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Evolver is a self-evolving AI agent framework that uses gene expression programming to autonomously improve agent behaviors through a continuous five-step loop of scanning, selecting, mutating, validating, and solidifying. It functions as an auditable evolution system that records every mutation and selection step, and can translate natural-language problems into executable Python code for automated grading and evaluation.
The main features of evomap/evolver are: Self-Evolving Agent Frameworks, Event-Driven Agent Loops, Three-Tier Memory Architectures, Shared Skill Distribution, Code Generation Agents, Gene Expression Programming Engines, Natural Language to Code Generators, Agent Memory Management.
Projects with overlapping indexed features include: memodb-io/acontext — Acontext is an LLM orchestration backend and agent memory framework designed to manage session state and knowledge for… memtensor/memos — MemOS is an open-source persistent memory layer for AI agents and large language models, providing a self-hosted… opensquilla/opensquilla — OpenSquilla is an LLM agent orchestration framework designed to coordinate multi-step AI workflows and tool execution… virgiliojr94/book-to-skill — Book-to-skill converts technical books and other documents into structured skills for Claude Code and other coding… ibm-granite/granite-code-models — Granite Code Models is a family of transformer-based foundational models designed for software engineering and logical… alchaincyf/hermes-agent-orange-book — This repository is a practical written guide, structured as a 21-section tutorial book, to an open-source AI agent…
Acontext is an LLM orchestration backend and agent memory framework designed to manage session state and knowledge for AI agents. It functions as a context manager and orchestration layer that integrates model providers with a secure code sandbox and a zero-knowledge data store. The project is distinguished by its approach to knowledge distillation, capturing agent learnings as reusable Markdown skills and structured memory files. It provides a secure execution environment where shell commands and scripts run in isolated containers with the ability to mount these persistent skill files direct
MemOS is an open-source persistent memory layer for AI agents and large language models, providing a self-hosted server that stores and retrieves structured memory across sessions. It enables AI systems to recall user preferences, history, and context without retraining, using a graph-based API and a web management interface for viewing, editing, and organizing memory items, skills, traces, and knowledge bases. The system distinguishes itself through a portable memory interchange protocol that allows memory to be transferred between different AI models, devices, and applications, along with a
OpenSquilla is an LLM agent orchestration framework designed to coordinate multi-step AI workflows and tool execution using directed acyclic graphs. It functions as a centralized system for managing specialized skill packages and executing complex reasoning sequences. The project distinguishes itself through a routing gateway that directs tasks to different AI providers based on complexity, cost, and performance. It utilizes a multi-tier AI memory system that organizes working, episodic, and semantic knowledge using local embeddings and SQLite, alongside a secure execution sandbox that isolat
Book-to-skill converts technical books and other documents into structured skills for Claude Code and other coding assistants. Instead of producing a plain summary, it distills a PDF, a folder of documents, or a set of sources into frameworks, decision rules, anti-patterns, a glossary, and per-chapter reference files. All extraction and analysis runs on the user's own machine, so source documents are never uploaded. The structuring cost is paid once during conversion. When a question comes up later, only the relevant chapter file is loaded into context, which keeps answers grounded in the sou