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Step-by-Step Guide to Build AI Agent using LangGraph | Python Tutorial

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Introduction

One of the limitations of conventional zero-shot prompting methods is their effectiveness on simple tasks compared to more complex tasks that involve multiple steps and reasoning. For instance, in the task of writing a blog post, the process entails several steps: researching the topic from credible sources, creating an outline with headings and subsections, writing an initial draft, analyzing and improving the draft, and finally proofreading and editing to generate polished content. Asking a large language model (LLM) to produce a full blog post with a single prompt may not yield the best results.

In contrast, planning for each subtask and deploying individual LLMs that can interact with one another to tackle the overarching task can yield higher quality outputs. This iterative and multi-step approach, which instructs LLMs to solve complex tasks with high accuracy, is referred to as agentic workflow. Various studies have demonstrated that the accuracy of responses produced by agentic applications significantly surpasses that of traditional zero-shot prompting methodologies.

To facilitate the implementation of agentic workflows, the LangChain team developed a Python library named LangGraph. This tool allows us to implement agentic workflows as directed graphs. In this article, we will provide a step-by-step guide on building a reactive agent workflow based on the thought-action-observation loop using LangGraph. The tutorial is particularly beneficial for individuals who are new to LangGraph. The agent will utilize available tools to retrieve financial data about specified stocks to respond to user queries. We will use the open-source LLM Llama 3.1, available through the Gro API, as our agent's decision-making and response-generating LLM.

Setting Up the Environment

At the start of our coding journey, we will organize the entire code within a Jupyter notebook. A sample image showcasing what a reactive agent looks like will be included, illustrating that whenever a user query comes in, it is processed through the thought-action-observation loop.

Initially, we will load the LLM using the Gro API after obtaining an API key from the Grock console. We will work specifically with the Llama 3.1 model and install the necessary dependencies, including the langchain-grock module to integrate our LLM with tools.

Next, we'll define various financial tools that will be leveraged by our LLM to extract the most recent financial information about stocks. These tools will retrieve details such as total debt, total revenue, EBITDA, and other crucial metrics.

Tool Definitions

We will import the necessary libraries and decorate the functions that retrieve specific information about stock tickers with a tool decorator from LangChain. Each tool will have a defined structure, which ensures LLMs can understand how to utilize them effectively.

  1. Company Address: Retrieves the address of the specified company.
  2. Full-Time Employees: Provides the number of full-time employees at the company.
  3. Last Close Price: Returns the last closing price of the stock.
  4. EBITDA: Obtains the Earnings Before Interest, Taxes, Depreciation, and Amortization.
  5. Total Debt: Retrieves the total debt of the specified company.
  6. Total Revenue: Returns the total revenue generated by the company.
  7. Debt to Equity Ratio: Provides the debt-to-equity ratio of the company.

The tools will be collected into a list and bound to our LLM, ensuring that every time the LLM is invoked, it can decide which tools to call based on the user's query.

Constructing the Agent

We will define our reactive agent using LangGraph by following these steps:

  1. Import the required classes.
  2. Create the agent state to manage the current status and chat history.
  3. Build two primary nodes—Llama3 (for calling the LLM) and Action (for executing tool calls).
  4. Establish conditional edges that will determine the workflow depending on whether tool calls are needed.
  5. Set the entry point for the graph to Llama3.

Querying the Agent

After constructing the agent, we will be able to query it with various financial questions. The agent will first receive the human query, process it through the defined nodes, and determine whether to use any tools. If necessary, it will execute the relevant tool calls and return the data to generate a coherent response.

Example Queries

  1. Simple Query (No Tool Calls): "What’s up, Llama 3.1?"—no tool calls are generated.
  2. EBITDA Query: "What is the EBITDA of Nvidia?"—the agent will call the respective tool for response generation.
  3. Multiple Tool Calls: "What are the EBITDA and debt-to-equity ratio of Google?"—the agent will execute multiple tool calls sequentially.

By visualizing the agent's behavior through these examples, we gain insights into its operational flow, the interaction with tools, and the final results generated based on user queries.

In this tutorial, various aspects of implementing an AI agent with LangGraph have been covered in detail, steered towards effective practices for harnessing the power of LLMs and financial tools in a cohesive manner.

Conclusion

By following the steps outlined in this guide, you can successfully create a responsive AI agent that interacts with financial data sources efficiently. This workflow benefits from the strengths of both LLMs and structured decision-making, thus enhancing the capabilities of automated systems in tackling complex tasks.

Keywords

agentic workflow, LangChain, LangGraph, Python, LLM, financial tools, Gro API, implementation, directed graph, reactive agent.

FAQ

What is an agentic workflow?

Agentic workflow refers to a structured approach to managing complex tasks by leveraging multiple AI agents that interact with each other to yield high-quality results.

What are the main components of LangGraph?

LangGraph comprises nodes (representing agents or functions), edges (control flow mechanisms), and conditional edges that enable decision-making based on outputs from previous nodes.

Can I use other LLMs besides Llama 3.1 with LangGraph?

Yes, you can use other models available through Gro API that suit your specific requirements.

What types of information can the financial tools retrieve?

The financial tools can retrieve various metrics, including company address, full-time employees, last close price, EBITDA, total revenue, total debt, and debt-to-equity ratio.

How does the querying process work in the agent?

The process involves sending a user query to the agent, which checks if tool calls are required and executes them accordingly while maintaining a history of all messages exchanged.