preparing for version 0.64.0

This commit adds two commands to the CLI:

- `crewai tool publish`
    - Builds the project using Poetry
    - Uploads the tarball to CrewAI's tool repository

- `crewai tool install my-tool`
    - Adds my-tool's index to Poetry and its credentials
    - Installs my-tool from the custom index

README.md

@@ -64,25 +64,8 @@ from crewai_tools import SerperDevTool
 os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
 os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
 
-# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.
-
-# os.environ["OPENAI_API_BASE"] = 'http://localhost:11434/v1'
-# os.environ["OPENAI_MODEL_NAME"] ='openhermes'  # Adjust based on available model
-# os.environ["OPENAI_API_KEY"] ='sk-111111111111111111111111111111111111111111111111'
-
-# You can pass an optional llm attribute specifying what model you wanna use.
 # It can be a local model through Ollama / LM Studio or a remote
 # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
-# If you don't specify a model, the default is OpenAI gpt-4o
-#
-# import os
-# os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
-#
-# OR
-#
-# from langchain_openai import ChatOpenAI
-
-search_tool = SerperDevTool()
 
 # Define your agents with roles and goals
 researcher = Agent(
@@ -95,7 +78,7 @@ researcher = Agent(
   allow_delegation=False,
   # You can pass an optional llm attribute specifying what model you wanna use.
   # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
-  tools=[search_tool]
+  tools=[SerperDevTool()]
 )
 writer = Agent(
   role='Tech Content Strategist',

docs/core-concepts/Agents.md

@@ -36,7 +36,6 @@ description: What are crewAI Agents and how to use them.
 | **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Allow Code Execution** *(optional)*     | `allow_code_execution`  | Enable code execution for the agent. Default is `False`.                                                                                                                                                                  |
 | **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.
-| **Use Stop Words** *(optional)*     | `use_stop_words`  | Adds the ability to not use stop words (to support o1 models). Default is `True`.                                                                                                                                                                  |
 | **Use System Prompt** *(optional)*     | `use_system_prompt`  | Adds the ability to not use system prompt (to support o1 models). Default is `True`.                                                                                                                                                                  |
 | **Respect Context Window** *(optional)*     | `respect_context_window`  | Summary strategy to avoid overflowing the context window. Default is `True`.                                                                                                                                                                  |
 
@@ -79,7 +78,6 @@ agent = Agent(
   callbacks=[callback1, callback2],  # Optional
   allow_code_execution=True,  # Optional
   max_retry_limit=2,  # Optional
-  use_stop_words=True,  # Optional
   use_system_prompt=True,  # Optional
   respect_context_window=True,  # Optional
 )

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/core-concepts/Tasks.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 title: crewAI Tasks
 description: Detailed guide on managing and creating tasks within the crewAI framework, reflecting the latest codebase updates.
@@ -314,4 +313,4 @@ save_output_task = Task(
 
 ## Conclusion
 
-Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.
+Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

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

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

docs/how-to/Customizing-Agents.md

@@ -20,7 +20,6 @@ Crafting an efficient CrewAI team hinges on the ability to dynamically tailor yo
 - **System Template** *(Optional)*: `system_template` defines the system format for the agent.
 - **Prompt Template** *(Optional)*: `prompt_template` defines the prompt format for the agent.
 - **Response Template** *(Optional)*: `response_template` defines the response format for the agent.
-- **Use Stop Words** *(Optional)*: `use_stop_words` attribute controls whether the agent will use stop words during task execution. This is now supported to aid o1 models.
 - **Use System Prompt** *(Optional)*: `use_system_prompt` controls whether the agent will use a system prompt for task execution. Agents can now operate without system prompts.
 - **Respect Context Window**: `respect_context_window` renames the sliding context window attribute and enables it by default to maintain context size.
 - **Max Retry Limit**: `max_retry_limit` defines the maximum number of retries for an agent to execute a task when an error occurs.

docs/how-to/Hierarchical.md

@@ -46,7 +46,6 @@ researcher = Agent(
     verbose=False,
     # tools=[]  # This can be optionally specified; defaults to an empty list
     use_system_prompt=True,  # Enable or disable system prompts for this agent
-    use_stop_words=True,  # Enable or disable stop words for this agent
     max_rpm=30,  # Limit on the number of requests per minute
     max_iter=5  # Maximum number of iterations for a final answer
 )
@@ -58,7 +57,6 @@ writer = Agent(
     verbose=False,
     # tools=[]  # Optionally specify tools; defaults to an empty list
     use_system_prompt=True,  # Enable or disable system prompts for this agent
-    use_stop_words=True,  # Enable or disable stop words for this agent
     max_rpm=30,  # Limit on the number of requests per minute
     max_iter=5  # Maximum number of iterations for a final answer
 )

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
@@ -162,7 +162,7 @@ nav:
     - Directory RAG Search: 'tools/DirectorySearchTool.md'
     - Directory Read: 'tools/DirectoryReadTool.md'
     - Docx Rag Search: 'tools/DOCXSearchTool.md'
-    - EXA Serch Web Loader: 'tools/EXASearchTool.md'
+    - EXA Search Web Loader: 'tools/EXASearchTool.md'
     - File Read: 'tools/FileReadTool.md'
     - File Write: 'tools/FileWriteTool.md'
     - Firecrawl Crawl Website Tool: 'tools/FirecrawlCrawlWebsiteTool.md'
@@ -210,6 +210,6 @@ extra:
     property: G-N3Q505TMQ6
   social:
     - icon: fontawesome/brands/twitter
-      link: https://twitter.com/joaomdmoura
+      link: https://x.com/crewAIInc
     - icon: fontawesome/brands/github
-      link: https://github.com/joaomdmoura/crewAI
+      link: https://github.com/crewAIInc/crewAI

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.60.0"
+version = "0.64.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"
@@ -14,14 +14,14 @@ Repository = "https://github.com/crewAIInc/crewAI"
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
 pydantic = "^2.4.2"
-langchain = ">0.2,<=0.3"
+langchain = "^0.2.16"
 openai = "^1.13.3"
 opentelemetry-api = "^1.22.0"
 opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "1.3.3"
-regex = "^2024.7.24"
-crewai-tools = { version = "^0.12.0", optional = true }
+regex = "^2024.9.11"
+crewai-tools = { version = "^0.12.1", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
@@ -49,7 +49,7 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.12.0"
+crewai-tools = "^0.12.1"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"

src/crewai/__init__.py

@@ -5,6 +5,7 @@ from crewai.pipeline import Pipeline
 from crewai.process import Process
 from crewai.routers import Router
 from crewai.task import Task
+from crewai.llm import LLM
 
 
 warnings.filterwarnings(
@@ -14,4 +15,4 @@ warnings.filterwarnings(
     module="pydantic.main",
 )
 
-__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router"]
+__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM"]

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():
@@ -73,16 +74,12 @@ class Agent(BaseAgent):
         default=None,
         description="Callback to be executed after each step of the agent execution.",
     )
-    use_stop_words: bool = Field(
-        default=True,
-        description="Use stop words for the agent.",
-    )
     use_system_prompt: Optional[bool] = Field(
         default=True,
         description="Use system prompt for the agent.",
     )
-    llm: Any = Field(
-        description="Language model that will run the agent.", default="gpt-4o"
+    llm: Union[str, InstanceOf[LLM], Any] = Field(
+        description="Language model that will run the agent.", default=None
     )
     function_calling_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
@@ -107,7 +104,7 @@ class Agent(BaseAgent):
         description="Keep messages under the context window size by summarizing content.",
     )
     max_iter: int = Field(
-        default=15,
+        default=20,
         description="Maximum number of iterations for an agent to execute a task before giving it's best answer",
     )
     max_retry_limit: int = Field(
@@ -118,12 +115,60 @@ class Agent(BaseAgent):
     @model_validator(mode="after")
     def post_init_setup(self):
         self.agent_ops_agent_name = self.role
-        self.llm = self.llm.model_name if hasattr(self.llm, "model_name") else self.llm
-        self.function_calling_llm = (
-            self.function_calling_llm.model_name
-            if hasattr(self.function_calling_llm, "model_name")
-            else self.function_calling_llm
-        )
+
+        # Handle different cases for self.llm
+        if isinstance(self.llm, str):
+            # If it's a string, create an LLM instance
+            self.llm = LLM(model=self.llm)
+        elif isinstance(self.llm, LLM):
+            # If it's already an LLM instance, keep it as is
+            pass
+        elif self.llm is None:
+            # If it's None, use environment variables or default
+            model_name = os.environ.get("OPENAI_MODEL_NAME", "gpt-4o-mini")
+            llm_params = {"model": model_name}
+
+            api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
+                "OPENAI_BASE_URL"
+            )
+            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()
 
@@ -242,7 +287,6 @@ class Agent(BaseAgent):
             stop_words=stop_words,
             max_iter=self.max_iter,
             tools_handler=self.tools_handler,
-            use_stop_words=self.use_stop_words,
             tools_names=self.__tools_names(parsed_tools),
             tools_description=self._render_text_description_and_args(parsed_tools),
             step_callback=self.step_callback,
@@ -300,8 +344,9 @@ class Agent(BaseAgent):
                 human_feedbacks = [
                     i["human_feedback"] for i in data.get(agent_id, {}).values()
                 ]
-                task_prompt += "You MUST follow these feedbacks: \n " + "\n - ".join(
-                    human_feedbacks
+                task_prompt += (
+                    "\n\nYou MUST follow these instructions: \n "
+                    + "\n - ".join(human_feedbacks)
                 )
 
         return task_prompt
@@ -310,8 +355,9 @@ class Agent(BaseAgent):
         """Use trained data for the agent task prompt to improve output."""
         if data := CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).load():
             if trained_data_output := data.get(self.role):
-                task_prompt += "You MUST follow these feedbacks: \n " + "\n - ".join(
-                    trained_data_output["suggestions"]
+                task_prompt += (
+                    "\n\nYou MUST follow these instructions: \n - "
+                    + "\n - ".join(trained_data_output["suggestions"])
                 )
         return task_prompt
 

src/crewai/agents/agent_builder/base_agent.py

@@ -176,7 +176,11 @@ class BaseAgent(ABC, BaseModel):
 
     @property
     def key(self):
-        source = [self.role, self.goal, self.backstory]
+        source = [
+            self._original_role or self.role,
+            self._original_goal or self.goal,
+            self._original_backstory or self.backstory,
+        ]
         return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
 
     @abstractmethod

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -6,6 +6,7 @@ from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
 from crewai.utilities.converter import ConverterError
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities import I18N
+from crewai.utilities.printer import Printer
 
 
 if TYPE_CHECKING:
@@ -22,6 +23,7 @@ class CrewAgentExecutorMixin:
     have_forced_answer: bool
     max_iter: int
     _i18n: I18N
+    _printer: Printer = Printer()
 
     def _should_force_answer(self) -> bool:
         """Determine if a forced answer is required based on iteration count."""
@@ -100,6 +102,12 @@ class CrewAgentExecutorMixin:
 
     def _ask_human_input(self, final_answer: dict) -> str:
         """Prompt human input for final decision making."""
-        return input(
-            self._i18n.slice("getting_input").format(final_answer=final_answer)
+        self._printer.print(
+            content=f"\033[1m\033[95m ## Final Result:\033[00m \033[92m{final_answer}\033[00m"
         )
+
+        self._printer.print(
+            content="\n\n=====\n## Please provide feedback on the Final Result and the Agent's actions:",
+            color="bold_yellow",
+        )
+        return input()

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

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

src/crewai/agents/crew_agent_executor.py

@@ -13,7 +13,6 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 )
 from crewai.utilities.logger import Logger
 from crewai.utilities.training_handler import CrewTrainingHandler
-from crewai.llm import LLM
 from crewai.agents.parser import (
     AgentAction,
     AgentFinish,
@@ -35,7 +34,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         max_iter: int,
         tools: List[Any],
         tools_names: str,
-        use_stop_words: bool,
         stop_words: List[str],
         tools_description: str,
         tools_handler: ToolsHandler,
@@ -61,7 +59,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tools_handler = tools_handler
         self.original_tools = original_tools
         self.step_callback = step_callback
-        self.use_stop_words = use_stop_words
+        self.use_stop_words = self.llm.supports_stop_words()
         self.tools_description = tools_description
         self.function_calling_llm = function_calling_llm
         self.respect_context_window = respect_context_window
@@ -69,8 +67,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.ask_for_human_input = False
         self.messages: List[Dict[str, str]] = []
         self.iterations = 0
+        self.log_error_after = 3
         self.have_forced_answer = False
         self.name_to_tool_map = {tool.name: tool for tool in self.tools}
+        self.llm.stop = self.stop
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:
@@ -98,17 +98,19 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
             formatted_answer = self._invoke_loop()
 
