--- title: "rebuff vs deep-searcher" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/protectai-rebuff-vs-zilliztech-deep-searcher" tools: ["protectai-rebuff", "zilliztech-deep-searcher"] --- # rebuff vs deep-searcher *GraphCanon updated Jul 12, 2026* ## Verdict Pick rebuff when rebuff is primarily TypeScript; deep-searcher is Python; pick deep-searcher when deep-searcher is primarily Python; rebuff is TypeScript. [rebuff](https://playground.rebuff.ai) reports 1.5k GitHub stars, 137 forks, and 33 open issues, last pushed Aug 7, 2024. [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 [rebuff's repository](https://github.com/protectai/rebuff) and [deep-searcher's repository](https://github.com/zilliztech/deep-searcher). | | [rebuff](/tools/protectai-rebuff.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Tagline | LLM Prompt Injection Detector | Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python. | | Stars | 1,511 | 7,941 | | Forks | 137 | 768 | | Open issues | 33 | 53 | | Language | TypeScript | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | LLM Frameworks, Vector Databases, Evaluation & Observability | AI Agents, Vector Databases, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rebuff](/tools/protectai-rebuff.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Slowing (36%) | | Days since push | 703d | 234d | | Archived on GitHub | Yes | No | | Open issues (now) | 33 | 53 | | Full report | [trust report](/tools/protectai-rebuff/trust.md) | [trust report](/tools/zilliztech-deep-searcher/trust.md) | ## Shared compatibility - **Python**: [rebuff](/tools/protectai-rebuff.md) - Python runtime; [deep-searcher](/tools/zilliztech-deep-searcher.md) - Python runtime ## Decision facts: deep-searcher - **Pricing:** freemium ## Choose when ### Choose rebuff if… - rebuff is primarily TypeScript; deep-searcher is Python. - Tags unique to rebuff: llmops, prompt-injection, llm, prompts. - Also covers Evaluation & Observability. ### Choose deep-searcher if… - deep-searcher is primarily Python; rebuff is TypeScript. - Tags unique to deep-searcher: grok, deepseek-r1, deepseek, claude. - Also covers AI Agents. - 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 rebuff - rebuff is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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 rebuff and deep-searcher? rebuff: LLM Prompt Injection Detector. 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 rebuff over deep-searcher? Choose rebuff over deep-searcher when rebuff is primarily TypeScript; deep-searcher is Python; Tags unique to rebuff: llmops, prompt-injection, llm, prompts; Also covers Evaluation & Observability. ### When should I choose deep-searcher over rebuff? Choose deep-searcher over rebuff when deep-searcher is primarily Python; rebuff is TypeScript; Tags unique to deep-searcher: grok, deepseek-r1, deepseek, claude; Also covers AI Agents; 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 rebuff? rebuff is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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 rebuff or deep-searcher more popular on GitHub? deep-searcher has more GitHub stars (7,941 vs 1,511). Stars measure visibility, not whether either tool fits your constraints. ### Are rebuff and deep-searcher open source? Yes - both are open-source projects on GitHub (rebuff: Apache-2.0, deep-searcher: Apache-2.0). ### Where can I find alternatives to rebuff or deep-searcher? GraphCanon lists graph-backed alternatives at [rebuff alternatives](/tools/protectai-rebuff/alternatives) and [deep-searcher alternatives](/tools/zilliztech-deep-searcher/alternatives) ([rebuff markdown twin](/tools/protectai-rebuff/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/protectai-rebuff-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, rebuff or deep-searcher? rebuff: Archived. 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 rebuff and deep-searcher? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rebuff trust report](/tools/protectai-rebuff/trust); [deep-searcher trust report](/tools/zilliztech-deep-searcher/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=protectai-rebuff`](/api/graphcanon/graph?tool=protectai-rebuff) - 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/_