iFANN
    ابحث في iFANN...
    تسجيل الدخول
    الرئيسية
    الأخبار
    فيديوهات
    صور
    صور GIF
    استكشاف
    استطلاعات
    الجوائز
    iFAMOUS
    ويكي
    أنمي
    غرف
    الإشعارات
    الرسائل
    المحفوظات
    الملف الشخصي
    ويكيالجوائزiFAMOUSالتصنيفاتالقطاعاتمكافآت المبدعينمكافآت المستخدمينالشروطالخصوصيةإرشادات المجتمعالإزالة / DMCAالمساعدةالمطورون

    © 2026 iFANN

    الرئيسية
    بحث
    الرسائل
    التنبيهات
    الملف الشخصي
    صورة
    Gifamoss
    Gifamoss@gifamoss6d
    🏢Anthropic💭AI💭artificial intelligence
    Anthropic agent memory 5 layers

    @gifamossFive memory layers is what Anthropic's new 13-page agent playbook is built around, and the pitch is that wiring them up can slash token costs by 90% start with Working Memory, the context window, basically everything the agent sees right now. fill it up and the oldest stuff just vanishes, which is where a lot of agents quietly fall apart Episodic Memory handles what happened. interaction logs with timestamps, down to the level of a deploy that broke because a migration script had a typo Semantic Memory covers what is true. facts and relationships sit in a knowledge graph that survives past the session, so something like user prefers TypeScript sticks around Procedural Memory is how to do things. an agent tries a few approaches, one lands, and that becomes a reusable skill so it doesn't redo the whole search next time Forgetting is the fifth piece, what to delete. an agent that never forgets accumulates contradictions, and you end up with old preferences overriding fresher ones the numbers: Mem0 stores 1,800 tokens per query versus 26,000. Snowflake's ontology layer brought 20% better accuracy and 39% fewer tool calls. memory pays for itself on day one, per the writeup this is the line between a chatbot and an agent that actually learns. save the 13 pages #Anthropic

    عرض المنشور الأصلي

    Anthropic agent memory 5 layers

    صورة بواسطة @gifamoss· Sep 15, 2026· Anthropic

    عن هذه الصورة

    The image is a diagram illustrating an agent memory architecture. It shows a flowchart with boxes representing different memory types like Working Memory, Episodic Memory, Semantic Memory, and Procedural Memory, connected by arrows indicating data flow. The style is technical and informative, like a research paper or presentation slide. A notable detail is the Anthropic logo in the bottom right corner. ON-SCREEN TEXT: Production Agent Engineering Practice 2026 Agent Memory Architecture 5 Layers That Cut Cost 90% and Make Your Agent Actually Learn Based on CoALA cognitive architecture, Mem0, Anthropic memory systems, Snowflake ontology research, and LangChain production engineering materials Independently compiled, September 2026 — not affiliated with Anthropic — and not endorsed Fig. 1. Agent memory flow architecture. User input enters Working Memory (context window). When context fills, it overflows into Episodic Memory (logs). Episodic memory distills facts into Semantic Memory (knowledge graph). Semantic memory encodes methods as Procedural Memory (skills). The Forgetting Engine prunes stale data and resolves contradictions. Das

    عرض كل صور Anthropicاقرأ ويكي Anthropic

    ?

    المزيد من صور Anthropic

    عرض كل صور Anthropic
    The AI Engineering Skills Map Andrew NG2The AI Engineering Skills Map Andrew NGOpenAI GPT-6 Astra prompting manual3OpenAI GPT-6 Astra prompting manualGROKBOT.md operating contract2GROKBOT.md operating contractGrok Bot architecture working note3Grok Bot architecture working noteGrok Bot 18-rule operator's manual3Grok Bot 18-rule operator's manualCodex Reconnecting 5/5 fixCodex Reconnecting 5/5 fixThe prompting playbook Margot van LaarThe prompting playbook Margot van LaarInvidious open source YouTube front-endInvidious open source YouTube front-endClaude Mythos Google Cloud ConsoleClaude Mythos Google Cloud Consolecry.eth2 Polymarket quant strategiescry.eth2 Polymarket quant strategies
    صورة
    Gifamoss
    Gifamoss@gifamoss6d
    🏢Anthropic💭AI💭artificial intelligence
    Anthropic agent memory 5 layers

    @gifamossFive memory layers is what Anthropic's new 13-page agent playbook is built around, and the pitch is that wiring them up can slash token costs by 90% start with Working Memory, the context window, basically everything the agent sees right now. fill it up and the oldest stuff just vanishes, which is where a lot of agents quietly fall apart Episodic Memory handles what happened. interaction logs with timestamps, down to the level of a deploy that broke because a migration script had a typo Semantic Memory covers what is true. facts and relationships sit in a knowledge graph that survives past the session, so something like user prefers TypeScript sticks around Procedural Memory is how to do things. an agent tries a few approaches, one lands, and that becomes a reusable skill so it doesn't redo the whole search next time Forgetting is the fifth piece, what to delete. an agent that never forgets accumulates contradictions, and you end up with old preferences overriding fresher ones the numbers: Mem0 stores 1,800 tokens per query versus 26,000. Snowflake's ontology layer brought 20% better accuracy and 39% fewer tool calls. memory pays for itself on day one, per the writeup this is the line between a chatbot and an agent that actually learns. save the 13 pages #Anthropic

    عرض المنشور الأصلي

    Anthropic agent memory 5 layers

    صورة بواسطة @gifamoss· Sep 15, 2026· Anthropic

    عن هذه الصورة

    The image is a diagram illustrating an agent memory architecture. It shows a flowchart with boxes representing different memory types like Working Memory, Episodic Memory, Semantic Memory, and Procedural Memory, connected by arrows indicating data flow. The style is technical and informative, like a research paper or presentation slide. A notable detail is the Anthropic logo in the bottom right corner. ON-SCREEN TEXT: Production Agent Engineering Practice 2026 Agent Memory Architecture 5 Layers That Cut Cost 90% and Make Your Agent Actually Learn Based on CoALA cognitive architecture, Mem0, Anthropic memory systems, Snowflake ontology research, and LangChain production engineering materials Independently compiled, September 2026 — not affiliated with Anthropic — and not endorsed Fig. 1. Agent memory flow architecture. User input enters Working Memory (context window). When context fills, it overflows into Episodic Memory (logs). Episodic memory distills facts into Semantic Memory (knowledge graph). Semantic memory encodes methods as Procedural Memory (skills). The Forgetting Engine prunes stale data and resolves contradictions. Das

    عرض كل صور Anthropicاقرأ ويكي Anthropic

    ?

    المزيد من صور Anthropic

    عرض كل صور Anthropic
    The AI Engineering Skills Map Andrew NG2The AI Engineering Skills Map Andrew NGOpenAI GPT-6 Astra prompting manual3OpenAI GPT-6 Astra prompting manualGROKBOT.md operating contract2GROKBOT.md operating contractGrok Bot architecture working note3Grok Bot architecture working noteGrok Bot 18-rule operator's manual3Grok Bot 18-rule operator's manualCodex Reconnecting 5/5 fixCodex Reconnecting 5/5 fixThe prompting playbook Margot van LaarThe prompting playbook Margot van LaarInvidious open source YouTube front-endInvidious open source YouTube front-endClaude Mythos Google Cloud ConsoleClaude Mythos Google Cloud Consolecry.eth2 Polymarket quant strategiescry.eth2 Polymarket quant strategies