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Use when wiring honcho() from @tanstack/ai-memory/honcho — a hosted memory adapter where recall is a dialectic answer over the user's representation (no discrete fragments). Requires the optional @honcho-ai/sdk peer.
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Use when wiring hindsight() from @tanstack/ai-memory/hindsight — a hosted memory adapter that buckets memory per conversation and exposes retain/recall/reflect tools to the model. Requires the optional @vectorize-io/hindsight-client peer.
Use when wiring mem0() from @tanstack/ai-memory/mem0 — a hosted memory adapter that talks to a mem0 server over plain HTTP (no SDK peer). Requires a running mem0 server.
Use when wiring redis() from @tanstack/ai-memory/redis in production — covers client setup (ioredis or node-redis via fromNodeRedis), the storage model, client-side ranking limits, and troubleshooting.
Use when wiring memoryMiddleware from @tanstack/ai-memory into a chat() call — covers the recall/save adapter contract, scope shape and server-side scope security, the recall-inject / deferred-save lifecycle, choosing an adapter (inMemory, redis, hindsight, mem0, honcho), and devtools events.
Use when wiring inMemory() from @tanstack/ai-memory/in-memory — explains setup, options (embedder, extract, topK/minScore), when to pick it (dev/tests/single-process demos), and what NOT to use it for (multi-process or persistent).
Entry point for TanStack AI skills. Routes to chat-experience, tool-calling, media-generation, structured-outputs, adapter-configuration, ag-ui-protocol, middleware, custom-backend-integration, and debug-logging. Use chat() not streamText(), openaiText() not createOpenAI(), toServerSentEventsResponse() not manual SSE, middleware hooks not onEnd callbacks.
| name | tanstack-ai-memory-honcho |
| description | Use when wiring honcho() from @tanstack/ai-memory/honcho — a hosted memory adapter where recall is a dialectic answer over the user's representation (no discrete fragments). Requires the optional @honcho-ai/sdk peer. |
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.
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().
user — user peer id (falls back to scope.userId, then 'demo-user').baseURL — Honcho server URL (default HONCHO_URL or http://localhost:8001).workspaceId — default HONCHO_APP_NAME or 'ai-memory'.apiKey — default HONCHO_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.