complete validation

docs/concepts/llms.mdx

@@ -25,100 +25,7 @@ By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables i
 - `OPENAI_API_BASE`
 - `OPENAI_API_KEY`
 
-### 2. Updating YAML files
-
-You can update the `agents.yml` file to refer to the LLM you want to use:
-
-```yaml Code
-researcher:
-    role: Research Specialist
-    goal: Conduct comprehensive research and analysis to gather relevant information,
-        synthesize findings, and produce well-documented insights.
-    backstory: A dedicated research professional with years of experience in academic
-        investigation, literature review, and data analysis, known for thorough and
-        methodical approaches to complex research questions.
-    verbose: true
-    llm: openai/gpt-4o
-    # llm: azure/gpt-4o-mini
-    # llm: gemini/gemini-pro
-    # llm: anthropic/claude-3-5-sonnet-20240620
-    # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
-    # llm: mistral/mistral-large-latest
-    # llm: ollama/llama3:70b
-    # llm: groq/llama-3.2-90b-vision-preview
-    # llm: watsonx/meta-llama/llama-3-1-70b-instruct
-    # ...
-```
-
-Keep in mind that you will need to set certain ENV vars depending on the model you are
-using to account for the credentials or set a custom LLM object like described below.
-Here are some of the required ENV vars for some of the LLM integrations:
-
-<AccordionGroup>
-    <Accordion title="OpenAI">
-    ```python Code
-    OPENAI_API_KEY=<your-api-key>
-    OPENAI_API_BASE=<optional-custom-base-url>
-    OPENAI_MODEL_NAME=<openai-model-name>
-    OPENAI_ORGANIZATION=<your-org-id>       # OPTIONAL
-    OPENAI_API_BASE=<openaiai-api-base>     # OPTIONAL
-    ```
-    </Accordion>
-
-    <Accordion title="Anthropic">
-    ```python Code
-    ANTHROPIC_API_KEY=<your-api-key>
-    ```
-    </Accordion>
-
-    <Accordion title="Google">
-    ```python Code
-    GEMINI_API_KEY=<your-api-key>
-    ```
-    </Accordion>
-
-    <Accordion title="Azure">
-    ```python Code
-    AZURE_API_KEY=<your-api-key>                    # "my-azure-api-key"
-    AZURE_API_BASE=<your-resource-url>              # "https://example-endpoint.openai.azure.com"
-    AZURE_API_VERSION=<api-version>                 # "2023-05-15"
-    AZURE_AD_TOKEN=<your-azure-ad-token>            # Optional
-    AZURE_API_TYPE=<your-azure-api-type>            # Optional
-    ```
-    </Accordion>
-
-    <Accordion title="AWS Bedrock">
-    ```python Code
-    AWS_ACCESS_KEY_ID=<your-access-key>
-    AWS_SECRET_ACCESS_KEY=<your-secret-key>
-    AWS_DEFAULT_REGION=<your-region>
-    ```
-    </Accordion>
-
-    <Accordion title="Mistral">
-    ```python Code
-    MISTRAL_API_KEY=<your-api-key>
-    ```
-    </Accordion>
-
-    <Accordion title="Groq">
-    ```python Code
-    GROQ_API_KEY=<your-api-key>
-    ```
-    </Accordion>
-
-    <Accordion title="IBM watsonx.ai">
-    ```python Code
-    WATSONX_URL=<your-url>                          # (required) Base URL of your WatsonX instance
-    WATSONX_APIKEY=<your-apikey>                    # (required) IBM cloud API key
-    WATSONX_TOKEN=<your-token>                      # (required) IAM auth token (alternative to APIKEY)
-    WATSONX_PROJECT_ID=<your-project-id>            # (optional) Project ID of your WatsonX instance
-    WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>     # (optional) ID of deployment space for deployed models
-    ```
-    </Accordion>
-</AccordionGroup>
-
-### 3. Custom LLM Objects
+### 2. Custom LLM Objects
 
 Pass a custom LLM implementation or object from another library.
 
@@ -195,7 +102,7 @@ When configuring an LLM for your agent, you have access to a wide range of param
 
 These are examples of how to configure LLMs for your agent.
 
