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

@@ -204,6 +204,12 @@ npm install praisonai
 | ↳ 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) |
@@ -261,6 +267,16 @@ npm install praisonai
 | ↳ 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) |
@@ -278,6 +294,12 @@ npm install praisonai
 | ↳ 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) |
@@ -857,21 +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:
 
@@ -905,7 +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)
@@ -981,6 +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
@@ -1730,124 +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 |
@@ -2388,6 +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
@@ -2475,6 +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>=2.3.79" \
+    "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>=2.3.79" \
+    "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>=2.3.79" \
+    "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/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/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/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/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

@@ -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":
@@ -91,6 +100,10 @@ def __getattr__(name):
     elif name == "SkillLoader":
         from praisonaiagents.skills import SkillLoader
         return SkillLoader
+    # Memory module (imports chromadb)
+    elif name == "Memory":
+        from praisonaiagents.memory.memory import Memory
+        return Memory
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 # Planning mode support
@@ -257,6 +270,8 @@ __all__ = [
     'AutoAgents',
     'Session',
     'Memory',
+    'db',
+    'obs',
     'display_interaction',
     'display_self_reflection',
     'display_instruction',

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

@@ -252,7 +252,12 @@ class Agent:
         history_in_context: Optional[int] = None,
         auto_save: Optional[str] = None,
         skills: Optional[List[str]] = None,
-        skills_dirs: 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.
 
@@ -393,6 +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
@@ -615,6 +631,11 @@ Your Goal: {self.goal}
         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."""
@@ -651,8 +672,45 @@ Your Goal: {self.goal}
                 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.
         
@@ -1321,6 +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
@@ -1571,8 +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
@@ -1913,7 +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
         
@@ -2010,6 +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
@@ -2039,6 +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:
@@ -2083,6 +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()
@@ -2122,6 +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)
@@ -2137,6 +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
@@ -2225,11 +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/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}")

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

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

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

@@ -0,0 +1,439 @@
+"""
+Middleware System for PraisonAI Agents.
+
+Provides wrap_model_call and wrap_tool_call patterns for intercepting
+and modifying agent behavior at model/tool call boundaries.
+
+Zero Performance Impact:
+- All middleware is optional
+- Empty middleware chain is O(1) fast path
+- No overhead when middleware not registered
+
+Usage:
+    from praisonaiagents.hooks import before_model, wrap_model_call, wrap_tool_call
+    
+    @before_model
+    def add_context(request):
+        request.messages.append({"role": "system", "content": "Extra"})
+        return request
+    
+    @wrap_model_call
+    def retry_on_error(request, call_next):
+        for _ in range(3):
+            try:
+                return call_next(request)
+            except Exception:
+                pass
+        raise RuntimeError("All retries failed")
+    
+    agent = Agent(name="Test", hooks=[add_context, retry_on_error])
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, TypeVar, Generic
+from functools import wraps
+
+
+@dataclass
+class InvocationContext:
+    """Context passed through middleware chain.
+    
+    Contains identifiers for multi-agent safety and optional metadata.
+    """
+    agent_id: str
+    run_id: str
+    session_id: str
+    tool_name: Optional[str] = None
+    model_name: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ModelRequest:
+    """Request data for model calls."""
+    messages: List[Dict[str, Any]]
+    model: str
+    temperature: float = 1.0
+    context: Optional[InvocationContext] = None
+    tools: Optional[List[Any]] = None
+    extra: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ModelResponse:
+    """Response data from model calls."""
+    content: str
+    model: str
+    usage: Optional[Dict[str, int]] = None
+    context: Optional[InvocationContext] = None
+    tool_calls: Optional[List[Any]] = None
+    extra: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ToolRequest:
+    """Request data for tool calls."""
+    tool_name: str
+    arguments: Dict[str, Any]
+    context: Optional[InvocationContext] = None
+    extra: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ToolResponse:
+    """Response data from tool calls."""
+    tool_name: str
+    result: Any
+    error: Optional[str] = None
+    context: Optional[InvocationContext] = None
+    extra: Dict[str, Any] = field(default_factory=dict)
+
+
+# Type aliases for middleware functions
+T = TypeVar('T')
+BeforeModelFn = Callable[[ModelRequest], ModelRequest]
+AfterModelFn = Callable[[ModelResponse], ModelResponse]
+WrapModelCallFn = Callable[[ModelRequest, Callable[[ModelRequest], ModelResponse]], ModelResponse]
+BeforeToolFn = Callable[[ToolRequest], ToolRequest]
+AfterToolFn = Callable[[ToolResponse], ToolResponse]
+WrapToolCallFn = Callable[[ToolRequest, Callable[[ToolRequest], ToolResponse]], ToolResponse]
+
+
+class MiddlewareChain:
+    """Synchronous middleware chain executor.
+    
+    Composes middleware functions in registration order.
+    First registered middleware wraps all subsequent ones.
+    """
+    
+    def __init__(self, middlewares: Optional[List[Callable]] = None):
+        self._middlewares = middlewares or []
+    
+    def execute(self, request: Any, final_handler: Callable[[Any], Any]) -> Any:
+        """Execute the middleware chain with a final handler.
+        
+        Args:
+            request: The request object to pass through
+            final_handler: The actual operation to perform
+            
+        Returns:
+            The result from the chain
+        """
+        if not self._middlewares:
+            return final_handler(request)
+        
+        # Build the chain from inside out
+        # Last middleware wraps final_handler
+        # First middleware is outermost
+        def build_chain(index: int) -> Callable[[Any], Any]:
+            if index >= len(self._middlewares):
+                return final_handler
+            
+            middleware = self._middlewares[index]
+            next_handler = build_chain(index + 1)
+            
+            def handler(req: Any) -> Any:
+                return middleware(req, next_handler)
+            
+            return handler
+        
+        chain = build_chain(0)
+        return chain(request)
+    
+    def __len__(self) -> int:
+        return len(self._middlewares)
+
+
+class AsyncMiddlewareChain:
+    """Asynchronous middleware chain executor.
+    
+    Same as MiddlewareChain but for async middleware and handlers.
+    """
+    
+    def __init__(self, middlewares: Optional[List[Callable]] = None):
+        self._middlewares = middlewares or []
+    
+    async def execute(self, request: Any, final_handler: Callable) -> Any:
+        """Execute the async middleware chain.
+        
+        Args:
+            request: The request object to pass through
+            final_handler: The actual async operation to perform
+            
+        Returns:
+            The result from the chain
+        """
+        if not self._middlewares:
+            return await final_handler(request)
+        
+        # Build the chain from inside out
+        def build_chain(index: int) -> Callable:
+            if index >= len(self._middlewares):
+                return final_handler
+            
+            middleware = self._middlewares[index]
+            next_handler = build_chain(index + 1)
+            
+            async def handler(req: Any) -> Any:
+                return await middleware(req, next_handler)
+            
+            return handler
+        
+        chain = build_chain(0)
+        return await chain(request)
+    
+    def __len__(self) -> int:
+        return len(self._middlewares)
+
+
+# Decorator functions for tagging middleware
+
+def before_model(func: BeforeModelFn) -> BeforeModelFn:
+    """Decorator to mark a function as a before_model hook.
+    
+    The function receives a ModelRequest and should return a (possibly modified) ModelRequest.
+    
+    Example:
+        @before_model
+        def add_context(request):
+            request.messages.append({"role": "system", "content": "Extra"})
+            return request
+    """
+    func._hook_type = 'before_model'
+    return func
+
+
+def after_model(func: AfterModelFn) -> AfterModelFn:
+    """Decorator to mark a function as an after_model hook.
+    
+    The function receives a ModelResponse and should return a (possibly modified) ModelResponse.
+    
+    Example:
+        @after_model
+        def log_response(response):
+            print(f"Model returned: {response.content[:50]}")
+            return response
+    """
+    func._hook_type = 'after_model'
+    return func
+
+
+def wrap_model_call(func: WrapModelCallFn) -> WrapModelCallFn:
+    """Decorator to mark a function as a wrap_model_call middleware.
+    
+    The function receives (request, call_next) and should call call_next(request)
+    to continue the chain, or return early to short-circuit.
+    
+    Example:
+        @wrap_model_call
+        def retry_on_error(request, call_next):
+            for _ in range(3):
+                try:
+                    return call_next(request)
+                except Exception:
+                    pass
+            raise RuntimeError("All retries failed")
+    """
+    func._hook_type = 'wrap_model_call'
+    return func
+
+
+def before_tool(func: BeforeToolFn) -> BeforeToolFn:
+    """Decorator to mark a function as a before_tool hook.
+    
+    The function receives a ToolRequest and should return a (possibly modified) ToolRequest.
+    
+    Example:
+        @before_tool
+        def validate_args(request):
+            if 'dangerous' in request.arguments:
+                raise ValueError("Dangerous argument detected")
+            return request
+    """
+    func._hook_type = 'before_tool'
+    return func
+
+
+def after_tool(func: AfterToolFn) -> AfterToolFn:
+    """Decorator to mark a function as an after_tool hook.
+    
+    The function receives a ToolResponse and should return a (possibly modified) ToolResponse.
+    
+    Example:
+        @after_tool
+        def log_result(response):
+            print(f"Tool {response.tool_name} returned: {response.result}")
+            return response
+    """
+    func._hook_type = 'after_tool'
+    return func
+
+
+def wrap_tool_call(func: WrapToolCallFn) -> WrapToolCallFn:
+    """Decorator to mark a function as a wrap_tool_call middleware.
+    
+    The function receives (request, call_next) and should call call_next(request)
+    to continue the chain, or return early to short-circuit.
+    
+    Example:
+        @wrap_tool_call
+        def retry_flaky_tool(request, call_next):
+            last_error = None
+            for _ in range(3):
+                try:
+                    return call_next(request)
+                except Exception as e:
+                    last_error = e
+            raise last_error
+    """
+    func._hook_type = 'wrap_tool_call'
+    return func
+
+
+def get_hook_type(func: Callable) -> Optional[str]:
+    """Get the hook type of a decorated function."""
+    return getattr(func, '_hook_type', None)
+
+
+def is_middleware(func: Callable) -> bool:
+    """Check if a function is a middleware (wrap_* type)."""
+    hook_type = get_hook_type(func)
+    return hook_type in ('wrap_model_call', 'wrap_tool_call')
+
+
+def categorize_hooks(hooks: List[Callable]) -> Dict[str, List[Callable]]:
+    """Categorize hooks by their type.
+    
+    Returns:
+        Dict with keys: before_model, after_model, wrap_model_call,
+                       before_tool, after_tool, wrap_tool_call
+    """
+    categories = {
+        'before_model': [],
+        'after_model': [],
+        'wrap_model_call': [],
+        'before_tool': [],
+        'after_tool': [],
+        'wrap_tool_call': [],
+    }
+    
+    for hook in hooks or []:
+        hook_type = get_hook_type(hook)
+        if hook_type and hook_type in categories:
+            categories[hook_type].append(hook)
+    
+    return categories
+
+
+class MiddlewareManager:
+    """Manages middleware for an agent.
+    
+    Provides methods to execute before/after hooks and middleware chains
+    for both model and tool calls.
+    """
+    
+    def __init__(self, hooks: Optional[List[Callable]] = None):
+        self._hooks = hooks or []
+        self._categorized = categorize_hooks(self._hooks)
+        
+        # Pre-build chains for efficiency
+        self._model_chain = MiddlewareChain(self._categorized['wrap_model_call'])
+        self._tool_chain = MiddlewareChain(self._categorized['wrap_tool_call'])
+    
+    @property
+    def has_model_hooks(self) -> bool:
+        """Check if any model hooks are registered."""
+        return bool(
+            self._categorized['before_model'] or
+            self._categorized['after_model'] or
+            self._categorized['wrap_model_call']
+        )
+    
+    @property
+    def has_tool_hooks(self) -> bool:
+        """Check if any tool hooks are registered."""
+        return bool(
+            self._categorized['before_tool'] or
+            self._categorized['after_tool'] or
+            self._categorized['wrap_tool_call']
+        )
+    
+    def run_before_model(self, request: ModelRequest) -> ModelRequest:
+        """Run all before_model hooks."""
+        for hook in self._categorized['before_model']:
+            request = hook(request)
+        return request
+    
+    def run_after_model(self, response: ModelResponse) -> ModelResponse:
+        """Run all after_model hooks (in reverse order)."""
+        for hook in reversed(self._categorized['after_model']):
+            response = hook(response)
+        return response
+    
+    def run_before_tool(self, request: ToolRequest) -> ToolRequest:
+        """Run all before_tool hooks."""
+        for hook in self._categorized['before_tool']:
+            request = hook(request)
+        return request
+    
+    def run_after_tool(self, response: ToolResponse) -> ToolResponse:
+        """Run all after_tool hooks (in reverse order)."""
+        for hook in reversed(self._categorized['after_tool']):
+            response = hook(response)
+        return response
+    
+    def execute_model_call(
+        self,
+        request: ModelRequest,
+        model_fn: Callable[[ModelRequest], ModelResponse]
+    ) -> ModelResponse:
+        """Execute a model call with all hooks and middleware.
+        
+        Order:
+        1. before_model hooks (in order)
+        2. wrap_model_call middleware chain
+        3. actual model call
+        4. after_model hooks (in reverse order)
+        """
+        # Fast path: no hooks
+        if not self.has_model_hooks:
+            return model_fn(request)
+        
+        # Run before hooks
+        request = self.run_before_model(request)
+        
+        # Build wrapped handler that includes after hooks
+        def wrapped_model_fn(req: ModelRequest) -> ModelResponse:
+            response = model_fn(req)
+            return self.run_after_model(response)
+        
+        # Execute through middleware chain
+        return self._model_chain.execute(request, wrapped_model_fn)
+    
+    def execute_tool_call(
+        self,
+        request: ToolRequest,
+        tool_fn: Callable[[ToolRequest], ToolResponse]
+    ) -> ToolResponse:
+        """Execute a tool call with all hooks and middleware.
+        
+        Order:
+        1. before_tool hooks (in order)
+        2. wrap_tool_call middleware chain
+        3. actual tool call
+        4. after_tool hooks (in reverse order)
+        """
+        # Fast path: no hooks
+        if not self.has_tool_hooks:
+            return tool_fn(request)
+        
+        # Run before hooks
+        request = self.run_before_tool(request)
+        
+        # Build wrapped handler that includes after hooks
+        def wrapped_tool_fn(req: ToolRequest) -> ToolResponse:
+            response = tool_fn(req)
+            return self.run_after_tool(response)
+        
+        # Execute through middleware chain
+        return self._tool_chain.execute(request, wrapped_tool_fn)

