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fast-agent

MCP, ACP and Skills support for building production coding agents — interactive or automated.

fast-agent is an Apache-licensed Python framework for building and running LLM agents with full MCP (Model Context Protocol) and ACP support. It ships with an interactive shell mode, Skills management, and multi-model routing — making it a practical platform for coding agents, workflow automation, and agent evaluation across Claude, Codex, HuggingFace, and local models.

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About fast-agent

fast-agent provides a flexible runtime for building LLM-powered agents with first-class support for MCP servers, ACP (Agent Communication Protocol), and reusable Skills. Developers can launch an interactive coding agent session with a single uvx fast-agent-mcp@latest -x command, connect to any MCP server over stdio or HTTP with OAuth, and manage skills via a built-in /skills command. The framework targets the full spectrum of agentic use cases — from exploratory coding sessions to fully automated CI pipelines and evaluation harnesses.

Unlike general-purpose agent frameworks that treat MCP as an afterthought, fast-agent is designed around the protocol from the ground up — supporting Sampling, Elicitations, and other advanced MCP features that most frameworks omit. It supports multiple model providers out of the box (Anthropic Claude, OpenAI Codex, HuggingFace Inference, llama.cpp, and generic local models) and ships with preconfigured packs for common workflows like --pack hf-dev for HuggingFace development or --pack codex for OpenAI Codex-optimized agents. The --smart flag enables automatic subagent routing and compaction strategies for long-running sessions.

fast-agent fits developers who want a coding agent framework that stays close to the MCP standard while remaining lightweight and composable. It works equally well as a daily driver for interactive development (fast-agent --model opus -x --smart) and as an evaluation harness for testing agent behavior across model providers. With 3,700+ GitHub stars, an active Discord community, and regular releases under the Apache 2.0 license, it has become a credible alternative for teams who find LangChain too heavyweight or CrewAI too opinionated for terminal-first coding workflows.

Pricing & Platform Specs

Pricing Summary

100% free and open-source MCP-native autonomous AI agent framework (MIT/Apache-2.0 license, $0 software license). Self-hosted with zero seat fees. Operates on Bring Your Own Key (BYOK) model supporting Anthropic Claude, OpenAI, Google Gemini, DeepSeek, or completely free $0 local inference via Ollama/vLLM.

Supported Platforms

Python 3.10+. macOS, Linux, Windows. Terminal/CLI. Works with any MCP-compatible server (stdio or HTTP+OAuth).

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Community experience

Sources & verification

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Verification dates are editorial checks. Routine CMS saves and automatic updatedAt timestamps do not advance them.

FAQ

What is fast-agent?

fast-agent is an Apache-licensed Python framework for building and running LLM agents with full MCP (Model Context Protocol) and ACP support. It ships with an interactive shell mode, Skills management, and multi-model routing — making it a practical platform for coding agents, workflow automation, and agent evaluation across Claude, Codex, HuggingFace, and local models.

Is fast-agent free?

Yes — fast-agent is open source and free to use. 100% free and open-source MCP-native autonomous AI agent framework (MIT/Apache-2.0 license, $0 software license). Self-hosted with zero seat fees. Operates on Bring Your Own Key (BYOK) model supporting Anthropic Claude, OpenAI, Google Gemini, DeepSeek, or completely free $0 local inference via Ollama/vLLM.

Is fast-agent open source?

Yes — fast-agent is open source.

Is fast-agent still maintained?

Yes — fast-agent is active. Its listing was last verified on August 26, 2026.

What are the best fast-agent alternatives?

The first editor-selected fast-agent alternatives are mcp-agent, SmoLAgents, Agno, and more.

How does fast-agent score in our review?

The published editorial review lists fast-agent at 83/100 overall across speed, privacy, and developer experience. Check the review's evidence status and test metadata for its verification level.