preparing for new verison

Flows

docs/core-concepts/Flows.md

@@ -0,0 +1,556 @@
+# 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.

docs/index.md

@@ -58,6 +58,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     LLMs
                 </a>
             </li>
+            <!-- <li>
+                <a href="./core-concepts/Flows">
+                    Flows
+                </a>
+            </li> -->
             <li>
                 <a href="./core-concepts/Pipeline">
                     Pipeline
@@ -160,7 +165,7 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
             </li>
         </ul>
     </div>
-    <div style="width:30%">
+    <!-- <div style="width:30%">
         <h2>Examples</h2>
         <ul>
             <li>
@@ -198,6 +203,26 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Landing Page Generator
                 </a>
             </li>
+            <li>
+                <a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/email_auto_responder_flow">
+                    Email Auto Responder Flow
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/lead-score-flow">
+                    Lead Score Flow
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/write_a_book_with_flows">
+                    Write a Book Flow
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/meeting_assistant_flow">
+                    Meeting Assistant Flow
+                </a>
+            </li>
         </ul>
-    </div>
+    </div> -->
 </div>

poetry.lock

@@ -24,13 +24,13 @@ langchain = ["langchain (==0.2.14)"]
 
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+    {file = "aiohappyeyeballs-2.4.2.tar.gz", hash = "sha256:4ca893e6c5c1f5bf3888b04cb5a3bee24995398efef6e0b9f747b5e89d84fd74"},
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 [[package]]
@@ -390,17 +390,17 @@ lxml = ["lxml"]
 
 [[package]]
 name = "boto3"
-version = "1.35.27"
+version = "1.35.28"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "boto3-1.35.27.tar.gz", hash = "sha256:10d0fe15670b83a3f26572ab20d9152a064cee4c54b5ea9a1eeb1f0c3b807a7b"},
+    {file = "boto3-1.35.28-py3-none-any.whl", hash = "sha256:dc088b86a14f17d3cd2e96915c6ccfd31bce640dfe9180df579ed311bc6bf0fc"},
+    {file = "boto3-1.35.28.tar.gz", hash = "sha256:8960fc458b9ba3c8a9890a607c31cee375db821f39aefaec9ff638248e81644a"},
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 [package.dependencies]
-botocore = ">=1.35.27,<1.36.0"
+botocore = ">=1.35.28,<1.36.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -409,13 +409,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
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-version = "1.35.27"
+version = "1.35.28"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
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-    {file = "botocore-1.35.27.tar.gz", hash = "sha256:f68875c26cd57a9d22c0f7a981ecb1636d7ce4d0e35797e04765b53e7bfed3e7"},
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+    {file = "botocore-1.35.28.tar.gz", hash = "sha256:115d13f2172d8e9fa92e8d913f0e80092b97624d190f46772ed2930d4a355d55"},
 ]
 
 [package.dependencies]
@@ -1637,30 +1637,30 @@ xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
 
 [[package]]
 name = "google-cloud-bigquery"
-version = "3.25.0"
+version = "3.26.0"
 description = "Google BigQuery API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "google_cloud_bigquery-3.26.0-py2.py3-none-any.whl", hash = "sha256:e0e9ad28afa67a18696e624cbccab284bf2c0a3f6eeb9eeb0426c69b943793a8"},
+    {file = "google_cloud_bigquery-3.26.0.tar.gz", hash = "sha256:edbdc788beea659e04c0af7fe4dcd6d9155344b98951a0d5055bd2f15da4ba23"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=2.11.1,<3.0.0dev", extras = ["grpc"]}
 google-auth = ">=2.14.1,<3.0.0dev"
-google-cloud-core = ">=1.6.0,<3.0.0dev"
-google-resumable-media = ">=0.6.0,<3.0dev"
+google-cloud-core = ">=2.4.1,<3.0.0dev"
+google-resumable-media = ">=2.0.0,<3.0dev"
 packaging = ">=20.0.0"
-python-dateutil = ">=2.7.2,<3.0dev"
+python-dateutil = ">=2.7.3,<3.0dev"
 requests = ">=2.21.0,<3.0.0dev"
 
 [package.extras]
-all = ["Shapely (>=1.8.4,<3.0.0dev)", "db-dtypes (>=0.3.0,<2.0.0dev)", "geopandas (>=0.9.0,<1.0dev)", "google-cloud-bigquery-storage (>=2.6.0,<3.0.0dev)", "grpcio (>=1.47.0,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "importlib-metadata (>=1.0.0)", "ipykernel (>=6.0.0)", "ipython (>=7.23.1,!=8.1.0)", "ipywidgets (>=7.7.0)", "opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20b0)", "opentelemetry-sdk (>=1.1.0)", "pandas (>=1.1.0)", "proto-plus (>=1.15.0,<2.0.0dev)", "protobuf (>=3.19.5,!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev)", "pyarrow (>=3.0.0)", "tqdm (>=4.7.4,<5.0.0dev)"]
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-ipython = ["ipykernel (>=6.0.0)", "ipython (>=7.23.1,!=8.1.0)"]
+ipython = ["bigquery-magics (>=0.1.0)"]
 ipywidgets = ["ipykernel (>=6.0.0)", "ipywidgets (>=7.7.0)"]
 opentelemetry = ["opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20b0)", "opentelemetry-sdk (>=1.1.0)"]
 pandas = ["db-dtypes (>=0.3.0,<2.0.0dev)", "importlib-metadata (>=1.0.0)", "pandas (>=1.1.0)", "pyarrow (>=3.0.0)"]
@@ -2844,13 +2844,13 @@ langchain-core = ">=0.2.38,<0.3.0"
 
 [[package]]
 name = "langsmith"
-version = "0.1.128"
+version = "0.1.129"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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-    {file = "langsmith-0.1.128.tar.gz", hash = "sha256:3299e17a659f3c47725c97c47f4445fc34113ac668becce425919866fbcb6ec2"},
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 ]
 
 [package.dependencies]
@@ -3144,13 +3144,13 @@ pyyaml = ">=5.1"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.37"
+version = "9.5.38"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3612,13 +3612,13 @@ files = [
 
 [[package]]
 name = "neo4j"
-version = "5.24.0"
+version = "5.25.0"
 description = "Neo4j Bolt driver for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -3745,13 +3745,13 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.48.0"
+version = "1.50.1"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -4943,13 +4943,13 @@ dev = ["build", "flake8", "mypy", "pytest", "twine"]
 
 [[package]]
 name = "pyright"
-version = "1.1.382.post0"
+version = "1.1.382.post1"
 description = "Command line wrapper for pyright"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -6951,103 +6951,103 @@ test = ["pytest"]
 
