Merge branch 'main' into feat/make-llm-call-magical

docs/concepts/cli.mdx

@@ -12,7 +12,7 @@ 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
 ```
 
@@ -20,7 +20,7 @@ pip install crewai
 
 The basic structure of a CrewAI CLI command is:
 
-```shell
+```shell Terminal
 crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 ```
 
@@ -30,7 +30,7 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 
 Create a new crew or flow.
 
-```shell
+```shell Terminal
 crewai create [OPTIONS] TYPE NAME
 ```
 
@@ -38,7 +38,7 @@ crewai create [OPTIONS] TYPE NAME
 - `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
 ```
@@ -63,7 +63,7 @@ crewai version --tools
 
 Train the crew for a specified number of iterations.
 
-```shell
+```shell Terminal
 crewai train [OPTIONS]
 ```
 
@@ -71,7 +71,7 @@ crewai train [OPTIONS]
 - `-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
 ```
 
@@ -79,14 +79,14 @@ crewai train -n 10 -f my_training_data.pkl
 
 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
 ```
 
@@ -94,7 +94,7 @@ crewai replay -t task_123456
 
 Retrieve your latest crew.kickoff() task outputs.
 
-```shell
+```shell Terminal
 crewai log-tasks-outputs
 ```
 
@@ -102,7 +102,7 @@ crewai log-tasks-outputs
 
 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
 ```
@@ -122,7 +122,7 @@ crewai reset-memories --all
 
 Test the crew and evaluate the results.
 
-```shell
+```shell Terminal
 crewai test [OPTIONS]
 ```
 
@@ -130,7 +130,7 @@ crewai test [OPTIONS]
 - `-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
 ```
 
@@ -138,7 +138,7 @@ crewai test -n 5 -m gpt-3.5-turbo
 
 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/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. 
+Now let's get you set up! 🚀
-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 shutil
 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 @@ class Agent(BaseAgent):
                 }
             )["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

src/crewai/agents/crew_agent_executor.py

@@ -3,6 +3,8 @@ import re
 from dataclasses import dataclass
 from typing import Any, Callable, Dict, List, Optional, Union
 
+from litellm.exceptions import AuthenticationError as LiteLLMAuthenticationError
+
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.parser import (
@@ -13,6 +15,7 @@ from crewai.agents.parser import (
     OutputParserException,
 )
 from crewai.agents.tools_handler import ToolsHandler
+from crewai.llm import LLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N, Printer
@@ -54,7 +57,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         callbacks: List[Any] = [],
     ):
         self._i18n: I18N = I18N()
-        self.llm = llm
+        self.llm: LLM = llm
         self.task = task
         self.agent = agent
         self.crew = crew
@@ -80,10 +83,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tool_name_to_tool_map: Dict[str, BaseTool] = {
             tool.name: tool for tool in self.tools
         }
-        if self.llm.stop:
+        self.stop = stop_words
-            self.llm.stop = list(set(self.llm.stop + self.stop))
+        self.llm.stop = list(set(self.llm.stop + self.stop))
-        else:
-            self.llm.stop = self.stop
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:
@@ -98,7 +99,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_start_logs()
 
         self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
-        formatted_answer = self._invoke_loop()
+
+        try:
+            formatted_answer = self._invoke_loop()
+        except Exception as e:
+            raise e
 
         if self.ask_for_human_input:
             formatted_answer = self._handle_human_feedback(formatted_answer)
@@ -124,7 +129,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self._enforce_rpm_limit()
 
                 answer = self._get_llm_response()
-
                 formatted_answer = self._process_llm_response(answer)
 
                 if isinstance(formatted_answer, AgentAction):
@@ -145,10 +149,40 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 if self._is_context_length_exceeded(e):
                     self._handle_context_length()
                     continue
+                elif self._is_litellm_authentication_error(e):
+                    self._handle_litellm_auth_error(e)
+                    raise e
+                else:
+                    self._printer.print(
+                        content=f"Unhandled exception: {e}",
+                        color="red",
+                    )
+            finally:
+                self.iterations += 1
 
         self._show_logs(formatted_answer)
         return formatted_answer
 
+    def _is_litellm_authentication_error(self, exception: Exception) -> bool:
+        """Check if the exception is a litellm authentication error."""
+        if LiteLLMAuthenticationError and isinstance(
+            exception, LiteLLMAuthenticationError
+        ):
+            return True
+
+        return False
+
+    def _handle_litellm_auth_error(self, exception: Exception) -> None:
+        """Handle litellm authentication error by informing the user and exiting."""
+        self._printer.print(
+            content="Authentication error with litellm occurred. Please check your API key and configuration.",
+            color="red",
+        )
+        self._printer.print(
+            content=f"Error details: {exception}",
+            color="red",
+        )
+
     def _has_reached_max_iterations(self) -> bool:
         """Check if the maximum number of iterations has been reached."""
         return self.iterations >= self.max_iter
@@ -160,10 +194,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def _get_llm_response(self) -> str:
         """Call the LLM and return the response, handling any invalid responses."""
-        answer = self.llm.call(
+        try:
-            self.messages,
+            answer = self.llm.call(
-            callbacks=self.callbacks,
+                self.messages,
-        )
+                callbacks=self.callbacks,
+            )
+        except Exception as e:
+            self._printer.print(
+                content=f"Error during LLM call: {e}",
+                color="red",
+            )
+            raise e
 
         if not answer:
             self._printer.print(
@@ -184,7 +225,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
                     answer = answer.split("Observation:")[0].strip()
 
