WIP fixed mypy src types

README.md

@@ -254,7 +254,7 @@ pip install dist/*.tar.gz
 
 CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
 
-It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. We don't offer a way to disable it now, but we will in the future.
+There is NO data being collected on the prompts, tasks descriptions agents backstories or goals nor tools usage, no API calls, nor responses nor any data that is being processed by the agents, nor any secrets and env vars.
 
 Data collected includes:
 
@@ -279,7 +279,7 @@ Data collected includes:
 - Tools names available
   - Understand out of the publically available tools, which ones are being used the most so we can improve them
 
-Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
+Users can opt-in sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews.
 
 ## License
 

docs/getting-started/Start-a-New-CrewAI-Project-Template-Method.md

@@ -20,7 +20,7 @@ Before getting started with CrewAI, make sure that you have installed it via pip
 $ pip install crewai crewai-tools
 ```
 
-### Virtual Environments
+### Virtual Environemnts
 It is highly recommended that you use virtual environments to ensure that your CrewAI project is isolated from other projects and dependencies. Virtual environments provide a clean, separate workspace for each project, preventing conflicts between different versions of packages and libraries. This isolation is crucial for maintaining consistency and reproducibility in your development process. You have multiple options for setting up virtual environments depending on your operating system and Python version:
 
 1. Use venv (Python's built-in virtual environment tool):
@@ -244,10 +244,6 @@ def run():
 
 To run your project, use the following command:
 
-```shell
-$ crewai run
-```
-or
 ```shell
 $ poetry run my_project
 ```

docs/how-to/Create-Custom-Tools.md

@@ -7,7 +7,6 @@ description: Comprehensive guide on crafting, using, and managing custom tools w
 This guide provides detailed instructions on creating custom tools for the crewAI framework and how to efficiently manage and utilize these tools, incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools, enabling agents to perform a wide range of actions.
 
 ### Prerequisites
-
 Before creating your own tools, ensure you have the crewAI extra tools package installed:
 
 ```bash
@@ -32,7 +31,7 @@ class MyCustomTool(BaseTool):
 
