19 best RAG & memory GitHub repos to use
Retrieval, embeddings, vector stores, and the memory layers that let an agent remember something between runs. The unglamorous half of every AI product that works.
Find GitHub repos worth using by topic, language, license and how active they are. Navid's picks come first, and each one opens its own page.
This is a list of the best RAG & memory GitHub repos.
In fact, it has 19 of them, with Navid's picks first.
So if you want RAG & memory GitHub repos worth your time, you'll love this list.
Retrieval, embeddings, vector stores, and the memory layers that let an agent remember something between runs. The unglamorous half of every AI product that works.
Here's what's inside:
- Supabase by supabase
- Claude Mem by thedotmack
- Ragflow by infiniflow
- Docling by docling-project
- Anything Llm by mintplex-labs
- LlamaIndex by LlamaIndex
- Langgraph by langchain-ai
- Deeptutor by HKUDS
- Rag_techniques by NirDiamant
- Agentmemory by rohitg00
- TencentDB Agent Memory by TencentCloud
- Typesense by typesense
- 12 Factor Agents by humanlayer
- Semantica by semantica-agi
- Jina Reader by Jina AI
- Cocoindex by cocoindex-io
- MegaParse by quivrhq
- Atomic by kenforthewin
- BuildingAI by bidingcc
Each one comes with what it covers and who it's for.
What are the best RAG & memory GitHub repos?
Here's the list at a glance.
- Owner
- supabase
- Stars
- ★ 111k
- Owner
- thedotmack
- Stars
- ★ 95k
- Owner
- infiniflow
- Stars
- ★ 91k
- Owner
- docling-project
- Stars
- ★ 67k
- Owner
- mintplex-labs
- Stars
- ★ 66k
- Owner
- LlamaIndex
- Stars
- ★ 52k
- Owner
- langchain-ai
- Stars
- ★ 42k
- Owner
- HKUDS
- Stars
- ★ 40k
- Owner
- NirDiamant
- Stars
- ★ 30k
- Owner
- rohitg00
- Stars
- ★ 29k
- Owner
- TencentCloud
- Stars
- ★ 27k
- Owner
- typesense
- Stars
- ★ 27k
- Owner
- humanlayer
- Stars
- ★ 26k
- Owner
- semantica-agi
- Stars
- ★ 13k
- Owner
- Jina AI
- Stars
- ★ 12k
- Owner
- cocoindex-io
- Stars
- ★ 12k
- Owner
- quivrhq
- Stars
- ★ 7.4k
- Owner
- kenforthewin
- Stars
- ★ 2k
- Owner
- bidingcc
- Stars
- ★ 1.9k
Top 19 RAG & memory GitHub repos
1. Supabase by supabase
Supabase is the Postgres development platform. We're building the features of Firebase using enterprise-grade open source tools. [x] Hosted Postgres Database. Docs [x] Authentication and Authorization. Docs [x] Auto-generated APIs. [x] REST. Docs [x] GraphQL. Docs [x] Realtime subscriptions. Docs [x] Functions. [x] Database Functions. Docs [x] Edge Functions Docs [x] File Storage. Docs [x] AI + Vector/Embeddings Toolkit. Docs [x] Dashboard
Watch "releases" of this repo to get notified of major updates.
To see how to Contribute, visit Getting Started Community Forum. Best for: help with building, discussion about database best practices. GitHub Issues. Best for: bugs and errors you encounter using Supabase. Email Support. Best for: problems with your database or infrastructure. Discord. Best for: sharing your applications and hanging out with the community.
Stars: 111k
Language: TypeScript
License: Apache-2.0
View Supabase on GitHub · More about Supabase
2. Claude Mem by thedotmack
Claude-Mem seamlessly preserves context across sessions by automatically capturing tool usage observations, generating semantic summaries, and making them available to future sessions. This enables Claude to maintain continuity of knowledge about projects even after sessions end or reconnect.
Restart Claude Code. Context from previous sessions will automatically appear in new sessions.
Note: Claude-Mem is also published on npm, but npm install -g claude-mem installs the SDK/library only, it does not register the plugin hooks or set up the worker service. Always install via npx claude-mem install or the /plugin commands above.
