sim/packages/python-sdk

Sim Python SDK

The official Python SDK for Sim, allowing you to execute workflows programmatically from your Python applications.

Server compatibility

0.2.x talks to the v2 API and has no fallback to the older endpoints, so it requires a Sim deployment that serves POST /api/v2/workflows/{id}/execute. That surface is newer than the endpoints 0.1.x used, and a deployment can also have it switched off — a self-hosted build serves /api/v2 only when the operator enables V2_API. Where it is unavailable every v2 route answers 404, so execute_workflow raises SimStudioError('HTTP 404: Not Found') — enable or upgrade the v2 API on the server, or pin simstudio-sdk<0.2, which keeps using /api/workflows/{id}/execute and /api/jobs/{id}.

Upgrading from 0.1.x to 0.2.0

0.2.0 is a breaking release.

• Requests move to /api/v2. execute_workflow posts to /api/v2/workflows/{workflow_id}/execute, sends the workflow input nested under input, and carries async / executionTimeoutSeconds in the body instead of the X-Execution-Mode and X-Execution-Timeout-Seconds headers.
• AsyncExecutionResult.job_id is now run_id, and execution_id has been removed from that dataclass. Replace result.job_id with result.run_id.
• get_job_status(job_id) is legacy. It still calls /api/jobs/{job_id} and only resolves IDs from a 0.1.x async execution. For runs started by 0.2.x, use get_workflow_run(workflow_id, run_id), which reads /api/v2/workflows/{workflow_id}/runs/{run_id}.
• WorkflowExecutionResult.success is derived from the run status rather than read from the response body, and is True only for completed and paused runs — so a run cancelled while it was in flight now reports success=False, as it did before the v2 migration. The new WorkflowExecutionResult.status field carries the server's terminal status ('completed', 'failed', 'paused' or 'cancelled'), which is how you tell a cancelled run from a failed one.
• metadata is now built by the SDK, with the keys duration, runId, startTime and endTime. The v2 response carries no execution logs or trace spans, so logs and trace_spans are always None; the pre-v2 metadata['executionId'] is now metadata['runId'].

Note one deliberate difference from the TypeScript SDK: a failed synchronous run throws there, but here it returns normally with error set and status='failed'.

Installation

pip install simstudio-sdk

Quick Start

import os
from simstudio import SimStudioClient

# Initialize the client
client = SimStudioClient(
    api_key=os.getenv("SIM_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 Reference

SimStudioClient

Constructor

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

• api_key (str): Your Sim API key
• base_url (str, optional): Base URL for the Sim API (defaults to https://sim.ai)

Methods

execute_workflow(workflow_id, input=None, *, timeout=30.0, stream=None, selected_outputs=None, async_execution=None, execution_timeout_seconds=None)

Execute a workflow with optional input data.

# With dict input (sent as the v2 input object)
result = client.execute_workflow("workflow-id", {"message": "Hello, world!"})

# With primitive input (sent as { input: { input: value } })
result = client.execute_workflow("workflow-id", "NVDA")

# With options (keyword-only arguments)
result = client.execute_workflow(
    "workflow-id",
    {"message": "Hello"},
    timeout=60.0,
    async_execution=True,
    execution_timeout_seconds=3600,
)

Parameters:

• workflow_id (str): The ID of the workflow to execute
• input (any, optional): Input data to pass to the workflow. Dicts become the v2 input object; primitives and lists become { input: value } inside it. File objects are automatically converted to base64.
• timeout (float, keyword-only): Timeout in seconds (default: 30.0)
• stream (bool, keyword-only): Enable streaming responses
• selected_outputs (list, keyword-only): Block outputs to stream (e.g., ["agent1.content"])
• async_execution (bool, keyword-only): Execute asynchronously and return a run ID
• execution_timeout_seconds (int, keyword-only): Server-side async execution cap from 1 to 604800 seconds. Requires async_execution=True and cannot extend the account policy.

Returns: WorkflowExecutionResult or AsyncExecutionResult

get_workflow_status(workflow_id)

Get the status of a workflow (deployment status, etc.).

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

Parameters:

• workflow_id (str): The ID of the workflow

Returns: WorkflowStatus

validate_workflow(workflow_id)

Validate that a workflow is ready for execution.

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

Parameters:

• workflow_id (str): The ID of the workflow

Returns: bool

execute_workflow_sync(workflow_id, input=None, *, timeout=30.0, stream=None, selected_outputs=None)

Execute a workflow synchronously (ensures non-async mode).

result = client.execute_workflow_sync("workflow-id", {"data": "some input"}, timeout=60.0)

Parameters:

• workflow_id (str): The ID of the workflow to execute
• input (any, optional): Input data to pass to the workflow
• timeout (float, keyword-only): Timeout in seconds (default: 30.0)
• stream (bool, keyword-only): Enable streaming responses
• selected_outputs (list, keyword-only): Block outputs to stream (e.g., ["agent1.content"])

Returns: WorkflowExecutionResult

get_workflow_run(workflow_id, run_id, *, include_output=None, selected_outputs=None)

Get the status and optional outputs of a workflow run. Use the run ID returned by async execution.

