Anthropic Integration

Anthropic provides state-of-the-art language models with a focus on safety and sustainability. GateFlow integrates seamlessly with Anthropic's API to provide optimized routing, cost control, and observability.

Current Models

Chat Models

Model	Context	Output	Input $/1M	Output $/1M
`claude-opus-4-5-20251107`	200k	8,192	$15.00	$75.00
`claude-opus-4-1-20250805`	200k	8,192	$12.00	$60.00
`claude-sonnet-4-5-20250929`	200k	8,192	$3.00	$15.00
`claude-sonnet-4-20250514`	200k	8,192	$2.50	$12.50
`claude-haiku-4-5-20251015`	200k	8,192	$0.25	$1.25

Setup

Navigate to Dashboard: Go to Settings → Providers
Add Provider: Click "Add Provider" and select Anthropic
Enter API Key: Paste your Anthropic API key
Configure Settings:
- Region: Select preferred data center (e.g., us-east-1)
- Sustainability Mode: Enable to prioritize low-carbon regions
- Default Model: Set default model for chat completions
Save: The provider is now available for routing

Configuration Options

json

{
  "provider": "anthropic",
  "api_key": "your-anthropic-api-key",
  "region": "us-east-1",
  "sustainability_mode": true,
  "default_model": "claude-sonnet-4-5-20250929",
  "quality_threshold": 0.95,
  "carbon_budget": 50
}

Best Practices

Cost Optimization: Use claude-haiku-4-5-20251015 for high-volume, low-latency tasks.
Quality Tasks: Use claude-opus-4-5-20251107 for complex reasoning and analysis.
Sustainability: Enable sustainability mode to route requests to low-carbon data centers.

Example Usage

python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.gateflow.ai/v1",
    api_key="gw_prod_your_key_here"
)

# Use Anthropic for complex reasoning
response = client.chat.completions.create(
    model="claude-opus-4-5-20251107",
    messages=[{"role": "user", "content": "Solve this complex problem"}],
    routing_mode="sustain_optimized"
)

print(response.choices[0].message.content)
print(f"Model used: {response.model}")
print(f"Carbon footprint: {response.sustainability.carbon_gco2e} gCO₂e")
print(f"Carbon saved: {response.sustainability.carbon_saved_gco2e} gCO₂e")

Anthropic-Specific Features

Tool Use

Anthropic models support function calling with GateFlow's unified interface:

python

# Define tools (works across all providers)
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather information for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                }
            }
        }
    }
]

response = client.chat.completions.create(
    model="claude-opus-4-5-20251107",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools,
    routing_mode="sustain_optimized"
)

Vision Support

python

# Multi-modal input with vision
response = client.chat.completions.create(
    model="claude-opus-4-5-20251107",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Analyze this image"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/image.jpg"
                    }
                }
            ]
        }
    ],
    routing_mode="sustain_optimized"
)

Sustainability Features

Anthropic models through GateFlow offer:

Carbon-Optimized Routing: Automatically select the most energy-efficient data center
Model Efficiency: Claude models are optimized for low energy consumption
Time-Shifted Execution: Defer non-urgent requests to low-carbon periods
Request Batching: Combine multiple requests for reduced overhead
Automatic Model Selection: Choose the most efficient Claude model for your task

Model Selection Guide

Use Case	Recommended Model	Key Features	Sustainability Benefits
Complex reasoning	`claude-opus-4-5-20251107`	200K context, vision	Highest efficiency per token
Production use	`claude-sonnet-4-5-20250929`	Balanced performance	Best quality-to-carbon ratio
Fast responses	`claude-haiku-4-5-20251015`	250ms latency	Lowest carbon footprint
Cost-effective	`claude-sonnet-4-20250514`	Balanced quality	Optimized for efficiency

Sustainability Best Practices

Optimization Strategies

Right-size your model: Use claude-haiku-4-5-20251015 for simple tasks instead of Opus models
Enable Sustain Mode: Let GateFlow automatically choose the most efficient Anthropic model
Use time-shifting: Defer non-urgent requests to low-carbon periods
Batch requests: Process multiple items in single API calls to reduce overhead
Combine with caching: Cache frequent Anthropic requests for maximum savings

Configuration Example

python

# Configure Anthropic provider with sustainability settings
response = client.chat.completions.create(
    model="anthropic:auto",  # Let GateFlow choose most efficient Anthropic model
    messages=[{"role": "user", "content": "Process this sustainably"}],
    routing_mode="sustain_optimized",
    minimum_quality_score=8,  # Balance quality and efficiency
    region_preference="us-west"  # Prioritize low-carbon regions
)

Performance Characteristics

Latency Comparison

Fastest: claude-haiku-4-5-20251015 (250ms)
Balanced: claude-sonnet-4-5-20250929 (1,400ms)
Advanced: claude-opus-4-5-20251107 (2,000ms)

