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 xinhuangcs · MCP Server · ★ 123
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is agentmaker safe to install? View the security audit →
agentmaker "Four lines of code, an agent with memory." A general-purpose Python framework for building LLM agents and multi-agent systems, with tools, memory, retrieval / RAG, context engineering, guardrails, human-in-the-loop, and observability built in. Async-first, fully typed, and easy to debug: a built-in LLM debugger pinpoints a failed run's first bad step, root cause, and fix. Highlights One agent, many recipes: a single agent loop for chat and tool use, plus plan-and-execute and reflection workflows, declarative agent specs, and multi-agent orchestration. Any LLM provider: native OpenAI, Anthropic, and Gemini, plus DeepSeek, Moonshot, Zhipu, local mode
| Stars | 123 |
| Forks | 4 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.4621004417235/100 |
| Open Issues | 4 |
| Last Updated | 2026-08-20 |
| Created | 2026-07-08 |
| Platforms | mcp, python |
| Est. Tokens | ~15k |
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agentmaker is A general-purpose Python framework for building LLM agents and multi-agent systems. "Four lines of code, an agent with memory.". It is categorized as a MCP Server with 123 GitHub stars.
agentmaker is primarily written in Python. It covers topics such as agent, agent-framework, ai-agents.
You can find installation instructions and usage details in the agentmaker GitHub repository at github.com/xinhuangcs/agentmaker. The project has 123 stars and 4 forks, indicating an active community.
agentmaker is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to agentmaker on Agent Skills Hub include WorkflowAI, oreilly-ai-agents, captain-claw. 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: