---
title: Python SDK Quickstart
description: 'Get started with Mem0 quickly!'
icon: "python"
iconType: "solid"
---

<Snippet file="security-compliance.mdx" />

> Welcome to the Mem0 quickstart guide. This guide will help you get up and running with Mem0 in no time.

## Installation

To install Mem0, you can use pip. Run the following command in your terminal:

```bash
pip install mem0ai
```

## Basic Usage

### Initialize Mem0

<Tabs>
  <Tab title="Basic">
```python
import os
from mem0 import Memory

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

m = Memory()
```
  </Tab>
  <Tab title="Async">
```python
import os
from mem0 import AsyncMemory

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

m = AsyncMemory()
```
  </Tab>
  <Tab title="Advanced">
If you want to run Mem0 in production, initialize using the following method:

Run Qdrant first:

```bash
docker pull qdrant/qdrant

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

Then, instantiate memory with qdrant server:

```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)
```
  </Tab>

<Tab title="Advanced (Graph Memory)">

```python
import os
from mem0 import Memory

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

config = {
    "graph_store": {
        "provider": "neo4j",
        "config": {
            "url": "neo4j+s://---",
            "username": "neo4j",
            "password": "---"
        }
    }
}

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

</Tab>
</Tabs>


### Store a Memory

<CodeGroup>
```python Code
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about a 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."}
]

# Store inferred memories (default behavior)
result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})

# Store raw messages without inference
# result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"}, infer=False)
```

```json Output
{
    "results": [
        {
            "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
            "memory": "User is planning to watch a movie tonight.",
            "metadata": {
                "category": "movie_recommendations"
            },
            "event": "ADD"
        },
        {
            "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
            "memory": "User is not a big fan of thriller movies.",
            "metadata": {
                "category": "movie_recommendations"
            },
            "event": "ADD"
        },
        {
            "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
            "memory": "User loves sci-fi movies.",
            "metadata": {
                "category": "movie_recommendations"
            },
            "event": "ADD"
        }
    ]
}
```
</CodeGroup>

### Retrieve Memories

<CodeGroup>
```python Code
# Get all memories
all_memories = m.get_all(user_id="alice")
```

```json Output
{
    "results": [
            {
        "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
        "memory": "User is planning to watch a movie tonight.",
        "hash": "1a271c007316c94377175ee80e746a19",
        "created_at": "2025-02-27T16:33:20.557Z",
        "updated_at": "2025-02-27T16:33:27.051Z",
        "metadata": {
            "category": "movie_recommendations"
        },
        "user_id": "alice"
        },
        {
        "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
        "memory": "User loves sci-fi movies.",
        "hash": "285d07801ae42054732314853e9eadd7",
        "created_at": "2025-02-27T16:33:20.560Z",
        "updated_at": None,
        "metadata": {
            "category": "movie_recommendations"
        },
        "user_id": "alice"
        },
        {
        "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
        "memory": "User is not a big fan of thriller movies.",
        "hash": "285d07801ae42054732314853e9eadd7",
        "created_at": "2025-02-27T16:33:20.560Z",
        "updated_at": None,
        "metadata": {
            "category": "movie_recommendations"
        },
        "user_id": "alice"
        }
    ]
}
```
</CodeGroup>


<br />

<CodeGroup>
```python Code
# Get a single memory by ID
specific_memory = m.get("892db2ae-06d9-49e5-8b3e-585ef9b85b8e")
```

```json Output
{
  "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
  "memory": "User is planning to watch a movie tonight.",
  "hash": "1a271c007316c94377175ee80e746a19",
  "created_at": "2025-02-27T16:33:20.557Z",
  "updated_at": None,
  "metadata": {
    "category": "movie_recommendations"
  },
  "user_id": "alice"
}
```
</CodeGroup>

### Search Memories

<CodeGroup>
```python Code
related_memories = m.search(query="What do you know about me?", user_id="alice")
```

```json Output
{
  "results": [
    {
      "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
      "memory": "User is planning to watch a movie tonight.",
      "hash": "1a271c007316c94377175ee80e746a19",
      "created_at": "2025-02-27T16:33:20.557Z",
      "updated_at": None,
      "score": 0.38920719231944799,
      "metadata": {
        "category": "movie_recommendations"
      },
      "user_id": "alice"
    },
    {
      "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
      "memory": "User loves sci-fi movies.",
      "hash": "285d07801ae42054732314853e9eadd7",
      "created_at": "2025-02-27T16:33:20.560Z",
      "updated_at": None,
      "score": 0.36869761478135689,
      "metadata": {
        "category": "movie_recommendations"
      },
      "user_id": "alice"
    },
    {
      "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
      "memory": "User is not a big fan of thriller movies.",
      "hash": "285d07801ae42054732314853e9eadd7",
      "created_at": "2025-02-27T16:33:20.560Z",
      "updated_at": None,
      "score": 0.33855272141248272,
      "metadata": {
        "category": "movie_recommendations"
      },
      "user_id": "alice"
    }
  ]
}
```
</CodeGroup>

