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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A benchmark built to evaluate and improve agent capabilities for supporting legal work.
| Date | Stars |
|---|---|
| 2026-07-31 | 581 |
| 2026-08-03 | 591 |
| 2026-08-06 | 591 |
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<p align="center">
<img src="docs/assets/lab-hero.png" alt="Harvey LAB" width="100%">
</p>
<p align="center">
<strong>Legal Agent Benchmark (LAB): An open-source benchmark for evaluating agents on real legal work.</strong>
</p>
<p align="center">
<a href="https://github.com/harveyai/harvey-labs/tags"><img alt="Latest version" src="https://img.shields.io/github/v/tag/harveyai/harvey-labs?display_name=tag&sort=semver&style=flat-square&label=version"></a>
<img alt="License: MIT" src="https://img.shields.io/badge/license-MIT-green?style=flat-square">
<img alt="Legal practice areas" src="https://img.shields.io/badge/legal%20practice%20areas-24%20%2B%20contracting-0E7C7B?style=flat-square">
<img alt="Tasks" src="https://img.shields.io/badge/tasks-1671-4F46E5?style=flat-square">
<a href="https://github.com/harveyai/harvey-labs/actions/workflows/validate-task-schema.yml"><img alt="Test suite" src="https://github.com/harveyai/harvey-labs/actions/workflows/validate-task-schema.yml/badge.svg?branch=main"></a>
</p>
Harvey LAB is an open-source project aimed at benchmarking LLM agents' abilities to perform legal work in realistic environments.
LAB consists of two parts: a dataset of *tasks* containing agent instructions, documents, and rubrics as well as an *execution harness* for running and evaluating agents against those tasks.
LAB is an ongoing project and we expect to consistently add to and refine the task set and execution harness.
Read the announcement post: [Introducing Harvey's Legal Agent Benchmark](https://www.harvey.ai/blog/introducing-harveys-legal-agent-benchmark)
## Getting Started
Start with the full walkthrough in **[docs/tutorial.md](docs/tutorial.md)** — it takes one realistic M&A data-room assignment end to end: setup, task inspection, agent run, scoring, report review, and comparison dashboards.
## Additional Documentation
| Guide | Description |
|---|---|
| [Architecture](docs/architecture.md) | Task model, harness, tools, adapters, reports, and sweeps |
| [Evaluation Methodology](docs/eval-strategies.md) | All-pass rubric scoring and LLM judge behavior |
| [Contributing](CONTRIBUTING.md) | Add tasks, model adapters, evaluation improvements, and docs |
## Citation
If you use Harvey LAB in your research, please cite it as:
```bibtex
@misc{harveylab2026,
title = {Harvey LAB: The Legal Agent Benchmark},
author = {{Harvey AI}},
year = {2026},
version = {v1.0},
url = {https://github.com/harveyai/harvey-labs/tree/v1.0},
note = {Announcement: \url{https://www.harvey.ai/blog/introducing-harveys-legal-agent-benchmark}}
}
```
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Enes Yilmaz · Apple · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:606d62f53a71e223, llm:description: 'A benchmark built to evaluate and improve agent capabilities for supporting legal work.'
matched fp:606d62f53a71e223, llm:description: 'A benchmark built to evaluate and improve agent capabilities for supporting legal work.'
matched fp:606d62f53a71e223, llm:description: 'A benchmark built to evaluate and improve agent capabilities for supporting legal work.'