fix: address valkey integration issues from code review

- Fix JSON encoding consistency in ValkeyStateStore to prevent type confusion - Add TTL guards for ExpirySet/ExpiryType to prevent cryptic RuntimeError - Add warning for score threshold limitation (cosine distance only) - Fix metadata persistence in KnowledgeDocument (now properly round-tripped) - Improve exception handling - only catch specific 'index not found' errors - Fix URL parameter handling in registry factories (reject with clear error) - Add input validation for vector dimensions and k parameter Co-authored-by: Mervin Praison <[email protected]>
2026-11-08 08:11:07 -10:00 · 2026-05-20 17:45:00 +00:00
318 changed files with 3636 additions and 19274 deletions
@@ -1,158 +0,0 @@
-name: Cross-Repo Integration Tests
-
-on:
-  push:
-    branches: [ main ]
-    paths:
-      - 'src/praisonai/praisonai/integration/**'
-      - 'src/praisonai/tests/integration/test_aiui_*'
-      - 'src/praisonai/pyproject.toml'
-  pull_request:
-    branches: [ main ]
-    paths:
-      - 'src/praisonai/praisonai/integration/**'
-      - 'src/praisonai/tests/integration/test_aiui_*'
-      - 'src/praisonai/pyproject.toml'
-  workflow_dispatch:
-
-jobs:
-  cross-repo-integration:
-    runs-on: ubuntu-latest
-    timeout-minutes: 30
-    
-    steps:
-    - name: Checkout PraisonAI
-      uses: actions/checkout@v4
-      with:
-        path: PraisonAI
-        ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
-        
-    - name: Checkout PraisonAIUI
-      uses: actions/checkout@v4
-      with:
-        repository: MervinPraison/PraisonAIUI
-        path: PraisonAIUI
-        ref: main
-        
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.11'
-        
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        # Install PraisonAI packages in development mode
-        cd PraisonAI/src/praisonai-agents
-        pip install -e .
-        cd ../praisonai
-        pip install -e ".[ui,dev]"
-        # Install PraisonAIUI in development mode
-        cd ../../../PraisonAIUI
-        pip install -e .
-        
-    - name: Verify versions
-      run: |
-        python -c "import praisonaiagents; print(f'praisonaiagents: {praisonaiagents.__version__}')"
-        python -c "import praisonai; print(f'praisonai: {praisonai.__version__}')"
-        python -c "import praisonaiui; print(f'praisonaiui: {praisonaiui.__version__}')"
-        
-    - name: Run PraisonAI integration tests
-      run: |
-        cd PraisonAI/src/praisonai
-        python -m pytest tests/integration/test_aiui_* -v --timeout=300
-        
-    - name: Run PraisonAIUI integration tests
-      run: |
-        cd PraisonAIUI
-        if [ -f tests/integration/test_agentic_roundtrip.py ]; then
-          python -m pytest tests/integration/test_agentic_roundtrip.py -v --timeout=300
-        fi
-        if [ -f tests/test_feature_sdk_backends.py ]; then
-          python -m pytest tests/test_feature_sdk_backends.py -v --timeout=300
-        fi
-        
-    - name: Test Pattern C CLI
-      run: |
-        cd PraisonAI/src/praisonai
-        # Test that ui-gateway command exists
-        python -m praisonai serve --help | grep ui-gateway
-        
-    - name: Test public API exports
-      run: |
-        cd PraisonAI/src/praisonai
-        python -c "from praisonai import run_integrated_gateway; print('✓ run_integrated_gateway imported')"
-        python -c "from praisonai import configure_host; print('✓ configure_host imported')"
-        
-    - name: Integration smoke test
-      env:
-        PRAISONAI_TEST_MODE: "1"
-        PYTHONPATH: ${{ github.workspace }}/PraisonAI/src/praisonai:${{ github.workspace }}/PraisonAI/src/praisonai-agents:${{ github.workspace }}/PraisonAIUI
-      run: |
-        cd PraisonAI/src/praisonai
-        python -c "
-        import sys
-        sys.path.insert(0, '.')
-        from praisonai.integration.host_app import configure_host
-        from praisonai import run_integrated_gateway
-        print('✓ Integration imports successful')
-        
-        # Test configure_host with new parameters
-        try:
-            configure_host(
-                style='dashboard', 
-                context_paths=['AGENTS.md'],
-                title='Test App'
-            )
-            print('✓ configure_host with new parameters works')
-        except Exception as e:
-            print(f'× configure_host failed: {e}')
-            sys.exit(1)
-        "
-        
-  # Optional job with API key for agentic tests (if secret is available)
-  agentic-integration:
-    runs-on: ubuntu-latest
-    timeout-minutes: 30
-    if: ${{ vars.RUN_AGENTIC_TESTS == 'true' }}
-    needs: cross-repo-integration
-    
-    steps:
-    - name: Checkout PraisonAI
-      uses: actions/checkout@v4
-      with:
-        path: PraisonAI
-        ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
-        
-    - name: Checkout PraisonAIUI
-      uses: actions/checkout@v4
-      with:
-        repository: MervinPraison/PraisonAIUI
-        path: PraisonAIUI
-        ref: main
-        
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.11'
-        
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        cd PraisonAI/src/praisonai-agents
-        pip install -e .
-        cd ../praisonai
-        pip install -e ".[ui,dev]"
-        cd ../../../PraisonAIUI
-        pip install -e .
-        
-    - name: Run agentic integration test
-      env:
-        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-      run: |
-        cd PraisonAI/src/praisonai
-        if [ -n "$OPENAI_API_KEY" ]; then
-          python -m pytest tests/integration/test_aiui_host_agentic.py -v --timeout=600
-        else
-          echo "OPENAI_API_KEY not available, skipping agentic tests"
-        fi
@@ -382,7 +382,7 @@ praisonai flow

 Open **http://localhost:7861** — use the **Agent** and **Agent Team** components to create sequential or parallel workflows. Connect Chat Input → Agent Team → Chat Output for instant multi-agent pipelines.

-> 📖 [Full Flow docs](https://docs.praison.ai/docs/concepts/agentflow) — visual agent building, component reference, and deployment
+> 📖 [Full Flow docs](https://docs.praison.ai/docs/concepts/flow) — visual agent building, component reference, and deployment

 ### 8. PraisonAI UI 🤖 (Clean Chat)

@@ -40,7 +40,6 @@ Methods:
 * <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>() -> Optional[bool]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">checkpoints</a>(value: Optional[bool]) -> None</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">clone_for_channel</a>() -> 'Agent'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">close</a>() -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">console</a>() -> Optional[Any]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">context_manager</a>() -> Optional[Any]</code>
@@ -172,7 +171,6 @@ Methods:
 * <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">safe_run</a>(**kwargs) -> ToolResult</code>
 * <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate</a>() -> bool</code>
 * <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_class</a>() -> bool</code>
-* <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_schema_roundtrip</a>() -> bool</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">__call__</a>(*args, **kwargs) -> Any</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">check_availability</a>() -> tuple[bool, str]</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">injected_params</a>() -> Dict[str, Any]</code>
@@ -479,10 +477,8 @@ Methods:
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">edit_skill</a>(name: str, content: str) -> dict</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_allowed_tools</a>(name: str) -> List[str]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_available_skills</a>() -> List[SkillMetadata]</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_available_skills_by_state</a>(state: SkillState) -> List[LoadedSkill]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_instructions</a>(name: str) -> Optional[str]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_skill</a>(name: str) -> Optional[LoadedSkill]</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_skills_diagnostics</a>() -> Dict[str, ValidationResult]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_user_invocable_skills</a>() -> List[LoadedSkill]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">invoke</a>(name: str, raw_args: str = '', session_id: Optional[str] = None, shell_exec: bool = False) -> Optional[str]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">load_resources</a>(name: str) -> bool</code>
@@ -491,7 +487,6 @@ Methods:
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">skill_names</a>() -> List[str]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">skills</a>() -> List[LoadedSkill]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">to_prompt</a>() -> str</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">validate_skill_capabilities</a>(skill_name: str, force_refresh: bool = False) -> ValidationResult</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">write_skill_file</a>(name: str, file_path: str, file_content: str) -> dict</code>
 * <code title="class SkillMetadata">SkillMetadata.<a href="./src/praisonai-agents/praisonaiagents/skills/models.py">from_properties</a>(props: SkillProperties) -> 'SkillMetadata'</code>
 * <code title="class SkillProperties">SkillProperties.<a href="./src/praisonai-agents/praisonaiagents/skills/models.py">to_dict</a>() -> dict</code>
@@ -607,13 +602,6 @@ Methods:
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">set_tool_budget</a>(tool_name: str, max_tokens: int, protected: bool = False) -> None</code>
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">truncate_tool_output</a>(tool_name: str, output: str) -> str</code>

-# UI
-
-Types:
-```python
-from praisonaiagents import A2UI
-```
-
 # Config

 Types:
@@ -768,7 +756,7 @@ Methods:

 Types:
 ```python
-from praisonai import Agent, AgentApp, AgentOS, AnthropicManagedAgent, CloudProvider, DB, Deploy, DeployConfig, DeployType, HostedAgent, HostedAgentConfig, LocalAgent, LocalAgentConfig, LocalManagedAgent, LocalManagedConfig, ManagedAgent, ManagedConfig, PraisonAI, __version__, arun, run, run_integrated_gateway
+from praisonai import Agent, AgentApp, AgentOS, AnthropicManagedAgent, CloudProvider, DB, Deploy, DeployConfig, DeployType, HostedAgent, HostedAgentConfig, LocalAgent, LocalAgentConfig, LocalManagedAgent, LocalManagedConfig, ManagedAgent, ManagedConfig, PraisonAI, __version__
 ```

 # CLI
@@ -873,7 +861,6 @@ Methods:
 * <code title="cli">praisonai doctor packaging <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 skills <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>
@@ -905,24 +892,11 @@ Methods:
 * <code title="cli">praisonai gateway send <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway start <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway status <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
-* <code title="cli">praisonai gateway stop <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway uninstall <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai github triage <a href="./src/praisonai/praisonai/cli/commands/github.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 kanban block <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban boards <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban comment <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban complete <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban create <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban dispatch <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban link <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban list <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban move <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban reclaim <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban show <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban unblock <a href="./src/praisonai/praisonai/cli/commands/kanban.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>
@@ -1080,7 +1054,6 @@ Methods:
 * <code title="cli">praisonai serve status <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve stop <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve ui <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
-* <code title="cli">praisonai serve ui-gateway <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve unified <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai session delete <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
 * <code title="cli">praisonai session export <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=4.6.52" \
+    "praisonai>=4.6.43" \
    "praisonai[chat]" \
    "embedchain[github,youtube]"

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=4.6.52" \
+    "praisonai>=4.6.43" \
    "praisonai[ui]" \
    "praisonai[chat]" \
    "praisonai[realtime]" \
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=4.6.52" \
+    "praisonai>=4.6.43" \
    "praisonai[ui]" \
    "praisonai[crewai]"

@@ -35,7 +35,7 @@ def run_quick_comparison():
        prompt="What is 2+2?",
        iterations=2,
        paths=["openai_sdk", "praisonai_agent"],
-        verbose=True
+        output="verbose"
    )
    
    # Print comparison table
@@ -56,7 +56,7 @@ def run_full_benchmark():
    report = handler.run_full_benchmark(
        prompt="Hi",
        iterations=3,
-        verbose=True
+        output="verbose"
    )
    
    # Print full report
@@ -78,7 +78,7 @@ def run_agent_benchmark():
        prompt="Explain Python in one sentence",
        iterations=3,
        paths=["openai_sdk", "praisonai_agent"],
-        verbose=True
+        output="verbose"
    )
    
    # Show timeline diagrams
@@ -22,7 +22,7 @@ custom_modes = {
 }

 # Create handler with verbose output
-handler = CompareHandler(verbose=True)
+handler = CompareHandler(output="verbose")

 # Example: Manually create comparison results for analysis
 # (useful for testing without API calls)
@@ -22,7 +22,7 @@ modes = parse_modes("basic,tools,planning")
 print(f"\nParsed modes: {modes}")

 # Create compare handler
-handler = CompareHandler(verbose=True)
+handler = CompareHandler(output="verbose")

 # Compare modes (this will run actual agent calls)
 # Uncomment to run:
@@ -15,7 +15,7 @@ agent = Agent(
 # With array [preset, overrides]
 agent_override = Agent(
    instructions="You are a helpful assistant.",
-    memory=MemoryConfig(backend="redis", user_id="custom_user"),
+    memory=["redis", {"user_id": "custom_user"}],
 )

 if __name__ == "__main__":
@@ -2,7 +2,7 @@
 from praisonaiagents import Agent
 from praisonaiagents import AgentFlow, Task
 from praisonaiagents.workflows.workflow_configs import (
-    WorkflowPlanningConfig, WorkflowMemoryConfig,
+    WorkflowOutputConfig, WorkflowPlanningConfig, WorkflowMemoryConfig,
 )

 # Full-featured workflow with all consolidated params
@@ -32,7 +32,7 @@ workflow = AgentFlow(
        ),
    ],
    # Workflow-level consolidated params
-    output="verbose",
+    output=WorkflowOutputConfig(output="verbose", stream=True),
    planning=WorkflowPlanningConfig(enabled=True, reasoning=True),
    memory=WorkflowMemoryConfig(backend="file"),
    # Agent-like params
@@ -15,20 +15,8 @@ workflow = AgentFlow(
    ],
    output="verbose",
    planning=True,
-    variables={"topic": "AI agents"},
 )

 if __name__ == "__main__":
-    import os
-    import sys
-
-    if not os.environ.get("OPENAI_API_KEY"):
-        print("Set OPENAI_API_KEY to run this example.")
-        sys.exit(0)
-    if sys.platform == "win32":
-        try:
-            sys.stdout.reconfigure(encoding="utf-8")
-        except Exception:
-            pass
-    result = workflow.run()
+    result = workflow.run(variables={"topic": "AI agents"})
    print(result)
@@ -24,7 +24,7 @@ if has_api_key:
        input_text="What is 2 + 2?",
        expected_output="4",
        num_iterations=3,  # Run 3 times for statistical significance
-        verbose=True
+        output="verbose"
    )

