# How to Configure CacheOptimizer for Different LLM Providers in Headroom

> Configure CacheOptimizer for various LLM providers in Headroom. Learn how Headroom's CacheOptimizerRegistry automatically optimizes cache strategies for different LLM services.

- Repository: [Tejas Chopra/headroom](https://github.com/chopratejas/headroom)
- Tags: how-to-guide
- Published: 2026-06-14

---

**Headroom automatically configures provider-specific cache optimizers through the `CacheOptimizerRegistry`, which maps LLM providers to specialized strategies using `CacheConfig` passed to the `HeadroomClient`.**

Headroom is an open-source LLM caching framework that ships with a provider-aware optimizer architecture. The system automatically adjusts caching strategies based on whether you are calling OpenAI, Anthropic, or Google APIs, ensuring maximum cache hit rates without manual prompt engineering.

## Understanding the CacheOptimizer Architecture

The caching system is built on a modular framework where each LLM provider has a dedicated optimizer implementation. All optimizers inherit from `BaseCacheOptimizer` defined in [`headroom/cache/base.py`](https://github.com/chopratejas/headroom/blob/main/headroom/cache/base.py).

### Provider-Specific Implementations

Headroom includes three concrete optimizer classes:

- **`OpenAICacheOptimizer`** ([`headroom/cache/openai.py`](https://github.com/chopratejas/headroom/blob/main/headroom/cache/openai.py)): Implements prefix stabilization for OpenAI's automatic prefix caching
- **`AnthropicCacheOptimizer`** ([`headroom/cache/anthropic.py`](https://github.com/chopratejas/headroom/blob/main/headroom/cache/anthropic.py)): Manages prefix and suffix optimization using Anthropic's cache-control token scheme
- **`GoogleCacheOptimizer`** ([`headroom/cache/google.py`](https://github.com/chopratejas/headroom/blob/main/headroom/cache/google.py)): Handles prefix stabilization similar to OpenAI but with Google-specific TTL and discount constants

The `CacheOptimizerRegistry` in [`headroom/cache/registry.py`](https://github.com/chopratejas/headroom/blob/main/headroom/cache/registry.py) maintains a central mapping of provider names to these classes. The registry automatically instantiates and caches optimizer objects to avoid recreating heavy resources like NER models or sentence-transformer pipelines.

## Provider-Specific Optimization Strategies

Each optimizer implements strategies tailored to the provider's caching mechanics.

### OpenAI Prefix Stabilization

OpenAI automatically caches the first approximately 1,024 tokens of every prompt. The `OpenAICacheOptimizer` extracts dynamic content—such as timestamps, UUIDs, and dates—and moves it to the end of the request. It normalizes whitespace and strips random IDs to ensure the cached prefix remains stable across requests.

Key configuration parameters:
- **`dynamic_detection_tiers`**: Array of detection methods (`["regex", "ner", "semantic"]`)
- **`min_tokens_for_caching`**: Minimum tokens required to trigger optimization (default: 1024)
- **`ttl_seconds`**: Time-to-live for cached entries

### Anthropic Cache-Control Tokens

Anthropic exposes explicit cache-control tokens that mark which prompt sections are cacheable. The `AnthropicCacheOptimizer` inserts these tokens and manages suffix boundaries.

Key configuration parameters:
- **`cache_control`**: Boolean to enable cache-control token insertion
- **`suffix_token_count`**: Number of tokens to reserve for the dynamic suffix
- **`dynamic_detection_tiers`**: Content detection methods for stabilization

### Google Cache Behavior

The `GoogleCacheOptimizer` follows the same prefix-stabilization pattern as OpenAI but applies Google-specific constants for TTL and pricing discounts. It resides in [`headroom/cache/google.py`](https://github.com/chopratejas/headroom/blob/main/headroom/cache/google.py).

## Configuring the CacheOptimizerRegistry

The registry populates automatically on import. You can retrieve optimizers explicitly or let Headroom handle selection automatically.

To fetch a specific optimizer manually:

```python
from headroom.cache.registry import CacheOptimizerRegistry
from headroom.cache.base import CacheConfig

# Configure detection tiers

config = CacheConfig(dynamic_detection_tiers=["regex", "ner"])

# Retrieve Google optimizer

google_opt = CacheOptimizerRegistry.get("google", config=config)

```

The registry returns cached instances by default. Pass `cached=False` to force instantiation of a new optimizer:

```python
fresh_optimizer = CacheOptimizerRegistry.get("anthropic", config=config, cached=False)

```

To check available providers:

```python
is_available = CacheOptimizerRegistry.is_registered("openai")

```

## Integrating with HeadroomClient

The `HeadroomClient` in [`headroom/client.py`](https://github.com/chopratejas/headroom/blob/main/headroom/client.py) serves as the primary entry point. It accepts a `cache_config` parameter and automatically lookups the appropriate optimizer from the registry (see implementation around lines 340-352).

