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 skills/tanstack/ai/tanstack-ai-memory-mem0npx skills add TanStack/ai --skill tanstack-ai-memory-mem0git clone --depth 1 https://github.com/TanStack/aiWhat 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 | $0.00051 | $0.00404 |
| Opus 5 | $0.00026 | $0.00202 |
| Sonnet 5 | $0.00010 | $0.00081 |
| Haiku 4.5 | $0.00005 | $0.00040 |
Grade A, and why
tanstack-ai-memory-mem0 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 yesterday.
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.
What it actually says
mem0 Memory Adapter
Hosted recall/save adapter backed by a mem0 server. mem0 owns extraction and ranking
server-side. Talks to the server over plain HTTP — no SDK peer dependency.
Setup
import { memoryMiddleware } from '@tanstack/ai-memory'
import { mem0 } from '@tanstack/ai-memory/mem0'
const memory = mem0({ user: currentUserId }) // baseUrl defaults to MEM0_URL
memoryMiddleware({ adapter: memory, scope })
Requires a running mem0 server (self-hosted or hosted). Point it via baseUrl (or
MEM0_URL); pass apiKey (or MEM0_ADMIN_API_KEY) when secured.
Options
user— mem0user_id(falls back toscope.userId, then'demo-user').baseUrl— mem0 server URL (defaultMEM0_URLorhttp://localhost:8000).apiKey— bearer token (defaultMEM0_ADMIN_API_KEY).rerank(defaulttrue),threshold(default0.1) — search tuning.
Scope fields: requests send user_id and run_id (scope.threadId). tenantId
and namespace are not sent — encode multi-tenant isolation into user if needed.
save posts the { user, assistant } turn to /memories; recall queries /search
and renders the results into the system prompt. mem0 exposes no LLM tools.
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.
- yesterday First seen · 37 lines · 51 tokens per session scan A cfd6851b370e
tanstack-ai-memory-mem0 is a skill published in the GitHub repository TanStack/ai (3,045 stars, last pushed 2d ago), licensed MIT. It adds 51 tokens to every session and 404 once invoked, about $0.0003 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.
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