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Workshop: Building Intelligent Memory

Graph Databases for AI Agent Context and Retrieval

Welcome to this hands-on workshop on building intelligent memory systems for AI agents using Neo4j and Pydantic AI!

Open in GitHub Codespaces

Memory graph visualization

Features

A full-stack chat application that uses Pydantic AI to query a Neo4j graph database of world news. The application features a modern Next.js frontend with Chakra UI and a Python FastAPI backend powered by Pydantic AI.

  • 🤖 Intelligent chat agent powered by Pydantic AI and OpenAI GPT-4
  • 📊 Neo4j graph database for storing and querying world news
  • 🔍 Vector Search: Semantic search using OpenAI embeddings and Neo4j vector indexes
  • 🌍 Geospatial Search: Find news near specific locations using latitude/longitude and radius
  • 📅 Time-Based Search: Filter news by date ranges with support for relative periods (last_week, last_month, etc.)
  • 🧠 Memory System: Learns and remembers user preferences from conversations
  • 🗂️ Conversation Threads: Persistent conversation history with automatic title generation
  • 🔧 Procedural Memory: Tracks reasoning steps, tool calls, and agent decision-making process
  • 🎨 Memory Graph Visualization: Interactive graph view of user memory, threads, and reasoning steps using NVL
  • 🛠️ Schema Tools: Database schema inspection, natural language to Cypher query generation, and direct Cypher execution
  • 💬 Interactive chat interface built with Next.js and Chakra UI
  • 🔎 Search news by topic, keywords, people, organizations, or locations
  • 📰 Sample world news dataset included
  • 🚀 Real-time communication between frontend and backend
  • 🔄 Toggle memory on/off to control preference learning
  • 🔁 Intelligent retry logic for handling empty search results

Agent chat

Full stack app with agent chat interface.

Agent chat UI

Inspect agent context

Detailed view of agent context for each turn including system prompt, user message, preference memory, tools available, and model used.

Inspect agent context

Inspect reasoning steps

Detailed view of reasoning steps including tool calls, arguments, and tool call results.

Inspect reasoning steps

Memory graph visualization

Interactive graph visualization to inspect the memory graph.

View memory graph visualzation

Episodic memory construction

Entity extraction, resolution and context management for user preferences

User profile memory visualization

Architecture

┌─────────────────────┐
│   Next.js Frontend  │
│   (Chakra UI)       │
└──────────┬──────────┘
           │
           │ HTTP/REST
           │
┌──────────▼──────────┐
│  FastAPI Backend    │
│  (Pydantic AI)      │
└────┬────────────┬───┘
     │            │
     │            │ (Optional)
     │            │
┌────▼─────┐  ┌──▼──────────┐
│  Neo4j   │  │   Neo4j     │
│  News    │  │   Memory    │
│  Graph   │  │  (Optional) │
└──────────┘  └─────────────┘

Prerequisites

  • Python 3.10+ - Backend runtime
  • uv - Fast Python package installer and resolver (recommended) or pip
  • Node.js 18+ - Frontend runtime
  • Neo4j 5.x - Graph database (one instance required, two recommended for memory features)
  • OpenAI API Key - For Pydantic AI agent and preference extraction

Quick Start with GitHub Codespaces

The easiest way to get started is using GitHub Codespaces. Everything is pre-configured and ready to go!

  1. Open in Codespaces

    • Click the "Code" button on GitHub
    • Select "Codespaces" tab
    • Click "Create codespace on main"

  2. Wait for automatic setup (takes 2-3 minutes)

    • Installs uv, Python, Node.js, and all dependencies
    • Starts Neo4j via Docker Compose
    • Initializes sample news data

  3. Configure your API key

    # Edit backend/.env and add your OpenAI API key
OPENAI_API_KEY=your_key_here

  4. Start the application

    # Terminal 1: Start backend
make backend

# Terminal 2: Start frontend
make frontend

  5. Access the application

    • Frontend: http://localhost:3000
    • Backend API: http://localhost:8000/docs
    • Neo4j Browser: http://localhost:7474 (neo4j/password)

See .devcontainer/README.md for more details on the Codespaces setup.

