remove extra folder

Added the langchain "Tool" functionality by creating a python function and then adding the functionality of that function to the tool by 'func' variable in the 'Tool' function.

.pre-commit-config.yaml

@@ -1,11 +1,11 @@
 repos:
-
   - repo: https://github.com/psf/black-pre-commit-mirror
     rev: 23.12.1
     hooks:
       - id: black
         language_version: python3.11
         files: \.(py)$
+        exclude: 'src/crewai/cli/templates/(crew|main)\.py'
 
   - repo: https://github.com/pycqa/isort
     rev: 5.13.2

README.md

@@ -49,36 +49,29 @@ To get started with CrewAI, follow these simple steps:
 pip install crewai
 ```
 
-If you want to also install crewai-tools, which is a package with tools that can be used by the agents, but more dependencies, you can install it with:
+If you want to also install crewai-tools, which is a package with tools that can be used by the agents, but more dependencies, you can install it with, example bellow uses it:
 
 ```shell
 pip install 'crewai[tools]'
 ```
 
-The example below also uses DuckDuckGo's Search. You can install it with `pip` too:
-
-```shell
-pip install duckduckgo-search
-```
-
 ### 2. Setting Up Your Crew
 
 ```python
 import os
 from crewai import Agent, Task, Crew, Process
+from crewai_tools import SerperDevTool
 
 os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
 
 # You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.
-# osOPENAI_API_BASE='http://localhost:11434/v1'
-# OPENAI_MODEL_NAME='openhermes'  # Adjust based on available model
-# OPENAI_API_KEY='sk-111111111111111111111111111111111111111111111111'
 
-# Install duckduckgo-search for this example:
-# !pip install -U duckduckgo-search
+# os.environ["OPENAI_API_BASE"] = 'http://localhost:11434/v1'
+# os.environ["OPENAI_MODEL_NAME"] ='openhermes'  # Adjust based on available model
+# os.environ["OPENAI_API_KEY"] ='sk-111111111111111111111111111111111111111111111111'
 
-from langchain_community.tools import DuckDuckGoSearchRun
-search_tool = DuckDuckGoSearchRun()
+search_tool = SerperDevTool()
 
 # Define your agents with roles and goals
 researcher = Agent(

docs/core-concepts/Collaboration.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 title: How Agents Collaborate in CrewAI
 description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.

docs/core-concepts/Crews.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 title: crewAI Crews
 description: Understanding and utilizing crews in the crewAI framework.
@@ -96,3 +95,4 @@ Once your crew is assembled, initiate the workflow with the `kickoff()` method.
 # Start the crew's task execution
 result = my_crew.kickoff()
 print(result)
+```

docs/core-concepts/Tasks.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 title: crewAI Tasks
 description: Overview and management of tasks within the crewAI framework.

docs/core-concepts/Tools.md

@@ -1,4 +1,3 @@
-```
 ---
 title: crewAI Tools
 description: Understanding and leveraging tools within the crewAI framework for agent collaboration and task execution.
@@ -39,13 +38,13 @@ from crewai_tools import (
 )
 
 # Set up API keys
-os.environ["SERPER_API_KEY"] = "Your Key"
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
 os.environ["OPENAI_API_KEY"] = "Your Key"
 
 # Instantiate tools
 docs_tool = DirectoryReadTool(directory='./blog-posts')
 file_tool = FileReadTool()
-search_tool = SeperDevTool()
+search_tool = SerperDevTool()
 web_rag_tool = WebsiteSearchTool()
 
 # Create agents
@@ -53,7 +52,7 @@ researcher = Agent(
     role='Market Research Analyst',
     goal='Provide up-to-date market analysis of the AI industry',
     backstory='An expert analyst with a keen eye for market trends.',
-    tools=[search_tool, website_rag],
+    tools=[search_tool, web_rag_tool],
     verbose=True
 )
 
@@ -182,7 +181,7 @@ from unstructured.partition.html import partition_html
 
     # Annotate the function with the tool decorator from crewAI
 @tool("Integration with a given API")
-def integtation_tool(argument: str) -> str:
+def integration_tool(argument: str) -> str:
     """Integration with a given API"""
     # Code here
     return resutls # string to be sent back to the agent
@@ -192,7 +191,7 @@ agent = Agent(
     role='Research Analyst',
     goal='Provide up-to-date market analysis',
     backstory='An expert analyst with a keen eye for market trends.',
-    tools=[integtation_tool]
+    tools=[integration_tool]
 )
 ```
 
@@ -229,4 +228,4 @@ agent = Agent(
 ```
 
 ## Conclusion
-Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem.
+Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem.

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

@@ -65,4 +65,27 @@ agent = Agent(
     backstory='An expert analyst with a keen eye for market trends.',
     tools=[integtation_tool]
 )
-```
+```
+
+
+
+### Using the `Tool` function from langchain
+
+For another simple approach, create a function in python directly with the required attributes and a functional logic.
+
+```python
+def combine(a, b):
+    return a + b
+```
+
+Then you can add that function into the your tool by using 'func' variable in the Tool function.
+
+```python
+from langchain.agents import Tool
+
+math_tool = Tool(
+            name="Math tool",
+            func=math_tool,
+            description="Useful for adding two numbers together, in other words combining them."
+        )
+```

docs/how-to/Human-Input-on-Execution.md

@@ -1,4 +1,8 @@
-```markdown
+---
+title: Human Input on Execution
+description: Comprehensive guide on integrating CrewAI with human input during execution in complex decision-making processes or when needed help during complex tasks. 
+---
+
 # Human Input in Agent Execution
 
 Human input plays a pivotal role in several agent execution scenarios, enabling agents to seek additional information or clarification when necessary. This capability is invaluable in complex decision-making processes or when agents need more details to complete a task effectively.
@@ -87,4 +91,4 @@ result = crew.kickoff()
 
 print("######################")
 print(result)
-```
+```

docs/index.md

@@ -44,7 +44,7 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                 </a>
             </li>
             <li>
-                <a href="./how-to/Create Custom Tools">
+                <a href="./how-to/Create-Custom-Tools">
                     Create Custom Tools
                 </a>
             </li>

docs/telemetry/Telemetry.md

@@ -1,4 +1,7 @@
-```markdown
+---
+title: Telemetry
+description: Understanding the telemetry data collected by CrewAI and how it contributes to the enhancement of the library.
+---
 ## 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.
@@ -9,24 +12,16 @@ It's pivotal to understand that **NO data is collected** concerning prompts, tas
 - **Version of CrewAI**: Assessing the adoption rate of our latest version helps us understand user needs and guide our updates.
 - **Python Version**: Identifying the Python versions our users operate with assists in prioritizing our support efforts for these versions.
 - **General OS Information**: Details like the number of CPUs and the operating system type (macOS, Windows, Linux) enable us to focus our development on the most used operating systems and explore the potential for OS-specific features.
-- **Platform-Specific Information**: Collection of `platform`, `platform_release`, `platform_system`, and `platform_version` provides a comprehensive overview of the users' operating systems.
 - **Number of Agents and Tasks in a Crew**: Ensures our internal testing mirrors real-world scenarios, helping us guide users towards best practices.
-- **Crew Process Utilization**: Understanding how crews are utilized aids in directing our development focus. Attributes such as `crew_process` including execution details are collected for this purpose.
+- **Crew Process Utilization**: Understanding how crews are utilized aids in directing our development focus.
 - **Memory and Delegation Use by Agents**: Insights into how these features are used help evaluate their effectiveness and future.
 - **Task Execution Mode**: Knowing whether tasks are executed in parallel or sequentially influences our emphasis on enhancing parallel execution capabilities.
-- **Language Model Utilization**: Supports our goal to improve support for the most popular languages among our users. This includes the collection of `crew_language` to indicate the default language model being used.
+- **Language Model Utilization**: Supports our goal to improve support for the most popular languages among our users.
 - **Roles of Agents within a Crew**: Understanding the various roles agents play aids in crafting better tools, integrations, and examples.
-- **Tool Usage**: Identifying which tools are most frequently used allows us to prioritize improvements in those areas. This includes the collection of tool names used by agents and tasks.
-- **Crew and Agent Specific Attributes**: Information such as `crew_id`, `verbose?`, `max_iter`, `max_rpm`, `i18n.language`, and `llm` attributes (like `name`, `model_name`, `base_url`, `model`, `top_k`, `temperature`) are collected to enhance our understanding and support of the ecosystem.
+- **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. 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.
-
-### Transparency and Review
-To foster greater transparency, users can inquire about the telemetry data being collected by contacting our support team. This initiative aims to provide users with the opportunity to understand the scope of our data collection practices better and aligns with our commitment to transparency and trust.
-
-By ensuring the documentation is up-to-date with these changes, we aim to maintain transparency with our users, fostering trust, and promoting an understanding of how telemetry data is used to enhance CrewAI.
-```

docs/tools/CSVSearchTool.md

@@ -0,0 +1,37 @@
+# CSVSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+
+This tool is used to perform a RAG (Retrieval-Augmented Generation) search within a CSV file's content. It allows users to semantically search for queries in the content of a specified CSV file. This feature is particularly useful for extracting information from large CSV datasets where traditional search methods might be inefficient. All tools with "Search" in their name, including CSVSearchTool, are RAG tools designed for searching different sources of data.
+
+## Installation
+
+Install the crewai_tools package
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+
+```python
+from crewai_tools import CSVSearchTool
+
+# Initialize the tool with a specific CSV file. This setup allows the agent to only search the given CSV file.
+tool = CSVSearchTool(csv='path/to/your/csvfile.csv')
+
+# OR
+
+# Initialize the tool without a specific CSV file. Agent  will need to provide the CSV path at runtime.
+tool = CSVSearchTool()
+```
+
+## Arguments
+
+- `csv` : The path to the CSV file you want to search. This is a mandatory argument if the tool was initialized without a specific CSV file; otherwise, it is optional.

docs/tools/DOCXSearchTool.md

@@ -0,0 +1,35 @@
+# DOCXSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+The DOCXSearchTool is a RAG tool designed for semantic searching within DOCX documents. It enables users to effectively search and extract relevant information from DOCX files using query-based searches. This tool is invaluable for data analysis, information management, and research tasks, streamlining the process of finding specific information within large document collections.
+
+## Installation
+Install the crewai_tools package by running the following command in your terminal:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+The following example demonstrates initializing the DOCXSearchTool to search within any DOCX file's content or with a specific DOCX file path.
+
+```python
+from crewai_tools import DOCXSearchTool
+
+# Initialize the tool to search within any DOCX file's content
+tool = DOCXSearchTool()
+
+# OR
+
+# Initialize the tool with a specific DOCX file, so the agent can only search the content of the specified DOCX file
+tool = DOCXSearchTool(docx='path/to/your/document.docx')
+```
+
+## Arguments
+- `docx`: An optional file path to a specific DOCX document you wish to search. If not provided during initialization, the tool allows for later specification of any DOCX file's content path for searching.

docs/tools/DirectoryReadTool.md

@@ -0,0 +1,36 @@
+# DirectoryReadTool
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+The DirectoryReadTool is a highly efficient utility designed for the comprehensive listing of directory contents. It recursively navigates through the specified directory, providing users with a detailed enumeration of all files, including those nested within subdirectories. This tool is indispensable for tasks requiring a thorough inventory of directory structures or for validating the organization of files within directories.
+
+## Installation
+Install the `crewai_tools` package to use the DirectoryReadTool in your project. If you haven't added this package to your environment, you can easily install it with pip using the following command:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+This installs the latest version of the `crewai_tools` package, allowing access to the DirectoryReadTool and other utilities.
+
+## Example
+The DirectoryReadTool is simple to use. The code snippet below shows how to set up and use the tool to list the contents of a specified directory:
+
+```python
+from crewai_tools import DirectoryReadTool
+
+# Initialize the tool so the agent can read any directory's content it learns about during execution
+tool = DirectoryReadTool()
+
+# OR
+
+# Initialize the tool with a specific directory, so the agent can only read the content of the specified directory
+tool = DirectoryReadTool(directory='/path/to/your/directory')
+```
+
+## Arguments
+The DirectoryReadTool requires minimal configuration for use. The essential argument for this tool is as follows:
+
+- `directory`: **Optional** A argument that specifies the path to the directory whose contents you wish to list. It accepts both absolute and relative paths, guiding the tool to the desired directory for content listing.

docs/tools/DirectorySearchTool.md

@@ -0,0 +1,33 @@
+# DirectorySearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+This tool is designed to perform a semantic search for queries within the content of a specified directory. Utilizing the RAG (Retrieval-Augmented Generation) methodology, it offers a powerful means to semantically navigate through the files of a given directory. The tool can be dynamically set to search any directory specified at runtime or can be pre-configured to search within a specific directory upon initialization.
+
+## Installation
+To start using the DirectorySearchTool, you need to install the crewai_tools package. Execute the following command in your terminal:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+The following examples demonstrate how to initialize the DirectorySearchTool for different use cases and how to perform a search:
+
+```python
+from crewai_tools import DirectorySearchTool
+
+# To enable searching within any specified directory at runtime
+tool = DirectorySearchTool()
+
+# Alternatively, to restrict searches to a specific directory
+tool = DirectorySearchTool(directory='/path/to/directory')
+```
+
+## Arguments
+- `directory` : This string argument specifies the directory within which to search. It is mandatory if the tool has not been initialized with a directory; otherwise, the tool will only search within the initialized directory.

docs/tools/FileReadTool.md

@@ -0,0 +1,32 @@
+# FileReadTool
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+The FileReadTool is a versatile component of the crewai_tools package, designed to streamline the process of reading and retrieving content from files. It is particularly useful in scenarios such as batch text file processing, runtime configuration file reading, and data importation for analytics. This tool supports various text-based file formats including `.txt`, `.csv`, `.json` and more, and adapts its functionality based on the file type, for instance, converting JSON content into a Python dictionary for easy use.
+
+## Installation
+Install the crewai_tools package to use the FileReadTool in your projects:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+To get started with the FileReadTool:
+
+```python
+from crewai_tools import FileReadTool
+
+# Initialize the tool to read any files the agents knows or lean the path for
+file_read_tool = FileReadTool()
+
+# OR
+
+# Initialize the tool with a specific file path, so the agent can only read the content of the specified file
+file_read_tool = FileReadTool(file_path='path/to/your/file.txt')
+```
+
+## Arguments
+- `file_path`: The path to the file you want to read. It accepts both absolute and relative paths. Ensure the file exists and you have the necessary permissions to access it.

docs/tools/GitHubSearchTool.md

@@ -0,0 +1,42 @@
+# GitHubSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+The GitHubSearchTool is a Read, Append, and Generate (RAG) tool specifically designed for conducting semantic searches within GitHub repositories. Utilizing advanced semantic search capabilities, it sifts through code, pull requests, issues, and repositories, making it an essential tool for developers, researchers, or anyone in need of precise information from GitHub.
+
+## Installation
+To use the GitHubSearchTool, first ensure the crewai_tools package is installed in your Python environment:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+This command installs the necessary package to run the GitHubSearchTool along with any other tools included in the crewai_tools package.
+
+## Example
+Here’s how you can use the GitHubSearchTool to perform semantic searches within a GitHub repository:
+```python
+from crewai_tools import GitHubSearchTool
+
+# Initialize the tool for semantic searches within a specific GitHub repository
+tool = GitHubSearchTool(
+	github_repo='https://github.com/example/repo',
+	content_types=['code', 'issue'] # Options: code, repo, pr, issue
+)
+
+# OR
+
+# Initialize the tool for semantic searches within a specific GitHub repository, so the agent can search any repository if it learns about during its execution
+tool = GitHubSearchTool(
+	content_types=['code', 'issue'] # Options: code, repo, pr, issue
+)
+```
+
+## Arguments
+- `github_repo` : The URL of the GitHub repository where the search will be conducted. This is a mandatory field and specifies the target repository for your search.
+- `content_types` : Specifies the types of content to include in your search. You must provide a list of content types from the following options: `code` for searching within the code, `repo` for searching within the repository's general information, `pr` for searching within pull requests, and `issue` for searching within issues. This field is mandatory and allows tailoring the search to specific content types within the GitHub repository.

