fastapi-taskflow 0.5.0

Turn FastAPI BackgroundTasks into a production-ready task system. Retries, control, resiliency and visibility without workers or brokers.

Turn FastAPI BackgroundTasks into a production-ready task system.
Retries, control, resiliency and visibility without workers or brokers.

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FastAPI's BackgroundTasks handles simple fire-and-forget work well. But in real applications you quickly hit the same gaps: tasks fail silently, you have no visibility into what ran, and nothing survives a restart.

fastapi-taskflow is a thin layer on top of what you already have. It does not compete with Celery, ARQ, Taskiq, or Dramatiq. It is built for teams who are already using FastAPI's native background tasks and want retries, resilience, status tracking, and a live dashboard without adding infrastructure.

Features

• Automatic retries with configurable delay and exponential backoff
• Task IDs and full lifecycle tracking: PENDING, RUNNING, SUCCESS, FAILED, INTERRUPTED
• Live admin dashboard over SSE at /tasks/dashboard
• SQLite persistence out of the box, Redis as an optional extra
• Pending task requeue: unfinished tasks at shutdown are re-dispatched on startup
• requeue_on_interrupt: opt-in requeue for idempotent tasks interrupted mid-execution
• Idempotency keys: prevent duplicate execution of the same logical operation
• Multi-instance support: atomic requeue claiming, shared task history across instances
• task_log(message, level=, **extra): structured log entries with level filtering and arbitrary extra fields
• get_task_context(): access task metadata (task_id, attempt, tags) from any code path inside a running task
• Tags: attach key/value labels at enqueue time, forwarded to every log and lifecycle event
• Pluggable observers: FileLogger, StdoutLogger, InMemoryLogger, and custom TaskObserver implementations
• Argument encryption: Fernet-based at-rest encryption for task args and kwargs
• Trace context propagation: OpenTelemetry spans flow from the request into background execution (Python 3.11+)
• Concurrency controls: opt-in semaphore for async tasks and dedicated thread pool for sync tasks
• Zero-migration injection: keep your existing BackgroundTasks annotations
• Both sync and async task functions supported

Installation

pip install fastapi-taskflow

With Redis backend:

pip install "fastapi-taskflow[redis]"

With argument encryption:

pip install "fastapi-taskflow[encryption]"

Quick start

from fastapi import BackgroundTasks, FastAPI
from fastapi_taskflow import TaskAdmin, TaskManager

task_manager = TaskManager(snapshot_db="tasks.db", snapshot_interval=30.0)
app = FastAPI()

# auto_install=True patches FastAPI's BackgroundTasks injection so existing
# route signatures work without any changes.
TaskAdmin(app, task_manager, auto_install=True)


@task_manager.task(retries=3, delay=1.0, backoff=2.0)
def send_email(address: str) -> None:
    ...


@app.post("/signup")
def signup(email: str, background_tasks: BackgroundTasks):
    task_id = background_tasks.add_task(send_email, address=email)
    return {"task_id": task_id}

uvicorn examples.basic_app:app --reload

curl -X POST "http://localhost:8000/signup?email=[e-mail]"
curl "http://localhost:8000/tasks"
curl "http://localhost:8000/tasks/metrics"
open "http://localhost:8000/tasks/dashboard"

Injection patterns

Three ways to get a ManagedBackgroundTasks instance into your routes:

# Pattern 1: keep the native annotation (requires auto_install=True)
def route(background_tasks: BackgroundTasks):
    task_id = background_tasks.add_task(my_func, arg)

# Pattern 2: explicit managed type (also requires auto_install=True)
from fastapi_taskflow import ManagedBackgroundTasks

def route(background_tasks: ManagedBackgroundTasks):
    task_id = background_tasks.add_task(my_func, arg)

# Pattern 3: explicit Depends — no install() required
from fastapi import Depends

def route(tasks=Depends(task_manager.get_tasks)):
    task_id = tasks.add_task(my_func, arg)

Decorator options

Parameter	Type	Default	Description
retries	int	0	Additional attempts after the first failure
delay	float	0.0	Seconds before the first retry
backoff	float	1.0	Multiplier applied to delay on each retry
persist	bool	False	Save this task for requeue on restart
name	str	function name	Override the name shown in the dashboard
requeue_on_interrupt	bool	False	Requeue this task if it was mid-execution at shutdown. Only set for idempotent tasks.

Idempotency keys

Pass an idempotency_key to add_task() to prevent the same logical operation from running twice. If a non-failed task with the same key already exists, the original task_id is returned and the task is not enqueued again.

task_id = tasks.add_task(send_notification, order_id, idempotency_key="order-42-notified")

Useful for handling retried HTTP requests, duplicate webhook deliveries, or double-clicks.

