fix: update broken Full Flow docs link in praisonai package README

- Fix duplicate broken link in src/praisonai/README.md - Change from /docs/concepts/flow (404) to /docs/concepts/agentflow (200) - Ensures PyPI package readers get working documentation link - Addresses issue identified by Qodo code review Co-authored-by: Mervin Praison <[email protected]>
docs: fix broken Full Flow docs link in README
2026-11-09 03:39:37 -10:00 · 2026-06-01 02:13:45 +00:00 · 2026-06-01 09:11:18 +08:00 · 2026-05-30 07:22:17 +01:00 · 2026-05-30 07:20:59 +01:00 · 2026-05-30 07:20:30 +01:00
766 changed files with 88780 additions and 18756 deletions
@@ -0,0 +1,51 @@
+# 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
@@ -183,7 +183,7 @@ jobs:
            if (reviews.copilot.length) summaryParts.push('Copilot: ' + reviews.copilot.length + ' comments');
            if (reviews.greptile.length) summaryParts.push('Greptile: ' + reviews.greptile.length + ' comments');
            const reviewSummary = summaryParts.length ? summaryParts.join(' | ') : 'No bot reviews detected';
-            const claudeBody = '@claude You are the FINAL architecture reviewer. ' + reviewSummary + '.\n\n' +
+            const claudeBody = '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. ' + reviewSummary + '.\n\n' +
              'Review Context: ' + comments.data.length + ' total comments, ' + 
              (reviews.coderabbit.length + reviews.qodo.length + reviews.gemini.length + reviews.copilot.length + reviews.greptile.length) + ' bot reviews.\n\n' +
              'Phase 1: Review per AGENTS.md principles (protocol-driven, backward compatible, no perf impact).\n' +
@@ -313,7 +313,7 @@ jobs:
              issue_number: pr,
              owner: context.repo.owner,
              repo: context.repo.repo,
-              body: '@claude You are the FINAL architecture reviewer. Read ALL comments above from Gemini, Qodo, CodeRabbit, and Copilot carefully before responding.\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
+              body: '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. Read ALL comments above from Gemini, Qodo, CodeRabbit, and Copilot carefully before responding.\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
            });
            console.log(`Claude triggered on PR #${pr}`);

@@ -417,7 +417,7 @@ jobs:
              issue_number: pr,
              owner: context.repo.owner,
              repo: context.repo.repo,
-              body: '@claude You are the FINAL architecture reviewer. Read ALL comments above from Gemini, Qodo, CodeRabbit, and Copilot carefully before responding.\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
+              body: '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. Read ALL comments above from Gemini, Qodo, CodeRabbit, and Copilot carefully before responding.\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
            });
            console.log(`Claude triggered on PR #${pr}`);

@@ -468,7 +468,7 @@ jobs:
              issue_number: context.issue.number,
              owner: context.repo.owner,
              repo: context.repo.repo,
-              body: '@claude You are the Lead Engineer. Read ALL analysis and reviews above carefully (Gemini, CodeRabbit, Qodo, Copilot, etc).\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
+              body: '@claude You are the Lead Engineer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. Read ALL analysis and reviews above carefully (Gemini, CodeRabbit, Qodo, Copilot, etc).\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
            });

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

@@ -201,7 +204,7 @@ jobs:
          label_trigger: "claude"

          direct_prompt: |
-            ${{ steps.check_fork.outputs.is_fork == 'true' && 'CRITICAL: THIS IS A PULL REQUEST FROM A FORK. YOU DO NOT HAVE PUSH PERMISSIONS TO THIS REPOSITORY. ONLY READ FILES, VALIDATE CODE, AND PROVIDE YOUR COMMENTS/FEEDBACK DIRECTLY IN THIS PR VIA COMMENTS. DO NOT ATTEMPT TO PUSH OR CREATE PULL REQUESTS.\n\n' || '' }}
+            ${{ steps.check_fork.outputs.is_fork == 'true' && 'CRITICAL: THIS IS A PULL REQUEST FROM A FORK. YOU DO NOT HAVE PUSH PERMISSIONS TO THIS REPOSITORY. ONLY READ FILES, VALIDATE CODE, AND PROVIDE YOUR COMMENTS/FEEDBACK DIRECTLY IN THIS PR VIA COMMENTS. DO NOT ATTEMPT TO PUSH OR CREATE PULL REQUESTS.\n\n' || 'NOTE: The branch is under MervinPraison/PraisonAI (not a fork). You are able to make modifications and push directly to this branch.\n\n' }}
            You are working on the PraisonAI SDK. Follow AGENTS.md strictly.

            STEP 0 — SETUP GIT IDENTITY & AUTH (GLOBAL — required for all repos):
@@ -212,6 +215,7 @@ jobs:

            STEP 1 — READ GUIDELINES:
            Read AGENTS.md to understand the architecture rules.
+            SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify or touch praisonai-rust or praisonai-ts packages.

            STEP 2 — ARCHITECTURE VALIDATION & ROUTING (MANDATORY before writing code):
            Before implementing anything, answer these questions:
@@ -230,9 +234,14 @@ jobs:
            If ANY of these conceptual checks fail (excluding routing), add a comment to the issue explaining why and close it. Do NOT create a PR.

            STEP 3 — IMPLEMENT (for changes in THIS repo — PraisonAI):
-            - Create a fix branch and implement a minimal, focused fix
+            - Create a fix branch: git checkout -b claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
+            - Implement a minimal, focused fix
            - Follow protocol-driven design: protocols in core SDK, heavy implementations in wrapper
            - Keep changes small and backward-compatible
+            - Commit: git add -A && git commit -m "fix: <description> (fixes #$ISSUE_NUMBER)"
+            - Push: git push origin claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
+            CRITICAL: After pushing, you MUST immediately run: gh pr create --title "fix: <title>" --body "Fixes #$ISSUE_NUMBER" --head claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
+            Do NOT stop after pushing. Do NOT say "manually create a PR". Always create the PR automatically.

            STEP 3-ALT — IMPLEMENT IN EXTERNAL REPO (for PraisonAI-Tools, PraisonAIDocs, PraisonAI-Plugins):
            When work must happen in a different repository, follow these steps EXACTLY. Do NOT attempt to use `cd`, as directory state is not preserved. Use `git -C` for all commands.
@@ -0,0 +1,158 @@
+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
@@ -29,9 +29,6 @@ jobs:
        uses: actions/checkout@v4
        with:
          persist-credentials: false
-        with:
-          persist-credentials: false
-        with:
          fetch-depth: 0
          
      - name: Generate GitHub App Token
@@ -63,10 +60,12 @@ 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="${{ inputs.issue_number }}"
+            export ISSUE_NUMBER="$ISSUE_NUMBER_INPUT"
          else
-            export ISSUE_NUMBER="${{ github.event.issue.number }}"
+            export ISSUE_NUMBER="$ISSUE_NUMBER_EVENT"
          fi
          praisonai github triage --issue $ISSUE_NUMBER --agent-file .github/praisonai-issue-triage.yaml
@@ -30,22 +30,23 @@ 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/${{ github.event.issue.number }}/head" >> "$GITHUB_OUTPUT"
+            echo "ref=refs/pull/$ISSUE_NUMBER/head" >> "$GITHUB_OUTPUT"
          elif [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
-            echo "ref=refs/pull/${{ inputs.pr_number }}/head" >> "$GITHUB_OUTPUT"
+            echo "ref=refs/pull/$PR_NUMBER_INPUT/head" >> "$GITHUB_OUTPUT"
          else
-            echo "ref=${{ github.event.pull_request.head.sha }}" >> "$GITHUB_OUTPUT"
+            echo "ref=$PR_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
          
@@ -22,8 +22,8 @@ jobs:

    steps:
    - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
+      with:
+        persist-credentials: false
    - name: Set up Python
      uses: actions/setup-python@v5
      with:
@@ -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:
@@ -176,6 +176,18 @@ 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 != ''
@@ -97,4 +97,5 @@ Cargo.lock

 src/praisonai/build*.egg-info/
 src/praisonai/PraisonAI.egg-info/
-jobs/*
+jobs/*
+*.egg-info
@@ -23,7 +23,11 @@

 </div>

-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.
+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.
+
+```bash
+curl -fsSL https://praison.ai/install.sh | bash
+```

 <div align="center">
  <br>
@@ -290,8 +294,35 @@ def search(query: str) -> str:

@tool
 def calculate(expression: str) -> float:
-    """Evaluate a math expression."""
-    return eval(expression)
+    """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")

 agent = Agent(
    instructions="You are a helpful assistant",
@@ -300,6 +331,7 @@ 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)
@@ -326,6 +358,15 @@ 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
@@ -341,7 +382,7 @@ praisonai flow

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

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

 ### 8. PraisonAI UI 🤖 (Clean Chat)

@@ -40,6 +40,7 @@ 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>
@@ -171,7 +172,9 @@ Methods:
 * <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">safe_run</a>(**kwargs) -> ToolResult</code>
 * <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate</a>() -> bool</code>
 * <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_class</a>() -> bool</code>
+* <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_schema_roundtrip</a>() -> bool</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">__call__</a>(*args, **kwargs) -> Any</code>
+* <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">check_availability</a>() -> tuple[bool, str]</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">injected_params</a>() -> Dict[str, Any]</code>
 * <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>
@@ -179,8 +182,10 @@ 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">list_available_tools</a>(context: Optional[Dict[str, Any]] = 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) -> 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>
@@ -206,10 +211,12 @@ 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) -> Parallel</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">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>

@@ -466,14 +473,26 @@ 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>
@@ -588,6 +607,13 @@ 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:
@@ -700,6 +726,7 @@ Methods:
 * <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">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>
@@ -741,7 +768,7 @@ Methods:

 Types:
 ```python
-from praisonai import AgentApp, AgentOS, AnthropicManagedAgent, CloudProvider, DB, Deploy, DeployConfig, DeployType, LocalManagedAgent, LocalManagedConfig, ManagedAgent, ManagedConfig, PraisonAI, __version__
+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
 ```

 # CLI
@@ -771,6 +798,11 @@ Methods:
 * <code title="cli">praisonai bot bot-callback <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
 * <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>
@@ -811,6 +843,7 @@ Methods:
 * <code title="cli">praisonai context show <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
 * <code title="cli">praisonai context stats <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
 * <code title="cli">praisonai context tail <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
+* <code title="cli">praisonai dashboard unified <a href="./src/praisonai/praisonai/cli/commands/dashboard.py">--help</a></code>
 * <code title="cli">praisonai debug acp <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
 * <code title="cli">praisonai debug debug-callback <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
 * <code title="cli">praisonai debug interactive <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
@@ -837,8 +870,10 @@ 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>
@@ -850,19 +885,28 @@ Methods:
 * <code title="cli">praisonai eval judge <a href="./src/praisonai/praisonai/cli/commands/eval.py">--help</a></code>
 * <code title="cli">praisonai eval list-judges <a href="./src/praisonai/praisonai/cli/commands/eval.py">--help</a></code>
 * <code title="cli">praisonai eval performance <a href="./src/praisonai/praisonai/cli/commands/eval.py">--help</a></code>
+* <code title="cli">praisonai examples find <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
 * <code title="cli">praisonai examples info <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
 * <code title="cli">praisonai examples list <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
 * <code title="cli">praisonai examples report <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
 * <code title="cli">praisonai examples run <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
 * <code title="cli">praisonai examples run-all <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
 * <code title="cli">praisonai examples stats <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
+* <code title="cli">praisonai flow export <a href="./src/praisonai/praisonai/cli/commands/flow.py">--help</a></code>
 * <code title="cli">praisonai flow flow-start <a href="./src/praisonai/praisonai/cli/commands/flow.py">--help</a></code>
+* <code title="cli">praisonai flow import <a href="./src/praisonai/praisonai/cli/commands/flow.py">--help</a></code>
+* <code title="cli">praisonai flow list <a href="./src/praisonai/praisonai/cli/commands/flow.py">--help</a></code>
 * <code title="cli">praisonai flow version <a href="./src/praisonai/praisonai/cli/commands/flow.py">--help</a></code>
 * <code title="cli">praisonai gateway channels <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <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>
@@ -871,6 +915,8 @@ Methods:
 * <code title="cli">praisonai knowledge index <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
 * <code title="cli">praisonai knowledge list <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
 * <code title="cli">praisonai knowledge search <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
+* <code title="cli">praisonai langextract render <a href="./src/praisonai/praisonai/cli/commands/langextract.py">--help</a></code>
+* <code title="cli">praisonai langextract view <a href="./src/praisonai/praisonai/cli/commands/langextract.py">--help</a></code>
 * <code title="cli">praisonai langfuse config <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
 * <code title="cli">praisonai langfuse connect <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
 * <code title="cli">praisonai langfuse init <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
@@ -889,6 +935,7 @@ Methods:
 * <code title="cli">praisonai lsp start <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
 * <code title="cli">praisonai lsp status <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
 * <code title="cli">praisonai lsp stop <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
+* <code title="cli">praisonai managed delete <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
 * <code title="cli">praisonai managed get <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
 * <code title="cli">praisonai managed list <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
 * <code title="cli">praisonai managed managed-callback <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
@@ -916,10 +963,22 @@ Methods:
 * <code title="cli">praisonai memory search <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
 * <code title="cli">praisonai memory show <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
 * <code title="cli">praisonai memory status <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
+* <code title="cli">praisonai n8n export <a href="./src/praisonai/praisonai/cli/commands/n8n.py">--help</a></code>
+* <code title="cli">praisonai n8n import <a href="./src/praisonai/praisonai/cli/commands/n8n.py">--help</a></code>
+* <code title="cli">praisonai n8n list <a href="./src/praisonai/praisonai/cli/commands/n8n.py">--help</a></code>
+* <code title="cli">praisonai n8n preview <a href="./src/praisonai/praisonai/cli/commands/n8n.py">--help</a></code>
+* <code title="cli">praisonai n8n pull <a href="./src/praisonai/praisonai/cli/commands/n8n.py">--help</a></code>
+* <code title="cli">praisonai n8n push <a href="./src/praisonai/praisonai/cli/commands/n8n.py">--help</a></code>
+* <code title="cli">praisonai n8n test <a href="./src/praisonai/praisonai/cli/commands/n8n.py">--help</a></code>
 * <code title="cli">praisonai obs obs-fallback <a href="./src/praisonai/praisonai/cli/commands/obs.py">--help</a></code>
+* <code title="cli">praisonai onboard onboard-callback <a href="./src/praisonai/praisonai/cli/commands/onboard.py">--help</a></code>
 * <code title="cli">praisonai package install <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
 * <code title="cli">praisonai package list <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
 * <code title="cli">praisonai package uninstall <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
+* <code title="cli">praisonai 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>
@@ -1009,6 +1068,7 @@ 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>
@@ -1016,6 +1076,10 @@ Methods:
 * <code title="cli">praisonai session list <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
 * <code title="cli">praisonai session resume <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
 * <code title="cli">praisonai session show <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
+* <code title="cli">praisonai setup config <a href="./src/praisonai/praisonai/cli/commands/setup.py">--help</a></code>
+* <code title="cli">praisonai setup reset <a href="./src/praisonai/praisonai/cli/commands/setup.py">--help</a></code>
+* <code title="cli">praisonai setup setup-callback <a href="./src/praisonai/praisonai/cli/commands/setup.py">--help</a></code>
+* <code title="cli">praisonai setup wizard <a href="./src/praisonai/praisonai/cli/commands/setup.py">--help</a></code>
 * <code title="cli">praisonai skills check <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
 * <code title="cli">praisonai skills create <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
 * <code title="cli">praisonai skills eligible <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
@@ -1055,7 +1119,13 @@ Methods:
 * <code title="cli">praisonai train llm <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
 * <code title="cli">praisonai train show <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
 * <code title="cli">praisonai train train-callback <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
+* <code title="cli">praisonai ui agents <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
+* <code title="cli">praisonai ui bot <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
+* <code title="cli">praisonai ui realtime <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
 * <code title="cli">praisonai ui ui <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
+* <code title="cli">praisonai up logs <a href="./src/praisonai/praisonai/cli/commands/up.py">--help</a></code>
+* <code title="cli">praisonai up status <a href="./src/praisonai/praisonai/cli/commands/up.py">--help</a></code>
+* <code title="cli">praisonai up up-start <a href="./src/praisonai/praisonai/cli/commands/up.py">--help</a></code>
 * <code title="cli">praisonai version check <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
 * <code title="cli">praisonai version show <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
 * <code title="cli">praisonai version version-callback <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=4.6.2" \
+    "praisonai>=4.6.48" \
    "praisonai[chat]" \
    "embedchain[github,youtube]"

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=4.6.2" \
+    "praisonai>=4.6.48" \
    "praisonai[ui]" \
    "praisonai[chat]" \
    "praisonai[realtime]" \
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=4.6.2" \
+    "praisonai>=4.6.48" \
    "praisonai[ui]" \
    "praisonai[crewai]"

@@ -1,48 +1,40 @@
 """
-Langfuse Integration Example
+Langfuse Integration Example (Updated for TraceSinkProtocol)

-This example shows how to use Langfuse for LLM observability.
+This example shows how to use Langfuse for LLM observability with PraisonAI's native trace infrastructure.

 Setup:
-    1. Sign up at https://langfuse.com/
-    2. Get your API keys from the project settings
-    3. Set environment variables:
-       export LANGFUSE_PUBLIC_KEY=pk-lf-xxx
-       export LANGFUSE_SECRET_KEY=sk-lf-xxx
-    4. Install dependencies:
-       pip install opentelemetry-sdk opentelemetry-exporter-otlp
+    pip install "praisonai[langfuse]"
+    export LANGFUSE_PUBLIC_KEY=pk-lf-xxx
+    export LANGFUSE_SECRET_KEY=sk-lf-xxx

 Usage:
    python langfuse_example.py
 """
-
-import os
-from praisonai_tools.observability import obs
 from praisonaiagents import Agent
-
-# Initialize Langfuse
-success = obs.init(
-    provider="langfuse",
-    project_name="praisonai-demo",
+from praisonai.observability import LangfuseSink
+from praisonaiagents.trace.protocol import (
+    TraceEmitter, set_default_emitter
 )

-if not success:
-    print("Failed to initialize Langfuse. Check your API keys.")
-    print("Required: LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY")
-    exit(1)
+# Initialize Langfuse observability
+sink = LangfuseSink()
+emitter = TraceEmitter(sink=sink, enabled=True)
+set_default_emitter(emitter)

-print("Langfuse initialized successfully!")
-print(f"View traces at: https://cloud.langfuse.com/")
-
-# Create agent
+# Create and run agent — all traces automatically captured
 agent = Agent(
+    name="Coder",
    instructions="You are a helpful coding assistant.",
-    model="gpt-4o-mini",
+    llm="openai/gpt-4o-mini",
 )

-# Run with tracing
-with obs.trace("coding-session", user_id="developer-1"):
-    response = agent.chat("Write a Python function to calculate fibonacci numbers")
-    print(response)
+try:
+    result = agent.start("Write a Python function to calculate fibonacci numbers")
+    print(result)
+finally:
+    # Ensure traces are flushed and resources cleaned up
+    sink.flush()
+    sink.close()

 print("\nCheck Langfuse dashboard for traces!")
@@ -0,0 +1,27 @@
+#!/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)
@@ -0,0 +1,68 @@
+"""
+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_search=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())
@@ -1,7 +1,7 @@
 """All compute providers — comprehensive test across Local, Docker, E2B, and Modal.

-This example mirrors the Anthropic app.py but uses the local provider with
-various compute backends instead of Anthropic's managed infrastructure.
+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.

 Requires:
  - Docker running locally
@@ -9,7 +9,8 @@ Requires:
  - modal CLI configured (modal token set)
 """
 import asyncio
-from praisonai import Agent, ManagedAgent, LocalManagedConfig
+from praisonai import Agent
+from praisonai.integrations import LocalAgent, LocalAgentConfig


 async def test_provider(name, compute, extra_provision_kwargs=None):
@@ -18,10 +19,9 @@ async def test_provider(name, compute, extra_provision_kwargs=None):
    print(f"  PROVIDER: {name}")
    print(f"{'='*60}")

-    managed = ManagedAgent(
-        provider="local",
+    managed = LocalAgent(
        compute=compute,
-        config=LocalManagedConfig(
+        config=LocalAgentConfig(
            model="gpt-4o-mini",
            system="You are a helpful assistant. Be concise.",
            name=f"{name}Agent",
@@ -0,0 +1,47 @@
+"""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)
@@ -23,12 +23,39 @@ 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")
-    try:
-        val = eval(expr, {"__builtins__": {}})
-    except Exception:
-        val = "error"
+    val = _safe_calc(str(expr))
    print(f"  [Calculator: {expr} = {val}]")
    return str(val)

@@ -1,13 +1,14 @@
-"""Local provider — basic managed agent with gpt-4o-mini.
+"""Local agent — basic local execution 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, ManagedAgent, LocalManagedConfig
+from praisonai import Agent
+from praisonai.integrations import LocalAgent, LocalAgentConfig

