Gifamoss@gifamoss

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Anthropic agent memory 5 layers

Five 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

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Gifamoss@gifamoss

Folgen

Anthropic agent memory 5 layers

Five 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

3d