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Cross-user reads are impossible by construction; you don't write tenant checks.",{"type":15,"tag":24,"props":308,"children":309},{},[310,312,318,320,326,328,334],{"type":21,"value":311},"The same capabilities are available over REST at ",{"type":15,"tag":57,"props":313,"children":315},{"className":314},[],[316],{"type":21,"value":317},"/ai-conversations",{"type":21,"value":319},", ",{"type":15,"tag":57,"props":321,"children":323},{"className":322},[],[324],{"type":21,"value":325},"/ai-memory",{"type":21,"value":327},", and ",{"type":15,"tag":57,"props":329,"children":331},{"className":330},[],[332],{"type":21,"value":333},"/ai-working-context",{"type":21,"value":335}," for non-JS clients (mobile, third-party integrations). This guide focuses on the JavaScript surface — that's where Actions live.",{"type":15,"tag":66,"props":337,"children":339},{"id":338},"the-mental-model-in-one-picture",[340],{"type":21,"value":341},"The Mental Model In One Picture",{"type":15,"tag":24,"props":343,"children":344},{},[345],{"type":21,"value":346},"If you take away one thing from this guide, take this:",{"type":15,"tag":348,"props":349,"children":351},"pre",{"code":350}," Conversation Memory\n ────────────── ──────\n (short-term, (long-term, bi-temporal,\n verbatim, replayable) distilled, recallable)\n\n ┌──────────────────────┐ ┌──────────────────────┐\n │ user: \"I prefer │ │ atom: \"User prefers │\n │ meetings on │ ───► │ morning meetings.\" │\n │ Tuesday mornings.\" │ │ │\n │ │ │ importance: 4 │\n │ ai: \"Got it!\" │ │ validFrom: 2026... │\n │ │ │ confidence: 0.95 │\n └──────────────────────┘ └──────────────────────┘\n ▲ │\n │ │\n │ extraction │ recall\n │ (event-driven, │ (BY_TOPIC,\n │ runs on close) │ BY_ENTITY,\n │ │ TIMELINE)\n │ ▼\n │ ┌──────────────────────┐\n └────────────────────────────┤ Working Context │\n │ = summary │\n │ + relevant atoms │\n │ + recent turns │\n │ ──► next prompt │\n └──────────────────────┘\n",[352],{"type":15,"tag":57,"props":353,"children":354},{"__ignoreMap":7},[355],{"type":21,"value":350},{"type":15,"tag":24,"props":357,"children":358},{},[359,361,366],{"type":21,"value":360},"A ",{"type":15,"tag":44,"props":362,"children":363},{},[364],{"type":21,"value":365},"conversation",{"type":21,"value":367}," is an append-only stream of messages — the verbatim record of what was said. It is short-term: you may keep it forever, but you would not feed all of it back into the LLM every turn.",{"type":15,"tag":24,"props":369,"children":370},{},[371,372,377],{"type":21,"value":360},{"type":15,"tag":44,"props":373,"children":374},{},[375],{"type":21,"value":376},"memory atom",{"type":21,"value":378}," is a single, distilled fact extracted from a conversation (or written directly). It is structured: it has a category, an importance score, a validity window, and links back to the source messages. Atoms live forever and accumulate into the user's long-term memory.",{"type":15,"tag":24,"props":380,"children":381},{},[382,384,389,391,396],{"type":21,"value":383},"The bridge between them is ",{"type":15,"tag":44,"props":385,"children":386},{},[387],{"type":21,"value":388},"extraction",{"type":21,"value":390}," — when a conversation closes, the platform runs an LLM over its content, produces atoms, and writes them to memory. You don't write that code; the platform does it for you. You just configure a ",{"type":15,"tag":44,"props":392,"children":393},{},[394],{"type":21,"value":395},"binding",{"type":21,"value":397}," that says \"conversations in this namespace produce atoms in this memory space.\"",{"type":15,"tag":24,"props":399,"children":400},{},[401,403,408],{"type":21,"value":402},"When the user starts a new conversation, the ",{"type":15,"tag":44,"props":404,"children":405},{},[406],{"type":21,"value":407},"working context builder",{"type":21,"value":409}," assembles a prompt block: the rolling summary of past turns, the relevant atoms recalled by topic, and the recent message tail. You feed that into the LLM and the LLM behaves as if it remembers.",{"type":15,"tag":24,"props":411,"children":412},{},[413],{"type":21,"value":414},"That's the whole story. Everything below is mechanics.",{"type":15,"tag":66,"props":416,"children":418},{"id":417},"quick-start-a-chatbot-with-memory",[419],{"type":21,"value":420},"Quick Start: A Chatbot With Memory",{"type":15,"tag":24,"props":422,"children":423},{},[424],{"type":21,"value":425},"Let's build the simplest useful case end-to-end. We'll write an action that powers a daily check-in chatbot. 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Find or create today's conversation\n var existing = conv.listConversations({}).find(function(c) {\n return c.namespace === \"daily-checkin\" && c.status === \"open\";\n });\n var conversation = existing || conv.createConversation({\n namespace: \"daily-checkin\",\n title: \"Daily check-in\",\n metadata: { date: new Date().toISOString().slice(0, 10) }\n });\n\n // 2. Find or create the memory space we extract into\n var space = memory.listMemorySpaces({}).find(function(s) {\n return s.name === \"checkin\";\n });\n if (!space) {\n space = memory.createMemorySpace({ name: \"checkin\" });\n }\n\n // 3. Assemble the working context: rolling summary +\n // semantic recall + recent turns\n var ctx = wc.buildWorkingContext(conversation.id, {\n memorySpaceId: space.id,\n recallQuery: arguments.userMessage,\n recentTurns: 8,\n recallLimit: 6,\n alwaysOnCategoryNames: [\"identity\", \"preference\", \"goal\"]\n });\n\n // 4. 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