AIエージェント
agno
Build, run, and manage full agent platforms — not just single agents.
★ 42.6k スターApache-2.0
ガイドを見る →fast-agentは、evalstateが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: evalstate/fast-agent）。GitHubスターは3,928、フォークは446で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月8日です。
Code, build and evaluate LLM agents with full MCP, Skills, ACP and A2A support.
Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.
Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.
Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.
Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.
End-to-end tested MCP features including Sampling and Elicitations, plus MCP Apps and the OpenAI Apps SDK.
SKILL.md-based skills managed with the /skills command, with a built-in registry covering LSP, hooks, compaction and automation.
Chain, Parallel, Router, Orchestrator, Evaluator-Optimizer, MAKER and Agents-as-Tools patterns ready to compose.
Run shell commands with ! inside the session and connect to MCP servers with /connect over stdio or streamable HTTP with OAuth.
Vision, PDF and structured-output support, well tested across providers.
Any setup can run inside ACP clients via fast-agent-acp, with A2A support for agent-to-agent work.
Get the source so you can inspect, configure and run it locally.
git clone https://github.com/evalstate/fast-agent.git
cd fast-agentInstall the package as a uv tool so the CLI and shell aliases are available.
uv tool install -U fast-agent-mcpLaunch with shell support and a model of your choice, then connect MCP servers with /connect.
fast-agent --model opus -xx # or quick-try: uvx fast-agent-mcp@latest -xReaders working through the AI Agents shelf also stop at Guardrails, Programmable guardrails to keep LLM systems safe and on rails. Also on this shelf: council-of-high-intelligence — Convene 18 legendary thinkers as an AI council that debates your hardest questions — inside Claude Code. Readers working through the AI Agents shelf also stop at AP2, an open protocol for secure AI-driven payments.
It is maintained by evalstate on GitHub (evalstate/fast-agent), where the source, issues and releases live.
Yes — it is free and open source under Apache-2.0, which permits use, modification and commercial redistribution, with licence/notice retention.
Yes — most use needs your own LLM provider key (Anthropic, OpenAI, Google or another supported provider), unless you point it at a local model via Ollama or llama.cpp.
Compared with LangChain or the OpenAI Agents SDK, fast-agent's distinguishing focus is: code, build and evaluate LLM agents with full MCP, Skills, ACP and A2A support. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.
Clone https://github.com/evalstate/fast-agent (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.
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