update readme.md

* Update LLM-Connections.md

* Update LLM-Connections.md

---------

Co-authored-by: João Moura <joaomdmoura@gmail.com>

README.md

@@ -8,11 +8,11 @@
 
 <h3>
 
-[Homepage](https://www.crewai.io/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Examples](https://github.com/joaomdmoura/crewai-examples) | [Discord](https://discord.com/invite/X4JWnZnxPb)
+[Homepage](https://www.crewai.com/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Examples](https://github.com/crewAIInc/crewAI-examples) | [Discourse](https://community.crewai.com)
 
 </h3>
 
-[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/joaomdmoura/crewAI)
+[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/crewAIInc/crewAI)
 [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
 
 </div>
@@ -154,12 +154,12 @@ In addition to the sequential process, you can use the hierarchical process, whi
 
 ## Examples
 
-You can test different real life examples of AI crews in the [crewAI-examples repo](https://github.com/joaomdmoura/crewAI-examples?tab=readme-ov-file):
+You can test different real life examples of AI crews in the [crewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
 
-- [Landing Page Generator](https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator)
+- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
 - [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
-- [Trip Planner](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner)
+- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
-- [Stock Analysis](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis)
+- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
 
 ### Quick Tutorial
 
@@ -167,19 +167,19 @@ You can test different real life examples of AI crews in the [crewAI-examples re
 
 ### Write Job Descriptions
 
-[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
 
 [![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
 ### Trip Planner
 
-[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
 
 [![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
 
 ### Stock Analysis
 
-[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
 
 [![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
 
@@ -191,13 +191,12 @@ Please refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-
 
 ## How CrewAI Compares
 
+**CrewAI's Advantage**: CrewAI is built with production in mind. It offers the flexibility of Autogen's conversational agents and the structured process approach of ChatDev, but without the rigidity. CrewAI's processes are designed to be dynamic and adaptable, fitting seamlessly into both development and production workflows.
+
 - **Autogen**: While Autogen does good in creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
 
 - **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
 
-**CrewAI's Advantage**: CrewAI is built with production in mind. It offers the flexibility of Autogen's conversational agents and the structured process approach of ChatDev, but without the rigidity. CrewAI's processes are designed to be dynamic and adaptable, fitting seamlessly into both development and production workflows.
-
-
 ## Contribution
 
 CrewAI is open-source and we welcome contributions. If you're looking to contribute, please:
@@ -285,3 +284,39 @@ Users can opt-in to Further Telemetry, sharing the complete telemetry data by se
 ## License
 
 CrewAI is released under the MIT License.
+
+## Frequently Asked Questions (FAQ)
+
+### Q: What is CrewAI?
+A: CrewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. It enables agents to work together seamlessly, tackling complex tasks through collaborative intelligence.
+
+### Q: How do I install CrewAI?
+A: You can install CrewAI using pip:
+```shell
+pip install crewai
+```
+For additional tools, use:
+```shell
+pip install 'crewai[tools]'
+```
+
+### Q: Can I use CrewAI with local models?
+A: Yes, CrewAI supports various LLMs, including local models. You can configure your agents to use local models via tools like Ollama & LM Studio. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details.
+
+### Q: What are the key features of CrewAI?
+A: Key features include role-based agent design, autonomous inter-agent delegation, flexible task management, process-driven execution, output saving as files, and compatibility with both open-source and proprietary models.
+
+### Q: How does CrewAI compare to other AI orchestration tools?
+A: CrewAI is designed with production in mind, offering flexibility similar to Autogen's conversational agents and structured processes like ChatDev, but with more adaptability for real-world applications.
+
+### Q: Is CrewAI open-source?
+A: Yes, CrewAI is open-source and welcomes contributions from the community.
+
+### Q: Does CrewAI collect any data?
+A: CrewAI uses anonymous telemetry to collect usage data for improvement purposes. No sensitive data (like prompts, task descriptions, or API calls) is collected. Users can opt-in to share more detailed data by setting `share_crew=True` on their Crews.
+
+### Q: Where can I find examples of CrewAI in action?
+A: You can find various real-life examples in the [crewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), including trip planners, stock analysis tools, and more.
+
+### Q: How can I contribute to CrewAI?
+A: Contributions are welcome! You can fork the repository, create a new branch for your feature, add your improvement, and send a pull request. Check the Contribution section in the README for more details.

docs/core-concepts/Pipeline.md

@@ -33,7 +33,7 @@ Each input creates its own run, flowing through all stages of the pipeline. Mult
 ## Pipeline Attributes
 
 | Attribute  | Parameters | Description                                                                                     |
-| :--------- | :--------- | :------------------------------------------------------------------------------------ |
+| :--------- | :--------- | :---------------------------------------------------------------------------------------------- |
 | **Stages** | `stages`   | A list of crews, lists of crews, or routers representing the stages to be executed in sequence. |
 
 ## Creating a Pipeline
@@ -239,7 +239,7 @@ email_router = Router(
             pipeline=normal_pipeline
         )
     },
-    default=Pipeline(stages=[normal_pipeline])  # Default to just classification if no urgency score
+    default=Pipeline(stages=[normal_pipeline])  # Default to just normal if no urgency score
 )
 
 # Use the router in a main pipeline

docs/core-concepts/Tasks.md

@@ -131,6 +131,7 @@ research_agent = Agent(
   verbose=True
 )
 
+# to perform a semantic search for a specified query from a text's content across the internet
 search_tool = SerperDevTool()
 
 task = Task(

docs/getting-started/Start-a-New-CrewAI-Project-Template-Method.md

@@ -17,40 +17,12 @@ Before we start, there are a couple of things to note:
 Before getting started with CrewAI, make sure that you have installed it via pip:
 
 ```shell
-$ pip install crewai crewai-tools
+$ pip install 'crewai[tools]'
 ```
 
-### Virtual Environments
-It is highly recommended that you use virtual environments to ensure that your CrewAI project is isolated from other projects and dependencies. Virtual environments provide a clean, separate workspace for each project, preventing conflicts between different versions of packages and libraries. This isolation is crucial for maintaining consistency and reproducibility in your development process. You have multiple options for setting up virtual environments depending on your operating system and Python version:
-
-1. Use venv (Python's built-in virtual environment tool):
-   venv is included with Python 3.3 and later, making it a convenient choice for many developers. It's lightweight and easy to use, perfect for simple project setups.
-
-   To set up virtual environments with venv, refer to the official [Python documentation](https://docs.python.org/3/tutorial/venv.html).
-
-2. Use Conda (A Python virtual environment manager):
-   Conda is an open-source package manager and environment management system for Python. It's widely used by data scientists, developers, and researchers to manage dependencies and environments in a reproducible way.
-
-   To set up virtual environments with Conda, refer to the official [Conda documentation](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html).
-
-3. Use Poetry (A Python package manager and dependency management tool):
-   Poetry is an open-source Python package manager that simplifies the installation of packages and their dependencies. Poetry offers a convenient way to manage virtual environments and dependencies.
-   Poetry is CrewAI's preferred tool for package / dependency management in CrewAI.
-
-### Code IDEs
-
-Most users of CrewAI use a Code Editor / Integrated Development Environment (IDE) for building their Crews. You can use any code IDE of your choice. See below for some popular options for Code Editors / Integrated Development Environments (IDE):
-
-- [Visual Studio Code](https://code.visualstudio.com/) - Most popular
-- [PyCharm](https://www.jetbrains.com/pycharm/)
-- [Cursor AI](https://cursor.com)
-
-Pick one that suits your style and needs.
-
 ## Creating a New Project
-In this example, we will be using Venv as our virtual environment manager.
+In this example, we will be using poetry as our virtual environment manager.
 
-To set up a virtual environment, run the following CLI command:
 To create a new CrewAI project, run the following CLI command:
 
 ```shell

docs/how-to/LLM-Connections.md

@@ -143,13 +143,12 @@ os.environ["OPENAI_MODEL_NAME"]='mistral-small'
 from langchain_community.chat_models.solar import SolarChat
 ```
 ```sh
-os.environ["SOLAR_API_BASE"]='https://api.upstage.ai/v1/solar'
+os.environ[SOLAR_API_BASE]="https://api.upstage.ai/v1/solar"
-os.environ["SOLAR_API_KEY"]='your-solar-api-key'
+os.environ[SOLAR_API_KEY]="your-solar-api-key"
-```
 
 # Free developer API key available here: https://console.upstage.ai/services/solar
 # Langchain Example: https://github.com/langchain-ai/langchain/pull/18556
-
+```
 
 ### Cohere
 ```python

docs/how-to/Langtrace-Observability.md

@@ -7,10 +7,14 @@ description: How to monitor cost, latency, and performance of CrewAI Agents usin
 
 Langtrace is an open-source, external tool that helps you set up observability and evaluations for Large Language Models (LLMs), LLM frameworks, and Vector Databases. While not built directly into CrewAI, Langtrace can be used alongside CrewAI to gain deep visibility into the cost, latency, and performance of your CrewAI Agents. This integration allows you to log hyperparameters, monitor performance regressions, and establish a process for continuous improvement of your Agents.
 
+![Overview of a select series of agent session runs](..%2Fassets%2Flangtrace1.png)
+![Overview of agent traces](..%2Fassets%2Flangtrace2.png)
+![Overview of llm traces in details](..%2Fassets%2Flangtrace3.png)
+
 ## Setup Instructions
 
 1. Sign up for [Langtrace](https://langtrace.ai/) by visiting [https://langtrace.ai/signup](https://langtrace.ai/signup).
-2. Create a project and generate an API key.
+2. Create a project, set the project type to crewAI & generate an API key.
 3. Install Langtrace in your CrewAI project using the following commands:
 
 ```bash
@@ -32,58 +36,29 @@ langtrace.init(api_key='<LANGTRACE_API_KEY>')
 from crewai import Agent, Task, Crew
 ```
 
-2. Create your CrewAI agents and tasks as usual.
-
-3. Use Langtrace's tracking functions to monitor your CrewAI operations. For example:
-
-```python
-with langtrace.trace("CrewAI Task Execution"):
-    result = crew.kickoff()
-```
-
 ### Features and Their Application to CrewAI
 
 1. **LLM Token and Cost Tracking**
+
    - Monitor the token usage and associated costs for each CrewAI agent interaction.
-   - Example:
-     ```python
-     with langtrace.trace("Agent Interaction"):
-         agent_response = agent.execute(task)
-     ```
 
 2. **Trace Graph for Execution Steps**
+
    - Visualize the execution flow of your CrewAI tasks, including latency and logs.
    - Useful for identifying bottlenecks in your agent workflows.
 
