Goai
Go SDK for AI apps: one type-safe API for text generation, streaming, tools and MCP across 25+ LLM providers.
If your backend is Go and you touch more than one model vendor, GoAI deletes the glue code you would otherwise maintain per provider. GenerateObject[T] and channel-based StreamText with auto MaxSteps loops are the parts that actually change how you write code; the MCP client with OAuth 2.1 is the reason to pick it over a homemade abstraction. Skip it if you are not in Go or expect a vector database or RAG pipeline in the box.
Verified 2h ago · liveness 67/100 · cite: rightaichoice.com/tools/goai
- Go developers who want one SDK instead of six provider clients
- Serverless functions and microservices with tight cold-start and memory budgets
- Teams building tool-using agents with automatic MaxSteps loops
- Go apps that need to consume existing MCP servers as callable tools
- Python, TypeScript or Java teams — this is Go-only
- Projects expecting a GUI, dashboard or hosted web console
- Teams needing a built-in vector database, RAG pipeline or document loaders
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Skip GoAI SDK if your codebase is not Go, or if you need a managed platform that ships a dashboard, vector store, and RAG pipeline rather than a library you wire up yourself.
GoAI itself is MIT-licensed and free, but every model call is billed by the provider you point it at — a multi-provider SDK makes it easy to spread spend across several invoices with no single bill to watch.
The SDK itself is $0 under the MIT License, which puts it well below commercial AI orchestration platforms that charge per seat or per workflow — but the real cost comparison is your total model spend, which GoAI neither adds to nor controls. Against Vercel AI SDK (also free, TypeScript-only) it competes on cold-start and memory for Go serverless; against LangChainGo (free, ~10 providers) it competes on provider count and generic typed output. For a Go team, cost of adoption is engineer time,
In short
Goai — Go SDK for AI apps: one type-safe API for text generation, streaming, tools and MCP across 25+ LLM providers. Best for Go developers who want one SDK instead of six provider clients, Serverless functions and microservices with tight cold-start and memory budgets, Teams building tool-using agents with automatic MaxSteps loops. Free to use.
What's new in Goai
Checked 5 days agoAcross the latest 4 updates: 4 feature updates.
v0.9.0 — Provider-neutral file upload and remote file references
Adds a FileUploader interface and RemoteFileRef for OpenAI, Anthropic, and Google Gemini, with compat providers mapping file parts to native OpenAI shapes.
v0.8.5 — Requesty provider, Anthropic native structured output, per-step Reasoning
Adds an OpenAI-compatible LLM gateway provider plus Anthropic native structured output, per-step reasoning, and Gemini schema sanitization.
v0.8.0 — MCP OAuth 2.1 + PKCE and NewTool typed constructor
Brings native OAuth for remote MCP servers, a NewTool[In] typed constructor, and surfaces lifecycle hook panics as *PanicError.
v0.7.0 — Cloudflare Workers AI and FPT Smart Cloud providers
Adds two providers with OpenAI-compatible chat and embeddings; FPT supports both Global and Japan regions.
What people actually say about Goai — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
9 mentions across 4 sources (Hacker News, Product Hunt, App Store, Lemmy) · researched Jul 3, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Unified API across 25+ LLM providers in one Go library.
- +Only 2 core dependencies – stdlib only, minimal bloat.
- +Generics-based type safety for structured object generation.
- +Streaming, tool loops, and MCP support built-in.
- +24x faster cold start compared to alternatives.
- −Community feedback is extremely sparse for a production tool.
- −Most online mentions are about other tools built on Goai, not Goai itself.
- −No real-world reliability or bug reports available.
- −Support resources like forums or documentation are unvalidated.
- −Integration examples beyond core text generation are not widely shared.
- • LLM provider API costs are not included – users pay per-token to providers.
- • No mention of paid support or enterprise add-ons.
