by daydreamsai · MCP Server · ★ 619
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is daydreams safe to install? View the security audit →
Daydreams - AI Agent Framework Notice: This agent framework is no longer the core focus as features are already obsolete. The Daydreams core focus is on Agentic Commerce and not the agent harness. Check out the lucid-agents repo: https://github.com/daydreamsai/lucid-agents We recommend the Pi agent harness for building agents and incorporating lucid-agents in it. Finally, TypeScript agents that scale and compose 🌐 Website • ⚡ Quick Start • 📖 Documentation • 💬 Discord 🎯 The Problem Every AI Developer Faces You build an AI agent. It works great in testing. Then you need to
npm install @daydreamsai/core @ai-sdk/openai zod
npx create-daydreams-agent my-agentTaken from the project's README on GitHub. Check the security audit before running it.
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 →
| Stars | 619 |
| Forks | 138 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65.5891072074702/100 |
| Open Issues | 25 |
| Last Updated | 2026-10-09 |
| Created | 2024-12-20 |
| Platforms | mcp, node |
| Est. Tokens | ~18k |
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daydreams is Daydreams is a set of tools for building agents for commerce. It is categorized as a MCP Server with 619 GitHub stars.
daydreams is primarily written in TypeScript. It covers topics such as 8004, agent-framework, agent-frameworks.
The README on GitHub (github.com/daydreamsai/daydreams) gives: npm install @daydreamsai/core @ai-sdk/openai zod , then npx create-daydreams-agent my-agent. Usage details are in the same README.
daydreams is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to daydreams on Agent Skills Hub include sre, aser, py-gpt. 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: