SKILL.md
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-memoryPython 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