docs/tools/JSONSearchTool.md

@@ -0,0 +1,33 @@
+# JSONSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+This tool is used to perform a RAG search within a JSON file's content. It allows users to initiate a search with a specific JSON path, focusing the search operation within that particular JSON file. If the path is provided at initialization, the tool restricts its search scope to the specified JSON file, thereby enhancing the precision of search results.
+
+## Installation
+Install the crewai_tools package by executing the following command in your terminal:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+Below are examples demonstrating how to use the JSONSearchTool for searching within JSON files. You can either search any JSON content or restrict the search to a specific JSON file.
+
+```python
+from crewai_tools import JSONSearchTool
+
+# Example 1: Initialize the tool for a general search across any JSON content. This is useful when the path is known or can be discovered during execution.
+tool = JSONSearchTool()
+
+# Example 2: Initialize the tool with a specific JSON path, limiting the search to a particular JSON file.
+tool = JSONSearchTool(json_path='./path/to/your/file.json')
+```
+
+## Arguments
+- `json_path` (str): An optional argument that defines the path to the JSON file to be searched. This parameter is only necessary if the tool is initialized without a specific JSON path. Providing this argument restricts the search to the specified JSON file.

docs/tools/MDXSearchTool.md

@@ -0,0 +1,35 @@
+# MDXSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+The MDX Search Tool, a key component of the `crewai_tools` package, is designed for advanced market data extraction, offering invaluable support to researchers and analysts requiring immediate market insights in the AI sector. With its ability to interface with various data sources and tools, it streamlines the process of acquiring, reading, and organizing market data efficiently.
+
+## Installation
+To utilize the MDX Search Tool, ensure the `crewai_tools` package is installed. If not already present, install it using the following command:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+Configuring and using the MDX Search Tool involves setting up environment variables and utilizing the tool within a crewAI project for market research. Here's a simple example:
+
+```python
+from crewai_tools import MDXSearchTool
+
+# Initialize the tool so the agent can search any MDX content if it learns about during its execution
+tool = MDXSearchTool()
+
+# OR
+
+# Initialize the tool with a specific MDX file path for exclusive search within that document
+tool = MDXSearchTool(mdx='path/to/your/document.mdx')
+```
+
+## Arguments
+- mdx: **Optional** The MDX path for the search. Can be provided at initialization

docs/tools/PDFSearchTool.md

@@ -0,0 +1,35 @@
+# PDFSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+The PDFSearchTool is a RAG tool designed for semantic searches within PDF content. It allows for inputting a search query and a PDF document, leveraging advanced search techniques to find relevant content efficiently. This capability makes it especially useful for extracting specific information from large PDF files quickly.
+
+## Installation
+To get started with the PDFSearchTool, first, ensure the crewai_tools package is installed with the following command:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+Here's how to use the PDFSearchTool to search within a PDF document:
+
+```python
+from crewai_tools import PDFSearchTool
+
+# Initialize the tool allowing for any PDF content search if the path is provided during execution
+tool = PDFSearchTool()
+
+# OR
+
+# Initialize the tool with a specific PDF path for exclusive search within that document
+tool = PDFSearchTool(pdf='path/to/your/document.pdf')
+```
+
+## Arguments
+- `pdf`: **Optinal** The PDF path for the search. Can be provided at initialization or within the `run` method's arguments. If provided at initialization, the tool confines its search to the specified document.

docs/tools/PGSearchTool.md

@@ -0,0 +1,34 @@
+# PGSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+This tool is designed to facilitate semantic searches within PostgreSQL database tables. Leveraging the RAG (Retrieve and Generate) technology, the PGSearchTool provides users with an efficient means of querying database table content, specifically tailored for PostgreSQL databases. It simplifies the process of finding relevant data through semantic search queries, making it an invaluable resource for users needing to perform advanced queries on extensive datasets within a PostgreSQL database.
+
+## Installation
+To install the `crewai_tools` package and utilize the PGSearchTool, execute the following command in your terminal:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+Below is an example showcasing how to use the PGSearchTool to conduct a semantic search on a table within a PostgreSQL database:
+
+```python
+from crewai_tools import PGSearchTool
+
+# Initialize the tool with the database URI and the target table name
+tool = PGSearchTool(db_uri='postgresql://user:password@localhost:5432/mydatabase', table_name='employees')
+
+```
+
+## Arguments
+The PGSearchTool requires the following arguments for its operation:
+
+- `db_uri`: A string representing the URI of the PostgreSQL database to be queried. This argument is mandatory and must include the necessary authentication details and the location of the database.
+- `table_name`: A string specifying the name of the table within the database on which the semantic search will be performed. This argument is mandatory.

docs/tools/ScrapeWebsiteTool.md

@@ -0,0 +1,30 @@
+# ScrapeWebsiteTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+A tool designed to extract and read the content of a specified website. It is capable of handling various types of web pages by making HTTP requests and parsing the received HTML content. This tool can be particularly useful for web scraping tasks, data collection, or extracting specific information from websites.
+
+## Installation
+Install the crewai_tools package
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+```python
+from crewai_tools import ScrapeWebsiteTool
+
+# To enable scrapping any website it finds during it's execution
+tool = ScrapeWebsiteTool()
+
+# Initialize the tool with the website URL, so the agent can only scrap the content of the specified website
+tool = ScrapeWebsiteTool(website_url='https://www.example.com')
+```
+
+## Arguments
+- `website_url` : Mandatory website URL to read the file. This is the primary input for the tool, specifying which website's content should be scraped and read.

docs/tools/SeleniumScrapingTool.md

@@ -0,0 +1,36 @@
+# SeleniumScrapingTool
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+This tool is designed for efficient web scraping, enabling users to extract content from web pages. It supports targeted scraping by allowing the specification of a CSS selector for desired elements. The flexibility of the tool enables it to be used on any website URL provided by the user, making it a versatile tool for various web scraping needs.
+
+## Installation
+Install the crewai_tools package
+```
+pip install 'crewai[tools]'
+```
+
+## Example
+```python
+from crewai_tools import SeleniumScrapingTool
+
+# Example 1: Scrape any website it finds during its execution
+tool = SeleniumScrapingTool()
+
+# Example 2: Scrape the entire webpage
+tool = SeleniumScrapingTool(website_url='https://example.com')
+
+# Example 3: Scrape a specific CSS element from the webpage
+tool = SeleniumScrapingTool(website_url='https://example.com', css_element='.main-content')
+
+# Example 4: Scrape using optional parameters for customized scraping
+tool = SeleniumScrapingTool(website_url='https://example.com', css_element='.main-content', cookie={'name': 'user', 'value': 'John Doe'})
+```
+
+## Arguments
+- `website_url`: Mandatory. The URL of the website to scrape.
+- `css_element`: Mandatory. The CSS selector for a specific element to scrape from the website.
+- `cookie`: Optional. A dictionary containing cookie information. This parameter allows the tool to simulate a session with cookie information, providing access to content that may be restricted to logged-in users.
+- `wait_time`: Optional. The number of seconds the tool waits after loading the website and after setting a cookie, before scraping the content. This allows for dynamic content to load properly.

docs/tools/SerperDevTool.md

@@ -0,0 +1,33 @@
+# SerperDevTool Documentation
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+This tool is designed to perform a semantic search for a specified query from a text's content across the internet. It utilizes the [serper.dev](https://serper.dev) API to fetch and display the most relevant search results based on the query provided by the user.
+
+## Installation
+To incorporate this tool into your project, follow the installation instructions below:
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+The following example demonstrates how to initialize the tool and execute a search with a given query:
+
+```python
+from crewai_tools import SerperDevTool
+
+# Initialize the tool for internet searching capabilities
+tool = SerperDevTool()
+```
+
+## Steps to Get Started
+To effectively use the `SerperDevTool`, follow these steps:
+
+1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
+2. **API Key Acquisition**: Acquire a `serper.dev` API key by registering for a free account at `serper.dev`.
+3. **Environment Configuration**: Store your obtained API key in an environment variable named `SERPER_API_KEY` to facilitate its use by the tool.
+
+## Conclusion
+By integrating the `SerperDevTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.

docs/tools/TXTSearchTool.md

@@ -0,0 +1,37 @@
+# TXTSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+This tool is used to perform a RAG (Retrieval-Augmented Generation) search within the content of a text file. It allows for semantic searching of a query within a specified text file's content, making it an invaluable resource for quickly extracting information or finding specific sections of text based on the query provided.
+
+## Installation
+To use the TXTSearchTool, you first need to install the crewai_tools package. This can be done using pip, a package manager for Python. Open your terminal or command prompt and enter the following command:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+This command will download and install the TXTSearchTool along with any necessary dependencies.
+
+## Example
+The following example demonstrates how to use the TXTSearchTool to search within a text file. This example shows both the initialization of the tool with a specific text file and the subsequent search within that file's content.
+
+```python
+from crewai_tools import TXTSearchTool
+
+# Initialize the tool to search within any text file's content the agent learns about during its execution
+tool = TXTSearchTool()
+
+# OR
+
+# Initialize the tool with a specific text file, so the agent can search within the given text file's content
+tool = TXTSearchTool(txt='path/to/text/file.txt')
+```
+
+## Arguments
+- `txt` (str): **Optinal**. The path to the text file you want to search. This argument is only required if the tool was not initialized with a specific text file; otherwise, the search will be conducted within the initially provided text file.

docs/tools/WebsiteSearchTool.md

@@ -0,0 +1,35 @@
+# WebsiteSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+This tool is specifically crafted for conducting semantic searches within the content of a particular website. Leveraging a Retrieval-Augmented Generation (RAG) model, it navigates through the information provided on a given URL. Users have the flexibility to either initiate a search across any website known or discovered during its usage or to concentrate the search on a predefined, specific website.
+
+## Installation
+Install the crewai_tools package by executing the following command in your terminal:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+To utilize the WebsiteSearchTool for different use cases, follow these examples:
+
+```python
+from crewai_tools import WebsiteSearchTool
+
+# To enable the tool to search any website the agent comes across or learns about during its operation
+tool = WebsiteSearchTool()
+
+# OR
+
+# To restrict the tool to only search within the content of a specific website.
+tool = WebsiteSearchTool(website='https://example.com')
+```
+
+## Arguments
+- `website` : An optional argument that specifies the valid website URL to perform the search on. This becomes necessary if the tool is initialized without a specific website. In the `WebsiteSearchToolSchema`, this argument is mandatory. However, in the `FixedWebsiteSearchToolSchema`, it becomes optional if a website is provided during the tool's initialization, as it will then only search within the predefined website's content.

docs/tools/XMLSearchTool copy.md

@@ -0,0 +1,35 @@
+# XMLSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+The XMLSearchTool is a cutting-edge RAG tool engineered for conducting semantic searches within XML files. Ideal for users needing to parse and extract information from XML content efficiently, this tool supports inputting a search query and an optional XML file path. By specifying an XML path, users can target their search more precisely to the content of that file, thereby obtaining more relevant search outcomes.
+
+## Installation
+To start using the XMLSearchTool, you must first install the crewai_tools package. This can be easily done with the following command:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+Here are two examples demonstrating how to use the XMLSearchTool. The first example shows searching within a specific XML file, while the second example illustrates initiating a search without predefining an XML path, providing flexibility in search scope.
+
+```python
+from crewai_tools.tools.xml_search_tool import XMLSearchTool
+
+# Allow agents to search within any XML file's content as it learns about their paths during execution
+tool = XMLSearchTool()
+
+# OR
+
+# Initialize the tool with a specific XML file path for exclusive search within that document
+tool = XMLSearchTool(xml='path/to/your/xmlfile.xml')
+```
+
+## Arguments
+- `xml`: This is the path to the XML file you wish to search. It is an optional parameter during the tool's initialization but must be provided either at initialization or as part of the `run` method's arguments to execute a search.

docs/tools/XMLSearchTool.md

@@ -0,0 +1,35 @@
+# XMLSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+The XMLSearchTool is a cutting-edge RAG tool engineered for conducting semantic searches within XML files. Ideal for users needing to parse and extract information from XML content efficiently, this tool supports inputting a search query and an optional XML file path. By specifying an XML path, users can target their search more precisely to the content of that file, thereby obtaining more relevant search outcomes.
+
+## Installation
+To start using the XMLSearchTool, you must first install the crewai_tools package. This can be easily done with the following command:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+Here are two examples demonstrating how to use the XMLSearchTool. The first example shows searching within a specific XML file, while the second example illustrates initiating a search without predefining an XML path, providing flexibility in search scope.
+
+```python
+from crewai_tools.tools.xml_search_tool import XMLSearchTool
+
+# Allow agents to search within any XML file's content as it learns about their paths during execution
+tool = XMLSearchTool()
+
+# OR
+
+# Initialize the tool with a specific XML file path for exclusive search within that document
+tool = XMLSearchTool(xml='path/to/your/xmlfile.xml')
+```
+
+## Arguments
+- `xml`: This is the path to the XML file you wish to search. It is an optional parameter during the tool's initialization but must be provided either at initialization or as part of the `run` method's arguments to execute a search.

docs/tools/YoutubeChannelSearchTool.md

@@ -0,0 +1,35 @@
+# YoutubeChannelSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+This tool is designed to perform semantic searches within a specific Youtube channel's content. Leveraging the RAG (Retrieval-Augmented Generation) methodology, it provides relevant search results, making it invaluable for extracting information or finding specific content without the need to manually sift through videos. It streamlines the search process within Youtube channels, catering to researchers, content creators, and viewers seeking specific information or topics.
+
+## Installation
+To utilize the YoutubeChannelSearchTool, the `crewai_tools` package must be installed. Execute the following command in your shell to install:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+To begin using the YoutubeChannelSearchTool, follow the example below. This demonstrates initializing the tool with a specific Youtube channel handle and conducting a search within that channel's content.
+
+```python
+from crewai_tools import YoutubeChannelSearchTool
+
+# Initialize the tool to search within any Youtube channel's content the agent learns about during its execution
+tool = YoutubeChannelSearchTool()
+
+# OR
+
+# Initialize the tool with a specific Youtube channel handle to target your search
+tool = YoutubeChannelSearchTool(youtube_channel_handle='@exampleChannel')
+```
+
+## Arguments
+- `youtube_channel_handle` : A mandatory string representing the Youtube channel handle. This parameter is crucial for initializing the tool to specify the channel you want to search within. The tool is designed to only search within the content of the provided channel handle.

docs/tools/YoutubeVideoSearchTool.md

@@ -0,0 +1,38 @@
+# YoutubeVideoSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+
+This tool is part of the `crewai_tools` package and is designed to perform semantic searches within Youtube video content, utilizing Retrieval-Augmented Generation (RAG) techniques. It is one of several "Search" tools in the package that leverage RAG for different sources. The YoutubeVideoSearchTool allows for flexibility in searches; users can search across any Youtube video content without specifying a video URL, or they can target their search to a specific Youtube video by providing its URL.
+
+## Installation
+
+To utilize the YoutubeVideoSearchTool, you must first install the `crewai_tools` package. This package contains the YoutubeVideoSearchTool among other utilities designed to enhance your data analysis and processing tasks. Install the package by executing the following command in your terminal:
+
+```
+pip install 'crewai[tools]'
+```
+
+## Example
+
+To integrate the YoutubeVideoSearchTool into your Python projects, follow the example below. This demonstrates how to use the tool both for general Youtube content searches and for targeted searches within a specific video's content.
+
+```python
+from crewai_tools import YoutubeVideoSearchTool
+
+# General search across Youtube content without specifying a video URL, so the agent can search within any Youtube video content it learns about irs url during its operation
+tool = YoutubeVideoSearchTool()
+
+# Targeted search within a specific Youtube video's content
+tool = YoutubeVideoSearchTool(youtube_video_url='https://youtube.com/watch?v=example')
+```
+## Arguments
+
+The YoutubeVideoSearchTool accepts the following initialization arguments:
+
+- `youtube_video_url`: An optional argument at initialization but required if targeting a specific Youtube video. It specifies the Youtube video URL path you want to search within.

