GPT Researcher vs MiroThinker
GPT Researcher: GPT Researcher is an open-source LLM agent designed for deep, structured research on any topic using web and local sources. It produces detailed reports with citations by running parallel research agents, scraping relevant sources, filtering content, and synthesizing findings. Supports local document research, customizable report formats, and multiple LLM backends including OpenAI, Anthropic, and Ollama.; MiroThinker: MiroThinker is an open-source deep research agent optimized for research and prediction, achieving leading performance on challenging benchmarks like GAIA. It advances tool-augmented reasoning through interactive scaling, handling deep and frequent agent-environment interactions.
GPT Researcher is a model-agnostic orchestration pipeline that works with OpenAI, Anthropic, or local models. MiroThinker provides open-weights research-agent models (30B/235B), the MiroFlow framework, and training datasets.
TL;DR
- You want an Apache-2.0 agent that plans sub-questions, searches the web and local documents in parallel, and writes 2,000+ word reports with citations.
- You want to use it as a Python library, standalone web app, or MCP-connected research skill with PDF/Word export.
- You want a general-purpose super-agent harness with a bash sandbox, persistent memory, and Feishu/WeCom/Slack bots; DeerFlow is built for that.
- You do not want per-report search API (e.g. Tavily) and multi-call LLM token costs.
- You want open-weights research-agent models (30B and 235B, 256K context) trained specifically for up to 400 tool calls per task.
- You are researching or fine-tuning tool-use agents and want the MiroFlow harness, reproducible GAIA/HLE benchmarks, and the 147k-sample MiroVerse dataset.
- You do not have multi-GPU infrastructure to host 30B/235B models and just want an off-the-shelf research app using commercial APIs; use GPT Researcher.
- You want a document RAG platform for enterprise PDFs rather than an open-weights research model; use RAGFlow or MaxKB.
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