> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# AutoGen v0.2 YAML Tools

> Wire YAML-declared tools into AutoGen v0.2 assistant + user_proxy

YAML `tools:` under `framework: autogen` (v0.2) are now fully wired into the AutoGen chat — the assistant advertises them to the LLM and the user\_proxy executes them.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "AutoGen v0.2 YAML Tools"
        YAML[📄 YAML tools:] --> Adapter[🔧 AutoGenAdapter]
        Adapter --> LLM[🧠 assistant.register_for_llm]
        Adapter --> Exec[⚙️ user_proxy.register_for_execution]
        LLM --> Chat[✅ AutoGen chat loop]
        Exec --> Chat
    end

    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef adapter fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef process fill:#F59E0B,stroke:#7C90A0,color:#fff
    classDef output fill:#10B981,stroke:#7C90A0,color:#fff

    class YAML input
    class Adapter adapter
    class LLM,Exec process
    class Chat output
```

## Quick Start

<Steps>
  <Step title="Declare the tool in YAML">
    Reference the tool by name under a role's `tools:` list.

    ```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    framework: autogen
    topic: Weather report

    roles:
      weather_agent:
        role: Meteorologist
        goal: Report weather for a city
        backstory: Reliability-focused agent.
        tools:
          - get_weather
        tasks:
          report:
            description: Report the weather for {topic}
            expected_output: One paragraph forecast.
    ```
  </Step>

  <Step title="Define the tool and run">
    Define `get_weather` as a plain callable with a `__name__` and a docstring, then run the YAML.

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonai import PraisonAI

    def get_weather(city: str) -> str:
        """Return the current weather for a city."""
        return f"It is sunny and 22°C in {city}."

    praison = PraisonAI(agent_file="agents.yaml", tools=[get_weather])
    praison.run()
    ```
  </Step>
</Steps>

***

## How It Works

The adapter registers each callable on both AutoGen v0.2 agents so the LLM can call it and the user\_proxy can run it.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant LLM
    participant Assistant as assistant
    participant Proxy as user_proxy
    participant Tool

    LLM->>Assistant: request tool call
    Assistant->>Proxy: dispatch (register_for_execution)
    Proxy->>Tool: run callable
    Tool-->>Proxy: result
    Proxy-->>LLM: tool result
```

| Step             | Method                                          | Purpose                           |
| ---------------- | ----------------------------------------------- | --------------------------------- |
| Advertise schema | `assistant.register_for_llm(name, description)` | The LLM learns the tool exists    |
| Execute call     | `user_proxy.register_for_execution(name)`       | The user\_proxy runs the callable |
| Description      | First line of `__doc__`, else `Tool <name>`     | Shown to the LLM                  |

***

## Skip Rules

Tools that cannot be wired are logged and skipped — never silently dropped.

| Situation                                      | Behaviour                                             |
| ---------------------------------------------- | ----------------------------------------------------- |
| Non-callable tool                              | Skipped with a warning                                |
| No resolvable `__name__` / `name`              | Skipped with a warning                                |
| Duplicate tool name on the shared `user_proxy` | First callable kept, duplicate skipped with a warning |

Duplicate handling prevents AutoGen's last-write-wins `_function_map` from silently overwriting an earlier tool when two agents declare a same-named callable.

***

## Timeout Translation

AutoGen v0.2 runs tools inside its chat loop and expects a string to hand back to the LLM. When a per-call tool timeout raises `ToolTimeoutError`, the adapter's `_wrap_tool_for_execution` catches it and returns `"Error: tool '<name>' timed out (…)."` so the conversation keeps moving instead of aborting.

See [Tool Configuration → Wrapper-Level Tool Timeout](/docs/configuration/tool-config#wrapper-level-tool-timeout-yaml-cli-defense-in-depth) for the full timeout contract.

***

## Best Practices

<AccordionGroup>
  <Accordion title="Give tools a clear __name__ and docstring">
    The tool name comes from `__name__` (or a `name` attribute), and the LLM-facing description is the first line of the docstring. Named functions with a one-line docstring wire cleanly.

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    def get_weather(city: str) -> str:
        """Return the current weather for a city."""
        return f"Sunny in {city}."
    ```
  </Accordion>

  <Accordion title="Don't rely on lambdas">
    Lambdas have no `__name__`, so the adapter cannot resolve a name and skips them with a warning. Use `def`.
  </Accordion>

  <Accordion title="Avoid same-named tools across agents on one run">
    Two agents declaring a tool with the same `__name__` share one `user_proxy`; only the first callable is registered for execution. Give each tool a distinct name.
  </Accordion>
</AccordionGroup>

***

## Related

<CardGroup cols={2}>
  <Card title="AutoGen Tool Registry Migration" icon="arrows-rotate" href="/docs/features/tool-registry-autogen-migration">
    Migrate tool registries into AutoGen
  </Card>

  <Card title="AutoGen LLM config_list" icon="list" href="/docs/features/llm-autogen-config-list">
    Configure the AutoGen LLM config\_list
  </Card>
</CardGroup>