Stars: 95k
Language: TypeScript
License: Apache-2.0
Install:
bash npx claude-mem install
View Claude Mem on GitHub · More about Claude Mem
3. Ragflow by infiniflow
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.
Try our cloud service at https://cloud.ragflow.io. 2026-06-15 Support multiple chat channels such as Feishu, Discord, Telegram, Line, etc. 2026-04-24 Supports DeepSeek v4. 2026-03-24 RAGFlow Skill on OpenClaw, Provides an official skill for accessing RAGFlow datasets via OpenClaw. 2025-12-26 Supports 'Memory' for AI agent. 2025-11-19 Supports Gemini 3 Pro. 2025-11-12 Supports data synchronization from Confluence, S3, Notion, Discord, Google Drive. 2025-10-23 Supports MinerU & Docling as document parsing methods. 2025-10-15 Supports orchestrable ingestion pipeline. 2025-08-08 Supports OpenAI's latest GPT-5 series models. 2025-08-01 Supports agentic workflow and MCP. 2025-05-23 Adds a Python/JavaScript code executor component to Agent. 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.
Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new releases! Deep document understanding-based knowledge extraction from unstructured data with complicated formats. Finds "needle in a data haystack" of literally unlimited tokens. Intelligent and explainable. Plenty of template options to choose from. Visualization of text chunking to allow human intervention. Quick view of the key references and traceable citations to support grounded answers. Supports Word, Slides, Excel, TXT, images, scanned copies, structured data, web pages, and more. Streamlined RAG orchestration catered to both personal and large businesses. Configurable LLMs as well as embedding models. Multiple recall paired with fused re-ranking. Intuitive APIs for seamless integration with business. CPU = 4 cores RAM = 16 GB Disk = 50 GB Docker = 24.0.0 & Docker Compose = v2.26.1 Python = 3.13 gVisor: Required only if you intend to use the code executor (sandbox) feature of RAGFlow.
Stars: 91k
Language: Go
License: Apache-2.0
Install:
bash git clone https://github.com/infiniflow/ragflow.git
View Ragflow on GitHub · More about Ragflow
4. Docling by docling-project
Docling simplifies document processing by parsing diverse formats, including advanced PDF understanding, and providing seamless integrations with the generative AI ecosystem. Parsing of [multiple document formats][supportedformats] including PDF, DOCX, PPTX, XLSX, HTML, EPUB, WAV, MP3, WebVTT, Box Notes, email formats (EML, MSG), images (PNG, TIFF, JPEG,.), LaTeX, DocLang, plain text, and more Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more A unified, expressive [DoclingDocument][doclingdocument] representation format Various [export formats][supportedformats] and options, including Markdown, HTML, WebVTT, DocLang, DocTags and lossless JSON Support for several application-specific XML schemas including DocLang, USPTO patents, JATS articles, and XBRL financial reports. Local execution capabilities for sensitive data and air-gapped environments Plug-and-play [integrations][integrations] incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI Extensive OCR support for scanned PDFs and images Support for several Visual Language Models, such as (GraniteDocling) Audio support with Automatic Speech Recognition (ASR) models Connect to any agent using the MCP server Run Docling as a service with the API server (docling-serve) Simple and convenient CLI Parsing of video files (MP4, AVI, MOV, MKV, and WebM) with an ASR transcript and representative keyframes Parsing of ODF (OpenDocument Format) files for text documents (.odt), spreadsheets (.ods), and presentations (.odp) Parsing of XBRL (eXtensible Business Reporting Language) documents for financial reports Parsing of email files (.eml,.msg) Parsing of EPUB (Electronic Publication) files for e-books Parsing of plain-text files (.txt,.text) and Markdown supersets (.qmd,.Rmd) Chart understanding (Barchart, Piechart, LinePlot): convert them into tables or code and add detailed descriptions Metadata extraction, including title, authors, references & language Complex chemistry understanding (Molecular structures)
Note: Python 3.9 support was dropped in docling version 2.70.0. Please use Python 3.10 or higher.
Works on macOS, Linux and Windows environments for both x8664 and arm64 architectures.
Stars: 67k
Language: Python
License: MIT
Install:
bash pip install docling
View Docling on GitHub · More about Docling
5. Anything Llm by mintplex-labs
We are also working on Open Computer which gives an entire computer environment for AI Agents to use.