Installation

1. Neo4j Setup

Set up a second instance for memory/preferences features. The easiest way to get a Neo4j instance is to create a free tier Neo4j Aura instance. Other options:

Option A: Docker Compose (Recommended - includes both instances)

Use the provided docker-compose.yml:

# Start just the news graph instance
docker-compose up -d neo4j

# Or uncomment neo4j-memory service in docker-compose.yml and start both
docker-compose up -d

The memory instance (if enabled) will be available at:

  • HTTP: http://localhost:7475
  • Bolt: bolt://localhost:7688
  • Auth: neo4j/memorypass

Option B: Docker (Manual setup for single instance)

# News graph instance (required)
docker run -d \
  --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/password \
  neo4j:5.14

# Memory instance (optional - for preferences and threads)
docker run -d \
  --name neo4j-memory \
  -p 7475:7474 -p 7688:7687 \
  -e NEO4J_AUTH=neo4j/memorypass \
  neo4j:5.14

Option C: Neo4j Desktop

Download from neo4j.com/download and create one or two databases.

2. Backend Setup

First, install uv if you haven't already:

# macOS and Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or via pip
pip install uv

Then set up the backend:

cd backend

# Create virtual environment and install dependencies
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies from pyproject.toml
uv pip install -e .

# Or install from requirements.txt (alternative)
# uv pip install -r requirements.txt

# Configure environment variables
# Copy the example file and edit with your values:
cp .env.example .env

# Required settings:
# - NEO4J_URI=bolt://localhost:7687  # News graph instance
# - NEO4J_USERNAME=neo4j
# - NEO4J_PASSWORD=password
# - OPENAI_API_KEY=your-openai-api-key
#
# Optional settings:
# - OPENAI_MODEL=gpt-4o  # AI model to use (default: gpt-4o)
# - ENVIRONMENT=development  # Used for sample data initialization safety
#
# Optional (for memory/preferences/threads features):
# - MEMORY_NEO4J_URI=bolt://localhost:7688  # Separate instance for memory
# - MEMORY_NEO4J_USERNAME=neo4j
# - MEMORY_NEO4J_PASSWORD=memorypass
#
# Optional (CORS configuration):
# - CORS_ALLOW_ORIGINS=http://localhost:3000,https://example.com
#   (If not set, allows all origins)
#
# If MEMORY_NEO4J_URI is not set, memory, preferences, threads, and
# procedural memory features will be disabled

Alternatively, use uv run to automatically manage the virtual environment:

# Install dependencies (creates venv automatically if needed)
uv pip install -e .

# Run commands without activating venv
uv run uvicorn app.main:app --reload

3. Frontend Setup

cd frontend

# Install dependencies
npm install

# Configure environment variables
cp .env.example .env
# Default API URL is http://localhost:8000

Running the Application

Quick Start with Makefile

The project includes a Makefile with convenient commands:

# Start backend (automatically uses uv)
make backend

# Start frontend
make frontend

# Install all dependencies
make install

# Start Neo4j with Docker
make docker-up

# Initialize sample news data
make init-sample-data

# View all available commands
make help

Manual Start

Start Backend Server

cd backend

# Option 1: Using uv run (recommended - automatically manages virtual environment)
uv run uvicorn app.main:app --reload

# Option 2: Activate virtual environment manually
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uvicorn app.main:app --reload

The backend will be available at http://localhost:8000

Start Frontend Development Server

cd frontend
npm run dev

The frontend will be available at http://localhost:3000

Initialize Sample Data

Before using the application, you need to populate the news database with sample data:

# Option 1: Using Make (recommended - sets ENVIRONMENT automatically)
make init-sample-data

# Option 2: Using uv run directly
cd backend
uv run python initialize_sample_data.py

# Option 3: With activated virtual environment
cd backend
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
python initialize_sample_data.py

Important:

  • This script will delete all existing data in the news database
  • It requires ENVIRONMENT=development or ENVIRONMENT=test in your .env file (or set via command line)
  • You'll be prompted to confirm before any data is deleted
  • The Make command automatically sets the ENVIRONMENT variable

Once the sample data is initialized:

  1. Open http://localhost:3000 in your browser
  2. (Optional) Enable the "Memory" toggle to activate preference learning
  3. Start chatting with the news agent!

