I've had good success with something along these lines but perhaps a bit more raw:<p><pre><code> - claude takes a -p option
- i have a bunch of tiny scripts, each script is an agent but it only does one tiny task
- scripts can be composed in a unix pipeline
</code></pre>
For example:<p><pre><code> $ git diff --staged | ai-commit-msg | git commit -F -
</code></pre>
Where ai-commit-msg is a tiny agent:<p><pre><code> #!/usr/bin/env bash
# ai-commit-msg: stdin=git diff, stdout=conventional commit message
# Usage: git diff --staged | ai-commit-msg
set -euo pipefail
source "${AGENTS_DIR:-$HOME/.agents}/lib/agent-lib.sh"
SYSTEM=$(load_skills \
core/unix-output.md \
core/be-concise.md \
domain/git.md \
output/plain-text.md)
SYSTEM+=$'\n\nTask: Given a git diff on stdin, output a single conventional commit message. One line only.'
run_agent "$SYSTEM"
</code></pre>
And you can see to keep the agents themselves tiny, they rely on a little lib to load the various skills and optionally apply some guard / post-exec validator. Those validators are usually simple grep or whatever to make sure there were no writes outside a given dir but sometimes they can be to enforce output correctness (always jq in my examples so far...). In theory the guard could be another claude -p call if i needed a semantic instruction.
I really like this idea. Gonna need an "Awesome Axe" page that collects agents.<p>One idea I'm thinking of is, after an agent has been in use for a while, and built up and understanding of the task, would be something like, "Write a Python script to replace this agent."<p>I could imagine this would work with agents that are processing log files or other semi-structured data for example.
It's exciting to see so much experimentation when it comes to form factors for agent orchestration!<p>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.
This is interesting. I'd be curious to see a bunch more working examples. Personally I like the chat model because I iterate heavily on planning specs and have a lot of back and forth before implementation.<p>I could see using this once the plan is defined and switching back to chat while iterating on post-implementation cleanup and refactoring.
Nice! I’ll try this soon, and I’m afraid I’ll end up using it a lot.<p>@jrswab, do you think it would be feasible to limit outgoing connections to a whitelist of domains, URLs, or IP addresses?<p>I’d like to automate some of my email, calendar, or timesheet tasks, but I’m concerned that a prompt injection could end up exfiltrating or deleting data. In fact, that’s the main reason why I’m not using Openclaw or similar projects with real data yet.
This is what I've been trying to get nanobot to do, so thanks for sharing this. I plan to use this for workflow definitions like filesystems.<p>I have a known workflow to create an RPG character with steps, lets automate some of the boilerplate by having a succession of LLMs read my preferences about each step and apply their particular pieces of data to that step of the workflow, outputting their result to successive subdirectories, so I can pub/sub the entire process and make edits to intermediate files to tweak results as I desire.<p>Now that's cool!
I will give it a try, I like the idea of being closer to the metal.<p>A Proper self-contained, self improving AI@home with the AI as the OS is my end goal, I have a nice high spec but older laptop I am currently using as a sacrificial pawn experimenting with this, but there is a big gap in my knowledge and I'm still working through GPT2 level stuff, also resources are tight when you're retired. I guess someone will get there this year the way things are going, but I'm happy to have fun until then.
Great work! Kind of reminds me of ell (<a href="https://github.com/MadcowD/ell" rel="nofollow">https://github.com/MadcowD/ell</a>), 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.
Axe treats LLM agents like Unix programs—small, composable, version-controllable. Are we finally doing AI the Unix way?
