DeepResearch vs gpt-researcher

Common questions

What is the difference between DeepResearch and gpt-researcher?

DeepResearch: Tongyi Deep Research, the Leading Open-source Deep Research Agent. gpt-researcher: An autonomous agent that conducts deep research on any data using any LLM providers. See the comparison table for live GitHub stats and shared categories.

When should I choose DeepResearch over gpt-researcher?

Choose DeepResearch over gpt-researcher when Pricing: No specific pricing info provided; deployment could vary depending on your cloud service provider or if you self-host on local servers.; Requirements: Min 16 GB RAM; Requires substantial computational resources due to its size and complexity.; Recommended for users with access to robust hardware infrastructure, either through cloud services like Aliyun's Bailian or local deployment.; Both tools conduct deep research using LLMs, but they may use different LLM providers or offer unique functionalities; Tags unique to DeepResearch: llm, artificial-intelligence, alibaba, deep-research; Also covers LLM Frameworks; When you need to perform sophisticated long-term horizon tasks that require in-depth information seeking.

When should I choose gpt-researcher over DeepResearch?

Choose gpt-researcher over DeepResearch when Requirements: Min 4 GB RAM; - A Python environment needs to be set up.; - Google Gemini (Nano Banana) integration for AI-generated images requires specific setup and keys.; Both tools conduct deep research using LLMs, but they may use different LLM providers or offer unique functionalities; Tags unique to gpt-researcher: llms, deepresearch, ai, python; gpt-researcher ships Docker support for self-hosted deployment; - You need to generate objective and detailed research reports beyond 2,000 words using both web sources and local documents.

When should I avoid DeepResearch?

Avoid using it for tasks requiring quick responses as it might suffer from slower response times due to its complex architecture designed for deep information-seeking. Not suitable for less demanding or simpler tasks where smaller and more efficient models can perform adequately without the overhead of DeepResearch's extensive capability.

When should I avoid gpt-researcher?

- Your project requires real-time or interactive research with immediate feedback, as GPT Researcher focuses on in-depth analysis rather than quick responses. - You are working within a restricted network environment where web scraping is not permitted, since the tool relies heavily on online sources for data gathering.

Is DeepResearch or gpt-researcher more popular on GitHub?

gpt-researcher has more GitHub stars (28,146 vs 19,621). Stars measure visibility, not whether either tool fits your constraints.

Are DeepResearch and gpt-researcher open source?

Yes - both are open-source projects on GitHub (DeepResearch: Apache-2.0, gpt-researcher: Apache-2.0).

Where can I find alternatives to DeepResearch or gpt-researcher?

GraphCanon lists graph-backed alternatives at /tools/alibaba-nlp-deepresearch/alternatives and /tools/assafelovic-gpt-researcher/alternatives (/tools/alibaba-nlp-deepresearch/alternatives.md, /tools/assafelovic-gpt-researcher/alternatives.md), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at /compare/alibaba-nlp-deepresearch-vs-assafelovic-gpt-researcher.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, DeepResearch or gpt-researcher?

DeepResearch: Slowing. gpt-researcher: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for DeepResearch and gpt-researcher?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepResearch: /tools/alibaba-nlp-deepresearch/trust; gpt-researcher: /tools/assafelovic-gpt-researcher/trust.