removing logs

Co-authored-by: João Moura <joaomdmoura@gmail.com>

README.md

@@ -64,25 +64,8 @@ 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.
-
-# 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'
-
-# You can pass an optional llm attribute specifying what model you wanna use.
 # It can be a local model through Ollama / LM Studio or a remote
 # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
-# If you don't specify a model, the default is OpenAI gpt-4o
-#
-# import os
-# os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
-#
-# OR
-#
-# from langchain_openai import ChatOpenAI
-
-search_tool = SerperDevTool()
 
 # Define your agents with roles and goals
 researcher = Agent(
@@ -95,7 +78,7 @@ researcher = Agent(
   allow_delegation=False,
   # You can pass an optional llm attribute specifying what model you wanna use.
   # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
-  tools=[search_tool]
+  tools=[SerperDevTool()]
 )
 writer = Agent(
   role='Tech Content Strategist',

docs/core-concepts/LLMs.md

@@ -0,0 +1,155 @@
+# Large Language Models (LLMs) in crewAI
+
+## Introduction
+Large Language Models (LLMs) are the backbone of intelligent agents in the crewAI framework. This guide will help you understand, configure, and optimize LLM usage for your crewAI projects.
+
+## Table of Contents
+- [Key Concepts](#key-concepts)
+- [Configuring LLMs for Agents](#configuring-llms-for-agents)
+  - [1. Default Configuration](#1-default-configuration)
+  - [2. String Identifier](#2-string-identifier)
+  - [3. LLM Instance](#3-llm-instance)
+  - [4. Custom LLM Objects](#4-custom-llm-objects)
+- [Connecting to OpenAI-Compatible LLMs](#connecting-to-openai-compatible-llms)
+- [LLM Configuration Options](#llm-configuration-options)
+- [Using Ollama (Local LLMs)](#using-ollama-local-llms)
+- [Changing the Base API URL](#changing-the-base-api-url)
+- [Best Practices](#best-practices)
+- [Troubleshooting](#troubleshooting)
+
+## Key Concepts
+- **LLM**: Large Language Model, the AI powering agent intelligence
+- **Agent**: A crewAI entity that uses an LLM to perform tasks
+- **Provider**: A service that offers LLM capabilities (e.g., OpenAI, Anthropic, Ollama, [more providers](https://docs.litellm.ai/docs/providers))
+
+## Configuring LLMs for Agents
+
+crewAI offers flexible options for setting up LLMs:
+
+### 1. Default Configuration
+By default, crewAI uses the `gpt-4o-mini` model. It uses environment variables if no LLM is specified:
+- `OPENAI_MODEL_NAME` (defaults to "gpt-4o-mini" if not set)
+- `OPENAI_API_BASE`
+- `OPENAI_API_KEY`
+
+### 2. String Identifier
+```python
+agent = Agent(llm="gpt-4o", ...)
+```
+
+### 3. LLM Instance
+List of [more providers](https://docs.litellm.ai/docs/providers).
+```python
+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.
+
+## 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:
+
+1. Using environment variables:
+```python
+import os
+
+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
+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
+
+When configuring an LLM for your agent, you have access to a wide range of parameters:
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `model` | str | The name of the model to use (e.g., "gpt-4", "gpt-3.5-turbo", "ollama/llama3.1", [more providers](https://docs.litellm.ai/docs/providers)) |
+| `timeout` | float, int | Maximum time (in seconds) to wait for a response |
+| `temperature` | float | Controls randomness in output (0.0 to 1.0) |
+| `top_p` | float | Controls diversity of output (0.0 to 1.0) |
+| `n` | int | Number of completions to generate |
+| `stop` | str, List[str] | Sequence(s) to stop generation |
+| `max_tokens` | int | Maximum number of tokens to generate |
+| `presence_penalty` | float | Penalizes new tokens based on their presence in the text so far |
+| `frequency_penalty` | float | Penalizes new tokens based on their frequency in the text so far |
+| `logit_bias` | Dict[int, float] | Modifies likelihood of specified tokens appearing in the completion |
+| `response_format` | Dict[str, Any] | Specifies the format of the response (e.g., {"type": "json_object"}) |
+| `seed` | int | Sets a random seed for deterministic results |
+| `logprobs` | bool | Whether to return log probabilities of the output tokens |
+| `top_logprobs` | int | Number of most likely tokens to return the log probabilities for |
+| `base_url` | str | The base URL for the API endpoint |
+| `api_version` | str | The version of the API to use |
+| `api_key` | str | Your API key for authentication |
+
+Example:
+```python
+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, ...)
+```
+
+## Using Ollama (Local LLMs)
+
+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
+agent = Agent(
+    llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"),
+    ...
+)
+```
+
+## Changing the Base API URL
+
+You can change the base API URL for any LLM provider by setting the `base_url` parameter:
+
+```python
+llm = LLM(
+    model="custom-model-name",
+    base_url="https://api.your-provider.com/v1",
+    api_key="your-api-key"
+)
+agent = Agent(llm=llm, ...)
+```
+
+This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider.
+
+## Best Practices
+1. **Choose the right model**: Balance capability and cost.
+2. **Optimize prompts**: Clear, concise instructions improve output.
+3. **Manage tokens**: Monitor and limit token usage for efficiency.
+4. **Use appropriate temperature**: Lower for factual tasks, higher for creative ones.
+5. **Implement error handling**: Gracefully manage API errors and rate limits.
+
+## Troubleshooting
+- **API Errors**: Check your API key, network connection, and rate limits.
+- **Unexpected Outputs**: Refine your prompts and adjust temperature or top_p.
+- **Performance Issues**: Consider using a more powerful model or optimizing your queries.
+- **Timeout Errors**: Increase the `timeout` parameter or optimize your input.

docs/core-concepts/Memory.md

@@ -28,7 +28,7 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
 ## Implementing Memory in Your Crew
 
 When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
-By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI embeddings by default, but you can change it by setting `embedder` to a different model.
+By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI embeddings by default, but you can change it by setting `embedder` to a different model. It's also possible to initialize the memory instance with your own instance.
 
 The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using the EmbedChain package.
 The **Long-Term Memory** uses SQLite3 to store task results. Currently, there is no way to override these storage implementations.
@@ -50,6 +50,45 @@ my_crew = Crew(
 )
 ```
 
+### Example: Use Custom Memory Instances e.g FAISS as the VectorDB
+
+```python
+from crewai import Crew, Agent, Task, Process
+
+# Assemble your crew with memory capabilities
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process="Process.sequential",
+    memory=True,
+    long_term_memory=EnhanceLongTermMemory(
+        storage=LTMSQLiteStorage(
+            db_path="/my_data_dir/my_crew1/long_term_memory_storage.db"
+        )
+    ),
+    short_term_memory=EnhanceShortTermMemory(
+        storage=CustomRAGStorage(
+            crew_name="my_crew",
+            storage_type="short_term",
+            data_dir="//my_data_dir",
+            model=embedder["model"],
+            dimension=embedder["dimension"],
+        ),
+    ),
+    entity_memory=EnhanceEntityMemory(
+        storage=CustomRAGStorage(
+            crew_name="my_crew",
+            storage_type="entities",
+            data_dir="//my_data_dir",
+            model=embedder["model"],
+            dimension=embedder["dimension"],
+        ),
+    ),
+    verbose=True,
+)
+```
+
+
 ## Additional Embedding Providers
 
 ### Using OpenAI embeddings (already default)

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

@@ -176,7 +176,7 @@ This will install the dependencies specified in the `pyproject.toml` file.
 
 Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{variable}` will be replaced by the value of the variable in the `main.py` file.
 
-#### agents.yaml
+#### tasks.yaml
 
 ```yaml
 research_task:

docs/how-to/LLM-Connections.md

@@ -5,10 +5,10 @@ description: Comprehensive guide on integrating CrewAI with various Large Langua
 
 ## Connect CrewAI to LLMs
 
-CrewAI now uses LiteLLM to connect to a wide variety of Language Models (LLMs). This integration provides extensive versatility, allowing you to use models from numerous providers with a simple, unified interface.
+CrewAI uses LiteLLM to connect to a wide variety of Language Models (LLMs). This integration provides extensive versatility, allowing you to use models from numerous providers with a simple, unified interface.
 
 !!! note "Default LLM"
-    By default, CrewAI uses OpenAI's GPT-4 model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4") for language processing. You can easily configure your agents to use a different model or provider as described in this guide.
+    By default, CrewAI uses the `gpt-4o-mini` model. This is determined by the `OPENAI_MODEL_NAME` environment variable, which defaults to "gpt-4o-mini" if not set. You can easily configure your agents to use a different model or provider as described in this guide.
 
