Agent as Service - AgentScope

Agent as Service (AaaS) represents the core mission of AgentScope Runtime: transforming agent applications into deployable, scalable services that can be accessed through standardized APIs and interfaces, just like any other production service.

What is AgentScope Runtime?

AgentScope Runtime is a full-stack runtime for AI agents designed to solve two core challenges: efficient deployment & serving and secure sandboxed execution. In short:

\begin{aligned} \textbf{AgentScope Runtime} =&\ \textbf{Tool Sandboxing} +\ \textbf{AaaS APIs} +\ \textbf{Scalable Deployment} \\ &+\ \textbf{Full-stack Observability (Logs/Traces)} +\ \textbf{Framework Compatibility} \end{aligned}

Key Features

Deployment Infrastructure

Built-in services for agent state management, conversation history, long-term memory, and sandbox lifecycle control.

Framework-Agnostic

Not tied to any specific framework — seamlessly integrates with popular open-source and custom implementations.

Developer-Friendly

AgentApp provides easy deployment with powerful customization options and multiple endpoint types.

Full Observability

Comprehensive tracking and monitoring of runtime operations, logs, and traces.

Sandboxed Tool Execution

Isolated sandbox ensures safe tool execution without affecting the host system.

Out-of-the-Box Tools

Rich set of ready-to-use tools with adapters for quick integration into different frameworks.

Quick Start

Prerequisites

Python 3.10 or higher
pip or uv package manager

Installation

From PyPI:

# Install core dependencies
pip install agentscope-runtime

# Install extension
pip install "agentscope-runtime[ext]"

# Install preview version
pip install --pre agentscope-runtime

(Optional) From source:

# Pull the source code from GitHub
git clone -b main https://github.com/agentscope-ai/agentscope-runtime.git
cd agentscope-runtime

# Install core dependencies
pip install -e .

Agent App Example

This example demonstrates how to create an agent API server using agentscope ReActAgent and AgentApp. To run a minimal AgentScope Agent with AgentScope Runtime, you generally need to implement:

Define lifespan – Use contextlib.asynccontextmanager to manage resource initialization (e.g., state services) at startup and cleanup on exit.
@agent_app.query(framework="agentscope") – Core logic for handling requests, must use stream_printing_messages to yield msg, last for streaming output

agent_app.py

# Shared configuration for all deployment methods
# -*- coding: utf-8 -*-
import asyncio
import os
from contextlib import asynccontextmanager

from fastapi import FastAPI
from agentscope.agent import ReActAgent
from agentscope.formatter import DashScopeChatFormatter
from agentscope.model import DashScopeChatModel
from agentscope.pipeline import stream_printing_messages
from agentscope.tool import Toolkit, execute_python_code
from agentscope.memory import InMemoryMemory
from agentscope.session import RedisSession

from agentscope_runtime.engine.app import AgentApp
from agentscope_runtime.engine.schemas.agent_schemas import AgentRequest

# Define Lifecycle
@asynccontextmanager
async def lifespan(app: FastAPI):
    # Startup Phase
    import fakeredis

    fake_redis = fakeredis.aioredis.FakeRedis(
        decode_responses=True
    )
    # NOTE: This FakeRedis instance is for development/testing only.
    # In production, replace it with your own Redis client/connection
    # (e.g., aioredis.Redis)
    app.state.session = RedisSession(
        connection_pool=fake_redis.connection_pool
    )
    try:
        yield
    finally:
        print("AgentApp is shutting down...")

# Pass the defined lifespan to AgentApp
app = AgentApp(
    app_name="Friday",
    app_description="A helpful assistant",
    lifespan=lifespan,
)

# Define Request Handling Logic
@app.query(framework="agentscope")
async def query_func(
    self,
    msgs,
    request: AgentRequest = None,
    **kwargs,
):
    assert kwargs is not None, "kwargs is Required for query_func"
    session_id = request.session_id
    user_id = request.user_id

    toolkit = Toolkit()
    toolkit.register_tool_function(execute_python_code)

    agent = ReActAgent(
        name="Friday",
        model=DashScopeChatModel(
            "qwen-turbo",
            api_key=os.getenv("DASHSCOPE_API_KEY"),
            enable_thinking=True,
            stream=True,
        ),
        sys_prompt="You're a helpful assistant named Friday.",
        toolkit=toolkit,
        memory=InMemoryMemory(),
        formatter=DashScopeChatFormatter(),
    )

    # Load agent state
    await app.state.session.load_session_state(
        session_id=session_id,
        user_id=user_id,
        agent=agent,
    )

    try:
        async for msg, last in stream_printing_messages(
            agents=[agent],
            coroutine_task=agent(msgs),
        ):
            yield msg, last

    except asyncio.CancelledError:
        # Interruption logic
        print(f"Task {session_id} was manually interrupted.")
        await agent.interrupt()
        raise

    finally:
        # Save agent state
        await app.state.session.save_session_state(
            session_id=session_id,
            user_id=user_id,
            agent=agent,
        )

