Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Local-first MCP server giving AI agents contextual knowledge about the people in your life
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。
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": {
"people-context": {
"command": "uvx",
"args": [
"people-context"
]
}
}
}在终端中运行这条命令
claude mcp add people-context -- uvx people-context粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"people-context": {
"command": "uvx",
"args": [
"people-context"
]
}
}
}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": {
"people-context": {
"type": "stdio",
"command": "uvx",
"args": [
"people-context"
]
}
}
}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。
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7日
注册名称已通过 GitHub 账户 JinyangWang27 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。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
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(1.5.0)于昨天进入注册中心。
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 Python (uv)。
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。
English | 简体中文
Your agent already remembers your codebase. Now it can remember your people.
people-context is a local-first MCP server and CLI that gives AI agents durable memory about the people in your life: who someone is, how you know them, what you last agreed, and how they like to be talked to. One SQLite file on your machine. No account, no cloud, no network calls.
Ask an assistant "how should I approach Priya about the reporting delay?" and it has nothing: it does not know which Priya, that she is your counterpart at a partner org, that you agreed a new deadline last week, or that she prefers a short email over a call. That knowledge lives in your head, your inbox, and a notes file the agent cannot see.
people-context keeps it in one place the agent can query through narrow tools:
It is opinionated about trust: writes are audited, forget is a real delete, sensitive records sit behind an operator-only gate that a prompt cannot open, and ordinary commands never touch the network.
A packaged fictional dataset is the fastest way to see identity resolution, graph traversal, and bounded context without touching real data:
uvx --from people-context pctx demo --reset
The demo always writes its own dedicated database at {XDG_DATA_HOME or ~/.local/share}/people-context/demo.db. It ignores --db, PEOPLE_CONTEXT_DB, the config file, and --reset replaces only that file plus its -wal/-shm companions, so a real database is never read or modified. Seeding writes audited fictional people, handles, affiliations, facts, interactions, and a connected relationship graph, then prints the path-targeted server command and concrete tool calls that use the ids it just created:
Demo database: /home/you/.local/share/people-context/demo.db
Start MCP server: people-context-mcp --db /home/you/.local/share/people-context/demo.db resolve_person {"query": "Amina Hassan"} get_relationship_graph {"person_id": " ", "depth": 2} find_connection {"person_a": " ", "person_b": " "}
Person ids are generated per seed, so the printed values differ from the placeholders above. Start the printed server command in an MCP client and run the printed calls verbatim. See docs/cli.md.
Requires Python 3.11+ and uv. Pick your client; each is one step.
Claude Code
claude plugin marketplace add JinyangWang27/people-context
claude plugin install people-context@people-context-plugins
Restart Claude Code or run /reload-plugins. You get the server plus /people-context:who, /people-context:remember, and /people-context:reminders. Details: docs/claude-code-plugin.md.
Claude Desktop
Download people-context.mcpb from the latest release and open it. Claude Desktop installs the pinned release with its own uv runtime. Details: docs/desktop-and-editors.md.
Codex
codex plugin marketplace add JinyangWang27/people-context
codex plugin add people-context@people-context-plugins
Start a new Codex session. Details: docs/codex-plugin.md.
Cursor, Windsurf, VS Code, or any MCP client
Add the stdio server to your client's MCP config (.cursor/mcp.json, ~/.codeium/windsurf/mcp_config.json, .vscode/mcp.json, ...):
{
"mcpServers": { "people-context": { "command": "uvx", "args": ["--from", "people-context", "people-context"] } } }
Or let the CLI write it: uvx --from people-context pctx setup cursor (also windsurf, vscode, claude-desktop; add --dry-run to preview). VS Code uses a servers key with "type": "stdio". Per-editor snippets: docs/desktop-and-editors.md.
OpenClaw
openclaw plugins install clawhub:openclaw-plugin-people-context
The native plugin talks to the opt-in loopback HTTP server. Details: docs/openclaw-plugin.md.
CLI only
uv tool install people-context
pctx init # seed your own record, optionally import a vCard, then connect a client pctx --help
people-context and people-context-mcp are the server commands; pctx is the human-operated CLI.
Then try, in your agent:
Who is Amina?
Remember that Amina from Open City Lab prefers short emails and hates surprise calls.
What should I know before my meeting with Daniel tomorrow?
The second one is a single remember tool call: the name is resolved, the person is created only if nobody matches, and the affiliation and preference are recorded in one audited transaction. Ambiguous names come back as candidates, never a guess.
Or, without an agent: pctx remember "Amina Hassan" "prefers short emails" --org "Open City Lab" and pctx brief "Amina Hassan". Five worked scenarios live in docs/use-cases.
Prefer a table to a terminal? pctx browse --open opens a local page over the same database:
It binds 127.0.0.1 only, every request needs the per-launch token printed in its URL, and every write goes through the same audited use cases as the CLI and MCP tools. Sensitive records stay hidden unless the operator enables them. It runs until you press Done or Ctrl-C. See docs/cli.md.
| It remembers | It never does |
|---|---|
| Names, nicknames, aliases, and handles | Upload anything, anywhere |
| Relationships with a canonical, extensible vocabulary | Store raw imported emails, chats, or files |
| Organisations, roles, and time-bounded affiliations | Let a model enable sensitive disclosure or full export |
| Durable facts, observations, and traits with evidence | Commit imported or agent-extracted data without your review |
| Concise interaction summaries and a per-person timeline | Log private values or keep a soft-deleted copy after forget |
| Reminders, follow-ups, and your communication philosophy | Make a network request outside pctx reindex --semantic |
people-context | Assistant memory (ChatGPT, Claude) | Memory platforms (Mem0 and similar) | |
|---|---|---|---|
| Where data lives | One SQLite file you own | Vendor account | Vendor platform or your own deployment |
| Works offline | Yes | No | Self-hosted only |
| Knows *people* as first-class records | Identity, relationships, roles, graph, guidance | Free-text notes | Free-text or vector memories |
| Explains a match | Ranked candidates with a reason; ambiguity is surfaced | No | Similarity score |
| Import review gate | Stage, review, commit | n/a | Automatic extraction |
| Deletion | Hard delete plus audit redaction in one transaction | Request to vendor | API delete |
| Backup and move | pctx sync push / pull bundle | n/a | Deployment-specific |
The dated, sourced version with vendor documentation links is in docs/privacy-and-safety.md.
This project executes local Python with the launching user's filesystem permissions. Ordinary MCP discovery excludes elevated sensitive context and full export. Operator-gated tools require process environment flags; models cannot enable them through arguments. Vault export is intentionally CLI-only.
The database is plaintext SQLite by default. On Unix-like systems a new one is created 0600, so other local accounts cannot read it. That is a boundary between accounts, not encryption, so pair it with full-disk encryption or opt into SQLCipher at-rest encryption (uv sync --extra encrypted, key read only from PEOPLE_CONTEXT_DB_KEY). See database file permissions and optional at-rest encryption.
people-context-mcp --http --host 127.0.0.1 --port 8765. Unauthenticated and local-only by design; prefer stdio. See docs/cli.md.uv sync --extra semantic && pctx reindex --semantic downloads a pinned multilingual Model2Vec model once; server startup and search stay cache-only.pctx export-vault --output ~/PeopleVault writes a deterministic, browsable vault, and a read-only Obsidian plugin renders live briefs. See docs/obsidian-plugin.md.pctx import stage SOURCE PATH then review and commit, over email, mbox, vCard, .ics, LinkedIn, Outlook, and WhatsApp exports. Agents can stage extracted candidates the same way. See [docs/import.md](d只显示了开头部分;其余内容请在仓库中阅读