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.

Quick Start

1. Declare the tool in YAML

   Reference the tool by name under a role’s tools: list.

   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.
   
2. Define the tool and run

   Define get_weather as a plain callable with a __name__ and a docstring, then run the YAML.

   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()
   

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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.

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

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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.

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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 for the full timeout contract.

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Best Practices

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.

def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"Sunny in {city}."

Don't rely on lambdas

Lambdas have no __name__, so the adapter cannot resolve a name and skips them with a warning. Use def.

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.

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Related

AutoGen Tool Registry Migration

Migrate tool registries into AutoGen

AutoGen LLM config_list

Configure the AutoGen LLM config_list