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 lunary-ai · Agent Tool · ★ 56
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is abso safe to install? View the security audit →
Drop-in replacement for OpenAI Abso provides a unified interface for calling various LLMs while maintaining full type safety. Features OpenAI-compatible API 🔁 (drop in replacement) Call any LLM provider (OpenAI, Anthropic, Groq, Ollama, etc.) Lightweight & Fast ⚡ Embeddings support 🧮 Unified tool calling 🛠️ Tokenizer and cost calculation (soon) 🔢 Smart routing (soon) Providers ❌
| Stars | 56 |
| Forks | 8 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 70.9008782412928/100 |
| Open Issues | 4 |
| Last Updated | 2025-06-02 |
| Created | 2025-02-08 |
| Platforms | gemini, node |
| Est. Tokens | ~13k |
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abso is TypeScript SDK to call 100+ LLM Providers in OpenAI format.. It is categorized as a Agent Tool with 56 GitHub stars.
abso is primarily written in TypeScript. It covers topics such as ai, anthropic, azure-openai.
You can find installation instructions and usage details in the abso GitHub repository at github.com/lunary-ai/abso. The project has 56 stars and 8 forks, indicating an active community.
abso is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to abso on Agent Skills Hub include kea-research, toolmate, SimplerLLM. 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: