fix: address review feedback - import ordering and docstring migration

- Move ScheduleParser import before logger assignment (PEP 8 E402)
- Migrate comprehensive docstring from base.py to shared.py
- Preserve original examples (*/6h, */30s) and doctests
- Use explicit re-export syntax to avoid noqa suppressions

Co-authored-by: Mervin Praison <[email protected]>

.env.example

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

.github/workflows/claude.yml

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

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

@@ -1,158 +0,0 @@
-name: Cross-Repo Integration Tests
-
-on:
-  push:
-    branches: [ main ]
-    paths:
-      - 'src/praisonai/praisonai/integration/**'
-      - 'src/praisonai/tests/integration/test_aiui_*'
-      - 'src/praisonai/pyproject.toml'
-  pull_request:
-    branches: [ main ]
-    paths:
-      - 'src/praisonai/praisonai/integration/**'
-      - 'src/praisonai/tests/integration/test_aiui_*'
-      - 'src/praisonai/pyproject.toml'
-  workflow_dispatch:
-
-jobs:
-  cross-repo-integration:
-    runs-on: ubuntu-latest
-    timeout-minutes: 30
-    
-    steps:
-    - name: Checkout PraisonAI
-      uses: actions/checkout@v4
-      with:
-        path: PraisonAI
-        ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
-        
-    - name: Checkout PraisonAIUI
-      uses: actions/checkout@v4
-      with:
-        repository: MervinPraison/PraisonAIUI
-        path: PraisonAIUI
-        ref: main
-        
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.11'
-        
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        # Install PraisonAI packages in development mode
-        cd PraisonAI/src/praisonai-agents
-        pip install -e .
-        cd ../praisonai
-        pip install -e ".[ui,dev]"
-        # Install PraisonAIUI in development mode
-        cd ../../../PraisonAIUI
-        pip install -e .
-        
-    - name: Verify versions
-      run: |
-        python -c "import praisonaiagents; print(f'praisonaiagents: {praisonaiagents.__version__}')"
-        python -c "import praisonai; print(f'praisonai: {praisonai.__version__}')"
-        python -c "import praisonaiui; print(f'praisonaiui: {praisonaiui.__version__}')"
-        
-    - name: Run PraisonAI integration tests
-      run: |
-        cd PraisonAI/src/praisonai
-        python -m pytest tests/integration/test_aiui_* -v --timeout=300
-        
-    - name: Run PraisonAIUI integration tests
-      run: |
-        cd PraisonAIUI
-        if [ -f tests/integration/test_agentic_roundtrip.py ]; then
-          python -m pytest tests/integration/test_agentic_roundtrip.py -v --timeout=300
-        fi
-        if [ -f tests/test_feature_sdk_backends.py ]; then
-          python -m pytest tests/test_feature_sdk_backends.py -v --timeout=300
-        fi
-        
-    - name: Test Pattern C CLI
-      run: |
-        cd PraisonAI/src/praisonai
-        # Test that ui-gateway command exists
-        python -m praisonai serve --help | grep ui-gateway
-        
-    - name: Test public API exports
-      run: |
-        cd PraisonAI/src/praisonai
-        python -c "from praisonai import run_integrated_gateway; print('✓ run_integrated_gateway imported')"
-        python -c "from praisonai import configure_host; print('✓ configure_host imported')"
-        
-    - name: Integration smoke test
-      env:
-        PRAISONAI_TEST_MODE: "1"
-        PYTHONPATH: ${{ github.workspace }}/PraisonAI/src/praisonai:${{ github.workspace }}/PraisonAI/src/praisonai-agents:${{ github.workspace }}/PraisonAIUI
-      run: |
-        cd PraisonAI/src/praisonai
-        python -c "
-        import sys
-        sys.path.insert(0, '.')
-        from praisonai.integration.host_app import configure_host
-        from praisonai import run_integrated_gateway
-        print('✓ Integration imports successful')
-        
-        # Test configure_host with new parameters
-        try:
-            configure_host(
-                style='dashboard', 
-                context_paths=['AGENTS.md'],
-                title='Test App'
-            )
-            print('✓ configure_host with new parameters works')
-        except Exception as e:
-            print(f'× configure_host failed: {e}')
-            sys.exit(1)
-        "
-        
-  # Optional job with API key for agentic tests (if secret is available)
-  agentic-integration:
-    runs-on: ubuntu-latest
-    timeout-minutes: 30
-    if: ${{ vars.RUN_AGENTIC_TESTS == 'true' }}
-    needs: cross-repo-integration
-    
-    steps:
-    - name: Checkout PraisonAI
-      uses: actions/checkout@v4
-      with:
-        path: PraisonAI
-        ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
-        
-    - name: Checkout PraisonAIUI
-      uses: actions/checkout@v4
-      with:
-        repository: MervinPraison/PraisonAIUI
-        path: PraisonAIUI
-        ref: main
-        
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.11'
-        
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        cd PraisonAI/src/praisonai-agents
-        pip install -e .
-        cd ../praisonai
-        pip install -e ".[ui,dev]"
-        cd ../../../PraisonAIUI
-        pip install -e .
-        
-    - name: Run agentic integration test
-      env:
-        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-      run: |
-        cd PraisonAI/src/praisonai
-        if [ -n "$OPENAI_API_KEY" ]; then
-          python -m pytest tests/integration/test_aiui_host_agentic.py -v --timeout=600
-        else
-          echo "OPENAI_API_KEY not available, skipping agentic tests"
-        fi

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

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

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

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

.github/workflows/test-windows.yml

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

README.md

@@ -294,35 +294,8 @@ def search(query: str) -> str:
 
 @tool
 def calculate(expression: str) -> float:
-    """Safely evaluate a numeric arithmetic expression."""
-    import ast
-    import operator
-    
-    # Define allowed operations
-    _OPS = {
-        ast.Add: operator.add,
-        ast.Sub: operator.sub,
-        ast.Mult: operator.mul,
-        ast.Div: operator.truediv,
-        ast.Pow: operator.pow,
-        ast.USub: operator.neg,
-        ast.UAdd: operator.pos,
-    }
-    
-    def _safe_eval(node):
-        if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
-            return node.value
-        elif isinstance(node, ast.BinOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_safe_eval(node.left), _safe_eval(node.right))
-        elif isinstance(node, ast.UnaryOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_safe_eval(node.operand))
-        else:
-            raise ValueError("Unsupported expression")
-    
-    try:
-        return _safe_eval(ast.parse(expression, mode="eval").body)
-    except (ValueError, SyntaxError, TypeError, ZeroDivisionError, OverflowError):
-        raise ValueError("Invalid arithmetic expression")
+    """Evaluate a math expression."""
+    return eval(expression)
 
 agent = Agent(
     instructions="You are a helpful assistant",
@@ -331,7 +304,6 @@ agent = Agent(
 agent.start("Search for AI news and calculate 15*4")
 ```
 
-> ⚠️ **Security Note:** Never use `eval()`, `exec()`, or `subprocess` in tool functions that process LLM-generated or user-supplied input. Always validate and sanitize inputs to prevent code injection attacks.
 > 📖 [Full tools docs](https://docs.praison.ai/docs/tools/tools) — BaseTool, tool packages, 100+ built-in tools
 
 ### 5. Persistence (Databases)
@@ -358,15 +330,6 @@ pip install "praisonai[claw]"
 praisonai claw
 ```
 
-#### Required Environment Variables
-
-Copy `.env.example` to `.env` and configure the following variables:
-
-| Variable | Required | Description |
-|----------|----------|-------------|
-| `OPENAI_API_KEY` | Yes | OpenAI API key for all LLM calls |
-| `TAVILY_API_KEY` | Yes (Claw) | Tavily key for the built-in web-search tool. Get one free at https://app.tavily.com |
-
 Open **http://localhost:8082** — the dashboard comes with 13 built-in pages: Chat, Agents, Memory, Knowledge, Channels, Guardrails, Cron, and more. Add messaging channels directly from the UI.
 
