preparing verison 0.5.5

README.md

@@ -30,6 +30,7 @@
 - [How CrewAI Compares](#how-crewai-compares)
 - [Contribution](#contribution)
 - [Hire CrewAI](#hire-crewai)
+- [Telemetry](#telemetry)
 - [License](#license)
 
 ## Why CrewAI?
@@ -62,7 +63,7 @@ from crewai import Agent, Task, Crew, Process
 os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
 
 # You can choose to use a local model through Ollama for example. See ./docs/how-to/llm-connections.md for more information.
-# from langchain.llms import Ollama
+# from langchain_community.llms import Ollama
 # ollama_llm = Ollama(model="openhermes")
 
 # Install duckduckgo-search for this example:
@@ -86,10 +87,12 @@ researcher = Agent(
   # model like OpenAI, Mistral, Antrophic or others (https://python.langchain.com/docs/integrations/llms/)
   #
   # Examples:
+  #
+  # from langchain_community.llms import Ollama
   # llm=ollama_llm # was defined above in the file
-  # llm=OpenAI(model_name="gpt-3.5", temperature=0.7)
-  # For the OpenAI model you would need to import
-  # from langchain_openai import OpenAI
+  #
+  # from langchain_openai import ChatOpenAI
+  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
 )
 writer = Agent(
   role='Tech Content Strategist',
@@ -241,6 +244,24 @@ pip install dist/*.tar.gz
 We're a company developing crewAI and crewAI Enterprise, we for a limited time are offer consulting with selected customers, to get them early access to our enterprise solution
 If you are interested on having access to it and hiring weekly hours with our team, feel free to email us at [joao@crewai.com](mailto:joao@crewai.com).
 
+## Telemetry
+
+CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
+
+There is NO data being collected on the prompts, tasks descriptions agents backstories or goals nor tools usage, no API calls, nor responses nor any data that is being processed by the agents, nor any secrets and env vars.
+
+Data collected includes:
+- Version of crewAI
+- Version of Python
+- General OS (e.g. number of CPUs, macOS/Windows/Linux)
+- Number of agents and tasks in a crew
+- Crew Process being used
+- If Agents are using memory or allowing delegation
+- If Tasks are being executed in parallel or sequentially
+- Language model being used
+- Roles of agents in a crew
+- Tools names available
+
 ## License
 
 CrewAI is released under the MIT License.

docs/core-concepts/Crews.md

@@ -0,0 +1,75 @@
+---
+title: crewAI Crews
+description: Understanding and utilizing crews in the crewAI framework.
+---
+
+## What is a Crew?
+!!! note "Definition of a Crew"
+    A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
+
+## Crew Attributes
+
+| Attribute            | Description                                                  |
+| :------------------- | :----------------------------------------------------------- |
+| **Tasks**            | A list of tasks assigned to the crew.                        |
+| **Agents**           | A list of agents that are part of the crew.                  |
+| **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. |
+| **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.                   |
+
+!!! 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.
+
+## Creating a Crew
+
+!!! note "Crew Composition"
+    When assembling a crew, you combine agents with complementary roles and tools, assign tasks, and select a process that dictates their execution order and interaction.
+
+### Example: Assembling a Crew
+
+```python
+from crewai import Crew, Agent, Task, Process
+from langchain_community.tools import DuckDuckGoSearchRun
+
+# Define agents with specific roles and tools
+researcher = Agent(
+    role='Senior Research Analyst',
+    goal='Discover innovative AI technologies',
+    tools=[DuckDuckGoSearchRun()]
+)
+
+writer = Agent(
+    role='Content Writer',
+    goal='Write engaging articles on AI discoveries'
+)
+
+# Create tasks for the agents
+research_task = Task(description='Identify breakthrough AI technologies', agent=researcher)
+write_article_task = Task(description='Draft an article on the latest AI technologies', agent=writer)
+
+# Assemble the crew with a sequential process
+my_crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, write_article_task],
+    process=Process.sequential,
+    verbose=True
+)
+```
+
+## Crew Execution Process
+
+- **Sequential Process**: Tasks are executed one after another, allowing for a linear flow of work.
+- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding.
+
+### Kicking Off a Crew
+
+Once your crew is assembled, initiate the workflow with the `kickoff()` method. This starts the execution process according to the defined process flow.
+
+```python
+# Start the crew's task execution
+result = my_crew.kickoff()
+print(result)
+```

