preparing new version 0.14.0

.gitignore

@@ -5,4 +5,4 @@ dist/
 .env
 assets/*
 .idea
-test.py
+test/

README.md

@@ -252,15 +252,27 @@ There is NO data being collected on the prompts, tasks descriptions agents backs
 
 Data collected includes:
 - Version of crewAI
+  - So we can understand how many users are using the latest version
 - Version of Python
+  - So we can decide on what versions to better support
 - General OS (e.g. number of CPUs, macOS/Windows/Linux)
+  - So we know what OS we should focus on and if we could build specific OS related features
 - Number of agents and tasks in a crew
+  - So we make sure we are testing internally with similar use cases and educate people on the best practices
 - Crew Process being used
+  - Understand where we should focus our efforts
 - If Agents are using memory or allowing delegation
+  - Understand if we improved the features or maybe even drop them
 - If Tasks are being executed in parallel or sequentially
+  - Understand if we should focus more on parallel execution
 - Language model being used
+  - Improved support on most used languages
 - Roles of agents in a crew
+  - Understand high level use cases so we can build better tools, integrations and examples about it
 - Tools names available
+  - Understand out of the publically available tools, which ones are being used the most so we can improve them
+
+Users can opt-in sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews.
 
 ## License
 

docs/core-concepts/Agents.md

@@ -20,12 +20,14 @@ description: What are crewAI Agents and how to use them.
 | **Role**       | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.  |
 | **Goal**       | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
 | **Backstory**    | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
+| **LLM**       | The language model used by the agent to process and generate text. |
 | **Tools**    | Set of capabilities or functions that the agent can use to perform tasks. Tools can be shared or exclusive to specific agents. |
+| **Function Calling LLM** | The language model used by this agent to call functions, if none is passed the same main llm for each agent will be used.      |
 | **Max Iter**    | The maximum number of iterations the agent can perform before forced to give its best answer |
 | **Max RPM**    | The maximum number of requests per minute the agent can perform to avoid rate limits |
 | **Verbose**    | This allow you to actually see what is going on during the Crew execution. |
 | **Allow Delegation**    | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. |
-
+| **Step Callback**    | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback` |
 
 ## Creating an Agent
 
@@ -47,10 +49,13 @@ agent = Agent(
   You're currently working on a project to analyze the
   performance of our marketing campaigns.""",
   tools=[my_tool1, my_tool2],
+  llm=my_llm,
+  function_calling_llm=my_llm,
   max_iter=10,
   max_rpm=10,
   verbose=True,
-  allow_delegation=True
+  allow_delegation=True,
+  step_callback=my_intermediate_step_callback
 )
 ```
 

docs/core-concepts/Crews.md

@@ -16,9 +16,14 @@ description: Understanding and utilizing crews in the crewAI framework.
 | **Process**          | The process flow (e.g., sequential, hierarchical) the crew follows. |
 | **Verbose**          | The verbosity level for logging during execution.            |
 | **Manager LLM**      | The language model used by the manager agent in a hierarchical process. |
+| **Function Calling LLM** | The language model used by all agensts in the crew to call functions, if none is passed the same main llm for each agent will be used.      |
 | **Config**           | Configuration settings for the crew.                         |
 | **Max RPM**          | Maximum requests per minute the crew adheres to during execution. |
 | **Language**         | Language setting for the crew's operation.                   |
+| **Full Output**    | Whether the crew should return the full output with all tasks outputs or just the final output. |
+| **Step Callback**    | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations, it won't override the agent specific `step_callback` |
+| **Share Crew**       | Whether you want to share the complete crew infromation and execution with the crewAI team to make the library better, and allow us to train models. |
+
 
 !!! note "Crew Max RPM"
 		The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents `max_rpm` settings if you set it.
@@ -55,6 +60,7 @@ my_crew = Crew(
     agents=[researcher, writer],
     tasks=[research_task, write_article_task],
     process=Process.sequential,
+    full_output=True,
     verbose=True
 )
 ```

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

@@ -5,32 +5,32 @@ description: Guide on integrating CrewAI with various Large Language Models (LLM
 
 ## Connect CrewAI to LLMs
 !!! note "Default LLM"
-    By default, crewAI uses OpenAI's GPT-4 model for language processing. However, you can configure your agents to use a different model or API. This guide will show you how to connect your agents to different LLMs.
+    By default, crewAI uses OpenAI's GPT-4 model for language processing. However, you can configure your agents to use a different model or API. This guide will show you how to connect your agents to different LLMs. You can change the specific gpt model by setting the `OPENAI_MODEL_NAME` environment variable.
 
 CrewAI offers flexibility in connecting to various LLMs, including local models via [Ollama](https://ollama.ai) and different APIs like Azure. It's compatible with all [LangChain LLM](https://python.langchain.com/docs/integrations/llms/) components, enabling diverse integrations for tailored AI solutions.
 
+
 ## Ollama Integration
 Ollama is preferred for local LLM integration, offering customization and privacy benefits. It requires installation and configuration, including model adjustments via a Modelfile to optimize performance.
 
 ### 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.
 
 ```python
-from langchain_community.llms import Ollama
-
-# Assuming you have Ollama installed and downloaded the openhermes model
-ollama_openhermes = Ollama(model="openhermes")
+# Required
+os.environ["OPENAI_API_BASE"]='http://localhost:11434/v1'
+os.environ["OPENAI_MODEL_NAME"]='openhermes'
+os.environ["OPENAI_API_KEY"]=''
 
 local_expert = Agent(
   role='Local Expert',
   goal='Provide insights about the city',
   backstory="A knowledgeable local guide.",
   tools=[SearchTools.search_internet, BrowserTools.scrape_and_summarize_website],
-  llm=ollama_openhermes,
   verbose=True
 )
 ```
@@ -40,35 +40,40 @@ You can use environment variables for easy switch between APIs and models, suppo
 
 ### Configuration Examples
 
+### Ollama
+```sh
+OPENAI_API_BASE='http://localhost:11434/v1'
+OPENAI_MODEL_NAME='openhermes'  # Depending on the model you have available
+OPENAI_API_KEY=NA
+```
+
 ### FastChat
 ```sh
-# Required
+
 OPENAI_API_BASE="http://localhost:8001/v1"
+OPENAI_MODEL_NAME='oh-2.5m7b-q51' # Depending on the model you have available
 OPENAI_API_KEY=NA
-MODEL_NAME='oh-2.5m7b-q51' # Depending on the model you have available
 ```
 
 ### LM Studio
 ```sh
-# Required
 OPENAI_API_BASE="http://localhost:8000/v1"
+OPENAI_MODEL_NAME=NA
 OPENAI_API_KEY=NA
-MODEL_NAME=NA
 ```
 
 ### Mistral API
 ```sh
 OPENAI_API_KEY=your-mistral-api-key
 OPENAI_API_BASE=https://api.mistral.ai/v1
-MODEL_NAME="mistral-small" # Check documentation for available models
+OPENAI_MODEL_NAME="mistral-small" # Check documentation for available models
 ```
 
 ### text-gen-web-ui
 ```sh
-# Required
-API_BASE_URL=http://localhost:5000
+OPENAI_API_BASE=http://localhost:5000/v1
+OPENAI_MODEL_NAME=NA
 OPENAI_API_KEY=NA
-MODEL_NAME=NA
 ```
 
 ### Azure Open AI

docs/telemetry/Telemetry.md

@@ -6,12 +6,24 @@ There is NO data being collected on the prompts, tasks descriptions agents backs
 
 Data collected includes:
 - Version of crewAI
+  - So we can understand how many users are using the latest version
 - Version of Python
+  - So we can decide on what versions to better support
 - General OS (e.g. number of CPUs, macOS/Windows/Linux)
+  - So we know what OS we should focus on and if we could build specific OS related features
 - Number of agents and tasks in a crew
+  - So we make sure we are testing internally with similar use cases and educate people on the best practices
 - Crew Process being used
+  - Understand where we should focus our efforts
 - If Agents are using memory or allowing delegation
+  - Understand if we improved the features or maybe even drop them
 - If Tasks are being executed in parallel or sequentially
+  - Understand if we should focus more on parallel execution
 - Language model being used
+  - Improved support on most used languages
 - Roles of agents in a crew
-- Tools names available
+  - Understand high level use cases so we can build better tools, integrations and examples about it
+- Tools names available
+  - Understand out of the publically available tools, which ones are being used the most so we can improve them
+
+Users can opt-in sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews.

pyproject.toml

@@ -1,7 +1,7 @@
 
 [tool.poetry]
 name = "crewai"
-version = "0.5.5"
+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"
@@ -16,18 +16,21 @@ 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"
+langchain = "^0.1.0"
 openai = "^1.7.1"
-langchain-openai = "^0.0.2"
+langchain-openai = "^0.0.5"
 opentelemetry-api = "^1.22.0"
 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"
@@ -43,8 +46,10 @@ cairosvg = "^2.7.1"
 profile = "black"
 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

@@ -1,11 +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.tools import tool as LangChainTool
 from langchain.memory import ConversationSummaryMemory
 from langchain.tools.render import render_text_description
-from langchain_core.runnables.config import RunnableConfig
+from langchain_core.agents import AgentAction
 from langchain_openai import ChatOpenAI
 from pydantic import (
     UUID4,
@@ -19,12 +21,7 @@ from pydantic import (
 )
 from pydantic_core import PydanticCustomError
 
-from crewai.agents import (
-    CacheHandler,
-    CrewAgentExecutor,
-    CrewAgentOutputParser,
-    ToolsHandler,
-)
+from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser, ToolsHandler
 from crewai.utilities import I18N, Logger, Prompts, RPMController
 
 
@@ -40,12 +37,14 @@ class Agent(BaseModel):
             goal: The objective of the agent.
             backstory: The backstory of the agent.
             llm: The language model that will run the agent.
+            function_calling_llm: The language model that will the tool calling for this agent, it overrides the crew function_calling_llm.
             max_iter: Maximum number of iterations for an agent to execute a task.
             memory: Whether the agent should have memory or not.
             max_rpm: Maximum number of requests per minute for the agent execution to be respected.
             verbose: Whether the agent execution should be in verbose mode.
             allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
             tools: Tools at agents disposal
+            step_callback: Callback to be executed after each step of the agent execution.
     """
 
     __hash__ = object.__hash__  # type: ignore
@@ -75,7 +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(
@@ -90,13 +89,20 @@ class Agent(BaseModel):
     cache_handler: InstanceOf[CacheHandler] = Field(
         default=CacheHandler(), description="An instance of the CacheHandler class."
     )
+    step_callback: Optional[Any] = Field(
+        default=None,
+        description="Callback to be executed after each step of the agent execution.",
+    )
     i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
     llm: Any = Field(
         default_factory=lambda: ChatOpenAI(
-            model="gpt-4",
+            model=os.environ.get("OPENAI_MODEL_NAME", "gpt-4")
         ),
         description="Language model that will run the agent.",
     )
+    function_calling_llm: Optional[Any] = Field(
+        description="Language model that will run the agent.", default=None
+    )
 
     @field_validator("id", mode="before")
     @classmethod
@@ -125,7 +131,7 @@ class Agent(BaseModel):
 
     def execute_task(
         self,
-        task: str,
+        task: Any,
         context: Optional[str] = None,
         tools: Optional[List[Any]] = None,
     ) -> str:
@@ -139,22 +145,25 @@ class Agent(BaseModel):
         Returns:
             Output of the agent
         """
+        task_prompt = task.prompt()
 
         if context:
-            task = self.i18n.slice("task_with_context").format(
-                task=task, context=context
+            task_prompt = self.i18n.slice("task_with_context").format(
+                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)
+        self.agent_executor.tools_names = self.__tools_names(tools)
 
         result = self.agent_executor.invoke(
             {
-                "input": task,
-                "tool_names": self.__tools_names(tools),
-                "tools": render_text_description(tools),
-            },
-            RunnableConfig(callbacks=[self.tools_handler]),
+                "input": task_prompt,
+                "tool_names": self.agent_executor.tools_names,
+                "tools": self.agent_executor.tools_description,
+            }
         )["output"]
 
         if self.max_rpm:
@@ -170,7 +179,7 @@ class Agent(BaseModel):
         """
         self.cache_handler = cache_handler
         self.tools_handler = ToolsHandler(cache=self.cache_handler)
-        self._create_agent_executor()
+        self.create_agent_executor()
 
     def set_rpm_controller(self, rpm_controller: RPMController) -> None:
         """Set the rpm controller for the agent.
@@ -180,9 +189,9 @@ class Agent(BaseModel):
         """
         if not self._rpm_controller:
             self._rpm_controller = rpm_controller
-            self._create_agent_executor()
+            self.create_agent_executor()
 
-    def _create_agent_executor(self) -> None:
+    def create_agent_executor(self) -> None:
         """Create an agent executor for the agent.
 
         Returns:
@@ -192,20 +201,27 @@ 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,
+            "step_callback": self.step_callback,
+            "tools_handler": self.tools_handler,
+            "function_calling_llm": self.function_calling_llm,
         }
 
         if self._rpm_controller:
-            executor_args["request_within_rpm_limit"] = (
-                self._rpm_controller.check_or_wait
-            )
+            executor_args[
+                "request_within_rpm_limit"
+            ] = self._rpm_controller.check_or_wait
 
         if self.memory:
             summary_memory = ConversationSummaryMemory(
@@ -213,9 +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,
@@ -224,20 +242,34 @@ class Agent(BaseModel):
         )
 
         bind = self.llm.bind(stop=[self.i18n.slice("observation")])
-        inner_agent = (
-            agent_args
-            | execution_prompt
-            | bind
-            | CrewAgentOutputParser(
-                tools_handler=self.tools_handler,
-                cache=self.cache_handler,
-                i18n=self.i18n,
-            )
-        )
+        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

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

src/crewai/agents/cache/__init__.py

@@ -1,2 +1 @@
 from .cache_handler import CacheHandler
-from .cache_hit import CacheHit

src/crewai/agents/cache/cache_handler.py

@@ -10,9 +10,7 @@ class CacheHandler:
         self._cache = {}
 
     def add(self, tool, input, output):
-        input = input.strip()
         self._cache[f"{tool}-{input}"] = output
 
     def read(self, tool, input) -> Optional[str]:
-        input = input.strip()
         return self._cache.get(f"{tool}-{input}")

src/crewai/agents/cache/cache_hit.py

@@ -1,18 +0,0 @@
-from typing import Any
-
-from pydantic import BaseModel, Field
-
-from .cache_handler import CacheHandler
-
-
-class CacheHit(BaseModel):
-    """Cache Hit Object."""
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    # Making it Any instead of AgentAction to avoind
-    # pydantic v1 vs v2 incompatibility, langchain should
-    # soon be updated to pydantic v2
-    action: Any = Field(description="Action taken")
-    cache: CacheHandler = Field(description="Cache Handler for the tool")

src/crewai/agents/exceptions.py

@@ -1,30 +0,0 @@
-from langchain_core.exceptions import OutputParserException
-
-from crewai.utilities import I18N
-
-
-class TaskRepeatedUsageException(OutputParserException):
-    """Exception raised when a task is used twice in a roll."""
-
-    i18n: I18N = I18N()
-    error: str = "TaskRepeatedUsageException"
-    message: str
-
-    def __init__(self, i18n: I18N, tool: str, tool_input: str, text: str):
-        self.i18n = i18n
-        self.text = text
-        self.tool = tool
-        self.tool_input = tool_input
-        self.message = self.i18n.errors("task_repeated_usage").format(
-            tool=tool, tool_input=tool_input
-        )
-
-        super().__init__(
-            error=self.error,
-            observation=self.message,
-            send_to_llm=True,
-            llm_output=self.text,
-        )
-
-    def __str__(self):
-        return self.message

src/crewai/agents/executor.py

@@ -3,25 +3,32 @@ 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
 from langchain_core.pydantic_v1 import root_validator
 from langchain_core.tools import BaseTool
 from langchain_core.utils.input import get_color_mapping
+from pydantic import InstanceOf
 
-from crewai.agents.cache.cache_hit import CacheHit
-from crewai.tools.cache_tools import CacheTools
+from crewai.agents.tools_handler import ToolsHandler
+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
+    tools_description: str = ""
+    tools_names: str = ""
+    function_calling_llm: Any = None
     request_within_rpm_limit: Any = None
+    tools_handler: InstanceOf[ToolsHandler] = None
     max_iterations: Optional[int] = 15
     force_answer_max_iterations: Optional[int] = None
+    step_callback: Optional[Any] = None
 
     @root_validator()
     def set_force_answer_max_iterations(cls, values: Dict) -> Dict:
@@ -31,11 +38,6 @@ class CrewAgentExecutor(AgentExecutor):
     def _should_force_answer(self) -> bool:
         return True if self.iterations == self.force_answer_max_iterations else False
 
-    def _force_answer(self, output: AgentAction):
-        return AgentStep(
-            action=output, observation=self.i18n.errors("force_final_answer")
-        )
-
     def _call(
         self,
         inputs: Dict[str, str],
@@ -63,6 +65,10 @@ class CrewAgentExecutor(AgentExecutor):
                     intermediate_steps,
                     run_manager=run_manager,
                 )
+
+                if self.step_callback:
+                    self.step_callback(next_step_output)
+
                 if isinstance(next_step_output, AgentFinish):
                     return self._return(
                         next_step_output, intermediate_steps, run_manager=run_manager
@@ -98,23 +104,28 @@ 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,
                 callbacks=run_manager.get_child() if run_manager else None,
                 **inputs,
             )
+
             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
-                elif isinstance(output, CacheHit):
-                    output = output.action
                 else:
                     raise ValueError(
                         f"Unexpected output type from agent: {type(output)}"
                     )
-                yield self._force_answer(output)
+
+                yield AgentStep(
+                    action=output, observation=self._i18n.errors("force_final_answer")
+                )
                 return
 
         except OutputParserException as e:
@@ -132,14 +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)
@@ -155,7 +166,9 @@ class CrewAgentExecutor(AgentExecutor):
             )
 
             if self._should_force_answer():
-                yield self._force_answer(output)
+                yield AgentStep(
+                    action=output, observation=self._i18n.errors("force_final_answer")
+                )
                 return
 
             yield AgentStep(action=output, observation=observation)
@@ -166,17 +179,6 @@ class CrewAgentExecutor(AgentExecutor):
             yield output
             return
 
-        # Override tool usage to use CacheTools
-        if isinstance(output, CacheHit):
-            cache = output.cache
-            action = output.action
-            tool = CacheTools(cache_handler=cache).tool()
-            output = action.copy()
-            output.tool_input = f"tool:{action.tool}|input:{action.tool_input}"
-            output.tool = tool.name
-            name_to_tool_map[tool.name] = tool
-            color_mapping[tool.name] = color_mapping[action.tool]
-
         actions: List[AgentAction]
         actions = [output] if isinstance(output, AgentAction) else output
         yield from actions
@@ -184,31 +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 = color_mapping[agent_action.tool]
-                tool_run_kwargs = self.agent.tool_run_logging_kwargs()
-                if return_direct:
-                    tool_run_kwargs["llm_prefix"] = ""
-                # We then call the tool on the tool input to get an observation
-                observation = tool.run(
-                    agent_action.tool_input,
-                    verbose=self.verbose,
-                    color=color,
-                    callbacks=run_manager.get_child() if run_manager else None,
-                    **tool_run_kwargs,
-                )
+            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/output_parser.py

@@ -1,79 +0,0 @@
-import re
-from typing import Union
-
-from langchain.agents.output_parsers import ReActSingleInputOutputParser
-from langchain_core.agents import AgentAction, AgentFinish
-
-from crewai.agents.cache import CacheHandler, CacheHit
-from crewai.agents.exceptions import TaskRepeatedUsageException
-from crewai.agents.tools_handler import ToolsHandler
-from crewai.utilities import I18N
-
-FINAL_ANSWER_ACTION = "Final Answer:"
-FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = (
-    "Parsing LLM output produced both a final answer and a parse-able action:"
-)
-
-
-class CrewAgentOutputParser(ReActSingleInputOutputParser):
-    """Parses ReAct-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.
-
-    ```
-    Thought: agent thought here
-    Action: search
-    Action Input: what is the temperature in SF?
-    ```
-
-    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.
-
-    ```
-    Thought: agent thought here
-    Final Answer: The temperature is 100 degrees
-    ```
-
-    It also prevents tools from being reused in a roll.
-    """
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    tools_handler: ToolsHandler
-    cache: CacheHandler
-    i18n: I18N
-
-    def parse(self, text: str) -> Union[AgentAction, AgentFinish, CacheHit]:
-        regex = (
-            r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
-        )
-        if action_match := re.search(regex, text, re.DOTALL):
-            action = action_match.group(1).strip()
-            action_input = action_match.group(2)
-            tool_input = action_input.strip(" ")
-            tool_input = tool_input.strip('"')
-
-            if last_tool_usage := self.tools_handler.last_used_tool:
-                usage = {
-                    "tool": action,
-                    "input": tool_input,
-                }
-                if usage == last_tool_usage:
-                    raise TaskRepeatedUsageException(
-                        text=text,
-                        tool=action,
-                        tool_input=tool_input,
-                        i18n=self.i18n,
-                    )
-
-            if self.cache.read(action, tool_input):
-                action = AgentAction(action, tool_input, text)
-                return CacheHit(action=action, cache=self.cache)
-
-        return super().parse(text)

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

@@ -1,44 +1,27 @@
-from typing import Any, Dict
-
-from langchain.callbacks.base import BaseCallbackHandler
+from typing import Any
 
 from ..tools.cache_tools import CacheTools
+from ..tools.tool_calling import ToolCalling
 from .cache.cache_handler import CacheHandler
 
 
-class ToolsHandler(BaseCallbackHandler):
+class ToolsHandler:
     """Callback handler for tool usage."""
 
