No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by griptape-ai · Agent Tool · ★ 2.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is griptape safe to install? View the security audit →
Griptape is a Python framework designed to simplify the development of generative AI (genAI) applications. It offers a set of straightforward, flexible abstractions for working with areas such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and much more. Looking for a no-code experience? Check out Griptape Nodes, a visual desktop application for building and running AI workflows. 🛠️ Core Components 🏗️ Structures 🤖
| Stars | 2,579 |
| Forks | 260 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 66.4782690374526/100 |
| Open Issues | 92 |
| Last Updated | 2026-09-24 |
| Created | 2023-01-14 |
| Platforms | claude-code, python |
| Est. Tokens | ~15k |
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griptape is Modular Python framework for AI agents and workflows with chain-of-thought reasoning, tools, and memory.. It is categorized as a Agent Tool with 2.6k GitHub stars.
griptape is primarily written in Python. It covers topics such as ai, anthropic, claude.
You can find installation instructions and usage details in the griptape GitHub repository at github.com/griptape-ai/griptape. The project has 2.6k stars and 260 forks, indicating an active community.
griptape is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to griptape on Agent Skills Hub include cascadeflow, aide, zcf. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: