The Problem: Building AI applications today means making dozens of infrastructure decisions:

- Which vector store to use? - How to handle data ingestion and chunking? - Which embedding model to choose? - How to structure the RAG pipeline? - How to manage context and memory?

Each of these requires research, setup, and ongoing maintenance. The resulting stack is complex and brittle.

What Unbody Provides:

- A unified backend with built-in support for data ingestion, processing, and vector storage - Configurable components - bring your own vector store, LLM, or embedding model - A GraphQL API for querying your knowledge base - Multi-modal data processing out of the box - Plugin system for extending functionality

Our aim is to lower the burden of developing intelligent software that understands, reasons, and acts. As an analogy, we like to think of Unbody's layers like some of the fundamental components in intelligent systems:

- Perception: Data ingestion, parsing, and enhancement - Memory: Vector storage and content relationships - Reasoning: LLM integration and function execution - Action: APIs, SDKs, and external integrations

Current State:

- Alpha release with core features and architecture implemented - Built with Node.js/TypeScript - Documentation in progress - Unstable

Why Open Source: We believe AI infrastructure should be transparent and customizable. By open-sourcing Unbody, we aim to:

1. Validate our ideas 2. Improve based on community feedback 3. Allow developers to inspect and modify the code 4. Build a foundation for AI-native development that everyone can contribute to

GitHub: https://github.com/unbody-io/unbody Docs: https://unbody.io/docs Discord: https://discord.gg/UX8WKEsVPu

We're particularly interested in feedback from developers building AI applications. What infrastructure challenges are you facing? What would make your development process easier?

Would love to hear your thoughts and experiences!