Memograph

Name: Memograph
Author: Indhar01

作者：Indhar01版本 0.4.0i★ –i在你的电脑上运行

注册名称io.github.Indhar01/memograph

安装

概览

作者提供的说明（英文原文）

Graph-based memory system for LLMs with knowledge graphs and semantic search.

PopNx MCP 的三大支柱

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。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i非常新↓ 30/周 · ★ –

它能帮你做什么

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。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆
你需要填写：MEMOGRAPH_VAULT, ANTHROPIC_API_KEY, OPENAI_API_KEY

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： MEMOGRAPH_VAULT, MEMOGRAPH_READONLY, MEMOGRAPH_PROVIDER, MEMOGRAPH_MODEL, ANTHROPIC_API_KEY, OPENAI_API_KEY, OLLAMA_HOST

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

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "memograph": {
      "command": "uvx",
      "args": [
        "memograph",
        "memograph-mcp"
      ],
      "env": {
        "MEMOGRAPH_VAULT": "your-value",
        "MEMOGRAPH_READONLY": "your-value",
        "MEMOGRAPH_PROVIDER": "your-value",
        "MEMOGRAPH_MODEL": "your-value",
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value",
        "OLLAMA_HOST": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add memograph --env MEMOGRAPH_VAULT=your-value --env MEMOGRAPH_READONLY=your-value --env MEMOGRAPH_PROVIDER=your-value --env MEMOGRAPH_MODEL=your-value --env ANTHROPIC_API_KEY=your-value --env OPENAI_API_KEY=your-value --env OLLAMA_HOST=your-value -- uvx memograph memograph-mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "memograph": {
      "command": "uvx",
      "args": [
        "memograph",
        "memograph-mcp"
      ],
      "env": {
        "MEMOGRAPH_VAULT": "your-value",
        "MEMOGRAPH_READONLY": "your-value",
        "MEMOGRAPH_PROVIDER": "your-value",
        "MEMOGRAPH_MODEL": "your-value",
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value",
        "OLLAMA_HOST": "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。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "memograph": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "memograph",
        "memograph-mcp"
      ],
      "env": {
        "MEMOGRAPH_VAULT": "your-value",
        "MEMOGRAPH_READONLY": "your-value",
        "MEMOGRAPH_PROVIDER": "your-value",
        "MEMOGRAPH_MODEL": "your-value",
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value",
        "OLLAMA_HOST": "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。

需要你填写的值

MEMOGRAPH_VAULT 必填 env
Path to your MemoGraph vault directory (created if missing).
MEMOGRAPH_READONLY env
Set to 'true' to block destructive MCP tools (delete_memory, update_memory, bulk_create, batch_delete, batch_update). Recommended for shared or untrusted clients.
MEMOGRAPH_PROVIDER env
Optional LLM provider for query_with_context tool. Only needed if you want AI-generated answers.
MEMOGRAPH_MODEL env
Optional LLM model name (e.g. 'claude-sonnet-4-6'). Used together with MEMOGRAPH_PROVIDER.
ANTHROPIC_API_KEY 机密 env
API key for Anthropic Claude. Required when MEMOGRAPH_PROVIDER=claude or when using the [anthropic] extra.
OPENAI_API_KEY 机密 env
API key for OpenAI. Required when using the [openai] extra (embeddings or LLM).
OLLAMA_HOST env
Base URL for a local Ollama server (default http://localhost:11434). Only needed when MEMOGRAPH_PROVIDER=ollama or the [ollama] extra is installed.

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。

它能做什么

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。

基础检查i

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9日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 Indhar01 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
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“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 5759 个服务器的 0%
发布到注册中心的时间比此分类中其他 5759 个服务器的 19% 更新
基础检查结果等于或优于此分类中其他 5759 个服务器的 100%

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。

常见问题

Memograph 安全吗？

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。

它需要哪些密钥或代码？

你需要填写：MEMOGRAPH_VAULT, ANTHROPIC_API_KEY, OPENAI_API_KEY

它在我的电脑上运行还是在云端？

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）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.4.0）于5个月前进入注册中心。

哪些应用可以使用它？

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)。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：↓ 每周下载量i★ 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

Context7

Up-to-date docs for AI agents

作者：upstash广泛使用↓ 97.3万/周★ 6.4万3天前更新

电脑或云端

安装

Codebase Memory

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

作者：DeusData广泛使用↓ 1.8万/周★ 4.8万23天前更新

在你的电脑上运行

安装

Exa

Connect AI agents to Exa for web search, content fetching, and multi-step research.

作者：exa-labs广泛使用★ 52132个月前更新

在云端运行✓ exa.ai

安装

作者的 README

打开 README

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。

MemoGraph 🧠

MemoGraph turns a folder of markdown notes into a queryable, AI-ready knowledge graph. It solves the LLM memory problem — your AI assistants forget last Tuesday's decision, can't find a related note across two projects, and re-derive the same insight again and again — by giving them a persistent, navigable, attribution-friendly memory layer that lives in plain markdown files you control.

