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# Conflicts:
#	pyproject.toml
#	src/crewai/agent.py
#	src/crewai/crew.py
#	src/crewai/task.py
#	src/crewai/tools/tool_usage.py
#	src/crewai/utilities/evaluators/task_evaluator.py
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6 changes: 3 additions & 3 deletions .github/workflows/tests.yml
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Expand Up @@ -19,13 +19,13 @@ jobs:
- name: Setup Python
uses: actions/setup-python@v4
with:
python-version: "3.10"
python-version: "3.11.9"

- name: Install Requirements
run: |
set -e
pip install poetry
poetry lock &&
poetry install
- name: Run tests
run: poetry run pytest tests
run: poetry run pytest
6 changes: 5 additions & 1 deletion .gitignore
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Expand Up @@ -11,4 +11,8 @@ chroma.sqlite3
old_en.json
db/
test.py
rc-tests/*
rc-tests/*
*.pkl
temp/*
.vscode/*
crew_tasks_output.json
2 changes: 2 additions & 0 deletions README.md
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Expand Up @@ -129,6 +129,7 @@ crew = Crew(
agents=[researcher, writer],
tasks=[task1, task2],
verbose=2, # You can set it to 1 or 2 to different logging levels
process = Process.sequential
)

# Get your crew to work!
Expand Down Expand Up @@ -197,6 +198,7 @@ Please refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-

**CrewAI's Advantage**: CrewAI is built with production in mind. It offers the flexibility of Autogen's conversational agents and the structured process approach of ChatDev, but without the rigidity. CrewAI's processes are designed to be dynamic and adaptable, fitting seamlessly into both development and production workflows.


## Contribution

CrewAI is open-source and we welcome contributions. If you're looking to contribute, please:
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116 changes: 99 additions & 17 deletions docs/core-concepts/Agents.md
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Expand Up @@ -16,21 +16,24 @@ description: What are crewAI Agents and how to use them.

## Agent Attributes

| Attribute | Description |
| :------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Role** | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
| **Goal** | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
| **Backstory** | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
| **LLM** *(optional)* | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified. |
| **Tools** *(optional)* | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list. |
| **Function Calling LLM** *(optional)* | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`. |
| **Max Iter** *(optional)* | The maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`. |
| **Max RPM** *(optional)* | The maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`. |
| **max_execution_time** *(optional)* | Maximum execution time for an agent to execute a task It's optional and can be left unspecified, with a default value of `None`, menaning no max execution time |
| **Verbose** *(optional)* | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`. |
| **Allow Delegation** *(optional)* | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`. |
| **Step Callback** *(optional)* | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`. |
| **Cache** *(optional)* | Indicates if the agent should use a cache for tool usage. Default is `True`. |
| Attribute | Parameter | Description |
| :------------------------- | :---- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Role** | `role` | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
| **Goal** | `goal` | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
| **Backstory** | `backstory` | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
| **LLM** *(optional)* | `llm` | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified. |
| **Tools** *(optional)* | `tools` | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list. |
| **Function Calling LLM** *(optional)* | `function_calling_llm` | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`. |
| **Max Iter** *(optional)* | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`. |
| **Max RPM** *(optional)* | `max_rpm` | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`. |
| **Max Execution Time** *(optional)* | `max_execution_time` | Max Execution Time is the Maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time. |
| **Verbose** *(optional)* | `verbose` | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`. |
| **Allow Delegation** *(optional)* | `allow_delegation` | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`. |
| **Step Callback** *(optional)* | `step_callback` | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`. |
| **Cache** *(optional)* | `cache` | Indicates if the agent should use a cache for tool usage. Default is `True`. |
| **System Template** *(optional)* | `system_template` | Specifies the system format for the agent. Default is `None`. |
| **Prompt Template** *(optional)* | `prompt_template` | Specifies the prompt format for the agent. Default is `None`. |
| **Response Template** *(optional)* | `response_template` | Specifies the response format for the agent. Default is `None`. |

## Creating an Agent

Expand All @@ -56,12 +59,91 @@ agent = Agent(
function_calling_llm=my_llm, # Optional
max_iter=15, # Optional
max_rpm=None, # Optional
max_execution_time=None, # Optional
verbose=True, # Optional
allow_delegation=True, # Optional
step_callback=my_intermediate_step_callback, # Optional
cache=True # Optional
cache=True, # Optional
system_template=my_system_template, # Optional
prompt_template=my_prompt_template, # Optional
response_template=my_response_template, # Optional
config=my_config, # Optional
crew=my_crew, # Optional
tools_handler=my_tools_handler, # Optional
cache_handler=my_cache_handler, # Optional
callbacks=[callback1, callback2], # Optional
agent_executor=my_agent_executor # Optional
)
```

## Setting prompt templates

Prompt templates are used to format the prompt for the agent. You can use to update the system, regular and response templates for the agent. Here's an example of how to set prompt templates:

```python
agent = Agent(
role="{topic} specialist",
goal="Figure {goal} out",
backstory="I am the master of {role}",
system_template="""<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>""",
prompt_template="""<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>""",
response_template="""<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>""",
)
```

## Bring your Third Party Agents
!!! note "Extend your Third Party Agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the crewai's BaseAgent class."

BaseAgent includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.

CrewAI is a universal multi agent framework that allows for all agents to work together to automate tasks and solve problems.


