by lmnr-ai · Agent Tool · ★ 3.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/lmnr-ai/lmnr
pip install --upgrade 'lmnr[all]'
Taken from the project's README on GitHub. Check the security audit before running it.
Flagged: environment access. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
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 | 3,477 |
| Forks | 261 |
| Language | TypeScript |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 67/100 |
| Open Issues | 162 |
| Last Updated | 2026-11-07 |
| Created | 2024-08-29 |
| Platforms | node |
| Est. Tokens | ~15k |
Laminar Laminar is an open-source observability platform purpose-built for AI agents. [x] Tracing. Docs [x] OpenTelemetry-native powerful tracing SDK - 1 line of code to automatically trace Vercel AI SDK, Browser Use, Stagehand, LangChain, OpenAI, Anthropic, Gemini, and more. [x] Evals. Docs [x] Unopinionated, extensible SDK and CLI for running evals locally or in CI/CD pipeline.
These tools work well together with lmnr for enhanced workflows:
Looking for a lmnr alternative? If you're comparing lmnr 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.
AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework
AI observability platform for production LLM and agent systems.
AI Observability & Evaluation
Self-hosted control plane for AI agents: dispatch tasks, review runs, track spend, and operate OpenClaw, Claud
Observability and enforcement for AI agent harnesses. Capture every run and runtime reliability with policy en
[DEPRECATED] Legacy Klavis AI MCP integrations. See klavis.ai for RL environments and training data.
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: