GEPAResearch
A plugin for your agentic framework that optimizes code using the GEPA algorithm (Genetic-Pareto LLM-driven search). Currently supported on Claude Code, Codex, OpenClaw, and Hermes.
You give it a codebase. It discovers metrics to optimize, sets up the evaluation, and hands the search to GEPA -- a reflection-driven evolutionary optimizer that maintains a Pareto frontier of candidates and uses an LLM to propose targeted improvements from diagnostic feedback.
- GEPA-backed search. The optimization inner loop is
gepa.optimize_anything: LLM-driven reflection over rich side-info, Pareto-efficient candidate selection, and automatic stall/budget handling.
- Per-candidate git worktrees. Every candidate GEPA proposes is applied in an isolated worktree and committed if it passes gates -- full audit trail, safe rollback.
- Gating. Regression tests or safety checks can be wired up as a gate. Candidates that fail the gate score 0.0 and are discarded.
- Observability. A local dashboard renders the candidate lineage DAG (from
GEPAResult.parents) and per-task traces.
- Benchmark discovery. The
discover skill explores the repo, figures out what to measure, and instruments the evaluation.
Install
Common: git, uv, Python 3.10+.
1. Install the gepa-research CLI (non-Claude Code hosts)
Claude Code bundles its own copy. Every other host calls gepa-research as an external binary. The CLI is not published to PyPI -- install it directly from this GitHub repo (the package lives in the plugins/gepa-research/ subdirectory):
uv tool install "git+https://github.com/CyrusNuevoDia/gepa-research#subdirectory=plugins/gepa-research"
# or: pipx install "git+https://github.com/CyrusNuevoDia/gepa-research#subdirectory=plugins/gepa-research"
gepa-research --version # gepa-research-cli 0.2.2
To pin a release, append @<tag> to the repo URL (e.g. [email protected]#subdirectory=...).
2. Add the plugin
Claude Code
/plugin marketplace add CyrusNuevoDia/gepa-research
/plugin install gepa-research@CyrusNuevoDia-gepa-research
Invoke: /gepa-research:discover, /gepa-research:optimize.
Codex (requires 0.121.0-alpha.2 or newer -- npm install -g @openai/codex@alpha if you're on 0.120.0 stable)
codex marketplace add CyrusNuevoDia/gepa-research
Then /plugins → gepa-research → install. Invoke: $gepa-research discover, $gepa-research optimize.
OpenClaw
openclaw plugins install gepa-research --marketplace https://github.com/CyrusNuevoDia/gepa-research
Invoke: /discover, /optimize.
Hermes (per-skill install, no bundle support)
hermes skills install CyrusNuevoDia/gepa-research/plugins/gepa-research/skills/discover --force
hermes skills install CyrusNuevoDia/gepa-research/plugins/gepa-research/skills/optimize
--force on discover bypasses the SKILL.md scanner (it flags gepa-research's own install examples). Invoke: /discover, /optimize.
Usage
Two skills:
discover -- explores the repo, instruments the benchmark, runs baselineoptimize -- hands the benchmark to GEPA and backports candidates into the local graph
Invocation syntax depends on the host -- see the Install section above.
optimize accepts optional parameters:
| Parameter | Default | Description |
|---|
max-metric-calls | 50 | Total evaluator calls GEPA may make this run |
stall | 5 | Consecutive iterations with no improvement before auto-stopping |
Example (Claude Code): /gepa-research:optimize max-metric-calls=100 stall=10. Other hosts use their own invocation prefix.
Typical flow:
you: gepa-research:discover
gepa-research: explores repo, instruments benchmark, runs baseline
you: gepa-research:optimize
gepa-research: hands the seed candidate + evaluator to gepa.optimize_anything
GEPA proposes mutations via LLM reflection over side-info
each candidate is applied in an isolated git worktree, gate-checked, and committed on success
runs until budget or stall limit reached
Under the hood, each GEPA candidate gets its own git worktree branching from its parent. If the score improves and the gate passes, the candidate is committed. Otherwise it's discarded and the worktree is cleaned up.
Architecture