 [[package]]
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+version = "1.13.0"
 description = "Yet another URL library"
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 python-versions = ">=3.8"
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+    {file = "yarl-1.13.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:01d3941d416e71ce65f33393beb50e93c1c9e8e516971b6653c96df6eb599a2c"},
+    {file = "yarl-1.13.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:801fb5dfc05910cd5ef4806726e2129d8c9a16cdfa26a8166697da0861e59dfc"},
+    {file = "yarl-1.13.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:cdcdd49136d423ee5234c9360eae7063d3120a429ee984d7d9da821c012da4d7"},
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+    {file = "yarl-1.13.0-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:d42227711a4180d0c22cec30fd81d263d7bb378389d8e70b5f4c597e8abae202"},
+    {file = "yarl-1.13.0-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:ebb2236f8098205f59774a28e25a84601a4beb3e974157d418ee6c470d73e0dc"},
+    {file = "yarl-1.13.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:f997004ff530b5381290e82b212a93bd420fefe5a605872dc16fc7e4a7f4251e"},
+    {file = "yarl-1.13.0-cp38-cp38-win32.whl", hash = "sha256:b9648e5ae280babcac867b16e845ce51ed21f8c43bced2ca40cff7eee983d6d4"},
+    {file = "yarl-1.13.0-cp38-cp38-win_amd64.whl", hash = "sha256:f3ef76df654f3547dcb76ba550f9ca59826588eecc6bd7df16937c620df32060"},
+    {file = "yarl-1.13.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:92abbe37e3fb08935e0e95ac5f83f7b286a6f2575f542225ec7afde405ed1fa1"},
+    {file = "yarl-1.13.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:1932c7bfa537f89ad5ca3d1e7e05de3388bb9e893230a384159fb974f6e9f90c"},
+    {file = "yarl-1.13.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4483680e129b2a4250be20947b554cd5f7140fa9e5a1e4f1f42717cf91f8676a"},
+    {file = "yarl-1.13.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2f6f4a352d0beea5dd39315ab16fc26f0122d13457a7e65ad4f06c7961dcf87a"},
+    {file = "yarl-1.13.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8a67f20e97462dee8a89e9b997a78932959d2ed991e8f709514cb4160143e7b1"},
+    {file = "yarl-1.13.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cf4f3a87bd52f8f33b0155cd0f6f22bdf2092d88c6c6acbb1aee3bc206ecbe35"},
+    {file = "yarl-1.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:deb70c006be548076d628630aad9a3ef3a1b2c28aaa14b395cf0939b9124252e"},
+    {file = "yarl-1.13.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bf7a9b31729b97985d4a796808859dfd0e37b55f1ca948d46a568e56e51dd8fb"},
+    {file = "yarl-1.13.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:d807417ceebafb7ce18085a1205d28e8fcb1435a43197d7aa3fab98f5bfec5ef"},
+    {file = "yarl-1.13.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:9671d0d65f86e0a0eee59c5b05e381c44e3d15c36c2a67da247d5d82875b4e4e"},
+    {file = "yarl-1.13.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:13a9cd39e47ca4dc25139d3c63fe0dc6acf1b24f9d94d3b5197ac578fbfd84bf"},
+    {file = "yarl-1.13.0-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:acf8c219a59df22609cfaff4a7158a0946f273e3b03a5385f1fdd502496f0cff"},
+    {file = "yarl-1.13.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:12c92576633027f297c26e52aba89f6363b460f483d85cf7c14216eb55d06d02"},
+    {file = "yarl-1.13.0-cp39-cp39-win32.whl", hash = "sha256:c2518660bd8166e770b76ce92514b491b8720ae7e7f5f975cd888b1592894d2c"},
+    {file = "yarl-1.13.0-cp39-cp39-win_amd64.whl", hash = "sha256:db90702060b1cdb7c7609d04df5f68a12fd5581d013ad379e58e0c2e651d92b8"},
+    {file = "yarl-1.13.0-py3-none-any.whl", hash = "sha256:c7d35ff2a5a51bc6d40112cdb4ca3fd9636482ce8c6ceeeee2301e34f7ed7556"},
+    {file = "yarl-1.13.0.tar.gz", hash = "sha256:02f117a63d11c8c2ada229029f8bb444a811e62e5041da962de548f26ac2c40f"},
 ]
 
 [package.dependencies]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.64.0"
+version = "0.65.2"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"

src/crewai/agents/crew_agent_executor.py

@@ -70,7 +70,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.log_error_after = 3
         self.have_forced_answer = False
         self.name_to_tool_map = {tool.name: tool for tool in self.tools}
-        self.llm.stop = self.stop
+        if self.llm.stop:
+            self.llm.stop = list(set(self.llm.stop + self.stop))
+        else:
+            self.llm.stop = self.stop
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:

src/crewai/cli/cli.py

@@ -4,6 +4,7 @@ import click
 import pkg_resources
 
 from crewai.cli.create_crew import create_crew
+from crewai.cli.create_flow import create_flow
 from crewai.cli.create_pipeline import create_pipeline
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
@@ -26,19 +27,20 @@ def crewai():
 
 
 @crewai.command()
-@click.argument("type", type=click.Choice(["crew", "pipeline"]))
+@click.argument("type", type=click.Choice(["crew", "pipeline", "flow"]))
 @click.argument("name")
-@click.option(
-    "--router", is_flag=True, help="Create a pipeline with router functionality"
-)
-def create(type, name, router):
-    """Create a new crew or pipeline."""
+def create(type, name):
+    """Create a new crew, pipeline, or flow."""
     if type == "crew":
         create_crew(name)
     elif type == "pipeline":
-        create_pipeline(name, router)
+        create_pipeline(name)
+    elif type == "flow":
+        create_flow(name)
     else:
-        click.secho("Error: Invalid type. Must be 'crew' or 'pipeline'.", fg="red")
+        click.secho(
+            "Error: Invalid type. Must be 'crew', 'pipeline', or 'flow'.", fg="red"
+        )
 
