From 32fdd11c933ad83d744edc2beb44c24585b941a1 Mon Sep 17 00:00:00 2001
From: "Brandon Hancock (bhancock_ai)"
<109994880+bhancockio@users.noreply.github.com>
Date: Mon, 30 Sep 2024 19:52:56 -0400
Subject: [PATCH 1/3] Flow visualizer (#1375)
* Almost working!
* It fully works but not clean enought
* Working but not clean engouth
* Everything is workign
* WIP. Working on adding and & or to flows. In the middle of setting up template for flow as well
* template working
* Everything is working
* More changes and todos
* Add more support for @start
* Router working now
* minor tweak to
* minor tweak to conditions and event handling
* Update logs
* Too trigger happy with cleanup
* Added in Thiago fix
* Flow passing results again
* Working on docs.
* made more progress updates on docs
* Finished talking about controlling flows
* add flow output
* fixed flow output section
* add crews to flows section is looking good now
* more flow doc changes
* Update docs and add more examples
* drop visualizer
* save visualizer
* pyvis is beginning to work
* pyvis working
* it is working
* regular methods and triggers working. Need to work on router next.
* properly identifying router and router children nodes. Need to fix color
* children router working. Need to support loops
* curving cycles but need to add curve conditionals
* everythin is showing up properly need to fix curves
* all working. needs to be cleaned up
* adjust padding
* drop lib
* clean up prior to PR
* incorporate joao feedback
* final tweaks for joao
---
.gitignore | 3 +-
docs/core-concepts/Flows.md | 556 -----------
poetry.lock | 896 +++++++++++-------
pyproject.toml | 1 +
src/crewai/__init__.py | 7 +-
src/crewai/cli/create_flow.py | 2 +-
src/crewai/cli/templates/flow/.gitignore | 1 +
src/crewai/flow/__init__.py | 3 +
.../assets/crewai_flow_visual_template.html | 93 ++
src/crewai/flow/assets/crewai_logo.svg | 12 +
src/crewai/flow/flow.py | 22 +-
src/crewai/flow/flow_visualizer.py | 474 +++++++++
12 files changed, 1161 insertions(+), 909 deletions(-)
delete mode 100644 docs/core-concepts/Flows.md
create mode 100644 src/crewai/flow/__init__.py
create mode 100644 src/crewai/flow/assets/crewai_flow_visual_template.html
create mode 100644 src/crewai/flow/assets/crewai_logo.svg
create mode 100644 src/crewai/flow/flow_visualizer.py
diff --git a/.gitignore b/.gitignore
index 8f05dfc975..ad64db4e73 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,7 @@
.pytest_cache
__pycache__
dist/
+lib/
.env
assets/*
.idea
@@ -15,4 +16,4 @@ rc-tests/*
*.pkl
temp/*
.vscode/*
-crew_tasks_output.json
\ No newline at end of file
+crew_tasks_output.json
diff --git a/docs/core-concepts/Flows.md b/docs/core-concepts/Flows.md
deleted file mode 100644
index 57f432bc4a..0000000000
--- a/docs/core-concepts/Flows.md
+++ /dev/null
@@ -1,556 +0,0 @@
-# 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.
-
-## 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.