 ## Supported Providers
 
@@ -35,7 +35,11 @@ For a complete and up-to-date list of supported providers, please refer to the [
 
 ## Changing the LLM
 
-To use a different LLM with your CrewAI agents, you simply need to pass the model name as a string when initializing the agent. Here are some examples:
+To use a different LLM with your CrewAI agents, you have several options:
+
+### 1. Using a String Identifier
+
+Pass the model name as a string when initializing the agent:
 
 ```python
 from crewai import Agent
@@ -55,59 +59,105 @@ claude_agent = Agent(
     backstory="An AI assistant leveraging Anthropic's language model.",
     llm='claude-2'
 )
+```
 
-# Using Ollama's local Llama 2 model
-ollama_agent = Agent(
-    role='Local AI Expert',
-    goal='Process information using a local model',
-    backstory="An AI assistant running on local hardware.",
-    llm='ollama/llama2'
+### 2. Using the LLM Class
+
+For more detailed configuration, use the LLM class:
+
+```python
+from crewai import Agent, LLM
+
+llm = LLM(
+    model="gpt-4",
+    temperature=0.7,
+    base_url="https://api.openai.com/v1",
+    api_key="your-api-key-here"
 )
 
-# Using Google's Gemini model
-gemini_agent = Agent(
-    role='Google AI Expert',
-    goal='Generate creative content with Gemini',
-    backstory="An AI assistant powered by Google's advanced language model.",
-    llm='gemini-pro'
+agent = Agent(
+    role='Customized LLM Expert',
+    goal='Provide tailored responses',
+    backstory="An AI assistant with custom LLM settings.",
+    llm=llm
 )
 ```
 
-## Configuration
+## Configuration Options
 
-For most providers, you'll need to set up your API keys as environment variables. Here's how you can do it for some common providers:
+When configuring an LLM for your agent, you have access to a wide range of parameters:
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `model` | str | The name of the model to use (e.g., "gpt-4", "claude-2") |
+| `temperature` | float | Controls randomness in output (0.0 to 1.0) |
+| `max_tokens` | int | Maximum number of tokens to generate |
+| `top_p` | float | Controls diversity of output (0.0 to 1.0) |
+| `frequency_penalty` | float | Penalizes new tokens based on their frequency in the text so far |
+| `presence_penalty` | float | Penalizes new tokens based on their presence in the text so far |
+| `stop` | str, List[str] | Sequence(s) to stop generation |
+| `base_url` | str | The base URL for the API endpoint |
+| `api_key` | str | Your API key for authentication |
+
+For a complete list of parameters and their descriptions, refer to the LLM class documentation.
+
+## 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:
+
+### Using Environment Variables
 
 ```python
 import os
 
-# OpenAI
-os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
-
-# Anthropic
-os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"
-
-# Google (Vertex AI)
-os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/your/credentials.json"
-
-# Azure OpenAI
-os.environ["AZURE_API_KEY"] = "your-azure-api-key"
-os.environ["AZURE_API_BASE"] = "your-azure-endpoint"
-
-# AWS (Bedrock)
-os.environ["AWS_ACCESS_KEY_ID"] = "your-aws-access-key-id"
-os.environ["AWS_SECRET_ACCESS_KEY"] = "your-aws-secret-access-key"
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
+os.environ["OPENAI_MODEL_NAME"] = "your-model-name"
 ```
 
-For providers that require additional configuration or have specific setup requirements, please refer to the [LiteLLM documentation](https://docs.litellm.ai/docs/) for detailed instructions.
+### Using LLM Class Attributes
 
-## Using Local Models
+```python
+llm = LLM(
+    model="custom-model-name",
+    api_key="your-api-key",
+    base_url="https://api.your-provider.com/v1"
+)
+agent = Agent(llm=llm, ...)
+```
 
-For local models like those provided by Ollama, ensure you have the necessary software installed and running. For example, to use Ollama:
+## Using Local Models with Ollama
+
+For local models like those provided by Ollama:
 
 1. [Download and install Ollama](https://ollama.com/download)
 2. Pull the desired model (e.g., `ollama pull llama2`)
-3. Use the model in your CrewAI agent by specifying `llm='ollama/llama2'`
+3. Configure your agent:
+
+```python
+agent = Agent(
+    role='Local AI Expert',
+    goal='Process information using a local model',
+    backstory="An AI assistant running on local hardware.",
+    llm=LLM(model="ollama/llama2", base_url="http://localhost:11434")
+)
+```
+
+## Changing the Base API URL
+
+You can change the base API URL for any LLM provider by setting the `base_url` parameter:
+
+```python
+llm = LLM(
+    model="custom-model-name",
+    base_url="https://api.your-provider.com/v1",
+    api_key="your-api-key"
+)
+agent = Agent(llm=llm, ...)
+```
+
+This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider.
 
 ## Conclusion
 
-By leveraging LiteLLM, CrewAI now offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options.
+By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options.

docs/index.md

@@ -53,6 +53,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Crews
                 </a>
             </li>
+            <li>
+                <a href="./core-concepts/LLMs">
+                    LLMs
+                </a>
+            </li>
             <li>
                 <a href="./core-concepts/Pipeline">
                     Pipeline

mkdocs.yml

@@ -78,14 +78,14 @@ theme:
 
   palette:
     - scheme: default
-      primary: red
-      accent: red
+      primary: deep orange
+      accent: deep orange
       toggle:
         icon: material/brightness-7
         name: Switch to dark mode
     - scheme: slate
-      primary: red
-      accent: red
+      primary: deep orange
+      accent: deep orange
       toggle:
         icon: material/brightness-4
         name: Switch to light mode

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.60.0"
+version = "0.63.0"
 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"
@@ -14,14 +14,14 @@ Repository = "https://github.com/crewAIInc/crewAI"
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
 pydantic = "^2.4.2"
-langchain = ">0.2,<=0.3"
+langchain = "^0.2.16"
 openai = "^1.13.3"
 opentelemetry-api = "^1.22.0"
 opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "1.3.3"
-regex = "^2024.7.24"
-crewai-tools = { version = "^0.12.0", optional = true }
+regex = "^2024.9.11"
+crewai-tools = { version = "^0.12.1", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
@@ -49,7 +49,7 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.12.0"
+crewai-tools = "^0.12.1"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"

src/crewai/agent.py

@@ -1,6 +1,6 @@
 import os
 from inspect import signature
-from typing import Any, List, Optional
+from typing import Any, List, Optional, Union
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
 from crewai.agents import CacheHandler
@@ -12,6 +12,7 @@ from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.training_handler import CrewTrainingHandler
 from crewai.utilities.token_counter_callback import TokenCalcHandler
+from crewai.llm import LLM
 
 
 def mock_agent_ops_provider():
@@ -81,8 +82,8 @@ class Agent(BaseAgent):
         default=True,
         description="Use system prompt for the agent.",
     )
-    llm: Any = Field(
-        description="Language model that will run the agent.", default="gpt-4o"
+    llm: Union[str, InstanceOf[LLM], Any] = Field(
+        description="Language model that will run the agent.", default=None
     )
     function_calling_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
@@ -118,12 +119,58 @@ class Agent(BaseAgent):
     @model_validator(mode="after")
     def post_init_setup(self):
         self.agent_ops_agent_name = self.role
-        self.llm = self.llm.model_name if hasattr(self.llm, "model_name") else self.llm
-        self.function_calling_llm = (
-            self.function_calling_llm.model_name
-            if hasattr(self.function_calling_llm, "model_name")
-            else self.function_calling_llm
-        )
+
+        # Handle different cases for self.llm
+        if isinstance(self.llm, str):
+            # If it's a string, create an LLM instance
+            self.llm = LLM(model=self.llm)
+        elif isinstance(self.llm, LLM):
+            # 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")
+            llm_params = {"model": model_name}
+
+            api_base = os.environ.get("OPENAI_API_BASE")
+            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
+
+            self.llm = LLM(**llm_params)
+        else:
+            # For any other type, attempt to extract relevant attributes
+            llm_params = {
+                "model": getattr(self.llm, "model_name", None)
+                or getattr(self.llm, "deployment_name", None)
+                or str(self.llm),
+                "temperature": getattr(self.llm, "temperature", None),
+                "max_tokens": getattr(self.llm, "max_tokens", None),
+                "logprobs": getattr(self.llm, "logprobs", None),
+                "timeout": getattr(self.llm, "timeout", None),
+                "max_retries": getattr(self.llm, "max_retries", None),
+                "api_key": getattr(self.llm, "api_key", None),
+                "base_url": getattr(self.llm, "base_url", None),
+                "organization": getattr(self.llm, "organization", None),
+            }
+            # Remove None values to avoid passing unnecessary parameters
+            llm_params = {k: v for k, v in llm_params.items() if v is not None}
+            self.llm = LLM(**llm_params)
+
+        # Similar handling for function_calling_llm
+        if self.function_calling_llm:
+            if isinstance(self.function_calling_llm, str):
+                self.function_calling_llm = LLM(model=self.function_calling_llm)
+            elif not isinstance(self.function_calling_llm, LLM):
+                self.function_calling_llm = LLM(
+                    model=getattr(self.function_calling_llm, "model_name", None)
+                    or getattr(self.function_calling_llm, "deployment_name", None)
+                    or str(self.function_calling_llm)
+                )
+
         if not self.agent_executor:
             self._setup_agent_executor()
 

src/crewai/agents/agent_builder/utilities/base_output_converter.py

@@ -39,9 +39,3 @@ class OutputConverter(BaseModel, ABC):
     def to_json(self, current_attempt=1):
         """Convert text to json."""
         pass
-
-    @property
-    @abstractmethod
-    def is_gpt(self) -> bool:
-        """Return if llm provided is of gpt from openai."""
-        pass

src/crewai/agents/crew_agent_executor.py

@@ -13,7 +13,6 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 )
 from crewai.utilities.logger import Logger
 from crewai.utilities.training_handler import CrewTrainingHandler
-from crewai.llm import LLM
 from crewai.agents.parser import (
     AgentAction,
     AgentFinish,
@@ -104,11 +103,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         try:
             while not isinstance(formatted_answer, AgentFinish):
                 if not self.request_within_rpm_limit or self.request_within_rpm_limit():
-                    answer = LLM(
-                        self.llm,
-                        stop=self.stop if self.use_stop_words else None,
+                    answer = self.llm.call(
+                        self.messages,
                         callbacks=self.callbacks,
-                    ).call(self.messages)
+                    )
 
                     if not self.use_stop_words:
                         try:
@@ -146,10 +144,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                                 )
                                 self.have_forced_answer = True
                         self.messages.append(
-                            self._format_msg(formatted_answer.text, role="assistant")
+                            self._format_msg(formatted_answer.text, role="user")
                         )
         except OutputParserException as e:
-            self.messages.append({"role": "assistant", "content": e.error})
+            self.messages.append({"role": "user", "content": e.error})
             return self._invoke_loop(formatted_answer)
 
         except Exception as e:
@@ -182,7 +180,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             if isinstance(formatted_answer, AgentAction):
                 thought = re.sub(r"\n+", "\n", formatted_answer.thought)
                 formatted_json = json.dumps(
-                    json.loads(formatted_answer.tool_input),
+                    formatted_answer.tool_input,
                     indent=2,
                     ensure_ascii=False,
                 )
@@ -241,7 +239,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return tool_result
 
     def _summarize_messages(self) -> None:
-        llm = LLM(self.llm)
         messages_groups = []
 
         for message in self.messages:
@@ -251,7 +248,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
         summarized_contents = []
         for group in messages_groups:
-            summary = llm.call(
+            summary = self.llm.call(
                 [
                     self._format_msg(
                         self._i18n.slices("summarizer_system_message"), role="system"
@@ -259,7 +256,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     self._format_msg(
                         self._i18n.errors("sumamrize_instruction").format(group=group),
                     ),
-                ]
+                ],
+                callbacks=self.callbacks,
             )
             summarized_contents.append(summary)
 

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

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.61.1,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.63.0,<1.0.0" }
 
 
 [tool.poetry.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 = ">=0.55.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.63.0,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

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

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.63.0,<1.0.0" }
 
 
 [tool.poetry.scripts]

src/crewai/crew.py

@@ -22,6 +22,7 @@ from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache import CacheHandler
 from crewai.crews.crew_output import CrewOutput
+from crewai.llm import LLM
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
@@ -110,6 +111,18 @@ class Crew(BaseModel):
         default=False,
         description="Whether the crew should use memory to store memories of it's execution",
     )
+    short_term_memory: Optional[InstanceOf[ShortTermMemory]] = Field(
+        default=None,
+        description="An Instance of the ShortTermMemory to be used by the Crew",
+    )
+    long_term_memory: Optional[InstanceOf[LongTermMemory]] = Field(
+        default=None,
+        description="An Instance of the LongTermMemory to be used by the Crew",
+    )
+    entity_memory: Optional[InstanceOf[EntityMemory]] = Field(
+        default=None,
+        description="An Instance of the EntityMemory to be used by the Crew",
+    )
     embedder: Optional[dict] = Field(
         default={"provider": "openai"},
         description="Configuration for the embedder to be used for the crew.",
@@ -199,12 +212,15 @@ class Crew(BaseModel):
         if self.output_log_file:
             self._file_handler = FileHandler(self.output_log_file)
         self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
-        self.function_calling_llm = (
-            self.function_calling_llm.model_name
-            if self.function_calling_llm is not None
-            and hasattr(self.function_calling_llm, "model_name")
-            else self.function_calling_llm
-        )
+        if self.function_calling_llm:
+            if isinstance(self.function_calling_llm, str):
+                self.function_calling_llm = LLM(model=self.function_calling_llm)
+            elif not isinstance(self.function_calling_llm, LLM):
+                self.function_calling_llm = LLM(
+                    model=getattr(self.function_calling_llm, "model_name", None)
+                    or getattr(self.function_calling_llm, "deployment_name", None)
+                    or str(self.function_calling_llm)
+                )
         self._telemetry = Telemetry()
         self._telemetry.set_tracer()
         return self
@@ -213,11 +229,19 @@ class Crew(BaseModel):
     def create_crew_memory(self) -> "Crew":
         """Set private attributes."""
         if self.memory:
-            self._long_term_memory = LongTermMemory()
-            self._short_term_memory = ShortTermMemory(
-                crew=self, embedder_config=self.embedder
+            self._long_term_memory = (
+                self.long_term_memory if self.long_term_memory else LongTermMemory()
+            )
+            self._short_term_memory = (
+                self.short_term_memory
+                if self.short_term_memory
+                else ShortTermMemory(crew=self, embedder_config=self.embedder)
+            )
+            self._entity_memory = (
+                self.entity_memory
+                if self.entity_memory
+                else EntityMemory(crew=self, embedder_config=self.embedder)
             )
-            self._entity_memory = EntityMemory(crew=self, embedder_config=self.embedder)
         return self
 
     @model_validator(mode="after")
@@ -514,10 +538,6 @@ class Crew(BaseModel):
             asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
             for i in range(len(inputs))
         ]
-        tasks = [
-            asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
-            for i in range(len(inputs))
-        ]
 
         results = await asyncio.gather(*tasks)
 
@@ -592,9 +612,9 @@ class Crew(BaseModel):
             manager.tools = self.manager_agent.get_delegation_tools(self.agents)
         else:
             self.manager_llm = (
-                self.manager_llm.model_name
-                if hasattr(self.manager_llm, "model_name")
-                else self.manager_llm
+                getattr(self.manager_llm, "model_name", None)
+                or getattr(self.manager_llm, "deployment_name", None)
+                or self.manager_llm
             )
             manager = Agent(
                 role=i18n.retrieve("hierarchical_manager_agent", "role"),
@@ -605,6 +625,7 @@ class Crew(BaseModel):
                 verbose=self.verbose,
             )
             self.manager_agent = manager
+        manager.crew = self
 
     def _execute_tasks(
         self,
@@ -936,14 +957,17 @@ class Crew(BaseModel):
     def test(
         self,
         n_iterations: int,
-        openai_model_name: str,
+        openai_model_name: Optional[str] = None,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
-        """Test and evaluate the Crew with the given inputs for n iterations."""
+        """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
         self._test_execution_span = self._telemetry.test_execution_span(
-            self, n_iterations, inputs, openai_model_name
-        )
-        evaluator = CrewEvaluator(self, openai_model_name)
+            self,
+            n_iterations,
+            inputs,
+            openai_model_name,  # type: ignore[arg-type]
+        )  # 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)

src/crewai/llm.py

@@ -1,20 +1,96 @@
-from typing import Any, Dict, List
-from litellm import completion
+from typing import Any, Dict, List, Optional, Union
+import logging
 import litellm
+from litellm import get_supported_openai_params
 
 
 class LLM:
-    def __init__(self, model: str, stop: List[str] = [], callbacks: List[Any] = []):
-        self.stop = stop
+    def __init__(
+        self,
+        model: str,
+        timeout: Optional[Union[float, int]] = None,
+        temperature: Optional[float] = None,
+        top_p: Optional[float] = None,
+        n: Optional[int] = None,
+        stop: Optional[Union[str, List[str]]] = None,
+        max_completion_tokens: Optional[int] = None,
+        max_tokens: Optional[int] = None,
+        presence_penalty: Optional[float] = None,
+        frequency_penalty: Optional[float] = None,
+        logit_bias: Optional[Dict[int, float]] = None,
+        response_format: Optional[Dict[str, Any]] = None,
+        seed: Optional[int] = None,
+        logprobs: Optional[bool] = None,
+        top_logprobs: Optional[int] = None,
+        base_url: Optional[str] = None,
+        api_version: Optional[str] = None,
+        api_key: Optional[str] = None,
+        callbacks: List[Any] = [],
+        **kwargs,
+    ):
         self.model = model
+        self.timeout = timeout
+        self.temperature = temperature
+        self.top_p = top_p
+        self.n = n
+        self.stop = stop
+        self.max_completion_tokens = max_completion_tokens
+        self.max_tokens = max_tokens
+        self.presence_penalty = presence_penalty
+        self.frequency_penalty = frequency_penalty
+        self.logit_bias = logit_bias
+        self.response_format = response_format
+        self.seed = seed
+        self.logprobs = logprobs
+        self.top_logprobs = top_logprobs
+        self.base_url = base_url
+        self.api_version = api_version
+        self.api_key = api_key
+        self.callbacks = callbacks
+        self.kwargs = kwargs
+
+        litellm.drop_params = True
         litellm.callbacks = callbacks
 
-    def call(self, messages: List[Dict[str, str]]) -> Dict[str, Any]:
-        response = completion(
-            stop=self.stop, model=self.model, messages=messages, num_retries=5
-        )
-        return response["choices"][0]["message"]["content"]
+    def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
+        if callbacks and len(callbacks) > 0:
+            litellm.callbacks = callbacks
 
