RAG и базы знаний
trustgraph
The semantic intelligence layer for ontologies — governed, traceable knowledge for AI agents
★ 2,8 тыс. звёздApache-2.0
Смотреть руководство →pipeshub-ai — проект с открытым исходным кодом из категории «RAG и базы знаний» от pipeshub-ai (pipeshub-ai/pipeshub-ai на GitHub). У него 3 820 звёзд на GitHub и 595 форков, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 8 октября 2026.
The open-source, permission-aware context layer that lets agents search all your company knowledge.
Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.
Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.
Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.
Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.
Slack, Drive, Jira, GitHub, Microsoft 365 and many more sources synced and indexed automatically.
Results respect source permissions, with hybrid search, knowledge-graph retrieval and citations.
Agents can search, navigate and cite company knowledge through the Model Context Protocol.
Official Python, TypeScript and Go SDKs for building on the context layer.
Ready agents for tasks such as deep research on top of your workspace knowledge.
Run it on your own infrastructure with the one-line installer; your data stays under your control.
Get the source so you can inspect, configure and run it locally.
git clone https://github.com/pipeshub-ai/pipeshub-ai.git
cd pipeshub-aiRun the official installer on your server (review the script first if you prefer).
curl -fsSL https://get.pipeshub.com/install | bashClone the repo and run the same installer from the repo root, then connect your first sources in the web app.
git clone https://github.com/pipeshub-ai/pipeshub-ai.git
cd pipeshub-ai
./install.shIt is maintained by pipeshub-ai on GitHub (pipeshub-ai/pipeshub-ai), where the source, issues and releases live.
Yes — it is free and open source under Apache-2.0, which permits use, modification and commercial redistribution, with licence/notice retention.
The software is free and self-hosted; you configure your own LLM provider (cloud key or a local model via Ollama) for answer generation.
Compared with Glean or hosted enterprise search, pipeshub-ai's distinguishing focus is: the open-source, permission-aware context layer that lets agents search all your company knowledge. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.
Clone https://github.com/pipeshub-ai/pipeshub-ai (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.
The semantic intelligence layer for ontologies — governed, traceable knowledge for AI agents
All-in-one AI framework for semantic search, LLM orchestration and language model workflows.
Utopia — an open-source enterprise world model: a bitemporal knowledge substrate that learns from your documents and gives agents a governed, auditable memory
A trilingual e-commerce knowledge base agents can install.
Skills that let coding agents search and read your local documents.
A new state of the art for RAG: an open knowledge base for humans and agents.
Другие проекты из категории «RAG и базы знаний» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если pipeshub-ai вам не подходит.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Соседние проекты из категории «RAG и базы знаний» в каталоге, ближайшие по числу звёзд к pipeshub-ai — продолжайте изучать полку по обе стороны от него.
nano-graphrag: a small, hackable GraphRAG implementation in about 1,100 lines — no Microsoft GraphRAG bulk
Knowhere turns documents into structured, hierarchy-native chunks ready for agents and RAG.
Ragent (容纳智能体): a production-grade Java Agentic RAG platform with hybrid retrieval and an admin console
A personal knowledge base that builds itself — drop in sources, get a persistent interlinked wiki.
RAGApp: deploy a RAG app over your documents with Docker — configure it like a custom GPT
VideoRAG — chat with your videos, even hundreds of hours of them, with graph-driven retrieval.
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.