A2A-MCP-LangChain-Multi-Agent-Financial-Analysis-Chatbot

## A2A + MCP + LangChain = Powerful Multi-Agent Financial Analysis Chatbot! We're building a multi-agent financial analysis system where different AI agents work together to provide comprehensive stock market insights. Think of it like assembling a team of specialists: - Financial Expert Agent (gives advice and analysis) - Data Fetcher Agent (gets current stock prices) - News Agent (finds latest financial news) - Coordinator Agent (orchestrates everything) ### The Architecture: Three Main Components #### 1. A2A (Agent-to-Agent) Protocol: What it is: A way for AI agents to talk to each other Why we need it: Instead of one big AI doing everything, we have specialists Real-world analogy: Like having a financial advisor who can call a data analyst or news reporter when needed #### 2. MCP (Model Context Protocol): What it is: A way to give AI agents specific tools/functions Why we need it: Our agents need to DO things (fetch data, scrape news) Real-world analogy: Like giving your assistant access to specific databases and websites #### 3. LangChain Integration: What it is: A framework that coordinates multiple AI agents and tools Why we need it: It makes all the agents work together smoothly Real-world analogy: Like a project manager coordinating different departments Try this notebook code on SingleStore Notebooks. [Signup to SingleStore for free!](https://portal.singlestore.com/intention/cloud?utm_medium=referral&utm_source=pavan&utm_term=github&utm_content=MultiAgent)