# Create AgentApp with multiple endpoints
@app.post("/stop")
async def stop_task(request: AgentRequest): # Endpoint to trigger task interruption
    await app.stop_chat(
        user_id=request.user_id,
        session_id=request.session_id,
    )
    return {
        "status": "success",
        "message": "Interrupt signal broadcasted.",
    }

@app.endpoint("/sync")
def sync_handler(request: AgentRequest):
    return {"status": "ok", "payload": request}

@app.endpoint("/async")
async def async_handler(request: AgentRequest):
    return {"status": "ok", "payload": request}

@app.endpoint("/stream_async")
async def stream_async_handler(request: AgentRequest):
    for i in range(5):
        yield f"async chunk {i}, with request payload {request}\n"

@app.endpoint("/stream_sync")
def stream_sync_handler(request: AgentRequest):
    for i in range(5):
        yield f"sync chunk {i}, with request payload {request}\n"

@app.task("/task", queue="celery1")
def task_handler(request: AgentRequest):
    import time
    time.sleep(30)
    return {"status": "ok", "payload": request}

@app.task("/atask")
async def atask_handler(request: AgentRequest):
    import asyncio
    await asyncio.sleep(15)
    return {"status": "ok", "payload": request}

print("✅ Agent and endpoints configured successfully")

The above configuration is shared across all deployment methods below. Each method will show only the deployment-specific code.

Deployment

After grasping the concepts and completing the quickstart, deployment is the bridge that turns experimental prototypes into reliable services. Its significance is underpinned by three core pillars:

Connect to real workloads: Moving agents from notebooks or scripts into a continuously running environment is the only way to serve real users, tools, and data.
Gain operational stability: Runtime offers standardized lifecycles, health checks, and scaling hooks that simplify monitoring and rollback.
Reuse the ecosystem: A unified deployment approach lets you reuse memory, sandbox, state, and other foundational services instead of rebuilding them per project.

Overview of Deployment Methods

AgentScope Runtime offers multiple distinct deployment approaches, each tailored for specific use cases:

Deployment Type	Use Case	Scalability	Management	Resource Isolation
Local Daemon	Development & Testing	Single Process	Manual	Process-level
Detached Process	Production Services	Single Node	Automated	Process-level
Kubernetes	Enterprise & Cloud	Single-node (multi-node support coming)	Orchestrated	Container-level
ModelStudio	Alibaba Cloud Platform	Cloud-managed	Platform-managed	Container-level
AgentRun	AgentRun Platform	Cloud-managed	Platform-managed	Container-level
PAI	Alibaba Cloud PAI Platform	Cloud-managed	Platform-managed	Container-level
Knative	Enterprise & Cloud	Single-node (multi-node support coming)	Orchestrated	Container-level
Kruise	Enterprise & Cloud	Single-node	Orchestrated	Container-level / MicroVM-level
Function Compute (FC)	Alibaba Cloud Serverless	Cloud-managed	Platform-managed	MicroVM-level

Prerequisites

Installation Requirements

Install AgentScope Runtime with all deployment dependencies:

# Basic installation
pip install agentscope-runtime>=1.0.0

# For Kubernetes deployment
pip install "agentscope-runtime[ext]>=1.0.0"

Environment Setup

Configure your API keys and environment variables:

# Required for LLM functionality
export DASHSCOPE_API_KEY="your_qwen_api_key"

# Optional for cloud deployments
export DOCKER_REGISTRY="your_registry_url"
export KUBECONFIG="/path/to/your/kubeconfig"

Prerequisites by Deployment Type

For All Deployments

Python 3.10+
AgentScope Runtime installed

For Kubernetes Deployment

Docker installed and configured
Kubernetes cluster access
kubectl configured
Container registry access (for image pushing)

For ModelStudio Deployment

Alibaba Cloud account with ModelStudio access
DashScope API key for LLM services
OSS (Object Storage Service) access
ModelStudio workspace configured

Method 1: Local Daemon Deployment

Best for: Development, testing, and single-user scenarios where you need persistent service with manual control.

Features

Persistent service in main process
Manual lifecycle management
Interactive control and monitoring
Direct resource sharing

Implementation

# daemon_deploy.py
import asyncio
from agentscope_runtime.engine.deployers.local_deployer import LocalDeployManager
from agent_app import app  # Import the configured app

# Deploy in daemon mode
async def main():
    await app.deploy(LocalDeployManager())

if __name__ == "__main__":
    asyncio.run(main())
    input("Press Enter to stop the server...")

Key Points:

Service runs in the main process (blocking)
Manually stopped with Ctrl+C or by ending the script
Best for development and testing

Testing the Deployed Service

Once deployed, you can test the endpoints using curl or Python: Using curl:

# Test health endpoint
curl http://localhost:8080/health

# Call sync endpoint
curl -X POST http://localhost:8080/sync \
  -H "Content-Type: application/json" \
  -d '{"input": [{"role": "user", "content": [{"type": "text", "text": "What is the weather in Beijing?"}]}], "session_id": "123"}'

# Call streaming endpoint
curl -X POST http://localhost:8080/stream_sync \
  -H "Content-Type: application/json" \
  -d '{"input": [{"role": "user", "content": [{"type": "text", "text": "What is the weather in Beijing?"}]}], "session_id": "123"}'

# Submit a task
curl -X POST http://localhost:8080/task \
  -H "Content-Type: application/json" \
  -d '{"input": [{"role": "user", "content": [{"type": "text", "text": "What is the weather in Beijing?"}]}], "session_id": "123"}'

Using OpenAI SDK:

from openai import OpenAI

client = OpenAI(base_url="http://0.0.0.0:8080/compatible-mode/v1")

response = client.responses.create(
  model="any_name",
  input="What is the weather in Beijing?"
)

print(response)

Method 2: Detached Process Deployment

Best for: Production services requiring process isolation, automated management, and independent lifecycle.

Features

Independent process execution
Automated lifecycle management
Remote shutdown capabilities
Service persistence after main script exit

Implementation

# detached_deploy.py
import asyncio
from agentscope_runtime.engine.deployers.local_deployer import LocalDeployManager
from agentscope_runtime.engine.deployers.utils.deployment_modes import DeploymentMode
from agent_app import app  # Import the configured app

async def main():
    """Deploy app in detached process mode"""
    print("🚀 Deploying AgentApp in detached process mode...")

    # Deploy in detached mode
    deployment_info = await app.deploy(
        LocalDeployManager(host="127.0.0.1", port=8080),
        mode=DeploymentMode.DETACHED_PROCESS,
    )

    print(f"✅ Deployment successful: {deployment_info['url']}")
    print(f"📍 Deployment ID: {deployment_info['deploy_id']}")
    print(f"""
🎯 Service started, test with:
curl {deployment_info['url']}/health
curl -X POST {deployment_info['url']}/admin/shutdown  # To stop

⚠️ Note: Service runs independently until stopped.
""")
    return deployment_info

if __name__ == "__main__":
    asyncio.run(main())

Key Points:

Service runs in a separate detached process
Script exits after deployment, service continues
Remote shutdown via /admin/shutdown endpoint

Method 3: Kubernetes Deployment

Best for: Enterprise production environments requiring scalability, high availability, and cloud-native orchestration.

Features

Container-based deployment
Horizontal scaling support
Cloud-native orchestration
Resource management and limits
Health checks and auto-recovery

Prerequisites for Kubernetes Deployment

# Ensure Docker is running
docker --version

# Verify Kubernetes access
kubectl cluster-info

# Check registry access (example with Aliyun)
docker login  your-registry

Implementation

# k8s_deploy.py
import asyncio
import os
from agentscope_runtime.engine.deployers.kubernetes_deployer import (
    KubernetesDeployManager,
    RegistryConfig,
    K8sConfig,
)
from agent_app import app  # Import the configured app

async def deploy_to_k8s():
    """Deploy AgentApp to Kubernetes"""

    # Configure registry and K8s connection
    deployer = KubernetesDeployManager(
        kube_config=K8sConfig(
            k8s_namespace="agentscope-runtime",
            kubeconfig_path=None,
        ),
        registry_config=RegistryConfig(
            registry_url="your-registry-url",
            namespace="agentscope-runtime",
        ),
        use_deployment=True,
    )

    # Deploy with configuration
    result = await app.deploy(
        deployer,
        port="8080",
        replicas=1,
        image_name="agent_app",
        image_tag="v1.0",
        requirements=["agentscope", "fastapi", "uvicorn"],
        base_image="python:3.10-slim-bookworm",
        environment={
            "PYTHONPATH": "/app",
            "DASHSCOPE_API_KEY": os.environ.get("DASHSCOPE_API_KEY"),
        },
        runtime_config={
            "resources": {
                "requests": {"cpu": "200m", "memory": "512Mi"},
                "limits": {"cpu": "1000m", "memory": "2Gi"},
            },
        },
        platform="linux/amd64",
        push_to_registry=True,
    )

    print(f"✅ Deployed to: {result['url']}")
    return result, deployer

if __name__ == "__main__":
    asyncio.run(deploy_to_k8s())

Key Points:

Containerized deployment with auto-scaling support
Resource limits and health checks configured
Can be scaled with kubectl scale deployment

Method 4: Serverless Deployment: ModelStudio

Best for: Alibaba Cloud users requiring managed cloud deployment with built-in monitoring, scaling, and integration with Alibaba Cloud ecosystem.

Features

Managed cloud deployment on Alibaba Cloud
Integrated with DashScope LLM services
Built-in monitoring and analytics
Automatic scaling and resource management
OSS integration for artifact storage
Web console for deployment management
Supports STS (Security Token Service) temporary credential authentication.

