neuml/txtai

Health v2 · maintenance only

Adoption not part of the score

12909 stars · 883 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

txtai is an all-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows, built around an embeddings database combining vector indexes, graph networks and relational databases. It supports RAG pipelines, autonomous agents, multimodal indexing and exposes web and MCP APIs with bindings for JavaScript, Java, Rust and Go.

Use cases

• build semantic search over my documents
• create a RAG chatbot over a knowledge base
• index and search text, PDFs, audio and images with embeddings
• orchestrate LLM workflows and pipelines
• build autonomous AI agents that search a knowledge base
• run question-answering and summarization pipelines locally
• set up a self-hosted vector database with SQL support

When to choose

• you want an all-in-one Python framework combining vector search, RAG, pipelines and agents
• you need multimodal embeddings (text, documents, audio, images, video) with SQL querying
• you want batteries-included defaults to prototype AI search quickly
• you need MCP or web APIs with multi-language bindings

When to avoid

• you need a standalone production vector database at massive scale (consider dedicated vector stores)
• you only need a thin embedding library or raw transformer access
• your stack is not Python-centric and you don't want to run a service

Facets

framework · maturity active

vector-database search-engine rag agent-framework llm-inference nlp machine-learning artificial-intelligence large-language-models machine-learning python self-hosted cross-platform semantic-search embeddings-database llm-orchestration vector-search mcp workflows question-answering retrieval-augmented-generation search natural-language-processing ai-agents docker

Member repositories

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