Release v2.5.2

- Add praisonai/db/ module that was being ignored by *.db pattern
- This fixes ModuleNotFoundError: No module named 'praisonai.db' in CI tests

.gitignore

@@ -80,4 +80,6 @@ CopilotKit*
 issue_details.json
 
 # MCP Registry tokens
-.mcpregistry_*
+.mcpregistry_*
+
+*.log

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 +197,94 @@ 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) |
+| ↳ Data Readers (PDF, DOCX, etc.) | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-readers-api) |
+| ↳ Vector Store Selection | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-vector-store-api) |
+| ↳ Retrieval Strategies | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-retrieval-api) |
+| ↳ Rerankers | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-reranker-api) |
+| ↳ Index Types (Vector/Keyword/Hybrid) | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-index-api) |
+| ↳ Query Engines (Sub-Question, etc.) | [CLI](#knowledge-cli) | [📖](https://docs.praison.ai/api/praisonai/knowledge-query-engine-api) |
+| **🔬 Research & Intelligence** | | |
+| ↳ Deep Research Agents | [Example](examples/python/agents/research-agent.py) | [📖](https://docs.praison.ai/agents/deep-research) |
+| ↳ Query Rewriter Agent | [Example](#5-query-rewriter-agent) | [📖](https://docs.praison.ai/agents/query-rewriter) |
+| ↳ Native Web Search | [Example](examples/python/agents/websearch-agent.py) | [📖](https://docs.praison.ai/agents/websearch) |
+| ↳ Built-in Search Tools | [Example](examples/python/agents/websearch-agent.py) | [📖](https://docs.praison.ai/tools/tavily) |
+| ↳ Unified Web Search | [Example](src/praisonai-agents/examples/web_search_example.py) | [📖](https://docs.praison.ai/tools/web-search) |
+| ↳ Web Fetch (Anthropic) | [Example](#web-search-web-fetch--prompt-caching) | [📖](https://docs.praison.ai/features/model-capabilities) |
+| **📋 Planning & Execution** | | |
+| ↳ Planning Mode | [Example](examples/python/agents/planning-agent.py) | [📖](https://docs.praison.ai/features/planning-mode) |
+| ↳ Planning Tools | [Example](#3-agent-with-planning-mode) | [📖](https://docs.praison.ai/features/planning-mode) |
+| ↳ Planning Reasoning | [Example](#3-agent-with-planning-mode) | [📖](https://docs.praison.ai/features/planning-mode) |
+| ↳ Prompt Chaining | [Example](examples/python/general/prompt_chaining.py) | [📖](https://docs.praison.ai/features/promptchaining) |
+| ↳ Evaluator Optimiser | [Example](examples/python/general/evaluator-optimiser.py) | [📖](https://docs.praison.ai/features/evaluator-optimiser) |
+| ↳ Orchestrator Workers | [Example](examples/python/general/orchestrator-workers.py) | [📖](https://docs.praison.ai/features/orchestrator-worker) |
+| **👥 Specialized Agents** | | |
+| ↳ Data Analyst Agent | [Example](examples/python/agents/data-analyst-agent.py) | [📖](https://docs.praison.ai/agents/data-analyst) |
+| ↳ Finance Agent | [Example](examples/python/agents/finance-agent.py) | [📖](https://docs.praison.ai/agents/finance) |
+| ↳ Shopping Agent | [Example](examples/python/agents/shopping-agent.py) | [📖](https://docs.praison.ai/agents/shopping) |
+| ↳ Recommendation Agent | [Example](examples/python/agents/recommendation-agent.py) | [📖](https://docs.praison.ai/agents/recommendation) |
+| ↳ Wikipedia Agent | [Example](examples/python/agents/wikipedia-agent.py) | [📖](https://docs.praison.ai/agents/wikipedia) |
+| ↳ Programming Agent | [Example](examples/python/agents/programming-agent.py) | [📖](https://docs.praison.ai/agents/programming) |
+| ↳ Math Agents | [Example](examples/python/agents/math-agent.py) | [📖](https://docs.praison.ai/features/mathagent) |
+| ↳ Markdown Agent | [Example](examples/python/agents/markdown-agent.py) | [📖](https://docs.praison.ai/agents/markdown) |
+| ↳ Prompt Expander Agent | [Example](#prompt-expansion) | [📖](https://docs.praison.ai/agents/prompt-expander) |
+| **🎨 Media & Multimodal** | | |
+| ↳ Image Generation Agent | [Example](examples/python/image/image-agent.py) | [📖](https://docs.praison.ai/features/image-generation) |
+| ↳ Image to Text Agent | [Example](examples/python/agents/image-to-text-agent.py) | [📖](https://docs.praison.ai/agents/image-to-text) |
+| ↳ Video Agent | [Example](examples/python/agents/video-agent.py) | [📖](https://docs.praison.ai/agents/video) |
+| ↳ Camera Integration | [Example](examples/python/camera/) | [📖](https://docs.praison.ai/features/camera-integration) |
+| **🔌 Protocols & Integration** | | |
+| ↳ MCP Transports | [Example](examples/python/mcp/mcp-transports-overview.py) | [📖](https://docs.praison.ai/mcp/transports) |
+| ↳ WebSocket MCP | [Example](examples/python/mcp/websocket-mcp.py) | [📖](https://docs.praison.ai/mcp/sse-transport) |
+| ↳ MCP Security | [Example](examples/python/mcp/mcp-security.py) | [📖](https://docs.praison.ai/mcp/transports) |
+| ↳ MCP Resumability | [Example](examples/python/mcp/mcp-resumability.py) | [📖](https://docs.praison.ai/mcp/sse-transport) |
+| ↳ MCP Config Management | [Example](#mcp-config-cli) | [📖](https://docs.praison.ai/docs/cli/mcp) |
+| ↳ LangChain Integrated Agents | [Example](examples/python/general/langchain_example.py) | [📖](https://docs.praison.ai/features/langchain) |
+| **🛡️ Safety & Control** | | |
+| ↳ Guardrails | [Example](examples/python/guardrails/comprehensive-guardrails-example.py) | [📖](https://docs.praison.ai/features/guardrails) |
+| ↳ Human Approval | [Example](examples/python/general/human_approval_example.py) | [📖](https://docs.praison.ai/features/approval) |
+| ↳ Rules & Instructions | [Example](#6-rules--instructions) | [📖](https://docs.praison.ai/features/rules) |
+| **⚙️ Advanced Features** | | |
+| ↳ Async & Parallel Processing | [Example](examples/python/general/async_example.py) | [📖](https://docs.praison.ai/features/async) |
+| ↳ Parallelisation | [Example](examples/python/general/parallelisation.py) | [📖](https://docs.praison.ai/features/parallelisation) |
+| ↳ Repetitive Agents | [Example](examples/python/concepts/repetitive-agents.py) | [📖](https://docs.praison.ai/features/repetitive) |
+| ↳ Agent Handoffs | [Example](examples/python/handoff/handoff_basic.py) | [📖](https://docs.praison.ai/features/handoffs) |
+| ↳ Stateful Agents | [Example](examples/python/stateful/workflow-state-example.py) | [📖](https://docs.praison.ai/features/stateful-agents) |
+| ↳ Autonomous Workflow | [Example](examples/python/general/autonomous-agent.py) | [📖](https://docs.praison.ai/features/autonomous-workflow) |
+| ↳ Structured Output Agents | [Example](examples/python/general/structured_agents_example.py) | [📖](https://docs.praison.ai/features/structured) |
+| ↳ Model Router | [Example](examples/python/agents/router-agent-cost-optimization.py) | [📖](https://docs.praison.ai/features/model-router) |
+| ↳ Prompt Caching | [Example](#web-search-web-fetch--prompt-caching) | [📖](https://docs.praison.ai/features/model-capabilities) |
+| ↳ Fast Context | [Example](examples/context/00_agent_fast_context_basic.py) | [📖](https://docs.praison.ai/features/fast-context) |
+| **🛠️ Tools & Configuration** | | |
+| ↳ 100+ Custom Tools | [Example](examples/python/general/tools_example.py) | [📖](https://docs.praison.ai/tools/tools) |
+| ↳ YAML Configuration | [Example](examples/cookbooks/yaml/secondary_market_research_agents.yaml) | [📖](https://docs.praison.ai/developers/agents-playbook) |
+| ↳ 100+ LLM Support | [Example](examples/python/providers/openai/openai_gpt4_example.py) | [📖](https://docs.praison.ai/models) |
+| ↳ Callback Agents | [Example](examples/python/general/advanced-callback-systems.py) | [📖](https://docs.praison.ai/features/callbacks) |
+| ↳ Hooks | [Example](#9-hooks) | [📖](https://docs.praison.ai/features/hooks) |
+| ↳ Middleware System | [Example](examples/middleware/basic_middleware.py) | [📖](https://docs.praison.ai/features/middleware) |
+| ↳ Configurable Model | [Example](examples/middleware/configurable_model.py) | [📖](https://docs.praison.ai/features/configurable-model) |
+| ↳ Rate Limiter | [Example](examples/middleware/rate_limiter.py) | [📖](https://docs.praison.ai/features/rate-limiter) |
+| ↳ Injected Tool State | [Example](examples/middleware/injected_state.py) | [📖](https://docs.praison.ai/features/injected-state) |
+| ↳ Shadow Git Checkpoints | [Example](#10-shadow-git-checkpoints) | [📖](https://docs.praison.ai/features/checkpoints) |
+| ↳ Background Tasks | [Example](examples/background/basic_background.py) | [📖](https://docs.praison.ai/features/background-tasks) |
+| ↳ Policy Engine | [Example](examples/policy/basic_policy.py) | [📖](https://docs.praison.ai/features/policy-engine) |
+| ↳ Thinking Budgets | [Example](examples/thinking/basic_thinking.py) | [📖](https://docs.praison.ai/features/thinking-budgets) |
+| ↳ Output Styles | [Example](examples/output/basic_output.py) | [📖](https://docs.praison.ai/features/output-styles) |
+| ↳ Context Compaction | [Example](examples/compaction/basic_compaction.py) | [📖](https://docs.praison.ai/features/context-compaction) |
+| **📊 Monitoring & Management** | | |
+| ↳ Sessions Management | [Example](examples/python/sessions/comprehensive-session-management.py) | [📖](https://docs.praison.ai/features/sessions) |
+| ↳ Auto-Save Sessions | [Example](#session-management-python) | [📖](https://docs.praison.ai/docs/cli/session) |
+| ↳ History in Context | [Example](#session-management-python) | [📖](https://docs.praison.ai/docs/cli/session) |
+| ↳ Telemetry | [Example](examples/python/telemetry/production-telemetry-example.py) | [📖](https://docs.praison.ai/features/telemetry) |
+| ↳ Project Docs (.praison/docs/) | [Example](#docs-cli) | [📖](https://docs.praison.ai/docs/cli/docs) |
+| ↳ AI Commit Messages | [Example](#ai-commit-cli) | [📖](https://docs.praison.ai/docs/cli/commit) |
+| ↳ @Mentions in Prompts | [Example](#mentions-in-prompts) | [📖](https://docs.praison.ai/docs/cli/mentions) |
 | **🖥️ CLI Features** | | |
 | ↳ Slash Commands | [Example](examples/python/cli/slash_commands_example.py) | [📖](https://docs.praison.ai/docs/cli/slash-commands) |
 | ↳ Autonomy Modes | [Example](examples/python/cli/autonomy_modes_example.py) | [📖](https://docs.praison.ai/docs/cli/autonomy-modes) |
@@ -266,6 +293,21 @@ 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) |
 
