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 langchain-ai · Agent Tool · ★ 1.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is deepagentsjs safe to install? View the security audit →
The batteries-included agent harness. Using an LLM to call tools in a loop is the simplest form of an agent. This architecture, however, can yield agents that are "shallow" and fail
| Stars | 1,571 |
| Forks | 274 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69.8658283057196/100 |
| Open Issues | 72 |
| Last Updated | 2026-09-18 |
| Created | 2025-08-04 |
| Platforms | node |
| Est. Tokens | ~453k |
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deepagentsjs is Agent harness built with LangChain and LangGraph. Equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - well-equipped to handle complex agentic tasks.. It is categorized as a Agent Tool with 1.6k GitHub stars.
deepagentsjs is primarily written in TypeScript. It covers topics such as ai, deepagents, langchain.
You can find installation instructions and usage details in the deepagentsjs GitHub repository at github.com/langchain-ai/deepagentsjs. The project has 1.6k stars and 274 forks, indicating an active community.
deepagentsjs is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to deepagentsjs on Agent Skills Hub include openagent, ai-agents-from-zero, awesome-LangGraph. 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: