feat: change log to inside function

description fixed to markdown language instead of marketing search

docs/how-to/Force-Tool-Ouput-as-Result.md

@@ -0,0 +1,31 @@
+---
+title: Forcing Tool Output as Result
+description: Learn how to force tool output as the result in of an Agent's task in crewAI.
+---
+
+## Introduction
+In CrewAI, you can force the output of a tool as the result of an agent's task. This feature is useful when you want to ensure that the tool output is captured and returned as the task result, and avoid the agent modifying the output during the task execution.
+
+## Forcing Tool Output as Result
+To force the tool output as the result of an agent's task, you can set the `force_tool_output` parameter to `True` when creating the task. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
+
+Here's an example of how to force the tool output as the result of an agent's task:
+
+```python
+# ...
+# Define a custom tool that returns the result as the answer
+coding_agent =Agent(
+        role="Data Scientist",
+        goal="Product amazing resports on AI",
+        backstory="You work with data and AI",
+        tools=[MyCustomTool(result_as_answer=True)],
+    )
+# ...
+```
+
+### Workflow in Action
+
+1. **Task Execution**: The agent executes the task using the tool provided.
+2. **Tool Output**: The tool generates the output, which is captured as the task result.
+3. **Agent Interaction**: The agent my reflect and take learnings from the tool but the output is not modified.
+4. **Result Return**: The tool output is returned as the task result without any modifications.

docs/how-to/Start-a-New-CrewAI-Project.md

@@ -0,0 +1,137 @@
+---
+title: Starting a New CrewAI Project
+description: A comprehensive guide to starting a new CrewAI project, including the latest updates and project setup methods.
+---
+
+# Starting Your CrewAI Project
+
+Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started.
+
+## Prerequisites
+
+We assume you have already installed CrewAI. If not, please refer to the [installation guide](how-to/Installing-CrewAI.md) to install CrewAI and its dependencies.
+
+## Creating a New Project
+
+To create a new project, run the following CLI command:
+
+```shell
+$ crewai create my_project
+```
+
+This command will create a new project folder with the following structure:
+
+```shell
+my_project/
+├── .gitignore
+├── pyproject.toml
+├── README.md
+└── src/
+    └── my_project/
+        ├── __init__.py
+        ├── main.py
+        ├── crew.py
+        ├── tools/
+        │   ├── custom_tool.py
+        │   └── __init__.py
+        └── config/
+            ├── agents.yaml
+            └── tasks.yaml
+```
+
+You can now start developing your project by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of your project, and the `crew.py` file is where you define your agents and tasks.
+
+## Customizing Your Project
+
+To customize your project, you can:
+- Modify `src/my_project/config/agents.yaml` to define your agents.
+- Modify `src/my_project/config/tasks.yaml` to define your tasks.
+- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
+- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
+- Add your environment variables into the `.env` file.
+
+### Example: Defining Agents and Tasks
+
+#### agents.yaml
+
+```yaml
+researcher:
+  role: >
+    Job Candidate Researcher
+  goal: >
+    Find potential candidates for the job
+  backstory: >
+    You are adept at finding the right candidates by exploring various online
+    resources. Your skill in identifying suitable candidates ensures the best
+    match for job positions.
+```
+
+#### tasks.yaml
+
+```yaml
+research_candidates_task:
+  description: >
+    Conduct thorough research to find potential candidates for the specified job.
+    Utilize various online resources and databases to gather a comprehensive list of potential candidates.
+    Ensure that the candidates meet the job requirements provided.
+
+    Job Requirements:
+    {job_requirements}
+  expected_output: >
+    A list of 10 potential candidates with their contact information and brief profiles highlighting their suitability.
+```
+
+## Installing Dependencies
+
+To install the dependencies for your project, you can use Poetry. First, navigate to your project directory:
+
+```shell
+$ cd my_project
+$ poetry lock
+$ poetry install
+```
+
+This will install the dependencies specified in the `pyproject.toml` file.
+
+## Interpolating Variables
+
+Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{variable}` will be replaced by the value of the variable in the `main.py` file.
+
+#### agents.yaml
+
+```yaml
+research_task:
+  description: >
+    Conduct a thorough research about the customer and competitors in the context
+    of {customer_domain}.
+    Make sure you find any interesting and relevant information given the
+    current year is 2024.
+  expected_output: >
+    A complete report on the customer and their customers and competitors,
+    including their demographics, preferences, market positioning and audience engagement.
+```
+
+#### main.py
+
+```python
+# main.py
+def run():
+    inputs = {
+        "customer_domain": "crewai.com"
+    }
+    MyProjectCrew(inputs).crew().kickoff(inputs=inputs)
+```
+
+## Running Your Project
+
+To run your project, use the following command:
+
+```shell
+$ poetry run my_project
+```
+
+This will initialize your crew of AI agents and begin task execution as defined in your configuration in the `main.py` file.
+
+## Deploying Your Project
+
+The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.

docs/index.md

@@ -48,6 +48,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
     <div style="width:30%">
         <h2>How-To Guides</h2>
         <ul>
+            <li>
+                <a href="./how-to/Start-a-New-CrewAI-Project">
+                    Starting Your crewAI Project
+                </a>
+            </li>
             <li>
                 <a href="./how-to/Installing-CrewAI">
                     Installing crewAI
@@ -88,6 +93,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Coding Agents
                 </a>
             </li>
+            <li>
+                <a href="./how-to/Force-Tool-Ouput-as-Result">
+                    Forcing Tool Output as Result
+                </a>
+            </li>
             <li>
                 <a href="./how-to/Human-Input-on-Execution">
                     Human Input on Execution

docs/tools/MDXSearchTool.md

@@ -4,7 +4,7 @@
     The MDXSearchTool is in continuous development. Features may be added or removed, and functionality could change unpredictably as we refine the tool.
 
 ## Description
-The MDX Search Tool is a component of the `crewai_tools` package aimed at facilitating advanced market data extraction. This tool is invaluable for researchers and analysts seeking quick access to market insights, especially within the AI sector. It simplifies the task of acquiring, interpreting, and organizing market data by interfacing with various data sources.
+The MDX Search Tool is a component of the `crewai_tools` package aimed at facilitating advanced markdown language extraction. It enables users to effectively search and extract relevant information from MD files using query-based searches. This tool is invaluable for data analysis, information management, and research tasks, streamlining the process of finding specific information within large document collections.
 
 ## Installation
 Before using the MDX Search Tool, ensure the `crewai_tools` package is installed. If it is not, you can install it with the following command:
@@ -59,4 +59,4 @@ tool = MDXSearchTool(
         ),
     )
 )
-```
+```

mkdocs.yml

@@ -131,6 +131,7 @@ nav:
     - Using LangChain Tools: 'core-concepts/Using-LangChain-Tools.md'
     - Using LlamaIndex Tools: 'core-concepts/Using-LlamaIndex-Tools.md'
   - How to Guides:
+    - Starting Your crewAI Project: 'how-to/Start-a-New-CrewAI-Project.md'
     - Installing CrewAI: 'how-to/Installing-CrewAI.md'
     - Getting Started: 'how-to/Creating-a-Crew-and-kick-it-off.md'
     - Create Custom Tools: 'how-to/Create-Custom-Tools.md'
@@ -140,6 +141,7 @@ nav:
     - Connecting to any LLM: 'how-to/LLM-Connections.md'
     - Customizing Agents: 'how-to/Customizing-Agents.md'
     - Coding Agents: 'how-to/Coding-Agents.md'
+    - Forcing Tool Output as Result: 'how-to/Force-Tool-Ouput-as-Result.md'
     - Human Input on Execution: 'how-to/Human-Input-on-Execution.md'
     - Kickoff a Crew Asynchronously: 'how-to/Kickoff-async.md'
     - Kickoff a Crew for a List: 'how-to/Kickoff-for-each.md'

