> ## 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.

# 9. Feature Flag System for Gradual Rollouts

> Architecture Decision Record: 9. Feature Flag System for Gradual Rollouts

# 9. Feature Flag System for Gradual Rollouts

Date: 2025-10-13

## Status

Accepted

## Category

Infrastructure & Deployment

## Context

Production systems need safe feature deployment mechanisms:

* **Gradual Rollouts**: Enable features for subset of users
* **A/B Testing**: Compare feature variants
* **Emergency Disable**: Turn off problematic features instantly
* **Experimental Features**: Beta test without full deployment
* **Configuration**: Change behavior without code deployment

Hardcoded feature switches create problems:

* Code changes required to enable/disable features
* Cannot toggle features per environment
* No runtime configuration
* Requires redeployment for feature changes

## Decision

Implement environment-based feature flag system using **Pydantic settings with validation**.

### Architecture

```python theme={null}
class FeatureFlags(BaseSettings):
    # Pydantic AI Features
    enable_pydantic_ai_routing: bool = True
    pydantic_ai_confidence_threshold: float = Field(default=0.7, ge=0.0, le=1.0)

    # LLM Features
    enable_llm_fallback: bool = True
    llm_timeout_seconds: int = Field(default=60, ge=10, le=300)

    # Authorization
    enable_openfga: bool = True
    openfga_strict_mode: bool = False

    # Observability
    enable_langsmith: bool = False
    enable_trace_sampling: bool = False

    # Experimental
    enable_experimental_features: bool = False
    enable_multi_agent_collaboration: bool = False

    model_config = SettingsConfigDict(
        env_prefix="FF_",  # FF_ENABLE_PYDANTIC_AI_ROUTING=false
        env_file=".env",
    )
```

### Usage

```python theme={null}
from mcp_server_langgraph.core.feature_flags import feature_flags

if feature_flags.enable_pydantic_ai_routing:
    decision = await pydantic_agent.route_message(message)
else:
    decision = keyword_based_routing(message)
```

## Consequences

### Positive Consequences

* **Safe Rollouts**: Enable features incrementally
* **Environment-Specific**: Different flags per environment
* **Runtime Configuration**: No code changes to toggle features
* **Type Safety**: Pydantic validation prevents invalid values
* **Documentation**: Flags self-document with descriptions

### Negative Consequences

* **Code Complexity**: if/else checks throughout codebase
* **Testing Burden**: Must test with flags on/off
* **Configuration Sprawl**: Many environment variables

## Alternatives Considered

1. **LaunchDarkly**: Third-party service, cost, complexity
2. **Code-Based Toggles**: No runtime config, requires deployment
3. **Database Flags**: Requires database, slower

**Why Rejected**: Environment variables simplest for our needs

## Implementation

30+ feature flags across categories:

* Pydantic AI (3 flags)
* LLM (3 flags)
* Authorization (3 flags)
* Observability (4 flags)
* Performance (4 flags)
* Agent Behavior (3 flags)
* Security (4 flags)
* Experimental (3 flags)

## References

* Implementation: `src/mcp_server_langgraph/core/feature_flags.py:1-281`
* Related ADRs: [ADR-0005](https://github.com/vishnu2kmohan/mcp-server-langgraph/blob/main/adr/adr-0005-pydantic-ai-integration.md)