evo

Get started with autoresearch on any codebase - with two simple commands.

Do you want to do more with autoresearch or need a custom, hands-on deployment? Request access to evo platform or email hello@evo-hq.com.

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Try it · Install · How it works · Dashboard · Upgrading

A plugin for your agentic framework that optimizes code through experiments

You give it a codebase. It discovers metrics to optimize, sets up the evaluation, and starts running experiments in a loop -- trying things, keeping what improves the score, throwing away what doesn't.

Inspired by Karpathy's autoresearch -- where an LLM runs training experiments autonomously to beat its own best score. Autoresearch is a pure hill climb: try something, keep or revert, repeat on a single branch. Evo adds structure on top of that idea:

• Tree search over greedy hill climb. Multiple directions can fork from any committed node, so exploration doesn't collapse to one path.
• Parallel semi-autonomous agents. Spawn multiple subagents and run them simultaneously, each in its own git worktree. Each subagent reads traces, formulates hypotheses, and can run multiple iterations within its branch.
• Shared state. Failure traces, annotations, and discarded hypotheses are accessible to every agent before it decides what to try next.
• Gating. Regression tests or safety checks can be wired up as a gate. Experiments that don't pass get discarded.
• Observability. A dashboard to monitor your experiments.
• Benchmark discovery. The discover skill explores the repo, figures out what to measure, and instruments the evaluation.

Runs on Claude Code, Codex, Cursor, Kimi, OpenClaw, Hermes, Opencode, or Pi. Experiments run locally or on remote sandboxes — Modal, E2B, Daytona, AWS, Azure, SSH.

Try it

Two commands:

/evo:discover     # one-time code discovery: figures out benchmarks and creates gates against unintended changes
/evo:optimize     # run the loop

discover asks what to optimize, the benchmark command, and the metric direction. Skip the questions by seeding the answer:

/evo:discover make the JSON parser at src/parser.py faster

Then run the loop:

/evo:optimize

evo sizes each round to your benchmark's resource profile — one experiment at a time when a run needs the whole GPU or another exclusive resource, wider when runs are independent — and keeps going until the score stops improving. By default it runs unattended and pushes edits through parallel subagents; say so in plain language if you'd rather it pause after each round or hold to one experiment at a time.

Invocation syntax is host-specific: /evo: on Claude Code, $evo on Codex, / skill menu on Cursor, natural language on Hermes, Opencode, OpenClaw, and Pi.

Install

# 1. evo CLI
uv tool install evo-hq-cli

# 2. Host CLI (if you don't already have it)
npm install -g @anthropic-ai/claude-code     # or @openai/codex, openclaw, @earendil-works/pi-coding-agent
# Cursor: install from cursor.com (IDE), or `curl https://cursor.com/install -fsS | bash` for the cursor-agent CLI
# Kimi: `curl -fsSL https://code.kimi.com/install.sh | bash`, or `npm install -g @moonshot-ai/kimi-code`

# 3. Plugin + host hooks
evo install <host>     # claude-code | codex | cursor | hermes | kimi | opencode | openclaw | pi

For remote backends, install with the matching provider extra: uv tool install 'evo-hq-cli[modal]' (or [e2b], [daytona], [aws], [azure], [all]).

Codex hook trust

evo install codex trusts evo's hooks for you. To review them yourself first, pass --no-trust-hooks, then approve via /hooks inside codex.

How it works

Parallel

The orchestrator dispatches subagents in parallel. Each runs in its own isolated workspace, picks up shared state (failure traces, annotations, discarded hypotheses), forms a hypothesis, edits, and runs the benchmark. A subagent with iteration budget remaining continues on its branch within the same round when its prior edit warrants a follow-up.

Frontier strategy

After each round, the orchestrator selects which committed branch to extend next. Available strategies:

• argmax — extend the highest-scoring branch
• top_k — round-robin among the K best
• epsilon_greedy — best most of the time, random sometimes
• softmax — sample weighted by score
• pareto_per_task — keep specialists the aggregate hides, inspired by GEPA

Configure in the dashboard's Frontier tab, which lists each strategy's parameters.

Cross-cutting scans

Between rounds, RLM-inspired scan subagents read trace batches in parallel and surface compound failure patterns: gate-failure intersections, shared root causes across traces. Findings land in shared state, which the next round's subagents read at startup.

Gates

evo introduces gates: pass/fail checks that run on every experiment. An experiment that fails a gate is discarded even if its score beats the current best. Without gates, the search will find ways to return a constant, skip work, or trade correctness for speed.

Any command that exits zero on pass and non-zero on fail qualifies as a gate: a test suite, an invariant script, a score floor on a held-out slice of the benchmark. Gates inherit down the experiment tree: a gate registered at the root runs on every descendant. Narrower gates can be attached to specific branches.

When discover builds a benchmark from scratch, it attaches a held-out-slice score-floor gate automatically. When the benchmark already exists in the repo, gates are opt-in.

Where experiments run

Backend	Where	Install
worktree (default)	local git worktree per experiment	inc