mkdocs.yml

@@ -128,11 +128,32 @@ nav:
     - Collaboration: 'core-concepts/Collaboration.md'
   - How to Guides:
     - Getting Started: 'how-to/Creating-a-Crew-and-kick-it-off.md'
+    - Create Custom Tools: 'how-to/Create-Custom-Tools.md'
     - Using Sequential Process: 'how-to/Sequential.md'
     - Using Hierarchical Process: 'how-to/Hierarchical.md'
     - Connecting to any LLM: 'how-to/LLM-Connections.md'
     - Customizing Agents: 'how-to/Customizing-Agents.md'
     - Human Input on Execution: 'how-to/Human-Input-on-Execution.md'
+  - Tools Docs:
+    - Google Serper Search: 'tools/SerperDevTool.md'
+    - Scrape Website: 'tools/ScrapeWebsiteTool.md'
+    - Directory Read: 'tools/DirectoryReadTool.md'
+    - File Read: 'tools/FileReadTool.md'
+    - Selenium Scraper: 'tools/SeleniumScrapingTool.md'
+    - Directory RAG Search: 'tools/DirectorySearchTool.md'
+    - PDF RAG Search: 'tools/PDFSearchTool.md'
+    - TXT RAG Search: 'tools/TXTSearchTool.md'
+    - CSV RAG Search: 'tools/CSVSearchTool.md'
+    - XML RAG Search: 'tools/XMLSearchTool.md'
+    - JSON RAG Search: 'tools/JSONSearchTool.md'
+    - Docx Rag Search: 'tools/DOCXSearchTool.md'
+    - MDX RAG Search: 'tools/MDXSearchTool.md'
+    - PG RAG Search: 'tools/PGSearchTool.md'
+    - Website RAG Search: 'tools/WebsiteSearchTool.md'
+    - Github RAG Search: 'tools/GitHubSearchTool.md'
+    - Code Docs RAG Search: 'tools/CodeDocsSearchTool.md'
+    - Youtube Video RAG Search: 'tools/YoutubeVideoSearchTool.md'
+    - Youtube Chanel RAG Search: 'tools/YoutubeChannelSearchTool.md'
   - Examples:
     - Trip Planner Crew: https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner"
     - Create Instagram Post: https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post"

poetry.lock

@@ -1064,13 +1064,13 @@ files = [
 
 [[package]]
 name = "embedchain"
-version = "0.1.92"
+version = "0.1.94"
 description = "Simplest open source retrieval(RAG) framework"
 optional = false
 python-versions = ">=3.9,<=3.13"
 files = [
-    {file = "embedchain-0.1.92-py3-none-any.whl", hash = "sha256:2c5093f32beea5d5f86d43b5709893efd3dfb86b16604ff974a53bea1e48f08e"},
-    {file = "embedchain-0.1.92.tar.gz", hash = "sha256:936d9097daa623a394a382c5e55ffae02c6a5e389f7cca1ec43f266efc0ad2b9"},
+    {file = "embedchain-0.1.94-py3-none-any.whl", hash = "sha256:7abf46bb1cb0043f1a0b43878181363e687fd91299ba4e9edb77fdffd6be9425"},
+    {file = "embedchain-0.1.94.tar.gz", hash = "sha256:d835b0c29ae7471514976ac438394722b7a35f043c4b19df87852d1e202f1420"},
 ]
 
 [package.dependencies]
@@ -1176,13 +1176,13 @@ typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "flatbuffers"
-version = "23.5.26"
+version = "24.3.7"
 description = "The FlatBuffers serialization format for Python"
 optional = false
 python-versions = "*"
 files = [
-    {file = "flatbuffers-23.5.26-py2.py3-none-any.whl", hash = "sha256:c0ff356da363087b915fde4b8b45bdda73432fc17cddb3c8157472eab1422ad1"},
-    {file = "flatbuffers-23.5.26.tar.gz", hash = "sha256:9ea1144cac05ce5d86e2859f431c6cd5e66cd9c78c558317c7955fb8d4c78d89"},
+    {file = "flatbuffers-24.3.7-py2.py3-none-any.whl", hash = "sha256:80c4f5dcad0ee76b7e349671a0d657f2fbba927a0244f88dd3f5ed6a3694e1fc"},
+    {file = "flatbuffers-24.3.7.tar.gz", hash = "sha256:0895c22b9a6019ff2f4de2e5e2f7cd15914043e6e7033a94c0c6369422690f22"},
 ]
 
 [[package]]
@@ -1386,13 +1386,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-auth"
-version = "2.28.1"
+version = "2.28.2"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-auth-2.28.1.tar.gz", hash = "sha256:34fc3046c257cedcf1622fc4b31fc2be7923d9b4d44973d481125ecc50d83885"},
-    {file = "google_auth-2.28.1-py2.py3-none-any.whl", hash = "sha256:25141e2d7a14bfcba945f5e9827f98092716e99482562f15306e5b026e21aa72"},
+    {file = "google-auth-2.28.2.tar.gz", hash = "sha256:80b8b4969aa9ed5938c7828308f20f035bc79f9d8fb8120bf9dc8db20b41ba30"},
+    {file = "google_auth-2.28.2-py2.py3-none-any.whl", hash = "sha256:9fd67bbcd40f16d9d42f950228e9cf02a2ded4ae49198b27432d0cded5a74c38"},
 ]
 
 [package.dependencies]
@@ -1449,17 +1449,18 @@ xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
 
 [[package]]
 name = "google-cloud-bigquery"
-version = "3.17.2"
+version = "3.19.0"
 description = "Google BigQuery API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigquery-3.17.2.tar.gz", hash = "sha256:6e1cf669a40e567ab3289c7b5f2056363da9fcb85d9a4736ee90240d4a7d84ea"},
-    {file = "google_cloud_bigquery-3.17.2-py2.py3-none-any.whl", hash = "sha256:cdadf5283dca55a1a350bacf8c8a7466169d3cf46c5a0a3abc5e9aa0b0a51dee"},
+    {file = "google-cloud-bigquery-3.19.0.tar.gz", hash = "sha256:8e311dae49768e1501fcdc5e916bff4b7e169471e5707919f4a6f78a02b3b5a6"},
+    {file = "google_cloud_bigquery-3.19.0-py2.py3-none-any.whl", hash = "sha256:c6b8850247a4b132066e49f6e45f850c22824482838688d744a4398eea1120ed"},
 ]
 
 [package.dependencies]
-google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 google-cloud-core = ">=1.6.0,<3.0.0dev"
 google-resumable-media = ">=0.6.0,<3.0dev"
 packaging = ">=20.0.0"
@@ -1497,36 +1498,36 @@ grpc = ["grpcio (>=1.38.0,<2.0dev)", "grpcio-status (>=1.38.0,<2.0.dev0)"]
 
 [[package]]
 name = "google-cloud-resource-manager"
-version = "1.12.2"
+version = "1.12.3"
 description = "Google Cloud Resource Manager API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-resource-manager-1.12.2.tar.gz", hash = "sha256:2ede446a5087b236f0e1fb39cca3791bae97eb0d9125057401454b190d5572ee"},
-    {file = "google_cloud_resource_manager-1.12.2-py2.py3-none-any.whl", hash = "sha256:45abbb8911195cc831cc77c8e3be84decc271686579b332d4142af507f423ebf"},
+    {file = "google-cloud-resource-manager-1.12.3.tar.gz", hash = "sha256:809851824119834e4f2310b2c4f38621c1d16b2bb14d5b9f132e69c79d355e7f"},
+    {file = "google_cloud_resource_manager-1.12.3-py2.py3-none-any.whl", hash = "sha256:92be7d6959927b76d90eafc4028985c37975a46ded5466a018f02e8649e113d4"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-storage"
-version = "2.14.0"
+version = "2.15.0"
 description = "Google Cloud Storage API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-storage-2.14.0.tar.gz", hash = "sha256:2d23fcf59b55e7b45336729c148bb1c464468c69d5efbaee30f7201dd90eb97e"},
-    {file = "google_cloud_storage-2.14.0-py2.py3-none-any.whl", hash = "sha256:8641243bbf2a2042c16a6399551fbb13f062cbc9a2de38d6c0bb5426962e9dbd"},
+    {file = "google-cloud-storage-2.15.0.tar.gz", hash = "sha256:7560a3c48a03d66c553dc55215d35883c680fe0ab44c23aa4832800ccc855c74"},
+    {file = "google_cloud_storage-2.15.0-py2.py3-none-any.whl", hash = "sha256:5d9237f88b648e1d724a0f20b5cde65996a37fe51d75d17660b1404097327dd2"},
 ]
 
 [package.dependencies]
-google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev"
-google-auth = ">=2.23.3,<3.0dev"
+google-api-core = ">=2.15.0,<3.0.0dev"
+google-auth = ">=2.26.1,<3.0dev"
 google-cloud-core = ">=2.3.0,<3.0dev"
 google-crc32c = ">=1.0,<2.0dev"
 google-resumable-media = ">=2.6.0"
@@ -1635,13 +1636,13 @@ requests = ["requests (>=2.18.0,<3.0.0dev)"]
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.62.0"
+version = "1.63.0"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "googleapis-common-protos-1.62.0.tar.gz", hash = "sha256:83f0ece9f94e5672cced82f592d2a5edf527a96ed1794f0bab36d5735c996277"},
-    {file = "googleapis_common_protos-1.62.0-py2.py3-none-any.whl", hash = "sha256:4750113612205514f9f6aa4cb00d523a94f3e8c06c5ad2fee466387dc4875f07"},
+    {file = "googleapis-common-protos-1.63.0.tar.gz", hash = "sha256:17ad01b11d5f1d0171c06d3ba5c04c54474e883b66b949722b4938ee2694ef4e"},
+    {file = "googleapis_common_protos-1.63.0-py2.py3-none-any.whl", hash = "sha256:ae45f75702f7c08b541f750854a678bd8f534a1a6bace6afe975f1d0a82d6632"},
 ]
 
 [package.dependencies]
@@ -1740,13 +1741,13 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "griffe"
-version = "0.41.2"
+version = "0.42.0"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "griffe-0.41.2-py3-none-any.whl", hash = "sha256:a3e1824bc8d5c3190b7e05fbfde36e1e794d9c409c646783a57366c947d7816b"},
-    {file = "griffe-0.41.2.tar.gz", hash = "sha256:662094e8066c1340d6993880ac46bdb3c42e639fe2b93995459503079332ec8f"},
+    {file = "griffe-0.42.0-py3-none-any.whl", hash = "sha256:384df6b802a60f70e65fdb7e83f5b27e2da869a12eac85b25b55250012dbc263"},
+    {file = "griffe-0.42.0.tar.gz", hash = "sha256:fb83ee602701ffdf99c9a6bf5f0a5a3bd877364b3bffb2c451dc8fbd9645b0cf"},
 ]
 
 [package.dependencies]
@@ -1770,84 +1771,84 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4
 
