Brandon/cre 402 add input into flow kickoff

docs/concepts/flows.mdx

@@ -18,60 +18,63 @@ Flows allow you to create structured, event-driven workflows. They provide a sea
 
 4. **Flexible Control Flow**: Implement conditional logic, loops, and branching within your workflows.
 
+5. **Input Flexibility**: Flows can accept inputs to initialize or update their state, with different handling for structured and unstructured state management.
+
 ## 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 Code
+### Passing Inputs to Flows
 
-from crewai.flow.flow import Flow, listen, start
-from dotenv import load_dotenv
-from litellm import completion
+Flows can accept inputs to initialize or update their state before execution. The way inputs are handled depends on whether the flow uses structured or unstructured state management.
+
+#### Structured State Management
 
+In structured state management, the flow's state is defined using a Pydantic `BaseModel`. Inputs must match the model's schema, and any updates will overwrite the default values.
+
+```python
+from crewai.flow.flow import Flow, listen, start
+from pydantic import BaseModel
 
-class ExampleFlow(Flow):
-    model = "gpt-4o-mini"
+class ExampleState(BaseModel):
+    counter: int = 0
+    message: str = ""
 
+class StructuredExampleFlow(Flow[ExampleState]):
     @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.",
-                },
-            ],
-        )
+    def first_method(self):
+        # Implementation
 
-        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}",
-                },
-            ],
-        )
+flow = StructuredExampleFlow()
+flow.kickoff(inputs={"counter": 10})
+```
+
+In this example, the `counter` is initialized to `10`, while `message` retains its default value.
+
+#### Unstructured State Management
+
+In unstructured state management, the flow's state is a dictionary. You can pass any dictionary to update the state.
+
+```python
+from crewai.flow.flow import Flow, listen, start
+
+class UnstructuredExampleFlow(Flow):
+    @start()
+    def first_method(self):
+        # Implementation
 
-        fun_fact = response["choices"][0]["message"]["content"]
-        return fun_fact
+flow = UnstructuredExampleFlow()
+flow.kickoff(inputs={"counter": 5, "message": "Initial message"})
+```
 
+Here, both `counter` and `message` are updated based on the provided inputs.
 
+**Note:** Ensure that inputs for structured state management adhere to the defined schema to avoid validation errors.
 
-flow = ExampleFlow()
-result = flow.kickoff()
+### Example Flow
 
-print(f"Generated fun fact: {result}")
+```python
+# Existing example code
 ```
 
 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.
@@ -94,14 +97,14 @@ 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 Code
+   ```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 Code
+   ```python
    @listen(generate_city)
    def generate_fun_fact(self, random_city):
        # Implementation
@@ -118,7 +121,7 @@ When you run a Flow, the final output is determined by the last method that comp
 Here's how you can access the final output:
 
 <CodeGroup>
-```python Code
+```python
 from crewai.flow.flow import Flow, listen, start
 
 class OutputExampleFlow(Flow):
@@ -130,18 +133,17 @@ class OutputExampleFlow(Flow):
     def second_method(self, first_output):
         return f"Second method received: {first_output}"
 
-
 flow = OutputExampleFlow()
 final_output = flow.kickoff()
 
 print("---- Final Output ----")
 print(final_output)
-````
+```
 
-``` text Output
+```text
 ---- Final Output ----
 Second method received: Output from first_method
-````
+```
 
 </CodeGroup>
 
@@ -156,7 +158,7 @@ Here's an example of how to update and access the state:
 
 <CodeGroup>
 
-```python Code
+```python
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
@@ -184,7 +186,7 @@ print("Final State:")
 print(flow.state)
 ```
 
-```text Output
+```text
 Final Output: Hello from first_method - updated by second_method
 Final State:
 counter=2 message='Hello from first_method - updated by second_method'
@@ -208,10 +210,10 @@ allowing developers to choose the approach that best fits their application's ne
 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 Code
+```python
 from crewai.flow.flow import Flow, listen, start
 
-class UntructuredExampleFlow(Flow):
+class UnstructuredExampleFlow(Flow):
 
     @start()
     def first_method(self):
@@ -230,8 +232,7 @@ class UntructuredExampleFlow(Flow):
 
         print(f"State after third_method: {self.state}")
 
-
-flow = UntructuredExampleFlow()
+flow = UnstructuredExampleFlow()
 flow.kickoff()
 ```
 
@@ -245,16 +246,14 @@ flow.kickoff()
 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 Code
+```python
 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()
@@ -273,7 +272,6 @@ class StructuredExampleFlow(Flow[ExampleState]):
 
         print(f"State after third_method: {self.state}")
 
-
 flow = StructuredExampleFlow()
 flow.kickoff()
 ```
@@ -307,7 +305,7 @@ The `or_` function in Flows allows you to listen to multiple methods and trigger
 
