--- title: "core vs Prompt-Engineering-Guide" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/cheshire-cat-ai-core-vs-dair-ai-prompt-engineering-guide" tools: ["cheshire-cat-ai-core", "dair-ai-prompt-engineering-guide"] --- # core vs Prompt-Engineering-Guide *GraphCanon updated Jul 11, 2026* ## Verdict Pick core when core is primarily Python; Prompt-Engineering-Guide is MDX; pick Prompt-Engineering-Guide when prompt-Engineering-Guide is primarily MDX; core is Python. [core](https://cheshirecat.ai) reports 3.1k GitHub stars, 410 forks, and 4 open issues, last pushed Jul 8, 2026. [Prompt-Engineering-Guide](https://www.promptingguide.ai/) has 76k stars, 8.4k forks, and 274 open issues, last pushed Mar 11, 2026. Figures are from public GitHub metadata via [core's repository](https://github.com/cheshire-cat-ai/core) and [Prompt-Engineering-Guide's repository](https://github.com/dair-ai/Prompt-Engineering-Guide). | | [core](/tools/cheshire-cat-ai-core.md) | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | | --- | --- | --- | | Tagline | AI agent microservice | Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents | | Stars | 3,072 | 76,349 | | Forks | 410 | 8,361 | | Open issues | 4 | 274 | | Language | Python | MDX | | Adopt for | - | Decision-critical facts for Prompt-Engineering-Guide | | Persona | - | - | | Runtime | - | - | | License | GPL-3.0 | MIT | | Categories | LLM Frameworks, AI Agents, Vector Databases | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [core](/tools/cheshire-cat-ai-core.md) | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 2d | 121d | | Open issues (now) | 4 | 274 | | Security scan | 2 low (2 low) | No criticals | | Full report | [trust report](/tools/cheshire-cat-ai-core/trust.md) | [trust report](/tools/dair-ai-prompt-engineering-guide/trust.md) | ## Decision facts: Prompt-Engineering-Guide - **Adopt for:** Decision-critical facts for Prompt-Engineering-Guide ## Choose when ### Choose core if… - core is primarily Python; Prompt-Engineering-Guide is MDX. - License: core is GPL-3.0, Prompt-Engineering-Guide is MIT. - Tags unique to core: assistant, ag-ui-protocol, ai, docker. - Also covers Vector Databases. ### Choose Prompt-Engineering-Guide if… - Prompt-Engineering-Guide is primarily MDX; core is Python. - License: Prompt-Engineering-Guide is MIT, core is GPL-3.0. - Tags unique to Prompt-Engineering-Guide: llms, deep-learning, agents, generative-ai. - When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. ## When NOT to use core - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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. ## Common questions ### What is the difference between core and Prompt-Engineering-Guide? core: AI agent microservice. Prompt-Engineering-Guide: Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose core over Prompt-Engineering-Guide? Choose core over Prompt-Engineering-Guide when core is primarily Python; Prompt-Engineering-Guide is MDX; License: core is GPL-3.0, Prompt-Engineering-Guide is MIT; Tags unique to core: assistant, ag-ui-protocol, ai, docker; Also covers Vector Databases. ### When should I choose Prompt-Engineering-Guide over core? Choose Prompt-Engineering-Guide over core when Prompt-Engineering-Guide is primarily MDX; core is Python; License: Prompt-Engineering-Guide is MIT, core is GPL-3.0; Tags unique to Prompt-Engineering-Guide: llms, deep-learning, agents, generative-ai; When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. ### When should I avoid core? 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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. ### Is core or Prompt-Engineering-Guide more popular on GitHub? Prompt-Engineering-Guide has more GitHub stars (76,349 vs 3,072). Stars measure visibility, not whether either tool fits your constraints. ### Are core and Prompt-Engineering-Guide open source? Yes - both are open-source projects on GitHub (core: GPL-3.0, Prompt-Engineering-Guide: MIT). ### Where can I find alternatives to core or Prompt-Engineering-Guide? GraphCanon lists graph-backed alternatives at [core alternatives](/tools/cheshire-cat-ai-core/alternatives) and [Prompt-Engineering-Guide alternatives](/tools/dair-ai-prompt-engineering-guide/alternatives) ([core markdown twin](/tools/cheshire-cat-ai-core/alternatives.md), [Prompt-Engineering-Guide markdown twin](/tools/dair-ai-prompt-engineering-guide/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/cheshire-cat-ai-core-vs-dair-ai-prompt-engineering-guide.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, core or Prompt-Engineering-Guide? core: Very active. Prompt-Engineering-Guide: 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 core and Prompt-Engineering-Guide? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [core trust report](/tools/cheshire-cat-ai-core/trust); [Prompt-Engineering-Guide trust report](/tools/dair-ai-prompt-engineering-guide/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=cheshire-cat-ai-core`](/api/graphcanon/graph?tool=cheshire-cat-ai-core) - 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/_