Viability Score
How well maintained and how widely used is Goai? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- GenerateText — non-streaming text generation across 25+ providers
- StreamText — real-time streaming via Go channels with automatic tool loops
- GenerateObject[T] — type-safe structured output using Go generics
- StreamObject[T] — partial object streaming with typed results
- Embed and EmbedMany — text embeddings with auto-chunking
- GenerateImage — image generation via OpenAI, Google, Azure and Vertex AI
- Tool calling with custom tools and an automatic MaxSteps agent loop
- 26 provider-defined tools including web search, code execution and computer use
- MCP client over stdio, HTTP and SSE with automatic tool conversion
- MCP OAuth 2.1 with PKCE for remote MCP servers (v0.8.0)
- Provider-neutral file upload and remote file references (v0.9.0)
- Prompt caching with automatic cache control for Anthropic, Bedrock and MiniMax
- Langfuse and OpenTelemetry tracing for generations, tools and multi-step loops
- TokenSource / CachedTokenSource for auto-refreshing OAuth tokens
- zenflow — declarative YAML multi-agent orchestration built on GoAI
About Goai
GoAI SDK is an MIT-licensed Go library that collapses 25+ LLM providers into a single API surface. Install it with `go get github.com/zendev-sh/goai@latest`, point it at any of OpenAI, Anthropic, Google Gemini, Bedrock, Azure, Groq, Mistral, Cohere, DeepSeek, Ollama or vLLM, and switching vendors becomes a one-line change. The design is shaped after the Vercel AI SDK but rebuilt the Go way: generics for type safety, channels for streaming, interfaces for extensibility. The core runtime pulls in only golang.org/x/oauth2, with OpenTelemetry in a separate submodule and goleak confined to tests. The core functions cover the standard workflow. GenerateText handles non-streaming calls, StreamText pushes chunks over Go channels with automatic tool loops, and GenerateObject[T] / StreamObject[T] return typed structs instead of raw JSON. Embed and EmbedMany handle embeddings with auto-chunking, GenerateImage covers OpenAI, Google, Azure and Vertex AI. Tool calling runs a MaxSteps auto loop, and the SDK ships 26 provider-defined tools including web search, code execution, computer use and file search. The MCP client connects over stdio, HTTP or SSE and converts any MCP server's tools into GoAI tools — v0.8.0 added OAuth 2.1 with PKCE for remote servers. Recent releases also bring provider-neutral file upload and remote file references (v0.9.0), the Requesty gateway plus Anthropic native structured output and per-step Reasoning (v0.8.5), and Cloudflare Workers AI and FPT Smart Cloud (v0.7.0). Prompt caching with automatic cache control works across Anthropic, Bedrock and MiniMax, and Langfuse plus OpenTelemetry tracing cover generations, tools and multi-step loops. For teams writing Go services, this positions against LangChainGo and hand-rolled provider abstractions — the vendor benches 569µs cold start versus 13.9ms for the Vercel AI SDK, which matters for serverless. It is not a replacement for a full Python RAG stack.
Behind the Verdict
We'd reach for GoAI the moment a Go service needs to talk to two or more model vendors. That is where the per-provider SDK sprawl really bites, and where a single provider-neutral call site, a shared tool loop and one streaming pattern save a genuine amount of maintenance. The parts that earn their keep are the typed ones. GenerateObject[T] returning your actual struct rather than a map you hand-decode is a real quality-of-life jump, and StreamText pushing chunks through a channel fits how Go code already handles concurrency. Automatic MaxSteps tool loops mean an agent does not have to be hand-wired. If you already run MCP servers — a GitHub or filesystem server, say — the client over stdio, HTTP and SSE turns their tools into callable GoAI tools with no custom plumbing, and v0.8.0's OAuth 2.1 with PKCE finally makes remote MCP servers practical. The dependency story is the quiet argument for it. Core runtime needs only golang.org/x/oauth2, OTel is opt-in via a submodule, goleak is test-only. The vendor's 569µs cold start versus the Vercel AI SDK's 13.9ms is a self-reported benchmark, so take the exact numbers with a grain of salt — but the architectural reason behind them, no Node runtime, holds up. Serverless and tight memory budgets are exactly where that matters. Where it bites: Go-only, full stop. Python and TypeScript teams have no path here, and anyone expecting a GUI, a vector store, document loaders or an opinionated RAG pipeline is looking at the wrong tool. There is also no managed dashboard — you own keys, retries and cost tracking yourself. The ecosystem is young; the provider count is impressive, but the community around it is a fraction of LangChainGo's. Closest alternative is LangChainGo. Pick LangChainGo if you want chain abstractions and its
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Real-world workflow fit
Concrete scenarios for the personas Goai actually fits — and what changes day-one when you adopt it.
