Two Agent Chat

You are creating an AG2 two-agent chat -- the simplest multi-agent pattern.

Instructions

1. Ask the user for:

   • What each agent's role is
   • How many turns of conversation (default: 2-4)
   • Whether to summarize the result

2. Create the two-agent chat following this pattern:

Two-Agent Chat Pattern

import asyncio
from autogen import ConversableAgent, LLMConfig

llm_config = LLMConfig({"api_type": "anthropic", "model": "claude-sonnet-4-6"})

agent_a = ConversableAgent(
    name="agent_a",
    system_message="Your role and behavior instructions.",
    llm_config=llm_config,
)

agent_b = ConversableAgent(
    name="agent_b",
    system_message="Your role and behavior instructions.",
    llm_config=llm_config,
)


async def main():
    response = await agent_a.a_run(
        agent_b,
        message="Your task or question here",
        max_turns=2,
        summary_method="reflection_with_llm",
    )
    await response.process()
    print(await response.summary)


if __name__ == "__main__":
    asyncio.run(main())

Key Rules

• Use a_run (async) with .process() then .summary -- NOT initiate_chat
• max_turns controls conversation rounds (each turn = both agents speak)
• summary_method="reflection_with_llm" generates a summary; use "last_msg" for the raw last message
• Use LLMConfig({...}) -- NOT a raw dict like {"model": "..."}
• You can use different models per agent (e.g., fast model for one, capable for the other)

Common Patterns

• Creator + Reviewer: Draft content, get feedback, revise
• Student + Teacher: Ask questions, get explanations
• Interviewer + Expert: Deep-dive into a topic
• Debater A + Debater B: Explore both sides of an argument

Example

See examples/student_teacher.py for a student-teacher Q&A conversation.