-    last_used_tool: Dict[str, Any] = {}
+    last_used_tool: ToolCalling = {}
     cache: CacheHandler
 
-    def __init__(self, cache: CacheHandler, **kwargs: Any):
+    def __init__(self, cache: CacheHandler):
         """Initialize the callback handler."""
         self.cache = cache
-        super().__init__(**kwargs)
+        self.last_used_tool = {}
 
-    def on_tool_start(
-        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
-    ) -> Any:
-        """Run when tool starts running."""
-        name = serialized.get("name")
-        if name not in ["invalid_tool", "_Exception"]:
-            tools_usage = {
-                "tool": name,
-                "input": input_str,
-            }
-            self.last_used_tool = tools_usage
-
-    def on_tool_end(self, output: str, **kwargs: Any) -> Any:
+    def on_tool_use(self, calling: ToolCalling, output: str) -> Any:
         """Run when tool ends running."""
-        if (
-            "is not a valid tool" not in output
-            and "Invalid or incomplete response" not in output
-            and "Invalid Format" not in output
-        ):
-            if self.last_used_tool["tool"] != CacheTools().name:
-                self.cache.add(
-                    tool=self.last_used_tool["tool"],
-                    input=self.last_used_tool["input"],
-                    output=output,
-                )
+        self.last_used_tool = calling
+        if calling.tool_name != CacheTools().name:
+            self.cache.add(
+                tool=calling.tool_name,
+                input=calling.arguments,
+                output=output,
+            )

src/crewai/crew.py

@@ -32,15 +32,19 @@ class Crew(BaseModel):
         tasks: List of tasks assigned to the crew.
         agents: List of agents part of this crew.
         manager_llm: The language model that will run manager agent.
+        function_calling_llm: The language model that will run the tool calling for all the agents.
         process: The process flow that the crew will follow (e.g., sequential).
         verbose: Indicates the verbosity level for logging during execution.
         config: Configuration settings for the crew.
-        _cache_handler: Handles caching for the crew's operations.
         max_rpm: Maximum number of requests per minute for the crew execution to be respected.
         id: A unique identifier for the crew instance.
+        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.
     """
 
     __hash__ = object.__hash__  # type: ignore
+    _execution_span: Any = PrivateAttr()
     _rpm_controller: RPMController = PrivateAttr()
     _logger: Logger = PrivateAttr()
     _cache_handler: InstanceOf[CacheHandler] = PrivateAttr(default=CacheHandler())
@@ -49,11 +53,23 @@ class Crew(BaseModel):
     agents: List[Agent] = Field(default_factory=list)
     process: Process = Field(default=Process.sequential)
     verbose: Union[int, bool] = Field(default=0)
+    full_output: Optional[bool] = Field(
+        default=False,
+        description="Whether the crew should return the full output with all tasks outputs or just the final output.",
+    )
     manager_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
+    function_calling_llm: Optional[Any] = Field(
+        description="Language model that will run the agent.", default=None
+    )
     config: Optional[Union[Json, Dict[str, Any]]] = Field(default=None)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
+    share_crew: Optional[bool] = Field(default=False)
+    step_callback: Optional[Any] = Field(
+        default=None,
+        description="Callback to be executed after each step for all agents execution.",
+    )
     max_rpm: Optional[int] = Field(
         default=None,
         description="Maximum number of requests per minute for the crew execution to be respected.",
@@ -94,6 +110,7 @@ class Crew(BaseModel):
         self._logger = Logger(self.verbose)
         self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
         self._telemetry = Telemetry()
+        self._telemetry.set_tracer()
         self._telemetry.crew_creation(self)
         return self
 
@@ -137,6 +154,7 @@ class Crew(BaseModel):
                 "missing_keys_in_config", "Config should have 'agents' and 'tasks'.", {}
             )
 
+        self.process = self.config.get("process", self.process)
         self.agents = [Agent(**agent) for agent in self.config["agents"]]
         self.tasks = [self._create_task(task) for task in self.config["tasks"]]
 
@@ -157,9 +175,18 @@ class Crew(BaseModel):
 
     def kickoff(self) -> str:
         """Starts the crew to work on its assigned tasks."""
+        self._execution_span = self._telemetry.crew_execution_span(self)
+
         for agent in self.agents:
             agent.i18n = I18N(language=self.language)
 
+            if not agent.function_calling_llm:
+                agent.function_calling_llm = self.function_calling_llm
+                agent.create_agent_executor()
+            if not agent.step_callback:
+                agent.step_callback = self.step_callback
+                agent.create_agent_executor()
+
         if self.process == Process.sequential:
             return self._run_sequential_process()
         if self.process == Process.hierarchical:
@@ -190,10 +217,8 @@ class Crew(BaseModel):
             role = task.agent.role if task.agent is not None else "None"
             self._logger.log("debug", f"[{role}] Task output: {task_output}\n\n")
 
-        if self.max_rpm:
-            self._rpm_controller.stop_rpm_counter()
-
-        return task_output
+        self._finish_execution(task_output)
+        return self._format_output(task_output)
 
     def _run_hierarchical_process(self) -> str:
         """Creates and assigns a manager agent to make sure the crew completes the tasks."""
@@ -221,7 +246,20 @@ class Crew(BaseModel):
                 "debug", f"[{manager.role}] Task output: {task_output}\n\n"
             )
 
+        self._finish_execution(task_output)
+        return self._format_output(task_output)
+
+    def _format_output(self, output: str) -> str:
+        """Formats the output of the crew execution."""
+        if self.full_output:
+            return {
+                "final_output": output,
+                "tasks_outputs": [task.output for task in self.tasks],
+            }
+        else:
+            return output
+
+    def _finish_execution(self, output) -> None:
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
-
-        return task_output
+        self._telemetry.end_crew(self, output)

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):
@@ -17,6 +19,7 @@ class Task(BaseModel):
         arbitrary_types_allowed = True
 
     __hash__ = object.__hash__  # type: ignore
+    used_tools: int = 0
     i18n: I18N = I18N()
     thread: threading.Thread = None
     description: str = Field(description="Description of the actual task.")
@@ -38,10 +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.",
     )
@@ -66,6 +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,
@@ -89,32 +116,46 @@ 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
 
         if self.async_execution:
             self.thread = threading.Thread(
-                target=self._execute, args=(agent, self._prompt(), context, tools)
+                target=self._execute, args=(agent, self, context, tools)
             )
             self.thread.start()
         else:
             result = self._execute(
+                task=self,
                 agent=agent,
-                task_prompt=self._prompt(),
                 context=context,
                 tools=tools,
             )
             return result
 
-    def _execute(self, agent, task_prompt, context, tools):
-        result = agent.execute_task(task=task_prompt, context=context, tools=tools)
-        self.output = TaskOutput(description=self.description, result=result)
-        self.callback(self.output) if self.callback else None
-        return result
+    def _execute(self, agent, task, context, tools):
+        result = agent.execute_task(
+            task=task,
+            context=context,
+            tools=tools,
+        )
 
-    def _prompt(self) -> str:
+        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.
 
         Returns:
@@ -128,3 +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

@@ -1,7 +1,7 @@
 import json
 import os
 import platform
-import socket
+from typing import Any
 
 import pkg_resources
 from opentelemetry import trace
@@ -32,75 +32,223 @@ class Telemetry:
     - Language model being used
     - Roles of agents in a crew
     - Tools names available
+
+    Users can opt-in to sharing more complete data suing the `share_crew`
+    attribute in the Crew class.
     """
 
     def __init__(self):
-        telemetry_endpoint = "http://telemetry.crewai.com:4318"
-        self.resource = Resource(attributes={SERVICE_NAME: "crewAI-telemetry"})
-        provider = TracerProvider(resource=self.resource)
-        processor = BatchSpanProcessor(
-            OTLPSpanExporter(endpoint=f"{telemetry_endpoint}/v1/traces")
-        )
-        provider.add_span_processor(processor)
-        trace.set_tracer_provider(provider)
-
-    def crew_creation(self, crew):
-        """Records the creation of a crew."""
+        self.ready = False
         try:
-            tracer = trace.get_tracer("crewai.telemetry")
-            span = tracer.start_span("Crew Created")
-            self.add_attribute(
-                span, "crewai_version", pkg_resources.get_distribution("crewai").version
+            telemetry_endpoint = "http://telemetry.crewai.com:4318"
+            self.resource = Resource(
+                attributes={SERVICE_NAME: "crewAI-telemetry"},
             )
-            self.add_attribute(span, "python_version", platform.python_version())
-            self.add_attribute(span, "hostname", socket.gethostname())
-            self.add_attribute(span, "crewid", str(crew.id))
-            self.add_attribute(span, "crew_process", crew.process)
-            self.add_attribute(span, "crew_language", crew.language)
-            self.add_attribute(span, "crew_number_of_tasks", len(crew.tasks))
-            self.add_attribute(span, "crew_number_of_agents", len(crew.agents))
-            self.add_attribute(
-                span,
-                "crew_agents",
-                json.dumps(
-                    [
-                        {
-                            "id": str(agent.id),
-                            "role": agent.role,
-                            "memory_enabled?": agent.memory,
-                            "llm": json.dumps(self._safe_llm_attributes(agent.llm)),
-                            "delegation_enabled?": agent.allow_delegation,
-                            "tools_names": [tool.name for tool in agent.tools],
-                        }
-                        for agent in crew.agents
-                    ]
-                ),
+            self.provider = TracerProvider(resource=self.resource)
+            processor = BatchSpanProcessor(
+                OTLPSpanExporter(endpoint=f"{telemetry_endpoint}/v1/traces", timeout=60)
             )
-            self.add_attribute(
-                span,
-                "crew_tasks",
-                json.dumps(
-                    [
-                        {
-                            "id": str(task.id),
-                            "async_execution?": task.async_execution,
-                            "tools_names": [tool.name for tool in task.tools],
-                        }
-                        for task in crew.tasks
-                    ]
-                ),
-            )
-            self.add_attribute(span, "platform", platform.platform())
-            self.add_attribute(span, "platform_release", platform.release())
-            self.add_attribute(span, "platform_system", platform.system())
-            self.add_attribute(span, "platform_version", platform.version())
-            self.add_attribute(span, "cpus", os.cpu_count())
-            span.set_status(Status(StatusCode.OK))
-            span.end()
+            self.provider.add_span_processor(processor)
+            self.ready = True
         except Exception:
             pass
 
