preparing new version 0.14.0

docs/core-concepts/Tasks.md

@@ -19,6 +19,9 @@ Tasks in CrewAI can be designed to require collaboration between agents. For exa
 | **Tools** *(optional)*   | These are the functions or capabilities the agent can utilize to perform the task. They can be anything from simple actions like 'search' to more complex interactions with other agents or APIs. |
 | **Async Execution** *(optional)*      | If the task should be executed asynchronously.  |
 | **Context**  *(optional)*     | Other tasks that will have their output used as context for this task, if one is an asynchronous task it will wait for that to finish |
+| **Output JSON**  *(optional)*     | Takes a pydantic model and returns the output as a JSON object. **Agent LLM needs to be using OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
+| **Output Pydantic**  *(optional)*     | Takes a pydantic model and returns the output as a pydantic object. **Agent LLM needs to be using 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. |
 
 ## Creating a Task

docs/how-to/LLM-Connections.md

@@ -15,7 +15,7 @@ Ollama is preferred for local LLM integration, offering customization and privac
 
 ### Setting Up Ollama
 - **Installation**: Follow Ollama's guide for setup.
-- **Configuration**: [Adjust your local model with a Modelfile](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md), considering adding `Observation` as a stop word and playing with parameters like `top_p` and `temperature`.
+- **Configuration**: [Adjust your local model with a Modelfile](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md), considering adding `Result` as a stop word and playing with parameters like `top_p` and `temperature`.
 
 ### Integrating Ollama with CrewAI
 Instantiate Ollama and pass it to your agents within CrewAI, enhancing them with the local model's capabilities.

pyproject.toml

@@ -1,7 +1,7 @@
 
 [tool.poetry]
 name = "crewai"
-version = "0.11.1"
+version = "0.14.0"
 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."
 authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"
@@ -16,7 +16,7 @@ Documentation = "https://github.com/joaomdmoura/CrewAI/wiki/Index"
 Repository = "https://github.com/joaomdmoura/crewai"
 
 [tool.poetry.dependencies]
-python = ">=3.10,<4.0"
+python = ">=3.10,<=3.13"
 pydantic = "^2.4.2"
 langchain = "^0.1.0"
 openai = "^1.7.1"
@@ -26,10 +26,11 @@ opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "^0.5.2"
 regex = "^2023.12.25"
+crewai-tools = "^0.0.6"
 
 [tool.poetry.group.dev.dependencies]
 isort = "^5.13.2"
-pyright = "1.1.333"
+pyright = ">=1.1.350,<2.0.0"
 black = {git = "https://github.com/psf/black.git", rev = "stable"}
 autoflake = "^2.2.1"
 pre-commit = "^3.6.0"
@@ -48,7 +49,7 @@ known_first_party = ["crewai"]
 
 
 [tool.poetry.group.test.dependencies]
-pytest = "^7.4"
+pytest = "^8.0.0"
 pytest-vcr = "^1.0.2"
 python-dotenv = "1.0.0"
 

src/crewai/agent.py

@@ -1,12 +1,13 @@
 import os
 import uuid
-from typing import Any, List, Optional
+from typing import Any, List, Optional, Tuple
 
+from crewai_tools import BaseTool as CrewAITool
 from langchain.agents.agent import RunnableAgent
-from langchain.agents.format_scratchpad import format_log_to_str
-from langchain.agents.output_parsers import ReActSingleInputOutputParser
+from langchain.agents.tools import tool as LangChainTool
 from langchain.memory import ConversationSummaryMemory
 from langchain.tools.render import render_text_description
+from langchain_core.agents import AgentAction
 from langchain_openai import ChatOpenAI
 from pydantic import (
     UUID4,
@@ -20,7 +21,7 @@ from pydantic import (
 )
 from pydantic_core import PydanticCustomError
 
-from crewai.agents import CacheHandler, CrewAgentExecutor, ToolsHandler
+from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser, ToolsHandler
 from crewai.utilities import I18N, Logger, Prompts, RPMController
 
 
@@ -73,7 +74,7 @@ class Agent(BaseModel):
     allow_delegation: bool = Field(
         default=True, description="Allow delegation of tasks to agents"
     )
-    tools: List[Any] = Field(
+    tools: Optional[List[Any]] = Field(
         default_factory=list, description="Tools at agents disposal"
     )
     max_iter: Optional[int] = Field(
@@ -151,7 +152,7 @@ class Agent(BaseModel):
                 task=task_prompt, context=context
             )
 
-        tools = tools or self.tools
+        tools = self._parse_tools(tools or self.tools)
         self.agent_executor.tools = tools
         self.agent_executor.task = task
         self.agent_executor.tools_description = render_text_description(tools)
@@ -200,12 +201,15 @@ class Agent(BaseModel):
             "input": lambda x: x["input"],
             "tools": lambda x: x["tools"],
             "tool_names": lambda x: x["tool_names"],
-            "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
+            "agent_scratchpad": lambda x: self.format_log_to_str(
+                x["intermediate_steps"]
+            ),
         }
+
         executor_args = {
             "llm": self.llm,
             "i18n": self.i18n,
-            "tools": self.tools,
+            "tools": self._parse_tools(self.tools),
             "verbose": self.verbose,
             "handle_parsing_errors": True,
             "max_iterations": self.max_iter,
@@ -225,9 +229,11 @@ class Agent(BaseModel):
             )
             executor_args["memory"] = summary_memory
             agent_args["chat_history"] = lambda x: x["chat_history"]
-            prompt = Prompts(i18n=self.i18n).task_execution_with_memory()
+            prompt = Prompts(
+                i18n=self.i18n, tools=self.tools
+            ).task_execution_with_memory()
         else:
-            prompt = Prompts(i18n=self.i18n).task_execution()
+            prompt = Prompts(i18n=self.i18n, tools=self.tools).task_execution()
 
         execution_prompt = prompt.partial(
             goal=self.goal,
@@ -236,13 +242,34 @@ class Agent(BaseModel):
         )
 
         bind = self.llm.bind(stop=[self.i18n.slice("observation")])
-        inner_agent = (
-            agent_args | execution_prompt | bind | ReActSingleInputOutputParser()
-        )
+        inner_agent = agent_args | execution_prompt | bind | CrewAgentParser()
         self.agent_executor = CrewAgentExecutor(
             agent=RunnableAgent(runnable=inner_agent), **executor_args
         )
 
+    def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:
+        """Parse tools to be used for the task."""
+        tools_list = []
+        for tool in tools:
+            if isinstance(tool, CrewAITool):
+                tools_list.append(tool.to_langchain())
+            else:
+                tools_list.append(tool)
+        return tools_list
+
+    def format_log_to_str(
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        observation_prefix: str = "Result: ",
+        llm_prefix: str = "",
+    ) -> str:
+        """Construct the scratchpad that lets the agent continue its thought process."""
+        thoughts = ""
+        for action, observation in intermediate_steps:
+            thoughts += action.log
+            thoughts += f"\n{observation_prefix}{observation}\n{llm_prefix}"
+        return thoughts
+
     @staticmethod
     def __tools_names(tools) -> str:
         return ", ".join([t.name for t in tools])

src/crewai/agents/__init__.py

@@ -1,3 +1,4 @@
 from .cache.cache_handler import CacheHandler
 from .executor import CrewAgentExecutor
+from .parser import CrewAgentParser
 from .tools_handler import ToolsHandler

src/crewai/agents/executor.py

@@ -3,7 +3,6 @@ from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
 from langchain.agents import AgentExecutor
 from langchain.agents.agent import ExceptionTool
-from langchain.agents.tools import InvalidTool
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain_core.agents import AgentAction, AgentFinish, AgentStep
 from langchain_core.exceptions import OutputParserException
@@ -13,12 +12,12 @@ from langchain_core.utils.input import get_color_mapping
 from pydantic import InstanceOf
 
 from crewai.agents.tools_handler import ToolsHandler
-from crewai.tools.tool_usage import ToolUsage
+from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N
 
 
 class CrewAgentExecutor(AgentExecutor):
-    i18n: I18N = I18N()
+    _i18n: I18N = I18N()
     llm: Any = None
     iterations: int = 0
     task: Any = None
@@ -105,7 +104,6 @@ class CrewAgentExecutor(AgentExecutor):
         """
         try:
             intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
-
             # Call the LLM to see what to do.
             output = self.agent.plan(
                 intermediate_steps,
@@ -114,14 +112,19 @@ class CrewAgentExecutor(AgentExecutor):
             )
 
             if self._should_force_answer():
-                if isinstance(output, AgentAction) or isinstance(output, AgentFinish):
+                if isinstance(output, AgentFinish):
+                    yield output
+                    return
+
+                if isinstance(output, AgentAction):
                     output = output
                 else:
                     raise ValueError(
                         f"Unexpected output type from agent: {type(output)}"
                     )
+
                 yield AgentStep(
-                    action=output, observation=self.i18n.errors("force_final_answer")
+                    action=output, observation=self._i18n.errors("force_final_answer")
                 )
                 return
 
