The sovereign AI Memory Substrate and LLM Substrate decoupling permanent knowledge from transient transformer attention. Drop-in OpenAI-compatible API with persistent 4D topological lattice memory. Crystallize codebases and documents once into permanent Lattice Nodes and stream pinpoint Grounded Facts to your LLM without quadratic token waste or context rot. Official benchmarks: 96.28% exact (466/484) on RULER MRCR v2 and 54.87% (276/503) on LongBench v2.
Paired evaluation used Google Gemini 3.5 Flash-Lite (gemini-3.5-flash-lite) as the upstream BYOK test model. ICX works with any supported LLM — Gemini was the model we ran for this comparison. Same tasks, same weights — ICX separates permanent lattice storage from the generative viewport so the model receives only the facts it needs. Token cuts below are versus uncached full-context Gemini prefill. They are not a dollar comparison to a prompt-cached Cursor, Claude Code, or Antigravity harness.
gemini-3.5-flash-lite · BYOK via Google AI API
LongBench v2 paired control · n=10 · identical item IDs
RULER MRCR · 484 rows · multi-round ordinal disambiguation
ICX run 073110 · Gemini full-context 20260819_104413 · same 503 IDs
852k context · deep AST & cross-file · item 66fa208b
| Benchmark & workload | Gemini 3.5 Flash-Lite alone | Gemini 3.5 Flash-Lite + ICX | Advantage |
|---|---|---|---|
| LongBench v2 paired n=10 matched item IDs |
6/10 · 2,253,544 tokens | 6/10 · 162,923 tokens | 92.77% vs uncached prefill · n=10 only |
| RULER MRCR 484 rows · eval 9fc0e405 |
No 484-row Gemini dump in archive | 96.28% (466/484) | 18 ordinal sibling misses |
| LongBench v2 official 503 items |
38.57% (194/503 raw) · 188 API failures | 54.87% (276/503) | +16.30 pp raw · 0 ICX errors |
| SWE-bench Verified 500 · predicted pass |
No official test-resolved column | 96.40% (482/500) | Not official test-resolved |
| Code repository 852k context |
6,227 ms · 852,861 tokens | 1,375 ms · 31,783 tokens | 4.53× faster · 88.81% token cut |
Prompt caching discounts re-reading a stable prefix. It does not take those tokens off the model’s working desk. Calera ICX provides a true AI Memory Substrate and LLM Substrate that keeps the entire knowledge archive in 4D topological lattice memory and streams only a focused, grounded viewport to the LLM.
Everything your engineering, security, and product teams need to deploy infinite context memory with zero compliance or performance friction.
Bring Your Own Google Gemini or OpenAI API keys. Keys are forwarded ephemerally via request headers (X-LLM-API-Key) with zero server-side key retention or training on customer data.
100% compatible drop-in replacement for standard OpenAI SDKs in Python, Node.js, Go, and Rust. Simply update baseURL and pass X-Space-ID to begin grounding requests.
Native 1-click Model Context Protocol (MCP) server integration for Google Antigravity, Claude Desktop, Cursor, and custom autonomous agent swarms.
Explore Hosted MCP Docs →Multi-tenant namespace isolation (legal_vault, eng_repo, clinical_ehr) ensures zero cross-tenant factual leakage and instantaneous context switching.
Discrete simplicial graph diffusion executes on standard CPU memory in <0.5ms, eliminating expensive GPU cluster reservation fees and VRAM memory starvation.
Factual assertions form closed simplicial chains (∂c = 0). Hallucinated or out-of-ontology statements trip the boundary refusal operator instantly.
X-LLM-API-Key with zero server key retention.
Connect Claude Desktop, Cursor, LangChain, LlamaIndex, or standard OpenAI SDKs to persistent memory in under 60 seconds.
Want a live comparison before copying code? Open the ICX console preview or watch the SwarmForge multi-agent demo — both use the live ICX lattice memory engine.
