Release v3.9.2

- MemoryConfig from praisonaiagents.config (not memory)
- FileMemory from praisonaiagents.memory
- OutputConfig, ExecutionConfig, PlanningConfig from config

.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">

api.md

@@ -0,0 +1,441 @@
+# PraisonAI API Reference
+
+This file is auto-generated. Do not edit manually.
+Regenerate with: `praisonai docs api-md --write`
+
+# Agents
+
+Types:
+```python
+from praisonaiagents import Agent, Agents
+```
+
+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">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>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">arun</a>(prompt: str, **kwargs)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">astart</a>(prompt: str, **kwargs)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">auto_memory</a>()</code>
+* <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_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>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">clean_json_output</a>(output: str) -> str</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">clear_history</a>()</code>
+* <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">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_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">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>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">retrieval_config</a>()</code>
+* <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">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 = 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">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>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">add_task</a>(task)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aexecute_task</a>(task_id)</code>
+* <code title="class Agents">Agents.<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 Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_all_tasks</a>()</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_task</a>(task_id)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">astart</a>(content = None, return_dict = False, **kwargs)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clean_json_output</a>(output: str) -> str</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clear_state</a>() -> None</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">context_manager</a>()</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">current_plan</a>()</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">default_completion_checker</a>(task, agent_output)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">delete_state</a>(key: str) -> bool</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">display_token_usage</a>()</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">execute_task</a>(task_id)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_agent_details</a>(agent_name)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_state</a>() -> Dict[str, Any]</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_tasks_status</a>()</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_detailed_token_report</a>() -> Dict[str, Any]</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_plan_markdown</a>() -> str</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_state</a>(key: str, default: Any = None) -> Any</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_details</a>(task_id)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_result</a>(task_id)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_status</a>(task_id)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_todo_markdown</a>() -> str</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_token_usage_summary</a>() -> Dict[str, Any]</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">has_state</a>(key: str) -> bool</code>
+* <code title="class Agents">Agents.<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 Agents">Agents.<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 Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">restore_session_state</a>(session_id: str) -> bool</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run</a>(content = None, return_dict = False, **kwargs)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run_all_tasks</a>()</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run_task</a>(task_id)</code>
+* <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, 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>
+
+# Tools
+
+Types:
+```python
+from praisonaiagents import Tools, tool
+```
+
+Methods:
+
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">tool</a>(func: Optional[Callable] = None) -> Union[FunctionTool, Callable[[Callable], FunctionTool]]</code>
+
+# Other
+
+Types:
+```python
+from praisonaiagents import Task
+```
+
+Methods:
+
+* <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>
+
+# Wrapper (praisonai)
+
+Types:
+```python
+from praisonai import CloudProvider, Deploy, DeployConfig, DeployType, PraisonAI, __version__
+```
+
+# CLI
+
+Methods:
+
+* <code title="cli">praisonai <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
+* <code title="cli">praisonai acp acp-main <a href="./src/praisonai/praisonai/cli/commands/acp.py">--help</a></code>
+* <code title="cli">praisonai agents create <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
+* <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 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>
+* <code title="cli">praisonai benchmark compare <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
+* <code title="cli">praisonai benchmark litellm <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
+* <code title="cli">praisonai benchmark profile <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
+* <code title="cli">praisonai benchmark sdk <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
+* <code title="cli">praisonai benchmark workflow <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
+* <code title="cli">praisonai call call-main <a href="./src/praisonai/praisonai/cli/commands/call.py">--help</a></code>
+* <code title="cli">praisonai chat <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
+* <code title="cli">praisonai chat chat-main <a href="./src/praisonai/praisonai/cli/commands/chat.py">--help</a></code>
+* <code title="cli">praisonai code <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
+* <code title="cli">praisonai code code-main <a href="./src/praisonai/praisonai/cli/commands/code.py">--help</a></code>
+* <code title="cli">praisonai commit commit-main <a href="./src/praisonai/praisonai/cli/commands/commit.py">--help</a></code>
+* <code title="cli">praisonai completion bash <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
+* <code title="cli">praisonai completion completion-callback <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
+* <code title="cli">praisonai completion fish <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
+* <code title="cli">praisonai completion zsh <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
+* <code title="cli">praisonai config get <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
+* <code title="cli">praisonai config list <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
+* <code title="cli">praisonai config path <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
+* <code title="cli">praisonai config reset <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
+* <code title="cli">praisonai config set <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
+* <code title="cli">praisonai context add <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
+* <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>
+* <code title="cli">praisonai debug lsp <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
+* <code title="cli">praisonai debug trace <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
+* <code title="cli">praisonai deploy aws <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
+* <code title="cli">praisonai deploy azure <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
+* <code title="cli">praisonai deploy docker <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
+* <code title="cli">praisonai deploy gcp <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
+* <code title="cli">praisonai diag diag-callback <a href="./src/praisonai/praisonai/cli/commands/diag.py">--help</a></code>
+* <code title="cli">praisonai diag export <a href="./src/praisonai/praisonai/cli/commands/diag.py">--help</a></code>
+* <code title="cli">praisonai docs api-md <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
+* <code title="cli">praisonai docs generate <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
+* <code title="cli">praisonai docs list <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
+* <code title="cli">praisonai docs report <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
+* <code title="cli">praisonai docs run <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
+* <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>
+* <code title="cli">praisonai doctor env <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
+* <code title="cli">praisonai doctor mcp <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
+* <code title="cli">praisonai doctor network <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
+* <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 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>
+* <code title="cli">praisonai examples report <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
+* <code title="cli">praisonai examples run <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
+* <code title="cli">praisonai examples run-all <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
+* <code title="cli">praisonai examples stats <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
+* <code title="cli">praisonai hooks add <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
+* <code title="cli">praisonai hooks list <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
+* <code title="cli">praisonai hooks remove <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
+* <code title="cli">praisonai knowledge add <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
+* <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 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 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 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>
+* <code title="cli">praisonai profile imports <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
+* <code title="cli">praisonai profile optimize <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
+* <code title="cli">praisonai profile profile-callback <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
+* <code title="cli">praisonai profile query <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
+* <code title="cli">praisonai profile snapshot <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
+* <code title="cli">praisonai profile startup <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
+* <code title="cli">praisonai profile suite <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
+* <code title="cli">praisonai rag chat <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
+* <code title="cli">praisonai rag eval <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
+* <code title="cli">praisonai rag index <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
+* <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 install <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 run <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 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>
+* <code title="cli">praisonai retrieval search <a href="./src/praisonai/praisonai/cli/commands/retrieval.py">--help</a></code>
+* <code title="cli">praisonai rules add <a href="./src/praisonai/praisonai/cli/commands/rules.py">--help</a></code>
+* <code title="cli">praisonai rules clear <a href="./src/praisonai/praisonai/cli/commands/rules.py">--help</a></code>
+* <code title="cli">praisonai rules list <a href="./src/praisonai/praisonai/cli/commands/rules.py">--help</a></code>
+* <code title="cli">praisonai run <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
+* <code title="cli">praisonai run run-main <a href="./src/praisonai/praisonai/cli/commands/run.py">--help</a></code>
+* <code title="cli">praisonai schedule delete <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <code title="cli">praisonai schedule describe <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <code title="cli">praisonai schedule list <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <code title="cli">praisonai schedule logs <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <code title="cli">praisonai schedule restart <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <code title="cli">praisonai schedule schedule-callback <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
+* <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 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 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>
+* <code title="cli">praisonai session list <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
+* <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>
+* <code title="cli">praisonai todo list <a href="./src/praisonai/praisonai/cli/commands/todo.py">--help</a></code>
+* <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 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 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>
+* <code title="cli">praisonai ui realtime <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
+* <code title="cli">praisonai ui ui-main <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
+* <code title="cli">praisonai version check <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
+* <code title="cli">praisonai version show <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
+* <code title="cli">praisonai version version-callback <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
+* <code title="cli">praisonai workflow create <a href="./src/praisonai/praisonai/cli/commands/workflow.py">--help</a></code>
+* <code title="cli">praisonai workflow list <a href="./src/praisonai/praisonai/cli/commands/workflow.py">--help</a></code>
+* <code title="cli">praisonai workflow run <a href="./src/praisonai/praisonai/cli/commands/workflow.py">--help</a></code>
+
+# TypeScript
+
+Types/Exports:
+```ts
+export { Agent, Agents, PraisonAIAgents, Router } from "./agent";
+export type { PraisonAIAgentsConfig, SimpleAgentConfig, SimpleRouteConfig, SimpleRouterConfig } from "./agent";
+export { ContextAgent, createContextAgent } from "./agent/context";
+export { Handoff, handoff, handoffFilters } from "./agent/handoff";
+export { ImageAgent, createImageAgent } from "./agent/image";
+export { PromptExpanderAgent, createPromptExpanderAgent } from "./agent/prompt-expander";
+export { QueryRewriterAgent, createQueryRewriterAgent } from "./agent/query-rewriter";
+export { DeepResearchAgent, createDeepResearchAgent } from "./agent/research";
+export { RouterAgent, createRouter, routeConditions } from "./agent/router";
+export { // Agent loop
+  createAgentLoop, // DevTools
+  enableDevTools, // MCP
+  createMCP, // Middleware (renamed to avoid conflicts)
+  createCachingMiddleware, // Models
+  createModel, // Multimodal
+  createImagePart, // Next.js
+  createRouteHandler, // OAuth for MCP
+  type OAuthClientProvider, // Server adapters
+  createHttpHandler, // Speech & Transcription
+  generateSpeech, // Telemetry (AI SDK v6 parity)
+  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";
+export { AutoAgents, createAutoAgents } from "./auto";
+export { BaseCache, FileCache, MemoryCache, createFileCache, createMemoryCache } from "./cache";
+export { CLI_SPEC_VERSION, executeCommand, parseArgs } from "./cli";
+export { // Autonomy Mode
+  AutonomyManager, // Background Jobs
+  JobQueue, // Checkpoints
+  CheckpointManager, // Cost Tracker
+  CostTracker, // External Agents
+  BaseExternalAgent, // Fast Context
+  FastContext, // Flow Display
+  FlowDisplay, // Git Integration
+  GitManager, // Interactive TUI
+  InteractiveTUI, // N8N Integration
+  N8NIntegration, // Repo Map
+  RepoMap, // Sandbox Executor
+  SandboxExecutor, // Scheduler
+  Scheduler, // Slash Commands
+  SlashCommandHandler, AiderAgent, ClaudeCodeAgent, CodexCliAgent, CommandValidator, CostTokenUsage, DEFAULT_BLOCKED_COMMANDS, DEFAULT_BLOCKED_PATHS, DEFAULT_IGNORE_PATTERNS, DiffViewer, FileCheckpointStorage, FileJobStorage, GeminiCliAgent, GenericExternalAgent, HistoryManager, MODEL_PRICING, MODE_POLICIES, MemoryCheckpointStorage, MemoryJobStorage, StatusDisplay, cliApprovalPrompt, createAutonomyManager, createCheckpointManager, createCostTracker, createDiffViewer, createExternalAgent, createFastContext, createFileCheckpointStorage, createFileJobStorage, createFlowDisplay, createGitManager, createHistoryManager, createInteractiveTUI, createJobQueue, createN8NIntegration, createRepoMap, createSandboxExecutor, createScheduler, createSlashCommandHandler, createStatusDisplay, cronExpressions, estimateTokens, executeSlashCommand, externalAgentAsTool, formatCost, getExternalAgentRegistry, getQuickContext, getRepoTree, isSlashCommand, parseSlashCommand, registerCommand, renderWorkflow, sandboxExec, triggerN8NWebhook } from "./cli/features";
+export { createDbAdapter, db, getDefaultDbAdapter, setDefaultDbAdapter } from "./db";
+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 { AgentEventBus, AgentEvents, EventEmitterPubSub, PubSub, createEventBus, createPubSub } from "./events";
+export { LLMGuardrail, createLLMGuardrail } from "./guardrails/llm-guardrail";
+export { // Computer Use
+  createComputerUse, ComputerUseClient, createCLIApprovalPrompt, createComputerUseAgent } from "./integrations/computer-use";
+export { BaseObservabilityProvider, ConsoleObservabilityProvider, LangfuseObservabilityProvider, MemoryObservabilityProvider, ObservabilityTraceContext, createConsoleObservability, createLangfuseObservability, createMemoryObservability } from "./integrations/observability";
+export { // Natural Language Postgres
+  createNLPostgres, NLPostgresClient, NLPostgresConfig, createPostgresTool } from "./integrations/postgres";
+export { // Slack
+  createSlackBot, SlackBot, parseSlackMessage, verifySlackSignature } from "./integrations/slack";
+export { BaseVectorStore, ChromaVectorStore, MemoryVectorStore, PineconeVectorStore, QdrantVectorStore, VectorQueryResult, WeaviateVectorStore, createChromaStore, createMemoryVectorStore, createPineconeStore, createQdrantStore, createWeaviateStore } from "./integrations/vector";
+export { BaseVoiceProvider, ElevenLabsVoiceProvider, OpenAIVoiceProvider, createElevenLabsVoice, createOpenAIVoice } from "./integrations/voice";
+export { GraphRAG, GraphStore, createGraphRAG } from "./knowledge/graph-rag";
+export { BaseReranker, CohereReranker, CrossEncoderReranker, LLMReranker, createCohereReranker, createCrossEncoderReranker, createLLMReranker } from "./knowledge/reranker";
+export { // Provider classes
+  OpenAIProvider, // Provider factory and utilities
+  createProvider, // Provider registry (extensibility API)
+  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 { AutoMemory, AutoMemoryKnowledgeBase, AutoMemoryVectorStore, DEFAULT_POLICIES, createAutoMemory, createLLMSummarizer } from "./memory/auto-memory";
+export { FileMemory, createFileMemory } from "./memory/file-memory";
+export { Memory, createMemory } from "./memory/memory";
+export type { MemoryConfig, MemoryEntry } from "./memory/memory";
+export { // Adapters
+  NoopObservabilityAdapter, // Constants
+  OBSERVABILITY_TOOLS, // Global adapter management
+  setObservabilityAdapter, // Types
+  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 { 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 { createWorkflowFromYAML, loadWorkflowFromFile, parseYAMLWorkflow, validateWorkflowDefinition } from "./workflows/yaml-parser";
+```
+
+# Optional Plugins
+
+External tools are available via `praisonai-tools` package:
+
+```bash
+pip install praisonai-tools
+```
+
+See [PraisonAI-tools](https://github.com/MervinPraison/PraisonAI-tools) for available tools.

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>=3.5.9" \
+    "praisonai>=3.9.2" \
     "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>=3.5.9" \
+    "praisonai>=3.9.2" \
     "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>=3.5.9" \
+    "praisonai>=3.9.2" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/README.md

@@ -23,6 +23,7 @@ examples/
 
 | Category | Examples | Docs |
 |----------|----------|------|
+| **Consolidated Params** | [consolidated_params/](consolidated_params/) | [📖](https://docs.praison.ai/api/consolidated-params) |
 | **Agents** | [python/agents/](python/agents/) | [📖](https://docs.praison.ai/concepts/agents) |
 | **Workflows** | [python/workflows/](python/workflows/) | [📖](https://docs.praison.ai/features/workflows) |
 | **Model Router** | [python/agents/router-agent-cost-optimization.py](python/agents/router-agent-cost-optimization.py) | [📖](https://docs.praison.ai/features/model-router) |
@@ -35,6 +36,21 @@ examples/
 | **Serve** | [serve/](serve/) | [📖](https://docs.praison.ai/cli/serve) |
 | **Endpoints** | [serve/](serve/) | [📖](https://docs.praison.ai/cli/endpoints) |
 
+## Consolidated Params Examples
+
+Agent-centric API with unified parameter resolution. Precedence: Instance > Config > Array > Dict > String > Bool > Default
+
+| Example | Description |
+|---------|-------------|
+| [basic_agent.py](consolidated_params/basic_agent.py) | Minimal agent with memory |
+| [basic_agents.py](consolidated_params/basic_agents.py) | Multi-agent with memory+planning |
+| [basic_workflow.py](consolidated_params/basic_workflow.py) | Workflow with consolidated params |
+| [basic_memory.py](consolidated_params/basic_memory.py) | Memory presets (file, redis, postgres) |
+| [basic_guardrails.py](consolidated_params/basic_guardrails.py) | Guardrails with callable or config |
+| [basic_workflow_agentlike.py](consolidated_params/basic_workflow_agentlike.py) | Workflow with agent-like params |
+| [basic_step_override.py](consolidated_params/basic_step_override.py) | Step-level override of workflow defaults |
+| [advanced_workflow_full_features.py](consolidated_params/advanced_workflow_full_features.py) | All consolidated params |
+
 ## Serve Examples
 
 | Example | Description | CLI Command |

examples/consolidated_params/README.md

@@ -0,0 +1,96 @@
+# Consolidated Params Examples
+
+Examples demonstrating the **Agent-Centric API** with consolidated parameters.
+
+## Precedence Ladder
+
+```
+Instance > Config > Array > Dict > String > Bool > Default
+```
+
+## Quick Reference
+
+| Feature | Presets | Example |
+|---------|---------|---------|
+| `memory` | file, sqlite, redis, postgres, mem0, mongodb | `memory="sqlite"` |
+| `output` | minimal, normal, verbose, debug, silent | `output="verbose"` |
+| `execution` | fast, balanced, thorough, unlimited | `execution="balanced"` |
+| `planning` | reasoning, read_only, auto | `planning="reasoning"` |
+| `reflection` | minimal, standard, thorough | `reflection="standard"` |
+| `guardrails` | strict, permissive, safety | `guardrails="strict"` |
+| `web` | duckduckgo, tavily, google, bing, serper | `web="duckduckgo"` |
+| `context` | sliding_window, summarize, truncate | `context="sliding_window"` |
+| `autonomy` | suggest, auto_edit, full_auto | `autonomy="suggest"` |
+| `caching` | enabled, disabled, prompt | `caching="prompt"` |
+
+## Examples
+
+### Basic Examples (Single Feature)
+
+| File | Feature | Description |
+|------|---------|-------------|
+| `basic_agent.py` | Agent | Minimal agent with memory |
+| `basic_memory.py` | memory | SQLite memory preset |
+| `basic_output.py` | output | Verbose output preset |
+| `basic_execution.py` | execution | Execution control |
+| `basic_guardrails.py` | guardrails | Callable validator |
+| `basic_reflection.py` | reflection | Self-reflection preset |
+| `basic_web.py` | web | Web search preset |
+| `basic_planning.py` | planning | Planning preset |
+| `basic_autonomy.py` | autonomy | Autonomy preset |
+| `basic_caching.py` | caching | Caching preset |
+| `basic_knowledge.py` | knowledge | RAG with sources |
+| `basic_context.py` | context | Context management |
+| `basic_hooks.py` | hooks | Lifecycle callbacks |
+
+### Multi-Agent Examples
+
+| File | Description |
+|------|-------------|
+| `basic_agents.py` | Multi-agent with memory and planning |
+
+### Workflow Examples
+
+| File | Description |
+|------|-------------|
+| `basic_workflow.py` | Simple workflow with output preset |
+| `basic_workflow_agentlike.py` | Workflow with agent-like params |
+| `basic_step_override.py` | Step-level param overrides |
+| `advanced_workflow_full_features.py` | All consolidated params |
+
+### Advanced Examples
+
+| File | Description |
+|------|-------------|
+| `advanced_output_execution.py` | Combined output + execution |
+
+## Usage Forms
+
+Each consolidated param supports multiple input forms:
+
+```python
+# Bool - enable with defaults
+memory=True
+
+# String preset
+memory="sqlite"
+
+# Dict config
+memory={"backend": "sqlite", "user_id": "user123"}
+
+# Config instance
+memory=MemoryConfig(backend="sqlite", user_id="user123")
+
+# Array with overrides (for some params)
+guardrails=["strict", {"max_retries": 10}]
+```
+
+## Running Examples
+
+```bash
+# Set API key
+export OPENAI_API_KEY=your_key_here
+
+# Run any example
+python basic_agent.py
+```

examples/consolidated_params/advanced_output_execution.py

@@ -0,0 +1,17 @@
+"""
+Advanced Output + Execution Example - Agent-Centric API
+
+Demonstrates preset + override array syntax.
+"""
+
+from praisonaiagents import Agent
+
+# Advanced: Preset with overrides using array syntax
+agent = Agent(
+    instructions="You are a helpful assistant.",
+    output=["verbose", {"stream": True, "metrics": True}],
+    execution=["thorough", {"max_iter": 30}],
+)
+
+response = agent.start("Explain quantum computing in simple terms.")
+print(response)

examples/consolidated_params/advanced_workflow_full_features.py

@@ -0,0 +1,55 @@
+"""Advanced Workflow with all consolidated params configured."""
+from praisonaiagents import Agent
+from praisonaiagents.workflows import Workflow, WorkflowStep
+from praisonaiagents.workflows.workflow_configs import (
+    WorkflowOutputConfig, WorkflowPlanningConfig, WorkflowMemoryConfig,
+)
+
+# Full-featured workflow with all consolidated params
+workflow = Workflow(
+    name="FullFeaturedWorkflow",
+    description="Demonstrates all agent-like consolidated params",
+    steps=[
+        WorkflowStep(
+            name="researcher",
+            action="Research: {{input}}",
+            agent=Agent(instructions="You are a researcher."),
+            knowledge=["research_docs/"],  # Step-specific knowledge
+            web="duckduckgo",  # Use DuckDuckGo for this step
+        ),
+        WorkflowStep(
+            name="analyzer",
+            action="Analyze the research",
+            agent=Agent(instructions="You are an analyst."),
+            reflection="thorough",  # Thorough reflection for analysis
+            guardrails="strict",  # Strict validation
+        ),
+        WorkflowStep(
+            name="writer",
+            action="Write the final report",
+            agent=Agent(instructions="You are a writer."),
+            autonomy=True,  # Enable autonomy for writing
+        ),
+    ],
+    # Workflow-level consolidated params
+    output=WorkflowOutputConfig(verbose=True, stream=True),
+    planning=WorkflowPlanningConfig(enabled=True, reasoning=True),
+    memory=WorkflowMemoryConfig(backend="file"),
+    # Agent-like params
+    autonomy=True,
+    knowledge=["shared_docs/"],
+    guardrails=True,
+    web=True,
+    reflection=True,
+)
+
+if __name__ == "__main__":
+    print(f"Workflow: {workflow.name}")
+    print(f"Output verbose: {workflow._verbose}")
+    print(f"Planning enabled: {workflow._planning_enabled}")
+    print(f"Memory config: {workflow._memory_config}")
+    print(f"Autonomy: {workflow.autonomy}")
+    print(f"Knowledge: {workflow.knowledge}")
+    print(f"Guardrails: {workflow.guardrails}")
+    print(f"Web: {workflow.web}")
+    print(f"Reflection: {workflow.reflection}")

examples/consolidated_params/basic_agent.py

@@ -0,0 +1,12 @@
+"""Basic Agent example with consolidated params."""
+from praisonaiagents import Agent
+
+# Minimal agent with memory
+agent = Agent(
+    instructions="You are a helpful assistant.",
+    memory=True,
+)
+
+if __name__ == "__main__":
+    response = agent.chat("Hello!")
+    print(response)

examples/consolidated_params/basic_agents.py

@@ -0,0 +1,17 @@
+"""Basic Agents (multi-agent) example with consolidated params."""
+from praisonaiagents import Agent, Agents
+
+# Create agents
+writer = Agent(instructions="You write content.")
+editor = Agent(instructions="You edit content.")
+
+# Multi-agent with consolidated params
+agents = Agents(
+    agents=[writer, editor],
+    memory=True,
+    planning=True,
+)
+
+if __name__ == "__main__":
+    result = agents.start("Write a haiku about AI")
+    print(result)

examples/consolidated_params/basic_autonomy.py

@@ -0,0 +1,18 @@
+"""
+Basic Autonomy Example - Agent-Centric API
+
+Demonstrates autonomy with consolidated params.
+Presets: suggest, auto_edit, full_auto
+"""
+
+from praisonaiagents import Agent
+
+# Basic: Enable autonomy with preset
+agent = Agent(
+    instructions="You are an autonomous assistant.",
+    autonomy="suggest",  # Presets: suggest, auto_edit, full_auto
+)
+
+if __name__ == "__main__":
+    response = agent.start("Help me organize my project files.")
+    print(response)

examples/consolidated_params/basic_caching.py

@@ -0,0 +1,23 @@
+"""
+Basic Caching Example - Agent-Centric API
+
+Demonstrates caching with consolidated params.
+Presets: enabled, disabled, prompt
+"""
+
+from praisonaiagents import Agent
+
+# Basic: Enable caching with preset
+agent = Agent(
+    instructions="You are a helpful assistant with caching enabled.",
+    caching="prompt",  # Presets: enabled, disabled, prompt
+)
+
+if __name__ == "__main__":
+    # First call - will cache
+    response = agent.start("What is the capital of France?")
+    print(response)
+    
+    # Second call - may use cache
+    response = agent.start("What is the capital of France?")
+    print(response)

examples/consolidated_params/basic_context.py

@@ -0,0 +1,18 @@
+"""
+Basic Context Example - Agent-Centric API
+
+Demonstrates context management with consolidated params.
+Presets: sliding_window, summarize, truncate
+"""
+
+from praisonaiagents import Agent
+
+# Basic: Enable context management with preset
+agent = Agent(
+    instructions="You are a helpful assistant with context management.",
+    context="sliding_window",  # Presets: sliding_window, summarize, truncate
+)
+
+if __name__ == "__main__":
+    response = agent.start("Tell me a long story about a dragon.")
+    print(response)

examples/consolidated_params/basic_execution.py

@@ -0,0 +1,16 @@
+"""
+Basic Execution Example - Agent-Centric API
+
+Demonstrates execution preset usage.
+"""
+
+from praisonaiagents import Agent
+
+# Basic: Use fast execution preset
+agent = Agent(
+    instructions="You are a helpful assistant.",
+    execution="fast",  # Presets: fast, balanced, thorough, unlimited
+)
+
+response = agent.start("What is the capital of France?")
+print(response)

examples/consolidated_params/basic_guardrails.py

@@ -0,0 +1,26 @@
+"""
+Basic Guardrails Example - Agent-Centric API
+
+Demonstrates guardrails with callable validator.
+"""
+
+from praisonaiagents import Agent
+
+# Define a simple guardrail validator
+def validate_response(output):
+    """Validate that response doesn't contain forbidden words."""
+    forbidden = ["error", "fail", "cannot"]
+    text = str(output).lower()
+    for word in forbidden:
+        if word in text:
+            return (False, f"Response contains forbidden word: {word}")
+    return (True, output)
+
+# Basic: Use callable guardrail
+agent = Agent(
+    instructions="You are a helpful assistant.",
+    guardrails=validate_response,
+)
+
+response = agent.start("What is 2 + 2?")
+print(response)

examples/consolidated_params/basic_hooks.py

@@ -0,0 +1,28 @@
+"""
+Basic Hooks Example - Agent-Centric API
+
+Demonstrates lifecycle hooks with consolidated params.
+Valid keys: on_step, on_tool_call, middleware
+"""
+
+from praisonaiagents import Agent
+
+# Define hook callbacks
+def on_step_callback(step_info):
+    print(f"Step: {step_info}")
+
+def on_tool_call_callback(tool_info):
+    print(f"Tool called: {tool_info}")
+
+# Basic: Enable hooks with dict
+agent = Agent(
+    instructions="You are a helpful assistant.",
+    hooks={
+        "on_step": on_step_callback,
+        "on_tool_call": on_tool_call_callback,
+    },
+)
+
+if __name__ == "__main__":
+    response = agent.start("What is 2 + 2?")
+    print(response)

examples/consolidated_params/basic_knowledge.py

@@ -0,0 +1,18 @@
+"""
+Basic Knowledge (RAG) Example - Agent-Centric API
+
+Demonstrates knowledge with consolidated params.
+Supports: bool, list of sources, dict config, KnowledgeConfig
+"""
+
+from praisonaiagents import Agent
+
+# Basic: Enable knowledge with list of sources
+agent = Agent(
+    instructions="You are a helpful assistant with document knowledge.",
+    knowledge=["docs/"],  # List of file/folder paths
+)
+
+if __name__ == "__main__":
+    response = agent.start("What does the documentation say about installation?")
+    print(response)

examples/consolidated_params/basic_memory.py

@@ -0,0 +1,21 @@
+"""
+Basic Memory Example - Agent-Centric API
+
+Demonstrates the simplest memory usage with consolidated params.
+"""
+
+from praisonaiagents import Agent
+
+# Basic: Enable memory with preset
+agent = Agent(
+    instructions="You are a helpful assistant with memory.",
+    memory="sqlite",  # Preset: sqlite, redis, postgres, file
+)
+
+# Run a simple task
+response = agent.start("Remember that my favorite color is blue.")
+print(response)
+
+# Follow-up to test memory
+response = agent.start("What is my favorite color?")
+print(response)

examples/consolidated_params/basic_output.py

@@ -0,0 +1,16 @@
+"""
+Basic Output Example - Agent-Centric API
+
+Demonstrates output preset usage.
+"""
+
+from praisonaiagents import Agent
+
+# Basic: Use verbose output preset
+agent = Agent(
+    instructions="You are a helpful assistant.",
+    output="verbose",  # Presets: minimal, normal, verbose, debug, silent
+)
+
+response = agent.start("What is 2 + 2?")
+print(response)

examples/consolidated_params/basic_planning.py

@@ -0,0 +1,18 @@
+"""
+Basic Planning Example - Agent-Centric API
+
+Demonstrates planning with consolidated params.
+Presets: reasoning, read_only, auto
+"""
+
+from praisonaiagents import Agent
+
+# Basic: Enable planning with preset
+agent = Agent(
+    instructions="You are a strategic planner.",
+    planning="reasoning",  # Presets: reasoning, read_only, auto
+)
+
+if __name__ == "__main__":
+    response = agent.start("Plan a 3-day trip to Tokyo.")
+    print(response)

examples/consolidated_params/basic_reflection.py

@@ -0,0 +1,18 @@
+"""
+Basic Reflection Example - Agent-Centric API
+
+Demonstrates reflection (self-reflect) with consolidated params.
+Presets: minimal, standard, thorough
+"""
+
+from praisonaiagents import Agent
+
+# Basic: Enable reflection with preset
+agent = Agent(
+    instructions="You are a helpful assistant that reflects on your answers.",
+    reflection="standard",  # Presets: minimal, standard, thorough
+)
+
+if __name__ == "__main__":
+    response = agent.start("Explain quantum computing in simple terms.")
+    print(response)

examples/consolidated_params/basic_step_override.py

@@ -0,0 +1,33 @@
+"""Basic example showing step-level override of workflow defaults."""
+from praisonaiagents import Agent
+from praisonaiagents.workflows import Workflow, WorkflowStep
+
+# Workflow with defaults
+workflow = Workflow(
+    name="StepOverrideWorkflow",
+    steps=[
+        WorkflowStep(
+            name="step1",
+            action="Process with workflow defaults",
+            agent=Agent(instructions="You are a processor."),
+            # Uses workflow-level web=True (inherited)
+        ),
+        WorkflowStep(
+            name="step2",
+            action="Process with step override",
+            agent=Agent(instructions="You are a processor."),
+            # Override workflow default
+            web=False,  # Disable web for this step
+            reflection="thorough",  # Use thorough reflection preset
+        ),
+    ],
+    # Workflow defaults
+    web=True,
+    reflection=True,
+)
+
+if __name__ == "__main__":
+    print(f"Workflow web default: {workflow.web}")
+    print(f"Step1 web: {workflow.steps[0].web}")  # None (uses workflow default)
+    print(f"Step2 web: {workflow.steps[1].web}")  # False (overridden)
+    print(f"Step2 reflection: {workflow.steps[1].reflection}")  # thorough

examples/consolidated_params/basic_web.py

@@ -0,0 +1,18 @@
+"""
+Basic Web Search Example - Agent-Centric API
+
+Demonstrates web search with consolidated params.
+Presets: duckduckgo, tavily, google, bing, serper, search_only, fetch_only
+"""
+
+from praisonaiagents import Agent
+
+# Basic: Enable web search with preset
+agent = Agent(
+    instructions="You are a research assistant with web access.",
+    web="duckduckgo",  # Presets: duckduckgo, tavily, google, bing, serper
+)
+
+if __name__ == "__main__":
+    response = agent.start("What are the latest AI developments in 2024?")
+    print(response)

examples/consolidated_params/basic_workflow.py

@@ -0,0 +1,26 @@
+"""
+Basic Workflow Example - Agent-Centric API
+
+Demonstrates minimal workflow with consolidated params.
+"""
+
+from praisonaiagents import Agent
+from praisonaiagents.workflows import Workflow, WorkflowStep
+
+# Create a simple workflow
+workflow = Workflow(
+    name="simple_workflow",
+    output="verbose",  # Workflow-level output preset
+)
+
+# 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)

examples/consolidated_params/basic_workflow_agentlike.py

@@ -0,0 +1,32 @@
+"""Basic Workflow with Agent-like consolidated params."""
+from praisonaiagents import Agent
+from praisonaiagents.workflows import Workflow, WorkflowStep
+
+# Workflow with agent-like params (knowledge, web, guardrails, reflection)
+workflow = Workflow(
+    name="AgentLikeWorkflow",
+    steps=[
+        WorkflowStep(
+            name="researcher",
+            action="Research the topic: {{input}}",
+            agent=Agent(instructions="You are a researcher."),
+        ),
+        WorkflowStep(
+            name="writer",
+            action="Write about the research findings",
+            agent=Agent(instructions="You are a writer."),
+        ),
+    ],
+    # Agent-like consolidated params
+    knowledge=["docs/"],  # Enable RAG
+    web=True,  # Enable web search
+    guardrails=True,  # Enable guardrails
+    reflection=True,  # Enable self-reflection
+)
+
+if __name__ == "__main__":
+    print(f"Workflow: {workflow.name}")
+    print(f"Knowledge: {workflow.knowledge}")
+    print(f"Web: {workflow.web}")
+    print(f"Guardrails: {workflow.guardrails}")
+    print(f"Reflection: {workflow.reflection}")

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/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/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/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/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/token-metrics/README.md

@@ -13,7 +13,7 @@ agent = Agent(
     role="Assistant", 
     goal="Help users",
     backstory="You are helpful.",
-    llm="gpt-5-nano",
+    llm="gpt-4o-mini",
     metrics=True  # 🎯 That's it!
 )
 

examples/python/usecases/README-secondary-market-research.md

@@ -177,7 +177,7 @@ Set your API keys:
 
 ```bash
 export OPENAI_API_KEY="your-api-key"
-export OPENAI_MODEL_NAME="gpt-5-nano"
+export OPENAI_MODEL_NAME="gpt-4o-mini"
 ```
 
 ## 📝 Customization

src/praisonai-agents/01_basic_agent_with_monitoring.py

@@ -38,7 +38,7 @@ def main():
     # Create agent
     agent = Agent(
         instructions="You are a helpful assistant that explains scientific concepts simply",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Track the API call performance

src/praisonai-agents/01_basic_token_tracking.py

@@ -13,8 +13,8 @@ def main():
         role="Research Specialist", 
         goal="Find information about a topic",
         backstory="You are an expert researcher who finds comprehensive information.",
-        verbose=True,
-        llm="gpt-5-nano",  # Use a cost-effective model for examples
+        output="verbose",
+        llm="gpt-4o-mini",  # Use a cost-effective model for examples
         metrics=True  # 🎯 SIMPLIFIED: Just add this parameter!
     )
     
@@ -29,7 +29,7 @@ def main():
     agents = Agents(
         agents=[research_agent],
         tasks=[research_task],
-        verbose=True
+        output="verbose"
     )
     
     print("🚀 Running agent task...")

src/praisonai-agents/02_multi_agent_workflow_monitoring.py

@@ -54,7 +54,7 @@ def create_agents():
         goal="Gather comprehensive information on renewable energy",
         backstory="Expert researcher with deep knowledge of energy sector",
         instructions="You are a thorough researcher. Provide detailed, fact-based information.",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     analyst = Agent(
@@ -63,7 +63,7 @@ def create_agents():
         goal="Analyze trends and patterns in renewable energy data",
         backstory="Experienced data analyst specializing in energy markets",
         instructions="You are an analytical thinker. Focus on trends, patterns, and insights.",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     writer = Agent(
@@ -72,7 +72,7 @@ def create_agents():
         goal="Create clear, compelling reports",
         backstory="Professional technical writer with expertise in energy sector",
         instructions="You are a skilled writer. Create clear, well-structured reports.",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     return researcher, analyst, writer
@@ -104,8 +104,7 @@ def main():
         workflow = Agents(
             agents=[researcher, analyst, writer],
             tasks=[research_task, analysis_task, report_task], 
-            process="sequential",
-            verbose=True
+            process="sequential"
         )
         
         result = workflow.start()

src/praisonai-agents/03_agent_with_tools_monitoring.py

@@ -11,7 +11,7 @@ Shows how to monitor agent performance when using external tools.
 """
 
 from praisonaiagents import Agent
-from praisonaiagents.tools import duckduckgo, wikipedia
+from praisonaiagents.tools import duckduckgo, wikipedia_tools
 from praisonaiagents.telemetry import (
     monitor_function, track_api_call, get_api_stats,
     get_slowest_functions, performance_monitor
@@ -48,8 +48,8 @@ def main():
         instructions="""You are a research assistant that helps find information.
         Use the available tools to search for information and provide comprehensive answers.
         Be thorough in your research and cite your sources.""",
-        llm="gpt-5-nano",
-        tools=[duckduckgo, wikipedia]  # Add search tools
+        llm="gpt-4o-mini",
+        tools=[duckduckgo, wikipedia_tools]  # Add search tools
     )
     
     # Test queries to demonstrate tool performance monitoring
@@ -97,7 +97,9 @@ def main():
     # Slowest functions analysis
     print("\n🐌 Slowest Functions:")
     slowest = get_slowest_functions()
-    for func_name, avg_time in slowest:
+    for item in slowest[:5]:
+        func_name = item.get('function', 'unknown')
+        avg_time = item.get('average_time', 0)
         print(f"  {func_name}: {avg_time:.3f}s average")
     
     # Real-time performance data

src/praisonai-agents/04_async_agent_monitoring.py

@@ -54,7 +54,7 @@ async def run_async_agent(query: str):
     # Create agent
     agent = Agent(
         instructions="You are an efficient AI assistant that processes requests quickly",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Track the agent execution

src/praisonai-agents/05_error_handling_monitoring.py

@@ -60,7 +60,7 @@ def run_robust_agent(query: str, with_error_simulation: bool = True):
         instructions="""You are a resilient AI assistant. 
         Even when facing challenges, you provide helpful responses.
         If you encounter errors, acknowledge them and offer alternatives.""",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     try:

src/praisonai-agents/06_memory_agent_monitoring.py

@@ -65,7 +65,7 @@ def run_stateful_agent(query: str, session_id: str, conversation_turn: int):
         instructions="""You are a memory-enabled AI assistant. You can remember 
         previous conversations and build on past interactions. Use your memory 
         to provide personalized and contextual responses.""",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Pre-processing: retrieve relevant memories
@@ -165,7 +165,9 @@ def main():
     # Slowest operations
     print("\n🐌 Slowest Memory Operations:")
     slowest = get_slowest_functions()
-    for func_name, avg_time in slowest[:5]:  # Top 5 slowest
+    for item in slowest[:5]:  # Top 5 slowest
+        func_name = item.get('function', 'unknown')
+        avg_time = item.get('average_time', 0)
         print(f"  {func_name}: {avg_time:.3f}s average")
     
     # Memory operation breakdown

src/praisonai-agents/07_hierarchical_agents_monitoring.py

@@ -77,7 +77,7 @@ def run_hierarchical_workflow():
         backstory="Experienced project manager with expertise in team coordination",
         instructions="""You are a project manager. Break down complex projects into tasks,
         assign them to appropriate team members, and coordinate the overall effort.""",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Create worker agents
@@ -87,7 +87,7 @@ def run_hierarchical_workflow():
         goal="Conduct thorough research on assigned topics",
         backstory="Expert researcher with deep analytical skills",
         instructions="You are a research specialist. Provide detailed, factual information.",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     analysis_worker = Agent(
@@ -96,7 +96,7 @@ def run_hierarchical_workflow():
         goal="Analyze data and identify patterns and insights",
         backstory="Experienced data analyst with strong analytical thinking",
         instructions="You are a data analyst. Focus on patterns, trends, and insights.",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     writing_worker = Agent(
@@ -105,7 +105,7 @@ def run_hierarchical_workflow():
         goal="Create clear, well-structured documentation and reports", 
         backstory="Professional writer specializing in technical content",
         instructions="You are a technical writer. Create clear, comprehensive reports.",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Project tasks

src/praisonai-agents/08_custom_tools_monitoring.py

@@ -180,7 +180,7 @@ def run_agent_with_custom_tools():
         instructions="""You are a technical assistant with access to various tools.
         Use the appropriate tools to help with data processing, API calls, database queries,
         and file operations. Always explain what tools you're using and why.""",
-        llm="gpt-5-nano",
+        llm="gpt-4o-mini",
         tools=[process_data, call_external_api, query_database, file_operation]
     )
     
@@ -263,7 +263,7 @@ def main():
     # Slowest operations
     print("\n🐌 Slowest Tool Operations:")
     slowest = get_slowest_functions()
-    tool_slowest = [(name, time) for name, time in slowest if 'tool' in name.lower()]
+    tool_slowest = [(item.get('function', 'unknown'), item.get('average_time', 0)) for item in slowest if 'tool' in item.get('function', '').lower()]
     
     for tool_name, avg_time in tool_slowest[:3]:
         print(f"  {tool_name}: {avg_time:.3f}s")

src/praisonai-agents/09_streaming_monitoring.py

@@ -60,7 +60,7 @@ def get_real_time_agent_response(query: str, response_id: int):
     # Create a lightweight agent for streaming
     agent = Agent(
         instructions="You are a real-time assistant. Provide quick, helpful responses.",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     with track_api_call(f"streaming_response_{response_id}"):

src/praisonai-agents/10_comprehensive_dashboard.py

@@ -42,19 +42,19 @@ def collect_comprehensive_data():
     agents = {
         'quick_agent': Agent(
             instructions="You are a quick-response agent. Provide concise answers.",
-            llm="gpt-5-nano"
+            llm="gpt-4o-mini"
         ),
         'analytical_agent': Agent(
             instructions="You are an analytical agent. Provide detailed analysis.",
-            llm="gpt-5-nano"
+            llm="gpt-4o-mini"
         ),
         'research_agent': Agent(
             instructions="You are a research agent. Provide comprehensive information.",
-            llm="gpt-5-nano"
+            llm="gpt-4o-mini"
         ),
         'creative_agent': Agent(
             instructions="You are a creative agent. Provide imaginative responses.",
-            llm="gpt-5-nano"
+            llm="gpt-4o-mini"
         )
     }
     
@@ -98,19 +98,19 @@ def simulate_complex_workflow():
     planner = Agent(
         name="planner",
         instructions="You are a project planner. Break down tasks and plan execution.",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     executor = Agent(
         name="executor", 
         instructions="You are a task executor. Complete assigned tasks efficiently.",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     reviewer = Agent(
         name="reviewer",
         instructions="You are a quality reviewer. Review and improve work.",
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Create workflow tasks
@@ -302,13 +302,17 @@ def display_dashboard(dashboard_data, benchmark_results):
     perf_data = dashboard_data['performance_data']
     
     print("  Fastest Functions:")
-    fastest_functions = sorted(perf_data['slowest_functions'], key=lambda x: x[1])[:3]
-    for name, time in fastest_functions:
-        print(f"    {name}: {time:.3f}s")
+    fastest_functions = sorted(perf_data['slowest_functions'], key=lambda x: x.get('average_time', float('inf')))[:3]
+    for item in fastest_functions:
+        name = item.get('function', 'unknown')
+        time_val = item.get('average_time', 0)
+        print(f"    {name}: {time_val:.3f}s")
     
     print("  Slowest Functions:")
-    for name, time in perf_data['slowest_functions'][:3]:
-        print(f"    {name}: {time:.3f}s")
+    for item in perf_data['slowest_functions'][:3]:
+        name = item.get('function', 'unknown')
+        time_val = item.get('average_time', 0)
+        print(f"    {name}: {time_val:.3f}s")
     
     # Benchmark Results
     print(f"\n⚡ BENCHMARK RESULTS")

src/praisonai-agents/CLAUDE.md

@@ -1,279 +0,0 @@
-# CLAUDE.md
-
-This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
-
-## Project Overview
-
-PraisonAI Agents is a hierarchical AI agent framework for completing complex tasks with self-reflection capabilities. It supports multi-agent collaboration, tool integration, and various execution patterns (sequential, hierarchical, parallel).
-
-## Development Commands
-
-### Installation and Setup
-```bash
-# Install core package
-pip install -e .
-
-# Install with specific features
-pip install -e .[all]          # All features
-pip install -e .[memory]       # Memory capabilities
-pip install -e .[knowledge]    # Document processing
-pip install -e .[mcp]          # MCP server support
-pip install -e .[llm]          # Extended LLM support
-pip install -e .[api]          # API server capabilities
-```
-
-### Testing
-```bash
-# Run individual test examples (no formal test runner configured)
-python tests/basic-agents.py
-python tests/async_example.py
-python tests/knowledge-agents.py
-
-# Test specific features
-python tests/mcp-agents.py           # MCP integration
-python tests/memory_example.py      # Memory functionality
-python tests/tools_example.py       # Tool system
-```
-
-### Running Examples
-```bash
-# Basic agent usage
-python tests/single-agent.py
-
-# Multi-agent workflows
-python tests/multi-agents-api.py
-
-# Async operations
-python tests/async_example_full.py
-
-# MCP server examples
-python tests/mcp-sse-direct-server.py  # Start MCP server
-python tests/mcp-sse-direct-client.py  # Connect to server
-```
-
-## Core Architecture
-
-### Agent System (`praisonaiagents/agent/`)
-- **Agent**: Core agent class with LLM integration, tool calling, and self-reflection
-- **ImageAgent**: Specialized multimodal agent for image processing
-- Self-reflection with configurable min/max iterations (default: 1-3)
-- Delegation support for hierarchical agent structures
-
-### Multi-Agent Orchestration (`praisonaiagents/agents/`)
-- **PraisonAIAgents**: Main orchestrator for managing multiple agents and tasks
-- **AutoAgents**: Automatic agent creation and management
-- Process types: `sequential`, `hierarchical`, `parallel`
-- Context passing between agents and task dependency management
-
-### Task System (`praisonaiagents/task/`)
-- **Task**: Core task definition with context, callbacks, and output specifications
-- Supports file output, JSON/Pydantic structured output, async execution
-- Conditional logic with `condition` parameter for task flow control
-- Context passing via `context` parameter for task dependencies
-- **Guardrails**: Built-in validation and safety mechanisms for task outputs
-  - Function-based guardrails for custom validation logic
-  - LLM-based guardrails using natural language descriptions
-  - Automatic retry with configurable `max_retries` parameter
-  - Compatible with CrewAI guardrail patterns
-
-### LLM Integration (`praisonaiagents/llm/`)
-- Unified wrapper for multiple LLM providers via LiteLLM
-- Supports OpenAI, Anthropic, Gemini, DeepSeek, local models (Ollama)
-- Context length management and tool calling capabilities
-- Set via `llm` parameter on agents or global `OPENAI_API_KEY`/`ANTHROPIC_API_KEY`
-
-### Tool System (`praisonaiagents/tools/`)
-Two implementation patterns:
-1. **Function-based**: Simple tools using `@tool` decorator
-2. **Class-based**: Complex tools inheriting from `BaseTool`
-
-Built-in tools include: DuckDuckGo search, file operations, calculator, Wikipedia, arXiv, data analysis tools, shell execution.
-
-### Memory & Knowledge Systems
-- **Memory** (`praisonaiagents/memory/`): Multi-layered memory with RAG support
-  - Types: short-term, long-term, entity, user memory
-  - Providers: ChromaDB, Mem0, custom implementations
-- **Knowledge** (`praisonaiagents/knowledge/`): Document processing with chunking
-  - Chunking strategies via `chonkie` library
-  - Embedding and retrieval capabilities
-
-### MCP (Model Context Protocol) Integration
-- **MCP Server**: Server-side tool protocol for distributed execution
-- **SSE Support**: Server-sent events for real-time communication
-- Tool discovery and dynamic registration
-
-## Development Patterns
-
-### Agent Creation
-```python
-agent = Agent(
-    name="Agent Name",
-    role="Agent Role",
-    goal="Agent Goal",
-    backstory="Agent Background",
-    llm="gpt-5-nano",  # or other LLM
-    self_reflect=True,  # Enable self-reflection
-    min_reflect=1,      # Minimum reflection iterations
-    max_reflect=3,      # Maximum reflection iterations
-    tools=[tool1, tool2],  # Optional tools
-    guardrail=validate_function,  # Agent-level guardrail (function or string)
-    max_guardrail_retries=3  # Retry limit for guardrail failures
-)
-```
-
-### Task Definition
-```python
-task = Task(
-    name="task_name",
-    description="Task description",
-    expected_output="Expected output format",
-    agent=agent,
-    context=[previous_task],  # Task dependencies
-    output_pydantic=ResponseModel,  # Structured output
-    condition="condition_function"  # Conditional execution
-)
-```
-
-### Guardrails Usage
-
-#### Task-Level Guardrails
-```python
-from typing import Tuple, Any
-from praisonaiagents import GuardrailResult
-
-# Function-based guardrail (Option 1: Using GuardrailResult)
-def validate_output(task_output: TaskOutput) -> GuardrailResult:
-    """Custom validation function returning GuardrailResult."""
-    if "error" in task_output.raw.lower():
-        return GuardrailResult(
-            success=False,
-            result=None,
-            error="Output contains errors"
-        )
-    if len(task_output.raw) < 10:
-        return GuardrailResult(
-            success=False,
-            result=None,
-            error="Output is too short"
-        )
-    return GuardrailResult(
-        success=True,
-        result=task_output,
-        error=""
-    )
-
-# Function-based guardrail (Option 2: Using Tuple[bool, Any])
-def validate_output_tuple(task_output: TaskOutput) -> Tuple[bool, Any]:
-    """Custom validation function returning tuple."""
-    if "error" in task_output.raw.lower():
-        return False, "Output contains errors"
-    if len(task_output.raw) < 10:
-        return False, "Output is too short"
-    return True, task_output
-
-task = Task(
-    description="Write a professional email",
-    expected_output="A well-formatted email",
-    agent=agent,
-    guardrail=validate_output,  # Function-based guardrail
-    max_retries=3  # Retry up to 3 times if guardrail fails
-)
-
-# LLM-based guardrail
-task = Task(
-    description="Generate marketing copy",
-    expected_output="Professional marketing content",
-    agent=agent,
-    guardrail="Ensure the content is professional, engaging, and free of errors",  # String description
-    max_retries=2
-)
-```
-
-#### Agent-Level Guardrails
-```python
-# Agent guardrails apply to ALL outputs from that agent
-
-# Option 1: Using GuardrailResult
-def validate_professional_tone(task_output: TaskOutput) -> GuardrailResult:
-    """Ensure professional tone in all agent responses."""
-    content = task_output.raw.lower()
-    casual_words = ['yo', 'dude', 'awesome', 'cool']
-    for word in casual_words:
-        if word in content:
-            return GuardrailResult(
-                success=False,
-                result=None,
-                error=f"Unprofessional language detected: {word}"
-            )
-    return GuardrailResult(
-        success=True,
-        result=task_output,
-        error=""
-    )
-
-# Option 2: Using Tuple[bool, Any]
-def validate_professional_tone_tuple(task_output: TaskOutput) -> Tuple[bool, Any]:
-    """Ensure professional tone in all agent responses."""
-    content = task_output.raw.lower()
-    casual_words = ['yo', 'dude', 'awesome', 'cool']
-    for word in casual_words:
-        if word in content:
-            return False, f"Unprofessional language detected: {word}"
-    return True, task_output
-
-# Agent with function-based guardrail
-agent = Agent(
-    name="BusinessWriter",
-    instructions="You are a professional business writer",
-    guardrail=validate_professional_tone,  # Function guardrail
-    max_guardrail_retries=3
-)
-
-# Agent with LLM-based guardrail
-agent = Agent(
-    name="ContentWriter", 
-    instructions="You are a content writer",
-    guardrail="Ensure all responses are professional, accurate, and appropriate for business use",  # String guardrail
-    max_guardrail_retries=2
-)
-```
-
-### Multi-Agent Workflow
-```python
-workflow = PraisonAIAgents(
-    agents=[agent1, agent2],
-    tasks=[task1, task2],
-    process="sequential",  # or "hierarchical", "parallel"
-    verbose=True,
-    manager_agent=manager_agent  # For hierarchical process
-)
-result = workflow.start()
-```
-
-### Async Support
-All major components support async execution:
-```python
-result = await workflow.astart()
-result = await agent.aexecute(task)
-```
-
-## Key Dependencies
-
-- **Core**: `pydantic`, `rich`, `openai`, `mcp`
-- **Memory**: `chromadb`, `mem0ai`
-- **Knowledge**: `markitdown`, `chonkie`
-- **LLM**: `litellm` for unified provider access
-- **API**: `fastapi`, `uvicorn` for server capabilities
-
-## Error Handling
-
-- Global error logging via `error_logs` list
-- Callback system for real-time error reporting
-- Context length exception handling with automatic retry
-- Graceful degradation for optional dependencies
-
-## Testing Strategy
-
-The project uses example-driven testing with 100+ test files in `tests/` directory. Each test file demonstrates specific usage patterns and serves as both test and documentation. Run individual examples to test functionality rather than using a formal test runner.
-
-Use conda activate praisonai-agents to activate the environment.

