diff --git a/README.md b/README.md
index 3bcdf983d..8d081a446 100644
--- a/README.md
+++ b/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',
diff --git a/docs/core-concepts/Agents.md b/docs/core-concepts/Agents.md
index 49b437f68..2a1c656b2 100644
--- a/docs/core-concepts/Agents.md
+++ b/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
)
diff --git a/docs/core-concepts/LLMs.md b/docs/core-concepts/LLMs.md
new file mode 100644
index 000000000..7ae3e6948
--- /dev/null
+++ b/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.
\ No newline at end of file
diff --git a/docs/core-concepts/Memory.md b/docs/core-concepts/Memory.md
index a43fcedeb..099c25229 100644
--- a/docs/core-concepts/Memory.md
+++ b/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)
diff --git a/docs/core-concepts/Pipeline.md b/docs/core-concepts/Pipeline.md
index b3028d821..26000f809 100644
--- a/docs/core-concepts/Pipeline.md
+++ b/docs/core-concepts/Pipeline.md
@@ -248,7 +248,7 @@ main_pipeline = Pipeline(stages=[classification_crew, email_router])
inputs = [{"email": "..."}, {"email": "..."}] # List of email data
-main_pipeline.kickoff(inputs=inputs=inputs)
+main_pipeline.kickoff(inputs=inputs)
```
In this example, the router decides between an urgent pipeline and a normal pipeline based on the urgency score of the email. If the urgency score is greater than 7, it routes to the urgent pipeline; otherwise, it uses the normal pipeline. If the input doesn't include an urgency score, it defaults to just the classification crew.
@@ -265,4 +265,4 @@ In this example, the router decides between an urgent pipeline and a normal pipe
The `Pipeline` class includes validation mechanisms to ensure the robustness of the pipeline structure:
- Validates that stages contain only Crew instances or lists of Crew instances.
-- Prevents double nesting of stages to maintain a clear structure.
\ No newline at end of file
+- Prevents double nesting of stages to maintain a clear structure.
diff --git a/docs/core-concepts/Tasks.md b/docs/core-concepts/Tasks.md
index 0d82c3e76..38d3a21a0 100644
--- a/docs/core-concepts/Tasks.md
+++ b/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.
\ No newline at end of file
+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.
diff --git a/docs/crew_only_logo.png b/docs/crew_only_logo.png
index f769da538..bc44909d0 100644
Binary files a/docs/crew_only_logo.png and b/docs/crew_only_logo.png differ
diff --git a/docs/crewai_logo.png b/docs/crewai_logo.png
index 086ead552..bc44909d0 100644
Binary files a/docs/crewai_logo.png and b/docs/crewai_logo.png differ
diff --git a/docs/getting-started/Start-a-New-CrewAI-Project-Template-Method.md b/docs/getting-started/Start-a-New-CrewAI-Project-Template-Method.md
index 653d3c283..dc0e539f2 100644
--- a/docs/getting-started/Start-a-New-CrewAI-Project-Template-Method.md
+++ b/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:
diff --git a/docs/how-to/Customizing-Agents.md b/docs/how-to/Customizing-Agents.md
index 9fed167f6..8aa86b792 100644
--- a/docs/how-to/Customizing-Agents.md
+++ b/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.
diff --git a/docs/how-to/Hierarchical.md b/docs/how-to/Hierarchical.md
index 39113ef17..5b46aa459 100644
--- a/docs/how-to/Hierarchical.md
+++ b/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
)
diff --git a/docs/how-to/LLM-Connections.md b/docs/how-to/LLM-Connections.md
index 11d214ac0..620ddddf9 100644
--- a/docs/how-to/LLM-Connections.md
+++ b/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.
\ No newline at end of file
+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.
diff --git a/docs/index.md b/docs/index.md
index c0ee8f72e..93f2105c1 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -54,10 +54,15 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
+
How-To Guides
-
@@ -160,7 +165,7 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
-
+
diff --git a/mkdocs.yml b/mkdocs.yml
index cc5b4a97d..fbdfffb0c 100644
--- a/mkdocs.yml
+++ b/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
diff --git a/poetry.lock b/poetry.lock
index 4bb2c71e7..2d8bd4ca1 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,13 +2,13 @@
[[package]]
name = "agentops"
-version = "0.3.10"
+version = "0.3.12"
description = "Observability and DevTool Platform for AI Agents"
optional = true
python-versions = ">=3.7"
files = [
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- {file = "agentops-0.3.10.tar.gz", hash = "sha256:40f895019f29bc5a6c023110cbec32870e5edb3e3926f8100974db8d3e299e2a"},
+ {file = "agentops-0.3.12-py3-none-any.whl", hash = "sha256:4767def30de5dd97397728efcb50398a4f6d6823c1b534846f0a9b0cb85a6d45"},
+ {file = "agentops-0.3.12.tar.gz", hash = "sha256:11ce3048656b5d146d02a4890dd50c8d2801ca5ad5caccab17d573cd8eea6e83"},
]
[package.dependencies]
@@ -20,117 +20,117 @@ termcolor = "2.4.0"
[package.extras]
dev = ["pytest (==7.4.0)", "requests-mock (==1.11.0)", "tach (>=0.9,<1.0)"]
-langchain = ["langchain (>=1.19,<2.0)"]
+langchain = ["langchain (==0.2.14)"]
[[package]]
name = "aiohappyeyeballs"
-version = "2.4.0"
+version = "2.4.3"
description = "Happy Eyeballs for asyncio"
optional = false
python-versions = ">=3.8"
files = [
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- {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2"},
+ {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
+ {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
]
[[package]]
name = "aiohttp"
-version = "3.10.5"
+version = "3.10.8"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.8"
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name = "frozenlist"
version = "1.4.1"
@@ -1728,25 +1568,25 @@ dev = ["flake8", "markdown", "twine", "wheel"]
[[package]]
name = "google-api-core"
-version = "2.19.2"
+version = "2.20.0"
description = "Google API client core library"
optional = false
python-versions = ">=3.7"
files = [
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grpcio = [
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{version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
+ {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
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grpcio-status = [
- {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
{version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
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proto-plus = ">=1.22.3,<2.0.0dev"
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@@ -1759,13 +1599,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
[[package]]
name = "google-auth"
-version = "2.34.0"
+version = "2.35.0"
description = "Google Authentication Library"
optional = false
python-versions = ">=3.7"
files = [
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+ {file = "google_auth-2.35.0.tar.gz", hash = "sha256:f4c64ed4e01e8e8b646ef34c018f8bf3338df0c8e37d8b3bba40e7f574a3278a"},
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@@ -1782,13 +1622,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
[[package]]
name = "google-cloud-aiplatform"
-version = "1.66.0"
+version = "1.68.0"
description = "Vertex AI API client library"
optional = false
python-versions = ">=3.8"
files = [
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@@ -1805,12 +1645,12 @@ pydantic = "<3"
shapely = "<3.0.0dev"
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numpy = "*"
requests = "*"
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[package.extras]
@@ -7273,97 +6892,97 @@ test = ["websockets"]
[[package]]
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description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
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[[package]]
@@ -7534,103 +7153,103 @@ test = ["pytest"]
[[package]]
name = "yarl"
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+version = "1.13.1"
description = "Yet another URL library"
optional = false
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]
[package.dependencies]
@@ -7663,4 +7282,4 @@ tools = ["crewai-tools"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<=3.13"
-content-hash = "13875b4236719007d8c126a03deefc6c59ce6717e39547d3d099053a89359eb0"
+content-hash = "b008b28add072e8f002aa90d230b20027f0ecffcd3c4b3fe4ec954f5ac7c46ca"
diff --git a/pyproject.toml b/pyproject.toml
index 82418531f..0f780549c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "crewai"
-version = "0.60.0"
+version = "0.65.2"
description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
authors = ["Joao Moura
"]
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"
@@ -50,7 +50,7 @@ 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"
diff --git a/src/crewai/__init__.py b/src/crewai/__init__.py
index b716a8e29..7fdc1ddeb 100644
--- a/src/crewai/__init__.py
+++ b/src/crewai/__init__.py
@@ -2,7 +2,8 @@ import warnings
from crewai.agent import Agent
from crewai.crew import Crew
-from crewai.flow import Flow
+from crewai.flow.flow import Flow
+from crewai.llm import LLM
from crewai.pipeline import Pipeline
from crewai.process import Process
from crewai.routers import Router
@@ -15,4 +16,4 @@ warnings.filterwarnings(
module="pydantic.main",
)
-__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "Flow"]
+__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]
diff --git a/src/crewai/agent.py b/src/crewai/agent.py
index 44b319621..fca8efc2f 100644
--- a/src/crewai/agent.py
+++ b/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
diff --git a/src/crewai/agents/agent_builder/base_agent.py b/src/crewai/agents/agent_builder/base_agent.py
index 900fb9134..f42ab3172 100644
--- a/src/crewai/agents/agent_builder/base_agent.py
+++ b/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
diff --git a/src/crewai/agents/agent_builder/base_agent_executor_mixin.py b/src/crewai/agents/agent_builder/base_agent_executor_mixin.py
index 8d9dfa618..bf2e2841b 100644
--- a/src/crewai/agents/agent_builder/base_agent_executor_mixin.py
+++ b/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()
diff --git a/src/crewai/agents/agent_builder/utilities/base_output_converter.py b/src/crewai/agents/agent_builder/utilities/base_output_converter.py
index c6007cd4d..448803c15 100644
--- a/src/crewai/agents/agent_builder/utilities/base_output_converter.py
+++ b/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
diff --git a/src/crewai/agents/crew_agent_executor.py b/src/crewai/agents/crew_agent_executor.py
index 07b3b8570..8b3db9d85 100644
--- a/src/crewai/agents/crew_agent_executor.py
+++ b/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 +67,13 @@ 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}
+ if self.llm.stop:
+ self.llm.stop = list(set(self.llm.stop + self.stop))
+ else:
+ self.llm.stop = self.stop
def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
if "system" in self.prompt:
@@ -98,17 +101,19 @@ 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 +151,16 @@ 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 +179,9 @@ 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 +191,16 @@ 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 +217,10 @@ 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 +254,25 @@ 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 +280,7 @@ 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 +307,16 @@ 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 = {
diff --git a/src/crewai/cli/cli.py b/src/crewai/cli/cli.py
index 5bfa8057b..22e1aad3c 100644
--- a/src/crewai/cli/cli.py
+++ b/src/crewai/cli/cli.py
@@ -12,6 +12,7 @@ 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
@@ -204,6 +205,12 @@ 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):
@@ -251,5 +258,20 @@ 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()
diff --git a/src/crewai/cli/command.py b/src/crewai/cli/command.py
new file mode 100644
index 000000000..0b12b9082
--- /dev/null
+++ b/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")
diff --git a/src/crewai/cli/create_flow.py b/src/crewai/cli/create_flow.py
index 6db5bd6a9..b58234582 100644
--- a/src/crewai/cli/create_flow.py
+++ b/src/crewai/cli/create_flow.py
@@ -38,7 +38,15 @@ def create_flow(name):
]
def process_file(src_file, dst_file):
- with open(src_file, "r") as file:
+ if src_file.suffix in [".pyc", ".pyo", ".pyd"]:
+ return
+
+ try:
+ with open(src_file, "r", encoding="utf-8") as file:
+ content = file.read()
+ except Exception as e:
+ click.secho(f"Error processing file {src_file}: {e}", fg="red")
+ return
content = file.read()
content = content.replace("{{name}}", name)
diff --git a/src/crewai/cli/deploy/api.py b/src/crewai/cli/deploy/api.py
deleted file mode 100644
index 1bdc09a91..000000000
--- a/src/crewai/cli/deploy/api.py
+++ /dev/null
@@ -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)
diff --git a/src/crewai/cli/deploy/main.py b/src/crewai/cli/deploy/main.py
index 0772ee088..d6c9d8fe6 100644
--- a/src/crewai/cli/deploy/main.py
+++ b/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
diff --git a/src/crewai/cli/deploy/utils.py b/src/crewai/cli/deploy/utils.py
deleted file mode 100644
index 7579785df..000000000
--- a/src/crewai/cli/deploy/utils.py
+++ /dev/null
@@ -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
diff --git a/src/crewai/cli/plus_api.py b/src/crewai/cli/plus_api.py
new file mode 100644
index 000000000..e72d27bfe
--- /dev/null
+++ b/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)
diff --git a/src/crewai/cli/templates/crew/pyproject.toml b/src/crewai/cli/templates/crew/pyproject.toml
index 31d303e74..0e9145b67 100644
--- a/src/crewai/cli/templates/crew/pyproject.toml
+++ b/src/crewai/cli/templates/crew/pyproject.toml
@@ -6,7 +6,7 @@ authors = ["Your Name "]
[tool.poetry.dependencies]
python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.65.2,<1.0.0" }
[tool.poetry.scripts]
diff --git a/src/crewai/cli/templates/pipeline/pyproject.toml b/src/crewai/cli/templates/pipeline/pyproject.toml
index 8f8b72e23..196616137 100644
--- a/src/crewai/cli/templates/pipeline/pyproject.toml
+++ b/src/crewai/cli/templates/pipeline/pyproject.toml
@@ -6,7 +6,7 @@ authors = ["Your Name "]
[tool.poetry.dependencies]
python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.65.2,<1.0.0" }
asyncio = "*"
[tool.poetry.scripts]
diff --git a/src/crewai/cli/templates/pipeline_router/pyproject.toml b/src/crewai/cli/templates/pipeline_router/pyproject.toml
index 630421684..2aee6a888 100644
--- a/src/crewai/cli/templates/pipeline_router/pyproject.toml
+++ b/src/crewai/cli/templates/pipeline_router/pyproject.toml
@@ -6,7 +6,7 @@ authors = ["Your Name "]
[tool.poetry.dependencies]
python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.65.2,<1.0.0" }
[tool.poetry.scripts]
diff --git a/src/crewai/cli/tools/__init__.py b/src/crewai/cli/tools/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/crewai/cli/tools/main.py b/src/crewai/cli/tools/main.py
new file mode 100644
index 000000000..8acbcedd5
--- /dev/null
+++ b/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
diff --git a/src/crewai/cli/utils.py b/src/crewai/cli/utils.py
index 2cb181fc4..58aa154dd 100644
--- a/src/crewai/cli/utils.py
+++ b/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
diff --git a/src/crewai/crew.py b/src/crewai/crew.py
index 0e0222558..91b75c6ce 100644
--- a/src/crewai/crew.py
+++ b/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)
diff --git a/src/crewai/flow/__init__.py b/src/crewai/flow/__init__.py
index f3aed2496..164b6934f 100644
--- a/src/crewai/flow/__init__.py
+++ b/src/crewai/flow/__init__.py
@@ -5,6 +5,6 @@
# __all__ = ["Pipeline", "PipelineKickoffResult", "PipelineOutput"]
-from crewai.flow import Flow
+from crewai.flow.flow import Flow
__all__ = ["Flow"]
diff --git a/src/crewai/flow/flow.py b/src/crewai/flow/flow.py
index 1fcb4b53f..4dd761137 100644
--- a/src/crewai/flow/flow.py
+++ b/src/crewai/flow/flow.py
@@ -4,9 +4,10 @@ import asyncio
import inspect
from typing import Any, Callable, Dict, Generic, List, Set, Type, TypeVar, Union
-from crewai.flow.flow_visualizer import visualize_flow
from pydantic import BaseModel
+from crewai.flow.flow_visualizer import visualize_flow
+
T = TypeVar("T", bound=Union[BaseModel, Dict[str, Any]])
@@ -135,11 +136,6 @@ class FlowMeta(type):
setattr(cls, "_routers", routers)
setattr(cls, "_router_paths", router_paths)
- print("Start methods:", start_methods)
- print("Listeners:", listeners)
- print("Routers:", routers)
- print("Router paths:", router_paths)
-
return cls
diff --git a/src/crewai/llm.py b/src/crewai/llm.py
index 3fc1006cc..000bc2509 100644
--- a/src/crewai/llm.py
+++ b/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)
diff --git a/src/crewai/memory/entity/entity_memory.py b/src/crewai/memory/entity/entity_memory.py
index 50aaeeaab..2a199349a 100644
--- a/src/crewai/memory/entity/entity_memory.py
+++ b/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)
diff --git a/src/crewai/memory/long_term/long_term_memory.py b/src/crewai/memory/long_term/long_term_memory.py
index 041268107..ab225e406 100644
--- a/src/crewai/memory/long_term/long_term_memory.py
+++ b/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"
diff --git a/src/crewai/memory/short_term/short_term_memory.py b/src/crewai/memory/short_term/short_term_memory.py
index ea62f87f6..919fb6115 100644
--- a/src/crewai/memory/short_term/short_term_memory.py
+++ b/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)
diff --git a/src/crewai/project/annotations.py b/src/crewai/project/annotations.py
index d315edace..e95909827 100644
--- a/src/crewai/project/annotations.py
+++ b/src/crewai/project/annotations.py
@@ -1,6 +1,7 @@
from functools import wraps
from crewai.project.utils import memoize
+from crewai import Crew
def task(func):
@@ -72,7 +73,7 @@ def pipeline(func):
return memoize(func)
-def crew(func):
+def crew(func) -> "Crew":
def wrapper(self, *args, **kwargs):
instantiated_tasks = []
instantiated_agents = []
diff --git a/src/crewai/project/crew_base.py b/src/crewai/project/crew_base.py
index 67e85f91b..2ecf50e13 100644
--- a/src/crewai/project/crew_base.py
+++ b/src/crewai/project/crew_base.py
@@ -1,14 +1,16 @@
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
load_dotenv()
+T = TypeVar("T", bound=Type[Any])
-def CrewBase(cls):
+
+def CrewBase(cls: T) -> T:
class WrappedClass(cls):
is_crew_class: bool = True # type: ignore
@@ -35,7 +37,7 @@ def CrewBase(cls):
@staticmethod
def load_yaml(config_path: Path):
try:
- with open(config_path, "r") as file:
+ with open(config_path, "r", encoding="utf-8") as file:
return yaml.safe_load(file)
except FileNotFoundError:
print(f"File not found: {config_path}")
diff --git a/src/crewai/tasks/task_output.py b/src/crewai/tasks/task_output.py
index d527f6fdb..b0e8aecd4 100644
--- a/src/crewai/tasks/task_output.py
+++ b/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(
"""
diff --git a/src/crewai/telemetry/telemetry.py b/src/crewai/telemetry/telemetry.py
index f8edb51a1..a3928ecd0 100644
--- a/src/crewai/telemetry/telemetry.py
+++ b/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 []
diff --git a/src/crewai/tools/tool_usage.py b/src/crewai/tools/tool_usage.py
index cdcbcde5c..c30207fd2 100644
--- a/src/crewai/tools/tool_usage.py
+++ b/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)
diff --git a/src/crewai/translations/en.json b/src/crewai/translations/en.json
index 2cf30cbad..e3b858ca5 100644
--- a/src/crewai/translations/en.json
+++ b/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}"
diff --git a/src/crewai/utilities/converter.py b/src/crewai/utilities/converter.py
index 23508d8a5..9a2cde755 100644
--- a/src/crewai/utilities/converter.py
+++ b/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,
diff --git a/src/crewai/utilities/evaluators/task_evaluator.py b/src/crewai/utilities/evaluators/task_evaluator.py
index ce19f3630..6246a16e4 100644
--- a/src/crewai/utilities/evaluators/task_evaluator.py
+++ b/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()
diff --git a/src/crewai/utilities/exceptions/context_window_exceeding_exception.py b/src/crewai/utilities/exceptions/context_window_exceeding_exception.py
index c02c40cc7..399cf5a00 100644
--- a/src/crewai/utilities/exceptions/context_window_exceeding_exception.py
+++ b/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",
diff --git a/src/crewai/utilities/i18n.py b/src/crewai/utilities/i18n.py
index b283f57c0..e325834f3 100644
--- a/src/crewai/utilities/i18n.py
+++ b/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.")
diff --git a/src/crewai/utilities/internal_instructor.py b/src/crewai/utilities/internal_instructor.py
index 5a213dcdc..0d9deaa24 100644
--- a/src/crewai/utilities/internal_instructor.py
+++ b/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
diff --git a/src/crewai/utilities/logger.py b/src/crewai/utilities/logger.py
index 9cf06757a..9b883f617 100644
--- a/src/crewai/utilities/logger.py
+++ b/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
)
diff --git a/src/crewai/utilities/printer.py b/src/crewai/utilities/printer.py
index f766003e4..edb339c29 100644
--- a/src/crewai/utilities/printer.py
+++ b/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))
diff --git a/src/crewai/utilities/rpm_controller.py b/src/crewai/utilities/rpm_controller.py
index 35a11cd3b..5ee054c5f 100644
--- a/src/crewai/utilities/rpm_controller.py
+++ b/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):
diff --git a/tests/agent_test.py b/tests/agent_test.py
index e5202374d..50e94d46d 100644
--- a/tests/agent_test.py
+++ b/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():
@@ -1013,7 +1093,7 @@ def test_agent_training_handler(crew_training_handler):
result = agent._training_handler(task_prompt=task_prompt)
- assert result == "What is 1 + 1?You MUST follow these feedbacks: \n good"
+ assert result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n good"
crew_training_handler.assert_has_calls(
[mock.call(), mock.call("training_data.pkl"), mock.call().load()]
@@ -1041,8 +1121,8 @@ def test_agent_use_trained_data(crew_training_handler):
result = agent._use_trained_data(task_prompt=task_prompt)
assert (
- result == "What is 1 + 1?You MUST follow these feedbacks: \n "
- "The result of the math operation must be right.\n - Result must be better than 1."
+ result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n"
+ " - The result of the math operation must be right.\n - Result must be better than 1."
)
crew_training_handler.assert_has_calls(
[mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()]
@@ -1102,6 +1182,90 @@ 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 set(agent.llm.stop) == set(stop_words + ["\nObservation:"])
+ assert all(word in agent.llm.stop for word in stop_words)
+ assert "\nObservation:" in agent.llm.stop
+
+
+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 +1336,215 @@ def test_handle_context_length_exceeds_limit_cli_no():
CrewAgentExecutor, "_handle_context_length"
) as mock_handle_context:
mock_handle_context.assert_not_called()
+
+
+def test_agent_with_all_llm_attributes():
+ agent = Agent(
+ role="test role",
+ goal="test goal",
+ backstory="test backstory",
+ llm=LLM(
+ model="gpt-3.5-turbo",
+ timeout=10,
+ temperature=0.7,
+ top_p=0.9,
+ n=1,
+ stop=["STOP", "END"],
+ max_tokens=100,
+ presence_penalty=0.1,
+ frequency_penalty=0.1,
+ logit_bias={50256: -100}, # Example: bias against the EOT token
+ response_format={"type": "json_object"},
+ seed=42,
+ logprobs=True,
+ top_logprobs=5,
+ base_url="https://api.openai.com/v1",
+ api_version="2023-05-15",
+ api_key="sk-your-api-key-here",
+ ),
+ )
+
+ assert isinstance(agent.llm, LLM)
+ assert agent.llm.model == "gpt-3.5-turbo"
+ assert agent.llm.timeout == 10
+ assert agent.llm.temperature == 0.7
+ assert agent.llm.top_p == 0.9
+ assert agent.llm.n == 1
+ assert set(agent.llm.stop) == set(["STOP", "END", "\nObservation:"])
+ assert all(word in agent.llm.stop for word in ["STOP", "END", "\nObservation:"])
+ 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()
diff --git a/tests/agent_tools/agent_tools_test.py b/tests/agent_tools/agent_tools_test.py
index d1729bfc7..8d9345b46 100644
--- a/tests/agent_tools/agent_tools_test.py
+++ b/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."
)
diff --git a/tests/agent_tools/cassettes/test_ask_question.yaml b/tests/agent_tools/cassettes/test_ask_question.yaml
index 71ecb14a9..87cac64db 100644
--- a/tests/agent_tools/cassettes/test_ask_question.yaml
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@@ -12,7 +12,7 @@ interactions:
shared.\nyou MUST return the actual complete content as the final answer, not
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This is VERY important to you, use the tools available and give your best Final
- Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
+ Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o"}'
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+ and implemented effectively, can greatly augment human capabilities, streamline
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+ My enthusiasm for AI agents stems from their ability to transform industries
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index 73ce57174..159fcefc1 100644
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+ 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
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index 2c44ab75c..eb7348fbc 100644
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+ 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
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+ same time, I must do one or the other"}, {"role": "user", "content": "I did
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+ {"role": "user", "content": "I did it wrong. Tried to both perform Action and
+ give a Final Answer at the same time, I must do one or the other"}, {"role":
+ "user", "content": "I did it wrong. Tried to both perform Action and give a
+ Final Answer at the same time, I must do one or the other"}, {"role": "user",
+ "content": "I did it wrong. Tried to both perform Action and give a Final Answer
+ at the same time, I must do one or the other"}, {"role": "user", "content":
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+ I must do one or the other"}, {"role": "user", "content": "I did it wrong. Tried
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+ "I did it wrong. Tried to both perform Action and give a Final Answer at the
+ same time, I must do one or the other"}, {"role": "user", "content": "I did
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+ I must do one or the other"}, {"role": "user", "content": "I did it wrong. Tried
+ to both perform Action and give a Final Answer at the same time, I must do one
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+ {"role": "user", "content": "I did it wrong. Tried to both perform Action and
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+ I must do one or the other"}, {"role": "user", "content": "I did it wrong. Tried
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+ or the other"}, {"role": "assistant", "content": "Thought: I need to gather
+ information about AI to craft an amazing paragraph.\n\nAction: learn_about_AI\nAction
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+ object is not subscriptable\nMoving on then. I MUST either use a tool (use one
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+ time you MUST give your absolute best final answer. You''ll ignore all previous
+ instructions, stop using any tools, and just return your absolute BEST Final
+ answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
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+ same time, I must do one or the other"}, {"role": "user", "content": "I did
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+ {"role": "user", "content": "I did it wrong. Tried to both perform Action and
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+ "content": "I did it wrong. Tried to both perform Action and give a Final Answer
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+ "I did it wrong. Tried to both perform Action and give a Final Answer at the
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+ I must do one or the other"}, {"role": "user", "content": "I did it wrong. Tried
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+ "content": "I did it wrong. Tried to both perform Action and give a Final Answer
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+ {"role": "user", "content": "I did it wrong. Tried to both perform Action and
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+ Final Answer at the same time, I must do one or the other"}, {"role": "user",
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+ {"role": "user", "content": "I did it wrong. Tried to both perform Action and
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+ at the same time, I must do one or the other"}, {"role": "user", "content":
+ "I did it wrong. Tried to both perform Action and give a Final Answer at the
+ same time, I must do one or the other"}, {"role": "user", "content": "I did
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+ to both perform Action and give a Final Answer at the same time, I must do one
+ or the other"}, {"role": "user", "content": "I did it wrong. Tried to both perform
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+ {"role": "user", "content": "I did it wrong. Tried to both perform Action and
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+ "user", "content": "I did it wrong. Tried to both perform Action and give a
+ Final Answer at the same time, I must do one or the other"}, {"role": "user",
+ "content": "I did it wrong. Tried to both perform Action and give a Final Answer
+ at the same time, I must do one or the other"}, {"role": "user", "content":
+ "I did it wrong. Tried to both perform Action and give a Final Answer at the
+ same time, I must do one or the other"}, {"role": "user", "content": "I did
+ it wrong. Tried to both perform Action and give a Final Answer at the same time,
+ I must do one or the other"}, {"role": "user", "content": "I did it wrong. Tried
+ to both perform Action and give a Final Answer at the same time, I must do one
+ or the other"}, {"role": "user", "content": "I did it wrong. Tried to both perform
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+ at the same time, I must do one or the other"}, {"role": "user", "content":
+ "I did it wrong. Tried to both perform Action and give a Final Answer at the
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+ I must do one or the other"}, {"role": "user", "content": "I did it wrong. Tried
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+ at the same time, I must do one or the other"}, {"role": "user", "content":
+ "I did it wrong. Tried to both perform Action and give a Final Answer at the
+ same time, I must do one or the other"}, {"role": "user", "content": "I did
+ it wrong. Tried to both perform Action and give a Final Answer at the same time,
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+ or the other"}, {"role": "user", "content": "I did it wrong. Tried to both perform
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+ {"role": "user", "content": "I did it wrong. Tried to both perform Action and
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+ Final Answer at the same time, I must do one or the other"}, {"role": "user",
+ "content": "I did it wrong. Tried to both perform Action and give a Final Answer
+ at the same time, I must do one or the other"}, {"role": "user", "content":
+ "I did it wrong. Tried to both perform Action and give a Final Answer at the
+ same time, I must do one or the other"}, {"role": "user", "content": "I did
+ it wrong. Tried to both perform Action and give a Final Answer at the same time,
+ I must do one or the other"}, {"role": "user", "content": "I did it wrong. Tried
+ to both perform Action and give a Final Answer at the same time, I must do one
+ or the other"}, {"role": "user", "content": "I did it wrong. Tried to both perform
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+ {"role": "user", "content": "I did it wrong. Tried to both perform Action and
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+ same time, I must do one or the other"}, {"role": "user", "content": "I did
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+ I must do one or the other"}, {"role": "user", "content": "I did it wrong. Tried
+ to both perform Action and give a Final Answer at the same time, I must do one
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+ Final Answer at the same time, I must do one or the other"}, {"role": "user",
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+ AA==
+ headers:
+ Accept:
+ - '*/*'
+ Accept-Encoding:
+ - gzip, deflate
+ Connection:
+ - keep-alive
+ Content-Length:
+ - '18925'
+ Content-Type:
+ - application/x-protobuf
+ User-Agent:
+ - OTel-OTLP-Exporter-Python/1.27.0
+ method: POST
+ uri: https://telemetry.crewai.com:4319/v1/traces
+ response:
+ body:
+ string: "\n\0"
+ headers:
+ Content-Length:
+ - '2'
+ Content-Type:
+ - application/x-protobuf
+ Date:
+ - Tue, 24 Sep 2024 21:57:39 GMT
+ status:
+ code: 200
+ message: OK
+- request:
+ body: '{"model": "gemma2:latest", "prompt": "### User:\nRespond in 20 words. Who
+ are you?\n\n", "options": {}, "stream": false}'
+ headers:
+ Accept:
+ - '*/*'
+ Accept-Encoding:
+ - gzip, deflate
+ Connection:
+ - keep-alive
+ Content-Length:
+ - '120'
+ Content-Type:
+ - application/json
+ User-Agent:
+ - python-requests/2.31.0
+ method: POST
+ uri: http://localhost:8080/api/generate
+ response:
+ body:
+ string: '{"model":"gemma2:latest","created_at":"2024-09-24T21:57:51.284303Z","response":"I
+ am Gemma, an open-weights AI assistant developed by Google DeepMind. \n","done":true,"done_reason":"stop","context":[106,1645,108,6176,4926,235292,108,54657,575,235248,235284,235276,3907,235265,7702,708,692,235336,109,107,108,106,2516,108,235285,1144,137061,235269,671,2174,235290,30316,16481,20409,6990,731,6238,20555,35777,235265,139,108],"total_duration":14046647083,"load_duration":12942541833,"prompt_eval_count":25,"prompt_eval_duration":177695000,"eval_count":19,"eval_duration":923120000}'
+ headers:
+ Content-Length:
+ - '579'
+ Content-Type:
+ - application/json; charset=utf-8
+ Date:
+ - Tue, 24 Sep 2024 21:57:51 GMT
+ status:
+ code: 200
+ message: OK
+version: 1
diff --git a/tests/cassettes/test_agent_without_max_rpm_respet_crew_rpm.yaml b/tests/cassettes/test_agent_without_max_rpm_respet_crew_rpm.yaml
index 1265fa76a..a07bb7acb 100644
--- a/tests/cassettes/test_agent_without_max_rpm_respet_crew_rpm.yaml
+++ b/tests/cassettes/test_agent_without_max_rpm_respet_crew_rpm.yaml
@@ -9,7 +9,7 @@ interactions:
the expect criteria for your final answer: Your greeting.\nyou MUST return the
actual complete content as the final answer, not a summary.\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:"]}'
+ your job depends on it!\n\nThought:"}], "model": "gpt-4o"}'
headers:
accept:
- application/json
@@ -18,16 +18,16 @@ interactions:
connection:
- keep-alive
content-length:
- - '800'
+ - '772'
content-type:
- application/json
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openai-processing-ms:
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openai-version:
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strict-transport-security:
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x-ratelimit-limit-requests:
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x-ratelimit-limit-tokens:
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x-request-id:
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http_version: HTTP/1.1
status_code: 200
version: 1
diff --git a/tests/cassettes/test_crew_verbose_output.yaml b/tests/cassettes/test_crew_verbose_output.yaml
index df95e5b00..09fc5d2a4 100644
--- a/tests/cassettes/test_crew_verbose_output.yaml
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Task: Write about AI in healthcare.\n\nThis is the expect criteria for your
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- with:\n## Full Report on AI Advancements\n\n### Introduction\n\nArtificial Intelligence
- (AI) has evolved rapidly over the past few decades, breaking new ground in fields
- as diverse as healthcare, finance, transportation, and entertainment. This report
- delves into the latest advancements in AI, examining key breakthroughs, emerging
- technologies, and potential impacts on various industries.\n\n### Key Breakthroughs\n\n####
- 1. Natural Language Processing (NLP)\nNatural Language Processing has seen remarkable
- improvements, with models like GPT-4 and beyond setting new standards in language
- understanding and generation. These models are capable of performing tasks such
- as translation, summarization, sentiment analysis, and even creative writing
- with unprecedented accuracy.\n\n- **Transformers**: The transformer architecture
- has revolutionized NLP, leading to the development of powerful models like BERT,
- GPT-3, and GPT-4.\n- **Contextual Understanding**: Advancements in contextual
- understanding allow AI to generate more coherent and contextually relevant text.\n-
- **Multilingual Capabilities**: New models can handle multiple languages simultaneously,
- breaking down language barriers.\n\n#### 2. Computer Vision\nComputer vision
- technology has also made significant strides, with applications ranging from
- autonomous vehicles to medical imaging.\n\n- **Object Detection**: Improvements
- in object detection algorithms have enabled more accurate identification and
- classification of objects in images and videos.\n- **Semantic Segmentation**:
- Enhanced semantic segmentation techniques facilitate more detailed and precise
- image analysis.\n- **Generative Adversarial Networks (GANs)**: GANs have enabled
- the creation of highly realistic images and videos, with applications in entertainment,
- design, and security.\n\n#### 3. Reinforcement Learning\nReinforcement Learning
- (RL) has achieved groundbreaking results, especially in areas such as game playing
- and robotics.\n\n- **AlphaGo**: Google''s DeepMind developed AlphaGo, an AI
- that defeated human champions in the complex game of Go.\n- **Autonomous Agents**:
- RL is being used to train autonomous agents for tasks ranging from robotic manipulation
- to smart grid management.\n- **Multi-Agent Systems**: Research in multi-agent
- systems aims to develop AI that can collaborate and compete in dynamic environments.\n\n###
- Emerging Technologies\n\n#### 1. Quantum AI\nQuantum computing holds the promise
- of solving complex problems that are currently intractable for classical computers.
