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1---
2glob: "**/*.ts"
3description: "Langgraph Memory Management Guidelines"
4---
5# Memory Management Guidelines
6 
7## Purpose of Memory
8 
9Memory in AI applications allows agents to process, store, and effectively recall information from past interactions, enabling learning and adaptation to user preferences.
10 
11* **Short-term Memory (Thread-scoped):** Persists conversational turns (`messages`) within a single session for context and continuity. Managed as part of the agent's state.
12* **Long-term Memory (Cross-thread):** Retains information across different conversations or users. Stored in custom namespaces via the `Store` interface.
13* **Checkpointing:** Saves snapshots of the graph state at every "super-step" for continuity, debugging, and fault tolerance.
14 
15## Implementation Details (`src/memory/`)
16 
17* **MongoDB Integration:** MongoDB is the primary backend for memory persistence, including chat history, vector stores, and checkpoints.
18* **`MongoDBSaver` (`@langchain/langgraph-checkpoint-mongodb`):** Used for LangGraph checkpointing, ensuring graph state can be saved and reloaded.
19* **`MongoDBStore`:** Provides a generic key-value store interface over MongoDB for various data types, supporting long-term memory.
20* **`MongoDBChatMessageHistory`:** Manages the storage and retrieval of chat messages in MongoDB.
21* **`MongoDBAtlasVectorSearch`:** Integrates with MongoDB Atlas Vector Search for efficient semantic search over embeddings. Uses Google embeddings.
22 
23## Best Practices for Memory
24 
25* **Session Management:** Ensure unique `sessionId`s are used for different conversations to maintain isolated contexts.
26* **Environment Variables:** `MONGODB_ATLAS_URI` must be set for database connection.
27* **Vector Indexing:** Ensure vector search indexes are properly created and maintained for efficient vector lookups (e.g., `vector_index` in `src/memory/storage.ts`). Note that Google embeddings are typically 768 dimensions.
28* **Data Consistency:** Be mindful of data consistency when updating and retrieving memory components.
29* **Scalability:** Consider the implications of memory storage on scalability for large-scale deployments.
30* **Managing Conversation History:** Implement strategies to manage long conversation histories (e.g., trimming, summarizing) to prevent context window overflow and reduce costs. The `MessagesAnnotation` with `messagesStateReducer` is crucial for handling message updates and deletions.
31* **Memory Store Usage:** Use the `Store` interface for cross-thread persistence. Define clear namespaces and keys for organizing memories. Implement semantic search for natural language retrieval.
32* **Writing Memories:** Decide whether to write memories "on the hot path" (real-time, potentially impacting latency) or "in the background" (as a separate task).
33* **Memory Representation:** Consider how memories are presented to the LLM (e.g., as updated instructions, few-shot examples) to optimize its performance.
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