mirror of
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Merge branch 'main' into flow-type-linting-fices
This commit is contained in:
João Moura
273
README.md
273
README.md
@@ -1,10 +1,10 @@
|
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<div align="center">
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|
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# **crewAI**
|
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# **CrewAI**
|
||||
|
||||
🤖 **crewAI**: Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
|
||||
🤖 **CrewAI**: Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
|
||||
|
||||
<h3>
|
||||
|
||||
@@ -44,83 +44,222 @@ To get started with CrewAI, follow these simple steps:
|
||||
|
||||
### 1. Installation
|
||||
|
||||
Ensure you have Python >=3.10 <=3.13 installed on your system. CrewAI uses [Poetry](https://python-poetry.org/) for dependency management and package handling, offering a seamless setup and execution experience.
|
||||
|
||||
First, if you haven't already, install Poetry:
|
||||
|
||||
```bash
|
||||
pip install poetry
|
||||
```
|
||||
|
||||
Then, install CrewAI:
|
||||
|
||||
```shell
|
||||
pip install crewai
|
||||
```
|
||||
|
||||
If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command: pip install 'crewai[tools]'. This command installs the basic package and also adds extra components which require more dependencies to function."
|
||||
If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command:
|
||||
|
||||
```shell
|
||||
pip install 'crewai[tools]'
|
||||
```
|
||||
The command above installs the basic package and also adds extra components which require more dependencies to function.
|
||||
|
||||
### 2. Setting Up Your Crew
|
||||
### 2. Setting Up Your Crew with the YAML Configuration
|
||||
|
||||
To create a new CrewAI project, run the following CLI (Command Line Interface) command:
|
||||
|
||||
```shell
|
||||
crewai create crew <project_name>
|
||||
```
|
||||
|
||||
This command creates a new project folder with the following structure:
|
||||
|
||||
```
|
||||
my_project/
|
||||
├── .gitignore
|
||||
├── pyproject.toml
|
||||
├── README.md
|
||||
├── .env
|
||||
└── src/
|
||||
└── my_project/
|
||||
├── __init__.py
|
||||
├── main.py
|
||||
├── crew.py
|
||||
├── tools/
|
||||
│ ├── custom_tool.py
|
||||
│ └── __init__.py
|
||||
└── config/
|
||||
├── agents.yaml
|
||||
└── tasks.yaml
|
||||
```
|
||||
|
||||
You can now start developing your crew by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of the project, the `crew.py` file is where you define your crew, the `agents.yaml` file is where you define your agents, and the `tasks.yaml` file is where you define your tasks.
|
||||
|
||||
#### To customize your project, you can:
|
||||
|
||||
- Modify `src/my_project/config/agents.yaml` to define your agents.
|
||||
- Modify `src/my_project/config/tasks.yaml` to define your tasks.
|
||||
- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
|
||||
- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
|
||||
- Add your environment variables into the `.env` file.
|
||||
|
||||
#### Example of a simple crew with a sequential process:
|
||||
|
||||
Instatiate your crew:
|
||||
|
||||
```shell
|
||||
crewai create crew latest-ai-development
|
||||
```
|
||||
|
||||
Modify the files as needed to fit your use case:
|
||||
|
||||
**agents.yaml**
|
||||
|
||||
```yaml
|
||||
# src/my_project/config/agents.yaml
|
||||
researcher:
|
||||
role: >
|
||||
{topic} Senior Data Researcher
|
||||
goal: >
|
||||
Uncover cutting-edge developments in {topic}
|
||||
backstory: >
|
||||
You're a seasoned researcher with a knack for uncovering the latest
|
||||
developments in {topic}. Known for your ability to find the most relevant
|
||||
information and present it in a clear and concise manner.
|
||||
|
||||
reporting_analyst:
|
||||
role: >
|
||||
{topic} Reporting Analyst
|
||||
goal: >
|
||||
Create detailed reports based on {topic} data analysis and research findings
|
||||
backstory: >
|
||||
You're a meticulous analyst with a keen eye for detail. You're known for
|
||||
your ability to turn complex data into clear and concise reports, making
|
||||
it easy for others to understand and act on the information you provide.
|
||||
```
|
||||
|
||||
**tasks.yaml**
|
||||
|
||||
```yaml
|
||||
# src/my_project/config/tasks.yaml
|
||||
research_task:
|
||||
description: >
|
||||
Conduct a thorough research about {topic}
|
||||
Make sure you find any interesting and relevant information given
|
||||
the current year is 2024.
|
||||
expected_output: >
|
||||
A list with 10 bullet points of the most relevant information about {topic}
|
||||
agent: researcher
|
||||
|
||||
reporting_task:
|
||||
description: >
|
||||
Review the context you got and expand each topic into a full section for a report.
|
||||
Make sure the report is detailed and contains any and all relevant information.
|
||||
expected_output: >
|
||||
A fully fledge reports with the mains topics, each with a full section of information.
|
||||
Formatted as markdown without '```'
|
||||
agent: reporting_analyst
|
||||
output_file: report.md
|
||||
```
|
||||
|
||||
**crew.py**
|
||||
|
||||
```python
|
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import os
|
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from crewai import Agent, Task, Crew, Process
|
||||
# src/my_project/crew.py
|
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from crewai import Agent, Crew, Process, Task
|
||||
from crewai.project import CrewBase, agent, crew, task
|
||||
from crewai_tools import SerperDevTool
|
||||
|
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os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
|
||||
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
|
||||
@CrewBase
|
||||
class LatestAiDevelopmentCrew():
|
||||
"""LatestAiDevelopment crew"""
|
||||
|
||||
# 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/)
|
||||
@agent
|
||||
def researcher(self) -> Agent:
|
||||
return Agent(
|
||||
config=self.agents_config['researcher'],
|
||||
verbose=True,
|
||||
tools=[SerperDevTool()]
|
||||
)
|
||||
|
||||
# Define your agents with roles and goals
|
||||
researcher = Agent(
|
||||
role='Senior Research Analyst',
|
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goal='Uncover cutting-edge developments in AI and data science',
|
||||
backstory="""You work at a leading tech think tank.
|
||||
Your expertise lies in identifying emerging trends.
|
||||
You have a knack for dissecting complex data and presenting actionable insights.""",
|
||||
verbose=True,
|
||||
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=[SerperDevTool()]
|
||||
)
|
||||
writer = Agent(
|
||||
role='Tech Content Strategist',
|
||||
goal='Craft compelling content on tech advancements',
|
||||
backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
|
||||
You transform complex concepts into compelling narratives.""",
|
||||
verbose=True,
|
||||
allow_delegation=True
|
||||
)
|
||||
@agent
|
||||
def reporting_analyst(self) -> Agent:
|
||||
return Agent(
|
||||
config=self.agents_config['reporting_analyst'],
|
||||
verbose=True
|
||||
)
|
||||
|
||||
# Create tasks for your agents
|
||||
task1 = Task(
|
||||
description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
|
||||
Identify key trends, breakthrough technologies, and potential industry impacts.""",
|
||||
expected_output="Full analysis report in bullet points",
|
||||
agent=researcher
|
||||
)
|
||||
@task
|
||||
def research_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config['research_task'],
|
||||
)
|
||||
|
||||
task2 = Task(
|
||||
description="""Using the insights provided, develop an engaging blog
|
||||
post that highlights the most significant AI advancements.
|
||||
Your post should be informative yet accessible, catering to a tech-savvy audience.
|
||||
Make it sound cool, avoid complex words so it doesn't sound like AI.""",
|
||||
expected_output="Full blog post of at least 4 paragraphs",
|
||||
agent=writer
|
||||
)
|
||||
@task
|
||||
def reporting_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config['reporting_task'],
|
||||
output_file='report.md'
|
||||
)
|
||||
|
||||
# Instantiate your crew with a sequential process
|
||||
crew = Crew(
|
||||
agents=[researcher, writer],
|
||||
tasks=[task1, task2],
|
||||
verbose=True,
|
||||
process = Process.sequential
|
||||
)
|
||||
|
||||
# Get your crew to work!
|
||||
result = crew.kickoff()
|
||||
|
||||
print("######################")
|
||||
print(result)
|
||||
@crew
|
||||
def crew(self) -> Crew:
|
||||
"""Creates the LatestAiDevelopment crew"""
|
||||
return Crew(
|
||||
agents=self.agents, # Automatically created by the @agent decorator
|
||||
tasks=self.tasks, # Automatically created by the @task decorator
|
||||
process=Process.sequential,
|
||||
verbose=True,
|
||||
)
|
||||
```
|
||||
|
||||
**main.py**
|
||||
|
||||
```python
|
||||
#!/usr/bin/env python
|
||||
# src/my_project/main.py
|
||||
import sys
|
||||
from latest_ai_development.crew import LatestAiDevelopmentCrew
|
||||
|
||||
def run():
|
||||
"""
|
||||
Run the crew.
|
||||
"""
|
||||
inputs = {
|
||||
'topic': 'AI Agents'
|
||||
}
|
||||
LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
|
||||
```
|
||||
|
||||
### 3. Running Your Crew
|
||||
|
||||
Before running your crew, make sure you have the following keys set as environment variables in your `.env` file:
|
||||
|
||||
- An [OpenAI API key](https://platform.openai.com/account/api-keys) (or other LLM API key): `OPENAI_API_KEY=sk-...`
|
||||
- A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE`
|
||||
|
||||
Lock the dependencies and install them by using the CLI command but first, navigate to your project directory:
|
||||
|
||||
```shell
|
||||
cd my_project
|
||||
crewai install
|
||||
```
|
||||
|
||||
To run your crew, execute the following command in the root of your project:
|
||||
|
||||
```bash
|
||||
crewai run
|
||||
```
|
||||
|
||||
or
|
||||
|
||||
```bash
|
||||
python src/my_project/main.py
|
||||
```
|
||||
|
||||
You should see the output in the console and the `report.md` file should be created in the root of your project with the full final report.
