added docs

* Update llms.mdx (Gemini 2.0)

- Add Gemini 2.0 flash to Gemini table.
- Add link to 2 hosting paths for Gemini in Tip.
- Change to lower case model slugs vs names, user convenience.
- Add https://artificialanalysis.ai/ as alternate leaderboard.
- Move Gemma to "other" tab.

* Update llm.py (gemini 2.0)

Add setting for Gemini 2.0 context window to llm.py

---------

Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>

.github/workflows/linter.yml

@@ -13,4 +13,4 @@ jobs:
           pip install ruff
 
       - name: Run Ruff Linter
-        run: ruff check --exclude "templates","__init__.py"
+        run: ruff check

.github/workflows/stale.yml

@@ -1,5 +1,10 @@
 name: Mark stale issues and pull requests
 
+permissions:
+  contents: write
+  issues: write
+  pull-requests: write
+
 on:
   schedule:
     - cron: '10 12 * * *'
@@ -8,9 +13,6 @@ on:
 jobs:
   stale:
     runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: write
     steps:
     - uses: actions/stale@v9
       with:

.pre-commit-config.yaml

@@ -1,9 +1,7 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.4
+    rev: v0.8.2
     hooks:
       - id: ruff
         args: ["--fix"]
-        exclude: "templates"
       - id: ruff-format
-        exclude: "templates"

.ruff.toml

@@ -0,0 +1,9 @@
+exclude = [
+    "templates",
+    "__init__.py",
+]
+
+[lint]
+select = [
+    "I", # isort rules
+]

README.md

@@ -44,7 +44,7 @@ To get started with CrewAI, follow these simple steps:
 
 ### 1. Installation
 
-Ensure you have Python >=3.10 <=3.12 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <3.13 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, install CrewAI:
 

docs/concepts/crews.mdx

@@ -32,7 +32,6 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
 | **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
-| **Manager Callbacks** _(optional)_    | `manager_callbacks`    | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used.                                                                                                                            |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |

docs/concepts/knowledge.mdx

@@ -79,6 +79,55 @@ crew = Crew(
 result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
 ```
 
+
+Here's another example with the `CrewDoclingSource`
+```python Code
+from crewai import LLM, Agent, Crew, Process, Task
+from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
+
+# Create a knowledge source
+content_source = CrewDoclingSource(
+    file_paths=[
+        "https://lilianweng.github.io/posts/2024-11-28-reward-hacking",
+        "https://lilianweng.github.io/posts/2024-07-07-hallucination",
+    ],
+)
+
+# Create an LLM with a temperature of 0 to ensure deterministic outputs
+llm = LLM(model="gpt-4o-mini", temperature=0)
+
+# Create an agent with the knowledge store
+agent = Agent(
+    role="About papers",
+    goal="You know everything about the papers.",
+    backstory="""You are a master at understanding papers and their content.""",
+    verbose=True,
+    allow_delegation=False,
+    llm=llm,
+)
+task = Task(
+    description="Answer the following questions about the papers: {question}",
+    expected_output="An answer to the question.",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    verbose=True,
+    process=Process.sequential,
+    knowledge_sources=[
+        content_source
+    ],  # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
+)
+
+result = crew.kickoff(
+    inputs={
+        "question": "What is the reward hacking paper about? Be sure to provide sources."
+    }
+)
+```
+
 ## Knowledge Configuration
 
 ### Chunking Configuration

docs/concepts/llms.mdx

@@ -29,7 +29,7 @@ Large Language Models (LLMs) are the core intelligence behind CrewAI agents. The
 
 ## Available Models and Their Capabilities
 
-Here's a detailed breakdown of supported models and their capabilities:
+Here's a detailed breakdown of supported models and their capabilities, you can compare performance at [lmarena.ai](https://lmarena.ai/?leaderboard) and [artificialanalysis.ai](https://artificialanalysis.ai/):
 
 <Tabs>
   <Tab title="OpenAI">
@@ -43,13 +43,104 @@ Here's a detailed breakdown of supported models and their capabilities:
       1 token ≈ 4 characters in English. For example, 8,192 tokens ≈ 32,768 characters or about 6,000 words.
     </Note>
   </Tab>
+  <Tab title="Nvidia NIM">
+    | Model | Context Window | Best For |
+    |-------|---------------|-----------|
+    | nvidia/mistral-nemo-minitron-8b-8k-instruct | 8,192 tokens | State-of-the-art small language model delivering superior accuracy for chatbot, virtual assistants, and content generation. |
+    | nvidia/nemotron-4-mini-hindi-4b-instruct| 4,096 tokens | A bilingual Hindi-English SLM for on-device inference, tailored specifically for Hindi Language. |
+    | "nvidia/llama-3.1-nemotron-70b-instruct | 128k tokens | Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA in order to improve the helpfulness of LLM generated responses. |
+    | nvidia/llama3-chatqa-1.5-8b | 128k tokens | Advanced LLM to generate high-quality, context-aware responses for chatbots and search engines. |
+    | nvidia/llama3-chatqa-1.5-70b | 128k tokens | Advanced LLM to generate high-quality, context-aware responses for chatbots and search engines. |
+    | nvidia/vila | 128k tokens | Multi-modal vision-language model that understands text/img/video and creates informative responses |
+    | nvidia/neva-22| 4,096 tokens | Multi-modal vision-language model that understands text/images and generates informative responses |
+    | nvidia/nemotron-mini-4b-instruct | 8,192 tokens | General-purpose tasks |
+    | nvidia/usdcode-llama3-70b-instruct | 128k tokens | State-of-the-art LLM that answers OpenUSD knowledge queries and generates USD-Python code. |
+    | nvidia/nemotron-4-340b-instruct | 4,096 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
+    | meta/codellama-70b | 100k tokens | LLM capable of generating code from natural language and vice versa. |
+    | meta/llama2-70b | 4,096 tokens | Cutting-edge large language AI model capable of generating text and code in response to prompts. |
+    | meta/llama3-8b-instruct | 8,192 tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
+    | meta/llama3-70b-instruct | 8,192 tokens | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
+    | meta/llama-3.1-8b-instruct | 128k tokens | Advanced state-of-the-art model with language understanding, superior reasoning, and text generation. |
+    | meta/llama-3.1-70b-instruct | 128k tokens | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
+    | meta/llama-3.1-405b-instruct | 128k tokens | Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks. |
+    | meta/llama-3.2-1b-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
+    | meta/llama-3.2-3b-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
+    | meta/llama-3.2-11b-vision-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
+    | meta/llama-3.2-90b-vision-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
+    | meta/llama-3.1-70b-instruct | 128k tokens | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
+    | google/gemma-7b | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
+    | google/gemma-2b | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
+    | google/codegemma-7b | 8,192 tokens | Cutting-edge model built on Google's Gemma-7B specialized for code generation and code completion. |
+    | google/codegemma-1.1-7b | 8,192 tokens | Advanced programming model for code generation, completion, reasoning, and instruction following. |
+    | google/recurrentgemma-2b | 8,192 tokens | Novel recurrent architecture based language model for faster inference when generating long sequences. |
+    | google/gemma-2-9b-it | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
+    | google/gemma-2-27b-it | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
+    | google/gemma-2-2b-it | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
+    | google/deplot | 512 tokens | One-shot visual language understanding model that translates images of plots into tables. |
+    | google/paligemma | 8,192 tokens | Vision language model adept at comprehending text and visual inputs to produce informative responses. |
+    | mistralai/mistral-7b-instruct-v0.2 | 32k tokens | This LLM follows instructions, completes requests, and generates creative text. |
+    | mistralai/mixtral-8x7b-instruct-v0.1 | 8,192 tokens | An MOE LLM that follows instructions, completes requests, and generates creative text. |
+    | mistralai/mistral-large | 4,096 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
+    | mistralai/mixtral-8x22b-instruct-v0.1 | 8,192 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
+    | mistralai/mistral-7b-instruct-v0.3 | 32k tokens | This LLM follows instructions, completes requests, and generates creative text. |
+    | nv-mistralai/mistral-nemo-12b-instruct | 128k tokens | Most advanced language model for reasoning, code, multilingual tasks; runs on a single GPU. |
+    | mistralai/mamba-codestral-7b-v0.1 | 256k tokens | Model for writing and interacting with code across a wide range of programming languages and tasks. |