 3. **Dataset Curation with Manual Annotation**
+
    - Create datasets from your CrewAI task outputs for future training or evaluation.
-   - Example:
-     ```python
-     langtrace.log_dataset_item(task_input, agent_output, {"task_type": "research"})
-     ```
 
 4. **Prompt Versioning and Management**
+
    - Keep track of different versions of prompts used in your CrewAI agents.
    - Useful for A/B testing and optimizing agent performance.
 
 5. **Prompt Playground with Model Comparisons**
+
    - Test and compare different prompts and models for your CrewAI agents before deployment.
 
 6. **Testing and Evaluations**
    - Set up automated tests for your CrewAI agents and tasks.
-   - Example:
-     ```python
-     langtrace.evaluate(agent_output, expected_output, "accuracy")
-     ```
-
-## Monitoring New CrewAI Features
-
-CrewAI has introduced several new features that can be monitored using Langtrace:
-
-1. **Code Execution**: Monitor the performance and output of code executed by agents.
-   ```python
-   with langtrace.trace("Agent Code Execution"):
-       code_output = agent.execute_code(code_snippet)
-   ```
-
-2. **Third-party Agent Integration**: Track interactions with LlamaIndex, LangChain, and Autogen agents.

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "agentops"
@@ -390,17 +390,17 @@ lxml = ["lxml"]
 
 [[package]]
 name = "boto3"
-version = "1.35.13"
+version = "1.35.14"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.35.13-py3-none-any.whl", hash = "sha256:6e220eae161a4c0ed21e2561edcb0fd9603fa621692c50bc099db318ed3e3ad4"},
+    {file = "boto3-1.35.14-py3-none-any.whl", hash = "sha256:c3e138e9041d59cd34cdc28a587dfdc899dba02ea26ebc3e10fb4bc88e5cf31b"},
-    {file = "boto3-1.35.13.tar.gz", hash = "sha256:4af17bd7bada591ddaa835d774b242705210e5d45133e25bd73417daa42e53e7"},
+    {file = "boto3-1.35.14.tar.gz", hash = "sha256:7bc78d7140c353b10a637927fe4bc4c4d95a464d1b8f515d5844def2ee52cbd5"},
 ]
 
 [package.dependencies]
-botocore = ">=1.35.13,<1.36.0"
+botocore = ">=1.35.14,<1.36.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -409,13 +409,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.35.13"
+version = "1.35.14"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.35.13-py3-none-any.whl", hash = "sha256:dd8a8bb1946187c8eb902a3b856d3b24df63917e4f2c61a6bce7f3ea9f112761"},
+    {file = "botocore-1.35.14-py3-none-any.whl", hash = "sha256:24823135232f88266b66ae8e1d0f3d40872c14cd976781f7fe52b8f0d79035a0"},
-    {file = "botocore-1.35.13.tar.gz", hash = "sha256:f7ae62eab44d731a5ad8917788378316c79c7bceb530a8307ed0f3bca7037341"},
+    {file = "botocore-1.35.14.tar.gz", hash = "sha256:8515a2fc7ca5bcf0b10016ba05ccf2d642b7cb77d8773026ff2fa5aa3bf38d2e"},
 ]
 
 [package.dependencies]
@@ -428,13 +428,13 @@ crt = ["awscrt (==0.21.2)"]
 
 [[package]]
 name = "build"
-version = "1.2.1"
+version = "1.2.2"
 description = "A simple, correct Python build frontend"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "build-1.2.1-py3-none-any.whl", hash = "sha256:75e10f767a433d9a86e50d83f418e83efc18ede923ee5ff7df93b6cb0306c5d4"},
+    {file = "build-1.2.2-py3-none-any.whl", hash = "sha256:277ccc71619d98afdd841a0e96ac9fe1593b823af481d3b0cea748e8894e0613"},
-    {file = "build-1.2.1.tar.gz", hash = "sha256:526263f4870c26f26c433545579475377b2b7588b6f1eac76a001e873ae3e19d"},
+    {file = "build-1.2.2.tar.gz", hash = "sha256:119b2fb462adef986483438377a13b2f42064a2a3a4161f24a0cca698a07ac8c"},
 ]
 
 [package.dependencies]
@@ -849,13 +849,13 @@ cron = ["capturer (>=2.4)"]
 
