Tool profile
ZHangZHengEric/Sage
github.com
An extensible framework for building custom, self-evolving AI agents with recursive self-improvement.
What it does
ZHangZHengEric/Sage
Sage is an RSI agent harness that improves AI agents through task feedback, retaining each version and its evidence while leaving adoption to the user. It offers configurable agent packages, tools, skills, and multi-round recursive self-improvement, available as a desktop app, multi-user server, and Python SDK. It is aimed at developers and teams building and optimizing LLM agents.
Tags
Categories
A concise summary has not been added yet.
FAQ
Frequently asked questions
- What is ZHangZHengEric/Sage?
- Sage is an RSI agent harness that improves AI agents through task feedback, retaining each version and its evidence while leaving adoption to the user. It offers configurable agent packages, tools, skills, and multi-round recursive self-improvement, available as a desktop app, multi-user server, and Python SDK. It is aimed at developers and teams building and optimizing LLM agents.
- How much does ZHangZHengEric/Sage cost?
- ZHangZHengEric/Sage uses a Open source pricing model. Check the official website for current plans.
- What is ZHangZHengEric/Sage used for?
- ZHangZHengEric/Sage is listed on TolsAI under: Open Source.
- Where can I use ZHangZHengEric/Sage?
- ZHangZHengEric/Sage is available on its official website, github.com.
Explore
More in Open Source
An open-source agentic harness for a self-improving AI agent that plans and executes tasks you describe.
A vectorless, reasoning-based RAG engine that indexes documents as trees and retrieves with LLM reasoning.
A PyTorch-like Python library to build and auto-optimize LLM workflows such as chatbots, RAG and agents.
A modular web UI for generating images, video and audio with AI models like Stable Diffusion, Flux and Wan.
A step-by-step guide to designing AI systems built on LLMs, RAG, and AI agents.
Open-source AI pipeline engine for building, debugging and deploying LLM workflows and agent orchestration from your IDE.
Discussion
Discussion
Log in to join the discussion
No comments yet. Start the discussion.