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

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

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

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

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

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

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

@@ -1,8 +1,130 @@
 """
-PraisonAI Knowledge - Advanced knowledge management system with configurable features
+PraisonAI Knowledge - Advanced knowledge management system with configurable features.
+
+This module provides:
+- Document readers (ReaderProtocol, ReaderRegistry)
+- Vector stores (VectorStoreProtocol, VectorStoreRegistry)
+- Retrieval strategies (RetrieverProtocol, RetrievalStrategy)
+- Rerankers (RerankerProtocol, RerankerRegistry)
+- Index types (IndexProtocol, IndexType)
+- Query engines (QueryEngineProtocol, QueryMode)
 """
 
+# Core Knowledge class (always available)
 from praisonaiagents.knowledge.knowledge import Knowledge
 from praisonaiagents.knowledge.chunking import Chunking
 
-__all__ = ["Knowledge", "Chunking"] 
+# Lazy loading for protocols and registries to avoid import overhead
+_LAZY_IMPORTS = {
+    # Readers
+    "Document": ("praisonaiagents.knowledge.readers", "Document"),
+    "ReaderProtocol": ("praisonaiagents.knowledge.readers", "ReaderProtocol"),
+    "ReaderRegistry": ("praisonaiagents.knowledge.readers", "ReaderRegistry"),
+    "get_reader_registry": ("praisonaiagents.knowledge.readers", "get_reader_registry"),
+    "detect_source_kind": ("praisonaiagents.knowledge.readers", "detect_source_kind"),
+    
+    # Vector stores
+    "VectorRecord": ("praisonaiagents.knowledge.vector_store", "VectorRecord"),
+    "VectorStoreProtocol": ("praisonaiagents.knowledge.vector_store", "VectorStoreProtocol"),
+    "VectorStoreRegistry": ("praisonaiagents.knowledge.vector_store", "VectorStoreRegistry"),
+    "get_vector_store_registry": ("praisonaiagents.knowledge.vector_store", "get_vector_store_registry"),
+    "InMemoryVectorStore": ("praisonaiagents.knowledge.vector_store", "InMemoryVectorStore"),
+    
+    # Retrieval
+    "RetrievalResult": ("praisonaiagents.knowledge.retrieval", "RetrievalResult"),
+    "RetrievalStrategy": ("praisonaiagents.knowledge.retrieval", "RetrievalStrategy"),
+    "RetrieverProtocol": ("praisonaiagents.knowledge.retrieval", "RetrieverProtocol"),
+    "RetrieverRegistry": ("praisonaiagents.knowledge.retrieval", "RetrieverRegistry"),
+    "get_retriever_registry": ("praisonaiagents.knowledge.retrieval", "get_retriever_registry"),
+    "reciprocal_rank_fusion": ("praisonaiagents.knowledge.retrieval", "reciprocal_rank_fusion"),
+    "merge_adjacent_chunks": ("praisonaiagents.knowledge.retrieval", "merge_adjacent_chunks"),
+    
+    # Rerankers
+    "RerankResult": ("praisonaiagents.knowledge.rerankers", "RerankResult"),
+    "RerankerProtocol": ("praisonaiagents.knowledge.rerankers", "RerankerProtocol"),
+    "RerankerRegistry": ("praisonaiagents.knowledge.rerankers", "RerankerRegistry"),
+    "get_reranker_registry": ("praisonaiagents.knowledge.rerankers", "get_reranker_registry"),
+    "SimpleReranker": ("praisonaiagents.knowledge.rerankers", "SimpleReranker"),
+    
+    # Index
+    "IndexType": ("praisonaiagents.knowledge.index", "IndexType"),
+    "IndexStats": ("praisonaiagents.knowledge.index", "IndexStats"),
+    "IndexProtocol": ("praisonaiagents.knowledge.index", "IndexProtocol"),
+    "IndexRegistry": ("praisonaiagents.knowledge.index", "IndexRegistry"),
+    "get_index_registry": ("praisonaiagents.knowledge.index", "get_index_registry"),
+    "KeywordIndex": ("praisonaiagents.knowledge.index", "KeywordIndex"),
+    
+    # Query engine
+    "QueryMode": ("praisonaiagents.knowledge.query_engine", "QueryMode"),
+    "QueryResult": ("praisonaiagents.knowledge.query_engine", "QueryResult"),
+    "QueryEngineProtocol": ("praisonaiagents.knowledge.query_engine", "QueryEngineProtocol"),
+    "QueryEngineRegistry": ("praisonaiagents.knowledge.query_engine", "QueryEngineRegistry"),
+    "get_query_engine_registry": ("praisonaiagents.knowledge.query_engine", "get_query_engine_registry"),
+    "decompose_question": ("praisonaiagents.knowledge.query_engine", "decompose_question"),
+    "SimpleQueryEngine": ("praisonaiagents.knowledge.query_engine", "SimpleQueryEngine"),
+    "SubQuestionEngine": ("praisonaiagents.knowledge.query_engine", "SubQuestionEngine"),
+}
+
+
+def __getattr__(name: str):
+    """Lazy load protocols and registries."""
+    if name in _LAZY_IMPORTS:
+        module_path, attr_name = _LAZY_IMPORTS[name]
+        import importlib
+        module = importlib.import_module(module_path)
+        return getattr(module, attr_name)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+def __dir__():
+    """List available attributes."""
+    return list(_LAZY_IMPORTS.keys()) + ["Knowledge", "Chunking"]
+
+
+__all__ = [
+    # Core
+    "Knowledge",
+    "Chunking",
+    # Readers
+    "Document",
+    "ReaderProtocol",
+    "ReaderRegistry",
+    "get_reader_registry",
+    "detect_source_kind",
+    # Vector stores
+    "VectorRecord",
+    "VectorStoreProtocol",
+    "VectorStoreRegistry",
+    "get_vector_store_registry",
+    "InMemoryVectorStore",
+    # Retrieval
+    "RetrievalResult",
+    "RetrievalStrategy",
+    "RetrieverProtocol",
+    "RetrieverRegistry",
+    "get_retriever_registry",
+    "reciprocal_rank_fusion",
+    "merge_adjacent_chunks",
+    # Rerankers
+    "RerankResult",
+    "RerankerProtocol",
+    "RerankerRegistry",
+    "get_reranker_registry",
+    "SimpleReranker",
+    # Index
+    "IndexType",
+    "IndexStats",
+    "IndexProtocol",
+    "IndexRegistry",
+    "get_index_registry",
+    "KeywordIndex",
+    # Query engine
+    "QueryMode",
+    "QueryResult",
+    "QueryEngineProtocol",
+    "QueryEngineRegistry",
+    "get_query_engine_registry",
+    "decompose_question",
+    "SimpleQueryEngine",
+    "SubQuestionEngine",
+] 