Their chat feature is hard-coded to OpenAI. They add the anthropic and google provider sub-packages, import only the two they need, and switch the model constructor while the GenerateText and StreamText call sites stay unchanged.
Outcome: Provider swap lands in one line of code instead of rewriting a bespoke client, and they keep channel-based streaming behaviour identical across vendors.
They declare a Go struct for the agent's output, call GenerateObject[Recipe] with a Gemini model, register two custom tools plus the built-in code-execution tool, and set WithMaxSteps(5) so the agent loops until the task resolves.
Outcome: Structured, type-checked output with no JSON string parsing, and a multi-step tool loop that did not require a hand-written agent runtime.
They spin up an MCP client with mcp.NewStdioTransport, call Connect and ListTools, and run mcp.ConvertTools to map the server's tools into GoAI tools before passing them to GenerateText.
Outcome: An existing MCP server is usable from Go in minutes without re-implementing its tools, and remote servers can authenticate via the MCP OAuth 2.1 + PKCE flow added in v0.8.0.
Use Cases
- Build a chatbot that switches between OpenAI, Anthropic, and Google models by changing a single line of code.
- Generate typed structured objects with GenerateObject[T] on any provider instead of hand-parsing raw JSON.
- Stream real-time chat responses across providers with tool calling, including code execution.
- Connect any MCP server (stdio, HTTP, or SSE) to your Go agent and use its tools directly via ConvertTools.
- Set up OAuth token refresh and auto-refreshing credentials for provider auth using TokenSource.
- Run image generation through OpenAI, Google, Azure, or Vertex AI behind one call.
- Add automatic multi-step tool loops to agents with WithTools and WithMaxSteps.
- Keep serverless cold start and per-request memory low with a 569µs / 220KB footprint.
Models Under the Hood
as of 2026-10-18
Limitations
- GoAI SDK is an open-source Go client library, not a hosted service, so model access, rate limits, and costs still come from whichever provider you point it at.
- Provider capabilities differ behind the shared API, so structured output, file upload, image, video, and prompt caching should be tested against each provider you ship on.
- It is a Go SDK requiring Go tooling; OpenTelemetry tracing lives in a separate observability/otel submodule with its own go.mod.
- The cold-start and memory benchmarks (569µs/220KB vs Vercel AI SDK) are self-reported and should be reproduced on your own workload.
as of 2026-10-12
Verification history
We have re-verified Goai 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Goai tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source (MIT)
$0
Ideal for
Go teams of any size building AI features in-house who want to avoid per-seat orchestration licences and keep provider spend on their own accounts.
What this tier adds
Starting tier and only tier: full SDK source under the MIT License, with no licence fees or usage caps — you pay providers directly, not the project.
Where the pricing makes sense
The company stage and team size where Goai's pricing actually pencils out — and where peers do it cheaper.
The SDK itself is $0 under the MIT License, which puts it well below commercial AI orchestration platforms that charge per seat or per workflow — but the real cost comparison is your total model spend, which GoAI neither adds to nor controls. Against Vercel AI SDK (also free, TypeScript-only) it competes on cold-start and memory for Go serverless; against LangChainGo (free, ~10 providers) it competes on provider count and generic typed output. For a Go team, cost of adoption is engineer time,
Setup time & first value
How long it actually takes to get something useful out of Goai — broken out by persona, not the marketing-page minute.
