Code with HF Inference Providers and llama.cpp

Use the latest open weight models via Hugging Face Inference Providers:

uvx fast-agent-mcp@latest --pack hf-dev

This starts fast-agent pre-configured with a coding agent and filesystem search sub-agent.

By default you will be prompted for the coding model to use, with gpt-oss-120b used for search.

To change these defaults use:

uvx fast-agent-mcp@latest model setup

The agent has a minimal system prompt set of tools for accessing the shell, filesystem and fast-agent services. The system prompt includes AGENTS.md if present. Customise the agent by modifying .fast-agent/agent-cards/dev.md

Use /skills to discover, add, remove and update skills. Use /connect to connect to MCP Servers.

Use the compaction-strategies skill to set up your preferred compaction scheme (if any).

Installation

fast-agent required Python 3.13 or above. Install with:

uv tool install -U fast-agent-mcp

Or a specific version of python:

uv tool install --python 3.13 -U fast-agent-mcp

This installs the fast-agent executable.

llama.cpp

To use models hosted locally with llama.cpp, start llama-server with your chosen model and then run:

fast-agent model llamacpp

This reads the correct model parameters (e.g. context window size) from the llama.cpp server, and configures the fast-agent model settings correctly.

Create a model overlay file for future use, or start immediately with "Start now".