> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Strands Agents

> Add persistent long-term memory to AWS Strands agents with Mem0, as a native MemoryStore that plugs into the agent loop.

Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [Strands Agents](https://github.com/strands-agents/sdk-python), AWS's open-source SDK for building AI agents. The [`strands-mem0`](https://github.com/mem0ai/mem0/tree/main/integrations/strands-mem0) package ships a native `MemoryStore`, so recall and writes happen automatically inside the agent loop, not as tool calls the model has to remember.

## Overview

1. A `MemoryStore` the `MemoryManager` drives on every turn: it searches Mem0 and injects the results into the prompt, and writes memory back when extraction is enabled.
2. Server-side extraction: because the store implements `add_messages`, enabling `extraction` routes raw conversation turns to Mem0's own extraction pipeline, with no extra client-side model call.
3. Works with the hosted Mem0 Platform (an API key) or self-hosted Mem0 OSS (a config dict).

## Prerequisites

Before setting up Mem0 with Strands, ensure you have:

1. Installed the required packages:

```bash theme={null}
pip install strands-mem0
```

2. A valid API key:
   * <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-strands" rel="nofollow">Mem0 API Key</a> (set as `MEM0_API_KEY`)

## Basic Integration Example

Hand a `Mem0MemoryStore` to a `MemoryManager`, and the agent gets automatic recall and memory writes:

```python theme={null}
import os
from strands import Agent
from strands.memory import MemoryManager
from strands_mem0 import Mem0MemoryStore

os.environ["MEM0_API_KEY"] = "your-mem0-api-key"

# extraction=True routes conversation turns to Mem0's server-side extraction.
store = Mem0MemoryStore(user_id="alex", extraction=True)
agent = Agent(memory_manager=MemoryManager(stores=[store]))

agent("Remember I use Neovim and deploy on Fridays.")  # writes memory
print(agent("What editor do I use?"))                   # recalls it, injected automatically
```

Scope memories with any of `user_id`, `agent_id`, `run_id`, or `app_id` (`app_id` is platform-only). Pass `max_search_results` to bound how many memories are injected per turn.

## Self-hosted Mem0 (OSS)

To run against self-hosted Mem0 instead of the platform, pass a `config` dict:

```python theme={null}
store = Mem0MemoryStore(
    user_id="alex",
    extraction=True,
    config={
        "vector_store": {"provider": "qdrant", "config": {"host": "localhost", "port": 6333}},
    },
)
```

## Explicit memory tool

If you want the model to call memory explicitly instead of (or alongside) the automatic store, use the `mem0_memory` tool from `strands-agents-tools`. A store and the tool can share the same Mem0 backend and namespace.

## Learn more

* [strands-mem0 on GitHub](https://github.com/mem0ai/mem0/tree/main/integrations/strands-mem0)
* [Strands Agents documentation](https://strandsagents.com)