AIRAS - an open-source project for research automation

AIRAS is open-source software for automated research. It gives a coding agent (Claude Code, Cursor, or any MCP client) everything it needs to take a research topic through literature survey, hypothesis, experiments, and a finished paper, and it makes the paper's claims verifiable: the paper is preregistered in git before any experiment runs, every reported number is realized from run outputs, and CI re-checks all of it before the PDF of record is produced.

AIRAS ships as one PyPI package (airas) that provides:

• an MCP server with the research tools the flow needs (paper search, hypothesis and experimental design, experiment results, figure rendering, LaTeX, Overleaf, and the record/verification tools),

• a Claude Code plugin that bundles the server with the auto-research workflow skills,

• a small CLI (airas verify-record, airas verify-paper) that the experiment repository's CI uses as the verification gate.

Currently, it focuses on the automation of machine learning research.

Quick Start

No clone, no Docker. Only uv is required; uvx fetches the package on first run.

1. Install

AIRAS is meant to be driven from Claude Code through its plugin. The plugin installs the MCP server together with the auto-research workflow skills and the hooks that record the agent's state:

/plugin marketplace add airas-org/airas
/plugin install airas@airas

The MCP server can also be used on its own, without the skills and hooks. In Claude Code:

claude mcp add airas -- uvx airas

In any other MCP client, add it to the client's MCP configuration (e.g. .mcp.json):

{
  "mcpServers": {
    "airas": {
      "command": "uvx",
      "args": ["airas"]
    }
  }
}

Upgrading. uvx keeps the version it fetched the first time, so an existing install does not move to a new release on its own. Run uv cache clean airas (or uvx airas@latest) once to pick up the latest version.

2. Configure credentials

Credentials live in ~/.airas/credentials.json and are re-read on every tool call, so you can create or edit the file at any time:

mkdir -p ~/.airas
cat > ~/.airas/credentials.json <<'EOF'
{
  "GH_PERSONAL_ACCESS_TOKEN": "ghp_...",
  "SEYVAL_API_KEY": "..."
}
EOF
chmod 600 ~/.airas/credentials.json

3. Start a research project

In Claude Code, invoke the orchestrator skill and give it a topic:

/airas:auto-research

It walks through the flow below, asking you to settle the operational choices (repository visibility, execution platform, compute target) once up front. Other MCP clients can start from the server's start_research prompt or call the tools directly.

Related MCP server: pubmed-search-mcp

The auto-research flow

auto-research owns only the ordering and the rules that span steps. Each step is its own skill with a stated contract, so you can also invoke a single step on an existing repository.

What the flow guarantees

• Declare before you run. Nothing is dispatched before the preregistration commit exists, and every run descends from it. Fixes are committed on top, never rebased away.

• The record only grows. .research/record.json is an append-only tree of hypotheses, claims, designs, runs, and results. A reworded claim, a changed run condition, or a dropped result all fail the same check. A claim that misses its criterion is reported as a negative result, not rewritten.

• No experimental number is ever typed. Numbers reach the paper only through \airasval{...} references to a run's measured metric or declared parameter, rendered from the record. Anything else is marked \unverified{...}.

• The gate is enforced, not advisory. Branch protection makes the verification workflow a required check, so a red run cannot be pushed past.

• The repository is the state. Everything a later step needs is committed, so a fresh session can resume from the clone alone.

Execution platforms and LLMs

Experiment execution is under construction. The run-experiments skill currently drives the Seyval compute platform (bring-your-own Slurm compute) through Seyval's own MCP server, and the MCP tools for GitHub Actions execution (dispatch_experiment, get_workflow_runs, get_experiment_run_status) are not yet wired into the flow. Every other step, including the record and verification gate, works independently of the execution backend.

Generation steps need no LLM key: get_generation_prompt hands the agent the curated prompt and output schema, and the agent authors the artifact itself. The same steps also exist as backend-LLM tools (generate_hypothesis, generate_paper, ...) for use outside the flow; those need a provider key (get_available_llms lists the models your keys allow). Supported providers: OpenAI, Anthropic, Google Gemini, OpenRouter, Amazon Bedrock, and Vercel AI Gateway.

Companion repositories

AIRAS relies on three sibling repositories under the airas-org organization. Each keeps one piece of the workflow outside the agent's reach.

MCP tools

The auto-research flow uses the following tools; the skills above are thin contracts over them. The server exposes more, but these are the ones a research project goes through.

See the MCP documentation for descriptions, credentials per tool, and configuration options.

CLI

uvx airas                 # MCP server on stdio (default)
uvx airas verify-record   # check .research/record.json against run outputs, git history, and the platform
uvx airas verify-paper    # verify the paper's values and provenance and build its PDF (the CI gate)

verify-record and verify-paper are what the experiment repository's CI runs as the required check on the protected branch.

Development

cd backend
uv sync
uv run airas

Lint and type checks are wired through pre-commit (pre-commit install once per clone). See CONTRIBUTING.md.

Roadmap

AIRAS is developed in stages. Reliability comes first: an automated research pipeline is only worth scaling once its outputs can be trusted and reproduced.

1. Reliability (in progress). The record now guarantees that every number in the paper traces back to a declared run and its outputs, and CI enforces it. That covers the paper but not everything upstream of it: experiment code that games a benchmark, leaks test data, or deviates from the design still passes the gate. Closing that gap, from the experiment code and evaluation inputs back to the design, is the current focus.

• Preregistration: claims, criteria, and predicted results frozen in git before any experiment runs

• Append-only research record with numbers realized from run outputs and verified in CI

• Integrity of the experiment code and evaluation itself (benchmark hacking, data leakage, design deviation)

2. Reproducibility. When an agent drives the research, the agent's trajectory is part of the method. Keeping it, and being able to replay the workflow from it, is a necessary condition for a reproducible result.

• Persist the agent trajectory alongside the repository state

• Replay a research workflow from its recorded trajectory

3. Research quality. Once reliability and reproducibility are in place, raise the quality of the research itself: better hypotheses, stronger experimental designs, and more rigorous analysis.

In parallel: other fields. Machine learning is the first target because experiments are code. Alongside the stages above we are collaborating with other domains, such as life sciences, robotics, and materials science, to extend the same integrity model to their workflows.

Contact

We aim to build an operating system for automated research that enables humanity to discover scientific breakthroughs it has not yet reached.

If you are interested in this topic, please feel free to contact us at ulti4929@gmail.com.

About AutoRes

This OSS is developed as part of the AutoRes project.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributions

By contributing to this project, you agree that your contributions are subject to the Contributor License Agreement (CLA) and may be used, modified, redistributed, and relicensed by the project owner, including for commercial, enterprise, and SaaS offerings.

See CLA.md for details.

Related projects

• AI-Research-SKILLs (Orchestra Research, MIT) — a library of library-specific ML engineering skills. AIRAS's experiment template installs it on code-generation runners so agents get framework-level guidance (fine-tuning, distributed training, inference); AIRAS's get_library_docs MCP tool complements it by pointing agents at each library's living documentation (llms.txt endpoints).

Citation

If you use AIRAS in your research, please cite as follows:

@software{airas2025,
  author = {Toma Tanaka, Takumi Matsuzawa, Yuki Yoshino, Ilya Horiguchi, Shiro Takagi, Ryutaro Yamauchi, Wataru Kumagai},
  title = {AIRAS},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/airas-org/airas}
}