improve type hintings

* Preparing new version

docs/concepts/knowledge.mdx

@@ -146,81 +146,106 @@ Here are examples of how to use different types of knowledge sources:
 
 ### Text File Knowledge Source
 ```python
-from crewai.knowledge.source import CrewDoclingSource
+from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
 
 # Create a text file knowledge source
 text_source = CrewDoclingSource(
     file_paths=["document.txt", "another.txt"]
 )
 
-# Create knowledge with text file source
-knowledge = Knowledge(
-    collection_name="text_knowledge",
-    sources=[text_source]
+# Create crew with text file source on agents or crew level
+agent = Agent(
+    ...
+    knowledge_sources=[text_source]
+)
+
+crew = Crew(
+    ...
+    knowledge_sources=[text_source]
 )
 ```
 
 ### PDF Knowledge Source
 ```python
-from crewai.knowledge.source import PDFKnowledgeSource
+from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
 
 # Create a PDF knowledge source
 pdf_source = PDFKnowledgeSource(
     file_paths=["document.pdf", "another.pdf"]
 )
 
-# Create knowledge with PDF source
-knowledge = Knowledge(
-    collection_name="pdf_knowledge",
-    sources=[pdf_source]
+# Create crew with PDF knowledge source on agents or crew level
+agent = Agent(
+    ...
+    knowledge_sources=[pdf_source]
+)
+
+crew = Crew(
+    ...
+    knowledge_sources=[pdf_source]
 )
 ```
 
 ### CSV Knowledge Source
 ```python
-from crewai.knowledge.source import CSVKnowledgeSource
+from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
 
 # Create a CSV knowledge source
 csv_source = CSVKnowledgeSource(
     file_paths=["data.csv"]
 )
 
-# Create knowledge with CSV source
-knowledge = Knowledge(
-    collection_name="csv_knowledge",
-    sources=[csv_source]
+# Create crew with CSV knowledge source or on agent level
+agent = Agent(
+    ...
+    knowledge_sources=[csv_source]
+)
+
+crew = Crew(
+    ...
+    knowledge_sources=[csv_source]
 )
 ```
 
 ### Excel Knowledge Source
 ```python
-from crewai.knowledge.source import ExcelKnowledgeSource
+from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
 
 # Create an Excel knowledge source
 excel_source = ExcelKnowledgeSource(
     file_paths=["spreadsheet.xlsx"]
 )
 
-# Create knowledge with Excel source
-knowledge = Knowledge(
-    collection_name="excel_knowledge",
-    sources=[excel_source]
+# Create crew with Excel knowledge source on agents or crew level
+agent = Agent(
+    ...
+    knowledge_sources=[excel_source]
+)
+
+crew = Crew(
+    ...
+    knowledge_sources=[excel_source]
 )
 ```
 
 ### JSON Knowledge Source
 ```python
-from crewai.knowledge.source import JSONKnowledgeSource
+from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
 
 # Create a JSON knowledge source
 json_source = JSONKnowledgeSource(
     file_paths=["data.json"]
 )
 
-# Create knowledge with JSON source
-knowledge = Knowledge(
-    collection_name="json_knowledge",
-    sources=[json_source]
+# Create crew with JSON knowledge source on agents or crew level
+agent = Agent(
+    ...
+    knowledge_sources=[json_source]
+)
+
+crew = Crew(
+    ...
+    knowledge_sources=[json_source]
 )
 ```
 
@@ -232,7 +257,7 @@ Knowledge sources automatically chunk content for better processing.
 You can configure chunking behavior in your knowledge sources:
 
 ```python
-from crewai.knowledge.source import StringKnowledgeSource
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 
 source = StringKnowledgeSource(
     content="Your content here",

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.86.0"
+version = "0.95.0"
 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"

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.86.0"
+__version__ = "0.95.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -193,8 +193,6 @@ class Agent(BaseAgent):
 
         task_prompt = task.prompt()
 
-        print("task_prompt:", task_prompt)
-
         # If the task requires output in JSON or Pydantic format,
         # append specific instructions to the task prompt to ensure
         # that the final answer does not include any code block markers
@@ -336,6 +334,7 @@ class Agent(BaseAgent):
 
     def get_multimodal_tools(self) -> List[Tool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
+
         return [AddImageTool()]
 
     def get_code_execution_tools(self):

src/crewai/cli/cli.py

@@ -8,7 +8,6 @@ from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
 from crewai.cli.create_flow import create_flow
 from crewai.cli.crew_chat import run_chat
-from crewai.cli.fetch_chat_llm import fetch_chat_llm
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )

src/crewai/cli/crew_chat.py

@@ -2,16 +2,13 @@ import json
 import re
 import sys
 from pathlib import Path
-from typing import Any, Dict, List, Set, Tuple
+from typing import Any, Dict, List, Optional, Set, Tuple
 
 import click
 import tomli
 
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.cli.fetch_chat_llm import fetch_chat_llm
-from crewai.cli.fetch_crew_inputs import fetch_crew_inputs
 from crewai.crew import Crew
-from crewai.task import Task
+from crewai.llm import LLM
 from crewai.types.crew_chat import ChatInputField, ChatInputs
 from crewai.utilities.llm_utils import create_llm
 
@@ -23,25 +20,52 @@ def run_chat():
     Exits if crew_name or crew_description are missing.
     """
     crew, crew_name = load_crew_and_name()
-    click.secho("\nFetching the Chat LLM...", fg="cyan")
-    try:
-        chat_llm = create_llm(crew.chat_llm)
-    except Exception as e:
-        click.secho(f"Failed to retrieve Chat LLM: {e}", fg="red")
-        return
+    chat_llm = initialize_chat_llm(crew)
     if not chat_llm:
-        click.secho("No valid Chat LLM returned. Exiting.", fg="red")
         return
 
-    # Generate crew chat inputs automatically
     crew_chat_inputs = generate_crew_chat_inputs(crew, crew_name, chat_llm)
-    print("crew_inputs:", crew_chat_inputs)
-
-    # Generate a tool schema from the crew inputs
     crew_tool_schema = generate_crew_tool_schema(crew_chat_inputs)
-    print("crew_tool_schema:", crew_tool_schema)
+    system_message = build_system_message(crew_chat_inputs)
 
-    # Build initial system message
+    # Call the LLM to generate the introductory message
+    introductory_message = chat_llm.call(
+        messages=[{"role": "system", "content": system_message}]
+    )
+    click.secho(f"\nAssistant: {introductory_message}\n", fg="green")
+
+    messages = [
+        {"role": "system", "content": system_message},
+        {"role": "assistant", "content": introductory_message},
+    ]
+
+    available_functions = {
+        crew_chat_inputs.crew_name: create_tool_function(crew, messages),
+    }
+
+    click.secho(
+        "\nEntering an interactive chat loop with function-calling.\n"
+        "Type 'exit' or Ctrl+C to quit.\n",
+        fg="cyan",
+    )
+
+    chat_loop(chat_llm, messages, crew_tool_schema, available_functions)
+
+
+def initialize_chat_llm(crew: Crew) -> Optional[LLM]:
+    """Initializes the chat LLM and handles exceptions."""