 ### Using the `tool` Decorator
 
-Alternatively, you can use the tool decorator `@tool`. This approach allows you to define the tool's attributes and functionality directly within a function, offering a concise and efficient way to create specialized tools tailored to your needs.
+Alternatively, use the `tool` decorator for a direct approach to create tools. This requires specifying attributes and the tool's logic within a function.
 
 ```python
 from crewai_tools import tool

docs/how-to/Force-Tool-Ouput-as-Result.md

@@ -16,7 +16,7 @@ Here's an example of how to force the tool output as the result of an agent's ta
 # Define a custom tool that returns the result as the answer
 coding_agent =Agent(
         role="Data Scientist",
-        goal="Product amazing reports on AI",
+        goal="Product amazing resports on AI",
         backstory="You work with data and AI",
         tools=[MyCustomTool(result_as_answer=True)],
     )

docs/how-to/LLM-Connections.md

@@ -6,25 +6,33 @@ description: Comprehensive guide on integrating CrewAI with various Large Langua
 ## Connect CrewAI to LLMs
 
 !!! note "Default LLM"
-    By default, CrewAI uses OpenAI's GPT-4o model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4o") for language processing. You can configure your agents to use a different model or API as described in this guide.
-    By default, CrewAI uses OpenAI's GPT-4 model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4") for language processing. You can configure your agents to use a different model or API as described in this guide.
+    By default, CrewAI uses OpenAI's GPT-4 model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4o") for language processing. You can configure your agents to use a different model or API as described in this guide.
 
-CrewAI provides extensive versatility in integrating with various Language Models (LLMs), including local options through Ollama such as  Llama and Mixtral to cloud-based solutions like Azure. Its compatibility extends to all [LangChain LLM components](https://python.langchain.com/v0.2/docs/integrations/llms/), offering a wide range of integration possibilities for customized AI applications.
+CrewAI offers flexibility in connecting to various LLMs, including local models via [Ollama](https://ollama.ai) and different APIs like Azure. It's compatible with all [LangChain LLM](https://python.langchain.com/docs/integrations/llms/) components, enabling diverse integrations for tailored AI solutions.
 
-The platform supports connections to an array of Generative AI models, including:
+## CrewAI Agent Overview
 
- - OpenAI's suite of advanced language models
- - Anthropic's cutting-edge AI offerings
- - Ollama's diverse range of locally-hosted generative model & embeddings
- - LM Studio's diverse range of locally hosted generative models & embeddings
- - Groq's Super Fast LLM offerings
- - Azures' generative AI offerings
- - HuggingFace's generative AI offerings
+The `Agent` class is the cornerstone for implementing AI solutions in CrewAI. Here's a comprehensive overview of the Agent class attributes and methods:
 
-This broad spectrum of LLM options enables users to select the most suitable model for their specific needs, whether prioritizing local deployment, specialized capabilities, or cloud-based scalability.
+- **Attributes**:
+    - `role`: Defines the agent's role within the solution.
+    - `goal`: Specifies the agent's objective.
+    - `backstory`: Provides a background story to the agent.
+    - `cache` *Optional*: Determines whether the agent should use a cache for tool usage. Default is `True`.
+    - `max_rpm` *Optional*: Maximum number of requests per minute the agent's execution should respect. Optional.
+    - `verbose` *Optional*: Enables detailed logging of the agent's execution. Default is `False`.
+    - `allow_delegation` *Optional*: Allows the agent to delegate tasks to other agents, default is `True`.
+    - `tools`: Specifies the tools available to the agent for task execution. Optional.
+    - `max_iter` *Optional*: Maximum number of iterations for an agent to execute a task, default is 25.
+    - `max_execution_time` *Optional*: Maximum execution time for an agent to execute a task. Optional.
+    - `step_callback` *Optional*: Provides a callback function to be executed after each step. Optional.
+    - `llm` *Optional*: Indicates the Large Language Model the agent uses. By default, it uses the GPT-4 model defined in the environment variable "OPENAI_MODEL_NAME".
+    - `function_calling_llm` *Optional* :  Will turn the ReAct CrewAI agent into a function-calling agent.
+    - `callbacks` *Optional*: A list of callback functions from the LangChain library that are triggered during the agent's execution process.
+    - `system_template` *Optional*: Optional string to define the system format for the agent.
+    - `prompt_template` *Optional*: Optional string to define the prompt format for the agent.
+    - `response_template` *Optional*: Optional string to define the response format for the agent.
 
-## Changing the default LLM
-The default LLM is provided through the `langchain openai` package, which is installed by default when you install CrewAI. You can change this default LLM to a different model or API by setting the `OPENAI_MODEL_NAME` environment variable. This straightforward process allows you to harness the power of different OpenAI models, enhancing the flexibility and capabilities of your CrewAI implementation.
 ```python
 # Required
 os.environ["OPENAI_MODEL_NAME"]="gpt-4-0125-preview"
@@ -37,27 +45,30 @@ example_agent = Agent(
   verbose=True
 )
 ```
-## Ollama Local Integration
-Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, you will need the `langchain-ollama` package. You can then set the following environment variables to connect to your Ollama instance running locally on port 11434.
 
+## Ollama Integration
+Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, set the appropriate environment variables as shown below.
+
+### Setting Up Ollama
+- **Environment Variables Configuration**: To integrate Ollama, set the following environment variables:
 ```sh
-os.environ[OPENAI_API_BASE]='http://localhost:11434'
-os.environ[OPENAI_MODEL_NAME]='llama2'  # Adjust based on available model
-os.environ[OPENAI_API_KEY]='' # No API Key required for Ollama
+OPENAI_API_BASE='http://localhost:11434'
+OPENAI_MODEL_NAME='llama2'  # Adjust based on available model
+OPENAI_API_KEY=''
 ```
 
-## Ollama Integration Step by Step (ex. for using Llama 3.1 8B locally)
-1. [Download and install Ollama](https://ollama.com/download).   
-2. After setting up the Ollama, Pull the Llama3.1 8B model by typing following lines into your terminal ```ollama run llama3.1```.   
-3. Llama3.1 should now be served locally on `http://localhost:11434`
+## Ollama Integration (ex. for using Llama 2 locally)
+1. [Download Ollama](https://ollama.com/download).   
+2. After setting up the Ollama, Pull the Llama2 by typing following lines into the terminal ```ollama pull llama2```.   
+3. Enjoy your free Llama2 model that powered up by excellent agents from crewai.   
 ```
 from crewai import Agent, Task, Crew
-from langchain_ollama import ChatOllama
+from langchain.llms import Ollama
 import os
 os.environ["OPENAI_API_KEY"] = "NA"
 
 llm = Ollama(
-    model = "llama3.1",
+    model = "llama2",
     base_url = "http://localhost:11434")
 
 general_agent = Agent(role = "Math Professor",
@@ -87,14 +98,13 @@ There are a couple of different ways you can use HuggingFace to host your LLM.
 