    # Run evaluation
@@ -42,7 +42,7 @@ else:
        input_text="What is 2 + 2?",
        expected_output="4",
        num_iterations=1,
-        verbose=True
+        output="verbose"
    )
    
    # Test with mock output
@@ -27,7 +27,7 @@ if has_api_key:
        scoring_type="numeric",  # Score 1-10
        threshold=7.0,           # Pass if score >= 7
        num_iterations=2,
-        verbose=True
+        output="verbose"
    )

    # Run evaluation
@@ -45,7 +45,7 @@ if has_api_key:
        agent=agent,
        input_text="Tell me a joke",
        scoring_type="binary",  # Pass or Fail
-        verbose=True
+        output="verbose"
    )

    binary_result = binary_evaluator.run(print_summary=True)
@@ -26,7 +26,7 @@ if has_api_key:
        num_iterations=10,  # Run 10 benchmark iterations
        warmup_runs=2,      # 2 warmup runs before measurement
        track_memory=True,  # Track memory usage
-        verbose=True
+        output="verbose"
    )

    # Run evaluation
@@ -16,19 +16,8 @@ def search_web(query: str) -> str:


 def calculate(expression: str) -> str:
-    """Calculate a math expression safely."""
-    import ast, operator
-    _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-            ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-            ast.Mod: operator.mod, ast.Pow: operator.pow,
-            ast.USub: operator.neg, ast.UAdd: operator.pos}
-    def _ev(n):
-        if isinstance(n, ast.Expression): return _ev(n.body)
-        if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-        if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-        if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-        raise ValueError(f"Unsupported: {ast.dump(n)}")
-    return str(_ev(ast.parse(expression, mode="eval")))
+    """Calculate a math expression."""
+    return str(eval(expression))


 # Check if we have an API key
@@ -48,7 +37,7 @@ if has_api_key:
        input_text="Search for the weather in Paris and calculate 25 * 4",
        expected_tools=["search_web", "calculate"],  # Tools that should be called
        forbidden_tools=["delete_file"],  # Tools that should NOT be called
-        verbose=True
+        output="verbose"
    )

    # Run evaluation
@@ -70,7 +59,7 @@ evaluator = ReliabilityEvaluator(
    agent=mock_agent,
    expected_tools=["search_web", "calculate"],  # Tools that should be called
    forbidden_tools=["delete_file"],  # Tools that should NOT be called
-    verbose=True
+    output="verbose"
 )

 result2 = evaluator.evaluate_tool_calls(
@@ -184,7 +184,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about codes, provide the EXACT code from the documents.
 Be concise but accurate.""",
            knowledge=[temp_dir],
-            memory={"user_id": "compression_demo_user"},
+            user_id="compression_demo_user",
            output="verbose",
        )
        
@@ -164,7 +164,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about department codes, provide the EXACT code from the documents.
 Be specific about which department you're referring to.""",
            knowledge=[temp_dir],
-            memory={"user_id": "hierarchy_demo_user"},
+            user_id="hierarchy_demo_user",
            output="verbose",
        )
        
@@ -124,7 +124,7 @@ def main():
            # Show strategy selection for different corpus sizes
            print(f"\nStrategy selection by corpus size:")
            for size in [100, 1000, 10000, 50000, 100000]:
-                strategy = select_strategy(CorpusStats(file_count=max(1, size // 1000), total_tokens=size))
+                strategy = select_strategy(corpus_tokens=size)
                print(f"  {size:>6} tokens -> {strategy.value}")
                
        except ImportError as e:
@@ -142,7 +142,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about verification codes, provide the EXACT code from the documents.
 If information is not in the context, say 'Information not found in documents.'""",
            knowledge=[temp_dir],
-            memory={"user_id": "strategy_demo_user"},
+            user_id="strategy_demo_user",
            output="verbose",  # Use new consolidated param
        )
        
@@ -65,7 +65,7 @@ def main():
            name="PolicyExpert",
            instructions="You are a company policy expert. Answer questions based ONLY on the provided knowledge. Quote exact values when asked about codes or numbers.",
            knowledge=[temp_dir],  # Add directory with documents
-            memory={"user_id": "example_user"},
+            user_id="example_user",  # Required for mem0 backend
        )
        
        print("\n1. Testing Agent.chat() with context-required question:")
@@ -50,7 +50,7 @@ def main():
            name="HR_Assistant_User1",
            instructions="You are an HR assistant.",
            knowledge=[temp_dir],
-            memory={"user_id": "user_alice"},
+            user_id="user_alice",  # Knowledge scoped to Alice
        )
        
        response = user1_agent.chat("What is the policy on remote work?")
@@ -80,7 +80,7 @@ def main():
            name="Personal_HR_Bot",
            instructions="You are a personal HR assistant.",
            knowledge=[temp_dir],
-            memory={"user_id": "user_bob"},
+            user_id="user_bob",
            agent_id="hr_bot_v2",
        )
        
@@ -49,7 +49,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
            name="AgentA_Ingester",
            instructions="You ingest and store knowledge.",
            knowledge=[temp_dir],
-            memory={"user_id": "user_alice"},
+            user_id="user_alice",  # Scoped to Alice
            output="silent",
        )
        
@@ -103,7 +103,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
            name="AgentB_Querier",
            instructions="Answer questions based on the provided knowledge. If you don't have the information, say so.",
            knowledge=[temp_dir],
-            memory={"user_id": "user_alice"},
+            user_id="user_alice",  # Same scope as Agent A
            output="silent",
        )
        
@@ -17,10 +17,8 @@ from praisonaiagents import Agent
 agent = Agent(
    name="CockroachDB Agent",
    instructions="You are a helpful assistant.",
-    memory={
-        "session_id": "crdb-demo-session",
-        "db": {"database_url": os.getenv("COCKROACHDB_URL")},
-    },
+    db={"database_url": os.getenv("COCKROACHDB_URL")},
+    session_id="crdb-demo-session",
 )

 result = agent.start("What is distributed SQL? Explain in 2 sentences.")
@@ -18,10 +18,8 @@ from praisonaiagents import Agent
 agent = Agent(
    name="Neon Agent",
    instructions="You are a helpful assistant.",
-    memory={
-        "session_id": "neon-demo-session",
-        "db": {"database_url": os.getenv("NEON_DATABASE_URL")},
-    },
+    db={"database_url": os.getenv("NEON_DATABASE_URL")},
+    session_id="neon-demo-session",
 )

 # Option 2: Direct URL
@@ -20,10 +20,8 @@ from praisonaiagents import Agent
 agent = Agent(
    name="Supabase Agent",
    instructions="You are a helpful assistant.",
-    memory={
-        "session_id": "supabase-demo-session",
-        "db": {"database_url": os.getenv("SUPABASE_DATABASE_URL")},
-    },
+    db={"database_url": os.getenv("SUPABASE_DATABASE_URL")},
+    session_id="supabase-demo-session",
 )

 result = agent.start("What is Supabase? Explain in 2 sentences.")
@@ -18,13 +18,11 @@ from praisonaiagents import Agent
 agent = Agent(
    name="Turso Agent",
    instructions="You are a helpful assistant.",
-    memory={
-        "session_id": "turso-demo-session",
-        "db": {
-            "database_url": os.getenv("TURSO_DATABASE_URL"),
-            "auth_token": os.getenv("TURSO_AUTH_TOKEN"),
-        },
+    db={
+        "database_url": os.getenv("TURSO_DATABASE_URL"),
+        "auth_token": os.getenv("TURSO_AUTH_TOKEN"),
    },
+    session_id="turso-demo-session",
 )

 result = agent.start("What is edge computing? Explain in 2 sentences.")
@@ -19,43 +19,20 @@ import os

 def calculate(expression: str) -> str:
    """
-    A simple calculator tool that evaluates mathematical expressions safely.
-
+    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
    """
-    import ast
-    import operator
-
-    _OPS = {
-        ast.Add: operator.add,
-        ast.Sub: operator.sub,
-        ast.Mult: operator.mul,
-        ast.Div: operator.truediv,
-        ast.FloorDiv: operator.floordiv,
-        ast.Mod: operator.mod,
-        ast.Pow: operator.pow,
-        ast.USub: operator.neg,
-        ast.UAdd: operator.pos,
-    }
-
-    def _safe_eval(node):
-        if isinstance(node, ast.Expression):
-            return _safe_eval(node.body)
-        if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
-            return node.value
-        if isinstance(node, ast.UnaryOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_safe_eval(node.operand))
-        if isinstance(node, ast.BinOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_safe_eval(node.left), _safe_eval(node.right))
-        raise ValueError(f"Unsupported expression: {ast.dump(node)}")
-
    try:
-        tree = ast.parse(expression, mode="eval")
-        result = _safe_eval(tree)
+        # 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)}"
@@ -24,18 +24,12 @@ def main():
    def calculate(expression: str) -> str:
        """Safely evaluate a mathematical expression."""
        try:
-            import ast, operator
-            _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                    ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                    ast.Mod: operator.mod, ast.Pow: operator.pow,
-                    ast.USub: operator.neg, ast.UAdd: operator.pos}
-            def _ev(n):
-                if isinstance(n, ast.Expression): return _ev(n.body)
-                if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-                if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-                if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-                raise ValueError(f"Unsupported: {ast.dump(n)}")
-            return f"Result: {_ev(ast.parse(expression, mode='eval'))}"
+            # Only allow safe math operations
+            allowed = set("0123456789+-*/.(). ")
+            if not all(c in allowed for c in expression):
+                return "Error: Invalid characters in expression"
+            result = eval(expression)  # Safe due to character filtering
+            return f"Result: {result}"
        except Exception as e:
            return f"Error: {e}"
    
@@ -10,7 +10,8 @@ from praisonaiagents import Agent
 agent = Agent(
    name="FlexBot",
    instructions="You are a helpful assistant.",
-    llm={"model": "gpt-4o-mini", "configurable": True},
+    llm="gpt-4o-mini",
+    llm_config={"configurable": True}
 )

 if __name__ == "__main__":
@@ -21,7 +21,7 @@ agent = Agent(
    name="ContextBot",
    instructions="You help show context information.",
    tools=[show_context],
-    memory={"session_id": "my-session-123"},
+    session_id="my-session-123"
 )

 if __name__ == "__main__":
@@ -50,12 +50,10 @@ print("=== Qdrant Knowledge Store Demo (Agent-First) ===")
 agent = Agent(
    name="KnowledgeAssistant",
    instructions="You are a helpful assistant with access to technical documentation.",
+    knowledge=[doc_path],
    knowledge={
-        "sources": [doc_path],
-        "vector_store": {
-            "provider": "qdrant",
-            "url": "http://localhost:6333",
-        },
+        "vector_store": "qdrant",
+        "url": "http://localhost:6333"
    }
 )

@@ -57,8 +57,8 @@ def main():
    agent = Agent(
        name="SecureAgent",
        instructions="You are a file management assistant.",
+        policy=engine
    )
-    agent.policy = engine
    
    print("\n--- Agent with Policy Enforcement Created ---")
    print(f"Agent: {agent.name}")
@@ -21,36 +21,9 @@ def search_web(query: str) -> str:
    return f"Search results for: {query}"

 def calculate(expression: str) -> str:
-    """Calculate a mathematical expression safely."""
-    import ast
-    import operator
-
-    _OPS = {
-        ast.Add: operator.add,
-        ast.Sub: operator.sub,
-        ast.Mult: operator.mul,
-        ast.Div: operator.truediv,
-        ast.FloorDiv: operator.floordiv,
-        ast.Mod: operator.mod,
-        ast.Pow: operator.pow,
-        ast.USub: operator.neg,
-        ast.UAdd: operator.pos,
-    }
-
-    def _eval(node):
-        if isinstance(node, ast.Expression):
-            return _eval(node.body)
-        if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
-            return node.value
-        if isinstance(node, ast.UnaryOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_eval(node.operand))
-        if isinstance(node, ast.BinOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_eval(node.left), _eval(node.right))
-        raise ValueError(f"Unsupported expression: {ast.dump(node)}")
-
+    """Calculate a mathematical expression."""
    try:
-        tree = ast.parse(expression, mode="eval")
-        return f"Result: {_eval(tree)}"
+        return f"Result: {eval(expression)}"
    except Exception:
        return "Invalid expression"

@@ -1,27 +0,0 @@
-#!/usr/bin/env python3
-"""Minimal A2UI example — agent + send_a2ui_messages tool.
-
-Install:
-    pip install praisonaiagents[a2ui]
-
-Run:
-    python send_surface.py
-
-For live rendering, use PraisonAIUI with AG-UI (/agui) — see integrate-a2ui-frontend docs.
-"""
-
-from praisonaiagents import Agent
-from praisonaiagents.tools.a2ui_tools import send_a2ui_messages
-
-agent = Agent(
-    name="ui-assistant",
-    instructions=(
-        "When asked for UI, call send_a2ui_messages with A2UI v0.9 JSON "
-        "(createSurface on surface id 'main')."
-    ),
-    tools=[send_a2ui_messages],
-)
-
-if __name__ == "__main__":
-    result = agent.start("Show a simple welcome card on surface main")
-    print(result)
@@ -75,7 +75,7 @@ class ContinuousCameraMonitor:
            agents=[self.vision_agent],
            tasks=[task],
            process="sequential",
-            output="silent",  # Reduced verbosity for continuous monitoring
+            verbose=0  # Reduced verbosity for continuous monitoring
        )
        
        return agents.start()
@@ -8,7 +8,7 @@ Docs: https://docs.praison.ai/cli/git-integration
 from praisonai.cli.features import GitIntegrationHandler

 # Initialize
-handler = GitIntegrationHandler(verbose=True)
+handler = GitIntegrationHandler(output="verbose")
 git = handler.initialize(repo_path=".")

