autogen

library0.13.4pypypi✓ verified 85d ago

AG2, distributed via the 'autogen' PyPI package, is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. It aims to streamline the development and research of agentic AI, offering features such as multi-agent conversations, support for various large language models (LLMs) and tool use, autonomous and human-in-the-loop workflows. The library is actively maintained with frequent minor releases, currently at version 0.11.5, and is on a roadmap towards a major v1.0 release.

llm-agents ai-ml

pip install "ag2[openai]"

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · AUTOGEN

autogen

llm-agentspythonv0.13.4

Install

8.1s avg

Import

3803ms

Disk

75MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.13.4 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.940 runs

installs and imports cleanly · install 0.0s · import 3.964s · 76.3MB

glibc

py 3.10–3.940 runs

installs and imports cleanly · install 8.1s · import 3.642s · 76MB

75MB installed

● package 75MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

ConversableAgent

✓ from autogen import ConversableAgent

LLMConfig

✓ from autogen import LLMConfig

UserProxyAgent

✓ from autogen import UserProxyAgent

GroupChat

✓ from autogen import GroupChat

GroupChatManager

✓ from autogen import GroupChatManager

This quickstart demonstrates how to create a simple 'ConversableAgent' with an LLM configuration and run an interaction. It uses OpenAI's API, expecting `OPENAI_API_KEY` to be set as an environment variable. The `run()` method prepares the conversation, and `process()` executes it.

import os from autogen import ConversableAgent, LLMConfig # Set your OpenAI API key as an environment variable # Example: export OPENAI_API_KEY="YOUR_API_KEY" llm_config = LLMConfig( { "api_type": "openai", "model": "gpt-5-nano", # or any other supported model "api_key": os.environ.get("OPENAI_API_KEY", "") } ) # Create our LLM agent my_agent = ConversableAgent( name="poetic_assistant", system_message="You are a poetic AI assistant, respond in rhyme.", llm_config=llm_config, ) # Run the agent with a prompt and process the response response = my_agent.run( message="In one sentence, what's the big deal about AI?", max_turns=3, user_input=False, # Set to True for interactive input ) print(response.process().json())

Debug

Known issues

breakingThe current `autogen.agentchat` framework is being transitioned to maintenance mode. The `autogen.beta` framework (with `Agent` as the core class) will become the official `AG2 v1.0`. Expect deprecation notices and a migration guide in upcoming minor versions (v0.12, v0.13, v0.14) before v1.0.

fix

Monitor the official AG2 documentation and release roadmap for migration guides and updated API usage as v1.0 approaches. Begin experimenting with `autogen.beta` for new projects.

affects: v0.12+

gotchaStarting from `ag2` version 0.8, the `openai` package (and other LLM provider packages) is no longer installed by default. You must explicitly include it as an extra dependency during installation.

fix

Ensure you install AG2 with the necessary extras, e.g., `pip install "ag2[openai]"` for OpenAI support, or `pip install "ag2[gemini,anthropic]"` for multiple providers.

affects: 0.8.0+

gotchaWhen installing AG2 with extra dependencies on macOS, a 'no matches found' error may occur if the package name with extras is not enclosed in double quotes.

fix

Wrap the package and extras in double quotes: `pip install "ag2[openai]"`.

affects: All

gotchaAG2 agents, by default, prefer to perform code execution within a Docker container. If Docker is not running or properly configured, code execution tasks might fail.

fix

Ensure Docker Desktop or your Docker environment is running and accessible. For non-Docker code execution, configure `code_execution_config={'use_docker': False}` for `UserProxyAgent`.

affects: All

Errors

Common errors & fixes

pydantic_core._pydantic_core.ValidationError: 1 validation error for _LLMConfig

Incorrect or incomplete configuration for `LLMConfig`, often missing required fields or having invalid types.

fix

Review your `LLMConfig` dictionary. Ensure all necessary keys like `api_type`, `model`, and `api_key` are present and correctly formatted, matching the requirements of your chosen LLM provider.

UnboundLocalError: cannot access local variable 'task' where it is not associated with a value

This error typically arises in complex multi-agent orchestrations, particularly when a variable like 'task' is referenced before it has been assigned a value within a specific scope, potentially due to logic gaps in custom agent behaviors or conversation flows.

fix

Inspect the agent's reply logic or custom functions where the 'task' variable is used. Ensure 'task' is always initialized or passed correctly before being accessed. Debug the agent's conversation flow to pinpoint when and why 'task' might be unassigned.

TypeError on ToolCall return type

A type mismatch or unexpected return type from a tool call, potentially due to an API change or an issue in how the tool's output is processed by the agent.

fix

Upgrade to `ag2` version 0.11.1 or newer, as this specific `TypeError` was addressed in that release. If the issue persists with the latest version, verify the tool's expected return format against its actual output.

ValueError: Agent names in a GroupChat must be unique.

When creating a `GroupChat`, two or more agents were instantiated with identical names, which is not allowed for proper identification within the chat.

fix

Ensure that every agent participating in a `GroupChat` has a unique `name` attribute. For example: `agent1 = ConversableAgent(name="Coder", ...)` and `agent2 = ConversableAgent(name="Reviewer", ...)`.

Upgrade

Version history

0.13.4latest on PyPI · released Jun 12, 2026

Audit

Dependencies

pythonrequiredRequired Python version range

openaioptionalRequired for OpenAI LLM usage, included via 'openai' extra

Agent activity

22 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/ag2ai/ag2 ↗

packagehttps://pypi.org/project/autogen/ ↗

homepagehttps://ag2.ai ↗