Fix watson keys

docs/concepts/llms.mdx

@@ -25,55 +25,52 @@ By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables i
 - `OPENAI_API_BASE`
 - `OPENAI_API_KEY`
 
-### 2. Custom LLM Objects
+### 2. String Identifier
+
+```python Code
+agent = Agent(llm="gpt-4o", ...)
+```
+
+### 3. LLM Instance
+
+List of [more providers](https://docs.litellm.ai/docs/providers).
+
+```python Code
+from crewai import LLM
+
+llm = LLM(model="gpt-4", temperature=0.7)
+agent = Agent(llm=llm, ...)
+```
+
+### 4. Custom LLM Objects
 
 Pass a custom LLM implementation or object from another library.
 
-See below for examples.
-
-<Tabs>
-    <Tab title="String Identifier">
-    ```python Code
-    agent = Agent(llm="gpt-4o", ...)
-    ```
-    </Tab>
-
-    <Tab title="LLM Instance">
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(model="gpt-4", temperature=0.7)
-    agent = Agent(llm=llm, ...)
-    ```
-    </Tab>
-</Tabs>
-
 ## Connecting to OpenAI-Compatible LLMs
 
 You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
 
-<Tabs>
-    <Tab title="Using Environment Variables">
-    ```python Code
-    import os
+1. Using environment variables:
 
-    os.environ["OPENAI_API_KEY"] = "your-api-key"
-    os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
-    ```
-    </Tab>
-    <Tab title="Using LLM Class Attributes">
-    ```python Code
-    from crewai import LLM
+```python Code
+import os
 
-    llm = LLM(
-        model="custom-model-name",
-        api_key="your-api-key",
-        base_url="https://api.your-provider.com/v1"
-    )
-    agent = Agent(llm=llm, ...)
-    ```
-    </Tab>
-</Tabs>
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+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",
+    base_url="https://api.your-provider.com/v1"
+)
+agent = Agent(llm=llm, ...)
+```
 
 ## LLM Configuration Options
 
@@ -100,149 +97,55 @@ When configuring an LLM for your agent, you have access to a wide range of param
 | **api_key**          | `str`             | Your API key for authentication.                                                                 |
 
 
-These are examples of how to configure LLMs for your agent.
+## OpenAI Example Configuration
 
-<AccordionGroup>    
-    <Accordion title="OpenAI">
+```python Code
+from crewai import LLM
 
-    ```python Code
-    from crewai import LLM
+llm = LLM(
+    model="gpt-4",
+    temperature=0.8,
+    max_tokens=150,
+    top_p=0.9,
+    frequency_penalty=0.1,
+    presence_penalty=0.1,
+    stop=["END"],
+    seed=42,
+    base_url="https://api.openai.com/v1",
+    api_key="your-api-key-here"
+)
+agent = Agent(llm=llm, ...)
+```
 
-    llm = LLM(
-        model="gpt-4",
-        temperature=0.8,
-        max_tokens=150,
-        top_p=0.9,
-        frequency_penalty=0.1,
-        presence_penalty=0.1,
-        stop=["END"],
-        seed=42,
-        base_url="https://api.openai.com/v1",
-        api_key="your-api-key-here"
-    )
-    agent = Agent(llm=llm, ...)
-    ```
-    </Accordion>
+## Cerebras Example Configuration
 
-    <Accordion title="Cerebras">
+```python Code
+from crewai import LLM
 
-    ```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, ...) 
+```
 
-    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, ...) 
-    ```
-    </Accordion>
- 
-    <Accordion title="Ollama (Local LLMs)">
+## 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:
+1. Install Ollama: [ollama.ai](https://ollama.ai/)
+2. Run a model: `ollama run llama2`
+3. Configure agent:
 
-    ```python Code
-    from crewai import LLM
+```python Code
+from crewai import LLM
 
-    agent = Agent(
-        llm=LLM(
-            model="ollama/llama3.1", 
-            base_url="http://localhost:11434"
-        ),
-        ...
