Microsoft AutoGen
AI / LLMFreeVisit Site
An open-source framework from Microsoft Research for building multi-agent applications, where agents converse to solve tasks together.
Overview
AutoGen models AI work as a conversation between agents — a coder, a reviewer, a planner — that message each other until a task is done. It is research-grade and fast-moving, with AgentChat for high-level patterns and a lower-level Core API for custom orchestration. For enterprise teams it is a credible way to prototype multi-agent designs, but the usual warning applies hard here: conversational multi-agent systems are difficult to evaluate, can loop, and burn tokens unpredictably. Treat AutoGen builds as experiments until you have evals and cost controls around them.
Pros & Cons
Pros
- Mature multi-agent conversation patterns
- Backed by Microsoft Research
- Flexible high-level and low-level APIs
- Strong fit for experimentation
Cons
- Multi-agent conversations are hard to evaluate and debug
- Token costs can escalate without guardrails
- APIs have changed significantly between versions
Workflows that use Microsoft AutoGen
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