LangAlpha
经 AI Skill Hub 精选评估，LangAlpha 获评「推荐使用」。已获得 1.3k 颗 GitHub Star，这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 7.5 分，适合有一定技术背景的用户使用。
📚 深度解析
通过安装 LangAlpha，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。LangAlpha 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 LangAlpha 评为 AI 评分 7.5 分，属于同类工具中的优质选择。
📋 工具概览
LangAlpha 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
LangAlpha 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/ginlix-ai/LangAlpha
# 方式二：手动配置 claude_desktop_config.json
{
"mcpServers": {
"langalpha": {
"command": "npx",
"args": ["-y", "langalpha"]
}
}
}
# 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 LangAlpha 执行以下任务... Claude: [自动调用 LangAlpha 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": {
"langalpha": {
"command": "npx",
"args": ["-y", "langalpha"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
简介
<p align="center"> <img src="web/public/logo_words.png" alt="LangAlpha" height="120" /> <br> <strong>A vibe investing agent harness</strong> <br> LangAlpha is built to help interpret financial markets and support investment decisions. <br><br> <img src="https://img.shields.io/badge/python-3.12+-blue.svg" alt="Python 3.12+" /> <a href="https://github.com/langchain-ai/langchain"><img src="https://img.shields.io/badge/LangChain-1c3c3c?logo=langchain&logoColor=white" alt="LangChain" /></a> <img src="https://img.shields.io/badge/license-Apache%202.0-green.svg" alt="License" /> </p>
<p align="center"> <a href="#getting-started">Getting Started</a> • <a href="docs/api/README.md">API Docs</a> • <a href="src/ptc_agent/">Agent Core</a> • <a href="src/server/">Backend</a> • <a href="web/">Web</a> • <a href="libs/ptc-cli/">TUI</a> • <a href="skills/">Skills</a> • <a href="mcp_servers/">MCP</a> </p>
<p align="center"> <video src="https://github.com/user-attachments/assets/56ec23b5-e9af-46ab-8505-66a7dff822a4" autoplay loop muted playsinline width="900"></video> </p> <p align="center"><em>Pin a curated news brief from the dashboard, kick off idea generation, and dispatch parallel subagents to screen the market — then get five long/short pair-trade ideas in an inline interactive dashboard, calibrated to your book.</em></p>
Features Highlights
- Progressive Tool Discovery — Any MCP tools loaded as summary in context and full documentation dumped into the workspace, allowing the agent to discover and use tools truly on demand. Also supports binding json tools with skills and only expose to agent when skill is activated.
- Programmatic Tool Calling (PTC) — The agent writes and executes Python to process financial data from mcp servers instead of pouring raw data into the LLM context window, enabling complex multi-step analysis while dramatically reducing token waste.
- Financial data ecosystem — Multi-tier provider hierarchy with native tools for quick lookups and MCP servers for bulk data processing, charting, and multi-year analysis in sandboxes.
- Persistent workspaces — Each workspace maps to a dedicated sandbox with structured directories and a workspace notes file (
agent.md) that compounds research across sessions and threads. A separate long-term memory store (.agents/user/memory/,.agents/workspace/memory/) persists durable user preferences and cross-sandbox knowledge, and a user-managed memo store (.agents/user/memo/) lets you upload PDFs and markdown research notes that the agent can read on demand. - Skills for Financial Research — Pre-built workflows for DCF models, initiating coverage reports, earnings analysis, morning notes, document generation, and more — activatable by slash command or auto-detection.
- Finance Research Workbench — Web UI with inline financial charts, multi-format file viewer, TradingView charting, real-time WebSocket market data, shareable conversations, and subagent monitoring.
- Multi-provider model layer — Provider-agnostic LLM abstraction and automatic failover on error.
- Automations — Schedule recurring or one-shot tasks, or set price-triggered automations that fire when a stock or index hits a real-time price condition.
- Secretary — Flash agent doubles as a secretary: create and manage workspaces, dispatch deep PTC analyses in the background, monitor running tasks, and retrieve results — all through conversational commands with human-in-the-loop approval.
- Agent swarm — Parallel async subagents with isolated context windows, preloaded toolset/skills, mid-execution steering, checkpoint-based resume, and live progress monitoring in the UI.
- Live steering — Send follow-up messages while the agent/subagent is working to course-correct, clarify, or redirect without waiting for it to finish.
- Middleware stack — 25 composable layers handling skill loading, plan mode, multimodal input, auto-compaction, and context management support long-running agent sessions.
