This guide demonstrates distributed tracing setup using an enterprise search chatbot (similar to Glean) example that:
  • Connects to company data sources (Google Drive, Dropbox)
  • Enables natural language search across data via Slack or web interface

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System architecture

The application uses 5 microservices: System architecture showing microservice components
  1. API Gateway: Authenticates the users and routes API requests
  2. Planner: Creates execution plans for queries
  3. Intent detector: Analyzes query intent
  4. Answer generator: Creates prompts based on planner instructions and RAG context
  5. RAG pipeline: Retrieves relevant information from vector database

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Setting up the Maxim dashboard

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1. Create Maxim repository

Create a new repository called “Chatbot production”:

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2. Generate API key

1

Navigate to Settings

Navigate to Settings → API Keys
2

Generate Key

Generate and save new API key

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3. Install SDK

npm install @maximai/maxim-js
Python
pip install maxim-py
Go
go get github.com/maximhq/maxim-go
Java
compileOnly("ai.getmaxim:sdk:0.1.3")

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4. Initialize logger

Add this code to initialize the logger in each service: 
import { Maxim } from "@maximai/maxim-js";

const maxim = new Maxim({ apiKey: "api-key" });
const logger = await maxim.logger({ id: "log-repository-id" });

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5. Create trace in API gateway

Use cf-request-id as trace identifier: 
const trace = logger.trace({
    id: req.headers["cf-request-id"],
    name: "user-query",
    tags: {
        userId: req.body.userId,
        accountId: req.body.accountId
    },
});

trace.input("Hello, how are you?");
trace.output("I'm fine, thank you!");

trace.end();
You can get a hold of a trace in two ways: 
// Method 1: Using logger and trace ID
logger.traceTag("trace-id", "newTag", "newValue");
logger.traceEnd("trace-id");

// Method 2: Using trace object
const trace = logger.trace({ id: "trace-id" });
trace.addTag("newTag", "newValue");
trace.end();
You can manipulate every entity of Maxim observability framework (Span, Generation, Retrieval, Event) in the same way.

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6. Add spans in services

Create spans to track operations in each service: 
const trace = logger.trace({id: req.headers["cf-request-id"]});
const span = trace.span({
    id: uuid(),
    name: "plan-query",
    tags: {
        userId: req.body.userId,
        accountId: req.body.accountId
    },
});
When creating spans, consider adding relevant tags that provide context about the operation being performed. These tags help in filtering and analyzing traces later. Remember to end each span once its operation completes to ensure accurate timing measurements.

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7. Log LLM calls

1

Track LLM Interactions

const generation = span.generation({
    id: uuid(),
    name: "plan-query",
    provider: "openai",
    model: "gpt-3.5-turbo-16k",
    modelParameters: { temperature: 0.7 },
    tags: {
        userId: req.body.userId,
        accountId: req.body.accountId
    },
});
2

Log Response Data

generation.result({
    id: uuid(),
    object: "chat.completion",
    created: Date.now(),
    model: "gpt-3.5-turbo-16k",
    choices: [{
        index: 0,
        message: {
            role: "assistant",
            content: "response"
        },
        finish_reason: "stop"
    }],
    usage: {
        prompt_tokens: 100,
        completion_tokens: 50,
        total_tokens: 150
    }
});
Maxim currently supports OpenAI message format. to convert other messaging formats to OpenAI format in the SDK.

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View traces

Access your traces in the Maxim dashboard within seconds of logging. The dashboard shows:
  • Complete request lifecycle
  • Durations and relationships of the Entities (Span/Trace)
  • LLM generation details
  • Performance metrics