-<AccordionGroup>
+<AccordionGroup>    
     <Accordion title="OpenAI">
 
     ```python Code
@@ -226,10 +133,10 @@ These are examples of how to configure LLMs for your agent.
         model="cerebras/llama-3.1-70b",
         api_key="your-api-key-here"
     )
-    agent = Agent(llm=llm, ...)
+    agent = Agent(llm=llm, ...) 
     ```
     </Accordion>
-
+ 
     <Accordion title="Ollama (Local LLMs)">
 
     CrewAI supports using Ollama for running open-source models locally:
@@ -243,7 +150,7 @@ These are examples of how to configure LLMs for your agent.
 
     agent = Agent(
         llm=LLM(
-            model="ollama/llama3.1",
+            model="ollama/llama3.1", 
             base_url="http://localhost:11434"
         ),
         ...
@@ -257,7 +164,7 @@ These are examples of how to configure LLMs for your agent.
     from crewai import LLM
 
     llm = LLM(
-        model="groq/llama3-8b-8192",
+        model="groq/llama3-8b-8192", 
         api_key="your-api-key-here"
     )
     agent = Agent(llm=llm, ...)
@@ -282,7 +189,7 @@ These are examples of how to configure LLMs for your agent.
     from crewai import LLM
 
     llm = LLM(
-        model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
+        model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",  
         api_key="your-api-key-here"
     )
     agent = Agent(llm=llm, ...)
@@ -317,29 +224,6 @@ These are examples of how to configure LLMs for your agent.
     </Accordion>
 
     <Accordion title="IBM watsonx.ai">
-    You can use IBM Watson by seeting the following ENV vars:
-
-    ```python Code
-    WATSONX_URL=<your-url>
-    WATSONX_APIKEY=<your-apikey>
-    WATSONX_PROJECT_ID=<your-project-id>
-    ```
-
-    You can then define your agents llms by updating the `agents.yml`
-
-    ```yaml Code
-    researcher:
-        role: Research Specialist
-        goal: Conduct comprehensive research and analysis to gather relevant information,
-            synthesize findings, and produce well-documented insights.
-        backstory: A dedicated research professional with years of experience in academic
-            investigation, literature review, and data analysis, known for thorough and
-            methodical approaches to complex research questions.
-        verbose: true
-        llm: watsonx/meta-llama/llama-3-1-70b-instruct
-    ```
-
-    You can also set up agents more dynamically as a base level LLM instance, like bellow:
 
     ```python Code
     from crewai import LLM
@@ -363,7 +247,7 @@ These are examples of how to configure LLMs for your agent.
         api_key="your-api-key-here",
         base_url="your_api_endpoint"
     )
-    agent = Agent(llm=llm, ...)
+    agent = Agent(llm=llm, ...) 
     ```
     </Accordion>
 </AccordionGroup>

docs/concepts/memory.mdx

@@ -254,31 +254,6 @@ my_crew = Crew(
 )
 ```
 
-### Using Watson embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-# Note: Ensure you have installed and imported `ibm_watsonx_ai` for Watson embeddings to work.
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "watson",
-        "config": {
-            "model": "<model_name>",
-            "api_url": "<api_url>",
-            "api_key": "<YOUR_API_KEY>",
-            "project_id": "<YOUR_PROJECT_ID>",
-        }
-    }
-)
-```
-
 ### Resetting Memory
 
 ```shell

docs/quickstart.mdx

@@ -330,4 +330,4 @@ This will clear the crew's memory, allowing for a fresh start.
 
 ## Deploying Your Project
 
-The easiest way to deploy your crew is through [CrewAI Enterprise](http://app.crewai.com/), where you can deploy your crew in a few clicks.
+The easiest way to deploy your crew is through [CrewAI Enterprise](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.79.4"
+version = "0.76.9"
 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."
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"
@@ -16,7 +16,7 @@ dependencies = [
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
     "instructor>=1.3.3",
     "regex>=2024.9.11",
-    "crewai-tools>=0.14.0",
+    "crewai-tools>=0.13.4",
     "click>=8.1.7",
     "python-dotenv>=1.0.0",
     "appdirs>=1.4.4",
@@ -37,7 +37,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.14.0"]
+tools = ["crewai-tools>=0.13.4"]
 agentops = ["agentops>=0.3.0"]
 
