Compare commits

...

6 Commits

Author SHA1 Message Date
Rip&Tear
2f09652d85 feat: Enhance crew creation script 2024-10-05 20:31:38 +08:00
Brandon Hancock (bhancock_ai)
0dfe3bcb0a quick fixes (#1385)
* quick fixes

* add generic name

---------

Co-authored-by: João Moura <joaomdmoura@gmail.com>
2024-10-04 13:23:42 -03:00
Brandon Hancock (bhancock_ai)
3f81383285 Brandon/cre 291 flow improvements (#1390)
* Implement joao feedback

* update colors for crew nodes

* clean up

* more linting clean up

* round legend corners

---------

Co-authored-by: João Moura <joaomdmoura@gmail.com>
2024-10-04 13:22:46 -03:00
Brandon Hancock (bhancock_ai)
e8a49e7687 Brandon/cre 288 add telemetry to flows (#1391)
* Telemetry for flows

* store node names
2024-10-04 13:21:55 -03:00
Brandon Hancock (bhancock_ai)
ed48efb9aa add plotting to flows documentation (#1394) 2024-10-04 13:19:09 -03:00
Vini Brasil
c3291b967b Add --force option to crewai tool publish (#1383)
This commit adds an option to bypass Git remote validations when
publishing tools.
2024-10-04 11:02:50 -03:00
14 changed files with 1235 additions and 355 deletions

627
docs/core-concepts/Flows.md Normal file
View 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.

View File

@@ -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()

View File

@@ -21,6 +21,7 @@ def create_crew(name, parent_folder=None):
bold=True,
)
# Create necessary directories
if not folder_path.exists():
folder_path.mkdir(parents=True)
(folder_path / "tests").mkdir(exist_ok=True)
@@ -28,14 +29,47 @@ def create_crew(name, parent_folder=None):
(folder_path / "src" / folder_name).mkdir(parents=True)
(folder_path / "src" / folder_name / "tools").mkdir(parents=True)
(folder_path / "src" / folder_name / "config").mkdir(parents=True)
with open(folder_path / ".env", "w") as file:
file.write("OPENAI_API_KEY=YOUR_API_KEY")
else:
click.secho(
f"\tFolder {folder_name} already exists. Please choose a different name.",
fg="red",
f"\tFolder {folder_name} already exists. Updating .env file...",
fg="yellow",
)
return
# Path to the .env file
env_file_path = folder_path / ".env"
# Load existing environment variables if .env exists
env_vars = {}
if env_file_path.exists():
with open(env_file_path, "r") as file:
for line in file:
key_value = line.strip().split('=', 1)
if len(key_value) == 2:
env_vars[key_value[0]] = key_value[1]
# Prompt for keys/variables/LLM settings only if not already set
if 'OPENAI_API_KEY' not in env_vars:
if click.confirm("Do you want to enter your OPENAI_API_KEY?", default=True):
env_vars['OPENAI_API_KEY'] = click.prompt("Enter your OPENAI_API_KEY", type=str)
if 'ANTHROPIC_API_KEY' not in env_vars:
if click.confirm("Do you want to enter your ANTHROPIC_API_KEY?", default=False):
env_vars['ANTHROPIC_API_KEY'] = click.prompt("Enter your ANTHROPIC_API_KEY", type=str)
if 'GEMINI_API_KEY' not in env_vars:
if click.confirm("Do you want to specify your GEMINI_API_KEY?", default=True):
env_vars['GEMINI_API_KEY'] = click.prompt("Enter your GEMINI_API_KEY", type=str)
# Loop to add other environment variables
while click.confirm("Do you want to specify another environment variable?", default=False):
var_name = click.prompt("Enter the variable name", type=str)
var_value = click.prompt(f"Enter the value for {var_name}", type=str)
env_vars[var_name] = var_value
# Write the environment variables to .env file
with open(env_file_path, "w") as file:
for key, value in env_vars.items():
file.write(f"{key}={value}\n")
package_dir = Path(__file__).parent
templates_dir = package_dir / "templates" / "crew"

View File

@@ -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)

View File

@@ -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"

View File

@@ -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},
},
}

View File

@@ -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"
@@ -142,25 +141,30 @@ 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] = {}
_router_paths: Dict[str, List[str]] = {}
initial_state: Union[Type[T], T, None] = None
def __class_getitem__(cls, item):
def __class_getitem__(cls, item: Type[T]) -> Type["Flow"]:
class _FlowGeneric(cls):
_initial_state_T = item
_initial_state_T: Type[T] = item
_FlowGeneric.__name__ = f"{cls.__name__}[{item.__name__}]"
return _FlowGeneric
def __init__(self):
def __init__(self) -> None:
self._methods: Dict[str, Callable] = {}
self._state = self._create_initial_state()
self._state: T = self._create_initial_state()
self._completed_methods: Set[str] = set()
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(
"__"
@@ -190,6 +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)
@@ -205,11 +213,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
else:
return None # Or raise an exception if no methods were executed
async def _execute_start_method(self, start_method: str):
async def _execute_start_method(self, start_method: str) -> None:
result = await self._execute_method(self._methods[start_method])
await self._execute_listeners(start_method, result)
async def _execute_method(self, method: Callable, *args, **kwargs):
async def _execute_method(self, method: Callable, *args: Any, **kwargs: Any) -> Any:
result = (
await method(*args, **kwargs)
if asyncio.iscoroutinefunction(method)
@@ -218,7 +226,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
self._method_outputs.append(result) # Store the output
return result
async def _execute_listeners(self, trigger_method: str, result: Any):
async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
listener_tasks = []
if trigger_method in self._routers:
@@ -246,7 +254,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
# Run all listener tasks concurrently and wait for them to complete
await asyncio.gather(*listener_tasks)
async def _execute_single_listener(self, listener: str, result: Any):
async def _execute_single_listener(self, listener: str, result: Any) -> None:
try:
method = self._methods[listener]
sig = inspect.signature(method)
@@ -270,5 +278,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
traceback.print_exc()
def plot(self, filename: str = "crewai_flow_graph"):
def plot(self, filename: str = "crewai_flow") -> None:
self._telemetry.flow_plotting_span(
self.__class__.__name__, list(self._methods.keys())
)
plot_flow(self, filename)

View File

@@ -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)

View File

@@ -1,5 +1,4 @@
import base64
import os
import re
@@ -48,7 +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()

View File

@@ -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>
"""

View File

@@ -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 = []

View File

@@ -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,
)

View File

@@ -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

View File

@@ -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