Merge branch 'main' into intergrate-mem0

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/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 = "0.61.0"
+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"

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-mini"
+    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
@@ -118,17 +119,58 @@ class Agent(BaseAgent):
     @model_validator(mode="after")
     def post_init_setup(self):
         self.agent_ops_agent_name = self.role
-        self.llm = (
-            os.environ.get("OPENAI_MODEL_NAME")
-            or getattr(self.llm, "model_name", None)
-            or getattr(self.llm, "deployment_name", None)
-            or self.llm
-        )
-        self.function_calling_llm = (
-            getattr(self.function_calling_llm, "model_name", None)
-            or getattr(self.function_calling_llm, "deployment_name", None)
-            or 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/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:
@@ -127,6 +125,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         action_result = self._use_tool(formatted_answer)
                         formatted_answer.text += f"\nObservation: {action_result}"
                         formatted_answer.result = action_result
+                        print("formatted_answer", formatted_answer)
                         self._show_logs(formatted_answer)
 
                         if self.step_callback:
@@ -182,7 +181,7 @@ 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 +240,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return tool_result
 
     def _summarize_messages(self) -> None:
-        llm = LLM(self.llm)
         messages_groups = []
 
         for message in self.messages:
@@ -251,7 +249,7 @@ 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 +257,8 @@ 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 = ">=0.61.0,<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 = ">=0.61.0,<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 = ">=0.61.0,<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
@@ -116,6 +117,18 @@ class Crew(BaseModel):
         default=None,
         description="The memory provider to be used for the crew.",
     )
+    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.",
@@ -205,11 +218,15 @@ 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 = (
-            getattr(self.function_calling_llm, "model_name", None)
-            or getattr(self.function_calling_llm, "deployment_name", None)
-            or 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
@@ -218,11 +235,17 @@ 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(
-                memory_provider=self.memory_provider,
-                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(
+                    memory_provider=self.memory_provider,
+                    crew=self,
+                    embedder_config=self.embedder,
+                )
             )
             self._entity_memory = EntityMemory(
                 memory_provider=self.memory_provider,
@@ -950,9 +973,12 @@ class Crew(BaseModel):
     ) -> None:
         """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 +1,87 @@
-from typing import Any, Dict, List
-from litellm import completion
+from typing import Any, Dict, List, Optional, Union
+import logging
 import litellm
 
 
 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

src/crewai/memory/entity/entity_memory.py

@@ -11,7 +11,9 @@ class EntityMemory(Memory):
     Inherits from the Memory class.
     """
 
-    def __init__(self, memory_provider, crew=None, embedder_config=None):
+    def __init__(
+        self, memory_provider=None, crew=None, embedder_config=None, storage=None
+    ):
         self.memory_provider = memory_provider
         if self.memory_provider == "mem0":
             storage = Mem0Storage(
@@ -19,11 +21,15 @@ class EntityMemory(Memory):
                 crew=crew,
             )
         else:
-            storage = RAGStorage(
-                type="entities",
-                allow_reset=False,
-                embedder_config=embedder_config,
-                crew=crew,
+            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

@@ -14,13 +14,19 @@ class ShortTermMemory(Memory):
     MemoryItem instances.
     """
 
-    def __init__(self, memory_provider=None, crew=None, embedder_config=None):
+    def __init__(
+        self, memory_provider=None, crew=None, embedder_config=None, storage=None
+    ):
         self.memory_provider = memory_provider
         if self.memory_provider == "mem0":
             storage = Mem0Storage(type="short_term", crew=crew)
         else:
-            storage = RAGStorage(
-                type="short_term", embedder_config=embedder_config, crew=crew
+            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/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

@@ -72,7 +72,8 @@ class ToolUsage:
 
         # Set the maximum parsing attempts for bigger models
         if (
-            self._is_gpt(self.function_calling_llm)
+            self.function_calling_llm
+            and self._is_gpt(self.function_calling_llm)
             and self.function_calling_llm in OPENAI_BIGGER_MODELS
         ):
             self._max_parsing_attempts = 2
@@ -85,6 +86,7 @@ class ToolUsage:
     def use(
         self, calling: Union[ToolCalling, InstructorToolCalling], tool_string: str
     ) -> str:
+        print("calling", calling)
         if isinstance(calling, ToolUsageErrorException):
             error = calling.message
             if self.agent.verbose:
@@ -299,9 +301,9 @@ class ToolUsage:
 
