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-honchonpx skills add TanStack/ai --skill tanstack-ai-memory-honchogit 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 | $0.00055 | $0.00428 |
| Opus 5 | $0.00028 | $0.00214 |
| Sonnet 5 | $0.00011 | $0.00086 |
| Haiku 4.5 | $0.00006 | $0.00043 |
Grade A, and why
tanstack-ai-memory-honcho 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
Honcho Memory Adapter
Hosted recall/save adapter backed by Honcho. Honcho models memory as peers
exchanging messages in a session; recall returns a synthesized dialectic answer
over the user peer's representation (so there are no discrete fragments), and save
appends the turn's messages to the session.
Setup
import { memoryMiddleware } from '@tanstack/ai-memory'
import { honcho } from '@tanstack/ai-memory/honcho'
const memory = honcho({ user: currentUserId }) // baseURL defaults to HONCHO_URL
memoryMiddleware({ adapter: memory, scope })
@honcho-ai/sdk is an optional peer dependency, loaded lazily on first use — install
it where you use honcho().
Options
user— user peer id (falls back toscope.userId, then'demo-user').baseURL— Honcho server URL (defaultHONCHO_URLorhttp://localhost:8001).workspaceId— defaultHONCHO_APP_NAMEor'ai-memory'.apiKey— defaultHONCHO_API_KEY.assistantId— assistant peer id (default'assistant').
Scope fields: session key = {tenantId|_}__{threadId}; peer id is
{tenantId}__{user} when tenantId is set, otherwise user / scope.userId.
namespace is ignored.
recall calls the user peer's dialectic chat() and injects the answer as the system
prompt; Honcho 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.
- 2d ago First seen · 41 lines · 55 tokens per session scan A 71fc09e7bfad
tanstack-ai-memory-honcho is a skill published in the GitHub repository TanStack/ai (3,056 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 428 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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