Merge branch 'main' into bugfix/support-llm-managers-that-use-model

* before kickoff breaks if inputs are none.

* improve none type

* Fix failing tests

* add tests for new code

* Fix failing test

* drop extra comments

* clean up based on eduardo feedback

docs/concepts/flows.mdx

@@ -35,6 +35,8 @@ class ExampleFlow(Flow):
     @start()
     def generate_city(self):
         print("Starting flow")
+        # Each flow state automatically gets a unique ID
+        print(f"Flow State ID: {self.state['id']}")
 
         response = completion(
             model=self.model,
@@ -47,6 +49,8 @@ class ExampleFlow(Flow):
         )
 
         random_city = response["choices"][0]["message"]["content"]
+        # Store the city in our state
+        self.state["city"] = random_city
         print(f"Random City: {random_city}")
 
         return random_city
@@ -64,6 +68,8 @@ class ExampleFlow(Flow):
         )
 
         fun_fact = response["choices"][0]["message"]["content"]
+        # Store the fun fact in our state
+        self.state["fun_fact"] = fun_fact
         return fun_fact
 
 
@@ -76,7 +82,15 @@ print(f"Generated fun fact: {result}")
 
 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.
+Each Flow instance automatically receives a unique identifier (UUID) in its state, which helps track and manage flow executions. The state can also store additional data (like the generated city and fun fact) that persists throughout the flow's execution.
+
+When you run the Flow, it will:
+1. Generate a unique ID for the flow state
+2. Generate a random city and store it in the state
+3. Generate a fun fact about that city and store it in the state
+4. Print the results to the console
+
+The state's unique ID and stored data can be useful for tracking flow executions and maintaining context between tasks.
 
 **Note:** Ensure you have set up your `.env` file to store your `OPENAI_API_KEY`. This key is necessary for authenticating requests to the OpenAI API.
 
@@ -207,14 +221,17 @@ 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.
+Even with unstructured states, CrewAI Flows automatically generates and maintains a unique identifier (UUID) for each state instance.
 
 ```python Code
 from crewai.flow.flow import Flow, listen, start
 
-class UntructuredExampleFlow(Flow):
+class UnstructuredExampleFlow(Flow):
 
     @start()
     def first_method(self):
+        # The state automatically includes an 'id' field
+        print(f"State ID: {self.state['id']}")
         self.state.message = "Hello from structured flow"
         self.state.counter = 0
 
@@ -231,10 +248,12 @@ class UntructuredExampleFlow(Flow):
         print(f"State after third_method: {self.state}")
 
 
-flow = UntructuredExampleFlow()
+flow = UnstructuredExampleFlow()
 flow.kickoff()
 ```
 
+**Note:** The `id` field is automatically generated and preserved throughout the flow's execution. You don't need to manage or set it manually, and it will be maintained even when updating the state with new data.
+
 **Key Points:**
 
 - **Flexibility:** You can dynamically add attributes to `self.state` without predefined constraints.
@@ -245,12 +264,15 @@ 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.
 
