Clean up docs

README.md

@@ -351,7 +351,7 @@ pre-commit install
 ### Running Tests
 
 ```bash
-uv run pytest .
+uvx pytest
 ```
 
 ### Running static type checks

docs/concepts/agents.mdx

@@ -31,17 +31,16 @@ Think of an agent as a member of a team, with specific skills and a particular j
 | **Max RPM** *(optional)*   | `max_rpm`  | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
 | **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
 | **Verbose** *(optional)*   | `verbose`  | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
-| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`.                                                                                                      |
+| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`.
 | **Step Callback** *(optional)* | `step_callback`  | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                               |
 | **Cache** *(optional)*     | `cache`  | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
 | **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Prompt Template** *(optional)*     | `prompt_template`  | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Allow Code Execution** *(optional)*     | `allow_code_execution`  | Enable code execution for the agent. Default is `False`.                                                                                                                                                                  |
-| **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.                                                                                                                                                                  |
+| **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.
 | **Use System Prompt** *(optional)*     | `use_system_prompt`  | Adds the ability to not use system prompt (to support o1 models). Default is `True`.                                                                                                                                                                  |
 | **Respect Context Window** *(optional)*     | `respect_context_window`  | Summary strategy to avoid overflowing the context window. Default is `True`.                                                                                                                                                                  |
-| **Code Execution Mode** *(optional)* | `code_execution_mode` | Determines the mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution on the host machine). Default is `safe`.                                                                                                          |
 
 ## Creating an agent
 
@@ -84,7 +83,6 @@ agent = Agent(
   max_retry_limit=2,  # Optional
   use_system_prompt=True,  # Optional
   respect_context_window=True,  # Optional
-  code_execution_mode='safe',  # Optional, defaults to 'safe'
 )
 ```
 
@@ -158,4 +156,4 @@ crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
 ## Conclusion
 
 Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, 
-you can create sophisticated AI systems that leverage the power of collaborative intelligence. The `code_execution_mode` attribute provides flexibility in how agents execute code, allowing for both secure and direct execution options.
+you can create sophisticated AI systems that leverage the power of collaborative intelligence.

docs/concepts/cli.mdx

@@ -6,7 +6,7 @@ icon: terminal
 
 # CrewAI CLI Documentation
 
-The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
+The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews and pipelines.
 
 ## Installation
 
@@ -146,34 +146,3 @@ crewai run
 Make sure to run these commands from the directory where your CrewAI project is set up. 
 Some commands may require additional configuration or setup within your project structure.
 </Note>
-
-
-### 9. API Keys
-
-When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
-
-Once you've selected an LLM provider, you will be prompted for API keys.
-
-#### Initial API key providers
-
-The CLI will initially prompt for API keys for the following services:
-
-* OpenAI
-* Groq
-* Anthropic
-* Google Gemini
-
-When you select a provider, the CLI will prompt you to enter your API key.
-
-#### Other Options
-
-If you select option 6, you will be able to select from a list of LiteLLM supported providers.
-
-When you select a provider, the CLI will prompt you to enter the Key name and the API key.
-
-See the following link for each provider's key name:
-
-* [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
-
-
-

docs/concepts/llms.mdx

@@ -62,8 +62,6 @@ os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
 2. Using LLM class attributes:
 
 ```python Code
-from crewai import LLM
-
 llm = LLM(
     model="custom-model-name",
     api_key="your-api-key",
@@ -97,11 +95,9 @@ When configuring an LLM for your agent, you have access to a wide range of param
 | **api_key**          | `str`             | Your API key for authentication.                                                                 |
 
 
-## OpenAI Example Configuration
+Example:
 
 ```python Code
-from crewai import LLM
-
 llm = LLM(
     model="gpt-4",
     temperature=0.8,
@@ -116,31 +112,15 @@ llm = LLM(
 )
 agent = Agent(llm=llm, ...)
 ```
-
-## Cerebras Example Configuration
-
-```python Code
-from crewai import LLM
-
-llm = LLM(
-    model="cerebras/llama-3.1-70b",
-    base_url="https://api.cerebras.ai/v1",
-    api_key="your-api-key-here"
-)
-agent = Agent(llm=llm, ...) 
-```
-
 ## Using Ollama (Local LLMs)
 
-CrewAI supports using Ollama for running open-source models locally:
+crewAI supports using Ollama for running open-source models locally:
 
 1. Install Ollama: [ollama.ai](https://ollama.ai/)
 2. Run a model: `ollama run llama2`
 3. Configure agent:
 
 ```python Code
-from crewai import LLM
-
 agent = Agent(
     llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"),
     ...
@@ -152,8 +132,6 @@ agent = Agent(
 You can change the base API URL for any LLM provider by setting the `base_url` parameter:
 