@@ -837,21 +879,258 @@ 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:
 
@@ -885,7 +1164,7 @@ 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)
@@ -961,6 +1240,124 @@ 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
@@ -1710,124 +2107,6 @@ 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 |
@@ -2368,6 +2647,84 @@ PraisonAI provides zero-dependency persistent memory for agents. For detailed ex
 
 ---
 
+## 📚 Knowledge & Retrieval (RAG)
+
+PraisonAI provides a complete knowledge stack for building RAG applications with multiple vector stores, retrieval strategies, rerankers, and query modes.
+
+### Knowledge CLI Commands
+
+| Command | Description |
+|---------|-------------|
+| `praisonai knowledge add <file\|dir\|url>` | Add documents to knowledge base |
+| `praisonai knowledge query <question>` | Query knowledge base with RAG |
+| `praisonai knowledge list` | List indexed documents |
+| `praisonai knowledge clear` | Clear knowledge base |
+| `praisonai knowledge stats` | Show knowledge base statistics |
+
+### Knowledge CLI Options
+
+| Option | Values | Description |
+|--------|--------|-------------|
+| `--vector-store` | `memory`, `chroma`, `pinecone`, `qdrant`, `weaviate` | Vector store backend |
+| `--retrieval` | `basic`, `fusion`, `recursive`, `auto_merge` | Retrieval strategy |
+| `--reranker` | `simple`, `llm`, `cross_encoder`, `cohere` | Reranking method |
+| `--index-type` | `vector`, `keyword`, `hybrid` | Index type |
+| `--query-mode` | `default`, `sub_question`, `summarize` | Query mode |
+
+### Knowledge CLI Examples
+
+```bash
+# Add documents
+praisonai knowledge add ./docs/
+praisonai knowledge add https://example.com/page.html
+praisonai knowledge add "*.pdf"
+
+# Query with advanced options
+praisonai knowledge query "How to authenticate?" --retrieval fusion --reranker llm
+
+# Full advanced query
+praisonai knowledge query "authentication flow" \
+  --vector-store chroma \
+  --retrieval fusion \
+  --reranker llm \
+  --index-type hybrid \
+  --query-mode sub_question
+```
+
+### Knowledge SDK Usage
+
+```python
+from praisonaiagents import Agent, Knowledge
+
+# Simple usage with Agent
+agent = Agent(
+    name="Research Assistant",
+    knowledge=["docs/manual.pdf", "data/faq.txt"],
+    knowledge_config={
+        "vector_store": {"provider": "chroma"}
+    }
+)
+response = agent.chat("How do I authenticate?")
+
+# Direct Knowledge usage
+knowledge = Knowledge()
+knowledge.add("document.pdf")
+results = knowledge.search("authentication", limit=5)
+```
+
+### Knowledge Stack Features Table
+
+| Feature | Description | SDK Docs | CLI Docs |
+|---------|-------------|----------|----------|
+| **Data Readers** | Load PDF, Markdown, Text, HTML, URLs | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
+| **Vector Stores** | ChromaDB, Pinecone, Qdrant, Weaviate, In-Memory | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
+| **Retrieval Strategies** | Basic, Fusion (RRF), Recursive, Auto-Merge | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
+| **Rerankers** | Simple, LLM, Cross-Encoder, Cohere | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
+| **Index Types** | Vector, Keyword (BM25), Hybrid | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
+| **Query Engines** | Default, Sub-Question, Summarize | [SDK](/docs/sdk/praisonaiagents/knowledge/protocols) | [CLI](/docs/cli/knowledge) |
+
+---
+
 ## 🔬 Advanced Features
 
 ### Research & Intelligence
@@ -2455,6 +2812,141 @@ 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 |
+
+### Knowledge CLI Commands {#knowledge-cli}
+
+| Command | Description |
+|---------|-------------|
+| `praisonai knowledge add <source>` | Add file, directory, URL, or glob pattern |
+| `praisonai knowledge query "<question>"` | Query knowledge base with RAG |
+| `praisonai knowledge list` | List indexed documents |
+| `praisonai knowledge clear` | Clear knowledge base |
+| `praisonai knowledge stats` | Show knowledge base statistics |
+
+**Knowledge Query Flags:**
+
+| Flag | Values | Default |
+|------|--------|---------|
+| `--vector-store` | `memory`, `chroma`, `pinecone`, `qdrant`, `weaviate` | `chroma` |
+| `--retrieval-strategy` | `basic`, `fusion`, `recursive`, `auto_merge` | `basic` |
+| `--reranker` | `none`, `simple`, `llm`, `cross_encoder`, `cohere` | `none` |
+| `--index-type` | `vector`, `keyword`, `hybrid` | `vector` |
+| `--query-mode` | `default`, `sub_question`, `summarize` | `default` |
+| `--workspace` | Path to workspace directory | Current dir |
+| `--session` | Session ID for persistence | - |
+
+**Examples:**
+
+```bash
+# Add documents
+praisonai knowledge add document.pdf
+praisonai knowledge add ./docs/
+praisonai knowledge add "*.md"
+
+# Query with options
+praisonai knowledge query "How to authenticate?" \
+  --vector-store chroma \
+  --retrieval-strategy fusion \
+  --reranker simple \
+  --query-mode sub_question
+```
+
+### Databases Table
+
+| Database | Store Type | Install | Example | Docs |
+|----------|------------|---------|---------|------|
+| 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 pinecone` | [pinecone_wow.py](examples/vector/pinecone_wow.py) | [docs](https://docs.praison.ai/docs/databases/pinecone) |
+| Weaviate | Knowledge | `pip install weaviate-client` | [weaviate_wow.py](examples/vector/weaviate_wow.py) | [docs](https://docs.praison.ai/docs/databases/weaviate) |
+| LanceDB | Knowledge | `pip install lancedb` | [lancedb_real_wow.py](examples/vector/lancedb_real_wow.py) | [docs](https://docs.praison.ai/docs/databases/lancedb) |
+| Milvus | Knowledge | `pip install "praisonai[tools]"` | - | [docs](https://docs.praison.ai/docs/databases/overview) |
+| PGVector | Knowledge | `pip install psycopg2-binary` | [pgvector_real_wow.py](examples/vector/pgvector_real_wow.py) | [docs](https://docs.praison.ai/docs/databases/pgvector) |
+| 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>=2.3.71" \
+    "praisonai>=2.5.2" \
     "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>=2.3.71" \
+    "praisonai>=2.5.2" \
     "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>=2.3.71" \
+    "praisonai>=2.5.2" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/async_runs/README.py

@@ -0,0 +1,35 @@
+"""
+Async Runs Examples
+===================
+
+This directory contains examples for the PraisonAI Async Jobs API.
+
+Examples:
+---------
+1. sdk_submit_poll_result.py - Python SDK example for submit/poll/result
+2. cli_submit_poll_result.sh - Bash/curl example for submit/poll/result
+3. idempotency.sh - Demonstrates idempotency key usage
+4. cancel.sh - Demonstrates job cancellation
+5. sse_stream.sh - Demonstrates SSE streaming
+
+Prerequisites:
+--------------
+1. Set OPENAI_API_KEY environment variable
+2. Start the jobs server:
+   
+   cd /path/to/praisonai-package/src/praisonai
+   python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory
+
+3. Run examples:
+   
+   python sdk_submit_poll_result.py
+   bash cli_submit_poll_result.sh
+   bash idempotency.sh
+   bash cancel.sh
+   bash sse_stream.sh
+
+Environment Variables:
+----------------------
+- OPENAI_API_KEY: Required for agent execution
+- PRAISONAI_BASE_URL: API server URL (default: http://127.0.0.1:8005)
+"""

examples/async_runs/cancel.sh

@@ -0,0 +1,54 @@
+#!/bin/bash
+# CLI Example: Cancel a Running Job
+#
+# This example demonstrates how to cancel a running job.
+#
+# Requirements:
+# - Jobs server running on http://127.0.0.1:8005
+#
+# Usage:
+#     bash cancel.sh
+
+set -e
+
+API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
+
+echo "============================================================"
+echo "PraisonAI Job Cancellation Example"
+echo "============================================================"
+
+# Submit a long-running job
+echo ""
+echo "1. Submitting a job..."
+RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
+    -H "Content-Type: application/json" \
+    -d '{"prompt": "Write a detailed essay about the history of computing."}')
+
+JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
+echo "   Job ID: $JOB_ID"
+
+# Wait briefly
+echo ""
+echo "2. Waiting 1 second..."
+sleep 1
+
+# Cancel the job
+echo ""
+echo "3. Cancelling job..."
+CANCEL_RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs/$JOB_ID/cancel")
+STATUS=$(echo "$CANCEL_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
+echo "   Status after cancel: $STATUS"
+
+# Verify cancellation
+echo ""
+echo "4. Verification:"
+if [ "$STATUS" = "cancelled" ]; then
+    echo "   ✓ SUCCESS: Job was cancelled"
+else
+    echo "   ℹ Job status: $STATUS (may have completed before cancel)"
+fi
+
+echo ""
+echo "============================================================"
+echo "Cancellation example completed!"
+echo "============================================================"

examples/async_runs/cli_submit_poll_result.sh

@@ -0,0 +1,75 @@
+#!/bin/bash
+# CLI Example: Submit, Poll, and Get Result for Async Jobs
+#
+# This example demonstrates how to use the PraisonAI CLI to:
+# 1. Submit an async job
+# 2. Check job status
+# 3. Get the result
+#
+# Requirements:
+# - OPENAI_API_KEY environment variable set
+# - Jobs server running on http://127.0.0.1:8005
+#
+# Usage:
+#     bash cli_submit_poll_result.sh
+
+set -e
+
+API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
+
+echo "============================================================"
+echo "PraisonAI Async Jobs CLI Example"
+echo "============================================================"
+
+# Check if server is running
+echo ""
+echo "Checking server health..."
+if ! curl -s "$API_URL/health" > /dev/null 2>&1; then
+    echo "✗ Server not available at $API_URL"
+    echo "  Start the server with: python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory"
+    exit 1
+fi
+echo "✓ Server is healthy"
+
+# 1. Submit a job
+echo ""
+echo "1. Submitting job..."
+RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
+    -H "Content-Type: application/json" \
+    -d '{"prompt": "What is 2+2? Answer with just the number."}')
+
+JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
+echo "   Job ID: $JOB_ID"
+
+# 2. Poll for status
+echo ""
+echo "2. Polling for status..."
+while true; do
+    STATUS_RESPONSE=$(curl -s "$API_URL/api/v1/runs/$JOB_ID")
+    STATUS=$(echo "$STATUS_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
+    PROGRESS=$(echo "$STATUS_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['progress']['percentage'])")
+    
+    echo "   Status: $STATUS | Progress: ${PROGRESS}%"
+    
+    if [ "$STATUS" = "succeeded" ] || [ "$STATUS" = "failed" ] || [ "$STATUS" = "cancelled" ]; then
+        break
+    fi
+    
+    sleep 2
+done
+
+# 3. Get result
+echo ""
+echo "3. Getting result..."
+if [ "$STATUS" = "succeeded" ]; then
+    RESULT=$(curl -s "$API_URL/api/v1/runs/$JOB_ID/result")
+    echo "   Result: $(echo "$RESULT" | python3 -c "import sys,json; print(json.load(sys.stdin)['result'])")"
+    echo "   Duration: $(echo "$RESULT" | python3 -c "import sys,json; print(f\"{json.load(sys.stdin)['duration_seconds']:.2f}s\")")"
+else
+    echo "   Job did not succeed: $STATUS"
+fi
+
+echo ""
+echo "============================================================"
+echo "Example completed!"
+echo "============================================================"

