--- title: "Prompt-Engineering-Guide vs py-gpt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dair-ai-prompt-engineering-guide-vs-szczyglis-dev-py-gpt" tools: ["dair-ai-prompt-engineering-guide", "szczyglis-dev-py-gpt"] --- # Prompt-Engineering-Guide vs py-gpt *GraphCanon updated Jul 15, 2026* ## Verdict Pick Prompt-Engineering-Guide when prompt-Engineering-Guide is primarily MDX; py-gpt is Python; pick py-gpt when py-gpt is primarily Python; Prompt-Engineering-Guide is MDX. [Prompt-Engineering-Guide](https://www.promptingguide.ai/) reports 76k GitHub stars, 8.4k forks, and 274 open issues, last pushed Mar 11, 2026. [py-gpt](https://pygpt.net) has 1.9k stars, 333 forks, and 61 open issues, last pushed Feb 6, 2026. Figures are from public GitHub metadata via [Prompt-Engineering-Guide's repository](https://github.com/dair-ai/Prompt-Engineering-Guide) and [py-gpt's repository](https://github.com/szczyglis-dev/py-gpt). | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [py-gpt](/tools/szczyglis-dev-py-gpt.md) | | --- | --- | --- | | Tagline | Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents | 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 | 76,349 | 1,851 | | Forks | 8,361 | 333 | | Open issues | 274 | 61 | | Language | MDX | Python | | Adopt for | Decision-critical facts for Prompt-Engineering-Guide | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [py-gpt](/tools/szczyglis-dev-py-gpt.md) | | --- | --- | --- | | Days since push | 121d | 159d | | Open issues (now) | 274 | 61 | | Owner type | Organization | User | | Full report | [trust report](/tools/dair-ai-prompt-engineering-guide/trust.md) | [trust report](/tools/szczyglis-dev-py-gpt/trust.md) | ## Decision facts: Prompt-Engineering-Guide - **Adopt for:** Decision-critical facts for Prompt-Engineering-Guide ## Choose when ### Choose Prompt-Engineering-Guide if… - Prompt-Engineering-Guide is primarily MDX; py-gpt is Python. - License: Prompt-Engineering-Guide is MIT, py-gpt is Other. - Tags unique to Prompt-Engineering-Guide: agent, agents, ai-agents, chatgpt. - When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. ### Choose py-gpt if… - py-gpt is primarily Python; Prompt-Engineering-Guide is MDX. - License: py-gpt is Other, Prompt-Engineering-Guide is MIT. - Tags unique to py-gpt: ai, ai-assistant, artificial-intelligence, autonomous-agent. - Also covers Vector Databases. ## When NOT to use Prompt-Engineering-Guide - Avoid using if your focus is entirely on deep-learning frameworks without a need for detailed instructions or examples related to prompt crafting. - Not suitable when you require tools that go beyond guiding materials, such as custom prompts or direct software plugins provided by competitors focused more on practical implementation over learning. ## 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. ## Common questions ### What is the difference between Prompt-Engineering-Guide and py-gpt? Prompt-Engineering-Guide: Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents. 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 Prompt-Engineering-Guide over py-gpt? Choose Prompt-Engineering-Guide over py-gpt when Prompt-Engineering-Guide is primarily MDX; py-gpt is Python; License: Prompt-Engineering-Guide is MIT, py-gpt is Other; Tags unique to Prompt-Engineering-Guide: agent, agents, ai-agents, chatgpt; When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. ### When should I choose py-gpt over Prompt-Engineering-Guide? Choose py-gpt over Prompt-Engineering-Guide when py-gpt is primarily Python; Prompt-Engineering-Guide is MDX; License: py-gpt is Other, Prompt-Engineering-Guide is MIT; Tags unique to py-gpt: ai, ai-assistant, artificial-intelligence, autonomous-agent; Also covers Vector Databases. ### When should I avoid Prompt-Engineering-Guide? Avoid using if your focus is entirely on deep-learning frameworks without a need for detailed instructions or examples related to prompt crafting. Not suitable when you require tools that go beyond guiding materials, such as custom prompts or direct software plugins provided by competitors focused more on practical implementation over learning. ### 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 Prompt-Engineering-Guide or py-gpt more popular on GitHub? Prompt-Engineering-Guide has more GitHub stars (76,349 vs 1,851). Stars measure visibility, not whether either tool fits your constraints. ### Are Prompt-Engineering-Guide and py-gpt open source? Yes - both are open-source projects on GitHub (Prompt-Engineering-Guide: MIT, py-gpt: Other). ### Where can I find alternatives to Prompt-Engineering-Guide or py-gpt? GraphCanon lists graph-backed alternatives at [Prompt-Engineering-Guide alternatives](/tools/dair-ai-prompt-engineering-guide/alternatives) and [py-gpt alternatives](/tools/szczyglis-dev-py-gpt/alternatives) ([Prompt-Engineering-Guide markdown twin](/tools/dair-ai-prompt-engineering-guide/alternatives.md), [py-gpt markdown twin](/tools/szczyglis-dev-py-gpt/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/dair-ai-prompt-engineering-guide-vs-szczyglis-dev-py-gpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Prompt-Engineering-Guide or py-gpt? Prompt-Engineering-Guide: Slowing. 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 Prompt-Engineering-Guide and py-gpt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Prompt-Engineering-Guide trust report](/tools/dair-ai-prompt-engineering-guide/trust); [py-gpt trust report](/tools/szczyglis-dev-py-gpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dair-ai-prompt-engineering-guide`](/api/graphcanon/graph?tool=dair-ai-prompt-engineering-guide) - 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/_