# harness-kit

> A Python toolkit for building and evaluating AI agent harnesses, enabling structured testing and benchmarking of LLM-based agents.

harness-kit is an open-source Python library designed to help developers build, run, and evaluate AI agent harnesses. It provides a structured framework for defining tasks, running agents against those tasks, and measuring their performance systematically. The toolkit is hosted on GitHub and targets researchers and engineers who need reproducible, comparable benchmarks for LLM-powered agents.

- **Agent Harness Framework**: *Define custom harnesses that wrap any LLM-based agent, providing a consistent interface for task execution and evaluation.*
- **Task Definition**: *Structure tasks with inputs, expected outputs, and evaluation criteria to enable automated scoring of agent responses.*
- **Benchmarking Support**: *Run agents across multiple tasks and collect metrics to compare performance across models or configurations.*
- **Extensible Design**: *Add custom evaluators, task loaders, and agent adapters to fit a wide range of use cases and agent architectures.*
- **Open Source**: *Clone the repository from GitHub, install dependencies via pip, and start building harnesses with minimal setup.*
- **Python-Native**: *Built entirely in Python, making it easy to integrate with popular LLM libraries such as LangChain, OpenAI SDK, and others.*

## Features
- Agent harness framework
- Task definition and structuring
- LLM agent benchmarking
- Automated evaluation and scoring
- Extensible evaluators and adapters
- Python-native integration
- Open source

## Integrations
LangChain, OpenAI SDK, Python

## Platforms
WEB, API, DEVELOPER_SDK, CLI

## Pricing
Open Source

## Links
- Website: https://github.com/deepklarity/harness-kit
- Documentation: https://github.com/deepklarity/harness-kit/blob/main/README.md
- Repository: https://github.com/deepklarity/harness-kit
- EveryDev.ai: https://www.everydev.ai/tools/harness-kit