-    )
-    ```
-    </Accordion>
-
-    <Accordion title="Groq">
-
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="groq/llama3-8b-8192", 
-        base_url="https://api.groq.com/openai/v1", 
-        api_key="your-api-key-here"
-    )
-    agent = Agent(llm=llm, ...)
-    ```
-    </Accordion>
-
-    <Accordion title="Anthropic">
-
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="anthropic/claude-3-5-sonnet-20241022",
-        base_url="https://api.anthropic.com/v1",
-        api_key="your-api-key-here"
-    )
-    agent = Agent(llm=llm, ...)
-    ```
-    </Accordion>
-
-    <Accordion title="Fireworks">
-
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="fireworks/meta-llama-3.1-8b-instruct",
-        base_url="https://api.fireworks.ai/inference/v1",
-        api_key="your-api-key-here"
-    )
-    agent = Agent(llm=llm, ...)
-    ```
-    </Accordion>
-
-    <Accordion title="Gemini">
-
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="gemini/gemini-1.5-flash",
-        base_url="https://api.gemini.google.com/v1",
-        api_key="your-api-key-here"
-    )
-    agent = Agent(llm=llm, ...)
-    ```
-    </Accordion>
-
-    <Accordion title="Perplexity AI (pplx-api)">
-
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="perplexity/mistral-7b-instruct",
-        base_url="https://api.perplexity.ai/v1",
-        api_key="your-api-key-here"
-    )
-    agent = Agent(llm=llm, ...)
-    ```
-    </Accordion>
-
-    <Accordion title="IBM watsonx.ai">
-
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="watsonx/ibm/granite-13b-chat-v2",
-        base_url="https://api.watsonx.ai/v1",
-        api_key="your-api-key-here"
-    )
-    agent = Agent(llm=llm, ...)
-    ```
-    </Accordion>
-</AccordionGroup>
+agent = Agent(
+    llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"),
+    ...
+)
+```
 
 ## Changing the Base API URL
 

docs/concepts/memory.mdx

@@ -254,31 +254,6 @@ my_crew = Crew(
 )
 ```
 
-### Using Watson embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-# Note: Ensure you have installed and imported `ibm_watsonx_ai` for Watson embeddings to work.
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "watson",
-        "config": {
-            "model": "<model_name>",
-            "api_url": "<api_url>",
-            "api_key": "<YOUR_API_KEY>",
-            "project_id": "<YOUR_PROJECT_ID>",
-        }
-    }
-)
-```
-
 ### Resetting Memory
 
 ```shell

src/crewai/agent.py

@@ -8,6 +8,7 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.cli.constants import ENV_VARS
 from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.tools.agent_tools import AgentTools
@@ -130,8 +131,12 @@ class Agent(BaseAgent):
             # If it's already an LLM instance, keep it as is
             pass
         elif self.llm is None:
-            # If it's None, use environment variables or default
-            model_name = os.environ.get("OPENAI_MODEL_NAME", "gpt-4o-mini")
+            # Determine the model name from environment variables or use default
+            model_name = (
+                os.environ.get("OPENAI_MODEL_NAME")
+                or os.environ.get("MODEL")
+                or "gpt-4o-mini"
+            )
             llm_params = {"model": model_name}
 
             api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
@@ -140,9 +145,39 @@ class Agent(BaseAgent):
             if api_base:
                 llm_params["base_url"] = api_base
 
-            api_key = os.environ.get("OPENAI_API_KEY")
-            if api_key:
-                llm_params["api_key"] = api_key
+            # Iterate over all environment variables to find matching API keys or use defaults
+            for provider, env_vars in ENV_VARS.items():
+                for env_var in env_vars:
+                    # Check if the environment variable is set
+                    if "key_name" in env_var:
+                        env_value = os.environ.get(env_var["key_name"])
+                        if env_value:
+                            # Map key names containing "API_KEY" to "api_key"
+                            key_name = (
+                                "api_key"
+                                if "API_KEY" in env_var["key_name"]
+                                else env_var["key_name"]
+                            )
+                            # Map key names containing "API_BASE" to "api_base"
+                            key_name = (
+                                "api_base"
+                                if "API_BASE" in env_var["key_name"]
+                                else key_name
+                            )
+                            # Map key names containing "API_VERSION" to "api_version"
+                            key_name = (
+                                "api_version"
+                                if "API_VERSION" in env_var["key_name"]
+                                else key_name
+                            )
+                            llm_params[key_name] = env_value
+                    # Check for default values if the environment variable is not set
+                    elif env_var.get("default", False):
+                        for key, value in env_var.items():
+                            if key not in ["prompt", "key_name", "default"]:
+                                # Only add default if the key is already set in os.environ
+                                if key in os.environ:
+                                    llm_params[key] = value
 
             self.llm = LLM(**llm_params)
         else:

src/crewai/cli/config.py

@@ -1,38 +0,0 @@
-import json
-from pathlib import Path
-from pydantic import BaseModel, Field
-from typing import Optional
-
-DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json"
-
-class Settings(BaseModel):
-    tool_repository_username: Optional[str] = Field(None, description="Username for interacting with the Tool Repository")
-    tool_repository_password: Optional[str] = Field(None, description="Password for interacting with the Tool Repository")
-    config_path: Path = Field(default=DEFAULT_CONFIG_PATH, exclude=True)
-
-    def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data):
-        """Load Settings from config path"""
-        config_path.parent.mkdir(parents=True, exist_ok=True)
-
-        file_data = {}
-        if config_path.is_file():
-            try:
-                with config_path.open("r") as f:
-                    file_data = json.load(f)
-            except json.JSONDecodeError:
-                file_data = {}
-
-        merged_data = {**file_data, **data}
-        super().__init__(config_path=config_path, **merged_data)
-
-    def dump(self) -> None:
-        """Save current settings to settings.json"""
-        if self.config_path.is_file():
-            with self.config_path.open("r") as f:
-                existing_data = json.load(f)
-        else:
-            existing_data = {}
-
-        updated_data = {**existing_data, **self.model_dump(exclude_unset=True)}
-        with self.config_path.open("w") as f:
-            json.dump(updated_data, f, indent=4)

src/crewai/cli/constants.py

@@ -1,19 +1,168 @@
 ENV_VARS = {
-    'openai': ['OPENAI_API_KEY'],
-    'anthropic': ['ANTHROPIC_API_KEY'],
-    'gemini': ['GEMINI_API_KEY'],
-    'groq': ['GROQ_API_KEY'],
-    'ollama': ['FAKE_KEY'],
+    "openai": [
+        {
+            "prompt": "Enter your OPENAI API key (press Enter to skip)",
+            "key_name": "OPENAI_API_KEY",
+        }
+    ],
+    "anthropic": [
+        {
+            "prompt": "Enter your ANTHROPIC API key (press Enter to skip)",
+            "key_name": "ANTHROPIC_API_KEY",
+        }
+    ],
+    "gemini": [
+        {
+            "prompt": "Enter your GEMINI API key (press Enter to skip)",
+            "key_name": "GEMINI_API_KEY",
+        }
+    ],
+    "groq": [
+        {
+            "prompt": "Enter your GROQ API key (press Enter to skip)",
+            "key_name": "GROQ_API_KEY",
+        }
+    ],
+    "watson": [
+        {
+            "prompt": "Enter your WATSONX URL (press Enter to skip)",
+            "key_name": "WATSONX_URL",
+        },
+        {
+            "prompt": "Enter your WATSONX API Key (press Enter to skip)",
+            "key_name": "WATSONX_APIKEY",
+        },
+        {
+            "prompt": "Enter your WATSONX Project Id (press Enter to skip)",
+            "key_name": "WATSONX_PROJECT_ID",
+        },
+    ],
+    "ollama": [
+        {
+            "default": True,
+            "API_BASE": "http://localhost:11434",
+        }
+    ],
+    "bedrock": [
+        {
+            "prompt": "Enter your AWS Access Key ID (press Enter to skip)",
+            "key_name": "AWS_ACCESS_KEY_ID",
+        },
+        {
+            "prompt": "Enter your AWS Secret Access Key (press Enter to skip)",
+            "key_name": "AWS_SECRET_ACCESS_KEY",
+        },
+        {
