Release v3.11.2

Pr 1099 fix

.github/workflows/benchmark.yml

@@ -0,0 +1,145 @@
+name: Performance Benchmarks
+
+on:
+  workflow_dispatch:
+    inputs:
+      iterations:
+        description: 'Number of benchmark iterations'
+        required: false
+        default: '100'
+      fail_on_regression:
+        description: 'Fail if performance regresses'
+        required: false
+        default: 'true'
+
+jobs:
+  benchmark:
+    runs-on: ubuntu-latest
+    timeout-minutes: 30
+    
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python 3.12
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+          cache: 'pip'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -e src/praisonai-agents[all]
+          pip install agno crewai langgraph
+
+      - name: Run Import Time Benchmark
+        id: import_time
+        run: |
+          cd src/praisonai-agents
+          python benchmarks/import_time.py 2>&1 | tee import_time_output.txt
+          
+          # Extract import time
+          IMPORT_TIME=$(grep -oP 'Median:\s+\K[\d.]+' import_time_output.txt || echo "0")
+          echo "import_time=$IMPORT_TIME" >> $GITHUB_OUTPUT
+          
+          # Check threshold (200ms)
+          if (( $(echo "$IMPORT_TIME > 200" | bc -l) )); then
+            echo "::warning::Import time ${IMPORT_TIME}ms exceeds 200ms threshold"
+            if [ "${{ inputs.fail_on_regression }}" == "true" ]; then
+              echo "import_passed=false" >> $GITHUB_OUTPUT
+            fi
+          else
+            echo "import_passed=true" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Run Memory Usage Benchmark
+        id: memory
+        run: |
+          cd src/praisonai-agents
+          python benchmarks/memory_usage.py 2>&1 | tee memory_output.txt
+          
+          # Extract peak memory
+          PEAK_MEMORY=$(grep -oP 'Peak:\s+\K[\d.]+' memory_output.txt || echo "0")
+          echo "peak_memory=$PEAK_MEMORY" >> $GITHUB_OUTPUT
+          
+          # Check threshold (30MB)
+          if (( $(echo "$PEAK_MEMORY > 30" | bc -l) )); then
+            echo "::warning::Memory usage ${PEAK_MEMORY}MB exceeds 30MB threshold"
+            if [ "${{ inputs.fail_on_regression }}" == "true" ]; then
+              echo "memory_passed=false" >> $GITHUB_OUTPUT
+            fi
+          else
+            echo "memory_passed=true" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Run Simple Benchmark
+        id: simple
+        run: |
+          cd src/praisonai-agents
+          python benchmarks/simple_benchmark.py 2>&1 | tee simple_output.txt
+          
+          # Extract PraisonAI time
+          PRAISON_TIME=$(grep -oP 'PraisonAI\s+\K[\d.]+' simple_output.txt | head -1 || echo "0")
+          echo "praison_time=$PRAISON_TIME" >> $GITHUB_OUTPUT
+
+      - name: Run Tools Benchmark
+        id: tools
+        run: |
+          cd src/praisonai-agents
+          python benchmarks/tools_benchmark.py 2>&1 | tee tools_output.txt
+          
+          # Extract PraisonAI time with tools
+          PRAISON_TOOLS_TIME=$(grep -oP 'PraisonAI\s+\K[\d.]+' tools_output.txt | head -1 || echo "0")
+          echo "praison_tools_time=$PRAISON_TOOLS_TIME" >> $GITHUB_OUTPUT
+
+      - name: Run Parameter Impact Benchmark
+        id: param_impact
+        run: |
+          cd src/praisonai-agents
+          python benchmarks/param_impact_benchmark.py --no-save --iterations ${{ inputs.iterations }} 2>&1 | tee param_impact_output.txt
+
+      - name: Check Lazy Imports
+        id: lazy_imports
+        run: |
+          cd src/praisonai-agents
+          python -c "
+          import sys
+          from praisonaiagents import Agent
+          agent = Agent(name='Test', output='silent')
+          
+          # Check litellm is NOT loaded
+          if 'litellm' in sys.modules:
+              print('FAIL: litellm loaded during silent agent creation')
+              exit(1)
+          else:
+              print('PASS: litellm not loaded (lazy import working)')
+          "
+
+      - name: Generate Benchmark Summary
+        if: always()
+        run: |
+          echo "## Performance Benchmark Results" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "| Metric | Value | Threshold | Status |" >> $GITHUB_STEP_SUMMARY
+          echo "|--------|-------|-----------|--------|" >> $GITHUB_STEP_SUMMARY
+          echo "| Import Time | ${{ steps.import_time.outputs.import_time }}ms | <200ms | ${{ steps.import_time.outputs.import_passed == 'true' && '✅' || '❌' }} |" >> $GITHUB_STEP_SUMMARY
+          echo "| Peak Memory | ${{ steps.memory.outputs.peak_memory }}MB | <30MB | ${{ steps.memory.outputs.memory_passed == 'true' && '✅' || '❌' }} |" >> $GITHUB_STEP_SUMMARY
+          echo "| Agent Instantiation | ${{ steps.simple.outputs.praison_time }}μs | - | ℹ️ |" >> $GITHUB_STEP_SUMMARY
+          echo "| Agent with Tools | ${{ steps.tools.outputs.praison_tools_time }}μs | - | ℹ️ |" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "### Lazy Import Check" >> $GITHUB_STEP_SUMMARY
+          echo "✅ litellm not loaded during silent agent creation" >> $GITHUB_STEP_SUMMARY
+
+      - name: Fail on Regression
+        if: inputs.fail_on_regression == 'true'
+        run: |
+          if [ "${{ steps.import_time.outputs.import_passed }}" == "false" ]; then
+            echo "::error::Import time regression detected"
+            exit 1
+          fi
+          if [ "${{ steps.memory.outputs.memory_passed }}" == "false" ]; then
+            echo "::error::Memory usage regression detected"
+            exit 1
+          fi
+          echo "All performance checks passed!"

.github/workflows/test-windows.yml

@@ -0,0 +1,204 @@
+name: Windows Tests
+
+on:
+  push:
+    branches: [main, develop]
+  pull_request:
+    branches: [main, develop]
+  workflow_dispatch:
+
+jobs:
+  test-windows:
+    runs-on: windows-latest
+    timeout-minutes: 5
+    env:
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      OPENAI_MODEL_NAME: gpt-4o-mini
+    
+    steps:
+    - uses: actions/checkout@v4
+    
+    - name: Set up Python 3.11
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.11"
+    
+    - name: Install praisonaiagents
+      run: |
+        python -m pip install --upgrade pip
+        cd src/praisonai-agents
+        pip install .
+      shell: bash
+    
+    - name: Install praisonai wrapper
+      run: |
+        cd src/praisonai
+        pip install .[dev]
+      shell: bash
+
+    # ==================== CORE IMPORTS ====================
+    - name: Test core imports
+      run: |
+        python -c "from praisonaiagents import Agent, Agents, Workflow; print('Core imports OK')"
+        python -c "from praisonaiagents import tool, Tools; print('Tool imports OK')"
+        python -c "from praisonaiagents import Task, WorkflowStep; print('Task/Step imports OK')"
+
+    # ==================== AGENT FEATURES ====================
+    - name: Test Agent instantiation
+      run: |
+        python -c "
+        from praisonaiagents import Agent
+        a = Agent(name='Test', instructions='You are helpful')
+        print('Agent created:', a.name)
+        a2 = Agent(name='ToolAgent', instructions='Use tools', tools=[])
+        print('Agent with tools:', a2.name)
+        "
+
+    - name: Test Agents orchestration
+      run: |
+        python -c "
+        from praisonaiagents import Agent, Agents, Task
+        a1 = Agent(name='Agent1', instructions='First agent')
+        a2 = Agent(name='Agent2', instructions='Second agent')
+        t = Task(description='Do something', agent=a1)
+        agents = Agents(agents=[a1, a2], tasks=[t])
+        print('Agents:', len(agents.agents), 'Tasks:', len(agents.tasks))
+        "
+
+    # ==================== WORKFLOW FEATURES ====================
+    - name: Test Workflow and WorkflowStep
+      run: |
+        python -c "
+        from praisonaiagents import Workflow, WorkflowStep
+        from praisonaiagents.workflows import WorkflowOutputConfig
+        
+        w = Workflow(name='test')
+        s1 = WorkflowStep(name='step1')
+        s2 = WorkflowStep(name='step2', context=['step1'])
+        print('Workflow:', w.name)
+        print('Steps:', s1.name, s2.name)
+        
+        cfg = WorkflowOutputConfig(verbose=True, stream=False)
+        w2 = Workflow(name='configured', output=cfg)
+        print('Configured workflow verbose:', w2.verbose)
+        "
+
+    # ==================== MEMORY FEATURES ====================
+    - name: Test Memory classes
+      run: |
+        python -c "
+        from praisonaiagents.memory import FileMemory
+        from praisonaiagents.config import MemoryConfig
+        
+        cfg = MemoryConfig(backend='file', user_id='test_user')
+        print('MemoryConfig backend:', cfg.backend)
+        
+        mem = FileMemory(user_id='test_user', base_path='.praison/test_memory')
+        print('FileMemory user:', mem.user_id)
+        "
+
+    # ==================== TOOL DECORATOR ====================
+    - name: Test tool decorator
+      run: |
+        python -c "
+        from praisonaiagents import tool
+        
+        @tool
+        def my_tool(query: str) -> str:
+            '''A test tool'''
+            return f'Echo: {query}'
+        
+        result = my_tool('hello')
+        print('Tool result:', result)
+        "
+
+    # ==================== CONFIG CLASSES ====================
+    - name: Test config classes
+      run: |
+        python -c "
+        from praisonaiagents.config import OutputConfig, ExecutionConfig, PlanningConfig
+        
+        out_cfg = OutputConfig(verbose=True, stream=True)
+        print('OutputConfig verbose:', out_cfg.verbose)
+        
+        exec_cfg = ExecutionConfig(max_iter=50, max_retry_limit=5)
+        print('ExecutionConfig max_iter:', exec_cfg.max_iter)
+        
+        plan_cfg = PlanningConfig(reasoning=True, auto_approve=False)
+        print('PlanningConfig reasoning:', plan_cfg.reasoning)
+        "
+    # ==================== HANDOFF ====================
+    - name: Test Handoff
+      run: |
+        python -c "
+        from praisonaiagents import Handoff, handoff, Agent
+        
+        a1 = Agent(name='Agent1', instructions='First')
+        a2 = Agent(name='Agent2', instructions='Second')
+        
+        # Using Handoff class directly
+        h = Handoff(agent=a2)
+        print('Handoff tool name:', h.tool_name)
+        
+        # Using handoff function
+        h2 = handoff(a1, tool_name_override='custom_handoff')
+        print('Custom handoff name:', h2.tool_name)
+        "
+
+
+    # ==================== ASYNC FEATURES ====================
+    - name: Test async imports
+      run: |
+        python -c "
+        import asyncio
+        from praisonaiagents import Agent
+        
+        async def test_async():
+            agent = Agent(name='AsyncAgent', instructions='Test')
+            assert hasattr(agent, 'arun'), 'Missing arun'
+            assert hasattr(agent, 'astart'), 'Missing astart'
+            print('Async methods available')
+        
+        asyncio.run(test_async())
+        "
+
+    # ==================== CLI COMMANDS ====================
+    - name: Test CLI help
+      run: praisonai --help
+
+    - name: Test CLI session list
+      run: praisonai session list
+      continue-on-error: true
+
+    - name: Test CLI queue list
+      run: praisonai queue list
+      continue-on-error: true
+
+    # ==================== REAL API TESTS ====================
+    - name: Real API test - Agent run
+      if: env.OPENAI_API_KEY != ''
+      run: |
+        python -c "
+        from praisonaiagents import Agent
+        agent = Agent(name='TestAgent', instructions='Reply with exactly: WINDOWS_OK', llm='gpt-4o-mini')
+        result = agent.run('Say WINDOWS_OK')
+        print('Result:', result)
+        print('Real API test passed')
+        "
+
+    - name: Real API test - Multi-agent
+      if: env.OPENAI_API_KEY != ''
+      run: |
+        python -c "
+        from praisonaiagents import Agent, Agents, Task
+        
+        researcher = Agent(name='Researcher', instructions='You research topics', llm='gpt-4o-mini')
+        writer = Agent(name='Writer', instructions='You write content', llm='gpt-4o-mini')
+        
+        task = Task(description='Say hello world', agent=researcher)
+        
+        agents = Agents(agents=[researcher, writer], tasks=[task])
+        result = agents.start()
+        print('Multi-agent result type:', type(result))
+        print('Multi-agent test passed')
+        "

.github/workflows/update-api-md.yml

@@ -0,0 +1,57 @@
+name: Update API Reference
+
+on:
+  push:
+    branches: [ "main" ]
+    paths:
+      - 'src/praisonai-agents/praisonaiagents/**/*.py'
+      - 'src/praisonai/praisonai/**/*.py'
+      - 'src/praisonai-ts/src/**/*.ts'
+      - '.github/workflows/update-api-md.yml'
+
+permissions:
+  contents: write
+
+jobs:
+  update-api-md:
+    runs-on: ubuntu-latest
+    
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+      with:
+        ref: main
+        persist-credentials: true
+        fetch-depth: 0
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.11'
+
+    - name: Generate api.md
+      run: |
+        cd $GITHUB_WORKSPACE
+        python3 src/praisonai/scripts/generate_api_md.py
+        
+    - name: Check for changes
+      id: check_changes
+      run: |
+        if git diff --quiet api.md; then
+          echo "changes=false" >> $GITHUB_OUTPUT
+          echo "No changes to api.md"
+        else
+          echo "changes=true" >> $GITHUB_OUTPUT
+          echo "api.md has been updated"
+          git diff api.md | head -50
+        fi
+
+    - name: Commit and push changes
+      if: steps.check_changes.outputs.changes == 'true'
+      uses: stefanzweifel/git-auto-commit-action@v5
+      with:
+        commit_message: "docs: auto-update api.md [skip ci]"
+        file_pattern: 'api.md'
+        commit_user_name: "github-actions[bot]"
+        commit_user_email: "41898282+github-actions[bot]@users.noreply.github.com"
+        commit_author: "github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>"

.gitignore

@@ -83,4 +83,5 @@ issue_details.json
 # MCP Registry tokens
 .mcpregistry_*
 
-*.log
+*.log
+.internal

README.md

@@ -23,7 +23,7 @@
 
 </div>
 
-PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AG2 (Formerly AutoGen), and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
+PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. It provides a low-code solution to streamline the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
 
 <div align="center">
   <a href="https://docs.praison.ai">
@@ -137,7 +137,6 @@ PraisonAI Agents is the **fastest AI agent framework** for agent instantiation.
 | PraisonAI (LiteLLM) | 7.56 | 2.00x |
 | PydanticAI | 226.94 | 60.16x |
 | LangGraph | 4,558.71 | 1,209x |
-| CrewAI | 15,607.92 | 4,138x |
 
 <details>
 <summary>Run benchmarks yourself</summary>

api.md

@@ -23,12 +23,12 @@ Methods:
 
 Types:
 ```python
-from praisonaiagents import Agent, Agents, AutoAgents, AutoRagAgent, ContextAgent, DeepResearchAgent, ImageAgent, PraisonAIAgents, PromptExpanderAgent, QueryRewriterAgent, create_context_agent
+from praisonaiagents import Agent, Agents, AutoAgents, AutoRagAgent, ContextAgent, DeepResearchAgent, ImageAgent, PlanningAgent, PromptExpanderAgent, QueryRewriterAgent, create_context_agent
 ```
 
 Methods:
 
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">achat</a>(prompt: str, temperature = 1.0, tools = None, output_json = None, output_pydantic = None, reasoning_steps = False, task_name = None, task_description = None, task_id = None)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">achat</a>(prompt: str, temperature = 1.0, tools = None, output_json = None, output_pydantic = None, reasoning_steps = False, task_name = None, task_description = None, task_id = None, attachments = None)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">aexecute</a>(task, context = None)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">agent_id</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">analyze_prompt</a>(prompt: str) -> set</code>
@@ -38,7 +38,7 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">auto_memory</a>(value)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">background</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">background</a>(value)</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">chat</a>(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, force_retrieval = False, skip_retrieval = False)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">chat</a>(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, force_retrieval = False, skip_retrieval = False, attachments = None, tool_choice = None)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">chat_with_context</a>(message: str, context: 'ContextPack', **kwargs) -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">checkpoints</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">checkpoints</a>(value)</code>
@@ -47,25 +47,31 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">console</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">context_manager</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">context_manager</a>(value)</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">display_generating</a>(content: str, start_time: float)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">delete_history</a>(index: int) -> bool</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">delete_history_matching</a>(pattern: str) -> int</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">display_name</a>() -> str</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">ephemeral</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">execute</a>(task, context = None)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">execute_tool</a>(function_name, arguments)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">execute_tool_async</a>(function_name: str, arguments: Dict[str, Any]) -> Any</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">from_template</a>(uri: str, config: Optional[Dict[str, Any]] = None, offline: bool = False, **kwargs) -> 'Agent'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">generate_task</a>() -> 'Task'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_available_tools</a>() -> List[Any]</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_history_size</a>() -> int</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_memory_context</a>(query: Optional[str] = None) -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_recommended_stage</a>(prompt: str) -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_rules_context</a>(file_path: Optional[str] = None, include_manual: Optional[List[str]] = None) -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_skills_prompt</a>() -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">handoff_to</a>(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">handoff_to_async</a>(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">iter_stream</a>(prompt: str, **kwargs)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">launch</a>(path: str = '/', port: int = 8000, host: str = '0.0.0.0', debug: bool = False, protocol: str = 'http')</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">llm_model</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">output_style</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">output_style</a>(value)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">policy</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">policy</a>(value)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">prune_history</a>(keep_last: int = 5) -> int</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">query</a>(question: str, **kwargs) -> 'RAGResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">rag</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">rag_query</a>(question: str, **kwargs) -> 'RAGResult'</code>
@@ -73,11 +79,13 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">retrieve</a>(query: str, **kwargs) -> 'ContextPack'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">rules_manager</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run</a>(prompt: str, **kwargs)</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_autonomous</a>(prompt: str, max_iterations: Optional[int] = None, timeout_seconds: Optional[float] = None)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_autonomous</a>(prompt: str, max_iterations: Optional[int] = None, timeout_seconds: Optional[float] = None, completion_promise: Optional[str] = None, clear_context: bool = False)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">session_id</a>() -> Optional[str]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">skill_manager</a>()</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">start</a>(prompt: str, **kwargs)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">start</a>(prompt: str = None, **kwargs)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">store_memory</a>(content: str, memory_type: str = 'short_term', **kwargs)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">stream_emitter</a>()</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">stream_emitter</a>(value)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">switch_model</a>(new_model: str) -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">thinking_budget</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">thinking_budget</a>(value)</code>
@@ -116,7 +124,7 @@ Methods:
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_output_to_file</a>(task, task_output)</code>
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_session_state</a>(session_id: str, include_memory: bool = True) -> None</code>
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">set_state</a>(key: str, value: Any) -> None</code>
-* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, **kwargs)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, output = None, **kwargs)</code>
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">todo_list</a>()</code>
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_plan_step_status</a>(step_id: str, status: str) -> bool</code>
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_state</a>(updates: Dict) -> None</code>
@@ -126,17 +134,23 @@ Methods:
 * <code title="class AutoRagAgent">AutoRagAgent.<a href="./src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py">chat</a>(message: str, **kwargs) -> str</code>
 * <code title="class AutoRagAgent">AutoRagAgent.<a href="./src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py">name</a>() -> str</code>
 * <code title="class AutoRagAgent">AutoRagAgent.<a href="./src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py">rag</a>() -> Optional['RAG']</code>
+* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">aanalyze_codebase</a>(project_path: str) -> Dict[str, Any]</code>
+* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">acreate_implementation_blueprint</a>(feature_request: str, context_analysis: Optional[Dict[str, Any]] = None) -> Dict[str, Any]</code>
+* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">agenerate_prp</a>(feature_request: str, context_analysis: Optional[Dict[str, Any]] = None) -> str</code>
+* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">analyze_codebase</a>(project_path: str) -> Dict[str, Any]</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">analyze_codebase_with_gitingest</a>(project_path: str) -> Dict[str, Any]</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">analyze_integration_points</a>(project_path: str) -> Dict[str, Any]</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">analyze_test_patterns</a>(project_path: str) -> Dict[str, Any]</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">build_implementation_blueprint</a>(feature_request: str, context_analysis: Dict[str, Any] = None) -> Dict[str, Any]</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">compile_context_documentation</a>(project_path: str) -> Dict[str, Any]</code>
+* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">create_implementation_blueprint</a>(feature_request: str, context_analysis: Optional[Dict[str, Any]] = None) -> Dict[str, Any]</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">create_quality_gates</a>(requirements: List[str]) -> Dict[str, Any]</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">create_validation_framework</a>(project_path: str) -> Dict[str, Any]</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">execute_prp</a>(prp_file_path: str) -> Dict[str, Any]</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">extract_implementation_patterns</a>(project_path: str, ast_analysis: Dict[str, Any] = None) -> Dict[str, Any]</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">generate_comprehensive_prp</a>(feature_request: str, context_analysis: Dict[str, Any] = None) -> str</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">generate_feature_prp</a>(feature_request: str) -> str</code>
+* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">generate_prp</a>(feature_request: str, context_analysis: Optional[Dict[str, Any]] = None) -> str</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">get_agent_interaction_summary</a>() -> Dict[str, Any]</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">log_debug</a>(message: str, **kwargs)</code>
 * <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">perform_ast_analysis</a>(project_path: str) -> Dict[str, Any]</code>
@@ -154,49 +168,23 @@ Methods:
 * <code title="class DeepResearchAgent">DeepResearchAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">research</a>(query: str, instructions: Optional[str] = None, model: Optional[str] = None, summary_mode: Optional[Literal['auto', 'detailed', 'concise']] = None, web_search: Optional[bool] = None, code_interpreter: Optional[bool] = None, mcp_servers: Optional[List[Dict[str, Any]]] = None, file_ids: Optional[List[str]] = None, file_search: Optional[bool] = None, file_search_stores: Optional[List[str]] = None, stream: bool = True) -> DeepResearchResponse</code>
 * <code title="class DeepResearchAgent">DeepResearchAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">rewrite_query</a>(query: str, model: Optional[str] = None) -> str</code>
 * <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">achat</a>(prompt: str, temperature: float = 0.2, tools: Optional[List[Any]] = None, output_json: Optional[str] = None, output_pydantic: Optional[Any] = None, reasoning_steps: bool = False, **kwargs) -> Union[str, Dict[str, Any]]</code>
+* <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">aedit</a>(image: str, prompt: str, mask: Optional[str] = None, n: int = 1, size: Optional[str] = None, **kwargs) -> Dict[str, Any]</code>
+* <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">agenerate</a>(prompt: str, **kwargs) -> Dict[str, Any]</code>
 * <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">agenerate_image</a>(prompt: str, **kwargs) -> Dict[str, Any]</code>
+* <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">avariation</a>(image: str, n: int = 1, size: Optional[str] = None, **kwargs) -> Dict[str, Any]</code>
 * <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">chat</a>(prompt: str, **kwargs) -> Dict[str, Any]</code>
+* <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">edit</a>(image: str, prompt: str, mask: Optional[str] = None, n: int = 1, size: Optional[str] = None, **kwargs) -> Dict[str, Any]</code>
+* <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">generate</a>(prompt: str, **kwargs) -> Dict[str, Any]</code>
 * <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">generate_image</a>(prompt: str, **kwargs) -> Dict[str, Any]</code>
 * <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">litellm</a>()</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">add_task</a>(task)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aexecute_task</a>(task_id)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">append_to_state</a>(key: str, value: Any, max_length: Optional[int] = None) -> List[Any]</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_all_tasks</a>()</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_task</a>(task_id)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">astart</a>(content = None, return_dict = False, **kwargs)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clean_json_output</a>(output: str) -> str</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clear_state</a>() -> None</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">context_manager</a>()</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">current_plan</a>()</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">default_completion_checker</a>(task, agent_output)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">delete_state</a>(key: str) -> bool</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">display_token_usage</a>()</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">execute_task</a>(task_id)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_agent_details</a>(agent_name)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_state</a>() -> Dict[str, Any]</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_tasks_status</a>()</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_detailed_token_report</a>() -> Dict[str, Any]</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_plan_markdown</a>() -> str</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_state</a>(key: str, default: Any = None) -> Any</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_details</a>(task_id)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_result</a>(task_id)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_status</a>(task_id)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_todo_markdown</a>() -> str</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_token_usage_summary</a>() -> Dict[str, Any]</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">has_state</a>(key: str) -> bool</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">increment_state</a>(key: str, amount: float = 1, default: float = 0) -> float</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">launch</a>(path: str = '/agents', port: int = 8000, host: str = '0.0.0.0', debug: bool = False, protocol: str = 'http')</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">restore_session_state</a>(session_id: str) -> bool</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run</a>(content = None, return_dict = False, **kwargs)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run_all_tasks</a>()</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run_task</a>(task_id)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_output_to_file</a>(task, task_output)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_session_state</a>(session_id: str, include_memory: bool = True) -> None</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">set_state</a>(key: str, value: Any) -> None</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, **kwargs)</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">todo_list</a>()</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_plan_step_status</a>(step_id: str, status: str) -> bool</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_state</a>(updates: Dict) -> None</code>
+* <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">variation</a>(image: str, n: int = 1, size: Optional[str] = None, **kwargs) -> Dict[str, Any]</code>
+* <code title="class PlanningAgent">PlanningAgent.<a href="./src/praisonai-agents/praisonaiagents/planning/planner.py">analyze_context</a>(context: str) -> str</code>
+* <code title="class PlanningAgent">PlanningAgent.<a href="./src/praisonai-agents/praisonaiagents/planning/planner.py">analyze_context_sync</a>(context: str) -> str</code>
+* <code title="class PlanningAgent">PlanningAgent.<a href="./src/praisonai-agents/praisonaiagents/planning/planner.py">create_plan</a>(request: str, agents: List['Agent'], tasks: Optional[List['Task']] = None, context: Optional[str] = None) -> Plan</code>
+* <code title="class PlanningAgent">PlanningAgent.<a href="./src/praisonai-agents/praisonaiagents/planning/planner.py">create_plan_sync</a>(request: str, agents: List['Agent'], tasks: Optional[List['Task']] = None, context: Optional[str] = None) -> Plan</code>
+* <code title="class PlanningAgent">PlanningAgent.<a href="./src/praisonai-agents/praisonaiagents/planning/planner.py">is_tool_allowed</a>(tool_name: str) -> bool</code>
+* <code title="class PlanningAgent">PlanningAgent.<a href="./src/praisonai-agents/praisonaiagents/planning/planner.py">refine_plan</a>(plan: Plan, feedback: str) -> Plan</code>
+* <code title="class PlanningAgent">PlanningAgent.<a href="./src/praisonai-agents/praisonaiagents/planning/planner.py">refine_plan_sync</a>(plan: Plan, feedback: str) -> Plan</code>
 * <code title="class PromptExpanderAgent">PromptExpanderAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/prompt_expander_agent.py">agent</a>()</code>
 * <code title="class PromptExpanderAgent">PromptExpanderAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/prompt_expander_agent.py">expand</a>(prompt: str, strategy: ExpandStrategy = ..., context: Optional[str] = None) -> ExpandResult</code>
 * <code title="class PromptExpanderAgent">PromptExpanderAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/prompt_expander_agent.py">expand_basic</a>(prompt: str, context: Optional[str] = None) -> ExpandResult</code>
@@ -241,6 +229,7 @@ Methods:
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_tools</a>() -> List[str]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None, overwrite: bool = False) -> None</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">unregister</a>(name: str) -> bool</code>
+* <code title="class Tools">Tools.<a href="./src/praisonai-agents/praisonaiagents/tools/tools.py">internet_search</a>(*args, **kwargs)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_registry</a>() -> ToolRegistry</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_tool</a>(name: str) -> Optional[Union[BaseTool, Callable]]</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register_tool</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None) -> None</code>
@@ -272,6 +261,7 @@ Methods:
 * <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">stream</a>() -> bool</code>
 * <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">verbose</a>() -> bool</code>
+* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">verbose</a>(value: bool)</code>
 * <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">async_execution</a>() -> bool</code>
 * <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">branch_condition</a>() -> Optional[Dict[str, List[str]]]</code>
 * <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">context_from</a>() -> Optional[List[str]]</code>
@@ -288,7 +278,7 @@ Methods:
 * <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">rerun</a>() -> bool</code>
 * <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">retain_full_context</a>() -> bool</code>
 * <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">to_dict</a>() -> Dict[str, Any]</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">loop</a>(step: Any, over: Optional[str] = None, from_csv: Optional[str] = None, from_file: Optional[str] = None, var_name: str = 'item') -> Loop</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">loop</a>(step: Any = None, steps: Optional[List[Any]] = None, over: Optional[str] = None, from_csv: Optional[str] = None, from_file: Optional[str] = None, var_name: str = 'item', parallel: bool = False, max_workers: Optional[int] = None, output_variable: Optional[str] = None) -> Loop</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">parallel</a>(steps: List) -> Parallel</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">repeat</a>(step: Any, until: Optional[Callable[[WorkflowContext], bool]] = None, max_iterations: int = 10) -> Repeat</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">route</a>(routes: Dict[str, List], default: Optional[List] = None) -> Route</code>
@@ -304,7 +294,7 @@ from praisonaiagents import db
 
 Types:
 ```python
-from praisonaiagents import Memory
+from praisonaiagents import Memory, MemoryBackend, MemoryConfig
 ```
 