 ```python Code
-from crewai import LLM
-
 llm = LLM(
     model="custom-model-name",
     base_url="https://api.your-provider.com/v1",

docs/concepts/memory.mdx

@@ -105,48 +105,9 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "model": 'text-embedding-3-small'
-        }
-    }
-)
-```
-Alternatively, you can directly pass the OpenAIEmbeddingFunction to the embedder parameter.
-
-Example:
-```python Code
-from crewai import Crew, Agent, Task, Process
-from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"),
-)
-```
-
-### Using Ollama embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "ollama",
-        "config": {
-            "model": "mxbai-embed-large"
-        }
-    }
+    embedder=embedding_functions.OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
 )
 ```
 
@@ -161,20 +122,16 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "google",
-        "config": {
-            "api_key": "<YOUR_API_KEY>",
-            "model_name": "<model_name>"
-        }
-    }
+    embedder=embedding_functions.OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model_name="text-embedding-ada-002"
+    )
 )
 ```
 
 ### Using Azure OpenAI embeddings
 
 ```python Code
-from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
@@ -183,7 +140,7 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder=OpenAIEmbeddingFunction(
+    embedder=embedding_functions.OpenAIEmbeddingFunction(
         api_key="YOUR_API_KEY",
         api_base="YOUR_API_BASE_PATH",
         api_type="azure",
@@ -196,7 +153,6 @@ my_crew = Crew(
 ### Using Vertex AI embeddings
 