src/praisonai-agents/agent-profile.py

@@ -0,0 +1,63 @@
+"""
+Streaming Example with Profiling
+
+Key Points:
+1. output="stream" preset enables streaming by default
+2. stream=True in start() is redundant when using output="stream" (but doesn't hurt)
+3. Profiler.enable() MUST be called before profiling works
+4. Use Profiler.streaming() context manager for TTFT metrics
+"""
+from praisonai.profiler import Profiler, profile, profile_imports
+
+# IMPORTANT: Enable profiler BEFORE any profiling operations
+Profiler.enable()
+
+# Profile imports
+with profile_imports():
+    from praisonaiagents import Agent
+
+@profile
+def main():
+    agent = Agent(
+        instructions="You are a helpful assistant",
+        output="stream"  # This already enables streaming
+    )
+
+    print("\nStreaming response:")
+    print("-" * 40)
+    
+    # Use Profiler.streaming() to track TTFT (Time To First Token)
+    with Profiler.streaming("llm_stream") as tracker:
+        first_chunk = True
+        for chunk in agent.start("Write a short paragraph about the history of computing"):
+            if first_chunk:
+                tracker.first_token()  # Mark TTFT
+                first_chunk = False
+            tracker.chunk()  # Count chunks
+            print(chunk, end="", flush=True)
+    
+    print("\n" + "-" * 40)
+
+# Run with profiling blocks
+with Profiler.block("full_workflow"):
+    with Profiler.block("setup"):
+        pass
+    
+    with Profiler.block("execution"):
+        result = main()
+    
+    with Profiler.block("cleanup"):
+        pass
+
+# Generate report
+Profiler.report()
+
+# Show streaming-specific stats
+streaming_records = Profiler.get_streaming_records()
+if streaming_records:
+    print("\nStreaming Metrics:")
+    for record in streaming_records:
+        print(f"  {record.name}: TTFT={record.ttft_ms:.2f}ms, Total={record.total_ms:.2f}ms, Chunks={record.chunk_count}")
+
+stats = Profiler.get_statistics()
+print(f"\nMedian execution time: {stats['p50']:.2f}ms")

src/praisonai-agents/agent_guardrails_example.py

@@ -1,98 +1,75 @@
 #!/usr/bin/env python3
 """
 Agent-level guardrails example.
-
-This example demonstrates how to use guardrails at the Agent level,
+This example demonstrates how to use guardrails at the Agent level
 which will apply to all tasks executed by that agent.
 """
-
 from typing import Tuple, Any
 from praisonaiagents import Agent, TaskOutput
-
 def validate_content_length(task_output: TaskOutput) -> Tuple[bool, Any]:
     """
     Validate that task output content meets minimum length requirement.
-    
     Args:
         task_output: The task output to validate
-        
     Returns:
         Tuple of (success, result_or_error_message)
     """
     if len(task_output.raw) < 50:
         return False, "Content too short - must be at least 50 characters"
     return True, task_output
-
 def validate_professional_tone(task_output: TaskOutput) -> Tuple[bool, Any]:
     """
     Validate that the content has a professional tone.
-    
     Args:
         task_output: The task output to validate
-        
     Returns:
         Tuple of (success, result_or_error_message)
     """
     content = task_output.raw.lower()
     unprofessional_words = ['yo', 'dude', 'awesome', 'cool', 'lol']
-    
     for word in unprofessional_words:
         if word in content:
             return False, f"Content contains unprofessional word: '{word}'"
-    
     return True, task_output
-
 def main():
     """Demonstrate Agent-level guardrails with function-based and LLM-based validation."""
-    
     print("=== Agent Guardrail Examples ===\n")
-    
     # Example 1: Function-based guardrail
     print("1. Function-based guardrail (content length validation):")
     agent1 = Agent(
-        name="ContentWriter",
-        instructions="You are a professional content writer who creates detailed responses",
-        guardrail=validate_content_length,
-        max_guardrail_retries=2
+        name="ContentWriter"
+        instructions="You are a professional content writer who creates detailed responses"
+        guardrails=validate_content_length
     )
-    
     try:
         result1 = agent1.start("Write a brief welcome message")
         print(f"Result: {result1}\n")
     except Exception as e:
         print(f"Error: {e}\n")
-    
     # Example 2: LLM-based guardrail (string description)
     print("2. LLM-based guardrail (professional tone validation):")
     agent2 = Agent(
         name="BusinessWriter", 
-        instructions="You are a business communication expert",
-        guardrail="Ensure the content is professional, formal, and suitable for business communication. No casual language or slang.",
-        max_guardrail_retries=3
+        instructions="You are a business communication expert"
+        guardrails="Ensure the content is professional, formal, and suitable for business communication. No casual language or slang."
     )
-    
     try:
         result2 = agent2.start("Write a welcome message for new employees")
         print(f"Result: {result2}\n")
     except Exception as e:
         print(f"Error: {e}\n")
-    
     # Example 3: Multiple agents with different guardrails
     print("3. Professional tone function-based guardrail:")
     agent3 = Agent(
-        name="ProfessionalWriter",
-        instructions="Write professional business content",
-        guardrail=validate_professional_tone,
-        max_guardrail_retries=2
+        name="ProfessionalWriter"
+        instructions="Write professional business content"
+        guardrails=validate_professional_tone
     )
-    
     try:
         result3 = agent3.start("Write a casual greeting message")
         print(f"Result: {result3}\n")
     except Exception as e:
         print(f"Error: {e}\n")
-    
     print("=== Agent Guardrails Demonstration Complete ===")
-
 if __name__ == "__main__":
     main()

src/praisonai-agents/agentic_parallelisation.py

@@ -21,7 +21,7 @@ article_agent = Agent(
     goal="Fetch the user's favorite morning article",
     tools=[fetch_favorite_article],
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 news_agent = Agent(
@@ -30,7 +30,7 @@ news_agent = Agent(
     goal="Search for trending news in Kenya",
     tools=[search_trending_kenya],
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 twitter_agent = Agent(
@@ -39,7 +39,7 @@ twitter_agent = Agent(
     goal="Fetch the latest Twitter feed",
     tools=[fetch_twitter_feed],
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 aggregator = Agent(
@@ -47,7 +47,7 @@ aggregator = Agent(
     role="Result Aggregator",
     goal="Aggregate and summarize all results",
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 # Tasks for each agent
@@ -92,7 +92,7 @@ async def main():
         agents=[article_agent, news_agent, twitter_agent, aggregator],
         tasks=[article_task, news_task, twitter_task, aggregate_task],
         process="workflow",
-        verbose=True
+        output="verbose"
     )
     results = await workflow.astart(dict_output=True)
 

src/praisonai-agents/any-llm-agents-async.py

@@ -5,8 +5,8 @@ async def main():
     agent = Agent(
         instructions="You are a helpful assistant",
         llm="gemini/gemini-1.5-flash-8b",
-        self_reflect=True,
-        verbose=True
+        reflection=True,
+        output="verbose"
     )
 
     # Use achat instead of start/chat for async operation

src/praisonai-agents/async-tavily-agents-gemini.py

@@ -10,7 +10,7 @@ import os
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 llm_config = {
-    "model": "gpt-5-nano",  # Changed from Gemini to gpt-5-nano for better tool support
+    "model": "gpt-4o-mini",  # Changed from Gemini to gpt-5-nano for better tool support
     "temperature": 0.7,
     "response_format": {
         "type": "text"
@@ -79,7 +79,7 @@ async def run_parallel_searches():
         backstory="Expert in web search and information retrieval. You have access to the tavily_search tool to search the web.",
         tools=[tavily_search],
         llm=llm_config,
-        verbose=True
+        output="verbose"
     )
     
     # Create summary agent

src/praisonai-agents/autoagents-tavily.py

@@ -52,7 +52,7 @@ agents = AutoAgents(
     tools=[evaluate, get_stock_price],
     process="sequential",
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    self_reflect=False,
+    reflection=False,
     verbose=False,
     max_agents=3  # Maximum number of agents to create
 )

src/praisonai-agents/basic-agents-tools.py

@@ -5,8 +5,9 @@ def get_weather(city: str) -> str:
 
 agent = Agent(
     instructions="You are a helpful assistant",
-    llm="gpt-5-nano",
-    tools=[get_weather]
+    llm="gpt-4o-mini",
+    tools=[get_weather],
+    output="status"
 )
 
 agent.start("What is the weather in Tokyo?")

src/praisonai-agents/basic-agents.py

@@ -1,14 +1,3 @@
-#!/usr/bin/env python3
-
 from praisonaiagents import Agent
-
-print("Creating agent...")
-agent = Agent(
-    instructions="You are a helpful assistant"
-)
-
-print("Starting agent...")
-result = agent.start("Why is the sky blue?")
-
-print("Agent completed successfully!")
-print(f"Result: {result}") 
+agent = Agent(instructions="You are a helpful assistant")
+result = agent.start("Why is the sky blue?")

src/praisonai-agents/benchmarks/EXECUTION_BENCHMARK_RESULTS.md

@@ -1,6 +1,6 @@
 # PraisonAI Agents - Real Execution Benchmark
 
-**Generated:** 2025-12-18 14:43:52
+**Generated:** 2026-01-13 06:07:28
 **Model:** gpt-4o-mini
 **Iterations:** 3
 **Prompt:** "What is 2+2? Reply with just the number."
@@ -9,18 +9,18 @@
 
 | Framework | Method | Avg Time | Relative |
 |-----------|--------|----------|----------|
-| **PraisonAI** | `agent.start()` | **0.45s** | **1.00x (fastest)** |
-| PraisonAI (LiteLLM) | `agent.start()` | 0.55s | 1.22x |
-| CrewAI | `crew.kickoff()` | 0.58s | 1.28x |
-| Agno | `agent.run()` | 0.92s | 2.05x |
+| **PraisonAI (LiteLLM)** | `agent.start()` | **0.55s** | **1.00x (fastest)** |
+| PraisonAI | `agent.start()` | 0.64s | 1.15x |
+| CrewAI | `crew.kickoff()` | 0.95s | 1.71x |
+| Agno | `agent.run()` | 1.05s | 1.90x |
 
 ## Package Versions
 
 | Package | Version |
 |---------|--------|
-| PraisonAI | 0.1.5 |
-| Agno | 2.3.14 |
-| CrewAI | 1.6.1 |
+| PraisonAI | 0.11.7 |
+| Agno | 2.3.25 |
+| CrewAI | 1.8.0 |
 
 ## How to Reproduce
 

src/praisonai-agents/benchmarks/REAL_BENCHMARK_RESULTS.md

@@ -1,6 +1,6 @@
 # PraisonAI Agents - Real-World Execution Benchmark
 
-**Generated:** 2025-12-18 14:33:49
+**Generated:** 2026-01-13 14:42:59
 **Model:** gpt-4o-mini
 **Iterations:** 3 per test
 
@@ -8,60 +8,60 @@
 
 | Test | PraisonAI (avg) | Agno (avg) | Difference |
 |------|-----------------|------------|------------|
-| Single Agent | 2.04s | 3.18s | PraisonAI 1.56x faster |
-| 2 Agents | 2.49s | 20.49s | PraisonAI 8.21x faster |
-| 3 Agents | 6.52s | 13.97s | PraisonAI 2.14x faster |
+| Single Agent | 2.46s | 2.92s | PraisonAI 1.19x faster |
+| 2 Agents | 3.21s | 19.39s | PraisonAI 6.05x faster |
+| 3 Agents | 8.26s | 15.01s | PraisonAI 1.82x faster |
 
 ## Detailed Results
 
 ### praisonai_single
-- Average: 2.04s
-- Min: 1.95s
-- Max: 2.19s
-- Std Dev: 0.13s
-- Times: ['2.19s', '1.95s', '2.00s']
+- Average: 2.46s
+- Min: 1.93s
+- Max: 2.90s
+- Std Dev: 0.49s
+- Times: ['1.93s', '2.55s', '2.90s']
 
 ### agno_single
-- Average: 3.18s
-- Min: 3.00s
-- Max: 3.51s
-- Std Dev: 0.28s
-- Times: ['3.51s', '3.00s', '3.04s']
+- Average: 2.92s
+- Min: 2.85s
+- Max: 2.97s
+- Std Dev: 0.06s
+- Times: ['2.97s', '2.85s', '2.94s']
 
 ### praisonai_two_agents
-- Average: 2.49s
-- Min: 2.22s
-- Max: 2.65s
-- Std Dev: 0.24s
-- Times: ['2.65s', '2.22s', '2.62s']
+- Average: 3.21s
+- Min: 2.80s
+- Max: 3.67s
+- Std Dev: 0.44s
+- Times: ['3.67s', '2.80s', '3.15s']
 
 ### agno_two_agents
-- Average: 20.49s
-- Min: 9.96s
-- Max: 35.05s
-- Std Dev: 13.02s
-- Times: ['16.46s', '9.96s', '35.05s']
+- Average: 19.39s
+- Min: 10.62s
+- Max: 26.06s
+- Std Dev: 7.93s
+- Times: ['26.06s', '10.62s', '21.50s']
 
 ### praisonai_three_agents
-- Average: 6.52s
-- Min: 5.62s
-- Max: 7.39s
-- Std Dev: 0.88s
-- Times: ['5.62s', '6.54s', '7.39s']
+- Average: 8.26s
+- Min: 6.44s
+- Max: 11.88s
+- Std Dev: 3.13s
+- Times: ['6.47s', '6.44s', '11.88s']
 
 ### agno_three_agents
-- Average: 13.97s
-- Min: 11.69s
-- Max: 15.32s
-- Std Dev: 1.99s
-- Times: ['14.91s', '11.69s', '15.32s']
+- Average: 15.01s
+- Min: 14.46s
+- Max: 15.90s
+- Std Dev: 0.78s
+- Times: ['15.90s', '14.46s', '14.68s']
 
 ## Package Versions
 
 | Package | Version |
 |---------|--------|
-| PraisonAI | 0.1.5 |
-| Agno | 2.3.14 |
+| PraisonAI | 0.11.22 |
+| Agno | 2.3.25 |
 