 [[package]]
 name = "crewai-tools"
-version = "0.8.3"
+version = "0.12.0"
 description = "Set of tools for the crewAI framework"
 optional = false
 python-versions = "<=3.13,>=3.10"
 files = [
-    {file = "crewai_tools-0.8.3-py3-none-any.whl", hash = "sha256:a54a10c36b8403250e13d6594bd37db7e7deb3f9fabc77e8720c081864ae6189"},
+    {file = "crewai_tools-0.12.0-py3-none-any.whl", hash = "sha256:cd1fce27960a1dee99f63f3ae276797feed1b09443d6fdeb62c557855188cc50"},
-    {file = "crewai_tools-0.8.3.tar.gz", hash = "sha256:f0317ea1d926221b22fcf4b816d71916fe870aa66ed7ee2a0067dba42b5634eb"},
+    {file = "crewai_tools-0.12.0.tar.gz", hash = "sha256:1f327d4cf436d6f34cf440c0067fac7a67d5256dc279fe8d829219eddbf79ee1"},
 ]
 
 [package.dependencies]
@@ -2570,13 +2570,13 @@ langchain-core = ">=0.2.38,<0.3.0"
 
 [[package]]
 name = "langsmith"
-version = "0.1.115"
+version = "0.1.116"
 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-0.1.115-py3-none-any.whl", hash = "sha256:04e35cfd4c2d4ff1ea10bb577ff43957b05ebb3d9eb4e06e200701f4a2b4ac9f"},
+    {file = "langsmith-0.1.116-py3-none-any.whl", hash = "sha256:4b5ea64c81ba5ca309695c85dc3fb4617429a985129ed5d9eca00d1c9d6483f4"},
-    {file = "langsmith-0.1.115.tar.gz", hash = "sha256:3b775377d858d32354f3ee0dd1ed637068cfe9a1f13e7b3bfa82db1615cdffc9"},
+    {file = "langsmith-0.1.116.tar.gz", hash = "sha256:5ccd7f5c1840f7c507ab3ee56334a1391de28c8bf72669782e2d82cafeefffa7"},
 ]
 
 [package.dependencies]
@@ -4072,19 +4072,6 @@ files = [
     {file = "pyarrow-17.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:392bc9feabc647338e6c89267635e111d71edad5fcffba204425a7c8d13610d7"},
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-    {file = "pyarrow-17.0.0.tar.gz", hash = "sha256:4beca9521ed2c0921c1023e68d097d0299b62c362639ea315572a58f3f50fd28"},
 ]
 
 [package.dependencies]
@@ -5475,13 +5462,13 @@ typing-extensions = ">=3.7.4.3"
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20240905"
+version = "2.32.0.20240907"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
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+    {file = "yarl-1.10.0-py3-none-any.whl", hash = "sha256:99eaa7d53f509ba1c2fea8fdfec15ba3cd36caca31d57ec6665073b148b5f260"},
-    {file = "yarl-1.9.11.tar.gz", hash = "sha256:c7548a90cb72b67652e2cd6ae80e2683ee08fde663104528ac7df12d8ef271d2"},
+    {file = "yarl-1.10.0.tar.gz", hash = "sha256:3bf10a395adac62177ba8ea738617e8de6cbb1cea6aa5d5dd2accde704fc8195"},
 ]
 
 [package.dependencies]
@@ -6133,4 +6120,4 @@ tools = ["crewai-tools"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<=3.13"
-content-hash = "269990492b05eaecacda4b7164c98b8b5cf545e589faff8c9485a5297b8a745a"
+content-hash = "0675e966c403260634207bd30679662291cceac370137563681fa02a2538947b"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.54.0"
+version = "0.55.2"
 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"
@@ -8,8 +8,8 @@ packages = [{ include = "crewai", from = "src" }]
 
 [tool.poetry.urls]
 Homepage = "https://crewai.com"
-Documentation = "https://github.com/joaomdmoura/CrewAI/wiki/Index"
+Documentation = "https://docs.crewai.com"
-Repository = "https://github.com/joaomdmoura/crewai"
+Repository = "https://github.com/crewAIInc/crewAI"
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
@@ -21,7 +21,7 @@ opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "1.3.3"
 regex = "^2024.7.24"
-crewai-tools = { version = "^0.8.3", optional = true }
+crewai-tools = { version = "^0.12.0", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
@@ -47,7 +47,7 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.8.3"
+crewai-tools = "^0.12.0"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"

src/crewai/__init__.py

@@ -2,6 +2,7 @@ from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.pipeline import Pipeline
 from crewai.process import Process
+from crewai.routers import Router
 from crewai.task import Task
 
-__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline"]
+__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router"]

src/crewai/cli/authentication/constants.py

@@ -1,4 +1,4 @@
 ALGORITHMS = ["RS256"]
-AUTH0_DOMAIN = "dev-jzsr0j8zs0atl5ha.us.auth0.com"
+AUTH0_DOMAIN = "crewai.us.auth0.com"
-AUTH0_CLIENT_ID = "CZtyRHuVW80HbLSjk4ggXNzjg4KAt7Oe"
+AUTH0_CLIENT_ID = "DEVC5Fw6NlRoSzmDCcOhVq85EfLBjKa8"
-AUTH0_AUDIENCE = "https://dev-jzsr0j8zs0atl5ha.us.auth0.com/api/v2/"
+AUTH0_AUDIENCE = "https://crewai.us.auth0.com/api/v2/"

src/crewai/cli/cli.py

@@ -203,10 +203,11 @@ def deploy():
 