|
||||
|
||||
In addition to the sequential process, you can use the hierarchical process, which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. [See more about the processes here](https://docs.crewai.com/core-concepts/Processes/).
|
||||
|
||||
## Key Features
|
||||
@@ -131,13 +270,13 @@ In addition to the sequential process, you can use the hierarchical process, whi
|
||||
- **Processes Driven**: Currently only supports `sequential` task execution and `hierarchical` processes, but more complex processes like consensual and autonomous are being worked on.
|
||||
- **Save output as file**: Save the output of individual tasks as a file, so you can use it later.
|
||||
- **Parse output as Pydantic or Json**: Parse the output of individual tasks as a Pydantic model or as a Json if you want to.
|
||||
- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models, even ones running locally!
|
||||
- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models, even ones running locally!
|
||||
|
||||
|
||||
|
||||
## Examples
|
||||
|
||||
You can test different real life examples of AI crews in the [crewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
|
||||
You can test different real life examples of AI crews in the [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
|
||||
|
||||
- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
|
||||
- [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
|
||||
@@ -168,9 +307,9 @@ You can test different real life examples of AI crews in the [crewAI-examples re
|
||||
|
||||
## Connecting Your Crew to a Model
|
||||
|
||||
crewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.
|
||||
CrewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.
|
||||
|
||||
Please refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring you agents' connections to models.
|
||||
Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring you agents' connections to models.
|
||||
|
||||
## How CrewAI Compares
|
||||
|
||||
@@ -241,7 +380,7 @@ It's pivotal to understand that **NO data is collected** concerning prompts, tas
|
||||
|
||||
Data collected includes:
|
||||
|
||||
- Version of crewAI
|
||||
- Version of CrewAI
|
||||
- So we can understand how many users are using the latest version
|
||||
- Version of Python
|
||||
- So we can decide on what versions to better support
|
||||
@@ -266,7 +405,7 @@ Users can opt-in to Further Telemetry, sharing the complete telemetry data by se
|
||||
|
||||
## License
|
||||
|
||||
CrewAI is released under the MIT License.
|
||||
CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/blob/main/LICENSE).
|
||||
|
||||
## Frequently Asked Questions (FAQ)
|
||||
|
||||
@@ -299,7 +438,7 @@ A: Yes, CrewAI is open-source and welcomes contributions from the community.
|
||||
A: CrewAI uses anonymous telemetry to collect usage data for improvement purposes. No sensitive data (like prompts, task descriptions, or API calls) is collected. Users can opt-in to share more detailed data by setting `share_crew=True` on their Crews.
|
||||
|
||||
### Q: Where can I find examples of CrewAI in action?
|
||||
A: You can find various real-life examples in the [crewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), including trip planners, stock analysis tools, and more.
|
||||
A: You can find various real-life examples in the [CrewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), including trip planners, stock analysis tools, and more.
|
||||
|
||||
### Q: How can I contribute to CrewAI?
|
||||
A: Contributions are welcome! You can fork the repository, create a new branch for your feature, add your improvement, and send a pull request. Check the Contribution section in the README for more details.
|
||||
|
||||
@@ -85,20 +85,20 @@ Example:
|
||||
crewai replay -t task_123456
|
||||
```
|
||||
|
||||
### 5. log_tasks_outputs
|
||||
### 5. log-tasks-outputs
|
||||
|
||||
Retrieve your latest crew.kickoff() task outputs.
|
||||
|
||||
```
|
||||
crewai log_tasks_outputs
|
||||
crewai log-tasks-outputs
|
||||
```
|
||||
|
||||
### 6. reset_memories
|
||||
### 6. reset-memories
|
||||
|
||||
Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
|
||||
|
||||
```
|
||||
crewai reset_memories [OPTIONS]
|
||||
crewai reset-memories [OPTIONS]
|
||||
```
|
||||
|
||||
- `-l, --long`: Reset LONG TERM memory
|
||||
@@ -109,8 +109,8 @@ crewai reset_memories [OPTIONS]
|
||||
|
||||
Example:
|
||||
```
|
||||
crewai reset_memories --long --short
|
||||
crewai reset_memories --all
|
||||
crewai reset-memories --long --short
|
||||
crewai reset-memories --all
|
||||
```
|
||||
|
||||
### 7. test
|
||||
|
||||
627
docs/core-concepts/Flows.md
Normal file
627
docs/core-concepts/Flows.md
Normal file
@@ -0,0 +1,627 @@
|
||||
# CrewAI Flows
|
||||
|
||||
## Introduction
|
||||
|
||||
CrewAI Flows is a powerful feature designed to streamline the creation and management of AI workflows. Flows allow developers to combine and coordinate coding tasks and Crews efficiently, providing a robust framework for building sophisticated AI automations.
|
||||
|
||||
Flows allow you to create structured, event-driven workflows. They provide a seamless way to connect multiple tasks, manage state, and control the flow of execution in your AI applications. With Flows, you can easily design and implement multi-step processes that leverage the full potential of CrewAI's capabilities.
|
||||
|
||||
1. **Simplified Workflow Creation**: Easily chain together multiple Crews and tasks to create complex AI workflows.
|
||||
|
||||
2. **State Management**: Flows make it super easy to manage and share state between different tasks in your workflow.
|
||||
|
||||
3. **Event-Driven Architecture**: Built on an event-driven model, allowing for dynamic and responsive workflows.
|
||||
|
||||
4. **Flexible Control Flow**: Implement conditional logic, loops, and branching within your workflows.
|
||||
|
||||
## Getting Started
|
||||
|
||||
Let's create a simple Flow where you will use OpenAI to generate a random city in one task and then use that city to generate a fun fact in another task.
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
from crewai.flow.flow import Flow, listen, start
|
||||
from litellm import completion
|
||||
|
||||
|
||||
class ExampleFlow(Flow):
|
||||
model = "gpt-4o-mini"
|
||||
|
||||
@start()
|
||||
def generate_city(self):
|
||||
print("Starting flow")
|
||||
|
||||
response = completion(
|
||||
model=self.model,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Return the name of a random city in the world.",
|
||||
},
|
||||
],
|
||||
)
|
||||
|
||||
random_city = response["choices"][0]["message"]["content"]
|
||||
print(f"Random City: {random_city}")
|
||||
|
||||
return random_city
|
||||
|
||||
@listen(generate_city)
|
||||
def generate_fun_fact(self, random_city):
|
||||
response = completion(
|
||||
model=self.model,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"Tell me a fun fact about {random_city}",
|
||||
},
|
||||
],
|
||||
)
|
||||
|
||||
fun_fact = response["choices"][0]["message"]["content"]
|
||||
return fun_fact
|
||||
|
||||
|
||||
async def main():
|
||||
flow = ExampleFlow()
|
||||
result = await flow.kickoff()
|
||||
|
||||
print(f"Generated fun fact: {result}")
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
|
||||
|
||||
When you run the Flow, it will generate a random city and then generate a fun fact about that city. The output will be printed to the console.
|
||||
|
||||
### @start()
|
||||
|
||||
The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started.
|
||||
|
||||
### @listen()
|
||||
|
||||
The `@listen()` decorator is used to mark a method as a listener for the output of another task in the Flow. The method decorated with `@listen()` will be executed when the specified task emits an output. The method can access the output of the task it is listening to as an argument.
|
||||
|
||||
#### Usage
|
||||
|
||||
The `@listen()` decorator can be used in several ways:
|
||||
|
||||
1. **Listening to a Method by Name**: You can pass the name of the method you want to listen to as a string. When that method completes, the listener method will be triggered.
|
||||
|
||||
```python
|
||||
@listen("generate_city")
|
||||
def generate_fun_fact(self, random_city):
|
||||
# Implementation
|
||||
```
|
||||
|
||||
2. **Listening to a Method Directly**: You can pass the method itself. When that method completes, the listener method will be triggered.
|
||||
```python
|
||||
@listen(generate_city)
|
||||
def generate_fun_fact(self, random_city):
|
||||
# Implementation
|
||||
```
|
||||
|
||||
### Flow Output
|
||||
|
||||
Accessing and handling the output of a Flow is essential for integrating your AI workflows into larger applications or systems. CrewAI Flows provide straightforward mechanisms to retrieve the final output, access intermediate results, and manage the overall state of your Flow.
|
||||
|
||||
#### Retrieving the Final Output
|
||||
|
||||
When you run a Flow, the final output is determined by the last method that completes. The `kickoff()` method returns the output of this final method.
|
||||
|
||||
Here's how you can access the final output:
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from crewai.flow.flow import Flow, listen, start
|
||||
|
||||
class OutputExampleFlow(Flow):
|
||||
@start()
|
||||
def first_method(self):
|
||||
return "Output from first_method"
|
||||
|
||||
@listen(first_method)
|
||||
def second_method(self, first_output):
|
||||
return f"Second method received: {first_output}"
|
||||
|
||||
async def main():
|
||||
flow = OutputExampleFlow()
|
||||
final_output = await flow.kickoff()
|
||||
print("---- Final Output ----")
|
||||
print(final_output)
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
In this example, the `second_method` is the last method to complete, so its output will be the final output of the Flow. The `kickoff()` method will return this final output, which is then printed to the console.
|
||||
|
||||
The output of the Flow will be:
|
||||
|
||||
```
|
||||
---- Final Output ----
|
||||
Second method received: Output from first_method
|
||||
```
|
||||
|
||||
#### Accessing and Updating State
|
||||
|
||||
In addition to retrieving the final output, you can also access and update the state within your Flow. The state can be used to store and share data between different methods in the Flow. After the Flow has run, you can access the state to retrieve any information that was added or updated during the execution.
