Examples¶

This page contains examples and use cases for aipartnerupflow.

Quick Start with Example Tasks¶

The easiest way to get started is to use the built-in example tasks. These demonstrate various features like task trees, dependencies, priorities, and different statuses.

Initialize Example Tasks¶

# Initialize example tasks in the database
aipartnerupflow examples init

# Force re-initialization (even if examples already exist)
aipartnerupflow examples init --force

What gets created: - Tasks with different statuses (completed, failed, pending, in_progress) - Task trees with parent-child relationships - Tasks with different priorities - Tasks with dependencies - CrewAI task example (requires LLM key)

Auto-initialization: When the API server starts, it automatically initializes example tasks if the database is empty. This helps beginners get started quickly.

View Example Tasks¶

After initialization, you can view the example tasks:

# List all tasks
aipartnerupflow tasks list

# Check specific task status
aipartnerupflow tasks status example_root_001

# View task tree
aipartnerupflow tasks tree example_root_001

Execute Example Tasks¶

Example tasks are created in pending status, ready to be executed:

# Execute via CLI
aipartnerupflow run flow --tasks '[{"id": "example_root_001", ...}]'

# Or execute via API
curl -X POST http://localhost:8000/tasks \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tasks.create",
    "params": {
      "tasks": [{"id": "example_root_001", ...}]
    }
  }'

Note: For CrewAI example tasks, you'll need to provide an LLM API key:

# Via request header
curl -X POST http://localhost:8000/tasks \
  -H "X-LLM-API-KEY: openai:sk-your-key" \
  -H "Content-Type: application/json" \
  -d '{...}'

Example Task Structure¶

The example tasks demonstrate:

System Analysis Project - A task tree with parent-child relationships:
Root task: System Analysis Project
Child tasks: CPU Analysis, Memory Analysis
Dependent task: Disk Analysis (depends on CPU and Memory)
CrewAI Research Task - Demonstrates CrewAI integration (requires LLM key)
Multi-Level Analysis - A deeper task tree with multiple levels
Various Statuses - Tasks in different states (pending, in_progress, etc.)

Basic Examples¶

Examples are also available in the test cases:

Integration tests: tests/integration/
Extension tests: tests/extensions/

Example: Custom Task¶

from aipartnerupflow import ExecutableTask
from typing import Dict, Any

class MyCustomTask(ExecutableTask):
    id = "my_custom_task"
    name = "My Custom Task"
    description = "A custom task example"

    async def execute(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        # Your task logic here
        return {"result": "success"}

    def get_input_schema(self) -> Dict[str, Any]:
        return {
            "type": "object",
            "properties": {
                "input_field": {"type": "string"}
            }
        }

Example: Task Tree¶

from aipartnerupflow import TaskManager, TaskTreeNode, create_session

db = create_session()
task_manager = TaskManager(db)

# Create tasks
root = await task_manager.task_repository.create_task(
    name="root",
    user_id="user_123"
)

child1 = await task_manager.task_repository.create_task(
    name="child1",
    user_id="user_123",
    parent_id=root.id
)

child2 = await task_manager.task_repository.create_task(
    name="child2",
    user_id="user_123",
    parent_id=root.id,
    dependencies=[child1.id]  # child2 depends on child1
)

# Build and execute
tree = TaskTreeNode(root)
tree.add_child(TaskTreeNode(child1))
tree.add_child(TaskTreeNode(child2))

result = await task_manager.distribute_task_tree(tree)

Example: CrewAI Task with LLM Key¶

# Via API with header
import httpx

async with httpx.AsyncClient() as client:
    response = await client.post(
        "http://localhost:8000/tasks",
        headers={
            "Content-Type": "application/json",
            "X-LLM-API-KEY": "openai:sk-your-key"  # Provider-specific format
        },
        json={
            "jsonrpc": "2.0",
            "method": "tasks.create",
            "params": {
                "tasks": [{
                    "id": "crewai-task",
                    "name": "CrewAI Research Task",
                    "user_id": "user123",
                    "schemas": {"method": "crewai_executor"},
                    "params": {
                        "works": {
                            "agents": {
                                "researcher": {
                                    "role": "Research Analyst",
                                    "goal": "Research and analyze the given topic",
                                    "llm": "openai/gpt-4"
                                }
                            },
                            "tasks": {
                                "research": {
                                    "description": "Research the topic: {topic}",
                                    "agent": "researcher"
                                }
                            }
                        }
                    },
                    "inputs": {
                        "topic": "Artificial Intelligence"
                    }
                }]
            }
        }
    )

For more examples, see the test cases in the main repository.