+            "prompt": "Enter your AWS Region Name (press Enter to skip)",
+            "key_name": "AWS_REGION_NAME",
+        },
+    ],
+    "azure": [
+        {
+            "prompt": "Enter your Azure deployment name (must start with 'azure/')",
+            "key_name": "model",
+        },
+        {
+            "prompt": "Enter your AZURE API key (press Enter to skip)",
+            "key_name": "AZURE_API_KEY",
+        },
+        {
+            "prompt": "Enter your AZURE API base URL (press Enter to skip)",
+            "key_name": "AZURE_API_BASE",
+        },
+        {
+            "prompt": "Enter your AZURE API version (press Enter to skip)",
+            "key_name": "AZURE_API_VERSION",
+        },
+    ],
+    "cerebras": [
+        {
+            "prompt": "Enter your Cerebras model name (must start with 'cerebras/')",
+            "key_name": "model",
+        },
+        {
+            "prompt": "Enter your Cerebras API version (press Enter to skip)",
+            "key_name": "CEREBRAS_API_KEY",
+        },
+    ],
 }
 
-PROVIDERS = ['openai', 'anthropic', 'gemini', 'groq', 'ollama']
+
+PROVIDERS = [
+    "openai",
+    "anthropic",
+    "gemini",
+    "groq",
+    "ollama",
+    "watson",
+    "bedrock",
+    "azure",
+    "cerebras",
+]
 
 MODELS = {
-    'openai': ['gpt-4', 'gpt-4o', 'gpt-4o-mini', 'o1-mini', 'o1-preview'],
-    'anthropic': ['claude-3-5-sonnet-20240620', 'claude-3-sonnet-20240229', 'claude-3-opus-20240229', 'claude-3-haiku-20240307'],
-    'gemini': ['gemini-1.5-flash', 'gemini-1.5-pro', 'gemini-gemma-2-9b-it', 'gemini-gemma-2-27b-it'],
-    'groq': ['llama-3.1-8b-instant', 'llama-3.1-70b-versatile', 'llama-3.1-405b-reasoning', 'gemma2-9b-it', 'gemma-7b-it'],
-    'ollama': ['llama3.1', 'mixtral'],
+    "openai": ["gpt-4", "gpt-4o", "gpt-4o-mini", "o1-mini", "o1-preview"],
+    "anthropic": [
+        "claude-3-5-sonnet-20240620",
+        "claude-3-sonnet-20240229",
+        "claude-3-opus-20240229",
+        "claude-3-haiku-20240307",
+    ],
+    "gemini": [
+        "gemini/gemini-1.5-flash",
+        "gemini/gemini-1.5-pro",
+        "gemini/gemini-gemma-2-9b-it",
+        "gemini/gemini-gemma-2-27b-it",
+    ],
+    "groq": [
+        "groq/llama-3.1-8b-instant",
+        "groq/llama-3.1-70b-versatile",
+        "groq/llama-3.1-405b-reasoning",
+        "groq/gemma2-9b-it",
+        "groq/gemma-7b-it",
+    ],
+    "ollama": ["ollama/llama3.1", "ollama/mixtral"],
+    "watson": [
+        "watsonx/google/flan-t5-xxl",
+        "watsonx/google/flan-ul2",
+        "watsonx/bigscience/mt0-xxl",
+        "watsonx/eleutherai/gpt-neox-20b",
+        "watsonx/ibm/mpt-7b-instruct2",
+        "watsonx/bigcode/starcoder",
+        "watsonx/meta-llama/llama-2-70b-chat",
+        "watsonx/meta-llama/llama-2-13b-chat",
+        "watsonx/ibm/granite-13b-instruct-v1",
+        "watsonx/ibm/granite-13b-chat-v1",
+        "watsonx/google/flan-t5-xl",
+        "watsonx/ibm/granite-13b-chat-v2",
+        "watsonx/ibm/granite-13b-instruct-v2",
+        "watsonx/elyza/elyza-japanese-llama-2-7b-instruct",
+        "watsonx/ibm-mistralai/mixtral-8x7b-instruct-v01-q",
+    ],
+    "bedrock": [
+        "bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
+        "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+        "bedrock/anthropic.claude-3-haiku-20240307-v1:0",
+        "bedrock/anthropic.claude-3-opus-20240229-v1:0",
+        "bedrock/anthropic.claude-v2:1",
+        "bedrock/anthropic.claude-v2",
+        "bedrock/anthropic.claude-instant-v1",
+        "bedrock/meta.llama3-1-405b-instruct-v1:0",
+        "bedrock/meta.llama3-1-70b-instruct-v1:0",
+        "bedrock/meta.llama3-1-8b-instruct-v1:0",
+        "bedrock/meta.llama3-70b-instruct-v1:0",
+        "bedrock/meta.llama3-8b-instruct-v1:0",
+        "bedrock/amazon.titan-text-lite-v1",
+        "bedrock/amazon.titan-text-express-v1",
+        "bedrock/cohere.command-text-v14",
+        "bedrock/ai21.j2-mid-v1",
+        "bedrock/ai21.j2-ultra-v1",
+        "bedrock/ai21.jamba-instruct-v1:0",
+        "bedrock/meta.llama2-13b-chat-v1",
+        "bedrock/meta.llama2-70b-chat-v1",
+        "bedrock/mistral.mistral-7b-instruct-v0:2",
+        "bedrock/mistral.mixtral-8x7b-instruct-v0:1",
+    ],
 }
 
-JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
+JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"

src/crewai/cli/create_crew.py

@@ -1,11 +1,11 @@
+import shutil
 import sys
 from pathlib import Path
 