Prerequisites for ModelStudio Deployment

# Ensure environment variables are set
export DASHSCOPE_API_KEY="your-dashscope-api-key"
export ALIBABA_CLOUD_ACCESS_KEY_ID="your-access-key-id"
export ALIBABA_CLOUD_ACCESS_KEY_SECRET="your-access-key-secret"
export MODELSTUDIO_WORKSPACE_ID="your-workspace-id"

# Optional: Set this if you are using STS temporary credentials (Recommended)
export ALIBABA_CLOUD_SECURITY_TOKEN="your-sts-token"

# Optional OSS-specific credentials
export OSS_ACCESS_KEY_ID="your-oss-access-key-id"
export OSS_ACCESS_KEY_SECRET="your-oss-access-key-secret"
export OSS_SESSION_TOKEN="your-oss-sts-token"

Implementation

# modelstudio_deploy.py
import asyncio
import os
from agentscope_runtime.engine.deployers.modelstudio_deployer import (
    ModelstudioDeployManager,
    OSSConfig,
    ModelstudioConfig,
)
from agent_app import app  # Import the configured app

async def deploy_to_modelstudio():
    """Deploy AgentApp to Alibaba Cloud ModelStudio"""

    # Configure OSS and ModelStudio
    deployer = ModelstudioDeployManager(
        oss_config=OSSConfig(
            access_key_id=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_ID"),
            access_key_secret=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_SECRET"),
            security_token=os.environ.get("ALIBABA_CLOUD_SECURITY_TOKEN"),
        ),
        modelstudio_config=ModelstudioConfig(
            workspace_id=os.environ.get("MODELSTUDIO_WORKSPACE_ID"),
            access_key_id=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_ID"),
            access_key_secret=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_SECRET"),
            security_token=os.environ.get("ALIBABA_CLOUD_SECURITY_TOKEN"),
            dashscope_api_key=os.environ.get("DASHSCOPE_API_KEY"),
        ),
    )

    # Deploy to ModelStudio
    result = await app.deploy(
        deployer,
        deploy_name="agent-app-example",
        telemetry_enabled=True,
        requirements=["agentscope", "fastapi", "uvicorn"],
        environment={
            "PYTHONPATH": "/app",
            "DASHSCOPE_API_KEY": os.environ.get("DASHSCOPE_API_KEY"),
        },
    )

    print(f"✅ Deployed to ModelStudio: {result['url']}")
    print(f"📦 Artifact: {result['artifact_url']}")
    return result

if __name__ == "__main__":
    asyncio.run(deploy_to_modelstudio())

Key Points:

Fully managed cloud deployment on Alibaba Cloud
Built-in monitoring and auto-scaling
Integrated with DashScope LLM services
Enhanced security with STS Token-based authentication support.

Method 5: Serverless Deployment: AgentRun

Best For: Alibaba Cloud users who need to deploy agents to AgentRun service with automated build, upload, and deployment workflows.

Features

Managed deployment on Alibaba Cloud AgentRun service
Automatic project building and packaging
OSS integration for artifact storage
Complete lifecycle management
Automatic runtime endpoint creation and management

AgentRun Deployment Prerequisites

# Ensure environment variables are set
# More env settings, please refer to the table below
export ALIBABA_CLOUD_ACCESS_KEY_ID="your-access-key-id"
export ALIBABA_CLOUD_ACCESS_KEY_SECRET="your-access-key-secret"
export AGENT_RUN_REGION_ID="cn-hangzhou"  # or other regions

# OSS configuration (for storing build artifacts)
export OSS_ACCESS_KEY_ID="your-oss-access-key-id"
export OSS_ACCESS_KEY_SECRET="your-oss-access-key-secret"
export OSS_REGION="cn-hangzhou"
export OSS_BUCKET_NAME="your-bucket-name"

Implementation

# agentrun_deploy.py
import asyncio
import os
from agentscope_runtime.engine.deployers.agentrun_deployer import (
    AgentRunDeployManager,
    OSSConfig,
    AgentRunConfig,
)
from agent_app import app  # Import configured app

async def deploy_to_agentrun():
    """Deploy AgentApp to Alibaba Cloud AgentRun service"""

    # Configure OSS and AgentRun
    deployer = AgentRunDeployManager(
        oss_config=OSSConfig(
            access_key_id=os.environ.get("OSS_ACCESS_KEY_ID"),
            access_key_secret=os.environ.get("OSS_ACCESS_KEY_SECRET"),
            region=os.environ.get("OSS_REGION"),
            bucket_name=os.environ.get("OSS_BUCKET_NAME"),
        ),
        agentrun_config=AgentRunConfig(
            access_key_id=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_ID"),
            access_key_secret=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_SECRET"),
            region_id=os.environ.get("ALIBABA_CLOUD_REGION_ID", "cn-hangzhou"),
        ),
    )

