neo4j-agent-memory

Authoritative reference for the neo4j-agent-memory Python package — a Neo4j Labs project that gives AI agents three distinct memory layers (short-term, long-term, reasoning) in a single knowledge graph.

⚠️ Verify authoritative state before writing. Version numbers, extras, tool counts, and API surface change between releases. The values in this skill reflect a specific point in time. Before publishing anything version-sensitive, confirm against PyPI (https://pypi.org/project/neo4j-agent-memory/) and the GitHub README (https://github.com/neo4j-labs/agent-memory). PyPI is the authoritative source for version numbers — never infer.

When to Use

• Building AI agents that need persistent memory (short-term, long-term, reasoning traces) backed by Neo4j
• Using the neo4j-agent-memory Python package or the hosted NAMS service at memory.neo4jlabs.com
• Integrating agent memory with LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, OpenAI Agents, LlamaIndex, or Microsoft Agent Framework
• Writing documentation, tutorials, or positioning content about graph-native agent memory
• Comparing graph-native memory against vector-only approaches

When NOT to Use

• Plain Neo4j driver connections (no memory layer needed) → use neo4j-driver-python-skill
• Writing or optimizing Cypher queries → use neo4j-cypher-skill
• GraphRAG retrieval pipelines → use neo4j-graphrag-skill