 > 📖 [Full Claw docs](https://docs.praison.ai/docs/concepts/claw) — platform tokens, CLI options, Docker, and YAML agent mode
@@ -382,7 +345,7 @@ praisonai flow
 
 Open **http://localhost:7861** — use the **Agent** and **Agent Team** components to create sequential or parallel workflows. Connect Chat Input → Agent Team → Chat Output for instant multi-agent pipelines.
 
-> 📖 [Full Flow docs](https://docs.praison.ai/docs/concepts/agentflow) — visual agent building, component reference, and deployment
+> 📖 [Full Flow docs](https://docs.praison.ai/docs/concepts/flow) — visual agent building, component reference, and deployment
 
 ### 8. PraisonAI UI 🤖 (Clean Chat)
 

api.md

@@ -40,7 +40,6 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">chat_with_context</a>(message: str, context: 'ContextPack', **kwargs) -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">checkpoints</a>() -> Optional[bool]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">checkpoints</a>(value: Optional[bool]) -> None</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">clone_for_channel</a>() -> 'Agent'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">close</a>() -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">console</a>() -> Optional[Any]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">context_manager</a>() -> Optional[Any]</code>
@@ -172,7 +171,6 @@ Methods:
 * <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">safe_run</a>(**kwargs) -> ToolResult</code>
 * <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate</a>() -> bool</code>
 * <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_class</a>() -> bool</code>
-* <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_schema_roundtrip</a>() -> bool</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">__call__</a>(*args, **kwargs) -> Any</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">check_availability</a>() -> tuple[bool, str]</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">injected_params</a>() -> Dict[str, Any]</code>
@@ -185,7 +183,6 @@ Methods:
 * <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>
@@ -479,10 +476,8 @@ Methods:
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">edit_skill</a>(name: str, content: str) -> dict</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_allowed_tools</a>(name: str) -> List[str]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_available_skills</a>() -> List[SkillMetadata]</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_available_skills_by_state</a>(state: SkillState) -> List[LoadedSkill]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_instructions</a>(name: str) -> Optional[str]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_skill</a>(name: str) -> Optional[LoadedSkill]</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_skills_diagnostics</a>() -> Dict[str, ValidationResult]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_user_invocable_skills</a>() -> List[LoadedSkill]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">invoke</a>(name: str, raw_args: str = '', session_id: Optional[str] = None, shell_exec: bool = False) -> Optional[str]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">load_resources</a>(name: str) -> bool</code>
@@ -491,7 +486,6 @@ Methods:
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">skill_names</a>() -> List[str]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">skills</a>() -> List[LoadedSkill]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">to_prompt</a>() -> str</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">validate_skill_capabilities</a>(skill_name: str, force_refresh: bool = False) -> ValidationResult</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">write_skill_file</a>(name: str, file_path: str, file_content: str) -> dict</code>
 * <code title="class SkillMetadata">SkillMetadata.<a href="./src/praisonai-agents/praisonaiagents/skills/models.py">from_properties</a>(props: SkillProperties) -> 'SkillMetadata'</code>
 * <code title="class SkillProperties">SkillProperties.<a href="./src/praisonai-agents/praisonaiagents/skills/models.py">to_dict</a>() -> dict</code>
@@ -607,13 +601,6 @@ Methods:
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">set_tool_budget</a>(tool_name: str, max_tokens: int, protected: bool = False) -> None</code>
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">truncate_tool_output</a>(tool_name: str, output: str) -> str</code>
 
-# UI
-
-Types:
-```python
-from praisonaiagents import A2UI
-```
-
 # Config
 
 Types:
@@ -768,7 +755,7 @@ Methods:
 
 Types:
 ```python
-from praisonai import Agent, AgentApp, AgentOS, AnthropicManagedAgent, CloudProvider, DB, Deploy, DeployConfig, DeployType, HostedAgent, HostedAgentConfig, LocalAgent, LocalAgentConfig, LocalManagedAgent, LocalManagedConfig, ManagedAgent, ManagedConfig, PraisonAI, __version__, arun, run, run_integrated_gateway
+from praisonai import Agent, AgentApp, AgentOS, AnthropicManagedAgent, CloudProvider, DB, Deploy, DeployConfig, DeployType, HostedAgent, HostedAgentConfig, LocalAgent, LocalAgentConfig, LocalManagedAgent, LocalManagedConfig, ManagedAgent, ManagedConfig, PraisonAI, __version__
 ```
 
 # CLI
@@ -799,10 +786,6 @@ Methods:
 * <code title="cli">praisonai bot discord <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
 * <code title="cli">praisonai bot email <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
 * <code title="cli">praisonai bot install-daemon <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
-* <code title="cli">praisonai bot linear <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
-* <code title="cli">praisonai bot list <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
-* <code title="cli">praisonai bot purge <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
-* <code title="cli">praisonai bot replay <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
 * <code title="cli">praisonai bot slack <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
 * <code title="cli">praisonai bot start <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
 * <code title="cli">praisonai bot telegram <a href="./src/praisonai/praisonai/cli/commands/bot.py">--help</a></code>
@@ -870,10 +853,8 @@ Methods:
 * <code title="cli">praisonai doctor env <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor mcp <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor network <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
-* <code title="cli">praisonai doctor packaging <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor performance <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor selftest <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
-* <code title="cli">praisonai doctor skills <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor tools <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor troubleshoot <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai endpoints list <a href="./src/praisonai/praisonai/cli/commands/endpoints.py">--help</a></code>
@@ -905,24 +886,11 @@ Methods:
 * <code title="cli">praisonai gateway send <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway start <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway status <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
-* <code title="cli">praisonai gateway stop <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway uninstall <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai github triage <a href="./src/praisonai/praisonai/cli/commands/github.py">--help</a></code>
 * <code title="cli">praisonai hooks add <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
 * <code title="cli">praisonai hooks list <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
 * <code title="cli">praisonai hooks remove <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
-* <code title="cli">praisonai kanban block <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban boards <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban comment <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban complete <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban create <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban dispatch <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban link <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban list <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban move <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban reclaim <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban show <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban unblock <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
 * <code title="cli">praisonai knowledge add <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
 * <code title="cli">praisonai knowledge index <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
 * <code title="cli">praisonai knowledge list <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
@@ -1080,7 +1048,6 @@ Methods:
 * <code title="cli">praisonai serve status <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve stop <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve ui <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
-* <code title="cli">praisonai serve ui-gateway <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve unified <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai session delete <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
 * <code title="cli">praisonai session export <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.52" \
+    "praisonai>=4.6.35" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.52" \
+    "praisonai>=4.6.35" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.52" \
+    "praisonai>=4.6.35" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/benchmark/benchmark_example.py

