Introduction 👋
This isn’t just another stock market app. This is about building a smart assistant that helps you navigate the stock market like a pro! We’ll walk through creating a stockbroker agent that understands your requests, fetches real-time data, and even prepares purchase details. But here’s the twist: you, the human, remain in control with built-in safeguards before any real money is spent.
1. Laying the Foundation: LangGraph.js 🏗️
Think of LangGraph.js as the blueprint for our stockbroker agent. It defines how the agent processes information and makes decisions.
Key Concepts:
- Graph: Imagine a flowchart where each step represents a specific action, like fetching stock prices or preparing a purchase. This is the core of our agent’s logic.
- Nodes: These are the individual steps in our flowchart. We’ll use nodes to get stock information, analyze it, and even execute mock purchases.
- Tools: Our agent needs access to real-world data. We’ll equip it with tools to search the web, access financial APIs, and more.
2. Building the Brain: Our Agent’s Logic🧠
2.1. Understanding Your Requests 🗣️
Our agent needs to understand what you want. We’ll use a large language model (LLM) like GPT-4 to process your requests and figure out the best course of action.
Example:
You ask: “What’s Tesla’s current stock price?”
The agent understands this and knows to use its “price snapshot” tool.
2.2. Accessing Financial Data 📈
We’ll use APIs like Financial Datasets to fetch real-time stock prices, company financials, and other relevant information.
Example:
The agent uses the “income statement” tool to find Apple’s revenue for last year.
2.3. Preparing Purchase Details 📝
Let’s say you want to buy shares of Google. Our agent will use the information you provide (company, quantity, maximum price) to prepare a mock purchase order.
Surprising Fact: Even though our agent can prepare purchase details, it won’t execute any real trades without your explicit confirmation!
3. The Human Touch: Staying in Control 🧑💻
3.1. The Power of Interrupts 🛑
Before executing any mock purchases, our agent will always ask for your confirmation. This ensures that you’re fully in control and prevents any accidental trades.
Example:
You ask the agent to buy 2 shares of Microsoft.
The agent will prepare the mock purchase details and then ask: “Please confirm you want to purchase 2 shares of Microsoft.”
Only after you confirm will the agent proceed (in this case, simulating the purchase).
3.2. Building Trust Through Transparency 🤝
At every step, our agent will clearly communicate what it’s doing and why. This transparency builds trust and allows you to understand the decision-making process.
Practical Tip: Use clear and concise language when interacting with your agent to avoid misunderstandings.
4. Deploying Your Stockbroker Agent 🚀
We’ll use LangSmith, a platform for building and deploying language model applications, to bring our stockbroker agent to life.
4.1. Deploying to the Cloud ☁️
With a few clicks, we can deploy our agent to LangSmith Cloud, making it accessible from anywhere.
4.2. Creating a User Interface 💻
We’ll build a simple chat interface using Next.js so you can easily interact with your agent.
Practical Tip: Experiment with different prompts and see how your agent responds!
Resource Toolbox 🧰
Here are some resources to help you get started:
- LangGraph.js: https://js.langchain.com/docs/ Learn more about building language model applications with JavaScript.
- Financial Datasets API: https://www.financialdatasets.ai/ Access real-time and historical financial data.
- LangSmith: https://smith.langchain.com/ Deploy and manage your language model applications.
Conclusion 🎉
Congratulations! You’ve built a powerful stockbroker agent that combines the intelligence of language models with the irreplaceable judgment of a human. As you continue to explore the world of language model agents, remember that the possibilities are limitless. Keep experimenting, keep learning, and never stop building!
