Build a Strands Agent with Context Graph Memory

mkdir strands-memory-agent
cd strands-memory-agent
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

pip install neo4j-agent-memory[aws,strands] python-dotenv

# Neo4j Configuration
NEO4J_URI=neo4j+s://xxxx.databases.neo4j.io
NEO4J_USER=neo4j
NEO4J_PASSWORD=your-password

# AWS Configuration (uses default credentials if not set)
AWS_REGION=us-east-1

"""Strands Agent with Neo4j Context Graph memory."""

import os
from dotenv import load_dotenv
from strands import Agent
from neo4j_agent_memory.integrations.strands import context_graph_tools

# Load environment variables
load_dotenv()

# Create Context Graph tools for the agent
tools = context_graph_tools(
    neo4j_uri=os.environ["NEO4J_URI"],
    neo4j_user=os.environ.get("NEO4J_USER", "neo4j"),
    neo4j_password=os.environ["NEO4J_PASSWORD"],
    embedding_provider="bedrock",
    aws_region=os.environ.get("AWS_REGION", "us-east-1"),
)

# Create the agent with memory tools
agent = Agent(
    model="anthropic.claude-sonnet-4-20250514-v1:0",
    tools=tools,
    system_prompt="""You are a helpful assistant with persistent memory.

You have access to a knowledge graph that stores:
- Conversation history
- Entities (people, organizations, projects)
- Relationships between entities
- User preferences

Use your memory tools to:
1. Search for relevant context before answering questions
2. Store important facts the user shares
3. Remember user preferences
4. Track relationships between entities

Be conversational and reference memories when relevant.""",
)


def main():
    """Run an interactive chat session."""
    print("Neo4j Memory Agent")
    print("=" * 40)
    print("Chat with an AI that remembers!")
    print("Type 'quit' to exit.\n")

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ("quit", "exit", "q"):
            print("Goodbye!")
            break

        if not user_input:
            continue

        # Get response from agent
        response = agent(user_input)
        print(f"\nAssistant: {response}\n")


if __name__ == "__main__":
    main()

python agent.py

You: Hi, I'm Alex and I work at Acme Corp as a software engineer.
Assistant: Nice to meet you, Alex! I've stored that you work at Acme Corp
as a software engineer. How can I help you today?

You: What company do I work for?
Assistant: Based on my memory, you work at Acme Corp as a software engineer!

// See all entities
MATCH (e:Entity) RETURN e LIMIT 25

// See relationships
MATCH (e1:Entity)-[r]->(e2:Entity) RETURN e1, r, e2 LIMIT 25

// Find Person entities
MATCH (p:Entity:Person) RETURN p.name, p.created_at