WARM

Name: WARM
Author: writerslogic

作者：writerslogic版本 0.1.5i★ –i在你的电脑上运行

注册名称io.github.writerslogic/warm-mcp

安装

概览

作者提供的说明（英文原文）

Local persistent memory for AI agents with recall, search, forgetting, and encrypted storage.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
注册中心没有列出需要填写的密钥。

你需要

npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "warm": {
      "command": "npx",
      "args": [
        "-y",
        "writerslogic-warm-mcp"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add warm -- npx -y writerslogic-warm-mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "warm": {
      "command": "npx",
      "args": [
        "-y",
        "writerslogic-warm-mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "warm": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "writerslogic-warm-mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 writerslogic 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7266 个服务器的 0%
发布到注册中心的时间比此分类中其他 7266 个服务器的 84% 更新
基础检查结果等于或优于此分类中其他 7266 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

WARM 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.1.5）于7天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js, Python (uv)。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT21天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

WARM

WritersLogic Adaptive Retention & Memory.

Hybrid search, graph indexing, and vector-symbolic memory.

Local memory for agents and apps: full-text and vector search, a memory API and a vector-symbolic fact store in one embeddable Rust engine that makes no network calls.

Website · Docs · Get started · Standards and provenance · Results · Capabilities · Limits · Contribute

What it is

WARM is a Rust library that stores text, vectors and facts in a directory on your machine and finds them again. It ships with Node.js bindings, C, WASM and UniFFI bindings, an HTTP server with a Python client, and an MCP server. There are no service calls, no telemetry and no model downloads unless you turn on a feature that asks for one.

Capability	What you get
Document search	Chunking, stemmed BM25 over blocked postings, Boolean and phrase queries, hybrid fusion with embeddings you supply, metadata filters, byte offsets
Vector index	Graph index with a memory-mapped vertex file, an updatable delta for inserts and deletes, optional query-aware routing trained on sample queries
Memory API	`remember`, `recall`, `forget` and `explain` with time validity and supersession; conversation memory with source provenance
Storage	Atomic replace and delete, crash-safe journal and checkpoints, verified compaction, one writer per store
Security	Optional AES-256-GCM storage encryption with documented coverage and limits, signed audit log, COSE/SCITT provenance
Vector-symbolic layer	Sparse hypervectors with bind, bundle and permute, triples with multi-hop queries, Hopfield cleanup

Who it's for

You are building	What you get
An agent or assistant that needs memory	A memory API with forgetting that removes facts from the store, and an MCP server for editors and agent hosts
A local-first or offline app	Embedded search and storage in one directory, with no server process and optional encryption
A service that needs vector or text search	`warm-server` over HTTP with tenants and scoped keys, or the library in-process
Research on vector-symbolic memory	The bind/bundle algebra, meaning memory and capacity experiments with their result files

Headline results

Every row links to the committed file it comes from. These are single machines and stated protocols, not a substitute for testing your own data.

Result	Conditions	Source file
Bemula forgetting score 0.322; best published row 0.310 (LangMem)	Full split: 50 dialogues, 135 forget requests. Agent and judge both `deepseek-v4-flash-0731` through OpenRouter pinned to DeepSeek, temperature 0; the judge snapshot is inferred from the authors' run metadata. Recall k=5 hybrid with `text-embedding-3-small`. Compared with the paper's published DeepSeek rows; a same-day rerun of the suite's naive RAG baseline scored 0.075. One run.	`bemula_v1.json`
LongMemEval dev 89.75 of 100 correct (mean of 4 runs, range 87 to 93)	Official generation and judge prompts, `gpt-4o-2024-08-06` as reader and judge, top 10 context. Retrieval is the reference turn ranking over cached Qwen3 scores, not native conversation-memory recall. k was chosen on dev; there is no held-out result.	`longmemeval_official_dev_summary.json`
VIBE llama-128-ip strict recall@100 up to 0.99687 at 1,457 QPS	Official test queries, one thread, query-aware graph (ef=512). Neither frozen RoarGraph configuration reached 0.99. Behind at 0.95 and 0.99 QPS (see below).	`query_aware_graph_v1/README.md`
VIBE yi-128-ip at recall 0.95: 3,214 QPS against 1,953 (MLANN RF80)	Same protocol; compared only with the frozen RoarGraph and MLANN configurations measured there. Recall 0.99 was not reached.	`query_aware_graph_v1/README.md`
SIFT-1M at recall@10 0.95: 23,918 QPS against hnswlib 8,125 (2.94x, interval 2.75 to 3.01); 3.09x at 0.99, 1.40x faster build, 1.83x less serving RSS, 3.74x faster cold start	Core Ultra 9 185H at `4b7a849f`, 10,000 queries issued one at a time from one client thread, index built with 8 threads; WARM through the Rust API, hnswlib through its Python binding; Bonferroni-adjusted bootstrap intervals	`heavy-ann-sift-...-185h-22c-27g.json`
BM25 search on SciFact, NFCorpus and the memory corpus: 1.66x to 2.00x the single-thread QPS of the fastest of tantivy 0.27, Lucene 10.6 and SQLite FTS5, with 1.69x to 2.91x less serving memory and 3.1x to 5.7x faster cold start, at tied nDCG@10	Two machines (Core Ultra 9 185H at `4b7a849f`, Modal 8 vCPU at `7ba2d4fe`, same engine source), 5 and 3 interleaved serve rounds, Bonferroni-adjusted bootstrap intervals exclude parity for every win. Lexical only, one chunk per document	COMPARISON.md, `standard-text-beir-scifact-...-185h-22c-27g.json`
SciFact nDCG@10 hybrid 0.729, above its own BM25 (0.688) and dense (0.645)	5,183 documents, 300 queries, MiniLM embeddings supplied by the harness. NFCorpus: 0.355 against 0.323 and 0.317.	`public_scifact.json`, `public_nfcorpus.json`