 
 @crewai.command()

src/crewai/cli/create_flow.py

@@ -0,0 +1,86 @@
+from pathlib import Path
+
+import click
+
+
+def create_flow(name):
+    """Create a new flow."""
+    folder_name = name.replace(" ", "_").replace("-", "_").lower()
+    class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
+
+    click.secho(f"Creating flow {folder_name}...", fg="green", bold=True)
+
+    project_root = Path(folder_name)
+    if project_root.exists():
+        click.secho(f"Error: Folder {folder_name} already exists.", fg="red")
+        return
+
+    # Create directory structure
+    (project_root / "src" / folder_name).mkdir(parents=True)
+    (project_root / "src" / folder_name / "crews").mkdir(parents=True)
+    (project_root / "src" / folder_name / "tools").mkdir(parents=True)
+    (project_root / "tests").mkdir(exist_ok=True)
+
+    # Create .env file
+    with open(project_root / ".env", "w") as file:
+        file.write("OPENAI_API_KEY=YOUR_API_KEY")
+
+    package_dir = Path(__file__).parent
+    templates_dir = package_dir / "templates" / "flow"
+
+    # List of template files to copy
+    root_template_files = [".gitignore", "pyproject.toml", "README.md"]
+    src_template_files = ["__init__.py", "main.py"]
+    tools_template_files = ["tools/__init__.py", "tools/custom_tool.py"]
+
+    crew_folders = [
+        "poem_crew",
+    ]
+
+    def process_file(src_file, dst_file):
+        with open(src_file, "r") as file:
+            content = file.read()
+
+        content = content.replace("{{name}}", name)
+        content = content.replace("{{flow_name}}", class_name)
+        content = content.replace("{{folder_name}}", folder_name)
+
+        with open(dst_file, "w") as file:
+            file.write(content)
+
+    # Copy and process root template files
+    for file_name in root_template_files:
+        src_file = templates_dir / file_name
+        dst_file = project_root / file_name
+        process_file(src_file, dst_file)
+
+    # Copy and process src template files
+    for file_name in src_template_files:
+        src_file = templates_dir / file_name
+        dst_file = project_root / "src" / folder_name / file_name
+        process_file(src_file, dst_file)
+
+    # Copy tools files
+    for file_name in tools_template_files:
+        src_file = templates_dir / file_name
+        dst_file = project_root / "src" / folder_name / file_name
+        process_file(src_file, dst_file)
+
+    # Copy crew folders
+    for crew_folder in crew_folders:
+        src_crew_folder = templates_dir / "crews" / crew_folder
+        dst_crew_folder = project_root / "src" / folder_name / "crews" / crew_folder
+        if src_crew_folder.exists():
+            for src_file in src_crew_folder.rglob("*"):
+                if src_file.is_file():
+                    relative_path = src_file.relative_to(src_crew_folder)
+                    dst_file = dst_crew_folder / relative_path
+                    dst_file.parent.mkdir(parents=True, exist_ok=True)
+                    process_file(src_file, dst_file)
+        else:
+            click.secho(
+                f"Warning: Crew folder {crew_folder} not found in template.",
+                fg="yellow",
+            )
+
+    click.secho(f"Flow {name} created successfully!", fg="green", bold=True)

src/crewai/cli/templates/crew/pyproject.toml

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.64.0,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.65.2,<1.0.0" }
 
 
 [tool.poetry.scripts]

src/crewai/cli/templates/flow/.gitignore

@@ -0,0 +1,2 @@
+.env
+__pycache__/

src/crewai/cli/templates/flow/README.md

@@ -0,0 +1,57 @@
+# {{crew_name}} Crew
+
+Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.
+
+## Installation
+
+Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [Poetry](https://python-poetry.org/) for dependency management and package handling, offering a seamless setup and execution experience.
+
+First, if you haven't already, install Poetry:
+
+```bash
+pip install poetry
+```
+
+Next, navigate to your project directory and install the dependencies:
+
+1. First lock the dependencies and then install them:
+
+```bash
+crewai install
+```
+
+### Customizing
+
+**Add your `OPENAI_API_KEY` into the `.env` file**
+
+- Modify `src/{{folder_name}}/config/agents.yaml` to define your agents
+- Modify `src/{{folder_name}}/config/tasks.yaml` to define your tasks
+- Modify `src/{{folder_name}}/crew.py` to add your own logic, tools and specific args
+- Modify `src/{{folder_name}}/main.py` to add custom inputs for your agents and tasks
+
+## Running the Project
+
+To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
+
+```bash
+crewai run
+```
+
+This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
+
+This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
+
+## Understanding Your Crew
+
+The {{name}} Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew.
+
+## Support
+
+For support, questions, or feedback regarding the {{crew_name}} Crew or crewAI.
+
+- Visit our [documentation](https://docs.crewai.com)
+- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai)
+- [Join our Discord](https://discord.com/invite/X4JWnZnxPb)
+- [Chat with our docs](https://chatg.pt/DWjSBZn)
+
+Let's create wonders together with the power and simplicity of crewAI.