+Each state in CrewAI Flows automatically receives a unique identifier (UUID) to help track and manage state instances. This ID is automatically generated and managed by the Flow system.
+
 ```python Code
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
 
 class ExampleState(BaseModel):
+    # Note: 'id' field is automatically added to all states
     counter: int = 0
     message: str = ""
 
@@ -259,6 +281,8 @@ class StructuredExampleFlow(Flow[ExampleState]):
 
     @start()
     def first_method(self):
+        # Access the auto-generated ID if needed
+        print(f"State ID: {self.state.id}")
         self.state.message = "Hello from structured flow"
 
     @listen(first_method)
@@ -628,4 +652,4 @@ Also, check out our YouTube video on how to use flows in CrewAI below!
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
   allowfullscreen
-></iframe>
+></iframe>

pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm>=1.44.22",
+    "litellm==1.57.4",
     "instructor>=1.3.3",
 
     # Text Processing

src/crewai/crew.py

@@ -513,6 +513,8 @@ class Crew(BaseModel):
         inputs: Optional[Dict[str, Any]] = None,
     ) -> CrewOutput:
         for before_callback in self.before_kickoff_callbacks:
+            if inputs is None:
+                inputs = {}
             inputs = before_callback(inputs)
 
         """Starts the crew to work on its assigned tasks."""
@@ -674,6 +676,7 @@ class Crew(BaseModel):
         else:
             self.manager_llm = (
                 getattr(self.manager_llm, "model_name", None)
+                or getattr(self.manager_llm, "model", None)
                 or getattr(self.manager_llm, "deployment_name", None)
                 or self.manager_llm
             )

src/crewai/flow/flow.py

@@ -13,9 +13,10 @@ from typing import (
     Union,
     cast,
 )
+from uuid import uuid4
 
 from blinker import Signal
-from pydantic import BaseModel, ValidationError
+from pydantic import BaseModel, Field, ValidationError
 
 from crewai.flow.flow_events import (
     FlowFinishedEvent,
@@ -27,7 +28,12 @@ from crewai.flow.flow_visualizer import plot_flow
 from crewai.flow.utils import get_possible_return_constants
 from crewai.telemetry import Telemetry
 
-T = TypeVar("T", bound=Union[BaseModel, Dict[str, Any]])
+
+class FlowState(BaseModel):
+    """Base model for all flow states, ensuring each state has a unique ID."""
+    id: str = Field(default_factory=lambda: str(uuid4()), description="Unique identifier for the flow state")
+
+T = TypeVar("T", bound=Union[FlowState, Dict[str, Any]])
 
 
 def start(condition: Optional[Union[str, dict, Callable]] = None) -> Callable:
@@ -377,14 +383,37 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 self._methods[method_name] = getattr(self, method_name)
 
     def _create_initial_state(self) -> T:
+        # Handle case where initial_state is None but we have a type parameter
         if self.initial_state is None and hasattr(self, "_initial_state_T"):
-            return self._initial_state_T()  # type: ignore
+            state_type = getattr(self, "_initial_state_T")
+            if isinstance(state_type, type):
+                if issubclass(state_type, FlowState):
+                    return state_type()  # type: ignore
+                elif issubclass(state_type, BaseModel):
+                    # Create a new type that includes the ID field
+                    class StateWithId(state_type, FlowState):  # type: ignore
+                        pass
+                    return StateWithId()  # type: ignore
+
+        # Handle case where no initial state is provided
         if self.initial_state is None:
-            return {}  # type: ignore
-        elif isinstance(self.initial_state, type):
-            return self.initial_state()
-        else:
-            return self.initial_state
+            return {"id": str(uuid4())}  # type: ignore
+
+        # Handle case where initial_state is a type (class)
+        if isinstance(self.initial_state, type):
+            if issubclass(self.initial_state, FlowState):
+                return self.initial_state()  # type: ignore
+            elif issubclass(self.initial_state, BaseModel):
+                # Create a new type that includes the ID field
+                class StateWithId(self.initial_state, FlowState):  # type: ignore
+                    pass
+                return StateWithId()  # type: ignore
+
+        # Handle dictionary case
+        if isinstance(self.initial_state, dict) and "id" not in self.initial_state:
+            self.initial_state["id"] = str(uuid4())
+
+        return self.initial_state  # type: ignore
 
     @property
     def state(self) -> T:
@@ -396,10 +425,17 @@ class Flow(Generic[T], metaclass=FlowMeta):
         return self._method_outputs
 
     def _initialize_state(self, inputs: Dict[str, Any]) -> None:
-        if isinstance(self._state, BaseModel):
+        if isinstance(self._state, dict):
+            # Preserve the ID when updating unstructured state
+            current_id = self._state.get("id")
+            self._state.update(inputs)
+            if current_id:
+                self._state["id"] = current_id
+            elif "id" not in self._state:
+                self._state["id"] = str(uuid4())
+        elif isinstance(self._state, BaseModel):
             # Structured state
             try:
-
                 def create_model_with_extra_forbid(
                     base_model: Type[BaseModel],
                 ) -> Type[BaseModel]:
@@ -409,16 +445,28 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
                     return ModelWithExtraForbid
 