You write notes the way you already do. MemoGraph indexes them, builds a graph from [[wikilinks]], ranks them by salience, and serves them back to your LLM (or your team) on demand.

⚡ Try it in 60 seconds

pip install memograph

memograph quickstart

That's it. The quickstart command drops a small, interconnected sample vault on your disk (15 notes about Python development, with real wikilinks between them), ingests it, and runs three live demo queries so you can see the graph + hybrid retrieval working before you decide whether to commit. Try this query in particular:

memograph --vault ~/memograph-quickstart search "FastAPI dependency injection"

The vault contains a note titled FastAPI dependencies (about Depends(...)) — the words "dependency" and "injection" never appear in any note's title. MemoGraph still finds it, because hybrid retrieval understands "dependency injection" semantically and the wikilink graph stitches related notes together. That's the product, demonstrated in one query.

Re-run memograph quickstart --force any time to reset to a fresh demo. When you're ready, point MemoGraph at your real notes: memograph --vault ~/your-notes ingest.

What you get

As a solo user / knowledge worker

A vault of human-readable markdown files — nothing proprietary, no lock-in. Your notes outlive any tool.
Hybrid retrieval that combines keyword search, semantic similarity, and graph traversal so you find the right note even when you don't remember the exact words.
AI-assisted tagging, link suggestions, and gap detection that grow your knowledge base instead of letting it rot.
A CLI and a web UI for browsing, editing, and visualizing the graph.

As an AI agent / IDE user

A first-class Model Context Protocol (MCP) server with 30+ tools, working out of the box with Claude Desktop, Claude Code, Cursor, Cline, Windsurf, Continue, Zed, VS Code, Goose, Gemini CLI, OpenAI Codex CLI, and others.
Autonomous "auto-save" hooks that capture decisions and context from your AI conversations into the vault automatically.
Per-conversation memory recall — your assistant can pull "what did we decide last week about X" without you copy-pasting context every time.

As an enterprise / SaaS operator

Multi-tenant deployment with filesystem-level isolation per tenant, end-to-end isolation tests, and a warm-LRU kernel cache.
OIDC + API-key authentication with JWKS support (Auth0, Clerk, WorkOS, Keycloak, Azure AD, Okta), restrictive CORS, request-size caps, and rate limiting.
GDPR-compliant scheduled deletion: tombstone-with-grace-period flow, automatic final backups, daily reaper, and an audit log of every deletion.
Observability built in: OpenTelemetry traces + Prometheus /metrics, structured JSON logging with request IDs, and a separate /healthz / /readyz for orchestration.
Operations runbooks shipped with the code: install, SSO setup, RBAC, backup-restore, and GDPR procedures.

How consumers benefit

You want to…	MemoGraph gives you…
Stop your AI assistant from forgetting context across conversations	Persistent vault + MCP server, plus optional auto-save hooks
Find a note across thousands when you only half-remember it	Hybrid retrieval (keyword + semantic + graph) with salience ranking
Connect related ideas without manual cross-linking	AI link suggestions, backlink graph, BFS traversal
Discover what's missing in your knowledge base	Gap detector + topic clustering + learning-path suggestions
Self-host a memory backend for a team or product	Web UI, FastAPI HTTP API, OpenAPI v1 contract, Docker compose
Ship MemoGraph to multiple paying customers	Multi-tenant kernel registry, OIDC, quotas (roadmap), GDPR runbook
Survive an SOC 2 audit conversation	Audit log with user + tenant binding, observability, security workflow, compliance roadmap doc

✨ Capabilities at a glance

Core memory engine

Graph-based memory — bidirectional [[wikilinks]] build a navigable knowledge graph automatically.
Hybrid retrieval — keyword + semantic embeddings + graph traversal, combined and re-ranked.
Memory types inspired by cognitive science: episodic, semantic, procedural, fact.
Salience scoring (0–1) that decays over time and boosts on access.
Smart indexing — mtime-cached, only re-parses changed files.
Context compression — token-budget-aware windowing for LLM prompts.
Markdown-native vault — every memory is a .md file with YAML frontmatter; no proprietary format.

AI features

Smart Auto-Organization Engine — extract topics, people, action items, decisions, questions, sentiment, risks, ideas, and timeline events from memories.
AutoTagger — suggest tags via semantic analysis, structure detection, and pattern learning.
LinkSuggester — propose [[wikilinks]] to related notes; bidirectional opportunities included.
GapDetector — surface missing topics, weak coverage, isolated notes, and unmade links.
Knowledge analysis — vault stats, topic clustering, learning paths, connection analysis.

