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LangAlpha

by ginlix-ai1.7kPythonUpdated 2026-09-08

Claude Code for Financial Market

LangAlpha is an AI agent harness designed for financial market interpretation and investment decision support, positioning itself as 'vibe investing' for progressive research workflows. Unlike one-shot finance tools, it provides persistent workspaces where research compounds across sessions, mirroring how code agents like Claude Code maintain context over time. The agent employs Programmatic Tool Calling (PTC) to write and execute Python code in sandboxes for complex financial analysis, supports parallel subagents for concurrent research tasks, and includes 23 pre-built financial research skills ranging from DCF models to earnings analysis. Built on LangGraph with a multi-provider LLM layer, it integrates native financial data tools, MCP servers for bulk data processing, and a web-based research workbench with live charts, automations, and channel integrations for Slack, Discord, and Telegram.

Key Features

Persistent workspace architecture with agent.md notes file that compounds research across sessions and threads
Programmatic Tool Calling (PTC) - agent writes Python code in Daytona cloud sandboxes to process financial data locally, reducing token waste
23 pre-built financial research skills including DCF models, initiating coverage reports, earnings analysis, and comps analysis
Multi-tier financial data ecosystem: native tools for quick lookups, MCP servers for bulk processing, Yahoo Finance fallback requiring no API key
Parallel async subagent swarm with isolated context windows, mid-execution steering, and live progress monitoring
Time-based and price-triggered automations that execute agent tasks on cron schedules or when stocks hit target prices
Agent-drawn chart annotations on live TradingView/candlestick charts with trendlines, Fibonacci levels, and event markers
Multi-provider LLM support with automatic failover, encrypted BYOK credentials, and OAuth integration for ChatGPT, Claude, Kimi, GLM

Use Cases

  • 01Building persistent investment theses that evolve over weeks as new earnings, filings, and market data arrive
  • 02Running parallel subagents to screen sectors, analyze competitive dynamics, and generate long/short pair-trade ideas
  • 03Scheduling recurring pre-earnings analyses for watchlist stocks with automated delivery to Slack or Discord
  • 04Creating initiating coverage reports with DCF models, comps analysis, and PDF deliverables generated from agent code
  • 05Monitoring real-time price triggers to execute research tasks when stocks cross technical levels or percentage thresholds
  • 06Annotating live market charts with support/resistance levels, trendlines, and catalyst events for technical analysis

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LangAlpha — FAQ

What is LangAlpha?+

LangAlpha is an AI agent harness for financial market research that provides persistent workspaces where investment analysis compounds across sessions. Unlike one-shot Q&A tools, it maintains context, files, and research notes over time, allowing iterative thesis development similar to how code agents work in software development.

How do I install and run LangAlpha?+

Clone the repository, run 'make config' to configure your LLM provider and optional data sources, then 'make up' to start the Docker-based stack. The web UI runs at localhost:5173 and requires only Docker and an LLM API key (OpenAI, Anthropic, or others) to get started.

Do I need API keys for financial data?+

No API keys are required to start - Yahoo Finance provides free price history, fundamentals, and analyst data. For higher-quality data, fundamentals, macro economics, and options analytics, add an FMP_API_KEY (free tier available). Real-time WebSocket feeds require ginlix-data access, currently available on the hosted platform.

Which AI clients or platforms does LangAlpha work with?+

LangAlpha is a standalone agent system accessed via its own web UI at localhost:5173 or the hosted platform at langalpha.ai. It integrates outbound with Slack, Discord, Feishu, and Telegram for message-based interaction, and supports BYOK for OpenAI, Anthropic Claude, Gemini, DeepSeek, Qwen, Kimi, Doubao, GLM, and MiniMax LLM providers.

Is LangAlpha free and open source?+

Yes, LangAlpha is Apache 2.0 licensed and fully open source. You can self-host it with Docker and bring your own LLM API keys. A hosted version with managed infrastructure, real-time data feeds, and cloud sandboxes is available at langalpha.ai.

What are the sandbox requirements for code execution?+

LangAlpha defaults to Docker-based sandboxes when self-hosted, which work for PTC code execution but with reduced isolation. For production-grade security and cross-session workspace persistence, add a DAYTONA_API_KEY to use Daytona cloud sandboxes. The hosted platform includes cloud sandboxes by default.

How do I install LangAlpha?+

Open the source repository on GitHub and follow its README. LangAlpha is a agent — MCP Agents Market links you directly to the official repo.

Is LangAlpha free?+

LangAlpha is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.

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