-    def _call_callbacks(self, formatted_answer):
-        for callback in self.callbacks:
-            callback(formatted_answer)
+        try:
+            params = {
+                "model": self.model,
+                "messages": messages,
+                "timeout": self.timeout,
+                "temperature": self.temperature,
+                "top_p": self.top_p,
+                "n": self.n,
+                "stop": self.stop,
+                "max_tokens": self.max_tokens or self.max_completion_tokens,
+                "presence_penalty": self.presence_penalty,
+                "frequency_penalty": self.frequency_penalty,
+                "logit_bias": self.logit_bias,
+                "response_format": self.response_format,
+                "seed": self.seed,
+                "logprobs": self.logprobs,
+                "top_logprobs": self.top_logprobs,
+                "api_base": self.base_url,
+                "api_version": self.api_version,
+                "api_key": self.api_key,
+                **self.kwargs,
+            }
+            # Remove None values to avoid passing unnecessary parameters
+            params = {k: v for k, v in params.items() if v is not None}
+
+            response = litellm.completion(**params)
+            return response["choices"][0]["message"]["content"]
+        except Exception as e:
+            logging.error(f"LiteLLM call failed: {str(e)}")
+            raise  # Re-raise the exception after logging
+
+    def supports_function_calling(self) -> bool:
+        try:
+            params = get_supported_openai_params(model=self.model)
+            return "response_format" in params
+        except Exception as e:
+            logging.error(f"Failed to get supported params: {str(e)}")
+            return False

src/crewai/memory/entity/entity_memory.py

@@ -10,12 +10,13 @@ class EntityMemory(Memory):
     Inherits from the Memory class.
     """
 
-    def __init__(self, crew=None, embedder_config=None):
-        storage = RAGStorage(
-            type="entities",
-            allow_reset=False,
-            embedder_config=embedder_config,
-            crew=crew,
+    def __init__(self, crew=None, embedder_config=None, storage=None):
+        storage = (
+            storage
+            if storage
+            else RAGStorage(
+                type="entities", allow_reset=False, embedder_config=embedder_config, crew=crew
+            )
         )
         super().__init__(storage)
 

src/crewai/memory/long_term/long_term_memory.py

@@ -14,8 +14,8 @@ class LongTermMemory(Memory):
     LongTermMemoryItem instances.
     """
 
-    def __init__(self):
-        storage = LTMSQLiteStorage()
+    def __init__(self, storage=None):
+        storage = storage if storage else LTMSQLiteStorage()
         super().__init__(storage)
 
     def save(self, item: LongTermMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"

src/crewai/memory/short_term/short_term_memory.py

@@ -13,9 +13,13 @@ class ShortTermMemory(Memory):
     MemoryItem instances.
     """
 
-    def __init__(self, crew=None, embedder_config=None):
-        storage = RAGStorage(
-            type="short_term", embedder_config=embedder_config, crew=crew
+    def __init__(self, crew=None, embedder_config=None, storage=None):
+        storage = (
+            storage
+            if storage
+            else RAGStorage(
+                type="short_term", embedder_config=embedder_config, crew=crew
+            )
         )
         super().__init__(storage)
 

src/crewai/project/crew_base.py

@@ -35,7 +35,7 @@ def CrewBase(cls):
         @staticmethod
         def load_yaml(config_path: Path):
             try:
-                with open(config_path, "r") as file:
+                with open(config_path, "r", encoding="utf-8") as file:
                     return yaml.safe_load(file)
             except FileNotFoundError:
                 print(f"File not found: {config_path}")

src/crewai/tasks/task_output.py

@@ -35,7 +35,7 @@ class TaskOutput(BaseModel):
         return self
 
     @property
-    def json(self) -> str:
+    def json(self) -> Optional[str]:
         if self.output_format != OutputFormat.JSON:
             raise ValueError(
                 """

src/crewai/telemetry/telemetry.py

@@ -53,7 +53,8 @@ class Telemetry:
             self.resource = Resource(
                 attributes={SERVICE_NAME: "crewAI-telemetry"},
             )
-            self.provider = TracerProvider(resource=self.resource)
+            with suppress_warnings():
+                self.provider = TracerProvider(resource=self.resource)
 
             processor = BatchSpanProcessor(
                 OTLPSpanExporter(
@@ -116,8 +117,10 @@ class Telemetry:
                                     "max_iter": agent.max_iter,
                                     "max_rpm": agent.max_rpm,
                                     "i18n": agent.i18n.prompt_file,
-                                    "function_calling_llm": agent.function_calling_llm,
-                                    "llm": agent.llm,
+                                    "function_calling_llm": agent.function_calling_llm.model
+                                    if agent.function_calling_llm
+                                    else "",
+                                    "llm": agent.llm.model,
                                     "delegation_enabled?": agent.allow_delegation,
                                     "allow_code_execution?": agent.allow_code_execution,
                                     "max_retry_limit": agent.max_retry_limit,
@@ -181,8 +184,10 @@ class Telemetry:
                                     "verbose?": agent.verbose,
                                     "max_iter": agent.max_iter,
                                     "max_rpm": agent.max_rpm,
-                                    "function_calling_llm": agent.function_calling_llm,
-                                    "llm": agent.llm,
+                                    "function_calling_llm": agent.function_calling_llm.model
+                                    if agent.function_calling_llm
+                                    else "",
+                                    "llm": agent.llm.model,
                                     "delegation_enabled?": agent.allow_delegation,
                                     "allow_code_execution?": agent.allow_code_execution,
                                     "max_retry_limit": agent.max_retry_limit,
@@ -487,7 +492,7 @@ class Telemetry:
                                 "max_iter": agent.max_iter,
                                 "max_rpm": agent.max_rpm,
                                 "i18n": agent.i18n.prompt_file,
-                                "llm": agent.llm,
+                                "llm": agent.llm.model,
                                 "delegation_enabled?": agent.allow_delegation,
                                 "tools_names": [
                                     tool.name.casefold() for tool in agent.tools or []

src/crewai/tools/tool_usage.py

@@ -17,7 +17,7 @@ if os.environ.get("AGENTOPS_API_KEY"):
     except ImportError:
         pass
 
-OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o"]
+OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o", "o1-preview", "o1-mini"]
 
 
 class ToolUsageErrorException(Exception):
@@ -71,10 +71,12 @@ class ToolUsage:
         self.function_calling_llm = function_calling_llm
 
         # Set the maximum parsing attempts for bigger models
-        if self._is_gpt(self.function_calling_llm) and "4" in self.function_calling_llm:
-            if self.function_calling_llm in OPENAI_BIGGER_MODELS:
-                self._max_parsing_attempts = 2
-                self._remember_format_after_usages = 4
+        if (
+            self.function_calling_llm
+            and self.function_calling_llm in OPENAI_BIGGER_MODELS
+        ):
+            self._max_parsing_attempts = 2
+            self._remember_format_after_usages = 4
 
     def parse(self, tool_string: str):
         """Parse the tool string and return the tool calling."""
@@ -295,13 +297,6 @@ class ToolUsage:
             )
         return "\n--\n".join(descriptions)
 
-    def _is_gpt(self, llm) -> bool:
-        return (
-            "gpt" in str(llm).lower()
-            or "o1-preview" in str(llm).lower()
-            or "o1-mini" in str(llm).lower()
-        )
-
     def _tool_calling(
         self, tool_string: str
     ) -> Union[ToolCalling, InstructorToolCalling]:
@@ -309,7 +304,7 @@ class ToolUsage:
             if self.function_calling_llm:
                 model = (
                     InstructorToolCalling
-                    if self._is_gpt(self.function_calling_llm)
+                    if self.function_calling_llm.supports_function_calling()
                     else ToolCalling
                 )
                 converter = Converter(
@@ -327,7 +322,12 @@ class ToolUsage:
                     ),
                     max_attempts=1,
                 )
-                calling = converter.to_pydantic()
+                tool_object = converter.to_pydantic()
+                calling = ToolCalling(
+                    tool_name=tool_object["tool_name"],
+                    arguments=tool_object["arguments"],
+                    log=tool_string,  # type: ignore
+                )
 
                 if isinstance(calling, ConverterError):
                     raise calling

src/crewai/utilities/converter.py

@@ -2,7 +2,6 @@ import json
 import re
 from typing import Any, Optional, Type, Union
 
-from crewai.llm import LLM
 from pydantic import BaseModel, ValidationError
 
 from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
@@ -24,10 +23,10 @@ class Converter(OutputConverter):
     def to_pydantic(self, current_attempt=1):
         """Convert text to pydantic."""
         try:
-            if self.is_gpt:
+            if self.llm.supports_function_calling():
                 return self._create_instructor().to_pydantic()
             else:
-                return LLM(model=self.llm).call(
+                return self.llm.call(
                     [
                         {"role": "system", "content": self.instructions},
                         {"role": "user", "content": self.text},
@@ -43,11 +42,11 @@ class Converter(OutputConverter):
     def to_json(self, current_attempt=1):
         """Convert text to json."""
         try:
-            if self.is_gpt:
+            if self.llm.supports_function_calling():
                 return self._create_instructor().to_json()
             else:
                 return json.dumps(
-                    LLM(model=self.llm).call(
+                    self.llm.call(
                         [
                             {"role": "system", "content": self.instructions},
                             {"role": "user", "content": self.text},
@@ -78,7 +77,7 @@ class Converter(OutputConverter):
         )
 
         parser = CrewPydanticOutputParser(pydantic_object=self.model)
-        result = LLM(model=self.llm).call(
+        result = self.llm.call(
             [
                 {"role": "system", "content": self.instructions},
                 {"role": "user", "content": self.text},
@@ -86,15 +85,6 @@ class Converter(OutputConverter):
         )
         return parser.parse_result(result)
 