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

@@ -0,0 +1,332 @@
+"""
+Index Type Selection for PraisonAI Agents.
+
+This module defines index types and protocols.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by the wrapper layer.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+from enum import Enum
+import logging
+import math
+
+logger = logging.getLogger(__name__)
+
+
+class IndexType(str, Enum):
+    """Available index types."""
+    VECTOR = "vector"
+    KEYWORD = "keyword"
+    HYBRID = "hybrid"
+    GRAPH = "graph"
+
+
+@dataclass
+class IndexStats:
+    """Statistics about an index."""
+    index_type: IndexType
+    document_count: int
+    chunk_count: int
+    total_tokens: int = 0
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@runtime_checkable
+class IndexProtocol(Protocol):
+    """
+    Protocol for document indices.
+    
+    Implementations must provide methods for indexing and querying.
+    """
+    
+    name: str
+    index_type: IndexType
+    
+    def add_documents(
+        self,
+        texts: List[str],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        ids: Optional[List[str]] = None,
+        embeddings: Optional[List[List[float]]] = None
+    ) -> List[str]:
+        """
+        Add documents to the index.
+        
+        Args:
+            texts: List of text content
+            metadatas: Optional list of metadata dicts
+            ids: Optional list of IDs
+            embeddings: Optional pre-computed embeddings (for vector index)
+            
+        Returns:
+            List of document IDs
+        """
+        ...
+    
+    def query(
+        self,
+        query: str,
+        top_k: int = 10,
+        filter: Optional[Dict[str, Any]] = None,
+        query_embedding: Optional[List[float]] = None
+    ) -> List[Dict[str, Any]]:
+        """
+        Query the index.
+        
+        Args:
+            query: Search query
+            top_k: Number of results
+            filter: Optional metadata filter
+            query_embedding: Optional pre-computed query embedding
+            
+        Returns:
+            List of results with text, score, metadata
+        """
+        ...
+    
+    def delete(
+        self,
+        ids: Optional[List[str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
+        delete_all: bool = False
+    ) -> int:
+        """Delete documents from the index."""
+        ...
+    
+    def stats(self) -> IndexStats:
+        """Get index statistics."""
+        ...
+
+
+class IndexRegistry:
+    """Registry for index types."""
+    
+    _instance: Optional["IndexRegistry"] = None
+    
+    def __new__(cls) -> "IndexRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._indices: Dict[str, Callable[..., IndexProtocol]] = {}
+        return cls._instance
+    
+    def register(self, name: str, factory: Callable[..., IndexProtocol]) -> None:
+        """Register an index factory."""
+        self._indices[name] = factory
+    
+    def get(self, name: str, **kwargs) -> Optional[IndexProtocol]:
+        """Get an index by name."""
+        if name in self._indices:
+            try:
+                return self._indices[name](**kwargs)
+            except Exception as e:
+                logger.warning(f"Failed to initialize index '{name}': {e}")
+                return None
+        return None
+    
+    def list_indices(self) -> List[str]:
+        """List all registered index names."""
+        return list(self._indices.keys())
+    
+    def clear(self) -> None:
+        """Clear all registered indices."""
+        self._indices.clear()
+
+
+def get_index_registry() -> IndexRegistry:
+    """Get the global index registry instance."""
+    return IndexRegistry()
+
+
+class KeywordIndex:
+    """
+    Simple BM25-like keyword index.
+    
+    Pure Python implementation with no external dependencies.
+    """
+    
+    name: str = "keyword"
+    index_type: IndexType = IndexType.KEYWORD
+    
+    def __init__(self, k1: float = 1.5, b: float = 0.75, **kwargs):
+        self.k1 = k1
+        self.b = b
+        self._documents: Dict[str, Dict[str, Any]] = {}  # id -> {text, metadata, terms}
+        self._term_doc_freq: Dict[str, int] = {}  # term -> doc count
+        self._avg_doc_len: float = 0.0
+        self._total_docs: int = 0
+    
+    def add_documents(
+        self,
+        texts: List[str],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        ids: Optional[List[str]] = None,
+        embeddings: Optional[List[List[float]]] = None
+    ) -> List[str]:
+        """Add documents to the keyword index."""
+        import uuid
+        
+        metadatas = metadatas or [{} for _ in texts]
+        ids = ids or [str(uuid.uuid4()) for _ in texts]
+        
+        total_len = sum(len(doc["text"].split()) for doc in self._documents.values())
+        
+        for text, metadata, doc_id in zip(texts, metadatas, ids):
+            terms = self._tokenize(text)
+            term_freq = {}
+            for term in terms:
+                term_freq[term] = term_freq.get(term, 0) + 1
+            
+            self._documents[doc_id] = {
+                "text": text,
+                "metadata": metadata,
+                "terms": term_freq,
+                "length": len(terms)
+            }
+            
+            # Update document frequency
+            for term in set(terms):
+                self._term_doc_freq[term] = self._term_doc_freq.get(term, 0) + 1
+            
+            total_len += len(terms)
+        
+        self._total_docs = len(self._documents)
+        self._avg_doc_len = total_len / self._total_docs if self._total_docs > 0 else 0
+        
+        return ids
+    
+    def query(
+        self,
+        query: str,
+        top_k: int = 10,
+        filter: Optional[Dict[str, Any]] = None,
+        query_embedding: Optional[List[float]] = None
+    ) -> List[Dict[str, Any]]:
+        """Query using BM25 scoring."""
+        query_terms = self._tokenize(query)
+        
+        scores = []
+        for doc_id, doc in self._documents.items():
+            # Apply filter
+            if filter:
+                match = True
+                for key, value in filter.items():
+                    if doc["metadata"].get(key) != value:
+                        match = False
+                        break
+                if not match:
+                    continue
+            
+            score = self._bm25_score(query_terms, doc)
+            scores.append({
+                "id": doc_id,
+                "text": doc["text"],
+                "score": score,
+                "metadata": doc["metadata"]
+            })
+        
+        # Sort by score
+        scores.sort(key=lambda x: x["score"], reverse=True)
+        return scores[:top_k]
+    
+    def delete(
+        self,
+        ids: Optional[List[str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
+        delete_all: bool = False
+    ) -> int:
+        """Delete documents from the index."""
+        if delete_all:
+            count = len(self._documents)
+            self._documents.clear()
+            self._term_doc_freq.clear()
+            self._total_docs = 0
+            self._avg_doc_len = 0
+            return count
+        
+        deleted = 0
+        to_delete = []
+        
+        if ids:
+            to_delete = [id_ for id_ in ids if id_ in self._documents]
+        elif filter:
+            for doc_id, doc in self._documents.items():
+                match = True
+                for key, value in filter.items():
+                    if doc["metadata"].get(key) != value:
+                        match = False
+                        break
+                if match:
+                    to_delete.append(doc_id)
+        
+        for doc_id in to_delete:
+            doc = self._documents[doc_id]
+            # Update term frequencies
+            for term in doc["terms"]:
+                if term in self._term_doc_freq:
+                    self._term_doc_freq[term] -= 1
+                    if self._term_doc_freq[term] <= 0:
+                        del self._term_doc_freq[term]
+            del self._documents[doc_id]
+            deleted += 1
+        
+        self._total_docs = len(self._documents)
+        if self._total_docs > 0:
+            self._avg_doc_len = sum(d["length"] for d in self._documents.values()) / self._total_docs
+        else:
+            self._avg_doc_len = 0
+        
+        return deleted
+    
+    def stats(self) -> IndexStats:
+        """Get index statistics."""
+        return IndexStats(
+            index_type=self.index_type,
+            document_count=self._total_docs,
+            chunk_count=self._total_docs,
+            total_tokens=sum(d["length"] for d in self._documents.values()),
+            metadata={"unique_terms": len(self._term_doc_freq)}
+        )
+    
+    def _tokenize(self, text: str) -> List[str]:
+        """Simple tokenization."""
+        import re
+        # Lowercase and split on non-alphanumeric
+        tokens = re.findall(r'\b\w+\b', text.lower())
+        # Remove very short tokens
+        return [t for t in tokens if len(t) > 1]
+    
+    def _bm25_score(self, query_terms: List[str], doc: Dict[str, Any]) -> float:
+        """Calculate BM25 score."""
+        score = 0.0
+        doc_len = doc["length"]
+        term_freq = doc["terms"]
+        
+        for term in query_terms:
+            if term not in term_freq:
+                continue
+            
+            tf = term_freq[term]
+            df = self._term_doc_freq.get(term, 0)
+            
+            # IDF component
+            idf = math.log((self._total_docs - df + 0.5) / (df + 0.5) + 1)
+            
+            # TF component with length normalization
+            tf_norm = (tf * (self.k1 + 1)) / (tf + self.k1 * (1 - self.b + self.b * doc_len / self._avg_doc_len))
+            
+            score += idf * tf_norm
+        
+        return score
+
+
+# Register the keyword index by default
+def _register_default_indices():
+    """Register default indices."""
+    registry = get_index_registry()
+    registry.register("keyword", KeywordIndex)
+
+
+_register_default_indices()

