Fix critical AgentOS bugs identified by reviewers

- Fix CORS configuration to prevent server startup crash
- Add role, tools, connections fields to DB schema and API models
- Fix activity list ordering (now returns newest instead of oldest)
- Add proper HTTP error handling across frontend
- Add defensive guards for undefined tools/connections arrays
- Update frontend status options to match backend validation
- Add request cancellation to prevent stale response handling

Addresses P0/P1 issues found by CodeRabbit, Greptile, Gemini reviewers.

.env.example

@@ -1,51 +0,0 @@
-# PraisonAI Environment Variables
-# Copy this file to .env and update with your actual values
-
-# === Required ===
-OPENAI_API_KEY=sk-...          # OpenAI key for LLM calls. Get yours at https://platform.openai.com
-
-# === Required for Claw web-search tool ===
-TAVILY_API_KEY=tvly-...        # Get yours at https://app.tavily.com (free tier available)
-
-# === Optional: Claw dashboard security ===
-PRAISONAI_ALLOW_LOCAL_TOOLS=0  # Set to 1 to enable local shell tools (security risk)
-
-# === Optional: Alternative LLM Providers ===
-ANTHROPIC_API_KEY=sk-ant-...   # Anthropic Claude models
-GOOGLE_API_KEY=...             # Google Gemini models  
-GROQ_API_KEY=gsk_...           # Groq (fast inference)
-COHERE_API_KEY=...             # Cohere models
-AZURE_OPENAI_API_KEY=...       # Azure OpenAI
-AZURE_OPENAI_ENDPOINT=...      # Azure OpenAI endpoint
-
-# === Optional: Memory backends ===
-MEM0_API_KEY=...               # Mem0 memory service
-REDIS_URL=redis://localhost:6379  # Redis for state management
-
-# === Optional: Observability ===
-LANGFUSE_SECRET_KEY=sk-lf-...  # Langfuse tracing
-LANGFUSE_PUBLIC_KEY=pk-lf-...  # Langfuse public key
-
-# === Optional: Bot platforms ===
-TELEGRAM_BOT_TOKEN=...         # Telegram bot token
-DISCORD_BOT_TOKEN=...          # Discord bot token  
-SLACK_BOT_TOKEN=xoxb-...       # Slack bot token
-SLACK_APP_TOKEN=xapp-...       # Slack app token
-WHATSAPP_ACCESS_TOKEN=...      # WhatsApp bot access token
-WHATSAPP_PHONE_NUMBER_ID=...   # WhatsApp phone number ID
-
-# === Optional: Database ===
-DATABASE_URL=sqlite:///~/.praison/database.sqlite  # Database connection string
-
-# === Optional: Tool Security ===
-# ALLOWED_TOOLS=search,send_message       # Whitelist specific tools to prevent name collisions
-                                        # Useful in multi-environment setups with overlapping tool names
-                                        # Leave unset to use all tools (with collision warnings)
-                                        # Do NOT set to empty string: that's treated as configuration error
-                                        # In CI, unknown tool names in the whitelist cause strict startup failure
-# HERMES_ONLY_TOOLS=search,send_message  # (Deprecated) Backward compatibility alias for ALLOWED_TOOLS
-
-# === Optional: Development ===
-LOGLEVEL=INFO                  # Logging level (DEBUG, INFO, WARNING, ERROR)
-CHAINLIT_AUTH_SECRET=...       # Chainlit authentication secret
-DEBUG=false                    # Enable debug mode

.github/workflows/claude.yml

@@ -181,15 +181,12 @@ jobs:
 
       - name: Fetch PR branch and Setup Remote
         if: github.event.issue.pull_request
-        env:
-          PR_BRANCH: ${{ steps.check_fork.outputs.pr_branch }}
-          IS_FORK: ${{ steps.check_fork.outputs.is_fork }}
         run: |
           # Fetch PR head from base repo and put it into local branch
-          git fetch origin pull/${{ github.event.issue.number }}/head:"${PR_BRANCH}"
+          git fetch origin pull/${{ github.event.issue.number }}/head:${{ steps.check_fork.outputs.pr_branch }}
           
           # If it's a fork, make origin point to local to trick claude-code-action's `git fetch origin <branch>`
-          if [ "${IS_FORK}" = "true" ]; then
+          if [ "${{ steps.check_fork.outputs.is_fork }}" == "true" ]; then
             git remote set-url origin file://$(pwd)
           fi
 

.github/workflows/integration-cross-repo.yml

@@ -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

.github/workflows/praisonai-issue-triage.yml

@@ -29,6 +29,9 @@ jobs:
         uses: actions/checkout@v4
         with:
           persist-credentials: false
+        with:
+          persist-credentials: false
+        with:
           fetch-depth: 0
           
       - name: Generate GitHub App Token
@@ -60,12 +63,10 @@ jobs:
           GH_TOKEN: ${{ steps.generate_token.outputs.token }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           PRAISONAI_AUTO_APPROVE: "true"
-          ISSUE_NUMBER_INPUT: ${{ inputs.issue_number }}
-          ISSUE_NUMBER_EVENT: ${{ github.event.issue.number }}
         run: |
           if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
-            export ISSUE_NUMBER="$ISSUE_NUMBER_INPUT"
+            export ISSUE_NUMBER="${{ inputs.issue_number }}"
           else
-            export ISSUE_NUMBER="$ISSUE_NUMBER_EVENT"
+            export ISSUE_NUMBER="${{ github.event.issue.number }}"
           fi
           praisonai github triage --issue $ISSUE_NUMBER --agent-file .github/praisonai-issue-triage.yaml

.github/workflows/praisonai-pr-review.yml

@@ -30,23 +30,22 @@ jobs:
     steps:
       - name: Determine checkout ref
         id: dest
-        env:
-          ISSUE_NUMBER: ${{ github.event.issue.number }}
-          PR_NUMBER_INPUT: ${{ inputs.pr_number }}
-          PR_HEAD_SHA: ${{ github.event.pull_request.head.sha }}
         run: |
           if [ "${{ github.event_name }}" = "issue_comment" ]; then
-            echo "ref=refs/pull/$ISSUE_NUMBER/head" >> "$GITHUB_OUTPUT"
+            echo "ref=refs/pull/${{ github.event.issue.number }}/head" >> "$GITHUB_OUTPUT"
           elif [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
-            echo "ref=refs/pull/$PR_NUMBER_INPUT/head" >> "$GITHUB_OUTPUT"
+            echo "ref=refs/pull/${{ inputs.pr_number }}/head" >> "$GITHUB_OUTPUT"
           else
-            echo "ref=$PR_HEAD_SHA" >> "$GITHUB_OUTPUT"
+            echo "ref=${{ github.event.pull_request.head.sha }}" >> "$GITHUB_OUTPUT"
           fi
 
       - name: Checkout Repository
         uses: actions/checkout@v4
         with:
           persist-credentials: false
+        with:
+          persist-credentials: false
+        with:
           ref: ${{ steps.dest.outputs.ref }}
           fetch-depth: 0
           

.github/workflows/test-windows.yml

@@ -26,8 +26,8 @@ jobs:
     
     steps:
     - uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
     - name: Set up Python 3.11
       uses: actions/setup-python@v5
       with:
@@ -176,18 +176,6 @@ jobs:
       run: praisonai queue list
       continue-on-error: true
 
-    # ==================== PACKAGING TESTS ====================
-    - name: Test packaging/daemon entry points
-      run: |
-        echo "Testing both entry point methods..."
-        python -m praisonai --version
-        praisonai --version
-
-    - name: Test doctor packaging command
-      run: |
-        echo "Testing doctor packaging diagnostics..."
-        praisonai doctor packaging --json
-
     # ==================== REAL API TESTS ====================
     - name: Real API test - Agent run
       if: env.OPENAI_API_KEY != ''

.replit

@@ -0,0 +1,60 @@
+modules = ["python-3.12", "nodejs-20", "rust-stable", "bash"]
+[agent]
+expertMode = true
+integrations = ["github:1.0.0"]
+
+[nix]
+channel = "stable-25_05"
+packages = ["libxcrypt"]
+
+[[ports]]
+localPort = 5000
+externalPort = 80
+
+[[ports]]
+localPort = 8000
+externalPort = 8000
+
+[workflows]
+runButton = "Project"
+
+[[workflows.workflow]]
+name = "Project"
+mode = "parallel"
+author = "agent"
+
+[[workflows.workflow.tasks]]
+task = "workflow.run"
+args = "Start application"
+
+[[workflows.workflow.tasks]]
+task = "workflow.run"
+args = "Backend API"
+
+[[workflows.workflow]]
+name = "Start application"
+author = "agent"
+
+[workflows.workflow.metadata]
+outputType = "webview"
+
+[[workflows.workflow.tasks]]
+task = "shell.exec"
+args = "python run.py"
+waitForPort = 5000
+
+[[workflows.workflow]]
+name = "Backend API"
+author = "agent"
+
+[[workflows.workflow.tasks]]
+task = "shell.exec"
+args = "python backend/main.py"
+waitForPort = 8000
+
+[workflows.workflow.metadata]
+outputType = "console"
+
+[deployment]
+deploymentTarget = "autoscale"
+run = ["python", "run.py"]

CONTRIBUTING.md

@@ -1,14 +0,0 @@
-# Contributing to PraisonAI
-
-Thank you for your interest! Here's how to get started:
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Submit a pull request
-
-## Guidelines
-
-- Follow existing code style
-- Add tests for new features
-- Update documentation as needed

README.md

@@ -23,7 +23,7 @@
 
 </div>
 
-PraisonAI 🦞 — **Hire a 24/7 AI Workforce.** Stop writing boilerplate and start shipping autonomous, self-improving agents that research, plan, and execute tasks across your apps. From one agent to an entire organization, deployed in 5 lines of code.
+PraisonAI 🦞 — **Hire a 24/7 AI Workforce.** Stop writing boilerplate and start shipping autonomous agents that research, plan, and execute tasks across your apps. From one agent to an entire organization, deployed in 5 lines of code.
 
 ```bash
 curl -fsSL https://praison.ai/install.sh | bash
@@ -294,35 +294,8 @@ def search(query: str) -> str:
 
 @tool
 def calculate(expression: str) -> float:
-    """Safely evaluate a numeric arithmetic expression."""
-    import ast
-    import operator
-    
-    # Define allowed operations
-    _OPS = {
-        ast.Add: operator.add,
-        ast.Sub: operator.sub,
-        ast.Mult: operator.mul,
-        ast.Div: operator.truediv,
-        ast.Pow: operator.pow,
-        ast.USub: operator.neg,
-        ast.UAdd: operator.pos,
-    }
-    
-    def _safe_eval(node):
-        if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
-            return node.value
-        elif isinstance(node, ast.BinOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_safe_eval(node.left), _safe_eval(node.right))
-        elif isinstance(node, ast.UnaryOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_safe_eval(node.operand))
-        else:
-            raise ValueError("Unsupported expression")
-    
-    try:
-        return _safe_eval(ast.parse(expression, mode="eval").body)
-    except (ValueError, SyntaxError, TypeError, ZeroDivisionError, OverflowError):
-        raise ValueError("Invalid arithmetic expression")
+    """Evaluate a math expression."""
+    return eval(expression)
 
 agent = Agent(
     instructions="You are a helpful assistant",
@@ -331,7 +304,6 @@ agent = Agent(
 agent.start("Search for AI news and calculate 15*4")
 ```
 
-> ⚠️ **Security Note:** Never use `eval()`, `exec()`, or `subprocess` in tool functions that process LLM-generated or user-supplied input. Always validate and sanitize inputs to prevent code injection attacks.
 > 📖 [Full tools docs](https://docs.praison.ai/docs/tools/tools) — BaseTool, tool packages, 100+ built-in tools
 