 [[package]]
 name = "grpcio"
-version = "1.62.0"
+version = "1.62.1"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "grpcio-1.62.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:136ffd79791b1eddda8d827b607a6285474ff8a1a5735c4947b58c481e5e4271"},
-    {file = "grpcio-1.62.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:d6a56ba703be6b6267bf19423d888600c3f574ac7c2cc5e6220af90662a4d6b0"},
-    {file = "grpcio-1.62.0-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:4cd356211579043fce9f52acc861e519316fff93980a212c8109cca8f47366b6"},
-    {file = "grpcio-1.62.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e803e9b58d8f9b4ff0ea991611a8d51b31c68d2e24572cd1fe85e99e8cc1b4f8"},
-    {file = "grpcio-1.62.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f4c04fe33039b35b97c02d2901a164bbbb2f21fb9c4e2a45a959f0b044c3512c"},
-    {file = "grpcio-1.62.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:95370c71b8c9062f9ea033a0867c4c73d6f0ff35113ebd2618171ec1f1e903e0"},
-    {file = "grpcio-1.62.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:c912688acc05e4ff012c8891803659d6a8a8b5106f0f66e0aed3fb7e77898fa6"},
-    {file = "grpcio-1.62.0-cp310-cp310-win32.whl", hash = "sha256:821a44bd63d0f04e33cf4ddf33c14cae176346486b0df08b41a6132b976de5fc"},
-    {file = "grpcio-1.62.0-cp310-cp310-win_amd64.whl", hash = "sha256:81531632f93fece32b2762247c4c169021177e58e725494f9a746ca62c83acaa"},
-    {file = "grpcio-1.62.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:3fa15850a6aba230eed06b236287c50d65a98f05054a0f01ccedf8e1cc89d57f"},
-    {file = "grpcio-1.62.0-cp311-cp311-macosx_10_10_universal2.whl", hash = "sha256:36df33080cd7897623feff57831eb83c98b84640b016ce443305977fac7566fb"},
-    {file = "grpcio-1.62.0-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:7a195531828b46ea9c4623c47e1dc45650fc7206f8a71825898dd4c9004b0928"},
-    {file = "grpcio-1.62.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ab140a3542bbcea37162bdfc12ce0d47a3cda3f2d91b752a124cc9fe6776a9e2"},
-    {file = "grpcio-1.62.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7f9d6c3223914abb51ac564dc9c3782d23ca445d2864321b9059d62d47144021"},
-    {file = "grpcio-1.62.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:fbe0c20ce9a1cff75cfb828b21f08d0a1ca527b67f2443174af6626798a754a4"},
-    {file = "grpcio-1.62.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:38f69de9c28c1e7a8fd24e4af4264726637b72f27c2099eaea6e513e7142b47e"},
-    {file = "grpcio-1.62.0-cp311-cp311-win32.whl", hash = "sha256:ce1aafdf8d3f58cb67664f42a617af0e34555fe955450d42c19e4a6ad41c84bd"},
-    {file = "grpcio-1.62.0-cp311-cp311-win_amd64.whl", hash = "sha256:eef1d16ac26c5325e7d39f5452ea98d6988c700c427c52cbc7ce3201e6d93334"},
-    {file = "grpcio-1.62.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:8aab8f90b2a41208c0a071ec39a6e5dbba16fd827455aaa070fec241624ccef8"},
-    {file = "grpcio-1.62.0-cp312-cp312-macosx_10_10_universal2.whl", hash = "sha256:62aa1659d8b6aad7329ede5d5b077e3d71bf488d85795db517118c390358d5f6"},
-    {file = "grpcio-1.62.0-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:0d7ae7fc7dbbf2d78d6323641ded767d9ec6d121aaf931ec4a5c50797b886532"},
-    {file = "grpcio-1.62.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f359d635ee9428f0294bea062bb60c478a8ddc44b0b6f8e1f42997e5dc12e2ee"},
-    {file = "grpcio-1.62.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:77d48e5b1f8f4204889f1acf30bb57c30378e17c8d20df5acbe8029e985f735c"},
-    {file = "grpcio-1.62.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:662d3df5314ecde3184cf87ddd2c3a66095b3acbb2d57a8cada571747af03873"},
-    {file = "grpcio-1.62.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:92cdb616be44c8ac23a57cce0243af0137a10aa82234f23cd46e69e115071388"},
-    {file = "grpcio-1.62.0-cp312-cp312-win32.whl", hash = "sha256:0b9179478b09ee22f4a36b40ca87ad43376acdccc816ce7c2193a9061bf35701"},
-    {file = "grpcio-1.62.0-cp312-cp312-win_amd64.whl", hash = "sha256:614c3ed234208e76991992342bab725f379cc81c7dd5035ee1de2f7e3f7a9842"},
-    {file = "grpcio-1.62.0-cp37-cp37m-linux_armv7l.whl", hash = "sha256:7e1f51e2a460b7394670fdb615e26d31d3260015154ea4f1501a45047abe06c9"},
-    {file = "grpcio-1.62.0-cp37-cp37m-macosx_10_10_universal2.whl", hash = "sha256:bcff647e7fe25495e7719f779cc219bbb90b9e79fbd1ce5bda6aae2567f469f2"},
-    {file = "grpcio-1.62.0-cp37-cp37m-manylinux_2_17_aarch64.whl", hash = "sha256:56ca7ba0b51ed0de1646f1735154143dcbdf9ec2dbe8cc6645def299bb527ca1"},
-    {file = "grpcio-1.62.0-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2e84bfb2a734e4a234b116be208d6f0214e68dcf7804306f97962f93c22a1839"},
-    {file = "grpcio-1.62.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2c1488b31a521fbba50ae86423f5306668d6f3a46d124f7819c603979fc538c4"},
-    {file = "grpcio-1.62.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:98d8f4eb91f1ce0735bf0b67c3b2a4fea68b52b2fd13dc4318583181f9219b4b"},
-    {file = "grpcio-1.62.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:b3d3d755cfa331d6090e13aac276d4a3fb828bf935449dc16c3d554bf366136b"},
-    {file = "grpcio-1.62.0-cp37-cp37m-win_amd64.whl", hash = "sha256:a33f2bfd8a58a02aab93f94f6c61279be0f48f99fcca20ebaee67576cd57307b"},
-    {file = "grpcio-1.62.0-cp38-cp38-linux_armv7l.whl", hash = "sha256:5e709f7c8028ce0443bddc290fb9c967c1e0e9159ef7a030e8c21cac1feabd35"},
-    {file = "grpcio-1.62.0-cp38-cp38-macosx_10_10_universal2.whl", hash = "sha256:2f3d9a4d0abb57e5f49ed5039d3ed375826c2635751ab89dcc25932ff683bbb6"},
-    {file = "grpcio-1.62.0-cp38-cp38-manylinux_2_17_aarch64.whl", hash = "sha256:62ccb92f594d3d9fcd00064b149a0187c246b11e46ff1b7935191f169227f04c"},
-    {file = "grpcio-1.62.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:921148f57c2e4b076af59a815467d399b7447f6e0ee10ef6d2601eb1e9c7f402"},
-    {file = "grpcio-1.62.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f897b16190b46bc4d4aaf0a32a4b819d559a37a756d7c6b571e9562c360eed72"},
-    {file = "grpcio-1.62.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:1bc8449084fe395575ed24809752e1dc4592bb70900a03ca42bf236ed5bf008f"},
-    {file = "grpcio-1.62.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:81d444e5e182be4c7856cd33a610154fe9ea1726bd071d07e7ba13fafd202e38"},
-    {file = "grpcio-1.62.0-cp38-cp38-win32.whl", hash = "sha256:88f41f33da3840b4a9bbec68079096d4caf629e2c6ed3a72112159d570d98ebe"},
-    {file = "grpcio-1.62.0-cp38-cp38-win_amd64.whl", hash = "sha256:fc2836cb829895ee190813446dce63df67e6ed7b9bf76060262c55fcd097d270"},
-    {file = "grpcio-1.62.0-cp39-cp39-linux_armv7l.whl", hash = "sha256:fcc98cff4084467839d0a20d16abc2a76005f3d1b38062464d088c07f500d170"},
-    {file = "grpcio-1.62.0-cp39-cp39-macosx_10_10_universal2.whl", hash = "sha256:0d3dee701e48ee76b7d6fbbba18ba8bc142e5b231ef7d3d97065204702224e0e"},
-    {file = "grpcio-1.62.0-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:b7a6be562dd18e5d5bec146ae9537f20ae1253beb971c0164f1e8a2f5a27e829"},
-    {file = "grpcio-1.62.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:29cb592c4ce64a023712875368bcae13938c7f03e99f080407e20ffe0a9aa33b"},
-    {file = "grpcio-1.62.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1eda79574aec8ec4d00768dcb07daba60ed08ef32583b62b90bbf274b3c279f7"},
-    {file = "grpcio-1.62.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:7eea57444a354ee217fda23f4b479a4cdfea35fb918ca0d8a0e73c271e52c09c"},
-    {file = "grpcio-1.62.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:0e97f37a3b7c89f9125b92d22e9c8323f4e76e7993ba7049b9f4ccbe8bae958a"},
-    {file = "grpcio-1.62.0-cp39-cp39-win32.whl", hash = "sha256:39cd45bd82a2e510e591ca2ddbe22352e8413378852ae814549c162cf3992a93"},
-    {file = "grpcio-1.62.0-cp39-cp39-win_amd64.whl", hash = "sha256:b71c65427bf0ec6a8b48c68c17356cb9fbfc96b1130d20a07cb462f4e4dcdcd5"},
-    {file = "grpcio-1.62.0.tar.gz", hash = "sha256:748496af9238ac78dcd98cce65421f1adce28c3979393e3609683fcd7f3880d7"},
+    {file = "grpcio-1.62.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:179bee6f5ed7b5f618844f760b6acf7e910988de77a4f75b95bbfaa8106f3c1e"},
+    {file = "grpcio-1.62.1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:48611e4fa010e823ba2de8fd3f77c1322dd60cb0d180dc6630a7e157b205f7ea"},
+    {file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:b2a0e71b0a2158aa4bce48be9f8f9eb45cbd17c78c7443616d00abbe2a509f6d"},
+    {file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fbe80577c7880911d3ad65e5ecc997416c98f354efeba2f8d0f9112a67ed65a5"},
+    {file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:58f6c693d446964e3292425e1d16e21a97a48ba9172f2d0df9d7b640acb99243"},
+    {file = "grpcio-1.62.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:77c339403db5a20ef4fed02e4d1a9a3d9866bf9c0afc77a42234677313ea22f3"},
+    {file = "grpcio-1.62.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:b5a4ea906db7dec694098435d84bf2854fe158eb3cd51e1107e571246d4d1d70"},
+    {file = "grpcio-1.62.1-cp310-cp310-win32.whl", hash = "sha256:4187201a53f8561c015bc745b81a1b2d278967b8de35f3399b84b0695e281d5f"},
+    {file = "grpcio-1.62.1-cp310-cp310-win_amd64.whl", hash = "sha256:844d1f3fb11bd1ed362d3fdc495d0770cfab75761836193af166fee113421d66"},
+    {file = "grpcio-1.62.1-cp311-cp311-linux_armv7l.whl", hash = "sha256:833379943d1728a005e44103f17ecd73d058d37d95783eb8f0b28ddc1f54d7b2"},
+    {file = "grpcio-1.62.1-cp311-cp311-macosx_10_10_universal2.whl", hash = "sha256:c7fcc6a32e7b7b58f5a7d27530669337a5d587d4066060bcb9dee7a8c833dfb7"},
+    {file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:fa7d28eb4d50b7cbe75bb8b45ed0da9a1dc5b219a0af59449676a29c2eed9698"},
+    {file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:48f7135c3de2f298b833be8b4ae20cafe37091634e91f61f5a7eb3d61ec6f660"},
+    {file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71f11fd63365ade276c9d4a7b7df5c136f9030e3457107e1791b3737a9b9ed6a"},
+    {file = "grpcio-1.62.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:4b49fd8fe9f9ac23b78437da94c54aa7e9996fbb220bac024a67469ce5d0825f"},
+    {file = "grpcio-1.62.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:482ae2ae78679ba9ed5752099b32e5fe580443b4f798e1b71df412abf43375db"},
+    {file = "grpcio-1.62.1-cp311-cp311-win32.whl", hash = "sha256:1faa02530b6c7426404372515fe5ddf66e199c2ee613f88f025c6f3bd816450c"},
+    {file = "grpcio-1.62.1-cp311-cp311-win_amd64.whl", hash = "sha256:5bd90b8c395f39bc82a5fb32a0173e220e3f401ff697840f4003e15b96d1befc"},
+    {file = "grpcio-1.62.1-cp312-cp312-linux_armv7l.whl", hash = "sha256:b134d5d71b4e0837fff574c00e49176051a1c532d26c052a1e43231f252d813b"},
+    {file = "grpcio-1.62.1-cp312-cp312-macosx_10_10_universal2.whl", hash = "sha256:d1f6c96573dc09d50dbcbd91dbf71d5cf97640c9427c32584010fbbd4c0e0037"},
+    {file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:359f821d4578f80f41909b9ee9b76fb249a21035a061a327f91c953493782c31"},
+    {file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a485f0c2010c696be269184bdb5ae72781344cb4e60db976c59d84dd6354fac9"},
+    {file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b50b09b4dc01767163d67e1532f948264167cd27f49e9377e3556c3cba1268e1"},
+    {file = "grpcio-1.62.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:3227c667dccbe38f2c4d943238b887bac588d97c104815aecc62d2fd976e014b"},
+    {file = "grpcio-1.62.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:3952b581eb121324853ce2b191dae08badb75cd493cb4e0243368aa9e61cfd41"},
+    {file = "grpcio-1.62.1-cp312-cp312-win32.whl", hash = "sha256:83a17b303425104d6329c10eb34bba186ffa67161e63fa6cdae7776ff76df73f"},
+    {file = "grpcio-1.62.1-cp312-cp312-win_amd64.whl", hash = "sha256:6696ffe440333a19d8d128e88d440f91fb92c75a80ce4b44d55800e656a3ef1d"},
+    {file = "grpcio-1.62.1-cp37-cp37m-linux_armv7l.whl", hash = "sha256:e3393b0823f938253370ebef033c9fd23d27f3eae8eb9a8f6264900c7ea3fb5a"},
+    {file = "grpcio-1.62.1-cp37-cp37m-macosx_10_10_universal2.whl", hash = "sha256:83e7ccb85a74beaeae2634f10eb858a0ed1a63081172649ff4261f929bacfd22"},
+    {file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_aarch64.whl", hash = "sha256:882020c87999d54667a284c7ddf065b359bd00251fcd70279ac486776dbf84ec"},
+    {file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a10383035e864f386fe096fed5c47d27a2bf7173c56a6e26cffaaa5a361addb1"},
+    {file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:960edebedc6b9ada1ef58e1c71156f28689978188cd8cff3b646b57288a927d9"},
+    {file = "grpcio-1.62.1-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:23e2e04b83f347d0aadde0c9b616f4726c3d76db04b438fd3904b289a725267f"},
+    {file = "grpcio-1.62.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:978121758711916d34fe57c1f75b79cdfc73952f1481bb9583399331682d36f7"},
+    {file = "grpcio-1.62.1-cp37-cp37m-win_amd64.whl", hash = "sha256:9084086190cc6d628f282e5615f987288b95457292e969b9205e45b442276407"},
+    {file = "grpcio-1.62.1-cp38-cp38-linux_armv7l.whl", hash = "sha256:22bccdd7b23c420a27fd28540fb5dcbc97dc6be105f7698cb0e7d7a420d0e362"},
+    {file = "grpcio-1.62.1-cp38-cp38-macosx_10_10_universal2.whl", hash = "sha256:8999bf1b57172dbc7c3e4bb3c732658e918f5c333b2942243f10d0d653953ba9"},
+    {file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_aarch64.whl", hash = "sha256:d9e52558b8b8c2f4ac05ac86344a7417ccdd2b460a59616de49eb6933b07a0bd"},
+    {file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1714e7bc935780bc3de1b3fcbc7674209adf5208ff825799d579ffd6cd0bd505"},
+    {file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8842ccbd8c0e253c1f189088228f9b433f7a93b7196b9e5b6f87dba393f5d5d"},
+    {file = "grpcio-1.62.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:1f1e7b36bdff50103af95a80923bf1853f6823dd62f2d2a2524b66ed74103e49"},
+    {file = "grpcio-1.62.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:bba97b8e8883a8038606480d6b6772289f4c907f6ba780fa1f7b7da7dfd76f06"},
+    {file = "grpcio-1.62.1-cp38-cp38-win32.whl", hash = "sha256:a7f615270fe534548112a74e790cd9d4f5509d744dd718cd442bf016626c22e4"},
+    {file = "grpcio-1.62.1-cp38-cp38-win_amd64.whl", hash = "sha256:e6c8c8693df718c5ecbc7babb12c69a4e3677fd11de8886f05ab22d4e6b1c43b"},
+    {file = "grpcio-1.62.1-cp39-cp39-linux_armv7l.whl", hash = "sha256:73db2dc1b201d20ab7083e7041946910bb991e7e9761a0394bbc3c2632326483"},
+    {file = "grpcio-1.62.1-cp39-cp39-macosx_10_10_universal2.whl", hash = "sha256:407b26b7f7bbd4f4751dbc9767a1f0716f9fe72d3d7e96bb3ccfc4aace07c8de"},
+    {file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:f8de7c8cef9261a2d0a62edf2ccea3d741a523c6b8a6477a340a1f2e417658de"},
+    {file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9bd5c8a1af40ec305d001c60236308a67e25419003e9bb3ebfab5695a8d0b369"},
+    {file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:be0477cb31da67846a33b1a75c611f88bfbcd427fe17701b6317aefceee1b96f"},
+    {file = "grpcio-1.62.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:60dcd824df166ba266ee0cfaf35a31406cd16ef602b49f5d4dfb21f014b0dedd"},
+    {file = "grpcio-1.62.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:973c49086cabab773525f6077f95e5a993bfc03ba8fc32e32f2c279497780585"},
+    {file = "grpcio-1.62.1-cp39-cp39-win32.whl", hash = "sha256:12859468e8918d3bd243d213cd6fd6ab07208195dc140763c00dfe901ce1e1b4"},
+    {file = "grpcio-1.62.1-cp39-cp39-win_amd64.whl", hash = "sha256:b7209117bbeebdfa5d898205cc55153a51285757902dd73c47de498ad4d11332"},
+    {file = "grpcio-1.62.1.tar.gz", hash = "sha256:6c455e008fa86d9e9a9d85bb76da4277c0d7d9668a3bfa70dbe86e9f3c759947"},
 ]
 
 [package.extras]
-protobuf = ["grpcio-tools (>=1.62.0)"]
+protobuf = ["grpcio-tools (>=1.62.1)"]
 
 [[package]]
 name = "grpcio-status"
-version = "1.62.0"
+version = "1.62.1"
 description = "Status proto mapping for gRPC"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "grpcio-status-1.62.0.tar.gz", hash = "sha256:0d693e9c09880daeaac060d0c3dba1ae470a43c99e5d20dfeafd62cf7e08a85d"},
-    {file = "grpcio_status-1.62.0-py3-none-any.whl", hash = "sha256:3baac03fcd737310e67758c4082a188107f771d32855bce203331cd4c9aa687a"},
+    {file = "grpcio-status-1.62.1.tar.gz", hash = "sha256:3431c8abbab0054912c41df5c72f03ddf3b7a67be8a287bb3c18a3456f96ff77"},
+    {file = "grpcio_status-1.62.1-py3-none-any.whl", hash = "sha256:af0c3ab85da31669f21749e8d53d669c061ebc6ce5637be49a46edcb7aa8ab17"},
 ]
 
 [package.dependencies]
 googleapis-common-protos = ">=1.5.5"
-grpcio = ">=1.62.0"
+grpcio = ">=1.62.1"
 protobuf = ">=4.21.6"
 
 [[package]]
@@ -1956,13 +1957,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.21.3"
+version = "0.21.4"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.21.3-py3-none-any.whl", hash = "sha256:b183144336fdf2810a8c109822e0bb6ef1fd61c65da6fb60e8c3f658b7144016"},
-    {file = "huggingface_hub-0.21.3.tar.gz", hash = "sha256:26a15b604e4fc7bad37c467b76456543ec849386cbca9cd7e1e135f53e500423"},
+    {file = "huggingface_hub-0.21.4-py3-none-any.whl", hash = "sha256:df37c2c37fc6c82163cdd8a67ede261687d80d1e262526d6c0ce73b6b3630a7b"},
+    {file = "huggingface_hub-0.21.4.tar.gz", hash = "sha256:e1f4968c93726565a80edf6dc309763c7b546d0cfe79aa221206034d50155531"},
 ]
 
 [package.dependencies]
@@ -2047,18 +2048,18 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.2"
+version = "6.1.3"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_resources-6.1.2-py3-none-any.whl", hash = "sha256:9a0a862501dc38b68adebc82970140c9e4209fc99601782925178f8386339938"},
-    {file = "importlib_resources-6.1.2.tar.gz", hash = "sha256:308abf8474e2dba5f867d279237cd4076482c3de7104a40b41426370e891549b"},
+    {file = "importlib_resources-6.1.3-py3-none-any.whl", hash = "sha256:4c0269e3580fe2634d364b39b38b961540a7738c02cb984e98add8b4221d793d"},
+    {file = "importlib_resources-6.1.3.tar.gz", hash = "sha256:56fb4525197b78544a3354ea27793952ab93f935bb4bf746b846bb1015020f2b"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
+testing = ["jaraco.collections", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
 
 [[package]]
 name = "iniconfig"
@@ -2208,13 +2209,13 @@ tests = ["aiohttp", "duckdb", "pandas (>=1.4)", "polars (>=0.19)", "pytest", "py
 