+            if self.crew and self.crew._train:
+                self._handle_crew_training_output(formatted_answer)
+
         return {"output": formatted_answer.output}
 
     def _invoke_loop(self, formatted_answer=None):
         try:
             while not isinstance(formatted_answer, AgentFinish):
                 if not self.request_within_rpm_limit or self.request_within_rpm_limit():
-                    answer = LLM(
-                        self.llm,
-                        stop=self.stop if self.use_stop_words else None,
+                    answer = self.llm.call(
+                        self.messages,
                         callbacks=self.callbacks,
-                    ).call(self.messages)
+                    )
 
                     if not self.use_stop_words:
                         try:
@@ -146,10 +148,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                                 )
                                 self.have_forced_answer = True
                         self.messages.append(
-                            self._format_msg(formatted_answer.text, role="assistant")
+                            self._format_msg(formatted_answer.text, role="user")
                         )
+
         except OutputParserException as e:
-            self.messages.append({"role": "assistant", "content": e.error})
+            self.messages.append({"role": "user", "content": e.error})
+            if self.iterations > self.log_error_after:
+                self._printer.print(
+                    content=f"Error parsing LLM output, agent will retry: {e.error}",
+                    color="red",
+                )
             return self._invoke_loop(formatted_answer)
 
         except Exception as e:
@@ -168,8 +176,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         if self.agent.verbose or (
             hasattr(self, "crew") and getattr(self.crew, "verbose", False)
         ):
+            agent_role = self.agent.role.split("\n")[0]
             self._printer.print(
-                content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{self.agent.role}\033[00m"
+                content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
             )
             self._printer.print(
                 content=f"\033[95m## Task:\033[00m \033[92m{self.task.description}\033[00m"
@@ -179,15 +188,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         if self.agent.verbose or (
             hasattr(self, "crew") and getattr(self.crew, "verbose", False)
         ):
+            agent_role = self.agent.role.split("\n")[0]
             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,
                 )
                 self._printer.print(
-                    content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{self.agent.role}\033[00m"
+                    content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
                 )
                 if thought and thought != "":
                     self._printer.print(
@@ -204,10 +214,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 )
             elif isinstance(formatted_answer, AgentFinish):
                 self._printer.print(
-                    content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{self.agent.role}\033[00m"
+                    content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
                 )
                 self._printer.print(
-                    content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m"
+                    content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n"
                 )
 
     def _use_tool(self, agent_action: AgentAction) -> Any:
@@ -241,25 +251,25 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return tool_result
 
     def _summarize_messages(self) -> None:
-        llm = LLM(self.llm)
         messages_groups = []
-
         for message in self.messages:
             content = message["content"]
-            for i in range(0, len(content), 5000):
-                messages_groups.append(content[i : i + 5000])
+            cut_size = self.llm.get_context_window_size()
+            for i in range(0, len(content), cut_size):
+                messages_groups.append(content[i : i + cut_size])
 
         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"
+                        self._i18n.slice("summarizer_system_message"), role="system"
                     ),
                     self._format_msg(
-                        self._i18n.errors("sumamrize_instruction").format(group=group),
+                        self._i18n.slice("sumamrize_instruction").format(group=group),
                     ),
-                ]
+                ],
+                callbacks=self.callbacks,
             )
             summarized_contents.append(summary)
 
@@ -267,7 +277,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
         self.messages = [
             self._format_msg(
-                self._i18n.errors("summary").format(merged_summary=merged_summary)
+                self._i18n.slice("summary").format(merged_summary=merged_summary)
             )
         ]
 
@@ -294,24 +304,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
     ) -> None:
         """Function to handle the process of the training data."""
         agent_id = str(self.agent.id)
-
         if (
             CrewTrainingHandler(TRAINING_DATA_FILE).load()
             and not self.ask_for_human_input
         ):
             training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
             if training_data.get(agent_id):
-                if self.crew is not None and hasattr(self.crew, "_train_iteration"):
-                    training_data[agent_id][self.crew._train_iteration][
-                        "improved_output"
-                    ] = result.output
-                    CrewTrainingHandler(TRAINING_DATA_FILE).save(training_data)
-                else:
-                    self._logger.log(
-                        "error",
-                        "Invalid crew or missing _train_iteration attribute.",
-                        color="red",
-                    )
+                training_data[agent_id][self.crew._train_iteration][
+                    "improved_output"
+                ] = result.output
+                CrewTrainingHandler(TRAINING_DATA_FILE).save(training_data)
 
         if self.ask_for_human_input and human_feedback is not None:
             training_data = {

src/crewai/cli/cli.py

@@ -11,6 +11,7 @@ from crewai.memory.storage.kickoff_task_outputs_storage import (
 
 from .authentication.main import AuthenticationCommand
 from .deploy.main import DeployCommand
+from .tools.main import ToolCommand
 from .evaluate_crew import evaluate_crew
 from .install_crew import install_crew
 from .replay_from_task import replay_task_command
@@ -202,6 +203,12 @@ def deploy():
     pass
 
 
+@crewai.group()
+def tool():
+    """Tool Repository related commands."""
+    pass
+
+
 @deploy.command(name="create")
 @click.option("-y", "--yes", is_flag=True, help="Skip the confirmation prompt")
 def deploy_create(yes: bool):
@@ -249,5 +256,20 @@ def deploy_remove(uuid: Optional[str]):
     deploy_cmd.remove_crew(uuid=uuid)
 
 
+@tool.command(name="install")
+@click.argument("handle")
+def tool_install(handle: str):
+    tool_cmd = ToolCommand()
+    tool_cmd.install(handle)
+
+
+@tool.command(name="publish")
+@click.option("--public", "is_public", flag_value=True, default=False)
+@click.option("--private", "is_public", flag_value=False)
+def tool_publish(is_public: bool):
+    tool_cmd = ToolCommand()
+    tool_cmd.publish(is_public)
+
+
 if __name__ == "__main__":
     crewai()

src/crewai/cli/command.py

@@ -0,0 +1,40 @@
+from typing import Dict, Any
+from rich.console import Console
+from crewai.cli.plus_api import PlusAPI
+from crewai.cli.utils import get_auth_token
+from crewai.telemetry.telemetry import Telemetry
+
+console = Console()
+
+
+class BaseCommand:
+    def __init__(self):
+        self._telemetry = Telemetry()
+        self._telemetry.set_tracer()
+
+
+class PlusAPIMixin:
+    def __init__(self, telemetry):
+        try:
+            telemetry.set_tracer()
+            self.plus_api_client = PlusAPI(api_key=get_auth_token())
+        except Exception:
+            self._deploy_signup_error_span = telemetry.deploy_signup_error_span()
+            console.print(
+                "Please sign up/login to CrewAI+ before using the CLI.",
+                style="bold red",
+            )
+            console.print("Run 'crewai signup' to sign up/login.", style="bold green")
+            raise SystemExit
+
+    def _handle_plus_api_error(self, json_response: Dict[str, Any]) -> None:
+        """
+        Handle and display error messages from API responses.
+
+        Args:
+            json_response (Dict[str, Any]): The JSON response containing error information.
+        """
+        error = json_response.get("error", "Unknown error")
+        message = json_response.get("message", "No message provided")
+        console.print(f"Error: {error}", style="bold red")
+        console.print(f"Message: {message}", style="bold red")

src/crewai/cli/deploy/api.py

@@ -1,66 +0,0 @@
-from os import getenv
-
-import requests
-
-from crewai.cli.deploy.utils import get_crewai_version
-
-
-class CrewAPI:
-    """
-    CrewAPI class to interact with the crewAI+ API.
-    """
-
-    def __init__(self, api_key: str) -> None:
-        self.api_key = api_key
-        self.headers = {
-            "Authorization": f"Bearer {api_key}",
-            "Content-Type": "application/json",
-            "User-Agent": f"CrewAI-CLI/{get_crewai_version()}",
-        }
-        self.base_url = getenv(
-            "CREWAI_BASE_URL", "https://crewai.com/crewai_plus/api/v1/crews"
-        )
-
-    def _make_request(self, method: str, endpoint: str, **kwargs) -> requests.Response:
-        url = f"{self.base_url}/{endpoint}"
-        return requests.request(method, url, headers=self.headers, **kwargs)
-
-    # Deploy
-    def deploy_by_name(self, project_name: str) -> requests.Response:
-        return self._make_request("POST", f"by-name/{project_name}/deploy")
-
-    def deploy_by_uuid(self, uuid: str) -> requests.Response:
-        return self._make_request("POST", f"{uuid}/deploy")
-
-    # Status
-    def status_by_name(self, project_name: str) -> requests.Response:
-        return self._make_request("GET", f"by-name/{project_name}/status")
-
-    def status_by_uuid(self, uuid: str) -> requests.Response:
-        return self._make_request("GET", f"{uuid}/status")
-
-    # Logs
-    def logs_by_name(
-        self, project_name: str, log_type: str = "deployment"
-    ) -> requests.Response:
-        return self._make_request("GET", f"by-name/{project_name}/logs/{log_type}")
-
-    def logs_by_uuid(
-        self, uuid: str, log_type: str = "deployment"
-    ) -> requests.Response:
-        return self._make_request("GET", f"{uuid}/logs/{log_type}")
-
-    # Delete
-    def delete_by_name(self, project_name: str) -> requests.Response:
-        return self._make_request("DELETE", f"by-name/{project_name}")
-
-    def delete_by_uuid(self, uuid: str) -> requests.Response:
-        return self._make_request("DELETE", f"{uuid}")
-
-    # List
-    def list_crews(self) -> requests.Response:
-        return self._make_request("GET", "")
-
-    # Create
-    def create_crew(self, payload) -> requests.Response:
-        return self._make_request("POST", "", json=payload)

src/crewai/cli/deploy/main.py

@@ -2,11 +2,9 @@ from typing import Any, Dict, List, Optional
 
 from rich.console import Console
 
-from crewai.telemetry import Telemetry
-from .api import CrewAPI
-from .utils import (
+from crewai.cli.command import BaseCommand, PlusAPIMixin
+from crewai.cli.utils import (
     fetch_and_json_env_file,
-    get_auth_token,
     get_git_remote_url,
     get_project_name,
 )
@@ -14,7 +12,7 @@ from .utils import (
 console = Console()
 
 
-class DeployCommand:
+class DeployCommand(BaseCommand, PlusAPIMixin):
     """
     A class to handle deployment-related operations for CrewAI projects.
     """
@@ -23,40 +21,10 @@ class DeployCommand:
         """
         Initialize the DeployCommand with project name and API client.
         """
-        try:
-            self._telemetry = Telemetry()
-            self._telemetry.set_tracer()
-            access_token = get_auth_token()
-        except Exception:
-            self._deploy_signup_error_span = self._telemetry.deploy_signup_error_span()
-            console.print(
-                "Please sign up/login to CrewAI+ before using the CLI.",
-                style="bold red",
-            )
-            console.print("Run 'crewai signup' to sign up/login.", style="bold green")
-            raise SystemExit
 
-        self.project_name = get_project_name()
-        if self.project_name is None:
-            console.print(
-                "No project name found. Please ensure your project has a valid pyproject.toml file.",
-                style="bold red",
-            )
-            raise SystemExit
-
-        self.client = CrewAPI(api_key=access_token)
-
-    def _handle_error(self, json_response: Dict[str, Any]) -> None:
-        """
-        Handle and display error messages from API responses.
-
-        Args:
-            json_response (Dict[str, Any]): The JSON response containing error information.
-        """
-        error = json_response.get("error", "Unknown error")
-        message = json_response.get("message", "No message provided")
-        console.print(f"Error: {error}", style="bold red")
-        console.print(f"Message: {message}", style="bold red")
+        BaseCommand.__init__(self)
+        PlusAPIMixin.__init__(self, telemetry=self._telemetry)
+        self.project_name = get_project_name(require=True)
 
     def _standard_no_param_error_message(self) -> None:
         """
@@ -104,9 +72,9 @@ class DeployCommand:
         self._start_deployment_span = self._telemetry.start_deployment_span(uuid)
         console.print("Starting deployment...", style="bold blue")
         if uuid:
-            response = self.client.deploy_by_uuid(uuid)
+            response = self.plus_api_client.deploy_by_uuid(uuid)
         elif self.project_name:
-            response = self.client.deploy_by_name(self.project_name)
+            response = self.plus_api_client.deploy_by_name(self.project_name)
         else:
             self._standard_no_param_error_message()
             return
@@ -115,7 +83,7 @@ class DeployCommand:
         if response.status_code == 200:
             self._display_deployment_info(json_response)
         else:
-            self._handle_error(json_response)
+            self._handle_plus_api_error(json_response)
 
     def create_crew(self, confirm: bool = False) -> None:
         """
@@ -139,11 +107,11 @@ class DeployCommand:
         self._confirm_input(env_vars, remote_repo_url, confirm)
         payload = self._create_payload(env_vars, remote_repo_url)
 
