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ai-agent

LEANN: Private RAG App

Run a fast RAG app with 97% storage savings. For founders, backed by 12k+ GitHub stars
intermediateโฑ 30 minutes๐Ÿ’ต Free (self-hosted)
12,727 stars1,149 forksPythonUpdated 7/25/2026100% free ยท open source
What it is

Run a fast and private RAG app on your device.

What you can make with it

Build an offline-first chatbot that answers user queries with accuracy.

How it helps

With 97% storage savings, your device won't run out of space, ensuring your chatbot stays online.

Real use case example

"A founder wants to build a chatbot for their company's website. They install LEANN, add their company's knowledge base to the app, and then deploy it to their self-hosted server. After a 30-minute setup, the chatbot is live, answering user queries quickly and accurately without draining device resources."

If you're new

When you're just starting to learn about AI and RAG, this skill is a great place to begin.

If you're senior

When you need a high-performance RAG app that can run offline and won't consume too much storage space, this skill is a top choice.

Common confusion cleared up

Note that while LEANN offers a high degree of customization, it still requires some basic technical know-how to set up effectively.

Best inside these AI tools
Claude DesktopCursorCodex CLI
Pairs with
Stripe webhookNotion databaseLocal storage
Why we list it on WorkflowStacks: As a free and open-source skill, this is a cost-effective alternative for running private RAG apps.
What it does

LEANN allows you to run a fast and accurate Retrieval-Augmented Generation (RAG) application with significant storage savings, making it possible to deploy AI models on personal devices while maintaining privacy

When to use it
  • โ€ขWhen you need to deploy AI models on devices with limited storage capacity
  • โ€ขWhen you require a high level of data privacy for your AI application
  • โ€ขWhen you want to reduce the latency and improve the responsiveness of your RAG application
Quick start
  1. 1Install LEANN from the GitHub repository: https://github.com/StarTrail-org/LEANN
  2. 2Set up your Python environment to run LEANN, ensuring you have the required dependencies
  3. 3Prepare your dataset and configure LEANN to run your RAG application
  4. 4Test and fine-tune your LEANN-powered RAG application for optimal performance
Ready-to-paste prompt
python -m leann.run --model-name my_rag_model --dataset my_dataset --storage-size 0.03
Saves to your device

Topics

ai
faiss
gpt-oss
langchain
llama-index
llm
localstorage
offline-first
ollama
privacy
python
rag
retrieval-augmented-generation
vector-database
vector-search
vectors
What's inside โ€” free to inspect
No purchase needed

Read the entire source before you build โ€” unlike paid marketplaces that hide it behind a buy button.

11
top-level files
16
folders
81.3M
repo size
MIT
license
Key files
.pre-commit-config.yaml
README.md
File tree
.devcontainer/
.github/
.vscode/
apps/
assets/
benchmarks/
data/
docker/
docs/
examples/
packages/
scripts/
skills/
sky/
tests/
videos/
.gitignore
.gitmodules
.pre-commit-config.yaml
.python-version
CLAUDE.md
demo.ipynb
LICENSE
llms.txt
Quick Actions
Details
Creator
StarTrail-org
Language
Python
Category
ai-agent
Published
6/9/2025

Are you the creator of this tool? Claim your listing โ†’ and earn 85% of every sale.

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