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I built an open source deep research implementation using the OpenAI Agents SDK that was released 2 weeks ago. It works with any models that are compatible with the OpenAI API spec and can handle structured outputs, which includes Gemini, Ollama, DeepSeek and others.
The intention is for it to be a lightweight and extendable starting point, such that it's easy to add custom tools to the research loop such as local file search/retrieval or specific APIs.
It does the following:
Carries out initial research/planning on the query to understand the question / topic
Splits the research topic into sub-topics and sub-sections
Iteratively runs research on each sub-topic - this is done in async/parallel to maximise speed
Consolidates all findings into a single report with references
If using OpenAI models, includes a full trace of the workflow and agent calls in OpenAI's trace system
It has 2 modes:
Simple: runs the iterative researcher in a single loop without the initial planning step (for faster output on a narrower topic or question)
Deep: runs the planning step with multiple concurrent iterative researchers deployed on each sub-topic (for deeper / more expansive reports)
I'll post a pic of the architecture in the comments for clarity.
Some interesting findings:
gpt-4o-mini and other smaller models with large context windows work surprisingly well for the vast majority of the workflow. 4o-mini actually benchmarks similarly to o3-mini for tool selection tasks (check out the Berkeley Function Calling Leaderboard) and is way faster than both 4o and o3-mini. Since the research relies on retrieved findings rather than general world knowledge, the wider training set of larger models don't yield much benefit.
LLMs are terrible at following word count instructions. They are therefore better off being guided on a heuristic that they have seen in their training data (e.g. "length of a tweet", "a few paragraphs", "2 pages").
Despite having massive output token limits, most LLMs max out at ~1,500-2,000 output words as they haven't been trained to produce longer outputs. Trying to get it to produce the "length of a book", for example, doesn't work. Instead you either have to run your own training, or sequentially stream chunks of output across multiple LLM calls. You could also just concatenate the output from each section of a report, but you get a lot of repetition across sections. I'm currently working on a long writer so that it can produce 20-50 page detailed reports (instead of 5-15 pages with loss of detail in the final step).
Feel free to try it out, share thoughts and contribute. At the moment it can only use Serper or OpenAI's WebSearch tool for running SERP queries, but can easily expand this if there's interest.