 # Check if it's a git repo
@@ -8,7 +8,7 @@ Docs: https://docs.praison.ai/cli/repo-map
 from praisonai.cli.features import RepoMapHandler

 # Initialize with current directory
-handler = RepoMapHandler(verbose=True)
+handler = RepoMapHandler(output="verbose")
 repo_map = handler.initialize(root=".")

 # Get the repository map
@@ -9,7 +9,7 @@ from praisonai.cli.features import SandboxExecutorHandler
 from praisonai.cli.features.sandbox_executor import SandboxMode

 # Initialize with basic sandbox
-handler = SandboxExecutorHandler(verbose=True)
+handler = SandboxExecutorHandler(output="verbose")
 sandbox = handler.initialize(mode="basic")

 print(f"Sandbox mode: {handler.get_mode()}")
@@ -25,7 +25,8 @@ pdf_agent = Agent(
    goal="Help users understand and extract information from PDF documents",
    backstory="You are an expert at reading, analyzing, and answering questions about PDF documents. You provide accurate, detailed answers based on the document content.",
    instructions="Read the provided PDF document and answer questions about its content. Be specific and cite relevant sections when possible.",
-    knowledge={**config, "sources": ["document.pdf"]},  # Replace with your PDF path
+    knowledge=["document.pdf"],  # Replace with your PDF file path
+    knowledge=config,
    reflection=True,
    
    
@@ -1,8 +1,6 @@
-import tempfile
-import os
-import sys
-import logging
 from praisonaiagents import Agent, Task, AgentTeam
+import logging
+import os

 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -19,20 +17,14 @@ config = {
    }
 }

-# Create sample file in system temp directory
-_sample_path = os.path.join(tempfile.gettempdir(), "sample_knowledge.txt")
-if not os.path.exists(_sample_path):
-    with open(_sample_path, "w", encoding="utf-8") as f:
-        f.write("Mervin Praison is the creator of PraisonAI.\n")
-
 # Create an agent with knowledge capabilities
 knowledge_agent = Agent(
    name="KnowledgeAgent",
    role="Information Specialist",
    goal="Store and retrieve knowledge efficiently",
    backstory="Expert in managing and utilizing stored knowledge",
-    knowledge={**config, "sources": [_sample_path]},
-)
+    knowledge=["sample.pdf"],
+    knowledge=config)

 # Define a task for the agent
 knowledge_task = Task(
@@ -47,16 +39,8 @@ agents = AgentTeam(
    agents=[knowledge_agent],
    tasks=[knowledge_task],
    process="sequential",
+    user_id="user1"
 )

-if __name__ == "__main__":
-    if not os.environ.get("OPENAI_API_KEY"):
-        print("Set OPENAI_API_KEY to run this example.")
-        sys.exit(0)
-    if sys.platform == "win32":
-        try:
-            sys.stdout.reconfigure(encoding="utf-8")
-        except Exception:
-            pass
-    result = agents.start()
-    print(result)
+# Start execution
+result = agents.start()
@@ -138,7 +138,8 @@ def main():
    agent = Agent(
        name="AI Research Assistant",
        instructions="You are an AI research assistant. Use your knowledge base to answer questions about artificial intelligence topics.",
-        knowledge={**rerank_config, "sources": sample_documents},
+        knowledge=sample_documents,  # Will use default knowledge config
+        knowledge=rerank_config,  # Use reranking config
        llm="gpt-4o-mini"
    )
    
@@ -32,12 +32,8 @@ rag_task = Task(
 agents = AgentTeam(
    agents=[rag_agent],
    tasks=[rag_task],
+    user_id="user1"
 )

-if __name__ == "__main__":
-    import os
-    import sys
-    if not os.environ.get("OPENAI_API_KEY"):
-        print("Set OPENAI_API_KEY to run this example.")
-        sys.exit(0)
-    agents.start()
+# Start Agents
+agents.start()
@@ -16,7 +16,7 @@ agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    process="workflow",
-    execution={"max_iter": 30},
+    max_iter=30
 )

 agents.start()
@@ -61,6 +61,7 @@ def example_with_wrappers():
        role="Data Analyst",
        goal="Analyze data and provide insights",
        llm="gpt-4o-mini",
+        request_id="request_2"  # Unique request ID
    )
    
    # Operations are automatically tracked
@@ -49,18 +49,7 @@ def calculate_with_tracking(expression: str) -> str:
    time.sleep(0.05)
    
    try:
-        import ast, operator
-        _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                ast.Mod: operator.mod, ast.Pow: operator.pow,
-                ast.USub: operator.neg, ast.UAdd: operator.pos}
-        def _ev(n):
-            if isinstance(n, ast.Expression): return _ev(n.body)
-            if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-            if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-            if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-            raise ValueError(f"Unsupported: {ast.dump(n)}")
-        result = _ev(ast.parse(expression, mode="eval"))
+        result = eval(expression)
        return f"Result: {result}"
    except Exception as e:
        return f"Error: {str(e)}"
@@ -132,8 +132,7 @@ agents = AgentTeam(
    agents=[qa_generator, total_questions_evaluator, cot_generator, upload_to_huggingface],
    tasks=[generate_task, evaluate_total_questions_task, generate_cot_task, upload_to_huggingface_task],
    process="workflow",
-    execution={"max_iter": 30},
-    output="minimal",
+    max_iter=30, output="minimal"
 )

 agents.start()
@@ -18,7 +18,7 @@ openai_profile = AuthProfile(
    provider="openai",
    api_key=os.environ.get("OPENAI_API_KEY", "sk-..."),
    priority=1,  # Highest priority
-    rate_limit_rpm=100,
+    rate_limit=100,
 )

 anthropic_profile = AuthProfile(
@@ -26,7 +26,7 @@ anthropic_profile = AuthProfile(
    provider="anthropic",
    api_key=os.environ.get("ANTHROPIC_API_KEY", "sk-ant-..."),
    priority=2,  # Second priority
-    rate_limit_rpm=50,
+    rate_limit=50,
 )

 groq_profile = AuthProfile(
@@ -34,7 +34,7 @@ groq_profile = AuthProfile(
    provider="groq",
    api_key=os.environ.get("GROQ_API_KEY", "gsk-..."),
    priority=3,  # Third priority (fast but limited)
-    rate_limit_rpm=30,
+    rate_limit=30,
 )

 # Configure failover behavior
@@ -5,7 +5,7 @@ import os
 def main():
    # Initialize memory config
    memory_config = {
-        "backend": "sqlite",
+        "provider": "rag",
        "use_embedding": True,
        "storage": {
            "type": "sqlite",
@@ -105,13 +105,13 @@ def main():
        agents=[researcher, retriever],
        tasks=[store_task, verify_task, query_task, query_both_task],  # Use same verbose level as memory
        memory={
+            "provider": "rag",
            "embedder": {
                "provider": "openai",
                "config": {
                    "model": "text-embedding-3-small"
                }
-            },
-            "config": {"provider": "sqlite"},
+            }
        }
    )

@@ -6,7 +6,6 @@ built-in validation and safety mechanisms for secure agent operations.
 """

 from praisonaiagents import Agent, Task, AgentTeam
-from praisonaiagents.config.feature_configs import GuardrailConfig
 from praisonaiagents.tools import internet_search

 print("=== Production Guardrails Patterns Example ===\n")
@@ -39,8 +38,8 @@ production_agent = Agent(
    goal="Generate safe, compliant content for production use",
    backstory="Production-ready agent with built-in safety and compliance checks",
    tools=[internet_search],
-    guardrails=GuardrailConfig(validator=content_guardrail, max_retries=3),
-)
+    guardrails=content_guardrail,
+    max_retries=3)

 # Create secure task with validation
 secure_task = Task(
@@ -40,7 +40,7 @@ agent = Agent(
        "execute_command"
    ],
    memory=True,
-    web=True,
+    web_search=True,
    auto_approve_tools=True,
 )

@@ -36,18 +36,10 @@ def calculate(expression: str) -> dict:
        expression: Math expression (e.g., "2 + 2 * 3")
    """
    try:
-        import ast, operator
-        _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                ast.Mod: operator.mod, ast.Pow: operator.pow,
-                ast.USub: operator.neg, ast.UAdd: operator.pos}
-        def _ev(n):
-            if isinstance(n, ast.Expression): return _ev(n.body)
-            if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-            if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-            if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-            raise ValueError(f"Unsupported: {ast.dump(n)}")
-        return {"expression": expression, "result": _ev(ast.parse(expression, mode="eval"))}
+        allowed = set("0123456789+-*/.() ")
+        if not all(c in allowed for c in expression):
+            return {"error": "Invalid characters"}
+        return {"expression": expression, "result": eval(expression)}
    except Exception as e:
        return {"error": str(e)}

@@ -47,18 +47,12 @@ def calculate(expression: str) -> Dict[str, Any]:
        The result of the calculation
    """
    try:
-        import ast, operator
-        _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                ast.Mod: operator.mod, ast.Pow: operator.pow,
-                ast.USub: operator.neg, ast.UAdd: operator.pos}
-        def _ev(n):
-            if isinstance(n, ast.Expression): return _ev(n.body)
-            if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-            if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-            if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-            raise ValueError(f"Unsupported: {ast.dump(n)}")
-        result = _ev(ast.parse(expression, mode="eval"))
+        # 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)  # Use safer alternative in production
        return {"expression": expression, "result": result}
    except Exception as e:
        return {"error": str(e)}
@@ -49,18 +49,13 @@ def calculate(expression: str) -> Dict[str, Any]:
        The result of the calculation
    """
    try:
-        import ast, operator
-        _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                ast.Mod: operator.mod, ast.Pow: operator.pow,
-                ast.USub: operator.neg, ast.UAdd: operator.pos}
-        def _ev(n):
-            if isinstance(n, ast.Expression): return _ev(n.body)
-            if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-            if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-            if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-            raise ValueError(f"Unsupported: {ast.dump(n)}")
-        result = _ev(ast.parse(expression, mode="eval"))
+        # Safe evaluation of mathematical expressions
+        # In production, use a proper math parser
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return {"error": "Invalid characters in expression"}
+        
+        result = eval(expression)  # Note: Use safer alternative in production
        return {"expression": expression, "result": result}
    except Exception as e:
        return {"error": str(e)}
@@ -12,8 +12,8 @@ print("=== Advanced Graph Memory Integration Example ===\n")

 # Configure simple graph memory
 memory_config = {
+    "provider": "mem0",
    "config": {
-        "provider": "mem0",
        "vector_store": {
            "provider": "chroma",
            "config": {
@@ -60,8 +60,8 @@ query_task = Task(
 agents_system = AgentTeam(
    agents=[knowledge_agent],
    tasks=[build_task, query_task],
-    memory=memory_config,
-    output="verbose",
+    memory=True,
+    memory=memory_config, output="verbose"
 )

 print("Starting graph memory demonstration...")
@@ -12,8 +12,8 @@ print("=== Advanced Memory Search and Optimization Example ===\n")

 # Configure optimized memory settings
 memory_config = {
+    "provider": "mem0",
    "config": {
-        "provider": "mem0",
        "vector_store": {
            "provider": "chroma",
            "config": {
@@ -61,8 +61,8 @@ search_task = Task(
 agents_system = AgentTeam(
    agents=[research_agent],
    tasks=[build_memory_task, search_task],
-    memory=memory_config,
-    output="verbose",
+    memory=True,
+    memory=memory_config, output="verbose"
 )

 print("Starting memory search optimization demonstration...")
@@ -86,13 +86,13 @@ def main():
        agents=[researcher, retriever],
        tasks=[store_task, retrieve_task],
        memory={
+            "provider": "rag",
            "embedder": {
                "provider": "gemini",
                "config": {
                    "model": "text-embedding-004",
                }
-            },
-            "config": {"provider": "sqlite"},
+            }
        }
    )
    
@@ -152,7 +152,8 @@ def main():
    agents_system = AgentTeam(
        agents=[researcher],
        tasks=[build_knowledge_task, query_knowledge_task, expand_knowledge_task],
-        memory=memory_config,
+        memory=True,
+        memory=memory_config
    )
    
    print("\n🚀 Starting graph memory demonstration...")
@@ -30,7 +30,7 @@ def main():
    try:
        # Create memory instance
        memory = Memory(config={
-            "backend": "sqlite",
+            "provider": "rag",
            "use_embedding": False,
            "agent_name": "demo_agent",  # Used in trace events
        })
@@ -32,8 +32,9 @@ def init_agent():
    return Agent(
        name="Knowledge Agent",
        instructions="You answer questions based on the provided knowledge.",
-        knowledge={**config, "sources": ["kag-research-paper.pdf"]},
-        memory={"user_id": "user1"},
+        knowledge=["kag-research-paper.pdf"],
+        knowledge=config,
+        user_id="user1",
        llm="deepseek-r1"
    )

@@ -30,12 +30,9 @@ config = {
 agent = Agent(
    name="Knowledge Agent",
    instructions="You answer questions based on the provided knowledge.",
-    knowledge={
-        "sources": ["kag-research-paper.pdf"],
-        "vector_store": config["vector_store"],
-        "embedder": config["embedder"],
-    },
-    memory={"user_id": "user1"},
+    knowledge=["kag-research-paper.pdf"], # Indexing
+    knowledge=config,
+    user_id="user1",
    llm="deepseek-r1"
 )

@@ -31,9 +31,10 @@ llm_config = {
 agent = Agent(
    instructions="You are a helpful Assistant specialized in scientific explanations. "
                "Provide clear, accurate, and engaging responses.",
-    llm=llm_config,
-    output={"markdown": True},
-    reflection=True,
+    llm=llm_config,                  # Pass the detailed configuration
+    # Enable detailed output
+    markdown=True,                   # Format responses in markdown
+    reflection=True               # Enable self-reflection
 )

 # Test the agent
@@ -189,8 +189,8 @@ def main():
    business_system = AgentTeam(
        agents=[data_manager, knowledge_curator, business_analyst, customer_service],
        tasks=business_tasks,
-        memory=mongodb_memory_config,
-        output="verbose",
+        memory=True,
+        memory=mongodb_memory_config, output="verbose"
    )
    
