Deep Research Agent 用 Jev 做审核层

Sumanth @Sumanth_077原推发布于 2026.10.20

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I built a Deep Research Agent with Jev as the review layer! Nemotron does the research, Tavily for the web search, and Jev decides whether the research is actually good enough to return. The agent starts with a question, searches across multiple sources, and builds an initial answer from what it finds. But instead of immediately sending that answer back, it passes the research to Jev. Jev looks at the result as a decision problem: is the answer sufficiently supported, does it cover the original question, and is there enough evidence to stop researching? If the answer is no, the agent gets another research pass. So the workflow becomes: Research → Draft → Jev Review → Research again if needed → Final answer That separation is useful because the model doing the research doesn’t also get the final say on whether its own work is complete. For the stack, I used Nemotron through Nebius Token Factory for the main model, Tavily for the web search, LangChain to run the research loop, and LangSmith to trace what happens across each step. I built this while trying out the Nebius AI Builder Program. What made sense for this project is that the program is built around this kind of stack: open models through Token Factory, retrieval with Tavily, orchestration with LangChain, observability with LangSmith, and other components that can be swapped independently instead of locking the whole agent into one setup. The full project is open source. I’ve shared the GitHub repo in the comments!

这条是引用着另一条写的：x.com/sumanth_077/status/2107473852850909566