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Weather agent

Example of Pydantic AI with multiple tools which the LLM needs to call in turn to answer a question.

Demonstrates:

In this case the idea is a "weather" agent — the user can ask for the weather in multiple locations, the agent will use the get_lat_lng tool to get the latitude and longitude of the locations, then use the get_weather tool to get the weather for those locations.

Running the Example

To run this example properly, you might want to add two extra API keys (Note if either key is missing, the code will fall back to dummy data, so they're not required):

With dependencies installed and environment variables set, run:

python -m pydantic_ai_examples.weather_agent 
uv run -m pydantic_ai_examples.weather_agent

Example Code

Learn about Gateway weather_agent.py
"""Example of Pydantic AI with multiple tools which the LLM needs to call in turn to answer a question. In this case the idea is a "weather" agent — the user can ask for the weather in multiple cities, the agent will use the `get_lat_lng` tool to get the latitude and longitude of the locations, then use the `get_weather` tool to get the weather. Run with:  uv run -m pydantic_ai_examples.weather_agent """ from __future__ import annotations as _annotations import asyncio from dataclasses import dataclass from typing import Any import logfire from httpx import AsyncClient from pydantic import BaseModel from pydantic_ai import Agent, RunContext # 'if-token-present' means nothing will be sent (and the example will work) if you don't have logfire configured logfire.configure(send_to_logfire='if-token-present') logfire.instrument_pydantic_ai() @dataclass class Deps: client: AsyncClient weather_agent = Agent( 'gateway/openai:gpt-5-mini', # 'Be concise, reply with one sentence.' is enough for some models (like openai) to use # the below tools appropriately, but others like anthropic and gemini require a bit more direction. instructions='Be concise, reply with one sentence.', deps_type=Deps, retries=2, ) class LatLng(BaseModel): lat: float lng: float @weather_agent.tool async def get_lat_lng(ctx: RunContext[Deps], location_description: str) -> LatLng:  """Get the latitude and longitude of a location.  Args:  ctx: The context.  location_description: A description of a location.  """ # NOTE: the response here will be random, and is not related to the location description. r = await ctx.deps.client.get( 'https://demo-endpoints.pydantic.workers.dev/latlng', params={'location': location_description}, ) r.raise_for_status() return LatLng.model_validate_json(r.content) @weather_agent.tool async def get_weather(ctx: RunContext[Deps], lat: float, lng: float) -> dict[str, Any]:  """Get the weather at a location.  Args:  ctx: The context.  lat: Latitude of the location.  lng: Longitude of the location.  """ # NOTE: the responses here will be random, and are not related to the lat and lng. temp_response, descr_response = await asyncio.gather( ctx.deps.client.get( 'https://demo-endpoints.pydantic.workers.dev/number', params={'min': 10, 'max': 30}, ), ctx.deps.client.get( 'https://demo-endpoints.pydantic.workers.dev/weather', params={'lat': lat, 'lng': lng}, ), ) temp_response.raise_for_status() descr_response.raise_for_status() return { 'temperature': f'{temp_response.text} °C', 'description': descr_response.text, } async def main(): async with AsyncClient() as client: logfire.instrument_httpx(client, capture_all=True) deps = Deps(client=client) result = await weather_agent.run( 'What is the weather like in London and in Wiltshire?', deps=deps ) print('Response:', result.output) if __name__ == '__main__': asyncio.run(main())
weather_agent.py
"""Example of Pydantic AI with multiple tools which the LLM needs to call in turn to answer a question. In this case the idea is a "weather" agent — the user can ask for the weather in multiple cities, the agent will use the `get_lat_lng` tool to get the latitude and longitude of the locations, then use the `get_weather` tool to get the weather. Run with:  uv run -m pydantic_ai_examples.weather_agent """ from __future__ import annotations as _annotations import asyncio from dataclasses import dataclass from typing import Any import logfire from httpx import AsyncClient from pydantic import BaseModel from pydantic_ai import Agent, RunContext # 'if-token-present' means nothing will be sent (and the example will work) if you don't have logfire configured logfire.configure(send_to_logfire='if-token-present') logfire.instrument_pydantic_ai() @dataclass class Deps: client: AsyncClient weather_agent = Agent( 'openai:gpt-5-mini', # 'Be concise, reply with one sentence.' is enough for some models (like openai) to use # the below tools appropriately, but others like anthropic and gemini require a bit more direction. instructions='Be concise, reply with one sentence.', deps_type=Deps, retries=2, ) class LatLng(BaseModel): lat: float lng: float @weather_agent.tool async def get_lat_lng(ctx: RunContext[Deps], location_description: str) -> LatLng:  """Get the latitude and longitude of a location.  Args:  ctx: The context.  location_description: A description of a location.  """ # NOTE: the response here will be random, and is not related to the location description. r = await ctx.deps.client.get( 'https://demo-endpoints.pydantic.workers.dev/latlng', params={'location': location_description}, ) r.raise_for_status() return LatLng.model_validate_json(r.content) @weather_agent.tool async def get_weather(ctx: RunContext[Deps], lat: float, lng: float) -> dict[str, Any]:  """Get the weather at a location.  Args:  ctx: The context.  lat: Latitude of the location.  lng: Longitude of the location.  """ # NOTE: the responses here will be random, and are not related to the lat and lng. temp_response, descr_response = await asyncio.gather( ctx.deps.client.get( 'https://demo-endpoints.pydantic.workers.dev/number', params={'min': 10, 'max': 30}, ), ctx.deps.client.get( 'https://demo-endpoints.pydantic.workers.dev/weather', params={'lat': lat, 'lng': lng}, ), ) temp_response.raise_for_status() descr_response.raise_for_status() return { 'temperature': f'{temp_response.text} °C', 'description': descr_response.text, } async def main(): async with AsyncClient() as client: logfire.instrument_httpx(client, capture_all=True) deps = Deps(client=client) result = await weather_agent.run( 'What is the weather like in London and in Wiltshire?', deps=deps ) print('Response:', result.output) if __name__ == '__main__': asyncio.run(main())