 
 @deploy.command(name="create")
-def deploy_create():
+@click.option("-y", "--yes", is_flag=True, help="Skip the confirmation prompt")
+def deploy_create(yes: bool):
     """Create a Crew deployment."""
     deploy_cmd = DeployCommand()
-    deploy_cmd.create_crew()
+    deploy_cmd.create_crew(yes)
 
 
 @deploy.command(name="list")

src/crewai/cli/deploy/api.py

@@ -18,7 +18,7 @@ class CrewAPI:
             "User-Agent": f"CrewAI-CLI/{get_crewai_version()}",
         }
         self.base_url = getenv(
-            "CREWAI_BASE_URL", "https://dev.crewai.com/crewai_plus/api/v1/crews"
+            "CREWAI_BASE_URL", "https://crewai.com/crewai_plus/api/v1/crews"
         )
 
     def _make_request(self, method: str, endpoint: str, **kwargs) -> requests.Response:

src/crewai/cli/deploy/main.py

@@ -28,9 +28,7 @@ class DeployCommand:
             self._telemetry.set_tracer()
             access_token = get_auth_token()
         except Exception:
-            self._deploy_signup_error_span = self._telemetry.deploy_signup_error_span(
+            self._deploy_signup_error_span = self._telemetry.deploy_signup_error_span()
-                self
-            )
             console.print(
                 "Please sign up/login to CrewAI+ before using the CLI.",
                 style="bold red",
@@ -40,7 +38,10 @@ class DeployCommand:
 
         self.project_name = get_project_name()
         if self.project_name is None:
-            console.print("No project name found. Please ensure your project has a valid pyproject.toml file.", style="bold red")
+            console.print(
+                "No project name found. Please ensure your project has a valid pyproject.toml file.",
+                style="bold red",
+            )
             raise SystemExit
 
         self.client = CrewAPI(api_key=access_token)
@@ -100,7 +101,7 @@ class DeployCommand:
         Args:
             uuid (Optional[str]): The UUID of the crew to deploy.
         """
-        self._start_deployment_span = self._telemetry.start_deployment_span(self, uuid)
+        self._start_deployment_span = self._telemetry.start_deployment_span(uuid)
         console.print("Starting deployment...", style="bold blue")
         if uuid:
             response = self.client.deploy_by_uuid(uuid)
@@ -116,12 +117,12 @@ class DeployCommand:
         else:
             self._handle_error(json_response)
 
-    def create_crew(self) -> None:
+    def create_crew(self, confirm: bool) -> None:
         """
         Create a new crew deployment.
         """
-        self._create_crew_deployment_span = self._telemetry.create_crew_deployment_span(
+        self._create_crew_deployment_span = (
-            self
+            self._telemetry.create_crew_deployment_span()
         )
         console.print("Creating deployment...", style="bold blue")
         env_vars = fetch_and_json_env_file()
@@ -129,10 +130,13 @@ class DeployCommand:
 
         if remote_repo_url is None:
             console.print("No remote repository URL found.", style="bold red")
-            console.print("Please ensure your project has a valid remote repository.", style="yellow")
+            console.print(
+                "Please ensure your project has a valid remote repository.",
+                style="yellow",
+            )
             return
 
-        self._confirm_input(env_vars, remote_repo_url)
+        self._confirm_input(env_vars, remote_repo_url, confirm)
         payload = self._create_payload(env_vars, remote_repo_url)
 
         response = self.client.create_crew(payload)
@@ -141,14 +145,18 @@ class DeployCommand:
         else:
             self._handle_error(response.json())
 
-    def _confirm_input(self, env_vars: Dict[str, str], remote_repo_url: str) -> None:
+    def _confirm_input(
+        self, env_vars: Dict[str, str], remote_repo_url: str, confirm: bool
+    ) -> None:
         """
         Confirm input parameters with the user.
 
         Args:
             env_vars (Dict[str, str]): Environment variables.
             remote_repo_url (str): Remote repository URL.
+            confirm (bool): Whether to confirm input.
         """
+        if not confirm:
             input(f"Press Enter to continue with the following Env vars: {env_vars}")
             input(
                 f"Press Enter to continue with the following remote repository: {remote_repo_url}\n"
@@ -266,9 +274,7 @@ class DeployCommand:
             uuid (Optional[str]): The UUID of the crew to get logs for.
             log_type (str): The type of logs to retrieve (default: "deployment").
         """
-        self._get_crew_logs_span = self._telemetry.get_crew_logs_span(
+        self._get_crew_logs_span = self._telemetry.get_crew_logs_span(uuid, log_type)
-            self, uuid, log_type
-        )
         console.print(f"Fetching {log_type} logs...", style="bold blue")
 
         if uuid:
@@ -291,7 +297,7 @@ class DeployCommand:
         Args:
             uuid (Optional[str]): The UUID of the crew to remove.
         """
-        self._remove_crew_span = self._telemetry.remove_crew_span(self, uuid)
+        self._remove_crew_span = self._telemetry.remove_crew_span(uuid)
         console.print("Removing deployment...", style="bold blue")
 
         if uuid:

src/crewai/cli/templates/crew/pyproject.toml

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.54.0,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
 
 
 [tool.poetry.scripts]

src/crewai/cli/templates/pipeline/pyproject.toml

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.54.0,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

src/crewai/cli/templates/pipeline_router/pyproject.toml

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.54.0,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.55.2,<1.0.0" }
 
 
 [tool.poetry.scripts]

src/crewai/crew.py

@@ -584,7 +584,10 @@ class Crew(BaseModel):
             self.manager_agent.allow_delegation = True
             manager = self.manager_agent
             if manager.tools is not None and len(manager.tools) > 0:
-                raise Exception("Manager agent should not have tools")
+                self._logger.log(
+                    "warning", "Manager agent should not have tools", color="orange"
+                )
+                manager.tools = []
             manager.tools = self.manager_agent.get_delegation_tools(self.agents)
         else:
             manager = Agent(

src/crewai/project/annotations.py

@@ -103,7 +103,8 @@ def crew(func):
         for task_name in sorted_task_names:
             task_instance = tasks[task_name]()
             instantiated_tasks.append(task_instance)
-            if hasattr(task_instance, "agent"):
+            agent_instance = getattr(task_instance, "agent", None)
+            if agent_instance is not None:
                 agent_instance = task_instance.agent
                 if agent_instance.role not in agent_roles:
                     instantiated_agents.append(agent_instance)

src/crewai/task.py

@@ -6,7 +6,7 @@ import uuid
 from concurrent.futures import Future
 from copy import copy
 from hashlib import md5
-from typing import Any, Dict, List, Optional, Tuple, Type, Union
+from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union
 
 from opentelemetry.trace import Span
 from pydantic import (
@@ -108,6 +108,7 @@ class Task(BaseModel):
         description="A converter class used to export structured output",
         default=None,
     )
+    processed_by_agents: Set[str] = Field(default_factory=set)
 
     _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry)
     _execution_span: Optional[Span] = PrivateAttr(default=None)
@@ -241,6 +242,8 @@ class Task(BaseModel):
         self.prompt_context = context
         tools = tools or self.tools or []
 
+        self.processed_by_agents.add(agent.role)
+
         result = agent.execute_task(
             task=self,
             context=context,
@@ -273,9 +276,7 @@ class Task(BaseModel):
             content = (
                 json_output
                 if json_output
-                else pydantic_output.model_dump_json()
+                else pydantic_output.model_dump_json() if pydantic_output else result
-                if pydantic_output
-                else result
             )
             self._save_file(content)
 
@@ -310,8 +311,10 @@ class Task(BaseModel):
         """Increment the tools errors counter."""
         self.tools_errors += 1
 
-    def increment_delegations(self) -> None:
+    def increment_delegations(self, agent_name: Optional[str]) -> None:
         """Increment the delegations counter."""
+        if agent_name:
+            self.processed_by_agents.add(agent_name)
         self.delegations += 1
 
     def copy(self, agents: List["BaseAgent"]) -> "Task":

src/crewai/tools/tool_calling.py

@@ -1,8 +1,8 @@
 from typing import Any, Dict, Optional
 
+from pydantic import BaseModel, Field
 from pydantic import BaseModel as PydanticBaseModel
 from pydantic import Field as PydanticField
-from pydantic.v1 import BaseModel, Field
 
 
 class ToolCalling(BaseModel):

src/crewai/tools/tool_output_parser.py

@@ -5,7 +5,7 @@ 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 ValidationError
 
 
 class ToolOutputParser(PydanticOutputParser):

src/crewai/tools/tool_usage.py

@@ -8,6 +8,7 @@ from langchain_core.tools import BaseTool
 from langchain_openai import ChatOpenAI
 
 from crewai.agents.tools_handler import ToolsHandler
+from crewai.task import Task
 from crewai.telemetry import Telemetry
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 from crewai.utilities import I18N, Converter, ConverterError, Printer
@@ -51,7 +52,7 @@ class ToolUsage:
         original_tools: List[Any],
         tools_description: str,
         tools_names: str,
-        task: Any,
+        task: Task,
         function_calling_llm: Any,
         agent: Any,
         action: Any,
@@ -154,7 +155,10 @@ class ToolUsage:
                     "Delegate work to coworker",
                     "Ask question to coworker",
                 ]:
-                    self.task.increment_delegations()
+                    coworker = (
+                        calling.arguments.get("coworker") if calling.arguments else None
+                    )
+                    self.task.increment_delegations(coworker)
 
                 if calling.arguments:
                     try:
@@ -241,7 +245,7 @@ class ToolUsage:
             result = self._remember_format(result=result)  # type: ignore # "_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
         return result
 
