RAG・知識ベース
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AST-powered code intelligence engine — graph database plus hybrid semantic search across 62 languages, zero LLM dependency.
★ 91 スターNOASSERTION
ガイドを見る →txtaiは、neumlが公開しているオープンソースのRAG・知識ベースプロジェクトです（GitHub: neuml/txtai）。GitHubスターは13,000、フォークは922で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月8日です。
All-in-one AI framework for semantic search, LLM orchestration and language model workflows.
Decide whether your first job is semantic search over content, a RAG pipeline, or a language-model workflow such as summarization or transcription.
Run pip install txtai (Python 3.11+); install only the optional dependencies your components need to keep the footprint low.
Create an Embeddings instance and index text, documents, audio, images or video - sensible defaults get you running in a couple of lines.
Chain pipelines into workflows, ground an LLM with your embeddings for RAG, or connect components into an agent; optionally serve it all through the built-in API.
A union of sparse and dense vector indexes, graph networks and a relational database, enabling vector search with SQL, topic modeling, graph analysis and multimodal indexing.
Create embeddings for text, documents, audio, images and video, so mixed content lives in one searchable index.
Ready pipelines run LLM prompts, question-answering, labeling, transcription, translation, summarization and more.
Join pipelines together and aggregate business logic - from simple microservices to multi-model workflows.
Agents built on smolagents connect embeddings, pipelines, workflows and other agents, supporting Hugging Face, llama.cpp and LiteLLM-backed LLMs (OpenAI, Claude, Bedrock).
A built-in FastAPI web API and MCP API, with bindings for JavaScript, Java, Rust and Go, so applications in any language can use a txtai service.
Clone txtai for the examples and source; most users install from PyPI instead.
git clone https://github.com/neuml/txtai.git
cd txtaiInstall txtai into a Python 3.11+ virtual environment.
pip install txtaiPoint the built-in API at a YAML config and query it over HTTP.
CONFIG=app.yml uvicorn "txtai.api:app"
# then: curl "http://localhost:8000/search?query=positive"txtai is developed by NeuML, the company behind the project, with open-source contributors; NeuML also offers consulting and a hosted txtai.cloud.
Yes. It is open source under the Apache-2.0 license.
Not for local models: txtai runs Hugging Face and llama.cpp models on your machine. Cloud LLMs reached through LiteLLM (OpenAI, Claude, AWS Bedrock) use your own provider keys.
A vector database only stores and searches embeddings, and LangChain orchestrates external components; txtai bundles the embeddings database, pipelines, workflows and agents into one framework with batteries included.
Run pip install txtai, create an Embeddings index in a few lines, and work through the introductory notebook.
AST-powered code intelligence engine — graph database plus hybrid semantic search across 62 languages, zero LLM dependency.
All-in-one API platform for search, recommendations, RAG and analytics — self-hostable
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
Unstract — LLM-driven extraction of unstructured data, built for API deployments and ETL pipeline workflows.
The open-source, permission-aware context layer that lets agents search all your company knowledge.
Semantic search engine designed for files and code.
LibreAIカタログに掲載されたRAG・知識ベースプロジェクトの中で最もスターの多いもの — txtaiが合わなかった場合に検討したい、現実的な候補リストです。
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
カタログの中でtxtaiとスター数が近いRAG・知識ベースプロジェクト — 前後の棚もそのまま見て回れます。
The smallest vector index in the world: private RAG over everything on your laptop with 97% less storage.
Incremental engine that keeps RAG indexes and agent context fresh by processing only the delta.
Local knowledge-base Q&A over PDFs, Office files, images and web pages - offline and CPU-friendly.
Polyglot document intelligence in Rust — extract text, tables and structured data from 106 formats.
Unified framework for enterprise RAG pipelines with small, specialized models - local, private and secure.
PaperQA2 — high-accuracy RAG for answering questions from scientific papers, with citations you can check.