Found a paper with no code, or code you cannot trust? Ask your agent to build it with IRP. The agent reads the paper, records every number it claims, writes the implementation, runs it and compares the results. IRP does the fetching, running and record keeping, and refuses the shortcuts: a number typed by hand never counts, a shortened run never counts, and the report is written only from the evidence log.

IRP connects your agent (Claude Code, Codex or Cursor) to tools for paper intake, environments, runs, measurements and verdicts. You can use the same tools from your terminal. Everything runs locally; IRP never calls a model, and data goes only where you send it.

Quick start

Set up your agent

With uv installed, run:

uvx irp-agents setup

Choose your agents, review the proposed changes, and approve them. Setup adds IRP's MCP server and its workflow skill, with backups of existing configuration. Restart your agent afterward.

Setup supports Claude Code, Codex and Cursor. Other MCP clients can use manual registration.

Ask your agent

Build arXiv 2501.12345 and check Table 2.

You get back a working code folder plus a report: every claim, the paper's number, your number, and a verdict.

Use the terminal

Every tool is also a command. Fetch a paper into a new study and read its outline:

irp fetch 2501.12345 --study ~/studies/example
irp section index --study ~/studies/example

To install the irp command for regular use:

uv tool install irp-agents
irp --help

See the CLI guide for the full workflow from the terminal.

Check readiness

irp doctor

Doctor checks Python, uv, git, Docker, GPUs, disk space and your agents' registrations, and changes nothing.

Update IRP

IRP changes quickly. For an installed command:

irp update

With uvx, refresh your agent setup with uvx irp-agents@latest setup, review the plan, and restart your agent.

How IRP works

Your agent thinks; IRP acts and keeps the receipts. The agent reads the paper, writes code and decides what to run. Every action goes through one core, so the CLI and the MCP server behave the same, and every step lands in an append-only, hash-chained evidence log. The report is built from that log alone.

The workflow skill gives the agent six hard rules, and the tools enforce them wherever they can:

1. Never type, estimate or copy a number as a result.
2. Never change the paper's method to make a run succeed; record a guess instead.
3. Report "could not run" with the blocking error instead of pushing through.
4. Run the smallest faithful version first; shortened runs never count.
5. Record every claim before writing any code.
6. Ask before any paid compute.

The report

The report is the product: a person must be able to check every verdict without trusting the agent. It has five parts: the claim table, four verdicts (reproduced, close, not reproduced, could not run), the guess list, the method map from equations to code lines, and provenance (arXiv ID and version, file hashes, git commit, lock files, GPU, seeds, every command with its exit code, and a verify command that rechecks the hash chain).

The agent cannot finish until every claim has a verdict with evidence.

Example report.md (made-up numbers):

# Reproduction report: Example Paper (arXiv 2501.12345v2)

Result: 2 reproduced, 1 close, 1 could not run (of 4 claims)

| Claim | Where | Paper | Ours | Verdict |
| --- | --- | --- | --- | --- |
| c1 Test accuracy | Table 2, row 3 | 94.1% | 93.8% (3 seeds, std 0.2) | close |
| c2 Training loss | Figure 4 | 0.31 | 0.31 (3 seeds) | reproduced |
| c3 Speed-up | Section 5.2 | 2.0x | 2.1x | reproduced |
| c4 ImageNet top-1 | Table 3 | 81.2% | not run: needs 8 GPUs for 2 days | could not run |

## Guesses
- g1 Warmup length not stated: used 5 epochs. Why: as in cited paper [12].

## Method map
- Eq. 3 (loss) -> code/src/loss.py:41
- Algorithm 1, line 4 -> code/src/train.py:88

## Provenance
- Paper: arXiv 2501.12345v2, source and PDF hashes
- Code: git commit 3f2a9c1 (clean)
- Commands logged: 37, 37 with an exit code
- Evidence log: 112 records, head sha256:…, verified
- Verify: `irp verify --study ~/studies/example`

See evidence and report for every field.

What you can reproduce

IRP requires Python 3.11+ on macOS or Linux. Additional tools depend on the paper:

IRP supports computational papers: machine learning, algorithms and simulations. It does not support wet-lab work or anything weapons-related.

Showcases

Example reproductions — a paper built end to end with the evidence to check it — will be published here.

If you build a paper with IRP, share the report in an issue with the paper, your question, and what you found.

FAQ

Documentation

• Installation and setup: agent registration, updates and uninstall.
• CLI: every command, output and exit status.
• MCP contracts and agent prompts: tool results, errors and the generated tool catalog.
• Evidence and report: the log, the gate, verification.
• Paper intake, environments and compute.
• Benchmarks: how IRP is measured.
• Roadmap: v0.1 to v1.0 and the gates between them.

Report vulnerabilities through SECURITY.md.

Disclaimer

See DISCLAIMER.md for responsible-use guidance. IRP is for lawful research, education and the verification of published computational work. A "not reproduced" verdict is evidence about one run, not a judgment of the paper or its authors.

Contributing

Contributions are welcome. Open an issue to report a bug or suggest a feature, or send a pull request.

See CONTRIBUTING.md for development setup and checks, testing for verification lanes, and the architecture map for the project structure.

License

MIT

IRP-authored code is licensed under MIT. Third-party material retains its own licenses and notices; see Third-party notices. Papers and datasets keep their authors' licenses; IRP never redistributes them.