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 AnthonyAlcaraz/agentic-graph-rag-skills --skill graphiti-incremental-updategit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-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/anthonyalcaraz/agentic-graph-rag-skills/graphiti-incremental-update)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/graphiti-incremental-update"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/graphiti-incremental-update/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/anthonyalcaraz/agentic-graph-rag-skills/graphiti-incremental-update"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/graphiti-incremental-update.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00166 | $0.02073 |
| Opus 5 | $0.00083 | $0.01037 |
| Sonnet 5 | $0.00033 | $0.00415 |
| Haiku 4.5 | $0.00017 | $0.00207 |
Grade A, and why
graphiti-incremental-update 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 12d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graphiti Incremental-Update
Overview
A naive add-to-graph implementation re-extracts entities across the whole corpus, re-computes embeddings, and re-clusters everything every time new content arrives. At millions-of-nodes scale this is fatal: update latency grows with graph size, and the agent ends up waiting for re-indexing instead of reasoning.
Graphiti (Zep) ships a different pattern (Ch4 Example 4-8):
- Extract entities and relationships only from the new episode. No backfill, no full-corpus re-pass.
- Entity-resolve: match each extracted entity against existing graph nodes by canonical name, then alias, then fuzzy/embedding similarity. If matched, reuse the existing node id. If not, create new.
- Incremental-update: modify only the touched neighborhood. New edges, possibly updated node metadata, no global re-clustering.
The chapter quote: "incremental_update modifies only the impacted neighborhood: the few nodes and edges touched by the new entities and relationships. The rest of the graph stays untouched, which keeps write operations predictably fast."
The retrieval side mirrors this: instead of one big query over everything, run parallel partial queries (vector / graph walk / keyword) targeting recent / specific / textual subsets, then merge. Production-grade Reciprocal Rank Fusion lives in a sibling skill; this skill is the ingestion side.
When to Use
- Memory graph that grows continuously (every interaction adds nodes)
- DevOps incident streams: new alerts, new deployments, new comments arriving every few seconds
- Customer-support agents where each ticket is an "episode" — entities (customer, product, issue type) resolve to existing nodes, new edges are added
- Multi-agent systems where each agent contributes new content to a shared graph and no agent should block on full-graph re-processing
Phrases: "incremental update", "don't re-process the whole graph", "entity resolution", "deduplicate against existing nodes", "Graphiti pattern", "add an episode".
What ships with it
2 files 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.
- 12d ago First seen · 149 lines · 166 tokens per session scan A 45570473e621
graphiti-incremental-update is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 166 tokens to every session and 2,073 once invoked, about $0.0008 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 2026-08-31.
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