 ### Your own HuggingFace endpoint
 ```python
-from langchain_huggingface import HuggingFaceEndpoint,
+from langchain_community.llms import HuggingFaceEndpoint
 
 llm = HuggingFaceEndpoint(
-    repo_id="microsoft/Phi-3-mini-4k-instruct",
+    endpoint_url="<YOUR_ENDPOINT_URL_HERE>",
+    huggingfacehub_api_token="<HF_TOKEN_HERE>",
     task="text-generation",
-    max_new_tokens=512,
-    do_sample=False,
-    repetition_penalty=1.03,
+    max_new_tokens=512
 )
 
 agent = Agent(
@@ -105,50 +115,66 @@ agent = Agent(
 )
 ```
 
+### From HuggingFaceHub endpoint
+```python
+from langchain_community.llms import HuggingFaceHub
+
+llm = HuggingFaceHub(
+    repo_id="HuggingFaceH4/zephyr-7b-beta",
+    huggingfacehub_api_token="<HF_TOKEN_HERE>",
+    task="text-generation",
+)
+```
+
 ## OpenAI Compatible API Endpoints
 Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, Groq, and Mistral AI.
 
 ### Configuration Examples
 #### FastChat
 ```sh
-os.environ[OPENAI_API_BASE]="http://localhost:8001/v1"
-os.environ[OPENAI_MODEL_NAME]='oh-2.5m7b-q51'
-os.environ[OPENAI_API_KEY]=NA
+OPENAI_API_BASE="http://localhost:8001/v1"
+OPENAI_MODEL_NAME='oh-2.5m7b-q51'
+OPENAI_API_KEY=NA
 ```
 
 #### LM Studio
 Launch [LM Studio](https://lmstudio.ai) and go to the Server tab. Then select a model from the dropdown menu and wait for it to load. Once it's loaded, click the green Start Server button and use the URL, port, and API key that's shown (you can modify them). Below is an example of the default settings as of LM Studio 0.2.19:
 ```sh
-os.environ[OPENAI_API_BASE]="http://localhost:1234/v1"
-os.environ[OPENAI_API_KEY]="lm-studio"
+OPENAI_API_BASE="http://localhost:1234/v1"
+OPENAI_API_KEY="lm-studio"
 ```
 
 #### Groq API
 ```sh
-os.environ[OPENAI_API_KEY]=your-groq-api-key
-os.environ[OPENAI_MODEL_NAME]='llama3-8b-8192'
-os.environ[OPENAI_API_BASE]=https://api.groq.com/openai/v1
+OPENAI_API_KEY=your-groq-api-key
+OPENAI_MODEL_NAME='llama3-8b-8192'
+OPENAI_API_BASE=https://api.groq.com/openai/v1
 ```
 
 #### Mistral API
 ```sh
-os.environ[OPENAI_API_KEY]=your-mistral-api-key
-os.environ[OPENAI_API_BASE]=https://api.mistral.ai/v1
-os.environ[OPENAI_MODEL_NAME]="mistral-small"
+OPENAI_API_KEY=your-mistral-api-key
+OPENAI_API_BASE=https://api.mistral.ai/v1
+OPENAI_MODEL_NAME="mistral-small"
 ```
 
 ### Solar
-```sh
+```python
 from langchain_community.chat_models.solar import SolarChat
-```
-```sh
-os.environ[SOLAR_API_BASE]="https://api.upstage.ai/v1/solar"
-os.environ[SOLAR_API_KEY]="your-solar-api-key"
-```
+# Initialize language model
+os.environ["SOLAR_API_KEY"] = "your-solar-api-key"
+llm = SolarChat(max_tokens=1024)
 
 # Free developer API key available here: https://console.upstage.ai/services/solar
 # Langchain Example: https://github.com/langchain-ai/langchain/pull/18556
+```
 
+### text-gen-web-ui
+```sh
+OPENAI_API_BASE=http://localhost:5000/v1
+OPENAI_MODEL_NAME=NA
+OPENAI_API_KEY=NA
+```
 
