--- title: "Prompt-Engineering-Guide vs agentic-radar" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dair-ai-prompt-engineering-guide-vs-splx-ai-agentic-radar" tools: ["dair-ai-prompt-engineering-guide", "splx-ai-agentic-radar"] --- # Prompt-Engineering-Guide vs agentic-radar *GraphCanon updated Jul 11, 2026* ## Verdict Pick Prompt-Engineering-Guide when prompt-Engineering-Guide is primarily MDX; agentic-radar is Python; pick agentic-radar when agentic-radar 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. [agentic-radar](https://splx.ai) has 997 stars, 137 forks, and 15 open issues, last pushed Nov 27, 2025. Figures are from public GitHub metadata via [Prompt-Engineering-Guide's repository](https://github.com/dair-ai/Prompt-Engineering-Guide) and [agentic-radar's repository](https://github.com/splx-ai/agentic-radar). | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [agentic-radar](/tools/splx-ai-agentic-radar.md) | | --- | --- | --- | | Tagline | Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents | A security scanner for your LLM agentic workflows | | Stars | 76,349 | 997 | | Forks | 8,361 | 137 | | Open issues | 274 | 15 | | Language | MDX | Python | | Adopt for | Decision-critical facts for Prompt-Engineering-Guide | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, LLM Frameworks | Vector Databases, AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [agentic-radar](/tools/splx-ai-agentic-radar.md) | | --- | --- | --- | | Days since push | 121d | 225d | | Open issues (now) | 274 | 15 | | Full report | [trust report](/tools/dair-ai-prompt-engineering-guide/trust.md) | [trust report](/tools/splx-ai-agentic-radar/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; agentic-radar is Python. - License: Prompt-Engineering-Guide is MIT, agentic-radar is Apache-2.0. - 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. ### Choose agentic-radar if… - agentic-radar is primarily Python; Prompt-Engineering-Guide is MDX. - License: agentic-radar is Apache-2.0, Prompt-Engineering-Guide is MIT. - Tags unique to agentic-radar: ai, agentic-framework, agentic-workflow, agentic-ai. - 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 agentic-radar - Last GitHub push was 226 days ago (slowing maintenance, Nov 27, 2025). Validate activity before betting a new project on agentic-radar. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - 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. ## Common questions ### What is the difference between Prompt-Engineering-Guide and agentic-radar? Prompt-Engineering-Guide: Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents. agentic-radar: A security scanner for your LLM agentic workflows. See the comparison table for live GitHub stats and shared categories. ### When should I choose Prompt-Engineering-Guide over agentic-radar? Choose Prompt-Engineering-Guide over agentic-radar when Prompt-Engineering-Guide is primarily MDX; agentic-radar is Python; License: Prompt-Engineering-Guide is MIT, agentic-radar is Apache-2.0; 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 choose agentic-radar over Prompt-Engineering-Guide? Choose agentic-radar over Prompt-Engineering-Guide when agentic-radar is primarily Python; Prompt-Engineering-Guide is MDX; License: agentic-radar is Apache-2.0, Prompt-Engineering-Guide is MIT; Tags unique to agentic-radar: ai, agentic-framework, agentic-workflow, agentic-ai; 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 agentic-radar? Last GitHub push was 226 days ago (slowing maintenance, Nov 27, 2025). Validate activity before betting a new project on agentic-radar. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. 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. ### Is Prompt-Engineering-Guide or agentic-radar more popular on GitHub? Prompt-Engineering-Guide has more GitHub stars (76,349 vs 997). Stars measure visibility, not whether either tool fits your constraints. ### Are Prompt-Engineering-Guide and agentic-radar open source? Yes - both are open-source projects on GitHub (Prompt-Engineering-Guide: MIT, agentic-radar: Apache-2.0). ### Where can I find alternatives to Prompt-Engineering-Guide or agentic-radar? GraphCanon lists graph-backed alternatives at [Prompt-Engineering-Guide alternatives](/tools/dair-ai-prompt-engineering-guide/alternatives) and [agentic-radar alternatives](/tools/splx-ai-agentic-radar/alternatives) ([Prompt-Engineering-Guide markdown twin](/tools/dair-ai-prompt-engineering-guide/alternatives.md), [agentic-radar markdown twin](/tools/splx-ai-agentic-radar/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-splx-ai-agentic-radar.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 agentic-radar? Prompt-Engineering-Guide: Slowing. agentic-radar: 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 agentic-radar? 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); [agentic-radar trust report](/tools/splx-ai-agentic-radar/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/_