This will bring AnythingLLM's agent capabilities to a new level and a novel UX paradigm for AI Agent use.
AnythingLLM: The all-in-one AI app you were looking for. Chat with your docs, use AI Agents, hyper-configurable, multi-user, & no frustrating setup required.
Stars: 66k
Language: JavaScript
License: MIT
View Anything Llm on GitHub · More about Anything Llm
6. LlamaIndex by LlamaIndex
LlamaIndex OSS (by LlamaIndex) is an open-source framework to build agentic applications. Parse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. You can use LlamaParse with this framework or on its own; see LlamaParse below for signup and product links.
Building with LlamaIndex typically involves working with LlamaIndex core and a chosen set of integrations (or plugins). There are two ways to start building with LlamaIndex in Python:
- Starter: llama-index. A starter Python package that includes core LlamaIndex as well as a selection of integrations.
Stars: 52k
Language: Python
License: MIT
Install:
bash pip install llama-index-core
View LlamaIndex on GitHub · More about LlamaIndex
7. Langgraph by langchain-ai
Low-level orchestration framework for building stateful agents.
Trusted by companies shaping the future of agents, including Klarna, Replit, Elastic, and more, LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
If you're looking to quickly build agents, check out Deep Agents, a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
Stars: 42k
Language: Python
License: MIT
Install:
bash pip install -U langgraph
View Langgraph on GitHub · More about Langgraph
8. Deeptutor by HKUDS
We welcome any kinds of contributing! Vote on roadmap items or propose new ones at Roadmap, and see our Contributing Guide for branching strategy, coding standards, and how to get started.
[2026.8.13] v1.5.12, Web search rebuilt with six new providers (Doubao, Bocha, Zhipu, Firecrawl, Qianfan, Aliyun IQS), a LiteParse parsing engine, MCP servers that reconnect on credential change, and CodeBuddy + OrcaRouter.
[2026.8.10] v1.5.11, Prose around a DSML tool call stops vanishing, a truncated reply continues instead of ending, live memory usage in Settings, and LightRAG indexing off the event loop.
Stars: 40k
Language: Python
License: Apache-2.0
Install:
bash pip install -U deeptutor
View Deeptutor on GitHub · More about Deeptutor
9. Rag_techniques by NirDiamant
A community-driven hub of 42+ runnable notebooks covering RAG techniques from foundational to cutting-edge - the intuition, the code, and the references to build more accurate, context-rich retrieval systems.
Prompt to Production - my full course on building software with AI the way professionals do: the methods and paradigms behind reliable, efficient, modular production systems, taught systematically. 17 modules, each pairing a video lecture with a hands-on lab, from your first structured prompt to a working production system.
One npm install adds the module's AI assistant to your Claude Code, and it guides you through the tutorial as you build.
Stars: 30k
Language: Jupyter Notebook
License: Other
Install:
bash git clone https://github.com/NirDiamant/RAG_Techniques.git
View Rag_techniques on GitHub · More about Rag_techniques
10. Agentmemory by rohitg00
Your coding agent remembers everything. No more re-explaining. Built on iii engine
Persistent memory for Claude Code, GitHub Copilot CLI, Cursor, Gemini CLI, Codex CLI, Hermes, OpenClaw, pi, OpenCode, and any MCP client.
The gist extends Karpathy's LLM Wiki pattern with confidence scoring, lifecycle, knowledge graphs, and hybrid search: agentmemory is the implementation.
Stars: 29k
Language: TypeScript
License: Apache-2.0
Install:
bash npm install -g @agentmemory/agentmemory # once — bare agentmemory on PATH
View Agentmemory on GitHub · More about Agentmemory
11. TencentDB Agent Memory by TencentCloud
Installation · What is it? · Team Play · Technical Implementation · Benchmark · Roadmap
Latest: Team Memory Beta is evolving quickly, install it and start exploring in minutes.
Complete installation documentation (standalone Memory Hub deployment, Proxy + Claude Code / CodeBuddy usage, stop and cleanup, port reference, etc.) is available in INSTALL.md (中文: INSTALLCN.md).
