Catalog Systems Dedicated memory layers Neo4j Agent Memory (Neo4j Labs)

Neo4j Agent Memory (Neo4j Labs)

https://github.com/neo4j-labs/agent-memory

Neo4j Labs' graph-native agent memory library (Python and TypeScript) that stores conversations, builds knowledge/context graphs and records agent reasoning traces to learn from. Also available as NAMS, a hosted memory service (memory.neo4jlabs.com) with server-side extraction and no database to run, alongside the self-hosted Neo4j/Aura backend. Traction: Official vendor-lab project; packages published on PyPI and npm

Active · Verified 2026-10-29 · fresh as of 2026-10-29 · 17 cited facts, 0 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Type
Framework repo fix
Tier
T3
Section
Dedicated memory layers
Created
2026-01-06 repo fix
Latest release
last commit 2026-10-22 repo fix
License
Apache-2.0 repo fix
GitHub
https://github.com/neo4j-labs/agent-memory (592★) repo fix
Pricing
searched not found fix
Funding
searched not found fix
Freshness
2026-10-29 fix
Layer
agent-infra repo fix
Licensing
open-source repo fix
Deployment locus
hybrid repo fix
Platform vs product
platform repo fix
Maturity
beta repo fix
SDK languages
python, typescript repo fix
Protocols
mcp-server repo fix

Memory taxonomy

storage
graph
retrieval
similarity
persistence
long-term
update
extraction
unit
entity
governance
inspectable
conflict
none

When to use

Optimised for: searched not found

Anti-fit: searched not found

Pros & cons

Pros

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Cons

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Claims & capabilities

A graph-native memory system for AI agents and context graphs. Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j.

Technical surface

API surface
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Backend storage
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Deployment
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Embedding model
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Multi-tenancy
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MCP
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A2A
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OpenTelemetry
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Alternatives to Neo4j Agent Memory (Neo4j Labs) → live options ranked by shared design, licence, hosting and MCP support

Similar systems

Other dedicated memory layers in the catalog, ranked by inbound references.

  • Mem0 T1

    Universal memory layer for AI agents. Three concurrent stores (vector + graph + KV); LLM-extracted facts; concurrent retrieval via ThreadPoolExecutor.

  • Zep & Graphiti T1

    Bi-temporal knowledge graph (event time + ingestion time). Strong on chronological reasoning and contradiction tracking. Graphiti is the open-source core.

  • Cognee T1

    "Extract–Cognify–Load" pipeline that turns raw input into a typed, queryable knowledge graph for agent recall.

  • Hindsight (Vectorize) T1

    Standalone memory service from Vectorize. Open source. Biomimetic four-network design (World, Bank, Observation, Opinion). Ships an MCP memory server.

  • Letta / MemGPT T1

    Originated as MemGPT (UC Berkeley), where the model pages its own memory between context, recall and archival tiers. The product now centres on Letta Code, a stateful agent harness whose agents rewrite their own memory, skills and prompts over time.

  • Memori T2

    Structured, persistent memory layer for AI agents. Open source.

Sources (16 cited facts, 0 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).

What changed

Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)

  1. 2026-10-23 Added to the catalog #222

Related systems

References (1)

  • Neo4j depends on at runtime — Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j.