+                # Get current state as dict, preserving the ID if it exists
+                state_model = cast(BaseModel, self._state)
+                current_state = (
+                    state_model.model_dump()
+                    if hasattr(state_model, "model_dump")
+                    else state_model.dict()
+                    if hasattr(state_model, "dict")
+                    else {
+                        k: v
+                        for k, v in state_model.__dict__.items()
+                        if not k.startswith("_")
+                    }
+                )
+
                 ModelWithExtraForbid = create_model_with_extra_forbid(
                     self._state.__class__
                 )
                 self._state = cast(
-                    T, ModelWithExtraForbid(**{**self._state.model_dump(), **inputs})
+                    T, ModelWithExtraForbid(**{**current_state, **inputs})
                 )
             except ValidationError as e:
                 raise ValueError(f"Invalid inputs for structured state: {e}") from e
-        elif isinstance(self._state, dict):
-            self._state.update(inputs)
         else:
             raise TypeError("State must be a BaseModel instance or a dictionary.")
 

src/crewai/project/crew_base.py

@@ -1,4 +1,5 @@
 import inspect
+import logging
 from pathlib import Path
 from typing import Any, Callable, Dict, TypeVar, cast
 
@@ -7,12 +8,16 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
+logging.basicConfig(level=logging.WARNING)
+
 T = TypeVar("T", bound=type)
 
 """Base decorator for creating crew classes with configuration and function management."""
 
+
 def CrewBase(cls: T) -> T:
     """Wraps a class with crew functionality and configuration management."""
+
     class WrappedClass(cls):  # type: ignore
         is_crew_class: bool = True  # type: ignore
 
@@ -26,16 +31,9 @@ def CrewBase(cls: T) -> T:
 
         def __init__(self, *args, **kwargs):
             super().__init__(*args, **kwargs)
-
-            agents_config_path = self.base_directory / self.original_agents_config_path
-            tasks_config_path = self.base_directory / self.original_tasks_config_path
-
-            self.agents_config = self.load_yaml(agents_config_path)
-            self.tasks_config = self.load_yaml(tasks_config_path)
-
+            self.load_configurations()
             self.map_all_agent_variables()
             self.map_all_task_variables()
-
             # Preserve all decorated functions
             self._original_functions = {
                 name: method
@@ -51,7 +49,6 @@ def CrewBase(cls: T) -> T:
                     ]
                 )
             }
-
             # Store specific function types
             self._original_tasks = self._filter_functions(
                 self._original_functions, "is_task"
@@ -69,6 +66,44 @@ def CrewBase(cls: T) -> T:
                 self._original_functions, "is_kickoff"
             )
 
+        def load_configurations(self):
+            """Load agent and task configurations from YAML files."""
+            if isinstance(self.original_agents_config_path, str):
+                agents_config_path = (
+                    self.base_directory / self.original_agents_config_path
+                )
+                try:
+                    self.agents_config = self.load_yaml(agents_config_path)
+                except FileNotFoundError:
+                    logging.warning(
+                        f"Agent config file not found at {agents_config_path}. "
+                        "Proceeding with empty agent configurations."
+                    )
+                    self.agents_config = {}
+            else:
+                logging.warning(
+                    "No agent configuration path provided. Proceeding with empty agent configurations."
+                )
+                self.agents_config = {}
+
+            if isinstance(self.original_tasks_config_path, str):
+                tasks_config_path = (
+                    self.base_directory / self.original_tasks_config_path
+                )
+                try:
+                    self.tasks_config = self.load_yaml(tasks_config_path)
+                except FileNotFoundError:
+                    logging.warning(
+                        f"Task config file not found at {tasks_config_path}. "
+                        "Proceeding with empty task configurations."
+                    )
+                    self.tasks_config = {}
+            else:
+                logging.warning(
+                    "No task configuration path provided. Proceeding with empty task configurations."
+                )
+                self.tasks_config = {}
+
         @staticmethod
         def load_yaml(config_path: Path):
             try:

src/crewai/utilities/llm_utils.py

@@ -1,11 +1,7 @@
 import os
 from typing import Any, Dict, List, Optional, Union
 
-from packaging import version
-
 from crewai.cli.constants import DEFAULT_LLM_MODEL, ENV_VARS, LITELLM_PARAMS
-from crewai.cli.utils import read_toml
-from crewai.cli.version import get_crewai_version
 from crewai.llm import LLM
 
 

tests/agent_test.py

@@ -1495,7 +1495,7 @@ def test_agent_execute_task_basic():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=LLM(model="gpt-3.5-turbo"),
+        llm="gpt-4o-mini",
     )
 
     task = Task(

tests/cassettes/test_agent_execute_task_basic.yaml

@@ -2,14 +2,15 @@ interactions:
 - request:
     body: '{"messages": [{"role": "system", "content": "You are test role. test backstory\nYour
       personal goal is: test goal\nTo give my best complete final answer to the task
-      use the exact following format:\n\nThought: I now can give a great answer\nFinal
-      Answer: Your final answer must be the great and the most complete as possible,
-      it must be outcome described.\n\nI MUST use these formats, my job depends on
-      it!"}, {"role": "user", "content": "\nCurrent Task: Calculate 2 + 2\n\nThis
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+      respond using the exact following format:\n\nThought: I now can give a great
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+      ["\nObservation:"]}'
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@@ -18,16 +19,13 @@ interactions:
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@@ -37,29 +35,35 @@ interactions:
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+      - 1.59.6
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tests/cassettes/test_agent_execute_task_with_ollama.yaml

@@ -2,44 +2,457 @@ interactions:
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-      my job depends on it!\n\n### User:\n\nCurrent Task: Explain what AI is in one
-      sentence\n\nThis is the expect criteria for your final answer: A one-sentence
+      answer to the task respond using the exact following format:\n\nThought: I now
+      can give a great answer\nFinal Answer: Your final answer must be the great and
+      the most complete as possible, it must be outcome described.\n\nI MUST use these
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+      is in one sentence\n\nThis is the expect criteria for your final answer: A one-sentence
       explanation of AI\nyou MUST return the actual complete content as the final
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       available and give your best Final Answer, your job depends on it!\n\nThought:\n\n",
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+    content: '{"model":"llama3.2:3b","created_at":"2025-01-10T18:39:31.893206Z","response":"Final
+      Answer: Artificial Intelligence (AI) refers to the development of computer systems
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+      Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty.
+      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
+      THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF
+      ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
+      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
+      MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR
+      DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS
+      AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY
+      OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR
+      ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT,
+      TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
+      \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
+      EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
+      OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n
+      \   a. No trademark licenses are granted under this Agreement, and in connection
+      with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark
+      owned by or associated with the other or any of its affiliates, \\nexcept as
+      required for reasonable and customary use in describing and redistributing the
+      Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants
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+      and modifications of the Llama Materials that are made by you, as between you
+      and Meta,\\n    you are and will be the owner of such derivative works and modifications.\\n\\n
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+      The term of this Agreement will commence upon your acceptance of this Agreement
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+      may terminate this Agreement if you are in breach of any term or condition of
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+      International\\nSale of Goods does not apply to this Agreement. The courts of
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+      is committed to promoting safe and fair use of its tools and features, including
+      Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use
+      Policy (\u201C**Policy**\u201D). The most recent copy of this policy can be
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+      \       3. Human trafficking, exploitation, and sexual violence\\n        4.
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+      Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
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+    headers:
+      Content-Type:
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+      Date:
+      - Fri, 10 Jan 2025 18:39:31 GMT
+      Transfer-Encoding:
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+    status_code: 200
 version: 1

tests/cassettes/test_agent_with_ollama_llama3.yaml

@@ -1,36 +1,863 @@
 interactions:
 - request:
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-      which model are you?\n\n", "options": {"stop": ["\nObservation:"]}, "stream":
-      false}'
+    body: '{"model": "llama3.2:3b", "prompt": "### User:\nRespond in 20 words. Which
+      model are you?\n\n", "options": {"stop": ["\nObservation:"]}, "stream": false}'
     headers:
-      Accept:
+      accept:
       - '*/*'
-      Accept-Encoding:
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       - gzip, deflate
-      Connection:
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       - keep-alive
-      Content-Length:
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-      Content-Type:
-      - application/json
