fast-agent vs mcp-agent
Python LLM agent frameworks compared
fast-agent and mcp-agent are Python frameworks for building LLM agents that use MCP tools and composable workflows. fast-agent emphasizes a configurable framework and interactive CLI, while mcp-agent focuses on MCP connection management and offers Temporal-backed durable execution.
fast-agent: Build and Run LLM Agents and Workflows
A Python framework and CLI for building, testing, and running LLM agents that use MCP tools and compose into workflows. It suits developers who want both interactive agent development and a programmable framework.
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.
| fast-agent | mcp-agent | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 3.9k | 8.6k |
| Forks | 446 | 893 |
| Last analyzed | Oct 7, 2026 | Oct 3, 2026 |
Key differences
- fast-agent provides an interactive terminal CLI with shell commands and skills management; mcp-agent's CLI scaffolds projects and supports deployment.
- fast-agent lists native Anthropic, OpenAI, and Google providers, plus access to other providers through TensorZero; mcp-agent describes LLM integrations with tool access and structured-output helpers.
- mcp-agent supports asyncio or Temporal execution, with Temporal enabling durable workflows such as pause, resume, and retries; fast-agent describes workflow composition without listing Temporal execution.
- fast-agent highlights multimodal resources such as images and PDFs where supported by the provider; mcp-agent highlights configurable settings through YAML, environment variables, or Python.
- Both projects use Python and Apache-2.0 licensing. The supplied facts list 8.6k stars and 893 forks for mcp-agent, compared with 3.9k stars and 447 forks for fast-agent.
Choose fast-agent if you…
- want an interactive terminal workflow with shell commands and skills management.
- need to define agents in Python and compose patterns such as chains, routers, or agents-as-tools.
- want the option to run agent applications through ACP clients or as MCP servers.
Choose mcp-agent if you…
- need Temporal-backed durable execution with pause, resume, and retries.
- want MCP server connection lifecycle management and configuration through YAML, environment variables, or Python.
- need a CLI to scaffold projects and deploy using mcp-agent's deployment support.
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.