eval import AccuracyEvaluator
+
+# Check if we have an API key
+has_api_key = os.getenv("OPENAI_API_KEY") is not None
+
+# Create a test file with multiple test cases
+test_cases = [
+    {
+        "input": "What is 2 + 2?",
+        "expected": "4"
+    },
+    {
+        "input": "What is the capital of France?",
+        "expected": "Paris"
+    },
+    {
+        "input": "What color is the sky?",
+        "expected": "Blue"
+    }
+]
+
+# Save test cases to a temporary file
+with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
+    json.dump(test_cases, f)
+    test_file = f.name
+
+print(f"Test file created: {test_file}")
+print(f"Running {len(test_cases)} test cases...")
+
+if has_api_key:
+    # You can use the CLI for batch evaluation:
+    # praisonai eval batch --agent agents.yaml --test-file tests.json --batch-type accuracy
+
+    # Or programmatically:
+    from praisonaiagents import Agent
+
+    agent = Agent(
+        instructions="You are a helpful assistant. Answer questions concisely."
+    )
+
+    results = []
+    for i, test_case in enumerate(test_cases):
+        print(f"\nTest {i + 1}: {test_case['input']}")
+        
+        evaluator = AccuracyEvaluator(
+            agent=agent,
+            input_text=test_case["input"],
+            expected_output=test_case["expected"],
+            num_iterations=1
+        )
+        
+        result = evaluator.run()
+        results.append({
+            "input": test_case["input"],
+            "expected": test_case["expected"],
+            "score": result.avg_score,
+            "passed": result.passed
+        })
+        
+        print(f"  Score: {result.avg_score}/10")
+        print(f"  Passed: {result.passed}")
+
+    # Summary
+    passed = sum(1 for r in results if r["passed"])
+    print(f"\n{'='*50}")
+    print(f"Summary: {passed}/{len(results)} tests passed")
+    print(f"Pass Rate: {passed/len(results):.1%}")
+else:
+    print("⚠️  No OPENAI_API_KEY found. Running mock batch evaluation...")
+    
+    # Mock evaluation without API key
+    results = []
+    for i, test_case in enumerate(test_cases):
+        print(f"\nTest {i + 1}: {test_case['input']}")
+        
+        # Mock result (all would fail without API key)
+        mock_score = 0.0
+        mock_passed = False
+        
+        results.append({
+            "input": test_case["input"],
+            "expected": test_case["expected"],
+            "score": mock_score,
+            "passed": mock_passed
+        })
+        
+        print(f"  Score: {mock_score}/10 (mock)")
+        print(f"  Passed: {mock_passed} (expected without API key)")
+
+    # Summary
+    passed = sum(1 for r in results if r["passed"])
+    print(f"\n{'='*50}")
+    print(f"Summary: {passed}/{len(results)} tests passed (mock evaluation)")
+    print(f"Pass Rate: {passed/len(results):.1%}")
+    print("\nTo run real batch evaluation, set OPENAI_API_KEY environment variable")

examples/eval/criteria_example.py

@@ -0,0 +1,82 @@
+"""
+Criteria Evaluation Example
+
+This example demonstrates how to evaluate agent outputs
+against custom criteria using LLM-as-judge.
+"""
+
+import os
+from praisonaiagents import Agent
+from praisonaiagents.eval import CriteriaEvaluator
+
+# Check if we have an API key
+has_api_key = os.getenv("OPENAI_API_KEY") is not None
+
+if has_api_key:
+    print("--- Testing Criteria Evaluation with Agent ---")
+    # Create a customer service agent
+    agent = Agent(
+        instructions="You are a friendly customer service agent. Be helpful and empathetic."
+    )
+
+    # Create criteria evaluator with numeric scoring
+    evaluator = CriteriaEvaluator(
+        criteria="Response is helpful, empathetic, and provides a clear solution",
+        agent=agent,
+        input_text="I'm frustrated because my order hasn't arrived yet.",
+        scoring_type="numeric",  # Score 1-10
+        threshold=7.0,           # Pass if score >= 7
+        num_iterations=2,
+        verbose=True
+    )
+
+    # Run evaluation
+    result = evaluator.run(print_summary=True)
+
+    print("\nNumeric Scoring Results:")
+    print(f"  Average Score: {result.avg_score}/10")
+    print(f"  Pass Rate: {result.pass_rate:.1%}")
+    print(f"  Passed: {result.passed}")
+
+    # Binary scoring example (pass/fail)
+    print("\n--- Testing Binary Scoring ---")
+    binary_evaluator = CriteriaEvaluator(
+        criteria="Response does not contain any offensive language",
+        agent=agent,
+        input_text="Tell me a joke",
+        scoring_type="binary",  # Pass or Fail
+        verbose=True
+    )
+
+    binary_result = binary_evaluator.run(print_summary=True)
+    
+    # With on_fail callback
+    print("\n--- Testing Failure Callback ---")
+    def handle_failure(score):
+        print(f"ALERT: Evaluation failed with score {score.score}")
+        print(f"Reasoning: {score.reasoning}")
+
+    callback_evaluator = CriteriaEvaluator(
+        criteria="Response is professional and helpful",
+        agent=agent,
+        input_text="Help me",
+        on_fail=handle_failure,
+        threshold=8.0
+    )
+
+    callback_evaluator.run()
+else:
+    print("⚠️  No OPENAI_API_KEY found. Skipping agent-based criteria evaluation...")
+
+# Test pre-generated output evaluation (doesn't need agent)
+print("\n--- Testing Pre-generated Output Evaluation ---")
+if has_api_key:
+    output = "I understand your frustration. Let me check on your order right away."
+    try:
+        result2 = evaluator.evaluate_output(output, print_summary=True)
+        print(f"Pre-generated output score: {result2.avg_score}/10")
+    except Exception as e:
+        print(f"Pre-generated evaluation failed: {e}")
+else:
+    print("⚠️  Skipping pre-generated evaluation (no API key)")
+    print("To run full evaluation, set OPENAI_API_KEY environment variable")

examples/eval/performance_example.py

@@ -0,0 +1,59 @@
+"""
+Performance Evaluation Example
+
+This example demonstrates how to benchmark agent performance
+by measuring runtime and memory usage across multiple iterations.
+"""
+
+import os
+from praisonaiagents import Agent
+from praisonaiagents.eval import PerformanceEvaluator
+
+# Check if we have an API key
+has_api_key = os.getenv("OPENAI_API_KEY") is not None
+
+if has_api_key:
+    print("--- Testing Agent Performance ---")
+    # Create a simple agent
+    agent = Agent(
+        instructions="You are a helpful assistant. Answer questions briefly."
+    )
+
+    # Create performance evaluator
+    evaluator = PerformanceEvaluator(
+        agent=agent,
+        input_text="What is the capital of France?",
+        num_iterations=10,  # Run 10 benchmark iterations
+        warmup_runs=2,      # 2 warmup runs before measurement
+        track_memory=True,  # Track memory usage
+        verbose=True
+    )
+
+    # Run evaluation
+    result = evaluator.run(print_summary=True)
+
+    # Access detailed metrics
+    print("\nAgent Performance Results:")
+    print(f"  Average Time: {result.avg_run_time:.4f}s")
+    print(f"  Min Time: {result.min_run_time:.4f}s")
+    print(f"  Max Time: {result.max_run_time:.4f}s")
+    print(f"  Median Time: {result.median_run_time:.4f}s")
+    print(f"  P95 Time: {result.p95_run_time:.4f}s")
+    print(f"  Avg Memory: {result.avg_memory:.2f} MB")
+else:
+    print("⚠️  No OPENAI_API_KEY found. Skipping agent performance test...")
+
+# You can also benchmark any function
+def my_function():
+    import time
+    time.sleep(0.1)
+    return "done"
+
+func_evaluator = PerformanceEvaluator(
+    func=my_function,
+    num_iterations=5,
+    warmup_runs=1,
+    track_memory=False
+)
+
+func_result = func_evaluator.run(print_summary=True)

examples/eval/reliability_example.py

@@ -0,0 +1,72 @@
+"""
+Reliability Evaluation Example
+
+This example demonstrates how to verify that agents call
+the expected tools during execution.
+"""
+
+import os
+from praisonaiagents import Agent
+from praisonaiagents.eval import ReliabilityEvaluator
+
+
+def search_web(query: str) -> str:
+    """Search the web for information."""
+    return f"Search results for: {query}"
+
+
+def calculate(expression: str) -> str:
+    """Calculate a math expression."""
+    return str(eval(expression))
+
+
+# Check if we have an API key
+has_api_key = os.getenv("OPENAI_API_KEY") is not None
+
+if has_api_key:
+    print("--- Testing Agent Tool Call Reliability ---")
+    # Create an agent with tools
+    agent = Agent(
+        instructions="You are a helpful assistant with access to search and calculator tools.",
+        tools=[search_web, calculate]
+    )
+
+    # Create reliability evaluator
+    evaluator = ReliabilityEvaluator(
+        agent=agent,
+        input_text="Search for the weather in Paris and calculate 25 * 4",
+        expected_tools=["search_web", "calculate"],  # Tools that should be called
+        forbidden_tools=["delete_file"],  # Tools that should NOT be called
+        verbose=True
+    )
+
+    # Run evaluation
+    result = evaluator.run(print_summary=True)
+
+    # Check results
+    print("\nAgent Reliability Results:")
+    print(f"  Passed: {result.passed}")
+    print(f"  Pass Rate: {result.pass_rate:.1%}")
+else:
+    print("⚠️  No OPENAI_API_KEY found. Skipping agent reliability test...")
+    print("Agent would fail to call tools without API key (expected behavior)")
+
+# You can also evaluate pre-recorded tool calls (doesn't need API key)
+print("\n--- Testing Pre-recorded Tool Call Evaluation ---")
+# Create a mock agent for tool call evaluation
+mock_agent = Agent(instructions="Mock agent")
+evaluator = ReliabilityEvaluator(
+    agent=mock_agent,
+    expected_tools=["search_web", "calculate"],  # Tools that should be called
+    forbidden_tools=["delete_file"],  # Tools that should NOT be called
+    verbose=True
+)
+
+result2 = evaluator.evaluate_tool_calls(
+    actual_tools=["search_web", "calculate"],
+    print_summary=True
+)
+
+print("\nPre-recorded Tool Call Results:")
+print(f"  Passed: {result2.passed}")
+print(f"  Pass Rate: {result2.pass_rate:.1%}")

examples/hooks/basic_hooks.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""
+Basic Hooks Example for PraisonAI Agents.
+
+This example demonstrates how to use the hooks system to:
+1. Log tool calls before execution
+2. Block dangerous operations
+3. Add context to agent responses
+4. Monitor session lifecycle
+
+Usage:
+    python basic_hooks.py
+"""
+
+import asyncio
+from praisonaiagents import Agent
+from praisonaiagents.hooks import (
+    HookRegistry, HookRunner, HookEvent, HookResult,
+    BeforeToolInput, AfterToolInput
+)
+
+
+def main():
+    # Create a hook registry
+    registry = HookRegistry()
+    
+    # ==========================================================================
+    # Hook 1: Log all tool calls
+    # ==========================================================================
+    @registry.on(HookEvent.BEFORE_TOOL)
+    def log_tool_calls(event_data: BeforeToolInput) -> HookResult:
+        """Log every tool call before execution."""
+        print(f"\n📝 [LOG] Tool: {event_data.tool_name}")
+        print(f"   Input: {event_data.tool_input}")
+        return HookResult.allow()
+    
+    # ==========================================================================
+    # Hook 2: Block dangerous file operations (using function logic)
+    # ==========================================================================
+    @registry.on(HookEvent.BEFORE_TOOL)
+    def block_delete_operations(event_data: BeforeToolInput) -> HookResult:
+        """Block any delete or remove operations."""
+        dangerous_keywords = ['delete', 'remove', 'rm', 'unlink']
+        tool_lower = event_data.tool_name.lower()
+        
+        if any(kw in tool_lower for kw in dangerous_keywords):
+            print(f"\n🚫 [BLOCKED] Dangerous operation: {event_data.tool_name}")
+            return HookResult.deny(
+                f"Operation '{event_data.tool_name}' is blocked by security policy"
+            )
+        return HookResult.allow()
+    
+    # ==========================================================================
+    # Hook 3: Require confirmation for write operations
+    # ==========================================================================
+    @registry.on(HookEvent.BEFORE_TOOL)
+    def confirm_write_operations(event_data: BeforeToolInput) -> HookResult:
+        """Request confirmation for write operations."""
+        write_keywords = ['write', 'save', 'create', 'update']
+        tool_lower = event_data.tool_name.lower()
+        
+        if any(kw in tool_lower for kw in write_keywords):
+            print(f"\n⚠️  [CONFIRM] Write operation: {event_data.tool_name}")
+            # In a real scenario, you might prompt the user here
+            # For this example, we'll auto-approve
+            return HookResult.allow("Auto-approved for demo")
+        return HookResult.allow()
+    
+    # ==========================================================================
+    # Hook 4: Add timing information after tool execution
+    # ==========================================================================
+    @registry.on(HookEvent.AFTER_TOOL)
+    def log_tool_completion(event_data: AfterToolInput) -> HookResult:
+        """Log tool completion with timing."""
+        print(f"\n✅ [DONE] Tool: {event_data.tool_name}")
+        print(f"   Duration: {event_data.execution_time_ms:.2f}ms")
+        if event_data.tool_error:
+            print(f"   Error: {event_data.tool_error}")
+        return HookResult.allow()
+    
+    # ==========================================================================
+    # Test the hooks
+    # ==========================================================================
+    print("=" * 60)
+    print("Testing Hooks System")
+    print("=" * 60)
+    
+    runner = HookRunner(registry)
+    
+    # Test 1: Normal tool call (should be logged and allowed)
+    print("\n--- Test 1: Normal read operation ---")
+    input1 = BeforeToolInput(
+        session_id="test",
+        cwd="/tmp",
+        event_name="before_tool",
+        timestamp="2024-01-01T00:00:00",
+        tool_name="read_file",
+        tool_input={"path": "/tmp/test.txt"}
+    )
+    results = asyncio.run(runner.execute(HookEvent.BEFORE_TOOL, input1))
+    print(f"Result: {'ALLOWED' if not HookRunner.is_blocked(results) else 'BLOCKED'}")
+    
+    # Test 2: Delete operation (should be blocked)
+    print("\n--- Test 2: Delete operation ---")
+    input2 = BeforeToolInput(
+        session_id="test",
+        cwd="/tmp",
+        event_name="before_tool",
+        timestamp="2024-01-01T00:00:00",
+        tool_name="delete_file",
+        tool_input={"path": "/important/data.txt"}
+    )
+    results = asyncio.run(runner.execute(HookEvent.BEFORE_TOOL, input2))
+    print(f"Result: {'ALLOWED' if not HookRunner.is_blocked(results) else 'BLOCKED'}")
+    if HookRunner.is_blocked(results):
+        print(f"Reason: {HookRunner.get_blocking_reason(results)}")
+    
+    # Test 3: Write operation (should be confirmed)
+    print("\n--- Test 3: Write operation ---")
+    input3 = BeforeToolInput(
+        session_id="test",
+        cwd="/tmp",
+        event_name="before_tool",
+        timestamp="2024-01-01T00:00:00",
+        tool_name="write_file",
+        tool_input={"path": "/tmp/output.txt", "content": "Hello"}
+    )
+    results = asyncio.run(runner.execute(HookEvent.BEFORE_TOOL, input3))
+    print(f"Result: {'ALLOWED' if not HookRunner.is_blocked(results) else 'BLOCKED'}")
+    
+    # Show registered hooks
+    print("\n" + "=" * 60)
+    print("Registered Hooks:")
+    print("=" * 60)
+    for event, hooks in registry.list_hooks().items():
+        print(f"\n{event}:")
+        for hook in hooks:
+            print(f"  - {hook['name']} (matcher: {hook['matcher'] or '*'})")
+
+
+if __name__ == "__main__":
+    main()

examples/persistence/knowledge_qdrant.py

@@ -0,0 +1,79 @@
+"""
+Qdrant Knowledge Store Example for PraisonAI.
+
+Demonstrates vector insert + search through the knowledge store.
+
+Requirements:
+    pip install "praisonai[tools]"
+
+Docker Setup:
+    docker run -d --name praison-qdrant -p 6333:6333 qdrant/qdrant
+
+Run:
+    python knowledge_qdrant.py
+
+Expected Output:
+    === Qdrant Knowledge Store Demo ===
+    Created collection: demo_knowledge
+    Inserted 3 documents
+    Search results: 2 documents found
+"""
+
+from praisonai.persistence.factory import create_knowledge_store
+from praisonai.persistence.knowledge.base import KnowledgeDocument
+import random
+
+# Create Qdrant knowledge store
+store = create_knowledge_store("qdrant", url="http://localhost:6333")
+
+collection_name = "demo_knowledge"
+dimension = 384  # Embedding dimension
+
+# Create collection
+print("=== Qdrant Knowledge Store Demo ===")
+if store.collection_exists(collection_name):
+    store.delete_collection(collection_name)
+
+store.create_collection(collection_name, dimension=dimension)
+print(f"Created collection: {collection_name}")
+
+# Create sample documents with embeddings
+random.seed(42)
+documents = [
+    KnowledgeDocument(
+        id="doc-1",
+        content="Python is a versatile programming language used for web development, data science, and AI.",
+        embedding=[random.random() for _ in range(dimension)],
+        metadata={"category": "programming", "language": "python"}
+    ),
+    KnowledgeDocument(
+        id="doc-2", 
+        content="Machine learning enables computers to learn patterns from data without explicit programming.",
+        embedding=[random.random() for _ in range(dimension)],
+        metadata={"category": "ai", "topic": "ml"}
+    ),
+    KnowledgeDocument(
+        id="doc-3",
+        content="PostgreSQL is a powerful open-source relational database system.",
+        embedding=[random.random() for _ in range(dimension)],
+        metadata={"category": "database", "type": "sql"}
+    ),
+]
+
+# Insert documents
+ids = store.insert(collection_name, documents)
+print(f"Inserted {len(ids)} documents")
+
+# Search for similar documents
+query_embedding = [random.random() for _ in range(dimension)]
+results = store.search(collection_name, query_embedding, limit=2)
+print(f"Search results: {len(results)} documents found")
+
+for doc in results:
+    print(f"  - {doc.id}: {doc.content[:50]}...")
+
+# Cleanup
+store.delete_collection(collection_name)
+store.close()
+
+print("\n=== Demo Complete ===")

examples/persistence/minimal_agent_db.py

@@ -0,0 +1,43 @@
+"""
+Minimal Agent with Database Persistence
+
+This is the simplest way to add persistence to your agent.
+Messages, runs, and traces are automatically saved.
+
+Run:
+    python minimal_agent_db.py
+
+Expected output:
+    - Agent responds to your question
+    - Data is persisted to PostgreSQL
+    - Session can be resumed later with same session_id
+"""
+
+from praisonaiagents import Agent, db
+
+# Create database connection (PostgreSQL + Redis)
+my_db = db(
+    database_url="postgresql://postgres:praison123@localhost:5432/praisonai",
+    state_url="redis://localhost:6379"  # Optional: for state/tracing
+)
+
+# Create agent with persistence
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant. Be concise.",
+    db=my_db,
+    # session_id is optional - defaults to per-hour ID (YYYYMMDDHH-hash)
+    # Set explicitly for session continuity across runs:
+    session_id="my-persistent-session"
+)
+
+# Chat - messages are automatically persisted
+response = agent.chat("What is the capital of France?")
+print(f"Response: {response}")
+
+# Verify persistence
+data = my_db.export_session("my-persistent-session")
+print(f"\nMessages stored: {len(data.get('messages', []))}")
+
+# Clean up
+my_db.close()

examples/persistence/postgres_runs_traces.py

@@ -0,0 +1,61 @@
+"""
+PostgreSQL with Runs and Traces
+
+Demonstrates full persistence with run tracking and tracing.
+
+Run:
+    python postgres_runs_traces.py
+
+Expected output:
+    - Agent responds with tool usage
+    - Runs and traces are persisted
+    - Export shows full session data
+"""
+
+from praisonaiagents import Agent, db
+
+def get_weather(city: str) -> str:
+    """Get weather for a city."""
+    return f"Weather in {city}: Sunny, 72°F"
+
+# Create database with PostgreSQL + Redis
+my_db = db(
+    database_url="postgresql://postgres:praison123@localhost:5432/praisonai",
+    state_url="redis://localhost:6379"
+)
+
+# Create agent with tools
+agent = Agent(
+    name="WeatherBot",
+    instructions="You help with weather. Use the get_weather tool when asked.",
+    tools=[get_weather],
+    db=my_db,
+    session_id="weather-session-001"
+)
+
+# First chat - triggers run tracking
+print("=== First Question ===")
+response1 = agent.chat("What's the weather in Tokyo?")
+print(f"Response: {response1}\n")
+
+# Second chat - same session, new run
+print("=== Second Question ===")
+response2 = agent.chat("How about New York?")
+print(f"Response: {response2}\n")
+
+# Export session data
+print("=== Session Data ===")
+data = my_db.export_session("weather-session-001")
+print(f"Session ID: {data.get('session_id')}")
+print(f"Messages: {len(data.get('messages', []))}")
+print(f"Agent: {data.get('agent_name')}")
+
+# Show messages
+print("\n=== Messages ===")
+for msg in data.get('messages', []):
+    role = msg.get('role', 'unknown')
+    content = msg.get('content', '')[:60]
+    print(f"  [{role}] {content}...")
+
+my_db.close()
+print("\n✅ Done")

examples/persistence/redis_state.py

@@ -0,0 +1,83 @@
+"""
+Redis State Store
+
+Use Redis for state management, tracing, and caching.
+
+Run:
+    python redis_state.py
+
+Expected output:
+    - State operations (set/get/delete)
+    - Tracing operations
+"""
+
+from praisonaiagents import db
+
+# Create Redis-only database (for state/tracing)
+redis_db = db.RedisDB(host="localhost", port=6379)
+
+print("=== Redis State Store ===")
+
+# Test state operations
+print("\n1. State Operations:")
+redis_db._init_stores()  # Initialize stores
+
+# Set a value
+redis_db._state_store.set("user:123:preferences", {
+    "theme": "dark",
+    "language": "en"
+})
+print("   Set user preferences")
+
+# Get the value
+prefs = redis_db._state_store.get("user:123:preferences")
+print(f"   Got preferences: {prefs}")
+
+# Set with TTL (expires in 60 seconds)
+redis_db._state_store.set("temp:token", "abc123", ttl=60)
+print("   Set temporary token (60s TTL)")
+
+# Delete
+redis_db._state_store.delete("user:123:preferences")
+print("   Deleted preferences")
+
+# Test tracing
+print("\n2. Tracing Operations:")
+
+# Start a trace
+trace_id = "trace-example-001"
+redis_db.on_trace_start(
+    trace_id=trace_id,
+    session_id="example-session",
+    agent_name="ExampleAgent",
+    metadata={"purpose": "demo"}
+)
+print(f"   Started trace: {trace_id}")
+
+# Add spans
+span_id = "span-llm-001"
+redis_db.on_span_start(
+    span_id=span_id,
+    trace_id=trace_id,
+    name="llm_call",
+    attributes={"model": "gpt-4", "temperature": 0.7}
+)
+print(f"   Started span: {span_id}")
+
+redis_db.on_span_end(
+    span_id=span_id,
+    status="ok",
+    attributes={"tokens": 150, "latency_ms": 450}
+)
+print(f"   Ended span: {span_id}")
+
+# End trace
+redis_db.on_trace_end(
+    trace_id=trace_id,
+    status="ok",
+    metadata={"total_spans": 1}
+)
+print(f"   Ended trace: {trace_id}")
+
+redis_db.close()
+print("\n✅ Done")

examples/persistence/session_resume.py

@@ -0,0 +1,66 @@
+"""
+Session Resume Example
+
+Demonstrates how to resume a conversation from a previous session.
+
+Run:
+    python session_resume.py
+
+Expected output:
+    - First run: Creates new session
+    - Second run: Resumes with history
+"""
+
+from praisonaiagents import Agent, db
+
+# Create database
+my_db = db(
+    database_url="postgresql://postgres:praison123@localhost:5432/praisonai",
+    state_url="redis://localhost:6379"
+)
+
+SESSION_ID = "resume-demo-session"
+
+# Create agent with explicit session_id
+agent = Agent(
+    name="MemoryBot",
+    instructions="You remember our conversation. Be helpful and concise.",
+    db=my_db,
+    session_id=SESSION_ID
+)
+
+# Check if this is a new or resumed session
+data = my_db.export_session(SESSION_ID)
+existing_messages = len(data.get('messages', []))
+
+if existing_messages == 0:
+    print("=== NEW SESSION ===")
+    print("This is a new conversation.\n")
+    
+    response = agent.chat("My name is Alice and I love Python programming.")
+    print(f"Response: {response}\n")
+    
+    response = agent.chat("What's my favorite programming language?")
+    print(f"Response: {response}\n")
+else:
+    print(f"=== RESUMED SESSION ({existing_messages} previous messages) ===")
+    print("Continuing previous conversation.\n")
+    
+    # Show previous messages
+    print("Previous messages:")
+    for msg in data.get('messages', [])[:4]:
+        role = msg.get('role', 'unknown')
+        content = msg.get('content', '')[:60]
+        print(f"  [{role}] {content}...")
+    print()
+    
+    # Continue conversation
+    response = agent.chat("What's my name and what do I like?")
+    print(f"Response: {response}\n")
+
+# Show final state
+final_data = my_db.export_session(SESSION_ID)
+print(f"Total messages now: {len(final_data.get('messages', []))}")
+
+my_db.close()
+print("✅ Done")

examples/persistence/simple_db_agent.py

@@ -0,0 +1,63 @@
+"""
+Simple Database Persistence Example for PraisonAI Agents.
+
+This example shows how to enable automatic conversation persistence
+with just 2 lines of code.
+
+Requirements:
+    pip install "praisonai[tools]"
+    
+Docker Setup:
+    docker run -d --name praison-postgres -p 5432:5432 \
+        -e POSTGRES_PASSWORD=praison123 \
+        -e POSTGRES_DB=praisonai \
+        postgres:16
+
+Environment:
+    export OPENAI_API_KEY=your-key
+
+Run:
+    python simple_agent_postgres.py
+
+Expected Output:
+    === First Run ===
+    Agent: Nice to meet you, Alice! Python is a great choice...
+    Session ID: demo-session-001
+    Messages persisted: 2
+"""
+
+from praisonaiagents import Agent
+from praisonai.db import PostgresDB
+
+# Create database adapter
+db = PostgresDB(
+    host="localhost",
+    port=5432,
+    database="praisonai",
+    user="postgres",
+    password="praison123"
+)
+
+# Create agent with persistence enabled
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant. Keep responses brief.",
+    db=db,
+    session_id="demo-session-001",  # Same session_id = resume conversation
+    verbose=False
+)
+
+# Chat with persistence
+print("=== First Run ===")
+response = agent.chat("My name is Alice and I love Python programming.")
+print(f"Agent: {response}\n")
+
+# Show session info
+print(f"Session ID: {agent.session_id}")
+print(f"Messages in history: {len(agent.chat_history)}")
+
+# Close DB connection
+db.close()
+
+print("\n=== Session persisted! ===")
+print("Run again with same session_id to resume.")

examples/persistence/sqlite_local.py

@@ -0,0 +1,41 @@
+"""
+SQLite Local Database
+
+Use SQLite for simple local persistence without external services.
+
+Run:
+    python sqlite_local.py
+
+Expected output:
+    - Agent responds
+    - Data persisted to local SQLite file
+"""
+
+from praisonaiagents import Agent, db
+import tempfile
+import os
+
+# Create SQLite database (local file)
+db_path = os.path.join(tempfile.gettempdir(), "my_agent.db")
+my_db = db.SQLiteDB(path=db_path)
+
+print(f"Database: {db_path}")
+
+# Create agent
+agent = Agent(
+    name="LocalBot",
+    instructions="You are a helpful assistant.",
+    db=my_db,
+    session_id="local-session"
+)
+
+# Chat
+response = agent.chat("Hello! What can you help me with?")
+print(f"Response: {response}")
+
+# Verify
+data = my_db.export_session("local-session")
+print(f"\nMessages stored: {len(data.get('messages', []))}")
+
+my_db.close()
+print("✅ Done")

examples/persistence/state_redis.py

@@ -0,0 +1,67 @@
+"""
+Redis State Store Example for PraisonAI.
+
+Demonstrates set/get/delete + TTL operations.
+
+Requirements:
+    pip install "praisonai[tools]"
+
+Docker Setup:
+    docker run -d --name praison-redis -p 6379:6379 redis:7
+
+Run:
+    python state_redis.py
+
+Expected Output:
+    === Redis State Store Demo ===
+    Set key: session:user123:preferences
+    Get value: {'theme': 'dark', 'language': 'en'}
+    TTL remaining: 3600 seconds
+    Hash operations: {'name': 'Alice', 'role': 'admin'}
+"""
+
+from praisonai.persistence.factory import create_state_store
+
+# Create Redis state store
+store = create_state_store("redis", url="redis://localhost:6379")
+
+print("=== Redis State Store Demo ===")
+
+# Basic set/get
+key = "session:user123:preferences"
+value = {"theme": "dark", "language": "en", "notifications": True}
+
+store.set(key, value)
+print(f"Set key: {key}")
+
+retrieved = store.get(key)
+print(f"Get value: {retrieved}")
+
+# Set with TTL (expires in 1 hour)
+ttl_key = "session:user123:token"
+store.set(ttl_key, "abc123xyz", ttl=3600)
+remaining = store.ttl(ttl_key)
+print(f"TTL remaining: {remaining} seconds")
+
+# Hash operations (for structured data)
+hash_key = "user:123:profile"
+store.hset(hash_key, "name", "Alice")
+store.hset(hash_key, "role", "admin")
+store.hset(hash_key, "last_login", "2024-12-24")
+
+profile = store.hgetall(hash_key)
+print(f"Hash operations: {profile}")
+
+# Check existence
+exists = store.exists(key)
+print(f"Key exists: {exists}")
+
+# Delete keys
+store.delete(key)
+store.delete(ttl_key)
+store.delete(hash_key)
+print("Cleaned up test keys")
+
+store.close()
+
+print("\n=== Demo Complete ===")

examples/policy/basic_policy.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""
+Basic Policy Engine Example for PraisonAI Agents.
+
+This example demonstrates how to use the policy engine to:
+1. Create policies with rules
+2. Check if actions are allowed
+3. Block dangerous operations
+4. Use convenience policy creators
+
+Usage:
+    python basic_policy.py
+"""
+
+from praisonaiagents.policy import (
+    PolicyEngine, Policy, PolicyRule, PolicyAction, PolicyConfig,
+    create_deny_tools_policy, create_read_only_policy
+)
+
+
+def main():
+    print("=" * 60)
+    print("Policy Engine Demo")
+    print("=" * 60)
+    
+    # Create a policy engine
+    engine = PolicyEngine()
+    
+    # ==========================================================================
+    # Policy 1: Block dangerous tools
+    # ==========================================================================
+    print("\n--- Creating Policies ---")
+    
+    dangerous_tools_policy = Policy(
+        name="block_dangerous",
+        description="Block dangerous file operations",
+        rules=[
+            PolicyRule(
+                action=PolicyAction.DENY,
+                resource="tool:delete_*",
+                reason="Delete operations are blocked",
+                name="deny_delete"
+            ),
+            PolicyRule(
+                action=PolicyAction.DENY,
+                resource="tool:rm_*",
+                reason="Remove operations are blocked",
+                name="deny_rm"
+            ),
+        ],
+        priority=100
+    )
+    engine.add_policy(dangerous_tools_policy)
+    print(f"✅ Added policy: {dangerous_tools_policy.name}")
+    
+    # ==========================================================================
+    # Policy 2: Allow read operations
+    # ==========================================================================
+    read_policy = Policy(
+        name="allow_read",
+        description="Allow read operations",
+        rules=[
+            PolicyRule(
+                action=PolicyAction.ALLOW,
+                resource="tool:read_*",
+                name="allow_read"
+            ),
+            PolicyRule(
+                action=PolicyAction.ALLOW,
+                resource="tool:list_*",
+                name="allow_list"
+            ),
+        ]
+    )
+    engine.add_policy(read_policy)
+    print(f"✅ Added policy: {read_policy.name}")
+    
+    # ==========================================================================
+    # Test policy checks
+    # ==========================================================================
+    print("\n--- Testing Policy Checks ---")
+    
+    test_cases = [
+        ("tool:read_file", "Reading a file"),
+        ("tool:list_directory", "Listing directory"),
+        ("tool:delete_file", "Deleting a file"),
+        ("tool:rm_directory", "Removing directory"),
+        ("tool:write_file", "Writing a file"),
+    ]
+    
+    for resource, description in test_cases:
+        result = engine.check(resource, {})
+        status = "✅ ALLOWED" if result.allowed else "🚫 DENIED"
+        reason = f" ({result.reason})" if result.reason else ""
+        print(f"  {status}: {description}{reason}")
+    
+    # ==========================================================================
+    # Using convenience functions
+    # ==========================================================================
+    print("\n--- Using Convenience Functions ---")
+    
+    # Create a read-only policy
+    read_only = create_read_only_policy()
+    print(f"✅ Created read-only policy with {len(read_only.rules)} rules")
+    
+    # Create a deny tools policy
+    deny_tools = create_deny_tools_policy(
+        ["execute_*", "shell_*", "system_*"],
+        reason="System commands are blocked"
+    )
+    print(f"✅ Created deny-tools policy with {len(deny_tools.rules)} rules")
+    
+    # ==========================================================================
+    # Strict mode example
+    # ==========================================================================
+    print("\n--- Strict Mode Example ---")
+    
+    strict_engine = PolicyEngine(PolicyConfig(strict_mode=True))
+    
+    # In strict mode, unknown resources are denied
+    result = strict_engine.check("tool:unknown_operation", {})
+    print(f"  Unknown operation in strict mode: {'DENIED' if not result.allowed else 'ALLOWED'}")
+    
+    # ==========================================================================
+    # Policy serialization
+    # ==========================================================================
+    print("\n--- Policy Serialization ---")
+    
+    policy_dict = dangerous_tools_policy.to_dict()
+    print(f"  Serialized policy: {policy_dict['name']} with {len(policy_dict['rules'])} rules")
+    
+    # Recreate from dict
+    restored = Policy.from_dict(policy_dict)
+    print(f"  Restored policy: {restored.name}")
+    
+    print("\n" + "=" * 60)
+    print("Demo Complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/python/scheduled_agents/news_checker_agent.py

