Olakai Monitoring Integration

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;
}

npm install -g olakai-cli
olakai login

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

# 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

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

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

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

// 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 });
}

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}

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,
});

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;
}

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;
}

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

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

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

{ "My KPI": 42 }

{ "My KPI": "MyVariable" }

{ "My KPI": null }

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

--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

// 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},
))