docs/getting-started.md

@@ -1,95 +0,0 @@
-# Getting Started
-
-To get started with CrewAI, follow these simple steps:
-
-1. **Installation**:
-
-```shell
-pip install crewai
-```
-
-The example below also uses duckduckgo, so also install that
-```shell
-pip install duckduckgo-search
-```
-
-2. **Setting Up Your Crew**:
-
-```python
-import os
-from crewai import Agent, Task, Crew, Process
-
-os.environ["OPENAI_API_KEY"] = "YOUR KEY"
-
-# You can choose to use a local model through Ollama for example. See ./docs/llm-connections.md for more information.
-# from langchain.llms import Ollama
-# ollama_llm = Ollama(model="openhermes")
-
-# Install duckduckgo-search for this example:
-# !pip install -U duckduckgo-search
-
-from langchain_community.tools import DuckDuckGoSearchRun
-search_tool = DuckDuckGoSearchRun()
-
-# Define your agents with roles and goals
-researcher = Agent(
-  role='Senior Research Analyst',
-  goal='Uncover cutting-edge developments in AI and data science',
-  backstory="""You work at a leading tech think tank.
-  Your expertise lies in identifying emerging trends.
-  You have a knack for dissecting complex data and presenting
-  actionable insights.""",
-  verbose=True,
-  allow_delegation=False,
-  tools=[search_tool]
-  # You can pass an optional llm attribute specifying what mode you wanna use.
-  # It can be a local model through Ollama / LM Studio or a remote
-  # model like OpenAI, Mistral, Antrophic of others (https://python.langchain.com/docs/integrations/llms/)
-  #
-  # Examples:
-  # llm=ollama_llm # was defined above in the file
-  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
-)
-writer = Agent(
-  role='Tech Content Strategist',
-  goal='Craft compelling content on tech advancements',
-  backstory="""You are a renowned Content Strategist, known for
-  your insightful and engaging articles.
-  You transform complex concepts into compelling narratives.""",
-  verbose=True,
-  allow_delegation=True,
-  # (optional) llm=ollama_llm
-)
-
-# Create tasks for your agents
-task1 = Task(
-  description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-  Identify key trends, breakthrough technologies, and potential industry impacts.
-  Your final answer MUST be a full analysis report""",
-  agent=researcher
-)
-
-task2 = Task(
-  description="""Using the insights provided, develop an engaging blog
-  post that highlights the most significant AI advancements.
-  Your post should be informative yet accessible, catering to a tech-savvy audience.
-  Make it sound cool, avoid complex words so it doesn't sound like AI.
-  Your final answer MUST be the full blog post of at least 4 paragraphs.""",
-  agent=writer
-)
-
-# Instantiate your crew with a sequential process
-crew = Crew(
-  agents=[researcher, writer],
-  tasks=[task1, task2],
-  verbose=2, # You can set it to 1 or 2 to different logging levels
-)
-
-# Get your crew to work!
-result = crew.kickoff()
-
-print("######################")
-print(result)
-```
-
-Currently the only supported process is `Process.sequential`, where one task is executed after the other and the outcome of one is passed as extra content into this next.

docs/how-to/Creating-a-Crew-and-kick-it-off.md

@@ -4,7 +4,15 @@ description: A step-by-step guide to creating a cohesive CrewAI team for your pr
 ---
 
 ## Introduction
-Assembling a crew in CrewAI is akin to casting for a play, where each agent plays a unique role. This guide walks you through creating a crew, assigning roles and tasks, and activating them to work in harmony.
+Embarking on your CrewAI journey involves a few straightforward steps to set up your environment and initiate your AI crew. This guide ensures a seamless start.
+
+## Step 0: Installation
+Begin by installing CrewAI and any additional packages required for your project. For instance, the `duckduckgo-search` package is used in this example for enhanced search capabilities.
+
+```shell
+pip install crewai
+pip install duckduckgo-search
+```
 
