Sim
SDKs

Python

官方的 Python SDK 允许您通过 Python 应用程序以编程方式执行工作流。

Python SDK 支持 Python 3.8+，具备异步执行支持、自动速率限制（带指数退避）以及使用情况跟踪功能。

安装

使用 pip 安装 SDK：

pip install simstudio-sdk

快速开始

以下是一个简单的示例，帮助您快速入门：

from simstudio import SimStudioClient

# Initialize the client
client = SimStudioClient(
    api_key="your-api-key-here",
    base_url="https://sim.ai"  # optional, defaults to https://sim.ai
)

# Execute a workflow
try:
    result = client.execute_workflow("workflow-id")
    print("Workflow executed successfully:", result)
except Exception as error:
    print("Workflow execution failed:", error)

API 参考

SimStudioClient

构造函数

SimStudioClient(api_key: str, base_url: str = "https://sim.ai")

参数：

  • api_key (str): 您的 Sim API 密钥
  • base_url (str, 可选): Sim API 的基础 URL

方法

execute_workflow()

执行带有可选输入数据的工作流。

result = client.execute_workflow(
    "workflow-id",
    input_data={"message": "Hello, world!"},
    timeout=30.0  # 30 seconds
)

参数：

  • workflow_id (str): 要执行的工作流 ID
  • input_data (dict, optional): 传递给工作流的输入数据
  • timeout (float, optional): 超时时间（以秒为单位，默认值：30.0）
  • stream (bool, optional): 启用流式响应（默认值：False）
  • selected_outputs (list[str], optional): 以 blockName.attribute 格式阻止输出流（例如，["agent1.content"]）
  • async_execution (bool, optional): 异步执行（默认值：False）

返回值： WorkflowExecutionResult | AsyncExecutionResult

当 async_execution=True 时，立即返回任务 ID 以供轮询。否则，等待完成。

get_workflow_status()

获取工作流的状态（部署状态等）。

status = client.get_workflow_status("workflow-id")
print("Is deployed:", status.is_deployed)

参数：

  • workflow_id (str): 工作流的 ID

返回值： WorkflowStatus

validate_workflow()

验证工作流是否已准备好执行。

is_ready = client.validate_workflow("workflow-id")
if is_ready:
    # Workflow is deployed and ready
    pass

参数：

  • workflow_id (str): 工作流的 ID

返回值： bool

get_job_status()

获取异步任务执行的状态。

status = client.get_job_status("task-id-from-async-execution")
print("Status:", status["status"])  # 'queued', 'processing', 'completed', 'failed'
if status["status"] == "completed":
    print("Output:", status["output"])

参数：

  • task_id (str): 异步执行返回的任务 ID

返回值： Dict[str, Any]

响应字段：

  • success (bool): 请求是否成功
  • taskId (str): 任务 ID
  • status (str): 可能的值包括 'queued', 'processing', 'completed', 'failed', 'cancelled'
  • metadata (dict): 包含 startedAt, completedAt 和 duration
  • output (any, optional): 工作流输出（完成时）
  • error (any, optional): 错误详情（失败时）
  • estimatedDuration (int, optional): 估计持续时间（以毫秒为单位，处理中/排队时）
execute_with_retry()

使用指数退避在速率限制错误上自动重试执行工作流。

result = client.execute_with_retry(
    "workflow-id",
    input_data={"message": "Hello"},
    timeout=30.0,
    max_retries=3,           # Maximum number of retries
    initial_delay=1.0,       # Initial delay in seconds
    max_delay=30.0,          # Maximum delay in seconds
    backoff_multiplier=2.0   # Exponential backoff multiplier
)

参数：

  • workflow_id (str): 要执行的工作流 ID
  • input_data (dict, optional): 传递给工作流的输入数据
  • timeout (float, optional): 超时时间（以秒为单位）
  • stream (bool, optional): 启用流式响应
  • selected_outputs (list, optional): 阻止输出流
  • async_execution (bool, optional): 异步执行
  • max_retries (int, optional): 最大重试次数（默认值：3）
  • initial_delay (float, optional): 初始延迟时间（以秒为单位，默认值：1.0）
  • max_delay (float, optional): 最大延迟时间（以秒为单位，默认值：30.0）
  • backoff_multiplier (float, optional): 退避倍数（默认值：2.0）

返回值： WorkflowExecutionResult | AsyncExecutionResult

重试逻辑使用指数退避（1 秒 → 2 秒 → 4 秒 → 8 秒...），并带有 ±25% 的抖动以防止惊群效应。如果 API 提供了 retry-after 标头，则会使用该标头。

get_rate_limit_info()