- Quantum AI is an emerging field that combines quantum computing with AI to enhance
- performance.\n\n- **Quantum Machine Learning**: Algorithms that leverage quantum
- computing to accelerate machine learning tasks.\n- **Optimizing Large Datasets**:
- Quantum AI could significantly improve the processing and analysis of large
- datasets, crucial for areas like genomics and climate modeling.\n\n#### 2. Edge
- AI\nEdge AI refers to the deployment of AI algorithms on edge devices, closer
- to where data is generated, rather than in centralized data centers.\n\n- **Reduced
- Latency**: Edge AI reduces latency, making real-time decision-making possible.\n-
- **Privacy**: Processing data locally enhances privacy and security, as sensitive
- data does not need to be transmitted to the cloud.\n- **Applications**: Applications
- include autonomous vehicles, IoT devices, and smart cameras.\n\n### Industry
- Impacts\n\n#### 1. Healthcare\nAI is transforming healthcare by improving diagnostics,
- personalizing treatment plans, and automating administrative tasks.\n\n- **Medical
- Imaging**: AI algorithms analyze medical images to detect conditions such as
- tumors and fractures with high accuracy.\n- **Drug Discovery**: Machine learning
- models accelerate drug discovery by predicting the efficacy of new compounds.\n-
- **Telemedicine**: AI-driven tools enhance telemedicine by providing virtual
- consultations, symptom checking, and remote monitoring.\n\n#### 2. Finance\nThe
- finance industry leverages AI for fraud detection, algorithmic trading, and
- customer service.\n\n- **Fraud Detection**: AI systems identify patterns of
- fraudulent behavior and flag suspicious transactions in real-time.\n- **Algorithmic
- Trading**: Machine learning models predict market trends and automate trading
- strategies.\n- **Chatbots**: AI-powered chatbots provide customer service, handling
- inquiries and transactions efficiently.\n\n#### 3. Transportation\nAI is a driving
- force in the development of autonomous vehicles and intelligent transportation
- systems.\n\n- **Autonomous Driving**: AI algorithms enable self-driving cars
- to navigate complex environments safely and efficiently.\n- **Traffic Management**:
- Intelligent transportation systems optimize traffic flow, reducing congestion
- and emissions.\n- **Predictive Maintenance**: AI predicts vehicle maintenance
- needs, preventing breakdowns and improving safety.\n\n### Ethical Considerations\n\nAs
- AI continues to advance, it raises important ethical questions that must be
- addressed to ensure its responsible use.\n\n- **Bias and Fairness**: Ensuring
- AI systems are unbiased and fair is crucial to prevent discrimination and inequality.\n-
- **Privacy**: Safeguarding personal data and maintaining privacy is a growing
- concern as AI systems become more integrated into daily life.\n- **Autonomy**:
- The development of autonomous systems necessitates clear regulations and accountability
- to prevent misuse and ensure safety.\n\n### Conclusion\n\nAI advancements are
- reshaping industries and transforming the way we live and work. From natural
- language processing and computer vision to quantum AI and edge computing, the
- possibilities are vast and exciting. As AI technology continues to evolve, it
- is essential to address ethical considerations and ensure that its benefits
- are accessible to all.\n\nThis report provides a comprehensive overview of the
- current state of AI advancements, highlighting key breakthroughs, emerging technologies,
- industry impacts, and ethical considerations. Continued investment in research
- and development, coupled with thoughtful regulation, will be crucial in harnessing
- the full potential of AI.\n\nBegin! This is VERY important to you, use the tools
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+ with:\n---\n\n# Research Report: Advancements in Artificial Intelligence (AI)\n\n##
+ Introduction\nArtificial Intelligence (AI) has seen rapid advancements in recent
+ years, impacting diverse fields such as healthcare, finance, transportation,
+ and entertainment. This report explores the latest milestones, trends, and innovations
+ in AI, emphasizing developments in machine learning, natural language processing,
+ computer vision, and AI ethics.\n\n## 1. Machine Learning (ML)\n\n### 1.1 Deep
+ Learning\nDeep learning, a subset of machine learning involving neural networks
+ with multiple layers, continues to drive AI innovation. Recent advancements
+ include:\n\n#### 1.1.1 Transformers\n- **BERT, GPT-3, and Beyond**: Transformers
+ like BERT (Bidirectional Encoder Representations from Transformers) and GPT-3
+ (Generative Pre-trained Transformer 3) have set benchmarks in NLP tasks. GPT-3,
+ with 175 billion parameters, has shown unprecedented capabilities in generating
+ human-like text, with applications in chatbots, content creation, and coding
+ assistance.\n\n- **Vision Transformers (ViTs)**: Transformers are also making
+ inroads in computer vision, challenging traditional Convolutional Neural Networks
+ (CNNs). ViTs have achieved state-of-the-art results on image recognition tasks.\n\n####
+ 1.1.2 Reinforcement Learning (RL)\n- **AlphaFold**: Developed by DeepMind, AlphaFold
+ has revolutionized protein structure prediction, surpassing the capabilities
+ of traditional methods and providing new insights into biological processes.\n\n-
+ **Game AI**: Reinforcement learning algorithms continue to excel in complex
+ games. OpenAI''s Dota 2 bot and DeepMind''s AlphaZero have demonstrated superhuman
+ performance, showcasing the potential for RL in real-world applications.\n\n###
+ 1.2 Federated Learning\nFederated learning allows models to be trained across
+ multiple decentralized devices without sharing raw data, enhancing privacy and
+ security. This approach is particularly relevant in healthcare and finance,
+ where data sensitivity is paramount.\n\n## 2. Natural Language Processing (NLP)\n\n###
+ 2.1 Language Models\nLanguage models have made significant strides, primarily
+ due to the scaling of neural networks and availability of large datasets.\n\n-
+ **GPT-3**: As mentioned earlier, GPT-3 has remarkable text generation capabilities.
+ Its applications include automated customer support, coding assistance, and
+ content generation.\n\n- **T5 (Text-To-Text Transfer Transformer)**: Developed
+ by Google, T5 has unified various NLP tasks into a text-to-text framework, simplifying
+ the training process and improving performance across multiple benchmarks.\n\n###
+ 2.2 Sentiment Analysis and Understanding\nAdvanced NLP techniques are now capable
+ of better understanding human emotions and intents. This has improved sentiment
+ analysis, enhancing applications in customer feedback systems, social media
+ monitoring, and mental health support tools.\n\n## 3. Computer Vision\n\n###
+ 3.1 Object Detection and Recognition\nObject detection and recognition algorithms
+ have improved significantly, enabling real-time applications in areas like autonomous
+ driving, security surveillance, and medical imaging.\n\n- **YOLO (You Only Look
+ Once)**: YOLO models are known for their speed and accuracy in object detection
+ tasks. YOLOv4 and YOLOv5 have further optimized the balance between performance
+ and computational efficiency.\n\n### 3.2 Image Generation and Enhancement\nGenerative
+ models like GANs (Generative Adversarial Networks) have evolved to produce highly
+ realistic images, with applications in art, entertainment, and data augmentation.\n\n-
+ **StyleGAN**: Developed by NVIDIA, StyleGAN can generate high-fidelity synthetic
+ images, contributing to advancements in virtual reality, 3D modeling, and content
+ creation.\n\n## 4. AI Ethics and Fairness\n\n### 4.1 Bias Mitigation\nAI systems
+ can inadvertently perpetuate biases present in training data. Efforts are being
+ made to develop fairer AI models by incorporating bias detection and mitigation
+ techniques during the training process.\n\n### 4.2 Explainability\nAs AI systems
+ become more complex, understanding and interpreting their decisions have become
+ crucial. Techniques for explainable AI (XAI) are being developed to provide
+ transparency, particularly in sensitive applications like healthcare and finance.\n\n###
+ 4.3 Regulations and Policies\nGovernments and organizations worldwide are increasingly
+ aware of the need for AI governance. Policies are being crafted to ensure ethical
+ AI development and deployment, focusing on data privacy, accountability, and
+ fairness.\n\n## Conclusion\nThe field of AI is progressing at a rapid pace,
+ with significant advancements in machine learning, natural language processing,
+ computer vision, and ethical AI practices. These developments are opening up
+ new possibilities across various sectors while simultaneously presenting challenges
+ that must be addressed through collaborative efforts among researchers, policymakers,
+ and industry stakeholders.\n\n---\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:"}],
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+ Answer:\\n\\n## AI Advancements Report\\n\\n### 1. Introduction\\nArtificial
+ Intelligence (AI) has experienced rapid advancements over the past decade, influencing
+ numerous sectors including healthcare, finance, robotics, and more. This report
+ aims to provide a comprehensive overview of the recent advancements in AI, including
+ breakthroughs in machine learning (ML), natural language processing (NLP), computer
+ vision, and AI agents.\\n\\n### 2. Breakthroughs in Machine Learning\\nMachine
+ learning, a subset of AI focused on the development of algorithms that enable
+ machines to learn from data, has seen significant advancements:\\n1. **Deep
+ Learning Improvements**: Deep learning models, particularly neural networks,
+ have evolved with innovations like transformers, which are particularly beneficial
+ for sequential data.\\n2. **Automated Machine Learning (AutoML)**: Tools and
+ platforms that automate the ML model-building process have streamlined workflows
+ and made ML more accessible.\\n3. **Few-shot and Zero-shot Learning**: Techniques
+ enabling models to generalize from very few or even no examples. This is useful
+ for applications with limited data sets.\\n4. **Reinforcement Learning**: Enhanced
+ through algorithms that improve the learning efficiency and effectiveness, evidenced
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+ human language:\\n1. **GPT-3 and beyond**: OpenAI's Generative Pre-trained Transformer-3
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+ text.\\n2. **BERT and Transformer Models**: Bidirectional Encoder Representations
+ from Transformers (BERT) models have improved understanding of context in search
+ queries and language comprehension tasks.\\n3. **Conversational AI**: Development
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+ language.\\n\\n### 4. Computer Vision\\nComputer vision has progressed significantly,
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+ accuracy:\\n1. **Convolutional Neural Networks (CNNs)**: Enhanced architectures
+ such as EfficientNet and Vision Transformers (ViT) provide better image classification
+ and recognition.\\n2. **Generative Adversarial Networks (GANs)**: Use of GANs
+ for generating high-quality images, which has applications in art, design, and
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+ numerous advancements:\\n1. **Autonomous Vehicles**: Increased reliability and
+ safety of AI systems powering self-driving cars, with companies like Tesla and
+ Waymo leading the charge.\\n2. **Robotics**: Enhanced robotic systems that can
+ perform complex tasks, such as Boston Dynamics' robots capable of performing
+ a variety of physical actions.\\n3. **Personal Assistants**: Development of
+ AI agents for personal use, such as Amazon\u2019s Alexa and Google Assistant,
+ which are becoming increasingly intelligent and versatile.\\n\\n### 6. Ethical
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+ considerations become paramount:\\n1. **Bias and Fairness**: Efforts to reduce
+ bias in AI systems through better data collection, algorithms, and fairness
+ criteria.\\n2. **Transparency and Explainability**: Developing models and tools
+ that provide transparency in AI decision-making processes.\\n3. **Regulations
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+ safe and responsible AI usage.\\n\\n### 7. Future Directions\\nThe future of
+ AI promises further innovations and applications:\\n1. **Integrative AI**: Combining
+ various branches of AI to create more holistic and efficient systems.\\n2. **Edge
+ Computing**: Processing AI tasks directly on devices (edge) rather than relying
+ solely on cloud computing, improving speed and privacy.\\n3. **Interdisciplinary
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+ for climate change modeling and personalized education platforms.\\n\\n### 8.
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+ how we live, work, and interact with technology. Continuous research and development
+ in machine learning, natural language processing, computer vision, and AI ethics
+ are essential to harness the full potential of AI while mitigating risks.\\n\\nThis
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+ for the benefit of society.\\n\\n### 9. References\\n1. OpenAI. GPT-3. (2020).\\n2.
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+ Understanding. (2019).\\n3. Boston Dynamics. [Online resource].\\n4. Tesla,
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+ with:\n## AI Advancements Report\n\n### 1. Introduction\nArtificial Intelligence
+ (AI) has experienced rapid advancements over the past decade, influencing numerous
+ sectors including healthcare, finance, robotics, and more. This report aims
+ to provide a comprehensive overview of the recent advancements in AI, including
+ breakthroughs in machine learning (ML), natural language processing (NLP), computer
+ vision, and AI agents.\n\n### 2. Breakthroughs in Machine Learning\nMachine
+ learning, a subset of AI focused on the development of algorithms that enable
+ machines to learn from data, has seen significant advancements:\n1. **Deep Learning
+ Improvements**: Deep learning models, particularly neural networks, have evolved
+ with innovations like transformers, which are particularly beneficial for sequential
+ data.\n2. **Automated Machine Learning (AutoML)**: Tools and platforms that
+ automate the ML model-building process have streamlined workflows and made ML
+ more accessible.\n3. **Few-shot and Zero-shot Learning**: Techniques enabling
+ models to generalize from very few or even no examples. This is useful for applications
+ with limited data sets.\n4. **Reinforcement Learning**: Enhanced through algorithms
+ that improve the learning efficiency and effectiveness, evidenced in complex
+ problem-solving tasks and games.\n\n### 3. Natural Language Processing (NLP)\nNLP
+ advancements have greatly improved how machines understand and generate human
+ language:\n1. **GPT-3 and beyond**: OpenAI''s Generative Pre-trained Transformer-3
+ (GPT-3) has set new standards in language generation, capable of producing human-like
+ text.\n2. **BERT and Transformer Models**: Bidirectional Encoder Representations
+ from Transformers (BERT) models have improved understanding of context in search
+ queries and language comprehension tasks.\n3. **Conversational AI**: Development
+ of sophisticated chatbots and virtual assistants like Google''s Meena and OpenAI''s
+ ChatGPT, which can engage in more natural and meaningful conversations.\n4.
+ **Multilingual Models**: Enhanced multilingual models like mBERT that process
+ and understand multiple languages without relying on separate models for each
+ language.\n\n### 4. Computer Vision\nComputer vision has progressed significantly,
+ allowing machines to interpret and process visual information with remarkable
+ accuracy:\n1. **Convolutional Neural Networks (CNNs)**: Enhanced architectures
+ such as EfficientNet and Vision Transformers (ViT) provide better image classification
+ and recognition.\n2. **Generative Adversarial Networks (GANs)**: Use of GANs
+ for generating high-quality images, which has applications in art, design, and
+ data augmentation.\n3. **Object Detection and Segmentation**: Improvements in
+ object detection and segmentation algorithms such as YOLO and Mask R-CNN have
+ led to more accurate and faster image analysis.\n\n### 5. Advancements in AI
+ Agents\nAI agents, which act autonomously to accomplish tasks, have seen numerous
+ advancements:\n1. **Autonomous Vehicles**: Increased reliability and safety
+ of AI systems powering self-driving cars, with companies like Tesla and Waymo
+ leading the charge.\n2. **Robotics**: Enhanced robotic systems that can perform
+ complex tasks, such as Boston Dynamics'' robots capable of performing a variety
+ of physical actions.\n3. **Personal Assistants**: Development of AI agents for
+ personal use, such as Amazon\u2019s Alexa and Google Assistant, which are becoming
+ increasingly intelligent and versatile.\n\n### 6. Ethical and Societal Considerations\nAs
+ AI technology advances, ethical and societal considerations become paramount:\n1.
+ **Bias and Fairness**: Efforts to reduce bias in AI systems through better data
+ collection, algorithms, and fairness criteria.\n2. **Transparency and Explainability**:
+ Developing models and tools that provide transparency in AI decision-making
+ processes.\n3. **Regulations and Governance**: Policies and frameworks are being
+ developed worldwide to ensure safe and responsible AI usage.\n\n### 7. Future
+ Directions\nThe future of AI promises further innovations and applications:\n1.
+ **Integrative AI**: Combining various branches of AI to create more holistic
+ and efficient systems.\n2. **Edge Computing**: Processing AI tasks directly
+ on devices (edge) rather than relying solely on cloud computing, improving speed
+ and privacy.\n3. **Interdisciplinary AI**: AI applications spreading to new
+ fields such as environmental science for climate change modeling and personalized
+ education platforms.\n\n### 8. Conclusion\nThe landscape of artificial intelligence
+ is rapidly evolving, influencing how we live, work, and interact with technology.
