open-connector: Open-source auth gateway connecting 1500+ SaaS providers to AI agents through SDK, CLI, MCP, HTTP, and OpenAPI

by oomol-lab · MCP Server · ★ 6.1k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 69/100

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. 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.

agent-toolsai-agentsapi-gatewayautomationclicloudflare-workersconnectorsintegration-platformmcpmodel-context-protocol

Quick Facts

Stars6,068
Forks570
LanguageTypeScript
CategoryMCP Server
LicenseApache-2.0
Quality Score69/100
Open Issues6
Last Updated2026-10-22
Created2026-06-29
Platformscli, mcp, node
Est. Tokens~17k

About open-connector

OpenConnector English | 简体中文 OpenConnector is an open-source alternative to Composio for agent-ready SaaS auth, tools, and integrations. It is a connector layer for agents that need reliable access to user accounts in external apps. It handles auth, tool execution, and agent-ready integrations.

Compatible Skills

These tools work well together with open-connector for enhanced workflows:

  • saas-starter — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (60%)
  • qwen-gate — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • opengate — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)

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How this security grade is produced

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:

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