Meta-Harness

Health v2 · maintenance only

Flags: no_releases young

Adoption not part of the score

1464 stars · 140 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Meta-Harness is a Python framework for automated end-to-end search over task-specific model harnesses — the code surrounding a fixed base LLM that decides what to store, retrieve, and show while the model works. It includes an onboarding flow for adapting the search loop to new domains and two reference experiments from the accompanying paper: memory-system search for text classification and scaffold evolution for Terminal-Bench 2.0.

Use cases

• automatically optimize an LLM agent scaffold for a benchmark
• search over memory-system designs for text classification
• evolve terminal-bench agent harnesses to improve task scores
• apply harness optimization to a new domain with an onboarding flow
• diagnose agent failures from raw execution traces and propose fixes
• reproduce results from the Meta-Harness paper

When to choose

• you have a fixed base model and want to automatically improve the surrounding harness code
• you want to reproduce or extend the Meta-Harness paper experiments
• you need an LLM-driven search loop that reasons over full execution traces rather than compressed summaries
• you want a starting point for harness engineering on benchmarks like Terminal-Bench 2.0

When to avoid

• you need a production-ready agent framework with polished APIs and support guarantees
• you want to fine-tune model weights rather than optimize harness code
• your project cannot use Claude Code or requires a different proposer agent without adaptation work
• you need a lightweight library with minimal dependencies — the search loop is compute- and evaluation-intensive

Facets

framework · maturity active

agent-framework llm-inference machine-learning benchmarking developer-tools large-language-models machine-learning developer-tools python cli harness-engineering llm-agents automated-search scaffold-optimization terminal-bench research-code evolutionary-search ai-agents automation linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="stanford-iris-lab/meta-harness")