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 empower-ai · Agent Tool · ★ 217
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Empower Functions Empower Functions is a family of LLMs(large language models) that offer GPT-4 level capabilities for real-world "tool using" use cases, with full compatibility support to be served as a drop-in replacement. Live Demo • Huggingface Repo • Website • Discord Update New Empower Functions v1.1 We have just launched new v1.1 of the Empower Functions family. The updated v1.1 family has been fine-tuned based on Llama3.1 using an enhanced curated dataset. It has achieved state-of-the-art performance on the Berkeley Function Calling leader board: (captured on Sep 10, 2024) What is real world "tool using" use cases? "tool using" refers to the ability of LLMs to interact with external APIs by recognizing when a function needs to be called and then generating JSON containing the necessary arguments based on user inputs. This capability is essential for building conversational agents and applications that convert natural language into API calls, facilitating tasks such as weather inquiries, data extraction, and interactions with k
| Stars | 217 |
| Forks | 17 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 68.5630675664641/100 |
| Open Issues | 1 |
| Last Updated | 2024-10-05 |
| Created | 2024-05-10 |
| Platforms | python |
| Est. Tokens | ~87k |
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empower-functions is GPT-4 level function calling models for real-world tool using use cases. It is categorized as a Agent Tool with 217 GitHub stars.
empower-functions is primarily written in Python. It covers topics such as ai, function-calling, llama3.
You can find installation instructions and usage details in the empower-functions GitHub repository at github.com/empower-ai/empower-functions. The project has 217 stars and 17 forks, indicating an active community.
The top alternatives to empower-functions on Agent Skills Hub include llm-functions, dexto, llm-axe. 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.
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