by brucechou1983 · Agent Tool · ★ 97
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 97 |
| Forks | 7 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 65/100 |
| Last Updated | 2025-12-19 |
| Created | 2024-04-22 |
| Platforms | python |
| Est. Tokens | ~905k |
Rapidly build and deploy production-ready conversational AI agents using Chainlit and LangGraph. This powerful integration combines state-of-the-art language models with flexible workflow management, enabling developers to create sophisticated chatbots, virtual assistants, and interactive AI applications in minutes. Find a way to run your open source models? This is a free tool for you.
Looking for a chainlit_langgraph alternative? If you're comparing chainlit_langgraph with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
An example agent demonstrating streaming, tool use, and interactivity from your terminal. This agent builder c
This MCP server provides documentation about Strands Agents to your GenAI tools, so you can use your favorite
Python version of 📦 repopack (https://github.com/yamadashy/repopack) by yamadashy (https://github.com/yamadas
🤖 An open-source, AI agent-native research canvas application that performs real-time search with HITL (Human
Structured Data Extractor for AI Agents. Search your documents or the web for specific data and get it back in
[archived] c4 GenAI Suite
Explore other popular agent tool tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: