mcp-agent vs autogen
Python frameworks for building AI agents compared
mcp-agent and autogen are Python frameworks for building LLM-powered agents and multi-agent workflows. mcp-agent centers on MCP integrations and composable workflows, while autogen offers layered APIs and prototyping tools but is in maintenance mode.
mcp-agent: Build Agents with Model Context Protocol
A Python framework for building LLM agents around Model Context Protocol servers and composable workflow patterns. It suits developers who need MCP integrations, multi-agent workflows, or durable execution with Temporal.
autogen: Build Multi-Agent AI Applications
AutoGen is a Python framework for building AI agents that work independently or alongside people. It supports multi-agent orchestration, model and tool integrations, and prototyping, but is now in maintenance mode.
| mcp-agent | autogen | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | CC-BY-4.0 |
| Stars | 8.6k | 61.3k |
| Forks | 893 | 9.3k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- mcp-agent focuses on managing MCP server connections and composing workflows; autogen covers event-driven agents, higher-level chat patterns, model integrations, and prototyping tools.
- mcp-agent supports asyncio and Temporal execution, with Temporal workflows offering pause, resume, and retries; autogen provides local or distributed runtimes.
- mcp-agent can expose applications and agents as MCP servers; autogen supports connecting agents to MCP servers through its MCP workbench.
- mcp-agent is licensed under Apache-2.0; autogen is licensed under CC-BY-4.0.
- mcp-agent is not archived and describes cloud deployment as beta; autogen is in maintenance mode, with Microsoft recommending its successor for new projects.
- mcp-agent requires provider credentials and MCP configuration, and Temporal durability requires a Temporal runtime and worker setup; autogen's quickstart examples use OpenAI API credentials.
Choose mcp-agent if you…
- need MCP-focused agent integrations and reusable workflow patterns.
- want asyncio execution or durable workflows with Temporal.
- plan to expose an application or agent as an MCP server.
Choose autogen if you…
- maintain an existing application built with AutoGen APIs.
- want to prototype multi-agent workflows with AutoGen Studio or evaluate them with AutoGen Bench.
- need its event-driven APIs, higher-level AgentChat patterns, or model and tool integrations.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.