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

examples/compare/cli_compare_advanced.py

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

examples/compare/cli_compare_basic.py

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

examples/consolidated_params/advanced_memory.py

@@ -15,7 +15,7 @@ agent = Agent(
 # With array [preset, overrides]
 agent_override = Agent(
     instructions="You are a helpful assistant.",
-    memory=MemoryConfig(backend="redis", user_id="custom_user"),
+    memory=["redis", {"user_id": "custom_user"}],
 )
 
 if __name__ == "__main__":

examples/consolidated_params/advanced_workflow_full_features.py

@@ -2,7 +2,7 @@
 from praisonaiagents import Agent
 from praisonaiagents import AgentFlow, Task
 from praisonaiagents.workflows.workflow_configs import (
-    WorkflowPlanningConfig, WorkflowMemoryConfig,
+    WorkflowOutputConfig, WorkflowPlanningConfig, WorkflowMemoryConfig,
 )
 
 # Full-featured workflow with all consolidated params
@@ -32,7 +32,7 @@ workflow = AgentFlow(
         ),
     ],
     # Workflow-level consolidated params
-    output="verbose",
+    output=WorkflowOutputConfig(output="verbose", stream=True),
     planning=WorkflowPlanningConfig(enabled=True, reasoning=True),
     memory=WorkflowMemoryConfig(backend="file"),
     # Agent-like params

examples/consolidated_params/basic_workflow.py

@@ -15,20 +15,8 @@ workflow = AgentFlow(
     ],
     output="verbose",
     planning=True,
-    variables={"topic": "AI agents"},
 )
 
 if __name__ == "__main__":
-    import os
-    import sys
-
-    if not os.environ.get("OPENAI_API_KEY"):
-        print("Set OPENAI_API_KEY to run this example.")
-        sys.exit(0)
-    if sys.platform == "win32":
-        try:
-            sys.stdout.reconfigure(encoding="utf-8")
-        except Exception:
-            pass
-    result = workflow.run()
+    result = workflow.run(variables={"topic": "AI agents"})
     print(result)

examples/eval/accuracy_example.py

@@ -24,7 +24,7 @@ if has_api_key:
         input_text="What is 2 + 2?",
         expected_output="4",
         num_iterations=3,  # Run 3 times for statistical significance
-        verbose=True
+        output="verbose"
     )
 
     # Run evaluation
@@ -42,7 +42,7 @@ else:
         input_text="What is 2 + 2?",
         expected_output="4",
         num_iterations=1,
-        verbose=True
+        output="verbose"
     )
     
     # Test with mock output

examples/eval/criteria_example.py

@@ -27,7 +27,7 @@ if has_api_key:
         scoring_type="numeric",  # Score 1-10
         threshold=7.0,           # Pass if score >= 7
         num_iterations=2,
-        verbose=True
+        output="verbose"
     )
 
     # Run evaluation
@@ -45,7 +45,7 @@ if has_api_key:
         agent=agent,
         input_text="Tell me a joke",
         scoring_type="binary",  # Pass or Fail
-        verbose=True
+        output="verbose"
     )
 
     binary_result = binary_evaluator.run(print_summary=True)

examples/eval/performance_example.py

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

examples/eval/reliability_example.py

@@ -16,19 +16,8 @@ def search_web(query: str) -> str:
 
 
 def calculate(expression: str) -> str:
-    """Calculate a math expression safely."""
-    import ast, operator
-    _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-            ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-            ast.Mod: operator.mod, ast.Pow: operator.pow,
-            ast.USub: operator.neg, ast.UAdd: operator.pos}
-    def _ev(n):
-        if isinstance(n, ast.Expression): return _ev(n.body)
-        if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-        if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-        if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-        raise ValueError(f"Unsupported: {ast.dump(n)}")
-    return str(_ev(ast.parse(expression, mode="eval")))
+    """Calculate a math expression."""
+    return str(eval(expression))
 
 
 # Check if we have an API key
@@ -48,7 +37,7 @@ if has_api_key:
         input_text="Search for the weather in Paris and calculate 25 * 4",
         expected_tools=["search_web", "calculate"],  # Tools that should be called
         forbidden_tools=["delete_file"],  # Tools that should NOT be called
-        verbose=True
+        output="verbose"
     )
 
     # Run evaluation
@@ -70,7 +59,7 @@ evaluator = ReliabilityEvaluator(
     agent=mock_agent,
     expected_tools=["search_web", "calculate"],  # Tools that should be called
     forbidden_tools=["delete_file"],  # Tools that should NOT be called
-    verbose=True
+    output="verbose"
 )
 
 result2 = evaluator.evaluate_tool_calls(

examples/knowledge/compression_demo.py

@@ -184,7 +184,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about codes, provide the EXACT code from the documents.
 Be concise but accurate.""",
             knowledge=[temp_dir],
-            memory={"user_id": "compression_demo_user"},
+            user_id="compression_demo_user",
             output="verbose",
         )
         

examples/knowledge/hierarchical_summaries_demo.py

@@ -164,7 +164,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about department codes, provide the EXACT code from the documents.
 Be specific about which department you're referring to.""",
             knowledge=[temp_dir],
-            memory={"user_id": "hierarchy_demo_user"},
+            user_id="hierarchy_demo_user",
             output="verbose",
         )
         

examples/knowledge/large_corpus_strategy_demo.py

@@ -124,7 +124,7 @@ def main():
             # Show strategy selection for different corpus sizes
             print(f"\nStrategy selection by corpus size:")
             for size in [100, 1000, 10000, 50000, 100000]:
-                strategy = select_strategy(CorpusStats(file_count=max(1, size // 1000), total_tokens=size))
+                strategy = select_strategy(corpus_tokens=size)
                 print(f"  {size:>6} tokens -> {strategy.value}")
                 
         except ImportError as e:
@@ -142,7 +142,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about verification codes, provide the EXACT code from the documents.
 If information is not in the context, say 'Information not found in documents.'""",
             knowledge=[temp_dir],
-            memory={"user_id": "strategy_demo_user"},
+            user_id="strategy_demo_user",
             output="verbose",  # Use new consolidated param
         )
         

examples/knowledge/mem0_backend_example.py

@@ -65,7 +65,7 @@ def main():
             name="PolicyExpert",
             instructions="You are a company policy expert. Answer questions based ONLY on the provided knowledge. Quote exact values when asked about codes or numbers.",
             knowledge=[temp_dir],  # Add directory with documents
-            memory={"user_id": "example_user"},
+            user_id="example_user",  # Required for mem0 backend
         )
         
         print("\n1. Testing Agent.chat() with context-required question:")

examples/knowledge/scope_identifiers_example.py

@@ -50,7 +50,7 @@ def main():
             name="HR_Assistant_User1",
             instructions="You are an HR assistant.",
             knowledge=[temp_dir],
-            memory={"user_id": "user_alice"},
+            user_id="user_alice",  # Knowledge scoped to Alice
         )
         
         response = user1_agent.chat("What is the policy on remote work?")
@@ -80,7 +80,7 @@ def main():
             name="Personal_HR_Bot",
             instructions="You are a personal HR assistant.",
             knowledge=[temp_dir],
-            memory={"user_id": "user_bob"},
+            user_id="user_bob",
             agent_id="hr_bot_v2",
         )
         

examples/knowledge/scope_isolation_example.py

@@ -49,7 +49,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
             name="AgentA_Ingester",
             instructions="You ingest and store knowledge.",
             knowledge=[temp_dir],
-            memory={"user_id": "user_alice"},
+            user_id="user_alice",  # Scoped to Alice
             output="silent",
         )
         