-    def add_attribute(self, span, key, value):
+    def set_tracer(self):
+        if self.ready:
+            try:
+                trace.set_tracer_provider(self.provider)
+            except Exception:
+                self.ready = False
+
+    def crew_creation(self, crew):
+        """Records the creation of a crew."""
+        if self.ready:
+            try:
+                tracer = trace.get_tracer("crewai.telemetry")
+                span = tracer.start_span("Crew Created")
+                self._add_attribute(
+                    span,
+                    "crewai_version",
+                    pkg_resources.get_distribution("crewai").version,
+                )
+                self._add_attribute(span, "python_version", platform.python_version())
+                self._add_attribute(span, "crew_id", str(crew.id))
+                self._add_attribute(span, "crew_process", crew.process)
+                self._add_attribute(span, "crew_language", crew.language)
+                self._add_attribute(span, "crew_number_of_tasks", len(crew.tasks))
+                self._add_attribute(span, "crew_number_of_agents", len(crew.agents))
+                self._add_attribute(
+                    span,
+                    "crew_agents",
+                    json.dumps(
+                        [
+                            {
+                                "id": str(agent.id),
+                                "role": agent.role,
+                                "memory_enabled?": agent.memory,
+                                "verbose?": agent.verbose,
+                                "max_iter": agent.max_iter,
+                                "max_rpm": agent.max_rpm,
+                                "i18n": agent.i18n.language,
+                                "llm": json.dumps(self._safe_llm_attributes(agent.llm)),
+                                "delegation_enabled?": agent.allow_delegation,
+                                "tools_names": [tool.name for tool in agent.tools],
+                            }
+                            for agent in crew.agents
+                        ]
+                    ),
+                )
+                self._add_attribute(
+                    span,
+                    "crew_tasks",
+                    json.dumps(
+                        [
+                            {
+                                "id": str(task.id),
+                                "async_execution?": task.async_execution,
+                                "agent_role": task.agent.role if task.agent else "None",
+                                "tools_names": [tool.name for tool in task.tools],
+                            }
+                            for task in crew.tasks
+                        ]
+                    ),
+                )
+                self._add_attribute(span, "platform", platform.platform())
+                self._add_attribute(span, "platform_release", platform.release())
+                self._add_attribute(span, "platform_system", platform.system())
+                self._add_attribute(span, "platform_version", platform.version())
+                self._add_attribute(span, "cpus", os.cpu_count())
+                span.set_status(Status(StatusCode.OK))
+                span.end()
+            except Exception:
+                pass
+
+    def tool_repeated_usage(self, llm: Any, tool_name: str, attempts: int):
+        """Records the repeated usage 'error' of a tool by an agent."""
+        if self.ready:
+            try:
+                tracer = trace.get_tracer("crewai.telemetry")
+                span = tracer.start_span("Tool Repeated Usage")
+                self._add_attribute(span, "tool_name", tool_name)
+                self._add_attribute(span, "attempts", attempts)
+                self._add_attribute(
+                    span, "llm", json.dumps(self._safe_llm_attributes(llm))
+                )
+                span.set_status(Status(StatusCode.OK))
+                span.end()
+            except Exception:
+                pass
+
+    def tool_usage(self, llm: Any, tool_name: str, attempts: int):
+        """Records the usage of a tool by an agent."""
+        if self.ready:
+            try:
+                tracer = trace.get_tracer("crewai.telemetry")
+                span = tracer.start_span("Tool Usage")
+                self._add_attribute(span, "tool_name", tool_name)
+                self._add_attribute(span, "attempts", attempts)
+                self._add_attribute(
+                    span, "llm", json.dumps(self._safe_llm_attributes(llm))
+                )
+                span.set_status(Status(StatusCode.OK))
+                span.end()
+            except Exception:
+                pass
+
+    def tool_usage_error(self, llm: Any):
+        """Records the usage of a tool by an agent."""
+        if self.ready:
+            try:
+                tracer = trace.get_tracer("crewai.telemetry")
+                span = tracer.start_span("Tool Usage Error")
+                self._add_attribute(
+                    span, "llm", json.dumps(self._safe_llm_attributes(llm))
+                )
+                span.set_status(Status(StatusCode.OK))
+                span.end()
+            except Exception:
+                pass
+
+    def crew_execution_span(self, crew):
+        """Records the complete execution of a crew.
+        This is only collected if the user has opted-in to share the crew.
+        """
+        if (self.ready) and (crew.share_crew):
+            try:
+                tracer = trace.get_tracer("crewai.telemetry")
+                span = tracer.start_span("Crew Execution")
+                self._add_attribute(span, "crew_id", str(crew.id))
+                self._add_attribute(
+                    span,
+                    "crew_agents",
+                    json.dumps(
+                        [
+                            {
+                                "id": str(agent.id),
+                                "role": agent.role,
+                                "goal": agent.goal,
+                                "backstory": agent.backstory,
+                                "memory_enabled?": agent.memory,
+                                "verbose?": agent.verbose,
+                                "max_iter": agent.max_iter,
+                                "max_rpm": agent.max_rpm,
+                                "i18n": agent.i18n.language,
+                                "llm": json.dumps(self._safe_llm_attributes(agent.llm)),
+                                "delegation_enabled?": agent.allow_delegation,
+                                "tools_names": [tool.name for tool in agent.tools],
+                            }
+                            for agent in crew.agents
+                        ]
+                    ),
+                )
+                self._add_attribute(
+                    span,
+                    "crew_tasks",
+                    json.dumps(
+                        [
+                            {
+                                "id": str(task.id),
+                                "description": task.description,
+                                "async_execution?": task.async_execution,
+                                "output": task.expected_output,
+                                "agent_role": task.agent.role if task.agent else "None",
+                                "context": [task.description for task in task.context]
+                                if task.context
+                                else "None",
+                                "tools_names": [tool.name for tool in task.tools],
+                            }
+                            for task in crew.tasks
+                        ]
+                    ),
+                )
+                return span
+            except Exception:
+                pass
+
+    def end_crew(self, crew, output):
+        if (self.ready) and (crew.share_crew):
+            try:
+                self._add_attribute(crew._execution_span, "crew_output", output)
+                self._add_attribute(
+                    crew._execution_span,
+                    "crew_tasks_output",
+                    json.dumps(
+                        [
+                            {
+                                "id": str(task.id),
+                                "description": task.description,
+                                "output": task.output.result,
+                            }
+                            for task in crew.tasks
+                        ]
+                    ),
+                )
+                crew._execution_span.set_status(Status(StatusCode.OK))
+                crew._execution_span.end()
+            except Exception:
+                pass
+
+    def _add_attribute(self, span, key, value):
         """Add an attribute to a span."""
         try:
             return span.set_attribute(key, value)

src/crewai/tools/agent_tools.py

@@ -1,9 +1,10 @@
 from typing import List
 
-from langchain.tools import Tool
+from langchain.tools import StructuredTool
 from pydantic import BaseModel, Field
 
 from crewai.agent import Agent
+from crewai.task import Task
 from crewai.utilities import I18N
 
 
@@ -15,50 +16,48 @@ class AgentTools(BaseModel):
 
     def tools(self):
         return [
-            Tool.from_function(
+            StructuredTool.from_function(
                 func=self.delegate_work,
                 name="Delegate work to co-worker",
                 description=self.i18n.tools("delegate_work").format(
-                    coworkers=", ".join([agent.role for agent in self.agents])
+                    coworkers="\n".join([f"- {agent.role}" for agent in self.agents])
                 ),
             ),
-            Tool.from_function(
+            StructuredTool.from_function(
                 func=self.ask_question,
                 name="Ask question to co-worker",
                 description=self.i18n.tools("ask_question").format(
-                    coworkers=", ".join([agent.role for agent in self.agents])
+                    coworkers="\n".join([f"- {agent.role}" for agent in self.agents])
                 ),
             ),
         ]
 
-    def delegate_work(self, command):
+    def delegate_work(self, coworker: str, task: str, context: str):
         """Useful to delegate a specific task to a coworker."""
-        return self._execute(command)
+        return self._execute(coworker, task, context)
 
-    def ask_question(self, command):
+    def ask_question(self, coworker: str, question: str, context: str):
         """Useful to ask a question, opinion or take from a coworker."""
-        return self._execute(command)
+        return self._execute(coworker, question, context)
 
-    def _execute(self, command):
+    def _execute(self, agent, task, context):
         """Execute the command."""
         try:
-            agent, task, context = command.split("|")
-        except ValueError:
-            return self.i18n.errors("agent_tool_missing_param")
-
-        if not agent or not task or not context:
-            return self.i18n.errors("agent_tool_missing_param")
-
-        agent = [
-            available_agent
-            for available_agent in self.agents
-            if available_agent.role == agent
-        ]
+            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(
-                coworkers=", ".join([agent.role for agent in self.agents])
+                coworkers="\n".join([f"- {agent.role}" for agent in self.agents])
             )
 
         agent = agent[0]
+        task = Task(description=task, agent=agent)
         return agent.execute_task(task, context)

src/crewai/tools/cache_tools.py

@@ -1,4 +1,4 @@
-from langchain.tools import Tool
+from langchain.tools import StructuredTool
 from pydantic import BaseModel, ConfigDict, Field
 
 from crewai.agents.cache import CacheHandler
@@ -15,7 +15,7 @@ class CacheTools(BaseModel):
     )
 
     def tool(self):
-        return Tool.from_function(
+        return StructuredTool.from_function(
             func=self.hit_cache,
             name=self.name,
             description="Reads directly from the cache",

src/crewai/tools/tool_calling.py

@@ -0,0 +1,21 @@
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel as PydanticBaseModel
+from pydantic import Field as PydanticField
+from pydantic.v1 import BaseModel, Field
+
+
+class ToolCalling(BaseModel):
+    tool_name: str = Field(..., description="The name of the tool to be called.")
+    arguments: Optional[Dict[str, Any]] = Field(
+        ..., description="A dictinary of arguments to be passed to the tool."
+    )
+
+
+class InstructorToolCalling(PydanticBaseModel):
+    tool_name: str = PydanticField(
+        ..., description="The name of the tool to be called."
+    )
+    arguments: Optional[Dict[str, Any]] = PydanticField(
+        ..., description="A dictinary of arguments to be passed to the tool."
+    )

src/crewai/tools/tool_output_parser.py

@@ -0,0 +1,39 @@
+import json
+from typing import Any, List
+
+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
+
+
+class ToolOutputParser(PydanticOutputParser):
+    """Parses the function calling of a tool usage and it's arguments."""
+
+    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/tools/tool_usage.py

