Top Gen AI Frameworks for Go in 2026: A Hands-On Comparison

A developer compared five Go generative-AI frameworks — Genkit Go, Eino, Google ADK Go, tRPC-Agent-Go, and LangChainGo — running identical code against gemini-3.8-flash on Go 1.27. The writeup reports that Genkit Go, an official first-party Google SDK at v1.13.1, offers three abstraction levels (direct model calls, typed flows, and agents) under one OpenTelemetry tracing pipeline and Dev UI, while LangChainGo is now effectively unmaintained.

Go has quietly become the language of AI infrastructure. MCP servers, agent runtimes, model gateways, Kubernetes operators that talk to LLMs: a large share of that plumbing is written in Go, because Go is what you reach for when something has to be a small static binary that handles thousands of concurrent connections without drama. What was missing for a long time was the framework layer on top of the raw provider SDKs. In 2026 that gap is closed, and the question is no longer "Go or Python?" but "which Go framework?". This article covers the five frameworks a Go developer will actually run into in 2026: Genkit Go , Eino , Google ADK Go , tRPC-Agent-Go , and LangChainGo . Four of them are alive and moving fast. The fifth, LangChainGo, is the one most Go developers heard of first, and it is now effectively unmaintained. It stays in the list because you will find it in existing codebases, and you need to know what that means for you. Same rules as the Java https://xavidop.me/genkit/2026-04-16-top-java-genai-frameworks-2026/ and JavaScript/TypeScript https://xavidop.me/genkit/2026-04-16-top-jsts-genai-frameworks-2026/ editions: this is not a benchmark post. Tokens per second go stale in weeks. This is a developer experience and architecture comparison, with working code and the real output of that code for every framework, all against the same model, gemini-3.8-flash , on Go 1.27. Versions used: Genkit Go v1.13.1, Eino v0.9.20, Google ADK Go v2.4.0, tRPC-Agent-Go v1.11.2, LangChainGo v0.1.14. APIs as of September 2026 . Genkit was announced by Google at I/O 2024 as a TypeScript-first framework. The Go SDK arrived only three months later, with v0.1.0 in August 2024, and unlike the Java SDK, which is a community effort, Genkit Go is an official, first-party SDK maintained by the same google team that ships the TypeScript runtime. It reached 1.0 in September 2025 and sits at v1.13.1 as of September 2026, on a cadence of roughly one minor release per month. The direction is parity with TypeScript plus Go-native ergonomics: generics instead of Zod schemas, context.Context everywhere, plain net/http handlers for flows, and an agents package, still marked experimental, with session stores, multi-agent delegation, artifacts, and a Skills middleware. The monorepo genkit-ai/genkit has 6.4k stars and 80 commits in the last 30 days across all SDKs. If you want the long-form introduction, start with Stop Using Python for Your Gen AI Apps, Use Go and Genkit Instead https://xavidop.me/genkit/2026-05-04-stop-using-python-genai-use-genkit-go/ . Genkit Go gives you three levels of abstraction in a single SDK : direct model calls, typed flows, and agents, all traced by the same OpenTelemetry pipeline and all visible in the same Dev UI. On top of that sits a middleware layer retry, fallback, tool approval, filesystem, skills that wraps any of the three. No other Go framework gives you that combination. Supported languages: Go 1.25+ for this SDK. The same framework exists for TypeScript, Python, Dart/Flutter, and, unofficially, Java. import "github.com/genkit-ai/genkit/go/ai" "github.com/genkit-ai/genkit/go/genkit" "github.com/genkit-ai/genkit/go/plugins/googlegenai" g := genkit.Init ctx, genkit.WithPlugins &googlegenai.GoogleAI{} , genkit.WithDefaultModel "googleai/gemini-3.8-flash" text, err := genkit.GenerateText ctx, g, ai.WithPrompt "Explain the CAP theorem in two sentences." The CAP theorem states that a distributed data system can simultaneously provide at most two of three guarantees: Consistency, Availability, and Partition tolerance. Because network partitions are inevitable in the real world, systems must ultimately choose between returning the most up-to-date data Consistency or ensuring every request receives a response Availability . Flows are the heart of Genkit. A flow is a named, typed, traceable unit that you can call in-process, expose as an HTTP endpoint with one line, and run from the Dev UI. The JSON schema for the output comes from the Go struct, and GenerateData returns the struct, not a string you have to parse. type TranslateRequest struct { Text string json:"text" TargetLanguage string json:"target language" } type TranslateResponse struct { Translation string json:"translation" DetectedLanguage string json:"detected language" } translate := genkit.DefineFlow g, "translateText", func ctx context.Context, req TranslateRequest TranslateResponse, error { out, , err := genkit.GenerateData TranslateResponse ctx, g, ai.WithPrompt "Translate %q to %s and detect the source language.", req.Text, req.TargetLanguage return out, err } // Call it in-process... out, err := translate.Run ctx, TranslateRequest{Text: "Buenos días", TargetLanguage: "English"} fmt.Printf "%+v\n", out // ...or expose it over HTTP and to the Dev UI . mux := http.NewServeMux mux.HandleFunc "POST /translateText", genkit.Handler translate log.Fatal server.Start ctx, "127.0.0.1:3400", mux {Translation:Good morning DetectedLanguage:Spanish} INFO server listening addr=127.0.0.1:3400 curl -X POST http://127.0.0.1:3400/translateText \ -H 'Content-Type: application/json' \ -d '{"data":{"text":"Buenos días","target language":"English"}}' {"result":{"translation":"Good morning","detected language":"Spanish"}} Tools are typed functions. The input schema is inferred from the struct, and the tool loop model asks, tool runs, model answers happens inside Generate . type WeatherInput struct { City string json:"city" } weather := genkit.DefineTool g, "getWeather", "Returns the current weather for a city.", func ctx ai.ToolContext, in WeatherInput string, error { return fmt.Sprintf "Sunny, 24C in %s", in.City , nil } resp, err := genkit.Generate ctx, g, ai.WithPrompt "What's the weather like in Tokyo?" , ai.WithTools weather fmt.Println resp.Text The weather in Tokyo is currently sunny and 24°C. When you need memory across turns, the experimental agents package adds sessions on top of the same tools. Enable it with genkit.WithExperimental : import aix "github.com/genkit-ai/genkit/go/ai/exp" "github.com/genkit-ai/genkit/go/ai/exp/localstore" genkitx "github.com/genkit-ai/genkit/go/genkit/exp" advisor := genkitx.DefineAgent g, "travelAdvisor", aix.InlinePrompt{ ai.WithSystem "You are a helpful travel advisor. Use tools to give accurate advice. Be brief." , ai.WithTools weather , }, aix.WithSessionStore localstore.NewInMemorySessionStore any first, err := advisor.RunText ctx, "Should I pack a jacket for my trip to Lisbon?" second, err := advisor.RunText ctx, "Which city did I ask about?", aix.WithSessionID any first.SessionID It is currently sunny and 24°C 75°F in Lisbon. You won't need a heavy coat, but packing a light jacket or sweater is recommended for cooler evenings and coastal breezes. You asked about Lisbon. Multi-agent delegation, cross-vendor fallback, and tool approval are covered in Building Agents in Go: Where Genkit Shines vs ADK 2.0 https://xavidop.me/genkit/2026-08-06-building-agents-genkit-vs-adk-go/ . The same Genkit Developer UI that TypeScript developers use works with Go, with no code changes. Install the CLI and start your Go program through it: npm install -g genkit genkit start -- go run . Genkit Developer UI: http://localhost:4000 The flow above shows up as /flow/translateText next to every Gemini model the plugin registered, and you get: Generate and flow call, with latency, token counts, and the exact prompts. .prompt files live. This is the single biggest advantage Genkit has over every other framework in this list: a zero-config, local trace explorer that replaces Langfuse or LangSmith during development. Official plugins in the Go module: Google AI Gemini , Vertex AI , Anthropic , Ollama , and an OpenAI-compatible family compat oai with ready-made packages for OpenAI , xAI , DeepSeek , OpenRouter , Kimi , DashScope , and Z.ai . Vector stores: localvec local, no service needed , PostgreSQL/pgvector , AlloyDB , Pinecone , Weaviate , and Firebase Firestore . Plus MCP client and server , evaluators, and an A2UI plugin for streaming UI components. | ✅ Pros | ❌ Cons | |---|---| | Best-in-class Dev UI with local trace explorer | Agents package still marked experimental | | Multi-level abstractions: vanilla, flows, agents | Fewer vector stores than LangChainGo | | Middleware: retry, fallback, tool approval | Go 1.25+ required | | Official Google SDK, monthly releases | | | Typed output from Go structs, no parsing | | | Same framework in TS, Python, Dart, Java | | Eino pronounced "aino" comes from ByteDance's CloudWeGo team, the group behind Kitex and Hertz, the RPC and HTTP frameworks that run a large part of ByteDance's Go services. It was open-sourced in December 2024 after internal use, and it is now the most-starred LLM framework in Go at 13.1k stars, ahead of LangChainGo. Releases land every few days: v0.9.20 on September 20, 2026, with v0.10.0 already at alpha 35. Eino's own description is that it draws from LangChain and Google ADK but follows Go conventions. The framework splits in two: compose , for orchestration Chain, Graph, Workflow , and adk , an Agent Development Kit that borrows Google ADK's vocabulary of agents, runners, and events. Components models, tools, retrievers, indexers, loaders live in a separate repo, eino-ext , together with callback handlers for Langfuse, LangSmith, CozeLoop, and ByteDance's APMPlus. The 2026 roadmap is all agents: DeepAgent planning plus sub-agents , agent middleware, interrupt and resume for human-in-the-loop, skills, A2UI streaming components, and a turn loop with preemption. The English documentation on cloudwego.io is now a full eleven-chapter course, a big change from 2025 when most of it was Chinese-first. Orchestration with automatic stream handling . Every node in an Eino graph can be invoked or streamed, and the framework concatenates, boxes, merges, and copies streams as data moves between nodes. You build the graph once and get both Invoke and Stream for free. Graph inputs and outputs are generic types, so the compiler checks that the nodes fit together. Cross-cutting concerns go through callbacks at fixed points OnStart , OnEnd , OnError , plus the streaming variants that apply to components, graphs, and agents alike. Supported languages: Go only. The module declares go 1.18 , the lowest bar in this list. One thing to know: Eino depends on ByteDance's sonic JSON library, and on Go 1.27 it prints a warning at startup and falls back to encoding/json . Harmless, but noisy. Eino does not own the provider client. You create the genai client yourself and hand it to the component. import "github.com/cloudwego/eino-ext/components/model/gemini" "github.com/cloudwego/eino/schema" "google.golang.org/genai" client, err := genai.NewClient ctx, &genai.ClientConfig{APIKey: os.Getenv "GEMINI API KEY" } cm, err := gemini.NewChatModel ctx, &gemini.Config{Client: client, Model: "gemini-3.8-flash"} msg, err := cm.Generate ctx, schema.Message{ schema.UserMessage "Explain the CAP theorem in two sentences." , } fmt.Println msg.Content The CAP theorem states that a distributed data store can simultaneously provide at most two of three guarantees: Consistency, Availability, and Partition tolerance. Because network partitions are practically inevitable in distributed systems, architects must choose between keeping data strictly up-to-date Consistency or ensuring the system remains responsive Availability during a network failure. A chain is a linear pipeline. Here the callback handler is attached at invocation time and fires for the chain, the template, and the model. import "github.com/cloudwego/eino/callbacks" "github.com/cloudwego/eino/components/prompt" "github.com/cloudwego/eino/compose" template := prompt.FromMessages schema.FString, schema.SystemMessage "You are a translator. Reply with the translation only." , schema.UserMessage "Translate {text} to {language}." chain := compose.NewChain map string any, schema.Message chain.AppendChatTemplate template .AppendChatModel cm runnable, err := chain.Compile ctx tracer := callbacks.NewHandlerBuilder . OnStartFn func ctx context.Context, info callbacks.RunInfo, in callbacks.CallbackInput context.Context { log.Printf "start %s %s", info.Component, info.Type return ctx } . OnEndFn func ctx context.Context, info callbacks.RunInfo, out callbacks.CallbackOutput context.Context { log.Printf "end %s %s", info.Component, info.Type return ctx } . Build out, err := runnable.Invoke ctx, map string any{"text": "Buenos días", "language": "English"}, compose.WithCallbacks tracer fmt.Println out.Content start Chain start ChatTemplate Default end ChatTemplate Default start ChatModel Gemini end ChatModel Gemini end Chain Good morning Graphs are where Eino earns its name. Nodes are typed, edges are explicit, and plain Go functions slot in as lambda nodes: graph := compose.NewGraph map string any, string = graph.AddChatTemplateNode "template", template = graph.AddChatModelNode "generate", cm = graph.AddLambdaNode "shout", compose.InvokableLambda func ctx context.Context, msg schema.Message string, error { return strings.ToUpper strings.TrimSpace msg.Content , nil } = graph.AddEdge compose.START, "template" = graph.AddEdge "template", "generate" = graph.AddEdge "generate", "shout" = graph.AddEdge "shout", compose.END runnable, err := graph.Compile ctx out, err := runnable.Invoke ctx, map string any{"product": "a Go framework for LLM apps"} BUILD PRODUCTION-GRADE LLM APPS AT GO SPEED. Branches, parallel fan-out, and sub-graphs use the same API, and a compiled graph can be wrapped as a tool and handed to an agent. Tools are inferred from a Go struct with jsonschema tags. The agent runs the ReAct loop and emits one event per step, so you see the tool call, the tool result, and the final answer as separate messages. import "github.com/cloudwego/eino/adk" "github.com/cloudwego/eino/components/tool" "github.com/cloudwego/eino/components/tool/utils" type WeatherInput struct { City string json:"city" jsonschema:"description=The city to look up" } weather, err := utils.InferTool "get weather", "Returns the current weather for a city.", func ctx context.Context, in WeatherInput string, error { return fmt.Sprintf "Sunny, 24C in %s", in.City , nil } advisor, err := adk.NewChatModelAgent ctx, &adk.ChatModelAgentConfig{ Name: "travel advisor", Description: "Helps with trip planning and weather-based advice.", Instruction: "You are a helpful travel advisor. Use tools to give accurate advice. Be brief.", Model: cm, ToolsConfig: adk.ToolsConfig{ ToolsNodeConfig: compose.ToolsNodeConfig{Tools: tool.BaseTool{weather}}, }, } runner := adk.NewRunner ctx, adk.RunnerConfig{Agent: advisor} iter := runner.Query ctx, "Should I pack a jacket for my trip to Lisbon?" for { event, ok := iter.Next if ok { break } if event.Err = nil { log.Fatal event.Err } if event.Output == nil || event.Output.MessageOutput == nil { continue } msg, := event.Output.MessageOutput.GetMessage fmt.Printf " %s %s\n", msg.Role, msg.Content } assistant tool Sunny, 24C in Lisbon assistant It is currently sunny and 24°C 75°F in Lisbon. You won't need a heavy coat, but packing a light jacket or cardigan is a good idea for breezy coastal evenings. The first assistant event is the tool call itself, which is why its content is empty. Multi-agent workflows are one constructor away: pipeline, err := adk.NewSequentialAgent ctx, &adk.SequentialAgentConfig{ Name: "content pipeline", Description: "Research, then write.", SubAgents: adk.Agent{researcher, writer}, } researcher 1. Created at Google by Robert Griesemer, Rob Pike, and Ken Thompson; released in 2009. 