Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Markdown memory for AI agents. Files you can read, edit, grep, and commit. Not a database.
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。
AGENT_MEMORY_DIR, AGENT_MEMORY_SCOPE, AGENT_MEMORY_LOG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": {
"agent-memory": {
"command": "npx",
"args": [
"-y",
"@xultrax-web/agent-memory-mcp"
],
"env": {
"AGENT_MEMORY_DIR": "your-value",
"AGENT_MEMORY_SCOPE": "your-value",
"AGENT_MEMORY_LOG": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add agent-memory --env AGENT_MEMORY_DIR=your-value --env AGENT_MEMORY_SCOPE=your-value --env AGENT_MEMORY_LOG=your-value -- npx -y @xultrax-web/agent-memory-mcp运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"agent-memory": {
"command": "npx",
"args": [
"-y",
"@xultrax-web/agent-memory-mcp"
],
"env": {
"AGENT_MEMORY_DIR": "your-value",
"AGENT_MEMORY_SCOPE": "your-value",
"AGENT_MEMORY_LOG": "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。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"agent-memory": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@xultrax-web/agent-memory-mcp"
],
"env": {
"AGENT_MEMORY_DIR": "your-value",
"AGENT_MEMORY_SCOPE": "your-value",
"AGENT_MEMORY_LOG": "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。
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。
AGENT_MEMORY_DIR envAGENT_MEMORY_SCOPE envAGENT_MEMORY_LOG 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。
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 xultrax-web 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(0.15.0)于5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。你的电脑上还需要有 Node.js。
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。
Guardrails for AI coding agents — write the rule once, every tool obeys, destructive actions get blocked.
Your AI coding agent will eventually try to rm -rf the wrong folder, force-push to main, or delete the file you actually needed. agent-memory-mcp is the memory layer where you write the rule once, in plain markdown — and it _enforces itself_:
AGENTS.md, CLAUDE.md, .cursor/rules/, and .gemini/ automatically — Claude Code, Cursor, Cline, Copilot, Gemini and Windsurf read the same rules, no plugin.cat it, grep it, edit it in vim, commit it to git, sync it across machines with agent-memory sync. No database, no daemon, no cloud — and if the AI gets it wrong, you fix it in a text editor.Memory — the rules, recipes, decisions, and context that survive every session and every tool — is the substrate. Enforcement is the point. Reference implementation of the Compliance Receipt Protocol 1.0, so other MCP servers can adopt the same receipts and interoperate.
# After adding the server to your MCP client (see Install below), from your project:
agent-memory init # drop in a starter guardrail pack (protect main, no rm -rf, no prod-data destruction, …) agent-memory install-hooks # make hard rules actually BLOCK in Claude Code (soft rules → ask)
Your agent is now guarded across every tool. Write your own rules with save_rule — they enforce _and_ emit to AGENTS.md / CLAUDE.md / .cursor/rules / .gemini. (CLI commands assume a global install — npm i -g @xultrax-web/agent-memory-mcp — or prefix with npx -y.)
What v0.10 and below shipped: a great file-based memory store. What v0.11+ added: rules that _enforce themselves_. A rule memory type carries severity (hard / soft), scope, applies_when, matches regex patterns, enforce_on categories, and last_verified date. From those, the server projects companion files out to every AI tool and gates destructive operations via cryptographic receipts.
agent-memory save-rule no-emojis-ever \
--description "Never use emojis in commits, comments, or chat output." \ --severity hard \ --scope global \ --enforce-on commits,chat_responses \ --content "No emojis. Anywhere. Ever."
agent-memory emit-companions
| Target | Path | Auto-loaded by |
|---|---|---|
agents | AGENTS.md | Claude Code, Codex CLI, Cursor, Aider, Devin, Copilot, Gemini CLI, Windsurf, Amazon Q |
claude | CLAUDE.md | Claude Code (5-level hierarchy · managed/global/project/local/subdir) |
cursor | .cursor/rules/operator-hard.mdc (alwaysApply: true) + operator-conventions.mdc (agent-requested) | Cursor (MDC format) |
gemini | .gemini/instructions.md | Gemini CLI |
Set AGENT_MEMORY_AUTO_EMIT_DIR=/path/to/project and the server re-emits all four files automatically on every rule save.
check_action · the protocol enforcement point# Agent proposes an action · server matches against rule store
agent-memory check-action "delete the memory called old-project-notes" --type deletions
MCP shape:
{
"name": "check_action", "arguments": { "action": "delete the memory called old-project-notes", "action_type": "deletions", "session_id": "sess_abc", }, }
Tier 1 (deterministic, every client): action matched against rule.matches regex, filtered by rule.enforce_on. Hard violations block. Soft violations warn. Approved actions get a fresh receipt with 60s TTL.