|
||||
|
||||
Here's an example of how to update and access the state:
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from crewai.flow.flow import Flow, listen, start
|
||||
from pydantic import BaseModel
|
||||
|
||||
class ExampleState(BaseModel):
|
||||
counter: int = 0
|
||||
message: str = ""
|
||||
|
||||
class StateExampleFlow(Flow[ExampleState]):
|
||||
|
||||
@start()
|
||||
def first_method(self):
|
||||
self.state.message = "Hello from first_method"
|
||||
self.state.counter += 1
|
||||
|
||||
@listen(first_method)
|
||||
def second_method(self):
|
||||
self.state.message += " - updated by second_method"
|
||||
self.state.counter += 1
|
||||
return self.state.message
|
||||
|
||||
async def main():
|
||||
flow = StateExampleFlow()
|
||||
final_output = await flow.kickoff()
|
||||
print(f"Final Output: {final_output}")
|
||||
print("Final State:")
|
||||
print(flow.state)
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
In this example, the state is updated by both `first_method` and `second_method`. After the Flow has run, you can access the final state to see the updates made by these methods.
|
||||
|
||||
The output of the Flow will be:
|
||||
|
||||
```
|
||||
Final Output: Hello from first_method - updated by second_method
|
||||
Final State:
|
||||
counter=2 message='Hello from first_method - updated by second_method'
|
||||
```
|
||||
|
||||
By ensuring that the final method's output is returned and providing access to the state, CrewAI Flows make it easy to integrate the results of your AI workflows into larger applications or systems, while also maintaining and accessing the state throughout the Flow's execution.
|
||||
|
||||
## Flow State Management
|
||||
|
||||
Managing state effectively is crucial for building reliable and maintainable AI workflows. CrewAI Flows provides robust mechanisms for both unstructured and structured state management, allowing developers to choose the approach that best fits their application's needs.
|
||||
|
||||
### Unstructured State Management
|
||||
|
||||
In unstructured state management, all state is stored in the `state` attribute of the `Flow` class. This approach offers flexibility, enabling developers to add or modify state attributes on the fly without defining a strict schema.
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
from crewai.flow.flow import Flow, listen, start
|
||||
|
||||
class UntructuredExampleFlow(Flow):
|
||||
|
||||
@start()
|
||||
def first_method(self):
|
||||
self.state.message = "Hello from structured flow"
|
||||
self.state.counter = 0
|
||||
|
||||
@listen(first_method)
|
||||
def second_method(self):
|
||||
self.state.counter += 1
|
||||
self.state.message += " - updated"
|
||||
|
||||
@listen(second_method)
|
||||
def third_method(self):
|
||||
self.state.counter += 1
|
||||
self.state.message += " - updated again"
|
||||
|
||||
print(f"State after third_method: {self.state}")
|
||||
|
||||
|
||||
async def main():
|
||||
flow = UntructuredExampleFlow()
|
||||
await flow.kickoff()
|
||||
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
**Key Points:**
|
||||
|
||||
- **Flexibility:** You can dynamically add attributes to `self.state` without predefined constraints.
|
||||
- **Simplicity:** Ideal for straightforward workflows where state structure is minimal or varies significantly.
|
||||
|
||||
### Structured State Management
|
||||
|
||||
Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow. By using models like Pydantic's `BaseModel`, developers can define the exact shape of the state, enabling better validation and auto-completion in development environments.
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
from crewai.flow.flow import Flow, listen, start
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class ExampleState(BaseModel):
|
||||
counter: int = 0
|
||||
message: str = ""
|
||||
|
||||
|
||||
class StructuredExampleFlow(Flow[ExampleState]):
|
||||
|
||||
@start()
|
||||
def first_method(self):
|
||||
self.state.message = "Hello from structured flow"
|
||||
|
||||
@listen(first_method)
|
||||
def second_method(self):
|
||||
self.state.counter += 1
|
||||
self.state.message += " - updated"
|
||||
|
||||
@listen(second_method)
|
||||
def third_method(self):
|
||||
self.state.counter += 1
|
||||
self.state.message += " - updated again"
|
||||
|
||||
print(f"State after third_method: {self.state}")
|
||||
|
||||
|
||||
async def main():
|
||||
flow = StructuredExampleFlow()
|
||||
await flow.kickoff()
|
||||
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
**Key Points:**
|
||||
|
||||
- **Defined Schema:** `ExampleState` clearly outlines the state structure, enhancing code readability and maintainability.
|
||||
- **Type Safety:** Leveraging Pydantic ensures that state attributes adhere to the specified types, reducing runtime errors.
|
||||
- **Auto-Completion:** IDEs can provide better auto-completion and error checking based on the defined state model.
|
||||
|
||||
### Choosing Between Unstructured and Structured State Management
|
||||
|
||||
- **Use Unstructured State Management when:**
|
||||
|
||||
- The workflow's state is simple or highly dynamic.
|
||||
- Flexibility is prioritized over strict state definitions.
|
||||
- Rapid prototyping is required without the overhead of defining schemas.
|
||||
|
||||
- **Use Structured State Management when:**
|
||||
- The workflow requires a well-defined and consistent state structure.
|
||||
- Type safety and validation are important for your application's reliability.
|
||||
- You want to leverage IDE features like auto-completion and type checking for better developer experience.
|
||||
|
||||
By providing both unstructured and structured state management options, CrewAI Flows empowers developers to build AI workflows that are both flexible and robust, catering to a wide range of application requirements.
|
||||
|
||||
## Flow Control
|
||||
|
||||
### Conditional Logic
|
||||
|
||||
#### or
|
||||
|
||||
The `or_` function in Flows allows you to listen to multiple methods and trigger the listener method when any of the specified methods emit an output.
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from crewai.flow.flow import Flow, listen, or_, start
|
||||
|
||||
class OrExampleFlow(Flow):
|
||||
|
||||
@start()
|
||||
def start_method(self):
|
||||
return "Hello from the start method"
|
||||
|
||||
@listen(start_method)
|
||||
def second_method(self):
|
||||
return "Hello from the second method"
|
||||
|
||||
@listen(or_(start_method, second_method))
|
||||
def logger(self, result):
|
||||
print(f"Logger: {result}")
|
||||
|
||||
|
||||
async def main():
|
||||
flow = OrExampleFlow()
|
||||
await flow.kickoff()
|
||||
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
When you run this Flow, the `logger` method will be triggered by the output of either the `start_method` or the `second_method`. The `or_` function is to listen to multiple methods and trigger the listener method when any of the specified methods emit an output.
|
||||
|
||||
The output of the Flow will be:
|
||||
|
||||
```
|
||||
Logger: Hello from the start method
|
||||
Logger: Hello from the second method
|
||||
```
|
||||
|
||||
#### and
|
||||
|
||||
The `and_` function in Flows allows you to listen to multiple methods and trigger the listener method only when all the specified methods emit an output.
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from crewai.flow.flow import Flow, and_, listen, start
|
||||
|
||||
class AndExampleFlow(Flow):
|
||||
|
||||
@start()
|
||||
def start_method(self):
|
||||
self.state["greeting"] = "Hello from the start method"
|
||||
|
||||
@listen(start_method)
|
||||
def second_method(self):
|
||||
self.state["joke"] = "What do computers eat? Microchips."
|
||||
|
||||
@listen(and_(start_method, second_method))
|
||||
def logger(self):
|
||||
print("---- Logger ----")
|
||||
print(self.state)
|
||||
|
||||
|
||||
async def main():
|
||||
flow = AndExampleFlow()
|
||||
await flow.kickoff()
|
||||
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
When you run this Flow, the `logger` method will be triggered only when both the `start_method` and the `second_method` emit an output. The `and_` function is used to listen to multiple methods and trigger the listener method only when all the specified methods emit an output.
|
||||
|
||||
The output of the Flow will be:
|
||||
|
||||
```
|
||||
---- Logger ----
|
||||
{'greeting': 'Hello from the start method', 'joke': 'What do computers eat? Microchips.'}
|
||||
```
|
||||
|
||||
### Router
|
||||
|
||||
The `@router()` decorator in Flows allows you to define conditional routing logic based on the output of a method. You can specify different routes based on the output of the method, allowing you to control the flow of execution dynamically.