     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()
+            "gpt" in str(llm.model).lower()
+            or "o1-preview" in str(llm.model).lower()
+            or "o1-mini" in str(llm.model).lower()
         )
 
     def _tool_calling(
@@ -309,11 +311,16 @@ class ToolUsage:
     ) -> Union[ToolCalling, InstructorToolCalling]:
         try:
             if self.function_calling_llm:
+                print("self.function_calling_llm")
                 model = (
                     InstructorToolCalling
                     if self._is_gpt(self.function_calling_llm)
                     else ToolCalling
                 )
+                print("model", model)
+                print(
+                    "self.function_calling_llm.model", self.function_calling_llm.model
+                )
                 converter = Converter(
                     text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n### TEXT \n{tool_string}",
                     llm=self.function_calling_llm,
@@ -329,7 +336,15 @@ class ToolUsage:
                     ),
                     max_attempts=1,
                 )
-                calling = converter.to_pydantic()
+                print("converter", converter)
+                tool_object = converter.to_pydantic()
+                print("tool_object", tool_object)
+                calling = ToolCalling(
+                    tool_name=tool_object["tool_name"],
+                    arguments=tool_object["arguments"],
+                    log=tool_string,  # type: ignore
+                )
+                print("calling", calling)
 
                 if isinstance(calling, ConverterError):
                     raise calling

src/crewai/utilities/converter.py

@@ -27,7 +27,7 @@ class Converter(OutputConverter):
             if self.is_gpt:
                 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},
@@ -47,7 +47,7 @@ class Converter(OutputConverter):
                 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 +78,7 @@ 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},
@@ -90,9 +90,9 @@ class Converter(OutputConverter):
     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()
+            "gpt" in str(self.llm.model).lower()
+            or "o1-preview" in str(self.llm.model).lower()
+            or "o1-mini" in str(self.llm.model).lower()
         )
 
 
@@ -142,6 +142,7 @@ def handle_partial_json(
     converter_cls: Optional[Type[Converter]] = None,
 ) -> Union[dict, BaseModel, str]:
     match = re.search(r"({.*})", result, re.DOTALL)
+    print("handle_partial_json")
     if match:
         try:
             exported_result = model.model_validate_json(match.group(0))
@@ -170,8 +171,11 @@ def convert_with_instructions(
     agent: Any,
     converter_cls: Optional[Type[Converter]] = None,
 ) -> Union[dict, BaseModel, str]:
+    print("convert_with_instructions")
     llm = agent.function_calling_llm or agent.llm
+    print("llm", llm)
     instructions = get_conversion_instructions(model, llm)
+    print("instructions", instructions)
     converter = create_converter(
         agent=agent,
         converter_cls=converter_cls,
@@ -180,10 +184,11 @@ def convert_with_instructions(
         model=model,
         instructions=instructions,
     )
-
+    print("converter", converter)
     exported_result = (
         converter.to_pydantic() if not is_json_output else converter.to_json()
     )
+    print("exported_result", exported_result)
 
     if isinstance(exported_result, ConverterError):
         Printer().print(
@@ -203,12 +208,12 @@ def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
     return instructions
 
 
-def is_gpt(llm: Any) -> bool:
+def is_gpt(llm: LLM) -> 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()
+        "gpt" in str(llm.model).lower()
+        or "o1-preview" in str(llm.model).lower()
+        or "o1-mini" in str(llm.model).lower()
     )
 
 

src/crewai/utilities/evaluators/task_evaluator.py

@@ -93,9 +93,9 @@ class TaskEvaluator:
 
     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()
+            "gpt" in str(self.llm.model).lower()
+            or "o1-preview" in str(self.llm.model).lower()
+            or "o1-mini" in str(self.llm.model).lower()
         )
 
     def evaluate_training_data(

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

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-mini"
+    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 calculated area of the circle is approximately 78.5 square centimeters."
+        == result
+    )
+    assert "square centimeters" in result.lower()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -67,7 +155,7 @@ def test_agent_execution():
     )
 
     output = agent.execute_task(task)
-    assert output == "1 + 1 = 2"
+    assert output == "The result of 1 + 1 is 2."
 