Tier 2 (Sampling-enriched, shipped v0.11.7): for rules with applies_when natural-language conditions, the server uses MCP Sampling to ask the client's LLM whether the proposed action triggers the rule. Falls back to Tier 1 only if the client doesn't advertise Sampling capability. Works on Claude Desktop and VS Code Copilot; on Claude Code, Cursor, Cline, and Codex CLI you get Tier 1 only — which is enough to enforce the rules you've written.
But check_action only gates _this server's own_ tools. To enforce your rules on the agent's real actions — the shell commands and file writes it runs — `agent-memory install-hooks` wires a PreToolUse hook into Claude Code: a hard rule denies the matching tool call, a soft rule asks you. Now an rm -rf or a force-push to main is actually _blocked_, not just advised. Run agent-memory init first for a starter ruleset.
Receipts are short-lived, signed bearer tokens with caveats (Macaroon pattern · Birgisson et al., NDSS 2014). The novel protocol primitive: server-issued tokens that bind to action + session + rules-version-hash + expiry. Tampering breaks the signature. Rule changes invalidate every outstanding receipt (because rules_version is part of the signed payload).
import { issueReceipt, validateReceipt } from "@xultrax-web/agent-memory-mcp";
const r = issueReceipt({ caveats: [ { type: "action", value: "delete_memory" }, { type: "session", value: "sess_abc123" }, ], ttl_seconds: 60, });
const v = validateReceipt(r, { required_caveats: [{ type: "action", value: "delete_memory" }], }); if (!v.valid) throw new Error(v.reason);
Receipt-required `delete_memory` (v0.12.0 breaking change): calling delete_memory without a valid receipt is refused. The two-step pattern is check_action → delete_memory(name, receipt). The signing-key file lives at /.keyring/hmac-key (CRP 1.0) or /.keyring/ed25519-priv (CRP 1.1), 0600 perms on POSIX. Receipts are bound to their specific target (the action_hash caveat for delete memory ) and are single-use; the .keyring/ is never committed or synced, and rotate_key regenerates it after a suspected leak.
CRP 1.1 · Ed25519 federation (v0.13.0): flip CRP_SIGNING_MODE=ed25519 and the server signs with an asymmetric keypair instead of HMAC. The public key gets published at /.keyring/ed25519-pub, so other MCP servers can validate your receipts without sharing a secret. The protocol allows cross-server enforcement: server A issues a receipt for "delete X", server B validates and honors it.
audit · operational health for the rule storeagent-memory audit # pretty colored terminal output
agent-memory audit --json # structured JSON for tooling
Surfaces:
last_verified > 90 days, or never verifiedenforce_on AND an identical regex in matchescheck_action calls that blocked an action (spot over-aggressive rules)delete_memory without going through check_actionThe healthy flag is true iff no stale rules, no conflicts, no unreceipted ops.
The receipt protocol is documented standalone at docs/compliance-receipt-protocol-1.0.md. Other MCP servers can adopt the same format + validation rules to interoperate · agent-memory-mcp is the reference implementation. The spec covers: receipt structure, canonical encoding, signing (HMAC-SHA256 for 1.0, Ed25519 for 1.1), validation order, rules-version hashing, reserved caveat types, MCP integration patterns, security considerations, cross-server adoption, and test vectors.
.agent-memory/
├── MEMORY.md # auto-managed index ├── user-prefers-tabs.md ├── feedback-no-emoji-in-code.md ├── project-q3-launch-frozen.md └── reference-postgres-runbook.md
A memory file is just markdown with YAML frontmatter:
---
name: feedback-no-emoji-in-code description: User wants zero emoji in commits, comments, or output type: feedback
Hard rule. No emoji anywhere user-facing.
Why: prior contractor flooded the repo with them; user spent a weekend removing them.
How to apply: scrub before commit; reject any tool output that adds them automatically.
That's the whole format. No magic. Read it, edit it, ship it.
Most MCP clients have no persistent memory. The ones that do (Claude Code) store it where only that client can see it. The official server-memory and every community alternative use opaque structured backends. That's fine for some workflows — but it puts your data behind a layer you can't read with cat.
We chose markdown because:
If you want vector similarity search, semantic recall, or auto-relation extraction — use one of the database-backed memory MCPs. They're great at that. If you want memory that you can still read after a power outage, this is for you.
`
只显示了开头部分;其余内容请在仓库中阅读