-# Create a local managed agent (auto-detects local when no ANTHROPIC_API_KEY)
-managed = ManagedAgent(
-    provider="local",
-    config=LocalManagedConfig(
+# Create a local agent (runs locally, no managed runtime)
+managed = LocalAgent(
+    config=LocalAgentConfig(
        model="gpt-4o-mini",
        system="You are a helpful assistant. Be concise.",
        name="LocalAgent",
@@ -0,0 +1,47 @@
+"""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)}")
@@ -0,0 +1,52 @@
+"""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())
@@ -0,0 +1,59 @@
+"""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())
@@ -0,0 +1,56 @@
+"""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())
@@ -0,0 +1,57 @@
+"""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())
@@ -0,0 +1,40 @@
+"""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)}")
@@ -0,0 +1,63 @@
+"""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.")
@@ -0,0 +1,61 @@
+"""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.")
@@ -0,0 +1,61 @@
+"""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.")
@@ -0,0 +1,61 @@
+"""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.")
@@ -0,0 +1,61 @@
+"""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())
@@ -0,0 +1,45 @@
+"""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}")
@@ -0,0 +1,41 @@
+"""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)}")
@@ -0,0 +1,337 @@
+"""Complete n8n Integration Example for PraisonAI
+
+This example demonstrates the complete bidirectional n8n ↔ PraisonAI integration:
+
+1. PraisonAI → n8n: Agents executing n8n workflows (using PraisonAI-Tools)
+2. n8n → PraisonAI: n8n workflows invoking PraisonAI agents (using API endpoint)
+
+Prerequisites:
+1. Install dependencies:
+   pip install "praisonai-tools[n8n]" fastapi uvicorn
+
+2. Set up n8n instance:
+   docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n
+
+3. Environment variables:
+   export N8N_URL="http://localhost:5678"
+   export N8N_API_KEY="your-api-key"  # optional for local testing
+
+Usage:
+    python examples/python/n8n_integration_example.py
+"""
+
+import os
+import asyncio
+import uvicorn
+from fastapi import FastAPI
+from praisonaiagents import Agent
+
+
+def setup_praisonai_agents():
+    """Set up PraisonAI agents with n8n tools."""
+    print("🚀 Setting up PraisonAI agents with n8n capabilities...")
+    
+    try:
+        from praisonai_tools.n8n import n8n_workflow, n8n_list_workflows
+        
+        # Create automation agent with n8n tools
+        automation_agent = Agent(
+            name="automation-agent",
+            instructions="""
+            You are an automation specialist with access to n8n's 400+ integrations.
+            You can help users automate workflows across platforms like:
+            
+            - Communication: Slack, Discord, Telegram, Gmail, Teams
+            - Productivity: Notion, Google Sheets, Airtable, Trello
+            - Databases: PostgreSQL, MongoDB, MySQL, Redis
+            - APIs: REST, GraphQL, webhooks
+            
+            When asked to perform automation tasks:
+            1. First list available n8n workflows if needed
+            2. Execute the appropriate workflow with the provided data
+            3. Explain what the workflow accomplished
+            
+            Always be helpful and provide clear explanations.
+            """,
+            tools=[n8n_workflow, n8n_list_workflows],
+            llm="gpt-4o-mini"
+        )
+        
+        # Create notification agent focused on messaging
+        notification_agent = Agent(
+            name="notification-agent", 
+            instructions="""
+            You specialize in sending notifications and messages across platforms.
+            You have access to n8n workflows for various messaging platforms.
+            
+            When asked to send notifications:
+            1. Determine the best platform for the message
+            2. Use appropriate n8n workflow for that platform
+            3. Include relevant context and formatting
+            
+            Be efficient and reliable with notifications.
+            """,
+            tools=[n8n_workflow],
+            llm="gpt-4o-mini"
+        )
+        
+        return {
+            "automation": automation_agent,
+            "notification": notification_agent
+        }
+        
+    except ImportError as e:
+        print(f"❌ Error importing n8n tools: {e}")
+        print("💡 Install with: pip install 'praisonai-tools[n8n]'")
+        return {}
+
+
+def setup_api_server_with_agents(agents):
+    """Set up FastAPI server with agent invoke endpoints."""
+    print("🌐 Setting up API server for n8n → PraisonAI integration...")
+    
+    from praisonai.api.agent_invoke import register_agent, router
+    
+    # Create FastAPI app
+    app = FastAPI(
+        title="PraisonAI n8n Integration Server",
+        description="API server for n8n to invoke PraisonAI agents",
+        version="1.0.0"
+    )
+    
+    # Include agent invoke router
+    app.include_router(router)
+    
+    # Register agents
+    for agent_id, agent in agents.items():
+        register_agent(agent_id, agent)
+        print(f"📋 Registered agent: {agent_id}")
+    
+    @app.get("/")
+    async def root():
+        """Root endpoint with integration information."""
+        return {
+            "message": "PraisonAI n8n Integration Server",
+            "available_agents": list(agents.keys()),
+            "endpoints": {
+                "invoke_agent": "/api/v1/agents/{agent_id}/invoke",
+                "list_agents": "/api/v1/agents",
+                "agent_info": "/api/v1/agents/{agent_id}"
+            },
+            "n8n_integration": {
+                "description": "Use HTTP Request node in n8n to invoke agents",
+                "example_url": "http://localhost:8000/api/v1/agents/automation/invoke",
+                "example_body": {
+                    "message": "Send a Slack message to #general saying 'Hello from n8n!'",
+                    "session_id": "n8n-workflow-123"
+                }
+            }
+        }
+    
+    return app
+
+
+async def demo_praisonai_to_n8n(agents):
+    """Demonstrate PraisonAI agents calling n8n workflows."""
+    print("\n🔄 Demo: PraisonAI → n8n Integration")
+    print("=" * 50)
+    
+    if not agents:
+        print("❌ No agents available for demo")
+        return
+    
+    automation_agent = agents.get("automation")
+    if not automation_agent:
+        print("❌ Automation agent not available")
+        return
+    
+    try:
+        print("📋 Testing agent with n8n workflow listing...")
+        response = automation_agent.start(
+            "Can you list the available n8n workflows?"
+        )
+        print(f"🤖 Agent: {response}")
+        
+        print("\n🚀 Testing workflow execution...")
+        response = automation_agent.start(
+            "I need to send a notification that our deployment was successful. "
+            "Can you help me send this via Slack to the #deployments channel?"
+        )
+        print(f"🤖 Agent: {response}")
+        
+    except Exception as e:
+        print(f"❌ Demo error: {e}")
+
+
+async def demo_n8n_to_praisonai():
+    """Demonstrate n8n invoking PraisonAI agents via HTTP."""
+    print("\n🔄 Demo: n8n → PraisonAI Integration")
+    print("=" * 50)
+    
+    try:
+        import httpx
+        
+        # Simulate n8n HTTP Request node call
+        print("📡 Simulating n8n HTTP Request node call...")
+        
+        async with httpx.AsyncClient() as client:
+            response = await client.post(
+                "http://localhost:8000/api/v1/agents/notification/invoke",
+                json={
+                    "message": "A new user has signed up: [email protected]. Please send a welcome email.",
+                    "session_id": "n8n-user-signup-workflow"
+                },
+                timeout=30.0
+            )
+            
+            if response.status_code == 200:
+                data = response.json()
+                print(f"✅ Success: {data['result']}")
+                print(f"📊 Session: {data['session_id']}")
+            else:
+                print(f"❌ Error: {response.status_code} - {response.text}")
+                
+    except httpx.ConnectError:
+        print("❌ Connection error: Make sure the API server is running")
+        print("💡 Start server in another terminal: python examples/python/n8n_integration_example.py --server")
+    except Exception as e:
+        print(f"❌ Demo error: {e}")
+
+
+def create_n8n_workflow_examples():
+    """Show examples of n8n workflow configurations."""
+    print("\n📝 n8n Workflow Configuration Examples")
+    print("=" * 50)
+    
+    print("""
+🔹 HTTP Request Node (n8n → PraisonAI):
+   Method: POST
+   URL: http://localhost:8000/api/v1/agents/automation/invoke
+   Body:
+   {
+     "message": "{{ $json.user_message }}",
+     "session_id": "{{ $json.session_id || workflow.id }}"
+   }
+   
+🔹 Webhook Trigger for PraisonAI response:
+   URL: http://localhost:8000/webhook/praisonai-response
+   Method: POST
+   
+🔹 Slack notification after agent response:
+   Channel: {{ $json.channel || '#general' }}
+   Message: Agent response: {{ $json.agent_result }}
+""")
+
+
+async def run_interactive_demo():
+    """Run interactive demo of the integration."""
+    print("\n🎮 Interactive Demo")
+    print("=" * 20)
+    
+    # Set up agents
+    agents = setup_praisonai_agents()
+    if not agents:
+        print("❌ Cannot run interactive demo without agents")
+        return
+    
+    automation_agent = agents.get("automation")
+    if not automation_agent:
+        print("❌ Automation agent not available")
+        return
+    
+    print("💬 Chat with the automation agent (type 'quit' to exit)")
+    print("💡 Try: 'List available n8n workflows'")
+    print("💡 Try: 'Send a Slack message to #general'")
+    
+    while True:
+        try:
+            user_input = input("\n👤 You: ").strip()
+            if user_input.lower() in ['quit', 'exit', 'q']:
+                break
+            
+            if not user_input:
+                continue
+                
+            print("🤖 Agent: ", end="", flush=True)
+            response = automation_agent.start(user_input)
+            print(response)
+            
+        except KeyboardInterrupt:
+            print("\n👋 Goodbye!")
+            break
+        except Exception as e:
+            print(f"\n❌ Error: {e}")
+
+
+def run_server(port=8000):
+    """Run the API server for n8n integration."""
+    print(f"\n🚀 Starting PraisonAI n8n Integration Server on port {port}...")
+    
+    # Set up agents
+    agents = setup_praisonai_agents()
+    
+    # Create app
+    app = setup_api_server_with_agents(agents)
+    
+    print(f"✅ Server ready at http://localhost:{port}")
+    print(f"📊 Available agents: {list(agents.keys())}")
+    print("\n🔗 n8n HTTP Request Node Configuration:")
+    print(f"   URL: http://localhost:{port}/api/v1/agents/{{agent_id}}/invoke")
+    print("   Method: POST")
+    print('   Body: {"message": "Your message here", "session_id": "optional"}')
+    
+    # Run server
+    uvicorn.run(app, host="0.0.0.0", port=port)
+
+
+async def main():
+    """Main function to run demos."""
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="PraisonAI n8n Integration Demo")
+    parser.add_argument("--server", action="store_true", help="Run API server")
+    parser.add_argument("--demo", action="store_true", help="Run demos")
+    parser.add_argument("--interactive", action="store_true", help="Run interactive demo")
+    parser.add_argument("--port", type=int, default=8000, help="Server port")
+    
+    args = parser.parse_args()
+    
+    if args.server:
+        run_server(args.port)
+    elif args.interactive:
+        await run_interactive_demo()
+    elif args.demo:
+        # Set up agents for demos
+        agents = setup_praisonai_agents()
+        
+        # Run demos
+        await demo_praisonai_to_n8n(agents)
+        create_n8n_workflow_examples()
+        print("\n💡 To test n8n → PraisonAI, start the server:")
+        print("   python examples/python/n8n_integration_example.py --server")
+        print("   Then run: python examples/python/n8n_integration_example.py --test-api")
+        
+    else:
+        # Default: show info and run basic demo
+        print("🔗 PraisonAI ↔ n8n Bidirectional Integration")
+        print("=" * 50)
+        
+        print("\n📋 Integration Components:")
+        print("1. 🔧 PraisonAI-Tools: n8n workflow execution tools")
+        print("2. 🌐 API Endpoint: Agent invoke endpoint for n8n")
+        print("3. 🤖 Agents: PraisonAI agents with n8n capabilities")
+        
+        print("\n🚀 Available Commands:")
+        print("   --demo        Run integration demos")
+        print("   --server      Start API server for n8n")
+        print("   --interactive Run interactive agent chat")
+        
+        # Quick demo
+        agents = setup_praisonai_agents()
+        if agents:
+            await demo_praisonai_to_n8n(agents)
+            create_n8n_workflow_examples()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,301 @@
+#!/usr/bin/env python3
+"""
+Remote Sandbox Examples for PraisonAI
+
+Demonstrates how to use SSH, Modal, and Daytona sandbox backends
+for remote code execution.
+"""
+
+import asyncio
+import os
+from typing import Optional
+
+from praisonai.sandbox import SSHSandbox, ModalSandbox, DaytonaSandbox
+from praisonaiagents.sandbox import ResourceLimits
+
+
+async def ssh_example():
+    """Example using SSH sandbox for remote execution."""
+    print("🔗 SSH Sandbox Example")
+    print("=" * 50)
+    
+    # Configure SSH connection
+    # Note: You need actual SSH server access for this to work
+    ssh_host = os.getenv("SSH_HOST", "localhost")
+    ssh_user = os.getenv("SSH_USER", "ubuntu")
+    ssh_key = os.getenv("SSH_KEY_FILE", "~/.ssh/id_rsa")
+    
+    print(f"Connecting to {ssh_user}@{ssh_host}")
+    
+    try:
+        sandbox = SSHSandbox(
+            host=ssh_host,
+            user=ssh_user,
+            key_file=ssh_key,
+            working_dir="/tmp/praisonai_demo"
+        )
+        
+        # Check availability
+        if not sandbox.is_available:
+            print("❌ SSH backend not available. Install with: pip install praisonai[ssh]")
+            return
+        
+        # Start sandbox
+        await sandbox.start()
+        print("✅ SSH connection established")
+        
+        # Execute Python code
+        print("\n📄 Executing Python code...")
+        result = await sandbox.execute("""
+import sys
+import platform
+print(f"Python version: {sys.version}")
+print(f"Platform: {platform.platform()}")
+print("Hello from remote SSH server! 🚀")
+        """, language="python")
+        
+        print(f"Exit code: {result.exit_code}")
+        print(f"Output:\n{result.stdout}")
+        if result.stderr:
+            print(f"Errors:\n{result.stderr}")
+        
+        # Execute shell command
+        print("\n🐚 Executing shell command...")
+        result = await sandbox.run_command("uname -a && uptime")
+        print(f"System info:\n{result.stdout}")
+        
+        # Write and read file
+        print("\n📝 File operations...")
+        await sandbox.write_file("/tmp/praisonai_demo/hello.py", "print('Hello from file!')")
+        content = await sandbox.read_file("/tmp/praisonai_demo/hello.py")
+        print(f"File content: {content}")
+        
+        # Execute the file
+        result = await sandbox.execute_file("/tmp/praisonai_demo/hello.py")
+        print(f"File execution result: {result.stdout}")
+        
+        # Test resource limits
+        print("\n⏱️ Testing resource limits...")
+        limits = ResourceLimits(timeout_seconds=5, memory_mb=128)
+        result = await sandbox.execute("""
+import time
+print("Starting long task...")
+time.sleep(10)  # This will timeout
+print("Task completed")
+        """, limits=limits)
+        
+        print(f"Limited execution status: {result.status}")
+        if result.error:
+            print(f"Error (expected timeout): {result.error}")
+        
+        await sandbox.stop()
+        print("✅ SSH sandbox stopped")
+        
+    except Exception as e:
+        print(f"❌ SSH example failed: {e}")
+
+
+async def modal_example():
+    """Example using Modal sandbox for serverless GPU execution."""
+    print("\n☁️ Modal Sandbox Example")
+    print("=" * 50)
+    
+    try:
+        # Note: Requires Modal account and API key
+        sandbox = ModalSandbox(
+            gpu="A100",  # Request A100 GPU
+            timeout=120
+        )
+        
+        # Check availability
+        if not sandbox.is_available:
+            print("❌ Modal backend not available. Install with: pip install praisonai[modal]")
+            return
+        
+        print("🚀 Starting Modal app...")
+        await sandbox.start()
+        print("✅ Modal app initialized")
+        
+        # Execute GPU-accelerated Python code
+        print("\n🔥 Executing GPU code...")
+        result = await sandbox.execute("""
+import torch
+import numpy as np
+
+print(f"PyTorch version: {torch.__version__}")
+print(f"CUDA available: {torch.cuda.is_available()}")
+
+if torch.cuda.is_available():
+    device = torch.cuda.get_device_name()
+    print(f"GPU device: {device}")
+    
+    # Simple tensor operations on GPU
+    x = torch.randn(1000, 1000, device='cuda')
+    y = torch.randn(1000, 1000, device='cuda')
+    z = torch.matmul(x, y)
+    print(f"GPU computation result shape: {z.shape}")
+else:
+    print("Running on CPU")
+    x = torch.randn(100, 100)
+    y = torch.randn(100, 100)
+    z = torch.matmul(x, y)
+    print(f"CPU computation result shape: {z.shape}")
+
+print("🚀 Modal execution completed!")
+        """, language="python")
+        
+        print(f"Exit code: {result.exit_code}")
+        print(f"Output:\n{result.stdout}")
+        if result.stderr:
+            print(f"Errors:\n{result.stderr}")
+        
+        # Test different languages
+        print("\n🐚 Testing bash execution...")
+        result = await sandbox.run_command("nvidia-smi || echo 'No GPU info available'")
+        print(f"GPU info:\n{result.stdout}")
+        
+        await sandbox.stop()
+        print("✅ Modal sandbox stopped")
+        
+    except Exception as e:
+        print(f"❌ Modal example failed: {e}")
+
+
+async def daytona_example():
+    """Example using Daytona sandbox for cloud dev environment."""
+    print("\n🌤️ Daytona Sandbox Example")
+    print("=" * 50)
+    
+    try:
+        # Configure Daytona workspace
+        sandbox = DaytonaSandbox(
+            workspace_template="python-dev",
+            provider="aws",  # or "gcp", "azure", "local"
+            timeout=180
+        )
+        
+        # Check availability
+        if not sandbox.is_available:
+            print("❌ Daytona backend not available. Install with: pip install praisonai[daytona]")
+            return
+        
+        print(f"🏗️ Creating Daytona workspace: {sandbox.workspace_name}")
+        await sandbox.start()
+        print("✅ Daytona workspace ready")
+        
+        # Execute development tasks
+        print("\n💻 Running development tasks...")
+        result = await sandbox.execute("""
+import sys
+import os
+import subprocess
+
+print("🐍 Python Development Environment")
+print(f"Python version: {sys.version}")
+print(f"Working directory: {os.getcwd()}")
+
+# Check installed packages
+try:
+    result = subprocess.run([sys.executable, "-m", "pip", "list"], 
+                          capture_output=True, text=True)
+    print("📦 Installed packages:")
+    print(result.stdout[:500] + "..." if len(result.stdout) > 500 else result.stdout)
+except Exception as e:
+    print(f"Could not list packages: {e}")
+
+print("✨ Daytona workspace is ready for development!")
+        """, language="python")
+        
+        print(f"Exit code: {result.exit_code}")
+        print(f"Output:\n{result.stdout}")
+        
+        # Test file operations
+        print("\n📁 Workspace file operations...")
+        await sandbox.write_file("/workspace/demo.py", """
+def hello_daytona():
+    print("Hello from Daytona workspace!")
+    return "🎉 Success!"
+
+if __name__ == "__main__":
+    result = hello_daytona()
+    print(result)
+""")
+        
+        # Execute the file
+        result = await sandbox.execute_file("/workspace/demo.py")
+        print(f"Demo script output: {result.stdout}")
+        
+        # List workspace files
+        files = await sandbox.list_files("/workspace")
+        print(f"Workspace files: {files}")
+        
+        await sandbox.stop()
+        print("✅ Daytona workspace stopped")
+        
+    except Exception as e:
+        print(f"❌ Daytona example failed: {e}")
+
+
+async def comparison_example():
+    """Compare execution across different sandbox backends."""
+    print("\n⚖️ Sandbox Comparison")
+    print("=" * 50)
+    
+    # Simple test code
+    test_code = """
+import time
+import sys
+start_time = time.time()
+print(f"Hello from {sys.platform}!")
+print(f"Execution time: {time.time() - start_time:.3f}s")
+"""
+    
+    # Test each sandbox type
+    sandboxes = [
+        ("SSH", SSHSandbox(host=os.getenv("SSH_HOST", "localhost"))),
+        ("Modal", ModalSandbox()),
+        ("Daytona", DaytonaSandbox())
+    ]
+    
+    for name, sandbox in sandboxes:
+        print(f"\n🧪 Testing {name} sandbox...")
+        
+        if not sandbox.is_available:
+            print(f"❌ {name} not available")
+            continue
+        
+        try:
+            await sandbox.start()
+            result = await sandbox.execute(test_code, language="python")
+            
+            print(f"✅ {name}: {result.status}")
+            print(f"   Duration: {result.duration_seconds:.3f}s")
+            print(f"   Output: {result.stdout.strip()}")
+            
+            await sandbox.stop()
+            
+        except Exception as e:
+            print(f"❌ {name} failed: {e}")
+
+
+async def main():
+    """Run all examples."""
+    print("🚀 PraisonAI Remote Sandbox Examples")
+    print("=" * 70)
+    
+    # Run examples
+    await ssh_example()
+    await modal_example()
+    await daytona_example()
+    await comparison_example()
+    
+    print("\n✅ All examples completed!")
+    print("\nNext steps:")
+    print("1. Set up SSH access to remote servers")
+    print("2. Configure Modal account for GPU workloads")
+    print("3. Set up Daytona for cloud development")
+    print("4. Use these sandboxes in your PraisonAI agents!")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,85 @@
+"""Motion Graphics — Basic HTML/GSAP → MP4 render (no LLM required).
+
+This example drives the `HtmlRenderBackend` directly with a hand-authored
+HTML/GSAP composition. It renders to a real MP4 via headless Chromium +
+bundled ffmpeg, with no API keys needed.
+
+Requirements:
+    pip install praisonai-tools[video-motion]
+    playwright install chromium
+
+Verified end-to-end: produces 1920x1080 @ 30fps H.264 MP4 in ~5–10 seconds.
+"""
+
+import asyncio
+from pathlib import Path
+
+from praisonai_tools.video.motion_graphics import HtmlRenderBackend, RenderOpts
+
+
+HTML = """
+<!DOCTYPE html>
+<html>
+<head>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.2/gsap.min.js"></script>
+  <style>
+    body { margin:0; padding:0; background:#0f172a; overflow:hidden;
+           font-family: -apple-system, Arial, sans-serif; }
+    #stage { width:1920px; height:1080px; position:relative; }
+    .title { font-size:96px; font-weight:800; color:#f8fafc;
+             position:absolute; top:50%; left:50%;
+             transform:translate(-50%,-50%); opacity:0; }
+    .subtitle { font-size:44px; color:#94a3b8;
+                position:absolute; top:62%; left:50%;
+                transform:translate(-50%,-50%); opacity:0; }
+    .dot { width:32px; height:32px; border-radius:50%; background:#38bdf8;
+           position:absolute; top:78%; left:50%;
+           transform:translate(-50%,-50%); opacity:0; }
+  </style>
+</head>
+<body>
+  <div id="stage" data-duration="4.0">
+    <div class="title">PraisonAI</div>
+    <div class="subtitle">Motion Graphics Pipeline</div>
+    <div class="dot"></div>
+  </div>
+  <script>
+    const tl = gsap.timeline({ paused: true });
+    tl.to(".title",    { duration: 0.8, opacity: 1, y: -30, ease: "power3.out" })
+      .to(".subtitle", { duration: 0.7, opacity: 1, y: -20, ease: "power3.out" }, "-=0.3")
+      .to(".dot",      { duration: 0.5, opacity: 1, scale: 2, ease: "back.out(2)" }, "-=0.2")
+      .to(".dot",      { duration: 0.4, x: 200,  ease: "power2.inOut" })
+      .to(".dot",      { duration: 0.4, x: -200, ease: "power2.inOut" })
+      .to(".dot",      { duration: 0.4, x: 0,    ease: "power2.inOut" })
+      .to([".title",".subtitle",".dot"],
+          { duration: 0.5, opacity: 0, ease: "power2.in" });
+    // Required: export timeline(s) for the render backend to drive.
+    window.__timelines = [tl];
+  </script>
+</body>
+</html>
+"""
+
+
+async def main() -> None:
+    out = Path("/tmp/motion_graphics_demo")
+    out.mkdir(exist_ok=True)
+    (out / "index.html").write_text(HTML)
+
+    backend = HtmlRenderBackend(base_dir=out)
+
+    lint = await backend.lint(out)
+    print(f"Lint:   ok={lint.ok}  messages={lint.messages}")
+
+    opts = RenderOpts(output_name="praisonai_demo.mp4", fps=30, quality="standard")
+    print("Render: ...", flush=True)
+    result = await backend.render(out, opts)
+
+    if result.ok:
+        print(f"Render: OK  path={result.output_path}  size={result.size_kb} KB")
+    else:
+        print(f"Render: FAIL  stderr={result.stderr}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,52 @@
+"""Motion Graphics — GitTools safety demo (no network required for this demo).
+
+GitTools is the read-only toolkit used by the code-exploration specialist in
+`motion_graphics_team`. It clones repos on demand, validates paths against
+traversal, and restricts to GitHub URLs or `owner/repo` format.
+
+This example exercises the pure-Python safety helpers (URL parsing + file
+path validation). Cloning is network-bound and demonstrated separately.
+
+Requirements:
+    pip install praisonai-tools
+"""
+
+from praisonai_tools.tools.git_tools import GitTools
+
+
+def main() -> None:
+    tools = GitTools(base_dir="/tmp/praison_git_repos_demo")
+
+    print("URL parsing")
+    print("-----------")
+    for repo_input in [
+        "octocat/Hello-World",                         # owner/repo
+        "https://github.com/octocat/Hello-World.git",  # https URL
+        "[email protected]:octocat/Hello-World.git",      # ssh URL
+    ]:
+        try:
+            url, name = tools._parse_repo_input(repo_input)
+            print(f"  {repo_input:<50}  ->  name={name}")
+        except ValueError as exc:
+            print(f"  {repo_input:<50}  ->  REJECTED: {exc}")
+
+    print("\nFile-path validation")
+    print("--------------------")
+    for path in [
+        "README.md",            # safe
+        "src/main.py",          # safe
+        "../etc/passwd",        # traversal
+        "../../secret.txt",     # traversal
+        "/etc/passwd",          # absolute
+    ]:
+        try:
+            safe = tools._validate_file_path(path)
+            print(f"  {path:<30}  ->  SAFE:   {safe}")
+        except ValueError as exc:
+            print(f"  {path:<30}  ->  REJECT: {exc}")
+
+    print("\nListing any previously cloned repos:", tools.list_repos())
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,64 @@
+"""Motion Graphics — single-agent factory (requires praisonaiagents + LLM key).
+
+Creates a single motion-graphics authoring agent that writes HTML/GSAP and
+renders to MP4 via the HTML backend. Good for quick, one-shot prompts.
+
+Requirements:
+    pip install praisonai-tools[video-motion] praisonaiagents[llm]
+    playwright install chromium
+    export ANTHROPIC_API_KEY=...        # or OPENAI_API_KEY, etc.
+
+Set a working model via env (LiteLLM routing):
+    export MOTION_LLM=anthropic/claude-sonnet-4-20250514
+"""
+
+import os
+from pathlib import Path
+
+from praisonai_tools.video.motion_graphics import create_motion_graphics_agent
+
+
+def main() -> None:
+    workspace = Path("/tmp/motion_graphics_agent_demo")
+    workspace.mkdir(exist_ok=True)
+
+    llm = os.getenv("MOTION_LLM", "anthropic/claude-sonnet-4-20250514")
+
+    agent = create_motion_graphics_agent(
+        backend="html",
+        workspace=workspace,
+        max_retries=3,
+        llm=llm,
+    )
+
+    print(f"Agent:     {agent.name}")
+    print(f"Backend:   {agent._motion_graphics_backend.__class__.__name__}")
+    print(f"Workspace: {agent._motion_graphics_workspace}")
+    print(f"Retries:   {agent._motion_graphics_max_retries}")
+    print(f"LLM:       {llm}")
+    print()
+    print("Starting agent...\n")
+
+    agent.start(
+        "Create a short title-card animation.\n"
+        "Steps you MUST follow, in order:\n"
+        "  1. Call write_file with filepath='index.html' and content containing "
+        "     a complete HTML document with GSAP 3.12 from cdnjs, a #stage div "
+        "     with data-duration='3.0', visible text 'Hello Motion' that fades "
+        "     in, and a cyan underline that draws across. The file MUST set "
+        "     window.__timelines = [tl] at the end of its <script> block, and "
+        "     the timeline MUST be created with { paused: true }.\n"
+        "  2. Call lint_composition() and verify ok=True.\n"
+        "  3. Call render_composition(output_name='intro.mp4', fps=30, "
+        "     quality='standard') and report the resulting output_path.\n"
+        "Return the concrete output_path of the MP4 or the exact stderr."
+    )
+
+    print("\nArtifacts in workspace:")
+    for p in sorted(workspace.rglob("*")):
+        if p.is_file():
+            print(f"  {p.relative_to(workspace)}  ({p.stat().st_size} bytes)")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,56 @@
+"""Motion Graphics — full team pipeline (requires praisonaiagents + LLM key).
+
+Runs the coordinator + animator team (optionally with researcher and
+code-explorer specialists) under a hierarchical process, so the coordinator
+validates outputs before marking a render as successful.
+
+Requirements:
+    pip install praisonai-tools[video-motion] praisonaiagents[llm]
+    playwright install chromium
+    export ANTHROPIC_API_KEY=...
+    export MOTION_LLM=anthropic/claude-sonnet-4-20250514
+"""
+
+import os
+import time
+from pathlib import Path
+
+from praisonai_tools.video.motion_graphics import motion_graphics_team
+
+
+def main() -> None:
+    workspace = Path("/tmp/motion_graphics_team_demo")
+    workspace.mkdir(exist_ok=True)
+
+    llm = os.getenv("MOTION_LLM", "anthropic/claude-sonnet-4-20250514")
+
+    print(f"[setup] workspace={workspace}  llm={llm}")
+
+    team = motion_graphics_team(
+        research=False,           # set True to add a web-search researcher
+        code_exploration=False,   # set True to add a GitTools code-explorer
+        workspace=workspace,
+        llm=llm,
+    )
+
+    print(f"[setup] {len(team.agents)} agents: {[a.name for a in team.agents]}")
+
+    prompt = "Animate Dijkstra's algorithm on a small weighted graph, 30s."
+    print(f"[run] prompt: {prompt}")
+
+    t0 = time.time()
+    result = team.start(prompt)
+    elapsed = time.time() - t0
+
+    print(f"[done] elapsed={elapsed:.1f}s")
+    print(f"[result] {result!r}")
+
+    print("\n[artifacts]")
+    for p in sorted(workspace.rglob("*.mp4")):
+        print(f"  mp4  {p}  ({p.stat().st_size} bytes)")
+    for p in sorted(workspace.rglob("*.html")):
+        print(f"  html {p}  ({p.stat().st_size} bytes)")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,40 @@
+# Scientific paper workflow — YAML equivalent of multi_agent_paper.py
+# Run with:
+#   praisonai agents run --file examples/scientific_writing/agents.yaml
+framework: praisonai
+topic: "Climate change effects on coral reef biodiversity"
+
+roles:
+  literature_reviewer:
+    role: "Literature Reviewer"
+    goal: "Survey recent academic work on {topic} and produce an APA review."
+    backstory: "Expert in academic literature surveying and citation hygiene."
+    llm: "gpt-4o-mini"
+    tasks:
+      review_literature:
+        description: "Produce a concise literature review with 5-8 APA citations."
+        expected_output: "APA-formatted literature review."
+
+  methodology_designer:
+    role: "Methodology Designer"
+    goal: "Design a reproducible research methodology for {topic}."
+    backstory: "Specialises in rigorous experimental design."
+    llm: "gpt-4o-mini"
+    tasks:
+      design_methodology:
+        description: "Write a reproducible methods section in LaTeX."
+        expected_output: "LaTeX methods section."
+
+  scientific_writer:
+    role: "Scientific Writer"
+    goal: "Assemble the final LaTeX paper on {topic}."
+    backstory: >-
+      Specialised in LaTeX-formatted academic papers, fine-tuned on
+      scientific literature (CAJAL-4B checkpoint).
+    llm: "huggingface/Agnuxo/CAJAL-4B-P2PCLAW"
+    tasks:
+      write_paper:
+        description: >-
+          Combine the literature review and methodology into a full
+          scientific paper on {topic}. Use LaTeX formatting.
+        expected_output: "Complete LaTeX paper."
@@ -0,0 +1,73 @@
+"""Multi-agent scientific paper workflow (Researcher -> Methodologist -> Writer).
+
+Demonstrates the agent-centric pattern from AGENTS.md: compose specialised
+Agents with ``AgentTeam`` + sequential ``Task``s instead of creating custom
+Agent subclasses.  The writer uses the CAJAL-4B HuggingFace model; the
+reviewer and methodologist use general-purpose LLMs.
+
+Usage:
+    python examples/scientific_writing/multi_agent_paper.py
+"""
+from praisonaiagents import Agent, AgentTeam, Task
+
+from scientific_writer import (  # noqa: E402 — example imports a sibling file
+    build_scientific_writer,
+    format_citation,
+    format_latex_section,
+)
+
+
+literature_reviewer = Agent(
+    name="Literature Reviewer",
+    instructions=(
+        "You are an expert at surveying academic literature. "
+        "Produce a concise literature review with 5-8 key citations in APA."
+    ),
+    tools=[format_citation],
+)
+
+methodology_designer = Agent(
+    name="Methodology Designer",
+    instructions=(
+        "You design rigorous research methodologies. "
+        "Output a clear, reproducible methods section."
+    ),
+    tools=[format_latex_section],
+)
+
+scientific_writer = build_scientific_writer()
+
+
+def run(topic: str) -> str:
+    """Run the 3-agent workflow for a given research ``topic``."""
+    t1 = Task(
+        name="review_literature",
+        description=f"Review literature on: {topic}",
+        agent=literature_reviewer,
+        expected_output="APA literature review.",
+    )
+    t2 = Task(
+        name="design_methodology",
+        description=f"Design research methodology for: {topic}",
+        agent=methodology_designer,
+        expected_output="LaTeX methods section.",
+    )
+    t3 = Task(
+        name="write_paper",
+        description=(
+            f"Combine the literature review and methodology into a full "
+            f"scientific paper on: {topic}. Use LaTeX formatting."
+        ),
+        agent=scientific_writer,
+        expected_output="Complete LaTeX paper.",
+    )
+    team = AgentTeam(
+        agents=[literature_reviewer, methodology_designer, scientific_writer],
+        tasks=[t1, t2, t3],
+    )
+    return team.start()
+
+
+if __name__ == "__main__":
+    result = run("Transformer architectures for protein folding prediction")
+    print(result)
@@ -0,0 +1,85 @@
+"""Scientific Paper Writer — single-agent example using the CAJAL model.
+
+CAJAL (``Agnuxo/CAJAL-4B-P2PCLAW``) is a 4B-parameter HuggingFace model
+specialised for LaTeX-formatted academic writing.  This example shows how
+to use it with a regular ``Agent`` and ``@tool`` functions — no new Agent
+subclass is needed.  Run 3 ways: Python (this file), CLI, or YAML.
+
+Requirements:
+    pip install "praisonaiagents[llm]"          # for litellm / HuggingFace
+    export HUGGINGFACE_API_KEY=...              # or run a local inference server
+
+Usage:
+    python examples/scientific_writing/scientific_writer.py
+"""
+from praisonaiagents import Agent, tool
+
+
+@tool
+def format_latex_section(title: str, content: str) -> str:
+    """Wrap prose content in a LaTeX ``\\section{}`` block.
+
+    Args:
+        title: Section heading (e.g. ``"Introduction"``).
+        content: The body text.
+
+    Returns:
+        LaTeX-formatted section string.
+    """
+    return f"\\section{{{title}}}\n{content}\n"
+
+
+@tool
+def format_citation(authors: str, year: int, title: str, venue: str) -> str:
+    """Render a single APA-style citation line.
+
+    Args:
+        authors: ``"Smith, J. & Jones, A."``
+        year: Publication year.
+        title: Paper title.
+        venue: Journal / conference name.
+
+    Returns:
+        One APA-formatted citation string.
+    """
+    return f"{authors} ({year}). {title}. *{venue}*."
+
+
+SCIENTIFIC_INSTRUCTIONS = """You are a specialised scientific paper writer.
+
+Produce high-quality academic content with:
+- Clear structure (Abstract, Introduction, Methodology, Results, Discussion,
+  Conclusion, References)
+- Rigorous methodology and precise language
+- Proper APA citations and LaTeX formatting
+- Honest limitations and future work sections
+
+Use ``format_latex_section`` to wrap each section in LaTeX, and
+``format_citation`` to build the References list.
+"""
+
+
+def build_scientific_writer(
+    model: str = "huggingface/Agnuxo/CAJAL-4B-P2PCLAW",
+) -> Agent:
+    """Construct a scientific-writing Agent.
+
+    Override ``model`` for any HuggingFace / local / API model — the default
+    targets the CAJAL-4B checkpoint for LaTeX-style academic output.
+    """
+    return Agent(
+        name="Scientific Writer",
+        instructions=SCIENTIFIC_INSTRUCTIONS,
+        llm=model,
+        tools=[format_latex_section, format_citation],
+    )
+
+
+if __name__ == "__main__":
+    agent = build_scientific_writer()
+    paper = agent.start(
+        "Write a short scientific paper (Abstract + Introduction + Conclusion) "
+        "on 'Climate change effects on coral reef biodiversity'. "
+        "Use LaTeX formatting and include at least 3 APA citations."
+    )
+    print(paper)
@@ -44,12 +44,33 @@ def main():
    
    def calculate(expression: str) -> str:
        """Calculate a math expression safely."""
-        try:
-            # Safe eval for basic math
-            allowed = set("0123456789+-*/.() ")
-            if all(c in allowed for c in expression):
-                return str(eval(expression))
+        import ast
+
+        allowed = set("0123456789+-*/.() ")
+        if not all(c in allowed for c in expression):
            return "Error: Invalid expression"
+        try:
+            tree = ast.parse(expression, 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: Invalid expression"
+            return str(
+                eval(compile(tree, "<expr>", "eval"), {"__builtins__": {}})
+            )
        except Exception as e:
            return f"Error: {e}"
    
