Documentation

from farol import trace
from anthropic import Anthropic

client = Anthropic()

@trace(agent_name="my-agent", farol_key="frl_your_key_here")
def my_agent(task, run=None):
    run["topic"] = task

    with run.span("llm_call", type="llm") as span:
        response = client.messages.create(
            model="claude-haiku-4-5",
            max_tokens=1024,
            messages=[{"role": "user", "content": task}]
        )
        span.input_tokens = response.usage.input_tokens
        span.output_tokens = response.usage.output_tokens

    return response.content[0].text

from farol import trace
import httpx

@trace(agent_name="my-agent", farol_key="frl_your_key_here")
def my_agent(task, run=None):
    run["topic"] = task

    with run.span("llm_call", type="llm") as span:
        response = httpx.post(
            "https://api.anthropic.com/v1/messages",
            headers={
                "x-api-key": "your-anthropic-key",
                "anthropic-version": "2023-06-01"
            },
            json={
                "model": "claude-haiku-4-5",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": task}]
            }
        ).json()
        span.input_tokens = response["usage"]["input_tokens"]
        span.output_tokens = response["usage"]["output_tokens"]

    return response["content"][0]["text"]

from farol import trace
from openai import OpenAI

client = OpenAI(
    api_key="your-openai-key"
)

@trace(agent_name="my-agent", farol_key="frl_your_key_here")
def my_agent(task, run=None):
    run["topic"] = task

    with run.span("llm_call", type="llm") as span:
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": task}]
        )
        span.input_tokens = response.usage.prompt_tokens
        span.output_tokens = response.usage.completion_tokens

    return response.choices[0].message.content

from farol import trace
import google.generativeai as genai

genai.configure(api_key="your-gemini-key")
model = genai.GenerativeModel("gemini-1.5-flash")

@trace(agent_name="my-agent", farol_key="frl_your_key_here")
def my_agent(task, run=None):
    run["topic"] = task

    with run.span("llm_call", type="llm") as span:
        response = model.generate_content(task)
        span.input_tokens = response.usage_metadata.prompt_token_count
        span.output_tokens = response.usage_metadata.candidates_token_count

    return response.text

from farol import trace
from mistralai import Mistral

client = Mistral(api_key="your-mistral-key")

@trace(agent_name="my-agent", farol_key="frl_your_key_here")
def my_agent(task, run=None):
    run["topic"] = task

    with run.span("llm_call", type="llm") as span:
        response = client.chat.complete(
            model="mistral-small-latest",
            messages=[{"role": "user", "content": task}]
        )
        span.input_tokens = response.usage.prompt_tokens
        span.output_tokens = response.usage.completion_tokens

    return response.choices[0].message.content

from farol import trace
from openai import OpenAI

client = OpenAI(
    api_key="your-xai-key",
    base_url="https://api.x.ai/v1"
)

@trace(agent_name="my-agent", farol_key="frl_your_key_here")
def my_agent(task, run=None):
    run["topic"] = task

    with run.span("llm_call", type="llm") as span:
        response = client.chat.completions.create(
            model="grok-3-mini",
            messages=[{"role": "user", "content": task}]
        )
        span.input_tokens = response.usage.prompt_tokens
        span.output_tokens = response.usage.completion_tokens

    return response.choices[0].message.content

import { trace } from '@usefarol/sdk';

const myAgent = trace(
  async (run, task: string) => {
    run.topic = task;

    const span = run.startSpan('llm_call', { type: 'llm' });
    try {
      // call your LLM here (Anthropic, OpenAI, Gemini, etc.)
      const response = await yourLlm.call(task);
      span.inputTokens = response.usage.input_tokens;
      span.outputTokens = response.usage.output_tokens;
      run.inputTokens += response.usage.input_tokens;
      run.outputTokens += response.usage.output_tokens;
      span.end();
      return response.text;
    } catch (e) {
      span.end(e instanceof Error ? e : new Error(String(e)));
      throw e;
    }
  },
  {
    agentName: 'my-agent',
    farolKey: 'frl_your_key_here',
  }
);

const result = await myAgent('your task here');

Full examples for Anthropic, OpenAI and more on npm →