Harness Evolver

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.

Install

Claude Code Plugin (recommended)

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

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

Quick Start

What It Looks Like

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

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.

How It Works

Full feature list

Evolution Loop

Detailed loop with all sub-steps

Agents

Requirements

- LangSmith account + LANGSMITH_API_KEY
- Python 3.10+ · Git · Claude Code (or Cursor/Codex/Windsurf)

Dependencies installed automatically by the plugin hook or npx installer.

LangSmith traces any AI framework: LangChain/LangGraph (auto), OpenAI/Anthropic SDK (wrap_*, 2 lines), CrewAI/AutoGen (OpenTelemetry), any Python (@traceable).

Companion: LangSmith Tracing

For full observability into what each proposer does during evolution (every file read, edit, and commit), install the LangSmith tracing plugin:

With both plugins installed, the evolution loop traces to LangSmith as a hierarchy: iteration → proposers → tool calls.

References

- Meta-Harness: End-to-End Optimization of Model Harnesses — Lee et al., 2026
- Self-Organizing LLM Agents Outperform Designed Structures — Dochkina, 2026
- Hermes Agent Self-Evolution — NousResearch
- Agent Skills for Context Engineering — Koylan
- A-Evolve: Automated Agent Evolution — Amazon (5-stage evolution loop, git-tagged mutations)
- Meta Context Engineering via Agentic…