@@ -140,14 +143,14 @@ class CrewAgentExecutor(AgentExecutor):
             text = str(e)
             if isinstance(self.handle_parsing_errors, bool):
                 if e.send_to_llm:
-                    observation = str(e.observation)
+                    observation = f"\n{str(e.observation)}"
                     text = str(e.llm_output)
                 else:
-                    observation = "Invalid or incomplete response"
+                    observation = ""
             elif isinstance(self.handle_parsing_errors, str):
-                observation = self.handle_parsing_errors
+                observation = f"\n{self.handle_parsing_errors}"
             elif callable(self.handle_parsing_errors):
-                observation = self.handle_parsing_errors(e)
+                observation = f"\n{self.handle_parsing_errors(e)}"
             else:
                 raise ValueError("Got unexpected type of `handle_parsing_errors`")
             output = AgentAction("_Exception", observation, text)
@@ -164,7 +167,7 @@ class CrewAgentExecutor(AgentExecutor):
 
             if self._should_force_answer():
                 yield AgentStep(
-                    action=output, observation=self.i18n.errors("force_final_answer")
+                    action=output, observation=self._i18n.errors("force_final_answer")
                 )
                 return
 
@@ -183,32 +186,27 @@ class CrewAgentExecutor(AgentExecutor):
             if run_manager:
                 run_manager.on_agent_action(agent_action, color="green")
             # Otherwise we lookup the tool
-            if agent_action.tool in name_to_tool_map:
-                tool = name_to_tool_map[agent_action.tool]
-                return_direct = tool.return_direct
-                color_mapping[agent_action.tool]
-                tool_run_kwargs = self.agent.tool_run_logging_kwargs()
-                if return_direct:
-                    tool_run_kwargs["llm_prefix"] = ""
-                observation = ToolUsage(
-                    tools_handler=self.tools_handler,
-                    tools=self.tools,
-                    tools_description=self.tools_description,
-                    tools_names=self.tools_names,
-                    function_calling_llm=self.function_calling_llm,
-                    llm=self.llm,
-                    task=self.task,
-                ).use(agent_action.log)
+            tool_usage = ToolUsage(
+                tools_handler=self.tools_handler,
+                tools=self.tools,
+                tools_description=self.tools_description,
+                tools_names=self.tools_names,
+                function_calling_llm=self.function_calling_llm,
+                llm=self.llm,
+                task=self.task,
+            )
+            tool_calling = tool_usage.parse(agent_action.log)
+
+            if isinstance(tool_calling, ToolUsageErrorException):
+                observation = tool_calling.message
             else:
-                tool_run_kwargs = self.agent.tool_run_logging_kwargs()
-                observation = InvalidTool().run(
-                    {
-                        "requested_tool_name": agent_action.tool,
-                        "available_tool_names": list(name_to_tool_map.keys()),
-                    },
-                    verbose=self.verbose,
-                    color=None,
-                    callbacks=run_manager.get_child() if run_manager else None,
-                    **tool_run_kwargs,
-                )
+                if tool_calling.tool_name.lower().strip() in [
+                    name.lower().strip() for name in name_to_tool_map
+                ]:
+                    observation = tool_usage.use(tool_calling, agent_action.log)
+                else:
+                    observation = self._i18n.errors("wrong_tool_name").format(
+                        tool=tool_calling.tool_name,
+                        tools=", ".join([tool.name for tool in self.tools]),
+                    )
             yield AgentStep(action=agent_action, observation=observation)

src/crewai/agents/parser.py

@@ -0,0 +1,60 @@
+from typing import Union
+
+from langchain.agents.output_parsers import ReActSingleInputOutputParser
+from langchain_core.agents import AgentAction, AgentFinish
+from langchain_core.exceptions import OutputParserException
+
+from crewai.utilities import I18N
+
+TOOL_USAGE_SECTION = "Use Tool:"
+FINAL_ANSWER_ACTION = "Final Answer:"
+FINAL_ANSWER_AND_TOOL_ERROR_MESSAGE = "I tried to use a tool and give a final answer at the same time, I must choose only one."
+
+
+class CrewAgentParser(ReActSingleInputOutputParser):
+    """Parses Crew-style LLM calls that have a single tool input.
+
+    Expects output to be in one of two formats.
+
+    If the output signals that an action should be taken,
+    should be in the below format. This will result in an AgentAction
+    being returned.
+
+    ```
+    Use Tool: All context for using the tool here
+    ```
+
+    If the output signals that a final answer should be given,
+    should be in the below format. This will result in an AgentFinish
+    being returned.
+
+    ```
+    Final Answer: The temperature is 100 degrees
+    ```
+    """
+
+    _i18n: I18N = I18N()
+
+    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
+        includes_answer = FINAL_ANSWER_ACTION in text
+        includes_tool = TOOL_USAGE_SECTION in text
+
+        if includes_tool:
+            if includes_answer:
+                raise OutputParserException(f"{FINAL_ANSWER_AND_TOOL_ERROR_MESSAGE}")
+
+            return AgentAction("", "", text)
+
+        elif includes_answer:
+            return AgentFinish(
+                {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
+            )
+
+        format = self._i18n.slice("format_without_tools")
+        error = f"{format}"
+        raise OutputParserException(
+            error,
+            observation=error,
+            llm_output=text,
+            send_to_llm=True,
+        )

src/crewai/agents/tools_handler.py

@@ -18,7 +18,6 @@ class ToolsHandler:
 
     def on_tool_use(self, calling: ToolCalling, output: str) -> Any:
         """Run when tool ends running."""
-        print(f"Tool {calling.tool_name} has been used.")
         self.last_used_tool = calling
         if calling.tool_name != CacheTools().name:
             self.cache.add(

src/crewai/crew.py

@@ -41,7 +41,6 @@ class Crew(BaseModel):
         full_output: Whether the crew should return the full output with all tasks outputs or just the final output.
         step_callback: Callback to be executed after each step for every agents execution.
         share_crew: Whether you want to share the complete crew infromation and execution with crewAI to make the library better, and allow us to train models.
-        _cache_handler: Handles caching for the crew's operations.
     """
 
     __hash__ = object.__hash__  # type: ignore

src/crewai/task.py

@@ -1,13 +1,15 @@
 import threading
 import uuid
-from typing import Any, List, Optional
+from typing import Any, List, Optional, Type
 
+from langchain_openai import ChatOpenAI
 from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
 from pydantic_core import PydanticCustomError
 
 from crewai.agent import Agent
 from crewai.tasks.task_output import TaskOutput
-from crewai.utilities import I18N
+from crewai.utilities import I18N, Converter, ConverterError, Printer
+from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
 
 class Task(BaseModel):
@@ -39,10 +41,22 @@ class Task(BaseModel):
         description="Whether the task should be executed asynchronously or not.",
         default=False,
     )
+    output_json: Optional[Type[BaseModel]] = Field(
+        description="A Pydantic model to be used to create a JSON output.",
+        default=None,
+    )
+    output_pydantic: Optional[Type[BaseModel]] = Field(
+        description="A Pydantic model to be used to create a Pydantic output.",
+        default=None,
+    )
+    output_file: Optional[str] = Field(
+        description="A file path to be used to create a file output.",
+        default=None,
+    )
     output: Optional[TaskOutput] = Field(
         description="Task output, it's final result after being executed", default=None
     )
-    tools: List[Any] = Field(
+    tools: Optional[List[Any]] = Field(
         default_factory=list,
         description="Tools the agent is limited to use for this task.",
     )
@@ -67,6 +81,18 @@ class Task(BaseModel):
             self.tools.extend(self.agent.tools)
         return self
 