poetry.lock

@@ -840,13 +840,13 @@ files = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.4.7"
+version = "0.4.8"
 description = "Set of tools for the crewAI framework"
 optional = false
 python-versions = "<=3.13,>=3.10"
 files = [
-    {file = "crewai_tools-0.4.7-py3-none-any.whl", hash = "sha256:3ff04b2da07d2c48e72f898511295b4a10038dd3e4fe859baa93fec1fb8baf8e"},
-    {file = "crewai_tools-0.4.7.tar.gz", hash = "sha256:4502a5e0ab94a7dae6638d000768f80049918909ca5338cdebc280351b3ce003"},
+    {file = "crewai_tools-0.4.8-py3-none-any.whl", hash = "sha256:628b08515ee0e06c751da1dd66b0cff70c9b2644775891c8f59883cb5debfef4"},
+    {file = "crewai_tools-0.4.8.tar.gz", hash = "sha256:ae190bd187f980163523c86ee7e1eb2ed78896f935d6caff98908dd7ab6c982b"},
 ]
 
 [package.dependencies]
@@ -1054,13 +1054,13 @@ idna = ">=2.0.0"
 
 [[package]]
 name = "embedchain"
-version = "0.1.115"
+version = "0.1.116"
 description = "Simplest open source retrieval (RAG) framework"
 optional = false
 python-versions = "<=3.13,>=3.9"
 files = [
-    {file = "embedchain-0.1.115-py3-none-any.whl", hash = "sha256:6f1842edea7780e6b1d21d073b72b2c916f440411c32e758165c88141b2ac1ec"},
-    {file = "embedchain-0.1.115.tar.gz", hash = "sha256:8b259a3307e022924c99cf82b17789f141d157f11e53beb3bc82bf49efa2e31e"},
+    {file = "embedchain-0.1.116-py3-none-any.whl", hash = "sha256:388835d047f9ff4542ebf50e3fa633ef596db262cbe506195ee4976b91a49172"},
+    {file = "embedchain-0.1.116.tar.gz", hash = "sha256:3e4d6418df2e749c2bd3cd3153c3857cbecd7227afe40b87d5ac3df629c394b2"},
 ]
 
 [package.dependencies]
@@ -1075,6 +1075,7 @@ langchain = ">0.2,<=0.3"
 langchain-cohere = ">=0.1.4,<0.2.0"
 langchain-community = ">=0.2.6,<0.3.0"
 langchain-openai = ">=0.1.7,<0.2.0"
+memzero = ">=0.0.7,<0.0.8"
 openai = ">=1.1.1"
 posthog = ">=3.0.2,<4.0.0"
 pypdf = ">=4.0.1,<5.0.0"
@@ -2051,13 +2052,13 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
 
 [[package]]
 name = "identify"
-version = "2.5.36"
+version = "2.6.0"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "identify-2.5.36-py2.py3-none-any.whl", hash = "sha256:37d93f380f4de590500d9dba7db359d0d3da95ffe7f9de1753faa159e71e7dfa"},
-    {file = "identify-2.5.36.tar.gz", hash = "sha256:e5e00f54165f9047fbebeb4a560f9acfb8af4c88232be60a488e9b68d122745d"},
+    {file = "identify-2.6.0-py2.py3-none-any.whl", hash = "sha256:e79ae4406387a9d300332b5fd366d8994f1525e8414984e1a59e058b2eda2dd0"},
+    {file = "identify-2.6.0.tar.gz", hash = "sha256:cb171c685bdc31bcc4c1734698736a7d5b6c8bf2e0c15117f4d469c8640ae5cf"},
 ]
 
 [package.extras]
@@ -2498,13 +2499,13 @@ langchain-core = ">=0.2.10,<0.3.0"
 