 [tool.uv]
@@ -52,7 +52,7 @@ dev-dependencies = [
     "mkdocs-material-extensions>=1.3.1",
     "pillow>=10.2.0",
     "cairosvg>=2.7.1",
-    "crewai-tools>=0.14.0",
+    "crewai-tools>=0.13.4",
     "pytest>=8.0.0",
     "pytest-vcr>=1.0.2",
     "python-dotenv>=1.0.0",

src/crewai/__init__.py

@@ -14,5 +14,5 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.79.4"
+__version__ = "0.76.9"
 __all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]

src/crewai/agent.py

@@ -123,11 +123,6 @@ class Agent(BaseAgent):
     @model_validator(mode="after")
     def post_init_setup(self):
         self.agent_ops_agent_name = self.role
-        unnacepted_attributes = [
-            "AWS_ACCESS_KEY_ID",
-            "AWS_SECRET_ACCESS_KEY",
-            "AWS_REGION_NAME",
-        ]
 
         # Handle different cases for self.llm
         if isinstance(self.llm, str):
@@ -151,49 +146,39 @@ class Agent(BaseAgent):
             if api_base:
                 llm_params["base_url"] = api_base
 
-            set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
-
             # Iterate over all environment variables to find matching API keys or use defaults
             for provider, env_vars in ENV_VARS.items():
-                if provider == set_provider:
-                    for env_var in env_vars:
-                        if env_var["key_name"] in unnacepted_attributes:
-                            continue
-                        # Check if the environment variable is set
-                        if "key_name" in env_var:
-                            env_value = os.environ.get(env_var["key_name"])
-                            print(
-                                f"Checking env var {env_var['key_name']}: {env_value}"
+                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"]
                             )
-                            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
-                                )
-                                print(f"Mapped key name: {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:
-                                        print(f"Using default value for {key}: {value}")
-                                        llm_params[key] = value
+                            # 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:

src/crewai/agents/crew_agent_executor.py

@@ -117,15 +117,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         callbacks=self.callbacks,
                     )
 
-                    if answer is None or answer == "":
-                        self._printer.print(
-                            content="Received None or empty response from LLM call.",
-                            color="red",
-                        )
-                        raise ValueError(
-                            "Invalid response from LLM call - None or empty."
-                        )
-
                     if not self.use_stop_words:
                         try:
                             self._format_answer(answer)
@@ -332,9 +323,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             if self.crew is not None and hasattr(self.crew, "_train_iteration"):
                 train_iteration = self.crew._train_iteration
                 if agent_id in training_data and isinstance(train_iteration, int):
-                    training_data[agent_id][train_iteration]["improved_output"] = (
-                        result.output
-                    )
+                    training_data[agent_id][train_iteration][
+                        "improved_output"
+                    ] = result.output
                     training_handler.save(training_data)
                 else:
                     self._logger.log(
@@ -385,5 +376,4 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return CrewAgentParser(agent=self.agent).parse(answer)
 
     def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
-        prompt = prompt.rstrip()
         return {"role": role, "content": prompt}

src/crewai/cli/add_crew_to_flow.py

@@ -54,7 +54,7 @@ def create_embedded_crew(crew_name: str, parent_folder: Path) -> None:
 
     templates_dir = Path(__file__).parent / "templates" / "crew"
     config_template_files = ["agents.yaml", "tasks.yaml"]
-    crew_template_file = f"{folder_name}.py"  # Updated file name
+    crew_template_file = f"{folder_name}_crew.py"  # Updated file name
 
     for file_name in config_template_files:
         src_file = templates_dir / "config" / file_name

src/crewai/cli/provider.py

@@ -164,7 +164,7 @@ def fetch_provider_data(cache_file):
     - dict or None: The fetched provider data or None if the operation fails.
     """
     try:
-        response = requests.get(JSON_URL, stream=True, timeout=60)
+        response = requests.get(JSON_URL, stream=True, timeout=10)
         response.raise_for_status()
         data = download_data(response)
         with open(cache_file, "w") as f:

src/crewai/cli/templates/crew/crew.py

@@ -8,12 +8,9 @@ from crewai.project import CrewBase, agent, crew, task
 # from crewai_tools import SerperDevTool
 
