Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Durable AI memory: recall distilled facts in a later session or subagent, and remember new ones.
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.
KHWAN_API_KEY, KHWAN_CORE, KHWAN_USER, KHWAN_BASE_URLLos 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 …”.
Funciona en Claude para web, escritorio y móvil:
https://mcp.khwan.ai/mcp/{core}/{user}La dirección tiene partes {…} que solo tú conoces (como tu cuenta), así que cópiala y complétalas.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"khwan": {
"command": "uvx",
"args": [
"khwan-mcp"
],
"env": {
"KHWAN_API_KEY": "your-value",
"KHWAN_CORE": "your-value",
"KHWAN_USER": "your-value",
"KHWAN_BASE_URL": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add --transport http khwan 'https://mcp.khwan.ai/mcp/{core}/{user}'claude mcp add khwan --env KHWAN_API_KEY=your-value --env KHWAN_CORE=your-value --env KHWAN_USER=your-value --env KHWAN_BASE_URL=your-value -- uvx khwan-mcpCompleta los valores vacíos antes de ejecutarlo.
La dirección tiene partes {…} que solo tú conoces (como tu cuenta), así que cópiala y complétalas.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"khwan": {
"url": "https://mcp.khwan.ai/mcp/{core}/{user}"
}
}
}Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"khwan": {
"command": "uvx",
"args": [
"khwan-mcp"
],
"env": {
"KHWAN_API_KEY": "your-value",
"KHWAN_CORE": "your-value",
"KHWAN_USER": "your-value",
"KHWAN_BASE_URL": "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.
La dirección tiene partes {…} que solo tú conoces (como tu cuenta), así que cópiala y complétalas.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"khwan": {
"type": "http",
"url": "https://mcp.khwan.ai/mcp/{core}/{user}"
}
}
}Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"khwan": {
"type": "stdio",
"command": "uvx",
"args": [
"khwan-mcp"
],
"env": {
"KHWAN_API_KEY": "your-value",
"KHWAN_CORE": "your-value",
"KHWAN_USER": "your-value",
"KHWAN_BASE_URL": "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.
ChatGPT solo se conecta a servidores en internet:
https://mcp.khwan.ai/mcp/{core}/{user}La dirección tiene partes {…} que solo tú conoces (como tu cuenta), así que cópiala y complétalas.
Los nombres de los menús pueden variar un poco según el plan y con el tiempo.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
KHWAN_CORE envKHWAN_USER envKHWAN_BASE_URL envEsta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Todavía no hemos leído la lista de herramientas de este servidor; revisamos los endpoints por turnos.
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 el dominio khwan.ai (el registro comprueba quién es dueño del dominio)
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
La dirección necesita datos de tu propia cuenta, así que no podemos revisarla por ti
La dirección necesita datos de tu propia cuenta, así que no podemos revisarla por ti
Aún sin revisar; leemos la lista de herramientas cuando revisamos el endpoint
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: KHWAN_API_KEY
Funciona de las dos formas: instálalo en tu computadora o conéctate a la versión en la nube.
El repo de GitHub cambió por última vez –, y la última versión (0.3.12) llegó al registro hace 2 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. Se conecta por URL, así que también funcionan las apps que aceptan servidores MCP remotos, como ChatGPT en modo desarrollador.
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.
Durable memory that survives the session. An MCP server that plugs Khwan — a pure AI-memory layer — into Claude Code, Claude Desktop, or any MCP client.
Khwan never runs *your* model. The client is the model. Its job is to persist and distil what matters into a brain you can recall in a later session or seed a subagent with — a compact, bounded set of facts instead of a replayed transcript. One account can hold many isolated cores (brains), and — on paid plans — an isolated sub-brain per end-user.
Nothing on the read or write path costs you an inference call: khwan_recall and khwan_remember make none. Khwan does run one model of its own, on a schedule — a nightly pass that distils stored turns into standing lessons. A real production run was 8 brains and 245 stored turns distilled into 11 lessons, for $0.0094. That pass is Khwan's cost, not yours, and it is the whole difference from memory layers that compress, graph, or consolidate on every turn.