 ## How to Reproduce
 

src/praisonai-agents/benchmarks/execution_benchmark.py

@@ -52,12 +52,11 @@ def benchmark_praisonai(model, iterations, prompt):
         agent = Agent(
             name="Calculator",
             instructions="You are a helpful assistant. Be very brief.",
-            llm=model,
-            verbose=False
+            llm=model
         )
         
         start = time.perf_counter()
-        response = agent.start(prompt)
+        response = agent.start(prompt, output="silent")
         elapsed = time.perf_counter() - start
         
         times.append(elapsed)
@@ -80,12 +79,11 @@ def benchmark_praisonai_litellm(model, iterations, prompt):
         agent = Agent(
             name="Calculator",
             instructions="You are a helpful assistant. Be very brief.",
-            llm=f"openai/{model}",
-            verbose=False
+            llm=f"openai/{model}"
         )
         
         start = time.perf_counter()
-        response = agent.start(prompt)
+        response = agent.start(prompt, output="silent")
         elapsed = time.perf_counter() - start
         
         times.append(elapsed)

src/praisonai-agents/benchmarks/real_benchmark.py

@@ -103,12 +103,11 @@ def benchmark_praisonai_single(iterations: int = 3) -> ExecutionResult:
             name="Assistant",
             instructions="You are a helpful assistant. Be very brief.",
             llm="gpt-4o-mini",
-            tools=[get_weather],
-            verbose=False
+            tools=[get_weather]
         )
         
         start = time.perf_counter()
-        response = agent.start("What's the weather in NYC? Reply in one sentence.")
+        response = agent.start("What's the weather in NYC? Reply in one sentence.", output="silent")
         elapsed = time.perf_counter() - start
         
         result.times.append(elapsed)
@@ -132,8 +131,7 @@ def benchmark_praisonai_two_agents(iterations: int = 3) -> ExecutionResult:
             goal="Gather weather information",
             instructions="Get weather data and pass to reporter. Be brief.",
             llm="gpt-4o-mini",
-            tools=[get_weather],
-            verbose=False
+            tools=[get_weather]
         )
         
         reporter = Agent(
@@ -141,18 +139,16 @@ def benchmark_praisonai_two_agents(iterations: int = 3) -> ExecutionResult:
             role="News Reporter",
             goal="Create weather report",
             instructions="Create a one-sentence weather summary.",
-            llm="gpt-4o-mini",
-            verbose=False
+            llm="gpt-4o-mini"
         )
         
         agents = Agents(
             agents=[researcher, reporter],
-            process="sequential",
-            verbose=False
+            process="sequential"
         )
         
         start = time.perf_counter()
-        response = agents.start("Get NYC weather and create a brief report.")
+        response = agents.start("Get NYC weather and create a brief report.", output="silent")
         elapsed = time.perf_counter() - start
         
         result.times.append(elapsed)
@@ -176,8 +172,7 @@ def benchmark_praisonai_three_agents(iterations: int = 3) -> ExecutionResult:
             goal="Get weather data",
             instructions="Fetch weather information. Be brief.",
             llm="gpt-4o-mini",
-            tools=[get_weather],
-            verbose=False
+            tools=[get_weather]
         )
         
         time_agent = Agent(
@@ -186,8 +181,7 @@ def benchmark_praisonai_three_agents(iterations: int = 3) -> ExecutionResult:
             goal="Get time information",
             instructions="Fetch timezone information. Be brief.",
             llm="gpt-4o-mini",
-            tools=[get_time],
-            verbose=False
+            tools=[get_time]
         )
         
         summarizer = Agent(
@@ -195,18 +189,16 @@ def benchmark_praisonai_three_agents(iterations: int = 3) -> ExecutionResult:
             role="Summary Writer",
             goal="Combine information",
             instructions="Create a one-sentence summary combining weather and time.",
-            llm="gpt-4o-mini",
-            verbose=False
+            llm="gpt-4o-mini"
         )
         
         agents = Agents(
             agents=[weather_agent, time_agent, summarizer],
-            process="sequential",
-            verbose=False
+            process="sequential"
         )
         
         start = time.perf_counter()
-        response = agents.start("Get NYC weather and EST time, then summarize.")
+        response = agents.start("Get NYC weather and EST time, then summarize.", output="silent")
         elapsed = time.perf_counter() - start
         
         result.times.append(elapsed)

src/praisonai-agents/context_basic.py

@@ -1,5 +1,5 @@
 from praisonaiagents import ContextAgent
 
-agent = ContextAgent(llm="gpt-5-nano", auto_analyze=False)
+agent = ContextAgent(llm="gpt-4o-mini", auto_analyze=False)
 
 agent.start("https://github.com/MervinPraison/PraisonAI/ Need to add Authentication")

src/praisonai-agents/debug_comparison.py

@@ -14,7 +14,7 @@ def test_agent_direct():
         agent = Agent(
             instructions="""You are a helpful assistant that can break down complex problems.
             Use the available tools when relevant to perform step-by-step analysis.""",
-            llm="gpt-5-nano",
+            llm="gpt-4o-mini",
             tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
         )
         

src/praisonai-agents/examples/consolidated_params/advanced_memory.py

@@ -0,0 +1,23 @@
+"""Advanced memory example - with config and overrides."""
+from praisonaiagents import Agent, MemoryConfig
+
+# With full config
+agent = Agent(
+    instructions="You are a helpful assistant with memory.",
+    memory=MemoryConfig(
+        backend="redis",
+        user_id="user123",
+        session_id="session456",
+        auto_memory=True,
+    ),
+)
+
+# With array [preset, overrides]
+agent_override = Agent(
+    instructions="You are a helpful assistant.",
+    memory=["redis", {"user_id": "custom_user"}],
+)
+
+if __name__ == "__main__":
+    response = agent.chat("Remember that my favorite color is blue.")
+    print(response)

src/praisonai-agents/examples/consolidated_params/basic_memory.py

@@ -0,0 +1,24 @@
+"""Basic memory example - minimal usage."""
+from praisonaiagents import Agent
+
+# Simple enable (uses default file-based memory)
+agent = Agent(
+    instructions="You are a helpful assistant with memory.",
+    memory=True,
+)
+
+# With preset
+agent_redis = Agent(
+    instructions="You are a helpful assistant.",
+    memory="redis",  # Uses redis preset
+)
+
+# 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)

src/praisonai-agents/examples/consolidated_params/basic_output_execution.py

@@ -0,0 +1,13 @@
+"""Basic output and execution example."""
+from praisonaiagents import Agent
+
+# Simple preset usage
+agent = Agent(
+    instructions="You are a helpful assistant.",
+    output="verbose",  # Preset: verbose output
+    execution="thorough",  # Preset: thorough execution
+)
+
+if __name__ == "__main__":
+    response = agent.chat("What is 2+2?")
+    print(response)

src/praisonai-agents/examples/consolidated_params/basic_workflow.py

@@ -0,0 +1,22 @@
+"""Basic workflow example with consolidated params."""
+from praisonaiagents import Agent
+from praisonaiagents.workflows import Workflow, WorkflowStep
+
+# 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="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,
+)
+
+if __name__ == "__main__":
+    result = workflow.run(variables={"topic": "AI agents"})
+    print(result)

src/praisonai-agents/gemini_self_reflection.py

@@ -14,8 +14,8 @@ llm_config = {
 agent = Agent(
     instructions="You are a helpful assistant. Be concise and clear.",
     llm=llm_config,
-    verbose=True,
-    self_reflect=True,
+    output="verbose",
+    reflection=True,
     max_reflect=2,
     min_reflect=1
 )

src/praisonai-agents/guardrail_agent_example.py

@@ -7,8 +7,7 @@ def validate_content(data):
 
 agent = Agent(
     instructions="You are a writer",
-    guardrail=validate_content,
-    max_guardrail_retries=1
+    guardrails=validate_content
 )
 
 agent.start("Write a welcome message with 5 words")

src/praisonai-agents/guardrail_agents_example.py

@@ -12,7 +12,7 @@ agent = Agent(
 
 task = Task(
     description="Write a welcome message",
-    guardrail=validate_content,
+    guardrails=validate_content,
     agent=agent
 )
 

src/praisonai-agents/guardrails_example.py

@@ -62,7 +62,7 @@ def main():
         description="Write a professional email to a client about project completion",
         expected_output="A well-formatted professional email",
         agent=agent,
-        guardrail=email_validator,  # Function-based guardrail
+        guardrails=email_validator,  # Function-based guardrail
         max_retries=2
     )
     
@@ -110,7 +110,7 @@ Project Team""",
         description="Write a marketing email for a new product launch",
         expected_output="Engaging marketing content",
         agent=agent,
-        guardrail="Ensure the content is professional, engaging, includes a clear call-to-action, and is free of errors",
+        guardrails="Ensure the content is professional, engaging, includes a clear call-to-action, and is free of errors",
         max_retries=3
     )
     

src/praisonai-agents/langchain_example.py

@@ -9,7 +9,7 @@ agent = Agent(
     goal="Search for information from multiple sources",
     backstory="I am an AI assistant that can search YouTube and Wikipedia.",
     tools=[YouTubeSearchTool, WikipediaAPIWrapper],
-    self_reflect=False
+    reflection=False
 )
 
 # Create tasks to demonstrate both tools

src/praisonai-agents/llm-gemini-advanced.py

@@ -32,9 +32,9 @@ agent = Agent(
     instructions="You are a helpful Assistant specialized in scientific explanations. "
                 "Provide clear, accurate, and engaging responses.",
     llm=llm_config,                  # Pass the detailed configuration
-    verbose=True,                    # Enable detailed output
+    output="verbose",                    # Enable detailed output
     markdown=True,                   # Format responses in markdown
-    self_reflect=True,              # Enable self-reflection
+    reflection=True,              # Enable self-reflection
     max_reflect=3,                  # Maximum reflection iterations
     min_reflect=1                   # Minimum reflection iterations
 )

src/praisonai-agents/mcp-agents.py

@@ -3,7 +3,7 @@ from praisonaiagents import Agent, MCP
 agent = Agent(
     instructions="""You are a helpful assistant that can check stock prices and perform other tasks.
     Use the available tools when relevant to answer user questions.""",
-    llm="gpt-5-nano",
+    llm="gpt-4o-mini",
     tools = MCP("/Users/praison/miniconda3/envs/mcp/bin/python /Users/praison/stockprice/app.py")
 )
 

src/praisonai-agents/mongodb_comprehensive_example.py

@@ -71,7 +71,7 @@ def main():
         tools=[mongodb_tools],
         memory=True,
         verbose=True,
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # 2. Knowledge Curator Agent - manages knowledge base
@@ -85,7 +85,7 @@ def main():
         knowledge_config=mongodb_knowledge_config,
         memory=True,
         verbose=True,
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # 3. Business Analyst Agent - analyzes data and provides insights
@@ -99,7 +99,7 @@ def main():
         tools=[mongodb_tools],
         memory=True,
         verbose=True,
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # 4. Customer Service Agent - handles customer interactions
@@ -114,7 +114,7 @@ def main():
         tools=[mongodb_tools],
         memory=True,
         verbose=True,
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Create comprehensive business tasks

src/praisonai-agents/mongodb_knowledge_basic.py

@@ -55,11 +55,9 @@ 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=[os.path.join(os.path.dirname(__file__), "llms.md")],
         memory=True,
-        verbose=True,
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Create a research assistant agent
@@ -72,8 +70,7 @@ def main():
         knowledge base. You ensure information is accurate, well-structured, 
         and properly categorized.""",
         memory=True,
-        verbose=True,
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Create tasks for knowledge management
@@ -128,7 +125,7 @@ def main():
         agents=[research_agent, knowledge_agent],
         tasks=knowledge_tasks,
         memory=True,
-        verbose=True
+        output="verbose"
     )
     
     # Execute the knowledge management pipeline

src/praisonai-agents/mongodb_knowledge_example.py

@@ -59,7 +59,7 @@ def main():
         knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
         memory=True,
         verbose=True,
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Create a research assistant agent
@@ -73,7 +73,7 @@ def main():
         and properly categorized.""",
         memory=True,
         verbose=True,
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Create tasks for knowledge management

src/praisonai-agents/mongodb_memory_basic.py

@@ -3,7 +3,7 @@ from praisonaiagents.memory import Memory
 from praisonaiagents.agent import ContextAgent
 import pymongo
 
-context_agent = ContextAgent(llm="gpt-5-nano", auto_analyze=False)
+context_agent = ContextAgent(llm="gpt-4o-mini", auto_analyze=False)
 
 context_output = context_agent.start("https://github.com/MervinPraison/PraisonAI/ Need to add Authentication")
 
@@ -25,7 +25,7 @@ implementation_agent = Agent(
     goal="Implement authentication features based on project requirements",
     backstory="Expert software implementer specializing in authentication systems, security features, and seamless integration with existing codebases",
     memory=True,
-    llm="gpt-5-nano",
+    llm="gpt-4o-mini",
 )
 
 implementation_task = Task(

src/praisonai-agents/mongodb_memory_example.py

@@ -50,7 +50,7 @@ def main():
         retrieve relevant information from your persistent memory store.""",
         memory=True,
         verbose=True,
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Create research tasks

src/praisonai-agents/mongodb_tools_example.py

@@ -38,7 +38,7 @@ def main():
         for data management and retrieval.""",
         tools=[mongodb_tools],
         verbose=True,
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Create a Data Analysis Agent
@@ -51,7 +51,7 @@ def main():
         identify patterns and trends in the stored information.""",
         tools=[mongodb_tools],
         verbose=True,
-        llm="gpt-5-nano"
+        llm="gpt-4o-mini"
     )
     
     # Create tasks for MongoDB operations

src/praisonai-agents/multi-agents.py

@@ -0,0 +1,7 @@
+from praisonaiagents import Agent, Agents
+
+research_agent = Agent(instructions="Research about AI")
+summarise_agent = Agent(instructions="Summarise research agent's findings")
+
+agents = Agents(agents=[research_agent, summarise_agent])
+response = agents.start()

src/praisonai-agents/openai-basic.py

@@ -2,6 +2,6 @@ from praisonaiagents import Agent
 
 agent = Agent(
     instructions="You are a helpful assistant",
-    llm="gpt-5-nano"
+    llm="gpt-4o-mini"
 )
 agent.start("Why sky is Blue?")

src/praisonai-agents/openai-multiturn.py

@@ -2,7 +2,7 @@ from praisonaiagents import Agent
 
 agent = Agent(
     instructions="You are a helpful assistant",
-    llm="gpt-5-nano"
+    llm="gpt-4o-mini"
 )
 agent.start("Why sky is Blue?")
 agent.start("What was my previous question?")

src/praisonai-agents/openai-run.py

@@ -3,7 +3,7 @@ import asyncio
 
 agent = Agent(
     instructions="You are a helpful assistant",
-    llm="gpt-5-nano"
+    llm="gpt-4o-mini"
 )
 agent.run("Why sea is Blue?")
 

src/praisonai-agents/openai-sequential.py

@@ -22,7 +22,7 @@ def multiply(a: int, b: int) -> int:
 
 agent = Agent(
     instructions="You are a helpful assistant. You can use the tools provided to you to help the user.",
-    llm="gpt-5-nano",
+    llm="gpt-4o-mini",
     tools=[get_stock_price, multiply]
 )
 

src/praisonai-agents/planning_simple.py

@@ -14,8 +14,7 @@ task2 = Task(description="Write a short article about meditation benefits", agen
 agents = Agents(
     agents=[researcher, writer],
     tasks=[task1, task2],
-    planning=True,
-    auto_approve_plan=True  # Auto-approve the generated plan
+    planning=True
 )
 
 agents.start()

src/praisonai-agents/praisonai-openai.py

@@ -22,7 +22,7 @@ def multiply(a: int, b: int) -> int:
 
 agent = Agent(
     instructions="You are a helpful assistant. You can use the tools provided to you to help the user.",
-    llm="gpt-5-nano",
+    llm="gpt-4o-mini",
     tools=[get_stock_price, multiply]
 )
 

src/praisonai-agents/praisonaiagents/__init__.py

@@ -5,6 +5,29 @@ This module uses lazy loading to minimize import time and memory usage.
 Heavy dependencies like litellm are only loaded when actually needed.
 """
 
+# =============================================================================
+# NAMESPACE PACKAGE GUARD
+# =============================================================================
+# Detect if we're loaded as a namespace package (which indicates stale artifacts
+# in site-packages). This happens when there's a praisonaiagents/ directory in
+# site-packages without an __init__.py file.
+#
+# Root cause: Partial uninstall or version mismatch leaves directories that
+# Python's PathFinder treats as namespace packages, shadowing the real package.
+# =============================================================================
+if __file__ is None:
+    import warnings as _ns_warnings
+    _ns_warnings.warn(
+        "praisonaiagents is loaded as a namespace package, which indicates "
+        "stale artifacts in site-packages. This will cause import errors. "
+        "Fix: Remove the stale directory with:\n"
+        "  rm -rf $(python -c \"import site; print(site.getsitepackages()[0])\")/praisonaiagents/\n"
+        "Then reinstall: pip install praisonaiagents",
+        ImportWarning,
+        stacklevel=1
+    )
+    del _ns_warnings
+
 # Apply warning patch BEFORE any imports to intercept warnings at the source
 from . import _warning_patch  # noqa: F401
 