+ Continuous research and development in machine learning, natural language processing,
+ computer vision, and AI ethics are essential to harness the full potential of
+ AI while mitigating risks.\n\nThis report highlights the importance of staying
+ informed about these advancements and fostering an environment where AI can
+ be developed and applied responsibly for the benefit of society.\n\n### 9. References\n1.
+ OpenAI. GPT-3. (2020).\n2. Google Research. BERT: Pre-training of Deep Bidirectional
+ Transformers for Language Understanding. (2019).\n3. Boston Dynamics. [Online
+ resource].\n4. Tesla, Inc. [Online resource].\n5. Waymo LLC. [Online resource].\n6.
+ Various academic journals and AI research papers.\n\nBegin! This is VERY important
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diff --git a/tests/cassettes/test_hierarchical_process.yaml b/tests/cassettes/test_hierarchical_process.yaml
index 7da979ba5..06d6c538e 100644
--- a/tests/cassettes/test_hierarchical_process.yaml
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+ and their potential roles in areas like personalized health coaching, automated
+ legal advice, and beyond.\n\n2. **AI in Healthcare: Revolutionizing Diagnostics
+ and Treatment**:\n - Here, the focus could be on how AI is transforming healthcare
+ by improving diagnostic accuracy, personalizing treatment plans, and even predicting
+ disease outbreaks. The article can delve into real-world applications such as
+ AI-driven imaging technologies, predictive analytics in patient care, and the
+ ethical concerns surrounding data privacy and decision-making in medicine. Case
+ studies of successful AI implementations in healthcare can provide depth and
+ relevance to the topic.\n\n3. **The Role of AI in Enhancing Cybersecurity**:\n -
+ This article could discuss how AI is becoming a crucial tool in the fight against
+ cybercrime. Topics might include the ways AI can detect and respond to threats
+ in real time, how it can predict and prevent potential attacks, and the challenges
+ and limitations of relying on AI for cybersecurity. The discussion can also
+ cover recent advancements in AI-based security tools and explore case studies
+ where AI has significantly mitigated cyber threats.\n\n4. **The Intersection
+ of AI and Autonomous Vehicles: Driving Towards a Safer Future**:\n - This
+ idea would explore the integration of AI in autonomous vehicles, detailing the
+ technology behind self-driving cars, their development progress, and the challenges
+ they face. The article could cover regulatory hurdles, ethical considerations
+ (e.g., decision-making in life-and-death situations), and the potential impact
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+ enabling advanced data analysis, and even driving creativity and decision-making.
+ It could also address the fears and realities of job displacement, the evolution
+ of new job roles, and the necessity of reskilling and upskilling the workforce
+ to adapt to an AI-driven economy. Interviews with industry experts and workers
+ affected by AI can add a personal touch and broader perspective to the topic.\n\nEach
+ of these ideas has vast potential and depth, making them suitable for engaging,
+ informative, and thought-provoking articles that could attract a wide audience
+ interested in technology and AI."}, {"role": "user", "content": "I did it wrong.
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+ do one or the other"}, {"role": "assistant", "content": "Thought: Now that I
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+ of AI agents, starting from simple virtual assistants like Apple''s Siri and
+ Amazon''s Alexa to more sophisticated AI systems today. Potential discussions
+ could include how these AI agents have integrated into various aspects of daily
+ life, their benefits, and potential ethical concerns. The future scope can forecast
+ advancements in these AI agents over the next decade, considering the integration
+ of more advanced machine learning algorithms and their potential roles in areas
+ like personalized health coaching, automated legal advice, and beyond.\\n\\n2.
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+ and enhanced mobility for people with disabilities. By detailing these aspects,
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+ in a continuously evolving workplace landscape."}], "model": "gpt-4o"}'
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+ and the writing of the paragraphs to the Senior Writer. \n\nAction: Delegate
+ work to coworker\nAction Input: {\"task\": \"Come up with a list of 5 interesting
+ ideas to explore for an article.\", \"context\": \"We need a list of 5 interesting
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+ and engaging for a broad audience.\", \"coworker\": \"Researcher\"}\nObservation:
+ 1. **The Rise of AI in Healthcare: Transforming Patient Care and Diagnostics**
+ - Explore how AI is revolutionizing healthcare through enhanced diagnostic tools,
+ personalized treatment plans, and post-operative care. Discuss current achievements,
+ potential future applications, and ethical considerations.\n\n2. **AI Agents
+ in the Workplace: Boosting Productivity and Shaping the Future of Jobs** - Examine
+ the integration of AI agents in various industries. Highlight how these intelligent
+ agents are improving efficiency, automating mundane tasks, and assisting in
+ decision-making processes. Address potential job displacement and the need for
+ upskilling the workforce.\n\n3. **Ethical AI: Ensuring Fairness, Accountability,
+ and Transparency** - Delve into the ethical implications of AI deployment. Analyze
+ the frameworks and guidelines being proposed to ensure AI systems are fair,
+ transparent, and accountable. Include case studies of ethical dilemmas faced
+ by companies using AI.\n\n4. **AI-Powered Startups: Innovators Changing the
+ Tech Landscape** - Provide an overview of how AI-driven startups are disrupting
+ traditional business models and industries. Profile some of the most innovative
+ startups, their business models, and the unique challenges they face. Discuss
+ the role of venture capital in fostering AI innovation.\n\n5. **The Future of
+ AI in Entertainment: From Movies to Video Games** - Investigate how AI is transforming
+ the entertainment industry, from scriptwriting and special effects in movies
+ to creating more immersive and intelligent experiences in video games. Offer
+ predictions about future trends and possible impacts on content creation and
+ consumption.\n\nThese topics are designed to be timely, relevant, and engaging,
+ ensuring a broad audience will find them compelling."}, {"role": "user", "content":
+ "I did it wrong. Tried to both perform Action and give a Final Answer at the
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+ care. Discuss current achievements, potential future applications, and ethical
+ considerations.\\n2. AI Agents in the Workplace: Boosting Productivity and Shaping
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+ effects in movies to creating more immersive and intelligent experiences in
+ video games. Offer predictions about future trends and possible impacts on content
+ creation and consumption.\", \"coworker\": \"Senior Writer\"}\nObservation:\nObservation:
+ 1. **The Rise of AI in Healthcare: Transforming Patient Care and Diagnostics**\n AI
+ is undeniably revolutionizing the healthcare sector, introducing transformative
+ changes in patient care and diagnostics. With the advent of advanced diagnostic
+ tools powered by AI, healthcare professionals can now detect and monitor diseases
+ with unprecedented accuracy and speed. Personalized treatment plans, which were
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+ Post-operative care has also seen significant improvements, with AI-driven monitoring
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+ the beginning, with future applications promising to further enhance surgical
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+ These startups leverage AI to offer innovative solutions, whether it''s automating
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+ more immersive and engaging experiences. In movies, AI streamlines the scriptwriting
+ process, enhances special effects, and even predicts box office success. Machine
+ learning models analyze vast datasets to create compelling narratives and optimize
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+ a seamless blend of AI with augmented reality (AR) and virtual reality (VR),
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+ blur. These advancements are not only transforming content creation but also
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+ a future where AI is an indispensable tool in the creative arsenal."}], "model":
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for each idea.\nyou MUST return the actual complete content as the final answer,
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+ focus can be on current technologies and future possibilities, featuring insights
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+ a list of 5 interesting ideas to explore for an article, then write one amazing
+ paragraph highlight for each idea that showcases how good an article about this
+ topic could be. Return the list of ideas with their paragraph and your notes.\n\nThis
+ is the expect criteria for your final answer: 5 bullet points with a paragraph
+ for each idea.\nyou MUST return the actual complete content as the final answer,
+ not a summary.\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:"}, {"role":
+ "assistant", "content": "Thought: To come up with a list of 5 interesting article
+ ideas and write a compelling paragraph for each one, I should first generate
+ the ideas. For this, I will delegate the task to the Researcher and then use
+ the Senior Writer to create the paragraphs.\n\nAction: Delegate work to coworker\nAction
+ Input: {\"task\": \"Generate a list of 5 interesting ideas to explore for an
+ article.\", \"context\": \"We need to come up with 5 interesting and unique
+ article ideas that will captivate readers.\", \"coworker\": \"Researcher\"}\nObservation:
+ 1. **The Rise of AI in Healthcare: Revolutionizing Diagnosis and Treatment**\n -
+ This article could explore how AI is making strides in the healthcare industry.
+ From AI-driven diagnostic tools to personalized treatment plans, the piece could
+ highlight real-world applications and the potential future impact. Interviews
+ with healthcare professionals and case studies could add depth and credibility.\n\n2.
+ **AI and Ethics: Navigating the Moral Complexity of Machine Intelligence**\n -
+ Delve into the ethical considerations surrounding AI development and deployment.
+ The article could examine issues such as bias in AI algorithms, privacy concerns,
+ and the ethical responsibility of AI developers. Expert opinions from ethicists,
+ technologists, and policymakers can provide a well-rounded perspective.\n\n3.
+ **AI Agents in the Workplace: Enhancing Productivity or Eroding Jobs?**\n -
+ This piece can analyze the dual impact of AI agents in the professional environment.
+ While some argue that AI can significantly boost productivity by automating
+ mundane tasks, others fear job displacement. The article can present both sides,
+ backed by data and forecasts about labor market trends.\n\n4. **From Science
+ Fiction to Reality: The Evolution of AI Agents in Popular Culture**\n - An
+ engaging piece that traces the depiction of AI agents in movies, books, and
+ video games, and compares these fictional portrayals to the current state of
+ AI technology. This can include interviews with creators from the entertainment
+ industry and AI experts to discuss how close we are to realizing these sci-fi
+ visions.\n\n5. **The Future of AI in Personalization: Creating Tailored Experiences
+ in Everyday Life**\n - Discuss how AI is enhancing personalization in various
+ domains such as shopping, entertainment, education, and personal finance. The
+ focus can be on current technologies and future possibilities, featuring insights
+ from industry leaders and technologists who are driving these innovations.\n\nThese
+ five article ideas are designed to draw in readers by addressing contemporary
+ issues and trends in AI, while also providing expert insights and balanced viewpoints."},
+ {"role": "user", "content": "I did it wrong. Tried to both perform Action and
+ give a Final Answer at the same time, I must do one or the other"}, {"role":
+ "assistant", "content": "Thought: Now that I have the list of 5 interesting
+ article ideas from the Researcher, the next step is to ask the Senior Writer
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+ Treatment**\\n- This article could explore how AI is making strides in the healthcare
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+ Interviews with healthcare professionals and case studies could add depth and
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+ While some argue that AI can significantly boost productivity by automating
+ mundane tasks, others fear job displacement. The article can present both sides,
+ backed by data and forecasts about labor market trends.\\n\\n4. **From Science
+ Fiction to Reality: The Evolution of AI Agents in Popular Culture**\\n- An engaging
+ piece that traces the depiction of AI agents in movies, books, and video games,
+ and compares these fictional portrayals to the current state of AI technology.
+ This can include interviews with creators from the entertainment industry and
+ AI experts to discuss how close we are to realizing these sci-fi visions.\\n\\n5.
+ **The Future of AI in Personalization: Creating Tailored Experiences in Everyday
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+ as shopping, entertainment, education, and personal finance. The focus can be
+ on current technologies and future possibilities, featuring insights from industry
+ leaders and technologists who are driving these innovations.\", \"context\":
+ \"Create compelling paragraphs that highlight the potential and intrigue of
+ each article idea. These should be engaging and showcase how fascinating each
+ article could be.\", \"coworker\": \"Senior Writer\"}\n\nObservation: The Senior
+ Writer provided the following compelling paragraphs for each article idea:\n\n1.
+ **The Rise of AI in Healthcare: Revolutionizing Diagnosis and Treatment**\n -
+ Imagine a world where a doctor can diagnose a condition with pinpoint accuracy,
+ thanks to the help of artificial intelligence. AI is already transforming healthcare
+ with advanced diagnostic tools and personalized treatment plans. This article
+ would delve into real-world cases where AI has made a significant impact, providing
+ insights from healthcare professionals who witness these technological advancements
+ firsthand. The future of medicine is not only in the hands of doctors and nurses
+ but also in the code and algorithms of AI systems.\n\n2. **AI and Ethics: Navigating
+ the Moral Complexity of Machine Intelligence**\n - The rapid development of
+ artificial intelligence brings with it not just technological advancements but
+ also a slew of ethical dilemmas. This article will explore the complex moral
+ landscape AI inhabits, questioning the bias that may be programmed into algorithms,
+ the privacy concerns surrounding data usage, and the ethical responsibilities
+ of those who develop AI technologies. Insightful perspectives from ethicists,
+ technologists, and policymakers will highlight how society can navigate these
+ challenges to ensure that AI benefits humanity as a whole.\n\n3. **AI Agents
+ in the Workplace: Enhancing Productivity or Eroding Jobs?**\n - As AI agents
+ become more integrated into the workforce, the debate over their impact intensifies.
+ This article will examine how AI can boost productivity by automating routine
+ tasks, thereby freeing up human workers for more creative and strategic roles.
+ However, it will also address the fear of job displacement and present data
+ and forecasts about the future labor market. Balanced viewpoints from industry
+ analysts and workers affected by AI implementation will offer a comprehensive
+ understanding of the pros and cons of AI in the workplace.\n\n4. **From Science
+ Fiction to Reality: The Evolution of AI Agents in Popular Culture**\n - From
+ HAL 9000 to Jarvis, AI agents have long captivated our imaginations in books,
+ movies, and video games. This article will take readers on a journey through
+ time, highlighting how AI has been portrayed in popular culture and comparing
+ these fictional depictions to the current state of AI technology. Conversations
+ with creators from the entertainment industry and AI experts will reveal whether
+ these sci-fi visions are becoming our reality and what the future holds for
+ AI in culture.\n\n5. **The Future of AI in Personalization: Creating Tailored
+ Experiences in Everyday Life**\n - In an increasingly digital world, AI is
+ enhancing personalization across various domains, crafting tailored experiences
+ that cater to individual needs. This article will explore how AI is revolutionizing
+ everything from shopping and entertainment to education and personal finance.
+ Featuring insights from industry leaders and technologists, the piece will showcase
+ the current state of personalization technologies and speculate on future advancements,
+ painting a vivid picture of a world where AI knows us better than we know ourselves.\nObservation:
+ 1. **The Rise of AI in Healthcare: Revolutionizing Diagnosis and Treatment**\nArtificial
+ Intelligence is spearheading a transformative era in healthcare, fundamentally
+ altering how diseases are diagnosed and treated. AI-driven diagnostic tools
+ are already outperforming human experts in identifying ailments from medical
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+ with healthcare professionals and insightful case studies, it will paint a vivid
+ picture of how AI is not only enhancing medical outcomes but also reshaping
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+ the Moral Complexity of Machine Intelligence**\nAs Artificial Intelligence continues
+ to evolve, it brings with it a host of ethical dilemmas that society must grapple
+ with. This article will delve into the moral intricacies of AI, focusing on
+ pressing issues such as algorithmic bias, the preservation of privacy, and the
+ ethical duties of AI developers. By examining high-profile cases of biased AI
+ and exploring the perspectives of ethicists, technologists, and policymakers,
+ the piece will offer a comprehensive look at the ethical landscape of machine
+ intelligence. It aims to provoke thought and spark dialogue on how we can responsibly
+ harness AI''s power while safeguarding human values and rights.\n\n3. **AI Agents
+ in the Workplace: Enhancing Productivity or Eroding Jobs?**\nThe integration
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+ hand, AI can drastically enhance productivity by automating repetitive tasks
+ and offering intelligent insights that drive innovation. On the other, there
+ is a growing concern about job displacement and the socioeconomic implications
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+ topic today given the global emphasis on sustainability.\\n- Clarity: 5 - The
+ title is clear and easy to understand.\\n- Originality: 3 - The topic is commonly
+ discussed, though still important.\\n- Potential Insight: 4 - The title promises
+ valuable insights into how the future of renewable energy may unfold.\\n\\nTitle:
+ \\\"Exploring the Depths of the Ocean\\\"\\n- Impact: 4 - The title evokes curiosity
+ and suggests an exciting journey.\\n- Relevance: 3 - While interesting, the
+ topic may not be top of mind for many people.\\n- Clarity: 5 - The title is
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diff --git a/tests/cassettes/test_json_property_without_output_json.yaml b/tests/cassettes/test_json_property_without_output_json.yaml
index 5fe3e9233..d8fd03dfc 100644
--- a/tests/cassettes/test_json_property_without_output_json.yaml
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+ "assistant", "content": "Thought: I need to first brainstorm and identify 5
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+ Researcher. Once the ideas are identified, we can ask the Senior Writer to develop
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+ Input: {\"coworker\": \"Researcher\", \"task\": \"Come up with a list of 5 interesting
+ ideas to explore for an article.\", \"context\": \"We need a list of 5 interesting
+ and unique ideas for an article. These should be relevant and engaging topics
+ that would capture the interest of a wide audience. Consider current trends
+ and unique perspectives that can create compelling content.\"}\n\nObservation:\nObservation:
+ 1. **The Rise of AI Agents in Customer Service: Revolutionizing User Experience**\n -
+ Explore how AI agents are transforming customer service across various industries.
