A Go Developer's Take on AI Agent Development in 2025: Why I Explored Genkit

After building AgentRuntime, a comprehensive Go-based platform for AI agent deployment, I found myself questioning whether Go was the right choice for AI development in 2025. This led me to explore Google's Genkit framework, specifically genkit-go, and compare it with the broader AI development landscape. Here's what I learned.

Why I Started with Go (And Why I Had Doubts)

When I began developing AgentRuntime, Go seemed like a natural choice. The language's excellent concurrency model, memory efficiency, and fast compilation made it ideal for building robust agent infrastructure. I successfully implemented:

• Vector storage with sqlite-vec integration

• RAG (Retrieval Augmented Generation) systems

• PDF processing pipelines

• Real-time agent communication protocols

However, as the project grew, I encountered significant limitations. The Go ecosystem for AI development felt sparse compared to other languages. PDF processing required wrestling with CGO dependencies, multimodal support was limited, and integrating with various AI services often meant building everything from scratch.

This friction led me to investigate alternatives, including Google's genkit-go framework.

The Language Landscape: A Developer's Reality Check

The numbers don't lie. According to GitHub's Octoverse 2024 report, Python overtook JavaScript as the most popular language on the platform, with a 92% spike in Jupyter Notebook usage—clear indicators of AI and machine learning's growing influence. Meanwhile, 85% of Node.js developers now prefer TypeScript for business projects, and Go maintains steady growth with approximately 4.7 million developers worldwide.

Python: The AI Comfort Zone

Python remains the undisputed king of AI development. With frameworks like LangChain, rich library ecosystems, and seamless integration with ML models, Python offers the path of least resistance. Every AI service, from OpenAI to Anthropic, provides first-class Python SDKs.

Pros:

• Extensive libraries

• Rapid prototyping

• Community support

Cons:

• Performance overhead

• Deployment complexity

• Dependency management

TypeScript: The Full-Stack Champion

TypeScript with frameworks like genkit-js bridges the gap between AI capabilities and web development. For teams building user-facing AI applications, TypeScript offers end-to-end consistency and excellent developer experience.

Pros:

• Unified frontend/backend

• Rich web ecosystem

• Strong typing

Cons:

• Limited AI-specific libraries

• Performance for compute-heavy tasks

Go + genkit-go: The Performance Pragmatist

My experience with genkit-go revealed both promise and limitations. Google's framework brings structure to Go AI development, offering:

• Standardized patterns for RAG implementation

• Built-in support for various AI models

• Better integration with Google's AI services

However, genkit-go still inherits Go's fundamental challenge in the AI space: a smaller ecosystem. While the framework simplifies common AI patterns, you're still building more from scratch compared to Python alternatives.

The Verdict: Choosing Your AI Development Language in 2025

Here's a practical comparison of the three approaches:

What the Data Tells Us

Recent trends strongly support this comparison. GitHub's 2024 data shows a 59% surge in generative AI project contributions, with the majority happening outside the United States: particularly in India, Germany, Japan, and Singapore. This global expansion of AI development reinforces Python's dominance, as its accessibility makes it the go-to choice for developers entering the AI space worldwide.

TypeScript's growing adoption in enterprise settings (now preferred by 85% of Node.js developers) reflects its strength in building production-ready AI applications that need to integrate with existing web infrastructure. Meanwhile, Go's steady 4.7 million developer base demonstrates its solid footing in infrastructure and systems programming, even as it faces ecosystem challenges in AI-specific domains.

After months of experimentation, here's my practical guide:

Choose Python if:

• You're prototyping or building complex AI workflows

• Your team values rapid iteration over performance

• You need access to cutting-edge AI research and libraries

Choose TypeScript if:

• You're building AI-powered web applications

• Your team already has strong JavaScript/TypeScript expertise

• You prioritize unified full-stack development

Choose Go (with or without genkit-go) if:

• Performance and resource efficiency are critical

• You're building infrastructure or high-throughput systems

• You have specific requirements that justify the ecosystem trade-offs

Final Thoughts

While I continue developing AgentRuntime in Go, I've gained a deeper appreciation for the trade-offs involved in AI development language choice. Go excels at building robust, performant systems, but the AI ecosystem advantages of Python and TypeScript are real and significant.

The key insight? Don't choose a language in isolation. Consider your team's expertise, your project's specific requirements, and—most importantly—your willingness to build custom solutions versus leveraging existing ecosystems.

In 2025, the "best" language for AI development isn't about technical superiority. It's about finding the right balance between your goals, constraints, and the rich ecosystems each language offers.

Building AI agents taught me that sometimes the best tool isn't the one you're most comfortable with. It's the one that gets you to production fastest while meeting your performance requirements.