txtai

txtai is a free, open-source framework for developers building search and language-model applications. It is strongest when a project needs retrieval, pipelines and automation under one roof; teams seeking a ready-made hosted app may prefer a more managed product.

Overview

txtai combines sparse and dense vector indexes with graph networks and relational databases. Alongside vector search, it supports SQL, object storage, topic modeling, graph analysis and multimodal indexing. That breadth is useful when an application must connect different kinds of data and retrieval, but it also means teams will be building and operating a framework rather than adopting a finished end-user tool.

It can create embeddings for text, documents, audio, images and video. Local operation can avoid sending data to separate remote services, a meaningful option for teams with data-control needs. The framework can run locally or on a server, cluster or serverless platform; this flexibility puts deployment choices—and the work of configuring them—in the team's hands.

Key features

Search and pipelines

Hybrid search and result reranking complement txtai's vector, graph and relational capabilities. Language-model pipelines cover prompting, question answering, labeling, transcription, translation and summarization. This makes txtai a good fit for developers who want to compose retrieval and language tasks in one system, rather than assemble separate tools for each stage.

Workflows, agents and integrations

Workflows combine pipelines with business logic, while agents can connect embeddings, pipelines, workflows and other agents to address problems autonomously. Integrations include Hugging Face and llama.cpp models, as well as OpenAI, Claude and AWS Bedrock through LiteLLM. The Web and Model Context Protocol APIs, plus JavaScript, Java, Rust and Go bindings, offer ways to connect applications beyond Python, although installation and runtime still require Python 3.11 or later.

Deployment and security

Installation is through pip and PyPI, with Docker images for CPU and GPU use and environment-specific prerequisites for Linux, macOS and Windows. Documented serverless options include AWS Lambda, Google Cloud Functions, Azure Cloud Functions and Kubernetes with Knative. The API is open over HTTP by default, so deployments that need transport protection or access control should configure HTTPS and API-token authorization. The safeopen option is enabled by default for API and application runs, restricting local files to a configured directory and URLs to public addresses.

Pricing

txtai — free. The free plan is open source under Apache 2.0 and can be installed with pip or run with Docker. There are no published seat or usage caps in this plan. The cost advantage comes with the responsibility to install and operate the software yourself. txtai.cloud is a planned hosted offering, and implementation guidance, development support and AI consulting are offered by NeuML; custom pricing applies where no price is published.

Platforms

txtai supports API, Linux, macOS, Windows and self-hosted use. It can run locally, on a server or cluster, and in documented serverless environments. Local operation can keep data from being sent to separate remote services, but teams remain responsible for deployment and API configuration.

Who it's for

txtai suits developers and technical teams building semantic search, retrieval-augmented generation, language-model workflows or agents who want an open-source framework they can run on their own infrastructure. Its multimodal indexing and broad deployment options add room to shape a system around varied data and environments. It is a weaker fit for readers who want a turnkey hosted service, or who cannot take on Python-based installation and API security setup.

Pros and cons

• Broad retrieval in one framework: sparse and dense indexes, graph networks, relational data and SQL support can serve applications that need more than vector search alone.
• Wide task and media coverage: pipelines span common language tasks, and embeddings can be created for text, documents, audio, images and video.
• Deployment flexibility: local, server, cluster and serverless options let teams choose where workloads run.
• Technical setup is part of the deal: pip or Docker installation and Python 3.11+ make it a developer-oriented choice, not a no-setup app.
• Security needs attention: the API defaults to open HTTP, so teams should configure HTTPS and token authorization where needed.

Alternatives

For a broader directory of options, see Retrieval-Augmented Generation Tools.

• AnythingLLM is worth considering if you want Android or web access alongside self-hosted and API platforms; it also has a free self-hosted Docker option.
• Flowise may suit teams looking for a freemium visual workflow option with a stated free allowance of 2 Flows & Assistants, 100 Predictions / month and 5MB Storage.
• I3K RAG Enterprise is an alternative for teams seeking a self-hosted community edition under AGPL-3.0, with Linux x86_64, ARM64 or Windows support.
• RAGFlow offers a free plan with 5 apps, 1 team member, 0.1 GB dataset storage and 500 credits / month; its free plan does not include an API key.
• Dify is another option with a free Sandbox plan, including 200 message credits, one workspace, one member and five apps.
• Haystack Enterprise Platform may fit teams seeking a free Studio tier with one workspace, one user, 100 pipeline hours and cloud deployment.
• LlamaIndex is an alternative with a freemium offering; LlamaParse is described as commercial, while LlamaIndex and Workflows are open-source projects.
• OpenRAG offers a free AGPL-3.0 self-hosted option with no feature held back.

Verdict

Choose txtai if you want an open-source, self-hostable framework that brings search, language-model pipelines, workflows and agents together, and you can own the Python-based setup. Look elsewhere if you need a turnkey hosted product or prefer not to manage API security and deployment yourself.