src/crewai/cli/templates/flow/crews/poem_crew/config/agents.yaml

@@ -0,0 +1,11 @@
+poem_writer:
+  role: >
+    CrewAI Poem Writer
+  goal: >
+    Generate a funny, light heartedpoem about how CrewAI 
+    is awesome with a sentence count of {sentence_count}
+  backstory: >
+    You're a creative poet with a talent for capturing the essence of any topic
+    in a beautiful and engaging way. Known for your ability to craft poems that
+    resonate with readers, you bring a unique perspective and artistic flair to
+    every piece you write.

src/crewai/cli/templates/flow/crews/poem_crew/config/tasks.yaml

@@ -0,0 +1,7 @@
+write_poem:
+  description: >
+    Write a poem about how CrewAI is awesome.
+    Ensure the poem is engaging and adheres to the specified sentence count of {sentence_count}.
+  expected_output: >
+    A beautifully crafted poem about CrewAI, with exactly {sentence_count} sentences.
+  agent: poem_writer

src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py

@@ -0,0 +1,31 @@
+from crewai import Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+
+@CrewBase
+class PoemCrew():
+	"""Poem Crew"""
+
+	agents_config = 'config/agents.yaml'
+	tasks_config = 'config/tasks.yaml'
+
+	@agent
+	def poem_writer(self) -> Agent:
+		return Agent(
+			config=self.agents_config['poem_writer'],
+		)
+
+	@task
+	def write_poem(self) -> Task:
+		return Task(
+			config=self.tasks_config['write_poem'],
+		)
+
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the Research Crew"""
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=True,
+		)

src/crewai/cli/templates/flow/main.py

@@ -0,0 +1,54 @@
+#!/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")
+        print(f"State before poem: {self.state}")
+        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
+        
+        print(f"State after generate_poem: {self.state}")
+
+    @listen(generate_poem)
+    def save_poem(self):
+        print("Saving poem")
+        print(f"State before save_poem: {self.state}")
+        with open("poem.txt", "w") as f:
+            f.write(self.state.poem)
+        print(f"State after save_poem: {self.state}")
+
+async def run():
+    """
+    Run the flow.
+    """
+    poem_flow = PoemFlow()
+    await poem_flow.kickoff()
+
+
+def main():
+    asyncio.run(run())
+
+
+if __name__ == "__main__":
+    main()

src/crewai/cli/templates/flow/pyproject.toml

@@ -0,0 +1,18 @@
+[tool.poetry]
+name = "{{folder_name}}"
+version = "0.1.0"
+description = "{{name}} using crewAI"
+authors = ["Your Name <you@example.com>"]
+
+[tool.poetry.dependencies]
+python = ">=3.10,<=3.13"
+crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
+asyncio = "*"
+
+[tool.poetry.scripts]
+{{folder_name}} = "{{folder_name}}.main:main"
+run_crew = "{{folder_name}}.main:main"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

src/crewai/cli/templates/flow/tools/custom_tool.py

@@ -0,0 +1,12 @@
+from crewai_tools import BaseTool
+
+
+class MyCustomTool(BaseTool):
+    name: str = "Name of my tool"
+    description: str = (
+        "Clear description for what this tool is useful for, you agent will need this information to use it."
+    )
+
+    def _run(self, argument: str) -> str:
+        # Implementation goes here
+        return "this is an example of a tool output, ignore it and move along."

src/crewai/cli/templates/pipeline/pyproject.toml

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.64.0,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.65.2,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

src/crewai/cli/templates/pipeline_router/pyproject.toml

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.64.0,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.65.2,<1.0.0" }
 
