deepagents vs mcp-agent
Python LLM agent frameworks compared
deepagents and mcp-agent are Python frameworks for building LLM agents with tools and multi-step workflows. deepagents bundles planning, file access, delegation, and context management, while mcp-agent centers on MCP server integrations and composable workflows, including optional Temporal execution.
deepagents: Build Agents with Planning and Tooling Built In
Deep Agents is a Python agent harness for building LLM agents with planning, delegation, file access, and context management built in. It suits developers who need a more capable starting point than a minimal agent loop, while retaining control over models and tools.
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.
| deepagents | mcp-agent | |
|---|---|---|
| Language | Python | Python |
| License | MIT | Apache-2.0 |
| Stars | 29.9k | 8.6k |
| Forks | 4.2k | 893 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- deepagents provides built-in planning, file operations, sub-agent delegation, and context management; mcp-agent focuses on connecting to MCP servers and composing workflow patterns.
- deepagents supports pluggable file backends, sandboxed shell commands, and persistent memory; mcp-agent offers asyncio or Temporal execution, with Temporal supporting durable workflows such as pause and resume.
- deepagents is built on LangGraph and supports tool-calling models; mcp-agent manages MCP connections and can expose applications and agents as MCP servers.
- deepagents is licensed under MIT; mcp-agent is licensed under Apache-2.0.
- deepagents lists 29.9k stars and 4.2k forks, while mcp-agent lists 8.6k stars and 893 forks. Both projects are listed as not archived.
Choose deepagents if you…
- need built-in planning, file tools, context management, and sub-agent delegation.
- want to customize an agent harness built on LangGraph or use its built-in memory and human approval options.
- need file and shell access governed by pluggable backends and a chosen sandbox.
Choose mcp-agent if you…
- need to connect agents to MCP servers and manage their connection lifecycle.
- want reusable workflow patterns such as routers, map-reduce, or orchestrator-workers.
- need Temporal-backed durable execution or want to expose an application as an MCP server.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.