2. Statically typed, compiled language with built-in garbage collection. 3. Features native, lightweight concurrency through goroutines and channels. writer Created at Google by Robert Griesemer, Rob Pike, and Ken Thompson, the Go programming language was released in 2009. It is a statically typed, compiled language that features built-in garbage collection and native, lightweight concurrency through goroutines and channels. NewParallelAgent and NewLoopAgent complete the set, and DeepAgent adds planning and delegation on top. Eino's answer to observability during development is the Eino Dev plugin for GoLand and VS Code, backed by the devops module in eino-ext . It renders your chains and graphs visually inside the editor and lets you start execution from any node with mock input. It is an IDE feature rather than a browser Dev UI, so there is no trace explorer for a running service. For that you attach one of the callback handlers Langfuse, LangSmith, CozeLoop, APMPlus . Model components in eino-ext : OpenAI , Claude , Gemini , DeepSeek , Qwen , Ollama , OpenRouter , Ark Volcano Engine , and Qianfan Baidu , plus "agentic" variants of the main ones for the newer agentic message format. Retrievers and indexers: Elasticsearch 7, 8, 9 , Milvus , Qdrant , Redis , OpenSearch , Dify , and VikingDB . Tools: MCP , Google Search, Bing, DuckDuckGo, SearXNG, Wikipedia, HTTP request, command line, browser use, and sequential thinking. | ✅ Pros | ❌ Cons | |---|---| | Most active independent Go LLM framework 13.1k stars | Still 0.x with frequent alphas; expect API churn | | Typed graph orchestration with automatic streaming | No browser Dev UI; visual debugging lives in the IDE plugin | | Callback aspects for logging, tracing, and metrics | Verbose: config structs and explicit error checks everywhere | | ADK-style agents plus DeepAgent and interrupt/resume | ByteDance-flavoured corners Ark, VikingDB, CozeLoop | | Broad component ecosystem in eino-ext | sonic dependency warns on the newest Go toolchains | | Runs on Go 1.18+ | Chinese-first history still shows in some docs and examples | ADK, the Agent Development Kit, was announced at Google Cloud Next 2025 with Python as its first language. The Go SDK made its first tagged release, v0.1.0, in November 2025, went 1.0 on March 23, 2026, and shipped a new major module, google.golang.org/adk/v2 , on June 30, 2026. It is at v2.4.0 September 11, 2026 , has 8.8k stars, and logged over 100 commits in the last 30 days: the highest commit volume in this list. It is an official Google product with the Python, Java, Kotlin, and TypeScript SDKs as siblings. The design is the same as its siblings: everything is an agent, a workflow agent, or a tool. Gemini is the native model, with openaimodel for anything speaking the OpenAI wire format and an Apigee model for enterprise gateways. Deployment gravity points at Vertex AI Agent Engine , Cloud Run, and GKE, with Pub/Sub and Eventarc triggers built into the launcher. The minimum Go version is 1.26.6, the highest bar of the five. A deeper look at the low-level differences with Genkit is in Genkit Go vs ADK Go 2.0 https://xavidop.me/genkit/2026-08-05-genkit-go-vs-adk-go/ . ADK Go is an agent framework . There is no vanilla generation primitive and no flow abstraction: you always build an agent, give it to a runner, and iterate the events. In exchange you get sessions, memory, artifacts, workflow agents, agent-to-agent A2A protocol support, MCP toolsets, tool confirmation, and an embedded web UI, all first party. Supported languages: Go 1.26.6+. Same framework in Python primary , Java, Kotlin, and TypeScript. import "google.golang.org/adk/v2/agent" "google.golang.org/adk/v2/agent/llmagent" "google.golang.org/adk/v2/model/gemini" "google.golang.org/adk/v2/runner" "google.golang.org/adk/v2/tool" "google.golang.org/adk/v2/tool/functiontool" "google.golang.org/genai" model, err := gemini.NewModel ctx, "gemini-3.8-flash", &genai.ClientConfig{ APIKey: os.Getenv "GOOGLE API KEY" , } weather, err := functiontool.New functiontool.Config{ Name: "get weather", Description: "Returns the current weather for a city.", }, func agent.Context, in WeatherInput WeatherOutput, error { return WeatherOutput{Report: fmt.Sprintf "Sunny, 24C in %s", in.City }, nil } advisor, err := llmagent.New llmagent.Config{ Name: "travel advisor", Model: model, Instruction: "You are a helpful travel advisor. Use tools to give accurate advice. Be brief.", Tools: tool.Tool{weather}, } r, err := runner.NewInMemory "travel-app", advisor msg := genai.NewContentFromText "Should I pack a jacket for my trip to Lisbon?", genai.RoleUser for event, err := range r.Run ctx, "user-1", "session-1", msg, agent.RunConfig{} { if err = nil { log.Fatal err } if event.LLMResponse.Content == nil || event.IsFinalResponse { continue } for , part := range event.LLMResponse.Content.Parts { fmt.Print part.Text } } It is currently sunny and warm at 24°C 75°F in Lisbon. You won't need a heavy jacket during the day, but packing a light jacket or sweater is recommended for cooler evenings and coastal breezes. Two things to notice. The runner returns a Go 1.23 iterator, so you range over events with the error as the second value, which is the most idiomatic event loop of the five. And the function tool is generic: input and output structs give the schema, nothing is spelled out twice. Workflow agents compose LLM agents. The OutputKey writes an agent's answer into session state, and the next agent reads it back through a {placeholder} in its instruction. import "google.golang.org/adk/v2/agent/workflowagents/sequentialagent" researcher, err := llmagent.New llmagent.Config{ Name: "researcher", Model: model, Instruction: "List three key facts about the user's topic. Be terse.", OutputKey: "facts", // saved into session state } writer, err := llmagent.New llmagent.Config{ Name: "writer", Model: model, Instruction: "Write a two-sentence brief using these facts:\n{facts}", } pipeline, err := sequentialagent.New sequentialagent.Config{ AgentConfig: agent.Config{ Name: "content pipeline", SubAgents: agent.Agent{researcher, writer}, }, } researcher Origin: Developed at Google by Robert Griesemer, Rob Pike, and Ken Thompson; released as open source in 2009. Concurrency: Features built-in, lightweight concurrency primitives known as goroutines and channels, managed by its own runtime. Ecosystem Role: Statically typed and compiled, it is the primary language behind core cloud-native technologies, including Docker and Kubernetes. writer Developed at Google by Robert Griesemer, Rob Pike, and Ken Thompson and released as open source in 2009, Go is a statically typed, compiled language featuring built-in, lightweight concurrency primitives known as goroutines and channels that are managed by its own runtime. Owing to these strengths, it has become the primary language powering core cloud-native technologies, including Docker and Kubernetes. parallelagent and loopagent follow the same pattern. The full launcher turns any agent into a CLI with console and web modes. The web mode takes sub-launchers, and webui serves the Angular ADK Web app straight out of your Go binary via go:embed , so there is nothing to install: import "google.golang.org/adk/v2/cmd/launcher" "google.golang.org/adk/v2/cmd/launcher/full" l := full.NewLauncher config := &launcher.Config{AgentLoader: agent.NewSingleLoader advisor } if err := l.Execute ctx, config, os.Args 1: ; err = nil { log.Fatalf "%v\n\n%s", err, l.CommandLineSyntax } go run . web -port 8080 api webui Web servers starts on http://localhost:8080 api: you can access API using http://localhost:8080/api The root URL redirects to /ui/ , the Agent Development Kit Dev UI, and /api/list-apps answers "assistant" . The launcher also wires a2a , pubsub , and eventarc sub-servers, and an -otel to cloud flag exports traces to Google Cloud. Flags go before the sub-launcher names, or the sub-launcher rejects them. You can run ADK Go anywhere. But the session and memory services with persistence are the in-memory ones, a database-backed session store, and Vertex AI ; the evaluation tooling, the managed scaling, and the telemetry defaults all assume Google Cloud. Same trade-off as ADK Java and ADK Python: the best experience is on GCP. | ✅ Pros | ❌ Cons | |---|---| | Official Google support, highest commit velocity | Agent-only: no vanilla generation, no flows | | Idiomatic Go: generics for tools, iterators for events | Go 1.26.6+ required | | Embedded ADK Web UI, no extra install | Strong pull toward Vertex AI and GCP | | Sequential, parallel, and loop workflow agents | Gemini-first; other providers only through OpenAI-compatible endpoints | | A2A, MCP toolsets, tool confirmation, artifacts | No vector store components; RAG goes through tools or Vertex | | Same mental model as ADK Python/Java/TS | Config structs get long for real agents | tRPC-Agent-Go is Tencent's entry, built by the team behind tRPC, the RPC framework that underpins much of Tencent's backend. Development started in May 2025, the first public release v0.0.1 landed in August 2025, and the project reached 1.x at the end of December 2025. It is now at v1.11.2 August 20, 2026 , with 1.8k stars and 56 commits in the last 30 days. The README credits Tencent Yuanbao, Tencent Video, Tencent News, IMA, and QQ Music with real-world validation, and I have no reason to doubt it. The framework is unapologetically ADK-shaped : llmagent , runner , chainagent , parallelagent , cycleagent , sessions, memory, OutputKey , {placeholder} instructions. If you know ADK, you