Stars: 27k
Language: TypeScript
License: Other
Install:
bash git clone https://github.com/Tencent/TencentDB-Agent-Memory.git
View TencentDB Agent Memory on GitHub · More about TencentDB Agent Memory
12. Typesense by typesense
Typesense is a fast, typo-tolerant search engine for building delightful search experiences.
Here are a couple of live demos that show Typesense in action on large datasets: Search a 32M songs dataset from MusicBrainz: songs-search.typesense.org Search a 28M books dataset from OpenLibrary: books-search.typesense.org Search a 2M recipe dataset from RecipeNLG: recipe-search.typesense.org Search 1M Git commit messages from the Linux Kernel: linux-commits-search.typesense.org Spellchecker with type-ahead, with 333K English words: spellcheck.typesense.org An E-Commerce Store Browsing experience: ecommerce-store.typesense.org GeoSearch / Browsing experience: airbnb-geosearch.typesense.org Search / Browse xkcd comics by topic: xkcd-search.typesense.org Semantic / Hybrid search on 300K HN comments: hn-comments-search.typesense.org
Here's a quick example showcasing how you can create a collection, index a document and search it on Typesense.
Stars: 27k
Language: C++
License: GPL-3.0
Install:
bash pip install typesense
View Typesense on GitHub · More about Typesense
13. 12 Factor Agents by humanlayer
In the spirit of 12 Factor Apps. The source for this project is public at https://github.com/humanlayer/12-factor-agents, and I welcome your feedback and contributions. Let's figure this out together!
I've tried every agent framework out there, from the plug-and-play crew/langchains to the "minimalist" smolagents of the world to the "production grade" langraph, griptape, etc.
I've talked to a lot of really strong founders, in and out of YC, who are all building really impressive things with AI. Most of them are rolling the stack themselves. I don't see a lot of frameworks in production customer-facing agents.
Stars: 26k
Language: TypeScript
License: Other
View 12 Factor Agents on GitHub · More about 12 Factor Agents
14. Semantica by semantica-agi
Ingest your enterprise data, extract what matters, build a Context Graph and knowledge graph (KG), and run graph analytics and causal reasoning over all of it, with full decision provenance baked in. Explainable, traceable, and trustworthy by design.
Most AI agents act without a trail. They store embeddings, not meaning: context that can't be explained, decisions that can't be audited. In lending, that gap is a compliance exposure, not an inconvenience: an underwriting agent's approval has to survive a regulator's "why" months later.
Semantica sits underneath your LLM, vector store, and agent framework as a deterministic infrastructure layer: no LLM required for graph construction, reasoning, or provenance.
Stars: 13k
Language: Python
License: MIT
Install:
bash pip install semantica
View Semantica on GitHub · More about Semantica
15. Jina Reader by Jina AI
Feel free to use Reader API in production. It is free, stable and scalable. We are maintaining it actively as one of the core products of Jina AI. Check out rate limit
This repository is the open source branch of the codebase behind https://r.jina.ai and https://s.jina.ai. It runs in stateless or bucket-cached mode; the MongoDB-backed SaaS storage layer is not included here. 2026-04, Re-synchronized the open source branch with the SaaS code. The MongoDB-backed storage layer is stripped; the oss branch runs in stateless mode out of the box, with optional MinIO/S3-compatible bucket caching via docker compose. See Local development. 2025-12, Storage layer decoupled and binary file uploads landed. PDFs and MS Office documents (Word, Excel, PowerPoint) can now be POSTed directly via the file body field, no need to host them first. See cookbooks.md. 2025-03, Major refactor: Reader is no longer a Firebase application. The SaaS migrated off Firestore + Cloud Functions to a Cloud Run image with MongoDB Atlas, removing the platform-coupled bits and unblocking the local-Docker path above. 2024-05, s.jina.ai launched, extending Reader from URL markdown to search markdown. PDFs added the same month, any URL ending in.pdf is parsed with PDF.js and returned as markdown. 2024-04, Reader released and r.jina.ai went live as Jina AI's first SaaS API for converting URLs to LLM-friendly input. Web pages, rendered with headless Chrome, or fetched lightweight via curl-impersonate. Reader picks intelligently between the two. PDFs, any URL, parsed with PDF.js. See this NASA PDF result vs the original. MS Office documents, Word, Excel, PowerPoint, converted via LibreOffice and then processed as HTML/PDF. Images, captioned by a vision-language model, so your downstream text-only LLM gets just enough hints to reason about them.
