README

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:

bash
uv venv --python 3.12 .venv
source .venv/bin/activate
uv pip install -e '.[render]'
harness2-demo --output runs/demo --k 2


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:

bash
cp .env.example .env
 Edit .env with your Vertex AI project and model IDs.
gcloud auth application-default login


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.

bash
harness2-setup lab
harness2-check --bench lab
harness2 --bench lab --k 1 

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