 [[package]]
 name = "langchain"
-version = "0.1.10"
+version = "0.1.11"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain-0.1.10-py3-none-any.whl", hash = "sha256:dcc1c0968b8d946a812155584ecbbeda690c930c3ee27bb5ecc113d954f6cf1a"},
-    {file = "langchain-0.1.10.tar.gz", hash = "sha256:17951bcd6d74adc74aa081f260ef5514c449488815314420b7e0f8349f15d932"},
+    {file = "langchain-0.1.11-py3-none-any.whl", hash = "sha256:b5e678ac50d85370b9bc28f2c97ad5f029aac1c0cca79cac9354adf72741bc6e"},
+    {file = "langchain-0.1.11.tar.gz", hash = "sha256:03f08cae7cd3f341c54f1042b3fe24d88f39eba7b7eda942735d8ced13fe6da9"},
 ]
 
 [package.dependencies]
@@ -2223,9 +2224,9 @@ async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
 langchain-community = ">=0.0.25,<0.1"
-langchain-core = ">=0.1.28,<0.2"
+langchain-core = ">=0.1.29,<0.2"
 langchain-text-splitters = ">=0.0.1,<0.1"
-langsmith = ">=0.1.0,<0.2.0"
+langsmith = ">=0.1.17,<0.2.0"
 numpy = ">=1,<2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -2249,19 +2250,19 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.0.25"
+version = "0.0.27"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain_community-0.0.25-py3-none-any.whl", hash = "sha256:09b931ba710b1a10e449396d59f38575e0554acd527287937c33a2c4abdc6d83"},
-    {file = "langchain_community-0.0.25.tar.gz", hash = "sha256:b6c8c14cd6ec2635e51e3974bf78a8de3b959bbedb4af55aad164f8cf392f0c5"},
+    {file = "langchain_community-0.0.27-py3-none-any.whl", hash = "sha256:377a7429580a71d909012df5aae538d295fa6f21bc479e5dac6fd1589762b3ab"},
+    {file = "langchain_community-0.0.27.tar.gz", hash = "sha256:266dffbd4c1666db1889cad953fa5102d4debff782335353b6d78636a761778d"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1.28,<0.2.0"
+langchain-core = ">=0.1.30,<0.2.0"
 langsmith = ">=0.1.0,<0.2.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
@@ -2271,17 +2272,17 @@ tenacity = ">=8.1.0,<9.0.0"
 
 [package.extras]
 cli = ["typer (>=0.9.0,<0.10.0)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)", "zhipuai (>=1.0.7,<2.0.0)"]
+extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)", "zhipuai (>=1.0.7,<2.0.0)"]
 
 [[package]]
 name = "langchain-core"
-version = "0.1.28"
+version = "0.1.30"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain_core-0.1.28-py3-none-any.whl", hash = "sha256:f40ca31257e003eb404e6275345d13a1b3839df147153684bab56bb8d80162c6"},
-    {file = "langchain_core-0.1.28.tar.gz", hash = "sha256:04e761a513200b6e5b5818613821945799c07bc5349087d7692e50823107c9d6"},
+    {file = "langchain_core-0.1.30-py3-none-any.whl", hash = "sha256:c9643505e41d25ba8f20a2e8bf083d0f0d50b9a098d901511fff8df79f831ada"},
+    {file = "langchain_core-0.1.30.tar.gz", hash = "sha256:e13a016e55e7f082ff3eeeda2d0cb505b89a8830e3a23c1d134d0a89d7871894"},
 ]
 
 [package.dependencies]
@@ -2333,13 +2334,13 @@ extended-testing = ["lxml (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langsmith"
-version = "0.1.14"
+version = "0.1.23"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langsmith-0.1.14-py3-none-any.whl", hash = "sha256:ecb243057d2a43c2da0524fe395585bc3421bb5d24f1cdd53eb06fbe63e43a69"},
-    {file = "langsmith-0.1.14.tar.gz", hash = "sha256:b95f267d25681f4c9862bb68236fba8a57a60ec7921ecfdaa125936807e51bde"},
+    {file = "langsmith-0.1.23-py3-none-any.whl", hash = "sha256:69984268b9867cb31b875965b3f86b6f56ba17dd5454d487d3a1a999bdaeea69"},
+    {file = "langsmith-0.1.23.tar.gz", hash = "sha256:327c66ec0de8c1bc57bfa47bbc70a29ef749e97c3e5571b9baf754d1e0644220"},
 ]
 
 [package.dependencies]
@@ -2476,13 +2477,13 @@ files = [
 
 [[package]]
 name = "marshmallow"
-version = "3.21.0"
+version = "3.21.1"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "marshmallow-3.21.0-py3-none-any.whl", hash = "sha256:e7997f83571c7fd476042c2c188e4ee8a78900ca5e74bd9c8097afa56624e9bd"},
-    {file = "marshmallow-3.21.0.tar.gz", hash = "sha256:20f53be28c6e374a711a16165fb22a8dc6003e3f7cda1285e3ca777b9193885b"},
+    {file = "marshmallow-3.21.1-py3-none-any.whl", hash = "sha256:f085493f79efb0644f270a9bf2892843142d80d7174bbbd2f3713f2a589dc633"},
+    {file = "marshmallow-3.21.1.tar.gz", hash = "sha256:4e65e9e0d80fc9e609574b9983cf32579f305c718afb30d7233ab818571768c3"},
 ]
 
 [package.dependencies]
@@ -2563,13 +2564,13 @@ mkdocs = ">=1.1"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.12"
+version = "9.5.13"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.12-py3-none-any.whl", hash = "sha256:d6f0c269f015e48c76291cdc79efb70f7b33bbbf42d649cfe475522ebee61b1f"},
-    {file = "mkdocs_material-9.5.12.tar.gz", hash = "sha256:5f69cef6a8aaa4050b812f72b1094fda3d079b9a51cf27a247244c03ec455e97"},
+    {file = "mkdocs_material-9.5.13-py3-none-any.whl", hash = "sha256:5cbe17fee4e3b4980c8420a04cc762d8dc052ef1e10532abd4fce88e5ea9ce6a"},
+    {file = "mkdocs_material-9.5.13.tar.gz", hash = "sha256:d8e4caae576312a88fd2609b81cf43d233cdbe36860d67a68702b018b425bd87"},
 ]
 
 [package.dependencies]
@@ -3443,13 +3444,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "posthog"
-version = "3.4.2"
+version = "3.5.0"
 description = "Integrate PostHog into any python application."
 optional = false
 python-versions = "*"
 files = [
-    {file = "posthog-3.4.2-py2.py3-none-any.whl", hash = "sha256:c7e79b2e585d16e93749874bcbcdad78d857037398ce0d8d6c474a04d0bd3bbe"},
-    {file = "posthog-3.4.2.tar.gz", hash = "sha256:f0eafa663fbc4a942b49b6168a62a890635407044bbc7593051dcb9cc1208873"},
+    {file = "posthog-3.5.0-py2.py3-none-any.whl", hash = "sha256:3c672be7ba6f95d555ea207d4486c171d06657eb34b3ce25eb043bfe7b6b5b76"},
+    {file = "posthog-3.5.0.tar.gz", hash = "sha256:8f7e3b2c6e8714d0c0c542a2109b83a7549f63b7113a133ab2763a89245ef2ef"},
 ]
 
 [package.dependencies]
@@ -3579,47 +3580,47 @@ files = [
 
 [[package]]
 name = "pyarrow"
-version = "15.0.0"
+version = "15.0.1"
 description = "Python library for Apache Arrow"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pyarrow-15.0.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:0a524532fd6dd482edaa563b686d754c70417c2f72742a8c990b322d4c03a15d"},
-    {file = "pyarrow-15.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:60a6bdb314affa9c2e0d5dddf3d9cbb9ef4a8dddaa68669975287d47ece67642"},
-    {file = "pyarrow-15.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:66958fd1771a4d4b754cd385835e66a3ef6b12611e001d4e5edfcef5f30391e2"},
-    {file = "pyarrow-15.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1f500956a49aadd907eaa21d4fff75f73954605eaa41f61cb94fb008cf2e00c6"},
-    {file = "pyarrow-15.0.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:6f87d9c4f09e049c2cade559643424da84c43a35068f2a1c4653dc5b1408a929"},
-    {file = "pyarrow-15.0.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:85239b9f93278e130d86c0e6bb455dcb66fc3fd891398b9d45ace8799a871a1e"},
-    {file = "pyarrow-15.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:5b8d43e31ca16aa6e12402fcb1e14352d0d809de70edd185c7650fe80e0769e3"},
-    {file = "pyarrow-15.0.0-cp311-cp311-macosx_10_15_x86_64.whl", hash = "sha256:fa7cd198280dbd0c988df525e50e35b5d16873e2cdae2aaaa6363cdb64e3eec5"},
-    {file = "pyarrow-15.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8780b1a29d3c8b21ba6b191305a2a607de2e30dab399776ff0aa09131e266340"},
-    {file = "pyarrow-15.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fe0ec198ccc680f6c92723fadcb97b74f07c45ff3fdec9dd765deb04955ccf19"},
-    {file = "pyarrow-15.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:036a7209c235588c2f07477fe75c07e6caced9b7b61bb897c8d4e52c4b5f9555"},
-    {file = "pyarrow-15.0.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:2bd8a0e5296797faf9a3294e9fa2dc67aa7f10ae2207920dbebb785c77e9dbe5"},
-    {file = "pyarrow-15.0.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:e8ebed6053dbe76883a822d4e8da36860f479d55a762bd9e70d8494aed87113e"},
-    {file = "pyarrow-15.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:17d53a9d1b2b5bd7d5e4cd84d018e2a45bc9baaa68f7e6e3ebed45649900ba99"},
-    {file = "pyarrow-15.0.0-cp312-cp312-macosx_10_15_x86_64.whl", hash = "sha256:9950a9c9df24090d3d558b43b97753b8f5867fb8e521f29876aa021c52fda351"},
-    {file = "pyarrow-15.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:003d680b5e422d0204e7287bb3fa775b332b3fce2996aa69e9adea23f5c8f970"},
-    {file = "pyarrow-15.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f75fce89dad10c95f4bf590b765e3ae98bcc5ba9f6ce75adb828a334e26a3d40"},
-    {file = "pyarrow-15.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0ca9cb0039923bec49b4fe23803807e4ef39576a2bec59c32b11296464623dc2"},
-    {file = "pyarrow-15.0.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:9ed5a78ed29d171d0acc26a305a4b7f83c122d54ff5270810ac23c75813585e4"},
-    {file = "pyarrow-15.0.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:6eda9e117f0402dfcd3cd6ec9bfee89ac5071c48fc83a84f3075b60efa96747f"},
-    {file = "pyarrow-15.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:9a3a6180c0e8f2727e6f1b1c87c72d3254cac909e609f35f22532e4115461177"},
-    {file = "pyarrow-15.0.0-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:19a8918045993349b207de72d4576af0191beef03ea655d8bdb13762f0cd6eac"},
-    {file = "pyarrow-15.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:d0ec076b32bacb6666e8813a22e6e5a7ef1314c8069d4ff345efa6246bc38593"},
-    {file = "pyarrow-15.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5db1769e5d0a77eb92344c7382d6543bea1164cca3704f84aa44e26c67e320fb"},
-    {file = "pyarrow-15.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e2617e3bf9df2a00020dd1c1c6dce5cc343d979efe10bc401c0632b0eef6ef5b"},
-    {file = "pyarrow-15.0.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:d31c1d45060180131caf10f0f698e3a782db333a422038bf7fe01dace18b3a31"},
-    {file = "pyarrow-15.0.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:c8c287d1d479de8269398b34282e206844abb3208224dbdd7166d580804674b7"},
-    {file = "pyarrow-15.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:07eb7f07dc9ecbb8dace0f58f009d3a29ee58682fcdc91337dfeb51ea618a75b"},
-    {file = "pyarrow-15.0.0-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:47af7036f64fce990bb8a5948c04722e4e3ea3e13b1007ef52dfe0aa8f23cf7f"},
-    {file = "pyarrow-15.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:93768ccfff85cf044c418bfeeafce9a8bb0cee091bd8fd19011aff91e58de540"},
-    {file = "pyarrow-15.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f6ee87fd6892700960d90abb7b17a72a5abb3b64ee0fe8db6c782bcc2d0dc0b4"},
-    {file = "pyarrow-15.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:001fca027738c5f6be0b7a3159cc7ba16a5c52486db18160909a0831b063c4e4"},
-    {file = "pyarrow-15.0.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:d1c48648f64aec09accf44140dccb92f4f94394b8d79976c426a5b79b11d4fa7"},
-    {file = "pyarrow-15.0.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:972a0141be402bb18e3201448c8ae62958c9c7923dfaa3b3d4530c835ac81aed"},
-    {file = "pyarrow-15.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:f01fc5cf49081426429127aa2d427d9d98e1cb94a32cb961d583a70b7c4504e6"},
-    {file = "pyarrow-15.0.0.tar.gz", hash = "sha256:876858f549d540898f927eba4ef77cd549ad8d24baa3207cf1b72e5788b50e83"},
+    {file = "pyarrow-15.0.1-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:c2ddb3be5ea938c329a84171694fc230b241ce1b6b0ff1a0280509af51c375fa"},
+    {file = "pyarrow-15.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:7543ea88a0ff72f8e6baaf9bfdbec2c62aeabdbede9e4a571c71cc3bc43b6302"},
+    {file = "pyarrow-15.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1519e218a6941fc074e4501088d891afcb2adf77c236e03c34babcf3d6a0d1c7"},
+    {file = "pyarrow-15.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:28cafa86e1944761970d3b3fc0411b14ff9b5c2b73cd22aaf470d7a3976335f5"},
+    {file = "pyarrow-15.0.1-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:be5c3d463e33d03eab496e1af7916b1d44001c08f0f458ad27dc16093a020638"},
+    {file = "pyarrow-15.0.1-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:47b1eda15d3aa3f49a07b1808648e1397e5dc6a80a30bf87faa8e2d02dad7ac3"},
+    {file = "pyarrow-15.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:e524a31be7db22deebbbcf242b189063ab9a7652c62471d296b31bc6e3cae77b"},
+    {file = "pyarrow-15.0.1-cp311-cp311-macosx_10_15_x86_64.whl", hash = "sha256:a476fefe8bdd56122fb0d4881b785413e025858803cc1302d0d788d3522b374d"},
+    {file = "pyarrow-15.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:309e6191be385f2e220586bfdb643f9bb21d7e1bc6dd0a6963dc538e347b2431"},
+    {file = "pyarrow-15.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:83bc586903dbeb4365cbc72b602f99f70b96c5882e5dfac5278813c7d624ca3c"},
+    {file = "pyarrow-15.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:07e652daac6d8b05280cd2af31c0fb61a4490ec6a53dc01588014d9fa3fdbee9"},
+    {file = "pyarrow-15.0.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:abad2e08652df153a72177ce20c897d083b0c4ebeec051239e2654ddf4d3c996"},
+    {file = "pyarrow-15.0.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:cde663352bc83ad75ba7b3206e049ca1a69809223942362a8649e37bd22f9e3b"},
+    {file = "pyarrow-15.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:1b6e237dd7a08482a8b8f3f6512d258d2460f182931832a8c6ef3953203d31e1"},
+    {file = "pyarrow-15.0.1-cp312-cp312-macosx_10_15_x86_64.whl", hash = "sha256:7bd167536ee23192760b8c731d39b7cfd37914c27fd4582335ffd08450ff799d"},
+    {file = "pyarrow-15.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7c08bb31eb2984ba5c3747d375bb522e7e536b8b25b149c9cb5e1c49b0ccb736"},
+    {file = "pyarrow-15.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c0f9c1d630ed2524bd1ddf28ec92780a7b599fd54704cd653519f7ff5aec177a"},
+    {file = "pyarrow-15.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5186048493395220550bca7b524420471aac2d77af831f584ce132680f55c3df"},
+    {file = "pyarrow-15.0.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:31dc30c7ec8958da3a3d9f31d6c3630429b2091ede0ecd0d989fd6bec129f0e4"},
+    {file = "pyarrow-15.0.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:3f111a014fb8ac2297b43a74bf4495cc479a332908f7ee49cb7cbd50714cb0c1"},
+    {file = "pyarrow-15.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:a6d1f7c15d7f68f08490d0cb34611497c74285b8a6bbeab4ef3fc20117310983"},
+    {file = "pyarrow-15.0.1-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:9ad931b996f51c2f978ed517b55cb3c6078272fb4ec579e3da5a8c14873b698d"},
+    {file = "pyarrow-15.0.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:738f6b53ab1c2f66b2bde8a1d77e186aeaab702d849e0dfa1158c9e2c030add3"},
+    {file = "pyarrow-15.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2c1c3fc16bc74e33bf8f1e5a212938ed8d88e902f372c4dac6b5bad328567d2f"},
+    {file = "pyarrow-15.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1fa92512128f6c1b8dde0468c1454dd70f3bff623970e370d52efd4d24fd0be"},
+    {file = "pyarrow-15.0.1-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:b4157f307c202cbbdac147d9b07447a281fa8e63494f7fc85081da351ec6ace9"},
+    {file = "pyarrow-15.0.1-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:b75e7da26f383787f80ad76143b44844ffa28648fcc7099a83df1538c078d2f2"},
+    {file = "pyarrow-15.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:3a99eac76ae14096c209850935057b9e8ce97a78397c5cde8724674774f34e5d"},
+    {file = "pyarrow-15.0.1-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:dd532d3177e031e9b2d2df19fd003d0cc0520d1747659fcabbd4d9bb87de508c"},
+    {file = "pyarrow-15.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ce8c89848fd37e5313fc2ce601483038ee5566db96ba0808d5883b2e2e55dc53"},
+    {file = "pyarrow-15.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:862eac5e5f3b6477f7a92b2f27e560e1f4e5e9edfca9ea9da8a7478bb4abd5ce"},
+    {file = "pyarrow-15.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f0ea3a29cd5cb99bf14c1c4533eceaa00ea8fb580950fb5a89a5c771a994a4e"},
+    {file = "pyarrow-15.0.1-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:bb902f780cfd624b2e8fd8501fadab17618fdb548532620ef3d91312aaf0888a"},
+    {file = "pyarrow-15.0.1-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:4f87757f02735a6bb4ad2e1b98279ac45d53b748d5baf52401516413007c6999"},
+    {file = "pyarrow-15.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:efd3816c7fbfcbd406ac0f69873cebb052effd7cdc153ae5836d1b00845845d7"},
+    {file = "pyarrow-15.0.1.tar.gz", hash = "sha256:21d812548d39d490e0c6928a7c663f37b96bf764034123d4b4ab4530ecc757a9"},
 ]
 
