---
title: "txtai"
type: "tool"
slug: "neuml-txtai"
canonical_url: "https://www.graphcanon.com/tools/neuml-txtai"
github_url: "https://github.com/neuml/txtai"
homepage_url: "https://neuml.github.io/txtai"
stars: 12711
forks: 841
primary_language: "Python"
license: "Apache-2.0"
categories: ["vector-databases", "inference-serving", "llm-frameworks", "data-retrieval"]
tags: ["pipelines", "embeddings-database", "llm-orchestration", "semantic-search", "web-api", "retrieval-augmented-generation", "autonomous-agents"]
updated_at: "2026-07-07T18:31:34.138802+00:00"
---

# txtai

> All-in-one AI framework for semantic search, LLM orchestration and language model workflows

Txtai is a versatile AI framework that supports vector search with SQL, embeddings creation for various media types, LLM-powered pipelines, complex workflows in microservices or multi-model formats, intelligent agents, and API integration.

## Facts

- Repository: https://github.com/neuml/txtai
- Homepage: https://neuml.github.io/txtai
- Stars: 12,711 · Forks: 841 · Open issues: 9 · Watchers: 114
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2026-07-02T14:44:53+00:00

## Categories

- [Vector Databases](/categories/vector-databases.md)
- [Inference & Serving](/categories/inference-serving.md)
- [LLM Frameworks](/categories/llm-frameworks.md)
- [Data & Retrieval](/categories/data-retrieval.md)

## Tags

pipelines, embeddings-database, llm-orchestration, semantic-search, web-api, retrieval-augmented-generation, autonomous-agents

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- [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. (★ 116,702)
- [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch (★ 98,711)

## README (excerpt)

```text
<p align="center">
    <img src="https://raw.githubusercontent.com/neuml/txtai/master/logo.png"/>
</p>

<p align="center">
    <b>All-in-one AI framework</b>
</p>

<p align="center">
    <a href="https://github.com/neuml/txtai/releases">
        <img src="https://img.shields.io/github/release/neuml/txtai.svg?style=flat&color=success" alt="Version"/>
    </a>
    <a href="https://github.com/neuml/txtai">
        <img src="https://img.shields.io/github/last-commit/neuml/txtai.svg?style=flat&color=blue" alt="GitHub last commit"/>
    </a>
    <a href="https://github.com/neuml/txtai/issues">
        <img src="https://img.shields.io/github/issues/neuml/txtai.svg?style=flat&color=success" alt="GitHub issues"/>
    </a>
    <a href="https://join.slack.com/t/txtai/shared_invite/zt-37c1zfijp-Y57wMty6YOx_hyIHEQvQJA">
        <img src="https://img.shields.io/badge/slack-join-blue?style=flat&logo=slack&logocolor=white" alt="Join Slack"/>
    </a>
    <a href="https://github.com/neuml/txtai/actions?query=workflow%3Abuild">
        <img src="https://github.com/neuml/txtai/workflows/build/badge.svg" alt="Build Status"/>
    </a>
    <a href="https://coveralls.io/github/neuml/txtai?branch=master">
        <img src="https://img.shields.io/coverallsCoverage/github/neuml/txtai" alt="Coverage Status">
    </a>
</p>

txtai is an all-in-one AI framework for semantic search, LLM orchestration and language model workflows.




The key component of txtai is an embeddings database, which is a union of vector indexes (sparse and dense), graph networks and relational databases.

This foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications.

Build autonomous agents, retrieval augmented generation (RAG) processes, multi-model workflows and more.

Summary of txtai features:

- 🔎 Vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing
- 📄 Create embeddings for text, documents, audio, images and video
- 💡 Pipelines powered by language models that run LLM prompts, question-answering, labeling, transcription, translation, summarization and more
- ↪️️ Workflows to join pipelines together and aggregate business logic. txtai processes can be simple microservices or multi-model workflows.
- 🤖 Agents that intelligently connect embeddings, pipelines, workflows and other agents together to autonomously solve complex problems
- ⚙️ Web and Model Context Protocol (MCP) APIs. Bindings available for [JavaScript](https://github.com/neuml/txtai.js), [Java](https://github.com/neuml/txtai.java), [Rust](https://github.com/neuml/txtai.rs) and [Go](https://github.com/neuml/txtai.go).
- 🔋 Batteries included with defaults to get up and running fast
- ☁️ Run local or scale out with container orchestration

txtai is built with Python 3.10+, [Hugging Face Transformers](https://github.com/huggingface/transformers), [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and [FastAPI](https://github.com/tiangolo/fastapi). txtai is open-source under an Apache 2.0 license.

> [!NOTE]
>
> [NeuML](https://neuml.com) is the company behind txtai and we provide AI consulting services around our stack. [Schedule a meeting](https://cal.com/neuml/intro) or [send a message](mailto:info@neuml.com) to learn more.
>
> We're also building an easy and secure way to run hosted txtai applications with [txtai.cloud](https://txtai.cloud).

## Why txtai?




New vector databases, LLM frameworks and everything in between are sprouting up daily. Why build with txtai?

- Up and running in minutes with [pip](https://neuml.github.io/txtai/install/) or [Docker](https://neuml.github.io/txtai/cloud/)
```python
# Get started in a couple lines
import txtai

embeddings = txtai.Embeddings()
embeddings.index(["Correct", "Not what we hoped"])
embeddings.search("positive", 1)
#[(0, 0.29862046241760254)]
```
- Built-in API makes it easy to develop applications using your programming language of cho
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/neuml-txtai`](/api/graphcanon/tools/neuml-txtai)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_