unknown

Step-by-step walkthrough of building a deep research multi-agent system in Python using open-source models, smolagents, and Firecrawl. The pipeline works in three stages: a Kimi K2 model generates a detailed research plan from a user query, a second LLM call with structured output splits the plan into independent subtasks, and a coordinator agent dynamically spawns one sub-agent per subtask. Each sub-agent uses Firecrawl's MCP tools (search and scrape) to gather web data, then returns a markdown report to the coordinator, which synthesizes everything into a final comprehensive research document. The tutorial covers HuggingFace Inference Providers, Pydantic structured outputs, MCP tool integration in smolagents, and prompt templating for both coordinator and sub-agent roles.

Create an Open Deep Research Multi-Agent in Python (Step by Step)

At WeProDev squad we share all helpful and nice articles for software developers
 