 ```python Code
-from chromadb.utils.embedding_functions import GoogleVertexEmbeddingFunction
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
@@ -205,7 +161,7 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder=GoogleVertexEmbeddingFunction(
+    embedder=embedding_functions.GoogleVertexEmbeddingFunction(
         project_id="YOUR_PROJECT_ID",
         region="YOUR_REGION",
         api_key="YOUR_API_KEY",
@@ -225,32 +181,10 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "cohere",
-        "config": {
-            "api_key": "YOUR_API_KEY",
-            "model_name": "<model_name>"
-        }
-    }
-)
-```
-### Using HuggingFace embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "huggingface",
-        "config": {
-            "api_url": "<api_url>",
-        }
-    }
+    embedder=embedding_functions.CohereEmbeddingFunction(
+        api_key=YOUR_API_KEY,
+        model_name="<model_name>"
+    )
 )
 ```
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.76.2"
+version = "0.74.2"
 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."
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"

src/crewai/__init__.py

@@ -14,5 +14,5 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.76.2"
+__version__ = "0.74.2"
 __all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]

src/crewai/agent.py

@@ -1,8 +1,6 @@
 import os
-import shutil
-import subprocess
 from inspect import signature
-from typing import Any, List, Literal, Optional, Union
+from typing import Any, List, Optional, Union
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
@@ -114,10 +112,6 @@ class Agent(BaseAgent):
         default=2,
         description="Maximum number of retries for an agent to execute a task when an error occurs.",
     )
-    code_execution_mode: Literal["safe", "unsafe"] = Field(
-        default="safe",
-        description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
-    )
 
     @model_validator(mode="after")
     def post_init_setup(self):
@@ -179,9 +173,6 @@ class Agent(BaseAgent):
         if not self.agent_executor:
             self._setup_agent_executor()
 
-        if self.allow_code_execution:
-            self._validate_docker_installation()
-
         return self
 
     def _setup_agent_executor(self):
@@ -317,9 +308,7 @@ class Agent(BaseAgent):
         try:
             from crewai_tools import CodeInterpreterTool
 
-            # Set the unsafe_mode based on the code_execution_mode attribute
-            unsafe_mode = self.code_execution_mode == "unsafe"
-            return [CodeInterpreterTool(unsafe_mode=unsafe_mode)]
+            return [CodeInterpreterTool()]
         except ModuleNotFoundError:
             self._logger.log(
                 "info", "Coding tools not available. Install crewai_tools. "
@@ -419,25 +408,6 @@ class Agent(BaseAgent):
 
         return "\n".join(tool_strings)
 
-    def _validate_docker_installation(self) -> None:
-        """Check if Docker is installed and running."""
-        if not shutil.which("docker"):
-            raise RuntimeError(
-                f"Docker is not installed. Please install Docker to use code execution with agent: {self.role}"
-            )
-
-        try:
-            subprocess.run(
-                ["docker", "info"],
-                check=True,
-                stdout=subprocess.PIPE,
-                stderr=subprocess.PIPE,
-            )
-        except subprocess.CalledProcessError:
-            raise RuntimeError(
-                f"Docker is not running. Please start Docker to use code execution with agent: {self.role}"
-            )
-
     @staticmethod
     def __tools_names(tools) -> str:
         return ", ".join([t.name for t in tools])

src/crewai/cli/cli.py

@@ -32,12 +32,10 @@ def crewai():
 @crewai.command()
 @click.argument("type", type=click.Choice(["crew", "pipeline", "flow"]))
 @click.argument("name")
-@click.option("--provider", type=str, help="The provider to use for the crew")
-@click.option("--skip_provider", is_flag=True, help="Skip provider validation")
-def create(type, name, provider, skip_provider=False):
+def create(type, name):
     """Create a new crew, pipeline, or flow."""
     if type == "crew":
-        create_crew(name, provider, skip_provider)
+        create_crew(name)
     elif type == "pipeline":
         create_pipeline(name)
     elif type == "flow":

src/crewai/cli/create_crew.py

@@ -1,16 +1,8 @@
-import sys
 from pathlib import Path
-
 import click
-
-from crewai.cli.constants import ENV_VARS
-from crewai.cli.provider import (
-    PROVIDERS,
-    get_provider_data,
-    select_model,
-    select_provider,
-)
 from crewai.cli.utils import copy_template, load_env_vars, write_env_file
+from crewai.cli.provider import get_provider_data, select_provider, PROVIDERS
+from crewai.cli.constants import ENV_VARS
 
 
 def create_folder_structure(name, parent_folder=None):
@@ -22,19 +14,11 @@ def create_folder_structure(name, parent_folder=None):
     else:
         folder_path = Path(folder_name)
 
-    if folder_path.exists():
-        if not click.confirm(
-            f"Folder {folder_name} already exists. Do you want to override it?"
-        ):
-            click.secho("Operation cancelled.", fg="yellow")
-            sys.exit(0)
-        click.secho(f"Overriding folder {folder_name}...", fg="green", bold=True)
-    else:
-        click.secho(
-            f"Creating {'crew' if parent_folder else 'folder'} {folder_name}...",
-            fg="green",
-            bold=True,
-        )
+    click.secho(
+        f"Creating {'crew' if parent_folder else 'folder'} {folder_name}...",
+        fg="green",
+        bold=True,
+    )
 
     if not folder_path.exists():
         folder_path.mkdir(parents=True)
@@ -43,6 +27,11 @@ def create_folder_structure(name, parent_folder=None):
             (folder_path / "src" / folder_name).mkdir(parents=True)
