AMAP-ML/LongHorizon-Harness is an open-source Python framework designed to enable and evaluate AI agents in performing long-horizon, multi-step tasks across desktop applications and operating systems. It provides a standardized environment and harness for testing an agent’s ability to plan and execute sequences of actions, such as using software or navigating an OS, to achieve a complex goal. The project is actively maintained, with its latest release published in August 2026.
What it is
The LongHorizon-Harness is a specialized testing and execution framework focused on the challenge of long-horizon computer use by AI agents. Unlike simple API calls or single-step commands, long-horizon tasks require an agent to break down a high-level objective (like “create a presentation from a research paper”) into a sustained sequence of precise low-level actions across one or more applications. The framework provides the necessary infrastructure to simulate or interface with a desktop environment, define these complex tasks, and measure an agent’s performance in completing them reliably.
By offering a common harness, the project aims to standardize evaluation in this domain, allowing researchers and developers to benchmark different AI models and agent architectures against the same set of challenging, real-world computer-use scenarios. Its open-source nature and MIT license encourage community contribution and adoption for both research and practical agent development.
Key facts
| Attribute | Detail |
| :— | :— |
| GitHub Repository | AMAP-ML/LongHorizon-Harness |
| Primary Language | Python |
| License | MIT |
| Latest Release | v0.1.5 (Released August 14, 2026) |
| Availability | Install via `uv tool install lh-harness` or `pip install lh-harness` |
| Project Activity | Last code push was on August 14, 2026. The repository had 740 stars, 88 forks, and 23 open issues as of August 2026. |
How it compares
LongHorizon-Harness occupies a specific niche within the broader ecosystem of AI agent frameworks and benchmarks. It is distinct from general-purpose agent frameworks (like LangChain or AutoGen) by its exclusive focus on long-horizon tasks executed directly on a desktop computer interface. It is also different from typical AI benchmarks (which often test knowledge or reasoning on static datasets) because it evaluates an agent’s sequential decision-making and operational skill in a dynamic, interactive environment. While other projects may test computer use, this harness emphasizes the standardization and reproducibility of evaluations for complex, multi-step workflows.
FAQ
What does “long-horizon” mean in this context?
In the context of AI agents, “long-horizon” refers to tasks that require many sequential steps and sustained planning to complete. Instead of a single action, the agent must execute a lengthy series of correct actions (like opening apps, clicking, typing, navigating menus) over an extended period to achieve a final objective, making the task significantly more challenging than short, isolated commands.
Is LongHorizon-Harness an AI model or an agent itself?
No, LongHorizon-Harness is not an AI model. It is a framework or “harness”—a piece of software that provides an environment and tools to run and evaluate AI agents. You would typically integrate an existing AI model (like a large language model) with this harness to create an agent that can be tested on its computer-use capabilities.
What kind of tasks can be tested with this harness?
The harness is designed for tasks that involve real computer use, such as data analysis across spreadsheets and charting tools, document formatting and creation in office suites, complex web research and compilation, or system configuration. The specific suite of benchmark tasks is defined within the project’s code and documentation, focusing on scenarios that require interaction with standard desktop applications and the operating system.