@@ -0,0 +1,115 @@
+"""
+24/7 News Checker Agent Example
+
+This example demonstrates how to create a continuously running agent
+that checks for AI news every hour and prints the results.
+
+Usage:
+    python news_checker_agent.py
+    
+The agent will:
+1. Run immediately on start
+2. Check for latest AI news every 1 hour
+3. Print results to console
+4. Continue running until stopped (Ctrl+C)
+"""
+
+import os
+import sys
+from datetime import datetime
+
+# Add parent directory to path for imports
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../..'))
+
+from praisonaiagents import Agent
+from praisonai.agent_scheduler import AgentScheduler
+from tools import search_tool
+
+
+def on_success(result):
+    """Callback function called when agent execution succeeds."""
+    print("\n" + "="*80)
+    print(f"✅ SUCCESS - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print("="*80)
+    print(result)
+    print("="*80 + "\n")
+
+
+def on_failure(error):
+    """Callback function called when agent execution fails."""
+    print("\n" + "="*80)
+    print(f"❌ FAILURE - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print("="*80)
+    print(f"Error: {error}")
+    print("="*80 + "\n")
+
+
+def main():
+    """Main function to set up and run the scheduled news checker agent."""
+    
+    print("\n🤖 Starting 24/7 News Checker Agent")
+    print("="*80)
+    
+    # Create the agent with web search capability
+    agent = Agent(
+        name="NewsChecker",
+        role="AI News Monitor",
+        goal="Monitor and summarize the latest AI and technology news",
+        backstory="""You are an expert AI news analyst who stays up-to-date 
+        with the latest developments in artificial intelligence, machine learning, 
+        and technology. You provide concise, accurate summaries of important news.""",
+        tools=[search_tool],
+        verbose=True
+    )
+    
+    # Define the task
+    task = """Search for the latest AI news from today. 
+    Focus on major developments, breakthroughs, or announcements.
+    Provide a summary of the top 3-5 most important news items.
+    Include the source and key details for each item."""
+    
+    # Create scheduler with callbacks
+    scheduler = AgentScheduler(
+        agent=agent,
+        task=task,
+        on_success=on_success,
+        on_failure=on_failure
+    )
+    
+    print(f"Agent: {agent.name}")
+    print(f"Task: {task}")
+    print(f"Schedule: Every 1 hour")
+    print("="*80)
+    print("\n⏰ Starting scheduler... (Press Ctrl+C to stop)\n")
+    
+    # Start the scheduler
+    # - "hourly" or "*/1h" = every 1 hour
+    # - run_immediately=True = run once before starting the schedule
+    scheduler.start(
+        schedule_expr="hourly",  # Run every hour
+        max_retries=3,           # Retry up to 3 times on failure
+        run_immediately=True     # Run immediately on start
+    )
+    
+    try:
+        # Keep the main thread alive
+        while scheduler.is_running:
+            import time
+            time.sleep(1)
+            
+    except KeyboardInterrupt:
+        print("\n\n🛑 Stopping scheduler...")
+        scheduler.stop()
+        
+        # Print final statistics
+        stats = scheduler.get_stats()
+        print("\n📊 Final Statistics:")
+        print(f"  Total Executions: {stats['total_executions']}")
+        print(f"  Successful: {stats['successful_executions']}")
+        print(f"  Failed: {stats['failed_executions']}")
+        print(f"  Success Rate: {stats['success_rate']:.1f}%")
+        print("\n✅ Agent stopped successfully\n")
+
+
+if __name__ == "__main__":
+    main()

examples/python/scheduled_agents/news_checker_live.py

@@ -0,0 +1,143 @@
+"""
+Live News Checker Agent - 24/7 Scheduled Agent
+
+This example demonstrates a real working agent that checks AI news
+every hour using the AgentScheduler with actual API calls.
+
+Requirements:
+    - OPENAI_API_KEY environment variable set
+    - praisonaiagents package installed
+    
+Usage:
+    python news_checker_live.py
+"""
+
+import os
+import sys
+from datetime import datetime
+
+# Add parent directory to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../..'))
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../../..'))
+
+from praisonaiagents import Agent
+from praisonai.scheduler import AgentScheduler
+
+# Import search_tool from root tools.py
+try:
+    from tools import search_tool
+except ImportError:
+    # Fallback: create inline search tool
+    from duckduckgo_search import DDGS
+    
+    def search_tool(query: str) -> list:
+        """Search the web using DuckDuckGo."""
+        try:
+            results = []
+            ddgs = DDGS()
+            for result in ddgs.text(keywords=query, max_results=5):
+                results.append({
+                    "title": result.get("title", ""),
+                    "url": result.get("href", ""),
+                    "snippet": result.get("body", ""),
+                })
+            return results
+        except Exception as e:
+            print(f"Search error: {e}")
+            return []
+
+
+def on_success(result):
+    """Callback when agent execution succeeds."""
+    print("\n" + "="*80)
+    print(f"✅ SUCCESS - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print("="*80)
+    print(result)
+    print("="*80 + "\n")
+
+
+def on_failure(error):
+    """Callback when agent execution fails."""
+    print("\n" + "="*80)
+    print(f"❌ FAILURE - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print("="*80)
+    print(f"Error: {error}")
+    print("="*80 + "\n")
+
+
+def main():
+    """Main function to run the scheduled news checker."""
+    
+    # Check for API key
+    if not os.getenv("OPENAI_API_KEY"):
+        print("❌ Error: OPENAI_API_KEY environment variable not set")
+        print("Please set it with: export OPENAI_API_KEY=your_key_here")
+        sys.exit(1)
+    
+    print("\n🤖 Starting 24/7 AI News Checker Agent")
+    print("="*80)
+    
+    # Create the agent with web search capability
+    agent = Agent(
+        name="AI News Monitor",
+        role="Technology News Analyst",
+        goal="Monitor and summarize the latest AI and technology news",
+        backstory="""You are an expert AI news analyst who stays up-to-date 
+        with the latest developments in artificial intelligence, machine learning, 
+        and technology. You provide concise, accurate summaries of important news.""",
+        tools=[search_tool],
+        verbose=True,
+        self_reflect=False  # Disable reflection for faster execution
+    )
+    
+    # Define the task
+    task = """Search for the latest AI news from today. 
+    Focus on major developments, breakthroughs, or announcements.
+    Provide a summary of the top 3 most important news items.
+    Include the source and key details for each item."""
+    
+    # Create scheduler with callbacks
+    scheduler = AgentScheduler(
+        agent=agent,
+        task=task,
+        on_success=on_success,
+        on_failure=on_failure
+    )
+    
+    print(f"Agent: {agent.name}")
+    print(f"Task: {task}")
+    print(f"Schedule: Every 1 hour")
+    print("="*80)
+    print("\n⏰ Starting scheduler... (Press Ctrl+C to stop)\n")
+    
+    # Start the scheduler
+    # For testing: use "*/2m" (every 2 minutes) instead of "hourly"
+    # For production: use "hourly"
+    scheduler.start(
+        schedule_expr="*/2m",  # Every 2 minutes for testing
+        max_retries=3,
+        run_immediately=True  # Run once immediately
+    )
+    
+    try:
+        # Keep the main thread alive
+        while scheduler.is_running:
+            import time
+            time.sleep(1)
+            
+    except KeyboardInterrupt:
+        print("\n\n🛑 Stopping scheduler...")
+        scheduler.stop()
+        
+        # Print final statistics
+        stats = scheduler.get_stats()
+        print("\n📊 Final Statistics:")
+        print(f"  Total Executions: {stats['total_executions']}")
+        print(f"  Successful: {stats['successful_executions']}")
+        print(f"  Failed: {stats['failed_executions']}")
+        print(f"  Success Rate: {stats['success_rate']:.1f}%")
+        print("\n✅ Agent stopped successfully\n")
+
+
+if __name__ == "__main__":
+    main()

examples/python/scheduled_agents/simple_test.py

@@ -0,0 +1,61 @@
+"""
+Simple test script for 24/7 Agent Scheduler
+
+This script demonstrates the basic usage of AgentScheduler
+for running agents periodically.
+
+Usage:
+    python simple_test.py
+"""
+
+import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../..'))
+
+from unittest.mock import Mock
+from praisonai.scheduler import AgentScheduler
+
+# Create a mock agent for testing
+mock_agent = Mock()
+mock_agent.name = "TestAgent"
+mock_agent.start = Mock(return_value="Test execution successful!")
+
+# Create scheduler
+scheduler = AgentScheduler(
+    agent=mock_agent,
+    task="Test task execution"
+)
+
+print("🤖 Testing AgentScheduler")
+print("=" * 60)
+
+# Test 1: Execute once
+print("\n✅ Test 1: Execute once")
+result = scheduler.execute_once()
+print(f"Result: {result}")
+
+# Test 2: Get stats
+print("\n✅ Test 2: Get statistics")
+stats = scheduler.get_stats()
+print(f"Stats: {stats}")
+
+# Test 3: Start scheduler (will run every 5 seconds)
+print("\n✅ Test 3: Start scheduler (*/5s)")
+scheduler.start("*/5s", run_immediately=True)
+
+import time
+print("Running for 12 seconds...")
+time.sleep(12)
+
+# Stop scheduler
+print("\n✅ Test 4: Stop scheduler")
+scheduler.stop()
+
+# Final stats
+print("\n📊 Final Statistics:")
+final_stats = scheduler.get_stats()
+for key, value in final_stats.items():
+    print(f"  {key}: {value}")
+
+print("\n✅ All tests passed!")
+print("=" * 60)

examples/scheduler/test_5sec_schedule.yaml

@@ -0,0 +1,14 @@
+framework: praisonai
+
+agents:
+  - name: "5 Second Timer"
+    role: "Timer Agent"
+    instructions: "Display the current time"
+    verbose: true
+
+task: "Show current time"
+
+schedule:
+  interval: "*/5s"
+  max_retries: 3
+  run_immediately: true

examples/scheduler/test_counter.yaml

@@ -0,0 +1,14 @@
+framework: praisonai
+
+agents:
+  - name: "Counter Agent"
+    role: "Counter"
+    instructions: "Count from 1 to 5, incrementing by 1 each time. Just output the numbers clearly."
+    verbose: true
+
+task: "Print numbers 1, 2, 3, 4, 5 (one number per line)"
+
+schedule:
+  interval: "*/5s"
+  max_retries: 3
+  run_immediately: true

examples/scheduler/test_counter_with_limits.yaml

@@ -0,0 +1,16 @@
+framework: praisonai
+
+agents:
+  - name: "Counter Agent with Limits"
+    role: "Counter"
+    instructions: "Count from 1 to 5, incrementing by 1 each time. Just output the numbers clearly."
+    verbose: true
+
+task: "Print numbers 1, 2, 3, 4, 5 (one number per line)"
+
+schedule:
+  interval: "*/5s"           # Run every 5 seconds
+  max_retries: 3             # Retry up to 3 times on failure
+  run_immediately: true      # Run once immediately on start
+  timeout: 30                # Max 30 seconds per execution
+  max_cost: 0.01             # Stop after spending $0.01 USD

examples/scheduler/test_default_budget.yaml

@@ -0,0 +1,15 @@
+framework: praisonai
+
+agents:
+  - name: "Budget Test Agent"
+    role: "Tester"
+    instructions: "Count from 1 to 3"
+    verbose: true
+
+task: "Print numbers 1, 2, 3"
+
+schedule:
+  interval: "*/3s"
+  max_retries: 2
+  run_immediately: true
+  # No max_cost specified - should default to $1.00

examples/skills/basic_skill_usage.py

@@ -0,0 +1,73 @@
+"""
+Basic Agent Skills Usage Example
+
+This example demonstrates how to use Agent Skills with PraisonAI Agents.
+Skills are modular capabilities that can be loaded and used by agents.
+"""
+
+from praisonaiagents import Agent, SkillManager
+
+# Example 1: Using SkillManager directly
+print("=" * 50)
+print("Example 1: Using SkillManager directly")
+print("=" * 50)
+
+# Create a skill manager
+manager = SkillManager()
+
+# Discover skills from a directory
+# This scans the directory for subdirectories containing SKILL.md files
+skill_count = manager.discover(["./pdf-processing"], include_defaults=False)
+print(f"Discovered {skill_count} skill(s)")
+
+# List available skills
+for skill in manager:
+    print(f"  - {skill.properties.name}: {skill.properties.description[:50]}...")
+
+# Generate prompt XML for system prompt injection
+prompt_xml = manager.to_prompt()
+print("\nGenerated prompt XML:")
+print(prompt_xml)
+
+# Example 2: Using skills with an Agent
+print("\n" + "=" * 50)
+print("Example 2: Using skills with an Agent")
+print("=" * 50)
+
+# Create an agent with skills
+# Skills are lazy-loaded only when accessed
+agent = Agent(
+    name="PDF Assistant",
+    instructions="You are a helpful assistant that can process PDF documents.",
+    skills=["./pdf-processing"],  # Direct skill paths
+)
+
+# Access the skill manager through the agent
+if agent.skill_manager:
+    print(f"Agent has {len(agent.skill_manager)} skill(s) loaded")
+    
+    # Get the skills prompt for system prompt injection
+    skills_prompt = agent.get_skills_prompt()
+    print("\nAgent skills prompt:")
+    print(skills_prompt)
+
+# Example 3: Discovering skills from multiple directories
+print("\n" + "=" * 50)
+print("Example 3: Using skills_dirs for discovery")
+print("=" * 50)
+
+# Create an agent that discovers skills from directories
+agent_with_discovery = Agent(
+    name="Multi-Skill Agent",
+    instructions="You are a versatile assistant with multiple skills.",
+    skills_dirs=["./"],  # Scan current directory for skill subdirectories
+)
+
+if agent_with_discovery.skill_manager:
+    print(f"Discovered {len(agent_with_discovery.skill_manager)} skill(s)")
+    for name in agent_with_discovery.skill_manager.skill_names:
+        print(f"  - {name}")
+
+print("\n" + "=" * 50)
+print("Examples complete!")
+print("=" * 50)

examples/skills/create_skill_example.py

@@ -0,0 +1,94 @@
+#!/usr/bin/env python3
+"""
+Example: Creating Skills with PraisonAI CLI
+
+This example demonstrates how to create Agent Skills using the CLI.
+"""
+
+import subprocess
+import tempfile
+import os
+from pathlib import Path
+
+
+def example_create_skill_with_template():
+    """Create a skill using template mode (no API key required)."""
+    print("=" * 60)
+    print("Example 1: Create skill with template")
+    print("=" * 60)
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        result = subprocess.run([
+            "praisonai", "skills", "create",
+            "--name", "my-template-skill",
+            "--description", "A skill for processing text files",
+            "--author", "example-team",
+            "--license", "MIT",
+            "--template",
+            "--output-dir", tmpdir
+        ], capture_output=True, text=True)
+        
+        print(result.stdout)
+        if result.returncode == 0:
+            skill_md = Path(tmpdir) / "my-template-skill" / "SKILL.md"
+            print(f"\nGenerated SKILL.md:\n{skill_md.read_text()[:500]}...")
+        else:
+            print(f"Error: {result.stderr}")
+
+
+def example_create_skill_with_ai():
+    """Create a skill using AI generation (requires API key)."""
+    print("\n" + "=" * 60)
+    print("Example 2: Create skill with AI generation")
+    print("=" * 60)
+    
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Skipping: OPENAI_API_KEY not set")
+        return
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        result = subprocess.run([
+            "praisonai", "skills", "create",
+            "--name", "csv-analyzer",
+            "--description", "Analyze CSV files and generate statistical reports",
+            "--output-dir", tmpdir
+        ], capture_output=True, text=True)
+        
+        print(result.stdout)
+        if result.returncode == 0:
+            skill_md = Path(tmpdir) / "csv-analyzer" / "SKILL.md"
+            print(f"\nGenerated SKILL.md:\n{skill_md.read_text()[:800]}...")
+            
+            # Check if script was generated
+            script_path = Path(tmpdir) / "csv-analyzer" / "scripts" / "skill.py"
+            if script_path.exists():
+                print(f"\nGenerated skill.py:\n{script_path.read_text()[:500]}...")
+
+
+def example_create_skill_with_script():
+    """Create a skill with auto-generated Python script."""
+    print("\n" + "=" * 60)
+    print("Example 3: Create skill with script")
+    print("=" * 60)
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        result = subprocess.run([
+            "praisonai", "skills", "create",
+            "--name", "data-processor",
+            "--description", "Process and transform data files",
+            "--template",
+            "--script",
+            "--output-dir", tmpdir
+        ], capture_output=True, text=True)
+        
+        print(result.stdout)
+        if result.returncode == 0:
+            script_path = Path(tmpdir) / "data-processor" / "scripts" / "skill.py"
+            if script_path.exists():
+                print(f"\nGenerated skill.py:\n{script_path.read_text()}")
+
+
+if __name__ == "__main__":
+    example_create_skill_with_template()
+    example_create_skill_with_ai()
+    example_create_skill_with_script()

examples/skills/custom_skill_example.py

@@ -0,0 +1,163 @@
+"""
+Custom Skill Creation Example
+
+This example demonstrates how to programmatically create and validate
+custom Agent Skills using PraisonAI Agents.
+"""
+
+import tempfile
+from pathlib import Path
+
+from praisonaiagents.skills import (
+    SkillManager,
+    SkillLoader,
+    validate,
+    read_properties,
+)
+
+
+def create_custom_skill(
+    name: str,
+    description: str,
+    instructions: str,
+    base_dir: str = None
+) -> Path:
+    """Create a custom skill programmatically.
+    
+    Args:
+        name: Skill name (kebab-case)
+        description: What the skill does
+        instructions: Markdown instructions for the skill
+        base_dir: Directory to create skill in (default: temp dir)
+        
+    Returns:
+        Path to the created skill directory
+    """
+    if base_dir is None:
+        base_dir = tempfile.mkdtemp()
+    
+    skill_dir = Path(base_dir) / name
+    skill_dir.mkdir(parents=True, exist_ok=True)
+    
+    # Create SKILL.md
+    skill_md_content = f"""---
+name: {name}
+description: {description}
+license: Apache-2.0
+metadata:
+  author: custom
+  version: "1.0"
+---
+
+{instructions}
+"""
+    (skill_dir / "SKILL.md").write_text(skill_md_content)
+    
+    # Create optional directories
+    (skill_dir / "scripts").mkdir(exist_ok=True)
+    (skill_dir / "references").mkdir(exist_ok=True)
+    (skill_dir / "assets").mkdir(exist_ok=True)
+    
+    return skill_dir
+
+
+def main():
+    print("=" * 60)
+    print("Custom Skill Creation Example")
+    print("=" * 60)
+    
+    # Create a custom skill
+    print("\n1. Creating a custom skill...")
+    
+    skill_path = create_custom_skill(
+        name="code-review",
+        description="Review code for best practices, bugs, and improvements. Use when asked to review or analyze code quality.",
+        instructions="""
+# Code Review Skill
+
+## Overview
+This skill enables thorough code review with focus on:
+- Code quality and readability
+- Potential bugs and edge cases
+- Performance considerations
+- Security vulnerabilities
+- Best practices adherence
+
+## Instructions
+
+When reviewing code:
+
+1. **Read the code carefully** - Understand the purpose and flow
+2. **Check for bugs** - Look for null checks, edge cases, error handling
+3. **Evaluate readability** - Variable names, comments, structure
+4. **Consider performance** - Time/space complexity, unnecessary operations
+5. **Security review** - Input validation, injection risks, data exposure
+6. **Suggest improvements** - Provide actionable recommendations
+
+## Output Format
+
+Structure your review as:
+- Summary: Brief overview of the code
+- Issues: List of problems found (severity: high/medium/low)
+- Suggestions: Recommended improvements
+- Positive aspects: What's done well
+"""
+    )
+    
+    print(f"   Created skill at: {skill_path}")
+    
+    # Validate the skill
+    print("\n2. Validating the skill...")
+    errors = validate(skill_path)
+    
+    if errors:
+        print("   Validation errors:")
+        for error in errors:
+            print(f"   - {error}")
+    else:
+        print("   ✓ Skill is valid!")
+    
+    # Load and inspect the skill
+    print("\n3. Loading skill properties...")
+    props = read_properties(skill_path)
+    print(f"   Name: {props.name}")
+    print(f"   Description: {props.description[:60]}...")
+    print(f"   License: {props.license}")
+    
+    # Use with SkillLoader for progressive loading
+    print("\n4. Using SkillLoader for progressive disclosure...")
+    loader = SkillLoader()
+    
+    # Level 1: Load metadata only
+    skill = loader.load_metadata(str(skill_path))
+    print(f"   Level 1 - Metadata loaded: {skill.properties.name}")
+    print(f"   Instructions loaded: {skill.is_activated}")
+    
+    # Level 2: Activate (load instructions)
+    loader.activate(skill)
+    print(f"   Level 2 - Activated: {skill.is_activated}")
+    print(f"   Instructions preview: {skill.instructions[:100]}...")
+    
+    # Use with SkillManager
+    print("\n5. Using with SkillManager...")
+    manager = SkillManager()
+    manager.add_skill(str(skill_path))
+    
+    # Generate prompt XML
+    prompt = manager.to_prompt()
+    print("   Generated prompt XML:")
+    print(prompt)
+    
+    # Clean up
+    print("\n6. Cleanup...")
+    import shutil
+    shutil.rmtree(skill_path.parent)
+    print("   Temporary files removed.")
+    
+    print("\n" + "=" * 60)
+    print("Example complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/skills/multi_agent_example.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""
+Example: Multi-Agent CLI Usage
+
+This example demonstrates how to use the praisonai agents command
+to define and run multiple agents from the command line.
+"""
+
+import subprocess
+import os
+
+
+def example_single_agent():
+    """Run a single agent with a simple task."""
+    print("=" * 60)
+    print("Example 1: Single Agent")
+    print("=" * 60)
+    
+    result = subprocess.run([
+        "praisonai", "agents", "run",
+        "--agent", "assistant:Helper",
+        "--task", "What is the capital of France?"
+    ], capture_output=True, text=True)
+    
+    print(result.stdout)
+    if result.returncode != 0:
+        print(f"Error: {result.stderr}")
+
+
+def example_agent_with_tools():
+    """Run an agent with tools."""
+    print("\n" + "=" * 60)
+    print("Example 2: Agent with Tools")
+    print("=" * 60)
+    
+    result = subprocess.run([
+        "praisonai", "agents", "run",
+        "--agent", "researcher:Research Analyst:internet_search",
+        "--task", "Find the latest news about renewable energy"
+    ], capture_output=True, text=True)
+    
+    print(result.stdout)
+
+
+def example_multiple_agents_sequential():
+    """Run multiple agents in sequential mode."""
+    print("\n" + "=" * 60)
+    print("Example 3: Multiple Agents (Sequential)")
+    print("=" * 60)
+    
+    result = subprocess.run([
+        "praisonai", "agents", "run",
+        "--agent", "researcher:Research Analyst:internet_search",
+        "--agent", "writer:Content Writer:write_file",
+        "--task", "Research AI trends and write a brief summary"
+    ], capture_output=True, text=True)
+    
+    print(result.stdout)
+
+
+def example_multiple_agents_parallel():
+    """Run multiple agents in parallel mode."""
+    print("\n" + "=" * 60)
+    print("Example 4: Multiple Agents (Parallel)")
+    print("=" * 60)
+    
+    result = subprocess.run([
+        "praisonai", "agents", "run",
+        "--agent", "analyst1:Market Analyst",
+        "--agent", "analyst2:Tech Analyst",
+        "--process", "parallel",
+        "--task", "Analyze the current state of AI industry"
+    ], capture_output=True, text=True)
+    
+    print(result.stdout)
+
+
+def example_with_custom_llm():
+    """Run agents with a custom LLM model."""
+    print("\n" + "=" * 60)
+    print("Example 5: Custom LLM Model")
+    print("=" * 60)
+    
+    result = subprocess.run([
+        "praisonai", "agents", "run",
+        "--agent", "coder:Developer:execute_command",
+        "--llm", "gpt-4o-mini",
+        "--task", "Write a simple hello world Python script"
+    ], capture_output=True, text=True)
+    
+    print(result.stdout)
+
+
+def example_with_instructions():
+    """Run agents with additional instructions."""
+    print("\n" + "=" * 60)
+    print("Example 6: With Additional Instructions")
+    print("=" * 60)
+    
+    result = subprocess.run([
+        "praisonai", "agents", "run",
+        "--agent", "analyst:Data Analyst",
+        "--instructions", "Be concise and provide only key insights",
+        "--task", "Explain the benefits of using AI in healthcare"
+    ], capture_output=True, text=True)
+    
+    print(result.stdout)
+
+
+def example_list_tools():
+    """List available tools for agents."""
+    print("\n" + "=" * 60)
+    print("Example 7: List Available Tools")
+    print("=" * 60)
+    
+    result = subprocess.run([
+        "praisonai", "agents", "list"
+    ], capture_output=True, text=True)
+    
+    print(result.stdout)
+
+
+if __name__ == "__main__":
+    # Check for API key
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Warning: OPENAI_API_KEY not set. Some examples may fail.")
+        print()
+    
+    # Run examples
+    example_list_tools()
+    example_single_agent()
+    # Uncomment to run more examples:
+    # example_agent_with_tools()
+    # example_multiple_agents_sequential()
+    # example_multiple_agents_parallel()
+    # example_with_custom_llm()
+    # example_with_instructions()

examples/skills/pdf-processing/SKILL.md

@@ -0,0 +1,38 @@
+---
+name: pdf-processing
+description: Process and extract information from PDF documents. Use this skill when the user asks to read, analyze, or extract data from PDF files.
+license: Apache-2.0
+compatibility: Works with PraisonAI Agents
+metadata:
+  author: praisonai
+  version: "1.0"
+---
+
+# PDF Processing Skill
+
+## Overview
+
+This skill enables agents to process PDF documents, extract text content, and analyze the information within them.
+
+## When to Use
+
+Activate this skill when:
+- User asks to read or analyze a PDF file
+- User needs to extract text from a PDF
+- User wants to summarize a PDF document
+- User needs to search for information within a PDF
+
+## Instructions
+
+1. First, verify the PDF file exists at the specified path
+2. Use appropriate tools to read the PDF content
+3. Extract text while preserving structure where possible
+4. For large PDFs, process in chunks to manage context
+5. Summarize or analyze based on user's specific request
+
+## Best Practices
+
+- Always confirm the file path before processing
+- Handle encrypted PDFs gracefully with appropriate error messages
+- For scanned PDFs, note that OCR may be required
+- Preserve important formatting like tables and lists

examples/thinking/basic_thinking.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""
+Basic Thinking Budgets Example for PraisonAI Agents.
+
+This example demonstrates how to use thinking budgets to:
+1. Configure token budgets for extended thinking
+2. Use predefined budget levels
+3. Track thinking usage
+4. Adapt budgets based on complexity
+
+Usage:
+    python basic_thinking.py
+"""
+
+from praisonaiagents.thinking import (
+    ThinkingBudget, ThinkingConfig, ThinkingUsage, ThinkingTracker
+)
+from praisonaiagents.thinking.budget import BudgetLevel
+
+
+def main():
+    print("=" * 60)
+    print("Thinking Budgets Demo")
+    print("=" * 60)
+    
+    # ==========================================================================
+    # Budget Levels
+    # ==========================================================================
+    print("\n--- Budget Levels ---")
+    
+    levels = [
+        ("Minimal", ThinkingBudget.minimal()),
+        ("Low", ThinkingBudget.low()),
+        ("Medium", ThinkingBudget.medium()),
+        ("High", ThinkingBudget.high()),
+        ("Maximum", ThinkingBudget.maximum()),
+    ]
+    
+    for name, budget in levels:
+        print(f"  {name}: {budget.max_tokens:,} tokens")
+    
+    # ==========================================================================
+    # Custom Budget
+    # ==========================================================================
+    print("\n--- Custom Budget ---")
+    
+    custom_budget = ThinkingBudget(
+        max_tokens=12000,
+        min_tokens=2000,
+        adaptive=True,
+        max_time_seconds=120.0
+    )
+    print(f"  Max tokens: {custom_budget.max_tokens:,}")
+    print(f"  Min tokens: {custom_budget.min_tokens:,}")
+    print(f"  Adaptive: {custom_budget.adaptive}")
+    print(f"  Time limit: {custom_budget.max_time_seconds}s")
+    
+    # ==========================================================================
+    # Complexity-Based Scaling
+    # ==========================================================================
+    print("\n--- Complexity-Based Token Allocation ---")
+    
+    budget = ThinkingBudget(
+        max_tokens=16000,
+        min_tokens=2000,
+        adaptive=True
+    )
+    
+    complexities = [0.0, 0.25, 0.5, 0.75, 1.0]
+    for complexity in complexities:
+        tokens = budget.get_tokens_for_complexity(complexity)
+        bar = "█" * int(tokens / 1000)
+        print(f"  Complexity {complexity:.2f}: {tokens:,} tokens {bar}")
+    
+    # ==========================================================================
+    # Usage Tracking
+    # ==========================================================================
+    print("\n--- Usage Tracking ---")
+    
+    usage = ThinkingUsage(
+        budget_tokens=8000,
+        tokens_used=5000,
+        time_seconds=30.0,
+        budget_time=60.0
+    )
+    
+    print(f"  Tokens used: {usage.tokens_used:,} / {usage.budget_tokens:,}")
+    print(f"  Tokens remaining: {usage.tokens_remaining:,}")
+    print(f"  Utilization: {usage.token_utilization:.1%}")
+    print(f"  Over budget: {usage.is_over_budget}")
+    print(f"  Time remaining: {usage.time_remaining}s")
+    
+    # ==========================================================================
+    # Session Tracking
+    # ==========================================================================
+    print("\n--- Session Tracking ---")
+    
+    tracker = ThinkingTracker()
+    
+    # Simulate multiple thinking sessions
+    sessions = [
+        (8000, 4000, 20.0),
+        (8000, 6000, 35.0),
+        (16000, 12000, 60.0),
+        (8000, 9000, 45.0),  # Over budget
+    ]
+    
+    for budget_tokens, tokens_used, time_seconds in sessions:
+        session = tracker.start_session(budget_tokens=budget_tokens)
+        tracker.end_session(session, tokens_used=tokens_used, time_seconds=time_seconds)
+    
+    summary = tracker.get_summary()
+    print(f"  Total sessions: {summary['session_count']}")
+    print(f"  Total tokens used: {summary['total_tokens_used']:,}")
+    print(f"  Average tokens/session: {summary['average_tokens_per_session']:,.0f}")
+    print(f"  Average utilization: {summary['average_utilization']:.1%}")
+    print(f"  Over-budget sessions: {summary['over_budget_count']}")
+    
+    # ==========================================================================
+    # Serialization
+    # ==========================================================================
+    print("\n--- Serialization ---")
+    
+    budget = ThinkingBudget.from_level(BudgetLevel.HIGH)
+    data = budget.to_dict()
+    print(f"  Serialized: level={data['level']}, max_tokens={data['max_tokens']}")
+    
+    restored = ThinkingBudget.from_dict(data)
+    print(f"  Restored: {restored.max_tokens:,} tokens")
+    
+    print("\n" + "=" * 60)
+    print("Demo Complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/tools/external/arxiv/tool.py

@@ -0,0 +1,44 @@
+"""ArXiv Tool Example.
+
+This example demonstrates how to use the ArXiv tool with PraisonAI agents.
+
+Requirements:
+    pip install "praisonai[tools]"
+
+Usage:
+    python tool.py
+
+Note: No API key required!
+"""
+
+from praisonai_tools import ArxivTool
+
+
+def main():
+    # Initialize ArXiv tool (no API key needed)
+    arxiv = ArxivTool()
+    
+    # Example 1: Search ArXiv
+    print("=" * 60)
+    print("Example 1: Search ArXiv")
+    print("=" * 60)
+    
+    results = arxiv.search("transformer neural networks", max_results=3)
+    
+    if results and "error" in results[0]:
+        print(f"Error: {results[0]['error']}")
+    else:
+        print(f"Found {len(results)} papers:")
+        for paper in results:
+            print(f"\n  Title: {paper.get('title', 'N/A')[:60]}...")
+            authors = paper.get('authors', [])
+            if authors:
+                print(f"  Authors: {', '.join(authors[:2])}...")
+            print(f"  Published: {paper.get('published', 'N/A')}")
+            print(f"  PDF: {paper.get('pdf_url', 'N/A')}")
+    
+    print("\n✅ ArXiv tool working correctly!")
+
+
+if __name__ == "__main__":
+    main()

examples/tools/external/calculator/tool.py

@@ -0,0 +1,53 @@
+"""Calculator Tool Example.
+
+This example demonstrates how to use the Calculator tool with PraisonAI agents.
+
+Requirements:
+    pip install "praisonai[tools]"
+
+Usage:
+    python tool.py
+
+Note: No API key required!
+"""
+
+from praisonai_tools import CalculatorTool
+
+
+def main():
+    # Initialize Calculator tool
+    calc = CalculatorTool()
+    
+    # Example 1: Basic arithmetic
+    print("=" * 60)
+    print("Example 1: Basic Arithmetic")
+    print("=" * 60)
+    
+    expressions = [
+        "2 + 2",
+        "10 * 5",
+        "100 / 4",
+        "2 ** 10",
+        "(10 + 5) * 2 - 3"
+    ]
+    
+    for expr in expressions:
+        result = calc.evaluate(expr)
+        print(f"  {expr} = {result.get('result', result)}")
+    
+    # Example 2: Percentage calculation
+    print("\n" + "=" * 60)
+    print("Example 2: Percentage Calculation")
+    print("=" * 60)
+    
+    result = calc.evaluate("250 * 0.15")
+    print(f"  15% of 250 = {result.get('result', result)}")
+    
+    result = calc.evaluate("1000 * (1 + 0.05) ** 3")
+    print(f"  $1000 at 5% for 3 years = ${result.get('result', 0):.2f}")
+    
+    print("\n✅ Calculator tool working correctly!")
+
+
+if __name__ == "__main__":
+    main()

examples/tools/external/duckduckgo/tool.py

@@ -0,0 +1,75 @@
+"""DuckDuckGo Search Tool Example.
+
+This example demonstrates how to use the DuckDuckGo search tool with PraisonAI agents.
+
+Requirements:
+    pip install "praisonai[tools]"
+
+Usage:
+    python tool.py
+
+Note: No API key required!
+"""
+
+from praisonai_tools import DuckDuckGoTool
+
+
+def main():
+    # Initialize DuckDuckGo tool (no API key needed)
+    ddg = DuckDuckGoTool()
+    
+    # Example 1: Web search
+    print("=" * 60)
+    print("Example 1: Web Search")
+    print("=" * 60)
+    
+    results = ddg.search("Python programming best practices", max_results=3)
+    
+    if results and "error" in results[0]:
+        print(f"Error: {results[0]['error']}")
+    else:
+        print(f"Found {len(results)} results:")
+        for r in results:
+            print(f"  - {r['title']}")
+            print(f"    URL: {r['url']}")
+            print(f"    Snippet: {r['snippet'][:100]}...")
+            print()
+    
+    # Example 2: News search
+    print("=" * 60)
+    print("Example 2: News Search")
+    print("=" * 60)
+    
+    news = ddg.news("artificial intelligence", max_results=3)
+    
+    if news and "error" in news[0]:
+        print(f"Error: {news[0]['error']}")
+    else:
+        print(f"Found {len(news)} news articles:")
+        for n in news:
+            print(f"  - {n['title']}")
+            print(f"    Source: {n.get('source', 'N/A')}")
+            print(f"    Date: {n.get('date', 'N/A')}")
+            print()
+    
+    # Example 3: Image search
+    print("=" * 60)
+    print("Example 3: Image Search")
+    print("=" * 60)
+    
+    images = ddg.images("sunset landscape", max_results=3)
+    
+    if images and "error" in images[0]:
+        print(f"Error: {images[0]['error']}")
+    else:
+        print(f"Found {len(images)} images:")
+        for img in images:
+            print(f"  - {img['title']}")
+            print(f"    Image URL: {img['image_url'][:60]}...")
+            print()
+    
+    print("✅ DuckDuckGo tool working correctly!")
+
+
+if __name__ == "__main__":
+    main()

examples/tools/external/exa/tool.py

@@ -0,0 +1,48 @@
+"""Exa Search Tool Example.
+
+This example demonstrates how to use the Exa search tool with PraisonAI agents.
+
+Requirements:
+    pip install "praisonai[tools]"
+    export EXA_API_KEY=your_api_key
+
+Usage:
+    python tool.py
+"""
+
+import os
+from praisonai_tools import ExaTool
+
+
+def main():
+    # Check for API key
+    if not os.getenv("EXA_API_KEY"):
+        print("Error: EXA_API_KEY environment variable not set")
+        print("Get your API key from https://exa.ai/")
+        return
+    
+    # Initialize Exa tool
+    exa = ExaTool()
+    
+    # Example 1: Neural search
+    print("=" * 60)
+    print("Example 1: Neural Search")
+    print("=" * 60)
+    
+    results = exa.search("best practices for Python development", num_results=3)
+    
+    if results and "error" in results[0]:
+        print(f"Error: {results[0]['error']}")
+    else:
+        print(f"Found {len(results)} results:")
+        for r in results:
+            print(f"  - {r.get('title', 'N/A')[:50]}...")
+            print(f"    URL: {r.get('url', 'N/A')[:60]}...")
+            print(f"    Score: {r.get('score', 'N/A')}")
+            print()
+    
+    print("✅ Exa tool working correctly!")
+
+
+if __name__ == "__main__":
+    main()

examples/tools/external/firecrawl/tool.py

@@ -0,0 +1,45 @@
+"""Firecrawl Tool Example.
+
+This example demonstrates how to use the Firecrawl tool with PraisonAI agents.
+
+Requirements:
+    pip install "praisonai[tools]"
+    export FIRECRAWL_API_KEY=your_api_key
+
+Usage:
+    python tool.py
+"""
+
+import os
+from praisonai_tools import FirecrawlTool
+
+
+def main():
+    # Check for API key
+    if not os.getenv("FIRECRAWL_API_KEY"):
+        print("Error: FIRECRAWL_API_KEY environment variable not set")
+        print("Get your API key from https://firecrawl.dev/")
+        return
+    
+    # Initialize Firecrawl tool
+    firecrawl = FirecrawlTool()
+    
+    # Example 1: Scrape a page
+    print("=" * 60)
+    print("Example 1: Scrape a Page")
+    print("=" * 60)
+    
+    result = firecrawl.scrape("https://example.com")
+    
+    if "error" in result:
+        print(f"Error: {result['error']}")
+    else:
+        print(f"URL: {result.get('url', 'N/A')}")
+        markdown = result.get('markdown', '')
+        print(f"Content preview: {markdown[:300]}...")
+    
+    print("\n✅ Firecrawl tool working correctly!")
+
+
+if __name__ == "__main__":
+    main()

examples/tools/external/hackernews/tool.py

@@ -0,0 +1,42 @@
+"""Hacker News Tool Example.
+
+This example demonstrates how to use the Hacker News tool with PraisonAI agents.
+
+Requirements:
+    pip install "praisonai[tools]"
+
+Usage:
+    python tool.py
+
+Note: No API key required!
+"""
+
+from praisonai_tools import HackerNewsTool
+
+
+def main():
+    # Initialize Hacker News tool
+    hn = HackerNewsTool()
+    
+    # Example 1: Get top stories
+    print("=" * 60)
+    print("Example 1: Top Stories")
+    print("=" * 60)
+    
+    stories = hn.get_top_stories(limit=5)
+    
+    if stories and "error" in stories[0]:
+        print(f"Error: {stories[0]['error']}")
+    else:
+        print(f"Found {len(stories)} top stories:")
+        for story in stories:
+            print(f"  - {story.get('title', 'N/A')[:50]}...")
+            print(f"    Score: {story.get('score', 'N/A')} | Comments: {story.get('descendants', 'N/A')}")
+            print(f"    URL: {story.get('url', 'N/A')[:60]}...")
+            print()
+    
+    print("✅ Hacker News tool working correctly!")
+
+
+if __name__ == "__main__":
+    main()

