Overview

AutoGen is an open-source framework from Microsoft Research designed to simplify the orchestration of conversational AI agents. It provides a flexible architecture where multiple agents, each with distinct roles and capabilities, can interact to achieve a common goal. This framework is particularly well-suited for developers and researchers aiming to build complex AI applications that go beyond single-prompt interactions, such as automated code generation, data analysis, or intricate workflow automation AutoGen Getting Started guide.

The core concept behind AutoGen is the 'conversable agent,' which can send and receive messages, execute code, and perform tool calls. Developers can define various types of agents, including those backed by large language models (LLMs), human users, or custom functions. These agents can then engage in multi-turn conversations, exchanging information and iteratively refining solutions. This approach allows for the decomposition of complex problems into smaller, manageable sub-tasks, with each agent contributing its specialized knowledge or skill. For instance, a data scientist agent might analyze a dataset, a software engineer agent might write code based on the analysis, and a product manager agent might review the output for business relevance.

AutoGen abstracts away much of the complexity involved in managing inter-agent communication, state, and execution flow. It offers a Pythonic interface, allowing developers to define agents, their communication patterns, and their roles using familiar programming constructs AutoGen ConversableAgent API reference. This makes it a suitable tool for rapid prototyping of agentic workflows as well as for developing production-ready LLM applications that require sophisticated coordination. Its flexibility extends to integrating with various LLM providers, allowing users to choose models based on performance, cost, or specific task requirements. The framework's design facilitates experimentation with different agent configurations and communication strategies, which is valuable for research into emergent agent behaviors and cooperative AI systems. Compared to single-agent systems, multi-agent frameworks like AutoGen can offer enhanced problem-solving capabilities by distributing cognitive load and enabling specialized expertise to converge on a solution, as observed in discussions on general AI agent architectures arXiv paper on multi-agent collaboration.

Key features

  • Flexible Agent Creation: Define custom agents with specific roles, personas, and capabilities, which can be LLM-backed, human-in-the-loop, or function-calling agents AutoGen agent definition guide.
  • Multi-Agent Conversation: Orchestrate complex interactions and message exchanges between multiple agents to collaboratively solve tasks.
  • Configurable Communication Patterns: Specify how agents interact, including turn-taking, conditional responses, and dynamic group chats.
  • Tool Integration: Enable agents to use external tools, APIs, and code execution environments to perform actions beyond their inherent LLM capabilities.
  • Code Execution & Evaluation: Agents can generate and execute code (e.g., Python, Shell scripts) in sandboxed environments, facilitating iterative development and verification.
  • Human-in-the-Loop Support: Integrate human input and supervision into agent workflows, allowing for oversight and intervention when needed.
  • Diverse LLM Backend Support: Connect to various LLM providers and models, including OpenAI, Azure OpenAI, Google Gemini, and open-source models hosted via Ollama AutoGen LLM configuration details.
  • Task Automation: Automate complex workflows such as data analysis, software development, creative writing, and research by decomposing tasks among specialized agents.

Pricing

AutoGen is an open-source project and is free to use. The costs associated with running AutoGen applications primarily stem from the underlying large language model (LLM) providers and computational resources used for executing code. Developers incur charges based on token usage, API calls, and compute time from services like OpenAI, Azure OpenAI, or Google Cloud. As of May 2026, specific pricing for integrated LLM services can be found on their respective platforms.

Component Cost Structure Notes (as of May 2026)
AutoGen Framework Free (Open-Source) No direct cost for the framework itself. Available on GitHub AutoGen GitHub repository.
LLM API Usage Per token / Per API call Costs vary by provider (e.g., OpenAI API pricing OpenAI pricing guide, Anthropic Claude pricing Anthropic models overview).
Compute Resources Hourly / Per usage For running agents, executing code, and hosting applications (e.g., cloud VMs, serverless functions).

Common integrations

Alternatives

  • LangChain: A framework for developing applications powered by language models, offering modular components for chaining LLM calls, agents, and retrieval LangChain homepage.
  • LlamaIndex: Focuses on data ingestion, indexing, and retrieval augmented generation (RAG) to connect LLMs with external data sources LlamaIndex documentation portal.
  • CrewAI: A multi-agent framework built on LangChain, designed for orchestrating autonomous AI agents with defined roles, goals, and tools to accomplish complex tasks CrewAI project website.

Getting started

To begin using AutoGen, you typically install the Python package and then configure your LLM provider. The following example demonstrates a basic two-agent conversation where an Assistant Agent helps a User Proxy Agent with a task, such as writing a simple Python script.

# Install AutoGen
pip install pyautogen

# Configure your LLM settings (e.g., for OpenAI)
# Replace 'your-openai-api-key' with your actual key
# and 'gpt-4o' with your preferred model.
# Ensure to set environment variable OPENAI_API_KEY or provide it directly.

import autogen

config_list = [
    {
        "model": "gpt-4o",
        "api_key": "your-openai-api-key", # Or retrieve from os.environ.get("OPENAI_API_KEY")
    }
]

# Create an AssistantAgent named "assistant"
assistant = autogen.AssistantAgent(
    name="assistant",
    llm_config={
        "config_list": config_list,
        "temperature": 0.7,
    },
)

# Create a UserProxyAgent named "user_proxy"
# It will ask the user for input and execute code if needed.
user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="TERMINATE", # Asks for human input if agent runs out of options
    max_consecutive_auto_reply=10,
    is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("exit") or x.get("content", "").rstrip().endswith("quit"),
    code_execution_config={
        "work_dir": "coding", # Directory for code execution
        "use_docker": False, # Set to True to use Docker for sandboxed execution
    },
)

# Initiate the conversation
user_proxy.initiate_chat(
    assistant,
    message="Write a Python script to calculate the factorial of a number. Make sure the script includes a function definition and handles non-integer inputs gracefully."
)

This script sets up two agents: an AssistantAgent (backed by an LLM) and a UserProxyAgent. The UserProxyAgent acts as a proxy for a human user, sending a prompt to the assistant and potentially executing any code the assistant generates. The human_input_mode="TERMINATE" setting means the user will be prompted for input only if the conversation needs explicit termination or guidance, while code_execution_config enables the agent to run Python code in a specified directory. This allows for iterative development where the assistant can write code, the proxy can execute it, and the assistant can debug or refine it based on the execution output.