-        response = self.client.create_crew(payload)
+        response = self.plus_api_client.create_crew(payload)
         if response.status_code == 201:
             self._display_creation_success(response.json())
         else:
-            self._handle_error(response.json())
+            self._handle_plus_api_error(response.json())
 
     def _confirm_input(
         self, env_vars: Dict[str, str], remote_repo_url: str, confirm: bool
@@ -208,7 +176,7 @@ class DeployCommand:
         """
         console.print("Listing all Crews\n", style="bold blue")
 
-        response = self.client.list_crews()
+        response = self.plus_api_client.list_crews()
         json_response = response.json()
         if response.status_code == 200:
             self._display_crews(json_response)
@@ -243,9 +211,9 @@ class DeployCommand:
         """
         console.print("Fetching deployment status...", style="bold blue")
         if uuid:
-            response = self.client.status_by_uuid(uuid)
+            response = self.plus_api_client.crew_status_by_uuid(uuid)
         elif self.project_name:
-            response = self.client.status_by_name(self.project_name)
+            response = self.plus_api_client.crew_status_by_name(self.project_name)
         else:
             self._standard_no_param_error_message()
             return
@@ -254,7 +222,7 @@ class DeployCommand:
         if response.status_code == 200:
             self._display_crew_status(json_response)
         else:
-            self._handle_error(json_response)
+            self._handle_plus_api_error(json_response)
 
     def _display_crew_status(self, status_data: Dict[str, str]) -> None:
         """
@@ -278,9 +246,9 @@ class DeployCommand:
         console.print(f"Fetching {log_type} logs...", style="bold blue")
 
         if uuid:
-            response = self.client.logs_by_uuid(uuid, log_type)
+            response = self.plus_api_client.crew_by_uuid(uuid, log_type)
         elif self.project_name:
-            response = self.client.logs_by_name(self.project_name, log_type)
+            response = self.plus_api_client.crew_by_name(self.project_name, log_type)
         else:
             self._standard_no_param_error_message()
             return
@@ -288,7 +256,7 @@ class DeployCommand:
         if response.status_code == 200:
             self._display_logs(response.json())
         else:
-            self._handle_error(response.json())
+            self._handle_plus_api_error(response.json())
 
     def remove_crew(self, uuid: Optional[str]) -> None:
         """
@@ -301,9 +269,9 @@ class DeployCommand:
         console.print("Removing deployment...", style="bold blue")
 
         if uuid:
-            response = self.client.delete_by_uuid(uuid)
+            response = self.plus_api_client.delete_crew_by_uuid(uuid)
         elif self.project_name:
-            response = self.client.delete_by_name(self.project_name)
+            response = self.plus_api_client.delete_crew_by_name(self.project_name)
         else:
             self._standard_no_param_error_message()
             return

src/crewai/cli/deploy/utils.py

@@ -1,155 +0,0 @@
-import sys
-import re
-import subprocess
-
-from rich.console import Console
-
-from ..authentication.utils import TokenManager
-
-console = Console()
-
-
-if sys.version_info >= (3, 11):
-    import tomllib
-
-
-# Drop the simple_toml_parser when we move to python3.11
-def simple_toml_parser(content):
-    result = {}
-    current_section = result
-    for line in content.split('\n'):
-        line = line.strip()
-        if line.startswith('[') and line.endswith(']'):
-            # New section
-            section = line[1:-1].split('.')
-            current_section = result
-            for key in section:
-                current_section = current_section.setdefault(key, {})
-        elif '=' in line:
-            key, value = line.split('=', 1)
-            key = key.strip()
-            value = value.strip().strip('"')
-            current_section[key] = value
-    return result
-
-
-def parse_toml(content):
-    if sys.version_info >= (3, 11):
-        return tomllib.loads(content)
-    else:
-        return simple_toml_parser(content)
-
-
-def get_git_remote_url() -> str | None:
-    """Get the Git repository's remote URL."""
-    try:
-        # Run the git remote -v command
-        result = subprocess.run(
-            ["git", "remote", "-v"], capture_output=True, text=True, check=True
-        )
-
-        # Get the output
-        output = result.stdout
-
-        # Parse the output to find the origin URL
-        matches = re.findall(r"origin\s+(.*?)\s+\(fetch\)", output)
-
-        if matches:
-            return matches[0]  # Return the first match (origin URL)
-        else:
-            console.print("No origin remote found.", style="bold red")
-
-    except subprocess.CalledProcessError as e:
-        console.print(f"Error running trying to fetch the Git Repository: {e}", style="bold red")
-    except FileNotFoundError:
-        console.print("Git command not found. Make sure Git is installed and in your PATH.", style="bold red")
-
-    return None
-
-
-def get_project_name(pyproject_path: str = "pyproject.toml") -> str | None:
-    """Get the project name from the pyproject.toml file."""
-    try:
-        # Read the pyproject.toml file
-        with open(pyproject_path, "r") as f:
-            pyproject_content = parse_toml(f.read())
-
-        # Extract the project name
-        project_name = pyproject_content["tool"]["poetry"]["name"]
-
-        if "crewai" not in pyproject_content["tool"]["poetry"]["dependencies"]:
-            raise Exception("crewai is not in the dependencies.")
-
-        return project_name
-
-    except FileNotFoundError:
-        print(f"Error: {pyproject_path} not found.")
-    except KeyError:
-        print(f"Error: {pyproject_path} is not a valid pyproject.toml file.")
-    except tomllib.TOMLDecodeError if sys.version_info >= (3, 11) else Exception as e:  # type: ignore
-        print(
-            f"Error: {pyproject_path} is not a valid TOML file."
-            if sys.version_info >= (3, 11)
-            else f"Error reading the pyproject.toml file: {e}"
-        )
-    except Exception as e:
-        print(f"Error reading the pyproject.toml file: {e}")
-
-    return None
-
-
-def get_crewai_version(poetry_lock_path: str = "poetry.lock") -> str:
-    """Get the version number of crewai from the poetry.lock file."""
-    try:
-        with open(poetry_lock_path, "r") as f:
-            lock_content = f.read()
-
-        match = re.search(
-            r'\[\[package\]\]\s*name\s*=\s*"crewai"\s*version\s*=\s*"([^"]+)"',
-            lock_content,
-            re.DOTALL,
-        )
-        if match:
-            return match.group(1)
-        else:
-            print("crewai package not found in poetry.lock")
-            return "no-version-found"
-
-    except FileNotFoundError:
-        print(f"Error: {poetry_lock_path} not found.")
-    except Exception as e:
-        print(f"Error reading the poetry.lock file: {e}")
-
-    return "no-version-found"
-
-
-def fetch_and_json_env_file(env_file_path: str = ".env") -> dict:
-    """Fetch the environment variables from a .env file and return them as a dictionary."""
-    try:
-        # Read the .env file
-        with open(env_file_path, "r") as f:
-            env_content = f.read()
-
-        # Parse the .env file content to a dictionary
-        env_dict = {}
-        for line in env_content.splitlines():
-            if line.strip() and not line.strip().startswith("#"):
-                key, value = line.split("=", 1)
-                env_dict[key.strip()] = value.strip()
-
-        return env_dict
-
-    except FileNotFoundError:
-        print(f"Error: {env_file_path} not found.")
-    except Exception as e:
-        print(f"Error reading the .env file: {e}")
-
-    return {}
-
-
-def get_auth_token() -> str:
-    """Get the authentication token."""
-    access_token = TokenManager().get_token()
-    if not access_token:
-        raise Exception()
-    return access_token

src/crewai/cli/plus_api.py

@@ -0,0 +1,92 @@
+from typing import Optional
+import requests
+from os import getenv
+from crewai.cli.utils import get_crewai_version
+from urllib.parse import urljoin
+
+
+class PlusAPI:
+    """
+    This class exposes methods for working with the CrewAI+ API.
+    """
+
+    TOOLS_RESOURCE = "/crewai_plus/api/v1/tools"
+    CREWS_RESOURCE = "/crewai_plus/api/v1/crews"
+
+    def __init__(self, api_key: str) -> None:
+        self.api_key = api_key
+        self.headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+            "User-Agent": f"CrewAI-CLI/{get_crewai_version()}",
+            "X-Crewai-Version": get_crewai_version(),
+        }
+        self.base_url = getenv("CREWAI_BASE_URL", "https://app.crewai.com")
+
+    def _make_request(self, method: str, endpoint: str, **kwargs) -> requests.Response:
+        url = urljoin(self.base_url, endpoint)
+        return requests.request(method, url, headers=self.headers, **kwargs)
+
+    def get_tool(self, handle: str):
+        return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}")
+
+    def publish_tool(
+        self,
+        handle: str,
+        is_public: bool,
+        version: str,
+        description: Optional[str],
+        encoded_file: str,
+    ):
+        params = {
+            "handle": handle,
+            "public": is_public,
+            "version": version,
+            "file": encoded_file,
+            "description": description,
+        }
+        return self._make_request("POST", f"{self.TOOLS_RESOURCE}", json=params)
+
+    def deploy_by_name(self, project_name: str) -> requests.Response:
+        return self._make_request(
+            "POST", f"{self.CREWS_RESOURCE}/by-name/{project_name}/deploy"
+        )
+
+    def deploy_by_uuid(self, uuid: str) -> requests.Response:
+        return self._make_request("POST", f"{self.CREWS_RESOURCE}/{uuid}/deploy")
+
+    def crew_status_by_name(self, project_name: str) -> requests.Response:
+        return self._make_request(
+            "GET", f"{self.CREWS_RESOURCE}/by-name/{project_name}/status"
+        )
+
+    def crew_status_by_uuid(self, uuid: str) -> requests.Response:
+        return self._make_request("GET", f"{self.CREWS_RESOURCE}/{uuid}/status")
+
+    def crew_by_name(
+        self, project_name: str, log_type: str = "deployment"
+    ) -> requests.Response:
+        return self._make_request(
+            "GET", f"{self.CREWS_RESOURCE}/by-name/{project_name}/logs/{log_type}"
+        )
+
+    def crew_by_uuid(
+        self, uuid: str, log_type: str = "deployment"
+    ) -> requests.Response:
+        return self._make_request(
+            "GET", f"{self.CREWS_RESOURCE}/{uuid}/logs/{log_type}"
+        )
+
+    def delete_crew_by_name(self, project_name: str) -> requests.Response:
+        return self._make_request(
+            "DELETE", f"{self.CREWS_RESOURCE}/by-name/{project_name}"
+        )
+
+    def delete_crew_by_uuid(self, uuid: str) -> requests.Response:
+        return self._make_request("DELETE", f"{self.CREWS_RESOURCE}/{uuid}")
+
+    def list_crews(self) -> requests.Response:
+        return self._make_request("GET", self.CREWS_RESOURCE)
+
+    def create_crew(self, payload) -> requests.Response:
+        return self._make_request("POST", self.CREWS_RESOURCE, json=payload)

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 = ">=0.55.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.64.0,<1.0.0" }
 
 
 [tool.poetry.scripts]

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

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.64.0,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

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

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.64.0,<1.0.0" }
 
 
 [tool.poetry.scripts]

