---
title: "Configuration"
description: "Configure Mem0 with custom LLMs, vector stores, embedders, and rerankers for production deployments"
icon: "sliders"
iconType: "solid"
---

Mem0 is highly configurable, allowing you to customize every component of your memory system. Choose from **51+ supported providers** across LLMs, vector databases, embedders, and rerankers.

## Quick Start

The simplest setup uses OpenAI defaults:

```python
import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "your-api-key"
m = Memory()
```

For production or custom setups, configure specific components:

```python
from mem0 import Memory

config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "host": "localhost",
            "port": 6333
        }
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4.1-nano-2025-04-14",
            "temperature": 0.1
        }
    }
}

m = Memory.from_config(config)
```

## Configuration Components

Mem0 has four configurable components. Click any to see all supported providers and detailed configuration options.

<CardGroup cols={2}>
<Card title="LLMs" icon="message-bot" href="/components/llms/overview">
**17 providers** including OpenAI, Anthropic, Ollama, Groq, and more

Configure the language model for memory extraction and processing

</Card>

<Card title="Vector Databases" icon="hard-drive" href="/components/vectordbs/overview">
**25+ databases** including Qdrant, Chroma, Pinecone, Weaviate, and more

Choose where to store and retrieve memory embeddings

</Card>

<Card title="Embedding Models" icon="cube" href="/components/embedders/overview">
**9 providers** including OpenAI, HuggingFace, Ollama, and more

Select the model to convert memories into vector embeddings

</Card>

<Card title="Rerankers" icon="ranking-star" href="/components/rerankers/overview">
**4 models** including Cohere, Zero Entropy, and LLM-based

Improve search relevance by re-scoring retrieved memories

</Card>
</CardGroup>

## Configuration Recipes

### Production Setup with Qdrant

For production deployments, use a dedicated vector store:

<Steps>
<Step title="Start Qdrant">
```bash
docker pull qdrant/qdrant

docker run -p 6333:6333 -p 6334:6334 \
 -v $(pwd)/qdrant_storage:/qdrant/storage:z \
 qdrant/qdrant

````
</Step>

<Step title="Configure Mem0">
```python
import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "your-api-key"

config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "host": "localhost",
            "port": 6333,
        }
    }
}

m = Memory.from_config(config)
````

</Step>
</Steps>

### Fully Local Setup

Run Mem0 completely offline with Ollama (no external APIs):

<Card title="Local Setup with Ollama" icon="server" href="/examples/mem0-with-ollama">
  Step-by-step guide to run Mem0 with local LLM and embeddings
</Card>

### Multi-Cloud Setup

Mix providers from different clouds:

```python
config = {
    "llm": {
        "provider": "azure_openai",
        "config": {
            "api_key": "azure-key",
            "deployment_name": "gpt-4.1-nano-2025-04-14"
        }
    },
    "vector_store": {
        "provider": "pinecone",
        "config": {
            "api_key": "pinecone-key",
            "index_name": "mem0"
        }
    },
    "embedder": {
        "provider": "vertexai",
        "config": {
            "model": "textembedding-gecko@003"
        }
    }
}
```

### Graph Memory Setup

Enable relationship tracking with Neo4j:

```python
config = {
    "graph_store": {
        "provider": "neo4j",
        "config": {
            "url": "neo4j+s://your-instance.databases.neo4j.io",
            "username": "neo4j",
            "password": "your-password"
        }
    }
}

m = Memory.from_config(config)
```

<Card title="Graph Memory Guide" icon="diagram-project" href="/open-source/features/graph-memory">
  Learn how to use graph memory for relationship-based retrieval
</Card>

## Advanced Configuration

### Custom Prompts

Override default prompts for memory processing:

```python
config = {
    "custom_fact_extraction_prompt": """
    Extract key facts from the conversation.
    Focus on: preferences, decisions, and context.
    Output as a single sentence.
    """,
    "custom_update_memory_prompt": """
    Update the existing memory with new information.
    Preserve important context from the old memory.
    """
}
```

<CardGroup cols={2}>
<Card title="Custom Fact Extraction" icon="sparkles" href="/open-source/features/custom-fact-extraction-prompt">
Customize how memories are extracted from conversations
</Card>

<Card title="Custom Memory Updates" icon="pen" href="/open-source/features/custom-update-memory-prompt">
Control how existing memories are modified
</Card>
</CardGroup>

