Create openlit-observability.mdx

docs/how-to/openlit-observability.mdx

@@ -0,0 +1,134 @@
+---
+title: Agent Monitoring with OpenLIT
+description: Quickly start monitoring your Agents in just a single line of code with OpenTelemetry.
+icon: chart-line
+---
+
+# OpenLIT Overview
+
+[OpenLIT](https://github.com/openlit/openlit?src=crewai-docs) is an open-source tool that makes it simple to monitor the performance of AI agents, LLMs, VectorDBs, and GPUs with just **one** line of code. 
+It offers OpenTelemetry-native tracing and metrics to track important parameters like cost, latency, interactions and task sequences. 
+This setup enables you to track hyperparameters and monitor for performance issues, helping you find ways to enhance and fine-tune your agents over time.
+
+![Overview of a select series of agent session runs](/images/langtrace1.png)
+![Overview of agent traces](/images/langtrace2.png)
+![Overview of llm traces in details](/images/langtrace3.png)
+
+### Features
+
+- **Analytics Dashboard**: Monitor your Agents health and performance with detailed dashboards that track metrics, costs, and user interactions.
+- **OpenTelemetry-native Observability SDK**: Vendor-neutral SDKs to send traces and metrics to your existing observability tools like Grafana, DataDog and more.
+- **Cost Tracking for Custom and Fine-Tuned Models**: Tailor cost estimations for specific models using custom pricing files for precise budgeting.
+- **Exceptions Monitoring Dashboard**: Quickly spot and resolve issues by tracking common exceptions and errors with a monitoring dashboard.
+- **Compliance and Security**: Detect potential threats such as profanity and PII leaks.
+- **Prompt Injection Detection**: Identify potential code injection and secret leaks.
+- **API Keys and Secrets Management**: Securely handle your LLM API keys and secrets centrally, avoiding insecure practices.
+- **Prompt Management**: Manage and version Agent prompts using PromptHub for consistent and easy access across Agents.
+- **Model Playground** Test and compare different models for your CrewAI agents before deployment.
+
+## Setup Instructions
+
+<Steps>
+    <Step title="Deploy OpenLIT">
+      <Steps>
+        <Step title="Git Clone OpenLIT Repository">
+          ```shell
+          git clone [email protected]:openlit/openlit.git
+          ```
+        </Step>
+        <Step title="Start Docker Compose">
+          From the root directory of the [OpenLIT Repo](https://github.com/openlit/openlit), Run the below command:
+          ```shell
+          docker compose up -d
+          ```
+        </Step>
+      </Steps>
+    </Step>
+    <Step title="Install OpenLIT SDK">
+      ```shell
+      pip install openlit
+      ```
+    </Step>
+    <Step title="Initialize OpenLIT in Your Application">
+      Add the following two lines to your application code:
+      <Tabs>
+      <Tab title="Setup using function arguments">
+      <Tabs>
+        <Tab title="Python">
+          ```python
+          import openlit
+          openlit.init(otlp_endpoint="http://127.0.0.1:4318")
+          ```
+
+          Example Usage for monitoring `OpenAI` Usage:
+
+          ```python
+          from openai import OpenAI
+          import openlit
+    
+          openlit.init(otlp_endpoint="http://127.0.0.1:4318")
+    
+          client = OpenAI(
+              api_key="YOUR_OPENAI_KEY"
+          )
+    
+          chat_completion = client.chat.completions.create(
+              messages=[
+                  {
+                      "role": "user",
+                      "content": "What is LLM Observability",
+                  }
+              ],
+              model="gpt-3.5-turbo",
+          )
+          ```
+        </Tab>
+        </Tabs>
+
+      </Tab>
+      <Tab title="Setup using Environment Variables">
+
+      <Tabs>
+        <Tab title="Python">
+           Add the following two lines to your application code:
+      ```python
+      import openlit
+
+      openlit.init()
+      ```
+
+      Run the following command to configure the OTEL export endpoint:
+      ```shell
+      export OTEL_EXPORTER_OTLP_ENDPOINT = "http://127.0.0.1:4318"
+      ```
+
+      Example Usage for monitoring `OpenAI` Usage:
+
+      ```python
+      from openai import OpenAI
+      import openlit
+
+      openlit.init()
+
+      client = OpenAI(
+          api_key="YOUR_OPENAI_KEY"
+      )
+
+      chat_completion = client.chat.completions.create(
+          messages=[
+              {
+                  "role": "user",
+                  "content": "What is LLM Observability",
+              }
+          ],
+          model="gpt-3.5-turbo",
+      )
+      ```
+        </Tab>
+        </Tabs>
+      </Tab>
+      </Tabs>
+      Refer to OpenLIT [Python SDK repository](https://github.com/openlit/openlit/tree/main/sdk/python) for more advanced configurations and use cases.
+    </Step>
+</Steps> 
+