AG2 (AutoGen) — Multi-Agent Conversation Framework

Overview

AG2 is the community continuation of Microsoft's AutoGen. Since v1.0 (current: 1.1.1, September 2026) it is a new, async-first framework and is not backward compatible with classic AutoGen:

You see in code	You have	Install
from ag2 import Agent, from ag2.config import OpenAIConfig	AG2 v1	pip install "ag2[openai]"
from autogen import ConversableAgent, GroupChat, UserProxyAgent	classic AutoGen / AG2 0.x	pip install ag2-classic (its README also shows autogen[openai])

Classic is in maintenance mode (security fixes only). Do not mix the two APIs in one file. Provider extras for v1: ag2[openai], ag2[anthropic], ag2[gemini], ag2[ollama] and others. Docs: https://docs.ag2.ai (v1) and https://classic.docs.ag2.ai (classic).

Instructions

AG2 v1: an agent with a tool

import asyncio
from ag2 import Agent, tool
from ag2.config import OpenAIConfig      # also AnthropicConfig, GeminiConfig, OllamaConfig, ...


@tool
async def count_lines(path: str) -> str:
    """Count the lines in a source file inside the current project."""
    with open(path, encoding="utf-8") as f:
        return f"{path}: {sum(1 for _ in f)} lines"


reviewer = Agent(
    "reviewer",
    prompt="You review Python code. Use tools to inspect files before judging them.",
    config=OpenAIConfig("gpt-4o-mini"),   # key is read from OPENAI_API_KEY
    tools=[count_lines],
)


async def main() -> None:
    reply = await reviewer.ask("How big is app/main.py?")
    print(reply.body)
    follow_up = await reply.ask("Is that too large for one module?")  # keeps the history
    print(follow_up.body)


asyncio.run(main())

• agent.ask(...) returns a reply; reply.body is the text and reply.ask(...) continues the same conversation.
• A tool is a typed function with a docstring decorated with @tool; the signature and docstring become the schema the model sees.
• Stream or observe a turn with async with agent.run("...") as run: run.start(); async for event in run.stream... and await run.result().
• Human approval: pass hitl_hook= to Agent; it receives a HumanInputRequest (from ag2.events) and returns a HumanMessage(content=...).
• Test without an LLM: from ag2.testing import TestConfig and pass TestConfig("scripted reply") (or ToolCallEvents) as config.

AG2 v1: several agents

v1 replaces GroupChat, swarm and nested chats with a Network: a Hub registers agents, and they talk over typed channels (conversation for free two-party chat, consulting for one question and one answer, discussion for round-robin among N agents, workflow for a TransitionGraph of conditional handoffs, the closest analogue to GroupChat). Read the group-chat migration guide on docs.ag2.ai before writing this part; the classes live in ag2.network (Hub, Channel, TransitionGraph, Handoff).

Classic AutoGen (ag2-classic): two agents and group chat

Only for existing code that imports autogen.

from autogen import ConversableAgent, LLMConfig, UserProxyAgent, register_function
from autogen.agentchat import run_group_chat
from autogen.agentchat.group.patterns import AutoPattern

llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")   # key from OPENAI_API_KEY

engineer = ConversableAgent(
    name="engineer",
    system_message="You write clean, tested Python. Explain design decisions briefly.",
    llm_config=llm_config,
)
runner = UserProxyAgent(
    name="runner",
    human_input_mode="NEVER",              # NEVER / ALWAYS / TERMINATE
    max_consecutive_auto_reply=10,
    is_termination_msg=lambda m: "TERMINATE" in (m.get("content") or ""),
    code_execution_config={"work_dir": "workspace", "use_docker": True},
)
result = runner.initiate_chat(engineer, message="Write a FastAPI /health endpoint with a pytest test.", max_turns=6)

reviewer = ConversableAgent(name="reviewer", system_message="You review code for bugs and security issues.", llm_config=llm_config)
pattern = AutoPattern(
    agents=[engineer, reviewer],
    initial_agent=engineer,
    group_manager_args={"name": "manager", "llm_config": llm_config},
)
response = run_group_chat(pattern=pattern, messages="Build a rate limiter for our API.", max_rounds=12)

Tools in classic: register_function(fn, caller=engineer, executor=runner, description="..."). The caller proposes the call, the executor runs it. Classic also has LLMConfig.from_json(path="OAI_CONFIG_LIST") for a config file. Only the register and run_group_chat calls above were checked against the classic README; check other classic details at classic.docs.ag2.ai.

Examples

Example 1: Single agent that inspects a repository

User request: "Make me an AG2 agent that can count lines in files and answer questions about them."

Install with python -m venv .venv && .venv/bin/pip install "ag2[openai]", save the v1 code above as reviewer.py, set OPENAI_API_KEY in the environment and run .venv/bin/python reviewer.py. The agent calls count_lines for app/main.py, then prints a sentence such as "app/main.py has 212 lines". The follow-up question reuses the same history.

Example 2: Unit-test an agent without calling an LLM

User request: "I want to test my AG2 tool wiring in CI without an API key."

from ag2 import Agent
from ag2.events import ToolCallEvent
from ag2.testing import TestConfig

config = TestConfig(
    ToolCallEvent(name="count_lines", arguments='{"path": "app/main.py"}'),
    "app/main.py has 212 lines",
)
agent = Agent("reviewer", prompt="Use tools.", config=config, tools=[count_lines])
reply = await agent.ask("How big is app/main.py?")
assert "212" in reply.body

The scripted model requests the tool, AG2 runs the real count_lines, and the scripted second turn is the answer. Each ask consumes scripted turns, so supply enough for follow-ups.

Guidelines

• Check which API a file uses (ag2 vs autogen imports) before editing; v1 and classic are different libraries.
• Keep keys in environment variables, never in code or a committed OAI_CONFIG_LIST.
• Give each agent a narrow prompt and only the tools it needs; vague prompts produce unfocused loops.
• Always bound loops: max_turns / max_rounds and max_consecutive_auto_reply in classic, and watch token usage in group chats.
• Classic code execution runs model-written code: keep use_docker: True, and never point work_dir at a real project.
• Require human approval (hitl_hook in v1, human_input_mode="ALWAYS" or "TERMINATE" in classic) before irreversible actions.
• Do not migrate a working classic system to v1 unprompted; v1 is a rewrite of orchestration, not a version bump.