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autoresearch vs evo

Karpathy's autoresearch loop as an installable skill for Claude Code, OpenCode and Codex: constraint + mechanical metric + autonomous modify→verify→keep/discard iteration. — versus — Karpathy-style autoresearch on any codebase: /evo:discover instruments the benchmark, /evo:optimize runs tree search with parallel subagents in worktrees. Plugin for Claude Code, Codex & co.

The curated verdict

Same lineage — Karpathy's autoresearch loop for coding agents. The skill is a single-branch keep/discard hill-climb; evo adds tree search, parallel worktree subagents, shared failure traces, gates and a dashboard. Start with the skill, graduate to evo.

autoresearchevo
Stars5.4k1.3k
Forks397100
LanguageShellPython
LicenseMITApache-2.0
Last activity27 days ago3 days ago
Topicsskills, codingcoding, skills
Curated connections32

autoresearch — the curator's take

The compounding-gains loop, packaged: pick a metric a machine can check, let the agent mutate-verify-keep against it for hours, and small wins stack — the Karpathy recipe without writing the harness yourself. Works across three agent CLIs. The discipline it demands is the catch: without a truly mechanical metric the loop optimizes noise, and unattended iteration burns real tokens — set budgets before you set it loose.

evo — the curator's take

Where the autoresearch skill is a greedy hill-climb on one branch, evo is the industrialized version: tree search over committed nodes, parallel subagents in isolated git worktrees sharing failure traces, regression gates that discard bad experiments, frontier strategies (argmax to GEPA-inspired pareto), and a dashboard. Benchmark discovery is the sleeper feature — it figures out what to measure before optimizing. Runs on 8 agent hosts, local or remote sandboxes (Modal, E2B, Daytona, AWS). NOT for codebases without a mechanical metric — no benchmark, no loop — and unattended parallel agents editing your repo demand real gates; the hosted 'evo platform' beta hints where the business model lands.