+    try:
+        return create_llm(crew.chat_llm)
+    except Exception as e:
+        click.secho(
+            f"Unable to find a Chat LLM. Please make sure you set chat_llm on the crew: {e}",
+            fg="red",
+        )
+        return None
+
+
+def build_system_message(crew_chat_inputs: ChatInputs) -> str:
+    """Builds the initial system message for the chat."""
     required_fields_str = (
         ", ".join(
             f"{field.name} (desc: {field.description or 'n/a'})"
@@ -50,7 +74,7 @@ def run_chat():
         or "(No required fields detected)"
     )
 
-    system_message = (
+    return (
         "You are a helpful AI assistant for the CrewAI platform. "
         "Your primary purpose is to assist users with the crew's specific tasks. "
         "You can answer general questions, but should guide users back to the crew's purpose afterward. "
@@ -62,30 +86,24 @@ def run_chat():
         "If a user asks a question outside the crew's scope, provide a brief answer and remind them of the crew's purpose. "
         "After calling the tool, be prepared to take user feedback and make adjustments as needed. "
         "If you are ever unsure about a user's request or need clarification, ask the user for more information."
+        "Before doing anything else, introduce yourself with a friendly message like: 'Hey! I'm here to help you with [crew's purpose]. Could you please provide me with [inputs] so we can get started?' "
+        "For example: 'Hey! I'm here to help you with uncovering and reporting cutting-edge developments through thorough research and detailed analysis. Could you please provide me with a topic you're interested in? This will help us generate a comprehensive research report and detailed analysis.'"
         f"\nCrew Name: {crew_chat_inputs.crew_name}"
         f"\nCrew Description: {crew_chat_inputs.crew_description}"
     )
 
-    messages = [
-        {"role": "system", "content": system_message},
-    ]
 
-    # Create a wrapper function that captures 'crew' and 'messages' from the enclosing scope
+def create_tool_function(crew: Crew, messages: List[Dict[str, str]]) -> Any:
+    """Creates a wrapper function for running the crew tool with messages."""
+
     def run_crew_tool_with_messages(**kwargs):
         return run_crew_tool(crew, messages, **kwargs)
 
-    # Prepare available_functions with the wrapper function
-    available_functions = {
-        crew_chat_inputs.crew_name: run_crew_tool_with_messages,
-    }
+    return run_crew_tool_with_messages
 
-    click.secho(
-        "\nEntering an interactive chat loop with function-calling.\n"
-        "Type 'exit' or Ctrl+C to quit.\n",
-        fg="cyan",
-    )
 
-    # Main chat loop
+def chat_loop(chat_llm, messages, crew_tool_schema, available_functions):
+    """Main chat loop for interacting with the user."""
     while True:
         try:
             user_input = click.prompt("You", type=str)
@@ -93,20 +111,14 @@ def run_chat():
                 click.echo("Exiting chat. Goodbye!")
                 break
 
-            # Append user message
             messages.append({"role": "user", "content": user_input})
-
-            # Invoke the LLM, passing tools and available_functions
             final_response = chat_llm.call(
                 messages=messages,
                 tools=[crew_tool_schema],
                 available_functions=available_functions,
             )
 
-            # Append assistant's reply
             messages.append({"role": "assistant", "content": final_response})
-
-            # Display assistant's reply
             click.secho(f"\nAssistant: {final_response}\n", fg="green")
 
         except KeyboardInterrupt:
@@ -165,7 +177,7 @@ def run_crew_tool(crew: Crew, messages: List[Dict[str, str]], **kwargs):
     """
     try:
         # Serialize 'messages' to JSON string before adding to kwargs
-        kwargs['crew_chat_messages'] = json.dumps(messages)
+        kwargs["crew_chat_messages"] = json.dumps(messages)
 
         # Run the crew with the provided inputs
         crew_output = crew.kickoff(inputs=kwargs)
@@ -184,7 +196,7 @@ def run_crew_tool(crew: Crew, messages: List[Dict[str, str]], **kwargs):
 def load_crew_and_name() -> Tuple[Crew, str]:
     """
     Loads the crew by importing the crew class from the user's project.
-    
+
     Returns:
         Tuple[Crew, str]: A tuple containing the Crew instance and the name of the crew.
     """
@@ -258,9 +270,7 @@ def generate_crew_chat_inputs(crew: Crew, crew_name: str, chat_llm) -> ChatInput
     crew_description = generate_crew_description_with_ai(crew, chat_llm)
 
     return ChatInputs(
-        crew_name=crew_name,
-        crew_description=crew_description,
-        inputs=input_fields
+        crew_name=crew_name, crew_description=crew_description, inputs=input_fields
     )
 
 
@@ -307,18 +317,31 @@ def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) ->
     placeholder_pattern = re.compile(r"\{(.+?)\}")
 
     for task in crew.tasks:
-        if f"{{{input_name}}}" in task.description or f"{{{input_name}}}" in task.expected_output:
+        if (
+            f"{{{input_name}}}" in task.description
+            or f"{{{input_name}}}" in task.expected_output
+        ):
             # Replace placeholders with input names
-            task_description = placeholder_pattern.sub(lambda m: m.group(1), task.description)
-            expected_output = placeholder_pattern.sub(lambda m: m.group(1), task.expected_output)
+            task_description = placeholder_pattern.sub(
+                lambda m: m.group(1), task.description
+            )
+            expected_output = placeholder_pattern.sub(
+                lambda m: m.group(1), task.expected_output
+            )
             context_texts.append(f"Task Description: {task_description}")
             context_texts.append(f"Expected Output: {expected_output}")
     for agent in crew.agents:
-        if f"{{{input_name}}}" in agent.role or f"{{{input_name}}}" in agent.goal or f"{{{input_name}}}" in agent.backstory:
+        if (
+            f"{{{input_name}}}" in agent.role
+            or f"{{{input_name}}}" in agent.goal
+            or f"{{{input_name}}}" in agent.backstory
+        ):
             # Replace placeholders with input names
             agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role)
             agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal)
-            agent_backstory = placeholder_pattern.sub(lambda m: m.group(1), agent.backstory)
+            agent_backstory = placeholder_pattern.sub(
+                lambda m: m.group(1), agent.backstory
+            )
             context_texts.append(f"Agent Role: {agent_role}")
             context_texts.append(f"Agent Goal: {agent_goal}")
             context_texts.append(f"Agent Backstory: {agent_backstory}")
@@ -357,8 +380,12 @@ def generate_crew_description_with_ai(crew: Crew, chat_llm) -> str:
 
     for task in crew.tasks:
         # Replace placeholders with input names
-        task_description = placeholder_pattern.sub(lambda m: m.group(1), task.description)
-        expected_output = placeholder_pattern.sub(lambda m: m.group(1), task.expected_output)
+        task_description = placeholder_pattern.sub(
+            lambda m: m.group(1), task.description
+        )
+        expected_output = placeholder_pattern.sub(
+            lambda m: m.group(1), task.expected_output
+        )
         context_texts.append(f"Task Description: {task_description}")
         context_texts.append(f"Expected Output: {expected_output}")
     for agent in crew.agents:

src/crewai/cli/fetch_chat_llm.py

@@ -1,81 +0,0 @@
-import json
-import subprocess
-
-import click
-from packaging import version
-
-from crewai.cli.utils import read_toml
-from crewai.cli.version import get_crewai_version
-from crewai.llm import LLM
-
-
-def fetch_chat_llm() -> LLM:
-    """
-    Fetch the chat LLM by running "uv run fetch_chat_llm" (or your chosen script name),
-    parsing its JSON stdout, and returning an LLM instance.
-
-    This expects the script "fetch_chat_llm" to print out JSON that represents the
-    LLM parameters (e.g., by calling something like: print(json.dumps(llm.to_dict()))).
-
-    Any error, whether from the subprocess or JSON parsing, will raise a RuntimeError.
-    """
-
-    # You may change this command to match whatever's in your pyproject.toml [project.scripts].