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

@@ -0,0 +1,351 @@
+"""
+Query Engine Patterns for PraisonAI Agents.
+
+This module defines query modes and protocols.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by the wrapper layer.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+from enum import Enum
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class QueryMode(str, Enum):
+    """Available query modes."""
+    DEFAULT = "default"
+    SUB_QUESTION = "sub_question"
+    SQL = "sql"
+    ROUTER = "router"
+    SUMMARIZE = "summarize"
+
+
+@dataclass
+class QueryResult:
+    """
+    Result from a query operation.
+    
+    Attributes:
+        answer: The synthesized answer
+        sources: List of source documents/chunks used
+        sub_questions: Optional list of sub-questions (for sub_question mode)
+        metadata: Additional metadata
+    """
+    answer: str
+    sources: List[Dict[str, Any]] = field(default_factory=list)
+    sub_questions: Optional[List[str]] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "answer": self.answer,
+            "sources": self.sources,
+            "sub_questions": self.sub_questions,
+            "metadata": self.metadata
+        }
+
+
+@runtime_checkable
+class QueryEngineProtocol(Protocol):
+    """
+    Protocol for query engines.
+    
+    Implementations must provide a query method that takes a question
+    and returns an answer with sources.
+    """
+    
+    name: str
+    mode: QueryMode
+    
+    def query(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        **kwargs
+    ) -> QueryResult:
+        """
+        Query the knowledge base.
+        
+        Args:
+            question: The user's question
+            context: Optional pre-retrieved context
+            **kwargs: Additional engine-specific options
+            
+        Returns:
+            QueryResult with answer and sources
+        """
+        ...
+    
+    async def aquery(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        **kwargs
+    ) -> QueryResult:
+        """Async version of query."""
+        ...
+
+
+class QueryEngineRegistry:
+    """Registry for query engines."""
+    
+    _instance: Optional["QueryEngineRegistry"] = None
+    
+    def __new__(cls) -> "QueryEngineRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._engines: Dict[str, Callable[..., QueryEngineProtocol]] = {}
+        return cls._instance
+    
+    def register(self, name: str, factory: Callable[..., QueryEngineProtocol]) -> None:
+        """Register a query engine factory."""
+        self._engines[name] = factory
+    
+    def get(self, name: str, **kwargs) -> Optional[QueryEngineProtocol]:
+        """Get a query engine by name."""
+        if name in self._engines:
+            try:
+                return self._engines[name](**kwargs)
+            except Exception as e:
+                logger.warning(f"Failed to initialize query engine '{name}': {e}")
+                return None
+        return None
+    
+    def list_engines(self) -> List[str]:
+        """List all registered engine names."""
+        return list(self._engines.keys())
+    
+    def clear(self) -> None:
+        """Clear all registered engines."""
+        self._engines.clear()
+
+
+def get_query_engine_registry() -> QueryEngineRegistry:
+    """Get the global query engine registry instance."""
+    return QueryEngineRegistry()
+
+
+def decompose_question(question: str) -> List[str]:
+    """
+    Simple deterministic question decomposition.
+    
+    Splits complex questions into sub-questions without LLM.
+    Used as fallback when LLM is not available.
+    
+    Args:
+        question: The complex question
+        
+    Returns:
+        List of sub-questions
+    """
+    sub_questions = []
+    
+    # Split on common conjunctions
+    conjunctions = [" and ", " also ", " additionally ", " furthermore ", " moreover "]
+    parts = [question]
+    
+    for conj in conjunctions:
+        new_parts = []
+        for part in parts:
+            new_parts.extend(part.split(conj))
+        parts = new_parts
+    
+    # Clean up parts
+    for part in parts:
+        part = part.strip()
+        if part:
+            # Ensure it ends with a question mark
+            if not part.endswith("?"):
+                part = part + "?"
+            # Capitalize first letter
+            part = part[0].upper() + part[1:] if len(part) > 1 else part.upper()
+            sub_questions.append(part)
+    
+    # If no decomposition happened, return original
+    if len(sub_questions) <= 1:
+        return [question]
+    
+    return sub_questions
+
+
+def synthesize_answer(
+    question: str,
+    contexts: List[str],
+    max_context_length: int = 4000
+) -> str:
+    """
+    Simple answer synthesis without LLM.
+    
+    Concatenates relevant context with the question.
+    Used as fallback when LLM is not available.
+    
+    Args:
+        question: The original question
+        contexts: List of context strings
+        max_context_length: Maximum total context length
+        
+    Returns:
+        Synthesized answer string
+    """
+    if not contexts:
+        return "No relevant information found."
+    
+    # Truncate contexts to fit
+    total_length = 0
+    selected_contexts = []
+    
+    for ctx in contexts:
+        if total_length + len(ctx) > max_context_length:
+            remaining = max_context_length - total_length
+            if remaining > 100:
+                selected_contexts.append(ctx[:remaining] + "...")
+            break
+        selected_contexts.append(ctx)
+        total_length += len(ctx)
+    
+    # Format as a simple answer
+    context_text = "\n\n".join(selected_contexts)
+    
+    return f"Based on the available information:\n\n{context_text}"
+
+
+class SimpleQueryEngine:
+    """
+    Simple query engine for testing and fallback.
+    
+    Uses basic context concatenation without LLM synthesis.
+    """
+    
+    name: str = "simple"
+    mode: QueryMode = QueryMode.DEFAULT
+    
+    def __init__(self, **kwargs):
+        pass
+    
+    def query(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        **kwargs
+    ) -> QueryResult:
+        """Simple query that returns context as answer."""
+        contexts = context or []
+        answer = synthesize_answer(question, contexts)
+        
+        return QueryResult(
+            answer=answer,
+            sources=[{"text": ctx} for ctx in contexts],
+            metadata={"mode": self.mode.value}
+        )
+    
+    async def aquery(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        **kwargs
+    ) -> QueryResult:
+        """Async version (just calls sync)."""
+        return self.query(question, context, **kwargs)
+
+
+class SubQuestionEngine:
+    """
+    Sub-question decomposition query engine.
+    
+    Decomposes complex questions into simpler sub-questions.
+    Uses deterministic fallback when LLM is not available.
+    """
+    
+    name: str = "sub_question"
+    mode: QueryMode = QueryMode.SUB_QUESTION
+    
+    def __init__(self, llm: Optional[Any] = None, **kwargs):
+        self.llm = llm
+    
+    def query(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        retriever: Optional[Any] = None,
+        **kwargs
+    ) -> QueryResult:
+        """Query with sub-question decomposition."""
+        # Decompose question
+        if self.llm:
+            sub_questions = self._decompose_with_llm(question)
+        else:
+            sub_questions = decompose_question(question)
+        
+        # Gather context for each sub-question
+        all_contexts = list(context or [])
+        sub_answers = []
+        
+        for sub_q in sub_questions:
+            if retriever:
+                # Retrieve for each sub-question
+                try:
+                    results = retriever.retrieve(sub_q, top_k=3)
+                    for r in results:
+                        if hasattr(r, 'text'):
+                            all_contexts.append(r.text)
+                        elif isinstance(r, dict):
+                            all_contexts.append(r.get('text', str(r)))
+                except Exception as e:
+                    logger.warning(f"Retrieval failed for sub-question: {e}")
+            
+            # Simple sub-answer (would use LLM in full implementation)
+            sub_answers.append(f"Sub-question: {sub_q}")
+        
+        # Synthesize final answer
+        answer = synthesize_answer(question, all_contexts)
+        
+        return QueryResult(
+            answer=answer,
+            sources=[{"text": ctx} for ctx in all_contexts[:10]],
+            sub_questions=sub_questions,
+            metadata={"mode": self.mode.value, "sub_question_count": len(sub_questions)}
+        )
+    
+    async def aquery(
+        self,
+        question: str,
+        context: Optional[List[str]] = None,
+        **kwargs
+    ) -> QueryResult:
+        """Async version (just calls sync)."""
+        return self.query(question, context, **kwargs)
+    
+    def _decompose_with_llm(self, question: str) -> List[str]:
+        """Decompose question using LLM."""
+        try:
+            prompt = f"""Break down this complex question into simpler sub-questions that can be answered independently.
+            
+Question: {question}
+
+Return only the sub-questions, one per line, without numbering or bullets."""
+            
+            response = self.llm.chat(prompt)
+            if response:
+                lines = response.strip().split("\n")
+                return [line.strip() for line in lines if line.strip()]
+        except Exception as e:
+            logger.warning(f"LLM decomposition failed: {e}")
+        
+        return decompose_question(question)
+
+
+# Register default engines
+def _register_default_engines():
+    """Register default query engines."""
+    registry = get_query_engine_registry()
+    registry.register("default", SimpleQueryEngine)
+    registry.register("simple", SimpleQueryEngine)
+    registry.register("sub_question", SubQuestionEngine)
+
+
+_register_default_engines()