 import click
 
-from crewai.cli.constants import ENV_VARS
+from crewai.cli.constants import ENV_VARS, MODELS
 from crewai.cli.provider import (
-    PROVIDERS,
     get_provider_data,
     select_model,
     select_provider,
@@ -29,20 +29,20 @@ def create_folder_structure(name, parent_folder=None):
             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,
-        )
+        shutil.rmtree(folder_path)  # Delete the existing folder and its contents
 
-    if not folder_path.exists():
-        folder_path.mkdir(parents=True)
-        (folder_path / "tests").mkdir(exist_ok=True)
-        if not parent_folder:
-            (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)
+    click.secho(
+        f"Creating {'crew' if parent_folder else 'folder'} {folder_name}...",
+        fg="green",
+        bold=True,
+    )
+
+    folder_path.mkdir(parents=True)
+    (folder_path / "tests").mkdir(exist_ok=True)
+    if not parent_folder:
+        (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)
 
     return folder_path, folder_name, class_name
 
@@ -92,7 +92,10 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
 
         existing_provider = None
         for provider, env_keys in ENV_VARS.items():
-            if any(key in env_vars for key in env_keys):
+            if any(
+                "key_name" in details and details["key_name"] in env_vars
+                for details in env_keys
+            ):
                 existing_provider = provider
                 break
 
@@ -118,47 +121,48 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
                 "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"
-            )
+        # Check if the selected provider has predefined models
+        if selected_provider in MODELS and MODELS[selected_provider]:
+            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",
+                )
+            env_vars["MODEL"] = selected_model
 
-        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="",
-            )
+        # Check if the selected provider requires API keys
+        if selected_provider in ENV_VARS:
+            provider_env_vars = ENV_VARS[selected_provider]
+            for details in provider_env_vars:
+                if details.get("default", False):
+                    # Automatically add default key-value pairs
+                    for key, value in details.items():
+                        if key not in ["prompt", "key_name", "default"]:
+                            env_vars[key] = value
+                elif "key_name" in details:
+                    # Prompt for non-default key-value pairs
+                    prompt = details["prompt"]
+                    key_name = details["key_name"]
+                    api_key_value = click.prompt(prompt, default="", show_default=False)
 
-        api_key_value = ""
-        click.echo(
-            f"Enter your {selected_provider.capitalize()} API key (press Enter to skip): ",
-            nl=False,
-        )
-        try:
-            api_key_value = input()
-        except (KeyboardInterrupt, EOFError):
-            api_key_value = ""
+                    if api_key_value.strip():
+                        env_vars[key_name] = api_key_value
 
-        if api_key_value.strip():
-            env_vars = {api_key_var: api_key_value}
+        if env_vars:
             write_env_file(folder_path, env_vars)
-            click.secho("API key saved to .env file", fg="green")
+            click.secho("API keys and model saved to .env file", fg="green")
         else:
             click.secho(
-                "No API key provided. Skipping .env file creation.", fg="yellow"
+                "No API keys provided. Skipping .env file creation.", fg="yellow"
             )
 
-        env_vars["MODEL"] = selected_model
-        click.secho(f"Selected model: {selected_model}", fg="green")
+        click.secho(f"Selected model: {env_vars.get('MODEL', 'N/A')}", fg="green")
 
     package_dir = Path(__file__).parent
     templates_dir = package_dir / "templates" / "crew"

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

@@ -48,4 +48,4 @@ class {{crew_name}}Crew():
 			process=Process.sequential,
 			verbose=True,
 			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
-		)
+		)

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

@@ -1,7 +1,11 @@
 #!/usr/bin/env python
 import sys
+import warnings
+
 from {{folder_name}}.crew import {{crew_name}}Crew
 
+warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
+
 # 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.
 # Replace with inputs you want to test with, it will automatically

src/crewai/cli/tools/main.py

@@ -1,15 +1,17 @@
 import base64
 import os
+import platform
 import subprocess
 import tempfile
 from pathlib import Path
+from netrc import netrc
+import stat
 
 import click
 from rich.console import Console
 
 from crewai.cli import git
 from crewai.cli.command import BaseCommand, PlusAPIMixin
-from crewai.cli.config import Settings
 from crewai.cli.utils import (
     get_project_description,
     get_project_name,
@@ -151,16 +153,26 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             raise SystemExit
 
         login_response_json = login_response.json()
-
-        settings = Settings()
-        settings.tool_repository_username = login_response_json["credential"]["username"]
-        settings.tool_repository_password = login_response_json["credential"]["password"]
-        settings.dump()
+        self._set_netrc_credentials(login_response_json["credential"])
 
         console.print(
             "Successfully authenticated to the tool repository.", style="bold green"
         )
 
+    def _set_netrc_credentials(self, credentials, netrc_path=None):
+        if not netrc_path:
+            netrc_filename = "_netrc" if platform.system() == "Windows" else ".netrc"
+            netrc_path = Path.home() / netrc_filename
+            netrc_path.touch(mode=stat.S_IRUSR | stat.S_IWUSR, exist_ok=True)
+
+        netrc_instance = netrc(file=netrc_path)
+        netrc_instance.hosts["app.crewai.com"] = (credentials["username"], "", credentials["password"])
+
+        with open(netrc_path, 'w') as file:
+            file.write(str(netrc_instance))
+
+        console.print(f"Added credentials to {netrc_path}", style="bold green")
+
     def _add_package(self, tool_details):
         tool_handle = tool_details["handle"]
         repository_handle = tool_details["repository"]["handle"]
@@ -175,11 +187,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             tool_handle,
         ]
         add_package_result = subprocess.run(
-            add_package_command,
-            capture_output=False,
-            env=self._build_env_with_credentials(repository_handle),
-            text=True,
-            check=True
+            add_package_command, capture_output=False, text=True, check=True
         )
 
         if add_package_result.stderr:
@@ -198,13 +206,3 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
                 "[bold yellow]Tip:[/bold yellow] Navigate to a different directory and try again."