+    | microsoft/phi-3-mini-128k-instruct | 128K tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3-mini-4k-instruct | 4,096 tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3-small-8k-instruct | 8,192 tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3-small-128k-instruct | 128K tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3-medium-4k-instruct | 4,096 tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3-medium-128k-instruct | 128K tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3.5-mini-instruct | 128K tokens | Lightweight multilingual LLM powering AI applications in latency bound, memory/compute constrained environments |
+    | microsoft/phi-3.5-moe-instruct | 128K tokens | Advanced LLM based on Mixture of Experts architecure to deliver compute efficient content generation |
+    | microsoft/kosmos-2 | 1,024 tokens | Groundbreaking multimodal model designed to understand and reason about visual elements in images. |
+    | microsoft/phi-3-vision-128k-instruct | 128k tokens | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
+    | microsoft/phi-3.5-vision-instruct | 128k tokens | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
+    | databricks/dbrx-instruct | 12k tokens | A general-purpose LLM with state-of-the-art performance in language understanding, coding, and RAG. |
+    | snowflake/arctic | 1,024 tokens | Delivers high efficiency inference for enterprise applications focused on SQL generation and coding. |
+    | aisingapore/sea-lion-7b-instruct | 4,096 tokens | LLM to represent and serve the linguistic and cultural diversity of Southeast Asia |
+    | ibm/granite-8b-code-instruct | 4,096 tokens | Software programming LLM for code generation, completion, explanation, and multi-turn conversion. |
+    | ibm/granite-34b-code-instruct | 8,192 tokens | Software programming LLM for code generation, completion, explanation, and multi-turn conversion. |
+    | ibm/granite-3.0-8b-instruct | 4,096 tokens | Advanced Small Language Model supporting RAG, summarization, classification, code, and agentic AI |
+    | ibm/granite-3.0-3b-a800m-instruct | 4,096 tokens | Highly efficient Mixture of Experts model for RAG, summarization, entity extraction, and classification |
+    | mediatek/breeze-7b-instruct | 4,096 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
+    | upstage/solar-10.7b-instruct | 4,096 tokens | Excels in NLP tasks, particularly in instruction-following, reasoning, and mathematics. |
+    | writer/palmyra-med-70b-32k | 32k tokens | Leading LLM for accurate, contextually relevant responses in the medical domain. |
+    | writer/palmyra-med-70b | 32k tokens | Leading LLM for accurate, contextually relevant responses in the medical domain. |
+    | writer/palmyra-fin-70b-32k | 32k tokens | Specialized LLM for financial analysis, reporting, and data processing |
+    | 01-ai/yi-large | 32k tokens | Powerful model trained on English and Chinese for diverse tasks including chatbot and creative writing. |
+    | deepseek-ai/deepseek-coder-6.7b-instruct | 2k tokens | Powerful coding model offering advanced capabilities in code generation, completion, and infilling |
+    | rakuten/rakutenai-7b-instruct | 1,024 tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
+    | rakuten/rakutenai-7b-chat | 1,024 tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
+    | baichuan-inc/baichuan2-13b-chat | 4,096 tokens | Support Chinese and English chat, coding, math, instruction following, solving quizzes |
+
+    <Note>
+      NVIDIA's NIM support for models is expanding continuously! For the most up-to-date list of available models, please visit build.nvidia.com.
+    </Note>
+  </Tab>
+  <Tab title="Gemini">
+    | Model | Context Window | Best For |
+    |-------|---------------|-----------|
+    | gemini-2.0-flash-exp | 1M tokens | Higher quality at faster speed, multimodal model, good for most tasks |
+    | gemini-1.5-flash | 1M tokens | Balanced multimodal model, good for most tasks |
+    | gemini-1.5-flash-8B | 1M tokens | Fastest, most cost-efficient, good for high-frequency tasks |
+    | gemini-1.5-pro | 2M tokens | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
+
+    <Tip>
+      Google's Gemini models are all multimodal, supporting audio, images, video and text, supporting context caching, json schema, function calling, etc.
+
+      These models are available via API_KEY from 
+      [The Gemini API](https://ai.google.dev/gemini-api/docs) and also from 
+      [Google Cloud Vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/migrate/migrate-google-ai) as part of the
+      [Model Garden](https://cloud.google.com/vertex-ai/generative-ai/docs/model-garden/explore-models).
+    </Tip>
+  </Tab>
   <Tab title="Groq">
     | Model | Context Window | Best For |
     |-------|---------------|-----------|
     | Llama 3.1 70B/8B | 131,072 tokens | High-performance, large context tasks |
     | Llama 3.2 Series | 8,192 tokens | General-purpose tasks |
     | Mixtral 8x7B | 32,768 tokens | Balanced performance and context |
-    | Gemma Series | 8,192 tokens | Efficient, smaller-scale tasks |
 
     <Tip>
       Groq is known for its fast inference speeds, making it suitable for real-time applications.
@@ -60,7 +151,7 @@ Here's a detailed breakdown of supported models and their capabilities:
     |----------|---------------|--------------|
     | Deepseek Chat | 128,000 tokens | Specialized in technical discussions |
     | Claude 3 | Up to 200K tokens | Strong reasoning, code understanding |
-    | Gemini | Varies by model | Multimodal capabilities |
+    | Gemma Series | 8,192 tokens | Efficient, smaller-scale tasks |
 
     <Info>
       Provider selection should consider factors like:
@@ -128,10 +219,10 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
         # llm: anthropic/claude-2.1
         # llm: anthropic/claude-2.0
 
-        # Google Models - Good for general tasks
-        # llm: gemini/gemini-pro
+        # Google Models - Strong reasoning, large cachable context window, multimodal
         # llm: gemini/gemini-1.5-pro-latest
-        # llm: gemini/gemini-1.0-pro-latest
+        # llm: gemini/gemini-1.5-flash-latest
+        # llm: gemini/gemini-1.5-flash-8b-latest
 
         # AWS Bedrock Models - Enterprise-grade
         # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
@@ -350,13 +441,18 @@ Learn how to get the most out of your LLM configuration:
 
   <Accordion title="Google">
     ```python Code
+    # Option 1. Gemini accessed with an API key.
+    # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
+
+    # Option 2. Vertex AI IAM credentials for Gemini, Anthropic, and anything in the Model Garden.
+    # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
     ```
 
     Example usage:
     ```python Code
     llm = LLM(
-        model="gemini/gemini-pro",
+        model="gemini/gemini-1.5-pro-latest",
         temperature=0.7
     )
     ```
@@ -412,6 +508,20 @@ Learn how to get the most out of your LLM configuration:
     ```
   </Accordion>
 
+  <Accordion title="Nvidia NIM">
+    ```python Code
+    NVIDIA_API_KEY=<your-api-key>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="nvidia_nim/meta/llama3-70b-instruct",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
   <Accordion title="Groq">
     ```python Code
     GROQ_API_KEY=<your-api-key>
@@ -502,20 +612,6 @@ Learn how to get the most out of your LLM configuration:
     ```
   </Accordion>
 
