---
id: existential-birds/beagle/pydantic-ai-agent-creation
version: "37dfb1d3"
license: Apache-2.0
install: manual
updated: 2026-07-21
---

# pydantic-ai-agent-creation — Set up PydanticAI agents with full type safety, Pydantic model validation, and configurable dependencies. Choose from OpenAI, Anthropic, Google, and other LLM providers, then define structured output schemas and inject external services. Execute synchronously, asynchronously, or via streaming.

Publisher: existential-birds · Stars: 74 · Updated: 2026-07-21

Install (manual): `git clone https://github.com/existential-birds/beagle`

## SKILL.md

# Creating PydanticAI Agents

## Quick Start

```python
from pydantic_ai import Agent

# Minimal agent (text output)
agent = Agent('openai:gpt-4o')
result = agent.run_sync('Hello!')
print(result.output)  # str
```

## Model Selection

Model strings follow `provider:model-name` format:

```python
# OpenAI
agent = Agent('openai:gpt-4o')
agent = Agent('openai:gpt-4o-mini')

# Anthropic
agent = Agent('anthropic:claude-sonnet-4-5')
agent = Agent('anthropic:claude-haiku-4-5')

# Google
agent = Agent('google-gla:gemini-2.0-flash')
agent = Agent('google-vertex:gemini-2.0-flash')

# Others: groq:, mistral:, cohere:, bedrock:, etc.
```

## Structured Outputs

Use Pydantic models for validated, typed responses:

```python
from pydantic import BaseModel
from pydantic_ai import Agent

class CityInfo(BaseModel):
    city: str
    country: str
    population: int

agent = Agent('openai:gpt-4o', output_type=CityInfo)
result = agent.run_sync('Tell me about Paris')
print(result.output.city)  # "Paris"
print(result.output.population)  # int, validated
```

## Agent Configuration

```python
from pydantic_ai import Agent
from pydantic_ai.settings import ModelSettings

agent = Agent(
    'openai:gpt-4o',
    output_type=MyOutput,           # Structured output type
    deps_type=MyDeps,               # Dependency injection type
    instructions='You are helpful.',  # Static instructions
    retries=2,                      # Retry attempts for validation
    name='my-agent',                # For logging/tracing
    model_settings=ModelSettings(   # Provider settings
        temperature=0.7,
        max_tokens=1000
    ),
    end_strategy='early',           # How to handle tool calls with results
)
```

## Running Agents

Three execution methods:

```python
# Async (preferred)
result = await agent.run('prompt', deps=my_deps)

# Sync (convenience)
result = agent.run_sync('prompt', deps=my_deps)

# Streaming
async with agent.run_stream('prompt') as response:
    async for chunk in response.stream_output():
        print(chunk, end='')
```

## Instructions vs System Prompts

```python
# Instructions: Concatenated, for agent behavior
agent = Agent(
    'openai:gpt-4o',
    instructions='You are a helpful assistant. Be concise.'
)

# Dynamic instructions via decorator
@agent.instructions
def add_context(ctx: RunContext[MyDeps]) -> str:
    return f"User ID: {ctx.deps.user_id}"

# System prompts: Static, for model context
agent = Agent(
    'openai:gpt-4o',
    system_prompt=['You are an expert.', 'Always cite sources.']
)
```

## Common Patterns

### Parameterized Agent (Type-Safe)

```python
from dataclasses import dataclass
from pydantic_ai import Agent, RunContext

@dataclass
class Deps:
    api_key: str
    user_id: int

agent: Agent[Deps, str] = Agent(
    'openai:gpt-4o',
    deps_type=Deps,
)

# deps is now required and type-checked
result = agent.run_sync('Hello', deps=Deps(api_key='...', user_id=123))
```

### No Dependencies (Satisfy Type Checker)

```python
# Option 1: Explicit type annotation
agent: Agent[None, str] = Agent('openai:gpt-4o')

# Option 2: Pass deps=None
result = agent.run_sync('Hello', deps=None)
```

## Verification gates

Run these in order before depending on an agent in production code:

1. **Smoke run** — Execute `agent.run_sync('Reply with OK.')` (or `await agent.run(...)` in async code). **Pass:** the call completes without raising and `result.output` is present.
2. **Structured output** — If you set `output_type`, prompt for a response that should satisfy the schema. **Pass:** `result.output` is an instance of your Pydantic model; repeated validation failures mean tightening instructions or `retries`, not adding features yet.
3. **Dependencies** — If you set `deps_type`, call `run` / `run_sync` with `deps=` of that type. **Pass:** the invocation type-checks and completes (or fails only for model/API reasons, not a missing or wrong `deps` value).

## Decision Framework

| Scenario | Configuration |
|----------|--------------|
| Simple text responses | `Agent(model)` |
| Structured data extraction | `Agent(model, output_type=MyModel)` |
| Need external services | Add `deps_type=MyDeps` |
| Validation retries needed | Increase `retries=3` |
| Debugging/monitoring | Set `instrument=True` |


[View on SkillFed](https://skillfed.io/existential-birds/beagle/pydantic-ai-agent-creation) · [View on GitHub](https://github.com/existential-birds/beagle)