> For the complete documentation index, see [llms.txt](https://developer.harness.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://developer.harness.io/cloud-cost-management/integrations/sdk-integrations/open-source-sdks/langchain.md).

# LangChain / LangGraph

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

* **Already using LangSmith (no code changes):** Turn on LangSmith's OpenTelemetry export and point it at Harness. Go to [Route existing traces (LangSmith)](#route-existing-traces-langsmith) below.
* **Not using LangSmith:** Add the OpenInference instrumentation to your application code. Go to [Instrument your app (OpenInference)](#instrument-your-app-openinference) below.

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](/cloud-cost-management/new-to-cacm/ai-cost-management/quickstart.md) for those steps. Replace `<YOUR_TOKEN>` below with that token and adjust the endpoint for your Harness cluster.

{% hint style="info" %}
**GENAI SEMANTIC CONVENTIONS REQUIRED**

Cost is calculated from OpenTelemetry traces with GenAI semantic conventions. Go to the [GenAI Span Attribute Reference](/cloud-cost-management/references/ai-cost-management/genai-span-attribute-reference.md) to review the attributes CACM reads.
{% endhint %}

***

### Route existing traces (LangSmith) <a href="#route-existing-traces-langsmith" id="route-existing-traces-langsmith"></a>

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](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_opentelemetry) to review the export options.

Set these environment variables, then restart the application:

```bash
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
```

{% hint style="info" %}
**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`:

```bash
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>"
```

{% endhint %}

***

### Instrument your app (OpenInference) <a href="#instrument-your-app-openinference" id="instrument-your-app-openinference"></a>

Use this method if you are not using LangSmith and need to instrument the application yourself. The [OpenInference LangChain instrumentation](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-langchain) exports traces to Harness with a few lines of setup code.

Install the instrumentation library:

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

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

```python
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 <a href="#reduce-trace-data-volume" id="reduce-trace-data-volume"></a>

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 <a href="#verify-traces-in-cost-explorer" id="verify-traces-in-cost-explorer"></a>

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 <a href="#next-steps" id="next-steps"></a>

* Go to [Supported Providers and Frameworks](/cloud-cost-management/references/ai-cost-management/supported-providers-and-frameworks.md) to check native GenAI export support.
* Go to the [GenAI Span Attribute Reference](/cloud-cost-management/references/ai-cost-management/genai-span-attribute-reference.md) to review the exact attributes CACM reads.
* Go to [AI Cost Troubleshooting](/cloud-cost-management/resources/ai-cost-troubleshooting.md) if traces do not appear or show no cost.