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 en este repositorio
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.
Resumen de Subagents
Lo que la gente pregunta sobre LongHorizon-Harness
¿Qué es AMAP-ML/LongHorizon-Harness?
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AMAP-ML/LongHorizon-Harness es subagents para el ecosistema de Claude AI. 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. Tiene 1.4k estrellas en GitHub y su última actualización registrada es del 2026-08-20.
¿Cómo se instala LongHorizon-Harness?
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Puedes instalar LongHorizon-Harness clonando el repositorio (https://github.com/AMAP-ML/LongHorizon-Harness) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar AMAP-ML/LongHorizon-Harness?
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Nuestro agente de seguridad ha analizado AMAP-ML/LongHorizon-Harness y le ha asignado un Trust Score de 97/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene AMAP-ML/LongHorizon-Harness?
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AMAP-ML/LongHorizon-Harness es mantenido por AMAP-ML. La última actividad registrada en GitHub es del 2026-08-20, con 41 issues abiertos.
¿Hay alternativas a LongHorizon-Harness?
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Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.
Despliega LongHorizon-Harness en tu cloud
Lleva este repo a producción en minutos. Cada plataforma genera su propio entorno con variables de entorno editables.
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