README - Mem0 — ContextQMD

<div align="center"> <h1>strands-mem0</h1> <h3>Persistent long-term memory for Strands Agents, backed by Mem0</h3> <p> A community <a href="https://strandsagents.com/">Strands Agents</a> integration that plugs <a href="https://mem0.ai">Mem0</a> in as a first-class <code>MemoryStore</code>. </p> </div>

strands-mem0 gives Strands agents durable memory that survives across sessions, backed by Mem0. Where the mem0_memory tool is called explicitly by the model, Mem0MemoryStore plugs into the agent loop directly: the manager recalls context and injects it automatically, and writes new memories, either verbatim or by extracting facts from the conversation.

Automatic recall + injection — relevant memories are searched and prepended to the prompt every turn, no tool call required.
Server-side extraction — raw conversation turns are handed to Mem0, which distills and de-duplicates facts on its own pipeline (no extra client-side model call).
Hosted or self-hosted — the managed Mem0 Platform by default, or your own Mem0 OSS backend via a config dict.

Install

bash

pip install strands-mem0

Usage

python

from strands import Agent
from strands.memory import MemoryManager
from strands_mem0 import Mem0MemoryStore

# Recall + write, distilling facts from the conversation via Mem0's server-side extraction.
store = Mem0MemoryStore(user_id="alex", writable=True, extraction=True)
agent = Agent(memory_manager=MemoryManager(stores=[store]))

# The agent now recalls from and writes to Mem0 without any explicit tool call.
agent("Remember that I prefer dark-mode dashboards and only drink oat milk.")
agent("How do I like my dashboards?")  # recalls the stored preference

Set MEM0_API_KEY for the hosted platform (get one at app.mem0.ai), or pass api_key=.... For a self-hosted Mem0 OSS backend, pass a config=... dict instead.

How it works

Mem0MemoryStore implements all three MemoryStore hooks:

Method	Maps to	When it runs
`search(query)`	`mem0.search(query, filters={...})`	Every turn, to recall and inject context
`add(content)`	`mem0.add(content, infer=False)`	The `add_memory` tool / a client-side extractor — stores a fact verbatim
`add_messages(messages)`	`mem0.add(rendered_turns, infer=True)`	Extraction — renders conversation turns to text, then hands them to Mem0's server-side extraction

Because add_messages is implemented, enabling extraction routes conversation turns straight to Mem0's own extraction pipeline. A store that only implemented add would instead need a client-side ModelExtractor (an extra model call) to distill facts first.

Configuration

Argument	Default	Description
`user_id` / `agent_id` / `run_id` / `app_id`	(at least one required)	Mem0 entity scope that owns the memories
`name`	`"mem0"`	Store identifier, used to target it from memory tools
`writable`	`True`	Whether the manager may write to the store
`extraction`	`None`	Automatic extraction (`bool` or `ExtractionConfig`)
`max_search_results`	`None`	Default result cap per search (falls back to 5)
`metadata`	`None`	Default metadata merged into every write
`api_key` / `host`	env	Mem0 platform key / base URL (`api_key` defaults to `$MEM0_API_KEY`)
`config`	`None`	Mem0 OSS config dict for a self-hosted backend

The explicit tool

For the model-called tool (store / retrieve / get / delete), use the mem0_memory tool from strands-agents-tools. The store and the tool share one Mem0 backend and namespace.

Development

The package lives under python/ (monorepo-style layout matching the Strands extension-template).

bash

cd python
pip install hatch
hatch run test        # pytest (no live server required — mocked client)
hatch run prepare     # format + lint + typecheck + test

License

Apache-2.0. Mem0 is a trademark of its respective owner. Strands Agents is a project of its respective authors.