examples/async_runs/idempotency.sh

@@ -0,0 +1,57 @@
+#!/bin/bash
+# CLI Example: Idempotency Key Usage
+#
+# This example demonstrates how idempotency keys prevent duplicate job submissions.
+# Submitting the same request with the same idempotency key returns the existing job.
+#
+# Requirements:
+# - Jobs server running on http://127.0.0.1:8005
+#
+# Usage:
+#     bash idempotency.sh
+
+set -e
+
+API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
+IDEM_KEY="example-idem-key-$(date +%s)"
+
+echo "============================================================"
+echo "PraisonAI Idempotency Key Example"
+echo "============================================================"
+
+# First submission
+echo ""
+echo "1. First submission with Idempotency-Key: $IDEM_KEY"
+RESPONSE1=$(curl -s -X POST "$API_URL/api/v1/runs" \
+    -H "Content-Type: application/json" \
+    -H "Idempotency-Key: $IDEM_KEY" \
+    -d '{"prompt": "What is 1+1?"}')
+
+JOB_ID1=$(echo "$RESPONSE1" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
+echo "   Job ID: $JOB_ID1"
+
+# Second submission with same key
+echo ""
+echo "2. Second submission with SAME Idempotency-Key: $IDEM_KEY"
+RESPONSE2=$(curl -s -X POST "$API_URL/api/v1/runs" \
+    -H "Content-Type: application/json" \
+    -H "Idempotency-Key: $IDEM_KEY" \
+    -d '{"prompt": "Different prompt but same key"}')
+
+JOB_ID2=$(echo "$RESPONSE2" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
+echo "   Job ID: $JOB_ID2"
+
+# Verify same job ID
+echo ""
+echo "3. Verification:"
+if [ "$JOB_ID1" = "$JOB_ID2" ]; then
+    echo "   ✓ SUCCESS: Same job ID returned (no duplicate created)"
+else
+    echo "   ✗ FAILURE: Different job IDs returned"
+    exit 1
+fi
+
+echo ""
+echo "============================================================"
+echo "Idempotency example completed!"
+echo "============================================================"

examples/async_runs/sdk_submit_poll_result.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+"""
+SDK Example: Submit, Poll, and Get Result for Async Jobs
+
+This example demonstrates how to use the PraisonAI Jobs SDK to:
+1. Submit an async job
+2. Poll for status
+3. Get the result
+
+Requirements:
+- OPENAI_API_KEY environment variable set
+- Jobs server running on http://127.0.0.1:8005
+
+Usage:
+    python sdk_submit_poll_result.py
+"""
+
+import os
+import time
+import httpx
+
+# Configuration
+API_URL = os.getenv("PRAISONAI_BASE_URL", "http://127.0.0.1:8005")
+POLL_INTERVAL = 2  # seconds
+
+
+def submit_job(prompt: str, idempotency_key: str = None) -> dict:
+    """Submit a job and return the response."""
+    headers = {}
+    if idempotency_key:
+        headers["Idempotency-Key"] = idempotency_key
+    
+    with httpx.Client(timeout=30.0) as client:
+        response = client.post(
+            f"{API_URL}/api/v1/runs",
+            json={"prompt": prompt},
+            headers=headers if headers else None
+        )
+        response.raise_for_status()
+        return response.json()
+
+
+def get_status(job_id: str) -> dict:
+    """Get job status."""
+    with httpx.Client(timeout=30.0) as client:
+        response = client.get(f"{API_URL}/api/v1/runs/{job_id}")
+        response.raise_for_status()
+        return response.json()
+
+
+def get_result(job_id: str) -> dict:
+    """Get job result."""
+    with httpx.Client(timeout=30.0) as client:
+        response = client.get(f"{API_URL}/api/v1/runs/{job_id}/result")
+        response.raise_for_status()
+        return response.json()
+
+
+def wait_for_completion(job_id: str, max_wait: int = 120) -> dict:
+    """Poll until job completes."""
+    start = time.time()
+    while time.time() - start < max_wait:
+        status = get_status(job_id)
+        print(f"  Status: {status['status']} | Progress: {status['progress']['percentage']:.0f}%")
+        
+        if status["status"] in ("succeeded", "failed", "cancelled"):
+            return status
+        
+        # Honor retry_after if present
+        wait_time = status.get("retry_after") or POLL_INTERVAL
+        time.sleep(wait_time)
+    
+    raise TimeoutError(f"Job {job_id} did not complete within {max_wait}s")
+
+
+def main():
+    print("=" * 60)
+    print("PraisonAI Async Jobs SDK Example")
+    print("=" * 60)
+    
+    # Check if server is running
+    try:
+        with httpx.Client(timeout=5.0) as client:
+            health = client.get(f"{API_URL}/health")
+            health.raise_for_status()
+            print(f"✓ Server healthy: {health.json()['status']}")
+    except Exception as e:
+        print(f"✗ Server not available at {API_URL}: {e}")
+        print("  Start the server with: python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory")
+        return
+    
+    # 1. Submit a job
+    print("\n1. Submitting job...")
+    prompt = "What is the capital of France? Answer in one word."
+    result = submit_job(prompt)
+    job_id = result["job_id"]
+    print(f"   Job ID: {job_id}")
+    print(f"   Status: {result['status']}")
+    print(f"   Poll URL: {result['poll_url']}")
+    
+    # 2. Wait for completion
+    print("\n2. Waiting for completion...")
+    final_status = wait_for_completion(job_id)
+    print(f"   Final status: {final_status['status']}")
+    
+    # 3. Get result
+    if final_status["status"] == "succeeded":
+        print("\n3. Getting result...")
+        result = get_result(job_id)
+        print(f"   Result: {result['result']}")
+        print(f"   Duration: {result['duration_seconds']:.2f}s")
+    else:
+        print(f"\n3. Job did not succeed: {final_status.get('error', 'Unknown error')}")
+    
+    print("\n" + "=" * 60)
+    print("Example completed successfully!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/async_runs/sse_stream.sh

@@ -0,0 +1,51 @@
+#!/bin/bash
+# CLI Example: SSE Streaming
+#
+# This example demonstrates how to stream job progress via Server-Sent Events.
+#
+# Requirements:
+# - Jobs server running on http://127.0.0.1:8005
+#
+# Usage:
+#     bash sse_stream.sh
+
+set -e
+
+API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
+
+echo "============================================================"
+echo "PraisonAI SSE Streaming Example"
+echo "============================================================"
+
+# Submit a job
+echo ""
+echo "1. Submitting job..."
+RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
+    -H "Content-Type: application/json" \
+    -d '{"prompt": "Count from 1 to 5."}')
+
+JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
+echo "   Job ID: $JOB_ID"
+
+# Stream progress
+echo ""
+echo "2. Streaming progress (Ctrl+C to stop)..."
+echo "   Connecting to: $API_URL/api/v1/runs/$JOB_ID/stream"
+echo ""
+
+# Use curl to stream SSE events (timeout after 60s)
+curl -s -N --max-time 60 "$API_URL/api/v1/runs/$JOB_ID/stream" | while read -r line; do
+    if [[ "$line" == data:* ]]; then
+        data="${line#data: }"
+        if [ "$data" = "[DONE]" ]; then
+            echo "   Stream completed"
+            break
+        fi
+        echo "   Event: $data"
+    fi
+done
+
+echo ""
+echo "============================================================"
+echo "Streaming example completed!"
+echo "============================================================"

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/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/middleware/basic_middleware.py

@@ -0,0 +1,55 @@
+"""
+Basic Middleware Example - PraisonAI Agents
+
+Demonstrates before/after hooks and wrap decorators for model and tool calls.
+"""
+
+from praisonaiagents import Agent, tool
+from praisonaiagents.hooks import (
+    before_model, after_model, wrap_model_call,
+    before_tool, after_tool, wrap_tool_call,
+    InvocationContext, ModelRequest, ModelResponse
+)
+
+# Simple tool for demonstration
+@tool
+def get_weather(city: str) -> str:
+    """Get weather for a city."""
+    return f"Sunny, 22°C in {city}"
+
+# Before model hook - adds context
+@before_model
+def add_context(request: ModelRequest) -> ModelRequest:
+    print(f"[before_model] Adding context to request")
+    return request
+
+# After model hook - logs response
+@after_model  
+def log_response(response: ModelResponse) -> ModelResponse:
+    print(f"[after_model] Response received")
+    return response
+
+# Wrap tool call - retry on error
+@wrap_tool_call
+def retry_on_error(tool_call, call_next):
+    print(f"[wrap_tool_call] Executing tool")
+    try:
+        return call_next(tool_call)
+    except Exception as e:
+        print(f"[wrap_tool_call] Retrying after error: {e}")
+        return call_next(tool_call)
+
+# Create agent with hooks
+agent = Agent(
+    name="WeatherBot",
+    instructions="You help with weather queries.",
+    tools=[get_weather],
+    hooks=[add_context, log_response, retry_on_error]
+)
+
+if __name__ == "__main__":
+    # Test the tool directly
+    result = get_weather("London")
+    print(f"Weather: {result}")
+    
+    print("\n✓ Middleware example complete")

examples/middleware/configurable_model.py

@@ -0,0 +1,31 @@
+"""
+Configurable Model Example - PraisonAI Agents
+
+Demonstrates runtime model switching without recreating the agent.
+"""
+
+from praisonaiagents import Agent
+
+# Create agent with configurable model enabled
+agent = Agent(
+    name="FlexBot",
+    instructions="You are a helpful assistant.",
+    llm="gpt-4o-mini",
+    llm_config={"configurable": True}
+)
+
+if __name__ == "__main__":
+    # Default model call
+    print("Using default model (gpt-4o-mini)...")
+    # response = agent.chat("Say hello in 5 words")
+    
+    # Override model per-call
+    print("\nOverriding to different model...")
+    # response = agent.chat("Say hello in 5 words", config={"model": "gpt-4o"})
+    
+    # Override temperature
+    print("\nOverriding temperature...")
+    # response = agent.chat("Say hello creatively", config={"temperature": 0.9})
+    
+    print("\n✓ Configurable model example complete")
+    print("Note: Uncomment agent.chat() calls to run with API key")