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

@@ -0,0 +1,286 @@
+"""
+Data Reader Adapter Protocol for PraisonAI Agents.
+
+This module defines the protocol for document readers and a registry system.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by the wrapper layer (praisonai.adapters.readers).
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+import os
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Document:
+    """
+    Lightweight document representation.
+    
+    Attributes:
+        content: The text content of the document
+        metadata: Optional metadata dict (source, filename, page, etc.)
+        doc_id: Optional unique identifier
+        embedding: Optional pre-computed embedding
+    """
+    content: str
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    doc_id: Optional[str] = None
+    embedding: Optional[List[float]] = None
+    
+    def __post_init__(self):
+        if self.doc_id is None:
+            import uuid
+            self.doc_id = str(uuid.uuid4())
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "content": self.content,
+            "metadata": self.metadata,
+            "doc_id": self.doc_id,
+            "embedding": self.embedding
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "Document":
+        """Create from dictionary."""
+        return cls(
+            content=data.get("content", ""),
+            metadata=data.get("metadata", {}),
+            doc_id=data.get("doc_id"),
+            embedding=data.get("embedding")
+        )
+
+
+@runtime_checkable
+class ReaderProtocol(Protocol):
+    """
+    Protocol for document readers.
+    
+    Implementations must provide:
+    - name: Identifier for the reader
+    - supported_extensions: List of file extensions this reader handles
+    - load(): Method to load documents from a source
+    """
+    
+    name: str
+    supported_extensions: List[str]
+    
+    def load(
+        self, 
+        source: str, 
+        *, 
+        metadata: Optional[Dict[str, Any]] = None
+    ) -> List[Document]:
+        """
+        Load documents from a source.
+        
+        Args:
+            source: File path, URL, or other source identifier
+            metadata: Optional metadata to attach to all documents
+            
+        Returns:
+            List of Document objects
+        """
+        ...
+    
+    def can_handle(self, source: str) -> bool:
+        """
+        Check if this reader can handle the given source.
+        
+        Args:
+            source: File path, URL, or other source identifier
+            
+        Returns:
+            True if this reader can handle the source
+        """
+        ...
+
+
+class ReaderRegistry:
+    """
+    Registry for document readers.
+    
+    Provides lazy loading and automatic reader selection based on source type.
+    """
+    
+    _instance: Optional["ReaderRegistry"] = None
+    
+    def __new__(cls) -> "ReaderRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._readers: Dict[str, Callable[[], ReaderProtocol]] = {}
+            cls._instance._extension_map: Dict[str, str] = {}
+            cls._instance._initialized_readers: Dict[str, ReaderProtocol] = {}
+        return cls._instance
+    
+    def register(
+        self, 
+        name: str, 
+        factory: Callable[[], ReaderProtocol],
+        extensions: Optional[List[str]] = None
+    ) -> None:
+        """
+        Register a reader factory.
+        
+        Args:
+            name: Unique name for the reader
+            factory: Callable that returns a ReaderProtocol instance
+            extensions: List of file extensions this reader handles
+        """
+        self._readers[name] = factory
+        if extensions:
+            for ext in extensions:
+                ext_lower = ext.lower().lstrip(".")
+                self._extension_map[ext_lower] = name
+    
+    def get(self, name: str) -> Optional[ReaderProtocol]:
+        """
+        Get a reader by name (lazy initialization).
+        
+        Args:
+            name: Reader name
+            
+        Returns:
+            ReaderProtocol instance or None
+        """
+        if name in self._initialized_readers:
+            return self._initialized_readers[name]
+        
+        if name in self._readers:
+            try:
+                reader = self._readers[name]()
+                self._initialized_readers[name] = reader
+                return reader
+            except Exception as e:
+                logger.warning(f"Failed to initialize reader '{name}': {e}")
+                return None
+        return None
+    
+    def get_for_source(self, source: str) -> Optional[ReaderProtocol]:
+        """
+        Get the best reader for a given source.
+        
+        Args:
+            source: File path, URL, or other source identifier
+            
+        Returns:
+            Best matching ReaderProtocol or None
+        """
+        source_kind = detect_source_kind(source)
+        
+        if source_kind == "file":
+            ext = os.path.splitext(source)[1].lower().lstrip(".")
+            if ext in self._extension_map:
+                return self.get(self._extension_map[ext])
+        elif source_kind == "url":
+            # Try URL reader
+            if "url" in self._readers:
+                return self.get("url")
+        elif source_kind == "directory":
+            if "directory" in self._readers:
+                return self.get("directory")
+        
+        # Fallback to auto reader if available
+        if "auto" in self._readers:
+            return self.get("auto")
+        
+        return None
+    
+    def list_readers(self) -> List[str]:
+        """List all registered reader names."""
+        return list(self._readers.keys())
+    
+    def list_extensions(self) -> Dict[str, str]:
+        """List all registered extensions and their readers."""
+        return dict(self._extension_map)
+    
+    def clear(self) -> None:
+        """Clear all registered readers (mainly for testing)."""
+        self._readers.clear()
+        self._extension_map.clear()
+        self._initialized_readers.clear()
+
+
+def get_reader_registry() -> ReaderRegistry:
+    """Get the global reader registry instance."""
+    return ReaderRegistry()
+
+
+def detect_source_kind(source: str) -> str:
+    """
+    Detect the kind of source without importing heavy libraries.
+    
+    Args:
+        source: File path, URL, directory, or glob pattern
+        
+    Returns:
+        One of: "file", "url", "directory", "glob", "unknown"
+    """
+    if not isinstance(source, str):
+        return "unknown"
+    
+    source = source.strip()
+    
+    # Check for URL
+    if source.startswith(("http://", "https://", "ftp://", "s3://", "gs://")):
+        return "url"
+    
+    # Check for glob pattern
+    if "*" in source or "?" in source or "[" in source:
+        return "glob"
+    
+    # Check if it's a directory
+    if os.path.isdir(source):
+        return "directory"
+    
+    # Check if it's a file
+    if os.path.isfile(source):
+        return "file"
+    
+    # Check if it looks like a file path (has extension)
+    if os.path.splitext(source)[1]:
+        return "file"
+    
+    return "unknown"
+
+
+def get_file_extension(source: str) -> str:
+    """
+    Get the file extension from a source path.
+    
+    Args:
+        source: File path or URL
+        
+    Returns:
+        Lowercase extension without dot, or empty string
+    """
+    # Handle URLs by extracting path
+    if source.startswith(("http://", "https://")):
+        from urllib.parse import urlparse
+        path = urlparse(source).path
+        return os.path.splitext(path)[1].lower().lstrip(".")
+    
+    return os.path.splitext(source)[1].lower().lstrip(".")
+
+
+# Default extension mappings (reader name -> extensions)
+DEFAULT_EXTENSION_MAPPINGS = {
+    "text": ["txt", "text"],
+    "markdown": ["md", "markdown"],
+    "json": ["json", "jsonl"],
+    "csv": ["csv", "tsv"],
+    "html": ["html", "htm"],
+    "xml": ["xml"],
+    "pdf": ["pdf"],
+    "docx": ["docx", "doc"],
+    "xlsx": ["xlsx", "xls"],
+    "pptx": ["pptx", "ppt"],
+    "image": ["jpg", "jpeg", "png", "gif", "bmp", "tiff", "webp"],
+    "audio": ["mp3", "wav", "ogg", "m4a", "flac"],
+    "video": ["mp4", "avi", "mov", "mkv", "webm"],
+}

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

@@ -0,0 +1,196 @@
+"""
+Reranker Protocol for PraisonAI Agents.
+
+This module defines the protocol for rerankers and a registry system.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by praisonai-tools or wrapper layer.
+"""
+
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RerankResult:
+    """
+    Result from a reranking operation.
+    
+    Attributes:
+        text: The text content
+        score: Reranking score (higher is more relevant)
+        original_index: Original position before reranking
+        metadata: Optional metadata
+    """
+    text: str
+    score: float
+    original_index: int
+    metadata: Dict[str, Any] = None
+    
+    def __post_init__(self):
+        if self.metadata is None:
+            self.metadata = {}
+
+
+@runtime_checkable
+class RerankerProtocol(Protocol):
+    """
+    Protocol for rerankers.
+    
+    Implementations must provide a rerank method that takes a query
+    and a list of documents and returns reranked results.
+    """
+    
+    name: str
+    
+    def rerank(
+        self,
+        query: str,
+        documents: List[str],
+        top_k: Optional[int] = None,
+        **kwargs
+    ) -> List[RerankResult]:
+        """
+        Rerank documents based on relevance to query.
+        
+        Args:
+            query: The search query
+            documents: List of document texts to rerank
+            top_k: Optional limit on results (None = return all)
+            **kwargs: Additional reranker-specific options
+            
+        Returns:
+            List of RerankResult objects sorted by relevance
+        """
+        ...
+    
+    async def arerank(
+        self,
+        query: str,
+        documents: List[str],
+        top_k: Optional[int] = None,
+        **kwargs
+    ) -> List[RerankResult]:
+        """Async version of rerank."""
+        ...
+
+
+class RerankerRegistry:
+    """
+    Registry for rerankers.
+    """
+    
+    _instance: Optional["RerankerRegistry"] = None
+    
+    def __new__(cls) -> "RerankerRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._rerankers: Dict[str, Callable[..., RerankerProtocol]] = {}
+        return cls._instance
+    
+    def register(
+        self, 
+        name: str, 
+        factory: Callable[..., RerankerProtocol]
+    ) -> None:
+        """Register a reranker factory."""
+        self._rerankers[name] = factory
+    
+    def get(
+        self, 
+        name: str, 
+        **kwargs
+    ) -> Optional[RerankerProtocol]:
+        """Get a reranker by name."""
+        if name in self._rerankers:
+            try:
+                return self._rerankers[name](**kwargs)
+            except Exception as e:
+                logger.warning(f"Failed to initialize reranker '{name}': {e}")
+                return None
+        return None
+    
+    def list_rerankers(self) -> List[str]:
+        """List all registered reranker names."""
+        return list(self._rerankers.keys())
+    
+    def clear(self) -> None:
+        """Clear all registered rerankers."""
+        self._rerankers.clear()
+
+
+def get_reranker_registry() -> RerankerRegistry:
+    """Get the global reranker registry instance."""
+    return RerankerRegistry()
+
+
+class SimpleReranker:
+    """
+    Simple keyword-based reranker for testing and fallback.
+    
+    Uses term frequency overlap for scoring. No external dependencies.
+    """
+    
+    name: str = "simple"
+    
+    def __init__(self, **kwargs):
+        pass
+    
+    def rerank(
+        self,
+        query: str,
+        documents: List[str],
+        top_k: Optional[int] = None,
+        **kwargs
+    ) -> List[RerankResult]:
+        """Rerank using simple term overlap."""
+        query_terms = set(query.lower().split())
+        
+        results = []
+        for i, doc in enumerate(documents):
+            doc_terms = set(doc.lower().split())
+            
+            # Calculate Jaccard similarity
+            if not query_terms or not doc_terms:
+                score = 0.0
+            else:
+                intersection = len(query_terms & doc_terms)
+                union = len(query_terms | doc_terms)
+                score = intersection / union if union > 0 else 0.0
+            
+            results.append(RerankResult(
+                text=doc,
+                score=score,
+                original_index=i
+            ))
+        
+        # Sort by score descending
+        results.sort(key=lambda x: x.score, reverse=True)
+        
+        if top_k is not None:
+            results = results[:top_k]
+        
+        return results
+    
+    async def arerank(
+        self,
+        query: str,
+        documents: List[str],
+        top_k: Optional[int] = None,
+        **kwargs
+    ) -> List[RerankResult]:
+        """Async version (just calls sync)."""
+        return self.rerank(query, documents, top_k, **kwargs)
+
+
+# Register the simple reranker by default
+def _register_default_rerankers():
+    """Register default rerankers."""
+    registry = get_reranker_registry()
+    registry.register("simple", SimpleReranker)
+
+
+_register_default_rerankers()

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

@@ -0,0 +1,285 @@
+"""
+Retrieval Strategy Patterns for PraisonAI Agents.
+
+This module defines protocols and strategies for document retrieval.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by the wrapper layer.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+from enum import Enum
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class RetrievalStrategy(str, Enum):
+    """Available retrieval strategies."""
+    BASIC = "basic"
+    FUSION = "fusion"
+    RECURSIVE = "recursive"
+    AUTO_MERGE = "auto_merge"
+    HYBRID = "hybrid"
+
+
+@dataclass
+class RetrievalResult:
+    """
+    Result from a retrieval operation.
+    
+    Attributes:
+        text: The retrieved text content
+        score: Relevance score (0-1)
+        metadata: Source metadata
+        doc_id: Document identifier
+        chunk_index: Index of chunk within document
+    """
+    text: str
+    score: float
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    doc_id: Optional[str] = None
+    chunk_index: Optional[int] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "text": self.text,
+            "score": self.score,
+            "metadata": self.metadata,
+            "doc_id": self.doc_id,
+            "chunk_index": self.chunk_index
+        }
+
+
+@runtime_checkable
+class RetrieverProtocol(Protocol):
+    """
+    Protocol for document retrievers.
+    
+    Implementations must provide a retrieve method that takes a query
+    and returns relevant documents.
+    """
+    
+    name: str
+    strategy: RetrievalStrategy
+    
+    def retrieve(
+        self,
+        query: str,
+        top_k: int = 10,
+        filter: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ) -> List[RetrievalResult]:
+        """
+        Retrieve relevant documents for a query.
+        
+        Args:
+            query: The search query
+            top_k: Maximum number of results
+            filter: Optional metadata filter
+            **kwargs: Additional retriever-specific options
+            
+        Returns:
+            List of RetrievalResult objects sorted by relevance
+        """
+        ...
+    
+    async def aretrieve(
+        self,
+        query: str,
+        top_k: int = 10,
+        filter: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ) -> List[RetrievalResult]:
+        """Async version of retrieve."""
+        ...
+
+
+class RetrieverRegistry:
+    """
+    Registry for retrieval strategies.
+    """
+    
+    _instance: Optional["RetrieverRegistry"] = None
+    
+    def __new__(cls) -> "RetrieverRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._retrievers: Dict[str, Callable[..., RetrieverProtocol]] = {}
+        return cls._instance
+    
+    def register(
+        self, 
+        name: str, 
+        factory: Callable[..., RetrieverProtocol]
+    ) -> None:
+        """Register a retriever factory."""
+        self._retrievers[name] = factory
+    
+    def get(
+        self, 
+        name: str, 
+        **kwargs
+    ) -> Optional[RetrieverProtocol]:
+        """Get a retriever by name."""
+        if name in self._retrievers:
+            try:
+                return self._retrievers[name](**kwargs)
+            except Exception as e:
+                logger.warning(f"Failed to initialize retriever '{name}': {e}")
+                return None
+        return None
+    
+    def list_retrievers(self) -> List[str]:
+        """List all registered retriever names."""
+        return list(self._retrievers.keys())
+    
+    def clear(self) -> None:
+        """Clear all registered retrievers."""
+        self._retrievers.clear()
+
+
+def get_retriever_registry() -> RetrieverRegistry:
+    """Get the global retriever registry instance."""
+    return RetrieverRegistry()
+
+
+def reciprocal_rank_fusion(
+    result_lists: List[List[RetrievalResult]],
+    k: int = 60
+) -> List[RetrievalResult]:
+    """
+    Reciprocal Rank Fusion (RRF) algorithm for combining multiple result lists.
+    
+    Pure Python implementation with no external dependencies.
+    
+    Args:
+        result_lists: List of result lists from different retrievers
+        k: RRF constant (default 60)
+        
+    Returns:
+        Fused and re-ranked list of results
+    """
+    # Track scores by document ID
+    fused_scores: Dict[str, float] = {}
+    doc_map: Dict[str, RetrievalResult] = {}
+    
+    for results in result_lists:
+        for rank, result in enumerate(results):
+            doc_key = result.doc_id or result.text[:100]  # Use text prefix as fallback key
+            
+            # RRF score: 1 / (k + rank)
+            rrf_score = 1.0 / (k + rank + 1)
+            
+            if doc_key in fused_scores:
+                fused_scores[doc_key] += rrf_score
+            else:
+                fused_scores[doc_key] = rrf_score
+                doc_map[doc_key] = result
+    
+    # Sort by fused score
+    sorted_keys = sorted(fused_scores.keys(), key=lambda x: fused_scores[x], reverse=True)
+    
+    # Build result list with updated scores
+    fused_results = []
+    for key in sorted_keys:
+        result = doc_map[key]
+        fused_results.append(RetrievalResult(
+            text=result.text,
+            score=fused_scores[key],
+            metadata=result.metadata,
+            doc_id=result.doc_id,
+            chunk_index=result.chunk_index
+        ))
+    
+    return fused_results
+
+
+def merge_adjacent_chunks(
+    results: List[RetrievalResult],
+    max_gap: int = 1
+) -> List[RetrievalResult]:
+    """
+    Merge adjacent chunks from the same document.
+    
+    Args:
+        results: List of retrieval results
+        max_gap: Maximum gap between chunk indices to merge
+        
+    Returns:
+        Merged results
+    """
+    if not results:
+        return results
+    
+    # Group by document ID
+    doc_groups: Dict[str, List[RetrievalResult]] = {}
+    for result in results:
+        doc_id = result.doc_id or "unknown"
+        if doc_id not in doc_groups:
+            doc_groups[doc_id] = []
+        doc_groups[doc_id].append(result)
+    
+    merged_results = []
+    
+    for doc_id, chunks in doc_groups.items():
+        # Sort by chunk index
+        chunks_with_index = [c for c in chunks if c.chunk_index is not None]
+        chunks_without_index = [c for c in chunks if c.chunk_index is None]
+        
+        if not chunks_with_index:
+            merged_results.extend(chunks_without_index)
+            continue
+        
+        chunks_with_index.sort(key=lambda x: x.chunk_index or 0)
+        
+        # Merge adjacent chunks
+        current_group = [chunks_with_index[0]]
+        
+        for chunk in chunks_with_index[1:]:
+            last_chunk = current_group[-1]
+            last_idx = last_chunk.chunk_index or 0
+            curr_idx = chunk.chunk_index or 0
+            
+            if curr_idx - last_idx <= max_gap:
+                current_group.append(chunk)
+            else:
+                # Merge current group and start new one
+                merged_results.append(_merge_chunk_group(current_group))
+                current_group = [chunk]
+        
+        # Don't forget the last group
+        if current_group:
+            merged_results.append(_merge_chunk_group(current_group))
+        
+        merged_results.extend(chunks_without_index)
+    
+    # Sort by score
+    merged_results.sort(key=lambda x: x.score, reverse=True)
+    return merged_results
+
+
+def _merge_chunk_group(chunks: List[RetrievalResult]) -> RetrievalResult:
+    """Merge a group of chunks into a single result."""
+    if len(chunks) == 1:
+        return chunks[0]
+    
+    # Combine text
+    merged_text = "\n".join(c.text for c in chunks)
+    
+    # Average score
+    avg_score = sum(c.score for c in chunks) / len(chunks)
+    
+    # Use first chunk's metadata
+    metadata = chunks[0].metadata.copy()
+    metadata["merged_chunks"] = len(chunks)
+    
+    return RetrievalResult(
+        text=merged_text,
+        score=avg_score,
+        metadata=metadata,
+        doc_id=chunks[0].doc_id,
+        chunk_index=chunks[0].chunk_index
+    )

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

@@ -0,0 +1,422 @@
+"""
+Vector Store Adapter Protocol for PraisonAI Agents.
+
+This module defines the protocol for vector stores and a registry system.
+NO heavy imports - only stdlib and typing.
+
+Implementations are provided by praisonai-tools or wrapper layer.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Protocol, runtime_checkable
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class VectorRecord:
+    """
+    A record in a vector store.
+    
+    Attributes:
+        id: Unique identifier
+        text: The text content
+        embedding: The vector embedding
+        metadata: Optional metadata dict
+        score: Optional similarity score (for query results)
+    """
+    id: str
+    text: str
+    embedding: List[float]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    score: Optional[float] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "id": self.id,
+            "text": self.text,
+            "embedding": self.embedding,
+            "metadata": self.metadata,
+            "score": self.score
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "VectorRecord":
+        """Create from dictionary."""
+        return cls(
+            id=data["id"],
+            text=data["text"],
+            embedding=data["embedding"],
+            metadata=data.get("metadata", {}),
+            score=data.get("score")
+        )
+
+
+@runtime_checkable
+class VectorStoreProtocol(Protocol):
+    """
+    Protocol for vector stores.
+    
+    Implementations must provide methods for:
+    - Adding vectors
+    - Querying by vector similarity
+    - Deleting vectors
+    - Getting count
+    """
+    
+    name: str
+    
+    def add(
+        self,
+        texts: List[str],
+        embeddings: List[List[float]],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        ids: Optional[List[str]] = None,
+        namespace: Optional[str] = None
+    ) -> List[str]:
+        """
+        Add vectors to the store.
+        
+        Args:
+            texts: List of text content
+            embeddings: List of embedding vectors
+            metadatas: Optional list of metadata dicts
+            ids: Optional list of IDs (auto-generated if not provided)
+            namespace: Optional namespace for multi-tenant isolation
+            
+        Returns:
+            List of IDs for the added vectors
+        """
+        ...
+    
+    def query(
+        self,
+        embedding: List[float],
+        top_k: int = 10,
+        namespace: Optional[str] = None,
+        filter: Optional[Dict[str, Any]] = None
+    ) -> List[VectorRecord]:
+        """
+        Query vectors by similarity.
+        
+        Args:
+            embedding: Query embedding vector
+            top_k: Number of results to return
+            namespace: Optional namespace filter
+            filter: Optional metadata filter
+            
+        Returns:
+            List of VectorRecord objects sorted by similarity
+        """
+        ...
+    
+    def delete(
+        self,
+        ids: Optional[List[str]] = None,
+        namespace: Optional[str] = None,
+        filter: Optional[Dict[str, Any]] = None,
+        delete_all: bool = False
+    ) -> int:
+        """
+        Delete vectors from the store.
+        
+        Args:
+            ids: Optional list of IDs to delete
+            namespace: Optional namespace filter
+            filter: Optional metadata filter
+            delete_all: If True, delete all vectors
+            
+        Returns:
+            Number of vectors deleted
+        """
+        ...
+    
+    def count(self, namespace: Optional[str] = None) -> int:
+        """
+        Get the number of vectors in the store.
+        
+        Args:
+            namespace: Optional namespace filter
+            
+        Returns:
+            Number of vectors
+        """
+        ...
+    
+    def get(
+        self,
+        ids: List[str],
+        namespace: Optional[str] = None
+    ) -> List[VectorRecord]:
+        """
+        Get vectors by ID.
+        
+        Args:
+            ids: List of IDs to retrieve
+            namespace: Optional namespace filter
+            
+        Returns:
+            List of VectorRecord objects
+        """
+        ...
+
+
+class VectorStoreRegistry:
+    """
+    Registry for vector stores.
+    
+    Provides lazy loading and configuration-based initialization.
+    """
+    
+    _instance: Optional["VectorStoreRegistry"] = None
+    
+    def __new__(cls) -> "VectorStoreRegistry":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._stores: Dict[str, Callable[..., VectorStoreProtocol]] = {}
+            cls._instance._initialized_stores: Dict[str, VectorStoreProtocol] = {}
+        return cls._instance
+    
+    def register(
+        self, 
+        name: str, 
+        factory: Callable[..., VectorStoreProtocol]
+    ) -> None:
+        """
+        Register a vector store factory.
+        
+        Args:
+            name: Unique name for the store (e.g., "chroma", "pinecone")
+            factory: Callable that returns a VectorStoreProtocol instance
+        """
+        self._stores[name] = factory
+    
+    def get(
+        self, 
+        name: str, 
+        config: Optional[Dict[str, Any]] = None,
+        namespace: Optional[str] = None
+    ) -> Optional[VectorStoreProtocol]:
+        """
+        Get a vector store by name (lazy initialization).
+        
+        Args:
+            name: Store name
+            config: Optional configuration dict
+            namespace: Optional namespace for multi-tenant isolation
+            
+        Returns:
+            VectorStoreProtocol instance or None
+        """
+        cache_key = f"{name}:{namespace or 'default'}"
+        
+        if cache_key in self._initialized_stores:
+            return self._initialized_stores[cache_key]
+        
+        if name in self._stores:
+            try:
+                store = self._stores[name](config=config, namespace=namespace)
+                self._initialized_stores[cache_key] = store
+                return store
+            except Exception as e:
+                logger.warning(f"Failed to initialize vector store '{name}': {e}")
+                return None
+        return None
+    
+    def list_stores(self) -> List[str]:
+        """List all registered store names."""
+        return list(self._stores.keys())
+    
+    def clear(self) -> None:
+        """Clear all registered stores (mainly for testing)."""
+        self._stores.clear()
+        self._initialized_stores.clear()
+
+
+def get_vector_store_registry() -> VectorStoreRegistry:
+    """Get the global vector store registry instance."""
+    return VectorStoreRegistry()
+
+
+class InMemoryVectorStore:
+    """
+    Pure Python in-memory vector store for testing and default usage.
+    
+    Uses cosine similarity for queries. No external dependencies.
+    """
+    
+    name: str = "memory"
+    
+    def __init__(
+        self, 
+        config: Optional[Dict[str, Any]] = None,
+        namespace: Optional[str] = None
+    ):
+        self.config = config or {}
+        self.default_namespace = namespace or "default"
+        self._data: Dict[str, Dict[str, VectorRecord]] = {}  # namespace -> id -> record
+    
+    def _get_namespace_data(self, namespace: Optional[str] = None) -> Dict[str, VectorRecord]:
+        """Get or create namespace data."""
+        ns = namespace or self.default_namespace
+        if ns not in self._data:
+            self._data[ns] = {}
+        return self._data[ns]
+    
+    def add(
+        self,
+        texts: List[str],
+        embeddings: List[List[float]],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        ids: Optional[List[str]] = None,
+        namespace: Optional[str] = None
+    ) -> List[str]:
+        """Add vectors to the store."""
+        import uuid
+        
+        ns_data = self._get_namespace_data(namespace)
+        metadatas = metadatas or [{} for _ in texts]
+        ids = ids or [str(uuid.uuid4()) for _ in texts]
+        
+        result_ids = []
+        for i, (text, embedding, metadata, id_) in enumerate(zip(texts, embeddings, metadatas, ids)):
+            record = VectorRecord(
+                id=id_,
+                text=text,
+                embedding=embedding,
+                metadata=metadata
+            )
+            ns_data[id_] = record
+            result_ids.append(id_)
+        
+        return result_ids
+    
+    def query(
+        self,
+        embedding: List[float],
+        top_k: int = 10,
+        namespace: Optional[str] = None,
+        filter: Optional[Dict[str, Any]] = None
+    ) -> List[VectorRecord]:
+        """Query vectors by cosine similarity."""
+        ns_data = self._get_namespace_data(namespace)
+        
+        if not ns_data:
+            return []
+        
+        # Calculate cosine similarity for all records
+        results = []
+        for record in ns_data.values():
+            # Apply metadata filter if provided
+            if filter:
+                match = True
+                for key, value in filter.items():
+                    if record.metadata.get(key) != value:
+                        match = False
+                        break
+                if not match:
+                    continue
+            
+            score = self._cosine_similarity(embedding, record.embedding)
+            results.append(VectorRecord(
+                id=record.id,
+                text=record.text,
+                embedding=record.embedding,
+                metadata=record.metadata,
+                score=score
+            ))
+        
+        # Sort by score descending and return top_k
+        results.sort(key=lambda x: x.score or 0, reverse=True)
+        return results[:top_k]
+    
+    def delete(
+        self,
+        ids: Optional[List[str]] = None,
+        namespace: Optional[str] = None,
+        filter: Optional[Dict[str, Any]] = None,
+        delete_all: bool = False
+    ) -> int:
+        """Delete vectors from the store."""
+        ns = namespace or self.default_namespace
+        
+        if delete_all:
+            if ns in self._data:
+                count = len(self._data[ns])
+                self._data[ns] = {}
+                return count
+            return 0
+        
+        if ns not in self._data:
+            return 0
+        
+        ns_data = self._data[ns]
+        deleted = 0
+        
+        if ids:
+            for id_ in ids:
+                if id_ in ns_data:
+                    del ns_data[id_]
+                    deleted += 1
+        elif filter:
+            to_delete = []
+            for id_, record in ns_data.items():
+                match = True
+                for key, value in filter.items():
+                    if record.metadata.get(key) != value:
+                        match = False
+                        break
+                if match:
+                    to_delete.append(id_)
+            for id_ in to_delete:
+                del ns_data[id_]
+                deleted += 1
+        
+        return deleted
+    
+    def count(self, namespace: Optional[str] = None) -> int:
+        """Get the number of vectors in the store."""
+        ns = namespace or self.default_namespace
+        if ns in self._data:
+            return len(self._data[ns])
+        return 0
+    
+    def get(
+        self,
+        ids: List[str],
+        namespace: Optional[str] = None
+    ) -> List[VectorRecord]:
+        """Get vectors by ID."""
+        ns_data = self._get_namespace_data(namespace)
+        results = []
+        for id_ in ids:
+            if id_ in ns_data:
+                results.append(ns_data[id_])
+        return results
+    
+    @staticmethod
+    def _cosine_similarity(a: List[float], b: List[float]) -> float:
+        """Calculate cosine similarity between two vectors."""
+        if len(a) != len(b):
+            return 0.0
+        
+        dot_product = sum(x * y for x, y in zip(a, b))
+        norm_a = sum(x * x for x in a) ** 0.5
+        norm_b = sum(x * x for x in b) ** 0.5
+        
+        if norm_a == 0 or norm_b == 0:
+            return 0.0
+        
+        return dot_product / (norm_a * norm_b)
+
+
+# Register the in-memory store by default
+def _register_default_stores():
+    """Register default vector stores."""
+    registry = get_vector_store_registry()
+    registry.register("memory", InMemoryVectorStore)
+
+
+_register_default_stores()

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

@@ -27,6 +27,7 @@ from .model_router import (
     TaskComplexity,
     create_routing_agent
 )
+from .rate_limiter import RateLimiter
 
 __all__ = [
     "LLM", 
@@ -46,5 +47,6 @@ __all__ = [
     "ModelRouter",
     "ModelProfile",
     "TaskComplexity",
-    "create_routing_agent"
+    "create_routing_agent",
+    "RateLimiter"
 ]

