Skip to main content

A framework for evaluating and optimizing agents and models using sandboxed environments.

Project description

Harbor

Docs Cookbook

Harbor is a framework from the creators of Terminal-Bench for evaluating and optimizing agents and language models. You can use Harbor to:

  • Evaluate arbitrary agents like Claude Code, OpenHands, Codex CLI, and more.
  • Build and share your own benchmarks and environments.
  • Conduct experiments in thousands of environments in parallel through providers like Daytona, Modal, LangSmith, Blaxel, and Novita Sandbox.
  • Generate rollouts for RL optimization.

Check out the Harbor Cookbook for end-to-end examples and guides.

Installation

uv tool install harbor

or

pip install harbor

Example: Running Terminal-Bench-2.0

Harbor is the official harness for Terminal-Bench-2.0:

export ANTHROPIC_API_KEY=<YOUR-KEY> 
harbor run --dataset terminal-bench@2.0 \
   --agent claude-code \
   --model anthropic/claude-opus-4-1 \
   --n-concurrent 4 

This will launch the benchmark locally using Docker. To run it on a cloud provider (like Daytona) pass the --env flag as below:

export ANTHROPIC_API_KEY=<YOUR-KEY> 
export DAYTONA_API_KEY=<YOUR-KEY>
harbor run --dataset terminal-bench@2.0 \
   --agent claude-code \
   --model anthropic/claude-opus-4-1 \
   --n-concurrent 100 \
   --env daytona

To see all supported agents, and other options run:

harbor run --help

To explore all supported third party benchmarks (like SWE-Bench and Aider Polyglot) run:

harbor datasets list

To evaluate an agent and model one of these datasets, you can use the following command:

harbor run -d "<dataset@version>" -m "<model>" -a "<agent>"

Citation

If you use Harbor in academic work, please cite it using the “Cite this repository” button on GitHub or the following BibTeX entry:

@software{Harbor_Framework,
author = {{Harbor Framework Team}},
month = jan,
title = {{Harbor: A framework for evaluating and optimizing agents and models in container environments}},
url = {https://github.com/harbor-framework/harbor},
year = {2026}
}

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

harbor-0.15.0.tar.gz (1.3 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

harbor-0.15.0-py3-none-any.whl (1.5 MB view details)

Uploaded Python 3

File details

Details for the file harbor-0.15.0.tar.gz.

File metadata

  • Download URL: harbor-0.15.0.tar.gz
  • Upload date:
  • Size: 1.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.10.9 {"installer":{"name":"uv","version":"0.10.9","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

File hashes

Hashes for harbor-0.15.0.tar.gz
Algorithm Hash digest
SHA256 406b20ce520fc1ec12bc9b05396cc91756bb6379fb565a5bc2f2b6759e94cb12
MD5 fcd4d13f3f09b859bad395918a03be2b
BLAKE2b-256 e0af9927af345cd7d80e4346539e2340a1cd795861b12fb5d5ffc7b0788a7401

See more details on using hashes here.

File details

Details for the file harbor-0.15.0-py3-none-any.whl.

File metadata

  • Download URL: harbor-0.15.0-py3-none-any.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.10.9 {"installer":{"name":"uv","version":"0.10.9","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

File hashes

Hashes for harbor-0.15.0-py3-none-any.whl
Algorithm Hash digest
SHA256 a6f7f703d9b008920f07751ae27f6c049afe908bee8199aa6bcc911f6c2aab1b
MD5 0b1ae65392321e45c5fab3a16f8ae573
BLAKE2b-256 df5a6e860d3bdbb09c56296d4b57fcebc22f8eb014b06eb3810f9e0a8ab718f7

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page