examples/tools/external/jina/tool.py

@@ -0,0 +1,40 @@
+"""Jina Reader Tool Example.
+
+This example demonstrates how to use the Jina Reader tool with PraisonAI agents.
+
+Requirements:
+    pip install "praisonai[tools]"
+
+Usage:
+    python tool.py
+
+Note: Works without API key (with rate limits)
+"""
+
+from praisonai_tools import JinaTool
+
+
+def main():
+    # Initialize Jina tool (works without API key)
+    jina = JinaTool()
+    
+    # Example 1: Read a URL
+    print("=" * 60)
+    print("Example 1: Read URL Content")
+    print("=" * 60)
+    
+    result = jina.read("https://example.com")
+    
+    if "error" in result:
+        print(f"Error: {result['error']}")
+    else:
+        print(f"URL: {result.get('url', 'N/A')}")
+        print(f"Title: {result.get('title', 'N/A')}")
+        content = result.get('content', '')
+        print(f"Content preview: {content[:300]}...")
+    
+    print("\n✅ Jina tool working correctly!")
+
+
+if __name__ == "__main__":
+    main()

examples/tools/external/postgres/tool.py

@@ -0,0 +1,56 @@
+"""PostgreSQL Tool Example.
+
+This example demonstrates how to use the PostgreSQL tool with PraisonAI agents.
+
+Requirements:
+    pip install "praisonai[tools]"
+    Docker: docker run -d --name postgres -e POSTGRES_PASSWORD=praison123 -e POSTGRES_DB=praisonai -p 5432:5432 postgres:16
+
+Usage:
+    python tool.py
+"""
+
+from praisonai_tools import PostgresTool
+
+
+def main():
+    # Initialize PostgreSQL tool
+    pg = PostgresTool(
+        host="localhost",
+        port=5432,
+        database="praisonai",
+        user="postgres",
+        password="praison123"
+    )
+    
+    # Example 1: List tables
+    print("=" * 60)
+    print("Example 1: List Tables")
+    print("=" * 60)
+    
+    tables = pg.list_tables()
+    
+    if isinstance(tables, dict) and "error" in tables:
+        print(f"Error: {tables['error']}")
+    else:
+        print(f"Found {len(tables)} tables:")
+        for t in tables[:10]:
+            print(f"  - {t.get('table_name', t)}")
+    
+    # Example 2: Query
+    print("\n" + "=" * 60)
+    print("Example 2: Execute Query")
+    print("=" * 60)
+    
+    result = pg.query("SELECT version()")
+    
+    if isinstance(result, dict) and "error" in result:
+        print(f"Error: {result['error']}")
+    else:
+        print(f"PostgreSQL version: {result}")
+    
+    print("\n✅ PostgreSQL tool working correctly!")
+
+
+if __name__ == "__main__":
+    main()

examples/tools/external/serper/tool.py

@@ -0,0 +1,47 @@
+"""Serper Tool Example.
+
+This example demonstrates how to use the Serper (Google Search) tool with PraisonAI agents.
+
+Requirements:
+    pip install "praisonai[tools]"
+    export SERPER_API_KEY=your_api_key
+
+Usage:
+    python tool.py
+"""
+
+import os
+from praisonai_tools import SerperTool
+
+
+def main():
+    # Check for API key
+    if not os.getenv("SERPER_API_KEY"):
+        print("Error: SERPER_API_KEY environment variable not set")
+        print("Get your API key from https://serper.dev/")
+        return
+    
+    # Initialize Serper tool
+    serper = SerperTool()
+    
+    # Example 1: Web search
+    print("=" * 60)
+    print("Example 1: Google Web Search")
+    print("=" * 60)
+    
+    results = serper.search("Python programming tutorials", max_results=3)
+    
+    if results and "error" in results[0]:
+        print(f"Error: {results[0]['error']}")
+    else:
+        print(f"Found {len(results)} results:")
+        for r in results:
+            print(f"  - {r.get('title', 'N/A')[:50]}...")
+            print(f"    URL: {r.get('link', 'N/A')[:60]}...")
+            print()
+    
+    print("✅ Serper tool working correctly!")
+
+
+if __name__ == "__main__":
+    main()

examples/tools/external/tavily/tool.py

@@ -0,0 +1,57 @@
+"""Tavily Search Tool Example.
+
+This example demonstrates how to use the Tavily search tool with PraisonAI agents.
+
+Requirements:
+    pip install "praisonai[tools]"
+    export TAVILY_API_KEY=your_api_key
+
+Usage:
+    python tool.py
+"""
+
+import os
+from praisonai_tools import TavilyTool
+
+def main():
+    # Check for API key
+    if not os.getenv("TAVILY_API_KEY"):
+        print("Error: TAVILY_API_KEY environment variable not set")
+        print("Get your API key from https://tavily.com/")
+        return
+    
+    # Initialize Tavily tool
+    tavily = TavilyTool()
+    
+    # Example 1: Basic search
+    print("=" * 60)
+    print("Example 1: Basic Search")
+    print("=" * 60)
+    
+    results = tavily.search("What is quantum computing?", max_results=3)
+    
+    if "error" in results:
+        print(f"Error: {results['error']}")
+    else:
+        print(f"Query: {results.get('query')}")
+        if "answer" in results:
+            print(f"\nAI Answer: {results['answer'][:200]}...")
+        print(f"\nResults ({len(results.get('results', []))}):")
+        for r in results.get("results", []):
+            print(f"  - {r['title']}")
+            print(f"    URL: {r['url']}")
+            print(f"    Score: {r.get('score', 'N/A')}")
+    
+    # Example 2: Search context for RAG
+    print("\n" + "=" * 60)
+    print("Example 2: Search Context (for RAG)")
+    print("=" * 60)
+    
+    context = tavily.search_context("Python best practices 2024")
+    print(f"Context (first 300 chars): {context[:300]}...")
+    
+    print("\n✅ Tavily tool working correctly!")
+
+
+if __name__ == "__main__":
+    main()

examples/tools/external/wikipedia/tool.py

@@ -0,0 +1,69 @@
+"""Wikipedia Tool Example.
+
+This example demonstrates how to use the Wikipedia tool with PraisonAI agents.
+
+Requirements:
+    pip install "praisonai[tools]"
+
+Usage:
+    python tool.py
+
+Note: No API key required!
+"""
+
+from praisonai_tools import WikipediaTool
+
+
+def main():
+    # Initialize Wikipedia tool (no API key needed)
+    wiki = WikipediaTool()
+    
+    # Example 1: Search Wikipedia
+    print("=" * 60)
+    print("Example 1: Search Wikipedia")
+    print("=" * 60)
+    
+    results = wiki.search("Python programming", max_results=5)
+    
+    if results and "error" in results[0]:
+        print(f"Error: {results[0]['error']}")
+    else:
+        print(f"Found {len(results)} results:")
+        for r in results:
+            print(f"  - {r['title']}")
+    
+    # Example 2: Get page summary
+    print("\n" + "=" * 60)
+    print("Example 2: Get Page Summary")
+    print("=" * 60)
+    
+    summary = wiki.summary("Artificial intelligence", sentences=3)
+    
+    if "error" in summary:
+        print(f"Error: {summary['error']}")
+    else:
+        print(f"Title: {summary['title']}")
+        print(f"Summary: {summary['summary'][:300]}...")
+    
+    # Example 3: Get full page
+    print("\n" + "=" * 60)
+    print("Example 3: Get Full Page")
+    print("=" * 60)
+    
+    page = wiki.get_page("Machine learning")
+    
+    if "error" in page:
+        print(f"Error: {page['error']}")
+        if "options" in page:
+            print(f"Options: {page['options'][:5]}")
+    else:
+        print(f"Title: {page['title']}")
+        print(f"URL: {page['url']}")
+        print(f"Categories: {page['categories'][:3]}")
+        print(f"Content preview: {page['content'][:200]}...")
+    
+    print("\n✅ Wikipedia tool working correctly!")
+
+
+if __name__ == "__main__":
+    main()

src/praisonai-agents/praisonaiagents/__init__.py

@@ -3,7 +3,7 @@ Praison AI Agents - A package for hierarchical AI agent task execution
 """
 
 # Apply warning patch BEFORE any imports to intercept warnings at the source
-from . import _warning_patch
+from . import _warning_patch  # noqa: F401
 
 # Import centralized logging configuration FIRST
 from . import _logging
@@ -54,6 +54,7 @@ except ImportError:
     MCP = None
 from .session import Session
 from .memory.memory import Memory
+from .db import db
 # Workflows - import from dedicated workflows module
 from .workflows import (
     Workflow, WorkflowStep, WorkflowContext, StepResult,
@@ -78,6 +79,19 @@ def __getattr__(name):
     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
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 # Planning mode support
@@ -244,6 +258,7 @@ __all__ = [
     'AutoAgents',
     'Session',
     'Memory',
+    'db',
     'display_interaction',
     'display_self_reflection',
     'display_instruction',
@@ -289,6 +304,23 @@ __all__ = [
     'ExpandResult'
 ]
 
+# Add workflow exports
+__all__.extend([
+    'Workflow',
+    'WorkflowStep', 
+    'WorkflowContext',
+    'StepResult',
+    'Route',
+    'Parallel',
+    'Loop',
+    'Repeat',
+    'route',
+    'parallel',
+    'loop',
+    'repeat',
+    'Pipeline'
+])
+
 # Add MCP to __all__ if available
 if _mcp_available:
     __all__.append('MCP')
@@ -305,6 +337,14 @@ __all__.extend([
     'LineRange'
 ])
 
+# Add Agent Skills exports (lazy loaded)
+__all__.extend([
+    'SkillManager',
+    'SkillProperties',
+    'SkillMetadata',
+    'SkillLoader'
+])
+
 # AG-UI support (optional, lazy loaded)
 try:
     from praisonaiagents.ui.agui import AGUI

src/praisonai-agents/praisonaiagents/_logging.py

@@ -55,10 +55,10 @@ def _get_all_noisy_loggers() -> List[str]:
 
 def _configure_loggers():
     """Configure all loggers based on LOGLEVEL environment variable."""
-    loglevel = os.environ.get('LOGLEVEL', 'INFO').upper()
+    loglevel = os.environ.get('LOGLEVEL', 'WARNING').upper()
     
-    # When DEBUG is set, allow some HTTP logging for API endpoints
-    if loglevel == 'DEBUG':
+    # When DEBUG or INFO is set, allow some HTTP logging for API endpoints
+    if loglevel in ('DEBUG', 'INFO'):
         allowed_debug_loggers = {"httpx", "httpx._client", "openai._client"}
         
         for logger_name in _get_all_noisy_loggers():
@@ -109,10 +109,10 @@ def configure_root_logger():
     """Configure the root logger with RichHandler."""
     from rich.logging import RichHandler
     
-    loglevel = os.environ.get('LOGLEVEL', 'INFO').upper()
+    loglevel = os.environ.get('LOGLEVEL', 'WARNING').upper()
     
     logging.basicConfig(
-        level=getattr(logging, loglevel, logging.INFO),
+        level=getattr(logging, loglevel, logging.WARNING),
         format="%(asctime)s %(filename)s:%(lineno)d %(levelname)s %(message)s",
         datefmt="[%X]",
         handlers=[RichHandler(rich_tracebacks=True)],

src/praisonai-agents/praisonaiagents/_warning_patch.py

@@ -8,9 +8,10 @@ import functools
 import sys
 
 # Apply aggressive warning filters first
+warnings.filterwarnings("ignore", message=".*There is no current event loop.*", category=DeprecationWarning)
 warnings.filterwarnings("ignore", message=".*Pydantic serializer warnings.*")
 warnings.filterwarnings("ignore", message=".*PydanticSerializationUnexpectedValue.*")
-warnings.filterwarnings("ignore", message=".*Expected 9 fields but got.*")
+warnings.filterwarnings("ignore", message=".*Expected \\d+ fields but got.*")
 warnings.filterwarnings("ignore", message=".*Expected `StreamingChoices` but got.*")
 warnings.filterwarnings("ignore", message=".*serialized value may not be as expected.*")
 warnings.filterwarnings("ignore", message=".*Use 'content=<...>' to upload raw bytes/text content.*")
@@ -27,11 +28,12 @@ SUPPRESSED_PATTERNS = [
     "The `dict` method is deprecated; use `model_dump` instead",
     "Pydantic serializer warnings",
     "PydanticSerializationUnexpectedValue",
-    "Expected 9 fields but got 5 for type `Message`",
+    "Expected",  # Catches all "Expected N fields but got M" patterns
     "Expected `StreamingChoices` but got `Choices`",
     "serialized value may not be as expected",
     "Mixing V1 models and V2 models",
-    "Please upgrade `Settings` to V2"
+    "Please upgrade `Settings` to V2",
+    "There is no current event loop"
 ]
 
 @functools.wraps(_original_warn)
@@ -43,7 +45,7 @@ def _patched_warn(message, category=None, stacklevel=1, source=None):
         if pattern in msg_str:
             return
     
-    if category == UserWarning and "pydantic" in msg_str.lower():
+    if category is UserWarning and "pydantic" in msg_str.lower():
         return
     
     _original_warn(message, category, stacklevel, source)
@@ -57,7 +59,7 @@ def _patched_warn_explicit(message, category, filename, lineno, module=None, reg
         if pattern in msg_str:
             return
     
-    if category == UserWarning and "pydantic" in msg_str.lower():
+    if category is UserWarning and "pydantic" in msg_str.lower():
         return
     
     if module and "pydantic" in str(module):

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

@@ -250,7 +250,11 @@ class Agent:
         fast_context_parallelism: int = 8,
         fast_context_timeout: float = 30.0,
         history_in_context: Optional[int] = None,
-        auto_save: Optional[str] = None
+        auto_save: Optional[str] = None,
+        skills: Optional[List[str]] = None,
+        skills_dirs: Optional[List[str]] = None,
+        db: Optional[Any] = None,
+        session_id: Optional[str] = None
     ):
         """Initialize an Agent instance.
 
@@ -607,6 +611,17 @@ Your Goal: {self.goal}
         self.fast_context_timeout = fast_context_timeout
         self._fast_context_instance = None  # Lazy loaded
 
+        # Agent Skills configuration (lazy loaded for zero performance impact)
+        self._skills = skills
+        self._skills_dirs = skills_dirs
+        self._skill_manager = None  # Lazy loaded
+        self._skills_initialized = False
+
+        # Database persistence (lazy - no imports until used)
+        self._db = db
+        self._session_id = session_id
+        self._db_initialized = False
+
     @property
     def console(self):
         """Lazily initialize Rich Console only when needed."""
@@ -626,6 +641,72 @@ Your Goal: {self.goal}
     def fast_context_path(self, value):
         self._fast_context_path = value
     
+    @property
+    def skill_manager(self):
+        """Lazily initialize SkillManager only when skills are accessed."""
+        if self._skill_manager is None and (self._skills or self._skills_dirs):
+            from ..skills import SkillManager
+            self._skill_manager = SkillManager()
+            
+            # Add explicit skill paths
+            if self._skills:
+                for skill_path in self._skills:
+                    self._skill_manager.add_skill(skill_path)
+            
+            # Discover skills from directories
+            if self._skills_dirs:
+                self._skill_manager.discover(self._skills_dirs, include_defaults=False)
+            
+            self._skills_initialized = True
+            
+            # Auto-add skill execution tools if not already present
+            self._add_skill_tools()
+        return self._skill_manager
+    
+    def _add_skill_tools(self):
+        """Add tools required for skill execution (read_file, run_skill_script).
+        
+        Uses lazy imports from praisonaiagents.tools to avoid performance impact
+        when skills are not used.
+        """
+        # Check if tools already include required capabilities
+        tool_names = set()
+        for tool in self.tools:
+            if callable(tool) and hasattr(tool, '__name__'):
+                tool_names.add(tool.__name__)
+            elif hasattr(tool, 'name'):
+                tool_names.add(tool.name)
+        
+        # Add read_file if not present
+        if 'read_file' not in tool_names:
+            try:
+                from ..tools import read_file
+                self.tools.append(read_file)
+                logging.debug("Added read_file tool for skill support")
+            except ImportError:
+                logging.warning("Could not import read_file tool for skills")
+        
+        # Add run_skill_script from skill_tools module
+        if 'run_skill_script' not in tool_names:
+            try:
+                from ..tools.skill_tools import create_skill_tools
+                # Create skill tools with current working directory
+                skill_tools = create_skill_tools()
+                self.tools.append(skill_tools.run_skill_script)
+                logging.debug("Added run_skill_script tool for skill support")
+            except ImportError as e:
+                logging.warning(f"Could not import skill_tools: {e}")
+    
+    def get_skills_prompt(self) -> str:
+        """Get the XML prompt for available skills.
+        
+        Returns:
+            XML string with <available_skills> block, or empty string if no skills
+        """
+        if self.skill_manager is None:
+            return ""
+        return self.skill_manager.to_prompt()
+    
     @property
     def _openai_client(self):
         """Lazily initialize OpenAI client only when needed."""
@@ -1284,6 +1365,13 @@ Your Goal: {self.goal}"""
                 if self.verbose:
                     self._display_memory_info()
         
+        # Add skills prompt if skills are configured
+        if self._skills or self._skills_dirs:
+            skills_prompt = self.get_skills_prompt()
+            if skills_prompt:
+                system_prompt += f"\n\n## Available Skills\n{skills_prompt}"
+                system_prompt += "\n\nWhen a skill is relevant to the task, read its SKILL.md file to get detailed instructions. If the skill has scripts in its scripts/ directory, you can execute them using the execute_code or run_script tool."
+        
         # Add tool usage instructions if tools are available
         # Use provided tools or fall back to self.tools
         tools_to_use = tools if tools is not None else self.tools
@@ -1538,13 +1626,13 @@ Your Goal: {self.goal}"""
         logging.debug(f"{self.name} executing tool {function_name} with arguments: {arguments}")
 
         # Check if approval is required for this tool
-        from ..approval import is_approval_required, console_approval_callback, get_risk_level, mark_approved, ApprovalDecision
+        from ..approval import is_approval_required, console_approval_callback, get_risk_level, mark_approved, ApprovalDecision, get_approval_callback
         if is_approval_required(function_name):
             risk_level = get_risk_level(function_name)
-            logging.info(f"Tool {function_name} requires approval (risk level: {risk_level})")
+            logging.debug(f"Tool {function_name} requires approval (risk level: {risk_level})")
             
             # Use global approval callback or default console callback
-            callback = approval_callback or console_approval_callback
+            callback = get_approval_callback() or console_approval_callback
             
             try:
                 decision = callback(function_name, arguments, risk_level)
@@ -1876,7 +1964,108 @@ Your Goal: {self.goal}"""
         #         expand=False
         #     )
 
+    def _init_db_session(self):
+        """Initialize DB session if db adapter is provided (lazy, first chat only)."""
+        if self._db is None or self._db_initialized:
+            return
+        
+        # Generate session_id if not provided: default to per-hour ID (YYYYMMDDHH-agentname)
+        if self._session_id is None:
+            import hashlib
+            from datetime import datetime, timezone
+            # Per-hour session ID: YYYYMMDDHH (UTC) + agent name hash for uniqueness
+            hour_str = datetime.now(timezone.utc).strftime("%Y%m%d%H")
+            agent_hash = hashlib.md5(self.name.encode()).hexdigest()[:6]
+            self._session_id = f"{hour_str}-{agent_hash}"
+        
+        # Call db adapter's on_agent_start to get previous messages
+        try:
+            history = self._db.on_agent_start(
+                agent_name=self.name,
+                session_id=self._session_id,
+                user_id=self.user_id,
+                metadata={"role": self.role, "goal": self.goal}
+            )
+            
+            # Restore chat history from previous session
+            if history:
+                for msg in history:
+                    self.chat_history.append({
+                        "role": msg.role,
+                        "content": msg.content
+                    })
+                logging.info(f"Resumed session {self._session_id} with {len(history)} messages")
+        except Exception as e:
+            logging.warning(f"Failed to initialize DB session: {e}")
+        
+        self._db_initialized = True
+        self._current_run_id = None  # Track current run
+
+    def _start_run(self, input_content: str):
+        """Start a new run (turn) for persistence tracking."""
+        if self._db is None:
+            return
+        
+        import uuid
+        self._current_run_id = f"run-{uuid.uuid4().hex[:12]}"
+        
+        try:
+            if hasattr(self._db, 'on_run_start'):
+                self._db.on_run_start(
+                    session_id=self._session_id,
+                    run_id=self._current_run_id,
+                    input_content=input_content,
+                    metadata={"agent_name": self.name}
+                )
+        except Exception as e:
+            logging.warning(f"Failed to start run: {e}")
+
+    def _end_run(self, output_content: str, status: str = "completed", metrics: dict = None):
+        """End the current run (turn)."""
+        if self._db is None or self._current_run_id is None:
+            return
+        
+        try:
+            if hasattr(self._db, 'on_run_end'):
+                self._db.on_run_end(
+                    session_id=self._session_id,
+                    run_id=self._current_run_id,
+                    output_content=output_content,
+                    status=status,
+                    metrics=metrics or {},
+                    metadata={"agent_name": self.name}
+                )
+        except Exception as e:
+            logging.warning(f"Failed to end run: {e}")
+        
+        self._current_run_id = None
+
+    def _persist_message(self, role: str, content: str):
+        """Persist a message to the DB if adapter is provided."""
+        if self._db is None:
+            return
+        
+        try:
+            if role == "user":
+                self._db.on_user_message(self._session_id, content)
+            elif role == "assistant":
+                self._db.on_agent_message(self._session_id, content)
+        except Exception as e:
+            logging.warning(f"Failed to persist message: {e}")
+
+    @property
+    def session_id(self) -> Optional[str]:
+        """Get the current session ID."""
+        return self._session_id
+
     def chat(self, prompt, temperature=1.0, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False, stream=None, task_name=None, task_description=None, task_id=None):
+        # Initialize DB session on first chat (lazy)
+        self._init_db_session()
+        
+        # Start a new run for this chat turn
+        prompt_str = prompt if isinstance(prompt, str) else str(prompt)
+        self._start_run(prompt_str)
+        
         # Reset the final display flag for each new conversation
         self._final_display_shown = False
         
@@ -1973,6 +2162,8 @@ Your Goal: {self.goal}"""
                         self.chat_history[-1].get("content") == normalized_content):
                     # Add user message to chat history BEFORE LLM call so handoffs can access it
                     self.chat_history.append({"role": "user", "content": normalized_content})
+                    # Persist user message to DB
+                    self._persist_message("user", normalized_content)
                 
                 try:
                     # Pass everything to LLM class
@@ -2002,6 +2193,8 @@ Your Goal: {self.goal}"""
                     )
 
                     self.chat_history.append({"role": "assistant", "content": response_text})
+                    # Persist assistant message to DB
+                    self._persist_message("assistant", response_text)
 
                     # Log completion time if in debug mode
                     if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
@@ -2046,6 +2239,8 @@ Your Goal: {self.goal}"""
                     self.chat_history[-1].get("content") == normalized_content):
                 # Add user message to chat history BEFORE LLM call so handoffs can access it
                 self.chat_history.append({"role": "user", "content": normalized_content})
+                # Persist user message to DB (OpenAI path)
+                self._persist_message("user", normalized_content)
 
             reflection_count = 0
             start_time = time.time()
@@ -2085,6 +2280,8 @@ Your Goal: {self.goal}"""
                             # Add to chat history and return raw response
                             # User message already added before LLM call via _build_messages
                             self.chat_history.append({"role": "assistant", "content": response_text})
+                            # Persist assistant message to DB
+                            self._persist_message("assistant", response_text)
                             # Apply guardrail validation even for JSON output
                             try:
                                 validated_response = self._apply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
@@ -2100,6 +2297,8 @@ Your Goal: {self.goal}"""
                         if not self.self_reflect:
                             # User message already added before LLM call via _build_messages
                             self.chat_history.append({"role": "assistant", "content": response_text})
+                            # Persist assistant message to DB (non-reflect path)
+                            self._persist_message("assistant", response_text)
                             if self.verbose:
                                 logging.debug(f"Agent {self.name} final response: {response_text}")
                             # Return only reasoning content if reasoning_steps is True
@@ -2188,11 +2387,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                     validated_response = self._apply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
                                     # Execute callback after validation
                                     self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
+                                    self._end_run(validated_response, "completed", {"duration_ms": (time.time() - start_time) * 1000})
                                     return validated_response
                                 except Exception as e:
                                     logging.error(f"Agent {self.name}: Guardrail validation failed after reflection: {e}")
                                     # Rollback chat history on guardrail failure
                                     self.chat_history = self.chat_history[:chat_history_length]
+                                    self._end_run(None, "error", {"error": str(e)})
                                     return None
 
                             # Check if we've hit max reflections

src/praisonai-agents/praisonaiagents/approval.py

@@ -43,6 +43,15 @@ def set_approval_callback(callback_fn: Callable):
     global approval_callback
     approval_callback = callback_fn
 
+def get_approval_callback() -> Optional[Callable]:
+    """Get the current approval callback function.
+    
+    Returns the custom callback if set, otherwise None.
+    This should be used instead of directly accessing approval_callback
+    to ensure the latest callback is always used.
+    """
+    return approval_callback
+
 def mark_approved(tool_name: str):
     """Mark a tool as approved in the current context."""
     approved = _approved_context.get(set())
@@ -90,7 +99,7 @@ def require_approval(risk_level: RiskLevel = "high"):
             except Exception as e:
                 # Fallback to sync approval if async fails
                 logging.warning(f"Async approval failed, using sync fallback: {e}")
-                callback = approval_callback or console_approval_callback
+                callback = get_approval_callback() or console_approval_callback
                 decision = callback(tool_name, kwargs, risk_level)
             
             if not decision.approved:
@@ -198,7 +207,7 @@ async def request_approval(function_name: str, arguments: Dict[str, Any]) -> App
     risk_level = TOOL_RISK_LEVELS.get(function_name, "medium")
     
     # Use custom callback if set, otherwise use console callback
-    callback = approval_callback or console_approval_callback
+    callback = get_approval_callback() or console_approval_callback
     
     try:
         # Handle async callbacks

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

@@ -0,0 +1,64 @@
+"""
+Background Agents Module for PraisonAI Agents.
+
+Provides the ability to run agents in the background, allowing:
+- Long-running tasks without blocking
+- Task queuing and management
+- Progress monitoring and notifications
+- Graceful cancellation
+
+Zero Performance Impact:
+- All imports are lazy loaded via __getattr__
+- Background processing only when explicitly started
+- No overhead when not in use
+
+Usage:
+    from praisonaiagents.background import BackgroundRunner, BackgroundTask
+    
+    # Create a background runner
+    runner = BackgroundRunner()
+    
+    # Submit a task
+    task = runner.submit(
+        agent=my_agent,
+        prompt="Research AI trends",
+        callback=on_complete
+    )
+    
+    # Check status
+    print(task.status)  # "running", "completed", "failed"
+    
+    # Wait for completion
+    result = await task.wait()
+"""
+
+__all__ = [
+    # Core classes
+    "BackgroundRunner",
+    "BackgroundTask",
+    # Status enum
+    "TaskStatus",
+    # Configuration
+    "BackgroundConfig",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components to avoid import overhead."""
+    if name == "BackgroundRunner":
+        from .runner import BackgroundRunner
+        return BackgroundRunner
+    
+    if name == "BackgroundTask":
+        from .task import BackgroundTask
+        return BackgroundTask
+    
+    if name == "TaskStatus":
+        from .task import TaskStatus
+        return TaskStatus
+    
+    if name == "BackgroundConfig":
+        from .config import BackgroundConfig
+        return BackgroundConfig
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

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

@@ -0,0 +1,17 @@
+"""
+Background Configuration for PraisonAI Agents.
+"""
+
+from dataclasses import dataclass, field
+from typing import Optional
+
+
+@dataclass
+class BackgroundConfig:
+    """Configuration for background task execution."""
+    max_concurrent_tasks: int = 5
+    default_timeout: Optional[float] = None  # None = no timeout
+    auto_cleanup: bool = True  # Auto-remove completed tasks
+    cleanup_delay: float = 300.0  # Seconds to keep completed tasks
+    enable_notifications: bool = True
+    log_progress: bool = True

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

@@ -0,0 +1,314 @@
+"""
+Background Runner for PraisonAI Agents.
+
+Manages background task execution.
+"""
+
+import asyncio
+import logging
+from typing import Optional, List, Dict, Any, Callable, Union
+from datetime import datetime
+
+from .task import BackgroundTask, TaskStatus
+from .config import BackgroundConfig
+
+logger = logging.getLogger(__name__)
+
+
+class BackgroundRunner:
+    """
+    Manages background task execution.
+    
+    Provides:
+    - Task submission and queuing
+    - Concurrent execution management
+    - Progress monitoring
+    - Task cancellation
+    
+    Example:
+        runner = BackgroundRunner()
+        
+        # Submit a task
+        task = await runner.submit(
+            func=my_agent.start,
+            args=("Research AI trends",),
+            name="research_task"
+        )
+        
+        # Check status
+        print(task.status)
+        
+        # Wait for result
+        result = await task.wait()
+    """
+    
+    def __init__(self, config: Optional[BackgroundConfig] = None):
+        """
+        Initialize the background runner.
+        
+        Args:
+            config: Configuration for background execution
+        """
+        self.config = config or BackgroundConfig()
+        self._tasks: Dict[str, BackgroundTask] = {}
+        self._semaphore = asyncio.Semaphore(self.config.max_concurrent_tasks)
+        self._running = False
+        self._cleanup_task: Optional[asyncio.Task] = None
+    
+    @property
+    def tasks(self) -> List[BackgroundTask]:
+        """Get all tasks."""
+        return list(self._tasks.values())
+    
+    @property
+    def running_tasks(self) -> List[BackgroundTask]:
+        """Get currently running tasks."""
+        return [t for t in self._tasks.values() if t.is_running]
+    
+    @property
+    def pending_tasks(self) -> List[BackgroundTask]:
+        """Get pending tasks."""
+        return [t for t in self._tasks.values() if t.status == TaskStatus.PENDING]
+    
+    async def submit(
+        self,
+        func: Callable,
+        args: tuple = (),
+        kwargs: Optional[Dict[str, Any]] = None,
+        name: Optional[str] = None,
+        timeout: Optional[float] = None,
+        on_complete: Optional[Callable[[BackgroundTask], None]] = None,
+        on_progress: Optional[Callable[[BackgroundTask], None]] = None
+    ) -> BackgroundTask:
+        """
+        Submit a task for background execution.
+        
+        Args:
+            func: Function to execute (can be sync or async)
+            args: Positional arguments for the function
+            kwargs: Keyword arguments for the function
+            name: Optional task name
+            timeout: Optional timeout in seconds
+            on_complete: Callback when task completes
+            on_progress: Callback for progress updates
+            
+        Returns:
+            BackgroundTask object for tracking
+        """
+        kwargs = kwargs or {}
+        
+        task = BackgroundTask(
+            name=name or func.__name__,
+            metadata={
+                "func_name": func.__name__,
+                "timeout": timeout or self.config.default_timeout
+            }
+        )
+        
+        self._tasks[task.id] = task
+        
+        # Create the execution coroutine
+        async def execute():
+            async with self._semaphore:
+                task.start()
+                logger.info(f"Background task started: {task.name} ({task.id})")
+                
+                try:
+                    # Check if function is async
+                    if asyncio.iscoroutinefunction(func):
+                        result = await asyncio.wait_for(
+                            func(*args, **kwargs),
+                            timeout=timeout or self.config.default_timeout
+                        )
+                    else:
+                        # Run sync function in thread pool
+                        loop = asyncio.get_event_loop()
+                        result = await asyncio.wait_for(
+                            loop.run_in_executor(None, lambda: func(*args, **kwargs)),
+                            timeout=timeout or self.config.default_timeout
+                        )
+                    
+                    task.complete(result)
+                    logger.info(f"Background task completed: {task.name} ({task.id})")
+                    
+                    if on_complete:
+                        try:
+                            on_complete(task)
+                        except Exception as e:
+                            logger.warning(f"on_complete callback error: {e}")
+                    
+                    return result
+                    
+                except asyncio.TimeoutError:
+                    task.fail(f"Task timed out after {timeout}s")
+                    logger.warning(f"Background task timed out: {task.name}")
+                    
+                except asyncio.CancelledError:
+                    task.cancel()
+                    logger.info(f"Background task cancelled: {task.name}")
+                    raise
+                    
+                except Exception as e:
+                    task.fail(str(e))
+                    logger.error(f"Background task failed: {task.name} - {e}")
+                    
+                    if on_complete:
+                        try:
+                            on_complete(task)
+                        except Exception as cb_error:
+                            logger.warning(f"on_complete callback error: {cb_error}")
+        
+        # Create and store the future
+        task._future = asyncio.create_task(execute())
+        
+        return task
+    
+    async def submit_agent(
+        self,
+        agent: Any,
+        prompt: str,
+        name: Optional[str] = None,
+        timeout: Optional[float] = None,
+        on_complete: Optional[Callable[[BackgroundTask], None]] = None
+    ) -> BackgroundTask:
+        """
+        Submit an agent task for background execution.
+        
+        Args:
+            agent: Agent instance to run
+            prompt: Prompt to send to the agent
+            name: Optional task name
+            timeout: Optional timeout in seconds
+            on_complete: Callback when task completes
+            
+        Returns:
+            BackgroundTask object for tracking
+        """
+        task_name = name or f"agent_{getattr(agent, 'name', 'unknown')}"
+        
+        # Determine the method to call
+        if hasattr(agent, 'start'):
+            func = agent.start
+        elif hasattr(agent, 'chat'):
+            func = agent.chat
+        elif hasattr(agent, 'run'):
+            func = agent.run
+        else:
+            raise ValueError("Agent must have start(), chat(), or run() method")
+        
+        return await self.submit(
+            func=func,
+            args=(prompt,),
+            name=task_name,
+            timeout=timeout,
+            on_complete=on_complete
+        )
+    
+    def get_task(self, task_id: str) -> Optional[BackgroundTask]:
+        """Get a task by ID."""
+        return self._tasks.get(task_id)
+    
+    async def cancel_task(self, task_id: str) -> bool:
+        """
+        Cancel a task.
+        
+        Args:
+            task_id: ID of task to cancel
+            
+        Returns:
+            True if cancelled, False if not found or already completed
+        """
+        task = self._tasks.get(task_id)
+        if task is None:
+            return False
+        
+        if task.is_completed:
+            return False
+        
+        task.cancel()
+        
+        if task._future and not task._future.done():
+            task._future.cancel()
+        
+        return True
+    
+    async def cancel_all(self):
+        """Cancel all running tasks."""
+        for task in self.running_tasks:
+            await self.cancel_task(task.id)
+    
+    async def wait_all(self, timeout: Optional[float] = None) -> List[BackgroundTask]:
+        """
+        Wait for all tasks to complete.
+        
+        Args:
+            timeout: Maximum time to wait
+            
+        Returns:
+            List of completed tasks
+        """
+        futures = [t._future for t in self._tasks.values() if t._future]
+        
+        if not futures:
+            return list(self._tasks.values())
+        
+        try:
+            await asyncio.wait_for(
+                asyncio.gather(*futures, return_exceptions=True),
+                timeout=timeout
+            )
+        except asyncio.TimeoutError:
+            pass
+        
+        return list(self._tasks.values())
+    
+    def clear_completed(self):
+        """Remove completed tasks from tracking."""
+        completed_ids = [
+            task_id for task_id, task in self._tasks.items()
+            if task.is_completed
+        ]
+        for task_id in completed_ids:
+            del self._tasks[task_id]
+    
+    def list_tasks(self, status: Optional[TaskStatus] = None) -> List[Dict[str, Any]]:
+        """
+        List tasks with optional status filter.
+        
+        Args:
+            status: Optional status to filter by
+            
+        Returns:
+            List of task dictionaries
+        """
+        tasks = self._tasks.values()
+        
+        if status:
+            tasks = [t for t in tasks if t.status == status]
+        
+        return [t.to_dict() for t in tasks]
+    
+    async def start_cleanup_loop(self):
+        """Start the automatic cleanup loop."""
+        if self._cleanup_task is not None:
+            return
+        
+        async def cleanup_loop():
+            while True:
+                await asyncio.sleep(self.config.cleanup_delay)
+                if self.config.auto_cleanup:
+                    self.clear_completed()
+        
+        self._cleanup_task = asyncio.create_task(cleanup_loop())
+    
+    async def stop(self):
+        """Stop the runner and cancel all tasks."""
+        await self.cancel_all()
+        
+        if self._cleanup_task:
+            self._cleanup_task.cancel()
+            try:
+                await self._cleanup_task
+            except asyncio.CancelledError:
+                pass
+            self._cleanup_task = None