-        self.iterations += 1
         return self._format_answer(answer)
 
     def _handle_agent_action(

src/crewai/llm.py

@@ -142,7 +142,6 @@ class LLM:
         self.temperature = temperature
         self.top_p = top_p
         self.n = n
-        self.stop = stop
         self.max_completion_tokens = max_completion_tokens
         self.max_tokens = max_tokens
         self.presence_penalty = presence_penalty
@@ -160,6 +159,14 @@ class LLM:
 
         litellm.drop_params = True
 
+        # Normalize self.stop to always be a List[str]
+        if stop is None:
+            self.stop: List[str] = []
+        elif isinstance(stop, str):
+            self.stop = [stop]
+        else:
+            self.stop = stop
+
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
 

src/crewai/utilities/llm_utils.py

@@ -24,12 +24,10 @@ def create_llm(
 
     # 1) If llm_value is already an LLM object, return it directly
     if isinstance(llm_value, LLM):
-        print("LLM value is already an LLM object")
         return llm_value
 
     # 2) If llm_value is a string (model name)
     if isinstance(llm_value, str):
-        print("LLM value is a string")
         try:
             created_llm = LLM(model=llm_value)
             return created_llm
@@ -39,12 +37,10 @@ def create_llm(
 
     # 3) If llm_value is None, parse environment variables or use default
     if llm_value is None:
-        print("LLM value is None")
         return _llm_via_environment_or_fallback()
 
     # 4) Otherwise, attempt to extract relevant attributes from an unknown object
     try:
-        print("LLM value is an unknown object")
         # Extract attributes with explicit types
         model = (
             getattr(llm_value, "model_name", None)

tests/agent_test.py

@@ -16,7 +16,7 @@ from crewai.tools import tool
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
 from crewai.tools.tool_usage_events import ToolUsageFinished
-from crewai.utilities import RPMController
+from crewai.utilities import Printer, RPMController
 from crewai.utilities.events import Emitter
 
 
@@ -1600,3 +1600,103 @@ def test_agent_with_knowledge_sources():
 
         # Assert that the agent provides the correct information
         assert "red" in result.raw.lower()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_litellm_auth_error_handling():
+    """Test that LiteLLM authentication errors are handled correctly and not retried."""
+    from litellm import AuthenticationError as LiteLLMAuthenticationError
+
+    # Create an agent with a mocked LLM and max_retry_limit=0
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-4"),
+        max_retry_limit=0,  # Disable retries for authentication errors
+    )
+
+    # Create a task
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+        agent=agent,
+    )
+
+    # Mock the LLM call to raise LiteLLMAuthenticationError
+    with (
+        patch.object(LLM, "call") as mock_llm_call,
+        pytest.raises(LiteLLMAuthenticationError, match="Invalid API key"),
+    ):
+        mock_llm_call.side_effect = LiteLLMAuthenticationError(
+            message="Invalid API key", llm_provider="openai", model="gpt-4"
+        )
+        agent.execute_task(task)
+
+    # Verify the call was only made once (no retries)
+    mock_llm_call.assert_called_once()
+
+
+def test_crew_agent_executor_litellm_auth_error():
+    """Test that CrewAgentExecutor properly identifies and handles LiteLLM authentication errors."""
+    from litellm import AuthenticationError as LiteLLMAuthenticationError
+
+    from crewai.agents.tools_handler import ToolsHandler
+    from crewai.utilities import Printer
+
+    # Create an agent and executor with max_retry_limit=0
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-4", api_key="invalid_api_key"),
+    )
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+        agent=agent,
+    )
+
+    # Create executor with all required parameters
+    executor = CrewAgentExecutor(
+        agent=agent,
+        task=task,
+        llm=agent.llm,
+        crew=None,
+        prompt={"system": "You are a test agent", "user": "Execute the task: {input}"},
+        max_iter=5,
+        tools=[],
+        tools_names="",
+        stop_words=[],
+        tools_description="",
+        tools_handler=ToolsHandler(),
+    )
+
+    # Mock the LLM call to raise LiteLLMAuthenticationError
+    with (
+        patch.object(LLM, "call") as mock_llm_call,
+        patch.object(Printer, "print") as mock_printer,
+        pytest.raises(LiteLLMAuthenticationError, match="Invalid API key"),
+    ):
+        mock_llm_call.side_effect = LiteLLMAuthenticationError(
+            message="Invalid API key", llm_provider="openai", model="gpt-4"
+        )
+        executor.invoke(
+            {
+                "input": "test input",
+                "tool_names": "",
+                "tools": "",
+            }
+        )
+
+    # Verify error handling
+    mock_printer.assert_any_call(
+        content="Authentication error with litellm occurred. Please check your API key and configuration.",
+        color="red",
+    )
+    mock_printer.assert_any_call(
+        content="Error details: litellm.AuthenticationError: Invalid API key",
+        color="red",
+    )
+    # Verify the call was only made once (no retries)
+    mock_llm_call.assert_called_once()

