Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Durable AI memory: recall distilled facts in a later session or subagent, and remember new ones.
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。
KHWAN_API_KEY, KHWAN_CORE, KHWAN_USER, KHWAN_BASE_URL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 …”。
适用于网页版、桌面版和手机版 Claude:
https://mcp.khwan.ai/mcp/{core}/{user}地址中有只有你知道的 {…} 部分(例如你的账户),请复制后自行填写。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 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"
}
}
}
}在终端中运行这条命令
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-mcp运行前请先填写空着的值。
地址中有只有你知道的 {…} 部分(例如你的账户),请复制后自行填写。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"khwan": {
"url": "https://mcp.khwan.ai/mcp/{core}/{user}"
}
}
}粘贴到项目中的 .cursor/mcp.json,或 ~/.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"
}
}
}
}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。
地址中有只有你知道的 {…} 部分(例如你的账户),请复制后自行填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"khwan": {
"type": "http",
"url": "https://mcp.khwan.ai/mcp/{core}/{user}"
}
}
}粘贴到项目中的 .vscode/mcp.json
{
"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"
}
}
}
}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。
ChatGPT 只能连接互联网上的服务器:
https://mcp.khwan.ai/mcp/{core}/{user}地址中有只有你知道的 {…} 部分(例如你的账户),请复制后自行填写。
菜单名称可能因套餐和版本不同而略有差异。
你的选择通过 Cookie 保存在这台设备上,无需账户。
KHWAN_CORE envKHWAN_USER envKHWAN_BASE_URL 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。
我们还没有读取这个服务器的工具列表;我们会轮流检查各个端点。
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
注册名称已通过域名 khwan.ai 验证(注册中心会核实域名所有权)
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
这个地址需要填入你自己的账户信息,所以我们无法替你检查
这个地址需要填入你自己的账户信息,所以我们无法替你检查
尚未检查;我们会在检查端点时读取工具列表
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:KHWAN_API_KEY
两种方式都可以:安装在你的电脑上,或者连接云端版本。
GitHub 仓库最后更新于–,最新版本(0.3.12)于2个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
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
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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。
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.