 @CrewBase
-class {{crew_name}}():
+class {{crew_name}}Crew():
 	"""{{crew_name}} crew"""
 
-	agents_config = 'config/agents.yaml'
-	tasks_config = 'config/tasks.yaml'
-
 	@agent
 	def researcher(self) -> Agent:
 		return Agent(

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

@@ -2,7 +2,7 @@
 import sys
 import warnings
 
-from {{folder_name}}.crew import {{crew_name}}
+from {{folder_name}}.crew import {{crew_name}}Crew
 
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
@@ -18,7 +18,7 @@ def run():
     inputs = {
         'topic': 'AI LLMs'
     }
-    {{crew_name}}().crew().kickoff(inputs=inputs)
+    {{crew_name}}Crew().crew().kickoff(inputs=inputs)
 
 
 def train():
@@ -29,7 +29,7 @@ def train():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
+        {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while training the crew: {e}")
@@ -39,7 +39,7 @@ def replay():
     Replay the crew execution from a specific task.
     """
     try:
-        {{crew_name}}().crew().replay(task_id=sys.argv[1])
+        {{crew_name}}Crew().crew().replay(task_id=sys.argv[1])
 
     except Exception as e:
         raise Exception(f"An error occurred while replaying the crew: {e}")
@@ -52,7 +52,7 @@ def test():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
+        {{crew_name}}Crew().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while replaying the crew: {e}")

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.79.4,<1.0.0"
+    "crewai[tools]>=0.76.9,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.79.4,<1.0.0",
+    "crewai[tools]>=0.76.9,<1.0.0",
 ]
 
 [project.scripts]

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 = ">=0.79.4,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.76.9,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = ["Your Name <you@example.com>"]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.79.4,<1.0.0"
+    "crewai[tools]>=0.76.9,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,6 +5,6 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.79.4"
+    "crewai[tools]>=0.76.9"
 ]
 

src/crewai/crew.py

@@ -445,14 +445,13 @@ class Crew(BaseModel):
         training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
 
         for agent in train_crew.agents:
-            if training_data.get(str(agent.id)):
-                result = TaskEvaluator(agent).evaluate_training_data(
-                    training_data=training_data, agent_id=str(agent.id)
-                )
+            result = TaskEvaluator(agent).evaluate_training_data(
+                training_data=training_data, agent_id=str(agent.id)
+            )
 
-                CrewTrainingHandler(filename).save_trained_data(
-                    agent_id=str(agent.role), trained_data=result.model_dump()
-                )
+            CrewTrainingHandler(filename).save_trained_data(
+                agent_id=str(agent.role), trained_data=result.model_dump()
+            )
 
     def kickoff(
         self,

src/crewai/flow/flow.py

@@ -131,6 +131,7 @@ class FlowMeta(type):
                 condition_type = getattr(attr_value, "__condition_type__", "OR")
                 listeners[attr_name] = (condition_type, methods)
 
+            # TODO: should we add a check for __condition_type__ 'AND'?
             elif hasattr(attr_value, "__is_router__"):
                 routers[attr_value.__router_for__] = attr_name
                 possible_returns = get_possible_return_constants(attr_value)
@@ -170,7 +171,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
     def __init__(self) -> None:
         self._methods: Dict[str, Callable] = {}
         self._state: T = self._create_initial_state()
-        self._method_execution_counts: Dict[str, int] = {}
+        self._executed_methods: Set[str] = set()
+        self._scheduled_tasks: Set[str] = set()
         self._pending_and_listeners: Dict[str, Set[str]] = {}
         self._method_outputs: List[Any] = []  # List to store all method outputs
 
@@ -215,20 +217,12 @@ class Flow(Generic[T], metaclass=FlowMeta):
         if isinstance(self._state, BaseModel):
             # Structured state management
             try:
-                # Define a function to create the dynamic class
-                def create_model_with_extra_forbid(
-                    base_model: Type[BaseModel],
-                ) -> Type[BaseModel]:
-                    class ModelWithExtraForbid(base_model):  # type: ignore
-                        model_config = base_model.model_config.copy()
-                        model_config["extra"] = "forbid"
+                M = self._state.__class__
 
-                    return ModelWithExtraForbid
-
-                # Create the dynamic class
-                ModelWithExtraForbid = create_model_with_extra_forbid(
-                    self._state.__class__
-                )
+                # Dynamically create a new model class with 'extra' set to 'forbid'
+                class ModelWithExtraForbid(M):
+                    model_config = M.model_config.copy()
+                    model_config["extra"] = "forbid"
 
                 # Create a new instance using the combined state and inputs
                 self._state = cast(
@@ -307,10 +301,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         )
         self._method_outputs.append(result)  # Store the output
 
-        # Track method execution counts
-        self._method_execution_counts[method_name] = (
-            self._method_execution_counts.get(method_name, 0) + 1
-        )
+        self._executed_methods.add(method_name)
 
         return result
 
@@ -320,34 +311,35 @@ class Flow(Generic[T], metaclass=FlowMeta):
         if trigger_method in self._routers:
             router_method = self._methods[self._routers[trigger_method]]
             path = await self._execute_method(
-                self._routers[trigger_method], router_method
-            )
+                trigger_method, router_method
+            )  # TODO: Change or not?
+            # Use the path as the new trigger method
             trigger_method = path
 
         for listener_name, (condition_type, methods) in self._listeners.items():
             if condition_type == "OR":
                 if trigger_method in methods:
-                    # Schedule the listener without preventing re-execution
-                    listener_tasks.append(
-                        self._execute_single_listener(listener_name, result)
-                    )
+                    if (
+                        listener_name not in self._executed_methods
+                        and listener_name not in self._scheduled_tasks
+                    ):
+                        self._scheduled_tasks.add(listener_name)
+                        listener_tasks.append(
+                            self._execute_single_listener(listener_name, result)
+                        )
             elif condition_type == "AND":
-                # Initialize pending methods for this listener if not already done
-                if listener_name not in self._pending_and_listeners:
-                    self._pending_and_listeners[listener_name] = set(methods)
-                # Remove the trigger method from pending methods
-                self._pending_and_listeners[listener_name].discard(trigger_method)
-                if not self._pending_and_listeners[listener_name]:
-                    # All required methods have been executed
-                    listener_tasks.append(
-                        self._execute_single_listener(listener_name, result)
-                    )
-                    # Reset pending methods for this listener
-                    self._pending_and_listeners.pop(listener_name, None)
+                if all(method in self._executed_methods for method in methods):
+                    if (
+                        listener_name not in self._executed_methods
+                        and listener_name not in self._scheduled_tasks
+                    ):
+                        self._scheduled_tasks.add(listener_name)
+                        listener_tasks.append(
+                            self._execute_single_listener(listener_name, result)
+                        )
 
         # Run all listener tasks concurrently and wait for them to complete
-        if listener_tasks:
-            await asyncio.gather(*listener_tasks)
+        await asyncio.gather(*listener_tasks)
 
     async def _execute_single_listener(self, listener_name: str, result: Any) -> None:
         try:
@@ -367,6 +359,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 # If listener does not expect parameters, call without arguments
                 listener_result = await self._execute_method(listener_name, method)
 
+            # Remove from scheduled tasks after execution
+            self._scheduled_tasks.discard(listener_name)
+
             # Execute listeners of this listener
             await self._execute_listeners(listener_name, listener_result)
         except Exception as e:

src/crewai/llm.py

@@ -1,10 +1,7 @@
-import io
-import logging
-import sys
-import warnings
 from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Union
-
+import logging
+import warnings
 import litellm
 from litellm import get_supported_openai_params
 
@@ -12,6 +9,9 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
 
+import sys
+import io
+
 
 class FilteredStream(io.StringIO):
     def write(self, s):

src/crewai/memory/storage/rag_storage.py

@@ -4,13 +4,13 @@ import logging
 import os
 import shutil
 import uuid
-from typing import Any, Dict, List, Optional, cast
-
-from chromadb import Documents, EmbeddingFunction, Embeddings
-from chromadb.api import ClientAPI
-from chromadb.api.types import validate_embedding_function
+from typing import Any, Dict, List, Optional
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities.paths import db_storage_path
+from chromadb.api import ClientAPI
+from chromadb.api.types import validate_embedding_function
+from chromadb import Documents, EmbeddingFunction, Embeddings
+from typing import cast
 
