---
title: "Configure the OSS Stack"
description: "Wire up Mem0 OSS with your preferred LLM, vector store, embedder, and reranker."
icon: "sliders"
---
# Configure Mem0 OSS Components
**Prerequisites**
- Python 3.10+ with `pip` available
- Running vector database (e.g., Qdrant, Postgres + pgvector) or access credentials for a managed store
- API keys for your chosen LLM, embedder, and reranker providers
Start from the Python quickstart if you still need the base CLI and repository.
## Install dependencies
```bash
pip install mem0ai
```
```bash
pip install qdrant-client openai
```
```bash
git clone https://github.com/mem0ai/mem0.git
cd mem0/examples/docker-compose
```
```bash
pip install -r requirements.txt
```
## Define your configuration
```python
from mem0 import Memory
config = {
"vector_store": {
"provider": "qdrant",
"config": {"host": "localhost", "port": 6333},
},
"llm": {
"provider": "openai",
"config": {"model": "gpt-4.1-mini", "temperature": 0.1},
},
"embedder": {
"provider": "vertexai",
"config": {"model": "textembedding-gecko@003"},
},
"reranker": {
"provider": "cohere",
"config": {"model": "rerank-english-v3.0"},
},
}
memory = Memory.from_config(config)
```
```bash
export QDRANT_API_KEY="..."
export OPENAI_API_KEY="..."
export COHERE_API_KEY="..."
```
```yaml
vector_store:
provider: qdrant
config:
host: localhost
port: 6333
llm:
provider: azure_openai
config:
api_key: ${AZURE_OPENAI_KEY}
deployment_name: gpt-4.1-mini
embedder:
provider: ollama
config:
model: nomic-embed-text
reranker:
provider: zero_entropy
config:
api_key: ${ZERO_ENTROPY_KEY}
```
```python
from mem0 import Memory
memory = Memory.from_config_file("config.yaml")
```
Run `memory.add(["Remember my favorite cafe in Tokyo."], user_id="alex")` and then `memory.search("favorite cafe", user_id="alex")`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
## Tune component settings
Name collections explicitly in production (`collection_name`) to isolate tenants and enable per-tenant retention policies.
Keep extraction temperatures ≤0.2 so advanced memories stay deterministic. Raise it only when you see missing facts.
Limit `top_k` to 10–20 results; sending more adds latency without meaningful gains.
Mixing managed and self-hosted components? Make sure every outbound provider call happens through a secure network path. Managed rerankers often require outbound internet even if your vector store is on-prem.
## Quick recovery
- Qdrant connection errors → confirm port `6333` is exposed and API key (if set) matches.
- Empty search results → verify the embedder model name; a mismatch causes dimension errors.
- `Unknown reranker` → update the SDK (`pip install --upgrade mem0ai`) to load the latest provider registry.