--- title: "Prompt-Engineering-Guide vs agentic_security" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dair-ai-prompt-engineering-guide-vs-msoedov-agentic-security" tools: ["dair-ai-prompt-engineering-guide", "msoedov-agentic-security"] --- # Prompt-Engineering-Guide vs agentic_security *GraphCanon updated Jul 11, 2026* ## Verdict Pick Prompt-Engineering-Guide when prompt-Engineering-Guide is primarily MDX; agentic_security is Python; pick agentic_security when agentic_security 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_security](https://agentic-security.vercel.app) has 1.9k stars, 267 forks, and 70 open issues, last pushed Jun 23, 2026. Figures are from public GitHub metadata via [Prompt-Engineering-Guide's repository](https://github.com/dair-ai/Prompt-Engineering-Guide) and [agentic_security's repository](https://github.com/msoedov/agentic_security). | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [agentic_security](/tools/msoedov-agentic-security.md) | | --- | --- | --- | | Tagline | Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents | Agentic LLM Vulnerability Scanner / AI red teaming kit 🧪 | | Stars | 76,349 | 1,923 | | Forks | 8,361 | 267 | | Open issues | 274 | 70 | | Language | MDX | Python | | Adopt for | Decision-critical facts for Prompt-Engineering-Guide | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | 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) | [agentic_security](/tools/msoedov-agentic-security.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 121d | 18d | | Open issues (now) | 274 | 70 | | Owner type | Organization | User | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/dair-ai-prompt-engineering-guide/trust.md) | [trust report](/tools/msoedov-agentic-security/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_security is Python. - License: Prompt-Engineering-Guide is MIT, agentic_security is Apache-2.0. - 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 agentic_security if… - agentic_security is primarily Python; Prompt-Engineering-Guide is MDX. - License: agentic_security is Apache-2.0, Prompt-Engineering-Guide is MIT. - Tags unique to agentic_security: agent-framework, agent-security, ai-red-team, llm-evaluation. - 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_security - 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 agentic_security? Prompt-Engineering-Guide: Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents. agentic_security: Agentic LLM Vulnerability Scanner / AI red teaming kit 🧪. See the comparison table for live GitHub stats and shared categories. ### When should I choose Prompt-Engineering-Guide over agentic_security? Choose Prompt-Engineering-Guide over agentic_security when Prompt-Engineering-Guide is primarily MDX; agentic_security is Python; License: Prompt-Engineering-Guide is MIT, agentic_security is Apache-2.0; 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 agentic_security over Prompt-Engineering-Guide? Choose agentic_security over Prompt-Engineering-Guide when agentic_security is primarily Python; Prompt-Engineering-Guide is MDX; License: agentic_security is Apache-2.0, Prompt-Engineering-Guide is MIT; Tags unique to agentic_security: agent-framework, agent-security, ai-red-team, llm-evaluation; 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_security? 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 agentic_security more popular on GitHub? Prompt-Engineering-Guide has more GitHub stars (76,349 vs 1,923). Stars measure visibility, not whether either tool fits your constraints. ### Are Prompt-Engineering-Guide and agentic_security open source? Yes - both are open-source projects on GitHub (Prompt-Engineering-Guide: MIT, agentic_security: Apache-2.0). ### Where can I find alternatives to Prompt-Engineering-Guide or agentic_security? GraphCanon lists graph-backed alternatives at [Prompt-Engineering-Guide alternatives](/tools/dair-ai-prompt-engineering-guide/alternatives) and [agentic_security alternatives](/tools/msoedov-agentic-security/alternatives) ([Prompt-Engineering-Guide markdown twin](/tools/dair-ai-prompt-engineering-guide/alternatives.md), [agentic_security markdown twin](/tools/msoedov-agentic-security/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-msoedov-agentic-security.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_security? Prompt-Engineering-Guide: Slowing. agentic_security: Active. 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_security? 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_security trust report](/tools/msoedov-agentic-security/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/_