strands-mongodb-store

A MongoDB Atlas Vector Search MemoryStore for Strands Agents — semantic long-term agent memory via Atlas $vectorSearch. No external vector database.

pip install strands-mongodb-store

from strands import Agent
from strands.memory import MemoryManager
from strands_mongodb_store import MongoDBMemoryStore

store = MongoDBMemoryStore(
    name="user-memories",
    connection_string="mongodb+srv://user:pass@cluster.mongodb.net",
    database_name="agent", collection_name="memory",
)
agent = Agent(memory_manager=MemoryManager(stores=[store]))

await store.add("The user prefers dark mode", metadata={"kind": "pref"})
hits = await store.search("what theme does the user like?")

How it works

• Semantic recall via Atlas $vectorSearch (approximate nearest-neighbor over a vector index), ranked by vectorSearchScore.
• You bring the embeddings. Default embedder is Amazon Bedrock Titan Text v2 (1024-dim, cosine); pass any embedder callable.
• Each add stores a document {_id, content, embedding, metadata, createdAt}; the Atlas vector index is created automatically if absent (create_index=True).

Requirements

MongoDB Atlas — Vector Search ($vectorSearch) is an Atlas feature; community/self-hosted MongoDB does not support it. For local development, use a managed store or the Postgres / DynamoDB memory stores.

The memory store (strands-mongodb-store) is distinct from the byte storage backend (strands-mongodb-storage). Also published as strands-store-mongodb.

License

MIT