Shared Selective Persistent Memory for Agentic LLM Systems
Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is token-inefficient and counterproductive: irrelevant context degrades generation quality. We introduce shared selective persistent memory, an architecture that identifies and retains four categories of reusable context (task specifications, data schemas, tool configurations, and o
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- PossiblePossibly related (embedding) · 67%NirDiamant/Agent_Memory_Techniques →
- PossiblePossibly related (embedding) · 63%joshuaswarren/remnic →
- PossiblePossibly related (embedding) · 62%LazyAGI/LazyLLM →
- PossiblePossibly related (embedding) · 29%thedotmack/claude-mem →
“Possibly related via embedding similarity 0.63 (not asserted). Timestamp check: artifact slightly before paper (-8d).”
- PossiblePossibly related (embedding) · 30%MemTensor/MemOS →
“Possibly related via embedding similarity 0.65 (not asserted). Timestamp check: artifact slightly before paper (-8d).”
- PossiblePossibly related (embedding) · 31%TencentCloud/TencentDB-Agent-Memory →
“Possibly related via embedding similarity 0.60 (not asserted). Timestamp check: artifact after paper (+3d).”
- PossiblePossibly related (embedding) · 31%MemoriLabs/Memori →
“Possibly related via embedding similarity 0.62 (not asserted). Timestamp check: artifact after paper (+38d).”
- PossiblePossibly related (embedding) · 56%NovasPlace/CSM →