file_log

Working output_log_file

docs/core-concepts/Tasks.md

@@ -23,6 +23,7 @@ Tasks in CrewAI can be designed to require collaboration between agents. For exa
 | **Output Pydantic**  *(optional)*     | Takes a pydantic model and returns the output as a pydantic object. **Agent LLM needs to be using an OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
 | **Output File**  *(optional)*     | Takes a file path and saves the output of the task on it. |
 | **Callback**  *(optional)*  | A function to be executed after the task is completed. |
+| **Human Input** *(optional)*   |   Indicates whether the agent should ask for feedback at the end of the task |
 
 ## Creating a Task
 
@@ -224,4 +225,4 @@ These validations help in maintaining the consistency and reliability of task ex
 
 ## Conclusion
 
-Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.
+Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

docs/how-to/Human-Input-on-Execution.md

@@ -1,6 +1,6 @@
 ---
 title: Human Input on Execution
-description: Comprehensive guide on integrating CrewAI with human input during execution in complex decision-making processes or when needed help during complex tasks. 
+description: Comprehensive guide on integrating CrewAI with human input during execution in complex decision-making processes or when needed help during complex tasks.
 ---
 
 # Human Input in Agent Execution
@@ -9,7 +9,7 @@ Human input plays a pivotal role in several agent execution scenarios, enabling
 
 ## Using Human Input with CrewAI
 
-Incorporating human input with CrewAI is straightforward, enhancing the agent's ability to make informed decisions. While the documentation previously mentioned using a "LangChain Tool" and a specific "DuckDuckGoSearchRun" tool from `langchain_community.tools`, it's important to clarify that the integration of such tools should align with the actual capabilities and configurations defined within your `Agent` class setup.
+Incorporating human input with CrewAI is straightforward, enhancing the agent's ability to make informed decisions. While the documentation previously mentioned using a "LangChain Tool" and a specific "DuckDuckGoSearchRun" tool from `langchain_community.tools`, it's important to clarify that the integration of such tools should align with the actual capabilities and configurations defined within your `Agent` class setup. Now it is a simple flag in the task itself that needs to be turned on.
 
 ### Example:
 
@@ -23,14 +23,10 @@ import os
 from crewai import Agent, Task, Crew
 from crewai_tools import SerperDevTool
 
-from langchain.agents import load_tools
-
 os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
 os.environ["OPENAI_API_KEY"] = "Your Key"
 
-
-# Loading Human Tools
-human_tools = load_tools(["human"])
+# Loading Tools
 search_tool = SerperDevTool()
 
 # Define your agents with roles, goals, and tools
@@ -44,7 +40,7 @@ researcher = Agent(
   ),
   verbose=True,
   allow_delegation=False,
-  tools=[search_tool]+human_tools # Passing human tools to the agent
+  tools=[search_tool]
 )
 writer = Agent(
   role='Tech Content Strategist',
@@ -67,6 +63,7 @@ task1 = Task(
   ),
   expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
   agent=researcher,
+  human_input=True, # setting the flag on for human input in this task
 )
 