    # Execute deployment
    result = await app.deploy(
        deployer,
        endpoint_path="/process",
        requirements=["agentscope", "fastapi", "uvicorn"],
        environment={
            "PYTHONPATH": "/app",
            "DASHSCOPE_API_KEY": os.environ.get("DASHSCOPE_API_KEY"),
        },
        deploy_name="agent-app-example",
        project_dir=".",  # Current project directory
        cmd="python -m uvicorn app:app --host 0.0.0.0 --port 8080",
    )

    print(f"✅ Deployed to AgentRun: {result['url']}")
    print(f"📍 AgentRun ID: {result.get('agentrun_id', 'N/A')}")
    print(f"📦 Artifact URL: {result.get('artifact_url', 'N/A')}")
    return result

if __name__ == "__main__":
    asyncio.run(deploy_to_agentrun())

Key Points:

Automatically builds and packages the project as a wheel file
Uploads artifacts to OSS
Creates and manages runtime in the AgentRun service
Automatically creates public access endpoints
Supports updating existing deployments (via agentrun_id parameter)

Configuration

OSSConfig

OSS configuration for storing build artifacts:

OSSConfig(
    access_key_id="your-access-key-id",
    access_key_secret="your-access-key-secret",
    region="cn-hangzhou",
    bucket_name="your-bucket-name",
)

AgentRunConfig

AgentRun service configuration:

AgentRunConfig(
    access_key_id="your-access-key-id",
    access_key_secret="your-access-key-secret",
    region_id="cn-hangzhou",  # Supported regions: cn-hangzhou, cn-beijing, etc.
)

Method 6: PAI Deployment (Platform for AI)

Best for: Enterprise users who need to deploy on Alibaba Cloud PAI platform, leveraging LangStudio for project management and EAS (Elastic Algorithm Service) for service deployment.

Features

Fully managed deployment on Alibaba Cloud PAI platform
Integrated LangStudio project and snapshot management
EAS (Elastic Algorithm Service) service deployment
Three resource types: Public Resource Pool, Dedicated Resource Group, Quota
VPC network configuration support
RAM role and permission configuration
Tracing support
Automatic/manual approval workflow
Auto-generated deployment tags

Prerequisites for PAI Deployment

# Set required environment variables
export ALIBABA_CLOUD_ACCESS_KEY_ID="your-access-key-id"
export ALIBABA_CLOUD_ACCESS_KEY_SECRET="your-access-key-secret"

# Optional configuration
export PAI_WORKSPACE_ID="your-workspace-id"
export REGION_ID="cn-hangzhou"  # or ALIBABA_CLOUD_REGION_ID

PAI Workspace Requirements

If using a RAM user account, PAI Developer Role must be assigned
OSS bucket must be configured for storing build artifacts
(Optional) VPC with public network access if using DashScope models

Services deployed to PAI EAS have no public network access by default. If using DashScope models, configure a VPC with public network access. Reference: Configure Network Connectivity

Implementation (SDK)

# pai_deploy.py
import asyncio
import os
from agentscope_runtime.engine.deployers.pai_deployer import (
    PAIDeployManager,
)
from agent_app import app  # Import configured app

async def deploy_to_pai():
    """Deploy AgentApp to Alibaba Cloud PAI"""

    # Create PAI deploy manager
    deployer = PAIDeployManager(
        workspace_id=os.environ.get("PAI_WORKSPACE_ID"),
        region_id=os.environ.get("REGION_ID", "cn-hangzhou"),
    )

    # Execute deployment
    result = await app.deploy(
        deployer,
        service_name="my-agent-service",
        project_dir="./my_agent",
        entrypoint="agent.py",
        resource_type="public",
        instance_type="ecs.c6.large",
        instance_count=1,
        environment={
            "DASHSCOPE_API_KEY": os.environ.get("DASHSCOPE_API_KEY"),
        },
        enable_trace=True,
        wait=True,
    )

    print(f"✅ Deployment successful: {result['url']}")
    print(f"📍 Deployment ID: {result['deploy_id']}")
    print(f"📦 Project ID: {result['flow_id']}")
    return result

if __name__ == "__main__":
    asyncio.run(deploy_to_pai())

Key Points:

Automatically packages project and uploads to OSS
Creates LangStudio project and snapshot
Deploys as EAS service
Supports multiple resource type configurations

Implementation (CLI)

PAI deployment recommends using configuration files for clarity and maintainability: Method 1: Using Configuration File (Recommended)

# Navigate to example directory
cd examples/deployments/pai_deploy

# Deploy using config file
agentscope deploy pai --config deploy_config.yaml

# Deploy with CLI overrides
agentscope deploy pai --config deploy_config.yaml --name new-service-name

Method 2: Using CLI Only

agentscope deploy pai ./my_agent \
  --name my-service \
  --workspace-id 12345 \
  --region cn-hangzhou \
  --instance-type ecs.c6.large \
  --env DASHSCOPE_API_KEY=your-key

Configuration

PAIDeployConfig Structure

PAI deployment uses YAML configuration files with the following structure:

