docs(integration): Add Elasticsearch APM Integration guide (#126)

Co-authored-by: Mahesh Babu Gorantla <mahesh@projexel.vet>

Author: maheshbabugorantla

img/integrations/elasticsearch-apm-trace-details.png

@@ -114,6 +114,7 @@
         "openllmetry/integrations/dash0",
         "openllmetry/integrations/datadog",
         "openllmetry/integrations/dynatrace",
+        "openllmetry/integrations/elasticsearch-apm",
         "openllmetry/integrations/gcp",
         "openllmetry/integrations/grafana",
         "openllmetry/integrations/highlight",

img/integrations/elasticsearch-apm.png

@@ -0,0 +1,291 @@
+---
+title: "LLM Observability with Elasticsearch APM Service"
+sidebarTitle: "Elasticsearch APM"
+---
+
+Connect OpenLLMetry to [Elastic APM](https://www.elastic.co/guide/en/apm/guide/current/index.html) to visualize LLM traces in Kibana's native APM interface. This integration uses OpenTelemetry Protocol (OTLP) to route traces from your application through an OpenTelemetry Collector to Elastic APM Server.
+
+<Note>
+  This integration requires an OpenTelemetry Collector to route traces between Traceloop OpenLLMetry client and Elastic APM Server.
+  Elastic APM Server 8.x+ supports OTLP natively.
+</Note>
+
+## Quick Start
+
+<Steps>
+  <Step title="Install OpenLLMetry">
+    Install the Traceloop SDK alongside your LLM provider client:
+
+    ```bash
+    pip install traceloop-sdk openai
+    ```
+  </Step>
+
+<Step title="Configure OpenTelemetry Collector">
+    Configure your OpenTelemetry Collector to receive traces from OpenLLMetry and forward them to APM Server.
+
+Create an `otel-collector-config.yaml` file:
+
+```yaml
+receivers:
+  otlp:
+    protocols:
+      http:
+        endpoint: localhost:4318
+      grpc:
+        endpoint: localhost:4317
+
+processors:
+  batch:
+    timeout: 10s
+    send_batch_size: 1024
+
+  memory_limiter:
+    check_interval: 1s
+    limit_mib: 512
+
+  resource:
+    attributes:
+      - key: service.name
+        action: upsert
+        value: your-service-name # Match this to app_name parameter value when calling Traceloop.init()
+
+exporters:
+  # Export to APM Server via OTLP
+  otlp/apm:
+    endpoint: http://localhost:8200 # APM Server Endpoint
+    tls:
+      insecure: true # Allow insecure connection from OTEL Collector to APM Server (for demo purposes)
+    compression: gzip
+
+  # Logging exporter for debugging (can ignore if not needed)
+  logging:
+    verbosity: normal # This is the verbosity of the logging
+    sampling_initial: 5
+    sampling_thereafter: 200
+
+  # Debug exporter to verify trace data
+  debug:
+    verbosity: detailed
+    sampling_initial: 10
+    sampling_thereafter: 10
+
+extensions:
+  health_check:
+    endpoint: localhost:13133 # Endpoint of OpenTelemetry Collector's health check extension
+
+service:
+  extensions: [health_check] # Enable health check extension
+
+  pipelines:
+    traces:
+      receivers: [otlp]
+      processors: [memory_limiter, batch, resource]
+      exporters: [otlp/apm, logging, debug]
+
+    metrics:
+      receivers: [otlp]
+      processors: [memory_limiter, batch, resource]
+      exporters: [otlp/apm, logging]
+
+    logs:
+      receivers: [otlp]
+      processors: [memory_limiter, batch, resource]
+      exporters: [otlp/apm, logging]
+```
+
+<Warning>
+In production, enable TLS and use APM Server secret tokens for authentication.
+Set `tls.insecure: false` and configure `headers: Authorization: Bearer <token>`.
+</Warning>
+    </Step>
+
+  <Step title="Initialize Traceloop">
+    Import and initialize Traceloop before any LLM imports:
+
+    ```python
+    from os import getenv
+
+    from traceloop.sdk import Traceloop
+    from openai import OpenAI
+
+    # Initialize Traceloop with OTLP endpoint
+    Traceloop.init(
+        app_name="your-service-name",
+        api_endpoint="http://localhost:4318"
+    )
+
+    # Traceloop must be initialized before importing the LLM client
+    # Traceloop instruments the OpenAI client automatically
+    client = OpenAI(api_key=getenv("OPENAI_API_KEY"))
+
+    # Make LLM calls - automatically traced
+    response = client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[{"role": "user", "content": "Hello!"}]
+    )
+    ```
+
+    <Note>
+      The `app_name` parameter sets the service name visible in Kibana APM's service list.
+    </Note>
+  </Step>
+
+  <Step title="View Traces in Kibana">
+    Navigate to Kibana's APM interface:
+
+    1. Open Kibana at `http://localhost:5601`
+    2. Go to **Observability → APM → Services**
+    3. Click on your service name (e.g., `your-service-name`)
+    4. View transactions and trace timelines with full LLM metadata
+
+    Each LLM call appears as a span containing:
+    - Model name (`gen_ai.request.model`)