### Update a Memory

<CodeGroup>
```python Code
result = m.update(memory_id="892db2ae-06d9-49e5-8b3e-585ef9b85b8e", data="I love India, it is my favorite country.")
```

```json Output
{'message': 'Memory updated successfully!'}
```
</CodeGroup>

### Memory History

<CodeGroup>
```python Code
history = m.history(memory_id="892db2ae-06d9-49e5-8b3e-585ef9b85b8e")
```

```json Output
[
  {
    "id": 39,
    "memory_id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
    "previous_value": "User is planning to watch a movie tonight.",
    "new_value": "I love India, it is my favorite country.",
    "action": "UPDATE",
    "created_at": "2025-02-27T16:33:20.557Z",
    "updated_at": "2025-02-27T16:33:27.051Z",
    "is_deleted": 0
  },
  {
    "id": 37,
    "memory_id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
    "previous_value": null,
    "new_value": "User is planning to watch a movie tonight.",
    "action": "ADD",
    "created_at": "2025-02-27T16:33:20.557Z",
    "updated_at": null,
    "is_deleted": 0
  }
]
```
</CodeGroup>

### Delete Memory

```python
# Delete a memory by id
m.delete(memory_id="892db2ae-06d9-49e5-8b3e-585ef9b85b8e")
# Delete all memories for a user
m.delete_all(user_id="alice")
```

### Reset Memory

```python
m.reset() # Reset all memories
```

## Configuration Parameters

Mem0 offers extensive configuration options to customize its behavior according to your needs. These configurations span across different components like vector stores, language models, embedders, and graph stores.

<AccordionGroup>
<Accordion title="Vector Store Configuration">
| Parameter    | Description                     | Default     |
|-------------|---------------------------------|-------------|
| `provider`   | Vector store provider (e.g., "qdrant") | "qdrant"   |
| `host`       | Host address                    | "localhost" |
| `port`       | Port number                     | 6333       |
</Accordion>

<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                      | All              |
| `api_key`             | API key to use                                | All              |
| `max_tokens`          | Tokens to generate                            | All              |
| `top_p`               | Probability threshold for nucleus sampling    | All              |
| `top_k`               | Number of highest probability tokens to keep  | All              |
| `http_client_proxies` | Allow proxy server settings                   | AzureOpenAI      |
| `models`              | List of models                                | Openrouter       |
| `route`               | Routing strategy                              | Openrouter       |
| `openrouter_base_url` | Base URL for Openrouter API                  | Openrouter       |
| `site_url`            | Site URL                                      | Openrouter       |
| `app_name`            | Application name                              | Openrouter       |
| `ollama_base_url`     | Base URL for Ollama API                      | Ollama           |
| `openai_base_url`     | Base URL for OpenAI API                      | OpenAI           |
| `azure_kwargs`        | Azure LLM args for initialization            | AzureOpenAI      |
| `deepseek_base_url`   | Base URL for DeepSeek API                    | DeepSeek         |
</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                        |
</Accordion>

<Accordion title="Graph Store Configuration">
| Parameter    | Description                     | Default     |
|-------------|---------------------------------|-------------|
| `provider`   | Graph store provider (e.g., "neo4j") | "neo4j"    |
| `url`        | Connection URL                  | None        |
| `username`   | Authentication username         | None        |
| `password`   | Authentication password         | None        |
</Accordion>

<Accordion title="General Configuration">
| Parameter         | Description                          | Default                    |
|------------------|--------------------------------------|----------------------------|
| `history_db_path` | Path to the history database         | "{mem0_dir}/history.db"    |
| `version`         | API version                          | "v1.1"                     |
| `custom_fact_extraction_prompt`   | Custom prompt for memory processing  | None                       |
| `custom_update_memory_prompt` | Custom prompt for update memory | None                |
</Accordion>

