RAG & Wissen
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AST-powered code intelligence engine — graph database plus hybrid semantic search across 62 languages, zero LLM dependency.
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Anleitung ansehen →txtai ist ein Open-Source-Projekt aus der Kategorie RAG & Wissen von neuml (neuml/txtai auf GitHub). Es hat 13.000 Sterne auf GitHub und 922 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 8. Oktober 2026 auf GitHub aktualisiert.
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
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The open-source, permission-aware context layer that lets agents search all your company knowledge.
Semantic search engine designed for files and code.
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Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Verwandte Projekte der Kategorie RAG & Wissen im Katalog, die txtai bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
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.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.