Be honest about the mechanism — an MCP adds to a host's context, it cannot replace the transcript the host already sends. So:
The token-smart pattern: seed once, remember durable facts (below), rather than running the full loop on every turn of a caching host. The full prepare → record loop still shines in a custom agent on a non-caching host, where replacing history with distilled memory bounds per-turn cost directly.
pip install khwan-mcp # or: uvx khwan-mcp
claude mcp add khwan --scope project \
-e KHWAN_CORE=default \ -- khwan-mcp
--scope project writes .mcp.json into the repo, so the setting travels with the project. Note what is not in that command: the key.
claude mcp add -e KHWAN_API_KEY=… writes the literal value into .mcp.json — a file whose whole point is being committed. Two ways to avoid that, and the second is the one that works everywhere:
Shell environment. Leave KHWAN_API_KEY out of the config entirely and export it in the shell that launches claude. The server inherits it.
export KHWAN_API_KEY=kwk_live_xxx
A launcher (works in the desktop app too). A desktop app is started from a dock or menu, not a login shell, so it inherits none of your shell exports and the approach above silently yields no key. Read it from a file instead:
mkdir -p ~/.khwan && chmod 700 ~/.khwan
printf 'KHWAN_API_KEY=kwk_live_xxx\n' > ~/.khwan/env && chmod 600 ~/.khwan/env
cat > ~/.khwan/khwan-mcp <<'SH' #!/bin/sh set -a [ -f "$HOME/.khwan/env" ] && . "$HOME/.khwan/env" set +a exec khwan-mcp "$@" SH chmod 700 ~/.khwan/khwan-mcp
Then point the config at the launcher and keep only non-secret settings inline:
claude mcp add khwan --scope project \
-e KHWAN_CORE=acme -e KHWAN_USER=Web \ -- ~/.khwan/khwan-mcp
.mcp.json is now safe to commit, and every new repo costs two lines instead of a pasted key. Anyone else on the team writes their own ~/.khwan/env.
Memory is only useful if the right project's memory comes back. Two axes, and both give complete isolation:
| selected by | free | paid | |
|---|---|---|---|
| core | KHWAN_CORE | 1 — default only | 5 (starter) → 25 (pro) |
| sub-brain | KHWAN_USER | 3 | unlimited |
A sub-brain is a full separate brain, not a filter: account::@web shares nothing with account::@api. So the two axes multiply, and a free account already holds four isolated brains — the core on its own, plus three sub-brains:
account default core, no KHWAN_USER brain 1
account::@web KHWAN_USER=web brain 2 account::@api KHWAN_USER=api brain 3 account::@docs KHWAN_USER=docs brain 4
Which means one-brain-per-project works on the free plan, for up to four projects — and it needs no `KHWAN_CORE` at all:
# in ~/code/web
claude mcp add khwan --scope project -e KHWAN_USER=web -- ~/.khwan/khwan-mcp
claude mcp add khwan --scope project -e KHWAN_USER=api -- ~/.khwan/khwan-mcp
Named cores are the paid axis. Reach for one when four brains stop being enough, or when you want them grouped per client rather than per repository:
# in ~/code/acme-web
claude mcp add khwan --scope project -e KHWAN_CORE=acme -e KHWAN_USER=Web -- ~/.khwan/khwan-mcp
claude mcp add khwan --scope project -e KHWAN_CORE=acme -e KHWAN_USER=Api -- ~/.khwan/khwan-mcp
Two things to know before you point KHWAN_CORE anywhere. A core must exist first — an unknown slug answers 404, not "created it for you" — and they are created in the dashboard. On the free plan there is nothing to point at: the cap of one is spent on default, so creating a named core answers 402. Leave KHWAN_CORE unset there and use KHWAN_USER. Sub-brains, by contrast, are created on first write.
