01
What is temporal validity in AI agent memory?
It is knowing which facts are still true after knowledge evolves. MemStrata retires contradicted values with a deterministic supersession rule — no similarity threshold, no LLM on the read path.
オープンソース · ローカルファースト
コードベース全体を 毎回 AI に送るのはもうやめよう。
MemStrata は AI コーディングツール向けのローカルなコンテキスト層です。中央値のユーザーで RAG 比 42% の入力トークン削減、月あたり $14 が手元に戻ります。
無料のオープンソースコア。Pro の節約額が購読料を上回らなければ返金します。
ダッシュボード — マネータブ
live
Saved this month ▲ +12% vs last
$14.62
Tokens vs. RAG 42%
Recall 87%
Precision 64%
24h survival 73%
Works with the tools you already use
Cursor Claude Code VS Code Windsurf Cline JetBrains Aider Copilot Cursor Claude Code VS Code Windsurf Cline JetBrains Aider Copilot
Capabilities
Built like a research product. Feels like magic.
Bento layout of the moat — hover any tile for a spotlight interaction.
Temporal validity ~0% stale-fact error
RAG has no model of time. We do.
Deterministic (subject, relation, object) supersession retires stale facts in a bi-temporal ledger — AUROC 0.59 proves cosine cannot tell contradiction from duplicate.
Explore Local-first 100% local index
Your code stays here
SQLite + DuckDB on-device. License check is a signed JWT only.
Explore Latency ~2.1s retrieval
Embedding floor, not LLM tax
No model on the read path. ~2.1s vs 16–18s for rerankers.
Explore Token economics 42% vs naive RAG
Median 42% fewer input tokens
Compress context per turn. Watch dollars tick up on the local Money tab — money-back if Pro savings fall short.
Explore Integrations 11+ tool surfaces
Cursor · Claude Code · VS Code · more
MCP, harness, and extension paths — honest coverage matrix, no oversell.
Explore Research P1–P5 portfolio
Five papers. One temporal spine.
From arXiv temporal validity through SWE-bench longitudinal, value-change ceilings, detect-and-flag logic bulk, and world-knowledge generalization.
Explore Scrollytelling
Scroll the story of temporal memory
Sticky visual layer · glass panels · research-backed answers
02
Why does RAG serve stale facts?
When a fact changes, old and new embeddings sit next to each other (cosine AUROC ~0.59 for contradiction vs duplicate). RAG retrieves both and has no structural way to choose the current value.
03
How is MemStrata different from Mem0 or Graphiti?
Same-stack local 7B tests show MemStrata’s CERTAIN spine at 1.000 on supersession and TEMPO axes. We optimize for never confidently wrong on evolved knowledge — not just long-dialogue recall leaderboards.
04
Does this cut AI coding token costs?
Yes. Median users see ~42% fewer input tokens vs naive RAG, with live savings on a local dashboard. Pro includes a money-back guarantee if measured savings fall short.
Workflow
仕組み
3 ステップ。最初の節約まで 60 秒未満。
MemStrata をローカルにインストール
pipx install memstrata-pro memstrata init
約 60 秒、完全にローカル
いつも通りにコーディング
MemStrata はコードベースのグラフを構築し、AI リクエストごとにコンテキストを自動でバックグラウンド圧縮します。
節約額が増えるのを見守る
ライブダッシュボードがターンごとに節約したトークンとドルを、4 つの指標の内訳とともに表示します。
Telemetry
節約した分を計測します
4 つの数値はすべてローカルのダッシュボードにあります。「最大 X%」ではなく、あなたの実測テレメトリだけ。
42%
RAG 比のトークン節約
アクティブユーザーの中央値。ダッシュボードに正確な数値が出ます。
Computed per AI turn: actual tokens sent ÷ what a naive top-5 BM25+embedding RAG pipeline would have sent. 42% is the cross-user median.
Full methodology →
87%
検索の再現率
ターンごとの中央値 — AI が参照し、かつ我々のコンテキストにあった要素。
After each turn, MemStrata parses the AI response for referenced symbols/files and checks how many were in the context it provided.
Full methodology →
64%
検索の精度
ターンごとの中央値 — 提供した要素のうち AI が実際に使ったもの。
High precision means focused context, not padding. Low precision shows up as higher token costs — caught and surfaced in the dashboard.
Full methodology →
73%
24 時間後の編集残存率
セッションごとの中央値 — 24 時間後もリポジトリに残る AI 編集。
Tracks whether AI-suggested edits survive the next day of development. A proxy for suggestion quality — bad suggestions get reverted quickly.