 [[package]]
 name = "langsmith"
-version = "0.1.83"
+version = "0.1.84"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.83-py3-none-any.whl", hash = "sha256:f54d8cd8479b648b6339f3f735d19292c3516d080f680933ecdca3eab4b67ed3"},
-    {file = "langsmith-0.1.83.tar.gz", hash = "sha256:5cdd947212c8ad19adb992c06471c860185a777daa6859bb47150f90daf64bf3"},
+    {file = "langsmith-0.1.84-py3-none-any.whl", hash = "sha256:01f3c6390dba26c583bac8dd0e551ce3d0509c7f55cad714db0b5c8d36e4c7ff"},
+    {file = "langsmith-0.1.84.tar.gz", hash = "sha256:5220c0439838b9a5bd320fd3686be505c5083dcee22d2452006c23891153bea1"},
 ]
 
 [package.dependencies]
@@ -2672,6 +2673,22 @@ files = [
     {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
 ]
 
+[[package]]
+name = "memzero"
+version = "0.0.7"
+description = "Long-term memory for AI Agents"
+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
+    {file = "memzero-0.0.7-py3-none-any.whl", hash = "sha256:65f6da88d46263dbc05621fcd01bd09616d0e7f082d55ed9899dc2152491ffd2"},
+    {file = "memzero-0.0.7.tar.gz", hash = "sha256:0c1f413d8ee0ade955fe9f8b8f5aff2cf58bc94869537aca62139db3d9f50725"},
+]
+
+[package.dependencies]
+httpx = ">=0.27.0,<0.28.0"
+posthog = ">=3.5.0,<4.0.0"
+pydantic = ">=2.7.3,<3.0.0"
+
 [[package]]
 name = "mergedeep"
 version = "1.3.4"
@@ -6073,4 +6090,4 @@ tools = ["crewai-tools"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<=3.13"
-content-hash = "4f3e5fddb5f0fc8fd143a8abe947ecac443213d595bd0eeed745ccb82dac2312"
+content-hash = "0dbf6f6e2e841fb3eec4ff87ea5d6b430f29702118fee91307983c6b2581e59e"

pyproject.toml

@@ -21,7 +21,7 @@ opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "1.3.3"
 regex = "^2023.12.25"
-crewai-tools = { version = "^0.4.7", optional = true }
+crewai-tools = { version = "^0.4.8", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
@@ -45,7 +45,7 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.4.7"
+crewai-tools = "^0.4.8"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"

src/crewai/agents/agent_builder/utilities/base_output_converter_base.py

@@ -27,7 +27,7 @@ class OutputConverter(BaseModel, ABC):
     model: Any = Field(description="The model to be used to convert the text.")
     instructions: str = Field(description="Conversion instructions to the LLM.")
     max_attempts: Optional[int] = Field(
-        description="Max number of attempts to try to get the output formated.",
+        description="Max number of attempts to try to get the output formatted.",
         default=3,
     )
 

src/crewai/cli/templates/config/tasks.yaml

@@ -12,4 +12,4 @@ reporting_task:
     Make sure the report is detailed and contains any and all relevant information.
   expected_output: >
     A fully fledge reports with the mains topics, each with a full section of information.
-    Formated as markdown with out '```'
+    Formatted as markdown without '```'

src/crewai/crew.py

@@ -30,6 +30,7 @@ from crewai.tools.agent_tools import AgentTools
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
+from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 try:
@@ -133,6 +134,10 @@ class Crew(BaseModel):
         default=False,
         description="output_log_file",
     )
+    planning: Optional[bool] = Field(
+        default=False,
+        description="Plan the crew execution and add the plan to the crew.",
+    )
 
     @field_validator("id", mode="before")
     @classmethod
@@ -340,6 +345,9 @@ class Crew(BaseModel):
 
             agent.create_agent_executor()
 