 
 [tool.poetry.scripts]

src/crewai/crews/crew_output.py

@@ -41,6 +41,14 @@ class CrewOutput(BaseModel):
             output_dict.update(self.pydantic.model_dump())
         return output_dict
 
+    def __getitem__(self, key):
+        if self.pydantic and hasattr(self.pydantic, key):
+            return getattr(self.pydantic, key)
+        elif self.json_dict and key in self.json_dict:
+            return self.json_dict[key]
+        else:
+            raise KeyError(f"Key '{key}' not found in CrewOutput.")
+
     def __str__(self):
         if self.pydantic:
             return str(self.pydantic)

src/crewai/flow/flow.py

@@ -0,0 +1,252 @@
+import asyncio
+import inspect
+from typing import Any, Callable, Dict, Generic, List, Set, Type, TypeVar, Union
+
+from pydantic import BaseModel
+
+T = TypeVar("T", bound=Union[BaseModel, Dict[str, Any]])
+
+
+def start(condition=None):
+    def decorator(func):
+        func.__is_start_method__ = True
+        if condition is not None:
+            if isinstance(condition, str):
+                func.__trigger_methods__ = [condition]
+                func.__condition_type__ = "OR"
+            elif (
+                isinstance(condition, dict)
+                and "type" in condition
+                and "methods" in condition
+            ):
+                func.__trigger_methods__ = condition["methods"]
+                func.__condition_type__ = condition["type"]
+            elif callable(condition) and hasattr(condition, "__name__"):
+                func.__trigger_methods__ = [condition.__name__]
+                func.__condition_type__ = "OR"
+            else:
+                raise ValueError(
+                    "Condition must be a method, string, or a result of or_() or and_()"
+                )
+        return func
+
+    return decorator
+
+
+def listen(condition):
+    def decorator(func):
+        if isinstance(condition, str):
+            func.__trigger_methods__ = [condition]
+            func.__condition_type__ = "OR"
+        elif (
+            isinstance(condition, dict)
+            and "type" in condition
+            and "methods" in condition
+        ):
+            func.__trigger_methods__ = condition["methods"]
+            func.__condition_type__ = condition["type"]
+        elif callable(condition) and hasattr(condition, "__name__"):
+            func.__trigger_methods__ = [condition.__name__]
+            func.__condition_type__ = "OR"
+        else:
+            raise ValueError(
+                "Condition must be a method, string, or a result of or_() or and_()"
+            )
+        return func
+
+    return decorator
+
+
+def router(method):
+    def decorator(func):
+        func.__is_router__ = True
+        func.__router_for__ = method.__name__
+        return func
+
+    return decorator
+
+
+def or_(*conditions):
+    methods = []
+    for condition in conditions:
+        if isinstance(condition, dict) and "methods" in condition:
+            methods.extend(condition["methods"])
+        elif isinstance(condition, str):
+            methods.append(condition)
+        elif callable(condition):
+            methods.append(getattr(condition, "__name__", repr(condition)))
+        else:
+            raise ValueError("Invalid condition in or_()")
+    return {"type": "OR", "methods": methods}
+
+
+def and_(*conditions):
+    methods = []
+    for condition in conditions:
+        if isinstance(condition, dict) and "methods" in condition:
+            methods.extend(condition["methods"])
+        elif isinstance(condition, str):
+            methods.append(condition)
+        elif callable(condition):
+            methods.append(getattr(condition, "__name__", repr(condition)))
+        else:
+            raise ValueError("Invalid condition in and_()")
+    return {"type": "AND", "methods": methods}
+
+
+class FlowMeta(type):
+    def __new__(mcs, name, bases, dct):
+        cls = super().__new__(mcs, name, bases, dct)
+
+        start_methods = []
+        listeners = {}
+        routers = {}
+
+        for attr_name, attr_value in dct.items():
+            if hasattr(attr_value, "__is_start_method__"):
+                start_methods.append(attr_name)
+                if hasattr(attr_value, "__trigger_methods__"):
+                    methods = attr_value.__trigger_methods__
+                    condition_type = getattr(attr_value, "__condition_type__", "OR")
+                    listeners[attr_name] = (condition_type, methods)
+            elif hasattr(attr_value, "__trigger_methods__"):
+                methods = attr_value.__trigger_methods__
+                condition_type = getattr(attr_value, "__condition_type__", "OR")
+                listeners[attr_name] = (condition_type, methods)
+            elif hasattr(attr_value, "__is_router__"):
+                routers[attr_value.__router_for__] = attr_name
+
+        setattr(cls, "_start_methods", start_methods)
+        setattr(cls, "_listeners", listeners)