```py
from crewai import Agent, Task, Crew
from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here.

from langchain.agents import load_tools

langchain_tools = load_tools(["google-serper"], llm=llm)

agent1 = CustomAgent(
role="backstory agent",
goal="who is {input}?",
backstory="agent backstory",
verbose=True,
)

task1 = Task(
expected_output="a short biography of {input}",
description="a short biography of {input}",
agent=agent1,
)

agent2 = Agent(
role="bio agent",
goal="summarize the short bio for {input} and if needed do more research",
backstory="agent backstory",
verbose=True,
)

task2 = Task(
description="a tldr summary of the short biography",
expected_output="5 bullet point summary of the biography",
agent=agent2,
context=[task1],
)

my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
```


## Conclusion
Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.
Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.
7 changes: 5 additions & 2 deletions docs/core-concepts/Collaboration.md
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Expand Up @@ -15,16 +15,19 @@ description: Exploring the dynamics of agent collaboration within the CrewAI fra
The `Crew` class has been enriched with several attributes to support advanced functionalities:

- **Language Model Management (`manager_llm`, `function_calling_llm`)**: Manages language models for executing tasks and tools, facilitating sophisticated agent-tool interactions. Note that while `manager_llm` is mandatory for hierarchical processes to ensure proper execution flow, `function_calling_llm` is optional, with a default value provided for streamlined tool interaction.
- **Custom Manager Agent (`manager_agent`)**: Allows specifying a custom agent as the manager instead of using the default manager provided by CrewAI.
- **Process Flow (`process`)**: Defines the execution logic (e.g., sequential, hierarchical) to streamline task distribution and execution.
- **Verbose Logging (`verbose`)**: Offers detailed logging capabilities for monitoring and debugging purposes. It supports both integer and boolean types to indicate the verbosity level. For example, setting `verbose` to 1 might enable basic logging, whereas setting it to True enables more detailed logs.
- **Rate Limiting (`max_rpm`)**: Ensures efficient utilization of resources by limiting requests per minute. Guidelines for setting `max_rpm` should consider the complexity of tasks and the expected load on resources.
- **Internationalization Support (`language`, `language_file`)**: Facilitates operation in multiple languages, enhancing global usability. Supported languages and the process for utilizing the `language_file` attribute for customization should be clearly documented.
- **Internationalization / Customization Support (`language`, `prompt_file`)**: Facilitates full customization of the inner prompts, enhancing global usability. Supported languages and the process for utilizing the `prompt_file` attribute for customization should be clearly documented. [Example of file](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json)
- **Execution and Output Handling (`full_output`)**: Distinguishes between full and final outputs for nuanced control over task results. Examples showcasing the difference in outputs can aid in understanding the practical implications of this attribute.
- **Callback and Telemetry (`step_callback`, `task_callback`)**: Integrates callbacks for step-wise and task-level execution monitoring, alongside telemetry for performance analytics. The purpose and usage of `task_callback` alongside `step_callback` for granular monitoring should be clearly explained.
- **Crew Sharing (`share_crew`)**: Enables sharing of crew information with CrewAI for continuous improvement and training models. The privacy implications and benefits of this feature, including how it contributes to model improvement, should be outlined.
- **Usage Metrics (`usage_metrics`)**: Stores all metrics for the language model (LLM) usage during all tasks' execution, providing insights into operational efficiency and areas for improvement. Detailed information on accessing and interpreting these metrics for performance analysis should be provided.
- **Memory Usage (`memory`)**: Indicates whether the crew should use memory to store memories of its execution, enhancing task execution and agent learning.
- **Embedder Configuration (`embedder`)**: Specifies the configuration for the embedder to be used by the crew for understanding and generating language. This attribute supports customization of the language model provider.
- **Cache Management (`cache`)**: Determines whether the crew should use a cache to store the results of tool executions, optimizing performance.
- **Output Logging (`output_log_file`)**: Specifies the file path for logging the output of the crew execution.

## Delegation: Dividing to Conquer
Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.
Expand All @@ -36,4 +39,4 @@ Setting up a crew involves defining the roles and capabilities of each agent. Cr
Consider a crew with a researcher agent tasked with data gathering and a writer agent responsible for compiling reports. The integration of advanced language model management and process flow attributes allows for more sophisticated interactions, such as the writer delegating complex research tasks to the researcher or querying specific information, thereby facilitating a seamless workflow.

## Conclusion
The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.
The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.
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