LongHorizon-Harness

> **The model determines what an agent can do in one round. LongHorizon-Harness engineers the loop around it: what to do next, how to verify the result in the real computer, what progress to preserve, and how to continue after failure or context refresh.** **A Loop Engineering system for Claude Code, Codex, OpenCode, and DeepSeek Harness. One-command install, ready to run.** LongHorizon-Harness turns existing agents into long-running computer-use systems. Across desktop apps and the terminal CLI, it continuously recovers the goal and verified state, selects the next bounded step, executes it with a fresh context, checks the actual result, and then checkpoints accepted progress or feeds failure evidence into the next round. It does not train a new model or replace an existing agent; it provides the durable execution loop around one. ## ✨ News - **[v0.1.7 · 2026-08-20]** A finished run is no longer a dead end: the workbench is now a conversation. Read the reply, type a follow-up, and the run continues on its own round ledger instead of replanning from scratch. A message you send mid-round is claimed by the very next round, so stopping and continuing never drops it. Also adds `--reasoning-effort` for every role (with `--manager-reasoning-effort` and friends to override one), forwarded to whichever backend exposes it. The transcript now reads in strict chronological order, and a graceful stop escalates to a force stop only when a worker ignores it. - **[v0.1.6 · 2026-08-15]** Added [OpenCode](https://github.com/anomalyco/opencode) CLI support. LongHorizon-Harness can now run `opencode run prompt` as `--agent opencode`, with role-scoped read/write permissions, OpenCode API endpoint overrides, normalized JSON results, and CLI/config/doctor integration. The Web workbench can select OpenCode Harness and its model independently for each role. - **[v0.1.5 · 2026-08-14]** Added phase-1 [DeepSeek Harness](https://github.com/deepseek-ai/deepseek-harness) CLI support. LongHorizon-Harness can now run `dsh --profile headless` as `--agent deepseek_harness`, with an isolated `DSH_HOME`, role-scoped read/write permissions, DeepSeek API endpoint overrides, normalized JSONL results, and CLI/config/doctor integration. The Web workbench can select DeepSeek Harness and its model independently for each role. GUI computer-use and MCP support will follow in a later phase; see [the CLI setup](#5-or-run-a-task-from-the-command-line). - **[v0.1.4 · 2026-08-11]** The new Dashboard has landed: a React/FastAPI workbench you can drive entirely from the browser. Start a task, choose a backend and model per role, answer approvals, send an instruction mid-run, and stop or restart a run. Launch it with `lh-harness web`; see [Run a task in the browser](#4-run-a-task-in-the-browser-recommended). - **[2026-08-10]** Added the Terminal-Bench 2.1 evaluation. - **[v0.1.3 · 2026-08-07]** Every run now ends with a plain-language reply that answers your task from the verified state alone. Tasks act on the directory you launched from by default, and the console reports each round as it happens. - **[2026-08-06]** LongHorizon-Harness reaches **#1** on the [Hugging Face Daily Papers weekly ranking](https://huggingface.co/papers/week/2026-W32). - **[v0.1.2 · 2026-08-06]** Adds unified computer-use plugin management, stronger auditor read-only checks and role isolation, reliable process cleanup, and expanded `doctor` diagnostics. See [Manage computer-use plugins](#manage-computer-use-plugins). > We’re iterating rapidly. Stay tuned! ## Video Demo https://github.com/user-attachments/assets/ca8b77ce-9220-4d85-a272-b346009b2454

Open the promotional video (1440p MP4)

