Harness Evolver

Point at any LLM agent codebase. Harness Evolver will autonomously improve it — prompts, routing, tools, architecture — using multi-agent evolution with LangSmith as the evaluation backend.

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Install

Claude Code Plugin (recommended)

/plugin marketplace add raphaelchristi/harness-evolver-marketplace
/plugin install harness-evolver

npx (first-time setup or non-Claude Code runtimes)

npx harness-evolver@latest

Works with Claude Code, Cursor, Codex, and Windsurf.

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Quick Start

cd my-llm-project
export LANGSMITH_API_KEY="lsv2_pt_..."
claude

/harness:setup      # explores project, configures LangSmith
/harness:health     # check dataset quality (auto-corrects issues)
/harness:evolve     # runs the optimization loop
/harness:status     # check progress (rich ASCII chart)
/harness:deploy     # tag, push, finalize

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What It Looks Like

Tested on a RAG agent (Agno framework, Gemini 3.1 Flash Lite, light mode):

xychart-beta
    title "agno-deepknowledge: 0.575 → 1.000 (+74%)"
    x-axis ["base", "v001", "v002", "v003", "v004", "v005", "v006", "v007"]
    y-axis "Correctness" 0 --> 1
    line [0.575, 0.575, 0.950, 0.950, 0.950, 0.950, 0.950, 1.0]
    bar [0.575, 0.333, 0.950, 0.720, 0.875, 0.680, 0.880, 1.0]

Iter	Score	Merged?	What the proposer did
baseline	0.575	—	Original agent — hallucinations, broken tool calls, no retry logic
v001	0.333	Yes	Anti-hallucination prompt (100% correct when API responded, but 60% hit rate limits)
v002	0.950	Yes	Breakthrough: inlined 17-line KB into prompt, eliminated vector search entirely. 5.7x faster, zero rate limits
v003	0.720	No	Attempted hybrid retrieval — regressed, rejected by constraint gate
v004	0.875	No	Response completeness fix — improved one case but regressed others
v005	0.680	No	Reduced tool calls — broke edge cases, rejected
v006	0.880	Yes	Evolution memory insight: combined v001's anti-hallucination with one-shot example from archive
v007	1.000	Yes	One-shot example injection + rubric-aligned responses — perfect on held-out

The line shows best score (only goes up — regressions aren't merged). The bars show each candidate's raw score. 4 merged, 3 rejected by gate checks. Not every iteration improves — that's the point.

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How It Works

LangSmith-Native	No custom scripts. Uses LangSmith Datasets, Experiments, and LLM-as-judge. Everything visible in the LangSmith UI.
Real Code Evolution	Proposers modify actual code in isolated git worktrees. Winners merge automatically.
Self-Organizing Proposers	Two-wave spawning, dynamic lenses from failure data, archive branching from losing candidates. Self-abstention when redundant.
Rubric-Based Evaluation	LLM-as-judge with justification-before-score, rubrics, few-shot calibration, pairwise comparison.
Smart Gating	Constraint gates, efficiency gate (cost/latency pre-merge), regression guards, Pareto selection, holdout enforcement, rate-limit early abort, stagnation detection.

Full feature list

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Evolution Loop