Supervise LangChain and LangGraph agents

pip install openinference-instrumentation-langchain opentelemetry-sdk opentelemetry-exporter-otlp-proto-http

export TOOLCAISE_AGENT_TOKEN=...   # the agent's credential from Dashboard, Agents
export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://toolcaise.com/api/v1/otlp/v1/traces
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer%20$TOOLCAISE_AGENT_TOKEN"
export OTEL_EXPORTER_OTLP_COMPRESSION=gzip
export OTEL_METRICS_EXPORTER=none OTEL_LOGS_EXPORTER=none
# Keep prompts and outputs out of the export. Toolcaise never reads them either way.
export OPENINFERENCE_HIDE_INPUTS=true OPENINFERENCE_HIDE_OUTPUTS=true

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

# Endpoint, credential and compression come from the OTEL_ environment variables above.
provider = TracerProvider()
provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
trace.set_tracer_provider(provider)
from openinference.instrumentation.langchain import LangChainInstrumentor

LangChainInstrumentor().instrument(tracer_provider=provider)

# Build and invoke your chain or graph exactly as before, for example:
# graph = builder.compile()
# graph.invoke({"messages": [("user", "Summarise today's incidents")]})

from toolcaise_agent import Toolcaise

toolcaise = Toolcaise()

@tool
def send_invoice(customer_id: str, amount: float) -> str:
    """Send an invoice."""
    return toolcaise.guarded_action(
        f"Send an invoice for {amount:.2f}",
        {"customer": customer_id, "amount": amount},
        lambda approved: billing.send(approved["customer"], approved["amount"]),
    )

with toolcaise.run("billing-agent"):
    graph.invoke({"messages": [("user", "Invoice this month's customers")]})