The suggestions were getting split at character level and not at sentence level (#436)

* fix the issue where the suggestions were split at character level

* Update contextual_memory.py

---------

Co-authored-by: rajib76 <rajib76@yahoo.com>
Co-authored-by: João Moura <joaomdmoura@gmail.com>

docs/core-concepts/Agents.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 title: crewAI Agents
 description: What are crewAI Agents and how to use them.
@@ -25,8 +24,9 @@ description: What are crewAI Agents and how to use them.
 | **LLM** *(optional)*       | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified.                                                         |
 | **Tools** *(optional)*     | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list.             |
 | **Function Calling LLM** *(optional)* | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`.                                                                                          |
-| **Max Iter** *(optional)*  | The maximum number of iterations the agent can perform before being forced to give its best answer. Default is `15`.                                                                                                                           |
+| **Max Iter** *(optional)*  | The maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`.                                                                                                                           |
 | **Max RPM** *(optional)*   | The maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
+| **max_execution_time** *(optional)*   | Maximum execution time for an agent to execute a task It's optional and can be left unspecified, with a default value of `None`, menaning no max execution time                                                                     |
 | **Verbose** *(optional)*   | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
 | **Allow Delegation** *(optional)* | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`.                                                                                                       |
 | **Step Callback** *(optional)* | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                               |
@@ -64,5 +64,4 @@ agent = Agent(
 ```
 
 ## Conclusion
-Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.
-```
+Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.

docs/core-concepts/Crews.md

@@ -27,6 +27,8 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Step Callback** *(optional)* | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
 | **Task Callback** *(optional)* | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
 | **Share Crew** *(optional)* | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
+| **Output Log File** *(optional)* | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently and it will be called logs.txt or passing a string with the full path and name of the file. |
+
 
 !!! note "Crew Max RPM"
     The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.

docs/core-concepts/Memory.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 title: crewAI Memory Systems
 description: Leveraging memory systems in the crewAI framework to enhance agent capabilities.

docs/core-concepts/Tasks.md

@@ -96,26 +96,26 @@ This is useful when you have a task that depends on the output of another task t
 # ...
 
 research_ai_task = Task(
-  description='Find and summarize the latest AI news',
-  expected_output='A bullet list summary of the top 5 most important AI news',
-  async_execution=True,
-  agent=research_agent,
-  tools=[search_tool]
+    description='Find and summarize the latest AI news',
+    expected_output='A bullet list summary of the top 5 most important AI news',
+    async_execution=True,
+    agent=research_agent,
+    tools=[search_tool]
 )
 
 research_ops_task = Task(
-  description='Find and summarize the latest AI Ops news',
-  expected_output='A bullet list summary of the top 5 most important AI Ops news',
-  async_execution=True,
-  agent=research_agent,
-  tools=[search_tool]
+    description='Find and summarize the latest AI Ops news',
+    expected_output='A bullet list summary of the top 5 most important AI Ops news',
+    async_execution=True,
+    agent=research_agent,
+    tools=[search_tool]
 )
 
 write_blog_task = Task(
-  description="Write a full blog post about the importance of AI and its latest news",
-  expected_output='Full blog post that is 4 paragraphs long',
-  agent=writer_agent,
-  context=[research_ai_task, research_ops_task]
+    description="Write a full blog post about the importance of AI and its latest news",
+    expected_output='Full blog post that is 4 paragraphs long',
+    agent=writer_agent,
+    context=[research_ai_task, research_ops_task]
 )
 
 #...
@@ -131,24 +131,24 @@ You can then use the `context` attribute to define in a future task that it shou
 #...
 
 list_ideas = Task(
-	description="List of 5 interesting ideas to explore for an article about AI.",
-	expected_output="Bullet point list of 5 ideas for an article.",
-	agent=researcher,
-	async_execution=True # Will be executed asynchronously
+    description="List of 5 interesting ideas to explore for an article about AI.",
+    expected_output="Bullet point list of 5 ideas for an article.",
+    agent=researcher,
+    async_execution=True # Will be executed asynchronously
 )
 
 list_important_history = Task(
-	description="Research the history of AI and give me the 5 most important events.",
-	expected_output="Bullet point list of 5 important events.",
-	agent=researcher,
-	async_execution=True # Will be executed asynchronously
+    description="Research the history of AI and give me the 5 most important events.",
+    expected_output="Bullet point list of 5 important events.",
+    agent=researcher,
+    async_execution=True # Will be executed asynchronously
 )
 
 write_article = Task(
-	description="Write an article about AI, its history, and interesting ideas.",
-	expected_output="A 4 paragraph article about AI.",
-	agent=writer,
-	context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
+    description="Write an article about AI, its history, and interesting ideas.",
+    expected_output="A 4 paragraph article about AI.",
+    agent=writer,
+    context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
 )
 
 #...
@@ -162,20 +162,20 @@ The callback function is executed after the task is completed, allowing for acti
 # ...
 
 def callback_function(output: TaskOutput):
-	# Do something after the task is completed
-	# Example: Send an email to the manager
-	print(f"""
-		Task completed!
-		Task: {output.description}
-		Output: {output.raw_output}
-	""")
+    # Do something after the task is completed
+    # Example: Send an email to the manager
+    print(f"""
+        Task completed!
+        Task: {output.description}
+        Output: {output.raw_output}
+    """)
 
 research_task = Task(
-  description='Find and summarize the latest AI news',
-	expected_output='A bullet list summary of the top 5 most important AI news',
-	agent=research_agent,
-  tools=[search_tool],
-	callback=callback_function
+    description='Find and summarize the latest AI news',
+    expected_output='A bullet list summary of the top 5 most important AI news',
+    agent=research_agent,
+    tools=[search_tool],
+    callback=callback_function
 )
 
 #...
@@ -188,27 +188,27 @@ Once a crew finishes running, you can access the output of a specific task by us
 ```python
 # ...
 task1 = Task(
-	description='Find and summarize the latest AI news',
-	expected_output='A bullet list summary of the top 5 most important AI news',
-	agent=research_agent,
-	tools=[search_tool]
+    description='Find and summarize the latest AI news',
+    expected_output='A bullet list summary of the top 5 most important AI news',
+    agent=research_agent,
+    tools=[search_tool]
 )
 