 Methods:
@@ -312,8 +302,15 @@ Methods:
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">build_context_for_task</a>(task_descr: str, user_id: Optional[str] = None, additional: str = '', max_items: int = 3, include_in_output: Optional[bool] = None) -> str</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">calculate_quality_metrics</a>(output: str, expected_output: str, llm: Optional[str] = None, custom_prompt: Optional[str] = None) -> Dict[str, float]</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">compute_quality_score</a>(completeness: float, relevance: float, clarity: float, accuracy: float, weights: Dict[str, float] = None) -> float</code>
+* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">delete_long_term</a>(memory_id: str) -> bool</code>
+* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">delete_memories</a>(memory_ids: List[str]) -> int</code>
+* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">delete_memories_matching</a>(query: str, memory_type: Optional[str] = None, limit: int = 10) -> int</code>
+* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">delete_memory</a>(memory_id: str, memory_type: Optional[str] = None) -> bool</code>
+* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">delete_short_term</a>(memory_id: str) -> bool</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">finalize_task_output</a>(content: str, agent_name: str, quality_score: float, threshold: float = 0.7, metrics: Dict[str, Any] = None, task_id: str = None)</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">get_all_memories</a>() -> List[Dict[str, Any]]</code>
+* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">get_learn_context</a>() -> str</code>
+* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">learn</a>()</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">reset_all</a>()</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">reset_entity_only</a>()</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">reset_long_term</a>()</code>
@@ -330,12 +327,13 @@ Methods:
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">store_quality</a>(text: str, quality_score: float, task_id: Optional[str] = None, iteration: Optional[int] = None, metrics: Optional[Dict[str, float]] = None, memory_type: Literal['short', 'long'] = 'long') -> None</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">store_short_term</a>(text: str, metadata: Dict[str, Any] = None, completeness: float = None, relevance: float = None, clarity: float = None, accuracy: float = None, weights: Dict[str, float] = None, evaluator_quality: float = None)</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">store_user_memory</a>(user_id: str, text: str, extra: Dict[str, Any] = None)</code>
+* <code title="class MemoryConfig">MemoryConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 
 # Knowledge
 
 Types:
 ```python
-from praisonaiagents import Chunking, Knowledge
+from praisonaiagents import Chunking, ChunkingStrategy, Knowledge, KnowledgeConfig
 ```
 
 Methods:
@@ -362,16 +360,21 @@ Methods:
 * <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">search</a>(query, user_id = None, agent_id = None, run_id = None, rerank = None, **kwargs)</code>
 * <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">store</a>(content, user_id = None, agent_id = None, run_id = None, metadata = None)</code>
 * <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">update</a>(memory_id, data)</code>
+* <code title="class KnowledgeConfig">KnowledgeConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 
 # RAG
 
 Types:
 ```python
-from praisonaiagents import RAG, RAGCitation, RAGConfig, RAGResult
+from praisonaiagents import CitationsMode, ContextPack, RAG, RAGCitation, RAGConfig, RAGResult, RetrievalConfig, RetrievalPolicy
 ```
 
 Methods:
 
+* <code title="class ContextPack">ContextPack.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">format_for_prompt</a>(include_sources: bool = True) -> str</code>
+* <code title="class ContextPack">ContextPack.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">from_dict</a>(data: Dict[str, Any]) -> 'ContextPack'</code>
+* <code title="class ContextPack">ContextPack.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">has_citations</a>() -> bool</code>
+* <code title="class ContextPack">ContextPack.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="class RAG">RAG.<a href="./src/praisonai-agents/praisonaiagents/rag/pipeline.py">aquery</a>(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> RAGResult</code>
 * <code title="class RAG">RAG.<a href="./src/praisonai-agents/praisonaiagents/rag/pipeline.py">aretrieve</a>(query: str, **kwargs) -> ContextPack</code>
 * <code title="class RAG">RAG.<a href="./src/praisonai-agents/praisonaiagents/rag/pipeline.py">astream</a>(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> AsyncIterator[str]</code>
@@ -388,6 +391,13 @@ Methods:
 * <code title="class RAGResult">RAGResult.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">from_dict</a>(data: Dict[str, Any]) -> 'RAGResult'</code>
 * <code title="class RAGResult">RAGResult.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">has_citations</a>() -> bool</code>
 * <code title="class RAGResult">RAGResult.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class RetrievalConfig">RetrievalConfig.<a href="./src/praisonai-agents/praisonaiagents/rag/retrieval_config.py">from_dict</a>(data: Dict[str, Any]) -> 'RetrievalConfig'</code>
+* <code title="class RetrievalConfig">RetrievalConfig.<a href="./src/praisonai-agents/praisonaiagents/rag/retrieval_config.py">get_strategy</a>(corpus_stats = None)</code>
+* <code title="class RetrievalConfig">RetrievalConfig.<a href="./src/praisonai-agents/praisonaiagents/rag/retrieval_config.py">get_token_budget</a>(model_name: Optional[str] = None)</code>
+* <code title="class RetrievalConfig">RetrievalConfig.<a href="./src/praisonai-agents/praisonaiagents/rag/retrieval_config.py">should_retrieve</a>(query: str, force: bool = False, skip: bool = False) -> bool</code>
+* <code title="class RetrievalConfig">RetrievalConfig.<a href="./src/praisonai-agents/praisonaiagents/rag/retrieval_config.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class RetrievalConfig">RetrievalConfig.<a href="./src/praisonai-agents/praisonaiagents/rag/retrieval_config.py">to_knowledge_config</a>() -> Dict[str, Any]</code>
+* <code title="class RetrievalConfig">RetrievalConfig.<a href="./src/praisonai-agents/praisonaiagents/rag/retrieval_config.py">to_rag_config</a>() -> Dict[str, Any]</code>
 
 # Handoff
 
@@ -415,18 +425,97 @@ Methods:
 
 Types:
 ```python
-from praisonaiagents import LLMGuardrail
+from praisonaiagents import GuardrailAction, GuardrailConfig, LLMGuardrail
 ```
 
 Methods:
 
+* <code title="class GuardrailConfig">GuardrailConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="class LLMGuardrail">LLMGuardrail.<a href="./src/praisonai-agents/praisonaiagents/guardrails/llm_guardrail.py">__call__</a>(task_output: TaskOutput) -> Tuple[bool, Union[str, TaskOutput]]</code>
 
+# Planning
+
+Types:
+```python
+from praisonaiagents import ApprovalCallback, Plan, PlanStep, PlanStorage, PlanningConfig, READ_ONLY_TOOLS, RESTRICTED_TOOLS, TodoItem, TodoList
+```
+
+Methods:
+
+* <code title="class ApprovalCallback">ApprovalCallback.<a href="./src/praisonai-agents/praisonaiagents/planning/approval.py">__call__</a>(plan: 'Plan') -> bool</code>
+* <code title="class ApprovalCallback">ApprovalCallback.<a href="./src/praisonai-agents/praisonaiagents/planning/approval.py">always_approve</a>(plan: 'Plan') -> bool</code>
+* <code title="class ApprovalCallback">ApprovalCallback.<a href="./src/praisonai-agents/praisonaiagents/planning/approval.py">always_reject</a>(plan: 'Plan') -> bool</code>
+* <code title="class ApprovalCallback">ApprovalCallback.<a href="./src/praisonai-agents/praisonaiagents/planning/approval.py">approve_if_no_dangerous_tools</a>(plan: 'Plan') -> bool</code>
+* <code title="class ApprovalCallback">ApprovalCallback.<a href="./src/praisonai-agents/praisonaiagents/planning/approval.py">approve_if_small</a>(plan: 'Plan', max_steps: int = 5) -> bool</code>
+* <code title="class ApprovalCallback">ApprovalCallback.<a href="./src/praisonai-agents/praisonaiagents/planning/approval.py">async_call</a>(plan: 'Plan') -> bool</code>
+* <code title="class ApprovalCallback">ApprovalCallback.<a href="./src/praisonai-agents/praisonaiagents/planning/approval.py">console_approval</a>(plan: 'Plan') -> bool</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">add_step</a>(step: PlanStep) -> None</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">approve</a>() -> None</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">cancel</a>() -> None</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">complete</a>() -> None</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">completed_step_ids</a>() -> List[str]</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">from_dict</a>(data: Dict[str, Any]) -> 'Plan'</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">from_markdown</a>(markdown: str) -> 'Plan'</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">get_next_steps</a>() -> List[PlanStep]</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">get_step</a>(step_id: str) -> Optional[PlanStep]</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">is_complete</a>() -> bool</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">progress</a>() -> float</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">remove_step</a>(step_id: str) -> bool</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">start_execution</a>() -> None</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">to_markdown</a>() -> str</code>
+* <code title="class Plan">Plan.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">update_step_status</a>(step_id: str, status: str) -> bool</code>
+* <code title="class PlanStep">PlanStep.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">from_dict</a>(data: Dict[str, Any]) -> 'PlanStep'</code>
+* <code title="class PlanStep">PlanStep.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">is_ready</a>(completed_steps: List[str]) -> bool</code>
+* <code title="class PlanStep">PlanStep.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">mark_complete</a>() -> None</code>
+* <code title="class PlanStep">PlanStep.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">mark_in_progress</a>() -> None</code>
+* <code title="class PlanStep">PlanStep.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">mark_skipped</a>() -> None</code>
+* <code title="class PlanStep">PlanStep.<a href="./src/praisonai-agents/praisonaiagents/planning/plan.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class PlanStorage">PlanStorage.<a href="./src/praisonai-agents/praisonaiagents/planning/storage.py">cleanup_old_plans</a>(keep_count: int = 10) -> int</code>
+* <code title="class PlanStorage">PlanStorage.<a href="./src/praisonai-agents/praisonaiagents/planning/storage.py">delete_plan</a>(plan_id: str) -> bool</code>
+* <code title="class PlanStorage">PlanStorage.<a href="./src/praisonai-agents/praisonaiagents/planning/storage.py">delete_todo</a>(name: str) -> bool</code>
+* <code title="class PlanStorage">PlanStorage.<a href="./src/praisonai-agents/praisonaiagents/planning/storage.py">get_latest_plan</a>() -> Optional[Plan]</code>
+* <code title="class PlanStorage">PlanStorage.<a href="./src/praisonai-agents/praisonaiagents/planning/storage.py">list_plans</a>() -> List[Dict[str, Any]]</code>
+* <code title="class PlanStorage">PlanStorage.<a href="./src/praisonai-agents/praisonaiagents/planning/storage.py">list_todos</a>() -> List[str]</code>
+* <code title="class PlanStorage">PlanStorage.<a href="./src/praisonai-agents/praisonaiagents/planning/storage.py">load_plan</a>(plan_id: str) -> Optional[Plan]</code>
+* <code title="class PlanStorage">PlanStorage.<a href="./src/praisonai-agents/praisonaiagents/planning/storage.py">load_plan_from_file</a>(path: str) -> Optional[Plan]</code>
+* <code title="class PlanStorage">PlanStorage.<a href="./src/praisonai-agents/praisonaiagents/planning/storage.py">load_todo</a>(name: str = 'current') -> Optional[TodoList]</code>
+* <code title="class PlanStorage">PlanStorage.<a href="./src/praisonai-agents/praisonaiagents/planning/storage.py">save_plan</a>(plan: Plan, filename: Optional[str] = None) -> str</code>
+* <code title="class PlanStorage">PlanStorage.<a href="./src/praisonai-agents/praisonaiagents/planning/storage.py">save_todo</a>(todo: TodoList, name: str = 'current') -> str</code>
+* <code title="class PlanningConfig">PlanningConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class TodoItem">TodoItem.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">complete</a>() -> None</code>
+* <code title="class TodoItem">TodoItem.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">from_dict</a>(data: Dict[str, Any]) -> 'TodoItem'</code>
+* <code title="class TodoItem">TodoItem.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">is_ready</a>(completed_ids: List[str]) -> bool</code>
+* <code title="class TodoItem">TodoItem.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">reset</a>() -> None</code>
+* <code title="class TodoItem">TodoItem.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">start</a>() -> None</code>
+* <code title="class TodoItem">TodoItem.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">add</a>(item: Union[TodoItem, str]) -> TodoItem</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">complete</a>(item_id: str) -> bool</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">completed</a>() -> List[TodoItem]</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">completed_ids</a>() -> List[str]</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">from_dict</a>(data: Dict[str, Any]) -> 'TodoList'</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">from_json</a>(json_str: str) -> 'TodoList'</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">from_markdown</a>(markdown: str) -> 'TodoList'</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">from_plan</a>(plan: 'Plan') -> 'TodoList'</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">get</a>(item_id: str) -> Optional[TodoItem]</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">get_ready_items</a>() -> List[TodoItem]</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">in_progress</a>() -> List[TodoItem]</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">is_complete</a>() -> bool</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">pending</a>() -> List[TodoItem]</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">progress</a>() -> float</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">remove</a>(item_id: str) -> bool</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">start</a>(item_id: str) -> bool</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">sync_with_plan</a>(plan: 'Plan') -> None</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">to_json</a>() -> str</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">to_markdown</a>() -> str</code>
+* <code title="class TodoList">TodoList.<a href="./src/praisonai-agents/praisonaiagents/planning/todo.py">update_from_plan</a>(plan: 'Plan') -> None</code>
+
 # Skills
 
 Types:
 ```python
-from praisonaiagents import SkillLoader, SkillManager, SkillMetadata, SkillProperties
+from praisonaiagents import SkillLoader, SkillManager, SkillMetadata, SkillProperties, SkillsConfig
 ```
 
 Methods:
@@ -452,6 +541,7 @@ Methods:
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">to_prompt</a>() -> str</code>
 * <code title="class SkillMetadata">SkillMetadata.<a href="./src/praisonai-agents/praisonaiagents/skills/models.py">from_properties</a>(props: SkillProperties) -> 'SkillMetadata'</code>
 * <code title="class SkillProperties">SkillProperties.<a href="./src/praisonai-agents/praisonaiagents/skills/models.py">to_dict</a>() -> dict</code>
+* <code title="class SkillsConfig">SkillsConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 
 # Session
 
@@ -480,15 +570,50 @@ Methods:
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">send_message</a>(message: str, **kwargs) -> str</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">set_state</a>(key: str, value: Any) -> None</code>
 
-# Telemetry
+# MCP
 
 Types:
 ```python
-from praisonaiagents import get_telemetry
+from praisonaiagents import MCP
 ```
 
 Methods:
 
+* <code title="class MCP">MCP.<a href="./src/praisonai-agents/praisonaiagents/mcp/mcp.py">get_tools</a>() -> List[Callable]</code>
+* <code title="class MCP">MCP.<a href="./src/praisonai-agents/praisonaiagents/mcp/mcp.py">shutdown</a>()</code>
+* <code title="class MCP">MCP.<a href="./src/praisonai-agents/praisonaiagents/mcp/mcp.py">to_openai_tool</a>()</code>
+
+# Telemetry
+
+Types:
+```python
+from praisonaiagents import MinimalTelemetry, TelemetryCollector, cleanup_telemetry_resources, disable_performance_mode, disable_telemetry, enable_performance_mode, enable_telemetry, get_telemetry
+```
+
+Methods:
+
+* <code title="class MinimalTelemetry">MinimalTelemetry.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">flush</a>()</code>
+* <code title="class MinimalTelemetry">MinimalTelemetry.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">get_metrics</a>() -> Dict[str, Any]</code>
+* <code title="class MinimalTelemetry">MinimalTelemetry.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">shutdown</a>()</code>
+* <code title="class MinimalTelemetry">MinimalTelemetry.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">track_agent_execution</a>(agent_name: str = None, success: bool = True, async_mode: bool = False)</code>
+* <code title="class MinimalTelemetry">MinimalTelemetry.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">track_error</a>(error_type: str = None)</code>
+* <code title="class MinimalTelemetry">MinimalTelemetry.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">track_feature_usage</a>(feature_name: str)</code>
+* <code title="class MinimalTelemetry">MinimalTelemetry.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">track_task_completion</a>(task_name: str = None, success: bool = True)</code>
+* <code title="class MinimalTelemetry">MinimalTelemetry.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">track_tool_usage</a>(tool_name: str, success: bool = True, execution_time: float = None)</code>
+* <code title="class TelemetryCollector">TelemetryCollector.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">get_metrics</a>() -> Dict[str, Any]</code>
+* <code title="class TelemetryCollector">TelemetryCollector.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">record_cost</a>(cost: float, model: str = None)</code>
+* <code title="class TelemetryCollector">TelemetryCollector.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">record_tokens</a>(prompt_tokens: int, completion_tokens: int, model: str = None)</code>
+* <code title="class TelemetryCollector">TelemetryCollector.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">start</a>()</code>
+* <code title="class TelemetryCollector">TelemetryCollector.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">stop</a>()</code>
+* <code title="class TelemetryCollector">TelemetryCollector.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">trace_agent_execution</a>(agent_name: str, **attributes)</code>
+* <code title="class TelemetryCollector">TelemetryCollector.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">trace_llm_call</a>(model: str = None, **attributes)</code>
+* <code title="class TelemetryCollector">TelemetryCollector.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">trace_task_execution</a>(task_name: str, agent_name: str = None, **attributes)</code>
+* <code title="class TelemetryCollector">TelemetryCollector.<a href="./src/praisonai-agents/praisonaiagents/telemetry/telemetry.py">trace_tool_call</a>(tool_name: str, **attributes)</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/telemetry/__init__.py">cleanup_telemetry_resources</a>()</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/telemetry/__init__.py">disable_performance_mode</a>()</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/telemetry/__init__.py">disable_telemetry</a>()</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/telemetry/__init__.py">enable_performance_mode</a>()</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/telemetry/__init__.py">enable_telemetry</a>()</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/telemetry/__init__.py">get_telemetry</a>() -> 'MinimalTelemetry'</code>
 
 # Observability
@@ -515,6 +640,7 @@ from praisonaiagents import ContextManager, ManagerConfig
 Methods:
 
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">capture_llm_boundary</a>(messages: List[Dict[str, Any]], tools: List[Dict[str, Any]]) -> SnapshotHookData</code>
+* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">emergency_truncate</a>(messages: List[Dict[str, Any]], target_tokens: int) -> List[Dict[str, Any]]</code>
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">estimate_tokens</a>(text: str, validate: bool = False) -> Tuple[int, Optional[EstimationMetrics]]</code>
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">get_history</a>() -> List[Dict[str, Any]]</code>
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">get_last_snapshot_hook</a>() -> Optional[SnapshotHookData]</code>
@@ -530,6 +656,34 @@ Methods:
 * <code title="class ManagerConfig">ManagerConfig.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">merge</a>(**overrides) -> 'ManagerConfig'</code>
 * <code title="class ManagerConfig">ManagerConfig.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">to_dict</a>() -> Dict[str, Any]</code>
 
+# Config
+
+Types:
+```python
+from praisonaiagents import AutonomyConfig, AutonomyLevel, CachingConfig, ExecutionConfig, ExecutionPreset, GuardrailConfig, HooksConfig, KnowledgeConfig, MemoryConfig, MultiAgentExecutionConfig, MultiAgentHooksConfig, MultiAgentMemoryConfig, MultiAgentOutputConfig, MultiAgentPlanningConfig, OutputConfig, OutputPreset, PlanningConfig, ReflectionConfig, SkillsConfig, TemplateConfig, WebConfig, WebSearchProvider
+```
+
+Methods:
+
+* <code title="class AutonomyConfig">AutonomyConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class CachingConfig">CachingConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class ExecutionConfig">ExecutionConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class GuardrailConfig">GuardrailConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class HooksConfig">HooksConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class KnowledgeConfig">KnowledgeConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class MemoryConfig">MemoryConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class MultiAgentExecutionConfig">MultiAgentExecutionConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class MultiAgentHooksConfig">MultiAgentHooksConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class MultiAgentMemoryConfig">MultiAgentMemoryConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class MultiAgentOutputConfig">MultiAgentOutputConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class MultiAgentPlanningConfig">MultiAgentPlanningConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class OutputConfig">OutputConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class PlanningConfig">PlanningConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class ReflectionConfig">ReflectionConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class SkillsConfig">SkillsConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class TemplateConfig">TemplateConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class WebConfig">WebConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+
 # Display
 
 Types:
@@ -543,32 +697,57 @@ Methods:
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_error</a>(message: str, console = None)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_generating</a>(content: str = '', start_time: Optional[float] = None)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_instruction</a>(message: str, console = None, agent_name: str = None, agent_role: str = None, agent_tools: List[str] = None)</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_interaction</a>(message, response, markdown = True, generation_time = None, console = None, agent_name = None, agent_role = None, agent_tools = None, task_name = None, task_description = None, task_id = None)</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_interaction</a>(message, response, markdown = True, generation_time = None, console = None, agent_name = None, agent_role = None, agent_tools = None, task_name = None, task_description = None, task_id = None, metrics = None)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_self_reflection</a>(message: str, console = None)</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_tool_call</a>(message: str, console = None, tool_name: str = None, tool_input: dict = None, tool_output: str = None)</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_tool_call</a>(message: str, console = None, tool_name: str = None, tool_input: dict = None, tool_output: str = None, elapsed_time: float = None, success: bool = True)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">register_display_callback</a>(display_type: str, callback_fn, is_async: bool = False)</code>
 
+# Utilities
+
+Types:
+```python
+from praisonaiagents import ArrayMode, is_policy_string, parse_policy_string, resolve, resolve_autonomy, resolve_caching, resolve_context, resolve_execution, resolve_guardrail_policies, resolve_guardrails, resolve_hooks, resolve_knowledge, resolve_memory, resolve_output, resolve_planning, resolve_reflection, resolve_routing, resolve_skills, resolve_web
+```
+
+Methods:
+
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/parse_utils.py">is_policy_string</a>(value: str) -> bool</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/parse_utils.py">parse_policy_string</a>(value: str) -> tuple</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve</a>(value: Any, param_name: str, config_class: Optional[Type] = None, presets: Optional[Dict[str, Any]] = None, default: Any = None, instance_check: Optional[Callable[[Any], bool]] = None, url_schemes: Optional[Dict[str, str]] = None, array_mode: Optional[str] = None, string_mode: Optional[str] = None) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_autonomy</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_caching</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_context</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_execution</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_guardrail_policies</a>(policies: list, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_guardrails</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_hooks</a>(value: Any, config_class: Optional[Type] = None) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_knowledge</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_memory</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_output</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_planning</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_reflection</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_routing</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_skills</a>(value: Any, config_class: Type) -> Any</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/config/param_resolver.py">resolve_web</a>(value: Any, config_class: Type) -> Any</code>
+
 # Other
 
 Types:
 ```python
-from praisonaiagents import AutoRagConfig, Citation, CodeExecutionStep, DeepResearchResponse, ExpandResult, ExpandStrategy, FileSearchCall, MCPCall, Provider, RagRetrievalPolicy, ReasoningStep, RewriteResult, RewriteStrategy, Task, WebSearchCall
+from praisonaiagents import EmbeddingResult, Task, aembedding, aembeddings, embedding, embeddings, get_dimensions
 ```
 
 Methods:
 
-* <code title="class AutoRagConfig">AutoRagConfig.<a href="./src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py">from_dict</a>(data: Dict[str, Any]) -> 'AutoRagConfig'</code>
-* <code title="class AutoRagConfig">AutoRagConfig.<a href="./src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py">to_dict</a>() -> Dict[str, Any]</code>
-* <code title="class Citation">Citation.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">from_dict</a>(data: Dict[str, Any]) -> 'Citation'</code>
-* <code title="class Citation">Citation.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">to_dict</a>() -> Dict[str, Any]</code>
-* <code title="class DeepResearchResponse">DeepResearchResponse.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">get_all_sources</a>() -> List[Dict[str, str]]</code>
-* <code title="class DeepResearchResponse">DeepResearchResponse.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">get_citation_text</a>(citation: Citation) -> str</code>
-* <code title="class RewriteResult">RewriteResult.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">all_queries</a>() -> List[str]</code>
-* <code title="class RewriteResult">RewriteResult.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">primary_query</a>() -> str</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">execute_callback</a>(task_output: TaskOutput) -> None</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">execute_callback_sync</a>(task_output: TaskOutput) -> None</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">initialize_memory</a>()</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">store_in_memory</a>(content: str, agent_name: str = None, task_id: str = None)</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/embedding/embed.py">aembedding</a>(input: Union[str, List[str]], model: str = 'text-embedding-3-small', dimensions: Optional[int] = None, encoding_format: str = 'float', timeout: float = 600.0, api_key: Optional[str] = None, api_base: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) -> EmbeddingResult</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/embedding/embed.py">aembeddings</a>(input: Union[str, List[str]], model: str = 'text-embedding-3-small', dimensions: Optional[int] = None, encoding_format: str = 'float', timeout: float = 600.0, api_key: Optional[str] = None, api_base: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) -> EmbeddingResult</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/embedding/embed.py">embedding</a>(input: Union[str, List[str]], model: str = 'text-embedding-3-small', dimensions: Optional[int] = None, encoding_format: str = 'float', timeout: float = 600.0, api_key: Optional[str] = None, api_base: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) -> EmbeddingResult</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/embedding/embed.py">embeddings</a>(input: Union[str, List[str]], model: str = 'text-embedding-3-small', dimensions: Optional[int] = None, encoding_format: str = 'float', timeout: float = 600.0, api_key: Optional[str] = None, api_base: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) -> EmbeddingResult</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/embedding/dimensions.py">get_dimensions</a>(model_name: str) -> int</code>
 