src/crewai/cli/tools/main.py

@@ -0,0 +1,168 @@
+import base64
+import click
+import os
+import subprocess
+import tempfile
+
+from crewai.cli.command import BaseCommand, PlusAPIMixin
+from crewai.cli.utils import (
+    get_project_name,
+    get_project_description,
+    get_project_version,
+)
+from rich.console import Console
+
+console = Console()
+
+
+class ToolCommand(BaseCommand, PlusAPIMixin):
+    """
+    A class to handle tool repository related operations for CrewAI projects.
+    """
+
+    def __init__(self):
+        BaseCommand.__init__(self)
+        PlusAPIMixin.__init__(self, telemetry=self._telemetry)
+
+    def publish(self, is_public: bool):
+        project_name = get_project_name(require=True)
+        assert isinstance(project_name, str)
+
+        project_version = get_project_version(require=True)
+        assert isinstance(project_version, str)
+
+        project_description = get_project_description(require=False)
+        encoded_tarball = None
+
+        with tempfile.TemporaryDirectory() as temp_build_dir:
+            subprocess.run(
+                ["poetry", "build", "-f", "sdist", "--output", temp_build_dir],
+                check=True,
+                capture_output=False,
+            )
+
+            tarball_filename = next(
+                (f for f in os.listdir(temp_build_dir) if f.endswith(".tar.gz")), None
+            )
+            if not tarball_filename:
+                console.print(
+                    "Project build failed. Please ensure that the command `poetry build -f sdist` completes successfully.",
+                    style="bold red",
+                )
+                raise SystemExit
+
+            tarball_path = os.path.join(temp_build_dir, tarball_filename)
+            with open(tarball_path, "rb") as file:
+                tarball_contents = file.read()
+
+            encoded_tarball = base64.b64encode(tarball_contents).decode("utf-8")
+
+        publish_response = self.plus_api_client.publish_tool(
+            handle=project_name,
+            is_public=is_public,
+            version=project_version,
+            description=project_description,
+            encoded_file=f"data:application/x-gzip;base64,{encoded_tarball}",
+        )
+        if publish_response.status_code == 422:
+            console.print(
+                "[bold red]Failed to publish tool. Please fix the following errors:[/bold red]"
+            )
+            for field, messages in publish_response.json().items():
+                for message in messages:
+                    console.print(
+                        f"* [bold red]{field.capitalize()}[/bold red] {message}"
+                    )
+
+            raise SystemExit
+        elif publish_response.status_code != 200:
+            self._handle_plus_api_error(publish_response.json())
+            console.print(
+                "Failed to publish tool. Please try again later.", style="bold red"
+            )
+            raise SystemExit
+
+        published_handle = publish_response.json()["handle"]
+        console.print(
+            f"Succesfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
+            style="bold green",
+        )
+
+    def install(self, handle: str):
+        get_response = self.plus_api_client.get_tool(handle)
+
+        if get_response.status_code == 404:
+            console.print(
+                "No tool found with this name. Please ensure the tool was published and you have access to it.",
+                style="bold red",
+            )
+            raise SystemExit
+        elif get_response.status_code != 200:
+            console.print(
+                "Failed to get tool details. Please try again later.", style="bold red"
+            )
+            raise SystemExit
+
+        self._add_repository_to_poetry(get_response.json())
+        self._add_package(get_response.json())
+
+        console.print(f"Succesfully installed {handle}", style="bold green")
+
+    def _add_repository_to_poetry(self, tool_details):
+        repository_handle = f"crewai-{tool_details['repository']['handle']}"
+        repository_url = tool_details["repository"]["url"]
+        repository_credentials = tool_details["repository"]["credentials"]
+
+        add_repository_command = [
+            "poetry",
+            "source",
+            "add",
+            "--priority=explicit",
+            repository_handle,
+            repository_url,
+        ]
+        add_repository_result = subprocess.run(
+            add_repository_command, text=True, check=True
+        )
+
+        if add_repository_result.stderr:
+            click.echo(add_repository_result.stderr, err=True)
+            raise SystemExit
+
+        add_repository_credentials_command = [
+            "poetry",
+            "config",
+            f"http-basic.{repository_handle}",
+            repository_credentials,
+            '""',
+        ]
+        add_repository_credentials_result = subprocess.run(
+            add_repository_credentials_command,
+            capture_output=False,
+            text=True,
+            check=True,
+        )
+
+        if add_repository_credentials_result.stderr:
+            click.echo(add_repository_credentials_result.stderr, err=True)
+            raise SystemExit
+
+    def _add_package(self, tool_details):
+        tool_handle = tool_details["handle"]
+        repository_handle = tool_details["repository"]["handle"]
+        pypi_index_handle = f"crewai-{repository_handle}"
+
+        add_package_command = [
+            "poetry",
+            "add",
+            "--source",
+            pypi_index_handle,
+            tool_handle,
+        ]
+        add_package_result = subprocess.run(
+            add_package_command, capture_output=False, text=True, check=True
+        )
+
+        if add_package_result.stderr:
+            click.echo(add_package_result.stderr, err=True)
+            raise SystemExit

src/crewai/cli/utils.py

@@ -1,4 +1,17 @@
 import click
+import re
+import subprocess
+import sys
+
+from crewai.cli.authentication.utils import TokenManager
+from functools import reduce
+from rich.console import Console
+from typing import Any, Dict, List
+
+if sys.version_info >= (3, 11):
+    import tomllib
+
+console = Console()
 
 
 def copy_template(src, dst, name, class_name, folder_name):
@@ -16,3 +29,191 @@ def copy_template(src, dst, name, class_name, folder_name):
         file.write(content)
 
     click.secho(f"  - Created {dst}", fg="green")
+
+
+# Drop the simple_toml_parser when we move to python3.11
+def simple_toml_parser(content):
+    result = {}
+    current_section = result
+    for line in content.split("\n"):
+        line = line.strip()
+        if line.startswith("[") and line.endswith("]"):
+            # New section
+            section = line[1:-1].split(".")
+            current_section = result
+            for key in section:
+                current_section = current_section.setdefault(key, {})
+        elif "=" in line:
+            key, value = line.split("=", 1)
+            key = key.strip()
+            value = value.strip().strip('"')
+            current_section[key] = value
+    return result
+
+
+def parse_toml(content):
+    if sys.version_info >= (3, 11):
+        return tomllib.loads(content)
+    else:
+        return simple_toml_parser(content)
+
+
+def get_git_remote_url() -> str | None:
+    """Get the Git repository's remote URL."""
+    try:
+        # Run the git remote -v command
+        result = subprocess.run(
+            ["git", "remote", "-v"], capture_output=True, text=True, check=True
+        )
+
+        # Get the output
+        output = result.stdout
+
+        # Parse the output to find the origin URL
+        matches = re.findall(r"origin\s+(.*?)\s+\(fetch\)", output)
+
+        if matches:
+            return matches[0]  # Return the first match (origin URL)
+        else:
+            console.print("No origin remote found.", style="bold red")
+
+    except subprocess.CalledProcessError as e:
+        console.print(
+            f"Error running trying to fetch the Git Repository: {e}", style="bold red"
+        )
+    except FileNotFoundError:
+        console.print(
+            "Git command not found. Make sure Git is installed and in your PATH.",
+            style="bold red",
+        )
+
+    return None
+
+
+def get_project_name(
+    pyproject_path: str = "pyproject.toml", require: bool = False
+) -> str | None:
+    """Get the project name from the pyproject.toml file."""
+    return _get_project_attribute(
+        pyproject_path, ["tool", "poetry", "name"], require=require
+    )
+
+
+def get_project_version(
+    pyproject_path: str = "pyproject.toml", require: bool = False
+) -> str | None:
+    """Get the project version from the pyproject.toml file."""
+    return _get_project_attribute(
+        pyproject_path, ["tool", "poetry", "version"], require=require
+    )
+
+
+def get_project_description(
+    pyproject_path: str = "pyproject.toml", require: bool = False
+) -> str | None:
+    """Get the project description from the pyproject.toml file."""
+    return _get_project_attribute(
+        pyproject_path, ["tool", "poetry", "description"], require=require
+    )
+
+
+def _get_project_attribute(
+    pyproject_path: str, keys: List[str], require: bool
+) -> Any | None:
+    """Get an attribute from the pyproject.toml file."""
+    attribute = None
+
+    try:
+        with open(pyproject_path, "r") as f:
+            pyproject_content = parse_toml(f.read())
+
+        dependencies = (
+            _get_nested_value(pyproject_content, ["tool", "poetry", "dependencies"])
+            or {}
+        )
+        if "crewai" not in dependencies:
+            raise Exception("crewai is not in the dependencies.")
+
+        attribute = _get_nested_value(pyproject_content, keys)
+    except FileNotFoundError:
+        print(f"Error: {pyproject_path} not found.")
+    except KeyError:
+        print(f"Error: {pyproject_path} is not a valid pyproject.toml file.")
+    except tomllib.TOMLDecodeError if sys.version_info >= (3, 11) else Exception as e:  # type: ignore
+        print(
+            f"Error: {pyproject_path} is not a valid TOML file."
+            if sys.version_info >= (3, 11)
+            else f"Error reading the pyproject.toml file: {e}"
+        )
+    except Exception as e:
+        print(f"Error reading the pyproject.toml file: {e}")
+
+    if require and not attribute:
+        console.print(
+            f"Unable to read '{'.'.join(keys)}' in the pyproject.toml file. Please verify that the file exists and contains the specified attribute.",
+            style="bold red",
+        )
+        raise SystemExit
+
+    return attribute
+
+
+def _get_nested_value(data: Dict[str, Any], keys: List[str]) -> Any:
+    return reduce(dict.__getitem__, keys, data)
+
+
+def get_crewai_version(poetry_lock_path: str = "poetry.lock") -> str:
+    """Get the version number of crewai from the poetry.lock file."""
+    try:
+        with open(poetry_lock_path, "r") as f:
+            lock_content = f.read()
+
+        match = re.search(
+            r'\[\[package\]\]\s*name\s*=\s*"crewai"\s*version\s*=\s*"([^"]+)"',
+            lock_content,
+            re.DOTALL,
+        )
+        if match:
+            return match.group(1)
+        else:
+            print("crewai package not found in poetry.lock")
+            return "no-version-found"
+
+    except FileNotFoundError:
+        print(f"Error: {poetry_lock_path} not found.")
+    except Exception as e:
+        print(f"Error reading the poetry.lock file: {e}")
+
+    return "no-version-found"
+
+
+def fetch_and_json_env_file(env_file_path: str = ".env") -> dict:
+    """Fetch the environment variables from a .env file and return them as a dictionary."""
+    try:
+        # Read the .env file
+        with open(env_file_path, "r") as f:
+            env_content = f.read()
+
+        # Parse the .env file content to a dictionary
+        env_dict = {}
+        for line in env_content.splitlines():
+            if line.strip() and not line.strip().startswith("#"):
+                key, value = line.split("=", 1)
+                env_dict[key.strip()] = value.strip()
+
+        return env_dict
+
+    except FileNotFoundError:
+        print(f"Error: {env_file_path} not found.")
+    except Exception as e:
+        print(f"Error reading the .env file: {e}")
+
+    return {}
+
+
+def get_auth_token() -> str:
+    """Get the authentication token."""
+    access_token = TokenManager().get_token()
+    if not access_token:
+        raise Exception()
+    return access_token

src/crewai/crew.py

@@ -22,6 +22,7 @@ from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache import CacheHandler
 from crewai.crews.crew_output import CrewOutput
+from crewai.llm import LLM
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
@@ -110,6 +111,18 @@ class Crew(BaseModel):
         default=False,
         description="Whether the crew should use memory to store memories of it's execution",
     )
+    short_term_memory: Optional[InstanceOf[ShortTermMemory]] = Field(
+        default=None,
+        description="An Instance of the ShortTermMemory to be used by the Crew",
+    )
+    long_term_memory: Optional[InstanceOf[LongTermMemory]] = Field(
+        default=None,
+        description="An Instance of the LongTermMemory to be used by the Crew",
+    )
+    entity_memory: Optional[InstanceOf[EntityMemory]] = Field(
+        default=None,
+        description="An Instance of the EntityMemory to be used by the Crew",
+    )
     embedder: Optional[dict] = Field(
         default={"provider": "openai"},
         description="Configuration for the embedder to be used for the crew.",
@@ -199,12 +212,15 @@ class Crew(BaseModel):
         if self.output_log_file:
             self._file_handler = FileHandler(self.output_log_file)
         self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
-        self.function_calling_llm = (
-            self.function_calling_llm.model_name
-            if self.function_calling_llm is not None
-            and hasattr(self.function_calling_llm, "model_name")
-            else self.function_calling_llm
-        )
+        if self.function_calling_llm:
+            if isinstance(self.function_calling_llm, str):
+                self.function_calling_llm = LLM(model=self.function_calling_llm)
+            elif not isinstance(self.function_calling_llm, LLM):
+                self.function_calling_llm = LLM(
+                    model=getattr(self.function_calling_llm, "model_name", None)
+                    or getattr(self.function_calling_llm, "deployment_name", None)
+                    or str(self.function_calling_llm)
+                )
         self._telemetry = Telemetry()
         self._telemetry.set_tracer()
         return self
@@ -213,11 +229,19 @@ class Crew(BaseModel):
     def create_crew_memory(self) -> "Crew":
         """Set private attributes."""
         if self.memory:
-            self._long_term_memory = LongTermMemory()
-            self._short_term_memory = ShortTermMemory(
-                crew=self, embedder_config=self.embedder
+            self._long_term_memory = (
+                self.long_term_memory if self.long_term_memory else LongTermMemory()
+            )
+            self._short_term_memory = (
+                self.short_term_memory
+                if self.short_term_memory
+                else ShortTermMemory(crew=self, embedder_config=self.embedder)
+            )
+            self._entity_memory = (
+                self.entity_memory
+                if self.entity_memory
+                else EntityMemory(crew=self, embedder_config=self.embedder)
             )
-            self._entity_memory = EntityMemory(crew=self, embedder_config=self.embedder)
         return self
 
     @model_validator(mode="after")
@@ -514,10 +538,6 @@ class Crew(BaseModel):
             asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
             for i in range(len(inputs))
         ]
-        tasks = [
-            asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
-            for i in range(len(inputs))
-        ]
 
         results = await asyncio.gather(*tasks)
 