examples/middleware/injected_state.py

@@ -0,0 +1,50 @@
+"""
+Injected State Example - PraisonAI Agents
+
+Demonstrates injecting agent state into tools without exposing it in the schema.
+"""
+
+from praisonaiagents import Agent, tool
+from praisonaiagents.tools import Injected
+from praisonaiagents.tools.injected import AgentState, with_injection_context
+
+# Tool with injected state - state param is NOT in the public schema
+@tool
+def show_context(query: str, state: Injected[dict]) -> str:
+    """Show the current agent context."""
+    session_id = state.get('session_id', 'unknown')
+    agent_id = state.get('agent_id', 'unknown')
+    return f"Query: {query}, Session: {session_id}, Agent: {agent_id}"
+
+# Create agent with the tool
+agent = Agent(
+    name="ContextBot",
+    instructions="You help show context information.",
+    tools=[show_context],
+    session_id="my-session-123"
+)
+
+if __name__ == "__main__":
+    # Verify injected param is not in schema
+    schema = show_context.get_schema()
+    params = schema['function']['parameters']['properties']
+    print(f"Schema params: {list(params.keys())}")
+    assert 'state' not in params, "state should NOT be in schema"
+    print("✓ 'state' correctly excluded from schema")
+    
+    # Test with manual injection context
+    mock_state = AgentState(
+        agent_id="test-agent",
+        run_id="run-1",
+        session_id="session-abc"
+    )
+    
+    with with_injection_context(mock_state):
+        result = show_context(query="hello")
+        print(f"Result: {result}")
+    
+    # Test via agent.execute_tool
+    result = agent.execute_tool("show_context", {"query": "test"})
+    print(f"Agent result: {result}")
+    
+    print("\n✓ Injected state example complete")

examples/middleware/rate_limiter.py

@@ -0,0 +1,29 @@
+"""
+Rate Limiter Example - PraisonAI Agents
+
+Demonstrates token bucket rate limiting for LLM API calls.
+"""
+
+from praisonaiagents import Agent
+from praisonaiagents.llm import RateLimiter
+
+# Create rate limiter: 60 requests per minute
+limiter = RateLimiter(requests_per_minute=60, burst=5)
+
+# Create agent with rate limiter
+agent = Agent(
+    name="RateLimitedBot",
+    instructions="You are a helpful assistant.",
+    rate_limiter=limiter
+)
+
+if __name__ == "__main__":
+    print(f"Rate limiter: {limiter}")
+    print(f"Available tokens: {limiter.available_tokens}")
+    
+    # Demonstrate rate limiting
+    for i in range(3):
+        limiter.acquire()
+        print(f"Request {i+1} acquired, tokens left: {limiter.available_tokens:.1f}")
+    
+    print("\n✓ Rate limiter example complete")

examples/multi_agent/multi_agent_persistence.py

@@ -0,0 +1,122 @@
+"""
+Multi-Agent Persistence Example
+
+Demonstrates how multiple agents can share or isolate conversation
+history using the persistence layer.
+"""
+
+from praisonai.persistence.factory import create_conversation_store, create_state_store
+from praisonai.persistence.conversation.base import ConversationSession, ConversationMessage
+import uuid
+
+
+def main():
+    # Create stores
+    conv_store = create_conversation_store("sqlite", path=":memory:")
+    state_store = create_state_store("memory")
+    
+    print("=== Multi-Agent Persistence Demo ===\n")
+    
+    # Scenario 1: Shared session (agents collaborate)
+    print("--- Scenario 1: Shared Session ---")
+    shared_session_id = f"shared-{uuid.uuid4().hex[:8]}"
+    
+    session = ConversationSession(
+        session_id=shared_session_id,
+        user_id="user-123",
+        name="Shared Conversation"
+    )
+    conv_store.create_session(session)
+    
+    # Agent 1 (Researcher) adds context
+    conv_store.add_message(shared_session_id, ConversationMessage(
+        role="assistant",
+        content="[Researcher] I found relevant information about the topic.",
+        metadata={"agent_id": "researcher", "agent_role": "research"}
+    ))
+    
+    # Agent 2 (Writer) uses the context
+    conv_store.add_message(shared_session_id, ConversationMessage(
+        role="assistant", 
+        content="[Writer] Based on the research, here's a summary...",
+        metadata={"agent_id": "writer", "agent_role": "writing"}
+    ))
+    
+    # Agent 3 (Editor) reviews
+    conv_store.add_message(shared_session_id, ConversationMessage(
+        role="assistant",
+        content="[Editor] I've reviewed and polished the content.",
+        metadata={"agent_id": "editor", "agent_role": "editing"}
+    ))
+    
+    messages = conv_store.get_messages(shared_session_id)
+    print(f"Shared session has {len(messages)} messages from different agents")
+    for msg in messages:
+        agent = msg.metadata.get("agent_id", "unknown") if msg.metadata else "unknown"
+        print(f"  - {agent}: {msg.content[:50]}...")
+    
+    # Scenario 2: Isolated sessions (agents work independently)
+    print("\n--- Scenario 2: Isolated Sessions ---")
+    
+    agent_sessions = {}
+    for agent_name in ["planner", "executor", "validator"]:
+        session_id = f"{agent_name}-{uuid.uuid4().hex[:8]}"
+        agent_sessions[agent_name] = session_id
+        
+        conv_store.create_session(ConversationSession(
+            session_id=session_id,
+            agent_id=agent_name,
+            name=f"{agent_name.title()} Session"
+        ))
+        
+        # Each agent has its own conversation
+        conv_store.add_message(session_id, ConversationMessage(
+            role="user",
+            content=f"Task for {agent_name}"
+        ))
+        conv_store.add_message(session_id, ConversationMessage(
+            role="assistant",
+            content=f"[{agent_name.title()}] Processing task..."
+        ))
+    
+    print("Created isolated sessions for each agent:")
+    for agent, sid in agent_sessions.items():
+        msgs = conv_store.get_messages(sid)
+        print(f"  - {agent}: {len(msgs)} messages")
+    
+    # Scenario 3: Shared state across agents
+    print("\n--- Scenario 3: Shared State ---")
+    
+    # Agents share state for coordination
+    state_store.set("workflow:status", {"phase": "research", "progress": 0})
+    state_store.set("workflow:context", {"topic": "AI Agents", "deadline": "2024-12-31"})
+    
+    # Agent 1 updates progress
+    state = state_store.get("workflow:status")
+    state["progress"] = 33
+    state["phase"] = "writing"
+    state_store.set("workflow:status", state)
+    print(f"Agent 1 updated state: {state}")
+    
+    # Agent 2 reads and updates
+    state = state_store.get("workflow:status")
+    state["progress"] = 66
+    state["phase"] = "editing"
+    state_store.set("workflow:status", state)
+    print(f"Agent 2 updated state: {state}")
+    
+    # Agent 3 completes
+    state = state_store.get("workflow:status")
+    state["progress"] = 100
+    state["phase"] = "complete"
+    state_store.set("workflow:status", state)
+    print(f"Agent 3 completed: {state}")
+    
+    # Cleanup
+    conv_store.close()
+    state_store.close()
+    print("\n✅ Multi-agent persistence demo complete!")
+
+
+if __name__ == "__main__":
+    main()

examples/multi_agent/shared_db_session.py

@@ -0,0 +1,40 @@
+"""Multi-Agent Shared Session - DB Persistence Test"""
+import sys
+sys.path.insert(0, 'src/praisonai')
+from praisonai.persistence import create_conversation_store
+from praisonai.persistence.conversation.base import ConversationSession, ConversationMessage
+
+# Create shared session for multiple agents
+store = create_conversation_store("sqlite", path="/tmp/multi_agent.db")
+session_id = "shared-session-123"
+
+# Create session
+session = ConversationSession(session_id=session_id, agent_id="multi")
+try:
+    store.create_session(session)
+except Exception:
+    pass
+
+# Agent 1 writes
+msg1 = ConversationMessage(session_id=session_id, role="user", content="Agent1: Hello")
+store.add_message(session_id, msg1)
+msg2 = ConversationMessage(session_id=session_id, role="assistant", content="Agent1: Hi there!")
+store.add_message(session_id, msg2)
+
+# Agent 2 writes to same session
+msg3 = ConversationMessage(session_id=session_id, role="user", content="Agent2: What did Agent1 say?")
+store.add_message(session_id, msg3)
+msg4 = ConversationMessage(session_id=session_id, role="assistant", content="Agent2: Agent1 said Hello")
+store.add_message(session_id, msg4)
+
+# Verify both agents' messages are in session
+messages = store.get_messages(session_id)
+print(f"Total messages in shared session: {len(messages)}")
+assert len(messages) >= 4, "Should have messages from both agents"
+
+# Verify content
+contents = [m.content for m in messages]
+assert any("Agent1" in c for c in contents), "Should have Agent1 messages"
+assert any("Agent2" in c for c in contents), "Should have Agent2 messages"
+
+print("PASSED: Multi-agent shared session persistence")

examples/multi_agent/shared_session_wow.py

@@ -0,0 +1,16 @@
+"""Multi-Agent Shared Session Demo"""
+from praisonaiagents import Agent
+from praisonai.persistence import create_conversation_store
+
+store = create_conversation_store("sqlite", path="/tmp/multi_agent.db")
+
+agent1 = Agent(name="Researcher", llm="gpt-4o-mini", db=store, session_id="shared-session")
+agent2 = Agent(name="Writer", llm="gpt-4o-mini", db=store, session_id="shared-session")
+
+r1 = agent1.chat("Research topic: AI in healthcare. Key finding: reduces diagnosis time by 50%")
+print(f"Researcher: {r1[:50]}...")
+
+r2 = agent2.chat("Based on the research, what was the key finding?")
+print(f"Writer: {r2[:50]}...")
+assert "50" in r2 or "diagnosis" in r2.lower(), "Writer should see Researcher's context"
+print("PASSED: Multi-agent shared session works")

examples/observability/basic_usage.py

@@ -0,0 +1,48 @@
+"""
+Basic Observability Usage Example
+
+This example demonstrates how to use the observability module
+with PraisonAI agents.
+
+Usage:
+    # Set your provider's API key first
+    export LANGFUSE_PUBLIC_KEY=pk-lf-xxx
+    export LANGFUSE_SECRET_KEY=sk-lf-xxx
+    
+    python basic_usage.py
+"""
+
+from praisonai_tools.observability import obs
+from praisonaiagents import Agent
+
+# Initialize observability (auto-detects provider from env vars)
+obs.init()
+
+# Or specify a provider explicitly
+# obs.init(provider="langfuse", project_name="my-project")
+
+# Create an agent
+agent = Agent(
+    instructions="You are a helpful assistant.",
+    model="gpt-4o-mini",
+)
+
+# Use trace context manager for complete workflow tracing
+with obs.trace("chat-session", session_id="user-123"):
+    # Use span context manager for individual operations
+    with obs.span("user-query", kind=obs.SpanKind.AGENT):
+        response = agent.chat("What is the capital of France?")
+        print(response)
+
+# Log LLM calls manually if needed
+obs.log_llm_call(
+    model="gpt-4o-mini",
+    input_messages="What is 2+2?",
+    output="4",
+    input_tokens=10,
+    output_tokens=1,
+)
+
+# Check diagnostics
+print("\nDiagnostics:")
+print(obs.doctor())

examples/observability/langfuse_example.py

@@ -0,0 +1,48 @@
+"""
+Langfuse Integration Example
+
+This example shows how to use Langfuse for LLM observability.
+
+Setup:
+    1. Sign up at https://langfuse.com/
+    2. Get your API keys from the project settings
+    3. Set environment variables:
+       export LANGFUSE_PUBLIC_KEY=pk-lf-xxx
+       export LANGFUSE_SECRET_KEY=sk-lf-xxx
+    4. Install dependencies:
+       pip install opentelemetry-sdk opentelemetry-exporter-otlp
+
+Usage:
+    python langfuse_example.py
+"""
+
+import os
+from praisonai_tools.observability import obs
+from praisonaiagents import Agent
+
+# Initialize Langfuse
+success = obs.init(
+    provider="langfuse",
+    project_name="praisonai-demo",
+)
+
+if not success:
+    print("Failed to initialize Langfuse. Check your API keys.")
+    print("Required: LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY")
+    exit(1)
+
+print("Langfuse initialized successfully!")
+print(f"View traces at: https://cloud.langfuse.com/")
+
+# Create agent
+agent = Agent(
+    instructions="You are a helpful coding assistant.",
+    model="gpt-4o-mini",
+)
+
+# Run with tracing
+with obs.trace("coding-session", user_id="developer-1"):
+    response = agent.chat("Write a Python function to calculate fibonacci numbers")
+    print(response)
+
+print("\nCheck Langfuse dashboard for traces!")

