fast-agent-mcp
Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support
Decision gist · record as of 2026-08-14
Yes, if you are building LLM agents with MCP server integration. The framework is actively maintained, has low install friction, permissive licensing, and no known vulnerabilities. The large dependency footprint is justified by multi-provider support and feature completeness. Install only if you need agent orchestration; it is not suitable for simple LLM API calls.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later (supports up to <3.15).
- An LLM provider API key (Anthropic, OpenAI, Google, or compatible) must be configured via environment or keyring to run agents.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 permits commercial and derivative use with attribution and patent protection. The license is permissive and widely compatible with most projects.
last release 2026-08-13 (1 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 157,584 downloads/mo, #10,750 on PyPI
Alternatives
Verify before relying
pip install fast-agent-mcp
from fast_agent import FastAgent
import asyncio
fast = FastAgent("Agent Example")
@fast.agent(instruction="Given an object, respond only with an estimate of its size.")
async def main():
async with fast.run() as agent:
await agent.interactive()
if __name__ == "__main__":
asyncio.run(main())- Whether all 37 runtime dependencies are required for basic usage or if subsets can be installed for specific providers
- Performance characteristics and latency overhead of MCP transport diagnostics and Streamable HTTP support
- Compatibility matrix with specific MCP server versions and transport types (stdio vs. HTTP)
What it is and what it does
fast-agent-mcp is a framework for building and orchestrating LLM agents that interact with Model Context Protocol (MCP) servers. It provides both a CLI interface and a Python API, supporting agents as simple decorators or complex workflows. The framework handles agent skill management, MCP server connections, and multi-model provider support (Anthropic, OpenAI, Google, Azure, Ollama, and others via TensorZero), with built-in features for structured outputs, vision, PDF handling, and shell integration.
The package is designed for rapid agent development with minimal boilerplate. Agents can be run interactively via a prompt-toolkit-powered terminal interface with streaming responses via rich, or programmatically in automation and server modes. It includes diagnostic tools for MCP transport inspection, OAuth-based secret management via keyring, and support for agent chaining and workflow composition. The framework emphasizes declarative configuration through YAML files and simple Python decorators, enabling developers to focus on prompt and MCP server composition rather than infrastructure.
Use it for
- Build a coding agent that uses MCP servers for file system access, LSP integration, and tool execution to assist with development tasks.
- Create a multi-step workflow where agents chain together (e.g., researcher → evaluator → optimizer) using shared MCP servers for data access.
- Set up an interactive CLI tool that lets users chat with an LLM agent backed by custom MCP servers for domain-specific tasks.
- Develop and test agent applications locally with model passthrough and playback LLMs before deploying to production.
- Expose an agent as an MCP server itself for use in other applications via stdio or HTTP transport with OAuth authentication.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building LLM agents with MCP server integration.
The framework is actively maintained, has low install friction, permissive licensing, and no known vulnerabilities. The large dependency footprint is justified by multi-provider support and feature completeness. Install only if you need agent orchestration; it is not suitable for simple LLM API calls.
Install
fast-agent-mcp on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance with a release 1 day old. The package has 37 runtime dependencies including fastapi, anthropic, openai, and google-genai, which is a substantial dependency footprint but typical for a multi-provider LLM framework.
Requires Python 3.12 or later (supports up to <3.15). An LLM provider API key (Anthropic, OpenAI, Google, or compatible) must be configured via environment or keyring to run agents.
License in practice
Apache License 2.0 permits commercial and derivative use with attribution and patent protection. The license is permissive and widely compatible with most projects.
Quickstart
pip install fast-agent-mcp
from fast_agent import FastAgent
import asyncio
fast = FastAgent("Agent Example")
@fast.agent(instruction="Given an object, respond only with an estimate of its size.")
async def main():
async with fast.run() as agent:
await agent.interactive()
if __name__ == "__main__":
asyncio.run(main())Verify before relying
- Whether all 37 runtime dependencies are required for basic usage or if subsets can be installed for specific providers
- Performance characteristics and latency overhead of MCP transport diagnostics and Streamable HTTP support
- Compatibility matrix with specific MCP server versions and transport types (stdio vs. HTTP)
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release <3.15,>=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 37 packagesa2a-sdkagent-client-protocolaiohttpanthropicdeprecatedemail-validatorfastapifastmcp-slimfilelockgoogle-genaihuggingface-hubjsonschemakeyringmcp-typesmcpmslexopenaiopentelemetry-exporter-otlp-proto-httpopentelemetry-instrumentation-anthropicopentelemetry-instrumentation-google-genaiopentelemetry-instrumentation-openaipillowprompt-toolkitpydantic-settingspydanticpyperclippython-frontmatterpyyamlrichruamel-yaml |
| Maintenance | Actively maintained 1 days since the last release |
| First released | |
| Downloads | 157,584 / month, #10,750 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: fast_agent_mcp-0.10.7-py3-none-any.whl
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