-  <Accordion title="Nvidia NIM">
-    ```python Code
-    NVIDIA_API_KEY=<your-api-key>
-    ```
-
-    Example usage:
-    ```python Code
-    llm = LLM(
-        model="nvidia_nim/meta/llama3-70b-instruct",
-        temperature=0.7
-    )
-    ```
-  </Accordion>
-
   <Accordion title="SambaNova">
     ```python Code
     SAMBANOVA_API_KEY=<your-api-key>

docs/installation.mdx

@@ -7,7 +7,7 @@ icon: wrench
 <Note>
   **Python Version Requirements**
   
-  CrewAI requires `Python >=3.10 and <=3.12`. Here's how to check your version:
+  CrewAI requires `Python >=3.10 and <3.13`. Here's how to check your version:
   ```bash
   python3 --version
   ```

pyproject.toml

@@ -3,7 +3,7 @@ name = "crewai"
 version = "0.86.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
-requires-python = ">=3.10,<=3.12"
+requires-python = ">=3.10,<3.13"
 authors = [
     { name = "Joao Moura", email = "joao@crewai.com" }
 ]
@@ -15,7 +15,6 @@ dependencies = [
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
     "instructor>=1.3.3",
     "regex>=2024.9.11",
-    "crewai-tools>=0.17.0",
     "click>=8.1.7",
     "python-dotenv>=1.0.0",
     "appdirs>=1.4.4",
@@ -27,9 +26,10 @@ dependencies = [
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
-    "chromadb>=0.5.18",
+    "chromadb>=0.5.23",
     "pdfplumber>=0.11.4",
     "openpyxl>=3.1.5",
+    "blinker>=1.9.0",
 ]
 
 [project.urls]
@@ -38,7 +38,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.14.0"]
+tools = ["crewai-tools>=0.17.0"]
 agentops = ["agentops>=0.3.0"]
 fastembed = ["fastembed>=0.4.1"]
 pdfplumber = [
@@ -51,10 +51,13 @@ openpyxl = [
     "openpyxl>=3.1.5",
 ]
 mem0 = ["mem0ai>=0.1.29"]
+docling = [
+    "docling>=2.12.0",
+]
 
 [tool.uv]
 dev-dependencies = [
-    "ruff>=0.4.10",
+    "ruff>=0.8.2",
     "mypy>=1.10.0",
     "pre-commit>=3.6.0",
     "mkdocs>=1.4.3",
@@ -64,7 +67,7 @@ dev-dependencies = [
     "mkdocs-material-extensions>=1.3.1",
     "pillow>=10.2.0",
     "cairosvg>=2.7.1",
-    "crewai-tools>=0.14.0",
+    "crewai-tools>=0.17.0",
     "pytest>=8.0.0",
     "pytest-vcr>=1.0.2",
     "python-dotenv>=1.0.0",

src/crewai/agent.py

@@ -23,27 +23,19 @@ from crewai.utilities.converter import generate_model_description
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
+agentops = None
 
-def mock_agent_ops_provider():
-    def track_agent(*args, **kwargs):
+try:
+    import agentops  # type: ignore # Name "agentops" is already defined
+    from agentops import track_agent  # type: ignore
+except ImportError:
+
+    def track_agent():
         def noop(f):
             return f
 
         return noop
 
-    return track_agent
-
-
-agentops = None
-
-if os.environ.get("AGENTOPS_API_KEY"):
-    try:
-        from agentops import track_agent
-    except ImportError:
-        track_agent = mock_agent_ops_provider()
-else:
-    track_agent = mock_agent_ops_provider()
-
 
 @track_agent()
 class Agent(BaseAgent):

src/crewai/agents/crew_agent_executor.py

@@ -144,7 +144,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                             formatted_answer
                         )
                         if self.step_callback:
-                          self.step_callback(tool_result)
+                            self.step_callback(tool_result)
 
                         formatted_answer.text += f"\nObservation: {tool_result.result}"
                         formatted_answer.result = tool_result.result
@@ -413,7 +413,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         """
         while self.ask_for_human_input:
             human_feedback = self._ask_human_input(formatted_answer.output)
-            print("Human feedback: ", human_feedback)
 
             if self.crew and self.crew._train:
                 self._handle_crew_training_output(formatted_answer, human_feedback)

src/crewai/agents/parser.py

@@ -1,5 +1,6 @@
 import re
 from typing import Any, Union
+
 from json_repair import repair_json
 
 from crewai.utilities import I18N

src/crewai/cli/authentication/main.py

@@ -5,9 +5,10 @@ from typing import Any, Dict
 import requests
 from rich.console import Console
 
+from crewai.cli.tools.main import ToolCommand
+
 from .constants import AUTH0_AUDIENCE, AUTH0_CLIENT_ID, AUTH0_DOMAIN
 from .utils import TokenManager, validate_token
-from crewai.cli.tools.main import ToolCommand
 
 console = Console()
 
@@ -79,7 +80,9 @@ class AuthenticationCommand:
                         style="yellow",
                     )
 
-                console.print("\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n")
+                console.print(
+                    "\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n"
+                )
                 return
 
             if token_data["error"] not in ("authorization_pending", "slow_down"):

src/crewai/cli/authentication/token.py

@@ -1,10 +1,9 @@
 from .utils import TokenManager
 
+
 def get_auth_token() -> str:
     """Get the authentication token."""
     access_token = TokenManager().get_token()
     if not access_token:
         raise Exception()
     return access_token
-
-

src/crewai/cli/cli.py

@@ -1,7 +1,7 @@
+from importlib.metadata import version as get_version
 from typing import Optional
 
 import click
-import pkg_resources
 
 from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
@@ -25,7 +25,7 @@ from .update_crew import update_crew
 
 
 @click.group()
-@click.version_option(pkg_resources.get_distribution("crewai").version)
+@click.version_option(get_version("crewai"))
 def crewai():
     """Top-level command group for crewai."""
 
@@ -52,16 +52,16 @@ def create(type, name, provider, skip_provider=False):
 def version(tools):
     """Show the installed version of crewai."""
     try:
-        crewai_version = pkg_resources.get_distribution("crewai").version
+        crewai_version = get_version("crewai")
     except Exception:
         crewai_version = "unknown version"
     click.echo(f"crewai version: {crewai_version}")
 
     if tools:
         try:
-            tools_version = pkg_resources.get_distribution("crewai-tools").version
+            tools_version = get_version("crewai")
             click.echo(f"crewai tools version: {tools_version}")
-        except pkg_resources.DistributionNotFound:
+        except Exception:
             click.echo("crewai tools not installed")
 
 

src/crewai/cli/command.py

@@ -1,8 +1,9 @@
 import requests
 from requests.exceptions import JSONDecodeError
 from rich.console import Console
-from crewai.cli.plus_api import PlusAPI
+
 from crewai.cli.authentication.token import get_auth_token
+from crewai.cli.plus_api import PlusAPI
 from crewai.telemetry.telemetry import Telemetry
 
 console = Console()

src/crewai/cli/config.py

@@ -1,13 +1,19 @@
 import json
 from pathlib import Path
-from pydantic import BaseModel, Field
 from typing import Optional
 
+from pydantic import BaseModel, Field
+
 DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json"
 
+
 class Settings(BaseModel):
-    tool_repository_username: Optional[str] = Field(None, description="Username for interacting with the Tool Repository")
-    tool_repository_password: Optional[str] = Field(None, description="Password for interacting with the Tool Repository")
+    tool_repository_username: Optional[str] = Field(
+        None, description="Username for interacting with the Tool Repository"
+    )
+    tool_repository_password: Optional[str] = Field(
+        None, description="Password for interacting with the Tool Repository"
+    )
     config_path: Path = Field(default=DEFAULT_CONFIG_PATH, exclude=True)
 
     def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data):

src/crewai/cli/plus_api.py

@@ -1,9 +1,11 @@
-from typing import Optional
-import requests
 from os import getenv
-from crewai.cli.version import get_crewai_version
+from typing import Optional
 from urllib.parse import urljoin
 
+import requests
+
+from crewai.cli.version import get_crewai_version
+
 
 class PlusAPI:
     """

src/crewai/cli/reset_memories_command.py

@@ -1,11 +1,12 @@
 import subprocess
+
 import click
 
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
-from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 
 
 def reset_memories_command(

src/crewai/cli/templates/crew/README.md

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
 
 ## Installation
 
-Ensure you have Python >=3.10 <=3.12 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, if you haven't already, install uv:
 

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

@@ -3,7 +3,7 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<=3.12"
+requires-python = ">=3.10,<3.13"
 dependencies = [
     "crewai[tools]>=0.86.0,<1.0.0"
 ]

src/crewai/cli/templates/crew/tools/custom_tool.py

@@ -10,7 +10,7 @@ class MyCustomToolInput(BaseModel):
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
     description: str = (
-        "Clear description for what this tool is useful for, you agent will need this information to use it."
+        "Clear description for what this tool is useful for, your agent will need this information to use it."
     )
     args_schema: Type[BaseModel] = MyCustomToolInput
 

src/crewai/cli/templates/flow/README.md

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
 
 ## Installation
 
-Ensure you have Python >=3.10 <=3.12 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, if you haven't already, install uv:
 

src/crewai/cli/templates/flow/main.py

@@ -5,7 +5,7 @@ from pydantic import BaseModel
 
 from crewai.flow.flow import Flow, listen, start
 
-from .crews.poem_crew.poem_crew import PoemCrew
+from {{folder_name}}.crews.poem_crew.poem_crew import PoemCrew
 
 
 class PoemState(BaseModel):

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

@@ -3,7 +3,7 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<=3.12"
+requires-python = ">=3.10,<3.13"
 dependencies = [
     "crewai[tools]>=0.86.0,<1.0.0",
 ]

src/crewai/cli/templates/flow/tools/custom_tool.py

@@ -13,7 +13,7 @@ class MyCustomToolInput(BaseModel):
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
     description: str = (
-        "Clear description for what this tool is useful for, you agent will need this information to use it."
+        "Clear description for what this tool is useful for, your agent will need this information to use it."
     )
     args_schema: Type[BaseModel] = MyCustomToolInput
 

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

@@ -1,17 +0,0 @@
-[tool.poetry]
-name = "{{folder_name}}"
-version = "0.1.0"
-description = "{{name}} using crewAI"
-authors = ["Your Name <you@example.com>"]
-
-[tool.poetry.dependencies]
-python = ">=3.10,<=3.12"
-crewai = { extras = ["tools"], version = ">=0.86.0,<1.0.0" }
-asyncio = "*"
-
-[tool.poetry.scripts]
-{{folder_name}} = "{{folder_name}}.main:main"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

src/crewai/cli/templates/tool/README.md

@@ -5,7 +5,7 @@ custom tools to power up your crews.
 