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
import random
|
||||
from crewai.flow.flow import Flow, listen, router, start
|
||||
from pydantic import BaseModel
|
||||
|
||||
class ExampleState(BaseModel):
|
||||
success_flag: bool = False
|
||||
|
||||
class RouterFlow(Flow[ExampleState]):
|
||||
|
||||
@start()
|
||||
def start_method(self):
|
||||
print("Starting the structured flow")
|
||||
random_boolean = random.choice([True, False])
|
||||
self.state.success_flag = random_boolean
|
||||
|
||||
@router(start_method)
|
||||
def second_method(self):
|
||||
if self.state.success_flag:
|
||||
return "success"
|
||||
else:
|
||||
return "failed"
|
||||
|
||||
@listen("success")
|
||||
def third_method(self):
|
||||
print("Third method running")
|
||||
|
||||
@listen("failed")
|
||||
def fourth_method(self):
|
||||
print("Fourth method running")
|
||||
|
||||
|
||||
async def main():
|
||||
flow = RouterFlow()
|
||||
await flow.kickoff()
|
||||
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
In the above example, the `start_method` generates a random boolean value and sets it in the state. The `second_method` uses the `@router()` decorator to define conditional routing logic based on the value of the boolean. If the boolean is `True`, the method returns `"success"`, and if it is `False`, the method returns `"failed"`. The `third_method` and `fourth_method` listen to the output of the `second_method` and execute based on the returned value.
|
||||
|
||||
When you run this Flow, the output will change based on the random boolean value generated by the `start_method`, but you should see an output similar to the following:
|
||||
|
||||
```
|
||||
Starting the structured flow
|
||||
Third method running
|
||||
```
|
||||
|
||||
## Adding Crews to Flows
|
||||
|
||||
Creating a flow with multiple crews in CrewAI is straightforward. You can generate a new CrewAI project that includes all the scaffolding needed to create a flow with multiple crews by running the following command:
|
||||
|
||||
```bash
|
||||
crewai create flow name_of_flow
|
||||
```
|
||||
|
||||
This command will generate a new CrewAI project with the necessary folder structure. The generated project includes a prebuilt crew called `poem_crew` that is already working. You can use this crew as a template by copying, pasting, and editing it to create other crews.
|
||||
|
||||
### Folder Structure
|
||||
|
||||
After running the `crewai create flow name_of_flow` command, you will see a folder structure similar to the following:
|
||||
|
||||
```
|
||||
name_of_flow/
|
||||
├── crews/
|
||||
│ └── poem_crew/
|
||||
│ ├── config/
|
||||
│ │ ├── agents.yaml
|
||||
│ │ └── tasks.yaml
|
||||
│ ├── poem_crew.py
|
||||
├── tools/
|
||||
│ └── custom_tool.py
|
||||
├── main.py
|
||||
├── README.md
|
||||
├── pyproject.toml
|
||||
└── .gitignore
|
||||
```
|
||||
|
||||
### Building Your Crews
|
||||
|
||||
In the `crews` folder, you can define multiple crews. Each crew will have its own folder containing configuration files and the crew definition file. For example, the `poem_crew` folder contains:
|
||||
|
||||
- `config/agents.yaml`: Defines the agents for the crew.
|
||||
- `config/tasks.yaml`: Defines the tasks for the crew.
|
||||
- `poem_crew.py`: Contains the crew definition, including agents, tasks, and the crew itself.
|
||||
|
||||
You can copy, paste, and edit the `poem_crew` to create other crews.
|
||||
|
||||
### Connecting Crews in `main.py`
|
||||
|
||||
The `main.py` file is where you create your flow and connect the crews together. You can define your flow by using the `Flow` class and the decorators `@start` and `@listen` to specify the flow of execution.
|
||||
|
||||
Here's an example of how you can connect the `poem_crew` in the `main.py` file:
|
||||
|
||||
```python
|
||||
#!/usr/bin/env python
|
||||
import asyncio
|
||||
from random import randint
|
||||
|
||||
from pydantic import BaseModel
|
||||
from crewai.flow.flow import Flow, listen, start
|
||||
from .crews.poem_crew.poem_crew import PoemCrew
|
||||
|
||||
class PoemState(BaseModel):
|
||||
sentence_count: int = 1
|
||||
poem: str = ""
|
||||
|
||||
class PoemFlow(Flow[PoemState]):
|
||||
|
||||
@start()
|
||||
def generate_sentence_count(self):
|
||||
print("Generating sentence count")
|
||||
# Generate a number between 1 and 5
|
||||
self.state.sentence_count = randint(1, 5)
|
||||
|
||||
@listen(generate_sentence_count)
|
||||
def generate_poem(self):
|
||||
print("Generating poem")
|
||||
poem_crew = PoemCrew().crew()
|
||||
result = poem_crew.kickoff(inputs={"sentence_count": self.state.sentence_count})
|
||||
|
||||
print("Poem generated", result.raw)
|
||||
self.state.poem = result.raw
|
||||
|
||||
@listen(generate_poem)
|
||||
def save_poem(self):
|
||||
print("Saving poem")
|
||||
with open("poem.txt", "w") as f:
|
||||
f.write(self.state.poem)
|
||||
|
||||
async def run():
|
||||
"""
|
||||
Run the flow.
|
||||
"""
|
||||
poem_flow = PoemFlow()
|
||||
await poem_flow.kickoff()
|
||||
|
||||
def main():
|
||||
asyncio.run(run())
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
```
|
||||
|
||||
In this example, the `PoemFlow` class defines a flow that generates a sentence count, uses the `PoemCrew` to generate a poem, and then saves the poem to a file. The flow is kicked off by calling the `kickoff()` method.
|
||||
|
||||
### Running the Flow
|
||||
|
||||
Before running the flow, make sure to install the dependencies by running:
|
||||
|
||||
```bash
|
||||
poetry install
|
||||
```
|
||||
|
||||
Once all of the dependencies are installed, you need to activate the virtual environment by running:
|
||||
|
||||
```bash
|
||||
poetry shell
|
||||
```
|
||||
|
||||
After activating the virtual environment, you can run the flow by executing one of the following commands:
|
||||
|
||||
```bash
|
||||
crewai flow run
|
||||
```
|
||||
|
||||
or
|
||||
|
||||
```bash
|
||||
poetry run run_flow
|
||||
```
|
||||
|
||||
The flow will execute, and you should see the output in the console.
|
||||
|
||||
## Plot Flows
|
||||
|
||||
Visualizing your AI workflows can provide valuable insights into the structure and execution paths of your flows. CrewAI offers a powerful visualization tool that allows you to generate interactive plots of your flows, making it easier to understand and optimize your AI workflows.
|
||||
|
||||
### What are Plots?
|
||||
|
||||
Plots in CrewAI are graphical representations of your AI workflows. They display the various tasks, their connections, and the flow of data between them. This visualization helps in understanding the sequence of operations, identifying bottlenecks, and ensuring that the workflow logic aligns with your expectations.
|
||||
|
||||
### How to Generate a Plot
|
||||
|
||||
CrewAI provides two convenient methods to generate plots of your flows:
|
||||
|
||||
#### Option 1: Using the `plot()` Method
|
||||
|
||||
If you are working directly with a flow instance, you can generate a plot by calling the `plot()` method on your flow object. This method will create an HTML file containing the interactive plot of your flow.
|
||||
|
||||
```python
|
||||
# Assuming you have a flow instance
|
||||
flow.plot("my_flow_plot")
|
||||
```
|
||||
|
||||
This will generate a file named `my_flow_plot.html` in your current directory. You can open this file in a web browser to view the interactive plot.
|
||||
|
||||
#### Option 2: Using the Command Line
|
||||
|
||||
If you are working within a structured CrewAI project, you can generate a plot using the command line. This is particularly useful for larger projects where you want to visualize the entire flow setup.
|
||||
|
||||
```bash
|
||||
crewai flow plot
|
||||
```
|
||||
|
||||
This command will generate an HTML file with the plot of your flow, similar to the `plot()` method. The file will be saved in your project directory, and you can open it in a web browser to explore the flow.
|
||||
|
||||
### Understanding the Plot
|
||||
|
||||
The generated plot will display nodes representing the tasks in your flow, with directed edges indicating the flow of execution. The plot is interactive, allowing you to zoom in and out, and hover over nodes to see additional details.
|
||||
|
||||
By visualizing your flows, you can gain a clearer understanding of the workflow's structure, making it easier to debug, optimize, and communicate your AI processes to others.
|
||||
|
||||
### Conclusion
|
||||
|
||||
Plotting your flows is a powerful feature of CrewAI that enhances your ability to design and manage complex AI workflows. Whether you choose to use the `plot()` method or the command line, generating plots will provide you with a visual representation of your workflows, aiding in both development and presentation.
|
||||
|
||||
## Next Steps
|
||||
|
||||
If you're interested in exploring additional examples of flows, we have a variety of recommendations in our examples repository. Here are four specific flow examples, each showcasing unique use cases to help you match your current problem type to a specific example:
|
||||
|
||||
1. **Email Auto Responder Flow**: This example demonstrates an infinite loop where a background job continually runs to automate email responses. It's a great use case for tasks that need to be performed repeatedly without manual intervention. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/email_auto_responder_flow)
|
||||
|
||||
2. **Lead Score Flow**: This flow showcases adding human-in-the-loop feedback and handling different conditional branches using the router. It's an excellent example of how to incorporate dynamic decision-making and human oversight into your workflows. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/lead-score-flow)
|
||||
|
||||
3. **Write a Book Flow**: This example excels at chaining multiple crews together, where the output of one crew is used by another. Specifically, one crew outlines an entire book, and another crew generates chapters based on the outline. Eventually, everything is connected to produce a complete book. This flow is perfect for complex, multi-step processes that require coordination between different tasks. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/write_a_book_with_flows)
|
||||
|
||||
4. **Meeting Assistant Flow**: This flow demonstrates how to broadcast one event to trigger multiple follow-up actions. For instance, after a meeting is completed, the flow can update a Trello board, send a Slack message, and save the results. It's a great example of handling multiple outcomes from a single event, making it ideal for comprehensive task management and notification systems. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/meeting_assistant_flow)
|
||||
|
||||
By exploring these examples, you can gain insights into how to leverage CrewAI Flows for various use cases, from automating repetitive tasks to managing complex, multi-step processes with dynamic decision-making and human feedback.