 task2 = Task(

log.txt

@@ -0,0 +1,57 @@
+agent=AI LLMs Senior Data Researcher
+2024-04-04 15:42:20: task=Conduct a thorough research about AI LLMs Make sure you find any interesting and relevant information given the current year is 2024.
+2024-04-04 15:42:20: status=started
+agent=AI LLMs Senior Data Researcher
+2024-04-04 15:44:25: task=1. AI is transforming the curriculum of LLM programs, with law schools incorporating AI-related topics in their courses. (Source: AI and Legal Education: A Primer on AI for Law Students)
+
+2. Quantitative analysis shows that AI has a significant influence on LLM programs. (Source: The Impact of AI on LLMs: A Quantitative Analysis)
+
+3. Leading law schools are integrating practical AI applications into their LLM curriculum. (Source: AI in LLMs: A Case Study on Leading Law Schools)
+
+4. Forecasts predict an increasing integration of AI in LLMs. (Source: The Future of LLMs: A Forecast on AI Integration)
+
+5. The influence of AI in legal practice is shaping the direction of LLM programs. (Source: Artificial Intelligence in Legal Practice: Implications for Law Schools)
+
+6. Emerging trends in AI and law include the use of AI in legal research and decision-making. (Source: The Future of AI in Law)
+
+7. Ethical considerations of using AI in legal practice are now a key component of LLM programs. (Source: AI and Legal Ethics: An Exploration)
+
+8. AI is significantly transforming legal research, a key skill for LLM students. (Source: Impacts of AI on Legal Research)
+
+9. AI's influence is evident in contract law, a major area of study in many LLM programs. (Source: The Role of AI in Contract Law)
+
+10. AI is reshaping the legal profession, influencing the skills taught in LLM programs. (Source: AI and the Future of Legal Practice)2024-04-04 15:44:25: status=completed
+agent=AI LLMs Reporting Analyst
+2024-04-04 15:44:25: task=Review the context you got and expand each topic into a full section for a report. Make sure the report is detailed and contains any and all relevant information.
+2024-04-04 15:44:25: status=started
+agent=AI LLMs Reporting Analyst
+2024-04-04 15:46:17: task=# AI Impact on LLM Programs and the Legal Profession: A Comprehensive Report
+
+## 1. Impact of AI on LLM Programs
+AI is revolutionizing LLM programs by facilitating personalized learning, automating administrative tasks, and providing a wealth of online resources for research. AI-powered tools analyze students' learning patterns and customize course materials to enhance understanding and retention. [Source: AI and Legal Education: A Primer on AI for Law Students]
+
+## 2. Integration of Practical AI Applications in LLM Curriculum
+Law schools are integrating AI into their curriculum to prepare students for the digital transformation in legal practice. This includes teaching students how to use AI for legal research, contract review, and case prediction. [Source: AI in LLMs: A Case Study on Leading Law Schools]
+
+## 3. Forecasts on AI Integration in LLMs
+The use of AI in LLM programs is expected to increase in the future. Universities are likely to invest more in AI technologies for teaching and research, and AI may become a standard part of the curriculum. [Source: The Future of LLMs: A Forecast on AI Integration]
+
+## 4. Influence of AI in Legal Practice
+AI is making legal practice more efficient and accurate. Software like ROSS and CaseText use AI to help lawyers conduct legal research, while tools like Kira Systems assist in contract review by identifying clauses that are unusual or missing. AI can also help predict case outcomes based on historical data. [Source: Artificial Intelligence in Legal Practice: Implications for Law Schools]
+
+## 5. Emerging Trends in AI and Law
+Emerging trends include AI for dispute resolution, predictive analytics for case outcomes, and AI for legal document automation. There's also an increasing focus on 'legaltech' startups that leverage AI to offer innovative legal services. [Source: The Future of AI in Law]
+
+## 6. Ethical Considerations in Using AI in Legal Practice
+While AI can improve efficiency, it also raises ethical questions. These include concerns about AI bias, the transparency of AI algorithms, and the potential for AI to replace human lawyers. Legal professionals need to navigate these ethical challenges as they integrate AI into their practice. [Source: AI and Legal Ethics: An Exploration]
+
+## 7. Transformation of Legal Research by AI
+AI is transforming legal research by making it faster and more comprehensive. AI tools can analyze vast amounts of legal texts and case law, identify relevant precedents, and even suggest arguments. This allows lawyers to focus more on strategy and less on manual research. [Source: Impacts of AI on Legal Research]
+
+## 8. AI's Influence in Contract Law
+AI is being used to review and analyze contracts quickly and accurately. AI can identify standard clauses, flag potential issues, and even suggest changes based on best practices. This reduces human error and makes contract review more efficient. [Source: The Role of AI in Contract Law]
+
+## 9. How AI is Reshaping the Legal Profession
+AI is reshaping the legal profession by automating routine tasks, aiding in legal research, and predicting case outcomes. This is changing the role of lawyers and requiring them to develop new skills, such as understanding how to use AI tools effectively. [Source: AI and the Future of Legal Practice]
+
+In conclusion, AI is having a profound impact on LLM programs and the legal profession as a whole. As AI technologies advance, their influence is likely to grow even stronger.2024-04-04 15:46:17: status=completed

