--- title: "kalliope vs Awesome-Prompt-Engineering" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kalliope-project-kalliope-vs-promptslab-awesome-prompt-engineering" tools: ["kalliope-project-kalliope", "promptslab-awesome-prompt-engineering"] --- # kalliope vs Awesome-Prompt-Engineering *GraphCanon updated Jul 11, 2026* ## Verdict Pick kalliope when kalliope is primarily Python; Awesome-Prompt-Engineering is TypeScript; pick Awesome-Prompt-Engineering when awesome-Prompt-Engineering is primarily TypeScript; kalliope is Python. [kalliope](https://kalliope-project.github.io/) reports 1.8k GitHub stars, 231 forks, and 27 open issues, last pushed Jul 2, 2023. [Awesome-Prompt-Engineering](https://discord.gg/m88xfYMbK6) has 6.2k stars, 723 forks, and 88 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [kalliope's repository](https://github.com/kalliope-project/kalliope) and [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering). | | [kalliope](/tools/kalliope-project-kalliope.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Tagline | Kalliope is a framework that will help you to create your own personal assistant. | This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc | | Stars | 1,762 | 6,150 | | Forks | 231 | 723 | | Open issues | 27 | 88 | | Language | Python | TypeScript | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | GPL-3.0 | Apache-2.0 | | Categories | Speech & Audio | LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [kalliope](/tools/kalliope-project-kalliope.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1104d | 0d | | Open issues (now) | 27 | 88 | | Full report | [trust report](/tools/kalliope-project-kalliope/trust.md) | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | ## Choose when ### Choose kalliope if… - kalliope is primarily Python; Awesome-Prompt-Engineering is TypeScript. - License: kalliope is GPL-3.0, Awesome-Prompt-Engineering is Apache-2.0. - Tags unique to kalliope: bot, bot-creation, home-automation, jarvis. ### Choose Awesome-Prompt-Engineering if… - Awesome-Prompt-Engineering is primarily TypeScript; kalliope is Python. - License: Awesome-Prompt-Engineering is Apache-2.0, kalliope is GPL-3.0. - Tags unique to Awesome-Prompt-Engineering: chatgpt, chatgpt-api, deep-learning, few-shot-learning. - Also covers LLM Frameworks, Model Training. ## When NOT to use kalliope - Last GitHub push was 1105 days ago (dormant maintenance, Jul 2, 2023). Validate activity before betting a new project on kalliope. ## When NOT to use Awesome-Prompt-Engineering - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between kalliope and Awesome-Prompt-Engineering? kalliope: Kalliope is a framework that will help you to create your own personal assistant.. Awesome-Prompt-Engineering: This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc. See the comparison table for live GitHub stats and shared categories. ### When should I choose kalliope over Awesome-Prompt-Engineering? Choose kalliope over Awesome-Prompt-Engineering when kalliope is primarily Python; Awesome-Prompt-Engineering is TypeScript; License: kalliope is GPL-3.0, Awesome-Prompt-Engineering is Apache-2.0; Tags unique to kalliope: bot, bot-creation, home-automation, jarvis. ### When should I choose Awesome-Prompt-Engineering over kalliope? Choose Awesome-Prompt-Engineering over kalliope when Awesome-Prompt-Engineering is primarily TypeScript; kalliope is Python; License: Awesome-Prompt-Engineering is Apache-2.0, kalliope is GPL-3.0; Tags unique to Awesome-Prompt-Engineering: chatgpt, chatgpt-api, deep-learning, few-shot-learning; Also covers LLM Frameworks, Model Training. ### When should I avoid kalliope? Last GitHub push was 1105 days ago (dormant maintenance, Jul 2, 2023). Validate activity before betting a new project on kalliope. ### When should I avoid Awesome-Prompt-Engineering? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is kalliope or Awesome-Prompt-Engineering more popular on GitHub? Awesome-Prompt-Engineering has more GitHub stars (6,150 vs 1,762). Stars measure visibility, not whether either tool fits your constraints. ### Are kalliope and Awesome-Prompt-Engineering open source? Yes - both are open-source projects on GitHub (kalliope: GPL-3.0, Awesome-Prompt-Engineering: Apache-2.0). ### Where can I find alternatives to kalliope or Awesome-Prompt-Engineering? GraphCanon lists graph-backed alternatives at [kalliope alternatives](/tools/kalliope-project-kalliope/alternatives) and [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) ([kalliope markdown twin](/tools/kalliope-project-kalliope/alternatives.md), [Awesome-Prompt-Engineering markdown twin](/tools/promptslab-awesome-prompt-engineering/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/kalliope-project-kalliope-vs-promptslab-awesome-prompt-engineering.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, kalliope or Awesome-Prompt-Engineering? kalliope: Dormant. Awesome-Prompt-Engineering: 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 kalliope and Awesome-Prompt-Engineering? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [kalliope trust report](/tools/kalliope-project-kalliope/trust); [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kalliope-project-kalliope`](/api/graphcanon/graph?tool=kalliope-project-kalliope) - 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/_