-    @property
-    def is_gpt(self) -> bool:
-        """Return if llm provided is of gpt from openai."""
-        return (
-            "gpt" in str(self.llm).lower()
-            or "o1-preview" in str(self.llm).lower()
-            or "o1-mini" in str(self.llm).lower()
-        )
-
 
 def convert_to_model(
     result: str,
@@ -180,7 +170,6 @@ def convert_with_instructions(
         model=model,
         instructions=instructions,
     )
-
     exported_result = (
         converter.to_pydantic() if not is_json_output else converter.to_json()
     )
@@ -197,21 +186,12 @@ def convert_with_instructions(
 
 def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
     instructions = "I'm gonna convert this raw text into valid JSON."
-    if not is_gpt(llm):
+    if llm.supports_function_calling():
         model_schema = PydanticSchemaParser(model=model).get_schema()
         instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
     return instructions
 
 
-def is_gpt(llm: Any) -> bool:
-    """Return if llm provided is of gpt from openai."""
-    return (
-        "gpt" in str(llm).lower()
-        or "o1-preview" in str(llm).lower()
-        or "o1-mini" in str(llm).lower()
-    )
-
-
 def create_converter(
     agent: Optional[Any] = None,
     converter_cls: Optional[Type[Converter]] = None,

src/crewai/utilities/evaluators/task_evaluator.py

@@ -78,7 +78,7 @@ class TaskEvaluator:
 
         instructions = "Convert all responses into valid JSON output."
 
-        if not self._is_gpt(self.llm):
+        if not self.llm.supports_function_calling():
             model_schema = PydanticSchemaParser(model=TaskEvaluation).get_schema()
             instructions = f"{instructions}\n\nReturn only valid JSON with the following schema:\n```json\n{model_schema}\n```"
 
@@ -91,13 +91,6 @@ class TaskEvaluator:
 
         return converter.to_pydantic()
 
-    def _is_gpt(self, llm) -> bool:
-        return (
-            "gpt" in str(self.llm).lower()
-            or "o1-preview" in str(self.llm).lower()
-            or "o1-mini" in str(self.llm).lower()
-        )
-
     def evaluate_training_data(
         self, training_data: dict, agent_id: str
     ) -> TrainingTaskEvaluation:
@@ -128,7 +121,7 @@ class TaskEvaluator:
         )
         instructions = "I'm gonna convert this raw text into valid JSON."
 
-        if not self._is_gpt(self.llm):
+        if not self.llm.supports_function_calling():
             model_schema = PydanticSchemaParser(
                 model=TrainingTaskEvaluation
             ).get_schema()

src/crewai/utilities/i18n.py

@@ -17,13 +17,13 @@ class I18N(BaseModel):
         """Load prompts from a JSON file."""
         try:
             if self.prompt_file:
-                with open(self.prompt_file, "r") as f:
+                with open(self.prompt_file, "r", encoding="utf-8") as f:
                     self._prompts = json.load(f)
             else:
                 dir_path = os.path.dirname(os.path.realpath(__file__))
                 prompts_path = os.path.join(dir_path, "../translations/en.json")
 
-                with open(prompts_path, "r") as f:
+                with open(prompts_path, "r", encoding="utf-8") as f:
                     self._prompts = json.load(f)
         except FileNotFoundError:
             raise Exception(f"Prompt file '{self.prompt_file}' not found.")

src/crewai/utilities/internal_instructor.py

@@ -42,6 +42,6 @@ class InternalInstructor:
         if self.instructions:
             messages.append({"role": "system", "content": self.instructions})
         model = self._client.chat.completions.create(
-            model=self.llm, response_model=self.model, messages=messages
+            model=self.llm.model, response_model=self.model, messages=messages
         )
         return model

src/crewai/utilities/logger.py

@@ -9,9 +9,9 @@ class Logger(BaseModel):
     verbose: bool = Field(default=False)
     _printer: Printer = PrivateAttr(default_factory=Printer)
 
-    def log(self, level, message, color="bold_green"):
+    def log(self, level, message, color="bold_yellow"):
         if self.verbose:
             timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
             self._printer.print(
-                f"[{timestamp}][{level.upper()}]: {message}", color=color
+                f"\n[{timestamp}][{level.upper()}]: {message}", color=color
             )

src/crewai/utilities/printer.py

@@ -15,6 +15,8 @@ class Printer:
             self._print_bold_blue(content)
         elif color == "yellow":
             self._print_yellow(content)
+        elif color == "bold_yellow":
+            self._print_bold_yellow(content)
         else:
             print(content)
 
@@ -35,3 +37,6 @@ class Printer:
 
     def _print_yellow(self, content):
         print("\033[93m {}\033[00m".format(content))
+
+    def _print_bold_yellow(self, content):
+        print("\033[1m\033[93m {}\033[00m".format(content))

src/crewai/utilities/rpm_controller.py

@@ -52,7 +52,7 @@ class RPMController(BaseModel):
             self._timer = None
 
     def _wait_for_next_minute(self):
-        time.sleep(1)
+        time.sleep(60)
         self._current_rpm = 0
 
     def _reset_request_count(self):

tests/agent_test.py

@@ -3,6 +3,7 @@
 from unittest import mock
 from unittest.mock import patch
 
+import os
 import pytest
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
@@ -16,6 +17,49 @@ from crewai_tools import tool
 from crewai.agents.parser import AgentAction
 
 
+def test_agent_llm_creation_with_env_vars():
+    # Store original environment variables
+    original_api_key = os.environ.get("OPENAI_API_KEY")
+    original_api_base = os.environ.get("OPENAI_API_BASE")
+    original_model_name = os.environ.get("OPENAI_MODEL_NAME")
+
+    # Set up environment variables
+    os.environ["OPENAI_API_KEY"] = "test_api_key"
+    os.environ["OPENAI_API_BASE"] = "https://test-api-base.com"
+    os.environ["OPENAI_MODEL_NAME"] = "gpt-4-turbo"
+
+    # Create an agent without specifying LLM
+    agent = Agent(role="test role", goal="test goal", backstory="test backstory")
+
+    # Check if LLM is created correctly
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-4-turbo"
+    assert agent.llm.api_key == "test_api_key"
+    assert agent.llm.base_url == "https://test-api-base.com"
+
+    # Clean up environment variables
+    del os.environ["OPENAI_API_KEY"]
+    del os.environ["OPENAI_API_BASE"]
+    del os.environ["OPENAI_MODEL_NAME"]
+
+    # Create an agent without specifying LLM
+    agent = Agent(role="test role", goal="test goal", backstory="test backstory")
+
+    # Check if LLM is created correctly
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model != "gpt-4-turbo"
+    assert agent.llm.api_key != "test_api_key"
+    assert agent.llm.base_url != "https://test-api-base.com"
+
+    # Restore original environment variables
+    if original_api_key:
+        os.environ["OPENAI_API_KEY"] = original_api_key
+    if original_api_base:
+        os.environ["OPENAI_API_BASE"] = original_api_base
+    if original_model_name:
+        os.environ["OPENAI_MODEL_NAME"] = original_model_name
+
+
 def test_agent_creation():
     agent = Agent(role="test role", goal="test goal", backstory="test backstory")
 
@@ -27,7 +71,7 @@ def test_agent_creation():
 
 def test_agent_default_values():
     agent = Agent(role="test role", goal="test goal", backstory="test backstory")
-    assert agent.llm == "gpt-4o"
+    assert agent.llm.model == "gpt-4o"
     assert agent.allow_delegation is False
 
 
@@ -35,7 +79,7 @@ def test_custom_llm():
     agent = Agent(
         role="test role", goal="test goal", backstory="test backstory", llm="gpt-4"
     )
-    assert agent.llm == "gpt-4"
+    assert agent.llm.model == "gpt-4"
 
 
 def test_custom_llm_with_langchain():
@@ -48,7 +92,51 @@ def test_custom_llm_with_langchain():
         llm=ChatOpenAI(temperature=0, model="gpt-4"),
     )
 
-    assert agent.llm == "gpt-4"
+    assert agent.llm.model == "gpt-4"
+
+
+def test_custom_llm_temperature_preservation():
+    from langchain_openai import ChatOpenAI
+
+    langchain_llm = ChatOpenAI(temperature=0.7, model="gpt-4")
+    agent = Agent(
+        role="temperature test role",
+        goal="temperature test goal",
+        backstory="temperature test backstory",
+        llm=langchain_llm,
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-4"
+    assert agent.llm.temperature == 0.7
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task():
+    from langchain_openai import ChatOpenAI
+    from crewai import Task
+
+    agent = Agent(
+        role="Math Tutor",
+        goal="Solve math problems accurately",
+        backstory="You are an experienced math tutor with a knack for explaining complex concepts simply.",
+        llm=ChatOpenAI(temperature=0.7, model="gpt-4o-mini"),
+    )
+
+    task = Task(
+        description="Calculate the area of a circle with radius 5 cm.",
+        expected_output="The calculated area of the circle in square centimeters.",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+
+    assert result is not None
+    assert (
+        "The area of the circle with a radius of 5 cm is approximately 78.5 square centimeters."
+        == result
+    )
+    assert "square centimeters" in result.lower()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -91,7 +179,7 @@ def test_agent_execution_with_tools():
         expected_output="The result of the multiplication.",
     )
     output = agent.execute_task(task)
-    assert output == "The result of the multiplication is 12."
+    assert output == "The result of 3 times 4 is 12"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -109,6 +197,7 @@ def test_logging_tool_usage():
         verbose=True,
     )
 
