Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O

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 Project at a Glance Field Value Package neo4j agent memory PyPI https://pypi.org/project/neo4j agent memory/ GitHub https://github.com/neo4j labs/agent memory Canonical docs https://neo4j.com/labs/agent memory/ Hosted service https://memory.neo4jlabs.com (NAMS — early access, not yet documented on official project pages) Hosted MCP endpoint https://memory.neo4jlabs.com/mcp (SSE, bearer auth) License Apache 2.0 Python 3.10+ Neo4j 5.20+ (required for vector indexes) Status Experimental (Neo4j Labs, community supported) Current version (at time of writing) 0.1.1 — always verify PyPI before citing What It Is (One Sentence) A graph native memory system for AI agents that stores conversations, builds knowledge graphs, and records agent reasoning — all as connected nodes in a single Neo4j database. Consumption Models neo4j agent memory ships in two consumption models. They are the same underlying project — the differences are how you run it, how you authenticate, and what's managed for you. Option What It Is When to Choose Self hosted library pip install neo4j agent memory + your own Neo4j (local / Docker / Aura). Full Python API, local MCP server, and framework integrations run in your process. Dev, on prem data, custom extraction pipelines, full control, bringing your own embeddings / LLMs. Hosted (NAMS) Managed service at https://memory.neo4jlabs.com . Per workspace isolated Neo4j Aura database, REST API, remote MCP endpoint, web console. Zero infra trials, sharing memory across agents / machines, demos, teams that don't want to run Neo4j. ⚠️ NAMS is reachable but not yet referenced in the GitHub README or neo4j.com/labs/agent memory/ . Treat it as early access / soft launched. Do not assert SLAs, pricing, or GA status in published content. See the Hosted Service (NAMS) section below for details. The Three Memory Types The defining architectural feature. Every piece of content describing the project should lead with this trinity. Memory Type Stores Color Convention Short Term Conversation messages, session history, sequential message chains, metadata filtered search, LLM powered summaries Green ( B2F2BB / 2F9E44 ) Long Term Entities (people, places, orgs), preferences, facts, and the relationships between them — built automatically from conversations via the POLE+O model Orange/Yellow ( FFEC99 / F08C00 ) Reasoning Decision traces, tool call provenance, thought action outcome chains — so the agent can learn from its own past reasoning patterns Purple ( D0BFFF / 9C36B5 ) Reasoning memory is the primary competitive differentiator. Most competing systems cover short term and long term but treat reasoning as an afterthought or omit it entirely. Lead with this when positioning. The POLE+O Model Long term memory uses the POLE+O entity framework — the canonical entity classification for this project: P erson O rganization L ocation E vent +O Object (anything that doesn't fit the core four — products, concepts, projects, etc.) When diagramming the data model, use ellipses for entity nodes and labeled arrows (UPPER SNAKE CASE) for relationships, consistent with Neo4j Browser conventions. Installation Core install plus extras. The extras pattern is pip install neo4j agent memory[<extra ] . Full extras list (subject to change — verify PyPI): all , anthropic , aws , bedrock , cli , crewai , extraction , full , fuzzy , gliner , google , google adk , langchain , llamaindex , mcp , microsoft agent , observability , openai , openai agents , opentelemetry , opik , pydantic ai , sentence transformers , spacy , strands , vertex ai . Python API (Quickstart) Canonical import pattern and basic usage. This is the shape to reproduce in tutorials and examples. Note the async context manager pattern ( async with MemoryClient(settings) as memory: ) — this is the canonical form. MCP Server Exposes memory as tools for MCP compatible AI assistants (Claude Desktop, Claude Code, Cursor, VS Code Copilot). Invocation The authoritative one liner (no install needed): Install local alternative: Transports and Profiles Tool Profiles Profile Tools Contents core 6 memory search , memory get context , memory store message , memory add entity , memory add preference , memory add fact extended (default) 16 Core + conversation history, entity details, graph export, relationship creation, reasoning traces, observations, read only Cypher As of v0.1.1, memory add fact accepts a metadata parameter, bringing it to parity with memory add entity . Claude Code Registration Claude Desktop Config For the hosted MCP endpoint at memory.neo4jlabs.com/mcp , see the Hosted Service (NAMS) section below — it uses SSE transport and bearer token auth, not a local uvx invocation. Hosted Service (NAMS) NAMS — Neo4j Agent Memory Service — is the managed deployment of neo4j agent memory at https://memory.neo4jlabs.com . It bundles the REST API, the MCP server, a web console, and per workspace