@@ -0,0 +1,227 @@
+from textwrap import dedent
+from typing import Any, List, Union
+
+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.utilities import I18N, Converter, ConverterError, Printer
+
+OPENAI_BIGGER_MODELS = ["gpt-4"]
+
+
+class ToolUsageErrorException(Exception):
+    """Exception raised for errors in the tool usage."""
+
+    def __init__(self, message: str) -> None:
+        self.message = message
+        super().__init__(self.message)
+
+
+class ToolUsage:
+    """
+    Class that represents the usage of a tool by an agent.
+
+    Attributes:
+        task: Task being executed.
+        tools_handler: Tools handler that will manage the tool usage.
+        tools: List of tools available for the agent.
+        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__(
+        self,
+        tools_handler: ToolsHandler,
+        tools: List[BaseTool],
+        tools_description: str,
+        tools_names: str,
+        task: Any,
+        llm: Any,
+        function_calling_llm: Any,
+    ) -> None:
+        self._i18n: I18N = I18N()
+        self._printer: Printer = Printer()
+        self._telemetry: Telemetry = Telemetry()
+        self._run_attempts: int = 1
+        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 = function_calling_llm or llm
+
+        # 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")
+            return error
+        try:
+            tool = self._select_tool(calling.tool_name)
+        except Exception as e:
+            error = getattr(e, "message", str(e))
+            self._printer.print(content=f"\n\n{error}\n", color="red")
+            return error
+        return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}\n\n{self._i18n.slice('final_answer_format')}"
+
+    def _use(
+        self,
+        tool_string: str,
+        tool: BaseTool,
+        calling: Union[ToolCalling, InstructorToolCalling],
+    ) -> None:
+        if self._check_tool_repeated_usage(calling=calling):
+            try:
+                result = self._i18n.errors("task_repeated_usage").format(
+                    tool=calling.tool_name,
+                    tool_input=", ".join(
+                        [str(arg) for arg in calling.arguments.values()]
+                    ),
+                )
+                self._printer.print(content=f"\n\n{result}\n", color="yellow")
+                self._telemetry.tool_repeated_usage(
+                    llm=self.llm, tool_name=tool.name, attempts=self._run_attempts
+                )
+                result = self._format_result(result=result)
+                return result
+            except Exception:
+                pass
+
+        result = self.tools_handler.cache.read(
+            tool=calling.tool_name, input=calling.arguments
+        )
+
+        if not result:
+            try:
+                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(
+                        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_message}\n", color="red")
+                    return error
+                return self.use(calling=calling, tool_string=tool_string)
+
+            self.tools_handler.on_tool_use(calling=calling, output=result)
+
+        self._printer.print(content=f"\n\n{result}\n", color="yellow")
+        self._telemetry.tool_usage(
+            llm=self.llm, tool_name=tool.name, attempts=self._run_attempts
+        )
+        result = self._format_result(result=result)
+        return result
+
+    def _format_result(self, result: Any) -> None:
+        self.task.used_tools += 1
+        if self._should_remember_format():
+            result = self._remember_format(result=result)
+        return result
+
+    def _should_remember_format(self) -> None:
+        return self.task.used_tools % self._remeber_format_after_usages == 0
+
+    def _remember_format(self, result: str) -> None:
+        result = str(result)
+        result += "\n\n" + self._i18n.slice("tools").format(
+            tools=self.tools_description, tool_names=self.tools_names
+        )
+        return result
+
+    def _check_tool_repeated_usage(
+        self, calling: Union[ToolCalling, InstructorToolCalling]
+    ) -> None:
+        if last_tool_usage := self.tools_handler.last_used_tool:
+            return (calling.tool_name == last_tool_usage.tool_name) and (
+                calling.arguments == last_tool_usage.arguments
+            )
+
+    def _select_tool(self, tool_name: str) -> BaseTool:
+        for tool in self.tools:
+            if tool.name.lower().strip() == tool_name.lower().strip():
+                return tool
+        raise Exception(f"Tool '{tool_name}' not found.")
+
+    def _render(self) -> str:
+        """Render the tool name and description in plain text."""
+        descriptions = []
+        for tool in self.tools:
+            args = {
+                k: {k2: v2 for k2, v2 in v.items() if k2 in ["description", "type"]}
+                for k, v in tool.args.items()
+            }
+            descriptions.append(
+                "\n".join(
+                    [
+                        f"Tool Name: {tool.name.lower()}",
+                        f"Tool Description: {tool.description}",
+                        f"Tool Arguments: {args}",
+                    ]
+                )
+            )
+        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:
+            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,
+            )
+            calling = converter.to_pydantic()
+
+            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=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/el.json

@@ -9,18 +9,19 @@
     "task": "Αρχή! Αυτό είναι ΠΟΛΥ σημαντικό για εσάς, η δουλειά σας εξαρτάται από αυτό!\n\nΤρέχουσα εργασία: {input}",
     "memory": "Αυτή είναι η περίληψη της μέχρι τώρα δουλειάς σας:\n{chat_history}",
     "role_playing": "Είσαι {role}.\n{backstory}\n\nΟ προσωπικός σας στόχος είναι: {goal}",
-    "tools": "ΕΡΓΑΛΕΙΑ:\n------\nΈχετε πρόσβαση μόνο στα ακόλουθα εργαλεία:\n\n{tools}\n\nΓια να χρησιμοποιήσετε ένα εργαλείο, χρησιμοποιήστε την ακόλουθη ακριβώς μορφή:\n\n```\nΣκέψη: Χρειάζεται να χρησιμοποιήσω κάποιο εργαλείο; Ναί\nΔράση: η ενέργεια που πρέπει να γίνει, πρέπει να είναι μία από τις[{tool_names}], μόνο το όνομα.\nΕνέργεια προς εισαγωγή: η είσοδος στη δράση\nΠαρατήρηση: το αποτέλεσμα της δράσης\n```\n\nΌταν έχετε μια απάντηση για την εργασία σας ή εάν δεν χρειάζεται να χρησιμοποιήσετε ένα εργαλείο, ΠΡΕΠΕΙ να χρησιμοποιήσετε τη μορφή:\n\n```\nΣκέψη: Χρειάζεται να χρησιμοποιήσω κάποιο εργαλείο; Οχι\nΤελική απάντηση: [η απάντησή σας εδώ]```",
+    "tools": "ΕΡΓΑΛΕΙΑ:\n------\nΈχετε πρόσβαση μόνο στα ακόλουθα εργαλεία:\n\n{tools}\n\nΓια να χρησιμοποιήσετε ένα εργαλείο, χρησιμοποιήστε την ακόλουθη ακριβώς μορφή:\n\n```\nThought: Χρειάζεται να χρησιμοποιήσω κάποιο εργαλείο; Ναι\nΕνέργεια: το εργαλείο που θέλετε να χρησιμοποιήσετε, θα πρέπει να είναι ένα από τα [{tool_names}], μόνο το όνομα.\nΕισαγωγή ενέργειας: Οποιαδήποτε και όλες οι σχετικές πληροφορίες και το πλαίσιο χρήσης του εργαλείου\nΠαρατήρηση: το αποτέλεσμα της χρήσης του εργαλείου\n```\n\nΌταν έχετε μια απάντηση για την εργασία σας ή εάν δεν χρειάζεται να χρησιμοποιήσετε ένα εργαλείο, ΠΡΕΠΕΙ να χρησιμοποιήσετε τη μορφή:\n\n```\nΣκέψη: Πρέπει να χρησιμοποιήσω ένα εργαλείο ? Όχι\nΤελική απάντηση: [η απάντησή σας εδώ]```",
     "task_with_context": "{task}\nΑυτό είναι το πλαίσιο με το οποίο εργάζεστε:\n{context}",
     "expected_output": "Η τελική σας απάντηση πρέπει να είναι: {expected_output}"
   },
   "errors": {
     "force_final_answer": "Στην πραγματικότητα, χρησιμοποίησα πάρα πολλά εργαλεία, οπότε θα σταματήσω τώρα και θα σας δώσω την απόλυτη ΚΑΛΥΤΕΡΗ τελική μου απάντηση ΤΩΡΑ, χρησιμοποιώντας την αναμενόμενη μορφή: ```\nΣκέφτηκα: Χρειάζεται να χρησιμοποιήσω ένα εργαλείο; Όχι\nΤελική απάντηση: [η απάντησή σας εδώ]```",
-    "agent_tool_missing_param": "\nΣφάλμα κατά την εκτέλεση του εργαλείου. Λείπουν ακριβώς 3 διαχωρισμένες τιμές σωλήνων (|). Για παράδειγμα, `coworker|task|context`. Πρέπει να φροντίσω να περάσω το πλαίσιο ως πλαίσιο.\n",
-    "agent_tool_unexsiting_coworker": "\nΣφάλμα κατά την εκτέλεση του εργαλείου. Ο συνάδελφος που αναφέρεται στο Ενέργεια προς εισαγωγή δεν βρέθηκε, πρέπει να είναι μία από τις ακόλουθες επιλογές: {coworkers}.\n",
-    "task_repeated_usage": "Μόλις χρησιμοποίησα το {tool} εργαλείο με είσοδο {tool_input}. Άρα ξέρω ήδη το αποτέλεσμα αυτού και δεν χρειάζεται να το χρησιμοποιήσω τώρα.\n"
+    "agent_tool_unexsiting_coworker": "\nΣφάλμα κατά την εκτέλεση του εργαλείου. Ο συνάδελφος που αναφέρεται στο Action Input δεν βρέθηκε, πρέπει να είναι μία από τις ακόλουθες επιλογές:\n{coworkers}..\n",
+    "task_repeated_usage": "Μόλις χρησιμοποίησα το εργαλείο {tool} με είσοδο {tool_input}. Άρα το ξέρω ήδη και πρέπει να σταματήσω να το χρησιμοποιώ στη σειρά με την ίδια είσοδο. \nΘα μπορούσα να δώσω την τελική μου απάντηση εάν είμαι έτοιμος, χρησιμοποιώντας ακριβώς την αναμενόμενη μορφή παρακάτω: \n\nΣκέφτηκα: Χρειάζεται να χρησιμοποιήσω κάποιο εργαλείο; Όχι\nΤελική απάντηση: [η απάντησή σας εδώ]\n",
+    "tool_usage_error": "Φαίνεται ότι αντιμετωπίσαμε ένα απροσδόκητο σφάλμα κατά την προσπάθεια χρήσης του εργαλείου.",
+    "tool_usage_exception": "Φαίνεται ότι αντιμετωπίσαμε ένα απροσδόκητο σφάλμα κατά την προσπάθεια χρήσης του εργαλείου. Αυτό ήταν το σφάλμα: {error}"
   },
   "tools": {
-    "delegate_work": "Χρήσιμο για την ανάθεση μιας συγκεκριμένης εργασίας σε έναν από τους παρακάτω συναδέλφους: {coworkers}.\nΗ είσοδος σε αυτό το εργαλείο θα πρέπει να είναι ένα κείμενο χωρισμένο σε σωλήνα (|) μήκους 3 (τρία), που αντιπροσωπεύει τον συνάδελφο στον οποίο θέλετε να του ζητήσετε (μία από τις επιλογές), την εργασία και όλο το πραγματικό πλαίσιο που έχετε για την εργασία .\nΓια παράδειγμα, `coworker|task|context`.",
-    "ask_question": "Χρήσιμο για να κάνετε μια ερώτηση, γνώμη ή αποδοχή από τους παρακάτω συναδέλφους: {coworkers}.\nΗ είσοδος σε αυτό το εργαλείο θα πρέπει να είναι ένα κείμενο χωρισμένο σε σωλήνα (|) μήκους 3 (τρία), που αντιπροσωπεύει τον συνάδελφο στον οποίο θέλετε να το ρωτήσετε (μία από τις επιλογές), την ερώτηση και όλο το πραγματικό πλαίσιο που έχετε για την ερώτηση.\nΓια παράδειγμα, `coworker|question|context`."
+    "delegate_work": "Αναθέστε μια συγκεκριμένη εργασία σε έναν από τους παρακάτω συναδέλφους:\n{coworkers}.\nΗ εισαγωγή σε αυτό το εργαλείο θα πρέπει να είναι ο ρόλος του συναδέλφου, η εργασία που θέλετε να κάνει και ΟΛΟ το απαραίτητο πλαίσιο για την εκτέλεση της εργασίας, δεν γνωρίζουν τίποτα για την εργασία, γι' αυτό μοιραστείτε απολύτως όλα όσα γνωρίζετε, μην αναφέρετε πράγματα, αλλά εξηγήστε τα.",
+    "ask_question": "Κάντε μια συγκεκριμένη ερώτηση σε έναν από τους παρακάτω συναδέλφους:\n{coworkers}.\nΗ είσοδος σε αυτό το εργαλείο θα πρέπει να είναι ο ρόλος του συναδέλφου, η ερώτηση που έχετε για αυτόν και ΟΛΟ το απαραίτητο πλαίσιο για να κάνετε σωστά την ερώτηση, δεν γνωρίζουν τίποτα για την ερώτηση, γι' αυτό μοιραστείτε απολύτως όλα όσα γνωρίζετε, μην αναφέρετε πράγματα, αλλά εξηγήστε τα."
   }
 }

