With Adaline Proxy

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The flow

1. SDK Configuration — Update your AI SDK’s baseUrl to point to Proxy
2. Header Addition — Add required Adaline headers for authentication, project and prompt identification
3. Transparent Proxying — Proxy forwards your requests to the actual AI provider
4. Automatic Telemetry — Responses are captured and logged as traces and spans in your Adaline project and prompt
5. Original Response — Your application receives the exact same response it would from the provider

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Benefits

• Minimal Code Changes — Works with existing AI SDK implementations by adding a couple of lines of code
• Automatic Observability — Captures traces and spans without manual logging
• Real-time Monitoring — Immediate visibility into AI application performance, including token usage and costs
• Continuous Evaluations — Setup one-click continuous AI evaluations for your AI applications
• Provider Agnostic — Supports all major AI providers
• Production Ready — Built for scale with high availability and security
• No Extra Costs — Proxy requests are billed as regular API Log requests to Adaline

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Quick start

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("OPENAI_API_KEY"),
    base_url="https://gateway.adaline.ai/v1/openai/",
)

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}],
    extra_headers={
        "adaline-api-key": os.getenv("ADALINE_API_KEY"),
        "adaline-project-id": os.getenv("ADALINE_PROJECT_ID"),
        "adaline-prompt-id": os.getenv("ADALINE_PROMPT_ID"),
        "adaline-trace-name": "my-workflow",
    },
)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: "https://gateway.adaline.ai/v1/openai/",
  defaultHeaders: {
    "adaline-api-key": process.env.ADALINE_API_KEY,
    "adaline-project-id": process.env.ADALINE_PROJECT_ID,
    "adaline-prompt-id": process.env.ADALINE_PROMPT_ID,
  },
});

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

import os
from anthropic import Anthropic

client = Anthropic(
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    base_url="https://gateway.adaline.ai/v1/anthropic/",
    default_headers={
        "adaline-api-key": os.getenv("ADALINE_API_KEY"),
        "adaline-project-id": os.getenv("ADALINE_PROJECT_ID"),
        "adaline-prompt-id": os.getenv("ADALINE_PROMPT_ID"),
    },
)

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
)

​

Supported providers

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Required headers

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Optional trace headers

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Optional span headers

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Group requests into a single trace

import uuid

trace_id = str(uuid.uuid4())

# First request
embedding = client.embeddings.create(
    model="text-embedding-3-small",
    input="User query",
    extra_headers={
        "adaline-api-key": os.getenv("ADALINE_API_KEY"),
        "adaline-project-id": os.getenv("ADALINE_PROJECT_ID"),
        "adaline-prompt-id": os.getenv("ADALINE_PROMPT_ID"),
        "adaline-trace-reference-id": trace_id,
        "adaline-span-name": "query-embedding",
    },
)

# Second request — same trace
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "..."}],
    extra_headers={
        "adaline-api-key": os.getenv("ADALINE_API_KEY"),
        "adaline-project-id": os.getenv("ADALINE_PROJECT_ID"),
        "adaline-prompt-id": os.getenv("ADALINE_PROMPT_ID"),
        "adaline-trace-reference-id": trace_id,
        "adaline-span-name": "chat-completion",
    },
)

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What gets captured automatically

• Request and response payloads
• Token usage (input and output) and cost
• Latency
• Model and provider information
• Errors and status codes

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Next steps