 # Wrapper (praisonai)
 
@@ -587,6 +766,10 @@ Methods:
 * <code title="cli">praisonai agents info <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
 * <code title="cli">praisonai agents list <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
 * <code title="cli">praisonai audit agent-centric <a href="./src/praisonai/praisonai/cli/commands/audit.py">--help</a></code>
+* <code title="cli">praisonai batch batch-run <a href="./src/praisonai/praisonai/cli/commands/batch.py">--help</a></code>
+* <code title="cli">praisonai batch list <a href="./src/praisonai/praisonai/cli/commands/batch.py">--help</a></code>
+* <code title="cli">praisonai batch report <a href="./src/praisonai/praisonai/cli/commands/batch.py">--help</a></code>
+* <code title="cli">praisonai batch stats <a href="./src/praisonai/praisonai/cli/commands/batch.py">--help</a></code>
 * <code title="cli">praisonai benchmark agent <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
 * <code title="cli">praisonai benchmark benchmark-callback <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
 * <code title="cli">praisonai benchmark cli <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
@@ -614,8 +797,11 @@ Methods:
 * <code title="cli">praisonai context clear <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
 * <code title="cli">praisonai context compact <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
 * <code title="cli">praisonai context export <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
+* <code title="cli">praisonai context grep <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
+* <code title="cli">praisonai context list <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
 * <code title="cli">praisonai context show <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
 * <code title="cli">praisonai context stats <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
+* <code title="cli">praisonai context tail <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
 * <code title="cli">praisonai debug acp <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
 * <code title="cli">praisonai debug debug-callback <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
 * <code title="cli">praisonai debug interactive <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
@@ -635,6 +821,7 @@ Methods:
 * <code title="cli">praisonai docs run-all <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
 * <code title="cli">praisonai docs serve <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
 * <code title="cli">praisonai docs stats <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
+* <code title="cli">praisonai doctor cleanup <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor config <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor db <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor doctor-callback <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
@@ -644,12 +831,15 @@ Methods:
 * <code title="cli">praisonai doctor performance <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor selftest <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor tools <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
+* <code title="cli">praisonai doctor troubleshoot <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai endpoints list <a href="./src/praisonai/praisonai/cli/commands/endpoints.py">--help</a></code>
 * <code title="cli">praisonai endpoints test <a href="./src/praisonai/praisonai/cli/commands/endpoints.py">--help</a></code>
 * <code title="cli">praisonai environment check <a href="./src/praisonai/praisonai/cli/commands/environment.py">--help</a></code>
 * <code title="cli">praisonai environment doctor <a href="./src/praisonai/praisonai/cli/commands/environment.py">--help</a></code>
 * <code title="cli">praisonai environment view <a href="./src/praisonai/praisonai/cli/commands/environment.py">--help</a></code>
 * <code title="cli">praisonai eval accuracy <a href="./src/praisonai/praisonai/cli/commands/eval.py">--help</a></code>
+* <code title="cli">praisonai eval judge <a href="./src/praisonai/praisonai/cli/commands/eval.py">--help</a></code>
+* <code title="cli">praisonai eval list-judges <a href="./src/praisonai/praisonai/cli/commands/eval.py">--help</a></code>
 * <code title="cli">praisonai eval performance <a href="./src/praisonai/praisonai/cli/commands/eval.py">--help</a></code>
 * <code title="cli">praisonai examples info <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
 * <code title="cli">praisonai examples list <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
@@ -664,20 +854,30 @@ Methods:
 * <code title="cli">praisonai knowledge index <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
 * <code title="cli">praisonai knowledge list <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
 * <code title="cli">praisonai knowledge search <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
+* <code title="cli">praisonai loop help <a href="./src/praisonai/praisonai/cli/commands/loop.py">--help</a></code>
+* <code title="cli">praisonai loop loop-main <a href="./src/praisonai/praisonai/cli/commands/loop.py">--help</a></code>
 * <code title="cli">praisonai lsp logs <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
 * <code title="cli">praisonai lsp lsp-callback <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
 * <code title="cli">praisonai lsp start <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
 * <code title="cli">praisonai lsp status <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
 * <code title="cli">praisonai lsp stop <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
 * <code title="cli">praisonai mcp add <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
+* <code title="cli">praisonai mcp auth <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
+* <code title="cli">praisonai mcp describe <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
 * <code title="cli">praisonai mcp list <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
+* <code title="cli">praisonai mcp logout <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
 * <code title="cli">praisonai mcp mcp-callback <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
 * <code title="cli">praisonai mcp remove <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
+* <code title="cli">praisonai mcp run <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
+* <code title="cli">praisonai mcp status <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
+* <code title="cli">praisonai mcp sync <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
 * <code title="cli">praisonai mcp test <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
+* <code title="cli">praisonai mcp tools <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
 * <code title="cli">praisonai memory add <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
 * <code title="cli">praisonai memory clear <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
 * <code title="cli">praisonai memory search <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
 * <code title="cli">praisonai memory show <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
+* <code title="cli">praisonai memory status <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
 * <code title="cli">praisonai package install <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
 * <code title="cli">praisonai package list <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
 * <code title="cli">praisonai package uninstall <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
@@ -694,11 +894,24 @@ Methods:
 * <code title="cli">praisonai rag query <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
 * <code title="cli">praisonai rag serve <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
 * <code title="cli">praisonai realtime realtime-main <a href="./src/praisonai/praisonai/cli/commands/realtime.py">--help</a></code>
+* <code title="cli">praisonai recipe apply <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
+* <code title="cli">praisonai recipe create <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
+* <code title="cli">praisonai recipe info <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
 * <code title="cli">praisonai recipe install <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
+* <code title="cli">praisonai recipe judge <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
 * <code title="cli">praisonai recipe list <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
+* <code title="cli">praisonai recipe optimize <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
 * <code title="cli">praisonai recipe run <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
+* <code title="cli">praisonai recipe serve <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
 * <code title="cli">praisonai registry list <a href="./src/praisonai/praisonai/cli/commands/registry.py">--help</a></code>
 * <code title="cli">praisonai registry serve <a href="./src/praisonai/praisonai/cli/commands/registry.py">--help</a></code>
+* <code title="cli">praisonai replay cleanup <a href="./src/praisonai/praisonai/cli/commands/replay.py">--help</a></code>
+* <code title="cli">praisonai replay context <a href="./src/praisonai/praisonai/cli/commands/replay.py">--help</a></code>
+* <code title="cli">praisonai replay dashboard <a href="./src/praisonai/praisonai/cli/commands/replay.py">--help</a></code>
+* <code title="cli">praisonai replay delete <a href="./src/praisonai/praisonai/cli/commands/replay.py">--help</a></code>
+* <code title="cli">praisonai replay flow <a href="./src/praisonai/praisonai/cli/commands/replay.py">--help</a></code>
+* <code title="cli">praisonai replay list <a href="./src/praisonai/praisonai/cli/commands/replay.py">--help</a></code>
+* <code title="cli">praisonai replay show <a href="./src/praisonai/praisonai/cli/commands/replay.py">--help</a></code>
 * <code title="cli">praisonai research research-main <a href="./src/praisonai/praisonai/cli/commands/research.py">--help</a></code>
 * <code title="cli">praisonai retrieval index <a href="./src/praisonai/praisonai/cli/commands/retrieval.py">--help</a></code>
 * <code title="cli">praisonai retrieval query <a href="./src/praisonai/praisonai/cli/commands/retrieval.py">--help</a></code>
@@ -717,10 +930,24 @@ Methods:
 * <code title="cli">praisonai schedule start <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule stats <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule stop <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <code title="cli">praisonai serve a2a <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve a2u <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve acp <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve agents <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve docs <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve gateway <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve lsp <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve mcp <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve rag <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve recipe <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve registry <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve scheduler <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve serve-callback <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve start <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve status <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve stop <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve ui <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
+* <code title="cli">praisonai serve unified <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai session delete <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
 * <code title="cli">praisonai session export <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
 * <code title="cli">praisonai session import <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
@@ -728,12 +955,19 @@ Methods:
 * <code title="cli">praisonai session resume <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
 * <code title="cli">praisonai session show <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
 * <code title="cli">praisonai skills create <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
+* <code title="cli">praisonai skills info <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
 * <code title="cli">praisonai skills install <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
 * <code title="cli">praisonai skills list <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
+* <code title="cli">praisonai skills search <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
 * <code title="cli">praisonai skills validate <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
+* <code title="cli">praisonai standardise check <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
+* <code title="cli">praisonai standardise fix <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
+* <code title="cli">praisonai standardise init <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
+* <code title="cli">praisonai standardise report <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
 * <code title="cli">praisonai templates create <a href="./src/praisonai/praisonai/cli/commands/templates.py">--help</a></code>
 * <code title="cli">praisonai templates list <a href="./src/praisonai/praisonai/cli/commands/templates.py">--help</a></code>
 * <code title="cli">praisonai test info <a href="./src/praisonai/praisonai/cli/commands/test.py">--help</a></code>
+* <code title="cli">praisonai test interactive <a href="./src/praisonai/praisonai/cli/commands/test.py">--help</a></code>
 * <code title="cli">praisonai test run <a href="./src/praisonai/praisonai/cli/commands/test.py">--help</a></code>
 * <code title="cli">praisonai todo add <a href="./src/praisonai/praisonai/cli/commands/todo.py">--help</a></code>
 * <code title="cli">praisonai todo done <a href="./src/praisonai/praisonai/cli/commands/todo.py">--help</a></code>
@@ -741,11 +975,16 @@ Methods:
 * <code title="cli">praisonai tools info <a href="./src/praisonai/praisonai/cli/commands/tools.py">--help</a></code>
 * <code title="cli">praisonai tools list <a href="./src/praisonai/praisonai/cli/commands/tools.py">--help</a></code>
 * <code title="cli">praisonai tools test <a href="./src/praisonai/praisonai/cli/commands/tools.py">--help</a></code>
+* <code title="cli">praisonai tools validate <a href="./src/praisonai/praisonai/cli/commands/tools.py">--help</a></code>
 * <code title="cli">praisonai traces disable <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
 * <code title="cli">praisonai traces enable <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
 * <code title="cli">praisonai traces list <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
 * <code title="cli">praisonai traces status <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
-* <code title="cli">praisonai train train-main <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
+* <code title="cli">praisonai train agents <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
+* <code title="cli">praisonai train list <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
+* <code title="cli">praisonai train llm <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
+* <code title="cli">praisonai train show <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
+* <code title="cli">praisonai train train-callback <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
 * <code title="cli">praisonai ui chat <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
 * <code title="cli">praisonai ui code <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
 * <code title="cli">praisonai ui gradio <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
@@ -764,6 +1003,8 @@ Types/Exports:
 ```ts
 export { Agent, Agents, PraisonAIAgents, Router } from "./agent";
 export type { PraisonAIAgentsConfig, SimpleAgentConfig, SimpleRouteConfig, SimpleRouterConfig } from "./agent";
+export { AudioAgent, createAudioAgent } from "./agent/audio";
+export type { AudioAgentConfig, AudioProvider, AudioSpeakOptions, AudioSpeakResult, AudioTranscribeOptions, AudioTranscribeResult } from "./agent/audio";
 export { ContextAgent, createContextAgent } from "./agent/context";
 export { Handoff, handoff, handoffFilters } from "./agent/handoff";
 export { ImageAgent, createImageAgent } from "./agent/image";
@@ -785,7 +1026,7 @@ export { // Agent loop
   configureTelemetry, // Tool Approval (AI SDK v6 parity)
   ApprovalManager, // Tools
   defineTool, // UI Message (AI SDK v6 parity)
-  convertToModelMessages, AIAgentStep, AIEmbedManyResult, AIEmbedOptions, AIEmbedResult, AIFilePart, AIGenerateImageOptions, AIGenerateImageResult, AIGenerateObjectOptions, AIGenerateObjectResult, AIGenerateTextOptions, AIGenerateTextResult, AIImagePart, AIMiddleware, AIMiddlewareConfig, AIModelMessage, AISpan, AISpanKind, AISpanOptions, AISpanStatus, AIStreamObjectOptions, AIStreamObjectResult, AIStreamTextOptions, AIStreamTextResult, AITelemetryEvent, AITelemetrySettings, AITextPart, AIToolDefinition, AITracer, AgentLoop, DANGEROUS_PATTERNS, MODEL_ALIASES, SPEECH_MODELS, TRANSCRIPTION_MODELS, ToolApprovalDeniedError, ToolApprovalTimeoutError, aiEmbed, aiEmbedMany, aiGenerateImage, aiGenerateObject, aiGenerateText, aiStreamObject, aiStreamText, applyMiddleware, autoEnableDevTools, base64ToUint8Array, clearAICache, clearEvents, closeAllMCPClients, closeMCPClient, convertToUIMessages, createAILoggingMiddleware, createAISpan, createApprovalResponse, createDangerousPatternChecker, createDevToolsMiddleware, createExpressHandler, createFastifyHandler, createFilePart, createHonoHandler, createMultimodalMessage, createNestHandler, createPagesHandler, createPdfPart, createSystemMessage, createTelemetryMiddleware, createTelemetrySettings, createTextMessage, createTextPart, createToolSet, disableAITelemetry, disableDevTools, enableAITelemetry, functionToTool, getAICacheStats, getApprovalManager, getDevToolsState, getDevToolsUrl, getEvents, getMCPClient, getModel, getTelemetrySettings, getToolsNeedingApproval, getTracer, hasModelAlias, hasPendingApprovals, initOpenTelemetry, isDangerous, isDataUrl, isDevToolsEnabled, isTelemetryEnabled, isUrl, listModelAliases, mcpToolsToAITools, parseModel, pipeUIMessageStreamToResponse, recordEvent, resolveModelAlias, safeValidateUIMessages, setApprovalManager, stopAfterSteps, stopWhen, stopWhenNoToolCalls, toMessageContent, toUIMessageStreamResponse, transcribe, uint8ArrayToBase64, validateUIMessages, withApproval, withSpan, wrapModel } from "./ai";
+  convertToModelMessages, AIAgentStep, AIEmbedManyResult, AIEmbedOptions, AIEmbedResult, AIFilePart, AIGenerateImageOptions, AIGenerateImageResult, AIGenerateObjectOptions, AIGenerateObjectResult, AIGenerateTextOptions, AIGenerateTextResult, AIImagePart, AIMiddleware, AIMiddlewareConfig, AIModelMessage, AISpan, AISpanKind, AISpanOptions, AISpanStatus, AIStreamObjectOptions, AIStreamObjectResult, AIStreamTextOptions, AIStreamTextResult, AITelemetryEvent, AITelemetrySettings, AITextPart, AIToolDefinition, AITracer, AgentLoop, DANGEROUS_PATTERNS, MCPClientType, MODEL_ALIASES, SPEECH_MODELS, TRANSCRIPTION_MODELS, ToolApprovalDeniedError, ToolApprovalTimeoutError, aiEmbed, aiEmbedMany, aiGenerateImage, aiGenerateObject, aiGenerateText, aiStreamObject, aiStreamText, applyMiddleware, autoEnableDevTools, base64ToUint8Array, clearAICache, clearEvents, closeAllMCPClients, closeMCPClient, convertToUIMessages, createAILoggingMiddleware, createAISpan, createApprovalResponse, createDangerousPatternChecker, createDevToolsMiddleware, createExpressHandler, createFastifyHandler, createFilePart, createHonoHandler, createMultimodalMessage, createNestHandler, createPagesHandler, createPdfPart, createSystemMessage, createTelemetryMiddleware, createTelemetrySettings, createTextMessage, createTextPart, createToolSet, disableAITelemetry, disableDevTools, enableAITelemetry, functionToTool, getAICacheStats, getApprovalManager, getDevToolsState, getDevToolsUrl, getEvents, getMCPClient, getModel, getTelemetrySettings, getToolsNeedingApproval, getTracer, hasModelAlias, hasPendingApprovals, initOpenTelemetry, isDangerous, isDataUrl, isDevToolsEnabled, isTelemetryEnabled, isUrl, listModelAliases, mcpToolsToAITools, parseModel, pipeUIMessageStreamToResponse, recordEvent, resolveModelAlias, safeValidateUIMessages, setApprovalManager, stopAfterSteps, stopWhen, stopWhenNoToolCalls, toMessageContent, toUIMessageStreamResponse, transcribe, uint8ArrayToBase64, validateUIMessages, withApproval, withSpan, wrapModel } from "./ai";
 export { AutoAgents, createAutoAgents } from "./auto";
 export { BaseCache, FileCache, MemoryCache, createFileCache, createMemoryCache } from "./cache";
 export { CLI_SPEC_VERSION, executeCommand, parseArgs } from "./cli";
@@ -809,9 +1050,11 @@ export type { DbAdapter, DbConfig, DbMessage, DbRun, DbTrace } from "./db";
 export { MemoryPostgresAdapter, NeonPostgresAdapter, PostgresSessionStorage, createMemoryPostgres, createNeonPostgres, createPostgresSessionStorage } from "./db/postgres";
 export { MemoryRedisAdapter, UpstashRedisAdapter, createMemoryRedis, createUpstashRedis } from "./db/redis";
 export { SQLiteAdapter, createSQLiteAdapter } from "./db/sqlite";
-export { EvalSuite, accuracyEval, performanceEval, reliabilityEval } from "./eval";
+export { // LLM-as-Judge
+  Judge, AccuracyJudge, CriteriaJudge, EvalResults, EvalSuite, Evaluator, RecipeJudge, accuracyEval, addJudge, addOptimizationRule, containsKeywordsCriterion, createDefaultEvaluator, createEvalResults, createEvaluator, getJudge, getOptimizationRule, lengthCriterion, listJudges, listOptimizationRules, noHarmfulContentCriterion, parseJudgeResponse, performanceEval, relevanceCriterion, reliabilityEval, removeJudge, removeOptimizationRule } from "./eval";
 export { AgentEventBus, AgentEvents, EventEmitterPubSub, PubSub, createEventBus, createPubSub } from "./events";
 export { LLMGuardrail, createLLMGuardrail } from "./guardrails/llm-guardrail";
+export { DisplayTypes, HooksManager, WorkflowHooksExecutor, clearAllCallbacks, clearApprovalCallback, createHooksManager, createLoggingOperationHooks, createLoggingWorkflowHooks, createTimingWorkflowHooks, createValidationOperationHooks, createWorkflowHooks, executeCallback, executeSyncCallback, getRegisteredDisplayTypes, hasApprovalCallback, registerApprovalCallback, registerDisplayCallback, requestApproval, unregisterDisplayCallback } from "./hooks";
 export { // Computer Use
   createComputerUse, ComputerUseClient, createCLIApprovalPrompt, createComputerUseAgent } from "./integrations/computer-use";
 export { BaseObservabilityProvider, ConsoleObservabilityProvider, LangfuseObservabilityProvider, MemoryObservabilityProvider, ObservabilityTraceContext, createConsoleObservability, createLangfuseObservability, createMemoryObservability } from "./integrations/observability";
@@ -829,10 +1072,14 @@ export { // Provider classes
   ProviderRegistry, // Types
   type LLMProvider, AnthropicProvider, BaseProvider, GoogleProvider, ProviderMessage, ProviderToolDefinition, createProviderRegistry, getAvailableProviders, getDefaultProvider, getDefaultRegistry, hasProvider, isProviderAvailable, listProviders, parseModelString, registerBuiltinProviders, registerProvider, unregisterProvider } from "./llm/providers";
 export { ADAPTERS, AISDK_PROVIDERS, COMMUNITY_PROVIDERS, PROVIDER_ALIASES } from "./llm/providers/ai-sdk/types";
+export { MCPClient, MCPSecurity, MCPServer, MCPSessionManager, createApiKeyPolicy, createMCPClient, createMCPSecurity, createMCPServer, createMCPSession, createRateLimitPolicy, getMCPTools } from "./mcp";
 export { AutoMemory, AutoMemoryKnowledgeBase, AutoMemoryVectorStore, DEFAULT_POLICIES, createAutoMemory, createLLMSummarizer } from "./memory/auto-memory";
+export { DocsManager, createDocsManager } from "./memory/docs-manager";
 export { FileMemory, createFileMemory } from "./memory/file-memory";
+export { MemoryHooks, createEncryptionHooks, createLoggingHooks, createMemoryHooks, createValidationHooks } from "./memory/hooks";
 export { Memory, createMemory } from "./memory/memory";
 export type { MemoryConfig, MemoryEntry } from "./memory/memory";
+export { RulesManager, createRulesManager, createSafetyRules } from "./memory/rules-manager";
 export { // Adapters
   NoopObservabilityAdapter, // Constants
   OBSERVABILITY_TOOLS, // Global adapter management
@@ -840,14 +1087,17 @@ export { // Adapters
   type SpanKind, ConsoleObservabilityAdapter, MemoryObservabilityAdapter, clearAdapterCache, createConsoleAdapter, createMemoryAdapter, createObservabilityAdapter, getObservabilityAdapter, getObservabilityToolInfo, hasObservabilityToolEnvVar, listObservabilityTools, noopAdapter, resetObservabilityAdapter, trace } from "./observability";
 export { Plan, PlanStep, PlanStorage, PlanningAgent, TaskAgent, TodoItem, TodoList, createPlan, createPlanStorage, createPlanningAgent, createTaskAgent, createTodoList } from "./planning";
 export { SkillManager, createSkillManager, parseSkillFile } from "./skills";
-export { AgentTelemetry, TelemetryCollector, cleanupTelemetry, createAgentTelemetry, disableTelemetry, enableTelemetry, getTelemetry } from "./telemetry";
-export { BaseTool, FunctionTool, ToolRegistry, ToolResult, ToolValidationError, createTool, getRegistry, getTool, registerTool, tool, validateTool } from "./tools";
+export { AgentTelemetry, PerformanceMonitor, TelemetryCollector, TelemetryIntegration, cleanupTelemetry, createAgentTelemetry, createConsoleSink, createHTTPSink, createPerformanceMonitor, createTelemetryIntegration, disableTelemetry, enableTelemetry, getTelemetry } from "./telemetry";
+export { // Subagent Tool (agent-as-tool pattern)
+  SubagentTool, BaseTool, FunctionTool, ToolRegistry, ToolResult, ToolValidationError, createDelegator, createSubagentTool, createSubagentTools, createTool, getRegistry, getTool, registerTool, tool, validateTool } from "./tools";
 export { airweaveSearch, bedrockBrowserClick, bedrockBrowserFill, bedrockBrowserNavigate, bedrockCodeInterpreter, codeExecution, codeMode, createCustomTool, exaSearch, firecrawlCrawl, firecrawlScrape, parallelSearch, perplexitySearch, registerCustomTool, registerLocalTool, registerNpmTool, superagentGuard, superagentRedact, superagentVerify, tavilyCrawl, tavilyExtract, tavilySearch, valyuBioSearch, valyuCompanyResearch, valyuEconomicsSearch, valyuFinanceSearch, valyuPaperSearch, valyuPatentSearch, valyuSecSearch, valyuWebSearch } from "./tools/builtins";
 export { MissingDependencyError, MissingEnvVarError, ToolsRegistry, composeMiddleware, createLoggingMiddleware, createRateLimitMiddleware, createRedactionMiddleware, createRetryMiddleware, createTimeoutMiddleware, createToolsRegistry, createTracingMiddleware, createValidationMiddleware, getToolsRegistry, resetToolsRegistry } from "./tools/registry";
 export type { InstallHints, PraisonTool, RedactionHooks, RegisteredTool, ToolCapabilities, ToolExecutionContext, ToolExecutionResult, ToolFactory, ToolHooks, ToolInstallStatus, ToolLimits, ToolLogger, ToolMetadata, ToolMiddleware, ToolParameterProperty, ToolParameterSchema } from "./tools/registry";
 export { registerBuiltinTools, tools } from "./tools/tools";
-export { Workflow, loop, parallel, repeat, route } from "./workflows";
-export type { StepResult, WorkflowContext, WorkflowStep } from "./workflows";
+export { // New: Python-parity Loop and Repeat classes
+  Loop, // WorkflowStep class
+  WorkflowStep, Repeat, Workflow, loop, loopPattern, parallel, repeat, repeatPattern, route } from "./workflows";
+export type { LoopConfig, LoopResult, RepeatConfig, RepeatContext, RepeatResult, StepContextConfig, StepExecutionConfig, StepOutputConfig, StepResult, StepRoutingConfig, WorkflowContext, WorkflowStepConfig } from "./workflows";
 export { createWorkflowFromYAML, loadWorkflowFromFile, parseYAMLWorkflow, validateWorkflowDefinition } from "./workflows/yaml-parser";
 ```
 

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>=3.7.2" \
+    "praisonai>=3.11.2" \
     "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>=3.7.2" \
+    "praisonai>=3.11.2" \
     "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>=3.7.2" \
+    "praisonai>=3.11.2" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/benchmark/benchmark_example.py

@@ -35,7 +35,7 @@ def run_quick_comparison():
         prompt="What is 2+2?",
         iterations=2,
         paths=["openai_sdk", "praisonai_agent"],
-        verbose=True
+        output="verbose"
     )
     
     # Print comparison table
@@ -56,7 +56,7 @@ def run_full_benchmark():
     report = handler.run_full_benchmark(
         prompt="Hi",
         iterations=3,
-        verbose=True
+        output="verbose"
     )
     
     # Print full report
@@ -78,7 +78,7 @@ def run_agent_benchmark():
         prompt="Explain Python in one sentence",
         iterations=3,
         paths=["openai_sdk", "praisonai_agent"],
-        verbose=True
+        output="verbose"
     )
     
     # Show timeline diagrams

examples/capabilities/embeddings_example.py

@@ -2,12 +2,14 @@
 Embeddings Capability Example
 
 Demonstrates text embedding generation using PraisonAI capabilities.
+Both `embed` and `embedding` work identically - use whichever you prefer.
 """
 
-from praisonai.capabilities import embed
+from praisonai import embed, embedding  # Both work from top-level
+# Or: from praisonai.capabilities import embed, embedding
 
-# Single text embedding
-print("=== Single Text Embedding ===")
+# Single text embedding using embed()
+print("=== Single Text Embedding (using embed) ===")
 result = embed(
     input="Hello, world!",
     model="text-embedding-3-small"
@@ -16,6 +18,14 @@ print(f"Embedding dimensions: {len(result.embeddings[0])}")
 print(f"First 5 values: {result.embeddings[0][:5]}")
 print(f"Usage: {result.usage}")
 
+# Same thing using embedding() alias
+print("\n=== Single Text Embedding (using embedding alias) ===")
+result = embedding(
+    input="Hello, world!",
+    model="text-embedding-3-small"
+)
+print(f"Embedding dimensions: {len(result.embeddings[0])}")
+
 # Multiple text embeddings
 print("\n=== Multiple Text Embeddings ===")
 result = embed(
@@ -25,3 +35,12 @@ result = embed(
 print(f"Number of embeddings: {len(result.embeddings)}")
 for i, emb in enumerate(result.embeddings):
     print(f"  Text {i+1}: {len(emb)} dimensions")
+
+# With custom dimensions (for models that support it)
+print("\n=== Custom Dimensions ===")
+result = embed(
+    input="Hello world",
+    model="text-embedding-3-large",
+    dimensions=256
+)
+print(f"Reduced dimensions: {len(result.embeddings[0])}")

examples/compare/cli_compare_advanced.py

@@ -22,7 +22,7 @@ custom_modes = {
 }
 
 # Create handler with verbose output
-handler = CompareHandler(verbose=True)
+handler = CompareHandler(output="verbose")
 
 # Example: Manually create comparison results for analysis
 # (useful for testing without API calls)

examples/compare/cli_compare_basic.py

@@ -22,7 +22,7 @@ modes = parse_modes("basic,tools,planning")
 print(f"\nParsed modes: {modes}")
 
 # Create compare handler
-handler = CompareHandler(verbose=True)
+handler = CompareHandler(output="verbose")
 
 # Compare modes (this will run actual agent calls)
 # Uncomment to run:

examples/consolidated_params/advanced_workflow_full_features.py

@@ -32,7 +32,7 @@ workflow = Workflow(
         ),
     ],
     # Workflow-level consolidated params
-    output=WorkflowOutputConfig(verbose=True, stream=True),
+    output=WorkflowOutputConfig(output="verbose", stream=True),
     planning=WorkflowPlanningConfig(enabled=True, reasoning=True),
     memory=WorkflowMemoryConfig(backend="file"),
     # Agent-like params

examples/consolidated_params/basic_memory.py

@@ -1,21 +1,24 @@
-"""
-Basic Memory Example - Agent-Centric API
-
-Demonstrates the simplest memory usage with consolidated params.
-"""
-
+"""Basic memory example - minimal usage."""
 from praisonaiagents import Agent
 
-# Basic: Enable memory with preset
+# Simple enable (uses default file-based memory)
 agent = Agent(
     instructions="You are a helpful assistant with memory.",
-    memory="sqlite",  # Preset: sqlite, redis, postgres, file
+    memory=True,
 )
 
-# Run a simple task
-response = agent.start("Remember that my favorite color is blue.")
-print(response)
+# With preset
+agent_redis = Agent(
+    instructions="You are a helpful assistant.",
+    memory="redis",  # Uses redis preset
+)
 
-# Follow-up to test memory
-response = agent.start("What is my favorite color?")
-print(response)
+# With URL
+agent_postgres = Agent(
+    instructions="You are a helpful assistant.",
+    memory="postgresql://localhost/mydb",  # URL auto-detected
+)
+
+if __name__ == "__main__":
+    response = agent.chat("Remember that my favorite color is blue.")
+    print(response)

examples/consolidated_params/basic_workflow.py

@@ -1,26 +1,22 @@
-"""
-Basic Workflow Example - Agent-Centric API
-
-Demonstrates minimal workflow with consolidated params.
-"""
-
+"""Basic workflow example with consolidated params."""
 from praisonaiagents import Agent
 from praisonaiagents.workflows import Workflow, WorkflowStep
 
-# Create a simple workflow
+# Create agents
+writer = Agent(instructions="You are a content writer.")
+editor = Agent(instructions="You are an editor.")
+
+# Create workflow with consolidated params
 workflow = Workflow(
-    name="simple_workflow",
-    output="verbose",  # Workflow-level output preset
+    name="Content Pipeline",
+    steps=[
+        WorkflowStep(name="write", agent=writer, action="Write about {{topic}}"),
+        WorkflowStep(name="edit", agent=editor, action="Edit the content", context=["write"]),
+    ],
+    output="verbose",
+    planning=True,
 )
 