 ## Step 1: Assemble Your Agents
 Begin by defining your agents with distinct roles and backstories. These elements not only add depth but also guide their task execution and interaction within the crew.

docs/how-to/Hierarchical.md

@@ -23,7 +23,11 @@ To utilize the hierarchical process, you must define a crew with a designated ma
 !!! note "Tools on the hierarchical process"
 	For tools when using the hierarchical process, you want to make sure to assign them to the agents instead of the tasks, as the manager will be the one delegating the tasks and the agents will be the ones executing them.
 
+!!! note "Manager LLM"
+	A manager will be automatically set for the crew, you don't need to define it. You do need to set the `manager_llm` parameter in the crew though.
+
 ```python
+from langchain_openai import ChatOpenAI
 from crewai import Crew, Process, Agent
 
 # Define your agents, no need to define a manager
@@ -42,6 +46,7 @@ writer = Agent(
 project_crew = Crew(
 	tasks=[...], # Tasks that that manager will figure out how to complete
 	agents=[researcher, writer],
+	manager_llm=ChatOpenAI(temperature=0, model="gpt-4"), # The manager's LLM that will be used internally
 	process=Process.hierarchical  # Designating the hierarchical approach
 )
 ```

docs/how-to/LLM-Connections.md

@@ -20,7 +20,7 @@ Ollama is preferred for local LLM integration, offering customization and privac
 Instantiate Ollama and pass it to your agents within CrewAI, enhancing them with the local model's capabilities.
 
 ```python
-from langchain.llms import Ollama
+from langchain_community.llms import Ollama
 
 # Assuming you have Ollama installed and downloaded the openhermes model
 ollama_openhermes = Ollama(model="openhermes")

docs/index.md

@@ -28,11 +28,31 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Processes
                 </a>
             </li>
+            <li>
+                <a href="./core-concepts/Crews">
+                    Crews
+                </a>
+            </li>
         </ul>
     </div>
     <div style="width:30%">
         <h2>How-To Guides</h2>
         <ul>
+            <li>
+                <a href="./how-to/Creating-a-Crew-and-kick-it-off">
+                    Getting Started
+                </a>
+            </li>
+            <li>
+                <a href="./how-to/how-to/Sequential">
+                    Using Sequential Process
+                </a>
+            </li>
+            <li>
+                <a href="./how-to/Hierarchical">
+                    Using Hierarchical Process
+                </a>
+            </li>
             <li>
                 <a href="./how-to/LLM-Connections">
                     Connecting to LLMs
@@ -43,11 +63,6 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Customizing Agents
                 </a>
             </li>
-            <li>
-                <a href="./how-to/Creating-a-Crew-and-kick-it-off">
-                    Creating a Crew and kick it off
-                </a>
-            </li>
             <li>
                 <a href="./how-to/Human-Input-on-Execution">
                     Human Input on Execution
@@ -58,6 +73,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
     <div style="width:30%">
         <h2>Examples</h2>
         <ul>
+            <li>
+                <a target='_blank' href="https://github.com/joaomdmoura/crewAI-examples/tree/main/prep-for-a-meeting">
+                    Prepare for meetings
+                </a>
+            </li>
             <li>
                 <a target='_blank' href="https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner">
                     Trip Planner Crew

docs/telemetry/Telemetry.md

@@ -0,0 +1,17 @@
+## Telemetry
+
+CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
+
+There is NO data being collected on the prompts, tasks descriptions agents backstories or goals nor tools usage, no API calls, nor responses nor any data that is being processed by the agents, nor any secrets and env vars.
+
+Data collected includes:
+- Version of crewAI
+- Version of Python
+- General OS (e.g. number of CPUs, macOS/Windows/Linux)