             (folder_path / "src" / folder_name / "tools").mkdir(parents=True)
             (folder_path / "src" / folder_name / "config").mkdir(parents=True)
+    else:
+        click.secho(
+            f"\tFolder {folder_name} already exists.",
+            fg="yellow",
+        )
 
     return folder_path, folder_name, class_name
 
@@ -81,84 +70,37 @@ def copy_template_files(folder_path, name, class_name, parent_folder):
             copy_template(src_file, dst_file, name, class_name, folder_path.name)
 
 
-def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
+def create_crew(name, parent_folder=None):
     folder_path, folder_name, class_name = create_folder_structure(name, parent_folder)
     env_vars = load_env_vars(folder_path)
-    if not skip_provider:
-        if not provider:
-            provider_models = get_provider_data()
-            if not provider_models:
-                return
 
-        existing_provider = None
-        for provider, env_keys in ENV_VARS.items():
-            if any(key in env_vars for key in env_keys):
-                existing_provider = provider
-                break
+    provider_models = get_provider_data()
+    if not provider_models:
+        return
 
-        if existing_provider:
-            if not click.confirm(
-                f"Found existing environment variable configuration for {existing_provider.capitalize()}. Do you want to override it?"
-            ):
-                click.secho("Keeping existing provider configuration.", fg="yellow")
-                return
+    selected_provider = select_provider(provider_models)
+    if not selected_provider:
+        return
+    provider = selected_provider
 
-        provider_models = get_provider_data()
-        if not provider_models:
-            return
+    # selected_model = select_model(provider, provider_models)
+    # if not selected_model:
+    #     return
+    # model = selected_model
 
-        while True:
-            selected_provider = select_provider(provider_models)
-            if selected_provider is None:  # User typed 'q'
-                click.secho("Exiting...", fg="yellow")
-                sys.exit(0)
-            if selected_provider:  # Valid selection
-                break
-            click.secho(
-                "No provider selected. Please try again or press 'q' to exit.", fg="red"
-            )
-
-        while True:
-            selected_model = select_model(selected_provider, provider_models)
-            if selected_model is None:  # User typed 'q'
-                click.secho("Exiting...", fg="yellow")
-                sys.exit(0)
-            if selected_model:  # Valid selection
-                break
-            click.secho(
-                "No model selected. Please try again or press 'q' to exit.", fg="red"
-            )
-
-        if selected_provider in PROVIDERS:
-            api_key_var = ENV_VARS[selected_provider][0]
-        else:
-            api_key_var = click.prompt(
-                f"Enter the environment variable name for your {selected_provider.capitalize()} API key",
-                type=str,
-                default="",
-            )
-
-        api_key_value = ""
-        click.echo(
-            f"Enter your {selected_provider.capitalize()} API key (press Enter to skip): ",
-            nl=False,
+    if provider in PROVIDERS:
+        api_key_var = ENV_VARS[provider][0]
+    else:
+        api_key_var = click.prompt(
+            f"Enter the environment variable name for your {provider.capitalize()} API key",
+            type=str,
         )
-        try:
-            api_key_value = input()
-        except (KeyboardInterrupt, EOFError):
-            api_key_value = ""
 
-        if api_key_value.strip():
-            env_vars = {api_key_var: api_key_value}
-            write_env_file(folder_path, env_vars)
-            click.secho("API key saved to .env file", fg="green")
-        else:
-            click.secho(
-                "No API key provided. Skipping .env file creation.", fg="yellow"
-            )
+    env_vars = {api_key_var: "YOUR_API_KEY_HERE"}
+    write_env_file(folder_path, env_vars)
 
-        env_vars["MODEL"] = selected_model
-        click.secho(f"Selected model: {selected_model}", fg="green")
+    # env_vars['MODEL'] = model
+    # click.secho(f"Selected model: {model}", fg="green")
 
     package_dir = Path(__file__).parent
     templates_dir = package_dir / "templates" / "crew"

src/crewai/cli/plot_flow.py

@@ -7,7 +7,7 @@ def plot_flow() -> None:
     """
     Plot the flow by running a command in the UV environment.
     """
-    command = ["uv", "run", "plot"]
+    command = ["uv", "run", "plot_flow"]
 
     try:
         result = subprocess.run(command, capture_output=False, text=True, check=True)

src/crewai/cli/provider.py

@@ -1,91 +1,67 @@
 import json
 import time
-from collections import defaultdict
-from pathlib import Path
-
-import click
 import requests
-
-from crewai.cli.constants import JSON_URL, MODELS, PROVIDERS
-
+from collections import defaultdict
+import click
+from pathlib import Path 
+from crewai.cli.constants import PROVIDERS, MODELS, JSON_URL
 
 def select_choice(prompt_message, choices):
     """
     Presents a list of choices to the user and prompts them to select one.
-
+    
     Args:
     - prompt_message (str): The message to display to the user before presenting the choices.
     - choices (list): A list of options to present to the user.
-
+    
     Returns:
-    - str: The selected choice from the list, or None if the user chooses to quit.
+    - str: The selected choice from the list, or None if the operation is aborted or an invalid selection is made.
     """
-
-    provider_models = get_provider_data()
-    if not provider_models:
-        return
     click.secho(prompt_message, fg="cyan")
     for idx, choice in enumerate(choices, start=1):
         click.secho(f"{idx}. {choice}", fg="cyan")
-    click.secho("q. Quit", fg="cyan")