On a caching host like Claude Code, prefer seed + remember over the per-turn loop:
"Call
khwan_recall(query=" ")and use the returnedseed_textas
context."
"That's a standing decision — call
khwan_remember(fact="…")."
Reinforce it in your project's CLAUDE.md, e.g.:
- At the start of a task, call `khwan_recall` to seed relevant memory.
khwan_remember.
Seeding a subagent is where the win is clearest — hand it a bounded brief instead of the whole transcript:
"Recall deploy memory with
khwan_recall(query="deploy runbook"), then spawn a
subagent whose brief is that
seed_textplus the task."
Claude Desktop and Claude Code keep separate MCP configuration — a server added to one is invisible to the other, and claude mcp add does not touch this file. Add to claude_desktop_config.json:
{
"mcpServers": { "khwan": { "command": "/Users/you/.khwan/khwan-mcp", "env": { "KHWAN_CORE": "acme", "KHWAN_USER": "Web" } } } }
Use an absolute path: a desktop app does not get your shell's PATH either, so a bare khwan-mcp may not resolve. One core is selected for the whole app — there is no per-project switch here, so choose a broad one.
| Var | Required | Purpose |
|---|---|---|
KHWAN_API_KEY | yes | Your key from the Khwan dashboard (kwk_live_…). |
KHWAN_CORE | no | Select a named core. Paid plans only — free has just default. |
KHWAN_USER | no | A separate brain inside the core — 3 on free, unlimited on paid. |
KHWAN_BASE_URL | no | Override the API base — e.g. http://127.0.0.1:8010 for a local engine. |
| Tool | When |
|---|---|
khwan_recall(query, limit=3) | seed a session/subagent — synthesised lessons + up to 3 relevant facts, as seed_text. |
khwan_remember(fact) | persist a durable fact/preference for future sessions. |
khwan_prepare(input) | full loop, before answering — memory context + a turn_token. |
khwan_record(turn_token, answer) | full loop, after answering — persists the turn so Khwan learns. |
khwan_memory(limit=20) | inspect what the brain currently remembers. |
khwan_cores() | list the isolated cores on the account. |
khwan_recall / khwan_remember are the token-smart pair for a caching host; khwan_prepare / khwan_record are the full loop for custom agents (pass the exact turn_token from prepare back into record).
khwan_recall returns at most three facts — that ceiling is the server's, so limit can lower it but not raise it — plus any lessons synthesis has distilled from many past turns. Lessons lead the seed_text: a rule earned over months outranks a single turn that happens to sit nearby in the index.
Retrieval applies a relevance floor, so an empty `facts` is an answer: the brain has nothing close to this question. Read it as "not known here" rather than as a failure, and do not fill the gap by leaning on whichever fact was nearest.
The floor is deliberately loose, because a memory wrongly dropped is invisible while a memory wrongly kept is not. Expect a returned fact to be *plausibly* related, not certainly relevant — read it before relying on it.
A new brain knows nothing, so its first weeks of recall are thin — while the answers are often already sitting in the host's own transcripts, unread. examples/backfill/ replays Claude Code transcripts into a brain: deterministic, no model calls, dry-run by default.
python3 examples/backfill/backfill_claude_code.py --map cores.json
The tools above are called *when Claude decides to*. For deterministic memory — no reliance on the model — use the hook preset in examples/claude-code-hooks/: a UserPromptSubmit hook injects memory on every prompt and a Stop hook records every answer.
⚠️ On a caching host this is the *thorough* option, not the *cheap* one — it
adds per-turn tokens. Prefer it when recall reliability matters more than token
cost (or on a non-caching client); otherwise use
khwan_recallat session
start.
github.com/khwanlabs/khwan-mcp — this server runs on your machine, with your key, reading what you type. Read it before you install it.
MIT — © Khwan Labs. See LICENSE.