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 mpaepper · Agent Tool · ★ 1.1k
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🔒 Is llm_agents safe to install? View the security audit →
LLM Agents Small library to build agents which are controlled by large language models (LLMs) which is heavily inspired by langchain. The goal was to get a better grasp of how such an agent works and understand it all in very few lines of code. Langchain is great, but it already has a few more files and abstraction layers, so I thought it would be nice to build the most important parts of a simple agent from scratch. Some more infos are in this Hacker News discussion from April 5th 2023 and the related blog post. How it works The agent works like this: It gets instructed by a prompt which tells it the basic way to solve a task using tools Tools are custom build components which the agent can use So far, I've implemented the ability to execute Python code in a REPL, to use the Google search and to search on Hacker News The agent runs in a loop of Thought, Action, Observation, Thought, ...
| Stars | 1,054 |
| Forks | 86 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67.9242825073159/100 |
| Open Issues | 4 |
| Last Updated | 2025-06-23 |
| Created | 2023-04-04 |
| Platforms | python |
| Est. Tokens | ~4k |
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llm_agents is Build agents which are controlled by LLMs. It is categorized as a Agent Tool with 1.1k GitHub stars.
llm_agents is primarily written in Python. It covers topics such as deep-learning, langchain, llms.
You can find installation instructions and usage details in the llm_agents GitHub repository at github.com/mpaepper/llm_agents. The project has 1.1k stars and 86 forks, indicating an active community.
llm_agents is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to llm_agents on Agent Skills Hub include prompttools, awesome-llms-fine-tuning, openinference. 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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