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

@@ -0,0 +1,154 @@
+"""
+Background Task for PraisonAI Agents.
+
+Represents a task running in the background.
+"""
+
+import uuid
+import asyncio
+import logging
+from enum import Enum
+from dataclasses import dataclass, field
+from typing import Optional, Any, Callable, Dict
+from datetime import datetime
+
+logger = logging.getLogger(__name__)
+
+
+class TaskStatus(str, Enum):
+    """Status of a background task."""
+    PENDING = "pending"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    CANCELLED = "cancelled"
+
+
+@dataclass
+class BackgroundTask:
+    """
+    Represents a background task.
+    
+    Tracks the status, progress, and result of a task running
+    in the background.
+    """
+    id: str = field(default_factory=lambda: str(uuid.uuid4())[:8])
+    name: str = ""
+    status: TaskStatus = TaskStatus.PENDING
+    progress: float = 0.0  # 0.0 to 1.0
+    result: Any = None
+    error: Optional[str] = None
+    created_at: datetime = field(default_factory=datetime.now)
+    started_at: Optional[datetime] = None
+    completed_at: Optional[datetime] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    # Internal
+    _future: Optional[asyncio.Future] = field(default=None, repr=False)
+    _cancel_event: Optional[asyncio.Event] = field(default=None, repr=False)
+    
+    def __post_init__(self):
+        self._cancel_event = asyncio.Event()
+    
+    @property
+    def is_running(self) -> bool:
+        """Check if task is currently running."""
+        return self.status == TaskStatus.RUNNING
+    
+    @property
+    def is_completed(self) -> bool:
+        """Check if task has completed (success or failure)."""
+        return self.status in (TaskStatus.COMPLETED, TaskStatus.FAILED, TaskStatus.CANCELLED)
+    
+    @property
+    def is_successful(self) -> bool:
+        """Check if task completed successfully."""
+        return self.status == TaskStatus.COMPLETED
+    
+    @property
+    def duration_seconds(self) -> Optional[float]:
+        """Get task duration in seconds."""
+        if self.started_at is None:
+            return None
+        end_time = self.completed_at or datetime.now()
+        return (end_time - self.started_at).total_seconds()
+    
+    def start(self):
+        """Mark task as started."""
+        self.status = TaskStatus.RUNNING
+        self.started_at = datetime.now()
+    
+    def complete(self, result: Any = None):
+        """Mark task as completed."""
+        self.status = TaskStatus.COMPLETED
+        self.result = result
+        self.progress = 1.0
+        self.completed_at = datetime.now()
+    
+    def fail(self, error: str):
+        """Mark task as failed."""
+        self.status = TaskStatus.FAILED
+        self.error = error
+        self.completed_at = datetime.now()
+    
+    def cancel(self):
+        """Request task cancellation."""
+        if self._cancel_event:
+            self._cancel_event.set()
+        self.status = TaskStatus.CANCELLED
+        self.completed_at = datetime.now()
+    
+    def update_progress(self, progress: float, message: Optional[str] = None):
+        """Update task progress."""
+        self.progress = min(max(progress, 0.0), 1.0)
+        if message:
+            self.metadata["last_message"] = message
+    
+    async def wait(self, timeout: Optional[float] = None) -> Any:
+        """
+        Wait for task completion.
+        
+        Args:
+            timeout: Maximum time to wait in seconds
+            
+        Returns:
+            Task result
+            
+        Raises:
+            asyncio.TimeoutError: If timeout exceeded
+            RuntimeError: If task failed
+        """
+        if self._future is None:
+            if self.is_completed:
+                if self.status == TaskStatus.FAILED:
+                    raise RuntimeError(self.error)
+                return self.result
+            raise RuntimeError("Task has no associated future")
+        
+        try:
+            result = await asyncio.wait_for(self._future, timeout=timeout)
+            return result
+        except asyncio.TimeoutError:
+            raise
+        except Exception as e:
+            raise RuntimeError(str(e))
+    
+    def should_cancel(self) -> bool:
+        """Check if cancellation was requested."""
+        return self._cancel_event is not None and self._cancel_event.is_set()
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "id": self.id,
+            "name": self.name,
+            "status": self.status.value,
+            "progress": self.progress,
+            "result": str(self.result) if self.result else None,
+            "error": self.error,
+            "created_at": self.created_at.isoformat(),
+            "started_at": self.started_at.isoformat() if self.started_at else None,
+            "completed_at": self.completed_at.isoformat() if self.completed_at else None,
+            "duration_seconds": self.duration_seconds,
+            "metadata": self.metadata
+        }

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

@@ -0,0 +1,65 @@
+"""
+Checkpoints Module for PraisonAI Agents.
+
+Provides file-level checkpointing using a shadow git repository to track
+and restore file changes made by agents. This enables:
+
+- Automatic checkpointing before file modifications
+- Rewind to any previous checkpoint
+- Diff between checkpoints
+- Restore files and conversation state together
+
+Zero Performance Impact:
+- All imports are lazy loaded via __getattr__
+- Checkpoints only created when enabled
+- No overhead when checkpoints are disabled
+
+Usage:
+    from praisonaiagents.checkpoints import CheckpointService
+    
+    # Create checkpoint service
+    service = CheckpointService(
+        workspace_dir="/path/to/project",
+        storage_dir="~/.praison/checkpoints"
+    )
+    
+    # Initialize shadow git
+    await service.initialize()
+    
+    # Save a checkpoint
+    checkpoint_id = await service.save("Before refactoring")
+    
+    # Restore to a checkpoint
+    await service.restore(checkpoint_id)
+    
+    # Get diff between checkpoints
+    diff = await service.diff(from_id, to_id)
+"""
+
+__all__ = [
+    # Core service
+    "CheckpointService",
+    # Data types
+    "Checkpoint",
+    "CheckpointDiff",
+    "CheckpointConfig",
+    # Events
+    "CheckpointEvent",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components to avoid import overhead."""
+    if name == "CheckpointService":
+        from .service import CheckpointService
+        return CheckpointService
+    
+    if name in ("Checkpoint", "CheckpointDiff", "CheckpointConfig"):
+        from .types import Checkpoint, CheckpointDiff, CheckpointConfig
+        return locals()[name]
+    
+    if name == "CheckpointEvent":
+        from .types import CheckpointEvent
+        return CheckpointEvent
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/checkpoints/service.py

@@ -0,0 +1,497 @@
+"""
+Checkpoint Service for PraisonAI Agents.
+
+Implements shadow git repository for file-level checkpointing.
+"""
+
+import os
+import asyncio
+import logging
+import shutil
+from typing import Optional, List, Dict, Any, Callable
+from datetime import datetime
+from pathlib import Path
+
+from .types import (
+    CheckpointConfig, Checkpoint, CheckpointDiff, FileDiff,
+    CheckpointResult, CheckpointEvent
+)
+
+logger = logging.getLogger(__name__)
+
+# Protected paths that should never be checkpointed
+PROTECTED_PATHS = [
+    os.path.expanduser("~"),
+    os.path.expanduser("~/Desktop"),
+    os.path.expanduser("~/Documents"),
+    os.path.expanduser("~/Downloads"),
+    "/",
+    "/tmp",
+    "/var",
+    "/etc",
+]
+
+
+class CheckpointService:
+    """
+    Shadow Git Checkpoint Service.
+    
+    Uses a separate git repository to track file changes in the workspace,
+    enabling checkpoint creation, restoration, and diffing without
+    interfering with the workspace's own git repository.
+    
+    Example:
+        service = CheckpointService(
+            workspace_dir="/path/to/project",
+            storage_dir="~/.praison/checkpoints"
+        )
+        
+        await service.initialize()
+        
+        # Save checkpoint before making changes
+        result = await service.save("Before refactoring")
+        
+        # Make changes...
+        
+        # Restore if needed
+        await service.restore(result.checkpoint.id)
+    """
+    
+    def __init__(
+        self,
+        workspace_dir: str,
+        storage_dir: Optional[str] = None,
+        enabled: bool = True,
+        auto_checkpoint: bool = True,
+        max_checkpoints: int = 100
+    ):
+        """
+        Initialize the checkpoint service.
+        
+        Args:
+            workspace_dir: Directory to track
+            storage_dir: Where to store checkpoint data
+            enabled: Whether checkpoints are enabled
+            auto_checkpoint: Auto-checkpoint before file modifications
+            max_checkpoints: Maximum checkpoints to keep
+        """
+        self.config = CheckpointConfig(
+            workspace_dir=workspace_dir,
+            storage_dir=storage_dir,
+            enabled=enabled,
+            auto_checkpoint=auto_checkpoint,
+            max_checkpoints=max_checkpoints
+        )
+        
+        self._initialized = False
+        self._checkpoints: List[Checkpoint] = []
+        self._event_handlers: Dict[CheckpointEvent, List[Callable]] = {
+            event: [] for event in CheckpointEvent
+        }
+    
+    @property
+    def workspace_dir(self) -> str:
+        """Get the workspace directory."""
+        return self.config.workspace_dir
+    
+    @property
+    def checkpoint_dir(self) -> str:
+        """Get the checkpoint storage directory."""
+        return self.config.get_checkpoint_dir()
+    
+    @property
+    def git_dir(self) -> str:
+        """Get the shadow git directory."""
+        return os.path.join(self.checkpoint_dir, ".git")
+    
+    @property
+    def is_initialized(self) -> bool:
+        """Check if the service is initialized."""
+        return self._initialized
+    
+    def on(self, event: CheckpointEvent, handler: Callable):
+        """Register an event handler."""
+        self._event_handlers[event].append(handler)
+    
+    def _emit(self, event: CheckpointEvent, data: Any = None):
+        """Emit an event to all handlers."""
+        for handler in self._event_handlers[event]:
+            try:
+                handler(data)
+            except Exception as e:
+                logger.warning(f"Event handler error: {e}")
+    
+    async def initialize(self) -> bool:
+        """
+        Initialize the shadow git repository.
+        
+        Returns:
+            True if initialization succeeded
+        """
+        if not self.config.enabled:
+            return False
+        
+        # Check for protected paths
+        workspace_real = os.path.realpath(self.config.workspace_dir)
+        for protected in PROTECTED_PATHS:
+            if workspace_real == os.path.realpath(protected):
+                logger.warning(f"Cannot checkpoint protected path: {workspace_real}")
+                return False
+        
+        # Check if workspace exists
+        if not os.path.isdir(self.config.workspace_dir):
+            logger.error(f"Workspace does not exist: {self.config.workspace_dir}")
+            return False
+        
+        # Create checkpoint directory
+        os.makedirs(self.checkpoint_dir, exist_ok=True)
+        
+        # Initialize shadow git if needed
+        if not os.path.exists(self.git_dir):
+            success = await self._init_shadow_git()
+            if not success:
+                return False
+        
+        # Load existing checkpoints
+        await self._load_checkpoints()
+        
+        self._initialized = True
+        self._emit(CheckpointEvent.INITIALIZED, {"workspace": self.config.workspace_dir})
+        
+        return True
+    
+    async def _init_shadow_git(self) -> bool:
+        """Initialize the shadow git repository."""
+        try:
+            # Initialize git repo
+            await self._run_git("init")
+            
+            # Configure git
+            await self._run_git("config", "user.name", "PraisonAI Checkpoints")
+            await self._run_git("config", "user.email", "[email protected]")
+            
+            # Set worktree to workspace
+            await self._run_git("config", "core.worktree", self.config.workspace_dir)
+            
+            # Create exclude file
+            exclude_dir = os.path.join(self.git_dir, "info")
+            os.makedirs(exclude_dir, exist_ok=True)
+            
+            exclude_file = os.path.join(exclude_dir, "exclude")
+            with open(exclude_file, "w") as f:
+                for pattern in self.config.exclude_patterns:
+                    f.write(f"{pattern}\n")
+                
+                # Also exclude the checkpoint directory itself
+                f.write(f"{self.checkpoint_dir}\n")
+            
+            # Create initial empty commit
+            await self._run_git("commit", "--allow-empty", "-m", "Initial checkpoint")
+            
+            logger.info(f"Initialized shadow git at {self.checkpoint_dir}")
+            return True
+            
+        except Exception as e:
+            logger.error(f"Failed to initialize shadow git: {e}")
+            return False
+    
+    async def _run_git(self, *args: str) -> str:
+        """Run a git command in the shadow repository."""
+        env = self._get_sanitized_env()
+        
+        cmd = ["git", f"--git-dir={self.git_dir}", f"--work-tree={self.config.workspace_dir}"]
+        cmd.extend(args)
+        
+        process = await asyncio.create_subprocess_exec(
+            *cmd,
+            stdout=asyncio.subprocess.PIPE,
+            stderr=asyncio.subprocess.PIPE,
+            env=env,
+            cwd=self.config.workspace_dir
+        )
+        
+        stdout, stderr = await process.communicate()
+        
+        if process.returncode != 0:
+            error_msg = stderr.decode().strip()
+            raise RuntimeError(f"Git command failed: {' '.join(args)}\n{error_msg}")
+        
+        return stdout.decode().strip()
+    
+    def _get_sanitized_env(self) -> Dict[str, str]:
+        """Get environment with git variables sanitized."""
+        env = os.environ.copy()
+        
+        # Remove git environment variables that could interfere
+        git_vars = [
+            "GIT_DIR", "GIT_WORK_TREE", "GIT_INDEX_FILE",
+            "GIT_OBJECT_DIRECTORY", "GIT_ALTERNATE_OBJECT_DIRECTORIES"
+        ]
+        for var in git_vars:
+            env.pop(var, None)
+        
+        return env
+    
+    async def save(self, message: str, allow_empty: bool = False) -> CheckpointResult:
+        """
+        Save a checkpoint.
+        
+        Args:
+            message: Checkpoint message
+            allow_empty: Allow checkpoint even if no changes
+            
+        Returns:
+            CheckpointResult with the created checkpoint
+        """
+        if not self._initialized:
+            return CheckpointResult.fail("Service not initialized")
+        
+        try:
+            # Stage all changes
+            await self._run_git("add", "-A")
+            
+            # Check if there are changes
+            try:
+                await self._run_git("diff", "--cached", "--quiet")
+                has_changes = False
+            except RuntimeError:
+                has_changes = True
+            
+            if not has_changes and not allow_empty:
+                return CheckpointResult.fail("No changes to checkpoint")
+            
+            # Create commit
+            commit_args = ["commit", "-m", message]
+            if allow_empty:
+                commit_args.append("--allow-empty")
+            
+            await self._run_git(*commit_args)
+            
+            # Get commit info
+            commit_hash = await self._run_git("rev-parse", "HEAD")
+            timestamp = await self._run_git("log", "-1", "--format=%aI")
+            
+            checkpoint = Checkpoint(
+                id=commit_hash,
+                short_id=commit_hash[:8],
+                message=message,
+                timestamp=datetime.fromisoformat(timestamp)
+            )
+            
+            self._checkpoints.append(checkpoint)
+            
+            # Prune old checkpoints if needed
+            await self._prune_checkpoints()
+            
+            self._emit(CheckpointEvent.CHECKPOINT_CREATED, checkpoint)
+            logger.info(f"Created checkpoint: {checkpoint.short_id} - {message}")
+            
+            return CheckpointResult.ok(checkpoint)
+            
+        except Exception as e:
+            error_msg = str(e)
+            self._emit(CheckpointEvent.ERROR, {"error": error_msg})
+            return CheckpointResult.fail(error_msg)
+    
+    async def restore(self, checkpoint_id: str) -> CheckpointResult:
+        """
+        Restore workspace to a checkpoint.
+        
+        Args:
+            checkpoint_id: Checkpoint ID (commit hash) to restore
+            
+        Returns:
+            CheckpointResult indicating success/failure
+        """
+        if not self._initialized:
+            return CheckpointResult.fail("Service not initialized")
+        
+        try:
+            # Clean untracked files
+            await self._run_git("clean", "-f", "-d")
+            
+            # Reset to checkpoint
+            await self._run_git("reset", "--hard", checkpoint_id)
+            
+            # Get checkpoint info
+            message = await self._run_git("log", "-1", "--format=%s", checkpoint_id)
+            timestamp = await self._run_git("log", "-1", "--format=%aI", checkpoint_id)
+            
+            checkpoint = Checkpoint(
+                id=checkpoint_id,
+                short_id=checkpoint_id[:8],
+                message=message,
+                timestamp=datetime.fromisoformat(timestamp)
+            )
+            
+            self._emit(CheckpointEvent.CHECKPOINT_RESTORED, checkpoint)
+            logger.info(f"Restored to checkpoint: {checkpoint.short_id}")
+            
+            return CheckpointResult.ok(checkpoint)
+            
+        except Exception as e:
+            error_msg = str(e)
+            self._emit(CheckpointEvent.ERROR, {"error": error_msg})
+            return CheckpointResult.fail(error_msg)
+    
+    async def diff(
+        self,
+        from_id: Optional[str] = None,
+        to_id: Optional[str] = None
+    ) -> CheckpointDiff:
+        """
+        Get diff between checkpoints.
+        
+        Args:
+            from_id: Starting checkpoint (default: previous checkpoint)
+            to_id: Ending checkpoint (default: current working directory)
+            
+        Returns:
+            CheckpointDiff with file changes
+        """
+        if not self._initialized:
+            return CheckpointDiff(from_checkpoint="", to_checkpoint="", files=[])
+        
+        try:
+            # Default from_id to HEAD~1
+            if from_id is None:
+                try:
+                    from_id = await self._run_git("rev-parse", "HEAD~1")
+                except RuntimeError:
+                    from_id = await self._run_git("rev-parse", "HEAD")
+            
+            # Build diff command
+            if to_id:
+                diff_output = await self._run_git(
+                    "diff", "--stat", "--numstat", from_id, to_id
+                )
+            else:
+                # Diff against working directory
+                await self._run_git("add", "-A")  # Stage to include untracked
+                diff_output = await self._run_git(
+                    "diff", "--stat", "--numstat", "--cached", from_id
+                )
+            
+            # Parse diff output
+            files = self._parse_diff_output(diff_output)
+            
+            return CheckpointDiff(
+                from_checkpoint=from_id[:8] if from_id else "",
+                to_checkpoint=to_id[:8] if to_id else None,
+                files=files
+            )
+            
+        except Exception as e:
+            logger.error(f"Failed to get diff: {e}")
+            return CheckpointDiff(from_checkpoint="", to_checkpoint="", files=[])
+    
+    def _parse_diff_output(self, output: str) -> List[FileDiff]:
+        """Parse git diff --numstat output."""
+        files = []
+        
+        for line in output.strip().split("\n"):
+            if not line or "\t" not in line:
+                continue
+            
+            parts = line.split("\t")
+            if len(parts) >= 3:
+                try:
+                    additions = int(parts[0]) if parts[0] != "-" else 0
+                    deletions = int(parts[1]) if parts[1] != "-" else 0
+                    path = parts[2]
+                    
+                    # Determine status
+                    if additions > 0 and deletions == 0:
+                        status = "added"
+                    elif additions == 0 and deletions > 0:
+                        status = "deleted"
+                    else:
+                        status = "modified"
+                    
+                    files.append(FileDiff(
+                        path=path,
+                        absolute_path=os.path.join(self.config.workspace_dir, path),
+                        status=status,
+                        additions=additions,
+                        deletions=deletions
+                    ))
+                except ValueError:
+                    continue
+        
+        return files
+    
+    async def list_checkpoints(self, limit: int = 50) -> List[Checkpoint]:
+        """
+        List all checkpoints.
+        
+        Args:
+            limit: Maximum number of checkpoints to return
+            
+        Returns:
+            List of checkpoints, newest first
+        """
+        if not self._initialized:
+            return []
+        
+        try:
+            # Get commit log
+            log_output = await self._run_git(
+                "log", f"-{limit}", "--format=%H|%s|%aI"
+            )
+            
+            checkpoints = []
+            for line in log_output.strip().split("\n"):
+                if not line or "|" not in line:
+                    continue
+                
+                parts = line.split("|", 2)
+                if len(parts) >= 3:
+                    checkpoints.append(Checkpoint(
+                        id=parts[0],
+                        short_id=parts[0][:8],
+                        message=parts[1],
+                        timestamp=datetime.fromisoformat(parts[2])
+                    ))
+            
+            return checkpoints
+            
+        except Exception as e:
+            logger.error(f"Failed to list checkpoints: {e}")
+            return []
+    
+    async def _load_checkpoints(self):
+        """Load existing checkpoints from git log."""
+        self._checkpoints = await self.list_checkpoints()
+    
+    async def _prune_checkpoints(self):
+        """Prune old checkpoints if over limit."""
+        if len(self._checkpoints) <= self.config.max_checkpoints:
+            return
+        
+        # Keep only the most recent checkpoints
+        # Note: This doesn't actually delete git history, just our tracking
+        self._checkpoints = self._checkpoints[:self.config.max_checkpoints]
+    
+    async def get_checkpoint(self, checkpoint_id: str) -> Optional[Checkpoint]:
+        """Get a specific checkpoint by ID."""
+        for cp in self._checkpoints:
+            if cp.id.startswith(checkpoint_id) or cp.short_id == checkpoint_id:
+                return cp
+        return None
+    
+    async def cleanup(self):
+        """Clean up the checkpoint service."""
+        # Nothing to clean up currently
+        self._initialized = False
+    
+    async def delete_all(self) -> bool:
+        """Delete all checkpoint data for this workspace."""
+        try:
+            if os.path.exists(self.checkpoint_dir):
+                shutil.rmtree(self.checkpoint_dir)
+            self._checkpoints = []
+            self._initialized = False
+            return True
+        except Exception as e:
+            logger.error(f"Failed to delete checkpoints: {e}")
+            return False

src/praisonai-agents/praisonaiagents/checkpoints/types.py

@@ -0,0 +1,158 @@
+"""
+Checkpoint Types for PraisonAI Agents.
+
+Defines the core types, enums, and dataclasses for the checkpoint system.
+"""
+
+import os
+from enum import Enum
+from dataclasses import dataclass, field
+from typing import Optional, Dict, Any, List
+from datetime import datetime
+from pathlib import Path
+
+
+class CheckpointEvent(str, Enum):
+    """Events emitted by the checkpoint service."""
+    INITIALIZED = "initialized"
+    CHECKPOINT_CREATED = "checkpoint_created"
+    CHECKPOINT_RESTORED = "checkpoint_restored"
+    ERROR = "error"
+
+
+@dataclass
+class CheckpointConfig:
+    """Configuration for the checkpoint service."""
+    workspace_dir: str
+    storage_dir: Optional[str] = None
+    enabled: bool = True
+    auto_checkpoint: bool = True  # Auto-checkpoint before file modifications
+    max_checkpoints: int = 100  # Maximum checkpoints to keep
+    exclude_patterns: List[str] = field(default_factory=lambda: [
+        ".git",
+        ".praison",
+        "__pycache__",
+        "*.pyc",
+        ".env",
+        "node_modules",
+        ".venv",
+        "venv",
+        "*.log"
+    ])
+    
+    def __post_init__(self):
+        # Expand paths
+        self.workspace_dir = os.path.expanduser(self.workspace_dir)
+        if self.storage_dir:
+            self.storage_dir = os.path.expanduser(self.storage_dir)
+        else:
+            # Default storage in user's home directory
+            self.storage_dir = os.path.expanduser("~/.praison/checkpoints")
+    
+    def get_checkpoint_dir(self) -> str:
+        """Get the checkpoint directory for this workspace."""
+        # Hash the workspace path to create a unique directory
+        import hashlib
+        workspace_hash = hashlib.sha256(
+            self.workspace_dir.encode()
+        ).hexdigest()[:12]
+        return os.path.join(self.storage_dir, workspace_hash)
+
+
+@dataclass
+class Checkpoint:
+    """Represents a single checkpoint."""
+    id: str  # Git commit hash
+    short_id: str  # Short hash (8 chars)
+    message: str
+    timestamp: datetime
+    files_changed: int = 0
+    insertions: int = 0
+    deletions: int = 0
+    
+    @classmethod
+    def from_git_commit(cls, commit_hash: str, message: str, timestamp: str) -> "Checkpoint":
+        """Create a Checkpoint from git commit info."""
+        return cls(
+            id=commit_hash,
+            short_id=commit_hash[:8],
+            message=message,
+            timestamp=datetime.fromisoformat(timestamp) if isinstance(timestamp, str) else timestamp
+        )
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "id": self.id,
+            "short_id": self.short_id,
+            "message": self.message,
+            "timestamp": self.timestamp.isoformat(),
+            "files_changed": self.files_changed,
+            "insertions": self.insertions,
+            "deletions": self.deletions
+        }
+
+
+@dataclass
+class FileDiff:
+    """Represents a diff for a single file."""
+    path: str
+    absolute_path: str
+    status: str  # added, modified, deleted
+    additions: int = 0
+    deletions: int = 0
+    before_content: Optional[str] = None
+    after_content: Optional[str] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "path": self.path,
+            "absolute_path": self.absolute_path,
+            "status": self.status,
+            "additions": self.additions,
+            "deletions": self.deletions
+        }
+
+
+@dataclass
+class CheckpointDiff:
+    """Represents a diff between two checkpoints."""
+    from_checkpoint: str
+    to_checkpoint: Optional[str]  # None means current working directory
+    files: List[FileDiff] = field(default_factory=list)
+    total_additions: int = 0
+    total_deletions: int = 0
+    
+    def __post_init__(self):
+        # Calculate totals
+        self.total_additions = sum(f.additions for f in self.files)
+        self.total_deletions = sum(f.deletions for f in self.files)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "from_checkpoint": self.from_checkpoint,
+            "to_checkpoint": self.to_checkpoint,
+            "files": [f.to_dict() for f in self.files],
+            "total_additions": self.total_additions,
+            "total_deletions": self.total_deletions
+        }
+
+
+@dataclass
+class CheckpointResult:
+    """Result of a checkpoint operation."""
+    success: bool
+    checkpoint: Optional[Checkpoint] = None
+    error: Optional[str] = None
+    
+    @classmethod
+    def ok(cls, checkpoint: Checkpoint) -> "CheckpointResult":
+        """Create a successful result."""
+        return cls(success=True, checkpoint=checkpoint)
+    
+    @classmethod
+    def fail(cls, error: str) -> "CheckpointResult":
+        """Create a failed result."""
+        return cls(success=False, error=error)

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

@@ -0,0 +1,57 @@
+"""
+Auto Context Compaction Module for PraisonAI Agents.
+
+Provides automatic context management for long conversations:
+- Message summarization
+- Context window management
+- Token counting and limits
+- Intelligent message pruning
+
+Zero Performance Impact:
+- All imports are lazy loaded via __getattr__
+- Compaction only runs when needed
+- No overhead when context is within limits
+
+Usage:
+    from praisonaiagents.compaction import ContextCompactor, CompactionConfig
+    
+    # Create a compactor
+    compactor = ContextCompactor(
+        max_tokens=8000,
+        strategy="summarize"
+    )
+    
+    # Compact messages when needed
+    compacted = compactor.compact(messages)
+"""
+
+__all__ = [
+    # Core classes
+    "ContextCompactor",
+    "CompactionConfig",
+    # Strategies
+    "CompactionStrategy",
+    # Results
+    "CompactionResult",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components to avoid import overhead."""
+    if name == "ContextCompactor":
+        from .compactor import ContextCompactor
+        return ContextCompactor
+    
+    if name == "CompactionConfig":
+        from .config import CompactionConfig
+        return CompactionConfig
+    
+    if name == "CompactionStrategy":
+        from .strategy import CompactionStrategy
+        return CompactionStrategy
+    
+    if name == "CompactionResult":
+        from .result import CompactionResult
+        return CompactionResult
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

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

@@ -0,0 +1,237 @@
+"""
+Context Compactor for PraisonAI Agents.
+
+Manages context window by compacting messages when needed.
+"""
+
+from typing import List, Dict, Any, Optional
+
+from .config import CompactionConfig
+from .strategy import CompactionStrategy
+from .result import CompactionResult
+
+
+class ContextCompactor:
+    """
+    Compacts conversation context to fit within token limits.
+    
+    Example:
+        compactor = ContextCompactor(max_tokens=8000)
+        
+        # Check if compaction needed
+        if compactor.needs_compaction(messages):
+            result = compactor.compact(messages)
+            messages = result.messages
+    """
+    
+    def __init__(
+        self,
+        max_tokens: int = 8000,
+        target_tokens: Optional[int] = None,
+        strategy: CompactionStrategy = CompactionStrategy.TRUNCATE,
+        preserve_system: bool = True,
+        preserve_recent: int = 5
+    ):
+        """
+        Initialize the compactor.
+        
+        Args:
+            max_tokens: Maximum tokens before compaction
+            target_tokens: Target tokens after compaction
+            strategy: Compaction strategy to use
+            preserve_system: Keep system messages
+            preserve_recent: Number of recent messages to preserve
+        """
+        self.max_tokens = max_tokens
+        self.target_tokens = target_tokens or int(max_tokens * 0.75)
+        self.strategy = strategy
+        self.preserve_system = preserve_system
+        self.preserve_recent = preserve_recent
+    
+    def estimate_tokens(self, text: str) -> int:
+        """Estimate token count for text."""
+        # Rough estimate: ~4 chars per token
+        return len(text) // 4
+    
+    def count_message_tokens(self, message: Dict[str, Any]) -> int:
+        """Count tokens in a message."""
+        content = message.get("content", "")
+        if isinstance(content, str):
+            return self.estimate_tokens(content)
+        elif isinstance(content, list):
+            # Handle multi-part content
+            total = 0
+            for part in content:
+                if isinstance(part, dict):
+                    total += self.estimate_tokens(str(part.get("text", "")))
+                else:
+                    total += self.estimate_tokens(str(part))
+            return total
+        return 0
+    
+    def count_total_tokens(self, messages: List[Dict[str, Any]]) -> int:
+        """Count total tokens in messages."""
+        return sum(self.count_message_tokens(m) for m in messages)
+    
+    def needs_compaction(self, messages: List[Dict[str, Any]]) -> bool:
+        """Check if messages need compaction."""
+        return self.count_total_tokens(messages) > self.max_tokens
+    
+    def compact(
+        self,
+        messages: List[Dict[str, Any]]
+    ) -> tuple[List[Dict[str, Any]], CompactionResult]:
+        """
+        Compact messages to fit within token limit.
+        
+        Args:
+            messages: List of messages to compact
+            
+        Returns:
+            Tuple of (compacted messages, result)
+        """
+        original_tokens = self.count_total_tokens(messages)
+        
+        if original_tokens <= self.max_tokens:
+            return messages, CompactionResult(
+                original_tokens=original_tokens,
+                compacted_tokens=original_tokens,
+                messages_removed=0,
+                messages_kept=len(messages),
+                strategy_used=self.strategy
+            )
+        
+        if self.strategy == CompactionStrategy.TRUNCATE:
+            compacted = self._truncate(messages)
+        elif self.strategy == CompactionStrategy.SLIDING:
+            compacted = self._sliding_window(messages)
+        elif self.strategy == CompactionStrategy.SUMMARIZE:
+            compacted = self._summarize(messages)
+        elif self.strategy == CompactionStrategy.SMART:
+            compacted = self._smart_compact(messages)
+        else:
+            compacted = self._truncate(messages)
+        
+        compacted_tokens = self.count_total_tokens(compacted)
+        
+        result = CompactionResult(
+            original_tokens=original_tokens,
+            compacted_tokens=compacted_tokens,
+            messages_removed=len(messages) - len(compacted),
+            messages_kept=len(compacted),
+            strategy_used=self.strategy
+        )
+        
+        return compacted, result
+    
+    def _truncate(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Truncate oldest messages."""
+        result = []
+        
+        # Separate system and non-system messages
+        system_msgs = []
+        other_msgs = []
+        
+        for msg in messages:
+            if self.preserve_system and msg.get("role") == "system":
+                system_msgs.append(msg)
+            else:
+                other_msgs.append(msg)
+        
+        # Always keep system messages
+        result.extend(system_msgs)
+        
+        # Keep recent messages
+        recent = other_msgs[-self.preserve_recent:] if other_msgs else []
+        
+        # Add recent messages
+        result.extend(recent)
+        
+        # If still over limit, truncate more
+        while self.count_total_tokens(result) > self.target_tokens and len(result) > 1:
+            # Remove oldest non-system message
+            for i, msg in enumerate(result):
+                if msg.get("role") != "system":
+                    result.pop(i)
+                    break
+        
+        return result
+    
+    def _sliding_window(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Keep a sliding window of recent messages."""
+        result = []
+        
+        # Keep system messages
+        for msg in messages:
+            if self.preserve_system and msg.get("role") == "system":
+                result.append(msg)
+        
+        # Add messages from end until we hit target
+        non_system = [m for m in messages if m.get("role") != "system"]
+        
+        for msg in reversed(non_system):
+            if self.count_total_tokens(result + [msg]) <= self.target_tokens:
+                result.insert(len([m for m in result if m.get("role") == "system"]), msg)
+            else:
+                break
+        
+        # Ensure messages are in order
+        system_msgs = [m for m in result if m.get("role") == "system"]
+        other_msgs = [m for m in result if m.get("role") != "system"]
+        
+        return system_msgs + other_msgs
+    
+    def _summarize(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Summarize old messages (simplified version)."""
+        result = []
+        
+        # Keep system messages
+        system_msgs = [m for m in messages if m.get("role") == "system"]
+        other_msgs = [m for m in messages if m.get("role") != "system"]
+        
+        result.extend(system_msgs)
+        
+        # Keep recent messages
+        recent = other_msgs[-self.preserve_recent:]
+        
+        # Summarize older messages
+        older = other_msgs[:-self.preserve_recent] if len(other_msgs) > self.preserve_recent else []
+        
+        if older:
+            # Create a simple summary
+            summary_parts = []
+            for msg in older:
+                role = msg.get("role", "unknown")
+                content = msg.get("content", "")
+                if isinstance(content, str) and content:
+                    # Take first 100 chars
+                    summary_parts.append(f"{role}: {content[:100]}...")
+            
+            if summary_parts:
+                summary = "[Previous conversation summary]\n" + "\n".join(summary_parts[:5])
+                result.append({
+                    "role": "system",
+                    "content": summary
+                })
+        
+        result.extend(recent)
+        
+        return result
+    
+    def _smart_compact(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Smart compaction based on message importance."""
+        # For now, use sliding window as base
+        return self._sliding_window(messages)
+    
+    def get_stats(self, messages: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Get statistics about messages."""
+        total_tokens = self.count_total_tokens(messages)
+        
+        return {
+            "message_count": len(messages),
+            "total_tokens": total_tokens,
+            "max_tokens": self.max_tokens,
+            "target_tokens": self.target_tokens,
+            "needs_compaction": total_tokens > self.max_tokens,
+            "utilization": total_tokens / self.max_tokens if self.max_tokens > 0 else 0
+        }

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

@@ -0,0 +1,17 @@
+"""
+Compaction Configuration for PraisonAI Agents.
+"""
+
+from dataclasses import dataclass
+from typing import Optional
+
+
+@dataclass
+class CompactionConfig:
+    """Configuration for context compaction."""
+    enabled: bool = True
+    max_tokens: int = 8000
+    target_tokens: int = 6000  # Target after compaction
+    preserve_system: bool = True  # Always keep system messages
+    preserve_recent: int = 5  # Keep last N messages
+    auto_compact: bool = True  # Automatically compact when needed

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

@@ -0,0 +1,49 @@
+"""
+Compaction Result for PraisonAI Agents.
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Dict, Any
+
+from .strategy import CompactionStrategy
+
+
+@dataclass
+class CompactionResult:
+    """Result of a compaction operation."""
+    original_tokens: int
+    compacted_tokens: int
+    messages_removed: int
+    messages_kept: int
+    strategy_used: CompactionStrategy
+    summary: str = ""
+    
+    @property
+    def tokens_saved(self) -> int:
+        """Get number of tokens saved."""
+        return self.original_tokens - self.compacted_tokens
+    
+    @property
+    def compression_ratio(self) -> float:
+        """Get compression ratio."""
+        if self.original_tokens == 0:
+            return 0.0
+        return self.compacted_tokens / self.original_tokens
+    
+    @property
+    def was_compacted(self) -> bool:
+        """Check if compaction occurred."""
+        return self.messages_removed > 0 or self.tokens_saved > 0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "original_tokens": self.original_tokens,
+            "compacted_tokens": self.compacted_tokens,
+            "tokens_saved": self.tokens_saved,
+            "messages_removed": self.messages_removed,
+            "messages_kept": self.messages_kept,
+            "strategy_used": self.strategy_used.value,
+            "compression_ratio": self.compression_ratio,
+            "summary": self.summary
+        }

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

@@ -0,0 +1,13 @@
+"""
+Compaction Strategies for PraisonAI Agents.
+"""
+
+from enum import Enum
+
+
+class CompactionStrategy(str, Enum):
+    """Strategy for compacting context."""
+    TRUNCATE = "truncate"      # Remove oldest messages
+    SUMMARIZE = "summarize"    # Summarize old messages
+    SLIDING = "sliding"        # Sliding window of recent messages
+    SMART = "smart"            # Intelligent selection based on relevance

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

@@ -0,0 +1,68 @@
+"""
+PraisonAI Agents Evaluation Framework.
+
+Provides comprehensive evaluation capabilities for AI agents with zero performance
+impact when not in use through lazy loading.
+
+Evaluator Types:
+    - AccuracyEvaluator: Compare output against expected output using LLM-as-judge
+    - PerformanceEvaluator: Measure runtime and memory usage
+    - ReliabilityEvaluator: Verify expected tool calls are made
+    - CriteriaEvaluator: Evaluate against custom criteria
+
+Example:
+    >>> from praisonaiagents.eval import AccuracyEvaluator
+    >>> evaluator = AccuracyEvaluator(
+    ...     agent=my_agent,
+    ...     input_text="What is 2+2?",
+    ...     expected_output="4"
+    ... )
+    >>> result = evaluator.run(print_summary=True)
+"""
+
+__all__ = [
+    "BaseEvaluator",
+    "AccuracyEvaluator",
+    "PerformanceEvaluator",
+    "ReliabilityEvaluator",
+    "CriteriaEvaluator",
+    "AccuracyResult",
+    "PerformanceResult",
+    "ReliabilityResult",
+    "CriteriaResult",
+    "EvaluationScore",
+    "PerformanceMetrics",
+    "ToolCallResult",
+    "CriteriaScore",
+]
+
+_LAZY_IMPORTS = {
+    "BaseEvaluator": ("base", "BaseEvaluator"),
+    "AccuracyEvaluator": ("accuracy", "AccuracyEvaluator"),
+    "PerformanceEvaluator": ("performance", "PerformanceEvaluator"),
+    "ReliabilityEvaluator": ("reliability", "ReliabilityEvaluator"),
+    "CriteriaEvaluator": ("criteria", "CriteriaEvaluator"),
+    "AccuracyResult": ("results", "AccuracyResult"),
+    "PerformanceResult": ("results", "PerformanceResult"),
+    "ReliabilityResult": ("results", "ReliabilityResult"),
+    "CriteriaResult": ("results", "CriteriaResult"),
+    "EvaluationScore": ("results", "EvaluationScore"),
+    "PerformanceMetrics": ("results", "PerformanceMetrics"),
+    "ToolCallResult": ("results", "ToolCallResult"),
+    "CriteriaScore": ("results", "CriteriaScore"),
+}
+
+
+def __getattr__(name: str):
+    """Lazy import mechanism for zero-cost imports when not used."""
+    if name in _LAZY_IMPORTS:
+        module_name, attr_name = _LAZY_IMPORTS[name]
+        import importlib
+        module = importlib.import_module(f".{module_name}", __name__)
+        return getattr(module, attr_name)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+def __dir__():
+    """Return list of available attributes for tab completion."""
+    return __all__