    # Execute the comprehensive business pipeline
@@ -55,10 +55,8 @@ 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={
-            **mongodb_knowledge_config,
-            "sources": [os.path.join(os.path.dirname(__file__), "llms.md")],
-        },
+        knowledge=mongodb_knowledge_config,
+        knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
        memory=True,
        llm="gpt-4o-mini"
    )
@@ -28,8 +28,8 @@ if not os.environ.get("OPENAI_API_KEY"):
 def main():
    # MongoDB memory configuration
    mongodb_memory_config = {
+        "provider": "mongodb",
        "config": {
-            "provider": "mongodb",
            "connection_string": "mongodb://localhost:27017/",  # Replace with your MongoDB connection string
            "database": "praisonai_memory",
            "use_vector_search": True,  # Enable Atlas Vector Search (requires MongoDB Atlas)
@@ -93,8 +93,8 @@ def main():
    research_system = AgentTeam(
        agents=[research_agent],
        tasks=research_tasks,
-        memory=mongodb_memory_config,
-        output="verbose",
+        memory=True,
+        memory=mongodb_memory_config, output="verbose"
    )
    
    # Execute the research pipeline
@@ -50,7 +50,7 @@ agent = Agent(
    name="TechSupport",
    instructions="You are a technical support assistant.",
    knowledge=[sample_doc],
-    memory={"user_id": "comparison_user"},
+    user_id="comparison_user",
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -12,7 +12,6 @@ Environment:
 """

 import os
-import tempfile
 from praisonaiagents import Agent, AutoRagAgent

 # Ensure API key is set
@@ -21,8 +20,8 @@ if not os.environ.get("OPENAI_API_KEY"):
    exit(1)

 # Create a sample document for testing
-sample_doc = os.path.join(tempfile.gettempdir(), "sample_doc.txt")
-with open(sample_doc, "w", encoding="utf-8") as f:
+sample_doc = "/tmp/sample_doc.txt"
+with open(sample_doc, "w") as f:
    f.write("""
 PraisonAI Framework Documentation

@@ -45,7 +44,7 @@ agent = Agent(
    name="DocBot",
    instructions="You are a helpful documentation assistant. Answer questions based on the provided context.",
    knowledge=[sample_doc],
-    memory={"user_id": "example_user"},
+    user_id="example_user",  # Required for RAG retrieval
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -38,7 +38,7 @@ agent = Agent(
    name="PolicyBot",
    instructions="You are a customer service assistant.",
    knowledge=[sample_doc],
-    memory={"user_id": "policy_user"},
+    user_id="policy_user",
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -44,7 +44,7 @@ agent = Agent(
    name="MLExpert",
    instructions="You are a machine learning expert. Answer questions based on the provided context.",
    knowledge=[sample_doc],
-    memory={"user_id": "rag_user"},
+    user_id="rag_user",
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -59,7 +59,7 @@ agent = Agent(
    name="APIDocBot",
    instructions="You are an API documentation assistant.",
    knowledge=[sample_doc],
-    memory={"user_id": "hybrid_user"},
+    user_id="hybrid_user",
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -44,7 +44,7 @@ agent = Agent(
    name="ResearchBot",
    instructions="You are a research assistant. Always cite your sources.",
    knowledge=[doc1, doc2],
-    memory={"user_id": "citation_user"},
+    user_id="citation_user",
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -28,7 +28,7 @@ def example_basic_persistence():
    agent = Agent(
        name="Assistant",
        instructions="You are a helpful assistant. Be very brief.",
-        memory={"session_id": session_id},
+        session_id=session_id,
        output="minimal",
    )
    
@@ -59,7 +59,7 @@ def example_session_restoration():
    agent1 = Agent(
        name="MemoryBot",
        instructions="You are a helpful assistant. Be very brief.",
-        memory={"session_id": session_id},
+        session_id=session_id,
        output="minimal",
    )
    
@@ -72,7 +72,7 @@ def example_session_restoration():
    agent2 = Agent(
        name="MemoryBot",
        instructions="You are a helpful assistant. Be very brief.",
-        memory={"session_id": session_id},
+        session_id=session_id,
        output="minimal",
    )
    
@@ -172,7 +172,7 @@ print("="*70)
 # Create sessions for different users working on the same project
 reviewer_session = Session(
    session_id="peer_review_001",
-    user_id="user_reviewer_01",
+    user_id="user_reviewer_01",  # Different user
    storage_path=session_dir
 )

@@ -14,12 +14,13 @@ def main():
    
    # Initialize memory with quality-based configuration
    memory_config = {
-        "backend": "sqlite",
-        "user_id": "quality_demo_user",
+        "provider": "rag",
+        "use_embedding": True,
+        "rag_db_path": "quality_memory_db"
    }
    
    memory = Memory(config=memory_config, verbose=5)
-    print(f"📦 Initialized memory system with backend: {memory_config['backend']}")
+    print(f"📦 Initialized memory system with provider: {memory_config['provider']}")
    
    # Create agent with memory
    agent = Agent(
@@ -27,6 +28,7 @@ def main():
        role="Information Quality Specialist",
        instructions="Store and retrieve information with quality assessment",
        memory=memory,
+        user_id="quality_demo_user"
    )
    
    print(f"🤖 Created agent: {agent.name}")
@@ -15,7 +15,7 @@ def main():
    # Create a session with persistent storage
    session = Session(
        session_id="demo_chat_001",
-        user_id="demo_user",
+        user_id="demo_user"
    )
    
    print(f"📝 Created session: {session}")
@@ -88,8 +88,9 @@ def main():
    workflow = AgentTeam(
        agents=[researcher, analyzer, writer],
        tasks=[research_task, analysis_task, report_task],
-        memory={"user_id": "research_project_001"},
+        memory=True,
        process="workflow",
+        user_id="research_project_001",
    )
    
    print(f"🤖 Created workflow with {len(workflow.agents)} agents and {len(workflow.tasks)} tasks")
@@ -14,7 +14,7 @@ def main():
        goal="Find information about a topic",
        backstory="You are an expert researcher who finds comprehensive information.",
        llm="gpt-4o-mini",  # Use a cost-effective model for examples
-        output={"metrics": True}  # Token metrics via consolidated output config
+        metrics=True  # 🎯 SIMPLIFIED: Just add this parameter!
    )
    
    # Create a simple task
@@ -14,7 +14,7 @@ def main():
        goal="Analyze data and provide insights",
        backstory="You are an experienced data analyst with expertise in market research.",
        llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable metrics tracking
    )
    
    writer_agent = Agent(
@@ -23,7 +23,7 @@ def main():
        goal="Create well-structured content",
        backstory="You are a skilled technical writer who creates clear, engaging content.",
        llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable metrics tracking
    )
    
    # Create multiple tasks
@@ -14,7 +14,7 @@ def main():
        goal="Conduct comprehensive market research",
        backstory="You are a seasoned market researcher with 10+ years of experience.",
        llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable automatic metrics tracking
    )
    
    strategy_agent = Agent(
@@ -23,7 +23,7 @@ def main():
        goal="Develop actionable business strategies",
        backstory="You are a strategic consultant who translates research into business plans.",
        llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable automatic metrics tracking
    )
    
    # Create tasks that will generate substantial token usage
@@ -22,8 +22,7 @@ rag_agent = Agent(
    
    You can use the internet_search function to search the web when needed.""",
    llm="gpt-4o",
-    output={"markdown": True},
-)
+    markdown=True)

 if __name__ == "__main__":
    print("🤖 Thai Recipe RAG Agent is ready!")
@@ -32,8 +32,9 @@ def init_agent():
    return Agent(
        name="Knowledge Agent",
        instructions="You answer questions based on the provided knowledge.",
-        knowledge={**config, "sources": ["kag-research-paper.pdf"]},
-        memory={"user_id": "user1"},
+        knowledge=["kag-research-paper.pdf"],
+        knowledge=config,
+        user_id="user1",
        llm="deepseek-r1"
    )

@@ -264,12 +264,13 @@ agents = AgentTeam(
    tasks=[tool_query_task, pentester_analysis_task, ciso_review_task],
    process="hierarchical",
    manager_llm="gpt-4o-mini",
+    memory=True,  # Enable shared memory for domain context
    memory={
-        "backend": "sqlite",
+        "provider": "rag",
        "config": {
            "collection_name": f"{domain.replace('.', '_')}_security_analysis"
-        },
-    },
+        }
+    }
 )

 # Main Execution Workflow
@@ -5,7 +5,7 @@ Demonstrates using should_run to conditionally execute agent steps
 based on input or previous results.
 """

-from praisonaiagents import Agent, AgentFlow, Task
+from praisonaiagents import Agent, Workflow, Task

 # Condition functions
 def is_technical(ctx) -> bool:
@@ -53,7 +53,7 @@ workflow = AgentFlow(
        # Step 4: Function - formats output
        format_output
    ],
-    llm="gpt-4o-mini",  # Used for action-based steps
+    default_llm="gpt-4o-mini"  # Used for action-based steps
 )

 if __name__ == "__main__":
@@ -207,7 +207,7 @@ def api_augmented_rag():
        instructions="""You are a financial assistant with access to market APIs.
        Provide accurate financial information and context.
        Always note that prices are subject to change.""",
-        memory={"user_id": "api_demo"},
+        user_id="api_demo"
    )
    
    queries = [
@@ -51,15 +51,13 @@ print("=" * 60)
 agent = Agent(
    name="Knowledge Agent",
    instructions="You are a helpful assistant that answers questions based on the provided knowledge. Be concise.",
-    knowledge={
-        "sources": [".praison/test_docs/overview.txt"],
-        "config": {
-            "policy": "always",
-            "top_k": 3,
-            "citations": True,
-            "max_context_tokens": 2000,
-        },
-    },
+    knowledge=[".praison/test_docs/overview.txt"],
+    retrieval_config={
+        "policy": "always",      # Always retrieve for this demo
+        "top_k": 3,              # Get top 3 chunks
+        "citations": True,       # Include citations
+        "max_context_tokens": 2000,
+    }
 )

 # Test 1: Basic chat with retrieval
@@ -60,7 +60,7 @@ print("=" * 50)
 agent_with_discovery = Agent(
    name="Multi-Skill Agent",
    instructions="You are a versatile assistant with multiple skills.",
-    skills=["./"],  # Scan current directory for skill subdirectories
+    skills_dirs=["./"],  # Scan current directory for skill subdirectories
 )

