Distributed Tracing

Overview

Anyway provides OpenTelemetry-compatible distributed tracing for your AI applications. Traces help you:

Debug issues by following requests through your system
Identify performance bottlenecks
Understand the flow of AI operations
Correlate LLM calls with your application logic

How Tracing Works

Every AI operation creates a trace composed of spans:

Trace: user-request
├── Span: validate-input (2ms)
├── Span: openai.chat.completions (1,234ms)
│   └── Attributes: model=gpt-4o, tokens=150, cost=$0.0035
├── Span: process-response (5ms)
└── Span: save-to-db (12ms)

Automatic Spans

The Anyway SDK automatically creates spans for supported LLM providers:

OpenAI API calls
Anthropic API calls
AWS Bedrock, Google Vertex AI, Cohere, Together AI
Vector DB operations (Pinecone, ChromaDB, Qdrant)

Custom Spans with Decorators

Structure your traces with workflow and task decorators:

Python
JavaScript

from anyway.sdk.decorators import workflow, task
from openai import OpenAI

client = OpenAI()

@task(name="validate_input")
def validate(user_input: str) -> str:
    # validation logic
    return user_input

@task(name="llm_call")
def call_llm(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

@workflow(name="process_query")
def process_query(user_input: str) -> str:
    validated = validate(user_input)
    result = call_llm(validated)
    return result

import { withWorkflow, withTask } from "@anyway-sh/node-server-sdk";
import OpenAI from "openai";

const client = new OpenAI();

async function validate(userInput: string) {
  return withTask({ name: "validate_input" }, async () => {
    // validation logic
    return userInput;
  });
}

async function callLlm(prompt: string) {
  return withTask({ name: "llm_call" }, async () => {
    const response = await client.chat.completions.create({
      model: "gpt-4o",
      messages: [{ role: "user", content: prompt }],
    });
    return response.choices[0].message.content;
  });
}

async function processQuery(userInput: string) {
  return withWorkflow({ name: "process_query" }, async () => {
    const validated = await validate(userInput);
    return callLlm(validated);
  });
}

Association Properties

Add metadata to traces for filtering and grouping:

Python
JavaScript

@workflow(name="user-query", association_properties={
    "user_id": "user-123",
    "team": "growth",
    "feature": "chatbot",
})
def handle_query(query: str):
    return call_llm(query)

await withWorkflow(
  {
    name: "user-query",
    associationProperties: {
      userId: "user-123",
      team: "growth",
      feature: "chatbot",
    },
  },
  async () => {
    return callLlm(query);
  },
);

Then filter traces in the dashboard by these properties.

Span Attributes

These attributes are automatically set for LLM spans:

Attribute	Description
`gen_ai.system`	Provider (openai, anthropic)
`gen_ai.request.model`	Requested model
`gen_ai.response.model`	Model used in response
`gen_ai.usage.prompt_tokens`	Input/prompt tokens
`gen_ai.usage.completion_tokens`	Output/completion tokens
`gen_ai.usage.cost`	Estimated cost in USD

Viewing Traces

In the Anyway Dashboard:

Navigate to Traces
Use filters to find specific traces:
- Time range
- Model
- Latency threshold
- Cost threshold
- Association properties
Click a trace to see the full span tree

Context Propagation

Anyway automatically propagates trace context across:

Async functions
Nested workflow/task calls
HTTP requests (with W3C trace context headers)

Next Steps

Cost Tracking

Monitor spending in real-time

Payments

Create payment links and accept payments

Getting Started

Python SDK

JavaScript SDK

Features

Billing

Distributed Tracing

Overview

How Tracing Works

Automatic Spans

Custom Spans with Decorators

Association Properties

Span Attributes

Viewing Traces

Context Propagation

Next Steps

Cost Tracking

Payments

Getting Started

Python SDK

JavaScript SDK

Features

Billing

​Overview

​How Tracing Works

​Automatic Spans

​Custom Spans with Decorators

​Association Properties

​Span Attributes

​Viewing Traces

​Context Propagation

​Next Steps

Cost Tracking

Payments

Overview

How Tracing Works

Automatic Spans

Custom Spans with Decorators

Association Properties

Span Attributes

Viewing Traces

Context Propagation

Next Steps