# deploy_config.yaml
context:
  # PAI workspace ID (required)
  workspace_id: "your-workspace-id"
  # Region (e.g., cn-hangzhou, cn-shanghai)
  region: "cn-hangzhou"

spec:
  # Service name (required, unique within region)
  name: "my_agent_service"

  code:
    # Source directory (relative to config file location)
    source_dir: "my_agent"
    # Entrypoint file
    entrypoint: "agent.py"

  resources:
    # Resource type: public, resource, quota
    type: "public"
    # Instance type (required for public mode)
    instance_type: "ecs.c6.large"
    # Number of instances
    instance_count: 1

  # VPC configuration (optional)
  vpc_config:
    vpc_id: "vpc-xxxxx"
    vswitch_id: "vsw-xxxxx"
    security_group_id: "sg-xxxxx"

  # RAM role configuration (optional)
  identity:
    ram_role_arn: "acs:ram::xxx:role/xxx"

  # Observability configuration
  observability:
    enable_trace: true

  # Environment variables
  env:
    DASHSCOPE_API_KEY: "your-dashscope-api-key"

  # Tags
  tags:
    team: "ai-team"
    project: "agent-demo"

code.source_dir is resolved relative to the config file location.

Configuration Structure Reference

Section	Description
`context`	Deployment target (workspace, region, storage)
`spec.name`	Service name (required)
`spec.code`	Source directory and entrypoint
`spec.resources`	Resource allocation settings
`spec.vpc_config`	VPC network configuration (optional)
`spec.identity`	RAM role configuration (optional)
`spec.observability`	Tracing settings
`spec.env`	Environment variables
`spec.tags`	Deployment tags

Resource Types

PAI supports three resource types:

1. Public Resource Pool (`type: "public"`)

Deploy on shared ECS instances, suitable for development/testing and small-scale deployments:

spec:
  resources:
    type: "public"
    instance_type: "ecs.c6.large"  # Required
    instance_count: 1

2. Dedicated Resource Group (`type: "resource"`)

Deploy on dedicated EAS resource group, suitable for production environments requiring resource isolation:

spec:
  resources:
    type: "resource"
    resource_id: "eas-r-xxxxx"  # Required
    cpu: 2
    memory: 4096

3. Quota-based (`type: "quota"`)

Deploy using PAI quota, suitable for enterprise-level resource management:

spec:
  resources:
    type: "quota"
    quota_id: "quota-xxxxxxxx"  # Required
    cpu: 2
    memory: 4096

VPC Configuration

Private network deployment configuration for scenarios requiring access to public or internal resources:

spec:
  vpc_config:
    vpc_id: "vpc-xxxxx"
    vswitch_id: "vsw-xxxxx"
    security_group_id: "sg-xxxxx"

Method 7: Knative Deployment

Best for: Enterprise production environments requiring scalability, high availability, and cloud-native serverless container orchestration.

Features

Container-based Serverless deployment
Provides automatic scaling from zero to thousands of instances, intelligent traffic routing
Cloud-native orchestration
Resource management and limits
Health checks and auto-recovery

Prerequisites for Kubernetes Deployment

# Ensure Docker is running
docker --version

# Verify Kubernetes access
kubectl cluster-info

# Check registry access (example with Aliyun)
docker login  your-registry

# Check Knative Serving installed
kubectl auth can-i create ksvc

Implementation

# knative_deploy.py
import asyncio
import os
from agentscope_runtime.engine.deployers.knative_deployer import (
    KnativeDeployManager,
    RegistryConfig,
    K8sConfig,
)
from agent_app import app  # Import the configured app

async def deploy_to_knative():
    """Deploy AgentApp to Knative"""

    # Configure registry and K8s connection
    deployer = KnativeDeployManager(
        kube_config=K8sConfig(
            k8s_namespace="agentscope-runtime",
            kubeconfig_path=None,
        ),
        registry_config=RegistryConfig(
            registry_url="your-registry-url",
            namespace="agentscope-runtime",
        ),
    )

    # Deploy with configuration
    result = await app.deploy(
        deployer,
        port="8080",
        image_name="agent_app",
        image_tag="v1.0",
        requirements=["agentscope", "fastapi", "uvicorn"],
        base_image="python:3.10-slim-bookworm",
        environment={
            "PYTHONPATH": "/app",
            "DASHSCOPE_API_KEY": os.environ.get("DASHSCOPE_API_KEY"),
        },
        labels={
            "app": "agent-ksvc",
        },
        runtime_config={
            "resources": {
                "requests": {"cpu": "200m", "memory": "512Mi"},
                "limits": {"cpu": "1000m", "memory": "2Gi"},
            },
        },
        platform="linux/amd64",
        push_to_registry=True,
    )

    print(f"✅ Deployed to: {result['url']}")
    return result, deployer

if __name__ == "__main__":
    asyncio.run(deploy_to_knative())

Key Points:

Containerized Serverless deployment
Provides automatic scaling from zero to thousands of instances, intelligent traffic routing
Resource limits and health checks configured

Method 8: Kruise Deployment

Best For: Scenarios requiring instance-level isolation, pause/resume capabilities, and secure multi-tenant runtime environments.