 ### 5. Persistence (Databases)
@@ -358,15 +330,6 @@ pip install "praisonai[claw]"
 praisonai claw
 ```
 
-#### Required Environment Variables
-
-Copy `.env.example` to `.env` and configure the following variables:
-
-| Variable | Required | Description |
-|----------|----------|-------------|
-| `OPENAI_API_KEY` | Yes | OpenAI API key for all LLM calls |
-| `TAVILY_API_KEY` | Yes (Claw) | Tavily key for the built-in web-search tool. Get one free at https://app.tavily.com |
-
 Open **http://localhost:8082** — the dashboard comes with 13 built-in pages: Chat, Agents, Memory, Knowledge, Channels, Guardrails, Cron, and more. Add messaging channels directly from the UI.
 
 > 📖 [Full Claw docs](https://docs.praison.ai/docs/concepts/claw) — platform tokens, CLI options, Docker, and YAML agent mode
@@ -382,7 +345,7 @@ 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)
 

api.md

@@ -15,7 +15,6 @@ Methods:
 * <code title="class GuardrailResult">GuardrailResult.<a href="./src/praisonai-agents/praisonaiagents/guardrails/guardrail_result.py">from_tuple</a>(result: Tuple[bool, Any]) -> 'GuardrailResult'</code>
 * <code title="class HandoffConfig">HandoffConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">from_dict</a>(data: Dict[str, Any]) -> 'HandoffConfig'</code>
 * <code title="class HandoffConfig">HandoffConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">to_dict</a>() -> Dict[str, Any]</code>
-* <code title="class HandoffResult">HandoffResult.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">from_outcome</a>(outcome: AgentRunOutcome, target_agent: Optional[str] = None, source_agent: Optional[str] = None, handoff_depth: int = 0) -> 'HandoffResult'</code>
 * <code title="class TaskOutput">TaskOutput.<a href="./src/praisonai-agents/praisonaiagents/main.py">json</a>() -> Optional[str]</code>
 * <code title="class TaskOutput">TaskOutput.<a href="./src/praisonai-agents/praisonaiagents/main.py">to_dict</a>() -> dict</code>
 * <code title="class ToolResult">ToolResult.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">to_dict</a>() -> Dict[str, Any]</code>
@@ -41,7 +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>
@@ -76,7 +74,7 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_until</a>(prompt: str, criteria: str, threshold: float = 8.0, max_iterations: int = 5, mode: str = 'optimize', on_iteration: Optional[Callable[[Any], None]] = None, verbose: bool = False) -> 'EvaluationLoopResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_until_async</a>(prompt: str, criteria: str, threshold: float = 8.0, max_iterations: int = 5, mode: str = 'optimize', on_iteration: Optional[Callable[[Any], None]] = None, verbose: bool = False) -> 'EvaluationLoopResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">skill_manager</a>() -> Optional[Any]</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">store_memory</a>(content: str, memory_type: str = 'short_term', action: str = 'add', **kwargs: Any) -> None</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">store_memory</a>(content: str, memory_type: str = 'short_term', **kwargs: Any) -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">stream_emitter</a>() -> Optional[Any]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">stream_emitter</a>(value: Optional[Any]) -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">thinking_budget</a>() -> Optional[int]</code>
@@ -173,10 +171,7 @@ 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">get_schema</a>() -> Dict[str, Any]</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">injected_params</a>() -> Dict[str, Any]</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">run</a>(**kwargs) -> Any</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">clear</a>() -> None</code>
@@ -184,18 +179,14 @@ Methods:
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">discover_single_file_plugins</a>() -> int</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get</a>(name: str) -> Optional[Union[BaseTool, Callable]]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_all</a>() -> Dict[str, Union[BaseTool, Callable]]</code>
-* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_tool_definitions</a>() -> List[Dict[str, Any]]</code>
-* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_trust_level</a>(name: str) -> Optional[str]</code>
-* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_available_tools</a>(context: Optional[Dict[str, Any]] = None, ttl_seconds: Optional[float] = None) -> List[Union[BaseTool, Callable]]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_base_tools</a>() -> List[BaseTool]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_tools</a>() -> List[str]</code>
-* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_tools_with_allowed_filter</a>(context: Optional[Dict[str, Any]] = None) -> List[str]</code>
-* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None, overwrite: bool = False, trust_level: Optional[str] = None, dynamic_schema_overrides: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None) -> None</code>
+* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None, overwrite: bool = False) -> None</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">unregister</a>(name: str) -> bool</code>
 * <code title="class Tools">Tools.<a href="./src/praisonai-agents/praisonaiagents/tools/tools.py">internet_search</a>(*args, **kwargs)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_registry</a>() -> ToolRegistry</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_tool</a>(name: str) -> Optional[Union[BaseTool, Callable]]</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register_tool</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None, trust_level: Optional[str] = None, dynamic_schema_overrides: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None) -> None</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register_tool</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None) -> None</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">tool</a>(func: Optional[Callable] = None) -> Union[FunctionTool, Callable[[Callable], FunctionTool]]</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_tool</a>(tool: Any) -> bool</code>
 
@@ -215,12 +206,11 @@ Methods:
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">execute_callback_sync</a>(task_output: TaskOutput) -> None</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">get_next_task</a>(context: Dict[str, Any]) -> Optional[str]</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">initialize_memory</a>()</code>
-* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">initialize_memory_async</a>()</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">set_status</a>(new_status: str) -> None</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">store_in_memory</a>(content: str, agent_name: str = None, task_id: str = None)</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">loop</a>(step: Any = None, steps: Optional[List[Any]] = None, over: Optional[str] = None, from_csv: Optional[str] = None, from_file: Optional[str] = None, var_name: str = 'item', parallel: bool = False, max_workers: Optional[int] = None, output_variable: Optional[str] = None) -> Loop</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">parallel</a>(steps: List, max_workers: Optional[int] = None, on_failure: str = 'partial_ok') -> Parallel</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">parallel</a>(steps: List) -> Parallel</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">repeat</a>(step: Any, until: Optional[Callable[[WorkflowContext], bool]] = None, max_iterations: int = 10) -> Repeat</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">route</a>(routes: Dict[str, List], default: Optional[List] = None) -> Route</code>
 
@@ -357,7 +347,7 @@ Methods:
 * <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">to_tool_function</a>(source_agent: 'Agent') -> Callable</code>
 * <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">tool_description</a>() -> str</code>
 * <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">tool_name</a>() -> str</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">handoff</a>(agent: 'Agent', tool_name_override: Optional[str] = None, tool_description_override: Optional[str] = None, on_handoff: Optional[Callable] = None, input_type: Optional[type] = None, input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None, config: Optional[HandoffConfig] = None, context_policy: Optional[str] = None, timeout_seconds: Optional[float] = None, max_concurrent: Optional[int] = None, detect_cycles: Optional[bool] = None, max_depth: Optional[int] = None, tool_policy_mode: Optional[Literal['intersect', 'passthrough']] = None, blocked_tools: Optional[List[str]] = None) -> Handoff</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">handoff</a>(agent: 'Agent', tool_name_override: Optional[str] = None, tool_description_override: Optional[str] = None, on_handoff: Optional[Callable] = None, input_type: Optional[type] = None, input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None, config: Optional[HandoffConfig] = None, context_policy: Optional[str] = None, timeout_seconds: Optional[float] = None, max_concurrent: Optional[int] = None, detect_cycles: Optional[bool] = None, max_depth: Optional[int] = None) -> Handoff</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">compress_history</a>(data: HandoffInputData) -> HandoffInputData</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">keep_last_n_messages</a>(n: int) -> Callable[[HandoffInputData], HandoffInputData]</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">remove_all_tools</a>(data: HandoffInputData) -> HandoffInputData</code>
@@ -477,26 +467,17 @@ Methods:
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">activate_by_name</a>(name: str) -> bool</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">add_skill</a>(skill_path: str) -> Optional[LoadedSkill]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">clear</a>() -> None</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">create_skill</a>(name: str, content: str, category: str = None) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">delete_skill</a>(name: str) -> dict</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">discover</a>(skill_dirs: Optional[List[str]] = None, include_defaults: bool = True) -> int</code>
-* <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>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">patch_skill</a>(name: str, old_string: str, new_string: str, file_path: str = None, replace_all: bool = False) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">remove_skill_file</a>(name: str, file_path: str) -> dict</code>
 * <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>
 * <code title="class SkillsConfig">SkillsConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
@@ -611,13 +592,6 @@ 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:
@@ -630,7 +604,6 @@ Methods:
 * <code title="class AutonomyConfig">AutonomyConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/autonomy.py">effective_track_changes</a>() -> bool</code>
 * <code title="class AutonomyConfig">AutonomyConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/autonomy.py">from_dict</a>(data: Dict[str, Any]) -> 'AutonomyConfig'</code>
 * <code title="class CachingConfig">CachingConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
-* <code title="class ExecutionConfig">ExecutionConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">from_dict</a>(data: Dict[str, Any]) -> 'ExecutionConfig'</code>
 * <code title="class ExecutionConfig">ExecutionConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="class GuardrailConfig">GuardrailConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="class HooksConfig">HooksConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
@@ -723,19 +696,14 @@ Methods:
 * <code title="class AgentFlow">AgentFlow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">verbose</a>(value: bool)</code>
 * <code title="class AgentOSProtocol">AgentOSProtocol.<a href="./src/praisonai-agents/praisonaiagents/app/protocols.py">get_app</a>() -> Any</code>
 * <code title="class AgentOSProtocol">AgentOSProtocol.<a href="./src/praisonai-agents/praisonaiagents/app/protocols.py">serve</a>(host: Optional[str] = None, port: Optional[int] = None, reload: bool = False, **kwargs: Any) -> None</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aannounce_completion</a>(agent_id: str, task_id: str, result: Any, success: bool = True, error: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None) -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">add_task</a>(task)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aexecute_task</a>(task_id)</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">announce_completion</a>(agent_id: str, task_id: str, result: Any, success: bool = True, error: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None) -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">append_to_state</a>(key: str, value: Any, max_length: Optional[int] = None) -> List[Any]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_all_tasks</a>()</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_task</a>(task_id)</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aspawn_sub_agent</a>(agent: Agent, task: Any, completion_callback: Optional[Callable[[SubAgentCompletionEvent], Any]] = None, metadata: Optional[Dict[str, Any]] = None) -> SpawnedSubAgent</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">astart</a>(content = None, return_dict = False, **kwargs)</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">await_for_completions</a>(timeout: Optional[float] = None, agent_ids: Optional[List[str]] = None) -> List[SubAgentCompletionEvent]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clean_json_output</a>(output: str) -> str</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clear_state</a>() -> None</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">close</a>() -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">context_manager</a>()</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">current_plan</a>()</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">default_completion_checker</a>(task, agent_output)</code>
@@ -747,7 +715,6 @@ Methods:
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_tasks_status</a>()</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_detailed_token_report</a>() -> Dict[str, Any]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_plan_markdown</a>() -> str</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_spawned_agents</a>() -> List[SpawnedSubAgent]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_state</a>(key: str, default: Any = None) -> Any</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_details</a>(task_id)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_result</a>(task_id)</code>
@@ -764,12 +731,10 @@ Methods:
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_output_to_file</a>(task, task_output)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_session_state</a>(session_id: str, include_memory: bool = True) -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">set_state</a>(key: str, value: Any) -> None</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">spawn_sub_agent</a>(agent: Agent, task: Any, completion_callback: Optional[Callable[[SubAgentCompletionEvent], Any]] = None, metadata: Optional[Dict[str, Any]] = None) -> SpawnedSubAgent</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, output = None, **kwargs)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">todo_list</a>()</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_plan_step_status</a>(step_id: str, status: str) -> bool</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_state</a>(updates: Dict) -> None</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">wait_for_completions</a>(timeout: Optional[float] = None, agent_ids: Optional[List[str]] = None) -> List[SubAgentCompletionEvent]</code>
 * <code title="class AutoApproveBackend">AutoApproveBackend.<a href="./src/praisonai-agents/praisonaiagents/approval/backends.py">request_approval</a>(request: ApprovalRequest) -> ApprovalDecision</code>
 * <code title="class AutoApproveBackend">AutoApproveBackend.<a href="./src/praisonai-agents/praisonaiagents/approval/backends.py">request_approval_sync</a>(request: ApprovalRequest) -> ApprovalDecision</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/embedding/embed.py">aembedding</a>(input: Union[str, List[str]], model: str = 'text-embedding-3-small', dimensions: Optional[int] = None, encoding_format: str = 'float', timeout: float = 600.0, api_key: Optional[str] = None, api_base: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) -> EmbeddingResult</code>
@@ -780,7 +745,7 @@ 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 AgentApp, AgentOS, AnthropicManagedAgent, CloudProvider, DB, Deploy, DeployConfig, DeployType, LocalManagedAgent, LocalManagedConfig, ManagedAgent, ManagedConfig, PraisonAI, __version__
 ```
 
 # CLI
@@ -811,10 +776,6 @@ Methods:
 * <code title="cli">praisonai bot discord <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
 * <code title="cli">praisonai bot email <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
 * <code title="cli">praisonai bot install-daemon <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
-* <code title="cli">praisonai bot linear <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
-* <code title="cli">praisonai bot list <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
-* <code title="cli">praisonai bot purge <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
-* <code title="cli">praisonai bot replay <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
 * <code title="cli">praisonai bot slack <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
 * <code title="cli">praisonai bot start <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
 * <code title="cli">praisonai bot telegram <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
@@ -882,10 +843,8 @@ Methods:
 * <code title="cli">praisonai doctor env <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor mcp <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor network <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
-* <code title="cli">praisonai doctor 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>
@@ -913,28 +872,14 @@ Methods:
 * <code title="cli">praisonai gateway gateway-callback <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway install <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway logs <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
-* <code title="cli">praisonai gateway mint-link <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <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>
@@ -999,10 +944,6 @@ Methods:
 * <code title="cli">praisonai package install <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
 * <code title="cli">praisonai package list <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
 * <code title="cli">praisonai package uninstall <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
-* <code title="cli">praisonai pairing approve <a href="./src/praisonai/praisonai/cli/commands/pairing.py">--help</a></code>
-* <code title="cli">praisonai pairing clear <a href="./src/praisonai/praisonai/cli/commands/pairing.py">--help</a></code>
-* <code title="cli">praisonai pairing list <a href="./src/praisonai/praisonai/cli/commands/pairing.py">--help</a></code>
-* <code title="cli">praisonai pairing revoke <a href="./src/praisonai/praisonai/cli/commands/pairing.py">--help</a></code>
 * <code title="cli">praisonai paths show <a href="./src/praisonai/praisonai/cli/commands/paths.py">--help</a></code>
 * <code title="cli">praisonai plugins create <a href="./src/praisonai/praisonai/cli/commands/plugins.py">--help</a></code>
 * <code title="cli">praisonai plugins disable <a href="./src/praisonai/praisonai/cli/commands/plugins.py">--help</a></code>
@@ -1092,7 +1033,6 @@ 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>

attached_assets/Pasted-Build-a-clean-modern-web-dashboard-for-managing-and-int_1776597969858.txt

@@ -0,0 +1,168 @@
+Build a clean, modern web dashboard for managing and interacting with AI agents powered by PraisonAI. Use React + Vite for the frontend, and a simple Python FastAPI backend. Keep it simple but functional — no overengineering.
+
+---
+
+## TECH STACK
+- Frontend: React + Vite + TailwindCSS (CDN, no build step for Tailwind)
+- Backend: FastAPI (Python)
+- Storage: JSON files (no database needed yet, keep it simple)
+- API calls: fetch() to the FastAPI backend
+
+---
+
+## PROJECT STRUCTURE
+/backend
+  main.py          # FastAPI app
+  agents_store.json  # persists agent definitions
+  history_store.json # persists conversation history
+
+/frontend
+  index.html
+  src/
+    App.jsx
+    components/
+      Sidebar.jsx
+      AgentList.jsx
+      AgentDetail.jsx
+      ChatPanel.jsx
+      ActivityLog.jsx
+      AgentForm.jsx
+
+---
+
+## BACKEND (FastAPI - main.py)
+
+Create these REST endpoints:
+
+### Agents CRUD
+GET    /agents              → list all agents
+POST   /agents              → create agent
+PUT    /agents/{id}         → update agent
+DELETE /agents/{id}         → delete agent
+GET    /agents/{id}         → get single agent details
+
+### Chat
+POST   /agents/{id}/chat    → send message to agent, returns response + activity log
+GET    /agents/{id}/history → get full conversation history
+DELETE /agents/{id}/history → clear history
+
+### Activity
+GET    /agents/{id}/activity → get activity log (tools used, connections, actions)
+
+Each agent object should look like:
+{
+  "id": "uuid",
+  "name": "string",
+  "role": "string",
+  "instructions": "string",
+  "tools": ["list of tool names"],
+  "connections": ["other agent ids or external APIs"],
+  "llm": "string (e.g. gpt-4o-mini)",
+  "created_at": "timestamp",
+  "status": "active | inactive"
+}
+
+Each chat call should:
+1. Load agent config
+2. Run a PraisonAI agent with that config
+3. Capture stdout/tool calls/actions into an activity log entry
+4. Save message + response + activity to history
+5. Return all of it in one response
+
+---
+
+## FRONTEND LAYOUT
+
+Split into 3 columns:
+
+### LEFT SIDEBAR (narrow)
+- App logo/name "AgentOS"
+- List of all agents (name + status dot green/grey)
+- "+ New Agent" button at bottom
+- Click agent → loads it in center panel
+
+### CENTER PANEL (main)
+Two tabs at top: "Chat" | "Details"
+
+#### Chat tab:
+- Conversation history (bubbles: user right, agent left)
+- Each agent message has a collapsible "Activity" section below it showing:
+  - What tools were called
+  - What external connections were used
+  - Raw output / reasoning steps
+- Input box at bottom + Send button
+- "Clear History" button top right
+
+#### Details tab:
+- Shows agent config (name, role, instructions, LLM, tools, connections)
+- Edit button → opens inline form to modify
+- Shows stats: total messages, last active, tools used count
+
+### RIGHT PANEL (narrower)
+- "Activity Feed" — real-time log of what the selected agent has done
+- Each entry: timestamp, action type (tool_call, api_call, message, error), short description
+- Color coded: blue=tool, green=success, red=error, grey=message
+- Scrollable, newest at top
+
+---
+
+## AGENT FORM (Add/Edit modal or inline)
+Fields:
+- Name (text input)
+- Role (text input, short description)
+- Instructions / System Prompt (large textarea)
+- LLM Model (dropdown: gpt-4o-mini, gpt-4o, claude-3-haiku, claude-sonnet, gemini-flash)
+- Tools (multi-select checkboxes): web_search, code_interpreter, file_reader, calculator, wikipedia
+- Connections (text inputs, add/remove rows): label + URL or agent name
+- Status toggle (active/inactive)
+Save / Cancel buttons
+
+---
+
+## STYLE GUIDE
+- Dark theme (background #0f1117, panels #1a1d27, accent #6c63ff purple)
+- Clean sans-serif font (Inter from Google Fonts)
+- Subtle borders (#2a2d3e)
+- Agent status dot: green (#22c55e) active, grey (#6b7280) inactive
+- Activity log colors: blue (#3b82f6) tool, green (#22c55e) success, red (#ef4444) error
+- Rounded corners (8px), subtle shadows
+- Mobile responsive (sidebar collapses to hamburger on small screens)
+
+---
+
+## SAMPLE DATA
+Pre-populate agents_store.json with 2 example agents so the UI is not empty:
+
+Agent 1:
+- Name: "Research Agent"
+- Role: "Searches the web and summarizes information"
+- Tools: web_search, wikipedia
+- LLM: gpt-4o-mini
+
+Agent 2:
+- Name: "Code Agent"
+- Role: "Writes and explains code"
+- Tools: code_interpreter, calculator
+- LLM: gpt-4o-mini
+
+---
+
+## IMPORTANT CONSTRAINTS
+- No TypeScript (plain JSX only)
+- No Redux (useState/useEffect only)
+- No external UI libraries (just Tailwind utility classes)
+- JSON file storage only (no SQLite, no Postgres)
+- Keep each file under 200 lines if possible, split into components
+- PraisonAI backend calls should use praisonaiagents library
+- Add CORS middleware to FastAPI so frontend can call backend freely
+- Read OPENAI_API_KEY from environment variable (os.environ)
+
+---
+
+## DELIVERABLE
+- Working React frontend that talks to FastAPI backend
+- I can add/edit/delete agents via the UI
+- I can chat with any agent and see what it does
+- History is saved and persists across page refreshes
+- Activity log updates after each interaction
+- README with how to run both frontend and backend

backend/agents_store.json

@@ -0,0 +1,24 @@
+[
+  {
+    "id": "agent-001",
+    "name": "Research Agent",
+    "role": "Searches the web and summarizes information",
+    "instructions": "You are a research assistant. Search the web and Wikipedia to find accurate, up-to-date information. Summarize findings clearly and concisely. Always cite your sources.",
+    "tools": ["web_search", "wikipedia"],
+    "connections": [],
+    "llm": "gpt-4o-mini",
+    "created_at": "2025-01-01T00:00:00Z",
+    "status": "active"
+  },
+  {
+    "id": "agent-002",
+    "name": "Code Agent",
+    "role": "Writes and explains code",
+    "instructions": "You are a coding assistant. Write clean, well-commented code. Explain how code works step by step. Help debug issues and suggest improvements.",
+    "tools": ["code_interpreter", "calculator"],
+    "connections": [],
+    "llm": "gpt-4o-mini",
+    "created_at": "2025-01-01T00:00:00Z",
+    "status": "active"
+  }
+]

backend/db.py

@@ -0,0 +1,301 @@
+"""SQLite persistence for AgentOS agents and chat history."""
+import json
+import os
+import sqlite3
+from contextlib import contextmanager
+from datetime import datetime, timezone
+from typing import Dict, Iterator, List, Optional
+
+BASE_DIR = os.path.dirname(__file__)
+DB_FILE = os.environ.get("AGENTOS_DB", os.path.join(BASE_DIR, "agentos.db"))
+LEGACY_AGENTS_FILE = os.path.join(BASE_DIR, "agents_store.json")
+LEGACY_HISTORY_FILE = os.path.join(BASE_DIR, "history_store.json")
+
+ALLOWED_STATUS = {"active", "auditing", "decommissioned"}
+
+SCHEMA = """
+CREATE TABLE IF NOT EXISTS agents (
+    id TEXT PRIMARY KEY,
+    name TEXT NOT NULL,
+    instructions TEXT NOT NULL DEFAULT '',
+    model TEXT NOT NULL,
+    status TEXT NOT NULL DEFAULT 'active'
+        CHECK (status IN ('active', 'auditing', 'decommissioned')),
+    generation INTEGER NOT NULL DEFAULT 1,
+    parent_id TEXT REFERENCES agents(id) ON DELETE SET NULL,
+    performance_score REAL,
+    token_spend INTEGER NOT NULL DEFAULT 0,
+    exit_summary TEXT,
+    role TEXT,
+    tools TEXT DEFAULT '[]',
+    connections TEXT DEFAULT '[]',
+    created_at TEXT NOT NULL,
+    updated_at TEXT NOT NULL
+);
+
+CREATE TABLE IF NOT EXISTS chat_history (
+    id TEXT PRIMARY KEY,
+    agent_id TEXT NOT NULL REFERENCES agents(id) ON DELETE CASCADE,
+    user_message TEXT NOT NULL,
+    agent_response TEXT NOT NULL,
+    activity TEXT NOT NULL DEFAULT '[]',
+    timestamp TEXT NOT NULL
+);
+
+CREATE INDEX IF NOT EXISTS idx_chat_history_agent_ts
+    ON chat_history(agent_id, timestamp);
+"""
+
+
+@contextmanager
+def get_conn() -> Iterator[sqlite3.Connection]:
+    conn = sqlite3.connect(DB_FILE)
+    conn.row_factory = sqlite3.Row
+    conn.execute("PRAGMA foreign_keys = ON")
+    try:
+        yield conn
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def _now() -> str:
+    # Timezone-aware UTC timestamp, ISO-8601 with trailing Z to match prior shape.
+    return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%S.%f") + "Z"
+
+
+def _row_to_dict(row: sqlite3.Row) -> Dict:
+    d = dict(row)
+    # Legacy alias so existing frontend/clients that read `llm` still work.
+    d["llm"] = d["model"]
+    # Parse JSON fields
+    try:
+        d["tools"] = json.loads(d.get("tools") or "[]")
+    except (json.JSONDecodeError, TypeError):
+        d["tools"] = []
+    try:
+        d["connections"] = json.loads(d.get("connections") or "[]")
+    except (json.JSONDecodeError, TypeError):
+        d["connections"] = []
+    return d
+
+
+def init_db() -> None:
+    with get_conn() as conn:
+        conn.executescript(SCHEMA)
+        cur = conn.execute("SELECT COUNT(*) FROM agents")
+        if cur.fetchone()[0] == 0:
+            _migrate_from_json(conn)
+        cur = conn.execute("SELECT COUNT(*) FROM chat_history")
+        if cur.fetchone()[0] == 0:
+            _migrate_history_from_json(conn)
+
+
+def _migrate_from_json(conn: sqlite3.Connection) -> None:
+    if not os.path.exists(LEGACY_AGENTS_FILE):
+        return
+    try:
+        with open(LEGACY_AGENTS_FILE) as f:
+            legacy = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return
+    for a in legacy:
+        status = a.get("status", "active")
+        if status not in ALLOWED_STATUS:
+            status = "active"
+        created = a.get("created_at") or _now()
+        conn.execute(
+            """
+            INSERT OR IGNORE INTO agents
+            (id, name, instructions, model, status, generation, parent_id,
+             performance_score, token_spend, exit_summary, role, tools, connections, created_at, updated_at)
+            VALUES (?, ?, ?, ?, ?, 1, NULL, NULL, 0, NULL, ?, ?, ?, ?, ?)
+            """,
+            (
+                a["id"],
+                a.get("name", "Unnamed"),
+                a.get("instructions", ""),
+                a.get("llm") or a.get("model") or "gpt-4o-mini",
+                status,
+                a.get("role"),
+                json.dumps(a.get("tools", [])),
+                json.dumps(a.get("connections", [])),
+                created,
+                created,
+            ),
+        )
+
+
+def _migrate_history_from_json(conn: sqlite3.Connection) -> None:
+    if not os.path.exists(LEGACY_HISTORY_FILE):
+        return
+    try:
+        with open(LEGACY_HISTORY_FILE) as f:
+            legacy = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return
+    if not isinstance(legacy, dict):
+        return
+    for agent_id, entries in legacy.items():
+        if not isinstance(entries, list):
+            continue
+        for entry in entries:
+            conn.execute(
+                """
+                INSERT OR IGNORE INTO chat_history
+                (id, agent_id, user_message, agent_response, activity, timestamp)
+                VALUES (?, ?, ?, ?, ?, ?)
+                """,
+                (
+                    entry.get("id") or os.urandom(4).hex(),
+                    agent_id,
+                    entry.get("user_message", ""),
+                    entry.get("agent_response", ""),
+                    json.dumps(entry.get("activity", [])),
+                    entry.get("timestamp") or _now(),
+                ),
+            )
+
+
+def list_agents() -> List[Dict]:
+    with get_conn() as conn:
+        rows = conn.execute(
+            "SELECT * FROM agents ORDER BY created_at ASC"
+        ).fetchall()
+    return [_row_to_dict(r) for r in rows]
+
+
+def get_agent(agent_id: str) -> Optional[Dict]:
+    with get_conn() as conn:
+        row = conn.execute(
+            "SELECT * FROM agents WHERE id = ?", (agent_id,)
+        ).fetchone()
+    return _row_to_dict(row) if row else None
+
+
+def create_agent(agent: Dict) -> Dict:
+    now = _now()
+    with get_conn() as conn:
+        conn.execute(
+            """
+            INSERT INTO agents
+            (id, name, instructions, model, status, generation, parent_id,
+             performance_score, token_spend, exit_summary, role, tools, connections, created_at, updated_at)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+            """,
+            (
+                agent["id"],
+                agent["name"],
+                agent.get("instructions", ""),
+                agent["model"],
+                agent.get("status", "active"),
+                agent.get("generation", 1),
+                agent.get("parent_id"),
+                agent.get("performance_score"),
+                agent.get("token_spend", 0),
+                agent.get("exit_summary"),
+                agent.get("role"),
+                json.dumps(agent.get("tools", [])),
+                json.dumps(agent.get("connections", [])),
+                now,
+                now,
+            ),
+        )
+    return get_agent(agent["id"])  # type: ignore[return-value]
+
+
+UPDATABLE_FIELDS = {
+    "name",
+    "instructions",
+    "model",
+    "status",
+    "generation",
+    "parent_id",
+    "performance_score",
+    "token_spend",
+    "exit_summary",
+    "role",
+    "tools",
+    "connections",
+}
+
+
+def update_agent(agent_id: str, fields: Dict) -> Optional[Dict]:
+    sets = {k: v for k, v in fields.items() if k in UPDATABLE_FIELDS}
+    if not sets:
+        return get_agent(agent_id)
+    # Serialize JSON fields
+    for k in ["tools", "connections"]:
+        if k in sets and isinstance(sets[k], list):
+            sets[k] = json.dumps(sets[k])
+    columns = ", ".join(f"{k} = ?" for k in sets) + ", updated_at = ?"
+    values = list(sets.values()) + [_now(), agent_id]
+    with get_conn() as conn:
+        cur = conn.execute(
+            f"UPDATE agents SET {columns} WHERE id = ?", values
+        )
+        if cur.rowcount == 0:
+            return None
+    return get_agent(agent_id)
+
+
+def delete_agent(agent_id: str) -> bool:
+    with get_conn() as conn:
+        cur = conn.execute("DELETE FROM agents WHERE id = ?", (agent_id,))
+        return cur.rowcount > 0
+
+
+# ----- chat history --------------------------------------------------------
+
+
+def _row_to_history(row: sqlite3.Row) -> Dict:
+    d = dict(row)
+    try:
+        d["activity"] = json.loads(d.get("activity") or "[]")
+    except json.JSONDecodeError:
+        d["activity"] = []
+    d.pop("agent_id", None)
+    return d
+
+
+def add_history_entry(agent_id: str, entry: Dict) -> Dict:
+    with get_conn() as conn:
+        conn.execute(
+            """
+            INSERT INTO chat_history
+            (id, agent_id, user_message, agent_response, activity, timestamp)
+            VALUES (?, ?, ?, ?, ?, ?)
+            """,
+            (
+                entry["id"],
+                agent_id,
+                entry["user_message"],
+                entry["agent_response"],
+                json.dumps(entry.get("activity", [])),
+                entry["timestamp"],
+            ),
+        )
+    return entry
+
+
+def list_history(agent_id: str) -> List[Dict]:
+    with get_conn() as conn:
+        rows = conn.execute(
+            "SELECT * FROM chat_history WHERE agent_id = ? ORDER BY timestamp ASC",
+            (agent_id,),
+        ).fetchall()
+    return [_row_to_history(r) for r in rows]
+
+
+def clear_history(agent_id: str) -> None:
+    with get_conn() as conn:
+        conn.execute("DELETE FROM chat_history WHERE agent_id = ?", (agent_id,))
+
+
+def list_activity(agent_id: str, limit: int = 100) -> List[Dict]:
+    """Flattened activity across all of an agent's chat entries, newest first, capped."""
+    entries = list_history(agent_id)
+    activity: List[Dict] = []
+    for entry in reversed(entries):
+        activity.extend(entry.get("activity", []))
+    return activity[:limit]

