ICE - Infinite Context Engine

What it does

Production LLM systems break when context runs out - agents forget tool results, copilots lose prior sessions, multi-tenant apps risk data leakage. ICE sits between your app and any LLM (OpenAI, Anthropic, Gemini, Ollama) as a drop-in memory layer. Zero code changes. Your existing SDK works as-is. ✦ Persistent cross-session recall ✦ Agent tool-result continuity ✦ Kernel-level multi-tenant isolation ✦ Sovereign / on-prem deployment B2B sales only.

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ICE runs entirely within your application process. No external servers, database daemons, or network round-trips required. Core memory operations are handled by an optimized, compiled binary kernel, bypassing Python runtime overhead for microsecond speeds. Surgical control over cognitive defaults, budgets, and query limits via a clean SDK validated with strict Pydantic models. Standard LLMs forget everything once the context window fills up. ICE provides a high-performance Virtual Memory Management (VMM) layer that runs directly inside your application for functionally infinite context. Boots instantly with an embedded DuckDB file (`~/.ice/ice_memory.db`). No Docker, no PostgreSQL, no…from dopove.com

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  Context GatewayMar 2026 · github.com · ▲220

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  DeltaMemoryFeb 2026 · deltamemory.com · ▲104

  Most AI agents forget everything between sessions. We built DeltaMemory to fix that. It's a cognitive memory layer that gives agents persistent recall, automatic fact extraction, and temporal reasoning — via a single SDK call. Key numbers: - 89% accuracy on LoCoMo long-term conversation benchmark - 50ms p50 retrieval latency - 97% cost reduction vs raw token re-processing Open source SDKs, works with any LLM stack. Currently in early access. Happy to answer questions about the architecture, benchmark methodology, or how we handle knowledge graphs.

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  Mnexium AIJan 2026 · mnexium.com · ▲83

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  Spydr2025 · spydr.dev · ▲139

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  Core – open source memory graph for LLMs – shareable, user owned2025 · github.com · ▲112

  I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…

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