--- title: "natasha vs deep-searcher" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/natasha-natasha-vs-zilliztech-deep-searcher" tools: ["natasha-natasha", "zilliztech-deep-searcher"] --- # natasha vs deep-searcher *GraphCanon updated Jul 11, 2026* ## Verdict Pick natasha when license: natasha is MIT, deep-searcher is Apache-2.0; pick deep-searcher when license: deep-searcher is Apache-2.0, natasha is MIT. [natasha](https://github.com/natasha/natasha) reports 1.3k GitHub stars, 120 forks, and 35 open issues, last pushed Apr 13, 2026. [deep-searcher](https://zilliztech.github.io/deep-searcher/) has 7.9k stars, 768 forks, and 53 open issues, last pushed Nov 19, 2025. Figures are from public GitHub metadata via [natasha's repository](https://github.com/natasha/natasha) and [deep-searcher's repository](https://github.com/zilliztech/deep-searcher). | | [natasha](/tools/natasha-natasha.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Tagline | Solves basic Russian NLP tasks, API for lower level Natasha projects | Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python. | | Stars | 1,342 | 7,941 | | Forks | 120 | 768 | | Open issues | 35 | 53 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Computer Vision, Vector Databases | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [natasha](/tools/natasha-natasha.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 88d | 234d | | Open issues (now) | 35 | 53 | | Full report | [trust report](/tools/natasha-natasha/trust.md) | [trust report](/tools/zilliztech-deep-searcher/trust.md) | ## Decision facts: deep-searcher - **Pricing:** freemium ## Choose when ### Choose natasha if… - License: natasha is MIT, deep-searcher is Apache-2.0. - Tags unique to natasha: embeddings, morphology, ner, nlp. - Also covers Computer Vision. ### Choose deep-searcher if… - License: deep-searcher is Apache-2.0, natasha is MIT. - Tags unique to deep-searcher: agent, agentic-rag, claude, deep-research. - Also covers AI Agents, LLM Frameworks. - deep-searcher ships Docker support for self-hosted deployment. - - When you need an open-source alternative for reasoning and searching on private data, avoiding closed systems like Claude or Grok. ## When NOT to use natasha - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use deep-searcher - - If you need a tool that supports web crawling out-of-the-box, as DeepSearcher currently lacks this feature, although it is on their future plans. - - When your project prioritizes using specific vector databases other than Milvus; while there are future plans to support more, these are not yet implemented. - - For rapid setup without additional configuration or dependency management; DeepSearcher requires detailed setup and optional dependencies for full functionality. ## Common questions ### What is the difference between natasha and deep-searcher? natasha: Solves basic Russian NLP tasks, API for lower level Natasha projects. deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.. See the comparison table for live GitHub stats and shared categories. ### When should I choose natasha over deep-searcher? Choose natasha over deep-searcher when License: natasha is MIT, deep-searcher is Apache-2.0; Tags unique to natasha: embeddings, morphology, ner, nlp; Also covers Computer Vision. ### When should I choose deep-searcher over natasha? Choose deep-searcher over natasha when License: deep-searcher is Apache-2.0, natasha is MIT; Tags unique to deep-searcher: agent, agentic-rag, claude, deep-research; Also covers AI Agents, LLM Frameworks; deep-searcher ships Docker support for self-hosted deployment; - When you need an open-source alternative for reasoning and searching on private data, avoiding closed systems like Claude or Grok. ### When should I avoid natasha? Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid deep-searcher? - If you need a tool that supports web crawling out-of-the-box, as DeepSearcher currently lacks this feature, although it is on their future plans. - When your project prioritizes using specific vector databases other than Milvus; while there are future plans to support more, these are not yet implemented. - For rapid setup without additional configuration or dependency management; DeepSearcher requires detailed setup and optional dependencies for full functionality. ### Is natasha or deep-searcher more popular on GitHub? deep-searcher has more GitHub stars (7,941 vs 1,342). Stars measure visibility, not whether either tool fits your constraints. ### Are natasha and deep-searcher open source? Yes - both are open-source projects on GitHub (natasha: MIT, deep-searcher: Apache-2.0). ### Where can I find alternatives to natasha or deep-searcher? GraphCanon lists graph-backed alternatives at [natasha alternatives](/tools/natasha-natasha/alternatives) and [deep-searcher alternatives](/tools/zilliztech-deep-searcher/alternatives) ([natasha markdown twin](/tools/natasha-natasha/alternatives.md), [deep-searcher markdown twin](/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 [this comparison](/compare/natasha-natasha-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, natasha or deep-searcher? natasha: Steady. 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 natasha and deep-searcher? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [natasha trust report](/tools/natasha-natasha/trust); [deep-searcher trust report](/tools/zilliztech-deep-searcher/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=natasha-natasha`](/api/graphcanon/graph?tool=natasha-natasha) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_