+    assert agent.llm.model == "gpt-4o"
     assert agent.tools_handler.last_used_tool == {}
     task = Task(
         description="What is 3 times 4?",
@@ -121,7 +210,8 @@ def test_logging_tool_usage():
     tool_usage = InstructorToolCalling(
         tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
     )
-    assert output == "The result of 3 times 4 is 12."
+
+    assert output == "12"
     assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
     assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
 
@@ -182,7 +272,7 @@ def test_cache_hitting():
         task = Task(
             description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
             agent=agent,
-            expected_output="The result of the multiplication.",
+            expected_output="The number that is the result of the multiplication tool.",
         )
         output = agent.execute_task(task)
         assert output == "0"
@@ -275,7 +365,7 @@ def test_agent_execution_with_specific_tools():
         expected_output="The result of the multiplication.",
     )
     output = agent.execute_task(task=task, tools=[multiplier])
-    assert output == "The result of the multiplication of 3 times 4 is 12."
+    assert output == "The result of 3 times 4 is 12."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -459,7 +549,7 @@ def test_agent_moved_on_after_max_iterations():
         task=task,
         tools=[get_final_answer],
     )
-    assert output == "The final answer is 42."
+    assert output == "42"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -750,27 +840,14 @@ def test_agent_function_calling_llm():
     )
     tasks = [essay]
     crew = Crew(agents=[agent1], tasks=tasks)
-    from unittest.mock import patch, Mock
+    from unittest.mock import patch
     import instructor
 
-    with patch.object(instructor, "from_litellm") as mock_from_litellm:
-        mock_client = Mock()
-        mock_from_litellm.return_value = mock_client
-        mock_chat = Mock()
-        mock_client.chat = mock_chat
-        mock_completions = Mock()
-        mock_chat.completions = mock_completions
-        mock_create = Mock()
-        mock_completions.create = mock_create
-
+    with patch.object(
+        instructor, "from_litellm", wraps=instructor.from_litellm
+    ) as mock_from_litellm:
         crew.kickoff()
-
         mock_from_litellm.assert_called()
-        mock_create.assert_called()
-        calls = mock_create.call_args_list
-        assert any(
-            call.kwargs.get("model") == "gpt-4o" for call in calls
-        ), "Instructor was not created with the expected model"
 
 
 def test_agent_count_formatting_error():
@@ -1102,6 +1179,88 @@ def test_agent_max_retry_limit():
         )
 
 
+def test_agent_with_llm():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo", temperature=0.7),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-3.5-turbo"
+    assert agent.llm.temperature == 0.7
+
+
+def test_agent_with_custom_stop_words():
+    stop_words = ["STOP", "END"]
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo", stop=stop_words),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.stop == stop_words
+
+
+def test_agent_with_callbacks():
+    def dummy_callback(response):
+        pass
+
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo", callbacks=[dummy_callback]),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert len(agent.llm.callbacks) == 1
+    assert agent.llm.callbacks[0] == dummy_callback
+
+
+def test_agent_with_additional_kwargs():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(
+            model="gpt-3.5-turbo",
+            temperature=0.8,
+            top_p=0.9,
+            presence_penalty=0.1,
+            frequency_penalty=0.1,
+        ),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-3.5-turbo"
+    assert agent.llm.temperature == 0.8
+    assert agent.llm.top_p == 0.9
+    assert agent.llm.presence_penalty == 0.1
+    assert agent.llm.frequency_penalty == 0.1
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call():
+    llm = LLM(model="gpt-3.5-turbo")
+    messages = [{"role": "user", "content": "Say 'Hello, World!'"}]
+
+    response = llm.call(messages)
+    assert "Hello, World!" in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_error():
+    llm = LLM(model="non-existent-model")
+    messages = [{"role": "user", "content": "This should fail"}]
+
+    with pytest.raises(Exception):
+        llm.call(messages)
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_handle_context_length_exceeds_limit():
     agent = Agent(
@@ -1172,3 +1331,214 @@ def test_handle_context_length_exceeds_limit_cli_no():
             CrewAgentExecutor, "_handle_context_length"
         ) as mock_handle_context:
             mock_handle_context.assert_not_called()
+
+
+def test_agent_with_all_llm_attributes():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(
+            model="gpt-3.5-turbo",
+            timeout=10,
+            temperature=0.7,
+            top_p=0.9,
+            n=1,
+            stop=["STOP", "END"],
+            max_tokens=100,
+            presence_penalty=0.1,
+            frequency_penalty=0.1,
+            logit_bias={50256: -100},  # Example: bias against the EOT token
+            response_format={"type": "json_object"},
+            seed=42,
+            logprobs=True,
+            top_logprobs=5,
+            base_url="https://api.openai.com/v1",
+            api_version="2023-05-15",
+            api_key="sk-your-api-key-here",
+        ),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-3.5-turbo"
+    assert agent.llm.timeout == 10
+    assert agent.llm.temperature == 0.7
+    assert agent.llm.top_p == 0.9
+    assert agent.llm.n == 1
+    assert agent.llm.stop == ["STOP", "END"]
+    assert agent.llm.max_tokens == 100
+    assert agent.llm.presence_penalty == 0.1
+    assert agent.llm.frequency_penalty == 0.1
+    assert agent.llm.logit_bias == {50256: -100}
+    assert agent.llm.response_format == {"type": "json_object"}
+    assert agent.llm.seed == 42
+    assert agent.llm.logprobs
+    assert agent.llm.top_logprobs == 5
+    assert agent.llm.base_url == "https://api.openai.com/v1"
+    assert agent.llm.api_version == "2023-05-15"
+    assert agent.llm.api_key == "sk-your-api-key-here"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_all_attributes():
+    llm = LLM(
+        model="gpt-3.5-turbo",
+        temperature=0.7,
+        max_tokens=50,
+        stop=["STOP"],
+        presence_penalty=0.1,
+        frequency_penalty=0.1,
+    )
+    messages = [{"role": "user", "content": "Say 'Hello, World!' and then say STOP"}]
+
+    response = llm.call(messages)
+    assert "Hello, World!" in response
+    assert "STOP" not in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_with_ollama_gemma():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(
+            model="ollama/gemma2:latest",
+            base_url="http://localhost:8080",
+        ),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "ollama/gemma2:latest"
+    assert agent.llm.base_url == "http://localhost:8080"
+
+    task = "Respond in 20 words. Who are you?"
+    response = agent.llm.call([{"role": "user", "content": task}])
+
+    assert response
+    assert len(response.split()) <= 25  # Allow a little flexibility in word count
+    assert "Gemma" in response or "AI" in response or "language model" in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_ollama_gemma():
+    llm = LLM(
+        model="ollama/gemma2:latest",
+        base_url="http://localhost:8080",
+        temperature=0.7,
+        max_tokens=30,
+    )
+    messages = [{"role": "user", "content": "Respond in 20 words. Who are you?"}]
+
+    response = llm.call(messages)
+
+    assert response
+    assert len(response.split()) <= 25  # Allow a little flexibility in word count
+    assert "Gemma" in response or "AI" in response or "language model" in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_basic():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo"),
+    )
+
+    task = Task(
+        description="Calculate 2 + 2",
+        expected_output="The result of the calculation",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert "4" in result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_context():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo"),
+    )
+
+    task = Task(
+        description="Summarize the given context in one sentence",
+        expected_output="A one-sentence summary",
+        agent=agent,
+    )
+
+    context = "The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet."
+
+    result = agent.execute_task(task, context=context)
+    assert len(result.split(".")) == 3
+    assert "fox" in result.lower() and "dog" in result.lower()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_tool():
+    @tool
+    def dummy_tool(query: str) -> str:
+        """Useful for when you need to get a dummy result for a query."""
+        return f"Dummy result for: {query}"
+
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo"),
+        tools=[dummy_tool],
+    )
+
+    task = Task(
+        description="Use the dummy tool to get a result for 'test query'",
+        expected_output="The result from the dummy tool",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert "Dummy result for: test query" in result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_custom_llm():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo", temperature=0.7, max_tokens=50),
+    )
+
+    task = Task(
+        description="Write a haiku about AI",
+        expected_output="A haiku (3 lines, 5-7-5 syllable pattern) about AI",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert result.startswith(
+        "Artificial minds,\nLearning, evolving, creating,\nFuture in circuits."
+    )
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_ollama():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="ollama/gemma2:latest", base_url="http://localhost:8080"),
+    )
+
+    task = Task(
+        description="Explain what AI is in one sentence",
+        expected_output="A one-sentence explanation of AI",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert len(result.split(".")) == 2
+    assert "AI" in result or "artificial intelligence" in result.lower()

tests/agent_tools/agent_tools_test.py

@@ -24,7 +24,7 @@ def test_delegate_work():
 
