rui-ye

rui-ye / OpenSeeker

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OpenSeeker: A search agent with open-source data and models

65
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100% credibility
Found Mar 17, 2026 at 65 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

OpenSeeker is an open-source project that provides training data, models, and evaluation tools for building AI agents capable of advanced web searching and page browsing to answer complex questions.

How It Works

1
🔍 Discover OpenSeeker

You hear about OpenSeeker, a smart helper that searches the web deeply to answer tricky questions, shared by researchers on places like GitHub.

2
🛠️ Prepare your space

You get your computer ready by creating a simple workspace for the assistant to live in.

3
🧠 Bring home the brain

You download the clever thinking model that powers the search magic.

4
🌐 Connect web helpers

You link easy web search and page-reading services so the assistant can explore the internet.

5
🚀 Launch the assistant

With one simple start, your personal search expert comes alive and is ready to work.

6
❓ Ask tough questions

You feed it lists of hard questions, and it browses and thinks to find answers.

7
📊 Review the results

You check how spot-on the answers are with built-in scoring tools.

🎉 Master searcher ready

Now you have a top-notch web research buddy that beats big company tools for complex info hunts.

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Star Growth

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AI-Generated Review

What is OpenSeeker?

OpenSeeker is a Python-based open-source search agent that deploys advanced web querying and page analysis for complex questions. You get a ready-to-run 30B model fine-tuned on 11.7K examples, plus full training data and models on Hugging Face, tackling info-seeking tasks like multi-hop research. Run the model server via a shell script, feed datasets to generate answers, and evaluate accuracy with built-in scripts—no proprietary APIs needed.

Why is it gaining traction?

It beats industrial search agents like Tongyi DeepResearch on benchmarks (48.4% vs 46.7% on BrowseComp-ZH) using pure open-source data and models, letting devs replicate SOTA without black-box training. Batched search via Serper and goal-directed page visits with Jina summaries make agent chains efficient and observable. Early adopters hook on the academic transparency for custom fine-tuning.

Who should use this?

AI researchers benchmarking or extending search agents on datasets like BrowseComp. Devs building RAG pipelines needing web tools beyond basic retrieval. Teams evaluating agent perf on Chinese/English queries without cloud costs.

Verdict

Promising for open-source search agent work—strong benchmarks and setup docs make it usable now, despite 65 stars and 1.0% credibility signaling early maturity. Fork and tweak if you're experimenting; skip for production until more adoption.

(198 words)

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