From 50fe5080e6f10b8cdf56079ffe2c6542a4ce5779 Mon Sep 17 00:00:00 2001
From: Tony Kipkemboi <iamtonykipkemboi@gmail.com>
Date: Wed, 26 Mar 2025 11:28:02 -0400
Subject: [PATCH] docs: update theme to mint and modify opik observability doc

---
 docs/docs.json                     |  2 +-
 docs/how-to/opik-observability.mdx | 87 ++++++++++++++++--------------
 2 files changed, 47 insertions(+), 42 deletions(-)

diff --git a/docs/docs.json b/docs/docs.json
index cced352cf..dc3acdaa6 100644
--- a/docs/docs.json
+++ b/docs/docs.json
@@ -1,6 +1,6 @@
 {
   "$schema": "https://mintlify.com/docs.json",
-  "theme": "palm",
+  "theme": "mint",
   "name": "CrewAI",
   "colors": {
     "primary": "#EB6658",
diff --git a/docs/how-to/opik-observability.mdx b/docs/how-to/opik-observability.mdx
index a1d128b8f..b87c1513d 100644
--- a/docs/how-to/opik-observability.mdx
+++ b/docs/how-to/opik-observability.mdx
@@ -8,6 +8,10 @@ icon: meteor
 
 With [Comet Opik](https://www.comet.com/docs/opik/), debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
 
+<Frame caption="Opik Agent Dashboard">
+  <img src="/images/opik-crewai-dashboard.png" alt="Opik agent monitoring example with CrewAI" />
+</Frame>
+
 Opik provides comprehensive support for every stage of your CrewAI application development:
 
 - **Log Traces and Spans**: Automatically track LLM calls and application logic to debug and analyze development and production systems. Manually or programmatically annotate, view, and compare responses across projects.
@@ -27,7 +31,7 @@ For this guide we will use CrewAI’s quickstart example.
 <Steps>
     <Step title="Install required packages">
       ```shell
-      %pip install crewai crewai-tools opik --upgrade
+      pip install crewai crewai-tools opik --upgrade
       ```
     </Step>
     <Step title="Configure Opik">
@@ -35,8 +39,10 @@ For this guide we will use CrewAI’s quickstart example.
       import opik
       opik.configure(use_local=False)
       ```
+    </Step>
     <Step title="Prepare environment">
       First, we set up our API keys for our LLM-provider as environment variables:
+  
       ```python
       import os
       import getpass
@@ -47,53 +53,56 @@ For this guide we will use CrewAI’s quickstart example.
     </Step>
     <Step title="Using CrewAI">
       The first step is to create our project. We will use an example from CrewAI’s documentation:
+
       ```python
       from crewai import Agent, Crew, Task, Process
 
 
-class YourCrewName:
-    def agent_one(self) -> Agent:
-        return Agent(
-            role="Data Analyst",
-            goal="Analyze data trends in the market",
-            backstory="An experienced data analyst with a background in economics",
-            verbose=True,
-        )
+      class YourCrewName:
+          def agent_one(self) -> Agent:
+              return Agent(
+                  role="Data Analyst",
+                  goal="Analyze data trends in the market",
+                  backstory="An experienced data analyst with a background in economics",
+                  verbose=True,
+              )
 
-    def agent_two(self) -> Agent:
-        return Agent(
-            role="Market Researcher",
-            goal="Gather information on market dynamics",
-            backstory="A diligent researcher with a keen eye for detail",
-            verbose=True,
-        )
+          def agent_two(self) -> Agent:
+              return Agent(
+                  role="Market Researcher",
+                  goal="Gather information on market dynamics",
+                  backstory="A diligent researcher with a keen eye for detail",
+                  verbose=True,
+              )
 
-    def task_one(self) -> Task:
-        return Task(
-            name="Collect Data Task",
-            description="Collect recent market data and identify trends.",
-            expected_output="A report summarizing key trends in the market.",
-            agent=self.agent_one(),
-        )
+          def task_one(self) -> Task:
+              return Task(
+                  name="Collect Data Task",
+                  description="Collect recent market data and identify trends.",
+                  expected_output="A report summarizing key trends in the market.",
+                  agent=self.agent_one(),
+              )
 
-    def task_two(self) -> Task:
-        return Task(
-            name="Market Research Task",
-            description="Research factors affecting market dynamics.",
-            expected_output="An analysis of factors influencing the market.",
-            agent=self.agent_two(),
-        )
+          def task_two(self) -> Task:
+              return Task(
+                  name="Market Research Task",
+                  description="Research factors affecting market dynamics.",
+                  expected_output="An analysis of factors influencing the market.",
+                  agent=self.agent_two(),
+              )
 
-    def crew(self) -> Crew:
-        return Crew(
-            agents=[self.agent_one(), self.agent_two()],
-            tasks=[self.task_one(), self.task_two()],
-            process=Process.sequential,
-            verbose=True,
-        )
+          def crew(self) -> Crew:
+              return Crew(
+                  agents=[self.agent_one(), self.agent_two()],
+                  tasks=[self.task_one(), self.task_two()],
+                  process=Process.sequential,
+                  verbose=True,
+              )
 
       ```
+
       Now we can import Opik’s tracker and run our crew:
+  
       ```python
       from opik.integrations.crewai import track_crewai
 
@@ -109,10 +118,6 @@ class YourCrewName:
       - Agent interactions and task execution flow
       - Performance metrics like latency and token usage
       - Evaluation metrics (built-in or custom)
-
-      <Frame caption="Opik Agent Dashboard">
-        <img src="/images/opik-crewai-dashboard.png" alt="Opik agent monitoring example with CrewAI" />
-      </Frame>
     </Step>
 </Steps>