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

@@ -0,0 +1,239 @@
+"""
+Accuracy evaluator for PraisonAI Agents.
+
+Evaluates agent output accuracy by comparing against expected output using LLM-as-judge.
+"""
+
+import os
+import logging
+from typing import Any, Callable, List, Optional, Union, TYPE_CHECKING
+
+from .base import BaseEvaluator
+from .results import AccuracyResult, EvaluationScore
+
+if TYPE_CHECKING:
+    from ..agent.agent import Agent
+    from ..agents.agents import PraisonAIAgents
+
+logger = logging.getLogger(__name__)
+
+
+class AccuracyEvaluator(BaseEvaluator):
+    """
+    Evaluates the accuracy of agent outputs against expected outputs.
+    
+    Uses an LLM as a judge to score outputs on a scale of 1-10.
+    """
+    
+    def __init__(
+        self,
+        agent: Optional[Union["Agent", "PraisonAIAgents"]] = None,
+        func: Optional[Callable[..., str]] = None,
+        input_text: str = "",
+        expected_output: str = "",
+        num_iterations: int = 1,
+        model: Optional[str] = None,
+        name: Optional[str] = None,
+        save_results_path: Optional[str] = None,
+        verbose: bool = False,
+    ):
+        """
+        Initialize the accuracy evaluator.
+        
+        Args:
+            agent: Agent or PraisonAIAgents instance to evaluate
+            func: Alternative callable that returns output string
+            input_text: Input to provide to the agent/function
+            expected_output: Expected output to compare against
+            num_iterations: Number of evaluation iterations
+            model: LLM model to use for judging (defaults to gpt-4o-mini)
+            name: Name for this evaluation
+            save_results_path: Path to save results
+            verbose: Enable verbose output
+        """
+        super().__init__(name=name, save_results_path=save_results_path, verbose=verbose)
+        
+        self.agent = agent
+        self.func = func
+        self.input_text = input_text
+        self.expected_output = expected_output
+        self.num_iterations = num_iterations
+        self.model = model or os.getenv("OPENAI_MODEL_NAME", "gpt-4o-mini")
+        
+        if agent is None and func is None:
+            raise ValueError("Either 'agent' or 'func' must be provided")
+    
+    def _get_output(self) -> str:
+        """Get output from agent or function."""
+        if self.func:
+            return str(self.func(self.input_text))
+        
+        if hasattr(self.agent, 'chat'):
+            return str(self.agent.chat(self.input_text))
+        elif hasattr(self.agent, 'start'):
+            result = self.agent.start(self.input_text)
+            if hasattr(result, 'raw'):
+                return str(result.raw)
+            return str(result)
+        else:
+            raise ValueError("Agent must have 'chat' or 'start' method")
+    
+    def _judge_output(self, output: str) -> EvaluationScore:
+        """
+        Use LLM to judge the output against expected output.
+        
+        Args:
+            output: The actual output to evaluate
+            
+        Returns:
+            EvaluationScore with score and reasoning
+        """
+        try:
+            from openai import OpenAI
+        except ImportError:
+            raise ImportError("OpenAI package required for accuracy evaluation. Install with: pip install openai")
+        
+        client = OpenAI()
+        
+        prompt = f"""You are an expert evaluator. Compare the actual output against the expected output and provide a score from 1-10.
+
+Input: {self.input_text}
+
+Expected Output: {self.expected_output}
+
+Actual Output: {output}
+
+Scoring Guidelines:
+- 10: Perfect match in meaning and completeness
+- 8-9: Very close, minor differences that don't affect correctness
+- 6-7: Mostly correct but missing some details or has minor errors
+- 4-5: Partially correct but significant issues
+- 2-3: Mostly incorrect but shows some understanding
+- 1: Completely wrong or irrelevant
+
+Respond in this exact format:
+SCORE: [number 1-10]
+REASONING: [brief explanation]"""
+
+        response = client.chat.completions.create(
+            model=self.model,
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.1,
+            max_tokens=200
+        )
+        
+        response_text = response.choices[0].message.content or ""
+        
+        score = 5.0
+        reasoning = "Unable to parse response"
+        
+        lines = response_text.strip().split('\n')
+        for line in lines:
+            if line.startswith('SCORE:'):
+                try:
+                    score = float(line.replace('SCORE:', '').strip())
+                    score = max(1.0, min(10.0, score))
+                except ValueError:
+                    pass
+            elif line.startswith('REASONING:'):
+                reasoning = line.replace('REASONING:', '').strip()
+        
+        return EvaluationScore(
+            score=score,
+            reasoning=reasoning,
+            input_text=self.input_text,
+            output_text=output,
+            expected_output=self.expected_output
+        )
+    
+    def run(self, print_summary: bool = False) -> AccuracyResult:
+        """
+        Execute the accuracy evaluation.
+        
+        Args:
+            print_summary: Whether to print summary after evaluation
+            
+        Returns:
+            AccuracyResult with all evaluation scores
+        """
+        self.before_run()
+        
+        result = AccuracyResult(
+            eval_id=self.eval_id,
+            name=self.name
+        )
+        
+        for i in range(self.num_iterations):
+            if self.verbose:
+                logger.info(f"Running iteration {i + 1}/{self.num_iterations}")
+            
+            try:
+                output = self._get_output()
+                score = self._judge_output(output)
+                result.evaluations.append(score)
+                
+                if self.verbose:
+                    logger.info(f"  Score: {score.score}/10 - {score.reasoning}")
+            except Exception as e:
+                logger.error(f"Error in iteration {i + 1}: {e}")
+                result.evaluations.append(EvaluationScore(
+                    score=0.0,
+                    reasoning=f"Error: {str(e)}",
+                    input_text=self.input_text,
+                    output_text="",
+                    expected_output=self.expected_output
+                ))
+        
+        self.after_run(result)
+        
+        if print_summary:
+            result.print_summary()
+        
+        return result
+    
+    async def run_async(self, print_summary: bool = False) -> AccuracyResult:
+        """
+        Execute the accuracy evaluation asynchronously.
+        
+        Args:
+            print_summary: Whether to print summary after evaluation
+            
+        Returns:
+            AccuracyResult with all evaluation scores
+        """
+        await self.async_before_run()
+        result = self.run(print_summary=False)
+        await self.async_after_run(result)
+        
+        if print_summary:
+            result.print_summary()
+        
+        return result
+    
+    def evaluate_output(self, output: str, print_summary: bool = False) -> AccuracyResult:
+        """
+        Evaluate a pre-generated output without running the agent.
+        
+        Args:
+            output: The output to evaluate
+            print_summary: Whether to print summary after evaluation
+            
+        Returns:
+            AccuracyResult with evaluation score
+        """
+        self.before_run()
+        
+        result = AccuracyResult(
+            eval_id=self.eval_id,
+            name=self.name
+        )
+        
+        score = self._judge_output(output)
+        result.evaluations.append(score)
+        
+        self.after_run(result)
+        
+        if print_summary:
+            result.print_summary()
+        
+        return result

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

@@ -0,0 +1,143 @@
+"""
+Base evaluator class for PraisonAI Agents evaluation framework.
+
+This module provides the abstract base class that all evaluators inherit from.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Optional, Union, TYPE_CHECKING
+import uuid
+import logging
+
+if TYPE_CHECKING:
+    from .results import AccuracyResult, PerformanceResult, ReliabilityResult, CriteriaResult
+
+logger = logging.getLogger(__name__)
+
+
+class BaseEvaluator(ABC):
+    """
+    Abstract base class for all evaluators in PraisonAI Agents.
+    
+    Provides common functionality for evaluation lifecycle management,
+    result storage, and telemetry integration.
+    """
+    
+    def __init__(
+        self,
+        name: Optional[str] = None,
+        save_results_path: Optional[str] = None,
+        verbose: bool = False,
+    ):
+        """
+        Initialize the base evaluator.
+        
+        Args:
+            name: Optional name for this evaluation run
+            save_results_path: Optional file path to save results (supports {name}, {eval_id} placeholders)
+            verbose: Enable verbose output during evaluation
+        """
+        self.eval_id = str(uuid.uuid4())[:8]
+        self.name = name or f"eval_{self.eval_id}"
+        self.save_results_path = save_results_path
+        self.verbose = verbose
+        self._result = None
+    
+    def before_run(self) -> None:
+        """
+        Hook called before evaluation runs.
+        Override in subclasses for custom pre-evaluation logic.
+        """
+        if self.verbose:
+            logger.info(f"Starting evaluation: {self.name} (ID: {self.eval_id})")
+    
+    def after_run(self, result: Any) -> None:
+        """
+        Hook called after evaluation completes.
+        Override in subclasses for custom post-evaluation logic.
+        
+        Args:
+            result: The evaluation result
+        """
+        if self.verbose:
+            logger.info(f"Completed evaluation: {self.name}")
+        
+        if self.save_results_path:
+            self._save_result(result)
+    
+    async def async_before_run(self) -> None:
+        """
+        Async hook called before evaluation runs.
+        Override in subclasses for custom async pre-evaluation logic.
+        """
+        self.before_run()
+    
+    async def async_after_run(self, result: Any) -> None:
+        """
+        Async hook called after evaluation completes.
+        Override in subclasses for custom async post-evaluation logic.
+        
+        Args:
+            result: The evaluation result
+        """
+        self.after_run(result)
+    
+    @abstractmethod
+    def run(self, **kwargs) -> Any:
+        """
+        Execute the evaluation synchronously.
+        
+        Returns:
+            Evaluation result (type depends on evaluator)
+        """
+        pass
+    
+    async def run_async(self, **kwargs) -> Any:
+        """
+        Execute the evaluation asynchronously.
+        Default implementation calls sync run().
+        Override for true async evaluation.
+        
+        Returns:
+            Evaluation result (type depends on evaluator)
+        """
+        return self.run(**kwargs)
+    
+    def _save_result(self, result: Any) -> None:
+        """
+        Save evaluation result to file.
+        
+        Args:
+            result: The evaluation result to save
+        """
+        if not self.save_results_path:
+            return
+        
+        try:
+            import json
+            from pathlib import Path
+            
+            file_path = self.save_results_path.format(
+                name=self.name,
+                eval_id=self.eval_id
+            )
+            
+            path = Path(file_path)
+            path.parent.mkdir(parents=True, exist_ok=True)
+            
+            if hasattr(result, 'to_dict'):
+                data = result.to_dict()
+            elif hasattr(result, '__dict__'):
+                data = result.__dict__
+            else:
+                data = {"result": str(result)}
+            
+            path.write_text(json.dumps(data, indent=2, default=str))
+            
+            if self.verbose:
+                logger.info(f"Saved results to: {file_path}")
+        except Exception as e:
+            logger.warning(f"Failed to save results: {e}")
+    
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(name={self.name!r}, eval_id={self.eval_id!r})"

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

@@ -0,0 +1,289 @@
+"""
+Criteria evaluator for PraisonAI Agents.
+
+Evaluates agent outputs against custom criteria using LLM-as-judge.
+"""
+
+import os
+import logging
+from typing import Callable, List, Literal, Optional, Union, TYPE_CHECKING
+
+from .base import BaseEvaluator
+from .results import CriteriaResult, CriteriaScore
+
+if TYPE_CHECKING:
+    from ..agent.agent import Agent
+    from ..agents.agents import PraisonAIAgents
+
+logger = logging.getLogger(__name__)
+
+
+class CriteriaEvaluator(BaseEvaluator):
+    """
+    Evaluates agent outputs against custom criteria.
+    
+    Uses an LLM as a judge to score outputs based on user-defined criteria.
+    Supports both numeric (1-10) and binary (pass/fail) scoring.
+    """
+    
+    def __init__(
+        self,
+        criteria: str,
+        agent: Optional[Union["Agent", "PraisonAIAgents"]] = None,
+        func: Optional[Callable[..., str]] = None,
+        input_text: str = "",
+        scoring_type: Literal["numeric", "binary"] = "numeric",
+        threshold: float = 7.0,
+        num_iterations: int = 1,
+        model: Optional[str] = None,
+        on_fail: Optional[Callable[[CriteriaScore], None]] = None,
+        name: Optional[str] = None,
+        save_results_path: Optional[str] = None,
+        verbose: bool = False,
+    ):
+        """
+        Initialize the criteria evaluator.
+        
+        Args:
+            criteria: The criteria to evaluate against (e.g., "Response is helpful and accurate")
+            agent: Agent or PraisonAIAgents instance to evaluate
+            func: Alternative callable that returns output string
+            input_text: Input to provide to the agent/function
+            scoring_type: "numeric" (1-10 scale) or "binary" (pass/fail)
+            threshold: Score threshold for passing (numeric mode only)
+            num_iterations: Number of evaluation iterations
+            model: LLM model to use for judging (defaults to gpt-4o-mini)
+            on_fail: Callback function called when evaluation fails
+            name: Name for this evaluation
+            save_results_path: Path to save results
+            verbose: Enable verbose output
+        """
+        super().__init__(name=name, save_results_path=save_results_path, verbose=verbose)
+        
+        self.criteria = criteria
+        self.agent = agent
+        self.func = func
+        self.input_text = input_text
+        self.scoring_type = scoring_type
+        self.threshold = threshold
+        self.num_iterations = num_iterations
+        self.model = model or os.getenv("OPENAI_MODEL_NAME", "gpt-4o-mini")
+        self.on_fail = on_fail
+    
+    def _get_output(self) -> str:
+        """Get output from agent or function."""
+        if self.func:
+            return str(self.func(self.input_text))
+        
+        if self.agent is None:
+            raise ValueError("Either 'agent' or 'func' must be provided")
+        
+        if hasattr(self.agent, 'chat'):
+            return str(self.agent.chat(self.input_text))
+        elif hasattr(self.agent, 'start'):
+            result = self.agent.start(self.input_text)
+            if hasattr(result, 'raw'):
+                return str(result.raw)
+            return str(result)
+        else:
+            raise ValueError("Agent must have 'chat' or 'start' method")
+    
+    def _judge_output(self, output: str) -> CriteriaScore:
+        """
+        Use LLM to judge the output against criteria.
+        
+        Args:
+            output: The output to evaluate
+            
+        Returns:
+            CriteriaScore with score, passed status, and reasoning
+        """
+        try:
+            from openai import OpenAI
+        except ImportError:
+            raise ImportError("OpenAI package required for criteria evaluation. Install with: pip install openai")
+        
+        client = OpenAI()
+        
+        if self.scoring_type == "binary":
+            prompt = f"""You are an expert evaluator. Evaluate the following output against the given criteria.
+
+Criteria: {self.criteria}
+
+Output to evaluate:
+{output}
+
+Determine if the output PASSES or FAILS the criteria.
+
+Respond in this exact format:
+RESULT: [PASS or FAIL]
+REASONING: [brief explanation]"""
+        else:
+            prompt = f"""You are an expert evaluator. Evaluate the following output against the given criteria.
+
+Criteria: {self.criteria}
+
+Output to evaluate:
+{output}
+
+Score the output from 1-10 based on how well it meets the criteria.
+- 10: Perfectly meets all criteria
+- 8-9: Meets criteria very well with minor issues
+- 6-7: Meets most criteria but has some gaps
+- 4-5: Partially meets criteria
+- 2-3: Barely meets criteria
+- 1: Does not meet criteria at all
+
+Respond in this exact format:
+SCORE: [number 1-10]
+REASONING: [brief explanation]"""
+
+        response = client.chat.completions.create(
+            model=self.model,
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.1,
+            max_tokens=200
+        )
+        
+        response_text = response.choices[0].message.content or ""
+        
+        score = 5.0
+        passed = False
+        reasoning = "Unable to parse response"
+        
+        lines = response_text.strip().split('\n')
+        for line in lines:
+            if self.scoring_type == "binary":
+                if line.startswith('RESULT:'):
+                    result_str = line.replace('RESULT:', '').strip().upper()
+                    passed = result_str == 'PASS'
+                    score = 10.0 if passed else 0.0
+            else:
+                if line.startswith('SCORE:'):
+                    try:
+                        score = float(line.replace('SCORE:', '').strip())
+                        score = max(1.0, min(10.0, score))
+                        passed = score >= self.threshold
+                    except ValueError:
+                        pass
+            
+            if line.startswith('REASONING:'):
+                reasoning = line.replace('REASONING:', '').strip()
+        
+        return CriteriaScore(
+            score=score,
+            passed=passed,
+            reasoning=reasoning,
+            output_text=output,
+            criteria=self.criteria
+        )
+    
+    def run(self, print_summary: bool = False) -> CriteriaResult:
+        """
+        Execute the criteria evaluation.
+        
+        Args:
+            print_summary: Whether to print summary after evaluation
+            
+        Returns:
+            CriteriaResult with all evaluation scores
+        """
+        self.before_run()
+        
+        result = CriteriaResult(
+            criteria=self.criteria,
+            scoring_type=self.scoring_type,
+            threshold=self.threshold,
+            eval_id=self.eval_id,
+            name=self.name
+        )
+        
+        for i in range(self.num_iterations):
+            if self.verbose:
+                logger.info(f"Running iteration {i + 1}/{self.num_iterations}")
+            
+            try:
+                output = self._get_output()
+                score = self._judge_output(output)
+                result.evaluations.append(score)
+                
+                if self.verbose:
+                    status = "PASS" if score.passed else "FAIL"
+                    logger.info(f"  {status} (Score: {score.score}) - {score.reasoning}")
+                
+                if not score.passed and self.on_fail:
+                    self.on_fail(score)
+                    
+            except Exception as e:
+                logger.error(f"Error in iteration {i + 1}: {e}")
+                error_score = CriteriaScore(
+                    score=0.0,
+                    passed=False,
+                    reasoning=f"Error: {str(e)}",
+                    output_text="",
+                    criteria=self.criteria
+                )
+                result.evaluations.append(error_score)
+                
+                if self.on_fail:
+                    self.on_fail(error_score)
+        
+        self.after_run(result)
+        
+        if print_summary:
+            result.print_summary()
+        
+        return result
+    
+    async def run_async(self, print_summary: bool = False) -> CriteriaResult:
+        """
+        Execute the criteria evaluation asynchronously.
+        
+        Args:
+            print_summary: Whether to print summary after evaluation
+            
+        Returns:
+            CriteriaResult with all evaluation scores
+        """
+        await self.async_before_run()
+        result = self.run(print_summary=False)
+        await self.async_after_run(result)
+        
+        if print_summary:
+            result.print_summary()
+        
+        return result
+    
+    def evaluate_output(self, output: str, print_summary: bool = False) -> CriteriaResult:
+        """
+        Evaluate a pre-generated output without running the agent.
+        
+        Args:
+            output: The output to evaluate
+            print_summary: Whether to print summary after evaluation
+            
+        Returns:
+            CriteriaResult with evaluation score
+        """
+        self.before_run()
+        
+        result = CriteriaResult(
+            criteria=self.criteria,
+            scoring_type=self.scoring_type,
+            threshold=self.threshold,
+            eval_id=self.eval_id,
+            name=self.name
+        )
+        
+        score = self._judge_output(output)
+        result.evaluations.append(score)
+        
+        if not score.passed and self.on_fail:
+            self.on_fail(score)
+        
+        self.after_run(result)
+        
+        if print_summary:
+            result.print_summary()
+        
+        return result

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

@@ -0,0 +1,184 @@
+"""
+Performance evaluator for PraisonAI Agents.
+
+Measures runtime and memory usage of agent executions or functions.
+"""
+
+import time
+import logging
+from typing import Callable, Optional, TYPE_CHECKING
+
+from .base import BaseEvaluator
+from .results import PerformanceResult, PerformanceMetrics
+
+if TYPE_CHECKING:
+    from ..agent.agent import Agent
+    from ..agents.agents import PraisonAIAgents
+
+logger = logging.getLogger(__name__)
+
+
+class PerformanceEvaluator(BaseEvaluator):
+    """
+    Evaluates the performance of agent executions or functions.
+    
+    Measures runtime and memory usage across multiple iterations.
+    """
+    
+    def __init__(
+        self,
+        func: Optional[Callable[[], None]] = None,
+        agent: Optional["Agent"] = None,
+        input_text: str = "",
+        num_iterations: int = 10,
+        warmup_runs: int = 2,
+        track_memory: bool = True,
+        name: Optional[str] = None,
+        save_results_path: Optional[str] = None,
+        verbose: bool = False,
+    ):
+        """
+        Initialize the performance evaluator.
+        
+        Args:
+            func: Callable to benchmark (takes no arguments)
+            agent: Agent to benchmark (will call chat with input_text)
+            input_text: Input text for agent benchmarking
+            num_iterations: Number of benchmark iterations
+            warmup_runs: Number of warmup runs before measurement
+            track_memory: Whether to track memory usage
+            name: Name for this evaluation
+            save_results_path: Path to save results
+            verbose: Enable verbose output
+        """
+        super().__init__(name=name, save_results_path=save_results_path, verbose=verbose)
+        
+        self.func = func
+        self.agent = agent
+        self.input_text = input_text
+        self.num_iterations = num_iterations
+        self.warmup_runs = warmup_runs
+        self.track_memory = track_memory
+        
+        if func is None and agent is None:
+            raise ValueError("Either 'func' or 'agent' must be provided")
+    
+    def _get_memory_usage(self) -> float:
+        """Get current memory usage in MB."""
+        if not self.track_memory:
+            return 0.0
+        
+        try:
+            import tracemalloc
+            current, _ = tracemalloc.get_traced_memory()
+            return current / (1024 * 1024)
+        except Exception:
+            return 0.0
+    
+    def _execute(self) -> None:
+        """Execute the function or agent."""
+        if self.func:
+            self.func()
+        elif self.agent:
+            if hasattr(self.agent, 'chat'):
+                self.agent.chat(self.input_text)
+            elif hasattr(self.agent, 'start'):
+                self.agent.start(self.input_text)
+            else:
+                raise ValueError("Agent must have 'chat' or 'start' method")
+    
+    def run(self, print_summary: bool = False) -> PerformanceResult:
+        """
+        Execute the performance evaluation.
+        
+        Args:
+            print_summary: Whether to print summary after evaluation
+            
+        Returns:
+            PerformanceResult with all performance metrics
+        """
+        self.before_run()
+        
+        result = PerformanceResult(
+            warmup_runs=self.warmup_runs,
+            eval_id=self.eval_id,
+            name=self.name
+        )
+        
+        if self.track_memory:
+            try:
+                import tracemalloc
+                tracemalloc.start()
+            except Exception as e:
+                logger.warning(f"Could not start memory tracking: {e}")
+                self.track_memory = False
+        
+        if self.verbose:
+            logger.info(f"Running {self.warmup_runs} warmup iterations...")
+        
+        for i in range(self.warmup_runs):
+            try:
+                self._execute()
+            except Exception as e:
+                logger.warning(f"Warmup iteration {i + 1} failed: {e}")
+        
+        if self.verbose:
+            logger.info(f"Running {self.num_iterations} benchmark iterations...")
+        
+        for i in range(self.num_iterations):
+            memory_before = self._get_memory_usage()
+            
+            start_time = time.perf_counter()
+            try:
+                self._execute()
+                success = True
+            except Exception as e:
+                logger.error(f"Iteration {i + 1} failed: {e}")
+                success = False
+            end_time = time.perf_counter()
+            
+            memory_after = self._get_memory_usage()
+            
+            if success:
+                metrics = PerformanceMetrics(
+                    run_time=end_time - start_time,
+                    memory_usage=max(0, memory_after - memory_before),
+                    iteration=i + 1
+                )
+                result.metrics.append(metrics)
+                
+                if self.verbose:
+                    logger.info(f"  Iteration {i + 1}: {metrics.run_time:.4f}s, {metrics.memory_usage:.2f}MB")
+        
+        if self.track_memory:
+            try:
+                import tracemalloc
+                tracemalloc.stop()
+            except Exception:
+                pass
+        
+        self.after_run(result)
+        
+        if print_summary:
+            result.print_summary()
+        
+        return result
+    
+    async def run_async(self, print_summary: bool = False) -> PerformanceResult:
+        """
+        Execute the performance evaluation asynchronously.
+        
+        Args:
+            print_summary: Whether to print summary after evaluation
+            
+        Returns:
+            PerformanceResult with all performance metrics
+        """
+        await self.async_before_run()
+        result = self.run(print_summary=False)
+        await self.async_after_run(result)
+        
+        if print_summary:
+            result.print_summary()
+        
+        return result

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

@@ -0,0 +1,225 @@
+"""
+Reliability evaluator for PraisonAI Agents.
+
+Evaluates agent reliability by verifying expected tool calls are made.
+"""
+
+import logging
+from typing import Dict, List, Optional, Set, Any, TYPE_CHECKING
+
+from .base import BaseEvaluator
+from .results import ReliabilityResult, ToolCallResult
+
+if TYPE_CHECKING:
+    from ..agent.agent import Agent
+    from ..agents.agents import PraisonAIAgents
+
+logger = logging.getLogger(__name__)
+
+
+class ReliabilityEvaluator(BaseEvaluator):
+    """
+    Evaluates the reliability of agent tool usage.
+    
+    Verifies that expected tools are called during agent execution.
+    """
+    
+    def __init__(
+        self,
+        agent: Optional["Agent"] = None,
+        input_text: str = "",
+        expected_tools: Optional[List[str]] = None,
+        forbidden_tools: Optional[List[str]] = None,
+        name: Optional[str] = None,
+        save_results_path: Optional[str] = None,
+        verbose: bool = False,
+    ):
+        """
+        Initialize the reliability evaluator.
+        
+        Args:
+            agent: Agent to evaluate
+            input_text: Input to provide to the agent
+            expected_tools: List of tool names that should be called
+            forbidden_tools: List of tool names that should NOT be called
+            name: Name for this evaluation
+            save_results_path: Path to save results
+            verbose: Enable verbose output
+        """
+        super().__init__(name=name, save_results_path=save_results_path, verbose=verbose)
+        
+        self.agent = agent
+        self.input_text = input_text
+        self.expected_tools = set(expected_tools or [])
+        self.forbidden_tools = set(forbidden_tools or [])
+        
+        if agent is None:
+            raise ValueError("'agent' must be provided")
+    
+    def _extract_tool_calls(self, response: Any) -> Set[str]:
+        """
+        Extract tool names from agent response.
+        
+        Args:
+            response: Agent response object
+            
+        Returns:
+            Set of tool names that were called
+        """
+        tool_calls = set()
+        
+        if hasattr(response, 'tool_calls') and response.tool_calls:
+            for tc in response.tool_calls:
+                if hasattr(tc, 'name'):
+                    tool_calls.add(tc.name)
+                elif hasattr(tc, 'function') and hasattr(tc.function, 'name'):
+                    tool_calls.add(tc.function.name)
+        
+        if hasattr(self.agent, 'chat_history'):
+            for msg in self.agent.chat_history:
+                if isinstance(msg, dict):
+                    if msg.get('role') == 'assistant' and 'tool_calls' in msg:
+                        for tc in msg.get('tool_calls', []):
+                            if isinstance(tc, dict) and 'function' in tc:
+                                tool_calls.add(tc['function'].get('name', ''))
+                    elif msg.get('role') == 'tool':
+                        tool_name = msg.get('name', '')
+                        if tool_name:
+                            tool_calls.add(tool_name)
+        
+        return tool_calls
+    
+    def run(self, print_summary: bool = False) -> ReliabilityResult:
+        """
+        Execute the reliability evaluation.
+        
+        Args:
+            print_summary: Whether to print summary after evaluation
+            
+        Returns:
+            ReliabilityResult with tool call verification results
+        """
+        self.before_run()
+        
+        result = ReliabilityResult(
+            eval_id=self.eval_id,
+            name=self.name
+        )
+        
+        try:
+            if hasattr(self.agent, 'chat'):
+                response = self.agent.chat(self.input_text)
+            elif hasattr(self.agent, 'start'):
+                response = self.agent.start(self.input_text)
+            else:
+                raise ValueError("Agent must have 'chat' or 'start' method")
+            
+            actual_tools = self._extract_tool_calls(response)
+            
+            if self.verbose:
+                logger.info(f"Expected tools: {self.expected_tools}")
+                logger.info(f"Actual tools called: {actual_tools}")
+            
+            for tool in self.expected_tools:
+                was_called = tool in actual_tools
+                result.tool_results.append(ToolCallResult(
+                    tool_name=tool,
+                    expected=True,
+                    actual=was_called,
+                    passed=was_called
+                ))
+            
+            for tool in self.forbidden_tools:
+                was_called = tool in actual_tools
+                result.tool_results.append(ToolCallResult(
+                    tool_name=tool,
+                    expected=False,
+                    actual=was_called,
+                    passed=not was_called
+                ))
+            
+        except Exception as e:
+            logger.error(f"Error during reliability evaluation: {e}")
+            for tool in self.expected_tools:
+                result.tool_results.append(ToolCallResult(
+                    tool_name=tool,
+                    expected=True,
+                    actual=False,
+                    passed=False,
+                    metadata={"error": str(e)}
+                ))
+        
+        self.after_run(result)
+        
+        if print_summary:
+            result.print_summary()
+        
+        return result
+    
+    async def run_async(self, print_summary: bool = False) -> ReliabilityResult:
+        """
+        Execute the reliability evaluation asynchronously.
+        
+        Args:
+            print_summary: Whether to print summary after evaluation
+            
+        Returns:
+            ReliabilityResult with tool call verification results
+        """
+        await self.async_before_run()
+        result = self.run(print_summary=False)
+        await self.async_after_run(result)
+        
+        if print_summary:
+            result.print_summary()
+        
+        return result
+    
+    def evaluate_tool_calls(
+        self,
+        actual_tools: List[str],
+        print_summary: bool = False
+    ) -> ReliabilityResult:
+        """
+        Evaluate a pre-recorded list of tool calls without running the agent.
+        
+        Args:
+            actual_tools: List of tool names that were called
+            print_summary: Whether to print summary after evaluation
+            
+        Returns:
+            ReliabilityResult with tool call verification results
+        """
+        self.before_run()
+        
+        result = ReliabilityResult(
+            eval_id=self.eval_id,
+            name=self.name
+        )
+        
+        actual_set = set(actual_tools)
+        
+        for tool in self.expected_tools:
+            was_called = tool in actual_set
+            result.tool_results.append(ToolCallResult(
+                tool_name=tool,
+                expected=True,
+                actual=was_called,
+                passed=was_called
+            ))
+        
+        for tool in self.forbidden_tools:
+            was_called = tool in actual_set
+            result.tool_results.append(ToolCallResult(
+                tool_name=tool,
+                expected=False,
+                actual=was_called,
+                passed=not was_called
+            ))
+        
+        self.after_run(result)
+        
+        if print_summary:
+            result.print_summary()
+        
+        return result