 
 @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 the multiplication is 12."
+
+    assert output == "The result of multiplying 3 by 4 is 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 number that is 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 is 12."
+    assert output == "The result of multiplying 3 and 4 is 12."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -329,7 +419,7 @@ def test_agent_powered_by_new_o_model_family_that_uses_tool():
         expected_output="The number of customers",
     )
     output = agent.execute_task(task=task, tools=[comapny_customer_data])
-    assert output == "The company has 42 customers"
+    assert output == "42"
 
 
 @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"])
@@ -726,7 +816,7 @@ def test_agent_step_callback():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_function_calling_llm():
-    llm = "gpt-4o"
+    llm = "gpt-4"
 
     @tool
     def learn_about_AI() -> str:
@@ -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 +1331,215 @@ 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 len(result.split("\n")) == 3
+    assert result.startswith(
+        "Artificial minds,\nSilent, yet they learn and grow,\nFuture in their code."
+    )
+
+
+@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 misconception that I might \"hate\" AI agents, the reality is much more nuanced. As an expert in technology research, especially in the realm of AI, I have a deep appreciation for both the potential and the challenges that AI agents present.\n\nAI agents, which can be broadly defined as autonomous software entities that perform tasks on behalf of users or other programs, are transforming numerous aspects of our daily lives and industries. Here are a few key points to consider:\n\n1. **Advantages of AI Agents**:\n   - **Automation and Efficiency**: AI agents can handle repetitive and time-consuming tasks efficiently, freeing up human workers to focus on more complex and creative activities.\n   - **Availability**: Unlike humans, AI agents can operate 24/7 without breaks, providing continuous service and support.\n   - **Scalability**: AI agents can be deployed across different platforms and industries, scaling solutions quickly and effectively.\n   - **Data Analysis and Insights**: They can process vast amounts of data rapidly, providing insights that would be difficult, if not impossible, for humans to derive on their own.\n\n2. **Challenges and Concerns**:\n   - **Ethical Implications**: There are significant concerns regarding the ethical use of AI agents, particularly related to privacy, bias, and decision-making transparency.\n   - **Job Displacement**: While AI agents increase efficiency, they also raise concerns about potential job displacement in certain sectors.\n   - **Dependence and Reliability**: Over-reliance on AI agents could lead to vulnerabilities if the technology fails or is compromised.\n\n3. **Looking Forward**:\n   - **Collaboration Between Humans and AI**: The best outcomes are likely to come from a hybrid approach where AI agents augment human capabilities rather than replace them outright.\n   - **Regulation and Standards**: There is a growing need for clear regulations and standards to ensure the ethical development and deployment of AI agents.\n   - **Continuous Improvement**: The technology behind AI agents is constantly evolving. Continuous research and development are essential to address current limitations and maximize benefits.\n\nIn conclusion, my perspective on AI agents is not one of disdain but rather a cautious optimism. They hold incredible promise for transforming various sectors and improving efficiencies, but this potential comes with significant responsibilities. It's essential to address the ethical, social, and technical challenges to harness the full benefits of AI agents while mitigating potential downsides. Thus, thoughtful consideration and ongoing dialogue about the role of AI agents in society are crucial."
+        == 'AI agents are a significant advancement in the field of artificial intelligence, offering immense potential and diverse applications across various sectors. However, the perception that I "hate" them is not entirely accurate. My stance on AI agents is more nuanced and analytical rather than emotional. Here\'s a comprehensive take:\n\n1. **Capabilities and Applications**:\n   - **Automation**: AI agents excel in performing repetitive and mundane tasks, allowing human workers to focus on more complex and creative activities.\n   - **Efficiency**: They can process vast amounts of data at speeds incomprehensible to humans, leading to quicker decision-making and problem-solving.\n   - **Personalization**: In customer service, AI agents provide personalized experiences by analyzing customer data and predicting preferences.\n   - **Healthcare**: AI agents assist in diagnosing diseases, recommending treatments, and even predicting outbreaks by analyzing medical data.\n\n2. **Benefits**:\n   - **Increased Productivity**: By automating routine tasks, businesses can significantly improve productivity and reduce operational costs.\n   - **24/7 Availability**: Unlike human workers, AI agents can operate continuously without breaks, providing constant support and monitoring.\n   - **Data-Driven Insights**: AI agents can uncover patterns and insights from data that might be missed by human analysts.\n\n3. **Challenges and Concerns**:\n   - **Job Displacement**: There is a legitimate concern about AI agents replacing human jobs, particularly in roles involving routine and predictable tasks.\n   - **Bias and Fairness**: AI agents can perpetuate and even amplify existing biases found in the data they are trained on, leading to unfair or discriminatory outcomes.\n   - **Transparency**: The decision-making process of AI agents can sometimes be opaque, making it difficult to understand how they arrive at certain conclusions (known as the "black box" problem).\n   - **Security**: AI systems can be vulnerable to hacking and other malicious activities, posing significant security risks.\n\n4. **Ethical Considerations**:\n   - **Accountability**: Who is responsible when an AI agent makes a mistake? This question remains complex and is a major ethical consideration.\n   - **Privacy**: The extensive use of data by AI agents raises concerns about user privacy and the potential for misuse of sensitive information.\n   - **Consent**: Users must be informed and provide consent regarding how their data is used by AI agents.\n\n5. **Future Directions**:\n   - **Regulation and Governance**: Developing robust regulatory frameworks to oversee the deployment and use of AI agents is crucial.\n   - **Continuous Improvement**: Ongoing research is needed to enhance the capabilities of AI agents while addressing their current limitations and ethical concerns.\n   - **Collaboration**: Encouraging collaboration between AI agents and human workers can lead to hybrid systems that leverage the strengths of both.\n\nIn summary, while AI agents offer numerous benefits and transformative potential, they also come with a set of challenges and ethical questions that must be carefully managed. My analysis focuses on a balanced view, recognizing both the opportunities and the complexities associated with AI agents. \n\nThus, it\'s not about hating AI agents, but rather critically evaluating their impact and advocating for responsible and fair implementation. This ensures that while we harness their capabilities, we also address and mitigate any negative repercussions.'
     )
 
 
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
 
     assert (
         result
-        == "It's not accurate to say that I hate AI agents. My stance is more nuanced and rooted in a deep understanding of their capabilities, limitations, and the potential impact on various sectors.\n\nAI agents are software entities that perform tasks autonomously on behalf of users. They are designed to mimic human decision-making and problem-solving abilities. The advances in machine learning, natural language processing, and data analytics have significantly improved the performance of AI agents, making them valuable tools for automation, customer service, data analysis, and more.\n\nHowever, my concerns about AI agents stem from several key points:\n\n1. **Ethical Considerations**: AI agents can perpetuate biases present in their training data. If the data used to train these agents contain biases, whether related to gender, race, or other factors, the AI can replicate and even amplify these biases in its operations. This has serious ethical implications for fairness and equality.\n\n2. **Job Displacement**: While AI agents can enhance efficiency and productivity, they can also displace human workers. Many routine and repetitive tasks previously performed by humans are now automated, leading to job losses in certain sectors. This aspect calls for a balanced approach where human roles are redefined rather than completely eliminated.\n\n3. **Transparency and Accountability**: AI agents often operate as \"black boxes,\" meaning their decision-making processes are not transparent. If an AI agent makes an error—whether in financial transactions, medical diagnoses, or legal decisions—it can be challenging to understand why the error occurred and who is responsible. Ensuring transparency and accountability is crucial for trust and reliability.\n\n4. **Security and Privacy**: AI agents often deal with vast amounts of personal data. Ensuring the security and privacy of this data is paramount. There are risks associated with data breaches and the misuse of personal information, which can have significant repercussions for individuals and organizations.\n\nDespite these concerns, I also recognize the immense potential of AI agents to drive innovation, improve efficiency, and provide solutions to complex problems. The key is to develop and deploy AI responsibly, with robust regulatory frameworks and ethical guidelines.\n\nIn conclusion, I don't hate AI agents. Instead, I advocate for a balanced perspective that acknowledges their benefits while addressing their challenges. By doing so, we can harness the power of AI agents for the greater good, ensuring they contribute positively to society."
+        == "While it might have circulated that I \"hate\" AI agents, I think it's more nuanced than that. AI agents are incredibly sophisticated pieces of technology that carry immense potential for transforming multiple sectors like healthcare, finance, and customer service.\n\nFirst, let's define what AI agents are. AI agents are autonomous entities designed to perform specific tasks, often mimicking human decision-making and problem-solving abilities. They can range from simple chatbots to complex systems capable of managing large-scale operations.\n\nThe Pros:\n1. Efficiency: AI agents can complete tasks faster and more accurately than humans. For instance, they can instantaneously process large datasets to provide real-time insights.\n2. Scalability: With AI agents, services can be scaled without the proportional increase in cost, which is particularly beneficial for businesses looking to grow without extensive human resource expenditures.\n3. 24/7 Availability: Unlike human workers, AI agents can operate around the clock, increasing productivity and availability, particularly in customer service domains.\n4. Data Handling: AI agents can handle and process data at speeds and volumes that far exceed human capabilities, enabling smarter business decisions and innovations.\n\nHowever, there are some drawbacks and concerns:\n1. Ethical Issues: AI agents often face ethical dilemmas, especially when their decision-making processes are opaque. Bias in AI systems can perpetuate existing inequalities, making transparency and fairness critical issues.\n2. Job Displacement: One significant concern is the potential for AI agents to displace human workers, particularly in industries reliant on routine, repetitive tasks.\n3. Dependency: Over-reliance on AI can lead to skill erosion among human employees and vulnerabilities in cases where technology fails or is compromised.\n4. Security: Given their access to sensitive data, AI agents can be attractive targets for cyber-attacks. Ensuring robust security measures is essential.\n\nSo, while I recognize the transformative power and benefits of AI agents, it's crucial to approach them with a balanced perspective. Adequate governance, ethical considerations, and transparency are needed to harness their potential responsibly. It's not a matter of hating them, but rather advocating for their responsible and fair use in society."
     )
 