-    def _should_remember_format(self) -> None:
+    def _should_remember_format(self) -> bool:
         return self.task.used_tools % self._remember_format_after_usages == 0
 
     def _remember_format(self, result: str) -> None:
@@ -353,10 +357,10 @@ class ToolUsage:
                     return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
                         f'{self._i18n.errors("tool_arguments_error")}'
                     )
-                calling = ToolCalling(  # type: ignore # Unexpected keyword argument "log" for "ToolCalling"
+                calling = ToolCalling(
                     tool_name=tool.name,
                     arguments=arguments,
-                    log=tool_string,
+                    log=tool_string,  # type: ignore
                 )
         except Exception as e:
             self._run_attempts += 1
@@ -404,19 +408,19 @@ class ToolUsage:
                         '"' + value.replace('"', '\\"') + '"'
                     )  # Re-encapsulate with double quotes
                 elif value.isdigit():  # Check if value is a digit, hence integer
-                    formatted_value = value
+                    value = value
                 elif value.lower() in [
                     "true",
                     "false",
                     "null",
                 ]:  # Check for boolean and null values
-                    formatted_value = value.lower()
+                    value = value.lower()
                 else:
                     # Assume the value is a string and needs quotes
-                    formatted_value = '"' + value.replace('"', '\\"') + '"'
+                    value = '"' + value.replace('"', '\\"') + '"'
 
                 # Rebuild the entry with proper quoting
-                formatted_entry = f'"{key}": {formatted_value}'
+                formatted_entry = f'"{key}": {value}'
                 formatted_entries.append(formatted_entry)
 
             # Reconstruct the JSON string

src/crewai/utilities/config.py

@@ -23,17 +23,16 @@ def process_config(
     # Copy values from config (originally from YAML) to the model's attributes.
     # Only copy if the attribute isn't already set, preserving any explicitly defined values.
     for key, value in config.items():
-        if key not in model_class.model_fields:
+        if key not in model_class.model_fields or values.get(key) is not None:
             continue
-        if values.get(key) is not None:
+
-            continue
+        if isinstance(value, dict):
-        if isinstance(value, (str, int, float, bool, list)):
-            values[key] = value
-        elif isinstance(value, dict):
             if isinstance(values.get(key), dict):
                 values[key].update(value)
             else:
                 values[key] = value
+        else:
+            values[key] = value
 
     # Remove the config from values to avoid duplicate processing
     values.pop("config", None)

src/crewai/utilities/crew_pydantic_output_parser.py

@@ -5,8 +5,7 @@ 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, ValidationError
-from pydantic import BaseModel
 
 
 class CrewPydanticOutputParser(PydanticOutputParser):

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -1,14 +1,14 @@
 from collections import defaultdict
 
-from langchain_openai import ChatOpenAI
-from pydantic import BaseModel, Field
-from rich.console import Console
-from rich.table import Table
-
 from crewai.agent import Agent
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
+from rich.box import HEAVY_EDGE
+from rich.console import Console
+from rich.table import Table
 
 
 class TaskEvaluationPydanticOutput(BaseModel):
@@ -77,50 +77,72 @@ class CrewEvaluator:
     def print_crew_evaluation_result(self) -> None:
         """
         Prints the evaluation result of the crew in a table.
-        A Crew with 2 tasks using the command crewai test -n 2
+        A Crew with 2 tasks using the command crewai test -n 3
         will output the following table:
 
-                        Task Scores
+                        Tasks Scores
                     (1-10 Higher is better)
-            ┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
+        ┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
-            ┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃
+        ┃ Tasks/Crew/Agents  ┃ Run 1 ┃ Run 2 ┃ Run 3 ┃ Avg. Total ┃ Agents                       ┃
-            ┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
+        ┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
-            │ Task 1     │ 10.0  │ 9.0   │ 9.5        │
+        │ Task 1             │ 9.0   │ 10.0  │ 9.0   │ 9.3        │ - AI LLMs Senior Researcher  │
-            │ Task 2     │ 9.0   │ 9.0   │ 9.0        │
+        │                    │       │       │       │            │ - AI LLMs Reporting Analyst  │
-            │ Crew       │ 9.5   │ 9.0   │ 9.2        │
+        │                    │       │       │       │            │                              │
-            └────────────┴───────┴───────┴────────────┘
+        │ Task 2             │ 9.0   │ 9.0   │ 9.0   │ 9.0        │ - AI LLMs Senior Researcher  │
+        │                    │       │       │       │            │ - AI LLMs Reporting Analyst  │
+        │                    │       │       │       │            │                              │
+        │ Crew               │ 9.0   │ 9.5   │ 9.0   │ 9.2        │                              │
+        │ Execution Time (s) │ 42    │ 79    │ 52    │ 57         │                              │
+        └────────────────────┴───────┴───────┴───────┴────────────┴──────────────────────────────┘
         """
         task_averages = [
             sum(scores) / len(scores) for scores in zip(*self.tasks_scores.values())
         ]
         crew_average = sum(task_averages) / len(task_averages)
 
-        # Create a table
+        table = Table(title="Tasks Scores \n (1-10 Higher is better)", box=HEAVY_EDGE)
-        table = Table(title="Tasks Scores \n (1-10 Higher is better)")
 
-        # Add columns for the table
+        table.add_column("Tasks/Crew/Agents", style="cyan")
-        table.add_column("Tasks/Crew")
         for run in range(1, len(self.tasks_scores) + 1):
-            table.add_column(f"Run {run}")
+            table.add_column(f"Run {run}", justify="center")
-        table.add_column("Avg. Total")
+        table.add_column("Avg. Total", justify="center")
+        table.add_column("Agents", style="green")
 
