--- title: "deep-research vs cherche" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dzhng-deep-research-vs-raphaelsty-cherche" tools: ["dzhng-deep-research", "raphaelsty-cherche"] --- # deep-research vs cherche *GraphCanon updated Sep 20, 2026* ## Verdict Pick deep-research if deep-research is an AI-powered research assistant that leverages search engines, web scraping, and large language models to conduct iterative and in-depth exploration of topics; pick cherche if cherche is a Python library for neural search and information retrieval tasks, supporting both simple and semantic retrievers. It is ideal for projects needing robust search functionalities enhanced with neural-based. [deep-research](https://github.com/dzhng/deep-research) reports 20k GitHub stars, 2.0k forks, and 93 open issues, last pushed Apr 11, 2026. [cherche](https://github.com/raphaelsty/cherche) has 332 stars, 14 forks, and 4 open issues, last pushed Jun 1, 2024. Figures are from public GitHub metadata via [deep-research's repository](https://github.com/dzhng/deep-research) and [cherche's repository](https://github.com/raphaelsty/cherche). | | [deep-research](/tools/dzhng-deep-research.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Tagline | An AI-powered research assistant that refines its topic focus over time using search engines, web scraping, and large language models. | Neural Search | | Stars | 19,571 | 332 | | Forks | 1,993 | 14 | | Open issues | 93 | 4 | | Language | TypeScript | Python | | Adopt for | Deep-research is an AI-powered research assistant that leverages search engines, web scraping, and large language models to conduct iterative and in-depth exploration of topics. | Cherche is a Python library for neural search and information retrieval tasks, supporting both simple and semantic retrievers. It is ideal for projects needing robust search functionalities enhanced with neural-based and | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, Data & Retrieval | Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deep-research](/tools/dzhng-deep-research.md) | [cherche](/tools/raphaelsty-cherche.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 129d | 840d | | Open issues (now) | 93 | 4 | | Stars delta | +195 (30d) | 0 (30d) | | Open issues delta | +3 (30d) | 0 (30d) | | Full report | [trust report](/tools/dzhng-deep-research/trust.md) | [trust report](/tools/raphaelsty-cherche/trust.md) | ## Decision facts: deep-research - **Requirements:** Requires Docker - **Adopt for:** Deep-research is an AI-powered research assistant that leverages search engines, web scraping, and large language models to conduct iterative and in-depth exploration of topics. ## Decision facts: cherche - **Adopt for:** Cherche is a Python library for neural search and information retrieval tasks, supporting both simple and semantic retrievers. It is ideal for projects needing robust search functionalities enhanced with neural-based and ## Choose when ### Choose deep-research if… - deep-research is primarily TypeScript; cherche is Python. - Requirements: Requires Docker. - Tags unique to deep-research: agent, ai, gpt, o3-mini. - Also covers AI Agents. - deep-research ships Docker support for self-hosted deployment. - When you need a tool that can refine its topic focus over time through repeated iterations. ### Choose cherche if… - cherche is primarily Python; deep-research is TypeScript. - Tags unique to cherche: bm25, flashtext, information retrieval, machine-learning. - Use Cherche when you need to implement neural search functionalities that go beyond traditional keyword matching. It is suitable for projects that require a deep understanding of the semantic meaning, ## When NOT to use deep-research - When you prefer a language other than TypeScript, as deep-research specifically requires a Node.js environment. - If your use case does not necessitate the use of both Firecrawl and OpenAI APIs, preferring instead solutions with more API flexibility or that do not require API keys. ## When NOT to use cherche - Avoid Cherche if your project has strict limitations on external dependencies or if the project's scope is limited to basic keyword search capabilities only, as it includes both simple and advanced - Cherche may not be the best option if your project prioritizes using a specific technology stack, such as a particular framework for machine learning, that the library does not well integrate with - If your project strictly avoids using libraries licensed under the MIT license, you should consider other options. Cherche's MIT license might not align with the licensing requirements of other - Do not use Cherche for projects that have explicit requirements for operating environments that are not supported by Cherche, such as specific versions of Python or operating systems. ## Common questions ### What is the difference between deep-research and cherche? deep-research: An AI-powered research assistant that refines its topic focus over time using search engines, web scraping, and large language models.. cherche: Neural Search. See the comparison table for live GitHub stats and shared categories. ### When should I choose deep-research over cherche? Choose deep-research over cherche when deep-research is primarily TypeScript; cherche is Python; Requirements: Requires Docker; Tags unique to deep-research: agent, ai, gpt, o3-mini; Also covers AI Agents; deep-research ships Docker support for self-hosted deployment; When you need a tool that can refine its topic focus over time through repeated iterations. ### When should I choose cherche over deep-research? Choose cherche over deep-research when cherche is primarily Python; deep-research is TypeScript; Tags unique to cherche: bm25, flashtext, information retrieval, machine-learning; Use Cherche when you need to implement neural search functionalities that go beyond traditional keyword matching. It is suitable for projects that require a deep understanding of the semantic meaning,. ### When should I avoid deep-research? When you prefer a language other than TypeScript, as deep-research specifically requires a Node.js environment. If your use case does not necessitate the use of both Firecrawl and OpenAI APIs, preferring instead solutions with more API flexibility or that do not require API keys. ### When should I avoid cherche? Avoid Cherche if your project has strict limitations on external dependencies or if the project's scope is limited to basic keyword search capabilities only, as it includes both simple and advanced Cherche may not be the best option if your project prioritizes using a specific technology stack, such as a particular framework for machine learning, that the library does not well integrate with If your project strictly avoids using libraries licensed under the MIT license, you should consider other options. Cherche's MIT license might not align with the licensing requirements of other Do not use Cherche for projects that have explicit requirements for operating environments that are not supported by Cherche, such as specific versions of Python or operating systems. ### Is deep-research or cherche more popular on GitHub? deep-research has more GitHub stars (19,571 vs 332). Stars measure visibility, not whether either tool fits your constraints. ### Are deep-research and cherche open source? Yes - both are open-source projects on GitHub (deep-research: MIT, cherche: MIT). ### Where can I find alternatives to deep-research or cherche? GraphCanon lists graph-backed alternatives at [deep-research alternatives](/tools/dzhng-deep-research/alternatives) and [cherche alternatives](/tools/raphaelsty-cherche/alternatives) ([deep-research markdown twin](/tools/dzhng-deep-research/alternatives.md), [cherche markdown twin](/tools/raphaelsty-cherche/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/dzhng-deep-research-vs-raphaelsty-cherche.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deep-research or cherche? deep-research: Slowing. cherche: Dormant. 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 deep-research and cherche? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deep-research trust report](/tools/dzhng-deep-research/trust); [cherche trust report](/tools/raphaelsty-cherche/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dzhng-deep-research`](/api/graphcanon/graph?tool=dzhng-deep-research) - 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/_