+        setattr(cls, "_routers", routers)
+
+        return cls
+
+
+class Flow(Generic[T], metaclass=FlowMeta):
+    _start_methods: List[str] = []
+    _listeners: Dict[str, tuple[str, List[str]]] = {}
+    _routers: Dict[str, str] = {}
+    initial_state: Union[Type[T], T, None] = None
+
+    def __class_getitem__(cls, item):
+        class _FlowGeneric(cls):
+            _initial_state_T = item
+
+        return _FlowGeneric
+
+    def __init__(self):
+        self._methods: Dict[str, Callable] = {}
+        self._state = 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
+
+        for method_name in dir(self):
+            if callable(getattr(self, method_name)) and not method_name.startswith(
+                "__"
+            ):
+                self._methods[method_name] = getattr(self, method_name)
+
+    def _create_initial_state(self) -> T:
+        if self.initial_state is None and hasattr(self, "_initial_state_T"):
+            return self._initial_state_T()  # type: ignore
+        if self.initial_state is None:
+            return {}  # type: ignore
+        elif isinstance(self.initial_state, type):
+            return self.initial_state()
+        else:
+            return self.initial_state
+
+    @property
+    def state(self) -> T:
+        return self._state
+
+    @property
+    def method_outputs(self) -> List[Any]:
+        """Returns the list of all outputs from executed methods."""
+        return self._method_outputs
+
+    async def kickoff(self) -> Any:
+        if not self._start_methods:
+            raise ValueError("No start method defined")
+
+        # Create tasks for all start methods
+        tasks = [
+            self._execute_start_method(start_method)
+            for start_method in self._start_methods
+        ]
+
+        # Run all start methods concurrently
+        await asyncio.gather(*tasks)
+
+        # Return the final output (from the last executed method)
+        if self._method_outputs:
+            return self._method_outputs[-1]
+        else:
+            return None  # Or raise an exception if no methods were executed
+
+    async def _execute_start_method(self, start_method: str):
+        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):
+        result = (
+            await method(*args, **kwargs)
+            if asyncio.iscoroutinefunction(method)
+            else method(*args, **kwargs)
+        )
+        self._method_outputs.append(result)  # Store the output
+        return result
+
+    async def _execute_listeners(self, trigger_method: str, result: Any):
+        listener_tasks = []
+
+        if trigger_method in self._routers:
+            router_method = self._methods[self._routers[trigger_method]]
+            path = await self._execute_method(router_method)
+            # Use the path as the new trigger method
+            trigger_method = path
+
+        for listener, (condition_type, methods) in self._listeners.items():
+            if condition_type == "OR":
+                if trigger_method in methods:
+                    listener_tasks.append(
+                        self._execute_single_listener(listener, result)
+                    )
+            elif condition_type == "AND":
+                if listener not in self._pending_and_listeners:
+                    self._pending_and_listeners[listener] = set()
+                self._pending_and_listeners[listener].add(trigger_method)
+                if set(methods) == self._pending_and_listeners[listener]:
+                    listener_tasks.append(
+                        self._execute_single_listener(listener, result)
+                    )
+                    del self._pending_and_listeners[listener]
+
+        # 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):
+        try:
+            method = self._methods[listener]
+            sig = inspect.signature(method)
+            params = list(sig.parameters.values())
+
+            # Exclude 'self' parameter
+            method_params = [p for p in params if p.name != "self"]
+
+            if method_params:
+                # If listener expects parameters, pass the result
+                listener_result = await self._execute_method(method, result)
+            else:
+                # If listener does not expect parameters, call without arguments
+                listener_result = await self._execute_method(method)
+
+            # Execute listeners of this listener
+            await self._execute_listeners(listener, listener_result)
+        except Exception as e:
+            print(f"[Flow._execute_single_listener] Error in method {listener}: {e}")
+            import traceback
+
+            traceback.print_exc()

src/crewai/project/annotations.py

@@ -1,6 +1,7 @@
 from functools import wraps
 
 from crewai.project.utils import memoize
+from crewai import Crew
 
 
 def task(func):
@@ -72,7 +73,7 @@ def pipeline(func):
     return memoize(func)
 
