crewai-memory-dynamodb

An Amazon DynamoDB StorageBackend for CrewAI's unified Memory — hierarchical scopes, categories, metadata filters, importance/recency, and native vector search via DynamoDB SearchVectors. No external vector database.

pip install crewai-memory-dynamodb

from crewai import Crew
from crewai.memory import Memory
from crewai_memory_dynamodb import DynamoDBMemoryBackend

backend = DynamoDBMemoryBackend(table_name="crewai-memory", dimensions=3072)   # match your embedder
memory = Memory(storage=backend)                                               # CrewAI does LLM analysis, scoping, scoring
crew = Crew(agents=[...], tasks=[...], memory=memory)

memory.remember("The customer prefers email over phone", scope="/customers/acme", categories=["preference"])
memory.recall("how should we contact acme?", scope="/customers")

Or register it once for every Crew(memory=True):

from crewai.memory.storage.factory import set_memory_storage_factory
set_memory_storage_factory(lambda spec: DynamoDBMemoryBackend("crewai-memory", dimensions=3072))

How it works

• One table: PK = scope path, SK = record id, plus a by_id GSI and a vector index on embedding (cosine, dimensions) whose search schema declares PK as an inline filter. Auto-created (PAY_PER_REQUEST); the vector index can only be declared at creation, so use a new table to change dimensions.
• search runs native SearchVectors once per concrete scope under the requested prefix (DynamoDB allows only equality on a string search-schema attribute), merges by similarity (1 - distance), then applies categories / metadata_filter / min_score on the candidates (oversampled ×3 when filtering).
• Scope tree, category counts, list_records, delete(older_than=...), reset, touch_records are query/scan based — sized for agent-memory volumes.
• dimensions must equal the Memory embedder's output size (CrewAI default text-embedding-3-large = 3072; Bedrock Titan v2 = 1024).

Requires boto3>=1.43.78 and a region where DynamoDB vector search is available. Permissions: DescribeTable, CreateTable, GetItem, PutItem, DeleteItem, BatchWriteItem, Query, Scan, SearchVectors.

Docs: https://skamalj.github.io/agentstate-reducer/ · part of crewai-memory

License

MIT