 #...
 
 crew = Crew(
-	agents=[research_agent],
-	tasks=[task1, task2, task3],
-	verbose=2
+    agents=[research_agent],
+    tasks=[task1, task2, task3],
+    verbose=2
 )
 
 result = crew.kickoff()
 
 # Returns a TaskOutput object with the description and results of the task
 print(f"""
-	Task completed!
-	Task: {task1.output.description}
-	Output: {task1.output.raw_output}
+    Task completed!
+    Task: {task1.output.description}
+    Output: {task1.output.raw_output}
 """)
 ```
 

docs/how-to/LLM-Connections.md

@@ -110,6 +110,16 @@ OPENAI_API_BASE=https://api.mistral.ai/v1
 OPENAI_MODEL_NAME="mistral-small"
 ```
 
+### Solar
+```sh
+from langchain_community.chat_models.solar import SolarChat
+# Initialize language model
+os.environ["SOLAR_API_KEY"] = "your-solar-api-key"
+llm = SolarChat(max_tokens=1024)
+
+Free developer API key available here: https://console.upstage.ai/services/solar
+Langchain Example: https://github.com/langchain-ai/langchain/pull/18556
+```
 ### text-gen-web-ui
 ```sh
 OPENAI_API_BASE=http://localhost:5000/v1
@@ -128,9 +138,9 @@ Free developer API key available here: https://cohere.com/
 Langchain Documentation: https://python.langchain.com/docs/integrations/chat/cohere
 ```
 
-### Azure Open AI
-Azure's OpenAI API needs a distinct setup, utilizing the `langchain_openai` component for Azure-specific configurations.
 
