--- title: "datafog-python vs deep-searcher" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datafog-datafog-python-vs-zilliztech-deep-searcher" tools: ["datafog-datafog-python", "zilliztech-deep-searcher"] --- # datafog-python vs deep-searcher *GraphCanon updated Sep 20, 2026* ## Verdict Pick datafog-python if datafog-python is an offline PII firewall for AI agents and LLM apps, featuring fast local detection and redaction of PII with minimal dependencies; pick deep-searcher if deepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license. [datafog-python](https://datafog.ai) reports 72 GitHub stars, 14 forks, and 8 open issues, last pushed Sep 10, 2026. [deep-searcher](https://zilliztech.github.io/deep-searcher/) has 8.1k stars, 775 forks, and 53 open issues, last pushed Nov 19, 2025. Figures are from public GitHub metadata via [datafog-python's repository](https://github.com/DataFog/datafog-python) and [deep-searcher's repository](https://github.com/zilliztech/deep-searcher). | | [datafog-python](/tools/datafog-datafog-python.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Tagline | Offline PII firewall for AI agents and LLM apps | Open Source Deep Research Alternative to Reason and Search on Private Data. | | Stars | 72 | 8,060 | | Forks | 14 | 775 | | Open issues | 8 | 53 | | Language | Python | Python | | Adopt for | datafog-python is an offline PII firewall for AI agents and LLM apps, featuring fast local detection and redaction of PII with minimal dependencies. | DeepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [datafog-python](/tools/datafog-datafog-python.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 2d | 272d | | Open issues (now) | 8 | 53 | | Stars delta | +6 (30d) | +59 (30d) | | Open issues delta | +2 (30d) | 0 (30d) | | Full report | [trust report](/tools/datafog-datafog-python/trust.md) | [trust report](/tools/zilliztech-deep-searcher/trust.md) | ## Shared compatibility - **Python**: [datafog-python](/tools/datafog-datafog-python.md) - Python runtime; [deep-searcher](/tools/zilliztech-deep-searcher.md) - Python runtime ## Decision facts: datafog-python - **Adopt for:** datafog-python is an offline PII firewall for AI agents and LLM apps, featuring fast local detection and redaction of PII with minimal dependencies. ## Decision facts: deep-searcher - **Adopt for:** DeepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license. ## Choose when ### Choose datafog-python if… - License: datafog-python is MIT, deep-searcher is Apache-2.0. - Tags unique to datafog-python: agent-security, anonymization, claude-code, compliance. - If you require rapid, offline detection and redaction of personally identifiable information without network calls or extensive dependencies. ### Choose deep-searcher if… - License: deep-searcher is Apache-2.0, datafog-python is MIT. - Tags unique to deep-searcher: agent, agentic-rag, deep-research, llm. - Also covers Vector Databases. - deep-searcher ships Docker support for self-hosted deployment. - When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3. ## When NOT to use datafog-python - When your application needs cloud-based processing capabilities beyond local pii detection and redaction offered by datafog-python. - If the need arises for advanced networked security features such as real-time threat intelligence updates, which datafog-python with its offline nature does not provide. ## When NOT to use deep-searcher - Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems. - Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily. ## Common questions ### What is the difference between datafog-python and deep-searcher? datafog-python: Offline PII firewall for AI agents and LLM apps. 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 datafog-python over deep-searcher? Choose datafog-python over deep-searcher when License: datafog-python is MIT, deep-searcher is Apache-2.0; Tags unique to datafog-python: agent-security, anonymization, claude-code, compliance; If you require rapid, offline detection and redaction of personally identifiable information without network calls or extensive dependencies. ### When should I choose deep-searcher over datafog-python? Choose deep-searcher over datafog-python when License: deep-searcher is Apache-2.0, datafog-python is MIT; Tags unique to deep-searcher: agent, agentic-rag, deep-research, llm; Also covers Vector Databases; deep-searcher ships Docker support for self-hosted deployment; When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3. ### When should I avoid datafog-python? When your application needs cloud-based processing capabilities beyond local pii detection and redaction offered by datafog-python. If the need arises for advanced networked security features such as real-time threat intelligence updates, which datafog-python with its offline nature does not provide. ### When should I avoid deep-searcher? Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems. Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily. ### Is datafog-python or deep-searcher more popular on GitHub? deep-searcher has more GitHub stars (8,060 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are datafog-python and deep-searcher open source? Yes - both are open-source projects on GitHub (datafog-python: MIT, deep-searcher: Apache-2.0). ### Where can I find alternatives to datafog-python or deep-searcher? GraphCanon lists graph-backed alternatives at [datafog-python alternatives](/tools/datafog-datafog-python/alternatives) and [deep-searcher alternatives](/tools/zilliztech-deep-searcher/alternatives) ([datafog-python markdown twin](/tools/datafog-datafog-python/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/datafog-datafog-python-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, datafog-python or deep-searcher? datafog-python: Very active. 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 datafog-python and deep-searcher? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [datafog-python trust report](/tools/datafog-datafog-python/trust); [deep-searcher trust report](/tools/zilliztech-deep-searcher/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datafog-datafog-python`](/api/graphcanon/graph?tool=datafog-datafog-python) - 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/_