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Integrate Olakai into Existing AI Code

This skill guides you through adding Olakai monitoring to an existing AI agent or LLM-powered application with minimal code changes.

For full SDK documentation, see: https://app.olakai.ai/llms.txt

Prerequisites

• Existing working AI agent/application using OpenAI, Anthropic, or other LLM
• Olakai CLI installed and authenticated (npm install -g olakai-cli && olakai login)
• Olakai API key for your agent (get via CLI: olakai agents get AGENT_ID --json | jq '.apiKey')
• Node.js 18+ (for TypeScript) or Python 3.7+ (for Python)

Note: Each agent can have its own API key. Create one with olakai agents create --name "Name" --with-api-key

Why Custom KPIs Are Essential

Adding monitoring is only the first step. The real value of Olakai comes from tracking custom KPIs specific to your agent's business purpose.

Without KPIs configured:

• Only basic token counts and request data
• No aggregated business KPIs on dashboard
• No alerting capabilities
• No ROI tracking

With KPIs configured:

• Custom KPIs (items processed, success rates, quality scores)
• Trend analysis and performance dashboards
• Threshold-based alerting
• Business value calculations

Plan to configure at least 2-4 KPIs that answer: "How do I know this agent is performing well?"

KPIs are unique per agent. If adding monitoring to an agent that needs the same KPIs as another already-configured agent, you must still create new KPI definitions for this agent. KPIs cannot be shared or reused across agents.

Understanding the customData to KPI Pipeline

Before adding monitoring, understand how custom data flows through Olakai:

SDK customData → CustomDataConfig (Schema) → Context Variable → KPI Formula → kpiData

Critical Rules

IMPORTANT: The SDK accepts any JSON in customData, but only fields registered as CustomDataConfigs are processed. Unregistered fields are stored but cannot be used in KPIs.

Quick Start (5-Minute Integration)

For TypeScript/JavaScript

1. Install the SDK:

npm install @olakai/sdk

2. Add tracking after your LLM call:

Before:

import OpenAI from "openai";
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: userMessage }],
});

After:

import OpenAI from "openai";
import { olakaiConfig, olakai } from "@olakai/sdk";

olakaiConfig({ apiKey: process.env.OLAKAI_API_KEY });

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: userMessage }],
});

// Track the interaction (fire-and-forget)
olakai("event", "ai_activity", {
  prompt: userMessage,
  response: response.choices[0].message.content,
  tokens: response.usage?.total_tokens,
  userEmail: user.email,
  task: "Customer Experience",
});

For Python

1. Install the SDK:

pip install olakai-sdk

2. Add tracking after your LLM call:

Before:

from openai import OpenAI
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": user_message}],
)

After:

from openai import OpenAI
from olakaisdk import olakai_config, olakai, OlakaiEventParams

olakai_config(os.getenv("OLAKAI_API_KEY"))
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": user_message}],
)

# Track the interaction
olakai("event", "ai_activity", OlakaiEventParams(
    prompt=user_message,
    response=response.choices[0].message.content,
    tokens=response.usage.total_tokens,
    userEmail=user.email,
    task="Customer Experience",
))

Detailed Integration Guide

Step 1: Identify Your Integration Pattern

Pattern A: Single LLM Client You have one OpenAI/Anthropic client used throughout your app. Use the fire-and-forget olakai() call after each completion.

Pattern B: Multiple LLM Calls per Request Your agent makes several LLM calls to complete one task. Use manual event tracking to aggregate calls into a single event.

Pattern C: Streaming Responses You stream LLM responses to users. Track after the stream completes with the full accumulated response.

Pattern D: Third-Party LLM (not OpenAI/Anthropic) You use Perplexity, Groq, local models, etc. Use manual event tracking via olakai() or olakai_event().

Step 2: Install and Configure

TypeScript Setup

// lib/olakai.ts - Initialize once at app startup
import { olakaiConfig } from "@olakai/sdk";

olakaiConfig({
  apiKey: process.env.OLAKAI_API_KEY!,
  debug: process.env.NODE_ENV === "development",
});

Python Setup

# lib/olakai.py - Initialize once at app startup
import os
from olakaisdk import olakai_config

olakai_config(
    api_key=os.getenv("OLAKAI_API_KEY"),
    debug=os.getenv("DEBUG") == "true"
)

Step 3: Add Context to Calls

Adding User Information

TypeScript:

olakai("event", "ai_activity", {
  prompt: userMessage,
  response: aiResponse,
  userEmail: user.email,
  task: "Customer Experience",
});

Python:

olakai("event", "ai_activity", OlakaiEventParams(
    prompt=user_message,
    response=ai_response,
    userEmail=user.email,
    task="Customer Experience",
))

Grouping Events by Conversation (chatId)

For assistive AI (chatbots/copilots), use chatId to group multiple turns of a conversation together. This is required for CHAT-scoped KPIs that analyze the full conversation.

olakai("event", "ai_activity", {
  prompt: userMessage,
  response: aiResponse,
  chatId: conversationId,  // groups turns in the same conversation
  userEmail: user.email,
});

When to use chatId: If your agent handles multi-turn conversations and you want KPIs that evaluate the entire conversation (e.g., sentiment scoring, satisfaction), pass a consistent chatId across all turns.

Adding Custom Data

IMPORTANT: Only send fields you've registered as CustomDataConfigs (Step 5.3). Unregistered fields are stored but cannot be used in KPIs.

Only send data you'll use in KPIs or for filtering. Don't duplicate fields already tracked by the platform (session ID, agent ID, user email, timestamps, token count, model, provider — all tracked automatically).

TypeScript:

olakai("event", "ai_activity", {
  prompt: userMessage,
  response: aiResponse,
  userEmail: user.email,
  customData: {
    // Only include fields registered as CustomDataConfigs
    Department: user.department,
    ProjectId: currentProject.id,
    Priority: ticket.priority,
  },
});

Step 4: Handle Agentic Workflows

If your agent makes multiple LLM calls per task, aggregate them into a single event.

taskExecutionId — Critical for multi-agent workflows. If multiple agents collaborate on the same task, the orchestrator must generate ONE taskExecutionId and pass it to all agents. This is how Olakai correlates cross-agent work as a single logical task.

async function processDocument(doc: Document): Promise<string> {
  const startTime = Date.now();
  let totalTokens = 0;

  // Step 1: Extract
  const extraction = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "user", content: `Extract from: ${doc.content}` }],
  });
  totalTokens += extraction.usage?.total_tokens ?? 0;

  // Step 2: Analyze
  const analysis = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "user", content: `Analyze: ${extraction.choices[0].message.content}` }],
  });
  totalTokens += analysis.usage?.total_tokens ?? 0;

  const result = analysis.choices[0].message.content ?? "";

  // Track the complete workflow as ONE event
  olakai("event", "ai_activity", {
    prompt: `Process document: ${doc.title}`,
    response: result,
    tokens: totalTokens,
    requestTime: Date.now() - startTime,
    taskExecutionId: crypto.randomUUID(),
    task: "Data Processing & Analysis",
    customData: {
      DocumentType: doc.type,
      StepCount: 2,
      Success: 1,
    },
  });

  return result;
}

Step 5: Configure Custom KPIs (Essential for Value)

This step is required to get real value from Olakai. Without KPIs, you're only tracking events — not gaining actionable insights.

5.1 Install CLI (if not already)

npm install -g olakai-cli
olakai login

5.2 Register Your Agent

olakai agents create \
  --name "Document Processor" \
  --description "Processes and summarizes documents" \
  --workflow WORKFLOW_ID \
  --with-api-key

5.2.1 Ensure Agent Has a Workflow

Every agent MUST belong to a workflow, even if it's the only agent.

# Check if agent has a workflow
olakai agents get YOUR_AGENT_ID --json | jq '.workflowId'

# If null, create a workflow and associate:
olakai workflows create --name "Your Workflow Name" --json
olakai agents update YOUR_AGENT_ID --workflow WORKFLOW_ID

5.3 Create Custom Data Configs FIRST

IMPORTANT: Create configs for ALL fields you send in customData. Only registered fields can be used in KPIs. CustomDataConfigs are agent-scoped.

olakai custom-data create --agent-id YOUR_AGENT_ID --name "DocumentType" --type STRING
olakai custom-data create --agent-id YOUR_AGENT_ID --name "StepCount" --type NUMBER
olakai custom-data create --agent-id YOUR_AGENT_ID --name "Success" --type NUMBER

# Verify all configs exist for this agent
olakai custom-data list --agent-id YOUR_AGENT_ID

5.4 Create KPIs

olakai kpis create \
  --name "Documents Processed" \
  --agent-id YOUR_AGENT_ID \
  --calculator-id formula \
  --formula "IF(Success = 1, 1, 0)" \
  --aggregation SUM

olakai kpis create \
  --name "Avg Steps per Document" \
  --agent-id YOUR_AGENT_ID \
  --calculator-id formula \
  --formula "StepCount" \
  --aggregation AVERAGE

5.5 Update SDK Code to Match

After creating configs, ensure your SDK code sends exactly those field names:

customData: {
  DocumentType: doc.type,     // Matches CustomDataConfig "DocumentType"
  StepCount: 2,               // Matches CustomDataConfig "StepCount"
  Success: true ? 1 : 0,      // Matches CustomDataConfig "Success"
}

Framework-Specific Integrations

Next.js API Routes

// app/api/chat/route.ts
import { NextRequest, NextResponse } from "next/server";
import { olakai } from "@olakai/sdk";
import { auth } from "@/auth";

export async function POST(req: NextRequest) {
  const session = await auth();
  if (!session?.user) {
    return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
  }

  const { message } = await req.json();

  const response = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "user", content: message }],
  });

  olakai("event", "ai_activity", {
    prompt: message,
    response: response.choices[0].message.content,
    userEmail: session.user.email!,
    task: "Customer Experience",
  });

  return NextResponse.json({ reply: response.choices[0].message.content });
}

FastAPI (Python)

from fastapi import FastAPI, Depends
from olakaisdk import olakai_config, olakai, OlakaiEventParams

app = FastAPI()