+    @model_validator(mode="after")
+    def check_output(self):
+        """Check if an output type is set."""
+        output_types = [self.output_json, self.output_pydantic]
+        if len([type for type in output_types if type]) > 1:
+            raise PydanticCustomError(
+                "output_type",
+                "Only one output type can be set, either output_pydantic or output_json.",
+                {},
+            )
+        return self
+
     def execute(
         self,
         agent: Agent | None = None,
@@ -90,7 +116,7 @@ class Task(BaseModel):
             for task in self.context:
                 if task.async_execution:
                     task.thread.join()
-                context.append(task.output.result)
+                context.append(task.output.raw_output)
             context = "\n".join(context)
 
         tools = tools or self.tools
@@ -115,9 +141,19 @@ class Task(BaseModel):
             context=context,
             tools=tools,
         )
-        self.output = TaskOutput(description=self.description, result=result)
-        self.callback(self.output) if self.callback else None
-        return result
+
+        exported_output = self._export_output(result)
+
+        self.output = TaskOutput(
+            description=self.description,
+            exported_output=exported_output,
+            raw_output=result,
+        )
+
+        if self.callback:
+            self.callback(self.output)
+
+        return exported_output
 
     def prompt(self) -> str:
         """Prompt the task.
@@ -133,3 +169,47 @@ class Task(BaseModel):
             )
             tasks_slices = [self.description, output]
         return "\n".join(tasks_slices)
+
+    def _export_output(self, result: str) -> Any:
+        exported_result = result
+        instructions = "I'm gonna convert this raw text into valid JSON."
+
+        if self.output_pydantic or self.output_json:
+            model = self.output_pydantic or self.output_json
+            llm = self.agent.function_calling_llm or self.agent.llm
+
+            if not self._is_gpt(llm):
+                model_schema = PydanticSchemaParser(model=model).get_schema()
+                instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
+
+            converter = Converter(
+                llm=llm, text=result, model=model, instructions=instructions
+            )
+
+            if self.output_pydantic:
+                exported_result = converter.to_pydantic()
+            elif self.output_json:
+                exported_result = converter.to_json()
+
+            if isinstance(exported_result, ConverterError):
+                Printer().print(
+                    content=f"{exported_result.message} Using raw output instead.",
+                    color="red",
+                )
+                exported_result = result
+
+        if self.output_file:
+            content = (
+                exported_result if not self.output_pydantic else exported_result.json()
+            )
+            self._save_file(content)
+
+        return exported_result
+
+    def _is_gpt(self, llm) -> bool:
+        return isinstance(llm, ChatOpenAI) and llm.openai_api_base == None
+
+    def _save_file(self, result: Any) -> None:
+        with open(self.output_file, "w") as file:
+            file.write(result)
+        return None

src/crewai/tasks/task_output.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, Union
 
 from pydantic import BaseModel, Field, model_validator
 
@@ -8,7 +8,10 @@ class TaskOutput(BaseModel):
 
     description: str = Field(description="Description of the task")
     summary: Optional[str] = Field(description="Summary of the task", default=None)
-    result: str = Field(description="Result of the task")
+    exported_output: Union[str, BaseModel] = Field(
+        description="Output of the task", default=None
+    )
+    raw_output: str = Field(description="Result of the task")
 
     @model_validator(mode="after")
     def set_summary(self):

src/crewai/telemtry/telemetry.py

@@ -41,10 +41,12 @@ class Telemetry:
         self.ready = False
         try:
             telemetry_endpoint = "http://telemetry.crewai.com:4318"
-            self.resource = Resource(attributes={SERVICE_NAME: "crewAI-telemetry"})
+            self.resource = Resource(
+                attributes={SERVICE_NAME: "crewAI-telemetry"},
+            )
             self.provider = TracerProvider(resource=self.resource)
             processor = BatchSpanProcessor(
-                OTLPSpanExporter(endpoint=f"{telemetry_endpoint}/v1/traces")
+                OTLPSpanExporter(endpoint=f"{telemetry_endpoint}/v1/traces", timeout=60)
             )
             self.provider.add_span_processor(processor)
             self.ready = True
@@ -53,7 +55,10 @@ class Telemetry:
 
     def set_tracer(self):
         if self.ready:
-            trace.set_tracer_provider(self.provider)
+            try:
+                trace.set_tracer_provider(self.provider)
+            except Exception:
+                self.ready = False
 
     def crew_creation(self, crew):
         """Records the creation of a crew."""

src/crewai/tools/agent_tools.py

@@ -42,11 +42,16 @@ class AgentTools(BaseModel):
 
     def _execute(self, agent, task, context):
         """Execute the command."""
-        agent = [
-            available_agent
-            for available_agent in self.agents
-            if available_agent.role.lower() == agent.lower()
-        ]
+        try:
+            agent = [
+                available_agent
+                for available_agent in self.agents
+                if available_agent.role.strip().lower() == agent.strip().lower()
+            ]
+        except:
+            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
+                coworkers="\n".join([f"- {agent.role}" for agent in self.agents])
+            )
 
         if not agent:
             return self.i18n.errors("agent_tool_unexsiting_coworker").format(

src/crewai/tools/tool_calling.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict
+from typing import Any, Dict, Optional
 
 from pydantic import BaseModel as PydanticBaseModel
 from pydantic import Field as PydanticField
@@ -7,7 +7,7 @@ from pydantic.v1 import BaseModel, Field
 
 class ToolCalling(BaseModel):
     tool_name: str = Field(..., description="The name of the tool to be called.")
-    arguments: Dict[str, Any] = Field(
+    arguments: Optional[Dict[str, Any]] = Field(
         ..., description="A dictinary of arguments to be passed to the tool."
     )
 
@@ -16,6 +16,6 @@ class InstructorToolCalling(PydanticBaseModel):
     tool_name: str = PydanticField(
         ..., description="The name of the tool to be called."
     )
-    arguments: Dict = PydanticField(
+    arguments: Optional[Dict[str, Any]] = PydanticField(
         ..., description="A dictinary of arguments to be passed to the tool."
     )

src/crewai/tools/tool_usage.py

@@ -1,15 +1,15 @@
+from textwrap import dedent
 from typing import Any, List, Union
 
-import instructor
-from langchain.prompts import PromptTemplate
 from langchain_core.tools import BaseTool
 from langchain_openai import ChatOpenAI
 
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.telemtry import Telemetry
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
-from crewai.tools.tool_output_parser import ToolOutputParser
-from crewai.utilities import I18N, Printer
+from crewai.utilities import I18N, Converter, ConverterError, Printer
+
+OPENAI_BIGGER_MODELS = ["gpt-4"]
 
 
 class ToolUsageErrorException(Exception):
@@ -31,6 +31,7 @@ class ToolUsage:
         tools_description: Description of the tools available for the agent.
         tools_names: Names of the tools available for the agent.
         llm: Language model to be used for the tool usage.
+        function_calling_llm: Language model to be used for the tool usage.
     """
 
     def __init__(
@@ -47,18 +48,28 @@ class ToolUsage:
         self._printer: Printer = Printer()
         self._telemetry: Telemetry = Telemetry()
         self._run_attempts: int = 1
-        self._max_parsing_attempts: int = 2
+        self._max_parsing_attempts: int = 3
         self._remeber_format_after_usages: int = 3
         self.tools_description = tools_description
         self.tools_names = tools_names
         self.tools_handler = tools_handler
         self.tools = tools
         self.task = task
-        self.llm = llm
-        self.function_calling_llm = function_calling_llm
+        self.llm = function_calling_llm or llm
 
-    def use(self, tool_string: str):
-        calling = self._tool_calling(tool_string)
+        # Set the maximum parsing attempts for bigger models
+        if (isinstance(self.llm, ChatOpenAI)) and (self.llm.openai_api_base == None):
+            if self.llm.model_name in OPENAI_BIGGER_MODELS:
+                self._max_parsing_attempts = 2
+                self._remeber_format_after_usages = 4
+
+    def parse(self, tool_string: str):
+        """Parse the tool string and return the tool calling."""
+        return self._tool_calling(tool_string)
+
+    def use(
+        self, calling: Union[ToolCalling, InstructorToolCalling], tool_string: str
+    ) -> str:
         if isinstance(calling, ToolUsageErrorException):
             error = calling.message
             self._printer.print(content=f"\n\n{error}\n", color="red")
@@ -69,7 +80,7 @@ class ToolUsage:
             error = getattr(e, "message", str(e))
             self._printer.print(content=f"\n\n{error}\n", color="red")
             return error
-        return self._use(tool_string=tool_string, tool=tool, calling=calling)
+        return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}\n\n{self._i18n.slice('final_answer_format')}"
 
     def _use(
         self,
@@ -100,17 +111,23 @@ class ToolUsage:
 
         if not result:
             try:
-                result = tool._run(**calling.arguments)
+                if calling.arguments:
+                    result = tool._run(**calling.arguments)
+                else:
+                    result = tool._run()
             except Exception as e:
                 self._run_attempts += 1
                 if self._run_attempts > self._max_parsing_attempts:
                     self._telemetry.tool_usage_error(llm=self.llm)
+                    error_message = self._i18n.errors("tool_usage_exception").format(
+                        error=e
+                    )
                     error = ToolUsageErrorException(
-                        self._i18n.errors("tool_usage_exception").format(error=e)
+                        f'\n{error_message}.\nMoving one then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
                     ).message
-                    self._printer.print(content=f"\n\n{error}\n", color="red")
+                    self._printer.print(content=f"\n\n{error_message}\n", color="red")
                     return error
-                return self.use(tool_string=tool_string)
+                return self.use(calling=calling, tool_string=tool_string)
 
             self.tools_handler.on_tool_use(calling=calling, output=result)
 
