taskforceai 0.3.0

Official Python SDK for TaskForceAI multi-agent orchestration

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

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"])

# Bring your own OpenRouter key (unlocks premium models)
task_id = client.submit_task(
    "Draft a quarterly strategy update.",
    open_router_key="sk-or-your-openrouter-key",
)

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

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

```python
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, open_router_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

```bash
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