-
-    while True:
-        choice = click.prompt(
-            "Enter the number of your choice or 'q' to quit", type=str
-        )
-
-        if choice.lower() == "q":
-            return None
-
-        try:
-            selected_index = int(choice) - 1
-            if 0 <= selected_index < len(choices):
-                return choices[selected_index]
-        except ValueError:
-            pass
-
-        click.secho(
-            "Invalid selection. Please select a number between 1 and 6 or 'q' to quit.",
-            fg="red",
-        )
-
+    try:
+        selected_index = click.prompt("Enter the number of your choice", type=int) - 1
+    except click.exceptions.Abort:
+        click.secho("Operation aborted by the user.", fg="red")
+        return None
+    if not (0 <= selected_index < len(choices)):
+        click.secho("Invalid selection.", fg="red")
+        return None
+    return choices[selected_index]
 
 def select_provider(provider_models):
     """
     Presents a list of providers to the user and prompts them to select one.
-
+    
     Args:
     - provider_models (dict): A dictionary of provider models.
-
+    
     Returns:
-    - str: The selected provider
-    - None: If user explicitly quits
+    - str: The selected provider, or None if the operation is aborted or an invalid selection is made.
     """
     predefined_providers = [p.lower() for p in PROVIDERS]
     all_providers = sorted(set(predefined_providers + list(provider_models.keys())))
 
-    provider = select_choice(
-        "Select a provider to set up:", predefined_providers + ["other"]
-    )
-    if provider is None:  # User typed 'q'
+    provider = select_choice("Select a provider to set up:", predefined_providers + ['other'])
+    if not provider:
         return None
+    provider = provider.lower()
 
-    if provider == "other":
+    if provider == 'other':
         provider = select_choice("Select a provider from the full list:", all_providers)
-        if provider is None:  # User typed 'q'
+        if not provider:
             return None
-
-    return provider.lower() if provider else False
-
+    return provider
 
 def select_model(provider, provider_models):
     """
     Presents a list of models for a given provider to the user and prompts them to select one.
-
+    
     Args:
     - provider (str): The provider for which to select a model.
     - provider_models (dict): A dictionary of provider models.
-
+    
     Returns:
     - str: The selected model, or None if the operation is aborted or an invalid selection is made.
     """
@@ -100,49 +76,37 @@ def select_model(provider, provider_models):
         click.secho(f"No models available for provider '{provider}'.", fg="red")
         return None
 
-    selected_model = select_choice(
-        f"Select a model to use for {provider.capitalize()}:", available_models
-    )
+    selected_model = select_choice(f"Select a model to use for {provider.capitalize()}:", available_models)
     return selected_model
 
-
 def load_provider_data(cache_file, cache_expiry):
     """
     Loads provider data from a cache file if it exists and is not expired. If the cache is expired or corrupted, it fetches the data from the web.
-
+    
     Args:
     - cache_file (Path): The path to the cache file.
     - cache_expiry (int): The cache expiry time in seconds.
-
+    
     Returns:
     - dict or None: The loaded provider data or None if the operation fails.
     """
     current_time = time.time()
-    if (
-        cache_file.exists()
-        and (current_time - cache_file.stat().st_mtime) < cache_expiry
-    ):
+    if cache_file.exists() and (current_time - cache_file.stat().st_mtime) < cache_expiry:
         data = read_cache_file(cache_file)
         if data:
             return data
-        click.secho(
-            "Cache is corrupted. Fetching provider data from the web...", fg="yellow"
-        )
+        click.secho("Cache is corrupted. Fetching provider data from the web...", fg="yellow")
     else:
-        click.secho(
-            "Cache expired or not found. Fetching provider data from the web...",
-            fg="cyan",
-        )
+        click.secho("Cache expired or not found. Fetching provider data from the web...", fg="cyan")
     return fetch_provider_data(cache_file)
 
-
 def read_cache_file(cache_file):
     """
     Reads and returns the JSON content from a cache file. Returns None if the file contains invalid JSON.
-
+    
     Args:
     - cache_file (Path): The path to the cache file.
-
+    
     Returns:
     - dict or None: The JSON content of the cache file or None if the JSON is invalid.
     """
@@ -152,14 +116,13 @@ def read_cache_file(cache_file):
     except json.JSONDecodeError:
         return None
 
-
 def fetch_provider_data(cache_file):
     """
     Fetches provider data from a specified URL and caches it to a file.
-
+    
     Args:
     - cache_file (Path): The path to the cache file.
-
+    
     Returns:
     - dict or None: The fetched provider data or None if the operation fails.
     """
@@ -176,42 +139,38 @@ def fetch_provider_data(cache_file):
         click.secho("Error parsing provider data. Invalid JSON format.", fg="red")
     return None
 
-
 def download_data(response):
     """
     Downloads data from a given HTTP response and returns the JSON content.
-
+    
     Args:
     - response (requests.Response): The HTTP response object.
-
+    
     Returns:
     - dict: The JSON content of the response.
     """
-    total_size = int(response.headers.get("content-length", 0))
+    total_size = int(response.headers.get('content-length', 0))
     block_size = 8192
     data_chunks = []
-    with click.progressbar(
-        length=total_size, label="Downloading", show_pos=True
-    ) as progress_bar:
+    with click.progressbar(length=total_size, label='Downloading', show_pos=True) as progress_bar:
