Configure the OSS Stack

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

Define your configuration

• Python
• 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

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.