From scratch (raw fetch)

Use this when the provider isn’t auto-instrumented (local Ollama, vLLM, a self-hosted inference server, a partner API). You emit every span yourself.

Node.js
Python

import trodo from 'trodo-node';

trodo.init({ siteId: process.env.TRODO_SITE_ID });

const LLM_URL = 'http://ollama.internal/api/chat';

export async function answer(userId, question) {
  const { result } = await trodo.wrapAgent('raw-http-agent', async (run) => {
    run.setInput({ question });

    const body = {
      model: 'llama3.1:70b',
      messages: [{ role: 'user', content: question }],
    };

    // Option A — withSpan + setLlm.  Explicit control over span timing.
    const respA = await trodo.withSpan({ kind: 'llm', name: 'ollama.chat' }, async (span) => {
      span.setInput(body);
      const r = await fetch(LLM_URL, { method: 'POST', body: JSON.stringify(body) }).then((x) => x.json());
      span.setLlm({
        model: r.model,
        provider: 'ollama',
        inputTokens: r.prompt_eval_count,
        outputTokens: r.eval_count,
      });
      span.setOutput(r);
      return r;
    });

    // Option B — trackLlmCall.  One-shot, less code.
    const r = await fetch(LLM_URL, { method: 'POST', body: JSON.stringify(body) }).then((x) => x.json());
    await trodo.trackLlmCall({
      model: r.model,
      provider: 'ollama',
      inputTokens: r.prompt_eval_count,
      outputTokens: r.eval_count,
      prompt: body,
      completion: r,
      metadata: { endpoint: '/api/chat' },
    });

    run.setOutput({ answer: r.message?.content });
    return r.message?.content;
  }, { distinctId: userId });

  return result;
}

import trodo, os, httpx

trodo.init(site_id=os.environ["TRODO_SITE_ID"])
LLM_URL = "http://ollama.internal/api/chat"

def answer(user_id, question):
    with trodo.wrap_agent("raw-http-agent", distinct_id=user_id) as run:
        run.set_input({"question": question})
        body = {"model": "llama3.1:70b", "messages": [{"role": "user", "content": question}]}

        # Option A — span + set_llm
        with trodo.span("ollama.chat", kind="llm") as span:
            span.set_input(body)
            r = httpx.post(LLM_URL, json=body).json()
            span.set_llm(
                model=r["model"],
                provider="ollama",
                input_tokens=r["prompt_eval_count"],
                output_tokens=r["eval_count"],
            )
            span.set_output(r)

        # Option B — track_llm_call
        r = httpx.post(LLM_URL, json=body).json()
        trodo.track_llm_call(
            model=r["model"],
            provider="ollama",
            input_tokens=r["prompt_eval_count"],
            output_tokens=r["eval_count"],
            prompt=body,
            completion=r,
            metadata={"endpoint": "/api/chat"},
        )

        run.set_output({"answer": r["message"]["content"]})
        return r["message"]["content"]

Option A vs Option B

Both land identical spans in the database. Pick by style:

	`withSpan` + `setLlm`	`trackLlmCall`
Shape	Wrapper around the call	Call-then-record
Errors	Thrown exceptions set `status='error'` automatically	You have to catch + record yourself
Prefer for	The call is the span’s unit of work	You already have the response object

Custom pricing

If the pricing table doesn’t cover your model — self-hosted, zero-cost, negotiated rate — pass cost explicitly:

await trodo.trackLlmCall({
  model: 'llama3.1:70b',
  provider: 'ollama-self-hosted',
  inputTokens: 1200,
  outputTokens: 320,
  cost: 0,   // self-hosted, don't bill
});

​Option A vs Option B

​Custom pricing

​See also

Option A vs Option B

Custom pricing

See also