integrate

Integrate Olakai monitoring into AI code by wrapping LLM clients.

13.9k|1.2k|Updated Oct 30, 2025

Search Tags:#monitoring #integration #ai #sdk #governance #kpi #olakai

Ready to run

npx skills add https://github.com/andrewyng/context-hub --skill integrate-andrewyng

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: integrate
Source: https://github.com/andrewyng/context-hub/tree/main/content/olakai/skills/integrate
Command: npx skills add https://github.com/andrewyng/context-hub --skill integrate-andrewyng

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @olakai/sdk, olakai-cli, openai, @anthropic-ai/sdk, and includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill helps you integrate Olakai's monitoring and KPI tracking into your existing AI applications with minimal code changes, enabling you to understand and improve agent performance.

Core Features & Use Cases

Seamless Integration: Wrap your LLM client with Olakai's SDK for automatic data capture.
Custom KPI Configuration: Define and track business-specific Key Performance Indicators relevant to your agent's purpose.
End-to-End Validation: Guides you through the process of setting up, testing, and validating your integration.
Use Case: Integrate Olakai into a customer support chatbot to track conversation success rates, user satisfaction, and resolution times, providing actionable insights for improvement.

Quick Start

Install the Olakai SDK for your language and add the olakai("event", ...) call after your LLM client interaction.

Frequently Asked Questions about integrate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I add monitoring to my AI code to track LLM agent performance?▼

To add monitoring to your AI code, wrap your existing LLM client with the Olakai SDK. This enables automatic data capture and allows you to configure custom KPIs for end-to-end validation of your agent performance.

Can I use Olakai monitoring with both Python and TypeScript AI applications?▼

Yes, Olakai monitoring supports both TypeScript/JavaScript and Python AI applications. The integration provides SDKs for both environments to ensure seamless event tracking and custom data enrichment.

How do I configure custom KPIs for my AI chatbot validation?▼

You can configure custom KPIs for AI chatbot validation by using the Olakai CLI. This guides you through agent registration and custom data configuration to track business-specific metrics like conversation success rates.

What is the best way to group user conversations when tracking LLM events?▼

The best way to group user conversations when tracking LLM events is by using the `chatId` parameter. This feature within the Olakai SDK allows you to group conversations and enrich data for performance analysis.

Do I need the Olakai CLI to set up AI monitoring and agent registration?▼

Yes, you need the Olakai CLI to set up AI monitoring. The CLI is required to guide you through agent registration, custom data configuration, and KPI creation for comprehensive performance tracking.

Does Olakai monitoring work with OpenAI and Anthropic SDKs?▼

Yes, Olakai monitoring works with both OpenAI and Anthropic SDKs. You can wrap these LLM clients with the Olakai SDK to capture interaction data and track custom KPIs automatically.