Overview
AutoGen (by Microsoft) is an open-source framework that enables developers to build LLM applications by composing multiple AI agents that converse with each other to accomplish complex tasks. It provides a unified multi-agent conversation abstraction where agents can integrate LLMs, tools, and even humans, supporting autonomous or human-in-the-loop workflows.
Pricing
Open Source (Free)
- Framework available on GitHub under permissive open-source licensing (MIT in the original repo, Apache 2.0 for the AG2 evolution).
- No per-seat fees; costs are mainly underlying model/API usage (e.g., OpenAI, Azure OpenAI) and infrastructure.
- Suitable for researchers, developers, and teams who want full control over deployments.
Key Features
Multi-Agent Conversation Framework – Supports multiple conversable agents that exchange messages to jointly solve tasks, including static and dynamic group conversations.microsoft.
Customizable Agents & Tools – Agents can be configured to use LLMs, tools/functions, code execution, and human input, with pluggable reply functions and tool integrations.
Event-Driven, Asynchronous Runtime – Asynchronous, event-driven architecture for scalable workflows and long-running, reactive agents.
Flexible Autonomy & Human-in-the-Loop – Configure conversations to be fully autonomous or require human approval at key steps via user proxy agents.microsoft.
Observability & Debugging – Built-in tracing, logging, and integration with OpenTelemetry for monitoring agent interactions and system behavior.
Best Use Cases
Code Generation & Debugging – Multi-agent coding setups where one agent writes code, another reviews/tests, and a third handles execution and fixes.
Research & Question Answering – Teams of agents for retrieval, reasoning, and summarization across documents, web data, or domain-specific corpora.
Optimization & Planning Tasks – Complex domains like supply-chain optimization, decision-making, or workflow planning with specialized collaborating agents.
Prototype Multi-Agent Systems – Rapid experimentation with agent patterns (group chat, hierarchical chat, FSM-style transitions) in Python.
Hybrid Human–AI Workflows – Human-supervised systems where a UserProxyAgent routes tasks and approves key actions.
Pros
✅ Mature multi-agent primitives – Rich conversation patterns (group chat, hierarchical, FSM-style flows) for sophisticated orchestration.microsoft.
✅ Highly configurable agents – Combine LLMs, tools, and humans with custom reply logic and tool integrations.
✅ Open-source and widely adopted – Active ecosystem, research backing, and extensive examples and docs.
✅ Strong observability – Built-in tracing and OpenTelemetry support help debug and monitor complex runs.
Cons
❌ Developer-oriented – Requires Python development skills; not a no-code platform for business users.microsoft.
❌ Complexity for large setups – Designing robust multi-agent topologies and prompts can be challenging at scale.
❌ Separate infra & model costs – While AutoGen is free, production usage incurs LLM API and infrastructure expenses.
Official Website
AutoGen – Microsoft Research project page: https://www.microsoft.com/en-us/research/project/autogen/microsoft
GitHub repository: https://github.com/microsoft/autogengithub
Release Date (initial paper/framework): August–September 2023
Last Updated: December 2025