olakai-sdk 0.6.0

Auto-instrumentation SDK for monitoring and tracking LLM usage (OpenAI, Anthropic, etc.)

Olakai Python SDK

Automatic instrumentation for LLM monitoring and tracking - Monitor your AI applications with zero code changes.

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What's New in v0.5.0 🎉

Automatic instrumentation is here! No more manual decorators or payload construction. Just install, configure once, and monitor all your LLM calls automatically.

• ✅ Auto-instrument OpenAI - One line to monitor all OpenAI calls
• ✅ Zero code changes - Works with existing OpenAI code
• ✅ Automatic data extraction - Tokens, costs, models, API keys
• ✅ Streaming support - Handles both regular and streaming responses
• ✅ Context-based metadata - Add user/session data with context managers
• ✅ Server-focused - Designed for backend Python applications

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Quick Start (30 seconds)

Installation

pip install olakai-sdk
pip install openai  # Install OpenAI SDK separately

Basic Usage

from olakaisdk import olakai_config, instrument_openai
from openai import OpenAI

# 1. Configure Olakai (one-time setup)
olakai_config("your-olakai-api-key")

# 2. Auto-instrument OpenAI
instrument_openai()

# 3. Use OpenAI normally - monitoring happens automatically!
client = OpenAI(api_key="your-openai-key")
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

# That's it! Your call is now tracked with:
# - Token counts (input/output)
# - Model name
# - API key (for cost tracking)
# - Latency
# - Request/response content

Check your Olakai dashboard to see the tracked data!

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Features

Automatic Tracking

After calling instrument_openai(), the SDK automatically captures:

• Token usage - Prompt tokens, completion tokens, total tokens
• Cost tracking - API key identification for backend cost calculation
• Model information - Which model was used (gpt-4, gpt-3.5-turbo, etc.)
• Latency - Request duration in milliseconds
• Content - Prompts and responses (configurable)
• Errors - Automatic error tracking with context

Context-Based Metadata

Add user and session metadata using context managers:

from olakaisdk import olakai_context

with olakai_context(
    userEmail="[e-mail]",
    chatId="session-123",
    task="Customer Support"
):
    # All OpenAI calls within this context include the metadata
    response = client.chat.completions.create(...)

Streaming Support

Works seamlessly with OpenAI's streaming API:

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True  # Streaming is automatically handled!
)

for chunk in response:
    print(chunk.choices[0].delta.content, end="")

# Telemetry is sent after stream completes

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Installation Options

# Basic installation
pip install olakai-sdk

# With OpenAI support
pip install olakai-sdk[openai]

# For development
pip install olakai-sdk[dev]

Requirements: Python 3.7+

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Usage Examples

Minimal Example

from olakaisdk import olakai_config, instrument_openai
from openai import OpenAI

olakai_config("olakai-api-key")
instrument_openai()

client = OpenAI(api_key="openai-key")
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

With User Context

from olakaisdk import olakai_config, instrument_openai, olakai_context
from openai import OpenAI

olakai_config("olakai-api-key")
instrument_openai()

client = OpenAI(api_key="openai-key")

# Add user metadata
with olakai_context(
    userEmail="[e-mail]",
    chatId="support-session-456",
    task="Customer Support",
    subTask="password-reset"
):
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "user", "content": "How do I reset my password?"}
        ]
    )
    print(response.choices[0].message.content)

With Custom Dimensions and Metrics

with olakai_context(
    userEmail="[e-mail]",
    task="Content Generation",
    customDimensions={
        "environment": "production",
        "region": "us-east-1",
        "user_tier": "premium"
    },
    customMetrics={
        "user_id": 12345.0,
        "session_length": 45.5
    }
):
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Write a blog post"}]
    )

Nested Contexts

Contexts can be nested, with inner contexts overriding outer values:

# Outer context applies to all calls
with olakai_context(task="Customer Service", userEmail="[e-mail]"):

    # Inner context overrides specific fields
    with olakai_context(subTask="billing-inquiry"):
        response = client.chat.completions.create(...)
        # Has task="Customer Service", subTask="billing-inquiry"

    # Back to outer context
    with olakai_context(subTask="technical-support"):
        response = client.chat.completions.create(...)
        # Has task="Customer Service", subTask="technical-support"

Async Support

Works with async OpenAI calls:

import asyncio
from openai import AsyncOpenAI

async def main():
    olakai_config("olakai-api-key")
    instrument_openai()

    client = AsyncOpenAI(api_key="openai-key")

    with olakai_context(userEmail="[e-mail]"):
        response = await client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": "Hello async world!"}]
        )
        print(response.choices[0].message.content)

asyncio.run(main())

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Configuration

Initialize the SDK

from olakaisdk import olakai_config

# Basic configuration
olakai_config("your-api-key")

# With custom endpoint
olakai_config("your-api-key", endpoint="https://custom.olakai.ai")

# With debug logging
olakai_config("your-api-key", debug=True)

Instrumentation Options

from olakaisdk import instrument_openai

# Default: capture everything
instrument_openai()

# Customize what to capture
instrument_openai(
    capture_inputs=True,      # Capture prompts/messages
    capture_outputs=True,     # Capture responses
    capture_api_keys=True     # Track API keys for cost analysis
)

Privacy Controls

Disable input/output capture for sensitive data:

instrument_openai(
    capture_inputs=False,    # Don't send prompts
    capture_outputs=False,   # Don't send responses
    capture_api_keys=True    # Still track tokens and costs
)

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API Reference

Primary API (v0.5.0)

olakai_config(api_key, endpoint="https://app.olakai.ai", debug=False)

Initialize the Olakai SDK. Must be called before instrumentation.

Parameters:

• api_key (str): Your Olakai API key
• endpoint (str, optional): API endpoint URL
• debug (bool, optional): Enable debug logging

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instrument_openai(capture_inputs=True, capture_outputs=True, capture_api_keys=True)

Auto-instrument OpenAI SDK for monitoring.

Parameters:

• capture_inputs (bool): Capture prompt/messages
• capture_outputs (bool): Capture responses
• capture_api_keys (bool): Track API keys for cost analysis

Raises:

• RuntimeError: If SDK not configured with olakai_config()
• ImportError: If OpenAI SDK not installed

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olakai_context(**metadata)

Context manager to add metadata to LLM calls.

Parameters:

• userEmail (str, optional): User email for tracking
• chatId (str, optional): Session/chat identifier
• task (str, optional): High-level task category
• subTask (str, optional): Specific subtask
• customDimensions (dict, optional): String metadata
• customMetrics (dict, optional): Numeric metadata

Example:

with olakai_context(userEmail="[e-mail]", task="Support"):
    # Your OpenAI calls here
    pass

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uninstrument_openai()

Remove OpenAI instrumentation. Restores original OpenAI behavior.

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is_instrumented()

Check if OpenAI is currently instrumented.

Returns: bool

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Legacy API (Deprecated)

The v0.4.0 decorator-based API is still available but will be removed in v1.0.0:

• @olakai_monitor() - Manual decorator (use instrument_openai() instead)
• olakai_report() - Manual reporting (use auto-instrumentation instead)
• olakai() - Low-level API (use auto-instrumentation instead)

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How It Works

Under the Hood

1. Monkey Patching: instrument_openai() wraps OpenAI's chat.completions.create methods
2. Data Extraction: Automatically extracts tokens, model, latency from responses
3. Context Merging: Combines context metadata with extracted data
4. Async Telemetry: Sends data to Olakai API without blocking your code
5. Error Handling: Captures errors without affecting your application

Data Flow

Your Code → OpenAI API → Response
    ↓                        ↓
Olakai Context      Extract Telemetry
    ↓                        ↓
    └──→ Merge & Send to Olakai API (async)

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Migration from v0.4.0

Old Way (v0.4.0)

from olakaisdk import olakai_config, olakai_monitor
from openai import OpenAI

olakai_config("api-key")

@olakai_monitor(
    userEmail="[e-mail]",
    task="Support",
    customDimensions={"model": "gpt-4"}
)
def get_response(prompt):
    client = OpenAI(api_key="openai-key")
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

result = get_response("Hello")

New Way (v0.5.0)

from olakaisdk import olakai_config, instrument_openai, olakai_context
from openai import OpenAI

olakai_config("api-key")
instrument_openai()  # ← One-time setup

client = OpenAI(api_key="openai-key")

def get_response(prompt):
    # No decorator needed!
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

# Add metadata with context when needed
with olakai_context(userEmail="[e-mail]", task="Support"):
    result = get_response("Hello")

Key Improvements:

• ✅ No decorators needed
• ✅ Model name automatically captured
• ✅ Tokens automatically captured
• ✅ Works with existing OpenAI code
• ✅ Cleaner, more maintainable code

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Dashboard & Analytics

After setting up monitoring, visit your Olakai dashboard to see:

• Usage Analytics - API calls, tokens, trends over time
• Cost Tracking - Per-API-key usage for ROI analysis
• User Insights - Individual user behavior patterns
• Task Performance - Monitor different tasks and success rates
• Model Comparison - Compare performance across models
• Custom Metrics - Visualize your custom dimensions and metrics

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Best Practices

Do This ✅

• Initialize once: Call olakai_config() at app startup
• Instrument early: Call instrument_openai() before creating clients
• Use contexts: Add metadata with olakai_context() for rich analytics
• Track users: Always include userEmail when possible
• Organize tasks: Use consistent task and subTask names
• Custom dimensions: Track environment, region, features with customDimensions

Avoid This ❌

• Don't skip configuration: Always call olakai_config() first
• Don't log secrets: Never include passwords in prompts/responses
• Don't instrument twice: Check is_instrumented() before re-instrumenting
• Don't use decorators: The old @olakai_monitor() API is deprecated

Security Tips

• Store API keys in environment variables
• Use capture_inputs=False / capture_outputs=False for sensitive data
• Review dashboard access controls
• Consider GDPR/privacy requirements for user tracking

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Troubleshooting

SDK not initialized error

RuntimeError: Olakai SDK not initialized. Call olakai_config() first.

Solution: Call olakai_config() before instrument_openai().

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OpenAI not installed error

ImportError: OpenAI SDK not installed. Install with: pip install openai

Solution: pip install openai

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No data in dashboard

Possible causes:

1. Check API key is correct
2. Enable debug mode: olakai_config("key", debug=True)
3. Verify network connectivity
4. Check instrumentation: is_instrumented() should return True

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Streaming not working

Make sure you're iterating through the entire stream:

response = client.chat.completions.create(..., stream=True)

# ✅ Correct - iterate fully
for chunk in response:
    print(chunk.choices[0].delta.content)
# Telemetry sent after loop completes

# ❌ Wrong - don't break early
for chunk in response:
    if some_condition:
        break  # Telemetry won't be sent!

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Examples

See USAGE.md for more detailed examples and use cases.

Try the sample script:

python examples/basic_example.py

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Development

Setup

git clone https://github.com/olakai/olakai-sdk-python
cd olakai-sdk-python
pip install -e ".[dev]"

Run Tests

pytest
pytest tests/test_instrumentation.py -v

Code Quality

./tests/check.sh

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Support & Community

• Documentation: Olakai Docs
• Support: [e-mail]
• Issues: GitHub Issues
• Changelog: CHANGELOG.md

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License

MIT © Olakai

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What's Next?

• 🚀 Anthropic instrumentation (Claude support)
• 🚀 Google AI instrumentation (Gemini support)
• 🚀 Local model support (Ollama, LM Studio)
• 🚀 Enhanced streaming analytics
• 🚀 Cost optimization recommendations

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Ready to monitor your AI application?

pip install olakai-sdk openai

from olakaisdk import olakai_config, instrument_openai
olakai_config("your-api-key")
instrument_openai()
# Start building! 🚀

Happy monitoring!