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 dkondo · Agent Tool · ★ 52
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is agent-tackle-box safe to install? View the security audit →
agent-tackle-box A toolkit for developing AI agents. agent-debugger is a terminal debugger for LangGraph/LangChain agents. It combines agent-level visibility (state, messages, tool calls, store snapshots, and semantic breakpoints) with Python-level debugging (line breakpoints, stepping, stack, and locals) in one Textual UI. See projects/agent-debugger/README.md for full usage, commands, and extension details.
| Stars | 52 |
| Forks | 4 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 50.8409259574957/100 |
| Last Updated | 2026-05-11 |
| Created | 2026-02-15 |
| Platforms | python |
| Est. Tokens | ~32k |
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** THIS REPO HAS MOVED TO https://github.com/langchain-ai/langchainjs/tree/main/libs/langchain-mcp-adapters **
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agent-tackle-box is A toolkit for developing AI agents, including agent-debugger: Terminal debugger for LangGraph & LangChain agents. Debug LLM agents with state inspection, tool calls, semantic breakpoints, and Python p. It is categorized as a Agent Tool with 52 GitHub stars.
agent-tackle-box is primarily written in Python. It covers topics such as agent-debugger, agent-development, agent-development-environment.
You can find installation instructions and usage details in the agent-tackle-box GitHub repository at github.com/dkondo/agent-tackle-box. The project has 52 stars and 4 forks, indicating an active community.
agent-tackle-box is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to agent-tackle-box on Agent Skills Hub include langchainjs-mcp-adapters, langchain_data_agent, Upwork-AI-jobs-applier. 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: