--- title: "Prompt-Engineering-Guide vs aigis" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dair-ai-prompt-engineering-guide-vs-killertcell428-aigis" tools: ["dair-ai-prompt-engineering-guide", "killertcell428-aigis"] --- # Prompt-Engineering-Guide vs aigis *GraphCanon updated Jul 15, 2026* ## Verdict Pick Prompt-Engineering-Guide when prompt-Engineering-Guide is primarily MDX; aigis is Python; pick aigis when aigis 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. [aigis](https://pypi.org/project/pyaigis/) has 51 stars, 8 forks, and 8 open issues, last pushed Jul 14, 2026. Figures are from public GitHub metadata via [Prompt-Engineering-Guide's repository](https://github.com/dair-ai/Prompt-Engineering-Guide) and [aigis's repository](https://github.com/killertcell428/aigis). | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [aigis](/tools/killertcell428-aigis.md) | | --- | --- | --- | | Tagline | Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents | Deterministic, zero-dependency Python firewall for AI agents, MCP rug-pull, memory poisoning, indirect injection, exfil channels. 44 compliance templates (US/CN/JP/EU). | | Stars | 76,349 | 51 | | Forks | 8,361 | 8 | | Open issues | 274 | 8 | | 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) | [aigis](/tools/killertcell428-aigis.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 121d | 1d | | Open issues (now) | 274 | 8 | | Owner type | Organization | User | | Full report | [trust report](/tools/dair-ai-prompt-engineering-guide/trust.md) | [trust report](/tools/killertcell428-aigis/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; aigis is Python. - License: Prompt-Engineering-Guide is MIT, aigis 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 aigis if… - aigis is primarily Python; Prompt-Engineering-Guide is MDX. - License: aigis is Apache-2.0, Prompt-Engineering-Guide is MIT. - Tags unique to aigis: ai-agent, ai-security, compliance, cybersecurity. - Also covers Vector Databases. - aigis ships Docker support for self-hosted deployment. ## 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 aigis - 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 aigis? Prompt-Engineering-Guide: Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents. aigis: Deterministic, zero-dependency Python firewall for AI agents, MCP rug-pull, memory poisoning, indirect injection, exfil channels. 44 compliance templates (US/CN/JP/EU).. See the comparison table for live GitHub stats and shared categories. ### When should I choose Prompt-Engineering-Guide over aigis? Choose Prompt-Engineering-Guide over aigis when Prompt-Engineering-Guide is primarily MDX; aigis is Python; License: Prompt-Engineering-Guide is MIT, aigis 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 aigis over Prompt-Engineering-Guide? Choose aigis over Prompt-Engineering-Guide when aigis is primarily Python; Prompt-Engineering-Guide is MDX; License: aigis is Apache-2.0, Prompt-Engineering-Guide is MIT; Tags unique to aigis: ai-agent, ai-security, compliance, cybersecurity; Also covers Vector Databases; aigis ships Docker support for self-hosted deployment. ### 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 aigis? 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 aigis more popular on GitHub? Prompt-Engineering-Guide has more GitHub stars (76,349 vs 51). Stars measure visibility, not whether either tool fits your constraints. ### Are Prompt-Engineering-Guide and aigis open source? Yes - both are open-source projects on GitHub (Prompt-Engineering-Guide: MIT, aigis: Apache-2.0). ### Where can I find alternatives to Prompt-Engineering-Guide or aigis? GraphCanon lists graph-backed alternatives at [Prompt-Engineering-Guide alternatives](/tools/dair-ai-prompt-engineering-guide/alternatives) and [aigis alternatives](/tools/killertcell428-aigis/alternatives) ([Prompt-Engineering-Guide markdown twin](/tools/dair-ai-prompt-engineering-guide/alternatives.md), [aigis markdown twin](/tools/killertcell428-aigis/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-killertcell428-aigis.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 aigis? Prompt-Engineering-Guide: Slowing. aigis: Very 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 aigis? 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); [aigis trust report](/tools/killertcell428-aigis/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/_