-    command = ["uv", "run", "fetch_chat_llm"]
-
-    crewai_version = get_crewai_version()
-    min_required_version = "0.87.0"  # Adjust as needed
-
-    pyproject_data = read_toml()
-
-    # If old poetry-based setup is detected and version is below min_required_version
-    if pyproject_data.get("tool", {}).get("poetry") and (
-        version.parse(crewai_version) < version.parse(min_required_version)
-    ):
-        click.secho(
-            f"You are running an older version of crewAI ({crewai_version}) that uses poetry pyproject.toml.\n"
-            f"Please run `crewai update` to transition your pyproject.toml to use uv.",
-            fg="red",
-        )
-
-    # Initialize a reference to your LLM
-    llm_instance = None
-
-    try:
-        result = subprocess.run(command, capture_output=True, text=True, check=True)
-        stdout_lines = result.stdout.strip().splitlines()
-
-        # Find the line that contains the JSON data
-        json_line = next(
-            (
-                line
-                for line in stdout_lines
-                if line.startswith("{") and line.endswith("}")
-            ),
-            None,
-        )
-
-        if not json_line:
-            raise RuntimeError(
-                "No valid JSON output received from `fetch_chat_llm` command."
-            )
-
-        try:
-            llm_data = json.loads(json_line)
-            llm_instance = LLM.from_dict(llm_data)
-        except json.JSONDecodeError as e:
-            raise RuntimeError(
-                f"Unable to parse JSON from `fetch_chat_llm` output: {e}\nOutput: {repr(json_line)}"
-            ) from e
-
-    except subprocess.CalledProcessError as e:
-        raise RuntimeError(f"An error occurred while fetching chat LLM: {e}") from e
-    except Exception as e:
-        raise RuntimeError(
-            f"An unexpected error occurred while fetching chat LLM: {e}"
-        ) from e
-
-    if not llm_instance:
-        raise RuntimeError("Failed to create a valid LLM from `fetch_chat_llm` output.")
-
-    return llm_instance

src/crewai/cli/fetch_crew_inputs.py

@@ -1,86 +0,0 @@
-import json
-import subprocess
-from typing import Optional
-
-import click
-from packaging import version
-
-from crewai.cli.utils import read_toml
-from crewai.cli.version import get_crewai_version
-from crewai.types.crew_chat import ChatInputs
-
-
-def fetch_crew_inputs() -> Optional[ChatInputs]:
-    """
-    Fetch the crew's ChatInputs (a structure containing crew_description and input fields)
-    by running "uv run fetch_chat_inputs", which prints JSON representing a ChatInputs object.
-
-    This function will parse that JSON and return a ChatInputs instance.
-    If the output is empty or invalid, an empty ChatInputs object is returned.
-    """
-
-    command = ["uv", "run", "fetch_chat_inputs"]
-    crewai_version = get_crewai_version()
-    min_required_version = "0.87.0"
-
-    pyproject_data = read_toml()
-    crew_name = pyproject_data.get("project", {}).get("name", None)
-
-    # If you're on an older poetry-based setup and version < min_required_version
-    if pyproject_data.get("tool", {}).get("poetry") and (
-        version.parse(crewai_version) < version.parse(min_required_version)
-    ):
-        click.secho(
-            f"You are running an older version of crewAI ({crewai_version}) that uses poetry pyproject.toml.\n"
-            f"Please run `crewai update` to update your pyproject.toml to use uv.",
-            fg="red",
-        )
-
-    try:
-        result = subprocess.run(command, capture_output=True, text=True, check=True)
-        stdout_lines = result.stdout.strip().splitlines()
-
-        # Find the line that contains the JSON data
-        json_line = next(
-            (
-                line
-                for line in stdout_lines
-                if line.startswith("{") and line.endswith("}")
-            ),
-            None,
-        )
-
-        if not json_line:
-            click.echo(
-                "No valid JSON output received from `fetch_chat_inputs` command.",
-                err=True,
-            )
-            return None
-
-        try:
-            raw_data = json.loads(json_line)
-            chat_inputs = ChatInputs(**raw_data)
-            if crew_name:
-                chat_inputs.crew_name = crew_name
-            return chat_inputs
-        except json.JSONDecodeError as e:
-            click.echo(
-                f"Unable to parse JSON from `fetch_chat_inputs` output: {e}\nOutput: {repr(json_line)}",
-                err=True,
-            )
-            return None
-
-    except subprocess.CalledProcessError as e:
-        click.echo(f"An error occurred while fetching chat inputs: {e}", err=True)
-        click.echo(e.output, err=True, nl=True)
-
-        if pyproject_data.get("tool", {}).get("poetry"):
-            click.secho(
-                "It's possible that you are using an old version of crewAI that uses poetry.\n"
-                "Please run `crewai update` to update your pyproject.toml to use uv.",
-                fg="yellow",
-            )
-    except Exception as e:
-        click.echo(f"An unexpected error occurred: {e}", err=True)
-
-    return None

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

@@ -1,10 +1,8 @@
 #!/usr/bin/env python
 import sys
-import json
 import warnings
 
 from {{folder_name}}.crew import {{crew_name}}
-from crewai.utilities.llm_utils import create_llm
 
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
@@ -15,26 +13,12 @@ warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
 def run():
     """
-    Run the crew, allowing CLI overrides for required inputs.
-    Usage example:
-        uv run run_crew -- --topic="New Topic" --some_other_field="Value"
+    Run the crew.
     """
-    # Default inputs
     inputs = {
         'topic': 'AI LLMs'
-        # Add any other default fields here
     }
-
-    # 1) Gather overrides from sys.argv
-    #    sys.argv might look like: ['run_crew', '--topic=NewTopic']
-    #    But be aware that if you're calling "uv run run_crew", sys.argv might have
-    #    additional items. So we typically skip the first 1 or 2 items to get only overrides.
-    overrides = parse_cli_overrides(sys.argv[1:])
-
-    # 2) Merge the overrides into defaults
-    inputs.update(overrides)
-
-    # 3) Kick off the crew with final inputs
+    
     try:
         {{crew_name}}().crew().kickoff(inputs=inputs)
     except Exception as e:
@@ -76,93 +60,3 @@ def test():
 
     except Exception as e:
         raise Exception(f"An error occurred while testing the crew: {e}")
-
-def fetch_inputs():
-    """
-    Command that gathers required placeholders/inputs from the Crew, then
-    prints them as JSON to stdout so external scripts can parse them easily.
-    """
-    try:
-        crew = {{crew_name}}().crew()
-        crew_inputs = crew.fetch_inputs()
-        json_string = json.dumps(list(crew_inputs))
-        print(json_string)
-    except Exception as e:
-        raise Exception(f"An error occurred while fetching inputs: {e}")
-
-def fetch_chat_llm():
-    """
-    Command that fetches the 'chat_llm' property from the Crew,
-    instantiates it via create_llm(),
-    and prints the resulting LLM as JSON (using LLM.to_dict()) to stdout.
-    """
-    try:
-        crew = {{crew_name}}().crew()
-        raw_chat_llm = getattr(crew, "chat_llm", None)
-
-        if not raw_chat_llm:
-            # If the crew doesn't have chat_llm, fallback to create_llm(None)
-            final_llm = create_llm(None)
-        else:
-            # raw_chat_llm might be a dict, or an LLM, or something else
-            final_llm = create_llm(raw_chat_llm)
-
-        if final_llm:
-            # Print the final LLM as JSON, so fetch_chat_llm.py can parse it
-            from crewai.llm import LLM  # Import here to avoid circular references
-
-            # Make sure it's an instance of the LLM class:
-            if isinstance(final_llm, LLM):
-                print(json.dumps(final_llm.to_dict()))
-            else:
-                # If somehow it's not an LLM, try to interpret as a dict
-                # or revert to an empty fallback
-                if isinstance(final_llm, dict):
-                    print(json.dumps(final_llm))
-                else:
-                    print(json.dumps({}))
-        else:
-            print(json.dumps({}))
-    except Exception as e:
-        raise Exception(f"An error occurred while fetching chat LLM: {e}")
-
-# TODO: Talk to Joao about making using LLM calls to analyze the crew
-# and generate all of this information automatically
-def fetch_chat_inputs():
-    """
-    Command that fetches the 'chat_inputs' property from the Crew,
-    and prints it as JSON to stdout.