 if agent_with_discovery.skill_manager:
@@ -1,222 +0,0 @@
-# Multi-Channel Bot Deployment Guide
-
-This guide explains how to set up multiple bots on the same platform (e.g., multiple Telegram bots) for different roles using PraisonAI, similar to Hermes-style AI workforces.
-
-## Overview
-
-PraisonAI supports running multiple bots on the same platform, each routing to different agents with specialized knowledge and capabilities. This is useful for:
-
- **Role-based bots**: `@company_cfo_bot`, `@company_ops_bot`, `@company_content_bot`
- **Department-specific agents**: Each with different knowledge brains and tool access
- **Workflow separation**: Different bots handle different types of requests
-
-## Quick Start with Onboard Wizard
-
-The easiest way to set up multiple channels is using the onboard wizard:
-
-```bash
-praisonai onboard
-```
-
-The wizard now supports:
-1. **Multi-channel setup**: Add multiple bots for the same platform
-2. **Role-based naming**: Automatically generates `TELEGRAM_<ROLE>_BOT_TOKEN` environment variables
-3. **Agent routing**: Each channel routes to a different agent
-
-### Onboard Wizard Flow
-
-1. **Choose platform**: Start with your first platform (e.g., telegram)
-2. **Add more channels**: Wizard asks "Add another bot channel?"
-3. **Specify role**: Enter role name (e.g., "cfo", "ops", "content")
-4. **Configure tokens**: Each channel gets its own environment variable
-5. **Test connections**: Wizard tests each bot separately
-
-## Environment Variable Convention
-
-For multiple bots on the same platform, use this naming convention:
-
-| Channel | Environment Variable | Bot Purpose |
-|---------|---------------------|-------------|
-| `telegram_cfo` | `TELEGRAM_CFO_BOT_TOKEN` | CFO-related tasks |
-| `telegram_ops` | `TELEGRAM_OPS_BOT_TOKEN` | Operations tasks |
-| `telegram_content` | `TELEGRAM_CONTENT_BOT_TOKEN` | Content creation |
-| `telegram_support` | `TELEGRAM_SUPPORT_BOT_TOKEN` | Customer support |
-
-### Setting Up Tokens
-
-1. **Create bots in @BotFather**:
-   ```text
-   /newbot
-   Bot name: Company CFO Bot
-   Username: @company_cfo_bot
-   -> Copy token
-   
-   /newbot
-   Bot name: Company Ops Bot
-   Username: @company_ops_bot
-   -> Copy token
-   ```
-
-2. **Set environment variables**:
-   ```bash
-   export TELEGRAM_CFO_BOT_TOKEN="123456789:ABC..."
-   export TELEGRAM_OPS_BOT_TOKEN="987654321:DEF..."
-   export TELEGRAM_CONTENT_BOT_TOKEN="456789123:GHI..."
-   ```
-
-3. **Or use `~/.praisonai/.env`**:
-   ```env
-   TELEGRAM_CFO_BOT_TOKEN=123456789:ABC...
-   TELEGRAM_OPS_BOT_TOKEN=987654321:DEF...
-   TELEGRAM_CONTENT_BOT_TOKEN=456789123:GHI...
-   TELEGRAM_ALLOWED_USERS=123456789,987654321
-   ```
-
-## Example Configuration
-
-See `multi-telegram-hermes-workforce.yaml` for a complete example.
-
-### Key Features
-
-1. **Multiple Channels**:
-   ```yaml
-   channels:
-     telegram_cfo:
-       platform: telegram
-       token: ${TELEGRAM_CFO_BOT_TOKEN}
-       routes:
-         default: cfo
-     
-     telegram_ops:
-       platform: telegram
-       token: ${TELEGRAM_OPS_BOT_TOKEN}
-       routes:
-         default: ops
-   ```
-
-2. **Specialized Agents**:
-   ```yaml
-   agents:
-     cfo:
-       instructions: "You are a CFO agent. Help with financial analysis..."
-       model: gpt-4o-mini
-     
-     ops:
-       instructions: "You are an ops agent. Help with system monitoring..."
-       model: gpt-4o-mini
-   ```
-
-## Validation with Doctor
-
-Use the doctor command to validate your multi-channel setup:
-
-```bash
-praisonai doctor
-```
-
-The doctor checks for:
- ✅ **Unique tokens**: Each channel uses a different bot token
- ✅ **Naming convention**: Follows `PLATFORM_ROLE_BOT_TOKEN` pattern  
- ✅ **Missing tokens**: All referenced environment variables are set
- ❌ **Duplicate tokens**: Same token used by multiple channels (fails)
-
-## Deployment
-
-### Start Gateway
-
-```bash
-# Start the multi-bot gateway
-praisonai gateway start --config multi-telegram-hermes-workforce.yaml
-
-# Or install as daemon
-praisonai onboard  # Wizard installs daemon automatically
-praisonai gateway status
-```
-
-### Verify Setup
-
-1. **Check health**: `curl http://127.0.0.1:8765/health`
-2. **View dashboard**: `praisonai claw` → http://127.0.0.1:8082
-3. **Test bots**: Send messages to each bot to verify routing
-
-## Common Patterns
-
-### Basic Multi-Role Setup
-```yaml
-# Single platform, multiple roles
-channels:
-  telegram_support:
-    token: ${TELEGRAM_SUPPORT_BOT_TOKEN}
-    routes:
-      default: support_agent
-  
-  telegram_sales:
-    token: ${TELEGRAM_SALES_BOT_TOKEN}
-    routes:
-      default: sales_agent
-```
-
-### Cross-Platform Workforce
-```yaml
-# Multiple platforms, multiple roles
-channels:
-  telegram_cfo:
-    platform: telegram
-    token: ${TELEGRAM_CFO_BOT_TOKEN}
-    routes:
-      default: cfo
-  
-  discord_ops:
-    platform: discord
-    token: ${DISCORD_OPS_BOT_TOKEN}
-    routes:
-      default: ops
-  
-  slack_content:
-    platform: slack
-    token: ${SLACK_CONTENT_BOT_TOKEN}
-    routes:
-      default: content
-```
-
-## Troubleshooting
-
-### Common Issues
-
-1. **"Token is used by multiple channels"**
-   - Each bot needs its own unique token from @BotFather
-   - Check environment variables for duplicates
-
-2. **"Channel skipped - no token"**
-   - Set the environment variable: `export TELEGRAM_CFO_BOT_TOKEN="..."`
-   - Or update `~/.praisonai/.env`
-
-3. **"Bot not responding"**
-   - Verify token is correct: `praisonai doctor`
-   - Check bot is not already running elsewhere
-   - Ensure allowlist includes your user ID
-
-### Debugging Commands
-
-```bash
-# Check configuration
-praisonai doctor
-
-# View logs
-praisonai gateway logs
-
-# Test connection
-curl -H "Authorization: Bearer $GATEWAY_AUTH_TOKEN" \
-     http://127.0.0.1:8765/info
-```
-
-## Migration from Single-Bot Setup
-
-If you currently have a single bot setup, you can add more channels:
-
-1. **Run onboard wizard**: `praisonai onboard`
-2. **Add new channels**: Wizard will preserve existing setup
-3. **Update configuration**: New channels added to existing `bot.yaml`
-4. **Restart gateway**: `praisonai gateway restart`
-
-The wizard preserves backward compatibility with single-bot configurations.
@@ -1,19 +0,0 @@
-# A2UI canvas agent — YAML example
-#
-# Usage:
-#   pip install praisonaiagents[a2ui]
-#   praisonai agent run --file a2ui-canvas.yaml --prompt "Show a welcome card"
-#
-# For live A2UI rendering, use PraisonAIUI (AG-UI / surfaces):
-#   pip install praisonaiui
-#   See examples/python/a2ui/send_surface.py and PraisonAIUI example 29.
-
-agent:
-  name: "ui-assistant"
-  instructions: |
-    You are a helpful assistant. When the user asks for UI elements
-    (buttons, cards, forms), call send_a2ui_messages with valid A2UI v0.9 JSON.
-    Use surface id "main" unless told otherwise.
-  llm: gpt-4o-mini
-  tools:
-    - send_a2ui_messages
@@ -1,40 +0,0 @@
-# Example: Agent with MCP Tool Integration
-#
-# This example shows how to configure an agent with MCP (Model Context Protocol)
-# servers for additional tool capabilities.
-#
-# Usage:
-#   praisonai workflow run --file examples/yaml/agent-with-mcp.yaml
-
-agent:
-  name: mcp_assistant
-  instructions: |
-    You are a helpful assistant with access to filesystem and time tools.
-    Use the MCP tools when needed to help users with file operations or time queries.
-  llm: gpt-4o-mini
-
-# MCP server configuration
-mcp:
-  servers:
-    filesystem:
-      # Filesystem access for the agent
-      command: npx
-      args:
-        - "-y"
-        - "@modelcontextprotocol/server-filesystem"
-        - "."
-      env: {}
-      enabled: true
-    
-    time:
-      # Time and date utilities
-      command: npx
-      args:
-        - "-y" 
-        - "@modelcontextprotocol/server-time"
-      env: {}
-      enabled: true
-
-# NOTE: Per-server tool filtering (tools.include/exclude) is not yet supported
-# by the MCP server config schema. Tool filtering should be applied after
-# calling mcp.get_tools() in your application code.
@@ -1,66 +0,0 @@
-# Multi-Telegram Channel Hermes Workforce Example
-# Generated by `praisonai onboard` with multi-channel support
-# Start with: praisonai gateway start --config multi-telegram-hermes-workforce.yaml
-
-gateway:
-  host: "127.0.0.1"
-  port: 8765
-
-# Legacy single-agent block for backward compatibility
-agent:
-  name: "assistant"
-  instructions: "You are a helpful AI assistant. You can search the web, manage schedules, and remember past conversations. Use your tools proactively."
-  model: "gpt-4o-mini"
-
-# Multi-agent workforce with different specialized roles
-agents:
-  cfo:
-    instructions: "You are a CFO agent. You are a helpful AI assistant. Use your tools proactively to help with CFO-related tasks like financial analysis, budget planning, and cost optimization."
-    model: gpt-4o-mini
-    memory: false
-
-  ops:
-    instructions: "You are an ops agent. You are a helpful AI assistant. Use your tools proactively to help with ops-related tasks like system monitoring, deployment, and infrastructure management."
-    model: gpt-4o-mini
-    memory: false
-
-  content:
-    instructions: "You are a content agent. You are a helpful AI assistant. Use your tools proactively to help with content-related tasks like writing, editing, and content strategy."
-    model: gpt-4o-mini
-    memory: false
-
-# Multiple Telegram channels - each with unique tokens and routing
-channels:
-  telegram_cfo:
-    platform: telegram
-    token: ${TELEGRAM_CFO_BOT_TOKEN}
-    group_policy: mention_only
-    allowed_users: ${TELEGRAM_ALLOWED_USERS}
-    # auto_approve_tools: true
-                               # Chat bots cannot show CLI approval prompts; safe tools auto-approved by default.
-    routes:
-      default: cfo
-
-  telegram_ops:
-    platform: telegram
-    token: ${TELEGRAM_OPS_BOT_TOKEN}
-    group_policy: mention_only
-    allowed_users: ${TELEGRAM_ALLOWED_USERS}
-    # auto_approve_tools: true
-                               # Chat bots cannot show CLI approval prompts; safe tools auto-approved by default.
-    routes:
-      default: ops
-
-  telegram_content:
-    platform: telegram
-    token: ${TELEGRAM_CONTENT_BOT_TOKEN}
-    group_policy: mention_only
-    allowed_users: ${TELEGRAM_ALLOWED_USERS}
-    # auto_approve_tools: true
-                               # Chat bots cannot show CLI approval prompts; safe tools auto-approved by default.
-    routes:
-      default: content
-
-# Legacy routing block
-routing:
-  default: assistant
@@ -93,7 +93,7 @@ def example_3_execute_yaml_directly():
    result = manager.execute_yaml(
        yaml_file,
        input_data="The future of AI",
-        verbose=True,
+        output="verbose"
    )
    
    print(f"Status: {result['status']}")
@@ -95,5 +95,4 @@ chroma_db/
 .windsurf
 .ruff_cache
 *.sfdx
-.sfdx/
-.cursor/
+.sfdx/
@@ -79,20 +79,10 @@ def get_time(timezone: Literal["est", "pst"]) -> str:


 def calculate(expression: str) -> str:
-    """Calculate a math expression safely."""
+    """Calculate a math expression."""
    try:
-        import ast, operator
-        _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                ast.Mod: operator.mod, ast.Pow: operator.pow,
-                ast.USub: operator.neg, ast.UAdd: operator.pos}
-        def _ev(n):
-            if isinstance(n, ast.Expression): return _ev(n.body)
-            if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-            if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-            if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-            raise ValueError(f"Unsupported: {ast.dump(n)}")
-        return f"Result: {_ev(ast.parse(expression, mode='eval'))}"
+        result = eval(expression)
+        return f"Result: {result}"
    except Exception:
        return "Error: Invalid expression"

@@ -343,7 +343,6 @@ _LAZY_IMPORTS = {
    # UI (optional)
    'AGUI': ('praisonaiagents.ui.agui', 'AGUI'),
    'A2A': ('praisonaiagents.ui.a2a', 'A2A'),
-    'A2UI': ('praisonaiagents.ui.a2ui', 'A2UI'),
    
    # Feature configs
    'MemoryConfig': ('praisonaiagents.config.feature_configs', 'MemoryConfig'),
@@ -384,7 +383,6 @@ _LAZY_IMPORTS = {
    'MultiAgentExecutionConfig': ('praisonaiagents.config.feature_configs', 'MultiAgentExecutionConfig'),
    'MultiAgentPlanningConfig': ('praisonaiagents.config.feature_configs', 'MultiAgentPlanningConfig'),
    'MultiAgentMemoryConfig': ('praisonaiagents.config.feature_configs', 'MultiAgentMemoryConfig'),
-    'ToolSearchConfig': ('praisonaiagents.config.feature_configs', 'ToolSearchConfig'),
    
    # Parameter resolver
    'resolve': ('praisonaiagents.config.param_resolver', 'resolve'),
@@ -816,6 +814,6 @@ def __dir__():
 # - Workflow, Task, Route, Parallel, Loop, Repeat, Pipeline, etc.
 # - MCP, FlowDisplay, track_workflow, FastContext, etc.
 # - Plan, PlanStep, TodoList, PlanningAgent, ApprovalCallback, etc.
-# - RAG, RAGConfig, RAGResult, AGUI, A2A, A2UI, etc.
+# - RAG, RAGConfig, RAGResult, AGUI, A2A, etc.
 # - All telemetry, display, and utility functions
 # ============================================================================
@@ -556,7 +556,6 @@ class Agent(SandboxMixin, UnifiedExecutionMixin, ToolExecutionMixin, ChatHandler
        backend: Optional[Any] = None,  # External managed agent backend (e.g., ManagedAgentIntegration)
        cli_backend: Optional[Union[str, Any]] = None,  # CLI backend for delegating turns (e.g., "claude-code")
        interrupt_controller: Optional['InterruptController'] = None,  # G2: Cooperative cancellation
-        tool_search: Optional[Union[bool, str, Dict[str, Any], 'ToolSearchConfig']] = False,  # Progressive tool disclosure
        sandbox: Optional[Union[bool, 'SandboxConfig']] = None,  # Sandbox for safe code execution
    ):
        """Initialize an Agent instance.
@@ -661,14 +660,6 @@ class Agent(SandboxMixin, UnifiedExecutionMixin, ToolExecutionMixin, ChatHandler
                When provided, agent delegates entire conversation turns to the CLI tool
                instead of using the built-in LLM. Enables session continuity and 
                tool integration through external AI coding assistants.
-            tool_search: Progressive tool disclosure configuration. Accepts:
-                - bool: False=disabled (default), True=auto mode
-                - str: Mode ("auto", "on", "off")  
-                - Dict[str, Any]: Config overrides (e.g. {"threshold_pct": 15})
-                - ToolSearchConfig: Custom configuration
-                When enabled, replaces large tool schemas with bridge tools (tool_search,
-                tool_describe, tool_call) to save context. Core SDK tools never defer.
-                Auto mode activates based on token threshold. Opt-in feature.

        Raises:
            ValueError: If all of name, role, goal, backstory, and instructions are None.
@@ -1439,34 +1430,6 @@ class Agent(SandboxMixin, UnifiedExecutionMixin, ToolExecutionMixin, ChatHandler
            web_search = False
            web_fetch = False
        
-        # ─────────────────────────────────────────────────────────────────────
-        # Resolve TOOL_SEARCH param
-        # ─────────────────────────────────────────────────────────────────────
-        # Fast path: None/False -> disabled (zero overhead)
-        if tool_search is None or tool_search is False:
-            self._tool_search_config = None
-        elif tool_search is True:
-            # True -> auto mode with defaults
-            from ..tools.tool_search import ToolSearchConfig as _ToolSearchConfig
-            self._tool_search_config = _ToolSearchConfig(enabled="auto")
-        elif isinstance(tool_search, str):
-            # String mode ("auto", "on", "off")
-            from ..tools.tool_search import ToolSearchConfig as _ToolSearchConfig
-            self._tool_search_config = _ToolSearchConfig.from_raw(tool_search)
-        elif isinstance(tool_search, dict):
-            # Dict -> config overrides
-            from ..tools.tool_search import ToolSearchConfig as _ToolSearchConfig
-            self._tool_search_config = _ToolSearchConfig(**tool_search)
-        else:
-            from ..tools.tool_search import ToolSearchConfig as _ToolSearchConfig
-            if isinstance(tool_search, _ToolSearchConfig):
-                self._tool_search_config = tool_search
-            else:
-                raise TypeError(
-                    "tool_search must be False/None, True, a mode string, "
-                    "a dict of ToolSearchConfig fields, or ToolSearchConfig"
-                )
-        
        # ============================================================
        # END CONSOLIDATED PARAMS EXTRACTION
        # ============================================================
@@ -1989,130 +1952,6 @@ Your Goal: {self.goal}
        # Sandbox configuration - initialize SandboxMixin
        super().__init__(sandbox=sandbox)

-    def __deepcopy__(self, memo: dict) -> "Agent":
-        """Custom deepcopy that creates fresh threading primitives.
-
-        threading.RLock (self.__cache_lock) and threading.Lock (self._cost_lock)
-        cannot be deep-copied on CPython < 3.13.  This hook deep-copies every
-        other attribute normally and replaces the locks with new instances so
-        that copy.deepcopy(agent) works in any Python version.
-        """
-        import copy
-        cls = self.__class__
-        result = cls.__new__(cls)
-        memo[id(self)] = result
-        for k, v in self.__dict__.items():
-            if k in ("_Agent__cache_lock",):
-                object.__setattr__(result, k, threading.RLock())
-            elif k == "_cost_lock":
-                object.__setattr__(result, k, threading.Lock())
-            else:
-                object.__setattr__(result, k, copy.deepcopy(v, memo))
-        return result
-
-    def clone_for_channel(self) -> "Agent":
-        """Return a fully independent copy of this agent for a gateway channel.
-        
-        This method safely clones an agent for use in multi-channel scenarios
-        by avoiding deepcopy issues with tools/handoffs and creating fresh
-        instances of mutable objects like interrupt_controller.
-        
-        Returns:
-            Agent: A new Agent instance with copied configuration but fresh
-                  locks, interrupt controller, and shallow-copied tools.
-        """
-        import copy
-        from ..tools.base import BaseTool
-        
-        # Build kwargs for new Agent instance, copying core attributes
-        clone_kwargs = {
-            # Core identity
-            'name': self.name,
-            'role': self.role, 
-            'goal': self.goal,
-            'backstory': self.backstory,
-            'instructions': self.instructions,
-            
-            # LLM configuration 
-            'llm': self.llm,
-            'base_url': getattr(self, 'base_url', None),
-            'api_key': getattr(self, 'api_key', None),
-            'auth': getattr(self, 'auth', None),
-            
-            # Shallow copy tools to avoid deepcopy issues with nested objects
-            'tools': list(self.tools) if self.tools else None,
-            
-            # Skip handoffs entirely - they shouldn't be shared across channels
-            # and can contain nested Agent instances that cause RLock issues
-            'handoffs': None,
-            
-            # Feature configurations - check for actual stored config objects
-            'memory': getattr(self, '_memory_config', None),
-            'knowledge': getattr(self, '_knowledge_config', None), 
-            'planning': getattr(self, '_planning_config', None),
-            'reflection': getattr(self, '_reflection_config', None),
-            'guardrails': getattr(self, '_guardrails_config', None),
-            'web': getattr(self, '_web_config', None),
-            'context': getattr(self, '_context_config', None),
-            'autonomy': getattr(self, '_autonomy_config', None),
-            'output': getattr(self, '_output_config', None),
-            'execution': getattr(self, '_execution_config', None),
-            'templates': getattr(self, '_template_config', None),
-            'caching': getattr(self, '_caching_config', None),
-            'hooks': getattr(self, '_hooks_config', None),
-            'skills': getattr(self, '_skills_config', None),
-            'approval': getattr(self, '_approval_config', None),
-            'learn': getattr(self, '_learn_config', None),
-            'tool_search': getattr(self, '_tool_search_config', None),
-            
-            # Tool configuration
-            'tool_timeout': getattr(self, '_tool_timeout', None),
-            'parallel_tool_calls': getattr(self, 'parallel_tool_calls', False),
-            
-            # CLI backend
-            'cli_backend': getattr(self, '_cli_backend', None),
-            
-            # Create fresh interrupt controller to avoid shared state
-            'interrupt_controller': None,  # Let new instance create its own
-            
-            # Sandbox config
-            'sandbox': getattr(self, '_sandbox_config', None),
-        }
-        
-        # Handle deprecated parameters for backward compatibility
-        import warnings
-        
-        # Check for deprecated standalone attributes and emit warnings
-        deprecated_attrs = [
-            ('allow_code_execution', 'execution'),
-            ('code_execution_mode', 'execution'), 
-            ('auto_save', 'memory'),
-            ('rate_limiter', 'execution'),
-            ('allow_delegation', 'handoffs'),
-            ('verification_hooks', 'autonomy')
-        ]
-        
-        for old_attr, new_param in deprecated_attrs:
-            if hasattr(self, old_attr):
-                value = getattr(self, old_attr)
-                if value is not None and value != False:  # Skip None/False defaults
-                    warnings.warn(
-                        f"Deprecated attribute '{old_attr}' found in agent clone. "
-                        f"Use '{new_param}=' parameter instead.",
-                        DeprecationWarning,
-                        stacklevel=2
-                    )
-                    # For basic compatibility, include in clone_kwargs if not already set
-                    if old_attr == 'allow_code_execution' and clone_kwargs['execution'] is None:
-                        from ..config.execution import ExecutionConfig
-                        clone_kwargs['execution'] = ExecutionConfig(code_execution=value)
-                    elif old_attr == 'auto_save' and clone_kwargs['memory'] is None:
-                        from ..config.memory import MemoryConfig  
-                        clone_kwargs['memory'] = MemoryConfig(auto_save=value)
-        
-        # Create new Agent instance
-        return self.__class__(**{k: v for k, v in clone_kwargs.items() if v is not None})
-
    @property
    def _telemetry(self):
        """Lazy-loaded telemetry instance for performance."""
@@ -4777,57 +4616,60 @@ Answer:"""
                error=f"Agent guardrail validation error: {str(e)}"
            )

-    def _validate_with_guardrail(self, response_text):
-        """Validate response with guardrail. Returns (success, result, error)."""
+    def _apply_guardrail_with_retry(self, response_text, prompt, temperature=1.0, tools=None, task_name=None, task_description=None, task_id=None):
+        """Apply guardrail validation with retry logic.
+        
+        Args:
+            response_text: The response to validate
+            prompt: Original prompt for regeneration if needed
+            temperature: Temperature for regeneration
+            tools: Tools for regeneration
+            
+        Returns:
+            str: The validated response text or None if validation fails after retries
+        """
        if not self._guardrail_fn:
-            return True, response_text, None
+            return response_text
            
        from ..main import TaskOutput
        
-        task_output = TaskOutput(
-            description="Agent response output",
-            raw=response_text,
-            agent=self.name
-        )
-        
-        guardrail_result = self._process_guardrail(task_output)
-        
-        if guardrail_result.success:
-            # Return the potentially modified result
-            if guardrail_result.result and hasattr(guardrail_result.result, 'raw'):
-                return True, guardrail_result.result.raw, None
-            elif guardrail_result.result:
-                return True, str(guardrail_result.result), None
-            else:
-                return True, response_text, None
-        else:
-            return False, None, guardrail_result.error
-
-    def _apply_guardrail_with_retry(self, response_text, prompt, temperature=1.0, tools=None, task_name=None, task_description=None, task_id=None):
-        """Apply guardrail validation with retry logic (sync version)."""
        retry_count = 0
        current_response = response_text
        
        while retry_count <= self.max_guardrail_retries:
-            success, result, error = self._validate_with_guardrail(current_response)
+            # Create TaskOutput object
+            task_output = TaskOutput(
+                description="Agent response output",
+                raw=current_response,
+                agent=self.name
+            )
            
-            if success:
+            # Process guardrail
+            guardrail_result = self._process_guardrail(task_output)
+            
+            if guardrail_result.success:
                logging.info(f"Agent {self.name}: Guardrail validation passed")
-                return result
+                # Return the potentially modified result
+                if guardrail_result.result and hasattr(guardrail_result.result, 'raw'):
+                    return guardrail_result.result.raw
+                elif guardrail_result.result:
+                    return str(guardrail_result.result)
+                else:
+                    return current_response
            
            # Guardrail failed
            if retry_count >= self.max_guardrail_retries:
                raise Exception(
                    f"Agent {self.name} response failed guardrail validation after {self.max_guardrail_retries} retries. "
-                    f"Last error: {error}"
+                    f"Last error: {guardrail_result.error}"
                )
            
            retry_count += 1
-            logging.warning(f"Agent {self.name}: Guardrail validation failed (retry {retry_count}/{self.max_guardrail_retries}): {error}")
+            logging.warning(f"Agent {self.name}: Guardrail validation failed (retry {retry_count}/{self.max_guardrail_retries}): {guardrail_result.error}")
            
            # Regenerate response for retry
            try:
-                retry_prompt = f"{prompt}\n\nNote: Previous response failed validation due to: {error}. Please provide an improved response."
+                retry_prompt = f"{prompt}\n\nNote: Previous response failed validation due to: {guardrail_result.error}. Please provide an improved response."
                response = self._chat_completion([{"role": "user", "content": retry_prompt}], temperature, tools, task_name=task_name, task_description=task_description, task_id=task_id)
                if response and response.choices:
                    content = response.choices[0].message.content
@@ -4836,46 +4678,10 @@ Answer:"""
                    raise Exception("Failed to generate retry response")
            except Exception as e:
                logging.error(f"Agent {self.name}: Error during guardrail retry: {e}")
-                raise Exception(f"Agent {self.name} guardrail retry failed: {e}")
-        
-        return current_response
-
-    async def _aapply_guardrail_with_retry(self, response_text, prompt, temperature=1.0, tools=None, task_name=None, task_description=None, task_id=None):
-        """Apply guardrail validation with retry logic (async version)."""
-        retry_count = 0
-        current_response = response_text
-        
-        while retry_count <= self.max_guardrail_retries:
-            success, result, error = self._validate_with_guardrail(current_response)
-            
-            if success:
-                logging.info(f"Agent {self.name}: Guardrail validation passed")
-                return result
-            
-            # Guardrail failed
-            if retry_count >= self.max_guardrail_retries:
+                # If we can't regenerate, fail the guardrail
                raise Exception(
-                    f"Agent {self.name} response failed guardrail validation after {self.max_guardrail_retries} retries. "
-                    f"Last error: {error}"
+                    f"Agent {self.name} guardrail retry failed: {e}"
                )
-            
-            retry_count += 1
-            logging.warning(f"Agent {self.name}: Guardrail validation failed (retry {retry_count}/{self.max_guardrail_retries}): {error}")
-            
-            # Regenerate response for retry (async version)
-            try:
-                retry_prompt = f"{prompt}\n\nNote: Previous response failed validation due to: {error}. Please provide an improved response."
-                response = await self._execute_unified_achat_completion([{"role": "user", "content": retry_prompt}], temperature, tools, task_name=task_name, task_description=task_description, task_id=task_id)
-                if response and hasattr(response, 'choices') and response.choices:
-                    content = response.choices[0].message.content
-                    current_response = content.strip() if content else ""
-                elif isinstance(response, str):
-                    current_response = response.strip()
-                else:
-                    raise Exception("Failed to generate retry response")
-            except Exception as e:
-                logging.error(f"Agent {self.name}: Error during guardrail retry: {e}")
-                raise Exception(f"Agent {self.name} guardrail retry failed: {e}")
        
        return current_response
    
@@ -331,7 +331,6 @@ Your Goal: {self.goal}"""
        - Callable functions
        - String function names
        - Objects with to_openai_tool() method
-        - Tool Search progressive disclosure (if enabled)
        
        Args:
            tools: List of tools in various formats or None to use self.tools
@@ -345,14 +344,10 @@ Your Goal: {self.goal}"""
        if not tools:
            return []
        
-        # Check cache first - include tool_search config in cache key for safety
+        # Check cache first
        tools_key = self._get_tools_cache_key(tools)
-        tool_search_enabled = getattr(self, '_tool_search_config', None) is not None
-        cache_key = f"{tools_key}:tool_search={tool_search_enabled}"
-        cached_entry = self._cache_get(self._formatted_tools_cache, cache_key)
-        if cached_entry is not None:
-            cached_tools, cached_metadata = cached_entry
-            self._tool_search_metadata = cached_metadata
+        cached_tools = self._cache_get(self._formatted_tools_cache, tools_key)
+        if cached_tools is not None:
            return cached_tools
            
        formatted_tools = []
@@ -404,46 +399,9 @@ Your Goal: {self.goal}"""
                logging.error(f"Tools are not JSON serializable: {e}")
                return []
        
-        # Apply tool search assembly if enabled (after formatting, before caching)
-        if hasattr(self, '_tool_search_config') and self._tool_search_config is not None:
-            try:
-                from ..tools.tool_search import assemble_tool_defs
-                # Get context length from LLM config if available
-                context_length = getattr(self, '_context_window_size', None)
-                
-                # Assemble tools with bridge mode check
-                assembled_tools, metadata = assemble_tool_defs(
-                    tool_defs=formatted_tools,
-                    config=self._tool_search_config,
-                    context_length=context_length
-                )
-                
-                # Store metadata for bridge tool dispatch
-                self._tool_search_metadata = metadata
-                formatted_tools = assembled_tools
-                
-            except ImportError:
-                # Tool search module not available, continue with original tools
-                logging.warning("Tool search requested but tool_search module not available")
-        
-        # Strip __praisonai_deferrable__ from provider-facing tool payloads
-        # Keep the marker only for internal tool classification
-        cleaned_tools = []
-        for tool in formatted_tools:
-            if isinstance(tool, dict) and "__praisonai_deferrable__" in tool:
-                tool_copy = tool.copy()
-                tool_copy.pop("__praisonai_deferrable__", None)
-                cleaned_tools.append(tool_copy)
-            else:
-                cleaned_tools.append(tool)
-        
-        # Cache the formatted tools with LRU eviction, including tool search metadata
-        self._cache_put(
-            self._formatted_tools_cache,
-            cache_key,
-            (cleaned_tools, getattr(self, "_tool_search_metadata", None)),
-        )
-        return cleaned_tools
+        # Cache the formatted tools with LRU eviction
+        self._cache_put(self._formatted_tools_cache, tools_key, formatted_tools)
+        return formatted_tools

    def _build_multimodal_prompt(
        self, 
@@ -549,7 +507,7 @@ Your Goal: {self.goal}"""
            emit_events=True
        )

-    def _chat_completion(self, messages, temperature=1.0, tools=None, stream=None, reasoning_steps=False, task_name=None, task_description=None, task_id=None, response_format=None, _retry_depth=0):
+    def _chat_completion(self, messages, temperature=1.0, tools=None, stream=True, reasoning_steps=False, task_name=None, task_description=None, task_id=None, response_format=None, _retry_depth=0):
        start_time = time.time()

        # --- Context compaction (opt-in via ExecutionConfig.context_compaction) ---
@@ -619,39 +577,6 @@ Your Goal: {self.goal}"""
        # Use the new _format_tools_for_completion helper method
        formatted_tools = self._format_tools_for_completion(tools)

-        # Smart fallback for streaming: try streaming first, fall back to non-streaming if unsupported
-        if stream is None:
-            # Auto-detect: prefer streaming for better UX, fallback if adapter doesn't support it
-            try:
-                # First attempt: try with streaming enabled for better user experience
-                stream_callback = self.stream_emitter.emit if hasattr(self, 'stream_emitter') else None
-                final_response = self._execute_unified_chat_completion(
-                    messages=messages,
-                    temperature=temperature,
-                    tools=formatted_tools,
-                    stream=True,  # Try streaming first
-                    reasoning_steps=reasoning_steps,
-                    task_name=task_name,
-                    task_description=task_description,
-                    task_id=task_id,
-                    response_format=response_format,
-                    stream_callback=stream_callback,
-                    emit_events=True
-                )
-                return final_response
-            except ValueError as e:
-                if "Streaming is not supported" in str(e):
-                    # Fallback: retry with non-streaming for sync adapters
-                    logging.debug(f"{self.name}: Streaming not supported by adapter, falling back to non-streaming")
-                    stream = False  # Set for the main execution below
-                else:
-                    raise  # Re-raise if it's a different ValueError
-            except Exception:
-                # For any other exception, fall back to non-streaming
-                logging.debug(f"{self.name}: Streaming attempt failed, falling back to non-streaming")
-                stream = False  # Set for the main execution below
-        
-        # If stream was explicitly set or fallback occurred, use the specified/fallback value
        try:
            # NEW: Unified protocol dispatch path (Issue #1304, #1362)
            # UNIFIED: Single protocol-driven dispatch path (fixes DRY violation)
@@ -849,7 +774,7 @@ Your Goal: {self.goal}"""
        messages, 
        temperature=1.0, 
        tools=None, 
-        stream=None,
+        stream=True,
        reasoning_steps=False,
        task_name=None,
        task_description=None,
@@ -881,47 +806,6 @@ Your Goal: {self.goal}"""
            # Cache the dispatcher
            self._unified_dispatcher = dispatcher
        
-        # Smart fallback for streaming: try streaming first, fall back to non-streaming if unsupported
-        if stream is None:
-            # Auto-detect: prefer streaming for better UX, fallback if adapter doesn't support it
-            try:
-                # First attempt: try with streaming enabled for better user experience
-                if stream_callback is None and hasattr(self, 'stream_emitter'):
-                    stream_callback = getattr(self.stream_emitter, 'emit', None)
-                final_response = self._unified_dispatcher.chat_completion(
-                    messages=messages,
-                    tools=tools,
-                    tool_choice=getattr(self, 'tool_choice', None),
-                    temperature=temperature,
-                    max_tokens=getattr(self, 'max_tokens', None),
-                    stream=True,  # Try streaming first
-                    response_format=response_format,
-                    execute_tool_fn=getattr(self, 'execute_tool', None),
-                    console=self.console if (self.verbose or True) else None,  # Enable console for streaming
-                    display_fn=self._display_generating if self.verbose else None,
-                    stream_callback=stream_callback,
-                    emit_events=emit_events,
-                    verbose=self.verbose,
-                    max_iterations=10,
-                    reasoning_steps=reasoning_steps,
-                    task_name=task_name,
-                    task_description=task_description,
-                    task_id=task_id,
-                    agent_name=getattr(self, 'name', 'assistant')
-                )
-                return final_response
-            except ValueError as e:
-                if "Streaming is not supported" in str(e):
-                    # Fallback: retry with non-streaming for sync adapters
-                    logging.debug(f"Agent: Streaming not supported by adapter, falling back to non-streaming")
-                    stream = False  # Set for the main execution below
-                else:
-                    raise  # Re-raise if it's a different ValueError
-            except Exception:
-                # For any other exception, fall back to non-streaming
-                logging.debug(f"Agent: Streaming attempt failed, falling back to non-streaming")
-                stream = False  # Set for the main execution below
-
        # Execute unified dispatch with all necessary parameters
        # Includes all parameters from both legacy paths to ensure full compatibility
        try:
@@ -973,7 +857,7 @@ Your Goal: {self.goal}"""
        messages, 
        temperature=1.0, 
        tools=None, 
-        stream=True,  # Async methods keep stream=True default (async adapters support streaming vs sync smart fallback)
+        stream=True,
        reasoning_steps=False,
        task_name=None,
        task_description=None,
@@ -2195,7 +2079,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                    
                    # Apply guardrail validation for custom LLM response
                    try:
-                        validated_response = await self._aapply_guardrail_with_retry(response_text, prompt, temperature, tools, task_name, task_description, task_id)
+                        validated_response = self._apply_guardrail_with_retry(response_text, prompt, temperature, tools, task_name, task_description, task_id)
                        # Execute callback after validation
                        self._execute_callback_and_display(normalized_content, validated_response, time.time() - start_time, task_name, task_description, task_id)
                        return await self._atrigger_after_agent_hook(prompt, validated_response, start_time)
@@ -2303,216 +2187,22 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                            task_id=task_id,
                            response_format=response_format
                        )
-                        
-                        # Process response - mirror sync _chat_impl behavior (lines ~1544-1602)
-                        if not response:
-                            # Rollback chat history on response failure
-                            self._truncate_chat_history(chat_history_length)
-                            return None
-
-                        # Extract response content using the same method as sync
-                        response_text = self._extract_llm_response_content(response)
-                        if isinstance(response_text, str):
-                            response_text = response_text.strip()
-
-                        # Handle output_json or output_pydantic if specified
-                        if output_json or output_pydantic:
-                            # Add to chat history and return raw response
-                            # User message already added before LLM call via _build_messages
-                            self._append_to_chat_history({"role": "assistant", "content": response_text})
-                            # Persist assistant message to DB
-                            self._persist_message("assistant", response_text)
-                            # Apply guardrail validation even for JSON output
-                            try:
-                                validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
-                                # Execute callback after validation
-                                self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
-                                return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
-                            except Exception as e:
-                                logging.error(f"Agent {self.name}: Guardrail validation failed for JSON output: {e}")
-                                # Rollback chat history on guardrail failure
-                                self._truncate_chat_history(chat_history_length)
-                                return None
-
-                        # For regular responses (no self-reflection)
-                        if not self.self_reflect:
-                            # User message already added before LLM call via _build_messages
-                            self._append_to_chat_history({"role": "assistant", "content": response_text})
-                            # Persist assistant message to DB (non-reflect path)
-                            self._persist_message("assistant", response_text)
-                            if self.verbose:
-                                logging.debug(f"Agent {self.name} final response: {response_text}")
-                            # Return only reasoning content if reasoning_steps is True
-                            if reasoning_steps and hasattr(response, 'choices') and response.choices and hasattr(response.choices[0].message, 'reasoning_content') and response.choices[0].message.reasoning_content:
-                                # Apply guardrail to reasoning content
-                                try:
-                                    validated_reasoning = await self._aapply_guardrail_with_retry(response.choices[0].message.reasoning_content, original_prompt, temperature, tools, task_name, task_description, task_id)
-                                    # Execute callback after validation
-                                    self._execute_callback_and_display(original_prompt, validated_reasoning, time.time() - start_time, task_name, task_description, task_id)
-                                    return await self._atrigger_after_agent_hook(original_prompt, validated_reasoning, start_time)
-                                except Exception as e:
-                                    logging.error(f"Agent {self.name}: Guardrail validation failed for reasoning content: {e}")
-                                    # Rollback chat history on guardrail failure
-                                    self._truncate_chat_history(chat_history_length)
-                                    return None
-                            else:
-                                # Apply guardrail to regular response content
-                                try:
-                                    validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
-                                    # Execute callback after validation
-                                    self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
-                                    return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
-                                except Exception as e:
-                                    logging.error(f"Agent {self.name}: Guardrail validation failed: {e}")
-                                    # Rollback chat history on guardrail failure
-                                    self._truncate_chat_history(chat_history_length)
-                                    return None
-                        
-                        # If self-reflection is enabled, implement reflection logic
-                        if self.self_reflect:
-                            # Implement async self-reflection similar to sync path
-                            reflection_prompt = f"""
-Reflect on your previous response: '{response_text}'.
-{self.reflect_prompt if self.reflect_prompt else "Identify any flaws, improvements, or actions."}
-Provide a "satisfactory" status ('yes' or 'no').
-Output MUST be JSON with 'reflection' and 'satisfactory'.
-                            """
-                            
-                            # Add reflection prompt to messages
-                            reflection_messages = messages + [
-                                {"role": "assistant", "content": response_text},
-                                {"role": "user", "content": reflection_prompt}
-                            ]
-                            
-                            reflection_count = 0
-                            
-                            while True:
-                                try:
-                                    # Check if OpenAI client is available for self-reflection
-                                    if self._openai_client is None:
-                                        # For custom LLMs, self-reflection with structured output is not supported
-                                        if self.verbose:
-                                            _get_display_functions()['display_self_reflection'](f"Agent {self.name}: Self-reflection with structured output is not supported for custom LLM providers. Skipping reflection.", console=self.console)
-                                        # Return the original response without reflection
-                                        self._append_to_chat_history({"role": "assistant", "content": response_text})
-                                        # Persist assistant message to DB
-                                        self._persist_message("assistant", response_text)
-                                        try:
-                                            validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
-                                            # Execute callback after validation
-                                            self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
-                                            return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
-                                        except Exception as e:
-                                            logging.error(f"Agent {self.name}: Guardrail validation failed: {e}")
-                                            # Rollback chat history on guardrail failure
-                                            self._truncate_chat_history(chat_history_length)
-                                            return None
-                                    
-                                    reflection_response = await self._openai_client.async_client.beta.chat.completions.parse(
-                                        model=self.reflect_llm if self.reflect_llm else self.llm,
-                                        messages=reflection_messages,
-                                        temperature=temperature,
-                                        response_format=_get_display_functions()['ReflectionOutput']
-                                    )
-                                    
-                                    reflection_output = reflection_response.choices[0].message.parsed
-                                    
-                                    if self.verbose:
-                                        _get_display_functions()['display_self_reflection'](f"Agent {self.name} self reflection (using {self.reflect_llm if self.reflect_llm else self.llm}): reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'", console=self.console)
-                                    
-                                    # Only consider satisfactory after minimum reflections
-                                    if reflection_output.satisfactory == "yes" and reflection_count >= self.min_reflect - 1:
-                                        if self.verbose:
-                                            _get_display_functions()['display_self_reflection']("Agent marked the response as satisfactory after meeting minimum reflections", console=self.console)
-                                        # Add to chat history and return
-                                        self._append_to_chat_history({"role": "assistant", "content": response_text})
-                                        # Persist assistant message to DB
-                                        self._persist_message("assistant", response_text)
-                                        try:
-                                            validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
-                                            # Execute callback after validation
-                                            self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
-                                            return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
-                                        except Exception as e:
-                                            logging.error(f"Agent {self.name}: Guardrail validation failed after reflection: {e}")
-                                            # Rollback chat history on guardrail failure
-                                            self._truncate_chat_history(chat_history_length)
-                                            return None
-                                    
-                                    # Check if we've hit max reflections
-                                    if reflection_count >= self.max_reflect - 1:
-                                        if self.verbose:
-                                            _get_display_functions()['display_self_reflection']("Maximum reflection count reached, returning current response", console=self.console)
-                                        # Add to chat history and return
-                                        self._append_to_chat_history({"role": "assistant", "content": response_text})
-                                        # Persist assistant message to DB
-                                        self._persist_message("assistant", response_text)
-                                        try:
-                                            validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
-                                            # Execute callback after validation
-                                            self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
-                                            return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
-                                        except Exception as e:
-                                            logging.error(f"Agent {self.name}: Guardrail validation failed after max reflections: {e}")
-                                            # Rollback chat history on guardrail failure
-                                            self._truncate_chat_history(chat_history_length)
-                                            return None
-                                    
-                                    # Regenerate response based on reflection
-                                    regenerate_messages = reflection_messages + [
-                                        {"role": "assistant", "content": f"Self Reflection: {reflection_output.reflection} Satisfactory?: {reflection_output.satisfactory}"},
-                                        {"role": "user", "content": "Now regenerate your response using the reflection you made"}
-                                    ]
-                                    
-                                    new_response = await self._execute_unified_achat_completion(
-                                        messages=regenerate_messages,
-                                        temperature=temperature,
-                                        tools=formatted_tools,
-                                        stream=stream,
-                                        reasoning_steps=reasoning_steps,
-                                        task_name=task_name,
-                                        task_description=task_description,
-                                        task_id=task_id,
-                                        response_format=response_format
-                                    )
-                                    
-                                    if new_response:
-                                        new_response_text = self._extract_llm_response_content(new_response)
-                                        if isinstance(new_response_text, str):
-                                            response_text = new_response_text.strip()
-                                        # Update reflection_messages to include the new response for next iteration
-                                        reflection_messages = regenerate_messages + [
-                                            {"role": "assistant", "content": response_text}
-                                        ]
-                                    
-                                    reflection_count += 1
-                                    
-                                except Exception as e:
-                                    if self.verbose:
-                                        _get_display_functions()['display_error'](f"Error in parsing self-reflection json {e}. Retrying", console=self.console)
-                                    logging.error("Reflection parsing failed.", exc_info=True)
-                                    reflection_count += 1
-                                    if reflection_count >= self.max_reflect:
-                                        # Return original response after max reflection attempts
-                                        self._append_to_chat_history({"role": "assistant", "content": response_text})
-                                        # Persist assistant message to DB
-                                        self._persist_message("assistant", response_text)
-                                        try:
-                                            validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
-                                            # Execute callback after validation
-                                            self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
-                                            return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
-                                        except Exception as guard_e:
-                                            logging.error(f"Agent {self.name}: Guardrail validation failed after reflection error: {guard_e}")
-                                            # Rollback chat history on guardrail failure
-                                            self._truncate_chat_history(chat_history_length)
-                                            return None
-                                    continue
-                        
-                        # This should never be reached due to the returns above
-                        # But adding as safety fallback
-                        logging.warning(f"Agent {self.name}: Unexpected code path reached in unified dispatch")
-                        return None
+                        return await self._finalize_unified_achat_response(
+                            response=response,
+                            original_prompt=original_prompt,
+                            temperature=temperature,
+                            tools=tools,
+                            output_json=output_json,
+                            output_pydantic=output_pydantic,
+                            reasoning_steps=reasoning_steps,
+                            stream=stream,
+                            messages=messages,
+                            chat_history_length=chat_history_length,
+                            start_time=start_time,
+                            task_name=task_name,
+                            task_description=task_description,
+                            task_id=task_id,
+                        )
                    else:
                        # LEGACY: Check if OpenAI client is available
                        if self._openai_client is None:
@@ -2649,7 +2339,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                        
                        # Apply guardrail validation for OpenAI client response
                        try:
-                            validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
+                            validated_response = self._apply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
                            # Execute callback after validation
                            self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
                            return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
@@ -403,33 +403,9 @@ class MemoryMixin:
                    self._session_store.add_user_message(self._session_id, content)
                elif role == "assistant":
                    self._session_store.add_assistant_message(self._session_id, content)
-                self._persist_session_stats()
            except Exception as e:
                logging.warning(f"Failed to persist message to session store: {e}")

