AutoGen

AutoGen was among the earliest and most influential multi-agent frameworks, popularizing the concept of complex LLM problem-solving through structured multi-agent dialogue rather than rigid chains. The v0.4 rewrite decoupled the framework into a three-tier architecture: Core (an asynchronous, actor-based message passing runtime), AgentChat (high-level conversational patterns), and Extensions (pluggable toolkits, execution environments, and cloud runtimes). Its key strengths include dual Python/C# support, first-class Model Context Protocol (MCP) server integration, distributed execution via gRPC, and built-in Docker/Jupyter sandboxed code execution. AutoGen also natively integrates with OpenTelemetry for distributed tracing of inter-agent messaging, making it robust for local experimentation and developer tooling. However, AutoGen faces limitations when applied to strict, production-grade business processes. Because control flow is governed by conversational messages rather than explicit deterministic graphs, enforcing rigid branching, cycles, and checkpoints can require extensive prompt engineering. Without carefully configured termination conditions, agents can enter expensive recursive feedback loops. Additionally, Microsoft placed the core `microsoft/autogen` repository into community maintenance mode while redirecting enterprise focus toward the Microsoft Agent Framework (and community-led AG2). AutoGen remains best suited for exploratory multi-agent research, collaborative code generation and review loops, interactive simulation teams, and developers who need low-level asynchronous message-passing infrastructure.