Open deep research has emerged as a significant application in the realm of agent-based systems, gaining popularity with products from companies like OpenAI, Anthropic, Perplexity, and Google, as well as numerous open-source implementations. It involves a flexible, multi-agent approach to research that adapts its strategies based on the user's request, enabling detailed and comprehensive reports by leveraging various models, search tools, and MCP servers. The process features three main phases: scoping to gather necessary context, research using a supervisor and sub-agents to delve into sub-topics, and report writing to compile the findings into a coherent deliverable. The system's architecture allows parallelized research efforts, ensuring depth and efficiency, and emphasizes context engineering to manage token usage and enhance performance. The open deep research platform, built on LangGraph, encourages experimentation and customization, offering users the opportunity to test and deploy their agents via the Open Agent Platform.