Memory Features (Optional)

Memory features (user preferences and conversation threads) require a separate Neo4j instance. If you don't configure MEMORY_NEO4J_URI, these features will be automatically disabled and the application will work fine without them.

To enable memory features:

  1. Set up a second Neo4j instance (see Neo4j Setup above)
  2. Configure MEMORY_NEO4J_URI, MEMORY_NEO4J_USERNAME, and MEMORY_NEO4J_PASSWORD in your .env file
  3. Restart the backend server

The schema will be automatically initialized when the backend starts.

Agent Capabilities

The news agent includes several intelligent features to provide better responses:

Intelligent Retry Logic

The agent automatically retries searches when results are empty or insufficient:

  • Automatic Retries: Tools retry up to 3 times when returning empty results
  • Parameter Adjustments: Automatically increases search limits on retries
  • Transparent Logging: All retry attempts are logged and visible in the API response
  • Fallback Strategies: Agent learns from failed attempts and adjusts its approach

Multi-Step Reasoning

The agent can perform multi-iteration reasoning for complex queries:

  • Reasoning Iterations: Multiple rounds of tool calls and analysis (configurable, default: 1)
  • Quality Evaluation: Automatically evaluates if tool results are sufficient
  • Adaptive Search: Tries different approaches when initial searches fail
  • Context Preservation: Maintains conversation context across reasoning iterations

Tool Selection Intelligence

The agent intelligently selects from 10 available tools based on your query:

  • Semantic Understanding: Uses vector search for concept-based queries
  • Structured Filtering: Uses topic/location/time filters for specific criteria
  • Schema Exploration: Can inspect database structure for complex queries
  • Cypher Generation: Converts natural language to Cypher for advanced data needs

Response Features

  • Detailed Reasoning Steps: See exactly how the agent arrived at its answer
  • Tool Call Transparency: View all tool calls with arguments and outputs
  • Agent Context: Inspect system prompt, model used, and available tools
  • Conversation Context: Long thread histories are automatically summarized

Usage Examples

Here are some questions you can ask the news agent:

General Queries:

  • "What are the latest news?"
  • "Tell me about artificial intelligence"
  • "What news do you have about climate change?"
  • "Show me news about space exploration"
  • "What are the available news topics?"

Time-Based Queries:

  • "Show me news from last week"
  • "What happened in the last 7 days?"
  • "Find articles from November 2024"

Location-Based Queries:

  • "What news is happening near the United States?"
  • "Show me news near Europe"

Combined Queries:

  • "What's the latest news about climate change from last month?"
  • "Find AI news from last week"

Vector Search

The agent uses semantic vector search to find relevant articles by meaning, not just keyword matches. This allows for more intelligent and context-aware searches.

How it works:

  1. Your query is converted to an embedding using OpenAI's text-embedding-3-small model
  2. Neo4j's vector index finds the most similar articles based on cosine similarity
  3. Results include a similarity score showing how relevant each article is

Example semantic queries:

  • "Find articles about climate change impacts" - finds articles about global warming, extreme weather, environmental policy, etc.
  • "What's happening with artificial intelligence?" - finds articles about AI, machine learning, neural networks, automation, etc.
  • "Tell me about innovations in healthcare" - finds articles about medical technology, treatments, health research, etc.

Benefits over keyword search:

  • Understands concepts and relationships, not just exact words
  • Handles synonyms automatically
  • Finds contextually related articles
  • No need to craft perfect keyword queries

Geospatial Search

Find news articles about locations within a specified distance from geographic coordinates.

How it works:

  1. Provide latitude, longitude, and search radius in kilometers
  2. Neo4j's geospatial functions find all locations within the radius
  3. Returns articles related to those locations, sorted by distance
  4. Uses a point index on Geo.location for optimal performance

Example queries:

  • "What news is happening near San Francisco?" (37.7749, -122.4194)
  • "Show me news within 500km of London" (51.5074, -0.1278)
  • "Find news about locations near New York" (40.7128, -74.0060)

Note: The agent needs to know or infer coordinates for locations. In the future, this could be enhanced with a geocoding service to convert location names to coordinates automatically.

