martian-ares 0.0.2

Agentic Research and Evaluation Suite

ARES: Agentic Research & Evaluation Suite

ARES is an RL-first framework for training and evaluating LLM agents, especially coding agents.

It is a modern gym: the environment layer powering RL research.

Quick Start

Pre-requisites

• Python >= 3.12

Getting Started

Install with uv:

uv add martian-ares

ARES comes packaged with useful presets for different code agent & environment configurations. List them with:

uv run python -c "import ares; print(ares.list_presets())"

You can get started by using this minimal loop to run mini-swe-agent on SWE-bench Verified sequentially.

Note: to run this particular example you will need:

• Docker (with the daemon running)
• A Martian API key (see below)

import asyncio

import ares
from ares import llms

async def main():
    # This requires `CHAT_COMPLETION_API_KEY` to be set with a Martian API key--see below.
    agent = llms.ChatCompletionCompatibleLLMClient(model="openai/gpt-5-mini")

    async with ares.make("sbv-mswea") as env:
        ts = await env.reset()
        while not ts.last():
            action = await agent(ts.observation)   # observation = LLM request
            ts = await env.step(action)            # action = LLM response
            print(f"{action}\n{ts}")

if __name__ == "__main__":
    asyncio.run(main())

To run the example above you'll need a Martian API key set in your .env file. To get a key:

1. Go to https://app.withmartian.com
2. on the Billing tab, add a payment method + top up some credits.
3. on the API Keys tab create an API key.
4. write CHAT_COMPLETION_API_KEY={your-key} in your .env

Alternatively, you can use another chat completions-compatible endpoint by setting both:

• CHAT_COMPLETION_API_BASE_URL
• CHAT_COMPLETION_API_KEY

Next Steps

1. Check out the examples
2. Read the docs to understand ARES and its key abstractions