Sample code for the article on Pydantic AI

pydantic-ai/README.md

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+# Pydantic AI: Build Type-Safe LLM Agents in Python
+
+This folder provides the code examples for the Real Python tutorial [Pydantic AI: Build Type-Safe LLM Agents in Python](https://realpython.com/pydantic-ai/).

pydantic-ai/cats.py

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+import requests
+from pydantic_ai import Agent
+
+agent = Agent(
+    "google-gla:gemini-2.5-flash",
+    system_prompt="Help users with cat breeds. Be concise.",
+)
+
+
+@agent.tool_plain
+def find_breed_info(breed_name: str) -> dict:
+    """Find information about a cat breed."""
+    response = requests.get("https://api.thecatapi.com/v1/breeds")
+    response.raise_for_status()
+    json_response = response.json()
+    for breed in json_response:
+        if breed["name"] == breed_name:
+            return breed
+    return {"error": "Breed not found"}
+
+
+result = agent.run_sync("Tell me about the Siamese cats.")
+print(result.output)

pydantic-ai/city.py

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+from pydantic import BaseModel
+from pydantic_ai import Agent
+
+
+class CityInfo(BaseModel):
+    name: str
+    country: str
+    population: int
+    fun_fact: str
+
+
+agent = Agent("google-gla:gemini-2.5-flash", output_type=CityInfo)
+
+result = agent.run_sync("Tell me about Tokyo")
+print(result.output)
+
+
+print(f"{result.output.name}, {result.output.country}")
+print(f"Population: {result.output.population:,}")
+print(f"Fun fact: {result.output.fun_fact}")

pydantic-ai/first_agent.py

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+from pydantic_ai import Agent
+
+agent = Agent(
+    "google-gla:gemini-2.5-flash",
+    system_prompt="You're a Python Expert. Reply in one sentence.",
+)
+
+result = agent.run_sync("What is Pydantic AI?")
+print(result.output)

pydantic-ai/users.py

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+import requests
+from pydantic import BaseModel
+from pydantic_ai import Agent, RunContext
+
+
+class UserDatabase:
+    """Simulate a user database using the JSONPlaceholder users API."""
+
+    _base_url = "https://jsonplaceholder.typicode.com"
+
+    def get_user_info(self, user_id: int) -> dict:
+        response = requests.get(f"{self._base_url}/users/{user_id}")
+        response.raise_for_status()
+        return response.json()
+
+
+class UserSummary(BaseModel):
+    name: str
+    email: str
+    company: str
+
+
+agent = Agent(
+    "google-gla:gemini-2.5-flash",
+    output_type=UserSummary,
+    deps_type=UserDatabase,
+    system_prompt=(
+        "You retrieve user information from an external database. "
+        "Use the available tools to gather user info, "
+        "then return a structured summary."
+    ),
+)
+
+
+@agent.tool
+def fetch_user(ctx: RunContext[UserDatabase], user_id: int) -> str:
+    """Fetch user profile from the service."""
+    try:
+        user = ctx.deps.get_user_info(user_id)
+        return str(user)
+    except requests.HTTPError:
+        return f"User with ID {user_id} not found"
+
+
+db = UserDatabase()
+result = agent.run_sync(
+    "Get a summary for user 7",
+    deps=db,
+)  # Inject the database
+print(f"Name: {result.output.name}")
+print(f"Email: {result.output.email}")
+print(f"Company: {result.output.company}")