You Can Learn Deep Research AI Agent Design & Launch In 25 Min | Kimi K2 0905, LangChain, OpenSource

Building a custom deep research agent requires a multi-agent architecture consisting of a clarifier, a supervisor for task delegation, and specialized researchers. This framework leverages shared state management to track progress and context across nodes while utilizing tools like the Model Context Protocol (MCP) and web search APIs to execute complex research tasks. Performance benchmarks across Kimi K2, Claude 4, GPT-4.0, and GPT-5 reveal significant trade-offs: while GPT-4.0 offers the fastest execution at 28 seconds, it struggles with instruction adherence and source attribution. Claude 4 provides the most structured and thorough reports in 82 seconds, whereas Kimi K2 excels at following complex instructions despite longer processing times. Deploying these agents via FastAPI on Google Cloud and Next.js on Vercel enables developers to integrate automated, high-quality research capabilities directly into their own products.