Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add neo4j-contrib/neo4j-skills --skill neo4j-agent-memory-skillgit clone --depth 1 https://github.com/neo4j-contrib/neo4j-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/neo4j-contrib/neo4j-skills/neo4j-agent-memory-skill)<a href="https://agentmods.dev/skills/neo4j-contrib/neo4j-skills/neo4j-agent-memory-skill"><img src="https://agentmods.dev/badge/skills/neo4j-contrib/neo4j-skills/neo4j-agent-memory-skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/neo4j-contrib/neo4j-skills/neo4j-agent-memory-skill"><img src="https://agentmods.dev/badge/skills/neo4j-contrib/neo4j-skills/neo4j-agent-memory-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 340 uvx/uv tool run commands without ==version create a rug-pull risk.Fix: Pin the version: uvx package-name==1.2.3
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00223 | $0.05714 |
| Opus 5.5 | $0.00089 | $0.02286 |
| Sonnet 5.5 | $0.00045 | $0.01143 |
| Haiku 4.5 | $0.00022 | $0.00571 |
Grade A, and why
neo4j-agent-memory-skill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 14d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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 (Streamable HTTP, 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) | 0.7.0 (2026-10-07) — always verify PyPI before citing |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 14d ago Changed 700919aae316
- 27d ago Changed 2fe1c6545ffc
- 1mo ago First seen · 428 lines · 223 tokens per session scan A 69f34429efbd
neo4j-agent-memory-skill is a skill published in the GitHub repository neo4j-contrib/neo4j-skills (117 stars, last pushed 2d ago), licensed MIT. It adds 223 tokens to every session and 5,714 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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