How to Build a Multi-Agent AI Research Team Using LangGraph and Gemini for Automated Reports

Overview of the Multi-Agent Research Team

This tutorial demonstrates how to build a multi-agent AI research team system leveraging LangGraph and Google’s Gemini API. The system comprises role-specific agents including Researcher, Analyst, Writer, and Supervisor, each tasked with a distinct phase of the research workflow. Together, these agents collaborate to gather data, analyze insights, synthesize comprehensive reports, and manage the overall workflow.

Setting Up the Environment

To start, install the necessary libraries such as LangGraph and LangChain's Google Gemini integration. Securely input the Google API key using the getpass module to authenticate the Gemini language model without exposing sensitive credentials.

Defining Shared Data Structures

Two TypedDict classes are created to maintain structured shared state and standardized responses across all agents:

• AgentState tracks messages, current workflow status, research topic, findings, and the final report.
• AgentResponse standardizes each agent's output.

A helper function initializes the Gemini LLM with configurable parameters like model type and temperature.

Creating Specialized Agents

Researcher Agent

The Research Specialist AI deeply analyzes the given topic, identifies key research areas, and suggests directions for further investigation. It uses a structured prompt to ensure clear, bullet-pointed responses and updates shared state with its findings.

Analyst Agent

The Data Analyst AI examines research findings, identifies patterns, trends, and key metrics, and provides data-driven conclusions with actionable recommendations. It focuses on quantitative analysis and evidence-based insights.

Writer Agent

The Report Writer AI synthesizes the research and analysis into a professional, well-structured report including an executive summary, findings, and conclusions. The agent ensures clarity and accessibility of complex information.

Supervisor Agent

The Supervisor AI orchestrates the workflow by coordinating tasks between agents, ensuring quality standards, and deciding the next steps based on progress. It routes the workflow among Researcher, Analyst, Writer, or finishes the process.

Assembling the Workflow

The multi-agent workflow graph connects all agents logically. The system uses LangGraph's StateGraph to manage transitions and MemorySaver to persist conversation history. The entry point is set to the Supervisor agent, which manages the flow.

Running the Research Team

The process is initiated with a research topic and an optional thread ID. The system streams execution step-by-step, allowing real-time observation of each agent's output. The final state includes collected findings and a final report.

Additional Features

• Interactive Sessions: Run multiple research topics interactively, with options to view full reports.
• Custom Agents: Create new agents with specific roles and instructions to extend the framework.
• Graph Visualization: Visualize the team workflow graph to understand agent connections.
• Performance Monitoring: Track runtime, message count, findings, and report length to evaluate efficiency.
• Quick Start Demo: Run predefined topics to showcase the system’s capabilities.

This modular AI research assistant framework streamlines complex research tasks by automating data gathering, analysis, and report generation with minimal human intervention. It is adaptable for customization and real-world deployment.