-    """
-    try:
-        crew = {{crew_name}}().crew()
-        raw_chat_inputs = getattr(crew, "chat_inputs", None)
-
-        if raw_chat_inputs:
-            # Convert to dictionary to print JSON
-            print(json.dumps(raw_chat_inputs.model_dump()))
-        else:
-            # If crew.chat_inputs is None or empty, print an empty JSON
-            print(json.dumps({}))
-    except Exception as e:
-        raise Exception(f"An error occurred while fetching chat inputs: {e}")
-    
-    
-def parse_cli_overrides(args_list) -> dict:
-    """
-    Parse arguments in the form of --key=value from a list of CLI arguments.
-    Return them as a dict. For example:
-    ['--topic=AI LLMs', '--username=John'] => {'topic': 'AI LLMs', 'username': 'John'}
-    """
-    overrides = {}
-    for arg in args_list:
-        if arg.startswith("--"):
-            # remove the leading --
-            trimmed = arg[2:]
-            if "=" in trimmed:
-                key, val = trimmed.split("=", 1)
-                overrides[key] = val
-            else:
-                # If someone passed something like --topic (no =),
-                # either handle differently or ignore
-                pass
-    return overrides

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]>=0.86.0,<1.0.0"
+    "crewai[tools]>=0.95.0,<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]>=0.86.0,<1.0.0",
+    "crewai[tools]>=0.95.0,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.86.0"
+    "crewai[tools]>=0.95.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -209,10 +209,6 @@ class Crew(BaseModel):
         default=None,
         description="LLM used to handle chatting with the crew.",
     )
-    chat_inputs: Optional[ChatInputs] = Field(
-        default=None,
-        description="Holds descriptions of the crew as well as named inputs for chat usage.",
-    )
     _knowledge: Optional[Knowledge] = PrivateAttr(
         default=None,
     )

src/crewai/llm.py

@@ -86,6 +86,9 @@ CONTEXT_WINDOW_USAGE_RATIO = 0.75
 def suppress_warnings():
     with warnings.catch_warnings():
         warnings.filterwarnings("ignore")
+        warnings.filterwarnings(
+            "ignore", message="open_text is deprecated*", category=DeprecationWarning
+        )
 
         # Redirect stdout and stderr
         old_stdout = sys.stdout
@@ -143,67 +146,11 @@ class LLM:
         self.callbacks = callbacks
         self.context_window_size = 0
 
-        # For safety, we disable passing init params to next calls
         litellm.drop_params = True
 
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
 
-    def to_dict(self) -> dict:
-        """
-        Return a dict of all relevant parameters for serialization.
-        """
-        return {
-            "model": self.model,
-            "timeout": self.timeout,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-            "n": self.n,
-            "stop": self.stop,
-            "max_completion_tokens": self.max_completion_tokens,
-            "max_tokens": self.max_tokens,
-            "presence_penalty": self.presence_penalty,
-            "frequency_penalty": self.frequency_penalty,
-            "logit_bias": self.logit_bias,
-            "response_format": self.response_format,
-            "seed": self.seed,
-            "logprobs": self.logprobs,
-            "top_logprobs": self.top_logprobs,
-            "base_url": self.base_url,
-            "api_version": self.api_version,
-            "api_key": self.api_key,
-            "callbacks": self.callbacks,
-        }
-
-    @classmethod
-    def from_dict(cls, data: dict) -> "LLM":
-        """
-        Create an LLM instance from a dict.
-        We assume the dict has all relevant keys that match what's in the constructor.
-        """
-        known_fields = {}
-        known_fields["model"] = data.pop("model", None)
-        known_fields["timeout"] = data.pop("timeout", None)
-        known_fields["temperature"] = data.pop("temperature", None)
-        known_fields["top_p"] = data.pop("top_p", None)
-        known_fields["n"] = data.pop("n", None)
-        known_fields["stop"] = data.pop("stop", None)
-        known_fields["max_completion_tokens"] = data.pop("max_completion_tokens", None)
-        known_fields["max_tokens"] = data.pop("max_tokens", None)
-        known_fields["presence_penalty"] = data.pop("presence_penalty", None)
-        known_fields["frequency_penalty"] = data.pop("frequency_penalty", None)
-        known_fields["logit_bias"] = data.pop("logit_bias", None)
-        known_fields["response_format"] = data.pop("response_format", None)
-        known_fields["seed"] = data.pop("seed", None)
-        known_fields["logprobs"] = data.pop("logprobs", None)
-        known_fields["top_logprobs"] = data.pop("top_logprobs", None)
-        known_fields["base_url"] = data.pop("base_url", None)
-        known_fields["api_version"] = data.pop("api_version", None)
-        known_fields["api_key"] = data.pop("api_key", None)
-        known_fields["callbacks"] = data.pop("callbacks", None)
-
-        return cls(**known_fields, **data)
-
     def call(
         self,
         messages: List[Dict[str, str]],
@@ -227,7 +174,7 @@ class LLM:
         :return: Final text response from the LLM or the tool result
         """
         with suppress_warnings():
-            if callbacks:
+            if callbacks and len(callbacks) > 0:
                 self.set_callbacks(callbacks)
 
             try:
@@ -255,7 +202,6 @@ class LLM:
                     "tools": tools,  # pass the tool schema
                 }
 
-                # Remove None values
                 params = {k: v for k, v in params.items() if v is not None}
 
                 response = litellm.completion(**params)
@@ -274,42 +220,36 @@ class LLM:
                 function_name = tool_call.function.name
 
                 if function_name in available_functions:
-                    # Parse arguments
                     try:
                         function_args = json.loads(tool_call.function.arguments)
                     except json.JSONDecodeError as e:
                         logging.warning(f"Failed to parse function arguments: {e}")
-                        return text_response  # Fallback to text response
+                        return text_response
 
                     fn = available_functions[function_name]
                     try:
                         # Call the actual tool function
                         result = fn(**function_args)
 
-                        print(f"Result from function '{function_name}': {result}")
-
-                        # Return the result directly
                         return result
 
                     except Exception as e:
                         logging.error(
                             f"Error executing function '{function_name}': {e}"
                         )
-                        return text_response  # Fallback to text response
+                        return text_response
 
                 else:
                     logging.warning(
                         f"Tool call requested unknown function '{function_name}'"
                     )
-                    return text_response  # Fallback to text response
+                    return text_response
 
             except Exception as e:
-                # Check if context length was exceeded, otherwise log
                 if not LLMContextLengthExceededException(
                     str(e)
                 )._is_context_limit_error(str(e)):
                     logging.error(f"LiteLLM call failed: {str(e)}")
-                # Re-raise the exception
                 raise
 
     def supports_function_calling(self) -> bool:
@@ -349,34 +289,51 @@ class LLM:
         Attempt to keep a single set of callbacks in litellm by removing old
         duplicates and adding new ones.
