https://docs.langchain.com/oss/javascript/langgraph/memory#long-term-memory 

NOTE: this experience should include Voyage auto-embedding support for MongoDB Vector Store

Use Case

As a... LangGraph JS Developer I want... A persisted Store API to manage data across different conversation threads and sessions. So that... Agents can retain user facts, preferences, and learned instructions (Long-term Memory) without resetting context between interactions.

User Experience

Developers gain a unified interface (Store) to save/retrieve JSON documents via custom namespaces (not just Thread IDs). End-users experience agents that remember them over time.

If bug: N/A

Dependencies

• Upstream: LangGraph State management, Embedding/Vector provider interfaces.

• Downstream: Persistent storage adapters (Postgres, Redis, etc).

Risks/Unknowns

• Latency: "Hot path" memory updates (during generation) may slow down responses.

• Context Overload: Retrieving too many memories via search may exceed LLM token limits.

• Data Drift: JSON schemas for user profiles may become corrupted or bloated over time without strict validation.

Acceptance Criteria

Implementation Requirements

• Implement BaseStore interface: get, put, delete, search.

• Support hierarchical namespaces (e.g., [userId, "memories"]).

• Support Semantic Search (vector similarity) and Metadata Filtering.

Testing Requirements

• Unit tests for CRUD operations on InMemoryStore.

• Mocked tests for vector search/embedding generation.

Documentation Requirements

• API documentation for Store.

• Examples for "Hot Path" vs "Background" memory updates.

Follow Up Requirements

• Implement backend-specific stores (Postgres, Redis).

• Parity check with LangGraph Python BaseStore behavior.