Chroma is an open-source AI-native database designed to store, query, and manage vector embeddings. It enables developers and researchers working with AI and machine learning applications to efficiently handle high-dimensional data generated from language models, image models, and other machine learning pipelines. Built for flexibility and ease of integration, Chroma is well-suited for prototyping LLM-based applications, building retrieval-augmented generation (RAG) systems, and supporting semantic search use cases.

Chroma is particularly useful for teams building AI-driven applications who need fast, lightweight infrastructure for storing embeddings without the complexity of managing external vector databases. It supports in-memory and persistent modes, has a simple Python client, and integrates well with tools like LangChain.

Key benefits include:

• Embedded directly in Python applications for low-latency querying

• Schema-less design with automatic metadata handling

• Open-source and local-first, ideal for privacy-conscious and offline use cases

What are the main features of Chroma?

Lightweight, embedded vector database

Chroma runs as a local, embedded database within Python applications, eliminating the need for external services or infrastructure.

• Fully in-process operation for minimal latency

• No server setup required – Chroma works out of the box with Python

• Designed to support rapid development and testing of AI prototypes

Flexible metadata and schema management

Chroma uses a schema-less architecture that automatically stores and indexes metadata alongside vector embeddings.

• Store arbitrary key-value metadata with each embedding

• Supports filtering, grouping, and querying based on metadata fields

• Allows rich semantic queries combining vectors and metadata

Built-in similarity search and filtering

Chroma provides native support for similarity search, enabling fast retrieval of relevant vectors based on distance metrics.

• k-NN (k-nearest neighbors) queries with cosine similarity

• Real-time vector insertion and retrieval

• Efficient search for large embedding sets, both in memory and on disk

Persistence and durability options

While Chroma runs in memory by default, it supports persistent storage for production or large-scale applications.

• Toggle between ephemeral and persistent modes

• Save and load vector collections from disk

• Use in both development (in-memory) and deployment (persistent) environments

Developer-friendly Python client

Chroma offers a simple and intuitive Python API, making it easy to integrate into AI workflows.

• CRUD operations for documents, metadata, and embeddings

• Seamless integration with frameworks like LangChain and FastAPI

• Minimal boilerplate – optimized for rapid prototyping

Why choose Chroma?

• Open-source and local-first: Offers transparency and control over data, especially for teams concerned about privacy and vendor lock-in

• Optimized for AI use cases: Purpose-built for embeddings, unlike general-purpose databases

• Low-latency and lightweight: Ideal for fast prototyping and small-scale deployments without infrastructure overhead

• Flexible and schema-less: Handles structured and unstructured data with minimal setup

• Strong developer support: Active open-source community and clean Python integration make it easy to use and extend