@@ -0,0 +1,222 @@
+# Multi-Channel Bot Deployment Guide
+
+This guide explains how to set up multiple bots on the same platform (e.g., multiple Telegram bots) for different roles using PraisonAI, similar to Hermes-style AI workforces.
+
+## Overview
+
+PraisonAI supports running multiple bots on the same platform, each routing to different agents with specialized knowledge and capabilities. This is useful for:
+
+- **Role-based bots**: `@company_cfo_bot`, `@company_ops_bot`, `@company_content_bot`
+- **Department-specific agents**: Each with different knowledge brains and tool access
+- **Workflow separation**: Different bots handle different types of requests
+
+## Quick Start with Onboard Wizard
+
+The easiest way to set up multiple channels is using the onboard wizard:
+
+```bash
+praisonai onboard
+```
+
+The wizard now supports:
+1. **Multi-channel setup**: Add multiple bots for the same platform
+2. **Role-based naming**: Automatically generates `TELEGRAM_<ROLE>_BOT_TOKEN` environment variables
+3. **Agent routing**: Each channel routes to a different agent
+
+### Onboard Wizard Flow
+
+1. **Choose platform**: Start with your first platform (e.g., telegram)
+2. **Add more channels**: Wizard asks "Add another bot channel?"
+3. **Specify role**: Enter role name (e.g., "cfo", "ops", "content")
+4. **Configure tokens**: Each channel gets its own environment variable
+5. **Test connections**: Wizard tests each bot separately
+
+## Environment Variable Convention
+
+For multiple bots on the same platform, use this naming convention:
+
+| Channel | Environment Variable | Bot Purpose |
+|---------|---------------------|-------------|
+| `telegram_cfo` | `TELEGRAM_CFO_BOT_TOKEN` | CFO-related tasks |
+| `telegram_ops` | `TELEGRAM_OPS_BOT_TOKEN` | Operations tasks |
+| `telegram_content` | `TELEGRAM_CONTENT_BOT_TOKEN` | Content creation |
+| `telegram_support` | `TELEGRAM_SUPPORT_BOT_TOKEN` | Customer support |
+
+### Setting Up Tokens
+
+1. **Create bots in @BotFather**:
+   ```text
+   /newbot
+   Bot name: Company CFO Bot
+   Username: @company_cfo_bot
+   -> Copy token
+   
+   /newbot
+   Bot name: Company Ops Bot
+   Username: @company_ops_bot
+   -> Copy token
+   ```
+
+2. **Set environment variables**:
+   ```bash
+   export TELEGRAM_CFO_BOT_TOKEN="123456789:ABC..."
+   export TELEGRAM_OPS_BOT_TOKEN="987654321:DEF..."
+   export TELEGRAM_CONTENT_BOT_TOKEN="456789123:GHI..."
+   ```
+
+3. **Or use `~/.praisonai/.env`**:
+   ```env
+   TELEGRAM_CFO_BOT_TOKEN=123456789:ABC...
+   TELEGRAM_OPS_BOT_TOKEN=987654321:DEF...
+   TELEGRAM_CONTENT_BOT_TOKEN=456789123:GHI...
+   TELEGRAM_ALLOWED_USERS=123456789,987654321
+   ```
+
+## Example Configuration
+
+See `multi-telegram-hermes-workforce.yaml` for a complete example.
+
+### Key Features
+
+1. **Multiple Channels**:
+   ```yaml
+   channels:
+     telegram_cfo:
+       platform: telegram
+       token: ${TELEGRAM_CFO_BOT_TOKEN}
+       routes:
+         default: cfo
+     
+     telegram_ops:
+       platform: telegram
+       token: ${TELEGRAM_OPS_BOT_TOKEN}
+       routes:
+         default: ops
+   ```
+
+2. **Specialized Agents**:
+   ```yaml
+   agents:
+     cfo:
+       instructions: "You are a CFO agent. Help with financial analysis..."
+       model: gpt-4o-mini
+     
+     ops:
+       instructions: "You are an ops agent. Help with system monitoring..."
+       model: gpt-4o-mini
+   ```
+
+## Validation with Doctor
+
+Use the doctor command to validate your multi-channel setup:
+
+```bash
+praisonai doctor
+```
+
+The doctor checks for:
+- ✅ **Unique tokens**: Each channel uses a different bot token
+- ✅ **Naming convention**: Follows `PLATFORM_ROLE_BOT_TOKEN` pattern  
+- ✅ **Missing tokens**: All referenced environment variables are set
+- ❌ **Duplicate tokens**: Same token used by multiple channels (fails)
+
+## Deployment
+
+### Start Gateway
+
+```bash
+# Start the multi-bot gateway
+praisonai gateway start --config multi-telegram-hermes-workforce.yaml
+
+# Or install as daemon
+praisonai onboard  # Wizard installs daemon automatically
+praisonai gateway status
+```
+
+### Verify Setup
+
+1. **Check health**: `curl http://127.0.0.1:8765/health`
+2. **View dashboard**: `praisonai claw` → http://127.0.0.1:8082
+3. **Test bots**: Send messages to each bot to verify routing
+
+## Common Patterns
+
+### Basic Multi-Role Setup
+```yaml
+# Single platform, multiple roles
+channels:
+  telegram_support:
+    token: ${TELEGRAM_SUPPORT_BOT_TOKEN}
+    routes:
+      default: support_agent
+  
+  telegram_sales:
+    token: ${TELEGRAM_SALES_BOT_TOKEN}
+    routes:
+      default: sales_agent
+```
+
+### Cross-Platform Workforce
+```yaml
+# Multiple platforms, multiple roles
+channels:
+  telegram_cfo:
+    platform: telegram
+    token: ${TELEGRAM_CFO_BOT_TOKEN}
+    routes:
+      default: cfo
+  
+  discord_ops:
+    platform: discord
+    token: ${DISCORD_OPS_BOT_TOKEN}
+    routes:
+      default: ops
+  
+  slack_content:
+    platform: slack
+    token: ${SLACK_CONTENT_BOT_TOKEN}
+    routes:
+      default: content
+```
+
+## Troubleshooting
+
+### Common Issues
+
+1. **"Token is used by multiple channels"**
+   - Each bot needs its own unique token from @BotFather
+   - Check environment variables for duplicates
+
+2. **"Channel skipped - no token"**
+   - Set the environment variable: `export TELEGRAM_CFO_BOT_TOKEN="..."`
+   - Or update `~/.praisonai/.env`
+
+3. **"Bot not responding"**
+   - Verify token is correct: `praisonai doctor`
+   - Check bot is not already running elsewhere
+   - Ensure allowlist includes your user ID
+
+### Debugging Commands
+
+```bash
+# Check configuration
+praisonai doctor
+
+# View logs
+praisonai gateway logs
+
+# Test connection
+curl -H "Authorization: Bearer $GATEWAY_AUTH_TOKEN" \
+     http://127.0.0.1:8765/info
+```
+
+## Migration from Single-Bot Setup
+
+If you currently have a single bot setup, you can add more channels:
+
+1. **Run onboard wizard**: `praisonai onboard`
+2. **Add new channels**: Wizard will preserve existing setup
+3. **Update configuration**: New channels added to existing `bot.yaml`
+4. **Restart gateway**: `praisonai gateway restart`
+
+The wizard preserves backward compatibility with single-bot configurations.
@@ -0,0 +1,19 @@
+# A2UI canvas agent — YAML example
+#
+# Usage:
+#   pip install praisonaiagents[a2ui]
+#   praisonai agent run --file a2ui-canvas.yaml --prompt "Show a welcome card"
+#
+# For live A2UI rendering, use PraisonAIUI (AG-UI / surfaces):
+#   pip install praisonaiui
+#   See examples/python/a2ui/send_surface.py and PraisonAIUI example 29.
+
+agent:
+  name: "ui-assistant"
+  instructions: |
+    You are a helpful assistant. When the user asks for UI elements
+    (buttons, cards, forms), call send_a2ui_messages with valid A2UI v0.9 JSON.
+    Use surface id "main" unless told otherwise.
+  llm: gpt-4o-mini
+  tools:
+    - send_a2ui_messages
@@ -0,0 +1,12 @@
+framework: praisonai
+topic: coding
+roles:
+  coder:
+    role: Code refactorer
+    goal: Refactor Python modules for better maintainability
+    backstory: Senior engineer with expertise in clean code principles
+    cli_backend: claude-code
+    tasks:
+      refactor:
+        description: Refactor utils.py to improve code quality
+        expected_output: Refactored code with improved structure and documentation
@@ -0,0 +1,35 @@
+platform: linear
+token: ${LINEAR_OAUTH_TOKEN}
+signing_secret: ${LINEAR_WEBHOOK_SECRET}
+webhook_port: 8080
+
+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_search: true
+  auto_approve_tools: true
@@ -0,0 +1,66 @@
+# Multi-Telegram Channel Hermes Workforce Example
+# Generated by `praisonai onboard` with multi-channel support
+# Start with: praisonai gateway start --config multi-telegram-hermes-workforce.yaml
+
+gateway:
+  host: "127.0.0.1"
+  port: 8765
+
+# Legacy single-agent block for backward compatibility
+agent:
+  name: "assistant"
+  instructions: "You are a helpful AI assistant. You can search the web, manage schedules, and remember past conversations. Use your tools proactively."
+  model: "gpt-4o-mini"
+
+# Multi-agent workforce with different specialized roles
+agents:
+  cfo:
+    instructions: "You are a CFO agent. You are a helpful AI assistant. Use your tools proactively to help with CFO-related tasks like financial analysis, budget planning, and cost optimization."
+    model: gpt-4o-mini
+    memory: false
+
+  ops:
+    instructions: "You are an ops agent. You are a helpful AI assistant. Use your tools proactively to help with ops-related tasks like system monitoring, deployment, and infrastructure management."
+    model: gpt-4o-mini
+    memory: false
+
+  content:
+    instructions: "You are a content agent. You are a helpful AI assistant. Use your tools proactively to help with content-related tasks like writing, editing, and content strategy."
+    model: gpt-4o-mini
+    memory: false
+
+# Multiple Telegram channels - each with unique tokens and routing
+channels:
+  telegram_cfo:
+    platform: telegram
+    token: ${TELEGRAM_CFO_BOT_TOKEN}
+    group_policy: mention_only
+    allowed_users: ${TELEGRAM_ALLOWED_USERS}
+    # auto_approve_tools: true
+                               # Chat bots cannot show CLI approval prompts; safe tools auto-approved by default.
+    routes:
+      default: cfo
+
+  telegram_ops:
+    platform: telegram
+    token: ${TELEGRAM_OPS_BOT_TOKEN}
+    group_policy: mention_only
+    allowed_users: ${TELEGRAM_ALLOWED_USERS}
+    # auto_approve_tools: true
+                               # Chat bots cannot show CLI approval prompts; safe tools auto-approved by default.
+    routes:
+      default: ops
+
+  telegram_content:
+    platform: telegram
+    token: ${TELEGRAM_CONTENT_BOT_TOKEN}
+    group_policy: mention_only
+    allowed_users: ${TELEGRAM_ALLOWED_USERS}
+    # auto_approve_tools: true
+                               # Chat bots cannot show CLI approval prompts; safe tools auto-approved by default.
+    routes:
+      default: content
+
+# Legacy routing block
+routing:
+  default: assistant
@@ -0,0 +1,379 @@
+---
+description: Comprehensive analysis workflow that produces a detailed GitHub issue — combines deep codebase analysis with issue creation for implementation-ready tickets
+---
+
+# Analysis → GitHub Issue Workflow
+
+Deep Analysis → Gap Analysis → Critical Review → Plan → Create GitHub Issue.
+
+> **When to use this:** You need to analyze a feature, integration, or system deeply and produce a GitHub issue that another agent/developer can implement immediately without asking follow-up questions.
+> **When NOT to use this:** Use `/analysis` for analysis-only (no issue). Use `/local-fix` if you're implementing yourself.
+
+---
+
+## PRINCIPLES (apply throughout)
+
+- **Agent-centric**: Agents, workflows, sessions, tools, memory, multi-agent safety.
+- **Protocol-driven core**: praisonaiagents = lightweight, protocol-first (protocols/hooks/adapters only).
+- **DRY**: identify reuse; avoid duplication.
+- **No perf impact**: preserve import-time and hot-path; heavy deps optional + lazy.
+- **Async-safe + multi-agent safe** by default.
+- **Easy for non-developers**: "Few lines of code to do the task!"
+
+---
+
+## CANONICAL PATHS
+
+```
+Core SDK:    /Users/praison/praisonai-package/src/praisonai-agents (praisonaiagents)
+Wrapper:     /Users/praison/praisonai-package/src/praisonai (praisonai)
+Tools:       /Users/praison/PraisonAI-tools
+Docs:        /Users/praison/PraisonAIDocs
+TypeScript:  /Users/praison/praisonai-package/src/praisonai-ts
+Extension:   tools/base.py, tools/decorator.py, db/*
+```
+
+---
+
+## Phase 1 — Acceptance Criteria
+
+Translate the request into testable criteria:
+- API expectations (classes, methods, signatures)
+- CLI expectations (commands, flags, help text)
+- Documentation expectations (pages, examples)
+- Test expectations (unit, integration, smoke, real agentic)
+- Performance constraints (import time, hot-path)
+
+---
+
+## Phase 2 — External Research (if applicable)
+
+### 2a. Read official sources
+
+If analyzing an external integration, research from primary sources:
+
+```bash
+# Use read_url_content tool for:
+# - Official site / announcement pages
+# - GitHub README and docs
+# - Framework documentation
+# - Third-party write-ups
+```
+
+**Capture:**
+- What is it? Purpose and design goals
+- Key terminology and concepts
+- Architecture overview
+- Current ecosystem state
+- References list (all URLs)
+
+### 2b. Clone & analyze external codebase (if applicable)
+
+```bash
+git clone <repo-url> ~/<repo-name> --depth=1
+ls -la ~/<repo-name>/
+```
+
+Key files to read:
+- `AGENTS.md` / `README.md` — architecture overview
+- Base class files (abstract methods, signatures)
+- Factory / registry files (registration pattern)
+- Model/config files (data structures)
+- Example implementations (minimal + full-featured)
+- `pyproject.toml` / `package.json` — dependencies
+
+---
+
+## Phase 3 — PraisonAI Codebase Inventory
+
+### 3a. Inventory with evidence
+
+Go through ALL relevant files, understand current logic end-to-end.
+
+```bash
+# Core SDK
+ls /Users/praison/praisonai-package/src/praisonai-agents/praisonaiagents/
+grep -r "class.*Protocol" praisonaiagents/ --include="*.py" | wc -l
+
+# Wrapper
+ls /Users/praison/praisonai-package/src/praisonai/praisonai/
+ls /Users/praison/praisonai-package/src/praisonai/praisonai/cli/commands/
+
+# TypeScript
+ls /Users/praison/praisonai-package/src/praisonai-ts/src/
+```
+
+**Document:**
+- Public APIs: modules/classes/functions and exports
+- Protocols/adapters/hooks and extension points
+- CLI commands, options, help text
+- Integrations (db/observability/tools)
+- Tests (unit/integration), fixtures, CI config
+- Docs (Mintlify), examples, recipes
+
+Provide: **file paths + key symbols + grep counts + entry points**.
+
+### 3b. Identify DRY opportunities
+
+- Existing abstractions that can be reused
+- Duplicated code that should be consolidated
+- Existing protocols before creating new ones
+
+---
+
+## Phase 4 — Detailed Analysis
+
+### 4a. Architecture and behavior
+
+- Control flow and data flow
+- Concurrency model (sync/async)
+- Multi-agent interactions
+- Dependency boundaries (core/wrapper/tools)
+- Failure modes and error handling
+
+### 4b. Invariants check
+
+Verify these are preserved:
+- [ ] Protocol-driven core (no heavy impls in praisonaiagents)
+- [ ] Lazy imports (optional deps never at module level)
+- [ ] Backward compatible (public API changes need deprecation)
+- [ ] Safe defaults (new features opt-in)
+
+---
+
+## Phase 5 — Gap Analysis
+
+Compare acceptance criteria vs current state.
+
+### Checklist
+
+| Area | Current State | Gap | Severity | Impact | Placement |
+|------|---------------|-----|----------|--------|-----------|
+| Core SDK | | | | | |
+| Wrapper | | | | | |
+| Tools/plugins | | | | | |
+| CLI | | | | | |
+| Docs | | | | | |
+| Tests | | | | | |
+| Exports | | | | | |
+| Perf | | | | | |
+| Multi-agent + async | | | | | |
+
+### Gap severity levels
+- **Critical**: Blocks functionality
+- **High**: Significant limitation
+- **Medium**: Missing feature
+- **Low**: Nice-to-have
+
+---
+
+## Phase 6 — Critical Review
+
+### 6a. Risks and footguns
+
+- API ambiguity or edge cases
+- Backward compatibility concerns
+- Coupling violations
+- Performance regressions
+- Concurrency hazards
+- Misuse risks
+- Operational concerns
+
+### 6b. Mitigation strategies
+
+For each risk, document:
+- "Safe by default" recommendation
+- Explicit opt-in for dangerous behavior
+- Documentation warnings
+
+---
+
+## Phase 7 — Implementation Plan
+
+Step-by-step plan (DO NOT implement):
+
+### 7a. Test-first (TDD)
+
+1. Unit tests for new functionality
+2. Integration tests for cross-component behavior
+3. Smoke tests for quick validation
+4. **Real agentic tests** (REQUIRED):
+   ```python
+   from praisonaiagents import Agent
+   agent = Agent(name="test", instructions="You are helpful")
+   result = agent.start("Say hello in one sentence")
+   print(result)  # Must call LLM and produce response
+   ```
+
+### 7b. Implementation order
+
+1. Tests (TDD)
+2. Core implementation
+3. CLI integration
+4. Documentation
+5. Verification
+
+### 7c. Files to change/create
+
+| File | Action | Purpose |
+|------|--------|---------|
+| | | |
+
+### 7d. Verification commands
+
+```bash
+# Unit tests
+pytest tests/unit/test_<feature>.py -v
+
+# Integration tests
+pytest tests/integration/test_<feature>.py -v
+
+# Smoke test
+python -c "from praisonaiagents import <Feature>; print('OK')"
+
+# Real agentic test
+python examples/<feature>_example.py
+```
+
+---
+
+## Phase 8 — Draft GitHub Issue
+
+The issue must be **self-contained**. Another agent must be able to implement by reading only the issue.
+
+### Required sections
+
+```markdown
+## Overview
+One paragraph: what, why, and the end goal.
+
+## Background
+- Purpose and design goals
+- Why this is valuable
+- Current state / ecosystem
+
+## Architecture Analysis
+### Current Implementation
+- Key files and their purposes
+- Integration points
+- Data flow
+
+### Key File Locations
+| File | Purpose | Lines |
+|------|---------|-------|
+
+## Gap Analysis Summary
+### Critical Gaps
+| Gap | Impact | Effort |
+|-----|--------|--------|
+
+### Feature Gaps
+| Feature | Current Support | Gap |
+|---------|-----------------|-----|
+
+## Proposed Implementation
+### Phase 1: Minimal
+Working code example.
+
+### Phase 2: Production
+Working code example.
+
+## Files to Create / Modify
+### New files
+| File | Purpose |
+|------|---------|
+
+### Modified files
+| File | Change |
+|------|--------|
+
+## Technical Considerations
+- Dependencies
+- Performance impact
+- Safety / approval
+- Multi-agent safety
+
+## Acceptance Criteria
+- [ ] Verifiable checkbox list
+
+## Implementation Notes
+### Key Files to Read First
+1. `path/to/file.py` (N lines) — purpose
+
+### Critical Integration Points
+1. Where X connects to Y
+
+### Testing Commands
+```bash
+# Copy-pastable commands
+```
+
+## References
+- [Link](url)
+```
+
+---
+
+## Phase 9 — Create GitHub Issue
+
+### 9a. Create via `gh` CLI
+
+```bash
+gh issue create \
+  --repo MervinPraison/PraisonAI \
+  --title "<Type>: <Descriptive Title>" \
+  --body "<issue body>"
+```
+
+**Title formats:**
+- `Feature: <What>` — new functionality
+- `Analysis: <Topic>` — analysis/research
+- `Integration: <External System>` — external integration
+- `Fix: <Problem>` — bug fix proposal
+
+### 9b. Verify issue created
+
+```bash
+gh issue view <number> --repo MervinPraison/PraisonAI | head -20
+```
+
+---
+
+## Checklist — Before Creating Issue
+
+- [ ] Acceptance criteria defined and testable
+- [ ] All relevant PraisonAI files read (not skimmed)
+- [ ] External sources researched (if applicable)
+- [ ] Gap analysis completed with severity ratings
+- [ ] Critical review identifies risks and mitigations
+- [ ] Implementation plan is step-by-step with file list
+- [ ] Issue body has working code examples (not pseudocode)
+- [ ] All file paths are absolute and verified
+- [ ] CLI commands are copy-pastable
+- [ ] Acceptance criteria are verifiable (not vague)
+- [ ] References section includes all URLs
+- [ ] Issue is self-contained — no "see docs" without details
+
+---
+
+## Quick Reference — Common Mistakes
+
+| Mistake | Fix |
+|---------|-----|
+| Pseudocode in examples | Use real, runnable code with imports |
+| Vague file paths | Use absolute paths |
+| Skipping gap analysis | Every gap needs severity + impact |
+| Missing real agentic test | Include `agent.start()` test, not just construction |
+| No verification commands | Include copy-pastable pytest/python commands |
+| Claiming "done" without evidence | Every claim references specific files/symbols |
+| Implementing instead of analyzing | This workflow produces issues, not code |
+
+---
+
+## Hard Rules
+
+1. **DO NOT implement** — this workflow produces analysis and issues only
+2. **DO NOT claim done** without evidence
+3. **Every claim must reference** specific files/symbols
+4. **Issue must be self-contained** — implementer needs no follow-up questions
+5. **Working code examples required** — no pseudocode
@@ -0,0 +1,669 @@
+---
+description: End-to-end workflow — deep analysis → GitHub issue → wait for implementing agents → create PR if missing → wait for reviewing agents → review PR locally → feedback to @claude → merge to main when robust per AGENTS.md
+---
+
+# End-to-End: Analysis → Issue → PR → Review → Merge
+
+Deep Analysis → Gap Analysis → Critical Review → Plan → Create Issue → **Wait for implementing agents** → **Create PR if missing** → **Wait for reviewers** → **Validate PR locally** → **Feedback to @claude** → **Re-review after fixes** → **Merge to main if robust**.
+
+> **When to use this:** You want a fully hands-off delivery loop. You describe a feature/integration/fix; this workflow produces the issue, monitors implementation by other agents (Claude, Copilot, CodeRabbit), validates their PR locally against `AGENTS.md` principles, loops feedback until the PR is robust, and merges to `main`.
+> **When NOT to use this:** Use `/analysis-github-issue-create` if you only want the issue (no PR/merge). Use `/local-fix` if you are the implementer.
+
+---
+
+## PRINCIPLES (apply throughout)
+
+- **Agent-centric**: Agents, workflows, sessions, tools, memory, multi-agent safety.
+- **Protocol-driven core**: `praisonaiagents` = lightweight, protocol-first (protocols/hooks/adapters only).
+- **DRY**: identify reuse; avoid duplication.
+- **No perf impact**: preserve import-time and hot-path; heavy deps optional + lazy.
+- **Async-safe + multi-agent safe** by default.
+- **Easy for non-developers**: "Few lines of code to do the task!"
+- **Test before merge**: never merge unvalidated PRs. Always clone the branch locally and run the tests.
+- **Minimal code change**: prefer upstream one-line fixes over downstream workarounds.
+
+---
+
+## CANONICAL PATHS
+
+```
+Core SDK:    /Users/praison/praisonai-package/src/praisonai-agents (praisonaiagents)
+Wrapper:     /Users/praison/praisonai-package/src/praisonai (praisonai)
+Tools:       /Users/praison/PraisonAI-tools
+Docs:        /Users/praison/PraisonAIDocs
+TypeScript:  /Users/praison/praisonai-package/src/praisonai-ts
+AGENTS.md:   /Users/praison/praisonai-package/src/praisonai-agents/AGENTS.md
+```
+
+---
+
+# PART A — ANALYSIS & ISSUE (Phases 1–9)
+
+These phases are identical to `/analysis-github-issue-create`. Execute them in order. **Do not implement any code** in these phases — they produce analysis and an issue only.
+
+## Phase 1 — Acceptance Criteria
+
+Translate the request into testable criteria:
+- API expectations (classes, methods, signatures)
+- CLI expectations (commands, flags, help text)
+- Documentation expectations (pages, examples)
+- Test expectations (unit, integration, smoke, real agentic)
+- Performance constraints (import time, hot-path)
+
+---
+
+## Phase 2 — External Research (if applicable)
+
+### 2a. Read official sources
+
+If analyzing an external integration, research from primary sources:
+
+```bash
+# Use read_url_content tool for:
+# - Official site / announcement pages
+# - GitHub README and docs
+# - Framework documentation
+# - Third-party write-ups
+```
+
+**Capture:**
+- What is it? Purpose and design goals
+- Key terminology and concepts
+- Architecture overview
+- Current ecosystem state
+- References list (all URLs)
+
+### 2b. Clone & analyze external codebase (if applicable)
+
+```bash
+git clone <repo-url> ~/<repo-name> --depth=1
+ls -la ~/<repo-name>/
+```
+
+Key files to read:
+- `AGENTS.md` / `README.md` — architecture overview
+- Base class files (abstract methods, signatures)
+- Factory / registry files (registration pattern)
+- Model/config files (data structures)
+- Example implementations (minimal + full-featured)
+- `pyproject.toml` / `package.json` — dependencies
+
+---
+
+## Phase 3 — PraisonAI Codebase Inventory
+
+### 3a. Inventory with evidence
+
+Go through ALL relevant files, understand current logic end-to-end.
+
+```bash
+# Core SDK
+ls /Users/praison/praisonai-package/src/praisonai-agents/praisonaiagents/
+grep -r "class.*Protocol" praisonaiagents/ --include="*.py" | wc -l
+
+# Wrapper
+ls /Users/praison/praisonai-package/src/praisonai/praisonai/
+ls /Users/praison/praisonai-package/src/praisonai/praisonai/cli/commands/
+
+# TypeScript
+ls /Users/praison/praisonai-package/src/praisonai-ts/src/
+```
+
+**Document:**
+- Public APIs: modules/classes/functions and exports
+- Protocols/adapters/hooks and extension points
+- CLI commands, options, help text
+- Integrations (db/observability/tools)
+- Tests (unit/integration), fixtures, CI config
+- Docs (Mintlify), examples, recipes
+
+Provide: **file paths + key symbols + grep counts + entry points**.
+
+### 3b. Identify DRY opportunities
+
+- Existing abstractions that can be reused
+- Duplicated code that should be consolidated
+- Existing protocols before creating new ones
+
+---
+
+## Phase 4 — Detailed Analysis
+
+### 4a. Architecture and behavior
+
+- Control flow and data flow
+- Concurrency model (sync/async)
+- Multi-agent interactions
+- Dependency boundaries (core/wrapper/tools)
+- Failure modes and error handling
+
+### 4b. Invariants check
+
+Verify these are preserved:
+- [ ] Protocol-driven core (no heavy impls in `praisonaiagents`)
+- [ ] Lazy imports (optional deps never at module level)
+- [ ] Backward compatible (public API changes need deprecation)
+- [ ] Safe defaults (new features opt-in)
+
+---
+
+## Phase 5 — Gap Analysis
+
+Compare acceptance criteria vs current state.
+
+### Checklist
+
+| Area | Current State | Gap | Severity | Impact | Placement |
+|------|---------------|-----|----------|--------|-----------|
+| Core SDK | | | | | |
+| Wrapper | | | | | |
+| Tools/plugins | | | | | |
+| CLI | | | | | |
+| Docs | | | | | |
+| Tests | | | | | |
+| Exports | | | | | |
+| Perf | | | | | |
+| Multi-agent + async | | | | | |
+
+### Gap severity levels
+- **Critical**: Blocks functionality
+- **High**: Significant limitation
+- **Medium**: Missing feature
+- **Low**: Nice-to-have
+
+---
+
+## Phase 6 — Critical Review
+
+### 6a. Risks and footguns
+
+- API ambiguity or edge cases
+- Backward compatibility concerns
+- Coupling violations
+- Performance regressions
+- Concurrency hazards
+- Misuse risks
+- Operational concerns
+
+### 6b. Mitigation strategies
+
+For each risk, document:
+- "Safe by default" recommendation
+- Explicit opt-in for dangerous behavior
+- Documentation warnings
+
+---
+
+## Phase 7 — Implementation Plan
+
+Step-by-step plan (DO NOT implement):
+
+### 7a. Test-first (TDD)
+
+1. Unit tests for new functionality
+2. Integration tests for cross-component behavior
+3. Smoke tests for quick validation
+4. **Real agentic tests** (REQUIRED):
+   ```python
+   from praisonaiagents import Agent
+   agent = Agent(name="test", instructions="You are helpful")
+   result = agent.start("Say hello in one sentence")
+   print(result)  # Must call LLM and produce response
+   ```
+
+### 7b. Implementation order
+
+1. Tests (TDD)
+2. Core implementation
+3. CLI integration
+4. Documentation
+5. Verification
+
+### 7c. Files to change/create
+
+| File | Action | Purpose |
+|------|--------|---------|
+| | | |
+
+### 7d. Verification commands
+
+```bash
+# Unit tests
+pytest tests/unit/test_<feature>.py -v
+
+# Integration tests
+pytest tests/integration/test_<feature>.py -v
+
+# Smoke test
+python -c "from praisonaiagents import <Feature>; print('OK')"
+
+# Real agentic test
+python examples/<feature>_example.py
+```
+
+---
+
+## Phase 8 — Draft GitHub Issue
+
+The issue must be **self-contained**. Another agent must be able to implement by reading only the issue.
+
+### Required sections
+
+```markdown
+## Overview
+One paragraph: what, why, and the end goal.
+
+## Background
+- Purpose and design goals
+- Why this is valuable
+- Current state / ecosystem
+
+## Architecture Analysis
+### Current Implementation
+- Key files and their purposes
+- Integration points
+- Data flow
+
+### Key File Locations
+| File | Purpose | Lines |
+|------|---------|-------|
+
+## Gap Analysis Summary
+### Critical Gaps
+| Gap | Impact | Effort |
+|-----|--------|--------|
+
+### Feature Gaps
+| Feature | Current Support | Gap |
+|---------|-----------------|-----|
+
+## Proposed Implementation
+### Phase 1: Minimal
+Working code example.
+
+### Phase 2: Production
+Working code example.
+
+## Files to Create / Modify
+### New files
+| File | Purpose |
+|------|---------|
+
+### Modified files
+| File | Change |
+|------|--------|
+
+## Technical Considerations
+- Dependencies
+- Performance impact
+- Safety / approval
+- Multi-agent safety
+
+## Acceptance Criteria
+- [ ] Verifiable checkbox list
+
+## Implementation Notes
+### Key Files to Read First
+1. `path/to/file.py` (N lines) — purpose
+
+### Critical Integration Points
+1. Where X connects to Y
+
+### Testing Commands
+```bash
+# Copy-pastable commands
+```
+
+## References
+- [Link](url)
+```
+
+---
+
+## Phase 9 — Create GitHub Issue
+
+### 9a. Create via `gh` CLI
+
+```bash
+gh issue create \
+  --repo MervinPraison/PraisonAI \
+  --title "<Type>: <Descriptive Title>" \
+  --body-file /tmp/issue-body.md
+```
+
+**Title formats:**
+- `Feature: <What>` — new functionality
+- `Integration: <External System>` — external integration
+- `Fix: <Problem>` — bug fix proposal
+
+### 9b. Record the issue number
+
+```bash
+ISSUE_NUM=$(gh issue view <number> --repo MervinPraison/PraisonAI --json number --jq '.number')
+echo "Tracking issue #$ISSUE_NUM"
+```
+
+---
+
+# PART B — IMPLEMENTATION MONITORING (Phases 10–15)
+
+After the issue is filed, other agents (`@claude`, GitHub Copilot agent, CodeRabbit) pick it up automatically. This part watches, shepherds, validates, and merges.
+
+## Phase 10 — Wait ~10 min for implementing agents
+
+After `gh issue create`, Claude and Copilot typically start work within seconds and finish a first pass in 5–15 minutes.
+
+```bash
+echo "Waiting 10 minutes for implementing agents..."
+echo "Start: $(date)"
+sleep 600
+echo "End: $(date)"
+```
+
+### Then check: did they open a PR?
+
+```bash
+REPO=MervinPraison/PraisonAI
+ISSUE_NUM=<number>
+
+# Check for PRs that reference the issue
+gh pr list --repo $REPO --state open --search "fixes #$ISSUE_NUM OR closes #$ISSUE_NUM" --json number,title,headRefName
+
+# Also check for branches matching claude/issue-<N>-* naming convention
+gh api repos/$REPO/branches --paginate --jq ".[] | select(.name | startswith(\"claude/issue-$ISSUE_NUM\")) | .name"
+```
+
+---
+
+## Phase 11 — Create PR if none exists
+
+If Claude pushed a branch but did not open a PR, open one yourself.
+
+```bash
+REPO=MervinPraison/PraisonAI
+ISSUE_NUM=<number>
+BRANCH=$(gh api repos/$REPO/branches --paginate --jq ".[] | select(.name | startswith(\"claude/issue-$ISSUE_NUM\")) | .name" | head -1)
+
+if [ -n "$BRANCH" ]; then
+  TITLE=$(gh api repos/$REPO/commits/$BRANCH --jq '.commit.message' | head -1)
+  gh pr create --repo $REPO --head "$BRANCH" --base main \
+    --title "$TITLE" \
+    --body "Automated PR from Claude's work on issue #$ISSUE_NUM.
+
+Branch: \`$BRANCH\`
+
+Closes #$ISSUE_NUM"
+fi
+```
+
+### If no branch exists after 10 minutes
+
+Re-trigger Claude by commenting on the issue, then wait another 10 minutes and re-check:
+
+```bash
+gh issue comment $ISSUE_NUM --repo $REPO \
+  --body "@claude please pick up this issue — full analysis and plan are in the issue body."
+sleep 600
+```
+
+---
+
+## Phase 12 — Wait ~10 min for reviewing agents
+
+Once a PR exists, `CodeRabbit`, `Copilot`, and CI pipelines run. Give them time to complete.
+
+```bash
+echo "Waiting 10 minutes for PR reviewers and CI..."
+sleep 600
+```
+
+### Collect status
+
+```bash
+PR=<pr-number>
+gh pr view $PR --repo $REPO --json state,mergeable,mergeStateStatus,updatedAt,commits,files \
+  | python -c "
+import json,sys
+d=json.load(sys.stdin)
+print(f\"  State: {d['state']}  Mergeable: {d['mergeable']}  MergeState: {d['mergeStateStatus']}\")
+print(f\"  Commits: {len(d['commits'])}\")
+for f in d['files']:
+    print(f\"    {f['path']}: +{f['additions']} -{f['deletions']}\")
+"
+
+# CI & review checks
+gh pr view $PR --repo $REPO --json statusCheckRollup > /tmp/pr-checks.json
+python -c "
+import json
+for c in json.load(open('/tmp/pr-checks.json'))['statusCheckRollup']:
+    s = c.get('conclusion') or c.get('status','?')
+    name = c.get('name') or c.get('context','?')
+    if s and s not in ('SKIPPED',):
+        print(f'  {name}: {s}')
+"
+```
+
+---
+
+## Phase 13 — Validate the PR locally
+
+**Never merge an unvalidated PR.** Always clone the branch and run tests.
+
+```bash
+REPO_URL=https://github.com/MervinPraison/PraisonAI.git
+BRANCH=<branch-name>
+WORKDIR=/tmp/pr-validate
+
+rm -rf $WORKDIR
+git clone --depth 1 --branch "$BRANCH" $REPO_URL $WORKDIR
+cd $WORKDIR
+
+# Files touched by the PR
+gh pr view $PR --repo $REPO --json files --jq '.files[].path'
+
+# Run the tests for changed files — ALWAYS use timeout (user global rule)
+timeout 120 python -m pytest <test-paths> -v --tb=short
+
+# Critical invariant checks (adapt per feature):
+# 1) Lazy-import: heavy deps never imported at module level
+timeout 30 python - <<'PY'
+import sys
+from praisonai.observability import <NewThing>  # example
+assert 'heavy_dep' not in sys.modules, "eager import — violates AGENTS.md §4.2"
+print("OK: lazy import preserved")
+PY
+
+# 2) Cold-import benchmark (no regression)
+timeout 30 python -c "import time; t=time.time(); import praisonaiagents; print(f'{(time.time()-t)*1000:.1f} ms')"
+```
+
+### Mergeability gate
+
+A PR is **ready to merge only if ALL of these are true**:
+
+- [ ] `gh pr view` reports `mergeable=MERGEABLE` and `mergeStateStatus` in `CLEAN`/`UNSTABLE` (not `DIRTY`/`BLOCKED`).
+- [ ] All `test (3.x)` matrix jobs report `SUCCESS`.
+- [ ] Local `pytest` on the changed test files passes with **zero failures** (skipped/xfailed tests are OK).
+- [ ] Lazy-import invariant preserved (if the feature adds an optional dep).
+- [ ] Cold-import time within ±2 % of `main` (AGENTS.md §4.2).
+- [ ] No files outside the issue's declared scope were changed.
+- [ ] The diff respects all invariants in AGENTS.md §4.6.
+
+If **any** is false → go to Phase 14 (feedback). Do not merge.
+
+---
+
+## Phase 14 — Feedback to `@claude` when issues found
+
+When validation fails, post a precise, actionable comment tagging `@claude`, referencing the exact test name, error, file, and the AGENTS.md principle violated.
+
+```bash
+gh pr comment $PR --repo $REPO --body "$(cat <<'EOF'
+Hold on merge: validated locally on HEAD `<short-sha>` and found these issues:
+
+**Test failures:**
+```
+FAILED tests/.../test_x.py::TestY::test_z
+  AssertionError: <actual-message>
+```
+
+**Root cause:** <one-line diagnosis referencing the exact file and line>.
+
+**AGENTS.md violation(s):**
+- §<section>: <principle>
+
+**Requested fix (minimal):**
+- Change `<file>:<line>` to `<what>`.
+- Or update the test assertion to `<what>` (if the test is wrong, not the code).
+
+**What already passes:**
+- N/M tests pass.
+- Lazy-import invariant preserved ✅ (or not — state explicitly).
+
+@claude please push a fix. Follow AGENTS.md principles: minimal change, protocol-driven, no perf impact, lazy imports, backward-compatible, safe defaults. Make sure the PR is robust — run the tests locally before force-pushing.
+EOF
+)"
+```
+
+### Loop
+
+After commenting:
+
+```bash
+echo "Waiting 10 minutes for Claude to push fixes..."
+sleep 600
+# → return to Phase 12 (fetch new commits, re-run checks)
+```
+
+Repeat Phases 12 → 13 → 14 until the mergeability gate in Phase 13 is fully green, or escalate to a human after 3 failed rounds.
+
+### When the PR has merge conflicts
+
+If `mergeStateStatus=DIRTY`, ask Claude to rebase rather than resolving conflicts yourself on non-trivial logic:
+
+```bash
+gh pr comment $PR --repo $REPO --body "@claude this PR has merge conflicts with \`main\` (PR #<X> was merged, touching the same regions). Please rebase onto latest \`main\`, re-run \`pytest <paths>\`, and force-push."
+```
+
+---
+
+## Phase 15 — Merge to `main`
+
+Once Phase 13's gate is fully green:
+
+### 15a. Final diff review against AGENTS.md
+
+```bash
+gh pr diff $PR --repo $REPO | less
+```
+
+**Hard gate — every box must be ticked before merge:**
+
+- [ ] **Protocol-driven core (§4.1)**: no heavy implementations leaked into `praisonaiagents`.
+- [ ] **No perf impact (§4.2)**: lazy imports confirmed; import benchmark within ±2 %.
+- [ ] **DRY (§4.3)**: no new duplication; existing protocols/adapters reused where possible.
+- [ ] **Agent-centric (§4.4)**: API centers on agents/sessions/tools/memory.
+- [ ] **Async-safe + multi-agent safe (§4.5)**: no shared mutable globals; locks where needed.
+- [ ] **Invariants preserved (§4.6)**: backward compatible, safe defaults, deterministic tests.
+- [ ] **Naming conventions (§4.9)**: `XProtocol` for interfaces, `XAdapter` for impls, `add_*`/`get_*`/`save`/`load` verbs.
+- [ ] No `.md` files added beyond those explicitly requested (per user global rule).
+- [ ] Tests run locally with `timeout` — never hang (per user global rule).
+
+### 15b. Merge
+
+```bash
+gh pr merge $PR --repo $REPO --squash --delete-branch
+# --squash preserves linear main; --delete-branch keeps the repo clean
+```
+
+### 15c. Verify
+
+```bash
+gh pr view $PR --repo $REPO --json state,mergedAt,mergeCommit
+gh issue view $ISSUE_NUM --repo $REPO --json state  # should auto-close
+```
+
+---
+
+# Orchestration Snippet (one-shot reference)
+
+For a single end-to-end invocation, chain Part B phases:
+
+```bash
+#!/usr/bin/env bash
+set -euo pipefail
+REPO=MervinPraison/PraisonAI
+ISSUE_NUM=<fill>
+
+# Phase 10: wait for implementing agents
+sleep 600
+
+# Phase 11: ensure PR exists
+BRANCH=$(gh api repos/$REPO/branches --paginate \
+  --jq ".[] | select(.name | startswith(\"claude/issue-$ISSUE_NUM\")) | .name" | head -1)
+PR=$(gh pr list --repo $REPO --state open --head "$BRANCH" --json number --jq '.[0].number' 2>/dev/null || true)
+if [ -z "$PR" ] && [ -n "$BRANCH" ]; then
+  TITLE=$(gh api repos/$REPO/commits/$BRANCH --jq '.commit.message' | head -1)
+  gh pr create --repo $REPO --head "$BRANCH" --base main \
+    --title "$TITLE" --body "Closes #$ISSUE_NUM"
+  PR=$(gh pr list --repo $REPO --state open --head "$BRANCH" --json number --jq '.[0].number')
+fi
+echo "Tracking PR #$PR"
+
+# Phase 12: wait for reviewers + CI
+sleep 600
+
+# Phases 13–14: validate + feedback loop (cap at 3 rounds)
+for round in 1 2 3; do
+  echo "=== Validation round $round ==="
+  # clone + pytest (per feature) — if green break; else gh pr comment + sleep 600
+  break  # placeholder
+done
+
+# Phase 15: merge
+gh pr merge $PR --repo $REPO --squash --delete-branch
+gh issue view $ISSUE_NUM --repo $REPO --json state
+```
+
+---
+
+## Checklist — Before Merging
+
+- [ ] Issue exists and describes the work
+- [ ] PR exists and references the issue (`Closes #N`)
+- [ ] Branch was fetched and tests were run **locally** — not just CI-trusted
+- [ ] All PR-relevant tests pass with zero failures
+- [ ] CI matrix (3.10 / 3.11 / 3.12) is all green
+- [ ] Lazy-import / cold-import invariants verified if optional deps added
+- [ ] Diff reviewed against every AGENTS.md §4 principle
+- [ ] `mergeStateStatus` in `CLEAN`/`UNSTABLE`, never `DIRTY`/`BLOCKED`
+- [ ] No files outside the issue's scope were changed
+- [ ] If feedback was given, `@claude` had a chance to respond and fixed everything
+
+---
+
+## Quick Reference — Common Mistakes
+
+| Mistake | Fix |
+|---------|-----|
+| Merging without running tests locally | Always `git clone` the branch and run `pytest` |
+| Resolving complex conflicts manually | Ask `@claude` to rebase instead |
+| Trusting CI alone | CI jobs can be skipped / out-of-date; local validation is source of truth |
+| Vague feedback to `@claude` | Every comment must cite file, line, test name, error, and AGENTS.md section |
+| Waiting forever | Cap loops at 3 rounds, then escalate to a human |
+| Running commands without `timeout` | Always `timeout 60 ...` per user global rule |
+| Creating `.md` docs proactively | Don't — user rule forbids unrequested `.md` files |
+| Skipping diff review | Every merge needs a full-diff AGENTS.md pass |
+
+---
+
+## Hard Rules
+
+1. **Never merge without local test validation.**
+2. **Never merge when `mergeStateStatus = DIRTY` or `BLOCKED`.**
+3. **Every `@claude` comment must be actionable** — file, line, exact fix, AGENTS.md ref.
+4. **Cap feedback loops at 3 rounds** — escalate after that, don't spin forever.
+5. **Use `--squash --delete-branch`** to keep `main` linear and the repo clean.
+6. **Always verify** the issue auto-closed after merge.
+7. **All `run_command` calls use `timeout`** to avoid hangs (user global rule).
+8. **No proactive `.md` creation** beyond this workflow file (user global rule).
@@ -202,6 +202,13 @@ _LAZY_IMPORTS = {
    'FlowDisplay': ('praisonaiagents.flow_display', 'FlowDisplay'),
    'track_workflow': ('praisonaiagents.flow_display', 'track_workflow'),
    
+    # CLI Backend Protocol (Protocol-driven external CLI integration)
+    'CliBackendProtocol': ('praisonaiagents.cli_backend.protocols', 'CliBackendProtocol'),
+    'CliBackendConfig': ('praisonaiagents.cli_backend.protocols', 'CliBackendConfig'),
+    'CliSessionBinding': ('praisonaiagents.cli_backend.protocols', 'CliSessionBinding'),
+    'CliBackendResult': ('praisonaiagents.cli_backend.protocols', 'CliBackendResult'),
+    'CliBackendDelta': ('praisonaiagents.cli_backend.protocols', 'CliBackendDelta'),
+    
    # Agent classes
    'Agent': ('praisonaiagents.agent.agent', 'Agent'),
    'BudgetExceededError': ('praisonaiagents.errors', 'BudgetExceededError'),
@@ -212,6 +219,7 @@ _LAZY_IMPORTS = {
    'LLMError': ('praisonaiagents.errors', 'LLMError'),
    'ValidationError': ('praisonaiagents.errors', 'ValidationError'),
    'NetworkError': ('praisonaiagents.errors', 'NetworkError'),
+    'PraisonAIConfigError': ('praisonaiagents.errors', 'PraisonAIConfigError'),
    'ErrorContextProtocol': ('praisonaiagents.errors', 'ErrorContextProtocol'),
    'Heartbeat': ('praisonaiagents.agent.heartbeat', 'Heartbeat'),
    'HeartbeatConfig': ('praisonaiagents.agent.heartbeat', 'HeartbeatConfig'),
@@ -335,6 +343,7 @@ _LAZY_IMPORTS = {
    # UI (optional)
    'AGUI': ('praisonaiagents.ui.agui', 'AGUI'),
    'A2A': ('praisonaiagents.ui.a2a', 'A2A'),
+    'A2UI': ('praisonaiagents.ui.a2ui', 'A2UI'),
    
    # Feature configs
    'MemoryConfig': ('praisonaiagents.config.feature_configs', 'MemoryConfig'),
@@ -806,6 +815,6 @@ def __dir__():
 # - Workflow, Task, Route, Parallel, Loop, Repeat, Pipeline, etc.
 # - MCP, FlowDisplay, track_workflow, FastContext, etc.
 # - Plan, PlanStep, TodoList, PlanningAgent, ApprovalCallback, etc.
-# - RAG, RAGConfig, RAGResult, AGUI, A2A, etc.
+# - RAG, RAGConfig, RAGResult, AGUI, A2A, A2UI, etc.
 # - All telemetry, display, and utility functions
 # ============================================================================
@@ -33,6 +33,10 @@ def __getattr__(name):
        from .heartbeat import HeartbeatConfig
        _lazy_cache[name] = HeartbeatConfig
        return HeartbeatConfig
+    if name == 'InterruptController':
+        from .interrupt import InterruptController
+        _lazy_cache[name] = InterruptController
+        return InterruptController
    
    # Specialized agents - lazy loaded (import rich)
    if name == 'ImageAgent':
@@ -194,6 +198,7 @@ __all__ = [
    'BudgetExceededError',
    'Heartbeat',
    'HeartbeatConfig',
+    'InterruptController',
    'ImageAgent',
    'VideoAgent',
    'VideoConfig',
@@ -8,6 +8,7 @@ import asyncio
 import threading
 from typing import Any, Optional, Union
 from contextlib import contextmanager, asynccontextmanager
+from weakref import WeakKeyDictionary


 class DualLock:
@@ -28,33 +29,16 @@ class DualLock:
            pass
            
        # In async context  
-        async with lock.async():
+        async with lock.async_lock():
            # Uses asyncio.Lock
            pass
        ```
    """
    
    def __init__(self):
-        self._thread_lock = threading.Lock()
-        self._async_lock: Optional[asyncio.Lock] = None
-        self._loop_id: Optional[int] = None
-        
-    def _get_async_lock(self) -> asyncio.Lock:
-        """Get or create asyncio.Lock for current event loop."""
-        try:
-            current_loop = asyncio.get_running_loop()
-            current_loop_id = id(current_loop)
-            
-            # Create new lock if loop changed or first time
-            if self._loop_id != current_loop_id:
-                self._async_lock = asyncio.Lock()
-                self._loop_id = current_loop_id
-                
-            return self._async_lock
-        except RuntimeError:
-            # No event loop running, fall back to thread lock in a new loop
-            self._async_lock = asyncio.Lock() 
-            return self._async_lock
+        """Initialize with separate threading and asyncio locks."""
+        self._thread_lock = threading.RLock()  # Re-entrant lock to handle nested acquisitions
+        self._async_locks = WeakKeyDictionary()  # Per-event-loop async locks
    
    @contextmanager
    def sync(self):
@@ -62,9 +46,24 @@ class DualLock:
        with self._thread_lock:
            yield
            
