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+# 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/].