> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/Arize-ai/openinference/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain

> Python auto-instrumentation for LangChain

Python auto-instrumentation library for LangChain.

These traces are fully OpenTelemetry compatible and can be sent to an OpenTelemetry collector for viewing, such as [Arize Phoenix](https://github.com/Arize-ai/phoenix).

## Compatibility

This instrumentation works with:

* **LangChain 1.x** (`langchain>=1.0.0`): Modern agent framework built on LangGraph
* **LangChain Classic** (`langchain-classic>=1.0.0`): Legacy chains and tools (formerly `langchain 0.x`)
* All LangChain partner packages (`langchain-openai`, `langchain-anthropic`, `langchain-google-vertexai`, etc.)

The instrumentation hooks into `langchain-core`, which is the shared foundation used by all LangChain packages.

## Installation

### For LangChain 1.x (Recommended for New Projects)

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

### For LangChain Classic (Legacy Applications)

```bash theme={null}
pip install openinference-instrumentation-langchain langchain-classic langchain-openai
```

### For Both (Migration Scenarios)

```bash theme={null}
pip install openinference-instrumentation-langchain langchain langchain-classic langchain-openai
```

## Quickstart

### Example with LangChain 1.x (New Agent Framework)

Install packages needed for this demonstration:

```bash theme={null}
pip install openinference-instrumentation-langchain langchain langchain-openai arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp
```

Start the Phoenix app in the background as a collector. By default, it listens on `http://localhost:6006`:

```bash theme={null}
python -m phoenix.server.main serve
```

The Phoenix app does not send data over the internet. It only operates locally on your machine.

### Setup instrumentation

```python theme={null}
from langchain.agents import create_agent
from langchain_openai import ChatOpenAI
from openinference.instrumentation.langchain import LangChainInstrumentor
from opentelemetry import trace as trace_api
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor

endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = trace_sdk.TracerProvider()
trace_api.set_tracer_provider(tracer_provider)
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))

LangChainInstrumentor().instrument()
```

### Configure OpenAI credentials

```python theme={null}
import os

os.environ["OPENAI_API_KEY"] = "<your openai key>"
```

### Create and run an agent

```python theme={null}
def get_weather(city: str) -> str:
    """Get the weather for a city."""
    return f"The weather in {city} is sunny!"

model = ChatOpenAI(model="gpt-4")
agent = create_agent(model, tools=[get_weather])
result = agent.invoke({"messages": [{"role": "user", "content": "What's the weather in Paris?"}]})
print(result)
```

### Example with LangChain Classic (Legacy Chains)

For legacy applications using LangChain Classic:

```python theme={null}
from langchain_classic.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI

# ... (same instrumentation setup as above)

prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
llm = LLMChain(llm=OpenAI(), prompt=prompt, metadata={"category": "jokes"})
completion = llm.predict(adjective="funny", metadata={"variant": "funny"})
print(completion)
```

Visit the Phoenix app at `http://localhost:6006` to see the traces.

## More Info

* [OpenInference and Phoenix documentation](https://docs.arize.com/phoenix)
* [How to customize spans to track sessions, metadata, etc.](https://github.com/Arize-ai/openinference/tree/main/python/openinference-instrumentation#customizing-spans)
* [How to account for private information and span payload customization](https://github.com/Arize-ai/openinference/tree/main/python/openinference-instrumentation#tracing-configuration)
* [PyPI package](https://pypi.org/project/openinference-instrumentation-langchain/)