Build a LLM Market Copilot MVP with LangChain + EODHD + Streamlit (Part 2)

2026-02-11 · Source: AI Advances - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, FinTech & Digital Financial Services · Depth: Intermediate, long

Summary

This article details the development of a Streamlit application for a Large Language Model (LLM) Market Copilot MVP, building upon an existing LangChain agent that uses EODHD for financial data. The focus is on creating a user-friendly interface for the market agent, moving from a notebook-based approach to a demo-ready product surface. The UI features a query-first design, allowing users to input natural language questions like "Give me a 60-day brief on AAPL. Include fundamentals and 5 headlines." It employs a two-pane layout, displaying a narrative brief on the left and tool-backed numerical artifacts (prices, fundamentals, risk metrics, headlines) on the right. The architecture emphasizes a clean separation between the Streamlit UI layer and the `copilot.py` backend logic, ensuring reusability and maintainability. The article also covers practical considerations such as input normalization, error handling, managing tool costs, and preventing output drift.

Key takeaway

For AI Product Managers or Machine Learning Engineers building financial intelligence tools, this approach demonstrates how to quickly transition from an agent prototype to a functional MVP. By separating UI from backend logic and prioritizing a query-first design with visible, tool-backed metrics, you can create a compelling internal demo that builds trust and facilitates rapid feedback, informing future feature development like multi-ticker comparisons or API integration.

Key insights

A Streamlit UI can effectively wrap a LangChain agent for a demo-ready, data-validated market copilot.

Principles

• Separate UI from backend logic for reusability.
• Prioritize query-first design for user experience.
• Display tool-backed metrics alongside narrative for trust.

Method

Build a Streamlit app with a query input and optional parameters, then render narrative briefs and structured tool outputs in a two-pane layout by extracting artifacts from agent messages.

In practice

• Use `st.set_page_config(layout="wide")` for dual-pane output.
• Implement `normalize_ticker()` for robust input handling.
• Cache tool results to improve performance and reduce API calls.

Topics

• LLM Market Copilot
• LangChain Agents
• Streamlit UI
• Financial Data APIs
• MVP Development

Best for: Machine Learning Engineer, Software Engineer, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Advances - Medium.