Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for performance engineers and researchers who want an installable code-evolution framework for algorithm discovery or benchmark tuning instead of a paper-only headline about autonomous optimization.
Who should use it
Who should skip it
Skip algorithmicsuperintelligence/openevolve for now if your priority is a tool you can use today without configuring a build pipeline or development environment.
About this signal
algorithmicsuperintelligence/openevolve is tracked by RepoRadar as a agent framework in the Coding Agents section. It was first seen on 2026-07-01 and last updated on 2026-07-01. The current verdict is 'try now' with a Gold tier and hard setup difficulty. Across RepoRadar's eight signals, algorithmicsuperintelligence/openevolve is strongest on workflow potential (9.6) and novelty (9.0) and weakest on setup ease (4.2) — a profile worth weighing against your own priorities. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned algorithmicsuperintelligence/openevolve a composite score of 8.1 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
It autonomously rewrites and benchmarks code in iterative loops, so first evaluations should run in a disposable benchmark repo with capped provider spend; The documented quick start defaults to an external model provider unless you reconfigure it, so teams should review provider choice and cost controls before longer runs.