 
 @contextlib.contextmanager
@@ -21,11 +21,9 @@ def suppress_logging(
     logger = logging.getLogger(logger_name)
     original_level = logger.getEffectiveLevel()
     logger.setLevel(level)
-    with (
-        contextlib.redirect_stdout(io.StringIO()),
-        contextlib.redirect_stderr(io.StringIO()),
-        contextlib.suppress(UserWarning),
-    ):
+    with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr(
+        io.StringIO()
+    ), contextlib.suppress(UserWarning):
         yield
     logger.setLevel(original_level)
 
@@ -115,52 +113,12 @@ class RAGStorage(BaseRAGStorage):
                 self.embedder_config = embedding_functions.HuggingFaceEmbeddingServer(
                     url=config.get("api_url"),
                 )
-            elif provider == "watson":
-                try:
-                    import ibm_watsonx_ai.foundation_models as watson_models
-                    from ibm_watsonx_ai import Credentials
-                    from ibm_watsonx_ai.metanames import (
-                        EmbedTextParamsMetaNames as EmbedParams,
-                    )
-                except ImportError as e:
-                    raise ImportError(
-                        "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
-                    ) from e
-
-                class WatsonEmbeddingFunction(EmbeddingFunction):
-                    def __call__(self, input: Documents) -> Embeddings:
-                        if isinstance(input, str):
-                            input = [input]
-
-                        embed_params = {
-                            EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
-                            EmbedParams.RETURN_OPTIONS: {"input_text": True},
-                        }
-
-                        embedding = watson_models.Embeddings(
-                            model_id=config.get("model"),
-                            params=embed_params,
-                            credentials=Credentials(
-                                api_key=config.get("api_key"), url=config.get("api_url")
-                            ),
-                            project_id=config.get("project_id"),
-                        )
-
-                        try:
-                            embeddings = embedding.embed_documents(input)
-                            return cast(Embeddings, embeddings)
-
-                        except Exception as e:
-                            print("Error during Watson embedding:", e)
-                            raise e
-
-                self.embedder_config = WatsonEmbeddingFunction()
             else:
                 raise Exception(
-                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface, watson]"
+                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface]"
                 )
         else:
-            validate_embedding_function(self.embedder_config)
+            validate_embedding_function(self.embedder_config)  # type: ignore # used for validating embedder_config if defined a embedding function/class
             self.embedder_config = self.embedder_config
 
     def _initialize_app(self):