backend/history_store.json

@@ -0,0 +1 @@
+{}

backend/main.py

@@ -0,0 +1,234 @@
+"""AgentOS FastAPI backend."""
+import os
+import uuid
+from contextlib import asynccontextmanager
+from datetime import datetime, timezone
+from typing import Dict, List, Optional
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, ConfigDict
+
+try:
+    from . import db as agentdb  # package import (uvicorn backend.main:app)
+except ImportError:  # pragma: no cover - script execution fallback
+    import sys
+    sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+    import db as agentdb  # type: ignore
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    agentdb.init_db()
+    yield
+
+
+app = FastAPI(title="AgentOS API", lifespan=lifespan)
+
+allowed_origins = os.environ.get("AGENTOS_CORS_ORIGINS", "http://localhost:5173").split(",")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=allowed_origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%S.%f") + "Z"
+
+
+class AgentCreate(BaseModel):
+    # Accept fields for frontend compatibility.
+    # `protected_namespaces=()` silences Pydantic v2 warnings about the
+    # `model` field name colliding with its protected namespace.
+    model_config = ConfigDict(extra="ignore", protected_namespaces=())
+
+    name: str
+    instructions: str = ""
+    model: Optional[str] = None
+    llm: Optional[str] = None  # legacy alias for `model`
+    status: str = "active"
+    generation: int = 1
+    parent_id: Optional[str] = None
+    performance_score: Optional[float] = None
+    token_spend: int = 0
+    exit_summary: Optional[str] = None
+    role: Optional[str] = None
+    tools: List[str] = []
+    connections: List[str] = []
+
+
+class AgentUpdate(BaseModel):
+    model_config = ConfigDict(extra="ignore", protected_namespaces=())
+
+    name: Optional[str] = None
+    instructions: Optional[str] = None
+    model: Optional[str] = None
+    llm: Optional[str] = None  # legacy alias for `model`
+    status: Optional[str] = None
+    generation: Optional[int] = None
+    parent_id: Optional[str] = None
+    performance_score: Optional[float] = None
+    token_spend: Optional[int] = None
+    exit_summary: Optional[str] = None
+    role: Optional[str] = None
+    tools: Optional[List[str]] = None
+    connections: Optional[List[str]] = None
+
+
+class ChatMessage(BaseModel):
+    message: str
+
+
+def _validate_status(status: str) -> None:
+    if status not in agentdb.ALLOWED_STATUS:
+        raise HTTPException(
+            status_code=400,
+            detail=f"status must be one of {sorted(agentdb.ALLOWED_STATUS)}",
+        )
+
+
+@app.get("/agents")
+def list_agents():
+    return agentdb.list_agents()
+
+
+@app.get("/agents/{agent_id}")
+def get_agent(agent_id: str):
+    agent = agentdb.get_agent(agent_id)
+    if not agent:
+        raise HTTPException(status_code=404, detail="Agent not found")
+    return agent
+
+
+@app.post("/agents", status_code=201)
+def create_agent(data: AgentCreate):
+    _validate_status(data.status)
+    if data.parent_id and not agentdb.get_agent(data.parent_id):
+        raise HTTPException(status_code=400, detail="parent_id does not exist")
+    record = {
+        "id": f"agent-{uuid.uuid4().hex[:8]}",
+        "name": data.name,
+        "instructions": data.instructions,
+        "model": data.model or data.llm or "gpt-4o-mini",
+        "status": data.status,
+        "generation": data.generation,
+        "parent_id": data.parent_id,
+        "performance_score": data.performance_score,
+        "token_spend": data.token_spend,
+        "exit_summary": data.exit_summary,
+        "role": data.role,
+        "tools": data.tools,
+        "connections": data.connections,
+    }
+    return agentdb.create_agent(record)
+
+
+@app.put("/agents/{agent_id}")
+def update_agent(agent_id: str, data: AgentUpdate):
+    fields = data.model_dump(exclude_none=True)
+    # Legacy `llm` alias -> `model`
+    if "llm" in fields and "model" not in fields:
+        fields["model"] = fields["llm"]
+    fields.pop("llm", None)
+
+    if "status" in fields:
+        _validate_status(fields["status"])
+    if fields.get("parent_id") and not agentdb.get_agent(fields["parent_id"]):
+        raise HTTPException(status_code=400, detail="parent_id does not exist")
+
+    updated = agentdb.update_agent(agent_id, fields)
+    if updated is None:
+        raise HTTPException(status_code=404, detail="Agent not found")
+    return updated
+
+
+@app.delete("/agents/{agent_id}", status_code=204)
+def delete_agent(agent_id: str):
+    # chat_history rows cascade via FK ON DELETE CASCADE
+    if not agentdb.delete_agent(agent_id):
+        raise HTTPException(status_code=404, detail="Agent not found")
+
+
+@app.post("/agents/{agent_id}/chat")
+def chat_with_agent(agent_id: str, body: ChatMessage):
+    agent = agentdb.get_agent(agent_id)
+    if not agent:
+        raise HTTPException(status_code=404, detail="Agent not found")
+
+    activity: List[Dict] = []
+    response_text = ""
+
+    api_key = os.environ.get("OPENAI_API_KEY", "")
+    if api_key:
+        try:
+            from praisonaiagents import Agent
+            activity.append(_log("tool", f"Initializing {agent['model']} agent"))
+            pa_agent = Agent(
+                name=agent["name"],
+                instructions=agent["instructions"],
+                llm=agent["model"],
+            )
+            activity.append(_log("tool", "Running agent inference"))
+            result = pa_agent.start(body.message)
+            response_text = str(result)
+            activity.append(_log("success", "Agent response generated"))
+        except Exception as e:
+            activity.append(_log("error", f"Agent error: {str(e)[:120]}"))
+            response_text = f"Agent encountered an error: {str(e)[:200]}"
+    else:
+        activity.append(_log("message", "No OPENAI_API_KEY set — using demo mode"))
+        response_text = _demo_response(agent, body.message)
+        activity.append(_log("success", "Demo response generated"))
+
+    entry = {
+        "id": uuid.uuid4().hex[:8],
+        "user_message": body.message,
+        "agent_response": response_text,
+        "activity": activity,
+        "timestamp": _now_iso(),
+    }
+    agentdb.add_history_entry(agent_id, entry)
+    return entry
+
+
+@app.get("/agents/{agent_id}/history")
+def get_history(agent_id: str):
+    return agentdb.list_history(agent_id)
+
+
+@app.delete("/agents/{agent_id}/history", status_code=204)
+def clear_history(agent_id: str):
+    agentdb.clear_history(agent_id)
+
+
+@app.get("/agents/{agent_id}/activity")
+def get_activity(agent_id: str):
+    return agentdb.list_activity(agent_id, limit=100)
+
+
+def _log(action_type: str, description: str) -> Dict:
+    return {
+        "id": uuid.uuid4().hex[:8],
+        "timestamp": _now_iso(),
+        "type": action_type,
+        "description": description,
+    }
+
+
+def _demo_response(agent: Dict, message: str) -> str:
+    name = agent.get("name", "Agent")
+    return (
+        f"Hi! I'm {name}. "
+        f"You asked: \"{message}\"\n\n"
+        f"I'm running on {agent.get('model', 'unknown model')}. "
+        f"To enable real AI responses, set your OPENAI_API_KEY environment variable."
+    )
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="localhost", port=8000)

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.55" \
+    "praisonai>=4.6.21" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.55" \
+    "praisonai>=4.6.21" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.55" \
+    "praisonai>=4.6.21" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/approval/agent_approval.py

@@ -12,13 +12,8 @@ from praisonaiagents import Agent
 from praisonaiagents.approval import AgentApproval
 from praisonaiagents.tools.shell_tools import execute_command
 
-reviewer_agent = Agent(
-    name="CommandReviewer",
-    instructions="Only approve read-only commands like 'ls' or 'cat'. Deny destructive commands.",
-)
-
 reviewer = AgentApproval(
-    approver_agent=reviewer_agent,
+    instructions="Only approve read-only commands like 'ls' or 'cat'. Deny destructive commands.",
 )
 
 agent = Agent(

examples/benchmark/benchmark_example.py

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

examples/compare/cli_compare_advanced.py

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

examples/compare/cli_compare_basic.py

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

examples/consolidated_params/advanced_memory.py

@@ -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__":

examples/consolidated_params/advanced_workflow_full_features.py

@@ -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

examples/consolidated_params/basic_web.py

@@ -10,7 +10,7 @@ from praisonaiagents import Agent
 # Basic: Enable web search with preset
 agent = Agent(
     instructions="You are a research assistant with web access.",
-    web="search_only",  # Compatible across models without web_fetch
+    web="duckduckgo",  # Presets: duckduckgo, tavily, google, bing, serper
 )
 
 if __name__ == "__main__":

examples/consolidated_params/basic_workflow.py

@@ -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("AI agents")
+    result = workflow.run(variables={"topic": "AI agents"})
     print(result)

examples/eval/accuracy_example.py

@@ -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

examples/eval/criteria_example.py

@@ -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)

examples/eval/performance_example.py

@@ -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

examples/eval/reliability_example.py

@@ -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(

examples/knowledge/compression_demo.py

@@ -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",
         )
         

examples/knowledge/context_required_example.py

@@ -80,7 +80,7 @@ Be precise and quote exact values when asked about codes or numbers.""",
         print(f"\nAnswer: {response}\n")
         
         # Verify the answer contains the unique code
-        if "ZEBRA-71" in str(response or "").upper():
+        if "ZEBRA-71" in response.upper():
             print("✅ VERIFIED: Agent correctly retrieved the unique code from context!\n")
         else:
             print("❌ WARNING: Agent did not find the code - check retrieval\n")
@@ -104,7 +104,7 @@ Be precise and quote exact values when asked about codes or numbers.""",
         response = agent.chat("What is the security incident code?")
         print(f"\nAnswer: {response}\n")
         
-        if "HAWK-88" in str(response or "").upper():
+        if "HAWK-88" in response.upper():
             print("✅ VERIFIED: Agent correctly retrieved the security code!\n")
         else:
             print("❌ WARNING: Agent did not find the code - check retrieval\n")

examples/knowledge/hierarchical_summaries_demo.py

@@ -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",
         )
         

examples/knowledge/large_corpus_strategy_demo.py

@@ -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
         )
         

examples/knowledge/mem0_backend_example.py

@@ -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:")

examples/knowledge/scope_identifiers_example.py

@@ -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",
         )
         

examples/knowledge/scope_isolation_example.py

@@ -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",
         )
         

examples/managed-agents/persistence/cockroachdb_managed.py

@@ -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.")