             )
             raise SystemExit
-
-    def _build_env_with_credentials(self, repository_handle: str):
-        repository_handle = repository_handle.upper().replace("-", "_")
-        settings = Settings()
-
-        env = os.environ.copy()
-        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(settings.tool_repository_username or "")
-        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(settings.tool_repository_password or "")
-
-        return env

src/crewai/memory/contextual/contextual_memory.py

@@ -34,7 +34,6 @@ class ContextualMemory:
         formatted_results = "\n".join(
             [f"- {result['context']}" for result in stm_results]
         )
-        print("formatted_results stm", formatted_results)
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
     def _fetch_ltm_context(self, task) -> Optional[str]:
@@ -54,8 +53,6 @@ class ContextualMemory:
         formatted_results = list(dict.fromkeys(formatted_results))
         formatted_results = "\n".join([f"- {result}" for result in formatted_results])  # type: ignore # Incompatible types in assignment (expression has type "str", variable has type "list[str]")
 
-        print("formatted_results ltm", formatted_results)
-
         return f"Historical Data:\n{formatted_results}" if ltm_results else ""
 
     def _fetch_entity_context(self, query) -> str:
@@ -67,5 +64,4 @@ class ContextualMemory:
         formatted_results = "\n".join(
             [f"- {result['context']}" for result in em_results]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
         )
-        print("formatted_results em", formatted_results)
         return f"Entities:\n{formatted_results}" if em_results else ""

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -70,7 +70,7 @@ class KickoffTaskOutputsSQLiteStorage:
                         task.expected_output,
                         json.dumps(output, cls=CrewJSONEncoder),
                         task_index,
-                        json.dumps(inputs, cls=CrewJSONEncoder),
+                        json.dumps(inputs),
                         was_replayed,
                     ),
                 )

src/crewai/memory/storage/rag_storage.py

@@ -4,13 +4,13 @@ import logging
 import os
 import shutil
 import uuid
-from typing import Any, Dict, List, Optional, cast
-
-from chromadb import Documents, EmbeddingFunction, Embeddings
-from chromadb.api import ClientAPI
-from chromadb.api.types import validate_embedding_function
+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
@@ -21,11 +21,9 @@ def suppress_logging(
     logger = logging.getLogger(logger_name)
     original_level = logger.getEffectiveLevel()
     logger.setLevel(level)
-    with (
-        contextlib.redirect_stdout(io.StringIO()),
-        contextlib.redirect_stderr(io.StringIO()),
-        contextlib.suppress(UserWarning),
-    ):
+    with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr(
+        io.StringIO()
+    ), contextlib.suppress(UserWarning):
         yield
     logger.setLevel(original_level)
 
@@ -115,52 +113,12 @@ class RAGStorage(BaseRAGStorage):
                 self.embedder_config = embedding_functions.HuggingFaceEmbeddingServer(
                     url=config.get("api_url"),
                 )
-            elif provider == "watson":
-                try:
-                    import ibm_watsonx_ai.foundation_models as watson_models
-                    from ibm_watsonx_ai import Credentials
-                    from ibm_watsonx_ai.metanames import (
-                        EmbedTextParamsMetaNames as EmbedParams,
-                    )
-                except ImportError as e:
-                    raise ImportError(
-                        "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
-                    ) from e
-
-                class WatsonEmbeddingFunction(EmbeddingFunction):
-                    def __call__(self, input: Documents) -> Embeddings:
-                        if isinstance(input, str):
-                            input = [input]
-
-                        embed_params = {
-                            EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
-                            EmbedParams.RETURN_OPTIONS: {"input_text": True},
-                        }
-
-                        embedding = watson_models.Embeddings(
-                            model_id=config.get("model"),
-                            params=embed_params,
-                            credentials=Credentials(
-                                api_key=config.get("api_key"), url=config.get("api_url")
-                            ),
-                            project_id=config.get("project_id"),
-                        )
-
-                        try:
-                            embeddings = embedding.embed_documents(input)
-                            return cast(Embeddings, embeddings)
-
-                        except Exception as e:
-                            print("Error during Watson embedding:", e)
-                            raise e
-
-                self.embedder_config = WatsonEmbeddingFunction()
             else:
                 raise Exception(
-                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface, watson]"
