Praison AI

Planning Agent

Learn how to create AI agents for trip planning and itinerary generation.

Build detailed travel itineraries — flights, hotels, and day-by-day plans — with a single Agent that searches the web.

python
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="TravelPlanner",
    instructions="You are a travel planning agent. Create detailed itineraries.",
    tools=[duckduckgo],
)

agent.start("Plan a 3-day trip to Tokyo")
Diagram

Planning Agent

📋 Travel Request

🤖 Agent

🧠 Web Search

✅ Itinerary

Travel planning agent with web search for finding flights, hotels, and creating itineraries.

Quick Start

  1. Simple Usage

    Attach a search tool and ask for a plan.

    python
    from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="TravelPlanner",
    instructions="You are a travel planning agent. Create detailed itineraries.",
    tools=[duckduckgo],
)

agent.start("Plan a 3-day trip to Tokyo")
  2. With Configuration

    Enable memory so the agent adjusts an existing itinerary.

    python
    from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="TravelPlanner",
    instructions="Create and refine travel itineraries with a budget.",
    tools=[duckduckgo],
    memory=True,
)

agent.start("Plan a 3-day Tokyo trip, then swap day 2 for a budget option.")

How It Works

Diagram
Web SearchAgentUserWeb SearchAgentUser“Plan a 3-day trip to Tokyo”Look up flights, hotels, activitiesOptions and pricesDay-by-day itinerary

Simple

Agents: 1 — Single agent with search tool handles research and planning.

Workflow

  1. Receive travel request
  2. Search for flights and hotels
  3. Generate detailed itinerary

Setup

bash
pip install praisonaiagents praisonai duckduckgo-search
export OPENAI_API_KEY="your-key"

Run — Python

python
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="TravelPlanner",
    instructions="You are a travel planning agent. Create detailed itineraries.",
    tools=[duckduckgo]
)

result = agent.start("Plan a 3-day trip to Tokyo")
print(result)

Run — CLI

bash
praisonai "Plan a weekend trip to Paris" --web-search

Run — agents.yaml

yaml
framework: praisonai
topic: Travel Planning
roles:
  travel_planner:
    role: Travel Planning Specialist
    goal: Create comprehensive travel plans
    backstory: You are an expert travel planner
    tools:
      - duckduckgo
    tasks:
      plan_trip:
        description: Plan a 3-day trip to Tokyo
        expected_output: A detailed itinerary
bash
praisonai agents.yaml

Serve API

python
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="TravelPlanner",
    instructions="You are a travel planning agent.",
    tools=[duckduckgo]
)

agent.launch(port=8080)
bash
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Plan a weekend getaway to Barcelona"}'

Advanced Workflow (All Features)

Agents: 1 — Single agent with memory, persistence, structured output, and session resumability.

Workflow

  1. Initialize session for trip tracking
  2. Configure SQLite persistence for travel history
  3. Search and plan with structured output
  4. Store itinerary in memory for modifications
  5. Resume session for trip updates

Setup

bash
pip install praisonaiagents praisonai duckduckgo-search pydantic
export OPENAI_API_KEY="your-key"

Run — Python

python
from praisonaiagents import Agent, Task, AgentTeam, Session
from praisonaiagents.tools import duckduckgo
from pydantic import BaseModel

# Structured output schema
class Itinerary(BaseModel):
    destination: str
    duration: str
    daily_plans: list[str]
    estimated_cost: str
    recommendations: list[str]

# Create session for trip tracking
session = Session(session_id="trip-001", user_id="user-1")

# Agent with memory and tools
agent = Agent(
    name="TravelPlanner",
    instructions="Create structured travel itineraries.",
    tools=[duckduckgo],
    memory=True
)

# Task with structured output
task = Task(
    description="Plan a 3-day trip to Tokyo with budget",
    expected_output="Structured itinerary",
    agent=agent,
    output_pydantic=Itinerary
)

# Run with SQLite persistence
agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)

# Resume later
session2 = Session(session_id="trip-001", user_id="user-1")
history = session2.search_memory("Tokyo")

Run — CLI

bash
praisonai "Plan a trip to Tokyo" --web-search --memory --verbose

Run — agents.yaml

yaml
framework: praisonai
topic: Travel Planning
memory: true
memory_config:
  provider: sqlite
  db_path: travel.db
roles:
  travel_planner:
    role: Travel Planning Specialist
    goal: Create structured travel plans
    backstory: You are an expert travel planner
    tools:
      - duckduckgo
    memory: true
    tasks:
      plan_trip:
        description: Plan a 3-day trip to Tokyo with budget
        expected_output: Structured itinerary
        output_json:
          destination: string
          duration: string
          daily_plans: array
          estimated_cost: string
          recommendations: array
bash
praisonai agents.yaml --verbose

Serve API

python
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="TravelPlanner",
    instructions="Create structured travel itineraries.",
    tools=[duckduckgo],
    memory=True
)

agent.launch(port=8080)
bash
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Plan a trip to Paris", "session_id": "trip-001"}'

Monitor / Verify

bash
praisonai "test planning" --web-search --verbose

Cleanup

bash
rm -f travel.db

Features Demonstrated

FeatureImplementation
WorkflowMulti-step travel planning
DB PersistenceSQLite via memory_config
Observability--verbose flag
ToolsDuckDuckGo search
ResumabilitySession with session_id
Structured OutputPydantic Itinerary model

Best Practices

State constraints up front

Include dates, budget, and party size in the prompt. The agent searches more effectively when it knows the boundaries instead of guessing.

Use structured output for itineraries

Define a Pydantic schema with destination, daily_plans, and estimated_cost so the plan renders cleanly in a UI or calendar.

Enable memory for trip revisions

Set memory=True so the agent tweaks an existing itinerary — swapping a hotel or a day — without rebuilding it from scratch.

Combine with Research for deep destination info

Hand off to the Research Agent when a trip needs visa rules or in-depth local guides beyond quick search snippets.

Related

Card

Research destinations in depth before planning.

 Card

Compare prices for flights, hotels, and gear.