Neo4j Aura databases. ⚠️ Verify against the live service before citing. NAMS is not documented on the GitHub README or neo4j.com/labs/agent memory/ . Endpoint shapes, tool counts, auth flows, and limits can change without a release note. Before publishing anything NAMS specific, re check the live site and the OpenAPI spec at /openapi.json . Surface Base URL: https://memory.neo4jlabs.com Web console: root URL — workspace management, memory browsing, entity visualization REST API: https://memory.neo4jlabs.com/v1/ — OpenAPI spec at /openapi.json ; covers conversations, entities, observations, reasoning traces, and read only Cypher MCP endpoint: https://memory.neo4jlabs.com/mcp — SSE transport, exposes the hosted tool set, bearer token auth Auth API keys , prefixed nams , created and rotated from the web console — used as a bearer token for REST and MCP Auth0 OAuth2 (PKCE) + scoped JWTs for interactive user flows Don't mix these with the self hosted library's password Neo4j credential — they serve different sides of the stack. Storage Model Each workspace is backed by an isolated Neo4j Aura database , provisioned on demand. Bring your own Neo4j is supported as an alternative, configured per workspace. Rate Limits Usage counters are tracked per API key / workspace. Exact limits are not publicly documented — check the console or re verify against the service before committing customers to numbers. Claude Code Registration (Hosted MCP) Claude Desktop Config (Hosted MCP) Framework Integrations All integrations live under neo4j agent memory.integrations.<framework . Install the matching extra. Framework Install Extra Import LangChain [langchain] from neo4j agent memory.integrations.langchain import Neo4jAgentMemory Pydantic AI [pydantic ai] from neo4j agent memory.integrations.pydantic ai import MemoryDependency Google ADK [google adk] from neo4j agent memory.integrations.google adk import Neo4jMemoryService AWS Strands [strands] from neo4j agent memory.integrations.strands import context graph tools CrewAI [crewai] from neo4j agent memory.integrations.crewai import Neo4jCrewMemory LlamaIndex [llamaindex] from neo4j agent memory.integrations.llamaindex import Neo4jLlamaIndexMemory OpenAI Agents [openai agents] from neo4j agent memory.integrations.openai agents import ... Microsoft Agent Framework [microsoft agent] from neo4j agent memory.integrations.microsoft agent import Neo4jMicrosoftMemory Entity Extraction Pipeline Multi stage extraction (cost/quality tradeoff from fastest → most accurate): 1. spaCy — fast statistical NER, cheapest, broad but imprecise coverage 2. GLiNER — zero shot entity extraction with typed schemas; GLiREL for relationships 3. LLM fallback — most accurate, most expensive; used when structure is rich or ambiguous Enrichment is a separate background stage: Wikipedia and Diffbot can hydrate extracted entities with additional context. Deduplication (v0.1.1+) auto merges duplicate facts and preferences using subject/predicate matching plus embedding similarity (threshold ~0.95), and updates confidence rather than creating new nodes. Tuned via DeduplicationConfig . Configuration objects to know: ExtractionConfig , DeduplicationConfig , MemoryIntegration , SessionStrategy . Positioning Language These phrasings are load bearing. Use them verbatim when possible. Core Taglines "Graph handles understanding; vector handles similarity." "Vector stores give you recall. The graph gives you understanding." "Three memory types, one knowledge graph." Category Framing Anchor to the Foundation Capital "AI's Trillion Dollar Opportunity: Context Graphs" thesis when the forum warrants it. neo4j agent memory is positioned as the complete implementation of the context graph category — it covers all three memory layers, not just two. The context graph coexists with domain data in the same Neo4j instance (not a bolted on external system). This is a key conceptual/visual point for architecture diagrams. Do Say "graph native memory" "context graph" "three distinct memory layers" "reasoning traces as first class graph nodes" "learn from past reasoning" "build knowledge graphs automatically from conversations" "Neo4j Labs project" / "experimental" / "community supported" Don't Say Don't name specific competitors (Mem0, Zep, Letta, Cognee, Supermemory) in published content. Reframe comparisons around capabilities, not product names. Don't call it "production ready" (it's a Labs project — see the neo4j labs brand skill for the full voice guide). Don't say "officially supported" or imply SLAs. Common Corrections to Watch For When editing or reviewing content about this project, check for: 1. Outdated version numbers — anyone writing "v0.1.0" today may be working from stale notes; verify PyPI. 2. Wrong canonical docs URL — it's neo4j.com/labs/agent memory , not a Vercel preview URL. 3. Inferred API surface — if code samples weren't run, flag them; prefer patterns from the GitHub README or official examples. 4. Missing "L

neo4j-agent-memory-skill