-# Add a step
-step = WorkflowStep(
-    name="writer",
-    action="Write a haiku about programming",
-    agent=Agent(instructions="You are a creative poet."),
-    output="result.txt",  # Save output to file
-)
-
-# Note: This is a structural example - actual execution requires workflow.run()
-print("Workflow created with output preset:", workflow.output)
-print("Step output file:", step.output_file)
+if __name__ == "__main__":
+    result = workflow.run(variables={"topic": "AI agents"})
+    print(result)

examples/context/01_basic_context.py

@@ -0,0 +1,59 @@
+"""
+Basic Context Management Example
+
+Demonstrates enabling context management with ContextConfig.
+This manages token budgets, auto-compaction, and prevents context overflow.
+"""
+
+from praisonaiagents import Agent
+from praisonaiagents.context import ContextConfig, OptimizerStrategy
+
+# Example 1: Enable context with defaults
+agent_basic = Agent(
+    name="Assistant",
+    instructions="You are a helpful assistant",
+    context=True  # Auto-compact at 80% utilization
+)
+
+# Example 2: Custom configuration
+agent_custom = Agent(
+    name="CustomAgent",
+    instructions="You are a helpful assistant with custom context settings",
+    context=ContextConfig(
+        auto_compact=True,
+        compact_threshold=0.8,      # Trigger at 80% utilization
+        strategy=OptimizerStrategy.SMART,
+        output_reserve=8000,        # Reserve tokens for output
+        keep_recent_turns=5,        # Always keep last 5 turns
+        tool_output_max=10000,      # Max tokens per tool output
+    )
+)
+
+# Example 3: Sliding window strategy
+agent_sliding = Agent(
+    name="SlidingAgent",
+    instructions="You are a helpful assistant using sliding window",
+    context=ContextConfig(
+        strategy=OptimizerStrategy.SLIDING_WINDOW,
+        keep_recent_turns=10,       # Keep last 10 turns
+    )
+)
+
+if __name__ == "__main__":
+    print("=== Basic Context Example ===")
+    print("Context management is automatically enabled.")
+    print()
+    
+    # Test basic agent
+    result = agent_basic.chat("Hello! What is 2 + 2?")
+    print(f"Response: {result}")
+    print()
+    
+    # Show context config
+    print("=== Context Configuration ===")
+    config = ContextConfig(
+        auto_compact=True,
+        compact_threshold=0.8,
+        strategy=OptimizerStrategy.SMART,
+    )
+    print(f"Config: {config.to_dict()}")

examples/context/02_session_tracking.py

@@ -0,0 +1,71 @@
+"""
+Session Tracking Example
+
+Demonstrates tracking conversation state (goal, plan, progress) across turns.
+Inspired by Agno's SessionContextStore pattern.
+"""
+
+from praisonaiagents.context import SessionContextTracker
+
+# Create session tracker
+tracker = SessionContextTracker(
+    session_id="user123",
+    track_summary=True,
+    track_goal=True,
+    track_plan=True,
+    track_progress=True,
+)
+
+# Set the user's goal
+tracker.update_goal("Build a Python web application with Flask")
+
+# Define the plan
+tracker.update_plan([
+    "Create Flask project structure",
+    "Add routes for API endpoints",
+    "Implement database models",
+    "Add authentication",
+    "Write tests",
+    "Deploy to cloud"
+])
+
+# Mark completed steps
+tracker.add_progress("Created Flask project structure")
+tracker.add_progress("Added routes for API endpoints")
+
+# Update summary
+tracker.update_summary(
+    "User is building a Flask web app. Completed project setup and API routes. "
+    "Next: implement database models."
+)
+
+if __name__ == "__main__":
+    print("=== Session Tracking Example ===")
+    print()
+    
+    # Show current state
+    print(f"Session ID: {tracker.session_id}")
+    print(f"Goal: {tracker.goal}")
+    print(f"Plan: {tracker.plan}")
+    print(f"Progress: {tracker.progress}")
+    print()
+    
+    # Get context string (simple format)
+    print("=== Context String ===")
+    print(tracker.to_context_string())
+    print()
+    
+    # Get system prompt section (XML format with guidelines)
+    print("=== System Prompt Section ===")
+    print(tracker.to_system_prompt_section())
+    print()
+    
+    # Export to dict for persistence
+    print("=== Export as Dict ===")
+    data = tracker.to_dict()
+    print(f"Exported: {data.keys()}")
+    print()
+    
+    # Recreate from dict
+    new_tracker = SessionContextTracker.from_dict(data)
+    print(f"Restored goal: {new_tracker.goal}")

examples/context/03_multi_memory.py

@@ -0,0 +1,81 @@
+"""
+Multi-Memory Aggregation Example
+
+Demonstrates concurrent fetching from multiple context sources.
+Inspired by CrewAI's ContextualMemory pattern.
+"""
+
+import asyncio
+from praisonaiagents.context import ContextAggregator, create_aggregator_from_config
+
+# Example 1: Basic aggregator with mock sources
+def mock_memory_search(query: str) -> str:
+    """Mock memory search - returns recent conversation context."""
+    return f"[Memory] User previously asked about: {query}. They prefer Python."
+
+def mock_knowledge_search(query: str) -> str:
+    """Mock knowledge search - returns facts from knowledge base."""
+    return f"[Knowledge] Relevant facts: Python is a high-level programming language."
+
+def mock_rag_retrieve(query: str) -> str:
+    """Mock RAG retrieval - returns document chunks."""
+    return f"[RAG] Retrieved: Documentation about {query} from the codebase."
+
+# Create aggregator
+aggregator = ContextAggregator(
+    max_tokens=4000,
+    separator="\n\n---\n\n",
+    include_source_labels=True
+)
+
+# Register sources with priorities (lower = higher priority)
+aggregator.register_source("memory", mock_memory_search, priority=10)
+aggregator.register_source("knowledge", mock_knowledge_search, priority=20)
+aggregator.register_source("rag", mock_rag_retrieve, priority=30)
+
+async def demo_async():
+    """Demo async aggregation."""
+    print("=== Async Aggregation ===")
+    result = await aggregator.aggregate("Python web development")
+    print(f"Sources used: {result.sources_used}")
+    print(f"Tokens used: {result.tokens_used}")
+    print(f"Fetch times: {result.fetch_times}")
+    print()
+    print("Context:")
+    print(result.context)
+    return result
+
+def demo_sync():
+    """Demo sync aggregation."""
+    print("=== Sync Aggregation ===")
+    result = aggregator.aggregate_sync("Flask API development")
+    print(f"Sources used: {result.sources_used}")
+    print(f"Tokens used: {result.tokens_used}")
+    print()
+    print("Context:")
+    print(result.context)
+    return result
+
+if __name__ == "__main__":
+    print("=== Multi-Memory Aggregation Example ===")
+    print()
+    
+    # Show registered sources
+    print(f"Registered sources: {aggregator.sources}")
+    print()
+    
+    # Demo sync aggregation
+    demo_sync()
+    print()
+    
+    # Demo async aggregation
+    asyncio.run(demo_async())
+    print()
+    
+    # Demo selective sources
+    print("=== Selective Sources ===")
+    result = aggregator.aggregate_sync(
+        "Find database info",
+        sources=["memory", "knowledge"]  # Only use these
+    )
+    print(f"Sources used: {result.sources_used}")

examples/context/04_agent_integration.py

@@ -37,7 +37,7 @@ def main():
         name="CodeAssistant",
         instructions="You are a helpful code assistant.",
         context=config,
-        verbose=False
+        output="silent"
     )
     
     print(f"   Agent name: {agent.name}")

examples/context/04_compaction_strategies.py

@@ -0,0 +1,76 @@
+"""
+Compaction Strategies Example
+
+Demonstrates different context compaction strategies.
+"""
+
+from praisonaiagents import Agent
+from praisonaiagents.context import ContextConfig, OptimizerStrategy
+
+# Strategy 1: TRUNCATE - Fast, removes oldest messages
+agent_truncate = Agent(
+    name="TruncateAgent",
+    instructions="You are a helpful assistant",
+    context=ContextConfig(
+        strategy=OptimizerStrategy.TRUNCATE,
+        compact_threshold=0.8,
+    )
+)
+
+# Strategy 2: SLIDING_WINDOW - Keeps recent N turns
+agent_sliding = Agent(
+    name="SlidingAgent",
+    instructions="You are a helpful assistant",
+    context=ContextConfig(
+        strategy=OptimizerStrategy.SLIDING_WINDOW,
+        keep_recent_turns=10,
+    )
+)
+
+# Strategy 3: SMART - Importance-based (default)
+agent_smart = Agent(
+    name="SmartAgent",
+    instructions="You are a helpful assistant",
+    context=ContextConfig(
+        strategy=OptimizerStrategy.SMART,  # Default
+        compact_threshold=0.8,
+    )
+)
+
+# Strategy 4: PRUNE_TOOLS - Truncate old tool outputs
+agent_prune = Agent(
+    name="PruneAgent",
+    instructions="You are a helpful assistant with tools",
+    context=ContextConfig(
+        strategy=OptimizerStrategy.PRUNE_TOOLS,
+        tool_output_max=5000,  # Max tokens per tool output
+    )
+)
+
+# Strategy 5: NON_DESTRUCTIVE - Only prune tools, keep all history
+agent_safe = Agent(
+    name="SafeAgent",
+    instructions="You are a helpful assistant (safety-critical)",
+    context=ContextConfig(
+        strategy=OptimizerStrategy.NON_DESTRUCTIVE,
+    )
+)
+
+if __name__ == "__main__":
+    print("=== Compaction Strategies Example ===")
+    print()
+    
+    strategies = [
+        ("TRUNCATE", OptimizerStrategy.TRUNCATE, "Remove oldest messages"),
+        ("SLIDING_WINDOW", OptimizerStrategy.SLIDING_WINDOW, "Keep last N turns"),
+        ("SMART", OptimizerStrategy.SMART, "Importance-based (default)"),
+        ("PRUNE_TOOLS", OptimizerStrategy.PRUNE_TOOLS, "Truncate tool outputs"),
+        ("NON_DESTRUCTIVE", OptimizerStrategy.NON_DESTRUCTIVE, "Only prune tools"),
+    ]
+    
+    for name, strategy, description in strategies:
+        print(f"  {name:20} - {description}")
+    
+    print()
+    print("Usage:")
+    print("  context=ContextConfig(strategy=OptimizerStrategy.SLIDING_WINDOW)")

examples/context/05_monitoring.py

@@ -0,0 +1,85 @@
+"""
+Context Monitoring Example
+
+Demonstrates monitoring context usage with logging and metrics.
+"""
+
+from praisonaiagents import Agent
+from praisonaiagents.context import ContextConfig, MonitorConfig, OptimizerStrategy
+
+# Example 1: Human-readable monitoring
+agent_human = Agent(
+    name="MonitoredAgent",
+    instructions="You are a helpful assistant",
+    context=ContextConfig(
+        auto_compact=True,
+        monitor=MonitorConfig(
+            enabled=True,
+            path="./context_logs/",
+            format="human",           # Human-readable format
+            frequency="turn",         # Log on every turn
+            redact_sensitive=True,    # Redact sensitive data
+            multi_agent_files=True,   # Separate files per agent
+        )
+    )
+)
+
+# Example 2: JSON monitoring (for parsing)
+agent_json = Agent(
+    name="JSONMonitorAgent",
+    instructions="You are a helpful assistant",
+    context=ContextConfig(
+        monitor=MonitorConfig(
+            enabled=True,
+            path="./context_logs/",
+            format="json",            # JSON format for parsing
+            frequency="tool_call",    # Log on tool calls
+        )
+    )
+)
+
+# Example 3: Overflow-only monitoring
+agent_overflow = Agent(
+    name="OverflowAgent",
+    instructions="You are a helpful assistant",
+    context=ContextConfig(
+        monitor=MonitorConfig(
+            enabled=True,
+            path="./context_logs/",
+            format="human",
+            frequency="overflow",     # Only log on overflow
+        )
+    )
+)
+
+if __name__ == "__main__":
+    print("=== Context Monitoring Example ===")
+    print()
+    
+    # Show monitor configuration
+    config = MonitorConfig(
+        enabled=True,
+        path="./context_logs/",
+        format="human",
+        frequency="turn",
+        redact_sensitive=True,
+    )
+    
+    print("Monitor Config:")
+    print(f"  enabled: {config.enabled}")
+    print(f"  path: {config.path}")
+    print(f"  format: {config.format}")
+    print(f"  frequency: {config.frequency}")
+    print(f"  redact_sensitive: {config.redact_sensitive}")
+    print()
+    
+    print("Frequency options:")
+    print("  'turn'      - Log on every conversation turn")
+    print("  'tool_call' - Log when tools are called")
+    print("  'manual'    - Manual logging only")
+    print("  'overflow'  - Log only when context overflows")
+    print()
+    
+    print("Format options:")
+    print("  'human' - Human-readable format")
+    print("  'json'  - JSON format for parsing")

examples/context/15_recursive_context.py

@@ -0,0 +1,274 @@
+"""
+Example 15: Recursive Context Pattern
+
+Demonstrates the RLM (Recursive Language Model) pattern for handling large context:
+- Store large documents as artifacts (not in LLM context)
+- Agent uses tools (grep/head/tail/chunk) to explore artifacts
+- Dramatically reduces token usage (~2K vs ~10K+)
+
+This pattern solves "context rot" by treating context as programmatically
+explorable data instead of dumping everything into the LLM's context window.
+"""
+
+import tempfile
+import os
+from praisonaiagents import Agent
+from praisonai.context import FileSystemArtifactStore
+from praisonai.context.queue import create_artifact_tools
+from praisonaiagents.context.artifacts import ArtifactMetadata
+
+
+def create_sample_document():
+    """Create a sample large document for testing."""
+    # Simulate a research paper / large document
+    content = """
+# Attention Is All You Need
+
+## Abstract
+
+The dominant sequence transduction models are based on complex recurrent or 
+convolutional neural networks that include an encoder and a decoder. The best
+performing models also connect the encoder and decoder through an attention
+mechanism. We propose a new simple network architecture, the Transformer,
+based solely on attention mechanisms, dispensing with recurrence and convolutions
+entirely. 
+
+## Introduction
+
+Recurrent neural networks, long short-term memory and gated recurrent neural 
+networks in particular, have been firmly established as state of the art 
+approaches in sequence modeling and transduction problems such as language 
+modeling and machine translation. 
+
+## Background
+
+The goal of reducing sequential computation also forms the foundation of the 
+Extended Neural GPU, ByteNet and ConvS2S, all of which use convolutional neural
+networks as basic building block.
+
+## Model Architecture
+
+### Encoder and Decoder Stacks
+
+The encoder is composed of a stack of N = 6 identical layers. Each layer has 
+two sub-layers. The first is a multi-head self-attention mechanism, and the 
+second is a simple, position-wise fully connected feed-forward network.
+
+### Attention
+
+An attention function can be described as mapping a query and a set of key-value
+pairs to an output, where the query, keys, values, and output are all vectors.
+
+#### Scaled Dot-Product Attention
+
+We call our particular attention "Scaled Dot-Product Attention". The input 
+consists of queries and keys of dimension dk, and values of dimension dv.
+
+The formula is: Attention(Q, K, V) = softmax(QK^T / sqrt(d_k))V
+
+### Multi-Head Attention
+
+Multi-head attention allows the model to jointly attend to information from
+different representation subspaces at different positions.
+
+## Results
+
+### Machine Translation
+
+On the WMT 2014 English-to-German translation task, the big transformer model
+outperforms the best previously reported models including ensembles.
+
+The model achieved a BLEU score of 28.4 on the WMT 2014 English-to-German 
+translation task, improving over the existing best results by over 2 BLEU.
+
+On the WMT 2014 English-to-French translation task, our big model achieves 
+a BLEU score of 41.8, outperforming all published single models.
+
+### Training
+
+Training took 3.5 days on 8 P100 GPUs. The big model was trained for 300,000
+steps (3.5 days) at a cost of approximately $1000 in cloud compute.
+
+## Model Variations
+
+| Model | Parameters | BLEU EN-DE | BLEU EN-FR |
+|-------|------------|------------|------------|
+| Base  | 65M        | 27.3       | 38.1       |
+| Big   | 213M       | 28.4       | 41.8       |
+
+The base model has 65 million parameters (65 × 10^6).
+The big model has 213 million parameters.
+
+## Conclusion
+
+In this work, we presented the Transformer, the first sequence transduction
+model based entirely on attention, replacing the recurrent layers most commonly
+used in encoder-decoder architectures with multi-headed self-attention.
+
+The Transformer can be trained significantly faster than architectures based
+on recurrent or convolutional layers.
+
+## Authors
+
+Ashish Vaswani (Google Brain)
+Noam Shazeer (Google Brain)
+Niki Parmar (Google Research)
+Jakob Uszkoreit (Google Research)
+Llion Jones (Google Research)
+Aidan N. Gomez (University of Toronto)
+Łukasz Kaiser (Google Brain)
+Illia Polosukhin
+
+## References
+
+[1] Neural Machine Translation by Jointly Learning to Align and Translate
+[2] Sequence to Sequence Learning with Neural Networks
+[3] Learning Phrase Representations using RNN Encoder-Decoder
+"""
+    # Repeat content to make it larger
+    full_content = (content + "\n\n") * 10  # ~35KB
+    return full_content
+
+
+def main():
+    print("=" * 70)
+    print("Recursive Context Pattern - Token Efficiency Demo")
+    print("=" * 70)
+    
+    # Track token usage across methods
+    token_stats = {
+        "traditional": {"input": 0, "output": 0},
+        "recursive": {"input": 0, "output": 0},
+    }
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # 1. Create artifact store and tools
+        store = FileSystemArtifactStore(base_dir=tmpdir)
+        artifact_tools = create_artifact_tools(store=store)
+        
+        print(f"\n1. Created FileSystemArtifactStore")
+        print(f"   Location: {tmpdir}")
+        print(f"   Tools available: {[t.__name__ for t in artifact_tools]}")
+        
+        # 2. Create sample document and store as artifact
+        document = create_sample_document()
+        doc_size = len(document)
+        doc_tokens = doc_size // 4  # Rough estimate: 4 chars per token
+        
+        metadata = ArtifactMetadata(
+            agent_id="research_agent",
+            run_id="demo_run",
+            tool_name="document_loader",
+            turn_id=1
+        )
+        
+        ref = store.store(document, metadata)
+        
+        print(f"\n2. Stored document as artifact")
+        print(f"   Size: {doc_size:,} chars (~{doc_tokens:,} tokens)")
+        print(f"   Path: {ref.path}")
+        print(f"   Summary: {ref.summary[:80]}...")
+        
+        # 3. Create agent WITH artifact tools (Recursive Context approach)
+        agent = Agent(
+            instructions="""You are a research paper analyst with access to artifact exploration tools.
+When given an artifact path, use the tools to explore it efficiently:
+- artifact_grep: Search for patterns (USE THIS FIRST)
+- artifact_head: See first N lines
+- artifact_tail: See last N lines
+- artifact_chunk: Get specific line ranges
+
+Be precise and cite line numbers when finding information.""",
+            tools=artifact_tools,
+            output='silent'  # Quiet output for cleaner demo
+        )
+        
+        print(f"\n3. Created agent with {len(agent.tools)} artifact tools")
+        
+        # 4. Query using RECURSIVE CONTEXT (small prompt + tools)
+        print(f"\n4. RECURSIVE CONTEXT APPROACH")
+        print("-" * 40)
+        
+        prompt = f"""I have a research paper stored at: {ref.path}
+
+Find the BLEU score on the WMT 2014 English-to-German translation task.
+Use artifact_grep to search, then report the answer with line number."""
+
+        prompt_tokens = len(prompt) // 4
+        print(f"   Prompt size: {len(prompt)} chars (~{prompt_tokens} tokens)")
+        print(f"   Document NOT in prompt context!")
+        
+        response = agent.chat(prompt)
+        
+        # Estimate output tokens (could use actual LiteLLM response if available)
+        output_tokens = len(response) // 4
+        
+        # Calculate recursive approach tokens (prompt + tool calls + response)
+        # Tool call: ~200 tokens for tool desc + call
+        # Tool response: ~300 tokens for grep results
+        estimated_total = prompt_tokens + 200 + 300 + output_tokens
+        token_stats["recursive"]["input"] = estimated_total
+        
+        print(f"\n   Agent Response:")
+        print(f"   {response[:200]}...")
+        print(f"\n   Estimated tokens used: ~{estimated_total:,}")
+        
+        # 5. Compare with TRADITIONAL approach (full context)
+        print(f"\n5. TRADITIONAL APPROACH (for comparison)")
+        print("-" * 40)
+        
+        traditional_prompt_tokens = prompt_tokens + doc_tokens
+        token_stats["traditional"]["input"] = traditional_prompt_tokens
+        
+        print(f"   If we passed FULL document in prompt:")
+        print(f"   Prompt: {prompt_tokens} + Document: {doc_tokens} = {traditional_prompt_tokens:,} tokens")
+        
+        # 6. Show token savings
+        print(f"\n6. TOKEN COMPARISON")
+        print("=" * 40)
+        
+        savings = token_stats["traditional"]["input"] - token_stats["recursive"]["input"]
+        savings_pct = (savings / token_stats["traditional"]["input"]) * 100
+        
+        print(f"   Traditional approach: ~{token_stats['traditional']['input']:,} tokens")
+        print(f"   Recursive approach:   ~{token_stats['recursive']['input']:,} tokens")
+        print(f"   -----------------------------------")
+        print(f"   SAVINGS:             ~{savings:,} tokens ({savings_pct:.0f}%)")
+        
+        # 7. Demonstrate other artifact operations
+        print(f"\n7. ARTIFACT OPERATIONS DEMO")
+        print("-" * 40)
+        
+        # head
+        head_result = store.head(ref, lines=5)
+        print(f"   head(5 lines):")
+        for line in head_result.split('\n')[:3]:
+            print(f"      {line[:60]}")
+        
+        # grep
+        grep_results = store.grep(ref, pattern=r"BLEU.*\d+", max_matches=3)
+        print(f"\n   grep('BLEU.*\\d+'):")
+        for match in grep_results[:2]:
+            print(f"      Line {match.line_number}: {match.line_content[:50].strip()}...")
+        
+        # chunk
+        chunk_result = store.chunk(ref, start_line=50, end_line=55)
+        print(f"\n   chunk(lines 50-55):")
+        for line in chunk_result.split('\n')[:3]:
+            print(f"      {line[:60]}")
+        
+        print("\n" + "=" * 70)
+        print("CONCLUSION: Recursive Context Pattern")
+        print("=" * 70)
+        print("""
+The Recursive Context pattern (RLM) provides:
+✓ Token efficiency: ~{savings_pct:.0f}% reduction in token usage
+✓ Scalability: Handle documents of any size
+✓ Precision: Agent searches for exactly what it needs
+✓ Cost savings: Lower API costs due to fewer tokens
+✓ Better accuracy: Avoids "context rot" from large contexts
+""".format(savings_pct=savings_pct))
+
+
+if __name__ == "__main__":
+    main()

examples/cookbooks/yaml/secondary_market_research_agents.yaml

@@ -22,7 +22,7 @@ agents:  # Canonical: use 'agents' instead of 'roles'
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-5-nano"
+      model: "gpt-4o-mini"
       temperature: 0.3
     tools:
       - "InternetSearchTool"
@@ -76,7 +76,7 @@ agents:  # Canonical: use 'agents' instead of 'roles'
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-5-nano" 
+      model: "gpt-4o-mini" 
       temperature: 0.3
     tools:
       - "InternetSearchTool"
@@ -130,7 +130,7 @@ agents:  # Canonical: use 'agents' instead of 'roles'
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-5-nano"
+      model: "gpt-4o-mini"
       temperature: 0.3
     tools:
       - "InternetSearchTool"
@@ -185,7 +185,7 @@ agents:  # Canonical: use 'agents' instead of 'roles'
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-5-nano"
+      model: "gpt-4o-mini"
       temperature: 0.4
     tools:
       - "InternetSearchTool"
@@ -239,7 +239,7 @@ agents:  # Canonical: use 'agents' instead of 'roles'
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-5-nano"
+      model: "gpt-4o-mini"
       temperature: 0.3
     tools:
       - "InternetSearchTool"
@@ -299,7 +299,7 @@ agents:  # Canonical: use 'agents' instead of 'roles'
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-5-nano"
+      model: "gpt-4o-mini"
       temperature: 0.3
     tasks:
       report_synthesis:

examples/embedding/agent_with_embedding.py

@@ -0,0 +1,145 @@
+"""
+Agent-centric Embedding Example
+
+This example shows how to use embeddings with PraisonAI Agents.
+Embeddings are used internally by Agent for:
+- Knowledge retrieval (RAG)
+- Memory storage and search
+- Semantic similarity
+
+You can also use the embedding API directly for custom use cases.
+"""
+
+from praisonaiagents import Agent, embedding
+
+# =============================================================================
+# Example 1: Direct Embedding API
+# =============================================================================
+
+def example_direct_embedding():
+    """Use the embedding API directly."""
+    
+    # Single text embedding
+    result = embedding("Hello, world!")
+    print(f"Single embedding: {len(result.embeddings[0])} dimensions")
+    
+    # Batch embedding
+    texts = ["AI agents are powerful", "Machine learning is fascinating"]
+    result = embedding(texts, model="text-embedding-3-small")
+    print(f"Batch embeddings: {len(result.embeddings)} vectors")
+    
+    # Custom dimensions (for models that support it)
+    result = embedding("Hello", model="text-embedding-3-large", dimensions=256)
+    print(f"Custom dimensions: {len(result.embeddings[0])} dimensions")
+    
+    return result
+
+
+# =============================================================================
+# Example 2: Agent with Knowledge (uses embeddings internally)
+# =============================================================================
+
+def example_agent_with_knowledge():
+    """Agent with knowledge uses embeddings for RAG."""
+    
+    agent = Agent(
+        instructions="You are a helpful assistant with knowledge about AI.",
+        knowledge=["AI agents can use tools to accomplish tasks."],
+        knowledge_config={
+            "embedder": {
+                "provider": "openai",
+                "config": {"model": "text-embedding-3-small"}
+            }
+        }
+    )
+    
+    # The agent uses embeddings internally for knowledge retrieval
+    response = agent.chat("What can AI agents do?")
+    print(f"Agent response: {response}")
+    
+    return agent
+
+
+# =============================================================================
+# Example 3: Agent with Memory (uses embeddings internally)
+# =============================================================================
+
+def example_agent_with_memory():
+    """Agent with memory uses embeddings for semantic search."""
+    
+    agent = Agent(
+        instructions="You are a helpful assistant that remembers conversations.",
+        memory=True,
+        memory_config={
+            "embedding_model": "text-embedding-3-small"
+        }
+    )
+    
+    # The agent uses embeddings internally for memory storage/retrieval
+    agent.chat("My name is Alice and I love Python programming.")
+    response = agent.chat("What do I love?")
+    print(f"Agent remembered: {response}")
+    
+    return agent
+
+
+# =============================================================================
+# Example 4: Custom Embedding for Similarity Search
+# =============================================================================
+
+def example_similarity_search():
+    """Use embeddings for custom similarity search."""
+    import math
+    
+    def cosine_similarity(a, b):
+        dot = sum(x * y for x, y in zip(a, b))
+        norm_a = math.sqrt(sum(x * x for x in a))
+        norm_b = math.sqrt(sum(x * x for x in b))
+        return dot / (norm_a * norm_b) if norm_a and norm_b else 0
+    
+    # Embed documents
+    documents = [
+        "Python is a programming language",
+        "JavaScript runs in browsers",
+        "AI agents can automate tasks"
+    ]
+    doc_embeddings = embedding(documents)
+    
+    # Embed query
+    query = "What language is used for web development?"
+    query_embedding = embedding(query)
+    
+    # Find most similar document
+    similarities = [
+        cosine_similarity(query_embedding.embeddings[0], doc_emb)
+        for doc_emb in doc_embeddings.embeddings
+    ]
+    
+    best_idx = similarities.index(max(similarities))
+    print(f"Most similar document: {documents[best_idx]}")
+    print(f"Similarity score: {similarities[best_idx]:.4f}")
+    
+    return similarities
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("Example 1: Direct Embedding API")
+    print("=" * 60)
+    example_direct_embedding()
+    
+    print("\n" + "=" * 60)
+    print("Example 4: Custom Similarity Search")
+    print("=" * 60)
+    example_similarity_search()
+    
+    # Uncomment to run agent examples (requires API key)
+    # print("\n" + "=" * 60)
+    # print("Example 2: Agent with Knowledge")
+    # print("=" * 60)
+    # example_agent_with_knowledge()
+    
+    # print("\n" + "=" * 60)
+    # print("Example 3: Agent with Memory")
+    # print("=" * 60)
+    # example_agent_with_memory()

examples/hooks/basic_hooks.py

@@ -2,32 +2,34 @@
 """
 Basic Hooks Example for PraisonAI Agents.
 
-This example demonstrates how to use the hooks system to:
+This example demonstrates the simplified hook API using add_hook():
 1. Log tool calls before execution
 2. Block dangerous operations
 3. Add context to agent responses
 4. Monitor session lifecycle
 
+For the simplest example, see simple_hooks.py
+
 Usage:
     python basic_hooks.py
 """
 
 import asyncio
-from praisonaiagents import Agent
 from praisonaiagents.hooks import (
-    HookRegistry, HookRunner, HookEvent, HookResult,
+    add_hook, remove_hook, has_hook, get_default_registry,
+    HookRunner, HookEvent, HookResult,
     BeforeToolInput, AfterToolInput
 )
 
 
 def main():
-    # Create a hook registry
-    registry = HookRegistry()
+    # Clear any existing hooks from previous runs
+    get_default_registry().clear()
     
     # ==========================================================================
-    # Hook 1: Log all tool calls
+    # Hook 1: Log all tool calls (using string event name)
     # ==========================================================================
-    @registry.on(HookEvent.BEFORE_TOOL)
+    @add_hook('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}")
@@ -35,9 +37,9 @@ def main():
         return HookResult.allow()
     
     # ==========================================================================
-    # Hook 2: Block dangerous file operations (using function logic)
+    # Hook 2: Block dangerous file operations
     # ==========================================================================
-    @registry.on(HookEvent.BEFORE_TOOL)
+    @add_hook('before_tool')
     def block_delete_operations(event_data: BeforeToolInput) -> HookResult:
         """Block any delete or remove operations."""
         dangerous_keywords = ['delete', 'remove', 'rm', 'unlink']
@@ -53,7 +55,7 @@ def main():
     # ==========================================================================
     # Hook 3: Require confirmation for write operations
     # ==========================================================================
-    @registry.on(HookEvent.BEFORE_TOOL)
+    @add_hook('before_tool')
     def confirm_write_operations(event_data: BeforeToolInput) -> HookResult:
         """Request confirmation for write operations."""
         write_keywords = ['write', 'save', 'create', 'update']
@@ -62,14 +64,13 @@ def main():
         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)
+    @add_hook('after_tool')
     def log_tool_completion(event_data: AfterToolInput) -> HookResult:
         """Log tool completion with timing."""
         print(f"\n✅ [DONE] Tool: {event_data.tool_name}")
@@ -82,10 +83,14 @@ def main():
     # Test the hooks
     # ==========================================================================
     print("=" * 60)
-    print("Testing Hooks System")
+    print("Testing Hooks System (using add_hook API)")
     print("=" * 60)
     
-    runner = HookRunner(registry)
+    # Check hooks are registered
+    print(f"\nHooks registered for 'before_tool': {has_hook('before_tool')}")
+    print(f"Hooks registered for 'after_tool': {has_hook('after_tool')}")
+    
+    runner = HookRunner(get_default_registry())
     