         """
-        callback_types = [type(callback) for callback in callbacks]
-        for callback in litellm.success_callback[:]:
-            if type(callback) in callback_types:
-                litellm.success_callback.remove(callback)
+        with suppress_warnings():
+            callback_types = [type(callback) for callback in callbacks]
+            for callback in litellm.success_callback[:]:
+                if type(callback) in callback_types:
+                    litellm.success_callback.remove(callback)
 
-        for callback in litellm._async_success_callback[:]:
-            if type(callback) in callback_types:
-                litellm._async_success_callback.remove(callback)
+            for callback in litellm._async_success_callback[:]:
+                if type(callback) in callback_types:
+                    litellm._async_success_callback.remove(callback)
 
-        litellm.callbacks = callbacks
+            litellm.callbacks = callbacks
 
     def set_env_callbacks(self):
         """
         Sets the success and failure callbacks for the LiteLLM library from environment variables.
+
+        This method reads the `LITELLM_SUCCESS_CALLBACKS` and `LITELLM_FAILURE_CALLBACKS`
+        environment variables, which should contain comma-separated lists of callback names.
+        It then assigns these lists to `litellm.success_callback` and `litellm.failure_callback`,
+        respectively.
+
+        If the environment variables are not set or are empty, the corresponding callback lists
+        will be set to empty lists.
+
+        Example:
+            LITELLM_SUCCESS_CALLBACKS="langfuse,langsmith"
+            LITELLM_FAILURE_CALLBACKS="langfuse"
+
+        This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
+        `litellm.failure_callback` to ["langfuse"].
         """
-        success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
-        success_callbacks = []
-        if success_callbacks_str:
-            success_callbacks = [
-                cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
-            ]
+        with suppress_warnings():
+            success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
+            success_callbacks = []
+            if success_callbacks_str:
+                success_callbacks = [
+                    cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
+                ]
 
-        failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
-        failure_callbacks = []
-        if failure_callbacks_str:
-            failure_callbacks = [
-                cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
-            ]
+            failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
+            failure_callbacks = []
+            if failure_callbacks_str:
+                failure_callbacks = [
+                    cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
+                ]
 
-        litellm.success_callback = success_callbacks
-        litellm.failure_callback = failure_callbacks
+                litellm.success_callback = success_callbacks
+                litellm.failure_callback = failure_callbacks

src/crewai/task.py

@@ -41,6 +41,7 @@ from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter, convert_to_model
 from crewai.utilities.i18n import I18N
+from crewai.utilities.printer import Printer
 
 
 class Task(BaseModel):
@@ -133,7 +134,6 @@ class Task(BaseModel):
         default=3, description="Maximum number of retries when guardrail fails"
     )
     retry_count: int = Field(default=0, description="Current number of retries")
-
     start_time: Optional[datetime.datetime] = Field(
         default=None, description="Start time of the task execution"
     )
@@ -391,10 +391,14 @@ class Task(BaseModel):
                     )
 
                 self.retry_count += 1
-                context = (
-                    f"### Previous attempt failed validation: {guardrail_result.error}\n\n\n"
-                    f"### Previous result:\n{task_output.raw}\n\n\n"
-                    "Try again, making sure to address the validation error."
+                context = self.i18n.errors("validation_error").format(
+                    guardrail_result_error=guardrail_result.error,
+                    task_output=task_output.raw,
+                )
+                printer = Printer()
+                printer.print(
+                    content=f"Guardrail blocked, retrying, due to: {guardrail_result.error}\n",
+                    color="yellow",
                 )
                 return self._execute_core(agent, context, tools)
 
@@ -427,9 +431,7 @@ class Task(BaseModel):
             content = (
                 json_output
                 if json_output
-                else pydantic_output.model_dump_json()
-                if pydantic_output
-                else result
+                else pydantic_output.model_dump_json() if pydantic_output else result
             )
             self._save_file(content)
 
@@ -449,11 +451,12 @@ class Task(BaseModel):
         tasks_slices = [self.description, output]
         return "\n".join(tasks_slices)
 
-
-    def interpolate_inputs_and_add_conversation_history(self, inputs: Dict[str, Union[str, int, float]]) -> None:
+    def interpolate_inputs_and_add_conversation_history(
+        self, inputs: Dict[str, Union[str, int, float]]
+    ) -> None:
         """Interpolate inputs into the task description, expected output, and output file path.
            Add conversation history if present.
-        
+
         Args:
             inputs: Dictionary mapping template variables to their values.
                    Supported value types are strings, integers, and floats.
@@ -493,16 +496,15 @@ class Task(BaseModel):
                     input_string=self._original_output_file, inputs=inputs
                 )
             except (KeyError, ValueError) as e:
-                raise ValueError(f"Error interpolating output_file path: {str(e)}") from e
-                
+                raise ValueError(
+                    f"Error interpolating output_file path: {str(e)}"
+                ) from e
+
         if "crew_chat_messages" in inputs and inputs["crew_chat_messages"]:
-            # Fetch the conversation history instruction using self.i18n.slice
             conversation_instruction = self.i18n.slice(
                 "conversation_history_instruction"
             )
-            print("crew_chat_messages:", inputs["crew_chat_messages"])
 
-            # Ensure that inputs["crew_chat_messages"] is a string
             crew_chat_messages_json = str(inputs["crew_chat_messages"])
 
             try:
@@ -511,15 +513,15 @@ class Task(BaseModel):
                 print("An error occurred while parsing crew chat messages:", e)
                 raise
 
-            # Process the messages to build conversation history
             conversation_history = "\n".join(
                 f"{msg['role'].capitalize()}: {msg['content']}"
                 for msg in crew_chat_messages
                 if isinstance(msg, dict) and "role" in msg and "content" in msg
             )
 
-            # Add the instruction and conversation history to the description
-            self.description += f"\n\n{conversation_instruction}\n\n{conversation_history}"
+            self.description += (
+                f"\n\n{conversation_instruction}\n\n{conversation_history}"
+            )
 
     def interpolate_only(
         self, input_string: Optional[str], inputs: Dict[str, Union[str, int, float]]

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Optional, Union
+from typing import Optional
 
 from pydantic import Field
 
@@ -54,12 +54,12 @@ class BaseAgentTool(BaseTool):
     ) -> str:
         """
         Execute delegation to an agent with case-insensitive and whitespace-tolerant matching.
-        
+
         Args:
             agent_name: Name/role of the agent to delegate to (case-insensitive)
             task: The specific question or task to delegate
             context: Optional additional context for the task execution
-            
+
         Returns:
             str: The execution result from the delegated agent or an error message
                  if the agent cannot be found

src/crewai/tools/base_tool.py

@@ -1,12 +1,23 @@
+import warnings
 from abc import ABC, abstractmethod
 from inspect import signature
 from typing import Any, Callable, Type, get_args, get_origin
 
-from pydantic import BaseModel, ConfigDict, Field, create_model, validator
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    PydanticDeprecatedSince20,
+    create_model,
+    validator,
+)
 from pydantic import BaseModel as PydanticBaseModel
 
 from crewai.tools.structured_tool import CrewStructuredTool
 
+# Ignore all "PydanticDeprecatedSince20" warnings globally
+warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
+
 
 class BaseTool(BaseModel, ABC):
     class _ArgsSchemaPlaceholder(PydanticBaseModel):

src/crewai/translations/en.json

@@ -35,7 +35,8 @@
     "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.",
     "wrong_tool_name": "You tried to use the tool {tool}, but it doesn't exist. You must use one of the following tools, use one at time: {tools}.",
     "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts these inputs: {tool_inputs}",
-    "agent_tool_execution_error": "Error executing task with agent '{agent_role}'. Error: {error}"
+    "agent_tool_execution_error": "Error executing task with agent '{agent_role}'. Error: {error}",
+    "validation_error": "### Previous attempt failed validation: {guardrail_result_error}\n\n\n### Previous result:\n{task_output}\n\n\nTry again, making sure to address the validation error."