+### Azure Open AI Configuration
+For Azure OpenAI API integration, set the following environment variables:
 ```sh
 AZURE_OPENAI_VERSION="2022-12-01"
 AZURE_OPENAI_DEPLOYMENT=""

docs/index.md

@@ -33,6 +33,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Crews
                 </a>
             </li>
+            <li>
+                <a href="./core-concepts/Memory">
+                    Memory
+                </a>
+            </li>
         </ul>
     </div>
     <div style="width:30%">

docs/tools/GitHubSearchTool.md

@@ -22,15 +22,15 @@ from crewai_tools import GithubSearchTool
 
 # Initialize the tool for semantic searches within a specific GitHub repository
 tool = GithubSearchTool(
-	github_repo='https://github.com/example/repo',
-	content_types=['code', 'issue'] # Options: code, repo, pr, issue
+    github_repo='https://github.com/example/repo',
+    content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 
 # OR
 
 # Initialize the tool for semantic searches within a specific GitHub repository, so the agent can search any repository if it learns about during its execution
 tool = GithubSearchTool(
-	content_types=['code', 'issue'] # Options: code, repo, pr, issue
+    content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 ```
 

mkdocs.yml

@@ -126,6 +126,7 @@ nav:
     - Processes: 'core-concepts/Processes.md'
     - Crews: 'core-concepts/Crews.md'
     - Collaboration: 'core-concepts/Collaboration.md'
+    - Memory: 'core-concepts/Memory.md'
   - How to Guides:
     - Getting Started: 'how-to/Creating-a-Crew-and-kick-it-off.md'
     - Create Custom Tools: 'how-to/Create-Custom-Tools.md'

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
 
 [[package]]
 name = "aiohttp"
@@ -4962,13 +4962,13 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.
 
 [[package]]
 name = "typing-extensions"
-version = "4.10.0"
+version = "4.11.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
-    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
+    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
 ]
 
 [[package]]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.27.0"
+version = "0.28.1"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"
@@ -23,7 +23,7 @@ opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "^0.5.2"
 regex = "^2023.12.25"
-crewai-tools = { version = "^0.1.4", optional = true }
+crewai-tools = { version = "^0.1.5", optional = true }
 click = "^8.1.7"
 python-dotenv = "1.0.0"
 embedchain = "^0.1.98"
@@ -44,7 +44,7 @@ mkdocs-material = {extras = ["imaging"], version = "^9.5.7"}
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai_tools = "^0.1.4"
+crewai-tools = "^0.1.5"
 
 [tool.isort]
 profile = "black"

src/crewai/agent.py

@@ -88,7 +88,11 @@ class Agent(BaseModel):
         default_factory=list, description="Tools at agents disposal"
     )
     max_iter: Optional[int] = Field(
-        default=15, description="Maximum iterations for an agent to execute a task"
+        default=25, description="Maximum iterations for an agent to execute a task"
+    )
+    max_execution_time: Optional[int] = Field(
+        default=None,
+        description="Maximum execution time for an agent to execute a task",
     )
     agent_executor: InstanceOf[CrewAgentExecutor] = Field(
         default=None, description="An instance of the CrewAgentExecutor class."
@@ -156,9 +160,12 @@ class Agent(BaseModel):
     def set_agent_executor(self) -> "Agent":
         """set agent executor is set."""
         if hasattr(self.llm, "model_name"):
-            self.llm.callbacks = [
-                TokenCalcHandler(self.llm.model_name, self._token_process)
-            ]
+            token_handler = TokenCalcHandler(self.llm.model_name, self._token_process)
+            if isinstance(self.llm.callbacks, list):
+                self.llm.callbacks.append(token_handler)
+            else:
+                self.llm.callbacks = [token_handler]
+
         if not self.agent_executor:
             if not self.cache_handler:
                 self.cache_handler = CacheHandler()
@@ -273,6 +280,7 @@ class Agent(BaseModel):
             "original_tools": tools,
             "handle_parsing_errors": True,
             "max_iterations": self.max_iter,
+            "max_execution_time": self.max_execution_time,
             "step_callback": self.step_callback,
             "tools_handler": self.tools_handler,
             "function_calling_llm": self.function_calling_llm,

src/crewai/cli/templates/README.md

@@ -23,7 +23,7 @@ poetry install
 ```
 ### Customizing
 
-**Add you `OPENAI_API_KEY` on the `.env` file**
+**Add your `OPENAI_API_KEY` into the `.env` file**
 
 - Modify `src/{{folder_name}}/config/agents.yaml` to define your agents
 - Modify `src/{{folder_name}}/config/tasks.yaml` to define your tasks

src/crewai/crew.py

@@ -25,7 +25,7 @@ from crewai.process import Process
 from crewai.task import Task
 from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools import AgentTools
-from crewai.utilities import I18N, Logger, RPMController
+from crewai.utilities import I18N, Logger, RPMController, FileHandler
 
 
 class Crew(BaseModel):
@@ -55,6 +55,7 @@ class Crew(BaseModel):
     _execution_span: Any = PrivateAttr()
     _rpm_controller: RPMController = PrivateAttr()
     _logger: Logger = PrivateAttr()
+    _file_handler: FileHandler = PrivateAttr()
     _cache_handler: InstanceOf[CacheHandler] = PrivateAttr(default=CacheHandler())
     _short_term_memory: Optional[InstanceOf[ShortTermMemory]] = PrivateAttr()
     _long_term_memory: Optional[InstanceOf[LongTermMemory]] = PrivateAttr()
@@ -115,6 +116,10 @@ class Crew(BaseModel):
         default=None,
         description="Path to the language file to be used for the crew.",
     )
+    output_log_file: Optional[Union[bool, str]] = Field(
+        default=False,
+        description="output_log_file",
+    )
 
