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 ginlix-ai · MCP Server · ★ 1.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LangAlpha safe to install? View the security audit →
A vibe investing agent harness LangAlpha is built to help interpret financial markets and support investment decisions. Getting Started • API Docs • Agent Core • Backend • Web • TUI • Skills • MCP Pin a curated news brief from the dashboard, kick off idea generation, and dispatch parallel subagents to screen the market — then get five long/short pair-trade ideas in an inline interactive dashboard, calibrated to your book. Why LangAlpha Every AI finance tool today treats investing as one-shot: ask a question, get
| Stars | 1,771 |
| Forks | 290 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 63.6716823701554/100 |
| Open Issues | 30 |
| Last Updated | 2026-09-22 |
| Created | 2026-01-18 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~22k |
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LangAlpha is Claude Code for Financial Market. It is categorized as a MCP Server with 1.8k GitHub stars.
LangAlpha is primarily written in Python. It covers topics such as agent, investment, langchain.
You can find installation instructions and usage details in the LangAlpha GitHub repository at github.com/ginlix-ai/LangAlpha. The project has 1.8k stars and 290 forks, indicating an active community.
LangAlpha is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to LangAlpha on Agent Skills Hub include open-ptc-agent, AI-Infra-Guard, casibase. 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: