AgentScope Go

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AgentScope Go is a Go library for building LLM applications with agents, tools and multi-agent workflows. It implements the core concepts of AgentScope through Go interfaces, contexts and channels, and can be embedded in a service or command-line program.

• Tool-using assistants: connect model calls to Go functions, manage context and request human confirmation for tool execution. Use AskUser to collect structured choices through your own frontend.
• Multi-agent workflows: coordinate agents with pipelines, message routing and leader/worker teams.
• Applications with memory: combine retrieval, conversation state and memory middleware with your own data sources.
• Services and experiments: expose an HTTP service or browser UI, record model responses, evaluate runs and inspect traces.

You need Go 1.25+ and, for the program below, an Anthropic API key and an available model ID. Other adapters are listed under Model providers.

Install the latest tagged release from the community module path. Applications using github.com/alanfokco/agentscope-go/v2 need to update their import prefix; see the module migration notes.

From a new directory:

mkdir agentscope-demo
cd agentscope-demo
go mod init example.com/agentscope-demo
go get github.com/agentscope-ai/agentscope-go/v2@latest

Save this as main.go:

package main

import (
	"context"
	"fmt"
	"log"
	"os"
	"time"

	"github.com/agentscope-ai/agentscope-go/v2/pkg/agentscope/agent"
	"github.com/agentscope-ai/agentscope-go/v2/pkg/agentscope/model"
)

func main() {
	cm, err := model.NewAnthropicChatModel(&model.AnthropicConfig{
		SecretAPIKey:    model.NewSecretStr(os.Getenv("ANTHROPIC_API_KEY")),
		Model:           os.Getenv("ANTHROPIC_MODEL"),
		MaxOutputTokens: 1024,
	})
	if err != nil {
		log.Fatal(err)
	}

	assistant := agent.NewUnifiedAgent(
		"assistant", "You are a helpful assistant. Keep answers concise.", cm,
	)

	ctx, cancel := context.WithTimeout(context.Background(), time.Minute)
	defer cancel()

	reply, err := assistant.Reply(ctx, "What is an AI agent? Explain in one sentence.")
	if err != nil {
		log.Fatal(err)
	}
	if text := reply.GetTextContent("\n"); text != nil {
		fmt.Println(*text)
	}
}

Set your key and a model ID available to your account, then run the program. These shell commands use Bash/Zsh syntax; in PowerShell, set environment variables with $env:NAME = 'value'.

export ANTHROPIC_API_KEY='your-api-key'
export ANTHROPIC_MODEL='your-model-id'
go mod tidy
go run .

The program makes a model API request and prints its reply. To add function calling, see the tool example; for configuration and next steps, see Getting started.

Adapters are available for OpenAI (Chat Completions and Responses), Anthropic, DashScope, DeepSeek, Gemini, Moonshot, xAI and Ollama. See provider configuration and the adapter source for options and defaults.

Tool calling, multimodal input, thinking and usage reporting depend on the provider and model. Provider ChatStream methods expose response chunks; UnifiedAgent.ReplyStream exposes lifecycle events and currently uses non-streaming model calls internally.

For local models, configure the context window and request timeout for your server and device. Model cards describe model capabilities; they do not configure the server. See edge deployment and the tracked local-model limitations.

Examples run from a repository checkout, separately from the application above. This worker-pool demo uses simulated jobs and needs no model API key:

git clone https://github.com/agentscope-ai/agentscope-go.git
cd agentscope-go
go run ./examples/agent_pool

Start with agent_v2 for function tools, model_call for direct model streaming, or webui for a browser interface. Check each example's source for its model choice, environment variables and required services or runtimes.

The examples guide provides running instructions and links to the same catalog.

Stability varies by package. The core model, message, agent, tool, permission, formatter and error APIs have documented stability commitments; other packages include experimental interfaces. Read STABILITY.md for the exact scope and remaining hardening work. Features on main may not be in a release.

For deployments that execute shell commands or access files, configure and test the appropriate workspace backend and permissions. Permission checks alone do not provide complete process, network or resource isolation. Deployment requirements depend on the selected providers and backends.

Bug reports, regression tests, documentation and focused feature contributions are welcome. For a bug, include the version or commit, configuration and a minimal reproduction with secrets removed. For a larger feature, open an issue to discuss the use case and API before starting a broad implementation.

Read CONTRIBUTING.md for setup and the PR process, and AGENTS.md for validation and review requirements. Small PRs with a clear purpose are easier to review; there is no need to take on a whole subsystem. Please follow our Code of Conduct.

Report suspected vulnerabilities privately through SECURITY.md, not in a public issue.

Licensed under Apache-2.0. For research using AgentScope, see AgentScope: A Flexible yet Robust Multi-Agent Platform.