@@ -103,7 +103,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
             name="AgentB_Querier",
             instructions="Answer questions based on the provided knowledge. If you don't have the information, say so.",
             knowledge=[temp_dir],
-            memory={"user_id": "user_alice"},
+            user_id="user_alice",  # Same scope as Agent A
             output="silent",
         )
         

examples/managed-agents/persistence/cockroachdb_managed.py

@@ -17,10 +17,8 @@ from praisonaiagents import Agent
 agent = Agent(
     name="CockroachDB Agent",
     instructions="You are a helpful assistant.",
-    memory={
-        "session_id": "crdb-demo-session",
-        "db": {"database_url": os.getenv("COCKROACHDB_URL")},
-    },
+    db={"database_url": os.getenv("COCKROACHDB_URL")},
+    session_id="crdb-demo-session",
 )
 
 result = agent.start("What is distributed SQL? Explain in 2 sentences.")

examples/managed-agents/persistence/neon_managed.py

@@ -18,10 +18,8 @@ from praisonaiagents import Agent
 agent = Agent(
     name="Neon Agent",
     instructions="You are a helpful assistant.",
-    memory={
-        "session_id": "neon-demo-session",
-        "db": {"database_url": os.getenv("NEON_DATABASE_URL")},
-    },
+    db={"database_url": os.getenv("NEON_DATABASE_URL")},
+    session_id="neon-demo-session",
 )
 
 # Option 2: Direct URL

examples/managed-agents/persistence/supabase_direct_managed.py

@@ -20,10 +20,8 @@ from praisonaiagents import Agent
 agent = Agent(
     name="Supabase Agent",
     instructions="You are a helpful assistant.",
-    memory={
-        "session_id": "supabase-demo-session",
-        "db": {"database_url": os.getenv("SUPABASE_DATABASE_URL")},
-    },
+    db={"database_url": os.getenv("SUPABASE_DATABASE_URL")},
+    session_id="supabase-demo-session",
 )
 
 result = agent.start("What is Supabase? Explain in 2 sentences.")

examples/managed-agents/persistence/turso_managed.py

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

examples/mcp/multi_server.py

@@ -19,43 +19,20 @@ import os
 
 def calculate(expression: str) -> str:
     """
-    A simple calculator tool that evaluates mathematical expressions safely.
-
+    A simple calculator tool that evaluates mathematical expressions.
+    
     Args:
         expression: A mathematical expression to evaluate (e.g., "2 + 2")
-
+    
     Returns:
         The result of the calculation
     """
-    import ast
-    import operator
-
-    _OPS = {
-        ast.Add: operator.add,
-        ast.Sub: operator.sub,
-        ast.Mult: operator.mul,
-        ast.Div: operator.truediv,
-        ast.FloorDiv: operator.floordiv,
-        ast.Mod: operator.mod,
-        ast.Pow: operator.pow,
-        ast.USub: operator.neg,
-        ast.UAdd: operator.pos,
-    }
-
-    def _safe_eval(node):
-        if isinstance(node, ast.Expression):
-            return _safe_eval(node.body)
-        if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
-            return node.value
-        if isinstance(node, ast.UnaryOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_safe_eval(node.operand))
-        if isinstance(node, ast.BinOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_safe_eval(node.left), _safe_eval(node.right))
-        raise ValueError(f"Unsupported expression: {ast.dump(node)}")
-
     try:
-        tree = ast.parse(expression, mode="eval")
-        result = _safe_eval(tree)
+        # Safe evaluation of mathematical expressions
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Invalid characters in expression"
+        result = eval(expression)
         return f"Result: {result}"
     except Exception as e:
         return f"Error: {str(e)}"

examples/mcp_server/custom_tools_server.py

@@ -24,18 +24,12 @@ def main():
     def calculate(expression: str) -> str:
         """Safely evaluate a mathematical expression."""
         try:
-            import ast, operator
-            _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                    ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                    ast.Mod: operator.mod, ast.Pow: operator.pow,
-                    ast.USub: operator.neg, ast.UAdd: operator.pos}
-            def _ev(n):
-                if isinstance(n, ast.Expression): return _ev(n.body)
-                if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-                if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-                if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-                raise ValueError(f"Unsupported: {ast.dump(n)}")
-            return f"Result: {_ev(ast.parse(expression, mode='eval'))}"
+            # Only allow safe math operations
+            allowed = set("0123456789+-*/.(). ")
+            if not all(c in allowed for c in expression):
+                return "Error: Invalid characters in expression"
+            result = eval(expression)  # Safe due to character filtering
+            return f"Result: {result}"
         except Exception as e:
             return f"Error: {e}"
     

examples/middleware/configurable_model.py

@@ -10,7 +10,8 @@ from praisonaiagents import Agent
 agent = Agent(
     name="FlexBot",
     instructions="You are a helpful assistant.",
-    llm={"model": "gpt-4o-mini", "configurable": True},
+    llm="gpt-4o-mini",
+    llm_config={"configurable": True}
 )
 
 if __name__ == "__main__":

examples/middleware/injected_state.py

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

examples/persistence/knowledge_qdrant.py

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

examples/policy/basic_policy.py

@@ -57,8 +57,8 @@ def main():
     agent = Agent(
         name="SecureAgent",
         instructions="You are a file management assistant.",
+        policy=engine
     )
-    agent.policy = engine
     
     print("\n--- Agent with Policy Enforcement Created ---")
     print(f"Agent: {agent.name}")

examples/python/a2a/a2a-server.py

@@ -21,36 +21,9 @@ def search_web(query: str) -> str:
     return f"Search results for: {query}"
 
 def calculate(expression: str) -> str:
-    """Calculate a mathematical expression safely."""
-    import ast
-    import operator
-
-    _OPS = {
-        ast.Add: operator.add,
-        ast.Sub: operator.sub,
-        ast.Mult: operator.mul,
-        ast.Div: operator.truediv,
-        ast.FloorDiv: operator.floordiv,
-        ast.Mod: operator.mod,
-        ast.Pow: operator.pow,
-        ast.USub: operator.neg,
-        ast.UAdd: operator.pos,
-    }
-
-    def _eval(node):
-        if isinstance(node, ast.Expression):
-            return _eval(node.body)
-        if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
-            return node.value
-        if isinstance(node, ast.UnaryOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_eval(node.operand))
-        if isinstance(node, ast.BinOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_eval(node.left), _eval(node.right))
-        raise ValueError(f"Unsupported expression: {ast.dump(node)}")
-
+    """Calculate a mathematical expression."""
     try:
-        tree = ast.parse(expression, mode="eval")
-        return f"Result: {_eval(tree)}"
+        return f"Result: {eval(expression)}"
     except Exception:
         return "Invalid expression"
 

examples/python/a2ui/send_surface.py

@@ -1,27 +0,0 @@
-#!/usr/bin/env python3
-"""Minimal A2UI example — agent + send_a2ui_messages tool.
-
-Install:
-    pip install praisonaiagents[a2ui]
-
-Run:
-    python send_surface.py
-
-For live rendering, use PraisonAIUI with AG-UI (/agui) — see integrate-a2ui-frontend docs.
-"""
-
-from praisonaiagents import Agent
-from praisonaiagents.tools.a2ui_tools import send_a2ui_messages
-
-agent = Agent(
-    name="ui-assistant",
-    instructions=(
-        "When asked for UI, call send_a2ui_messages with A2UI v0.9 JSON "
-        "(createSurface on surface id 'main')."
-    ),
-    tools=[send_a2ui_messages],
-)
-
-if __name__ == "__main__":
-    result = agent.start("Show a simple welcome card on surface main")
-    print(result)

examples/python/camera/camera-continuous.py

@@ -75,7 +75,7 @@ class ContinuousCameraMonitor:
             agents=[self.vision_agent],
             tasks=[task],
             process="sequential",
-            output="silent",  # Reduced verbosity for continuous monitoring
+            verbose=0  # Reduced verbosity for continuous monitoring
         )
         
         return agents.start()

examples/python/cli/git_integration_example.py

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

examples/python/cli/repo_map_example.py

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

examples/python/cli/sandbox_execution_example.py

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

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

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

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

@@ -1,8 +1,6 @@
-import tempfile
-import os
-import sys
-import logging
 from praisonaiagents import Agent, Task, AgentTeam
+import logging
+import os
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -19,20 +17,14 @@ config = {
     }
 }
 
