# Datadog Tracing

>[ddtrace](https://github.com/DataDog/dd-trace-py) is a Datadog application performance monitoring (APM) library which provides an integration to monitor your LangChain application.

Key features of the ddtrace integration for LangChain:
- Traces: Capture LangChain requests, parameters, prompt-completions, and help visualize LangChain operations.
- Metrics: Capture LangChain request latency, errors, and token/cost usage (for OpenAI LLMs and Chat Models).
- Logs: Store prompt completion data for each LangChain operation.
- Dashboard: Combine metrics, logs, and trace data into a single plane to monitor LangChain requests.
- Monitors: Provide alerts in response to spikes in LangChain request latency or error rate.

Note: The ddtrace LangChain integration currently provides tracing for LLMs, Chat Models, Text Embedding Models, Chains, and Vectorstores.

## Installation and Setup

1. Enable APM and StatsD in your Datadog Agent, along with a Datadog API key. For example, in Docker:

```
docker run -d --cgroupns host \
              --pid host \
              -v /var/run/docker.sock:/var/run/docker.sock:ro \
              -v /proc/:/host/proc/:ro \
              -v /sys/fs/cgroup/:/host/sys/fs/cgroup:ro \
              -e DD_API_KEY=<DATADOG_API_KEY> \
              -p 127.0.0.1:8126:8126/tcp \
              -p 127.0.0.1:8125:8125/udp \
              -e DD_DOGSTATSD_NON_LOCAL_TRAFFIC=true \
              -e DD_APM_ENABLED=true \
              gcr.io/datadoghq/agent:latest
```

2. Install the Datadog APM Python library.

```
pip install ddtrace>=1.17
```


3. The LangChain integration can be enabled automatically when you prefix your LangChain Python application command with `ddtrace-run`:

```
DD_SERVICE="my-service" DD_ENV="staging" DD_API_KEY=<DATADOG_API_KEY> ddtrace-run python <your-app>.py
```

**Note**: If the Agent is using a non-default hostname or port, be sure to also set `DD_AGENT_HOST`, `DD_TRACE_AGENT_PORT`, or `DD_DOGSTATSD_PORT`.

Additionally, the LangChain integration can be enabled programmatically by adding `patch_all()` or `patch(langchain=True)` before the first import of `langchain` in your application.

Note that using `ddtrace-run` or `patch_all()` will also enable the `requests` and `aiohttp` integrations which trace HTTP requests to LLM providers, as well as the `openai` integration which traces requests to the OpenAI library.

```python
from ddtrace import config, patch

# Note: be sure to configure the integration before calling ``patch()``!
# eg. config.langchain["logs_enabled"] = True

patch(langchain=True)

# to trace synchronous HTTP requests
# patch(langchain=True, requests=True)

# to trace asynchronous HTTP requests (to the OpenAI library)
# patch(langchain=True, aiohttp=True)

# to include underlying OpenAI spans from the OpenAI integration
# patch(langchain=True, openai=True)patch_all
```

See the [APM Python library documentation][https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html] for more advanced usage.


## Configuration

See the [APM Python library documentation][https://ddtrace.readthedocs.io/en/stable/integrations.html#langchain] for all the available configuration options.


### Log Prompt & Completion Sampling

To enable log prompt and completion sampling, set the `DD_LANGCHAIN_LOGS_ENABLED=1` environment variable. By default, 10% of traced requests will emit logs containing the prompts and completions.

To adjust the log sample rate, see the [APM library documentation][https://ddtrace.readthedocs.io/en/stable/integrations.html#langchain].

**Note**: Logs submission requires `DD_API_KEY` to be specified when running `ddtrace-run`.


## Troubleshooting

Need help? Create an issue on [ddtrace](https://github.com/DataDog/dd-trace-py) or contact [Datadog support][https://docs.datadoghq.com/help/].