yiming-qing

yiming-qing / Research-Agent---1st-place-in-Alibaba-Cloud-Data-AI-Competition

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Research Agent for multi-hop reasoning QA — 1st place in Alibaba Cloud Data+AI Global Competition (College Track). Built on PAI-LangStudio with Qwen models, featuring ReAct reasoning, dual-engine search, and web content extraction.

13
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100% credibility
Found Mar 16, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This open-source project is a research agent that answers complex questions through web searching, page analysis, and multi-step reasoning, earning first place in Alibaba Cloud's Data+AI competition.

How It Works

1
🏆 Discover the winning research helper

You come across this smart assistant that took first place in a major AI contest for solving tough research puzzles.

2
🔗 Connect helpful services

You link it to thinking power and search tools so it can explore the web on your behalf.

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🚀 Bring it to life

You launch your assistant online, and it’s ready to tackle any question you throw at it.

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❓ Ask a challenging question

You type in a complex query that requires digging through facts from multiple places.

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🔍 Watch it research deeply

It breaks down the problem, searches the internet, visits sites, verifies info, and reasons step by step.

✅ Receive the perfect answer

You get a clear, accurate response based on solid evidence, saving you hours of work.

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Star Growth

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AI-Generated Review

What is Research-Agent---1st-place-in-Alibaba-Cloud-Data-AI-Competition?

This Python-based research agent handles complex multi-hop QA by searching the web with dual engines (Google/Serper for English, Ali IQS for Chinese), extracting page content, and reasoning via Qwen models on PAI-LangStudio. Users POST natural language questions to its FastAPI endpoint—like "Who designed the scoring system for that chess game?"—and get precise text answers after autonomous planning and verification. It's a battle-tested github research ai tool from the 1st place winner in Alibaba Cloud's Data+AI Competition (College Track), solving unreliable single-shot LLM queries for fact-heavy research data tasks.

Why is it gaining traction?

Unlike basic research agent langchain setups or ChatGPT plugins, it enforces deep multi-round searches (8-15 typical), cross-validates sources, and normalizes answers for exact matches—proven on 100+ competition problems. Devs dig the open source research agent prompt engineering for handling bilingual queries, timeouts, and content filters without babysitting. As a github research repo, it hooks those scouting research agent copilot alternatives with ready eval scripts for question.jsonl datasets.

Who should use this?

AI engineers building custom research agents for multi-hop QA in data competitions or internal tools. Data teams needing a github research tools baseline for fact-checking reports or knowledge bases. Devs prototyping research github copilot flows, especially with Chinese/English web data.

Verdict

Grab it as a solid research agent open source template—excellent docs and eval setup make forking easy, despite low 13 stars and 1.0% credibility signaling early maturity. Test locally with curl before production; ideal starter for agent experiments, not heavy deployment yet.

(198 words)

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