-# Create sample file in system temp directory
-_sample_path = os.path.join(tempfile.gettempdir(), "sample_knowledge.txt")
-if not os.path.exists(_sample_path):
-    with open(_sample_path, "w", encoding="utf-8") as f:
-        f.write("Mervin Praison is the creator of PraisonAI.\n")
-
 # Create an agent with knowledge capabilities
 knowledge_agent = Agent(
     name="KnowledgeAgent",
     role="Information Specialist",
     goal="Store and retrieve knowledge efficiently",
     backstory="Expert in managing and utilizing stored knowledge",
-    knowledge={**config, "sources": [_sample_path]},
-)
+    knowledge=["sample.pdf"],
+    knowledge=config)
 
 # Define a task for the agent
 knowledge_task = Task(
@@ -47,16 +39,8 @@ agents = AgentTeam(
     agents=[knowledge_agent],
     tasks=[knowledge_task],
     process="sequential",
+    user_id="user1"
 )
 
-if __name__ == "__main__":
-    if not os.environ.get("OPENAI_API_KEY"):
-        print("Set OPENAI_API_KEY to run this example.")
-        sys.exit(0)
-    if sys.platform == "win32":
-        try:
-            sys.stdout.reconfigure(encoding="utf-8")
-        except Exception:
-            pass
-    result = agents.start()
-    print(result)
+# Start execution
+result = agents.start()

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

@@ -138,7 +138,8 @@ def main():
     agent = Agent(
         name="AI Research Assistant",
         instructions="You are an AI research assistant. Use your knowledge base to answer questions about artificial intelligence topics.",
-        knowledge={**rerank_config, "sources": sample_documents},
+        knowledge=sample_documents,  # Will use default knowledge config
+        knowledge=rerank_config,  # Use reranking config
         llm="gpt-4o-mini"
     )
     

examples/python/concepts/rag-agents.py

@@ -32,12 +32,8 @@ rag_task = Task(
 agents = AgentTeam(
     agents=[rag_agent],
     tasks=[rag_task],
+    user_id="user1"
 )
 
-if __name__ == "__main__":
-    import os
-    import sys
-    if not os.environ.get("OPENAI_API_KEY"):
-        print("Set OPENAI_API_KEY to run this example.")
-        sys.exit(0)
-    agents.start()
+# Start Agents
+agents.start()

examples/python/concepts/repetitive-agents.py

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

examples/python/custom_tools/example_latency_tracking.py

@@ -61,6 +61,7 @@ def example_with_wrappers():
         role="Data Analyst",
         goal="Analyze data and provide insights",
         llm="gpt-4o-mini",
+        request_id="request_2"  # Unique request ID
     )
     
     # Operations are automatically tracked

examples/python/custom_tools/tools_with_latency.py

@@ -49,18 +49,7 @@ def calculate_with_tracking(expression: str) -> str:
     time.sleep(0.05)
     
     try:
-        import ast, operator
-        _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                ast.Mod: operator.mod, ast.Pow: operator.pow,
-                ast.USub: operator.neg, ast.UAdd: operator.pos}
-        def _ev(n):
-            if isinstance(n, ast.Expression): return _ev(n.body)
-            if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-            if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-            if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-            raise ValueError(f"Unsupported: {ast.dump(n)}")
-        result = _ev(ast.parse(expression, mode="eval"))
+        result = eval(expression)
         return f"Result: {result}"
     except Exception as e:
         return f"Error: {str(e)}"

examples/python/data/cot-agents.py

@@ -132,8 +132,7 @@ agents = AgentTeam(
     agents=[qa_generator, total_questions_evaluator, cot_generator, upload_to_huggingface],
     tasks=[generate_task, evaluate_total_questions_task, generate_cot_task, upload_to_huggingface_task],
     process="workflow",
-    execution={"max_iter": 30},
-    output="minimal",
+    max_iter=30, output="minimal"
 )
 
 agents.start()

examples/python/failover_example.py

@@ -18,7 +18,7 @@ openai_profile = AuthProfile(
     provider="openai",
     api_key=os.environ.get("OPENAI_API_KEY", "sk-..."),
     priority=1,  # Highest priority
-    rate_limit_rpm=100,
+    rate_limit=100,
 )
 
 anthropic_profile = AuthProfile(
@@ -26,7 +26,7 @@ anthropic_profile = AuthProfile(
     provider="anthropic",
     api_key=os.environ.get("ANTHROPIC_API_KEY", "sk-ant-..."),
     priority=2,  # Second priority
-    rate_limit_rpm=50,
+    rate_limit=50,
 )
 
 groq_profile = AuthProfile(
@@ -34,7 +34,7 @@ groq_profile = AuthProfile(
     provider="groq",
     api_key=os.environ.get("GROQ_API_KEY", "gsk-..."),
     priority=3,  # Third priority (fast but limited)
-    rate_limit_rpm=30,
+    rate_limit=30,
 )
 
 # Configure failover behavior

examples/python/general/memory_example.py

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

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

@@ -6,7 +6,6 @@ built-in validation and safety mechanisms for secure agent operations.
 """
 
 from praisonaiagents import Agent, Task, AgentTeam
-from praisonaiagents.config.feature_configs import GuardrailConfig
 from praisonaiagents.tools import internet_search
 
 print("=== Production Guardrails Patterns Example ===\n")
@@ -39,8 +38,8 @@ production_agent = Agent(
     goal="Generate safe, compliant content for production use",
     backstory="Production-ready agent with built-in safety and compliance checks",
     tools=[internet_search],
-    guardrails=GuardrailConfig(validator=content_guardrail, max_retries=3),
-)
+    guardrails=content_guardrail,
+    max_retries=3)
 
 # Create secure task with validation
 secure_task = Task(

examples/python/linear_agent_example.py

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

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

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

examples/python/mcp/mcp-tools-server-sse.py

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

examples/python/mcp/mcp-tools-server.py

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

examples/python/mcp_server_example.py

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

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

@@ -12,8 +12,8 @@ print("=== Advanced Graph Memory Integration Example ===\n")
 
 # Configure simple graph memory
 memory_config = {
+    "provider": "mem0",
     "config": {
-        "provider": "mem0",
         "vector_store": {
             "provider": "chroma",
             "config": {
@@ -60,8 +60,8 @@ query_task = Task(
 agents_system = AgentTeam(
     agents=[knowledge_agent],
     tasks=[build_task, query_task],
-    memory=memory_config,
-    output="verbose",
+    memory=True,
+    memory=memory_config, output="verbose"
 )
 
 print("Starting graph memory demonstration...")

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

@@ -12,8 +12,8 @@ print("=== Advanced Memory Search and Optimization Example ===\n")
 
 # Configure optimized memory settings
 memory_config = {
+    "provider": "mem0",
     "config": {
-        "provider": "mem0",
         "vector_store": {
             "provider": "chroma",
             "config": {
@@ -61,8 +61,8 @@ search_task = Task(
 agents_system = AgentTeam(
     agents=[research_agent],
     tasks=[build_memory_task, search_task],
-    memory=memory_config,
-    output="verbose",
+    memory=True,
+    memory=memory_config, output="verbose"
 )
 
 print("Starting memory search optimization demonstration...")

