Skip to content

Introduction to LangGraph

LangGraph is a framework to create sophisticated agents that can reason, plan, and execute complex tasks. More LangGraph is a powerful framework for building stateful, multi-actor applications with Large Language Models (LLMs). It’s built on top of LangChain and allows one to define their agent's workflow as a graph. Think of each node in the graph as a step in a flowchart (a function or a tool) and the edges as the paths or decisions that connect these steps. This structure gives explicit, programmatic control over agent's reasoning process, making it easier to build, debug, and maintain.

1. What to Expect in this series

In this series, focus will be on harnessing the power of LangGraph within the Google Cloud ecosystem. This guide will use Google's powerful Gemini models as our agent's brain and leverage tools like Google Search for accessing real-time information. It will help build the understanding step-by-step, focusing on the "why" alongside the "how."

Goal is to create a simple, easy-to-follow guide for engineers, developers, and tech enthusiasts who are new to building with agentic AI. Let's keep things practical, helping one build a robust LangGraph application from the ground up.

2. Why Gemini and the Google Ecosystem?

Choosing the right tools is crucial when starting a new project. I feel the Google ecosystem, particularly the Gemini family of models, is an excellent choice for starting into Agentic AI development as it's powerful, got matured and variety of options and it has a free tier : )

Generous Free Tier

Google AI Studio provides a generous free tier for accessing the Gemini models. This allows one to experiment, build, and run applications without any initial financial commitment. The free usage limits are more than sufficient for learning, development, and even small-scale deployments.

Top-Tier Performance

Gemini Models are at the forefront of the AI race, holding their own against other top models from OpenAI and Anthropic.

Rich Multimodality

Google provides powerful models that can understand and process not just text, but also images, audio, and video. This opens up a vast range of possibilities for building advanced, multimodal agents in future projects.

3. Do I Need a Framework to Build an AI Agent?

This is a question I see a lot. While can certainly build agents from scratch, frameworks like LangGraph offer significant advantages, especially when starting out in this journey of Agentic AI development:

  • Accelerated Development: Frameworks handle the boilerplate code and low-level complexities, letting one focus on the agent's core logic. Frameworks help avoid reinventing the wheel.
  • Managing Complexity: As agent's capabilities grow, so does its complexity. LangGraph's graph structure provides a clear, visual way to manage complex workflows. I find the reusability of graphs to be a huge plus.

Flow Chart

graph TD
    A[Start: User Question] --> B{Agent};
    B --> C{Tool Node: Search};
    C --> B;
    B --> D[End: Final Answer];
  • Visualizing the Flow: The graph paradigm makes it intuitive to understand the agent's decision-making process.
  • Trade-offs to Consider: Like any framework (e.g., Spring Boot for web development), there are trade-offs. The absolute latest LLM features might not be integrated overnight. And for highly latency-sensitive applications, one might still want to consider a "raw" implementation for maximum control.

I've really enjoyed my time experimenting with LangGraph and thought I'd share my learning path in the hope that it helps one on theirs. Let's get started! :)