Comparison
hello-agents vs py-gpt
Verdict
Pick hello-agents when requirements: Min 4 GB RAM; Python knowledge assumed; pick py-gpt when tags unique to py-gpt: ai, ai-assistant, artificial-intelligence, autonomous-agent.
Markdown twin · hello-agents alternatives · py-gpt alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | hello-agents | py-gpt |
|---|---|---|
| Maintenance | Very active (0d since push) As of 4d · github_public_v1 | Slowing (159d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4d · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 4d · osv@v1 | No lockfile (source not queried) As of today · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- hello-agents
- Course on building intelligent agents from scratch
- py-gpt
- Desktop AI Assistant powered by GPT-5, GPT-4, o1, o3, Gemini, Claude, Ollama, DeepSeek, Perplexity, Grok, Bielik, chat, vision, voice, RAG, image and video generation, agents, tools, MCP, plugins, spe
Stars
- hello-agents
- 65k
- py-gpt
- 1.9k
Forks
- hello-agents
- 8.1k
- py-gpt
- 333
Open issues
- hello-agents
- 144
- py-gpt
- 61
Language
- hello-agents
- Python
- py-gpt
- Python
Adopt for
- hello-agents
- hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods.
- py-gpt
- -
Persona
- hello-agents
- -
- py-gpt
- -
Runtime
- hello-agents
- -
- py-gpt
- -
License
- hello-agents
- hello-agents is covered under an unconventional license which may require further review before usage.
- py-gpt
- Other
Last pushed
- hello-agents
- Jul 10, 2026
- py-gpt
- Feb 6, 2026
Categories
- hello-agents
- AI Agents, LLM Frameworks
- py-gpt
- AI Agents, LLM Frameworks, Vector Databases
Trust and health
Maintenance
- hello-agents
- Very active (96%)
- py-gpt
- Slowing (36%)
Days since push
- hello-agents
- 0d
- py-gpt
- 159d
Open issues (now)
- hello-agents
- 144
- py-gpt
- 61
Owner type
- hello-agents
- Organization
- py-gpt
- User
Full report
- hello-agents
- Trust report
- py-gpt
- Trust report
Choose hello-agents if…
- 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 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.
Choose py-gpt if…
- Tags unique to py-gpt: ai, ai-assistant, artificial-intelligence, autonomous-agent.
- Also covers Vector Databases.
- Leaner open-issue backlog (61).
When NOT to use py-gpt
- Last GitHub push was 159 days ago (slowing maintenance, Feb 6, 2026). Validate activity before betting a new project on py-gpt.
- 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.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (datawhalechina/hello-agents) · observed Jul 11, 2026
- GitHub forks (datawhalechina/hello-agents) · observed Jul 11, 2026
- Last push (datawhalechina/hello-agents) · observed Jul 10, 2026
- License file (Other) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (szczyglis-dev/py-gpt) · observed Jul 15, 2026
- GitHub forks (szczyglis-dev/py-gpt) · observed Jul 15, 2026
- Last push (szczyglis-dev/py-gpt) · observed Feb 6, 2026
- License file (Other) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: hello-agents 65k · py-gpt 1.9k (synced Jul 11, 2026).
Common questions
- What is the difference between hello-agents and py-gpt?
- hello-agents: Course on building intelligent agents from scratch. py-gpt: Desktop AI Assistant powered by GPT-5, GPT-4, o1, o3, Gemini, Claude, Ollama, DeepSeek, Perplexity, Grok, Bielik, chat, vision, voice, RAG, image and video generation, agents, tools, MCP, plugins, spe. See the comparison table for live GitHub stats and shared categories.
- When should I choose hello-agents over py-gpt?
- Choose hello-agents over py-gpt when 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 choose py-gpt over hello-agents?
- Choose py-gpt over hello-agents when Tags unique to py-gpt: ai, ai-assistant, artificial-intelligence, autonomous-agent; Also covers Vector Databases; Leaner open-issue backlog (61).
- 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 py-gpt?
- Last GitHub push was 159 days ago (slowing maintenance, Feb 6, 2026). Validate activity before betting a new project on py-gpt. 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Is hello-agents or py-gpt more popular on GitHub?
- hello-agents has more GitHub stars (65,432 vs 1,851). Stars measure visibility, not whether either tool fits your constraints.
- Are hello-agents and py-gpt open source?
- Yes - both are open-source projects on GitHub (hello-agents: Other, py-gpt: Other).
- Where can I find alternatives to hello-agents or py-gpt?
- GraphCanon lists graph-backed alternatives at hello-agents alternatives and py-gpt alternatives (hello-agents markdown twin, py-gpt markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, hello-agents or py-gpt?
- hello-agents: Very active. py-gpt: 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 py-gpt?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: hello-agents trust report; py-gpt trust report.