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

@@ -86,13 +86,13 @@ def main():
         agents=[researcher, retriever],
         tasks=[store_task, retrieve_task],
         memory={
+            "provider": "rag",
             "embedder": {
                 "provider": "gemini",
                 "config": {
                     "model": "text-embedding-004",
                 }
-            },
-            "config": {"provider": "sqlite"},
+            }
         }
     )
     

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

@@ -152,7 +152,8 @@ def main():
     agents_system = AgentTeam(
         agents=[researcher],
         tasks=[build_knowledge_task, query_knowledge_task, expand_knowledge_task],
-        memory=memory_config,
+        memory=True,
+        memory=memory_config
     )
     
     print("\n🚀 Starting graph memory demonstration...")

examples/python/memory_with_tracing_example.py

@@ -30,7 +30,7 @@ def main():
     try:
         # Create memory instance
         memory = Memory(config={
-            "backend": "sqlite",
+            "provider": "rag",
             "use_embedding": False,
             "agent_name": "demo_agent",  # Used in trace events
         })

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

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

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

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

examples/python/models/googlegemini/any-llm-agents-advanced.py

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

examples/python/mongodb/mongodb_comprehensive_example.py

@@ -189,8 +189,8 @@ def main():
     business_system = AgentTeam(
         agents=[data_manager, knowledge_curator, business_analyst, customer_service],
         tasks=business_tasks,
-        memory=mongodb_memory_config,
-        output="verbose",
+        memory=True,
+        memory=mongodb_memory_config, output="verbose"
     )
     
     # Execute the comprehensive business pipeline

examples/python/mongodb/mongodb_knowledge_example.py

@@ -55,10 +55,8 @@ def main():
         retrieve information from a comprehensive MongoDB knowledge base. You excel 
         at finding relevant information, synthesizing knowledge from multiple sources, 
         and providing accurate, context-aware responses.""",
-        knowledge={
-            **mongodb_knowledge_config,
-            "sources": [os.path.join(os.path.dirname(__file__), "llms.md")],
-        },
+        knowledge=mongodb_knowledge_config,
+        knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
         memory=True,
         llm="gpt-4o-mini"
     )

examples/python/mongodb/mongodb_memory_example.py

@@ -28,8 +28,8 @@ if not os.environ.get("OPENAI_API_KEY"):
 def main():
     # MongoDB memory configuration
     mongodb_memory_config = {
+        "provider": "mongodb",
         "config": {
-            "provider": "mongodb",
             "connection_string": "mongodb://localhost:27017/",  # Replace with your MongoDB connection string
             "database": "praisonai_memory",
             "use_vector_search": True,  # Enable Atlas Vector Search (requires MongoDB Atlas)
@@ -93,8 +93,8 @@ def main():
     research_system = AgentTeam(
         agents=[research_agent],
         tasks=research_tasks,
-        memory=mongodb_memory_config,
-        output="verbose",
+        memory=True,
+        memory=mongodb_memory_config, output="verbose"
     )
     
     # Execute the research pipeline

examples/python/rag/agent_vs_auto_rag.py

@@ -50,7 +50,7 @@ agent = Agent(
     name="TechSupport",
     instructions="You are a technical support assistant.",
     knowledge=[sample_doc],
-    memory={"user_id": "comparison_user"},
+    user_id="comparison_user",
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/rag/auto_rag_agent_basic.py

@@ -12,7 +12,6 @@ Environment:
 """
 
 import os
-import tempfile
 from praisonaiagents import Agent, AutoRagAgent
 
 # Ensure API key is set
@@ -21,8 +20,8 @@ if not os.environ.get("OPENAI_API_KEY"):
     exit(1)
 
 # Create a sample document for testing
-sample_doc = os.path.join(tempfile.gettempdir(), "sample_doc.txt")
-with open(sample_doc, "w", encoding="utf-8") as f:
+sample_doc = "/tmp/sample_doc.txt"
+with open(sample_doc, "w") as f:
     f.write("""
 PraisonAI Framework Documentation
 
@@ -45,7 +44,7 @@ agent = Agent(
     name="DocBot",
     instructions="You are a helpful documentation assistant. Answer questions based on the provided context.",
     knowledge=[sample_doc],
-    memory={"user_id": "example_user"},
+    user_id="example_user",  # Required for RAG retrieval
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/rag/auto_rag_agent_policy.py

@@ -38,7 +38,7 @@ agent = Agent(
     name="PolicyBot",
     instructions="You are a customer service assistant.",
     knowledge=[sample_doc],
-    memory={"user_id": "policy_user"},
+    user_id="policy_user",
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/rag/rag_basic.py

@@ -44,7 +44,7 @@ agent = Agent(
     name="MLExpert",
     instructions="You are a machine learning expert. Answer questions based on the provided context.",
     knowledge=[sample_doc],
-    memory={"user_id": "rag_user"},
+    user_id="rag_user",
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/rag/rag_hybrid.py

@@ -59,7 +59,7 @@ agent = Agent(
     name="APIDocBot",
     instructions="You are an API documentation assistant.",
     knowledge=[sample_doc],
-    memory={"user_id": "hybrid_user"},
+    user_id="hybrid_user",
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/rag/rag_with_citations.py

@@ -44,7 +44,7 @@ agent = Agent(
     name="ResearchBot",
     instructions="You are a research assistant. Always cite your sources.",
     knowledge=[doc1, doc2],
-    memory={"user_id": "citation_user"},
+    user_id="citation_user",
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/session/session_persistence_example.py

@@ -28,7 +28,7 @@ def example_basic_persistence():
     agent = Agent(
         name="Assistant",
         instructions="You are a helpful assistant. Be very brief.",
-        memory={"session_id": session_id},
+        session_id=session_id,
         output="minimal",
     )
     
@@ -59,7 +59,7 @@ def example_session_restoration():
     agent1 = Agent(
         name="MemoryBot",
         instructions="You are a helpful assistant. Be very brief.",
-        memory={"session_id": session_id},
+        session_id=session_id,
         output="minimal",
     )
     
@@ -72,7 +72,7 @@ def example_session_restoration():
     agent2 = Agent(
         name="MemoryBot",
         instructions="You are a helpful assistant. Be very brief.",
-        memory={"session_id": session_id},
+        session_id=session_id,
         output="minimal",
     )
     

examples/python/sessions/comprehensive-session-management.py

@@ -172,7 +172,7 @@ print("="*70)
 # Create sessions for different users working on the same project
 reviewer_session = Session(
     session_id="peer_review_001",
-    user_id="user_reviewer_01",
+    user_id="user_reviewer_01",  # Different user
     storage_path=session_dir
 )
 

examples/python/stateful/memory-quality-example.py

@@ -14,12 +14,13 @@ def main():
     
     # Initialize memory with quality-based configuration
     memory_config = {
-        "backend": "sqlite",
-        "user_id": "quality_demo_user",
+        "provider": "rag",
+        "use_embedding": True,
+        "rag_db_path": "quality_memory_db"
     }
     
     memory = Memory(config=memory_config, verbose=5)
-    print(f"📦 Initialized memory system with backend: {memory_config['backend']}")
+    print(f"📦 Initialized memory system with provider: {memory_config['provider']}")
     
