Ross ROSS = Recommend OSS · open-source software intelligence for agents

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

github.com/evo-hq/evo · homepage · Python · Apache-2.0 (permissive) 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

Full methodology

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

Member repositories

RepositoryRoleHealth v2
evo-hq/evomain76

For agents

markdown · JSON · MCP: product_card(name="evo-hq/evo")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem