Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Self-hosted memory for LLMs in TriliumNext, structured by the server and editable by you.
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。
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。
选择你使用的 AI 应用,然后复制设置。
TRILIUM_BASE_URL, TRILIUM_ETAPI_TOKEN, TRILIUM_PASSWORD, BRAINLLM_MODE, BRAINLLM_TZ, BRAINLLM_OWNER_PASSWORD, BRAINLLM_OAUTH_SECRET, BRAINLLM_PUBLIC_URL, BRAINLLM_ALLOW_UNAUTHENTICATED_HTTP, BRAINLLM_TRUST_PROXY标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
你需要
你用的是哪个应用?
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": {
"brainllm": {
"command": "npx",
"args": [
"-y",
"brainllm"
],
"env": {
"TRILIUM_BASE_URL": "your-value",
"TRILIUM_ETAPI_TOKEN": "your-value",
"TRILIUM_PASSWORD": "your-value",
"BRAINLLM_MODE": "your-value",
"BRAINLLM_TZ": "your-value",
"BRAINLLM_OWNER_PASSWORD": "your-value",
"BRAINLLM_OAUTH_SECRET": "your-value",
"BRAINLLM_PUBLIC_URL": "your-value",
"BRAINLLM_ALLOW_UNAUTHENTICATED_HTTP": "your-value",
"BRAINLLM_TRUST_PROXY": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add brainllm --env TRILIUM_BASE_URL=your-value --env TRILIUM_ETAPI_TOKEN=your-value --env TRILIUM_PASSWORD=your-value --env BRAINLLM_MODE=your-value --env BRAINLLM_TZ=your-value --env BRAINLLM_OWNER_PASSWORD=your-value --env BRAINLLM_OAUTH_SECRET=your-value --env BRAINLLM_PUBLIC_URL=your-value --env BRAINLLM_ALLOW_UNAUTHENTICATED_HTTP=your-value --env BRAINLLM_TRUST_PROXY=your-value -- npx -y brainllm运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"brainllm": {
"command": "npx",
"args": [
"-y",
"brainllm"
],
"env": {
"TRILIUM_BASE_URL": "your-value",
"TRILIUM_ETAPI_TOKEN": "your-value",
"TRILIUM_PASSWORD": "your-value",
"BRAINLLM_MODE": "your-value",
"BRAINLLM_TZ": "your-value",
"BRAINLLM_OWNER_PASSWORD": "your-value",
"BRAINLLM_OAUTH_SECRET": "your-value",
"BRAINLLM_PUBLIC_URL": "your-value",
"BRAINLLM_ALLOW_UNAUTHENTICATED_HTTP": "your-value",
"BRAINLLM_TRUST_PROXY": "your-value"
}
}
}
}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。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"brainllm": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"brainllm"
],
"env": {
"TRILIUM_BASE_URL": "your-value",
"TRILIUM_ETAPI_TOKEN": "your-value",
"TRILIUM_PASSWORD": "your-value",
"BRAINLLM_MODE": "your-value",
"BRAINLLM_TZ": "your-value",
"BRAINLLM_OWNER_PASSWORD": "your-value",
"BRAINLLM_OAUTH_SECRET": "your-value",
"BRAINLLM_PUBLIC_URL": "your-value",
"BRAINLLM_ALLOW_UNAUTHENTICATED_HTTP": "your-value",
"BRAINLLM_TRUST_PROXY": "your-value"
}
}
}
}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。
BRAINLLM_MODE envBRAINLLM_TZ envBRAINLLM_PUBLIC_URL envBRAINLLM_ALLOW_UNAUTHENTICATED_HTTP envBRAINLLM_TRUST_PROXY env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。
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6日
注册名称已通过 GitHub 账户 miisodev 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
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。
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。
你需要填写:TRILIUM_BASE_URL, TRILIUM_ETAPI_TOKEN, TRILIUM_PASSWORD, BRAINLLM_OWNER_PASSWORD, BRAINLLM_OAUTH_SECRET
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)。
GitHub 仓库最后更新于–,最新版本(13.3.1)于3天前进入注册中心。
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。
同一分类中有:Context7, Codebase Memory, Exa
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
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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。
Give Claude a memory that survives the session.
Self-hosted memory for LLMs in TriliumNext Notes, structured by the server and editable by you, served over the Model Context Protocol.
Site · Connect to Claude · How it works · Docs · Privacy
LLMs forget. Every session starts from zero: who you are, what you're working on, what was decided yesterday, what went wrong last week. BrainLLM fixes that.
BrainLLM is an MCP server that gives an LLM a real, persistent memory — stored in TriliumNext Notes, a mature open-source knowledge base you self-host and own. The model opens each session by loading who you are and what's live, writes durable facts the moment they surface, wires knowledge together as a typed graph, and closes each session with a log, a diary entry, and a database backup. The next session picks up exactly where the last one ended.
It's a single Bun/TypeScript service with two dependencies (the MCP SDK and Zod), speaking to Trilium exclusively through its public ETAPI. Your memory lives in *your* Trilium instance — inspectable, editable, and portable, never locked inside a vendor's black box.
Anyone running an LLM as a long-running collaborator, not a one-shot Q&A tool — across coding, research, personal knowledge management, or day-to-day operational tracking — who wants that collaborator to actually remember: who you are, what's in flight, what was decided, without you re-explaining it every session. If you're comfortable self-hosting one small service and a Trilium instance, BrainLLM turns "the model forgot everything again" into a solved problem. It works with any MCP-capable client (Claude Desktop, Claude Code, or anything else speaking MCP), not just one product.