Token Limits

All models: 200K context window
Output limits: 8,192 tokens

Pricing Overview

Input prices: $0.25-$15.00 per 1M tokens
Output prices: $1.25-$75.00 per 1M tokens

Integration with Other GateFlow Features

Multi-Provider Fallbacks

python

# Configure Anthropic as primary with fallbacks
response = client.chat.completions.create(
    model="claude-opus-4-5-20251107",  # Primary: Anthropic
    messages=[{"role": "user", "content": "Important request"}],
    fallback_providers=["openai", "mistral"],  # Fallback chain
    routing_mode="sustain_optimized"
)

Semantic Caching

python

# Cache frequent Anthropic requests
response = client.chat.completions.create(
    model="claude-sonnet-4-5-20250929",
    messages=[{"role": "user", "content": "Frequently asked question"}],
    cache_ttl_seconds=3600,  # Cache for 1 hour
    embedding_model="text-embedding-3-small"  # Use for semantic matching
)

Troubleshooting

"Anthropic API key not configured"

Solution: Add your Anthropic API key in the GateFlow Dashboard under Settings → Providers.

"Model not found: claude-3-opus-20240229"

Solution: Use current models like claude-opus-4-5-20251107 instead of deprecated models.

"Rate limit exceeded"

Solution:

Check your Anthropic account limits
Configure fallbacks to other providers
Enable request queuing in GateFlow settings
Use claude-haiku-4-5-20251015 for high-volume applications

"Carbon savings lower than expected"

Solution:

Verify Sustain Mode is properly configured
Check grid carbon intensity in your region
Try different Anthropic models for better efficiency
Enable time-shifted execution for non-urgent requests

Migration from Direct Anthropic API

Key Differences

Feature	Direct Anthropic API	GateFlow Anthropic Integration
API Format	Anthropic-specific	OpenAI-compatible
Authentication	Anthropic API key	GateFlow API key
Model Names	`claude-3-opus`	`claude-opus-4-5-20251107`
Carbon Tracking	Manual	Automatic
Multi-provider	No	Yes
Fallbacks	Manual	Automatic
Sustainability	Basic	Advanced optimization

Migration Example

Before (Direct Anthropic API):

python

import anthropic
client = anthropic.Anthropic(api_key="your-anthropic-api-key")
response = client.messages.create(
    model="claude-3-opus-20240229",
    messages=[{"role": "user", "content": "Hello from Anthropic!"}]
)

After (GateFlow Integration):

python

from openai import OpenAI
client = OpenAI(
    base_url="https://api.gateflow.ai/v1",
    api_key="gw_prod_your_gateflow_key"
)
response = client.chat.completions.create(
    model="claude-opus-4-5-20251107",  # Use current models
    messages=[{"role": "user", "content": "Hello from Anthropic via GateFlow with sustainability benefits!"}],
    routing_mode="sustain_optimized"  # Enable carbon optimization
)

Next Steps

Explore OpenAI Integration - Versatile AI models
Try Google Gemini Models - Large context capabilities
Configure Sustain Mode - Automatic carbon optimization
View Provider Analytics - Monitor your Anthropic carbon savings

Anthropic Integration ​

Current Models ​

Chat Models ​

Setup ​

Configuration Options ​

Best Practices ​

Example Usage ​

Anthropic-Specific Features ​

Tool Use ​

Vision Support ​

Sustainability Features ​

Model Selection Guide ​

Sustainability Best Practices ​

Optimization Strategies ​

Configuration Example ​

Performance Characteristics ​

Latency Comparison ​

Token Limits ​

Pricing Overview ​

Integration with Other GateFlow Features ​

Multi-Provider Fallbacks ​

Semantic Caching ​

Troubleshooting ​

"Anthropic API key not configured" ​

"Model not found: claude-3-opus-20240229" ​

"Rate limit exceeded" ​

"Carbon savings lower than expected" ​

Migration from Direct Anthropic API ​

Key Differences ​

Migration Example ​

Next Steps ​

Anthropic Integration

Current Models

Chat Models

Setup

Configuration Options

Best Practices

Example Usage

Anthropic-Specific Features

Tool Use

Vision Support

Sustainability Features

Model Selection Guide

Sustainability Best Practices

Optimization Strategies

Configuration Example

Performance Characteristics

Latency Comparison

Token Limits

Pricing Overview

Integration with Other GateFlow Features

Multi-Provider Fallbacks

Semantic Caching

Troubleshooting

"Anthropic API key not configured"

"Model not found: claude-3-opus-20240229"

"Rate limit exceeded"

"Carbon savings lower than expected"

Migration from Direct Anthropic API

Key Differences

Migration Example

Next Steps