-    def _persist_session_stats(self):
-        """Flush agent cost/token stats into session JSON metadata."""
-        store = getattr(self, "_session_store", None)
-        session_id = getattr(self, "_session_id", None)
-        if store is None or session_id is None:
-            return
-        if not hasattr(store, "update_session_metadata"):
-            return
-        try:
-            summary = getattr(self, "cost_summary", {}) or {}
-            llm = getattr(self, "llm", None)
-            model = str(llm) if llm is not None else None
-            store.update_session_metadata(
-                session_id,
-                model=model,
-                total_tokens=summary.get("tokens_in", 0) + summary.get("tokens_out", 0),
-                cost=summary.get("cost", 0),
-                source=getattr(self, "_session_source", "chat"),
-                agent_id=getattr(self, "agent_id", None) or getattr(self, "_agent_id", None),
-            )
-        except Exception as e:
-            logging.debug(f"Session stats persist skipped: {e}")
-
    def session_id(self) -> Optional[str]:
        """Get the current session ID."""
        return self._session_id
@@ -26,21 +26,6 @@ if TYPE_CHECKING:
 class ToolExecutionMixin:
    """Mixin providing toolexecution methods for the Agent class."""

-    def _get_existing_stream_emitter(self):
-        """Return an already-initialized stream emitter without creating one."""
-        emitter = getattr(self, "_stream_emitter", None)
-        if emitter is not None:
-            return emitter
-
-        # Support name-mangled private attributes across class renames/inheritance.
-        for cls in type(self).mro():
-            mangled = f"_{cls.__name__}__stream_emitter"
-            if hasattr(self, mangled):
-                emitter = getattr(self, mangled, None)
-                if emitter is not None:
-                    return emitter
-        return None
-
    def _resolve_tool_names(self, tool_names):
        """Resolve tool names to actual tool instances from registry.
        
@@ -135,12 +120,6 @@ class ToolExecutionMixin:
        """
        logging.debug(f"{self.name} executing tool {function_name} with arguments: {arguments}")
        
-        # Handle bridge tool unwrapping BEFORE trace/stream/hooks (design invariant #6)
-        # Only intercept when tool_search is active; otherwise fall through to real tool execution
-        if (getattr(self, '_tool_search_config', None) is not None and
-                function_name in ("tool_search", "tool_describe", "tool_call")):
-            return self._handle_bridge_tool_call(function_name, arguments, tool_call_id)
-        
        # NOTE: tool_call callback is triggered by display_tool_call in openai_client.py
        # Do NOT call it here to avoid duplicate output
        
@@ -181,9 +160,8 @@ class ToolExecutionMixin:
        
        # Emit TOOL_CALL_START to stream_emitter (for AIUI/AG-UI consumers)
        # Zero overhead when no callbacks registered
-        _stream_emitter = self._get_existing_stream_emitter()
-        if _stream_emitter is not None and _stream_emitter.has_callbacks:
-            _stream_emitter.emit(StreamEvent(
+        if hasattr(self, '_Agent__stream_emitter') and getattr(self, "_Agent__stream_emitter", None) is not None and getattr(self, "_Agent__stream_emitter", None).has_callbacks:
+            getattr(self, "_Agent__stream_emitter", None).emit(StreamEvent(
                type=StreamEventType.TOOL_CALL_START,
                timestamp=_tool_start_perf,
                tool_call={
@@ -297,10 +275,10 @@ class ToolExecutionMixin:
            
            # Emit TOOL_CALL_RESULT to stream_emitter (for AIUI/AG-UI consumers)
            # Zero overhead when no callbacks registered
-            if _stream_emitter is not None and _stream_emitter.has_callbacks:
+            if hasattr(self, '_Agent__stream_emitter') and getattr(self, "_Agent__stream_emitter", None) is not None and getattr(self, "_Agent__stream_emitter", None).has_callbacks:
                # Truncate result for stream event (keep it reasonable for UI display)
                result_summary = str(result)[:500] if result else None
-                _stream_emitter.emit(StreamEvent(
+                getattr(self, "_Agent__stream_emitter", None).emit(StreamEvent(
                    type=StreamEventType.TOOL_CALL_RESULT,
                    timestamp=_time.perf_counter(),
                    tool_call={
@@ -312,18 +290,6 @@ class ToolExecutionMixin:
                    agent_id=self.name,
                    metadata={"duration_ms": _duration_ms},
                ))
-                _stream_emitter.emit(StreamEvent(
-                    type=StreamEventType.TOOL_CALL_END,
-                    timestamp=_time.perf_counter(),
-                    tool_call={
-                        "name": function_name,
-                        "arguments": arguments,
-                        "result": result_summary,
-                        "id": tool_call_id,
-                    },
-                    agent_id=self.name,
-                    metadata={"duration_ms": _duration_ms},
-                ))
            
            # Trigger AFTER_TOOL hook
            from ..hooks import HookEvent, AfterToolInput
@@ -960,97 +926,4 @@ class ToolExecutionMixin:
    def pending_approval_count(self) -> int:
        """Number of approval requests still waiting."""
        return len(self._pending_approvals)
-    
-    def _handle_bridge_tool_call(self, function_name: str, arguments: Dict[str, Any], tool_call_id: Optional[str] = None) -> Any:
-        """
-        Handle bridge tool calls (tool_search, tool_describe, tool_call).
-        
-        This implements the tool search unwrapping logic before trace/stream/hooks
-        as required by design invariant #6.
-        
-        Args:
-            function_name: Bridge tool name
-            arguments: Arguments passed to bridge tool
-            tool_call_id: Optional tool call ID
-            
-        Returns:
-            Result of bridge tool execution or unwrapped real tool call
-        """
-        # Ensure tool search metadata is available
-        if not hasattr(self, '_tool_search_metadata') or self._tool_search_metadata is None:
-            return "Tool search not available or not in bridge mode"
-        
-        metadata = self._tool_search_metadata
-        
-        # Check if we're in bridge mode
-        if not metadata.get("bridge_mode", False):
-            return "Tool search not in bridge mode"
-        
-        # Get deferrable tools from metadata
-        deferrable_tools = metadata.get("deferrable_tools", [])
-        
-        if function_name == "tool_search":
-            # Handle tool_search bridge call
-            try:
-                from ..tools.tool_search import dispatch_tool_search
-                query = arguments.get("query", "")
-                limit = arguments.get("limit", None)
-                
-                result = dispatch_tool_search(
-                    query=query,
-                    limit=limit, 
-                    deferrable_tools=deferrable_tools,
-                    config=self._tool_search_config
-                )
-                return json.dumps(result, indent=2)
-            except ImportError:
-                return "Tool search module not available"
-            except Exception as e:
-                logging.error(f"Error in tool_search: {e}")
-                return f"Error searching tools: {e}"
-        
-        elif function_name == "tool_describe":
-            # Handle tool_describe bridge call
-            try:
-                from ..tools.tool_search import dispatch_tool_describe
-                tool_name = arguments.get("tool_name", "")
-                
-                result = dispatch_tool_describe(
-                    tool_name=tool_name,
-                    deferrable_tools=deferrable_tools
-                )
-                return json.dumps(result, indent=2)
-            except ImportError:
-                return "Tool search module not available"
-            except Exception as e:
-                logging.error(f"Error in tool_describe: {e}")
-                return f"Error describing tool: {e}"
-        
-        elif function_name == "tool_call":
-            # Handle tool_call bridge - unwrap and recurse with real tool
-            try:
-                from ..tools.tool_search import resolve_underlying_call
-                
-                # Unwrap the real tool call
-                real_function_name, real_arguments = resolve_underlying_call(function_name, arguments)
-                
-                # Validate that the real tool is in our deferrable set (security check)
-                deferrable_names = {
-                    tool_def.get("function", {}).get("name", "") 
-                    for tool_def in deferrable_tools
-                }
-                
-                if real_function_name not in deferrable_names:
-                    return f"Tool '{real_function_name}' is not available for execution"
-                
-                # Recursively execute the real tool (this will go through normal execution path)
-                return self.execute_tool(real_function_name, real_arguments, tool_call_id)
-                
-            except ImportError:
-                return "Tool search module not available"
-            except Exception as e:
-                logging.error(f"Error in tool_call unwrap: {e}")
-                return f"Error executing tool: {e}"
-        
-        else:
-            return f"Unknown bridge tool: {function_name}"
+
@@ -15,9 +15,9 @@ from enum import Enum

 # Import token tracking
 try:
-    from ..telemetry.token_collector import get_token_collector
+    from ..telemetry.token_collector import _token_collector
 except ImportError:
-    get_token_collector = None
+    _token_collector = None

 # Import async utility for hot-path usage
 try:
@@ -1609,25 +1609,25 @@ class AgentTeam:
            original_on_task_start = self.on_task_start
            original_on_task_complete = self.on_task_complete
            
-            def composed_on_task_start(task, task_id):
+            def composed_on_task_start(task):
                try:
-                    verbose_task_start_callback(task, task_id)
+                    verbose_task_start_callback(task)
                except Exception as e:
                    logging.debug(f"Error in verbose task start callback: {e}")
                if original_on_task_start:
                    try:
-                        original_on_task_start(task, task_id)
+                        original_on_task_start(task)
                    except Exception as e:
                        logging.debug(f"Error in original task start callback: {e}")
            
-            def composed_on_task_complete(task, task_output):
+            def composed_on_task_complete(task):
                try:
-                    verbose_task_complete_callback(task, task_output)
+                    verbose_task_complete_callback(task)
                except Exception as e:
                    logging.debug(f"Error in verbose task complete callback: {e}")
                if original_on_task_complete:
                    try:
-                        original_on_task_complete(task, task_output)
+                        original_on_task_complete(task)
                    except Exception as e:
                        logging.debug(f"Error in original task complete callback: {e}")
            
@@ -1826,19 +1826,18 @@ class AgentTeam:

    def get_token_usage_summary(self) -> Dict[str, Any]:
        """Get a summary of token usage across all agents and tasks."""
-        if not get_token_collector:
+        if not _token_collector:
            return {"error": "Token tracking not available"}
        
-        return get_token_collector().get_session_summary()
+        return _token_collector.get_session_summary()
    
    def get_detailed_token_report(self) -> Dict[str, Any]:
        """Get a detailed token usage report."""
-        if not get_token_collector:
+        if not _token_collector:
            return {"error": "Token tracking not available"}
        
-        collector = get_token_collector()
-        summary = collector.get_session_summary()
-        recent = collector.get_recent_interactions(limit=20)
+        summary = _token_collector.get_session_summary()
+        recent = _token_collector.get_recent_interactions(limit=20)
        
        # Calculate cost estimates (example rates)
        cost_per_1k_input = 0.0005  # $0.0005 per 1K input tokens
@@ -1862,11 +1861,11 @@ class AgentTeam:
    
    def display_token_usage(self):
        """Display token usage in a formatted table."""
-        if not get_token_collector:
+        if not _token_collector:
            print("Token tracking not available")
            return
        
-        summary = get_token_collector().get_session_summary()
+        summary = _token_collector.get_session_summary()
        
        print("\n" + "="*50)
        print("TOKEN USAGE SUMMARY")
@@ -39,7 +39,6 @@ __all__ = [
    "CachingConfig",
    "HooksConfig",
    "SkillsConfig",
-    "ToolSearchConfig",
    # Type aliases
    "MemoryParam",
    "KnowledgeParam",
@@ -123,7 +122,6 @@ _MODULE_MAP = {
    "CachingConfig": "feature_configs",
    "HooksConfig": "feature_configs",
    "SkillsConfig": "feature_configs",
-    "ToolSearchConfig": "feature_configs",
    "MemoryParam": "feature_configs",
    "KnowledgeParam": "feature_configs",
    "PlanningParam": "feature_configs",