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+      content-length:
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+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.57.4
     method: POST
     uri: http://localhost:11434/api/generate
   response:
-    body:
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+    content: '{"model":"llama3.2:3b","created_at":"2025-01-10T18:37:01.552946Z","response":"I''m
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       Content-Type:
       - application/json; charset=utf-8
       Date:
-      - Thu, 02 Jan 2025 20:07:07 GMT
-    status:
-      code: 200
-      message: OK
+      - Fri, 10 Jan 2025 18:37:01 GMT
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: '{"name": "llama3.2:3b"}'
+    headers:
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+      content-type:
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+      - localhost:11434
+      user-agent:
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+    method: POST
+    uri: http://localhost:11434/api/show
+  response:
+    content: "{\"license\":\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version
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+      and conditions for use, reproduction, distribution \\nand modification of the
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+      be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
+      \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
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+      other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
+      distribute, copy, create derivative works \\nof, and make modifications to the
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+      thereof), \\nor a product or service (including another AI model) that contains
+      any of them, you shall (A) provide\\na copy of this Agreement with any such
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+      or any derivative works thereof, from a Licensee as part\\nof an integrated
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+      \       iii. You must retain in all copies of the Llama Materials that you distribute
+      the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed
+      as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
+      Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
+      \       iv. Your use of the Llama Materials must comply with applicable laws
+      and regulations\\n(including trade compliance laws and regulations) and adhere
+      to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy),
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+      Additional Commercial Terms. If, on the Llama 3.2 version release date, the
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+      Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly
+      active users in the preceding calendar month, you must request \\na license
+      from Meta, which Meta may grant to you in its sole discretion, and you are not
+      authorized to\\nexercise any of the rights under this Agreement unless or until
+      Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty.
+      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
+      THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF
+      ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
+      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
+      MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR
+      DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS
+      AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY
+      OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR
+      ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT,
+      TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
+      \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
+      EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
+      OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n
+      \   a. No trademark licenses are granted under this Agreement, and in connection
+      with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark
+      owned by or associated with the other or any of its affiliates, \\nexcept as
+      required for reasonable and customary use in describing and redistributing the
+      Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants
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+      \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s
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+      All goodwill arising out of your use of the Mark \\nwill inure to the benefit
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+      and modifications of the Llama Materials that are made by you, as between you
+      and Meta,\\n    you are and will be the owner of such derivative works and modifications.\\n\\n
+      \   c. If you institute litigation or other proceedings against Meta or any
+      entity (including a cross-claim or\\n    counterclaim in a lawsuit) alleging
+      that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n
+      \   of any of the foregoing, constitutes infringement of intellectual property
+      or other rights owned or licensable\\n    by you, then any licenses granted
+      to you under this Agreement shall terminate as of the date such litigation or\\n
+      \   claim is filed or instituted. You will indemnify and hold harmless Meta
+      from and against any claim by any third\\n    party arising out of or related
+      to your use or distribution of the Llama Materials.\\n\\n6. Term and Termination.