Interfaces

Python API — MemoryKernel with sync, async, and batch variants.
CLI — 24+ commands for ingest, search, batch ops, import, export, backup, and AI features.
MCP server — 30+ tools, stdio transport, drop-in for any MCP-compatible client.
Web UI — React + D3 graph visualization, search, and editing (FastAPI backend + Vite frontend).
HTTP API — versioned /api/v1/, OpenAPI snapshot in CI, ready for service-to-service integration.

Enterprise & SaaS readiness

Multi-tenancy with filesystem-isolated tenants, an LRU registry of warm kernels, per-tenant audit logs, and end-to-end isolation tests gating release.
Authentication via OIDC (JWKS) or hashed API keys; per-route auth scope; identity bound into the audit log.
Web hardening — restrictive CORS, slowapi rate limiting, request-size caps, structured JSON logging with request IDs, info-leak-free 500 handler.
Storage hardening — path-traversal-safe vault writes, vault size soft/hard caps, schema-versioned cache files.
Scheduled deletion for GDPR Art. 17: tombstone with configurable grace period, automatic final backup, daily reaper script, cancel-before-grace endpoint.
Observability — OpenTelemetry FastAPI/asyncio auto-instrumentation, Prometheus /metrics, OTLP export.
Reliability — concurrency audit, stress tests for concurrent writes, versioned backup format with integrity checks.
Distribution — pinned-and-locked dependencies, Docker compose for self-host, security workflow (bandit + pip-audit).

See docs/INSTALL_ENTERPRISE.md, docs/SSO_SETUP.md, docs/GDPR_RUNBOOK.md, docs/BACKUP_RESTORE_RUNBOOK.md, docs/OBSERVABILITY_GUIDE.md, and docs/RBAC_GUIDE.md for the operator-facing details.

🚀 Quick Start

Hosting it yourself? docs/HOSTING_GUIDE.md

covers four genuinely-free paths — Oracle Free Tier, Cloudflare

Tunnel + your hardware (recommended for most), GCP always-free

stitch, and GitHub-repo-as-vault. Workspace identity via OIDC and

Drive-as-portability-backup are documented in

docs/GOOGLE_WORKSPACE_SETUP.md.

Installation

pip install memograph

Install with optional dependencies:

# For OpenAI support

pip install memograph[openai]

For Anthropic Claude support

pip install memograph[anthropic]

For Ollama support

pip install memograph[ollama]

For embedding support

pip install memograph[embeddings]

Install everything

pip install memograph[all]

Python Usage

from memograph import MemoryKernel, MemoryType

Initialize the kernel attached to your vault path

kernel = MemoryKernel("~/my-vault")

Ingest all notes in the vault

stats = kernel.ingest() print(f"Indexed {stats['indexed']} memories.")

Programmatically add a new memory

kernel.remember( title="Meeting Note", content="Decided to use BFS graph traversal for retrieval.", memory_type=MemoryType.EPISODIC, tags=["design", "retrieval"] )

Retrieve context for an LLM query

context = kernel.context_window( query="how does retrieval work?", tags=["retrieval"], depth=2, top_k=8 )

print(context)

🔌 MCP Server (Model Context Protocol)

MemoGraph includes a full-featured MCP server for seamless integration with AI assistants like Cline and Claude Desktop.

📖 New to MemoGraph MCP? See the [MCP User Guide](docs/MCP_USER_GUIDE.md) for practical usage instructions and examples!

🚨 Having connection issues? See [Setup & Troubleshooting Guide](docs/MCP_SETUP_TROUBLESHOOTING.md) - Common fixes for "cannot connect" errors!

19 Available Tools

Category	Tools	Description
Search	`search_vault`, `query_with_context`	Semantic search and context retrieval
Create	`create_memory`, `import_document`	Add memories and import documents
Read	`list_memories`, `get_memory`, `get_vault_info`	Browse and retrieve memories
Update	`update_memory`	Modify existing memories
Delete	`delete_memory`	Remove memories by ID
Analytics	`get_vault_stats`	Vault statistics and insights
Discovery	`list_available_tools`	List all available tools
Autonomous	`auto_hook_query`, `auto_hook_response`, `configure_autonomous_mode`, `get_autonomous_config`	Autonomous memory management
Graph	`relate_memories`, `search_by_graph`, `find_path`	Graph-native linking and traversal
Bulk	`bulk_create`	Create multiple memories in one call

Supported Clients

MemoGraph's MCP server is a stdio server — it runs alongside any MCP-compatible agentic CLI or editor. The full setup cookbook (config-file paths, format quirks, verification steps) lives in [docs/MCP_CLIENTS.md](docs/MCP_CLIENTS.md):

Client	Format	Quick reference

| Claude Code (CLI) | mcpServers | [claude_code_config.json](memograph/mcp/

只显示了开头部分；其余内容请在仓库中阅读