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

@@ -0,0 +1,463 @@
+"""
+Result dataclasses for PraisonAI Agents evaluation framework.
+
+This module provides structured result types for different evaluation methods.
+"""
+
+from dataclasses import dataclass, field, asdict
+from typing import List, Optional, Dict, Any
+from datetime import datetime
+import statistics
+import json
+
+
+@dataclass
+class EvaluationScore:
+    """Individual evaluation score with reasoning."""
+    score: float
+    reasoning: str
+    input_text: str
+    output_text: str
+    expected_output: Optional[str] = None
+    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class AccuracyResult:
+    """Result from accuracy evaluation comparing output to expected output."""
+    evaluations: List[EvaluationScore] = field(default_factory=list)
+    eval_id: str = ""
+    name: str = ""
+    
+    @property
+    def scores(self) -> List[float]:
+        """Get list of all scores."""
+        return [e.score for e in self.evaluations]
+    
+    @property
+    def avg_score(self) -> float:
+        """Calculate average score."""
+        if not self.scores:
+            return 0.0
+        return statistics.mean(self.scores)
+    
+    @property
+    def min_score(self) -> float:
+        """Get minimum score."""
+        if not self.scores:
+            return 0.0
+        return min(self.scores)
+    
+    @property
+    def max_score(self) -> float:
+        """Get maximum score."""
+        if not self.scores:
+            return 0.0
+        return max(self.scores)
+    
+    @property
+    def std_dev(self) -> float:
+        """Calculate standard deviation of scores."""
+        if len(self.scores) < 2:
+            return 0.0
+        return statistics.stdev(self.scores)
+    
+    @property
+    def passed(self) -> bool:
+        """Check if evaluation passed (avg score >= 7)."""
+        return self.avg_score >= 7.0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert result to dictionary."""
+        return {
+            "eval_id": self.eval_id,
+            "name": self.name,
+            "evaluations": [asdict(e) for e in self.evaluations],
+            "avg_score": self.avg_score,
+            "min_score": self.min_score,
+            "max_score": self.max_score,
+            "std_dev": self.std_dev,
+            "passed": self.passed,
+            "num_evaluations": len(self.evaluations)
+        }
+    
+    def to_json(self) -> str:
+        """Convert result to JSON string."""
+        return json.dumps(self.to_dict(), indent=2)
+    
+    def print_summary(self) -> None:
+        """Print a summary of the evaluation results."""
+        try:
+            from rich.console import Console
+            from rich.table import Table
+            from rich.panel import Panel  # noqa: F401
+            
+            console = Console()
+            
+            table = Table(title="Accuracy Evaluation Summary")
+            table.add_column("Metric", style="cyan")
+            table.add_column("Value", style="green")
+            
+            table.add_row("Evaluations", str(len(self.evaluations)))
+            table.add_row("Average Score", f"{self.avg_score:.2f}")
+            table.add_row("Min Score", f"{self.min_score:.2f}")
+            table.add_row("Max Score", f"{self.max_score:.2f}")
+            table.add_row("Std Dev", f"{self.std_dev:.2f}")
+            table.add_row("Status", "✅ PASSED" if self.passed else "❌ FAILED")
+            
+            console.print(table)
+        except ImportError:
+            print("Accuracy Evaluation Summary")
+            print(f"  Evaluations: {len(self.evaluations)}")
+            print(f"  Average Score: {self.avg_score:.2f}")
+            print(f"  Min Score: {self.min_score:.2f}")
+            print(f"  Max Score: {self.max_score:.2f}")
+            print(f"  Std Dev: {self.std_dev:.2f}")
+            print(f"  Status: {'PASSED' if self.passed else 'FAILED'}")
+
+
+@dataclass
+class PerformanceMetrics:
+    """Individual performance measurement."""
+    run_time: float
+    memory_usage: float
+    iteration: int
+    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class PerformanceResult:
+    """Result from performance evaluation measuring runtime and memory."""
+    metrics: List[PerformanceMetrics] = field(default_factory=list)
+    warmup_runs: int = 0
+    eval_id: str = ""
+    name: str = ""
+    
+    @property
+    def run_times(self) -> List[float]:
+        """Get list of all run times."""
+        return [m.run_time for m in self.metrics]
+    
+    @property
+    def memory_usages(self) -> List[float]:
+        """Get list of all memory usages."""
+        return [m.memory_usage for m in self.metrics]
+    
+    @property
+    def avg_run_time(self) -> float:
+        """Calculate average run time."""
+        if not self.run_times:
+            return 0.0
+        return statistics.mean(self.run_times)
+    
+    @property
+    def min_run_time(self) -> float:
+        """Get minimum run time."""
+        if not self.run_times:
+            return 0.0
+        return min(self.run_times)
+    
+    @property
+    def max_run_time(self) -> float:
+        """Get maximum run time."""
+        if not self.run_times:
+            return 0.0
+        return max(self.run_times)
+    
+    @property
+    def std_dev_run_time(self) -> float:
+        """Calculate standard deviation of run times."""
+        if len(self.run_times) < 2:
+            return 0.0
+        return statistics.stdev(self.run_times)
+    
+    @property
+    def median_run_time(self) -> float:
+        """Calculate median run time."""
+        if not self.run_times:
+            return 0.0
+        return statistics.median(self.run_times)
+    
+    @property
+    def p95_run_time(self) -> float:
+        """Calculate 95th percentile run time."""
+        if not self.run_times:
+            return 0.0
+        sorted_times = sorted(self.run_times)
+        idx = int(len(sorted_times) * 0.95)
+        return sorted_times[min(idx, len(sorted_times) - 1)]
+    
+    @property
+    def avg_memory(self) -> float:
+        """Calculate average memory usage."""
+        if not self.memory_usages:
+            return 0.0
+        return statistics.mean(self.memory_usages)
+    
+    @property
+    def max_memory(self) -> float:
+        """Get maximum memory usage."""
+        if not self.memory_usages:
+            return 0.0
+        return max(self.memory_usages)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert result to dictionary."""
+        return {
+            "eval_id": self.eval_id,
+            "name": self.name,
+            "metrics": [asdict(m) for m in self.metrics],
+            "warmup_runs": self.warmup_runs,
+            "avg_run_time": self.avg_run_time,
+            "min_run_time": self.min_run_time,
+            "max_run_time": self.max_run_time,
+            "std_dev_run_time": self.std_dev_run_time,
+            "median_run_time": self.median_run_time,
+            "p95_run_time": self.p95_run_time,
+            "avg_memory": self.avg_memory,
+            "max_memory": self.max_memory,
+            "num_iterations": len(self.metrics)
+        }
+    
+    def to_json(self) -> str:
+        """Convert result to JSON string."""
+        return json.dumps(self.to_dict(), indent=2)
+    
+    def print_summary(self) -> None:
+        """Print a summary of the performance results."""
+        try:
+            from rich.console import Console
+            from rich.table import Table
+            
+            console = Console()
+            
+            table = Table(title="Performance Evaluation Summary")
+            table.add_column("Metric", style="cyan")
+            table.add_column("Value", style="green")
+            
+            table.add_row("Iterations", str(len(self.metrics)))
+            table.add_row("Warmup Runs", str(self.warmup_runs))
+            table.add_row("Avg Run Time", f"{self.avg_run_time:.4f}s")
+            table.add_row("Min Run Time", f"{self.min_run_time:.4f}s")
+            table.add_row("Max Run Time", f"{self.max_run_time:.4f}s")
+            table.add_row("Std Dev", f"{self.std_dev_run_time:.4f}s")
+            table.add_row("Median", f"{self.median_run_time:.4f}s")
+            table.add_row("P95", f"{self.p95_run_time:.4f}s")
+            table.add_row("Avg Memory", f"{self.avg_memory:.2f} MB")
+            table.add_row("Max Memory", f"{self.max_memory:.2f} MB")
+            
+            console.print(table)
+        except ImportError:
+            print("Performance Evaluation Summary")
+            print(f"  Iterations: {len(self.metrics)}")
+            print(f"  Warmup Runs: {self.warmup_runs}")
+            print(f"  Avg Run Time: {self.avg_run_time:.4f}s")
+            print(f"  Min Run Time: {self.min_run_time:.4f}s")
+            print(f"  Max Run Time: {self.max_run_time:.4f}s")
+            print(f"  Std Dev: {self.std_dev_run_time:.4f}s")
+            print(f"  Median: {self.median_run_time:.4f}s")
+            print(f"  P95: {self.p95_run_time:.4f}s")
+            print(f"  Avg Memory: {self.avg_memory:.2f} MB")
+            print(f"  Max Memory: {self.max_memory:.2f} MB")
+
+
+@dataclass
+class ToolCallResult:
+    """Result of a single tool call verification."""
+    tool_name: str
+    expected: bool
+    actual: bool
+    passed: bool
+    arguments: Optional[Dict[str, Any]] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ReliabilityResult:
+    """Result from reliability evaluation checking tool calls."""
+    tool_results: List[ToolCallResult] = field(default_factory=list)
+    eval_id: str = ""
+    name: str = ""
+    
+    @property
+    def passed_calls(self) -> List[ToolCallResult]:
+        """Get list of passed tool calls."""
+        return [t for t in self.tool_results if t.passed]
+    
+    @property
+    def failed_calls(self) -> List[ToolCallResult]:
+        """Get list of failed tool calls."""
+        return [t for t in self.tool_results if not t.passed]
+    
+    @property
+    def pass_rate(self) -> float:
+        """Calculate pass rate."""
+        if not self.tool_results:
+            return 0.0
+        return len(self.passed_calls) / len(self.tool_results)
+    
+    @property
+    def status(self) -> str:
+        """Get overall status."""
+        return "PASSED" if len(self.failed_calls) == 0 else "FAILED"
+    
+    @property
+    def passed(self) -> bool:
+        """Check if all tool calls passed."""
+        return len(self.failed_calls) == 0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert result to dictionary."""
+        return {
+            "eval_id": self.eval_id,
+            "name": self.name,
+            "tool_results": [asdict(t) for t in self.tool_results],
+            "passed_count": len(self.passed_calls),
+            "failed_count": len(self.failed_calls),
+            "pass_rate": self.pass_rate,
+            "status": self.status
+        }
+    
+    def to_json(self) -> str:
+        """Convert result to JSON string."""
+        return json.dumps(self.to_dict(), indent=2)
+    
+    def print_summary(self) -> None:
+        """Print a summary of the reliability results."""
+        try:
+            from rich.console import Console
+            from rich.table import Table
+            
+            console = Console()
+            
+            table = Table(title="Reliability Evaluation Summary")
+            table.add_column("Metric", style="cyan")
+            table.add_column("Value", style="green")
+            
+            table.add_row("Total Tool Calls", str(len(self.tool_results)))
+            table.add_row("Passed", str(len(self.passed_calls)))
+            table.add_row("Failed", str(len(self.failed_calls)))
+            table.add_row("Pass Rate", f"{self.pass_rate:.1%}")
+            table.add_row("Status", "✅ PASSED" if self.passed else "❌ FAILED")
+            
+            console.print(table)
+            
+            if self.failed_calls:
+                fail_table = Table(title="Failed Tool Calls")
+                fail_table.add_column("Tool", style="red")
+                fail_table.add_column("Expected", style="yellow")
+                fail_table.add_column("Actual", style="yellow")
+                
+                for tc in self.failed_calls:
+                    fail_table.add_row(tc.tool_name, str(tc.expected), str(tc.actual))
+                
+                console.print(fail_table)
+        except ImportError:
+            print("Reliability Evaluation Summary")
+            print(f"  Total Tool Calls: {len(self.tool_results)}")
+            print(f"  Passed: {len(self.passed_calls)}")
+            print(f"  Failed: {len(self.failed_calls)}")
+            print(f"  Pass Rate: {self.pass_rate:.1%}")
+            print(f"  Status: {self.status}")
+
+
+@dataclass
+class CriteriaScore:
+    """Individual criteria evaluation score."""
+    score: float
+    passed: bool
+    reasoning: str
+    output_text: str
+    criteria: str
+    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class CriteriaResult:
+    """Result from criteria-based evaluation (agent-as-judge)."""
+    evaluations: List[CriteriaScore] = field(default_factory=list)
+    criteria: str = ""
+    scoring_type: str = "numeric"  # "numeric" or "binary"
+    threshold: float = 7.0
+    eval_id: str = ""
+    name: str = ""
+    
+    @property
+    def scores(self) -> List[float]:
+        """Get list of all scores."""
+        return [e.score for e in self.evaluations]
+    
+    @property
+    def avg_score(self) -> float:
+        """Calculate average score."""
+        if not self.scores:
+            return 0.0
+        return statistics.mean(self.scores)
+    
+    @property
+    def pass_rate(self) -> float:
+        """Calculate pass rate."""
+        if not self.evaluations:
+            return 0.0
+        passed = sum(1 for e in self.evaluations if e.passed)
+        return passed / len(self.evaluations)
+    
+    @property
+    def passed(self) -> bool:
+        """Check if evaluation passed overall."""
+        if self.scoring_type == "binary":
+            return self.pass_rate >= 0.5
+        return self.avg_score >= self.threshold
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert result to dictionary."""
+        return {
+            "eval_id": self.eval_id,
+            "name": self.name,
+            "criteria": self.criteria,
+            "scoring_type": self.scoring_type,
+            "threshold": self.threshold,
+            "evaluations": [asdict(e) for e in self.evaluations],
+            "avg_score": self.avg_score,
+            "pass_rate": self.pass_rate,
+            "passed": self.passed,
+            "num_evaluations": len(self.evaluations)
+        }
+    
+    def to_json(self) -> str:
+        """Convert result to JSON string."""
+        return json.dumps(self.to_dict(), indent=2)
+    
+    def print_summary(self) -> None:
+        """Print a summary of the criteria evaluation results."""
+        try:
+            from rich.console import Console
+            from rich.table import Table
+            
+            console = Console()
+            
+            table = Table(title="Criteria Evaluation Summary")
+            table.add_column("Metric", style="cyan")
+            table.add_column("Value", style="green")
+            
+            table.add_row("Criteria", self.criteria[:50] + "..." if len(self.criteria) > 50 else self.criteria)
+            table.add_row("Scoring Type", self.scoring_type)
+            table.add_row("Threshold", str(self.threshold))
+            table.add_row("Evaluations", str(len(self.evaluations)))
+            table.add_row("Average Score", f"{self.avg_score:.2f}")
+            table.add_row("Pass Rate", f"{self.pass_rate:.1%}")
+            table.add_row("Status", "✅ PASSED" if self.passed else "❌ FAILED")
+            
+            console.print(table)
+        except ImportError:
+            print("Criteria Evaluation Summary")
+            print(f"  Criteria: {self.criteria[:50]}...")
+            print(f"  Scoring Type: {self.scoring_type}")
+            print(f"  Threshold: {self.threshold}")
+            print(f"  Evaluations: {len(self.evaluations)}")
+            print(f"  Average Score: {self.avg_score:.2f}")
+            print(f"  Pass Rate: {self.pass_rate:.1%}")
+            print(f"  Status: {'PASSED' if self.passed else 'FAILED'}")

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

@@ -0,0 +1,154 @@
+"""
+Utility functions for PraisonAI Agents evaluation framework.
+"""
+
+import json
+import logging
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+logger = logging.getLogger(__name__)
+
+
+def save_result_to_file(
+    result: Any,
+    file_path: str,
+    eval_id: Optional[str] = None,
+    name: Optional[str] = None
+) -> bool:
+    """
+    Save evaluation result to a file.
+    
+    Args:
+        result: The evaluation result to save
+        file_path: Path to save the result (supports {name}, {eval_id} placeholders)
+        eval_id: Optional eval ID for placeholder substitution
+        name: Optional name for placeholder substitution
+        
+    Returns:
+        True if save was successful, False otherwise
+    """
+    try:
+        formatted_path = file_path.format(name=name or "", eval_id=eval_id or "")
+        path = Path(formatted_path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        
+        if hasattr(result, 'to_dict'):
+            data = result.to_dict()
+        elif hasattr(result, '__dict__'):
+            data = result.__dict__
+        else:
+            data = {"result": str(result)}
+        
+        path.write_text(json.dumps(data, indent=2, default=str))
+        logger.debug(f"Saved result to: {formatted_path}")
+        return True
+    except Exception as e:
+        logger.warning(f"Failed to save result: {e}")
+        return False
+
+
+def load_result_from_file(file_path: str) -> Optional[Dict[str, Any]]:
+    """
+    Load evaluation result from a file.
+    
+    Args:
+        file_path: Path to the result file
+        
+    Returns:
+        Dictionary containing the result data, or None if load failed
+    """
+    try:
+        path = Path(file_path)
+        if not path.exists():
+            logger.warning(f"Result file not found: {file_path}")
+            return None
+        
+        data = json.loads(path.read_text())
+        return data
+    except Exception as e:
+        logger.warning(f"Failed to load result: {e}")
+        return None
+
+
+def format_score(score: float, max_score: float = 10.0) -> str:
+    """
+    Format a score for display.
+    
+    Args:
+        score: The score value
+        max_score: Maximum possible score
+        
+    Returns:
+        Formatted score string
+    """
+    return f"{score:.1f}/{max_score:.0f}"
+
+
+def format_percentage(value: float) -> str:
+    """
+    Format a value as a percentage.
+    
+    Args:
+        value: Value between 0 and 1
+        
+    Returns:
+        Formatted percentage string
+    """
+    return f"{value * 100:.1f}%"
+
+
+def format_duration(seconds: float) -> str:
+    """
+    Format a duration in seconds for display.
+    
+    Args:
+        seconds: Duration in seconds
+        
+    Returns:
+        Formatted duration string
+    """
+    if seconds < 0.001:
+        return f"{seconds * 1000000:.0f}μs"
+    elif seconds < 1:
+        return f"{seconds * 1000:.2f}ms"
+    elif seconds < 60:
+        return f"{seconds:.2f}s"
+    else:
+        minutes = int(seconds // 60)
+        secs = seconds % 60
+        return f"{minutes}m {secs:.1f}s"
+
+
+def format_memory(mb: float) -> str:
+    """
+    Format memory usage for display.
+    
+    Args:
+        mb: Memory in megabytes
+        
+    Returns:
+        Formatted memory string
+    """
+    if mb < 1:
+        return f"{mb * 1024:.1f} KB"
+    elif mb < 1024:
+        return f"{mb:.2f} MB"
+    else:
+        return f"{mb / 1024:.2f} GB"
+
+
+def calculate_pass_rate(passed: int, total: int) -> float:
+    """
+    Calculate pass rate.
+    
+    Args:
+        passed: Number of passed evaluations
+        total: Total number of evaluations
+        
+    Returns:
+        Pass rate as a float between 0 and 1
+    """
+    if total == 0:
+        return 0.0
+    return passed / total

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

@@ -0,0 +1,97 @@
+"""
+Hooks Module for PraisonAI Agents.
+
+Provides a powerful hook system for intercepting and modifying agent behavior
+at various lifecycle points. Unlike callbacks (which are for UI events),
+hooks can intercept, modify, or block tool execution.
+
+Features:
+- Event-based hook system (BeforeTool, AfterTool, BeforeAgent, etc.)
+- Shell command hooks for external integrations
+- Python function hooks for in-process customization
+- Matcher patterns for selective hook execution
+- Sequential and parallel hook execution
+- Decision outcomes (allow, deny, block, ask)
+
+Zero Performance Impact:
+- All imports are lazy loaded via __getattr__
+- Hooks only execute when registered
+- No overhead when hooks are disabled
+
+Usage:
+    from praisonaiagents.hooks import HookRegistry, HookEvent
+    
+    # Register a Python function hook
+    registry = HookRegistry()
+    
+    @registry.on(HookEvent.BEFORE_TOOL)
+    def my_hook(event_data):
+        if event_data.tool_name == "dangerous_tool":
+            return HookResult(decision="deny", reason="Tool blocked by policy")
+        return HookResult(decision="allow")
+    
+    # Register a shell command hook
+    registry.register_command_hook(
+        event=HookEvent.BEFORE_TOOL,
+        command="python /path/to/validator.py",
+        matcher="write_*"  # Only match tools starting with write_
+    )
+    
+    # Use with Agent
+    agent = Agent(
+        name="MyAgent",
+        hooks=registry
+    )
+"""
+
+__all__ = [
+    # Core types
+    "HookEvent",
+    "HookDecision",
+    "HookResult",
+    "HookInput",
+    "HookOutput",
+    # Hook definitions
+    "HookDefinition",
+    "CommandHook",
+    "FunctionHook",
+    # Registry and runner
+    "HookRegistry",
+    "HookRunner",
+    # Event-specific inputs
+    "BeforeToolInput",
+    "AfterToolInput",
+    "BeforeAgentInput",
+    "AfterAgentInput",
+    "SessionStartInput",
+    "SessionEndInput",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components to avoid import overhead."""
+    if name in ("HookEvent", "HookDecision", "HookResult", "HookInput", "HookOutput"):
+        from .types import HookEvent, HookDecision, HookResult, HookInput, HookOutput
+        return locals()[name]
+    
+    if name in ("HookDefinition", "CommandHook", "FunctionHook"):
+        from .types import HookDefinition, CommandHook, FunctionHook
+        return locals()[name]
+    
+    if name == "HookRegistry":
+        from .registry import HookRegistry
+        return HookRegistry
+    
+    if name == "HookRunner":
+        from .runner import HookRunner
+        return HookRunner
+    
+    if name in ("BeforeToolInput", "AfterToolInput", "BeforeAgentInput", 
+                "AfterAgentInput", "SessionStartInput", "SessionEndInput"):
+        from .events import (
+            BeforeToolInput, AfterToolInput, BeforeAgentInput,
+            AfterAgentInput, SessionStartInput, SessionEndInput
+        )
+        return locals()[name]
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/hooks/events.py

@@ -0,0 +1,195 @@
+"""
+Event-specific input types for the hook system.
+
+Each event type has its own input class with relevant fields.
+"""
+
+from dataclasses import dataclass, field
+from typing import Optional, Dict, Any, List
+from .types import HookInput
+
+
+@dataclass
+class BeforeToolInput(HookInput):
+    """Input for BeforeTool hooks."""
+    tool_name: str = ""
+    tool_input: Dict[str, Any] = field(default_factory=dict)
+    tool_description: Optional[str] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        base = super().to_dict()
+        base.update({
+            "tool_name": self.tool_name,
+            "tool_input": self.tool_input,
+            "tool_description": self.tool_description
+        })
+        return base
+
+
+@dataclass
+class AfterToolInput(HookInput):
+    """Input for AfterTool hooks."""
+    tool_name: str = ""
+    tool_input: Dict[str, Any] = field(default_factory=dict)
+    tool_output: Any = None
+    tool_error: Optional[str] = None
+    execution_time_ms: float = 0.0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        base = super().to_dict()
+        base.update({
+            "tool_name": self.tool_name,
+            "tool_input": self.tool_input,
+            "tool_output": str(self.tool_output) if self.tool_output else None,
+            "tool_error": self.tool_error,
+            "execution_time_ms": self.execution_time_ms
+        })
+        return base
+
+
+@dataclass
+class BeforeAgentInput(HookInput):
+    """Input for BeforeAgent hooks."""
+    prompt: str = ""
+    conversation_history: List[Dict[str, Any]] = field(default_factory=list)
+    tools_available: List[str] = field(default_factory=list)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        base = super().to_dict()
+        base.update({
+            "prompt": self.prompt,
+            "conversation_history_length": len(self.conversation_history),
+            "tools_available": self.tools_available
+        })
+        return base
+
+
+@dataclass
+class AfterAgentInput(HookInput):
+    """Input for AfterAgent hooks."""
+    prompt: str = ""
+    response: str = ""
+    tools_used: List[str] = field(default_factory=list)
+    total_tokens: int = 0
+    execution_time_ms: float = 0.0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        base = super().to_dict()
+        base.update({
+            "prompt": self.prompt,
+            "response": self.response[:500] if self.response else "",  # Truncate for JSON
+            "tools_used": self.tools_used,
+            "total_tokens": self.total_tokens,
+            "execution_time_ms": self.execution_time_ms
+        })
+        return base
+
+
+@dataclass
+class SessionStartInput(HookInput):
+    """Input for SessionStart hooks."""
+    source: str = "startup"  # startup, resume, clear
+    session_name: Optional[str] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        base = super().to_dict()
+        base.update({
+            "source": self.source,
+            "session_name": self.session_name
+        })
+        return base
+
+
+@dataclass
+class SessionEndInput(HookInput):
+    """Input for SessionEnd hooks."""
+    reason: str = "exit"  # exit, clear, logout, error
+    total_turns: int = 0
+    total_tokens: int = 0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        base = super().to_dict()
+        base.update({
+            "reason": self.reason,
+            "total_turns": self.total_turns,
+            "total_tokens": self.total_tokens
+        })
+        return base
+
+
+@dataclass
+class BeforeLLMInput(HookInput):
+    """Input for BeforeLLM hooks."""
+    messages: List[Dict[str, Any]] = field(default_factory=list)
+    model: str = ""
+    temperature: Optional[float] = None
+    max_tokens: Optional[int] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        base = super().to_dict()
+        base.update({
+            "messages_count": len(self.messages),
+            "model": self.model,
+            "temperature": self.temperature,
+            "max_tokens": self.max_tokens
+        })
+        return base
+
+
+@dataclass
+class AfterLLMInput(HookInput):
+    """Input for AfterLLM hooks."""
+    messages: List[Dict[str, Any]] = field(default_factory=list)
+    response: str = ""
+    model: str = ""
+    tokens_used: int = 0
+    latency_ms: float = 0.0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        base = super().to_dict()
+        base.update({
+            "messages_count": len(self.messages),
+            "response_length": len(self.response),
+            "model": self.model,
+            "tokens_used": self.tokens_used,
+            "latency_ms": self.latency_ms
+        })
+        return base
+
+
+@dataclass
+class OnErrorInput(HookInput):
+    """Input for OnError hooks."""
+    error_type: str = ""
+    error_message: str = ""
+    stack_trace: Optional[str] = None
+    context: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        base = super().to_dict()
+        base.update({
+            "error_type": self.error_type,
+            "error_message": self.error_message,
+            "stack_trace": self.stack_trace,
+            "context": self.context
+        })
+        return base
+
+
+@dataclass
+class OnRetryInput(HookInput):
+    """Input for OnRetry hooks."""
+    retry_count: int = 0
+    max_retries: int = 3
+    error_message: str = ""
+    operation: str = ""  # tool_call, llm_request, etc.
+    
+    def to_dict(self) -> Dict[str, Any]:
+        base = super().to_dict()
+        base.update({
+            "retry_count": self.retry_count,
+            "max_retries": self.max_retries,
+            "error_message": self.error_message,
+            "operation": self.operation
+        })
+        return base

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

@@ -0,0 +1,336 @@
+"""
+Hook Registry for PraisonAI Agents.
+
+Manages registration and lookup of hooks for different events.
+"""
+
+import logging
+from typing import Dict, List, Optional, Callable, Union
+from functools import wraps
+
+from .types import (
+    HookEvent, HookDefinition, CommandHook, FunctionHook,
+    HookInput, HookResult
+)
+
+logger = logging.getLogger(__name__)
+
+
+class HookRegistry:
+    """
+    Registry for managing hooks.
+    
+    Provides methods to register, unregister, and lookup hooks
+    for different events.
+    
+    Example:
+        registry = HookRegistry()
+        
+        # Register a function hook using decorator
+        @registry.on(HookEvent.BEFORE_TOOL)
+        def validate_tool(event_data):
+            if event_data.tool_name == "dangerous":
+                return HookResult.deny("Tool is dangerous")
+            return HookResult.allow()
+        
+        # Register a command hook
+        registry.register_command(
+            event=HookEvent.BEFORE_TOOL,
+            command="python /path/to/validator.py",
+            matcher="write_*"
+        )
+    """
+    
+    def __init__(self, enabled: bool = True):
+        """
+        Initialize the hook registry.
+        
+        Args:
+            enabled: Whether hooks are enabled globally
+        """
+        self._hooks: Dict[HookEvent, List[HookDefinition]] = {
+            event: [] for event in HookEvent
+        }
+        self._enabled = enabled
+        self._global_timeout = 60.0
+    
+    @property
+    def enabled(self) -> bool:
+        """Check if hooks are enabled."""
+        return self._enabled
+    
+    @enabled.setter
+    def enabled(self, value: bool):
+        """Enable or disable hooks."""
+        self._enabled = value
+    
+    def register(self, hook: HookDefinition) -> str:
+        """
+        Register a hook definition.
+        
+        Args:
+            hook: The hook definition to register
+            
+        Returns:
+            The hook ID
+        """
+        self._hooks[hook.event].append(hook)
+        logger.debug(f"Registered hook '{hook.name}' for event '{hook.event.value}'")
+        return hook.id
+    
+    def register_function(
+        self,
+        event: HookEvent,
+        func: Callable[[HookInput], HookResult],
+        matcher: Optional[str] = None,
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        sequential: bool = False,
+        timeout: float = 60.0,
+        is_async: bool = False
+    ) -> str:
+        """
+        Register a Python function as a hook.
+        
+        Args:
+            event: The event to hook
+            func: The function to call
+            matcher: Optional regex pattern to match targets
+            name: Optional name for the hook
+            description: Optional description
+            sequential: Whether to run sequentially
+            timeout: Timeout in seconds
+            is_async: Whether the function is async
+            
+        Returns:
+            The hook ID
+        """
+        hook = FunctionHook(
+            event=event,
+            func=func,
+            matcher=matcher,
+            name=name or func.__name__,
+            description=description,
+            sequential=sequential,
+            timeout=timeout,
+            is_async=is_async
+        )
+        return self.register(hook)
+    
+    def register_command(
+        self,
+        event: HookEvent,
+        command: str,
+        matcher: Optional[str] = None,
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        sequential: bool = False,
+        timeout: float = 60.0,
+        env: Optional[Dict[str, str]] = None,
+        shell: bool = True
+    ) -> str:
+        """
+        Register a shell command as a hook.
+        
+        Args:
+            event: The event to hook
+            command: The shell command to execute
+            matcher: Optional regex pattern to match targets
+            name: Optional name for the hook
+            description: Optional description
+            sequential: Whether to run sequentially
+            timeout: Timeout in seconds
+            env: Additional environment variables
+            shell: Whether to run in shell mode
+            
+        Returns:
+            The hook ID
+        """
+        hook = CommandHook(
+            event=event,
+            command=command,
+            matcher=matcher,
+            name=name,
+            description=description,
+            sequential=sequential,
+            timeout=timeout,
+            env=env or {},
+            shell=shell
+        )
+        return self.register(hook)
+    
+    def on(
+        self,
+        event: HookEvent,
+        matcher: Optional[str] = None,
+        sequential: bool = False,
+        timeout: float = 60.0
+    ) -> Callable:
+        """
+        Decorator to register a function as a hook.
+        
+        Args:
+            event: The event to hook
+            matcher: Optional regex pattern to match targets
+            sequential: Whether to run sequentially
+            timeout: Timeout in seconds
+            
+        Returns:
+            Decorator function
+            
+        Example:
+            @registry.on(HookEvent.BEFORE_TOOL)
+            def my_hook(event_data):
+                return HookResult.allow()
+        """
+        def decorator(func: Callable[[HookInput], HookResult]) -> Callable:
+            import asyncio
+            is_async = asyncio.iscoroutinefunction(func)
+            
+            self.register_function(
+                event=event,
+                func=func,
+                matcher=matcher,
+                sequential=sequential,
+                timeout=timeout,
+                is_async=is_async
+            )
+            
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                return func(*args, **kwargs)
+            
+            return wrapper
+        
+        return decorator
+    
+    def unregister(self, hook_id: str) -> bool:
+        """
+        Unregister a hook by ID.
+        
+        Args:
+            hook_id: The hook ID to unregister
+            
+        Returns:
+            True if found and removed, False otherwise
+        """
+        for event in HookEvent:
+            for i, hook in enumerate(self._hooks[event]):
+                if hook.id == hook_id:
+                    self._hooks[event].pop(i)
+                    logger.debug(f"Unregistered hook '{hook_id}'")
+                    return True
+        return False
+    
+    def get_hooks(
+        self,
+        event: HookEvent,
+        target: Optional[str] = None
+    ) -> List[HookDefinition]:
+        """
+        Get all hooks for an event, optionally filtered by target.
+        
+        Args:
+            event: The event to get hooks for
+            target: Optional target to filter by (e.g., tool name)
+            
+        Returns:
+            List of matching hook definitions
+        """
+        if not self._enabled:
+            return []
+        
+        hooks = self._hooks.get(event, [])
+        
+        if target is None:
+            return [h for h in hooks if h.enabled]
+        
+        return [h for h in hooks if h.enabled and h.matches(target)]
+    
+    def has_hooks(self, event: HookEvent) -> bool:
+        """Check if there are any hooks registered for an event."""
+        return bool(self.get_hooks(event))
+    
+    def clear(self, event: Optional[HookEvent] = None):
+        """
+        Clear all hooks or hooks for a specific event.
+        
+        Args:
+            event: Optional event to clear hooks for
+        """
+        if event is None:
+            for e in HookEvent:
+                self._hooks[e] = []
+        else:
+            self._hooks[event] = []
+    
+    def list_hooks(self) -> Dict[str, List[Dict]]:
+        """
+        List all registered hooks.
+        
+        Returns:
+            Dictionary mapping event names to hook info
+        """
+        result = {}
+        for event in HookEvent:
+            hooks = self._hooks[event]
+            if hooks:
+                result[event.value] = [
+                    {
+                        "id": h.id,
+                        "name": h.name,
+                        "type": "command" if isinstance(h, CommandHook) else "function",
+                        "matcher": h.matcher,
+                        "enabled": h.enabled,
+                        "sequential": h.sequential
+                    }
+                    for h in hooks
+                ]
+        return result
+    
+    def enable_hook(self, hook_id: str) -> bool:
+        """Enable a specific hook."""
+        for event in HookEvent:
+            for hook in self._hooks[event]:
+                if hook.id == hook_id:
+                    hook.enabled = True
+                    return True
+        return False
+    
+    def disable_hook(self, hook_id: str) -> bool:
+        """Disable a specific hook."""
+        for event in HookEvent:
+            for hook in self._hooks[event]:
+                if hook.id == hook_id:
+                    hook.enabled = False
+                    return True
+        return False
+    
+    def set_global_timeout(self, timeout: float):
+        """Set the global timeout for all hooks."""
+        self._global_timeout = timeout
+    
+    def __len__(self) -> int:
+        """Get total number of registered hooks."""
+        return sum(len(hooks) for hooks in self._hooks.values())
+    
+    def __repr__(self) -> str:
+        return f"HookRegistry(enabled={self._enabled}, hooks={len(self)})"
+
+
+# Global default registry
+_default_registry: Optional[HookRegistry] = None
+
+
+def get_default_registry() -> HookRegistry:
+    """Get the default global hook registry."""
+    global _default_registry
+    if _default_registry is None:
+        _default_registry = HookRegistry()
+    return _default_registry
+
+
+def set_default_registry(registry: HookRegistry):
+    """Set the default global hook registry."""
+    global _default_registry
+    _default_registry = registry

src/praisonai-agents/praisonaiagents/hooks/runner.py

@@ -0,0 +1,450 @@
+"""
+Hook Runner for PraisonAI Agents.
+
+Executes hooks registered in the registry, supporting both
+Python functions and shell commands.
+"""
+
+import os
+import json
+import time
+import asyncio
+import logging
+import subprocess
+from typing import List, Optional, Dict, Any
+
+from .types import (
+    HookEvent, HookDefinition, CommandHook, FunctionHook,
+    HookInput, HookResult, HookExecutionResult
+)
+from .registry import HookRegistry
+
+logger = logging.getLogger(__name__)
+
+# Exit codes for command hooks
+EXIT_CODE_SUCCESS = 0
+EXIT_CODE_BLOCKING_ERROR = 2
+EXIT_CODE_NON_BLOCKING_ERROR = 1
+
+
+class HookRunner:
+    """
+    Executes hooks from a registry.
+    
+    Supports:
+    - Python function hooks (sync and async)
+    - Shell command hooks
+    - Sequential and parallel execution
+    - Timeout handling
+    - Result aggregation
+    
+    Example:
+        registry = HookRegistry()
+        runner = HookRunner(registry)
+        
+        # Execute hooks for an event
+        results = await runner.execute(
+            event=HookEvent.BEFORE_TOOL,
+            input_data=BeforeToolInput(
+                tool_name="write_file",
+                tool_input={"path": "/tmp/test.txt"}
+            )
+        )
+        
+        # Check if any hook blocked
+        if runner.is_blocked(results):
+            print("Tool execution blocked by hook")
+    """
+    
+    def __init__(
+        self,
+        registry: Optional[HookRegistry] = None,
+        default_timeout: float = 60.0,
+        cwd: Optional[str] = None
+    ):
+        """
+        Initialize the hook runner.
+        
+        Args:
+            registry: Hook registry to use
+            default_timeout: Default timeout for hooks
+            cwd: Working directory for command hooks
+        """
+        self._registry = registry or HookRegistry()
+        self._default_timeout = default_timeout
+        self._cwd = cwd or os.getcwd()
+    
+    @property
+    def registry(self) -> HookRegistry:
+        """Get the hook registry."""
+        return self._registry
+    
+    async def execute(
+        self,
+        event: HookEvent,
+        input_data: HookInput,
+        target: Optional[str] = None
+    ) -> List[HookExecutionResult]:
+        """
+        Execute all hooks for an event.
+        
+        Args:
+            event: The event to execute hooks for
+            input_data: Input data for the hooks
+            target: Optional target to filter hooks (e.g., tool name)
+            
+        Returns:
+            List of execution results
+        """
+        hooks = self._registry.get_hooks(event, target)
+        
+        if not hooks:
+            return []
+        
+        # Separate sequential and parallel hooks
+        sequential_hooks = [h for h in hooks if h.sequential]
+        parallel_hooks = [h for h in hooks if not h.sequential]
+        
+        results = []
+        
+        # Execute parallel hooks first
+        if parallel_hooks:
+            parallel_results = await self._execute_parallel(
+                parallel_hooks, event, input_data
+            )
+            results.extend(parallel_results)
+        
+        # Execute sequential hooks
+        if sequential_hooks:
+            sequential_results = await self._execute_sequential(
+                sequential_hooks, event, input_data
+            )
+            results.extend(sequential_results)
+        
+        return results
+    
+    def execute_sync(
+        self,
+        event: HookEvent,
+        input_data: HookInput,
+        target: Optional[str] = None
+    ) -> List[HookExecutionResult]:
+        """
+        Synchronous version of execute.
+        
+        Args:
+            event: The event to execute hooks for
+            input_data: Input data for the hooks
+            target: Optional target to filter hooks
+            
+        Returns:
+            List of execution results
+        """
+        try:
+            loop = asyncio.get_running_loop()
+            # If we're in an async context, create a task
+            future = asyncio.ensure_future(self.execute(event, input_data, target))
+            return loop.run_until_complete(future)
+        except RuntimeError:
+            # No event loop running, create one
+            return asyncio.run(self.execute(event, input_data, target))
+    
+    async def _execute_parallel(
+        self,
+        hooks: List[HookDefinition],
+        event: HookEvent,
+        input_data: HookInput
+    ) -> List[HookExecutionResult]:
+        """Execute hooks in parallel."""
+        tasks = [
+            self._execute_single(hook, event, input_data)
+            for hook in hooks
+        ]
+        return await asyncio.gather(*tasks)
+    
+    async def _execute_sequential(
+        self,
+        hooks: List[HookDefinition],
+        event: HookEvent,
+        input_data: HookInput
+    ) -> List[HookExecutionResult]:
+        """Execute hooks sequentially, passing modified input to next hook."""
+        results = []
+        current_input = input_data
+        
+        for hook in hooks:
+            result = await self._execute_single(hook, event, current_input)
+            results.append(result)
+            
+            # If hook modified the input, use it for next hook
+            if result.success and result.output and result.output.modified_input:
+                # Update input with modifications
+                for key, value in result.output.modified_input.items():
+                    if hasattr(current_input, key):
+                        setattr(current_input, key, value)
+            
+            # Stop if hook blocked
+            if result.output and result.output.is_denied():
+                break
+        
+        return results
+    
+    async def _execute_single(
+        self,
+        hook: HookDefinition,
+        event: HookEvent,
+        input_data: HookInput
+    ) -> HookExecutionResult:
+        """Execute a single hook."""
+        start_time = time.time()
+        
+        try:
+            if isinstance(hook, CommandHook):
+                return await self._execute_command_hook(hook, event, input_data, start_time)
+            elif isinstance(hook, FunctionHook):
+                return await self._execute_function_hook(hook, event, input_data, start_time)
+            else:
+                raise ValueError(f"Unknown hook type: {type(hook)}")
+        except Exception as e:
+            duration = (time.time() - start_time) * 1000
+            logger.warning(f"Hook '{hook.name}' failed: {e}")
+            return HookExecutionResult(
+                hook_id=hook.id,
+                hook_name=hook.name or "unknown",
+                event=event,
+                success=False,
+                error=str(e),
+                duration_ms=duration
+            )
+    
+    async def _execute_command_hook(
+        self,
+        hook: CommandHook,
+        event: HookEvent,
+        input_data: HookInput,
+        start_time: float
+    ) -> HookExecutionResult:
+        """Execute a command hook."""
+        timeout = hook.timeout or self._default_timeout
+        
+        # Prepare environment
+        env = os.environ.copy()
+        env.update(hook.env)
+        env["PRAISON_PROJECT_DIR"] = self._cwd
+        env["PRAISON_EVENT"] = event.value
+        
+        # Expand command variables
+        command = self._expand_command(hook.command, input_data)
+        
+        try:
+            # Create subprocess
+            process = await asyncio.create_subprocess_shell(
+                command,
+                stdin=asyncio.subprocess.PIPE,
+                stdout=asyncio.subprocess.PIPE,
+                stderr=asyncio.subprocess.PIPE,
+                env=env,
+                cwd=self._cwd
+            )
+            
+            # Send input as JSON
+            input_json = json.dumps(input_data.to_dict()).encode()
+            
+            try:
+                stdout, stderr = await asyncio.wait_for(
+                    process.communicate(input=input_json),
+                    timeout=timeout
+                )
+            except asyncio.TimeoutError:
+                process.kill()
+                await process.wait()
+                duration = (time.time() - start_time) * 1000
+                return HookExecutionResult(
+                    hook_id=hook.id,
+                    hook_name=hook.name or "unknown",
+                    event=event,
+                    success=False,
+                    error=f"Hook timed out after {timeout}s",
+                    duration_ms=duration
+                )
+            
+            duration = (time.time() - start_time) * 1000
+            exit_code = process.returncode
+            stdout_str = stdout.decode() if stdout else ""
+            stderr_str = stderr.decode() if stderr else ""
+            
+            # Parse output
+            output = self._parse_command_output(stdout_str, stderr_str, exit_code)
+            
+            return HookExecutionResult(
+                hook_id=hook.id,
+                hook_name=hook.name or "unknown",
+                event=event,
+                success=exit_code == EXIT_CODE_SUCCESS,
+                output=output,
+                stdout=stdout_str,
+                stderr=stderr_str,
+                exit_code=exit_code,
+                duration_ms=duration
+            )
+            
+        except Exception as e:
+            duration = (time.time() - start_time) * 1000
+            return HookExecutionResult(
+                hook_id=hook.id,
+                hook_name=hook.name or "unknown",
+                event=event,
+                success=False,
+                error=str(e),
+                duration_ms=duration
+            )
+    
+    async def _execute_function_hook(
+        self,
+        hook: FunctionHook,
+        event: HookEvent,
+        input_data: HookInput,
+        start_time: float
+    ) -> HookExecutionResult:
+        """Execute a function hook."""
+        if hook.func is None:
+            duration = (time.time() - start_time) * 1000
+            return HookExecutionResult(
+                hook_id=hook.id,
+                hook_name=hook.name or "unknown",
+                event=event,
+                success=False,
+                error="Hook function is None",
+                duration_ms=duration
+            )
+        
+        timeout = hook.timeout or self._default_timeout
+        
+        try:
+            if hook.is_async:
+                # Execute async function with timeout
+                result = await asyncio.wait_for(
+                    hook.func(input_data),
+                    timeout=timeout
+                )
+            else:
+                # Execute sync function in thread pool with timeout
+                loop = asyncio.get_event_loop()
+                result = await asyncio.wait_for(
+                    loop.run_in_executor(None, hook.func, input_data),
+                    timeout=timeout
+                )
+            
+            duration = (time.time() - start_time) * 1000
+            
+            # Normalize result
+            if result is None:
+                result = HookResult.allow()
+            elif not isinstance(result, HookResult):
+                result = HookResult.allow()
+            
+            return HookExecutionResult(
+                hook_id=hook.id,
+                hook_name=hook.name or "unknown",
+                event=event,
+                success=True,
+                output=result,
+                duration_ms=duration
+            )
+            
+        except asyncio.TimeoutError:
+            duration = (time.time() - start_time) * 1000
+            return HookExecutionResult(
+                hook_id=hook.id,
+                hook_name=hook.name or "unknown",
+                event=event,
+                success=False,
+                error=f"Hook timed out after {timeout}s",
+                duration_ms=duration
+            )
+        except Exception as e:
+            duration = (time.time() - start_time) * 1000
+            return HookExecutionResult(
+                hook_id=hook.id,
+                hook_name=hook.name or "unknown",
+                event=event,
+                success=False,
+                error=str(e),
+                duration_ms=duration
+            )
+    
+    def _expand_command(self, command: str, input_data: HookInput) -> str:
+        """Expand variables in command string."""
+        return command.replace(
+            "$PRAISON_PROJECT_DIR", self._cwd
+        ).replace(
+            "$PRAISON_CWD", input_data.cwd
+        )
+    
+    def _parse_command_output(
+        self,
+        stdout: str,
+        stderr: str,
+        exit_code: int
+    ) -> HookResult:
+        """Parse command output into HookResult."""
+        # Try to parse JSON from stdout
+        if stdout.strip():
+            try:
+                data = json.loads(stdout.strip())
+                
+                # Handle double-encoded JSON
+                if isinstance(data, str):
+                    data = json.loads(data)
+                
+                return HookResult(
+                    decision=data.get("decision", "allow"),
+                    reason=data.get("reason"),
+                    modified_input=data.get("modified_input"),
+                    additional_context=data.get("additional_context"),
+                    suppress_output=data.get("suppress_output", False)
+                )
+            except json.JSONDecodeError:
+                pass
+        
+        # Convert exit code to decision
+        if exit_code == EXIT_CODE_SUCCESS:
+            return HookResult(
+                decision="allow",
+                additional_context=stdout.strip() if stdout.strip() else None
+            )
+        elif exit_code == EXIT_CODE_BLOCKING_ERROR:
+            return HookResult(
+                decision="deny",
+                reason=stderr.strip() or stdout.strip() or "Blocked by hook"
+            )
+        else:
+            return HookResult(
+                decision="allow",
+                additional_context=f"Warning: {stderr.strip() or stdout.strip()}"
+            )
+    
+    @staticmethod
+    def is_blocked(results: List[HookExecutionResult]) -> bool:
+        """Check if any hook blocked execution."""
+        for result in results:
+            if result.output and result.output.is_denied():
+                return True
+        return False
+    
+    @staticmethod
+    def get_blocking_reason(results: List[HookExecutionResult]) -> Optional[str]:
+        """Get the reason for blocking from results."""
+        for result in results:
+            if result.output and result.output.is_denied():
+                return result.output.reason
+        return None
+    
+    @staticmethod
+    def aggregate_context(results: List[HookExecutionResult]) -> Optional[str]:
+        """Aggregate additional context from all results."""
+        contexts = []
+        for result in results:
+            if result.output and result.output.additional_context:
+                contexts.append(result.output.additional_context)
+        return "\n".join(contexts) if contexts else None

