--- title: "datafog-python vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datafog-datafog-python-vs-datawhalechina-hello-agents" tools: ["datafog-datafog-python", "datawhalechina-hello-agents"] --- # datafog-python vs hello-agents *GraphCanon updated Jul 15, 2026* ## Verdict Pick datafog-python when license: datafog-python is MIT, hello-agents is Other; pick hello-agents when license: hello-agents is Other, datafog-python is MIT. [datafog-python](https://datafog.ai) reports 67 GitHub stars, 14 forks, and 6 open issues, last pushed Jul 14, 2026. [hello-agents](https://hello-agents.datawhale.cc) has 65k stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [datafog-python's repository](https://github.com/DataFog/datafog-python) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [datafog-python](/tools/datafog-datafog-python.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | Offline PII firewall for AI agents and LLM apps: fast local detection and redaction, Claude Code hook, LiteLLM guardrail. Zero network calls, one dependency. | Course on building intelligent agents from scratch | | Stars | 67 | 65,432 | | Forks | 14 | 8,109 | | Open issues | 6 | 144 | | Language | Python | Python | | Adopt for | - | hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. | | Persona | - | - | | Runtime | - | - | | License | MIT | hello-agents is covered under an unconventional license which may require further review before usage. | | Categories | AI Agents, Computer Vision, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [datafog-python](/tools/datafog-datafog-python.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Open issues (now) | 6 | 144 | | Full report | [trust report](/tools/datafog-datafog-python/trust.md) | [trust report](/tools/datawhalechina-hello-agents/trust.md) | ## Decision facts: hello-agents - **Requirements:** Min 4 GB RAM; Python knowledge assumed - **Adopt for:** hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. - **License detail:** hello-agents is covered under an unconventional license which may require further review before usage. ## Choose when ### Choose datafog-python if… - License: datafog-python is MIT, hello-agents is Other. - Tags unique to datafog-python: agent-security, ai-agents, anonymization, claude code. - Also covers Computer Vision. ### Choose hello-agents if… - License: hello-agents is Other, datafog-python is MIT. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, llm, rag, tutorial. - You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ## When NOT to use datafog-python - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use hello-agents - Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. - Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ## Common questions ### What is the difference between datafog-python and hello-agents? datafog-python: Offline PII firewall for AI agents and LLM apps: fast local detection and redaction, Claude Code hook, LiteLLM guardrail. Zero network calls, one dependency.. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose datafog-python over hello-agents? Choose datafog-python over hello-agents when License: datafog-python is MIT, hello-agents is Other; Tags unique to datafog-python: agent-security, ai-agents, anonymization, claude code; Also covers Computer Vision. ### When should I choose hello-agents over datafog-python? Choose hello-agents over datafog-python when License: hello-agents is Other, datafog-python is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, llm, rag, tutorial; You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### When should I avoid datafog-python? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid hello-agents? Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ### Is datafog-python or hello-agents more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 67). Stars measure visibility, not whether either tool fits your constraints. ### Are datafog-python and hello-agents open source? Yes - both are open-source projects on GitHub (datafog-python: MIT, hello-agents: Other). ### Where can I find alternatives to datafog-python or hello-agents? GraphCanon lists graph-backed alternatives at [datafog-python alternatives](/tools/datafog-datafog-python/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([datafog-python markdown twin](/tools/datafog-datafog-python/alternatives.md), [hello-agents markdown twin](/tools/datawhalechina-hello-agents/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-datawhalechina-hello-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, datafog-python or hello-agents? datafog-python: Very active. hello-agents: Very active. 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 hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [datafog-python trust report](/tools/datafog-datafog-python/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/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/_