从上一次 API 响应中获取当前的速率限制信息。

rate_limit_info = client.get_rate_limit_info()
if rate_limit_info:
    print("Limit:", rate_limit_info.limit)
    print("Remaining:", rate_limit_info.remaining)
    print("Reset:", datetime.fromtimestamp(rate_limit_info.reset))

返回值： RateLimitInfo | None

get_usage_limits()

获取您的账户当前的使用限制和配额信息。

limits = client.get_usage_limits()
print("Sync requests remaining:", limits.rate_limit["sync"]["remaining"])
print("Async requests remaining:", limits.rate_limit["async"]["remaining"])
print("Current period cost:", limits.usage["currentPeriodCost"])
print("Plan:", limits.usage["plan"])

返回值： UsageLimits

响应结构：

{
    "success": bool,
    "rateLimit": {
        "sync": {
            "isLimited": bool,
            "limit": int,
            "remaining": int,
            "resetAt": str
        },
        "async": {
            "isLimited": bool,
            "limit": int,
            "remaining": int,
            "resetAt": str
        },
        "authType": str  # 'api' or 'manual'
    },
    "usage": {
        "currentPeriodCost": float,
        "limit": float,
        "plan": str  # e.g., 'free', 'pro'
    }
}
set_api_key()

更新 API 密钥。

client.set_api_key("new-api-key")
set_base_url()

更新基础 URL。

client.set_base_url("https://my-custom-domain.com")
close()

关闭底层 HTTP 会话。

client.close()

数据类

WorkflowExecutionResult

@dataclass
class WorkflowExecutionResult:
    success: bool
    output: Optional[Any] = None
    error: Optional[str] = None
    logs: Optional[List[Any]] = None
    metadata: Optional[Dict[str, Any]] = None
    trace_spans: Optional[List[Any]] = None
    total_duration: Optional[float] = None

AsyncExecutionResult

@dataclass
class AsyncExecutionResult:
    success: bool
    task_id: str
    status: str  # 'queued'
    created_at: str
    links: Dict[str, str]  # e.g., {"status": "/api/jobs/{taskId}"}

WorkflowStatus

@dataclass
class WorkflowStatus:
    is_deployed: bool
    deployed_at: Optional[str] = None
    is_published: bool = False
    needs_redeployment: bool = False

RateLimitInfo

@dataclass
class RateLimitInfo:
    limit: int
    remaining: int
    reset: int
    retry_after: Optional[int] = None

UsageLimits

@dataclass
class UsageLimits:
    success: bool
    rate_limit: Dict[str, Any]
    usage: Dict[str, Any]

SimStudioError

class SimStudioError(Exception):
    def __init__(self, message: str, code: Optional[str] = None, status: Optional[int] = None):
        super().__init__(message)
        self.code = code
        self.status = status

常见错误代码：

  • UNAUTHORIZED: 无效的 API 密钥
  • TIMEOUT: 请求超时
  • RATE_LIMIT_EXCEEDED: 超出速率限制
  • USAGE_LIMIT_EXCEEDED: 超出使用限制
  • EXECUTION_ERROR: 工作流执行失败

示例

基本工作流执行

使用您的 API 密钥设置 SimStudioClient。

检查工作流是否已部署并准备好执行。

使用您的输入数据运行工作流。

处理执行结果并处理任何错误。

import os
from simstudio import SimStudioClient

client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))

def run_workflow():
    try:
        # Check if workflow is ready
        is_ready = client.validate_workflow("my-workflow-id")
        if not is_ready:
            raise Exception("Workflow is not deployed or ready")

        # Execute the workflow
        result = client.execute_workflow(
            "my-workflow-id",
            input_data={
                "message": "Process this data",
                "user_id": "12345"
            }
        )

        if result.success:
            print("Output:", result.output)
            print("Duration:", result.metadata.get("duration") if result.metadata else None)
        else:
            print("Workflow failed:", result.error)
            
    except Exception as error:
        print("Error:", error)

run_workflow()

错误处理

处理工作流执行过程中可能发生的不同类型的错误：

from simstudio import SimStudioClient, SimStudioError
import os

client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))

def execute_with_error_handling():
    try:
        result = client.execute_workflow("workflow-id")
        return result
    except SimStudioError as error:
        if error.code == "UNAUTHORIZED":
            print("Invalid API key")
        elif error.code == "TIMEOUT":
            print("Workflow execution timed out")
        elif error.code == "USAGE_LIMIT_EXCEEDED":
            print("Usage limit exceeded")
        elif error.code == "INVALID_JSON":
            print("Invalid JSON in request body")
        else:
            print(f"Workflow error: {error}")
        raise
    except Exception as error:
        print(f"Unexpected error: {error}")
        raise