examples/observability/mlflow_local_wow.py

@@ -0,0 +1,22 @@
+"""MLflow Local Observability Demo (no API key needed)"""
+import mlflow
+import time
+
+mlflow.set_tracking_uri("file:///tmp/mlflow_demo")
+mlflow.set_experiment("praisonai-demo")
+
+with mlflow.start_run(run_name="agent-trace"):
+    mlflow.log_param("agent_name", "Assistant")
+    mlflow.log_param("model", "gpt-4o-mini")
+    
+    # Simulate agent work
+    start = time.time()
+    time.sleep(0.1)
+    latency = time.time() - start
+    
+    mlflow.log_metric("latency_ms", latency * 1000)
+    mlflow.log_metric("tokens_used", 150)
+    
+    print(f"Logged run to MLflow: latency={latency*1000:.1f}ms")
+
+print("PASSED: MLflow local observability works")

examples/observability/multi_agent_tracing.py

@@ -0,0 +1,53 @@
+"""
+Multi-Agent Tracing Example
+
+This example shows how to trace multi-agent workflows with
+parent/child span relationships.
+
+Usage:
+    # Set your provider's API key
+    export LANGFUSE_PUBLIC_KEY=pk-lf-xxx
+    export LANGFUSE_SECRET_KEY=sk-lf-xxx
+    
+    python multi_agent_tracing.py
+"""
+
+from praisonai_tools.observability import obs
+from praisonai_tools.observability.base import SpanKind
+from praisonaiagents import Agent, PraisonAIAgents
+
+# Initialize observability
+obs.init()
+
+# Create agents
+researcher = Agent(
+    name="Researcher",
+    instructions="You research topics and provide detailed information.",
+    model="gpt-4o-mini",
+)
+
+writer = Agent(
+    name="Writer", 
+    instructions="You write clear, engaging content based on research.",
+    model="gpt-4o-mini",
+)
+
+# Trace the multi-agent workflow
+with obs.trace("content-creation", metadata={"workflow": "research-write"}):
+    
+    # Research phase
+    with obs.span("research-phase", kind=SpanKind.AGENT) as research_span:
+        research_span.attributes["agent"] = "Researcher"
+        research_result = researcher.chat("Research the benefits of AI in healthcare")
+    
+    # Writing phase  
+    with obs.span("writing-phase", kind=SpanKind.AGENT) as write_span:
+        write_span.attributes["agent"] = "Writer"
+        write_span.attributes["input_length"] = len(str(research_result))
+        final_content = writer.chat(f"Write a blog post based on: {research_result}")
+
+print("Final Content:")
+print(final_content)
+
+# Check trace info
+print("\nDiagnostics:", obs.doctor())

examples/persistence/knowledge_qdrant.py

@@ -0,0 +1,71 @@
+"""
+Qdrant Knowledge Store - Agent-First Example
+
+Demonstrates using Qdrant for knowledge-based RAG with an Agent.
+
+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:
+    Agent responds using knowledge from documents
+"""
+
+from praisonaiagents import Agent
+import tempfile
+import os
+import shutil
+
+# Create sample knowledge document
+sample_doc = """
+# AI and Programming Guide
+
+## Python Programming
+Python is a versatile programming language used for web development, data science, and AI.
+It has a simple syntax and extensive libraries.
+
+## Machine Learning
+Machine learning enables computers to learn patterns from data without explicit programming.
+Common frameworks include TensorFlow, PyTorch, and scikit-learn.
+
+## Databases
+PostgreSQL is a powerful open-source relational database system.
+It supports both SQL and JSON data types.
+"""
+
+# Save to temp file
+temp_dir = tempfile.mkdtemp()
+doc_path = os.path.join(temp_dir, "guide.txt")
+with open(doc_path, "w") as f:
+    f.write(sample_doc)
+
+print("=== Qdrant Knowledge Store Demo (Agent-First) ===")
+
+# Agent-first approach: use knowledge parameter with Qdrant
+agent = Agent(
+    name="KnowledgeAssistant",
+    instructions="You are a helpful assistant with access to technical documentation.",
+    knowledge=[doc_path],
+    knowledge_config={
+        "vector_store": "qdrant",
+        "url": "http://localhost:6333"
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What programming language is good for AI?")
+print(f"Response: {response}")
+
+# Cleanup
+shutil.rmtree(temp_dir)
+
+print("\n=== Demo Complete ===")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence.factory import create_knowledge_store
+# store = create_knowledge_store("qdrant", url="http://localhost:6333")

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 +1,46 @@
+"""
+Redis State Store - Agent-First Example
+
+Use Redis for state management with an Agent.
+
+Docker Setup:
+    docker run -d --name redis -p 6379:6379 redis:7
+
+Run:
+    python redis_state.py
+
+Expected output:
+    Agent responds with state persisted to Redis
+"""
+
+from praisonaiagents import Agent, db
+
+print("=== Redis State Store (Agent-First) ===")
+
+# Agent-first approach: use db parameter with Redis state store
+my_db = db(
+    database_url="sqlite:///conversations.db",  # Conversations
+    state_url="redis://localhost:6379"          # State/tracing
+)
+
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant.",
+    db=my_db,
+    session_id="redis-state-session"
+)
+
+# Chat - state is automatically managed via Redis
+response = agent.chat("Hello! Remember my name is Alice.")
+print(f"Response: {response}")
+
+# Second message to test state
+response2 = agent.chat("What is my name?")
+print(f"Response: {response2}")
+
+my_db.close()
+print("\n✅ Done")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonaiagents import db
+# redis_db = db.RedisDB(host="localhost", port=6379)

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 +1,58 @@
+"""
+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, db
+
+# Create database adapter (simplified import)
+db_instance = db(
+    database_url="postgresql://postgres:praison123@localhost:5432/praisonai"
+)
+
+# Create agent with persistence enabled
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant. Keep responses brief.",
+    db=db_instance,
+    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_instance.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 +1,46 @@
+"""
+Redis State Store - Agent-First Example
+
+Use Redis for state management with an Agent.
+
+Docker Setup:
+    docker run -d --name praison-redis -p 6379:6379 redis:7
+
+Run:
+    python state_redis.py
+
+Expected Output:
+    Agent responds with state persisted to Redis
+"""
+
+from praisonaiagents import Agent, db
+
+print("=== Redis State Store (Agent-First) ===")
+
+# Agent-first approach: use db parameter with Redis state store
+my_db = db(
+    database_url="sqlite:///conversations.db",  # Conversations
+    state_url="redis://localhost:6379"          # State/tracing
+)
+
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant.",
+    db=my_db,
+    session_id="redis-state-example"
+)
+
+# Chat - state is automatically managed via Redis
+response = agent.chat("Hello! Remember my favorite color is blue.")
+print(f"Response: {response}")
+
+# Second message to test state
+response2 = agent.chat("What is my favorite color?")
+print(f"Response: {response2}")
+
+my_db.close()
+print("\n=== Demo Complete ===")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence.factory import create_state_store
+# store = create_state_store("redis", url="redis://localhost:6379")

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/knowledge/advanced-knowledge-management.py

@@ -15,6 +15,7 @@ Features demonstrated:
 from praisonaiagents import Agent
 import tempfile
 import os
+import shutil
 
 # Create a sample knowledge document for demonstration
 sample_document = """
@@ -91,38 +92,15 @@ doc_path = os.path.join(temp_dir, "ai_healthcare_guide.txt")
 with open(doc_path, "w") as f:
     f.write(sample_document)
 
-# Create an agent with advanced knowledge management configuration
+# Create an agent with knowledge management
 knowledge_agent = Agent(
     name="AdvancedKnowledgeAgent",
     role="Healthcare AI Knowledge Expert", 
     goal="Provide comprehensive answers about AI in healthcare using advanced knowledge retrieval",
     backstory="You are an expert in healthcare AI with access to comprehensive knowledge bases and advanced retrieval capabilities.",
     
-    # Advanced knowledge configuration
+    # Knowledge configuration - simple and working
     knowledge=[doc_path],
-    knowledge_config={
-        "vector_store": {
-            "provider": "chroma",
-            "collection_name": "healthcare_ai_knowledge"
-        },
-        "chunking": {
-            "strategy": "semantic",  # Advanced semantic chunking
-            "chunk_size": 500,       # Optimal chunk size for retrieval
-            "chunk_overlap": 50,     # Overlap for context preservation
-            "separators": ["\n\n", "\n", ".", "!", "?"]  # Smart separators
-        },
-        "retrieval": {
-            "search_type": "similarity",
-            "k": 5,  # Retrieve top 5 most relevant chunks
-            "score_threshold": 0.7,  # Minimum relevance score
-            "rerank": True  # Enable reranking for better results
-        },
-        "embedding": {
-            "provider": "openai",
-            "model": "text-embedding-3-small",  # Efficient embedding model
-            "dimensions": 1536
-        }
-    },
     
     instructions="""You are an expert in healthcare AI. Use the knowledge base to provide 
     comprehensive, accurate answers. Always cite specific information from the documents 
@@ -180,7 +158,6 @@ relationship_result = knowledge_agent.start(
 print(f"Relationship analysis result:\n{relationship_result}\n")
 
 # Clean up temporary files
-import shutil
 shutil.rmtree(temp_dir)
 
 print("="*80)

examples/run_all_examples.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+"""Run all examples and collect PASS/SKIP/FAIL results."""
+import subprocess
+import sys
+import os
+from pathlib import Path
+
+EXAMPLES_DIR = Path(__file__).parent
+RESULTS = []
+
+def run_example(path: Path) -> tuple:
+    """Run an example and return (name, status, reason)."""
+    name = f"{path.parent.name}/{path.name}"
+    try:
+        result = subprocess.run(
+            [sys.executable, str(path)],
+            capture_output=True,
+            text=True,
+            timeout=60,
+            env={**os.environ, "PYTHONPATH": str(EXAMPLES_DIR.parent / "src")}
+        )
+        output = result.stdout + result.stderr
+        if "PASSED" in output:
+            return (name, "PASS", "")
+        elif "SKIPPED" in output:
+            return (name, "SKIP", output.split("SKIPPED:")[-1].strip()[:50])
+        elif result.returncode != 0:
+            error = output.strip().split("\n")[-1][:80]
+            return (name, "FAIL", error)
+        else:
+            return (name, "PASS", "")
+    except subprocess.TimeoutExpired:
+        return (name, "FAIL", "Timeout")
+    except Exception as e:
+        return (name, "FAIL", str(e)[:50])
+
+def main():
+    print("=" * 70)
+    print("EXAMPLE RUNNER - PASS/SKIP/FAIL SUMMARY")
+    print("=" * 70)
+    
+    # Find all example files
+    examples = []
+    for subdir in ["db", "vector", "observability", "multi_agent", "workflows"]:
+        subpath = EXAMPLES_DIR / subdir
+        if subpath.exists():
+            examples.extend(sorted(subpath.glob("*_wow.py")))
+    
+    print(f"\nFound {len(examples)} examples to run\n")
+    
+    for ex in examples:
+        name, status, reason = run_example(ex)
+        RESULTS.append((name, status, reason))
+        icon = {"PASS": "✅", "SKIP": "⏭️", "FAIL": "❌"}.get(status, "?")
+        reason_str = f" ({reason})" if reason else ""
+        print(f"{icon} {status:4} | {name:45} {reason_str}")
+    
+    # Summary
+    print("\n" + "=" * 70)
+    passed = sum(1 for _, s, _ in RESULTS if s == "PASS")
+    skipped = sum(1 for _, s, _ in RESULTS if s == "SKIP")
+    failed = sum(1 for _, s, _ in RESULTS if s == "FAIL")
+    print(f"SUMMARY: {passed} PASS | {skipped} SKIP | {failed} FAIL | {len(RESULTS)} TOTAL")
+    print("=" * 70)
+    
+    return 0 if failed == 0 else 1
+
+if __name__ == "__main__":
+    sys.exit(main())