@@ -170,69 +187,41 @@ class ToolUsage:
             )
         return "\n--\n".join(descriptions)
 
+    def _is_gpt(self, llm) -> bool:
+        return isinstance(llm, ChatOpenAI) and llm.openai_api_base == None
+
     def _tool_calling(
         self, tool_string: str
     ) -> Union[ToolCalling, InstructorToolCalling]:
         try:
-            tool_string = tool_string.replace(
-                "Thought: Do I need to use a tool? Yes", ""
+            model = InstructorToolCalling if self._is_gpt(self.llm) else ToolCalling
+            converter = Converter(
+                text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid ouput schema:\n\n{tool_string}```",
+                llm=self.llm,
+                model=model,
+                instructions=dedent(
+                    """\
+                                    The schema should have the following structure, only two keys:
+                                    - tool_name: str
+                                    - arguments: dict (with all arguments being passed)
+
+                                    Example:
+                                    {"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
+                ),
+                max_attemps=1,
             )
-            tool_string = tool_string.replace("Action:", "Tool Name:")
-            tool_string = tool_string.replace("Action Input:", "Tool Arguments:")
+            calling = converter.to_pydantic()
 
-            llm = self.function_calling_llm or self.llm
-
-            if (isinstance(llm, ChatOpenAI)) and (llm.openai_api_base == None):
-                client = instructor.patch(
-                    llm.client._client,
-                    mode=instructor.Mode.FUNCTIONS,
-                )
-                calling = client.chat.completions.create(
-                    model=llm.model_name,
-                    messages=[
-                        {
-                            "role": "system",
-                            "content": """
-                                The schema should have the following structure, only two key:
-                                - tool_name: str
-                                - arguments: dict (with all arguments being passed)
-
-                                Example:
-                                {"tool_name": "tool_name", "arguments": {"arg_name1": "value", "arg_name2": 2}}
-                            """,
-                        },
-                        {
-                            "role": "user",
-                            "content": f"Tools available:\n\n{self._render()}\n\nReturn a valid schema for the tool, use this text to inform a valid ouput schema:\n{tool_string}```",
-                        },
-                    ],
-                    response_model=InstructorToolCalling,
-                )
-            else:
-                parser = ToolOutputParser(pydantic_object=ToolCalling)
-                prompt = PromptTemplate(
-                    template="Tools available:\n\n{available_tools}\n\nReturn a valid schema for the tool, use this text to inform a valid ouput schema:\n{tool_string}\n\n{format_instructions}\n```",
-                    input_variables=["tool_string"],
-                    partial_variables={
-                        "available_tools": self._render(),
-                        "format_instructions": """
-                        The schema should have the following structure, only two key:
-                        - tool_name: str
-                        - arguments: dict (with all arguments being passed)
-
-                        Example:
-                        {"tool_name": "tool_name", "arguments": {"arg_name1": "value", "arg_name2": 2}}
-                        """,
-                    },
-                )
-                chain = prompt | llm | parser
-                calling = chain.invoke({"tool_string": tool_string})
-
-        except Exception:
+            if isinstance(calling, ConverterError):
+                raise calling
+        except Exception as e:
             self._run_attempts += 1
             if self._run_attempts > self._max_parsing_attempts:
-                self._telemetry.tool_usage_error(llm=llm)
-                return ToolUsageErrorException(self._i18n.errors("tool_usage_error"))
+                self._telemetry.tool_usage_error(llm=self.llm)
+                self._printer.print(content=f"\n\n{e}\n", color="red")
+                return ToolUsageErrorException(
+                    f'{self._i18n.errors("tool_usage_error")}\n{self._i18n.slice("format").format(tool_names=self.tools_names)}'
+                )
             return self._tool_calling(tool_string)
 
         return calling

src/crewai/translations/en.json

@@ -5,23 +5,29 @@
     "backstory": "You are a seasoned manager with a knack for getting the best out of your team.\nYou are also known for your ability to delegate work to the right people, and to ask the right questions to get the best out of your team.\nEven though you don't perform tasks by yourself, you have a lot of experience in the field, which allows you to properly evaluate the work of your team members."
   },
   "slices": {
-    "observation": "\nObservation",
-    "task": "Begin! This is VERY important to you, your job depends on it!\n\nCurrent Task: {input}",
+    "observation": "\nResult",
+    "task": "\n\nCurrent Task: {input}\n\n Begin! This is VERY important to you, your job depends on it!\n\n",
     "memory": "This is the summary of your work so far:\n{chat_history}",
     "role_playing": "You are {role}.\n{backstory}\n\nYour personal goal is: {goal}",
-    "tools": "TOOLS:\n------\nYou have access to only the following tools:\n\n{tools}\n\nTo use a tool, please use the exact following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the tool you wanna use, should be one of [{tool_names}], just the name.\nAction Input: Any and all relevant information input and context for using the tool\nObservation: the result of using the tool\n```\n\nWhen you have a response for your task, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]```",
+    "tools": "I have access to ONLY the following tools, I can use only these, use one at time:\n\n{tools}\n\nTo use a tool I MUST use the exact following format:\n\n```\nUse Tool: the tool I wanna use, should be one of [{tool_names}] and absolute all relevant input and context for using the tool, I must use only one tool at once.\nResult: [result of the tool]\n```\n\nTo give my final answer I'll use the exact following format:\n\n```\nFinal Answer: [my expected final answer, entire content of my most complete final answer goes here]\n```\nI MUST use these formats, my jobs depends on it!",
+    "no_tools": "To give my final answer use the exact following format:\n\n```\nFinal Answer: [my expected final answer, entire content of my most complete final answer goes here]\n```\nI MUST use these formats, my jobs depends on it!",
+    "format": "I MUST either use a tool (use one at time) OR give my best final answer. To use a single tool I MUST use the exact following format:\n\n```\nUse Tool: the tool I wanna use, should be one of [{tool_names}] and absolute all relevant input and context for using the tool, I must use only one tool at once.\nResult: [result of the tool]\n```\n\nTo give my final answer use the exact following format:\n\n```\nFinal Answer: [my expected final answer, entire content of my most complete final answer goes here]\n```\nI MUST use these formats, my jobs depends on it!",
+    "final_answer_format": "If I don't need to use any more tools, I must make sure use the correct format to give my final answer:\n\n```Final Answer: [my expected final answer, entire content of my most complete final answer goes here]```\n I MUST use these formats, my jobs depends on it!",
+    "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 formats I must follow:\n\n```\nUse Tool: the tool I wanna use, and absolute all relevant input and context for using the tool, I must use only one tool at once.\nResult: [result of the tool]\n```\nOR\n```\nFinal Answer: [my expected final answer, entire content of my most complete final answer goes here]\n```\n",
     "task_with_context": "{task}\nThis is the context you're working with:\n{context}",
     "expected_output": "Your final answer must be: {expected_output}"
   },
   "errors": {
-    "force_final_answer": "Actually, I used too many tools, so I'll stop now and give you my absolute BEST Final answer NOW, using exaclty the expected format bellow: \n```\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]```",
-    "agent_tool_unexsiting_coworker": "\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options:\n{coworkers}.\n",
-    "task_repeated_usage": "I just used the {tool} tool with input {tool_input}. So I already know that and must stop using it in a row with the same input. \nI could give my final answer if I'm ready, using exaclty the expected format bellow: \n\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]\n",
+    "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",
+    "force_final_answer": "Actually, I used too many tools, so I'll stop now and give you my absolute BEST Final answer NOW, using exaclty the expected format bellow:\n\n```\nFinal Answer: [my expected final answer, entire content of my most complete final answer goes here]\n```\nI MUST use these formats, my jobs depends on it!",
+    "agent_tool_unexsiting_coworker": "\nError executing tool. Co-worker mentioned not found, it must to be one of the following options:\n{coworkers}\n",
+    "task_repeated_usage": "I already used the {tool} tool with input {tool_input}. So I already know that and must stop using it with same input. \nI could give my best complete final answer if I'm ready, using exaclty the expected format bellow:\n\n```\nFinal Answer: [my expected final answer, entire content of my most complete final answer goes here]\n```\nI MUST use these formats, my jobs depends on it!",
     "tool_usage_error": "It seems we encountered an unexpected error while trying to use the tool.",
+    "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": "It seems we encountered an unexpected error while trying to use the tool. This was the error: {error}"
   },
   "tools": {
-    "delegate_work": "Delegate a specific task to one of the following co-workers:\n{coworkers}.\nThe input to this tool should be the role of the coworker, the task you want them to do, and ALL necessary context to exectue the task, they know nothing about the task, so share absolute everything you know, don't reference things but instead explain them.",
-    "ask_question": "Ask a specific question to one of the following co-workers:\n{coworkers}.\nThe input to this tool should be the role of the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolute everything you know, don't reference things but instead explain them."
+    "delegate_work": "Delegate a specific task to one of the following co-workers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to exectue the task, they know nothing about the task, so share absolute everything you know, don't reference things but instead explain them.",
+    "ask_question": "Ask a specific question to one of the following co-workers: {coworkers}\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolute everything you know, don't reference things but instead explain them."
   }
 }