上下文管理器的使用

将客户端用作上下文管理器以自动处理资源清理：

from simstudio import SimStudioClient
import os

# Using context manager to automatically close the session
with SimStudioClient(api_key=os.getenv("SIM_API_KEY")) as client:
    result = client.execute_workflow("workflow-id")
    print("Result:", result)
# Session is automatically closed here

批量工作流执行

高效地执行多个工作流：

from simstudio import SimStudioClient
import os

client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))

def execute_workflows_batch(workflow_data_pairs):
    """Execute multiple workflows with different input data."""
    results = []
    
    for workflow_id, input_data in workflow_data_pairs:
        try:
            # Validate workflow before execution
            if not client.validate_workflow(workflow_id):
                print(f"Skipping {workflow_id}: not deployed")
                continue
                
            result = client.execute_workflow(workflow_id, input_data)
            results.append({
                "workflow_id": workflow_id,
                "success": result.success,
                "output": result.output,
                "error": result.error
            })
            
        except Exception as error:
            results.append({
                "workflow_id": workflow_id,
                "success": False,
                "error": str(error)
            })
    
    return results

# Example usage
workflows = [
    ("workflow-1", {"type": "analysis", "data": "sample1"}),
    ("workflow-2", {"type": "processing", "data": "sample2"}),
]

results = execute_workflows_batch(workflows)
for result in results:
    print(f"Workflow {result['workflow_id']}: {'Success' if result['success'] else 'Failed'}")

异步工作流执行

为长时间运行的任务异步执行工作流：

import os
import time
from simstudio import SimStudioClient

client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))

def execute_async():
    try:
        # Start async execution
        result = client.execute_workflow(
            "workflow-id",
            input_data={"data": "large dataset"},
            async_execution=True  # Execute asynchronously
        )

        # Check if result is an async execution
        if hasattr(result, 'task_id'):
            print(f"Task ID: {result.task_id}")
            print(f"Status endpoint: {result.links['status']}")

            # Poll for completion
            status = client.get_job_status(result.task_id)

            while status["status"] in ["queued", "processing"]:
                print(f"Current status: {status['status']}")
                time.sleep(2)  # Wait 2 seconds
                status = client.get_job_status(result.task_id)

            if status["status"] == "completed":
                print("Workflow completed!")
                print(f"Output: {status['output']}")
                print(f"Duration: {status['metadata']['duration']}")
            else:
                print(f"Workflow failed: {status['error']}")

    except Exception as error:
        print(f"Error: {error}")

execute_async()

速率限制与重试

通过指数退避自动处理速率限制：

import os
from simstudio import SimStudioClient, SimStudioError

client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))

def execute_with_retry_handling():
    try:
        # Automatically retries on rate limit
        result = client.execute_with_retry(
            "workflow-id",
            input_data={"message": "Process this"},
            max_retries=5,
            initial_delay=1.0,
            max_delay=60.0,
            backoff_multiplier=2.0
        )

        print(f"Success: {result}")
    except SimStudioError as error:
        if error.code == "RATE_LIMIT_EXCEEDED":
            print("Rate limit exceeded after all retries")

            # Check rate limit info
            rate_limit_info = client.get_rate_limit_info()
            if rate_limit_info:
                from datetime import datetime
                reset_time = datetime.fromtimestamp(rate_limit_info.reset)
                print(f"Rate limit resets at: {reset_time}")

execute_with_retry_handling()

使用监控

监控您的账户使用情况和限制：

import os
from simstudio import SimStudioClient

client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))

def check_usage():
    try:
        limits = client.get_usage_limits()

        print("=== Rate Limits ===")
        print("Sync requests:")
        print(f"  Limit: {limits.rate_limit['sync']['limit']}")
        print(f"  Remaining: {limits.rate_limit['sync']['remaining']}")
        print(f"  Resets at: {limits.rate_limit['sync']['resetAt']}")
        print(f"  Is limited: {limits.rate_limit['sync']['isLimited']}")

        print("\nAsync requests:")
        print(f"  Limit: {limits.rate_limit['async']['limit']}")
        print(f"  Remaining: {limits.rate_limit['async']['remaining']}")
        print(f"  Resets at: {limits.rate_limit['async']['resetAt']}")
        print(f"  Is limited: {limits.rate_limit['async']['isLimited']}")

        print("\n=== Usage ===")
        print(f"Current period cost: ${limits.usage['currentPeriodCost']:.2f}")
        print(f"Limit: ${limits.usage['limit']:.2f}")
        print(f"Plan: {limits.usage['plan']}")

        percent_used = (limits.usage['currentPeriodCost'] / limits.usage['limit']) * 100
        print(f"Usage: {percent_used:.1f}%")

        if percent_used > 80:
            print("⚠️  Warning: You are approaching your usage limit!")

