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Discover top open-source software, updated regularly with real-world adoption signals.
Embedding database for building LLM apps with memory
Chroma is an embedding database that enables Python and JavaScript developers to add semantic search and memory to LLM applications with a simple 4-function API.
Chroma is an embedding database designed to make building LLM applications with memory straightforward and scalable. Whether you're prototyping in a notebook or deploying to production, Chroma provides the same simple API for storing documents, generating embeddings, and retrieving semantically similar content.
With just four core functions—create, add, query, and get—developers can implement semantic search, retrieval-augmented generation (RAG), and "chat your data" workflows in minutes. Chroma handles tokenization, embedding generation, and indexing automatically, though you can bring your own embeddings and models when needed. The database integrates seamlessly with LangChain, LlamaIndex, and popular embedding providers including OpenAI, Cohere, and Sentence Transformers.
Chroma runs in-memory for rapid prototyping, supports persistent storage for development, and scales to client-server deployments for production workloads. The project is fully typed, tested, and documented, with Apache 2.0 licensing. A managed Chroma Cloud service offers serverless vector and full-text search for teams seeking a hosted solution. Built primarily in Rust with Python and TypeScript clients, Chroma delivers performance without sacrificing developer experience.
When teams consider Chroma, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Retrieval-Augmented Generation (RAG)
Query relevant documents from your knowledge base and inject them into LLM context windows for grounded, factual responses
Semantic Document Search
Enable natural language queries across internal documentation, returning conceptually similar content rather than keyword matches
Conversational Memory for Chatbots
Store conversation history as embeddings to retrieve relevant context and maintain coherent multi-turn dialogues
Content Recommendation Systems
Match user queries or preferences to similar articles, products, or media using embedding similarity
Embeddings convert text, images, or audio into numerical vectors that capture semantic meaning. Vector databases like Chroma store these embeddings and enable fast similarity search, allowing you to find conceptually related content rather than exact keyword matches.
Yes. While Chroma uses Sentence Transformers by default, you can provide your own embeddings from OpenAI, Cohere, or custom models. You can also pass embeddings directly when adding documents.
Chroma runs in-memory for prototyping, supports persistent local storage for development, and offers client-server mode for production deployments. The managed Chroma Cloud service provides serverless scaling without infrastructure management.
Yes. Chroma has native integrations with both LangChain (Python and JavaScript) and LlamaIndex, making it straightforward to use as the retrieval layer in LLM orchestration frameworks.
Chroma is licensed under Apache 2.0, which permits commercial use, modification, and distribution with minimal restrictions.
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