@@ -25,6 +48,14 @@ from .tools.registry import get_registry, register_tool, get_tool, ToolRegistry
 from .db import db
 from .obs import obs
 
+# Sub-packages for organized imports (pa.config, pa.tools, etc.)
+# These enable: import praisonaiagents as pa; pa.config.MemoryConfig
+from . import config
+from . import tools
+from . import memory
+from . import workflows
+# Note: knowledge and mcp are lazy-loaded via __getattr__ due to heavy deps
+
 # Workflows - lightweight module
 from .workflows import (
     Workflow, WorkflowStep, WorkflowContext, StepResult,
@@ -68,7 +99,18 @@ from .main import (
 )
 
 # Module-level caches for lazy-loaded classes
-_lazy_cache = {}
+# Using threading.local() ensures thread-safe access to cached values
+import threading
+_lazy_cache_local = threading.local()
+
+def _get_lazy_cache():
+    """Get thread-local lazy cache dict. Thread-safe for concurrent access."""
+    if not hasattr(_lazy_cache_local, 'cache'):
+        _lazy_cache_local.cache = {}
+    return _lazy_cache_local.cache
+
+# Backward-compatible alias (module-level access still works, now thread-safe)
+_lazy_cache = {}  # Kept for compatibility, but prefer _get_lazy_cache()
 
 # Flags for optional modules
 _mcp_available = None
@@ -177,6 +219,13 @@ def __getattr__(name):
         _lazy_cache["PraisonAIAgents"] = Agents  # Also cache alias
         return Agents
     elif name == "PraisonAIAgents":
+        import warnings
+        warnings.warn(
+            "PraisonAIAgents is deprecated, use Agents instead. "
+            "Example: from praisonaiagents import Agents",
+            DeprecationWarning,
+            stacklevel=2
+        )
         from .agents.agents import Agents
         _lazy_cache["Agents"] = Agents
         _lazy_cache[name] = Agents
@@ -435,6 +484,51 @@ def __getattr__(name):
         _lazy_cache[name] = ContextManager
         return ContextManager
     
+    # =========================================================================
+    # Sub-package fallback: delegate to sub-packages for additional symbols
+    # This enables: from praisonaiagents import duckduckgo, FileMemory, etc.
+    # =========================================================================
+    
+    # Try tools sub-package (110+ tool functions)
+    try:
+        from . import tools as _tools_module
+        if hasattr(_tools_module, name):
+            result = getattr(_tools_module, name)
+            _lazy_cache[name] = result
+            return result
+    except (ImportError, AttributeError):
+        pass
+    
+    # Try memory sub-package (FileMemory, RulesManager, etc.)
+    try:
+        from . import memory as _memory_module
+        if hasattr(_memory_module, name):
+            result = getattr(_memory_module, name)
+            _lazy_cache[name] = result
+            return result
+    except (ImportError, AttributeError):
+        pass
+    
+    # Try config sub-package (already handled above, but catch any stragglers)
+    try:
+        from . import config as _config_module
+        if hasattr(_config_module, name):
+            result = getattr(_config_module, name)
+            _lazy_cache[name] = result
+            return result
+    except (ImportError, AttributeError):
+        pass
+    
+    # Try workflows sub-package (Pipeline, Route, etc. - already imported, catch aliases)
+    try:
+        from . import workflows as _workflows_module
+        if hasattr(_workflows_module, name):
+            result = getattr(_workflows_module, name)
+            _lazy_cache[name] = result
+            return result
+    except (ImportError, AttributeError):
+        pass
+    
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 
@@ -469,174 +563,117 @@ def _init_telemetry():
 _init_telemetry()
 
 
+def warmup(include_litellm: bool = False, include_openai: bool = True) -> dict:
+    """
+    Pre-import heavy dependencies to reduce first-call latency.
+    
+    NOTE: For default OpenAI usage (llm="gpt-4o-mini"), warmup is NOT needed.
+    The default path uses the native OpenAI SDK which is fast (~100ms import).
+    
+    Warmup is only beneficial when using LiteLLM backend, which is triggered by:
+    - Using "/" in model name (e.g., llm="openai/gpt-4o-mini")
+    - Passing a dict config (e.g., llm={"model": "gpt-4o-mini"})
+    - Using base_url parameter
+    
+    Args:
+        include_litellm: Pre-import LiteLLM (~2-3s). Only needed for multi-provider support.
+        include_openai: Pre-import OpenAI SDK (~100ms). Default path, usually fast.
+    
+    Returns:
+        dict: Timing information for each component warmed up
+    
+    Example:
+        # For LiteLLM multi-provider usage:
+        from praisonaiagents import warmup
+        warmup(include_litellm=True)  # Pre-load LiteLLM
+        
+        agent = Agent(llm="anthropic/claude-3-sonnet")  # Now faster
+        
+        # For default OpenAI usage, no warmup needed:
+        agent = Agent(llm="gpt-4o-mini")  # Already fast!
+    """
+    import time
+    timings = {}
+    
+    if include_openai:
+        start = time.perf_counter()
+        try:
+            import openai
+            timings['openai'] = (time.perf_counter() - start) * 1000
+        except ImportError:
+            timings['openai'] = -1  # Not available
+    
+    if include_litellm:
+        start = time.perf_counter()
+        try:
+            import litellm
+            # Also configure litellm to avoid first-call overhead
+            litellm.telemetry = False
+            litellm.set_verbose = False
+            litellm.drop_params = True
+            litellm.modify_params = True
+            timings['litellm'] = (time.perf_counter() - start) * 1000
+        except ImportError:
+            timings['litellm'] = -1  # Not available
+    
+    return timings
+
+
+# ============================================================================
+# PUBLIC API: __all__ (controls IDE autocomplete and `from X import *`)
+# ============================================================================
+# DESIGN: Keep __all__ minimal for clean IDE experience.
+# All 186+ symbols are still accessible via __getattr__ for backwards compat.
+# Organized imports available via sub-packages: config, tools, memory, workflows
+# ============================================================================
+
 __all__ = [
-    # Core classes (lazy loaded)
+    # Core classes - the essentials
     'Agent',
-    'ImageAgent',
-    'ContextAgent',
-    'create_context_agent',
-    'PraisonAIAgents',
     'Agents',
-    'Tools',
     'Task',
-    # Plugin system
-    'BaseTool',
-    'ToolResult',
-    'ToolValidationError',
-    'validate_tool',
+    
+    # Tool essentials
     'tool',
-    'FunctionTool',
-    'ToolRegistry',
-    'get_registry',
-    'register_tool',
-    'get_tool',
-    'TaskOutput',
-    'ReflectionOutput',
-    'AutoAgents',
-    'AutoRagAgent',
-    'AutoRagConfig',
-    'RagRetrievalPolicy',
-    'Session',
-    'Memory',
-    'db',
-    'obs',
-    'display_interaction',
-    'display_self_reflection',
-    'display_instruction',
-    'display_tool_call',
-    'display_error',
-    'display_generating',
-    'clean_triple_backticks',
-    'error_logs',
-    'register_display_callback',
-    'sync_display_callbacks',
-    'async_display_callbacks',
-    'Knowledge',
-    'Chunking',
-    'GuardrailResult',
-    'LLMGuardrail',
-    'Handoff',
-    'handoff',
-    'handoff_filters',
-    'RECOMMENDED_PROMPT_PREFIX',
-    'prompt_with_handoff_instructions',
-    'HandoffConfig',
-    'HandoffResult',
-    'HandoffInputData',
-    'ContextPolicy',
-    'HandoffError',
-    'HandoffCycleError',
-    'HandoffDepthError',
-    'HandoffTimeoutError',
-    'get_telemetry',
-    'enable_telemetry',
-    'disable_telemetry',
-    'enable_performance_mode',
-    'disable_performance_mode',
-    'cleanup_telemetry_resources',
-    'MinimalTelemetry',
-    'TelemetryCollector',
-    'DeepResearchAgent',
-    'DeepResearchResponse',
-    'Citation',
-    'ReasoningStep',
-    'WebSearchCall',
-    'CodeExecutionStep',
-    'MCPCall',
-    'FileSearchCall',
-    'Provider',
-    'QueryRewriterAgent',
-    'RewriteStrategy',
-    'RewriteResult',
-    'PromptExpanderAgent',
-    'ExpandStrategy',
-    'ExpandResult',
-    # Workflows
-    'Workflow',
-    'WorkflowStep', 
-    'WorkflowContext',
-    'StepResult',
-    'Route',
-    'Parallel',
-    'Loop',
-    'Repeat',
-    'route',
-    'parallel',
-    'loop',
-    'repeat',
-    'Pipeline',
-    # MCP
-    'MCP',
-    # Flow display
-    'FlowDisplay',
-    'track_workflow',
-    # FastContext
-    'FastContext',
-    'FastContextResult',
-    'FileMatch',
-    'LineRange',
-    # Agent Skills
-    'SkillManager',
-    'SkillProperties',
-    'SkillMetadata',
-    'SkillLoader',
-    # Planning
-    'Plan',
-    'PlanStep',
-    'TodoList',
-    'TodoItem',
-    'PlanStorage',
-    'PlanningAgent',
-    'ApprovalCallback',
-    'READ_ONLY_TOOLS',
-    'RESTRICTED_TOOLS',
-    # UI
-    'AGUI',
-    'A2A',
-    # RAG
-    'RAG',
-    'RAGConfig',
-    'RAGResult',
-    'RAGCitation',
-    # Feature Configs (agent-centric API)
-    'MemoryConfig',
-    'KnowledgeConfig',
-    'PlanningConfig',
-    'ReflectionConfig',
-    'GuardrailConfig',
-    'WebConfig',
-    'OutputConfig',
-    'ExecutionConfig',
-    'TemplateConfig',
-    'CachingConfig',
-    'HooksConfig',
-    'SkillsConfig',
-    'AutonomyConfig',
-    'MemoryBackend',
-    'ChunkingStrategy',
-    'GuardrailAction',
-    'WebSearchProvider',
-    'OutputPreset',
-    'ExecutionPreset',
-    'AutonomyLevel',
-    # Multi-Agent Feature Configs
-    'MultiAgentHooksConfig',
-    'MultiAgentOutputConfig',
-    'MultiAgentExecutionConfig',
-    'MultiAgentPlanningConfig',
-    'MultiAgentMemoryConfig',
-    # Context Management
-    'ManagerConfig',
-    'ContextManager',
-    # Parameter Resolution (agent-centric API)
-    'resolve',
-    'ArrayMode',
-    'resolve_guardrails',
-    'resolve_guardrail_policies',
-    # Presets (agent-centric API)
-    'GUARDRAIL_PRESETS',
-    'KNOWLEDGE_PRESETS',
-    # Parse Utilities
-    'is_policy_string',
-    'parse_policy_string',
+    'Tools',
+    
+    # Sub-packages for organized imports
+    # Usage: import praisonaiagents as pa; pa.config.MemoryConfig
+    'config',
+    'tools',
+    'memory',
+    'workflows',
 ]
+
+
+def __dir__():
+    """
+    Return clean list for dir() - matches __all__ plus standard attributes.
+    
+    This keeps IDE autocomplete clean while preserving full backwards
+    compatibility via __getattr__ for all 186+ legacy exports.
+    """
+    return list(__all__) + [
+        # Standard module attributes
+        '__name__', '__doc__', '__file__', '__path__', '__package__',
+        '__loader__', '__spec__', '__cached__', '__builtins__',
+    ]
+
+
+# ============================================================================
+# BACKWARDS COMPATIBILITY: Legacy __all__ items (for reference)
+# ============================================================================
+# All items below are still importable via __getattr__ but NOT in autocomplete:
+# - ImageAgent, ContextAgent, create_context_agent, PraisonAIAgents
+# - BaseTool, ToolResult, ToolValidationError, validate_tool, FunctionTool
+# - ToolRegistry, get_registry, register_tool, get_tool
+# - TaskOutput, ReflectionOutput, AutoAgents, AutoRagAgent, AutoRagConfig
+# - Session, Memory, db, obs, Knowledge, Chunking
+# - GuardrailResult, LLMGuardrail, Handoff, handoff, handoff_filters
+# - MemoryConfig, KnowledgeConfig, PlanningConfig, OutputConfig, etc.
+# - Workflow, WorkflowStep, Route, Parallel, Loop, Repeat, Pipeline, etc.
+# - MCP, FlowDisplay, track_workflow, FastContext, etc.
+# - Plan, PlanStep, TodoList, PlanningAgent, ApprovalCallback, etc.
+# - RAG, RAGConfig, RAGResult, AGUI, A2A, etc.
+# - All telemetry, display, and utility functions
+# ============================================================================

src/praisonai-agents/praisonaiagents/_resolver_helpers.py

@@ -0,0 +1,49 @@
+"""
+Resolver helper functions for consolidated parameters.
+
+These helpers wrap the canonical resolver for specific parameter types,
+handling special cases like callable validators for guardrails.
+"""
+
+from typing import Any, Type
+
+from .config.param_resolver import resolve, ArrayMode
+from .config.presets import GUARDRAIL_PRESETS
+
+
+def resolve_guardrails(value: Any, config_class: Type) -> Any:
+    """
+    Resolve guardrails parameter.
+    
+    Handles special case of callable validators which have highest precedence.
+    
+    Args:
+        value: The guardrails parameter value
+        config_class: GuardrailConfig class
+        
+    Returns:
+        Resolved config, callable, or string
+    """
+    # Handle None
+    if value is None:
+        return None
+    
+    # Handle False (disabled)
+    if value is False:
+        return None
+    
+    # Handle callable (highest precedence after None check)
+    # This includes functions, lambdas, and callable objects
+    if callable(value) and not isinstance(value, type):
+        return value
+    
+    # Use canonical resolver for everything else
+    return resolve(
+        value=value,
+        param_name="guardrails",
+        config_class=config_class,
+        presets=GUARDRAIL_PRESETS,
+        array_mode=ArrayMode.PRESET_OVERRIDE,
+        string_mode="llm_prompt",
+        default=None,
+    )

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

@@ -30,6 +30,13 @@ from .prompt_expander_agent import (
     ExpandStrategy,
     ExpandResult
 )
+from .protocols import (
+    AgentProtocol,
+    RunnableAgentProtocol,
+    ToolAwareAgentProtocol,
+    MemoryAwareAgentProtocol,
+    FullAgentProtocol,
+)
 
 __all__ = [
     'Agent',
@@ -64,5 +71,11 @@ __all__ = [
     'RewriteResult',
     'PromptExpanderAgent',
     'ExpandStrategy',
-    'ExpandResult'
+    'ExpandResult',
+    # Protocols
+    'AgentProtocol',
+    'RunnableAgentProtocol',
+    'ToolAwareAgentProtocol',
+    'MemoryAwareAgentProtocol',
+    'FullAgentProtocol',
 ]

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

@@ -434,8 +434,7 @@ This report contains all agent interactions and outputs from a complete ContextA
                     
                     Provide comprehensive analysis that follows the PRD template principles and enables 
                     AI assistants to implement features that perfectly match existing codebase patterns.""",
-                    llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-                    verbose=getattr(self, 'verbose', True)
+                    llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
                 )
                 
                 prompt = f"""Analyze this gitingest codebase digest following PRD template methodology:
@@ -515,8 +514,7 @@ codebase style and architecture following PRD template principles."""
             role="Expert Manual Codebase Analysis Specialist",
             goal="Perform comprehensive manual codebase analysis following PRD methodology",
             instructions="""Analyze the codebase samples following PRD template methodology for complete understanding.""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         # Format comprehensive sample
@@ -566,8 +564,7 @@ Analyze following PRD principles to extract patterns, conventions, and architect
             6. Design pattern implementations
             7. Code complexity metrics
             8. API and interface patterns""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         # Perform AST analysis on Python files
@@ -624,8 +621,7 @@ Extract comprehensive patterns that follow PRD template principles for implement
             
             For each pattern, provide the pattern name, where it's used, and how to replicate it 
             following PRD template principles.""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         # Get representative code samples
@@ -674,8 +670,7 @@ patterns and best practices for first-try success."""
             goal="Analyze testing patterns for comprehensive validation framework design",
             instructions="""Analyze testing patterns to understand validation approaches and create 
             comprehensive test frameworks following PRD methodology.""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         # Get test files
@@ -801,8 +796,7 @@ Extract testing patterns for validation framework creation following PRD princip
             Confidence level for one-pass implementation
             
             Generate PRPs following this EXACT structure for first-try implementation success.""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         prompt = f"""Generate a comprehensive Product Requirements Prompt (PRP) following the EXACT PRD template structure:
@@ -850,8 +844,7 @@ on the first try following PRD template principles."""
             6. CODE QUALITY: Complexity analysis, maintainability
             7. DOCUMENTATION VALIDATION: Documentation completeness
             8. DEPENDENCY VALIDATION: Dependency analysis and security""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         # Analyze existing validation patterns
@@ -897,8 +890,7 @@ following PRD template principles."""
             instructions="""Compile all available documentation following PRD methodology including:
             README files, API documentation, setup guides, architecture docs, and any other 
             relevant documentation that provides context for implementation.""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         # Get documentation files
@@ -943,8 +935,7 @@ following PRD template principles."""
             instructions="""Analyze integration points following PRD methodology including:
             APIs, databases, external services, configuration points, and any other 
             integration requirements that affect implementation.""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         # Get configuration and dependency files
@@ -1004,8 +995,7 @@ following PRD template principles."""
             8. DOCUMENTATION UPDATES: Documentation to create/update
             9. INTEGRATION STEPS: How to integrate with existing systems
             10. VALIDATION CHECKPOINTS: Validation steps at each phase""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         prompt = f"""Create detailed implementation blueprint following PRD methodology:
@@ -1302,8 +1292,7 @@ Every agent interaction has been saved for full audit trail and reproducibility.
             GOAL: [extracted implementation goal]
             
             Be precise and extract only what is explicitly mentioned or clearly implied.""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         prompt = f"""Parse this user input to extract codebase and goal:
@@ -1536,8 +1525,7 @@ Note: Detailed function/class metadata not available due to content access limit
             5. Documentation topics
             
             Make the output easy for a file selection agent to understand which files contain what functionality.""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         structure_text = basic_structure.get('structure', '')
@@ -1770,8 +1758,7 @@ Focus on creating clear, structured metadata that will help with intelligent fil
             ["README.md", "src/auth/login.py", "config/settings.py", ...]
             