### Reranking for Better Search

Add reranking to improve search relevance:

```python
config = {
    "rerank": {
        "provider": "cohere",
        "config": {
            "model": "rerank-english-v3.0",
            "top_k": 5
        }
    }
}
```

    <Card title="Reranker-Enhanced Search" icon="arrow-up-arrow-down" href="/open-source/features/reranker-search">
  Learn how reranking improves memory search accuracy
</Card>

### History Database

Configure where operation history is stored:

```python
config = {
    "history_db_path": "/custom/path/to/history.db"
}
```

## All Configuration Options

<AccordionGroup>
<Accordion title="LLM Configuration">
| Parameter              | Description                                   | Provider          |
|-----------------------|-----------------------------------------------|-------------------|
| `provider`            | LLM provider (e.g., "openai", "anthropic")    | All              |
| `model`               | Model to use                                  | All              |
| `temperature`         | Temperature of the model (0.0-2.0)            | All              |
| `api_key`             | API key to use                                | Most             |
| `max_tokens`          | Maximum tokens to generate                    | All              |
| `top_p`               | Nucleus sampling threshold                    | All              |
| `top_k`               | Top-k sampling parameter                      | Some             |
| `ollama_base_url`     | Base URL for Ollama API                      | Ollama           |
| `openai_base_url`     | Base URL for OpenAI API                      | OpenAI           |
| `azure_kwargs`        | Azure-specific initialization args           | Azure OpenAI     |

**See all 17 LLM providers:** [LLMs Overview](/components/llms/overview)

</Accordion>

<Accordion title="Vector Store Configuration">
Common parameters (provider-specific options vary):

| Parameter         | Description                | Example     |
| ----------------- | -------------------------- | ----------- |
| `provider`        | Vector store provider      | "qdrant"    |
| `host`            | Host address               | "localhost" |
| `port`            | Port number                | 6333        |
| `collection_name` | Collection/index name      | "memories"  |
| `api_key`         | API key (for cloud stores) | "your-key"  |

**See all 25+ vector stores:** [Vector Databases Overview](/components/vectordbs/overview)

</Accordion>

<Accordion title="Embedder Configuration">
| Parameter    | Description                     | Default                      |
|-------------|---------------------------------|------------------------------|
| `provider`   | Embedding provider              | "openai"                     |
| `model`      | Embedding model to use          | "text-embedding-3-small"     |
| `api_key`    | API key for embedding service   | None                        |

**See all 9 embedder providers:** [Embedders Overview](/components/embedders/overview)

</Accordion>

<Accordion title="Reranker Configuration">
| Parameter    | Description                     | Example                      |
|-------------|---------------------------------|------------------------------|
| `provider`   | Reranker provider               | "cohere"                     |
| `model`      | Reranker model to use           | "rerank-english-v3.0"       |
| `top_k`      | Number of results to return     | 5                           |
| `api_key`    | API key for reranker service    | "your-key"                  |

**See all reranker options:** [Rerankers Overview](/components/rerankers/overview)

</Accordion>

<Accordion title="Graph Store Configuration">
| Parameter    | Description                     | Example                      |
|-------------|---------------------------------|------------------------------|
| `provider`   | Graph store provider            | "neo4j"                      |
| `url`        | Connection URL                  | "neo4j+s://..."             |
| `username`   | Authentication username         | "neo4j"                     |
| `password`   | Authentication password         | "your-password"             |

**Learn more:** [Graph Memory Overview](/open-source/features/graph-memory)

</Accordion>

<Accordion title="General Configuration">
| Parameter         | Description                          | Default                    |
|------------------|--------------------------------------|----------------------------|
| `history_db_path` | Path to the history database         | "{mem0_dir}/history.db"    |
| `custom_fact_extraction_prompt`   | Custom prompt for memory extraction  | None                       |
| `custom_update_memory_prompt` | Custom prompt for memory updates | None                |
</Accordion>
</AccordionGroup>

## Next Steps

<CardGroup cols={3}>
<Card title="Python Quickstart" icon="python" href="/open-source/python-quickstart">
Get started with the Python SDK
</Card>

<Card title="Self-Hosting Features" icon="server" href="/open-source/features/overview">
  Explore OSS-specific capabilities
</Card>

<Card title="Examples" icon="book" href="/examples">
See configuration examples in action
</Card>
</CardGroup>