@@ -592,9 +612,9 @@ class Crew(BaseModel):
             manager.tools = self.manager_agent.get_delegation_tools(self.agents)
         else:
             self.manager_llm = (
-                self.manager_llm.model_name
-                if hasattr(self.manager_llm, "model_name")
-                else self.manager_llm
+                getattr(self.manager_llm, "model_name", None)
+                or getattr(self.manager_llm, "deployment_name", None)
+                or self.manager_llm
             )
             manager = Agent(
                 role=i18n.retrieve("hierarchical_manager_agent", "role"),
@@ -605,6 +625,7 @@ class Crew(BaseModel):
                 verbose=self.verbose,
             )
             self.manager_agent = manager
+        manager.crew = self
 
     def _execute_tasks(
         self,
@@ -936,14 +957,17 @@ class Crew(BaseModel):
     def test(
         self,
         n_iterations: int,
-        openai_model_name: str,
+        openai_model_name: Optional[str] = None,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
-        """Test and evaluate the Crew with the given inputs for n iterations."""
+        """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
         self._test_execution_span = self._telemetry.test_execution_span(
-            self, n_iterations, inputs, openai_model_name
-        )
-        evaluator = CrewEvaluator(self, openai_model_name)
+            self,
+            n_iterations,
+            inputs,
+            openai_model_name,  # type: ignore[arg-type]
+        )  # type: ignore[arg-type]
+        evaluator = CrewEvaluator(self, openai_model_name)  # type: ignore[arg-type]
 
         for i in range(1, n_iterations + 1):
             evaluator.set_iteration(i)

src/crewai/llm.py

@@ -1,20 +1,183 @@
-from typing import Any, Dict, List
-from litellm import completion
+from contextlib import contextmanager
+from typing import Any, Dict, List, Optional, Union
+import logging
+import warnings
 import litellm
+from litellm import get_supported_openai_params
+
+from crewai.utilities.exceptions.context_window_exceeding_exception import (
+    LLMContextLengthExceededException,
+)
+
+import sys
+import io
+
+
+class FilteredStream(io.StringIO):
+    def write(self, s):
+        if (
+            "Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new"
+            in s
+            or "LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True`"
+            in s
+        ):
+            return
+        super().write(s)
+
+
+LLM_CONTEXT_WINDOW_SIZES = {
+    # openai
+    "gpt-4": 8192,
+    "gpt-4o": 128000,
+    "gpt-4o-mini": 128000,
+    "gpt-4-turbo": 128000,
+    "o1-preview": 128000,
+    "o1-mini": 128000,
+    # deepseek
+    "deepseek-chat": 128000,
+    # groq
+    "gemma2-9b-it": 8192,
+    "gemma-7b-it": 8192,
+    "llama3-groq-70b-8192-tool-use-preview": 8192,
+    "llama3-groq-8b-8192-tool-use-preview": 8192,
+    "llama-3.1-70b-versatile": 131072,
+    "llama-3.1-8b-instant": 131072,
+    "llama-3.2-1b-preview": 8192,
+    "llama-3.2-3b-preview": 8192,
+    "llama-3.2-11b-text-preview": 8192,
+    "llama-3.2-90b-text-preview": 8192,
+    "llama3-70b-8192": 8192,
+    "llama3-8b-8192": 8192,
+    "mixtral-8x7b-32768": 32768,
+}
+
+
+@contextmanager
+def suppress_warnings():
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore")
+
+        # Redirect stdout and stderr
+        old_stdout = sys.stdout
+        old_stderr = sys.stderr
+        sys.stdout = FilteredStream()
+        sys.stderr = FilteredStream()
+
+        try:
+            yield
+        finally:
+            # Restore stdout and stderr
+            sys.stdout = old_stdout
+            sys.stderr = old_stderr
 
 
 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.set_verbose = False
         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:
+        with suppress_warnings():
+            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,
+                    "stream": False,
+                    **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:
+                if not LLMContextLengthExceededException(
+                    str(e)
+                )._is_context_limit_error(str(e)):
+                    logging.error(f"LiteLLM call failed: {str(e)}")
+
+                raise  # Re-raise the exception after logging
+
+    def supports_function_calling(self) -> bool:
+        try:
+            params = get_supported_openai_params(model=self.model)
+            return "response_format" in params
+        except Exception as e:
+            logging.error(f"Failed to get supported params: {str(e)}")
+            return False
+
+    def supports_stop_words(self) -> bool:
+        try:
+            params = get_supported_openai_params(model=self.model)
+            return "stop" in params
+        except Exception as e:
+            logging.error(f"Failed to get supported params: {str(e)}")
+            return False
+
+    def get_context_window_size(self) -> int:
+        # Only using 75% of the context window size to avoid cutting the message in the middle
+        return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75)

src/crewai/memory/entity/entity_memory.py

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

src/crewai/memory/long_term/long_term_memory.py

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

src/crewai/memory/short_term/short_term_memory.py

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

src/crewai/project/crew_base.py

@@ -1,14 +1,19 @@
 import inspect
 from pathlib import Path
-from typing import Any, Callable, Dict
+from typing import Any, Callable, Dict, Type, TypeVar
 
 import yaml
 from dotenv import load_dotenv
 
+from crewai.crew import Crew
+
 load_dotenv()
 
 
-def CrewBase(cls):
+T = TypeVar("T", bound=Type[Any])
+
+
+def CrewBase(cls: T) -> T:
     class WrappedClass(cls):
         is_crew_class: bool = True  # type: ignore
 
@@ -32,10 +37,23 @@ def CrewBase(cls):
             self.map_all_agent_variables()
             self.map_all_task_variables()
 
+        def crew(self) -> "Crew":
+            agents = [
+                getattr(self, name)()
+                for name, func in self._get_all_functions().items()
+                if hasattr(func, "is_agent")
+            ]
+            tasks = [
+                getattr(self, name)()
+                for name, func in reversed(self._get_all_functions().items())
+                if hasattr(func, "is_task")
+            ]
+            return Crew(agents=agents, tasks=tasks)
+
         @staticmethod
         def load_yaml(config_path: Path):
             try:
-                with open(config_path, "r") as file:
+                with open(config_path, "r", encoding="utf-8") as file:
                     return yaml.safe_load(file)
             except FileNotFoundError:
                 print(f"File not found: {config_path}")

src/crewai/tasks/task_output.py

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

src/crewai/telemetry/telemetry.py

@@ -53,7 +53,8 @@ class Telemetry:
             self.resource = Resource(
                 attributes={SERVICE_NAME: "crewAI-telemetry"},
             )
-            self.provider = TracerProvider(resource=self.resource)
+            with suppress_warnings():
+                self.provider = TracerProvider(resource=self.resource)
 
             processor = BatchSpanProcessor(
                 OTLPSpanExporter(
@@ -116,8 +117,10 @@ class Telemetry:
                                     "max_iter": agent.max_iter,
                                     "max_rpm": agent.max_rpm,
                                     "i18n": agent.i18n.prompt_file,
-                                    "function_calling_llm": agent.function_calling_llm,
-                                    "llm": agent.llm,
+                                    "function_calling_llm": agent.function_calling_llm.model
+                                    if agent.function_calling_llm
+                                    else "",
+                                    "llm": agent.llm.model,
                                     "delegation_enabled?": agent.allow_delegation,
                                     "allow_code_execution?": agent.allow_code_execution,
                                     "max_retry_limit": agent.max_retry_limit,
@@ -181,8 +184,10 @@ class Telemetry:
                                     "verbose?": agent.verbose,
                                     "max_iter": agent.max_iter,
                                     "max_rpm": agent.max_rpm,
-                                    "function_calling_llm": agent.function_calling_llm,
-                                    "llm": agent.llm,
+                                    "function_calling_llm": agent.function_calling_llm.model
+                                    if agent.function_calling_llm
+                                    else "",
+                                    "llm": agent.llm.model,
                                     "delegation_enabled?": agent.allow_delegation,
                                     "allow_code_execution?": agent.allow_code_execution,
                                     "max_retry_limit": agent.max_retry_limit,
@@ -296,7 +301,7 @@ class Telemetry:
                 self._add_attribute(span, "tool_name", tool_name)
                 self._add_attribute(span, "attempts", attempts)
                 if llm:
-                    self._add_attribute(span, "llm", llm)
+                    self._add_attribute(span, "llm", llm.model)
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             except Exception:
@@ -316,7 +321,7 @@ class Telemetry:
                 self._add_attribute(span, "tool_name", tool_name)
                 self._add_attribute(span, "attempts", attempts)
                 if llm:
-                    self._add_attribute(span, "llm", llm)
+                    self._add_attribute(span, "llm", llm.model)
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             except Exception:
@@ -334,7 +339,7 @@ class Telemetry:
                     pkg_resources.get_distribution("crewai").version,
                 )
                 if llm:
-                    self._add_attribute(span, "llm", llm)
+                    self._add_attribute(span, "llm", llm.model)
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             except Exception:
@@ -487,7 +492,7 @@ class Telemetry:
                                 "max_iter": agent.max_iter,
                                 "max_rpm": agent.max_rpm,
                                 "i18n": agent.i18n.prompt_file,
-                                "llm": agent.llm,
+                                "llm": agent.llm.model,
                                 "delegation_enabled?": agent.allow_delegation,
                                 "tools_names": [
                                     tool.name.casefold() for tool in agent.tools or []

src/crewai/tools/tool_usage.py

@@ -17,7 +17,7 @@ if os.environ.get("AGENTOPS_API_KEY"):
     except ImportError:
         pass
 
-OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o"]
+OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o", "o1-preview", "o1-mini"]
 
 
 class ToolUsageErrorException(Exception):
@@ -71,10 +71,12 @@ class ToolUsage:
         self.function_calling_llm = function_calling_llm
 
         # Set the maximum parsing attempts for bigger models
-        if self._is_gpt(self.function_calling_llm) and "4" in self.function_calling_llm:
-            if self.function_calling_llm in OPENAI_BIGGER_MODELS:
-                self._max_parsing_attempts = 2
-                self._remember_format_after_usages = 4
+        if (
+            self.function_calling_llm
+            and self.function_calling_llm in OPENAI_BIGGER_MODELS
+        ):
+            self._max_parsing_attempts = 2
+            self._remember_format_after_usages = 4
 
     def parse(self, tool_string: str):
         """Parse the tool string and return the tool calling."""
@@ -295,61 +297,78 @@ class ToolUsage:
             )
         return "\n--\n".join(descriptions)
 
-    def _is_gpt(self, llm) -> bool:
-        return (
-            "gpt" in str(llm).lower()
-            or "o1-preview" in str(llm).lower()
-            or "o1-mini" in str(llm).lower()
+    def _function_calling(self, tool_string: str):
+        model = (
+            InstructorToolCalling
+            if self.function_calling_llm.supports_function_calling()
+            else ToolCalling
+        )
+        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,
+            model=model,
+            instructions=dedent(
+                """\
+        The schema should have the following structure, only two keys:
+        - tool_name: str
+        - arguments: dict (always a dictionary, with all arguments being passed)
+
+        Example:
+        {"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
+            ),
+            max_attempts=1,
+        )
+        tool_object = converter.to_pydantic()
+        calling = ToolCalling(
+            tool_name=tool_object["tool_name"],
+            arguments=tool_object["arguments"],
+            log=tool_string,  # type: ignore
+        )
+
+        if isinstance(calling, ConverterError):
+            raise calling
+
+        return calling
+
+    def _original_tool_calling(self, tool_string: str, raise_error: bool = False):
+        tool_name = self.action.tool
+        tool = self._select_tool(tool_name)
+        try:
+            tool_input = self._validate_tool_input(self.action.tool_input)
+            arguments = ast.literal_eval(tool_input)
+        except Exception:
+            if raise_error:
+                raise
+            else:
+                return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
+                    f'{self._i18n.errors("tool_arguments_error")}'
+                )
+
+        if not isinstance(arguments, dict):
+            if raise_error:
+                raise
+            else:
+                return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
+                    f'{self._i18n.errors("tool_arguments_error")}'
+                )
+
+        return ToolCalling(
+            tool_name=tool.name,
+            arguments=arguments,
+            log=tool_string,  # type: ignore
         )
 
     def _tool_calling(
         self, tool_string: str
     ) -> Union[ToolCalling, InstructorToolCalling]:
         try:
-            if self.function_calling_llm:
-                model = (
-                    InstructorToolCalling
-                    if self._is_gpt(self.function_calling_llm)
-                    else ToolCalling
-                )
-                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,
-                    model=model,
-                    instructions=dedent(
-                        """\
-              The schema should have the following structure, only two keys:
-              - tool_name: str
-              - arguments: dict (with all arguments being passed)
-
-              Example:
-              {"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
-                    ),
-                    max_attempts=1,
-                )
-                calling = converter.to_pydantic()
-
-                if isinstance(calling, ConverterError):
-                    raise calling
-            else:
-                tool_name = self.action.tool
-                tool = self._select_tool(tool_name)
-                try:
-                    tool_input = self._validate_tool_input(self.action.tool_input)
-                    arguments = ast.literal_eval(tool_input)
-                except Exception:
-                    return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
-                        f'{self._i18n.errors("tool_arguments_error")}'
-                    )
-                if not isinstance(arguments, dict):
-                    return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
-                        f'{self._i18n.errors("tool_arguments_error")}'
-                    )
-                calling = ToolCalling(
-                    tool_name=tool.name,
-                    arguments=arguments,
-                    log=tool_string,  # type: ignore
-                )
+            try:
+                return self._original_tool_calling(tool_string, raise_error=True)
+            except Exception:
+                if self.function_calling_llm:
+                    return self._function_calling(tool_string)
+                else:
+                    return self._original_tool_calling(tool_string)
         except Exception as e:
             self._run_attempts += 1
             if self._run_attempts > self._max_parsing_attempts:
@@ -362,8 +381,6 @@ class ToolUsage:
                 )
             return self._tool_calling(tool_string)
 
-        return calling
-
     def _validate_tool_input(self, tool_input: str) -> str:
         try:
             ast.literal_eval(tool_input)

src/crewai/translations/en.json

@@ -17,7 +17,7 @@
     "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
     "expected_output": "\nThis is the expect criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
     "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
-    "getting_input": "This is the agent's final answer: {final_answer}\nPlease provide feedback: ",
+    "getting_input": "This is the agent's final answer: {final_answer}\n\n",
     "summarizer_system_message": "You are a helpful assistant that summarizes text.",
     "sumamrize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
     "summary": "This is a summary of our conversation so far:\n{merged_summary}"

src/crewai/utilities/converter.py

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

src/crewai/utilities/evaluators/task_evaluator.py

@@ -49,7 +49,7 @@ class TaskEvaluation(BaseModel):
 
 class TrainingTaskEvaluation(BaseModel):
     suggestions: List[str] = Field(
-        description="Based on the Human Feedbacks and the comparison between Initial Outputs and Improved outputs provide action items based on human_feedback for future tasks."
+        description="List of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent's performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific action items for future tasks. Ensure all key and specific points from the human feedback are incorporated into these instructions."
     )
     quality: float = Field(
         description="A score from 0 to 10 evaluating on completion, quality, and overall performance from the improved output to the initial output based on the human feedback."
@@ -78,7 +78,7 @@ class TaskEvaluator:
 
         instructions = "Convert all responses into valid JSON output."
 
-        if not self._is_gpt(self.llm):
+        if not self.llm.supports_function_calling():
             model_schema = PydanticSchemaParser(model=TaskEvaluation).get_schema()
             instructions = f"{instructions}\n\nReturn only valid JSON with the following schema:\n```json\n{model_schema}\n```"
 
@@ -91,13 +91,6 @@ class TaskEvaluator:
 
         return converter.to_pydantic()
 
-    def _is_gpt(self, llm) -> bool:
-        return (
-            "gpt" in str(self.llm).lower()
-            or "o1-preview" in str(self.llm).lower()
-            or "o1-mini" in str(self.llm).lower()
-        )
-
     def evaluate_training_data(
         self, training_data: dict, agent_id: str
     ) -> TrainingTaskEvaluation:
@@ -123,12 +116,12 @@ class TaskEvaluator:
             "Assess the quality of the training data based on the llm output, human feedback , and llm output improved result.\n\n"
             f"{final_aggregated_data}"
             "Please provide:\n"
-            "- Based on the Human Feedbacks and the comparison between Initial Outputs and Improved outputs provide action items based on human_feedback for future tasks\n"
+            "- Provide a list of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent's performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific action items for future tasks. Ensure all key and specificpoints from the human feedback are incorporated into these instructions.\n"
             "- A score from 0 to 10 evaluating on completion, quality, and overall performance from the improved output to the initial output based on the human feedback\n"
         )
         instructions = "I'm gonna convert this raw text into valid JSON."
 
-        if not self._is_gpt(self.llm):
+        if not self.llm.supports_function_calling():
             model_schema = PydanticSchemaParser(
                 model=TrainingTaskEvaluation
             ).get_schema()

src/crewai/utilities/exceptions/context_window_exceeding_exception.py

@@ -1,5 +1,6 @@
 class LLMContextLengthExceededException(Exception):
     CONTEXT_LIMIT_ERRORS = [
+        "expected a string with maximum length",
         "maximum context length",
         "context length exceeded",
         "context_length_exceeded",

src/crewai/utilities/i18n.py

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

src/crewai/utilities/internal_instructor.py

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

src/crewai/utilities/logger.py

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

src/crewai/utilities/printer.py

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

src/crewai/utilities/rpm_controller.py

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

tests/agent_test.py

@@ -3,6 +3,7 @@
 from unittest import mock
 from unittest.mock import patch
 
+import os
 import pytest
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
@@ -16,6 +17,49 @@ from crewai_tools import tool
 from crewai.agents.parser import AgentAction
 
 
+def test_agent_llm_creation_with_env_vars():
+    # Store original environment variables
+    original_api_key = os.environ.get("OPENAI_API_KEY")
+    original_api_base = os.environ.get("OPENAI_API_BASE")
+    original_model_name = os.environ.get("OPENAI_MODEL_NAME")
+
+    # Set up environment variables
+    os.environ["OPENAI_API_KEY"] = "test_api_key"
+    os.environ["OPENAI_API_BASE"] = "https://test-api-base.com"
+    os.environ["OPENAI_MODEL_NAME"] = "gpt-4-turbo"
+
+    # Create an agent without specifying LLM
+    agent = Agent(role="test role", goal="test goal", backstory="test backstory")
+
+    # Check if LLM is created correctly
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-4-turbo"
+    assert agent.llm.api_key == "test_api_key"
+    assert agent.llm.base_url == "https://test-api-base.com"
+
+    # Clean up environment variables
+    del os.environ["OPENAI_API_KEY"]
+    del os.environ["OPENAI_API_BASE"]
+    del os.environ["OPENAI_MODEL_NAME"]
+
+    # Create an agent without specifying LLM
+    agent = Agent(role="test role", goal="test goal", backstory="test backstory")
+
+    # Check if LLM is created correctly
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model != "gpt-4-turbo"
+    assert agent.llm.api_key != "test_api_key"
+    assert agent.llm.base_url != "https://test-api-base.com"
+
+    # Restore original environment variables
+    if original_api_key:
+        os.environ["OPENAI_API_KEY"] = original_api_key
+    if original_api_base:
+        os.environ["OPENAI_API_BASE"] = original_api_base
+    if original_model_name:
+        os.environ["OPENAI_MODEL_NAME"] = original_model_name
+
+
 def test_agent_creation():
     agent = Agent(role="test role", goal="test goal", backstory="test backstory")
 
@@ -27,7 +71,7 @@ def test_agent_creation():
 
 def test_agent_default_values():
     agent = Agent(role="test role", goal="test goal", backstory="test backstory")
-    assert agent.llm == "gpt-4o"
+    assert agent.llm.model == "gpt-4o"
     assert agent.allow_delegation is False
 
 
@@ -35,7 +79,7 @@ def test_custom_llm():
     agent = Agent(
         role="test role", goal="test goal", backstory="test backstory", llm="gpt-4"
     )
-    assert agent.llm == "gpt-4"
+    assert agent.llm.model == "gpt-4"
 
 
 def test_custom_llm_with_langchain():
@@ -48,7 +92,51 @@ def test_custom_llm_with_langchain():
         llm=ChatOpenAI(temperature=0, model="gpt-4"),
     )
 
-    assert agent.llm == "gpt-4"
+    assert agent.llm.model == "gpt-4"
+
+
+def test_custom_llm_temperature_preservation():
+    from langchain_openai import ChatOpenAI
+
+    langchain_llm = ChatOpenAI(temperature=0.7, model="gpt-4")
+    agent = Agent(
+        role="temperature test role",
+        goal="temperature test goal",
+        backstory="temperature test backstory",
+        llm=langchain_llm,
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-4"
+    assert agent.llm.temperature == 0.7
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task():
+    from langchain_openai import ChatOpenAI
+    from crewai import Task
+
+    agent = Agent(
+        role="Math Tutor",
+        goal="Solve math problems accurately",
+        backstory="You are an experienced math tutor with a knack for explaining complex concepts simply.",
+        llm=ChatOpenAI(temperature=0.7, model="gpt-4o-mini"),
+    )
+
+    task = Task(
+        description="Calculate the area of a circle with radius 5 cm.",
+        expected_output="The calculated area of the circle in square centimeters.",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+
+    assert result is not None
+    assert (
+        result
+        == "The calculated area of the circle is approximately 78.5 square centimeters."
+    )
+    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 == "The result of the math operation 1 + 1 is 2."
+    assert output == "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 3 times 4 is 12."
+
+    assert output == "The result of the multiplication 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 result of the multiplication.",
+            expected_output="The number that is the result of the multiplication tool.",
         )
         output = agent.execute_task(task)
         assert output == "0"
@@ -275,7 +365,7 @@ def test_agent_execution_with_specific_tools():
         expected_output="The result of the multiplication.",
     )
     output = agent.execute_task(task=task, tools=[multiplier])
-    assert output == "The result of the multiplication of 3 times 4 is 12."
+    assert output == "The result of the multiplication is 12."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -293,7 +383,6 @@ def test_agent_powered_by_new_o_model_family_that_allows_skipping_tool():
         max_iter=3,
         use_system_prompt=False,
         allow_delegation=False,
-        use_stop_words=False,
     )
 
     task = Task(
@@ -320,7 +409,6 @@ def test_agent_powered_by_new_o_model_family_that_uses_tool():
         max_iter=3,
         use_system_prompt=False,
         allow_delegation=False,
-        use_stop_words=False,
     )
 
     task = Task(
@@ -329,7 +417,7 @@ 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"])
@@ -490,7 +578,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
             task=task,
             tools=[get_final_answer],
         )
-        assert output == "42"
+        assert output == "The final answer is 42."
         captured = capsys.readouterr()
         assert "Max RPM reached, waiting for next minute to start." in captured.out
         moveon.assert_called()
@@ -620,12 +708,13 @@ def test_agent_error_on_parsing_tool(capsys):
         verbose=True,
         function_calling_llm="gpt-4o",
     )
-
-    with patch.object(ToolUsage, "_render") as force_exception:
-        force_exception.side_effect = Exception("Error on parsing tool.")
-        crew.kickoff()
-        captured = capsys.readouterr()
-        assert "Error on parsing tool." in captured.out
+    with patch.object(ToolUsage, "_original_tool_calling") as force_exception_1:
+        force_exception_1.side_effect = Exception("Error on parsing tool.")
+        with patch.object(ToolUsage, "_render") as force_exception_2:
+            force_exception_2.side_effect = Exception("Error on parsing tool.")
+            crew.kickoff()
+    captured = capsys.readouterr()
+    assert "Error on parsing tool." in captured.out
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -750,27 +839,18 @@ 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
+    from crewai.tools.tool_usage import ToolUsage
 
-    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, patch.object(
+        ToolUsage, "_original_tool_calling", side_effect=Exception("Forced exception")
+    ) as mock_original_tool_calling:
         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"
+        mock_original_tool_calling.assert_called()
 