     @field_validator("id", mode="before")
     @classmethod
@@ -145,6 +150,8 @@ class Crew(BaseModel):
         """Set private attributes."""
         self._cache_handler = CacheHandler()
         self._logger = Logger(self.verbose)
+        if self.output_log_file:
+            self._file_handler = FileHandler(self.output_log_file)
         self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
         self._telemetry = Telemetry()
         self._telemetry.set_tracer()
@@ -273,11 +280,16 @@ class Crew(BaseModel):
                     task.tools += AgentTools(agents=agents_for_delegation).tools()
 
             role = task.agent.role if task.agent is not None else "None"
-            self._logger.log("debug", f"== Working Agent: {role}", color="bold_yellow")
+            self._logger.log("debug", f"== Working Agent: {role}", color="bold_purple")
             self._logger.log(
-                "info", f"== Starting Task: {task.description}", color="bold_yellow"
+                "info", f"== Starting Task: {task.description}", color="bold_purple"
             )
 
+            if self.output_log_file:
+                self._file_handler.log(
+                    agent=role, task=task.description, status="started"
+                )
+
             output = task.execute(context=task_output)
             if not task.async_execution:
                 task_output = output
@@ -285,6 +297,9 @@ class Crew(BaseModel):
             role = task.agent.role if task.agent is not None else "None"
             self._logger.log("debug", f"== [{role}] Task output: {task_output}\n\n")
 
+            if self.output_log_file:
+                self._file_handler.log(agent=role, task=task_output, status="completed")
+
         self._finish_execution(task_output)
         return self._format_output(task_output)
 
@@ -306,12 +321,22 @@ class Crew(BaseModel):
             self._logger.log("debug", f"Working Agent: {manager.role}")
             self._logger.log("info", f"Starting Task: {task.description}")
 
+            if self.output_log_file:
+                self._file_handler.log(
+                    agent=manager.role, task=task.description, status="started"
+                )
+
             task_output = task.execute(
                 agent=manager, context=task_output, tools=manager.tools
             )
 
             self._logger.log("debug", f"[{manager.role}] Task output: {task_output}")
 
+            if self.output_log_file:
+                self._file_handler.log(
+                    agent=manager.role, task=task_output, status="completed"
+                )
+
         self._finish_execution(task_output)
         return self._format_output(task_output), manager._token_process.get_summary()
 

src/crewai/memory/contextual/contextual_memory.py

@@ -43,7 +43,7 @@ class ContextualMemory:
         formatted_results = "\n".join(
             [f"{result['metadata']['suggestions']}" for result in ltm_results]
         )
-        formatted_results = list(set(formatted_results))
+        formatted_results = list(set(formatted_results.split('\n')))
         return f"Historical Data:\n{formatted_results}" if ltm_results else ""
 
     def _fetch_entity_context(self, query) -> str:

src/crewai/task.py

@@ -13,7 +13,23 @@ from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
 
 class Task(BaseModel):
-    """Class that represent a task to be executed."""
+    """Class that represents a task to be executed.
+
+    Each task must have a description, an expected output and an agent responsible for execution.
+
+    Attributes:
+        agent: Agent responsible for task execution. Represents entity performing task.
+        async_execution: Boolean flag indicating asynchronous task execution.
+        callback: Function/object executed post task completion for additional actions.
+        config: Dictionary containing task-specific configuration parameters.
+        context: List of Task instances providing task context or input data.
+        description: Descriptive text detailing task's purpose and execution.
+        expected_output: Clear definition of expected task outcome.
+        output_file: File path for storing task output.
+        output_json: Pydantic model for structuring JSON output.
+        output_pydantic: Pydantic model for task output.
+        tools: List of tools/resources limited for task execution.
+    """
 
     class Config:
         arbitrary_types_allowed = True

src/crewai/tools/tool_usage.py

@@ -101,7 +101,7 @@ class ToolUsage:
                 result = self._i18n.errors("task_repeated_usage").format(
                     tool_names=self.tools_names
                 )
-                self._printer.print(content=f"\n\n{result}\n", color="yellow")
+                self._printer.print(content=f"\n\n{result}\n", color="purple")
                 self._telemetry.tool_repeated_usage(
                     llm=self.function_calling_llm,
                     tool_name=tool.name,
@@ -178,7 +178,7 @@ class ToolUsage:
                     calling=calling, output=result, should_cache=should_cache
                 )
 