src/crewai/agents/executor.py

@@ -18,6 +18,7 @@ from crewai.utilities import I18N
 
 class CrewAgentExecutor(AgentExecutor):
     _i18n: I18N = I18N()
+    should_ask_for_human_input: bool = False
     llm: Any = None
     iterations: int = 0
     task: Any = None
@@ -54,6 +55,9 @@ class CrewAgentExecutor(AgentExecutor):
             [tool.name for tool in self.tools], excluded_colors=["green", "red"]
         )
         intermediate_steps: List[Tuple[AgentAction, str]] = []
+        # Allowing human input given task setting
+        if self.task.human_input:
+            self.should_ask_for_human_input = True
         # Let's start tracking the number of iterations and time elapsed
         self.iterations = 0
         time_elapsed = 0.0
@@ -169,8 +173,24 @@ class CrewAgentExecutor(AgentExecutor):
 
         # If the tool chosen is the finishing tool, then we end and return.
         if isinstance(output, AgentFinish):
-            yield output
-            return
+            if self.should_ask_for_human_input:
+                # Making sure we only ask for it once, so disabling for the next thought loop
+                self.should_ask_for_human_input = False
+                human_feedback = self._ask_human_input(output.return_values["output"])
+                action = AgentAction(
+                    tool="Human Input", tool_input=human_feedback, log=output.log
+                )
+                yield AgentStep(
+                    action=action,
+                    observation=self._i18n.slice("human_feedback").format(
+                        human_feedback=human_feedback
+                    ),
+                )
+                return
+
+            else:
+                yield output
+                return
 
         actions: List[AgentAction]
         actions = [output] if isinstance(output, AgentAction) else output
@@ -203,3 +223,9 @@ class CrewAgentExecutor(AgentExecutor):
                         tools=", ".join([tool.name for tool in self.tools]),
                     )
             yield AgentStep(action=agent_action, observation=observation)
+
+    def _ask_human_input(self, final_answer: dict) -> str:
+        """Get human input."""
+        return input(
+            self._i18n.slice("getting_input").format(final_answer=final_answer)
+        )

src/crewai/cli/cli.py

@@ -1,5 +1,5 @@
 import click
-
+import pkg_resources
 from .create_crew import create_crew
 
 
@@ -15,5 +15,22 @@ def create(project_name):
     create_crew(project_name)
 
 
+@crewai.command()
+@click.option(
+    "--tools", is_flag=True, help="Show the installed version of crewai tools"
+)
+def version(tools):
+    """Show the installed version of crewai."""
+    crewai_version = pkg_resources.get_distribution("crewai").version
+    click.echo(f"crewai version: {crewai_version}")
+
+    if tools:
+        try:
+            tools_version = pkg_resources.get_distribution("crewai[tools]").version
+            click.echo(f"crewai tools version: {tools_version}")
+        except pkg_resources.DistributionNotFound:
+            click.echo("crewai tools not installed")
+
+
 if __name__ == "__main__":
     crewai()

src/crewai/crew.py

@@ -22,7 +22,7 @@ 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):
@@ -49,6 +49,7 @@ 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())
     model_config = ConfigDict(arbitrary_types_allowed=True)
     tasks: List[Task] = Field(default_factory=list)
@@ -88,6 +89,10 @@ class Crew(BaseModel):
         default="en",
         description="Language used for the crew, defaults to English.",
     )
+    output_log_file: Optional[Union[bool, str]] = Field(
+        default=False,
+        description="Will create a log file with the output of the crew execution.",
+    )
 
