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If you are building generative-AI features in Go, you are choosing between three different kinds of tools: application frameworks that orchestrate LLM workflows, provider SDKs that wrap one vendor’s API, and local-model clients that talk to a model running on your own machine. They solve different problems, so the useful question is not “which is best” but “which layer do I need.” This guide maps the options as of October 2026, explains what the official documentation establishes about each, and flags where the evidence stops.
Read this as a documentation-based comparison, not a benchmark. It does not include timed tests, throughput or latency measurements, or reliability data, and no framework is declared a winner on performance. The Go project’s own AI guide warns that this space changes quickly, so treat package names and version numbers below as a snapshot to confirm before you pin them in production.
The three layers, and which one you need
Most Go developers are really asking one of four questions: how do I find packages for AI services, how do I call a hosted service, how do I call a model I run myself, and how do I build a full LLM application. The table below separates the options by the layer they occupy. Items in the same layer are alternatives to each other; items in different layers often work together.
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| Layer | Options named in the Go project’s AI guide or official docs | What it is for | Scope limits |
|---|---|---|---|
| Application frameworks | Genkit Go, LangChainGo, CloudWeGo Eino | Multi-step LLM workflows, abstractions over models, tools and integrations | Provider coverage, maturity and feature parity differ by framework and must be checked individually |
| Provider SDKs | Google GenAI Go SDK (google.golang.org/genai), openai-go (github.com/openai/openai-go/v3) |
Official clients for one vendor’s model API | Not an orchestration layer; calls are made directly against that provider’s API |
| Local-model clients | Ollama’s Go API, which uses the Ollama service’s localhost REST API | Running models on your own machine and calling them from Go | Inference runs on the local machine; the sources reviewed do not set hardware requirements |
A practical reading of this table: a provider SDK is the thinnest option and gives you the provider’s newest API surface first, a framework adds application-level structure at the cost of another dependency, and a local client trades hosted capacity for control over where computation happens.
Application frameworks
Genkit Go
The Go project’s AI guide describes Genkit Go as an open-source framework from Google for building AI-powered applications. It is the framework the guide names alongside Google’s provider SDK as a starting point. The guide does not establish which model integrations, tracing features or deployment targets are current, so confirm those in the Genkit Go documentation for the version you plan to use.
LangChainGo
The Go project’s AI guide identifies LangChainGo as the Go implementation of LangChain. The name invites an assumption that it mirrors the Python library. Do not assume that. Check which chains, agents, vector-store and model integrations exist in the Go module itself, because feature parity with Python LangChain is not established by the sources reviewed.
CloudWeGo Eino
The Eino project repository describes itself as an LLM and AI application development framework written in Go. Before adopting it, check its current components, provider integrations, release activity and the Go versions it supports in the project’s own documentation. Repository listings establish what the project is, not how mature it is.
Provider SDKs
Google GenAI Go SDK for Gemini
Google’s documentation for the Gemini API states: “When building with the Gemini API, we recommend using the Google GenAI SDK.” For Go, the import path is google.golang.org/genai. Google says the SDK reached general availability in May 2025 and is the actively maintained library for Gemini API access.
The SDK’s repository README says it supports both the Gemini Developer API and the Gemini Enterprise Agent Platform APIs, and that it supports the Interactions API. Multimodal input combining text and images is documented. The same README warns that the arguments to Models.GenerateVideos are changing and recommends pinning to a version below 2.0.0 so that updates do not break your build.
OpenAI Go (openai-go)
The openai-go repository is the official Go library for the OpenAI API. Its documentation covers the Responses API and includes integration examples for Amazon Bedrock and Azure OpenAI. Those examples show that the same client code can be pointed at different deployment options, but the repository does not claim full feature parity across all of them, so check the specific endpoint you need.
The import path currently shown is github.com/openai/openai-go/v3. Because the library’s Go version requirement changes between releases, see the version notes below before choosing a release.
Local models with Ollama
The Go project’s AI guide describes Ollama as a way for Go applications to reach a locally running model service through a localhost REST API. The model computation happens on your machine rather than on a hosted provider, which matters for data residency, offline use and per-request cost, and it shifts the cost onto your own hardware.
The sources reviewed establish the access path, not the hardware you need. The amount of memory, the GPU and the disk space required depend on the specific model you run, and you should take those figures from that model’s own documentation rather than from this guide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Criteria that separate the options
The projects document different roles, so compare them on the following axes rather than on a single ranking:
- Provider breadth and coupling: a provider SDK ties your code to one vendor; a framework may abstract several providers but depends on its own integration quality.
- Application-level abstractions: does the option provide workflows, tools or memory, or only direct API calls?
- Maintenance and release compatibility: check the latest release date, the Go version required, and whether the project is actively maintained.
- Deployment target: hosted API, cloud platform, or local service.
- API surface and ergonomics: whether the newest provider features reach the Go client, and how well the types and examples fit your code.
- Custom integration code: how much glue you must write yourself to connect models, tools and your own data.
These axes come from the roles the projects describe. They are not measured results, and no standardized performance comparison exists in the official sources reviewed for this guide.
Version and migration checks
Moving from the old Gemini Go library
Google’s Gemini documentation lists the older Go library google.golang.org/generative-ai as not actively maintained and names google.golang.org/genai as its replacement. Google states that legacy libraries were deprecated as of November 30, 2025. If your code still imports the older path, migrate to the new module, since the older library will not receive maintenance.
Choosing an openai-go release
The openai-go repository says releases from v3.45.0 require Go 1.25 or later, and it points users on Go 1.22 through 1.24 to v3.44.0 as the final release that supports those versions. Go’s support windows and the library’s requirements both change, so confirm the figures in the repository’s current README before you fix a version in go.mod.
Decision guide
- Calling one hosted model and need the newest provider features? Start with the provider’s Go SDK: the Google GenAI Go SDK for Gemini, or openai-go for OpenAI.
- Building a multi-step application that calls tools, chains steps or switches between providers? Evaluate Genkit Go, LangChainGo and Eino against the checklist above, and prototype the one whose abstractions match your workflow.
- Need the model to run on your own machine? Use Ollama’s localhost API, and select a model based on the hardware figures in that model’s documentation.
- Mixing a framework with a provider? Many frameworks call provider APIs underneath, so a framework and a provider SDK can coexist in one codebase. Confirm that the framework’s provider integration supports the exact API you need.
What is not established
No official source reviewed for this guide provides a controlled benchmark comparing latency, throughput or production reliability across these Go options, and no named adoption statistics were found. Any article or table that ranks these tools on speed is working from measurements the sources do not provide. Choose based on the layer you need, the provider you are committed to, and the maintenance status you can verify today.
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