google-research / harness2

Recursive Agent Harnessing for an Open World
A **harness** is the system around a model that enables it to act as an agent:
prompts, tools, memory, skills, orchestration, and more. **Harnessing** is the
process of optimizing that system.
Harness² is a plug-and-play framework for **recursive agent harnessing** in an
open world, where agents encounter tasks unseen during harness design. It
connects two levels of recursion while keeping model weights fixed:
* **Task-level recursion** refines the execution harness for the current task
 through a contrastive **Propose–Probe–Compose** step: propose edits, probe
 their effects on the same task, and compose a refined harness from behavioral
 contrasts between executions, without ground-truth feedback.
* **Harnessing-level recursion** updates a persistent *improvement harness*
 with adopted harnesses and evidence-supported lessons from each task to guide
 future refinement.
Across three professional benchmarks and four model–harness configurations, a
single Harness² step improves GLM-5 and Gemini 3.5 Flash over their base
harnesses, with gains of up to 11.5 points on WorkBuddy-Bench, 4.5 points on
JobBench, and 11.8 percentage points on LAB's partial pass rate. Increasing the
task-level recursion budget brings further gains.
The release supports **parallel and sequential recursion**, **OpenCode and
Codex executors**, and **JobBench, LAB, and WorkBuddy-Bench**.
## Video overview
https://github.com/user-attachments/assets/76b10548-b14b-4c8f-9aa5-a93662df4d56
## Contents
- Video overview
- Harness components
- Install
- Run a benchmark
- Experiments
- Web access
- Results and resume
- Citation
- Contributing
- License
- Disclaimer
## Harness components
Harness² exposes eight editable components around the model: **system prompt,
guardrails, memory, tool guidance, subagents, skills, scripts, and plugins**.
They control context construction, the tool interface, delegation, and behavior
inside the tool loop.
## Install
Use **Linux, Python 3.12, and uv**.
From the repository root:
 [code] 
The local demo runs both recursion modes with deterministic editors and real
Python executions. It requires no model credentials.
For model-backed benchmarks, install **Node.js 20+, the Google Cloud CLI**, and
the benchmark dependencies. LAB needs Pandoc and
Poppler; WorkBuddy-Bench needs Docker. Then configure your models:
 [code] 
See the setup guide for model settings, authentication
alternatives, and the Codex executor.
## Run a benchmark
The following example installs LAB and runs one task, then evaluates and reports
its result. Model-backed runs incur API costs; start with a small task selection.
 [code] 
Sequential recursion runs the same commands with `--mode sequential`. `--k` is
the task-level recursion budget; see Experiments. Judging and
reporting must use the same benchmark, executor, subset, `--k`, `--mode`, and
seed as the refinement run. Add `--dry-run` to `harness2` to preview task
selection without model call