Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Self-hosted memory for LLMs in TriliumNext, structured by the server and editable by you.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
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_PROXYValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
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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": {
"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"
}
}
}
}Run this in your terminal
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 brainllmFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.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"
}
}
}
}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.
Paste into .vscode/mcp.json in your project
{
"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"
}
}
}
}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.
BRAINLLM_MODE envBRAINLLM_TZ envBRAINLLM_PUBLIC_URL envBRAINLLM_ALLOW_UNAUTHENTICATED_HTTP envBRAINLLM_TRUST_PROXY envThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
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 6, 2026
The registry name was verified with the GitHub account miisodev, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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.
You need to fill in: TRILIUM_BASE_URL, TRILIUM_ETAPI_TOKEN, TRILIUM_PASSWORD, BRAINLLM_OWNER_PASSWORD, BRAINLLM_OAUTH_SECRET
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).
The GitHub repo last changed –, and the latest version (13.3.1) reached the registry 3 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Context7, Codebase Memory, Exa
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
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.
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.
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
Only the beginning is shown; read the rest in the repo