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?

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?

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.

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

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

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.

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.

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/

There is no "session" concept?

I’m having trouble understanding when/where I would use this? Is this a replacement for pi or codex?

Is the axe drawing actually a hammer?

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.

The Unix-philosophy framing resonates — focused, composable, single-purpose agents are genuinely safer architecturally than monolithic long-lived sessions with massive context windows.

That said, composability introduces its own attack surface. When agents chain together via pipes or tool calls, each handoff is a trust boundary. A compromised upstream output becomes a prompt injection vector for the next agent in the chain.

This is one of the patterns we stress-test at audn.ai (https://audn.ai) — we do adversarial testing of AI agents and MCP tool chains. The depth-limited sub-agent delegation you mention is exactly the kind of structure where multi-step drift and argument injection can cause real damage. A malicious intermediate output can poison a downstream agent's context in ways that are really hard to audit after the fact.

The small binary / minimal deps approach is great for reducing supply chain risk. Have you thought about trust boundaries between agents when piping? Would be curious whether there's a signing or validation layer planned between agent handoffs.