LangAlpha

Why we included this project

LangAlpha treats investing as an iterative process rather than a one-shot question-and-answer exchange. Each research goal gets its own persistent workspace, so the agent can refine a thesis over weeks, revisit positions, and layer new analysis on earlier work. The core is built on LangGraph, and the agent writes and executes Python in isolated sandboxes, which keeps multi-step financial analysis in code instead of overloading the LLM context window. Developers and small teams who want structured, repeatable research, whether DCF models, initiating coverage, earnings analysis, or screen-driven pair-trade ideas, get book-aware candidates in an inline dashboard rather than one-off answers.