---
title: "hello-agents vs awesome-hacking-lists"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-taielab-awesome-hacking-lists"
tools: ["datawhalechina-hello-agents", "taielab-awesome-hacking-lists"]
---

# hello-agents vs awesome-hacking-lists

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick hello-agents when requirements: Min 4 GB RAM; Python knowledge assumed; pick awesome-hacking-lists when tags unique to awesome-hacking-lists: agents, ai, aiagent, awesome-list.

[hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [awesome-hacking-lists](https://github.com/taielab/awesome-hacking-lists) has 1.4k stars, 264 forks, and 2 open issues, last pushed Dec 4, 2025. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [awesome-hacking-lists's repository](https://github.com/taielab/awesome-hacking-lists).

| | [hello-agents](/tools/datawhalechina-hello-agents.md) | [awesome-hacking-lists](/tools/taielab-awesome-hacking-lists.md) |
| --- | --- | --- |
| Tagline | Course on building intelligent agents from scratch | A curated collection of top-tier penetration testing tools and productivity utilities across multiple domains. Join us to explore, contribute, and enhance your hacking toolkit! |
| Stars | 65,432 | 1,362 |
| Forks | 8,109 | 264 |
| Open issues | 144 | 2 |
| Language | 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 | hello-agents is covered under an unconventional license which may require further review before usage. | - |
| Categories | LLM Frameworks, AI Agents | LLM Frameworks, AI Agents, Inference & Serving |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [hello-agents](/tools/datawhalechina-hello-agents.md) | [awesome-hacking-lists](/tools/taielab-awesome-hacking-lists.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Slowing (36%) |
| Days since push | 0d | 219d |
| Open issues (now) | 144 | 2 |
| Owner type | Organization | User |
| Security scan | No lockfile | No MCP manifest |
| Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/taielab-awesome-hacking-lists/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 hello-agents if…

- Requirements: Min 4 GB RAM; Python knowledge assumed.
- Tags unique to hello-agents: llm, rag, tutorial, agent.
- You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up.

### Choose awesome-hacking-lists if…

- Tags unique to awesome-hacking-lists: agents, ai, aiagent, awesome-list.
- Also covers Inference & Serving.
- Leaner open-issue backlog (2).

## 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.

## When NOT to use awesome-hacking-lists

- Last GitHub push was 220 days ago (slowing maintenance, Dec 4, 2025). Validate activity before betting a new project on awesome-hacking-lists.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

## Common questions

### What is the difference between hello-agents and awesome-hacking-lists?

hello-agents: Course on building intelligent agents from scratch. awesome-hacking-lists: A curated collection of top-tier penetration testing tools and productivity utilities across multiple domains. Join us to explore, contribute, and enhance your hacking toolkit!. See the comparison table for live GitHub stats and shared categories.

### When should I choose hello-agents over awesome-hacking-lists?

Choose hello-agents over awesome-hacking-lists when Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: llm, rag, tutorial, agent; 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 choose awesome-hacking-lists over hello-agents?

Choose awesome-hacking-lists over hello-agents when Tags unique to awesome-hacking-lists: agents, ai, aiagent, awesome-list; Also covers Inference & Serving; Leaner open-issue backlog (2).

### 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.

### When should I avoid awesome-hacking-lists?

Last GitHub push was 220 days ago (slowing maintenance, Dec 4, 2025). Validate activity before betting a new project on awesome-hacking-lists. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

### Is hello-agents or awesome-hacking-lists more popular on GitHub?

hello-agents has more GitHub stars (65,432 vs 1,362). Stars measure visibility, not whether either tool fits your constraints.

### Are hello-agents and awesome-hacking-lists open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to hello-agents or awesome-hacking-lists?

GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [awesome-hacking-lists alternatives](/tools/taielab-awesome-hacking-lists/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [awesome-hacking-lists markdown twin](/tools/taielab-awesome-hacking-lists/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/datawhalechina-hello-agents-vs-taielab-awesome-hacking-lists.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, hello-agents or awesome-hacking-lists?

hello-agents: Very active. awesome-hacking-lists: 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 hello-agents and awesome-hacking-lists?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [awesome-hacking-lists trust report](/tools/taielab-awesome-hacking-lists/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=datawhalechina-hello-agents`](/api/graphcanon/graph?tool=datawhalechina-hello-agents)
- 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/_