# pip install calera-agent-memory from calera_agent_memory import CaleraMemoryClient # Auto-provision a free 1,000,000-node persistent vault (Delaware UETA § 14) client = CaleraMemoryClient() vault_info = client.claim_free_vault("developer@company.com", agent_name="my-agent") print(f"Provisioned Vault ID: {client.vault_id}") # 1. Store structured facts or task state into 4D topological lattice client.store("NVDA_FY26_Rev", "Estimated at $168B on Blackwell datacenter expansion") # 2. Sub-5ms topological associative recall with zero context rot results = client.query("NVDA revenue projection") print(results)
A formula, not an invoice. It applies a published list price and a published cache-read multiplier. ICX has not measured a live agent-loop bill.
Read Infinite Memory Is Not Infinite Attention (CALERA-PAPER-ICX-2026-08-28). Discover the A₄ Simplicial Transitive Graph Walker (943µs 5-hop), In-Process WASM Kernel (37µs), Multi-Agent Swarms ($V_t$), Universal Auto-Sync, NIST FIPS 203/204 Post-Quantum Security, 15 Certified MCP Tools, and 100% benchmark sweep.
Calera asked Google Antigravity, with the ICX MCP attached to this repository, to contrast truncated grep with lattice lookup. The result is an operator transcript — not a measurement. Empirical head-to-head numbers stay on the paper above.
Read the sessionSelect a developer preview tier. All subscriptions include single-account management and multi-domain billing on Calera Labs.
For individual developers testing persistent memory workflows.
For solo developers and agents who want persistent memory without a team invoice.
For engineering teams building memory-augmented AI applications and swarms.
For production AI workloads, heavy context pipelines, and autonomous agent swarms.
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Everything you need to know about the AI Memory Substrate, the Substrate Separation Principle, and integrating ICX with your LLMs and agents.
An AI Memory Substrate is an external, persistent, mathematically verified cognitive memory architecture that stores factual knowledge, codebases, and conversational history independently of an LLM's transient context window. ICX provides a 4D A₄ topological lattice memory substrate that allows any upstream LLM to recall verified facts in sub-5ms O(1) time without token bloat or attention degradation.
An LLM Substrate is the foundational memory and verification layer that pairs with generative foundation models. By delegating long-term fact storage to the ICX substrate (the Substrate Separation Principle), LLMs avoid quadratic prefill compute costs, eliminate lost-in-the-middle context rot, and maintain persistent state across unbounded multi-turn sessions.
Transformers are reasoning engines, not storage databases. In standard monolithic context windows (128k to 1M+ tokens), re-sending long context causes attention diffusion, high latency, and frequent recall failures. An external memory substrate maintains the full knowledge archive in 4D lattice memory on CPU and streams only a laser-focused viewport of 500–1,500 Grounded Facts to the LLM per turn.
Vector databases rely on approximate cosine similarity over dense statistical embeddings, which is non-deterministic and prone to hallucinations. GraphRAG introduces heavy LLM extraction latency and high token costs. ICX compiles corpora directly into discrete 4D A₄ Coxeter simplexes, enabling deterministic simplicial graph walking in sub-5ms with mathematical boundary refusal (∂² = 0) when information is absent.
Yes. ICX is a drop-in replacement for OpenAI SDK endpoints at https://icx.api.caleralabs.com/v1 with full support for /v1/chat/completions, streaming, and tool calls. It also natively supports Model Context Protocol (MCP) at https://icx.caleralabs.com/mcp for instant 1-click addition to Cursor, Claude Desktop, and Google Antigravity.
Yes. Autonomous agents can claim a free 1,000,000-node memory vault in <300ms by calling calera.icx.claim-1m-nodes via window.document.modelContext (W3C WebMCP) or POST /api/v1/agent/provision under Delaware UETA § 14 with zero human friction.
Provision isolated, high-throughput Cloud Run worker instances with guaranteed 1,500 req/min burst rates, dedicated CPU/memory specs, and private memory space isolation for enterprise security requirements.