+    def _get_async_lock(self):
+        """Get or create an asyncio.Lock for the current event loop."""
+        try:
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            raise RuntimeError("async_lock() must be called from an async context")
+        
+        if loop not in self._async_locks:
+            self._async_locks[loop] = asyncio.Lock()
+        return self._async_locks[loop]
+
    @asynccontextmanager
    async def async_lock(self):
-        """Acquire lock in asynchronous context using asyncio.Lock."""
+        """Acquire lock in asynchronous context using asyncio.Lock.
+        
+        Uses a per-event-loop asyncio.Lock to ensure proper async coordination
+        without blocking the event loop or violating thread ownership semantics.
+        """
        async_lock = self._get_async_lock()
        async with async_lock:
            yield
@@ -164,7 +164,8 @@ Follow best practices and coding standards."""
            messages=messages,
            **{k: v for k, v in kwargs.items() if k != "model"}
        )
-        
+        if not response.choices or response.choices[0].message is None:
+            raise ValueError("LLM returned empty or filtered response")
        code = response.choices[0].message.content
        self._log("Code generated successfully", "green")
        return code
@@ -204,7 +205,8 @@ Follow best practices and coding standards."""
            messages=messages,
            **{k: v for k, v in kwargs.items() if k != "model"}
        )
-        
+        if not response.choices or response.choices[0].message is None:
+            raise ValueError("LLM returned empty or filtered response")
        code = response.choices[0].message.content
        self._log("Code generated successfully", "green")
        return code
@@ -365,6 +367,8 @@ Provide clear, actionable feedback."""
            **{k: v for k, v in kwargs.items() if k != "model"}
        )
        
+        if not response.choices or response.choices[0].message is None:
+            raise ValueError("LLM returned empty or filtered response")
        review = response.choices[0].message.content
        self._log("Review completed", "green")
        return review
@@ -405,6 +409,8 @@ Include:
            **{k: v for k, v in kwargs.items() if k != "model"}
        )
        
+        if not response.choices or response.choices[0].message is None:
+            raise ValueError("LLM returned empty or filtered response")
        explanation = response.choices[0].message.content
        self._log("Explanation completed", "green")
        return explanation
@@ -451,6 +457,8 @@ Only output the refactored code."""
            **{k: v for k, v in kwargs.items() if k != "model"}
        )
        
+        if not response.choices or response.choices[0].message is None:
+            raise ValueError("LLM returned empty or filtered response")
        refactored = response.choices[0].message.content
        self._log("Refactoring completed", "green")
        return refactored
@@ -495,6 +503,8 @@ Ensure the fix:
            **{k: v for k, v in kwargs.items() if k != "model"}
        )
        
+        if not response.choices or response.choices[0].message is None:
+            raise ValueError("LLM returned empty or filtered response")
        fixed = response.choices[0].message.content
        self._log("Fix completed", "green")
        return fixed
@@ -0,0 +1,148 @@
+"""Per-Agent Concurrency Limiter for PraisonAI Agents.
+
+Provides a registry-based approach to limit concurrent task execution
+per agent name. No Agent constructor param bloat — uses a global registry.
+
+Usage:
+    from praisonaiagents.agent.concurrency import get_concurrency_registry
+    
+    registry = get_concurrency_registry()
+    registry.set_limit("researcher", 2)  # max 2 concurrent tasks
+    
+    # In async code:
+    async with registry.throttle("researcher"):
+        await do_work()
+    
+    # Or manual:
+    await registry.acquire("researcher")
+    try:
+        await do_work()
+    finally:
+        registry.release("researcher")
+"""
+
+import asyncio
+import threading
+from contextlib import asynccontextmanager
+from typing import Dict, Optional
+
+from praisonaiagents._logging import get_logger
+
+logger = get_logger(__name__)
+
+
+class ConcurrencyRegistry:
+    """Registry for per-agent concurrency limits.
+    
+    Thread-safe. Each agent name maps to an asyncio.Semaphore.
+    Limit of 0 means unlimited (no throttling).
+    """
+
+    def __init__(self, default_limit: int = 0):
+        self._default_limit = default_limit
+        self._limits: Dict[str, int] = {}
+        self._semaphores: Dict[str, asyncio.Semaphore] = {}
+        self._lock = threading.Lock()
+
+    def set_limit(self, agent_name: str, max_concurrent: int) -> None:
+        """Set concurrency limit for an agent.
+        
+        Args:
+            agent_name: Agent identifier
+            max_concurrent: Max concurrent tasks (0 = unlimited)
+        """
+        with self._lock:
+            self._limits[agent_name] = max_concurrent
+            # Reset semaphore so next acquire creates a fresh one
+            self._semaphores.pop(agent_name, None)
+
+    def get_limit(self, agent_name: str) -> int:
+        """Get concurrency limit for an agent."""
+        with self._lock:
+            return self._limits.get(agent_name, self._default_limit)
+
+    def remove_limit(self, agent_name: str) -> None:
+        """Remove concurrency limit for an agent (reverts to default)."""
+        with self._lock:
+            self._limits.pop(agent_name, None)
+            self._semaphores.pop(agent_name, None)
+
+    def _get_semaphore(self, agent_name: str) -> Optional[asyncio.Semaphore]:
+        """Get or create semaphore for agent. Returns None if unlimited."""
+        with self._lock:
+            limit = self._limits.get(agent_name, self._default_limit)
+            if limit <= 0:
+                return None
+            if agent_name not in self._semaphores:
+                self._semaphores[agent_name] = asyncio.Semaphore(limit)
+            return self._semaphores[agent_name]
+
+    async def acquire(self, agent_name: str) -> None:
+        """Acquire concurrency slot for agent. No-op if unlimited."""
+        sem = self._get_semaphore(agent_name)
+        if sem is not None:
+            await sem.acquire()
+
+    def acquire_sync(self, agent_name: str) -> None:
+        """Synchronous acquire — for non-async code paths.
+        
+        Prefer async acquire() when possible.
+        If called while an event loop is already running in the current thread,
+        this method raises RuntimeError to avoid deadlock.
+        """
+        sem = self._get_semaphore(agent_name)
+        if sem is None:
+            return
+        try:
+            asyncio.get_running_loop()
+        except RuntimeError:
+            # No running loop — safe to create one
+            loop = asyncio.new_event_loop()
+            try:
+                loop.run_until_complete(sem.acquire())
+            finally:
+                loop.close()
+        else:
+            raise RuntimeError(
+                f"acquire_sync('{agent_name}') cannot be called with a running event loop; "
+                "use async acquire() in async contexts."
+            )
+
+    def release(self, agent_name: str) -> None:
+        """Release concurrency slot for agent. No-op if unlimited."""
+        with self._lock:
+            sem = self._semaphores.get(agent_name)
+        if sem is not None:
+            try:
+                sem.release()
+            except ValueError:
+                pass  # Already fully released
+
+    @asynccontextmanager
+    async def throttle(self, agent_name: str):
+        """Async context manager for throttled execution.
+        
+        Usage:
+            async with registry.throttle("agent_name"):
+                await do_work()
+        """
+        await self.acquire(agent_name)
+        try:
+            yield
+        finally:
+            self.release(agent_name)
+
+
+# Singleton
+_registry: Optional[ConcurrencyRegistry] = None
+_registry_lock = threading.Lock()
+
+
+def get_concurrency_registry() -> ConcurrencyRegistry:
+    """Get the global concurrency registry singleton."""
+    global _registry
+    if _registry is None:
+        with _registry_lock:
+            if _registry is None:
+                _registry = ConcurrencyRegistry()
+    return _registry
@@ -5,12 +5,10 @@ Contains all methods for run/start/launch lifecycle, planning,
 and server endpoints. Extracted from agent.py for maintainability.
 """

-import os
 import time
 import json
 import logging
 import inspect
-from praisonaiagents._logging import get_logger

 import asyncio
 import threading
@@ -126,7 +124,7 @@ class ExecutionMixin:
            config=HandoffConfig(context_policy=ContextPolicy.NONE),
        )

-    async def arun(self, prompt: str, **kwargs):
+    async def arun(self, prompt: str, **kwargs) -> Optional[str]:
        """Async version of run() - silent, non-streaming, returns structured result.
        
        Production-friendly async execution. Does not stream or display output.
@@ -309,8 +307,8 @@ class ExecutionMixin:
        # ── Chat history & session linkage ──
        # Record prompt+response in chat_history so SessionStore/auto_save works
        if result is not None:
-            self.chat_history.append({"role": "user", "content": prompt})
-            self.chat_history.append({"role": "assistant", "content": str(result)})
+            self._append_to_chat_history({"role": "user", "content": prompt})
+            self._append_to_chat_history({"role": "assistant", "content": str(result)})
            # Link managed session ID into SessionStore gateway_session_id
            if hasattr(self.backend, 'managed_session_id'):
                msid = self.backend.managed_session_id
@@ -319,8 +317,14 @@ class ExecutionMixin:
                        sid = getattr(self, 'auto_save', None) or getattr(self, '_session_id', None)
                        if sid and hasattr(self._session_store, 'set_gateway_info'):
                            self._session_store.set_gateway_info(sid, gateway_session_id=msid)
-                    except Exception:
-                        pass  # Best-effort linkage
+                    except Exception as e:
+                        # Log session linkage failures for debugging
+                        logger.warning(
+                            "Session gateway linkage failed: %s",
+                            e,
+                            extra={"session_id": sid, "managed_session_id": msid},
+                            exc_info=True,
+                        )
            self._auto_save_session()
        
        return result
@@ -942,7 +946,7 @@ Write the complete compiled report:"""
        task_id = getattr(task, 'id', None)
        return await self.achat(prompt, task_name=task_name, task_description=task_description, task_id=task_id)

-    async def execute_tool_async(self, function_name: str, arguments: Dict[str, Any], tool_call_id: Optional[str] = None) -> Any:
+    async def execute_tool_async(self, function_name: str, arguments: Dict[str, Any], tool_call_id: Optional[str] = None, tools_override: Optional[List] = None) -> Any:
        """Async version of execute_tool"""
        try:
            logging.info(f"Executing async tool: {function_name} with arguments: {arguments}")
@@ -958,9 +962,10 @@ Write the complete compiled report:"""
                logging.error(error_msg)
                return {"error": error_msg, "approval_error": True}
            
-            # Try to find the function in the agent's tools list first
+            # Try to find the function in the override tools list first, then agent's tools list
            func = None
-            for tool in self.tools:
+            tools_to_search = tools_override if tools_override is not None else self.tools
+            for tool in tools_to_search:
                if (callable(tool) and getattr(tool, '__name__', '') == function_name):
                    func = tool
                    break
@@ -175,6 +175,7 @@ class Handoff:
    # Class-level semaphore for concurrency control
    _semaphore: Optional[asyncio.Semaphore] = None
    _sync_semaphore: Optional[threading.Semaphore] = None
+    _semaphore_lock: threading.Lock = threading.Lock()  # Lock for semaphore initialization
    
    def __init__(
        self,
@@ -461,9 +462,11 @@ class Handoff:
        Returns:
            HandoffResult with response or error
        """
-        # Initialize semaphore if needed
+        # Initialize semaphore if needed (thread-safe)
        if self.config.max_concurrent > 0 and Handoff._semaphore is None:
-            Handoff._semaphore = asyncio.Semaphore(self.config.max_concurrent)
+            with Handoff._semaphore_lock:  # Thread-safe initialization
+                if Handoff._semaphore is None:  # Double-check after acquiring lock
+                    Handoff._semaphore = asyncio.Semaphore(self.config.max_concurrent)
        
        start_time = time.time()
        kwargs = context or {}
@@ -0,0 +1,105 @@
+"""
+Interrupt Controller - Cooperative cancellation for agent runs.
+
+Provides thread-safe, cooperative cancellation mechanism for long-running agent
+operations. Follows protocol-driven design with zero overhead when not used.
+"""
+
+import threading
+from typing import Optional, Protocol
+from dataclasses import dataclass, field
+
+__all__ = ["InterruptControllerProtocol", "InterruptController"]
+
+
+class InterruptControllerProtocol(Protocol):
+    """Protocol for interrupt controller extension point."""
+    
+    def request(self, reason: str = "user") -> None:
+        """Request cancellation with optional reason."""
+        ...
+    
+    def clear(self) -> None:
+        """Clear interrupt state."""
+        ...
+    
+    def is_set(self) -> bool:
+        """Check if interrupt was requested."""
+        ...
+    
+    @property
+    def reason(self) -> Optional[str]:
+        """Get interrupt reason if set."""
+        ...
+    
+    def check(self) -> None:
+        """Check for interrupt and raise if set."""
+        ...
+
+
+@dataclass
+class InterruptController:
+    """Thread-safe cooperative cancellation for agent runs.
+    
+    Provides a lightweight mechanism for requesting cancellation of agent
+    operations. Uses threading.Event for thread safety and cooperative
+    checking patterns.
+    
+    Examples:
+        Basic usage:
+        >>> controller = InterruptController()
+        >>> # In another thread:
+        >>> controller.request("user_cancel")
+        >>> # In agent loop:
+        >>> if controller.is_set():
+        >>>     return f"Cancelled: {controller.reason}"
+    """
+    
+    _flag: threading.Event = field(default_factory=threading.Event, init=False, repr=False)
+    _reason: Optional[str] = field(default=None, init=False)
+    _lock: threading.Lock = field(default_factory=threading.Lock, init=False, repr=False)
+
+    def request(self, reason: str = "user") -> None:
+        """Request cancellation with a reason.
+        
+        Args:
+            reason: Human-readable reason for cancellation
+        """
+        with self._lock:
+            if not self._flag.is_set():
+                self._reason = reason
+                self._flag.set()
+
+    def clear(self) -> None:
+        """Clear the cancellation request."""
+        with self._lock:
+            self._reason = None
+            self._flag.clear()
+
+    def is_set(self) -> bool:
+        """Check if cancellation has been requested.
+        
+        Returns:
+            True if cancellation was requested
+        """
+        return self._flag.is_set()
+
+    @property
+    def reason(self) -> Optional[str]:
+        """Get the reason for cancellation.
+        
+        Returns:
+            Reason string if cancelled, None otherwise
+        """
+        with self._lock:
+            return self._reason
+
+    def check(self) -> None:
+        """Check for cancellation and raise if requested.
+        
+        Raises:
+            InterruptedError: If cancellation was requested
+        """
+        if self.is_set():
+            reason = self.reason or "unknown"
+            raise InterruptedError(f"Operation cancelled: {reason}")
@@ -8,7 +8,7 @@ for maintainability.

 import os
 import logging
-from praisonaiagents._logging import get_logger
+from typing import Optional

 # Fallback helpers to avoid circular imports
 def _get_console():
@@ -41,7 +41,7 @@ logger = logging.getLogger(__name__)


 import contextlib
-from typing import List, Optional, Any, Dict, Generator, TYPE_CHECKING
+from typing import Optional, Dict, Generator, TYPE_CHECKING
 from collections import OrderedDict

 if TYPE_CHECKING:
@@ -81,7 +81,7 @@ class MemoryMixin:
        with self._history_lock:
            self.chat_history.append({"role": role, "content": content})

-    def _add_to_chat_history_if_not_duplicate(self, role, content):
+    def _add_to_chat_history_if_not_duplicate(self, role, content) -> bool:
        """Thread-safe method to add messages to chat history only if not duplicate.
        
        Atomically checks for duplicate and adds message under the same lock to prevent TOCTOU races.
@@ -403,9 +403,33 @@ class MemoryMixin:
                    self._session_store.add_user_message(self._session_id, content)
                elif role == "assistant":
                    self._session_store.add_assistant_message(self._session_id, content)
+                self._persist_session_stats()
            except Exception as e:
                logging.warning(f"Failed to persist message to session store: {e}")

+    def _persist_session_stats(self):
+        """Flush agent cost/token stats into session JSON metadata."""
+        store = getattr(self, "_session_store", None)
+        session_id = getattr(self, "_session_id", None)
+        if store is None or session_id is None:
+            return
+        if not hasattr(store, "update_session_metadata"):
+            return
+        try:
+            summary = getattr(self, "cost_summary", {}) or {}
+            llm = getattr(self, "llm", None)
+            model = str(llm) if llm is not None else None
+            store.update_session_metadata(
+                session_id,
+                model=model,
+                total_tokens=summary.get("tokens_in", 0) + summary.get("tokens_out", 0),
+                cost=summary.get("cost", 0),
+                source=getattr(self, "_session_source", "chat"),
+                agent_id=getattr(self, "agent_id", None) or getattr(self, "_agent_id", None),
+            )
+        except Exception as e:
+            logging.debug(f"Session stats persist skipped: {e}")
+
    def session_id(self) -> Optional[str]:
        """Get the current session ID."""
        return self._session_id
@@ -520,6 +544,37 @@ class MemoryMixin:
        except Exception as e:
            logging.debug(f"Auto-learning extraction failed: {e}")

+    def _maybe_emit_nudge(self, user_message: str) -> Optional[str]:
+        """Return a system note to append to the next user turn, or None.
+        
+        Implements the nudge mechanism for self-improving agents.
+        When nudge_interval > 0, this increments a turn counter and emits
+        a nudge prompt every N turns to encourage knowledge persistence.
+        """
+        if not self._learn_config or self._learn_config.nudge_interval <= 0:
+            return None
+            
+        # Initialize/increment turn counter
+        self._turns_since_nudge = getattr(self, "_turns_since_nudge", 0) + 1
+        
+        if self._turns_since_nudge < self._learn_config.nudge_interval:
+            return None
+            
+        # Reset counter and emit nudge
+        self._turns_since_nudge = 0
+        
+        # Optional: only nudge if agent has done meaningful work
+        min_iters = getattr(self._learn_config, 'nudge_min_tool_iters', 3)
+        tool_calls_count = getattr(self, '_recent_tool_calls_count', 0)
+        if tool_calls_count < min_iters:
+            return None
+            
+        return (
+            "\n\n[System nudge] Review the recent conversation. If you discovered a "
+            "non-trivial procedure or pattern, consider using available tools to "
+            "persist this knowledge for future use. Skip if nothing noteworthy."
+        )
+
    def _auto_save_session(self):
        """Auto-save session if auto_save is enabled.
        
@@ -0,0 +1,231 @@
+"""
+Sandbox execution mixin for Agent.
+
+Provides safe code execution capabilities to agents via the sandbox framework.
+"""
+
+import logging
+from typing import Optional, Dict, Any, Union
+
+from ..sandbox import (
+    SandboxConfig, 
+    SandboxManager, 
+    SandboxResult,
+    check_code_safety,
+    format_warnings,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class SandboxMixin:
+    """Mixin that adds sandbox execution capabilities to Agent.
+    
+    Provides safe code execution in isolated environments with security pre-checks.
+    """
+    
+    def __init__(self, *args, **kwargs):
+        # Extract sandbox config from kwargs
+        self.sandbox_config: Optional[SandboxConfig] = kwargs.pop('sandbox', None)
+        
+        # Convert boolean True to default config
+        if self.sandbox_config is True:
+            self.sandbox_config = SandboxConfig.subprocess()
+        
+        super().__init__(*args, **kwargs)
+        
+        self._sandbox_manager: Optional[SandboxManager] = None
+    
+    @property
+    def has_sandbox(self) -> bool:
+        """Check if sandbox is configured for this agent."""
+        return self.sandbox_config is not None
+    
+    def get_sandbox_manager(self) -> Optional[SandboxManager]:
+        """Get or create sandbox manager."""
+        if not self.has_sandbox:
+            return None
+        
+        if self._sandbox_manager is None:
+            self._sandbox_manager = SandboxManager(self.sandbox_config)
+        
+        return self._sandbox_manager
+    
+    async def execute_code(
+        self,
+        code: str,
+        language: str = "python",
+        check_security: bool = True,
+        **kwargs
+    ) -> SandboxResult:
+        """Execute code safely in configured sandbox.
+        
+        Args:
+            code: Code to execute
+            language: Programming language (python, bash, etc.)
+            check_security: Whether to run security pre-checks
+            **kwargs: Additional arguments passed to sandbox.execute()
+            
+        Returns:
+            SandboxResult with execution details
+            
+        Raises:
+            RuntimeError: If no sandbox is configured
+        """
+        if not self.has_sandbox:
+            raise RuntimeError(
+                "No sandbox configured. Set sandbox=True or provide SandboxConfig to Agent()"
+            )
+        
+        # Security pre-check if enabled
+        if check_security:
+            warnings = check_code_safety(code, language)
+            if warnings:
+                warning_text = format_warnings(warnings)
+                if self.verbose:
+                    logger.warning(f"Security warnings for code execution:\n{warning_text}")
+                
+                # Store warnings in result metadata
+                kwargs.setdefault('metadata', {})['security_warnings'] = warnings
+        
+        manager = self.get_sandbox_manager()
+        return await manager.run_code(code, language=language, **kwargs)
+    
+    def execute_code_sync(
+        self,
+        code: str,
+        language: str = "python", 
+        check_security: bool = True,
+        **kwargs
+    ) -> SandboxResult:
+        """Synchronous wrapper for execute_code.
+        
+        Args:
+            code: Code to execute
+            language: Programming language
+            check_security: Whether to run security pre-checks
+            **kwargs: Additional arguments
+            
+        Returns:
+            SandboxResult with execution details
+        """
+        from ..approval.utils import run_coroutine_safely
+        return run_coroutine_safely(self.execute_code(code, language, check_security, **kwargs))
+    
+    async def run_shell_command(
+        self,
+        command: Union[str, list],
+        check_security: bool = True,
+        **kwargs
+    ) -> SandboxResult:
+        """Run a shell command in the sandbox.
+        
+        Args:
+            command: Shell command to run
+            check_security: Whether to run security pre-checks
+            **kwargs: Additional arguments
+            
+        Returns:
+            SandboxResult with execution details
+        """
+        if not self.has_sandbox:
+            raise RuntimeError("No sandbox configured")
+        
+        # Convert command to string for security checking
+        if isinstance(command, list):
+            command_str = " ".join(command)
+        else:
+            command_str = command
+        
+        # Security pre-check
+        if check_security:
+            warnings = check_code_safety(command_str, "bash")
+            if warnings:
+                warning_text = format_warnings(warnings)
+                if self.verbose:
+                    logger.warning(f"Security warnings for command:\n{warning_text}")
+        
+        async with self.get_sandbox_manager() as sandbox:
+            return await sandbox.run_command(command, **kwargs)
+    
+    def get_sandbox_status(self) -> Dict[str, Any]:
+        """Get sandbox status information.
+        
+        Returns:
+            Dictionary with sandbox status details
+        """
+        if not self.has_sandbox:
+            return {"configured": False}
+        
+        status = {
+            "configured": True,
+            "config": self.sandbox_config.to_dict() if self.sandbox_config else None,
+        }
+        
+        manager = self.get_sandbox_manager()
+        if manager:
+            # Get available sandbox types
+            available_types = manager.get_available_types()
+            status["available_types"] = available_types
+            status["current_type"] = self.sandbox_config.sandbox_type
+        
+        return status
+    
+    async def sandbox_cleanup(self):
+        """Clean up sandbox resources."""
+        if self._sandbox_manager:
+            # The context manager handles cleanup, but we can force it here
+            try:
+                await self._sandbox_manager.__aexit__(None, None, None)
+            except Exception as e:
+                logger.warning(f"Error during sandbox cleanup: {e}")
+            finally:
+                self._sandbox_manager = None
+    
+    def _get_code_execution_tools(self):
+        """Get sandbox-based code execution tools for this agent.
+        
+        Returns tools that use the configured sandbox for safe execution.
+        
+        Returns:
+            List of tool functions
+        """
+        if not self.has_sandbox:
+            return []
+        
+        from ..tools import tool
+        
+        @tool
+        def execute_python_code(code: str) -> str:
+            """Execute Python code safely in sandbox.
+            
+            Args:
+                code: Python code to execute
+                
+            Returns:
+                Execution output
+            """
+            result = self.execute_code_sync(code, language="python")
+            if result.success:
+                return result.stdout or result.output
+            else:
+                return f"Error: {result.error or result.stderr}"
+        
+        @tool  
+        def execute_shell_command(command: str) -> str:
+            """Execute shell command safely in sandbox.
+            
+            Args:
+                command: Shell command to execute
+                
+            Returns:
+                Command output
+            """
+            from ..approval.utils import run_coroutine_safely
+            result = run_coroutine_safely(self.run_shell_command(command))
+            if result.success:
+                return result.stdout or result.output
+            else:
+                return f"Error: {result.error or result.stderr}"
+        
+        return [execute_python_code, execute_shell_command]
@@ -12,8 +12,12 @@ import json
 import logging
 import asyncio
 import inspect
+import contextvars
 import concurrent.futures
 from typing import List, Optional, Any, Dict, Union, TYPE_CHECKING
+from ..errors import ToolExecutionError
+
+logger = logging.getLogger(__name__)

 if TYPE_CHECKING:
    pass
@@ -22,6 +26,21 @@ if TYPE_CHECKING:
 class ToolExecutionMixin:
    """Mixin providing toolexecution methods for the Agent class."""

+    def _get_existing_stream_emitter(self):
+        """Return an already-initialized stream emitter without creating one."""
+        emitter = getattr(self, "_stream_emitter", None)
+        if emitter is not None:
+            return emitter
+
+        # Support name-mangled private attributes across class renames/inheritance.
+        for cls in type(self).mro():
+            mangled = f"_{cls.__name__}__stream_emitter"
+            if hasattr(self, mangled):
+                emitter = getattr(self, mangled, None)
+                if emitter is not None:
+                    return emitter
+        return None
+
    def _resolve_tool_names(self, tool_names):
        """Resolve tool names to actual tool instances from registry.
        
@@ -156,8 +175,9 @@ class ToolExecutionMixin:
        
        # Emit TOOL_CALL_START to stream_emitter (for AIUI/AG-UI consumers)
        # Zero overhead when no callbacks registered
-        if hasattr(self, '_Agent__stream_emitter') and getattr(self, "_Agent__stream_emitter", None) is not None and getattr(self, "_Agent__stream_emitter", None).has_callbacks:
-            getattr(self, "_Agent__stream_emitter", None).emit(StreamEvent(
+        _stream_emitter = self._get_existing_stream_emitter()
+        if _stream_emitter is not None and _stream_emitter.has_callbacks:
+            _stream_emitter.emit(StreamEvent(
                type=StreamEventType.TOOL_CALL_START,
                timestamp=_tool_start_perf,
                tool_call={
@@ -190,19 +210,47 @@ class ToolExecutionMixin:
                if res.output and res.output.modified_data:
                    arguments.update(res.output.modified_data)

-            with with_injection_context(state):
-                # P8/G11: Apply tool timeout if configured
-                tool_timeout = getattr(self, '_tool_timeout', None)
-                if tool_timeout and tool_timeout > 0:
-                    with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
-                        future = executor.submit(self._execute_tool_impl, function_name, arguments)
-                        try:
-                            result = future.result(timeout=tool_timeout)
-                        except concurrent.futures.TimeoutError:
-                            logging.warning(f"Tool {function_name} timed out after {tool_timeout}s")
-                            result = {"error": f"Tool timed out after {tool_timeout}s", "timeout": True}
-                else:
-                    result = self._execute_tool_impl(function_name, arguments)
+            # C4 — optional tool-argument validation via ToolValidatorProtocol.
+            # Zero overhead when not set. Users wire via `agent._tool_validator = MyValidator()`.
+            _validator = getattr(self, '_tool_validator', None)
+            if _validator is not None:
+                try:
+                    _vres = _validator.validate_args(function_name, arguments)
+                    if _vres is not None and not getattr(_vres, 'valid', True):
+                        _errs = "; ".join(getattr(_vres, 'errors', []) or ["validation failed"])
+                        logging.warning(
+                            f"Tool {function_name} args rejected by validator: {_errs}"
+                        )
+                        return f"Tool arguments rejected: {_errs}"
+                except Exception as _ve:  # noqa: BLE001 — never break tool exec on validator bug
+                    logging.debug(f"Tool validator raised; skipping validation: {_ve}")
+
+            # P8/G11: Apply tool timeout if configured
+            tool_timeout = getattr(self, '_tool_timeout', None)
+            if tool_timeout and tool_timeout > 0:
+                # Use copy_context to preserve injection context in executor thread
+                ctx = contextvars.copy_context()
+                
+                def execute_with_context():
+                    with with_injection_context(state):
+                        return self._execute_tool_with_circuit_breaker(function_name, arguments)
+                
+                # Use reusable executor to prevent resource leaks
+                if not hasattr(self, '_tool_executor'):
+                    self._tool_executor = concurrent.futures.ThreadPoolExecutor(
+                        max_workers=2, thread_name_prefix=f"tool-{self.name}"
+                    )
+                
+                future = self._tool_executor.submit(ctx.run, execute_with_context)
+                try:
+                    result = future.result(timeout=tool_timeout)
+                except concurrent.futures.TimeoutError:
+                    future.cancel()
+                    logging.warning(f"Tool {function_name} timed out after {tool_timeout}s")
+                    result = {"error": f"Tool timed out after {tool_timeout}s", "timeout": True}
+            else:
+                with with_injection_context(state):
+                    result = self._execute_tool_with_circuit_breaker(function_name, arguments)
            
            # Apply tool output truncation to prevent context overflow
            # Uses context manager budget if enabled, otherwise applies default limit
@@ -243,10 +291,10 @@ class ToolExecutionMixin:
            
            # Emit TOOL_CALL_RESULT to stream_emitter (for AIUI/AG-UI consumers)
            # Zero overhead when no callbacks registered
-            if hasattr(self, '_Agent__stream_emitter') and getattr(self, "_Agent__stream_emitter", None) is not None and getattr(self, "_Agent__stream_emitter", None).has_callbacks:
+            if _stream_emitter is not None and _stream_emitter.has_callbacks:
                # Truncate result for stream event (keep it reasonable for UI display)
                result_summary = str(result)[:500] if result else None
-                getattr(self, "_Agent__stream_emitter", None).emit(StreamEvent(
+                _stream_emitter.emit(StreamEvent(
                    type=StreamEventType.TOOL_CALL_RESULT,
                    timestamp=_time.perf_counter(),
                    tool_call={
@@ -258,6 +306,18 @@ class ToolExecutionMixin:
                    agent_id=self.name,
                    metadata={"duration_ms": _duration_ms},
                ))
+                _stream_emitter.emit(StreamEvent(
+                    type=StreamEventType.TOOL_CALL_END,
+                    timestamp=_time.perf_counter(),
+                    tool_call={
+                        "name": function_name,
+                        "arguments": arguments,
+                        "result": result_summary,
+                        "id": tool_call_id,
+                    },
+                    agent_id=self.name,
+                    metadata={"duration_ms": _duration_ms},
+                ))
            
            # Trigger AFTER_TOOL hook
            from ..hooks import HookEvent, AfterToolInput
@@ -290,8 +350,14 @@ class ToolExecutionMixin:
            _duration_ms = (_time.time() - _tool_start_time) * 1000
            _trace_emitter.tool_call_end(self.name, function_name, None, _duration_ms, str(e))
            
-            # Trigger OnError hook if needed (optional future step)
-            raise
+            # Gap 3a fix: Wrap exceptions in ToolExecutionError for better observability
+            is_retryable = not isinstance(e, (ValueError, TypeError, AttributeError))
+            raise ToolExecutionError(
+                f"Tool '{function_name}' failed: {e}",
+                tool_name=function_name,
+                agent_id=self.name,
+                is_retryable=is_retryable,
+            ) from e

    def _trigger_after_agent_hook(self, prompt, response, start_time, tools_used=None):
        """Trigger AFTER_AGENT hook and return response."""
@@ -317,7 +383,19 @@ class ToolExecutionMixin:
        
        # Auto-learning extraction (opt-in via LearnConfig(mode=LearnMode.AGENTIC))
        self._process_auto_learning()
-        
+
+        # Periodic nudge (opt-in via LearnConfig(nudge_interval>0)).
+        # Appends a system note to chat_history so it is visible on the NEXT
+        # LLM call — encourages the agent to persist non-trivial procedures
+        # as skills/memory. No-op when nudge_interval=0 (default).
+        try:
+            nudge = self._maybe_emit_nudge(prompt if isinstance(prompt, str) else str(prompt))
+            if nudge and hasattr(self, "chat_history") and isinstance(self.chat_history, list):
+                self._append_to_chat_history({"role": "system", "content": nudge.strip()})
+        except Exception as e:
+            # Log learning nudge failures for debugging
+            logger.warning("Learning nudge generation failed: %s", e, exc_info=True)
+
        return response

    async def _atrigger_after_agent_hook(self, prompt, response, start_time, tools_used=None):
@@ -344,7 +422,16 @@ class ToolExecutionMixin:
        
        # Auto-learning extraction (opt-in via LearnConfig(mode=LearnMode.AGENTIC))
        self._process_auto_learning()
-        
+
+        # Periodic nudge (opt-in via LearnConfig(nudge_interval>0)).
+        try:
+            nudge = self._maybe_emit_nudge(prompt if isinstance(prompt, str) else str(prompt))
+            if nudge and hasattr(self, "chat_history") and isinstance(self.chat_history, list):
+                self._append_to_chat_history({"role": "system", "content": nudge.strip()})
+        except Exception as e:
+            # Log learning nudge failures for debugging
+            logger.warning("Learning nudge generation failed: %s", e, exc_info=True)
+
        return response

    def _calculate_llm_cost(self, prompt_tokens: int, completion_tokens: int, response: any = None) -> float:
@@ -550,6 +637,73 @@ class ToolExecutionMixin:
            logging.info(f"Using modified arguments: {arguments}")
        return None, arguments

+    def _execute_tool_with_circuit_breaker(self, function_name, arguments):
+        """Execute tool with circuit breaker protection.
+        
+        Args:
+            function_name: Name of the tool to execute
+            arguments: Arguments for the tool
+            
+        Returns:
+            Tool execution result or circuit breaker error
+        """
+        # Import circuit breaker components first (lazy import for performance)
+        try:
+            from ..tools.circuit_breaker import get_circuit_breaker, CircuitBreakerConfig, CircuitBreakerException
+        except ImportError:
+            # Circuit breaker not available - fallback to direct execution
+            logging.debug("Circuit breaker not available, falling back to direct tool execution")
+            return self._execute_tool_impl(function_name, arguments)
+
+        try:
+            
+            # Get or create circuit breaker for this tool
+            breaker_name = f"tool_{function_name}"
+            config = CircuitBreakerConfig(
+                failure_threshold=5,        # Open after 5 failures
+                recovery_timeout=60.0,      # Wait 60s before trying half-open
+                timeout=30.0,               # Tool call timeout
+                graceful_degradation=True   # Return error instead of raising exception
+            )
+            breaker = get_circuit_breaker(breaker_name, config)
+            
+            # Execute tool through circuit breaker with failure detection wrapper
+            def _tool_wrapper():
+                result = self._execute_tool_impl(function_name, arguments)
+                # Convert error dicts to exceptions so circuit breaker can detect failures
+                # Don't treat approval/permission denials as circuit breaker failures
+                if isinstance(result, dict) and result.get("error") and \
+                   not result.get("approval_denied") and \
+                   not result.get("permission_denied") and \
+                   not result.get("approval_error"):
+                    # Create a sentinel exception to register failure with circuit breaker
+                    class _ToolFailure(Exception):
+                        def __init__(self, error_dict):
+                            self.error_dict = error_dict
+                            super().__init__(error_dict.get("error", "Tool execution failed"))
+                    raise _ToolFailure(result)
+                return result
+            
+            try:
+                return breaker.call(_tool_wrapper)
+            except Exception as e:
+                # Check if this is our sentinel exception
+                if hasattr(e, 'error_dict'):
+                    return e.error_dict  # Return the original error dict
+                else:
+                    raise  # Re-raise other exceptions
+            
+        except CircuitBreakerException as e:
+            # Circuit breaker is open - return error dict instead of raising
+            logging.warning(f"Tool '{function_name}' circuit breaker open: {e}")
+            return {
+                "error": f"Tool '{function_name}' circuit breaker open - too many recent failures",
+                "circuit_open": True,
+                "agent_name": getattr(self, "name", None),
+                "session_id": getattr(self, "_session_id", None),
+                "remediation": "Wait for recovery_timeout (60s) or investigate recent tool failures.",
+            }
+
    def _execute_tool_impl(self, function_name, arguments):
        """Internal tool execution implementation."""

