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# How to Build an LLM Market Copilot MVP with LangChain, APIs, and Streamlit

**[freeCodeCamp](https://daily.dev/sources/freecodecamp)** · 35 min read · 3 upvotes · 0 comments

## Summary

A step-by-step guide to building a market copilot MVP that turns natural-language stock queries into structured market briefs. The architecture separates a LangChain/LangGraph ReAct agent backend (copilot.py) from a Streamlit UI shell (app.py). The agent calls EODHD API-backed tools for price returns, fundamentals, news headlines, and risk metrics, then composes a concise Markdown brief. Tool outputs are extracted from agent messages and passed directly to the UI as auditable artifacts, avoiding redundant API calls. Three demo workflows are shown: baseline brief, risk-first analysis, and news-only context. Practical notes cover ticker normalization, missing data handling, output drift prevention, and extension paths like multi-ticker comparison, scheduled briefs, caching, and FastAPI wrapping.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.freecodecamp.org/news/build-an-llm-market-copilot-with-langchain/>

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---

Tags: [#fintech](https://daily.dev/tags/fintech), [#langchain](https://daily.dev/tags/langchain), [#llm](https://daily.dev/tags/llm), [#python](https://daily.dev/tags/python)

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