Basic usage with auto-detection:

```python
from headroom.client import HeadroomClient

# Provider auto-detected as "openai"

client = HeadroomClient(model="gpt-4o-mini")

```

Explicit provider configuration with custom settings:

```python
from headroom.client import HeadroomClient
from headroom.cache.base import CacheConfig

# Anthropic configuration with cache-control

anthropic_cfg = CacheConfig(
    provider="anthropic",
    dynamic_detection_tiers=["regex", "ner"],
    cache_control=True,
    suffix_token_count=64,
    ttl_seconds=1800
)

client = HeadroomClient(
    model="claude-3-opus-20240229",
    cache_config=anthropic_cfg
)

```

Google configuration with extended TTL:

```python
google_cfg = CacheConfig(
    provider="google",
    ttl_seconds=3600,
    dynamic_detection_tiers=["regex"]
)

client = HeadroomClient(
    model="gemini-1.5-pro",
    cache_config=google_cfg
)

```

## Creating Custom Optimizers

For experimental or proprietary algorithms, extend `BaseCacheOptimizer` and register it manually:

```python
from headroom.cache.registry import CacheOptimizerRegistry
from headroom.cache.base import BaseCacheOptimizer

class ExperimentalOptimizer(BaseCacheOptimizer):
    def optimize(self, messages):
        # Implementation logic

        return optimized_messages
    
    def analyze_cache_potential(self, messages):
        # Analysis logic

        return metrics

# Register with override protection

CacheOptimizerRegistry.register(
    "experimental",
    ExperimentalOptimizer,
    override=False
)

# Use in client

client = HeadroomClient(
    model="gpt-4o-mini",
    cache_config=CacheConfig(provider="experimental")
)

```

The registry rejects duplicate registrations unless you explicitly pass `override=True`.

## Complete Working Example

This end-to-end example demonstrates configuring the Anthropic optimizer with full debugging enabled:

```python
from headroom.client import HeadroomClient
from headroom.cache.base import CacheConfig

# Configure for Anthropic with aggressive caching

config = CacheConfig(
    provider="anthropic",
    dynamic_detection_tiers=["regex", "ner", "semantic"],
    cache_control=True,
    suffix_token_count=128,
    ttl_seconds=1800
)

# Initialize client

client = HeadroomClient(
    model="claude-3-sonnet-20240229",
    cache_config=config
)

# Execute request

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the current weather?"}
]

response = client.chat(messages)

# Access cache metrics

if hasattr(response, 'cache_result'):
    print(f"Prefix stabilized: {response.cache_result.metrics.prefix_changed_from_previous}")
    print(f"Estimated savings: {response.cache_result.metrics.estimated_savings_percent}%")

```

## Summary

- **Headroom** provides distinct optimizer implementations for OpenAI, Anthropic, and Google in [`headroom/cache/openai.py`](https://github.com/chopratejas/headroom/blob/main/headroom/cache/openai.py), [`headroom/cache/anthropic.py`](https://github.com/chopratejas/headroom/blob/main/headroom/cache/anthropic.py), and [`headroom/cache/google.py`](https://github.com/chopratejas/headroom/blob/main/headroom/cache/google.py)
- The **`CacheOptimizerRegistry`** ([`headroom/cache/registry.py`](https://github.com/chopratejas/headroom/blob/main/headroom/cache/registry.py)) automatically maps provider strings to optimizer classes and caches instances for performance
- Configure optimizations using **`CacheConfig`** with provider-specific parameters like `cache_control`, `suffix_token_count`, and `dynamic_detection_tiers`
- **`HeadroomClient`** ([`headroom/client.py`](https://github.com/chopratejas/headroom/blob/main/headroom/client.py)) integrates the optimizer automatically when you pass `cache_config`, auto-detecting the provider from the model name
- Extend **`BaseCacheOptimizer`** to create custom caching strategies and register them via `CacheOptimizerRegistry.register()`

## Frequently Asked Questions

### How does Headroom automatically detect which LLM provider I'm using?

Headroom inspects the `model` parameter passed to `HeadroomClient` and maps known model name patterns to provider identifiers. For example, models starting with `gpt-` map to `openai`, `claude-` maps to `anthropic`, and `gemini-` maps to `google`. You can override this auto-detection by explicitly setting the `provider` field in `CacheConfig`.

### Can I use the same CacheConfig across different LLM providers?

While the `CacheConfig` class is reusable across providers, specific parameters like `cache_control` (Anthropic-specific) or `min_tokens_for_caching` (OpenAI-specific) are ignored by optimizers that do not support them. For best results, create separate configurations tailored to each provider's caching capabilities.

### What happens if I request the same provider optimizer multiple times?

The `CacheOptimizerRegistry` caches optimizer instances by default to avoid the overhead of reloading NER models or sentence transformers. When you call `CacheOptimizerRegistry.get("openai", config=config)` multiple times with the same configuration, you receive the same instance unless you pass `cached=False`.

### How do I disable the cache optimizer for specific requests?

To bypass caching entirely, initialize `HeadroomClient` without passing a `cache_config` parameter, or set `cache_config=None`. Alternatively, you can instantiate the client with a configuration that sets `dynamic_detection_tiers=[]` and disables provider-specific features like `cache_control=False`, though this still runs the optimizer pipeline without modifications.