Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Self-hosted memory for LLMs in TriliumNext, structured by the server and editable by you.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
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_PROXYLos valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en 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"
}
}
}
}Ejecuta esto en tu 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 brainllmCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.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"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"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"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
BRAINLLM_MODE envBRAINLLM_TZ envBRAINLLM_PUBLIC_URL envBRAINLLM_ALLOW_UNAUTHENTICATED_HTTP envBRAINLLM_TRUST_PROXY envEsta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub miisodev, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
Debes completar: TRILIUM_BASE_URL, TRILIUM_ETAPI_TOKEN, TRILIUM_PASSWORD, BRAINLLM_OWNER_PASSWORD, BRAINLLM_OAUTH_SECRET
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (13.3.1) llegó al registro hace 3 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Context7, Codebase Memory, Exa
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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
Solo se muestra el principio; lee el resto en el repo