Igor Lins e Silva
f20a1a30fe
Part of #875. Bring the VitePress site into line with the new README and the reproducibility scorecard: drop category-error comparisons, drop retracted claims, retain only metrics and caveats that survive audit. website/index.md - New tagline matches README (local-first, verbatim, pluggable backend, 96.6% R@5 raw, zero API calls). - Replace the "MemPalace hybrid 100% / Supermemory ~99% / Mastra 94.87% / Mem0 ~85%" comparison table with a single honest table showing MemPalace's own retrieval-recall numbers (raw 96.6%, hybrid v4 held-out 98.4%). Add an explicit sentence explaining why we no longer publish a cross-system table on the landing page (retrieval recall vs QA accuracy are different metrics). - Soften the "ChromaDB-powered vector search" feature blurb to be backend-agnostic, since the retrieval layer is pluggable. website/reference/benchmarks.md - Full rewrite of the retrieval-recall tables. No more "100%" headline; honest held-out 98.4% R@5 replaces it. Added the model-agnostic rerank result (99.2% R@5 / 100% R@10 with minimax-m2.7 via Ollama) to show the pipeline is not Haiku-specific. - Drop the LoCoMo "Hybrid v5 + Sonnet rerank (top-50) 100%" row. With per-conversation session counts of 19-32 and top_k=50, the retrieval stage returns every session by construction — the number measures an LLM's reading comprehension, not retrieval. - Drop the cross-system comparison tables. Link out to each project's own research page (Mastra, Mem0, Supermemory) for their published numbers and metric definitions. - Rewrite reproduction commands to use the correct repository and demonstrate the new --llm-backend ollama flag. website/concepts/the-palace.md - Remove the "+34%" row / paragraph. Wing/room filtering is standard metadata filtering in the vector store, not a novel retrieval mechanism — the April-7 note already retracted that framing; this finishes the retraction on the website where it had remained. website/guide/searching.md - Same treatment for "34% retrieval improvement". Reframe as operational scoping, not a novel boost. website/reference/contributing.md - Update the "palace structure matters" bullet to reflect the same framing: scoping-not-magic. website/concepts/knowledge-graph.md - Replace the MemPalace-vs-Zep feature matrix with a short "related work" note that links to Zep's own documentation for authoritative details on their deployment model. Avoids claims we cannot verify at source.
89 lines
3.2 KiB
Markdown
89 lines
3.2 KiB
Markdown
---
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layout: home
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hero:
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name: MemPalace
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text: Give your AI a memory.
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tagline: "Local-first AI memory. Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls."
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image:
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src: /mempalace_logo.png
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alt: MemPalace
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actions:
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- theme: brand
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text: Get Started
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link: /guide/getting-started
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- theme: alt
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text: Architecture →
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link: /concepts/the-palace
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- theme: alt
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text: GitHub ↗
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link: https://github.com/MemPalace/mempalace
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features:
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- icon:
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src: /icons/file-text.svg
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alt: Verbatim Storage
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title: Verbatim Storage
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details: Store source text directly instead of extracting facts up front. The raw benchmark result comes from retrieving verbatim content.
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- icon:
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src: /icons/building-2.svg
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alt: Palace Structure
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title: Palace Structure
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details: Wings and rooms give retrieval useful structure. In the project benchmarks, narrowing search scope outperformed flat search.
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- icon:
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src: /icons/search.svg
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alt: Semantic Search
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title: Semantic Search
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details: Vector search over verbatim content lets the model retrieve past discussions by topic, project, or room. Backend is pluggable.
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- icon:
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src: /icons/git-merge.svg
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alt: Knowledge Graph
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title: Knowledge Graph
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details: Temporal entity-relationship triples in SQLite. Facts can be added, queried, and invalidated over time.
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- icon:
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src: /icons/wrench.svg
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alt: 19 MCP Tools
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title: 19 MCP Tools
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details: MCP tools expose search, filing, knowledge graph, graph navigation, and diary operations to compatible clients.
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- icon:
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src: /icons/shield-check.svg
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alt: Zero Cloud
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title: Zero Cloud
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details: Core storage and retrieval run locally. Optional reranking features can add an API dependency but are not required for the benchmark path.
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---
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<style>
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:root {
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--vp-home-hero-name-color: transparent;
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--vp-home-hero-name-background: linear-gradient(
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135deg,
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#4f46e5 0%,
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#06b6d4 50%,
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#8b5cf6 100%
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);
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}
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</style>
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<div style="max-width: 688px; margin: 0 auto; padding: 48px 24px 0;">
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## Verbatim Retrieval First
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MemPalace stores source text and retrieves it with semantic search. The benchmarked raw mode does not require an LLM at any stage — no extraction, no rerank, no summarisation.
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**LongMemEval retrieval recall (500 questions):**
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| Mode | R@5 | LLM required |
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| Raw (semantic search over verbatim text) | **96.6%** | None |
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| Hybrid v4, held-out 450q | **98.4%** | None |
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The raw 96.6% reproduces on any machine with the committed dataset: result JSONLs, the `seed=42` train/held-out split, and the `--mode raw` / `--held-out` runners are all in the `benchmarks/` directory of the repo.
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We deliberately do not publish a side-by-side comparison against other memory systems on this page. Retrieval recall (R@5) and end-to-end QA accuracy are different metrics and are not comparable; where MemPalace can be fairly compared on the same metric, we link to the other project's published source.
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<div style="text-align: center; padding-top: 16px;">
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<a href="./reference/benchmarks" style="color: var(--vp-c-brand-1); font-weight: 500;">Full benchmark methodology →</a>
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</div>
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</div>
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