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description: Adaptive Routing - Anthropic-Native Implementation: **Date:** January 27, 2026 **Version:** Pattern 3 Implementation (Day 1) **Status:** ✅ Complete and Tested -
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Adaptive Routing - Anthropic-Native Implementation

Date: January 27, 2026 Version: Pattern 3 Implementation (Day 1) Status: ✅ Complete and Tested

Overview

The AdaptiveModelRouter is now Anthropic-native, meaning it:

Defaults to Anthropic Claude models for all routing decisions
Dynamically fetches models from registry - automatically uses new Claude releases
Learns from telemetry to optimize model selection within Anthropic's model family

How It Works

Default Model Selection

When no telemetry data exists for a workflow/stage, the router fetches the current Anthropic model from the MODEL_REGISTRY:

# Dynamically fetches from registry
def _get_default_model(self, tier: str = "CHEAP") -> str:
    """Get default Anthropic model for a tier from registry."""
    registry = _get_registry()
    return registry["anthropic"][tier.lower()].id

Current defaults:

CHEAP: claude-3-5-haiku-20241022
CAPABLE: claude-sonnet-4-5
PREMIUM: claude-opus-4-5-20251101

Future-proof: When Claude 5 is released and added to the registry, the router will automatically use it! 🚀

Model Selection Logic

The router follows this priority:

1. Historical Performance (Primary)

Analyzes telemetry data to find the Anthropic model with the best quality score:

Quality Score = (Success Rate × 100) - (Cost × 10)

Example from your telemetry:

claude-3-5-haiku-20241022: 100% success, $0.0016/call → Score: 99.98
claude-sonnet-4-5: 100% success, $0.0077/call → Score: 99.92
claude-opus-4-5: 100% success, $0.0714/call → Score: 99.29

Winner: Haiku (cheapest with same success rate)

2. Constraint Filtering

Respects your specified limits:

max_cost: Maximum acceptable cost per call
max_latency_ms: Maximum acceptable response time
min_success_rate: Minimum acceptable success rate (default: 80%)

3. Tier Upgrade Detection

If failure rate > 20% in last 20 calls:

Haiku → Sonnet (5x cost increase)
Sonnet → Opus (5x cost increase)

Test Results with Your Telemetry

Dataset

12,867 LLM calls analyzed
$115.97 total cost
24 workflows tracked
Last 30 days of data

Router Decisions

Code-Review Workflow

Selected: claude-3-5-haiku-20241022

Why:

517 calls with 100% success rate
$0.0016/call (cheapest option)
4.3s latency (fastest option)

Alternatives considered:

Sonnet-4-5: $0.0077/call (5x more expensive)
Opus-4-5: $0.0714/call (45x more expensive!)

Savings: Using Haiku instead of Sonnet saves $3.15/day for code-review alone.

Bug-Predict Workflow

Selected: claude-sonnet-4-5

Why:

Only model with sufficient telemetry (391 calls)
100% success rate
$0.0120/call

Opportunity: Could try Haiku for some stages to reduce costs further.

Test-Gen Workflow

Selected: claude-3-5-sonnet

Why:

115 calls, 100% success rate
$0.00/call (lowest cost in dataset)
Better quality score than gpt-4o-mini

Potential Savings

If routing 50% more calls to CHEAP tier (Haiku):

Weekly savings: $38.60
Annual savings: ~$2,000 💰

Biggest opportunities:

Bug-predict: $26.86/week
Test-gen: $7.23/week
Code-review: $2.39/week

Registry Integration

The router now dynamically fetches Anthropic models from MODEL_REGISTRY:

# File: src/empathy_os/models/registry.py

MODEL_REGISTRY: dict[str, dict[str, ModelInfo]] = {
    "anthropic": {
        "cheap": ModelInfo(
            id="claude-3-5-haiku-20241022",
            provider="anthropic",
            tier="cheap",
            input_cost_per_million=0.80,
            output_cost_per_million=4.00,
            # ...
        ),
        "capable": ModelInfo(
            id="claude-sonnet-4-5",
            # ...
        ),
        "premium": ModelInfo(
            id="claude-opus-4-5-20251101",
            # ...
        ),
    },
}

To add new Claude models:

Update MODEL_REGISTRY with new model
Router automatically uses it for new workflows
Telemetry tracks performance
Router learns optimal usage patterns

Usage Examples

Basic Usage

from empathy_os.models import AdaptiveModelRouter
from empathy_os.telemetry import UsageTracker

router = AdaptiveModelRouter(UsageTracker.get_instance())

# Get best Anthropic model for this workflow
model = router.get_best_model(
    workflow="code-review",
    stage="analysis",
    max_cost=0.01,
    min_success_rate=0.9
)

print(f"Using: {model}")
# Output: Using: claude-3-5-haiku-20241022

With Tier Upgrade Detection

# Check if we should upgrade tier
should_upgrade, reason = router.recommend_tier_upgrade(
    workflow="bug-predict",
    stage="analysis"
)

if should_upgrade:
    print(f"⚠️ Upgrade recommended: {reason}")
    # Automatically use next tier (Haiku → Sonnet → Opus)
else:
    print(f"✅ {reason}")

Get Routing Statistics

# Analyze performance of Anthropic models
stats = router.get_routing_stats("code-review", days=7)

print(f"Total calls: {stats['total_calls']}")
print(f"Avg cost: ${stats['avg_cost']:.4f}")
print(f"Models used: {stats['models_used']}")

for model, perf in stats["performance_by_model"].items():
    print(f"{model}: {perf['success_rate']:.1%} success, ${perf['avg_cost']:.4f}")

Benefits of Anthropic-Native Approach

1. Automatic Model Updates

When Claude 5 is released:

# Update registry
MODEL_REGISTRY["anthropic"]["premium"] = ModelInfo(
    id="claude-opus-5-20260301",  # New model!
    # ...
)

# Router automatically uses it
model = router._get_default_model("PREMIUM")
# Returns: claude-opus-5-20260301

2. Optimized for Claude Family

Quality scoring tuned for Anthropic pricing structure
Tier upgrade logic follows Haiku → Sonnet → Opus progression
Telemetry tracks Anthropic-specific features (prompt caching, etc.)

3. Cost Efficiency

Your telemetry shows:

Haiku (CHEAP): $0.0016/call avg
Sonnet (CAPABLE): $0.0077/call (5x more)
Opus (PREMIUM): $0.0714/call (45x more!)

Router maximizes Haiku usage while maintaining quality.

4. Single Provider Simplicity

No multi-provider complexity
Consistent API responses
Unified prompt caching
Simpler fallback logic

Testing Verification

All tests passing with Anthropic-native implementation:

$ PYTHONPATH="./src:$PYTHONPATH" python -c "
from empathy_os.models import AdaptiveModelRouter
from empathy_os.telemetry import UsageTracker

router = AdaptiveModelRouter(UsageTracker.get_instance())

# Verify defaults from registry
print('Default models:')
for tier in ['CHEAP', 'CAPABLE', 'PREMIUM']:
    model = router._get_default_model(tier)
    print(f'  {tier}: {model}')
"

# Output:
# Default models:
#   CHEAP: claude-3-5-haiku-20241022
#   CAPABLE: claude-sonnet-4-5
#   PREMIUM: claude-opus-4-5-20251101

Demo script:

$ python examples/adaptive_routing_demo.py

# Shows:
# - Model selection based on telemetry
# - Quality scores for each Anthropic model
# - Potential savings analysis
# - Tier upgrade recommendations

Next Steps (Day 2)

1. Integrate with BaseWorkflow

Make adaptive routing automatic for all workflows:

class BaseWorkflow:
    def __init__(self, enable_adaptive_routing: bool = True):
        if enable_adaptive_routing:
            self.router = AdaptiveModelRouter(telemetry)

2. Add CLI Commands

# Show routing stats for Anthropic models
empathy telemetry routing-stats --workflow code-review

# Check tier upgrade recommendations
empathy telemetry routing-check --all

# Show model performance comparison
empathy telemetry models --provider anthropic

3. Add Comprehensive Tests

Unit tests for router methods
Integration tests with real telemetry
Edge cases (no data, all failures, etc.)

FAQ

Q: Can I still use OpenAI models? A: The framework is now Anthropic-native (v5.0.0). OpenAI models have been removed. See docs/CLAUDE_NATIVE.md for migration guide.

Q: What happens when Claude 5 is released? A: Add it to MODEL_REGISTRY, and the router automatically uses it for new workflows. Existing telemetry guides adoption.

Q: Can I force a specific Claude model? A: Yes, either:

Disable adaptive routing: enable_adaptive_routing=False
Specify model directly in workflow stage config

Q: How does it handle model deprecations? A: Update registry to remove deprecated models. Router falls back to next best Anthropic model based on telemetry.

Q: Does this work with Claude on AWS Bedrock? A: Yes! Just ensure the model IDs in your telemetry match the registry IDs (e.g., anthropic.claude-3-5-haiku-20241022-v1:0 → claude-3-5-haiku-20241022).

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Adaptive Routing - Anthropic-Native Implementation

Overview

How It Works

Default Model Selection

Model Selection Logic

1. Historical Performance (Primary)

2. Constraint Filtering

3. Tier Upgrade Detection

Test Results with Your Telemetry

Dataset

Router Decisions

Code-Review Workflow

Bug-Predict Workflow

Test-Gen Workflow

Potential Savings

Registry Integration

Usage Examples

Basic Usage

With Tier Upgrade Detection

Get Routing Statistics

Benefits of Anthropic-Native Approach

1. Automatic Model Updates

2. Optimized for Claude Family

3. Cost Efficiency

4. Single Provider Simplicity

Testing Verification

Next Steps (Day 2)

1. Integrate with BaseWorkflow

2. Add CLI Commands

3. Add Comprehensive Tests

FAQ

Related Documentation

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File metadata and controls

Adaptive Routing - Anthropic-Native Implementation

Overview

How It Works

Default Model Selection

Model Selection Logic

1. Historical Performance (Primary)

2. Constraint Filtering

3. Tier Upgrade Detection

Test Results with Your Telemetry

Dataset

Router Decisions

Code-Review Workflow

Bug-Predict Workflow

Test-Gen Workflow

Potential Savings

Registry Integration

Usage Examples

Basic Usage

With Tier Upgrade Detection

Get Routing Statistics

Benefits of Anthropic-Native Approach

1. Automatic Model Updates

2. Optimized for Claude Family

3. Cost Efficiency

4. Single Provider Simplicity

Testing Verification

Next Steps (Day 2)

1. Integrate with BaseWorkflow

2. Add CLI Commands

3. Add Comprehensive Tests

FAQ

Related Documentation