Full methodology →
Integrations
あなたのスタックで動く？
正直な対応表。注意点も明記 — クローズドなエコシステムを誇張しません。
| Tool | MCP | Harness | Extension |
|---|---|---|---|
| Cursor | — | ||
| Windsurf | — | ||
| VS Code | — | ||
| Claude Code | — | ||
| Cline | — | ||
| Continue.dev | |||
| JetBrains AI | — | — | |
| GitHub Copilot Chat | — | ||
| Aider | — | — | |
| Codex CLI | — | — | |
| Zed | — |
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返金保証
Auto-credited if savings < subscription cost
節約できたときだけ利益を得ます
各請求サイクルの終わりに、ダッシュボードの実測節約額を合計します。購読料を下回れば、その差額を次回の請求に充当します。フォームもサポート問い合わせも不要 — 自動で領収書に反映されます。
1 か月 MemStrata を使わなければ請求はゼロ。実際に節約できたときだけ利益を得ます。
Privacy
コードはマシンから出ません
ライセンスサーバーは署名付きトークンで購読を確認するだけ — それ以外は何も送りません。
Your machine — all local
AI tool
Claude Code, Cursor, VS Code…
Core
MemStrata harness
localhost:8080
compresses context
Your LLM provider
Anthropic, OpenAI, Ollama…
Local SQLite + DuckDB
· index.db · telemetry.db — your code stays here
License server
memstrata.dev/lic
Pricing
シンプルで誠実な料金
永久に無料・オープンソース。有料プランはアクティブな harness と返金保証を追加します。
無料
オープンソース $0 ずっと
MIT ライセンスのオープンソースコア。ずっとあなたのもの。
- Open-source MIT core
- Local MCP server
- Local dashboard
- Browser extension — chat capture
- Community support (GitHub)
一番人気
Pro
$14.99 CAD /mo
＋税
アクティブなコンテキスト harness ＋ V6 メモリエンジン。
- Everything in Free
- Active context harness — all AI tools
- VS Code + browser augmentation
- V6 memory engine — verified retrieval
- Money tab — full savings metrics
- 60-day money-back guarantee
Pro+
$16.99 CAD /mo
＋税
Pro のすべてに加え、V7 のツール自動生成。
- Everything in Pro
- V7 tool autogeny — auto tool gen + JIT routing
- Earliest access to new engines
- Priority support
無料のオープンソースコアを永久に利用 — アカウント不要。 全機能を比較 →
Research
Backed by science — not vibes
Temporal validity, marker-free evaluation, and a five-paper program on agent memory under knowledge evolution.
0.59
Cosine AUROC
~0%
Stale-fact error
0.95–1.0
Evolving accuracy
~2.1s
Retrieval latency
1 Published arXiv preprint · cs.CL / cs.AI
Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledge
Deterministic supersession that RAG cannot match by construction
RAG gives agents access to accumulated knowledge but has no model of time. When a fact changes, cosine similarity surfaces both stale and current values nearly equally (AUROC 0.59 for contradiction vs duplicate). MemStrata stores facts like RAG, then retires contradicted values with a deterministic (subject, relation, object) supersession rule in a bi-temporal ledger — no similarity threshold, no LLM on the read path. Across six local benchmarks with a 7B model, MemStrata ties RAG on static knowledge and reaches 0.95–1.00 accuracy on evolving knowledge where RAG reaches 0.20–0.47. Stale-fact-error drops from 15–40% (RAG, when forced to answer) to ~0%.
- Cosine AUROC 0.59 — similarity cannot separate contradictions from duplicates
- Evolving knowledge accuracy 0.95–1.00 vs RAG 0.20–0.47
- Stale-fact-error ~0% vs RAG 15–40% when required to answer
- Retrieval ~2.1s (embedding floor) vs ~16–18s LLM-rerank baselines
temporal validitystale-fact errorbi-temporal ledgerRAG
Blog
All 10 posts → Explainers that drive SEO & understanding
Product + research
8 min read Paper 1, 2
Cut AI Coding Token Costs with Local Context Compression (Without Sending Your Repo Upstream)
MemStrata is the local context layer for AI coding tools: median 42% fewer input tokens vs naive RAG, money-back on Pro, and research-backed temporal memory so cheaper context is still correct context.
token costs AI coding local-first
Competitive analysis
11 min read Paper 1, 4, 5
MemStrata vs Mem0, Graphiti, and Letta: Same-Stack Comparison on Temporal Axes
Apples-to-apples local 7B evaluation: MemStrata’s deterministic supersession hits 1.000 on MemArch supersession/poisoning/TEMPO axes where flat RAG and several agent-memory systems still serve stale values.
Mem0 Graphiti Letta
Paper 2
8 min read Paper 2, 1
SWE-bench Longitudinal: Temporal Memory Beats RAG on Real Code Histories
On 130 marker-free scenarios extracted from SWE-bench Lite/Verified buggy→fixed histories, MemStrata temporal_v6 hits 0.908 accuracy vs ~0.57 RAG — with stale-fact-error 0.023 vs 0.262.
SWE-bench code agents benchmarks
Ship more. Spend less on tokens.
Install in under a minute. Keep coding the way you already do. Watch the savings compound — with research-backed temporal memory underneath.