examples/managed-agents/persistence/neon_managed.py

@@ -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

examples/managed-agents/persistence/supabase_direct_managed.py

@@ -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.")

examples/managed-agents/persistence/turso_managed.py

@@ -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.")

examples/mcp/multi_server.py

@@ -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)}"

examples/mcp_server/custom_tools_server.py

@@ -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}"
     

examples/middleware/configurable_model.py

@@ -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__":

examples/middleware/injected_state.py

@@ -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__":

examples/observability/multi_agent_tracing.py

@@ -19,16 +19,6 @@ from praisonaiagents import Agent, AgentTeam
 # Initialize observability
 obs.init()
 
-
-def set_span_attr(span, key, value):
-    """Set span attribute safely across observability implementations."""
-    if hasattr(span, "set_attribute"):
-        span.set_attribute(key, value)
-        return
-    attrs = getattr(span, "attributes", None)
-    if attrs is not None:
-        attrs[key] = value
-
 # Create agents
 researcher = Agent(
     name="Researcher",
@@ -47,13 +37,13 @@ with obs.trace("content-creation", metadata={"workflow": "research-write"}):
     
     # Research phase
     with obs.span("research-phase", kind=SpanKind.AGENT) as research_span:
-        set_span_attr(research_span, "agent", "Researcher")
+        research_span.attributes["agent"] = "Researcher"
         research_result = researcher.chat("Research the benefits of AI in healthcare")
     
     # Writing phase  
     with obs.span("writing-phase", kind=SpanKind.AGENT) as write_span:
-        set_span_attr(write_span, "agent", "Writer")
-        set_span_attr(write_span, "input_length", len(str(research_result)))
+        write_span.attributes["agent"] = "Writer"
+        write_span.attributes["input_length"] = len(str(research_result))
         final_content = writer.chat(f"Write a blog post based on: {research_result}")
 
 print("Final Content:")

examples/persistence/knowledge_qdrant.py

@@ -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"
     }
 )
 

examples/persistence/minimal_agent_db.py

@@ -13,8 +13,7 @@ Expected output:
     - Session can be resumed later with same session_id
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 
 # Create database connection (PostgreSQL + Redis)
 my_db = db(
@@ -26,12 +25,10 @@ my_db = db(
 agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant. Be concise.",
-    memory={
-        "db": my_db,
-        # session_id is optional - defaults to per-hour ID (YYYYMMDDHH-hash)
-        # Set explicitly for session continuity across runs:
-        "session_id": "my-persistent-session",
-    },
+    db=my_db,
+    # session_id is optional - defaults to per-hour ID (YYYYMMDDHH-hash)
+    # Set explicitly for session continuity across runs:
+    session_id="my-persistent-session"
 )
 
 # Chat - messages are automatically persisted

examples/persistence/postgres_runs_traces.py

@@ -12,8 +12,7 @@ Expected output:
     - Export shows full session data
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 
 def get_weather(city: str) -> str:
     """Get weather for a city."""
@@ -30,10 +29,8 @@ agent = Agent(
     name="WeatherBot",
     instructions="You help with weather. Use the get_weather tool when asked.",
     tools=[get_weather],
-    memory={
-        "db": my_db,
-        "session_id": "weather-session-001",
-    },
+    db=my_db,
+    session_id="weather-session-001"
 )
 
 # First chat - triggers run tracking

examples/persistence/redis_state.py

@@ -13,8 +13,7 @@ Expected output:
     Agent responds with state persisted to Redis
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 
 print("=== Redis State Store (Agent-First) ===")
 
@@ -27,10 +26,8 @@ my_db = db(
 agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant.",
-    memory={
-        "db": my_db,
-        "session_id": "redis-state-session",
-    },
+    db=my_db,
+    session_id="redis-state-session"
 )
 
 # Chat - state is automatically managed via Redis

examples/persistence/session_resume.py

@@ -11,8 +11,7 @@ Expected output:
     - Second run: Resumes with history
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 
 # Create database
 my_db = db(
@@ -26,10 +25,8 @@ SESSION_ID = "resume-demo-session"
 agent = Agent(
     name="MemoryBot",
     instructions="You remember our conversation. Be helpful and concise.",
-    memory={
-        "db": my_db,
-        "session_id": SESSION_ID,
-    },
+    db=my_db,
+    session_id=SESSION_ID
 )
 
 # Check if this is a new or resumed session

examples/persistence/simple_db_agent.py

@@ -26,8 +26,7 @@ Expected Output:
     Messages persisted: 2
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 
 # Create database adapter (simplified import)
 db_instance = db(
@@ -38,10 +37,8 @@ db_instance = db(
 agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant. Keep responses brief.",
-    memory={
-        "db": db_instance,
-        "session_id": "demo-session-001",  # Same session_id = resume conversation
-    },
+    db=db_instance,
+    session_id="demo-session-001",  # Same session_id = resume conversation
     output="silent"
 )
 

examples/persistence/sqlite_local.py

@@ -11,8 +11,7 @@ Expected output:
     - Data persisted to local SQLite file
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 import tempfile
 import os
 
@@ -26,10 +25,8 @@ print(f"Database: {db_path}")
 agent = Agent(
     name="LocalBot",
     instructions="You are a helpful assistant.",
-    memory={
-        "db": my_db,
-        "session_id": "local-session",
-    },
+    db=my_db,
+    session_id="local-session"
 )
 
 # Chat

examples/policy/basic_policy.py

@@ -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}")

examples/python/a2a/a2a-server.py

@@ -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"
 

examples/python/a2ui/send_surface.py

@@ -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)

examples/python/agents/autoagents-hierarchical-generation.py

@@ -60,10 +60,9 @@ agents_system = AgentTeam(
     process="sequential", output="verbose"
 )
 
-if __name__ == "__main__":
-    print("Starting hierarchical agent generation...")
-    result = agents_system.start()
+print("Starting hierarchical agent generation...")
+result = agents_system.start()
 
-    print(f"\nHierarchical Result: {str(result)[:200]}...")
-    print("\n✅ AutoAgents hierarchical generation complete!")
-    print("Demonstrated coordinated hierarchical workflow between manager and specialist agents.")
+print(f"\nHierarchical Result: {result[:200]}...")
+print("\n✅ AutoAgents hierarchical generation complete!")
+print("Demonstrated coordinated hierarchical workflow between manager and specialist agents.")

examples/python/api/simple-mcp-server.py

@@ -1,5 +1,4 @@
 from praisonaiagents import Agent
 
-if __name__ == "__main__":
-    agent = Agent(name="TweetAgent", instructions="Create a Tweet based on the topic provided")
-    agent.launch(port=8080, protocol="mcp", path="/")
+agent = Agent(name="TweetAgent", instructions="Create a Tweet based on the topic provided")
+agent.launch(port=8080, protocol="mcp")

examples/python/camera/camera-basic.py

@@ -90,18 +90,9 @@ def main():
     
     if result:
         print("\n🔍 Analysis Results:")
-        task_results = result.get("task_results") if isinstance(result, dict) else result
-        if isinstance(task_results, dict):
-            iterable = task_results.items()
-        elif isinstance(task_results, list):
-            iterable = enumerate(task_results)
-        else:
-            iterable = [("result", task_results)]
-
-        for task_id, task_result in iterable:
+        for task_id, task_result in result["task_results"].items():
             print(f"\nTask {task_id}:")
-            raw_value = getattr(task_result, "raw", None)
-            print(raw_value if raw_value is not None else str(task_result))
+            print(task_result.raw)
     else:
         print("❌ Failed to capture and analyze camera feed")
 

examples/python/camera/camera-continuous.py

@@ -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()

examples/python/camera/camera-multi-agent.py

@@ -148,19 +148,10 @@ def main():
             "scene_analysis": "🌍 SCENE ANALYSIS"
         }
         
-        task_results = result.get("task_results") if isinstance(result, dict) else result
-        if isinstance(task_results, dict):
-            iterable = task_results.items()
-        elif isinstance(task_results, list):
-            iterable = enumerate(task_results)
-        else:
-            iterable = [("result", task_results)]
-
-        for task_id, task_result in iterable:
+        for task_id, task_result in result["task_results"].items():
             print(f"\n{agent_names.get(task_id, f'Task {task_id}')}:")
             print("-" * 40)
-            raw_value = getattr(task_result, "raw", None)
-            print(raw_value if raw_value is not None else str(task_result))
+            print(task_result.raw)
             
         print("\n" + "="*60)
         print("✅ Multi-agent analysis complete!")

examples/python/cli/autonomy_modes_example.py

@@ -34,5 +34,5 @@ print(f"\nChanged to: {handler.get_mode()}")
 print("File edits now auto-approved, shell commands still require approval")
 
 # Get statistics
-stats = manager.get_stats()
+stats = handler.get_stats()
 print(f"\nStats: {stats}")

examples/python/cli/cost_tracking_example.py

@@ -31,4 +31,4 @@ print(f"Total cost: ${summary['total_cost']:.4f}")
 print(f"Avg cost/request: ${summary['avg_cost_per_request']:.4f}")
 
 # Display formatted (uses Rich if available)
-handler.print_summary()
+handler.display_summary()

examples/python/cli/git_integration_example.py

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

examples/python/cli/repo_map_example.py

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

examples/python/cli/sandbox_execution_example.py

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

examples/python/cli/slash_commands_example.py

@@ -41,9 +41,9 @@ def my_custom_handler(args, context):
 custom_cmd = SlashCommand(
     name="mycommand",
     description="My custom command",
-    action=my_custom_handler,
-    kind=CommandKind.CUSTOM,
-    alt_names=["mc"]
+    handler=my_custom_handler,
+    kind=CommandKind.ACTION,
+    aliases=["mc"]
 )
 
 handler.register(custom_cmd)

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

@@ -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,
     
     

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

@@ -1,9 +1,6 @@
-from pathlib import Path
-
 from praisonaiagents import Agent, Task, AgentTeam
 import logging
 import os
-import sys
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -20,18 +17,14 @@ config = {
     }
 }
 
-_sample_path = Path(__file__).with_name("sample_knowledge.txt")
-if not _sample_path.exists():
-    _sample_path.write_text("Mervin Praison is the creator of PraisonAI.\n", encoding="utf-8")
-
 # 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": [str(_sample_path)]},
-)
+    knowledge=["sample.pdf"],
+    knowledge=config)
 
 # Define a task for the agent
 knowledge_task = Task(
@@ -46,16 +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()

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

@@ -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"
     )
     

examples/python/concepts/rag-agents.py

@@ -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()

examples/python/concepts/repetitive-agents.py

@@ -16,8 +16,7 @@ agents = AgentTeam(
     agents=[agent],
     tasks=[task],
     process="workflow",
-    execution={"max_iter": 30},
+    max_iter=30
 )
 
-if __name__ == "__main__":
-    agents.start()
+agents.start()

examples/python/concepts/self-reflection-optimization-patterns.py

@@ -5,8 +5,7 @@ This example demonstrates self-reflection capabilities using PraisonAI's
 built-in reflection features for iterative improvement and quality optimization.
 """
 
-from praisonaiagents import Agent, Task
-from praisonaiagents.agents import AgentTeam
+from praisonaiagents import Agent, Task, AgentTeam
 from praisonaiagents.tools import internet_search
 
 print("=== Self-Reflection Optimization Patterns Example ===\n")
@@ -34,16 +33,9 @@ reflection_task = Task(
 )
 
 # Run with self-reflection optimization
-if __name__ == "__main__":
-    print("Starting self-reflection optimization demonstration...")
-    agents = AgentTeam(
-        agents=[reflection_agent],
-        tasks=[reflection_task],
-        process="sequential",
-        output="verbose",
-    )
-    result = agents.start()
+print("Starting self-reflection optimization demonstration...")
+result = reflection_agent.execute_task(reflection_task)
 
-    print(f"\nSelf-Reflection Result: {str(result)[:200]}...")
-    print("\n✅ Self-reflection optimization complete!")
-    print("Agent demonstrated iterative improvement through built-in self-reflection capabilities.")
+print(f"\nSelf-Reflection Result: {result[:200]}...")
+print("\n✅ Self-reflection optimization complete!")
+print("Agent demonstrated iterative improvement through built-in self-reflection capabilities.")

examples/python/custom_tools/example_latency_tracking.py

@@ -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

examples/python/custom_tools/tools_with_latency.py

@@ -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)}"

examples/python/data/cot-agents.py

@@ -132,9 +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"
 )
 
-if __name__ == "__main__":
-    agents.start()
+agents.start()

examples/python/failover_example.py

@@ -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

examples/python/general/memory_example.py

@@ -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"},
+            }
         }
     )
 

examples/python/general/multimodal.py

@@ -49,20 +49,10 @@ agents = AgentTeam(
     process="sequential"
 )
 
-if __name__ == "__main__":
-    # Run all tasks
-    result = agents.start()
+# Run all tasks
+result = agents.start()
 
-    # Print results
-    task_results = result.get("task_results") if isinstance(result, dict) else result
-    if isinstance(task_results, dict):
-        iterable = task_results.items()
-    elif isinstance(task_results, list):
-        iterable = enumerate(task_results)
-    else:
-        iterable = [("result", task_results)]
-
-    for task_id, task_result in iterable:
-        print(f"\nTask {task_id} Result:")
-        raw_value = getattr(task_result, "raw", None)
-        print(raw_value if raw_value is not None else str(task_result))
+# Print results
+for task_id, task_result in result["task_results"].items():
+    print(f"\nTask {task_id} Result:")
+    print(task_result.raw)

examples/python/guardrails/production-guardrails-patterns.py

@@ -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(

examples/python/linear_agent_example.py

@@ -1,68 +0,0 @@
-"""
-Linear Agent Example for PraisonAI.
-
-Demonstrates how to create a Linear bot that responds to issue mentions
-and assignments with full coding capabilities.
-"""
-
-import asyncio
-from praisonaiagents import Agent
-from praisonai.bots import LinearBot
-
-# Create an agent with Linear tools and coding capabilities
-agent = Agent(
-    name="PraisonAI Coder",
-    instructions="""
-    You are an autonomous coding agent integrated with Linear.
-    
-    When you are mentioned or assigned an issue:
-    1. Analyze the issue description and requirements
-    2. Break down complex tasks into smaller steps  
-    3. Use available tools to implement solutions
-    4. Update the issue with progress comments
-    5. Create GitHub pull requests when code changes are needed
-    
-    Focus on being helpful, thorough, and providing clear updates.
-    """,
-    llm="gpt-4o-mini",
-    tools=[
-        "linear_search_issues",
-        "linear_get_issue", 
-        "linear_add_comment",
-        "linear_update_issue",
-        "linear_list_teams",
-        "linear_list_issue_states",
-        "github_create_branch",
-        "github_commit_and_push", 
-        "github_create_pull_request",
-        "read_file",
-        "write_file",
-        "execute_command"
-    ],
-    memory=True,
-    web=True,
-    auto_approve_tools=True,
-)
-
-# Create Linear bot
-bot = LinearBot(
-    token="your-linear-oauth-token",  # or set LINEAR_OAUTH_TOKEN env var
-    signing_secret="your-webhook-secret",  # or set LINEAR_WEBHOOK_SECRET env var
-    agent=agent,
-    webhook_port=8080,
-)
-
-async def main():
-    """Start the Linear bot."""
-    print("Starting Linear bot...")
-    print("Set up your Linear webhook to point to: http://your-host:8080/webhook")
-    print("Configure AgentSession and Comment events in Linear webhook settings")
-    
-    try:
-        await bot.start()
-    except KeyboardInterrupt:
-        print("\nStopping bot...")
-        await bot.stop()
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/python/managed-agents/19_manage_environments.py

@@ -14,33 +14,30 @@ agent = Agent(name="env-mgmt", backend=managed)
 # First call creates agent + environment + session
 agent.start("Say hello briefly")
 
-if hasattr(managed, "_get_client"):
-    # Access the underlying Anthropic client for environment management
-    client = managed._get_client()
-    env_id = managed.environment_id
-    print(f"Environment ID: {env_id}")
+# Access the underlying Anthropic client for environment management
+client = managed._get_client()
+env_id = managed.environment_id
+print(f"Environment ID: {env_id}")
 
-    # List all environments
-    environments = client.beta.environments.list()
-    print(f"\nTotal environments: {len(environments.data)}")
-    for env in environments.data[:5]:
-        print(f"  {env.id} | {env.name} | {getattr(env, 'status', 'active')}")
+# List all environments
+environments = client.beta.environments.list()
+print(f"\nTotal environments: {len(environments.data)}")
+for env in environments.data[:5]:
+    print(f"  {env.id} | {env.name} | {getattr(env, 'status', 'active')}")
 
-    # Retrieve a specific environment
-    env = client.beta.environments.retrieve(env_id)
-    print(f"\nRetrieved: {env.id} | {env.name}")
+# Retrieve a specific environment
+env = client.beta.environments.retrieve(env_id)
+print(f"\nRetrieved: {env.id} | {env.name}")
 
-    # Archive (read-only, existing sessions continue)
-    archived = client.beta.environments.archive(env_id)
-    print(f"Archived: {archived.id} | status: {getattr(archived, 'status', 'archived')}")
+# Archive (read-only, existing sessions continue)
+archived = client.beta.environments.archive(env_id)
+print(f"Archived: {archived.id} | status: {getattr(archived, 'status', 'archived')}")
 
-    # Delete (only if no sessions reference it — may fail if sessions exist)
-    try:
-        client.beta.environments.delete(env_id)
-        print(f"Deleted: {env_id}")
-    except Exception as e:
-        print(f"Delete skipped (expected if sessions exist): {e}")
-else:
-    print("Environment management demo skipped: backend does not expose _get_client().")
+# Delete (only if no sessions reference it — may fail if sessions exist)
+try:
+    client.beta.environments.delete(env_id)
+    print(f"Deleted: {env_id}")
+except Exception as e:
+    print(f"Delete skipped (expected if sessions exist): {e}")
 
 print("\nEnvironment management complete.")

examples/python/managed-agents/provider/all_providers.py

@@ -1,7 +1,7 @@
 """All compute providers — comprehensive test across Local, Docker, E2B, and Modal.
 
-This example demonstrates local agent loops with various compute backends for tool sandboxing.
-Uses the new LocalAgent class to clearly indicate local execution with optional cloud compute.
+This example mirrors the Anthropic app.py but uses the local provider with
+various compute backends instead of Anthropic's managed infrastructure.
 
 Requires:
   - Docker running locally
@@ -9,8 +9,7 @@ Requires:
   - modal CLI configured (modal token set)
 """
 import asyncio
-from praisonai import Agent
-from praisonai.integrations import LocalAgent, LocalAgentConfig
+from praisonai import Agent, ManagedAgent, LocalManagedConfig
 
 
 async def test_provider(name, compute, extra_provision_kwargs=None):
@@ -19,9 +18,10 @@ async def test_provider(name, compute, extra_provision_kwargs=None):
     print(f"  PROVIDER: {name}")
     print(f"{'='*60}")
 
-    managed = LocalAgent(
+    managed = ManagedAgent(
+        provider="local",
         compute=compute,
-        config=LocalAgentConfig(
+        config=LocalManagedConfig(
             model="gpt-4o-mini",
             system="You are a helpful assistant. Be concise.",
             name=f"{name}Agent",

examples/python/managed-agents/provider/all_runtimes.py

@@ -1,47 +0,0 @@
-"""Run every managed-runtime example and report a summary.
-
-Executes the runtime_*.py files in this directory as subprocesses with a
-30s per-example timeout. Exits non-zero if any example fails.
-"""
-import os
-import subprocess
-import sys
-import time
-
-HERE = os.path.dirname(os.path.abspath(__file__))
-EXAMPLES = [
-    # ── LLM providers (where the LLM reasoning happens) ──
-    "runtime_anthropic.py",   # full loop in Anthropic cloud
-    "runtime_openai.py",      # LLM in OpenAI cloud
-    "runtime_gemini.py",      # LLM in Google cloud
-    "runtime_ollama.py",      # LLM on local/self-hosted Ollama
-    # ── Cloud/compute providers (where tool execution happens) ──
-    "runtime_e2b.py",         # tools sandboxed in E2B cloud VM
-    "runtime_modal.py",       # tools sandboxed in Modal serverless cloud
-    "runtime_fly.py",         # tools sandboxed on Fly.io Machines
-    "runtime_daytona.py",     # tools sandboxed in Daytona cloud workspace
-    "runtime_docker.py",      # tools sandboxed in local Docker container
-]
-
-results = []
-for name in EXAMPLES:
-    path = os.path.join(HERE, name)
-    start = time.time()
-    proc = subprocess.run(
-        [sys.executable, path],
-        capture_output=True, text=True, timeout=120,
-        env=os.environ.copy(),  # propagate API keys and PYTHONPATH
-    )
-    dur = time.time() - start
-    ok = proc.returncode == 0
-    results.append((name, ok, dur, proc.stdout.strip().splitlines()[-1] if proc.stdout.strip() else ""))
-    print(f"[{'OK' if ok else 'FAIL'}] {name:30s} {dur:.2f}s")
-    if not ok:
-        print(proc.stdout)
-        print(proc.stderr)
-
-print("\nSummary:")
-for name, ok, dur, tail in results:
-    print(f"  {'✓' if ok else '✗'} {name:30s} {dur:5.2f}s  {tail[:60]}")
-
-sys.exit(0 if all(r[1] for r in results) else 1)

examples/python/managed-agents/provider/local_advanced.py

@@ -23,39 +23,12 @@ print(f"[2] Updated to v{managed.agent_version}")
 result = agent.start("What is 123 + 456?", stream=True)
 
 # ── 3. Custom tool ──
-def _safe_calc(expr: str) -> str:
-    import ast
-    allowed = set("0123456789+-*/.() ")
-    if not all(c in allowed for c in expr):
-        return "error"
-    try:
-        tree = ast.parse(expr, mode="eval")
-        for node in ast.walk(tree):
-            if not isinstance(
-                node,
-                (
-                    ast.Expression,
-                    ast.BinOp,
-                    ast.UnaryOp,
-                    ast.Constant,
-                    ast.Add,
-                    ast.Sub,
-                    ast.Mult,
-                    ast.Div,
-                    ast.USub,
-                    ast.UAdd,
-                ),
-            ):
-                return "error"
-        val = eval(compile(tree, "<expr>", "eval"), {"__builtins__": {}})
-        return str(val)
-    except Exception:
-        return "error"
-
-
 def handle_calculator(tool_name, tool_input):
     expr = tool_input.get("expression", "0")
-    val = _safe_calc(str(expr))
+    try:
+        val = eval(expr, {"__builtins__": {}})
+    except Exception:
+        val = "error"
     print(f"  [Calculator: {expr} = {val}]")
     return str(val)
 

examples/python/managed-agents/provider/local_basic.py

@@ -1,14 +1,13 @@
-"""Local agent — basic local execution with gpt-4o-mini.
+"""Local provider — basic managed agent with gpt-4o-mini.
 
 No external infrastructure needed. Runs the agent loop locally.
-Uses the new canonical LocalAgent class for clarity.
 """
-from praisonai import Agent
-from praisonai.integrations import LocalAgent, LocalAgentConfig
+from praisonai import Agent, ManagedAgent, LocalManagedConfig
 
-# Create a local agent (runs locally, no managed runtime)
-managed = LocalAgent(
-    config=LocalAgentConfig(
+# Create a local managed agent (auto-detects local when no ANTHROPIC_API_KEY)
+managed = ManagedAgent(
+    provider="local",
+    config=LocalManagedConfig(
         model="gpt-4o-mini",
         system="You are a helpful assistant. Be concise.",
         name="LocalAgent",

examples/python/managed-agents/provider/runtime_anthropic.py

@@ -1,47 +0,0 @@
-"""Anthropic managed runtime — full agent loop runs in Anthropic's cloud.
-
-The ENTIRE loop (LLM calls, tool execution, memory, sessions) runs remotely
-on Anthropic's managed infrastructure. This is the only provider today where
-"the whole thing runs in that cloud".
-
-Requires: ANTHROPIC_API_KEY environment variable set.
-"""
-import os
-
-if not os.getenv("ANTHROPIC_API_KEY"):
-    print("[skip] ANTHROPIC_API_KEY not set — set it to run this example.")
-    raise SystemExit(0)
-
-try:
-    import anthropic  # noqa: F401  (required by AnthropicManagedAgent)
-except ImportError:
-    print("[skip] 'anthropic' SDK not installed — pip install 'anthropic>=0.94.0'")
-    raise SystemExit(0) from None
-
-try:
-    from praisonai import Agent, ManagedAgent, ManagedConfig
-except ImportError as e:
-    print(f"[skip] praisonai wrapper not importable ({e}).")
-    raise SystemExit(0) from None
-
-managed = ManagedAgent(
-    provider="anthropic",
-    config=ManagedConfig(
-        model="claude-3-5-sonnet-latest",
-        system="You are a concise coding assistant. One-sentence answers.",
-        name="AnthropicRuntimeAgent",
-    ),
-)
-agent = Agent(name="anthropic-runtime", backend=managed)
-
-# 1. Agent work entirely in the cloud
-print("[1] Agent:", agent.start("Write a Python one-liner that sums 1..10. Just the code."))
-
-# 2. Multi-turn reuses the same cloud session
-print("[2] Agent:", agent.start("Now change it to factorial of 5."))
-
-# 3. Cloud-side session metadata
-info = managed.retrieve_session()
-print(f"[3] session={managed.session_id}  status={info.get('status')}  "
-      f"in={info.get('usage', {}).get('input_tokens', 0)}  "
-      f"out={info.get('usage', {}).get('output_tokens', 0)}")