|
||||
@@ -208,7 +208,7 @@ my_crew = Crew(
|
||||
|
||||
### Resetting Memory
|
||||
```sh
|
||||
crewai reset_memories [OPTIONS]
|
||||
crewai reset-memories [OPTIONS]
|
||||
```
|
||||
|
||||
#### Resetting Memory Options
|
||||
|
||||
@@ -276,12 +276,13 @@ def tool_install(handle: str):
|
||||
|
||||
|
||||
@tool.command(name="publish")
|
||||
@click.option("--force", is_flag=True, show_default=True, default=False, help="Bypasses Git remote validations")
|
||||
@click.option("--public", "is_public", flag_value=True, default=False)
|
||||
@click.option("--private", "is_public", flag_value=False)
|
||||
def tool_publish(is_public: bool):
|
||||
def tool_publish(is_public: bool, force: bool):
|
||||
tool_cmd = ToolCommand()
|
||||
tool_cmd.login()
|
||||
tool_cmd.publish(is_public)
|
||||
tool_cmd.publish(is_public, force)
|
||||
|
||||
|
||||
@crewai.group()
|
||||
|
||||
@@ -2,6 +2,8 @@ from pathlib import Path
|
||||
|
||||
import click
|
||||
|
||||
from crewai.telemetry import Telemetry
|
||||
|
||||
|
||||
def create_flow(name):
|
||||
"""Create a new flow."""
|
||||
@@ -15,6 +17,10 @@ def create_flow(name):
|
||||
click.secho(f"Error: Folder {folder_name} already exists.", fg="red")
|
||||
return
|
||||
|
||||
# Initialize telemetry
|
||||
telemetry = Telemetry()
|
||||
telemetry.flow_creation_span(class_name)
|
||||
|
||||
# Create directory structure
|
||||
(project_root / "src" / folder_name).mkdir(parents=True)
|
||||
(project_root / "src" / folder_name / "crews").mkdir(parents=True)
|
||||
|
||||
@@ -59,8 +59,8 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
|
||||
finally:
|
||||
os.chdir(old_directory)
|
||||
|
||||
def publish(self, is_public: bool):
|
||||
if not git.Repository().is_synced():
|
||||
def publish(self, is_public: bool, force: bool = False):
|
||||
if not git.Repository().is_synced() and not force:
|
||||
console.print(
|
||||
"[bold red]Failed to publish tool.[/bold red]\n"
|
||||
"Local changes need to be resolved before publishing. Please do the following:\n"
|
||||
|
||||
@@ -2,6 +2,7 @@ DARK_GRAY = "#333333"
|
||||
CREWAI_ORANGE = "#FF5A50"
|
||||
GRAY = "#666666"
|
||||
WHITE = "#FFFFFF"
|
||||
BLACK = "#000000"
|
||||
|
||||
COLORS = {
|
||||
"bg": WHITE,
|
||||
@@ -16,31 +17,43 @@ COLORS = {
|
||||
|
||||
NODE_STYLES = {
|
||||
"start": {
|
||||
"color": COLORS["start"],
|
||||
"color": CREWAI_ORANGE,
|
||||
"shape": "box",
|
||||
"font": {"color": COLORS["text"]},
|
||||
"font": {"color": WHITE},
|
||||
"margin": {"top": 10, "bottom": 8, "left": 10, "right": 10},
|
||||
},
|
||||
"method": {
|
||||
"color": COLORS["method"],
|
||||
"color": DARK_GRAY,
|
||||
"shape": "box",
|
||||
"font": {"color": COLORS["text"]},
|
||||
"font": {"color": WHITE},
|
||||
"margin": {"top": 10, "bottom": 8, "left": 10, "right": 10},
|
||||
},
|
||||
"router": {
|
||||
"color": {
|
||||
"background": COLORS["router"],
|
||||
"border": COLORS["router_border"],
|
||||
"background": DARK_GRAY,
|
||||
"border": CREWAI_ORANGE,
|
||||
"highlight": {
|
||||
"border": COLORS["router_border"],
|
||||
"background": COLORS["router"],
|
||||
"border": CREWAI_ORANGE,
|
||||
"background": DARK_GRAY,
|
||||
},
|
||||
},
|
||||
"shape": "box",
|
||||
"font": {"color": COLORS["text"]},
|
||||
"font": {"color": WHITE},
|
||||
"borderWidth": 3,
|
||||
"borderWidthSelected": 4,
|
||||
"shapeProperties": {"borderDashes": [5, 5]},
|
||||
"margin": {"top": 10, "bottom": 8, "left": 10, "right": 10},
|
||||
},
|
||||
"crew": {
|
||||
"color": {
|
||||
"background": WHITE,
|
||||
"border": CREWAI_ORANGE,
|
||||
},
|
||||
"shape": "box",
|
||||
"font": {"color": BLACK},
|
||||
"borderWidth": 3,
|
||||
"borderWidthSelected": 4,
|
||||
"shapeProperties": {"borderDashes": False},
|
||||
"margin": {"top": 10, "bottom": 8, "left": 10, "right": 10},
|
||||
},
|
||||
}
|
||||
|
||||
@@ -1,7 +1,5 @@
|
||||
# flow.py
|
||||
|
||||
# flow.py
|
||||
|
||||
import asyncio
|
||||
import inspect
|
||||
from typing import Any, Callable, Dict, Generic, List, Set, Type, TypeVar, Union
|
||||
@@ -9,6 +7,8 @@ from typing import Any, Callable, Dict, Generic, List, Set, Type, TypeVar, Union
|
||||
from pydantic import BaseModel
|
||||
|
||||
from crewai.flow.flow_visualizer import plot_flow
|
||||
from crewai.flow.utils import get_possible_return_constants
|
||||
from crewai.telemetry import Telemetry
|
||||
|
||||
T = TypeVar("T", bound=Union[BaseModel, Dict[str, Any]])
|
||||
|
||||
@@ -63,12 +63,10 @@ def listen(condition):
|
||||
return decorator
|
||||
|
||||
|
||||
def router(method, paths=None):
|
||||
def router(method):
|
||||
def decorator(func):
|
||||
func.__is_router__ = True
|
||||
func.__router_for__ = method.__name__
|
||||
if paths:
|
||||
func.__router_paths__ = paths
|
||||
return func
|
||||
|
||||
return decorator
|
||||
@@ -124,10 +122,11 @@ class FlowMeta(type):
|
||||
listeners[attr_name] = (condition_type, methods)
|
||||
elif hasattr(attr_value, "__is_router__"):
|
||||
routers[attr_value.__router_for__] = attr_name
|
||||
if hasattr(attr_value, "__router_paths__"):
|
||||
router_paths[attr_name] = attr_value.__router_paths__
|
||||
possible_returns = get_possible_return_constants(attr_value)
|
||||
if possible_returns:
|
||||
router_paths[attr_name] = possible_returns
|
||||
|
||||
# **Register router as a listener to its triggering method**
|
||||
# Register router as a listener to its triggering method
|
||||
trigger_method_name = attr_value.__router_for__
|
||||
methods = [trigger_method_name]
|
||||
condition_type = "OR"
|
||||
@@ -142,6 +141,8 @@ class FlowMeta(type):
|
||||
|
||||
|
||||
class Flow(Generic[T], metaclass=FlowMeta):
|
||||
_telemetry = Telemetry()
|
||||
|
||||
_start_methods: List[str] = []
|
||||
_listeners: Dict[str, tuple[str, List[str]]] = {}
|
||||
_routers: Dict[str, str] = {}
|
||||
@@ -162,6 +163,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
self._pending_and_listeners: Dict[str, Set[str]] = {}
|
||||
self._method_outputs: List[Any] = [] # List to store all method outputs
|
||||
|
||||
self._telemetry.flow_creation_span(self.__class__.__name__)
|
||||
|
||||
for method_name in dir(self):
|
||||
if callable(getattr(self, method_name)) and not method_name.startswith(
|
||||
"__"
|
||||
@@ -191,6 +194,10 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
if not self._start_methods:
|
||||
raise ValueError("No start method defined")
|
||||
|
||||
self._telemetry.flow_execution_span(
|
||||
self.__class__.__name__, list(self._methods.keys())
|
||||
)
|
||||
|
||||
# Create tasks for all start methods
|
||||
tasks = [
|
||||
self._execute_start_method(start_method)
|
||||
@@ -271,5 +278,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
|
||||
|
||||
traceback.print_exc()
|
||||
|
||||
def plot(self, filename: str = "crewai_flow_graph") -> None:
|
||||
def plot(self, filename: str = "crewai_flow") -> None:
|
||||
self._telemetry.flow_plotting_span(
|
||||
self.__class__.__name__, list(self._methods.keys())
|
||||
)
|
||||
|
||||
plot_flow(self, filename)
|
||||
|
||||
@@ -30,6 +30,22 @@ class FlowPlot:
|
||||
layout=None,
|
||||
)
|
||||
|
||||
# Set options to disable physics
|
||||
net.set_options(
|
||||
"""
|
||||
var options = {
|
||||
"nodes": {
|
||||
"font": {
|
||||
"multi": "html"
|
||||
}
|
||||
},
|
||||
"physics": {
|
||||
"enabled": false
|
||||
}
|
||||
}
|
||||
"""
|
||||
)
|
||||
|
||||
# Calculate levels for nodes
|
||||
node_levels = calculate_node_levels(self.flow)
|
||||
|
||||
@@ -42,24 +58,13 @@ class FlowPlot:
|
||||
# Add edges to the network
|
||||
add_edges(net, self.flow, node_positions, self.colors)
|
||||
|
||||
# Set options to disable physics
|
||||
net.set_options(
|
||||
"""
|
||||
var options = {
|
||||
"physics": {
|
||||
"enabled": false
|
||||
}
|
||||
}
|
||||
"""
|
||||
)
|
||||
|
||||
network_html = net.generate_html()
|
||||
final_html_content = self._generate_final_html(network_html)
|
||||
|
||||
# Save the final HTML content to the file
|
||||
with open(f"{filename}.html", "w", encoding="utf-8") as f:
|
||||
f.write(final_html_content)
|
||||
print(f"Graph saved as {filename}.html")
|
||||
print(f"Plot saved as {filename}.html")
|
||||
|
||||
self._cleanup_pyvis_lib()
|
||||
|
||||
@@ -94,6 +99,6 @@ class FlowPlot:
|
||||
print(f"Error cleaning up {lib_folder}: {e}")
|
||||
|
||||
|
||||
def plot_flow(flow, filename="flow_graph"):
|
||||
def plot_flow(flow, filename="flow_plot"):