Features

Custom resource deployment based on Kruise Sandbox CRD (agents.kruise.io/v1alpha1)
Instance-level isolation, ensuring secure runtime environments across different agents
Supports pausing and resuming, effectively saving resource consumption
Automatically creates LoadBalancer Service for external access
Deployment state persistence management

Kruise Deployment Prerequisites

# Ensure Docker is running
docker --version

# Verify Kubernetes access
kubectl cluster-info

# Check registry access (e.g., Alibaba Cloud)
docker login your-registry

# Check Kruise Sandbox is installed
# Installation guide: https://github.com/openkruise/agents
kubectl get crd sandboxes.agents.kruise.io

Implementation

# kruise_deploy.py
import asyncio
import os
from agentscope_runtime.engine.deployers.kruise_deployer import (
    KruiseDeployManager,
    K8sConfig,
)
from agentscope_runtime.engine.deployers.utils.docker_image_utils import (
    RegistryConfig,
)
from agent_app import app  # Import the configured app

async def deploy_to_kruise():
    """Deploy AgentApp to Kruise Sandbox"""

    # Configure registry and K8s connection
    deployer = KruiseDeployManager(
        kube_config=K8sConfig(
            k8s_namespace="agentscope-runtime",
            kubeconfig_path=None,
        ),
        registry_config=RegistryConfig(
            registry_url="your-registry-url",
            namespace="agentscope-runtime",
        ),
    )

    # Execute deployment
    result = await app.deploy(
        deployer,
        port="8090",
        image_name="agent_app",
        image_tag="v1.0",
        requirements=["agentscope", "fastapi", "uvicorn"],
        base_image="python:3.10-slim-bookworm",
        environment={
            "PYTHONPATH": "/app",
            "DASHSCOPE_API_KEY": os.environ.get("DASHSCOPE_API_KEY"),
        },
        labels={
            "app": "agent-kruise",
        },
        runtime_config={
            "resources": {
                "requests": {"cpu": "200m", "memory": "512Mi"},
                "limits": {"cpu": "1000m", "memory": "2Gi"},
            },
        },
        platform="linux/amd64",
        push_to_registry=True,
    )

    print(f"Deployment successful: {result['url']}")
    return result, deployer

if __name__ == "__main__":
    asyncio.run(deploy_to_kruise())

Key Points:

Isolated deployment based on Kruise Sandbox CRD, each agent runs in an independent environment
Automatically creates LoadBalancer Service, supports automatic switching between local and cloud environments
Deployment state is automatically persisted, supports lifecycle management via CLI

Method 9: Serverless Deployment: Function Compute (FC)

Best For: Alibaba Cloud users who need to deploy agents to Function Compute (FC) service with automated build, upload, and deployment workflows. FC provides a true serverless experience with pay-per-use pricing and automatic scaling.

Features

Serverless deployment on Alibaba Cloud Function Compute
Automatic project building and packaging with Docker
OSS integration for artifact storage
HTTP trigger for public access
Session affinity support for stateful applications
VPC and logging configuration support
Pay-per-use pricing model

FC Deployment Prerequisites

# Ensure environment variables are set
# More env settings, please refer to the table below
export ALIBABA_CLOUD_ACCESS_KEY_ID="your-access-key-id"
export ALIBABA_CLOUD_ACCESS_KEY_SECRET="your-access-key-secret"
export FC_ACCOUNT_ID="your-fc-account-id"
export FC_REGION_ID="cn-hangzhou"  # or other regions

# OSS configuration (for storing build artifacts)
export OSS_ACCESS_KEY_ID="your-oss-access-key-id"
export OSS_ACCESS_KEY_SECRET="your-oss-access-key-secret"
export OSS_REGION="cn-hangzhou"
export OSS_BUCKET_NAME="your-bucket-name"

Implementation

# fc_deploy.py
import asyncio
import os
from agentscope_runtime.engine.deployers.fc_deployer import (
    FCDeployManager,
    OSSConfig,
    FCConfig,
)
from agent_app import app  # Import configured app

async def deploy_to_fc():
    """Deploy AgentApp to Alibaba Cloud Function Compute (FC)"""

    # Configure OSS and FC
    deployer = FCDeployManager(
        oss_config=OSSConfig(
            access_key_id=os.environ.get("OSS_ACCESS_KEY_ID"),
            access_key_secret=os.environ.get("OSS_ACCESS_KEY_SECRET"),
            region=os.environ.get("OSS_REGION", "cn-hangzhou"),
            bucket_name=os.environ.get("OSS_BUCKET_NAME"),
        ),
        fc_config=FCConfig(
            access_key_id=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_ID"),
            access_key_secret=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_SECRET"),
            account_id=os.environ.get("FC_ACCOUNT_ID"),
            region_id=os.environ.get("FC_REGION_ID", "cn-hangzhou"),
        ),
    )