+      The term of this Agreement will commence upon your acceptance of this Agreement
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+      may terminate this Agreement if you are in breach of any term or condition of
+      this\\nAgreement. Upon termination of this Agreement, you shall delete and cease
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+      International\\nSale of Goods does not apply to this Agreement. The courts of
+      California shall have exclusive jurisdiction of\\nany dispute arising out of
+      this Agreement.\\n**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed
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+      Transfer-Encoding:
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@@ -1,7 +1,6 @@
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+    body: '{"messages": [{"role": "user", "content": "Hello, world!"}], "model": "gpt-4o-mini"}'
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+      - '286'
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+      - req_18f5593ddf37824bb9a7690407170dc0
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-      "model": "gpt-4o-mini", "stream": false}'
+      "model": "gpt-4o-mini"}'
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tests/crew_test.py

@@ -16,6 +16,7 @@ from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.process import Process
+from crewai.project import crew
 from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.output_format import OutputFormat
@@ -3474,3 +3475,119 @@ def test_crew_guardrail_feedback_in_context():
 
     # Verify task retry count
     assert task.retry_count == 1, "Task should have been retried once"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_before_kickoff_callback():
+    from crewai.project import CrewBase, agent, before_kickoff, crew, task
+
+    @CrewBase
+    class TestCrewClass:
+        agents_config = None
+        tasks_config = None
+
+        def __init__(self):
+            self.inputs_modified = False
+
+        @before_kickoff
+        def modify_inputs(self, inputs):
+
+            self.inputs_modified = True
+            inputs["modified"] = True
+            return inputs
+
+        @agent
+        def my_agent(self):
+            return Agent(
+                role="Test Agent",
+                goal="Test agent goal",
+                backstory="Test agent backstory",
+            )
+
+        @task
+        def my_task(self):
+            task = Task(
+                description="Test task description",
+                expected_output="Test expected output",
+                agent=self.my_agent(),  # Use the agent instance
+            )
+            return task
+
+        @crew
+        def crew(self):
+            return Crew(agents=self.agents, tasks=self.tasks)
+
+    test_crew_instance = TestCrewClass()
+
+    crew = test_crew_instance.crew()
+
+    # Verify that the before_kickoff_callbacks are set
+    assert len(crew.before_kickoff_callbacks) == 1
+
+    # Prepare inputs
+    inputs = {"initial": True}
+
+    # Call kickoff
+    crew.kickoff(inputs=inputs)
+
+    # Check that the before_kickoff function was called and modified inputs
+    assert test_crew_instance.inputs_modified
+    assert inputs.get("modified") == True
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_before_kickoff_without_inputs():
+    from crewai.project import CrewBase, agent, before_kickoff, crew, task
+
+    @CrewBase
+    class TestCrewClass:
+        agents_config = None
+        tasks_config = None
+
+        def __init__(self):
+            self.inputs_modified = False
+            self.received_inputs = None
+
+        @before_kickoff
+        def modify_inputs(self, inputs):
+            self.inputs_modified = True
+            inputs["modified"] = True
+            self.received_inputs = inputs
+            return inputs
+
+        @agent
+        def my_agent(self):
+            return Agent(
+                role="Test Agent",
+                goal="Test agent goal",
+                backstory="Test agent backstory",
+            )
+
+        @task
+        def my_task(self):
+            return Task(
+                description="Test task description",
+                expected_output="Test expected output",
+                agent=self.my_agent(),
+            )
+
+        @crew
+        def crew(self):
+            return Crew(agents=self.agents, tasks=self.tasks)
+
+    # Instantiate the class
+    test_crew_instance = TestCrewClass()
+    # Build the crew
+    crew = test_crew_instance.crew()
+    # Verify that the before_kickoff_callback is registered
+    assert len(crew.before_kickoff_callbacks) == 1
+
+    # Call kickoff without passing inputs
+    output = crew.kickoff()
+
+    # Check that the before_kickoff function was called
+    assert test_crew_instance.inputs_modified
+
+    # Verify that the inputs were initialized and modified inside the before_kickoff method
+    assert test_crew_instance.received_inputs is not None
+    assert test_crew_instance.received_inputs.get("modified") is True

tests/flow_test.py

@@ -3,6 +3,7 @@
 import asyncio
 
 import pytest
+from pydantic import BaseModel
 
 from crewai.flow.flow import Flow, and_, listen, or_, router, start
 
@@ -265,6 +266,81 @@ def test_flow_with_custom_state():
     assert flow.counter == 2
 
 
+def test_flow_uuid_unstructured():
+    """Test that unstructured (dictionary) flow states automatically get a UUID that persists."""
+    initial_id = None
+
+    class UUIDUnstructuredFlow(Flow):
+        @start()
+        def first_method(self):
+            nonlocal initial_id
+            # Verify ID is automatically generated
