Open-source DeepResearch – Freeing our search agents

Yesterday, OpenAI released Deep Research , a system that browses the web to summarize content and answer questions based on the summary. The system is impressive and blew our minds when we tried it for the first time.

One of the main results in the blog post is a strong improvement of performances on the General AI Assistants benchmark (GAIA) , a benchmark we’ve been playing with recently as well, where they successfully reached near 67% correct answers on 1-shot on average, and 47.6% on especially challenging “level 3” questions that involve multiple steps of reasoning and tool usage (see below for a presentation of GAIA).

DeepResearch is composed of an LLM (which can be selected from the current list of LLMs provided by OpenAI, 4o, o1, o3, etc) and an internal “agentic framework” which guide the LLM to use tools like web search and organize its actions in steps.

2. A simple text inspector, to be able to read a bunch of text file format , find it here .

However, this is only the beginning, and there are a lot of things to improve! Our open tools can be made better, the smolagents framework can also be tuned, and we’d love to explore the performance of better open models to support the agent.

While powerful LLMs are now freely available in open-source (see e.g. the recent DeepSeek R1 model ), OpenAI didn’t disclose much about the agentic framework underlying Deep Research…

So we decided to embark on a 24-hour mission to reproduce their results and open-source the needed framework along the way!

An Agent framework is a layer on top of an LLM to make said LLM execute actions (like browse the web or read PDF documents), and organize its operations in a series of steps. For a quick intro to agents, check this great interview by Andrew Ng and our introduction blog post to the smolagents library. For a more detailed dive in agents you can subscribe to our agents course that starts in just a few days: link here .