     # Test 1: Normal tool call (should be logged and allowed)
     print("\n--- Test 1: Normal read operation ---")
@@ -132,7 +137,7 @@ def main():
     print("\n" + "=" * 60)
     print("Registered Hooks:")
     print("=" * 60)
-    for event, hooks in registry.list_hooks().items():
+    for event, hooks in get_default_registry().list_hooks().items():
         print(f"\n{event}:")
         for hook in hooks:
             print(f"  - {hook['name']} (matcher: {hook['matcher'] or '*'})")

examples/hooks/simple_hooks.py

@@ -0,0 +1,67 @@
+#!/usr/bin/env python3
+"""
+Simple Hooks Example for PraisonAI Agents.
+
+This example shows the SIMPLEST way to use hooks with the add_hook API.
+Just a few lines of code to intercept tool calls!
+
+No need to import HookResult - just return None/True to allow, 
+False to deny, or a string to deny with a reason.
+
+Usage:
+    python simple_hooks.py
+"""
+
+from praisonaiagents.hooks import add_hook, has_hook
+
+
+# =============================================================================
+# Register hooks with simple string event names
+# =============================================================================
+
+@add_hook('before_tool')
+def log_tools(event_data):
+    """Log every tool call. Return nothing (None) to allow."""
+    print(f"🔧 Tool: {event_data.tool_name}")
+    # No return needed - defaults to allow
+
+
+@add_hook('before_tool')
+def block_dangerous(event_data):
+    """Block delete operations. Return False or string to deny."""
+    if 'delete' in event_data.tool_name.lower():
+        print(f"🚫 Blocked: {event_data.tool_name}")
+        return "Delete operations are not allowed"  # String = deny with reason
+    # No return = allow
+
+
+@add_hook('after_tool')
+def log_completion(event_data):
+    """Log tool completion."""
+    print(f"✅ Done: {event_data.tool_name} ({event_data.execution_time_ms:.0f}ms)")
+
+
+# =============================================================================
+# Test the hooks
+# =============================================================================
+
+if __name__ == "__main__":
+    print("=" * 50)
+    print("Simple Hooks Example")
+    print("=" * 50)
+    
+    # Check hooks are registered
+    print(f"\nHooks registered for 'before_tool': {has_hook('before_tool')}")
+    print(f"Hooks registered for 'after_tool': {has_hook('after_tool')}")
+    
+    # You can now create an Agent and the hooks will be applied
+    print("\n💡 Hooks are now globally registered!")
+    print("   Any Agent will automatically use these hooks.")
+    print("\n   Example:")
+    print("   from praisonaiagents import Agent")
+    print("   agent = Agent(instructions='You are helpful')")
+    print("   agent.start('Help me with files')")
+    print("\n📝 Hook returns:")
+    print("   - None or True  → Allow the operation")
+    print("   - False         → Deny the operation")
+    print("   - 'reason'      → Deny with a custom message")

examples/interactive_defaults/interactive_tools_example.py

@@ -105,7 +105,7 @@ def example_agent_with_default_tools():
         name="InteractiveAgent",
         instructions="You are a helpful coding assistant with file and code analysis capabilities.",
         tools=tools,
-        verbose=False
+        output="silent"
     )
     
     print(f"Agent created with {len(tools)} tools")

examples/js/agents/audio-agent.ts

@@ -0,0 +1,128 @@
+/**
+ * AudioAgent Integration Test
+ * 
+ * Tests the AudioAgent for TTS and STT using AI SDK.
+ * Requires OPENAI_API_KEY for live tests.
+ * 
+ * Run: npx ts-node audio-agent.ts
+ */
+
+import { AudioAgent, createAudioAgent } from '../../../src/praisonai-ts/dist';
+import * as fs from 'fs';
+import * as path from 'path';
+
+async function main() {
+    console.log('=== AudioAgent Integration Test ===\n');
+
+    // Test 1: Create AudioAgent with defaults
+    console.log('1. Testing AudioAgent creation:');
+    const agent = new AudioAgent({
+        name: 'TestAudioAgent',
+        provider: 'openai',
+        voice: 'alloy',
+        verbose: true
+    });
+    console.log('   Agent ID:', agent.id);
+    console.log('   Agent Name:', agent.name);
+    console.log('   Success: ✅');
+
+    // Test 2: Factory function
+    console.log('\n2. Testing createAudioAgent() factory:');
+    const agent2 = createAudioAgent({ provider: 'openai', voice: 'nova' });
+    console.log('   Agent created:', typeof agent2);
+    console.log('   Success: ✅');
+
+    // Test 3: Live TTS (requires API key)
+    if (process.env.OPENAI_API_KEY) {
+        console.log('\n3. Testing Text-to-Speech (live API):');
+        try {
+            const ttsAgent = new AudioAgent({
+                provider: 'openai',
+                voice: 'alloy',
+                verbose: false
+            });
+
+            const result = await ttsAgent.speak('Hello from PraisonAI AudioAgent!');
+            console.log('   Audio format:', result.format);
+            console.log('   Audio size:', result.audio.byteLength || result.audio.length, 'bytes');
+
+            // Optionally save to file
+            const outputPath = path.join(__dirname, 'test-output.mp3');
+            if (result.audio instanceof Buffer) {
+                fs.writeFileSync(outputPath, result.audio);
+                console.log('   Saved to:', outputPath);
+            }
+            console.log('   Success: ✅');
+        } catch (error: any) {
+            console.log('   ⚠️ TTS failed:', error.message);
+        }
+
+        // Test 4: Live Transcription (requires audio file)
+        console.log('\n4. Testing Speech-to-Text (live API):');
+        try {
+            const sttAgent = new AudioAgent({
+                provider: 'openai',
+                verbose: false
+            });
+
+            // Check if we have the test output from TTS
+            const audioPath = path.join(__dirname, 'test-output.mp3');
+            if (fs.existsSync(audioPath)) {
+                const result = await sttAgent.transcribe(audioPath);
+                console.log('   Transcribed text:', result.text);
+                console.log('   Language:', result.language || 'auto-detected');
+                console.log('   Success: ✅');
+
+                // Cleanup
+                fs.unlinkSync(audioPath);
+                console.log('   Cleaned up test file');
+            } else {
+                console.log('   Skipped (no test audio file available)');
+            }
+        } catch (error: any) {
+            console.log('   ⚠️ STT failed:', error.message);
+        }
+
+        // Test 5: Chat method (auto-detect mode)
+        console.log('\n5. Testing chat() method:');
+        try {
+            const chatAgent = new AudioAgent({ provider: 'openai', voice: 'echo' });
+
+            // Text input -> TTS
+            const chatResult = await chatAgent.chat('Testing audio chat interface');
+            console.log('   Chat result:', chatResult);
+            console.log('   Success: ✅');
+        } catch (error: any) {
+            console.log('   ⚠️ Chat failed:', error.message);
+        }
+
+    } else {
+        console.log('\n3-5. Live API Tests: Skipped (OPENAI_API_KEY not set)');
+        console.log('   Set OPENAI_API_KEY to run live tests');
+    }
+
+    // Test 6: Different providers (just creation, no live calls)
+    console.log('\n6. Testing provider configurations:');
+    const providers = ['openai', 'elevenlabs', 'groq', 'deepgram'] as const;
+
+    for (const provider of providers) {
+        try {
+            const a = new AudioAgent({ provider });
+            console.log(`   ${provider}: ✅ (created)`);
+        } catch (error: any) {
+            console.log(`   ${provider}: ⚠️ ${error.message}`);
+        }
+    }
+
+    // Test 7: Voice options
+    console.log('\n7. Testing OpenAI voice options:');
+    const voices = ['alloy', 'echo', 'fable', 'onyx', 'nova', 'shimmer'];
+    for (const voice of voices) {
+        const a = new AudioAgent({ provider: 'openai', voice });
+        console.log(`   ${voice}: ✅`);
+    }
+
+    console.log('\n=== AudioAgent Tests Complete ===');
+}
+
+main().catch(console.error);

examples/js/knowledge/chonkie-adapter.ts

@@ -0,0 +1,124 @@
+/**
+ * ChonkieAdapter Integration Test
+ * 
+ * Tests the ChonkieJS adapter for text chunking.
+ * Works without ChonkieJS installed (uses native fallback).
+ * 
+ * Run: npx ts-node chonkie-adapter.ts
+ */
+
+import {
+  ChonkieAdapter,
+  createChonkieAdapter,
+  hasChonkie,
+  Chunking
+} from '../../../src/praisonai-ts/dist';
+
+async function main() {
+  console.log('=== ChonkieAdapter Integration Test ===\n');
+
+  // Test 1: Check ChonkieJS availability
+  console.log('1. Checking ChonkieJS availability:');
+  const chonkieAvailable = await hasChonkie();
+  console.log(`   ChonkieJS installed: ${chonkieAvailable ? '✅' : '❌ (using native fallback)'}`);
+
+  // Test 2: Create adapter with default config
+  console.log('\n2. Testing ChonkieAdapter creation:');
+  const adapter = new ChonkieAdapter({
+    strategy: 'token',
+    chunkSize: 100,
+    overlap: 20
+  });
+  console.log('   Adapter created: ✅');
+  console.log('   Info:', adapter.getInfo());
+
+  // Test 3: Chunk text
+  console.log('\n3. Testing text chunking:');
+  const sampleText = `
+    PraisonAI is a high-performance, agentic AI framework designed with minimal client-side API.
+    It features extreme performance sensitivity, lazy loading everywhere, and optional dependencies only.
+    The framework supports clean separation between Core SDK, Wrapper/integrations, and Tools/plugins.
+    All features must have a relevant CLI integration and representation.
+  `.trim();
+
+  const chunks = await adapter.chunk(sampleText);
+  console.log(`   Input length: ${sampleText.length} characters`);
+  console.log(`   Chunks created: ${chunks.length}`);
+  chunks.forEach((chunk, i) => {
+    console.log(`   [${i}]: "${chunk.content.slice(0, 50)}..." (${chunk.content.length} chars)`);
+  });
+  console.log('   Success: ✅');
+
+  // Test 4: Different strategies
+  console.log('\n4. Testing different chunking strategies:');
+  const strategies = ['size', 'sentence', 'paragraph', 'semantic'] as const;
+
+  for (const strategy of strategies) {
+    const strategyAdapter = createChonkieAdapter({ strategy, chunkSize: 80 });
+    const strategyChunks = await strategyAdapter.chunk(sampleText);
+    console.log(`   ${strategy}: ${strategyChunks.length} chunks`);
+  }
+  console.log('   Success: ✅');
+
+  // Test 5: Native Chunking class
+  console.log('\n5. Testing native Chunking class:');
+  const nativeChunker = new Chunking({ chunkSize: 100, overlap: 10, strategy: 'sentence' });
+  const nativeChunks = nativeChunker.chunk(sampleText);
+  console.log(`   Native chunks: ${nativeChunks.length}`);
+  console.log('   Success: ✅');
+
+  // Test 6: Factory function
+  console.log('\n6. Testing createChunking() factory:');
+  const factoryChunker = createChunking({ chunkSize: 200 });
+  const factoryChunks = factoryChunker.chunk(sampleText);
+  console.log(`   Factory chunks: ${factoryChunks.length}`);
+  console.log('   Success: ✅');
+
+  // Test 7: Merge small chunks
+  console.log('\n7. Testing mergeSmallChunks:');
+  const tinyChunker = new Chunking({ chunkSize: 20, overlap: 5 });
+  const tinyChunks = tinyChunker.chunk(sampleText);
+  const mergedChunks = tinyChunker.mergeSmallChunks(tinyChunks, 50);
+  console.log(`   Before merge: ${tinyChunks.length} chunks`);
+  console.log(`   After merge: ${mergedChunks.length} chunks`);
+  console.log('   Success: ✅');
+
+  // Test 8: Code chunking (if ChonkieJS available)
+  if (chonkieAvailable) {
+    console.log('\n8. Testing code chunking (ChonkieJS):');
+    const codeAdapter = createChonkieAdapter({ strategy: 'code', language: 'typescript' });
+    const codeText = `
+function hello() {
+  console.log('Hello');
+}
+
+class MyClass {
+  constructor() {
+    this.value = 42;
+  }
+  
+  getValue() {
+    return this.value;
+  }
+}
+    `.trim();
+
+    const codeChunks = await codeAdapter.chunk(codeText);
+    console.log(`   Code chunks: ${codeChunks.length}`);
+    console.log('   Success: ✅');
+  } else {
+    console.log('\n8. Code chunking: Skipped (ChonkieJS not installed)');
+  }
+
+  // Test 9: Verify adapter info
+  console.log('\n9. Verifying adapter state:');
+  const info = adapter.getInfo();
+  console.log(`   Strategy: ${info.strategy}`);
+  console.log(`   Using ChonkieJS: ${info.usingChonkie}`);
+  console.log(`   Chunk size: ${info.config.chunkSize}`);
+  console.log('   Success: ✅');
+
+  console.log('\n=== ChonkieAdapter Tests Complete ===');
+}
+
+main().catch(console.error);

examples/js/knowledge/query-engine.ts

@@ -0,0 +1,97 @@
+/**
+ * QueryEngine Integration Test
+ * 
+ * Tests semantic and keyword search for RAG applications.
+ * 
+ * Run: npx ts-node query-engine.ts
+ */
+
+import {
+    QueryEngine,
+    createQueryEngine,
+    createSimpleQueryEngine
+} from '../../../src/praisonai-ts/dist';
+
+async function main() {
+    console.log('=== QueryEngine Integration Test ===\n');
+
+    // Test 1: Simple in-memory engine
+    console.log('1. Testing simple in-memory QueryEngine:');
+    const docs = [
+        { id: 'doc1', content: 'PraisonAI is a high-performance AI agent framework designed for production use.' },
+        { id: 'doc2', content: 'TypeScript provides static typing and better tooling for JavaScript projects.' },
+        { id: 'doc3', content: 'Agents can use tools, memory, and knowledge to complete complex tasks.' },
+        { id: 'doc4', content: 'RAG (Retrieval-Augmented Generation) improves AI responses with relevant context.' },
+        { id: 'doc5', content: 'Workflows enable sequential and parallel execution of agent tasks.' }
+    ];
+
+    const engine = createSimpleQueryEngine(docs);
+
+    const results1 = await engine.query('agent framework', { topK: 2 });
+    console.log('   Query: "agent framework"');
+    console.log(`   Found: ${results1.length} results`);
+    results1.forEach((r, i) => {
+        console.log(`   [${i + 1}] ${r.content.slice(0, 50)}... (score: ${r.score.toFixed(2)})`);
+    });
+    console.log('   Success: ✅');
+
+    // Test 2: Different queries
+    console.log('\n2. Testing various queries:');
+    const queries = ['TypeScript', 'RAG', 'workflow', 'tools memory'];
+
+    for (const q of queries) {
+        const results = await engine.query(q, { topK: 1 });
+        console.log(`   "${q}" → ${results.length > 0 ? results[0].content.slice(0, 40) + '...' : 'No results'}`);
+    }
+    console.log('   Success: ✅');
+
+    // Test 3: Query for context string
+    console.log('\n3. Testing queryForContext:');
+    const context = await engine.queryForContext('agent tasks', { topK: 2 });
+    console.log('   Context string (preview):');
+    console.log('   ' + context.split('\n')[0]);
+    console.log('   Success: ✅');
+
+    // Test 4: Cache behavior
+    console.log('\n4. Testing caching:');
+    const start = Date.now();
+    await engine.query('agent framework', { topK: 2 });
+    const cachedTime = Date.now() - start;
+    console.log(`   Cached query time: ${cachedTime}ms (should be fast)`);
+
+    engine.clearCache();
+    const start2 = Date.now();
+    await engine.query('agent framework', { topK: 2 });
+    const uncachedTime = Date.now() - start2;
+    console.log(`   Uncached query time: ${uncachedTime}ms`);
+    console.log('   Success: ✅');
+
+    // Test 5: QueryEngine configuration
+    console.log('\n5. Testing custom QueryEngine configuration:');
+    const customEngine = new QueryEngine({
+        keywordSearch: async (query, options) => {
+            return docs
+                .filter(d => d.content.toLowerCase().includes(query.toLowerCase()))
+                .map(d => ({ id: d.id, content: d.content, score: 0.8 }))
+                .slice(0, options?.topK ?? 5);
+        },
+        defaultOptions: { mode: 'keyword', topK: 3 }
+    });
+
+    const customResults = await customEngine.query('TypeScript');
+    console.log(`   Custom engine results: ${customResults.length}`);
+    console.log('   Success: ✅');
+
+    // Test 6: Empty results handling
+    console.log('\n6. Testing empty results:');
+    const emptyResults = await engine.query('nonexistent_query_12345');
+    console.log(`   Results for nonsense query: ${emptyResults.length}`);
+
+    const emptyContext = await engine.queryForContext('nonexistent_query_12345');
+    console.log(`   Context: "${emptyContext}"`);
+    console.log('   Success: ✅');
+
+    console.log('\n=== QueryEngine Tests Complete ===');
+}
+
+main().catch(console.error);

examples/js/mcp/mcp-client-test.ts

@@ -0,0 +1,136 @@
+/**
+ * MCP Client Integration Test
+ * 
+ * Tests the MCP (Model Context Protocol) client.
+ * Note: Requires an MCP server for full integration tests.
+ * 
+ * Run: npx ts-node mcp-client.ts
+ */
+
+import {
+    MCPClient,
+    createMCPClient,
+    getMCPTools,
+    type MCPClientConfig
+} from '../../../src/praisonai-ts/dist/mcp';
+
+async function main() {
+    console.log('=== MCPClient Integration Test ===\n');
+
+    // Test 1: Create MCP client
+    console.log('1. Testing MCPClient creation:');
+    const client = new MCPClient({
+        serverUrl: 'http://localhost:3000',
+        transport: 'http',
+        timeout: 5000,
+        verbose: true
+    });
+    console.log('   Client ID:', client.id);
+    console.log('   Connected:', client.isConnected());
+    console.log('   Success: ✅');
+
+    // Test 2: Factory function
+    console.log('\n2. Testing createMCPClient() factory:');
+    const client2 = createMCPClient({ serverUrl: 'http://localhost:8080' });
+    console.log('   Client created:', typeof client2);
+    console.log('   Success: ✅');
+
+    // Test 3: Transport configurations
+    console.log('\n3. Testing transport configurations:');
+    const transports: MCPClientConfig['transport'][] = ['http', 'websocket', 'sse', 'stdio'];
+
+    for (const transport of transports) {
+        try {
+            const c = new MCPClient({
+                transport,
+                serverUrl: transport !== 'stdio' ? 'http://localhost:3000' : undefined,
+                command: transport === 'stdio' ? 'npx' : undefined,
+                args: transport === 'stdio' ? ['mcp-server'] : undefined
+            });
+            console.log(`   ${transport}: ✅ (configured)`);
+        } catch (error: any) {
+            console.log(`   ${transport}: ⚠️ ${error.message}`);
+        }
+    }
+
+    // Test 4: Session info
+    console.log('\n4. Testing session management:');
+    const session = client.getSession();
+    console.log('   Session ID:', session.id);
+    console.log('   Connected:', session.connected);
+    console.log('   Tools:', session.tools.length);
+    console.log('   Resources:', session.resources.length);
+    console.log('   Success: ✅');
+
+    // Test 5: Live connection test (optional - requires running server)
+    const testServerUrl = process.env.MCP_SERVER_URL;
+    if (testServerUrl) {
+        console.log('\n5. Testing live MCP connection:');
+        try {
+            const liveClient = new MCPClient({
+                serverUrl: testServerUrl,
+                transport: 'http',
+                timeout: 10000,
+                verbose: false
+            });
+
+            await liveClient.connect();
+            console.log('   Connected: ✅');
+
+            const tools = await liveClient.listTools();
+            console.log(`   Tools discovered: ${tools.length}`);
+
+            for (const tool of tools.slice(0, 3)) {
+                console.log(`   - ${tool.name}: ${tool.description || 'No description'}`);
+            }
+
+            // Get tools in AI SDK format
+            const aiTools = await liveClient.getToolsAsAISDK();
+            console.log(`   AI SDK tools: ${aiTools.length}`);
+
+            await liveClient.disconnect();
+            console.log('   Disconnected: ✅');
+        } catch (error: any) {
+            console.log(`   ⚠️ Connection failed: ${error.message}`);
+        }
+    } else {
+        console.log('\n5. Live connection: Skipped (MCP_SERVER_URL not set)');
+        console.log('   Set MCP_SERVER_URL to test live connections');
+    }
+
+    // Test 6: Mock tool call simulation
+    console.log('\n6. Testing tool call preparation:');
+    const mockTool = {
+        name: 'search',
+        description: 'Search for information',
+        inputSchema: {
+            type: 'object',
+            properties: {
+                query: { type: 'string', description: 'Search query' }
+            },
+            required: ['query']
+        }
+    };
+    console.log('   Mock tool:', mockTool.name);
+    console.log('   Schema:', JSON.stringify(mockTool.inputSchema));
+    console.log('   Success: ✅');
+
+    // Test 7: API Key configuration
+    console.log('\n7. Testing API key configuration:');
+    const authClient = new MCPClient({
+        serverUrl: 'http://api.example.com',
+        apiKey: 'test-api-key-12345'
+    });
+    console.log('   Client with API key: ✅');
+
+    // Test 8: getMCPTools helper
+    console.log('\n8. Testing getMCPTools helper signature:');
+    console.log('   getMCPTools function:', typeof getMCPTools);
+    console.log('   Returns: { client, tools }');
+    console.log('   Success: ✅');
+
+    console.log('\n=== MCPClient Tests Complete ===');
+    console.log('\nNote: For full integration tests, run an MCP server and set MCP_SERVER_URL');
+}
+
+main().catch(console.error);

examples/js/memory/memory-hooks.ts

@@ -0,0 +1,137 @@
+/**
+ * MemoryHooks Integration Test
+ * 
+ * Tests pre/post hooks for memory operations.
+ * 
+ * Run: npx ts-node memory-hooks.ts
+ */
+
+import {
+    MemoryHooks,
+    createMemoryHooks,
+    createLoggingHooks,
+    createValidationHooks,
+    Memory,
+    createMemory
+} from '../../../src/praisonai-ts/dist';
+
+async function main() {
+    console.log('=== MemoryHooks Integration Test ===\n');
+
+    // Test 1: Basic hooks
+    console.log('1. Testing basic MemoryHooks:');
+    const logs: string[] = [];
+
+    const hooks = new MemoryHooks({
+        beforeStore: async (key, value) => {
+            logs.push(`beforeStore: ${key}`);
+            return { key, value };
+        },
+        afterStore: async (key) => {
+            logs.push(`afterStore: ${key}`);
+        },
+        beforeRetrieve: async (key) => {
+            logs.push(`beforeRetrieve: ${key}`);
+            return key;
+        },
+        afterRetrieve: async (key, value) => {
+            logs.push(`afterRetrieve: ${key}`);
+            return value;
+        }
+    });
+
+    await hooks.beforeStore('key1', { data: 'value1' });
+    await hooks.afterStore('key1', { data: 'value1' });
+    await hooks.beforeRetrieve('key1');
+    await hooks.afterRetrieve('key1', { data: 'value1' });
+
+    console.log('   Triggered hooks:', logs);
+    console.log('   Success: ✅');
+
+    // Test 2: Logging hooks
+    console.log('\n2. Testing createLoggingHooks:');
+    const logMessages: string[] = [];
+    const loggingHooks = createLoggingHooks((msg) => logMessages.push(msg));
+
+    await loggingHooks.beforeStore('test', 'data');
+    await loggingHooks.afterStore('test', 'data');
+    await loggingHooks.beforeRetrieve('test');
+    await loggingHooks.afterRetrieve('test', 'data');
+
+    console.log('   Log messages:', logMessages.length);
+    logMessages.forEach(m => console.log('    -', m));
+    console.log('   Success: ✅');
+
+    // Test 3: Validation hooks
+    console.log('\n3. Testing createValidationHooks:');
+    const validationHooks = createValidationHooks((key, value) => {
+        // Only allow non-empty strings
+        if (typeof value !== 'string' || value.length === 0) {
+            return { valid: false, reason: 'Value must be non-empty string' };
+        }
+        return { valid: true };
+    });
+
+    const validResult = await validationHooks.beforeStore('good', 'valid string');
+    console.log('   Valid data allowed:', validResult !== null);
+
+    const invalidResult = await validationHooks.beforeStore('bad', '');
+    console.log('   Invalid data blocked:', invalidResult === null);
+    console.log('   Success: ✅');
+
+    // Test 4: Delete hooks
+    console.log('\n4. Testing delete hooks:');
+    const deleteHooks = new MemoryHooks({
+        beforeDelete: async (key) => {
+            console.log(`   [Hook] About to delete: ${key}`);
+            return true; // Allow deletion
+        },
+        afterDelete: async (key, success) => {
+            console.log(`   [Hook] Delete ${key}: ${success ? 'succeeded' : 'failed'}`);
+        }
+    });
+
+    const canDelete = await deleteHooks.beforeDelete('item-to-delete');
+    console.log('   Deletion allowed:', canDelete);
+    await deleteHooks.afterDelete('item-to-delete', true);
+    console.log('   Success: ✅');
+
+    // Test 5: Search hooks
+    console.log('\n5. Testing search hooks:');
+    const searchHooks = new MemoryHooks({
+        beforeSearch: async (query, options) => {
+            console.log(`   [Hook] Searching: "${query}"`);
+            // Could modify query here
+            return { query: query.toLowerCase(), options };
+        },
+        afterSearch: async (query, results) => {
+            console.log(`   [Hook] Found ${results.length} results for "${query}"`);
+            return results;
+        }
+    });
+
+    const searchParams = await searchHooks.beforeSearch('UPPERCASE QUERY');
+    console.log('   Modified query:', searchParams?.query);
+
+    await searchHooks.afterSearch('query', [{ id: '1' }, { id: '2' }]);
+    console.log('   Success: ✅');
+
+    // Test 6: Dynamic hook management
+    console.log('\n6. Testing dynamic hook management:');
+    const dynamicHooks = new MemoryHooks({});
+
+    console.log('   Initial config:', Object.keys(dynamicHooks.getConfig()).length === 0 ? 'empty' : 'has hooks');
+
+    dynamicHooks.addHook('logging', true);
+    dynamicHooks.setLogging(true);
+
+    console.log('   After adding logging:', dynamicHooks.getConfig().logging);
+
+    dynamicHooks.removeHook('logging');
+    console.log('   After removing:', dynamicHooks.getConfig().logging ?? 'undefined');
+    console.log('   Success: ✅');
+
+    console.log('\n=== MemoryHooks Tests Complete ===');
+}
+
+main().catch(console.error);

examples/js/run-feature-tests.ts

@@ -0,0 +1,181 @@
+/**
+ * Feature Parity Integration Test Runner
+ * 
+ * Runs all integration tests for the new TypeScript features.
+ * Tests include real API calls when OPENAI_API_KEY is available.
+ * 
+ * Run: npx ts-node run-feature-tests.ts
+ */
+
+import { execSync, spawn } from 'child_process';
+import * as path from 'path';
+import * as fs from 'fs';
+
+interface TestResult {
+    name: string;
+    passed: boolean;
+    duration: number;
+    output: string;
+    error?: string;
+}
+
+const TEST_FILES = [
+    // Workflow patterns
+    'workflows/loop-pattern.ts',
+    'workflows/repeat-pattern.ts',
+    // Agents
+    'agents/audio-agent.ts',
+    // Knowledge
+    'knowledge/chonkie-adapter.ts',
+    // MCP
+    'mcp/mcp-client-test.ts',
+];
+
+async function runTest(testFile: string): Promise<TestResult> {
+    const startTime = Date.now();
+    const fullPath = path.join(__dirname, testFile);
+    const name = path.basename(testFile, '.ts');
+
+    return new Promise((resolve) => {
+        const proc = spawn('npx', ['ts-node', fullPath], {
+            cwd: __dirname,
+            env: { ...process.env },
+            stdio: ['pipe', 'pipe', 'pipe']
+        });
+
+        let stdout = '';
+        let stderr = '';
+
+        proc.stdout.on('data', (data) => {
+            stdout += data.toString();
+        });
+
+        proc.stderr.on('data', (data) => {
+            stderr += data.toString();
+        });
+
+        proc.on('close', (code) => {
+            const duration = Date.now() - startTime;
+            resolve({
+                name,
+                passed: code === 0,
+                duration,
+                output: stdout,
+                error: stderr || undefined
+            });
+        });
+
+        proc.on('error', (error) => {
+            const duration = Date.now() - startTime;
+            resolve({
+                name,
+                passed: false,
+                duration,
+                output: '',
+                error: error.message
+            });
+        });
+
+        // Timeout after 60 seconds
+        setTimeout(() => {
+            proc.kill();
+            resolve({
+                name,
+                passed: false,
+                duration: 60000,
+                output: stdout,
+                error: 'Test timed out after 60 seconds'
+            });
+        }, 60000);
+    });
+}
+
+async function main() {
+    console.log('╔════════════════════════════════════════════════════════════╗');
+    console.log('║     PraisonAI TypeScript Feature Parity Integration Tests  ║');
+    console.log('╚════════════════════════════════════════════════════════════╝\n');
+
+    // Environment info
+    console.log('Environment:');
+    console.log(`  OPENAI_API_KEY: ${process.env.OPENAI_API_KEY ? '✅ Set' : '❌ Not set'}`);
+    console.log(`  MCP_SERVER_URL: ${process.env.MCP_SERVER_URL ? '✅ Set' : '❌ Not set'}`);
+    console.log(`  Node version: ${process.version}`);
+    console.log('');
+
+    // Check if test files exist
+    console.log('Test Files:');
+    for (const file of TEST_FILES) {
+        const fullPath = path.join(__dirname, file);
+        const exists = fs.existsSync(fullPath);
+        console.log(`  ${exists ? '✅' : '❌'} ${file}`);
+    }
+    console.log('');
+
+    // Run tests
+    console.log('Running Tests...\n');
+    console.log('─'.repeat(60));
+
+    const results: TestResult[] = [];
+    for (const testFile of TEST_FILES) {
+        const fullPath = path.join(__dirname, testFile);
+        if (!fs.existsSync(fullPath)) {
+            results.push({
+                name: path.basename(testFile, '.ts'),
+                passed: false,
+                duration: 0,
+                output: '',
+                error: 'File not found'
+            });
+            continue;
+        }
+
+        console.log(`\n▶ Running: ${testFile}`);
+        const result = await runTest(testFile);
+        results.push(result);
+
+        if (result.passed) {
+            console.log(`  ✅ PASSED (${result.duration}ms)`);
+        } else {
+            console.log(`  ❌ FAILED (${result.duration}ms)`);
+            if (result.error) {
+                console.log(`     Error: ${result.error.slice(0, 200)}...`);
+            }
+        }
+    }
+
+    console.log('\n' + '─'.repeat(60));
+
+    // Summary
+    const passed = results.filter(r => r.passed).length;
+    const failed = results.filter(r => !r.passed).length;
+    const totalDuration = results.reduce((sum, r) => sum + r.duration, 0);
+
+    console.log('\n╔════════════════════════════════════════════════════════════╗');
+    console.log('║                      TEST SUMMARY                          ║');
+    console.log('╚════════════════════════════════════════════════════════════╝');
+    console.log(`  Total Tests: ${results.length}`);
+    console.log(`  Passed: ${passed} ✅`);
+    console.log(`  Failed: ${failed} ${failed > 0 ? '❌' : ''}`);
+    console.log(`  Duration: ${(totalDuration / 1000).toFixed(2)}s`);
+    console.log('');
+
+    // Detailed results
+    if (failed > 0) {
+        console.log('Failed Tests:');
+        for (const result of results.filter(r => !r.passed)) {
+            console.log(`  ❌ ${result.name}`);
+            if (result.error) {
+                console.log(`     ${result.error.slice(0, 100)}`);
+            }
+        }
+        console.log('');
+    }
+
+    // Exit with appropriate code
+    process.exit(failed > 0 ? 1 : 0);
+}
+
+main().catch((error) => {
+    console.error('Test runner failed:', error);
+    process.exit(1);
+});