diff --git a/poetry.lock b/poetry.lock
index 432f2661b6..2d8bd4ca1a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -24,113 +24,113 @@ langchain = ["langchain (==0.2.14)"]
[[package]]
name = "aiohappyeyeballs"
-version = "2.4.2"
+version = "2.4.3"
description = "Happy Eyeballs for asyncio"
optional = false
python-versions = ">=3.8"
files = [
- {file = "aiohappyeyeballs-2.4.2-py3-none-any.whl", hash = "sha256:8522691d9a154ba1145b157d6d5c15e5c692527ce6a53c5e5f9876977f6dab2f"},
- {file = "aiohappyeyeballs-2.4.2.tar.gz", hash = "sha256:4ca893e6c5c1f5bf3888b04cb5a3bee24995398efef6e0b9f747b5e89d84fd74"},
+ {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
+ {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
]
[[package]]
name = "aiohttp"
-version = "3.10.6"
+version = "3.10.8"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.8"
files = [
- {file = "aiohttp-3.10.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:682836fc672972cc3101cc9e30d49c5f7e8f1d010478d46119fe725a4545acfd"},
- {file = "aiohttp-3.10.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:289fa8a20018d0d5aa9e4b35d899bd51bcb80f0d5f365d9a23e30dac3b79159b"},
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[package.extras]
tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
+[[package]]
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+description = "Annotate AST trees with source code positions"
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+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
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name = "async-timeout"
version = "4.0.3"
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[[package]]
name = "boto3"
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+version = "1.35.29"
description = "The AWS SDK for Python"
optional = false
python-versions = ">=3.8"
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@@ -2390,6 +2431,44 @@ mistralai = ["mistralai (>=0.1.8,<0.2.0)"]
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@@ -2422,6 +2501,25 @@ more-itertools = "*"
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name = "jeepney"
version = "0.8.0"
@@ -2560,6 +2658,22 @@ files = [
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jsonpointer = ">=1.9"
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name = "jsonpointer"
version = "3.0.0"
@@ -2864,13 +2978,13 @@ requests = ">=2,<3"
[[package]]
name = "litellm"
-version = "1.48.2"
+version = "1.48.7"
description = "Library to easily interface with LLM API providers"
optional = false
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
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tests = ["pytest", "pytz", "simplejson"]
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@@ -3144,13 +3272,13 @@ pyyaml = ">=5.1"
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name = "mkdocs-material"
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description = "Documentation that simply works"
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python-versions = ">=3.8"
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pyarrow = ["pyarrow (>=1.0.0)"]
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name = "nodeenv"
version = "1.9.1"
@@ -3745,13 +3891,13 @@ sympy = "*"
[[package]]
name = "openai"
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description = "The official Python library for the openai API"
optional = false
python-versions = ">=3.7.1"
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@@ -4167,6 +4313,21 @@ files = [
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dev = ["jinja2"]
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version = "0.12.1"
@@ -4456,6 +4617,20 @@ nodeenv = ">=0.11.1"
pyyaml = ">=5.1"
virtualenv = ">=20.10.0"
+[[package]]
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+optional = false
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@@ -4579,6 +4754,20 @@ all = ["apache-bookkeeper-client (>=4.16.1)", "fastavro (>=1.9.2)", "grpcio (>=1
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functions = ["apache-bookkeeper-client (>=4.16.1)", "grpcio (>=1.60.0)", "prometheus-client", "protobuf (>=3.6.1,<=3.20.3)", "ratelimit"]
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+description = "Safely evaluate AST nodes without side effects"
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name = "py"
version = "1.11.0"
@@ -4869,13 +5058,13 @@ torch = ["torch"]
[[package]]
name = "pymdown-extensions"
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description = "Extension pack for Python Markdown."
optional = false
python-versions = ">=3.8"
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@@ -4918,13 +5107,13 @@ files = [
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description = "Wrappers to call pyproject.toml-based build backend hooks."