## Loop Engineering for real computer environments. Give LongHorizon-Harness an outcome. It repeatedly turns the remaining work into a bounded step, performs that step on the right computer surface, checks what actually happened, and carries the verified result into the next round. ```mermaid flowchart LR S["Original goal +
verified state"] --> P["Plan the next
bounded step"] P --> A["Act in a desktop app or CLI
with fresh context"] A --> V["Verify files, UI, logs, and tests
in the real environment"] V -->|Pass| C["Checkpoint
verified progress"] V -->|Fail| R["Record evidence
and recover"] C --> D{"Task complete?"} R --> S D -->|No| S D -->|Yes| F["Verified result"] ``` This is **Loop Engineering**: designing the execution, verification, correction, and recovery loop around the agent — not just the prompt for a single turn. ### One loop. Three focused responsibilities. The roles are implementation boundaries inside the loop, not three agents independently growing their own versions of the task. | Loop responsibility | Role | What it owns | |---|---|---| | **State and next step** | **Manager** | Rebuilds each round from the original goal, verified progress, failure evidence, and remaining work | | ⚡ **Action** | **Executor** | Starts with a fresh context and completes one clearly defined step in a desktop app or the CLI | | **Ground truth** | **Auditor** | Independently inspects the actual files, interfaces, logs, and tests instead of trusting the Executor's claim | Only results that pass independent verification become trusted task state. A rejected result remains evidence, not progress. When a context is refreshed, an action fails, or a deliverable does not pass inspection, the next round starts from the original goal and the last verified checkpoint, then continues from what remains. ## Desktop apps and CLI. One continuous task. LongHorizon-Harness supports both GUI and CLI workflows. | ️ Operate the desktop | ⌨️ Work in the terminal | |---|---| | Click, type, scroll, and browse | Write and modify code | | Operate spreadsheets | ▶️ Run commands and scripts | | Edit documents | Install dependencies and environments | | Use design software | Configure and debug systems | | Operate 3D tools | Process files and data | 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. ## 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. | | Layer | Supported choices | |---|---|---| | | **Models** | Claude, GPT, Qwen, and other models exposed by an agent backend | | | **Agent backends** | Claude Code, Codex CLI, OpenCode, DeepSeek Harness (`dsh`, CLI-only in phase 1), and custom `AgentAdapter` implementations | | ️ | **Role assignment** | The Manager, Executor, and Auditor can each use a different model or backend | | ️ | **Execution environments** | Local, with a pluggable `Environment` protocol | 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: | Task domain | What the tasks involve | |---|---| | **Web Frontend** | Developing, fixing, and validating websites and web applications through browser interaction, developer tools, and code changes | | **Data Analysis & Visualization** | Processing data, producing charts and dashboards, and checking analytical results and visual deliverables | | ️ **Operations & Debugging** | Investigating logs, networks, performance, and service failures; configuring, diagnosing, and repairing systems | | **Design & Image Processing** | Editing visual assets, matching design references, processing images, and verifying final visual quality | | **Games & Interaction** | Building, operating, and debugging games or interactive applications; checking interaction logic and runtime behavior | | **Documents & Presentations** | Editing documents and slide decks, including content, formatting, references, layout, and final delivery | | **Spatial Reasoning** | Completing tasks involving spatial relationships, geometry, precise placement, and 3D operations | | ️ **Desktop & System Settings** | Operating desktop applications, files, and system settings across multi-application workflows | | **Research & Education** | Completing literature research, coursework, teaching materials, forms, and research-support workflows | | **Creative Production** | Producing presentations, video, audio, and other media while coordinating assets across tools | | ⚙️ **Engineering & Computing** | Using CAD, EDA, scientific software, development tools, and cloud or DevOps toolchains | | **Personal Services** | Handling event ticketing, everyday services, games, and visual-search workflows | | ️ **Administration & Compliance** | Completing office, legal, policy-sensitive form, institutional, and safety-aware submission workflows | | **Business & Finance** | Handling market analysis, procurement, loans, sales, reimbursements, and cross-application enterprise workflows | | **Healthcare** | Completing medical quality-control, insurance, immunization, and structured health-form workflows | ### Same model. Same execution backend. Only the harness changes.

~50% → ~80%

GUI + CLI completion
WeaveBench

3×

Full desktop-task completion
OSWorld 2.0

69.7% → 77.2%

Code + CLI success
Terminal-Bench 2.1 · 24% fewer tokens ### Full benchmark results and experimental settings | Benchmark | Metric | Claude Code | **LongHorizon-Harness** | Gain | |---|---|:-:|:-:|:-:| | **WeaveBench** (114 tasks) | PassRate | 51.8 | **80.7** | **+28.9** | | **WeaveBench** | Overall | 0.702 | **0.835** | +0.133 | | **OSWorld 2.0** (108 tasks) | Binary | 2.8 | **8.3** | **3.0×** | | **OSWorld 2.0** | Partial | 21.5 | **35.2** | **+13.7** | | **Terminal-Bench 2.1** | Success rate | 69.7 | **77.2** | **+7.5** | All rows use Qwen 3.7-Plus as the backbone and Claude Code as the execution backend. Full result tables and case trajectories are available on the [LongHorizon-Harness project website](https://lh-harness.pages.dev). ## One command. Full visibility. ### Installation Steps 1–2 are once per machine; step 3 is once per project. Then run tasks from the browser (step 4) or the command line (step 5). #### Requirements | | Needed for | |---|---| | [uv](https://docs.astral.sh/uv/getting-started/installation/) | The recommended isolated install. Skip it if you prefer pip. | | Python 3.10 or later | Running the harness. `uv tool install` brings its own; a pip install uses yours. | | One agent runtime on `PATH`: [`codex`](https://github.com/openai/codex#installing-and-running-codex-cli), [`claude`](https://docs.anthropic.com/en/docs/claude-code/getting-started), [`opencode`](https://github.com/anomalyco/opencode), or [`dsh`](https://github.com/deepseek-ai/deepseek-harness) | Actually executing the work. Install more than one if you want to mix backends across roles. | | [Node.js](https://nodejs.org) 20 or later | The npm-distributed computer-use plugins. DeepSeek Harness itself currently requires Node.js `^22.19.0` or `>=24.0.0`. | > **Platform status:*