         for chunk in response.iter_content(block_size):
             if chunk:
                 data_chunks.append(chunk)
                 progress_bar.update(len(chunk))
-    data_content = b"".join(data_chunks)
-    return json.loads(data_content.decode("utf-8"))
-
+    data_content = b''.join(data_chunks)
+    return json.loads(data_content.decode('utf-8'))
 
 def get_provider_data():
     """
     Retrieves provider data from a cache file, filters out models based on provider criteria, and returns a dictionary of providers mapped to their models.
-
+    
     Returns:
     - dict or None: A dictionary of providers mapped to their models or None if the operation fails.
     """
-    cache_dir = Path.home() / ".crewai"
+    cache_dir = Path.home() / '.crewai'
     cache_dir.mkdir(exist_ok=True)
-    cache_file = cache_dir / "provider_cache.json"
-    cache_expiry = 24 * 3600
+    cache_file = cache_dir / 'provider_cache.json'
+    cache_expiry = 24 * 3600 
 
     data = load_provider_data(cache_file, cache_expiry)
     if not data:
@@ -220,8 +179,8 @@ def get_provider_data():
     provider_models = defaultdict(list)
     for model_name, properties in data.items():
         provider = properties.get("litellm_provider", "").strip().lower()
-        if "http" in provider or provider == "other":
+        if 'http' in provider or provider == 'other':
             continue
         if provider:
             provider_models[provider].append(model_name)
-    return provider_models
+    return provider_models

src/crewai/cli/templates/crew/main.py

@@ -3,7 +3,7 @@ import sys
 from {{folder_name}}.crew import {{crew_name}}Crew
 
 # This main file is intended to be a way for you to run your
-# crew locally, so refrain from adding unnecessary logic into this file.
+# crew locally, so refrain from adding necessary logic into this file.
 # Replace with inputs you want to test with, it will automatically
 # interpolate any tasks and agents information
 

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.76.2,<1.0.0"
+    "crewai[tools]>=0.74.2,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.76.2,<1.0.0",
+    "crewai[tools]>=0.74.2,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/pipeline/pyproject.toml

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.76.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.74.2,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

src/crewai/cli/templates/pipeline_router/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = ["Your Name <you@example.com>"]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.76.2,<1.0.0"
+    "crewai[tools]>=0.74.2,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,6 +5,6 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.76.2"
+    "crewai[tools]>=0.74.2"
 ]
 

src/crewai/crew.py

@@ -435,16 +435,15 @@ class Crew(BaseModel):
         self, n_iterations: int, filename: str, inputs: Optional[Dict[str, Any]] = {}
     ) -> None:
         """Trains the crew for a given number of iterations."""
-        train_crew = self.copy()
-        train_crew._setup_for_training(filename)
+        self._setup_for_training(filename)
 
         for n_iteration in range(n_iterations):
-            train_crew._train_iteration = n_iteration
-            train_crew.kickoff(inputs=inputs)
+            self._train_iteration = n_iteration
+            self.kickoff(inputs=inputs)
 
         training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
 
-        for agent in train_crew.agents:
+        for agent in self.agents:
             result = TaskEvaluator(agent).evaluate_training_data(
                 training_data=training_data, agent_id=str(agent.id)
             )
@@ -988,19 +987,17 @@ class Crew(BaseModel):
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
-        test_crew = self.copy()
-
-        self._test_execution_span = test_crew._telemetry.test_execution_span(
-            test_crew,
+        self._test_execution_span = self._telemetry.test_execution_span(
+            self,
             n_iterations,
             inputs,
             openai_model_name,  # type: ignore[arg-type]
         )  # type: ignore[arg-type]
-        evaluator = CrewEvaluator(test_crew, openai_model_name)  # type: ignore[arg-type]
+        evaluator = CrewEvaluator(self, openai_model_name)  # type: ignore[arg-type]
 
         for i in range(1, n_iterations + 1):
             evaluator.set_iteration(i)
-            test_crew.kickoff(inputs=inputs)
+            self.kickoff(inputs=inputs)
 
         evaluator.print_crew_evaluation_result()
 

src/crewai/memory/entity/entity_memory.py

@@ -16,7 +16,7 @@ class EntityMemory(Memory):
             if storage
             else RAGStorage(
                 type="entities",
-                allow_reset=True,
+                allow_reset=False,
                 embedder_config=embedder_config,
                 crew=crew,
             )

src/crewai/memory/storage/rag_storage.py

@@ -8,9 +8,6 @@ from typing import Any, Dict, List, Optional
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities.paths import db_storage_path
 from chromadb.api import ClientAPI
-from chromadb.api.types import validate_embedding_function
-from chromadb import Documents, EmbeddingFunction, Embeddings
-from typing import cast
 
 
 @contextlib.contextmanager
@@ -44,93 +41,16 @@ class RAGStorage(BaseRAGStorage):
         self.agents = agents
 
         self.type = type
-
+        self.embedder_config = embedder_config or self._create_embedding_function()
         self.allow_reset = allow_reset
         self._initialize_app()
 
-    def _set_embedder_config(self):
-        import chromadb.utils.embedding_functions as embedding_functions
-
-        if self.embedder_config is None:
-            self.embedder_config = self._create_default_embedding_function()
-
-        if isinstance(self.embedder_config, dict):
-            provider = self.embedder_config.get("provider")