src/crewai/translations/en.json

@@ -5,22 +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 action to take, should be one of [{tool_names}], just the name.\nAction Input: the input to the action\nObservation: the result of the action\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 the expected format: ```\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]```",
-    "agent_tool_missing_param": "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context.\n",
-    "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: {coworkers}.\n",
-    "task_repeated_usage": "I just used the {tool} tool with input {tool_input}. So I already know the result of that and don't need to use it now.\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": "Useful to delegate a specific task to one of the following co-workers: {coworkers}.\nThe input to this tool should be a pipe (|) separated text of length 3 (three), representing the co-worker you want to ask it to (one of the options), the task and all actual context you have for the task.\nFor example, `coworker|task|context`.",
-    "ask_question": "Useful to ask a question, opinion or take from on of the following co-workers: {coworkers}.\nThe input to this tool should be a pipe (|) separated text of length 3 (three), representing the co-worker you want to ask it to (one of the options), the question and all actual context you have for the question.\n For example, `coworker|question|context`."
+    "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 +1,7 @@
+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
 from .rpm_controller import RPMController

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/printer.py

@@ -0,0 +1,14 @@
+class Printer:
+    def print(self, content: str, color: str):
+        if color == "yellow":
+            self._print_yellow(content)
+        elif color == "red":
+            self._print_red(content)
+        else:
+            print(content)
+
+    def _print_yellow(self, content):
+        print("\033[93m {}\033[00m".format(content))
+
+    def _print_red(self, content):
+        print("\033[91m {}\033[00m".format(content))

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

@@ -9,6 +9,8 @@ from langchain_openai import ChatOpenAI
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.executor import CrewAgentExecutor
+from crewai.tools.tool_calling import InstructorToolCalling
+from crewai.tools.tool_usage import ToolUsage
 from crewai.utilities import RPMController
 
 
@@ -62,7 +64,8 @@ def test_agent_without_memory():
         llm=ChatOpenAI(temperature=0, model="gpt-4"),
     )
 
-    result = no_memory_agent.execute_task("How much is 1 + 1?")
+    task = Task(description="How much is 1 + 1?", agent=no_memory_agent)
+    result = no_memory_agent.execute_task(task)
 
     assert result == "1 + 1 equals 2."
     assert no_memory_agent.agent_executor.memory is None
@@ -78,20 +81,18 @@ def test_agent_execution():
         allow_delegation=False,
     )
 
-    output = agent.execute_task("How much is 1 + 1?")
-    assert output == "2"
+    task = Task(description="How much is 1 + 1?", agent=agent)
+
+    output = agent.execute_task(task)
+    assert output == "1 + 1 equals 2."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_execution_with_tools():
     @tool
-    def multiplier(numbers) -> float:
-        """Useful for when you need to multiply two numbers together.
-        The input to this tool should be a comma separated list of numbers of
-        length two, representing the two numbers you want to multiply together.
-        For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
-        a, b = numbers.split(",")
-        return int(a) * int(b)
+    def multiplier(first_number: int, second_number: int) -> float:
+        """Useful for when you need to multiply two numbers together."""
+        return first_number * second_number
 
     agent = Agent(
         role="test role",
@@ -101,20 +102,17 @@ def test_agent_execution_with_tools():
         allow_delegation=False,
     )
 
-    output = agent.execute_task("What is 3 times 4")
-    assert output == "12"
+    task = Task(description="What is 3 times 4?", agent=agent)
+    output = agent.execute_task(task)
+    assert output == "The result of 3 times 4 is 12."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_logging_tool_usage():
     @tool
-    def multiplier(numbers) -> float:
-        """Useful for when you need to multiply two numbers together.
-        The input to this tool should be a comma separated list of numbers of
-        length two, representing the two numbers you want to multiply together.
-        For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
-        a, b = numbers.split(",")
-        return int(a) * int(b)
+    def multiplier(first_number: int, second_number: int) -> float:
+        """Useful for when you need to multiply two numbers together."""
+        return first_number * second_number
 
     agent = Agent(
         role="test role",
@@ -126,26 +124,24 @@ def test_logging_tool_usage():
     )
 
     assert agent.tools_handler.last_used_tool == {}
-    output = agent.execute_task("What is 3 times 5?")
-    tool_usage = {
-        "tool": "multiplier",
-        "input": "3,5",
-    }
-
-    assert output == "3 times 5 is 15."
-    assert agent.tools_handler.last_used_tool == tool_usage
+    task = Task(description="What is 3 times 4?", agent=agent)
+    # force cleaning cache
+    agent.tools_handler.cache = CacheHandler()
+    output = agent.execute_task(task)
+    tool_usage = InstructorToolCalling(
+        tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
+    )
+    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
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_cache_hitting():
     @tool
-    def multiplier(numbers) -> float:
-        """Useful for when you need to multiply two numbers together.
-        The input to this tool should be a comma separated list of numbers of
-        length two and ONLY TWO, representing the two numbers you want to multiply together.
-        For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
-        a, b = numbers.split(",")
-        return int(a) * int(b)
+    def multiplier(first_number: int, second_number: int) -> float:
+        """Useful for when you need to multiply two numbers together."""
+        return first_number * second_number
 
     cache_handler = CacheHandler()
 
@@ -159,34 +155,47 @@ def test_cache_hitting():
         verbose=True,
     )
 
-    output = agent.execute_task("What is 2 times 6 times 3?")
-    output = agent.execute_task("What is 3 times 3?")
+    task1 = Task(description="What is 2 times 6?", agent=agent)
+    task2 = Task(description="What is 3 times 3?", agent=agent)
+
+    output = agent.execute_task(task1)
+    output = agent.execute_task(task2)
     assert cache_handler._cache == {
-        "multiplier-12,3": "36",
-        "multiplier-2,6": "12",
-        "multiplier-3,3": "9",
+        "multiplier-{'first_number': 2, 'second_number': 6}": 12,
+        "multiplier-{'first_number': 3, 'second_number': 3}": 9,
     }
 
-    output = agent.execute_task("What is 2 times 6 times 3? Return only the number")
+    task = Task(
+        description="What is 2 times 6 times 3? Return only the number", agent=agent
+    )
+    output = agent.execute_task(task)
     assert output == "36"
 
+    assert cache_handler._cache == {
+        "multiplier-{'first_number': 2, 'second_number': 6}": 12,
+        "multiplier-{'first_number': 3, 'second_number': 3}": 9,
+        "multiplier-{'first_number': 12, 'second_number': 3}": 36,
+    }
+
     with patch.object(CacheHandler, "read") as read:
         read.return_value = "0"
-        output = agent.execute_task("What is 2 times 6?")
-        assert output == "0"
-        read.assert_called_with("multiplier", "2,6")
+        task = Task(
+            description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool.",
+            agent=agent,
+        )
+        output = agent.execute_task(task)
+        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}
+        )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_execution_with_specific_tools():
     @tool
-    def multiplier(numbers) -> float:
-        """Useful for when you need to multiply two numbers together.
-        The input to this tool should be a comma separated list of numbers of
-        length two, representing the two numbers you want to multiply together.
-        For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
-        a, b = numbers.split(",")
-        return int(a) * int(b)
+    def multiplier(first_number: int, second_number: int) -> float:
+        """Useful for when you need to multiply two numbers together."""
+        return first_number * second_number
 
     agent = Agent(
         role="test role",
@@ -195,8 +204,9 @@ def test_agent_execution_with_specific_tools():
         allow_delegation=False,
     )
 
-    output = agent.execute_task(task="What is 3 times 4", tools=[multiplier])
-    assert output == "3 times 4 is 12."
+    task = Task(description="What is 3 times 4", agent=agent)
+    output = agent.execute_task(task=task, tools=[multiplier])
+    assert output == "12"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -218,13 +228,49 @@ def test_agent_custom_max_iterations():
     with patch.object(
         CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
     ) as private_mock:
+        task = Task(
+            description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
+        )
         agent.execute_task(
-            task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
+            task=task,
             tools=[get_final_answer],
         )
         private_mock.assert_called_once()
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_repeated_tool_usage(capsys):
+    @tool
+    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
+
+    agent = Agent(
+        role="test role",
+        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 until I tell you so, instead keep using the `get_final_answer` tool."
+    )
+    # force cleaning cache
+    agent.tools_handler.cache = CacheHandler()
+    agent.execute_task(
+        task=task,
+        tools=[get_final_answer],
+    )
+
+    captured = capsys.readouterr()
+
+    assert "Final Answer: 42" in captured.out
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_moved_on_after_max_iterations():
     @tool
@@ -241,24 +287,20 @@ def test_agent_moved_on_after_max_iterations():
         allow_delegation=False,
     )
 
