evo-hq/evo
turns your codebase into an autoresearch loop — discovers what to measure, instruments the benchmark, then runs tree search with parallel subagents. observed · 2026-08-28
Health v2 · maintenance only
76/100
- Activity 93
- Release rhythm 93
- Longevity 10
Flags: young
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 2.5
- age_days: 150
- days_rel: 47
- days_push: 47
- n_releases_24m: 19
Adoption not part of the score
1421 stars · 109 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Evo is a plugin for agentic coding frameworks (Claude Code, Codex, Cursor, etc.) that turns a codebase into an autoresearch loop: it discovers what to measure, instruments benchmarks, and runs tree search with parallel subagents in isolated git worktrees. Experiments that improve a metric are kept, regressions are discarded, and gating plus a dashboard provide safety and observability.
Use cases
- automatically optimize my codebase with an LLM agent loop
- run parallel AI agents to improve a benchmark score
- discover what metrics to optimize in my repo
- set up an autoresearch hill climb with tree search
- optimize a JSON parser speed automatically
- run experiments in git worktrees with subagents
- add regression gates to autonomous code experiments
- monitor autonomous optimization experiments on a dashboard
When to choose
- you use Claude Code, Codex, Cursor, or a similar agentic CLI and want autonomous code optimization
- you have a measurable benchmark or metric and want an LLM to iteratively improve it
- you want parallel exploration via tree search instead of a single greedy hill climb
- you need regression gates and observability around autonomous experiments
When to avoid
- you have no objective metric or benchmark to optimize against
- your codebase cannot run safely in automated experiments or sandboxes
- you need a hosted inference-routing product rather than a local optimization loop (the evo-hq.com Router is a separate offering)
- you want fully deterministic, non-LLM optimization
Facets
plugin · maturity active
agent-framework benchmarking developer-tools cli workflow-automation llm-inference developer-tools artificial-intelligence performance python cli cross-platform cloud autoresearch evolutionary-algorithms tree-search claude-code codex code-optimization subagents git-worktrees llm-agents experiment-loop automation ai-agents docker
3 sources
- readme: https://github.com/evo-hq/evo · fetched 2026-08-28 · 9ec35193c090
- homepage: https://evo-hq.com · fetched 2026-08-29 · 66f2287281d6
- site_page: https://evo-hq.com/docs · fetched 2026-08-29 · a84993152190
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| evo-hq/evo | main | 76 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem