Show HN: Axe – A 12MB binary that replaces your AI framework
I built Axe because I got tired of every AI tool trying to be a chatbot. Most frameworks want a long-lived session with a massive context window doing everything at once. That's expensive, slow, and fragile. Good software is small, focused, and composable... AI agents should be too. Axe treats LLM agents like Unix programs. Each agent is a TOML config with a focused job. Such as code reviewer, log analyzer, commit message writer. You can run them from the CLI, pipe data in, get results out. You can use pipes to chain them together. Or trigger from cron, git hooks, CI. What Axe is: - 12MB binary, two dependencies. no framework, no Python, no Docker (unless you want it) - Stdin piping, something like `git diff | axe run reviewer` just works - Sub-agent delegation. Where agents call other agents via tool use, depth-limited - Persistent memory. If you want, agents can remember across runs without you managing state - MCP support. Axe can connect any MCP server to your agents - Built-in tools. Such as web_search and url_fetch out of the box - Multi-provider. Bring what you love to use.. Anthropic, OpenAI, Ollama, or anything in models.dev format - Path-sandboxed file ops. Keeps agents locked to a working directory Written in Go. No daemon, no GUI. What would you automate first?
Discussion Highlights (20 comments)
armcat
Great work! Kind of reminds me of ell ( https://github.com/MadcowD/ell ), which had this concept of treating prompts as small individual programs and you can pipe them together. Not sure if that particular tool is being maintained anymore, but your Axe tool caters to that audience of small short-lived composable AI agents.
a1o
Is the axe drawing actually a hammer?
nthypes
There is no "session" concept?
mark_l_watson
If I have time I want to try this today because it matches my LLM-based work style, especially when I am using local models: I have command line tools that help me generated large one-shot prompts that I just paste into an Ollama repl - then I check back in a while. It looks like Axe works the same way: fire off a request and later look at the results.
punkpeye
What are some things you've automated using Axe?
bensyverson
It's exciting to see so much experimentation when it comes to form factors for agent orchestration! The first question that comes to mind is: how do you think about cost control? Putting a ton in a giant context window is expensive, but unintentionally fanning out 10 agents with a slightly smaller context window is even more expensive. The answer might be "well, don't do that," and that certainly maps to the UNIX analogy, where you're given powerful and possibly destructive tools, and it's up to you to construct the workflow carefully. But I'm curious how you would approach budget when using Axe.
ufish235
Why is this comment an ad?
jedbrooke
looks interesting, I agree that chat is not always the right interface for agents, and a LLM boosted cli sometimes feels like the right paradigm (especially for dev related tasks). how would you say this compares to similar tools like google’s dotprompt? https://google.github.io/dotprompt/getting-started/
Lliora
12MB for an "AI framework replacement"? That's either brilliant compression or someone's redefining "framework" to mean "toy model that works on my laptop." Show me the benchmarks on actual workloads, not the readme poetry.
0xbadcafebee
Nice. There's another one also written in Go ( https://github.com/tbckr/sgpt ), but i'll try this one too. I love that open source creates multiple solutions and you can choose the one that fits you best
zrail
Looks pretty interesting! Tiny note: there's a typo in your repo description.
TSiege
This looks really interesting. I'm curious to learn more about security around this project. There's a small section, but I wonder if there's more to be aware of like prompt injection
saberience
I’m having trouble understanding when/where I would use this? Is this a replacement for pi or codex?
Orchestrion
The Unix-style framing resonates a lot. One thing I’ve noticed when experimenting with agent pipelines is that the “single-purpose agent” model tends to make both cost control and reasoning easier. Each agent only gets the context it actually needs, which keeps prompts small and behavior easier to predict. Where it gets interesting is when the pipeline starts producing artifacts instead of just text — reports, logs, generated files, etc. At that point the workflow starts looking less like a chat session and more like a series of composable steps producing intermediate outputs. That’s where the Unix analogy feels particularly strong: small tools, small contexts, and explicit data flowing between steps. Curious if you’ve experimented with workflows where agents produce artifacts (files, reports, etc.) rather than just returning text.
let_rec
Is there Gemini support?
dumbfounder
Now what we need is a chat interface to develop these config files.
btbuildem
I really like seeing the movement away from MCP across the various projects. Here the composition of the new with the old (the ol' unix composability) seems to um very nicely. OP, what have you used this on in practice, with success?
hamandcheese
> Each agent is a TOML config with a focused job. Such as code reviewer, log analyzer, commit message writer. You can run them from the CLI, pipe data in, get results out. I'm a bit skeptical of this approach, at least for building general purpose coding agents. If the agents were humans, it would be absolutely insane to assign such fine-grained responsibilities to multiple people and ask them to collaborate.
swaminarayan
Axe treats LLM agents like Unix programs—small, composable, version-controllable. Are we finally doing AI the Unix way?
reacharavindh
Reminded me of this from my bookmarks. https://github.com/chr15m/runprompt