+- Number of agents and tasks in a crew
+- Crew Process being used
+- If Agents are using memory or allowing delegation
+- If Tasks are being executed in parallel or sequentially
+- Language model being used
+- Roles of agents in a crew
+- Tools names available

mkdocs.yml

@@ -124,9 +124,10 @@ nav:
     - Tasks: 'core-concepts/Tasks.md'
     - Tools: 'core-concepts/Tools.md'
     - Processes: 'core-concepts/Processes.md'
+    - Crews: 'core-concepts/Crews.md'
     - Collaboration: 'core-concepts/Collaboration.md'
   - How to Guides:
-    - Creating a Crew Automation: 'how-to/Creating-a-Crew-and-kick-it-off.md'
+    - Getting Started: 'how-to/Creating-a-Crew-and-kick-it-off.md'
     - Using Sequential Process: 'how-to/Sequential.md'
     - Using Hierarchical Process: 'how-to/Hierarchical.md'
     - Connecting to any LLM: 'how-to/LLM-Connections.md'
@@ -139,6 +140,9 @@ nav:
     - Game Generator: https://github.com/joaomdmoura/crewAI-examples/tree/main/game-builder-crew"
     - Drafting emails with LangGraph: https://github.com/joaomdmoura/crewAI-examples/tree/main/CrewAI-LangGraph"
     - Landing Page Generator: https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator"
+    - Prepare for meetings: https://github.com/joaomdmoura/crewAI-examples/tree/main/prep-for-a-meeting"
+  - Telemetry: 'telemetry/Telemetry.md'
+
 extra_css:
   - stylesheets/output.css
   - stylesheets/extra.css

poetry.lock

@@ -202,6 +202,17 @@ files = [
 [package.extras]
 dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
+[[package]]
+name = "backoff"
+version = "2.2.1"
+description = "Function decoration for backoff and retry"
+optional = false
+python-versions = ">=3.7,<4.0"
+files = [
+    {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"},
+    {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"},
+]
+
 [[package]]
 name = "black"
 version = "24.1.1"
@@ -528,6 +539,23 @@ files = [
     {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
 ]
 
+[[package]]
+name = "deprecated"
+version = "1.2.14"
+description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "Deprecated-1.2.14-py2.py3-none-any.whl", hash = "sha256:6fac8b097794a90302bdbb17b9b815e732d3c4720583ff1b198499d78470466c"},
+    {file = "Deprecated-1.2.14.tar.gz", hash = "sha256:e5323eb936458dccc2582dc6f9c322c852a775a27065ff2b0c4970b9d53d01b3"},
+]
+
+[package.dependencies]
+wrapt = ">=1.10,<2"
+
+[package.extras]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
+
 [[package]]
 name = "distlib"
 version = "0.3.8"
@@ -683,6 +711,23 @@ python-dateutil = ">=2.8.1"
 [package.extras]
 dev = ["flake8", "markdown", "twine", "wheel"]
 
+[[package]]
+name = "googleapis-common-protos"
+version = "1.62.0"
+description = "Common protobufs used in Google APIs"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "googleapis-common-protos-1.62.0.tar.gz", hash = "sha256:83f0ece9f94e5672cced82f592d2a5edf527a96ed1794f0bab36d5735c996277"},
+    {file = "googleapis_common_protos-1.62.0-py2.py3-none-any.whl", hash = "sha256:4750113612205514f9f6aa4cb00d523a94f3e8c06c5ad2fee466387dc4875f07"},
+]
+
+[package.dependencies]
+protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0"
+
+[package.extras]
+grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
+
 [[package]]
 name = "greenlet"
 version = "3.0.3"
@@ -768,6 +813,72 @@ files = [
 [package.dependencies]
 colorama = ">=0.4"
 
+[[package]]
+name = "grpcio"
+version = "1.60.1"
+description = "HTTP/2-based RPC framework"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "grpcio-1.60.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:14e8f2c84c0832773fb3958240c69def72357bc11392571f87b2d7b91e0bb092"},
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+    {file = "pydantic_core-2.16.2.tar.gz", hash = "sha256:0ba503850d8b8dcc18391f10de896ae51d37fe5fe43dbfb6a35c5c5cad271a06"},
 ]
 
 [package.dependencies]
@@ -2387,23 +2631,6 @@ brotli = ["brotli (==1.0.9)", "brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotl
 secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
 socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