 ### Cohere
 ```python
@@ -164,11 +190,10 @@ llm = ChatCohere()
 ### Azure Open AI Configuration
 For Azure OpenAI API integration, set the following environment variables:
 ```sh
-
-os.environ[AZURE_OPENAI_DEPLOYMENT] = "You deployment"
-os.environ["OPENAI_API_VERSION"] = "2023-12-01-preview"
-os.environ["AZURE_OPENAI_ENDPOINT"] = "Your Endpoint"
-os.environ["AZURE_OPENAI_API_KEY"] = "<Your API Key>"
+AZURE_OPENAI_VERSION="2022-12-01"
+AZURE_OPENAI_DEPLOYMENT=""
+AZURE_OPENAI_ENDPOINT=""
+AZURE_OPENAI_KEY=""
 ```
 
 ### Example Agent with Azure LLM
@@ -191,5 +216,6 @@ azure_agent = Agent(
   llm=azure_llm
 )
 ```
+
 ## Conclusion
 Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.

docs/telemetry/Telemetry.md

@@ -5,7 +5,7 @@ description: Understanding the telemetry data collected by CrewAI and how it con
 
 ## Telemetry
 
-CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users. We don't offer a way to disable it now, but we will in the future.
+CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users.
 
 It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy.
 
@@ -22,7 +22,7 @@ It's pivotal to understand that **NO data is collected** concerning prompts, tas
 - **Tool Usage**: Identifying which tools are most frequently used allows us to prioritize improvements in those areas.
 
 ### Opt-In Further Telemetry Sharing
-Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
+Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. This opt-in approach respects user privacy and aligns with data protection standards by ensuring users have control over their data sharing preferences. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
 
 ### Updates and Revisions
 We are committed to maintaining the accuracy and transparency of our documentation. Regular reviews and updates are performed to ensure our documentation accurately reflects the latest developments of our codebase and telemetry practices. Users are encouraged to review this section for the most current information on our data collection practices and how they contribute to the improvement of CrewAI.

docs/tools/CodeInterpreterTool.md

@@ -1,9 +1,9 @@
 # CodeInterpreterTool
 
 ## Description
-This tool enables the Agent to execute Python 3 code that it has generated autonomously. The code is run in a secure, isolated environment, ensuring safety regardless of the content. 
+This tool is used to give the Agent the ability to run code (Python3) from the code generated by the Agent itself. The code is executed in a sandboxed environment, so it is safe to run any code.
 
-This functionality is particularly valuable as it allows the Agent to create code, execute it within the same ecosystem, obtain the results, and utilize that information to inform subsequent decisions and actions.
+It is incredible useful since it allows the Agent to generate code, run it in the same environment, get the result and use it to make decisions.
 
 ## Requirements
 

docs/tools/ComposioTool.md

@@ -2,7 +2,7 @@
 
 ## Description
 
-This tools is a wrapper around the composio set of tools and gives your agent access to a wide variety of tools from the composio SDK.
+This tools is a wrapper around the composio toolset and gives your agent access to a wide variety of tools from the composio SDK.
 
 ## Installation
 
@@ -19,7 +19,7 @@ after the installation is complete, either run `composio login` or export your c
 
 The following example demonstrates how to initialize the tool and execute a github action:
 
-1. Initialize Composio tools
+1. Initialize toolset
 
 ```python
 from composio import App

docs/tools/SerperDevTool.md

@@ -40,9 +40,10 @@ The `SerperDevTool` comes with several parameters that will be passed to the API
 - **locale**: Optional. Specify the locale for the search results.
 - **n_results**: Number of search results to return. Default is `10`.
 
-The values for `country`, `location`, `locale` and `search_url` can be found on the [Serper Playground](https://serper.dev/playground).
+The values for `country`, `location`, `lovale` and `search_url` can be found on the [Serper Playground](https://serper.dev/playground).
 
 ## Example with Parameters
+
 Here is an example demonstrating how to use the tool with additional parameters:
 
 ```python

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.46.0"
+version = "0.41.1"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"

src/crewai/agents/executor.py

@@ -1,8 +1,6 @@
 import threading
 import time
-from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
-import click
-
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
 from langchain.agents import AgentExecutor
 from langchain.agents.agent import ExceptionTool
@@ -13,21 +11,12 @@ from langchain_core.tools import BaseTool
 from langchain_core.utils.input import get_color_mapping
 from pydantic import InstanceOf
 
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.chains.summarize import load_summarize_chain
-
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.tools_handler import ToolsHandler
-
-
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N
 from crewai.utilities.constants import TRAINING_DATA_FILE
-from crewai.utilities.exceptions.context_window_exceeding_exception import (
-    LLMContextLengthExceededException,
-)
 from crewai.utilities.training_handler import CrewTrainingHandler
-from crewai.utilities.logger import Logger
 
 
 class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
@@ -51,8 +40,6 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
     system_template: Optional[str] = None
     prompt_template: Optional[str] = None
     response_template: Optional[str] = None
-    _logger: Logger = Logger(verbose_level=2)
-    _fit_context_window_strategy: Optional[Literal["summarize"]] = "summarize"
 
     def _call(
         self,
@@ -144,7 +131,7 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
 
             # Call the LLM to see what to do.
-            output = self.agent.plan(
+            output = self.agent.plan(  # type: ignore #  Incompatible types in assignment (expression has type "AgentAction | AgentFinish | list[AgentAction]", variable has type "AgentAction")
                 intermediate_steps,
                 callbacks=run_manager.get_child() if run_manager else None,
                 **inputs,
@@ -198,27 +185,6 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             yield AgentStep(action=output, observation=observation)
             return
 
-        except Exception as e:
-            if LLMContextLengthExceededException(str(e))._is_context_limit_error(
-                str(e)
-            ):
-                output = self._handle_context_length_error(
-                    intermediate_steps, run_manager, inputs
-                )
-
-                if isinstance(output, AgentFinish):
-                    yield output
-                elif isinstance(output, list):
-                    for step in output:
-                        yield step
-                return
-
-            yield AgentStep(
-                action=AgentAction("_Exception", str(e), str(e)),
-                observation=str(e),
-            )
-            return
-
         # If the tool chosen is the finishing tool, then we end and return.
         if isinstance(output, AgentFinish):
             if self.should_ask_for_human_input:
@@ -269,7 +235,6 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
                 agent=self.crew_agent,
                 action=agent_action,
             )
-
             tool_calling = tool_usage.parse(agent_action.log)
 
             if isinstance(tool_calling, ToolUsageErrorException):
@@ -315,91 +280,3 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             CrewTrainingHandler(TRAINING_DATA_FILE).append(
                 self.crew._train_iteration, agent_id, training_data
             )
-
-    def _handle_context_length(
-        self, intermediate_steps: List[Tuple[AgentAction, str]]
-    ) -> List[Tuple[AgentAction, str]]:
-        text = intermediate_steps[0][1]
-        original_action = intermediate_steps[0][0]
-
-        text_splitter = RecursiveCharacterTextSplitter(
-            separators=["\n\n", "\n"],
-            chunk_size=8000,
-            chunk_overlap=500,
-        )
-
-        if self._fit_context_window_strategy == "summarize":
-            docs = text_splitter.create_documents([text])
-            self._logger.log(
-                "debug",
-                "Summarizing Content, it is recommended to use a RAG tool",
-                color="bold_blue",
-            )
-            summarize_chain = load_summarize_chain(
-                self.llm, chain_type="map_reduce", verbose=True
-            )
-            summarized_docs = []
-            for doc in docs:
-                summary = summarize_chain.invoke(
-                    {"input_documents": [doc]}, return_only_outputs=True
-                )
-
-                summarized_docs.append(summary["output_text"])
-
-            formatted_results = "\n\n".join(summarized_docs)
-            summary_step = AgentStep(
-                action=AgentAction(
-                    tool=original_action.tool,
-                    tool_input=original_action.tool_input,
-                    log=original_action.log,
-                ),
-                observation=formatted_results,
-            )
-            summary_tuple = (summary_step.action, summary_step.observation)
-            return [summary_tuple]
-
-        return intermediate_steps
-
-    def _handle_context_length_error(
-        self,
-        intermediate_steps: List[Tuple[AgentAction, str]],
-        run_manager: Optional[CallbackManagerForChainRun],
-        inputs: Dict[str, str],
-    ) -> Union[AgentFinish, List[AgentStep]]:
-        self._logger.log(
-            "debug",
-            "Context length exceeded. Asking user if they want to use summarize prompt to fit, this will reduce context length.",
-            color="yellow",
-        )
-        user_choice = click.confirm(
-            "Context length exceeded. Do you want to summarize the text to fit models context window?"
-        )
-        if user_choice:
-            self._logger.log(
-                "debug",
-                "Context length exceeded. Using summarize prompt to fit, this will reduce context length.",
-                color="bold_blue",
-            )
-            intermediate_steps = self._handle_context_length(intermediate_steps)
-
-            output = self.agent.plan(
-                intermediate_steps,
-                callbacks=run_manager.get_child() if run_manager else None,
-                **inputs,
-            )
-
-            if isinstance(output, AgentFinish):
-                return output
-            elif isinstance(output, AgentAction):
-                return [AgentStep(action=output, observation=None)]
-            else:
-                return [AgentStep(action=action, observation=None) for action in output]
-        else:
-            self._logger.log(
-                "debug",
-                "Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
-                color="red",
-            )
-            raise SystemExit(
-                "Context length exceeded and user opted not to summarize. Consider using smaller text or RAG tools from crewai_tools."
-            )

