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Engineering Short & Long Term Agent Memory using MongoDB

A hands-on lab exploring how to build agents that remember — within a session, across sessions, and at scale — using MongoDB as the memory backend.

What you'll build

Notebook Memory type Key primitive
01-short-term-memory.ipynb Session / Working memory MongoDBSaver (LangGraph checkpointer)
02-long-term-memory.ipynb Episodic + Semantic memory VoyageAI embeddings + $vectorSearch
03-semantic-cache.ipynb Semantic Cache VoyageAI embeddings + $vectorSearch

Concepts covered

Short-Term Memory
├── Working memory    — LLM context window
└── Session memory    — LangGraph checkpoints in MongoDB (MongoDBSaver)

Long-Term Memory
├── Episodic          — records of past interactions
├── Semantic          — facts, preferences, entity memory
└── Procedural        — (referenced in slides)

Semantic Cache       — skip the LLM for semantically equivalent queries

The full memory pipeline: Aggregate → Encode → Store → Organise → Retrieve

Prerequisites

Getting started

  1. Open the repo in a Codespace (or Dev Container).
  2. Add your secrets in Codespace settings (or a .env file for local):
    • VOYAGE_API_KEY
    • ANTHROPIC_API_KEY
  3. Open the lab/ folder and run the notebooks in order.

The devcontainer automatically starts a local MongoDB Atlas-compatible instance and runs the seed script to populate the listings dataset.

Stack

  • Runtime: TypeScript (tslab kernel in Jupyter)
  • LLM: Claude Haiku via @langchain/anthropic
  • Embeddings: VoyageAI voyage-4-large / voyage-4-lite
  • Orchestration: LangGraph (@langchain/langgraph)
  • Database: MongoDB Atlas local (mongodb/mongodb-atlas-local)