Where it's behind

Result	Conditions	Source file
ann-benchmarks at recall 0.95: sift 2,819 QPS against faiss-ivfpqfs 3,788 (0.74x); glove 1,019 against ScaNN 1,535 (0.66x)	Official ann-benchmarks runner, each run pinned to one CPU, AVX2 host	`ann_benchmarks_harness_wl02_v1.json`
VIBE llama-128-ip QPS: 24.6% behind RoarGraph at recall 0.95, 54.8% behind MLANN RF80 at 0.99	One thread, official test queries	`query_aware_graph_v1/README.md`
SIFT-1M index file 1,389 MiB against hnswlib's 752 (0.54x, no interval); peak build RSS 0.94x of hnswlib's	Same run as the SIFT-1M row above	`docs/benchmarks/GAPS.md`
BEIR FiQA (57,600 documents) single-thread BM25 QPS: 1,269 against Lucene 10.6's 1,625 (0.78x); p50 and p99 latency also behind	Core Ultra 9 185H at `4b7a849f`, 5 interleaved serve rounds; serving memory (1.26x less than SQLite FTS5) and cold start (2.37x faster than tantivy) still win	`standard-text-beir-fiqa-...-185h-22c-27g.json`
Text ingest: peak build memory 1.1x to 2.1x SQLite FTS5's on every corpus; ingest time behind SQLite FTS5 on SciFact, NFCorpus and memory (0.46x to 0.97x), tied on FiQA	Same runs, both machines	`docs/benchmarks/GAPS.md`
glove-100-angular with the sparse vector path: recall@10 0.252 at 33.7 QPS against FAISS HNSW 0.832 at 4,111 QPS	One thread, Apple M4; the sparse path is experimental	`public_glove-100-angular.json`

Method, intervals and every loss: docs/benchmarks and the benchmarks pages.

_Generated from benchmarks/results/competitive/ by warm-bench table; method and caveats in docs/benchmarks/COMPARISON.md. W = WARM ahead (95% interval excludes parity), T = tie, L = a comparator is ahead, - = no verdict (unpinned source revision, no interval, or not comparable). Rows are compared only within one machine._

Dataset	Machine	Metric	WARM	Best other	Advantage
sift-128-euclidean	`linux-x86_64-intel-ultra-9-185h-22c-27g`	QPS @ recall@10 >= 0.95 (QPS)	23,918	hnswlib 8,125	2.94x	W
sift-128-euclidean	`linux-x86_64-intel-ultra-9-185h-22c-27g`	p99 latency @ 0.95 (us)	97	hnswlib 251	2.60x	W
sift-128-euclidean	`linux-x86_64-intel-ultra-9-185h-22c-27g`	Build time (s)	121.44	hnswlib 170.02	1.40x	W
sift-128-euclidean	`linux-x86_64-intel-ultra-9-185h-22c-27g`	RSS, serving (MiB)	490	hnswlib 898	1.83x	W
sift-128-euclidean	`linux-x86_64-intel-ultra-9-185h-22c-27g`	Index size (MiB)	1,389.1	hnswlib 751.8	0.54x	-
beir:fiqa	`linux-x86_64-intel-ultra-9-185h-22c-27g`	nDCG@10	0.2654	SQLite FTS5 0.2678	0.99x	T
beir:fiqa	`linux-x86_64-intel-ultra-9-185h-22c-27g`	QPS (single thread) (QPS)	1,269	Apache Lucene 10.6.0 (JVM) 1,625	0.78x	L
beir:fiqa	`linux-x86_64-intel-ultra-9-185h-22c-27g`	p99 latency (us)	1,828	Apache Lucene 10.6.0 (JVM) 1,486	0.81x	L
beir:fiqa	`linux-x86_64-intel-ultra-9-185h-22c-27g`	Ingest/build time (s)	1.55	SQLite FTS5 1.44	0.93x	T
beir:fiqa	`linux-x86_64-intel-ultra-9-185h-22c-27g`	Peak RSS, serving (MiB)	31	SQLite FTS5 39	1.26x	W
beir:fiqa	`linux-x86_64-intel-ultra-9-185h-22c-27g`	On-disk size (MiB)	9.7	Apache Lucene 10.6.0 (JVM) 12.1	1.25x	W
memory	`modal-x86_64-8vcpu-32g`	nDCG@10	0.4561	Apache Lucene 10.6.0 (JVM) 0.4627	0.99x	T
memory	`modal-x86_64-8vcpu-32g`	QPS (single thread) (QPS)	4,138	tantivy 2,185	1.89x	W
memory	`modal-x86_64-8vcpu-32g`	p99 latency (us)	533	tantivy 798	1.50x	W
memory	`modal-x86_64-8vcpu-32g`	Ingest/build time (s)	1.91	SQLite FTS5 1.75	0.92x	L
memory	`modal-x86_64-8vcpu-32g`	Peak RSS, serving (MiB)	17	SQLite FTS5 41	2.42x	W
memory	`modal-x86_64-8vcpu-32g`	On-disk size (MiB)	3.5	Apache Lucene 10.6.0 (JVM) 10.3	2.92x	W

只显示了开头部分；其余内容请在仓库中阅读

WARM

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

常见问题

替代选择

Context7

Codebase Memory

Exa

作者的 README

WARM

What it is

Who it's for

Headline results

Where it's behind