AIDE: AI-Driven Exploration in the Space of Code. The machine Learning engineering agent that automates AI R&D.

Curated list of AutoResearch use cases with optimization traces and open source implementations

A Claude Code plugin that makes Claude Code self-improve — learning from your corrections, not just remembering them (also support codex now)

The official repository of "Position: Agentic Evolution is the Path to Evolving LLMs".

Make your agents improve themselves. Reflexio is an AI agent self-improvement harness that enables your AI agents to continuously learn from real user interactions.

A 27-chapter hands-on tutorial for building an autonomous AI agent from zero in Python. Agent loop, tool system, memory, skills, MCP, multi-platform gateway, and self-evolution — inspired by Hermes Agent.

Curated real-world use cases for Hermes Agent — the self-improving AI agent from Nous Research. Backed by primary sources.

A selective learning and memory substrate for agentic systems — typed, revisable, decayable memory with competence learning and trust-aware retrieval.

CLI for managing AI agent squads. Status, memory, goals, feedback, and dashboard for your autonomous agents.

A token-efficient open-source AI assistant that remembers, adapts, and improves — secured, self-hosted, and entirely yours.

Ralph Loop Optimizer: an AI-driven framework that turns any evaluatable codebase into a self-improving optimization loop for strategies, models, prompts, and workflows

Browser-native agent · profiles · tools — by aratech | Zero installs, isolated, secured and self-evolving agent.

The skill OS for Codex, Claude Code, and Gemini CLI. One pool, one router, one feedback loop — across all three hosts. Per-turn semantic top-K with dynamic context sizing, session-end self-evaluation, and evidence-blended re-ranking that gets better the more you use it.

Local-first AI operating system. Persistent memory, 178 cognitive modules, multi-model reasoning. Runs on your hardware, not theirs. I am releasing SOMA to the world as I have reached the technical ceiling of what I am capable of so treat her good and enjoy.

Autonomous multi-agent coding system on local LLMs. No frameworks, no API costs. Plan → Build → Test → Fix with Qwen 80B + 7B on your own GPUs.

An autonomous content marketing team. Talk to your agents on Telegram. Always on. Auto-improving.

OpenClaw fork with real-time token tracking, self-improving crons, persistent memory, and budget-aware model routing. 262+ commits ahead. The €850 bill was the motivation.

Self-programming autonomous AI agent on Raspberry Pi Zero 2W. 960+ cycles, 47 self-built tools, 417 essays, 1077 knowledge files. Zero API cost. Live: seed-brain.vercel.app

Code for "Evolutionary System Prompt Learning for Reinforcement Learning in LLMs" (arxiv.org/abs/2602.14697)

Open-source Runtime Assurance SDK for AI agents in production