 
@@ -52,7 +52,7 @@ def test_ask_question():
 
     assert (
         result
-        == "As a researcher specialized in technology with a particular focus on AI and AI agents, my stance on AI agents isn't rooted in emotional responses like hate or love. Rather, it is grounded in objective analysis and thoughtful consideration of their capabilities, ethics, and impact on society.\n\nAI agents are powerful tools that can transform various industries, from healthcare and finance to customer service and manufacturing. They have the potential to increase efficiency, improve decision-making, and even solve complex problems that were previously insurmountable. For instance, AI agents can analyze vast amounts of data far more quickly and accurately than humans, leading to advancements in medical research and diagnostics.\n\nHowever, it is equally important to recognize the ethical and societal implications of AI agents. Issues such as data privacy, algorithmic bias, and the potential displacement of jobs need to be carefully managed. As a researcher, my role is to study these aspects comprehensively, provide balanced insights, and advocate for responsible development and deployment of AI technologies.\n\nSo, to address your question directly: I don't hate AI agents. I appreciate their potential and am keenly aware of their challenges. My goal is to contribute to a future where AI agents are designed and used ethically and effectively to benefit humanity."
+        == 'As a researcher specialized in technology, particularly in artificial intelligence (AI) and AI agents, my perspective is shaped by the extensive analysis and study I engage in daily. AI agents are neither inherently good nor bad; they are tools with the potential for both positive and negative impacts depending on how they are designed, implemented, and used.\n\nI do not "hate" AI agents. In fact, I find them fascinating and hold an appreciation for their capabilities and the potential they have to transform various sectors, from healthcare and education to finance and entertainment. AI agents can offer significant benefits, such as improving efficiency, personalizing user experiences, and even tackling complex problems that are beyond human capacity to solve alone.\n\nHowever, it is essential to approach AI with a critical eye. There are legitimate concerns around privacy, security, ethical use, and the socio-economic implications of AI deployment. Ensuring that AI is developed responsibly and is aligned with ethical standards is crucial for minimizing potential negatives.\n\nIn summary, my stance on AI agents is informed by a balanced view that recognizes both their potential and the necessity for responsible stewardship. The enthusiasm I have for their capabilities is tempered by a commitment to understanding and addressing the challenges they present.'
     )
 
 
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
 
     assert (
         result
-        == "As an expert researcher specialized in technology, I have a profound appreciation for AI and AI agents. They represent a pinnacle of human innovation and have the potential to greatly enhance various aspects of our lives. AI agents can assist in automating mundane tasks, providing insights through data analysis, and even offering companionship to those in need. However, it's also essential to approach AI with a balanced perspective, acknowledging both its strengths and the ethical considerations it raises. In short, I don't hate AI agents; I recognize their immense potential and value while being mindful of the responsibilities that come with their development and deployment."
+        == "As an expert researcher specialized in technology and AI, I do not harbor feelings of hate towards AI agents. In fact, the notion of hate or love for a technology is somewhat misplaced. AI agents are tools created to solve specific problems and enhance our capabilities. They can transform industries, improve efficiencies, and even save lives in fields such as healthcare. My enthusiasm and interest in AI agents come from their potential to innovate and drive progress. While it is essential to be mindful of ethical considerations and responsible use, it is equally important to remain objective. My goal as a researcher is to contribute to balanced and insightful analysis, exploring both the benefits and challenges of AI, and not to personalize or emotionally charge the discussion about technological tools."
     )
 
