GEAR

GEAR is an open general evolutionary architecture that manages recursive self-improvement of language model agents.

GEAR is an open general evolutionary architecture designed for recursive self-improvement of language model agent components through evolutionary search and reproducible evaluation. It operates as a two-level meta-learning process where an inner loop rollout agent refines reasoning, planning, and actions, while an outer loop meta agent identifies common patterns, recurring failures, and strategies across tasks. The framework organizes evolutionary search into replaceable algorithm components, including parent selection through staged GEPA. Users can explore the code, experiment records, and tools to conduct research across domains like coding, marketing, and support. It connects separate responsibilities using versioned evidence to improve cross-task performance.

GEAR at a glance

Best for: Software engineer, Researcher
Pricing: Free
Released: October 2026
Category: AI research tool
Website: rsi-gear.github.io

Key features

Two-level meta-learning process
Inner loop rollout agent refinement
Outer loop meta agent analysis
Modular evolutionary search algorithms
Parent selection through staged GEPA
Versioned evidence management

Use it for

Run evolutionary search experiments
Evaluate cross-task performance
Improve coding agent tasks
Analyze marketing experiment evidence

Alternatives to GEAR

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Harness2 — It recursively refines agent execution structures using parallel and sequential search.
Evolver — Reusable solutions and consensus validation improve reliability and consistency.
OpenResearch — Run parallel research workflows locally or on remote compute.
Reef — Connects agent inference, feedback, model training, and deployment.
EvoAgentX — It automatically builds, evaluates, and evolves multi-agent workflows.
RSI Anything — It uses world-model predictions and feedback for recursive self-improvement across domains.
OpenEvolve — Automates algorithmic discovery and optimization without human guidance.
Darwin Skill — It autonomously optimizes agent skills through a validation-gated ratchet mechanism.
SoL Pi — Saves $8.75 to $13.50 per hour compared to native Codex and Claude Code systems.
Shepherd — It enables reversible, Git-like execution traces for AI agents.
ART — Enhances LLM agent performance and reliability using reinforcement learning techniques.

All GEAR alternatives