New ask Hacker News story: Ask HN: Anyone using knowledge graphs for LLM agent memory/context management?
Ask HN: Anyone using knowledge graphs for LLM agent memory/context management?
2 by mbbah | 0 comments on Hacker News.
I’m building infrastructure for LLM agents and copilots that need to reason and operate over time—not just in single prompts. One core challenge I keep hitting: managing evolving memory and context. RAG works for retrieval, and scratchpads are fine for short-term reasoning—but once agents need to maintain structured knowledge, track state, or coordinate multi-step tasks, things get messy fast; the context becomes less and less interpretable. I’m experimenting with a shared memory layer built on a knowledge graph: - Agents can ingest structured/unstructured data into it - Memory updates dynamically as agents act - Devs can observe, query, and refine the graph. - It supports high-level task modeling and dependency tracking (pre/postconditions) My questions: - Are you building agents that need persistent memory or task context? - Have you tried structured memory (graphs, JSON stores, etc.) or stuck with embeddings/scratchpads? - Would something like a graph-based memory actually help, or is it overkill for most real-world use? I’m in the thick of validating this idea and would love to hear what’s working (or breaking) for others building with LLMs today. Thanks in advance HNers!
2 by mbbah | 0 comments on Hacker News.
I’m building infrastructure for LLM agents and copilots that need to reason and operate over time—not just in single prompts. One core challenge I keep hitting: managing evolving memory and context. RAG works for retrieval, and scratchpads are fine for short-term reasoning—but once agents need to maintain structured knowledge, track state, or coordinate multi-step tasks, things get messy fast; the context becomes less and less interpretable. I’m experimenting with a shared memory layer built on a knowledge graph: - Agents can ingest structured/unstructured data into it - Memory updates dynamically as agents act - Devs can observe, query, and refine the graph. - It supports high-level task modeling and dependency tracking (pre/postconditions) My questions: - Are you building agents that need persistent memory or task context? - Have you tried structured memory (graphs, JSON stores, etc.) or stuck with embeddings/scratchpads? - Would something like a graph-based memory actually help, or is it overkill for most real-world use? I’m in the thick of validating this idea and would love to hear what’s working (or breaking) for others building with LLMs today. Thanks in advance HNers!
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