 
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
 
     assert (
         result
-        == "In addressing your query about AI agents, it's crucial to clarify my stance. While I don't \"hate\" AI agents, I have a nuanced perspective that balances both their potential benefits and inherent drawbacks. This balanced view is essential for understanding the complex role that AI agents play in today's technology landscape.\n\nAI agents are remarkable tools that can automate tasks, provide intelligent recommendations, and enhance user experiences. They harness advancements in machine learning, natural language processing, and big data analytics to perform a wide range of functions, from personal assistants like Siri and Alexa to sophisticated customer service bots and autonomous systems in various industries.\n\nThe Potential Benefits of AI Agents:\n1. **Automation and Efficiency**: AI agents can handle repetitive and mundane tasks, freeing up human workers to focus on more creative and high-value activities. This can lead to increased productivity and operational efficiency in businesses.\n2. **Data-Driven Insights**: By analyzing vast amounts of data, AI agents can provide actionable insights that inform decision-making processes in areas like finance, healthcare, and marketing.\n3. **Personalization**: AI agents can tailor recommendations and services based on individual user preferences and behaviors, enhancing user satisfaction and engagement.\n4. **24/7 Availability**: Unlike human workers, AI agents can operate continuously without the need for breaks, providing round-the-clock support and services.\n\nHowever, there are also several challenges and concerns associated with AI agents that cannot be overlooked:\n\n1. **Ethical Considerations**: The deployment of AI agents raises ethical questions about privacy, surveillance, and the potential for bias in their algorithms. Ensuring that AI systems are transparent and fair is a critical issue.\n2. **Job Displacement**: While AI agents can increase efficiency, they also have the potential to displace human workers, leading to concerns about unemployment and the need for workforce reskilling.\n3. **Trust and Reliability**: Building trust in AI systems is essential. Users need to be confident that AI agents will perform their tasks accurately and reliably, without unexpected failures or errors.\n4. **Control and Accountability**: Determining who is accountable when AI agents make errors or cause harm is a complex issue, especially when these systems operate autonomously or with minimal human oversight.\n\nIn summary, my view on AI agents is not one of antagonism but of cautious optimism. I recognize the transformative potential of AI agents in various domains, but I also emphasize the importance of addressing the ethical, economic, and technical challenges they present. Our goal should be to develop and deploy AI agents in a way that maximizes their benefits while mitigating their risks. This balanced approach will ensure that AI agents can be a positive force in society, driving innovation and improving quality of life without compromising ethical standards or societal values."
+        == "AI agents, like any technology, have both significant advantages and notable challenges. Initially, the skepticism surrounding AI agents might stem from concerns about their implementation, ethical considerations, or potential impact on jobs. However, let's delve into a comprehensive analysis of their roles and implications.\n\nFirstly, AI agents are software entities that perform tasks autonomously on behalf of a user or another program with some degree of intelligence and learning capability. These agents can interpret data, learn from patterns, and even make decisions based on the information they process. They are essential across various sectors, including healthcare, finance, customer service, and logistics.\n\nPositive Aspects:\n1. **Efficiency and Productivity:** AI agents can process large volumes of data faster than humans, enabling quicker decision-making. This efficiency can significantly boost productivity in organizations by automating repetitive tasks, allowing human workers to focus on more strategic activities.\n\n2. **24/7 Operation:** Unlike humans, AI agents can work around the clock without fatigue. This capability is crucial for industries that require continuous operation, such as monitoring systems in cybersecurity or customer service through chatbots.\n\n3. **Personalization:** AI agents can tailor experiences to individual users by learning from their behavior. For instance, recommendation systems used by services like Netflix or Amazon provide personalized content, enhancing user satisfaction and engagement.\n\n4. **Accuracy and Precision:** AI agents, when properly trained, can perform tasks with a high degree of accuracy, reducing the likelihood of human error. This precision is especially valuable in fields such as medical diagnostics, where accurate analysis can be life-saving.\n\nChallenges and Concerns:\n1. **Ethical Issues:** The deployment of AI agents raises ethical questions regarding privacy, data security, and potential biases in decision-making. It is essential to ensure that AI systems are transparent, fair, and respect user privacy.\n\n2. **Job Displacement:** As AI agents automate more tasks, there is a genuine concern about job loss, particularly in roles that involve routine and repetitive tasks. It is critical to address this issue by reskilling and upskilling the workforce to adapt to the changing job landscape.\n\n3. **Dependence on Data:** AI agents rely heavily on large datasets to learn and make decisions. If the data is biased or incomplete, the AI’s performance and reliability can be compromised. This dependence also raises questions about data ownership and governance.\n\n4. **Complexity and Accountability:** Understanding and managing AI agents can be complex, and determining accountability in the event of failures or erroneous decisions can be challenging. Developing robust frameworks for monitoring and accountability is crucial.\n\nIn conclusion, while there are valid concerns associated with AI agents, they also offer transformative potential in improving efficiency, personalization, and accuracy in various applications. It is important to engage in balanced discussions, address ethical considerations, and develop policies to mitigate potential negative impacts while leveraging the benefits of AI technology. Hence, my stance is not of outright hatred, but one of cautious optimism, advocating for responsible development and deployment of AI agents."
     )
 
 
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
 
     assert (
         result
-        == "No, I do not hate AI agents. In fact, I appreciate their potential and the positive impact they can have in various fields. AI agents can perform tasks that are either too mundane or too complex for humans, thereby increasing productivity and allowing us to focus on more creative and strategic activities. Moreover, they can process and analyze vast amounts of data much more quickly and accurately than humans, leading to better decision-making and innovations. As a researcher specialized in technology, I am always excited to see advancements in AI and AI agents, as they push the boundaries of what technology can achieve. So, yes, I do love the possibilities they bring to our world."
+        == "While I understand there are mixed feelings about AI agents, I do not hate them. In fact, I see significant potential in AI and AI agents to transform various industries and improve our daily lives. My enthusiasm for AI comes from appreciating the advancements it's bringing, such as personalized recommendations, improved diagnostics in healthcare, and more efficient business processes.\n\nHowever, it's essential to approach AI with a balanced perspective. Ethical considerations, privacy concerns, and the potential for job displacement are critical issues that need to be addressed. I believe in responsible AI development, where the focus is on maximizing benefits while minimizing potential harms.\n\nSo, in short, I love the potential and capabilities of AI agents when developed and implemented responsibly and ethically."
     )
 
 

tests/agent_tools/cassettes/test_ask_question.yaml

@@ -12,7 +12,7 @@ interactions:
       shared.\nyou MUST return the actual complete content as the final answer, not
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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"}'
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@@ -50,37 +50,36 @@ interactions:
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   response:
-    content: "{\n  \"id\": \"chatcmpl-A8itGodu0IQ3bZNxRStGoqFBPnObt\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1726642546,\n  \"model\": \"gpt-4o-2024-05-13\",\n
+    content: "{\n  \"id\": \"chatcmpl-AAWjmuMRGqp9cs5FAx9HQBlWHW4EB\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1727072486,\n  \"model\": \"gpt-4o-2024-05-13\",\n
       \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
       \"assistant\",\n        \"content\": \"Thought: I now can give a great answer\\nFinal
-      Answer: As a researcher specialized in technology with a particular focus on
-      AI and AI agents, my stance on AI agents isn't rooted in emotional responses
-      like hate or love. Rather, it is grounded in objective analysis and thoughtful
-      consideration of their capabilities, ethics, and impact on society.\\n\\nAI
-      agents are powerful tools that can transform various industries, from healthcare
-      and finance to customer service and manufacturing. They have the potential to
-      increase efficiency, improve decision-making, and even solve complex problems
-      that were previously insurmountable. For instance, AI agents can analyze vast
-      amounts of data far more quickly and accurately than humans, leading to advancements
-      in medical research and diagnostics.\\n\\nHowever, it is equally important to
-      recognize the ethical and societal implications of AI agents. Issues such as
-      data privacy, algorithmic bias, and the potential displacement of jobs need
-      to be carefully managed. As a researcher, my role is to study these aspects
-      comprehensively, provide balanced insights, and advocate for responsible development
-      and deployment of AI technologies.\\n\\nSo, to address your question directly:
-      I don't hate AI agents. I appreciate their potential and am keenly aware of
-      their challenges. My goal is to contribute to a future where AI agents are designed
-      and used ethically and effectively to benefit humanity.\\n\\n\",\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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+      Answer: As a researcher specialized in technology, particularly in artificial
+      intelligence (AI) and AI agents, my perspective is shaped by the extensive analysis
+      and study I engage in daily. AI agents are neither inherently good nor bad;
+      they are tools with the potential for both positive and negative impacts depending
+      on how they are designed, implemented, and used.\\n\\nI do not \\\"hate\\\"
+      AI agents. In fact, I find them fascinating and hold an appreciation for their
+      capabilities and the potential they have to transform various sectors, from
+      healthcare and education to finance and entertainment. AI agents can offer significant
+      benefits, such as improving efficiency, personalizing user experiences, and
+      even tackling complex problems that are beyond human capacity to solve alone.\\n\\nHowever,
+      it is essential to approach AI with a critical eye. There are legitimate concerns
+      around privacy, security, ethical use, and the socio-economic implications of
+      AI deployment. Ensuring that AI is developed responsibly and is aligned with
+      ethical standards is crucial for minimizing potential negatives.\\n\\nIn summary,
+      my stance on AI agents is informed by a balanced view that recognizes both their
+      potential and the necessity for responsible stewardship. The enthusiasm I have
+      for their capabilities is tempered by a commitment to understanding and addressing
+      the challenges they present.\",\n        \"refusal\": null\n      },\n      \"logprobs\":
+      null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      199,\n    \"completion_tokens\": 253,\n    \"total_tokens\": 452,\n    \"completion_tokens_details\":
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tests/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml

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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: While I understand there are mixed feelings about AI agents, I do not
+      hate them. In fact, I see significant potential in AI and AI agents to transform
+      various industries and improve our daily lives. My enthusiasm for AI comes from
+      appreciating the advancements it's bringing, such as personalized recommendations,
+      improved diagnostics in healthcare, and more efficient business processes.\\n\\nHowever,
+      it's essential to approach AI with a balanced perspective. Ethical considerations,
+      privacy concerns, and the potential for job displacement are critical issues
+      that need to be addressed. I believe in responsible AI development, where the
+      focus is on maximizing benefits while minimizing potential harms.\\n\\nSo, in
+      short, I love the potential and capabilities of AI agents when developed and
+      implemented responsibly and ethically.\",\n        \"refusal\": null\n      },\n
+      \     \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n
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tests/agent_tools/cassettes/test_ask_question_with_wrong_co_worker_variable.yaml

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+      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o"}'
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+      Answer: As an expert researcher specialized in technology and AI, I do not harbor
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+      problems and enhance our capabilities. They can transform industries, improve
+      efficiencies, and even save lives in fields such as healthcare. My enthusiasm
+      and interest in AI agents come from their potential to innovate and drive progress.
+      While it is essential to be mindful of ethical considerations and responsible
+      use, it is equally important to remain objective. My goal as a researcher is
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+      and challenges of AI, and not to personalize or emotionally charge the discussion
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+      sectors. However, the perception that I \\\"hate\\\" them is not entirely accurate.
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+      and reduce operational costs.\\n   - **24/7 Availability**: Unlike human workers,
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+      predictable tasks.\\n   - **Bias and Fairness**: AI agents can perpetuate and
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+      the potential for misuse of sensitive information.\\n   - **Consent**: Users
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+      Answer: \\n\\nWhile it might have circulated that I \\\"hate\\\" AI agents,
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+      sectors like healthcare, finance, and customer service.\\n\\nFirst, let's define
+      what AI agents are. AI agents are autonomous entities designed to perform specific
+      tasks, often mimicking human decision-making and problem-solving abilities.
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+      far exceed human capabilities, enabling smarter business decisions and innovations.\\n\\nHowever,
+      there are some drawbacks and concerns:\\n1. Ethical Issues: AI agents often
+      face ethical dilemmas, especially when their decision-making processes are opaque.
+      Bias in AI systems can perpetuate existing inequalities, making transparency
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+      roles and implications.\\n\\nFirstly, AI agents are software entities that perform
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tests/cassettes/test_agent_with_ollama_gemma.yaml

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+      practical challenges of this shift.\n   - **Interest:** Readers will find it
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tests/cassettes/test_custom_converter_cls.yaml

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