   },
   "tools": {
     "delegate_work": "Delegate a specific task to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolute everything you know, don't reference things but instead explain them.",

src/crewai/types/crew_chat.py

@@ -1,4 +1,4 @@
-from typing import List, Optional
+from typing import List
 
 from pydantic import BaseModel, Field
 
@@ -14,10 +14,7 @@ class ChatInputField(BaseModel):
     """
 
     name: str = Field(..., description="The name of the input field")
-    description: str = Field(
-        ...,
-        description="A short description of the input field",
-    )
+    description: str = Field(..., description="A short description of the input field")
 
 
 class ChatInputs(BaseModel):
@@ -36,8 +33,7 @@ class ChatInputs(BaseModel):
 
     crew_name: str = Field(..., description="The name of the crew")
     crew_description: str = Field(
-        ...,
-        description="A description of the crew's purpose",
+        ..., description="A description of the crew's purpose"
     )
     inputs: List[ChatInputField] = Field(
         default_factory=list, description="A list of input fields for the crew"

src/crewai/utilities/internal_instructor.py

@@ -31,10 +31,10 @@ class InternalInstructor:
             import instructor
             from litellm import completion
 
-        self._client = instructor.from_litellm(
-            completion,
-            mode=instructor.Mode.TOOLS,
-        )
+            self._client = instructor.from_litellm(
+                completion,
+                mode=instructor.Mode.TOOLS,
+            )
 
     def to_json(self):
         model = self.to_pydantic()

src/crewai/utilities/llm_utils.py

@@ -34,7 +34,6 @@ def create_llm(
     if isinstance(llm_value, str):
         try:
             created_llm = LLM(model=llm_value)
-            print(f"LLM created with model='{llm_value}'")
             return created_llm
         except Exception as e:
             print(f"Failed to instantiate LLM with model='{llm_value}': {e}")
@@ -68,45 +67,13 @@ def create_llm(
             api_key=api_key,
             base_url=base_url,
         )
-        print(
-            "LLM created with extracted parameters; "
-            f"model='{model}'"
-        )
+        print("LLM created with extracted parameters; " f"model='{model}'")
         return created_llm
     except Exception as e:
         print(f"Error instantiating LLM from unknown object type: {e}")
         return None
 
 
-def create_chat_llm() -> Optional[LLM]:
-    """
-    Creates a Chat LLM with additional checks, such as verifying crewAI version
-    or reading from pyproject.toml. Then calls `create_llm(None, default_model)`.
-
-    Args:
-        default_model (str): Fallback model if not set in environment.
-
-    Returns:
-        An instance of LLM or None if instantiation fails.
-    """
-    print("[create_chat_llm] Checking environment and version info...")
-
-    crewai_version = get_crewai_version()
-    min_required_version = "0.87.0"  # Update to latest if needed
-
-    pyproject_data = read_toml()
-    if pyproject_data.get("tool", {}).get("poetry") and (
-        version.parse(crewai_version) < version.parse(min_required_version)
-    ):
-        print(
-            f"You are running an older version of crewAI ({crewai_version}) that uses poetry.\n"
-            "Please run `crewai update` to switch to uv-based builds."
-        )
-
-    # After checks, simply call create_llm with None (meaning "use env or fallback"):
-    return create_llm(None)
-
-
 def _llm_via_environment_or_fallback() -> Optional[LLM]:
     """
     Helper function: if llm_value is None, we load environment variables or fallback default model.
@@ -174,22 +141,25 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
     set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
 
     if set_provider in ENV_VARS:
-        for env_var in ENV_VARS[set_provider]:
-            key_name = env_var.get("key_name")
-            if key_name and key_name not in UNACCEPTED_ATTRIBUTES:
-                env_value = os.environ.get(key_name)
-                if env_value:
-                    # Map environment variable names to recognized parameters
-                    param_key = _normalize_key_name(key_name.lower())
-                    llm_params[param_key] = env_value
-            elif isinstance(env_var, dict):
-                if env_var.get("default", False):
-                    for key, value in env_var.items():
-                        if key not in ["prompt", "key_name", "default"]:
-                            if key in os.environ:
-                                llm_params[key] = os.environ[key]
-            else:
-                print(f"Expected env_var to be a dictionary, but got {type(env_var)}")
+        env_vars_for_provider = ENV_VARS[set_provider]
+        if isinstance(env_vars_for_provider, (list, tuple)):
+            for env_var in env_vars_for_provider:
+                key_name = env_var.get("key_name")
+                if key_name and key_name not in UNACCEPTED_ATTRIBUTES:
+                    env_value = os.environ.get(key_name)
+                    if env_value:
+                        # Map environment variable names to recognized parameters
+                        param_key = _normalize_key_name(key_name.lower())
+                        llm_params[param_key] = env_value
+                elif isinstance(env_var, dict):
+                    if env_var.get("default", False):
+                        for key, value in env_var.items():
+                            if key not in ["prompt", "key_name", "default"]:
+                                llm_params[key.lower()] = value
+                else:
+                    print(
+                        f"Expected env_var to be a dictionary, but got {type(env_var)}"
+                    )
 
     # Remove None values
     llm_params = {k: v for k, v in llm_params.items() if v is not None}
@@ -197,10 +167,11 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
     # Try creating the LLM
     try:
         new_llm = LLM(**llm_params)
-        print(f"LLM created with model='{model_name}'")
         return new_llm
     except Exception as e:
-        print(f"Error instantiating LLM from environment/fallback: {type(e).__name__}: {e}")
+        print(
+            f"Error instantiating LLM from environment/fallback: {type(e).__name__}: {e}"
+        )
         return None
 
 

src/crewai/utilities/planning_handler.py

@@ -1,4 +1,3 @@
-import json
 import logging
 from typing import Any, List, Optional
 
@@ -78,10 +77,10 @@ class CrewPlanner:
     def _get_agent_knowledge(self, task: Task) -> List[str]:
         """
         Safely retrieve knowledge source content from the task's agent.
