Google ADK for Go: An Honest Look at the Agent Framework Landscape

On November 7, 2025, Google announced that the Agent Development Kit (ADK) now supports Go, joining Python and Java. This is a real announcement you can verify on the Google Developers Blog.

What does this mean for developers choosing between agent frameworks? Here's an honest assessment of what we know and don't know.

What Google ADK Actually Offers

Based on Google's documentation and announcement, ADK provides:

Why Go?

Google's choice to add Go support makes sense for several reasons:

• Concurrency: Go's goroutines and channels are well-suited for parallel agent operations
• Type Safety: Static typing catches errors at compile time
• Deployment: Go compiles to single binaries with no runtime dependencies
• Google's Infrastructure: Much of Google's backend infrastructure uses Go

JetBrains' Go Ecosystem Report suggests Go developers have relatively high AI tool adoption, though exact figures vary by survey methodology.

Framework Comparison: What We Can Verify

Here's what we can actually verify about the major frameworks:

Claim	Evidence Level	Notes
LangChain has largest community	Well-supported	GitHub stars, npm/pip downloads verifiable
CrewAI is multi-agent focused	Well-supported	Core design philosophy is verifiable
Google ADK supports Go/Python/Java	Well-supported	Official announcement and repos exist
Microsoft unified AutoGen + Semantic Kernel	Partially supported	Some announcements; details still emerging
"80% of enterprises use agents"	Unverified	Various surveys claim this; methodology unclear
"96% plan to expand agent use"	Unverified	Survey-based; selection bias likely

Framework-by-Framework Analysis

How to Think About Framework Choice

Rather than declaring a "winner," here's a framework for thinking about your choice:

Consider LangChain/LangGraph If...

• You want the largest ecosystem and community support
• You're Python-first and okay with framework abstractions
• You value extensive documentation and examples
• You need many pre-built integrations

Consider CrewAI If...

• Multi-agent collaboration is your primary use case
• You want intuitive role-based agent definition
• You prefer a simpler mental model
• You're okay with a smaller ecosystem

Consider Google ADK If...

• You're already on Google Cloud
• You have Go or Java expertise
• You want code-first without framework magic
• You value type safety and compile-time checks

Consider Microsoft's Offering If...

• You're an Azure customer
• You need enterprise support and SLAs
• You have .NET expertise
• Commercial licensing works for your situation

What We Don't Know

The Case for Multi-Agent Systems (With Caveats)

The theoretical case for multi-agent architectures is reasonable:

• Specialization: Agents focused on specific tasks may perform better than generalist agents
• Parallel execution: Multiple agents can work concurrently
• Failure isolation: One agent failing doesn't necessarily break the whole system
• Modularity: Easier to test, update, and replace individual agents

But there are counterarguments:

• Coordination overhead: Agents communicating adds complexity and latency
• Reliability compounds: If each agent is 95% reliable, a 10-step pipeline is ~60% reliable
• Debugging complexity: Multi-agent systems are harder to debug than single agents
• Cost: More agents means more API calls and higher costs

The right architecture depends on your specific problem. "Multi-agent is always better" is not supported by evidence.

Getting Started with Google ADK for Go

If you want to experiment with Google ADK for Go:

Resources:

• Repository: github.com/google/adk-go
• Documentation: google.github.io/adk-docs/
• Announcement: Google Developers Blog (November 7, 2025)

The Bottom Line

Google ADK for Go is a legitimate addition to the agent framework landscape. It offers a code-first approach with multi-language support and strong Google Cloud integration.

Is it "better" than LangChain, CrewAI, or Microsoft's offerings? That depends entirely on your context: your team's skills, your cloud provider, your use case, and your preferences around abstraction levels.

The honest answer is that all these frameworks are relatively new, production data is limited, and best practices are still emerging. Choose based on your constraints, run pilots, measure results, and be willing to change if something isn't working.