 
-def crew(func):
+def crew(func) -> "Crew":
     def wrapper(self, *args, **kwargs):
         instantiated_tasks = []
         instantiated_agents = []

src/crewai/project/crew_base.py

@@ -5,11 +5,8 @@ from typing import Any, Callable, Dict, Type, TypeVar
 import yaml
 from dotenv import load_dotenv
 
-from crewai.crew import Crew
-
 load_dotenv()
 
-
 T = TypeVar("T", bound=Type[Any])
 
 
@@ -37,19 +34,6 @@ def CrewBase(cls: T) -> T:
             self.map_all_agent_variables()
             self.map_all_task_variables()
 
-        def crew(self) -> "Crew":
-            agents = [
-                getattr(self, name)()
-                for name, func in self._get_all_functions().items()
-                if hasattr(func, "is_agent")
-            ]
-            tasks = [
-                getattr(self, name)()
-                for name, func in reversed(self._get_all_functions().items())
-                if hasattr(func, "is_task")
-            ]
-            return Crew(agents=agents, tasks=tasks)
-
         @staticmethod
         def load_yaml(config_path: Path):
             try:

tests/agent_test.py

@@ -1093,7 +1093,7 @@ def test_agent_training_handler(crew_training_handler):
 
     result = agent._training_handler(task_prompt=task_prompt)
 
-    assert result == "What is 1 + 1?You MUST follow these feedbacks: \n good"
+    assert result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n good"
 
     crew_training_handler.assert_has_calls(
         [mock.call(), mock.call("training_data.pkl"), mock.call().load()]
@@ -1121,8 +1121,8 @@ def test_agent_use_trained_data(crew_training_handler):
     result = agent._use_trained_data(task_prompt=task_prompt)
 
     assert (
-        result == "What is 1 + 1?You MUST follow these feedbacks: \n "
-        "The result of the math operation must be right.\n - Result must be better than 1."
+        result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n"
+        " - The result of the math operation must be right.\n - Result must be better than 1."
     )
     crew_training_handler.assert_has_calls(
         [mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()]
@@ -1205,7 +1205,7 @@ def test_agent_with_custom_stop_words():
     )
 
     assert isinstance(agent.llm, LLM)
-    assert agent.llm.stop == stop_words
+    assert agent.llm.stop == stop_words + ["\nObservation:"]
 
 
 def test_agent_with_callbacks():
@@ -1368,7 +1368,7 @@ def test_agent_with_all_llm_attributes():
     assert agent.llm.temperature == 0.7
     assert agent.llm.top_p == 0.9
     assert agent.llm.n == 1
-    assert agent.llm.stop == ["STOP", "END"]
+    assert agent.llm.stop == ["STOP", "END", "\nObservation:"]
     assert agent.llm.max_tokens == 100
     assert agent.llm.presence_penalty == 0.1
     assert agent.llm.frequency_penalty == 0.1

tests/utilities/evaluators/test_task_evaluator.py

@@ -50,11 +50,12 @@ def test_evaluate_training_data(converter_mock):
                 text="Assess the quality of the training data based on the llm output, human feedback , and llm "
                 "output improved result.\n\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback "
                 "1\n\nImproved Output:\nImproved output 1\n\nInitial Output:\nInitial output 2\n\nHuman "
-                "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\nPlease provide:\n- "
-                "Based on the Human Feedbacks and the comparison between Initial Outputs and Improved outputs "
-                "provide action items based on human_feedback for future tasks\n- A score from 0 to 10 evaluating "
-                "on completion, quality, and overall performance from the improved output to the initial output "
-                "based on the human feedback\n",
+                "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\nPlease provide:\n- Provide "
+                "a list of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent's "
+                "performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific "
+                "action items for future tasks. Ensure all key and specificpoints from the human feedback are "
+                "incorporated into these instructions.\n- A score from 0 to 10 evaluating on completion, quality, and "
+                "overall performance from the improved output to the initial output based on the human feedback\n",
                 model=TrainingTaskEvaluation,
                 instructions="I'm gonna convert this raw text into valid JSON.\n\nThe json should have the "
                 "following structure, with the following keys:\n{\n    suggestions: List[str],\n    quality: float,\n    final_summary: str\n}",