Configure the OSS Stack - Mem0

Mem0 OSS works out of the box with OpenAI defaults. Point it at your own LLM, embedder, and vector store by passing a config when you create Memory. The Python SDK also supports a reranker and graph memory.

<Info> **Prerequisites** - Python 3.10+ (`pip`) or Node.js 18+ (`npm`) - A running vector store such as Qdrant or Postgres + pgvector (Python's default Qdrant and Node's in-memory store need nothing extra) - API keys for your chosen LLM and embedder providers </Info> <Tip> New to Mem0 OSS? Run the <Link href="/open-source/python-quickstart">Python</Link> or <Link href="/open-source/node-quickstart">Node.js</Link> quickstart first, then come back to swap in your own providers. </Tip>

Install dependencies

<CodeGroup> ```bash pip pip install mem0ai ```

bash

npm install mem0ai

</CodeGroup>

Using Qdrant as your vector store? Install its Python client (the Node SDK talks to Qdrant over REST) and run the server locally:

bash

pip install qdrant-client   # Python only
docker run -p 6333:6333 qdrant/qdrant

Define your configuration

Each component takes a provider and a config. Keys are snake_case in Python and camelCase in TypeScript. Pass the config when you create Memory:

<CodeGroup> ```python Python from mem0 import Memory

config = { "vector_store": { "provider": "qdrant", "config": {"host": "localhost", "port": 6333}, }, "llm": { "provider": "openai", "config": {"model": "gpt-5-mini", "temperature": 0.1}, }, "embedder": { "provider": "openai", "config": {"model": "text-embedding-3-small"}, }, "reranker": { "provider": "cohere", "config": {"model": "rerank-v3.5"}, }, }

memory = Memory.from_config(config)


```ts Node.js
import { Memory } from "mem0ai/oss";

const memory = new Memory({
  llm: {
    provider: "openai",
    config: { apiKey: process.env.OPENAI_API_KEY || "", model: "gpt-5-mini", temperature: 0.1 },
  },
  embedder: {
    provider: "openai",
    config: { apiKey: process.env.OPENAI_API_KEY || "", model: "text-embedding-3-small" },
  },
  vectorStore: {
    provider: "qdrant",
    config: { host: "localhost", port: 6333, collectionName: "memories" },
  },
});

</CodeGroup>

Set your provider keys as environment variables:

bash

export OPENAI_API_KEY="..."
export COHERE_API_KEY="..."   # Python reranker only

<Note> The TypeScript OSS SDK configures the LLM, embedder, vector store, and history store. Reranker and graph memory are Python-only today. </Note>

Prefer a config file? Load YAML into Python's from_config:

python

import yaml
from mem0 import Memory

with open("config.yaml") as f:
    config = yaml.safe_load(f)

memory = Memory.from_config(config)

<Info icon="check"> Verify it works: add a memory and search it back. `memory.add(...)` followed by `memory.search(...)` should populate your vector store and return the memory as a top hit. </Info>

Available providers

Change the provider string to switch backends. The most common options:

Component	Python	TypeScript
LLM	`openai`, `anthropic`, `gemini`, `groq`, `ollama`, `aws_bedrock`, `azure_openai`, `litellm`	`openai`, `anthropic`, `gemini`, `groq`, `ollama`, `aws_bedrock`, `azure_openai`, `mistral`, `deepseek`
Embedder	`openai`, `gemini`, `azure_openai`, `ollama`, `huggingface`, `vertexai`, `aws_bedrock`	`openai`, `gemini`, `azure_openai`, `ollama`
Vector store	`qdrant`, `pgvector`, `chroma`, `pinecone`, `redis`, `weaviate`, `milvus`, `elasticsearch`	`memory`, `qdrant`, `pgvector`, `redis`, `supabase`, `azure-ai-search`, `vectorize`, `milvus`

See the full catalog in <Link href="/components/llms/overview">Components</Link>.

Tune component settings

<AccordionGroup> <Accordion title="Vector store collections"> Name collections explicitly in production (`collection_name` / `collectionName`) to isolate tenants and enable per-tenant retention policies. </Accordion> <Accordion title="LLM extraction temperature"> Keep extraction temperature at or below 0.2 so memories stay deterministic. Raise it only when you see facts being missed. </Accordion> <Accordion title="Reranker depth (Python)"> Limit `top_k` to 10 to 20 results. Sending more adds latency without meaningful gains. </Accordion> </AccordionGroup> <Warning> Mixing managed and self-hosted components? Make sure every outbound provider call has a secure network path. Managed rerankers and embedders often require outbound internet even if your vector store is on-prem. </Warning>

Quick recovery

Qdrant connection errors: confirm port 6333 is exposed and the API key (if set) matches.
Empty search results: verify the embedder model name. A mismatch causes dimension errors.
Unknown reranker (Python): upgrade the SDK with pip install --upgrade mem0ai to load the latest provider registry.
Cannot find module (Node): import from the OSS entry point, import { Memory } from "mem0ai/oss", not "mem0ai".