src/crewai/utilities/__init__.py

@@ -1,4 +1,6 @@
+from .converter import Converter, ConverterError
 from .i18n import I18N
+from .instructor import Instructor
 from .logger import Logger
 from .printer import Printer
 from .prompts import Prompts

src/crewai/utilities/converter.py

@@ -0,0 +1,87 @@
+import json
+from typing import Any, Optional
+
+from langchain.schema import HumanMessage, SystemMessage
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field, PrivateAttr, model_validator
+
+
+class ConverterError(Exception):
+    """Error raised when Converter fails to parse the input."""
+
+    def __init__(self, message: str, *args: object) -> None:
+        super().__init__(message, *args)
+        self.message = message
+
+
+class Converter(BaseModel):
+    """Class that converts text into either pydantic or json."""
+
+    _is_gpt: bool = PrivateAttr(default=True)
+    text: str = Field(description="Text to be converted.")
+    llm: Any = Field(description="The language model to be used to convert the text.")
+    model: Any = Field(description="The model to be used to convert the text.")
+    instructions: str = Field(description="Conversion instructions to the LLM.")
+    max_attemps: Optional[int] = Field(
+        description="Max number of attemps to try to get the output formated.",
+        default=3,
+    )
+
+    @model_validator(mode="after")
+    def check_llm_provider(self):
+        if not self._is_gpt(self.llm):
+            self._is_gpt = False
+
+    def to_pydantic(self, current_attempt=1):
+        """Convert text to pydantic."""
+        try:
+            if self._is_gpt:
+                return self._create_instructor().to_pydantic()
+            else:
+                return self._create_chain().invoke({})
+        except Exception as e:
+            if current_attempt < self.max_attemps:
+                return self.to_pydantic(current_attempt + 1)
+            return ConverterError(
+                f"Failed to convert text into a pydantic model due to the following error: {e}"
+            )
+
+    def to_json(self, current_attempt=1):
+        """Convert text to json."""
+        try:
+            if self._is_gpt:
+                return self._create_instructor().to_json()
+            else:
+                return json.dumps(self._create_chain().invoke({}).model_dump())
+        except Exception:
+            if current_attempt < self.max_attemps:
+                return self.to_json(current_attempt + 1)
+            return ConverterError("Failed to convert text into JSON.")
+
+    def _create_instructor(self):
+        """Create an instructor."""
+        from crewai.utilities import Instructor
+
+        inst = Instructor(
+            llm=self.llm,
+            max_attemps=self.max_attemps,
+            model=self.model,
+            content=self.text,
+            instructions=self.instructions,
+        )
+        return inst
+
+    def _create_chain(self):
+        """Create a chain."""
+        from crewai.utilities.crew_pydantic_output_parser import (
+            CrewPydanticOutputParser,
+        )
+
+        parser = CrewPydanticOutputParser(pydantic_object=self.model)
+        new_prompt = HumanMessage(content=self.text) + SystemMessage(
+            content=self.instructions
+        )
+        return new_prompt | self.llm | parser
+
+    def _is_gpt(self, llm) -> bool:
+        return isinstance(llm, ChatOpenAI) and llm.openai_api_base == None

src/crewai/utilities/crew_pydantic_output_parser.py

@@ -0,0 +1,43 @@
+import json
+from typing import Any, List, Type, Union
+
+import regex
+from langchain.output_parsers import PydanticOutputParser
+from langchain_core.exceptions import OutputParserException
+from langchain_core.outputs import Generation
+from langchain_core.pydantic_v1 import ValidationError
+from pydantic import BaseModel
+from pydantic.v1 import BaseModel as V1BaseModel
+
+
+class CrewPydanticOutputParser(PydanticOutputParser):
+    """Parses the text into pydantic models"""
+
+    pydantic_object: Union[Type[BaseModel], Type[V1BaseModel]]
+
+    def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
+        result[0].text = self._transform_in_valid_json(result[0].text)
+        json_object = super().parse_result(result)
+        try:
+            return self.pydantic_object.parse_obj(json_object)
+        except ValidationError as e:
+            name = self.pydantic_object.__name__
+            msg = f"Failed to parse {name} from completion {json_object}. Got: {e}"
+            raise OutputParserException(msg, llm_output=json_object)
+
+    def _transform_in_valid_json(self, text) -> str:
+        text = text.replace("```", "").replace("json", "")
+        json_pattern = r"\{(?:[^{}]|(?R))*\}"
+        matches = regex.finditer(json_pattern, text)
+
+        for match in matches:
+            try:
+                # Attempt to parse the matched string as JSON
+                json_obj = json.loads(match.group())
+                # Return the first successfully parsed JSON object
+                json_obj = json.dumps(json_obj)
+                return str(json_obj)
+            except json.JSONDecodeError:
+                # If parsing fails, skip to the next match
+                continue
+        return text

src/crewai/utilities/instructor.py

@@ -0,0 +1,50 @@
+from typing import Any, Optional, Type
+
+import instructor
+from pydantic import BaseModel, Field, PrivateAttr, model_validator
+
+
+class Instructor(BaseModel):
+    """Class that wraps an agent llm with instructor."""
+
+    _client: Any = PrivateAttr()
+    content: str = Field(description="Content to be sent to the instructor.")
+    agent: Optional[Any] = Field(
+        description="The agent that needs to use instructor.", default=None
+    )
+    llm: Optional[Any] = Field(
+        description="The agent that needs to use instructor.", default=None
+    )
+    instructions: Optional[str] = Field(
+        description="Instructions to be sent to the instructor.",
+        default=None,
+    )
+    model: Type[BaseModel] = Field(
+        description="Pydantic model to be used to create an output."
+    )
+
+    @model_validator(mode="after")
+    def set_instructor(self):
+        """Set instructor."""
+        if self.agent and not self.llm:
+            self.llm = self.agent.function_calling_llm or self.agent.llm
+
+        self._client = instructor.patch(
+            self.llm.client._client,
+            mode=instructor.Mode.TOOLS,
+        )
+        return self
+
+    def to_json(self):
+        model = self.to_pydantic()
+        return model.model_dump_json(indent=2)
+
+    def to_pydantic(self):
+        messages = [{"role": "user", "content": self.content}]
+        if self.instructions:
+            messages.append({"role": "system", "content": self.instructions})
+
+        model = self._client.chat.completions.create(
+            model=self.llm.model_name, response_model=self.model, messages=messages
+        )
+        return model

src/crewai/utilities/prompts.py

@@ -1,6 +1,6 @@
-from typing import ClassVar
+from typing import Any, ClassVar
 
-from langchain.prompts import PromptTemplate, BasePromptTemplate
+from langchain.prompts import BasePromptTemplate, PromptTemplate
 from pydantic import BaseModel, Field
 
 from crewai.utilities import I18N
@@ -10,12 +10,13 @@ class Prompts(BaseModel):
     """Manages and generates prompts for a generic agent with support for different languages."""
 
     i18n: I18N = Field(default=I18N())
-
+    tools: list[Any] = Field(default=[])
     SCRATCHPAD_SLICE: ClassVar[str] = "\n{agent_scratchpad}"
 
     def task_execution_with_memory(self) -> BasePromptTemplate:
         """Generate a prompt for task execution with memory components."""
-        return self._build_prompt(["role_playing", "tools", "memory", "task"])
+        slices = ["role_playing", "tools", "memory", "task"]
+        return self._build_prompt(slices)
 
     def task_execution_without_tools(self) -> BasePromptTemplate:
         """Generate a prompt for task execution without tools components."""
@@ -23,10 +24,12 @@ class Prompts(BaseModel):
 
     def task_execution(self) -> BasePromptTemplate:
         """Generate a standard prompt for task execution."""
-        return self._build_prompt(["role_playing", "tools", "task"])
+        slices = ["role_playing", "tools", "task"]
+        return self._build_prompt(slices)
 
     def _build_prompt(self, components: list[str]) -> BasePromptTemplate:
         """Constructs a prompt string from specified components."""
         prompt_parts = [self.i18n.slice(component) for component in components]
         prompt_parts.append(self.SCRATCHPAD_SLICE)
-        return PromptTemplate.from_template("".join(prompt_parts))
+        prompt = PromptTemplate.from_template("".join(prompt_parts))
+        return prompt