tests/flow_test.py

@@ -1,264 +0,0 @@
-"""Test Flow creation and execution basic functionality."""
-
-import asyncio
-
-import pytest
-from crewai.flow.flow import Flow, and_, listen, or_, router, start
-
-
-def test_simple_sequential_flow():
-    """Test a simple flow with two steps called sequentially."""
-    execution_order = []
-
-    class SimpleFlow(Flow):
-        @start()
-        def step_1(self):
-            execution_order.append("step_1")
-
-        @listen(step_1)
-        def step_2(self):
-            execution_order.append("step_2")
-
-    flow = SimpleFlow()
-    flow.kickoff()
-
-    assert execution_order == ["step_1", "step_2"]
-
-
-def test_flow_with_multiple_starts():
-    """Test a flow with multiple start methods."""
-    execution_order = []
-
-    class MultiStartFlow(Flow):
-        @start()
-        def step_a(self):
-            execution_order.append("step_a")
-
-        @start()
-        def step_b(self):
-            execution_order.append("step_b")
-
-        @listen(step_a)
-        def step_c(self):
-            execution_order.append("step_c")
-
-        @listen(step_b)
-        def step_d(self):
-            execution_order.append("step_d")
-
-    flow = MultiStartFlow()
-    flow.kickoff()
-
-    assert "step_a" in execution_order
-    assert "step_b" in execution_order
-    assert "step_c" in execution_order
-    assert "step_d" in execution_order
-    assert execution_order.index("step_c") > execution_order.index("step_a")
-    assert execution_order.index("step_d") > execution_order.index("step_b")
-
-
-def test_cyclic_flow():
-    """Test a cyclic flow that runs a finite number of iterations."""
-    execution_order = []
-
-    class CyclicFlow(Flow):
-        iteration = 0
-        max_iterations = 3
-
-        @start("loop")
-        def step_1(self):
-            if self.iteration >= self.max_iterations:
-                return  # Do not proceed further
-            execution_order.append(f"step_1_{self.iteration}")
-
-        @listen(step_1)
-        def step_2(self):
-            execution_order.append(f"step_2_{self.iteration}")
-
-        @router(step_2)
-        def step_3(self):
-            execution_order.append(f"step_3_{self.iteration}")
-            self.iteration += 1
-            if self.iteration < self.max_iterations:
-                return "loop"
-
-            return "exit"
-
-    flow = CyclicFlow()
-    flow.kickoff()
-
-    expected_order = []
-    for i in range(flow.max_iterations):
-        expected_order.extend([f"step_1_{i}", f"step_2_{i}", f"step_3_{i}"])
-
-    assert execution_order == expected_order
-
-
-def test_flow_with_and_condition():
-    """Test a flow where a step waits for multiple other steps to complete."""
-    execution_order = []
-
-    class AndConditionFlow(Flow):
-        @start()
-        def step_1(self):
-            execution_order.append("step_1")
-
-        @start()
-        def step_2(self):
-            execution_order.append("step_2")
-
-        @listen(and_(step_1, step_2))
-        def step_3(self):
-            execution_order.append("step_3")
-
-    flow = AndConditionFlow()
-    flow.kickoff()
-
-    assert "step_1" in execution_order
-    assert "step_2" in execution_order
-    assert execution_order[-1] == "step_3"
-    assert execution_order.index("step_3") > execution_order.index("step_1")
-    assert execution_order.index("step_3") > execution_order.index("step_2")
-
-
-def test_flow_with_or_condition():
-    """Test a flow where a step is triggered when any of multiple steps complete."""
-    execution_order = []
-
-    class OrConditionFlow(Flow):
-        @start()
-        def step_a(self):
-            execution_order.append("step_a")
-
-        @start()
-        def step_b(self):
-            execution_order.append("step_b")
-
-        @listen(or_(step_a, step_b))
-        def step_c(self):
-            execution_order.append("step_c")
-
-    flow = OrConditionFlow()
-    flow.kickoff()
-
-    assert "step_a" in execution_order or "step_b" in execution_order
-    assert "step_c" in execution_order
-    assert execution_order.index("step_c") > min(
-        execution_order.index("step_a"), execution_order.index("step_b")
-    )
-
-
-def test_flow_with_router():
-    """Test a flow that uses a router method to determine the next step."""
-    execution_order = []
-
-    class RouterFlow(Flow):
-        @start()
-        def start_method(self):
-            execution_order.append("start_method")
-
-        @router(start_method)
-        def router(self):
-            execution_order.append("router")
-            # Ensure the condition is set to True to follow the "step_if_true" path
-            condition = True
-            return "step_if_true" if condition else "step_if_false"
-
-        @listen("step_if_true")
-        def truthy(self):
-            execution_order.append("step_if_true")
-
-        @listen("step_if_false")
-        def falsy(self):
-            execution_order.append("step_if_false")
-
-    flow = RouterFlow()
-    flow.kickoff()
-
-    assert execution_order == ["start_method", "router", "step_if_true"]
-
-
-def test_async_flow():
-    """Test an asynchronous flow."""
-    execution_order = []
-
-    class AsyncFlow(Flow):
-        @start()
-        async def step_1(self):
-            execution_order.append("step_1")
-            await asyncio.sleep(0.1)
-
-        @listen(step_1)
-        async def step_2(self):
-            execution_order.append("step_2")
-            await asyncio.sleep(0.1)
-
-    flow = AsyncFlow()
-    asyncio.run(flow.kickoff_async())
-
-    assert execution_order == ["step_1", "step_2"]
-
-
-def test_flow_with_exceptions():
-    """Test flow behavior when exceptions occur in steps."""
-    execution_order = []
-
-    class ExceptionFlow(Flow):
-        @start()
-        def step_1(self):
-            execution_order.append("step_1")
-            raise ValueError("An error occurred in step_1")
-
-        @listen(step_1)
-        def step_2(self):
-            execution_order.append("step_2")
-
-    flow = ExceptionFlow()
-
-    with pytest.raises(ValueError):
-        flow.kickoff()
-
-    # Ensure step_2 did not execute
-    assert execution_order == ["step_1"]
-
-
-def test_flow_restart():
-    """Test restarting a flow after it has completed."""
-    execution_order = []
-
-    class RestartableFlow(Flow):
-        @start()
-        def step_1(self):
-            execution_order.append("step_1")
-
-        @listen(step_1)
-        def step_2(self):
-            execution_order.append("step_2")
-
-    flow = RestartableFlow()
-    flow.kickoff()
-    flow.kickoff()  # Restart the flow
-
-    assert execution_order == ["step_1", "step_2", "step_1", "step_2"]
-
-
-def test_flow_with_custom_state():
-    """Test a flow that maintains and modifies internal state."""
-
-    class StateFlow(Flow):
-        def __init__(self):
-            super().__init__()
-            self.counter = 0
-
-        @start()
-        def step_1(self):
-            self.counter += 1
-
-        @listen(step_1)
-        def step_2(self):
-            self.counter *= 2
-            assert self.counter == 2
-
-    flow = StateFlow()
-    flow.kickoff()
-    assert flow.counter == 2