optional = false
python-versions = ">=3.7"
files = [
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+ {file = "yarl-1.13.1.tar.gz", hash = "sha256:ec8cfe2295f3e5e44c51f57272afbd69414ae629ec7c6b27f5a410efc78b70a0"},
]
[package.dependencies]
@@ -7080,4 +7282,4 @@ tools = ["crewai-tools"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<=3.13"
-content-hash = "3ce4d197b40731bf96ab6a2f7918e419a967612b31880e627962d5a1f76cd202"
+content-hash = "b008b28add072e8f002aa90d230b20027f0ecffcd3c4b3fe4ec954f5ac7c46ca"
diff --git a/pyproject.toml b/pyproject.toml
index 6f00121f43..0f780549c9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,6 +32,7 @@ json-repair = "^0.25.2"
auth0-python = "^4.7.1"
poetry = "^1.8.3"
litellm = "^1.44.22"
+pyvis = "^0.3.2"
[tool.poetry.extras]
tools = ["crewai-tools"]
diff --git a/src/crewai/__init__.py b/src/crewai/__init__.py
index 6698deeb26..7fdc1ddeb7 100644
--- a/src/crewai/__init__.py
+++ b/src/crewai/__init__.py
@@ -1,12 +1,13 @@
import warnings
+
from crewai.agent import Agent
from crewai.crew import Crew
+from crewai.flow.flow import Flow
+from crewai.llm import LLM
from crewai.pipeline import Pipeline
from crewai.process import Process
from crewai.routers import Router
from crewai.task import Task
-from crewai.llm import LLM
-
warnings.filterwarnings(
"ignore",
@@ -15,4 +16,4 @@
module="pydantic.main",
)
-__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM"]
+__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]
diff --git a/src/crewai/cli/create_flow.py b/src/crewai/cli/create_flow.py
index 57292d68cd..b582345822 100644
--- a/src/crewai/cli/create_flow.py
+++ b/src/crewai/cli/create_flow.py
@@ -38,7 +38,7 @@ def create_flow(name):
]
def process_file(src_file, dst_file):
- if src_file.suffix in ['.pyc', '.pyo', '.pyd']:
+ if src_file.suffix in [".pyc", ".pyo", ".pyd"]:
return
try:
diff --git a/src/crewai/cli/templates/flow/.gitignore b/src/crewai/cli/templates/flow/.gitignore
index d50a09fc91..02dc677b97 100644
--- a/src/crewai/cli/templates/flow/.gitignore
+++ b/src/crewai/cli/templates/flow/.gitignore
@@ -1,2 +1,3 @@
.env
__pycache__/
+lib/
diff --git a/src/crewai/flow/__init__.py b/src/crewai/flow/__init__.py
new file mode 100644
index 0000000000..b8c530bc34
--- /dev/null
+++ b/src/crewai/flow/__init__.py
@@ -0,0 +1,3 @@
+from crewai.flow.flow import Flow
+
+__all__ = ["Flow"]
diff --git a/src/crewai/flow/assets/crewai_flow_visual_template.html b/src/crewai/flow/assets/crewai_flow_visual_template.html
new file mode 100644
index 0000000000..f175ef1a74
--- /dev/null
+++ b/src/crewai/flow/assets/crewai_flow_visual_template.html
@@ -0,0 +1,93 @@
+
+
+
+
+ {{ title }}
+
+
+
+
+
+
+
+
+
+
+
+
", network_html, re.DOTALL)
+ if match:
+ network_body = match.group(1)
+ else:
+ network_body = ""
+
+ # Read the custom template
+ current_dir = os.path.dirname(__file__)
+ template_path = os.path.join(
+ current_dir, "assets", "crewai_flow_visual_template.html"
+ )
+ with open(template_path, "r", encoding="utf-8") as f:
+ html_template = f.read()
+
+ # Generate the legend items HTML
+ legend_items = [
+ {"label": "Start Method", "color": self.colors["start"]},
+ {"label": "Method", "color": self.colors["method"]},
+ {
+ "label": "Router",
+ "color": self.colors["router"],
+ "border": self.colors["router_border"],
+ "dashed": True,
+ },
+ {"label": "Trigger", "color": self.colors["edge"], "dashed": False},
+ {"label": "AND Trigger", "color": self.colors["edge"], "dashed": True},
+ {
+ "label": "Router Trigger",
+ "color": self.colors["router_edge"],
+ "dashed": True,
+ },
+ ]
+
+ legend_items_html = ""
+ for item in legend_items:
+ if "border" in item:
+ legend_items_html += f"""
+