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The Crypto Analyst Agent is the missing middle ground. It is a LangGraph-orchestrated multi-agent system that maintains live WebSocket connections to Binance and Coinbase, calculates MACD, Bollinger Bands, RSI, and VWAP in parallel on every candle close, detects price and volume anomalies using statistical thresholds and targeted LLM analysis, synthesises findings into structured trading signals, delivers formatted alerts to Slack and Telegram, and validates strategies.

In this assignment, you will design and implement a fully agentic RAG pipeline in Python using LangGraph — a stateful, graph-based orchestration framework built on top of LangChain.

In this assignment, students are required to design and develop a fully functional AI-powered Financial Analyst platform using LangGraph and modern AI engineering practices.

LangGraph is gaining significant traction for building robust, stateful AI agents and multi-agent systems. Its graph-based approach allows for complex workflows, cyclical processes, and better control over LLM interactions. Here's a list of top 20 trending LangGraph projects or assignment ideas for AI Engineers and LLM Developers, drawing inspiration from current trends and real-world applications: Core LangGraph Project Assignments Intelligent SQL Agent Overview: Translates

Struggling with LangGraph’s graph-based data models or writing efficient graph queries? You’re not alone. LangGraph assignments often...

In this blog, we’ll enhance our LangGraph chatbot by adding Human-in-the-Loop (HITL) capabilities. Learn how to pause AI workflows for expert input using interrupt(), seamlessly blend human reasoning into your LLM responses, and build more reliable, production-ready chat agents — all on top of the memory-powered chatbot we built previously.

Want your chatbot to remember what you said earlier? In this beginner-friendly guide, we build on our previous LangGraph chatbot and add memory using MemorySaver. Learn how to persist conversation history, manage sessions using threads, and inspect state — step-by-step.

Ready to give your chatbot superpowers? In this beginner-friendly guide, we’ll show you how to integrate external tools like web search into your LangGraph-powered chatbot. Learn what tools are, why they matter, and how to add them step-by-step — no prior AI experience needed!

Curious about how to build smart, memory-capable AI workflows? This beginner-friendly blog demystifies LangGraph, a powerful tool for creating stateful applications with large language models. We break down real code step-by-step, explain key concepts like state, nodes, and graphs in plain English, and help you build your own chatbot — no prior graph theory needed!