+        if self.planning:
+            self._handle_crew_planning()
+
         metrics = []
 
         if self.process == Process.sequential:
@@ -421,6 +429,14 @@ class Crew(BaseModel):
 
         return results
 
+    def _handle_crew_planning(self):
+        """Handles the Crew planning."""
+        self._logger.log("info", "Planning the crew execution")
+        result = CrewPlanner(self.tasks)._handle_crew_planning()
+
+        for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
+            task.description += step_plan
+
     def _run_sequential_process(self) -> Union[str, Dict[str, Any]]:
         """Executes tasks sequentially and returns the final output."""
         task_output = None

src/crewai/task.py

@@ -13,8 +13,7 @@ from pydantic_core import PydanticCustomError
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
-from crewai.utilities.converter import ConverterError
-from crewai.utilities.converter import Converter
+from crewai.utilities.converter import Converter, ConverterError
 from crewai.utilities.i18n import I18N
 from crewai.utilities.printer import Printer
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
@@ -179,15 +178,13 @@ class Task(BaseModel):
         agent: BaseAgent | None = None,
         context: Optional[str] = None,
         tools: Optional[List[Any]] = None,
-    ) -> str | None:
+    ) -> Any:
         """Execute the task.
 
         Returns:
             Output of the task.
         """
 
-        self._execution_span = self._telemetry.task_started(self)
-
         agent = agent or self.agent
         if not agent:
             raise Exception(
@@ -195,6 +192,8 @@ class Task(BaseModel):
                 "and should be executed in a Crew using a specific process that support that, like hierarchical."
             )
 
+        self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self)
+
         if self.context:
             internal_context = []
             for task in self.context:
@@ -292,7 +291,7 @@ class Task(BaseModel):
         copied_data = {k: v for k, v in copied_data.items() if v is not None}
 
         cloned_context = (
-            [task.copy() for task in self.context] if self.context else None
+            [task.copy(agents) for task in self.context] if self.context else None
         )
 
         def get_agent_by_role(role: str) -> Union["BaseAgent", None]:
@@ -310,17 +309,17 @@ class Task(BaseModel):
 
         return copied_task
 
-    def _create_converter(self, *args, **kwargs) -> Converter: # type: ignore
-      converter = self.agent.get_output_converter(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
-        *args, **kwargs
-      )
-      if self.converter_cls:
-        converter = self.converter_cls(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
-          *args, **kwargs
-        )          
-      return converter
+    def _create_converter(self, *args, **kwargs) -> Converter:  # type: ignore
+        converter = self.agent.get_output_converter(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
+            *args, **kwargs
+        )
+        if self.converter_cls:
+            converter = self.converter_cls(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
+                *args, **kwargs
+            )
+        return converter
 
-    def _export_output(self, result: str) -> Any:
+    def _export_output(self, result: str) -> Any:  # TODO: Refactor and fix type hints
         exported_result = result
         instructions = "I'm gonna convert this raw text into valid JSON."
 
@@ -350,7 +349,7 @@ class Task(BaseModel):
                 model_schema = PydanticSchemaParser(model=model).get_schema()  # type: ignore # Argument "model" to "PydanticSchemaParser" has incompatible type "type[BaseModel] | None"; expected "type[BaseModel]"
                 instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
 
-            converter = self._create_converter( # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
+            converter = self._create_converter(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
                 llm=llm, text=result, model=model, instructions=instructions
             )
 

src/crewai/telemetry/telemetry.py

@@ -156,18 +156,35 @@ class Telemetry:
             except Exception:
                 pass
 
-    def task_started(self, task: Task) -> Span | None:
+    def task_started(self, crew: Crew, task: Task) -> Span | None:
         """Records task started in a crew."""
         if self.ready:
             try:
                 tracer = trace.get_tracer("crewai.telemetry")
                 span = tracer.start_span("Task Execution")
 
+                created_span = tracer.start_span("Task Created")
+
+                self._add_attribute(created_span, "task_id", str(task.id))
+
+                if crew.share_crew:
+                    self._add_attribute(
+                        created_span, "formatted_description", task.description
+                    )
+                    self._add_attribute(
+                        created_span, "formatted_expected_output", task.expected_output
+                    )
+
+                created_span.set_status(Status(StatusCode.OK))
+                created_span.end()
+
                 self._add_attribute(span, "task_id", str(task.id))
-                self._add_attribute(span, "formatted_description", task.description)
-                self._add_attribute(
-                    span, "formatted_expected_output", task.expected_output
-                )
+
+                if crew.share_crew:
+                    self._add_attribute(span, "formatted_description", task.description)
+                    self._add_attribute(
+                        span, "formatted_expected_output", task.expected_output
+                    )
 
                 return span
             except Exception:

src/crewai/tools/tool_calling.py

@@ -8,7 +8,7 @@ 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."
+        ..., description="A dictionary of arguments to be passed to the tool."
     )
 