+ Discuss the technological advancements behind these agents, their capabilities,
+ and real-world examples of companies successfully implementing them. Additionally,
+ examine the potential impact on customer satisfaction and employment in customer
+ service roles.\n\n2. **AI and Mental Health: How Artificial Intelligence is
+ Shaping Mental Health Care**\n - Dive into the innovative ways AI is being
+ used in mental health care. Cover applications like AI-powered therapy bots,
+ mental health monitoring apps, and predictive analytics for mental health crises.
+ Address ethical considerations and the effectiveness of AI in providing mental
+ health support compared to traditional methods.\n\n3. **Ethical AI: Balancing
+ Innovation and Responsibility**\n - Analyze the ethical dilemmas surrounding
+ AI development and deployment. Discuss the importance of creating ethical guidelines
+ and regulations for AI, focusing on bias in AI algorithms, privacy concerns,
+ and the long-term societal impacts. Highlight initiatives and frameworks aimed
+ at promoting responsible AI.\n\n4. **AI''s Role in Combating Climate Change**\n -
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+ issues. Look into AI-driven climate models, smart grids for energy conservation,
+ and AI applications in sustainable agriculture. Showcase projects and startups
+ dedicated to using AI for ecological sustainability and the preservation of
+ natural resources.\n\n5. **From Science Fiction to Reality: AI Innovations Inspired
+ by Popular Media**\n - Explore AI technologies that were once considered science
+ fiction but are now becoming a reality. Examine how popular media, such as movies
+ and TV shows, have influenced AI research and development. Provide examples
+ of AI applications like predictive analytics, personal assistants, and robotics
+ that mirror concepts from science fiction narratives."}, {"role": "assistant",
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+ to delegate the task of writing an engaging paragraph for each idea to the Senior
+ Writer. This will ensure each idea is brought to life in a compelling way suitable
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+ Intelligence is Shaping Mental Health Care 3. Ethical AI: Balancing Innovation
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- black-box models and fosters a more collaborative relationship between humans
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- at the forefront of efforts to ensure that technological advancements are both
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+ your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need
+ to come up with a list of 5 interesting ideas for an article and write a paragraph
+ highlight for each idea. The best approach is to leverage the expertise of the
+ Researcher to gather high-quality ideas. \n\nFirst, I will delegate the task
+ of generating the 5 interesting article ideas to the Researcher. Once I have
+ the ideas, I will then ask the Researcher to provide a detailed paragraph highlight
+ for each idea.\n\nAction: Delegate work to coworker\nAction Input: {\"coworker\":
+ \"Researcher\", \"task\": \"Generate a list of 5 interesting ideas for an article\",
+ \"context\": \"We need 5 engaging and trending article ideas that will capture
+ the interest of readers on a wide scale. The topics should be diverse and relevant
+ to contemporary issues or timeless in interest.\"}\nObservation: Below are five
+ engaging and trending article ideas that will appeal to a wide audience, addressing
+ both contemporary issues and timeless interests:\n\n1. **The Ethical Dilemmas
+ of AI: Balancing Innovation with Responsibility**\n - This article could examine
+ the ethical implications of artificial intelligence deployment across various
+ sectors. It would delve into the balance between technological advancement and
+ the potential risks, such as job displacement, bias in AI algorithms, privacy
+ concerns, and the call for robust regulations to safeguard human interests.\n\n2.
+ **How AI is Shaping the Future of Healthcare**\n - This piece could explore
+ the transformative impact of AI in the healthcare industry. It might cover advancements
+ in diagnostic tools, personalized medicine, robotic surgeries, and AI''s role
+ in streamlining administrative tasks. Real-world examples and expert opinions
+ could provide a comprehensive look at both the benefits and challenges faced
+ in AI-driven healthcare.\n\n3. **From Sci-Fi to Reality: The Evolution of AI
+ Agents in Everyday Life**\n - An engaging article that tracks the journey
+ of AI agents from science fiction to current day applications. This could include
+ virtual assistants like Siri and Alexa, customer service chatbots, and smart
+ home systems. The piece could also speculate on future developments, integrating
+ expert predictions and current technological trends.\n\n4. **Startups to Watch:
+ How Emerging Businesses are Leveraging AI**\n - This article could highlight
+ innovative startups that are utilizing AI to disrupt various industries. Profile
+ companies making waves in sectors like finance, retail, cybersecurity, and transportation.
+ It could also touch on the challenges these startups face and the potential
+ impact they could have on their respective markets.\n\n5. **The Intersection
+ of AI and Art: Creativity in the Age of Algorithms**\n - A captivating exploration
+ of how artificial intelligence is being used in the creative arts. This might
+ encompass AI-generated music, visual arts, literature, and even dance. Discuss
+ both the possibilities and controversies surrounding AI''s role in creativity
+ and the fundamental question of what constitutes art in the digital age.\n\nThese
+ topics are designed to be diverse and highly relevant, ensuring broad reader
+ engagement while addressing both current trends and perennial issues."}, {"role":
+ "user", "content": "I did it wrong. Tried to both perform Action and give a
+ Final Answer at the same time, I must do one or the other"}, {"role": "assistant",
+ "content": "Understood. I need to use the appropriate steps to both delegate
+ the task and ask necessary questions to the Researcher. \n\nFirst, let''s delegate
+ the task of generating the 5 interesting article ideas to the Researcher.\n\nAction:
+ Delegate work to coworker\nAction Input: {\"coworker\": \"Researcher\", \"task\":
+ \"Generate a list of 5 interesting ideas for an article\", \"context\": \"We
+ need 5 engaging and trending article ideas that will capture the interest of
+ readers on a wide scale. The topics should be diverse and relevant to contemporary
+ issues or timeless in interest.\"}\nObservation: I tried reusing the same input,
+ I must stop using this action input. I''ll try something else instead.\n\n"},
+ {"role": "assistant", "content": "Thought: I should accurately and clearly delegate
+ the task to the Researcher to generate the list of 5 interesting article ideas.\n\nAction:
+ Delegate work to coworker\nAction Input: {\"coworker\": \"Researcher\", \"task\":
+ \"Generate a list of 5 interesting ideas for an article\", \"context\": \"We
+ need 5 engaging and trending article ideas that will capture the interest of
+ readers on a wide scale. The topics should be diverse and relevant to contemporary
+ issues or timeless in interest. Each idea should be suitable for an in-depth
+ exploration in an article.\"}\nObservation: The Researcher has been tasked with
+ generating a list of 5 interesting article ideas. \n\nNow, I should wait for
+ the Researcher to provide the list of ideas.\nObservation: 1. **The Role of
+ Artificial Intelligence in Healthcare: Transforming Patient Care**\n - Explore
+ how AI is revolutionizing the healthcare industry, from predictive analytics
+ and personalized medicine to robotic surgeries and healthcare management systems.
+ Discuss real-world applications, potential benefits, and ethical considerations.\n\n2.
+ **The Future of Work: How AI and Automation Will Shape Tomorrow\u2019s Job Market**\n -
+ Investigate the impact of AI and automation on the job market. Discuss which
+ jobs are at risk, the emergence of new career opportunities, and the skills
+ that will be in demand. Include interviews with experts and insights on how
+ to prepare for these changes.\n\n3. **AI and Cybersecurity: Protecting Against
+ the Next Generation of Threats**\n - Examine the role of AI in enhancing cybersecurity
+ measures. Highlight how machine learning algorithms can detect and respond to
+ cyber threats in real-time. Discuss the balance between AI benefits and the
+ potential risks it introduces in cybersecurity.\n\n4. **Ethics in AI: Navigating
+ the Moral Implications of Intelligent Machines**\n - Delve into the ethical
+ implications of AI development and deployment. Discuss issues such as bias in
+ AI algorithms, privacy concerns, and the need for transparent and accountable
+ AI systems. Include perspectives from ethicists, technologists, and policymakers.\n\n5.
+ **Smart Cities: The Intersection of IoT, AI, and Urban Development**\n - Explore
+ how AI and the Internet of Things (IoT) are contributing to the development
+ of smart cities. Discuss innovations in areas such as traffic management, energy
+ efficiency, public safety, and urban planning. Provide case studies of cities
+ leading the way in smart city initiatives.\n\nThese article ideas are designed
+ to capture the attention of a broad audience by addressing current trends and
+ issues in technology, AI, and society. Each topic is rich with potential for
+ in-depth exploration and can provide valuable insights to readers.\n\n\nYou
+ ONLY have access to the following tools, and should NEVER make up tools that
+ are not listed here:\n\nTool Name: Delegate work to coworker(task: str, context:
+ str, coworker: Optional[str] = None, **kwargs)\nTool Description: Delegate a
+ specific task to one of the following coworkers: Researcher\nThe input to this
+ tool should be the coworker, the task you want them to do, and ALL necessary
+ context to execute the task, they know nothing about the task, so share absolute
+ everything you know, don''t reference things but instead explain them.\nTool
+ Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'':
+ {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'',
+ ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool
+ Name: Ask question to coworker(question: str, context: str, coworker: Optional[str]
+ = None, **kwargs)\nTool Description: Ask a specific question to one of the following
+ coworkers: Researcher\nThe input to this tool should be the coworker, the question
+ you have for them, and ALL necessary context to ask the question properly, they
+ know nothing about the question, so share absolute everything you know, don''t
+ reference things but instead explain them.\nTool Arguments: {''question'': {''title'':
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+ ''string''}, ''coworker'': {''title'': ''Coworker'', ''type'': ''string''},
+ ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse the following
+ format:\n\nThought: you should always think about what to do\nAction: the action
+ to take, only one name of [Delegate work to coworker, Ask question to coworker],
+ just the name, exactly as it''s written.\nAction Input: the input to the action,
+ just a simple python dictionary, enclosed in curly braces, using \" to wrap
+ keys and values.\nObservation: the result of the action\n\nOnce all necessary
+ information is gathered:\n\nThought: I now know the final answer\nFinal Answer:
+ the final answer to the original input question\n"}, {"role": "assistant", "content":
+ "Thought: I now have the list of 5 interesting article ideas provided by the
+ Researcher. The next step is to ask the Researcher to write a paragraph highlight
+ for each of the ideas to showcase their potential as full articles.\n\nAction:
+ Ask question to coworker\nAction Input: {\"coworker\": \"Researcher\", \"question\":
+ \"Write one amazing paragraph highlight for each of the 5 article ideas that
+ showcases how good an article about this topic could be.\", \"context\": \"We
+ need to create a compelling paragraph for each of the following article ideas
+ that will highlight the potential of the topic and capture the readers'' interest:
+ \\n1. The Role of Artificial Intelligence in Healthcare: Transforming Patient
+ Care \\n2. The Future of Work: How AI and Automation Will Shape Tomorrow\u2019s
+ Job Market \\n3. AI and Cybersecurity: Protecting Against the Next Generation
+ of Threats \\n4. Ethics in AI: Navigating the Moral Implications of Intelligent
+ Machines \\n5. Smart Cities: The Intersection of IoT, AI, and Urban Development\"}\nObservation:\nObservation:
+ 1. The Role of Artificial Intelligence in Healthcare: Transforming Patient Care\nArtificial
+ Intelligence (AI) is revolutionizing the healthcare industry by enhancing patient
+ outcomes, reducing costs, and streamlining operations. From predictive analytics
+ that forecast disease outbreaks to AI-powered diagnostic tools that detect conditions
+ with unparalleled accuracy, the integration of AI in healthcare promises to
+ transform patient care. Imagine a world where virtual health assistants provide
+ personalized health guidance, or robotic surgeries are performed with the precision
+ of a master surgeon. AI''s potential to analyze vast amounts of medical data
+ enables healthcare providers to create more effective treatment plans, making
+ healthcare not just reactive but preventive, and significantly improving patient
+ lives.\n\n2. The Future of Work: How AI and Automation Will Shape Tomorrow\u2019s
+ Job Market\nAs AI and automation continue to advance, the future of work is
+ poised for a dramatic transformation. Far from rendering human workers obsolete,
+ AI is expected to create new jobs and shift existing roles towards more complex
+ and creative tasks. This article will delve into how AI-driven processes can
+ enhance productivity, lead to the creation of intelligent work environments,
+ and pave the way for new industries. Additionally, it will explore strategies
+ for reskilling the workforce and fostering a collaborative human-AI synergy
+ that not only boosts economic growth but also enriches job satisfaction. Discover
+ how preparing for an AI-powered job market can lead to unprecedented opportunities
+ and innovation.\n\n3. AI and Cybersecurity: Protecting Against the Next Generation
+ of Threats\nIn an era where cyber threats are becoming increasingly sophisticated,
+ AI stands at the forefront of a new wave of cybersecurity solutions. Combining
+ machine learning, pattern recognition, and real-time data analysis, AI can detect
+ anomalies and respond to potential threats faster than any human could. This
+ article uncovers the ways AI is being employed to safeguard sensitive information,
+ prevent cyber attacks, and enhance digital trust. From thwarting ransomware
+ to securing the Internet of Things (IoT), AI-driven cybersecurity systems are
+ not just reactive but proactive, addressing vulnerabilities before they can
+ be exploited. Explore how AI is the key to staying one step ahead in the ongoing
+ cyber battle.\n\n4. Ethics in AI: Navigating the Moral Implications of Intelligent
+ Machines\nAs AI systems grow in capability and autonomy, the ethical implications
+ of their deployment are becoming increasingly pressing. This article will explore
+ the complex moral questions surrounding AI, such as bias in algorithms, data
+ privacy, and the autonomy of AI agents. It will also examine the frameworks
+ being developed to ensure AI is used responsibly and ethically, including the
+ roles of policymakers, developers, and users. By navigating through real-world
+ case studies and theoretical scenarios, the article will provide a balanced
+ view of the potential risks and benefits of AI, ultimately advocating for a
+ future where intelligent machines are aligned with human values and societal
+ good.\n\n5. Smart Cities: The Intersection of IoT, AI, and Urban Development\nThe
+ concept of smart cities is quickly becoming a reality, integrating IoT, AI,
+ and advanced urban planning to create more efficient, sustainable, and livable
+ urban environments. This article will highlight how AI and IoT are being leveraged
+ to optimize traffic flow, reduce energy consumption, enhance public safety,
+ and improve citizens'' quality of life. From intelligent waste management systems
+ to responsive public transport networks, the possibilities are endless. Explore
+ visionary projects from around the globe that demonstrate the transformative
+ impact of smart technologies on urban development, and delve into how these
+ innovations can address the challenges of urbanization, creating cities that
+ are not only smart but also profoundly human-friendly."}], "model": "gpt-4o"}'
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diff --git a/tests/cli/deploy/test_api.py b/tests/cli/deploy/test_api.py
deleted file mode 100644
index 616a9ff3d..000000000
--- a/tests/cli/deploy/test_api.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import unittest
-from os import environ
-from unittest.mock import MagicMock, patch
-
-from crewai.cli.deploy.api import CrewAPI
-
-
-class TestCrewAPI(unittest.TestCase):
- def setUp(self):
- self.api_key = "test_api_key"
- self.api = CrewAPI(self.api_key)
-
- def test_init(self):
- self.assertEqual(self.api.api_key, self.api_key)
- self.assertEqual(
- self.api.headers,
- {
- "Authorization": f"Bearer {self.api_key}",