 [package.dependencies]
@@ -3901,13 +3902,13 @@ torch = ["torch"]
 
 [[package]]
 name = "pymdown-extensions"
-version = "10.7"
+version = "10.7.1"
 description = "Extension pack for Python Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pymdown_extensions-10.7-py3-none-any.whl", hash = "sha256:6ca215bc57bc12bf32b414887a68b810637d039124ed9b2e5bd3325cbb2c050c"},
-    {file = "pymdown_extensions-10.7.tar.gz", hash = "sha256:c0d64d5cf62566f59e6b2b690a4095c931107c250a8c8e1351c1de5f6b036deb"},
+    {file = "pymdown_extensions-10.7.1-py3-none-any.whl", hash = "sha256:f5cc7000d7ff0d1ce9395d216017fa4df3dde800afb1fb72d1c7d3fd35e710f4"},
+    {file = "pymdown_extensions-10.7.1.tar.gz", hash = "sha256:c70e146bdd83c744ffc766b4671999796aba18842b268510a329f7f64700d584"},
 ]
 
 [package.dependencies]
@@ -3998,13 +3999,13 @@ files = [
 
 [[package]]
 name = "pyright"
-version = "1.1.352"
+version = "1.1.353"
 description = "Command line wrapper for pyright"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pyright-1.1.352-py3-none-any.whl", hash = "sha256:0040cf173c6a60704e553bfd129dfe54de59cc76d0b2b80f77cfab4f50701d64"},
-    {file = "pyright-1.1.352.tar.gz", hash = "sha256:a621c0dfbcf1291b3610641a07380fefaa1d0e182890a1b2a7f13b446e8109a9"},
+    {file = "pyright-1.1.353-py3-none-any.whl", hash = "sha256:8d7e6719d0be4fd9f4a37f010237c6a74d91ec1e7c81de634c2f3f9965f8ab43"},
+    {file = "pyright-1.1.353.tar.gz", hash = "sha256:24343bbc2a4f997563f966b6244a2e863473f1d85af6d24abcb366fcbb4abca9"},
 ]
 
 [package.dependencies]
@@ -4038,13 +4039,13 @@ files = [
 
 [[package]]
 name = "pytest"
-version = "8.1.0"
+version = "8.1.1"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.1.0-py3-none-any.whl", hash = "sha256:ee32db7af8de4629a455806befa90559f307424c07b8413ccfc30bf5b221dd7e"},
-    {file = "pytest-8.1.0.tar.gz", hash = "sha256:f8fa04ab8f98d185113ae60ea6d79c22f8143b14bc1caeced44a0ab844928323"},
+    {file = "pytest-8.1.1-py3-none-any.whl", hash = "sha256:2a8386cfc11fa9d2c50ee7b2a57e7d898ef90470a7a34c4b949ff59662bb78b7"},
+    {file = "pytest-8.1.1.tar.gz", hash = "sha256:ac978141a75948948817d360297b7aae0fcb9d6ff6bc9ec6d514b85d5a65c044"},
 ]
 
 [package.dependencies]
@@ -4324,13 +4325,13 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "requests-oauthlib"
-version = "1.3.1"
+version = "1.4.0"
 description = "OAuthlib authentication support for Requests."
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
-    {file = "requests-oauthlib-1.3.1.tar.gz", hash = "sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a"},
-    {file = "requests_oauthlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5"},
+    {file = "requests-oauthlib-1.4.0.tar.gz", hash = "sha256:acee623221e4a39abcbb919312c8ff04bd44e7e417087fb4bd5e2a2f53d5e79a"},
+    {file = "requests_oauthlib-1.4.0-py2.py3-none-any.whl", hash = "sha256:7a3130d94a17520169e38db6c8d75f2c974643788465ecc2e4b36d288bf13033"},
 ]
 
 [package.dependencies]
@@ -4560,60 +4561,60 @@ files = [
 
 [[package]]
 name = "sqlalchemy"
-version = "2.0.27"
+version = "2.0.28"
 description = "Database Abstraction Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "SQLAlchemy-2.0.27-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d04e579e911562f1055d26dab1868d3e0bb905db3bccf664ee8ad109f035618a"},
-    {file = "SQLAlchemy-2.0.27-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fa67d821c1fd268a5a87922ef4940442513b4e6c377553506b9db3b83beebbd8"},
-    {file = "SQLAlchemy-2.0.27-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6c7a596d0be71b7baa037f4ac10d5e057d276f65a9a611c46970f012752ebf2d"},
-    {file = "SQLAlchemy-2.0.27-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:954d9735ee9c3fa74874c830d089a815b7b48df6f6b6e357a74130e478dbd951"},
-    {file = "SQLAlchemy-2.0.27-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:5cd20f58c29bbf2680039ff9f569fa6d21453fbd2fa84dbdb4092f006424c2e6"},
-    {file = "SQLAlchemy-2.0.27-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:03f448ffb731b48323bda68bcc93152f751436ad6037f18a42b7e16af9e91c07"},
-    {file = "SQLAlchemy-2.0.27-cp310-cp310-win32.whl", hash = "sha256:d997c5938a08b5e172c30583ba6b8aad657ed9901fc24caf3a7152eeccb2f1b4"},
-    {file = "SQLAlchemy-2.0.27-cp310-cp310-win_amd64.whl", hash = "sha256:eb15ef40b833f5b2f19eeae65d65e191f039e71790dd565c2af2a3783f72262f"},
-    {file = "SQLAlchemy-2.0.27-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6c5bad7c60a392850d2f0fee8f355953abaec878c483dd7c3836e0089f046bf6"},
-    {file = "SQLAlchemy-2.0.27-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a3012ab65ea42de1be81fff5fb28d6db893ef978950afc8130ba707179b4284a"},
-    {file = "SQLAlchemy-2.0.27-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dbcd77c4d94b23e0753c5ed8deba8c69f331d4fd83f68bfc9db58bc8983f49cd"},
-    {file = "SQLAlchemy-2.0.27-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d177b7e82f6dd5e1aebd24d9c3297c70ce09cd1d5d37b43e53f39514379c029c"},
-    {file = "SQLAlchemy-2.0.27-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:680b9a36029b30cf063698755d277885d4a0eab70a2c7c6e71aab601323cba45"},
-    {file = "SQLAlchemy-2.0.27-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:1306102f6d9e625cebaca3d4c9c8f10588735ef877f0360b5cdb4fdfd3fd7131"},
-    {file = "SQLAlchemy-2.0.27-cp311-cp311-win32.whl", hash = "sha256:5b78aa9f4f68212248aaf8943d84c0ff0f74efc65a661c2fc68b82d498311fd5"},
-    {file = "SQLAlchemy-2.0.27-cp311-cp311-win_amd64.whl", hash = "sha256:15e19a84b84528f52a68143439d0c7a3a69befcd4f50b8ef9b7b69d2628ae7c4"},
-    {file = "SQLAlchemy-2.0.27-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:0de1263aac858f288a80b2071990f02082c51d88335a1db0d589237a3435fe71"},
-    {file = "SQLAlchemy-2.0.27-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ce850db091bf7d2a1f2fdb615220b968aeff3849007b1204bf6e3e50a57b3d32"},
-    {file = "SQLAlchemy-2.0.27-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8dfc936870507da96aebb43e664ae3a71a7b96278382bcfe84d277b88e379b18"},
-    {file = "SQLAlchemy-2.0.27-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c4fbe6a766301f2e8a4519f4500fe74ef0a8509a59e07a4085458f26228cd7cc"},
-    {file = "SQLAlchemy-2.0.27-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:4535c49d961fe9a77392e3a630a626af5baa967172d42732b7a43496c8b28876"},
-    {file = "SQLAlchemy-2.0.27-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:0fb3bffc0ced37e5aa4ac2416f56d6d858f46d4da70c09bb731a246e70bff4d5"},
-    {file = "SQLAlchemy-2.0.27-cp312-cp312-win32.whl", hash = "sha256:7f470327d06400a0aa7926b375b8e8c3c31d335e0884f509fe272b3c700a7254"},
-    {file = "SQLAlchemy-2.0.27-cp312-cp312-win_amd64.whl", hash = "sha256:f9374e270e2553653d710ece397df67db9d19c60d2647bcd35bfc616f1622dcd"},
-    {file = "SQLAlchemy-2.0.27-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:e97cf143d74a7a5a0f143aa34039b4fecf11343eed66538610debc438685db4a"},
-    {file = "SQLAlchemy-2.0.27-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d7b5a3e2120982b8b6bd1d5d99e3025339f7fb8b8267551c679afb39e9c7c7f1"},
-    {file = "SQLAlchemy-2.0.27-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e36aa62b765cf9f43a003233a8c2d7ffdeb55bc62eaa0a0380475b228663a38f"},
-    {file = "SQLAlchemy-2.0.27-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:5ada0438f5b74c3952d916c199367c29ee4d6858edff18eab783b3978d0db16d"},
-    {file = "SQLAlchemy-2.0.27-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:b1d9d1bfd96eef3c3faedb73f486c89e44e64e40e5bfec304ee163de01cf996f"},
-    {file = "SQLAlchemy-2.0.27-cp37-cp37m-win32.whl", hash = "sha256:ca891af9f3289d24a490a5fde664ea04fe2f4984cd97e26de7442a4251bd4b7c"},
-    {file = "SQLAlchemy-2.0.27-cp37-cp37m-win_amd64.whl", hash = "sha256:fd8aafda7cdff03b905d4426b714601c0978725a19efc39f5f207b86d188ba01"},
-    {file = "SQLAlchemy-2.0.27-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:ec1f5a328464daf7a1e4e385e4f5652dd9b1d12405075ccba1df842f7774b4fc"},
-    {file = "SQLAlchemy-2.0.27-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:ad862295ad3f644e3c2c0d8b10a988e1600d3123ecb48702d2c0f26771f1c396"},
-    {file = "SQLAlchemy-2.0.27-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:48217be1de7d29a5600b5c513f3f7664b21d32e596d69582be0a94e36b8309cb"},
-    {file = "SQLAlchemy-2.0.27-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e56afce6431450442f3ab5973156289bd5ec33dd618941283847c9fd5ff06bf"},
-    {file = "SQLAlchemy-2.0.27-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:611068511b5531304137bcd7fe8117c985d1b828eb86043bd944cebb7fae3910"},
-    {file = "SQLAlchemy-2.0.27-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:b86abba762ecfeea359112b2bb4490802b340850bbee1948f785141a5e020de8"},
-    {file = "SQLAlchemy-2.0.27-cp38-cp38-win32.whl", hash = "sha256:30d81cc1192dc693d49d5671cd40cdec596b885b0ce3b72f323888ab1c3863d5"},
-    {file = "SQLAlchemy-2.0.27-cp38-cp38-win_amd64.whl", hash = "sha256:120af1e49d614d2525ac247f6123841589b029c318b9afbfc9e2b70e22e1827d"},
-    {file = "SQLAlchemy-2.0.27-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:d07ee7793f2aeb9b80ec8ceb96bc8cc08a2aec8a1b152da1955d64e4825fcbac"},
-    {file = "SQLAlchemy-2.0.27-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:cb0845e934647232b6ff5150df37ceffd0b67b754b9fdbb095233deebcddbd4a"},
-    {file = "SQLAlchemy-2.0.27-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fc19ae2e07a067663dd24fca55f8ed06a288384f0e6e3910420bf4b1270cc51"},
-    {file = "SQLAlchemy-2.0.27-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b90053be91973a6fb6020a6e44382c97739736a5a9d74e08cc29b196639eb979"},
-    {file = "SQLAlchemy-2.0.27-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:2f5c9dfb0b9ab5e3a8a00249534bdd838d943ec4cfb9abe176a6c33408430230"},
-    {file = "SQLAlchemy-2.0.27-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:33e8bde8fff203de50399b9039c4e14e42d4d227759155c21f8da4a47fc8053c"},
-    {file = "SQLAlchemy-2.0.27-cp39-cp39-win32.whl", hash = "sha256:d873c21b356bfaf1589b89090a4011e6532582b3a8ea568a00e0c3aab09399dd"},
-    {file = "SQLAlchemy-2.0.27-cp39-cp39-win_amd64.whl", hash = "sha256:ff2f1b7c963961d41403b650842dc2039175b906ab2093635d8319bef0b7d620"},
-    {file = "SQLAlchemy-2.0.27-py3-none-any.whl", hash = "sha256:1ab4e0448018d01b142c916cc7119ca573803a4745cfe341b8f95657812700ac"},
-    {file = "SQLAlchemy-2.0.27.tar.gz", hash = "sha256:86a6ed69a71fe6b88bf9331594fa390a2adda4a49b5c06f98e47bf0d392534f8"},
+    {file = "SQLAlchemy-2.0.28-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e0b148ab0438f72ad21cb004ce3bdaafd28465c4276af66df3b9ecd2037bf252"},
+    {file = "SQLAlchemy-2.0.28-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:bbda76961eb8f27e6ad3c84d1dc56d5bc61ba8f02bd20fcf3450bd421c2fcc9c"},
+    {file = "SQLAlchemy-2.0.28-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:feea693c452d85ea0015ebe3bb9cd15b6f49acc1a31c28b3c50f4db0f8fb1e71"},
+    {file = "SQLAlchemy-2.0.28-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5da98815f82dce0cb31fd1e873a0cb30934971d15b74e0d78cf21f9e1b05953f"},
+    {file = "SQLAlchemy-2.0.28-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:4a5adf383c73f2d49ad15ff363a8748319ff84c371eed59ffd0127355d6ea1da"},
+    {file = "SQLAlchemy-2.0.28-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:56856b871146bfead25fbcaed098269d90b744eea5cb32a952df00d542cdd368"},
+    {file = "SQLAlchemy-2.0.28-cp310-cp310-win32.whl", hash = "sha256:943aa74a11f5806ab68278284a4ddd282d3fb348a0e96db9b42cb81bf731acdc"},
+    {file = "SQLAlchemy-2.0.28-cp310-cp310-win_amd64.whl", hash = "sha256:c6c4da4843e0dabde41b8f2e8147438330924114f541949e6318358a56d1875a"},
+    {file = "SQLAlchemy-2.0.28-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:46a3d4e7a472bfff2d28db838669fc437964e8af8df8ee1e4548e92710929adc"},
+    {file = "SQLAlchemy-2.0.28-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:0d3dd67b5d69794cfe82862c002512683b3db038b99002171f624712fa71aeaa"},
+    {file = "SQLAlchemy-2.0.28-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c61e2e41656a673b777e2f0cbbe545323dbe0d32312f590b1bc09da1de6c2a02"},
+    {file = "SQLAlchemy-2.0.28-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0315d9125a38026227f559488fe7f7cee1bd2fbc19f9fd637739dc50bb6380b2"},
+    {file = "SQLAlchemy-2.0.28-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:af8ce2d31679006e7b747d30a89cd3ac1ec304c3d4c20973f0f4ad58e2d1c4c9"},
+    {file = "SQLAlchemy-2.0.28-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:81ba314a08c7ab701e621b7ad079c0c933c58cdef88593c59b90b996e8b58fa5"},
+    {file = "SQLAlchemy-2.0.28-cp311-cp311-win32.whl", hash = "sha256:1ee8bd6d68578e517943f5ebff3afbd93fc65f7ef8f23becab9fa8fb315afb1d"},
+    {file = "SQLAlchemy-2.0.28-cp311-cp311-win_amd64.whl", hash = "sha256:ad7acbe95bac70e4e687a4dc9ae3f7a2f467aa6597049eeb6d4a662ecd990bb6"},
+    {file = "SQLAlchemy-2.0.28-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d3499008ddec83127ab286c6f6ec82a34f39c9817f020f75eca96155f9765097"},
+    {file = "SQLAlchemy-2.0.28-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:9b66fcd38659cab5d29e8de5409cdf91e9986817703e1078b2fdaad731ea66f5"},
+    {file = "SQLAlchemy-2.0.28-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bea30da1e76cb1acc5b72e204a920a3a7678d9d52f688f087dc08e54e2754c67"},
+    {file = "SQLAlchemy-2.0.28-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:124202b4e0edea7f08a4db8c81cc7859012f90a0d14ba2bf07c099aff6e96462"},
+    {file = "SQLAlchemy-2.0.28-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:e23b88c69497a6322b5796c0781400692eca1ae5532821b39ce81a48c395aae9"},
+    {file = "SQLAlchemy-2.0.28-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4b6303bfd78fb3221847723104d152e5972c22367ff66edf09120fcde5ddc2e2"},
+    {file = "SQLAlchemy-2.0.28-cp312-cp312-win32.whl", hash = "sha256:a921002be69ac3ab2cf0c3017c4e6a3377f800f1fca7f254c13b5f1a2f10022c"},
+    {file = "SQLAlchemy-2.0.28-cp312-cp312-win_amd64.whl", hash = "sha256:b4a2cf92995635b64876dc141af0ef089c6eea7e05898d8d8865e71a326c0385"},
+    {file = "SQLAlchemy-2.0.28-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:8e91b5e341f8c7f1e5020db8e5602f3ed045a29f8e27f7f565e0bdee3338f2c7"},
+    {file = "SQLAlchemy-2.0.28-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:45c7b78dfc7278329f27be02c44abc0d69fe235495bb8e16ec7ef1b1a17952db"},
+    {file = "SQLAlchemy-2.0.28-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3eba73ef2c30695cb7eabcdb33bb3d0b878595737479e152468f3ba97a9c22a4"},
+    {file = "SQLAlchemy-2.0.28-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:5df5d1dafb8eee89384fb7a1f79128118bc0ba50ce0db27a40750f6f91aa99d5"},
+    {file = "SQLAlchemy-2.0.28-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:2858bbab1681ee5406650202950dc8f00e83b06a198741b7c656e63818633526"},
+    {file = "SQLAlchemy-2.0.28-cp37-cp37m-win32.whl", hash = "sha256:9461802f2e965de5cff80c5a13bc945abea7edaa1d29360b485c3d2b56cdb075"},
+    {file = "SQLAlchemy-2.0.28-cp37-cp37m-win_amd64.whl", hash = "sha256:a6bec1c010a6d65b3ed88c863d56b9ea5eeefdf62b5e39cafd08c65f5ce5198b"},
+    {file = "SQLAlchemy-2.0.28-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:843a882cadebecc655a68bd9a5b8aa39b3c52f4a9a5572a3036fb1bb2ccdc197"},
+    {file = "SQLAlchemy-2.0.28-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:dbb990612c36163c6072723523d2be7c3eb1517bbdd63fe50449f56afafd1133"},
+    {file = "SQLAlchemy-2.0.28-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bd7e4baf9161d076b9a7e432fce06217b9bd90cfb8f1d543d6e8c4595627edb9"},
+    {file = "SQLAlchemy-2.0.28-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e0a5354cb4de9b64bccb6ea33162cb83e03dbefa0d892db88a672f5aad638a75"},
+    {file = "SQLAlchemy-2.0.28-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:fffcc8edc508801ed2e6a4e7b0d150a62196fd28b4e16ab9f65192e8186102b6"},
+    {file = "SQLAlchemy-2.0.28-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:aca7b6d99a4541b2ebab4494f6c8c2f947e0df4ac859ced575238e1d6ca5716b"},
+    {file = "SQLAlchemy-2.0.28-cp38-cp38-win32.whl", hash = "sha256:8c7f10720fc34d14abad5b647bc8202202f4948498927d9f1b4df0fb1cf391b7"},
+    {file = "SQLAlchemy-2.0.28-cp38-cp38-win_amd64.whl", hash = "sha256:243feb6882b06a2af68ecf4bec8813d99452a1b62ba2be917ce6283852cf701b"},
+    {file = "SQLAlchemy-2.0.28-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:fc4974d3684f28b61b9a90fcb4c41fb340fd4b6a50c04365704a4da5a9603b05"},
+    {file = "SQLAlchemy-2.0.28-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:87724e7ed2a936fdda2c05dbd99d395c91ea3c96f029a033a4a20e008dd876bf"},
+    {file = "SQLAlchemy-2.0.28-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68722e6a550f5de2e3cfe9da6afb9a7dd15ef7032afa5651b0f0c6b3adb8815d"},
+    {file = "SQLAlchemy-2.0.28-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:328529f7c7f90adcd65aed06a161851f83f475c2f664a898af574893f55d9e53"},
+    {file = "SQLAlchemy-2.0.28-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:df40c16a7e8be7413b885c9bf900d402918cc848be08a59b022478804ea076b8"},
+    {file = "SQLAlchemy-2.0.28-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:426f2fa71331a64f5132369ede5171c52fd1df1bd9727ce621f38b5b24f48750"},
+    {file = "SQLAlchemy-2.0.28-cp39-cp39-win32.whl", hash = "sha256:33157920b233bc542ce497a81a2e1452e685a11834c5763933b440fedd1d8e2d"},
+    {file = "SQLAlchemy-2.0.28-cp39-cp39-win_amd64.whl", hash = "sha256:2f60843068e432311c886c5f03c4664acaef507cf716f6c60d5fde7265be9d7b"},
+    {file = "SQLAlchemy-2.0.28-py3-none-any.whl", hash = "sha256:78bb7e8da0183a8301352d569900d9d3594c48ac21dc1c2ec6b3121ed8b6c986"},
+    {file = "SQLAlchemy-2.0.28.tar.gz", hash = "sha256:dd53b6c4e6d960600fd6532b79ee28e2da489322fcf6648738134587faf767b6"},
 ]
 
