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Overview

The Trip Planning tutorial shows how to:

  • Execute workflow steps in parallel
  • Integrate with external APIs
  • Combine data from multiple sources
  • Generate personalized itineraries using AI

Follow each step of the tutorial:

Let’s break down the task into its core components:

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1. Input Schema

First, we define what inputs our task expects:

input_schema:
  type: object
  properties:
    locations:
      type: array
      items:
        type: string

This schema specifies that our task expects an array of location strings (e.g., ["New York", "London", "Paris"]).

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2. Tools Configuration

Next, we define the external tools our task will use:

- name: weather
  type: integration
  integration:
    provider: weather
    setup:
      openweathermap_api_key: YOUR_OPENWEATHERMAP_API_KEY

- name: internet_search
  type: integration
  integration:
    provider: brave
    setup:
      brave_api_key: YOUR_BRAVE_API_KEY

We’re using two integrations:

  • The weather integration to fetch current weather conditions
  • The brave search integration to find tourist attractions

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3. Main Workflow Steps

1

Get Weather Data

- over: $ steps[0].input.locations
  map:
    tool: weather
    arguments:
      location: $ _

This step:

  • Iterates over each location in the input array
  • Calls the weather API for each location
2

Search for Tourist Attractions

- over: $ steps[0].input.locations
  map:
    tool: internet_search
    arguments:
      query: $ 'tourist attractions in ' + _

This step:

  • Iterates over the locations again
  • Searches for tourist attractions in each location
  • Constructs a search query by concatenating “tourist attractions in ” with the location
3

Combine Data

- evaluate:
    zipped: |-
      $ list(
        zip(
          steps[0].input.locations,
          [output['result'] for output in steps[0].output],
          steps[1].output
        )
      )

This step:

  • Combines the data from previous steps into tuples
  • Each tuple contains: (location, weather_data, attractions_data)
  • Uses Python’s zip function to align the data
4

Generate Itineraries

- over: $ _['zipped']
  parallelism: 3
  # Inside the map step, each `_` represents the current element in the list
  # which is a tuple of (location, weather, attractions)
  map:
    prompt:
    - role: system
      content: >-
        $ f'''You are {agent.name}. Your task is to create a detailed itinerary
        for visiting tourist attractions in some locations.
        The user will give you the following information for each location:

        - The location
        - The current weather condition
        - The top tourist attractions'''
    - role: user
      content: >-
        $ f'''Location: "{_[0]}"
        Weather: "{_[1]}"
        Attractions: "{_[2]}"'''
    unwrap: true

This step:

  • Processes up to 3 locations in parallel
  • For each location tuple:
    • Sends a prompt to the AI model
    • Includes location, weather, and attraction data
    • Generates a personalized itinerary
5

Format Final Output

- evaluate:
    final_plan: |-
      $ '\\n---------------\\n'.join(activity for activity in _)

This step:

  • Combines all itineraries into a single output
  • Separates each itinerary with a divider

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Usage

Here’s how to use this task with the Julep SDK:

from julep import Client
import time
import yaml

# Initialize the client
client = Client(api_key=JULEP_API_KEY)

# Create the agent
agent = client.agents.create(
  name="Julep Trip Planning Agent",
  about="A Julep agent that can generate a detailed itinerary for visiting tourist attractions in some locations, considering the current weather conditions.",
)

# Load the task definition
with open('trip_planning_task.yaml', 'r') as file:
  task_definition = yaml.safe_load(file)

# Create the task
task = client.tasks.create(
  agent_id=AGENT_ID,
  **task_definition
)

# Create the execution
execution = client.executions.create(
    task_id=task.id,
    input={
        "locations": ["New York", "London", "Paris", "Tokyo", "Sydney"]
    }
)

# Wait for the execution to complete
while (result := client.executions.get(execution.id)).status not in ['succeeded', 'failed']:
    print(result.status)
    time.sleep(1)

# Print the result
if result.status == "succeeded":
    print(result.output)
else:
    print(f"Error: {result.error}")

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Example Output

An example output when running this task with an input of ["New York", "London", "Paris", "Tokyo", "Sydney"]:

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Next Steps

  • Try this task yourself, check out the full example, see the trip-planning cookbook.
  • To learn more about the integrations used in this task, check out the integrations page.

Also See:

  1. Input Schema
  2. Tools Configuration
  3. Workflow Steps
  4. Running the Task