+                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface]"
                 )
         else:
-            validate_embedding_function(self.embedder_config)
+            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):

src/crewai/telemetry/telemetry.py

@@ -21,7 +21,7 @@ with suppress_warnings():
 
 
 from opentelemetry import trace  # noqa: E402
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter # noqa: E402
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter  # noqa: E402
 from opentelemetry.sdk.resources import SERVICE_NAME, Resource  # noqa: E402
 from opentelemetry.sdk.trace import TracerProvider  # noqa: E402
 from opentelemetry.sdk.trace.export import BatchSpanProcessor  # noqa: E402
@@ -48,10 +48,6 @@ class Telemetry:
     def __init__(self):
         self.ready = False
         self.trace_set = False
-
-        if os.getenv("OTEL_SDK_DISABLED", "false").lower() == "true":
-            return
-
         try:
             telemetry_endpoint = "https://telemetry.crewai.com:4319"
             self.resource = Resource(

src/crewai/utilities/crew_json_encoder.py

@@ -2,14 +2,13 @@ from datetime import datetime, date
 import json
 from uuid import UUID
 from pydantic import BaseModel
-from decimal import Decimal
 
 
 class CrewJSONEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, BaseModel):
             return self._handle_pydantic_model(obj)
-        elif isinstance(obj, UUID) or isinstance(obj, Decimal):
+        elif isinstance(obj, UUID):
             return str(obj)
 
         elif isinstance(obj, datetime) or isinstance(obj, date):

tests/cli/config_test.py

@@ -1,109 +0,0 @@
-import unittest
-import json
-import tempfile
-import shutil
-from pathlib import Path
-from crewai.cli.config import Settings
-
-class TestSettings(unittest.TestCase):
-    def setUp(self):
-        self.test_dir = Path(tempfile.mkdtemp())
-        self.config_path = self.test_dir / "settings.json"
-
-    def tearDown(self):
-        shutil.rmtree(self.test_dir)
-
-    def test_empty_initialization(self):
-        settings = Settings(config_path=self.config_path)
-        self.assertIsNone(settings.tool_repository_username)
-        self.assertIsNone(settings.tool_repository_password)
-
-    def test_initialization_with_data(self):
-        settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="user1"
-        )
-        self.assertEqual(settings.tool_repository_username, "user1")
-        self.assertIsNone(settings.tool_repository_password)
-
-    def test_initialization_with_existing_file(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            json.dump({"tool_repository_username": "file_user"}, f)
-
-        settings = Settings(config_path=self.config_path)
-        self.assertEqual(settings.tool_repository_username, "file_user")
-
-    def test_merge_file_and_input_data(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            json.dump({
-                "tool_repository_username": "file_user",
-                "tool_repository_password": "file_pass"
-            }, f)
-
-        settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="new_user"
-        )
-        self.assertEqual(settings.tool_repository_username, "new_user")
-        self.assertEqual(settings.tool_repository_password, "file_pass")
-
-    def test_dump_new_settings(self):
-        settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="user1"
-        )
-        settings.dump()
-
-        with self.config_path.open("r") as f:
-            saved_data = json.load(f)
-
-        self.assertEqual(saved_data["tool_repository_username"], "user1")
-
-    def test_update_existing_settings(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            json.dump({"existing_setting": "value"}, f)
-
-        settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="user1"
-        )
-        settings.dump()
-
-        with self.config_path.open("r") as f:
-            saved_data = json.load(f)
-
-        self.assertEqual(saved_data["existing_setting"], "value")
-        self.assertEqual(saved_data["tool_repository_username"], "user1")
-
-    def test_none_values(self):
-        settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username=None
-        )
-        settings.dump()
-
-        with self.config_path.open("r") as f:
-            saved_data = json.load(f)
-
-        self.assertIsNone(saved_data.get("tool_repository_username"))
-
-    def test_invalid_json_in_config(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            f.write("invalid json")
-
-        try:
-            settings = Settings(config_path=self.config_path)
-            self.assertIsNone(settings.tool_repository_username)
-        except json.JSONDecodeError:
-            self.fail("Settings initialization should handle invalid JSON")
-
-    def test_empty_config_file(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        self.config_path.touch()
-
-        settings = Settings(config_path=self.config_path)
-        self.assertIsNone(settings.tool_repository_username)

tests/cli/tools/test_main.py

@@ -82,7 +82,6 @@ def test_install_success(mock_get, mock_subprocess_run):
         capture_output=False,
         text=True,
         check=True,
-        env=unittest.mock.ANY
     )
 
     assert "Succesfully installed sample-tool" in output