Overview

Monitor, troubleshoot, and evaluate your LLM-powered applications (e.g. chatbot, data extraction tool, etc) built using LangChain.

Use LLM Observability to investigate the root cause of issues, monitor operational performance, and evaluate the quality, privacy, and safety of your LLM applications.

See the LLM Observability tracing view video for an example of how you can investigate a trace.

Get cost estimation, prompt and completion sampling, error tracking, performance metrics, and more out of LangChain Python library requests using Datadog metrics and APM.

Setup

LLM Observability: Get end-to-end visibility into your LLM application using LangChain

You can enable LLM Observability in different environments. Follow the appropriate setup based on your scenario:

Installation for Python

If you do not have the Datadog Agent:

1. Install the ddtrace package:

   pip install ddtrace
   

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2. Start your application with the following command, enabling Agentless mode:

    DD_SITE=<YOUR_DATADOG_SITE> DD_API_KEY=<YOUR_API_KEY> DD_LLMOBS_ENABLED=1 DD_LLMOBS_AGENTLESS_ENABLED=1 DD_LLMOBS_ML_APP=<YOUR_ML_APP_NAME> ddtrace-run python <YOUR_APP>.py
   

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If you already have the Datadog Agent installed:

1. Make sure the Agent is running and that APM and StatsD are enabled. For example, use the following command with 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
   

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2. Install the ddtrace package if it isn’t installed yet:

   pip install ddtrace
   

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3. Start your application using the ddtrace-run command to automatically enable tracing:

     DD_SITE=<YOUR_DATADOG_SITE> DD_API_KEY=<YOUR_API_KEY> DD_LLMOBS_ENABLED=1 DD_LLMOBS_ML_APP=<YOUR_ML_APP_NAME> ddtrace-run python <YOUR_APP>.py
   

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Note: If the Agent is running on a custom host or port, set DD_AGENT_HOST and DD_TRACE_AGENT_PORT accordingly.

If you are running LLM Observability in a serverless environment (AWS Lambda):

1. Install the Datadog-Python and Datadog-Extension Lambda layers as part of your AWS Lambda setup.

2. Enable LLM Observability by setting the following environment variables:

    DD_SITE=<YOUR_DATADOG_SITE> DD_API_KEY=<YOUR_API_KEY> DD_LLMOBS_ENABLED=1 DD_LLMOBS_ML_APP=<YOUR_ML_APP_NAME>
   

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Note: In serverless environments, Datadog automatically flushes spans when the Lambda function finishes running.

Automatic LangChain Tracing

LangChain integration is automatically enabled when LLM Observability is configured. This captures latency, errors, input/output messages, and token usage for LangChain operations.

The following methods are traced for both synchronous and asynchronous LangChain operations:

• LLMs: llm.invoke(), llm.ainvoke()
• Chat Models: chat_model.invoke(), chat_model.ainvoke()
• Chains/LCEL: chain.invoke(), chain.ainvoke(), chain.batch(), chain.abatch()
• Embeddings: OpenAIEmbeddings.embed_documents(), OpenAIEmbeddings.embed_query()

No additional setup is required for these methods.

Validation

Validate that LLM Observability is properly capturing spans by checking your application logs for successful span creation. You can also run the following command to check the status of the ddtrace integration:

ddtrace-run --info

Look for the following message to confirm the setup:

Agent error: None

Debugging

If you encounter issues during setup, enable debug logging by passing the --debug flag:

ddtrace-run --debug

This will display any errors related to data transmission or instrumentation, including issues with LangChain traces.

APM: Get Usage Metrics for your python Applications

1. Enable APM and StatsD in your Datadog Agent. 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
   

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2. Install the Datadog APM Python library.

   pip install ddtrace>=1.17
   

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3. 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
   

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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.

See the APM Python library documentation for more advanced usage.

Configuration

See the APM Python library documentation for all the available configuration options.

Validation

Validate that the APM Python library can communicate with your Agent using:

ddtrace-run --info

You should see the following output:

    Agent error: None

Debug Logging

Pass the --debug flag to ddtrace-run to enable debug logging.

ddtrace-run --debug

This displays any errors sending data:

ERROR:ddtrace.internal.writer.writer:failed to send, dropping 1 traces to intake at http://localhost:8126/v0.5/traces after 3 retries ([Errno 61] Connection refused)

Data Collected

Metrics

Events

The LangChain integration does not include any events.

Service Checks

The LangChain integration does not include any service checks.

Troubleshooting

Need help? Contact Datadog support.