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txtai

neuml.github.io

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Spec score 8.3 #6 of 49 in LLM Application Development Frameworks

What it is

txtai is an open-source framework for semantic search, language-model orchestration, and language-model workflows. Its embeddings database brings together sparse and dense vector indexes, graph networks, and relational databases. It supports vector search with SQL, object storage, topic modeling, graph analysis, and multimodal indexing, and can create embeddings from text, documents, audio, images, and video. Pipelines cover prompts, question answering, labeling, transcription, translation, and summarization; workflows combine pipelines with business logic, while agents can coordinate embeddings, pipelines, workflows, and other agents. Web and Model Context Protocol APIs are available, alongside JavaScript, Java, Rust, and Go bindings. txtai can run locally or be deployed on a server, cluster, or serverless environment. Install with pip or use Docker; Python 3.11 or later is supported. The software is free under the Apache 2.0 license. The API is open over HTTP by default, with HTTPS and token authorization options documented.

Spec sheet

Purpose
txtai is an all-in-one AI framework for semantic search, LLM orchestration, and language model workflows.Read 5 Oct 2026 · neuml.github.io
Product
txtai is an all-in-one AI framework for semantic search, LLM orchestration, and language model workflows.Read 5 Oct 2026 · neuml.github.io
Deployment
txtai API instances and workflows can run locally, on a server, on a cluster, or serverless; documented serverless options include AWS Lambda, Google Cloud Functions, Azure Cloud Functions, and Kubernetes with Knative.Read 5 Oct 2026 · neuml.github.io
Integrations
The documentation describes agent support for Hugging Face and llama.cpp models, and OpenAI, Claude, and AWS Bedrock through LiteLLM.Read 5 Oct 2026 · neuml.github.io
Security
The API is open over HTTP by default; the documentation describes HTTPS options, API-token authorization, and a safeopen option enabled by default for API and application runs.Read 5 Oct 2026 · neuml.github.io
Support
NeuML offers implementation guidance, development support, and AI consulting, with meetings or email as contact options.Read 5 Oct 2026 · neuml.com
License
txtai is open-source under the Apache 2.0 license.Read 5 Oct 2026 · neuml.github.io
API security
The API is open over HTTP by default; the security guide recommends enabling HTTPS and documents API-token authorization.Read 5 Oct 2026 · neuml.github.io
APIs and bindings
txtai provides Web and Model Context Protocol APIs, with bindings for JavaScript, Java, Rust, and Go.Read 5 Oct 2026 · neuml.github.io
Embeddings database
Its embeddings database combines sparse and dense vector indexes, graph networks, and relational databases.Read 5 Oct 2026 · neuml.github.io
Features
It supports vector search, multimodal embeddings, language-model pipelines, workflows, and autonomous agents.Read 5 Oct 2026 · neuml.github.io
Formats
It can create embeddings for text, documents, audio, images, and video.Read 5 Oct 2026 · neuml.github.io
Hosted offering
The official pages describe txtai.cloud as a planned effort for hosted txtai applications, without listing plan pricing.Read 5 Oct 2026 · neuml.github.io
Install and runtime
The documented install is via pip and PyPI, Python 3.11+ is supported, and Docker images are available for CPU and GPU installs.Read 5 Oct 2026 · neuml.github.io
License and price
txtai is open-source under the Apache 2.0 license; the official pages reviewed describe pip installation and self-hosting and do not list a software price.Read 5 Oct 2026 · neuml.github.io
Local operation
txtai can run locally, and its documentation says this avoids shipping data to disparate remote services.Read 5 Oct 2026 · neuml.github.io
Local use
The maker says txtai can run locally, avoiding the need to send data to separate remote services.Read 5 Oct 2026 · neuml.github.io
Media support
txtai can create embeddings for text, documents, audio, images, and video.Read 5 Oct 2026 · neuml.github.io
Operating systems
The installation guide provides environment-specific prerequisites for Linux, macOS, and Windows.Read 5 Oct 2026 · neuml.github.io
Pipelines
Language model pipelines can handle prompts, question answering, labeling, transcription, translation, and summarization.Read 5 Oct 2026 · neuml.github.io
Requirements
The installation guide says Python 3.11 or later is supported.Read 5 Oct 2026 · neuml.github.io
Safe file and URL access
The safeopen option, enabled by default for the API and applications, restricts local files to a configured directory and URLs to public addresses.Read 5 Oct 2026 · neuml.github.io
Search and data
Its embeddings database combines vector indexes, graph networks, and relational databases, and supports vector search with SQL, object storage, topic modeling, graph analysis, and multimodal indexing.Read 5 Oct 2026 · neuml.github.io
Workflows and agents
Workflows join pipelines and business logic, while agents connect embeddings, pipelines, workflows, and other agents to solve problems autonomously.Read 5 Oct 2026 · neuml.github.io

Plans and prices

txtai Free Open-source under Apache 2.0 · Install via pip or run with Docker Read 5 Oct 2026 · neuml.github.io

Every txtai plan and price →

Strong points and limits

Strong points

  • Free and open-source under Apache 2.0
  • Supports text, document, audio, image, and video embeddings
  • Offers pipelines, workflows, and autonomous agents
  • Runs locally, on servers, clusters, or serverless
  • Bindings available for JavaScript, Java, Rust, and Go

Limits

  • Requires Python 3.11 or later
  • API is open over HTTP by default
  • Hosted txtai.cloud is described as planned

Who it suits

It suits developers and teams building semantic search or language-model workflows who want local operation or self-managed deployment. Its API and language bindings also suit projects that need to connect txtai with other software.

Verdict

txtai combines search, embeddings, language-model pipelines, and workflow tools in a self-hostable framework. Its breadth comes with a Python runtime requirement, and API deployments need attention to the documented security options.

Where Specifiction places it

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