---
title: GoAI SDK vs LangChainGo vs eino — Go LLM Libraries Compared
description: Compare GoAI SDK with LangChainGo, eino, and lingoose. Provider support, Go generics, dependencies, tool calling, streaming — side-by-side comparison.
---

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

GoAI SDK is a Go-native AI SDK supporting 25+ 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 | **25+** | ~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 | **Minimal** (stdlib + oauth2) | 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 (core uses stdlib + oauth2 only; optional OTel in separate module)
- 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 **3.1× 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](https://github.com/zendev-sh/goai/tree/main/bench)

## Code Comparison

### Text Generation

**GoAI SDK:**

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

**LangChainGo:**

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

### Structured Output

**GoAI SDK:**

```go
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:**

```go
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

```go
// 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

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

[Installation guide →](/getting-started/installation)

[View all 25+ providers →](/providers/)

[Examples →](/examples)