BrainLLM began in February 2026 as a personal experiment: could an LLM operate a real, self-hosted memory reliably enough to be trusted as its own memory — orienting, writing, connecting, and closing sessions without a human doing the filing? Through sustained daily, production use the answer held. The design has settled, the failure modes have been found and fixed, and the project has graduated from experiment to something efficient and stable enough to share — so it's now open source. It still runs the author's own sessions every day; what you're reading is the same code, not a demo. It also still carries the shape of one person's daily use — see Adapting to your environment for exactly what that means and what to change.
The model supplies content. The server owns form.
Placement, naming, labels, deduplication, relation bookkeeping, lifecycle aging, archival, date stamping, HTML sanitization, backups — and structure itself — are all deterministic server policy, never delegated to the LLM. The model never chooses a parent note, never sets a label, never checks for duplicates, never stamps a date. That division is what makes the memory *reliable*: every guarantee is enforced at the tool layer, not requested via prompt.
template() and held on write: a new thread requires its goal, thread/diary/session entries open with an identification line (which LLM, which environment, which session type), threads carry exactly one Resolution (owned by resolve()), duplicate section headings are detected, and Last updated stamps are server-maintained.graph() renders the whole relation graph (or any note's neighborhood) as a Mermaid flowchart, maintained as a native Trilium note.day() serves the previous session, its change log and everything touched since in a single call.shape= at creation.maintain(deep=true) flags dated prose that creeps in. Every note except a log carries an icon, set by kind and backfilled by the sweep.deletionCatchupDays in the lifecycle policy), defaulting to Trilium's 7-day retention.consistency(subject="…") finds every note asserting about a fact however it is phrased, no regex guessing; consistency(pattern, staleAfterDays=N) surfaces figures held in exactly one untouched note, the ones that rot silently because nothing disagrees with them, and skips sessions and diaries, which are history rather than claims; diff(since="today") reviews every note a session changed in one call; revise(find="Phase 2", within="tr") edits, inserts or removes a table row named by a few words inside it; read(ids=[…]) batches a multi-note orientation into one round trip — each note's size up front, or a single section= across them; and split(noteId, sections=[…], into="…") is the write half of the oversized-note problem — it lifts whole sections into a new note and leaves a pointer back. Information notes can carry a #mandate marker so a scoped session finds the one note it must obey without reading every note's prose.revise(noteId, edits=[…], dryRun=true) shows sequential match counts and sanitized before/after content with no revision, labels or gate writes. Revision failures stop content overwrites. consistency() reports read failures and scan coverage; case-insensitive comparisons retain spelling variants without false conflicts. Incomplete scans cannot establish absence or unanimity.revise(find, within="tr", mode="remove", closure={thread, body}) removes a register row and records its closure in that thread's entry for the day, closure={note, table?, row?} moves a row into an explicitly selected register table; destination cells must match the table's shape. Destination-first ordering preserves recoverability, and moves=[{find, row?}] batches closures with preflight and per-row progress, and append={table, row} adds a row with no anchor to find; within= also accepts a row as a reader sees it ("Phase 2 | Claude | Open"), and a table row whose cells disagree with its header is flagged at write time and by maintain(deep).revise(find, within="tr", mode="remove", all=true) removes every row an anchor sits in, in one write and previewable with dryRun=true; remember(kind="sources", revision=[{source, from}]) renames a Sources key when a source moves instead of adding a second row; a dated-thread append returns previousRun, its author's previous block and run number; read(ids, sizes=true) plans a bulk read from sizes and section sizes alone; and diff(since="today", summaryOnly=true) lists what changed without the lines.close() seals its day's records into a digest chained to the previous day's, and maintain(deep) reports any sealed record that was rewritten afterwards. Appends stay legal; edits to history do not go unnoticed.At bootstrap, BrainLLM builds a five-area tree in Trilium. Every note the tools create is typed, labeled, dated, and placed by server policy:
BrainLLM (#brainLlmRoot)
├── 👤 Master Biography · Goals · Preferences (the user — maintained singletons) ├── 🤖 LLM Responsibilities · Protocols · Self-correction · Diary/ (the assistant's self-model + daily diary) ├── 🗂️ Memory Sessions/ · Threads/ (daily session logs + multi-session work) ├── 📚 Knowledge Master/ · Domains/ /{ Sources, info } (learned facts beyond/contra training) └── 💡 Insights Logs/ · Graph · Claims (the brain's record of itself)
| Note class | Kinds | Behavior |
|---|---|---|
| Singletons | biography, goals, preferences, responsibilities, protocols, selfcorrection (+ each domain's Sources note) | Exactly one maintained note; edited in place; hold *current-state truth* |
| Authored records | diary, session, dated threadEntry | One per calendar day and parent; every write lands as a timestamped addendum block — chronology is the point |
| Generated logs | log | One per calendar day; regenerated in place from the change feed |
| Collections | thread, user, information, domain | Titled notes, deduplicated by normalized title within their scope |
A session follows an enforced protocol: start() orients (who you are, live threads, what changed since last time) → the model works, writing durable facts *as they surface* → session() → addendum() → maintain() → remarks() → diary() → `close
只显示了开头部分;其余内容请在仓库中阅读