-            config = self.embedder_config.get("config", {})
-            model_name = config.get("model")
-            if provider == "openai":
-                self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
-                    api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
-                    model_name=model_name,
-                )
-            elif provider == "azure":
-                self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
-                    api_key=config.get("api_key"),
-                    api_base=config.get("api_base"),
-                    api_type=config.get("api_type", "azure"),
-                    api_version=config.get("api_version"),
-                    model_name=model_name,
-                )
-            elif provider == "ollama":
-                from openai import OpenAI
-
-                class OllamaEmbeddingFunction(EmbeddingFunction):
-                    def __call__(self, input: Documents) -> Embeddings:
-                        client = OpenAI(
-                            base_url="http://localhost:11434/v1",
-                            api_key=config.get("api_key", "ollama"),
-                        )
-                        try:
-                            response = client.embeddings.create(
-                                input=input, model=model_name
-                            )
-                            embeddings = [item.embedding for item in response.data]
-                            return cast(Embeddings, embeddings)
-                        except Exception as e:
-                            raise e
-
-                self.embedder_config = OllamaEmbeddingFunction()
-            elif provider == "vertexai":
-                self.embedder_config = (
-                    embedding_functions.GoogleVertexEmbeddingFunction(
-                        model_name=model_name,
-                        api_key=config.get("api_key"),
-                    )
-                )
-            elif provider == "google":
-                self.embedder_config = (
-                    embedding_functions.GoogleGenerativeAiEmbeddingFunction(
-                        model_name=model_name,
-                        api_key=config.get("api_key"),
-                    )
-                )
-            elif provider == "cohere":
-                self.embedder_config = embedding_functions.CohereEmbeddingFunction(
-                    model_name=model_name,
-                    api_key=config.get("api_key"),
-                )
-            elif provider == "huggingface":
-                self.embedder_config = embedding_functions.HuggingFaceEmbeddingServer(
-                    url=config.get("api_url"),
-                )
-            else:
-                raise Exception(
-                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface]"
-                )
-        else:
-            validate_embedding_function(self.embedder_config)  # type: ignore # used for validating embedder_config if defined a embedding function/class
-            self.embedder_config = self.embedder_config
-
     def _initialize_app(self):
         import chromadb
-        from chromadb.config import Settings
 
-        self._set_embedder_config()
         chroma_client = chromadb.PersistentClient(
-            path=f"{db_storage_path()}/{self.type}/{self.agents}",
-            settings=Settings(allow_reset=self.allow_reset),
+            path=f"{db_storage_path()}/{self.type}/{self.agents}"
         )
-
         self.app = chroma_client
 
         try:
@@ -202,15 +122,11 @@ class RAGStorage(BaseRAGStorage):
             if self.app:
                 self.app.reset()
         except Exception as e:
-            if "attempt to write a readonly database" in str(e):
-                # Ignore this specific error
-                pass
-            else:
-                raise Exception(
-                    f"An error occurred while resetting the {self.type} memory: {e}"
-                )
+            raise Exception(
+                f"An error occurred while resetting the {self.type} memory: {e}"
+            )
 
-    def _create_default_embedding_function(self):
+    def _create_embedding_function(self):
         import chromadb.utils.embedding_functions as embedding_functions
 
         return embedding_functions.OpenAIEmbeddingFunction(

tests/crew_test.py

@@ -9,7 +9,6 @@ from unittest.mock import MagicMock, patch
 import instructor
 import pydantic_core
 import pytest
-
 from crewai.agent import Agent
 from crewai.agents.cache import CacheHandler
 from crewai.crew import Crew
@@ -498,7 +497,6 @@ def test_cache_hitting_between_agents():
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_api_calls_throttling(capsys):
     from unittest.mock import patch
-
     from crewai_tools import tool
 
     @tool
@@ -781,14 +779,11 @@ def test_async_task_execution_call_count():
     list_important_history.output = mock_task_output
     write_article.output = mock_task_output
 
-    with (
-        patch.object(
-            Task, "execute_sync", return_value=mock_task_output
-        ) as mock_execute_sync,
-        patch.object(
-            Task, "execute_async", return_value=mock_future
-        ) as mock_execute_async,
-    ):
+    with patch.object(
+        Task, "execute_sync", return_value=mock_task_output
+    ) as mock_execute_sync, patch.object(
+        Task, "execute_async", return_value=mock_future
+    ) as mock_execute_async:
         crew.kickoff()
 
         assert mock_execute_async.call_count == 2
@@ -1110,7 +1105,6 @@ def test_dont_set_agents_step_callback_if_already_set():
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_function_calling_llm():
     from unittest.mock import patch
-
     from crewai_tools import tool
 
     llm = "gpt-4o"
@@ -1454,6 +1448,52 @@ def test_crew_does_not_interpolate_without_inputs():
