🧊 ICE by Dopove: The Virtual Memory Manager for LLMs

Building AI agents is easy. Keeping them coherent in production is a nightmare.

If you are building long-lived agents or enterprise copilots, you’ve likely hit the same wall: the LLM works perfectly in the demo, but degrades in production because it can’t reliably hold, retrieve, and attend to the right context over a long workflow.

Right now, most teams respond by duct-taping together a custom memory stack—Postgres + pgvector + Redis + session IDs + summarization loops. It’s fragile, expensive to debug, and leads to specific, bleeding-neck failures:

• Agents hit Step 10 and forget the tool output from Step 2.

• You shove more context into the prompt, your token bills explode, and the model still suffers from "Lost in the Middle" amnesia.

• You live in constant fear of multi-tenant data leakage.

Try ICE (Indexless Context Engine).

ICE is a drop-in Virtual Memory Management (VMM) layer that sits between your existing application and any upstream LLM (OpenAI, Anthropic, Gemini, Ollama).

The best part? Zero migration friction. ICE is 100% API compatible. You literally just change your base URL. You keep your SDKs, your prompts, and your orchestration frameworks (LangGraph, LlamaIndex, etc.).

What ICE handles under the hood:

• Cures Agentic Amnesia: ICE natively understands tool calls. It pins critical, recent tool-results to the active window and safely archives older steps to a Semantic Ledger. Multi-step agents never lose their train of thought.

• BYODB (Bring Your Own Database): Don't migrate your data. Point ICE at your existing PostgreSQL (pgvector) and Redis clusters. You own the storage and the infrastructure; we just provide the memory OS.

• Glass-Box Observability: Engineers hate black boxes. ICE natively exports OpenTelemetry (OTel) traces and includes a dry-run API. You can see the exact token receipts and injected context before it ever hits the LLM. You never lose control of the prompt.

• Precision Paging: We don't do dumb summarization. ICE dynamically pages raw, high-signal context into the prompt exactly when the agent needs it, keeping your physical token count ruthlessly low while guaranteeing perfect historical recall.

• Kernel-Level Multi-Tenancy: Isolation isn't an app-level afterthought. ICE enforces multi-tenancy at the database layer via PostgreSQL Row-Level Security (RLS). Cross-tenant leakage is mathematically impossible.

• Sovereign / VPC Deployment: Built for regulated enterprises where your data and memory state cannot leave your own boundary.

Planned Future Features:

We are actively building the next generation of LLM physics to completely eliminate token bottlenecks and compute waste.

• True Infinite Input (Zero-Token Cost): We are moving beyond text prompts. ICE is developing direct KV-Cache memory mapping. Soon, models won't "read" context; ICE will inject it directly into the model's neural state, unlocking instant 100-Billion token horizons with zero input API costs.

• Multi-Tenant Batch Routing: To slash enterprise inference bills, ICE will aggregate queries across your entire application and process them in single, massive inference batches—while using our strict VMM isolation to guarantee tenant data never crosses streams.

• Hardware Defiance: Our ultimate goal is O(1) memory mapping efficiency, allowing you to run 1,000 concurrent, memory-dense AI sessions on a single edge device without degrading performance.

If you are shipping persistent enterprise copilots or multi-tenant AI products, stop rebuilding brittle Postgres/Redis memory workarounds.

Read the docs, contact us, and drop ICE into your stack today:
👉 https://dopove.com/ice