API endpoints

Method	Path	Description
GET	/tasks	List all tasks
GET	/tasks/{task_id}	Single task detail
GET	/tasks/metrics	Aggregated stats
GET	/tasks/dashboard	Live HTML dashboard
POST	/tasks/{task_id}/retry	Retry a failed or interrupted task

Multi-instance deployments

fastapi-taskflow supports running multiple instances behind a load balancer when a shared backend is configured.

Same host, multiple processes -- use SQLite. All instances share the same file. Requeue claiming is atomic so only one instance picks up each task on restart.

Multiple hosts -- use Redis. All instances share the same Redis instance. Idempotency keys, requeue claiming, and completed task history all work across hosts.

from fastapi_taskflow.backends import RedisBackend

task_manager = TaskManager(
    snapshot_backend=RedisBackend("redis://localhost:6379/0"),
    requeue_pending=True,
)

Dashboard in multi-instance deployments -- the dashboard shows live tasks for the instance it is connected to. Completed tasks from all instances are visible via the shared backend (with a short cache window). For accurate live task visibility, route dashboard traffic to a single instance using sticky sessions at the load balancer. See the multi-instance guide for examples.

Known caveats:

• Live PENDING and RUNNING tasks from other instances are not visible in real time. Each instance only holds its own in-memory state.
• SQLite multi-instance only works when all processes share the same file path on the same host. It does not work across separate machines.
• Tasks in RUNNING state at the time of a hard crash (SIGKILL, OOM) cannot be recovered. Only clean shutdowns trigger the pending store write.

Structured task logging

task_log() accepts an optional level= and arbitrary **extra keyword fields. Extras are forwarded to observers as structured fields rather than embedded in the message string.

from fastapi_taskflow import get_task_context, task_log

@task_manager.task(retries=3)
def process_order(order_id: int) -> None:
    ctx = get_task_context()
    task_log("Processing order", order_id=order_id, attempt=ctx.attempt if ctx else 0)
    task_log("Payment gateway error", level="warning", order_id=order_id, code=503)

get_task_context() returns a TaskContext with task_id, func_name, attempt, and tags from any code path inside a running task.

Observability

Pass one or more observers to loggers= to receive structured LogEvent and LifecycleEvent objects for every task_log() call and status transition:

from fastapi_taskflow import FileLogger, StdoutLogger, TaskManager

task_manager = TaskManager(
    snapshot_db="tasks.db",
    loggers=[
        FileLogger("tasks.log", log_lifecycle=True),
        StdoutLogger(log_lifecycle=True, min_level="warning"),
    ],
)

Built-in observers:

Observer	Description
FileLogger	Rotating plain text file. Works with tail -f, grep, and log shippers.
StdoutLogger	Prints to stdout. Suitable for containers with a log agent.
InMemoryLogger	Captures events in memory for test assertions.

The log_file shorthand on TaskManager remains fully supported and creates a FileLogger internally.

Tags

Attach key/value labels to a task at enqueue time. Tags flow through to every log and lifecycle event.

task_id = tasks.add_task(
    send_email,
    address=email,
    tags={"user_id": str(user_id), "source": "signup"},
)

Argument encryption

When tasks carry sensitive data, encrypt args and kwargs at rest with Fernet:

import os
from fastapi_taskflow import TaskManager

task_manager = TaskManager(
    snapshot_db="tasks.db",
    encrypt_args_key=os.environ["TASK_ENCRYPTION_KEY"],
)

Generate a key:

python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"

Args and kwargs are encrypted at add_task() time and decrypted only inside the executor. They are never stored in plain text in the task store, the database, or any log file. Requires pip install "fastapi-taskflow[encryption]".

Concurrency controls

By default, tasks share the event loop and thread pool with request handlers. Under burst task load this can increase request latency. Both controls are opt-in and do not change existing behaviour when not set.

max_concurrent_tasks — caps how many async tasks hold event loop time simultaneously via an asyncio.Semaphore. Tasks waiting for a slot are parked without blocking the loop.

max_sync_threads — runs sync task functions in a dedicated ThreadPoolExecutor, isolated from the default pool used by sync request handlers.

task_manager = TaskManager(
    snapshot_db="tasks.db",
    max_concurrent_tasks=10,
    max_sync_threads=8,
)

Both default to None. When not set, execution is identical to previous versions.

File logging

In addition to the observer system, a plain text log file can be configured directly on TaskManager:

task_manager = TaskManager(
    snapshot_db="tasks.db",
    log_file="tasks.log",
    log_lifecycle=True,
)

Each line has the format [task_id] [func_name] 2026-01-01T12:00:00 message. For multi-process or multi-host deployments see the file logging guide.

What this is not

fastapi-taskflow does not compete with Celery, ARQ, Taskiq, or Dramatiq. Those tools are built for distributed workers, message brokers, and high-throughput task routing. This library is for teams using FastAPI's native BackgroundTasks who want retries, visibility, and resilience without adding worker infrastructure.

License

MIT