Simply prepend https://s.jina.ai/ to your search query. Note that if you are using this in the code, make sure to encode your search query first, e.g. if your query is Who will win 2024 US presidential election? then your url should look like:
Stars: 12k
Language: TypeScript
License: Apache-2.0
Install:
bash docker pull ghcr.io/jina-ai/reader:oss
View Jina Reader on GitHub · More about Jina Reader
16. Cocoindex by cocoindex-io
CocoIndex turns codebases, meeting notes, inboxes, Slack, PDFs, and videos into live, continuously fresh context for your AI agents and LLM apps to reason over effectively, with minimal incremental processing. Get your production AI agent ready in 10 minutes with reliable, continuously fresh data, no stale batches, no context gap
Declare what should be in your target, CocoIndex keeps it in sync forever, recomputing only the Δ.
Run once to backfill. Re-run anytime, only the changed files re-embed.
Stars: 12k
Language: Rust
License: Apache-2.0
Install:
bash pip install -U cocoindex
View Cocoindex on GitHub · More about Cocoindex
17. MegaParse by quivrhq
MegaParse is a powerful and versatile parser that can handle various types of documents with ease. Whether you're dealing with text, PDFs, Powerpoint presentations, Word documents MegaParse has got you covered. Focus on having no information loss during parsing. Versatile Parser: MegaParse is a powerful and versatile parser that can handle various types of documents with ease. No Information Loss: Focus on having no information loss during parsing. Fast and Efficient: Designed with speed and efficiency at its core. Wide File Compatibility: Supports Text, PDF, Powerpoint presentations, Excel, CSV, Word documents. Open Source: Freedom is beautiful, and so is MegaParse. Open source and free to use. Files: PDF Powerpoint Word Content: Tables TOC Headers Footers Images
Note: The model supported by MegaParse Vision are the multimodal ones such as claude 3.5, claude 4, gpt-4o and gpt-4.
See localhost:8000/docs for more info on the different endpoints!
Stars: 7.4k
Language: Python
License: Apache-2.0
Install:
bash pip install megaparse
View MegaParse on GitHub · More about MegaParse
18. Atomic by kenforthewin
Atomic Cloud, Atomic, hosted at your own subdomain. Try it out for free. Everything below still self-hosts forever.
A personal knowledge base that turns markdown notes into a semantically-connected, AI-augmented knowledge graph.
Atomic stores knowledge as atoms, markdown notes that are automatically chunked, embedded, tagged, and linked by semantic similarity. Your atoms can be synthesized into wiki articles, explored on a spatial canvas, and queried through an agentic chat interface.
Stars: 2k
Language: Rust
License: MIT
Install:
bash git clone https://github.com/kenforthewin/atomic.git
View Atomic on GitHub · More about Atomic
19. BuildingAI by bidingcc
BuildingAI is an enterprise-grade open-source intelligent agent platform designed for AI developers, AI entrepreneurs, and forward-thinking organizations. Through a visual configuration interface (Do It Yourself), you can build native enterprise AI applications without code. The platform offers native capabilities such as intelligent agents, MCP, RAG pipelines, knowledge bases, large-model aggregation, and context engineering, along with user registration, membership subscriptions, compute billing, and other business operations.
Deploying BuildingAI with Docker is the simplest and most stable option. Ensure that Docker and Docker Compose are already installed on your device.
Wait for images to be pulled and the project to build. Depending on your device performance and network conditions, this usually takes about 5, 10 minutes. You can check the build progress in the Node.js container logs; once an accessible URL appears, the project has started successfully.
Stars: 1.9k
Language: TypeScript
License: Apache-2.0
Install:
bash docker compose up -d
Final thoughts on RAG & memory GitHub repos
The best RAG & memory repo is the one you come back to. Pick 2 or 3 from this list, give them a month, and keep the ones you look forward to.
Navid Moazzez
AI business strategist & AI OS builder
Navid Moazzez helps creators and founders master AI and build their own AI Operating System (AI OS) to automate their business and life.
Navid.me is reader-supported. When you buy through links on this site, I may earn an affiliate commission. Learn more.
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