For a Go developer already on Go 1.25: minutes. Run `go get github.com/zendev-sh/goai@latest`, set OPENAI_API_KEY in your environment (the SDK auto-resolves keys from env, so no explicit WithAPIKey is needed), import the provider sub-package you want, and run the main.go from the Quick Start. Provider packages ship with the core install, so there is no second dependency step. First value is a
Switching to or from Goai
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a bespoke multi-provider wrapper: map each existing client to a GoAI provider sub-package import and replace hand-rolled retry/stream code with StreamText and channel iteration.
- →From direct provider SDKs (openai-go, anthropic-sdk-go): keep the call sites that build messages, but move model selection to provider.Chat(model) constructors and reuse GoAI's shared options.
- →From Vercel AI SDK (TypeScript): the eight core functions map one-to-one, but the rewrite is Go — expect to reimplement route handlers and tool definitions natively.
- →From LangChainGo: replace chain composition with Go's own control flow; GenerateObject[T] and WithTools/WithMaxSteps cover the structured-output and agent-loop chains you used.
- →From Python LangChain/OpenAI scripts: port the workflow, not the abstraction — GoAI's surface is smaller and expects plain Go concurrency for orchestration.
- ↗To Vercel AI SDK: if your team moves to TypeScript, the API shape is deliberately similar, but you rewrite the Go concurrency and generics layer by hand.
- ↗To direct provider SDKs: drop GoAI when you standardise on a single vendor and no longer need the shared abstraction — the provider sub-packages are thin, so the call sites translate directly.
- ↗To LangChainGo: reach for it if you need chain/agent abstractions that resemble Python LangChain, accepting fewer supported providers (~10) and heavier dependencies.
- ↗To a managed AI platform: if you want dashboards, vector stores, and RAG managed for you, move the orchestration out of your Go service entirely and keep Go as a thin API client.
- ↗To zenflow: if you want the same underlying model calls driven by declarative YAML multi-agent workflows, layer zenflow on top rather than replacing GoAI.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Goai”, and we withheld 6: 6 could not be judged, because “Goai” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Goai.
Official links
Tools that pair well with Goai
Common stack mates teams adopt alongside Goai, with the specific reason each pairing earns its keep.
Vercel AI SDK
Open-source TypeScript AI SDK for streaming AI apps and agents across 100+ models with built-in fallbacks
Guidance
Guidance is an open-source Python library for constrained LLM generation with regex, context-free grammars, and inline control flow.
Agnes AI
Agnes AI puts text, image and video generation behind one OpenAI-compatible API — free to start.
Featured Head-to-Head Comparisons
Goai vs Spider Cloud
Choose Spider Cloud if you need high-volume web scraping with AI extraction for RAG pipelines; it offers a robust crawling API with 1,000+ ready-made scrapers and browser-based AI commands. Choose GoAI if you're a Go developer seeking a unified, fast SDK to access 25+ LLM providers with minimal dependencies and efficient streaming. They solve different problems and can complement each other.
Goai vs Voyage Ai
Choose Voyage AI if you need high-accuracy domain-specific embeddings and rerankers for enterprise RAG on finance/legal data with long-context support. Choose Goai if you're a Go developer who wants a lightweight, free SDK to call 25+ LLM providers with streaming, structured output, and agent tooling — no proprietary embeddings required.
Goai vs Temporal Ai
If your priority is building reliable, fault-tolerant AI agents or multi-step microservices that survive failures, Temporal's durable execution is unmatched. For Go developers needing a lean, fast SDK to call 25+ LLMs with streaming and structured output, Goai is the clear choice. They solve different problems — pick by your stack and needs.
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Vercel AI SDK
Open-source TypeScript AI SDK for streaming AI apps and agents across 100+ models with built-in fallbacks
Frequently Asked Questions
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