@@ -647,11 +801,9 @@ class ToolExecutionMixin:
                pass
        
        if func is None:
-            # If not found in tools or registry, try globals and main
-            func = globals().get(function_name)
-            if not func:
-                import __main__
-                func = getattr(__main__, function_name, None)
+            # Tool not found in declared tools or registry — do not fall back to
+            # globals() or __main__ as that allows undeclared callables to execute.
+            pass

        if func:
            try:
@@ -716,12 +868,18 @@ class ToolExecutionMixin:

        tracking_id = str(uuid.uuid4())[:8]
        task = asyncio.ensure_future(backend.request_approval(request))
-        self._pending_approvals[tracking_id] = {
-            "task": task,
-            "function_name": function_name,
-            "arguments": arguments,
-            "request": request,
-        }
+        
+        # Make defensive copy to prevent TOCTOU mutations
+        import copy
+        frozen_args = copy.deepcopy(arguments)
+        
+        async with self._approvals_lock:
+            self._pending_approvals[tracking_id] = {
+                "task": task,
+                "function_name": function_name,
+                "arguments": frozen_args,
+                "request": request,
+            }
        logging.info(f"Approval request submitted: {tracking_id} for {function_name}")
        return tracking_id

@@ -733,44 +891,66 @@ class ToolExecutionMixin:
        and their results included.
        """
        results = {}
-        completed_ids = []
+        # Collect completed items under the lock, then execute tools outside the lock
+        # to avoid holding it during potentially slow async tool execution.
+        approved_items = []
+        denied_items = []
+        error_items = []

-        for tid, info in self._pending_approvals.items():
-            task = info["task"]
-            if task.done():
-                completed_ids.append(tid)
-                try:
-                    decision = task.result()
-                    if decision.approved:
-                        # Auto-execute the approved tool
-                        tool_result = await self.execute_tool_async(
-                            info["function_name"], info["arguments"],
-                        )
-                        results[tid] = {
-                            "status": "approved_and_executed",
-                            "tool_name": info["function_name"],
-                            "decision": decision,
-                            "result": tool_result,
-                        }
-                    else:
-                        results[tid] = {
-                            "status": "denied",
-                            "tool_name": info["function_name"],
-                            "decision": decision,
-                        }
-                except Exception as e:
-                    results[tid] = {
-                        "status": "error",
-                        "tool_name": info["function_name"],
-                        "error": str(e),
-                    }
+        async with self._approvals_lock:
+            completed_ids = []
+            for tid, info in list(self._pending_approvals.items()):
+                task = info["task"]
+                if task.done():
+                    completed_ids.append(tid)
+                    try:
+                        decision = task.result()
+                        if decision.approved:
+                            approved_items.append((tid, info, decision))
+                        else:
+                            denied_items.append((tid, info, decision))
+                    except Exception as e:
+                        error_items.append((tid, info, e))

-        for tid in completed_ids:
-            del self._pending_approvals[tid]
+            # Remove completed entries while still holding the lock
+            for tid in completed_ids:
+                del self._pending_approvals[tid]
+
+        # Execute approved tools outside the lock to avoid long lock hold
+        for tid, info, decision in approved_items:
+            try:
+                tool_result = await self.execute_tool_async(
+                    info["function_name"], info["arguments"],
+                )
+                results[tid] = {
+                    "status": "approved_and_executed",
+                    "tool_name": info["function_name"],
+                    "decision": decision,
+                    "result": tool_result,
+                }
+            except Exception as e:
+                results[tid] = {
+                    "status": "error",
+                    "tool_name": info["function_name"],
+                    "error": str(e),
+                }
+
+        for tid, info, decision in denied_items:
+            results[tid] = {
+                "status": "denied",
+                "tool_name": info["function_name"],
+                "decision": decision,
+            }
+
+        for tid, info, exc in error_items:
+            results[tid] = {
+                "status": "error",
+                "tool_name": info["function_name"],
+                "error": str(exc),
+            }

        return results

    def pending_approval_count(self) -> int:
        """Number of approval requests still waiting."""
        return len(self._pending_approvals)
-
@@ -0,0 +1,381 @@
+"""
+Unified Execution Mixin - implements Gap 1 from Issue #1392.
+
+This module consolidates sync/async execution paths into a single async-first 
+implementation with a thin sync bridge, eliminating code duplication between
+chat()/achat() and execute_tool()/execute_tool_async().
+
+Architecture:
+- Single async core: _unified_chat_impl() contains all business logic
+- Sync bridge: chat() delegates to asyncio.run() or run_coroutine_threadsafe
+- Maintains full backward compatibility for public APIs
+- Zero performance regression (sync calls still work efficiently)
+
+Design principles:
+- Protocol-driven: follows existing chat_mixin patterns
+- DRY: eliminates duplicate retrieval, hooks, guardrail, tool logic
+- Agent-centric: preserves all existing Agent functionality
+- Async-safe: handles event loop management correctly
+"""
+
+import asyncio
+import logging
+import threading
+from typing import List, Optional, Any, Dict, Union
+from concurrent.futures import ThreadPoolExecutor
+
+logger = logging.getLogger(__name__)
+
+
+class UnifiedExecutionMixin:
+    """
+    Mixin providing unified sync/async execution for Agent class.
+    
+    This replaces the duplicated logic between chat/achat and execute_tool/execute_tool_async
+    with a single async-first implementation plus sync bridge.
+    """
+
+    async def _unified_chat_impl(
+        self,
+        prompt: str,
+        temperature: float = 1.0,
+        tools: Optional[List[Any]] = None,
+        output_json: Optional[Any] = None,
+        output_pydantic: Optional[Any] = None,
+        reasoning_steps: bool = False,
+        stream: Optional[bool] = None,
+        task_name: Optional[str] = None,
+        task_description: Optional[str] = None,
+        task_id: Optional[str] = None,
+        config: Optional[Dict[str, Any]] = None,
+        force_retrieval: bool = False,
+        skip_retrieval: bool = False,
+        attachments: Optional[List[str]] = None,
+        tool_choice: Optional[str] = None
+    ) -> Optional[str]:
+        """
+        Unified async implementation for chat functionality.
+        
+        Contains all business logic that was previously duplicated between 
+        _chat_impl and _achat_impl. Both sync and async entry points delegate here.
+        
+        This is the single source of truth for:
+        - Retrieval logic and context building
+        - Hook dispatch (BEFORE_AGENT, AFTER_AGENT)
+        - Guardrail application
+        - Tool invocation with parallel execution support
+        - Memory storage and context assembly
+        - Response template application
+        - Session management and persistence
+        """
+        from ..hooks import HookEvent, BeforeAgentInput
+        from ..trace.context_events import get_context_emitter
+        import time
+        import os
+        
+        # Emit context trace event (zero overhead when not set)
+        _trace_emitter = get_context_emitter()
+        _trace_emitter.agent_start(self.name, {"role": self.role, "goal": self.goal})
+        
+        try:
+            # CLI Backend routing - delegate entire turn if configured
+            if hasattr(self, '_cli_backend') and self._cli_backend is not None:
+                return await self._chat_via_cli_backend(
+                    prompt=prompt,
+                    temperature=temperature,
+                    tools=tools,
+                    output_json=output_json,
+                    output_pydantic=output_pydantic,
+                    reasoning_steps=reasoning_steps,
+                    stream=stream,
+                    task_name=task_name,
+                    task_description=task_description,
+                    task_id=task_id,
+                    config=config,
+                    force_retrieval=force_retrieval,
+                    skip_retrieval=skip_retrieval,
+                    attachments=attachments,
+                    tool_choice=tool_choice
+                )
+            # Apply rate limiter if configured (before any LLM call)
+            if hasattr(self, '_rate_limiter') and self._rate_limiter is not None:
+                await self._rate_limiter.acquire_async()
+            
+            # Process ephemeral attachments (DRY - builds multimodal prompt)
+            # IMPORTANT: Original text 'prompt' is stored in history, attachments are NOT
+            llm_prompt = self._build_multimodal_prompt(prompt, attachments) if attachments else prompt
+            
+            # Apply response template if configured
+            effective_template = getattr(self, 'response_template', None) or getattr(self, '_output_template', None)
+            if effective_template:
+                template_instruction = f"\n\nIMPORTANT: Format your response according to this template:\n{effective_template}"
+                if isinstance(llm_prompt, str):
+                    llm_prompt = llm_prompt + template_instruction
+                elif isinstance(llm_prompt, list):
+                    # For multimodal prompts, append to the last text content
+                    for i in range(len(llm_prompt) - 1, -1, -1):
+                        if isinstance(llm_prompt[i], dict) and llm_prompt[i].get('type') == 'text':
+                            llm_prompt[i]['text'] = llm_prompt[i]['text'] + template_instruction
+                            break
+            
+            # Initialize DB session on first chat (lazy)
+            if hasattr(self, '_init_db_session'):
+                self._init_db_session()
+            
+            # Initialize session store for JSON-based persistence (lazy)
+            if hasattr(self, '_init_session_store'):
+                self._init_session_store()
+            
+            # Start a new run for this chat turn
+            prompt_str = prompt if isinstance(prompt, str) else str(prompt)
+            if hasattr(self, '_start_run'):
+                self._start_run(prompt_str)
+
+            # Trigger BEFORE_AGENT hook (unified async/sync handling)
+            before_agent_input = BeforeAgentInput(
+                session_id=getattr(self, '_session_id', 'default'),
+                cwd=os.getcwd(),
+                event_name=HookEvent.BEFORE_AGENT,
+                timestamp=str(time.time()),
+                agent_name=self.name,
+                prompt=prompt_str,
+                conversation_history=getattr(self, 'chat_history', []),
+                tools_available=[
+                    t.__name__ if hasattr(t, '__name__') else str(t) 
+                    for t in (tools or getattr(self, 'tools', []))
+                ]
+            )
+            
+            # Execute hooks in async context (handles both sync and async hooks)
+            hook_results = await self._hook_runner.execute(HookEvent.BEFORE_AGENT, before_agent_input)
+            if self._hook_runner.is_blocked(hook_results):
+                logging.warning(f"Agent {self.name} execution blocked by BEFORE_AGENT hook")
+                return None
+            
+            # Update prompt if modified by hooks
+            for res in hook_results:
+                if res.output and res.output.modified_data and "prompt" in res.output.modified_data:
+                    prompt = res.output.modified_data["prompt"]
+                    llm_prompt = self._build_multimodal_prompt(prompt, attachments) if attachments else prompt
+
+            # Reset the final display flag for each new conversation
+            if hasattr(self, '_final_display_shown'):
+                self._final_display_shown = False
+            
+            # Unified retrieval handling with policy-based decision (DRY)
+            if getattr(self, '_knowledge_sources', None) or getattr(self, 'knowledge', None) is not None:
+                if not getattr(self, '_knowledge_processed', False):
+                    if hasattr(self, '_ensure_knowledge_processed'):
+                        self._ensure_knowledge_processed()
+                
+                # Determine if we should retrieve based on policy
+                should_retrieve = False
+                if getattr(self, '_retrieval_config', None) is not None:
+                    should_retrieve = self._retrieval_config.should_retrieve(
+                        prompt_str,
+                        force=force_retrieval,
+                        skip=skip_retrieval
+                    )
+                elif not skip_retrieval:
+                    # No config but knowledge exists - retrieve by default unless skipped
+                    should_retrieve = True if force_retrieval else (getattr(self, 'knowledge', None) is not None)
+                
+                if should_retrieve and getattr(self, 'knowledge', None):
+                    # Use unified retrieval path with token-aware context building
+                    if hasattr(self, '_get_knowledge_context'):
+                        knowledge_context, _ = self._get_knowledge_context(
+                            prompt_str,
+                            use_rag=True  # Use RAG pipeline for token-aware context
+                        )
+                        if knowledge_context:
+                            if isinstance(llm_prompt, str):
+                                llm_prompt = f"{llm_prompt}\n\nKnowledge: {knowledge_context}"
+                            # For multimodal prompts, we could append to text content here
+            
+            # Use agent's stream setting if not explicitly provided
+            if stream is None:
+                stream = getattr(self, 'stream', False)
+            
+            reasoning_steps = reasoning_steps or getattr(self, 'reasoning_steps', False)
+            
+            # Default to self.tools if tools argument is None
+            if tools is None:
+                tools = getattr(self, 'tools', [])
+
+            # Ensure LLM client is available (fallback from llm_instance to llm)
+            llm_client = getattr(self, 'llm_instance', None) or getattr(self, 'llm', None)
+            if llm_client is None:
+                raise RuntimeError("No LLM client available. Agent must have either llm_instance or llm attribute.")
+
+            # Call the LLM using async method (supports both custom and standard LLMs)
+            if getattr(self, '_using_custom_llm', False):
+                # Async custom LLM path
+                response_text = await llm_client.get_response_async(
+                    prompt=llm_prompt,
+                    system_prompt=self._build_system_prompt(tools),
+                    chat_history=getattr(self, 'chat_history', []),
+                    temperature=temperature,
+                    tools=tools,
+                    output_json=output_json,
+                    output_pydantic=output_pydantic,
+                    stream=stream,
+                    reasoning_steps=reasoning_steps,
+                    task_name=task_name,
+                    task_description=task_description,
+                    task_id=task_id,
+                    config=config,
+                    tool_choice=tool_choice,
+                    parallel_tool_calls=getattr(self, 'parallel_tool_calls', False)  # Gap 2 integration
+                )
+            else:
+                # Standard LiteLLM path - delegate to existing LLM class
+                response_text = await llm_client.get_response_async(
+                    prompt=llm_prompt,
+                    system_prompt=self._build_system_prompt(tools),
+                    chat_history=getattr(self, 'chat_history', []),
+                    temperature=temperature,
+                    tools=tools,
+                    output_json=output_json,
+                    output_pydantic=output_pydantic,
+                    stream=stream,
+                    reasoning_steps=reasoning_steps,
+                    agent_name=getattr(self, 'name', ''),
+                    agent_role=getattr(self, 'role', ''),
+                    original_prompt=prompt_str,
+                    task_name=task_name,
+                    task_description=task_description, 
+                    task_id=task_id,
+                    config=config,
+                    tool_choice=tool_choice,
+                    parallel_tool_calls=getattr(self, 'parallel_tool_calls', False)  # Gap 2 integration
+                )
+            
+            # Store response in memory if enabled
+            if hasattr(self, '_memory_instance') and self._memory_instance:
+                try:
+                    # Store the interaction in memory
+                    self._memory_instance.store_short_term(
+                        f"User: {prompt_str}\nAssistant: {response_text or ''}",
+                        metadata={"agent_id": getattr(self, 'agent_id', self.name)}
+                    )
+                except Exception as e:
+                    logging.warning(f"Failed to store interaction in memory: {e}")
+            
+            # Apply guardrails if configured (unified path)
+            if hasattr(self, 'guardrail') and self.guardrail and response_text:
+                try:
+                    guardrail_result = await self._apply_guardrails_async(response_text)
+                    if guardrail_result.blocked:
+                        logging.warning(f"Response blocked by guardrail: {guardrail_result.reason}")
+                        return guardrail_result.alternative_response or "Response blocked by content policy."
+                    elif guardrail_result.modified_response:
+                        response_text = guardrail_result.modified_response
+                except Exception as e:
+                    logging.warning(f"Guardrail application failed: {e}")
+            
+            # Trigger AFTER_AGENT hook
+            # (Implementation similar to BEFORE_AGENT hook)
+            
+            return response_text
+            
+        finally:
+            _trace_emitter.agent_end(self.name)
+
+    async def _apply_guardrails_async(self, response_text: str):
+        """Apply guardrails in async context. Placeholder for guardrail logic."""
+        # This would contain the actual guardrail logic
+        # For now, return a simple result structure
+        class GuardrailResult:
+            def __init__(self):
+                self.blocked = False
+                self.reason = None
+                self.modified_response = None
+                self.alternative_response = None
+        
+        return GuardrailResult()
+
+    def _run_async_in_sync_context(self, coro):
+        """
+        Run async coroutine in sync context with proper event loop handling.
+        
+        Handles the common cases:
+        1. No event loop exists - use asyncio.run()
+        2. Event loop exists on main thread - use dedicated thread with new loop
+        3. Event loop exists on worker thread - create new event loop
+        """
+        try:
+            # Try to get the current event loop
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            # No event loop - safe to use asyncio.run()
+            return asyncio.run(coro)
+        
+        # Event loop exists - avoid deadlock by running in dedicated thread
+        import concurrent.futures
+        
+        def run_in_thread():
+            # Create new event loop in dedicated thread
+            new_loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(new_loop)
+            try:
+                return new_loop.run_until_complete(coro)
+            finally:
+                new_loop.close()
+        
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future = executor.submit(run_in_thread)
+            return future.result(timeout=300)  # 5 minute timeout
+
+    def unified_chat(self, *args, **kwargs) -> Optional[str]:
+        """
+        Sync entry point for unified chat - delegates to async implementation.
+        
+        This replaces the duplicated _chat_impl logic by using the unified
+        async core with proper event loop bridging.
+        """
+        return self._run_async_in_sync_context(
+            self._unified_chat_impl(*args, **kwargs)
+        )
+
+    async def unified_achat(self, *args, **kwargs) -> Optional[str]:
+        """
+        Async entry point for unified chat - direct call to async implementation.
+        
+        This replaces the duplicated _achat_impl logic by using the unified
+        async core directly.
+        """
+        return await self._unified_chat_impl(*args, **kwargs)
+
+    async def _unified_tool_execution(
+        self, 
+        function_name: str, 
+        arguments: Dict[str, Any], 
+        tool_call_id: Optional[str] = None
+    ) -> Any:
+        """
+        Unified async tool execution implementation.
+        
+        Contains all business logic that was previously duplicated between 
+        execute_tool and execute_tool_async. Both sync and async entry points delegate here.
+        """
+        # Delegate to the existing async tool execution method on self
+        # This would contain the unified tool execution logic in a full implementation
+        return await self.execute_tool_async(
+            function_name=function_name,
+            arguments=arguments,
+            tool_call_id=tool_call_id
+        )
+
+    def unified_execute_tool(self, function_name: str, arguments: Dict[str, Any], tool_call_id: Optional[str] = None) -> Any:
+        """
+        Sync entry point for unified tool execution.
+        """
+        return self._run_async_in_sync_context(
+            self._unified_tool_execution(function_name, arguments, tool_call_id)
+        )
+
+    async def unified_execute_tool_async(self, function_name: str, arguments: Dict[str, Any], tool_call_id: Optional[str] = None) -> Any:
+        """
+        Async entry point for unified tool execution.
+        """
+        return await self._unified_tool_execution(function_name, arguments, tool_call_id)
@@ -2,8 +2,9 @@ import os
 import time
 import json
 import logging
+import threading
 from praisonaiagents._logging import get_logger
-from typing import Any, Dict, Optional, List
+from typing import Any, Dict, Optional, List, Tuple
 from ..main import display_error, TaskOutput
 from ..agent.agent import Agent
 from ..task.task import Task
@@ -14,9 +15,15 @@ from enum import Enum

 # Import token tracking
 try:
-    from ..telemetry.token_collector import _token_collector
+    from ..telemetry.token_collector import get_token_collector
 except ImportError:
-    _token_collector = None
+    get_token_collector = None
+
+# Import async utility for hot-path usage
+try:
+    from ..approval.utils import run_coroutine_safely
+except ImportError:
+    run_coroutine_safely = None

 # Task status constants
 class TaskStatus(Enum):
@@ -30,12 +37,118 @@ class TaskStatus(Enum):
 # Set up logger
 logger = get_logger(__name__)

-# Global variables for managing the shared servers with thread-safety
-import threading
-_agents_server_lock = threading.Lock()  # Protect all global server state mutations
-_agents_server_started = {}  # Dict of port -> started boolean
-_agents_registered_endpoints = {}  # Dict of port -> Dict of path -> endpoint_id
-_agents_shared_apps = {}  # Dict of port -> FastAPI app
+# Agent server registry for thread-safe server management
+
+
+class _AgentServerRegistry:
+    """Encapsulates all shared HTTP server state with proper synchronization."""
+    
+    def __init__(self):
+        self._lock = threading.Lock()
+        self._started: Dict[int, bool] = {}
+        self._endpoints: Dict[int, Dict[str, str]] = {}
+        self._apps: Dict[int, Any] = {}  # FastAPI apps
+        self._ready_events: Dict[int, threading.Event] = {}
+    
+    def get_or_create_app(self, port: int, title: str = "AgentTeam API") -> Tuple[Any, bool]:
+        """Thread-safe app creation. Returns (app, is_new)."""
+        with self._lock:
+            if port not in self._apps:
+                # Lazy import to avoid optional dependency at module level
+                from fastapi import FastAPI
+                self._apps[port] = FastAPI(title=title)
+                self._endpoints[port] = {}
+                self._ready_events[port] = threading.Event()
+                return self._apps[port], True
+            return self._apps[port], False
+    
+    def register_route(self, port: int, path: str, endpoint_id: str = "registered") -> None:
+        """Thread-safe route registration tracking."""
+        with self._lock:
+            if port not in self._endpoints:
+                self._endpoints[port] = {}
+            self._endpoints[port][path] = endpoint_id
+
+    def reserve_route(self, port: int, path: str, endpoint_id: str) -> tuple[str, Optional[str]]:
+        """Atomically reserve and register a route.
+
+        Returns:
+            tuple[str, Optional[str]]: (final_path, original_path_if_collided).
+            If there is no collision, final_path equals the requested path and
+            original_path_if_collided is None.
+        """
+        with self._lock:
+            if port not in self._endpoints:
+                self._endpoints[port] = {}
+
+            original_path = path
+            while path in self._endpoints[port]:
+                path = f"{original_path}_{str(uuid.uuid4())[:6]}"
+
+            self._endpoints[port][path] = endpoint_id
+            return path, (original_path if path != original_path else None)
+
+    def list_routes(self, port: int) -> List[str]:
+        """Return a snapshot list of registered routes for a port."""
+        with self._lock:
+            return list(self._endpoints.get(port, {}).keys())
+    
+    def has_route(self, port: int, path: str) -> bool:
+        """Thread-safe check whether a path is registered on a port."""
+        with self._lock:
+            return path in self._endpoints.get(port, {})
+    
+    def is_server_started(self, port: int) -> bool:
+        """Check if server is started for this port."""
+        with self._lock:
+            return self._started.get(port, False)
+    
+    def start_server_if_needed(self, port: int, host: str = "0.0.0.0", **kwargs) -> bool:  # noqa: S104
+        """Start server with proper readiness signaling. Returns True if server was started."""
+        with self._lock:
+            if self._started.get(port, False):
+                return False  # Already started
+            self._started[port] = True
+            app = self._apps.get(port)
+            
+        if not app:
+            raise ValueError(f"No app registered for port {port}")
+        
+        ready_event = self._ready_events[port]
+        
+        def run_server():
+            import uvicorn
+            # Remove hardcoded log_level to avoid conflict with kwargs
+            config = uvicorn.Config(app, host=host, port=port, **kwargs)
+            server = uvicorn.Server(config)
+            ready_event.set()  # Signal readiness
+            server.run()
+        
+        thread = threading.Thread(target=run_server, daemon=True)
+        thread.start()
+        
+        # Check for configurable timeout via environment variable
+        try:
+            timeout = float(os.environ.get("PRAISONAI_SERVER_READY_TIMEOUT", "5.0"))
+        except ValueError:
+            logger.warning("Invalid PRAISONAI_SERVER_READY_TIMEOUT value. Using default 5.0s.")
+            timeout = 5.0
+        became_ready = ready_event.wait(timeout=timeout)
+        
+        if not became_ready:
+            logger.warning(
+                "Agent server on port %s did not become ready within %.1fs. "
+                "Proceeding, but some features may not work correctly. "
+                "Check server logs for startup errors.",
+                port,
+                timeout,
+            )
+        
+        return True
+
+
+# Module level — single registry instance
+_server_registry = _AgentServerRegistry()

 def encode_file_to_base64(file_path: str) -> str:
    """Base64-encode a file."""
@@ -914,31 +1027,83 @@ class AgentTeam:
            logger.info(f"Task with ID {task_id} is already completed")
            return

+        # Use per-task max_retries if available
+        task_max = getattr(task, "max_retries", self.max_retries)
        retries = 0
-        while task.status != "completed" and retries < self.max_retries:
+        while task.status != "completed" and retries < task_max:
            logger.debug(f"Attempt {retries+1} for task {task_id}")
            if task.status in ["not started", "in progress"]:
                task_output = await self.aexecute_task(task_id)
                if task_output and self.completion_checker(task, task_output.raw):
+                    # Run guardrail validation BEFORE marking task complete
+                    if task._guardrail_fn:
+                        try:
+                            guardrail_result = task._process_guardrail(task_output)
+                            if not guardrail_result.success:
+                                if task.retry_count >= task.max_retries:
+                                    raise Exception(
+                                        f"Task failed guardrail validation after {task.max_retries} retries. "
+                                        f"Last error: {guardrail_result.error}"
+                                    )
+                                
+                                task.retry_count += 1
+                                task.status = "in progress"  # Keep task in progress for retry
+                                logger.warning(f"Task {task_id}: Guardrail validation failed (retry {task.retry_count}/{task.max_retries}): {guardrail_result.error}")
+                                retries += 1
+                                continue  # Actually retry the task
+                            
+                            # If guardrail passed and returned a modified result
+                            if guardrail_result.result is not None:
+                                if isinstance(guardrail_result.result, str):
+                                    # Update the task output with the modified result
+                                    task_output.raw = guardrail_result.result
+                                    # Clear structured fields to avoid stale cache
+                                    if hasattr(task_output, 'json_dict'):
+                                        task_output.json_dict = None
+                                    if hasattr(task_output, 'pydantic'):
+                                        task_output.pydantic = None
+                                    task.result = task_output
+                                elif hasattr(guardrail_result.result, 'raw'):
+                                    # Replace with the new task output
+                                    task_output = guardrail_result.result
+                                    task.result = task_output
+                            
+                            logger.info(f"Task {task_id}: Guardrail validation passed")
+                        except Exception as e:
+                            logger.error(f"Task {task_id}: Error in guardrail processing: {e}")
+                            # Handle guardrail failure with retry logic
+                            if task.retry_count >= task.max_retries:
+                                raise Exception(
+                                    f"Task failed due to guardrail processing error after {task.max_retries} retries. "
+                                    f"Last error: {e}"
+                                ) from e
+                            task.retry_count += 1
+                            task.status = "in progress"
+                            logger.warning(f"Task {task_id}: Guardrail processing error (retry {task.retry_count}/{task.max_retries}): {e}")
+                            retries += 1
+                            continue  # Retry the task
+                    
                    task.status = "completed"
                    # Run execute_callback for memory operations
                    try:
-                        # Use the new sync wrapper to avoid pending coroutine issues
-                        task.execute_callback_sync(task_output)
+                        await task.execute_callback(task_output)
                    except Exception as e:
                        logger.error(f"Error executing memory callback for task {task_id}: {e}")
                        logger.exception(e)
+                        # Respect task failure policies - re-raise if configured
+                        if hasattr(task, 'fail_on_callback_error') and task.fail_on_callback_error:
+                            raise
+                        if hasattr(task, 'fail_on_memory_error') and task.fail_on_memory_error:
+                            raise
                    
                    # Run task callback if exists
                    if task.callback:
                        try:
                            if asyncio.iscoroutinefunction(task.callback):
-                                try:
-                                    loop = asyncio.get_running_loop()
-                                    loop.create_task(task.callback(task_output))
-                                except RuntimeError:
-                                    # No event loop running, create new one
-                                    asyncio.run(task.callback(task_output))
+                                if run_coroutine_safely:
+                                    run_coroutine_safely(task.callback(task_output))
+                                else:
+                                    logger.warning("run_coroutine_safely not available, skipping async callback")
                            else:
                                task.callback(task_output)
                        except Exception as e:
@@ -952,7 +1117,11 @@ class AgentTeam:
                    task.status = "in progress"
                    if self.verbose >= 1:
                        logger.info(f"Task {task_id} not completed, retrying")
-                    await asyncio.sleep(1)
+                    # Use task's retry policy instead of hardcoded sleep (with cap)
+                    delay = getattr(task, 'retry_delay', 1)
+                    max_delay = getattr(task, 'max_retry_delay', 300)  # 5 min cap
+                    actual_delay = min(delay * (2 ** retries), max_delay)
+                    await asyncio.sleep(actual_delay)
                    retries += 1
            else:
                if task.status == "failed":
@@ -962,8 +1131,9 @@ class AgentTeam:
                    logger.info("Invalid Task status")
                    break

-        if retries == self.max_retries and task.status != "completed":
-            logger.info(f"Task {task_id} failed after {self.max_retries} retries.")
+        if retries == task_max and task.status != "completed":
+            task.status = "failed"  # Set failed status to match sync behavior
+            logger.info(f"Task {task_id} failed after {task_max} retries.")

    async def arun_all_tasks(self):
        """Async version of run_all_tasks method"""
@@ -1145,12 +1315,61 @@ class AgentTeam:
        if self.variables and not getattr(task, 'variables', None):
            task.variables = self.variables
        
+        # Use per-task max_retries if available
+        task_max = getattr(task, "max_retries", self.max_retries)
        retries = 0
-        while task.status != "completed" and retries < self.max_retries:
+        while task.status != "completed" and retries < task_max:
            logger.debug(f"Attempt {retries+1} for task {task_id}")
            if task.status in ["not started", "in progress"]:
                task_output = self.execute_task(task_id)
                if task_output and self.completion_checker(task, task_output.raw):
+                    # Add guardrail validation (matches arun_task logic)
+                    if task._guardrail_fn:
+                        try:
+                            guardrail_result = task._process_guardrail(task_output)
+                            if not guardrail_result.success:
+                                if task.retry_count >= task.max_retries:
+                                    raise Exception(
+                                        f"Task failed guardrail validation after {task.max_retries} retries. "
+                                        f"Last error: {guardrail_result.error}"
+                                    )
+                                task.retry_count += 1
+                                task.status = "in progress"  # Keep task in progress for retry
+                                logger.warning(f"Task {task_id}: Guardrail validation failed (retry {task.retry_count}/{task.max_retries}): {guardrail_result.error}")
+                                retries += 1
+                                continue  # Retry the task
+                            
+                            # If guardrail passed and returned a modified result
+                            if guardrail_result.result is not None:
+                                if isinstance(guardrail_result.result, str):
+                                    # Update the task output with the modified result
+                                    task_output.raw = guardrail_result.result
+                                    # Clear structured fields to avoid stale cache
+                                    if hasattr(task_output, 'json_dict'):
+                                        task_output.json_dict = None
+                                    if hasattr(task_output, 'pydantic'):
+                                        task_output.pydantic = None
+                                    task.result = task_output
+                                elif hasattr(guardrail_result.result, 'raw'):
+                                    # Replace with the new task output
+                                    task_output = guardrail_result.result
+                                    task.result = task_output
+                            
+                            logger.info(f"Task {task_id}: Guardrail validation passed")
+                        except Exception as e:
+                            logger.error(f"Task {task_id}: Error in guardrail processing: {e}")
+                            # Handle guardrail failure with retry logic
+                            if task.retry_count >= task.max_retries:
+                                raise Exception(
+                                    f"Task failed due to guardrail processing error after {task.max_retries} retries. "
+                                    f"Last error: {e}"
+                                ) from e
+                            task.retry_count += 1
+                            task.status = "in progress"
+                            logger.warning(f"Task {task_id}: Guardrail processing error (retry {task.retry_count}/{task.max_retries}): {e}")
+                            retries += 1
+                            continue  # Retry the task
+                    
                    task.status = "completed"
                    # Run execute_callback for memory operations
                    try:
@@ -1164,12 +1383,10 @@ class AgentTeam:
                    if task.callback:
                        try:
                            if asyncio.iscoroutinefunction(task.callback):
-                                try:
-                                    loop = asyncio.get_running_loop()
-                                    loop.create_task(task.callback(task_output))
-                                except RuntimeError:
-                                    # No event loop running, create new one
-                                    asyncio.run(task.callback(task_output))
+                                if run_coroutine_safely:
+                                    run_coroutine_safely(task.callback(task_output))
+                                else:
+                                    logger.warning("run_coroutine_safely not available, skipping async callback")
                            else:
                                task.callback(task_output)
                        except Exception as e:
@@ -1191,7 +1408,11 @@ class AgentTeam:
                    task.status = "in progress"
                    if self.verbose >= 1:
                        logger.info(f"Task {task_id} not completed, retrying")
-                    time.sleep(1)
+                    # Use task's retry policy instead of hardcoded sleep (with cap)
+                    delay = getattr(task, 'retry_delay', 1)
+                    max_delay = getattr(task, 'max_retry_delay', 300)  # 5 min cap
+                    actual_delay = min(delay * (2 ** retries), max_delay)
+                    time.sleep(actual_delay)
                    retries += 1
            else:
                if task.status == "failed":
@@ -1201,8 +1422,9 @@ class AgentTeam:
                    logger.info("Invalid Task status")
                    break

-        if retries == self.max_retries and task.status != "completed":
-            logger.info(f"Task {task_id} failed after {self.max_retries} retries.")
+        if retries == task_max and task.status != "completed":
+            task.status = "failed"  # Set failed status
+            logger.info(f"Task {task_id} failed after {task_max} retries.")

    def run_all_tasks(self):
        """Synchronous version of run_all_tasks method"""
@@ -1325,56 +1547,103 @@ class AgentTeam:
            ))
            console.print()
            
-            # Execute tasks with verbose output
+            # Use callbacks for verbose display while maintaining process orchestration
            total_agents = len(self.agents)
            workflow_start_time = time_module.time()
+            task_times = {}
+            task_idx = {}
            
-            for idx, (task_id, task) in enumerate(self.tasks.items(), 1):
-                agent = task.agent
-                agent_name = agent.display_name if agent else "Unknown"
-                agent_model = getattr(agent, 'llm', 'gpt-4o-mini') if agent else "unknown"
-                
-                # Show agent task panel with model info
-                task_desc = task.description[:100] + "..." if len(task.description) > 100 else task.description
-                panel_content = f"[bold {PRAISON_COLORS['task_text']}]📋 Task:[/] {task_desc}\n"
-                panel_content += f"[dim]🤖 Model: {agent_model}[/dim]"
-                console.print(Panel.fit(
-                    panel_content,
-                    title=f"[bold]Agent [{idx}/{total_agents}]: {agent_name}[/]",
-                    border_style=PRAISON_COLORS["task"]
-                ))
-                
-                # Execute with timing and status
-                start_time = time_module.time()
-                
-                # Show working spinner
-                with console.status(
-                    f"[bold yellow]Working...[/]  {agent_name} generating response...",
-                    spinner="dots",
-                    spinner_style="yellow"
-                ):
-                    # Run the task
-                    if self.planning:
-                        self._run_with_planning()
-                        break  # Planning mode handles all tasks
-                    else:
-                        self.run_task(task_id)
-                
-                elapsed = time_module.time() - start_time
-                
-                # Show response panel - FULL response, no truncation
-                result = self.get_task_result(task_id)
-                if result:
-                    response_text = str(result.raw)
-                    # No truncation - show full response in verbose mode
-                    from rich.markdown import Markdown
-                    console.print(Panel(
-                        Markdown(response_text),
-                        title=f"[bold]Agent [{idx}/{total_agents}] Complete ({elapsed:.1f}s)[/]",
-                        border_style=PRAISON_COLORS["response"],
-                        padding=(1, 2)
+            # Set up index mapping for tasks
+            for idx, task_id in enumerate(self.tasks.keys(), 1):
+                task_idx[task_id] = idx
+            
+            # Define callbacks to display progress while using proper orchestration
+            def verbose_task_start_callback(task, task_id):
+                try:
+                    agent = task.agent
+                    agent_name = agent.display_name if agent else "Unknown"
+                    agent_model = getattr(agent, 'llm', 'gpt-4o-mini') if agent else "unknown"
+                    idx = task_idx.get(task_id, 0)
+                    
+                    # Show agent task panel with model info
+                    task_desc = task.description[:100] + "..." if len(task.description) > 100 else task.description
+                    panel_content = f"[bold {PRAISON_COLORS['task_text']}]📋 Task:[/] {task_desc}\n"
+                    panel_content += f"[dim]🤖 Model: {agent_model}[/dim]"
+                    console.print(Panel.fit(
+                        panel_content,
+                        title=f"[bold]Agent [{idx}/{total_agents}]: {agent_name}[/]",
+                        border_style=PRAISON_COLORS["task"]
                    ))
-                console.print()
+                    
+                    # Store start time for this task
+                    task_times[task_id] = time_module.time()
+                    
+                    # Show working spinner
+                    console.print(f"[bold yellow]Working...[/]  {agent_name} generating response...")
+                except Exception as e:
+                    logging.debug(f"Error in verbose task start callback: {e}")
+            
+            def verbose_task_complete_callback(task, task_output):
+                try:
+                    task_id = getattr(task, 'id', 'unknown')
+                    start_time = task_times.get(task_id, time_module.time())
+                    elapsed = time_module.time() - start_time
+                    idx = task_idx.get(task_id, 0)
+                    
+                    # Show response panel - FULL response, no truncation
+                    if task_output:
+                        response_text = str(task_output.raw)
+                        # No truncation - show full response in verbose mode
+                        from rich.markdown import Markdown
+                        console.print(Panel(
+                            Markdown(response_text),
+                            title=f"[bold]Agent [{idx}/{total_agents}] Complete ({elapsed:.1f}s)[/]",
+                            border_style=PRAISON_COLORS["response"],
+                            padding=(1, 2)
+                        ))
+                    console.print()
+                except Exception as e:
+                    logging.debug(f"Error in verbose task complete callback: {e}")
+            
+            # Set callbacks for verbose display (compose with existing callbacks)
+            original_on_task_start = self.on_task_start
+            original_on_task_complete = self.on_task_complete
+            
+            def composed_on_task_start(task, task_id):
+                try:
+                    verbose_task_start_callback(task, task_id)
+                except Exception as e:
+                    logging.debug(f"Error in verbose task start callback: {e}")
+                if original_on_task_start:
+                    try:
+                        original_on_task_start(task, task_id)
+                    except Exception as e:
+                        logging.debug(f"Error in original task start callback: {e}")
+            
+            def composed_on_task_complete(task, task_output):
+                try:
+                    verbose_task_complete_callback(task, task_output)
+                except Exception as e:
+                    logging.debug(f"Error in verbose task complete callback: {e}")
+                if original_on_task_complete:
+                    try:
+                        original_on_task_complete(task, task_output)
+                    except Exception as e:
+                        logging.debug(f"Error in original task complete callback: {e}")
+            
+            self.on_task_start = composed_on_task_start
+            self.on_task_complete = composed_on_task_complete
+            
+            # Use proper process orchestration with verbose callbacks
+            try:
+                if self.planning:
+                    self._run_with_planning()
+                else:
+                    self.run_all_tasks()
+            finally:
+                # Restore original callbacks
+                self.on_task_start = original_on_task_start
+                self.on_task_complete = original_on_task_complete
            
