Monitor and log LLM API calls from OpenAI and Anthropic to the Coolhand analytics platform.

pip install coolhand

1. Get API Key: Visit coolhandlabs.com to create a free account
2. Install: pip install coolhand
3. Configure: Set COOLHAND_API_KEY and import coolhand in your app
4. Deploy: Your AI calls are now automatically monitored!

Set it and forget it! Monitor ALL AI API calls across your entire application with minimal configuration.

import coolhand  # Auto-initializes and starts monitoring

# That's it! ALL AI API calls are now automatically monitored:
# ✅ OpenAI SDK calls
# ✅ Anthropic API calls
# ✅ ANY library making AI API calls via httpx

# Your existing code works unchanged:
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)
# The request and response have been automatically logged to Coolhand!

Why Automatic Monitoring:

• Zero refactoring - No code changes to existing services
• Complete coverage - Monitors all AI libraries using httpx automatically
• Security built-in - Automatic credential sanitization
• Performance optimized - Negligible overhead via async logging
• Future-proof - Automatically captures new AI calls added by your team

from coolhand import Coolhand

coolhand_client = Coolhand(
    api_key='your-api-key',
    silent=False,  # Enable verbose logging
)

import coolhand
from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
# Request automatically logged to Coolhand!

import coolhand
from anthropic import Anthropic

client = Anthropic()
response = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.content[0].text)
# Request automatically logged to Coolhand!

import coolhand
from openai import OpenAI

client = OpenAI()
stream = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")
# Complete streamed response automatically logged to Coolhand!

The monitor captures:

• Request Data: Method, URL, headers, request body
• Response Data: Status code, headers, response body
• Timing: Request timestamp, response timestamp, duration
• LLM-Specific: Model used, token counts, streaming status

Headers containing API keys are automatically sanitized for security.

Coolhand monitors HTTP requests made via httpx, which is used by:

• OpenAI Python SDK
• Anthropic Python SDK
• Any other library using httpx for HTTP requests

1. When you import coolhand, it automatically patches httpx
2. Requests to OpenAI and Anthropic APIs are intercepted
3. Request and response data are captured (credentials sanitized)
4. Data is sent to Coolhand asynchronously
5. Your application continues without interruption

For non-LLM endpoints, requests pass through unchanged with zero overhead.

Collect user feedback on LLM responses to improve your AI outputs. The FeedbackService lets you capture thumbs up/down ratings, explanations, and corrections.

Frontend Feedback Widget: For browser-based feedback collection, see coolhand-js - an accessible, lightweight JavaScript widget that leverages best UX practices to capture actionable user feedback on any AI output.

from coolhand import Coolhand

# Initialize with your API key
ch = Coolhand(api_key='your-api-key')

# Submit positive feedback
ch.create_feedback({
    'llm_request_log_id': 12345,  # From Coolhand logs
    'like': True,
    'explanation': 'Very helpful response!'
})

# Using original output for fuzzy matching
ch.create_feedback({
    'original_output': 'The capital of France is London.',
    'like': False,
    'revised_output': 'The capital of France is Paris.'
})

*At least one matching field is recommended to link feedback to the original request.

from coolhand import Coolhand

Coolhand(
    api_key='your-api-key',
    silent=False  # Enable verbose logging
)

Or via environment variable:

export COOLHAND_SILENT=false

Sign up for free at coolhandlabs.com to get your API key and start monitoring your LLM usage.

What you get:

• Complete LLM request and response logging
• Usage analytics and insights
• No credit card required to start

• API keys in request headers are automatically redacted
• No sensitive data is exposed in logs
• All data is sent via HTTPS to Coolhand servers

• Frontend (Feedback Collection Widget): coolhand-js - Frontend feedback widget for collecting user feedback on AI outputs
• Ruby: coolhand gem - Coolhand monitoring for Ruby applications
• Node.js: coolhand-node package - Coolhand monitoring for Node.js applications

• Questions? Create an issue
• Contribute? Submit a pull request
• Support? Visit coolhandlabs.com

Apache-2.0