 [package.dependencies]
@@ -5022,13 +5023,13 @@ socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
 [[package]]
 name = "uvicorn"
-version = "0.27.1"
+version = "0.28.0"
 description = "The lightning-fast ASGI server."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "uvicorn-0.27.1-py3-none-any.whl", hash = "sha256:5c89da2f3895767472a35556e539fd59f7edbe9b1e9c0e1c99eebeadc61838e4"},
-    {file = "uvicorn-0.27.1.tar.gz", hash = "sha256:3d9a267296243532db80c83a959a3400502165ade2c1338dea4e67915fd4745a"},
+    {file = "uvicorn-0.28.0-py3-none-any.whl", hash = "sha256:6623abbbe6176204a4226e67607b4d52cc60ff62cda0ff177613645cefa2ece1"},
+    {file = "uvicorn-0.28.0.tar.gz", hash = "sha256:cab4473b5d1eaeb5a0f6375ac4bc85007ffc75c3cc1768816d9e5d589857b067"},
 ]
 
 [package.dependencies]

pyproject.toml

@@ -1,7 +1,6 @@
-
 [tool.poetry]
 name = "crewai"
-version = "0.19.0"
+version = "0.22.5"
 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"
@@ -9,9 +8,8 @@ packages = [
     { include = "crewai", from = "src" },
 ]
 
-
 [tool.poetry.urls]
-Homepage = "https://crewai.io"
+Homepage = "https://crewai.com"
 Documentation = "https://github.com/joaomdmoura/CrewAI/wiki/Index"
 Repository = "https://github.com/joaomdmoura/crewai"
 
@@ -28,6 +26,7 @@ instructor = "^0.5.2"
 regex = "^2023.12.25"
 crewai-tools = { version = "^0.0.15", optional = true }
 click = "^8.1.7"
+python-dotenv = "1.0.0"
 
 [tool.poetry.extras]
 tools = ["crewai-tools"]
@@ -56,6 +55,9 @@ pytest = "^8.0.0"
 pytest-vcr = "^1.0.2"
 python-dotenv = "1.0.0"
 
+[tool.poetry.scripts]
+crewai = "crewai.cli.cli:crewai"
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"

src/crewai/agent.py

@@ -7,6 +7,7 @@ from langchain.agents.tools import tool as LangChainTool
 from langchain.memory import ConversationSummaryMemory
 from langchain.tools.render import render_text_description
 from langchain_core.agents import AgentAction
+from langchain_core.callbacks import BaseCallbackHandler
 from langchain_openai import ChatOpenAI
 from pydantic import (
     UUID4,
@@ -36,6 +37,7 @@ class Agent(BaseModel):
             role: The role of the agent.
             goal: The objective of the agent.
             backstory: The backstory of the agent.
+            config: Dict representation of agent configuration.
             llm: The language model that will run the agent.
             function_calling_llm: The language model that will the tool calling for this agent, it overrides the crew function_calling_llm.
             max_iter: Maximum number of iterations for an agent to execute a task.
@@ -45,6 +47,7 @@ class Agent(BaseModel):
             allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
             tools: Tools at agents disposal
             step_callback: Callback to be executed after each step of the agent execution.
+            callbacks: A list of callback functions from the langchain library that are triggered during the agent's execution process
     """
 
     __hash__ = object.__hash__  # type: ignore
@@ -63,6 +66,10 @@ class Agent(BaseModel):
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Objective of the agent")
     backstory: str = Field(description="Backstory of the agent")
+    config: Optional[Dict[str, Any]] = Field(
+        description="Configuration for the agent",
+        default=None,
+    )
     max_rpm: Optional[int] = Field(
         default=None,
         description="Maximum number of requests per minute for the agent execution to be respected.",
@@ -105,6 +112,13 @@ class Agent(BaseModel):
     function_calling_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
+    callbacks: Optional[List[InstanceOf[BaseCallbackHandler]]] = Field(
+        default=None, description="Callback to be executed"
+    )
+
+    def __init__(__pydantic_self__, **data):
+        config = data.pop("config", {})
+        super().__init__(**config, **data)
 
     @field_validator("id", mode="before")
     @classmethod
@@ -114,6 +128,14 @@ class Agent(BaseModel):
                 "may_not_set_field", "This field is not to be set by the user.", {}
             )
 
+    @model_validator(mode="after")
+    def set_attributes_based_on_config(self) -> "Agent":
+        """Set attributes based on the agent configuration."""
+        if self.config:
+            for key, value in self.config.items():
+                setattr(self, key, value)
+        return self
+
     @model_validator(mode="after")
     def set_private_attrs(self):
         """Set private attributes."""
@@ -228,6 +250,7 @@ class Agent(BaseModel):
             "step_callback": self.step_callback,
             "tools_handler": self.tools_handler,
             "function_calling_llm": self.function_calling_llm,
+            "callbacks": self.callbacks,
         }
 
         if self._rpm_controller:
@@ -301,3 +324,6 @@ class Agent(BaseModel):
     @staticmethod
     def __tools_names(tools) -> str:
         return ", ".join([t.name for t in tools])
+
+    def __repr__(self):
+        return f"Agent(role={self.role}, goal={self.goal}, backstory={self.backstory})"

src/crewai/cli/cli.py

@@ -0,0 +1,19 @@
+import click
+
+from .create_crew import create_crew
+
+
+@click.group()
+def crewai():
+    """Top-level command group for crewai."""
+
+
+@crewai.command()
+@click.argument("project_name")
+def create(project_name):
+    """Create a new crew."""
+    create_crew(project_name)
+
+
+if __name__ == "__main__":
+    crewai()

src/crewai/cli/create_crew.py

@@ -0,0 +1,79 @@
+import os
+from pathlib import Path
+
+import click
+
+
+def create_crew(name):
+    """Create a new crew."""
+    folder_name = name.replace(" ", "_").replace("-", "_").lower()
+    class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
+
+    click.secho(f"Creating folder {folder_name}...", fg="green", bold=True)
+
+    if not os.path.exists(folder_name):
+        os.mkdir(folder_name)
+        os.mkdir(folder_name + "/tests")
+        os.mkdir(folder_name + "/src")
+        os.mkdir(folder_name + "/src/tools")
+        os.mkdir(folder_name + "/src/config")
+        with open(folder_name + "/.env", "w") as file:
+            file.write("OPENAI_API_KEY=YOUR_API_KEY")
+    else:
+        click.secho(
+            f"\tFolder {folder_name} already exists. Please choose a different name.",
+            fg="red",
+        )
+        return
+
+    package_dir = Path(__file__).parent
+    templates_dir = package_dir / "templates"
+
+    # List of template files to copy
+    root_template_files = [
+        ".gitignore",
+        "pyproject.toml",
+        "README.md",
+    ]
+    tools_template_files = ["tools/custom_tool.py", "tools/__init__.py"]
+    config_template_files = ["config/agents.yaml", "config/tasks.yaml"]
+    src_template_files = ["__init__.py", "main.py", "crew.py"]
+
+    for file_name in root_template_files:
+        src_file = templates_dir / file_name
+        dst_file = Path(folder_name) / file_name
+        copy_template(src_file, dst_file, name, class_name, folder_name)
+
+    for file_name in src_template_files:
+        src_file = templates_dir / file_name
+        dst_file = Path(folder_name) / "src" / file_name
+        copy_template(src_file, dst_file, name, class_name, folder_name)
+
+    for file_name in tools_template_files:
+        src_file = templates_dir / file_name
+        dst_file = Path(folder_name) / "src" / file_name
+        copy_template(src_file, dst_file, name, class_name, folder_name)
+
+    for file_name in config_template_files:
+        src_file = templates_dir / file_name
+        dst_file = Path(folder_name) / "src" / file_name
+        copy_template(src_file, dst_file, name, class_name, folder_name)
+
+    click.secho(f"Crew {name} created successfully!", fg="green", bold=True)
+
+
+def copy_template(src, dst, name, class_name, folder_name):
+    """Copy a file from src to dst."""
+    with open(src, "r") as file:
+        content = file.read()
+
+    # Interpolate the content
+    content = content.replace("{{name}}", name)
+    content = content.replace("{{crew_name}}", class_name)
+    content = content.replace("{{folder_name}}", folder_name)
+
+    # Write the interpolated content to the new file
+    with open(dst, "w") as file:
+        file.write(content)
+
+    click.secho(f"  - Created {dst}", fg="green")

src/crewai/cli/templates/.gitignore

@@ -0,0 +1,2 @@
+.env
+__pycache__/

src/crewai/cli/templates/README.md

@@ -0,0 +1,57 @@
+# {{crew_name}} Crew
+
+Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.
+
+## Installation
+
+Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [Poetry](https://python-poetry.org/) for dependency management and package handling, offering a seamless setup and execution experience.
+
+First, if you haven't already, install Poetry:
+
+```bash
+pip install poetry
+```
+
+Next, navigate to your project directory and install the dependencies:
+
+1. First lock the dependencies and then install them:
+```bash
+poetry lock
+```
+```bash
+poetry install
+```
+### Customizing
+
+**Add you `OPENAI_API_KEY` on the `.env` file**
+
+- Modify `src/{{folder_name}}/config/agents.yaml` to define your agents
+- Modify `src/{{folder_name}}/config/tasks.yaml` to define your tasks
+- Modify `src/{{folder_name}}/crew.py` to add your own logic, tools and specific args
+- Modify `src/{{folder_name}}/main.py` to add custom inputs for your agents and tasks
+
+## Running the Project
+
+To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
+
+```bash
+poetry run {{folder_name}}
+```
+
+This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
+
+This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folser
+
+## Understanding Your Crew
+
+The {{name}} Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew.
+
+## Support
+
+For support, questions, or feedback regarding the {{crew_name}} Crew or crewAI.
+- Visit our [documentation](https://docs.crewai.com)
+- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai)
+- [Joing our Discord](https://discord.com/invite/X4JWnZnxPb)
+- [Chat wtih our docs](https://chatg.pt/DWjSBZn)
+
+Let's create wonders together with the power and simplicity of crewAI.