     assert (
         result
-        == "While it's a common perception that I might \"hate\" AI agents, my actual stance is much more nuanced and guided by an in-depth understanding of their potential and limitations. As an expert researcher in technology, I recognize that AI agents are a significant advancement in the field of computing and artificial intelligence, offering numerous benefits and applications across various sectors. Here's a detailed take on AI agents:\n\n**Advantages of AI Agents:**\n1. **Automation and Efficiency:** AI agents can automate repetitive tasks, thus freeing up human workers for more complex and creative work. This leads to significant efficiency gains in industries such as customer service (chatbots), data analysis, and even healthcare (AI diagnostic tools).\n\n2. **24/7 Availability:** Unlike human workers, AI agents can operate continuously without fatigue. This is particularly beneficial in customer service environments where support can be provided around the clock.\n\n3. **Data Handling and Analysis:** AI agents can process and analyze vast amounts of data more quickly and accurately than humans. This ability is invaluable in fields like finance, where AI can detect fraudulent activities, or in marketing, where consumer data can be analyzed to improve customer engagement strategies.\n\n4. **Personalization:** AI agents can provide personalized experiences by learning from user interactions. For example, recommendation systems on platforms like Netflix and Amazon use AI agents to suggest content or products tailored to individual preferences.\n\n5. **Scalability:** AI agents can be scaled up easily to handle increasing workloads, making them ideal for businesses experiencing growth or variable demand.\n\n**Challenges and Concerns:**\n1. **Ethical Implications:** The deployment of AI agents raises significant ethical questions, including issues of bias, privacy, and the potential for job displacement. It’s crucial to address these concerns by incorporating transparent, fair, and inclusive practices in AI development and deployment.\n\n2. **Dependability and Error Rates:** While AI agents are generally reliable, they are not infallible. Errors, especially in critical areas like healthcare or autonomous driving, can have severe consequences. Therefore, rigorous testing and validation are essential.\n\n3. **Lack of Understanding:** Many users and stakeholders may not fully understand how AI agents work, leading to mistrust or misuse. Improving AI literacy and transparency can help build trust in these systems.\n\n4. **Security Risks:** AI agents can be vulnerable to cyber-attacks. Ensuring robust cybersecurity measures are in place is vital to protect sensitive data and maintain the integrity of AI systems.\n\n5. **Regulation and Oversight:** The rapid development of AI technology often outpaces regulatory frameworks. Effective governance is needed to ensure AI is used responsibly and ethically.\n\nIn summary, while I thoroughly understand the transformative potential of AI agents and their numerous advantages, I also recognize the importance of addressing the associated challenges. It's not about hating AI agents, but rather advocating for their responsible and ethical use to ensure they benefit society as a whole. My critical perspective is rooted in a desire to see AI agents implemented in ways that maximize their benefits while minimizing potential harms."
+        == "While I understand the concerns and skepticism surrounding AI agents, I wouldn't say that I hate them. My standpoint is more nuanced. AI agents, which are software entities that perform tasks autonomously using machine learning and other AI technologies, have tremendous potential to revolutionize various sectors.\n\nOn the positive side, AI agents can significantly enhance efficiency and productivity. For example, in customer service, AI agents can handle routine inquiries, allowing human agents to focus on more complex issues. In healthcare, they can assist in diagnosing diseases, thus speeding up the decision-making process and potentially saving lives. In finance, AI agents can automate trading, detect fraudulent activities, and provide personalized financial advice.\n\nHowever, there are legitimate concerns that need to be addressed. One major issue is the ethical implications of deploying AI agents. These include data privacy, biases in decision-making algorithms, and the lack of transparency in how these agents operate. Another concern is the potential job displacement that could result from increased automation. While AI agents can handle many tasks more efficiently than humans, this could lead to significant job losses in certain sectors.\n\nMoreover, there's the matter of reliability and accountability. AI agents, despite their advanced capabilities, are not infallible. They can make mistakes, and when they do, it can be challenging to pinpoint where things went wrong and who is responsible. This raises important questions about oversight and governance.\n\nIn summary, while I am cautious about the unchecked deployment of AI agents due to these ethical and practical concerns, I also recognize their potential to bring about significant positive changes. The key lies in finding a balanced approach that maximizes their benefits while mitigating their risks. This includes rigorous testing, continuous monitoring, and establishing clear ethical guidelines and policies to govern their use. \n\nBy addressing these challenges head-on, we can harness the power of AI agents in a way that is both innovative and responsible."
     )
 
 
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
 
     assert (
         result
-        == "As an expert researcher in technology, particularly in the field of AI and AI agents, it is essential to clarify that my perspective is not one of hatred but rather critical analysis. My evaluation of AI agents is grounded in a balanced view of their advantages and the challenges they present. \n\nAI agents represent a significant leap in technological progress with a wide array of applications across industries. They can perform tasks ranging from customer service interactions, data analysis, complex simulations, to even personal assistance. Their ability to learn and adapt makes them powerful tools for enhancing productivity and innovation.\n\nHowever, there are considerable challenges and ethical concerns associated with their deployment. These include privacy issues, job displacement, and the potential for biased decision-making driven by flawed algorithms. Furthermore, the security risks posed by AI agents, such as how they can be manipulated or hacked, are critical concerns that cannot be ignored.\n\nIn essence, while I do recognize the transformative potential of AI agents, I remain vigilant about their implications. It is vital to ensure that their development is guided by robust ethical standards and stringent regulations to mitigate risks. My view is not rooted in hatred but in a deep commitment to responsible and thoughtful technological advancement. \n\nI hope this clarifies my stance on AI agents and underscores the importance of critical engagement with emerging technologies."
+        == 'AI agents are specialized software entities that perform tasks autonomously on behalf of users. They leverage artificial intelligence to process inputs, learn from experiences, and make decisions, mimicking human-like behavior. Despite their transformative potential, I don\'t "hate" AI agents; rather, I hold a nuanced view that acknowledges both their advantages and limitations.\n\nAdvantages of AI Agents:\n1. **Efficiency and Productivity**: AI agents can handle repetitive tasks efficiently, freeing up human workers to focus on more complex and creative activities.\n2. **24/7 Operation**: Unlike humans, AI agents can work around the clock without breaks, significantly increasing productivity and service availability.\n3. **Data Processing**: They can process and analyze vast amounts of data quickly and accurately, supporting better decision-making.\n4. **Personalization**: AI agents can tailor services and recommendations based on user behavior and preferences, improving customer satisfaction.\n\nLimitations and Concerns:\n1. **Ethical Issues**: The deployment of AI agents raises concerns about data privacy, surveillance, and the potential for bias in decision-making algorithms.\n2. **Job Displacement**: There is legitimate concern about AI agents replacing human jobs, especially in industries where tasks are routine and repetitive.\n3. **Dependence on Data Quality**: AI agents\' performance hinges on the quality and quantity of data they are trained on. Poor data quality can lead to erroneous outcomes.\n4. **Complexity in Implementation**: Developing and maintaining AI agents requires significant technical expertise and resources. Problems can arise from their complexity, leading to potential failures.\n\nIn conclusion, while I don\'t "hate" AI agents, I am cautious of their broad and uncritical adoption. It’s essential to strike a balance between leveraging their capabilities and addressing the ethical, social, and technical challenges they present.'
     )
 
 
@@ -52,7 +52,7 @@ def test_ask_question():
 
     assert (
         result
-        == "No, I do not hate AI agents; in fact, I find them incredibly fascinating and useful. As a researcher specializing in technology, particularly in AI and AI agents, I appreciate their potential to revolutionize various industries by automating tasks, providing deep insights through data analysis, and even enhancing decision-making processes. AI agents can streamline operations, improve efficiency, and contribute to advancements in fields like healthcare, finance, and cybersecurity. While they do present challenges, such as ethical considerations and the need for robust security measures, the benefits and potential for positive impact are immense. Therefore, my stance is one of strong support and enthusiasm for AI agents and their future developments."
+        == "As a researcher specializing in technology and AI, I don't hate AI agents. In fact, I find them incredibly fascinating and beneficial. AI agents have the potential to transform various industries, improve efficiencies, and offer new solutions to complex problems. Their ability to learn, adapt, and perform tasks that were once thought to require human intelligence is remarkable. While it's important to consider ethical implications and ensure that AI systems are designed and deployed responsibly, I believe their overall positive impact on society and technology is significant. So to clarify, I don't hate AI agents; rather, I am quite enthusiastic about their potential and the advancements they bring to the field of technology."
     )
 
 
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
 
     assert (
         result
-        == "I do not hate AI agents; in fact, I appreciate them for their immense potential and the numerous benefits they bring to various fields. My passion for AI agents stems from their ability to streamline processes, enhance decision-making, and provide innovative solutions to complex problems. They significantly contribute to advancements in healthcare, finance, education, and many other sectors, making tasks more efficient and freeing up human capacities for more creative and strategic endeavors. So, to answer your question, I love AI agents because of the positive impact they have on our world and their capability to drive technological progress."
+        == "As an expert researcher specialized in technology and AI, my perspective on AI agents is shaped by both their potential and limitations. AI agents are tools designed to perform tasks, analyze data, and assist in various domains efficiently and accurately. They have the capability to revolutionize industries by automating complex processes, enhancing decision-making, and providing personalized experiences. For instance, in healthcare, AI agents can help in diagnosing diseases with high precision, while in finance, they can predict market trends and prevent fraud.\n\nHowever, my appreciation for AI agents does not mean I am blind to their challenges. There are valid concerns related to privacy, ethical use, and the potential displacement of jobs. The development and deployment of AI should be approached with caution, ensuring transparency, fairness, and accountability.\n\nIn conclusion, I value the advancements AI agents bring to the table and acknowledge their profound impact on society. My interest lies in leveraging their potential responsibly while addressing the associated ethical and societal challenges. So, while I love the capabilities and innovations brought forth by AI agents, I remain critically aware of the need for responsible development and use."
     )
 
