by airbytehq · MCP Server · ★ 119
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🐙 Drop-in tools that give AI agents reliable, permission-aware access to external systems.
| Stars | 119 |
| Forks | 10 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 40.2/100 |
| Open Issues | 8 |
| Last Updated | 2026-05-05 |
| Created | 2025-11-26 |
| Platforms | gemini, mcp, python |
| Est. Tokens | ~5305k |
These tools work well together with airbyte-agent-sdk for enhanced workflows:
Looking for a airbyte-agent-sdk alternative? If you're comparing airbyte-agent-sdk with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Open-source protocol suite standardizing LLM, Vector, Graph, and Embedding infrastructure across LangChain, Ll
Open-source framework for building AI agents in Salesforce, with built-in memory, tools, and security
Task Planning and Tracking toolset for Pydantic AI agents, enabling hierarchical task management with subtasks
File Storage & Sandbox Backends for Pydantic AI: console tools for file operations, Docker-isolated sandboxes
Guardrail capabilities for Pydantic AI — cost tracking, prompt injection detection, PII filtering, secret reda
Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling
Explore other popular mcp server tools:
airbyte-agent-sdk is 🐙 Drop-in tools that give AI agents reliable, permission-aware access to external systems.. It is categorized as a MCP Server with 119 GitHub stars.
airbyte-agent-sdk is primarily written in Python. It covers topics such as ai, ai-agents, airbyte.
You can find installation instructions and usage details in the airbyte-agent-sdk GitHub repository at github.com/airbytehq/airbyte-agent-sdk. The project has 119 stars and 10 forks, indicating an active community.
The top alternatives to airbyte-agent-sdk on Agent Skills Hub include corpusos, aiAgentStudio, pydantic-ai-todo. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.