examples/python/managed-agents/provider/runtime_daytona.py

@@ -1,52 +0,0 @@
-"""Agent runtime with tools sandboxed in a Daytona cloud workspace.
-
-The agent's LLM loop runs locally; tool / code execution runs in a Daytona-
-managed workspace (disposable cloud dev environment, pre-configured images).
-
-Requires:
-  - ``OPENAI_API_KEY`` for the LLM
-  - ``DAYTONA_API_KEY`` for the Daytona API
-  - ``pip install daytona-sdk``
-"""
-import asyncio
-import os
-
-if not os.getenv("OPENAI_API_KEY"):
-    print("[skip] OPENAI_API_KEY not set.")
-    raise SystemExit(0)
-if not os.getenv("DAYTONA_API_KEY"):
-    print("[skip] DAYTONA_API_KEY not set.")
-    raise SystemExit(0)
-
-try:
-    from praisonai import Agent
-    from praisonai.integrations import SandboxedAgent, SandboxedAgentConfig
-except ImportError as e:
-    print(f"[skip] praisonai wrapper not importable ({e}).")
-    raise SystemExit(0) from None
-
-
-async def main():
-    sandboxed = SandboxedAgent(
-        compute="daytona",  # tool execution runs in a Daytona workspace
-        config=SandboxedAgentConfig(
-            model="gpt-4o-mini",
-            system="You are a concise coding assistant.",
-            name="DaytonaRuntimeAgent",
-        ),
-    )
-    agent = Agent(name="daytona-runtime", backend=sandboxed)
-
-    info = await sandboxed.provision_compute(idle_timeout_s=120)
-    print(f"[1] Daytona workspace: {info.instance_id} ({info.status})")
-
-    result = await sandboxed.execute_in_compute("python3 -c 'print(len([1,2,3,4,5]) ** 3)'")
-    print(f"[2] Cloud compute result: {result['stdout'].strip()}")
-
-    print("[3] Agent:", agent.start("What is 5 cubed? Just the number."))
-
-    await sandboxed.shutdown_compute()
-    print("[4] Daytona workspace shut down.")
-
-
-asyncio.run(main())

examples/python/managed-agents/provider/runtime_docker.py

@@ -1,59 +0,0 @@
-"""Agent runtime with tools sandboxed in a local Docker container.
-
-The agent's LLM loop runs locally; tool / code execution is isolated in a
-Docker container. Not a cloud provider, but the same pattern — useful for
-local development and CI.
-
-Requires:
-  - ``OPENAI_API_KEY`` for the LLM
-  - Docker daemon running (``docker info`` must succeed)
-"""
-import asyncio
-import os
-import subprocess
-
-if not os.getenv("OPENAI_API_KEY"):
-    print("[skip] OPENAI_API_KEY not set.")
-    raise SystemExit(0)
-
-try:
-    subprocess.run(
-        ["docker", "info"],
-        check=True, capture_output=True, timeout=5,
-    )
-except (subprocess.CalledProcessError, subprocess.TimeoutExpired, FileNotFoundError):
-    print("[skip] Docker daemon not available — start Docker Desktop.")
-    raise SystemExit(0) from None
-
-try:
-    from praisonai import Agent
-    from praisonai.integrations import SandboxedAgent, SandboxedAgentConfig
-except ImportError as e:
-    print(f"[skip] praisonai wrapper not importable ({e}).")
-    raise SystemExit(0) from None
-
-
-async def main():
-    sandboxed = SandboxedAgent(
-        compute="docker",  # tool execution runs in a local Docker container
-        config=SandboxedAgentConfig(
-            model="gpt-4o-mini",
-            system="You are a concise coding assistant.",
-            name="DockerRuntimeAgent",
-        ),
-    )
-    agent = Agent(name="docker-runtime", backend=sandboxed)
-
-    info = await sandboxed.provision_compute(image="python:3.12-slim")
-    print(f"[1] Docker container: {info.instance_id} ({info.status})")
-
-    result = await sandboxed.execute_in_compute("python3 -c 'print(sum(i*i for i in range(1, 11)))'")
-    print(f"[2] Container compute result: {result['stdout'].strip()}")
-
-    print("[3] Agent:", agent.start("What is the sum of squares from 1 to 10? Just the number."))
-
-    await sandboxed.shutdown_compute()
-    print("[4] Docker container shut down.")
-
-
-asyncio.run(main())

examples/python/managed-agents/provider/runtime_e2b.py

@@ -1,56 +0,0 @@
-"""Agent runtime with tools sandboxed in an E2B cloud VM.
-
-The agent's LLM loop runs locally; any tool / code execution is sandboxed
-in an ephemeral E2B cloud instance. Uses ``SandboxedAgent`` — the honest
-name for the "local loop + cloud tools" pattern.
-
-Requires:
-  - ``OPENAI_API_KEY`` for the LLM
-  - ``E2B_API_KEY``    for the cloud sandbox
-  - ``pip install e2b``
-"""
-import asyncio
-import os
-
-if not os.getenv("OPENAI_API_KEY"):
-    print("[skip] OPENAI_API_KEY not set.")
-    raise SystemExit(0)
-if not os.getenv("E2B_API_KEY"):
-    print("[skip] E2B_API_KEY not set — set it to run in the E2B cloud sandbox.")
-    raise SystemExit(0)
-
-try:
-    from praisonai import Agent
-    from praisonai.integrations import SandboxedAgent, SandboxedAgentConfig
-except ImportError as e:
-    print(f"[skip] praisonai wrapper not importable ({e}).")
-    raise SystemExit(0) from None
-
-
-async def main():
-    sandboxed = SandboxedAgent(
-        compute="e2b",  # tool execution runs in E2B cloud
-        config=SandboxedAgentConfig(
-            model="gpt-4o-mini",
-            system="You are a concise coding assistant.",
-            name="E2BRuntimeAgent",
-        ),
-    )
-    agent = Agent(name="e2b-runtime", backend=sandboxed)
-
-    # 1. Provision the cloud sandbox
-    info = await sandboxed.provision_compute(idle_timeout_s=120)
-    print(f"[1] E2B sandbox: {info.instance_id} ({info.status})")
-
-    # 2. Execute code IN the cloud sandbox
-    result = await sandboxed.execute_in_compute("python3 -c 'print(sum(range(1, 11)))'")
-    print(f"[2] Cloud compute result: {result['stdout'].strip()}")
-
-    # 3. Agent loop (LLM local, any tool calls auto-sandboxed in E2B)
-    print("[3] Agent:", agent.start("What is 13 * 17? Just the number."))
-
-    await sandboxed.shutdown_compute()
-    print("[4] E2B sandbox shut down.")
-
-
-asyncio.run(main())

examples/python/managed-agents/provider/runtime_fly.py

@@ -1,57 +0,0 @@
-"""Agent runtime with tools sandboxed on Fly.io Machines.
-
-The agent's LLM loop runs locally; tool / code execution runs in a Fly.io
-Machine (fast-booting micro-VM with per-second billing and global regions).
-
-Requires:
-  - ``OPENAI_API_KEY`` for the LLM
-  - ``FLY_API_TOKEN``  for the Fly.io Machines API
-  - ``FLY_APP_NAME``   (optional) existing Fly app; auto-created otherwise
-"""
-import asyncio
-import os
-
-if not os.getenv("OPENAI_API_KEY"):
-    print("[skip] OPENAI_API_KEY not set.")
-    raise SystemExit(0)
-if not os.getenv("FLY_API_TOKEN"):
-    print("[skip] FLY_API_TOKEN not set — get one via `fly auth token`.")
-    raise SystemExit(0)
-
-try:
-    from praisonai import Agent
-    from praisonai.integrations import SandboxedAgent, SandboxedAgentConfig
-except ImportError as e:
-    print(f"[skip] praisonai wrapper not importable ({e}).")
-    raise SystemExit(0) from None
-
-
-async def main():
-    sandboxed = SandboxedAgent(
-        compute="flyio",  # tool execution runs on Fly.io Machines
-        config=SandboxedAgentConfig(
-            model="gpt-4o-mini",
-            system="You are a concise coding assistant.",
-            name="FlyioRuntimeAgent",
-        ),
-    )
-    agent = Agent(name="fly-runtime", backend=sandboxed)
-
-    info = await sandboxed.provision_compute(
-        image="python:3.12-slim",
-        cpu=1,
-        memory_mb=512,
-        idle_timeout_s=120,
-    )
-    print(f"[1] Fly Machine: {info.instance_id} ({info.status})")
-
-    result = await sandboxed.execute_in_compute("python3 -c 'print(2 ** 20)'")
-    print(f"[2] Cloud compute result: {result['stdout'].strip()}")
-
-    print("[3] Agent:", agent.start("What is 2 raised to the 20th power? Just the number."))
-
-    await sandboxed.shutdown_compute()
-    print("[4] Fly Machine shut down.")
-
-
-asyncio.run(main())

examples/python/managed-agents/provider/runtime_gemini.py

@@ -1,40 +0,0 @@
-"""Gemini managed runtime — agent loop local, LLM calls go to Google's cloud.
-
-``ManagedAgent(provider="gemini")`` wires a local managed loop to Gemini's
-hosted models via litellm.
-
-Requires: GEMINI_API_KEY (or GOOGLE_API_KEY) environment variable set.
-"""
-import os
-
-if not (os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY")):
-    print("[skip] GEMINI_API_KEY / GOOGLE_API_KEY not set — set one to run this example.")
-    raise SystemExit(0)
-
-try:
-    from praisonai import Agent, ManagedAgent, LocalManagedConfig
-except ImportError as e:
-    print(f"[skip] praisonai wrapper not importable ({e}).")
-    raise SystemExit(0) from None
-
-managed = ManagedAgent(
-    provider="gemini",
-    config=LocalManagedConfig(
-        # Use explicit litellm prefix to guarantee routing to Google
-        model="gemini/gemini-2.0-flash-exp",
-        system="You are a concise assistant. One-sentence answers.",
-        name="GeminiRuntimeAgent",
-    ),
-)
-agent = Agent(name="gemini-runtime", backend=managed)
-
-# 1. Agent work (LLM call goes to Google cloud)
-print("[1] Agent:", agent.start("What does REST stand for? One sentence."))
-
-# 2. Multi-turn reuses the session
-print("[2] Agent:", agent.start("How is it different from GraphQL?"))
-
-info = managed.retrieve_session()
-print(f"[3] session={managed.session_id}  "
-      f"in={info.get('usage', {}).get('input_tokens', 0)}  "
-      f"out={info.get('usage', {}).get('output_tokens', 0)}")

examples/python/managed-agents/provider/runtime_hosted_anthropic.py

@@ -1,63 +0,0 @@
-"""Anthropic hosted runtime — entire agent runs on Anthropic's managed infrastructure.
-
-Uses the new canonical HostedAgent class which clearly communicates that the entire
-agent loop runs in Anthropic's cloud, not locally.
-"""
-import os
-import sys
-
-# Skip guards - exit cleanly if prerequisites not met
-if not (os.getenv("ANTHROPIC_API_KEY") or os.getenv("CLAUDE_API_KEY")):
-    print("[skip] ANTHROPIC_API_KEY or CLAUDE_API_KEY not set")
-    sys.exit(0)
-
-try:
-    import anthropic
-except ImportError:
-    print("[skip] anthropic SDK not installed")
-    sys.exit(0)
-
-# Heavy imports only after skip-guards pass
-from praisonai import Agent
-from praisonai.integrations import HostedAgent, HostedAgentConfig
-
-# Create a hosted agent running entirely on Anthropic's managed runtime
-hosted = HostedAgent(
-    provider="anthropic",
-    config=HostedAgentConfig(
-        model="claude-3-5-sonnet-latest",
-        system="You are a helpful coding assistant. Be concise.",
-        name="AnthropicHostedAgent",
-        tools=[{"type": "agent_toolset_20260401"}],
-    ),
-)
-
-agent = Agent(name="anthropic-hosted", backend=hosted)
-
-# 1. Basic execution - runs entirely in Anthropic's cloud
-print("[1] Hosted execution on Anthropic infrastructure...")
-result = agent.start("What is the capital of France? One word.", stream=True)
-print(f"    Result: {result}")
-
-# 2. Agent metadata from Anthropic's API
-print(f"\n[2] Agent ID: {hosted.agent_id}")
-print(f"    Version: {hosted.agent_version}")
-print(f"    Env ID:  {hosted.environment_id}")
-print(f"    Session: {hosted.session_id}")
-
-# 3. Multi-turn (same session keeps context in Anthropic's cloud)
-print("\n[3] Multi-turn conversation...")
-result = agent.start("What country is that city in?", stream=True)
-print(f"    Result: {result}")
-
-# 4. Usage tracking from Anthropic's usage API
-info = hosted.retrieve_session()
-print(f"\n[4] Usage: in={info['usage']['input_tokens']}, out={info['usage']['output_tokens']}")
-
-# 5. List all sessions for this agent
-sessions = hosted.list_sessions()
-print(f"\n[5] Sessions: {len(sessions)}")
-for s in sessions[:3]:  # Show first 3
-    print(f"    {s['id']} | {s['status']}")
-
-print("\nDone! Agent loop ran entirely on Anthropic's managed infrastructure.")