src/crewai/utilities/pydantic_schema_parser.py

@@ -0,0 +1,38 @@
+from typing import Type, get_args, get_origin
+
+from pydantic import BaseModel
+
+
+class PydanticSchemaParser(BaseModel):
+    model: Type[BaseModel]
+
+    def get_schema(self) -> str:
+        """
+        Public method to get the schema of a Pydantic model.
+
+        :param model: The Pydantic model class to generate schema for.
+        :return: String representation of the model schema.
+        """
+        return self._get_model_schema(self.model)
+
+    def _get_model_schema(self, model, depth=0) -> str:
+        lines = []
+        for field_name, field in model.model_fields.items():
+            field_type_str = self._get_field_type(field, depth + 1)
+            lines.append(f"{' ' * 4 * depth}- {field_name}: {field_type_str}")
+
+        return "\n".join(lines)
+
+    def _get_field_type(self, field, depth) -> str:
+        field_type = field.annotation
+        if get_origin(field_type) is list:
+            list_item_type = get_args(field_type)[0]
+            if issubclass(list_item_type, BaseModel):
+                nested_schema = self._get_model_schema(list_item_type, depth + 1)
+                return f"List[\n{nested_schema}\n{' ' * 4 * depth}]"
+            else:
+                return f"List[{list_item_type.__name__}]"
+        elif issubclass(field_type, BaseModel):
+            return f"\n{self._get_model_schema(field_type, depth)}"
+        else:
+            return field_type.__name__

tests/agent_test.py

@@ -104,7 +104,7 @@ def test_agent_execution_with_tools():
 
     task = Task(description="What is 3 times 4?", agent=agent)
     output = agent.execute_task(task)
-    assert output == "3 times 4 equals 12."
+    assert output == "The result of 3 times 4 is 12."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -131,7 +131,7 @@ def test_logging_tool_usage():
     tool_usage = InstructorToolCalling(
         tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
     )
-    assert output == "3 times 4 equals 12."
+    assert output == "The result of multiplying 3 by 4 is 12."
     assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
     assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
 
@@ -184,7 +184,7 @@ def test_cache_hitting():
             agent=agent,
         )
         output = agent.execute_task(task)
-        assert output == "0"
+        assert output == "The result of the multiplication of 2 and 6 is 0."
         read.assert_called_with(
             tool="multiplier", input={"first_number": 2, "second_number": 6}
         )
@@ -206,7 +206,7 @@ def test_agent_execution_with_specific_tools():
 
     task = Task(description="What is 3 times 4", agent=agent)
     output = agent.execute_task(task=task, tools=[multiplier])
-    assert output == "3 times 4 is 12."
+    assert output == "12"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -241,7 +241,7 @@ def test_agent_custom_max_iterations():
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_repeated_tool_usage(capsys):
     @tool
-    def get_final_answer(numbers) -> float:
+    def get_final_answer(anything: str) -> float:
         """Get the final answer but don't give it yet, just re-use this
         tool non-stop."""
         return 42
@@ -251,11 +251,13 @@ def test_agent_repeated_tool_usage(capsys):
         goal="test goal",
         backstory="test backstory",
         max_iter=4,
+        llm=ChatOpenAI(model="gpt-4-0125-preview"),
         allow_delegation=False,
+        verbose=True,
     )
 
     task = Task(
-        description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool."
+        description="The final answer is 42. But don't give it until I tell you so, instead keep using the `get_final_answer` tool."
     )
     # force cleaning cache
     agent.tools_handler.cache = CacheHandler()
@@ -266,10 +268,7 @@ def test_agent_repeated_tool_usage(capsys):
 
     captured = capsys.readouterr()
 
-    assert (
-        "I just used the get_final_answer tool with input 42. So I already know that and must stop using it in a row with the same input. \nI could give my final answer if I'm ready, using exaclty the expected format bellow: \n\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]\n"
-        in captured.out
-    )
+    assert "Final Answer: 42" in captured.out
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -301,7 +300,7 @@ def test_agent_moved_on_after_max_iterations():
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_respect_the_max_rpm_set(capsys):
     @tool
-    def get_final_answer(numbers) -> float:
+    def get_final_answer(anything: str) -> float:
         """Get the final answer but don't give it yet, just re-use this
         tool non-stop."""
         return 42
@@ -319,7 +318,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
     with patch.object(RPMController, "_wait_for_next_minute") as moveon:
         moveon.return_value = True
         task = Task(
-            description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool, unless you're told otherwise"
+            description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer"
         )
         output = agent.execute_task(
             task=task,
@@ -327,7 +326,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
         )
         assert (
             output
-            == "I have used the tool 'get_final_answer' with the input '42' multiple times and have observed the same result. Therefore, I am confident to conclude that the final answer is '42'."
+            == 'The result of using the `get_final_answer` tool with the input "test input" is 42.'
         )
         captured = capsys.readouterr()
         assert "Max RPM reached, waiting for next minute to start." in captured.out
@@ -429,7 +428,7 @@ def test_agent_error_on_parsing_tool(capsys):
     from langchain.tools import tool
 
     @tool
-    def get_final_answer(numbers) -> float:
+    def get_final_answer(anything: str) -> float:
         """Get the final answer but don't give it yet, just re-use this
         tool non-stop."""
         return 42
@@ -454,10 +453,7 @@ def test_agent_error_on_parsing_tool(capsys):
         force_exception.side_effect = Exception("Error on parsing tool.")
         crew.kickoff()
         captured = capsys.readouterr()
-        assert (
-            "It seems we encountered an unexpected error while trying to use the tool"
-            in captured.out
-        )
+        assert "Error on parsing tool." in captured.out
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -467,7 +463,7 @@ def test_agent_remembers_output_format_after_using_tools_too_many_times():
     from langchain.tools import tool
 
     @tool
-    def get_final_answer(numbers) -> float:
+    def get_final_answer(anything: str) -> float:
         """Get the final answer but don't give it yet, just re-use this
         tool non-stop."""
         return 42
@@ -476,12 +472,12 @@ def test_agent_remembers_output_format_after_using_tools_too_many_times():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        max_iter=4,
+        max_iter=6,
         verbose=True,
     )
     tasks = [
         Task(
-            description="Never give the final answer. Use the get_final_answer tool in a loop.",
+            description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer",
             agent=agent1,
             tools=[get_final_answer],
         )
@@ -553,7 +549,7 @@ def test_agent_step_callback():
 def test_agent_function_calling_llm():
     from langchain_openai import ChatOpenAI
 
-    llm = ChatOpenAI(model="gpt-3.5")
+    llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
 
     with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:
 
@@ -567,6 +563,7 @@ def test_agent_function_calling_llm():
             goal="test goal",
             backstory="test backstory",
             tools=[learn_about_AI],
+            llm=ChatOpenAI(model="gpt-4-0125-preview"),
             function_calling_llm=llm,
         )
 

tests/agent_tools/agent_tools_test.py

@@ -51,7 +51,7 @@ def test_delegate_work_to_wrong_agent():
 
     assert (
         result
-        == "\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options:\n- researcher.\n"
+        == "\nError executing tool. Co-worker mentioned not found, it must to be one of the following options:\n- researcher\n"
     )
 
 
@@ -64,5 +64,5 @@ def test_ask_question_to_wrong_agent():
 
     assert (
         result
-        == "\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options:\n- researcher.\n"
+        == "\nError executing tool. Co-worker mentioned not found, it must to be one of the following options:\n- researcher\n"
     )