|
||||
visualizer = FlowPlot(flow)
|
||||
visualizer.plot(filename)
|
||||
|
||||
@@ -47,7 +47,7 @@ class HTMLTemplateHandler:
|
||||
"""
|
||||
return legend_items_html
|
||||
|
||||
def generate_final_html(self, network_body, legend_items_html, title="Flow Graph"):
|
||||
def generate_final_html(self, network_body, legend_items_html, title="Flow Plot"):
|
||||
html_template = self.read_template()
|
||||
logo_svg_base64 = self.encode_logo()
|
||||
|
||||
|
||||
@@ -2,6 +2,12 @@ def get_legend_items(colors):
|
||||
return [
|
||||
{"label": "Start Method", "color": colors["start"]},
|
||||
{"label": "Method", "color": colors["method"]},
|
||||
{
|
||||
"label": "Crew Method",
|
||||
"color": colors["bg"],
|
||||
"border": colors["start"],
|
||||
"dashed": False,
|
||||
},
|
||||
{
|
||||
"label": "Router",
|
||||
"color": colors["router"],
|
||||
@@ -22,9 +28,10 @@ def generate_legend_items_html(legend_items):
|
||||
legend_items_html = ""
|
||||
for item in legend_items:
|
||||
if "border" in item:
|
||||
style = "dashed" if item["dashed"] else "solid"
|
||||
legend_items_html += f"""
|
||||
<div class="legend-item">
|
||||
<div class="legend-color-box" style="background-color: {item['color']}; border: 2px dashed {item['border']};"></div>
|
||||
<div class="legend-color-box" style="background-color: {item['color']}; border: 2px {style} {item['border']}; border-radius: 5px;"></div>
|
||||
<div>{item['label']}</div>
|
||||
</div>
|
||||
"""
|
||||
@@ -32,14 +39,14 @@ def generate_legend_items_html(legend_items):
|
||||
style = "dashed" if item["dashed"] else "solid"
|
||||
legend_items_html += f"""
|
||||
<div class="legend-item">
|
||||
<div class="legend-{style}" style="border-bottom: 2px {style} {item['color']};"></div>
|
||||
<div class="legend-{style}" style="border-bottom: 2px {style} {item['color']}; border-radius: 5px;"></div>
|
||||
<div>{item['label']}</div>
|
||||
</div>
|
||||
"""
|
||||
else:
|
||||
legend_items_html += f"""
|
||||
<div class="legend-item">
|
||||
<div class="legend-color-box" style="background-color: {item['color']};"></div>
|
||||
<div class="legend-color-box" style="background-color: {item['color']}; border-radius: 5px;"></div>
|
||||
<div>{item['label']}</div>
|
||||
</div>
|
||||
"""
|
||||
|
||||
@@ -1,3 +1,48 @@
|
||||
import ast
|
||||
import inspect
|
||||
import textwrap
|
||||
|
||||
|
||||
def get_possible_return_constants(function):
|
||||
try:
|
||||
source = inspect.getsource(function)
|
||||
except OSError:
|
||||
# Can't get source code
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"Error retrieving source code for function {function.__name__}: {e}")
|
||||
return None
|
||||
|
||||
try:
|
||||
# Remove leading indentation
|
||||
source = textwrap.dedent(source)
|
||||
# Parse the source code into an AST
|
||||
code_ast = ast.parse(source)
|
||||
except IndentationError as e:
|
||||
print(f"IndentationError while parsing source code of {function.__name__}: {e}")
|
||||
print(f"Source code:\n{source}")
|
||||
return None
|
||||
except SyntaxError as e:
|
||||
print(f"SyntaxError while parsing source code of {function.__name__}: {e}")
|
||||
print(f"Source code:\n{source}")
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"Unexpected error while parsing source code of {function.__name__}: {e}")
|
||||
print(f"Source code:\n{source}")
|
||||
return None
|
||||
|
||||
return_values = []
|
||||
|
||||
class ReturnVisitor(ast.NodeVisitor):
|
||||
def visit_Return(self, node):
|
||||
# Check if the return value is a constant (Python 3.8+)
|
||||
if isinstance(node.value, ast.Constant):
|
||||
return_values.append(node.value.value)
|
||||
|
||||
ReturnVisitor().visit(code_ast)
|
||||
return return_values
|
||||
|
||||
|
||||
def calculate_node_levels(flow):
|
||||
levels = {}
|
||||
queue = []
|
||||
|
||||
@@ -1,3 +1,6 @@
|
||||
import ast
|
||||
import inspect
|
||||
|
||||
from .utils import (
|
||||
build_ancestor_dict,
|
||||
build_parent_children_dict,
|
||||
@@ -6,6 +9,70 @@ from .utils import (
|
||||
)
|
||||
|
||||
|
||||
def method_calls_crew(method):
|
||||
"""Check if the method calls `.crew()`."""
|
||||
try:
|
||||
source = inspect.getsource(method)
|
||||
source = inspect.cleandoc(source)
|
||||
tree = ast.parse(source)
|
||||
except Exception as e:
|
||||
print(f"Could not parse method {method.__name__}: {e}")
|
||||
return False
|
||||
|
||||
class CrewCallVisitor(ast.NodeVisitor):
|
||||
def __init__(self):
|
||||
self.found = False
|
||||
|
||||
def visit_Call(self, node):
|
||||
if isinstance(node.func, ast.Attribute):
|
||||
if node.func.attr == "crew":
|
||||
self.found = True
|
||||
self.generic_visit(node)
|
||||
|
||||
visitor = CrewCallVisitor()
|
||||
visitor.visit(tree)
|
||||
return visitor.found
|
||||
|
||||
|
||||
def add_nodes_to_network(net, flow, node_positions, node_styles):
|
||||
def human_friendly_label(method_name):
|
||||
return method_name.replace("_", " ").title()
|
||||
|
||||
for method_name, (x, y) in node_positions.items():
|
||||
method = flow._methods.get(method_name)
|
||||
if hasattr(method, "__is_start_method__"):
|
||||
node_style = node_styles["start"]
|
||||
elif hasattr(method, "__is_router__"):
|
||||
node_style = node_styles["router"]
|
||||
elif method_calls_crew(method):
|
||||
node_style = node_styles["crew"]
|
||||
else:
|
||||
node_style = node_styles["method"]
|
||||
|
||||
node_style = node_style.copy()
|
||||
label = human_friendly_label(method_name)
|
||||
|
||||
node_style.update(
|
||||
{
|
||||
"label": label,
|
||||
"shape": "box",
|
||||
"font": {
|
||||
"multi": "html",
|
||||
"color": node_style.get("font", {}).get("color", "#FFFFFF"),
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
net.add_node(
|
||||
method_name,
|
||||
x=x,
|
||||
y=y,
|
||||
fixed=True,
|
||||
physics=False,
|
||||
**node_style,
|
||||
)
|
||||
|
||||
|
||||
def compute_positions(flow, node_levels, y_spacing=150, x_spacing=150):
|
||||
level_nodes = {}
|
||||
node_positions = {}
|
||||
@@ -109,24 +176,3 @@ def add_edges(net, flow, node_positions, colors):
|
||||
"smooth": edge_smooth,
|
||||
}
|
||||
net.add_edge(router_method_name, listener_name, **edge_style)
|
||||
|
||||
|
||||
def add_nodes_to_network(net, flow, node_positions, node_styles):
|
||||
for method_name, (x, y) in node_positions.items():
|
||||
method = flow._methods.get(method_name)
|
||||
if hasattr(method, "__is_start_method__"):
|
||||
node_style = node_styles["start"]
|
||||
elif hasattr(method, "__is_router__"):
|
||||
node_style = node_styles["router"]
|
||||
else:
|
||||
node_style = node_styles["method"]
|
||||
|
||||
net.add_node(
|
||||
method_name,
|
||||
label=method_name,
|
||||
x=x,
|
||||
y=y,
|
||||
fixed=True,
|
||||
physics=False,
|
||||
**node_style,
|
||||
)
|
||||
|
||||
@@ -5,8 +5,8 @@ import json
|
||||
import os
|
||||
import platform
|
||||
import warnings
|
||||
from typing import TYPE_CHECKING, Any, Optional
|
||||
from contextlib import contextmanager
|
||||
from typing import TYPE_CHECKING, Any, Optional
|
||||
|
||||
|
||||
@contextmanager
|
||||
@@ -21,7 +21,9 @@ with suppress_warnings():
|
||||
|
||||
|
||||
from opentelemetry import trace # noqa: E402
|
||||
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter # noqa: E402
|
||||
from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
|
||||
OTLPSpanExporter, # noqa: E402
|
||||
)
|
||||
from opentelemetry.sdk.resources import SERVICE_NAME, Resource # noqa: E402
|
||||
from opentelemetry.sdk.trace import TracerProvider # noqa: E402
|
||||
from opentelemetry.sdk.trace.export import BatchSpanProcessor # noqa: E402
|
||||
@@ -117,9 +119,11 @@ class Telemetry:
|
||||
"max_iter": agent.max_iter,
|
||||
"max_rpm": agent.max_rpm,
|
||||
"i18n": agent.i18n.prompt_file,
|
||||
"function_calling_llm": agent.function_calling_llm.model
|
||||
if agent.function_calling_llm
|
||||
else "",
|
||||
"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,
|
||||
@@ -145,9 +149,9 @@ class Telemetry:
|
||||
"expected_output": task.expected_output,
|
||||
"async_execution?": task.async_execution,
|
||||
"human_input?": task.human_input,
|
||||
"agent_role": task.agent.role
|
||||
if task.agent
|
||||
else "None",
|
||||
"agent_role": (
|
||||
task.agent.role if task.agent else "None"
|
||||
),
|
||||
"agent_key": task.agent.key if task.agent else None,
|
||||
"context": (
|
||||
[task.description for task in task.context]
