I've created fast-agent to make building my own products easier - and remove the friction between defining Prompts, MCP Servers and their composition. It uses a simple, declarative style that's easy to work with and source control - with inbuilt support for the patterns in the Building Effective Agents paper.

Because you can "warm-up" and interact with Agents before, during or after the workflows, it's easy to diagnose and tune Agent prompts and behaviour for later runs. Being able to set these workflows up makes LLM Context Management and Tool Selection a lot easier and can vastly improve output quality for little effort.

For MCP Server developers you can see how different models interpret tool descriptions. There's also MCP Roots support, and it comes bundled with a ChatGPT style data-analysis tool (`fast-agent bootstrap data-analysis`) as one of the demonstrations.

One of the thing I am most looking forward to is combining MCP data retrieval with Anthropic's Citations API - I think that's going to be an incredibly important feature in a lot of scenarios.

It's been forked from, and and builds upon Sarmad Qadri's mcp-agent framework, and we're collaborating to keep the projects in-sync.

Anyway, I'd love to hear your thoughts and feedback on this project, and eager to hear from potential users, contributors and collaborators.