M4A

Unbody

Memory API for AI applications with built-in RAG capabilities

freemiumbetaapiragdeveloper-toolsvector-searchopen-source
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Memory Types

semantic, contextual, document

Integrations

langchain, openai, anthropic, google, weaviate


Overview


Unbody is a developer-focused memory API that simplifies building AI applications with persistent memory and retrieval-augmented generation (RAG). It provides a unified API layer over various data sources and vector databases, making it easy to ingest, index, and retrieve information for LLM applications without managing complex infrastructure.


The platform acts as a bridge between your data sources (documents, websites, APIs) and AI models, automatically handling chunking, embedding, indexing, and retrieval. Unbody abstracts away the complexity of vector databases and RAG pipelines while providing flexibility for customization.


Key Features


  • **Unified API**: Single API for multiple data sources and vector databases
  • **Automatic Indexing**: Automatically chunks and embeds documents
  • **GraphQL Interface**: Flexible querying with GraphQL
  • **Multi-Source Support**: Ingest from files, URLs, APIs, and databases
  • **Vector Search**: Built-in semantic search capabilities
  • **RAG Ready**: Pre-configured for retrieval-augmented generation
  • **Managed Infrastructure**: Hosted option eliminates DevOps overhead
  • **Open Source**: Self-hostable with full code access

    • When to Use Unbody


    Unbody is ideal for:

  • Developers building RAG applications without infrastructure expertise
  • Teams wanting to prototype AI features quickly
  • Applications requiring memory across multiple data sources
  • Projects needing unified search across diverse content types
  • Startups wanting managed infrastructure for AI memory
  • Developers who want RAG without managing vector databases

    • Pros


  • Significantly reduces development time for RAG applications
  • Abstracts complexity of vector databases
  • Flexible GraphQL API for powerful queries
  • Both hosted and self-hosted options available
  • Good documentation and developer experience
  • Open-source core with commercial hosting

    • Cons


  • Still in beta with potential breaking changes
  • Smaller community compared to established alternatives
  • Limited customization of underlying RAG pipeline
  • GraphQL learning curve for some developers
  • Hosted version has vendor lock-in concerns
  • Performance may not match specialized vector databases

    • Pricing


  • **Free Tier**: 10k documents, 1M tokens/month
  • **Starter**: $49/month for 50k documents
  • **Pro**: $199/month for 200k documents
  • **Self-Hosted**: Free with open-source license

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