<Accordion title="Complete Configuration Example">
```python
config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "host": "localhost",
            "port": 6333
        }
    },
    "llm": {
        "provider": "openai",
        "config": {
            "api_key": "your-api-key",
            "model": "gpt-4"
        }
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "api_key": "your-api-key",
            "model": "text-embedding-3-small"
        }
    },
    "graph_store": {
        "provider": "neo4j",
        "config": {
            "url": "neo4j+s://your-instance",
            "username": "neo4j",
            "password": "password"
        }
    },
    "history_db_path": "/path/to/history.db",
    "version": "v1.1",
    "custom_fact_extraction_prompt": "Optional custom prompt for fact extraction for memory",
    "custom_update_memory_prompt": "Optional custom prompt for update memory"
}
```
</Accordion>
</AccordionGroup>

## Run Mem0 Locally

Please refer to the example [Mem0 with Ollama](../examples/mem0-with-ollama) to run Mem0 locally.


## Chat Completion

Mem0 can be easily integrated into chat applications to enhance conversational agents with structured memory. Mem0's APIs are designed to be compatible with OpenAI's, with the goal of making it easy to leverage Mem0 in applications you may have already built.

If you have a `Mem0 API key`, you can use it to initialize the client. Alternatively, you can initialize Mem0 without an API key if you're using it locally.

Mem0 supports several language models (LLMs) through integration with various [providers](https://litellm.vercel.app/docs/providers).

## Use Mem0 Platform

```python
from mem0.proxy.main import Mem0

client = Mem0(api_key="m0-xxx")

# First interaction: Storing user preferences
messages = [
  {
    "role": "user",
    "content": "I love indian food but I cannot eat pizza since allergic to cheese."
  },
]
user_id = "alice"
chat_completion = client.chat.completions.create(messages=messages, model="gpt-4o-mini", user_id=user_id)
# Memory saved after this will look like: "Loves Indian food. Allergic to cheese and cannot eat pizza."

# Second interaction: Leveraging stored memory
messages = [
  {
    "role": "user",
    "content": "Suggest restaurants in San Francisco to eat.",
  }
]

chat_completion = client.chat.completions.create(messages=messages, model="gpt-4o-mini", user_id=user_id)
print(chat_completion.choices[0].message.content)
# Answer: You might enjoy Indian restaurants in San Francisco, such as Amber India, Dosa, or Curry Up Now, which offer delicious options without cheese.
```

In this example, you can see how the second response is tailored based on the information provided in the first interaction. Mem0 remembers the user's preference for Indian food and their cheese allergy, using this information to provide more relevant and personalized restaurant suggestions in San Francisco.

### Use Mem0 OSS

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

client = Mem0(config=config)

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "What's the capital of France?",
        }
    ],
    model="gpt-4o",
)
```

## APIs

Get started with using Mem0 APIs in your applications. For more details, refer to the [Platform](../platform/quickstart).

Here is an example of how to use Mem0 APIs:

```python
import os
from mem0 import MemoryClient

os.environ["MEM0_API_KEY"] = "your-api-key"

client = MemoryClient() # get api_key from https://app.mem0.ai/

# Store messages
messages = [
    {"role": "user", "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts."},
    {"role": "assistant", "content": "Hello Alex! I've noted that you're a vegetarian and have a nut allergy. I'll keep this in mind for any food-related recommendations or discussions."}
]
result = client.add(messages, user_id="alex")
print(result)

# Retrieve memories
all_memories = client.get_all(user_id="alex")
print(all_memories)

# Search memories
query = "What do you know about me?"
related_memories = client.search(query, user_id="alex")

# Get memory history
history = client.history(memory_id="m1")
print(history)
```


## Contributing

We welcome contributions to Mem0! Here's how you can contribute:

1. Fork the repository and create your branch from `main`.
2. Clone the forked repository to your local machine.
3. Install the project dependencies:

   ```bash
   poetry install
   ```

4. Install pre-commit hooks:

   ```bash
   pip install pre-commit  # If pre-commit is not already installed
   pre-commit install
   ```

5. Make your changes and ensure they adhere to the project's coding standards.

6. Run the tests locally:

   ```bash
   poetry run pytest
   ```

7. If all tests pass, commit your changes and push to your fork.
8. Open a pull request with a clear title and description.

Please make sure your code follows our coding conventions and is well-documented. We appreciate your contributions to make Mem0 better!


If you have any questions, please feel free to reach out to us using one of the following methods:

<Snippet file="get-help.mdx" />