-        # Add rows for each task
+        for task_index, task in enumerate(self.crew.tasks):
-        for task_index in range(len(task_averages)):
             task_scores = [
                 self.tasks_scores[run][task_index]
                 for run in range(1, len(self.tasks_scores) + 1)
             ]
             avg_score = task_averages[task_index]
+            agents = list(task.processed_by_agents)
+
+            # Add the task row with the first agent
             table.add_row(
-                f"Task {task_index + 1}", *map(str, task_scores), f"{avg_score:.1f}"
+                f"Task {task_index + 1}",
+                *[f"{score:.1f}" for score in task_scores],
+                f"{avg_score:.1f}",
+                f"- {agents[0]}" if agents else "",
             )
 
-        # Add a row for the crew average
+            # Add rows for additional agents
+            for agent in agents[1:]:
+                table.add_row("", "", "", "", "", f"- {agent}")
+
+            # Add a blank separator row if it's not the last task
+            if task_index < len(self.crew.tasks) - 1:
+                table.add_row("", "", "", "", "", "")
+
+        # Add Crew and Execution Time rows
         crew_scores = [
             sum(self.tasks_scores[run]) / len(self.tasks_scores[run])
             for run in range(1, len(self.tasks_scores) + 1)
         ]
-        table.add_row("Crew", *map(str, crew_scores), f"{crew_average:.1f}")
+        table.add_row(
+            "Crew",
+            *[f"{score:.2f}" for score in crew_scores],
+            f"{crew_average:.1f}",
+            "",
+        )
 
         run_exec_times = [
             int(sum(tasks_exec_times))
@@ -128,11 +150,9 @@ class CrewEvaluator:
         ]
         execution_time_avg = int(sum(run_exec_times) / len(run_exec_times))
         table.add_row(
-            "Execution Time (s)",
+            "Execution Time (s)", *map(str, run_exec_times), f"{execution_time_avg}", ""
-            *map(str, run_exec_times),
-            f"{execution_time_avg}",
         )
-        # Display the table in the terminal
+
         console = Console()
         console.print(table)
 

tests/utilities/evaluators/test_crew_evaluator_handler.py

@@ -1,7 +1,6 @@
 from unittest import mock
 
 import pytest
-
 from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.task import Task
@@ -80,6 +79,7 @@ class TestCrewEvaluator:
     @mock.patch("crewai.utilities.evaluators.crew_evaluator_handler.Console")
     @mock.patch("crewai.utilities.evaluators.crew_evaluator_handler.Table")
     def test_print_crew_evaluation_result(self, table, console, crew_planner):
+        # Set up task scores and execution times
         crew_planner.tasks_scores = {
             1: [10, 9, 8],
             2: [9, 8, 7],
@@ -89,22 +89,45 @@ class TestCrewEvaluator:
             2: [55, 33, 67],
         }
 
+        # Mock agents and assign them to tasks
+        crew_planner.crew.agents = [
+            mock.Mock(role="Agent 1"),
+            mock.Mock(role="Agent 2"),
+        ]
+        crew_planner.crew.tasks = [
+            mock.Mock(
+                agent=crew_planner.crew.agents[0], processed_by_agents=["Agent 1"]
+            ),
+            mock.Mock(
+                agent=crew_planner.crew.agents[1], processed_by_agents=["Agent 2"]
+            ),
+        ]
+
+        # Run the method
         crew_planner.print_crew_evaluation_result()
 
+        # Verify that the table is created with the appropriate structure and rows
         table.assert_has_calls(
             [
-                mock.call(title="Tasks Scores \n (1-10 Higher is better)"),
+                mock.call(
-                mock.call().add_column("Tasks/Crew"),
+                    title="Tasks Scores \n (1-10 Higher is better)", box=mock.ANY
-                mock.call().add_column("Run 1"),
+                ),  # Title and styling
-                mock.call().add_column("Run 2"),
+                mock.call().add_column("Tasks/Crew/Agents", style="cyan"),  # Columns
-                mock.call().add_column("Avg. Total"),
+                mock.call().add_column("Run 1", justify="center"),
-                mock.call().add_row("Task 1", "10", "9", "9.5"),
+                mock.call().add_column("Run 2", justify="center"),
-                mock.call().add_row("Task 2", "9", "8", "8.5"),
+                mock.call().add_column("Avg. Total", justify="center"),
-                mock.call().add_row("Task 3", "8", "7", "7.5"),
+                mock.call().add_column("Agents", style="green"),
-                mock.call().add_row("Crew", "9.0", "8.0", "8.5"),
+                # Verify rows for tasks with agents
-                mock.call().add_row("Execution Time (s)", "135", "155", "145"),
+                mock.call().add_row("Task 1", "10.0", "9.0", "9.5", "- Agent 1"),
+                mock.call().add_row("", "", "", "", "", ""),  # Blank row between tasks
+                mock.call().add_row("Task 2", "9.0", "8.0", "8.5", "- Agent 2"),
+                # Add crew averages and execution times
+                mock.call().add_row("Crew", "9.00", "8.00", "8.5", ""),
+                mock.call().add_row("Execution Time (s)", "135", "155", "145", ""),
             ]
         )
+
+        # Ensure the console prints the table
         console.assert_has_calls([mock.call(), mock.call().print(table())])
 
     def test_evaluate(self, crew_planner):