Running the UI

You can build multi-turn chat applications for your agent with Gradio, a framework for building AI web applications entirely in python. Gradio comes with built-in chat components and agent support so the entire UI will be implemented in a single python file!

Here's what the UI looks like for the weather agent:

pip install gradio>=5.9.0 python/uv-run -m pydantic_ai_examples.weather_agent_gradio

UI Code

weather_agent_gradio.py
from __future__ import annotations as _annotations import json from httpx import AsyncClient from pydantic import BaseModel from pydantic_ai import ToolCallPart, ToolReturnPart from pydantic_ai_examples.weather_agent import Deps, weather_agent try: import gradio as gr except ImportError as e: raise ImportError( 'Please install gradio with `pip install gradio`. You must use python>=3.10.' ) from e TOOL_TO_DISPLAY_NAME = {'get_lat_lng': 'Geocoding API', 'get_weather': 'Weather API'} client = AsyncClient() deps = Deps(client=client) async def stream_from_agent(prompt: str, chatbot: list[dict], past_messages: list): chatbot.append({'role': 'user', 'content': prompt}) yield gr.Textbox(interactive=False, value=''), chatbot, gr.skip() async with weather_agent.run_stream( prompt, deps=deps, message_history=past_messages ) as result: for message in result.new_messages(): for call in message.parts: if isinstance(call, ToolCallPart): call_args = call.args_as_json_str() metadata = { 'title': f'🛠️ Using {TOOL_TO_DISPLAY_NAME[call.tool_name]}', } if call.tool_call_id is not None: metadata['id'] = call.tool_call_id gr_message = { 'role': 'assistant', 'content': 'Parameters: ' + call_args, 'metadata': metadata, } chatbot.append(gr_message) if isinstance(call, ToolReturnPart): for gr_message in chatbot: if ( gr_message.get('metadata', {}).get('id', '') == call.tool_call_id ): if isinstance(call.content, BaseModel): json_content = call.content.model_dump_json() else: json_content = json.dumps(call.content) gr_message['content'] += f'\nOutput: {json_content}' yield gr.skip(), chatbot, gr.skip() chatbot.append({'role': 'assistant', 'content': ''}) async for message in result.stream_text(): chatbot[-1]['content'] = message yield gr.skip(), chatbot, gr.skip() past_messages = result.all_messages() yield gr.Textbox(interactive=True), gr.skip(), past_messages async def handle_retry(chatbot, past_messages: list, retry_data: gr.RetryData): new_history = chatbot[: retry_data.index] previous_prompt = chatbot[retry_data.index]['content'] past_messages = past_messages[: retry_data.index] async for update in stream_from_agent(previous_prompt, new_history, past_messages): yield update def undo(chatbot, past_messages: list, undo_data: gr.UndoData): new_history = chatbot[: undo_data.index] past_messages = past_messages[: undo_data.index] return chatbot[undo_data.index]['content'], new_history, past_messages def select_data(message: gr.SelectData) -> str: return message.value['text'] with gr.Blocks() as demo: gr.HTML(  """ <div style="display: flex; justify-content: center; align-items: center; gap: 2rem; padding: 1rem; width: 100%">  <img src="https://ai.pydantic.dev/img/logo-white.svg" style="max-width: 200px; height: auto">  <div>  <h1 style="margin: 0 0 1rem 0">Weather Assistant</h1>  <h3 style="margin: 0 0 0.5rem 0">  This assistant answer your weather questions.  </h3>  </div> </div> """ ) past_messages = gr.State([]) chatbot = gr.Chatbot( label='Packing Assistant', type='messages', avatar_images=(None, 'https://ai.pydantic.dev/img/logo-white.svg'), examples=[ {'text': 'What is the weather like in Miami?'}, {'text': 'What is the weather like in London?'}, ], ) with gr.Row(): prompt = gr.Textbox( lines=1, show_label=False, placeholder='What is the weather like in New York City?', ) generation = prompt.submit( stream_from_agent, inputs=[prompt, chatbot, past_messages], outputs=[prompt, chatbot, past_messages], ) chatbot.example_select(select_data, None, [prompt]) chatbot.retry( handle_retry, [chatbot, past_messages], [prompt, chatbot, past_messages] ) chatbot.undo(undo, [chatbot, past_messages], [prompt, chatbot, past_messages]) if __name__ == '__main__': demo.launch()