文瀾
AI Skill Hub 强烈推荐：文瀾 是一款优质的MCP工具。AI 综合评分 8.0 分，在同类工具中表现稳健。如果你正在寻找可靠的MCP工具解决方案，这是一个值得深入了解的选择。
📚 深度解析
通过安装 文瀾，你的 AI 助手将获得额外的工具调用能力，可以用自然语言直接操控该工具的功能，无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置，永久增强"——配置完成后，每次与 AI 对话时都可以无缝调用这些工具。
在技术实现上，MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信，工具的功能以"工具列表"的形式暴露给 AI 模型，AI 可以按需调用。文瀾 提供了结构化的工具调用接口，使 AI 模型能够精确地理解和使用每个功能点，显著降低 AI 在工具使用上的错误率。
与传统的 API 集成相比，MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON，即可让 AI 获得全新能力。AI Skill Hub 将 文瀾 评为 AI 评分 8.0 分，属于同类工具中的优质选择。
📋 工具概览
文瀾 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
📖 中文文档
文瀾 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
- 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
- 提供结构化工具调用接口，显著降低 AI 集成复杂度
- 支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
- 可与其他 MCP 工具组合叠加，构建完整 AI 工作站
- 轻量无侵入设计，不影响现有系统架构
- 在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
- 通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
- 将多个 MCP 工具组合使用，构建个人专属 AI 工作站
# 方式一：通过 Claude Code CLI 一键安装
claude skill install https://github.com/7xuanlu/wenlan
# 方式二：手动配置 claude_desktop_config.json
{
"mcpServers": {
"--": {
"command": "npx",
"args": ["-y", "wenlan"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
- 确认已安装 Node.js（v18 或以上版本）
- 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
- 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
- 保存配置文件并重启 Claude 客户端
- 重启后，在对话中即可使用本工具
# 安装后在 Claude 对话中直接使用 # 示例： 用户: 请帮我用 文瀾 执行以下任务... Claude: [自动调用 文瀾 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入："列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"__": {
"command": "npx",
"args": ["-y", "wenlan"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
简介
<p align="center"> <picture> <source media="(max-width: 600px)" srcset="./docs/assets/readme-banner-mobile.png"> <img src="./docs/assets/readme-banner.png" alt="Wenlan: your source-backed knowledge base, built to compound." width="100%"> </picture> </p>
Useful work with AI shouldn't disappear when a conversation ends. Wenlan builds the right pages and keeps them current as sources change, asking only when judgment is needed.
<p align="center"> English | <a href="./README.zh-Hans.md">简体中文</a> | <a href="./README.zh-Hant.md">繁體中文</a> | <a href="./README.es-ES.md">Español</a> </p>
<p align="center"> <a href="https://github.com/7xuanlu/wenlan/actions/workflows/ci.yml?query=branch%3Amain"><img alt="CI" src="https://github.com/7xuanlu/wenlan/actions/workflows/ci.yml/badge.svg?branch=main&event=push"></a> <a href="https://github.com/7xuanlu/wenlan/releases/latest"><img alt="Latest release" src="https://img.shields.io/github/v/release/7xuanlu/wenlan?sort=semver&label=release"></a> <a href="#license"><img alt="License: Apache-2.0 and AGPL-3.0" src="https://img.shields.io/badge/license-Apache--2.0%20%2B%20AGPL--3.0-blue.svg"></a> </p>
<p align="center"> <a href="#start-in-30-seconds">Get started</a> · <a href="#what-does-wenlan-build">What is this?</a> · <a href="#what-can-it-do">Capabilities</a> · <a href="#how-does-it-work">Daily workflow</a> · <a href="#evaluation">Evaluation</a> · <a href="#learn-more">Learn more</a> </p>
https://github.com/user-attachments/assets/d8b2ad4a-f97a-4a15-97a8-9105478de18a
<p align="center"> <sub>A maintained Page in the desktop app: open any citation to inspect the Source or Memory behind the claim.</sub> </p>
---
<a id="quickstart"></a> <a id="start-in-30-seconds"></a>
Capabilities
- Chat import: Bring in ChatGPT or Claude export ZIPs; Wenlan automatically skips conversations already imported.
- Document Sources: Ingest one
.md,.txt, or text-extractable.pdffile; recurse through a folder of them; or index Markdown from an Obsidian vault. - Incremental sync: Regular file and folder Sources track changes in the background; Obsidian vaults stay read-only and resync on demand.
- Atomic Memory: MCP clients save one complete decision, lesson, correction, preference, or fact, with provenance and supersession recording where it came from and what it replaces.
- Typed enrichment: A configured model classifies each Memory, then adds the structured fields defined for its type, plus dates, tags, retrieval cues, and graph links.
- Source-backed Pages: Distill related Sources and Memories into Markdown Pages with source references and
[[wikilinks]]; the daemon can verify and record per-claim citations. - Citation-gated refresh: Automatic refresh rejects citation-poor drafts; machine Pages update while human edits become reviewable revisions.
- Hybrid retrieval: FTS5 finds exact words, local BGE embeddings find meaning, and RRF fuses their ranks; graph links can add context.
- Retrieval channels: Optional Page, episodic, and per-fact channels widen recall; cross-encoder reranking can improve precision.