src/crewai/cli/cli.py

@@ -9,7 +9,6 @@ from .create_crew import create_crew
 from .evaluate_crew import evaluate_crew
 from .replay_from_task import replay_task_command
 from .reset_memories_command import reset_memories_command
-from .run_crew import run_crew
 from .train_crew import train_crew
 
 
@@ -148,12 +147,5 @@ def test(n_iterations: int, model: str):
     evaluate_crew(n_iterations, model)
 
 
-@crewai.command()
-def run():
-    """Run the crew."""
-    click.echo("Running the crew")
-    run_crew()
-
-
 if __name__ == "__main__":
     crewai()

src/crewai/cli/run_crew.py

@@ -1,23 +0,0 @@
-import subprocess
-
-import click
-
-
-def run_crew() -> None:
-    """
-    Run the crew by running a command in the Poetry environment.
-    """
-    command = ["poetry", "run", "run_crew"]
-
-    try:
-        result = subprocess.run(command, capture_output=False, text=True, check=True)
-
-        if result.stderr:
-            click.echo(result.stderr, err=True)
-
-    except subprocess.CalledProcessError as e:
-        click.echo(f"An error occurred while running the crew: {e}", err=True)
-        click.echo(e.output, err=True)
-
-    except Exception as e:
-        click.echo(f"An unexpected error occurred: {e}", err=True)

src/crewai/cli/templates/README.md

@@ -34,10 +34,6 @@ poetry install
 
 To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
 
-```bash
-$ crewai run
-```
-or
 ```bash
 poetry run {{folder_name}}
 ```

src/crewai/cli/templates/pyproject.toml

@@ -6,11 +6,10 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.46.0" }
+crewai = { extras = ["tools"], version = "^0.41.1" }
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"
-run_crew = "{{folder_name}}.main:run"
 train = "{{folder_name}}.main:train"
 replay = "{{folder_name}}.main:replay"
 test = "{{folder_name}}.main:test"

src/crewai/tools/tool_usage.py

@@ -16,7 +16,7 @@ try:
 except ImportError:
     agentops = None
 
-OPENAI_BIGGER_MODELS = ["gpt-4o"]
+OPENAI_BIGGER_MODELS = ["gpt-4"]
 
 
 class ToolUsageErrorException(Exception):

src/crewai/utilities/__init__.py

@@ -7,9 +7,6 @@ from .parser import YamlParser
 from .printer import Printer
 from .prompts import Prompts
 from .rpm_controller import RPMController
-from .exceptions.context_window_exceeding_exception import (
-    LLMContextLengthExceededException,
-)
 
 __all__ = [
     "Converter",
@@ -22,5 +19,4 @@ __all__ = [
     "Prompts",
     "RPMController",
     "YamlParser",
-    "LLMContextLengthExceededException",
 ]

src/crewai/utilities/exceptions/context_window_exceeding_exception.py

@@ -1,26 +0,0 @@
-class LLMContextLengthExceededException(Exception):
-    CONTEXT_LIMIT_ERRORS = [
-        "maximum context length",
-        "context length exceeded",
-        "context_length_exceeded",
-        "context window full",
-        "too many tokens",
-        "input is too long",
-        "exceeds token limit",
-    ]
-
-    def __init__(self, error_message: str):
-        self.original_error_message = error_message
-        super().__init__(self._get_error_message(error_message))
-
-    def _is_context_limit_error(self, error_message: str) -> bool:
-        return any(
-            phrase.lower() in error_message.lower()
-            for phrase in self.CONTEXT_LIMIT_ERRORS
-        )
-
-    def _get_error_message(self, error_message: str):
-        return (
-            f"LLM context length exceeded. Original error: {error_message}\n"
-            "Consider using a smaller input or implementing a text splitting strategy."
-        )

src/crewai/utilities/token_counter_callback.py

@@ -10,24 +10,24 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
 class TokenCalcHandler(BaseCallbackHandler):
     model_name: str = ""
     token_cost_process: TokenProcess
-    encoding: tiktoken.Encoding
 
     def __init__(self, model_name, token_cost_process):
         self.model_name = model_name
         self.token_cost_process = token_cost_process
-        try:
-            self.encoding = tiktoken.encoding_for_model(self.model_name)
-        except KeyError:
-            self.encoding = tiktoken.get_encoding("cl100k_base")
 
     def on_llm_start(
         self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
     ) -> None:
+        try:
+            encoding = tiktoken.encoding_for_model(self.model_name)
+        except KeyError:
+            encoding = tiktoken.get_encoding("cl100k_base")
+
         if self.token_cost_process is None:
             return
 
         for prompt in prompts:
-            self.token_cost_process.sum_prompt_tokens(len(self.encoding.encode(prompt)))
+            self.token_cost_process.sum_prompt_tokens(len(encoding.encode(prompt)))
 
     async def on_llm_new_token(self, token: str, **kwargs) -> None:
         self.token_cost_process.sum_completion_tokens(1)

tests/agent_test.py

@@ -7,7 +7,6 @@ import pytest
 from langchain.tools import tool
 from langchain_core.exceptions import OutputParserException
 from langchain_openai import ChatOpenAI
-from langchain.schema import AgentAction
 
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
@@ -1015,75 +1014,3 @@ def test_agent_max_retry_limit():
                 ),
             ]
         )
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_handle_context_length_exceeds_limit():
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-    )
-    original_action = AgentAction(
-        tool="test_tool", tool_input="test_input", log="test_log"
-    )
-
-    with patch.object(
-        CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
-    ) as private_mock:
-        task = Task(
-            description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
-            expected_output="The final answer",
-        )
-        agent.execute_task(
-            task=task,
-        )
-        private_mock.assert_called_once()
-        with patch("crewai.agents.executor.click") as mock_prompt:
-            mock_prompt.return_value = "y"
-            with patch.object(
-                CrewAgentExecutor, "_handle_context_length"
-            ) as mock_handle_context:
-                mock_handle_context.side_effect = ValueError(
-                    "Context length limit exceeded"
-                )
-
-                long_input = "This is a very long input. " * 10000
-
-                # Attempt to handle context length, expecting the mocked error
-                with pytest.raises(ValueError) as excinfo:
-                    agent.agent_executor._handle_context_length(
-                        [(original_action, long_input)]
-                    )
-
-                assert "Context length limit exceeded" in str(excinfo.value)
-                mock_handle_context.assert_called_once()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_handle_context_length_exceeds_limit_cli_no():
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-    )
-    task = Task(description="test task", agent=agent, expected_output="test output")
-
-    with patch.object(
-        CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
-    ) as private_mock:
-        task = Task(
-            description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
-            expected_output="The final answer",
-        )
-        agent.execute_task(
-            task=task,
-        )
-        private_mock.assert_called_once()
-        with patch("crewai.agents.executor.click") as mock_prompt:
-            mock_prompt.return_value = "n"
-            pytest.raises(SystemExit)
-            with patch.object(
-                CrewAgentExecutor, "_handle_context_length"
-            ) as mock_handle_context:
-                mock_handle_context.assert_not_called()

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tests/cassettes/test_handle_context_length_exceeds_limit_cli_no.yaml

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tests/crew_test.py

@@ -632,18 +632,21 @@ def test_sequential_async_task_execution_completion():
     list_ideas = Task(
         description="Give me a list of 5 interesting ideas to explore for an article, what makes them unique and interesting.",
         expected_output="Bullet point list of 5 important events.",
+        max_retry_limit=3,
         agent=researcher,
         async_execution=True,
     )
     list_important_history = Task(
         description="Research the history of AI and give me the 5 most important events that shaped the technology.",
         expected_output="Bullet point list of 5 important events.",
+        max_retry_limit=3,
         agent=researcher,
         async_execution=True,
     )
     write_article = Task(
         description="Write an article about the history of AI and its most important events.",
         expected_output="A 4 paragraph article about AI.",
+        max_retry_limit=3,
         agent=writer,
         context=[list_ideas, list_important_history],
     )