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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
An agent capable of self-evolving and dynamically hardening security
| Date | Stars |
|---|---|
| 2026-07-31 | 2568 |
| 2026-08-05 | 2576 |
| 2026-08-06 | 2576 |
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<div align="center"> <p align="center"> <img src="assets/omniagent-logo.png" alt="OmniAgent" width="200"> </p> # OmniAgent An agent capable of omni-self-evolving and dynamically hardening security <p align="center"> <a href="https://yeqing17-2026.github.io/OmniAgent/">Website</a> • <a href="https://docs.omniagent.dev">Docs (on the way)</a> • <a href="README.md">English</a> • <a href="README_CN.md">中文</a> <img src="https://img.shields.io/badge/Python-3.11+-blue?style=for-the-badge&logo=python&logoColor=white" alt="Python"> <img src="https://img.shields.io/badge/License-GPL--3.0-blue?style=for-the-badge" alt="License"> <img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=for-the-badge" alt="PRs Welcome"> </p> </div> **OmniAgent** is an open-source self-evolving Agent framework inspired by OpenClaw. It's the only agent that implements full-dimensional self-evolution (**OmniEvolve**): - **Proactive Memory**: A dual-path alignment mechanism based on explicit interactive feedback and implicit LLM induction enables proactive memory and self-evolving - **Skill Self-Evolution**: Through automatic creation, inspection, and repair of skills during interaction, skills evolve in real-time - **Context Self-Evolution**: Built on a multi-layer information stack architecture, leveraging real-time user interaction feedback and LLM summarization feedback to continuously update memory and user preferences — achieving self-evolving Personalization Context - **BrainModel Self-Evolution**: Through a novel online reinforcement learning feedback loop, the BrainModel iterates dynamically during interaction Together, these enable full-dimensional (Skill, Context, BrainModel) self-evolution of the Agent. Additionally, **Hyper Harness** and **Deep Reflexion** modules enhance system safety and task success rate: - **Hyper-Harness**: An efficient, safe, and intelligent execution scaffold that provides systematic support for complex tasks - **Deep Reflexion**: A dual-layer reflective architecture — real-time risk interception and failure-to-insight conversion — providing a robust guarantee for task success rate --- **OmniAgent** V.S. **OpenClaw** V.S. **Hermes** | Dimension | OpenClaw | Hermes | * OmniAgent | | :--- | :--- | :--- | :--- | | **Skill Evolution** | Static skills, no evolution | **Periodic** post-execution evolution (slow to take effect) | **Real-time** self-evolution during execution (fast to take effect) | | **Skill Injection** | User Message | User Message | User Message (saves 90% token cost) | | **Context Evolution** | Static context assembly, no evolution (weak) | Prompt-instruction-based evolution (weak) | Real-time interaction feedback + LLM summarization self-evolution (strong) | | **BrainModel Evolution** | Fixed model, no evolution | Fixed model, no evolution | Self-deployed model, online RL evolution | | **Harness Safety** | Static security scanning (bypassable) | Skill trust-level policy, static scanning (bypassable) | **Tool & Skill** trust-level policy + four-layer dynamic security scanning (unbypassable) | | **Hyper-Harness** | None (slow) | None (slow) | Dynamic multi-agent + dynamic concurrent tool execution (fast) | | **Agent-Loop** | ReAct single loop (low success rate) | ReAct single loop (low success rate) | Dual-layer Deep Reflexion loop (high success rate) | ## Core Features **OmniEvolve (Full-Dimensional Self-Evolution)**: The agent evolves continuously through interaction, and safety hardens dynamically. - **Proactive Memory**: Based on a multi-layer information stack, a dual-path alignment mechanism of explicit user feedback and implicit LLM induction enables autonomous precipitation and continuous self-evolution of user profiles and memory - **Skill Self-Evolution**: Through pattern extraction from high-frequency action sequences, skills are natively auto-generated; leveraging dual-path feedback from user interaction and LLM d
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8b9cbb1ec8b05e92, llm:Repository topics: agent, hermes, open-claw; description: 'An agent capable of self-evolving and dynamically hardening security'
matched fp:8b9cbb1ec8b05e92, llm:Repository topics: agent, hermes, open-claw; description: 'An agent capable of self-evolving and dynamically hardening security'