// rag Β· Zleap-AI/SAG

SAG

A new state of the art for RAG: an open knowledge base for humans and agents.

πŸ’Ž Hidden gemβ˜… 2.5k starsβ‘‚ 167 forksMITupdated 2026-10
β–¦ visual β€” this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

SAG is an open-source rag & knowledge project by Zleap-AI (Zleap-AI/SAG on GitHub). It has 2,517 GitHub stars and 167 forks, is released under the MIT license, and was last updated on GitHub on 4 October 2026.

Repository
Zleap-AI/SAG
GitHub stars
2,517
Forks
167
License
MIT
Last updated on GitHub
4 October 2026
Listing
Hidden gem β€” a shorter page for a lesser-known project
Official site
Official site
// how it works

How it works

  1. Install SAG

    Set up the Python package.

  2. Build the knowledge base

    Ingest your documents.

  3. Query it

    Ask questions through the new architecture.

  4. Serve humans and agents

    Expose the knowledge base to both.

// features

Why people love it

New retrieval architecture

Beyond standard vector search.

GraphRAG techniques

Knowledge graphs meet retrieval.

Open knowledge base

Inspectable and modifiable.

Human and agent UX

Built for both audiences.

Python native

Fits data pipelines.

MIT licensed

Free for any use.

Advertisement
// run it

Run it yourself

  1. Step 1 β€” Clone the repo

    Download the source and move into the project folder.

    git clone https://github.com/Zleap-AI/SAG.git
    cd SAG
  2. Step 2 β€” Check the README

    Every project documents its own setup; start with install requirements.

    cat README.md | head -60
  3. Step 3 β€” Run it

    Follow the project’s launch command β€” Docker, pip, npm, or a binary release.

    # see README for the exact launch command
// use cases

What you can build

Advertisement
// faq

Questions, answered

Who maintains SAG?

It is an open-source project at Zleap-AI/SAG with 167 forks.

Is it free for commercial use?

Yes. MIT licensed.

Is it really state of the art?

That is the project’s claim; test it against your own benchmarks.

How is it different from LlamaIndex?

It proposes an original retrieval architecture rather than a framework.

What data can it ingest?

Documents for knowledge-base construction; see the docs.

// keep exploring

Similar tools

AI Agents

Llama Index

LlamaIndex is the document processing platform for AI

β˜… 52.4k starsMIT
View guide β†’
AI Agents

TencentDB-Agent-Memory

TencentDB Agent Memory is a team-level memory hub for AI Agents β€” turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill,…

β˜… 27.7k starsUnknown
View guide β†’
AI Agents

mempalace

The best-benchmarked open-source AI memory system. And it's free.

β˜… 59.4k starsMIT
View guide β†’
RAG & Knowledge

autoflow

Graph RAG conversational knowledge base built on TiDB Serverless vector storage

β˜… 3.0k starsApache-2.0
View guide β†’
RAG & Knowledge

Langchain-Chatchat

Local knowledge-base RAG and agent app built on LangChain with ChatGLM, Qwen and Llama models

β˜… 38.7k starsApache-2.0
View guide β†’
RAG & Knowledge

knowledge-agent-template

Starter template for AI agents that stay synced with your knowledge base.

β˜… 1.1k starsMIT
View guide β†’
// alternatives

Alternatives in RAG & Knowledge

The most-starred other RAG & Knowledge projects in the LibreAI catalogue β€” the realistic shortlist if SAG is not the right fit.

Get SAG

Free and open β€” grab the source, star the repo, and start creating.