Merge branch 'main' into devin/1737479945-fix-conditional-index

docs/concepts/cli.mdx

@@ -12,15 +12,15 @@ The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you
 
 To use the CrewAI CLI, make sure you have CrewAI installed:
 
-```shell
+```shell Terminal
 pip install crewai
 ```
 
 ## Basic Usage
 
 The basic structure of a CrewAI CLI command is:
 
-```shell
+```shell Terminal
 crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 ```
 
@@ -30,15 +30,15 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 
 Create a new crew or flow.
 
-```shell
+```shell Terminal
 crewai create [OPTIONS] TYPE NAME
 ```
 
 - `TYPE`: Choose between "crew" or "flow"
 - `NAME`: Name of the crew or flow
 
 Example:
-```shell
+```shell Terminal
 crewai create crew my_new_crew
 crewai create flow my_new_flow
 ```
@@ -47,14 +47,14 @@ crewai create flow my_new_flow
 
 Show the installed version of CrewAI.
 
-```shell
+```shell Terminal
 crewai version [OPTIONS]
 ```
 
 - `--tools`: (Optional) Show the installed version of CrewAI tools
 
 Example:
-```shell
+```shell Terminal
 crewai version
 crewai version --tools
 ```
@@ -63,46 +63,46 @@ crewai version --tools
 
 Train the crew for a specified number of iterations.
 
-```shell
+```shell Terminal
 crewai train [OPTIONS]
 ```
 
 - `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
 - `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
 
 Example:
-```shell
+```shell Terminal
 crewai train -n 10 -f my_training_data.pkl
 ```
 
 ### 4. Replay
 
 Replay the crew execution from a specific task.
 
-```shell
+```shell Terminal
 crewai replay [OPTIONS]
 ```
 
 - `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
 
 Example:
-```shell    
+```shell Terminal    
 crewai replay -t task_123456
 ```
 
 ### 5. Log-tasks-outputs
 
 Retrieve your latest crew.kickoff() task outputs.
 
-```shell
+```shell Terminal
 crewai log-tasks-outputs
 ```
 
 ### 6. Reset-memories
 
 Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
 
-```shell
+```shell Terminal
 crewai reset-memories [OPTIONS]
 ```
 
@@ -113,7 +113,7 @@ crewai reset-memories [OPTIONS]
 - `-a, --all`: Reset ALL memories
 
 Example:
-```shell
+```shell Terminal
 crewai reset-memories --long --short
 crewai reset-memories --all
 ```
@@ -122,23 +122,23 @@ crewai reset-memories --all
 
 Test the crew and evaluate the results.
 
-```shell
+```shell Terminal
 crewai test [OPTIONS]
 ```
 
 - `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
 - `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
 
 Example:
-```shell
+```shell Terminal    
 crewai test -n 5 -m gpt-3.5-turbo
 ```
 
 ### 8. Run
 
 Run the crew.
 
-```shell
+```shell Terminal
 crewai run
 ```
 <Note>
@@ -153,7 +153,7 @@ Starting in version `0.98.0`, when you run the `crewai chat` command, you start
 
 After receiving the results, you can continue interacting with the assistant for further instructions or questions.
 
-```shell
+```shell Terminal
 crewai chat
 ```
 <Note>

docs/concepts/llms.mdx

@@ -243,6 +243,9 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
         # llm: bedrock/amazon.titan-text-express-v1
         # llm: bedrock/meta.llama2-70b-chat-v1
 
+        # Amazon SageMaker Models - Enterprise-grade
+        # llm: sagemaker/<my-endpoint>
+
         # Mistral Models - Open source alternative
         # llm: mistral/mistral-large-latest
         # llm: mistral/mistral-medium-latest
@@ -506,6 +509,21 @@ Learn how to get the most out of your LLM configuration:
     )
     ```
   </Accordion>
+
+  <Accordion title="Amazon SageMaker">
+    ```python Code
+    AWS_ACCESS_KEY_ID=<your-access-key>
+    AWS_SECRET_ACCESS_KEY=<your-secret-key>
+    AWS_DEFAULT_REGION=<your-region>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="sagemaker/<my-endpoint>"
+    )
+    ```
+  </Accordion>
 
   <Accordion title="Mistral">
     ```python Code

docs/installation.mdx

@@ -15,10 +15,48 @@ icon: wrench
   If you need to update Python, visit [python.org/downloads](https://python.org/downloads)
 </Note>
 
+# Setting Up Your Environment
+
+Before installing CrewAI, it's recommended to set up a virtual environment. This helps isolate your project dependencies and avoid conflicts.
+
+<Steps>
+    <Step title="Create a Virtual Environment">
+        Choose your preferred method to create a virtual environment:
+
+        **Using venv (Python's built-in tool):**
+        ```shell Terminal
+        python3 -m venv .venv
+        ```
+
+        **Using conda:**
+        ```shell Terminal
+        conda create -n crewai-env python=3.12
+        ```
+    </Step>
+
+    <Step title="Activate the Virtual Environment">
+        Activate your virtual environment based on your platform:
+
+        **On macOS/Linux (venv):**
+        ```shell Terminal
+        source .venv/bin/activate
+        ```
+
+        **On Windows (venv):**
+        ```shell Terminal
+        .venv\Scripts\activate
+        ```
+
+        **Using conda (all platforms):**
+        ```shell Terminal
+        conda activate crewai-env
+        ```
+    </Step>
+</Steps>
+
 # Installing CrewAI
 
-CrewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently. 
-Let's get you set up! 🚀
+Now let's get you set up! 🚀
 
 <Steps>
     <Step title="Install CrewAI">
@@ -72,9 +110,9 @@ Let's get you set up! 🚀
 
 # Creating a New Project
 
-<Info>
+<Tip>
   We recommend using the YAML Template scaffolding for a structured approach to defining agents and tasks.
-</Info>
+</Tip>
 
 <Steps>
   <Step title="Generate Project Structure">
@@ -101,10 +139,22 @@ Let's get you set up! 🚀
             │   └── __init__.py
             └── config/
                 ├── agents.yaml
+                ├── config.yaml
                 └── tasks.yaml
     ```
     </Frame>
-  </Step>   
+  </Step>
+
+  <Step title="Install Additional Tools">
+    You can install additional tools using UV:
+    ```shell Terminal
+    uv add <tool-name>
+    ```
+
+    <Tip>
+      UV is our preferred package manager as it's significantly faster than pip and provides better dependency resolution.
+    </Tip>
+  </Step>
 
   <Step title="Customize Your Project">
     Your project will contain these essential files:

docs/tools/composiotool.mdx

@@ -1,5 +1,5 @@
 ---
-title: Composio
+title: Composio Tool
 description: Composio provides 250+ production-ready tools for AI agents with flexible authentication management.
 icon: gear-code
 ---
@@ -75,7 +75,8 @@ filtered_action_enums = toolset.find_actions_by_use_case(
 )
 
 tools = toolset.get_tools(actions=filtered_action_enums)
-```<Tip>Set `advanced` to True to get actions for complex use cases</Tip>
+```
+<Tip>Set `advanced` to True to get actions for complex use cases</Tip>
 
 - Using specific tools:
 

src/crewai/agent.py

@@ -3,6 +3,7 @@
 import subprocess
 from typing import Any, Dict, List, Literal, Optional, Union
 
+from litellm import AuthenticationError as LiteLLMAuthenticationError
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
 from crewai.agents import CacheHandler
@@ -261,6 +262,9 @@ def execute_task(
                 }
             )["output"]
         except Exception as e:
+            if isinstance(e, LiteLLMAuthenticationError):
+                # Do not retry on authentication errors
+                raise e
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
                 raise e