already know most of it. What it adds on top is where it gets interesting: a GraphAgent that is a faithful LangGraph in Go state schemas, conditional edges, checkpoints, time travel , an AG-UI server for streaming agent events to frontends, A2A, MCP, Anthropic-style Agent Skills with sandboxed execution, agent self-evolution, an evaluation package, model failover and hedging, prompt caching, and OpenTelemetry traces and metrics with Langfuse examples. RAG is covered by a knowledge module with embedders for OpenAI, Gemini, Ollama, and Hugging Face, and stores from in-memory and SQLite-vec up to pgvector, Elasticsearch, Milvus, Qdrant, and TcVector. The GraphAgent . Nobody else in Go ships a LangGraph-equivalent state graph as a first-class agent type that plugs into the same runner, sessions, and telemetry as the LLM agents. Supported languages: Go only, go 1.21+ . Model providers: OpenAI and anything OpenAI-compatible DeepSeek, Qwen, and friends through variants , Anthropic , Gemini , Bedrock , Ollama , Hugging Face , and Hunyuan . import "trpc.group/trpc-go/trpc-agent-go/agent/llmagent" "trpc.group/trpc-go/trpc-agent-go/model" "trpc.group/trpc-go/trpc-agent-go/model/gemini" "trpc.group/trpc-go/trpc-agent-go/runner" "trpc.group/trpc-go/trpc-agent-go/tool" "trpc.group/trpc-go/trpc-agent-go/tool/function" type weatherReq struct { City string json:"city" jsonschema:"description=The city to look up,required" } type weatherRsp struct { Report string json:"report" } func getWeather ctx context.Context, req weatherReq weatherRsp, error { return weatherRsp{Report: fmt.Sprintf "Sunny, 24C in %s", req.City }, nil } m, err := gemini.New ctx, "gemini-3.8-flash" // reads GEMINI API KEY / GOOGLE API KEY weather := function.NewFunctionTool getWeather, function.WithName "get weather" , function.WithDescription "Returns the current weather for a city." advisor := llmagent.New "travel advisor", llmagent.WithModel m , llmagent.WithInstruction "You are a helpful travel advisor. Use tools to give accurate advice. Be brief." , llmagent.WithTools tool.Tool{weather} r := runner.NewRunner "travel-app", advisor events, err := r.Run ctx, "user-1", "session-1", model.NewUserMessage "Should I pack a jacket for my trip to Lisbon?" for evt := range events { if evt.Error = nil { log.Fatalf "%s", evt.Error.Message } if evt.Response == nil || len evt.Response.Choices == 0 { continue } if evt.IsFinalResponse { fmt.Println evt.Response.Choices 0 .Message.Content } } It is currently sunny and warm in Lisbon at 24°C 75°F , so you will not need a heavy coat. However, packing a light jacket or sweater is recommended for cooler, breezy evenings near the coast. Events arrive on a channel and use OpenAI's response shape Choices 0 .Message or Choices 0 .Delta when streaming , which is familiar but means digging through nested structs for every message. The chain agent is ADK's sequential agent under another name, OutputKey and {facts} included: import "trpc.group/trpc-go/trpc-agent-go/agent" "trpc.group/trpc-go/trpc-agent-go/agent/chainagent" researcher := llmagent.New "researcher", llmagent.WithModel m , llmagent.WithInstruction "List three key facts about the user's topic. Be terse." , llmagent.WithOutputKey "facts" writer := llmagent.New "writer", llmagent.WithModel m , llmagent.WithInstruction "Write a two-sentence brief using these facts:\n{facts}" pipeline := chainagent.New "content pipeline", chainagent.WithSubAgents agent.Agent{researcher, writer} researcher 1. Developed at Google by Robert Griesemer, Rob Pike, and Ken Thompson; released in 2009. 2. Statically typed, compiled language featuring automatic garbage collection. 3. Native concurrency support using lightweight threads called goroutines and channels. writer Developed at Google by Robert Griesemer, Rob Pike, and Ken Thompson and released in 2009, Go is a statically typed, compiled language featuring automatic garbage collection. It is designed with native concurrency support, utilizing channels and lightweight threads called goroutines. A state graph with a routing node, a conditional edge, and two LLM nodes. Short questions go to the quick node, long ones to the deep node. import "trpc.group/trpc-go/trpc-agent-go/agent/graphagent" "trpc.group/trpc-go/trpc-agent-go/graph" sg := graph.NewStateGraph graph.MessagesStateSchema sg.AddNode "route", func ctx context.Context, state graph.State any, error { return nil, nil // pure routing node, no state change } sg.AddLLMNode "quick", m, "Answer in one short sentence.", nil sg.AddLLMNode "deep", m, "Answer thoroughly. Follow the length the user asks for.", nil sg.SetEntryPoint "route" sg.AddConditionalEdges "route", func ctx context.Context, state graph.State string, error { input, := state graph.StateKeyUserInput . string if len strings.Fields input 8 { return "deep", nil } return "quick", nil }, map string string{"quick": "quick", "deep": "deep"} sg.SetFinishPoint "quick" sg.SetFinishPoint "deep" g, err := sg.Compile router, err := graphagent.New "router", g r := runner.NewRunner "graph-app", router Asked "What is Go?" and then a request for two paragraphs on goroutines and channels: Go is an ancient strategy board game, as well as an open-source programming language developed by Google. Unlike operating system threads that require megabytes of memory and expensive kernel-level context switching, goroutines are extremely lightweight user-space constructs that start with just a few kilobytes of stack space. ... The same graph package gives you checkpoints, interrupts for human input, sub-graphs, map-reduce fan-out, per-node callbacks, and a visualization export. It is the most complete workflow engine of the five, and it is the reason to look at this framework even if you never use its LLM agents. | ✅ Pros | ❌ Cons | |---|---| | GraphAgent: a real LangGraph for Go | Smallest community of the five 1.8k stars | | ADK mental model without the GCP gravity | No Dev UI; observability means OpenTelemetry plus Langfuse | | AG-UI, A2A, MCP, Agent Skills, evaluation | Huge option surface llmagent alone has dozens | | Model failover and hedging built in | OpenAI-shaped event structs are verbose to consume | | OpenTelemetry traces and metrics first party | Tencent-flavoured corners Hunyuan, tRPC integration | | Runs on Go 1.21+ | Documentation partly Chinese-first | LangChainGo was the first. Travis Cline started it in February 2023, weeks after LangChain Python went viral, and for two years it was the default answer to "how do I call an LLM from Go". It has 9.7k stars, 17 LLM providers, and 15 vector stores, and the LangChain names chains , prompts , agents , memory , outputparser make it instantly readable to anyone coming from Python. And it has stalled. The last release, v0.1.14, is from October 20, 2025. The last commit to main is from January 11, 2026. It is still 0.1.x after three and a half years. There are 243 open issues and 174 open pull requests. The repository is not archived, but nobody is merging. That would be survivable if the code kept working. It does not: googleai.New ctx with no options targets gemini-2.0-flash , and Google turned that model off: default model: gemini-2.0-flash err: googleapi: Error 404: This model models/gemini-2.0-flash is no longer available. Please update your code to use models/gemini-3.6-flash for the latest features and improvements. llms/googleai is built on google/generative-ai-go , whose support ended on November 30, 2025. One, two, three, four, five. err: error in stream mode: invalid character ' ' looking for beginning of value Call ctx, string string, error : no schema, no typed arguments. The full teardown, with a capstone app built in both frameworks, is in LangChainGo vs Genkit Go: Where Genkit Shines https://xavidop.me/genkit/2026-09-11-langchaingo-vs-genkit-go/ . None of this means the basics stopped working. Pin a current model and the classic API still runs: import "github.com/tmc/langchaingo/llms" "github.com/tmc/langchaingo/llms/googleai" llm, err := googleai.New ctx, googleai.WithDefaultModel "gemini-3.8-flash" out, err := llms.GenerateFromSinglePrompt ctx, llm, "Explain the CAP theorem in two sentences." The CAP theorem states that a distributed data store can simultaneously provide at most two of three guarantees: Consistency, Availability, and Partition tolerance. Because network partitions are inevitable in real-world systems, architects must fundamentally choose between serving the most up-to-date data Consistency or keeping the system operational Availability when a network failure occurs. import "github.com/tmc/langchaingo/chains" "github.com/tmc/langchaingo/prompts" prompt := prompts.NewPromptTemplate "Translate {{.text}} to {{.language}}. Reply with the translation only.", string{"text", "language"} chain := chains.NewLLMChain llm, prompt out, err := chains.Predict ctx, chain, map string any{ "text": "Buenos días", "language": "English", } Good morning Tools are strings in, strings out. The agent is a ReAct loop driven by prompt text: import "github.com/tmc/langchaingo/agents" "github.com/tmc/langchaingo/tools" type WeatherTool struct{} func WeatherTool Name string { return "get weather" } func WeatherTool Description string { return "Returns the current weather. Input: a city name." } func WeatherTool Call ctx context.Context, city string string, error { return fmt.Sprintf "Sunny, 24C in %s", city , nil } agent := agents.NewOneShotAgent llm, tools.Tool{WeatherTool{}}, agents.WithMaxIterations 3 out, err := chains.Run ctx, agents.NewExecutor agent , "What's the weather like in Tokyo?" The weather in Tokyo is currently sunny with a temperature of 24°C. If you are on LangChainGo today, the move is not urgent, but it is inevitable: pin your models, avoid streaming on Gemini, and plan the migration. The concept-by-concept guide is in Migrating from LangChainGo to Genkit Go https://xavidop.me/genkit/2026-09-11-langchaingo-to-genkit-go-migration/ . | ✅ Pros | ❌ Cons | |---|---| | Largest catalog: 17 providers, 15 vector stores | Unmaintained since January 2026, still 0.1.x | | LangChain vocabulary, easy for Python teams | Gemini provider on a retired SDK; streaming broken | | Conversation memory is ergonomic | No middleware, retry, fallback, or OpenTelemetry | | Runs on Go 1.24+ | Tools are untyped strings | | Many existing tutorials and examples | Every vector store needs an external service | | Framework | DX Highlights | Shortcomings | |---|---|---| | Genkit Go | Dev UI with trace explorer. Typed output from structs. Flows are one line from HTTP. | Agents still experimental | | Eino | Typed graphs, automatic streaming, callbacks everywhere. | Verbose config structs; 0.x churn | | ADK Go | Iterator-based event loop, generic tools, embedded web UI. | Agent-only; Go 1.26.6 | | tRPC-Agent-Go | GraphAgent, ADK mental model, resilience built in. | OpenAI-shaped events; option overload | | LangChainGo | Familiar names, biggest catalog. | Unmaintained; broken Gemini streaming | Genkit Go is the only framework here with all three levels in one SDK: vanilla generation , typed flows , and agents . Eino covers generation and orchestration, plus agents through its ADK module. ADK Go and tRPC-Agent-Go are agent frameworks: every call goes through an agent and a runner. LangChainGo has chains and agents, but no typed pipeline and no session-aware agent. | Framework | Local Dev | Production | |---|---|---| | Genkit Go | Dev UI with trace explorer | OpenTelemetry export, Google Cloud plugin, any OTEL collector | | Eino | Eino Dev plugin for GoLand and VS Code graph rendering, node debugging | Callback handlers: Langfuse, LangSmith, CozeLoop, APMPlus | | ADK Go | Embedded ADK Web UI | OpenTelemetry to Google Cloud -otel to cloud | | tRPC-Agent-Go | Events on a channel, no UI | OpenTelemetry traces and metrics, Langfuse | | LangChainGo | Logging callbacks only | Nothing built in | | Framework | Model Providers | Vector Stores | |---|---|---| | Genkit Go | Gemini Google AI, Vertex AI , Anthropic, Ollama, OpenAI-compatible family OpenAI, xAI, DeepSeek, OpenRouter, Kimi, DashScope, Z.ai | localvec, pgvector, AlloyDB, Pinecone, Weaviate, Firestore | | Eino | OpenAI, Claude, Gemini, DeepSeek, Qwen, Ollama, OpenRouter, Ark, Qianfan | Elasticsearch, Milvus, Qdrant, Redis, OpenSearch, Dify, VikingDB | | ADK Go | Gemini, OpenAI-compatible, Apigee | None as components Vertex AI Search via tools | | tRPC-Agent-Go | OpenAI-compatible, Anthropic, Gemini, Bedrock, Ollama, Hugging Face, Hunyuan | In-memory, SQLite-vec, pgvector, Elasticsearch, Milvus, Qdrant, TcVector | | LangChainGo | 17, including OpenAI, Anthropic, Google AI, Vertex, Bedrock, Cohere, Mistral, Ollama | 15, all external services | | Framework | Min Go | Tools | Streaming | Style | |---|---|---|---|---| | Genkit Go | 1.25 | Generic, schema from struct | Callback per chunk | Functional options ai.With... | | Eino | 1.18 | Generic utils.InferTool | StreamReader , automatic across nodes | Config structs plus builder chains | | ADK Go | 1.26.6 | Generic functiontool.New | iter.Seq2 over events | Config structs | | tRPC-Agent-Go | 1.21 | Generic plus jsonschema tags | Channel of events | Functional options | | LangChainGo | 1.24 | Untyped strings | Callback per chunk fails on Gemini | Interfaces plus option funcs | Only ADK Go uses range-over-func iterators for its event loop. Genkit and Eino use generics the most naturally; LangChainGo predates all of it and shows its age. Numbers as of September 21, 2026: | Framework | Stars | Latest Release | Last Commit | Commits, Last 30 Days | |---|---|---|---|---| | Genkit Go | 6.4k monorepo | go/v1.13.1, Sep 3, 2026 | Sep 21, 2026 | 80 all SDKs | | Eino | 13.1k | v0.9.20, Sep 20, 2026 | Sep 20, 2026 | 7 on main , plus five v0.10 alphas | | ADK Go | 8.8k | v2.4.0, Sep 11, 2026 | Sep 21, 2026 | 100+ | | tRPC-Agent-Go | 1.8k | v1.11.2, Aug 20, 2026 | Sep 21, 2026 | 56 | | LangChainGo | 9.7k | v0.1.14, Oct 20, 2025 | Jan 11, 2026 | 0 | Stars measure the past. The last two columns measure the present, and they tell the story. Choose Genkit Go if: Choose Eino if: Choose Google ADK Go if: Choose tRPC-Agent-Go if: Stay on LangChainGo only if: Go's Gen AI framework landscape in 2026 is richer than most Go developers realise, and it has a clear shape. Genkit Go is the most versatile choice and the best developer experience, with the Dev UI as a category of its own. Eino is the ambitious independent, the framework to pick when orchestration is the hard part of your problem. Google ADK Go is the enterprise agent runtime, at its best on Google Cloud. tRPC-Agent-Go is the dark horse, with the best workflow engine of the five hiding behind a modest star count. And LangChainGo is the framework that got everyone started and now needs a farewell plan. If I had to pick one for a new Go service today with no cloud commitment, it would be Genkit Go, for the same reason as in TypeScript and Java: the combination of multiple abstraction levels, provider neutrality, and local observability shortens the loop between an idea and a working feature more than anything else on this list. Whatever you pick, pick it deliberately. Go finally has the tooling. Use it. Last updated: September 2026. Framework versions referenced: Genkit Go v1.13.1, Eino v0.9.20 with eino-ext gemini v0.1.36 , Google ADK Go v2.4.0, tRPC-Agent-Go v1.11.2, LangChainGo v0.1.14. Go 1.27.1, model gemini-3.8-flash.