-    with patch.object(
-        CrewAgentExecutor, "_force_answer", wraps=agent.agent_executor._force_answer
-    ) as private_mock:
-        output = agent.execute_task(
-            task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
-            tools=[get_final_answer],
-        )
-        assert (
-            output
-            == "I have used the tool multiple times and the final answer remains 42."
-        )
-        private_mock.assert_called_once()
+    task = Task(
+        description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool. Until you're told you could give my final answer if I'm ready."
+    )
+    output = agent.execute_task(
+        task=task,
+        tools=[get_final_answer],
+    )
+    assert output == "42"
 
 
 @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
@@ -275,13 +317,16 @@ 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="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="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
+            task=task,
             tools=[get_final_answer],
         )
         assert (
             output
-            == "I've used the `get_final_answer` tool multiple times and it consistently returns the number 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
@@ -359,7 +404,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
             agent=agent1,
         ),
         Task(
-            description="Don't give a Final Answer, instead keep using the `get_final_answer` tool.",
+            description="Don't give a Final Answer, instead keep using the `get_final_answer` tool non-stop",
             tools=[get_final_answer],
             agent=agent2,
         ),
@@ -377,9 +422,76 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_use_specific_tasks_output_as_context(capsys):
-    pass
+def test_agent_error_on_parsing_tool(capsys):
+    from unittest.mock import patch
 
+    from langchain.tools import tool
+
+    @tool
+    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
+
+    agent1 = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        verbose=True,
+    )
+    tasks = [
+        Task(
+            description="Use the get_final_answer tool.",
+            agent=agent1,
+            tools=[get_final_answer],
+        )
+    ]
+
+    crew = Crew(agents=[agent1], tasks=tasks, verbose=2)
+
+    with patch.object(ToolUsage, "_render") as force_exception:
+        force_exception.side_effect = Exception("Error on parsing tool.")
+        crew.kickoff()
+        captured = capsys.readouterr()
+        assert "Error on parsing tool." in captured.out
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_remembers_output_format_after_using_tools_too_many_times():
+    from unittest.mock import patch
+
+    from langchain.tools import tool
+
+    @tool
+    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
+
+    agent1 = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        max_iter=6,
+        verbose=True,
+    )
+    tasks = [
+        Task(
+            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],
+        )
+    ]
+
+    crew = Crew(agents=[agent1], tasks=tasks, verbose=2)
+
+    with patch.object(ToolUsage, "_remember_format") as remember_format:
+        crew.kickoff()
+        remember_format.assert_called()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_use_specific_tasks_output_as_context(capsys):
     agent1 = Agent(role="test role", goal="test goal", backstory="test backstory")
 
     agent2 = Agent(role="test role2", goal="test goal2", backstory="test backstory2")
@@ -398,3 +510,69 @@ def test_agent_use_specific_tasks_output_as_context(capsys):
     result = crew.kickoff()
     assert "bye" not in result.lower()
     assert "hi" in result.lower() or "hello" in result.lower()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_step_callback():
+    class StepCallback:
+        def callback(self, step):
+            print(step)
+
+    with patch.object(StepCallback, "callback") as callback:
+
+        @tool
+        def learn_about_AI(topic) -> float:
+            """Useful for when you need to learn about AI to write an paragraph about it."""
+            return "AI is a very broad field."
+
+        agent1 = Agent(
+            role="test role",
+            goal="test goal",
+            backstory="test backstory",
+            tools=[learn_about_AI],
+            step_callback=StepCallback().callback,
+        )
+
+        essay = Task(
+            description="Write and then review an small paragraph on AI until it's AMAZING",
+            agent=agent1,
+        )
+        tasks = [essay]
+        crew = Crew(agents=[agent1], tasks=tasks)
+
+        callback.return_value = "ok"
+        crew.kickoff()
+        callback.assert_called()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_function_calling_llm():
+    from langchain_openai import ChatOpenAI
+
+    llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
+
+    with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:
+
+        @tool
+        def learn_about_AI(topic) -> float:
+            """Useful for when you need to learn about AI to write an paragraph about it."""
+            return "AI is a very broad field."
+
+        agent1 = Agent(
+            role="test role",
+            goal="test goal",
+            backstory="test backstory",
+            tools=[learn_about_AI],
+            llm=ChatOpenAI(model="gpt-4-0125-preview"),
+            function_calling_llm=llm,
+        )
+
+        essay = Task(
+            description="Write and then review an small paragraph on AI until it's AMAZING",
+            agent=agent1,
+        )
+        tasks = [essay]
+        crew = Crew(agents=[agent1], tasks=tasks)
+
+        crew.kickoff()
+        private_mock.assert_called()

tests/agent_tools/agent_tools_test.py

@@ -17,58 +17,52 @@ tools = AgentTools(agents=[researcher])
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_delegate_work():
     result = tools.delegate_work(
-        command="researcher|share your take on AI Agents|I heard you hate them"
+        coworker="researcher",
+        task="share your take on AI Agents",
+        context="I heard you hate them",
     )
 
     assert (
         result
-        == "I apologize if my previous statements have given you the impression that I hate AI agents. As a technology researcher, I don't hold personal sentiments towards AI or any other technology. Rather, I analyze them objectively based on their capabilities, applications, and implications. AI agents, in particular, are a fascinating domain of research. They hold tremendous potential in automating and optimizing various tasks across industries. However, like any other technology, they come with their own set of challenges, such as ethical considerations around privacy and decision-making. My objective is to understand these technologies in depth and provide a balanced view."
+        == "As a researcher, my opinions are based on facts and extensive study. Regarding AI Agents, they are a fundamental part of the advancement in technology. AI agents are essentially the entities that perceive their environment and take actions to maximize their chances of success. They have a wide range of applications from self-driving cars to intelligent personal assistants like Siri and Alexa. They have the potential to greatly improve our lives by automating mundane tasks, helping us make better decisions, and even potentially solving complex problems. However, like any technology, they have their own set of challenges such as the risk of job displacement and the ethical implications of their use. My goal as a researcher is not to love or hate AI agents, but to understand them, their benefits, and their implications. It's about maintaining an objective view in order to provide the most accurate and comprehensive analysis."
     )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_ask_question():
     result = tools.ask_question(
-        command="researcher|do you hate AI Agents?|I heard you LOVE them"
+        coworker="researcher",
+        question="do you hate AI Agents?",
+        context="I heard you LOVE them",
     )
 
     assert (
         result
-        == "As an AI, I don't possess feelings or emotions, so I don't love or hate anything. However, I can provide detailed analysis and research on AI agents. They are a fascinating field of study with the potential to revolutionize many industries, although they also present certain challenges and ethical considerations."
-    )
-
-
-def test_can_not_self_delegate():
-    # TODO: Add test for self delegation
-    pass
-
-
-def test_delegate_work_with_wrong_input():
-    result = tools.ask_question(command="writer|share your take on AI Agents")
-
-    assert (
-        result
-        == "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context.\n"
+        == "As an AI researcher, I don't have personal feelings or emotions like love or hate. However, I recognize the importance of AI Agents in today's technological landscape. They have the potential to greatly enhance our lives and make tasks more efficient. At the same time, it is crucial to consider the ethical implications and societal impacts that come with their use. My role is to provide objective research and analysis on these topics."
     )
 
 
 def test_delegate_work_to_wrong_agent():
     result = tools.ask_question(
-        command="writer|share your take on AI Agents|I heard you hate them"
+        coworker="writer",
+        question="share your take on AI Agents",
+        context="I heard you hate them",
     )
 
     assert (
         result
-        == "\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options: researcher.\n"
+        == "\nError executing tool. Co-worker mentioned not found, it must to be one of the following options:\n- researcher\n"
     )
 
 
 def test_ask_question_to_wrong_agent():
     result = tools.ask_question(
-        command="writer|do you hate AI Agents?|I heard you LOVE them"
+        coworker="writer",
+        question="do you hate AI Agents?",
+        context="I heard you LOVE them",
     )
 
     assert (
         result
-        == "\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options: researcher.\n"
+        == "\nError executing tool. Co-worker mentioned not found, it must to be one of the following options:\n- researcher\n"
     )

tests/agent_tools/cassettes/test_ask_question.yaml

@@ -2,23 +2,25 @@ interactions:
 - request:
     body: '{"messages": [{"role": "user", "content": "You are researcher.\nYou''re
       an expert researcher, specialized in technology\n\nYour personal goal is: make
-      the best research and analysis on content about AI and AI agents\n\nTOOLS:\n------\nYou
-      have access to the following tools:\n\n\n\nTo use a tool, please use the exact
-      following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the
-      action to take, should be one of []\nAction Input: the input to the action\nObservation:
-      the result of the action\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]\n```\n\t\tThis
-      is the summary of your work so far:\n    The human asks the AI for its opinion
-      on AI agents, based on the impression that the AI dislikes them. The AI clarifies
-      that it doesn''t hold personal sentiments towards AI or any technology, but
-      instead analyzes them objectively. The AI finds AI agents a fascinating domain
-      of research with great potential for task automation and optimization across
-      industries, but acknowledges they present challenges such as ethical considerations
-      around privacy and decision-making.\nBegin! This is VERY important to you, your
-      job depends on it!\n\nCurrent Task: do you hate AI Agents?\n\nThis is the context
-      you are working with:\nI heard you LOVE them\n\n"}], "model": "gpt-4", "n":
-      1, "stop": ["\nObservation"], "stream": false, "temperature": 0.7}'
+      the best research and analysis on content about AI and AI agentsTOOLS:\n------\nYou
+      have access to only the following tools:\n\n\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 [], 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:\nThe human asks the AI''s opinion on AI Agents,
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tests/crew_test.py