-[[package]]
-name = "urllib3"
-version = "2.2.0"
-description = "HTTP library with thread-safe connection pooling, file post, and more."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
-    {file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
-]
-
-[package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
-h2 = ["h2 (>=4,<5)"]
-socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
-zstd = ["zstandard (>=0.18.0)"]
-
 [[package]]
 name = "vcrpy"
 version = "6.0.1"
@@ -2416,7 +2643,7 @@ files = [
 
 [package.dependencies]
 PyYAML = "*"
-urllib3 = {version = "<2", markers = "platform_python_implementation == \"PyPy\" or python_version < \"3.10\""}
+urllib3 = {version = "<2", markers = "platform_python_implementation == \"PyPy\""}
 wrapt = "*"
 yarl = "*"
 
@@ -2692,5 +2919,5 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.9,<4.0"
-content-hash = "4445741acbc0829e52a688edfc6e0811b25e51517ce45512e187bd42eea18521"
+python-versions = ">=3.10,<4.0"
+content-hash = "2be3f98f57af7ea47f0985000be807e67c1bb3a95598b637e50f2cec54d11c80"

pyproject.toml

@@ -1,7 +1,7 @@
 
 [tool.poetry]
 name = "crewai"
-version = "0.5.0"
+version = "0.5.5"
 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"
@@ -16,11 +16,14 @@ Documentation = "https://github.com/joaomdmoura/CrewAI/wiki/Index"
 Repository = "https://github.com/joaomdmoura/crewai"
 
 [tool.poetry.dependencies]
-python = ">=3.9,<4.0"
+python = ">=3.10,<4.0"
 pydantic = "^2.4.2"
 langchain = "0.1.0"
 openai = "^1.7.1"
 langchain-openai = "^0.0.2"
+opentelemetry-api = "^1.22.0"
+opentelemetry-sdk = "^1.22.0"
+opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 
 [tool.poetry.group.dev.dependencies]
 isort = "^5.13.2"

src/crewai/agents/executor.py

@@ -33,7 +33,7 @@ class CrewAgentExecutor(AgentExecutor):
 
     def _force_answer(self, output: AgentAction):
         return AgentStep(
-            action=output, observation=self.i18n.errors("used_too_many_tools")
+            action=output, observation=self.i18n.errors("force_final_answer")
         )
 
     def _call(
@@ -106,7 +106,7 @@ class CrewAgentExecutor(AgentExecutor):
                 **inputs,
             )
             if self._should_force_answer():
-                if isinstance(output, AgentAction):
+                if isinstance(output, AgentAction) or isinstance(output, AgentFinish):
                     output = output
                 elif isinstance(output, CacheHit):
                     output = output.action

src/crewai/crew.py

@@ -19,6 +19,7 @@ from crewai.agent import Agent
 from crewai.agents.cache import CacheHandler
 from crewai.process import Process
 from crewai.task import Task
+from crewai.telemtry import Telemetry
 from crewai.tools.agent_tools import AgentTools
 from crewai.utilities import I18N, Logger, RPMController
 
@@ -30,6 +31,7 @@ class Crew(BaseModel):
     Attributes:
         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.
         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.
@@ -47,6 +49,9 @@ class Crew(BaseModel):
     agents: List[Agent] = Field(default_factory=list)
     process: Process = Field(default=Process.sequential)
     verbose: Union[int, bool] = Field(default=0)
+    manager_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)
     max_rpm: Optional[int] = Field(
@@ -88,6 +93,19 @@ class Crew(BaseModel):
         self._cache_handler = CacheHandler()
         self._logger = Logger(self.verbose)
         self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
+        self._telemetry = Telemetry()
+        self._telemetry.crew_creation(self)
+        return self
+
+    @model_validator(mode="after")
+    def check_manager_llm(self):
+        """Validates that the language model is set when using hierarchical process."""
+        if self.process == Process.hierarchical and not self.manager_llm:
+            raise PydanticCustomError(
+                "missing_manager_llm",
+                "Attribute `manager_llm` is required when using hierarchical process.",
+                {},
+            )
         return self
 
