Merge branch 'main' into bugfix/event-emitter-fix

docs/docs.json

@@ -1,6 +1,6 @@
 {
   "$schema": "https://mintlify.com/docs.json",
-  "theme": "mint",
+  "theme": "palm",
   "name": "CrewAI",
   "colors": {
     "primary": "#EB6658",

docs/how-to/opik-observability.mdx

@@ -8,10 +8,6 @@ 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.
@@ -31,7 +27,7 @@ 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">
@@ -39,10 +35,8 @@ 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
@@ -53,56 +47,53 @@ 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
 
@@ -118,6 +109,10 @@ For this guide we will use CrewAI’s quickstart example.
       - 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>