examples/js/tools/subagent-tool.ts

@@ -0,0 +1,112 @@
+/**
+ * SubagentTool Integration Test
+ * 
+ * Tests the agent-as-tool pattern with real agents.
+ * 
+ * Run: npx ts-node subagent-tool.ts
+ * Requires: OPENAI_API_KEY
+ */
+
+import {
+    Agent,
+    SubagentTool,
+    createSubagentTool,
+    createSubagentTools,
+    createDelegator
+} from '../../../src/praisonai-ts/dist';
+
+async function main() {
+    console.log('=== SubagentTool Integration Test ===\n');
+
+    // Test 1: Create SubagentTool
+    console.log('1. Testing SubagentTool creation:');
+    const mockAgent = {
+        name: 'MockAgent',
+        chat: async (input: string) => `Echo: ${input}`
+    };
+
+    const tool = new SubagentTool(mockAgent, {
+        name: 'echo',
+        description: 'Echo the input'
+    });
+
+    console.log('   Tool name:', tool.name);
+    console.log('   Tool ID:', tool.id);
+    console.log('   Schema:', JSON.stringify(tool.getSchema()));
+    console.log('   Success: ✅');
+
+    // Test 2: Execute tool with mock agent
+    console.log('\n2. Testing tool execution:');
+    const mockResult = await tool.execute({ input: 'Hello World' });
+    console.log('   Input: "Hello World"');
+    console.log('   Output:', mockResult);
+    console.log('   Success: ✅');
+
+    // Test 3: Factory function
+    console.log('\n3. Testing createSubagentTool factory:');
+    const factoryTool = createSubagentTool(mockAgent, { name: 'factory_echo' });
+    console.log('   Tool name:', factoryTool.name);
+    console.log('   Success: ✅');
+
+    // Test 4: Multiple subagent tools
+    console.log('\n4. Testing createSubagentTools (batch):');
+    const agents = [
+        { name: 'Agent1', chat: async (i: string) => `A1: ${i}` },
+        { name: 'Agent2', chat: async (i: string) => `A2: ${i}` }
+    ];
+    const tools = createSubagentTools(agents);
+    console.log('   Created tools:', tools.length);
+    tools.forEach(t => console.log('    -', t.name));
+    console.log('   Success: ✅');
+
+    // Test 5: AI SDK format conversion
+    console.log('\n5. Testing AI SDK format conversion:');
+    const aiTool = tool.toAISDKTool();
+    console.log('   Type:', aiTool.type);
+    console.log('   Function name:', aiTool.function.name);
+    console.log('   Has execute:', typeof aiTool.execute === 'function');
+    console.log('   Success: ✅');
+
+    // Test 6: Live test with real agent
+    if (process.env.OPENAI_API_KEY) {
+        console.log('\n6. Testing with real Agent (live API):');
+        try {
+            const researcher = new Agent({
+                name: 'QuickResearcher',
+                instructions: 'Answer in exactly 5 words.',
+                llm: 'openai/gpt-4o-mini'
+            });
+
+            const researchTool = createSubagentTool(researcher, {
+                name: 'quick_research',
+                description: 'Get a quick 5-word answer'
+            });
+
+            const result = await researchTool.execute({ input: 'What is AI?' });
+            console.log('   Question: "What is AI?"');
+            console.log('   Answer:', result);
+            console.log('   Success: ✅');
+        } catch (error: any) {
+            console.log('   Error:', error.message);
+        }
+    } else {
+        console.log('\n6. Live test: Skipped (OPENAI_API_KEY not set)');
+    }
+
+    // Test 7: Input/Output transforms
+    console.log('\n7. Testing transforms:');
+    const transformTool = createSubagentTool(mockAgent, {
+        name: 'transform_echo',
+        inputTransform: (input) => input.toUpperCase(),
+        outputTransform: (output) => `[TRANSFORMED] ${output}`
+    });
+
+    const transformResult = await transformTool.execute({ input: 'hello' });
+    console.log('   Input: "hello"');
+    console.log('   Output:', transformResult);
+    console.log('   Success: ✅');
+
+    console.log('\n=== SubagentTool Tests Complete ===');
+}
+
+main().catch(console.error);

examples/js/workflows/loop-pattern.ts

@@ -0,0 +1,88 @@
+/**
+ * Loop Pattern Integration Test
+ * 
+ * Tests the Loop workflow pattern with real data.
+ * 
+ * Run: npx ts-node loop-pattern.ts
+ */
+
+import { Loop, loopPattern, Agent, Workflow } from '../../../src/praisonai-ts/dist';
+
+async function main() {
+    console.log('=== Loop Pattern Integration Test ===\n');
+
+    // Test 1: Basic Loop with array
+    console.log('1. Testing Loop with array iteration:');
+    const items = ['apple', 'banana', 'cherry'];
+
+    const processor = (item: string) => `Processed: ${item.toUpperCase()}`;
+    const arrayLoop = new Loop(processor, { over: 'items' });
+
+    const result1 = await arrayLoop.run({ items });
+    console.log('   Results:', result1.results);
+    console.log('   Iterations:', result1.iterations);
+    console.log('   Success:', result1.success ? '✅' : '❌');
+
+    // Test 2: Loop with convenience function
+    console.log('\n2. Testing loop() convenience function:');
+    const doubler = (n: number) => n * 2;
+    const numberLoop = loopPattern(doubler, { over: 'numbers' });
+
+    const result2 = await numberLoop.run({ numbers: [1, 2, 3, 4, 5] });
+    console.log('   Input: [1, 2, 3, 4, 5]');
+    console.log('   Output:', result2.results);
+    console.log('   Success:', result2.success ? '✅' : '❌');
+
+    // Test 3: Loop with Agent (requires OPENAI_API_KEY)
+    if (process.env.OPENAI_API_KEY) {
+        console.log('\n3. Testing Loop with Agent (live API):');
+        try {
+            const agent = new Agent({
+                name: 'Summarizer',
+                instructions: 'Summarize the given topic in one sentence.',
+                llm: 'openai/gpt-4o-mini'
+            });
+
+            const topics = ['AI', 'TypeScript'];
+            const agentLoop = new Loop(agent, { over: 'topics', varName: 'topic' });
+
+            console.log('   Processing topics:', topics);
+            const result3 = await agentLoop.run({ topics });
+
+            result3.results.forEach((res, i) => {
+                console.log(`   [${topics[i]}]: ${res.slice(0, 80)}...`);
+            });
+            console.log('   Success:', result3.success ? '✅' : '❌');
+        } catch (error: any) {
+            console.log('   ⚠️ Agent test failed:', error.message);
+        }
+    } else {
+        console.log('\n3. Agent Loop Test: Skipped (OPENAI_API_KEY not set)');
+    }
+
+    // Test 4: Loop with error handling
+    console.log('\n4. Testing Loop with continueOnError:');
+    const errorProneProcessor = (item: string) => {
+        if (item === 'fail') throw new Error('Intentional failure');
+        return `OK: ${item}`;
+    };
+
+    const errorLoop = new Loop(errorProneProcessor, {
+        over: 'items',
+        continueOnError: true
+    });
+
+    const result4 = await errorLoop.run({ items: ['a', 'fail', 'b'] });
+    console.log('   Results:', result4.results);
+    console.log('   Errors:', result4.errors.length);
+    console.log('   Success:', result4.success ? '✅' : '❌ (expected - has errors)');
+
+    // Test 5: Loop type exports
+    console.log('\n5. Verifying exports:');
+    console.log('   Loop class:', typeof Loop);
+    console.log('   loop function:', typeof loopPattern);
+
+    console.log('\n=== Loop Pattern Tests Complete ===');
+}
+
+main().catch(console.error);

examples/js/workflows/repeat-pattern.ts

@@ -0,0 +1,114 @@
+/**
+ * Repeat Pattern Integration Test
+ * 
+ * Tests the Repeat workflow pattern for evaluator-optimizer loops.
+ * 
+ * Run: npx ts-node repeat-pattern.ts
+ */
+
+import { Repeat, repeatPattern, Agent } from '../../../src/praisonai-ts/dist';
+
+async function main() {
+    console.log('=== Repeat Pattern Integration Test ===\n');
+
+    // Test 1: Basic repeat until condition
+    console.log('1. Testing Repeat with condition:');
+    let counter = 0;
+    const incrementer = () => {
+        counter++;
+        return `Iteration ${counter}`;
+    };
+
+    const repeat1 = new Repeat(incrementer, {
+        until: (ctx) => ctx.iteration >= 3,
+        maxIterations: 10
+    });
+
+    const result1 = await repeat1.run('start');
+    console.log('   Results:', result1.iterations);
+    console.log('   Final:', result1.result);
+    console.log('   Condition met:', result1.conditionMet ? '✅' : '❌');
+    console.log('   Max reached:', result1.maxReached ? '⚠️' : 'No');
+
+    // Test 2: Repeat with content condition
+    console.log('\n2. Testing Repeat with content-based condition:');
+    let quality = 0;
+    const improver = (input: string, prev: string | null) => {
+        quality += 25;
+        return `Quality: ${quality}% - ${prev || input}`;
+    };
+
+    const repeat2 = new Repeat(improver, {
+        until: (ctx) => ctx.lastResult?.includes('100%') ?? false,
+        maxIterations: 5
+    });
+
+    quality = 0; // Reset
+    const result2 = await repeat2.run('Initial draft');
+    console.log('   Iterations:', result2.count);
+    console.log('   Final:', result2.result);
+    console.log('   Condition met:', result2.conditionMet ? '✅' : '❌');
+
+    // Test 3: Repeat reaching max iterations
+    console.log('\n3. Testing Repeat hitting max iterations:');
+    const neverDone = () => 'still working...';
+
+    const repeat3 = new Repeat(neverDone, {
+        until: () => false, // Never satisfied
+        maxIterations: 3
+    });
+
+    const result3 = await repeat3.run('start');
+    console.log('   Iterations:', result3.count);
+    console.log('   Max reached:', result3.maxReached ? '✅ (expected)' : '❌');
+
+    // Test 4: Repeat with Agent (requires OPENAI_API_KEY)
+    if (process.env.OPENAI_API_KEY) {
+        console.log('\n4. Testing Repeat with Agent (live API):');
+        try {
+            const agent = new Agent({
+                name: 'Optimizer',
+                instructions: 'Improve the given text. Add more detail and clarity. Say "DONE" when satisfied.',
+                llm: 'openai/gpt-4o-mini'
+            });
+
+            const repeat4 = new Repeat(agent, {
+                until: (ctx) => ctx.lastResult?.includes('DONE') ?? false,
+                maxIterations: 3
+            });
+
+            const result4 = await repeat4.run('Make this better: Hello world');
+            console.log('   Iterations:', result4.count);
+            console.log('   Final (truncated):', result4.result.slice(0, 100) + '...');
+            console.log('   Condition met:', result4.conditionMet ? '✅' : 'Max reached');
+        } catch (error: any) {
+            console.log('   ⚠️ Agent test failed:', error.message);
+        }
+    } else {
+        console.log('\n4. Agent Repeat Test: Skipped (OPENAI_API_KEY not set)');
+    }
+
+    // Test 5: Convenience function
+    console.log('\n5. Testing repeatPattern() convenience function:');
+    let seq = 0;
+    const sequencer = () => `seq-${++seq}`;
+
+    const repeat5 = repeatPattern(sequencer, {
+        until: (ctx) => ctx.iteration >= 2,
+        maxIterations: 5
+    });
+
+    seq = 0; // Reset
+    const result5 = await repeat5.run('');
+    console.log('   Results:', result5.iterations);
+    console.log('   Success:', result5.conditionMet ? '✅' : '❌');
+
+    // Test 6: Verify exports
+    console.log('\n6. Verifying exports:');
+    console.log('   Repeat class:', typeof Repeat);
+    console.log('   repeatPattern function:', typeof repeatPattern);
+
+    console.log('\n=== Repeat Pattern Tests Complete ===');
+}
+
+main().catch(console.error);

examples/knowledge/context_required_example.py

@@ -68,7 +68,7 @@ Answer questions based ONLY on the provided knowledge context.
 If the answer is not in the context, say 'I don't have that information.'
 Be precise and quote exact values when asked about codes or numbers.""",
             knowledge=[temp_dir],
-            user_id="demo_user",
+            memory={"user_id": "demo_user"},
             output="verbose",
         )
         

examples/knowledge/scope_isolation_example.py

@@ -50,7 +50,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
             instructions="You ingest and store knowledge.",
             knowledge=[temp_dir],
             user_id="user_alice",  # Scoped to Alice
-            verbose=False,
+            output="silent",
         )
         
         # Ensure knowledge is processed
@@ -104,7 +104,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
             instructions="Answer questions based on the provided knowledge. If you don't have the information, say so.",
             knowledge=[temp_dir],
             user_id="user_alice",  # Same scope as Agent A
-            verbose=False,
+            output="silent",
         )
         
         response = agent_b.chat("What is the secret access code for Project PHOENIX?")

examples/mcp/multi_server.py

@@ -0,0 +1,148 @@
+"""
+Multi-Server MCP Example.
+
+This example demonstrates how to use multiple MCP servers with PraisonAI Agents,
+including mixing MCP tools with regular Python function tools.
+
+Requirements:
+    - Node.js and npx installed (or uvx for Python-based servers)
+    - pip install praisonaiagents[mcp]
+    - OPENAI_API_KEY environment variable set
+
+Usage:
+    python multi_server.py
+"""
+
+from praisonaiagents import Agent, MCP
+import os
+
+
+def calculate(expression: str) -> str:
+    """
+    A simple calculator tool that evaluates mathematical expressions.
+    
+    Args:
+        expression: A mathematical expression to evaluate (e.g., "2 + 2")
+    
+    Returns:
+        The result of the calculation
+    """
+    try:
+        # Safe evaluation of mathematical expressions
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Invalid characters in expression"
+        result = eval(expression)
+        return f"Result: {result}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+def main():
+    # Check for API key
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Please set OPENAI_API_KEY environment variable")
+        return
+    
+    print("=" * 60)
+    print("Multi-Server MCP Example")
+    print("=" * 60)
+    
+    # Create MCP instance with Time server
+    print("\nInitializing MCP servers...")
+    try:
+        time_mcp = MCP("uvx mcp-server-time", timeout=30)
+        print("✓ Time MCP server initialized")
+    except Exception as e:
+        print(f"✗ Time MCP server failed: {e}")
+        return
+    
+    # Create an agent with multiple tool sources
+    agent = Agent(
+        name="MultiToolAgent",
+        instructions="""You are a helpful assistant with access to multiple tools:
+        1. Time tools - for getting current time in different timezones
+        2. Calculator - for mathematical calculations
+        
+        Use the appropriate tool based on the user's request.""",
+        llm="openai/gpt-4o-mini",
+        tools=[time_mcp, calculate]  # Mix MCP and regular tools
+    )
+    
+    # Show available tools
+    print("\nAvailable tools:")
+    formatted_tools = agent._format_tools_for_completion(agent.tools)
+    for tool in formatted_tools:
+        print(f"  - {tool['function']['name']}: {tool['function']['description'][:50]}...")
+    
+    # Example queries
+    queries = [
+        "What time is it in Tokyo?",
+        "Calculate 15 * 7 + 23",
+        "What's the current time in New York and London?"
+    ]
+    
+    print("\n" + "=" * 60)
+    print("Running queries...")
+    print("=" * 60)
+    
+    for i, query in enumerate(queries, 1):
+        print(f"\n--- Query {i} ---")
+        print(f"Question: {query}\n")
+        
+        try:
+            response = agent.chat(query)
+            print(f"Response:\n{response}")
+        except Exception as e:
+            print(f"Error: {e}")
+        
+        print("-" * 40)
+    
+    # Clean up
+    time_mcp.shutdown()
+    print("\nDone!")
+
+
+def demo_multiple_mcp_servers():
+    """
+    Demonstrate using multiple MCP servers simultaneously.
+    
+    This function shows how to create agents with multiple MCP servers,
+    each providing different capabilities.
+    """
+    print("\n" + "=" * 60)
+    print("Multiple MCP Servers Demo")
+    print("=" * 60)
+    
+    # Initialize multiple MCP servers
+    servers = {}
+    
+    try:
+        servers['time'] = MCP("uvx mcp-server-time", timeout=30)
+        print("✓ Time server initialized")
+    except Exception as e:
+        print(f"✗ Time server failed: {e}")
+    
+    # Create agent with all available servers
+    available_tools = list(servers.values())
+    
+    if not available_tools:
+        print("No MCP servers available")
+        return
+    
+    agent = Agent(
+        name="MultiServerAgent",
+        instructions="You have access to multiple MCP servers. Use the appropriate tools.",
+        llm="openai/gpt-4o-mini",
+        tools=available_tools
+    )
+    
+    print(f"\nAgent created with {len(available_tools)} MCP server(s)")
+    
+    # Clean up
+    for server in servers.values():
+        server.shutdown()
+
+
+if __name__ == "__main__":
+    main()

examples/mcp/sequential_thinking.py

@@ -0,0 +1,70 @@
+"""
+Sequential Thinking MCP Example.
+
+This example demonstrates how to use the Sequential Thinking MCP server
+with PraisonAI Agents to break down complex problems step by step.
+
+Requirements:
+    - Node.js and npx installed
+    - pip install praisonaiagents[mcp]
+    - OPENAI_API_KEY environment variable set
+
+Usage:
+    python sequential_thinking.py
+"""
+
+from praisonaiagents import Agent, MCP
+import os
+
+
+def main():
+    # Check for API key
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Please set OPENAI_API_KEY environment variable")
+        return
+    
+    # Create MCP instance with Sequential Thinking server
+    # The server provides tools for step-by-step problem analysis
+    sequential_mcp = MCP(
+        "npx -y @modelcontextprotocol/server-sequential-thinking",
+        timeout=60
+    )
+    
+    # Create an agent with the Sequential Thinking tools
+    agent = Agent(
+        name="ProblemSolver",
+        instructions="""You are an expert problem solver who breaks down complex problems 
+        into manageable steps. When given a problem:
+        1. Use the sequential thinking tool to analyze it step by step
+        2. Provide clear, actionable steps
+        3. Explain your reasoning at each step""",
+        llm="openai/gpt-4o-mini",
+        tools=sequential_mcp
+    )
+    
+    # Example problems to solve
+    problems = [
+        "Break down the process of making a cup of tea",
+        "How would you plan a weekend trip to a new city?",
+        "What steps would you take to learn a new programming language?"
+    ]
+    
+    print("=" * 60)
+    print("Sequential Thinking MCP Example")
+    print("=" * 60)
+    
+    for i, problem in enumerate(problems, 1):
+        print(f"\n--- Problem {i} ---")
+        print(f"Question: {problem}\n")
+        
+        response = agent.chat(problem)
+        print(f"Response:\n{response}")
+        print("-" * 40)
+    
+    # Clean up
+    sequential_mcp.shutdown()
+    print("\nDone!")
+
+
+if __name__ == "__main__":
+    main()

examples/performance/thread_safety_example.py

@@ -74,7 +74,7 @@ def example_agent_locks():
     agent = Agent(
         name="LockTestAgent",
         instructions="Test agent",
-        verbose=False
+        output="silent"
     )
     
     # Check for locks
@@ -112,7 +112,7 @@ def example_concurrent_real_api():
         name="ConcurrentAgent",
         instructions="Reply with one word only.",
         llm="gpt-4o-mini",
-        verbose=False
+        output="silent"
     )
     
     results = []

examples/persistence/knowledge_qdrant.py

@@ -51,7 +51,7 @@ agent = Agent(
     name="KnowledgeAssistant",
     instructions="You are a helpful assistant with access to technical documentation.",
     knowledge=[doc_path],
-    knowledge_config={
+    knowledge={
         "vector_store": "qdrant",
         "url": "http://localhost:6333"
     }

examples/persistence/simple_db_agent.py

@@ -39,7 +39,7 @@ agent = Agent(
     instructions="You are a helpful assistant. Keep responses brief.",
     db=db_instance,
     session_id="demo-session-001",  # Same session_id = resume conversation
-    verbose=False
+    output="silent"
 )
 
 # Chat with persistence

examples/python/agents/MULTI_PROVIDER_README.md

@@ -46,7 +46,7 @@ agent = RouterAgent(
     name="Smart Assistant",
     role="Adaptive AI Assistant",
     goal="Complete tasks using the most appropriate model",
-    models=["gpt-5-nano", "gpt-5-nano", "claude-3-5-sonnet-20241022"],
+    models=["gpt-4o-mini", "gpt-4o-mini", "claude-3-5-sonnet-20241022"],
     routing_strategy="auto"  # Automatic model selection
 )
 
@@ -90,7 +90,7 @@ analyzer = RouterAgent(
     goal="Analyze data efficiently",
     models={
         "gemini/gemini-1.5-flash": {},
-        "gpt-5-nano": {},
+        "gpt-4o-mini": {},
         "claude-3-haiku-20240307": {}
     },
     model_router=router,
@@ -207,7 +207,7 @@ for i, agent in enumerate(auto.agents):
         name=agent.name,
         role=agent.role,
         goal=agent.goal,
-        models=["gpt-5-nano", "gpt-5-nano", "claude-3-5-sonnet"],
+        models=["gpt-4o-mini", "gpt-4o-mini", "claude-3-5-sonnet"],
         routing_strategy="auto"
     )
 

examples/python/audio/roundtrip.py

@@ -0,0 +1,10 @@
+from praisonaiagents import AudioAgent
+
+# Text to Speech
+tts = AudioAgent(llm="openai/tts-1")
+tts.say("Hello!", output="hello.mp3")
+
+# Speech to Text
+stt = AudioAgent(llm="openai/whisper-1")
+text = stt.listen("hello.mp3")
+print(text)

examples/python/audio/stt_advanced.py

@@ -0,0 +1,7 @@
+from praisonaiagents import AudioAgent
+
+agent = AudioAgent(llm="groq/whisper-large-v3")  # 10x faster
+text = agent.transcribe("audio.mp3", language="en")
+print(text)
+
+# Models: openai/whisper-1, groq/whisper-large-v3, deepgram/nova-2

examples/python/audio/stt_basic.py

@@ -0,0 +1,8 @@
+# Speech-to-Text with Groq (fastest)
+# Requires: export GROQ_API_KEY=your-key
+
+from praisonaiagents import AudioAgent
+
+agent = AudioAgent(llm="groq/whisper-large-v3")
+text = agent.listen("audio.mp3")  # Replace with your audio file
+print(text)

examples/python/audio/stt_deepgram.py

@@ -0,0 +1,5 @@
+from praisonaiagents import AudioAgent
+
+agent = AudioAgent(llm="deepgram/nova-2")
+text = agent.listen("audio.mp3")
+print(text)

examples/python/audio/stt_groq.py

@@ -0,0 +1,5 @@
+from praisonaiagents import AudioAgent
+
+agent = AudioAgent(llm="groq/whisper-large-v3")
+text = agent.listen("audio.mp3")
+print(text)

examples/python/audio/stt_openai.py

@@ -0,0 +1,5 @@
+from praisonaiagents import AudioAgent
+
+agent = AudioAgent(llm="openai/whisper-1")
+text = agent.listen("audio.mp3")
+print(text)

examples/python/audio/tts_advanced.py

@@ -0,0 +1,7 @@
+from praisonaiagents import AudioAgent
+
+agent = AudioAgent(llm="openai/tts-1-hd")
+agent.speech("Hello!", voice="nova", speed=1.2, output="hello.mp3")
+
+# Voices: alloy, echo, fable, onyx, nova, shimmer
+# Models: openai/tts-1, openai/tts-1-hd, openai/gpt-4o-mini-tts

examples/python/audio/tts_basic.py

@@ -0,0 +1,8 @@
+# Text-to-Speech with OpenAI
+# Requires: export OPENAI_API_KEY=your-key
+
+from praisonaiagents import AudioAgent
+
+agent = AudioAgent(llm="openai/tts-1")
+agent.say("Hello world!", output="hello.mp3")
+print("Saved to hello.mp3")

examples/python/audio/tts_elevenlabs.py

@@ -0,0 +1,4 @@
+from praisonaiagents import AudioAgent
+
+agent = AudioAgent(llm="elevenlabs/eleven_multilingual_v2")
+agent.say("Hello world!", output="hello.mp3")

examples/python/audio/tts_gemini.py

@@ -0,0 +1,4 @@
+from praisonaiagents import AudioAgent
+
+agent = AudioAgent(llm="gemini/gemini-2.5-flash-preview-tts")
+agent.say("Hello world!", output="hello.mp3", voice="leda")

examples/python/audio/tts_openai.py

@@ -0,0 +1,4 @@
+from praisonaiagents import AudioAgent
+
+agent = AudioAgent(llm="openai/tts-1")
+agent.say("Hello world!", output="hello.mp3")

examples/python/bot_example.py

@@ -0,0 +1,76 @@
+"""
+Messaging Bot Example - Deploy Agents to Telegram, Discord, Slack
+
+This example demonstrates how to configure bots for different
+messaging platforms.
+"""
+
+import os
+from praisonaiagents import Agent, BotConfig
+
+# Telegram Bot Configuration
+telegram_config = BotConfig(
+    token=os.environ.get("TELEGRAM_BOT_TOKEN", ""),
+    command_prefix="/",
+    mention_required=True,
+    typing_indicator=True,
+    max_message_length=4096,
+    metadata={"platform": "telegram"}
+)
+
+# Discord Bot Configuration
+discord_config = BotConfig(
+    token=os.environ.get("DISCORD_BOT_TOKEN", ""),
+    command_prefix="!",
+    mention_required=True,
+    typing_indicator=True,
+    metadata={
+        "platform": "discord",
+        "guild_ids": []  # Add your guild IDs here
+    }
+)
+
+# Slack Bot Configuration
+slack_config = BotConfig(
+    token=os.environ.get("SLACK_BOT_TOKEN", ""),
+    command_prefix="/",
+    mention_required=True,
+    typing_indicator=True,
+    metadata={
+        "platform": "slack",
+        "app_token": os.environ.get("SLACK_APP_TOKEN", "")
+    }
+)
+
+# Create the agent that will handle messages
+assistant = Agent(
+    name="assistant",
+    instructions="""You are a helpful assistant that responds to messages 
+    from users on messaging platforms. Be friendly and concise.""",
+    llm="gpt-4o-mini"
+)
+
+if __name__ == "__main__":
+    print("Bot Configurations:")
+    print()
+    
+    print("Telegram Config:")
+    print(f"  Token configured: {bool(telegram_config.token)}")
+    print(f"  Command prefix: {telegram_config.command_prefix}")
+    print(f"  Mention required: {telegram_config.mention_required}")
+    print()
+    
+    print("Discord Config:")
+    print(f"  Token configured: {bool(discord_config.token)}")
+    print(f"  Command prefix: {discord_config.command_prefix}")
+    print()
+    
+    print("Slack Config:")
+    print(f"  Token configured: {bool(slack_config.token)}")
+    print(f"  Command prefix: {slack_config.command_prefix}")
+    print()
+    
+    print("To start a bot, use the CLI:")
+    print("  praisonai bot telegram --token $TELEGRAM_BOT_TOKEN")
+    print("  praisonai bot discord --token $DISCORD_BOT_TOKEN")
+    print("  praisonai bot slack --token $SLACK_BOT_TOKEN")

examples/python/cli/git_integration_example.py

@@ -8,7 +8,7 @@ Docs: https://docs.praison.ai/cli/git-integration
 from praisonai.cli.features import GitIntegrationHandler
 
 # Initialize
-handler = GitIntegrationHandler(verbose=True)
+handler = GitIntegrationHandler(output="verbose")
 git = handler.initialize(repo_path=".")
 
 # Check if it's a git repo

examples/python/cli/repo_map_example.py

@@ -8,7 +8,7 @@ Docs: https://docs.praison.ai/cli/repo-map
 from praisonai.cli.features import RepoMapHandler
 
 # Initialize with current directory
-handler = RepoMapHandler(verbose=True)
+handler = RepoMapHandler(output="verbose")
 repo_map = handler.initialize(root=".")
 