     @model_validator(mode="after")
@@ -106,6 +124,7 @@ class Crew(BaseModel):
         if self.agents:
             for agent in self.agents:
                 agent.set_cache_handler(self._cache_handler)
+                if self.max_rpm:
                     agent.set_rpm_controller(self._rpm_controller)
         return self
 
@@ -185,6 +204,7 @@ class Crew(BaseModel):
             goal=i18n.retrieve("hierarchical_manager_agent", "goal"),
             backstory=i18n.retrieve("hierarchical_manager_agent", "backstory"),
             tools=AgentTools(agents=self.agents).tools(),
+            llm=self.manager_llm,
             verbose=True,
         )
 

src/crewai/telemtry/__init__.py

@@ -0,0 +1 @@
+from .telemetry import Telemetry

src/crewai/telemtry/telemetry.py

@@ -0,0 +1,114 @@
+import json
+import os
+import platform
+import socket
+
+import pkg_resources
+from opentelemetry import trace
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
+from opentelemetry.sdk.resources import SERVICE_NAME, Resource
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+from opentelemetry.trace import Status, StatusCode
+
+
+class Telemetry:
+    """A class to handle anonymous telemetry for the crewai package.
+
+    The data being collected is for development purpose, all data is anonymous.
+
+    There is NO data being collected on the prompts, tasks descriptions
+    agents backstories or goals nor responses or any data that is being
+    processed by the agents, nor any secrets and env vars.
+
+    Data collected includes:
+    - Version of crewAI
+    - Version of Python
+    - General OS (e.g. number of CPUs, macOS/Windows/Linux)
+    - Number of agents and tasks in a crew
+    - Crew Process being used
+    - If Agents are using memory or allowing delegation
+    - If Tasks are being executed in parallel or sequentially
+    - Language model being used
+    - Roles of agents in a crew
+    - Tools names available
+    """
+
+    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."""
+        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, "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.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()
+        except Exception:
+            pass
+
+    def add_attribute(self, span, key, value):
+        """Add an attribute to a span."""
+        try:
+            return span.set_attribute(key, value)
+        except Exception:
+            pass
+
+    def _safe_llm_attributes(self, llm):
+        attributes = ["name", "model_name", "base_url", "model", "top_k", "temperature"]
+        safe_attributes = {k: v for k, v in vars(llm).items() if k in attributes}
+        safe_attributes["class"] = llm.__class__.__name__
+        return safe_attributes

src/crewai/translations/el.json

@@ -9,12 +9,12 @@
     "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```\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]```",
     "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": {
-    "used_too_many_tools": "Έχω χρησιμοποιήσει πάρα πολλά εργαλεία για αυτήν την εργασία. Θα σας δώσω την απόλυτη ΚΑΛΥΤΕΡΗ τελική μου απάντηση τώρα και δεν θα χρησιμοποιήσω άλλα εργαλεία.",
+    "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"

src/crewai/translations/en.json

@@ -9,12 +9,12 @@
     "task": "Begin! This is VERY important to you, your job depends on it!\n\nCurrent Task: {input}",
     "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": "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]```",
     "task_with_context": "{task}\nThis is the context you're working with:\n{context}",
     "expected_output": "Your final answer must be: {expected_output}"
   },
   "errors": {
-    "used_too_many_tools": "I've used too many tools for this task. I'm going to give you my absolute BEST Final answer now and not use any more tools.",
+    "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"

src/crewai/utilities/rpm_controller.py

@@ -14,10 +14,12 @@ class RPMController(BaseModel):
     _current_rpm: int = PrivateAttr(default=0)
     _timer: threading.Timer | None = PrivateAttr(default=None)
