WARM

Name: WARM
Author: writerslogic

by writerslogicversion 0.1.5i★ –iRuns on your computer

Registry nameio.github.writerslogic/warm-mcp

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

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

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account writerslogic, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7264 servers in this category
Published to the registry more recently than 84% of the other 7264 servers in this category
Basic checks equal to or better than 100% of the other 7264 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is WARM safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.5) reached the registry 7 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js, Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

WARM

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

WARM

What it is

Who it's for

Headline results

Where it's behind