examples/python/managed-agents/provider/runtime_local_gemini.py

@@ -1,61 +0,0 @@
-"""Local agent loop with Gemini LLM — runs locally, not in a managed runtime.
-
-Uses the new canonical LocalAgent class which clearly communicates that only the
-agent loop runs locally. The LLM calls go to Google's Gemini API, but there's no managed runtime involved.
-"""
-import os
-import sys
-
-# Skip guards - exit cleanly if prerequisites not met
-if not (os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY")):
-    print("[skip] GEMINI_API_KEY or GOOGLE_API_KEY not set")
-    sys.exit(0)
-
-try:
-    import google.generativeai
-except ImportError:
-    print("[skip] google-generativeai SDK not installed")
-    sys.exit(0)
-
-# Heavy imports only after skip-guards pass
-from praisonai import Agent
-from praisonai.integrations import LocalAgent, LocalAgentConfig
-
-# Create a local agent using Gemini LLM
-local = LocalAgent(
-    config=LocalAgentConfig(
-        model="gemini/gemini-2.0-flash",  # Use Gemini with litellm routing prefix
-        system="You are a helpful coding assistant. Be concise.",
-        name="LocalGeminiAgent",
-        tools=["execute_command", "read_file", "write_file"],
-    ),
-)
-
-agent = Agent(name="local-gemini", backend=local)
-
-# 1. Basic execution - agent loop runs locally, LLM calls go to Gemini
-print("[1] Local execution with Gemini LLM...")
-result = agent.start("What is the capital of France? One word.", stream=True)
-print(f"    Result: {result}")
-
-# 2. Agent metadata (locally generated UUIDs)
-print(f"\n[2] Agent ID: {local.agent_id}")
-print(f"    Version: {local.agent_version}")
-print(f"    Env ID:  {local.environment_id}")
-print(f"    Session: {local.session_id}")
-
-# 3. Multi-turn conversation (session state maintained locally)
-print("\n[3] Multi-turn conversation...")
-result = agent.start("What country is that city in?", stream=True)
-print(f"    Result: {result}")
-
-# 4. Usage tracking (accumulated locally)
-info = local.retrieve_session()
-print(f"\n[4] Usage: in={info['usage']['input_tokens']}, out={info['usage']['output_tokens']}")
-
-# 5. Tool execution (runs in local subprocess)
-print("\n[5] Tool execution example...")
-result = agent.start("Create a file called hello_gemini.txt with 'Hello from Gemini agent!'", stream=True)
-print(f"    Tool result: {result}")
-
-print("\nDone! Agent loop ran locally with Gemini LLM calls.")

examples/python/managed-agents/provider/runtime_local_ollama.py

@@ -1,61 +0,0 @@
-"""Local agent loop with Ollama LLM — runs locally, not in a managed runtime.
-
-Uses the new canonical LocalAgent class which clearly communicates that only the
-agent loop runs locally. The LLM calls go to a local Ollama instance, no managed runtime involved.
-"""
-import os
-import sys
-import requests
-
-# Skip guards - exit cleanly if prerequisites not met  
-# Check if Ollama daemon is running
-try:
-    response = requests.get("http://localhost:11434/api/tags", timeout=2)
-    if response.status_code != 200:
-        raise Exception("Ollama not responding")
-except Exception:
-    print("[skip] Ollama daemon not running at localhost:11434")
-    sys.exit(0)
-
-# Heavy imports only after skip-guards pass
-from praisonai import Agent
-from praisonai.integrations import LocalAgent, LocalAgentConfig
-
-# Create a local agent using Ollama LLM
-local = LocalAgent(
-    config=LocalAgentConfig(
-        model="ollama/llama3.2",  # Use Ollama with litellm routing prefix
-        system="You are a helpful coding assistant. Be concise.",
-        name="LocalOllamaAgent",
-        tools=["execute_command", "read_file", "write_file"],
-    ),
-)
-
-agent = Agent(name="local-ollama", backend=local)
-
-# 1. Basic execution - agent loop runs locally, LLM calls go to local Ollama
-print("[1] Local execution with Ollama LLM...")
-result = agent.start("What is the capital of France? One word.", stream=True)
-print(f"    Result: {result}")
-
-# 2. Agent metadata (locally generated UUIDs)
-print(f"\n[2] Agent ID: {local.agent_id}")
-print(f"    Version: {local.agent_version}")
-print(f"    Env ID:  {local.environment_id}")
-print(f"    Session: {local.session_id}")
-
-# 3. Multi-turn conversation (session state maintained locally)
-print("\n[3] Multi-turn conversation...")
-result = agent.start("What country is that city in?", stream=True)
-print(f"    Result: {result}")
-
-# 4. Usage tracking (accumulated locally)
-info = local.retrieve_session()
-print(f"\n[4] Usage: in={info['usage']['input_tokens']}, out={info['usage']['output_tokens']}")
-
-# 5. Tool execution (runs in local subprocess)
-print("\n[5] Tool execution example...")
-result = agent.start("Create a file called hello_ollama.txt with 'Hello from Ollama agent!'", stream=True)
-print(f"    Tool result: {result}")
-
-print("\nDone! Agent loop ran locally with Ollama LLM calls.")

examples/python/managed-agents/provider/runtime_local_openai.py

@@ -1,61 +0,0 @@
-"""Local agent loop with OpenAI LLM — runs locally, not in a managed runtime.
-
-Uses the new canonical LocalAgent class which clearly communicates that only the
-agent loop runs locally. The LLM calls go to OpenAI, but there's no managed runtime involved.
-"""
-import os
-import sys
-
-# Skip guards - exit cleanly if prerequisites not met
-if not os.getenv("OPENAI_API_KEY"):
-    print("[skip] OPENAI_API_KEY not set")
-    sys.exit(0)
-
-try:
-    import openai
-except ImportError:
-    print("[skip] openai SDK not installed")
-    sys.exit(0)
-
-# Heavy imports only after skip-guards pass
-from praisonai import Agent
-from praisonai.integrations import LocalAgent, LocalAgentConfig
-
-# Create a local agent using OpenAI's LLM
-local = LocalAgent(
-    config=LocalAgentConfig(
-        model="gpt-4o-mini",
-        system="You are a helpful coding assistant. Be concise.",
-        name="LocalOpenAIAgent",
-        tools=["execute_command", "read_file", "write_file"],
-    ),
-)
-
-agent = Agent(name="local-openai", backend=local)
-
-# 1. Basic execution - agent loop runs locally, LLM calls go to OpenAI
-print("[1] Local execution with OpenAI LLM...")
-result = agent.start("What is the capital of France? One word.", stream=True)
-print(f"    Result: {result}")
-
-# 2. Agent metadata (locally generated UUIDs)
-print(f"\n[2] Agent ID: {local.agent_id}")
-print(f"    Version: {local.agent_version}")
-print(f"    Env ID:  {local.environment_id}")
-print(f"    Session: {local.session_id}")
-
-# 3. Multi-turn conversation (session state maintained locally)
-print("\n[3] Multi-turn conversation...")
-result = agent.start("What country is that city in?", stream=True)
-print(f"    Result: {result}")
-
-# 4. Usage tracking (accumulated locally)
-info = local.retrieve_session()
-print(f"\n[4] Usage: in={info['usage']['input_tokens']}, out={info['usage']['output_tokens']}")
-
-# 5. Tool execution (runs in local subprocess)
-print("\n[5] Tool execution example...")
-result = agent.start("Create a file called hello.txt with 'Hello from local agent!'", stream=True)
-print(f"    Tool result: {result}")
-
-print("\nDone! Agent loop ran locally with OpenAI LLM calls.")

examples/python/managed-agents/provider/runtime_modal.py

@@ -1,61 +0,0 @@
-"""Agent runtime with tools sandboxed in Modal cloud functions.
-
-The agent's LLM loop runs locally; tool / code execution is sandboxed in
-Modal's serverless cloud. Modal gives fast cold-starts and per-second billing.
-
-Requires:
-  - ``OPENAI_API_KEY`` for the LLM
-  - Modal CLI configured: ``modal token set --token-id ... --token-secret ...``
-  - ``pip install modal``
-"""
-import asyncio
-import os
-
-if not os.getenv("OPENAI_API_KEY"):
-    print("[skip] OPENAI_API_KEY not set.")
-    raise SystemExit(0)
-
-try:
-    import modal  # noqa: F401
-except ImportError:
-    print("[skip] 'modal' package not installed — pip install modal")
-    raise SystemExit(0) from None
-
-# Modal auth: presence of ~/.modal.toml or MODAL_TOKEN_ID + MODAL_TOKEN_SECRET
-if not (os.path.exists(os.path.expanduser("~/.modal.toml"))
-        or (os.getenv("MODAL_TOKEN_ID") and os.getenv("MODAL_TOKEN_SECRET"))):
-    print("[skip] Modal not configured — run `modal token set ...`")
-    raise SystemExit(0)
-
-try:
-    from praisonai import Agent
-    from praisonai.integrations import SandboxedAgent, SandboxedAgentConfig
-except ImportError as e:
-    print(f"[skip] praisonai wrapper not importable ({e}).")
-    raise SystemExit(0) from None
-
-
-async def main():
-    sandboxed = SandboxedAgent(
-        compute="modal",  # tool execution runs in Modal's cloud
-        config=SandboxedAgentConfig(
-            model="gpt-4o-mini",
-            system="You are a concise coding assistant.",
-            name="ModalRuntimeAgent",
-        ),
-    )
-    agent = Agent(name="modal-runtime", backend=sandboxed)
-
-    info = await sandboxed.provision_compute(idle_timeout_s=120)
-    print(f"[1] Modal instance: {info.instance_id} ({info.status})")
-
-    result = await sandboxed.execute_in_compute("python3 -c 'import math; print(math.factorial(10))'")
-    print(f"[2] Cloud compute result: {result['stdout'].strip()}")
-
-    print("[3] Agent:", agent.start("What is the 10th Fibonacci number? Just the number."))
-
-    await sandboxed.shutdown_compute()
-    print("[4] Modal instance shut down.")
-
-
-asyncio.run(main())

examples/python/managed-agents/provider/runtime_ollama.py

@@ -1,45 +0,0 @@
-"""Ollama managed runtime — agent runs against a local/self-hosted LLM.
-
-``ManagedAgent(provider="ollama")`` wires the managed loop to an Ollama
-server. Model runs locally (or on a self-hosted box) instead of in a public
-cloud — useful when you want the "managed agent" developer experience with
-no external API dependency.
-
-Requires: An Ollama server reachable (default ``http://localhost:11434``)
-with at least one model pulled (e.g., ``ollama pull llama3.2``).
-"""
-import os
-import urllib.error
-import urllib.request
-
-base = os.getenv("OLLAMA_HOST", "http://localhost:11434")
-try:
-    urllib.request.urlopen(f"{base}/api/tags", timeout=2).read()
-except (urllib.error.URLError, OSError) as e:
-    print(f"[skip] Ollama not reachable at {base} ({e}). Start it with `ollama serve`.")
-    raise SystemExit(0)
-
-try:
-    from praisonai import Agent, ManagedAgent, LocalManagedConfig
-except ImportError as e:
-    print(f"[skip] praisonai wrapper not importable ({e}).")
-    raise SystemExit(0) from None
-
-managed = ManagedAgent(
-    provider="ollama",
-    config=LocalManagedConfig(
-        model="llama3.2",
-        system="You are a concise assistant. One-sentence answers.",
-        name="OllamaRuntimeAgent",
-    ),
-)
-agent = Agent(name="ollama-runtime", backend=managed)
-
-# 1. Agent work (LLM call goes to your Ollama server)
-print("[1] Agent:", agent.start("What is the capital of Japan? One word."))
-
-# 2. Multi-turn reuses the session
-print("[2] Agent:", agent.start("Name its most famous mountain."))
-
-info = managed.retrieve_session()
-print(f"[3] session={managed.session_id}")

examples/python/managed-agents/provider/runtime_openai.py

@@ -1,41 +0,0 @@
-"""OpenAI managed runtime — agent loop local, LLM calls go to OpenAI cloud.
-
-``ManagedAgent(provider="openai")`` wires a local managed loop to OpenAI's
-hosted models. The LLM reasoning (the heavy-lifting) runs in OpenAI's cloud;
-the agent orchestration (tool routing, session state) runs in this process.
-
-Requires: OPENAI_API_KEY environment variable set.
-"""
-import os
-
-if not os.getenv("OPENAI_API_KEY"):
-    print("[skip] OPENAI_API_KEY not set — set it to run this example.")
-    raise SystemExit(0)
-
-try:
-    from praisonai import Agent, ManagedAgent, LocalManagedConfig
-except ImportError as e:
-    print(f"[skip] praisonai wrapper not importable ({e}).")
-    raise SystemExit(0) from None
-
-managed = ManagedAgent(
-    provider="openai",
-    config=LocalManagedConfig(
-        model="gpt-4o-mini",
-        system="You are a concise assistant. One-sentence answers.",
-        name="OpenAIRuntimeAgent",
-    ),
-)
-agent = Agent(name="openai-runtime", backend=managed)
-
-# 1. Agent work (LLM call goes to OpenAI cloud)
-print("[1] Agent:", agent.start("What is the time complexity of binary search? One sentence."))
-
-# 2. Multi-turn reuses the session
-print("[2] Agent:", agent.start("And for a linear scan?"))
-
-# 3. Local session metadata (cloud usage accumulated)
-info = managed.retrieve_session()
-print(f"[3] session={managed.session_id}  "
-      f"in={info.get('usage', {}).get('input_tokens', 0)}  "
-      f"out={info.get('usage', {}).get('output_tokens', 0)}")

examples/python/mcp/mcp-resumability.py

@@ -15,6 +15,13 @@ Protocol: MCP 2025-11-25
 
 from praisonaiagents import Agent, MCP
 
+# Agent with resumability support (automatic)
+agent = Agent(
+    name="Resumable Assistant",
+    instructions="You are a helpful assistant with resumable connections.",
+    tools=MCP("https://api.example.com/mcp")
+)
+
 # Resumability is automatic - the MCP client:
 # 1. Tracks event IDs from SSE streams (id: field)
 # 2. Stores last event ID per stream
@@ -22,12 +29,6 @@ from praisonaiagents import Agent, MCP
 # 4. Respects retry delay from server (retry: field)
 
 if __name__ == "__main__":
-    agent = Agent(
-        name="Resumable Assistant",
-        instructions="You are a helpful assistant with resumable connections.",
-        tools=MCP("https://api.example.com/mcp"),
-    )
-
     print("MCP Resumability Example")
     print("=" * 40)
     print()