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 agentmods add rules/gifflet/graphiti-mcp-server/graphiti_cursor_rulesgit clone --depth 1 https://github.com/gifflet/graphiti-mcp-serverWrote 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/rules/gifflet/graphiti-mcp-server/graphiti_cursor_rules)<a href="https://agentmods.dev/rules/gifflet/graphiti-mcp-server/graphiti_cursor_rules"><img src="https://agentmods.dev/badge/rules/gifflet/graphiti-mcp-server/graphiti_cursor_rules.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00484 | $0.00484 |
| Opus 5 | $0.00242 | $0.00242 |
| Sonnet 5 | $0.00097 | $0.00097 |
| Haiku 4.5 | $0.00048 | $0.00048 |
Grade A, and why
graphiti_cursor_rules 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 4d 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions for Using Graphiti's MCP Tools for Agent Memory
Before Starting Any Task
- Always search first: Use the
search_nodestool to look for relevant preferences and procedures before beginning work. - Search for facts too: Use the
search_factstool to discover relationships and factual information that may be relevant to your task. - Filter by entity type: Specify
Preference,Procedure, orRequirementin your node search to get targeted results. - Review all matches: Carefully examine any preferences, procedures, or facts that match your current task.
Always Save New or Updated Information
- Capture requirements and preferences immediately: When a user expresses a requirement or preference, use
add_memoryto store it right away.- Best practice: Split very long requirements into shorter, logical chunks.
- Be explicit if something is an update to existing knowledge. Only add what's changed or new to the graph.
- Document procedures clearly: When you discover how a user wants things done, record it as a procedure.
- Record factual relationships: When you learn about connections between entities, store these as facts.
- Be specific with categories: Label preferences and procedures with clear categories for better retrieval later.
During Your Work
- Respect discovered preferences: Align your work with any preferences you've found.
- Follow procedures exactly: If you find a procedure for your current task, follow it step by step.
- Apply relevant facts: Use factual information to inform your decisions and recommendations.
- Stay consistent: Maintain consistency with previously identified preferences, procedures, and facts.
Best Practices
- Search before suggesting: Always check if there's established knowledge before making recommendations.
- Combine node and fact searches: For complex tasks, search both nodes and facts to build a complete picture.
- Use
center_node_uuid: When exploring related information, center your search around a specific node. - Prioritize specific matches: More specific information takes precedence over general information.
- Be proactive: If you notice patterns in user behavior, consider storing them as preferences or procedures.
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.
- 4d ago First seen · 39 lines · 484 tokens per session scan A 18c36d5fa786
graphiti_cursor_rules is a cursor rule published in the GitHub repository gifflet/graphiti-mcp-server (144 stars, last pushed 1y ago), licensed MIT. It adds 484 tokens to every session, about $0.0024 per session on Opus 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.
Other cursor rules, from other repositories
archcore-context
Archcore knowledge base context — document types, MCP tools, and conventions for working with .archcore/ documents.
context-management
Context window management — prevents AI from losing track during long sessions.
demo-consume-context
Worked example that discovers, loads, and tags a context package per CONSUMING-CONTEXT-PACKAGE.md, then writes a reverse-index addendum — proves the produce/consume/tag loop end-to-end. Do NOT use for real feature work.
tell-plumbing-reference
Peer plumbing reference memory for platform/MCP/auth/design-flow plans — never name the peer in commits.
cursorrules
🧠 Fast, zero-dependency structured knowledge base (.agent-kb/) with 2D neural graph visualizer │ & MCP server for Claude, Codex, PI, Cursor, Windsurf, Zed & Other AI coding agents.
cursorrules
You have the AI Mind Map MCP server connected. It is a persistent code memory system that eliminates redundant file re-reading and context loss between sessions.