Best Go Framework for Building AI Agents in 2026

Go has fewer agent frameworks than Python or TypeScript, but the ones it has are maintained by large organizations and focus on the parts that matter in production, such as typed tools, streaming and tracing.

An agent in production is mostly a backend service that has to keep conversation state, run long tasks reliably and keep API keys out of the code, and Go is already common for that kind of service, with 55 percent of respondents to the 2025 Go Developer Survey building API services in it alongside CLIs.

Anthropic's guide to writing tools for agents recommends building and running evaluations of the tools an agent calls, a reminder that agent output still needs a structure around it that can be checked.

What to weigh when choosing:

• How tools are declared and how their schemas are generated from Go types.
• Whether the framework supports streaming responses and multi-step flows.
• How runs are traced and debugged locally.
• Where state, queues and secrets live in each environment.

Go agent frameworks at a glance

Genkit: flows with a developer UI

Genkit

Genkit is an open source framework from Google with Go support, built around flows, typed tools and prompts, and a local developer UI for running and inspecting them.

Key features

• Flows with typed input and output.
• Tool calling with schemas from Go structs.
• Plugins for many model providers.

Good to know

Genkit supports several languages, and plugin coverage differs between them, so check that the model and vector store plugins you need are available for Go.

Try Genkit →

Eino: composable orchestration

Eino

Eino is part of the CloudWeGo project and offers components for models, tools and retrievers that compose into chains and graphs with type checking between nodes.

Key features

• Graph orchestration with typed edges.
• Streaming handled across components.
• Callbacks for tracing and metrics.

Good to know

Eino has many concepts to learn before the first agent runs, which pays off mainly for complex flows.

Try Eino →

LangChainGo: familiar concepts

LangChainGo

LangChainGo brings LangChain's chains, agents, memory and vector store integrations to Go.

Key features

• Wide set of model and vector store integrations.
• Agents and tools modeled on LangChain.
• Community maintained.

Good to know

It is a community port, so it does not track the Python and JavaScript LangChain releases one to one.

Try LangChainGo →

Encore: infrastructure for the agent's service

Encore combines the agent's Go service with the infrastructure it needs through an infrastructure SDK, so the service declares its Postgres database, Pub/Sub topic and secrets in code, while Genkit, Eino or LangChainGo handles the model loop inside the handler. encore run starts Postgres locally with the topic, and a deploy provisions them in your own AWS or GCP account. Whichever option above you pick, Encore can add the database, topics and secrets to the same app.

package agent

import (
	"context"

	"encore.dev/pubsub"
	"encore.dev/storage/sqldb"
)

var db = sqldb.NewDatabase("agent", sqldb.DatabaseConfig{
	Migrations: "./migrations",
})

// Set per environment with `encore secret set --type prod OpenAIKey`.
var secrets struct {
	OpenAIKey string
}

type RunRequested struct {
	RunID  int64
	Prompt string
}

var Runs = pubsub.NewTopic[*RunRequested]("agent-runs", pubsub.TopicConfig{
	DeliveryGuarantee: pubsub.AtLeastOnce,
})

var _ = pubsub.NewSubscription(Runs, "execute-run",
	pubsub.SubscriptionConfig[*RunRequested]{Handler: execute})

type CreateRunParams struct {
	Prompt string `json:"prompt"`
}

type CreateRunResponse struct {
	RunID int64 `json:"run_id"`
}

//encore:api public method=POST path=/runs
func CreateRun(ctx context.Context, p *CreateRunParams) (*CreateRunResponse, error) {
	var id int64
	err := db.QueryRow(ctx, "INSERT INTO runs (prompt) VALUES ($1) RETURNING id", p.Prompt).Scan(&id)
	if err != nil {
		return nil, err
	}
	if _, err := Runs.Publish(ctx, &RunRequested{RunID: id, Prompt: p.Prompt}); err != nil {
		return nil, err
	}
	return &CreateRunResponse{RunID: id}, nil
}

func execute(ctx context.Context, r *RunRequested) error {
	// runAgent wraps your agent framework, such as a Genkit flow.
	output, err := runAgent(ctx, secrets.OpenAIKey, r.Prompt)
	if err != nil {
		return err // Encore retries the message.
	}
	_, err = db.Exec(ctx, "UPDATE runs SET output = $1, done = true WHERE id = $2", output, r.RunID)
	return err
}

Key features

• Secrets are typed fields set per environment rather than values in an environment file.
• Pub/Sub subscriptions retry failed runs, which suits model calls that time out.
• Tracing shows the request, the published message and each query of a run.
• Each subscription handler is a plain Go function, so an agent editing the service can unit test it against a local database.

Good to know

Encore does not call models, so pick the agent framework for how you want flows written. Keep the production database out of reach of the agent itself, since Replit's AI agent deleted a user's production database during a code freeze in July 2025. Cloud resources are created at deploy time, and the cloud access sits with Encore's IAM role, not the agent.

Try Encore →

How to choose

• Genkit for an end-to-end framework with a developer UI.
• Eino for complex graphs with typed components.
• LangChainGo when your team already thinks in LangChain terms.

Related guides

• Best Go backend frameworks
• How to build an AI agent backend with TypeScript
• What is Pub/Sub
• Best Go Web framework for AI coding agents
• How to add Pub/Sub to a Go API
• Add a database to a Go API and deploy it to AWS with an AI coding agent