If you're looking for an agent implementation that is light on resources, but prefer to work in a terminal, consider also hax (https://usehax.dev/). Less than 2 MB fully static Linux binary, few MBs of RAM use in runtime. Works great with local models also (auto-detects config from running llama-server), besides the usual cloud providers.
I’ve been using GPT models via hax[1], a bare bones harness with minimal tools on my system and have the models rely on bash for the most part the last few months. It’s been a breath of fresh air amidst all these overengineered harnesses.
If I need special actions, I just make them CLI tools on my Mac and tell the AI model that such a tool exists(via a single line in its system prompt) and that’s it. It’s been very nice.
If anyone's looking for something a bit more minimal, I can't recommend hax [0] enough. No MCP, agent just gets a shell tool, simple config, whole thing's in C.
I don't understand MCP still? What can MCP do that a skill + cli can't? I've been using hax (https://usehax.dev) and haven't missed skills at all to be honest.
Honestly, the provided argument for it is rather weak. They are basically adding a way of running scripts that are contained within harness to execute harness's own tools (that's the Codemode). A coding agent can already compose any arbitrary logic by invoking shell scripts (or python scripts, or node scripts), etc - so this is just entirely unnecessary in the core, from my perspective.
If you feel that Pi has been drifting away from its original vision, try hax (https://usehax.dev/) - you might like it.
If you're looking for a coding agent that would fit nicely into resource-constrained environments (such as laptops, or tiny VPS servers, or tiny single-board computers, etc), and would also work great with local models - you might also like hax (https://usehax.dev/). 0.7 MB dynamically linked native C binary, few MBs of RAM usage when running, auto-discovers config from running local llama-server, and uses minimalist system prompt and tools for lean context usage.
If you like this list of "why?", you might also like this: https://usehax.dev/ (I am the author).
Most of the list applies, similar minimalist Unix tool approach, with some differences. Hax is written in C, the dynamically linked binary is even smaller (0.6 MB), MIT-licensed. No wasm though.
Important difference - fx is currently Vercel AI Gateway only - while hax does support multiple providers already (OpenAI API, ChatGPT/Codex subscription, Anthropic API, OpenRouter, OpenCode Zen/Go), and integrates well out of the box with local llama-server.
I am working on my own agent implementation (https://usehax.dev/), and I can praise OpenRouter for being seemingly the only provider endpoint that actually gives proper API for gathering available models and their capabilities (context length, image support, pricing, etc), reports exact routing details and total cost in the response, and provides an endpoint for obtaining current available credits (for "/usage"-like command). So for OpenRouter specifically I could deliver a really good experience out-of-the-box (always fresh models and metadata, exact pricing, etc).
You would think that these things would be standard fare, but they really are not. Other providers need metadata from a separate catalog, don't report pricing, don't really report available credits / usage either.
This is a coding agent implementation I am working on, which delivers what this promises (at least on the "lean" part), except it's actually fully open source, and even more lean (few MBs of runtime memory usage).
MIT-licensed, written in C, multi-provider / multi-model, minimalist approach to system prompt and tools (think kinda like pi, but with a bit more "batteries included", like subagents and background tasks out of the box), polished presentation, inspectable (usable transcript view), etc.