 ## Installing
 
-Ensure you have Python >=3.10 <=3.12 installed on your system. This project
+Ensure you have Python >=3.10 <3.13 installed on your system. This project
 uses [UV](https://docs.astral.sh/uv/) for dependency management and package
 handling, offering a seamless setup and execution experience.
 

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

@@ -3,7 +3,7 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
-requires-python = ">=3.10,<=3.12"
+requires-python = ">=3.10,<3.13"
 dependencies = [
     "crewai[tools]>=0.86.0"
 ]

src/crewai/cli/tools/main.py

@@ -117,7 +117,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
         published_handle = publish_response.json()["handle"]
         console.print(
-            f"Succesfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
+            f"Successfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
             style="bold green",
         )
 
@@ -138,7 +138,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
         self._add_package(get_response.json())
 
-        console.print(f"Succesfully installed {handle}", style="bold green")
+        console.print(f"Successfully installed {handle}", style="bold green")
 
     def login(self):
         login_response = self.plus_api_client.login_to_tool_repository()

src/crewai/cli/version.py

@@ -1,6 +1,6 @@
 import importlib.metadata
 
+
 def get_crewai_version() -> str:
     """Get the version number of CrewAI running the CLI"""
     return importlib.metadata.version("crewai")
-

src/crewai/crew.py

@@ -1,6 +1,5 @@
 import asyncio
 import json
-import os
 import uuid
 import warnings
 from concurrent.futures import Future
@@ -49,12 +48,10 @@ from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
-agentops = None
-if os.environ.get("AGENTOPS_API_KEY"):
-    try:
-        import agentops  # type: ignore
-    except ImportError:
-        pass
+try:
+    import agentops  # type: ignore
+except ImportError:
+    agentops = None
 
 
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
@@ -1032,6 +1029,7 @@ class Crew(BaseModel):
             agentops.end_session(
                 end_state="Success",
                 end_state_reason="Finished Execution",
+                is_auto_end=True,
             )
         self._telemetry.end_crew(self, final_string_output)
 

src/crewai/flow/flow.py

@@ -14,8 +14,15 @@ from typing import (
     cast,
 )
 
+from blinker import Signal
 from pydantic import BaseModel, ValidationError
 
+from crewai.flow.flow_events import (
+    FlowFinishedEvent,
+    FlowStartedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionStartedEvent,
+)
 from crewai.flow.flow_visualizer import plot_flow
 from crewai.flow.utils import get_possible_return_constants
 from crewai.telemetry import Telemetry
@@ -159,6 +166,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
     _routers: Dict[str, str] = {}
     _router_paths: Dict[str, List[str]] = {}
     initial_state: Union[Type[T], T, None] = None
+    event_emitter = Signal("event_emitter")
 
     def __class_getitem__(cls: Type["Flow"], item: Type[T]) -> Type["Flow"]:
         class _FlowGeneric(cls):  # type: ignore
@@ -253,6 +261,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Returns:
             The final output from the flow execution.
         """
+        self.event_emitter.send(
+            self,
+            event=FlowStartedEvent(
+                type="flow_started",
+                flow_name=self.__class__.__name__,
+            ),
+        )
+
         if inputs is not None:
             self._initialize_state(inputs)
         return asyncio.run(self.kickoff_async())
@@ -267,8 +283,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Returns:
             The final output from the flow execution.
         """
-        if inputs is not None:
-            self._initialize_state(inputs)
         if not self._start_methods:
             raise ValueError("No start method defined")
 
@@ -285,11 +299,19 @@ class Flow(Generic[T], metaclass=FlowMeta):
         # Run all start methods concurrently
         await asyncio.gather(*tasks)
 
-        # Return the final output (from the last executed method)
-        if self._method_outputs:
-            return self._method_outputs[-1]
-        else:
-            return None  # Or raise an exception if no methods were executed
+        # Determine the final output (from the last executed method)
+        final_output = self._method_outputs[-1] if self._method_outputs else None
+
+        self.event_emitter.send(
+            self,
+            event=FlowFinishedEvent(
+                type="flow_finished",
+                flow_name=self.__class__.__name__,
+                result=final_output,
+            ),
+        )
+
+        return final_output
 
     async def _execute_start_method(self, start_method_name: str) -> None:
         result = await self._execute_method(
@@ -352,6 +374,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
     async def _execute_single_listener(self, listener_name: str, result: Any) -> None:
         try:
             method = self._methods[listener_name]
+
+            self.event_emitter.send(
+                self,
+                event=MethodExecutionStartedEvent(
+                    type="method_execution_started",
+                    method_name=listener_name,
+                    flow_name=self.__class__.__name__,
+                ),
+            )
+
             sig = inspect.signature(method)
             params = list(sig.parameters.values())
 
@@ -367,6 +399,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 # If listener does not expect parameters, call without arguments
                 listener_result = await self._execute_method(listener_name, method)
 
