evalstate/fast-agent

> [!TIP]
> Please see https://fast-agent.ai for latest documentation.
**fast-agent** is a flexible way to interact with LLMs, excellent for use as a Coding Agent, Development Toolkit, Evaluation or Workflow platform.
To start an interactive session with shell support, install uv and run
To start coding with Hugging Face inference providers or use your OpenAI Codex plan:
Enter a shell with !, or run shell commands e.g. ! cd web && npm run build.
Manage skills with the /skills command, and connect to MCP Servers with /connect. The default **fast-agent** registry contains skills to let you set up LSP, Agent and Tool Hooks, Compaction strategies, Automation and more.
It's recommended to install **fast-agent** to set up the shell aliases and other tooling.
Use local models with the generic provider, or automatically create the correct configuration for llama.cpp:
Any **fast-agent** setup or program can be used with any ACP client - the simplest way is to use fast-agent-acp:
**fast-agent** enables you to create and interact with sophisticated multimodal Agents and Workflows in minutes. It is the first framework with complete, end-to-end tested MCP Feature support including Sampling and Elicitations.
fast-agent is CLI-first, with an optional prompt_toolkit-powered interactive terminal prompt (TUI-style input, completions, and in-terminal menus); responses can stream live to the terminal via rich without relying on full-screen curses UIs or external GUI overlays.
The simple declarative syntax lets you concentrate on composing your Prompts and MCP Servers to build effective agents.
Model support is comprehensive with native support for Anthropic, OpenAI and Google providers as well as Azure, Ollama, Deepseek and dozens of others via TensorZero. Structured Outputs, PDF and Vision support is simple to use and well tested. Passthrough and Playback LLMs enable rapid development and test of Python glue-code for your applications.
Recent features include:
- Agent Skills (SKILL.md)
- MCP Apps
- OpenAI Apps SDK
- Shell Mode
- Advanced MCP Transport Diagnsotics
- MCP Elicitations
fast-agent is the only tool that allows you to inspect Streamable HTTP Transport usage - a critical feature for ensuring reliable, compliant deployments. OAuth is supported with KeyRing storage for secrets. Use the fast-agent auth command to manage.
> [!IMPORTANT]
>
> Documentation is included in this repository under docs/. Use the docs helper script from the
> repository root to install, generate, build, serve, screenshot, and assess the site.
Agent Application Development
Prompts and configurations that define your Agent Applications are stored in simple files, with minimal boilerplate, enabling simple management and version control.
Chat with individual Agents and Components before, during and after workflow execution to tune and diagnose your application. Agents can request human input to get additional context for task completion.
Simple model selection makes testing Model MCP Server interaction painless. You can read more about the motivation behind this project here
Get started:
Start by installing the uv package manager for Python. Then:
With no configured model, an interactive run opens the model picker.
Automation and server runs must pass --model, set FAST_AGENT_MODEL, configure
default_model, or declare a model on the agent.
For packaged starter agents, use fast-agent go --pack --model .
This installs the pack into the selected fast-agent home if needed, then
starts go normally. --model is a fallback for cards without an explicit
model setting; a model declared directly in an AgentCard still wins.
Other quickstart examples include a Researcher Agent (with Evaluator-Optimizer workflow) and Data Analysis Agent (similar to the ChatGPT experience), demonstrating MCP Roots support.