- Knowledge graph: Typed entities, relations, and observations connect people, projects, claims, and supporting Memories.
- Human-in-the-loop review: Routine work stays automatic; protected conflicts, Page revisions, entity merges, and new vocabulary wait for judgment.
- Spaces: Keep work, personal, client, and repository knowledge inside an explicit retrieval scope.
- Local daemon + MCP: One lightweight Rust daemon remains the local source of truth. The desktop app and CLI call it directly; AI clients use small MCP connectors to reach the same knowledge.
- Custom integrations: The localhost HTTP API accepts prepared text, webpage content, and Memories from other capture workflows.
- Background maintenance: The daemon keeps working after the desktop app closes, running configured sync, enrichment, citation work, and eligible Page refresh.
- Model choice: Base retrieval stays local; enrichment and synthesis can use on-device Qwen, a local endpoint, or a configured cloud model.
- Inspectable ownership: Memories and graph data stay in local libSQL; Markdown, citations, revisions, git history, and Obsidian exports remain inspectable.
- Read-only health checks:
doctorverifies the runtime;lintfinds malformed citations, orphan links, broken embeddings, and search-index or graph integrity problems without rewriting knowledge.
---
<a id="how-wenlan-works"></a> <a id="how-does-it-work"></a>
Your data and uninstall
Nothing is locked in. Pages and session notes are Markdown under ~/.wenlan/; memories live in one libSQL database under the platform data directory (~/Library/Application Support/wenlan/ on macOS, ~/.local/share/wenlan/ on Linux, %LOCALAPPDATA%\wenlan\ on Windows). Copy those two folders to back up or move a Wenlan. An install upgraded from Origin still holds a full copy of its data in ~/.origin/ and in the sibling origin data folder (~/Library/Application Support/origin/ on macOS, ~/.local/share/origin/ on Linux, %LOCALAPPDATA%\origin\ on Windows); delete or copy those two as well.
To uninstall: the app's Run Wenlan in background at login toggle removes the launch registration — turn it off, quit, and delete Wenlan.app or run the Windows uninstaller, then delete the folders above. wenlan background off only stops the daemon and disables autostart; it does not remove the launch registration, so a CLI-only install should instead follow the daemon uninstall bullet in PRIVACY.md. The paths Wenlan writes are listed there.
---
<a id="evaluation"></a>
daemon crates (default-members — the desktop app is not compiled)
cargo build cargo test
Workflow guides
- Build a client project knowledge base for consulting
- Build an investment research knowledge base
- Build a product research knowledge base before writing a PRD
- Build an SRE incident knowledge base
- Build a business metric definition knowledge base: turn approved KPI specifications into a source-backed data dictionary with formula text, grain, exclusions, owners, revisions, and review state.
Daily workflow
The system above becomes a small daily loop: start with relevant knowledge, capture what matters while you work, close with a handoff, and let Wenlan refine what should return next time. Each pass leaves the same knowledge base sharper instead of creating another disconnected history.
The loop has four steps:
- Capture and find knowledge while you work.
/capture <thing>saves a decision, lesson, gotcha, or fact with its source./recall <query>retrieves only what is relevant instead of loading your whole history. - Find current knowledge. Open a relevant Page, search, or use
/recall <query>;/brief [topic]reads the Brief — the Space's rolling project snapshot, first written by/handoff— and a topic appends separately labeled context from that same Space. Clients without plugin commands use the equivalent page, search, recall, and brief tools. - Close the loop.
/handoffrecords what changed and applies typed item-level updates to the current Space Brief. - Keep the wiki current.
/distilldeliberately creates or refreshes pages. Between sessions, optional model-backed passes can enrich captures, connect related entities, and refresh eligible pages./lintchecks knowledge health;/curatebrings proposed revisions and any conflict-review items created by the optional reconcile pass to you.
高质量的开源MCP工具
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- CLI：直接 npm install -g / pip install，命令行调用
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
- 提供结构化工具调用接口，显著降低 AI 集成复杂度
- 支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
- 可与其他 MCP 工具组合叠加，构建完整 AI 工作站
- 轻量无侵入设计，不影响现有系统架构
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
👥 适合人群
🎯 使用场景
- 在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
- 通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
- 将多个 MCP 工具组合使用，构建个人专属 AI 工作站
⚖️ 优点与不足
- +Apache-2.0 协议，可免费商用
- +标准化 MCP 协议，生态互联性强
- +与 Claude 官方生态无缝对接
- +即插即用，配置简单快捷
- −依赖 Claude 客户端，非 Claude 用户无法使用
- −MCP 协议仍在持续演进，接口可能变更
- −需要一定的配置步骤
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
❓ 常见问题 FAQ
总体来看，文瀾 是一款质量优秀的MCP工具，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
| 原始名称 | wenlan |
| 原始描述 | 开源MCP工具：Wenlan (文瀾) is a personal knowledge base for the AI-native age. Your AI agents c。⭐46 · Rust |
| Topics | mcpai-memoryclaude |
| GitHub | https://github.com/7xuanlu/wenlan |
| License | Apache-2.0 |
| 语言 | Rust |
收录时间：2026-06-25 · 更新时间：2026-06-26 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。