LangGraph + SciPy: AI Agent for Statistical Decision Making

The intricate world of statistics and data science has always presented a formidable challenge, particularly when it comes to selecting the appropriate statistical test for a given dataset. With an overwhelming array of concepts, tests, and distributions, even seasoned professionals often found themselves consulting dense documentation or outdated cheat sheets. However, the advent of Large Language Models (LLMs) is rapidly transforming this landscape, offering a dynamic “second brain” capable of quickly distilling complex information and adapting it to specific needs.

Recognizing the inherent confusion in choosing the correct statistical test—a decision often contingent on variable types, underlying assumptions, and data distribution—a new AI assistant has been developed. This innovative tool leverages LangGraph, a powerful library designed for building sophisticated, multi-step LLM applications. Unlike rigid sequential processes, LangGraph structures AI workflows as a flexible graph, where each “node” represents a function or tool, enabling the agent to make dynamic decisions based on previous steps and conditions.

At its core, this statistical advisor agent operates on a clear, intelligent workflow. When presented with a statistics-related query, such as “How to compare the means of two groups,” the agent first assesses the question’s intent. It determines whether a direct answer suffices or if a deeper dive into documentation is required. Should the latter be the case, the agent employs a Retrieval-Augmented Generation (RAG) tool, querying an embedded knowledge base derived from SciPy’s extensive statistical documentation. This process involves chunking the documentation into manageable segments and converting them into numerical “embeddings,” which are then stored in a vector database like ChromaDB for efficient retrieval. The agent then leverages a powerful LLM, such as OpenAI’s GPT-4o, to synthesize the retrieved information into a coherent answer. Crucially, if the query pertains to a statistical test that can be performed using SciPy, the agent also provides a sample Python code snippet, directly translating theoretical advice into practical application.

The architectural elegance of the agent lies in LangGraph’s “state” management. Each function or “node” within the agent’s graph—from classifying the user’s intent to retrieving information, formulating a response, and generating code—reads from and writes to a central state dictionary. This shared state acts as the single source of truth, ensuring seamless communication and coordination across the entire workflow. For instance, the initial classification node identifies the user’s need, updating the state with an “intent” flag. Subsequent nodes then access this flag to determine whether to proceed with a documentation search or a direct response, ultimately contributing to the final answer and any accompanying code.

To make this powerful agent accessible, it has been integrated with a user-friendly front-end built using Streamlit, a popular Python library for rapid web application development. This interface allows users to input their statistical questions, providing a clear text box for queries and displaying the agent’s responses in a well-formatted manner. Users can also input their OpenAI API key, ensuring secure and personalized access to the underlying LLM.

The results are remarkably precise. For a question like, “What is the best test to compare two groups means?”, the agent accurately suggests the independent two-sample t-test for independent, normally distributed data, the Mann-Whitney U test for non-parametric cases, and the paired sample t-test for related groups. This demonstrates the agent’s ability to navigate nuanced statistical considerations and provide contextually relevant advice.

While LangGraph offers a highly flexible and scalable architecture—allowing developers to easily add new features by simply introducing new nodes—it demands a more structured approach to development. Unlike more abstracted frameworks such as Agno or CrewAI, LangGraph encourages developers to meticulously consider every step of the information flow, from initial query to final output. This deliberate design, though requiring a steeper learning curve, ultimately leads to cleaner, more robust, and adaptable AI agents, making it an ideal choice for complex, decision-driven applications where precision and scalability are paramount. The choice of framework ultimately hinges on the specific problem being solved and the desired degree of workflow control.