sim/apps/docs/content/docs/de/sdks/python.mdx

---
title: Python
---

import { Callout } from 'fumadocs-ui/components/callout'
import { Card, Cards } from 'fumadocs-ui/components/card'
import { Step, Steps } from 'fumadocs-ui/components/steps'
import { Tab, Tabs } from 'fumadocs-ui/components/tabs'

Das offizielle Python SDK für Sim ermöglicht es Ihnen, Workflows programmatisch aus Ihren Python-Anwendungen mithilfe des offiziellen Python SDKs auszuführen.

<Callout type="info">
  Das Python SDK unterstützt Python 3.8+ mit asynchroner Ausführungsunterstützung, automatischer Ratenbegrenzung mit exponentiellem Backoff und Nutzungsverfolgung.
</Callout>

## Installation

Installieren Sie das SDK mit pip:

```bash
pip install simstudio-sdk
```

## Schnellstart

Hier ist ein einfaches Beispiel für den Einstieg:

```python
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-Referenz

### SimStudioClient

#### Konstruktor

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

**Parameter:**
- `api_key` (str): Ihr Sim API-Schlüssel
- `base_url` (str, optional): Basis-URL für die Sim API

#### Methoden

##### execute_workflow()

Führt einen Workflow mit optionalen Eingabedaten aus.

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

**Parameter:**
- `workflow_id` (str): Die ID des auszuführenden Workflows
- `input_data` (dict, optional): Eingabedaten, die an den Workflow übergeben werden
- `timeout` (float, optional): Timeout in Sekunden (Standard: 30.0)
- `stream` (bool, optional): Streaming-Antworten aktivieren (Standard: False)
- `selected_outputs` (list[str], optional): Block-Ausgaben, die im `blockName.attribute`Format gestreamt werden sollen (z.B. `["agent1.content"]`)
- `async_execution` (bool, optional): Asynchron ausführen (Standard: False)

**Rückgabe:** `WorkflowExecutionResult | AsyncExecutionResult`

Wenn `async_execution=True`, wird sofort mit einer Task-ID zum Abfragen zurückgegeben. Andernfalls wird auf den Abschluss gewartet.

##### get_workflow_status()

Den Status eines Workflows abrufen (Bereitstellungsstatus usw.).

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

**Parameter:**
- `workflow_id` (str): Die ID des Workflows

**Rückgabe:** `WorkflowStatus`

##### validate_workflow()

Überprüfen, ob ein Workflow für die Ausführung bereit ist.

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

**Parameter:**
- `workflow_id` (str): Die ID des Workflows

**Rückgabe:** `bool`

##### get_job_status()

Den Status einer asynchronen Job-Ausführung abrufen.

```python
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"])
```

**Parameter:**
- `task_id` (str): Die Task-ID, die von der asynchronen Ausführung zurückgegeben wurde

**Rückgabe:** `Dict[str, Any]`

**Antwortfelder:**
- `success` (bool): Ob die Anfrage erfolgreich war
- `taskId` (str): Die Task-ID
- `status` (str): Einer der Werte `'queued'`, `'processing'`, `'completed'`, `'failed'`, `'cancelled'`
- `metadata` (dict): Enthält `startedAt`, `completedAt` und `duration`
- `output` (any, optional): Die Workflow-Ausgabe (wenn abgeschlossen)
- `error` (any, optional): Fehlerdetails (wenn fehlgeschlagen)
- `estimatedDuration` (int, optional): Geschätzte Dauer in Millisekunden (wenn in Bearbeitung/in Warteschlange)

##### execute_with_retry()

Einen Workflow mit automatischer Wiederholung bei Ratenbegrenzungsfehlern unter Verwendung von exponentiellem Backoff ausführen.

```python
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
)
```

**Parameter:**
- `workflow_id` (str): Die ID des auszuführenden Workflows
- `input_data` (dict, optional): Eingabedaten, die an den Workflow übergeben werden
- `timeout` (float, optional): Timeout in Sekunden
- `stream` (bool, optional): Streaming-Antworten aktivieren
- `selected_outputs` (list, optional): Block-Ausgaben zum Streamen
- `async_execution` (bool, optional): Asynchron ausführen
- `max_retries` (int, optional): Maximale Anzahl von Wiederholungen (Standard: 3)
- `initial_delay` (float, optional): Anfängliche Verzögerung in Sekunden (Standard: 1.0)
- `max_delay` (float, optional): Maximale Verzögerung in Sekunden (Standard: 30.0)
- `backoff_multiplier` (float, optional): Backoff-Multiplikator (Standard: 2.0)

**Rückgabewert:** `WorkflowExecutionResult | AsyncExecutionResult`

Die Wiederholungslogik verwendet exponentielles Backoff (1s → 2s → 4s → 8s...) mit ±25% Jitter, um den Thundering-Herd-Effekt zu vermeiden. Wenn die API einen `retry-after` Header bereitstellt, wird dieser stattdessen verwendet.

##### get_rate_limit_info()

Ruft die aktuellen Rate-Limit-Informationen aus der letzten API-Antwort ab.