     _lock: threading.Lock = PrivateAttr(default=None)
+    _shutdown_flag = False
 
     @model_validator(mode="after")
     def reset_counter(self):
         if self.max_rpm:
+            if not self._shutdown_flag:
                 self._lock = threading.Lock()
                 self._reset_request_count()
         return self
@@ -45,13 +47,13 @@ class RPMController(BaseModel):
 
     def _wait_for_next_minute(self):
         time.sleep(60)
-        with self._lock:
         self._current_rpm = 0
 
     def _reset_request_count(self):
         with self._lock:
             self._current_rpm = 0
         if self._timer:
+            self._shutdown_flag = True
             self._timer.cancel()
         self._timer = threading.Timer(60.0, self._reset_request_count)
         self._timer.start()

tests/agent_test.py

@@ -4,7 +4,7 @@ from unittest.mock import patch
 
 import pytest
 from langchain.tools import tool
-from langchain_openai import ChatOpenAI as OpenAI
+from langchain_openai import ChatOpenAI
 
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
@@ -24,7 +24,7 @@ def test_agent_creation():
 def test_agent_default_values():
     agent = Agent(role="test role", goal="test goal", backstory="test backstory")
 
-    assert isinstance(agent.llm, OpenAI)
+    assert isinstance(agent.llm, ChatOpenAI)
     assert agent.llm.model_name == "gpt-4"
     assert agent.llm.temperature == 0.7
     assert agent.llm.verbose == False
@@ -36,10 +36,10 @@ def test_custom_llm():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=OpenAI(temperature=0, model="gpt-4"),
+        llm=ChatOpenAI(temperature=0, model="gpt-4"),
     )
 
-    assert isinstance(agent.llm, OpenAI)
+    assert isinstance(agent.llm, ChatOpenAI)
     assert agent.llm.model_name == "gpt-4"
     assert agent.llm.temperature == 0
 
@@ -51,7 +51,7 @@ def test_agent_without_memory():
         goal="test goal",
         backstory="test backstory",
         memory=False,
-        llm=OpenAI(temperature=0, model="gpt-4"),
+        llm=ChatOpenAI(temperature=0, model="gpt-4"),
     )
 
     memory_agent = Agent(
@@ -59,7 +59,7 @@ def test_agent_without_memory():
         goal="test goal",
         backstory="test backstory",
         memory=True,
-        llm=OpenAI(temperature=0, model="gpt-4"),
+        llm=ChatOpenAI(temperature=0, model="gpt-4"),
     )
 
     result = no_memory_agent.execute_task("How much is 1 + 1?")

tests/crew_test.py

@@ -2,6 +2,7 @@
 
 import json
 
+import pydantic_core
 import pytest
 
 from crewai.agent import Agent
@@ -144,6 +145,8 @@ def test_crew_creation():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_hierarchical_process():
+    from langchain_openai import ChatOpenAI
+
     task = Task(
         description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
     )
@@ -151,6 +154,7 @@ def test_hierarchical_process():
     crew = Crew(
         agents=[researcher, writer],
         process=Process.hierarchical,
+        manager_llm=ChatOpenAI(temperature=0, model="gpt-4"),
         tasks=[task],
     )
 
@@ -175,6 +179,19 @@ def test_hierarchical_process():
     )
 
 
+def test_manager_llm_requirement_for_hierarchical_process():
+    task = Task(
+        description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
+    )
+
+    with pytest.raises(pydantic_core._pydantic_core.ValidationError):
+        Crew(
+            agents=[researcher, writer],
+            process=Process.hierarchical,
+            tasks=[task],
+        )
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_with_delegating_agents():
     tasks = [
@@ -339,6 +356,25 @@ def test_api_calls_throttling(capsys):
         moveon.assert_called()
 
 
+def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        allow_delegation=False,
+        verbose=True,
+    )
+
+    task = Task(
+        description="just say hi!",
+        agent=agent,
+    )
+
+    Crew(agents=[agent], tasks=[task], verbose=2)
+
+    assert agent._rpm_controller is None
+
+
 def test_async_task_execution():
     import threading
     from unittest.mock import patch