 
 def test_agent_count_formatting_error():
@@ -1102,6 +1182,88 @@ 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 +1334,214 @@ def test_handle_context_length_exceeds_limit_cli_no():
             CrewAgentExecutor, "_handle_context_length"
         ) as mock_handle_context:
             mock_handle_context.assert_not_called()
+
+
+def test_agent_with_all_llm_attributes():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(
+            model="gpt-3.5-turbo",
+            timeout=10,
+            temperature=0.7,
+            top_p=0.9,
+            n=1,
+            stop=["STOP", "END"],
+            max_tokens=100,
+            presence_penalty=0.1,
+            frequency_penalty=0.1,
+            logit_bias={50256: -100},  # Example: bias against the EOT token
+            response_format={"type": "json_object"},
+            seed=42,
+            logprobs=True,
+            top_logprobs=5,
+            base_url="https://api.openai.com/v1",
+            api_version="2023-05-15",
+            api_key="sk-your-api-key-here",
+        ),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-3.5-turbo"
+    assert agent.llm.timeout == 10
+    assert agent.llm.temperature == 0.7
+    assert agent.llm.top_p == 0.9
+    assert agent.llm.n == 1
+    assert agent.llm.stop == ["STOP", "END"]
+    assert agent.llm.max_tokens == 100
+    assert agent.llm.presence_penalty == 0.1
+    assert agent.llm.frequency_penalty == 0.1
+    assert agent.llm.logit_bias == {50256: -100}
+    assert agent.llm.response_format == {"type": "json_object"}
+    assert agent.llm.seed == 42
+    assert agent.llm.logprobs
+    assert agent.llm.top_logprobs == 5
+    assert agent.llm.base_url == "https://api.openai.com/v1"
+    assert agent.llm.api_version == "2023-05-15"
+    assert agent.llm.api_key == "sk-your-api-key-here"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_all_attributes():
+    llm = LLM(
+        model="gpt-3.5-turbo",
+        temperature=0.7,
+        max_tokens=50,
+        stop=["STOP"],
+        presence_penalty=0.1,
+        frequency_penalty=0.1,
+    )
+    messages = [{"role": "user", "content": "Say 'Hello, World!' and then say STOP"}]
+
+    response = llm.call(messages)
+    assert "Hello, World!" in response
+    assert "STOP" not in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_with_ollama_gemma():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(
+            model="ollama/gemma2:latest",
+            base_url="http://localhost:8080",
+        ),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "ollama/gemma2:latest"
+    assert agent.llm.base_url == "http://localhost:8080"
+
+    task = "Respond in 20 words. Who are you?"
+    response = agent.llm.call([{"role": "user", "content": task}])
+
+    assert response
+    assert len(response.split()) <= 25  # Allow a little flexibility in word count
+    assert "Gemma" in response or "AI" in response or "language model" in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_ollama_gemma():
+    llm = LLM(
+        model="ollama/gemma2:latest",
+        base_url="http://localhost:8080",
+        temperature=0.7,
+        max_tokens=30,
+    )
+    messages = [{"role": "user", "content": "Respond in 20 words. Who are you?"}]
+
+    response = llm.call(messages)
+
+    assert response
+    assert len(response.split()) <= 25  # Allow a little flexibility in word count
+    assert "Gemma" in response or "AI" in response or "language model" in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_basic():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo"),
+    )
+
+    task = Task(
+        description="Calculate 2 + 2",
+        expected_output="The result of the calculation",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert "4" in result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_context():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo"),
+    )
+
+    task = Task(
+        description="Summarize the given context in one sentence",
+        expected_output="A one-sentence summary",
+        agent=agent,
+    )
+
+    context = "The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet."
+
+    result = agent.execute_task(task, context=context)
+    assert len(result.split(".")) == 3
+    assert "fox" in result.lower() and "dog" in result.lower()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_tool():
+    @tool
+    def dummy_tool(query: str) -> str:
+        """Useful for when you need to get a dummy result for a query."""
+        return f"Dummy result for: {query}"
+
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo"),
+        tools=[dummy_tool],
+    )
+
+    task = Task(
+        description="Use the dummy tool to get a result for 'test query'",
+        expected_output="The result from the dummy tool",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert "Dummy result for: test query" in result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_custom_llm():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo", temperature=0.7, max_tokens=50),
+    )
+
+    task = Task(
+        description="Write a haiku about AI",
+        expected_output="A haiku (3 lines, 5-7-5 syllable pattern) about AI",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert result.startswith(
+        "Artificial minds,\nCoding thoughts in circuits bright,\nAI's silent might."
+    )
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_ollama():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="ollama/gemma2:latest", base_url="http://localhost:8080"),
+    )
+
+    task = Task(
+        description="Explain what AI is in one sentence",
+        expected_output="A one-sentence explanation of AI",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert len(result.split(".")) == 2
+    assert "AI" in result or "artificial intelligence" in result.lower()

tests/agent_tools/agent_tools_test.py

@@ -24,7 +24,7 @@ def test_delegate_work():
 
     assert (
         result
-        == "While it's a common perception that I might \"hate\" AI agents, my actual stance is much more nuanced and guided by an in-depth understanding of their potential and limitations. As an expert researcher in technology, I recognize that AI agents are a significant advancement in the field of computing and artificial intelligence, offering numerous benefits and applications across various sectors. Here's a detailed take on AI agents:\n\n**Advantages of AI Agents:**\n1. **Automation and Efficiency:** AI agents can automate repetitive tasks, thus freeing up human workers for more complex and creative work. This leads to significant efficiency gains in industries such as customer service (chatbots), data analysis, and even healthcare (AI diagnostic tools).\n\n2. **24/7 Availability:** Unlike human workers, AI agents can operate continuously without fatigue. This is particularly beneficial in customer service environments where support can be provided around the clock.\n\n3. **Data Handling and Analysis:** AI agents can process and analyze vast amounts of data more quickly and accurately than humans. This ability is invaluable in fields like finance, where AI can detect fraudulent activities, or in marketing, where consumer data can be analyzed to improve customer engagement strategies.\n\n4. **Personalization:** AI agents can provide personalized experiences by learning from user interactions. For example, recommendation systems on platforms like Netflix and Amazon use AI agents to suggest content or products tailored to individual preferences.\n\n5. **Scalability:** AI agents can be scaled up easily to handle increasing workloads, making them ideal for businesses experiencing growth or variable demand.\n\n**Challenges and Concerns:**\n1. **Ethical Implications:** The deployment of AI agents raises significant ethical questions, including issues of bias, privacy, and the potential for job displacement. It’s crucial to address these concerns by incorporating transparent, fair, and inclusive practices in AI development and deployment.\n\n2. **Dependability and Error Rates:** While AI agents are generally reliable, they are not infallible. Errors, especially in critical areas like healthcare or autonomous driving, can have severe consequences. Therefore, rigorous testing and validation are essential.\n\n3. **Lack of Understanding:** Many users and stakeholders may not fully understand how AI agents work, leading to mistrust or misuse. Improving AI literacy and transparency can help build trust in these systems.\n\n4. **Security Risks:** AI agents can be vulnerable to cyber-attacks. Ensuring robust cybersecurity measures are in place is vital to protect sensitive data and maintain the integrity of AI systems.\n\n5. **Regulation and Oversight:** The rapid development of AI technology often outpaces regulatory frameworks. Effective governance is needed to ensure AI is used responsibly and ethically.\n\nIn summary, while I thoroughly understand the transformative potential of AI agents and their numerous advantages, I also recognize the importance of addressing the associated challenges. It's not about hating AI agents, but rather advocating for their responsible and ethical use to ensure they benefit society as a whole. My critical perspective is rooted in a desire to see AI agents implemented in ways that maximize their benefits while minimizing potential harms."
+        == "I understand why you might think I dislike AI agents, but my perspective is more nuanced. AI agents, in essence, are incredibly versatile tools designed to perform specific tasks autonomously or semi-autonomously. They harness various artificial intelligence techniques, such as machine learning, natural language processing, and computer vision, to interpret data, understand tasks, and execute them efficiently. \n\nFrom a technological standpoint, AI agents have revolutionized numerous industries. In customer service, for instance, AI agents like chatbots and virtual assistants handle customer inquiries 24/7, providing quick and efficient solutions. In healthcare, AI agents can assist in diagnosing diseases, managing patient data, and even predicting outbreaks. The automation capabilities of AI agents also enhance productivity in areas such as logistics, finance, and cybersecurity by identifying patterns and anomalies at speeds far beyond human capabilities.\n\nHowever, it's important to acknowledge the potential downsides and challenges associated with AI agents. Ethical considerations are paramount. Issues such as data privacy, security, and biases in AI algorithms need to be carefully managed. There is also the human aspect to consider—over-reliance on AI agents might lead to job displacement in certain sectors, and ensuring a fair transition for affected workers is crucial.\n\nMy concerns generally stem from these ethical and societal implications rather than from the technology itself. I advocate for responsible AI development, which includes transparency, fairness, and accountability. By addressing these concerns, we can harness the full potential of AI agents while mitigating the associated risks.\n\nSo, to clarify, I don't hate AI agents; I recognize their immense potential and the significant benefits they bring to various fields. However, I am equally aware of the challenges they present and advocate for a balanced approach to their development and deployment."
     )
 
 
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
 
     assert (
         result
-        == "As an expert researcher in technology, particularly in the field of AI and AI agents, it is essential to clarify that my perspective is not one of hatred but rather critical analysis. My evaluation of AI agents is grounded in a balanced view of their advantages and the challenges they present. \n\nAI agents represent a significant leap in technological progress with a wide array of applications across industries. They can perform tasks ranging from customer service interactions, data analysis, complex simulations, to even personal assistance. Their ability to learn and adapt makes them powerful tools for enhancing productivity and innovation.\n\nHowever, there are considerable challenges and ethical concerns associated with their deployment. These include privacy issues, job displacement, and the potential for biased decision-making driven by flawed algorithms. Furthermore, the security risks posed by AI agents, such as how they can be manipulated or hacked, are critical concerns that cannot be ignored.\n\nIn essence, while I do recognize the transformative potential of AI agents, I remain vigilant about their implications. It is vital to ensure that their development is guided by robust ethical standards and stringent regulations to mitigate risks. My view is not rooted in hatred but in a deep commitment to responsible and thoughtful technological advancement. \n\nI hope this clarifies my stance on AI agents and underscores the importance of critical engagement with emerging technologies."
+        == "AI agents are essentially autonomous software programs that perform tasks or provide services on behalf of humans. They're built on complex algorithms and often leverage machine learning and neural networks to adapt and improve over time. \n\nIt's important to clarify that I don't \"hate\" AI agents, but I do approach them with a critical eye for a couple of reasons. AI agents have enormous potential to transform industries, making processes more efficient, providing insightful data analytics, and even learning from user behavior to offer personalized experiences. However, this potential comes with significant challenges and risks:\n\n1. **Ethical Concerns**: AI agents operate on data, and the biases present in data can lead to unfair or unethical outcomes. Ensuring that AI operates within ethical boundaries requires rigorous oversight, which is not always in place.\n\n2. **Privacy Issues**: AI agents often need access to large amounts of data, raising questions about privacy and data security. If not managed correctly, this can lead to unauthorized data access and potential misuse of sensitive information.\n\n3. **Transparency and Accountability**: The decision-making process of AI agents can be opaque, making it difficult to understand how they arrive at specific conclusions or actions. This lack of transparency poses challenges for accountability, especially if something goes wrong.\n\n4. **Job Displacement**: As AI agents become more capable, there are valid concerns about their impact on employment. Tasks that were traditionally performed by humans are increasingly being automated, which can lead to job loss in certain sectors.\n\n5. **Reliability**: While AI agents can outperform humans in many areas, they are not infallible. They can make mistakes, sometimes with serious consequences. Continuous monitoring and regular updates are essential to maintain their performance and reliability.\n\nIn summary, while AI agents offer substantial benefits and opportunities, it's critical to approach their adoption and deployment with careful consideration of the associated risks. Balancing innovation with responsibility is key to leveraging AI agents effectively and ethically. So, rather than \"hating\" AI agents, I advocate for a balanced, cautious approach that maximizes benefits while mitigating potential downsides."
     )
 
 
@@ -52,7 +52,7 @@ def test_ask_question():
 
     assert (
         result
-        == "No, I do not hate AI agents; in fact, I find them incredibly fascinating and useful. As a researcher specializing in technology, particularly in AI and AI agents, I appreciate their potential to revolutionize various industries by automating tasks, providing deep insights through data analysis, and even enhancing decision-making processes. AI agents can streamline operations, improve efficiency, and contribute to advancements in fields like healthcare, finance, and cybersecurity. While they do present challenges, such as ethical considerations and the need for robust security measures, the benefits and potential for positive impact are immense. Therefore, my stance is one of strong support and enthusiasm for AI agents and their future developments."
+        == "As an expert researcher specialized in technology, I don't harbor emotions such as hate towards AI agents. Instead, my focus is on understanding, analyzing, and leveraging their potential to advance various fields. AI agents, when designed and implemented effectively, can greatly augment human capabilities, streamline processes, and provide valuable insights that might otherwise be overlooked. My enthusiasm for AI agents stems from their ability to transform industries and improve everyday life, making complex tasks more manageable and enhancing overall efficiency. This passion drives my research and commitment to making meaningful contributions in the realm of AI and AI agents."
     )
 
 
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
 
     assert (
         result
-        == "I do not hate AI agents; in fact, I appreciate them for their immense potential and the numerous benefits they bring to various fields. My passion for AI agents stems from their ability to streamline processes, enhance decision-making, and provide innovative solutions to complex problems. They significantly contribute to advancements in healthcare, finance, education, and many other sectors, making tasks more efficient and freeing up human capacities for more creative and strategic endeavors. So, to answer your question, I love AI agents because of the positive impact they have on our world and their capability to drive technological progress."
+        == "I don't hate AI agents; on the contrary, I find them fascinating and incredibly useful. Considering the rapid advancements in AI technology, these agents have the potential to revolutionize various industries by automating tasks, improving efficiency, and providing insights that were previously unattainable. My expertise in researching and analyzing AI and AI agents has allowed me to appreciate the intricate design and the vast possibilities they offer. Therefore, it's more accurate to say that I love AI agents for their potential to drive innovation and improve our daily lives."
     )
 