     # Create agent with memory
     agent = Agent(
@@ -27,6 +28,7 @@ def main():
         role="Information Quality Specialist",
         instructions="Store and retrieve information with quality assessment",
         memory=memory,
+        user_id="quality_demo_user"
     )
     
     print(f"🤖 Created agent: {agent.name}")

examples/python/stateful/session-example.py

@@ -15,7 +15,7 @@ def main():
     # Create a session with persistent storage
     session = Session(
         session_id="demo_chat_001",
-        user_id="demo_user",
+        user_id="demo_user"
     )
     
     print(f"📝 Created session: {session}")

examples/python/stateful/workflow-state-example.py

@@ -88,8 +88,9 @@ def main():
     workflow = AgentTeam(
         agents=[researcher, analyzer, writer],
         tasks=[research_task, analysis_task, report_task],
-        memory={"user_id": "research_project_001"},
+        memory=True,
         process="workflow",
+        user_id="research_project_001",
     )
     
     print(f"🤖 Created workflow with {len(workflow.agents)} agents and {len(workflow.tasks)} tasks")

examples/python/token-metrics/01_basic_token_tracking.py

@@ -14,7 +14,7 @@ def main():
         goal="Find information about a topic",
         backstory="You are an expert researcher who finds comprehensive information.",
         llm="gpt-4o-mini",  # Use a cost-effective model for examples
-        output={"metrics": True}  # Token metrics via consolidated output config
+        metrics=True  # 🎯 SIMPLIFIED: Just add this parameter!
     )
     
     # Create a simple task

examples/python/token-metrics/02_session_metrics.py

@@ -14,7 +14,7 @@ def main():
         goal="Analyze data and provide insights",
         backstory="You are an experienced data analyst with expertise in market research.",
         llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable metrics tracking
     )
     
     writer_agent = Agent(
@@ -23,7 +23,7 @@ def main():
         goal="Create well-structured content",
         backstory="You are a skilled technical writer who creates clear, engaging content.",
         llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable metrics tracking
     )
     
     # Create multiple tasks

examples/python/token-metrics/03_cost_estimation.py

@@ -14,7 +14,7 @@ def main():
         goal="Conduct comprehensive market research",
         backstory="You are a seasoned market researcher with 10+ years of experience.",
         llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable automatic metrics tracking
     )
     
     strategy_agent = Agent(
@@ -23,7 +23,7 @@ def main():
         goal="Develop actionable business strategies",
         backstory="You are a strategic consultant who translates research into business plans.",
         llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable automatic metrics tracking
     )
     
     # Create tasks that will generate substantial token usage

examples/python/tools/exa-tool/rag_examples/agentic_rag/rag_agent.py

@@ -22,8 +22,7 @@ rag_agent = Agent(
     
     You can use the internet_search function to search the web when needed.""",
     llm="gpt-4o",
-    output={"markdown": True},
-)
+    markdown=True)
 
 if __name__ == "__main__":
     print("🤖 Thai Recipe RAG Agent is ready!")

examples/python/ui/ollama-rag-agents-streamlit.py

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

examples/python/usecases/domain-context-solution.py

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

examples/python/workflows/workflow_conditional.py

@@ -5,7 +5,7 @@ Demonstrates using should_run to conditionally execute agent steps
 based on input or previous results.
 """
 
-from praisonaiagents import Agent, AgentFlow, Task
+from praisonaiagents import Agent, Workflow, Task
 
 # Condition functions
 def is_technical(ctx) -> bool:

examples/python/workflows/workflow_mixed_steps.py

@@ -53,7 +53,7 @@ workflow = AgentFlow(
         # Step 4: Function - formats output
         format_output
     ],
-    llm="gpt-4o-mini",  # Used for action-based steps
+    default_llm="gpt-4o-mini"  # Used for action-based steps
 )
 
 if __name__ == "__main__":

examples/rag/external_sources.py

@@ -207,7 +207,7 @@ def api_augmented_rag():
         instructions="""You are a financial assistant with access to market APIs.
         Provide accurate financial information and context.
         Always note that prices are subject to change.""",
-        memory={"user_id": "api_demo"},
+        user_id="api_demo"
     )
     
     queries = [

examples/retrieval/basic_retrieval.py

@@ -51,15 +51,13 @@ print("=" * 60)
 agent = Agent(
     name="Knowledge Agent",
     instructions="You are a helpful assistant that answers questions based on the provided knowledge. Be concise.",
-    knowledge={
-        "sources": [".praison/test_docs/overview.txt"],
-        "config": {
-            "policy": "always",
-            "top_k": 3,
-            "citations": True,
-            "max_context_tokens": 2000,
-        },
-    },
+    knowledge=[".praison/test_docs/overview.txt"],
+    retrieval_config={
+        "policy": "always",      # Always retrieve for this demo
+        "top_k": 3,              # Get top 3 chunks
+        "citations": True,       # Include citations
+        "max_context_tokens": 2000,
+    }
 )
 
 # Test 1: Basic chat with retrieval

examples/scientific_writing/agents.yaml

@@ -1,40 +0,0 @@
-# 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."

examples/scientific_writing/multi_agent_paper.py

@@ -1,73 +0,0 @@
-"""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)

examples/scientific_writing/scientific_writer.py

@@ -1,85 +0,0 @@
-"""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)

examples/serve/mcp_http_server.py

@@ -44,33 +44,12 @@ def main():
     
     def calculate(expression: str) -> str:
         """Calculate a math expression safely."""
-        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__": {}})
-            )
+            # Safe eval for basic math
+            allowed = set("0123456789+-*/.() ")
+            if all(c in allowed for c in expression):
+                return str(eval(expression))
+            return "Error: Invalid expression"
         except Exception as e:
             return f"Error: {e}"
     

