Tutorials

Learn neo4j-agent-memory by building real applications. These tutorials guide you through complete, working examples from start to finish.

Tutorials are learning-oriented. They take you by the hand through a series of steps to complete a project. They focus on building a context graph—the personalized knowledge structure that makes AI agents truly intelligent.

Getting Started

Start here if you’re new to neo4j-agent-memory:

Tutorial What You’ll Build Time

Build Your First Memory-Enabled Agent

A working memory system that stores conversations, extracts entities automatically, and enables semantic search. You’ll see your context graph in Neo4j Browser.

~30 min

Building Real Applications

After completing the first tutorial, build complete applications:

Tutorial What You’ll Build Time

Add Conversation Memory to a Chatbot

A shopping assistant chatbot that remembers users, learns preferences (brands, sizes, budget), and provides personalized recommendations across sessions.

~45 min

Build a Knowledge Graph from Documents

Process financial documents to extract entities (companies, people, securities), discover relationships, and power intelligent Q&A with LLM integration.

~45 min

Build a Strands Agent with Context Graph

Create an AI agent using AWS Strands SDK with persistent memory backed by Neo4j. Uses Amazon Bedrock for LLM and embeddings.

~30 min

What You’ll Learn

These tutorials cover the core concepts of neo4j-agent-memory:

  • Context Graph: Build and query the knowledge structure that powers personalization

  • Entity Extraction: Automatically extract people, companies, products from text

  • Preference Learning: Capture and use user preferences for personalization

  • Semantic Search: Find information by meaning, not just keywords

  • Multi-Session Persistence: Memory that survives across conversations

  • Agent Integration: Connect memory to LLMs for intelligent responses

Enterprise Examples

Each tutorial includes examples for common enterprise use cases:

  • Financial Services: Client profiles, investment preferences, document analysis

  • Ecommerce Retail: Customer preferences, product recommendations, purchase history

Before You Start

Each tutorial assumes you have:

  • Python 3.10 or later installed

  • Basic familiarity with Python and async programming

  • Access to a Neo4j database (tutorials include setup instructions)

Tutorial Philosophy

These tutorials follow the Diataxis "learning-oriented" approach:

  • Step-by-step: Every action is explicit, with exact commands to run

  • Hands-on: You’ll build something real, not just read about concepts

  • One path: No choices or alternatives - just follow along

  • Complete: Each tutorial stands alone and produces a working result

If you’re looking for quick answers to specific problems, see the How-To Guides.

If you want to understand the concepts behind the library, see the Explanation section.

If you need to look up configuration options or API details, see the Reference section.

Getting Help

If you get stuck:

  1. Check the Configuration Reference for environment setup

  2. See the GitHub Discussions for community help

  3. Report issues at GitHub Issues