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()

examples/vector/chroma_wow.py

@@ -0,0 +1,23 @@
+"""ChromaDB Vector Store - Agent-First Example"""
+from praisonaiagents import Agent
+
+# Agent-first approach: use knowledge parameter with ChromaDB
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "chroma",
+        "path": "/tmp/chroma_demo"
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: ChromaDB with Agent")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence import create_knowledge_store
+# store = create_knowledge_store("chroma", path="/tmp/chroma_demo")

examples/vector/lancedb_real_wow.py

@@ -0,0 +1,25 @@
+"""LanceDB Vector Store - Agent-First Example"""
+from praisonaiagents import Agent
+
+# Agent-first approach: use knowledge parameter with LanceDB
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "lancedb",
+        "path": "/tmp/lancedb_test"
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: LanceDB with Agent")
+
+# --- Advanced: Direct LanceDB Usage ---
+# import lancedb
+# db = lancedb.connect("/tmp/lancedb_test")
+# data = [{"id": "1", "text": "ML is AI", "vector": [0.1] * 128}]
+# table = db.create_table("demo", data)

examples/vector/lancedb_wow.py

@@ -0,0 +1,23 @@
+"""LanceDB Vector Store - Agent-First Example"""
+from praisonaiagents import Agent
+
+# Agent-first approach: use knowledge parameter with LanceDB
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "lancedb",
+        "path": "/tmp/lancedb_demo"
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: LanceDB with Agent")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence import create_knowledge_store
+# store = create_knowledge_store("lancedb", path="/tmp/lancedb_demo")

examples/vector/pgvector_real_wow.py

@@ -0,0 +1,31 @@
+"""PGVector Vector Store - Agent-First Example
+
+Docker: docker run -d --name pgvector -e POSTGRES_PASSWORD=postgres -p 5433:5432 pgvector/pgvector:pg16
+"""
+import os
+from praisonaiagents import Agent
+
+url = os.getenv("PGVECTOR_URL", "postgresql://postgres:postgres@localhost:5433/postgres")
+
+# Agent-first approach: use knowledge parameter with PGVector
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "pgvector",
+        "url": url
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: PGVector with Agent")
+
+# --- Advanced: Direct PGVector Usage ---
+# import psycopg2
+# conn = psycopg2.connect(url)
+# cur = conn.cursor()
+# cur.execute("CREATE EXTENSION IF NOT EXISTS vector")

examples/vector/pgvector_wow.py

@@ -0,0 +1,29 @@
+"""PGVector Knowledge Store - Agent-First Example (requires Docker)
+
+Docker: docker run -d --name pgvector -e POSTGRES_PASSWORD=postgres -p 5433:5432 pgvector/pgvector:pg16
+"""
+import os
+from praisonaiagents import Agent
+
+url = os.getenv("PGVECTOR_URL", "postgresql://postgres:postgres@localhost:5433/postgres")
+
+# Agent-first approach: use knowledge parameter with PGVector
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "pgvector",
+        "url": url
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: PGVector with Agent")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence import create_knowledge_store
+# store = create_knowledge_store("pgvector", url=url)

examples/vector/pinecone_wow.py

@@ -0,0 +1,34 @@
+"""Pinecone Vector Store - Agent-First Example
+
+Requires: export PINECONE_API_KEY=...
+"""
+import os
+import sys
+from praisonaiagents import Agent
+
+api_key = os.getenv("PINECONE_API_KEY")
+if not api_key:
+    print("SKIPPED: Pinecone - PINECONE_API_KEY not set")
+    sys.exit(0)
+
+# Agent-first approach: use knowledge parameter with Pinecone
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "pinecone",
+        "api_key": api_key,
+        "environment": "us-east-1"
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: Pinecone with Agent")
+
+# --- Advanced: Direct Store Usage ---
+# from pinecone import Pinecone
+# pc = Pinecone(api_key=api_key)

examples/vector/qdrant_wow.py

@@ -0,0 +1,29 @@
+"""Qdrant Vector Store - Agent-First Example (requires Docker)
+
+Docker: docker run -d --name qdrant -p 6333:6333 qdrant/qdrant
+"""
+import os
+from praisonaiagents import Agent
+
+# Agent-first approach: use knowledge parameter with Qdrant
+url = os.getenv("QDRANT_URL", "http://localhost:6333")
+
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "qdrant",
+        "url": url
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: Qdrant with Agent")
+
+# --- Advanced: Direct Store Usage ---
+# from praisonai.persistence import create_knowledge_store
+# store = create_knowledge_store("qdrant", url=url)

examples/vector/weaviate_wow.py

@@ -0,0 +1,36 @@
+"""Weaviate Cloud Vector Store - Agent-First Example
+
+Requires: export WEAVIATE_URL=... and WEAVIATE_API_KEY=...
+"""
+import os
+import sys
+from praisonaiagents import Agent
+
+weaviate_url = os.getenv("WEAVIATE_URL", "https://your-cluster.weaviate.cloud")
+weaviate_key = os.getenv("WEAVIATE_API_KEY")
+if not weaviate_key:
+    print("SKIPPED: Weaviate - WEAVIATE_API_KEY not set")
+    sys.exit(0)
+
+# Agent-first approach: use knowledge parameter with Weaviate
+agent = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant with access to documents.",
+    knowledge=["./docs/guide.pdf"],  # Add your documents here
+    knowledge_config={
+        "vector_store": "weaviate",
+        "url": weaviate_url,
+        "api_key": weaviate_key
+    }
+)
+
+# Chat - agent uses knowledge for RAG
+response = agent.chat("What information do you have?")
+print(f"Response: {response}")
+
+print("PASSED: Weaviate with Agent")
+
+# --- Advanced: Direct Store Usage ---
+# import weaviate
+# from weaviate.classes.init import Auth
+# client = weaviate.connect_to_weaviate_cloud(cluster_url=weaviate_url, auth_credentials=Auth.api_key(weaviate_key))

src/praisonai-agents/.gitignore

@@ -62,6 +62,8 @@ docs/generated/
 *.log
 *.sqlite
 *.db
+# But keep the db module directory
+!praisonaiagents/db/
 
 # OS generated files
 .DS_Store

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
@@ -43,8 +43,7 @@ from .tools.base import BaseTool, ToolResult, ToolValidationError, validate_tool
 from .tools.decorator import tool, FunctionTool
 from .tools.registry import get_registry, register_tool, get_tool, ToolRegistry
 from .agents.autoagents import AutoAgents
-from .knowledge.knowledge import Knowledge
-from .knowledge.chunking import Chunking
+# Knowledge is lazy-loaded to avoid importing heavy deps (chromadb, mem0) at startup
 # MCP support (optional)
 try:
     from .mcp.mcp import MCP
@@ -53,7 +52,9 @@ except ImportError:
     _mcp_available = False
     MCP = None
 from .session import Session
-from .memory.memory import Memory
+# Memory is lazy-loaded to avoid importing chromadb at startup
+from .db import db
+from .obs import obs
 # Workflows - import from dedicated workflows module
 from .workflows import (
     Workflow, WorkflowStep, WorkflowContext, StepResult,
@@ -65,8 +66,16 @@ from .guardrails import GuardrailResult, LLMGuardrail
 
 # Fast Context support (lazy loaded to avoid performance impact)
 def __getattr__(name):
-    """Lazy load FastContext to avoid impacting package load time."""
-    if name == "FastContext":
+    """Lazy load heavy modules to avoid impacting package load time."""
+    # Knowledge module (imports chromadb, mem0)
+    if name == "Knowledge":
+        from praisonaiagents.knowledge.knowledge import Knowledge
+        return Knowledge
+    elif name == "Chunking":
+        from praisonaiagents.knowledge.chunking import Chunking
+        return Chunking
+    # FastContext support
+    elif name == "FastContext":
         from praisonaiagents.context.fast import FastContext
         return FastContext
     elif name == "FastContextResult":
@@ -78,6 +87,23 @@ 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
+    # Memory module (imports chromadb)
+    elif name == "Memory":
+        from praisonaiagents.memory.memory import Memory
+        return Memory
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 # Planning mode support
@@ -244,6 +270,8 @@ __all__ = [
     'AutoAgents',
     'Session',
     'Memory',
+    'db',
+    'obs',
     'display_interaction',
     'display_self_reflection',
     'display_instruction',
@@ -289,6 +317,23 @@ __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 +350,14 @@ __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/_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 +250,14 @@ 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,
+        hooks: Optional[List[Any]] = None,
+        llm_config: Optional[Dict[str, Any]] = None,
+        rate_limiter: Optional[Any] = None
     ):
         """Initialize an Agent instance.
 
@@ -391,6 +398,17 @@ class Agent:
         # Flag to track if final result has been displayed to prevent duplicates
         self._final_display_shown = False
         
+        # Store hooks for middleware system (zero overhead when empty)
+        self._hooks = hooks or []
+        self._middleware_manager = None  # Lazy init
+        
+        # Store llm_config for configurable model switching
+        self._llm_config = llm_config or {}
+        self._llm_configurable = self._llm_config.get('configurable', False)
+        
+        # Store rate limiter (optional, zero overhead when None)
+        self._rate_limiter = rate_limiter
+        
         # Store OpenAI client parameters for lazy initialization
         self._openai_api_key = api_key
         self._openai_base_url = base_url
@@ -607,6 +625,17 @@ 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 +655,72 @@ 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 +1379,13 @@ 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
@@ -1534,8 +1636,32 @@ Your Goal: {self.goal}"""
     def execute_tool(self, function_name, arguments):
         """
         Execute a tool dynamically based on the function name and arguments.
+        Injects agent state for tools with Injected[T] parameters.
         """
         logging.debug(f"{self.name} executing tool {function_name} with arguments: {arguments}")
+        
+        # Set up injection context for tools with Injected parameters
+        from ..tools.injected import AgentState, with_injection_context
+        state = AgentState(
+            agent_id=self.name,
+            run_id=getattr(self, '_current_run_id', 'unknown'),
+            session_id=getattr(self, '_session_id', None) or 'default',
+            last_user_message=self.chat_history[-1].get('content') if self.chat_history else None,
+            metadata={'agent_name': self.name}
+        )
+        
+        # Execute within injection context
+        return self._execute_tool_with_context(function_name, arguments, state)
+    
+    def _execute_tool_with_context(self, function_name, arguments, state):
+        """Execute tool within injection context."""
+        from ..tools.injected import with_injection_context
+        
+        with with_injection_context(state):
+            return self._execute_tool_impl(function_name, arguments)
+    
+    def _execute_tool_impl(self, function_name, arguments):
+        """Internal tool execution implementation."""
 
         # Check if approval is required for this tool
         from ..approval import is_approval_required, console_approval_callback, get_risk_level, mark_approved, ApprovalDecision, get_approval_callback
@@ -1876,7 +2002,112 @@ Your Goal: {self.goal}"""
         #         expand=False
         #     )
 
-    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):
+    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, config=None):
+        # Apply rate limiter if configured (before any LLM call)
+        if self._rate_limiter is not None:
+            self._rate_limiter.acquire()
+        
+        # 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 +2204,8 @@ 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 +2235,8 @@ 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 +2281,8 @@ 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 +2322,8 @@ 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 +2339,8 @@ 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 +2429,13 @@ 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/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/db/__init__.py

@@ -0,0 +1,85 @@
+"""
+Database adapter interface for PraisonAI Agents.
+
+This module provides the protocol and types for database persistence.
+Implementations are provided by the wrapper layer (praisonai.db).
+
+Usage (simplest - recommended):
+    from praisonaiagents import Agent, db
+    
+    agent = Agent(
+        name="Assistant",
+        db=db(database_url="postgresql://localhost/mydb"),  # db(...) shortcut
+        session_id="my-session"  # optional: defaults to per-hour ID
+    )
+    agent.chat("Hello!")  # auto-persists messages, runs, traces, tool calls
+
+Alternative (explicit backend):
+    db=db.PraisonDB(database_url="...")  # Auto-detect backend
+    db=db.PostgresDB(host="localhost")   # PostgreSQL
+    db=db.SQLiteDB(path="data.db")       # SQLite
+    db=db.RedisDB(host="localhost")      # Redis (state only)
+"""
+
+from .protocol import (
+    DbAdapter,
+    AsyncDbAdapter,
+    DbMessage,
+    DbToolCall,
+    DbRun,
+    DbSpan,
+    DbTrace,
+)
+
+
+class _LazyDbModule:
+    """
+    Lazy proxy for database backends.
+    
+    Allows `from praisonaiagents import db` without importing heavy deps.
+    Actual backend classes are loaded only when accessed.
+    """
+    
+    _BACKENDS = {
+        "PraisonDB": "praisonai.db.adapter",
+        "PostgresDB": "praisonai.db.adapter",
+        "SQLiteDB": "praisonai.db.adapter",
+        "RedisDB": "praisonai.db.adapter",
+    }
+    
+    def __getattr__(self, name: str):
+        if name in self._BACKENDS:
+            try:
+                import importlib
+                module = importlib.import_module(self._BACKENDS[name])
+                return getattr(module, name)
+            except ImportError as e:
+                raise ImportError(
+                    f"Database backend '{name}' requires the praisonai package. "
+                    f"Install with: pip install praisonai\n"
+                    f"Original error: {e}"
+                ) from e
+        raise AttributeError(f"module 'db' has no attribute {name!r}")
+    
+    def __repr__(self):
+        return "<module 'praisonaiagents.db' (lazy database backends)>"
+    
+    def __call__(self, **kwargs):
+        """Shortcut: db(...) is equivalent to db.PraisonDB(...)"""
+        return self.PraisonDB(**kwargs)
+
+
+# Singleton lazy module instance
+db = _LazyDbModule()
+
+
+__all__ = [
+    "DbAdapter",
+    "AsyncDbAdapter",
+    "DbMessage",
+    "DbToolCall",
+    "DbRun",
+    "DbSpan",
+    "DbTrace",
+    "db",
+]

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

@@ -0,0 +1,464 @@
+"""
+Database adapter protocol for PraisonAI Agents.
+
+This module uses only stdlib (typing, dataclasses) to define the interface.
+No external dependencies are imported here.
+
+Implementations are provided by the wrapper layer (praisonai.db).
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
+import time
+
+
+@dataclass
+class DbToolCall:
+    """A tool call to be persisted."""
+    tool_name: str
+    args: Dict[str, Any]
+    result: Any = None
+    call_id: Optional[str] = None
+    timestamp: float = field(default_factory=time.time)
+    duration_ms: Optional[float] = None
+    error: Optional[str] = None
+
+
+@dataclass
+class DbMessage:
+    """A message to be persisted."""
+    role: str  # "user", "assistant", "system", "tool"
+    content: str
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    timestamp: float = field(default_factory=time.time)
+    id: Optional[str] = None
+    tool_calls: Optional[List["DbToolCall"]] = None  # For assistant messages with tool calls
+    run_id: Optional[str] = None  # Group messages by run
+
+
+@dataclass
+class DbRun:
+    """A single agent run (turn) to be persisted."""
+    run_id: str
+    session_id: str
+    agent_name: str
+    started_at: float = field(default_factory=time.time)
+    ended_at: Optional[float] = None
+    status: str = "running"  # running, completed, error, cancelled
+    input_content: Optional[str] = None
+    output_content: Optional[str] = None
+    messages: List[DbMessage] = field(default_factory=list)
+    tool_calls: List[DbToolCall] = field(default_factory=list)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    metrics: Dict[str, Any] = field(default_factory=dict)  # tokens, latency, etc.
+
+
+@dataclass
+class DbSpan:
+    """A span for tracing/observability."""
+    span_id: str
+    trace_id: str
+    name: str
+    started_at: float = field(default_factory=time.time)
+    ended_at: Optional[float] = None
+    parent_span_id: Optional[str] = None
+    status: str = "running"  # running, ok, error
+    attributes: Dict[str, Any] = field(default_factory=dict)
+    events: List[Dict[str, Any]] = field(default_factory=list)
+
+
+@dataclass
+class DbTrace:
+    """A trace for observability (groups spans)."""
+    trace_id: str
+    session_id: Optional[str] = None
+    run_id: Optional[str] = None
+    agent_name: Optional[str] = None
+    user_id: Optional[str] = None
+    started_at: float = field(default_factory=time.time)
+    ended_at: Optional[float] = None
+    status: str = "running"  # running, ok, error
+    spans: List[DbSpan] = field(default_factory=list)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@runtime_checkable
+class DbAdapter(Protocol):
+    """
+    Protocol for database adapters that provide persistence for agents.
+    
+    Implementations must provide these methods. The agent will call them
+    at appropriate lifecycle points if a db adapter is provided.
+    
+    All methods are synchronous. Async support can be added via wrapper.
+    """
+    
+    def on_agent_start(
+        self,
+        agent_name: str,
+        session_id: str,
+        user_id: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> List[DbMessage]:
+        """
+        Called when agent starts a session.
+        
+        Args:
+            agent_name: Name of the agent
+            session_id: Session identifier
+            user_id: Optional user identifier
+            metadata: Optional session metadata
+            
+        Returns:
+            List of previous messages for this session (for resume)
+        """
+        ...
+    
+    def on_user_message(
+        self,
+        session_id: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when user sends a message.
+        
+        Args:
+            session_id: Session identifier
+            content: Message content
+            metadata: Optional message metadata
+        """
+        ...
+    
+    def on_agent_message(
+        self,
+        session_id: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when agent produces a response.
+        
+        Args:
+            session_id: Session identifier
+            content: Response content
+            metadata: Optional message metadata
+        """
+        ...
+    
+    def on_tool_call(
+        self,
+        session_id: str,
+        tool_name: str,
+        args: Dict[str, Any],
+        result: Any,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a tool is executed.
+        
+        Args:
+            session_id: Session identifier
+            tool_name: Name of the tool
+            args: Tool arguments
+            result: Tool result
+            metadata: Optional call metadata
+        """
+        ...
+    
+    def on_agent_end(
+        self,
+        session_id: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when agent session ends.
+        
+        Args:
+            session_id: Session identifier
+            metadata: Optional end metadata
+        """
+        ...
+    
+    def on_run_start(
+        self,
+        session_id: str,
+        run_id: str,
+        input_content: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a new run (turn) starts.
+        
+        Args:
+            session_id: Session identifier
+            run_id: Run identifier
+            input_content: User input for this run
+            metadata: Optional run metadata
+        """
+        ...
+    
+    def on_run_end(
+        self,
+        session_id: str,
+        run_id: str,
+        output_content: Optional[str] = None,
+        status: str = "completed",
+        metrics: Optional[Dict[str, Any]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a run (turn) ends.
+        
+        Args:
+            session_id: Session identifier
+            run_id: Run identifier
+            output_content: Agent output for this run
+            status: Run status (completed, error, cancelled)
+            metrics: Optional metrics (tokens, latency)
+            metadata: Optional run metadata
+        """
+        ...
+    
+    def get_runs(
+        self,
+        session_id: str,
+        limit: Optional[int] = None,
+    ) -> List["DbRun"]:
+        """
+        Get runs for a session.
+        
+        Args:
+            session_id: Session identifier
+            limit: Maximum number of runs to return
+            
+        Returns:
+            List of runs for this session
+        """
+        ...
+    
+    def export_session(
+        self,
+        session_id: str,
+    ) -> Dict[str, Any]:
+        """
+        Export a session as a dictionary (for JSONL export).
+        
+        Args:
+            session_id: Session identifier
+            
+        Returns:
+            Dictionary with session data, messages, runs
+        """
+        ...
+    
+    def import_session(
+        self,
+        data: Dict[str, Any],
+    ) -> str:
+        """
+        Import a session from a dictionary.
+        
+        Args:
+            data: Session data dictionary
+            
+        Returns:
+            Session ID of imported session
+        """
+        ...
+    
+    # --- Tracing/Observability ---
+    
+    def on_trace_start(
+        self,
+        trace_id: str,
+        session_id: Optional[str] = None,
+        run_id: Optional[str] = None,
+        agent_name: Optional[str] = None,
+        user_id: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a new trace starts.
+        
+        Args:
+            trace_id: Unique trace identifier
+            session_id: Optional session identifier
+            run_id: Optional run identifier
+            agent_name: Optional agent name
+            user_id: Optional user identifier
+            metadata: Optional trace metadata
+        """
+        ...
+    
+    def on_trace_end(
+        self,
+        trace_id: str,
+        status: str = "ok",
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a trace ends.
+        
+        Args:
+            trace_id: Trace identifier
+            status: Trace status (ok, error)
+            metadata: Optional end metadata
+        """
+        ...
+    
+    def on_span_start(
+        self,
+        span_id: str,
+        trace_id: str,
+        name: str,
+        parent_span_id: Optional[str] = None,
+        attributes: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a new span starts.
+        
+        Args:
+            span_id: Unique span identifier
+            trace_id: Parent trace identifier
+            name: Span name (e.g., "llm_call", "tool_execution")
+            parent_span_id: Optional parent span for nesting
+            attributes: Optional span attributes
+        """
+        ...
+    
+    def on_span_end(
+        self,
+        span_id: str,
+        status: str = "ok",
+        attributes: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Called when a span ends.
+        
+        Args:
+            span_id: Span identifier
+            status: Span status (ok, error)
+            attributes: Optional end attributes
+        """
+        ...
+    
+    def get_traces(
+        self,
+        session_id: Optional[str] = None,
+        user_id: Optional[str] = None,
+        limit: Optional[int] = None,
+    ) -> List["DbTrace"]:
+        """
+        Get traces with optional filters.
+        
+        Args:
+            session_id: Filter by session
+            user_id: Filter by user
+            limit: Maximum number of traces
+            
+        Returns:
+            List of traces
+        """
+        ...
+    
+    def close(self) -> None:
+        """Close the database connection."""
+        ...
+
+
+@runtime_checkable
+class AsyncDbAdapter(Protocol):
+    """
+    Async protocol for database adapters.
+    
+    Provides async variants of all DbAdapter methods for use with
+    async/await patterns and async backends like asyncpg.
+    """
+    
+    async def on_agent_start(
+        self,
+        agent_name: str,
+        session_id: str,
+        user_id: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> List["DbMessage"]:
+        """Async version of on_agent_start."""
+        ...
+    
+    async def on_user_message(
+        self,
+        session_id: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_user_message."""
+        ...
+    
+    async def on_agent_message(
+        self,
+        session_id: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_agent_message."""
+        ...
+    
+    async def on_tool_call(
+        self,
+        session_id: str,
+        tool_name: str,
+        args: Dict[str, Any],
+        result: Any,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_tool_call."""
+        ...
+    
+    async def on_agent_end(
+        self,
+        session_id: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_agent_end."""
+        ...
+    
+    async def on_run_start(
+        self,
+        session_id: str,
+        run_id: str,
+        input_content: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_run_start."""
+        ...
+    
+    async def on_run_end(
+        self,
+        session_id: str,
+        run_id: str,
+        output_content: Optional[str] = None,
+        status: str = "completed",
+        metrics: Optional[Dict[str, Any]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Async version of on_run_end."""
+        ...
+    
+    async def export_session(
+        self,
+        session_id: str,
+    ) -> Dict[str, Any]:
+        """Async version of export_session."""
+        ...
+    
+    async def import_session(
+        self,
+        data: Dict[str, Any],
+    ) -> str:
+        """Async version of import_session."""
+        ...
+    
+    async def close(self) -> None:
+        """Async close the database connection."""
+        ...

src/praisonai-agents/praisonaiagents/db/tests/__init__.py

@@ -0,0 +1 @@
+"""TDD tests for db integration."""

src/praisonai-agents/praisonaiagents/db/tests/test_db_integration.py

@@ -0,0 +1,246 @@
+"""
+TDD tests for PraisonAI Agents DB integration.
+
+Tests:
+1. Lazy import verification - no heavy deps on import
+2. Agent(db=MockDb) calls hook lifecycle in correct order
+3. Session resume injects history
+4. db=None behavior unchanged
+"""
+
+import sys
+import time
+
+
+def test_lazy_import_no_heavy_deps():
+    """Test that importing praisonaiagents.db does not import DB libs."""
+    print("=== Test: Lazy Import Verification ===")
+    
+    # Record modules before import
+    before_modules = set(sys.modules.keys())
+    
+    # Import only the db module (not full praisonaiagents which loads Knowledge)
+    from praisonaiagents.db import DbAdapter, DbMessage
+    
+    # Check that heavy DB libs are NOT loaded by db module itself
+    # Note: praisonaiagents main package loads qdrant/chroma via Knowledge
+    # but the db module should not add any additional heavy deps
+    heavy_libs = ['psycopg2', 'pymysql', 'redis']  # DB-specific libs
+    loaded_heavy = [lib for lib in heavy_libs if lib in sys.modules]
+    
+    if loaded_heavy:
+        print(f"  ❌ Heavy DB libs loaded by db module: {loaded_heavy}")
+        return False
+    
+    print("  ✅ No heavy DB libs loaded by praisonaiagents.db module")
+    return True
+
+
+def test_agent_accepts_db_parameter():
+    """Test that Agent accepts db and session_id parameters."""
+    print("\n=== Test: Agent Accepts db Parameter ===")
+    
+    import inspect
+    from praisonaiagents import Agent
+    
+    sig = inspect.signature(Agent.__init__)
+    params = list(sig.parameters.keys())
+    
+    if 'db' not in params:
+        print("  ❌ Agent does not accept 'db' parameter")
+        return False
+    
+    if 'session_id' not in params:
+        print("  ❌ Agent does not accept 'session_id' parameter")
+        return False
+    
+    print("  ✅ Agent accepts db and session_id parameters")
+    return True
+
+
+class MockDbAdapter:
+    """Mock DB adapter for testing hook lifecycle."""
+    
+    def __init__(self):
+        self.calls = []
+        self.history_to_return = []
+    
+    def on_agent_start(self, agent_name, session_id, user_id=None, metadata=None):
+        self.calls.append(('on_agent_start', agent_name, session_id))
+        return self.history_to_return
+    
+    def on_user_message(self, session_id, content, metadata=None):
+        self.calls.append(('on_user_message', session_id, content))
+    
+    def on_agent_message(self, session_id, content, metadata=None):
+        self.calls.append(('on_agent_message', session_id, content))
+    
+    def on_tool_call(self, session_id, tool_name, args, result, metadata=None):
+        self.calls.append(('on_tool_call', session_id, tool_name))
+    
+    def on_agent_end(self, session_id, metadata=None):
+        self.calls.append(('on_agent_end', session_id))
+    
+    def close(self):
+        self.calls.append(('close',))
+
+
+def test_db_hook_lifecycle():
+    """Test that Agent calls db hooks in correct order."""
+    print("\n=== Test: DB Hook Lifecycle ===")
+    
+    from praisonaiagents import Agent
+    
+    mock_db = MockDbAdapter()
+    
+    # Create agent with mock db
+    agent = Agent(
+        name="TestAgent",
+        instructions="Test agent",
+        db=mock_db,
+        session_id="test-session-001",
+        verbose=False
+    )
+    
+    # Verify db is stored
+    if agent._db is not mock_db:
+        print("  ❌ Agent did not store db adapter")
+        return False
+    
+    if agent._session_id != "test-session-001":
+        print("  ❌ Agent did not store session_id")
+        return False
+    
+    print("  ✅ Agent stores db and session_id correctly")
+    return True
+
+
+def test_db_none_unchanged_behavior():
+    """Test that db=None behavior is unchanged."""
+    print("\n=== Test: db=None Unchanged Behavior ===")
+    
+    from praisonaiagents import Agent
+    
+    # Create agent without db
+    agent = Agent(
+        name="TestAgent",
+        instructions="Test agent",
+        verbose=False
+    )
+    
+    # Verify db is None
+    if agent._db is not None:
+        print("  ❌ Agent._db should be None when not provided")
+        return False
+    
+    # Verify _init_db_session does nothing when db is None
+    agent._init_db_session()
+    
+    if agent._db_initialized:
+        print("  ❌ _db_initialized should remain False when db is None")
+        return False
+    
+    print("  ✅ db=None behavior unchanged")
+    return True
+
+
+def test_session_id_auto_generation():
+    """Test that session_id is auto-generated if not provided."""
+    print("\n=== Test: Session ID Auto-Generation ===")
+    
+    from praisonaiagents import Agent
+    
+    mock_db = MockDbAdapter()
+    
+    agent = Agent(
+        name="TestAgent",
+        instructions="Test agent",
+        db=mock_db,
+        # No session_id provided
+        verbose=False
+    )
+    
+    # Trigger db initialization
+    agent._init_db_session()
+    
+    if agent._session_id is None:
+        print("  ❌ session_id should be auto-generated")
+        return False
+    
+    if not agent._session_id.startswith("session-"):
+        print(f"  ❌ session_id format unexpected: {agent._session_id}")
+        return False
+    
+    print(f"  ✅ session_id auto-generated: {agent._session_id}")
+    return True
+
+
+def test_protocol_compliance():
+    """Test that DbAdapter protocol is properly defined."""
+    print("\n=== Test: DbAdapter Protocol Compliance ===")
+    
+    from praisonaiagents.db.protocol import DbAdapter, DbMessage
+    
+    # Check DbMessage fields
+    msg = DbMessage(role="user", content="test")
+    if msg.role != "user" or msg.content != "test":
+        print("  ❌ DbMessage fields not working")
+        return False
+    
+    # Check protocol methods
+    required_methods = [
+        'on_agent_start',
+        'on_user_message', 
+        'on_agent_message',
+        'on_tool_call',
+        'on_agent_end',
+        'close'
+    ]
+    
+    mock = MockDbAdapter()
+    for method in required_methods:
+        if not hasattr(mock, method):
+            print(f"  ❌ MockDbAdapter missing {method}")
+            return False
+    
+    # Check protocol isinstance
+    if not isinstance(mock, DbAdapter):
+        print("  ❌ MockDbAdapter should implement DbAdapter protocol")
+        return False
+    
+    print("  ✅ DbAdapter protocol properly defined")
+    return True
+
+
+def run_all_tests():
+    """Run all TDD tests."""
+    print("=" * 60)
+    print("PraisonAI Agents DB Integration - TDD Tests")
+    print("=" * 60)
+    
+    results = []
+    
+    results.append(("Lazy Import", test_lazy_import_no_heavy_deps()))
+    results.append(("Agent db Parameter", test_agent_accepts_db_parameter()))
+    results.append(("DB Hook Lifecycle", test_db_hook_lifecycle()))
+    results.append(("db=None Unchanged", test_db_none_unchanged_behavior()))
+    results.append(("Session ID Auto-Gen", test_session_id_auto_generation()))
+    results.append(("Protocol Compliance", test_protocol_compliance()))
+    
+    print("\n" + "=" * 60)
+    passed = sum(1 for _, r in results if r)
+    total = len(results)
+    print(f"Results: {passed}/{total} tests passed")
+    
+    for name, result in results:
+        status = "✅" if result else "❌"
+        print(f"  {status} {name}")
+    
+    print("=" * 60)
+    
+    return passed == total
+
+
+if __name__ == "__main__":
+    success = run_all_tests()
+    sys.exit(0 if success else 1)

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 +1,137 @@
+"""
+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",
+    # Middleware types
+    "InvocationContext",
+    "ModelRequest",
+    "ModelResponse",
+    "ToolRequest",
+    "ToolResponse",
+    # Middleware decorators
+    "before_model",
+    "after_model",
+    "wrap_model_call",
+    "before_tool",
+    "after_tool",
+    "wrap_tool_call",
+    # Middleware utilities
+    "MiddlewareChain",
+    "AsyncMiddlewareChain",
+    "MiddlewareManager",
+]
+
+
+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]
+    
+    # Middleware types
+    if name in ("InvocationContext", "ModelRequest", "ModelResponse", 
+                "ToolRequest", "ToolResponse"):
+        from .middleware import (
+            InvocationContext, ModelRequest, ModelResponse,
+            ToolRequest, ToolResponse
+        )
+        return locals()[name]
+    
+    # Middleware decorators
+    if name in ("before_model", "after_model", "wrap_model_call",
+                "before_tool", "after_tool", "wrap_tool_call"):
+        from .middleware import (
+            before_model, after_model, wrap_model_call,
+            before_tool, after_tool, wrap_tool_call
+        )
+        return locals()[name]
+    
+    # Middleware utilities
+    if name in ("MiddlewareChain", "AsyncMiddlewareChain", "MiddlewareManager"):
+        from .middleware import MiddlewareChain, AsyncMiddlewareChain, MiddlewareManager
+        return locals()[name]
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")