examples/skills/basic_skill_usage.py

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

examples/yaml/MULTI_CHANNEL_GUIDE.md

@@ -1,222 +0,0 @@
-# Multi-Channel Bot Deployment Guide
-
-This guide explains how to set up multiple bots on the same platform (e.g., multiple Telegram bots) for different roles using PraisonAI, similar to Hermes-style AI workforces.
-
-## Overview
-
-PraisonAI supports running multiple bots on the same platform, each routing to different agents with specialized knowledge and capabilities. This is useful for:
-
-- **Role-based bots**: `@company_cfo_bot`, `@company_ops_bot`, `@company_content_bot`
-- **Department-specific agents**: Each with different knowledge brains and tool access
-- **Workflow separation**: Different bots handle different types of requests
-
-## Quick Start with Onboard Wizard
-
-The easiest way to set up multiple channels is using the onboard wizard:
-
-```bash
-praisonai onboard
-```
-
-The wizard now supports:
-1. **Multi-channel setup**: Add multiple bots for the same platform
-2. **Role-based naming**: Automatically generates `TELEGRAM_<ROLE>_BOT_TOKEN` environment variables
-3. **Agent routing**: Each channel routes to a different agent
-
-### Onboard Wizard Flow
-
-1. **Choose platform**: Start with your first platform (e.g., telegram)
-2. **Add more channels**: Wizard asks "Add another bot channel?"
-3. **Specify role**: Enter role name (e.g., "cfo", "ops", "content")
-4. **Configure tokens**: Each channel gets its own environment variable
-5. **Test connections**: Wizard tests each bot separately
-
-## Environment Variable Convention
-
-For multiple bots on the same platform, use this naming convention:
-
-| Channel | Environment Variable | Bot Purpose |
-|---------|---------------------|-------------|
-| `telegram_cfo` | `TELEGRAM_CFO_BOT_TOKEN` | CFO-related tasks |
-| `telegram_ops` | `TELEGRAM_OPS_BOT_TOKEN` | Operations tasks |
-| `telegram_content` | `TELEGRAM_CONTENT_BOT_TOKEN` | Content creation |
-| `telegram_support` | `TELEGRAM_SUPPORT_BOT_TOKEN` | Customer support |
-
-### Setting Up Tokens
-
-1. **Create bots in @BotFather**:
-   ```text
-   /newbot
-   Bot name: Company CFO Bot
-   Username: @company_cfo_bot
-   -> Copy token
-   
-   /newbot
-   Bot name: Company Ops Bot
-   Username: @company_ops_bot
-   -> Copy token
-   ```
-
-2. **Set environment variables**:
-   ```bash
-   export TELEGRAM_CFO_BOT_TOKEN="123456789:ABC..."
-   export TELEGRAM_OPS_BOT_TOKEN="987654321:DEF..."
-   export TELEGRAM_CONTENT_BOT_TOKEN="456789123:GHI..."
-   ```
-
-3. **Or use `~/.praisonai/.env`**:
-   ```env
-   TELEGRAM_CFO_BOT_TOKEN=123456789:ABC...
-   TELEGRAM_OPS_BOT_TOKEN=987654321:DEF...
-   TELEGRAM_CONTENT_BOT_TOKEN=456789123:GHI...
-   TELEGRAM_ALLOWED_USERS=123456789,987654321
-   ```
-
-## Example Configuration
-
-See `multi-telegram-hermes-workforce.yaml` for a complete example.
-
-### Key Features
-
-1. **Multiple Channels**:
-   ```yaml
-   channels:
-     telegram_cfo:
-       platform: telegram
-       token: ${TELEGRAM_CFO_BOT_TOKEN}
-       routes:
-         default: cfo
-     
-     telegram_ops:
-       platform: telegram
-       token: ${TELEGRAM_OPS_BOT_TOKEN}
-       routes:
-         default: ops
-   ```
-
-2. **Specialized Agents**:
-   ```yaml
-   agents:
-     cfo:
-       instructions: "You are a CFO agent. Help with financial analysis..."
-       model: gpt-4o-mini
-     
-     ops:
-       instructions: "You are an ops agent. Help with system monitoring..."
-       model: gpt-4o-mini
-   ```
-
-## Validation with Doctor
-
-Use the doctor command to validate your multi-channel setup:
-
-```bash
-praisonai doctor
-```
-
-The doctor checks for:
-- ✅ **Unique tokens**: Each channel uses a different bot token
-- ✅ **Naming convention**: Follows `PLATFORM_ROLE_BOT_TOKEN` pattern  
-- ✅ **Missing tokens**: All referenced environment variables are set
-- ❌ **Duplicate tokens**: Same token used by multiple channels (fails)
-
-## Deployment
-
-### Start Gateway
-
-```bash
-# Start the multi-bot gateway
-praisonai gateway start --config multi-telegram-hermes-workforce.yaml
-
-# Or install as daemon
-praisonai onboard  # Wizard installs daemon automatically
-praisonai gateway status
-```
-
-### Verify Setup
-
-1. **Check health**: `curl http://127.0.0.1:8765/health`
-2. **View dashboard**: `praisonai claw` → http://127.0.0.1:8082
-3. **Test bots**: Send messages to each bot to verify routing
-
-## Common Patterns
-
-### Basic Multi-Role Setup
-```yaml
-# Single platform, multiple roles
-channels:
-  telegram_support:
-    token: ${TELEGRAM_SUPPORT_BOT_TOKEN}
-    routes:
-      default: support_agent
-  
-  telegram_sales:
-    token: ${TELEGRAM_SALES_BOT_TOKEN}
-    routes:
-      default: sales_agent
-```
-
-### Cross-Platform Workforce
-```yaml
-# Multiple platforms, multiple roles
-channels:
-  telegram_cfo:
-    platform: telegram
-    token: ${TELEGRAM_CFO_BOT_TOKEN}
-    routes:
-      default: cfo
-  
-  discord_ops:
-    platform: discord
-    token: ${DISCORD_OPS_BOT_TOKEN}
-    routes:
-      default: ops
-  
-  slack_content:
-    platform: slack
-    token: ${SLACK_CONTENT_BOT_TOKEN}
-    routes:
-      default: content
-```
-
-## Troubleshooting
-
-### Common Issues
-
-1. **"Token is used by multiple channels"**
-   - Each bot needs its own unique token from @BotFather
-   - Check environment variables for duplicates
-
-2. **"Channel skipped - no token"**
-   - Set the environment variable: `export TELEGRAM_CFO_BOT_TOKEN="..."`
-   - Or update `~/.praisonai/.env`
-
-3. **"Bot not responding"**
-   - Verify token is correct: `praisonai doctor`
-   - Check bot is not already running elsewhere
-   - Ensure allowlist includes your user ID
-
-### Debugging Commands
-
-```bash
-# Check configuration
-praisonai doctor
-
-# View logs
-praisonai gateway logs
-
-# Test connection
-curl -H "Authorization: Bearer $GATEWAY_AUTH_TOKEN" \
-     http://127.0.0.1:8765/info
-```
-
-## Migration from Single-Bot Setup
-
-If you currently have a single bot setup, you can add more channels:
-
-1. **Run onboard wizard**: `praisonai onboard`
-2. **Add new channels**: Wizard will preserve existing setup
-3. **Update configuration**: New channels added to existing `bot.yaml`
-4. **Restart gateway**: `praisonai gateway restart`
-
-The wizard preserves backward compatibility with single-bot configurations.

examples/yaml/a2ui-canvas.yaml

@@ -1,19 +0,0 @@
-# A2UI canvas agent — YAML example
-#
-# Usage:
-#   pip install praisonaiagents[a2ui]
-#   praisonai agent run --file a2ui-canvas.yaml --prompt "Show a welcome card"
-#
-# For live A2UI rendering, use PraisonAIUI (AG-UI / surfaces):
-#   pip install praisonaiui
-#   See examples/python/a2ui/send_surface.py and PraisonAIUI example 29.
-
-agent:
-  name: "ui-assistant"
-  instructions: |
-    You are a helpful assistant. When the user asks for UI elements
-    (buttons, cards, forms), call send_a2ui_messages with valid A2UI v0.9 JSON.
-    Use surface id "main" unless told otherwise.
-  llm: gpt-4o-mini
-  tools:
-    - send_a2ui_messages

examples/yaml/agent-with-mcp.yaml

@@ -1,40 +0,0 @@
-# Example: Agent with MCP Tool Integration
-#
-# This example shows how to configure an agent with MCP (Model Context Protocol)
-# servers for additional tool capabilities.
-#
-# Usage:
-#   praisonai workflow run --file examples/yaml/agent-with-mcp.yaml
-
-agent:
-  name: mcp_assistant
-  instructions: |
-    You are a helpful assistant with access to filesystem and time tools.
-    Use the MCP tools when needed to help users with file operations or time queries.
-  llm: gpt-4o-mini
-
-# MCP server configuration
-mcp:
-  servers:
-    filesystem:
-      # Filesystem access for the agent
-      command: npx
-      args:
-        - "-y"
-        - "@modelcontextprotocol/server-filesystem"
-        - "."
-      env: {}
-      enabled: true
-    
-    time:
-      # Time and date utilities
-      command: npx
-      args:
-        - "-y" 
-        - "@modelcontextprotocol/server-time"
-      env: {}
-      enabled: true
-
-# NOTE: Per-server tool filtering (tools.include/exclude) is not yet supported
-# by the MCP server config schema. Tool filtering should be applied after
-# calling mcp.get_tools() in your application code.

examples/yaml/linear-bot.yaml

@@ -1,35 +0,0 @@
-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