How to Build the Ultimate AI Automation with Multi-Agent Collaboration

In this article, Assaf Elovic introduces a method for building an autonomous research assistant using LangGraph with a team of specialized agents. The process is based on multi-agent workflows and leverages the GPT Researcher Python package for optimized, in-depth, and factual research report generation. The research team consists of seven LLM agents, each responsible for specific tasks such as planning, reviewing, revising, writing, and publishing the final report. LangGraph provides developers with a high degree of controllability and flexibility to create custom agents and flows. The architecture is divided into stages: planning the research, data collection and analysis, review and revision, writing the report, and finally publication. The process involves parallel work within a graph for each research task, which would be reviewed and revised based on predefined guidelines.