RUC-NLPIR/WebThinker

Health v2 · maintenance only

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Adoption not part of the score

1470 stars · 139 forks observed · 2026-09-27

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

WebThinker is a research framework that empowers large reasoning models (like DeepSeek-R1 and QwQ) with deep research capability, letting them autonomously search, click, and extract information from the web while reasoning. It also provides fine-tuned model checkpoints and report generation for complex research questions.

Use cases

• run deep research with open-source reasoning models
• generate research reports automatically from web sources
• let an LLM search and browse the web while reasoning
• evaluate reasoning models on GAIA, GPQA, HLE, or WebWalker benchmarks
• fine-tune DeepSeek-R1 or QwQ models for deep search tasks
• build an open-source alternative to proprietary deep research agents

When to choose

• you want an open-source deep research pipeline built on large reasoning models
• you need reproducible research code from a peer-reviewed NeurIPS paper
• you want to benchmark or fine-tune R1/QwQ models on deep search tasks

When to avoid

• you need a production-ready hosted research assistant with no setup
• you don't have GPU resources to run large reasoning models locally
• you only need simple web scraping without LLM reasoning

Facets

library · maturity active

rag agent-framework llm-inference search-engine nlp large-language-models python cross-platform deep-research deep-search reasoning-models report-generation web-search deepseek-r1 qwq neurips-2025 retrieval-augmented-generation ai-agents natural-language-processing research gpu

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markdown · JSON · MCP: product_card(name="RUC-NLPIR/WebThinker")