GoAI SDK vs LangChainGo vs eino vs lingoose — Go LLM Libraries Compared (2026)

GoAI SDK is a Go-native AI SDK supporting 20+ LLM providers with minimal dependencies. LangChainGo is a Go port of Python's LangChain. eino is ByteDance's Go AI framework inspired by Google ADK. lingoose is a minimal Go LLM library.

Feature Comparison

Feature	GoAI SDK	langchaingo	eino	lingoose
Providers	20+	~10	~8	~5
Go generics	✅	❌	✅	❌
Structured output	GenerateObject[T]	Manual JSON	Partial	❌
Auto tool loop	✅ MaxSteps	❌	✅	❌
Streaming	✅ channels	✅	✅	✅
Provider-defined tools	20 tools	—	—	—
Prompt caching	✅ auto	❌	❌	❌
Embeddings	✅ Embed / EmbedMany	✅	✅	✅
Image generation	✅ GenerateImage	❌	❌	❌
Dependencies	stdlib only	Heavy	Medium	Minimal
Inspired by	Vercel AI SDK	LangChain (Python)	Google ADK	—
License	MIT	MIT	Apache 2.0	MIT

When to use GoAI SDK

• You need to support multiple LLM providers with minimal code changes — switch providers by changing one line
• You want type-safe structured output with Go generics (GenerateObject[T], StreamObject[T])
• You need provider-defined tools: web search, code execution, computer use, file search across OpenAI, Anthropic, and Google
• You want minimal transitive dependencies (stdlib only, no heavy dependency tree)
• You're building production AI applications in Go from scratch
• You need automatic prompt caching for cost optimization
• You want the Vercel AI SDK experience in Go — same mental model, Go idioms

When to use LangChainGo

• You're migrating from a Python LangChain codebase and want familiar abstractions
• You need chain-based composition patterns familiar from the Python ecosystem
• Your team already has LangChain experience and wants to reuse that knowledge

When to use eino

• You're in the ByteDance/CloudWeGo ecosystem
• You need graph-based workflow composition (similar to Google ADK)
• You want tight integration with ByteDance infrastructure

When to use lingoose

• You want the smallest possible footprint
• Simple single-provider use case, no tool calling needed
• You're prototyping and need something lightweight

Benchmark: GoAI SDK vs Vercel AI SDK

Real benchmark data from the GoAI repository. Both sides use in-process mock servers serving identical SSE fixtures — no real API calls, no network jitter.

Environment: Apple M2, Go 1.26.0, Bun 1.3.10, AI SDK 5.0.124. Average of 3 independent sequential runs.

Benchmark	GoAI (Go)	Vercel AI SDK (TS/Bun)	Winner
Streaming throughput (100 chunks × 500B)	1.46 ms/op	1.62 ms/op	1.1× Go
Time to first chunk	320.7 μs	412.3 μs	1.3× Go
Cold start (median of 20 launches)	569.2 μs	13.89 ms	24.4× Go
Schema generation	3.6 μs/op	3.5 μs/op	1.0× TS
Memory per stream	220 KB	676 KB	3.1× Go
GenerateText (non-streaming)	55.7 μs/op	79.0 μs/op	1.4× Go

GoAI SDK starts 24× faster, uses 3× less memory, and processes streaming responses 1.3× faster to first token compared to the Vercel AI SDK running on Bun.

Schema generation is comparable — Go reflection vs Zod→JSON Schema are roughly equivalent in performance.

Source: github.com/zendev-sh/goai/tree/main/bench

Code Comparison

Text Generation

GoAI SDK:

result, err := goai.GenerateText(ctx,
    openai.Chat("gpt-4o"),
    goai.WithPrompt("Hello"),
)
fmt.Println(result.Text)

LangChainGo:

llm, err := openaillm.New()
resp, err := llms.GenerateFromSinglePrompt(ctx, llm, "Hello")
fmt.Println(resp)

Structured Output

GoAI SDK:

type Recipe struct {
    Name        string   `json:"name"`
    Ingredients []string `json:"ingredients"`
}

result, err := goai.GenerateObject[Recipe](ctx,
    openai.Chat("gpt-4o"),
    goai.WithPrompt("A pasta recipe"),
)
fmt.Println(result.Object.Name) // type-safe access

LangChainGo: Requires manual JSON schema definition and unmarshaling.

Streaming

GoAI SDK:

stream, err := goai.StreamText(ctx,
    openai.Chat("gpt-4o"),
    goai.WithPrompt("Tell me a story"),
)
for chunk := range stream.TextStream() {
    fmt.Print(chunk)
}

Switch Providers — One Line Change

// OpenAI
model := openai.Chat("gpt-4o")

// Anthropic
model := anthropic.Chat("claude-sonnet-4-20250514")

// Google Gemini
model := google.Chat("gemini-2.5-flash")

// AWS Bedrock
model := bedrock.Chat("anthropic.claude-sonnet-4-20250514-v1:0")

// All use the same GoAI functions:
result, err := goai.GenerateText(ctx, model, goai.WithPrompt("Hello"))

Getting Started

go get github.com/zendev-sh/goai@latest

Installation guide →

View all 20+ providers →

Examples →