Mira cada span de tus agentes.

// Cada callTool emite un span
import { wrapMcpClient, HttpSpanExporter } from '@lookspan/mcp';

const exporter = new HttpSpanExporter({
  endpoint: 'http://127.0.0.1:3100/api/ingest',
});

const { client } = wrapMcpClient(mcpClient, {
  exporter,
  agentId: 'mi-agente',
});

// Úsalo exactamente igual que antes
await client.callTool({ name: 'read_file', arguments: { path: '/tmp/foo.txt' } });

from lookspan import LookspanClient
from lookspan_langgraph import LookspanCallbackHandler

client = LookspanClient(endpoint="http://127.0.0.1:3100/api/ingest")
handler = LookspanCallbackHandler(client=client, agent_id="mi-agente")

# Pásalo como callback — LangGraph hace el resto
result = graph.invoke({"messages": []}, config={"callbacks": [handler]})
client.flush()

from crewai import Crew
from lookspan import LookspanClient
from lookspan_crewai import attach_lookspan

client = LookspanClient(endpoint="http://127.0.0.1:3100/api/ingest")
attach_lookspan(client, agent_id="research-crew")

# Una llamada instrumenta toda la crew
crew = Crew(agents=[...], tasks=[...])
crew.kickoff()
client.flush()

# Cualquier lenguaje que pueda hacer un POST
curl -X POST http://127.0.0.1:3100/api/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "source": "mi-agente",
    "spans": [{
      "traceId": "tr_abc123",
      "spanId": "sp_def456",
      "type": "llm_call",
      "name": "completion",
      "status": "ok",
      "framework": "custom",
      "model": "gpt-4o",
      "provider": "openai"
    }]
  }'

# Reutiliza tu instrumentación OpenTelemetry
export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://127.0.0.1:3100/v1/traces
export OTEL_EXPORTER_OTLP_PROTOCOL=http/json

# Los atributos gen_ai.* se mapean a modelo, proveedor y tokens.