Open Deep Research
AI Summary
Together AI's April 2025 technical blog post presenting their open-source Deep Research implementation: a plan-search-self-reflect-write pipeline that assigns different LLMs to planner, summarizer, JSON-extractor, and report-writer roles, uses Tavily for web retrieval, and generates Mermaid charts, cover images, and podcast audio alongside the report. They benchmark it against LangChain's open_deep_research and HuggingFace's SmolAgents on FRAMES, SimpleQA, and HotPotQA using an LLM-as-judge, and show an ablation proving multi-step search meaningfully outperforms single-step RAG.
Original excerpt
TL;DR: Deep Research is an emerging AI workflow that enhances web search by producing comprehensive, well-cited content on complex topics. We present Together AI's Open Deep Research, an LLM workflow that can answer complex questions that require multi-hop reasoning and generate research reports. It features a flexible architecture designed to educate developers and be extended by the community, focusing specifically on providing an in-depth understanding of specific topics rather than just short answers to complex questions.
Our Together Open Deep Research is built on top of two core components: a planning component and a self-reflection component. Deep Research starts with a first set of…
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What is "Open Deep Research" about?
Together AI's April 2025 technical blog post presenting their open-source Deep Research implementation: a plan-search-self-reflect-write pipeline that assigns different LLMs to planner, summarizer, JSON-extractor, and report-writer roles, uses Tavily for web retrieval, and generates Mermaid charts,…
Who wrote "Open Deep Research"?
"Open Deep Research" was written by Together AI. It is curated in the AI Deep Research vault on Burn 451, which covers ai research agents & methodology.
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