What is JamAI Base used for?

JamAI Base is an open-source RAG backend with a spreadsheet-like interface where cells can run LLM prompts, chain into pipelines and be queried through a REST API. It is a good fit for building RAG chatbots without custom orchestration, comparing prompts and models in a shared sheet, and generating structured data columns with an LLM.

Is JamAI Base open source?

Yes. JamAI Base is open source under the Apache-2.0 licence. Its source code is on GitHub at EmbeddedLLM/JamAIBase and is written mainly in Python.

Yes. JamAI Base is open source, so the software itself is free to use. Paid plans are also available, starting at $25 per month.

Can I self-host JamAI Base?

Yes. JamAI Base can be self-hosted on your own server or infrastructure.

What are some alternatives to JamAI Base?

Similar open-source tools in the AI Infrastructure category include Langflow, Dify and RAGFlow. SaaS products in the same category include Cohere, Unstructured and AI Stats.

Is JamAI Base actively maintained?

Yes. The most recent commit to JamAI Base was on 24 September 2026, and the latest release is v0.4, published on 13 February 2025. The project has 1.1k stars on GitHub.

JamAI Base: open-source AI infrastructure

About JamAI Base

JamAI Base is an open-source backend platform for retrieval-augmented generation that presents itself as a collaborative spreadsheet for AI. It combines an embedded SQLite database and a LanceDB vector database with managed memory and RAG, and orchestrates language models, embeddings and rerankers behind a simple REST API.

Its main building blocks are tables with different roles. Generative tables fill columns with LLM-generated data, action tables provide a real-time interaction layer between an application and the model, and knowledge tables store structured data and documents that supply context to the others. Cells can be chained into pipelines, prompts and models can be compared, and responses can be evaluated while teams work together on the same sheet.

The platform is written in Python with a Svelte front end and is licensed under Apache-2.0. It is meant to be self-hosted, with documentation and a migration guide from version 1 to 2, and its topics describe it as a backend-as-a-service for AI. It suits developers building chatbots and RAG applications who prefer to configure workflows in a table instead of writing orchestration code.

Key features

Spreadsheet-style interface for LLM workflows
Embedded SQLite and LanceDB vector store
Generative, action, and knowledge tables
Model, embedding, and reranker orchestration
Simple REST API
Collaboration on prompts and pipelines

Good fit for

→Building RAG chatbots without custom orchestration
→Comparing prompts and models in a shared sheet
→Generating structured data columns with an LLM

Built with: Python; Svelte
Tags: rag; llm; spreadsheet; vector-database; lancedb; backend-as-a-service; python; ai-workflows; svelte

JamAI Base: questions and answers

What is JamAI Base used for?: JamAI Base is an open-source RAG backend with a spreadsheet-like interface where cells can run LLM prompts, chain into pipelines and be queried through a REST API. It is a good fit for building RAG chatbots without custom orchestration, comparing prompts and models in a shared sheet, and generating structured data columns with an LLM.
Is JamAI Base open source?: Yes. JamAI Base is open source under the Apache-2.0 licence. Its source code is on GitHub at EmbeddedLLM/JamAIBase and is written mainly in Python.
Is JamAI Base free?: Yes. JamAI Base is open source, so the software itself is free to use. Paid plans are also available, starting at $25 per month.
Can I self-host JamAI Base?: Yes. JamAI Base can be self-hosted on your own server or infrastructure.
What are some alternatives to JamAI Base?: Similar open-source tools in the AI Infrastructure category include Langflow, Dify and RAGFlow. SaaS products in the same category include Cohere, Unstructured and AI Stats.
Is JamAI Base actively maintained?: Yes. The most recent commit to JamAI Base was on 24 September 2026, and the latest release is v0.4, published on 13 February 2025. The project has 1.1k stars on GitHub.