             interpolate_task_inputs.assert_not_called()
 
 
+# def test_crew_partial_inputs():
+#     agent = Agent(
+#         role="{topic} Researcher",
+#         goal="Express hot takes on {topic}.",
+#         backstory="You have a lot of experience with {topic}.",
+#     )
+
+#     task = Task(
+#         description="Give me an analysis around {topic}.",
+#         expected_output="{points} bullet points about {topic}.",
+#     )
+
+#     crew = Crew(agents=[agent], tasks=[task], inputs={"topic": "AI"})
+#     inputs = {"topic": "AI"}
+#     crew._interpolate_inputs(inputs=inputs)  # Manual call for now
+
+#     assert crew.tasks[0].description == "Give me an analysis around AI."
+#     assert crew.tasks[0].expected_output == "{points} bullet points about AI."
+#     assert crew.agents[0].role == "AI Researcher"
+#     assert crew.agents[0].goal == "Express hot takes on AI."
+#     assert crew.agents[0].backstory == "You have a lot of experience with AI."
+
+
+# def test_crew_invalid_inputs():
+#     agent = Agent(
+#         role="{topic} Researcher",
+#         goal="Express hot takes on {topic}.",
+#         backstory="You have a lot of experience with {topic}.",
+#     )
+
+#     task = Task(
+#         description="Give me an analysis around {topic}.",
+#         expected_output="{points} bullet points about {topic}.",
+#     )
+
+#     crew = Crew(agents=[agent], tasks=[task], inputs={"subject": "AI"})
+#     inputs = {"subject": "AI"}
+#     crew._interpolate_inputs(inputs=inputs)  # Manual call for now
+
+#     assert crew.tasks[0].description == "Give me an analysis around {topic}."
+#     assert crew.tasks[0].expected_output == "{points} bullet points about {topic}."
+#     assert crew.agents[0].role == "{topic} Researcher"
+#     assert crew.agents[0].goal == "Express hot takes on {topic}."
+#     assert crew.agents[0].backstory == "You have a lot of experience with {topic}."
+
+
 def test_task_callback_on_crew():
     from unittest.mock import MagicMock, patch
 
@@ -1730,10 +1770,7 @@ def test_manager_agent_with_tools_raises_exception():
 @patch("crewai.crew.Crew.kickoff")
 @patch("crewai.crew.CrewTrainingHandler")
 @patch("crewai.crew.TaskEvaluator")
-@patch("crewai.crew.Crew.copy")
-def test_crew_train_success(
-    copy_mock, task_evaluator, crew_training_handler, kickoff_mock
-):
+def test_crew_train_success(task_evaluator, crew_training_handler, kickoff):
     task = Task(
         description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
         expected_output="5 bullet points with a paragraph for each idea.",
@@ -1744,19 +1781,9 @@ def test_crew_train_success(
         agents=[researcher, writer],
         tasks=[task],
     )
-
-    # Create a mock for the copied crew
-    copy_mock.return_value = crew
-
     crew.train(
         n_iterations=2, inputs={"topic": "AI"}, filename="trained_agents_data.pkl"
     )
-
-    # Ensure kickoff is called on the copied crew
-    kickoff_mock.assert_has_calls(
-        [mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})]
-    )
-
     task_evaluator.assert_has_calls(
         [
             mock.call(researcher),
@@ -1795,6 +1822,10 @@ def test_crew_train_success(
         ]
     )
 
+    kickoff.assert_has_calls(
+        [mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})]
+    )
+
 
 def test_crew_train_error():
     task = Task(
@@ -1809,7 +1840,7 @@ def test_crew_train_error():
     )
 
     with pytest.raises(TypeError) as e:
-        crew.train()  # type: ignore purposefully throwing err
+        crew.train()
         assert "train() missing 1 required positional argument: 'n_iterations'" in str(
             e
         )
@@ -2505,9 +2536,8 @@ def test_conditional_should_execute():
 
 
 @mock.patch("crewai.crew.CrewEvaluator")
-@mock.patch("crewai.crew.Crew.copy")
 @mock.patch("crewai.crew.Crew.kickoff")
-def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
+def test_crew_testing_function(mock_kickoff, crew_evaluator):
     task = Task(
         description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
         expected_output="5 bullet points with a paragraph for each idea.",
@@ -2518,15 +2548,11 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
         agents=[researcher],
         tasks=[task],
     )
-
-    # Create a mock for the copied crew
-    copy_mock.return_value = crew
-
     n_iterations = 2
     crew.test(n_iterations, openai_model_name="gpt-4o-mini", inputs={"topic": "AI"})
 
-    # Ensure kickoff is called on the copied crew
-    kickoff_mock.assert_has_calls(
+    assert len(mock_kickoff.mock_calls) == n_iterations
+    mock_kickoff.assert_has_calls(
         [mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})]
     )
 

tests/tools/test_tool_usage.py

@@ -1,12 +1,12 @@
 import json
 import random
-from unittest.mock import MagicMock
+from unittest.mock import MagicMock, patch
 
 import pytest
 from crewai_tools import BaseTool
 from pydantic import BaseModel, Field
 
-from crewai import Agent, Task
+from crewai import Agent, Crew, Task
 from crewai.tools.tool_usage import ToolUsage
 
 
@@ -44,12 +44,36 @@ example_task = Task(
 )
 
 
+def test_random_number_tool_usage():
+    crew = Crew(
+        agents=[example_agent],
+        tasks=[example_task],
+    )
+
+    with patch.object(random, "randint", return_value=42):
+        result = crew.kickoff()
+
+    assert "42" in result.raw
+
+
 def test_random_number_tool_range():
     tool = RandomNumberTool()
     result = tool._run(1, 10)
     assert 1 <= result <= 10
 
 
+def test_random_number_tool_with_crew():
+    crew = Crew(
+        agents=[example_agent],
+        tasks=[example_task],
+    )
+
+    result = crew.kickoff()
+
+    # Check if the result contains a number between 1 and 100
+    assert any(str(num) in result.raw for num in range(1, 101))
+
+
 def test_random_number_tool_invalid_range():
     tool = RandomNumberTool()
     with pytest.raises(ValueError):