+    - Token usage (`gen_ai.usage.input_tokens`, `gen_ai.usage.output_tokens`)
+    - Prompts and completions (configurable)
+    - Request duration and latency
+  </Step>
+</Steps>
+
+## Environment Variables
+
+Configure OpenLLMetry behavior using environment variables:
+
+| Variable | Description | Default |
+|----------|-------------|---------|
+| `TRACELOOP_BASE_URL` | OpenTelemetry Collector endpoint | `http://localhost:4318` |
+| `TRACELOOP_TRACE_CONTENT` | Capture prompts/completions | `true` |
+
+
+<Warning>
+Set `TRACELOOP_TRACE_CONTENT=false` in production to prevent logging sensitive prompt content.
+</Warning>
+
+## Using Workflow Decorators
+
+For complex applications with multiple steps, use workflow decorators to create hierarchical traces:
+
+```python
+from os import getenv
+from traceloop.sdk import Traceloop
+from traceloop.sdk.decorators import workflow, task
+from openai import OpenAI
+
+Traceloop.init(
+  app_name="recipe-service",
+  api_endpoint="http://localhost:4318",
+)
+
+# Traceloop must be initialized before importing the LLM client
+# Traceloop instruments the OpenAI client automatically
+client = OpenAI(api_key=getenv("OPENAI_API_KEY"))
+
+@task(name="generate_recipe")
+def generate_recipe(dish: str):
+    """LLM call - creates a child span"""
+    response = client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[
+            {"role": "system", "content": "You are a chef."},
+            {"role": "user", "content": f"Recipe for {dish}"}
+        ]
+    )
+    return response.choices[0].message.content
+
+
+@workflow(name="recipe_workflow")
+def create_recipe(dish: str, servings: int):
+    """Parent workflow - creates the root transaction"""
+    recipe = generate_recipe(dish)
+    return {"recipe": recipe, "servings": servings}
+
+# Call the workflow
+result = create_recipe("pasta carbonara", 4)
+```
+
+In Kibana APM, you'll see:
+- `recipe_workflow.workflow` as the parent transaction
+- `generate_recipe.task` as a child span
+- `openai.chat.completions` as the LLM API span with full metadata
+
+
+## Example Trace Visualization
+
+### Trace View
+
+<Frame>
+  <img src="/img/integrations/elasticsearch-apm.png" />
+</Frame>
+
+### Trace Details
+
+<Frame>
+  <img src="/img/integrations/elasticsearch-apm-trace-details.png" />
+</Frame>
+
+## Captured Metadata
+
+OpenLLMetry automatically captures these attributes in each LLM span:
+
+**Request Attributes:**
+- `gen_ai.request.model` - Model identifier
+- `gen_ai.request.temperature` - Sampling temperature
+- `gen_ai.system` - Provider name (OpenAI, Anthropic, etc.)
+
+**Response Attributes:**
+- `gen_ai.response.model` - Actual model used
+- `gen_ai.response.id` - Unique response identifier
+- `gen_ai.response.finish_reason` - Completion reason
+
+**Token Usage:**
+- `gen_ai.usage.input_tokens` - Input token count
+- `gen_ai.usage.output_tokens` - Output token count
+- `llm.usage.total_tokens` - Total tokens
+
+**Content (if enabled):**
+- `gen_ai.prompt.{N}.content` - Prompt messages
+- `gen_ai.completion.{N}.content` - Generated completions
+
+## Production Considerations
+
+<Tabs>
+  <Tab title="Content Logging">
+    Disable prompt/completion logging in production:
+
+    ```bash
+    export TRACELOOP_TRACE_CONTENT=false
+    ```
+
+    This prevents sensitive data from being stored in Elasticsearch.
+  </Tab>
+
+  <Tab title="Sampling">
+    Configure sampling in the OpenTelemetry Collector to reduce trace volume:
+
+    ```yaml
+    processors:
+      probabilistic_sampler:
+        sampling_percentage: 10  # Sample 10% of traces
+    ```
+  </Tab>
+
+  <Tab title="Security">
+    Enable APM Server authentication:
+
+    ```yaml
+    exporters:
+      otlp/apm:
+        endpoint: https://localhost:8200
+        headers:
+          Authorization: "Bearer <secret-token>"
+        tls:
+          insecure: false
+    ```
+  </Tab>
+</Tabs>
+
+## Resources
+
+- [Elastic APM Documentation](https://www.elastic.co/docs/solutions/observability/apm)
+- [OpenTelemetry Collector Configuration](https://opentelemetry.io/docs/collector/configuration/)
+- [Traceloop SDK Configuration](https://www.traceloop.com/docs/openllmetry/configuration)

mint.json

@@ -20,6 +20,7 @@ in any observability platform that supports OpenTelemetry.
   <Card title="Dash0" href="/openllmetry/integrations/dash0"></Card>
   <Card title="Datadog" href="/openllmetry/integrations/datadog"></Card>
   <Card title="Dynatrace" href="/openllmetry/integrations/dynatrace"></Card>
+  <Card title="Elasticsearch APM" href="/openllmetry/integrations/elasticsearch-apm"></Card>
   <Card title="Google Cloud" href="/openllmetry/integrations/gcp"></Card>
   <Card title="Grafana Tempo" href="/openllmetry/integrations/grafana"></Card>
   <Card title="Highlight" href="/openllmetry/integrations/highlight"></Card>