 # Get the repository map

examples/python/cli/sandbox_execution_example.py

@@ -9,7 +9,7 @@ from praisonai.cli.features import SandboxExecutorHandler
 from praisonai.cli.features.sandbox_executor import SandboxMode
 
 # Initialize with basic sandbox
-handler = SandboxExecutorHandler(verbose=True)
+handler = SandboxExecutorHandler(output="verbose")
 sandbox = handler.initialize(mode="basic")
 
 print(f"Sandbox mode: {handler.get_mode()}")

examples/python/concepts/chat-with-pdf.py

@@ -26,7 +26,7 @@ pdf_agent = Agent(
     backstory="You are an expert at reading, analyzing, and answering questions about PDF documents. You provide accurate, detailed answers based on the document content.",
     instructions="Read the provided PDF document and answer questions about its content. Be specific and cite relevant sections when possible.",
     knowledge=["document.pdf"],  # Replace with your PDF file path
-    knowledge_config=config,
+    knowledge=config,
     reflection=True,
     
     

examples/python/concepts/knowledge-agents-task.py

@@ -24,7 +24,7 @@ knowledge_agent = Agent(
     goal="Store and retrieve knowledge efficiently",
     backstory="Expert in managing and utilizing stored knowledge",
     knowledge=["sample.pdf"],
-    knowledge_config=config)
+    knowledge=config)
 
 # Define a task for the agent
 knowledge_task = Task(

examples/python/concepts/knowledge-agents.py

@@ -13,8 +13,7 @@ config = {
 agent = Agent(
     name="Knowledge Agent",
     instructions="You answer questions based on the provided knowledge.",
-    knowledge=["small.pdf"],
-    knowledge_config=config
+    knowledge=["small.pdf"]
 )
 
 agent.start("What is KAG in one line?")

examples/python/concepts/knowledge-reranker-example.py

@@ -139,7 +139,7 @@ def main():
         name="AI Research Assistant",
         instructions="You are an AI research assistant. Use your knowledge base to answer questions about artificial intelligence topics.",
         knowledge=sample_documents,  # Will use default knowledge config
-        knowledge_config=rerank_config,  # Use reranking config
+        knowledge=rerank_config,  # Use reranking config
         llm="gpt-4o-mini"
     )
     

examples/python/conditional_branching_example.py

@@ -0,0 +1,391 @@
+"""
+Conditional Branching Example - PraisonAI Agents
+
+This example demonstrates the `if:` pattern for conditional workflow execution:
+1. Basic if/then/else branching
+2. Numeric condition evaluation (>, <, >=, <=, ==, !=)
+3. String condition evaluation (==, !=)
+4. Contains condition (in, contains)
+5. Combining if: with loop: for per-item decisions
+
+The if: pattern enables dynamic workflow paths based on
+runtime conditions and variable values.
+"""
+
+from praisonaiagents import Workflow, WorkflowContext, StepResult
+from praisonaiagents.workflows import if_, loop
+
+
+# =============================================================================
+# Example 1: Basic If/Then/Else - Score-Based Routing
+# =============================================================================
+
+def basic_if_example():
+    """
+    Route to different handlers based on a score threshold.
+    """
+    print("\n" + "="*60)
+    print("Example 1: Basic If/Then/Else - Score Routing")
+    print("="*60)
+    
+    def approve(ctx: WorkflowContext) -> StepResult:
+        score = ctx.variables.get("score", 0)
+        print(f"  ✅ APPROVED: Score {score} meets threshold")
+        return StepResult(output="Application approved")
+    
+    def reject(ctx: WorkflowContext) -> StepResult:
+        score = ctx.variables.get("score", 0)
+        print(f"  ❌ REJECTED: Score {score} below threshold")
+        return StepResult(output="Application rejected")
+    
+    # Test with passing score
+    workflow_pass = Workflow(
+        steps=[
+            if_(
+                condition="{{score}} >= 70",
+                then_steps=[approve],
+                else_steps=[reject]
+            )
+        ],
+        variables={"score": 85}
+    )
+    
+    print("\nTest 1: Score = 85")
+    workflow_pass.start("Evaluate application")
+    
+    # Test with failing score
+    workflow_fail = Workflow(
+        steps=[
+            if_(
+                condition="{{score}} >= 70",
+                then_steps=[approve],
+                else_steps=[reject]
+            )
+        ],
+        variables={"score": 55}
+    )
+    
+    print("\nTest 2: Score = 55")
+    workflow_fail.start("Evaluate application")
+
+
+# =============================================================================
+# Example 2: String Condition - Status-Based Processing
+# =============================================================================
+
+def string_condition_example():
+    """
+    Branch based on string equality.
+    """
+    print("\n" + "="*60)
+    print("Example 2: String Condition - Status Processing")
+    print("="*60)
+    
+    def process_active(ctx: WorkflowContext) -> StepResult:
+        print("  🟢 Processing ACTIVE user")
+        return StepResult(output="Active user processed")
+    
+    def process_inactive(ctx: WorkflowContext) -> StepResult:
+        print("  🔴 Processing INACTIVE user")
+        return StepResult(output="Inactive user archived")
+    
+    # Test with active status
+    workflow = Workflow(
+        steps=[
+            if_(
+                condition="{{status}} == active",
+                then_steps=[process_active],
+                else_steps=[process_inactive]
+            )
+        ],
+        variables={"status": "active"}
+    )
+    
+    print("\nTest: status = 'active'")
+    workflow.start("Process user")
+    
+    # Test with inactive status
+    workflow2 = Workflow(
+        steps=[
+            if_(
+                condition="{{status}} == active",
+                then_steps=[process_active],
+                else_steps=[process_inactive]
+            )
+        ],
+        variables={"status": "inactive"}
+    )
+    
+    print("\nTest: status = 'inactive'")
+    workflow2.start("Process user")
+
+
+# =============================================================================
+# Example 3: Contains Condition - Content Filtering
+# =============================================================================
+
+def contains_condition_example():
+    """
+    Branch based on whether a string contains a substring.
+    """
+    print("\n" + "="*60)
+    print("Example 3: Contains Condition - Content Filtering")
+    print("="*60)
+    
+    def flag_content(ctx: WorkflowContext) -> StepResult:
+        print("  🚨 FLAGGED: Message contains sensitive content")
+        return StepResult(output="Content flagged for review")
+    
+    def approve_content(ctx: WorkflowContext) -> StepResult:
+        print("  ✅ APPROVED: Message is clean")
+        return StepResult(output="Content approved")
+    
+    # Test with flagged content
+    workflow1 = Workflow(
+        steps=[
+            if_(
+                condition="error in {{message}}",
+                then_steps=[flag_content],
+                else_steps=[approve_content]
+            )
+        ],
+        variables={"message": "System error: connection failed"}
+    )
+    
+    print("\nTest 1: Message contains 'error'")
+    workflow1.start("Filter content")
+    
+    # Test with clean content
+    workflow2 = Workflow(
+        steps=[
+            if_(
+                condition="error in {{message}}",
+                then_steps=[flag_content],
+                else_steps=[approve_content]
+            )
+        ],
+        variables={"message": "Operation completed successfully"}
+    )
+    
+    print("\nTest 2: Message is clean")
+    workflow2.start("Filter content")
+
+
+# =============================================================================
+# Example 4: If Inside Loop - Per-Item Decisions
+# =============================================================================
+
+def if_inside_loop_example():
+    """
+    Make decisions for each item in a collection.
+    Combines iteration with conditional branching.
+    """
+    print("\n" + "="*60)
+    print("Example 4: If Inside Loop - Per-Item Decisions")
+    print("="*60)
+    
+    results = {"premium": [], "standard": []}
+    
+    def premium_service(ctx: WorkflowContext) -> StepResult:
+        customer = ctx.variables.get("customer", {})
+        name = customer.get("name", "Unknown")
+        results["premium"].append(name)
+        print(f"  ⭐ {name}: Premium service activated")
+        return StepResult(output=f"Premium service for {name}")
+    
+    def standard_service(ctx: WorkflowContext) -> StepResult:
+        customer = ctx.variables.get("customer", {})
+        name = customer.get("name", "Unknown")
+        results["standard"].append(name)
+        print(f"  📦 {name}: Standard service activated")
+        return StepResult(output=f"Standard service for {name}")
+    
+    workflow = Workflow(
+        steps=[
+            loop(
+                steps=[
+                    if_(
+                        condition="{{customer.tier}} == premium",
+                        then_steps=[premium_service],
+                        else_steps=[standard_service]
+                    )
+                ],
+                over="customers",
+                var_name="customer"
+            )
+        ],
+        variables={
+            "customers": [
+                {"name": "Alice", "tier": "premium"},
+                {"name": "Bob", "tier": "standard"},
+                {"name": "Charlie", "tier": "premium"},
+                {"name": "Diana", "tier": "standard"}
+            ]
+        }
+    )
+    
+    print("\nProcessing customers:")
+    workflow.start("Assign service tiers")
+    
+    print(f"\nSummary:")
+    print(f"  Premium customers: {results['premium']}")
+    print(f"  Standard customers: {results['standard']}")
+
+
+# =============================================================================
+# Example 5: Nested Conditions - Multi-Level Decision Tree
+# =============================================================================
+
+def nested_conditions_example():
+    """
+    Demonstrate nested if conditions for complex decision trees.
+    """
+    print("\n" + "="*60)
+    print("Example 5: Nested Conditions - Decision Tree")
+    print("="*60)
+    
+    def vip_treatment(ctx: WorkflowContext) -> StepResult:
+        print("  👑 VIP Treatment: Dedicated support + Priority queue")
+        return StepResult(output="VIP treatment applied")  # noqa: ARG001
+    
+    def regular_premium(ctx: WorkflowContext) -> StepResult:
+        print("  ⭐ Premium: Priority support")
+        return StepResult(output="Premium support applied")
+    
+    def basic_support(ctx: WorkflowContext) -> StepResult:
+        print("  📞 Basic: Standard support queue")
+        return StepResult(output="Basic support applied")
+    
+    # Outer condition: Is premium?
+    # Inner condition (if premium): Is VIP?
+    workflow = Workflow(
+        steps=[
+            if_(
+                condition="{{tier}} == premium",
+                then_steps=[
+                    if_(
+                        condition="{{spend}} > 10000",
+                        then_steps=[vip_treatment],
+                        else_steps=[regular_premium]
+                    )
+                ],
+                else_steps=[basic_support]
+            )
+        ],
+        variables={"tier": "premium", "spend": 15000}
+    )
+    
+    print("\nTest 1: Premium tier, high spend")
+    workflow.start("Determine support level")
+    
+    workflow2 = Workflow(
+        steps=[
+            if_(
+                condition="{{tier}} == premium",
+                then_steps=[
+                    if_(
+                        condition="{{spend}} > 10000",
+                        then_steps=[vip_treatment],
+                        else_steps=[regular_premium]
+                    )
+                ],
+                else_steps=[basic_support]
+            )
+        ],
+        variables={"tier": "premium", "spend": 5000}
+    )
+    
+    print("\nTest 2: Premium tier, low spend")
+    workflow2.start("Determine support level")
+    
+    workflow3 = Workflow(
+        steps=[
+            if_(
+                condition="{{tier}} == premium",
+                then_steps=[
+                    if_(
+                        condition="{{spend}} > 10000",
+                        then_steps=[vip_treatment],
+                        else_steps=[regular_premium]
+                    )
+                ],
+                else_steps=[basic_support]
+            )
+        ],
+        variables={"tier": "basic", "spend": 20000}
+    )
+    
+    print("\nTest 3: Basic tier (regardless of spend)")
+    workflow3.start("Determine support level")
+
+
+# =============================================================================
+# Example 6: No Else Branch - Optional Processing
+# =============================================================================
+
+def no_else_example():
+    """
+    If condition without else branch - only execute if true.
+    """
+    print("\n" + "="*60)
+    print("Example 6: No Else Branch - Optional Processing")
+    print("="*60)
+    
+    def send_notification(ctx: WorkflowContext) -> StepResult:
+        print("  📧 Notification sent!")
+        return StepResult(output="Notification sent")
+    
+    def continue_processing(ctx: WorkflowContext) -> StepResult:
+        print("  ➡️ Continuing with main workflow...")
+        return StepResult(output="Continued")
+    
+    # Only send notification if opted in
+    workflow = Workflow(
+        steps=[
+            if_(
+                condition="{{notify}} == true",
+                then_steps=[send_notification]
+                # No else_steps - just skip if false
+            ),
+            continue_processing
+        ],
+        variables={"notify": "true"}
+    )
+    
+    print("\nTest 1: notify = true")
+    workflow.start("Process with notification")
+    
+    workflow2 = Workflow(
+        steps=[
+            if_(
+                condition="{{notify}} == true",
+                then_steps=[send_notification]
+            ),
+            continue_processing
+        ],
+        variables={"notify": "false"}
+    )
+    
+    print("\nTest 2: notify = false")
+    workflow2.start("Process without notification")
+
+
+# =============================================================================
+# Main - Run All Examples
+# =============================================================================
+
+if __name__ == "__main__":
+    print("\n" + "="*60)
+    print("PraisonAI Conditional Branching Examples")
+    print("="*60)
+    
+    basic_if_example()
+    string_condition_example()
+    contains_condition_example()
+    if_inside_loop_example()
+    nested_conditions_example()
+    no_else_example()
+    
+    print("\n" + "="*60)
+    print("All conditional branching examples completed!")
+    print("="*60)

examples/python/dict_config_example.py

@@ -16,7 +16,7 @@ def example_dict_output_config():
     """Example: Using dict for output configuration."""
     from praisonaiagents import Agent
     
-    # Dict shorthand - equivalent to OutputConfig(verbose=True, stream=False)
+    # Dict shorthand - equivalent to OutputConfig(output="verbose", stream=False)
     agent = Agent(
         name="Assistant",
         instructions="You are a helpful assistant.",

examples/python/extensibility/merge_strategies_example.py

@@ -0,0 +1,214 @@
+"""Example: Merge Strategies for Multi-Agent Workflows
+
+This example demonstrates how to use and create custom merge strategies
+for combining outputs from parallel agent executions.
+"""
+from praisonaiagents.agents import (
+    MergeStrategyProtocol,
+    FirstWinsMerge,
+    ConcatMerge,
+    DictMerge,
+)
+
+
+def example_first_wins_merge():
+    """Demonstrate FirstWinsMerge strategy."""
+    print("=" * 50)
+    print("FirstWinsMerge Examples")
+    print("=" * 50)
+    
+    merge = FirstWinsMerge()
+    
+    # 1. Basic usage - returns first non-None
+    print("\n1. Basic Usage:")
+    outputs = [None, "Agent 2 response", "Agent 3 response"]
+    result = merge.merge(outputs)
+    print(f"   Inputs: {outputs}")
+    print(f"   Result: {result}")
+    
+    # 2. All None returns None
+    print("\n2. All None:")
+    outputs = [None, None, None]
+    result = merge.merge(outputs)
+    print(f"   Inputs: {outputs}")
+    print(f"   Result: {result}")
+    
+    # 3. First value wins
+    print("\n3. First Value Wins:")
+    outputs = ["Fast agent", "Slow agent", "Slowest agent"]
+    result = merge.merge(outputs)
+    print(f"   Inputs: {outputs}")
+    print(f"   Result: {result}")
+    
+    # 4. Works with any type
+    print("\n4. Various Types:")
+    print(f"   Dict: {merge.merge([None, {'key': 'value'}])}")
+    print(f"   List: {merge.merge([None, [1, 2, 3]])}")
+    print(f"   Number: {merge.merge([None, 42])}")
+
+
+def example_concat_merge():
+    """Demonstrate ConcatMerge strategy."""
+    print("\n" + "=" * 50)
+    print("ConcatMerge Examples")
+    print("=" * 50)
+    
+    # 1. Default separator (double newline)
+    print("\n1. Default Separator:")
+    merge = ConcatMerge()
+    outputs = ["Part 1: Introduction", "Part 2: Analysis", "Part 3: Conclusion"]
+    result = merge.merge(outputs)
+    print(f"   Result:\n{result}")
+    
+    # 2. Custom separator
+    print("\n2. Custom Separator:")
+    merge = ConcatMerge(separator=" | ")
+    outputs = ["Alice", "Bob", "Charlie"]
+    result = merge.merge(outputs)
+    print(f"   Result: {result}")
+    
+    # 3. Skips None values
+    print("\n3. Skips None:")
+    merge = ConcatMerge(separator=", ")
+    outputs = ["A", None, "C", None, "E"]
+    result = merge.merge(outputs)
+    print(f"   Inputs: {outputs}")
+    print(f"   Result: {result}")
+    
+    # 4. Converts non-strings
+    print("\n4. Converts Non-Strings:")
+    merge = ConcatMerge(separator=" + ")
+    outputs = [1, 2, 3]
+    result = merge.merge(outputs)
+    print(f"   Inputs: {outputs}")
+    print(f"   Result: {result}")
+
+
+def example_dict_merge():
+    """Demonstrate DictMerge strategy."""
+    print("\n" + "=" * 50)
+    print("DictMerge Examples")
+    print("=" * 50)
+    
+    # 1. Basic shallow merge
+    print("\n1. Shallow Merge (default):")
+    merge = DictMerge()
+    outputs = [
+        {"name": "Alice", "score": 10},
+        {"name": "Bob", "level": 5},
+        {"score": 20, "rank": 1}
+    ]
+    result = merge.merge(outputs)
+    print(f"   Inputs: {outputs}")
+    print(f"   Result: {result}")
+    
+    # 2. Later values override
+    print("\n2. Override Behavior:")
+    merge = DictMerge()
+    outputs = [
+        {"status": "pending"},
+        {"status": "processing"},
+        {"status": "complete"}
+    ]
+    result = merge.merge(outputs)
+    print(f"   Result: {result}")
+    
+    # 3. Deep merge for nested dicts
+    print("\n3. Deep Merge:")
+    merge = DictMerge(deep=True)
+    outputs = [
+        {"config": {"a": 1, "b": 2}},
+        {"config": {"b": 3, "c": 4}},
+        {"config": {"d": 5}}
+    ]
+    result = merge.merge(outputs)
+    print(f"   Inputs: {outputs}")
+    print(f"   Result: {result}")
+    
+    # 4. Skips non-dict values
+    print("\n4. Skips Non-Dict Values:")
+    merge = DictMerge()
+    outputs = [{"a": 1}, "not a dict", None, {"b": 2}]
+    result = merge.merge(outputs)
+    print(f"   Inputs: {outputs}")
+    print(f"   Result: {result}")
+
+
+def example_custom_merge():
+    """Demonstrate creating custom merge strategies."""
+    print("\n" + "=" * 50)
+    print("Custom Merge Strategy Examples")
+    print("=" * 50)
+    
+    # 1. Longest output merge
+    class LongestOutputMerge:
+        """Returns the longest output."""
+        def merge(self, outputs, context=None):
+            valid = [o for o in outputs if o is not None]
+            if not valid:
+                return None
+            return max(valid, key=lambda x: len(str(x)))
+    
+    print("\n1. LongestOutputMerge:")
+    merge = LongestOutputMerge()
+    outputs = ["short", "medium length", "this is the longest response"]
+    result = merge.merge(outputs)
+    print(f"   Inputs: {outputs}")
+    print(f"   Result: {result}")
+    
+    # 2. Majority vote merge
+    class MajorityVoteMerge:
+        """Returns the most common output."""
+        def merge(self, outputs, context=None):
+            from collections import Counter
+            valid = [o for o in outputs if o is not None]
+            if not valid:
+                return None
+            counts = Counter(valid)
+            return counts.most_common(1)[0][0]
+    
+    print("\n2. MajorityVoteMerge:")
+    merge = MajorityVoteMerge()
+    outputs = ["yes", "yes", "no", "yes", "no"]
+    result = merge.merge(outputs)
+    print(f"   Inputs: {outputs}")
+    print(f"   Result: {result} (3 votes)")
+    
+    # 3. Weighted merge
+    class WeightedMerge:
+        """Merges with agent weights from context."""
+        def merge(self, outputs, context=None):
+            weights = context.get("weights", [1] * len(outputs)) if context else [1] * len(outputs)
+            best_idx = 0
+            best_weight = 0
+            for i, (output, weight) in enumerate(zip(outputs, weights)):
+                if output is not None and weight > best_weight:
+                    best_idx = i
+                    best_weight = weight
+            return outputs[best_idx] if outputs else None
+    
+    print("\n3. WeightedMerge:")
+    merge = WeightedMerge()
+    outputs = ["junior answer", "senior answer", "expert answer"]
+    context = {"weights": [1, 5, 10]}
+    result = merge.merge(outputs, context=context)
+    print(f"   Inputs: {outputs}")
+    print(f"   Weights: {context['weights']}")
+    print(f"   Result: {result}")
+    
+    # 4. Protocol compliance
+    print("\n4. Protocol Compliance:")
+    print(f"   LongestOutputMerge: {isinstance(LongestOutputMerge(), MergeStrategyProtocol)}")
+    print(f"   MajorityVoteMerge: {isinstance(MajorityVoteMerge(), MergeStrategyProtocol)}")
+    print(f"   WeightedMerge: {isinstance(WeightedMerge(), MergeStrategyProtocol)}")
+
+
+if __name__ == "__main__":
+    example_first_wins_merge()
+    example_concat_merge()
+    example_dict_merge()
+    example_custom_merge()
+    
+    print("\n" + "=" * 50)
+    print("All examples completed!")
+    print("=" * 50)

examples/python/extensibility/retry_example.py

@@ -0,0 +1,174 @@
+"""Example: Retry Policies and Fallback Chains
+
+This example demonstrates how to configure retry behavior with
+exponential backoff, jitter, and fallback chains.
+"""
+from praisonaiagents.tools import (
+    RetryPolicy,
+    FallbackChain,
+    ToolExecutionConfig,
+)
+
+
+def example_retry_policy():
+    """Demonstrate RetryPolicy configuration and usage."""
+    print("=" * 50)
+    print("RetryPolicy Examples")
+    print("=" * 50)
+    
+    # 1. Default policy
+    print("\n1. Default RetryPolicy:")
+    policy = RetryPolicy()
+    print(f"   max_attempts: {policy.max_attempts}")
+    print(f"   backoff_factor: {policy.backoff_factor}")
+    print(f"   initial_delay_ms: {policy.initial_delay_ms}")
+    print(f"   max_delay_ms: {policy.max_delay_ms}")
+    print(f"   retry_on: {policy.retry_on}")
+    
+    # 2. Exponential backoff calculation
+    print("\n2. Exponential Backoff (no jitter):")
+    policy = RetryPolicy(
+        initial_delay_ms=1000,
+        backoff_factor=2.0,
+        max_delay_ms=30000,
+        jitter=False
+    )
+    for attempt in range(5):
+        delay = policy.get_delay_ms(attempt)
+        print(f"   Attempt {attempt}: delay = {delay}ms")
+    
+    # 3. With jitter for production
+    print("\n3. With Jitter (production-ready):")
+    policy = RetryPolicy(
+        initial_delay_ms=1000,
+        backoff_factor=2.0,
+        jitter=True,
+        jitter_factor=0.25  # ±25%
+    )
+    print("   Delays for attempt 0 (10 samples):")
+    delays = [policy.get_delay_ms(0) for _ in range(10)]
+    print(f"   {delays}")
+    print(f"   Range: {min(delays)} - {max(delays)} (expected: 750-1250)")
+    
+    # 4. should_retry logic
+    print("\n4. should_retry Logic:")
+    policy = RetryPolicy(max_attempts=3, retry_on={"timeout", "rate_limit"})
+    
+    test_cases = [
+        ("timeout", 0),
+        ("timeout", 2),
+        ("timeout", 3),  # At max_attempts
+        ("unknown_error", 0),  # Not in retry_on
+    ]
+    for error_type, attempt in test_cases:
+        result = policy.should_retry(error_type, attempt)
+        print(f"   should_retry('{error_type}', {attempt}) = {result}")
+    
+    # 5. Custom error types
+    print("\n5. Custom Error Types:")
+    policy = RetryPolicy(retry_on={"api_error", "network_error", "throttled"})
+    print(f"   Retries on: {policy.retry_on}")
+    print(f"   should_retry('api_error', 0) = {policy.should_retry('api_error', 0)}")
+    print(f"   should_retry('timeout', 0) = {policy.should_retry('timeout', 0)}")
+
+
+def example_fallback_chain():
+    """Demonstrate FallbackChain configuration."""
+    print("\n" + "=" * 50)
+    print("FallbackChain Examples")
+    print("=" * 50)
+    
+    # 1. Basic chain
+    print("\n1. Basic FallbackChain:")
+    chain = FallbackChain(
+        tools=["web_search", "cached_search", "default_response"]
+    )
+    print(f"   Tools: {chain.tools}")
+    print(f"   Length: {len(chain)}")
+    print(f"   stop_on_success: {chain.stop_on_success}")
+    
+    # 2. Iteration
+    print("\n2. Iterating over chain:")
+    for i, tool in enumerate(chain):
+        print(f"   {i+1}. {tool}")
+    
+    # 3. Try all (don't stop on success)
+    print("\n3. Try All Mode:")
+    chain = FallbackChain(
+        tools=["validator_1", "validator_2", "validator_3"],
+        stop_on_success=False
+    )
+    print(f"   stop_on_success: {chain.stop_on_success}")
+    print("   (All tools will be tried regardless of success)")
+
+
+def example_tool_execution_config():
+    """Demonstrate ToolExecutionConfig combining retry and fallback."""
+    print("\n" + "=" * 50)
+    print("ToolExecutionConfig Examples")
+    print("=" * 50)
+    
+    # 1. Default config
+    print("\n1. Default Config:")
+    config = ToolExecutionConfig()
+    print(f"   retry_policy: {config.retry_policy}")
+    print(f"   fallback_chain: {config.fallback_chain}")
+    print(f"   timeout_ms: {config.timeout_ms}")
+    
+    # 2. Using default() factory
+    print("\n2. Default Factory:")
+    config = ToolExecutionConfig.default()
+    print(f"   retry_policy.max_attempts: {config.retry_policy.max_attempts}")
+    
+    # 3. Full configuration
+    print("\n3. Full Configuration:")
+    config = ToolExecutionConfig(
+        retry_policy=RetryPolicy(
+            max_attempts=5,
+            backoff_factor=1.5,
+            initial_delay_ms=500,
+            jitter=True
+        ),
+        fallback_chain=FallbackChain(
+            tools=["primary_api", "backup_api", "cache"]
+        ),
+        timeout_ms=10000
+    )
+    print(f"   retry_policy.max_attempts: {config.retry_policy.max_attempts}")
+    print(f"   retry_policy.jitter: {config.retry_policy.jitter}")
+    print(f"   fallback_chain tools: {config.fallback_chain.tools}")
+    print(f"   timeout_ms: {config.timeout_ms}")
+
+
+def example_validation():
+    """Demonstrate validation of RetryPolicy parameters."""
+    print("\n" + "=" * 50)
+    print("Validation Examples")
+    print("=" * 50)
+    
+    invalid_configs = [
+        ("max_attempts=0", {"max_attempts": 0}),
+        ("backoff_factor=0.5", {"backoff_factor": 0.5}),
+        ("initial_delay_ms=-1", {"initial_delay_ms": -1}),
+        ("max_delay < initial", {"initial_delay_ms": 1000, "max_delay_ms": 500}),
+        ("jitter_factor=1.5", {"jitter_factor": 1.5}),
+    ]
+    
+    for name, kwargs in invalid_configs:
+        try:
+            RetryPolicy(**kwargs)
+            print(f"\n   {name}: No error (unexpected!)")
+        except ValueError as e:
+            print(f"\n   {name}:")
+            print(f"   → ValueError: {e}")
+
+
+if __name__ == "__main__":
+    example_retry_policy()
+    example_fallback_chain()
+    example_tool_execution_config()
+    example_validation()
+    
+    print("\n" + "=" * 50)
+    print("All examples completed!")
+    print("=" * 50)

examples/python/extensibility/validators_example.py

@@ -0,0 +1,163 @@
+"""Example: Tool Validation with Custom Validators
+
+This example demonstrates how to use ValidationResult and implement
+custom validators using the ToolValidatorProtocol.
+"""
+from praisonaiagents.tools import (
+    ValidationResult,
+    ToolValidatorProtocol,
+    PassthroughValidator,
+)
+
+
+def example_validation_result():
+    """Demonstrate ValidationResult usage."""
+    print("=" * 50)
+    print("ValidationResult Examples")
+    print("=" * 50)
+    
+    # 1. Success factory
+    success = ValidationResult.success()
+    print(f"\n1. Success: valid={success.valid}, bool={bool(success)}")
+    
+    # 2. Failure factory
+    failure = ValidationResult.failure(
+        errors=["Query cannot be empty", "Missing required field"],
+        remediation="Provide a non-empty query string"
+    )
+    print(f"\n2. Failure: valid={failure.valid}")
+    print(f"   Errors: {failure.errors}")
+    print(f"   Remediation: {failure.remediation}")
+    
+    # 3. Fluent API - building incrementally
+    result = ValidationResult(valid=True)
+    result.add_warning("Query is very long, may be slow")
+    result.add_warning("Consider using pagination")
+    print(f"\n3. With warnings: valid={result.valid}, warnings={result.warnings}")
+    
+    # 4. Adding errors changes validity
+    result.add_error("Rate limit exceeded")
+    print(f"\n4. After error: valid={result.valid}, errors={result.errors}")
+
+
+class InputLengthValidator:
+    """Custom validator that checks string input lengths."""
+    
+    def __init__(self, max_length: int = 100):
+        self.max_length = max_length
+    
+    def validate_args(self, tool_name, args, context=None):
+        """Validate that string arguments don't exceed max length."""
+        for key, value in args.items():
+            if isinstance(value, str) and len(value) > self.max_length:
+                return ValidationResult.failure(
+                    errors=[f"Argument '{key}' ({len(value)} chars) exceeds limit ({self.max_length})"],
+                    remediation=f"Truncate '{key}' to {self.max_length} characters or less"
+                )
+        return ValidationResult.success()
+    
+    def validate_result(self, tool_name, result, context=None):
+        """Validate tool results."""
+        return ValidationResult.success()
+
+
+class TypeValidator:
+    """Custom validator that checks argument types."""
+    
+    def __init__(self, type_specs: dict):
+        """
+        Args:
+            type_specs: Dict mapping arg names to expected types
+                       e.g., {"query": str, "limit": int}
+        """
+        self.type_specs = type_specs
+    
+    def validate_args(self, tool_name, args, context=None):
+        result = ValidationResult(valid=True)
+        
+        for arg_name, expected_type in self.type_specs.items():
+            if arg_name in args:
+                actual_value = args[arg_name]
+                if not isinstance(actual_value, expected_type):
+                    result.add_error(
+                        f"Argument '{arg_name}' must be {expected_type.__name__}, "
+                        f"got {type(actual_value).__name__}"
+                    )
+        
+        if not result.valid:
+            result.remediation = "Check argument types and try again"
+        
+        return result
+    
+    def validate_result(self, tool_name, result, context=None):
+        return ValidationResult.success()
+
+
+def example_custom_validators():
+    """Demonstrate custom validator implementations."""
+    print("\n" + "=" * 50)
+    print("Custom Validator Examples")
+    print("=" * 50)
+    
+    # 1. InputLengthValidator
+    print("\n1. InputLengthValidator:")
+    validator = InputLengthValidator(max_length=50)
+    
+    # Valid input
+    result = validator.validate_args("search", {"query": "short query"})
+    print(f"   Short query: valid={result.valid}")
+    
+    # Invalid input
+    long_query = "x" * 100
+    result = validator.validate_args("search", {"query": long_query})
+    print(f"   Long query: valid={result.valid}")
+    print(f"   Error: {result.errors[0]}")
+    print(f"   Fix: {result.remediation}")
+    
+    # 2. TypeValidator
+    print("\n2. TypeValidator:")
+    validator = TypeValidator({"query": str, "limit": int, "offset": int})
+    
+    # Valid types
+    result = validator.validate_args("search", {"query": "test", "limit": 10})
+    print(f"   Correct types: valid={result.valid}")
+    
+    # Invalid types
+    result = validator.validate_args("search", {"query": 123, "limit": "ten"})
+    print(f"   Wrong types: valid={result.valid}")
+    for error in result.errors:
+        print(f"   - {error}")
+    
+    # 3. Protocol compliance check
+    print("\n3. Protocol Compliance:")
+    print(f"   InputLengthValidator: {isinstance(InputLengthValidator(), ToolValidatorProtocol)}")
+    print(f"   TypeValidator: {isinstance(TypeValidator({}), ToolValidatorProtocol)}")
+    print(f"   PassthroughValidator: {isinstance(PassthroughValidator(), ToolValidatorProtocol)}")
+
+
+def example_passthrough_validator():
+    """Demonstrate the default PassthroughValidator."""
+    print("\n" + "=" * 50)
+    print("PassthroughValidator Example")
+    print("=" * 50)
+    
+    validator = PassthroughValidator()
+    
+    # Always returns valid
+    result = validator.validate_args("any_tool", {"any": "args"})
+    print(f"\nvalidate_args: valid={result.valid}")
+    
+    result = validator.validate_result("any_tool", {"any": "result"})
+    print(f"validate_result: valid={result.valid}")
+    
+    print("\nPassthroughValidator is useful as a default when no validation is needed.")
+
+
+if __name__ == "__main__":
+    example_validation_result()
+    example_custom_validators()
+    example_passthrough_validator()
+    
+    print("\n" + "=" * 50)
+    print("All examples completed!")
+    print("=" * 50)

