Chroma

Use Chroma as a vector database in Mem0 for local or cloud-hosted vector storage with built-in embedding support.

Chroma is an AI-native open-source vector database that simplifies building LLM apps by providing tools for storing, embedding, and searching embeddings with a focus on simplicity and speed. It supports both local deployment and cloud hosting through ChromaDB Cloud.

Usage

Python

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "test",
            "path": "db",
            # Optional: ChromaDB Cloud configuration
            # "api_key": "your-chroma-cloud-api-key",
            # "tenant": "your-chroma-cloud-tenant-id",
        }
    }
}

m = Memory.from_config(config)
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})

import { Memory } from 'mem0ai/oss';

// The Node.js client connects to a running Chroma server.
// Start one locally with: chroma run --host localhost --port 8000
const config = {
  vectorStore: {
    provider: 'chroma',
    config: {
      collectionName: 'memories',
      host: 'localhost',
      port: 8000,
      // Optional: ChromaDB Cloud configuration
      // apiKey: 'your-chroma-cloud-api-key',
      // tenant: 'your-chroma-cloud-tenant-id',
    },
  },
};

const memory = new Memory(config);
const messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });

The Node.js SDK uses the chromadb v3 client, which talks to a Chroma server over HTTP (local server or ChromaDB Cloud). Install it with npm install chromadb. Mem0 supplies the embeddings, so the collection is created without an embedding function.

Config

Here are the parameters available for configuring Chroma:

Python

Parameter	Description	Default Value
collection_name	The name of the collection	mem0
client	Custom client for Chroma	None
path	Path for the Chroma database	db
host	The host where the Chroma server is running	None
port	The port where the Chroma server is running	None
api_key	ChromaDB Cloud API key (for cloud usage)	None
tenant	ChromaDB Cloud tenant ID (for cloud usage)	None

TypeScript

Parameter	Description	Default Value
collectionName	The name of the collection	mem0
client	Pre-configured ChromaClient or CloudClient instance	None
host	The host where the Chroma server is running	None
port	The port where the Chroma server is running	None
ssl	Whether to use SSL when connecting to the Chroma server	false
path	Full URL of a Chroma server, e.g. http://localhost:8000 (alternative to host and port)	None
apiKey	ChromaDB Cloud API key (for cloud usage)	None
tenant	ChromaDB Cloud tenant ID (for cloud usage)	None
database	ChromaDB Cloud database name (for cloud usage)	mem0