-        
+
         Args:
             task: The task containing an agent with potential knowledge sources
-            
+
         Returns:
             List[str]: A list of knowledge source strings
         """
@@ -108,6 +107,6 @@ class CrewPlanner:
                     f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"',
                     f',\n                "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else ""
                 )
-            
+
             tasks_summary.append(task_summary)
         return " ".join(tasks_summary)

tests/agents/agent_builder/base_agent_test.py

@@ -7,7 +7,7 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tools.base_tool import BaseTool
 
 
-class TestAgent(BaseAgent):
+class MockAgent(BaseAgent):
     def execute_task(
         self,
         task: Any,
@@ -29,7 +29,7 @@ class TestAgent(BaseAgent):
 
 
 def test_key():
-    agent = TestAgent(
+    agent = MockAgent(
         role="test role",
         goal="test goal",
         backstory="test backstory",

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tests/cassettes/test_task_tools_override_agent_tools.yaml

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@@ -177,12 +177,12 @@ class TestDeployCommand(unittest.TestCase):
     def test_get_crew_status(self):
         mock_response = MagicMock()
         mock_response.status_code = 200
-        mock_response.json.return_value = {"name": "TestCrew", "status": "active"}
+        mock_response.json.return_value = {"name": "InternalCrew", "status": "active"}
         self.mock_client.crew_status_by_name.return_value = mock_response
 
         with patch("sys.stdout", new=StringIO()) as fake_out:
             self.deploy_command.get_crew_status()
-            self.assertIn("TestCrew", fake_out.getvalue())
+            self.assertIn("InternalCrew", fake_out.getvalue())
             self.assertIn("active", fake_out.getvalue())
 
     def test_get_crew_logs(self):

tests/crew_test.py

@@ -1846,7 +1846,9 @@ def test_crew_inputs_interpolate_both_agents_and_tasks_diff():
             Agent, "interpolate_inputs", wraps=agent.interpolate_inputs
         ) as interpolate_agent_inputs:
             with patch.object(
-                Task, "interpolate_inputs", wraps=task.interpolate_inputs
+                Task,
+                "interpolate_inputs_and_add_conversation_history",
+                wraps=task.interpolate_inputs_and_add_conversation_history,
             ) as interpolate_task_inputs:
                 execute.return_value = "ok"
                 crew.kickoff(inputs={"topic": "AI", "points": 5})
@@ -1873,7 +1875,9 @@ def test_crew_does_not_interpolate_without_inputs():
     crew = Crew(agents=[agent], tasks=[task])
 
     with patch.object(Agent, "interpolate_inputs") as interpolate_agent_inputs:
-        with patch.object(Task, "interpolate_inputs") as interpolate_task_inputs:
+        with patch.object(
+            Task, "interpolate_inputs_and_add_conversation_history"
+        ) as interpolate_task_inputs:
             crew.kickoff()
             interpolate_agent_inputs.assert_not_called()
             interpolate_task_inputs.assert_not_called()
@@ -3109,6 +3113,7 @@ def test_fetch_inputs():
         actual_placeholders == expected_placeholders
     ), f"Expected {expected_placeholders}, but got {actual_placeholders}"
 
+
 def test_task_tools_preserve_code_execution_tools():
     """
     Test that task tools don't override code execution tools when allow_code_execution=True
@@ -3359,3 +3364,117 @@ def test_multimodal_agent_live_image_analysis():
     assert isinstance(result.raw, str)
     assert len(result.raw) > 100  # Expecting a detailed analysis
     assert "error" not in result.raw.lower()  # No error messages in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_with_failing_task_guardrails():
+    """Test that crew properly handles failing guardrails and retries with validation feedback."""
+
+    def strict_format_guardrail(result: TaskOutput):
+        """Validates that the output follows a strict format:
+        - Must start with 'REPORT:'
+        - Must end with 'END REPORT'
+        """
+        content = result.raw.strip()
+
+        if not ("REPORT:" in content or "**REPORT:**" in content):
+            return (
+                False,
+                "Output must start with 'REPORT:' no formatting, just the word REPORT",
+            )
+
+        if not ("END REPORT" in content or "**END REPORT**" in content):
+            return (
+                False,
+                "Output must end with 'END REPORT' no formatting, just the word END REPORT",
+            )
+
+        return (True, content)
+
+    researcher = Agent(
+        role="Report Writer",
+        goal="Create properly formatted reports",
+        backstory="You're an expert at writing structured reports.",
+    )
+
+    task = Task(
+        description="""Write a report about AI with exactly 3 key points.""",
+        expected_output="A properly formatted report",
+        agent=researcher,
+        guardrail=strict_format_guardrail,
+        max_retries=3,
+    )
+
+    crew = Crew(
+        agents=[researcher],
+        tasks=[task],
+    )
+
+    result = crew.kickoff()
+
+    # Verify the final output meets all format requirements
+    content = result.raw.strip()
+    assert content.startswith("REPORT:"), "Output should start with 'REPORT:'"
+    assert content.endswith("END REPORT"), "Output should end with 'END REPORT'"
+
+    # Verify task output
+    task_output = result.tasks_output[0]
+    assert isinstance(task_output, TaskOutput)
+    assert task_output.raw == result.raw
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_guardrail_feedback_in_context():
+    """Test that guardrail feedback is properly appended to task context for retries."""
+
+    def format_guardrail(result: TaskOutput):
+        """Validates that the output contains a specific keyword."""
+        if "IMPORTANT" not in result.raw:
+            return (False, "Output must contain the keyword 'IMPORTANT'")
+        return (True, result.raw)
+
+    # Create execution contexts list to track contexts
+    execution_contexts = []
+
+    researcher = Agent(
+        role="Writer",
+        goal="Write content with specific keywords",
+        backstory="You're an expert at following specific writing requirements.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Write a short response.",
+        expected_output="A response containing the keyword 'IMPORTANT'",
+        agent=researcher,
+        guardrail=format_guardrail,
+        max_retries=2,
+    )
+
+    crew = Crew(agents=[researcher], tasks=[task])
+
+    with patch.object(Agent, "execute_task") as mock_execute_task:
+        # Define side_effect to capture context and return different responses
+        def side_effect(task, context=None, tools=None):
+            execution_contexts.append(context if context else "")
+            if len(execution_contexts) == 1:
+                return "This is a test response"
+            return "This is an IMPORTANT test response"
+
+        mock_execute_task.side_effect = side_effect
+
+        result = crew.kickoff()
+
+    # Verify that we had multiple executions
+    assert len(execution_contexts) > 1, "Task should have been executed multiple times"
+
+    # Verify that the second execution included the guardrail feedback
+    assert (
+        "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1]
+    ), "Guardrail feedback should be included in retry context"
+
+    # Verify final output meets guardrail requirements
+    assert "IMPORTANT" in result.raw, "Final output should contain required keyword"
+
+    # Verify task retry count
+    assert task.retry_count == 1, "Task should have been retried once"

tests/project_test.py

@@ -27,7 +27,7 @@ class SimpleCrew:
 
 
 @CrewBase
-class TestCrew:
+class InternalCrew:
     agents_config = "config/agents.yaml"
     tasks_config = "config/tasks.yaml"
 
@@ -84,7 +84,7 @@ def test_task_memoization():
 
 
 def test_crew_memoization():
-    crew = TestCrew()
+    crew = InternalCrew()
     first_call_result = crew.crew()
     second_call_result = crew.crew()
 
@@ -107,7 +107,7 @@ def test_task_name():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_before_kickoff_modification():
-    crew = TestCrew()
+    crew = InternalCrew()
     inputs = {"topic": "LLMs"}
     result = crew.crew().kickoff(inputs=inputs)
     assert "bicycles" in result.raw, "Before kickoff function did not modify inputs"
@@ -115,7 +115,7 @@ def test_before_kickoff_modification():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_after_kickoff_modification():
-    crew = TestCrew()
+    crew = InternalCrew()
     # Assuming the crew execution returns a dict
     result = crew.crew().kickoff({"topic": "LLMs"})
 
@@ -126,7 +126,7 @@ def test_after_kickoff_modification():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_before_kickoff_with_none_input():
-    crew = TestCrew()
+    crew = InternalCrew()
     crew.crew().kickoff(None)
     # Test should pass without raising exceptions
 

tests/task_test.py

@@ -722,7 +722,9 @@ def test_interpolate_inputs():
         output_file="/tmp/{topic}/output_{date}.txt",
     )
 
-    task.interpolate_inputs(inputs={"topic": "AI", "date": "2024"})
+    task.interpolate_inputs_and_add_conversation_history(
+        inputs={"topic": "AI", "date": "2024"}
+    )
     assert (
         task.description
         == "Give me a list of 5 interesting ideas about AI to explore for an article, what makes them unique and interesting."
@@ -730,7 +732,9 @@ def test_interpolate_inputs():
     assert task.expected_output == "Bullet point list of 5 interesting ideas about AI."
     assert task.output_file == "/tmp/AI/output_2024.txt"
 
-    task.interpolate_inputs(inputs={"topic": "ML", "date": "2025"})
+    task.interpolate_inputs_and_add_conversation_history(
+        inputs={"topic": "ML", "date": "2025"}
+    )
     assert (
         task.description
         == "Give me a list of 5 interesting ideas about ML to explore for an article, what makes them unique and interesting."