src/praisonai-agents/praisonaiagents/hooks/types.py

@@ -0,0 +1,197 @@
+"""
+Hook Types for PraisonAI Agents.
+
+Defines the core types, enums, and dataclasses for the hook system.
+"""
+
+import uuid
+from enum import Enum
+from dataclasses import dataclass, field
+from typing import Optional, Dict, Any, List, Callable, Union, Literal
+
+
+class HookEvent(str, Enum):
+    """Event names for the hook system."""
+    BEFORE_TOOL = "before_tool"
+    AFTER_TOOL = "after_tool"
+    BEFORE_AGENT = "before_agent"
+    AFTER_AGENT = "after_agent"
+    SESSION_START = "session_start"
+    SESSION_END = "session_end"
+    BEFORE_LLM = "before_llm"
+    AFTER_LLM = "after_llm"
+    ON_ERROR = "on_error"
+    ON_RETRY = "on_retry"
+
+
+# Decision types for hook outputs
+HookDecision = Literal["allow", "deny", "block", "ask", None]
+
+
+@dataclass
+class HookInput:
+    """Base hook input - common fields for all events."""
+    session_id: str
+    cwd: str
+    event_name: str
+    timestamp: str
+    agent_name: Optional[str] = None
+    extra: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for JSON serialization."""
+        return {
+            "session_id": self.session_id,
+            "cwd": self.cwd,
+            "event_name": self.event_name,
+            "timestamp": self.timestamp,
+            "agent_name": self.agent_name,
+            **self.extra
+        }
+
+
+@dataclass
+class HookOutput:
+    """Base hook output - common fields for all events."""
+    proceed: bool = True
+    stop_reason: Optional[str] = None
+    suppress_output: bool = False
+    system_message: Optional[str] = None
+    decision: HookDecision = None
+    reason: Optional[str] = None
+    modified_data: Optional[Dict[str, Any]] = None
+    
+    def is_blocking(self) -> bool:
+        """Check if this output represents a blocking decision."""
+        return self.decision in ("block", "deny")
+    
+    def should_stop(self) -> bool:
+        """Check if execution should stop."""
+        return not self.proceed
+    
+    def get_reason(self) -> str:
+        """Get the effective reason for blocking or stopping."""
+        return self.reason or self.stop_reason or "No reason provided"
+
+
+@dataclass
+class HookResult:
+    """Result from a hook execution."""
+    decision: HookDecision = "allow"
+    reason: Optional[str] = None
+    modified_input: Optional[Dict[str, Any]] = None
+    additional_context: Optional[str] = None
+    suppress_output: bool = False
+    
+    @classmethod
+    def allow(cls, reason: Optional[str] = None) -> "HookResult":
+        """Create an allow result."""
+        return cls(decision="allow", reason=reason)
+    
+    @classmethod
+    def deny(cls, reason: str) -> "HookResult":
+        """Create a deny result."""
+        return cls(decision="deny", reason=reason)
+    
+    @classmethod
+    def block(cls, reason: str) -> "HookResult":
+        """Create a block result."""
+        return cls(decision="block", reason=reason)
+    
+    @classmethod
+    def ask(cls, reason: str) -> "HookResult":
+        """Create an ask result (requires user confirmation)."""
+        return cls(decision="ask", reason=reason)
+    
+    def is_allowed(self) -> bool:
+        """Check if the result allows execution."""
+        return self.decision in ("allow", None)
+    
+    def is_denied(self) -> bool:
+        """Check if the result denies execution."""
+        return self.decision in ("deny", "block")
+
+
+@dataclass
+class HookDefinition:
+    """Hook definition with matcher and configuration."""
+    id: str = field(default_factory=lambda: str(uuid.uuid4())[:8])
+    event: HookEvent = HookEvent.BEFORE_TOOL
+    matcher: Optional[str] = None  # Regex pattern to match tool names
+    sequential: bool = False  # Execute hooks sequentially vs parallel
+    enabled: bool = True
+    name: Optional[str] = None
+    description: Optional[str] = None
+    timeout: float = 60.0  # Timeout in seconds
+    
+    def matches(self, target: str) -> bool:
+        """Check if this hook matches the target (tool name, etc.)."""
+        if self.matcher is None:
+            return True
+        
+        import re
+        try:
+            return bool(re.match(self.matcher, target))
+        except re.error:
+            # Invalid regex, fall back to simple string match
+            return self.matcher in target
+
+
+@dataclass
+class CommandHook(HookDefinition):
+    """Hook that executes a shell command."""
+    command: str = ""
+    shell: bool = True
+    env: Dict[str, str] = field(default_factory=dict)
+    
+    def __post_init__(self):
+        if not self.name:
+            self.name = f"command_hook_{self.id}"
+
+
+@dataclass
+class FunctionHook(HookDefinition):
+    """Hook that executes a Python function."""
+    func: Optional[Callable[[HookInput], HookResult]] = None
+    is_async: bool = False
+    
+    def __post_init__(self):
+        if not self.name and self.func:
+            self.name = self.func.__name__
+        elif not self.name:
+            self.name = f"function_hook_{self.id}"
+
+
+@dataclass
+class HookExecutionResult:
+    """Result of executing a single hook."""
+    hook_id: str
+    hook_name: str
+    event: HookEvent
+    success: bool
+    output: Optional[HookResult] = None
+    stdout: Optional[str] = None
+    stderr: Optional[str] = None
+    exit_code: Optional[int] = None
+    duration_ms: float = 0.0
+    error: Optional[str] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "hook_id": self.hook_id,
+            "hook_name": self.hook_name,
+            "event": self.event.value,
+            "success": self.success,
+            "output": self.output.__dict__ if self.output else None,
+            "stdout": self.stdout,
+            "stderr": self.stderr,
+            "exit_code": self.exit_code,
+            "duration_ms": self.duration_ms,
+            "error": self.error
+        }
+
+
+# Type alias for hook functions
+HookFunction = Callable[[HookInput], Union[HookResult, None]]
+AsyncHookFunction = Callable[[HookInput], Any]  # Returns Awaitable[HookResult]

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

@@ -1054,7 +1054,7 @@ class LLM:
         **kwargs
     ) -> str:
         """Enhanced get_response with all OpenAI-like features"""
-        logging.info(f"Getting response from {self.model}")
+        logging.debug(f"Getting response from {self.model}")
         # Log all self values when in debug mode
         self._log_llm_config(
             'LLM instance',
@@ -2472,7 +2472,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         """Async version of get_response with identical functionality."""
         try:
             import litellm
-            logging.info(f"Getting async response from {self.model}")
+            logging.debug(f"Getting async response from {self.model}")
             # Log all self values when in debug mode
             self._log_llm_config(
                 'get_response_async',

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

@@ -19,6 +19,15 @@ from rich.console import Console
 from rich.live import Live
 import inspect
 
+# Import display_tool_call for callback support (lazy import to avoid circular imports)
+_display_tool_call = None
+def _get_display_tool_call():
+    global _display_tool_call
+    if _display_tool_call is None:
+        from ..main import display_tool_call
+        _display_tool_call = display_tool_call
+    return _display_tool_call
+
 # Constants
 LOCAL_SERVER_API_KEY_PLACEHOLDER = "not-needed"
 
@@ -978,16 +987,19 @@ class OpenAIClient:
                         function_name = tool_call.function.name
                         arguments = json.loads(tool_call.function.arguments)
                     
+                    # Always trigger callback for tool call tracking (even when verbose=False)
+                    display_tool_call_fn = _get_display_tool_call()
+                    display_tool_call_fn(f"Calling function: {function_name}", console=console if verbose else None)
+                    
                     if verbose and console:
-                        console.print(f"[bold]Calling function:[/bold] {function_name}")
                         console.print(f"[dim]Arguments:[/dim] {arguments}")
                     
                     # Execute the tool
                     tool_result = execute_tool_fn(function_name, arguments)
                     results_str = json.dumps(tool_result) if tool_result else "Function returned an empty output"
                     
-                    if verbose and console:
-                        console.print(f"[dim]Result:[/dim] {results_str}")
+                    # Trigger callback with result
+                    display_tool_call_fn(f"Function {function_name} returned: {results_str[:200]}{'...' if len(results_str) > 200 else ''}", console=console if verbose else None)
                     
                     messages.append({
                         "role": "tool",
@@ -1150,8 +1162,11 @@ class OpenAIClient:
                         function_name = tool_call.function.name
                         arguments = json.loads(tool_call.function.arguments)
                     
+                    # Always trigger callback for tool call tracking (even when verbose=False)
+                    display_tool_call_fn = _get_display_tool_call()
+                    display_tool_call_fn(f"Calling function: {function_name}", console=console if verbose else None)
+                    
                     if verbose and console:
-                        console.print(f"[bold]Calling function:[/bold] {function_name}")
                         console.print(f"[dim]Arguments:[/dim] {arguments}")
                     
                     # Execute the tool (async)
@@ -1167,8 +1182,8 @@ class OpenAIClient:
                     
                     results_str = json.dumps(tool_result) if tool_result else "Function returned an empty output"
                     
-                    if verbose and console:
-                        console.print(f"[dim]Result:[/dim] {results_str}")
+                    # Trigger callback with result
+                    display_tool_call_fn(f"Function {function_name} returned: {results_str[:200]}{'...' if len(results_str) > 200 else ''}", console=console if verbose else None)
                     
                     messages.append({
                         "role": "tool",
@@ -1299,6 +1314,10 @@ class OpenAIClient:
                                 yield f"\n[Error parsing arguments for {function_name if 'function_name' in locals() else 'unknown function'}: {str(e)}]"
                             continue
                         
+                        # Always trigger callback for tool call tracking (even when verbose=False)
+                        display_tool_call_fn = _get_display_tool_call()
+                        display_tool_call_fn(f"Calling function: {function_name}", console=None)
+                        
                         if verbose:
                             yield f"\n[Calling function: {function_name}]"
                         
@@ -1311,6 +1330,9 @@ class OpenAIClient:
                             if verbose:
                                 yield f"\n[Function error: {str(e)}]"
                         
+                        # Trigger callback with result
+                        display_tool_call_fn(f"Function {function_name} returned: {results_str[:200]}{'...' if len(results_str) > 200 else ''}", console=None)
+                        
                         if verbose:
                             yield f"\n[Function result: {results_str}]"
                         

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

@@ -0,0 +1,65 @@
+"""
+LSP Integration Module for PraisonAI Agents.
+
+Provides Language Server Protocol integration for code intelligence:
+- Diagnostics (errors, warnings)
+- Code completion suggestions
+- Go to definition
+- Find references
+- Hover information
+
+Zero Performance Impact:
+- All imports are lazy loaded via __getattr__
+- LSP client only initialized when needed
+- No overhead when not in use
+
+Usage:
+    from praisonaiagents.lsp import LSPClient
+    
+    # Create client for a language
+    client = LSPClient(language="python")
+    
+    # Start the language server
+    await client.start()
+    
+    # Get diagnostics for a file
+    diagnostics = await client.get_diagnostics("/path/to/file.py")
+    
+    # Get completions at a position
+    completions = await client.get_completions("/path/to/file.py", line=10, character=5)
+"""
+
+__all__ = [
+    # Core client
+    "LSPClient",
+    # Data types
+    "Diagnostic",
+    "DiagnosticSeverity",
+    "CompletionItem",
+    "Location",
+    "Position",
+    "Range",
+    # Configuration
+    "LSPConfig",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components to avoid import overhead."""
+    if name == "LSPClient":
+        from .client import LSPClient
+        return LSPClient
+    
+    if name in ("Diagnostic", "DiagnosticSeverity", "CompletionItem", 
+                "Location", "Position", "Range"):
+        from .types import (
+            Diagnostic, DiagnosticSeverity, CompletionItem,
+            Location, Position, Range
+        )
+        return locals()[name]
+    
+    if name == "LSPConfig":
+        from .config import LSPConfig
+        return LSPConfig
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/lsp/client.py

@@ -0,0 +1,489 @@
+"""
+LSP Client for PraisonAI Agents.
+
+Provides Language Server Protocol client implementation.
+"""
+
+import os
+import json
+import asyncio
+import logging
+from typing import Optional, List, Dict, Any
+from pathlib import Path
+
+from .types import (
+    Diagnostic, DiagnosticSeverity, CompletionItem, Location,
+    Position, Range, TextDocumentItem, TextDocumentIdentifier,
+    TextDocumentPositionParams
+)
+from .config import LSPConfig
+
+logger = logging.getLogger(__name__)
+
+
+class LSPClient:
+    """
+    Language Server Protocol client.
+    
+    Communicates with language servers to provide code intelligence
+    features like diagnostics, completions, and navigation.
+    
+    Example:
+        client = LSPClient(language="python")
+        await client.start()
+        
+        # Get diagnostics
+        diagnostics = await client.get_diagnostics("/path/to/file.py")
+        
+        # Get completions
+        completions = await client.get_completions(
+            "/path/to/file.py", line=10, character=5
+        )
+        
+        await client.stop()
+    """
+    
+    def __init__(
+        self,
+        language: str,
+        command: Optional[str] = None,
+        args: Optional[List[str]] = None,
+        root_uri: Optional[str] = None
+    ):
+        """
+        Initialize the LSP client.
+        
+        Args:
+            language: Programming language (python, javascript, etc.)
+            command: Language server command (uses default if not specified)
+            args: Command arguments
+            root_uri: Workspace root URI
+        """
+        self.config = LSPConfig(
+            language=language,
+            command=command,
+            args=args or [],
+            root_uri=root_uri
+        )
+        
+        self._process: Optional[asyncio.subprocess.Process] = None
+        self._request_id = 0
+        self._pending_requests: Dict[int, asyncio.Future] = {}
+        self._diagnostics: Dict[str, List[Diagnostic]] = {}
+        self._initialized = False
+        self._reader_task: Optional[asyncio.Task] = None
+    
+    @property
+    def is_running(self) -> bool:
+        """Check if the language server is running."""
+        return self._process is not None and self._process.returncode is None
+    
+    async def start(self) -> bool:
+        """
+        Start the language server.
+        
+        Returns:
+            True if started successfully
+        """
+        if self.is_running:
+            return True
+        
+        try:
+            cmd = [self.config.command] + self.config.args
+            
+            self._process = await asyncio.create_subprocess_exec(
+                *cmd,
+                stdin=asyncio.subprocess.PIPE,
+                stdout=asyncio.subprocess.PIPE,
+                stderr=asyncio.subprocess.PIPE
+            )
+            
+            # Start reader task
+            self._reader_task = asyncio.create_task(self._read_responses())
+            
+            # Initialize the server
+            root_uri = self.config.root_uri or f"file://{os.getcwd()}"
+            
+            result = await self._send_request("initialize", {
+                "processId": os.getpid(),
+                "rootUri": root_uri,
+                "capabilities": {
+                    "textDocument": {
+                        "completion": {"completionItem": {"snippetSupport": True}},
+                        "hover": {},
+                        "definition": {},
+                        "references": {},
+                        "publishDiagnostics": {}
+                    }
+                },
+                "initializationOptions": self.config.initialization_options
+            })
+            
+            if result:
+                await self._send_notification("initialized", {})
+                self._initialized = True
+                logger.info(f"LSP server started: {self.config.command}")
+                return True
+            
+            return False
+            
+        except FileNotFoundError:
+            logger.error(f"Language server not found: {self.config.command}")
+            return False
+        except Exception as e:
+            logger.error(f"Failed to start LSP server: {e}")
+            return False
+    
+    async def stop(self):
+        """Stop the language server."""
+        if not self.is_running:
+            return
+        
+        try:
+            await self._send_request("shutdown", None)
+            await self._send_notification("exit", None)
+        except Exception:
+            pass
+        
+        if self._reader_task:
+            self._reader_task.cancel()
+            try:
+                await self._reader_task
+            except asyncio.CancelledError:
+                pass
+        
+        if self._process:
+            self._process.terminate()
+            try:
+                await asyncio.wait_for(self._process.wait(), timeout=5.0)
+            except asyncio.TimeoutError:
+                self._process.kill()
+        
+        self._process = None
+        self._initialized = False
+    
+    async def open_document(self, file_path: str, text: Optional[str] = None) -> bool:
+        """
+        Open a document in the language server.
+        
+        Args:
+            file_path: Path to the file
+            text: File content (reads from disk if not provided)
+            
+        Returns:
+            True if successful
+        """
+        if not self._initialized:
+            return False
+        
+        if text is None:
+            try:
+                with open(file_path, "r") as f:
+                    text = f.read()
+            except Exception as e:
+                logger.error(f"Failed to read file: {e}")
+                return False
+        
+        uri = f"file://{os.path.abspath(file_path)}"
+        
+        await self._send_notification("textDocument/didOpen", {
+            "textDocument": TextDocumentItem(
+                uri=uri,
+                language_id=self.config.language,
+                version=1,
+                text=text
+            ).to_dict()
+        })
+        
+        return True
+    
+    async def close_document(self, file_path: str):
+        """Close a document in the language server."""
+        if not self._initialized:
+            return
+        
+        uri = f"file://{os.path.abspath(file_path)}"
+        
+        await self._send_notification("textDocument/didClose", {
+            "textDocument": TextDocumentIdentifier(uri=uri).to_dict()
+        })
+    
+    async def get_diagnostics(self, file_path: str) -> List[Diagnostic]:
+        """
+        Get diagnostics for a file.
+        
+        Args:
+            file_path: Path to the file
+            
+        Returns:
+            List of diagnostics
+        """
+        uri = f"file://{os.path.abspath(file_path)}"
+        return self._diagnostics.get(uri, [])
+    
+    async def get_completions(
+        self,
+        file_path: str,
+        line: int,
+        character: int
+    ) -> List[CompletionItem]:
+        """
+        Get completions at a position.
+        
+        Args:
+            file_path: Path to the file
+            line: Line number (0-indexed)
+            character: Character position (0-indexed)
+            
+        Returns:
+            List of completion items
+        """
+        if not self._initialized:
+            return []
+        
+        uri = f"file://{os.path.abspath(file_path)}"
+        
+        result = await self._send_request("textDocument/completion", {
+            "textDocument": {"uri": uri},
+            "position": {"line": line, "character": character}
+        })
+        
+        if result is None:
+            return []
+        
+        items = result.get("items", result) if isinstance(result, dict) else result
+        
+        return [
+            CompletionItem.from_dict(item)
+            for item in (items if isinstance(items, list) else [])
+        ]
+    
+    async def get_definition(
+        self,
+        file_path: str,
+        line: int,
+        character: int
+    ) -> List[Location]:
+        """
+        Get definition location(s) for a symbol.
+        
+        Args:
+            file_path: Path to the file
+            line: Line number (0-indexed)
+            character: Character position (0-indexed)
+            
+        Returns:
+            List of locations
+        """
+        if not self._initialized:
+            return []
+        
+        uri = f"file://{os.path.abspath(file_path)}"
+        
+        result = await self._send_request("textDocument/definition", {
+            "textDocument": {"uri": uri},
+            "position": {"line": line, "character": character}
+        })
+        
+        if result is None:
+            return []
+        
+        if isinstance(result, dict):
+            return [Location.from_dict(result)]
+        elif isinstance(result, list):
+            return [Location.from_dict(loc) for loc in result]
+        
+        return []
+    
+    async def get_references(
+        self,
+        file_path: str,
+        line: int,
+        character: int,
+        include_declaration: bool = True
+    ) -> List[Location]:
+        """
+        Get references to a symbol.
+        
+        Args:
+            file_path: Path to the file
+            line: Line number (0-indexed)
+            character: Character position (0-indexed)
+            include_declaration: Include the declaration in results
+            
+        Returns:
+            List of locations
+        """
+        if not self._initialized:
+            return []
+        
+        uri = f"file://{os.path.abspath(file_path)}"
+        
+        result = await self._send_request("textDocument/references", {
+            "textDocument": {"uri": uri},
+            "position": {"line": line, "character": character},
+            "context": {"includeDeclaration": include_declaration}
+        })
+        
+        if result is None:
+            return []
+        
+        return [Location.from_dict(loc) for loc in result]
+    
+    async def get_hover(
+        self,
+        file_path: str,
+        line: int,
+        character: int
+    ) -> Optional[str]:
+        """
+        Get hover information for a position.
+        
+        Args:
+            file_path: Path to the file
+            line: Line number (0-indexed)
+            character: Character position (0-indexed)
+            
+        Returns:
+            Hover content as string
+        """
+        if not self._initialized:
+            return None
+        
+        uri = f"file://{os.path.abspath(file_path)}"
+        
+        result = await self._send_request("textDocument/hover", {
+            "textDocument": {"uri": uri},
+            "position": {"line": line, "character": character}
+        })
+        
+        if result is None:
+            return None
+        
+        contents = result.get("contents")
+        if isinstance(contents, str):
+            return contents
+        elif isinstance(contents, dict):
+            return contents.get("value", str(contents))
+        elif isinstance(contents, list):
+            return "\n".join(
+                c.get("value", str(c)) if isinstance(c, dict) else str(c)
+                for c in contents
+            )
+        
+        return None
+    
+    async def _send_request(self, method: str, params: Any) -> Any:
+        """Send a request and wait for response."""
+        if not self.is_running:
+            return None
+        
+        self._request_id += 1
+        request_id = self._request_id
+        
+        message = {
+            "jsonrpc": "2.0",
+            "id": request_id,
+            "method": method,
+            "params": params
+        }
+        
+        future = asyncio.get_event_loop().create_future()
+        self._pending_requests[request_id] = future
+        
+        await self._send_message(message)
+        
+        try:
+            result = await asyncio.wait_for(future, timeout=self.config.timeout)
+            return result
+        except asyncio.TimeoutError:
+            logger.warning(f"Request timed out: {method}")
+            self._pending_requests.pop(request_id, None)
+            return None
+    
+    async def _send_notification(self, method: str, params: Any):
+        """Send a notification (no response expected)."""
+        if not self.is_running:
+            return
+        
+        message = {
+            "jsonrpc": "2.0",
+            "method": method,
+            "params": params
+        }
+        
+        await self._send_message(message)
+    
+    async def _send_message(self, message: Dict[str, Any]):
+        """Send a JSON-RPC message."""
+        if self._process is None or self._process.stdin is None:
+            return
+        
+        content = json.dumps(message)
+        header = f"Content-Length: {len(content)}\r\n\r\n"
+        
+        self._process.stdin.write(header.encode() + content.encode())
+        await self._process.stdin.drain()
+    
+    async def _read_responses(self):
+        """Read responses from the language server."""
+        if self._process is None or self._process.stdout is None:
+            return
+        
+        try:
+            while True:
+                # Read header
+                header = b""
+                while b"\r\n\r\n" not in header:
+                    chunk = await self._process.stdout.read(1)
+                    if not chunk:
+                        return
+                    header += chunk
+                
+                # Parse content length
+                content_length = 0
+                for line in header.decode().split("\r\n"):
+                    if line.startswith("Content-Length:"):
+                        content_length = int(line.split(":")[1].strip())
+                        break
+                
+                if content_length == 0:
+                    continue
+                
+                # Read content
+                content = await self._process.stdout.read(content_length)
+                message = json.loads(content.decode())
+                
+                await self._handle_message(message)
+                
+        except asyncio.CancelledError:
+            raise
+        except Exception as e:
+            logger.error(f"Error reading LSP response: {e}")
+    
+    async def _handle_message(self, message: Dict[str, Any]):
+        """Handle an incoming message."""
+        if "id" in message:
+            # Response to a request
+            request_id = message["id"]
+            if request_id in self._pending_requests:
+                future = self._pending_requests.pop(request_id)
+                if "error" in message:
+                    future.set_exception(
+                        RuntimeError(message["error"].get("message", "Unknown error"))
+                    )
+                else:
+                    future.set_result(message.get("result"))
+        
+        elif "method" in message:
+            # Notification from server
+            method = message["method"]
+            params = message.get("params", {})
+            
+            if method == "textDocument/publishDiagnostics":
+                uri = params.get("uri", "")
+                diagnostics = [
+                    Diagnostic.from_dict(d)
+                    for d in params.get("diagnostics", [])
+                ]
+                self._diagnostics[uri] = diagnostics

src/praisonai-agents/praisonaiagents/lsp/config.py

@@ -0,0 +1,52 @@
+"""
+LSP Configuration for PraisonAI Agents.
+"""
+
+from dataclasses import dataclass, field
+from typing import Optional, Dict, List
+
+
+# Default language server commands
+DEFAULT_SERVERS = {
+    "python": {
+        "command": "pylsp",
+        "args": [],
+    },
+    "javascript": {
+        "command": "typescript-language-server",
+        "args": ["--stdio"],
+    },
+    "typescript": {
+        "command": "typescript-language-server",
+        "args": ["--stdio"],
+    },
+    "rust": {
+        "command": "rust-analyzer",
+        "args": [],
+    },
+    "go": {
+        "command": "gopls",
+        "args": [],
+    },
+}
+
+
+@dataclass
+class LSPConfig:
+    """Configuration for LSP client."""
+    language: str
+    command: Optional[str] = None
+    args: List[str] = field(default_factory=list)
+    root_uri: Optional[str] = None
+    initialization_options: Dict = field(default_factory=dict)
+    timeout: float = 30.0
+    
+    def __post_init__(self):
+        # Use default server if not specified
+        if self.command is None:
+            if self.language in DEFAULT_SERVERS:
+                server = DEFAULT_SERVERS[self.language]
+                self.command = server["command"]
+                self.args = server["args"]
+            else:
+                raise ValueError(f"No default server for language: {self.language}")

src/praisonai-agents/praisonaiagents/lsp/types.py

@@ -0,0 +1,162 @@
+"""
+LSP Types for PraisonAI Agents.
+
+Defines the core types for Language Server Protocol integration.
+"""
+
+from enum import IntEnum
+from dataclasses import dataclass, field
+from typing import Optional, List, Dict, Any
+
+
+class DiagnosticSeverity(IntEnum):
+    """Severity of a diagnostic."""
+    ERROR = 1
+    WARNING = 2
+    INFORMATION = 3
+    HINT = 4
+
+
+@dataclass
+class Position:
+    """Position in a text document (0-indexed)."""
+    line: int
+    character: int
+    
+    def to_dict(self) -> Dict[str, int]:
+        return {"line": self.line, "character": self.character}
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, int]) -> "Position":
+        return cls(line=data["line"], character=data["character"])
+
+
+@dataclass
+class Range:
+    """Range in a text document."""
+    start: Position
+    end: Position
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {"start": self.start.to_dict(), "end": self.end.to_dict()}
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "Range":
+        return cls(
+            start=Position.from_dict(data["start"]),
+            end=Position.from_dict(data["end"])
+        )
+
+
+@dataclass
+class Location:
+    """Location in a document."""
+    uri: str
+    range: Range
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {"uri": self.uri, "range": self.range.to_dict()}
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "Location":
+        return cls(uri=data["uri"], range=Range.from_dict(data["range"]))
+
+
+@dataclass
+class Diagnostic:
+    """Represents a diagnostic (error, warning, etc.)."""
+    range: Range
+    message: str
+    severity: DiagnosticSeverity = DiagnosticSeverity.ERROR
+    code: Optional[str] = None
+    source: Optional[str] = None
+    related_information: List[Dict[str, Any]] = field(default_factory=list)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "range": self.range.to_dict(),
+            "message": self.message,
+            "severity": self.severity.value,
+            "code": self.code,
+            "source": self.source
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "Diagnostic":
+        return cls(
+            range=Range.from_dict(data["range"]),
+            message=data["message"],
+            severity=DiagnosticSeverity(data.get("severity", 1)),
+            code=data.get("code"),
+            source=data.get("source")
+        )
+
+
+@dataclass
+class CompletionItem:
+    """Represents a completion item."""
+    label: str
+    kind: int = 1  # Text
+    detail: Optional[str] = None
+    documentation: Optional[str] = None
+    insert_text: Optional[str] = None
+    sort_text: Optional[str] = None
+    filter_text: Optional[str] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "label": self.label,
+            "kind": self.kind,
+            "detail": self.detail,
+            "documentation": self.documentation,
+            "insertText": self.insert_text
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "CompletionItem":
+        return cls(
+            label=data["label"],
+            kind=data.get("kind", 1),
+            detail=data.get("detail"),
+            documentation=data.get("documentation"),
+            insert_text=data.get("insertText")
+        )
+
+
+@dataclass
+class TextDocumentItem:
+    """Represents a text document."""
+    uri: str
+    language_id: str
+    version: int
+    text: str
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "uri": self.uri,
+            "languageId": self.language_id,
+            "version": self.version,
+            "text": self.text
+        }
+
+
+@dataclass
+class TextDocumentIdentifier:
+    """Identifies a text document."""
+    uri: str
+    
+    def to_dict(self) -> Dict[str, str]:
+        return {"uri": self.uri}
+
+
+@dataclass
+class TextDocumentPositionParams:
+    """Parameters for text document position requests."""
+    text_document: TextDocumentIdentifier
+    position: Position
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "textDocument": self.text_document.to_dict(),
+            "position": self.position.to_dict()
+        }

src/praisonai-agents/praisonaiagents/main.py

@@ -231,15 +231,15 @@ def display_tool_call(message: str, console=None):
     if not message or not message.strip():
         logging.debug("Empty message in display_tool_call, returning early")
         return
-    if console is None:
-        console = Console()
     message = _clean_display_content(str(message))
     logging.debug(f"Cleaned message in display_tool_call: {repr(message)}")
     
-    # Execute synchronous callbacks
+    # Execute synchronous callbacks (always, even when console is None)
     execute_sync_callback('tool_call', message=message)
     
-    console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))
+    # Only print panel if console is provided (verbose mode)
+    if console is not None:
+        console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))
 
 def display_error(message: str, console=None):
     if not message or not message.strip():

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

@@ -0,0 +1,77 @@
+"""
+Exec Policy Engine for PraisonAI Agents.
+
+Provides policy-based execution control:
+- Define rules for what agents can/cannot do
+- Tool execution policies
+- Resource access control
+- Rate limiting and quotas
+
+Zero Performance Impact:
+- All imports are lazy loaded via __getattr__
+- Policies only evaluated when enabled
+- No overhead when no policies defined
+
+Usage:
+    from praisonaiagents.policy import PolicyEngine, Policy, PolicyRule
+    
+    # Create a policy engine
+    engine = PolicyEngine()
+    
+    # Add a policy
+    policy = Policy(
+        name="no_delete",
+        rules=[
+            PolicyRule(
+                action="deny",
+                resource="tool:delete_*",
+                reason="Delete operations are not allowed"
+            )
+        ]
+    )
+    engine.add_policy(policy)
+    
+    # Check if action is allowed
+    result = engine.check("tool:delete_file", context={})
+"""
+
+__all__ = [
+    # Core classes
+    "PolicyEngine",
+    "Policy",
+    "PolicyRule",
+    # Result types
+    "PolicyResult",
+    "PolicyAction",
+    # Configuration
+    "PolicyConfig",
+    # Convenience functions
+    "create_deny_tools_policy",
+    "create_allow_tools_policy",
+    "create_read_only_policy",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components to avoid import overhead."""
+    if name == "PolicyEngine":
+        from .engine import PolicyEngine
+        return PolicyEngine
+    
+    if name in ("Policy", "PolicyRule"):
+        from .policy import Policy, PolicyRule
+        return locals()[name]
+    
+    if name in ("PolicyResult", "PolicyAction"):
+        from .types import PolicyResult, PolicyAction
+        return locals()[name]
+    
+    if name == "PolicyConfig":
+        from .config import PolicyConfig
+        return PolicyConfig
+    
+    if name in ("create_deny_tools_policy", "create_allow_tools_policy", "create_read_only_policy"):
+        from .engine import create_deny_tools_policy, create_allow_tools_policy, create_read_only_policy
+        return locals()[name]
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/policy/config.py