status = client.get_workflow_run(
    "workflow-id",
    "run-id",
    include_output=True,
    selected_outputs=["agent.content"]
)
print("Run status:", status["status"])

Parameters:

• workflow_id (str): The workflow ID
• run_id (str): The run ID returned from async execution
• include_output (bool, keyword-only): Include the final output for completed executions
• selected_outputs (list, keyword-only): Block output selectors to include

Returns: dict

get_job_status(job_id)

Get the status of a job created through the legacy async execution endpoint. New integrations should use get_workflow_run() with a run ID.

status = client.get_job_status("legacy-job-id")

Returns: dict

execute_with_retry(workflow_id, input=None, *, timeout=30.0, stream=None, selected_outputs=None, async_execution=None, max_retries=3, initial_delay=1.0, max_delay=30.0, backoff_multiplier=2.0)

Execute a workflow with automatic retry on rate limit errors.

result = client.execute_with_retry(
    "workflow-id",
    {"message": "Hello"},
    timeout=30.0,
    max_retries=3,
    initial_delay=1.0,
    max_delay=30.0,
    backoff_multiplier=2.0
)

Parameters:

• workflow_id (str): The ID of the workflow to execute
• input (any, optional): Input data to pass to the workflow
• timeout (float, keyword-only): Timeout in seconds (default: 30.0)
• stream (bool, keyword-only): Enable streaming responses
• selected_outputs (list, keyword-only): Block outputs to stream
• async_execution (bool, keyword-only): Execute asynchronously
• max_retries (int, keyword-only): Maximum retry attempts (default: 3)
• initial_delay (float, keyword-only): Initial delay in seconds (default: 1.0)
• max_delay (float, keyword-only): Maximum delay in seconds (default: 30.0)
• backoff_multiplier (float, keyword-only): Backoff multiplier (default: 2.0)

Returns: WorkflowExecutionResult or AsyncExecutionResult

get_rate_limit_info()

Get current rate limit information from the last API response.

rate_info = client.get_rate_limit_info()
if rate_info:
    print("Remaining requests:", rate_info.remaining)

Returns: RateLimitInfo or None

get_usage_limits()

Get current usage limits and quota information.

limits = client.get_usage_limits()
print("Current usage:", limits.usage)

Returns: UsageLimits

set_api_key(api_key)

Update the API key.

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

set_base_url(base_url)

Update the base URL.

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

close()

Close the underlying HTTP session.

client.close()

Data Classes

WorkflowExecutionResult

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

success is True only for the completed and paused statuses. status carries the server's terminal status verbatim, so a cancelled run (success=False, error=None) is distinguishable from a failed one.

WorkflowStatus

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

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

AsyncExecutionResult

@dataclass
class AsyncExecutionResult:
    success: bool
    run_id: str
    status_url: str
    message: str = ""
    async_execution: bool = True

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]

Examples

Basic Workflow Execution

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",
            {
                "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()

Error Handling

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

Context Manager Usage

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

Environment Configuration

import os
from simstudio import SimStudioClient

# Using environment variables
client = SimStudioClient(
    api_key=os.getenv("SIM_API_KEY"),
    base_url=os.getenv("SIM_BASE_URL", "https://sim.ai")
)

File Upload

File objects are automatically detected and converted to base64 format. Include them in your input under the field name matching your workflow's API trigger input format:

The SDK converts file objects to this format:

{
  'type': 'file',
  'data': 'data:mime/type;base64,base64data',
  'name': 'filename',
  'mime': 'mime/type'
}

Alternatively, you can manually provide files using the URL format:

{
  'type': 'url',
  'data': 'https://example.com/file.pdf',
  'name': 'file.pdf',
  'mime': 'application/pdf'
}

from simstudio import SimStudioClient
import os

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

# Upload a single file - include it under the field name from your API trigger
with open('document.pdf', 'rb') as f:
    result = client.execute_workflow(
        'workflow-id',
        {
            'documents': [f],  # Must match your workflow's "files" field name
            'instructions': 'Analyze this document'
        }
    )

# Upload multiple files
with open('doc1.pdf', 'rb') as f1, open('doc2.pdf', 'rb') as f2:
    result = client.execute_workflow(
        'workflow-id',
        {
            'attachments': [f1, f2],  # Must match your workflow's "files" field name
            'query': 'Compare these documents'
        }
    )

Batch Workflow Execution

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, workflow_input 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, workflow_input)
            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'}")

Getting Your API Key

1. Log in to your Sim account
2. Navigate to your workflow
3. Click on "Deploy" to deploy your workflow
4. Select or create an API key during the deployment process
5. Copy the API key to use in your application

Development

Running Tests

To run the tests locally:

1. Clone the repository and navigate to the Python SDK directory:

   cd packages/python-sdk
   

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install the package in development mode with test dependencies:

   pip install -e ".[dev]"
   

4. Run the tests:

   pytest tests/ -v
   

Code Quality

Run code quality checks:

# Code formatting
black simstudio/

# Linting
flake8 simstudio/ --max-line-length=100

# Type checking
mypy simstudio/

# Import sorting
isort simstudio/

Requirements

• Python 3.8+
• requests >= 2.25.0

License

Apache-2.0