Give your agents a memory that actually remembers.
The Neo4j Agent Memory Service is a hosted, graph-native memory layer for LLM agents - short-term conversations, long-term knowledge, and full reasoning traces - backed by Neo4j Aura with native vector search.
Get started in one command
Drop NAMS into your agent CLI with the nams-hooks plugin - memory turns on automatically.
claude plugin marketplace add neo4j-labs/nams-plugins@latest claude plugin install nams-hooks@nams-plugins
Then set NAMS_API_KEY (and NAMS_BASE_URL=https://memory.neo4jlabs.com) from your dashboard API keys.
gemini extensions install https://github.com/neo4j-labs/nams-plugins --ref latest
Then set NAMS_API_KEY (and NAMS_BASE_URL=https://memory.neo4jlabs.com) from your dashboard API keys.
codex plugin marketplace add neo4j-labs/nams-plugins@latest
Then set NAMS_API_KEY (and NAMS_BASE_URL=https://memory.neo4jlabs.com) from your dashboard API keys.
npm install -g @neo4j-labs/nams-plugins cp "$(npm root -g)/@neo4j-labs/nams-plugins/templates/opencode/.opencode/plugins/nams-hooks.js" \ .opencode/plugins/nams-hooks.js
Then set NAMS_API_KEY (and NAMS_BASE_URL=https://memory.neo4jlabs.com) from your dashboard API keys.
4 memory types
47 MCP tools
SKILL.md portable skill export
0 infrastructure to run
Everything your agent needs to remember
A complete memory layer - structured, searchable, and production-ready out of the box.
Four Memory Types
Short-term conversations, long-term knowledge graph, reasoning traces, and synthesized observations - all in one place.
Graph-Native by Design
Typed entities, typed relationships, entity merging, and cross-conversation history. Traversable, queryable, explainable.
Semantic Search Built In
HNSW vector indexes on messages, entities, observations and reflections. Hybrid vector + text with automatic fallback.
Fully Multi-Tenant
Per-workspace isolated databases on Neo4j Aura, provisioned on demand. Bring-your-own Neo4j also supported.
Production Auth & Metering
API keys, Auth0 SSO, scoped JWTs, rate limiting, real-time usage counters - ready for real workloads.
Bring Your Own Keys
Per-workspace LLM enrichment with your own provider key (BYOK), or point a workspace at your own external Neo4j - no lock-in.
How it works
Three steps from zero memory to a fully-remembering agent.
STEP 01
Connect
Point your agent at the NAMS MCP endpoint or REST API using your workspace API key.
STEP 02
Remember
Messages, entities, and tool calls are captured, extracted, and embedded automatically.
STEP 03
Recall
Query the graph, search semantically, or ask for the three-tier context for any conversation.
Turn memory into portable Agent Skills
Distill a scoped slice of a workspace's memory graph into a signed, provenance-grounded SKILL.md package that any agentskills.io runtime can load - every recommendation traces back to the decisions, entities, and messages it came from.
Grounded, not invented
Every claim cites the exact source nodes it came from. The graph is the authority - ungrounded claims are dropped.
Quality gates + human review
Grounding score, coverage, spec lint, and PII redaction must all pass. Nothing publishes without a draft -> in-review -> published sign-off.
Typed procedure graphs
Procedures become a schema-validated step DAG read from the real trace - script-backed steps with typed I/O, not hand-waved prose.
Cryptographic attestation
A dedicated signing key attests each package digest, so consumers can verify offline via JWKS that a skill is unmodified.
Drift detection
A background reconciler re-checks published skills against the live graph and flags stale, missing, or contradicted claims.
Composition & reuse
Shared sub-procedures are lifted into callable skills, so skills compose and reuse instead of duplicating logic.
Seven-stage pipeline
Scope
→
Extract
→
Consolidate
→
Synthesize
→
Gate
→
Package
→
Publish
Stop rebuilding memory from scratch.
NAMS handles extraction, embedding, compression, and retrieval - you keep building the agent.
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