@app.on_event("startup")
async def startup():
    olakai_config(os.getenv("OLAKAI_API_KEY"))

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

@app.post("/chat")
async def chat(message: str, user: User = Depends(get_current_user)):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": message}]
    )

    olakai("event", "ai_activity", OlakaiEventParams(
        prompt=message,
        response=response.choices[0].message.content,
        userEmail=user.email,
        task="Customer Experience",
    ))

    return {"reply": response.choices[0].message.content}

Handling Edge Cases

Streaming Responses

Track after the stream completes with the full response:

const stream = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: userMessage }],
  stream: true,
});

let fullResponse = "";
for await (const chunk of stream) {
  fullResponse += chunk.choices[0]?.delta?.content ?? "";
  res.write(chunk.choices[0]?.delta?.content ?? "");
}

// Track after stream completes
olakai("event", "ai_activity", {
  prompt: userMessage,
  response: fullResponse,
  userEmail: user.email,
});

Error Handling

try {
  const response = await openai.chat.completions.create({
    model: "gpt-4o",
    messages,
  });
  return response.choices[0].message.content;
} catch (error) {
  // Track the failed attempt
  olakai("event", "ai_activity", {
    prompt: messages[messages.length - 1].content,
    response: `Error: ${error instanceof Error ? error.message : "Unknown"}`,
    task: "Software Development",
    customData: { Success: 0 },
  });
  throw error;
}

Non-OpenAI Providers

For Anthropic, Perplexity, or other providers, use manual tracking:

import Anthropic from "@anthropic-ai/sdk";

const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });

async function callClaude(prompt: string): Promise<string> {
  const startTime = Date.now();

  const response = await anthropic.messages.create({
    model: "claude-sonnet-4-20250514",
    max_tokens: 1024,
    messages: [{ role: "user", content: prompt }],
  });

  const content = response.content[0].type === "text" ? response.content[0].text : "";

  olakai("event", "ai_activity", {
    prompt,
    response: content,
    tokens: response.usage.input_tokens + response.usage.output_tokens,
    requestTime: Date.now() - startTime,
    task: "Content Development",
  });

  return content;
}

Test-Validate-Iterate Cycle

Never assume your integration is working. Always validate by generating a test event and inspecting the actual data.

Step 1: Generate a Test Event

Run your application to trigger at least one LLM call.

Step 2: Fetch and Inspect the Event

olakai activity list --limit 1 --json
olakai activity get EVENT_ID --json

Step 3: Validate Each Component

Check customData is present:

olakai activity get EVENT_ID --json | jq '.customData'

Check KPIs are numeric (not strings or null):

olakai activity get EVENT_ID --json | jq '.kpiData'

CORRECT:

{ "My KPI": 42 }

WRONG (formula stored as string):

{ "My KPI": "MyVariable" }

Fix: olakai kpis update KPI_ID --formula "MyVariable"

WRONG (null value):

{ "My KPI": null }

Fix by ensuring:

1. CustomDataConfig exists: olakai custom-data create --agent-id ID --name "MyVariable" --type NUMBER
2. Field name case matches exactly (case-sensitive)
3. SDK actually sends the field in customData

Validation Flow

1. Trigger LLM call (generate event)
           ↓
2. Fetch: olakai activity get ID --json
           ↓
3. Event exists? NO → Check API key, SDK init, debug mode
           ↓
4. customData correct? NO → Fix SDK customData parameter
           ↓
5. kpiData numeric? NO → olakai kpis update ID --formula "X"
           ↓
6. kpiData not null? NO → Create CustomDataConfig, check field name case
           ↓
✅ Integration validated

KPI Formula Reference

Supported Operators

Common Formula Patterns

--formula "StepCount"                          # passthrough
--formula "SuccessRate * 100"                  # percentage conversion
--formula "IF(Success = 1, 1, 0)"              # conditional counting
--formula "IF(PII detected, 1, 0)"             # built-in variable
--formula "IF(ISDEFINED(MyField), MyField, 0)" # null-safe

Aggregation Types

Quick Reference

// TypeScript — initialize once
import { olakaiConfig, olakai } from "@olakai/sdk";
olakaiConfig({ apiKey: process.env.OLAKAI_API_KEY });

// Track any interaction
olakai("event", "ai_activity", {
  prompt: "input",
  response: "output",
  tokens: 1500,
  requestTime: 5000,
  userEmail: "user@example.com",
  chatId: "conversation-id",
  taskExecutionId: "uuid-shared-across-agents",
  task: "Data Processing & Analysis",
  customData: { StepCount: 3, Success: 1 },
});

# Python — initialize once
from olakaisdk import olakai_config, olakai, OlakaiEventParams
olakai_config(os.getenv("OLAKAI_API_KEY"))

# Track any interaction
olakai("event", "ai_activity", OlakaiEventParams(
    prompt="input",
    response="output",
    tokens=1500,
    requestTime=5000,
    userEmail="user@example.com",
    chatId="conversation-id",
    taskExecutionId="uuid-shared-across-agents",
    task="Data Processing & Analysis",
    customData={"StepCount": 3, "Success": 1},
))