     @field_validator("id", mode="before")
     @classmethod
@@ -118,6 +123,7 @@ class Crew(BaseModel):
         """Set private attributes."""
         self._cache_handler = CacheHandler()
         self._logger = Logger(self.verbose)
+        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()
@@ -236,6 +242,10 @@ class Crew(BaseModel):
             self._logger.log(
                 "info", f"== Starting Task: {task.description}", color="bold_yellow"
             )
+            if self._file_handler:
+                self._file_handler.log(
+                    agent=role, task=task.description, status="started"
+                )
 
             output = task.execute(context=task_output)
             if not task.async_execution:
@@ -243,6 +253,8 @@ 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._file_handler:
+                self._file_handler.log(agent=role, task=task_output, status="completed")
 
         self._finish_execution(task_output)
         return self._format_output(task_output)
@@ -264,12 +276,19 @@ class Crew(BaseModel):
         for task in self.tasks:
             self._logger.log("debug", f"Working Agent: {manager.role}")
             self._logger.log("info", f"Starting Task: {task.description}")
-
+            if self._file_handler:
+                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._file_handler:
+                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/task.py

@@ -70,6 +70,10 @@ class Task(BaseModel):
         frozen=True,
         description="Unique identifier for the object, not set by user.",
     )
+    human_input: Optional[bool] = Field(
+        description="Whether the task should have a human review the final answer of the agent",
+        default=False,
+    )
 
     def __init__(__pydantic_self__, **data):
         config = data.pop("config", {})

src/crewai/translations/en.json

@@ -15,7 +15,9 @@
     "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfy the expect criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n",
     "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task\nYour final answer must be the great and the most complete as possible, it must be outcome described\n\n",
     "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
-    "expected_output": "\nThis is the expect criteria for your final answer: {expected_output} \n you MUST return the actual complete content as the final answer, not a summary."
+    "expected_output": "\nThis is the expect criteria for your final answer: {expected_output} \n you MUST return the actual complete content as the final answer, not a summary.",
+    "human_feedback": "You got human feedback on your work, re-avaluate it and give a new Final Answer when ready.\n {human_feedback}",
+    "getting_input": "This is the agent final answer: {final_answer}\nPlease provide a feedback: "
   },
   "errors": {
     "unexpected_format": "\nSorry, I didn't use the expected format, I MUST either use a tool (use one at time) OR give my best final answer.\n",

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")

tests/agent_test.py

@@ -680,3 +680,30 @@ def test_agent_definition_based_on_dict():
     assert agent.backstory == "test backstory"
     assert agent.verbose == True
     assert agent.tools == []
+
+
+# test for human input
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_human_input():
+    from unittest.mock import patch
+
+    config = {
+        "role": "test role",
+        "goal": "test goal",
+        "backstory": "test backstory",
+    }
+
+    agent = Agent(config=config)
+
+    task = Task(
+        agent=agent,
+        description="Say the word: Hi",
+        expected_output="The word: Hi",
+        human_input=True,
+    )
+
+    with patch.object(CrewAgentExecutor, "_ask_human_input") as mock_human_input:
+        mock_human_input.return_value = "Hello"
+        output = agent.execute_task(task)
+        mock_human_input.assert_called_once()
+        assert output == "Hello"

tests/cassettes/test_agent_human_input.yaml

@@ -0,0 +1,381 @@
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+- request:
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+      use the exact following format:\n\nThought: I now can give a great answer\nFinal
+      Answer: my best complete final answer to the task.\nYour final answer must be
+      the great and the most complete as possible, it must be outcome described.\n\nI
+      MUST use these formats, my job depends on it!\n\nThought: \n\nCurrent Task:
+      Say the word: Hi\n\nThis is the expect criteria for your final answer: The word:
+      Hi \n you MUST return the actual complete content as the final answer, not a
+      summary.\n\nBegin! This is VERY important to you, use the tools available and
+      give your best Final Answer, your job depends on it!\n\nThought: \n"}], "model":
+      "gpt-4", "n": 1, "stop": ["\nObservation"], "stream": true, "temperature": 0.7}'
+    headers:
+      accept:
+      - application/json
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+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.10.10
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
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