Mem0MemoryStore

Mem0MemoryStore is a memory store backed by the Mem0 cloud API. It is the shared data layer used by Mem0MemoryRetriever, Mem0MemoryWriter, and the Mem0 Memory Tools.

Used by	Mem0MemoryRetriever, Mem0MemoryWriter, Mem0MemoryRetrieverTool, Mem0MemoryWriterTool
Optional init variables	api_key: Defaults to MEM0_API_KEY environment variable
API reference	Mem0
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mem0
Package name	mem0-haystack

Overview

Mem0MemoryStore wraps the Mem0 cloud API and provides two core methods:

• add_memories — stores a list of ChatMessage objects as memories in Mem0.
• search_memories — retrieves memories from Mem0 that are relevant to a query.

Scope memories with at least one Mem0 entity ID: user_id, run_id, agent_id, or app_id. These are runtime parameters, so a single store instance can serve multiple users or sessions.

The infer parameter on add_memories controls how Mem0 processes incoming messages:

• infer=True lets Mem0 extract memories from the messages automatically. This is useful when storing a full Agent turn.
• infer=False stores the supplied message text as-is. This is useful when the exact memory text has already been selected upstream.

Installation

Install the Mem0 integration:

bash

pip install mem0-haystack

Set your Mem0 API key:

bash

export MEM0_API_KEY="your-mem0-api-key"

Usage

On its own

python

from haystack.dataclasses import ChatMessage
from haystack_integrations.memory_stores.mem0 import Mem0MemoryStore

store = Mem0MemoryStore()

store.add_memories(
    messages=[ChatMessage.from_user("Alice prefers concise Python examples.")],
    user_id="alice",
    infer=False,
)

memories = store.search_memories(
    query="What does Alice prefer?",
    user_id="alice",
    top_k=3,
)
print([msg.text for msg in memories])

Scoping with multiple entity IDs

Mem0 supports narrowing the scope of reads and writes with user_id, run_id, agent_id, and app_id. Pass any combination at call time:

python

store.add_memories(
    messages=[ChatMessage.from_user("Alice is working on a documentation search system.")],
    user_id="alice",
    run_id="docs-assistant-session-1",
    infer=True,
)

memories = store.search_memories(
    query="What project is Alice working on?",
    user_id="alice",
    run_id="docs-assistant-session-1",
)
print([msg.text for msg in memories])

Retrieving all memories in scope

Pass query=None to return all memories matching the provided scope without a relevance search:

python

all_memories = store.search_memories(query=None, user_id="alice")
print([msg.text for msg in all_memories])