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LangChain / LangGraph

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Use this integration for LangChain or LangGraph applications. LangChain can emit GenAI traces two ways, and both quickstart paths lead here:

Both methods capture the full workflow: chains, tool calls, retries, and loops, not just the LLM call.

Before you start, generate an ingestion token and connect a provider. Go to the AI Cost Management Quickstart for those steps. Replace <YOUR_TOKEN> below with that token and adjust the endpoint for your Harness cluster.

GenAI semantic conventions required

Cost is calculated from OpenTelemetry traces with GenAI semantic conventions. Go to the GenAI Span Attribute Reference to review the attributes CACM reads.

Route existing traces (LangSmith)​

Use this method if LangSmith already traces your application. It needs no code changes: LangChain and LangGraph emit OTel when LangSmith's OpenTelemetry export is enabled with LANGSMITH_OTEL_ENABLED=true, so you add that flag and point the exporter at Harness. Go to LangSmith's OpenTelemetry support to review the export options.

Set these environment variables, then restart the application:

export LANGSMITH_OTEL_ENABLED=true
export OTEL_EXPORTER_OTLP_ENDPOINT=https://app.harness.io/udp-ingest/otel
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer <YOUR_TOKEN>"
export OTEL_TRACES_EXPORTER=otlp
export OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf
Alternative LangSmith configuration

Some LangSmith setups also set LANGSMITH_TRACING=true. If traces do not appear with the configuration above, try adding LANGSMITH_TRACING=true:

export LANGSMITH_OTEL_ENABLED=true
export LANGSMITH_TRACING=true
export OTEL_EXPORTER_OTLP_ENDPOINT=https://app.harness.io/udp-ingest/otel
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer <YOUR_TOKEN>"

Instrument your app (OpenInference)​

Use this method if you are not using LangSmith and need to instrument the application yourself. The OpenInference LangChain instrumentation exports traces to Harness with a few lines of setup code.

Install the instrumentation library:

pip install openinference-instrumentation-langchain \
  opentelemetry-sdk opentelemetry-exporter-otlp

Configure the OpenTelemetry exporter once at application startup, then add the framework instrumentor:

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
from openinference.instrumentation.langchain import LangChainInstrumentor

provider = TracerProvider()
provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="https://app.harness.io/udp-ingest/otel/v1/traces",
            headers={"Authorization": "Bearer <YOUR_TOKEN>"},
        )
    )
)
trace.set_tracer_provider(provider)

LangChainInstrumentor().instrument(tracer_provider=provider)

What this produces:

  • One trace per LangChain invocation (chain, agent, tool).
  • Nested spans for each step (LLM call, tool use, retrieval).
  • GenAI semantic conventions on LLM spans.
  • Cost calculated from token counts.

Reduce Trace Data Volume​

Large payloads and over-instrumentation inflate span volume and storage cost. To keep trace data manageable in high-traffic production:

  • Scope instrumentation to LLM calls: Instrument the model calls that carry cost, not every function in the application.
  • Sample a percentage of traces: Export a representative sample rather than every trace.

Verify Traces in Cost Explorer​

  1. Run the application so traces flow. They usually appear within a few minutes; allow up to about 20 minutes.
  2. Go to Cloud & AI Cost Management > Cost Explorer.
  3. Select the AI Traces view or group by Service Name, and find your service.
  4. Select a service row to open the Service Traces drawer and inspect the span waterfall.

Next Steps​