Your AI writing assistant remembers chapter 3 when it writes chapter 300 — and the prompt never gets bigger.
MCP server that gives your AI writing assistant a constant-size memory of a long document. Chapter 1000 costs the same tokens as chapter 10. Local-only: no account, no API key, no network.
claude mcp add longform-memory -e LONGFORM_MEMORY_PROJECT=my-novel -- npx -y longform-memory-mcp
Restart your client. Then say: "remember this chapter" and "recall context for chapter 12".
The problem it solves
Write anything long with an LLM — a novel, a manual, a course, a serialised story — and you hit the same wall twice.
Withhold the past and it contradicts itself. A character who died in chapter 12 speaks in chapter 30. The key planted in chapter 3 is never mentioned again.
Send the past and you cannot afford it. Pasting every prior summary is O(n): tens of thousands of tokens by chapter 100, and by chapter 1000 it does not fit at all. Filling the window also makes things worse — models skip the middle.
Both are the same problem wearing two hats: there is no budget ceiling.
memory in the prompt
│
61k ─┤ ╭──── just paste it all
│ ╭─────╯
30k ─┤ ╭─────────────╯
│ ╭─────────────╯
4k ─┤════════════╪═════════════╪═════════════╪════ longform-memory
└──────┬─────┴──────┬──────┴──────┬──────┴────
10 100 500 1000 chapter
Measured on a real 1000-chapter book: 61,331 tokens → 4,396, and going from chapter 500 to chapter 1000 grew the block by 2 tokens.
The six tools
tool
what it does
recall_context
The main one. One paste-ready block: entity states, recent chapters, a sampled skeleton of the whole arc, and older material retrieved as relevant — at a fixed token size, with per-section accounting.
remember_chapter
Folds a finished chapter in: summary, entity state changes, thread moves. Optionally stores the prose locally.
list_open_threads
What the document promised the reader and has not delivered, most urgent first, overdue flagged.
check_continuity
Characters who left the story and act again; threads past their deadline. Quotes the sentence it tripped on.
entity_card
One character/place/object: current state plus the chapter-by-chapter timeline of how it got there.
search_memory
Find chapters by what happened in them, reaching past the recent window. BM25 offline; true semantic search if you configure your own embedding endpoint.
The rhythm
┌─ recall_context(12) ──────► paste into your drafting context
│
├─ list_open_threads(12) ───► what's still owed
│
├─ …write chapter 12…
│
└─ remember_chapter(12, …) ─► summary · entity states · thread moves
Step four is where the value is made. Your assistant is the model doing the extraction — it just read the chapter, so it already knows what changed.
It never calls a model
This server has no LLM inside it. The client calling it already is one.
So remember_chapter does not run an extraction pass over your prose — it asks your assistant to hand over what it understood while reading. That single decision is why there is:
no API key to obtain, paste, rotate or leak
no account, no sign-up, no rate limit
no cost to anyone, at any usage level
no network call, ever
Privacy
Everything lives in plain files under ~/.longform-memory/<project>/:
~/.longform-memory/my-novel/
├── events.jsonl # append-only: summaries, entity states, threads
└── chapters/
├── 000001.txt # your prose, only if you passed `text`
└── 000002.txt
Readable, greppable, rm-able, and yours. No telemetry. The only outbound request this package can ever make is to an embedding endpoint you configure yourself — and if you configure none, the code path never runs.
Why an append-only log rather than SQLite: this server is launched by npx inside someone else's editor, and a native module that fails to build there is not a degraded experience — it is a server that never starts, and you would never find out why.
Configuration
variable
default
meaning
LONGFORM_MEMORY_PROJECT
default
Which document. One entry per book, or pass project per call.
LONGFORM_MEMORY_HOME
~/.longform-memory
Where memory is stored.
LONGFORM_MEMORY_EMBED_URL
—
OpenAI-compatible /embeddings endpoint. Optional.
LONGFORM_MEMORY_EMBED_KEY
—
Your key, for your endpoint.
LONGFORM_MEMORY_EMBED_MODEL
—
e.g. text-embedding-3-small.
All three embedding variables must be set together — a half-configured endpoint that quietly does nothing is worse than no feature. search_memory always states which mode actually ran.
What it deliberately does not do
No semantic contradiction detection. "Three towers in chapter 4, four in chapter 60" needs a reader. A detector guessing at that produces a panel full of false alarms, and a panel that cries wolf is a panel nobody reads. check_continuity covers the part a model is bad at — remembering exactly, across a thousand chapters.
No writing. It does not generate, rewrite or critique prose.
No cloud sync. One machine, one directory.
No automatic extraction. If your client does not send entity states and threads, you get summary storage — and the reply tells you so rather than pretending.
Use it as a library
The server is a thin adapter; everything is callable directly.
import { recallContext, rememberChapter } from 'longform-memory-mcp';
await rememberChapter({
project: 'my-novel',
chapter: 12,
summary: 'Ines burns the letter and lies to her brother about it.',
entities: [{ name: 'Ines', state: 'lying to Tomas', note: 'first outright lie' }],
threads: [{ summary: 'what the letter actually said', deadlineChapter: 30 }],
});
const { recall } = await recallContext({ project: 'my-novel', chapter: 13 });
console.log(recall.text); // paste-ready block
console.log(recall.sections); // { entity: { usedTokens, kept, dropped }, … }
The budgeting core is published separately, with no dependencies at all:
longform-memory (npm) · longform-memory (PyPI).
Development
npm install
npm test # 54 tests, including a real stdio protocol round-trip
npm run build
The test suite spawns the built binary and speaks JSON-RPC to it, because the failures that only appear there are the expensive ones — a non-executable bin, a schema the SDK rejects, a stray write to stdout corrupting the transport.
Tool descriptions tell a model what a call returns. They cannot tell it that
recalling after drafting is useless, or that a [T3] from one chapter must not
be quoted at another. That is sequencing and judgement, and it lives in a skill:
MCP server that gives your AI writing assistant a constant-size memory of a long document. Chapter 1000 costs the same tokens as chapter 10. Local-only: no account, no API key, no network.
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
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ตรวจสอบล่าสุดเมื่อ 2026-11-14 MCP server that gives your AI writing assistant a constant-size memory of a long document. Chapter 1000 costs the same tokens as chapter 10. Local-only: no account, no API key, no network.