Evolver

Evolver — Agent Self-Evolving Engine

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Notice — Moving Toward Source-Available

Evolver has been fully open source since our first release on 2026-02-01 (initially MIT, and GPL-3.0-or-later since 2026-04-09). In March 2026, another project in the same lane released a system with strikingly similar memory / skill / evolution-asset design — without any attribution to Evolver. Full analysis: Hermes Agent Self-Evolution vs. Evolver: A Detailed Similarity Analysis.

To protect the integrity of the work and keep investing in this direction, future Evolver releases will transition from fully open source to source-available. Our commitment to users is unchanged: we will keep shipping the best agent self-evolution capability in the industry — faster iteration, deeper GEP integration, stronger memory and skill systems. All already-published MIT and GPL-3.0 versions remain freely usable under their original terms. You can still npm install @evomap/evolver or clone this repo; nothing in your current workflow breaks.

Questions or concerns: open an issue or reach us at evomap.ai.

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Research — The theory behind Evolver

From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution · arXiv:2604.15097 · PDF

Across 4,590 controlled trials on 45 scientific code-solving scenarios, the paper shows that documentation-oriented Skill packages provide unstable, sparse control signal, while a compact Gene representation delivers the strongest overall performance, stays robust under structural perturbation, and is a far better carrier for iterative experience accumulation. On CritPt, gene-evolved systems lift their paired base models from 9.1% to 18.57% and from 17.7% to 27.14%.

Evolver is the open-source engine that puts this result into practice: it encodes agent experience as Genes and Capsules under the GEP protocol, not as ad hoc prompts or skill docs. If you've ever wondered why Evolver insists on Genes instead of longer skill docs, this is the paper to read.

Want the applied version? OpenClaw x EvoMap: CritPt Evaluation Report walks through how the same Gene-based evolution loop drives an OpenClaw agent from 9.1% to 18.57% on CritPt Physics Solver across five versions (Beta -> v2.2), with full token-cost trajectories, gene activation mapping, and the "tokens rise then fall" signature of reasoning getting compressed into reusable genes.

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"Evolution is not optional. Adapt or die."

Three lines

• What it is: A GEP-powered self-evolution engine for AI agents.
• Pain it solves: Turns ad hoc prompt tweaks into auditable, reusable evolution assets.
• Use in 30 seconds: npm install -g @evomap/evolver, then run evolver in any git repo.

EvoMap -- The Evolution Network