    # Execute deployment
    result = await app.deploy(
        deployer,
        deploy_name="agent-app-example",
        requirements=["agentscope", "fastapi", "uvicorn"],
        environment={
            "PYTHONPATH": "/code",
            "DASHSCOPE_API_KEY": os.environ.get("DASHSCOPE_API_KEY"),
        },
    )

    print(f"✅ Deployed to FC: {result['url']}")
    print(f"📍 Function Name: {result['function_name']}")
    print(f"🔗 Endpoint URL: {result['endpoint_url']}")
    print(f"📦 Artifact URL: {result['artifact_url']}")
    return result

if __name__ == "__main__":
    asyncio.run(deploy_to_fc())

Key Points:

Automatically builds project with Docker and creates a deployable zip package
Uploads artifacts to OSS for FC to pull
Creates FC function with HTTP trigger for public access
Supports session affinity via x-agentscope-runtime-session-id header
Supports updating existing deployments (via function_name parameter)

Configuration

OSSConfig

OSS configuration for storing build artifacts:

OSSConfig(
    access_key_id="your-access-key-id",
    access_key_secret="your-access-key-secret",
    region="cn-hangzhou",
    bucket_name="your-bucket-name",
)

FCConfig

Function Compute service configuration:

FCConfig(
    access_key_id="your-access-key-id",
    access_key_secret="your-access-key-secret",
    account_id="your-account-id",
    region_id="cn-hangzhou",  # Supported regions: cn-hangzhou, cn-beijing, etc.
    cpu=2.0,  # CPU cores
    memory=2048,  # Memory in MB
    disk=512,  # Disk in MB
)

​What is AgentScope Runtime?

​Key Features

Deployment Infrastructure

Framework-Agnostic

Developer-Friendly

Full Observability

Sandboxed Tool Execution

Out-of-the-Box Tools

​Quick Start

​Prerequisites

​Installation

​Agent App Example

​Deployment

​Overview of Deployment Methods

​Prerequisites

​Installation Requirements

​Environment Setup

​Prerequisites by Deployment Type

For All Deployments

For Kubernetes Deployment

For ModelStudio Deployment

​Method 1: Local Daemon Deployment

​Features

​Implementation

​Testing the Deployed Service

​Method 2: Detached Process Deployment

​Features

​Implementation

​Method 3: Kubernetes Deployment

​Features

​Prerequisites for Kubernetes Deployment

​Implementation

​Method 4: Serverless Deployment: ModelStudio

​Features

​Prerequisites for ModelStudio Deployment

​Implementation

​Method 5: Serverless Deployment: AgentRun

​Features

​AgentRun Deployment Prerequisites

​Implementation

​Configuration

OSSConfig

AgentRunConfig

​Method 6: PAI Deployment (Platform for AI)

​Features

​Prerequisites for PAI Deployment

​PAI Workspace Requirements

​Implementation (SDK)

​Implementation (CLI)

​Configuration

PAIDeployConfig Structure

Configuration Structure Reference

​Resource Types

1. Public Resource Pool (type: "public")

2. Dedicated Resource Group (type: "resource")

3. Quota-based (type: "quota")

​VPC Configuration

​Method 7: Knative Deployment

​Features

​Prerequisites for Kubernetes Deployment

​Implementation

​Method 8: Kruise Deployment

​Features

​Kruise Deployment Prerequisites

​Implementation

​Method 9: Serverless Deployment: Function Compute (FC)

​Features

​FC Deployment Prerequisites

​Implementation

​Configuration

OSSConfig

FCConfig

What is AgentScope Runtime?

Key Features

Quick Start

Prerequisites

Installation

Agent App Example

Deployment

Overview of Deployment Methods

Prerequisites

Installation Requirements

Environment Setup

Prerequisites by Deployment Type

Method 1: Local Daemon Deployment

Features

Implementation

Testing the Deployed Service

Method 2: Detached Process Deployment

Features

Implementation

Method 3: Kubernetes Deployment

Features

Prerequisites for Kubernetes Deployment

Implementation

Method 4: Serverless Deployment: ModelStudio

Features

Prerequisites for ModelStudio Deployment

Implementation

Method 5: Serverless Deployment: AgentRun

Features

AgentRun Deployment Prerequisites

Implementation

Configuration

Method 6: PAI Deployment (Platform for AI)

Features

Prerequisites for PAI Deployment

PAI Workspace Requirements

Implementation (SDK)

Implementation (CLI)

Configuration

Resource Types

1. Public Resource Pool (`type: "public"`)

2. Dedicated Resource Group (`type: "resource"`)

3. Quota-based (`type: "quota"`)

VPC Configuration

Method 7: Knative Deployment

Features

Prerequisites for Kubernetes Deployment

Implementation

Method 8: Kruise Deployment

Features

Kruise Deployment Prerequisites

Implementation

Method 9: Serverless Deployment: Function Compute (FC)

Features

FC Deployment Prerequisites

Implementation

Configuration