- Security & workspace vault — Encryption at rest via pgcrypto, automatic credential leak detection and redaction, sandboxed execution, and per-workspace secret storage for safe agent access
- Channel integrations — Use LangAlpha from Slack, Discord with complete feature support.
- Production-ready infrastructure — SSE-streamed agent activity with Redis-buffered reconnection replay, background execution decoupled from HTTP connections, and PostgreSQL-backed state persistence.
Getting Started
[!TIP] Don't want to self-host? Try the hosted version — it includes full data infrastructure (FMP, real-time market data, cloud sandboxes) out of the box. Bring your own LLM key (BYOK) and start immediately with no setup.
You can start LangAlpha with nothing but Docker — no API keys for data, no cloud sandbox. Just Docker for infrastructure and your own LLM subscription for the AI model.
git clone https://github.com/ginlix-ai/langalpha.git
cd langalpha
make config # interactive wizard — creates .env, configures LLM, data sources, sandbox, and search
make up # starts PostgreSQL, Redis, backend, and frontend- Frontend: http://localhost:5173
- Backend API: http://localhost:8000 (interactive docs at
/docs) - Verify:
curl http://localhost:8000/health
For the full experience, the wizard will prompt you for optional keys — or add them to .env later:
| Key | What It Unlocks |
|---|---|
DAYTONA_API_KEY | Persistent cloud sandboxes with cross-session workspace support ([daytona.io](https://www.daytona.io/)) |
FMP_API_KEY | High-quality fundamentals, macro, SEC filings, options ([free tier available](https://site.financialmodelingprep.com/)) |
SERPER_API_KEY or TAVILY_API_KEY | Web search |
LANGSMITH_API_KEY | Tracing and observability |
[!NOTE] Without external service keys you get a functional but reduced experience: Yahoo Finance provides free price history, fundamentals, earnings, and analyst data, but lacks real-time quotes, intraday tick data, macro economics, and options analytics. The Docker sandbox replaces Daytona cloud sandboxes — full PTC code execution works, but with a downgraded security and isolation. Add keys incrementally to unlock more capabilities.
Run make help to see all available commands. For manual setup without Docker, see CONTRIBUTING.md.
Channel Integrations
Use LangAlpha from the tools you already work in. The integration gateway relays messages between messaging platforms and the core agent, with each channel receiving responses in its native format. Channel integrations are available exclusively on our hosted service with one-click setup and quick account binding — visit integrations to get started.
| Feature | Slack | Discord | Feishu | Telegram | |
|---|---|---|---|---|---|
| Rich text / markdown | ✅ | ✅ | ✅ | ✅ | 🔜 |
| File upload (user → agent) | ✅ | ✅ | ✅ | ❌ | ➖ |
| File download (agent → user) | ✅ | ✅ | ✅ | ❌ | ➖ |
| Image rendering | ✅ | ✅ | ✅ | ❌ | ➖ |
| Human-in-the-loop interrupts | ✅ | ✅ | ✅ | ⚠️ | ➖ |
| Subagent tracking | ✅ | ✅ | ✅ | ✅ | 🔜 |
| Workspace / model selection | ✅ | ✅ | ✅ | ✅ | 🔜 |
| Automation delivery (outbound) | ✅ | ✅ | ❌ | ➖ | ➖ |
| Simplified account linking | ✅ | ✅ | ❌ | ❌ | ➖ |
| Slash commands | ✅ | ✅ | ✅ | ✅ | ➖ |
Slack and Discord offer native channels and thread-level groups, which map naturally to LangAlpha workspaces and threads — context is managed natively. Telegram and WhatsApp lack these primitives, so they run a simplified orchestration mode. Feishu has full messaging and card-based UI with OAuth coming soon. Telegram has partial support with full coverage coming soon. WhatsApp is planned.
高质量的开源MCP工具，具有较强的实用价值
- 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
- 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
- Python 依赖冲突：建议用 venv / uv 隔离环境
- 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
- 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
- Python 依赖冲突：建议用 venv / uv 隔离环境
👥 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
AI Skill Hub 点评：LangAlpha 的核心功能完整，质量良好。对于Claude Desktop / Claude Code 用户来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | LangAlpha |
| 原始描述 | 开源MCP工具：Claude Code for Finance。⭐1.3k · Python |
| Topics | mcpagentinvestmentlangchainlangraphllmpython |
| GitHub | https://github.com/ginlix-ai/LangAlpha |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-06-09 · 更新时间：2026-06-13 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。