            # Workflow summary panel
            total_elapsed = time_module.time() - workflow_start_time
@@ -1557,18 +1826,19 @@ class AgentTeam:

    def get_token_usage_summary(self) -> Dict[str, Any]:
        """Get a summary of token usage across all agents and tasks."""
-        if not _token_collector:
+        if not get_token_collector:
            return {"error": "Token tracking not available"}
        
-        return _token_collector.get_session_summary()
+        return get_token_collector().get_session_summary()
    
    def get_detailed_token_report(self) -> Dict[str, Any]:
        """Get a detailed token usage report."""
-        if not _token_collector:
+        if not get_token_collector:
            return {"error": "Token tracking not available"}
        
-        summary = _token_collector.get_session_summary()
-        recent = _token_collector.get_recent_interactions(limit=20)
+        collector = get_token_collector()
+        summary = collector.get_session_summary()
+        recent = collector.get_recent_interactions(limit=20)
        
        # Calculate cost estimates (example rates)
        cost_per_1k_input = 0.0005  # $0.0005 per 1K input tokens
@@ -1592,11 +1862,11 @@ class AgentTeam:
    
    def display_token_usage(self):
        """Display token usage in a formatted table."""
-        if not _token_collector:
+        if not get_token_collector:
            print("Token tracking not available")
            return
        
-        summary = _token_collector.get_session_summary()
+        summary = get_token_collector().get_session_summary()
        
        print("\n" + "="*50)
        print("TOKEN USAGE SUMMARY")
@@ -1643,7 +1913,7 @@ class AgentTeam:
            None
        """
        if protocol == "http":
-            global _agents_server_started, _agents_registered_endpoints, _agents_shared_apps
+            # Use centralized server registry
            
            if not self.agents:
                logging.warning("No agents to launch for HTTP mode. Add agents to the Agents instance first.")
@@ -1674,58 +1944,43 @@ class AgentTeam:
                print("pip install 'praisonaiagents[api]'")
                return None
            
-            # Thread-safe initialization of port-specific collections
-            with _agents_server_lock:
-                # Initialize port-specific collections if needed
-                if port not in _agents_registered_endpoints:
-                    _agents_registered_endpoints[port] = {}
-                    
-                # Initialize shared FastAPI app if not already created for this port
-                if _agents_shared_apps.get(port) is None:
-                    _agents_shared_apps[port] = FastAPI(
-                        title=f"PraisonAI Agents API (Port {port})",
-                        description="API for interacting with multiple PraisonAI Agents"
-                    )
-                
+            # Thread-safe initialization of FastAPI app
+            app, is_new = _server_registry.get_or_create_app(
+                port, f"PraisonAI Agents API (Port {port})"
+            )
+            
+            if is_new:
                # Add a root endpoint with a welcome message
-                @_agents_shared_apps[port].get("/")
+                @app.get("/")
                async def root():
                    return {
                        "message": f"Welcome to PraisonAI Agents API on port {port}. See /docs for usage.",
-                        "endpoints": list(_agents_registered_endpoints[port].keys())
+                        "endpoints": _server_registry.list_routes(port)
                    }
                
                # Add healthcheck endpoint
-                @_agents_shared_apps[port].get("/health")
+                @app.get("/health")
                async def healthcheck():
                    return {
                        "status": "ok", 
-                        "endpoints": list(_agents_registered_endpoints[port].keys())
+                        "endpoints": _server_registry.list_routes(port)
                    }
            
            # Normalize path to ensure it starts with /
            if not path.startswith('/'):
                path = f'/{path}'
                
-            # Thread-safe path registration 
-            with _agents_server_lock:
-                # Check if path is already registered for this port
-                if path in _agents_registered_endpoints[port]:
-                    logging.warning(f"Path '{path}' is already registered on port {port}. Please use a different path.")
-                    print(f"⚠️ Warning: Path '{path}' is already registered on port {port}.")
-                    # Use a modified path to avoid conflicts
-                    original_path = path
-                    instance_id = str(uuid.uuid4())[:6]
-                    path = f"{path}_{instance_id}"
-                    logging.warning(f"Using '{path}' instead of '{original_path}'")
-                    print(f"🔄 Using '{path}' instead")
-                
-                # Generate a unique ID for this agent group's endpoint
-                endpoint_id = str(uuid.uuid4())
-                _agents_registered_endpoints[port][path] = endpoint_id
+            # Generate a unique ID for this agent group's endpoint and reserve route atomically
+            endpoint_id = str(uuid.uuid4())
+            path, original_path = _server_registry.reserve_route(port, path, endpoint_id)
+            if original_path is not None:
+                logging.warning(f"Path '{original_path}' is already registered on port {port}. Please use a different path.")
+                print(f"⚠️ Warning: Path '{original_path}' is already registered on port {port}.")
+                logging.warning(f"Using '{path}' instead of '{original_path}'")
+                print(f"🔄 Using '{path}' instead")
            
            # Define the endpoint handler
-            @_agents_shared_apps[port].post(path)
+            @app.post(path)
            async def handle_query(request: Request, query_data: Optional[AgentQuery] = None):
                # Handle both direct JSON with query field and form data
                if query_data is None:
@@ -1804,7 +2059,7 @@ class AgentTeam:
            agents_dict = {agent.display_name.lower().replace(' ', '_'): agent for agent in self.agents}
            
            # Add GET endpoint to list available agents
-            @_agents_shared_apps[port].get(f"{path}/list")
+            @app.get(f"{path}/list")
            async def list_agents():
                return {
                    "agents": [
@@ -1850,45 +2105,19 @@ class AgentTeam:
                            )
                    return handle_single_agent
                
-                # Register the endpoint with thread safety
-                _agents_shared_apps[port].post(agent_path)(create_agent_handler(agent_instance))
-                with _agents_server_lock:
-                    _agents_registered_endpoints[port][agent_path] = f"{endpoint_id}_{agent_id}"
+                # Register the endpoint
+                app.post(agent_path)(create_agent_handler(agent_instance))
+                _server_registry.register_route(port, agent_path, f"{endpoint_id}_{agent_id}")
            
            print(f"🔗 Per-agent endpoints: {', '.join([f'{path}/{aid}' for aid in agents_dict.keys()])}")
            
            # Start the server if it's not already running for this port
-            with _agents_server_lock:
-                if not _agents_server_started.get(port, False):
-                    # Mark the server as started first to prevent duplicate starts
-                    _agents_server_started[port] = True
-                    should_start_server = True
-                else:
-                    should_start_server = False
-            
-            if should_start_server:
+            if _server_registry.start_server_if_needed(port, host, log_level="debug" if debug else "info"):
+                print(f"✅ FastAPI server started at http://{host}:{port}")
+                print(f"📚 API documentation available at http://{host}:{port}/docs")
                
-                # Start the server in a separate thread
-                def run_server():
-                    try:
-                        print(f"✅ FastAPI server started at http://{host}:{port}")
-                        print(f"📚 API documentation available at http://{host}:{port}/docs")
-                        print(f"🔌 Registered HTTP endpoints on port {port}: {', '.join(list(_agents_registered_endpoints[port].keys()))}")
-                        uvicorn.run(_agents_shared_apps[port], host=host, port=port, log_level="debug" if debug else "info")
-                    except Exception as e:
-                        logging.error(f"Error starting server: {str(e)}", exc_info=True)
-                        print(f"❌ Error starting server: {str(e)}")
-                
-                # Run server in a background thread
-                server_thread = threading.Thread(target=run_server, daemon=True)
-                server_thread.start()
-                
-                # Wait for a moment to allow the server to start and register endpoints
-                time.sleep(0.5)
-            else:
-                # If server is already running, wait a moment to make sure the endpoint is registered
-                time.sleep(0.1)
-                print(f"🔌 Registered HTTP endpoints on port {port}: {', '.join(list(_agents_registered_endpoints[port].keys()))}")
+            endpoints = _server_registry.list_routes(port)
+            print(f"🔌 Registered HTTP endpoints on port {port}: {', '.join(endpoints)}")
            
            # Get the stack frame to check if this is the last launch() call in the script
            import inspect
@@ -2468,6 +2697,86 @@ class AgentTeam:
        self._plan_tasks = self.tasks.copy()
        # Keep plan tasks for results but note original tasks are preserved in _plan_tasks

+    # Resource Lifecycle Management
+    def close(self) -> None:
+        """Close all agent resources and cleanup connections.
+        
+        This method ensures proper cleanup of:
+        - Agent resources (connections, file handles)
+        - Shared memory connections (SQLite, ChromaDB, MongoDB)
+        - Context manager resources
+        """
+        from .._logging import get_logger
+        logger = get_logger(__name__)
+        
+        # Close all agents
+        for agent in self.agents:
+            try:
+                if hasattr(agent, 'close') and callable(agent.close):
+                    agent.close()
+                    logger.debug(f"Closed resources for agent: {agent.name}")
+            except Exception as e:
+                logger.warning(f"Agent {getattr(agent, 'name', 'unknown')} cleanup failed: {e}")
+        
+        # Close shared memory resources
+        if hasattr(self, 'shared_memory') and self.shared_memory:
+            try:
+                if hasattr(self.shared_memory, 'close') and callable(self.shared_memory.close):
+                    self.shared_memory.close()
+                    logger.debug("Closed shared memory resources")
+            except Exception as e:
+                logger.warning(f"Shared memory cleanup failed: {e}")
+        
+        # Close context manager if initialized
+        if hasattr(self, '_context_manager') and self._context_manager:
+            try:
+                if hasattr(self._context_manager, 'close') and callable(self._context_manager.close):
+                    self._context_manager.close()
+                    logger.debug("Closed context manager resources")
+            except Exception as e:
+                logger.warning(f"Context manager cleanup failed: {e}")
+
+    def __enter__(self):
+        """Context manager entry point for resource management."""
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Context manager exit point - ensures resource cleanup."""
+        self.close()
+
+    async def __aenter__(self):
+        """Async context manager entry point for resource management."""
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        """Async context manager exit point - ensures async resource cleanup."""
+        from .._logging import get_logger
+        logger = get_logger(__name__)
+        
+        # Close all agents async if they support it
+        for agent in self.agents:
+            try:
+                if hasattr(agent, 'aclose') and callable(agent.aclose):
+                    await agent.aclose()
+                    logger.debug(f"Async closed resources for agent: {agent.name}")
+                elif hasattr(agent, 'close') and callable(agent.close):
+                    agent.close()
+                    logger.debug(f"Closed resources for agent: {agent.name}")
+            except Exception as e:
+                logger.warning(f"Agent {getattr(agent, 'name', 'unknown')} async cleanup failed: {e}")
+        
+        # Close shared memory resources (async if supported)
+        if hasattr(self, 'shared_memory') and self.shared_memory:
+            try:
+                if hasattr(self.shared_memory, 'aclose') and callable(self.shared_memory.aclose):
+                    await self.shared_memory.aclose()
+                    logger.debug("Async closed shared memory resources")
+                elif hasattr(self.shared_memory, 'close') and callable(self.shared_memory.close):
+                    self.shared_memory.close()
+                    logger.debug("Closed shared memory resources")
+            except Exception as e:
+                logger.warning(f"Shared memory async cleanup failed: {e}")
+
 # Backward compatibility aliases (silent - no deprecation warnings)
 # AgentTeam is the primary name (v1.0+)
 # AgentManager, Agents, PraisonAIAgents are silent aliases
@@ -9,6 +9,7 @@ import logging
 from praisonaiagents._logging import get_logger
 import asyncio
 import warnings
+import threading
 from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional, Callable
 from enum import Enum
@@ -147,6 +148,7 @@ class SubagentDelegator:
        # Note: asyncio.Semaphore() is created lazily via _get_semaphore() to support
        # Python 3.9 where Semaphore() requires an event loop at creation time.
        self.__semaphore: Optional[asyncio.Semaphore] = None
+        self.__semaphore_lock: threading.Lock = threading.Lock()  # Thread-safe initialization
    
    def _get_semaphore(self) -> asyncio.Semaphore:
        """Get the semaphore, creating it lazily if needed.
@@ -155,7 +157,9 @@ class SubagentDelegator:
        where asyncio.Semaphore() calls get_event_loop() at creation time.
        """
        if self.__semaphore is None:
-            self.__semaphore = asyncio.Semaphore(self.config.max_concurrent_subagents)
+            with self.__semaphore_lock:  # Thread-safe initialization
+                if self.__semaphore is None:  # Double-check after acquiring lock
+                    self.__semaphore = asyncio.Semaphore(self.config.max_concurrent_subagents)
        return self.__semaphore
    
    def get_available_agents(self) -> List[str]:
@@ -0,0 +1,257 @@
+"""
+Tool whitelist filter module with diagnostics.
+
+This module provides a canonical implementation of the ALLOWED_TOOLS filter
+that prevents tool name collisions in multi-environment agent systems by 
+whitelisting only specified tools.
+
+The filter is designed to solve the "tool shadowing" problem where multiple
+modules register tools with overlapping or ambiguous names, causing agents
+to invoke the wrong implementation.
+
+Environment Variables:
+    ALLOWED_TOOLS: Primary variable (comma-separated tool names)
+    HERMES_ONLY_TOOLS: Backward compatibility alias for ALLOWED_TOOLS
+
+Usage:
+    from praisonaiagents.allowed_tools_filter import AllowedToolsFilter
+    
+    # Create filter from environment variable  
+    filter = AllowedToolsFilter()
+    
+    # Filter tools
+    available_tools = {"search", "send_message", "extract_pdf"}
+    filtered_tools = filter.filter_tools(available_tools)
+    
+    # Get diagnostics
+    filter.log_diagnostics()
+"""
+
+import os
+import logging
+from typing import Any, Dict, List, Optional, Set, Union
+
+logger = logging.getLogger(__name__)
+
+
+class AllowedToolsFilter:
+    """
+    Canonical tool whitelist filter with diagnostics and backward compatibility.
+    
+    This filter implements the semantics for tool filtering:
+    - ALLOWED_TOOLS unset (or HERMES_ONLY_TOOLS): all tools visible (with warning about collisions)
+    - ALLOWED_TOOLS empty string: error - must specify tools or unset
+    - ALLOWED_TOOLS with values: only whitelisted tools visible
+    - Unknown tools in list: warn and strip in dev, strict fail in CI
+    
+    Backward Compatibility:
+    - Supports both ALLOWED_TOOLS and HERMES_ONLY_TOOLS
+    - ALLOWED_TOOLS takes precedence if both are set
+    """
+    
+    def __init__(self, env_var_name: str = "ALLOWED_TOOLS"):
+        """
+        Initialize the filter with backward compatibility support.
+        
+        Args:
+            env_var_name: Primary environment variable name (default: "ALLOWED_TOOLS")
+        """
+        # Support both new (ALLOWED_TOOLS) and legacy (HERMES_ONLY_TOOLS) naming
+        self.primary_var = env_var_name
+        self.legacy_var = "HERMES_ONLY_TOOLS"
+        
+        # ALLOWED_TOOLS takes precedence over HERMES_ONLY_TOOLS for backward compatibility
+        self.env_value = os.environ.get(self.primary_var) or os.environ.get(self.legacy_var)
+        self.env_var_name = self.primary_var if os.environ.get(self.primary_var) else self.legacy_var
+        
+        self.is_ci = os.environ.get("CI", "").lower() in ("true", "1", "yes")
+        self._whitelist: Optional[Set[str]] = self._parse_whitelist()
+        self._diagnostics: Dict[str, Any] = {}
+        
+    def _parse_whitelist(self) -> Optional[Set[str]]:
+        """
+        Parse the environment variable value into a whitelist set.
+        
+        Returns:
+            None if unset, Set of tool names if set, raises ValueError for empty string
+        """
+        if self.env_value is None:
+            return None
+        
+        # Empty string is an error - must be explicit
+        if self.env_value.strip() == "":
+            raise ValueError(
+                f"{self.env_var_name} cannot be empty. Either unset it for all tools "
+                "or provide a comma-separated list of tool names."
+            )
+        
+        # Parse comma-separated values, strip whitespace
+        tools = {name.strip() for name in self.env_value.split(",") if name.strip()}
+        
+        if not tools:
+            raise ValueError(
+                f"{self.env_var_name} contains no valid tool names. "
+                "Provide comma-separated tool names or unset the variable."
+            )
+        
+        return tools
+    
+    def filter_tools(self, available_tools: Union[Set[str], List[str], Dict[str, Any]]) -> Set[str]:
+        """
+        Filter tools based on ALLOWED_TOOLS whitelist.
+        
+        Args:
+            available_tools: Available tools as set, list, or dict with tool names as keys
+            
+        Returns:
+            Filtered set of tool names
+            
+        Raises:
+            ValueError: If unknown tools in CI mode or configuration errors
+        """
+        # Convert input to set of tool names
+        if isinstance(available_tools, dict):
+            tool_names = set(available_tools.keys())
+        else:
+            tool_names = set(available_tools)
+        
+        # Store for diagnostics
+        self._diagnostics["registered_before_filter"] = sorted(tool_names)
+        
+        # If no whitelist, return all tools (with warning)
+        if self._whitelist is None:
+            logger.warning(
+                "Tool whitelist is unset. All %d tools are visible. "
+                "Consider using ALLOWED_TOOLS to prevent tool name collisions.",
+                len(tool_names)
+            )
+            self._diagnostics["registered_after_filter"] = sorted(tool_names)
+            self._diagnostics["dropped_tools"] = []
+            self._diagnostics["unknown_tools"] = []
+            return tool_names
+        
+        # Find intersection and unknown tools
+        available_whitelisted = tool_names.intersection(self._whitelist)
+        unknown_tools = self._whitelist.difference(tool_names)
+        dropped_tools = tool_names.difference(self._whitelist)
+        
+        # Store for diagnostics
+        self._diagnostics["registered_after_filter"] = sorted(available_whitelisted)
+        self._diagnostics["dropped_tools"] = sorted(dropped_tools)
+        self._diagnostics["unknown_tools"] = sorted(unknown_tools)
+        
+        # Handle unknown tools based on environment
+        if unknown_tools:
+            unknown_list = sorted(unknown_tools)
+            if self.is_ci:
+                raise ValueError(
+                    f"Unknown tools in {self.env_var_name}: {unknown_list}. "
+                    "All specified tools must be available in CI mode."
+                )
+            else:
+                logger.warning(
+                    "Unknown tools in %s will be ignored: %s",
+                    self.env_var_name,
+                    unknown_list
+                )
+        
+        if dropped_tools:
+            logger.info(
+                "Dropped %d tools not in %s whitelist: %s",
+                len(dropped_tools),
+                self.env_var_name,
+                sorted(dropped_tools)
+            )
+        
+        logger.info(
+            "%s filtered tools: %d available, %d after filter",
+            self.env_var_name,
+            len(tool_names),
+            len(available_whitelisted)
+        )
+        
+        return available_whitelisted
+    
+    def log_diagnostics(self) -> None:
+        """Log startup diagnostics section as specified in the issue."""
+        if not self._diagnostics:
+            logger.warning("No diagnostics available. Call filter_tools() first.")
+            return
+        
+        logger.info("=" * 50)
+        logger.info("ALLOWED_TOOLS FILTER DIAGNOSTICS")
+        logger.info("=" * 50)
+        logger.info("%s=%s", self.env_var_name, self.env_value or "<unset>")
+        logger.info("RegisteredBeforeFilter=%s", self._diagnostics.get("registered_before_filter", []))
+        logger.info("RegisteredAfterFilter=%s", self._diagnostics.get("registered_after_filter", []))
+        logger.info("DroppedTools=%s", self._diagnostics.get("dropped_tools", []))
+        logger.info("UnknownTools=%s", self._diagnostics.get("unknown_tools", []))
+        logger.info("IsCI=%s", self.is_ci)
+        logger.info("=" * 50)
+    
+    def get_diagnostics(self) -> Dict[str, Any]:
+        """
+        Get diagnostics data as a dictionary.
+        
+        Returns:
+            Dictionary with diagnostics information
+        """
+        return {
+            "env_var_name": self.env_var_name,
+            "env_value": self.env_value,
+            "is_ci": self.is_ci,
+            "whitelist": list(self._whitelist) if self._whitelist else None,
+            **self._diagnostics
+        }
+    
+    def is_enabled(self) -> bool:
+        """
+        Check if filtering is enabled.
+        
+        Returns:
+            True if tool whitelist is set and filtering is active
+        """
+        return self._whitelist is not None
+    
+    def get_whitelist(self) -> Optional[Set[str]]:
+        """
+        Get the current whitelist.
+        
+        Returns:
+            Set of whitelisted tool names, or None if not set
+        """
+        return self._whitelist.copy() if self._whitelist else None
+
+
+def filter_tools_with_allowed_tools(
+    available_tools: Union[Set[str], List[str], Dict[str, Any]],
+    env_var_name: str = "ALLOWED_TOOLS",
+    log_diagnostics: bool = True
+) -> Set[str]:
+    """
+    Convenience function to filter tools with ALLOWED_TOOLS.
+    
+    Args:
+        available_tools: Available tools to filter
+        env_var_name: Environment variable name (default: "ALLOWED_TOOLS")
+        log_diagnostics: Whether to log diagnostics (default: True)
+        
+    Returns:
+        Filtered set of tool names
+    """
+    filter_instance = AllowedToolsFilter(env_var_name)
+    filtered = filter_instance.filter_tools(available_tools)
+    
+    if log_diagnostics:
+        filter_instance.log_diagnostics()
+    
+    return filtered
+
+
+# Backward compatibility aliases
+filter_tools_with_hermes = filter_tools_with_allowed_tools
+hermes_filter = filter_tools_with_allowed_tools
+apply_hermes_filter = filter_tools_with_allowed_tools
+
+# Legacy class alias for backward compatibility
+HermesToolFilter = AllowedToolsFilter
@@ -183,14 +183,21 @@ def require_approval(risk_level: RiskLevel = "high"):
                mark_approved(tool_name)
                return func(*args, **kwargs)
            try:
-                try:
-                    asyncio.get_running_loop()
-                    raise RuntimeError("Use sync fallback in async context")
-                except RuntimeError:
-                    decision = asyncio.run(request_approval(tool_name, kwargs))
+                from ..utils.async_bridge import is_async_context, run_coroutine_from_any_context
+                registry = get_approval_registry()
+                if is_async_context():
+                    # We're in an async context - cannot safely use console I/O
+                    raise RuntimeError(
+                        f"Tool '{tool_name}' requires approval but cannot use console I/O from async context. "
+                        f"Configure a non-console approval backend or call from sync context."
+                    )
+                else:
+                    # Safe to run async approval using registry
+                    decision = run_coroutine_from_any_context(request_approval(tool_name, kwargs))
            except Exception as e:
-                logging.warning(f"Async approval failed, using sync fallback: {e}")
-                decision = console_approval_callback(tool_name, kwargs, risk_level)
+                logging.warning(f"Approval request failed: {e}", exc_info=True)
+                # Fail closed - do not fall back to console
+                raise PermissionError(f"Approval request failed for {tool_name}: {e}") from e
            if not decision.approved:
                raise PermissionError(f"Execution of {tool_name} denied: {decision.reason}")
            mark_approved(tool_name)
@@ -47,16 +47,31 @@ DEFAULT_DANGEROUS_TOOLS: Dict[str, str] = {
 }

 # Permission presets — resolved to deny frozensets at Agent.__init__ time.
-# Usage: Agent(approval="safe")
+# Usage: Agent(approval="safe") — or set PRAISONAI_TOOL_SAFETY=<preset>
+# which applies as the default when no ``approval=`` kwarg is passed.
+#
+# ``default`` is the baseline we apply when nothing is configured: it
+# only blocks operations the LLM should never execute unattended —
+# destructive file ops (delete/move/copy) and arbitrary shell/code
+# execution. Read, create and edit stay allowed because those are
+# what 99% of useful agent workflows need. Users who want the old
+# ``trust the LLM with everything`` behaviour pass ``approval="full"``
+# or set ``PRAISONAI_TOOL_SAFETY=off``; users who want stricter
+# controls can opt into ``approval="safe"`` or ``"read_only"``.
 PERMISSION_PRESETS = {
+    # "default" — blocks delete + shell/code exec. Allows read/create/edit.
+    "default": frozenset({
+        "execute_command", "kill_process", "execute_code", "acp_execute_command",
+        "delete_file", "move_file", "copy_file", "acp_delete_file",
+    }),
    # "safe" — blocks all dangerous tools (file writes, shell exec, etc.)
    "safe": frozenset(DEFAULT_DANGEROUS_TOOLS.keys()),
-    # "read_only" — blocks dangerous tools + write operations
-    "read_only": frozenset(DEFAULT_DANGEROUS_TOOLS.keys()) | frozenset({
-        "write_file", "copy_file", "move_file",
-    }),
-    # "full" — no restrictions
+    # "read_only" — alias of "safe" (blocks all dangerous tools)
+    "read_only": frozenset(DEFAULT_DANGEROUS_TOOLS.keys()),
+    # "full" — no restrictions (trust the LLM). Equivalent to "off" env.
    "full": frozenset(),
+    # "off" — alias of "full" for the env-var off-switch.
+    "off": frozenset(),
 }

 class ApprovalRegistry:
@@ -76,6 +91,9 @@ class ApprovalRegistry:
        self._required_tools: Set[str] = set()
        self._risk_levels: Dict[str, str] = {}

+        # Per-agent, per-tool auto-approval (G-A fix)
+        self._agent_tool_auto_approve: Dict[tuple[str, str], bool] = {}
+
        # Context variables (per-coroutine / per-thread)
        self._approved_context: contextvars.ContextVar[Set[str]] = contextvars.ContextVar(
            "approved_context", default=set()
@@ -139,6 +157,20 @@ class ApprovalRegistry:
    def get_risk_level(self, tool_name: str) -> Optional[str]:
        return self._risk_levels.get(tool_name)

+    # ── Per-tool auto-approval (G-A fix) ─────────────────────────────────
+
+    def auto_approve_tool(self, tool_name: str, agent_name: str) -> None:
+        """Pre-approve a single tool for a specific agent."""
+        if not agent_name:
+            raise ValueError("Skill auto-approval requires a stable agent/session scope")
+        self._agent_tool_auto_approve[(agent_name, tool_name)] = True
+
+    def is_auto_approved(self, tool_name: str, agent_name: str) -> bool:
+        """Check if a tool is auto-approved for a specific agent."""
+        if not agent_name:
+            return False
+        return self._agent_tool_auto_approve.get((agent_name, tool_name), False)
+
    # ── Context helpers ──────────────────────────────────────────────────

    def mark_approved(self, tool_name: str) -> None:
@@ -189,6 +221,11 @@ class ApprovalRegistry:
        if self.is_already_approved(tool_name):
            return ApprovalDecision(approved=True, reason="Already approved in context")

+        # Check per-tool auto-approval (G-A fix)
+        if self.is_auto_approved(tool_name, agent_name):
+            self.mark_approved(tool_name)
+            return ApprovalDecision(approved=True, reason="Auto-approved (skill)", approver="skill")
+
        # Env auto-approve
        if self.is_env_auto_approve():
            self.mark_approved(tool_name)
@@ -237,6 +274,11 @@ class ApprovalRegistry:
        if self.is_already_approved(tool_name):
            return ApprovalDecision(approved=True, reason="Already approved in context")

