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Open-Source] Deep Research Assistant Built Solely for Gemini API

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Open-Source] Deep Research Assistant Built Solely for Gemini API

Hi everyone, I've been working on an open-source project called Deep Research OSS and would love to get some feedback from the community. I was frustrated by AI research tools that only performed a single/double-pass search and often missed important details. To solve this problem, I built a multi-agent pipeline that mimics how a human team works. First, it creates a broad plan. Then, it uses agents to conduct research. Finally, it has another agent perform a gap analysis to identify missing information and assign new tasks to dig deeper. Additionally, to avoid errors, AI is forced to use code execution for all numbers in the report. The whole thing is built specifically for the Gemini API to see how far it can be pushed. I've been using it for my own stock analysis, and the iterative process seems to generate much more comprehensive reports. It's still a work in progress, and I'm sure there are better ways to approach this. I'm looking for contributors and feedback to improve the pipeline and make it more robust. If you're interested in multi-agent systems or just want to help build a better research tool, you can check out the repo. https://github.com/zyakita/gemini-deep-research-oss

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, new · Missing: mac, macos, cursor
97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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