|
||||
@@ -184,9 +188,11 @@ class Telemetry:
|
||||
"verbose?": agent.verbose,
|
||||
"max_iter": agent.max_iter,
|
||||
"max_rpm": agent.max_rpm,
|
||||
"function_calling_llm": agent.function_calling_llm.model
|
||||
if agent.function_calling_llm
|
||||
else "",
|
||||
"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,
|
||||
@@ -210,9 +216,9 @@ class Telemetry:
|
||||
"id": str(task.id),
|
||||
"async_execution?": task.async_execution,
|
||||
"human_input?": task.human_input,
|
||||
"agent_role": task.agent.role
|
||||
if task.agent
|
||||
else "None",
|
||||
"agent_role": (
|
||||
task.agent.role if task.agent else "None"
|
||||
),
|
||||
"agent_key": task.agent.key if task.agent else None,
|
||||
"tools_names": [
|
||||
tool.name.casefold()
|
||||
@@ -568,3 +574,38 @@ class Telemetry:
|
||||
return span.set_attribute(key, value)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def flow_creation_span(self, flow_name: str):
|
||||
if self.ready:
|
||||
try:
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Flow Creation")
|
||||
self._add_attribute(span, "flow_name", flow_name)
|
||||
span.set_status(Status(StatusCode.OK))
|
||||
span.end()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def flow_plotting_span(self, flow_name: str, node_names: list[str]):
|
||||
if self.ready:
|
||||
try:
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Flow Plotting")
|
||||
self._add_attribute(span, "flow_name", flow_name)
|
||||
self._add_attribute(span, "node_names", json.dumps(node_names))
|
||||
span.set_status(Status(StatusCode.OK))
|
||||
span.end()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def flow_execution_span(self, flow_name: str, node_names: list[str]):
|
||||
if self.ready:
|
||||
try:
|
||||
tracer = trace.get_tracer("crewai.telemetry")
|
||||
span = tracer.start_span("Flow Execution")
|
||||
self._add_attribute(span, "flow_name", flow_name)
|
||||
self._add_attribute(span, "node_names", json.dumps(node_names))
|
||||
span.set_status(Status(StatusCode.OK))
|
||||
span.end()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
from datetime import datetime
|
||||
from datetime import datetime, date
|
||||
import json
|
||||
from uuid import UUID
|
||||
from pydantic import BaseModel
|
||||
@@ -11,8 +11,9 @@ class CrewJSONEncoder(json.JSONEncoder):
|
||||
elif isinstance(obj, UUID):
|
||||
return str(obj)
|
||||
|
||||
elif isinstance(obj, datetime):
|
||||
elif isinstance(obj, datetime) or isinstance(obj, date):
|
||||
return obj.isoformat()
|
||||
|
||||
return super().default(obj)
|
||||
|
||||
def _handle_pydantic_model(self, obj):
|
||||
|
||||
@@ -1,300 +1,344 @@
|
||||
from contextlib import contextmanager
|
||||
import tempfile
|
||||
import unittest
|
||||
import unittest.mock
|
||||
import os
|
||||
from contextlib import contextmanager
|
||||
|
||||
from pytest import raises
|
||||
from crewai.cli.tools.main import ToolCommand
|
||||
from io import StringIO
|
||||
from unittest.mock import patch, MagicMock
|
||||
|
||||
@contextmanager
|
||||
def in_temp_dir():
|
||||
original_dir = os.getcwd()
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
os.chdir(temp_dir)
|
||||
try:
|
||||
yield temp_dir
|
||||
finally:
|
||||
os.chdir(original_dir)
|
||||
|
||||
class TestToolCommand(unittest.TestCase):
|
||||
@contextmanager
|
||||
def in_temp_dir(self):
|
||||
original_dir = os.getcwd()
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
os.chdir(temp_dir)
|
||||
try:
|
||||
yield temp_dir
|
||||
finally:
|
||||
os.chdir(original_dir)
|
||||
|
||||
@patch("crewai.cli.tools.main.subprocess.run")
|
||||
def test_create_success(self, mock_subprocess):
|
||||
with self.in_temp_dir():
|
||||
tool_command = ToolCommand()
|
||||
|
||||
with patch.object(tool_command, "login") as mock_login, patch(
|
||||
"sys.stdout", new=StringIO()
|
||||
) as fake_out:
|
||||
tool_command.create("test-tool")
|
||||
output = fake_out.getvalue()
|
||||
|
||||
self.assertTrue(os.path.isdir("test_tool"))
|
||||
|
||||
self.assertTrue(os.path.isfile(os.path.join("test_tool", "README.md")))
|
||||
self.assertTrue(os.path.isfile(os.path.join("test_tool", "pyproject.toml")))
|
||||
self.assertTrue(
|
||||
os.path.isfile(
|
||||
os.path.join("test_tool", "src", "test_tool", "__init__.py")
|
||||
)
|
||||
)
|
||||
self.assertTrue(
|
||||
os.path.isfile(os.path.join("test_tool", "src", "test_tool", "tool.py"))
|
||||
)
|
||||
|
||||
with open(
|
||||
os.path.join("test_tool", "src", "test_tool", "tool.py"), "r"
|
||||
) as f:
|
||||
content = f.read()
|
||||
self.assertIn("class TestTool", content)
|
||||
|
||||
mock_login.assert_called_once()
|
||||
mock_subprocess.assert_called_once_with(["git", "init"], check=True)
|
||||
|
||||
self.assertIn("Creating custom tool test_tool...", output)
|
||||
|
||||
@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"},
|
||||
}
|
||||
mock_get.return_value = mock_get_response
|
||||
mock_subprocess_run.return_value = MagicMock(stderr=None)
|
||||
|
||||
@patch("crewai.cli.tools.main.subprocess.run")
|
||||
def test_create_success(mock_subprocess):
|
||||
with in_temp_dir():
|
||||
tool_command = ToolCommand()
|
||||
|
||||
with patch("sys.stdout", new=StringIO()) as fake_out:
|
||||
tool_command.install("sample-tool")
|
||||
with patch.object(tool_command, "login") as mock_login, patch(
|
||||
"sys.stdout", new=StringIO()
|
||||
) as fake_out:
|
||||
tool_command.create("test-tool")
|
||||
output = fake_out.getvalue()
|
||||
|
||||
mock_get.assert_called_once_with("sample-tool")
|
||||
mock_subprocess_run.assert_any_call(
|
||||
["poetry", "add", "--source", "crewai-sample-repo", "sample-tool"],
|
||||
capture_output=False,
|
||||
text=True,
|
||||
check=True,
|
||||
assert os.path.isdir("test_tool")
|
||||
assert os.path.isfile(os.path.join("test_tool", "README.md"))
|
||||
assert os.path.isfile(os.path.join("test_tool", "pyproject.toml"))
|
||||
assert os.path.isfile(
|
||||
os.path.join("test_tool", "src", "test_tool", "__init__.py")
|
||||
)
|
||||
assert os.path.isfile(os.path.join("test_tool", "src", "test_tool", "tool.py"))
|
||||
|
||||
self.assertIn("Succesfully installed sample-tool", output)
|
||||
with open(
|
||||
os.path.join("test_tool", "src", "test_tool", "tool.py"), "r"
|
||||
) as f:
|
||||
content = f.read()
|
||||
assert "class TestTool" in content
|
||||
|
||||
@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
|
||||
mock_login.assert_called_once()
|
||||
mock_subprocess.assert_called_once_with(["git", "init"], check=True)
|
||||
|
||||
tool_command = ToolCommand()
|
||||
assert "Creating custom tool test_tool..." in output
|
||||
|
||||
with patch("sys.stdout", new=StringIO()) as fake_out:
|
||||
with self.assertRaises(SystemExit):
|
||||
tool_command.install("non-existent-tool")
|
||||
output = fake_out.getvalue()
|
||||
@patch("crewai.cli.tools.main.subprocess.run")
|
||||
@patch("crewai.cli.plus_api.PlusAPI.get_tool")
|
||||
def test_install_success(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"},
|
||||
}
|
||||
mock_get.return_value = mock_get_response
|
||||
mock_subprocess_run.return_value = MagicMock(stderr=None)
|
||||
|
||||
mock_get.assert_called_once_with("non-existent-tool")
|
||||
self.assertIn("No tool found with this name", output)
|
||||
tool_command = ToolCommand()
|
||||
|
||||
@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
|
||||
with patch("sys.stdout", new=StringIO()) as fake_out:
|
||||
tool_command.install("sample-tool")
|
||||
output = fake_out.getvalue()
|
||||
|
||||
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"
|
||||
mock_get.assert_called_once_with("sample-tool")
|
||||
mock_subprocess_run.assert_any_call(
|
||||
["poetry", "add", "--source", "crewai-sample-repo", "sample-tool"],
|
||||
capture_output=False,
|
||||
text=True,
|
||||
check=True,
|
||||
)
|
||||
@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")
|
||||
@patch("crewai.cli.tools.main.git.Repository.is_synced", return_value=True)
|
||||
def test_publish_success(
|
||||
self,
|
||||
mock_is_synced,
|
||||
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
|
||||
|
||||
assert "Succesfully installed sample-tool" in output
|
||||
|
||||
@patch("crewai.cli.plus_api.PlusAPI.get_tool")
|
||||
def test_install_tool_not_found(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:
|
||||
try:
|
||||
tool_command.install("non-existent-tool")
|
||||
except SystemExit:
|