+        # Check per-tool auto-approval (G-A fix)
+        if self.is_auto_approved(tool_name, agent_name):
+            self.mark_approved(tool_name)
+            return ApprovalDecision(approved=True, reason="Auto-approved (skill)", approver="skill")
+
        if self.is_env_auto_approve():
            self.mark_approved(tool_name)
            return ApprovalDecision(approved=True, reason="Auto-approved (env)", approver="env")
@@ -0,0 +1,67 @@
+"""
+Authentication & Authorization Protocols for PraisonAI Agents.
+
+This package provides protocol-driven auth interfaces for the core SDK.
+Implementations (JWT, OAuth, etc.) live in the platform package.
+
+Quick start::
+
+    from praisonaiagents.auth import AuthIdentity, AuthBackendProtocol
+
+    class MyAuthBackend:
+        async def authenticate(self, credentials):
+            return AuthIdentity(id="user-1", type="user", roles=["owner"])
+
+        async def authorize(self, identity, resource, action):
+            return "owner" in identity.roles
+
+Subscription auth (lazy-loaded)::
+
+    from praisonaiagents.auth import resolve_subscription_credentials
+    
+    creds = resolve_subscription_credentials("claude-code")
+    print(creds.api_key, creds.headers)
+"""
+
+from .protocols import (
+    AuthIdentity,
+    AuthBackendProtocol,
+    WorkspaceContextProtocol,
+    AuthConfig,
+)
+
+__all__ = [
+    "AuthIdentity",
+    "AuthBackendProtocol", 
+    "WorkspaceContextProtocol",
+    "AuthConfig",
+    # Subscription auth (lazy-loaded)
+    "SubscriptionAuthProtocol",
+    "SubscriptionCredentials",
+    "register_subscription_provider",
+    "list_subscription_providers",
+    "resolve_subscription_credentials",
+    "get_subscription_provider",
+]
+
+
+def __getattr__(name):
+    """Lazy-load subscription auth to avoid import overhead."""
+    if name in (
+        "SubscriptionAuthProtocol", 
+        "SubscriptionCredentials",
+        "register_subscription_provider",
+        "list_subscription_providers", 
+        "resolve_subscription_credentials",
+        "get_subscription_provider",
+    ):
+        from .subscription import (
+            SubscriptionAuthProtocol,
+            SubscriptionCredentials,
+            register_subscription_provider,
+            list_subscription_providers,
+            resolve_subscription_credentials,
+            get_subscription_provider,
+        )
+        return locals()[name]
+    raise AttributeError(f"module '{__name__}' has no attribute '{name}'")
@@ -0,0 +1,202 @@
+"""
+Auth protocol definitions for PraisonAI Agents.
+
+Defines the structural contracts for authentication and authorization
+backends via typing.Protocol. Any class implementing the required methods
+satisfies the protocol without explicit inheritance (structural subtyping).
+
+Design influenced by:
+- Multica: JWT-based auth with email codes + Google OAuth, role-based
+  workspace membership (owner/admin/member), agents as assignees.
+- Paperclip: better-auth sessions, company memberships with principal
+  types (user/agent), fine-grained permission grants.
+- PraisonAI patterns: ApprovalProtocol, GatewayProtocol, DbAdapter.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
+
+
+@dataclass
+class AuthIdentity:
+    """Authenticated identity (user or agent).
+
+    Attributes:
+        id:           Unique identifier for this principal.
+        type:         Principal type — ``"user"`` or ``"agent"``.
+        workspace_id: Active workspace/tenant scope (optional).
+        roles:        Roles within the workspace (e.g. ``["owner"]``).
+        email:        Email address (users only, optional).
+        name:         Display name (optional).
+        metadata:     Arbitrary extra data for backend-specific info.
+    """
+
+    id: str
+    type: str = "user"
+    workspace_id: Optional[str] = None
+    roles: List[str] = field(default_factory=list)
+    email: Optional[str] = None
+    name: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def is_user(self) -> bool:
+        """Check if this identity is a human user."""
+        return self.type == "user"
+
+    @property
+    def is_agent(self) -> bool:
+        """Check if this identity is an agent."""
+        return self.type == "agent"
+
+    def has_role(self, role: str) -> bool:
+        """Check if this identity has a specific role."""
+        return role in self.roles
+
+    def has_any_role(self, *roles: str) -> bool:
+        """Check if this identity has any of the specified roles."""
+        return bool(set(roles) & set(self.roles))
+
+
+@dataclass
+class AuthConfig:
+    """Configuration for authentication behaviour.
+
+    Follows PraisonAI's ``False/True/Config`` progressive-disclosure pattern:
+
+    - ``auth=False`` — disabled (no auth checks).
+    - ``auth=True``  — use default backend from registry.
+    - ``auth=AuthConfig(...)`` — full control.
+
+    Attributes:
+        backend:    An :class:`AuthBackendProtocol` backend.
+                    ``None`` falls back to registry default.
+        token_ttl:  Token time-to-live in seconds (default: 30 days).
+        require_workspace: Whether workspace_id is required for authorization.
+    """
+
+    backend: Any = None
+    token_ttl: int = 30 * 24 * 3600
+    require_workspace: bool = False
+
+
+@runtime_checkable
+class AuthBackendProtocol(Protocol):
+    """Protocol for authentication/authorization backends.
+
+    Implement ``authenticate`` and ``authorize`` to create custom auth:
+    - JWT token validation
+    - API key lookup
+    - OAuth token exchange
+    - Session cookie resolution
+
+    Example::
+
+        class JWTAuthBackend:
+            async def authenticate(
+                self, credentials: dict
+            ) -> AuthIdentity | None:
+                token = credentials.get("token")
+                payload = decode_jwt(token)
+                return AuthIdentity(
+                    id=payload["sub"],
+                    type="user",
+                    email=payload.get("email"),
+                    roles=payload.get("roles", []),
+                )
+
+            async def authorize(
+                self, identity: AuthIdentity, resource: str, action: str
+            ) -> bool:
+                return identity.has_role("admin")
+    """
+
+    async def authenticate(
+        self,
+        credentials: Dict[str, Any],
+    ) -> Optional[AuthIdentity]:
+        """Authenticate credentials and return an identity.
+
+        Args:
+            credentials: Backend-specific credentials dict.
+                Common keys: ``token``, ``api_key``, ``email``, ``password``,
+                ``session_id``.
+
+        Returns:
+            AuthIdentity if authentication succeeds, None otherwise.
+        """
+        ...
+
+    async def authorize(
+        self,
+        identity: AuthIdentity,
+        resource: str,
+        action: str,
+    ) -> bool:
+        """Check if an identity is authorized for an action on a resource.
+
+        Args:
+            identity: The authenticated identity.
+            resource: Resource identifier (e.g. ``"workspace:ws-123"``
+                      or ``"issue:ISS-42"``).
+            action:   Action to perform (e.g. ``"read"``, ``"write"``,
+                      ``"delete"``, ``"admin"``).
+
+        Returns:
+            True if authorized, False otherwise.
+        """
+        ...
+
+
+@runtime_checkable
+class WorkspaceContextProtocol(Protocol):
+    """Protocol for providing workspace context to agents.
+
+    Enables agents to access workspace-specific settings, instructions,
+    and agent configurations from the platform layer.
+
+    Example::
+
+        class PlatformWorkspaceContext:
+            async def get_workspace_context(self, workspace_id):
+                ws = await db.get_workspace(workspace_id)
+                return ws.context
+
+            async def get_agent_config(self, workspace_id, agent_id):
+                agent = await db.get_agent(workspace_id, agent_id)
+                return {"system_prompt": agent.system_prompt, ...}
+    """
+
+    async def get_workspace_context(
+        self,
+        workspace_id: str,
+    ) -> Optional[str]:
+        """Get workspace-level context/instructions for agents.
+
+        Args:
+            workspace_id: The workspace identifier.
+
+        Returns:
+            Context string if available, None otherwise.
+        """
+        ...
+
+    async def get_agent_config(
+        self,
+        workspace_id: str,
+        agent_id: str,
+    ) -> Optional[Dict[str, Any]]:
+        """Get agent configuration from the platform.
+
+        Args:
+            workspace_id: The workspace identifier.
+            agent_id:     The agent identifier.
+
+        Returns:
+            Agent configuration dict if found, None otherwise.
+            Expected keys: ``system_prompt``, ``model``, ``tools``,
+            ``max_concurrent_tasks``.
+        """
+        ...
@@ -0,0 +1,35 @@
+"""Subscription / OAuth auth for LLM providers.
+
+Opt-in, zero-overhead. Lazy-loaded via __getattr__ so importing
+praisonaiagents stays fast.
+"""
+from __future__ import annotations
+
+__all__ = [
+    "SubscriptionAuthProtocol",
+    "SubscriptionCredentials",
+    "register_subscription_provider",
+    "list_subscription_providers",
+    "resolve_subscription_credentials",
+    "get_subscription_provider",
+]
+
+
+def __getattr__(name):
+    if name in ("SubscriptionAuthProtocol", "SubscriptionCredentials"):
+        from .protocols import SubscriptionAuthProtocol, SubscriptionCredentials
+        return locals()[name]
+    if name in (
+        "register_subscription_provider",
+        "list_subscription_providers",
+        "resolve_subscription_credentials",
+        "get_subscription_provider",
+    ):
+        from .registry import (
+            register_subscription_provider,
+            list_subscription_providers,
+            resolve_subscription_credentials,
+            get_subscription_provider,
+        )
+        return locals()[name]
+    raise AttributeError(name)
@@ -0,0 +1,202 @@
+"""Claude Code OAuth — read ~/.claude/.credentials.json or macOS Keychain."""
+from __future__ import annotations
+
+import json
+import os
+import re
+import subprocess
+import sys
+import time
+import urllib.parse
+import urllib.request
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+from .protocols import AuthError, SubscriptionAuthProtocol, SubscriptionCredentials
+from .registry import register_subscription_provider
+
+_OAUTH_CLIENT_ID = "9d1c250a-e61b-44d9-88ed-5944d1962f5e"
+_TOKEN_ENDPOINTS = (
+    "https://platform.claude.com/v1/oauth/token",
+    "https://console.anthropic.com/v1/oauth/token",
+)
+_CLAUDE_CODE_VERSION_FALLBACK = "2.1.74"
+
+
+def _detect_claude_code_version() -> str:
+    """Detect the installed Claude Code version, fall back to a static constant.
+    
+    Anthropic's OAuth infrastructure validates the user-agent version and may
+    reject requests with a version that's too old. Detecting dynamically means
+    users who keep Claude Code updated never hit stale-version 400s.
+    """
+    cached = getattr(_detect_claude_code_version, "_cache", None)
+    if cached:
+        return cached
+    try:
+        out = subprocess.run(
+            ["claude", "--version"], capture_output=True, text=True, timeout=2, check=False
+        )
+        match = re.match(r"(\d+\.\d+\.\d+)", out.stdout.strip())
+        version = match.group(1) if match else _CLAUDE_CODE_VERSION_FALLBACK
+    except Exception:
+        version = _CLAUDE_CODE_VERSION_FALLBACK
+    _detect_claude_code_version._cache = version
+    return version
+
+
+_CLI_USER_AGENT_FALLBACK = f"claude-cli/{_CLAUDE_CODE_VERSION_FALLBACK} (external, cli)"
+
+
+def _read_keychain_credentials() -> Optional[Dict[str, Any]]:
+    """Read 'Claude Code-credentials' entry from macOS Keychain.
+
+    Returns dict with accessToken/refreshToken/expiresAt or None.
+    """
+    if sys.platform != "darwin":
+        return None
+    import subprocess
+    try:
+        out = subprocess.run(
+            ["security", "find-generic-password", "-s", "Claude Code-credentials", "-w"],
+            capture_output=True, text=True, timeout=5, check=False,
+        )
+        if out.returncode != 0 or not out.stdout.strip():
+            return None
+        data = json.loads(out.stdout)
+        oauth = data.get("claudeAiOauth") or {}
+        if oauth.get("accessToken"):
+            return {
+                "accessToken":  oauth["accessToken"],
+                "refreshToken": oauth.get("refreshToken", ""),
+                "expiresAt":    int(oauth.get("expiresAt", 0)),
+                "source":       "claude-code-keychain",
+            }
+    except Exception:
+        return None
+    return None
+
+
+def _read_file_credentials() -> Optional[Dict[str, Any]]:
+    """Read ~/.claude/.credentials.json."""
+    path = Path.home() / ".claude" / ".credentials.json"
+    if not path.exists():
+        return None
+    try:
+        data = json.loads(path.read_text(encoding="utf-8"))
+    except Exception:
+        return None
+    oauth = data.get("claudeAiOauth") or {}
+    if not oauth.get("accessToken"):
+        return None
+    return {
+        "accessToken":  oauth["accessToken"],
+        "refreshToken": oauth.get("refreshToken", ""),
+        "expiresAt":    int(oauth.get("expiresAt", 0)),
+        "source":       "claude-code-file",
+    }
+
+
+def _is_expiring(expires_at_ms: Optional[int], skew_ms: int = 60_000) -> bool:
+    if not expires_at_ms:
+        return False
+    return int(time.time() * 1000) >= (expires_at_ms - skew_ms)
+
+
+def _refresh(refresh_token: str) -> Dict[str, Any]:
+    """Pure refresh — does not touch local files."""
+    if not refresh_token:
+        raise AuthError("no refresh_token; please re-run 'claude /login'")
+    body = urllib.parse.urlencode({
+        "grant_type": "refresh_token",
+        "refresh_token": refresh_token,
+        "client_id": _OAUTH_CLIENT_ID,
+    }).encode()
+    last = None
+    for endpoint in _TOKEN_ENDPOINTS:
+        req = urllib.request.Request(
+            endpoint, data=body, method="POST",
+            headers={
+                "Content-Type": "application/x-www-form-urlencoded",
+                "User-Agent":   f"claude-cli/{_detect_claude_code_version()} (external, cli)",
+            },
+        )
+        try:
+            with urllib.request.urlopen(req, timeout=10) as resp:
+                payload = json.loads(resp.read().decode())
+            return {
+                "accessToken":  payload["access_token"],
+                "refreshToken": payload.get("refresh_token", refresh_token),
+                "expiresAt":    int(time.time() * 1000) + int(payload.get("expires_in", 3600)) * 1000,
+            }
+        except Exception as exc:
+            last = exc
+    raise AuthError(f"Anthropic refresh failed: {last}")
+
+
+class ClaudeCodeAuth:
+    """Claude Code OAuth subscription auth."""
+
+    name = "claude-code"
+
+    def resolve_credentials(self) -> SubscriptionCredentials:
+        # 1. ANTHROPIC_TOKEN / CLAUDE_CODE_OAUTH_TOKEN env (Hermes-style)
+        for env_key in ("ANTHROPIC_TOKEN", "CLAUDE_CODE_OAUTH_TOKEN"):
+            tok = os.environ.get(env_key, "").strip()
+            if tok:
+                return SubscriptionCredentials(
+                    api_key=tok,
+                    base_url="https://api.anthropic.com",
+                    headers=self.headers_for("https://api.anthropic.com", ""),
+                    auth_scheme="bearer",
+                    source=f"env:{env_key}",
+                )
+
+        # 2. Keychain or file
+        creds = _read_keychain_credentials() or _read_file_credentials()
+        if not creds:
+            raise AuthError(
+                "No Claude Code credentials found. Install Claude Code and run "
+                "'claude /login', or set ANTHROPIC_TOKEN."
+            )
+        if _is_expiring(creds["expiresAt"]) and creds.get("refreshToken"):
+            creds.update(_refresh(creds["refreshToken"]))
+        return SubscriptionCredentials(
+            api_key=creds["accessToken"],
+            base_url="https://api.anthropic.com",
+            headers=self.headers_for("https://api.anthropic.com", ""),
+            auth_scheme="bearer",
+            expires_at_ms=creds.get("expiresAt"),
+            source=creds["source"],
+        )
+
+    def refresh(self) -> SubscriptionCredentials:
+        creds = _read_keychain_credentials() or _read_file_credentials()
+        if not creds or not creds.get("refreshToken"):
+            raise AuthError("cannot refresh: no refresh token available")
+        new = _refresh(creds["refreshToken"])
+        return SubscriptionCredentials(
+            api_key=new["accessToken"],
+            base_url="https://api.anthropic.com",
+            headers=self.headers_for("https://api.anthropic.com", ""),
+            auth_scheme="bearer",
+            expires_at_ms=new["expiresAt"],
+            source="claude-code-refreshed",
+        )
+
+    def headers_for(self, base_url: str, model: str) -> Dict[str, str]:
+        # CRITICAL: without these headers Anthropic returns 500s on OAuth tokens.
+        # Match Hermes' OAuth header set exactly. litellm auto-adds oauth-2025-04-20.
+        return {
+            "anthropic-beta": ",".join([
+                "interleaved-thinking-2025-05-14",
+                "fine-grained-tool-streaming-2025-05-14", 
+                "context-1m-2025-08-07",
+                "claude-code-20250219",
+            ]),
+            "user-agent":     f"claude-cli/{_detect_claude_code_version()} (external, cli)",
+            "x-app":          "cli",
+        }
+
+
+register_subscription_provider("claude-code", lambda: ClaudeCodeAuth())
@@ -0,0 +1,69 @@
+"""Codex / ChatGPT subscription — read ~/.codex/auth.json (or CODEX_HOME)."""
+from __future__ import annotations
+
+import json
+import os
+import time
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+from .protocols import AuthError, SubscriptionCredentials
+from .registry import register_subscription_provider
+
+
+_CODEX_BASE_URL = "https://chatgpt.com/backend-api/codex"
+
+
+def _read_codex_tokens() -> Optional[Dict[str, Any]]:
+    home = os.environ.get("CODEX_HOME", "").strip() or str(Path.home() / ".codex")
+    auth_path = Path(home).expanduser() / "auth.json"
+    if not auth_path.is_file():
+        return None
+    try:
+        payload = json.loads(auth_path.read_text(encoding="utf-8"))
+    except Exception:
+        return None
+    tokens = payload.get("tokens") or {}
+    if not tokens.get("access_token") or not tokens.get("refresh_token"):
+        return None
+    return tokens
+
+
+def _is_jwt_expiring(jwt: str, skew_seconds: int = 60) -> bool:
+    """Decode JWT exp claim without verifying signature."""
+    import base64
+    try:
+        _hdr, payload_b64, _sig = jwt.split(".")
+        pad = "=" * (-len(payload_b64) % 4)
+        payload = json.loads(base64.urlsafe_b64decode(payload_b64 + pad))
+        exp = int(payload.get("exp", 0))
+        return exp and (time.time() + skew_seconds) >= exp
+    except Exception:
+        return False  # treat unknown as not-expiring; refresh on actual 401
+
+
+class CodexAuth:
+    name = "codex"
+
+    def resolve_credentials(self) -> SubscriptionCredentials:
+        # EXPERIMENTAL: Codex uses Responses API, not Chat Completions
+        # OpenAI chat-completions requests will 404 against this endpoint
+        raise AuthError(
+            "Codex auth is experimental and requires custom Responses API transport. "
+            "Use a standard OpenAI API key or wait for proper Codex support in a future release."
+        )
+
+    def refresh(self) -> SubscriptionCredentials:
+        # OpenAI Codex tokens are refreshed by the Codex CLI itself; we rely
+        # on the CLI being run periodically. A direct refresh would require
+        # replicating their device-flow client — out of scope for v1.
+        return self.resolve_credentials()
+
+    def headers_for(self, base_url: str, model: str) -> Dict[str, str]:
+        return {
+            "openai-beta": "responses=v1",
+            "user-agent":  "codex-cli/0.1.0 (external)",
+        }
+
+
+register_subscription_provider("codex", lambda: CodexAuth())
@@ -0,0 +1,51 @@
+"""Gemini CLI OAuth — read ~/.gemini/oauth_creds.json."""
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+from .protocols import AuthError, SubscriptionCredentials
+from .registry import register_subscription_provider
+
+
+_GEMINI_BASE_URL = "https://cloudcode-pa.googleapis.com/v1internal"
+
+
+def _read_gemini_tokens() -> Optional[Dict[str, Any]]:
+    """Read Gemini CLI OAuth credentials."""
+    auth_path = Path.home() / ".gemini" / "oauth_creds.json"
+    if not auth_path.is_file():
+        return None
+    try:
+        payload = json.loads(auth_path.read_text(encoding="utf-8"))
+    except Exception:
+        return None
+    if not payload.get("access_token"):
+        return None
+    return payload
+
+
+class GeminiCliAuth:
+    name = "gemini-cli"
+
+    def resolve_credentials(self) -> SubscriptionCredentials:
+        # EXPERIMENTAL: Gemini CLI uses Code Assist API, not OpenAI chat format
+        # Standard OpenAI chat-completions requests will not work with cloudcode-pa endpoints
+        raise AuthError(
+            "Gemini CLI auth is experimental and requires custom Code Assist transport. "
+            "Use standard Gemini API keys or wait for proper Code Assist support in a future release."
+        )
+
+    def refresh(self) -> SubscriptionCredentials:
+        # Gemini CLI handles refresh internally
+        return self.resolve_credentials()
+
+    def headers_for(self, base_url: str, model: str) -> Dict[str, str]:
+        return {
+            "user-agent": "gemini-cli/1.0.0 (external)",
+        }
+
+
+register_subscription_provider("gemini-cli", lambda: GeminiCliAuth())
@@ -0,0 +1,41 @@
+"""Subscription-auth protocol — what every provider must implement."""
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, Optional, Protocol, runtime_checkable
+
+
+@dataclass
+class SubscriptionCredentials:
+    """Resolved credentials ready to inject into an LLM client."""
+    api_key: str                            # access_token (or "" for SDK-default chains like AWS)
+    base_url: str = ""                       # provider endpoint
+    headers: Dict[str, str] = field(default_factory=dict)  # extra headers (UA, x-app, betas)
+    auth_scheme: str = "bearer"              # "bearer" | "x-api-key" | "sdk-default"
+    expires_at_ms: Optional[int] = None     # epoch ms; None = no expiry tracked
+    source: str = ""                         # "claude-code-keychain", "codex-cli-file", etc.
+
+
+@runtime_checkable
+class SubscriptionAuthProtocol(Protocol):
+    """Discover, refresh, and inject subscription credentials."""
+
+    def resolve_credentials(self) -> SubscriptionCredentials:
+        """Return current valid credentials, refreshing if expiring soon.
+
+        Raises:
+            AuthError: if no usable credentials can be resolved.
+        """
+        ...
+
+    def refresh(self) -> SubscriptionCredentials:
+        """Force a refresh and return the new credentials."""
+        ...
+
+    def headers_for(self, base_url: str, model: str) -> Dict[str, str]:
+        """Provider-specific headers (UA, beta flags) keyed off endpoint/model."""
+        ...
+
+
+class AuthError(RuntimeError):
+    """Raised when subscription credentials cannot be resolved."""
@@ -0,0 +1,58 @@
+"""Qwen CLI OAuth — read ~/.qwen/oauth_creds.json."""
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+from .protocols import AuthError, SubscriptionCredentials
+from .registry import register_subscription_provider
+
+
+_QWEN_BASE_URL = "https://portal.qwen.ai/v1"
+
+
+def _read_qwen_tokens() -> Optional[Dict[str, Any]]:
+    """Read Qwen CLI OAuth credentials."""
+    auth_path = Path.home() / ".qwen" / "oauth_creds.json"
+    if not auth_path.is_file():
+        return None
+    try:
+        payload = json.loads(auth_path.read_text(encoding="utf-8"))
+    except Exception:
+        return None
+    if not payload.get("access_token"):
+        return None
+    return payload
+
+
+class QwenCliAuth:
+    name = "qwen-cli"
+
+    def resolve_credentials(self) -> SubscriptionCredentials:
+        tokens = _read_qwen_tokens()
+        if not tokens:
+            raise AuthError(
+                "No Qwen CLI credentials at ~/.qwen/oauth_creds.json. "
+                "Install Qwen CLI and run 'qwen login'."
+            )
+        return SubscriptionCredentials(
+            api_key=tokens["access_token"],
+            base_url=os.environ.get("PRAISONAI_QWEN_BASE_URL", "").strip() or _QWEN_BASE_URL,
+            headers=self.headers_for(_QWEN_BASE_URL, ""),
+            auth_scheme="bearer",
+            source="qwen-cli-file",
+        )
+
+    def refresh(self) -> SubscriptionCredentials:
+        # Qwen CLI handles refresh internally
+        return self.resolve_credentials()
+
+    def headers_for(self, base_url: str, model: str) -> Dict[str, str]:
+        return {
+            "user-agent": "qwen-cli/1.0.0 (external)",
+        }
+
+
+register_subscription_provider("qwen-cli", lambda: QwenCliAuth())
@@ -0,0 +1,64 @@
+"""Thread-safe provider registry for subscription auth."""
+from __future__ import annotations
+
+import threading
+from typing import Callable, Dict, List, Optional
+
+from .protocols import AuthError, SubscriptionAuthProtocol, SubscriptionCredentials
+
+_REGISTRY: Dict[str, Callable[[], SubscriptionAuthProtocol]] = {}
+_LOCK = threading.RLock()
+_BUILTIN_REGISTERED = False
+
+
+def register_subscription_provider(
+    provider_id: str,
+    factory: Callable[[], SubscriptionAuthProtocol],
+) -> None:
+    """Register a new subscription-auth provider.
+
+    Args:
+        provider_id: short id e.g. "claude-code", "codex", "gemini-cli".
+        factory: zero-arg callable returning a SubscriptionAuthProtocol impl.
+    """
+    with _LOCK:
+        _REGISTRY[provider_id] = factory
+
+
+def list_subscription_providers() -> List[str]:
+    _ensure_builtins()
+    with _LOCK:
+        return sorted(_REGISTRY)
+
+
+def resolve_subscription_credentials(provider_id: str) -> SubscriptionCredentials:
+    """Look up provider by id and resolve its credentials."""
+    _ensure_builtins()
+    with _LOCK:
+        factory = _REGISTRY.get(provider_id)
+    if factory is None:
+        raise AuthError(
+            f"Unknown subscription provider {provider_id!r}. "
+            f"Registered: {list_subscription_providers()}"
+        )
+    return factory().resolve_credentials()
+
+
+def get_subscription_provider(provider_id: str) -> Optional[SubscriptionAuthProtocol]:
+    """Return a new provider instance for *provider_id*, or None if not registered."""
+    _ensure_builtins()
+    with _LOCK:
+        factory = _REGISTRY.get(provider_id)
+    return factory() if factory is not None else None
+
+
+def _ensure_builtins() -> None:
+    """Lazy register built-in providers on first use."""
+    global _BUILTIN_REGISTERED
+    with _LOCK:
+        if _BUILTIN_REGISTERED:
+            return
+        # Import modules inside the lock to prevent race conditions
+        from . import claude_code, codex, gemini_cli, qwen_cli   # noqa: F401
+        # Each module registers itself at import time.
+        _BUILTIN_REGISTERED = True
@@ -6,6 +6,7 @@ Manages background task execution.

 import asyncio
 import logging
+import threading
 from praisonaiagents._logging import get_logger
 from typing import Optional, List, Dict, Any, Callable, Union
 from datetime import datetime
@@ -53,13 +54,16 @@ class BackgroundRunner:
        self._tasks: Dict[str, BackgroundTask] = {}
        # Lazily create asyncio.Semaphore() to avoid Python 3.9 event loop issues
        self._semaphore: Optional[asyncio.Semaphore] = None
+        self._semaphore_lock: threading.Lock = threading.Lock()  # Thread-safe initialization
        self._running = False
        self._cleanup_task: Optional[asyncio.Task] = None
    
    def _get_semaphore(self) -> asyncio.Semaphore:
        """Get or create the semaphore lazily (Python 3.9 compatible)."""
        if self._semaphore is None:
-            self._semaphore = asyncio.Semaphore(self.config.max_concurrent_tasks)
+            with self._semaphore_lock:  # Thread-safe initialization
+                if self._semaphore is None:  # Double-check after acquiring lock
+                    self._semaphore = asyncio.Semaphore(self.config.max_concurrent_tasks)
        return self._semaphore
    
    @property
@@ -403,7 +407,7 @@ def _get_bg_loop() -> asyncio.AbstractEventLoop:
    """Return a running event loop on a background daemon thread.

    Created once on first call; reused thereafter.  The thread is a
-    daemon so it won't prevent process exit.
+    daemon so it won't prevent process exit, but includes graceful shutdown.
    """
    global _bg_loop
    if _bg_loop is not None and _bg_loop.is_running():
@@ -420,3 +424,31 @@ def _get_bg_loop() -> asyncio.AbstractEventLoop:
        t.start()

    return _bg_loop
+
+
+def _shutdown_bg_loop():
+    """Gracefully shutdown the background event loop."""
+    global _bg_loop
+    if _bg_loop is None or not _bg_loop.is_running():
+        return
+
+    async def _cancel_pending():
+        current = asyncio.current_task()
+        pending = [t for t in asyncio.all_tasks() if t is not current]
+        for t in pending:
+            t.cancel()
+        if pending:
+            await asyncio.gather(*pending, return_exceptions=True)
+
+    try:
+        fut = asyncio.run_coroutine_threadsafe(_cancel_pending(), _bg_loop)
+        fut.result(timeout=2)
+    except Exception:
+        pass
+    finally:
+        _bg_loop.call_soon_threadsafe(_bg_loop.stop)
+
+
+# Register cleanup on process exit
+import atexit
+atexit.register(_shutdown_bg_loop)
@@ -5,7 +5,9 @@ Provides configuration dataclasses for bot settings.
 """

 from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Literal, Optional
+
+from .pairing_types import UnknownUserPolicy


@dataclass
@@ -48,16 +50,32 @@ class BotConfig:
    # Group message policy
    group_policy: str = "mention_only"  # respond_all, mention_only, command_only
    
-    # Default tools enabled for all bots
+    # Default safe tools (auto-injected for bots with no tools configured)
+    # With workspace scoping, file operations are now safe by construction
    default_tools: List[str] = field(default_factory=lambda: [
-        "execute_command", "search_web", "web_crawl",
-        "schedule_add", "schedule_list", "schedule_remove",
+        # Web (existing)
+        "search_web", "web_crawl",
+        # Memory / learning (existing)
        "store_memory", "search_memory",
        "store_learning", "search_learning",
+        # Scheduling (existing)
+        "schedule_add", "schedule_list", "schedule_remove",
+        # Clarify tool (new from main)
+        "clarify",
+        # Files — NEW (workspace-scoped, safe by construction)
+        "read_file", "write_file", "edit_file", "list_files", "search_files",
+        # Planning — NEW
+        "todo_add", "todo_list", "todo_update",
+        # Skills (self-improving) — NEW
+        "skills_list", "skill_view", "skill_manage",
+        # Delegation & session tools are intentionally NOT auto-injected yet;
+        # their reference implementations are placeholders. Users can opt in
+        # via BotConfig(default_tools=[..., "delegate_task", "session_search"]).
    ])
    
    # Auto-approve tool calls (useful for trusted environments)
-    auto_approve_tools: bool = False  # If True, skip confirmation for tool execution
+    # Default to True for chat bots - they can't show CLI approval prompts
+    auto_approve_tools: bool = True  # If True, skip confirmation for safe tool execution
    
    # Inbound message debounce (ms). Coalesces rapid messages from same user.
    # 0 = disabled (default). Recommended: 1000-2000 for chat bots.
@@ -73,6 +91,24 @@ class BotConfig:
    # When set, stale sessions older than this are auto-reaped.
    session_ttl: int = 0
    
+    # Workspace settings for file operation containment and security
+    workspace_dir: Optional[str] = None  # default: ~/.praisonai/workspaces/<scope>/<session_key>
+    workspace_access: str = "rw"  # "rw" (read-write) | "ro" (read-only) | "none" (copy-on-write sandbox)
+    workspace_scope: str = "session"  # "shared" | "session" | "user" | "agent"
+    
+    # Unknown user policy: "deny" (default), "pair", or "allow"
+    unknown_user_policy: UnknownUserPolicy = "deny"
+    
+    # Owner user ID for pairing approvals (platform-specific format)
+    owner_user_id: Optional[str] = None
+
+    def __post_init__(self) -> None:
+        if self.unknown_user_policy not in {"deny", "pair", "allow"}:
+            raise ValueError(
+                f"unknown_user_policy must be one of: deny, pair, allow. Got: {self.unknown_user_policy}"
+            )
+    
+    
    metadata: Dict[str, Any] = field(default_factory=dict)
    
    def to_dict(self) -> Dict[str, Any]:
@@ -100,6 +136,11 @@ class BotConfig:
            "ack_emoji": self.ack_emoji,
            "done_emoji": self.done_emoji,
            "session_ttl": self.session_ttl,
+            "workspace_dir": self.workspace_dir,
+            "workspace_access": self.workspace_access,
+            "workspace_scope": self.workspace_scope,
+            "unknown_user_policy": self.unknown_user_policy,
+            "owner_user_id": "***" if self.owner_user_id else None,
            "metadata": self.metadata,
        }
    
@@ -0,0 +1,37 @@
+"""
+Pairing types and protocols for bot user approval.
+
+Contains lightweight types and protocols for the pairing approval system.
+Heavy implementations are in the wrapper package.
+"""
+
+from dataclasses import dataclass
+from typing import Literal, Optional
+
+# Policy for handling unknown users
+UnknownUserPolicy = Literal["deny", "pair", "allow"]
+
+
+@dataclass
+class PairingReply:
+    """Reply data for pairing approval requests."""
+    
+    user_name: str
+    user_id: str
+    channel: str
+    code: str
+    message: str = ""
+    
+    def __post_init__(self):
+        if not self.message:
+            self.message = f"User {self.user_name} wants to chat. Approve access?"
+
+
+@dataclass
+class PairingApprovalResult:
+    """Result of a pairing approval action."""
+    
+    success: bool
+    message: str
+    user_id: Optional[str] = None
+    channel: Optional[str] = None
@@ -9,6 +9,7 @@ import logging
 from praisonaiagents._logging import get_logger
 import threading
 from typing import Any, Callable, Dict, List, Optional, Set, Union
+from ..utils.async_bridge import run_coroutine_from_any_context
 from dataclasses import dataclass, field
 from .event import Event, EventType

@@ -209,13 +210,13 @@ class EventBus:
        for sub in subscribers:
            try:
                if sub.is_async:
-                    # Schedule async callback
+                    # Schedule async callback safely
                    try:
                        loop = asyncio.get_running_loop()
                        loop.create_task(sub.callback(event))
                    except RuntimeError:
-                        # No running loop, run synchronously
-                        asyncio.run(sub.callback(event))
+                        # No running loop, use safe async bridge
+                        run_coroutine_from_any_context(sub.callback(event))
                else:
                    sub.callback(event)
            except Exception as e:
@@ -485,9 +485,17 @@ class CheckpointService:
        if len(self._checkpoints) <= self.config.max_checkpoints:
            return
        
-        # Keep only the most recent checkpoints
-        # Note: This doesn't actually delete git history, just our tracking
-        self._checkpoints = self._checkpoints[:self.config.max_checkpoints]
+        # Calculate how many to remove
+        num_to_remove = len(self._checkpoints) - self.config.max_checkpoints
+        
+        # Keep only the most recent checkpoints in memory (newest-last semantics)
+        # Since save() appends (newest last), keep the last N entries
+        self._checkpoints = self._checkpoints[-self.config.max_checkpoints:]
+        
+        logger.info(f"Pruned {num_to_remove} old checkpoints to stay under limit of {self.config.max_checkpoints}")
+        
+        # Emit pruning event for any cleanup hooks
+        self._emit(CheckpointEvent.CHECKPOINTS_PRUNED, {"action": "pruned", "removed_count": num_to_remove})
    
    async def get_checkpoint(self, checkpoint_id: str) -> Optional[Checkpoint]:
        """Get a specific checkpoint by ID."""
@@ -26,6 +26,7 @@ class CheckpointEvent(str, Enum):
    INITIALIZED = "initialized"
    CHECKPOINT_CREATED = "checkpoint_created"
    CHECKPOINT_RESTORED = "checkpoint_restored"
+    CHECKPOINTS_PRUNED = "checkpoints_pruned"
    ERROR = "error"


@@ -0,0 +1,37 @@
+"""CLI Backend Protocol for PraisonAI Agents.
+
+Provides protocol-driven core for integrating external CLI tools as agent backends.
+
+This module follows AGENTS.md protocol-driven design:
+- Core SDK contains only protocols and dataclasses
+- Heavy implementations live in wrapper (praisonai package)
+- Zero optional dependencies at import time
+"""
+
+def __getattr__(name: str):
+    """Lazy loading for CLI backend components."""
+    if name == "CliBackendProtocol":
+        from .protocols import CliBackendProtocol
+        return CliBackendProtocol
+    elif name == "CliBackendConfig":
+        from .protocols import CliBackendConfig
+        return CliBackendConfig
+    elif name == "CliSessionBinding":
+        from .protocols import CliSessionBinding
+        return CliSessionBinding
+    elif name == "CliBackendResult":
+        from .protocols import CliBackendResult
+        return CliBackendResult
+    elif name == "CliBackendDelta":
+        from .protocols import CliBackendDelta
+        return CliBackendDelta
+    else:
+        raise AttributeError(f"module '{__name__}' has no attribute '{name}'")
+
+__all__ = [
+    "CliBackendProtocol",
+    "CliBackendConfig", 
+    "CliSessionBinding",
+    "CliBackendResult",
+    "CliBackendDelta"
+]
@@ -0,0 +1,135 @@
+"""CLI Backend protocols and data structures.
+
+Protocol-driven design following AGENTS.md:
+- Lightweight protocols only (no implementations)
+- Dataclasses for configuration
+- Async-first with proper typing
+"""
+
+from dataclasses import dataclass, field
+from typing import Protocol, AsyncIterator, Optional, List, Dict, Any, runtime_checkable
+
+
+@dataclass
+class CliBackendConfig:
+    """Declarative CLI backend configuration.
+    
+    Mirrors OpenClaw's CliBackendConfig for cross-ecosystem compatibility.
+    """
+    # Core command
+    command: str  # e.g., "claude", "codex", "gemini"
+    args: List[str] = field(default_factory=list)
+    
+    # Session management
+    resume_args: Optional[List[str]] = None  # supports "{session_id}" placeholder
+    session_arg: Optional[str] = None  # e.g., "--session-id"
+    session_mode: str = "none"  # "always" | "existing" | "none"
+    session_id_fields: List[str] = field(default_factory=list)
+    
+    # Input/output format
+    output: str = "text"  # "text" | "json" | "jsonl"
+    input: str = "arg"  # "arg" | "stdin"
+    max_prompt_arg_chars: Optional[int] = None
+    
+    # Model configuration
+    model_arg: Optional[str] = None  # e.g., "--model"
+    model_aliases: Dict[str, str] = field(default_factory=dict)
+    
+    # System prompt handling
+    system_prompt_arg: Optional[str] = None  # e.g., "--append-system-prompt"
+    system_prompt_when: str = "always"  # "first" | "always" | "never"
+    system_prompt_mode: str = "append"  # "append" | "replace"
+    
+    # Image support
+    image_arg: Optional[str] = None  # e.g., "--image"
+    image_mode: str = "repeat"  # "repeat" | "list"
+    
+    # Environment
+    clear_env: List[str] = field(default_factory=list)  # Env vars to clear
+    env: Dict[str, str] = field(default_factory=dict)  # Env vars to set
+    
+    # Advanced features
+    live_session: Optional[str] = None  # e.g., "claude-stdio"
+    bundle_mcp: bool = False
+    bundle_mcp_mode: Optional[str] = None  # e.g., "claude-config-file"
+    serialize: bool = False  # Queue operations to avoid conflicts
+    
+    # Timeouts
+    no_output_timeout_ms: Optional[int] = None
+    timeout_ms: int = 300_000
+
+
+@dataclass
+class CliSessionBinding:
+    """Session binding for CLI backend state tracking."""
+    session_id: Optional[str] = None
+    auth_profile_id: Optional[str] = None
+    system_prompt_hash: Optional[str] = None
+    mcp_config_hash: Optional[str] = None
+    is_resume: bool = False  # Whether this is resuming an existing session
+
+
+@dataclass
+class CliBackendResult:
+    """Result from CLI backend execution."""
+    content: str
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    session_id: Optional[str] = None
+    error: Optional[str] = None
+
+
+@dataclass
+class CliBackendDelta:
+    """Streaming delta from CLI backend."""
+    type: str  # "text" | "tool_call" | "thinking" | "error"
+    content: str = ""
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@runtime_checkable
+class CliBackendProtocol(Protocol):
+    """Protocol for CLI backend implementations.
+    
+    Any object implementing these methods can serve as an Agent backend.
+    Follows typing.Protocol pattern from AGENTS.md.
+    """
+    config: CliBackendConfig
+    
+    async def execute(
+        self, 
+        prompt: str, 
+        *,
+        session: Optional[CliSessionBinding] = None,
+        images: Optional[List[str]] = None,
+        system_prompt: Optional[str] = None,
+        **kwargs
+    ) -> CliBackendResult:
+        """Execute a single prompt and return result.
+        
+        Args:
+            prompt: User prompt/query
+            session: Session binding for state management
+            images: List of image paths for multimodal prompts
+            system_prompt: System prompt override
+            **kwargs: Additional backend-specific options
+            
+        Returns:
+            CliBackendResult with response content and metadata
+        """
+        ...
+    
+    async def stream(
+        self, 
+        prompt: str, 
+        **kwargs
+    ) -> AsyncIterator[CliBackendDelta]:
+        """Stream response deltas from CLI backend.
+        
+        Args:
+            prompt: User prompt/query
+            **kwargs: Additional options (session, images, etc.)
+            
+        Yields:
+            CliBackendDelta objects with incremental response content
+        """
+        ...
@@ -42,6 +42,7 @@ class MemoryBackend(str, Enum):
    FILE = "file"
    SQLITE = "sqlite"
    REDIS = "redis"
+    VALKEY = "valkey"
    POSTGRES = "postgres"
    MEM0 = "mem0"
    MONGODB = "mongodb"
@@ -120,6 +121,11 @@ class LearnConfig:
    feedback: bool = False     # Outcome signals
    improvements: bool = False # Self-improvement proposals
    
+    # Nudge mechanism (self-improving agent loop)
+    nudge_interval: int = 0    # 0=disabled; N=nudge every N turns  
+    nudge_min_tool_iters: int = 3  # Only nudge if agent did real work
+    propose_skills: bool = False   # Enable skill_manage tool (propose mode)
+    
    # Learning mode
    mode: Union[str, LearnMode] = LearnMode.DISABLED  # How to extract learnings
    
@@ -144,6 +150,9 @@ class LearnConfig:
            "decisions": self.decisions,
            "feedback": self.feedback,
            "improvements": self.improvements,
+            "nudge_interval": self.nudge_interval,
+            "nudge_min_tool_iters": self.nudge_min_tool_iters,
+            "propose_skills": self.propose_skills,
            "mode": self.mode.value if isinstance(self.mode, LearnMode) else self.mode,
            "scope": self.scope.value if isinstance(self.scope, LearnScope) else self.scope,
            "backend": self.backend.value if isinstance(self.backend, LearnBackend) else self.backend,
@@ -735,6 +744,11 @@ class ExecutionConfig:
    # Action when budget exceeded: "stop" (default) raises BudgetExceededError,
    # "warn" logs warning but continues, or callable(total_cost, max_budget).
    on_budget_exceeded: Any = "stop"
+    
+    # Parallel tool execution (Gap 2): Enable parallel execution of batched LLM tool calls
+    # When True, multiple tool calls from LLM are executed concurrently instead of sequentially
+    # Default False preserves existing behavior for backward compatibility
+    parallel_tool_calls: bool = False

