- fx is a coding agent harness and CLI written in Zig, optimized for research and embeddability as part of larger systems.

- It focuses on minimalism and performance across the board, from system prompt design, to its tools, feature set, and 6.39mib binary.

- For end users, its CLI output style and form factor aims to be closer to a Unix shell than a heavy "IDE in the terminal" TUI.

- It's open source (Apache-2.0), model-agnostic, and suitable for both local and cloud inference.

- Designed for instant installation and embedding in resource constrained environments and agent sandboxes.

- fx cold starts in 10µs and does no unnecessary work or I/O prior to accepting user input, making it ideal for programmatic use.

- Optimal fx.wasm builds produced by the Zig toolchain, which further reduce fx's size, making the network stack pluggable.

- fx contributes single-digit megabytes of memory baseline, allowing you to pack many instances in one machine.

- fx preserves scroll history by default, produces minimal output, and makes sparing use of complex TUI or paints

- Minimal system prompt and tools, to save on token costs and to yield optimal time-to-first-token performance (TTFT).

- Small core, extended via skills, plugins, MCPs, with a Unix-like philosophy to extensibility.

- Designed to work with local models, gateways, direct provider API access or subscriptions.

Everything else in the harness is largely the same just Vercel-flavoured.

The portability benefit is also a bit over-sold imho. I wrote a harness in Go and it is as portable as this ... in fact it deploys straight into Vercel's own sandbox environment on demand without any issues.

That being said, did you say GLM 5.2 free? I need to look into that. GLM is remarkably capable model and that alone is worth using the harness in my books.

- written in rust, super fast startup and rendering

- implements token saving techniques

- plugins written in lua

No affiliation, just think it's a really well made piece of software.

I wonder why it's so large for a program written in Zig. It's basically just a loop that accepts user input, prepares the context, sends it to the LLM, parses the output, invokes the tools, and presents it all in the terminal. Add the built-in prompts and a few checks here and there (like blocking a write tool call before the file has been read first), and I'd expect a truly tiny native agent to be around 200-300 KB max.

I'm not sure harness is the right word either, but that seems to be what the industry has settled on so I'll concede on that, but surely the agent is the thing doing the work (which I guess is the model, or an instance of the model which is why agent is different?), whereas the harness is how the user interacts with the agent. We've had ways to describe that relationship before; client and server, frontend and backend, but again, I'll concede that the shiny new thing doesn't want to use boring old terminology, but I think some consistency and logic in the shiny new terminology is pretty important

That isn't specifically about fx of course, more of a general industry complaint

import json,sys;from subprocess import getoutput as sh;from urllib.request import Request as R,urlopen url=sys.argv[1];h=[];b=dict(model="gpt-5.6",input=h,tools=[dict(type="custom",name="sh")]) while p:=input("> "): h+=[dict(role="user",content=p)];H={"Content-Type":"application/json"} while True: o=(r:=json.load(urlopen(R(url,json.dumps(b).encode(),H))))["output"] h+=o;c=[i for i in o if i["type"]=="custom_tool_call"];z=r["usage"]["total_tokens"]/10500 if not c:print(o[-1]["content"][0]["text"],f'\n[{z:06.3f}%]');break h+=[dict(type="custom_tool_call_output",call_id=i["call_id"],output=sh(i["input"])) for i in c]

GLM 5.2 totally free even with a free Vercel account.

Make business sense too - fx is the entry point to bring more user to Vercel AI.

And every says it is the one :)

This binary is ~40kb and uses much less ram than fx.

The person who built this obviously has little understanding of harnesses.

You should have significantly less tools today with how good LLMs have become.

The start up time and binary size are quite literally the most useless stats to base a harness off of lol

Man what with Cloudflare, Vercel and all these tech companies just releasing pure slop.

Just use Pi. It's actually minimal and well thought out by people who actually understand agents.

I thought my `disc.sh` was good but this is better.

1. Why do we need yet another coding agent over the rest of them?

2. Is this going to be another Vercel Labs slop project that they will abandon like the others since this is super experimental?