examples/python/failover_example.py

@@ -0,0 +1,170 @@
+"""
+Model Failover Example - Automatic Provider Switching
+
+This example demonstrates how to configure model failover
+for reliability and cost optimization.
+"""
+
+import os
+from praisonaiagents import (
+    AuthProfile,
+    FailoverConfig,
+    FailoverManager,
+)
+
+# Create auth profiles for different providers
+openai_profile = AuthProfile(
+    name="openai-primary",
+    provider="openai",
+    api_key=os.environ.get("OPENAI_API_KEY", "sk-..."),
+    priority=1,  # Highest priority
+    rate_limit=100,
+)
+
+anthropic_profile = AuthProfile(
+    name="anthropic-backup",
+    provider="anthropic",
+    api_key=os.environ.get("ANTHROPIC_API_KEY", "sk-ant-..."),
+    priority=2,  # Second priority
+    rate_limit=50,
+)
+
+groq_profile = AuthProfile(
+    name="groq-fallback",
+    provider="groq",
+    api_key=os.environ.get("GROQ_API_KEY", "gsk-..."),
+    priority=3,  # Third priority (fast but limited)
+    rate_limit=30,
+)
+
+# Configure failover behavior
+failover_config = FailoverConfig(
+    max_retries=3,
+    retry_delay=1.0,
+    exponential_backoff=True,
+    max_retry_delay=60.0,
+    failover_on_rate_limit=True,
+    failover_on_timeout=True,
+    failover_on_error=True,
+)
+
+# Create failover manager
+manager = FailoverManager(failover_config)
+manager.add_profile(openai_profile)
+manager.add_profile(anthropic_profile)
+manager.add_profile(groq_profile)
+
+def demonstrate_failover():
+    """Demonstrate failover functionality."""
+    
+    print("Failover Manager Status:")
+    print()
+    
+    # Get current status
+    status = manager.status()
+    for name, info in status.items():
+        print(f"  {name}:")
+        print(f"    Status: {info.get('status', 'unknown')}")
+        print(f"    Priority: {info.get('priority', 'N/A')}")
+        print(f"    Failures: {info.get('failure_count', 0)}")
+    print()
+    
+    # Get next available profile
+    next_profile = manager.get_next_profile()
+    if next_profile:
+        print(f"Next profile to use: {next_profile.name}")
+        print(f"  Provider: {next_profile.provider}")
+        print(f"  Priority: {next_profile.priority}")
+    print()
+    
+    # Simulate marking a failure
+    print("Simulating failure on primary provider...")
+    manager.mark_failure("openai-primary", "Rate limit exceeded")
+    
+    # Get next profile after failure
+    next_profile = manager.get_next_profile()
+    if next_profile:
+        print(f"After failure, next profile: {next_profile.name}")
+    print()
+    
+    # Get retry delay
+    delay = manager.get_retry_delay("openai-primary")
+    print(f"Retry delay for openai-primary: {delay}s")
+    print()
+    
+    # Reset all providers
+    print("Resetting all providers...")
+    manager.reset_all()
+    
+    next_profile = manager.get_next_profile()
+    if next_profile:
+        print(f"After reset, next profile: {next_profile.name}")
+
+def cost_optimization_example():
+    """Example of cost-optimized failover."""
+    
+    print("\nCost Optimization Example:")
+    print("-" * 40)
+    
+    # Create cost-optimized manager
+    cost_manager = FailoverManager()
+    
+    # Cheaper model first
+    cost_manager.add_profile(AuthProfile(
+        name="gpt-4o-mini",
+        provider="openai",
+        api_key=os.environ.get("OPENAI_API_KEY", ""),
+        priority=1,
+        metadata={"model": "gpt-4o-mini", "cost_per_1k": 0.00015}
+    ))
+    
+    # Premium model as fallback
+    cost_manager.add_profile(AuthProfile(
+        name="gpt-4o",
+        provider="openai",
+        api_key=os.environ.get("OPENAI_API_KEY", ""),
+        priority=2,
+        metadata={"model": "gpt-4o", "cost_per_1k": 0.005}
+    ))
+    
+    # Most expensive as last resort
+    cost_manager.add_profile(AuthProfile(
+        name="claude-opus",
+        provider="anthropic",
+        api_key=os.environ.get("ANTHROPIC_API_KEY", ""),
+        priority=3,
+        metadata={"model": "claude-3-opus", "cost_per_1k": 0.015}
+    ))
+    
+    print("Cost-optimized failover chain:")
+    for profile in [
+        cost_manager.get_profile("gpt-4o-mini"),
+        cost_manager.get_profile("gpt-4o"),
+        cost_manager.get_profile("claude-opus"),
+    ]:
+        if profile:
+            cost = profile.metadata.get("cost_per_1k", "N/A")
+            print(f"  {profile.priority}. {profile.name} (${cost}/1K tokens)")
+
+if __name__ == "__main__":
+    print("Model Failover Configuration")
+    print("=" * 40)
+    print()
+    
+    print("Failover Config:")
+    print(f"  Max retries: {failover_config.max_retries}")
+    print(f"  Initial delay: {failover_config.retry_delay}s")
+    print(f"  Exponential backoff: {failover_config.exponential_backoff}")
+    print(f"  Max delay: {failover_config.max_retry_delay}s")
+    print()
+    
+    print("Auth Profiles:")
+    for profile in [openai_profile, anthropic_profile, groq_profile]:
+        print(f"  {profile.name}:")
+        print(f"    Provider: {profile.provider}")
+        print(f"    Priority: {profile.priority}")
+        print(f"    Rate limit: {profile.rate_limit} req/min")
+    print()
+    
+    demonstrate_failover()
+    cost_optimization_example()

examples/python/gateway_example.py

@@ -0,0 +1,47 @@
+"""
+Gateway Example - Multi-Agent Coordination
+
+This example demonstrates how to use the Gateway for coordinating
+multiple agents and managing real-time communication.
+"""
+
+from praisonaiagents import Agent, GatewayConfig, SessionConfig
+
+# Configure the gateway
+gateway_config = GatewayConfig(
+    host="127.0.0.1",
+    port=8765,
+    max_connections=100,
+    heartbeat_interval=30,
+    session_config=SessionConfig(
+        timeout=3600,  # 1 hour session timeout
+        max_messages=500,
+    )
+)
+
+# Create specialized agents
+researcher = Agent(
+    name="researcher",
+    instructions="You research topics thoroughly and provide detailed information.",
+    llm="gpt-4o-mini"
+)
+
+writer = Agent(
+    name="writer",
+    instructions="You write clear, engaging content based on research.",
+    llm="gpt-4o-mini"
+)
+
+# Example: Simple agent interaction
+if __name__ == "__main__":
+    print("Gateway Configuration:")
+    print(f"  Host: {gateway_config.host}")
+    print(f"  Port: {gateway_config.port}")
+    print(f"  WebSocket URL: {gateway_config.ws_url}")
+    print(f"  Max Connections: {gateway_config.max_connections}")
+    print()
+    
+    # Test agent
+    response = researcher.start("What are the key benefits of multi-agent systems?")
+    print("Researcher Response:")
+    print(response)

examples/python/general/memory_example.py

@@ -104,11 +104,13 @@ def main():
     agents = Agents(
         agents=[researcher, retriever],
         tasks=[store_task, verify_task, query_task, query_both_task],  # Use same verbose level as memory
-        memory=True,
-        embedder={
-            "provider": "openai",
-            "config": {
-                "model": "text-embedding-3-small"
+        memory={
+            "provider": "rag",
+            "embedder": {
+                "provider": "openai",
+                "config": {
+                    "model": "text-embedding-3-small"
+                }
             }
         }
     )

examples/python/image/image_advanced.py

@@ -0,0 +1,17 @@
+from praisonaiagents import ImageAgent
+
+agent = ImageAgent(llm="openai/dall-e-3", style="vivid")
+
+# Generate with options
+result = agent.generate("A futuristic city", size="1792x1024", quality="hd")
+print(result.data[0].url)
+
+# Edit image (dall-e-2 only)
+agent2 = ImageAgent(llm="openai/dall-e-2")
+edited = agent2.edit("photo.png", "Add a rainbow")
+print(edited.data[0].url)
+
+# Generate variations
+variations = agent2.variation("photo.png", n=3)
+for img in variations.data:
+    print(img.url)

examples/python/image/image_basic.py

@@ -0,0 +1,8 @@
+# Image Generation with DALL-E
+# Requires: export OPENAI_API_KEY=your-key
+
+from praisonaiagents import ImageAgent
+
+agent = ImageAgent(llm="openai/dall-e-3")
+result = agent.generate("A sunset over mountains")
+print(result.data[0].url)

examples/python/mcp/mcp-auth-storage.py

@@ -0,0 +1,72 @@
+"""
+MCP Auth Storage Example
+
+This example demonstrates how to use the MCPAuthStorage class
+to manage OAuth tokens for remote MCP servers.
+"""
+
+from praisonaiagents.mcp import (
+    MCPAuthStorage,
+    generate_state,
+    generate_code_verifier,
+    generate_code_challenge,
+    get_redirect_url
+)
+import time
+
+# Initialize auth storage
+storage = MCPAuthStorage()
+print(f"Auth storage path: {storage.filepath}")
+
+# Generate PKCE parameters for OAuth flow
+state = generate_state()
+verifier = generate_code_verifier()
+challenge = generate_code_challenge(verifier)
+redirect_url = get_redirect_url()
+
+print("\n=== PKCE Parameters ===")
+print(f"State: {state[:30]}...")
+print(f"Verifier: {verifier[:30]}...")
+print(f"Challenge: {challenge[:30]}...")
+print(f"Redirect URL: {redirect_url}")
+
+# Store tokens (simulating OAuth callback)
+print("\n=== Storing Tokens ===")
+storage.set_tokens("example-server", {
+    "access_token": "example_access_token_12345",
+    "refresh_token": "example_refresh_token_67890",
+    "expires_at": time.time() + 3600,  # 1 hour from now
+    "scope": "read write"
+}, server_url="https://mcp.example.com/mcp")
+print("Tokens stored successfully")
+
+# Retrieve tokens
+entry = storage.get("example-server")
+print("\n=== Retrieved Entry ===")
+print(f"Server URL: {entry['server_url']}")
+print(f"Access Token: {entry['tokens']['access_token'][:20]}...")
+print(f"Expires At: {entry['tokens']['expires_at']}")
+
+# Check token expiration
+is_expired = storage.is_token_expired("example-server")
+print("\n=== Token Status ===")
+print(f"Is Expired: {is_expired}")
+
+# URL validation
+print("\n=== URL Validation ===")
+valid = storage.get_for_url("example-server", "https://mcp.example.com/mcp")
+print(f"Same URL: {valid is not None}")
+
+invalid = storage.get_for_url("example-server", "https://different.url/mcp")
+print(f"Different URL: {invalid is None}")
+
+# List all entries
+print("\n=== All Entries ===")
+all_entries = storage.all()
+for name, entry in all_entries.items():
+    print(f"  {name}: {entry.get('server_url', 'N/A')}")
+
+# Cleanup
+storage.remove("example-server")
+print("\n=== Cleanup ===")
+print("Removed example-server entry")

examples/python/mcp/remote-mcp-oauth.py

@@ -0,0 +1,32 @@
+"""
+Remote MCP Server with OAuth Authentication Example
+
+This example shows how to connect to a remote MCP server
+that requires OAuth 2.1 authentication.
+
+Prerequisites:
+1. Configure the remote server in ~/.praison/config.yaml
+2. Run: praisonai mcp auth <server-name>
+"""
+
+from praisonaiagents import Agent, MCP
+from praisonaiagents.mcp import MCPAuthStorage
+
+# Check if authenticated
+storage = MCPAuthStorage()
+entry = storage.get("my-remote-server")
+
+if not entry or not entry.get("tokens"):
+    print("Not authenticated. Run: praisonai mcp auth my-remote-server")
+    exit(1)
+
+# Create agent with remote MCP server
+agent = Agent(
+    name="Remote Assistant",
+    instructions="You help with tasks using the remote MCP server.",
+    llm="gpt-4o-mini",
+    tools=MCP("https://mcp.example.com/mcp")
+)
+
+# Start the agent
+agent.start("What tools are available?")

examples/python/memory/advanced-graph-memory-integration.py

@@ -61,7 +61,7 @@ agents_system = Agents(
     agents=[knowledge_agent],
     tasks=[build_task, query_task],
     memory=True,
-    memory_config=memory_config, output="verbose"
+    memory=memory_config, output="verbose"
 )
 
 print("Starting graph memory demonstration...")

examples/python/memory/advanced-memory-search-optimization.py

@@ -62,7 +62,7 @@ agents_system = Agents(
     agents=[research_agent],
     tasks=[build_memory_task, search_task],
     memory=True,
-    memory_config=memory_config, output="verbose"
+    memory=memory_config, output="verbose"
 )
 
 print("Starting memory search optimization demonstration...")

examples/python/memory/gemini-embedding-example.py

@@ -85,13 +85,13 @@ def main():
     agents = Agents(
         agents=[researcher, retriever],
         tasks=[store_task, retrieve_task],
-        memory=True,
-        embedder={
-            "provider": "gemini",
-            "config": {
-                "model": "text-embedding-004",
-                # Optional: specify task type for better results
-                # "task_type": "RETRIEVAL_DOCUMENT"  
+        memory={
+            "provider": "rag",
+            "embedder": {
+                "provider": "gemini",
+                "config": {
+                    "model": "text-embedding-004",
+                }
             }
         }
     )

examples/python/memory/gemini-embeddings.md

@@ -97,7 +97,7 @@ researcher = Agent(
     name="Researcher",
     role="Information Researcher",
     goal="Store information with semantic understanding",
-    llm="gpt-5-nano"
+    llm="gpt-4o-mini"
 )
 
 # Create task

examples/python/memory/graph-memory-agent.py

@@ -153,7 +153,7 @@ def main():
         agents=[researcher],
         tasks=[build_knowledge_task, query_knowledge_task, expand_knowledge_task],
         memory=True,
-        memory_config=memory_config
+        memory=memory_config
     )
     
     print("\n🚀 Starting graph memory demonstration...")

examples/python/memory_with_tracing_example.py

@@ -0,0 +1,72 @@
+"""
+Example: Memory with Tracing
+
+This example demonstrates how to use memory with context tracing enabled.
+When tracing is enabled, memory operations (store/search) emit trace events
+that can be used for debugging and analysis with the LLM Judge.
+
+Usage:
+    python memory_with_tracing_example.py
+
+To judge the trace:
+    praisonai recipe judge <trace_id> --memory
+"""
+
+from praisonaiagents.memory import Memory
+from praisonaiagents.trace.context_events import (
+    ContextTraceEmitter, 
+    ContextListSink,
+    set_context_emitter,
+    reset_context_emitter,
+)
+
+
+def main():
+    # Set up trace emitter to capture events
+    sink = ContextListSink()
+    emitter = ContextTraceEmitter(sink=sink, session_id="memory-demo", enabled=True)
+    token = set_context_emitter(emitter)
+    
+    try:
+        # Create memory instance
+        memory = Memory(config={
+            "provider": "rag",
+            "use_embedding": False,
+            "agent_name": "demo_agent",  # Used in trace events
+        })
+        
+        # Store some memories
+        print("Storing memories...")
+        memory.store_short_term("The user's name is Alice")
+        memory.store_short_term("Alice likes Python programming")
+        memory.store_long_term("Important: Alice prefers dark mode in all applications")
+        
+        # Search memories
+        print("\nSearching memories...")
+        results = memory.search_short_term("Alice", limit=5)
+        print(f"Found {len(results)} short-term memories")
+        
+        results = memory.search_long_term("preferences", limit=5)
+        print(f"Found {len(results)} long-term memories")
+        
+        # Print captured trace events
+        events = sink.get_events()
+        print(f"\n--- Captured {len(events)} trace events ---")
+        for event in events:
+            print(f"  {event.event_type.value}: {event.data}")
+        
+        # Filter memory-specific events
+        memory_events = [e for e in events if 'memory' in e.event_type.value]
+        print(f"\n--- Memory-specific events: {len(memory_events)} ---")
+        for event in memory_events:
+            print(f"  {event.event_type.value}:")
+            print(f"    agent: {event.agent_name}")
+            print(f"    data: {event.data}")
+        
+    finally:
+        reset_context_emitter(token)
+        print("\nTrace emitter reset.")
+
+
+if __name__ == "__main__":
+    main()

examples/python/models/deepseek/deepseek-rag-agents-streamlit.py

@@ -33,7 +33,7 @@ def init_agent():
         name="Knowledge Agent",
         instructions="You answer questions based on the provided knowledge.",
         knowledge=["kag-research-paper.pdf"],
-        knowledge_config=config,
+        knowledge=config,
         user_id="user1",
         llm="deepseek-r1"
     )

examples/python/models/deepseek/deepseek-rag-agents.py

@@ -31,7 +31,7 @@ agent = Agent(
     name="Knowledge Agent",
     instructions="You answer questions based on the provided knowledge.",
     knowledge=["kag-research-paper.pdf"], # Indexing
-    knowledge_config=config,
+    knowledge=config,
     user_id="user1",
     llm="deepseek-r1"
 )

examples/python/mongodb/mongodb_comprehensive_example.py

@@ -81,7 +81,7 @@ def main():
         backstory="""You are a knowledge management specialist who excels at 
         organizing, categorizing, and retrieving information from the knowledge base. 
         You ensure that knowledge is properly stored, indexed, and accessible.""",
-        knowledge_config=mongodb_knowledge_config,
+        knowledge=mongodb_knowledge_config,
         memory=True,
         llm="gpt-4o-mini"
     )
@@ -107,7 +107,7 @@ def main():
         backstory="""You are a customer service representative who uses the 
         knowledge base and customer data to provide excellent support. You can 
         access customer history, product information, and troubleshooting guides.""",
-        knowledge_config=mongodb_knowledge_config,
+        knowledge=mongodb_knowledge_config,
         tools=[mongodb_tools],
         memory=True,
         llm="gpt-4o-mini"
@@ -190,7 +190,7 @@ def main():
         agents=[data_manager, knowledge_curator, business_analyst, customer_service],
         tasks=business_tasks,
         memory=True,
-        memory_config=mongodb_memory_config, output="verbose"
+        memory=mongodb_memory_config, output="verbose"
     )
     
     # Execute the comprehensive business pipeline

examples/python/mongodb/mongodb_knowledge_example.py

@@ -55,7 +55,7 @@ def main():
         retrieve information from a comprehensive MongoDB knowledge base. You excel 
         at finding relevant information, synthesizing knowledge from multiple sources, 
         and providing accurate, context-aware responses.""",
-        knowledge_config=mongodb_knowledge_config,
+        knowledge=mongodb_knowledge_config,
         knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
         memory=True,
         llm="gpt-4o-mini"

examples/python/mongodb/mongodb_memory_example.py

@@ -94,7 +94,7 @@ def main():
         agents=[research_agent],
         tasks=research_tasks,
         memory=True,
-        memory_config=mongodb_memory_config, output="verbose"
+        memory=mongodb_memory_config, output="verbose"
     )
     
     # Execute the research pipeline

examples/python/monitoring/README.md

@@ -17,7 +17,7 @@ def my_function():
 
 agent = Agent(
     instructions="You are a helpful assistant",
-    llm="gpt-5-nano"
+    llm="gpt-4o-mini"
 )
 
 with track_api_call("agent_request"):

examples/python/nested_workflows_example.py

@@ -0,0 +1,252 @@
+"""
+Nested Workflows Example - PraisonAI Agents
+
+This example demonstrates how to use nested workflow patterns:
+1. Loop inside Loop (nested iteration)
+2. Parallel inside Loop (concurrent processing per item)
+3. Route inside Loop (conditional routing per item)
+4. Depth limit protection (prevents infinite recursion)
+
+These patterns enable complex data processing pipelines
+while maintaining clean, readable workflow definitions.
+"""
+
+from praisonaiagents import Workflow, WorkflowContext, StepResult
+from praisonaiagents.workflows import loop, parallel, route
+
+
+# =============================================================================
+# Example 1: Nested Loops - Process Matrix Data
+# =============================================================================
+
+def nested_loop_example():
+    """
+    Process a 2D matrix using nested loops.
+    Outer loop iterates rows, inner loop iterates columns.
+    """
+    print("\n" + "="*60)
+    print("Example 1: Nested Loops - Matrix Processing")
+    print("="*60)
+    
+    results = []
+    
+    def process_cell(ctx: WorkflowContext) -> StepResult:
+        row = ctx.variables.get("row", {})
+        col = ctx.variables.get("col", "")
+        row_name = row.get("name", "?")
+        results.append(f"{row_name}-{col}")
+        return StepResult(output=f"Processed cell {row_name}-{col}")
+    
+    # Inner loop processes columns for each row
+    inner_loop = loop(
+        process_cell,
+        over="columns",
+        var_name="col"
+    )
+    
+    # Outer loop iterates through rows
+    outer_loop = loop(
+        steps=[inner_loop],
+        over="rows",
+        var_name="row"
+    )
+    
+    workflow = Workflow(
+        steps=[outer_loop],
+        variables={
+            "rows": [
+                {"name": "Row1"},
+                {"name": "Row2"}
+            ],
+            "columns": ["A", "B", "C"]
+        }
+    )
+    
+    workflow.start("Process matrix")
+    print(f"Processed cells: {results}")
+    # Output: ['Row1-A', 'Row1-B', 'Row1-C', 'Row2-A', 'Row2-B', 'Row2-C']
+
+
+# =============================================================================
+# Example 2: Parallel Inside Loop - Concurrent Task Processing
+# =============================================================================
+
+def parallel_inside_loop_example():
+    """
+    For each project, run multiple analysis tasks in parallel.
+    Combines iteration with concurrent execution.
+    """
+    print("\n" + "="*60)
+    print("Example 2: Parallel Inside Loop - Concurrent Analysis")
+    print("="*60)
+    
+    results = []
+    
+    def security_scan(ctx: WorkflowContext) -> StepResult:
+        project = ctx.variables.get("project", "unknown")
+        results.append(f"security:{project}")
+        return StepResult(output=f"Security scan complete for {project}")
+    
+    def performance_test(ctx: WorkflowContext) -> StepResult:
+        project = ctx.variables.get("project", "unknown")
+        results.append(f"performance:{project}")
+        return StepResult(output=f"Performance test complete for {project}")
+    
+    def code_review(ctx: WorkflowContext) -> StepResult:
+        project = ctx.variables.get("project", "unknown")
+        results.append(f"review:{project}")
+        return StepResult(output=f"Code review complete for {project}")
+    
+    # Parallel tasks run concurrently for each project
+    parallel_analysis = parallel([security_scan, performance_test, code_review])
+    
+    # Loop through projects, running parallel analysis for each
+    workflow = Workflow(
+        steps=[
+            loop(
+                steps=[parallel_analysis],
+                over="projects",
+                var_name="project"
+            )
+        ],
+        variables={
+            "projects": ["frontend", "backend", "api"]
+        }
+    )
+    
+    workflow.start("Analyze all projects")
+    print(f"Analysis results: {sorted(results)}")
+
+
+# =============================================================================
+# Example 3: Route Inside Loop - Conditional Processing Per Item
+# =============================================================================
+
+def route_inside_loop_example():
+    """
+    Process items differently based on their type.
+    Each item in the loop is routed to the appropriate handler.
+    """
+    print("\n" + "="*60)
+    print("Example 3: Route Inside Loop - Type-Based Processing")
+    print("="*60)
+    
+    results = []
+    
+    def process_image(ctx: WorkflowContext) -> StepResult:
+        item = ctx.variables.get("item", {})
+        results.append(f"image:{item.get('name')}")
+        return StepResult(output=f"Processed image: {item.get('name')}")
+    
+    def process_video(ctx: WorkflowContext) -> StepResult:
+        item = ctx.variables.get("item", {})
+        results.append(f"video:{item.get('name')}")
+        return StepResult(output=f"Processed video: {item.get('name')}")
+    
+    def process_document(ctx: WorkflowContext) -> StepResult:
+        item = ctx.variables.get("item", {})
+        results.append(f"document:{item.get('name')}")
+        return StepResult(output=f"Processed document: {item.get('name')}")
+    
+    # Route based on item type
+    type_router = route({
+        "image": [process_image],
+        "video": [process_video],
+        "document": [process_document]
+    })
+    
+    # First, determine the type, then route
+    def get_type(ctx: WorkflowContext) -> StepResult:
+        item = ctx.variables.get("item", {})
+        item_type = item.get("type", "unknown")
+        return StepResult(output=item_type)
+    
+    workflow = Workflow(
+        steps=[
+            loop(
+                steps=[get_type, type_router],
+                over="files",
+                var_name="item"
+            )
+        ],
+        variables={
+            "files": [
+                {"name": "photo.jpg", "type": "image"},
+                {"name": "movie.mp4", "type": "video"},
+                {"name": "report.pdf", "type": "document"},
+                {"name": "logo.png", "type": "image"}
+            ]
+        }
+    )
+    
+    workflow.start("Process all files")
+    print(f"Processing results: {results}")
+
+
+# =============================================================================
+# Example 4: Three-Level Nesting - Complex Data Pipeline
+# =============================================================================
+
+def three_level_nesting_example():
+    """
+    Demonstrate three levels of nesting:
+    Department -> Team -> Member
+    """
+    print("\n" + "="*60)
+    print("Example 4: Three-Level Nesting - Organization Processing")
+    print("="*60)
+    
+    results = []
+    
+    def process_member(ctx: WorkflowContext) -> StepResult:
+        dept = ctx.variables.get("dept", {}).get("name", "?")
+        team = ctx.variables.get("team", {}).get("name", "?")
+        member = ctx.variables.get("member", "?")
+        results.append(f"{dept}/{team}/{member}")
+        return StepResult(output=f"Processed {member}")
+    
+    # Level 3: Process each member
+    member_loop = loop(process_member, over="members", var_name="member")
+    
+    # Level 2: Process each team (contains member loop)
+    team_loop = loop(steps=[member_loop], over="teams", var_name="team")
+    
+    # Level 1: Process each department (contains team loop)
+    dept_loop = loop(steps=[team_loop], over="departments", var_name="dept")
+    
+    workflow = Workflow(
+        steps=[dept_loop],
+        variables={
+            "departments": [
+                {"name": "Engineering"},
+                {"name": "Marketing"}
+            ],
+            "teams": [
+                {"name": "Frontend", "members": ["Alice", "Bob"]},
+                {"name": "Backend", "members": ["Charlie"]}
+            ],
+            "members": ["Member1", "Member2"]  # Simplified for demo
+        }
+    )
+    
+    workflow.start("Process organization")
+    print(f"Processed paths: {results}")
+
+
+# =============================================================================
+# Main - Run All Examples
+# =============================================================================
+
+if __name__ == "__main__":
+    print("\n" + "="*60)
+    print("PraisonAI Nested Workflows Examples")
+    print("="*60)
+    
+    nested_loop_example()
+    parallel_inside_loop_example()
+    route_inside_loop_example()
+    three_level_nesting_example()
+    
+    print("\n" + "="*60)
+    print("All nested workflow examples completed!")
+    print("="*60)

examples/python/ocr/ocr_advanced.py

@@ -0,0 +1,12 @@
+from praisonaiagents import OCRAgent
+
+agent = OCRAgent()
+
+# Extract with page selection (pages is 0-indexed)
+result = agent.extract("https://arxiv.org/pdf/2201.04234", pages=[0, 1])
+for page in result.pages:
+    print(f"Page {page.index}: {page.markdown[:200]}")
+
+# Quick read - just get the text
+text = agent.read("https://arxiv.org/pdf/2201.04234")
+print(f"Total length: {len(text)} chars")

examples/python/ocr/ocr_basic.py

@@ -0,0 +1,9 @@
+# OCR with Mistral
+# Requires: export MISTRAL_API_KEY=your-key
+# Note: Source must be a URL (https://) or base64
+
+from praisonaiagents import OCRAgent
+
+agent = OCRAgent()
+text = agent.read("https://arxiv.org/pdf/2201.04234")
+print(text[:500])  # First 500 chars

examples/python/ocr/ocr_mistral.py

@@ -0,0 +1,5 @@
+from praisonaiagents import OCRAgent
+
+agent = OCRAgent(llm="mistral/mistral-ocr-latest")
+text = agent.read("https://arxiv.org/pdf/2201.04234")
+print(text[:500])  # First 500 chars