DeepResearch vs deep-searcher

Common questions

What is the difference between DeepResearch and deep-searcher?

DeepResearch: Tongyi Deep Research, the Leading Open-source Deep Research Agent. deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data. See the comparison table for live GitHub stats and shared categories.

When should I choose DeepResearch over deep-searcher?

Choose DeepResearch over deep-searcher 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.; Deep Searcher and DeepResearch both perform open-source deep research tasks, potentially differing in their feature sets or community support; Tags unique to DeepResearch: artificial-intelligence, alibaba, web-agent; Also covers LLM Frameworks; When you need to perform sophisticated long-term horizon tasks that require in-depth information seeking.

When should I choose deep-searcher over DeepResearch?

Choose deep-searcher over DeepResearch when Deep Searcher and DeepResearch both perform open-source deep research tasks, potentially differing in their feature sets or community support; Tags unique to deep-searcher: openai, claude, agentic-rag, milvus; Also covers Data & Retrieval, Vector Databases; deep-searcher ships Docker support for self-hosted deployment; - **When you need a flexible embedding option**: DeepSearcher supports multiple embedding models like Milvus for optimal selection.

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 deep-searcher?

- **If you require real-time web content integration only**: DeepSearcher primarily focuses on local/private data. Online content integration is possible but not its core functionality. - **When strict API dependency avoidance is needed**: DeepSearcher often relies on specific APIs (e.g., OpenAI) for LLM services, which might be a constraint in environments strictly avoiding third-党

Is DeepResearch or deep-searcher more popular on GitHub?

DeepResearch has more GitHub stars (19,621 vs 7,934). Stars measure visibility, not whether either tool fits your constraints.

Are DeepResearch and deep-searcher open source?

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

Where can I find alternatives to DeepResearch or deep-searcher?

GraphCanon lists graph-backed alternatives at /tools/alibaba-nlp-deepresearch/alternatives and /tools/zilliztech-deep-searcher/alternatives (/tools/alibaba-nlp-deepresearch/alternatives.md, /tools/zilliztech-deep-searcher/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-zilliztech-deep-searcher.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, DeepResearch or deep-searcher?

DeepResearch: Slowing. deep-searcher: Slowing. 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 deep-searcher?

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