```python
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))
```

**Rückgabewert:** `RateLimitInfo | None`

##### get_usage_limits()

Ruft aktuelle Nutzungslimits und Kontingentinformationen für dein Konto ab.

```python
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"])
```

**Rückgabewert:** `UsageLimits`

**Antwortstruktur:**

```python
{
    "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()

Aktualisiert den API-Schlüssel.

```python
client.set_api_key("new-api-key")
```

##### set_base_url()

Aktualisiert die Basis-URL.

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

##### close()

Schließt die zugrunde liegende HTTP-Sitzung.

```python
client.close()
```

## Datenklassen

### WorkflowExecutionResult

```python
@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

```python
@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

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

### RateLimitInfo

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

### UsageLimits

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

### SimStudioError

```python
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
```

**Häufige Fehlercodes:**
- `UNAUTHORIZED`: Ungültiger API-Schlüssel
- `TIMEOUT`: Zeitüberschreitung bei der Anfrage
- `RATE_LIMIT_EXCEEDED`: Ratengrenze überschritten
- `USAGE_LIMIT_EXCEEDED`: Nutzungsgrenze überschritten
- `EXECUTION_ERROR`: Workflow-Ausführung fehlgeschlagen

## Beispiele

### Grundlegende Workflow-Ausführung

<Steps>
  <Step title="Client initialisieren">
    Richten Sie den SimStudioClient mit Ihrem API-Schlüssel ein.
  </Step>
  <Step title="Workflow validieren">
    Prüfen Sie, ob der Workflow bereitgestellt und für die Ausführung bereit ist.
  </Step>
  <Step title="Workflow ausführen">
    Führen Sie den Workflow mit Ihren Eingabedaten aus.
  </Step>
  <Step title="Ergebnis verarbeiten">
    Verarbeiten Sie das Ausführungsergebnis und behandeln Sie eventuelle Fehler.
  </Step>
</Steps>

```python
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()
```

### Fehlerbehandlung

Behandeln Sie verschiedene Fehlertypen, die während der Workflow-Ausführung auftreten können:

```python
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
```

### Verwendung des Kontextmanagers

Verwenden Sie den Client als Kontextmanager, um die Ressourcenbereinigung automatisch zu handhaben:

---CODE-PLACEHOLDER-ef99d3dd509e04865d5b6b0e0e03d3f8---

### Batch-Workflow-Ausführung

Führen Sie mehrere Workflows effizient aus:

```python
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'}")
```

### Asynchrone Workflow-Ausführung

Führen Sie Workflows asynchron für lang laufende Aufgaben aus:

```python
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()
```

### Rate-Limiting und Wiederholungsversuche

Behandle Rate-Limits automatisch mit exponentiellem Backoff:

```python
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()
```

### Nutzungsüberwachung

Überwache deine Kontonutzung und -limits:

```python
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()
```

### Streaming-Workflow-Ausführung

Führe Workflows mit Echtzeit-Streaming-Antworten aus:

```python
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()
```

Die Streaming-Antwort folgt dem Server-Sent Events (SSE) Format:

```
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-Streaming-Beispiel:**

```python
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)
```

### Umgebungskonfiguration

Konfiguriere den Client mit Umgebungsvariablen:

<Tabs items={['Development', 'Production']}>
  <Tab value="Development">

    ```python
    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")
    )
    ```

  </Tab>
  <Tab value="Production">

    ```python
    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")
    )
    ```

  </Tab>
</Tabs>

## API-Schlüssel erhalten

<Steps>
  <Step title="Bei Sim anmelden">
    Navigiere zu [Sim](https://sim.ai) und melde dich bei deinem Konto an.
  </Step>
  <Step title="Öffne deinen Workflow">
    Navigiere zu dem Workflow, den du programmatisch ausführen möchtest.
  </Step>
  <Step title="Deploye deinen Workflow">
    Klicke auf "Deploy", um deinen Workflow zu deployen, falls dies noch nicht geschehen ist.
  </Step>
  <Step title="Erstelle oder wähle einen API-Schlüssel">
    Wähle während des Deployment-Prozesses einen API-Schlüssel aus oder erstelle einen neuen.
  </Step>
  <Step title="Kopiere den API-Schlüssel">
    Kopiere den API-Schlüssel zur Verwendung in deiner Python-Anwendung.
  </Step>
</Steps>

## Anforderungen

- Python 3.8+
- requests >= 2.25.0

## Lizenz

Apache-2.0