-        self._printer.print(content=f"\n\n{result}\n", color="yellow")
+        self._printer.print(content=f"\n\n{result}\n", color="purple")
         self._telemetry.tool_usage(
             llm=self.function_calling_llm,
             tool_name=tool.name,

src/crewai/utilities/__init__.py

@@ -5,3 +5,4 @@ from .logger import Logger
 from .printer import Printer
 from .prompts import Prompts
 from .rpm_controller import RPMController
+from .fileHandler import FileHandler

src/crewai/utilities/fileHandler.py

@@ -0,0 +1,20 @@
+import os
+from datetime import datetime
+
+
+class FileHandler:
+    """take care of file operations, currently it only logs messages to a file"""
+
+    def __init__(self, file_path):
+        if isinstance(file_path, bool):
+            self._path = os.path.join(os.curdir, "logs.txt")
+        elif isinstance(file_path, str):
+            self._path = file_path
+        else:
+            raise ValueError("file_path must be either a boolean or a string.")
+
+    def log(self, **kwargs):
+        now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        message = f"{now}: ".join([f"{key}={value}" for key, value in kwargs.items()])
+        with open(self._path, "a") as file:
+            file.write(message + "\n")

src/crewai/utilities/printer.py

@@ -1,24 +1,24 @@
 class Printer:
     def print(self, content: str, color: str):
-        if color == "yellow":
-            self._print_yellow(content)
+        if color == "purple":
+            self._print_purple(content)
         elif color == "red":
             self._print_red(content)
         elif color == "bold_green":
             self._print_bold_green(content)
-        elif color == "bold_yellow":
-            self._print_bold_yellow(content)
+        elif color == "bold_purple":
+            self._print_bold_purple(content)
         else:
             print(content)
 
-    def _print_bold_yellow(self, content):
-        print("\033[1m\033[93m {}\033[00m".format(content))
+    def _print_bold_purple(self, content):
+        print("\033[1m\033[95m {}\033[00m".format(content))
 
     def _print_bold_green(self, content):
         print("\033[1m\033[92m {}\033[00m".format(content))
 
-    def _print_yellow(self, content):
-        print("\033[93m {}\033[00m".format(content))
+    def _print_purple(self, content):
+        print("\033[95m {}\033[00m".format(content))
 
     def _print_red(self, content):
         print("\033[91m {}\033[00m".format(content))

tests/cassettes/test_crew_log_file_output.yaml

@@ -0,0 +1,160 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "user", "content": "You are Researcher. You''re
+      an expert researcher, specialized in technology, software engineering, AI and
+      startups. You work as a freelancer and is now working on doing research and
+      analysis for a new customer.\nYour personal goal is: Make the best research
+      and analysis on content about AI and AI agentsTo give my best complete final
+      answer to the task use the exact following format:\n\nThought: I now can give
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tests/crew_test.py

@@ -648,10 +648,10 @@ def test_agent_usage_metrics_are_captured_for_sequential_process():
     result = crew.kickoff()
     assert result == "Howdy!"
     assert crew.usage_metrics == {
-        "completion_tokens": 17,
-        "prompt_tokens": 161,
-        "successful_requests": 1,
-        "total_tokens": 178,
+        "completion_tokens": 51,
+        "prompt_tokens": 483,
+        "successful_requests": 3,
+        "total_tokens": 534,
     }
 
 
@@ -678,10 +678,10 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
     result = crew.kickoff()
     assert result == '"Howdy!"'
     assert crew.usage_metrics == {
-        "total_tokens": 1641,
-        "prompt_tokens": 1358,
-        "completion_tokens": 283,
-        "successful_requests": 3,
+        "total_tokens": 2592,
+        "prompt_tokens": 2048,
+        "completion_tokens": 544,
+        "successful_requests": 6,
     }
 
 
@@ -894,3 +894,21 @@ def test_disabled_memory_using_contextual_memory():
     with patch.object(ContextualMemory, "build_context_for_task") as contextual_mem:
         crew.kickoff()
         contextual_mem.assert_not_called()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_log_file_output(tmp_path):
+    test_file = tmp_path / "logs.txt"
+    tasks = [
+        Task(
+            description="Say Hi",
+            expected_output="The word: Hi",
+            agent=researcher,
+        )
+    ]
+
+    test_message = {"agent": "Researcher", "task": "Say Hi"}
+
+    crew = Crew(agents=[researcher], tasks=tasks, output_log_file=str(test_file))
+    crew.kickoff()
+    assert test_file.exists()