||||
pass
|
||||
output = fake_out.getvalue()
|
||||
|
||||
mock_get.assert_called_once_with("non-existent-tool")
|
||||
assert "No tool found with this name" in output
|
||||
|
||||
@patch("crewai.cli.plus_api.PlusAPI.get_tool")
|
||||
def test_install_api_error(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:
|
||||
try:
|
||||
tool_command.install("error-tool")
|
||||
except SystemExit:
|
||||
pass
|
||||
output = fake_out.getvalue()
|
||||
|
||||
mock_get.assert_called_once_with("error-tool")
|
||||
assert "Failed to get tool details" in output
|
||||
|
||||
@patch("crewai.cli.tools.main.git.Repository.is_synced", return_value=False)
|
||||
def test_publish_when_not_in_sync(mock_is_synced):
|
||||
with patch("sys.stdout", new=StringIO()) as fake_out, \
|
||||
raises(SystemExit):
|
||||
tool_command = ToolCommand()
|
||||
tool_command.publish(is_public=True)
|
||||
|
||||
mock_get_project_name.assert_called_with(require=True)
|
||||
mock_get_project_version.assert_called_with(require=True)
|
||||
mock_get_project_description.assert_called_with(require=False)
|
||||
mock_subprocess_run.assert_called_with(
|
||||
["poetry", "build", "-f", "sdist", "--output", unittest.mock.ANY],
|
||||
check=True,
|
||||
capture_output=False,
|
||||
)
|
||||
mock_open.assert_called_with(unittest.mock.ANY, "rb")
|
||||
mock_publish.assert_called_with(
|
||||
handle="sample-tool",
|
||||
is_public=True,
|
||||
version="1.0.0",
|
||||
description="A sample tool",
|
||||
encoded_file=unittest.mock.ANY,
|
||||
)
|
||||
assert "Local changes need to be resolved before publishing" in fake_out.getvalue()
|
||||
|
||||
@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.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")
|
||||
@patch("crewai.cli.tools.main.git.Repository.is_synced", return_value=False)
|
||||
def test_publish_when_not_in_sync_and_force(
|
||||
mock_is_synced,
|
||||
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, force=True)
|
||||
|
||||
mock_get_project_name.assert_called_with(require=True)
|
||||
mock_get_project_version.assert_called_with(require=True)
|
||||
mock_get_project_description.assert_called_with(require=False)
|
||||
mock_subprocess_run.assert_called_with(
|
||||
["poetry", "build", "-f", "sdist", "--output", unittest.mock.ANY],
|
||||
check=True,
|
||||
capture_output=False,
|
||||
)
|
||||
@patch("crewai.cli.tools.main.subprocess.run")
|
||||
@patch(
|
||||
"crewai.cli.tools.main.os.listdir", return_value=["sample-tool-1.0.0.tar.gz"]
|
||||
mock_open.assert_called_with(unittest.mock.ANY, "rb")
|
||||
mock_publish.assert_called_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.open",
|
||||
new_callable=unittest.mock.mock_open,
|
||||
read_data=b"sample tarball content",
|
||||
|
||||
@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")
|
||||
@patch("crewai.cli.tools.main.git.Repository.is_synced", return_value=True)
|
||||
def test_publish_success(
|
||||
mock_is_synced,
|
||||
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_with(require=True)
|
||||
mock_get_project_version.assert_called_with(require=True)
|
||||
mock_get_project_description.assert_called_with(require=False)
|
||||
mock_subprocess_run.assert_called_with(
|
||||
["poetry", "build", "-f", "sdist", "--output", unittest.mock.ANY],
|
||||
check=True,
|
||||
capture_output=False,
|
||||
)
|
||||
@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 complete operation", 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"
|
||||
mock_open.assert_called_with(unittest.mock.ANY, "rb")
|
||||
mock_publish.assert_called_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.subprocess.run")
|
||||
@patch(
|
||||
"crewai.cli.tools.main.os.listdir", return_value=["sample-tool-1.0.0.tar.gz"]
|
||||
|
||||
@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(
|
||||
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:
|
||||
try:
|
||||
tool_command.publish(is_public=True)
|
||||
except SystemExit:
|
||||
pass
|
||||
output = fake_out.getvalue()
|
||||
|
||||
mock_publish.assert_called_once()
|
||||
assert "Failed to complete operation" in output
|
||||
assert "Name is already taken" in 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(
|
||||
mock_publish,
|
||||
mock_open,
|
||||
mock_listdir,
|
||||
mock_subprocess_run,
|
||||
mock_get_project_description,
|
||||
mock_get_project_version,
|
||||
mock_get_project_name,
|
||||
):
|
||||
mock_response = MagicMock()
|
||||
mock_response.status_code = 500
|
||||
mock_response.json.return_value = {"error": "Internal Server Error"}
|
||||
mock_response.ok = False
|
||||
mock_publish.return_value = mock_response
|
||||
|
||||
tool_command = ToolCommand()
|
||||
|
||||
with patch("sys.stdout", new=StringIO()) as fake_out:
|
||||
try:
|
||||
tool_command.publish(is_public=True)
|
||||
except SystemExit:
|
||||
pass
|
||||
output = fake_out.getvalue()
|
||||
|
||||
mock_publish.assert_called_once()
|
||||
assert "Request to Enterprise API failed" in output
|
||||
|
||||
@patch("crewai.cli.plus_api.PlusAPI.login_to_tool_repository")
|
||||
@patch("crewai.cli.tools.main.subprocess.run")
|
||||
def test_login_success(mock_subprocess_run, mock_login):
|
||||
mock_login_response = MagicMock()
|
||||
mock_login_response.status_code = 200
|
||||
mock_login_response.json.return_value = {
|
||||
"repositories": [
|
||||
{
|
||||
"handle": "tools",
|
||||
"url": "https://example.com/repo",
|
||||
}
|
||||
],
|
||||
"credential": {"username": "user", "password": "pass"},
|
||||
}
|
||||
mock_login.return_value = mock_login_response
|
||||
|
||||
mock_subprocess_run.return_value = MagicMock(stderr=None)
|
||||
|
||||
tool_command = ToolCommand()
|
||||
|
||||
with patch("sys.stdout", new=StringIO()) as fake_out:
|
||||
tool_command.login()
|
||||
output = fake_out.getvalue()
|
||||
|
||||
mock_login.assert_called_once()
|
||||
mock_subprocess_run.assert_any_call(
|
||||
[
|
||||
"poetry",
|
||||
"source",
|
||||
"add",
|
||||
"--priority=explicit",
|
||||
"crewai-tools",
|
||||
"https://example.com/repo",
|
||||
],
|
||||
text=True,
|
||||
check=True,
|
||||
)
|
||||
@patch(
|
||||
"crewai.cli.tools.main.open",
|
||||
new_callable=unittest.mock.mock_open,
|
||||
read_data=b"sample tarball content",
|
||||
mock_subprocess_run.assert_any_call(
|
||||
[
|
||||
"poetry",
|
||||
"config",
|
||||
"http-basic.crewai-tools",
|
||||
"user",
|
||||
"pass",
|
||||
],
|
||||
capture_output=False,
|
||||
text=True,
|
||||
check=True,
|
||||
)
|
||||
@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_response = MagicMock()
|
||||
mock_response.status_code = 500
|
||||
mock_response.json.return_value = {"error": "Internal Server Error"}
|
||||
mock_response.ok = False
|
||||
mock_publish.return_value = mock_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("Request to Enterprise API failed", output)
|
||||
|
||||
@patch("crewai.cli.plus_api.PlusAPI.login_to_tool_repository")
|
||||
@patch("crewai.cli.tools.main.subprocess.run")
|
||||
def test_login_success(self, mock_subprocess_run, mock_login):
|
||||
mock_login_response = MagicMock()
|
||||
mock_login_response.status_code = 200
|
||||
mock_login_response.json.return_value = {
|
||||
"repositories": [
|
||||
{
|
||||
"handle": "tools",
|
||||
"url": "https://example.com/repo",
|
||||
}
|
||||
],
|
||||
"credential": {"username": "user", "password": "pass"},
|
||||
}
|
||||
mock_login.return_value = mock_login_response
|
||||
|
||||
mock_subprocess_run.return_value = MagicMock(stderr=None)
|
||||
|
||||
tool_command = ToolCommand()
|
||||
|
||||
with patch("sys.stdout", new=StringIO()) as fake_out:
|
||||
tool_command.login()
|
||||
output = fake_out.getvalue()
|
||||
|
||||
mock_login.assert_called_once()
|
||||
mock_subprocess_run.assert_any_call(
|
||||
[
|
||||
"poetry",
|
||||
"source",
|
||||
"add",
|
||||
"--priority=explicit",
|
||||
"crewai-tools",
|
||||
"https://example.com/repo",
|
||||
],
|
||||
text=True,
|
||||
check=True,
|
||||
)
|
||||
mock_subprocess_run.assert_any_call(
|
||||
[
|
||||
"poetry",
|
||||
"config",
|
||||
"http-basic.crewai-tools",
|
||||
"user",
|
||||
"pass",
|
||||
],
|
||||
capture_output=False,
|
||||
text=True,
|
||||
check=True,
|
||||
)
|
||||
self.assertIn("Succesfully authenticated to the tool repository", output)
|
||||
assert "Succesfully authenticated to the tool repository" in output
|
||||
|
||||
Reference in New Issue
Block a user