TaskForceAI Python SDK

The official Python client for TaskForceAI's multi-agent orchestration platform.

• ✅ Sync + async clients powered by httpx
• ✅ Automatic authentication with your TaskForceAI API key
• ✅ Convenience helpers for polling task completion
• ✅ Rich error handling with status codes and retry-ready exceptions
• ✅ Mock mode for development without an API key

Installation

python -m pip install taskforceai

Quick Start

from taskforceai import TaskForceAIClient

client = TaskForceAIClient(api_key="your-api-key")

task_id = client.submit_task("Analyze the security posture of this repository.")
status = client.wait_for_completion(task_id)

print(status["result"])

# Select a specific model (Pro/Super plans)
task_id = client.submit_task(
    "Draft a quarterly strategy update.",
    model_id="xai/grok-4.1"
)

# Bring your own Vercel AI Gateway key
task_id = client.submit_task(
    "Custom model run",
    vercel_ai_key="sk-vercel-your-gateway-key"
)

# Forward arbitrary TaskForceAI orchestration options
task_id = client.submit_task(
    "Do a full repository risk review",
    options={"agents": 4, "budget": 12},
)

Mock Mode

Build and test your integration without an API key using mock mode:

from taskforceai import TaskForceAIClient

# No API key required in mock mode
client = TaskForceAIClient(mock_mode=True)

result = client.run_task("Test your integration")
print(result.result)  # "This is a mock response. Configure your API key to get real results."

Mock mode simulates the full task lifecycle locally—no network requests are made. Tasks go through "processing" then "completed" states, making it easy to build UIs and test error handling before launch.

Async Variant

import asyncio
from taskforceai import AsyncTaskForceAIClient

async def main() -> None:
    async with AsyncTaskForceAIClient(api_key="your-api-key") as client:
        result = await client.run_task("Summarize the latest launch notes.")
        print(result["result"])

asyncio.run(main())

Streaming Task Updates

from taskforceai import TaskForceAIClient

client = TaskForceAIClient(api_key="your-api-key")
stream = client.run_task_stream("Map open security issues", poll_interval=0.5)

for status in stream:
    print(f"{status['status']}: {status.get('result')}")

# Cancel locally if needed
# stream.cancel()

Async projects can use AsyncTaskForceAIClient.stream_task_status() and iterate with async for status in stream for non-blocking workflows.

API Surface

Both clients expose the same methods:

• submit_task(prompt, *, options=None, silent=None, mock=None, model_id=None, vercel_ai_key=None) -> str
• get_task_status(task_id) -> dict
• get_task_result(task_id) -> dict
• wait_for_completion(task_id, poll_interval=2.0, max_attempts=150, on_status=None) -> dict
• run_task(prompt, ..., on_status=None) -> dict
• stream_task_status(task_id, ..., on_status=None) -> Iterator
• run_task_stream(prompt, ..., on_status=None) -> Iterator

Response Hooks & Rate-Limit Telemetry

Both clients accept response_hook= in their constructors. The hook is invoked with the raw httpx.Response (headers included) for every request, making it easy to track rate-limit headers, request IDs, or emit custom metrics without wrapping the SDK.

All responses mirror the REST API payloads. Errors raise TaskForceAIError, which includes status_code for quick branching.

Development

python -m pip install -e "packages/python-sdk[dev]"
pytest packages/python-sdk/tests
ruff format packages/python-sdk/src packages/python-sdk/tests -q
ruff check packages/python-sdk/src packages/python-sdk/tests
mypy --config-file packages/python-sdk/pyproject.toml packages/python-sdk/src

License

MIT