 
@@ -17,5 +17,5 @@ class InstructorToolCalling(PydanticBaseModel):
         ..., 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."
+        ..., description="A dictionary of arguments to be passed to the tool."
     )

src/crewai/tools/tool_usage.py

@@ -119,7 +119,7 @@ class ToolUsage:
                     attempts=self._run_attempts,
                 )
                 result = self._format_result(result=result)  # type: ignore #  "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
-                return result  # type: ignore # Fix the reutrn type of this function
+                return result  # type: ignore # Fix the return type of this function
 
             except Exception:
                 self.task.increment_tools_errors()

src/crewai/translations/en.json

@@ -16,7 +16,7 @@
     "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task\nYour final answer must be the great and the most complete as possible, it must be outcome described\n\n",
     "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
     "expected_output": "\nThis is the expect criteria for your final answer: {expected_output} \n you MUST return the actual complete content as the final answer, not a summary.",
-    "human_feedback": "You got human feedback on your work, re-avaluate it and give a new Final Answer when ready.\n {human_feedback}",
+    "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
     "getting_input": "This is the agent's final answer: {final_answer}\nPlease provide feedback: "
   },
   "errors": {

src/crewai/utilities/planning_handler.py

@@ -0,0 +1,64 @@
+from typing import List
+
+from pydantic import BaseModel
+
+from crewai.agent import Agent
+from crewai.task import Task
+
+
+class PlannerTaskPydanticOutput(BaseModel):
+    list_of_plans_per_task: List[str]
+
+
+class CrewPlanner:
+    def __init__(self, tasks: List[Task]):
+        self.tasks = tasks
+
+    def _handle_crew_planning(self):
+        """Handles the Crew planning by creating detailed step-by-step plans for each task."""
+        planning_agent = self._create_planning_agent()
+        tasks_summary = self._create_tasks_summary()
+
+        planner_task = self._create_planner_task(planning_agent, tasks_summary)
+
+        return planner_task.execute()
+
+    def _create_planning_agent(self) -> Agent:
+        """Creates the planning agent for the crew planning."""
+        return Agent(
+            role="Task Execution Planner",
+            goal=(
+                "Your goal is to create an extremely detailed, step-by-step plan based on the tasks and tools "
+                "available to each agent so that they can perform the tasks in an exemplary manner"
+            ),
+            backstory="Planner agent for crew planning",
+        )
+
+    def _create_planner_task(self, planning_agent: Agent, tasks_summary: str) -> Task:
+        """Creates the planner task using the given agent and tasks summary."""
+        return Task(
+            description=(
+                f"Based on these tasks summary: {tasks_summary} \n Create the most descriptive plan based on the tasks "
+                "descriptions, tools available, and agents' goals for them to execute their goals with perfection."
+            ),
+            expected_output="Step by step plan on how the agents can execute their tasks using the available tools with mastery",
+            agent=planning_agent,
+            output_pydantic=PlannerTaskPydanticOutput,
+        )
+
+    def _create_tasks_summary(self) -> str:
+        """Creates a summary of all tasks."""
+        tasks_summary = []
+        for idx, task in enumerate(self.tasks):
+            tasks_summary.append(
+                f"""
+                Task Number {idx + 1} - {task.description}
+                "task_description": {task.description}
+                "task_expected_output": {task.expected_output}
+                "agent": {task.agent.role if task.agent else "None"}
+                "agent_goal": {task.agent.goal if task.agent else "None"}
+                "task_tools": {task.tools}
+                "agent_tools": {task.agent.tools if task.agent else "None"}
+                """
+            )
+        return " ".join(tasks_summary)