Cool work!<p>Aside but 12 MB is ... large ... for such a thing. For reference, an entire HTTP (including crypto, TLS) stack with LLM API calls in Zig would net you a binary ~400 KB on ReleaseSmall (statically linked).<p>You can implement an entire language, compiler, and a VM in another 500 KB (or less!)<p>I don't think 12 MB is an impressive badge here?
it's written in golang. 12MB barely gets you "hello world" since everything is statically linked. With that in mind, the size is impressive.
golang doesn't statically link everything by default (anymore?), this is from FreeBSD:<p><pre><code> $ ls -l axe
-rwxr-xr-x 1 root wheel 12830781 Mar 12 22:38 axe*
$ ldd axe
axe:
libthr.so.3 => /lib/libthr.so.3 (0xe2e74a1d000)
libc.so.7 => /lib/libc.so.7 (0xe2e74c27000)
libsys.so.7 => /lib/libsys.so.7 (0xe2e75de6000)
[vdso] (0xe2e7366b000)</code></pre>
I know off topic, but is that mostly coming from the Go runtime (how large is that about?)
12 MB is not large; it's like 3 minutes of watching YouTube. Actual RAM consumption is only very weakly correlated to the binary size, and that's what matters.
It is large compared to a stripped Zig ReleaseSmall binary with no runtime. With agents, one can take this repo, and create an <i>extremely</i> small binary.<p>To your point, why even advertise the number? If that particular number is completely irrelevant in practical usage, why mention it? It seems like the point is to impress, hence my response.
“ MCP support. Axe can connect any MCP server to your agents”<p>I just don't see this in the readme… It is not in the Features section at least.<p>Anyway, i have MCP server that can post inline comments into Gitlab MR. Would like to try to hook it up to the code reviewer.
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.<p>OP, what have you used this on in practice, with success?
looks really cool, how does it differ from something like running claude headless with `claude -p`?
Reminded me of this from my bookmarks.<p><a href="https://github.com/chr15m/runprompt" rel="nofollow">https://github.com/chr15m/runprompt</a>
> 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.<p>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.
It is easier to trust in the correctness and reliability of an LLM when you treat it as a glorified NLP function with a very narrow scope and limited responsibilities. That is to say, LLMs rarely mess up specific low level instructions, compared to open-ended, long-horizon tasks.
Clankers are not humans.
This is the second time I've seen somebody use the word "clankers" in the last couple days to refer to AI. Is that a thing now? Where'd that come from?<p>Gonna be honest, it has taken away from the message both times I've seen it. It feels a bit like you're LARPing your favorite humans vs robots tv show.
I've been hearing the term in IRC and discords for about a year or more already.<p>I get that it can seem childish but when you compare that to the indolent people who are demanding AI, it cancels out.
It is a thing, i've been hearing it for at least 6 months. There's a lot of people who really hate AI and want nothing to do with it.
You can find the answers to both of your questions on Wikipedia: <a href="https://en.wikipedia.org/wiki/Clanker" rel="nofollow">https://en.wikipedia.org/wiki/Clanker</a>
What are some things you've automated using Axe?
I have a few flows I'm using it for and have a growing list of things I want to automate. Basically, if there is a process that takes a human to do (like creating drafts or running scripts with variable data) I make axe do it.<p>1. I have a flow where I pass in a youtube video and the first agent calls an api to get the transcript, the second converts that transcript into a blog-like post, and the third uploads that blog-like post to instapaper.<p>2. Blog post drafting: I talk into my phone's notes app which gets synced via syncthing. The first agent takes that text and looks for notes in my note system for related information, than passes my raw text and notes into the next to draft a blog post, a third agent takes out all the em dashes because I'm tired of taking them out. Once that's all done then I read and edit it to be exactly what I want.
Does it support the use of other OpenAI API compatible services like Openrouter?
Nice. There's another one also written in Go (<a href="https://github.com/tbckr/sgpt" rel="nofollow">https://github.com/tbckr/sgpt</a>), 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
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.<p>It looks like Axe works the same way: fire off a request and later look at the results.
The Unix-style framing resonates a lot.<p>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.<p>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.<p>That’s where the Unix analogy feels particularly strong: small tools, small contexts, and explicit data flowing between steps.<p>Curious if you’ve experimented with workflows where agents produce artifacts (files, reports, etc.) rather than just returning text.
> Curious if you’ve experimented with workflows where agents produce artifacts (files, reports, etc.) rather than just returning text.<p>Yes! I run a ghost blog (a blog that does not use my name) and have axe produce artifacts. The flow is: I send the first agent a text file of my brain dump (normally spoken) which it then searched my note system for related notes, saves it to a file, then passes everything to agent 2 which make that dump a blog draft and saves it to a file, agent 3 then takes that blog draft and cleans it up to how I like it and saves it. from that point I have to take it to publish after reading and making edits myself.
That’s a really nice pipeline. The “save to file between steps” pattern seems to appear very naturally once agents start doing multi-stage work.<p>One thing I’ve noticed when experimenting with similar workflows is that once artifacts start accumulating (drafts, logs, intermediate reports, etc.), you start running into small infrastructure questions pretty quickly:<p>– where intermediate artifacts live
– how later agents reference them
– how long they should persist
– whether they’re part of the workflow state or just temporary outputs<p>For small pipelines the filesystem works great, but as the number of steps grows it starts to look more like a little dataflow system than just a sequence of prompts.<p>Do you usually just keep everything as local files, or have you experimented with something like object storage or a shared artifact layer between agents?
In my prompting framework I have a workflow that the agent would scan all the artifacts in my closed/ folder and create a yyyymmdd-archive artifact which records all artifact name and their summaries, then just delete them. Since the framework is deeply integrated with git, the artifact can be digged up from git history via the recorded names.
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Now what we need is a chat interface to develop these config files.
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
I'm happy you brought this up. I've been thinking about this and working on a plan to make it as solid as possible. For now, the best way would be to run each agent in a docker container (there is an example Dockerfile in the repo) so any destructive actions will be contained to the container.<p>However, this does not help if a person gives access to something like Google Calendar and a prompt tells the LLM to be destructive against that account.
I really like the project, although I would prefer a json5 config, not toml, which I find annoying to reason about.
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).<p>how would you say this compares to similar tools like google’s dotprompt? <a href="https://google.github.io/dotprompt/getting-started/" rel="nofollow">https://google.github.io/dotprompt/getting-started/</a>
There is no "session" concept?
Is the axe drawing actually a hammer?
Looks like an axe to me. The cutting edge of the axe is embedded into the surface. And the handle attaches near the back of the head like an axe. Most hammers I've seen the handle attaches in the middle.
I believe it's actually trying to render a splitting maul, which people often confuse for an axe.
There are many different styles of axe and some don't flair out much.<p>[0]<a href="https://inchbyinch.de/wp-content/uploads/2017/08/0400-axe-types.jpeg" rel="nofollow">https://inchbyinch.de/wp-content/uploads/2017/08/0400-axe-ty...</a><p>[1]<a href="https://i.pinimg.com/originals/da/14/80/da148078cc1478ec6b255d3f5583e2a3.jpg" rel="nofollow">https://i.pinimg.com/originals/da/14/80/da148078cc1478ec6b25...</a>
Sure is. How weird.
amazing work my friend
I’m having trouble understanding when/where I would use this? Is this a replacement for pi or codex?
Is there Gemini support?
Looks pretty interesting!<p>Tiny note: there's a typo in your repo description.
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Why is this comment an ad?
This is the OP promoting their project — makes sense to me
How can it be an ad if it's not selling anything? Seems like a proud parent touting their child to me.
It's a Show HN. That's the point.
Because they had an AI write it. Their other comments seem organic but the one you’re responding to does not
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.
This is not an LLM but a Binary to run LLMs as single purpose agents that can chain together.
Putting heavy AI workloads in a 12MB binary means you either make savage cuts on model support or you lock users to strange minimal formats. If you care about ops, eventually you hit edge cases where the "just works" story collapses and you end up debugging missing layers or janky hardware support. If the goal is to experiment locally or run demos, 12MB is fine but pretending it fits broader deployment is a stretch unless they're pulling some wild tricks under the hood.