apflow
Distributed task orchestration for the AI-native era
apflow is a high-performance distributed task orchestration framework that scales from a single process to massive multi-node clusters. It provides a unified execution interface for 12+ built-in executors (HTTP, SSH, Docker, gRPC, MCP, LLM Agents) with automatic leader election, lease-based task ownership, and horizontal scaling. The framework includes a real-time GraphQL API with WebSocket subscriptions for live task tracking, a pluggable protocol registry (A2A, MCP, GraphQL), and flexible storage options (DuckDB for local, PostgreSQL for distributed). Built for the AI-native era, it seamlessly integrates with CrewAI and LLM-based task tree generation.
Features
Workflow Examples
Visualize how tasks are organized in trees and how dependencies control execution order
Get Started
High-performance distributed task orchestration framework.
$ pip install apflow[standard]from apflow.core.builders import TaskBuilder
from apflow import TaskManager, create_session
# Initialize task manager
db = create_session()
task_manager = TaskManager(db)
# Use TaskBuilder for clean task creation and execution
result = await (
TaskBuilder(task_manager, "rest_executor")
.with_name("fetch_data")
.with_input("url", "https://api.example.com/data")
.with_input("method", "GET")
.execute()
)
print(f"Result: {result.result}")