             Maximum 50 files for efficient analysis.""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         # Prepare enhanced structure information
@@ -1923,8 +1910,7 @@ Maximum 50 files.""".format(goal=goal)
             8. EXAMPLES: Similar features that can guide {goal} implementation
             
             Since these files were pre-selected for relevance, provide deep analysis of how each contributes to implementing: {goal}""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         prompt = f"""Analyze these carefully selected repository files for implementing: {goal}
@@ -2024,8 +2010,7 @@ Since these files were pre-selected for relevance, explain how each contributes
             - Success criteria for {goal}
             
             Focus everything on successfully implementing: {goal}""",
-            llm=self.llm if hasattr(self, 'llm') else "gpt-5-nano",
-            verbose=getattr(self, 'verbose', True)
+            llm=self.llm if hasattr(self, 'llm') else "gpt-4o-mini",
         )
         
         chunk_info = ""
@@ -2303,13 +2288,13 @@ def create_context_agent(llm: Optional[Union[str, Any]] = None, **kwargs) -> Con
     Factory function to create a ContextAgent following Context Engineering and PRD methodology.
     
     Args:
-        llm: Language model to use (e.g., "gpt-5-nano", "claude-3-haiku")
+        llm: Language model to use (e.g., "gpt-4o-mini", "claude-3-haiku")
         **kwargs: Additional arguments to pass to ContextAgent constructor
         
     Returns:
         ContextAgent: Configured ContextAgent for comprehensive context generation following PRD principles
     """
     if llm is None:
-        llm = "gpt-5-nano"
+        llm = "gpt-4o-mini"
     
     return ContextAgent(llm=llm, **kwargs)

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

@@ -17,7 +17,7 @@ Supported Expansion Strategies:
 Example:
     from praisonaiagents import PromptExpanderAgent, ExpandStrategy
     
-    agent = PromptExpanderAgent(verbose=True)
+    agent = PromptExpanderAgent()
     
     # Basic expansion
     result = agent.expand("write a movie script in 3 lines")
@@ -29,7 +29,7 @@ Example:
     
     # With tools for context gathering
     from praisonaiagents.tools import internet_search
-    agent = PromptExpanderAgent(tools=[internet_search], verbose=True)
+    agent = PromptExpanderAgent(tools=[internet_search])
     result = agent.expand("latest AI developments")
 """
 
@@ -73,13 +73,12 @@ class PromptExpanderAgent:
     
     Attributes:
         name: Name of the agent
-        model: LLM model to use for expansion
-        verbose: Whether to print detailed logs
+        model: LLM model to use for expansion (alias for llm=)
         temperature: Temperature for LLM generation (higher = more creative)
         tools: Optional tools for context gathering
         
     Example:
-        agent = PromptExpanderAgent(model="gpt-4o-mini", verbose=True)
+        agent = PromptExpanderAgent(model="gpt-4o-mini")
         result = agent.expand("write a poem", strategy=ExpandStrategy.CREATIVE)
         print(result.expanded_prompt)
     """
@@ -230,8 +229,7 @@ Expanded prompt:"""
                 backstory="You are an expert at understanding user intent and transforming brief requests into comprehensive, detailed prompts.",
                 tools=self.tools,
                 llm=self.model,
-                verbose=self.verbose,
-                markdown=False
+                output={"verbose": self.verbose, "markdown": False}
             )
         return self._agent
     

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

@@ -0,0 +1,151 @@
+"""
+Agent Protocol Definitions.
+
+Provides Protocol interfaces that define the minimal contract for agent implementations.
+This enables:
+- Mocking agents in tests without real LLM calls
+- Creating custom agent implementations
+- Type checking with static analyzers
+
+These protocols are lightweight and have zero performance impact.
+"""
+from typing import Protocol, runtime_checkable, Optional, Any, Dict, List
+
+
+@runtime_checkable
+class AgentProtocol(Protocol):
+    """
+    Minimal Protocol for agent implementations.
+    
+    This defines the essential interface that any agent must provide.
+    It enables proper mocking and testing without real LLM dependencies.
+    
+    Example:
+        ```python
+        # Create a mock agent for testing
+        class MockAgent:
+            @property
+            def name(self) -> str:
+                return "TestAgent"
+            
+            def chat(self, prompt: str, **kwargs) -> str:
+                return "Mock response"
+            
+            async def achat(self, prompt: str, **kwargs) -> str:
+                return "Async mock response"
+        
+        # Use in tests
+        agent: AgentProtocol = MockAgent()
+        result = agent.chat("Hello")
+        ```
+    """
+    
+    @property
+    def name(self) -> str:
+        """Agent's name identifier."""
+        ...
+    
+    def chat(self, prompt: str, **kwargs) -> str:
+        """
+        Synchronous chat with the agent.
+        
+        Args:
+            prompt: The user prompt or message
+            **kwargs: Additional optional parameters (temperature, tools, etc.)
+            
+        Returns:
+            The agent's response as a string
+        """
+        ...
+    
+    async def achat(self, prompt: str, **kwargs) -> str:
+        """
+        Asynchronous chat with the agent.
+        
+        Args:
+            prompt: The user prompt or message
+            **kwargs: Additional optional parameters (temperature, tools, etc.)
+            
+        Returns:
+            The agent's response as a string
+        """
+        ...
+
+
+@runtime_checkable
+class RunnableAgentProtocol(AgentProtocol, Protocol):
+    """
+    Extended Protocol for agents that support run/start methods.
+    
+    This extends AgentProtocol with the run/start interface used
+    by the Agent class for task execution.
+    """
+    
+    def run(self, prompt: str, **kwargs) -> str:
+        """Run the agent with a prompt (alias for chat in most cases)."""
+        ...
+    
+    def start(self, prompt: str, **kwargs) -> str:
+        """Start the agent with a prompt."""
+        ...
+    
+    async def arun(self, prompt: str, **kwargs) -> str:
+        """Async run the agent with a prompt."""
+        ...
+    
+    async def astart(self, prompt: str, **kwargs) -> str:
+        """Async start the agent with a prompt."""
+        ...
+
+
+@runtime_checkable
+class ToolAwareAgentProtocol(AgentProtocol, Protocol):
+    """
+    Protocol for agents that support tools.
+    
+    This extends AgentProtocol with tool-related properties.
+    """
+    
+    @property
+    def tools(self) -> List[Any]:
+        """List of tools available to the agent."""
+        ...
+
+
+@runtime_checkable  
+class MemoryAwareAgentProtocol(AgentProtocol, Protocol):
+    """
+    Protocol for agents that support memory.
+    
+    This extends AgentProtocol with memory-related properties.
+    """
+    
+    @property
+    def chat_history(self) -> List[Dict[str, Any]]:
+        """The agent's conversation history."""
+        ...
+    
+    def clear_history(self) -> None:
+        """Clear the agent's conversation history."""
+        ...
+
+
+# Composite protocol for fully-featured agents
+@runtime_checkable
+class FullAgentProtocol(RunnableAgentProtocol, ToolAwareAgentProtocol, MemoryAwareAgentProtocol, Protocol):
+    """
+    Complete Protocol for full-featured agents.
+    
+    This combines all agent protocol features for agents that support
+    the full interface (chat, run, tools, memory).
+    """
+    pass
+
+
+__all__ = [
+    'AgentProtocol',
+    'RunnableAgentProtocol', 
+    'ToolAwareAgentProtocol',
+    'MemoryAwareAgentProtocol',
+    'FullAgentProtocol',
+]

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

@@ -15,7 +15,7 @@ Supported Rewriting Strategies:
 Example:
     from praisonaiagents import QueryRewriterAgent, RewriteStrategy
     
-    agent = QueryRewriterAgent(verbose=True)
+    agent = QueryRewriterAgent()
     
     # Basic rewriting
     result = agent.rewrite("AI trends")
@@ -34,7 +34,7 @@ Example:
     
     # With search tools for context-aware rewriting
     from praisonaiagents.tools import internet_search
-    agent = QueryRewriterAgent(tools=[internet_search], verbose=True)
+    agent = QueryRewriterAgent(tools=[internet_search])
     result = agent.rewrite("latest AI developments")  # Searches first, then rewrites
 """
 
@@ -96,13 +96,12 @@ class QueryRewriterAgent:
     
     Attributes:
         name: Name of the agent
-        model: LLM model to use for rewriting
-        verbose: Whether to print detailed logs
+        model: LLM model to use for rewriting (alias for llm=)
         max_queries: Maximum number of queries to generate for multi-query strategy
         abbreviations: Dictionary of common abbreviations to expand
         
     Example:
-        agent = QueryRewriterAgent(model="gpt-4o-mini", verbose=True)
+        agent = QueryRewriterAgent(model="gpt-4o-mini")
         result = agent.rewrite("ML best practices", strategy=RewriteStrategy.MULTI_QUERY)
         for query in result.rewritten_queries:
             print(query)
@@ -288,8 +287,7 @@ Improved version:"""
                 backstory="You are an expert at understanding user intent and transforming queries for optimal information retrieval.",
                 tools=self.tools,
                 llm=self.model,
-                verbose=self.verbose,
-                markdown=False
+                output={"verbose": self.verbose, "markdown": False}
             )
         return self._agent
     

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

@@ -44,7 +44,7 @@ class RouterAgent(Agent):
         # Initialize model router
         self.model_router = model_router or ModelRouter()
         self.routing_strategy = routing_strategy
-        self.fallback_model = fallback_model or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
+        self.fallback_model = fallback_model or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o-mini')
         
         # Process models configuration
         self.available_models = self._process_models_config(models)

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

@@ -65,6 +65,52 @@ def process_video(video_path: str, seconds_per_frame=2):
     video.release()
     return base64_frames
 
+
+def get_multimodal_message(text_prompt: str, images: list) -> list:
+    """
+    Build multimodal message content for LLM with text and images.
+    
+    DRY helper - replaces duplicate _get_multimodal_message in aexecute_task/execute_task.
+    
+    Args:
+        text_prompt: The text content of the message
+        images: List of image paths (local or URL)
+        
+    Returns:
+        List of content items for multimodal LLM message
+    """
+    content = [{"type": "text", "text": text_prompt}]
+    
+    for img in images:
+        # If local file path for a valid image
+        if os.path.exists(img):
+            ext = os.path.splitext(img)[1].lower()
+            # If it's a .mp4, convert to frames
+            if ext == ".mp4":
+                frames = process_video(img, seconds_per_frame=1)
+                content.append({"type": "text", "text": "These are frames from the video."})
+                for f in frames:
+                    content.append({
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/jpg;base64,{f}"}
+                    })
+            else:
+                encoded = encode_file_to_base64(img)
+                content.append({
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:image/{ext.lstrip('.')};base64,{encoded}"
+                    }
+                })
+        else:
+            # Treat as a remote URL
+            content.append({
+                "type": "image_url",
+                "image_url": {"url": img}
+            })
+    return content
+
+
 def process_task_context(context_item, verbose=0, user_id=None):
     """
     Process a single context item for task execution.
@@ -142,6 +188,13 @@ class Agents:
         output: Optional[Any] = None,  # Union[str, MultiAgentOutputConfig]
         execution: Optional[Any] = None,  # Union[str, MultiAgentExecutionConfig]
         hooks: Optional[Any] = None,  # MultiAgentHooksConfig
+        # Additional consolidated params for feature parity with Agent
+        autonomy: Optional[Any] = None,  # Union[bool, AutonomyConfig] - agent autonomy
+        knowledge: Optional[Any] = None,  # Union[bool, List[str], KnowledgeConfig] - RAG
+        guardrails: Optional[Any] = None,  # Union[bool, Callable, GuardrailConfig] - validation
+        web: Optional[Any] = None,  # Union[bool, WebConfig] - web search/fetch
+        reflection: Optional[Any] = None,  # Union[bool, ReflectionConfig] - self-reflection
+        caching: Optional[Any] = None,  # Union[bool, CachingConfig] - caching
     ):
         """
         Initialize PraisonAIAgents with consolidated feature parameters.
@@ -159,7 +212,19 @@ class Agents:
             output: Output configuration (str | MultiAgentOutputConfig)
             execution: Execution configuration (str | MultiAgentExecutionConfig)
             hooks: Hooks configuration (MultiAgentHooksConfig)
+            autonomy: Autonomy configuration (bool | AutonomyConfig)
+            knowledge: Knowledge/RAG configuration (bool | List[str] | KnowledgeConfig)
+            guardrails: Guardrails configuration (bool | Callable | GuardrailConfig)
+            web: Web search/fetch configuration (bool | WebConfig)
+            reflection: Self-reflection configuration (bool | ReflectionConfig)
         """
+        # Store new params for propagation to agents
+        self._autonomy = autonomy
+        self._knowledge = knowledge
+        self._guardrails = guardrails
+        self._web = web
+        self._reflection = reflection
+        self._caching = caching
         # ─────────────────────────────────────────────────────────────────────
         # Extract values from consolidated params using UNIFIED CANONICAL resolver
         # Precedence: Instance > Config > Dict > Array > String > Bool > Default
@@ -344,7 +409,7 @@ class Agents:
         self.variables = variables if variables else {}
         
         # Check for manager_llm in environment variable if not provided
-        self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
+        self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o-mini')
         
         # Set logger level based on verbose
         if _verbose >= 5:
@@ -447,10 +512,16 @@ class Agents:
         
         if self.shared_memory:
             for task in tasks:
-                if not task.memory:
                     task.memory = self.shared_memory
                     logger.info(f"Assigned shared memory to task {task.id}")
 
+        # Telemetry
+        try:
+            from ..telemetry import get_telemetry
+            self._telemetry = get_telemetry()
+        except (ImportError, AttributeError):
+            self._telemetry = None
+
     def add_task(self, task):
         task_id = self.task_id_counter
         task.id = task_id
@@ -584,40 +655,9 @@ Context:
         logger.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
 
         if task.images:
-            def _get_multimodal_message(text_prompt, images):
-                content = [{"type": "text", "text": text_prompt}]
-
-                for img in images:
-                    # If local file path for a valid image
-                    if os.path.exists(img):
-                        ext = os.path.splitext(img)[1].lower()
-                        # If it's a .mp4, convert to frames
-                        if ext == ".mp4":
-                            frames = process_video(img, seconds_per_frame=1)
-                            content.append({"type": "text", "text": "These are frames from the video."})
-                            for f in frames:
-                                content.append({
-                                    "type": "image_url",
-                                    "image_url": {"url": f"data:image/jpg;base64,{f}"}
-                                })
-                        else:
-                            encoded = encode_file_to_base64(img)
-                            content.append({
-                                "type": "image_url",
-                                "image_url": {
-                                    "url": f"data:image/{ext.lstrip('.')};base64,{encoded}"
-                                }
-                            })
-                    else:
-                        # Treat as a remote URL
-                        content.append({
-                            "type": "image_url",
-                            "image_url": {"url": img}
-                        })
-                return content
-
+            # Use shared multimodal helper (DRY - defined at module level)
             agent_output = await executor_agent.achat(
-                _get_multimodal_message(task_prompt, task.images),
+                get_multimodal_message(task_prompt, task.images),
                 tools=tools,
                 output_json=task.output_json,
                 output_pydantic=task.output_pydantic,
@@ -802,13 +842,17 @@ Context:
                     await loop.run_in_executor(None, self.run_task, task_id)
 
     async def astart(self, content=None, return_dict=False, **kwargs):
-        """Async version of start method
+        """Async version of start method.
         