tests/cassettes/test_agent_error_on_parsing_tool.yaml

@@ -2,21 +2,21 @@ interactions:
 - request:
     body: '{"messages": [{"role": "system", "content": "You are test role. test backstory\nYour
       personal goal is: test goal\nYou ONLY have access to the following tools, and
-      should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer(*args:
+      should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool
-      Any, **kwargs: Any) -> Any\nTool Description: get_final_answer() - Get the final
+      Arguments: {}\nTool Description: Get the final answer but don''t give it yet,
-      answer but don''t give it yet, just re-use this         tool non-stop. \nTool
+      just re-use this\n        tool non-stop.\n\nIMPORTANT: Use the following format
-      Arguments: {}\n\nUse the following format:\n\nThought: you should always think
+      in your response:\n\n```\nThought: you should always think about what to do\nAction:
-      about what to do\nAction: the action to take, only one name of [get_final_answer],
+      the action to take, only one name of [get_final_answer], just the name, exactly
-      just the name, exactly as it''s written.\nAction Input: the input to the action,
+      as it''s written.\nAction Input: the input to the action, just a simple JSON
-      just a simple python dictionary, enclosed in curly braces, using \" to wrap
+      object, enclosed in curly braces, using \" to wrap keys and values.\nObservation:
-      keys and values.\nObservation: the result of the action\n\nOnce all necessary
+      the result of the action\n```\n\nOnce all necessary information is gathered,
-      information is gathered:\n\nThought: I now know the final answer\nFinal Answer:
+      return the following format:\n\n```\nThought: I now know the final answer\nFinal
-      the final answer to the original input question\n"}, {"role": "user", "content":
+      Answer: the final answer to the original input question\n```"}, {"role": "user",
-      "\nCurrent Task: Use the get_final_answer tool.\n\nThis is the expect criteria
+      "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis is the expect
-      for your final answer: The final answer\nyou MUST return the actual complete
+      criteria for your final answer: The final answer\nyou MUST return the actual
-      content as the final answer, not a summary.\n\nBegin! This is VERY important
+      complete content as the final answer, not a summary.\n\nBegin! This is VERY
-      to you, use the tools available and give your best Final Answer, your job depends
+      important to you, use the tools available and give your best Final Answer, your
-      on it!\n\nThought:"}], "model": "gpt-4o"}'
+      job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
     headers:
       accept:
       - application/json
@@ -25,16 +25,13 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1325'
+      - '1367'
       content-type:
       - application/json
-      cookie:
-      - _cfuvid=ePJSDFdHag2D8lj21_ijAMWjoA6xfnPNxN4uekvC728-1727226247743-0.0.1.1-604800000;
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       - api.openai.com
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-      - OpenAI/Python 1.47.0
+      - OpenAI/Python 1.59.6
       x-stainless-arch:
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       x-stainless-async:
@@ -44,30 +41,35 @@ interactions:
       x-stainless-os:
       - MacOS
       x-stainless-package-version:
-      - 1.47.0
+      - 1.59.6
       x-stainless-raw-response:
       - 'true'
+      x-stainless-retry-count:
+      - '0'
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.11.7
+      - 3.12.7
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     uri: https://api.openai.com/v1/chat/completions
   response:
-    content: "{\n  \"id\": \"chatcmpl-ABAtOWmVjvzQ9X58tKAUcOF4gmXwx\",\n  \"object\":
+    content: "{\n  \"id\": \"chatcmpl-AsXdf4OZKCZSigmN4k0gyh67NciqP\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1727226842,\n  \"model\": \"gpt-4o-2024-05-13\",\n
+      \"chat.completion\",\n  \"created\": 1737562383,\n  \"model\": \"gpt-4o-2024-08-06\",\n
       \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": \"Thought: I need to use the get_final_answer
+      \"assistant\",\n        \"content\": \"```\\nThought: I have to use the available
-      tool to determine the final answer.\\nAction: get_final_answer\\nAction Input:
+      tool to get the final answer. Let's proceed with executing it.\\nAction: get_final_answer\\nAction
-      {}\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n      \"finish_reason\":
+      Input: {}\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
-      \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 274,\n    \"completion_tokens\":
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
-      27,\n    \"total_tokens\": 301,\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\":
+      274,\n    \"completion_tokens\": 33,\n    \"total_tokens\": 307,\n    \"prompt_tokens_details\":
-      0\n    }\n  },\n  \"system_fingerprint\": \"fp_e375328146\"\n}\n"
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
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+      - 9060d43e3be1d690-IAD
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@@ -75,19 +77,27 @@ interactions:
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       - application/json
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-      - Wed, 25 Sep 2024 01:14:03 GMT
+      - Wed, 22 Jan 2025 16:13:03 GMT
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       - cloudflare
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