A new Go library called GoAI promises to simplify LLM integration for backend teams juggling multiple AI providers.
The open-source project provides a unified API across eight providers - OpenAI, Anthropic Claude, Google Gemini, xAI Grok, Mistral, Perplexity, and local models via Ollama. Switch providers by changing configuration, not rewriting integration code.
Why this matters
Go's emerging as the language of choice for production LLM infrastructure. Customer support platforms are handling 10,000+ daily queries with sub-200ms latency using Go's goroutines and channels. Assembled's production system processes millions of monthly LLM requests via custom Go code.
The pattern is clear: Python for prototyping, Go for scale. But until now, each LLM provider meant bespoke integration work.
GoAI's pitch: zero dependencies beyond Go's standard library, native context support for timeouts and cancellation, and structured errors instead of string-matching. The library weighs in light - no transitive dependency explosion.
client, _ := goai.New(
"anthropic",
"claude-3-opus",
goai.WithAPIKey(os.Getenv("ANTHROPIC_API_KEY")),
goai.WithTimeout(30*time.Second),
)
response, _ := client.Chat(ctx, "Your prompt")
The real test
Library fragmentation is already visible - inercia/go-llm offers similar multi-provider abstraction. The Go-LLM ecosystem needs consolidation, not more competing standards.
What GoAI has in its favour: intentionally minimal design that suits Go's philosophy. Enterprise teams won't adopt magic frameworks, but they will adopt clean interfaces that solve specific problems.
The question isn't whether Go belongs in LLM infrastructure - production deployments already prove it does. The question is whether a lightweight abstraction layer beats custom code.
Worth watching: adoption by teams already running Go-based LLM systems at scale. If Assembled-sized operations standardise on GoAI or similar tools, that signals the ecosystem maturing beyond bespoke implementations.
For now, it's one developer's attempt to DRY up a common pattern. Whether it becomes infrastructure or stays a GitHub curiosity depends on whether production teams see value in standardisation over control.