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

@@ -0,0 +1,147 @@
+"""
+Rate Limiter for PraisonAI Agents.
+
+Provides token bucket rate limiting for LLM API calls.
+
+Zero Performance Impact:
+- When rate_limiter is None, no overhead
+- Uses monotonic clock for accuracy
+- Injectable clock/sleep for deterministic testing
+
+Usage:
+    from praisonaiagents.llm import RateLimiter
+    from praisonaiagents import Agent
+    
+    limiter = RateLimiter(requests_per_minute=60)
+    agent = Agent(name="Test", rate_limiter=limiter)
+"""
+
+import time
+import asyncio
+from dataclasses import dataclass, field
+from typing import Optional, Callable
+
+
+@dataclass
+class RateLimiter:
+    """Token bucket rate limiter for API calls.
+    
+    Uses the token bucket algorithm:
+    - Tokens are added at a fixed rate (requests_per_minute / 60 per second)
+    - Each request consumes one token
+    - If no tokens available, wait until one is available
+    - Burst allows multiple requests in quick succession
+    
+    Args:
+        requests_per_minute: Maximum requests per minute
+        tokens_per_minute: Optional token-based rate limiting (for future use)
+        burst: Maximum burst size (default: 1)
+    
+    Example:
+        limiter = RateLimiter(requests_per_minute=60)  # 1 req/sec
+        limiter.acquire()  # Blocks if rate exceeded
+        
+        # Async usage
+        await limiter.acquire_async()
+    """
+    requests_per_minute: int
+    tokens_per_minute: Optional[int] = None
+    burst: int = 1
+    
+    # Internal state
+    _tokens: float = field(default=None, init=False, repr=False)
+    _last_update: float = field(default=None, init=False, repr=False)
+    _lock: asyncio.Lock = field(default=None, init=False, repr=False)
+    
+    # Injectable functions for testing
+    _get_time: Callable[[], float] = field(default=None, init=False, repr=False)
+    _sleep: Callable[[float], None] = field(default=None, init=False, repr=False)
+    _async_sleep: Callable[[float], None] = field(default=None, init=False, repr=False)
+    
+    def __post_init__(self):
+        """Initialize internal state."""
+        self._tokens = float(self.burst)
+        self._last_update = time.monotonic()
+        self._lock = asyncio.Lock()
+        
+        # Default implementations (can be overridden for testing)
+        if self._get_time is None:
+            self._get_time = time.monotonic
+        if self._sleep is None:
+            self._sleep = time.sleep
+        if self._async_sleep is None:
+            self._async_sleep = asyncio.sleep
+    
+    @property
+    def _rate(self) -> float:
+        """Tokens per second."""
+        return self.requests_per_minute / 60.0
+    
+    def _refill(self) -> None:
+        """Refill tokens based on elapsed time."""
+        now = self._get_time()
+        elapsed = now - self._last_update
+        self._tokens = min(self.burst, self._tokens + elapsed * self._rate)
+        self._last_update = now
+    
+    def _wait_time(self) -> float:
+        """Calculate time to wait for next token."""
+        if self._tokens >= 1.0:
+            return 0.0
+        return (1.0 - self._tokens) / self._rate
+    
+    def acquire(self) -> None:
+        """Acquire a token, blocking if necessary.
+        
+        This is the synchronous version. Use acquire_async() for async code.
+        """
+        self._refill()
+        
+        wait = self._wait_time()
+        if wait > 0:
+            self._sleep(wait)
+            self._refill()
+        
+        self._tokens -= 1.0
+    
+    async def acquire_async(self) -> None:
+        """Acquire a token asynchronously, waiting if necessary.
+        
+        This is the async version. Use acquire() for sync code.
+        """
+        async with self._lock:
+            self._refill()
+            
+            wait = self._wait_time()
+            if wait > 0:
+                await self._async_sleep(wait)
+                self._refill()
+            
+            self._tokens -= 1.0
+    
+    def try_acquire(self) -> bool:
+        """Try to acquire a token without blocking.
+        
+        Returns:
+            True if token acquired, False if rate limit exceeded
+        """
+        self._refill()
+        
+        if self._tokens >= 1.0:
+            self._tokens -= 1.0
+            return True
+        return False
+    
+    def reset(self) -> None:
+        """Reset the rate limiter to initial state."""
+        self._tokens = float(self.burst)
+        self._last_update = self._get_time()
+    
+    @property
+    def available_tokens(self) -> float:
+        """Get current available tokens (for monitoring)."""
+        self._refill()
+        return self._tokens
+    
+    def __repr__(self) -> str:
+        return f"RateLimiter(rpm={self.requests_per_minute}, burst={self.burst})"

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

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

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

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

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

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

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

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

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

@@ -0,0 +1,240 @@
+"""
+Observability interface for PraisonAI Agents.
+
+This module provides the protocol and types for observability/tracing.
+Implementations are provided by the wrapper layer (praisonai_tools.observability).
+
+Usage (simplest - recommended):
+    from praisonaiagents import Agent, obs
+    
+    agent = Agent(
+        name="Assistant",
+        observability=obs.auto(),  # Auto-detect from env vars
+    )
+    agent.chat("Hello!")  # Auto-traces to configured provider
+
+Alternative (explicit provider):
+    observability=obs.langfuse()     # Langfuse
+    observability=obs.langsmith()    # LangSmith
+    observability=obs.agentops()     # AgentOps
+    observability=obs.arize()        # Arize Phoenix
+    observability=obs.datadog()      # Datadog
+"""
+
+from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
+from dataclasses import dataclass, field
+import time
+
+
+@dataclass
+class Span:
+    """Represents a span in a trace."""
+    span_id: str
+    trace_id: str
+    name: str
+    parent_span_id: Optional[str] = None
+    start_time: float = field(default_factory=time.time)
+    end_time: Optional[float] = None
+    attributes: Dict[str, Any] = field(default_factory=dict)
+    status: str = "ok"
+
+
+@dataclass
+class Trace:
+    """Represents a complete trace."""
+    trace_id: str
+    session_id: Optional[str] = None
+    agent_name: Optional[str] = None
+    user_id: Optional[str] = None
+    start_time: float = field(default_factory=time.time)
+    end_time: Optional[float] = None
+    spans: List[Span] = field(default_factory=list)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    status: str = "ok"
+
+
+@runtime_checkable
+class ObservabilityAdapter(Protocol):
+    """Protocol for observability adapters."""
+    
+    def on_trace_start(
+        self,
+        trace_id: str,
+        session_id: Optional[str] = None,
+        agent_name: Optional[str] = None,
+        user_id: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Called when a trace starts."""
+        ...
+    
+    def on_trace_end(
+        self,
+        trace_id: str,
+        status: str = "ok",
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Called when a trace ends."""
+        ...
+    
+    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 span starts."""
+        ...
+    
+    def on_span_end(
+        self,
+        span_id: str,
+        status: str = "ok",
+        attributes: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Called when a span ends."""
+        ...
+    
+    def on_llm_call(
+        self,
+        span_id: str,
+        model: str,
+        messages: List[Dict[str, Any]],
+        response: Optional[str] = None,
+        tokens: Optional[Dict[str, int]] = None,
+        latency_ms: Optional[float] = None,
+    ) -> None:
+        """Called when an LLM call is made."""
+        ...
+    
+    def on_tool_call(
+        self,
+        span_id: str,
+        tool_name: str,
+        args: Dict[str, Any],
+        result: Any,
+        latency_ms: Optional[float] = None,
+    ) -> None:
+        """Called when a tool is invoked."""
+        ...
+    
+    def flush(self) -> None:
+        """Flush any pending data."""
+        ...
+    
+    def close(self) -> None:
+        """Close the adapter and release resources."""
+        ...
+
+
+class _LazyObsModule:
+    """
+    Lazy proxy for observability backends.
+    
+    Allows `from praisonaiagents import obs` without importing heavy deps.
+    Actual provider classes are loaded only when accessed.
+    """
+    
+    _PROVIDERS = {
+        "langfuse": "praisonai_tools.observability.providers.langfuse_provider",
+        "langsmith": "praisonai_tools.observability.providers.langsmith_provider",
+        "agentops": "praisonai_tools.observability.providers.agentops_provider",
+        "arize": "praisonai_tools.observability.providers.arize_phoenix_provider",
+        "phoenix": "praisonai_tools.observability.providers.arize_phoenix_provider",
+        "datadog": "praisonai_tools.observability.providers.datadog_provider",
+        "mlflow": "praisonai_tools.observability.providers.mlflow_provider",
+        "openlit": "praisonai_tools.observability.providers.openlit_provider",
+        "opik": "praisonai_tools.observability.providers.opik_provider",
+        "portkey": "praisonai_tools.observability.providers.portkey_provider",
+        "traceloop": "praisonai_tools.observability.providers.traceloop_provider",
+        "weave": "praisonai_tools.observability.providers.weave_provider",
+        "braintrust": "praisonai_tools.observability.providers.braintrust_provider",
+        "langtrace": "praisonai_tools.observability.providers.langtrace_provider",
+        "langwatch": "praisonai_tools.observability.providers.langwatch_provider",
+        "maxim": "praisonai_tools.observability.providers.maxim_provider",
+    }
+    
+    def __getattr__(self, name: str):
+        if name in self._PROVIDERS:
+            return self._create_provider_factory(name)
+        raise AttributeError(f"module 'obs' has no attribute {name!r}")
+    
+    def _create_provider_factory(self, provider_name: str):
+        """Create a factory function for a provider."""
+        def factory(**kwargs):
+            try:
+                import importlib
+                module = importlib.import_module(self._PROVIDERS[provider_name])
+                # Get the provider class (naming convention: XxxProvider)
+                class_name = f"{provider_name.title().replace('_', '')}Provider"
+                provider_cls = getattr(module, class_name, None)
+                if provider_cls:
+                    return provider_cls(**kwargs)
+                # Fallback: try to find any class ending with Provider
+                for attr_name in dir(module):
+                    if attr_name.endswith("Provider"):
+                        return getattr(module, attr_name)(**kwargs)
+                raise AttributeError(f"No provider class found in {module}")
+            except ImportError as e:
+                raise ImportError(
+                    f"Observability provider '{provider_name}' requires praisonai-tools. "
+                    f"Install with: pip install praisonai-tools\n"
+                    f"Original error: {e}"
+                ) from e
+        return factory
+    
+    def auto(self, **kwargs):
+        """
+        Auto-detect observability provider from environment variables.
+        
+        Checks for common env vars like LANGFUSE_PUBLIC_KEY, LANGSMITH_API_KEY, etc.
+        Returns None if no provider is detected.
+        """
+        import os
+        
+        # Check for common provider env vars
+        provider_env_map = {
+            "LANGFUSE_PUBLIC_KEY": "langfuse",
+            "LANGSMITH_API_KEY": "langsmith",
+            "AGENTOPS_API_KEY": "agentops",
+            "ARIZE_API_KEY": "arize",
+            "DATADOG_API_KEY": "datadog",
+            "MLFLOW_TRACKING_URI": "mlflow",
+            "OPENLIT_API_KEY": "openlit",
+            "OPIK_API_KEY": "opik",
+            "PORTKEY_API_KEY": "portkey",
+            "TRACELOOP_API_KEY": "traceloop",
+            "WANDB_API_KEY": "weave",
+            "BRAINTRUST_API_KEY": "braintrust",
+        }
+        
+        for env_var, provider in provider_env_map.items():
+            if os.getenv(env_var):
+                return self._create_provider_factory(provider)(**kwargs)
+        
+        return None
+    
+    def __repr__(self):
+        return "<module 'praisonaiagents.obs' (lazy observability backends)>"
+    
+    def __call__(self, provider: str = "auto", **kwargs):
+        """
+        Shortcut: obs("langfuse") or obs() for auto-detection.
+        """
+        if provider == "auto":
+            return self.auto(**kwargs)
+        return self._create_provider_factory(provider)(**kwargs)
+
+
+# Singleton lazy module instance
+obs = _LazyObsModule()
+
+
+__all__ = [
+    "ObservabilityAdapter",
+    "Span",
+    "Trace",
+    "obs",
+]