uv.lock

@@ -604,7 +604,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.79.4"
+version = "0.76.9"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -665,8 +665,8 @@ requires-dist = [
     { name = "auth0-python", specifier = ">=4.7.1" },
     { name = "chromadb", specifier = ">=0.4.24" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", specifier = ">=0.14.0" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.14.0" },
+    { name = "crewai-tools", specifier = ">=0.13.4" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.13.4" },
     { name = "instructor", specifier = ">=1.3.3" },
     { name = "json-repair", specifier = ">=0.25.2" },
     { name = "jsonref", specifier = ">=1.1.0" },
@@ -688,7 +688,7 @@ requires-dist = [
 [package.metadata.requires-dev]
 dev = [
     { name = "cairosvg", specifier = ">=2.7.1" },
-    { name = "crewai-tools", specifier = ">=0.14.0" },
+    { name = "crewai-tools", specifier = ">=0.13.4" },
     { name = "mkdocs", specifier = ">=1.4.3" },
     { name = "mkdocs-material", specifier = ">=9.5.7" },
     { name = "mkdocs-material-extensions", specifier = ">=1.3.1" },
@@ -707,7 +707,7 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.14.0"
+version = "0.13.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "beautifulsoup4" },
@@ -725,9 +725,9 @@ dependencies = [
     { name = "requests" },
     { name = "selenium" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9b/6d/4fa91b481b120f83bb58f365203d8aa8564e8ced1035d79f8aedb7d71e2f/crewai_tools-0.14.0.tar.gz", hash = "sha256:510f3a194bcda4fdae4314bd775521964b5f229ddbe451e5d9e0216cae57f4e3", size = 815892 }
+sdist = { url = "https://files.pythonhosted.org/packages/64/bd/eff7b633a0b28ff4ed115adde1499e3dcc683e4f0b5c378a4c6f5c0c1bf6/crewai_tools-0.13.4.tar.gz", hash = "sha256:b6ac527633b7018471d892c21ac96bc961a86b6626d996b1ed7d53cd481d4505", size = 816588 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/c8/ed/9f4e64e1507062957b0118085332d38b621c1000874baef2d1c4069bfd97/crewai_tools-0.14.0-py3-none-any.whl", hash = "sha256:0a804a828c29869c3af3253f4fc4c3967a3f80f06dab22e9bbe9526608a31564", size = 462980 },
+    { url = "https://files.pythonhosted.org/packages/6c/40/93cd347d854059cf5e54a81b70f896deea7ad1f03e9c024549eb323c4da5/crewai_tools-0.13.4-py3-none-any.whl", hash = "sha256:eda78fe3c4df57676259d8dd6b2610fa31f89b90909512f15893adb57fb9e825", size = 463703 },
 ]
 
 [[package]]