src/crewai/cli/templates/config/agents.yaml

@@ -0,0 +1,19 @@
+researcher:
+  role: >
+    {topic} Senior Data Researcher
+  goal: >
+    Uncover cutting-edge developments in {topic}
+  backstory: >
+    You're a seasoned researcher with a knack for uncovering the latest
+    developments in {topic}. Known for your ability to find the most relevant
+    information and present it in a clear and concise manner.
+
+reporting_analyst:
+  role: >
+    {topic} Reporting Analyst
+  goal: >
+    Create detailed reports based on {topic} data analysis and research findings
+  backstory: >
+    You're a meticulous analyst with a keen eye for detail. You're known for
+    your ability to turn complex data into clear and concise reports, making
+    it easy for others to understand and act on the information you provide.

src/crewai/cli/templates/config/tasks.yaml

@@ -0,0 +1,15 @@
+research_task:
+  description: >
+    Conduct a thorough research about {topic}
+    Make sure you find any interesting and relevant information given
+    the current year is 2024.
+  expected_output: >
+    A list with 10 bullet points of the most relevant information about {topic}
+
+reporting_task:
+  description: >
+    Review the context you got and expand each topic into a full section for a report.
+    Make sure the report is detailed and contains any and all relevant information.
+  expected_output: >
+    A fully fledge reports with the mains topics, each with a full section of information.
+    Formated as markdown with out '```'

src/crewai/cli/templates/crew.py

@@ -0,0 +1,55 @@
+from crewai import Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+
+# Uncomment the following line to use an example of a custom tool
+# from {{folder_name}}.tools.custom_tool import MyCustomTool
+
+# Check our tools documentations for more information on how to use them
+# from crewai_tools import SerperDevTool
+
+@CrewBase
+class {{crew_name}}Crew():
+	"""{{crew_name}} crew"""
+	agents_config = 'config/agents.yaml'
+	tasks_config = 'config/tasks.yaml'
+
+	@agent
+	def researcher(self) -> Agent:
+		return Agent(
+			config=self.agents_config['researcher'],
+			# tools=[MyCustomTool()], # Example of custom tool, loaded on the beginning of file
+			verbose=True
+		)
+
+	@agent
+	def reporting_analyst(self) -> Agent:
+		return Agent(
+			config=self.agents_config['reporting_analyst'],
+			verbose=True
+		)
+
+	@task
+	def research_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['research_task'],
+			agent=self.researcher()
+		)
+
+	@task
+	def reporting_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['reporting_task'],
+			agent=self.reporting_analyst(),
+			output_file='report.md'
+		)
+
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the {{crew_name}} crew"""
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=2,
+			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
+		)

src/crewai/cli/templates/main.py

@@ -0,0 +1,13 @@
+#!/usr/bin/env python
+from crew import {{crew_name}}Crew
+
+
+def run():
+    # Replace with your inputs, it will automatically interpolate any tasks and agents information
+    inputs = {
+        'topic': 'AI LLMs'
+    }
+    {{crew_name}}Crew().crew().kickoff(inputs=inputs)
+
+if __name__ == "__main__":
+    run()

src/crewai/cli/templates/pyproject.toml

@@ -0,0 +1,16 @@
+[tool.poetry]
+name = "{{folder_name}}"
+version = "0.1.0"
+description = "{{name}} using crewAI"
+authors = ["Your Name <you@example.com>"]
+
+[tool.poetry.dependencies]
+python = ">=3.10,<=3.13"
+crewai = {extras = ["tools"], version = "^0.22.2"}
+
+[tool.poetry.scripts]
+{{folder_name}} = "{{folder_name}}.main:run"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

src/crewai/cli/templates/tools/custom_tool.py

@@ -0,0 +1,10 @@
+from crewai_tools import BaseTool
+
+
+class MyCustomTool(BaseTool):
+    name: str = "Name of my tool"
+    description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
+
+    def _run(self, argument: str) -> str:
+        # Implementation goes here
+        return "this is an example of a tool output, ignore it and move along."

src/crewai/crew.py

@@ -2,6 +2,7 @@ import json
 import uuid
 from typing import Any, Dict, List, Optional, Union
 
+from langchain_core.callbacks import BaseCallbackHandler
 from pydantic import (
     UUID4,
     BaseModel,
@@ -32,6 +33,7 @@ class Crew(BaseModel):
         tasks: List of tasks assigned to the crew.
         agents: List of agents part of this crew.
         manager_llm: The language model that will run manager agent.
+        manager_callbacks: The callback handlers to be executed by the manager agent when hierarchical process is used
         function_calling_llm: The language model that will run the tool calling for all the agents.
         process: The process flow that the crew will follow (e.g., sequential).
         verbose: Indicates the verbosity level for logging during execution.
@@ -64,6 +66,10 @@ class Crew(BaseModel):
     manager_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
+    manager_callbacks: Optional[List[InstanceOf[BaseCallbackHandler]]] = Field(
+        default=None,
+        description="A list of callback handlers to be executed by the manager agent when hierarchical process is used",
+    )
     function_calling_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
@@ -226,15 +232,17 @@ class Crew(BaseModel):
                     task.tools += AgentTools(agents=agents_for_delegation).tools()
 
             role = task.agent.role if task.agent is not None else "None"
-            self._logger.log("debug", f"Working Agent: {role}")
-            self._logger.log("info", f"Starting Task: {task.description}")
+            self._logger.log("debug", f"== Working Agent: {role}", color="bold_yellow")
+            self._logger.log(
+                "info", f"== Starting Task: {task.description}", color="bold_yellow"
+            )
 
             output = task.execute(context=task_output)
             if not task.async_execution:
                 task_output = output
 
             role = task.agent.role if task.agent is not None else "None"
-            self._logger.log("debug", f"[{role}] Task output: {task_output}\n\n")
+            self._logger.log("debug", f"== [{role}] Task output: {task_output}\n\n")
 
         self._finish_execution(task_output)
         return self._format_output(task_output)
@@ -261,9 +269,7 @@ class Crew(BaseModel):
                 agent=manager, context=task_output, tools=manager.tools
             )
 
-            self._logger.log(
-                "debug", f"[{manager.role}] Task output: {task_output}\n\n"
-            )
+            self._logger.log("debug", f"[{manager.role}] Task output: {task_output}")
 
         self._finish_execution(task_output)
         return self._format_output(task_output), manager._token_process.get_summary()
@@ -287,3 +293,6 @@ class Crew(BaseModel):
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
         self._telemetry.end_crew(self, output)
+
+    def __repr__(self):
+        return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"

src/crewai/project/__init__.py

@@ -0,0 +1,2 @@
+from .annotations import agent, crew, task
+from .crew_base import CrewBase

src/crewai/project/annotations.py

@@ -0,0 +1,47 @@
+tasks_order = []
+
+
+def task(func):
+    func.is_task = True
+    tasks_order.append(func.__name__)
+    return func
+
+
+def agent(func):
+    func.is_agent = True
+    return func
+
+
+def crew(func):
+    def wrapper(self, *args, **kwargs):
+        instantiated_tasks = []
+        instantiated_agents = []
+
+        agent_roles = set()
+        # Iterate over tasks_order to maintain the defined order
+        for task_name in tasks_order:
+            possible_task = getattr(self, task_name)
+            if callable(possible_task):
+                task_instance = possible_task()
+                instantiated_tasks.append(task_instance)
+                if hasattr(task_instance, "agent"):
+                    agent_instance = task_instance.agent
+                    if agent_instance.role not in agent_roles:
+                        instantiated_agents.append(agent_instance)
+                        agent_roles.add(agent_instance.role)
+
+        # Instantiate any additional agents not already included by tasks
+        for attr_name in dir(self):
+            possible_agent = getattr(self, attr_name)
+            if callable(possible_agent) and hasattr(possible_agent, "is_agent"):
+                temp_agent_instance = possible_agent()
+                if temp_agent_instance.role not in agent_roles:
+                    instantiated_agents.append(temp_agent_instance)
+                    agent_roles.add(temp_agent_instance.role)
+
+        self.agents = instantiated_agents
+        self.tasks = instantiated_tasks
+
+        return func(self, *args, **kwargs)
+
+    return wrapper

src/crewai/project/crew_base.py

@@ -0,0 +1,45 @@
+import inspect
+import os
+from pathlib import Path
+
+import yaml
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+def CrewBase(cls):
+    class WrappedClass(cls):
+        is_crew_class = True
+
+        base_directory = None
+        for frame_info in inspect.stack():
+            if "site-packages" not in frame_info.filename:
+                base_directory = Path(frame_info.filename).parent.resolve()
+                break
+
+        if base_directory is None:
+            raise Exception(
+                "Unable to dynamically determine the project's base directory, you must run it from the project's root directory."
+            )
+
+        original_agents_config_path = getattr(
+            cls, "agents_config", "config/agents.yaml"
+        )
+        original_tasks_config_path = getattr(cls, "tasks_config", "config/tasks.yaml")
+
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, **kwargs)
+            self.agents_config = self.load_yaml(
+                os.path.join(self.base_directory, self.original_agents_config_path)
+            )
+            self.tasks_config = self.load_yaml(
+                os.path.join(self.base_directory, self.original_tasks_config_path)
+            )
+
+        @staticmethod
+        def load_yaml(config_path: str):
+            with open(config_path, "r") as file:
+                return yaml.safe_load(file)
+
+    return WrappedClass

src/crewai/task.py

@@ -28,6 +28,10 @@ class Task(BaseModel):
     expected_output: str = Field(
         description="Clear definition of expected output for the task."
     )
+    config: Optional[Dict[str, Any]] = Field(
+        description="Configuration for the agent",
+        default=None,
+    )
     callback: Optional[Any] = Field(
         description="Callback to be executed after the task is completed.", default=None
     )
@@ -67,6 +71,10 @@ class Task(BaseModel):
         description="Unique identifier for the object, not set by user.",
     )
 
+    def __init__(__pydantic_self__, **data):
+        config = data.pop("config", {})
+        super().__init__(**config, **data)
+
     @field_validator("id", mode="before")
     @classmethod
     def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
@@ -75,6 +83,14 @@ class Task(BaseModel):
                 "may_not_set_field", "This field is not to be set by the user.", {}
             )
 
+    @model_validator(mode="after")
+    def set_attributes_based_on_config(self) -> "Task":
+        """Set attributes based on the agent configuration."""
+        if self.config:
+            for key, value in self.config.items():
+                setattr(self, key, value)
+        return self
+
     @model_validator(mode="after")
     def check_tools(self):
         """Check if the tools are set."""
@@ -228,3 +244,6 @@ class Task(BaseModel):
         with open(self.output_file, "w") as file:
             file.write(result)
         return None
+
+    def __repr__(self):
+        return f"Task(description={self.description}, expected_output={self.expected_output})"

src/crewai/translations/en.json

@@ -25,7 +25,7 @@
     "tool_usage_error": "I encountered an error: {error}",
     "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.",
     "wrong_tool_name": "You tried to use the tool {tool}, but it doesn't exist. You must use one of the following tools, use one at time: {tools}.",
-    "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts there inputs: {tool_inputs}"
+    "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts these inputs: {tool_inputs}"
   },
   "tools": {
     "delegate_work": "Delegate a specific task to one of the following co-workers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to exectue the task, they know nothing about the task, so share absolute everything you know, don't reference things but instead explain them.",

src/crewai/utilities/converter.py

@@ -78,8 +78,8 @@ class Converter(BaseModel):
         )
 
         parser = CrewPydanticOutputParser(pydantic_object=self.model)
-        new_prompt = HumanMessage(content=self.text) + SystemMessage(
-            content=self.instructions
+        new_prompt = SystemMessage(content=self.instructions) + HumanMessage(
+            content=self.text
         )
         return new_prompt | self.llm | parser
 

src/crewai/utilities/logger.py

@@ -1,11 +1,16 @@
+from crewai.utilities.printer import Printer
+
+
 class Logger:
+    _printer = Printer()
+
     def __init__(self, verbose_level=0):
         verbose_level = (
             2 if isinstance(verbose_level, bool) and verbose_level else verbose_level
         )
         self.verbose_level = verbose_level
 
-    def log(self, level, message):
+    def log(self, level, message, color="bold_green"):
         level_map = {"debug": 1, "info": 2}
         if self.verbose_level and level_map.get(level, 0) <= self.verbose_level:
-            print(f"[{level.upper()}]: {message}")
+            self._printer.print(f"[{level.upper()}]: {message}", color=color)

src/crewai/utilities/printer.py

@@ -4,9 +4,19 @@ class Printer:
             self._print_yellow(content)
         elif color == "red":
             self._print_red(content)
+        elif color == "bold_green":
+            self._print_bold_green(content)
+        elif color == "bold_yellow":
+            self._print_bold_yellow(content)
         else:
             print(content)
 
+    def _print_bold_yellow(self, content):
+        print("\033[1m\033[93m {}\033[00m".format(content))
+
+    def _print_bold_green(self, content):
+        print("\033[1m\033[92m {}\033[00m".format(content))
+
     def _print_yellow(self, content):
         print("\033[93m {}\033[00m".format(content))
 

tests/agent_test.py

@@ -663,3 +663,20 @@ def test_agent_llm_uses_token_calc_handler_with_llm_has_model_name():
     assert (
         agent1.llm.callbacks[0].token_cost_process.__class__.__name__ == "TokenProcess"
     )
+
+
+def test_agent_definition_based_on_dict():
+    config = {
+        "role": "test role",
+        "goal": "test goal",
+        "backstory": "test backstory",
+        "verbose": True,
+    }
+
+    agent = Agent(config=config)
+
+    assert agent.role == "test role"
+    assert agent.goal == "test goal"
+    assert agent.backstory == "test backstory"
+    assert agent.verbose == True
+    assert agent.tools == []

tests/crew_test.py

@@ -222,12 +222,12 @@ def test_crew_verbose_output(capsys):
     crew.kickoff()
     captured = capsys.readouterr()
     expected_strings = [
-        "[DEBUG]: Working Agent: Researcher",
-        "[INFO]: Starting Task: Research AI advancements.",
-        "[DEBUG]: [Researcher] Task output:",
-        "[DEBUG]: Working Agent: Senior Writer",
-        "[INFO]: Starting Task: Write about AI in healthcare.",
-        "[DEBUG]: [Senior Writer] Task output:",
+        "[DEBUG]: == Working Agent: Researcher",
+        "[INFO]: == Starting Task: Research AI advancements.",
+        "[DEBUG]: == [Researcher] Task output:",
+        "[DEBUG]: == Working Agent: Senior Writer",
+        "[INFO]: == Starting Task: Write about AI in healthcare.",
+        "[DEBUG]: == [Senior Writer] Task output:",
     ]
 
     for expected_string in expected_strings:

tests/task_test.py

@@ -449,3 +449,16 @@ def test_increment_tool_errors():
         increment_tools_errors.return_value = None
         crew.kickoff()
         increment_tools_errors.assert_called_once
+
+
+def test_task_definition_based_on_dict():
+    config = {
+        "description": "Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work', check examples to based your evaluation.",
+        "expected_output": "The score of the title.",
+    }
+
+    task = Task(config=config)
+
+    assert task.description == config["description"]
+    assert task.expected_output == config["expected_output"]
+    assert task.agent is None