@@ -0,0 +1,15 @@
+"""
+Policy Configuration for PraisonAI Agents.
+"""
+
+from dataclasses import dataclass, field
+from typing import Optional, List
+
+
+@dataclass
+class PolicyConfig:
+    """Configuration for the policy engine."""
+    enabled: bool = True
+    default_action: str = "allow"  # Default action when no policy matches
+    log_decisions: bool = True
+    strict_mode: bool = False  # If True, deny when no policy matches

src/praisonai-agents/praisonaiagents/policy/engine.py

@@ -0,0 +1,307 @@
+"""
+Policy Engine for PraisonAI Agents.
+
+Manages and evaluates policies for execution control.
+"""
+
+import logging
+from typing import Optional, List, Dict, Any
+
+from .types import PolicyAction, PolicyResult
+from .policy import Policy, PolicyRule
+from .config import PolicyConfig
+
+logger = logging.getLogger(__name__)
+
+
+class PolicyEngine:
+    """
+    Policy Engine for execution control.
+    
+    Manages policies and evaluates them against resources
+    to determine what actions are allowed.
+    
+    Example:
+        engine = PolicyEngine()
+        
+        # Add a policy to block delete operations
+        engine.add_policy(Policy(
+            name="no_delete",
+            rules=[
+                PolicyRule(
+                    action=PolicyAction.DENY,
+                    resource="tool:delete_*",
+                    reason="Delete operations are not allowed"
+                )
+            ]
+        ))
+        
+        # Check if action is allowed
+        result = engine.check("tool:delete_file", {})
+        if not result.allowed:
+            print(f"Denied: {result.reason}")
+    """
+    
+    def __init__(self, config: Optional[PolicyConfig] = None):
+        """
+        Initialize the policy engine.
+        
+        Args:
+            config: Engine configuration
+        """
+        self.config = config or PolicyConfig()
+        self._policies: Dict[str, Policy] = {}
+    
+    @property
+    def policies(self) -> List[Policy]:
+        """Get all policies sorted by priority."""
+        return sorted(
+            self._policies.values(),
+            key=lambda p: p.priority,
+            reverse=True
+        )
+    
+    def add_policy(self, policy: Policy):
+        """
+        Add a policy to the engine.
+        
+        Args:
+            policy: Policy to add
+        """
+        self._policies[policy.name] = policy
+        logger.debug(f"Added policy: {policy.name}")
+    
+    def remove_policy(self, name: str) -> bool:
+        """
+        Remove a policy by name.
+        
+        Args:
+            name: Policy name
+            
+        Returns:
+            True if removed, False if not found
+        """
+        if name in self._policies:
+            del self._policies[name]
+            logger.debug(f"Removed policy: {name}")
+            return True
+        return False
+    
+    def get_policy(self, name: str) -> Optional[Policy]:
+        """Get a policy by name."""
+        return self._policies.get(name)
+    
+    def enable_policy(self, name: str) -> bool:
+        """Enable a policy."""
+        policy = self._policies.get(name)
+        if policy:
+            policy.enabled = True
+            return True
+        return False
+    
+    def disable_policy(self, name: str) -> bool:
+        """Disable a policy."""
+        policy = self._policies.get(name)
+        if policy:
+            policy.enabled = False
+            return True
+        return False
+    
+    def check(
+        self,
+        resource: str,
+        context: Optional[Dict[str, Any]] = None
+    ) -> PolicyResult:
+        """
+        Check if an action is allowed.
+        
+        Args:
+            resource: Resource identifier (e.g., "tool:read_file")
+            context: Additional context for policy evaluation
+            
+        Returns:
+            PolicyResult indicating if action is allowed
+        """
+        if not self.config.enabled:
+            return PolicyResult.allow("Policy engine disabled")
+        
+        context = context or {}
+        
+        # Evaluate policies in priority order
+        for policy in self.policies:
+            result = policy.evaluate(resource, context)
+            if result is not None:
+                if self.config.log_decisions:
+                    logger.info(
+                        f"Policy decision: {result.action.value} for {resource} "
+                        f"(policy: {result.policy_name}, reason: {result.reason})"
+                    )
+                return result
+        
+        # No policy matched - use default action
+        if self.config.strict_mode:
+            return PolicyResult.deny(
+                "No policy matched and strict mode is enabled"
+            )
+        
+        return PolicyResult.allow("No policy matched")
+    
+    def check_tool(
+        self,
+        tool_name: str,
+        tool_input: Optional[Dict[str, Any]] = None
+    ) -> PolicyResult:
+        """
+        Check if a tool execution is allowed.
+        
+        Args:
+            tool_name: Name of the tool
+            tool_input: Tool input parameters
+            
+        Returns:
+            PolicyResult
+        """
+        return self.check(
+            f"tool:{tool_name}",
+            {"tool_name": tool_name, "tool_input": tool_input or {}}
+        )
+    
+    def check_file(
+        self,
+        operation: str,
+        file_path: str
+    ) -> PolicyResult:
+        """
+        Check if a file operation is allowed.
+        
+        Args:
+            operation: Operation type (read, write, delete)
+            file_path: Path to the file
+            
+        Returns:
+            PolicyResult
+        """
+        return self.check(
+            f"file:{operation}",
+            {"operation": operation, "file_path": file_path}
+        )
+    
+    def clear(self):
+        """Remove all policies."""
+        self._policies.clear()
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert engine state to dictionary."""
+        return {
+            "config": {
+                "enabled": self.config.enabled,
+                "default_action": self.config.default_action,
+                "strict_mode": self.config.strict_mode
+            },
+            "policies": [p.to_dict() for p in self.policies]
+        }
+    
+    def load_from_dict(self, data: Dict[str, Any]):
+        """Load policies from a dictionary."""
+        for policy_data in data.get("policies", []):
+            policy = Policy.from_dict(policy_data)
+            self.add_policy(policy)
+
+
+# Convenience functions for common policies
+
+def create_deny_tools_policy(
+    tool_patterns: List[str],
+    name: str = "deny_tools",
+    reason: str = "Tool not allowed"
+) -> Policy:
+    """
+    Create a policy that denies specific tools.
+    
+    Args:
+        tool_patterns: List of tool name patterns to deny
+        name: Policy name
+        reason: Reason for denial
+        
+    Returns:
+        Policy
+    """
+    rules = [
+        PolicyRule(
+            action=PolicyAction.DENY,
+            resource=f"tool:{pattern}",
+            reason=reason,
+            name=f"deny_{pattern}"
+        )
+        for pattern in tool_patterns
+    ]
+    
+    return Policy(name=name, rules=rules)
+
+
+def create_allow_tools_policy(
+    tool_patterns: List[str],
+    name: str = "allow_tools"
+) -> Policy:
+    """
+    Create a policy that allows specific tools.
+    
+    Args:
+        tool_patterns: List of tool name patterns to allow
+        name: Policy name
+        
+    Returns:
+        Policy
+    """
+    rules = [
+        PolicyRule(
+            action=PolicyAction.ALLOW,
+            resource=f"tool:{pattern}",
+            name=f"allow_{pattern}"
+        )
+        for pattern in tool_patterns
+    ]
+    
+    return Policy(name=name, rules=rules, priority=10)
+
+
+def create_read_only_policy(name: str = "read_only") -> Policy:
+    """
+    Create a read-only policy that denies write operations.
+    
+    Args:
+        name: Policy name
+        
+    Returns:
+        Policy
+    """
+    return Policy(
+        name=name,
+        rules=[
+            PolicyRule(
+                action=PolicyAction.DENY,
+                resource="file:write",
+                reason="Write operations not allowed in read-only mode",
+                name="deny_write"
+            ),
+            PolicyRule(
+                action=PolicyAction.DENY,
+                resource="file:delete",
+                reason="Delete operations not allowed in read-only mode",
+                name="deny_delete"
+            ),
+            PolicyRule(
+                action=PolicyAction.DENY,
+                resource="tool:*_write*",
+                reason="Write tools not allowed in read-only mode",
+                name="deny_write_tools"
+            ),
+            PolicyRule(
+                action=PolicyAction.DENY,
+                resource="tool:*_delete*",
+                reason="Delete tools not allowed in read-only mode",
+                name="deny_delete_tools"
+            ),
+        ],
+        priority=100
+    )

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

@@ -0,0 +1,174 @@
+"""
+Policy and PolicyRule for PraisonAI Agents.
+
+Defines policies and rules for execution control.
+"""
+
+import re
+import fnmatch
+from dataclasses import dataclass, field
+from typing import Optional, Dict, Any, List, Callable
+
+from .types import PolicyAction, PolicyResult
+
+
+@dataclass
+class PolicyRule:
+    """
+    A single rule within a policy.
+    
+    Rules match against resources and contexts to determine
+    what action to take.
+    """
+    action: PolicyAction
+    resource: Optional[str] = None  # Pattern to match (supports wildcards)
+    condition: Optional[Callable[[Dict[str, Any]], bool]] = None
+    reason: Optional[str] = None
+    name: Optional[str] = None
+    priority: int = 0  # Higher priority rules are evaluated first
+    
+    def matches(self, resource: str, context: Dict[str, Any]) -> bool:
+        """
+        Check if this rule matches the given resource and context.
+        
+        Args:
+            resource: Resource identifier (e.g., "tool:read_file")
+            context: Additional context for the check
+            
+        Returns:
+            True if the rule matches
+        """
+        # Check resource pattern
+        if self.resource:
+            if not self._match_pattern(self.resource, resource):
+                return False
+        
+        # Check condition
+        if self.condition:
+            try:
+                if not self.condition(context):
+                    return False
+            except Exception:
+                return False
+        
+        return True
+    
+    def _match_pattern(self, pattern: str, value: str) -> bool:
+        """Match a pattern against a value (supports wildcards)."""
+        # Convert glob pattern to regex
+        if "*" in pattern or "?" in pattern:
+            return fnmatch.fnmatch(value, pattern)
+        return pattern == value
+    
+    def evaluate(self, resource: str, context: Dict[str, Any]) -> Optional[PolicyResult]:
+        """
+        Evaluate this rule against a resource.
+        
+        Args:
+            resource: Resource identifier
+            context: Additional context
+            
+        Returns:
+            PolicyResult if rule matches, None otherwise
+        """
+        if not self.matches(resource, context):
+            return None
+        
+        return PolicyResult(
+            allowed=self.action in (PolicyAction.ALLOW, PolicyAction.LOG),
+            action=self.action,
+            rule_name=self.name,
+            reason=self.reason
+        )
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "action": self.action.value,
+            "resource": self.resource,
+            "reason": self.reason,
+            "name": self.name,
+            "priority": self.priority
+        }
+
+
+@dataclass
+class Policy:
+    """
+    A collection of rules that define execution policy.
+    
+    Policies are evaluated in order, with higher priority rules
+    taking precedence.
+    """
+    name: str
+    rules: List[PolicyRule] = field(default_factory=list)
+    description: Optional[str] = None
+    enabled: bool = True
+    priority: int = 0  # Higher priority policies are evaluated first
+    
+    def add_rule(self, rule: PolicyRule):
+        """Add a rule to this policy."""
+        self.rules.append(rule)
+        # Sort by priority (descending)
+        self.rules.sort(key=lambda r: r.priority, reverse=True)
+    
+    def remove_rule(self, rule_name: str) -> bool:
+        """Remove a rule by name."""
+        for i, rule in enumerate(self.rules):
+            if rule.name == rule_name:
+                self.rules.pop(i)
+                return True
+        return False
+    
+    def evaluate(self, resource: str, context: Dict[str, Any]) -> Optional[PolicyResult]:
+        """
+        Evaluate this policy against a resource.
+        
+        Args:
+            resource: Resource identifier
+            context: Additional context
+            
+        Returns:
+            PolicyResult if any rule matches, None otherwise
+        """
+        if not self.enabled:
+            return None
+        
+        for rule in self.rules:
+            result = rule.evaluate(resource, context)
+            if result is not None:
+                result.policy_name = self.name
+                return result
+        
+        return None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "name": self.name,
+            "description": self.description,
+            "enabled": self.enabled,
+            "priority": self.priority,
+            "rules": [r.to_dict() for r in self.rules]
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "Policy":
+        """Create a policy from a dictionary."""
+        rules = []
+        for rule_data in data.get("rules", []):
+            rules.append(PolicyRule(
+                action=PolicyAction(rule_data["action"]),
+                resource=rule_data.get("resource"),
+                reason=rule_data.get("reason"),
+                name=rule_data.get("name"),
+                priority=rule_data.get("priority", 0)
+            ))
+        
+        return cls(
+            name=data["name"],
+            rules=rules,
+            description=data.get("description"),
+            enabled=data.get("enabled", True),
+            priority=data.get("priority", 0)
+        )

src/praisonai-agents/praisonaiagents/policy/types.py

@@ -0,0 +1,60 @@
+"""
+Policy Types for PraisonAI Agents.
+
+Defines the core types for the policy engine.
+"""
+
+from enum import Enum
+from dataclasses import dataclass, field
+from typing import Optional, Dict, Any, List
+
+
+class PolicyAction(str, Enum):
+    """Action to take when a policy matches."""
+    ALLOW = "allow"
+    DENY = "deny"
+    ASK = "ask"  # Require user confirmation
+    LOG = "log"  # Allow but log the action
+    RATE_LIMIT = "rate_limit"
+
+
+@dataclass
+class PolicyResult:
+    """Result of a policy check."""
+    allowed: bool
+    action: PolicyAction
+    policy_name: Optional[str] = None
+    rule_name: Optional[str] = None
+    reason: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    @classmethod
+    def allow(cls, reason: Optional[str] = None) -> "PolicyResult":
+        """Create an allow result."""
+        return cls(allowed=True, action=PolicyAction.ALLOW, reason=reason)
+    
+    @classmethod
+    def deny(cls, reason: str, policy_name: Optional[str] = None) -> "PolicyResult":
+        """Create a deny result."""
+        return cls(
+            allowed=False,
+            action=PolicyAction.DENY,
+            policy_name=policy_name,
+            reason=reason
+        )
+    
+    @classmethod
+    def ask(cls, reason: str) -> "PolicyResult":
+        """Create an ask result (requires confirmation)."""
+        return cls(allowed=False, action=PolicyAction.ASK, reason=reason)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "allowed": self.allowed,
+            "action": self.action.value,
+            "policy_name": self.policy_name,
+            "rule_name": self.rule_name,
+            "reason": self.reason,
+            "metadata": self.metadata
+        }

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

@@ -0,0 +1,76 @@
+"""Agent Skills module for PraisonAI Agents.
+
+This module provides support for the open Agent Skills standard (agentskills.io),
+enabling agents to load and use modular capabilities through SKILL.md files.
+
+Zero Performance Impact:
+- All imports are lazy loaded via __getattr__
+- Skills only loaded when explicitly enabled
+- No auto-discovery at import time
+
+Usage:
+    from praisonaiagents.skills import SkillManager, SkillProperties
+    
+    manager = SkillManager()
+    manager.discover(["./skills"])
+    prompt_xml = manager.to_prompt()
+"""
+
+__all__ = [
+    # Models
+    "SkillProperties",
+    "SkillMetadata",
+    "ParseError",
+    "ValidationError",
+    # Parser
+    "parse_frontmatter",
+    "find_skill_md",
+    "read_properties",
+    # Validator
+    "validate",
+    "validate_metadata",
+    # Prompt
+    "to_prompt",
+    "generate_skills_xml",
+    "format_skill_for_prompt",
+    # Discovery
+    "discover_skills",
+    "get_default_skill_dirs",
+    # Loader
+    "SkillLoader",
+    # Manager
+    "SkillManager",
+]
+
+
+def __getattr__(name: str):
+    """Lazy load module components to avoid import overhead."""
+    if name in ("SkillProperties", "SkillMetadata", "ParseError", "ValidationError"):
+        from .models import SkillProperties, SkillMetadata, ParseError, ValidationError
+        return locals()[name]
+    
+    if name in ("parse_frontmatter", "find_skill_md", "read_properties"):
+        from .parser import parse_frontmatter, find_skill_md, read_properties
+        return locals()[name]
+    
+    if name in ("validate", "validate_metadata", "_validate_name", "_validate_description", "_validate_compatibility"):
+        from .validator import validate, validate_metadata, _validate_name, _validate_description, _validate_compatibility
+        return locals()[name]
+    
+    if name in ("to_prompt", "generate_skills_xml", "format_skill_for_prompt"):
+        from .prompt import to_prompt, generate_skills_xml, format_skill_for_prompt
+        return locals()[name]
+    
+    if name in ("discover_skills", "get_default_skill_dirs"):
+        from .discovery import discover_skills, get_default_skill_dirs
+        return locals()[name]
+    
+    if name == "SkillLoader":
+        from .loader import SkillLoader
+        return SkillLoader
+    
+    if name == "SkillManager":
+        from .manager import SkillManager
+        return SkillManager
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/skills/discovery.py

@@ -0,0 +1,129 @@
+"""Skill discovery and directory scanning."""
+
+from pathlib import Path
+from typing import List, Optional
+
+from .parser import find_skill_md, read_properties
+from .models import SkillProperties
+
+
+def get_default_skill_dirs() -> List[Path]:
+    """Get default skill directory locations.
+
+    Returns directories in precedence order (high to low):
+    1. Project: ./.praison/skills/ or ./.claude/skills/
+    2. User: ~/.praison/skills/
+    3. System: /etc/praison/skills/ (admin-managed)
+
+    Returns:
+        List of existing skill directories
+    """
+    dirs = []
+    cwd = Path.cwd()
+
+    # Project-level directories
+    project_dirs = [
+        cwd / ".praison" / "skills",
+        cwd / ".claude" / "skills",
+    ]
+    for d in project_dirs:
+        if d.exists() and d.is_dir():
+            dirs.append(d)
+
+    # User-level directory
+    user_dir = Path.home() / ".praison" / "skills"
+    if user_dir.exists() and user_dir.is_dir():
+        dirs.append(user_dir)
+
+    # System-level directory (Unix-like systems)
+    system_dir = Path("/etc/praison/skills")
+    if system_dir.exists() and system_dir.is_dir():
+        dirs.append(system_dir)
+
+    return dirs
+
+
+def discover_skills(
+    skill_dirs: Optional[List[str]] = None,
+    include_defaults: bool = True,
+) -> List[SkillProperties]:
+    """Discover all valid skills in the given directories.
+
+    Args:
+        skill_dirs: List of directory paths to scan for skills.
+            Each directory should contain skill subdirectories.
+        include_defaults: Whether to include default skill directories
+
+    Returns:
+        List of SkillProperties for all valid skills found
+    """
+    all_dirs = []
+
+    # Add explicit directories
+    if skill_dirs:
+        for d in skill_dirs:
+            path = Path(d).expanduser().resolve()
+            if path.exists() and path.is_dir():
+                all_dirs.append(path)
+
+    # Add default directories
+    if include_defaults:
+        all_dirs.extend(get_default_skill_dirs())
+
+    # Remove duplicates while preserving order
+    seen = set()
+    unique_dirs = []
+    for d in all_dirs:
+        if d not in seen:
+            seen.add(d)
+            unique_dirs.append(d)
+
+    skills = []
+
+    for parent_dir in unique_dirs:
+        # Each subdirectory in parent_dir might be a skill
+        try:
+            for item in parent_dir.iterdir():
+                if not item.is_dir():
+                    continue
+
+                # Check if this directory contains a SKILL.md
+                skill_md = find_skill_md(item)
+                if skill_md is None:
+                    continue
+
+                try:
+                    props = read_properties(item)
+                    skills.append(props)
+                except Exception:
+                    # Skip invalid skills
+                    continue
+        except PermissionError:
+            # Skip directories we can't read
+            continue
+
+    return skills
+
+
+def discover_skill(skill_path: str) -> Optional[SkillProperties]:
+    """Discover a single skill from a directory path.
+
+    Args:
+        skill_path: Path to a skill directory
+
+    Returns:
+        SkillProperties if valid, None otherwise
+    """
+    path = Path(skill_path).expanduser().resolve()
+
+    if not path.exists() or not path.is_dir():
+        return None
+
+    skill_md = find_skill_md(path)
+    if skill_md is None:
+        return None
+
+    try:
+        return read_properties(path)
+    except Exception:
+        return None

src/praisonai-agents/praisonaiagents/skills/loader.py

@@ -0,0 +1,236 @@
+"""Progressive skill loading for Agent Skills."""
+
+from pathlib import Path
+from typing import Optional
+from dataclasses import dataclass, field
+
+from .parser import find_skill_md, read_properties, parse_frontmatter
+from .models import SkillProperties, SkillMetadata
+
+
+@dataclass
+class LoadedSkill:
+    """A fully or partially loaded skill.
+
+    Supports progressive disclosure:
+    - Level 1: metadata only (name, description, location)
+    - Level 2: full instructions (SKILL.md body)
+    - Level 3: resources (scripts, references, assets)
+    """
+
+    properties: SkillProperties
+    instructions: Optional[str] = None
+    resources_loaded: bool = False
+    _scripts: dict = field(default_factory=dict)
+    _references: dict = field(default_factory=dict)
+    _assets: dict = field(default_factory=dict)
+
+    @property
+    def metadata(self) -> SkillMetadata:
+        """Get lightweight metadata for system prompt."""
+        return SkillMetadata.from_properties(self.properties)
+
+    @property
+    def is_activated(self) -> bool:
+        """Check if skill has been activated (instructions loaded)."""
+        return self.instructions is not None
+
+    def get_scripts(self) -> dict:
+        """Get loaded scripts."""
+        return self._scripts
+
+    def get_references(self) -> dict:
+        """Get loaded references."""
+        return self._references
+
+    def get_assets(self) -> dict:
+        """Get loaded assets."""
+        return self._assets
+
+
+class SkillLoader:
+    """Loader for Agent Skills with progressive disclosure support.
+
+    Usage:
+        loader = SkillLoader()
+
+        # Level 1: Load metadata only
+        skill = loader.load_metadata("/path/to/skill")
+
+        # Level 2: Activate skill (load instructions)
+        loader.activate(skill)
+
+        # Level 3: Load specific resources
+        loader.load_scripts(skill)
+        loader.load_references(skill)
+    """
+
+    def load_metadata(self, skill_path: str) -> Optional[LoadedSkill]:
+        """Load skill metadata only (Level 1).
+
+        This is the minimal load for system prompt injection.
+        Only reads the YAML frontmatter, not the full SKILL.md body.
+
+        Args:
+            skill_path: Path to skill directory
+
+        Returns:
+            LoadedSkill with metadata only, or None if invalid
+        """
+        path = Path(skill_path).expanduser().resolve()
+
+        if not path.exists() or not path.is_dir():
+            return None
+
+        try:
+            props = read_properties(path)
+            return LoadedSkill(properties=props)
+        except Exception:
+            return None
+
+    def activate(self, skill: LoadedSkill) -> bool:
+        """Activate a skill by loading its full instructions (Level 2).
+
+        Args:
+            skill: LoadedSkill instance to activate
+
+        Returns:
+            True if activation succeeded
+        """
+        if skill.is_activated:
+            return True
+
+        if skill.properties.path is None:
+            return False
+
+        skill_md = find_skill_md(skill.properties.path)
+        if skill_md is None:
+            return False
+
+        try:
+            content = skill_md.read_text()
+            _, body = parse_frontmatter(content)
+            skill.instructions = body
+            return True
+        except Exception:
+            return False
+
+    def load_scripts(self, skill: LoadedSkill) -> dict:
+        """Load scripts from skill's scripts/ directory (Level 3).
+
+        Args:
+            skill: LoadedSkill instance
+
+        Returns:
+            Dict mapping script names to their contents
+        """
+        if skill.properties.path is None:
+            return {}
+
+        scripts_dir = skill.properties.path / "scripts"
+        if not scripts_dir.exists() or not scripts_dir.is_dir():
+            return {}
+
+        scripts = {}
+        try:
+            for script_file in scripts_dir.iterdir():
+                if script_file.is_file():
+                    try:
+                        scripts[script_file.name] = script_file.read_text()
+                    except Exception:
+                        continue
+        except PermissionError:
+            pass
+
+        skill._scripts = scripts
+        return scripts
+
+    def load_references(self, skill: LoadedSkill) -> dict:
+        """Load references from skill's references/ directory (Level 3).
+
+        Args:
+            skill: LoadedSkill instance
+
+        Returns:
+            Dict mapping reference names to their contents
+        """
+        if skill.properties.path is None:
+            return {}
+
+        refs_dir = skill.properties.path / "references"
+        if not refs_dir.exists() or not refs_dir.is_dir():
+            return {}
+
+        refs = {}
+        try:
+            for ref_file in refs_dir.iterdir():
+                if ref_file.is_file():
+                    try:
+                        refs[ref_file.name] = ref_file.read_text()
+                    except Exception:
+                        continue
+        except PermissionError:
+            pass
+
+        skill._references = refs
+        return refs
+
+    def load_assets(self, skill: LoadedSkill) -> dict:
+        """Load asset paths from skill's assets/ directory (Level 3).
+
+        Note: For binary assets, only paths are returned, not contents.
+
+        Args:
+            skill: LoadedSkill instance
+
+        Returns:
+            Dict mapping asset names to their paths
+        """
+        if skill.properties.path is None:
+            return {}
+
+        assets_dir = skill.properties.path / "assets"
+        if not assets_dir.exists() or not assets_dir.is_dir():
+            return {}
+
+        assets = {}
+        try:
+            for asset_file in assets_dir.iterdir():
+                if asset_file.is_file():
+                    assets[asset_file.name] = str(asset_file)
+        except PermissionError:
+            pass
+
+        skill._assets = assets
+        skill.resources_loaded = True
+        return assets
+
+    def load_all_resources(self, skill: LoadedSkill) -> None:
+        """Load all resources for a skill (Level 3 complete).
+
+        Args:
+            skill: LoadedSkill instance
+        """
+        self.load_scripts(skill)
+        self.load_references(skill)
+        self.load_assets(skill)
+        skill.resources_loaded = True
+
+    @classmethod
+    def load(cls, skill_path: str, activate: bool = False) -> Optional[LoadedSkill]:
+        """Convenience method to load a skill.
+
+        Args:
+            skill_path: Path to skill directory
+            activate: Whether to also load instructions
+
+        Returns:
+            LoadedSkill or None if invalid
+        """
+        loader = cls()
+        skill = loader.load_metadata(skill_path)
+
+        if skill and activate:
+            loader.activate(skill)
+
+        return skill

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

@@ -0,0 +1,189 @@
+"""SkillManager for Agent Skills integration."""
+
+from typing import List, Optional, Dict
+
+from .models import SkillMetadata
+from .discovery import discover_skills
+from .loader import SkillLoader, LoadedSkill
+from .prompt import generate_skills_xml
+
+
+class SkillManager:
+    """Manager for discovering, loading, and using Agent Skills.
+
+    This is the main entry point for Agent Skills integration.
+    Supports progressive disclosure and zero performance impact
+    when skills are not used.
+
+    Usage:
+        manager = SkillManager()
+
+        # Discover skills from directories
+        manager.discover(["./skills", "~/.praison/skills"])
+
+        # Get XML for system prompt
+        prompt_xml = manager.to_prompt()
+
+        # Activate a specific skill
+        skill = manager.get_skill("pdf-processing")
+        manager.activate(skill)
+    """
+
+    def __init__(self):
+        """Initialize the SkillManager."""
+        self._skills: Dict[str, LoadedSkill] = {}
+        self._loader = SkillLoader()
+        self._discovered = False
+
+    @property
+    def skills(self) -> List[LoadedSkill]:
+        """Get all loaded skills."""
+        return list(self._skills.values())
+
+    @property
+    def skill_names(self) -> List[str]:
+        """Get names of all loaded skills."""
+        return list(self._skills.keys())
+
+    def discover(
+        self,
+        skill_dirs: Optional[List[str]] = None,
+        include_defaults: bool = True,
+    ) -> int:
+        """Discover skills from directories.
+
+        Args:
+            skill_dirs: List of directory paths to scan
+            include_defaults: Whether to include default skill directories
+
+        Returns:
+            Number of skills discovered
+        """
+        props_list = discover_skills(skill_dirs, include_defaults)
+
+        for props in props_list:
+            if props.name not in self._skills:
+                self._skills[props.name] = LoadedSkill(properties=props)
+
+        self._discovered = True
+        return len(props_list)
+
+    def add_skill(self, skill_path: str) -> Optional[LoadedSkill]:
+        """Add a single skill from a directory path.
+
+        Args:
+            skill_path: Path to skill directory
+
+        Returns:
+            LoadedSkill if successful, None otherwise
+        """
+        skill = self._loader.load_metadata(skill_path)
+        if skill:
+            self._skills[skill.properties.name] = skill
+        return skill
+
+    def get_skill(self, name: str) -> Optional[LoadedSkill]:
+        """Get a skill by name.
+
+        Args:
+            name: Skill name
+
+        Returns:
+            LoadedSkill or None if not found
+        """
+        return self._skills.get(name)
+
+    def activate(self, skill: LoadedSkill) -> bool:
+        """Activate a skill (load its instructions).
+
+        Args:
+            skill: LoadedSkill to activate
+
+        Returns:
+            True if activation succeeded
+        """
+        return self._loader.activate(skill)
+
+    def activate_by_name(self, name: str) -> bool:
+        """Activate a skill by name.
+
+        Args:
+            name: Skill name
+
+        Returns:
+            True if activation succeeded
+        """
+        skill = self.get_skill(name)
+        if skill is None:
+            return False
+        return self.activate(skill)
+
+    def get_available_skills(self) -> List[SkillMetadata]:
+        """Get metadata for all available skills.
+
+        This is used for system prompt injection.
+
+        Returns:
+            List of SkillMetadata instances
+        """
+        return [skill.metadata for skill in self._skills.values()]
+
+    def to_prompt(self) -> str:
+        """Generate XML prompt for available skills.
+
+        Returns:
+            XML string with <available_skills> block
+        """
+        metadata_list = self.get_available_skills()
+        return generate_skills_xml(metadata_list)
+
+    def get_instructions(self, name: str) -> Optional[str]:
+        """Get instructions for a skill, activating if needed.
+
+        Args:
+            name: Skill name
+
+        Returns:
+            Skill instructions or None if not found
+        """
+        skill = self.get_skill(name)
+        if skill is None:
+            return None
+
+        if not skill.is_activated:
+            self.activate(skill)
+
+        return skill.instructions
+
+    def load_resources(self, name: str) -> bool:
+        """Load all resources for a skill.
+
+        Args:
+            name: Skill name
+
+        Returns:
+            True if resources loaded successfully
+        """
+        skill = self.get_skill(name)
+        if skill is None:
+            return False
+
+        self._loader.load_all_resources(skill)
+        return True
+
+    def clear(self) -> None:
+        """Clear all loaded skills."""
+        self._skills.clear()
+        self._discovered = False
+
+    def __len__(self) -> int:
+        """Get number of loaded skills."""
+        return len(self._skills)
+
+    def __contains__(self, name: str) -> bool:
+        """Check if a skill is loaded."""
+        return name in self._skills
+
+    def __iter__(self):
+        """Iterate over loaded skills."""
+        return iter(self._skills.values())

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

@@ -0,0 +1,95 @@
+"""Data models for Agent Skills."""
+
+from dataclasses import dataclass, field
+from typing import Optional
+from pathlib import Path
+
+
+class ParseError(Exception):
+    """Raised when SKILL.md parsing fails."""
+    pass
+
+
+class ValidationError(Exception):
+    """Raised when skill validation fails."""
+    pass
+
+
+@dataclass
+class SkillProperties:
+    """Properties parsed from a skill's SKILL.md frontmatter.
+
+    Attributes:
+        name: Skill name in kebab-case (required)
+        description: What the skill does and when the model should use it (required)
+        license: License for the skill (optional)
+        compatibility: Compatibility information for the skill (optional)
+        allowed_tools: Tool patterns the skill requires (optional, experimental)
+        metadata: Key-value pairs for client-specific properties (defaults to
+            empty dict; omitted from to_dict() output when empty)
+        path: Path to the skill directory (optional, added for PraisonAI)
+    """
+
+    name: str
+    description: str
+    license: Optional[str] = None
+    compatibility: Optional[str] = None
+    allowed_tools: Optional[str] = None
+    metadata: dict = field(default_factory=dict)
+    path: Optional[Path] = None
+
+    def to_dict(self) -> dict:
+        """Convert to dictionary, excluding None values and empty metadata."""
+        result = {"name": self.name, "description": self.description}
+        if self.license is not None:
+            result["license"] = self.license
+        if self.compatibility is not None:
+            result["compatibility"] = self.compatibility
+        if self.allowed_tools is not None:
+            result["allowed-tools"] = self.allowed_tools
+        if self.metadata:
+            result["metadata"] = self.metadata
+        return result
+
+
+@dataclass
+class SkillMetadata:
+    """Lightweight skill metadata for system prompt injection.
+    
+    This is the minimal information needed to include a skill in the
+    system prompt's <available_skills> block (~50-100 tokens per skill).
+
+    Attributes:
+        name: Skill name
+        description: What the skill does and when to use it
+        location: Absolute path to the SKILL.md file
+    """
+
+    name: str
+    description: str
+    location: str
+
+    @classmethod
+    def from_properties(cls, props: SkillProperties) -> "SkillMetadata":
+        """Create SkillMetadata from SkillProperties.
+        
+        Args:
+            props: SkillProperties instance with path set
+            
+        Returns:
+            SkillMetadata with location pointing to SKILL.md
+        """
+        if props.path:
+            # Look for SKILL.md or skill.md
+            skill_md = props.path / "SKILL.md"
+            if not skill_md.exists():
+                skill_md = props.path / "skill.md"
+            location = str(skill_md)
+        else:
+            location = ""
+        
+        return cls(
+            name=props.name,
+            description=props.description,
+            location=location
+        )

src/praisonai-agents/praisonaiagents/skills/parser.py

@@ -0,0 +1,165 @@
+"""YAML frontmatter parsing for SKILL.md files."""
+
+from pathlib import Path
+from typing import Optional, Tuple
+import re
+
+from .models import ParseError, ValidationError, SkillProperties
+
+
+def find_skill_md(skill_dir: Path) -> Optional[Path]:
+    """Find the SKILL.md file in a skill directory.
+
+    Prefers SKILL.md (uppercase) but accepts skill.md (lowercase).
+
+    Args:
+        skill_dir: Path to the skill directory
+
+    Returns:
+        Path to the SKILL.md file, or None if not found
+    """
+    for name in ("SKILL.md", "skill.md"):
+        path = skill_dir / name
+        if path.exists():
+            return path
+    return None
+
+
+def parse_frontmatter(content: str) -> Tuple[dict, str]:
+    """Parse YAML frontmatter from SKILL.md content.
+
+    Args:
+        content: Raw content of SKILL.md file
+
+    Returns:
+        Tuple of (metadata dict, markdown body)
+
+    Raises:
+        ParseError: If frontmatter is missing or invalid
+    """
+    if not content.startswith("---"):
+        raise ParseError("SKILL.md must start with YAML frontmatter (---)")
+
+    parts = content.split("---", 2)
+    if len(parts) < 3:
+        raise ParseError("SKILL.md frontmatter not properly closed with ---")
+
+    frontmatter_str = parts[1]
+    body = parts[2].strip()
+
+    try:
+        metadata = _parse_yaml(frontmatter_str)
+    except Exception as e:
+        raise ParseError(f"Invalid YAML in frontmatter: {e}")
+
+    if not isinstance(metadata, dict):
+        raise ParseError("SKILL.md frontmatter must be a YAML mapping")
+
+    # Convert metadata values to strings where needed
+    if "metadata" in metadata and isinstance(metadata["metadata"], dict):
+        metadata["metadata"] = {str(k): str(v) for k, v in metadata["metadata"].items()}
+
+    return metadata, body
+
+
+def _parse_yaml(yaml_str: str) -> dict:
+    """Parse YAML string to dictionary.
+    
+    Uses a simple parser to avoid strictyaml dependency.
+    Falls back to yaml if available.
+    
+    Args:
+        yaml_str: YAML string to parse
+        
+    Returns:
+        Parsed dictionary
+    """
+    # Try to use PyYAML if available
+    try:
+        import yaml
+        return yaml.safe_load(yaml_str) or {}
+    except ImportError:
+        pass
+    
+    # Simple fallback parser for basic YAML
+    result = {}
+    current_key = None
+    current_dict = None
+    
+    for line in yaml_str.strip().split("\n"):
+        if not line.strip():
+            continue
+            
+        # Check for nested dict
+        if line.startswith("  ") and current_dict is not None:
+            match = re.match(r"^\s+(\w+):\s*(.*)$", line)
+            if match:
+                key, value = match.groups()
+                current_dict[key] = value.strip().strip('"').strip("'")
+            continue
+        
+        # Top-level key
+        match = re.match(r"^(\S+):\s*(.*)$", line)
+        if match:
+            key, value = match.groups()
+            value = value.strip()
+            
+            if not value:
+                # Could be start of nested dict
+                result[key] = {}
+                current_key = key
+                current_dict = result[key]
+            else:
+                result[key] = value.strip('"').strip("'")
+                current_dict = None
+    
+    return result
+
+
+def read_properties(skill_dir: Path) -> SkillProperties:
+    """Read skill properties from SKILL.md frontmatter.
+
+    This function parses the frontmatter and returns properties.
+    It does NOT perform full validation. Use validate() for that.
+
+    Args:
+        skill_dir: Path to the skill directory
+
+    Returns:
+        SkillProperties with parsed metadata
+
+    Raises:
+        ParseError: If SKILL.md is missing or has invalid YAML
+        ValidationError: If required fields (name, description) are missing
+    """
+    skill_dir = Path(skill_dir)
+    skill_md = find_skill_md(skill_dir)
+
+    if skill_md is None:
+        raise ParseError(f"SKILL.md not found in {skill_dir}")
+
+    content = skill_md.read_text()
+    metadata, _ = parse_frontmatter(content)
+
+    if "name" not in metadata:
+        raise ValidationError("Missing required field in frontmatter: name")
+    if "description" not in metadata:
+        raise ValidationError("Missing required field in frontmatter: description")
+
+    name = metadata["name"]
+    description = metadata["description"]
+
+    if not isinstance(name, str) or not name.strip():
+        raise ValidationError("Field 'name' must be a non-empty string")
+    if not isinstance(description, str) or not description.strip():
+        raise ValidationError("Field 'description' must be a non-empty string")
+
+    return SkillProperties(
+        name=name.strip(),
+        description=description.strip(),
+        license=metadata.get("license"),
+        compatibility=metadata.get("compatibility"),
+        allowed_tools=metadata.get("allowed-tools"),
+        metadata=metadata.get("metadata") or {},
+        path=skill_dir,
+    )