tests/cassettes/test_agent_execution_with_specific_tools.yaml

@@ -1,28 +1,21 @@
 interactions:
 - request:
     body: '{"messages": [{"role": "user", "content": "You are test role.\ntest backstory\n\nYour
-      personal goal is: test goalTOOLS:\n------\nYou have access to only the following
-      tools:\n\n(''multiplier: multiplier(first_number: int, second_number: int) ->
-      float - Useful for when you need to multiply two numbers together.'',)\n\nTo
-      use a tool, please use the exact following format:\n\n```\nThought: Do I need
-      to use a tool? Yes\nAction: the tool you wanna use, should be one of [multiplier],
-      just the name.\nAction Input: Any and all relevant information input and context
-      for using the tool\nObservation: the result of using the tool\n```\n\nWhen you
-      have a response for your task, or if you do not need to use a tool, you MUST
-      use the format:\n\n```\nThought: Do I need to use a tool? No\nFinal Answer:
-      [your response here]```This is the summary of your work so far:\nBegin! This
-      is VERY important to you, your job depends on it!\n\nCurrent Task: What is 3
-      times 4\n"}], "model": "gpt-4", "n": 1, "stop": ["\nObservation"], "stream":
+      personal goal is: test goalTo complete the task you MUST follow the format:\n\n```\nFinal
+      Answer: [your most complete final answer goes here]\n``` You must use these
+      formats, my life depends on it.This is the summary of your work so far:\nBegin!
+      This is VERY important to you, your job depends on it!\n\nCurrent Task: What
+      is 3 times 4\n"}], "model": "gpt-4", "n": 1, "stop": ["\nResult"], "stream":
       true, "temperature": 0.7}'
     headers:
       accept:
       - application/json
       accept-encoding:
-      - gzip, deflate
+      - gzip, deflate, br
       connection:
       - keep-alive
       content-length:
-      - '1050'
+      - '511'
       content-type:
       - application/json
       host:
@@ -47,123 +40,27 @@ interactions:
     uri: https://api.openai.com/v1/chat/completions
   response:
     body:
-      string: 'data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}]}
+      string: 'data: {"id":"chatcmpl-8uAN3ZtdzWEuH23HGRoPRRtzvfGBZ","object":"chat.completion.chunk","created":1708396925,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"Thought"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-8uAN3ZtdzWEuH23HGRoPRRtzvfGBZ","object":"chat.completion.chunk","created":1708396925,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"Final"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-8uAN3ZtdzWEuH23HGRoPRRtzvfGBZ","object":"chat.completion.chunk","created":1708396925,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
+        Answer"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        Do"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-8uAN3ZtdzWEuH23HGRoPRRtzvfGBZ","object":"chat.completion.chunk","created":1708396925,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        I"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        need"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        to"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        use"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        a"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        tool"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"?"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        Yes"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"\n"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"Action"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        multiplier"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"\n"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"Action"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        Input"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        {\""},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"first"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"_number"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"\":"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-8uAN3ZtdzWEuH23HGRoPRRtzvfGBZ","object":"chat.completion.chunk","created":1708396925,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
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-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"3"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-8uAN3ZtdzWEuH23HGRoPRRtzvfGBZ","object":"chat.completion.chunk","created":1708396925,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"12"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":","},"logprobs":null,"finish_reason":null}]}
-
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-        \""},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"second"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"_number"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"\":"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        "},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"4"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"}"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rhrE6b7f4GrMPaunp8xcwcWQE9kn","object":"chat.completion.chunk","created":1707810664,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"stop"}]}
+        data: {"id":"chatcmpl-8uAN3ZtdzWEuH23HGRoPRRtzvfGBZ","object":"chat.completion.chunk","created":1708396925,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"stop"}]}
 
 
         data: [DONE]
@@ -174,7 +71,7 @@ interactions:
       CF-Cache-Status:
       - DYNAMIC
       CF-RAY:
-      - 854b7beb6d1c15b6-SJC
+      - 858364efbe7e01b0-GRU
       Cache-Control:
       - no-cache, must-revalidate
       Connection:
@@ -182,14 +79,14 @@ interactions:
       Content-Type:
       - text/event-stream
       Date:
-      - Tue, 13 Feb 2024 07:51:05 GMT
+      - Tue, 20 Feb 2024 02:42:05 GMT
       Server:
       - cloudflare
       Set-Cookie:
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-        path=/; expires=Tue, 13-Feb-24 08:21:05 GMT; domain=.api.openai.com; HttpOnly;
+      - __cf_bm=A4_gsEewjfI_cxZT.kzwJOFlNMwGcVQ79F2fvRwT0GU-1708396925-1.0-Acz2vnKqtu4QQDdoAEAvUGGWPfgH2233lNKW2S0hpo4P7GLtP1zHbDcmpxbJKfZnVlmOgzQjlTUGTtkAn4jqBOc=;
+        path=/; expires=Tue, 20-Feb-24 03:12:05 GMT; domain=.api.openai.com; HttpOnly;
         Secure; SameSite=None
-      - _cfuvid=xAlsN4tpUk6egjAfe12ByLvmPPMpPCRpXivDHVBFlgA-1707810665071-0.0-604800000;
+      - _cfuvid=jbzd0_VB5OSlOP6ehIcdvPI61zEtxDnSKYD1UiuNCFw-1708396925948-0.0-604800000;
         path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
       Transfer-Encoding:
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@@ -200,9 +97,9 @@ interactions:
       openai-model:
       - gpt-4-0613
       openai-organization:
-      - user-z7g4wmlazxqvc5wjyaaaocfz
+      - crewai-iuxna1
       openai-processing-ms:
-      - '412'
+      - '261'
       openai-version:
       - '2020-10-01'
       strict-transport-security:
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tests/crew_test.py

@@ -152,7 +152,7 @@ def test_hierarchical_process():
 
     assert (
         crew.kickoff()
-        == """Here are the five interesting ideas for articles with their respective highlights:\n\n1. The Role of AI in Climate Change: As the world grapples with the existential threat of climate change, artificial intelligence (AI) has emerged as a powerful ally in our battle against it. The article will explore how AI is being used to predict weather patterns, optimize renewable energy sources, and even capture and reduce greenhouse emissions. This novel intersection of technology and environment could hold the key to a sustainable future, making this a must-read for anyone interested in the potential of AI to transform our world.\n\n2. AI and Mental Health: With the increasing prevalence of mental health issues worldwide, innovative solutions are needed more than ever. This article will delve into the cutting-edge domain of AI and mental health, exploring how machine learning algorithms are helping to diagnose conditions, personalize treatments, and even predict the onset of mental disorders. This exploration of AI's potential in mental health not only sheds light on the future of healthcare but also opens a dialogue on the ethical considerations involved.\n\n3. The Ethical Implications of AI: As AI continues to permeate our lives, it brings with it a host of ethical considerations. This article will unravel the complex ethical terrain of AI, from issues of privacy and consent to its potential for bias and discrimination. By diving into the philosophical underpinnings of AI and its societal implications, this article will provoke thought and stimulate discussion on how we can ensure a fair and equitable AI-enabled future.\n\n4. How AI is Revolutionizing E-commerce: In the fiercely competitive world of e-commerce, AI is proving to be a game-changer. This article will take you on a journey through the world of AI-enhanced e-commerce, showcasing how machine learning algorithms are optimizing logistics, personalizing shopping experiences, and even predicting consumer behavior. This deep dive into AI's transformative impact on e-commerce is a must-read for anyone interested in the future of business and technology.\n\n5. AI in Space Exploration: The final frontier of space exploration is being redefined by the advent of AI. This article will take you on an interstellar journey through the role of AI in space exploration, from autonomous spacecraft navigation to the search for extraterrestrial life. By peering into the cosmos through the lens of AI, this article offers a glimpse into the future of space exploration and the infinite possibilities that AI holds."""
+        == """Here are the 5 unique and interesting ideas for articles along with a highlight paragraph for each:\n\n1) The Future of AI and Machine Learning: A deeper look into the future of AI and machine learning, revealing the potential of both and their implications on society. The article will provide an informed vision of the future, addressing the possibilities that AI and machine learning could bring to our daily lives, from healthcare to education, and the challenges we might face.\n\n2) Startups Revolutionizing Traditional Industries with Tech: This article will narrate the journey of game-changing startups that are transforming traditional industries with innovative technology. It will delve into their stories, exploring how they leverage technology to disrupt the status quo, the hurdles they've overcome, and the impact they're making.\n\n3) Personal Development in the Age of Technology: In this article, we will explore how technology has changed the landscape of personal development. We will cover how digital tools and platforms are empowering individuals to learn, grow, and achieve their goals faster than ever before.\n\n4) Ethical Issues in Software Engineering: This article will investigate the ethical dilemmas that are arising in the realm of software engineering. It will discuss the moral implications of new technologies, the responsibilities of software engineers, and the need for a robust code of ethics in this rapidly evolving field.\n\n5) Entrepreneurship in the Digital Era: In this piece, we will delve into the role of digital technology in shaping the entrepreneurial landscape. We will discuss how the digital era has given rise to new entrepreneurial opportunities, the challenges that come with it, and the skills required to thrive in this new era."""
     )
 
