CHChadMoran·Apr 29, 2025
Check out Fast Agent! (I have no affiliation with it, just use it).
https://github.com/evalstate/fast-agent
In Chain of Recursive Thoughts: Make AI think harder by making it argue with itselfevalstate
fast-agent
Python2025–2025
evalstate / fast-agent
fast-agent
Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support
3Distinct people
4Mentions
1Explicit recommenders
4KGitHub stars
What to know before exploring
What it is
Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
Fast Agent has this as a first-class citizen called "Evaluator Optimizer" pattern. Where it in a loop with a defined number of max refinements judge itself and give the output…
ChadMoran used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by week4 dated mentions
Observed mentions: 2025-03-06 16:34:20 UTC to 2025-04-29 20:42:59 UTC. Weeks start Monday, UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Mar 3 | 2025-03-03 00:00:00 UTC | 2025-03-10 00:00:00 UTC | 1 | 1 | 0 |
| Mar 10 | 2025-03-10 00:00:00 UTC | 2025-03-17 00:00:00 UTC | 0 | 0 | 0 |
| Mar 17 | 2025-03-17 00:00:00 UTC | 2025-03-24 00:00:00 UTC | 0 | 0 | 0 |
| Mar 24 | 2025-03-24 00:00:00 UTC | 2025-03-31 00:00:00 UTC | 1 | 0 | 1 |
| Mar 31 | 2025-03-31 00:00:00 UTC | 2025-04-07 00:00:00 UTC | 0 | 0 | 0 |
| Apr 7 | 2025-04-07 00:00:00 UTC | 2025-04-14 00:00:00 UTC | 0 | 0 | 0 |
| Apr 14 | 2025-04-14 00:00:00 UTC | 2025-04-21 00:00:00 UTC | 0 | 0 | 0 |
| Apr 21 | 2025-04-21 00:00:00 UTC | 2025-04-28 00:00:00 UTC | 0 | 0 | 0 |
| Apr 28 | 2025-04-28 00:00:00 UTC | 2025-05-05 00:00:00 UTC | 2 | 0 | 2 |
What Hacker News says
4 sourcesFast Agent has this as a first-class citizen called "Evaluator Optimizer" pattern. Where it in a loop with a defined number of max refinements judge itself and give the output a rating, demanding it improve it's output.
Highly encourage others to check out Fast Agent. It has been delightful to use. It has interactive chat mode which I love and it's really tight and easy to implement.
https://github.com/evalstate/fast-agent
In Chain of Recursive Thoughts: Make AI think harder by making it argue with itselfYou'd use a client which runs locally to coordinate between the LLM/agent and the available tools, similarly to how it's described here https://modelcontextprotocol.io/quickstart/client
There are a variety of available clients documented here https://modelcontextprotocol.io/clients
If you haven't tried any of these yet, the first place to start is Claude Desktop. If you'd like to write your own agents, consider https://github.com/evalstate/fast-agent
EDIT: I may have misunderstood your question. If you're asking "how can I make an API call to OpenAI, and have OpenAI call an MCP server I'm running as part of generating its response to me", the answer is "you can't". You'll want a proxy API that you call which is actually an MCP client, responsible for coordinating between the MCP servers and the OpenAI API upstream agent.
In OpenAI adds MCP support to Agents SDKShow HN: Fast-agent – Compose MCP enabled Agents and Workflows in minutes
Hello, HN.
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
Read the rest
-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.