Topic
Graph Based Memory Representation
6 episodes
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Overview: Directed Acyclic Graph
We finally slow down on directed acyclic graphs, or D A Gs, because this one quiet structure keeps showing up under workflows, agents, build systems, and half our control-stack arguments. We make it click as a map of prerequisites: arrows for order, no loops, and a scheduler that can see what can run now.
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Overview: Graph based Memory Representation
We finally slow down and explain graph-based memory representation, the thing we keep gesturing at whenever agent memory, receipts, and relationship-aware retrieval come up. We use one corkboard mental model to make nodes, edges, traversal, and the real trade-offs feel less mystical.
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2078778799064584535
A viral claim that Graph Engineering has displaced RAG at major AI labs, and what that actually means in practice.
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Graph Engineering Athropic Playbook
Anthropic's knowledge-graph engineering playbook replaces classical NLP pipelines (trained NER, relation classifiers, entity-resolution heuristics) with a sequence of Claude API structured-output calls. The entire extraction-resolution-assembly-querying loop becomes prompt-based, scaling from Haiku (high-volume extraction) to Sonnet (reasoning). The graph serves multi-agent systems as shared memory, grounding layer for evaluator-optimizer loops, and persistent world model across sessions. The paper maps this onto Anthropic's five canonical agent patterns and reports precision/recall against a gold set.
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How to Implement a Unified Memory From Scratch
Jessica and Cathy dig into a new post that walks through building a unified agent memory from scratch using knowledge graphs and MongoDB, unpacking what it actually takes to wire memory into a real agent harness. They tease apart where the post’s blueprint shines, where it overreaches, and who on earth should actually roll their own instead of reaching for an off-the-shelf tool.
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AI agent memory: MRAgent cuts token use up to 27x | VentureBeat
MRAgent from NUS replaces static retrieve-then-reason memory with active reconstruction during reasoning, cutting token use 27x over competing frameworks. The system treats memory as an interactive graph where agents dynamically refine retrieval paths based on intermediate evidence, using a three-layer Cue-Tag-Content structure and automated ingestion pipelines.