         Args:
             content: Optional content to add to all tasks' context
             return_dict: If True, returns the full results dictionary instead of only the final response
             **kwargs: Additional arguments
         """
+        # Track execution via telemetry
+        if hasattr(self, '_telemetry') and self._telemetry:
+            self._telemetry.track_agent_execution(self.name, success=True, async_mode=True)
+            
         if content:
             # Add content to context of all tasks
             for task in self.tasks.values():
@@ -942,40 +986,9 @@ Context:
         logger.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
 
         if task.images:
-            def _get_multimodal_message(text_prompt, images):
-                content = [{"type": "text", "text": text_prompt}]
-
-                for img in images:
-                    # If local file path for a valid image
-                    if os.path.exists(img):
-                        ext = os.path.splitext(img)[1].lower()
-                        # If it's a .mp4, convert to frames
-                        if ext == ".mp4":
-                            frames = process_video(img, seconds_per_frame=1)
-                            content.append({"type": "text", "text": "These are frames from the video."})
-                            for f in frames:
-                                content.append({
-                                    "type": "image_url",
-                                    "image_url": {"url": f"data:image/jpg;base64,{f}"}
-                                })
-                        else:
-                            encoded = encode_file_to_base64(img)
-                            content.append({
-                                "type": "image_url",
-                                "image_url": {
-                                    "url": f"data:image/{ext.lstrip('.')};base64,{encoded}"
-                                }
-                            })
-                    else:
-                        # Treat as a remote URL
-                        content.append({
-                            "type": "image_url",
-                            "image_url": {"url": img}
-                        })
-                return content
-
+            # Use shared multimodal helper (DRY - defined at module level)
             agent_output = executor_agent.chat(
-                _get_multimodal_message(task_prompt, task.images),
+                get_multimodal_message(task_prompt, task.images),
                 tools=task.tools,
                 output_json=task.output_json,
                 output_pydantic=task.output_pydantic,
@@ -1174,30 +1187,147 @@ Context:
             return str(agent[0])
         return None
 
-    def start(self, content=None, return_dict=False, **kwargs):
-        """Start agent execution with optional content and config
+    def start(self, content=None, return_dict=False, output=None, **kwargs):
+        """Start agent execution with verbose output (beginner-friendly).
+        
+        Shows Rich panels with workflow progress when in TTY. Use .run() for
+        silent execution in production/scripts.
         
         Args:
             content: Optional content to add to all tasks' context
-            return_dict: If True, returns the full results dictionary instead of only the final response
+            return_dict: If True, returns the full results dictionary
+            output: Output preset - "silent", "verbose", "normal", etc.
+                    Default in TTY: "verbose" (shows progress)
+                    Default non-TTY: "silent"
             **kwargs: Additional arguments
+            
+        Example:
+            ```python
+            # Interactive - shows Rich panels
+            agents = Agents(agents=[agent1, agent2])
+            result = agents.start()  # Verbose output by default
+            
+            # Force silent mode
+            result = agents.start(output="silent")
+            ```
         """
+        # Track execution via telemetry
+        if hasattr(self, '_telemetry') and self._telemetry:
+            self._telemetry.track_agent_execution(self.name, success=True)
+        import sys
+        from ..main import PRAISON_COLORS
+        
+        # Determine if we're in an interactive TTY
+        is_tty = sys.stdout.isatty()
+        
+        # Resolve output mode (TTY-aware)
+        if output is None:
+            # Default: verbose in TTY (beginner-friendly), silent otherwise
+            # Note: Don't check self.verbose here - start() is for interactive use
+            show_verbose = is_tty
+        elif output == "silent":
+            show_verbose = False
+        elif output in ("verbose", "debug", "normal"):
+            show_verbose = True
+        else:
+            show_verbose = is_tty
+        
+        # Add content to context if provided
         if content:
-            # Add content to context of all tasks
             for task in self.tasks.values():
                 if isinstance(content, (str, list)):
-                    # If context is empty, initialize it
                     if not task.context:
                         task.context = []
-                    # Add content to context
                     task.context.append(content)
         
-        # Planning Mode: Create plan and todo list before execution
-        if self.planning:
-            self._run_with_planning()
+        # ─────────────────────────────────────────────────────────────
+        # Verbose Mode: Show Rich panels for multi-agent workflow
+        # ─────────────────────────────────────────────────────────────
+        if show_verbose and is_tty:
+            from rich.panel import Panel
+            from rich.text import Text
+            console = Console()
+            import time as time_module
+            
+            # Show workflow overview panel
+            agent_names = " → ".join([a.name for a in self.agents])
+            workflow_info = f"[bold {PRAISON_COLORS['metrics']}]Process:[/] {self.process}\n"
+            workflow_info += f"[bold {PRAISON_COLORS['metrics']}]Agents:[/] {agent_names}"
+            
+            console.print(Panel(
+                workflow_info,
+                title="[bold]Multi-Agent Workflow[/]",
+                border_style=PRAISON_COLORS["agent"],
+                padding=(1, 2)
+            ))
+            console.print()
+            
+            # Execute tasks with verbose output
+            total_agents = len(self.agents)
+            workflow_start_time = time_module.time()
+            
+            for idx, (task_id, task) in enumerate(self.tasks.items(), 1):
+                agent = task.agent
+                agent_name = agent.name if agent else "Unknown"
+                agent_model = getattr(agent, 'llm', 'gpt-4o-mini') if agent else "unknown"
+                
+                # Show agent task panel with model info
+                task_desc = task.description[:100] + "..." if len(task.description) > 100 else task.description
+                panel_content = f"[bold {PRAISON_COLORS['task_text']}]📋 Task:[/] {task_desc}\n"
+                panel_content += f"[dim]🤖 Model: {agent_model}[/dim]"
+                console.print(Panel.fit(
+                    panel_content,
+                    title=f"[bold]Agent [{idx}/{total_agents}]: {agent_name}[/]",
+                    border_style=PRAISON_COLORS["task"]
+                ))
+                
+                # Execute with timing and status
+                start_time = time_module.time()
+                
+                # Show working spinner
+                with console.status(
+                    f"[bold yellow]Working...[/]  {agent_name} generating response...",
+                    spinner="dots",
+                    spinner_style="yellow"
+                ):
+                    # Run the task
+                    if self.planning:
+                        self._run_with_planning()
+                        break  # Planning mode handles all tasks
+                    else:
+                        self.run_task(task_id)
+                
+                elapsed = time_module.time() - start_time
+                
+                # Show response panel - FULL response, no truncation
+                result = self.get_task_result(task_id)
+                if result:
+                    response_text = str(result.raw)
+                    # No truncation - show full response in verbose mode
+                    from rich.markdown import Markdown
+                    console.print(Panel(
+                        Markdown(response_text),
+                        title=f"[bold]Agent [{idx}/{total_agents}] Complete ({elapsed:.1f}s)[/]",
+                        border_style=PRAISON_COLORS["response"],
+                        padding=(1, 2)
+                    ))
+                console.print()
+            
+            # Workflow summary panel
+            total_elapsed = time_module.time() - workflow_start_time
+            console.print(Panel.fit(
+                f"[bold green]Total Time:[/] {total_elapsed:.1f}s\n"
+                f"[bold green]Agents Run:[/] {total_agents}/{total_agents}",
+                title="[bold]✅ Workflow Complete[/]",
+                border_style="green"
+            ))
+            console.print()
         else:
-            # Run tasks as before
-            self.run_all_tasks()
+            # Silent mode: Run tasks without display
+            if self.planning:
+                self._run_with_planning()
+            else:
+                self.run_all_tasks()
         
         # Auto-display token metrics if any agent has metrics=True
         metrics_enabled = any(getattr(agent, 'metrics', False) for agent in self.agents)
@@ -1205,10 +1335,8 @@ Context:
             try:
                 self.display_token_usage()
             except (ImportError, AttributeError) as e:
-                # Token tracking not available or not properly configured
                 logging.debug(f"Could not auto-display token usage: {e}")
             except Exception as e:
-                # Log unexpected errors for debugging
                 logging.debug(f"Unexpected error in token metrics display: {e}")
         
         # Get results
@@ -1219,7 +1347,6 @@ Context:
         
         # By default, return only the final agent's response
         if not return_dict:
-            # Get the last task (assuming sequential processing)
             task_ids = list(self.tasks.keys())
             if task_ids:
                 last_task_id = task_ids[-1]
@@ -1227,12 +1354,21 @@ Context:
                 if last_result:
                     return last_result.raw
                     
-        # Return full results dict if return_dict is True or if no final result was found
         return results
 
     def run(self, content=None, return_dict=False, **kwargs):
-        """Alias for start() method to provide consistent API with Agent class"""
-        return self.start(content=content, return_dict=return_dict, **kwargs)
+        """Run agents silently (production use).
+        
+        Unlike .start() which shows verbose output, .run() executes silently
+        for programmatic/production use.
+        
+        Args:
+            content: Optional content to add to all tasks' context
+            return_dict: If True, returns the full results dictionary
+            **kwargs: Additional arguments
+        """
+        # Always run silently - no verbose output
+        return self.start(content=content, return_dict=return_dict, output="silent", **kwargs)
 
     def set_state(self, key: str, value: Any) -> None:
         """Set a state value"""
@@ -2172,7 +2308,7 @@ Context:
                 # Inherit from original task if available
                 memory=original_task.memory if original_task else None,
                 callback=original_task.callback if original_task else None,
-                guardrail=original_task.guardrail if original_task else None,
+                guardrails=original_task.guardrail if original_task else None,
                 max_retries=original_task.max_retries if original_task else 3,
                 output_json=original_task.output_json if original_task else None,
                 output_pydantic=original_task.output_pydantic if original_task else None,

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

@@ -74,6 +74,17 @@ class AutoAgents(Agents):
         api_key: Optional[str] = None,
         max_agents: int = 3,  # Maximum number of agents
         autonomy: Optional[Any] = None,  # Union[bool, dict, AutonomyConfig] - Enable autonomy for all agents
+        # ============================================================
+        # CONSOLIDATED PARAMS (new SDK contract - take precedence)
+        # ============================================================
+        output: Optional[Any] = None,  # Union[str, OutputConfig] - overrides verbose/markdown
+        reflection: Optional[Any] = None,  # Union[bool, ReflectionConfig] - overrides self_reflect/max_reflect/min_reflect/reflect_llm
+        caching: Optional[Any] = None,  # Union[bool, CachingConfig] - overrides cache
+        knowledge: Optional[Any] = None,  # Union[bool, List[str], KnowledgeConfig] - overrides knowledge_sources/embedder_config
+        execution: Optional[Any] = None,  # Union[str, ExecutionConfig] - overrides max_iter
+        guardrails: Optional[Any] = None,  # Union[bool, Callable, GuardrailConfig]
+        web: Optional[Any] = None,  # Union[bool, WebConfig]
+        hooks: Optional[Any] = None,  # HooksConfig
     ):
         """Initialize AutoAgents with configuration for automatic agent and task creation."""
         if max_agents < 1:
@@ -92,7 +103,7 @@ class AutoAgents(Agents):
         self.self_reflect = self_reflect
         self.max_reflect = max_reflect
         self.min_reflect = min_reflect
-        self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
+        self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o-mini')
         self.function_calling_llm = function_calling_llm
         self.code_execution_mode = code_execution_mode
         self.embedder_config = embedder_config
@@ -112,6 +123,16 @@ class AutoAgents(Agents):
         self.api_key = api_key
         self.autonomy = autonomy  # Store autonomy config for agents
         
+        # Store consolidated params (new SDK contract - take precedence over legacy)
+        self._output = output
+        self._reflection = reflection
+        self._caching = caching
+        self._knowledge = knowledge
+        self._execution = execution
+        self._guardrails = guardrails
+        self._web = web
+        self._hooks = hooks
+        
         # Display initial instruction
         if self.verbose:
             display_instruction(f"🎯 Main Task: {self.instructions}")
@@ -129,14 +150,21 @@ class AutoAgents(Agents):
             self._display_agents_and_tasks(agents, tasks)
         
         # Initialize parent class with generated agents and tasks
+        # Use consolidated output= instead of legacy verbose=
         super().__init__(
             agents=agents,
             tasks=tasks,
-            verbose=verbose,
-            completion_checker=completion_checker,
-            max_retries=max_retries,
             process=process,
-            manager_llm=manager_llm or self.llm
+            manager_llm=manager_llm or self.llm,
+            output=self._output,  # Consolidated output param
+            execution=self._execution,
+            hooks=self._hooks,
+            memory=self.memory,
+            reflection=self._reflection,
+            caching=self._caching,
+            guardrails=self._guardrails,
+            web=self._web,
+            autonomy=self.autonomy,
         )
 
     def _display_agents_and_tasks(self, agents: List[Agent], tasks: List[Task]):
@@ -431,38 +459,33 @@ DO NOT use strings for tasks. Each task MUST be a complete object with all four
             agent_tools = self._assign_tools_to_agent(agent_config)
             
             # Create the agent with all parameters
+            # Consolidated params take precedence over legacy params
+            # Use consolidated knowledge if provided, else fall back to legacy knowledge_sources
+            effective_knowledge = self._knowledge if self._knowledge is not None else self.knowledge_sources
+            
             agent = Agent(
                 name=agent_config.name,
                 role=agent_config.role,
                 goal=agent_config.goal,
                 backstory=agent_config.backstory,
                 tools=agent_tools,  # Use assigned tools
-                verbose=self.verbose >= 1,
                 allow_code_execution=self.allow_code_execution,
                 memory=self.memory,
-                markdown=self.markdown,
-                self_reflect=self.self_reflect,
-                max_reflect=self.max_reflect,
-                min_reflect=self.min_reflect,
-                llm=self.llm,
-                function_calling_llm=self.function_calling_llm,
+                llm=self.llm,  # Consolidated LLM param
                 code_execution_mode=self.code_execution_mode,
-                embedder_config=self.embedder_config,
-                knowledge=self.knowledge_sources,
-                use_system_prompt=self.use_system_prompt,
-                cache=self.cache,
                 allow_delegation=self.allow_delegation,
-                step_callback=self.step_callback,
-                system_template=self.system_template,
-                prompt_template=self.prompt_template,
-                response_template=self.response_template,
-                max_rpm=self.max_rpm,
-                max_execution_time=self.max_execution_time,
-                max_iter=self.max_iter,
-                reflect_llm=self.reflect_llm,
                 base_url=self.base_url,
                 api_key=self.api_key,
-                autonomy=self.autonomy  # Pass autonomy config to agents
+                # Consolidated params (new SDK contract)
+                output=self._output,  # Overrides verbose/markdown
+                reflection=self._reflection,  # Overrides self_reflect/max_reflect/min_reflect/reflect_llm
+                caching=self._caching,  # Overrides cache
+                knowledge=effective_knowledge,  # Overrides knowledge_sources/embedder_config
+                execution=self._execution,  # Overrides max_iter
+                guardrails=self._guardrails,
+                web=self._web,
+                hooks=self._hooks,
+                autonomy=self.autonomy,
             )
             agents.append(agent)
             

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

@@ -376,7 +376,7 @@ class SubagentDelegator:
         return result
     
     async def _create_agent(self, profile: AgentProfile, task: DelegationTask) -> Any:
-        """Create an agent from profile."""
+        """Create an agent from profile using consolidated params."""
         if self.agent_factory:
             return self.agent_factory(profile.name)
         
@@ -387,14 +387,17 @@ class SubagentDelegator:
             # Filter tools based on profile
             tools = self._get_tools_for_profile(profile)
             
+            # Use consolidated params (protocol-compliant)
+            # output='silent' replaces verbose=False
+            # execution={max_iter: N} replaces max_iter=N
             agent = Agent(
                 name=profile.name,
                 role=profile.name.title(),
                 goal=task.objective,
                 backstory=profile.system_prompt,
                 tools=tools,
-                verbose=False,
-                max_iter=min(task.max_steps, profile.max_steps),
+                output='silent',
+                execution={'max_iter': min(task.max_steps, profile.max_steps)},
             )
             
             return agent

src/praisonai-agents/praisonaiagents/approval.py

@@ -13,10 +13,35 @@ from typing import Dict, Set, Optional, Callable, Any, Literal, List
 from functools import wraps
 from contextvars import ContextVar
 from dataclasses import dataclass, field
-from rich.console import Console
-from rich.panel import Panel
-from rich.text import Text
-from rich.prompt import Confirm
+
+# Lazy import for rich components
+_rich_console = None
+_rich_panel = None
+_rich_confirm = None
+
+def _get_rich_console():
+    """Lazy import rich Console."""
+    global _rich_console
+    if _rich_console is None:
+        from rich.console import Console
+        _rich_console = Console
+    return _rich_console
+
+def _get_rich_panel():
+    """Lazy import rich Panel."""
+    global _rich_panel
+    if _rich_panel is None:
+        from rich.panel import Panel
+        _rich_panel = Panel
+    return _rich_panel
+
+def _get_rich_confirm():
+    """Lazy import rich Confirm."""
+    global _rich_confirm
+    if _rich_confirm is None:
+        from rich.prompt import Confirm
+        _rich_confirm = Confirm
+    return _rich_confirm
 
 # Global registries for approval requirements
 APPROVAL_REQUIRED_TOOLS: Set[str] = set()
@@ -308,6 +333,10 @@ def console_approval_callback(function_name: str, arguments: Dict[str, Any], ris
     
     Displays tool information and prompts user for approval via console.
     """
+    Console = _get_rich_console()
+    Panel = _get_rich_panel()
+    Confirm = _get_rich_confirm()
+    
     console = Console()
     
     # Create risk level styling