    except Exception as error:
        print(f"Error checking usage: {error}")

check_usage()

流式工作流执行

通过实时流式响应执行工作流：

from simstudio import SimStudioClient
import os

client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))

def execute_with_streaming():
    """Execute workflow with streaming enabled."""
    try:
        # Enable streaming for specific block outputs
        result = client.execute_workflow(
            "workflow-id",
            input_data={"message": "Count to five"},
            stream=True,
            selected_outputs=["agent1.content"]  # Use blockName.attribute format
        )

        print("Workflow result:", result)
    except Exception as error:
        print("Error:", error)

execute_with_streaming()

流式响应遵循服务器发送事件 (SSE) 格式：

data: {"blockId":"7b7735b9-19e5-4bd6-818b-46aae2596e9f","chunk":"One"}

data: {"blockId":"7b7735b9-19e5-4bd6-818b-46aae2596e9f","chunk":", two"}

data: {"event":"done","success":true,"output":{},"metadata":{"duration":610}}

data: [DONE]

Flask 流式示例：

from flask import Flask, Response, stream_with_context
import requests
import json
import os

app = Flask(__name__)

@app.route('/stream-workflow')
def stream_workflow():
    """Stream workflow execution to the client."""

    def generate():
        response = requests.post(
            'https://sim.ai/api/workflows/WORKFLOW_ID/execute',
            headers={
                'Content-Type': 'application/json',
                'X-API-Key': os.getenv('SIM_API_KEY')   
            },
            json={
                'message': 'Generate a story',
                'stream': True,
                'selectedOutputs': ['agent1.content']
            },
            stream=True
        )

        for line in response.iter_lines():
            if line:
                decoded_line = line.decode('utf-8')
                if decoded_line.startswith('data: '):
                    data = decoded_line[6:]  # Remove 'data: ' prefix

                    if data == '[DONE]':
                        break

                    try:
                        parsed = json.loads(data)
                        if 'chunk' in parsed:
                            yield f"data: {json.dumps(parsed)}\n\n"
                        elif parsed.get('event') == 'done':
                            yield f"data: {json.dumps(parsed)}\n\n"
                            print("Execution complete:", parsed.get('metadata'))
                    except json.JSONDecodeError:
                        pass

    return Response(
        stream_with_context(generate()),
        mimetype='text/event-stream'
    )

if __name__ == '__main__':
    app.run(debug=True)

环境配置

使用环境变量配置客户端：

import os
from simstudio import SimStudioClient

# Development configuration
client = SimStudioClient(
    api_key=os.getenv("SIM_API_KEY")
    base_url=os.getenv("SIM_BASE_URL", "https://sim.ai")
)
import os
from simstudio import SimStudioClient

# Production configuration with error handling
api_key = os.getenv("SIM_API_KEY")
if not api_key:
    raise ValueError("SIM_API_KEY environment variable is required")

client = SimStudioClient(
    api_key=api_key,
    base_url=os.getenv("SIM_BASE_URL", "https://sim.ai")
)

获取您的 API 密钥

前往 Sim 并登录您的账户。

前往您想要以编程方式执行的工作流。

如果尚未部署，请点击“部署”以部署您的工作流。

在部署过程中，选择或创建一个 API 密钥。

复制 API 密钥以在您的 Python 应用程序中使用。

系统要求

  • Python 3.8+
  • requests >= 2.25.0

许可证

Apache-2.0

角色和权限TypeScript
On this page

On this page

安装
快速开始
API 参考
SimStudioClient
构造函数
方法
execute_workflow()
get_workflow_status()
validate_workflow()
get_job_status()
execute_with_retry()
get_rate_limit_info()
get_usage_limits()
set_api_key()
set_base_url()
close()
数据类
WorkflowExecutionResult
AsyncExecutionResult
WorkflowStatus
RateLimitInfo
UsageLimits
SimStudioError
示例
基本工作流执行
错误处理
上下文管理器的使用
批量工作流执行
异步工作流执行
速率限制与重试
使用监控
流式工作流执行
环境配置
获取您的 API 密钥
系统要求
许可证