> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.site/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain

> Trace LangChain Python applications with OpenInference and send spans to Arize AX for LLM observability.

[LangChain](https://www.langchain.com/) is a Python framework for composing LLM applications — chains, agents, RAG, tool use. Arize AX captures every LangChain run — prompt templates, chat-model calls, retrievals, tools, and the runnable hierarchy that wraps them — via the [`openinference-instrumentation-langchain`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-langchain) package.

<CardGroup>
  <Card horizontal icon="https://storage.googleapis.com/arize-phoenix-assets/assets/images/phoenix-docs-images/gc.ico" href="http://colab.research.google.com/github/Arize-ai/tutorials/blob/main/python/llm/tracing/langchain/langchain-tracing.ipynb" title="LangChain Tracing Tutorial (Google Colab)" />
</CardGroup>

## Prerequisites

* Python 3.10+
* An Arize AX account ([sign up](https://arize.com/sign-up/))
* An `OPENAI_API_KEY` from the [OpenAI Platform](https://platform.openai.com/api-keys)

## Launch Arize AX

1. Sign in to your [Arize AX account](https://app.arize.com/).
2. From **Space Settings**, copy your **Space ID** and **API Key**. You will set them as `ARIZE_SPACE_ID` and `ARIZE_API_KEY` below.

## Install

```bash theme={null}
pip install arize-otel \
  openinference-instrumentation-langchain \
  langchain langchain-openai
```

## Configure credentials

```bash theme={null}
export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="langchain-tracing-example"
export OPENAI_API_KEY="<your-openai-api-key>"
```

## Setup tracing

```python theme={null}
# instrumentation.py
import os

from arize.otel import register
from openinference.instrumentation.langchain import LangChainInstrumentor

tracer_provider = register(
    space_id=os.environ["ARIZE_SPACE_ID"],
    api_key=os.environ["ARIZE_API_KEY"],
    project_name=os.environ["ARIZE_PROJECT_NAME"],
)

LangChainInstrumentor().instrument(tracer_provider=tracer_provider)
print("Arize AX tracing initialized for LangChain.")
```

## Run LangChain

```python theme={null}
# example.py

# Importing instrumentation first ensures tracing is set up
# before `langchain_openai` is imported.
from instrumentation import tracer_provider

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

# ChatOpenAI reads OPENAI_API_KEY from the environment.
prompt = ChatPromptTemplate.from_template(
    "Why is the {celestial_object} {color}? Answer in two sentences."
)
chain = prompt | ChatOpenAI(model="gpt-5.5")

response = chain.invoke({"celestial_object": "sky", "color": "blue"})

print(response.content)
```

### Expected output

```text wrap theme={null}
Arize AX tracing initialized for LangChain.
The sky appears blue because of Rayleigh scattering — molecules in the atmosphere scatter shorter (blue) wavelengths of sunlight more than longer ones, so the diffuse light reaching your eyes is dominated by blue. At sunrise and sunset, light travels through more atmosphere, so the blue is scattered out and reds dominate.
```

## Verify in Arize AX

1. Open your Arize AX space and select project **`langchain-tracing-example`**.
2. You should see a new trace within \~30 seconds containing a `RunnableSequence` parent span (the LCEL chain) wrapping `ChatPromptTemplate` and `ChatOpenAI` child spans, with the prompt, response, and token usage attached.
3. If no traces appear, see [Troubleshooting](#troubleshooting).

### Check from the skill, CLI, or SDK

Confirm spans are actually reaching your Arize AX project. Use whichever fits your workflow — the skill and CLI work for any framework; the SDK check is shown for each language.

<Tabs>
  <Tab title="Arize skill (agent)">
    Install the [Arize Skills](https://github.com/Arize-ai/arize-skills) plugin and let your coding agent check for you:

    ```bash theme={null}
    npx skills add Arize-ai/arize-skills
    ```

    Then prompt your agent:

    > Use the `arize-trace` skill to export and analyze recent traces from my project. Confirm spans are arriving, and summarize any errors or latency issues.
  </Tab>

  <Tab title="AX CLI">
    Export recent spans for your project — any rows mean traces are landing:

    ```bash theme={null}
    ax spans export "$ARIZE_PROJECT_NAME" --space "$ARIZE_SPACE_ID" \
      --limit 5 --stdout | jq 'length'
    ```

    A non-zero count confirms spans reached Arize AX. Run `ax auth login` first if you have not authenticated. See the [`ax spans` reference](/api-clients/cli/spans).
  </Tab>

  <Tab title="SDK">
    Query the project's spans and check that at least one came back.

    <CodeGroup>
      ```python Python theme={null}
      import os
      from arize import ArizeClient

      client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])
      resp = client.spans.list(
          project=os.environ["ARIZE_PROJECT_NAME"],
          space=os.environ["ARIZE_SPACE_ID"],
          limit=5,
      )
      count = len(resp.spans)
      print(
          f"{count} span(s) found" if count else "No spans yet — recheck setup"
      )
      ```

      ```typescript TypeScript theme={null}
      // Reads ARIZE_API_KEY from the environment.
      import { listSpans } from "@arizeai/ax-client";

      const { data: spans } = await listSpans({
        project: process.env.ARIZE_PROJECT_NAME!,
        space: process.env.ARIZE_SPACE_ID!,
        limit: 5,
      });
      const count = spans.length;
      console.log(
        count ? `${count} span(s) found` : "No spans yet — recheck setup",
      );
      ```

      ```go Go theme={null}
      client, err := arize.NewClient(
          arize.Config{APIKey: os.Getenv("ARIZE_API_KEY")},
      )
      if err != nil {
          log.Fatal(err)
      }
      resp, err := client.Spans.List(ctx, spans.ListRequest{
          Project: os.Getenv("ARIZE_PROJECT_NAME"),
          Space:   os.Getenv("ARIZE_SPACE_ID"),
          Limit:   5,
      })
      if err != nil {
          log.Fatal(err)
      }
      fmt.Printf("%d span(s) found\n", len(resp.Spans))
      ```
    </CodeGroup>

    SDK span references: [Python](/api-clients/python/version-8/client-resources/spans) · [TypeScript](/api-clients/typescript/version-1/client-resources/spans) · [Go](/api-clients/go/version-2/client-resources/spans).
  </Tab>
</Tabs>

## Troubleshooting

* **No traces in Arize AX.** Confirm `ARIZE_SPACE_ID` and `ARIZE_API_KEY` are set in the same shell that runs `example.py`. Enable OpenTelemetry debug logs with `export OTEL_LOG_LEVEL=debug` and re-run.
* **LangChain spans missing but other spans present.** `LangChainInstrumentor().instrument(...)` must run before any `langchain` import. Make sure `instrumentation.py` is the first import in your entry point.
* **`401` from OpenAI.** Verify `OPENAI_API_KEY` is set and has access to `gpt-5.5`. Swap for a model your key can call.
* **Other LLM providers.** Install the matching `langchain-<provider>` package (e.g. `langchain-anthropic`, `langchain-google-genai`) and replace `ChatOpenAI` with the equivalent chat model. The same `LangChainInstrumentor` covers every provider.

## Resources

<CardGroup>
  <Card icon="book-open" href="https://python.langchain.com/docs/introduction/" title="LangChain Python Documentation" horizontal />

  <Card icon="terminal" href="https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-langchain" title="OpenInference LangChain Instrumentor" horizontal />

  <Card icon="github" href="https://github.com/Arize-ai/tutorials/tree/main/python/llm/tracing/langchain" title="Arize AX LangChain Tutorials" horizontal />

  <Card icon="code" href="/ax/integrations/ts-js-agent-frameworks/langchain/langchain-js" title="LangChain.js Tracing" horizontal />
</CardGroup>