 
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
 
     assert (
         result
-        == "AI agents have emerged as a revolutionary force in today's technological landscape, and my stance on them is not rooted in hatred but in a critical, analytical perspective. Let's delve deeper into what makes AI agents both a boon and a bane in various contexts.\n\n**Benefits of AI Agents:**\n\n1. **Automation and Efficiency:**\n   AI agents excel at automating repetitive tasks, which frees up human resources for more complex and creative endeavors. They are capable of performing tasks rapidly and with high accuracy, leading to increased efficiency in operations.\n\n2. **Data Analysis and Decision Making:**\n   These agents can process vast amounts of data at speeds far beyond human capability. They can identify patterns and insights that would otherwise be missed, aiding in informed decision-making processes across industries like finance, healthcare, and logistics.\n\n3. **Personalization and User Experience:**\n   AI agents can personalize interactions on a scale that is impractical for humans. For example, recommendation engines in e-commerce or content platforms tailor suggestions to individual users, enhancing user experience and satisfaction.\n\n4. **24/7 Availability:**\n   Unlike human employees, AI agents can operate round-the-clock without the need for breaks, sleep, or holidays. This makes them ideal for customer service roles, providing consistent and immediate responses any time of the day.\n\n**Challenges and Concerns:**\n\n1. **Job Displacement:**\n   One of the major concerns is the displacement of jobs. As AI agents become more proficient at a variety of tasks, there is a legitimate fear of human workers being replaced, leading to unemployment and economic disruption.\n\n2. **Bias and Fairness:**\n   AI agents are only as good as the data they are trained on. If the training data contains biases, the AI agents can perpetuate or even exacerbate these biases, leading to unfair and discriminatory outcomes.\n\n3. **Privacy and Security:**\n   The use of AI agents often involves handling large amounts of personal data, raising significant privacy and security concerns. Unauthorized access or breaches could lead to severe consequences for individuals and organizations.\n\n4. **Accountability and Transparency:**\n   The decision-making processes of AI agents can be opaque, making it difficult to hold them accountable. This lack of transparency can lead to mistrust and ethical dilemmas, particularly when AI decisions impact human lives.\n\n5. **Ethical Considerations:**\n   The deployment of AI agents in sensitive areas, such as surveillance and law enforcement, raises ethical issues. The potential for misuse or overdependence on AI decision-making poses a threat to individual freedoms and societal norms.\n\nIn conclusion, while AI agents offer remarkable advantages in terms of efficiency, data handling, and user experience, they also bring significant challenges that need to be addressed carefully. My critical stance is driven by a desire to ensure that their integration into society is balanced, fair, and beneficial to all, without ignoring the potential downsides. Therefore, a nuanced approach is essential in leveraging the power of AI agents responsibly."
+        == "It's interesting that you've heard I dislike AI agents; I suspect there may have been a miscommunication. My thoughts on AI agents are more nuanced than a simple like or dislike.\n\nAI agents can be incredibly powerful tools with the potential to drastically transform various industries. Their ability to automate tasks, analyze vast amounts of data, and make predictions can lead to significant improvements in efficiency and innovation. For instance, in healthcare, AI agents can assist in diagnosing diseases by quickly analyzing medical images. In finance, they can help in fraud detection by swiftly recognizing suspicious patterns in transactions. The applications are virtually limitless and continually expanding.\n\nHowever, there are concerns that need to be addressed, which might have led to a perception that I \"hate\" AI agents. One concern is the ethical implications surrounding their deployment. Issues such as data privacy, algorithmic bias, and the potential for job displacement are significant. For example, if an AI system is trained on biased data, it may make unfair or discriminatory decisions, perpetuating existing societal inequalities. Moreover, as AI agents take over repetitive tasks, there's a real risk that many jobs could become obsolete, causing economic disruption.\n\nAdditionally, there's the matter of accountability. When an AI agent makes a decision, it's not always clear who is responsible if something goes wrong. This opacity poses challenges for regulatory frameworks and trust in these systems. \n\nBalancing the tremendous benefits AI agents can provide with the ethical and practical challenges they introduce is crucial. Rather than viewing AI agents as something to be liked or disliked, I see them as tools that need thoughtful integration and rigorous oversight to maximize their positive impact and minimize their risks. Therefore, while I am enthusiastic about the potential of AI agents, I advocate for a cautious and responsible approach to their development and deployment."
     )
 
 
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
 
     assert (
         result
-        == "As a researcher specialized in technology, particularly in AI and AI agents, my feelings toward them are far more nuanced than simply loving or hating them. AI agents represent a remarkable advancement in technology and hold tremendous potential for improving various aspects of our lives and industries. They can automate tedious tasks, provide intelligent data analysis, support decision-making, and even enhance our creative processes. These capabilities can drive efficiency, innovation, and economic growth.\n\nHowever, it is also crucial to acknowledge the challenges and ethical considerations posed by AI agents. Issues such as data privacy, security, job displacement, and the need for proper regulation are significant concerns that must be carefully managed. Moreover, the development and deployment of AI should be guided by principles that ensure fairness, transparency, and accountability.\n\nIn essence, I appreciate the profound impact AI agents can have, but I also recognize the importance of approaching their integration into society with thoughtful consideration and responsibility. Balancing enthusiasm with caution and ethical oversight is key to harnessing the full potential of AI while mitigating its risks."
+        == "As an expert researcher in technology with a specialization in AI and AI agents, my perspective is rooted in my deep understanding of their capabilities and potential. AI agents, like any technology, are tools that can be used for both beneficial and harmful purposes. Personally, I do not hate AI agents; rather, I recognize their immense potential to transform industries, improve efficiencies, and solve complex problems. However, I also acknowledge that they come with challenges that need to be carefully managed, such as ethical considerations, privacy concerns, and the potential for job displacement.\n\nThe reason you might have heard that I love them is likely because I am passionate about the potential that AI agents hold for advancing technology and aiding humanity. I believe that with responsible development, transparent governance, and thoughtful integration, AI agents can indeed bring about positive change. My enthusiasm should not be misconstrued as blind love but rather as a measured appreciation for their capabilities and a commitment to navigating their complexities responsibly."
     )
 
 

tests/agent_tools/cassettes/test_ask_question.yaml

@@ -12,7 +12,7 @@ interactions:
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       This is VERY important to you, use the tools available and give your best Final
-      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
+      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o"}'
     headers:
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@@ -40,7 +40,7 @@ interactions:
       x-stainless-os:
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-      - 1.45.0
+      - 1.47.0
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@@ -50,29 +50,28 @@ interactions:
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-      \"chat.completion\",\n  \"created\": 1726476290,\n  \"model\": \"gpt-4o-2024-05-13\",\n
+    content: "{\n  \"id\": \"chatcmpl-AAj5XXXw78AeZ5uNUvDiQGNKU2frE\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1727119963,\n  \"model\": \"gpt-4o-2024-05-13\",\n
       \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
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-      AI agents, I appreciate their potential to revolutionize various industries
-      by automating tasks, providing deep insights through data analysis, and even
-      enhancing decision-making processes. AI agents can streamline operations, improve
-      efficiency, and contribute to advancements in fields like healthcare, finance,
-      and cybersecurity. While they do present challenges, such as ethical considerations
-      and the need for robust security measures, the benefits and potential for positive
-      impact are immense. Therefore, my stance is one of strong support and enthusiasm
-      for AI agents and their future developments.\",\n        \"refusal\": null\n
-      \     },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n
-      \ ],\n  \"usage\": {\n    \"prompt_tokens\": 199,\n    \"completion_tokens\":
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+      \"assistant\",\n        \"content\": \"Thought: I now can give a great answer\\nFinal
+      Answer: As a researcher specializing in technology and AI, I don't hate AI agents.
+      In fact, I find them incredibly fascinating and beneficial. AI agents have the
+      potential to transform various industries, improve efficiencies, and offer new
+      solutions to complex problems. Their ability to learn, adapt, and perform tasks
+      that were once thought to require human intelligence is remarkable. While it's
+      important to consider ethical implications and ensure that AI systems are designed
+      and deployed responsibly, I believe their overall positive impact on society
+      and technology is significant. So to clarify, I don't hate AI agents; rather,
+      I am quite enthusiastic about their potential and the advancements they bring
+      to the field of technology.\",\n        \"refusal\": null\n      },\n      \"logprobs\":
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tests/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml

@@ -12,7 +12,7 @@ interactions:
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-      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
+      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o"}'
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+      Answer: As an expert researcher in technology with a specialization in AI and
+      AI agents, my perspective is rooted in my deep understanding of their capabilities
+      and potential. AI agents, like any technology, are tools that can be used for
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+      I recognize their immense potential to transform industries, improve efficiencies,
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+      privacy concerns, and the potential for job displacement.\\n\\nThe reason you
+      might have heard that I love them is likely because I am passionate about the
+      potential that AI agents hold for advancing technology and aiding humanity.
+      I believe that with responsible development, transparent governance, and thoughtful
+      integration, AI agents can indeed bring about positive change. My enthusiasm
+      should not be misconstrued as blind love but rather as a measured appreciation
+      for their capabilities and a commitment to navigating their complexities responsibly.\",\n
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+      This includes rigorous testing, continuous monitoring, and establishing clear
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