# CrewAI collaborative trip planner

- **Agent pattern:** Sequential two-agent crew
- **Search surfaces:** Flights, hotels, and maps
- **Saved artifact:** `crewai-trip-plan.md`


# What you'll build

A two-agent CrewAI trip planner. A researcher gathers current flight, hotel, and neighborhood evidence; a decision editor turns that packet into a practical four-night plan with an explicit budget.

**Default brief:** Plan Kyoto for two adults, flying SFO to KIX roughly 120 days from now. Keep flights and lodging under USD 4,000 and prefer a walkable, transit-friendly neighborhood.


# How the agent works

1.  **Search exact travel inputs.** The researcher uses airport codes, generated dates, occupancy, and currency with flights and hotels search.
2.  **Ground the neighborhood.** Maps search checks nearby places and gives the planner location-level evidence.
3.  **Edit the decision.** A second agent selects one flight and hotel, totals the estimate, and surfaces assumptions or price volatility.


# Core agent setup

This excerpt highlights the typed travel tools and the two-agent handoff. The full script defines the research and planning tasks, runtime dates, model, and saved report.

``` python
travel_tools = [
    flights_search(provider="crewai", default_params={"currency": "USD"}),
    hotels_search(provider="crewai", default_params={"currency": "USD"}),
    maps_search(provider="crewai", default_params={"gl": "jp"}),
]
researcher = crewai.Agent(
    role="Travel search researcher",
    goal="Collect current flight, hotel, and neighborhood evidence.",
    llm=llm,
    tools=travel_tools,
)
planner = crewai.Agent(
    role="Travel decision editor",
    goal="Turn verified options into a practical, budget-aware trip decision.",
    llm=llm,
)
crew = crewai.Crew(
    agents=[researcher, planner],
    tasks=[research_task, planning_task],
    process=crewai.Process.sequential,
)
report = str(crew.kickoff())
```

[View the complete `main.py` →](https://github.com/serpapi/serpapi-search-tools-python/blob/main/cookbook/crewai/main.py)


# Run the recipe

Set `SERPAPI_API_KEY` and `XAI_API_KEY` in the repository-root `.env`, then run:

``` bash
uv run --isolated --no-project --with 'serpapi-search-tools[crewai]' --with python-dotenv cookbook/crewai/main.py
```

Optional controls: `XAI_MODEL`, `XAI_BASE_URL`, `COOKBOOK_PROMPT`, and `COOKBOOK_OUTPUT_DIR`.

**Dates stay runnable:** The default prompt calculates departure and return dates at runtime. Supply `COOKBOOK_PROMPT` when you want a different origin, destination, party, or budget.


# Inspect the result

The recipe writes `cookbook-output/crewai-trip-plan.md`. Expect one recommended flight, one hotel, an estimated total, a compact four-day outline, source URLs, and assumptions that could change the price.

The researcher can take up to seven iterations; the editor gets a shorter three-iteration pass over the collected evidence.


# SerpApi tools used

[flights_search](../../reference/flights_search.md#serpapi_search_tools.flights_search) checks routes with exact airport identifiers and dates, [hotels_search](../../reference/hotels_search.md#serpapi_search_tools.hotels_search) uses the stay dates and occupancy, and [maps_search](../../reference/maps_search.md#serpapi_search_tools.maps_search) grounds the neighborhood recommendation in nearby places. Their typed inputs help the researcher collect options that match the trip constraints before the planner makes a decision.

**Framework pattern:** The researcher-and-planner roles follow CrewAI's Trip Planner example and use SerpApi flights, hotels, and maps tools.

- [Open the runnable recipe](https://github.com/serpapi/serpapi-search-tools-python/tree/main/cookbook/crewai)
- [View CrewAI's examples](https://github.com/crewAIInc/crewAI-examples)