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-hindsightnpx skills add TanStack/ai --skill tanstack-ai-memory-hindsightgit 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 | $0.00057 | $0.00445 |
| Opus 5 | $0.00028 | $0.00222 |
| Sonnet 5 | $0.00011 | $0.00089 |
| Haiku 4.5 | $0.00006 | $0.00044 |
Grade A, and why
tanstack-ai-memory-hindsight 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 2d 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.
What it actually says
Hindsight Memory Adapter
Hosted recall/save adapter backed by Hindsight. Hindsight owns extraction and
ranking server-side, buckets memory into per-conversation "banks"
({tenantId|_}__{user}__{threadId}), and — uniquely — exposes LLM tools through recall so the
model can retain/recall/reflect directly.
Setup
import { memoryMiddleware } from '@tanstack/ai-memory'
import { hindsight } from '@tanstack/ai-memory/hindsight'
const memory = hindsight({ user: currentUserId }) // baseUrl defaults to HINDSIGHT_URL
memoryMiddleware({ adapter: memory, scope })
@vectorize-io/hindsight-client is an optional peer dependency, loaded lazily on
first use — install it where you use hindsight().
Options
user— durable user id for the bank key (falls back toscope.userId).baseUrl— Hindsight server URL (defaultHINDSIGHT_URLorhttp://localhost:8888).budget— recall budget:'low' | 'mid' | 'high'(default'mid').onToolRetain/onToolRecall— callbacks fired when the model uses the memory tools.
Scope fields: bank id is {tenantId|_}__{user}__{threadId}. namespace is ignored.
Tools
recall returns hindsight_retain, hindsight_recall, and hindsight_reflect in its
tools plus a toolGuidance block. memoryMiddleware merges them into the run so the
model can manage long-term memory itself.
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.
- 2d ago First seen · 41 lines · 57 tokens per session scan A 120db339ad5a
tanstack-ai-memory-hindsight is a skill published in the GitHub repository TanStack/ai (3,056 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 445 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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