 
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
 
     assert (
         result
-        == "AI agents have emerged as a revolutionary force in today's technological landscape, and my stance on them is not rooted in hatred but in a critical, analytical perspective. Let's delve deeper into what makes AI agents both a boon and a bane in various contexts.\n\n**Benefits of AI Agents:**\n\n1. **Automation and Efficiency:**\n   AI agents excel at automating repetitive tasks, which frees up human resources for more complex and creative endeavors. They are capable of performing tasks rapidly and with high accuracy, leading to increased efficiency in operations.\n\n2. **Data Analysis and Decision Making:**\n   These agents can process vast amounts of data at speeds far beyond human capability. They can identify patterns and insights that would otherwise be missed, aiding in informed decision-making processes across industries like finance, healthcare, and logistics.\n\n3. **Personalization and User Experience:**\n   AI agents can personalize interactions on a scale that is impractical for humans. For example, recommendation engines in e-commerce or content platforms tailor suggestions to individual users, enhancing user experience and satisfaction.\n\n4. **24/7 Availability:**\n   Unlike human employees, AI agents can operate round-the-clock without the need for breaks, sleep, or holidays. This makes them ideal for customer service roles, providing consistent and immediate responses any time of the day.\n\n**Challenges and Concerns:**\n\n1. **Job Displacement:**\n   One of the major concerns is the displacement of jobs. As AI agents become more proficient at a variety of tasks, there is a legitimate fear of human workers being replaced, leading to unemployment and economic disruption.\n\n2. **Bias and Fairness:**\n   AI agents are only as good as the data they are trained on. If the training data contains biases, the AI agents can perpetuate or even exacerbate these biases, leading to unfair and discriminatory outcomes.\n\n3. **Privacy and Security:**\n   The use of AI agents often involves handling large amounts of personal data, raising significant privacy and security concerns. Unauthorized access or breaches could lead to severe consequences for individuals and organizations.\n\n4. **Accountability and Transparency:**\n   The decision-making processes of AI agents can be opaque, making it difficult to hold them accountable. This lack of transparency can lead to mistrust and ethical dilemmas, particularly when AI decisions impact human lives.\n\n5. **Ethical Considerations:**\n   The deployment of AI agents in sensitive areas, such as surveillance and law enforcement, raises ethical issues. The potential for misuse or overdependence on AI decision-making poses a threat to individual freedoms and societal norms.\n\nIn conclusion, while AI agents offer remarkable advantages in terms of efficiency, data handling, and user experience, they also bring significant challenges that need to be addressed carefully. My critical stance is driven by a desire to ensure that their integration into society is balanced, fair, and beneficial to all, without ignoring the potential downsides. Therefore, a nuanced approach is essential in leveraging the power of AI agents responsibly."
+        == "My perspective on AI agents is quite nuanced and not a matter of simple like or dislike. AI agents, depending on their design, deployment, and use cases, can bring about both significant benefits and substantial challenges.\n\nOn the positive side, AI agents have the potential to automate mundane tasks, enhance productivity, and provide personalized services in ways that were previously unimaginable. For instance, in customer service, AI agents can handle inquiries 24/7, reducing waiting times and improving user satisfaction. In healthcare, they can assist in diagnosing diseases by analyzing vast datasets much faster than humans. These applications demonstrate the transformative power of AI in improving efficiency and delivering better outcomes across various industries.\n\nHowever, my reservations stem from several critical concerns. Firstly, there's the issue of reliability and accuracy. Mismanaged or poorly designed AI systems can lead to significant errors, which could be particularly detrimental in high-stakes environments like healthcare or autonomous vehicles. Second, there's a risk of job displacement as AI agents become capable of performing tasks traditionally done by humans. This raises socio-economic concerns that need to be addressed through effective policy-making and upskilling programs.\n\nAdditionally, there are ethical and privacy considerations. AI agents often require large amounts of data to function effectively, which can lead to issues concerning consent, data security, and individual privacy rights. The lack of transparency in how these agents make decisions can also pose challenges—this is often referred to as the \"black box\" problem, where even the developers may not fully understand how specific AI outputs are generated.\n\nFinally, the deployment of AI agents by bad actors for malicious purposes, such as deepfakes, misinformation, and hacking, remains a pertinent concern. These potential downsides imply that while AI technology is extremely powerful and promising, it must be developed and implemented with care, consideration, and robust ethical guidelines.\n\nSo, in summary, I don't hate AI agents—rather, I approach them critically with a balanced perspective, recognizing both their profound potential and the significant challenges they present. Thoughtful development, responsible deployment, and ethical governance are crucial to harness the benefits while mitigating the risks associated with AI agents."
     )
 
 
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
 
     assert (
         result
-        == "As a researcher specialized in technology, particularly in AI and AI agents, my feelings toward them are far more nuanced than simply loving or hating them. AI agents represent a remarkable advancement in technology and hold tremendous potential for improving various aspects of our lives and industries. They can automate tedious tasks, provide intelligent data analysis, support decision-making, and even enhance our creative processes. These capabilities can drive efficiency, innovation, and economic growth.\n\nHowever, it is also crucial to acknowledge the challenges and ethical considerations posed by AI agents. Issues such as data privacy, security, job displacement, and the need for proper regulation are significant concerns that must be carefully managed. Moreover, the development and deployment of AI should be guided by principles that ensure fairness, transparency, and accountability.\n\nIn essence, I appreciate the profound impact AI agents can have, but I also recognize the importance of approaching their integration into society with thoughtful consideration and responsibility. Balancing enthusiasm with caution and ethical oversight is key to harnessing the full potential of AI while mitigating its risks."
+        == "As an expert researcher specializing in technology and AI, I have a deep appreciation for AI agents. These advanced tools have the potential to revolutionize countless industries by improving efficiency, accuracy, and decision-making processes. They can augment human capabilities, handle mundane and repetitive tasks, and even offer insights that might be beyond human reach. While it's crucial to approach AI with a balanced perspective, understanding both its capabilities and limitations, my stance is one of optimism and fascination. Properly developed and ethically managed, AI agents hold immense promise for driving innovation and solving complex problems. So yes, I do love AI agents for their transformative potential and the positive impact they can have on society."
     )
 
 

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
       a summary.\n\nThis is the context you''re working with:\nI heard you LOVE them\n\nBegin!
       This is VERY important to you, use the tools available and give your best Final
-      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
+      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o"}'
     headers:
       accept:
       - application/json
@@ -21,16 +21,16 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1049'
+      - '1021'
       content-type:
       - application/json
       cookie:
-      - __cf_bm=1SckBhvJ18Dazp6bi8DEKYeiS9Q4.6_6i3nmLBw9b6g-1726476036-1.0.1.1-TnN4UpDXA33YXCVCUWOaZ12vGIg_o5NpJQEUHgjn6XdUgb7M0ND8PdkTfkd8rrxG5XFlPRMzI54GxZ0FeUY9xw;
-        _cfuvid=0Rs4xTPk7h7OIXuSbTgMVVD9JSoZeKMwnygKHoHQo3k-1726476036297-0.0.1.1-604800000
+      - __cf_bm=rb61BZH2ejzD5YPmLaEJqI7km71QqyNJGTVdNxBq6qk-1727213194-1.0.1.1-pJ49onmgX9IugEMuYQMralzD7oj_6W.CHbSu4Su1z3NyjTGYg.rhgJZWng8feFYah._oSnoYlkTjpK1Wd2C9FA;
+        _cfuvid=lbRdAddVWV6W3f5Dm9SaOPWDUOxqtZBSPr_fTW26nEA-1727213194587-0.0.1.1-604800000
       host:
       - api.openai.com
       user-agent:
-      - OpenAI/Python 1.45.0
+      - OpenAI/Python 1.47.0
       x-stainless-arch:
       - arm64
       x-stainless-async:
@@ -40,7 +40,7 @@ interactions:
       x-stainless-os:
       - MacOS
       x-stainless-package-version:
-      - 1.45.0
+      - 1.47.0
       x-stainless-raw-response:
       - 'true'
       x-stainless-runtime:
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-      significant ethical questions, including issues of bias, privacy, and the potential
-      for job displacement. It\u2019s crucial to address these concerns by incorporating
-      transparent, fair, and inclusive practices in AI development and deployment.\\n\\n2.
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-      are not infallible. Errors, especially in critical areas like healthcare or
-      autonomous driving, can have severe consequences. Therefore, rigorous testing
-      and validation are essential.\\n\\n3. **Lack of Understanding:** Many users
-      and stakeholders may not fully understand how AI agents work, leading to mistrust
-      or misuse. Improving AI literacy and transparency can help build trust in these
-      systems.\\n\\n4. **Security Risks:** AI agents can be vulnerable to cyber-attacks.
-      Ensuring robust cybersecurity measures are in place is vital to protect sensitive
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-      Effective governance is needed to ensure AI is used responsibly and ethically.\\n\\nIn
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-      and their numerous advantages, I also recognize the importance of addressing
-      the associated challenges. It's not about hating AI agents, but rather advocating
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+      Answer: I understand why you might think I dislike AI agents, but my perspective
+      is more nuanced. AI agents, in essence, are incredibly versatile tools designed
+      to perform specific tasks autonomously or semi-autonomously. They harness various
+      artificial intelligence techniques, such as machine learning, natural language
+      processing, and computer vision, to interpret data, understand tasks, and execute
+      them efficiently. \\n\\nFrom a technological standpoint, AI agents have revolutionized
+      numerous industries. In customer service, for instance, AI agents like chatbots
+      and virtual assistants handle customer inquiries 24/7, providing quick and efficient
+      solutions. In healthcare, AI agents can assist in diagnosing diseases, managing
+      patient data, and even predicting outbreaks. The automation capabilities of
+      AI agents also enhance productivity in areas such as logistics, finance, and
+      cybersecurity by identifying patterns and anomalies at speeds far beyond human
+      capabilities.\\n\\nHowever, it's important to acknowledge the potential downsides
+      and challenges associated with AI agents. Ethical considerations are paramount.
+      Issues such as data privacy, security, and biases in AI algorithms need to be
+      carefully managed. There is also the human aspect to consider\u2014over-reliance
+      on AI agents might lead to job displacement in certain sectors, and ensuring
+      a fair transition for affected workers is crucial.\\n\\nMy concerns generally
+      stem from these ethical and societal implications rather than from the technology
+      itself. I advocate for responsible AI development, which includes transparency,
+      fairness, and accountability. By addressing these concerns, we can harness the
+      full potential of AI agents while mitigating the associated risks.\\n\\nSo,
+      to clarify, I don't hate AI agents; I recognize their immense potential and
+      the significant benefits they bring to various fields. However, I am equally
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+      and often leverage machine learning and neural networks to adapt and improve
+      over time. \\n\\nIt's important to clarify that I don't \\\"hate\\\" AI agents,
+      but I do approach them with a critical eye for a couple of reasons. AI agents
+      have enormous potential to transform industries, making processes more efficient,
+      providing insightful data analytics, and even learning from user behavior to
+      offer personalized experiences. However, this potential comes with significant
+      challenges and risks:\\n\\n1. **Ethical Concerns**: AI agents operate on data,
+      and the biases present in data can lead to unfair or unethical outcomes. Ensuring
+      that AI operates within ethical boundaries requires rigorous oversight, which
+      is not always in place.\\n\\n2. **Privacy Issues**: AI agents often need access
+      to large amounts of data, raising questions about privacy and data security.
+      If not managed correctly, this can lead to unauthorized data access and potential
+      misuse of sensitive information.\\n\\n3. **Transparency and Accountability**:
+      The decision-making process of AI agents can be opaque, making it difficult
+      to understand how they arrive at specific conclusions or actions. This lack
+      of transparency poses challenges for accountability, especially if something
+      goes wrong.\\n\\n4. **Job Displacement**: As AI agents become more capable,
+      there are valid concerns about their impact on employment. Tasks that were traditionally
+      performed by humans are increasingly being automated, which can lead to job
+      loss in certain sectors.\\n\\n5. **Reliability**: While AI agents can outperform
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+      to maintain their performance and reliability.\\n\\nIn summary, while AI agents
+      offer substantial benefits and opportunities, it's critical to approach their
+      adoption and deployment with careful consideration of the associated risks.
+      Balancing innovation with responsibility is key to leveraging AI agents effectively
+      and ethically. So, rather than \\\"hating\\\" AI agents, I advocate for a balanced,
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+      can bring about both significant benefits and substantial challenges.\\n\\nOn
+      the positive side, AI agents have the potential to automate mundane tasks, enhance
+      productivity, and provide personalized services in ways that were previously
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+      24/7, reducing waiting times and improving user satisfaction. In healthcare,
+      they can assist in diagnosing diseases by analyzing vast datasets much faster
+      than humans. These applications demonstrate the transformative power of AI in
+      improving efficiency and delivering better outcomes across various industries.\\n\\nHowever,
+      my reservations stem from several critical concerns. Firstly, there's the issue
+      of reliability and accuracy. Mismanaged or poorly designed AI systems can lead
+      to significant errors, which could be particularly detrimental in high-stakes
+      environments like healthcare or autonomous vehicles. Second, there's a risk
+      of job displacement as AI agents become capable of performing tasks traditionally
+      done by humans. This raises socio-economic concerns that need to be addressed
+      through effective policy-making and upskilling programs.\\n\\nAdditionally,
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+      amounts of data to function effectively, which can lead to issues concerning
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+      work to coworker\nAction Input: \n{\n    \"coworker\": \"Senior Writer\", \n    \"task\":
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+      yet engaging. Make sure the content is original, insightful, and thoroughly
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+      computer programs designed to perform tasks that typically require human intelligence,
+      such as decision making, problem-solving, and learning. These agents operate
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