Yahya/create anthropic

.github/CODEOWNERS

@@ -199,7 +199,7 @@ datadog_checks_base/tests/**/test_db_statements.py  @DataDog/database-monitoring
 # APM Integrations
 /langchain/         @DataDog/ml-observability @DataDog/agent-integrations @DataDog/documentation
 /openai/            @DataDog/ml-observability @DataDog/agent-integrations @DataDog/documentation
-
+/anthropic/         @DataDog/ml-observability @DataDog/agent-integrations @DataDog/documentation
 
 # Windows agent
 datadog_checks_base/datadog_checks/base/checks/win/                  @DataDog/windows-agent @DataDog/agent-integrations

.github/workflows/config/labeler.yml

@@ -313,6 +313,8 @@ integration/kyverno:
 - kyverno/**/*
 integration/langchain:
 - langchain/**/*
+integration/anthropic:
+- anthropic/**/*
 integration/lastpass:
 - lastpass/**/*
 integration/lighttpd:

anthropic/CHANGELOG.md

@@ -0,0 +1,7 @@
+# CHANGELOG - Anthropic
+
+## 1.0.0 / 2024-11-08
+
+***Added***:
+
+* Initial Release

anthropic/README.md

@@ -0,0 +1,127 @@
+# Anthropic
+
+## Overview
+Use the Anthropic integration to monitor, troubleshoot, and evaluate your LLM-powered applications, such as chatbots or data extraction tools.
+
+If you are building LLM applications, 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](https://imgix.datadoghq.com/video/products/llm-observability/expedite-troubleshooting.mp4?fm=webm&fit=max) for an example of how you can investigate a trace.
+
+## Setup
+
+### LLM Observability: Get end-to-end visibility into your LLM application using Anthropic
+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:
+
+  ```shell
+    pip install ddtrace
+  ```
+
+2.  Start your application using the following command to enable Agentless mode:
+
+  ```shell
+    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
+  ```
+
+##### 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:
+
+  ```shell
+  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. If you haven't already, install the `ddtrace` package:
+
+  ```shell
+    pip install ddtrace
+  ```
+
+3. To automatically enable tracing, start your application using the `ddtrace-run` command:
+
+  ```shell
+     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
+  ```
+
+**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:
+
+  ```shell
+     DD_SITE=<YOUR_DATADOG_SITE> DD_API_KEY=<YOUR_API_KEY> DD_LLMOBS_ENABLED=1 DD_LLMOBS_ML_APP=<YOUR_ML_APP_NAME>
+  ```
+
+**Note**: In serverless environments, Datadog automatically flushes spans at the end of the Lambda function.
+
+##### Automatic Anthropic tracing
+
+The Anthropic integration allows for automatic tracing of chat message calls made by the Anthropic Python SDK, capturing latency, errors, input/output messages, and token usage during Anthropic operations.
+
+The following methods are traced for both synchronous and asynchronous Anthropic operations:
+- Chat messages (including streamed calls): `Anthropic().messages.create()`, `AsyncAnthropic().messages.create()`
+- Streamed chat messages: `Anthropic().messages.stream()`, `AsyncAnthropic().messages.stream()`
+
+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 `dd-trace` integration:
+
+  ```shell
+  ddtrace-run --info
+  ```
+
+Look for the following message to confirm the setup:
+
+  ```shell
+  Agent error: None
+  ```
+
+##### Debugging
+
+If you encounter issues during setup, enable debug logging by passing the `--debug` flag:
+
+  ```shell
+  ddtrace-run --debug
+  ```
+
+This displays any errors related to data transmission or instrumentation, including issues with Anthropic traces.
+
+## Data Collected
+
+### Metrics
+
+The Anthropic integration does not include any custom metrics.
+
+### Service Checks
+
+The Anthropic integration does not include any service checks.
+
+### Events
+
+The Anthropic integration does not include any events.
+
+## Troubleshooting
+
+Need help? Contact [Datadog support][2].
+
+[1]: https://docs.datadoghq.com/integrations/anthropic/
+[2]: https://docs.datadoghq.com/help/
+

anthropic/assets/service_checks.json

@@ -0,0 +1 @@
+[]

anthropic/manifest.json

@@ -0,0 +1,51 @@
+{
+  "manifest_version": "2.0.0",
+  "app_uuid": "53fe7c3e-57eb-42ca-8e43-ec92c04b6160",
+  "app_id": "anthropic",
+  "display_on_public_website": true,
+  "tile": {
+    "overview": "README.md#Overview",
+    "configuration": "README.md#Setup",
+    "support": "README.md#Support",
+    "changelog": "CHANGELOG.md",
+    "description": "Monitor Anthropic usage and health at the application level",
+    "title": "Anthropic",
+    "media": [],
+    "classifier_tags": [
+      "Category::AI/ML",
+      "Category::Metrics",
+      "Submitted Data Type::Traces",
+      "Supported OS::Linux",
+      "Supported OS::Windows",
+      "Supported OS::macOS",
+      "Offering::Integration"
+    ]
+  },
+  "assets": {
+    "integration": {
+      "auto_install": false,
+      "source_type_id": 31102434,
+      "source_type_name": "Anthropic",
+      "events": {
+        "creates_events": false
+      },
+      "metrics": {
+        "prefix": "anthropic.",
+        "check": [],
+        "metadata_path": "metadata.csv"
+      },
+      "service_checks": {
+        "metadata_path": "assets/service_checks.json"
+      }
+    },
+    "dashboards": {
+      "LLM Observability Overview Dashboard": "assets/dashboards/llm_observability_overview_dashboard.json"
+    }
+  },
+  "author": {
+    "support_email": "help@datadoghq.com",
+    "name": "Datadog",
+    "homepage": "https://www.datadoghq.com",
+    "sales_email": "info@datadoghq.com"
+  }
+}

anthropic/metadata.csv

@@ -0,0 +1 @@
+metric_name,metric_type,interval,unit_name,per_unit_name,description,orientation,integration,short_name,curated_metric,sample_tags