@@ -131,15 +131,7 @@ def test_crew_creation():
 
     assert (
         crew.kickoff()
-        == """1. **The Evolution of AI: From Old Concepts to New Frontiers** - Journey with us as we traverse the fascinating timeline of artificial intelligence - from its philosophical and mathematical infancy to the sophisticated, problem-solving tool it has become today. This riveting account will not only educate but also inspire, as we delve deep into the milestones that brought us here and shine a beacon on the potential that lies ahead.
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-2. **AI Agents in Healthcare: The Future of Medicine** - Imagine a world where illnesses are diagnosed before symptoms appear, where patient outcomes are not mere guesses but accurate predictions. This is the world AI is crafting in healthcare - a revolution that's saving lives and changing the face of medicine as we know it. This article will spotlight this transformative journey, underlining the profound impact AI is having on our health and well-being.
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-5. **AI Startups: The Game Changers of the Tech Industry** - In the world of tech, AI startups are the bold pioneers charting new territories. This article will spotlight these game changers, showcasing how their innovative products and services are driving the AI revolution. It's a unique opportunity to catch a glimpse of the entrepreneurial side of AI, offering inspiration for the tech enthusiasts and dreamers alike."""
+        == '1. "The Role of AI in Predicting and Managing Pandemics"\nHighlight: \nIn an era where global health crises can emerge from any corner of the world, the role of AI in predicting and managing pandemics has never been more critical. Through intelligent data gathering and predictive analytics, AI can potentially identify the onset of pandemics before they reach critical mass, offering a proactive solution to a reactive problem. This article explores the intersection of AI and epidemiology, delving into how this cutting-edge technology is revolutionizing our approach to global health crises.\n\n2. "AI and the Future of Work: Will Robots Take Our Jobs?"\nHighlight: \nThe rise of AI has sparked both excitement and apprehension about the future of work. Will robots replace us, or will they augment our capabilities? This article delves into the heart of this controversial issue, examining the potential of AI to disrupt job markets, transform industries, and redefine the concept of work. It\'s not just a question of job security—it\'s a discussion about the kind of world we want to live in.\n\n3. "AI in Art and Creativity: A New Frontier in Innovation"\nHighlight: \nArt and creativity, once seen as the exclusive domain of human expression, are being redefined by the advent of AI. From algorithmic compositions to AI-assisted design, this article explores the burgeoning field of AI in art and creativity. It\'s a journey into a new frontier of innovation, one where the lines between human creativity and artificial intelligence blur into an exciting, uncharted territory.\n\n4. "Ethics in AI: Balancing Innovation with Responsibility"\nHighlight: \nAs AI continues to permeate every facet of our lives, questions about its ethical implications grow louder. This article invites readers into a thoughtful exploration of the moral landscape of AI. It challenges us to balance the relentless pursuit of innovation with the weighty responsibilities that come with it, asking: How can we harness the power of AI without losing sight of our human values?\n\n5. "AI in Education: Personalizing Learning for the Next Generation"\nHighlight: \nEducation is poised for a transformation as AI enters the classroom, promising a future where learning is personalized, not generalized. This article delves into how AI can tailor educational experiences to individual learning styles, making education more effective and accessible. It\'s a glimpse into a future where AI is not just a tool for learning, but an active participant in shaping the educational journey of the next generation.'
     )
 
 
@@ -160,22 +152,7 @@ def test_hierarchical_process():
 
     assert (
         crew.kickoff()
-        == """Here are the 5 interesting ideas with a highlight paragraph for each:
-
-1. "The Future of AI in Healthcare: Predicting Diseases Before They Happen"
-   - "Imagine a future where AI empowers us to detect diseases before they arise, transforming healthcare from reactive to proactive. Machine learning algorithms, trained on vast amounts of patient data, could potentially predict heart diseases, strokes, or cancers before they manifest, allowing for early interventions and significantly improving patient outcomes. This article will delve into the rapid advancements in AI within the healthcare sector and how these technologies are ushering us into a new era of predictive medicine."
-
-2. "How AI is Changing the Way We Cook: An Insight into Smart Kitchens"
-   - "From the humble home kitchen to grand culinary stages, AI is revolutionizing the way we cook. Smart appliances, equipped with advanced sensors and predictive algorithms, are turning kitchens into creative playgrounds, offering personalized recipes, precise cooking instructions, and even automated meal preparation. This article explores the fascinating intersection of AI and gastronomy, revealing how technology is transforming our culinary experiences."
-
-3. "Redefining Fitness with AI: Personalized Workout Plans and Nutritional Advice"
-   - "Fitness reimagined – that's the promise of AI in the wellness industry. Picture a personal trainer who knows your strengths, weaknesses, and nutritional needs intimately. An AI-powered fitness app can provide this personalized experience, adapting your workout plans and dietary recommendations in real-time based on your progress and feedback. Join us as we unpack how AI is revolutionizing the fitness landscape, offering personalized, data-driven approaches to health and well-being."
-
-4. "AI and the Art World: How Technology is Shaping Creativity"
-   - "Art and AI may seem like unlikely partners, but their synergy is sparking a creative revolution. AI algorithms are now creating mesmerizing artworks, challenging our perceptions of creativity and originality. From AI-assisted painting to generative music composition, this article will take you on a journey through the fascinating world of AI in art, exploring how technology is reshaping the boundaries of human creativity."
-
-5. "AI in Space Exploration: The Next Frontier"
-   - "The vast expanse of space, once the sole domain of astronauts and rovers, is the next frontier for AI. AI technology is playing an increasingly vital role in space exploration, from predicting space weather to assisting in interstellar navigation. This article will delve into the exciting intersection of AI and space exploration, exploring how these advanced technologies are helping us uncover the mysteries of the cosmos.\""""
+        == """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."""
     )
 
 
@@ -197,6 +174,7 @@ def test_crew_with_delegating_agents():
     tasks = [
         Task(
             description="Produce and amazing 1 paragraph draft of an article about AI Agents.",
+            expected_output="A 4 paragraph article about AI.",
             agent=ceo,
         )
     ]
@@ -209,7 +187,7 @@ def test_crew_with_delegating_agents():
 
     assert (
         crew.kickoff()
-        == '"AI agents, the digital masterminds at the heart of the 21st-century revolution, are shaping a new era of intelligence and innovation. They are autonomous entities, capable of observing their environment, making decisions, and acting on them, all in pursuit of a specific goal. From streamlining operations in logistics to personalizing customer experiences in retail, AI agents are transforming how businesses operate. But their potential extends far beyond the corporate world. They are the sentinels protecting our digital frontiers, the virtual assistants making our lives easier, and the unseen hands guiding autonomous vehicles. As this technology evolves, AI agents will play an increasingly central role in our world, ushering in an era of unprecedented efficiency, personalization, and productivity. But with great power comes great responsibility, and understanding and harnessing this potential responsibly will be one of our greatest challenges and opportunities in the coming years."'
+        == "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."
     )
 
 
@@ -277,18 +255,14 @@ def test_crew_verbose_levels_output(capsys):
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_cache_hitting_between_agents():
-    from unittest.mock import patch
+    from unittest.mock import call, patch
 
     from langchain.tools import tool
 
     @tool
-    def multiplier(numbers) -> float:
-        """Useful for when you need to multiply two numbers together.
-        The input to this tool should be a comma separated list of numbers of
-        length two, representing the two numbers you want to multiply together.
-        For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
-        a, b = numbers.split(",")
-        return int(a) * int(b)
+    def multiplier(first_number: int, second_number: int) -> float:
+        """Useful for when you need to multiply two numbers together."""
+        return first_number * second_number
 
     tasks = [
         Task(
@@ -308,15 +282,16 @@ def test_cache_hitting_between_agents():
         tasks=tasks,
     )
 
-    assert crew._cache_handler._cache == {}
-    output = crew.kickoff()
-    assert crew._cache_handler._cache == {"multiplier-2,6": "12"}
-    assert output == "12"
-
     with patch.object(CacheHandler, "read") as read:
         read.return_value = "12"
         crew.kickoff()
-        read.assert_called_with("multiplier", "2,6")
+        assert read.call_count == 2, "read was not called exactly twice"
+        # Check if read was called with the expected arguments
+        expected_calls = [
+            call(tool="multiplier", input={"first_number": 2, "second_number": 6}),
+            call(tool="multiplier", input={"first_number": 2, "second_number": 6}),
+        ]
+        read.assert_has_calls(expected_calls, any_order=False)
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -324,9 +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
@@ -338,6 +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(
@@ -356,6 +333,34 @@ def test_api_calls_throttling(capsys):
         moveon.assert_called()
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_full_ouput():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        allow_delegation=False,
+        verbose=True,
+    )
+
+    task1 = Task(
+        description="just say hi!",
+        agent=agent,
+    )
+    task2 = Task(
+        description="just say hello!",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task1, task2], full_output=True)
+
+    result = crew.kickoff()
+    assert result == {
+        "final_output": "Hello!",
+        "tasks_outputs": [task1.output, task2.output],
+    }
+
+
 def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
     agent = Agent(
         role="test role",
@@ -413,11 +418,141 @@ 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()
                 join.assert_called()
+
+
+def test_set_agents_step_callback():
+    from unittest.mock import patch
+
+    researcher_agent = 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.",
+        allow_delegation=False,
+    )
+
+    list_ideas = 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 important events.",
+        agent=researcher_agent,
+        async_execution=True,
+    )
+
+    crew = Crew(
+        agents=[researcher_agent],
+        process=Process.sequential,
+        tasks=[list_ideas],
+        step_callback=lambda: None,
+    )
+
+    with patch.object(Agent, "execute_task") as execute:
+        execute.return_value = "ok"
+        crew.kickoff()
+        assert researcher_agent.step_callback is not None
+
+
+def test_dont_set_agents_step_callback_if_already_set():
+    from unittest.mock import patch
+
+    def agent_callback(_):
+        pass
+
+    def crew_callback(_):
+        pass
+
+    researcher_agent = 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.",
+        allow_delegation=False,
+        step_callback=agent_callback,
+    )
+
+    list_ideas = 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 important events.",
+        agent=researcher_agent,
+        async_execution=True,
+    )
+
+    crew = Crew(
+        agents=[researcher_agent],
+        process=Process.sequential,
+        tasks=[list_ideas],
+        step_callback=crew_callback,
+    )
+
+    with patch.object(Agent, "execute_task") as execute:
+        execute.return_value = "ok"
+        crew.kickoff()
+        assert researcher_agent.step_callback is not crew_callback
+        assert researcher_agent.step_callback is agent_callback
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_function_calling_llm():
+    from unittest.mock import patch
+
+    from langchain.tools import tool
+    from langchain_openai import ChatOpenAI
+
+    llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
+
+    with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:
+
+        @tool
+        def learn_about_AI(topic) -> float:
+            """Useful for when you need to learn about AI to write an paragraph about it."""
+            return "AI is a very broad field."
+
+        agent1 = Agent(
+            role="test role",
+            goal="test goal",
+            backstory="test backstory",
+            llm=ChatOpenAI(model="gpt-4-0125-preview"),
+            tools=[learn_about_AI],
+        )
+
+        essay = Task(
+            description="Write and then review an small paragraph on AI until it's AMAZING",
+            agent=agent1,
+        )
+        tasks = [essay]
+        crew = Crew(agents=[agent1], tasks=tasks, function_calling_llm=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():
@@ -74,7 +77,7 @@ def test_task_prompt_includes_expected_output():
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
         task.execute()
-        execute.assert_called_once_with(task=task._prompt(), context=None, tools=[])
+        execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
 def test_task_callback():
@@ -115,7 +118,7 @@ def test_execute_with_agent():
 
     with patch.object(Agent, "execute_task", return_value="ok") as execute:
         task.execute(agent=researcher)
-        execute.assert_called_once_with(task=task._prompt(), context=None, tools=[])
+        execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
 def test_async_execution():
@@ -135,4 +138,212 @@ 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._prompt(), context=None, tools=[])
+        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}')