LongHorizon-Harness

Advancing Long-Horizon Agents for Real-World Tasks

Operate the whole computer like a human. Work across desktop apps and the command line for dozens of hours.

No state drift. Verifiable progress. Complex tasks carried through to completion.

Usage · What You Get · How It Works · Results · Project Website · 简体中文

The model determines what an agent can do in one round. LongHorizon-Harness determines whether that work can be verified, preserved, and continued until the task is actually complete.

Works with Claude Code, Codex, and OpenClaw. One-command install, ready to run.

LongHorizon-Harness is an execution, state-management, and result-verification system for long-horizon tasks. It does not train a new model or replace an existing agent. It runs on top of systems such as Codex and Claude Code, helping agents operate autonomously in real computer environments for extended periods and continuously move complex tasks forward.

Video Demo

https://github.com/user-attachments/assets/ca8b77ce-9220-4d85-a272-b346009b2454

Open the promotional video (1440p MP4)

Three roles. One trusted state.

LongHorizon-Harness separates planning, execution, and verification so that one growing context is not responsible for everything.

Only results that pass independent verification enter persistent task state. Even when the context is refreshed, an action fails, or a deliverable does not pass inspection, the system retains previously verified progress and continues from what remains.

Desktop apps and CLI. One continuous task.

LongHorizon-Harness supports both GUI and CLI workflows.

One task can begin in a browser, move to the command line for data processing, continue in desktop software to produce an artifact, and return to the terminal for validation or debugging. The goal, progress, and evidence remain under the same state-management system throughout.

lh-harness run --task @task.md --agent claude_code \
  --mcp-config /path/to/your/mcp.json \
  --mcp-add-dir /path/to/your/mcp/files

Any model. Any agent backend.

LongHorizon-Harness is not tied to a specific model or agent backend. Existing models and agents connect through configuration without changing their original workflows.

A lightweight AgentAdapter preserves each agent's native execution loop while LongHorizon-Harness coordinates role boundaries, verified task state, and cross-round progress around it.

Use one model for all three roles, or combine different models and backends to balance quality, speed, and cost.

Hundreds of real tasks. Measured gains.

LongHorizon-Harness is not demonstrated only on a handful of carefully selected success cases.

We ran it on hundreds of complex tasks across GUI, CLI, and mixed computer environments:

Same model. Same execution backend. Only the harness changes.

Full result tables and case trajectories are available on the LongHorizon-Harness project website.

One command. Full visibility.

Install LongHorizon-Harness:

uv tool install lh-harness

LongHorizon-Harness requires Python 3.10+ and at least one agent runtime: claude, codex, or openclaw.

Run a task:

lh-harness run \
  --task "Inspect the current directory and summarize its files."

Run a longer task from a file and open the Dashboard:

lh-harness run --task @task.md --dashboard

The Dashboard shows every round's plan, execution result, audit evidence, and reason for rework. It also provides human gates when a task completes, becomes blocked, needs input, or fails repeatedly.

Every run is stored in an isolated runs/<run-id>/ directory. The complete task state and audit trail make the agent's progress inspectable, recoverable, and reproducible.

pip install lh-harness

lh-harness run --task @task.md --dashboard      # Monitor a live run
lh-harness dashboard --runs-root ./runs         # Browse completed and active runs

Evaluation Reproduction

eval/ provides frozen reproduction suites for two benchmarks:

See each directory's README.md or README.zh-CN.md for environment setup, parameters, and launch commands. The nested cua_harness packages are frozen compatibility copies used for evaluation; new integrations should use src/lh_harness/.

Citation

@article{longhorizonharness2026,
  title={LongHorizon-Harness: Advancing Long-Horizon Agents for Real-World Tasks},
  author={Ziyu Ma and Hailang Huang and Shun Zou and Yong Wang and Shidong Yang and Yiming Hu and Fei Wei and XiangXiang Chu},
  journal={arXiv preprint arXiv:2608.01964},
  year   = {2026},
  url    = {https://arxiv.org/abs/2608.01964}
}