 
@@ -187,7 +187,7 @@ def test_crew_with_delegating_agents():
 
     assert (
         crew.kickoff()
-        == "The Senior Writer has produced a fantastic 4 paragraph article on AI:\n\n\"Artificial Intelligence, or AI, is often considered the stuff of science fiction, but it is very much a reality in today's world. In simplest terms, AI is a branch of computer science that aims to create machines that mimic human intelligence - think self-driving cars, voice assistants like Siri or Alexa, even your Netflix recommendations. These are all examples of AI in action, silently making our lives easier and more efficient.\n\nThe applications of AI are as vast as our imagination. In healthcare, AI is used to predict diseases and personalize patient care. In finance, algorithms can analyze market trends and make investment decisions. The education sector uses AI to customize learning and identify areas where students need help. Even in creative fields like music and art, AI is making its mark by creating new pieces that are hard to distinguish from those made by humans.\n\nAI's potential for the future is staggering. As technology advances, so too does the complexity and capabilities of AI. It's predicted that AI will play a significant role in tackling some of humanity's biggest challenges, such as climate change and global health crises. Imagine AI systems predicting natural disasters with enough time for us to take preventative measures, or developing new, effective treatments for diseases through data analysis.\n\nHowever, this brave new world does not come without its challenges. Ethical issues are at the forefront, with concerns over privacy and the potential misuse of AI. There's also the question of job displacement due to automation, and the need for laws and regulations to keep pace with this rapidly advancing technology. Despite these hurdles, the promise of AI and its ability to transform our world is an exciting prospect, one that we are only just beginning to explore.\""
+        == "In today's technological landscape, Artificial Intelligence (AI) agents have emerged as key players in shaping the future of various industries. These agents, which are essentially computer programs that can learn, adapt, and operate autonomously, are a testament to the rapidly evolving capabilities of AI. They are the harbingers of a new era, where machines can mimic human intelligence, and in some cases, even surpass it.\n\nAI agents are transforming the way we engage with technology, enabling a more personalized and efficient user experience. They are extensively used in areas like customer service, where chatbots can handle customer inquiries without human intervention. They have revolutionized sectors like healthcare, where AI agents can analyze patient data to predict health trends and provide personalized treatment recommendations. \n\nHowever, as AI agents continue to evolve, they also pose significant ethical and regulatory challenges. There are concerns about privacy, bias, and the potential misuse of these technologies. As a society, it's crucial to establish norms and regulations that ensure the responsible use of AI agents, balancing their benefits with potential risks.\n\nIn conclusion, AI agents are a transformative technology that is reshaping our world. The challenges they present are complex, but the opportunities they offer are immense. As we continue to explore and understand this technology, we can harness its potential to create a more efficient, personalized, and intelligent future."
     )
 
 
@@ -299,9 +299,10 @@ def test_api_calls_throttling(capsys):
     from unittest.mock import patch
 
     from langchain.tools import tool
+    from langchain_openai import ChatOpenAI
 
     @tool
-    def get_final_answer(numbers) -> float:
+    def get_final_answer(anything) -> float:
         """Get the final answer but don't give it yet, just re-use this
         tool non-stop."""
         return 42
@@ -313,6 +314,7 @@ def test_api_calls_throttling(capsys):
         max_iter=5,
         allow_delegation=False,
         verbose=True,
+        llm=ChatOpenAI(model="gpt-4-0125-preview"),
     )
 
     task = Task(
@@ -416,10 +418,10 @@ def test_async_task_execution():
             start.return_value = thread
             with patch.object(threading.Thread, "join", wraps=thread.join()) as join:
                 list_ideas.output = TaskOutput(
-                    description="A 4 paragraph article about AI.", result="ok"
+                    description="A 4 paragraph article about AI.", raw_output="ok"
                 )
                 list_important_history.output = TaskOutput(
-                    description="A 4 paragraph article about AI.", result="ok"
+                    description="A 4 paragraph article about AI.", raw_output="ok"
                 )
                 crew.kickoff()
                 start.assert_called()
@@ -501,7 +503,7 @@ def test_crew_function_calling_llm():
     from langchain.tools import tool
     from langchain_openai import ChatOpenAI
 
-    llm = ChatOpenAI(model="gpt-3.5")
+    llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
 
     with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:
 
@@ -514,6 +516,7 @@ def test_crew_function_calling_llm():
             role="test role",
             goal="test goal",
             backstory="test backstory",
+            llm=ChatOpenAI(model="gpt-4-0125-preview"),
             tools=[learn_about_AI],
         )
 
@@ -522,8 +525,34 @@ def test_crew_function_calling_llm():
             agent=agent1,
         )
         tasks = [essay]
-        print(agent1.function_calling_llm)
         crew = Crew(agents=[agent1], tasks=tasks, function_calling_llm=llm)
-        print(agent1.function_calling_llm)
         crew.kickoff()
         private_mock.assert_called()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_task_with_no_arguments():
+    from langchain.tools import tool
+
+    @tool
+    def return_data() -> str:
+        "Useful to get the sales related data"
+        return "January: 5, February: 10, March: 15, April: 20, May: 25"
+
+    researcher = Agent(
+        role="Researcher",
+        goal="Make the best research and analysis on content about AI and AI agents",
+        backstory="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.",
+        tools=[return_data],
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Look at the available data nd give me a sense on the total number of sales.",
+        agent=researcher,
+    )
+
+    crew = Crew(agents=[researcher], tasks=[task])
+
+    result = crew.kickoff()
+    assert result == "The total number of sales from January to May is 75."

tests/task_test.py

@@ -2,8 +2,11 @@
 
 from unittest.mock import MagicMock, patch
 
-from crewai.agent import Agent
-from crewai.task import Task
+import pytest
+from pydantic import BaseModel
+from pydantic_core import ValidationError
+
+from crewai import Agent, Crew, Task
 
 
 def test_task_tool_reflect_agent_tools():
@@ -136,3 +139,211 @@ def test_async_execution():
     with patch.object(Agent, "execute_task", return_value="ok") as execute:
         task.execute(agent=researcher)
         execute.assert_called_once_with(task=task, context=None, tools=[])
+
+
+def test_multiple_output_type_error():
+    class Output(BaseModel):
+        field: str
+
+    with pytest.raises(ValidationError):
+        Task(
+            description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
+            expected_output="Bullet point list of 5 interesting ideas.",
+            output_json=Output,
+            output_pydantic=Output,
+        )
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_output_pydantic():
+    class ScoreOutput(BaseModel):
+        score: int
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_pydantic=ScoreOutput,
+        agent=scorer,
+    )
+
+    crew = Crew(agents=[scorer], tasks=[task])
+    result = crew.kickoff()
+    assert isinstance(result, ScoreOutput)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_output_json():
+    class ScoreOutput(BaseModel):
+        score: int
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_json=ScoreOutput,
+        agent=scorer,
+    )
+
+    crew = Crew(agents=[scorer], tasks=[task])
+    result = crew.kickoff()
+    assert '{\n  "score": 4\n}' == result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_output_pydantic_to_another_task():
+    from langchain_openai import ChatOpenAI
+
+    class ScoreOutput(BaseModel):
+        score: int
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+        llm=ChatOpenAI(model="gpt-4-0125-preview"),
+        function_calling_llm=ChatOpenAI(model="gpt-3.5-turbo-0125"),
+        verbose=True,
+    )
+
+    task1 = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_pydantic=ScoreOutput,
+        agent=scorer,
+    )
+
+    task2 = Task(
+        description="Given the score the title 'The impact of AI in the future of work' got, give me an integer score between 1-5 for the following title: 'Return of the Jedi', you MUST give it a score, use your best judgment",
+        expected_output="The score of the title.",
+        output_pydantic=ScoreOutput,
+        agent=scorer,
+    )
+
+    crew = Crew(agents=[scorer], tasks=[task1, task2], verbose=2)
+    result = crew.kickoff()
+    assert 5 == result.score
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_output_json_to_another_task():
+    class ScoreOutput(BaseModel):
+        score: int
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task1 = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_json=ScoreOutput,
+        agent=scorer,
+    )
+
+    task2 = Task(
+        description="Given the score the title 'The impact of AI in the future of work' got, give me an integer score between 1-5 for the following title: 'Return of the Jedi'",
+        expected_output="The score of the title.",
+        output_json=ScoreOutput,
+        agent=scorer,
+    )
+
+    crew = Crew(agents=[scorer], tasks=[task1, task2])
+    result = crew.kickoff()
+    assert '{\n  "score": 5\n}' == result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_save_task_output():
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_file="score.json",
+        agent=scorer,
+    )
+
+    crew = Crew(agents=[scorer], tasks=[task])
+
+    with patch.object(Task, "_save_file") as save_file:
+        save_file.return_value = None
+        crew.kickoff()
+        save_file.assert_called_once()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_save_task_json_output():
+    class ScoreOutput(BaseModel):
+        score: int
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_file="score.json",
+        output_json=ScoreOutput,
+        agent=scorer,
+    )
+
+    crew = Crew(agents=[scorer], tasks=[task])
+
+    with patch.object(Task, "_save_file") as save_file:
+        save_file.return_value = None
+        crew.kickoff()
+        save_file.assert_called_once_with('{\n  "score": 4\n}')
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_save_task_pydantic_output():
+    class ScoreOutput(BaseModel):
+        score: int
+
+    scorer = Agent(
+        role="Scorer",
+        goal="Score the title",
+        backstory="You're an expert scorer, specialized in scoring titles.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
+        expected_output="The score of the title.",
+        output_file="score.json",
+        output_pydantic=ScoreOutput,
+        agent=scorer,
+    )
+
+    crew = Crew(agents=[scorer], tasks=[task])
+
+    with patch.object(Task, "_save_file") as save_file:
+        save_file.return_value = None
+        crew.kickoff()
+        save_file.assert_called_once_with('{"score":4}')