Time-Based Search

Filter news articles by date ranges using both explicit dates and convenient relative periods.

Supported formats:

  • Explicit dates: "2024-11-01", "2024-11-15"
  • Relative periods: "today", "yesterday", "last_week", "last_month", "last_7_days", "last_30_days"

Example queries:

  • "Show me news from last week"
  • "What happened between November 1st and November 10th, 2024?"
  • "Find articles from the last 7 days about climate change"
  • "Get news from last month"

How it works:

  1. Date strings are automatically parsed (both explicit and relative)
  2. Articles are filtered by their published date
  3. Results are sorted by publication date (newest first)

Memory System

Note: Memory features require a separate Neo4j instance. If not configured, these features are automatically disabled.

When memory is enabled and the memory toggle is on, the agent will:

  • Learn your preferences from conversations
  • Remember topics you're interested in
  • Adapt to your preferred detail level and writing style
  • Store preferences globally (shared across all users)
  • Maintain conversation threads/history

Example conversation with memory enabled:

User: "I prefer brief summaries and I'm mainly interested in climate change news"
Agent: [Provides brief climate change news and stores these preferences]

User: "What's new today?"
Agent: [Uses learned preferences to focus on climate change with brief summaries]

Managing Preferences:

  • Click "Clear Preferences" to reset all learned preferences
  • Use the API endpoint /preferences/list to view current preferences
  • Memory toggle controls both reading and writing preferences
  • Click "View Memory Graph" to see an interactive visualization of your memory

Configuration: To enable memory features, set these environment variables in .env:

  • MEMORY_NEO4J_URI - Connection URI for memory instance
  • MEMORY_NEO4J_USERNAME - Username (default: neo4j)
  • MEMORY_NEO4J_PASSWORD - Password

Memory Graph Visualization

The application includes a powerful graph visualization feature that displays your complete memory system:

  • Interactive Graph: View preferences, categories, threads, messages, reasoning steps, and tool calls
  • Color-Coded Nodes: Different colors for each node type (preferences, threads, messages, reasoning steps, tools)
  • Zoom Controls: Zoom in, out, and reset the view
  • Node Details: Click nodes to see detailed information
  • Real-time Updates: Refresh to see the latest memory state
  • Procedural Memory: Visualize how the agent reasons and which tools it uses

The graph shows:

  • Declarative Memory: User preferences and categories
  • Episodic Memory: Conversation threads and messages
  • Procedural Memory: Reasoning steps and tool usage patterns

Conversation Threads

The application maintains persistent conversation threads when memory features are enabled:

  • Automatic Thread Creation: New conversations automatically create threads
  • Thread History: Load and continue previous conversations
  • Auto-Generated Titles: Threads automatically receive descriptive titles after the first exchange
  • Thread Management: List, view, update, and delete threads via API
  • Message History: Full conversation context is preserved with reasoning steps
  • Conversation Summarization: Long threads are automatically summarized to maintain context

Schema Tools

Advanced database exploration tools for complex queries:

  • get_database_schema: Inspect the complete Neo4j schema including node labels, relationships, properties, constraints, and indexes
  • text2cypher: Convert natural language questions into Cypher queries using AI
  • execute_cypher: Execute custom read-only Cypher queries for advanced data exploration

Example usage:

User: "What's the structure of the database?"
Agent: [Uses get_database_schema to show all node types and relationships]

User: "Show me how many articles each organization is mentioned in"
Agent: [Uses text2cypher to generate appropriate Cypher query, then execute_cypher to run it]