@@ -865,7 +869,7 @@ def test_key():
 
     assert task.key == hash, "The key should be the hash of the description."
 
-    task.interpolate_inputs(inputs={"topic": "AI"})
+    task.interpolate_inputs_and_add_conversation_history(inputs={"topic": "AI"})
     assert (
         task.key == hash
     ), "The key should be the hash of the non-interpolated description."

tests/test_manager_llm_delegation.py

@@ -6,7 +6,7 @@ from crewai import Agent, Task
 from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
 
 
-class TestAgentTool(BaseAgentTool):
+class InternalAgentTool(BaseAgentTool):
     """Concrete implementation of BaseAgentTool for testing."""
 
     def _run(self, *args, **kwargs):
@@ -39,7 +39,7 @@ def test_agent_tool_role_matching(role_name, should_match):
     )
 
     # Create test agent tool
-    agent_tool = TestAgentTool(
+    agent_tool = InternalAgentTool(
         name="test_tool", description="Test tool", agents=[test_agent]
     )
 

tests/tools/test_base_tool.py

@@ -15,7 +15,7 @@ def test_creating_a_tool_using_annotation():
         my_tool.description
         == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
     )
-    assert my_tool.args_schema.schema()["properties"] == {
+    assert my_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }
     assert (
@@ -29,7 +29,7 @@ def test_creating_a_tool_using_annotation():
         converted_tool.description
         == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
     )
-    assert converted_tool.args_schema.schema()["properties"] == {
+    assert converted_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }
     assert (
@@ -54,7 +54,7 @@ def test_creating_a_tool_using_baseclass():
         my_tool.description
         == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
     )
-    assert my_tool.args_schema.schema()["properties"] == {
+    assert my_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }
     assert my_tool.run("What is the meaning of life?") == "What is the meaning of life?"
@@ -66,7 +66,7 @@ def test_creating_a_tool_using_baseclass():
         converted_tool.description
         == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
     )
-    assert converted_tool.args_schema.schema()["properties"] == {
+    assert converted_tool.args_schema.model_json_schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
     }
     assert (

tests/tools/test_structured_tool.py

@@ -25,7 +25,7 @@ def schema_class():
     return TestSchema
 
 
-class TestCrewStructuredTool:
+class InternalCrewStructuredTool:
     def test_initialization(self, basic_function, schema_class):
         """Test basic initialization of CrewStructuredTool"""
         tool = CrewStructuredTool(

tests/utilities/evaluators/test_crew_evaluator_handler.py

@@ -12,7 +12,7 @@ from crewai.utilities.evaluators.crew_evaluator_handler import (
 )
 
 
-class TestCrewEvaluator:
+class InternalCrewEvaluator:
     @pytest.fixture
     def crew_planner(self):
         agent = Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1")

tests/utilities/test_llm_utils.py

@@ -0,0 +1,96 @@
+import os
+from unittest.mock import patch
+
+import pytest
+from litellm.exceptions import BadRequestError
+
+from crewai.llm import LLM
+from crewai.utilities.llm_utils import create_llm
+
+
+def test_create_llm_with_llm_instance():
+    existing_llm = LLM(model="gpt-4o")
+    llm = create_llm(llm_value=existing_llm)
+    assert llm is existing_llm
+
+
+def test_create_llm_with_valid_model_string():
+    llm = create_llm(llm_value="gpt-4o")
+    assert isinstance(llm, LLM)
+    assert llm.model == "gpt-4o"
+
+
+def test_create_llm_with_invalid_model_string():
+    with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
+        llm = create_llm(llm_value="invalid-model")
+        llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
+
+
+def test_create_llm_with_unknown_object_missing_attributes():
+    class UnknownObject:
+        pass
+
+    unknown_obj = UnknownObject()
+    llm = create_llm(llm_value=unknown_obj)
+
+    # Attempt to call the LLM and expect it to raise an error due to missing attributes
+    with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
+        llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
+
+
+def test_create_llm_with_none_uses_default_model():
+    with patch.dict(os.environ, {}, clear=True):
+        with patch("crewai.cli.constants.DEFAULT_LLM_MODEL", "gpt-4o"):
+            llm = create_llm(llm_value=None)
+            assert isinstance(llm, LLM)
+            assert llm.model == "gpt-4o-mini"
+
+
+def test_create_llm_with_unknown_object():
+    class UnknownObject:
+        model_name = "gpt-4o"
+        temperature = 0.7
+        max_tokens = 1500
+
+    unknown_obj = UnknownObject()
+    llm = create_llm(llm_value=unknown_obj)
+    assert isinstance(llm, LLM)
+    assert llm.model == "gpt-4o"
+    assert llm.temperature == 0.7
+    assert llm.max_tokens == 1500
+
+
+def test_create_llm_from_env_with_unaccepted_attributes():
+    with patch.dict(
+        os.environ,
+        {
+            "OPENAI_MODEL_NAME": "gpt-3.5-turbo",
+            "AWS_ACCESS_KEY_ID": "fake-access-key",
+            "AWS_SECRET_ACCESS_KEY": "fake-secret-key",
+            "AWS_REGION_NAME": "us-west-2",
+        },
+    ):
+        llm = create_llm(llm_value=None)
+        assert isinstance(llm, LLM)
+        assert llm.model == "gpt-3.5-turbo"
+        assert not hasattr(llm, "AWS_ACCESS_KEY_ID")
+        assert not hasattr(llm, "AWS_SECRET_ACCESS_KEY")
+        assert not hasattr(llm, "AWS_REGION_NAME")
+
+
+def test_create_llm_with_partial_attributes():
+    class PartialAttributes:
+        model_name = "gpt-4o"
+        # temperature is missing
+
+    obj = PartialAttributes()
+    llm = create_llm(llm_value=obj)
+    assert isinstance(llm, LLM)
+    assert llm.model == "gpt-4o"
+    assert llm.temperature is None  # Should handle missing attributes gracefully
+
+
+def test_create_llm_with_invalid_type():
+    with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
+        llm = create_llm(llm_value=42)
+        llm.call(messages=[{"role": "user", "content": "Hello, world!"}])

tests/utilities/test_planning_handler.py

@@ -16,7 +16,7 @@ from crewai.utilities.planning_handler import (
 )
 
 
-class TestCrewPlanner:
+class InternalCrewPlanner:
     @pytest.fixture
     def crew_planner(self):
         tasks = [
@@ -115,13 +115,13 @@ class TestCrewPlanner:
             def __init__(self, name: str, description: str):
                 tool_data = {"name": name, "description": description}
                 super().__init__(**tool_data)
-            
+
             def __str__(self):
                 return self.name
-            
+
             def __repr__(self):
                 return self.name
-            
+
             def to_structured_tool(self):
                 return self
 
@@ -149,11 +149,11 @@ class TestCrewPlanner:
                 ]
             )
         )
-        
+
         # Create planner with the new task
         planner = CrewPlanner([task], None)
         tasks_summary = planner._create_tasks_summary()
-        
+
         # Verify task summary content
         assert isinstance(tasks_summary, str)
         assert task.description in tasks_summary

tests/utilities/test_training_handler.py

@@ -4,7 +4,7 @@ import unittest
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 
-class TestCrewTrainingHandler(unittest.TestCase):
+class InternalCrewTrainingHandler(unittest.TestCase):
     def setUp(self):
         self.handler = CrewTrainingHandler("trained_data.pkl")
 

uv.lock

@@ -631,7 +631,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.86.0"
+version = "0.95.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },