Google ADK for Python — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Google ADK for Python. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Jun 19, 2026 • Sentiment updated: Jun 25, 2026

GitHub Statistics

Community Sentiment

Community Buzz: As one user on GitHub said, 'ADK is gaia's runtime (agents, tools, sessions, MCP, plugins)', highlighting its importance in the ecosystem. Another user on Dev.to mentioned, 'I recently tried to make an AI agent answer more. It answered less.', showing the community's experimentation with the technology.

Pros & Cons

What People Love

Easy to use, Powerful features, Strong community support, as seen on GitHub and Dev.to

Common Complaints

Crashes and bugs, Difficulty with integration, Limited documentation

Biggest Positive: Easy to use

Biggest Negative: Crashes and bugs

Why Google ADK for Python Stands Out

ADK stands out from alternative AI agent frameworks with its code-first approach, flexible tool ecosystem, and modular architecture. The project's focus on simplicity, flexibility, and control makes it an attractive choice for developers looking to build sophisticated AI agents. By leveraging ADK's features, such as custom service registration and tool confirmation flow, developers can create agents that are tailored to their specific needs. Additionally, ADK's integration with Vertex AI and Cloud Run enables seamless deployment and management of agents, making it a valuable tool for building scalable AI applications.

Built With

Build a personalized search assistant — ADK's code-first approach and flexible tool ecosystem enable rapid development of custom search agents, Build a multi-agent system for task execution — ADK's modular architecture and A2A protocol integration facilitate seamless communication between agents, Build a research agent that summarizes long documents — ADK's support for Gemini models and tool confirmation flow enable accurate and efficient document analysis, Build a conversational AI model that learns from user interactions — ADK's agent config feature and Vertex AI integration enable easy deployment and training of conversational models, Build a scalable e-commerce chatbot that handles customer inquiries — ADK's deploy anywhere feature and Cloud Run support enable easy deployment and management of chatbots

Getting Started

  1. Install the latest stable version of ADK using `pip install google-adk`
  2. Explore the full documentation for detailed guides on building, evaluating, and deploying agents at https://google.github.io/adk-docs
  3. Define a single agent using the `Agent` class and specify its name, model, instruction, and tools
  4. Create a multi-agent system by defining individual agents and assigning them to a parent agent
  5. Try running the `root_agent` example to verify that ADK is working correctly and to see the agent in action

About

An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

Official site: https://google.github.io/adk-docs/

Category & Tags

Category: multi-agent

Tags: agent, agentic, agentic-ai, agents, agents-sdk, ai, ai-agents, aiagentframework, genai, genai-chatbot, llm, llms, multi-agent, multi-agent-systems, multi-agents, multi-agents-collaboration

Market Context

ADK is positioned as a key tool for building and deploying AI agents, with a strong presence on GitHub and Dev.to, competing with other AI development kits.