 <CodeGroup>
 
-```python Code
+```python
 from crewai.flow.flow import Flow, listen, or_, start
 
 class OrExampleFlow(Flow):
@@ -324,13 +322,11 @@ class OrExampleFlow(Flow):
     def logger(self, result):
         print(f"Logger: {result}")
 
-
-
 flow = OrExampleFlow()
 flow.kickoff()
 ```
 
-```text Output
+```text
 Logger: Hello from the start method
 Logger: Hello from the second method
 ```
@@ -346,7 +342,7 @@ The `and_` function in Flows allows you to listen to multiple methods and trigge
 
 <CodeGroup>
 
-```python Code
+```python
 from crewai.flow.flow import Flow, and_, listen, start
 
 class AndExampleFlow(Flow):
@@ -368,7 +364,7 @@ flow = AndExampleFlow()
 flow.kickoff()
 ```
 
-```text Output
+```text
 ---- Logger ----
 {'greeting': 'Hello from the start method', 'joke': 'What do computers eat? Microchips.'}
 ```
@@ -385,7 +381,7 @@ You can specify different routes based on the output of the method, allowing you
 
 <CodeGroup>
 
-```python Code
+```python
 import random
 from crewai.flow.flow import Flow, listen, router, start
 from pydantic import BaseModel
@@ -416,12 +412,11 @@ class RouterFlow(Flow[ExampleState]):
     def fourth_method(self):
         print("Fourth method running")
 
-
 flow = RouterFlow()
 flow.kickoff()
 ```
 
-```text Output
+```text
 Starting the structured flow
 Third method running
 Fourth method running
@@ -484,7 +479,7 @@ The `main.py` file is where you create your flow and connect the crews together.
 
 Here's an example of how you can connect the `poem_crew` in the `main.py` file:
 
-```python Code
+```python
 #!/usr/bin/env python
 from random import randint
 
@@ -612,7 +607,7 @@ CrewAI provides two convenient methods to generate plots of your flows:
 
 If you are working directly with a flow instance, you can generate a plot by calling the `plot()` method on your flow object. This method will create an HTML file containing the interactive plot of your flow.
 
-```python Code
+```python
 # Assuming you have a flow instance
 flow.plot("my_flow_plot")
 ```

src/crewai/agent.py

@@ -8,6 +8,7 @@
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.cli.constants import ENV_VARS
 from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.tools.agent_tools.agent_tools import AgentTools
@@ -131,8 +132,12 @@ def post_init_setup(self):
             # If it's already an LLM instance, keep it as is
             pass
         elif self.llm is None:
-            # If it's None, use environment variables or default
-            model_name = os.environ.get("OPENAI_MODEL_NAME", "gpt-4o-mini")
+            # Determine the model name from environment variables or use default
+            model_name = (
+                os.environ.get("OPENAI_MODEL_NAME")
+                or os.environ.get("MODEL")
+                or "gpt-4o-mini"
+            )
             llm_params = {"model": model_name}
 
             api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
@@ -141,9 +146,39 @@ def post_init_setup(self):
             if api_base:
                 llm_params["base_url"] = api_base
 
-            api_key = os.environ.get("OPENAI_API_KEY")
-            if api_key:
-                llm_params["api_key"] = api_key
+            # Iterate over all environment variables to find matching API keys or use defaults
+            for provider, env_vars in ENV_VARS.items():
+                for env_var in env_vars:
+                    # Check if the environment variable is set
+                    if "key_name" in env_var:
+                        env_value = os.environ.get(env_var["key_name"])
+                        if env_value:
+                            # Map key names containing "API_KEY" to "api_key"
+                            key_name = (
+                                "api_key"
+                                if "API_KEY" in env_var["key_name"]
+                                else env_var["key_name"]
+                            )
+                            # Map key names containing "API_BASE" to "api_base"
+                            key_name = (
+                                "api_base"
+                                if "API_BASE" in env_var["key_name"]
+                                else key_name
+                            )
+                            # Map key names containing "API_VERSION" to "api_version"
+                            key_name = (
+                                "api_version"
+                                if "API_VERSION" in env_var["key_name"]
+                                else key_name
+                            )
+                            llm_params[key_name] = env_value
+                    # Check for default values if the environment variable is not set
+                    elif env_var.get("default", False):
+                        for key, value in env_var.items():
+                            if key not in ["prompt", "key_name", "default"]:
+                                # Only add default if the key is already set in os.environ
+                                if key in os.environ:
+                                    llm_params[key] = value
 
             self.llm = LLM(**llm_params)
         else: