> ## Documentation Index
> Fetch the complete documentation index at: https://mcp-server-langgraph.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 14. Pydantic Type Safety Strategy

> Architecture Decision Record: 14. Pydantic Type Safety Strategy

# 14. Pydantic Type Safety Strategy

Date: 2025-10-13

## Status

Accepted

## Category

Core Architecture

## Context

Python's dynamic typing creates runtime errors that could be caught at development time:

* Invalid data passed to functions
* Missing required fields
* Type mismatches
* No IDE autocomplete

Production systems need:

* Runtime validation of untrusted input
* Type-safe API contracts
* Self-documenting code
* IDE support (autocomplete, type checking)

## Decision

Use **Pydantic BaseModel** for all data structures requiring validation.

### Implementation

```python theme={null}
from pydantic import BaseModel, Field, field_validator

class SessionData(BaseModel):
    session_id: str = Field(..., min_length=32)
    user_id: str = Field(...)
    roles: List[str] = Field(default_factory=list)
    created_at: str
    expires_at: str

    @field_validator('session_id')
    @classmethod
    def validate_session_id(cls, v: str) -> str:
        if len(v) < 32:
            raise ValueError('Session ID must be at least 32 characters')
        return v
```

## Consequences

### Positive Consequences

* **Runtime Validation**: Automatic validation of all fields
* **Type Safety**: mypy catches type errors
* **IDE Support**: Full autocomplete and type hints
* **Self-Documenting**: Field descriptions in schema
* **JSON Serialization**: Built-in model\_dump\_json()

### Negative Consequences

* **Performance**: Validation overhead (\~10-50µs per model)
* **Boilerplate**: More code than plain dicts
* **Learning Curve**: Pydantic-specific patterns

## Alternatives Considered

1. **dataclasses**: No validation, just structure
2. **TypedDict**: No runtime validation
3. **Plain dicts**: No type safety

## Usage Across Codebase

* **Session Management**: SessionData model (src/mcp\_server\_langgraph/auth/session.py:30)
* **Authentication**: UserData, AuthResponse models (src/mcp\_server\_langgraph/auth/user\_provider.py:26)
* **Configuration**: Settings, FeatureFlags (src/mcp\_server\_langgraph/core/config.py)
* **Compliance**: All GDPR, SOC 2, HIPAA models

## References

* Dependency: `pyproject.toml:44` - `pydantic>=2.5.3`
* Related ADRs: [ADR-0005](https://github.com/vishnu2kmohan/mcp-server-langgraph/blob/main/adr/adr-0005-pydantic-ai-integration.md)