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Strands Agents

docs/integrations/strands.mdx

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Integrate Mem0 with Strands Agents, AWS's open-source SDK for building AI agents. The 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
pip install strands-mem0
  1. 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
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
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