+            assert "id" in self.state
+            assert isinstance(self.state["id"], str)
+            # Store initial ID for comparison
+            initial_id = self.state["id"]
+            # Add some data to trigger state update
+            self.state["data"] = "example"
+
+        @listen(first_method)
+        def second_method(self):
+            # Ensure the ID persists after state updates
+            assert "id" in self.state
+            assert self.state["id"] == initial_id
+            # Update state again to verify ID preservation
+            self.state["more_data"] = "test"
+            assert self.state["id"] == initial_id
+
+    flow = UUIDUnstructuredFlow()
+    flow.kickoff()
+    # Verify ID persists after flow completion
+    assert flow.state["id"] == initial_id
+    # Verify UUID format (36 characters, including hyphens)
+    assert len(flow.state["id"]) == 36
+
+
+def test_flow_uuid_structured():
+    """Test that structured (Pydantic) flow states automatically get a UUID that persists."""
+    initial_id = None
+
+    class MyStructuredState(BaseModel):
+        counter: int = 0
+        message: str = "initial"
+
+    class UUIDStructuredFlow(Flow[MyStructuredState]):
+        @start()
+        def first_method(self):
+            nonlocal initial_id
+            # Verify ID is automatically generated and accessible as attribute
+            assert hasattr(self.state, "id")
+            assert isinstance(self.state.id, str)
+            # Store initial ID for comparison
+            initial_id = self.state.id
+            # Update some fields to trigger state changes
+            self.state.counter += 1
+            self.state.message = "updated"
+
+        @listen(first_method)
+        def second_method(self):
+            # Ensure the ID persists after state updates
+            assert hasattr(self.state, "id")
+            assert self.state.id == initial_id
+            # Update state again to verify ID preservation
+            self.state.counter += 1
+            self.state.message = "final"
+            assert self.state.id == initial_id
+
+    flow = UUIDStructuredFlow()
+    flow.kickoff()
+    # Verify ID persists after flow completion
+    assert flow.state.id == initial_id
+    # Verify UUID format (36 characters, including hyphens)
+    assert len(flow.state.id) == 36
+    # Verify other state fields were properly updated
+    assert flow.state.counter == 2
+    assert flow.state.message == "final"
+
+
 def test_router_with_multiple_conditions():
     """Test a router that triggers when any of multiple steps complete (OR condition),
     and another router that triggers only after all specified steps complete (AND condition).

tests/llm_test.py

@@ -1,3 +1,5 @@
+from time import sleep
+
 import pytest
 
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
@@ -5,24 +7,31 @@ from crewai.llm import LLM
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 
 
+# TODO: This test fails without print statement, which makes me think that something is happening asynchronously that we need to eventually fix and dive deeper into at a later date
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_llm_callback_replacement():
-    llm = LLM(model="gpt-4o-mini")
+    llm1 = LLM(model="gpt-4o-mini")
+    llm2 = LLM(model="gpt-4o-mini")
 
     calc_handler_1 = TokenCalcHandler(token_cost_process=TokenProcess())
     calc_handler_2 = TokenCalcHandler(token_cost_process=TokenProcess())
 
-    llm.call(
+    result1 = llm1.call(
         messages=[{"role": "user", "content": "Hello, world!"}],
         callbacks=[calc_handler_1],
     )
+    print("result1:", result1)
     usage_metrics_1 = calc_handler_1.token_cost_process.get_summary()
+    print("usage_metrics_1:", usage_metrics_1)
 
-    llm.call(
+    result2 = llm2.call(
         messages=[{"role": "user", "content": "Hello, world from another agent!"}],
         callbacks=[calc_handler_2],
     )
+    sleep(5)
+    print("result2:", result2)
     usage_metrics_2 = calc_handler_2.token_cost_process.get_summary()
+    print("usage_metrics_2:", usage_metrics_2)
 
     # The first handler should not have been updated
     assert usage_metrics_1.successful_requests == 1

uv.lock

@@ -720,7 +720,7 @@ requires-dist = [
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     { name = "jsonref", specifier = ">=1.1.0" },
-    { name = "litellm", specifier = ">=1.44.22" },
+    { name = "litellm", specifier = "==1.57.4" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.29" },
     { name = "openai", specifier = ">=1.13.3" },
     { name = "openpyxl", specifier = ">=3.1.5" },
@@ -2344,24 +2344,24 @@ wheels = [
 
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+version = "1.57.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
     { name = "click" },
+    { name = "httpx" },
     { name = "importlib-metadata" },
     { name = "jinja2" },
     { name = "jsonschema" },
     { name = "openai" },
     { name = "pydantic" },
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+    { url = "https://files.pythonhosted.org/packages/9f/72/35c8509cb2a37343c213b794420405cbef2e1fdf8626ee981fcbba3d7c5c/litellm-1.57.4-py3-none-any.whl", hash = "sha256:afe48924d8a36db801018970a101622fce33d117fe9c54441c0095c491511abb", size = 6592126 },
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@@ -3155,7 +3155,7 @@ wheels = [
 
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+version = "1.59.6"
 source = { registry = "https://pypi.org/simple" }
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@@ -3167,9 +3167,9 @@ dependencies = [
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