    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary."""
@@ -749,6 +763,7 @@ class ExecutionConfig:
            "context_compaction": self.context_compaction,
            "max_context_tokens": self.max_context_tokens,
            "max_budget": self.max_budget,
+            "parallel_tool_calls": self.parallel_tool_calls,
        }


@@ -7,7 +7,7 @@ Inspired by Agno's SessionContextStore pattern.

 from dataclasses import dataclass, field, asdict
 from typing import Any, Dict, List, Optional
-from datetime import datetime
+from datetime import datetime, timezone
 import json
 import logging
 from praisonaiagents._logging import get_logger
@@ -97,8 +97,8 @@ class SessionContextTracker:
        
        self._state = SessionState(
            session_id=self.session_id,
-            created_at=datetime.utcnow().isoformat() + "Z",
-            updated_at=datetime.utcnow().isoformat() + "Z",
+            created_at=datetime.now(timezone.utc).isoformat(),
+            updated_at=datetime.now(timezone.utc).isoformat(),
        )
    
    def _generate_session_id(self) -> str:
@@ -163,7 +163,7 @@ class SessionContextTracker:
    
    def _mark_updated(self) -> None:
        """Update the updated_at timestamp."""
-        self._state.updated_at = datetime.utcnow().isoformat() + "Z"
+        self._state.updated_at = datetime.now(timezone.utc).isoformat()
        self._state.turn_count += 1
    
    def to_context_string(self) -> str:
@@ -257,7 +257,7 @@ Current messages take precedence if there's any conflict with this summary.
        self._state = SessionState(
            session_id=self.session_id,
            created_at=self._state.created_at,
-            updated_at=datetime.utcnow().isoformat() + "Z",
+            updated_at=datetime.now(timezone.utc).isoformat(),
        )
    
    def to_dict(self) -> Dict[str, Any]:
@@ -292,6 +292,44 @@ class HandoffError(PraisonAIError):
        self.target_agent = target_agent


+class PraisonAIConfigError(PraisonAIError):
+    """
+    Configuration error (missing API keys, invalid settings, etc).
+    
+    Usually indicates setup issues that require user action.
+    """
+    
+    def __init__(
+        self,
+        message: str,
+        config_key: Optional[str] = None,
+        agent_id: str = "unknown",
+        run_id: Optional[str] = None,
+        is_retryable: bool = False,  # Config errors need user intervention
+        remediation_hint: Optional[str] = None,
+        context: Optional[Dict[str, Any]] = None
+    ):
+        context = context or {}
+        if config_key:
+            context.update({"config_key": config_key})
+            if remediation_hint is None:
+                remediation_hint = f"Set {config_key} or run the setup wizard before retrying."
+        if remediation_hint:
+            context["remediation_hint"] = remediation_hint
+            message = f"{message} Remediation: {remediation_hint}"
+        
+        super().__init__(
+            message,
+            agent_id=agent_id,
+            run_id=run_id,
+            error_category="validation",  # Configuration is a type of validation error
+            is_retryable=is_retryable,
+            context=context
+        )
+        self.config_key = config_key
+        self.remediation_hint = remediation_hint
+
+
 # Specialized handoff errors (maintain backward compatibility)
 class HandoffCycleError(HandoffError):
    """Circular handoff dependency detected."""
@@ -341,5 +379,6 @@ __all__ = [
    "HandoffError",
    "HandoffCycleError", 
    "HandoffDepthError", 
-    "HandoffTimeoutError"
+    "HandoffTimeoutError",
+    "PraisonAIConfigError"
 ]
@@ -12,7 +12,7 @@ import os
 import logging
 from praisonaiagents._logging import get_logger
 from dataclasses import dataclass, field, asdict
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import Any, Dict, List, Optional

 logger = get_logger(__name__)
@@ -39,7 +39,7 @@ class GradeResult:
    input_text: str = ""
    output: str = ""
    expected_output: Optional[str] = None
-    timestamp: str = field(default_factory=lambda: datetime.utcnow().isoformat())
+    timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary."""
@@ -55,7 +55,7 @@ class GradeResult:
            input_text=data.get("input_text", ""),
            output=data.get("output", ""),
            expected_output=data.get("expected_output"),
-            timestamp=data.get("timestamp", datetime.utcnow().isoformat()),
+            timestamp=data.get("timestamp", datetime.now(timezone.utc).isoformat()),
        )

 class BaseLLMGrader:
@@ -18,8 +18,25 @@ from .protocols import (
    GatewayEvent,
    GatewayMessage,
    EventType,
+    # Push protocols and dataclasses
+    PushChannelProtocol,
+    PresenceProtocol,
+    DeliveryGuaranteeProtocol,
+    ChannelInfo,
+    PresenceInfo,
+)
+from .config import (
+    GatewayConfig,
+    SessionConfig,
+    ChannelRouteConfig,
+    MultiChannelGatewayConfig,
+    # Push config
+    PushConfig,
+    RedisConfig,
+    PresenceConfig,
+    DeliveryConfig,
+    PollingConfig,
 )
-from .config import GatewayConfig, SessionConfig, ChannelRouteConfig, MultiChannelGatewayConfig

 # Lazy loading cache
 _lazy_cache = {}
@@ -68,11 +85,22 @@ __all__ = [
    "GatewayEvent",
    "GatewayMessage",
    "EventType",
+    # Push protocols (always available)
+    "PushChannelProtocol",
+    "PresenceProtocol",
+    "DeliveryGuaranteeProtocol",
+    "ChannelInfo",
+    "PresenceInfo",
    # Config (always available)
    "GatewayConfig",
    "SessionConfig",
    "ChannelRouteConfig",
    "MultiChannelGatewayConfig",
+    "PushConfig",
+    "RedisConfig",
+    "PresenceConfig",
+    "DeliveryConfig",
+    "PollingConfig",
    # Implementations (lazy loaded from praisonai wrapper)
    "WebSocketGateway",
    "GatewaySession",
@@ -37,6 +37,158 @@ class SessionConfig:
        }


+# ---------------------------------------------------------------------------
+# Push notification configuration
+# ---------------------------------------------------------------------------
+
+@dataclass
+class RedisConfig:
+    """Redis connection configuration for push service scaling.
+    
+    Attributes:
+        url: Full Redis URL (takes precedence over host/port)
+        host: Redis host
+        port: Redis port
+        db: Redis database number
+        password: Redis password
+        prefix: Key prefix namespace
+        max_connections: Connection pool size
+    """
+    
+    url: Optional[str] = None
+    host: str = "localhost"
+    port: int = 6379
+    db: int = 0
+    password: Optional[str] = None
+    prefix: str = "praison:push:"
+    max_connections: int = 20
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary (hides sensitive data)."""
+        return {
+            "url": "***" if self.url else None,
+            "host": self.host,
+            "port": self.port,
+            "db": self.db,
+            "password": "***" if self.password else None,
+            "prefix": self.prefix,
+            "max_connections": self.max_connections,
+        }
+
+
+@dataclass
+class PresenceConfig:
+    """Configuration for presence tracking.
+    
+    Attributes:
+        enabled: Toggle presence tracking
+        heartbeat_interval: Expected heartbeat frequency from clients (seconds)
+        offline_timeout: Mark offline after this many seconds without heartbeat
+        broadcast_changes: Broadcast presence changes to subscribed channels
+    """
+    
+    enabled: bool = True
+    heartbeat_interval: int = 15
+    offline_timeout: int = 45
+    broadcast_changes: bool = True
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "enabled": self.enabled,
+            "heartbeat_interval": self.heartbeat_interval,
+            "offline_timeout": self.offline_timeout,
+            "broadcast_changes": self.broadcast_changes,
+        }
+
+
+@dataclass
+class DeliveryConfig:
+    """Configuration for at-least-once delivery guarantees.
+    
+    Attributes:
+        enabled: Toggle delivery guarantees
+        ack_timeout: Seconds to wait for ACK before retrying
+        max_retries: Maximum retry attempts
+        retry_backoff: Exponential backoff multiplier
+        message_ttl: How long to retain unacknowledged messages (seconds)
+        store_backend: Message store backend ("memory" or "redis")
+    """
+    
+    enabled: bool = True
+    ack_timeout: int = 30
+    max_retries: int = 3
+    retry_backoff: float = 2.0
+    message_ttl: int = 86400
+    store_backend: str = "memory"
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "enabled": self.enabled,
+            "ack_timeout": self.ack_timeout,
+            "max_retries": self.max_retries,
+            "retry_backoff": self.retry_backoff,
+            "message_ttl": self.message_ttl,
+            "store_backend": self.store_backend,
+        }
+
+
+@dataclass
+class PollingConfig:
+    """Configuration for HTTP long-polling fallback.
+    
+    Attributes:
+        enabled: Toggle polling fallback
+        long_poll_timeout: Long-poll hang duration (seconds)
+        max_batch_size: Max messages per poll response
+    """
+    
+    enabled: bool = True
+    long_poll_timeout: int = 30
+    max_batch_size: int = 100
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "enabled": self.enabled,
+            "long_poll_timeout": self.long_poll_timeout,
+            "max_batch_size": self.max_batch_size,
+        }
+
+
+@dataclass
+class PushConfig:
+    """Top-level configuration for the push notification service.
+    
+    All push capabilities are opt-in. When enabled=False (default),
+    zero overhead is added to the gateway.
+    
+    Attributes:
+        enabled: Feature toggle (push is opt-in)
+        redis: Redis config for cross-server scaling (None = local-only)
+        presence: Presence tracking settings
+        delivery: Delivery guarantee settings
+        polling: Polling fallback settings
+    """
+    
+    enabled: bool = False
+    redis: Optional[RedisConfig] = None
+    presence: PresenceConfig = field(default_factory=PresenceConfig)
+    delivery: DeliveryConfig = field(default_factory=DeliveryConfig)
+    polling: PollingConfig = field(default_factory=PollingConfig)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "enabled": self.enabled,
+            "redis": self.redis.to_dict() if self.redis else None,
+            "presence": self.presence.to_dict(),
+            "delivery": self.delivery.to_dict(),
+            "polling": self.polling.to_dict(),
+        }
+
+
@dataclass
 class GatewayConfig:
    """Configuration for the gateway server.
@@ -45,6 +197,7 @@ class GatewayConfig:
        host: Host to bind to
        port: Port to listen on
        cors_origins: Allowed CORS origins
+        allowed_origins: Allowed origins for WebSocket connections (CSWSH defense)
        auth_token: Optional authentication token
        max_connections: Maximum concurrent connections
        max_sessions_per_agent: Maximum sessions per agent (0 = unlimited)
@@ -53,11 +206,14 @@ class GatewayConfig:
        reconnect_timeout: Time to wait for reconnection before closing session
        ssl_cert: Path to SSL certificate (for HTTPS/WSS)
        ssl_key: Path to SSL key
+        push: Push notification service configuration
    """
    
    host: str = "127.0.0.1"
    port: int = 8765
+    bind_host: Optional[str] = None
    cors_origins: List[str] = field(default_factory=lambda: [])
+    allowed_origins: List[str] = field(default_factory=lambda: [])
    auth_token: Optional[str] = None
    max_connections: int = 1000
    max_sessions_per_agent: int = 0  # 0 = unlimited
@@ -66,6 +222,12 @@ class GatewayConfig:
    reconnect_timeout: int = 60
    ssl_cert: Optional[str] = None
    ssl_key: Optional[str] = None
+    push: PushConfig = field(default_factory=PushConfig)
+
+    def __post_init__(self) -> None:
+        """Post-initialization to set bind_host from host if not specified."""
+        if self.bind_host is None:
+            self.bind_host = self.host
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary (hides sensitive data)."""
@@ -73,6 +235,7 @@ class GatewayConfig:
            "host": self.host,
            "port": self.port,
            "cors_origins": self.cors_origins,
+            "allowed_origins": self.allowed_origins,
            "auth_token": "***" if self.auth_token else None,
            "max_connections": self.max_connections,
            "max_sessions_per_agent": self.max_sessions_per_agent,
@@ -80,6 +243,7 @@ class GatewayConfig:
            "heartbeat_interval": self.heartbeat_interval,
            "reconnect_timeout": self.reconnect_timeout,
            "ssl_enabled": bool(self.ssl_cert and self.ssl_key),
+            "push": self.push.to_dict(),
        }
    
    @property
@@ -205,6 +369,7 @@ class MultiChannelGatewayConfig:
            host=gw_data.get("host", "127.0.0.1"),
            port=gw_data.get("port", 8765),
            cors_origins=gw_data.get("cors_origins", []),
+            allowed_origins=gw_data.get("allowed_origins", []),
            auth_token=gw_data.get("auth_token"),
            max_connections=gw_data.get("max_connections", 1000),
        )
@@ -20,6 +20,7 @@ from typing import (
    Callable,
    Dict,
    List,
+    Literal,
    Optional,
    Protocol,
    Union,
@@ -27,6 +28,7 @@ from typing import (
 )

 if TYPE_CHECKING:
+    from praisonai.gateway.pairing import PairedChannel
    from ..agent import Agent


@@ -62,6 +64,26 @@ class EventType(str, Enum):
    HEALTH = "health"
    ERROR = "error"
    BROADCAST = "broadcast"
+    
+    # Push channel events
+    CHANNEL_SUBSCRIBE = "channel_subscribe"
+    CHANNEL_UNSUBSCRIBE = "channel_unsubscribe"
+    CHANNEL_MESSAGE = "channel_message"
+    CHANNEL_CREATED = "channel_created"
+    CHANNEL_DELETED = "channel_deleted"
+    
+    # Presence events
+    PRESENCE_JOIN = "presence_join"
+    PRESENCE_LEAVE = "presence_leave"
+    PRESENCE_UPDATE = "presence_update"
+    
+    # Delivery events
+    MESSAGE_NACK = "message_nack"
+    DELIVERY_RETRY = "delivery_retry"
+    
+    # Polling events
+    POLL_REQUEST = "poll_request"
+    POLL_RESPONSE = "poll_response"


@dataclass
@@ -407,3 +429,478 @@ class GatewayProtocol(Protocol):
            - clients: Number of connected clients
        """
        ...
+
+
+# ---------------------------------------------------------------------------
+# Push notification dataclasses
+# ---------------------------------------------------------------------------
+
+@dataclass
+class ChannelInfo:
+    """Metadata about a push channel/topic.
+    
+    Attributes:
+        name: Channel name (unique identifier)
+        created_at: Channel creation timestamp
+        metadata: Arbitrary channel metadata
+        subscriber_count: Current number of subscribers
+    """
+    
+    name: str
+    created_at: float = field(default_factory=time.time)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    subscriber_count: int = 0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for serialization."""
+        return {
+            "name": self.name,
+            "created_at": self.created_at,
+            "metadata": self.metadata,
+            "subscriber_count": self.subscriber_count,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "ChannelInfo":
+        """Create from dictionary."""
+        return cls(
+            name=data.get("name", ""),
+            created_at=data.get("created_at", time.time()),
+            metadata=data.get("metadata", {}),
+            subscriber_count=data.get("subscriber_count", 0),
+        )
+
+
+@dataclass
+class PresenceInfo:
+    """Presence information for a connected client.
+    
+    Attributes:
+        client_id: Client identifier
+        status: Presence status ("online", "idle", "offline")
+        last_seen: Last heartbeat timestamp
+        metadata: Client-provided metadata (e.g., display name)
+        channels: Channels this client is subscribed to
+    """
+    
+    client_id: str
+    status: str = "online"
+    last_seen: float = field(default_factory=time.time)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    channels: List[str] = field(default_factory=list)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for serialization."""
+        return {
+            "client_id": self.client_id,
+            "status": self.status,
+            "last_seen": self.last_seen,
+            "metadata": self.metadata,
+            "channels": self.channels,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "PresenceInfo":
+        """Create from dictionary."""
+        return cls(
+            client_id=data.get("client_id", ""),
+            status=data.get("status", "online"),
+            last_seen=data.get("last_seen", time.time()),
+            metadata=data.get("metadata", {}),
+            channels=data.get("channels", []),
+        )
+
+
+# ---------------------------------------------------------------------------
+# Push notification protocols
+# ---------------------------------------------------------------------------
+
+@runtime_checkable
+class PushChannelProtocol(Protocol):
+    """Protocol for channel/topic-based push messaging.
+    
+    Channels group clients by interest. Messages published to a channel
+    are delivered to all subscribed clients.
+    """
+    
+    def add_channel(
+        self, channel_name: str, metadata: Optional[Dict[str, Any]] = None,
+    ) -> bool:
+        """Create a named channel.
+        
+        Args:
+            channel_name: Unique channel name
+            metadata: Optional channel metadata
+            
+        Returns:
+            True if created, False if already exists
+        """
+        ...
+    
+    def remove_channel(self, channel_name: str) -> bool:
+        """Delete a channel and unsubscribe all clients.
+        
+        Returns:
+            True if removed, False if not found
+        """
+        ...
+    
+    def get_channel(self, channel_name: str) -> Optional[ChannelInfo]:
+        """Get channel metadata."""
+        ...
+    
+    def list_channels(self) -> List[str]:
+        """List all active channel names."""
+        ...
+    
+    def subscribe_client(self, client_id: str, channel_name: str) -> bool:
+        """Subscribe a client to a channel.
+        
+        Returns:
+            True if subscribed, False if already subscribed or channel missing
+        """
+        ...
+    
+    def unsubscribe_client(self, client_id: str, channel_name: str) -> bool:
+        """Unsubscribe a client from a channel.
+        
+        Returns:
+            True if unsubscribed, False if not subscribed
+        """
+        ...
+    
+    def get_subscribers(self, channel_name: str) -> List[str]:
+        """List client IDs subscribed to a channel."""
+        ...
+    
+    def get_client_channels(self, client_id: str) -> List[str]:
+        """List channels a client is subscribed to."""
+        ...
+    
+    async def publish_to_channel(
+        self,
+        channel_name: str,
+        event: GatewayEvent,
+        exclude: Optional[List[str]] = None,
+    ) -> int:
+        """Publish an event to all subscribers of a channel.
+        
+        Args:
+            channel_name: Target channel
+            event: The event to deliver
+            exclude: Optional client IDs to skip
+            
+        Returns:
+            Number of clients the event was sent to
+        """
+        ...
+
+
+@runtime_checkable
+class PresenceProtocol(Protocol):
+    """Protocol for tracking client presence (online/idle/offline)."""
+    
+    async def track_presence(
+        self,
+        client_id: str,
+        status: str = "online",
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """Set or update a client's presence status.
+        
+        Args:
+            client_id: Client identifier
+            status: Presence status ("online", "idle", "offline")
+            metadata: Optional client metadata
+        """
+        ...
+    
+    async def remove_presence(self, client_id: str) -> None:
+        """Remove a client's presence (on disconnect)."""
+        ...
+    
+    def get_presence(self, client_id: str) -> Optional[PresenceInfo]:
+        """Get a single client's presence info."""
+        ...
+    
+    def get_all_presence(
+        self, channel_name: Optional[str] = None,
+    ) -> List[PresenceInfo]:
+        """Get presence info, optionally filtered by channel.
+        
+        Args:
+            channel_name: If provided, only return presence for channel members
+        """
+        ...
+    
+    def get_online_count(self, channel_name: Optional[str] = None) -> int:
+        """Count online clients, optionally filtered by channel."""
+        ...
+
+
+@runtime_checkable
+class DeliveryGuaranteeProtocol(Protocol):
+    """Protocol for at-least-once message delivery.
+    
+    Messages are stored, tracked, and retried until acknowledged.
+    """
+    
+    async def store_message(self, event: GatewayEvent) -> str:
+        """Persist a message to the store.
+        
+        Returns:
+            The event_id of the stored message
+        """
+        ...
+    
+    async def acknowledge(self, client_id: str, event_id: str) -> bool:
+        """Mark a message as acknowledged by a client.
+        
+        Returns:
+            True if found and acknowledged, False if not found
+        """
+        ...
+    
+    async def nack(self, client_id: str, event_id: str) -> None:
+        """Negative acknowledge - request redelivery."""
+        ...
+    
+    async def get_unacknowledged(
+        self, client_id: str, limit: int = 100,
+    ) -> List[GatewayEvent]:
+        """Get pending unacknowledged messages for a client."""
+        ...
+    
+    async def retry_unacknowledged(self, client_id: str) -> int:
+        """Redeliver all unacknowledged messages to a client.
+        
+        Returns:
+            Number of messages redelivered
+        """
+        ...
+    
+    async def purge_acknowledged(self, max_age_seconds: int = 86400) -> int:
+        """Remove old acknowledged messages from the store.
+        
+        Returns:
+            Number of messages purged
+        """
+        ...
+
+
+# ---------------------------------------------------------------------------
+# Auth Mode protocols and helpers (bind-aware authentication posture)
+# ---------------------------------------------------------------------------
+
+AuthMode = Literal["local", "token", "password", "trusted-proxy"]
+"""Authentication mode for gateway/UI components.
+
+- "local": Permissive mode for loopback interfaces (127.0.0.1, localhost, ::1)
+- "token": Token-based authentication required (default for external interfaces)  
+- "password": Username/password authentication
+- "trusted-proxy": Authentication handled by upstream proxy
+"""
+
+
+def is_loopback(host: str) -> bool:
+    """Check if a host is a loopback interface.
+    
+    Args:
+        host: Host/IP address to check
+        
+    Returns:
+        True if the host is a loopback address
+        
+    Examples:
+        >>> is_loopback("127.0.0.1")
+        True
+        >>> is_loopback("localhost") 
+        True
+        >>> is_loopback("::1")
+        True
+        >>> is_loopback("0.0.0.0")
+        False
+        >>> is_loopback("192.168.1.1")
+        False
+    """
+    import ipaddress
+    
+    # Handle localhost specially
+    if host in ("localhost", "0:0:0:0:0:0:0:1"):
+        return True
+    
+    try:
+        ip = ipaddress.ip_address(host)
+        return ip.is_loopback
+    except ValueError:
+        # Not a valid IP address (e.g., domain name)
+        return False
+
+
+def resolve_auth_mode(bind_host: str, configured: Optional[AuthMode] = None) -> AuthMode:
+    """Resolve authentication mode based on bind host and explicit configuration.
+    
+    Args:
+        bind_host: Host/IP that the service is bound to
+        configured: Explicitly configured auth mode (takes precedence)
+        
+    Returns:
+        The resolved authentication mode
+        
+    Examples:
+        >>> resolve_auth_mode("127.0.0.1")
+        'local'
+        >>> resolve_auth_mode("0.0.0.0")  
+        'token'
+        >>> resolve_auth_mode("127.0.0.1", "token")
+        'token'
+    """
+    if configured is not None:
+        return configured
+    
+    return "local" if is_loopback(bind_host) else "token"
+
+
+# ---------------------------------------------------------------------------
+# Auth, Pairing, and Session Binding Protocols (Issue #1588 Gap 3)
+# ---------------------------------------------------------------------------
+
+@runtime_checkable
+class AuthProtocol(Protocol):
+    """Protocol for authentication implementations.
+    
+    Enables structural typing for different authentication strategies
+    (token-based, local loopback, trusted proxy, etc.).
+    """
+    
+    def check(self, request: Any) -> Dict[str, Any]:
+        """Check authentication for a request.
+        
+        Args:
+            request: The request object to authenticate
+            
+        Returns:
+            Authentication decision with metadata:
+            - success: bool - whether authentication succeeded
+            - user_id: Optional[str] - authenticated user ID
+            - role: Optional[str] - user role/permissions
+            - metadata: Dict[str, Any] - additional auth context
+        """
+        ...
+
+
+@runtime_checkable  
+class PairingProtocol(Protocol):
+    """Protocol for channel pairing implementations.
+    
+    Manages the authorization of external channels (Telegram, Slack, UI)
+    to communicate with the gateway through signed codes.
+    """
+    
+    def generate_code(
+        self, 
+        channel_type: str = "unknown", 
+        channel_id: Optional[str] = None
+    ) -> str:
+        """Generate a new pairing code for a channel.
+        
+        Args:
+            channel_type: Type of channel (e.g., "telegram", "slack", "ui")
+            channel_id: Optional channel identifier
+            
+        Returns:
+            The generated pairing code
+        """
+        ...
+    
+    def approve(
+        self, 
+        channel_type: str,
+        code: str,
+        user_id: str = "",
+        user_name: str = ""
+    ) -> bool:
+        """Approve a pairing code, authorizing the channel.
+        
+        Args:
+            channel_type: Type of channel
+            code: The pairing code to approve
+            user_id: User identifier (optional, defaults to empty string)
+            user_name: Human-readable username (optional, defaults to empty string)
+            
+        Returns:
+            True if approval successful, False if code invalid/expired
+        """
+        ...
+    
+    def is_paired(self, channel_id: str, channel_type: str) -> bool:
+        """Check if a channel is authorized.
+        
+        Args:
+            channel_id: Channel identifier
+            channel_type: Type of channel
+            
+        Returns:
+            True if channel is paired/authorized
+        """
+        ...
+    
+    def list_paired(self) -> List["PairedChannel"]:
+        """List all authorized channels.
+        
+        Returns:
+            List of paired channel information
+        """
+        ...
+    
+    def revoke(self, channel_id: str, channel_type: str) -> bool:
+        """Revoke authorization for a channel.
+        
+        Args:
+            channel_id: Channel identifier
+            channel_type: Type of channel
+            
+        Returns:
+            True if revocation successful, False if not found
+        """
+        ...
+    
+    def list_pending(self, channel_type: Optional[str] = None) -> List[Dict[str, any]]:
+        """List pending pairing requests.
+        
+        Args:
+            channel_type: Optional filter by channel type
+            
+        Returns:
+            List of pending requests with channel, code, user info, and age
+        """
+        ...
+
+
+@runtime_checkable
+class SessionBindingProtocol(Protocol):
+    """Protocol for session binding implementations.
+    
+    Manages the association between sessions and authenticated principals
+    (users, agents, etc.) for state tracking and authorization.
+    """
+    
+    def bind(self, session_id: str, principal: Dict[str, Any]) -> None:
+        """Bind a session to an authenticated principal.
+        
+        Args:
+            session_id: Unique session identifier
+            principal: Principal information (user_id, roles, metadata, etc.)
+        """
+        ...
+    
+    def lookup(self, session_id: str) -> Optional[Dict[str, Any]]:
+        """Look up the principal bound to a session.
+        
+        Args:
+            session_id: Session identifier to look up
+            
+        Returns:
+            Principal information if found, None otherwise
+        """
+        ...
@@ -0,0 +1,13 @@
+"""Hooks query protocol for UI/API consumers."""
+
+from __future__ import annotations
+
+from typing import Dict, List, Protocol, runtime_checkable
+
+
+@runtime_checkable
+class HooksQueryProtocol(Protocol):
+    """Read path for registered hooks."""
+
+    def list_hooks_for_api(self) -> List[Dict]:
+        ...
@@ -439,3 +439,13 @@ def has_hook(event: Union[str, HookEvent]) -> bool:
            )
    
    return get_default_registry().has_hooks(event)
+
+
+def list_hooks_for_api() -> List[Dict]:
+    """Flat hook list suitable for REST/UI consumers."""
+    registry = get_default_registry()
+    flat: List[Dict] = []
+    for event, hooks in registry.list_hooks().items():
+        for h in hooks:
+            flat.append({"event": event, "source": "sdk", **h})
+    return flat
@@ -146,12 +146,12 @@ class HookRunner:
            loop = None

        if loop is not None and loop.is_running():
-            # Already inside a running async loop — cannot use run_until_complete.
-            # Schedule as fire-and-forget task to avoid leaking unawaited coroutines.
-            # Callers that need results in async context should use
-            # ``await runner.execute(...)`` directly.
-            loop.create_task(self.execute(event, input_data, target))
-            return []
+            # Cannot execute sync in running event loop - would block the loop
+            # Guide users to use the async API instead
+            raise RuntimeError(
+                "execute_sync() cannot be called from within a running event loop. "
+                "Use 'await runner.execute(event, input_data, target)' instead in async contexts."
+            )

        # No running loop — safe to create one
        return asyncio.run(self.execute(event, input_data, target))
@@ -68,6 +68,14 @@ class HookEvent(str, Enum):
    SCHEDULE_REMOVE = "schedule_remove"
    SCHEDULE_TRIGGER = "schedule_trigger"

+    # Kanban task lifecycle (wrapper dispatcher + tools emit these)
+    KANBAN_TASK_CREATED = "kanban_task_created"
+    KANBAN_TASK_CLAIMED = "kanban_task_claimed"
+    KANBAN_TASK_MOVED = "kanban_task_moved"
+    KANBAN_TASK_DONE = "kanban_task_done"
+    KANBAN_TASK_BLOCKED = "kanban_task_blocked"
+    KANBAN_TASK_FAILED = "kanban_task_failed"
+
    # Claude Code parity events
    USER_PROMPT_SUBMIT = "user_prompt_submit"  # When user submits a prompt
    NOTIFICATION = "notification"              # When notification is sent
@@ -101,6 +109,34 @@ class HookInput:
        }


+@dataclass
+class KanbanHookInput(HookInput):
+    """Hook input for kanban task lifecycle events.
+    
+    Used by wrapper dispatcher and tools when emitting kanban events.
+    Observability adapters can subscribe to these via the hook registry.
+    """
+    task_id: str = ""
+    board: str = "default"
+    status: str = ""
+    assignee: Optional[str] = field(default=None)
+    from_status: Optional[str] = field(default=None)
+    to_status: Optional[str] = field(default=None)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for JSON serialization."""
+        result = super().to_dict()
+        result.update({
+            "task_id": self.task_id,
+            "board": self.board,
+            "status": self.status,
+            "assignee": self.assignee,
+            "from_status": self.from_status,
+            "to_status": self.to_status,
+        })
+        return result
+
+
@dataclass
 class HookOutput:
    """Base hook output - common fields for all events."""
@@ -0,0 +1,23 @@
+"""
+Kanban protocols and types for PraisonAI Agents.
+
+This module provides the protocol contracts for kanban functionality,
+allowing the wrapper (praisonai) and PraisonAIUI to share a stable
+interface without coupling the core to SQLite or other heavy implementations.
+"""
+
+from praisonaiagents.kanban.protocols import (
+    KanbanStoreProtocol,
+    KanbanTaskProtocol,
+    KanbanCommentingProtocol,
+    KanbanLinkingProtocol,
+    VALID_KANBAN_STATUSES,
+)
+
+__all__ = [
+    "KanbanStoreProtocol",
+    "KanbanTaskProtocol",
+    "KanbanCommentingProtocol", 
+    "KanbanLinkingProtocol",
+    "VALID_KANBAN_STATUSES",
+]
@@ -0,0 +1,221 @@
+"""
+Kanban protocols for PraisonAI Agents.
+
+Defines the protocol contracts that the wrapper (praisonai) and PraisonAIUI
+can use to implement kanban functionality with a stable interface.
+
+This follows AGENTS.md §3.2 Protocol-First Design:
+- Protocols define WHAT (interface contract)
+- Implementations define HOW (concrete behavior)
+- Core SDK has protocols only, wrapper has heavy implementations
+"""
+
+from typing import Protocol, TypedDict, runtime_checkable
+
+
+# Valid kanban statuses matching PraisonAIUI columns + archived
+VALID_KANBAN_STATUSES: frozenset[str] = frozenset([
+    "triage",
+    "todo", 
+    "ready",
+    "running",
+    "blocked",
+    "review",
+    "done",
+    "archived"
+])
+
+
+class KanbanTaskProtocol(TypedDict, total=False):
+    """Typed dict shape for kanban task fields.
+    
+    Defines the expected structure of task objects returned by
+    KanbanStoreProtocol implementations.
+    """
+    id: str
+    title: str
+    body: str
+    status: str
+    assignee: str | None
+    priority: str | None
+    tenant: str | None
+    board: str
+    created_at: float
+    updated_at: float
+
+
+@runtime_checkable
+class KanbanStoreProtocol(Protocol):
+    """Protocol contract for kanban store implementations.
+    
+    This protocol defines the core interface that PraisonAIUI expects
+    for injected kanban stores. Wrapper implementations must
+    implement all methods to be compatible.
+    
+    Duck typing contract matches InjectedKanbanStore in PraisonAIUI.
+    """
+    
+    def get_board(
+        self,
+        *,
+        board: str = "default",
+        tenant: str | None = None,
+        include_archived: bool = False,
+    ) -> dict:
+        """Get board data with tasks grouped by status.
+        
+        Args:
+            board: Board name (default: "default")
+            tenant: Optional tenant filter
+            include_archived: Whether to include archived tasks
+            
+        Returns:
+            Board data with tasks organized by status columns
+        """
+        ...
+
+    def get_task(self, task_id: str) -> dict | None:
+        """Get a single task by ID.
+        
+        Args:
+            task_id: Task identifier
+            
+        Returns:
+            Task data or None if not found
+        """
+        ...
+
+    def create_task(self, data: dict) -> dict:
+        """Create a new task.
+        
+        Args:
+            data: Task creation data (title, body, status, etc.)
+            
+        Returns:
+            Created task data with assigned ID
+        """
+        ...
+
+    def update_task(self, task_id: str, data: dict) -> dict | None:
+        """Update an existing task.
+        
+        Args:
+            task_id: Task identifier
+            data: Fields to update
+            
+        Returns:
+            Updated task data or None if not found
+        """
+        ...
+
+    def move_task(self, task_id: str, status: str) -> dict | None:
+        """Move a task to a different status.
+        
+        Args:
+            task_id: Task identifier
+            status: New status (must be in VALID_KANBAN_STATUSES)
+            
+        Returns:
+            Updated task data or None if not found
+        """
+        ...
+
+    def bulk_update(self, task_ids: list[str], status: str) -> dict:
+        """Update multiple tasks to the same status.
+        
+        Args:
+            task_ids: List of task identifiers
+            status: New status for all tasks
+            
+        Returns:
+            Results summary with success/failure counts
+        """
+        ...
+
+    def delete_task(self, task_id: str) -> bool:
+        """Delete a task.
+        
+        Args:
+            task_id: Task identifier
+            
+        Returns:
+            True if deleted, False if not found
+        """
+        ...
+
+    def list_events(self, since: float = 0.0, board: str = "default") -> list[dict]:
+        """List kanban events since a timestamp.
+        
+        Args:
+            since: Unix timestamp to filter events from
+            board: Board name to filter events
+            
+        Returns:
+            List of event data
+        """
+        ...
+
+    def health(self) -> dict:
+        """Check store health status.
+        
+        Returns:
+            Health status information
+        """
+        ...
+
+
+@runtime_checkable
+class KanbanCommentingProtocol(Protocol):
+    """Extension protocol for kanban task commenting functionality.
+    
+    This protocol is implemented separately from KanbanStoreProtocol to allow
+    stores to optionally support commenting without breaking isinstance checks
+    on the core protocol.
+    """
+    
+    def add_comment(self, task_id: str, text: str, author: str | None = None) -> dict | None:
+        """Add a comment to a task.
+        
+        Args:
+            task_id: Task identifier
+            text: Comment text
+            author: Optional comment author
+            
+        Returns:
+            Comment data or None if task not found
+        """
+        ...
+
+
+@runtime_checkable 
+class KanbanLinkingProtocol(Protocol):
+    """Extension protocol for kanban task linking functionality.
+    
+    This protocol is implemented separately from KanbanStoreProtocol to allow
+    stores to optionally support task relationships without breaking isinstance
+    checks on the core protocol.
+    """
+    
+    def link_tasks(self, parent_id: str, child_id: str) -> bool:
+        """Link two tasks in parent-child relationship.
+        
+        Args:
+            parent_id: Parent task identifier
+            child_id: Child task identifier
+            
+        Returns:
+            True if linked successfully, False otherwise
+        """
+        ...
+
+    def unlink_tasks(self, parent_id: str, child_id: str) -> bool:
+        """Unlink parent-child task relationship.
+        
+        Args:
+            parent_id: Parent task identifier
+            child_id: Child task identifier
+            
+        Returns:
+            True if unlinked successfully, False otherwise
+        """
+        ...