The long-horizon computer-use harness. Run AI agents across desktop apps and the CLI for extended periods while preserving task state and making reliable progress on complex workflows. Features fresh-context execution, durable verified state, independent auditing, recoverable progress, and native Claude Code / Codex / OpenClaw integration.
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Healthy fork ratio
- ✓Clear description
- ✓Topics declared
git clone https://github.com/AMAP-ML/LongHorizon-Harness && cp LongHorizon-Harness/*.md ~/.claude/agents/5 items in this repository
Check OSWorld tasks. Validate the evaluation function, verify that the instruction is feasible given the task setup and agent-visible files, inspect setup artifacts when needed, and produce both markdown and structured JSON reports.
Analyze OSWorld-V2 agent trajectory logs and task results to produce actionable insights. Use this skill whenever the user wants to understand agent performance on OSWorld tasks — including analyzing trajectories, reviewing task results, finding error patterns, comparing code vs GUI strategies, identifying which tools/commands the agent used, or deciding which task types to scale up in the benchmark.
Migrate an agent from upstream OSWorld into this OSWorld-V2 repository, add matching evaluation entrypoints, and verify the integration.
Provision and verify an OSWorld-V2 checkout after clone. Use when the user asks for OSWorld-V2 setup, installation, onboarding, AWS provider setup, Docker provider setup, mocked website server setup, GitLab server setup, gated task download, CUA-Harness hybrid experiment setup, or a final runnable export block. The skill should install/configure the selected supported infrastructure where possible, ask for user confirmation or credentials when required, and report what is fully configured versus still blocked.
Reproduce CUA-Harness experiments on WeaveBench from a GitHub checkout. Use when the user wants an AI coding agent to set up dependencies, download WeaveBench assets, prepare the 120G VM, configure Qwen/Anthropic-compatible APIs, run smoke tests, launch full or subset evaluations, inspect logs, or summarize scores for this repository.
Subagents overview
What people ask about LongHorizon-Harness
What is AMAP-ML/LongHorizon-Harness?
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AMAP-ML/LongHorizon-Harness is subagents for the Claude AI ecosystem. The long-horizon computer-use harness. Run AI agents across desktop apps and the CLI for extended periods while preserving task state and making reliable progress on complex workflows. Features fresh-context execution, durable verified state, independent auditing, recoverable progress, and native Claude Code / Codex / OpenClaw integration. It has 1.4k GitHub stars and its last recorded update is dated 2026-08-20.
How do I install LongHorizon-Harness?
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You can install LongHorizon-Harness by cloning the repository (https://github.com/AMAP-ML/LongHorizon-Harness) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is AMAP-ML/LongHorizon-Harness safe to use?
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Our security agent has analyzed AMAP-ML/LongHorizon-Harness and assigned a Trust Score of 97/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains AMAP-ML/LongHorizon-Harness?
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AMAP-ML/LongHorizon-Harness is maintained by AMAP-ML. The last recorded GitHub activity is dated 2026-08-20, with 41 open issues.
Are there alternatives to LongHorizon-Harness?
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Yes. On ClaudeWave you can browse similar subagents at /categories/agents, sorted by popularity or recent activity.
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