Building a LangGraph Agent from Scratch: Integrating Gemini, ReAct, and LangChain

Building a LangGraph Agent from Scratch: Integrating Gemini, ReAct, and LangChain

As artificial intelligence evolves beyond static large language models into dynamic, goal-driven agents, a new wave of developers is turning to LangGraph — a powerful framework for orchestrating stateful, multi-step AI workflows. According to Google AI for Developers, LangGraph enables the creation of ReAct (Reasoning + Acting) agents that dynamically alternate between thinking and tool use, making them ideal for complex tasks like data retrieval, decision-making, and real-time interaction. By combining Google’s Gemini API with LangGraph’s stateful graph structure, developers can now build agents that reason through problems, plan actions, and adapt responses based on environmental feedback — all without hardcoded logic.

While the original tutorial from Towards Data Science provides a foundational overview of LangGraph’s architecture, the practical implementation detailed on Google’s official developer portal offers a more robust, production-ready blueprint. The ReAct agent example showcased there demonstrates how to chain together prompts, tool calls, and memory states within a directed graph, where each node represents a decision point — whether it’s generating a thought, invoking a calculator, searching the web, or returning a final answer. This modular design allows for easy debugging, scalability, and integration with external APIs, making it a preferred approach over monolithic agent designs.

Although Medium’s article on LangChain agents was inaccessible due to rate-limiting, its title suggests a complementary perspective: LangChain, a popular open-source framework for chaining LLM components, often serves as a precursor to LangGraph. Many developers begin with LangChain’s agent templates before migrating to LangGraph for more complex, stateful workflows. LangGraph’s advantage lies in its explicit handling of cycles and conditional branches — features that LangChain’s linear chains struggle to manage elegantly. For instance, a LangGraph agent can loop back to re-evaluate its reasoning after receiving new data, whereas traditional LangChain agents typically require external orchestration to achieve similar behavior.

From a systems engineering standpoint, constructing a LangGraph agent involves four core components: a language model (such as Gemini), a set of tools (e.g., search, code execution, database queries), a state schema to track conversation history and intermediate results, and a graph structure that defines transitions between states. Google’s documentation walks developers through defining these elements in Python, using LangGraph’s StateGraph class to map nodes and edges. Each node can be a function that calls the model, processes input, or invokes a tool — and transitions are governed by conditional logic based on the agent’s output.

Real-world applications of such agents span customer service automation, scientific research assistants, and financial analysts that cross-reference market data and regulatory documents. One notable use case involves a financial compliance agent that uses Gemini to interpret SEC filings, queries a company database via a tool, and then reasons whether a transaction violates anti-money laundering policies — all within a single, traceable workflow.

While Wikipedia’s entry on "Building" provides no direct technical insight, its broad definition of construction as "the process of assembling or forming something" serves as a fitting metaphor: LangGraph agents are not merely coded — they are engineered, brick by brick, with each node and edge representing a deliberate architectural choice. As the field moves toward autonomous AI systems, the ability to construct, audit, and refine these agents will become a critical skill for AI engineers.

For practitioners, the key takeaway is clear: LangGraph is not just another library — it’s a paradigm shift in agent design. By embracing statefulness, modularity, and iterative reasoning, developers can move beyond simple chatbots toward truly intelligent, adaptive systems. The integration of Gemini’s multimodal reasoning with LangGraph’s control flow creates a powerful synergy, positioning this combination at the forefront of next-generation AI agent development.