Open Research Multi-Agent System Tutorial
Alejandro AO’s YouTube video, “Create an Open Deep Research Multi-Agent in Python,” published on December 11, 2025, delves into building a multi-agent deep research system using open-source models and tools such as Firecrawl, Hugging Face, and smolagents. The concept is simple yet fascinating: create a comprehensive research pipeline that begins with a user-generated query, which is transformed into a detailed research plan using models like Kimi K2. This plan is then split into subtasks, each handled by sub-agents managed by a research coordinator. The coordinator compiles a final report by integrating findings from all sub-agents. The approach leverages Firecrawl’s web scraping and search functionalities and smolagents’ minimalistic, tool-integrating capabilities, emphasizing the robust possibilities of open-source resources.
Alejandro effortlessly guides viewers through setting up the environment, including obtaining API keys from Hugging Face and selecting models with sufficient context window capabilities. This choice is critical as agents generate considerable data in the form of function calls and messages. He also navigates through the potential errors and corrects them on the fly, showing resilience and encouraging experimentation.
The step-by-step tutorial, though lengthy, is comprehensive and insightful, aimed at enthusiasts eager to explore AI’s cutting-edge possibilities. Alejandro’s consideration of alternatives, like incorporating human-in-the-loop processes or using closed models, showcases a broader understanding of the practical intricacies involved in deploying a multi-agent system. He also suggests improvements, such as adding user feedback loops or utilizing different agent-creating frameworks, indicating his commitment to advancing AI research methodologies.
Overall, Alejandro AO’s video is an enlightening exploration into the realm of collaborative AI systems, creatively balancing educational value with technical proficiency, and making an impressive case for the potential of open-source tools in AI-driven research.