Project Structure

pydantic-ai-neo4j/
├── backend/
│   ├── app/
│   │   ├── __init__.py
│   │   ├── main.py                      # FastAPI application
│   │   ├── agent.py                     # Pydantic AI agent
│   │   ├── neo4j_client.py              # Neo4j database client (news)
│   │   ├── preferences_client.py        # Neo4j preferences client
│   │   ├── sessions_client.py           # Conversation threads management
│   │   ├── procedural_memory_client.py  # Reasoning steps and tool calls
│   │   └── memory_provider.py           # Memory/preference provider
│   ├── initialize_sample_data.py        # Sample data initialization script
│   ├── pyproject.toml                   # Project configuration and dependencies (uv)
│   ├── requirements.txt                 # Legacy requirements (optional)
│   ├── setup_preferences_db.py          # Preferences database setup script
│   └── MCP_INTEGRATION.md               # MCP server integration guide
├── frontend/
│   ├── app/
│   │   ├── layout.tsx                   # Root layout
│   │   ├── page.tsx                     # Home page
│   │   └── providers.tsx                # Chakra UI provider
│   ├── components/
│   │   ├── ChatInterface.tsx            # Main chat component
│   │   ├── Sidebar.tsx                  # Conversation sidebar
│   │   └── MemoryGraphView.tsx          # Memory graph visualization
│   ├── lib/
│   │   └── api.ts                       # API client
│   ├── package.json
│   ├── tsconfig.json
│   └── .env.example
├── workshop/                             # Workshop materials and exercises
│   ├── exercises/                        # Hands-on exercises
│   │   ├── exercise1_short_term_memory.md
│   │   ├── exercise2_user_preferences.md
│   │   └── exercise3_procedural_memory.md
│   ├── slides/                           # Workshop presentation
│   └── README.md                         # Workshop guide
└── README.md

API Endpoints

Backend API

General

  • GET / - Root endpoint
  • GET /health - Health check

Chat

  • POST /chat - Send message to agent 
    {
  "message": "What are the latest news?",
  "memory_enabled": false,
  "thread_id": "optional-thread-id"
}
    Returns chat response with reasoning steps, tool calls, and agent context

News

  • GET /categories - Get available news topics

Preferences (requires memory Neo4j instance)

  • GET /preferences/status - Get preference statistics
  • GET /preferences/list - List all stored preferences
  • GET /preferences/graph - Get complete memory graph for visualization (includes preferences, threads, messages, reasoning steps, and tool calls)
  • POST /preferences/clear - Clear all preferences
  • DELETE /preferences/{id} - Delete a specific preference

Conversation Threads (requires memory Neo4j instance)

  • GET /threads - List all conversation threads
  • GET /threads/last-active - Get the most recently active thread
  • GET /threads/{thread_id} - Get a specific thread with all messages
  • POST /threads - Create a new thread 
    {
  "title": "Optional title"
}
  • PUT /threads/{thread_id}/title - Update thread title 
    {
  "title": "New title"
}
  • DELETE /threads/{thread_id} - Delete a thread and all its messages

Neo4j Graph Schema

(:Article {
  title: string (indexed),
  abstract: string,
  published: string (indexed),
  url: string (indexed),
  byline: string,
  embedding: list (1536-dimensional vector, indexed with article_embedding_index)
})

(:Topic {name: string (indexed)})
(:Person {name: string (indexed)})
(:Organization {name: string (indexed)})
(:Geo {
  name: string (indexed),
  location: point (indexed)
})
(:Photo {
  url: string (indexed),
  caption: string
})

(:Article)-[:HAS_TOPIC]->(:Topic)
(:Article)-[:ABOUT_PERSON]->(:Person)
(:Article)-[:ABOUT_ORGANIZATION]->(:Organization)
(:Article)-[:ABOUT_GEO]->(:Geo)
(:Article)-[:HAS_PHOTO]->(:Photo)

Memory Database Schema

User preferences and conversation threads are stored in a separate Neo4j instance (if configured):

Preferences:

(:UserPreference {
  id: string (unique, indexed),
  category: string (indexed),
  preference: string,
  context: string,
  confidence: float,
  created_at: datetime (indexed),
  last_updated: datetime
})

(:PreferenceCategory {
  name: string (unique),
  description: string
})

(:UserPreference)-[:IN_CATEGORY]->(:PreferenceCategory)

Conversation Threads:

(:Thread {
  id: string (unique, indexed),
  title: string,
  created_at: datetime,
  updated_at: datetime,
  last_message_at: datetime (indexed)
})

(:Message {
  id: string (unique, indexed),
  thread_id: string (indexed),
  text: string,
  sender: string,
  timestamp: datetime (indexed),
  reasoning_steps: string (JSON),
  agent_context: string (JSON)
})

// Thread relationships
(:Thread)-[:HAS_MESSAGE]->(:Message)
(:Thread)-[:FIRST_MESSAGE]->(:Message)
(:Message)-[:NEXT_MESSAGE]->(:Message)

Procedural Memory (Reasoning Steps and Tool Calls):

(:ReasoningStep {
  id: string (unique, indexed),
  step_number: integer,
  reasoning_text: string,
  timestamp: datetime,
  message_id: string (indexed),
  thread_id: string (indexed)
})

(:ToolCall {
  id: string (unique, indexed),
  step_id: string (indexed),
  timestamp: datetime,
  arguments: string (JSON),
  output: string (JSON)
})

(:Tool {
  name: string (unique),
  description: string,
  created_at: datetime,
  last_used_at: datetime,
  usage_count: integer
})

// Procedural memory relationships
(:Message)-[:HAS_REASONING_STEP]->(:ReasoningStep)
(:ReasoningStep)-[:NEXT_STEP]->(:ReasoningStep)
(:ReasoningStep)-[:USES_TOOL]->(:ToolCall)
(:ToolCall)-[:INSTANCE_OF]->(:Tool)

Preference Categories:

  • topics_of_interest - News topics the user is interested in
  • detail_level - Preferred level of detail (brief, detailed, etc.)
  • writing_style - Preferred communication style
  • topic_dislikes - Topics to avoid or minimize
  • geographic_focus - Geographic regions of interest
  • news_sources - Preferred news sources
  • other - Other user preferences

Development

Backend Development

The backend uses:

  • FastAPI - Modern web framework
  • Pydantic AI - AI agent framework
  • Neo4j Python Driver - Database connectivity

To add new agent capabilities, edit backend/app/agent.py and add new tool functions.

Frontend Development

The frontend uses:

  • Next.js 14 - React framework with App Router
  • Chakra UI - Component library
  • TypeScript - Type safety

To modify the UI, edit components in frontend/components/.

Troubleshooting

Backend won't start

  • Ensure Python 3.10+ is installed
  • Install uv if using it: curl -LsSf https://astral.sh/uv/install.sh | sh
  • Check that all dependencies are installed: uv pip install -e .
  • Verify your .env file has correct credentials

Frontend shows "Disconnected"

  • Ensure the backend server is running on port 8000
  • Check that CORS is properly configured in backend/app/main.py
  • Verify the API URL in frontend/.env

Neo4j connection fails

  • Ensure Neo4j is running
  • Verify connection details in backend/.env
  • Check that the Neo4j bolt port (7687) is accessible

Agent responses are slow

  • OpenAI API calls may take a few seconds
  • Check your internet connection
  • Verify your OpenAI API key is valid
  • When memory is enabled, preference extraction adds a small overhead

Memory/Preferences not working

  • Check if MEMORY_NEO4J_URI is set in your .env file
  • Ensure the memory Neo4j instance is running and accessible
  • Verify the credentials (MEMORY_NEO4J_USERNAME and MEMORY_NEO4J_PASSWORD)
  • Look for initialization messages in the backend console
  • If memory features are not needed, simply don't set MEMORY_NEO4J_URI

Technologies Used

Backend

  • Python 3.10+
  • FastAPI - Modern web framework
  • Pydantic AI - AI agent framework
  • Neo4j Python Driver 5.14.1
  • OpenAI API (configurable model, defaults to GPT-4o)
  • uv - Fast Python package installer and resolver

Frontend

  • Next.js 14 - React framework with App Router
  • React 18
  • TypeScript
  • Chakra UI v3 - Component library
  • NVL - Graph visualization

Database

  • Neo4j 5.x (5.14.0 recommended)
  • Separate instances supported for news data and memory

Workshop Materials

The workshop/ directory contains:

  • Hands-on Exercises: Step-by-step exercises covering:

    • Exercise 1: Short-term memory with Pydantic AI
    • Exercise 2: User preferences and semantic memory
    • Exercise 3: Procedural memory and reasoning steps

  • Workshop Guide: Complete guide with setup instructions, concepts, and exercises

  • Presentation Slides: Workshop presentation materials

See workshop/README.md for the complete workshop guide.

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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Example of short-term, long-term, and procedural memory with Neo4j + Pydantic AI agent

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