Kartik
4.2 KiB
mem0-strands
Persistent long-term memory for Strands Agents, backed by Mem0
A community Strands Agents integration that plugs
Mem0 in as a first-class MemoryStore.
mem0-strands 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
pip install mem0-strands
Usage
from strands import Agent
from strands.memory import MemoryManager
from mem0_strands 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).
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.