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

**[WeProDev](https://daily.dev/sources/weprodev)** · [@michaelandish](https://daily.dev/michaelandish) · 0 upvotes · 0 comments

## Summary

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.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=vHBRmXpDIFY>

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#mcp](https://daily.dev/tags/mcp), [#python](https://daily.dev/tags/python)

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