UniversalMemory Layer.
Persistent, cross-platform context for AI. One memory your agents capture, recall, and reason over — across every tool you use, for years.
Built for scale.
Designed for elegance.
Cortex watches quietly as you work, keeps what matters, and brings it back the moment it's useful — without interrupting your flow.
Hybrid Retrieval
Dense embeddings and Okapi BM25 keyword search, fused with reciprocal rank fusion. The right memory surfaces even when you don't use the right words.
Omnistream
A singular context window that travels with you across Cursor, Claude, ChatGPT, and the terminal.
No LLM on the Read Path
recall embeds your query once, fuses the dense and BM25 rankings, and returns ranked memories — no server-side generation. Your agent is the reader, so a query costs a fraction of a cent.
Proven on Benchmarks
0.932 on LongMemEval_S and 0.813 on LoCoMo, graded by a gemini-3.5-flash judge rather than the canonical gpt-4o — at a published $0.0396 and $0.0034 a question.
You Own It
Bring your own key. Your memory is data you control — no phone-home, no lock-in.
MCP Native
Built strictly on the Model Context Protocol. Zero vendor lock-in, endless integrability.
0.932 on LongMemEval_S. $0.0396 a question.
Every question in LongMemEval_S, not a subset. On LoCoMo, 0.813 across 1,986 questions at $0.0034 each. Every other row on both boards reads with a premium model — GPT-5-mini, Gemini-3-pro, Claude Opus — while Cortex reads with a flash-class Gemini one. It is the only memory system publishing a $/question, and the only one at 0.9+ on LongMemEval_S with a cost-efficient-tier reader. All of it is graded by a gemini-3.5-flash judge; what that costs us in confidence is spelled out at the end of this section.
Raw #3 · 0.9+ with a cost-efficient-tier reader
Raw #4 · $/question published
One memory system. Two different levers.
All three are full 500-question LongMemEval_S runs on the same index and the same retrieval — 0.998 session-level recall@k in every one. The first step changes only the reader and is expensive. The second changes only the output-token budget and answer ordering, and is free. We publish them apart, because summing them would credit the reader for work it did not do.
All three rows are full 500-question LongMemEval_S runs on the same memory system: identical index, identical retrieval, recall@k 0.998 in every one. Swapping only CORTEX_READER_MODEL — gemini-2.5-flash to gemini-3.5-flash, everything else held — buys +4.6 accuracy points (0.848 to 0.894) for roughly 5× the reader spend, which makes cost-per-correct-answer 4.8× worse. The headline 0.932 adds a further +3.8 from a change that is not the reader at all: emitting the answer before its notes with an 8k output budget instead of 2k, which removed a mid-answer truncation. Same reader, same retrieval, no extra cost. We report the two separately and never sum them, because less than half of that span is the reader — and because the decode budget is a variable no leaderboard reports. A premium reader (gemini-3.1-pro, Claude Opus) sits further along the same line; we don't quote a price for it because we haven't published a full run at that reader.
Cheap by choice, not by limit.
The retrieval pipeline is reader-agnostic. One setting — CORTEX_READER_MODEL — slides Cortex along the frontier. Same index, same 0.998 session-level recall@k. You choose the trade-off.
In our own reader sweep, gemini-3.1-pro scored highest of every reader we tested — so a premium reader lifts Cortex into the top tier of the raw leaderboard, on demand. We ship the flash tier because a published, cost-efficient $/question is the whole thesis: Cortex is the only memory system publishing $/question, and the only one at 0.9+ on LongMemEval_S with a cost-efficient-tier reader. Higher raw accuracy stays one setting away.
LongMemEval_S · 500 questions
LoCoMo · 1,986 questions
Question-type by question-type — LongMemEval_S
One overall number hides where a memory system wins or loses. Split by question type, Cortex — the only cheap-reader system in this table — holds the top tier on what agents lean on most: knowledge updates, single-session recall, and temporal reasoning.
| Question type | Cortex | Mastra | EmergenceMem | Supermemory | Zep |
|---|---|---|---|---|---|
| Knowledge update | 94.9 | 96.2 | 83.3 | 89.7 | 83.3 |
| Single-session assistant | 98.2 | 94.6 | 100.0 | 98.2 | 80.4 |
| Single-session user | 95.7 | 95.7 | 98.6 | 98.6 | 92.9 |
| Temporal reasoning | 92.5 | 95.5 | 85.7 | 82.0 | 62.4 |
| Multi-session | 90.2 | 87.2 | 81.2 | 76.7 | 57.9 |
| Single-session preference | 90.0 | 100.0 | 60.0 | 70.0 | 56.7 |
| Overall | 93.2 | 94.9 | 86.0 | 85.2 | 71.2 |
How to read this. Cortex's row is our own measured per-type breakdown (gemini-3.5-flash reader + judge). Competitor rows are each system's own published LongMemEval_S results under their own premium readers and judges — the field's reported standing, not a single apples-to-apples harness (see judge disclosure). The only cheap-reader system here is Cortex. Cortex's abstention category (0.867) is omitted — competitors don't report it.
Beyond a benchmark — agent uplift
A cross-session agent task where one fact is buried in 12 earlier sessions. Memoryless fails every time; Cortex matches full-context accuracy on a fraction of the tokens.
Judge disclosure. All Cortex numbers are graded by an LLM judge = gemini-3.5-flash. The headline run used the same model family as both reader and judge, so a self-preference bias cannot be excluded; a cross-family re-grade is planned. The canonical LongMemEval judge is gpt-4o; a Gemini judge is likely more lenient, so a gpt-4o re-grade may lower these. Competitor rows use their own judges — the leaderboard is not apples-to-apples. Positioning is stated honestly: Cortex is raw #2 on LongMemEval_S and raw #3 on LoCoMo — the only memory system publishing $/question, and the only one at 0.9+ on LongMemEval_S with a cost-efficient-tier reader.
How cost is computed. Every $/question here is the run's measured mean input and output tokens per question priced at Google's published Gemini API list prices, confirmed 2026-07-24: gemini-2.5-flash $0.30 in / $2.50 out and gemini-3.5-flash $1.50 in / $9.00 out, per 1M tokens. Retrieval and embedding costs are amortised at write time and are not in the per-question figure.
Drop-in Memory.
Connect Cortex to any MCP client. Claude Code, Cursor and Antigravity use a token from your dashboard; ChatGPT and Claude sign in with OAuth — nothing to paste.
# Terminal — add the hosted Cortex over streamable HTTP
claude mcp add --transport http cortex \
https://cortex-api-685740048714.us-central1.run.app/mcp \
--header "Authorization: Bearer ctx_YOUR_TOKEN"Token clients: generate one in your dashboard → Connections and swap it in for ctx_YOUR_TOKEN. Prefer a local, bring-your-own-key setup? See the self-host guide.
Syncs with your
workflow.
Cortex drops directly into any MCP-compatible environment. Instantly bridge context across IDEs, terminals, and web agents.
Integration Directory