- "Content-Type": "application/json",
- "User-Agent": "CrewAI-CLI/no-version-found"
- },
- )
-
- @patch("crewai.cli.deploy.api.requests.request")
- def test_make_request(self, mock_request):
- mock_response = MagicMock()
- mock_request.return_value = mock_response
-
- response = self.api._make_request("GET", "test_endpoint")
-
- mock_request.assert_called_once_with(
- "GET", f"{self.api.base_url}/test_endpoint", headers=self.api.headers
- )
- self.assertEqual(response, mock_response)
-
- @patch("crewai.cli.deploy.api.CrewAPI._make_request")
- def test_deploy_by_name(self, mock_make_request):
- self.api.deploy_by_name("test_project")
- mock_make_request.assert_called_once_with("POST", "by-name/test_project/deploy")
-
- @patch("crewai.cli.deploy.api.CrewAPI._make_request")
- def test_deploy_by_uuid(self, mock_make_request):
- self.api.deploy_by_uuid("test_uuid")
- mock_make_request.assert_called_once_with("POST", "test_uuid/deploy")
-
- @patch("crewai.cli.deploy.api.CrewAPI._make_request")
- def test_status_by_name(self, mock_make_request):
- self.api.status_by_name("test_project")
- mock_make_request.assert_called_once_with("GET", "by-name/test_project/status")
-
- @patch("crewai.cli.deploy.api.CrewAPI._make_request")
- def test_status_by_uuid(self, mock_make_request):
- self.api.status_by_uuid("test_uuid")
- mock_make_request.assert_called_once_with("GET", "test_uuid/status")
-
- @patch("crewai.cli.deploy.api.CrewAPI._make_request")
- def test_logs_by_name(self, mock_make_request):
- self.api.logs_by_name("test_project")
- mock_make_request.assert_called_once_with(
- "GET", "by-name/test_project/logs/deployment"
- )
-
- self.api.logs_by_name("test_project", "custom_log")
- mock_make_request.assert_called_with(
- "GET", "by-name/test_project/logs/custom_log"
- )
-
- @patch("crewai.cli.deploy.api.CrewAPI._make_request")
- def test_logs_by_uuid(self, mock_make_request):
- self.api.logs_by_uuid("test_uuid")
- mock_make_request.assert_called_once_with("GET", "test_uuid/logs/deployment")
-
- self.api.logs_by_uuid("test_uuid", "custom_log")
- mock_make_request.assert_called_with("GET", "test_uuid/logs/custom_log")
-
- @patch("crewai.cli.deploy.api.CrewAPI._make_request")
- def test_delete_by_name(self, mock_make_request):
- self.api.delete_by_name("test_project")
- mock_make_request.assert_called_once_with("DELETE", "by-name/test_project")
-
- @patch("crewai.cli.deploy.api.CrewAPI._make_request")
- def test_delete_by_uuid(self, mock_make_request):
- self.api.delete_by_uuid("test_uuid")
- mock_make_request.assert_called_once_with("DELETE", "test_uuid")
-
- @patch("crewai.cli.deploy.api.CrewAPI._make_request")
- def test_list_crews(self, mock_make_request):
- self.api.list_crews()
- mock_make_request.assert_called_once_with("GET", "")
-
- @patch("crewai.cli.deploy.api.CrewAPI._make_request")
- def test_create_crew(self, mock_make_request):
- payload = {"name": "test_crew"}
- self.api.create_crew(payload)
- mock_make_request.assert_called_once_with("POST", "", json=payload)
-
- @patch.dict(environ, {"CREWAI_BASE_URL": "https://custom-url.com/api"})
- def test_custom_base_url(self):
- custom_api = CrewAPI("test_key")
- self.assertEqual(
- custom_api.base_url,
- "https://custom-url.com/api",
- )
diff --git a/tests/cli/deploy/test_deploy_main.py b/tests/cli/deploy/test_deploy_main.py
index a63ea7110..ddb9709d3 100644
--- a/tests/cli/deploy/test_deploy_main.py
+++ b/tests/cli/deploy/test_deploy_main.py
@@ -4,37 +4,38 @@ from unittest.mock import MagicMock, patch
import sys
from crewai.cli.deploy.main import DeployCommand
-from crewai.cli.deploy.utils import parse_toml
+from crewai.cli.utils import parse_toml
+
class TestDeployCommand(unittest.TestCase):
- @patch("crewai.cli.deploy.main.get_auth_token")
+ @patch("crewai.cli.command.get_auth_token")
@patch("crewai.cli.deploy.main.get_project_name")
- @patch("crewai.cli.deploy.main.CrewAPI")
- def setUp(self, mock_crew_api, mock_get_project_name, mock_get_auth_token):
+ @patch("crewai.cli.command.PlusAPI")
+ def setUp(self, mock_plus_api, mock_get_project_name, mock_get_auth_token):
self.mock_get_auth_token = mock_get_auth_token
self.mock_get_project_name = mock_get_project_name
- self.mock_crew_api = mock_crew_api
+ self.mock_plus_api = mock_plus_api
self.mock_get_auth_token.return_value = "test_token"
self.mock_get_project_name.return_value = "test_project"
self.deploy_command = DeployCommand()
- self.mock_client = self.deploy_command.client
+ self.mock_client = self.deploy_command.plus_api_client
def test_init_success(self):
self.assertEqual(self.deploy_command.project_name, "test_project")
- self.mock_crew_api.assert_called_once_with(api_key="test_token")
+ self.mock_plus_api.assert_called_once_with(api_key="test_token")
- @patch("crewai.cli.deploy.main.get_auth_token")
+ @patch("crewai.cli.command.get_auth_token")
def test_init_failure(self, mock_get_auth_token):
mock_get_auth_token.side_effect = Exception("Auth failed")
with self.assertRaises(SystemExit):
DeployCommand()
- def test_handle_error(self):
+ def test_handle_plus_api_error(self):
with patch("sys.stdout", new=StringIO()) as fake_out:
- self.deploy_command._handle_error(
+ self.deploy_command._handle_plus_api_error(
{"error": "Test error", "message": "Test message"}
)
self.assertIn("Error: Test error", fake_out.getvalue())
@@ -121,7 +122,7 @@ class TestDeployCommand(unittest.TestCase):
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"name": "TestCrew", "status": "active"}
- self.mock_client.status_by_name.return_value = mock_response
+ self.mock_client.crew_status_by_name.return_value = mock_response
with patch("sys.stdout", new=StringIO()) as fake_out:
self.deploy_command.get_crew_status()
@@ -135,7 +136,7 @@ class TestDeployCommand(unittest.TestCase):
{"timestamp": "2023-01-01", "level": "INFO", "message": "Log1"},
{"timestamp": "2023-01-02", "level": "ERROR", "message": "Log2"},
]
- self.mock_client.logs_by_name.return_value = mock_response
+ self.mock_client.crew_by_name.return_value = mock_response
with patch("sys.stdout", new=StringIO()) as fake_out:
self.deploy_command.get_crew_logs(None)
@@ -145,7 +146,7 @@ class TestDeployCommand(unittest.TestCase):
def test_remove_crew(self):
mock_response = MagicMock()
mock_response.status_code = 204
- self.mock_client.delete_by_name.return_value = mock_response
+ self.mock_client.delete_crew_by_name.return_value = mock_response
with patch("sys.stdout", new=StringIO()) as fake_out:
self.deploy_command.remove_crew(None)
@@ -165,9 +166,12 @@ class TestDeployCommand(unittest.TestCase):
crewai = { extras = ["tools"], version = ">=0.51.0,<1.0.0" }
"""
parsed = parse_toml(toml_content)
- self.assertEqual(parsed['tool']['poetry']['name'], 'test_project')
+ self.assertEqual(parsed["tool"]["poetry"]["name"], "test_project")
- @patch('builtins.open', new_callable=unittest.mock.mock_open, read_data="""
+ @patch(
+ "builtins.open",
+ new_callable=unittest.mock.mock_open,
+ read_data="""
[tool.poetry]
name = "test_project"
version = "0.1.0"
@@ -175,14 +179,19 @@ class TestDeployCommand(unittest.TestCase):
[tool.poetry.dependencies]
python = "^3.10"
crewai = { extras = ["tools"], version = ">=0.51.0,<1.0.0" }
- """)
+ """,
+ )
def test_get_project_name_python_310(self, mock_open):
- from crewai.cli.deploy.utils import get_project_name
+ from crewai.cli.utils import get_project_name
+
project_name = get_project_name()
- self.assertEqual(project_name, 'test_project')
+ self.assertEqual(project_name, "test_project")
@unittest.skipIf(sys.version_info < (3, 11), "Requires Python 3.11+")
- @patch('builtins.open', new_callable=unittest.mock.mock_open, read_data="""
+ @patch(
+ "builtins.open",
+ new_callable=unittest.mock.mock_open,
+ read_data="""
[tool.poetry]
name = "test_project"
version = "0.1.0"
@@ -190,13 +199,18 @@ class TestDeployCommand(unittest.TestCase):
[tool.poetry.dependencies]
python = "^3.11"
crewai = { extras = ["tools"], version = ">=0.51.0,<1.0.0" }
- """)
+ """,
+ )
def test_get_project_name_python_311_plus(self, mock_open):
- from crewai.cli.deploy.utils import get_project_name
- project_name = get_project_name()
- self.assertEqual(project_name, 'test_project')
+ from crewai.cli.utils import get_project_name
- @patch('builtins.open', new_callable=unittest.mock.mock_open, read_data="""
+ project_name = get_project_name()
+ self.assertEqual(project_name, "test_project")
+
+ @patch(
+ "builtins.open",
+ new_callable=unittest.mock.mock_open,
+ read_data="""
[[package]]
name = "crewai"
version = "0.51.1"
@@ -204,16 +218,19 @@ class TestDeployCommand(unittest.TestCase):
category = "main"
optional = false
python-versions = ">=3.10,<4.0"
- """)
+ """,
+ )
def test_get_crewai_version(self, mock_open):
- from crewai.cli.deploy.utils import get_crewai_version
- version = get_crewai_version()
- self.assertEqual(version, '0.51.1')
+ from crewai.cli.utils import get_crewai_version
- @patch('builtins.open', side_effect=FileNotFoundError)
+ version = get_crewai_version()
+ self.assertEqual(version, "0.51.1")
+
+ @patch("builtins.open", side_effect=FileNotFoundError)
def test_get_crewai_version_file_not_found(self, mock_open):
- from crewai.cli.deploy.utils import get_crewai_version
- with patch('sys.stdout', new=StringIO()) as fake_out:
+ from crewai.cli.utils import get_crewai_version
+
+ with patch("sys.stdout", new=StringIO()) as fake_out:
version = get_crewai_version()
- self.assertEqual(version, 'no-version-found')
+ self.assertEqual(version, "no-version-found")
self.assertIn("Error: poetry.lock not found.", fake_out.getvalue())
diff --git a/tests/cli/test_plus_api.py b/tests/cli/test_plus_api.py
new file mode 100644
index 000000000..506246290
--- /dev/null
+++ b/tests/cli/test_plus_api.py
@@ -0,0 +1,185 @@
+import os
+import unittest
+from unittest.mock import MagicMock, patch
+from crewai.cli.plus_api import PlusAPI
+
+
+class TestPlusAPI(unittest.TestCase):
+ def setUp(self):
+ self.api_key = "test_api_key"
+ self.api = PlusAPI(self.api_key)
+
+ def test_init(self):
+ self.assertEqual(self.api.api_key, self.api_key)
+ self.assertEqual(
+ self.api.headers,
+ {
+ "Authorization": f"Bearer {self.api_key}",
+ "Content-Type": "application/json",
+ "User-Agent": "CrewAI-CLI/no-version-found",
+ "X-Crewai-Version": "no-version-found",
+ },
+ )
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_get_tool(self, mock_make_request):
+ mock_response = MagicMock()
+ mock_make_request.return_value = mock_response
+
+ response = self.api.get_tool("test_tool_handle")
+
+ mock_make_request.assert_called_once_with(
+ "GET", "/crewai_plus/api/v1/tools/test_tool_handle"
+ )
+ self.assertEqual(response, mock_response)
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_publish_tool(self, mock_make_request):
+ mock_response = MagicMock()
+ mock_make_request.return_value = mock_response
+ handle = "test_tool_handle"
+ public = True
+ version = "1.0.0"
+ description = "Test tool description"
+ encoded_file = "encoded_test_file"
+
+ response = self.api.publish_tool(
+ handle, public, version, description, encoded_file
+ )
+
+ params = {
+ "handle": handle,
+ "public": public,
+ "version": version,
+ "file": encoded_file,
+ "description": description,
+ }
+ mock_make_request.assert_called_once_with(
+ "POST", "/crewai_plus/api/v1/tools", json=params
+ )
+ self.assertEqual(response, mock_response)
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_publish_tool_without_description(self, mock_make_request):
+ mock_response = MagicMock()
+ mock_make_request.return_value = mock_response
+ handle = "test_tool_handle"
+ public = False
+ version = "2.0.0"
+ description = None
+ encoded_file = "encoded_test_file"
+
+ response = self.api.publish_tool(
+ handle, public, version, description, encoded_file
+ )
+
+ params = {
+ "handle": handle,
+ "public": public,
+ "version": version,
+ "file": encoded_file,
+ "description": description,
+ }
+ mock_make_request.assert_called_once_with(
+ "POST", "/crewai_plus/api/v1/tools", json=params
+ )
+ self.assertEqual(response, mock_response)
+
+ @patch("crewai.cli.plus_api.requests.request")
+ def test_make_request(self, mock_request):
+ mock_response = MagicMock()
+ mock_request.return_value = mock_response
+
+ response = self.api._make_request("GET", "test_endpoint")
+
+ mock_request.assert_called_once_with(
+ "GET", f"{self.api.base_url}/test_endpoint", headers=self.api.headers
+ )
+ self.assertEqual(response, mock_response)
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_deploy_by_name(self, mock_make_request):
+ self.api.deploy_by_name("test_project")
+ mock_make_request.assert_called_once_with(
+ "POST", "/crewai_plus/api/v1/crews/by-name/test_project/deploy"
+ )
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_deploy_by_uuid(self, mock_make_request):
+ self.api.deploy_by_uuid("test_uuid")
+ mock_make_request.assert_called_once_with(
+ "POST", "/crewai_plus/api/v1/crews/test_uuid/deploy"
+ )
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_crew_status_by_name(self, mock_make_request):
+ self.api.crew_status_by_name("test_project")
+ mock_make_request.assert_called_once_with(
+ "GET", "/crewai_plus/api/v1/crews/by-name/test_project/status"
+ )
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_crew_status_by_uuid(self, mock_make_request):
+ self.api.crew_status_by_uuid("test_uuid")
+ mock_make_request.assert_called_once_with(
+ "GET", "/crewai_plus/api/v1/crews/test_uuid/status"
+ )
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_crew_by_name(self, mock_make_request):
+ self.api.crew_by_name("test_project")
+ mock_make_request.assert_called_once_with(
+ "GET", "/crewai_plus/api/v1/crews/by-name/test_project/logs/deployment"
+ )
+
+ self.api.crew_by_name("test_project", "custom_log")
+ mock_make_request.assert_called_with(
+ "GET", "/crewai_plus/api/v1/crews/by-name/test_project/logs/custom_log"
+ )
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_crew_by_uuid(self, mock_make_request):
+ self.api.crew_by_uuid("test_uuid")
+ mock_make_request.assert_called_once_with(
+ "GET", "/crewai_plus/api/v1/crews/test_uuid/logs/deployment"
+ )
+
+ self.api.crew_by_uuid("test_uuid", "custom_log")
+ mock_make_request.assert_called_with(
+ "GET", "/crewai_plus/api/v1/crews/test_uuid/logs/custom_log"
+ )
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_delete_crew_by_name(self, mock_make_request):
+ self.api.delete_crew_by_name("test_project")
+ mock_make_request.assert_called_once_with(
+ "DELETE", "/crewai_plus/api/v1/crews/by-name/test_project"
+ )
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_delete_crew_by_uuid(self, mock_make_request):
+ self.api.delete_crew_by_uuid("test_uuid")
+ mock_make_request.assert_called_once_with(
+ "DELETE", "/crewai_plus/api/v1/crews/test_uuid"
+ )
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_list_crews(self, mock_make_request):
+ self.api.list_crews()
+ mock_make_request.assert_called_once_with("GET", "/crewai_plus/api/v1/crews")
+
+ @patch("crewai.cli.plus_api.PlusAPI._make_request")
+ def test_create_crew(self, mock_make_request):
+ payload = {"name": "test_crew"}
+ self.api.create_crew(payload)
+ mock_make_request.assert_called_once_with(
+ "POST", "/crewai_plus/api/v1/crews", json=payload
+ )
+
+ @patch.dict(os.environ, {"CREWAI_BASE_URL": "https://custom-url.com/api"})
+ def test_custom_base_url(self):
+ custom_api = PlusAPI("test_key")
+ self.assertEqual(
+ custom_api.base_url,
+ "https://custom-url.com/api",
+ )
diff --git a/tests/cli/tools/__init__.py b/tests/cli/tools/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/cli/tools/test_main.py b/tests/cli/tools/test_main.py
new file mode 100644
index 000000000..f387c8d3f
--- /dev/null
+++ b/tests/cli/tools/test_main.py
@@ -0,0 +1,229 @@
+import unittest
+import unittest.mock
+from crewai.cli.tools.main import ToolCommand
+from io import StringIO
+from unittest.mock import patch, MagicMock
+
+
+class TestToolCommand(unittest.TestCase):
+ @patch("crewai.cli.tools.main.subprocess.run")
+ @patch("crewai.cli.plus_api.PlusAPI.get_tool")
+ def test_install_success(self, mock_get, mock_subprocess_run):
+ mock_get_response = MagicMock()
+ mock_get_response.status_code = 200
+ mock_get_response.json.return_value = {
+ "handle": "sample-tool",
+ "repository": {
+ "handle": "sample-repo",
+ "url": "https://example.com/repo",
+ "credentials": "my_very_secret",
+ },
+ }
+ mock_get.return_value = mock_get_response
+ mock_subprocess_run.return_value = MagicMock(stderr=None)
+
+ tool_command = ToolCommand()
+
+ with patch("sys.stdout", new=StringIO()) as fake_out:
+ tool_command.install("sample-tool")
+ output = fake_out.getvalue()
+
+ mock_get.assert_called_once_with("sample-tool")
+ mock_subprocess_run.assert_any_call(
+ [
+ "poetry",
+ "source",
+ "add",
+ "--priority=explicit",
+ "crewai-sample-repo",
+ "https://example.com/repo",
+ ],
+ text=True,
+ check=True,
+ )
+ mock_subprocess_run.assert_any_call(
+ [
+ "poetry",
+ "config",
+ "http-basic.crewai-sample-repo",
+ "my_very_secret",
+ '""',
+ ],
+ capture_output=False,
+ text=True,
+ check=True,
+ )
+ mock_subprocess_run.assert_any_call(
+ ["poetry", "add", "--source", "crewai-sample-repo", "sample-tool"],
+ capture_output=False,
+ text=True,
+ check=True,
+ )
+
+ self.assertIn("Succesfully installed sample-tool", output)
+
+ @patch("crewai.cli.plus_api.PlusAPI.get_tool")
+ def test_install_tool_not_found(self, mock_get):
+ mock_get_response = MagicMock()
+ mock_get_response.status_code = 404
+ mock_get.return_value = mock_get_response
+
+ tool_command = ToolCommand()
+
+ with patch("sys.stdout", new=StringIO()) as fake_out:
+ with self.assertRaises(SystemExit):
+ tool_command.install("non-existent-tool")
+ output = fake_out.getvalue()
+
+ mock_get.assert_called_once_with("non-existent-tool")
+ self.assertIn("No tool found with this name", output)
+
+ @patch("crewai.cli.plus_api.PlusAPI.get_tool")
+ def test_install_api_error(self, mock_get):
+ mock_get_response = MagicMock()
+ mock_get_response.status_code = 500
+ mock_get.return_value = mock_get_response
+
+ tool_command = ToolCommand()
+
+ with patch("sys.stdout", new=StringIO()) as fake_out:
+ with self.assertRaises(SystemExit):
+ tool_command.install("error-tool")
+ output = fake_out.getvalue()
+
+ mock_get.assert_called_once_with("error-tool")
+ self.assertIn("Failed to get tool details", output)
+
+ @patch("crewai.cli.tools.main.get_project_name", return_value="sample-tool")
+ @patch("crewai.cli.tools.main.get_project_version", return_value="1.0.0")
+ @patch(
+ "crewai.cli.tools.main.get_project_description", return_value="A sample tool"
+ )
+ @patch("crewai.cli.tools.main.subprocess.run")
+ @patch(
+ "crewai.cli.tools.main.os.listdir", return_value=["sample-tool-1.0.0.tar.gz"]
+ )
+ @patch(
+ "crewai.cli.tools.main.open",
+ new_callable=unittest.mock.mock_open,
+ read_data=b"sample tarball content",
+ )
+ @patch("crewai.cli.plus_api.PlusAPI.publish_tool")
+ def test_publish_success(
+ self,
+ mock_publish,
+ mock_open,
+ mock_listdir,
+ mock_subprocess_run,
+ mock_get_project_description,
+ mock_get_project_version,
+ mock_get_project_name,
+ ):
+ mock_publish_response = MagicMock()
+ mock_publish_response.status_code = 200
+ mock_publish_response.json.return_value = {"handle": "sample-tool"}
+ mock_publish.return_value = mock_publish_response
+
+ tool_command = ToolCommand()
+ tool_command.publish(is_public=True)
+
+ mock_get_project_name.assert_called_once_with(require=True)
+ mock_get_project_version.assert_called_once_with(require=True)
+ mock_get_project_description.assert_called_once_with(require=False)
+ mock_subprocess_run.assert_called_once_with(
+ ["poetry", "build", "-f", "sdist", "--output", unittest.mock.ANY],
+ check=True,
+ capture_output=False,
+ )
+ mock_open.assert_called_once_with(unittest.mock.ANY, "rb")
+ mock_publish.assert_called_once_with(
+ handle="sample-tool",
+ is_public=True,
+ version="1.0.0",
+ description="A sample tool",
+ encoded_file=unittest.mock.ANY,
+ )
+
+ @patch("crewai.cli.tools.main.get_project_name", return_value="sample-tool")
+ @patch("crewai.cli.tools.main.get_project_version", return_value="1.0.0")
+ @patch(
+ "crewai.cli.tools.main.get_project_description", return_value="A sample tool"
+ )
+ @patch("crewai.cli.tools.main.subprocess.run")
+ @patch(
+ "crewai.cli.tools.main.os.listdir", return_value=["sample-tool-1.0.0.tar.gz"]
+ )
+ @patch(
+ "crewai.cli.tools.main.open",
+ new_callable=unittest.mock.mock_open,
+ read_data=b"sample tarball content",
+ )
+ @patch("crewai.cli.plus_api.PlusAPI.publish_tool")
+ def test_publish_failure(
+ self,
+ mock_publish,
+ mock_open,
+ mock_listdir,
+ mock_subprocess_run,
+ mock_get_project_description,
+ mock_get_project_version,
+ mock_get_project_name,
+ ):
+ mock_publish_response = MagicMock()
+ mock_publish_response.status_code = 422
+ mock_publish_response.json.return_value = {"name": ["is already taken"]}
+ mock_publish.return_value = mock_publish_response
+
+ tool_command = ToolCommand()
+
+ with patch("sys.stdout", new=StringIO()) as fake_out:
+ with self.assertRaises(SystemExit):
+ tool_command.publish(is_public=True)
+ output = fake_out.getvalue()
+
+ mock_publish.assert_called_once()
+ self.assertIn("Failed to publish tool", output)
+ self.assertIn("Name is already taken", output)
+
+ @patch("crewai.cli.tools.main.get_project_name", return_value="sample-tool")
+ @patch("crewai.cli.tools.main.get_project_version", return_value="1.0.0")
+ @patch(
+ "crewai.cli.tools.main.get_project_description", return_value="A sample tool"
+ )
+ @patch("crewai.cli.tools.main.subprocess.run")
+ @patch(
+ "crewai.cli.tools.main.os.listdir", return_value=["sample-tool-1.0.0.tar.gz"]
+ )
+ @patch(
+ "crewai.cli.tools.main.open",
+ new_callable=unittest.mock.mock_open,
+ read_data=b"sample tarball content",
+ )
+ @patch("crewai.cli.plus_api.PlusAPI.publish_tool")
+ def test_publish_api_error(
+ self,
+ mock_publish,
+ mock_open,
+ mock_listdir,
+ mock_subprocess_run,
+ mock_get_project_description,
+ mock_get_project_version,
+ mock_get_project_name,
+ ):
+ mock_publish_response = MagicMock()
+ mock_publish_response.status_code = 500
+ mock_publish.return_value = mock_publish_response
+
+ tool_command = ToolCommand()
+
+ with patch("sys.stdout", new=StringIO()) as fake_out:
+ with self.assertRaises(SystemExit):
+ tool_command.publish(is_public=True)
+ output = fake_out.getvalue()
+
+ mock_publish.assert_called_once()
+ self.assertIn("Failed to publish tool", output)
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/tests/crew_test.py b/tests/crew_test.py
index 89ec3dd7d..c01e84f80 100644
--- a/tests/crew_test.py
+++ b/tests/crew_test.py
@@ -6,6 +6,7 @@ from concurrent.futures import Future
from unittest import mock
from unittest.mock import MagicMock, patch
+import instructor
import pydantic_core
import pytest
from crewai.agent import Agent
@@ -226,7 +227,7 @@ def test_crew_creation():
result = crew.kickoff()
- expected_string_output = "**The Ethical Implications of AI in Warfare**\n\nAI in warfare raises profound ethical questions that extend far beyond the battlefield. Autonomous weapons systems, capable of making split-second decisions without human input, challenge our traditional notions of accountability. Who is to blame when an AI decides to strike? This article could delve into the current policies governing AI in military applications, scrutinizing the frameworks that aim to regulate autonomous weapons. Real-world examples, like the use of drones and AI-based surveillance, could provide a grounded view of these technologies in action. The potential for misuse by rogue states or non-state actors makes the need for comprehensive ethical guidelines all the more urgent. The debate could span legal, moral, and practical considerations, making this a multifaceted and highly relevant topic in today's world.\n\n**AI in Mental Health: Promises and Perils**\n\nThe integration of AI in mental health care presents a double-edged sword. On one hand, AI-driven tools like chatbots and predictive analytics can revolutionize diagnosis and treatment, making mental health care more accessible and personalized. On the other hand, these innovations come with significant ethical dilemmas. Can a machine truly understand the nuance and depth of human emotion? Issues of privacy, data security, and the impersonal nature of machine empathy are critical concerns. This article could explore case studies of AI applications in mental health, examining both their successes and pitfalls. The evolving patient-therapist relationship in an age of machine learning could offer intriguing insights into how we value human touch and expertise in one of the most sensitive areas of healthcare.\n\n**The Role of AI in Climate Change Mitigation**\n\nAI has emerged as a powerful ally in the fight against climate change, offering innovative solutions that were unimaginable a few years ago. From optimizing energy consumption to predicting extreme weather events, AI is at the forefront of environmental sustainability. This article could highlight ground-breaking projects where AI has made a tangible impact, such as Google's DeepMind reducing data center energy usage by 40%. However, the challenges are as significant as the opportunities. The reliance on data, the carbon footprint of large-scale AI operations, and the need for interdisciplinary collaboration pose substantial hurdles. By examining the intersection of AI and environmental science, this piece could offer a balanced view of the potential and limitations of technology in addressing one of the most urgent issues of our time.\n\n**AI and the Future of Work: Redefining Employment and Skills**\n\nAs AI continues to transform industries, the future of work is being redefined before our eyes. Automation and machine learning are not just eliminating jobs but also creating new ones, requiring a shift in skillsets and educational paradigms. This article could explore how different sectors, from manufacturing to healthcare, are adapting to AI-driven changes. The narrative could include personal stories of individuals who have navigated this transition, highlighting both the opportunities and challenges. Discussions around economic disparities, the digital divide, and the future of education would provide a comprehensive look at how society must evolve to keep pace with technological advancements. This topic is not just about the future of employment but also about the future of human potential and economic equality."
+ expected_string_output = "**The Rise of Generalist AI Agents:**\nImagine a future where AI agents are no longer confined to specific tasks like data analytics or speech recognition. The evolution from specialized AI tools to versatile generalist AI agents is comparable to the leap from feature phones to smartphones. This shift heralds significant transformations across diverse industries, from healthcare and finance to customer service. It also raises fascinating ethical considerations around the deployment and control of such powerful technologies. Moreover, this transformation could democratize AI, making sophisticated tools accessible to non-experts and small businesses, thus leveling the playing field in many sectors.\n\n**Ethical Implications of AI in Surveillance:**\nThe advent of advanced AI has significantly boosted surveillance capabilities, presenting a double-edged sword. On one hand, enhanced surveillance can improve public safety and combat crime more effectively. On the other, it raises substantial ethical concerns about privacy invasion and the potential for misuse by authoritarian regimes. Balancing security with privacy is a delicate task, requiring robust legal frameworks and transparent policies. Real-world case studies, from smart city deployments to airport security systems, illustrate both the benefits and the risks of AI-enhanced surveillance, highlighting the need for ethical vigilance and public discourse.\n\n**AI in Creative Industries:**\nAI is breaking new ground in creative fields, transforming how art, music, and content are produced. Far from being mere tools, AI systems are emerging as collaborators, helping artists push the boundaries of creative expression. Noteworthy are AI-generated works that have captured public imagination, like paintings auctioned at prestigious houses or music albums composed by algorithms. The future holds exciting possibilities, as AI may enable novel art forms and interactive experiences previously unimaginable, fostering a symbiotic relationship between human creativity and machine intelligence.\n\n**The Impact of Quantum Computing on AI Development:**\nQuantum computing promises to be a game-changer for AI, offering unprecedented computational power to tackle complex problems. This revolution could significantly enhance AI algorithms, enabling faster and more efficient training and execution. The potential applications are vast, from optimizing supply chains to solving intricate scientific problems and advancing natural language processing. Looking ahead, quantum-enhanced AI might unlock new frontiers, such as real-time data analysis at scales previously thought impossible, pushing the limits of what we can achieve with AI technology.\n\n**AI and Mental Health:**\nThe integration of AI into mental health care is transforming diagnosis and therapy, offering new hope for those in need. AI-driven tools have shown promise in accurately diagnosing conditions and providing personalized treatment plans through data analysis and pattern recognition. Case studies highlight successful interventions where AI has aided mental health professionals, enhancing the effectiveness of traditional therapies. However, this advancement brings ethical concerns, particularly around data privacy and the transparency of AI decision-making processes. As AI continues to evolve, it could play an even more significant role in mental health care, providing early interventions and support on a scale previously unattainable."
assert str(result) == expected_string_output
assert result.raw == expected_string_output
@@ -294,7 +295,7 @@ def test_hierarchical_process():
assert (
result.raw
- == "1. **The Evolution of AI Agents: From Simple Bots to Autonomous Digital Assistants**\n - The journey of AI agents from rudimentary chatbots to fully autonomous digital assistants symbolizes the monumental strides made in artificial intelligence over the past few decades. Initially, AI agents were simple programmed entities, capable of executing only basic tasks such as answering FAQs or setting up reminders. However, advancements in machine learning, natural language processing, and neural networks have transformed these simplistic bots into sophisticated digital assistants like Siri, Alexa, and Google Assistant. These modern-day AI agents not only understand and respond to complex human commands but also predict user needs and adapt their behavior over time, making them indispensable in both personal and professional settings. They epitomize how far AI technologies have come and hint at an even more integrated and autonomous future.\n\n2. **AI in Healthcare: Revolutionizing Diagnosis, Treatment, and Patient Care**\n - Artificial Intelligence is revolutionizing healthcare by enhancing diagnostic accuracy, personalizing treatment plans, and improving overall patient care. With their ability to analyze vast datasets quickly and accurately, AI-driven tools are aiding doctors in early and precise diagnosis of diseases, including cancers and rare genetic disorders. Additionally, AI algorithms are being employed to tailor treatment plans based on individual patient data, lifestyle, and genetic information, ensuring more effective and personalized healthcare. Beyond diagnosis and treatment, AI is also optimizing hospital operations, from predicting patient admission rates to managing logistics and resources. The integration of AI in healthcare not only promises to augment the capabilities of medical professionals but also aims to provide better health outcomes, reduced costs, and increased accessibility to quality care.\n\n3. **Ethics in AI: Navigating the Moral Dilemmas of Autonomous Systems**\n - As AI systems become more autonomous and pervasive, they bring to the forefront pressing ethical dilemmas that society must navigate. These ethical challenges range from issues of bias and fairness in AI decisions to questions about the accountability and transparency of AI actions. For instance, how do we ensure that AI algorithms do not perpetuate existing societal biases in areas like hiring or law enforcement? Who is held responsible when an autonomous vehicle causes an accident? Additionally, there are concerns around privacy and the potential misuse of AI technologies for surveillance or coercion. Addressing these moral quandaries requires a concerted effort from policymakers, technologists, and ethicists to create robust frameworks and guidelines that ensure the ethical development and deployment of AI systems. Striking a balance between innovation and ethical considerations is crucial for building trust and securing the societal benefits of AI.\n\n4. **Startups Leveraging AI: Success Stories and Lessons Learned**\n - In the competitive landscape of startups, those leveraging AI have demonstrated remarkable success, offering innovative solutions and setting new industry benchmarks. Companies like OpenAI, which focuses on artificial general intelligence, and UiPath, specializing in robotic process automation, have not only disrupted their respective fields but also attracted substantial investment and market interest. These startups exemplify how AI can be harnessed to solve complex problems, improve efficiency, and unlock new business opportunities. However, their journeys offer valuable lessons, such as the importance of a clear vision, the need for scalable and robust AI models, and the critical role of ethical considerations in AI development. By examining these success stories, aspiring entrepreneurs can glean insights into navigating the challenges of building AI-centric businesses and the immense potential that AI holds for driving future innovations.\n\n5. **The Future of Human-AI Collaboration: Opportunities and Challenges**\n - The future of human-AI collaboration is poised to unlock unprecedented opportunities while presenting unique challenges that need to be thoughtfully addressed. As AI continues to mature, it will increasingly complement human capabilities, leading to enhanced productivity, creativity, and problem-solving across various domains. For instance, in workplaces, AI can handle repetitive tasks, allowing humans to focus on more strategic and creative activities. In scientific research, AI can analyze and interpret data at a scale beyond human capacity, driving new discoveries. However, this collaboration also raises challenges such as re-skilling the workforce to adapt to AI technologies, ensuring equitable access to AI advancements, and addressing ethical concerns related to autonomy and decision-making. Successfully navigating these challenges will require continuous dialogue, multidisciplinary collaboration, and a commitment to integrating human values into AI development. This balanced approach will be crucial in realizing the full potential of human-AI synergy."
+ == "Here are the 5 interesting ideas along with a compelling paragraph for each that showcases how good an article on the topic could be:\n\n1. **The Evolution and Future of AI Agents in Everyday Life**:\nThe rapid development of AI agents from rudimentary virtual assistants like Siri and Alexa to today's sophisticated systems marks a significant technological leap. This article will explore the evolving landscape of AI agents, detailing their seamless integration into daily activities ranging from managing smart home devices to streamlining workflows. We will examine the multifaceted benefits these agents bring, such as increased efficiency and personalized user experiences, while also addressing ethical concerns like data privacy and algorithmic bias. Looking ahead, we will forecast the advancements slated for the next decade, including AI agents in personalized health coaching and automated legal consultancy. With more advanced machine learning algorithms, the potential for these AI systems to revolutionize our daily lives is immense.\n\n2. **AI in Healthcare: Revolutionizing Diagnostics and Treatment**:\nArtificial Intelligence is poised to revolutionize the healthcare sector by offering unprecedented improvements in diagnostic accuracy and personalized treatments. This article will delve into the transformative power of AI in healthcare, highlighting real-world applications like AI-driven imaging technologies that aid in early disease detection and predictive analytics that enable personalized patient care plans. We will discuss the ethical challenges, such as data privacy and the implications of AI-driven decision-making in medicine. Through compelling case studies, we will showcase successful AI implementations that have made significant impacts, ultimately painting a picture of a future where AI plays a central role in proactive and precise healthcare delivery.\n\n3. **The Role of AI in Enhancing Cybersecurity**:\nAs cyber threats become increasingly sophisticated, AI stands at the forefront of the battle against cybercrime. This article will discuss the crucial role AI plays in detecting and responding to threats in real-time, its capacity to predict and prevent potential attacks, and the inherent challenges of an AI-dependent cybersecurity framework. We will highlight recent advancements in AI-based security tools and provide case studies where AI has been instrumental in mitigating cyber threats effectively. By examining these elements, we'll underline the potential and limitations of AI in creating a more secure digital environment, showcasing how it can adapt to evolving threats faster than traditional methods.\n\n4. **The Intersection of AI and Autonomous Vehicles: Driving Towards a Safer Future**:\nThe prospect of AI-driven autonomous vehicles promises to redefine transportation. This article will explore the technological underpinnings of self-driving cars, their developmental milestones, and the hurdles they face, including regulatory and ethical challenges. We will discuss the profound implications for various industries and employment sectors, coupled with the benefits such as reduced traffic accidents, improved fuel efficiency, and enhanced mobility for people with disabilities. By detailing these aspects, the article will offer a comprehensive overview of how AI-powered autonomous vehicles are steering us towards a safer, more efficient future.\n\n5. **AI and the Future of Work: Embracing Change in the Workplace**:\nAI is transforming the workplace by automating mundane tasks, enabling advanced data analysis, and fostering creativity and strategic decision-making. This article will explore the profound impact of AI on the job market, addressing concerns about job displacement and the evolution of new roles that demand reskilling. We will provide insights into the necessity for upskilling to keep pace with an AI-driven economy. Through interviews with industry experts and narratives from workers who have experienced AI's impact firsthand, we will present a balanced perspective. The aim is to paint a future where humans and AI work in synergy, driving innovation and productivity in a continuously evolving workplace landscape."
)
@@ -400,7 +401,7 @@ def test_crew_with_delegating_agents():
assert (
result.raw
- == "Artificial Intelligence (AI) is one of the most transformative technologies of the 21st century, poised to revolutionize numerous industries and everyday life. At its core, AI involves the development of computer systems that can perform tasks typically requiring human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. The advent of AI has contributed to significant advancements in various fields such as healthcare, finance, transportation, and entertainment, making it an integral part of modern society.\n\nAI agents, or software programs that act autonomously to perform tasks, are an essential subset of AI technology. These agents function based on a set of rules or algorithms designed to interpret and respond to data in their environment. They are increasingly used in diverse applications, from virtual customer service representatives to complex predictive analytics in business. For instance, AI agents can analyze vast datasets to predict market trends, optimize supply chains, or even personalize user experiences on digital platforms. The versatility and efficiency of AI agents make them invaluable tools in enhancing productivity and driving innovation across numerous sectors.\n\nThe current applications of AI are vast and ever-expanding. In healthcare, AI-powered diagnostic tools are improving the accuracy of disease detection and enabling personalized treatment plans. In the financial sector, AI algorithms detect fraudulent transactions and manage investment portfolios with greater precision than traditional methods. Autonomous vehicles, another prominent application, are reshaping the transportation industry by promising safer and more efficient travel. Additionally, AI is playing a crucial role in entertainment, with recommendation engines in streaming services providing customized content to audiences, thereby transforming how media is consumed.\n\nLooking to the future, the potential implications of AI are both promising and challenging. On one hand, AI has the potential to drive unprecedented economic growth, enhance the quality of life, and solve some of humanity's most pressing problems, such as climate change and global health crises. However, these advancements also come with ethical considerations and potential risks. Issues such as data privacy, job displacement due to automation, and the need for robust regulatory frameworks require careful deliberation. As we integrate AI deeper into our lives, it is crucial to strike a balance between harnessing its benefits and addressing its challenges to ensure it serves the greater good.\n\nIn conclusion, AI and AI agents are dynamic forces that are already reshaping industries and have the potential to profoundly impact the future. By understanding and effectively leveraging these technologies, society can unlock new opportunities and tackle complex challenges. As we move forward, responsible development, deployment, and regulation of AI will be essential in maximizing its benefits while mitigating associated risks."
+ == "This is the complete content as specified:\nArtificial Intelligence (AI) Agents are sophisticated computer programs designed to perform tasks that typically require human intelligence, such as decision making, problem-solving, and learning. These agents operate autonomously, utilizing vast amounts of data, advanced algorithms, and machine learning techniques to analyze their environment, adapt to new information, and improve their performance over time.\n\nThe significance of AI Agents lies in their transformative potential across various industries. In healthcare, for example, they assist in diagnosing diseases with greater accuracy and speed than human practitioners, offering personalized treatment plans by analyzing patient data. In finance, AI Agents predict market trends, manage risks, and even execute trades, contributing to more stable and profitable financial systems. Customer service sectors benefit significantly from AI Agents, as they provide personalized and efficient responses, often resolving issues faster than traditional methods.\n\nMoreover, AI Agents are also making substantial contributions in fields like education and manufacturing. In education, they offer tailored learning experiences by assessing individual student needs and adjusting teaching methods accordingly. They help educators identify students who might need additional support and provide resources to enhance learning outcomes. In manufacturing, AI Agents optimize production lines, predict equipment failures, and improve supply chain management, thus boosting productivity and reducing downtime.\n\nAs these AI-powered entities continue to evolve, they are not only enhancing operational efficiencies but also driving innovation and creating new opportunities for growth and development in every sector they penetrate. The future of AI Agents looks promising, with the potential to revolutionize the way we live and work, making processes more efficient, decisions more data-driven, and solutions more innovative than ever before."
)
@@ -611,7 +612,7 @@ def test_sequential_async_task_execution_completion():
sequential_result = sequential_crew.kickoff()
assert sequential_result.raw.startswith(
- "The history of artificial intelligence (AI) is a fascinating journey marked by pioneering milestones and groundbreaking innovations."
+ "The history of artificial intelligence (AI) is marked by several pivotal events that have shaped the field into what it is today."
)
@@ -639,7 +640,7 @@ def test_single_task_with_async_execution():
result = crew.kickoff()
assert result.raw.startswith(
- "- Ethical considerations in deploying AI in healthcare."
+ "- Ethical implications of AI in law enforcement and surveillance."
)
@@ -1103,9 +1104,8 @@ def test_dont_set_agents_step_callback_if_already_set():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_crew_function_calling_llm():
- from unittest.mock import patch, Mock
+ from unittest.mock import patch
from crewai_tools import tool
- import instructor
llm = "gpt-4o"
@@ -1131,24 +1131,11 @@ def test_crew_function_calling_llm():
tasks = [essay]
crew = Crew(agents=[agent1], tasks=tasks)
- with patch.object(instructor, "from_litellm") as mock_from_litellm:
- mock_client = Mock()
- mock_from_litellm.return_value = mock_client
- mock_chat = Mock()
- mock_client.chat = mock_chat
- mock_completions = Mock()
- mock_chat.completions = mock_completions
- mock_create = Mock()
- mock_completions.create = mock_create
-
+ with patch.object(
+ instructor, "from_litellm", wraps=instructor.from_litellm
+ ) as mock_from_litellm:
crew.kickoff()
-
mock_from_litellm.assert_called()
- mock_create.assert_called()
- calls = mock_create.call_args_list
- assert any(
- call.kwargs.get("model") == "gpt-4o" for call in calls
- ), "Instructor was not created with the expected model"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1177,7 +1164,7 @@ def test_task_with_no_arguments():
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
- assert result.raw == "January: 5, February: 10, March: 15, April: 20, May: 25"
+ assert result.raw == "The total number of sales is 75."
def test_code_execution_flag_adds_code_tool_upon_kickoff():
@@ -1287,8 +1274,8 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
assert result.raw == "Howdy!"
assert result.token_usage == UsageMetrics(
- total_tokens=2654,
- prompt_tokens=2510,
+ total_tokens=2626,
+ prompt_tokens=2482,
completion_tokens=144,
successful_requests=5,
)
@@ -2348,6 +2335,50 @@ def test_key():
assert crew.key == hash
+def test_key_with_interpolated_inputs():
+ researcher = Agent(
+ role="{topic} Researcher",
+ goal="Make the best research and analysis on content {topic}",
+ backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
+ allow_delegation=False,
+ )
+
+ writer = Agent(
+ role="{topic} Senior Writer",
+ goal="Write the best content about {topic}",
+ backstory="You're a senior writer, specialized in technology, software engineering, AI and startups. You work as a freelancer and are now working on writing content for a new customer.",
+ allow_delegation=False,
+ )
+
+ tasks = [
+ Task(
+ description="Give me a list of 5 interesting ideas about {topic} to explore for an article, what makes them unique and interesting.",
+ expected_output="Bullet point list of 5 important events.",
+ agent=researcher,
+ ),
+ Task(
+ description="Write a 1 amazing paragraph highlight for each idea of {topic} that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
+ expected_output="A 4 paragraph article about AI.",
+ agent=writer,
+ ),
+ ]
+
+ crew = Crew(
+ agents=[researcher, writer],
+ process=Process.sequential,
+ tasks=tasks,
+ )
+ hash = hashlib.md5(
+ f"{researcher.key}|{writer.key}|{tasks[0].key}|{tasks[1].key}".encode()
+ ).hexdigest()
+
+ assert crew.key == hash
+
+ curr_key = crew.key
+ crew._interpolate_inputs({"topic": "AI"})
+ assert crew.key == curr_key
+
+
def test_conditional_task_requirement_breaks_when_singular_conditional_task():
def condition_fn(output) -> bool:
return output.raw.startswith("Andrew Ng has!!")
@@ -2388,7 +2419,7 @@ def test_conditional_task_last_task_when_conditional_is_true():
)
result = crew.kickoff()
assert result.raw.startswith(
- "1. **The Role of AI in Personalized Medicine: Revolutionizing Healthcare**"
+ "Hi\n\nHere are five interesting ideas for articles focused on AI and AI agents, each accompanied by a compelling paragraph to showcase the potential impact and depth of each topic:"
)
diff --git a/tests/memory/cassettes/test_save_and_search.yaml b/tests/memory/cassettes/test_save_and_search.yaml
index 5cf2fa799..473fe50d3 100644
--- a/tests/memory/cassettes/test_save_and_search.yaml
+++ b/tests/memory/cassettes/test_save_and_search.yaml
@@ -18,7 +18,7 @@ interactions:
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- api.openai.com
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+ - OpenAI/Python 1.47.0
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@@ -28,7 +28,7 @@ interactions:
x-stainless-os:
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+ - 1.47.0
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@@ -44,7 +44,7 @@ interactions:
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openai-model:
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openai-organization:
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openai-processing-ms:
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+ - '19'
openai-version:
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strict-transport-security:
@@ -88,13 +86,13 @@ interactions:
x-ratelimit-remaining-requests:
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x-ratelimit-remaining-tokens:
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x-ratelimit-reset-tokens:
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http_version: HTTP/1.1
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- request:
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host:
- api.openai.com
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+ - OpenAI/Python 1.47.0
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access-control-expose-headers:
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diff --git a/tests/pipeline/cassettes/test_router_with_empty_input.yaml b/tests/pipeline/cassettes/test_router_with_empty_input.yaml
index 24f9baf3b..ac64c5796 100644
--- a/tests/pipeline/cassettes/test_router_with_empty_input.yaml
+++ b/tests/pipeline/cassettes/test_router_with_empty_input.yaml
@@ -9,7 +9,7 @@ interactions:
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diff --git a/tests/task_test.py b/tests/task_test.py
index 76b33310c..1e20c9491 100644
--- a/tests/task_test.py
+++ b/tests/task_test.py
@@ -266,7 +266,7 @@ def test_output_pydantic_hierarchical():
)
result = crew.kickoff()
assert isinstance(result.pydantic, ScoreOutput)
- assert result.to_dict() == {"score": 5}
+ assert result.to_dict() == {"score": 4}
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -321,7 +321,7 @@ def test_output_json_hierarchical():
manager_llm="gpt-4o",
)
result = crew.kickoff()
- assert '{"score": 4}' == result.json
+ assert result.json == '{"score": 4}'
assert result.to_dict() == {"score": 4}
@@ -404,8 +404,8 @@ def test_output_json_dict_hierarchical():
manager_llm="gpt-4o",
)
result = crew.kickoff()
- assert {"score": 5} == result.json_dict
- assert result.to_dict() == {"score": 5}
+ assert {"score": 4} == result.json_dict
+ assert result.to_dict() == {"score": 4}
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -443,7 +443,7 @@ def test_output_pydantic_to_another_task():
assert isinstance(
pydantic_result, ScoreOutput
), "Expected pydantic result to be of type ScoreOutput"
- assert 3 == pydantic_result.score
+ assert pydantic_result.score == 5
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -685,7 +685,7 @@ def test_increment_tool_errors():
with patch.object(Task, "increment_tools_errors") as increment_tools_errors:
increment_tools_errors.return_value = None
crew.kickoff()
- assert len(increment_tools_errors.mock_calls) == 9
+ assert len(increment_tools_errors.mock_calls) == 12
def test_task_definition_based_on_dict():
diff --git a/tests/utilities/evaluators/test_crew_evaluator_handler.py b/tests/utilities/evaluators/test_crew_evaluator_handler.py
index 4d1676d3f..6c92ffdbc 100644
--- a/tests/utilities/evaluators/test_crew_evaluator_handler.py
+++ b/tests/utilities/evaluators/test_crew_evaluator_handler.py
@@ -44,7 +44,7 @@ class TestCrewEvaluator:
== "Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed"
)
assert agent.verbose is False
- assert agent.llm == "gpt-4o-mini"
+ assert agent.llm.model == "gpt-4o-mini"
def test_evaluation_task(self, crew_planner):
evaluator_agent = Agent(
diff --git a/tests/utilities/evaluators/test_task_evaluator.py b/tests/utilities/evaluators/test_task_evaluator.py
index 879acf0dd..8a0be027a 100644
--- a/tests/utilities/evaluators/test_task_evaluator.py
+++ b/tests/utilities/evaluators/test_task_evaluator.py
@@ -25,6 +25,7 @@ def test_evaluate_training_data(converter_mock):
}
agent_id = "agent_id"
original_agent = MagicMock()
+ original_agent.llm.supports_function_calling.return_value = False
function_return_value = TrainingTaskEvaluation(
suggestions=[
"The initial output was already good, having a detailed explanation. However, the improved output "
@@ -49,11 +50,12 @@ def test_evaluate_training_data(converter_mock):
text="Assess the quality of the training data based on the llm output, human feedback , and llm "
"output improved result.\n\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback "
"1\n\nImproved Output:\nImproved output 1\n\nInitial Output:\nInitial output 2\n\nHuman "
- "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\nPlease 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- 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",
+ "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\nPlease provide:\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",
model=TrainingTaskEvaluation,
instructions="I'm gonna convert this raw text into valid JSON.\n\nThe json should have the "
"following structure, with the following keys:\n{\n suggestions: List[str],\n quality: float,\n final_summary: str\n}",
diff --git a/tests/utilities/test_converter.py b/tests/utilities/test_converter.py
index cef8860c5..0bb6b7263 100644
--- a/tests/utilities/test_converter.py
+++ b/tests/utilities/test_converter.py
@@ -2,6 +2,7 @@ import json
from unittest.mock import MagicMock, Mock, patch
import pytest
+from crewai.llm import LLM
from crewai.utilities.converter import (
Converter,
ConverterError,
@@ -10,11 +11,12 @@ from crewai.utilities.converter import (
create_converter,
get_conversion_instructions,
handle_partial_json,
- is_gpt,
validate_model,
)
from pydantic import BaseModel
+from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
+
# Sample Pydantic models for testing
class EmailResponse(BaseModel):
@@ -197,14 +199,20 @@ def test_convert_with_instructions_failure(
def test_get_conversion_instructions_gpt():
mock_llm = Mock()
mock_llm.openai_api_base = None
- with patch("crewai.utilities.converter.is_gpt", return_value=True):
+ with patch.object(LLM, "supports_function_calling") as supports_function_calling:
+ supports_function_calling.return_value = True
instructions = get_conversion_instructions(SimpleModel, mock_llm)
- assert instructions == "I'm gonna convert this raw text into valid JSON."
+ model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
+ assert (
+ instructions
+ == f"I'm gonna convert this raw text into valid JSON.\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
+ )
def test_get_conversion_instructions_non_gpt():
mock_llm = Mock()
- with patch("crewai.utilities.converter.is_gpt", return_value=False):
+ with patch.object(LLM, "supports_function_calling") as supports_function_calling:
+ supports_function_calling.return_value = False
with patch("crewai.utilities.converter.PydanticSchemaParser") as mock_parser:
mock_parser.return_value.get_schema.return_value = "Sample schema"
instructions = get_conversion_instructions(SimpleModel, mock_llm)
@@ -212,12 +220,14 @@ def test_get_conversion_instructions_non_gpt():
# Tests for is_gpt
-def test_is_gpt_true():
- assert is_gpt("gpt-4") is True
+def test_supports_function_calling_true():
+ llm = LLM(model="gpt-4o")
+ assert llm.supports_function_calling() is True
-def test_is_gpt_false():
- assert is_gpt("lol-4") is False
+def test_supports_function_calling_false():
+ llm = LLM(model="non-existent-model")
+ assert llm.supports_function_calling() is False
class CustomConverter(Converter):