Google ships Agent Development Kit for Python - 8,200 stars in two weeks
Google released the Agent Development Kit (ADK) for Python, a multi-agent orchestration framework that gained 8,200+ GitHub stars in its first two weeks. The framework supports MCP integration and works with any model provider.
Google released the Agent Development Kit (ADK) for Python in April, a framework for building multi-agent systems that can orchestrate complex tasks. The project gained 8,200+ GitHub stars in its first two weeks on GitHub, making it one of the most popular new AI projects of the month.
ADK provides a structured way to build agents that work together. You define individual agents with specific capabilities, then compose them into teams where a coordinator agent delegates tasks to specialists. The framework handles state management, message passing, error recovery, and result aggregation between agents. This coordinator-specialist pattern is becoming a standard architecture for complex agent workflows.
MCP integration is built in, which means ADK agents can connect to any MCP server for tool access. This includes file systems, databases, APIs, browser automation, and any other MCP-compatible service. The framework also supports custom tool definitions for project-specific needs, using the same JSON schema format that other MCP clients use.
The key design choice is model-agnostic operation. ADK works with Google's own models (Gemini) but also supports any OpenAI-compatible API, which means Ollama, vLLM, Anthropic, and other providers work without modification. You can even mix providers within a single agent team, using different models for different specialist roles based on their strengths.
The Python API follows familiar patterns. If you have built Flask apps or used LangChain, the structure will feel recognizable. Agents are Python classes with defined capabilities, and orchestration is expressed as function calls rather than complex configuration files or custom domain-specific languages.
The 8,200+ star count in two weeks signals genuine developer interest. The issues tab has active discussions about real deployment challenges, which is a good indicator that people are building with it rather than just starring it. Google's own documentation includes example projects that go beyond toy demos into realistic multi-agent scenarios.
For anyone evaluating agent frameworks, ADK joins a crowded field alongside LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, and others. Its strength is Google's engineering depth, tight MCP integration, and strong documentation. Whether you adopt ADK or not, the multi-agent patterns it implements are worth understanding as reference architectures.