+            self.event_emitter.send(
+                self,
+                event=MethodExecutionFinishedEvent(
+                    type="method_execution_finished",
+                    method_name=listener_name,
+                    flow_name=self.__class__.__name__,
+                ),
+            )
+
             # Execute listeners of this listener
             await self._execute_listeners(listener_name, listener_result)
         except Exception as e:

src/crewai/flow/flow_events.py

@@ -0,0 +1,33 @@
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Any, Optional
+
+
+@dataclass
+class Event:
+    type: str
+    flow_name: str
+    timestamp: datetime = field(init=False)
+
+    def __post_init__(self):
+        self.timestamp = datetime.now()
+
+
+@dataclass
+class FlowStartedEvent(Event):
+    pass
+
+
+@dataclass
+class MethodExecutionStartedEvent(Event):
+    method_name: str
+
+
+@dataclass
+class MethodExecutionFinishedEvent(Event):
+    method_name: str
+
+
+@dataclass
+class FlowFinishedEvent(Event):
+    result: Optional[Any] = None

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -1,8 +1,8 @@
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Dict, List, Union
+from typing import Dict, List, Optional, Union
 
-from pydantic import Field
+from pydantic import Field, field_validator
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
@@ -14,17 +14,28 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
     """Base class for knowledge sources that load content from files."""
 
     _logger: Logger = Logger(verbose=True)
-    file_path: Union[Path, List[Path], str, List[str]] = Field(
-        ..., description="The path to the file"
+    file_path: Optional[Union[Path, List[Path], str, List[str]]] = Field(
+        default=None,
+        description="[Deprecated] The path to the file. Use file_paths instead.",
+    )
+    file_paths: Optional[Union[Path, List[Path], str, List[str]]] = Field(
+        default_factory=list, description="The path to the file"
     )
     content: Dict[Path, str] = Field(init=False, default_factory=dict)
     storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
     safe_file_paths: List[Path] = Field(default_factory=list)
 
+    @field_validator("file_path", "file_paths", mode="before")
+    def validate_file_path(cls, v, values):
+        """Validate that at least one of file_path or file_paths is provided."""
+        if v is None and ("file_path" not in values or values.get("file_path") is None):
+            raise ValueError("Either file_path or file_paths must be provided")
+        return v
+
     def model_post_init(self, _):
         """Post-initialization method to load content."""
         self.safe_file_paths = self._process_file_paths()
-        self.validate_paths()
+        self.validate_content()
         self.content = self.load_content()
 
     @abstractmethod
@@ -32,13 +43,13 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
         """Load and preprocess file content. Should be overridden by subclasses. Assume that the file path is relative to the project root in the knowledge directory."""
         pass
 
-    def validate_paths(self):
+    def validate_content(self):
         """Validate the paths."""
         for path in self.safe_file_paths:
             if not path.exists():
                 self._logger.log(
                     "error",
-                    f"File not found: {path}. Try adding sources to the knowledge directory. If its inside the knowledge directory, use the relative path.",
+                    f"File not found: {path}. Try adding sources to the knowledge directory. If it's inside the knowledge directory, use the relative path.",
                     color="red",
                 )
                 raise FileNotFoundError(f"File not found: {path}")
@@ -59,13 +70,31 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
 
     def _process_file_paths(self) -> List[Path]:
         """Convert file_path to a list of Path objects."""
-        paths = (
-            [self.file_path]
-            if isinstance(self.file_path, (str, Path))
-            else self.file_path
-        )
+
+        # Check if old file_path is being used
+        if hasattr(self, "file_path") and self.file_path is not None:
+            self._logger.log(
+                "warning",
+                "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.",
+                color="yellow",
+            )
+            paths = (
+                [self.file_path]
+                if isinstance(self.file_path, (str, Path))
+                else self.file_path
+            )
+        else:
+            if self.file_paths is None:
+                raise ValueError("Your source must be provided with a file_paths: []")
+            paths = (
+                [self.file_paths]
+                if isinstance(self.file_paths, (str, Path))
+                else self.file_paths
+            )
 
         if not isinstance(paths, list):
-            raise ValueError("file_path must be a Path, str, or a list of these types")
+            raise ValueError(
+                "file_path/file_paths must be a Path, str, or a list of these types"
+            )
 
         return [self.convert_to_path(path) for path in paths]

src/crewai/knowledge/source/base_knowledge_source.py

@@ -21,7 +21,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
     collection_name: Optional[str] = Field(default=None)
 
     @abstractmethod
-    def load_content(self) -> Dict[Any, str]:
+    def validate_content(self) -> Any:
         """Load and preprocess content from the source."""
         pass
 

src/crewai/knowledge/source/crew_docling_source.py

@@ -0,0 +1,120 @@
+from pathlib import Path
+from typing import Iterator, List, Optional, Union
+from urllib.parse import urlparse
+
+from docling.datamodel.base_models import InputFormat
+from docling.document_converter import DocumentConverter
+from docling.exceptions import ConversionError
+from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker
+from docling_core.types.doc.document import DoclingDocument
+from pydantic import Field
+
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
+from crewai.utilities.logger import Logger
+
+
+class CrewDoclingSource(BaseKnowledgeSource):
+    """Default Source class for converting documents to markdown or json
+    This will auto support PDF, DOCX, and TXT, XLSX, Images, and HTML files without any additional dependencies and follows the docling package as the source of truth.
+    """
+
+    _logger: Logger = Logger(verbose=True)
+
+    file_path: Optional[List[Union[Path, str]]] = Field(default=None)
+    file_paths: List[Union[Path, str]] = Field(default_factory=list)
+    chunks: List[str] = Field(default_factory=list)
+    safe_file_paths: List[Union[Path, str]] = Field(default_factory=list)
+    content: List[DoclingDocument] = Field(default_factory=list)
+    document_converter: DocumentConverter = Field(
+        default_factory=lambda: DocumentConverter(
+            allowed_formats=[
+                InputFormat.MD,
+                InputFormat.ASCIIDOC,
+                InputFormat.PDF,
+                InputFormat.DOCX,
+                InputFormat.HTML,
+                InputFormat.IMAGE,
+                InputFormat.XLSX,
+                InputFormat.PPTX,
+            ]
+        )
+    )
+
+    def model_post_init(self, _) -> None:
+        if self.file_path:
+            self._logger.log(
+                "warning",
+                "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.",
+                color="yellow",
+            )
+            self.file_paths = self.file_path
+        self.safe_file_paths = self.validate_content()
+        self.content = self.load_content()
+
+    def load_content(self) -> List[DoclingDocument]:
+        try:
+            return self.convert_source_to_docling_documents()
+        except ConversionError as e:
+            self._logger.log(
+                "error",
+                f"Error loading content: {e}. Supported formats: {self.document_converter.allowed_formats}",
+                "red",
+            )
+            raise e
+        except Exception as e:
+            self._logger.log("error", f"Error loading content: {e}")
+            raise e
+
+    def add(self) -> None:
+        if self.content is None:
+            return
+        for doc in self.content:
+            new_chunks_iterable = self._chunk_doc(doc)
+            self.chunks.extend(list(new_chunks_iterable))
+        self._save_documents()
+
+    def convert_source_to_docling_documents(self) -> List[DoclingDocument]:
+        conv_results_iter = self.document_converter.convert_all(self.safe_file_paths)
+        return [result.document for result in conv_results_iter]
+
+    def _chunk_doc(self, doc: DoclingDocument) -> Iterator[str]:
+        chunker = HierarchicalChunker()
+        for chunk in chunker.chunk(doc):
+            yield chunk.text
+
+    def validate_content(self) -> List[Union[Path, str]]:
+        processed_paths: List[Union[Path, str]] = []
+        for path in self.file_paths:
+            if isinstance(path, str):
+                if path.startswith(("http://", "https://")):
+                    try:
+                        if self._validate_url(path):
+                            processed_paths.append(path)
+                        else:
+                            raise ValueError(f"Invalid URL format: {path}")
+                    except Exception as e:
+                        raise ValueError(f"Invalid URL: {path}. Error: {str(e)}")
+                else:
+                    local_path = Path(KNOWLEDGE_DIRECTORY + "/" + path)
+                    if local_path.exists():
+                        processed_paths.append(local_path)
+                    else:
+                        raise FileNotFoundError(f"File not found: {local_path}")
+            else:
+                # this is an instance of Path
+                processed_paths.append(path)
+        return processed_paths
+
+    def _validate_url(self, url: str) -> bool:
+        try:
+            result = urlparse(url)
+            return all(
+                [
+                    result.scheme in ("http", "https"),
+                    result.netloc,
+                    len(result.netloc.split(".")) >= 2,  # Ensure domain has TLD
+                ]
+            )
+        except Exception:
+            return False

src/crewai/knowledge/source/string_knowledge_source.py

@@ -13,9 +13,9 @@ class StringKnowledgeSource(BaseKnowledgeSource):
 
     def model_post_init(self, _):
         """Post-initialization method to validate content."""
-        self.load_content()
+        self.validate_content()
 
-    def load_content(self):
+    def validate_content(self):
         """Validate string content."""
         if not isinstance(self.content, str):
             raise ValueError("StringKnowledgeSource only accepts string content")

src/crewai/knowledge/storage/base_knowledge_storage.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Dict, Any, List, Optional
+from typing import Any, Dict, List, Optional
 
 
 class BaseKnowledgeStorage(ABC):

src/crewai/knowledge/storage/knowledge_storage.py

@@ -14,9 +14,9 @@ from chromadb.config import Settings
 
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
 from crewai.utilities import EmbeddingConfigurator
+from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
 from crewai.utilities.logger import Logger
 from crewai.utilities.paths import db_storage_path
-from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
 
 
 @contextlib.contextmanager
@@ -124,43 +124,60 @@ class KnowledgeStorage(BaseKnowledgeStorage):
         documents: List[str],
         metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
     ):
-        if self.collection:
-            try:
-                if metadata is None:
-                    metadatas: Optional[OneOrMany[chromadb.Metadata]] = None
-                elif isinstance(metadata, list):
-                    metadatas = [cast(chromadb.Metadata, m) for m in metadata]
-                else:
-                    metadatas = cast(chromadb.Metadata, metadata)
-
-                ids = [
-                    hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents
-                ]
-
-                self.collection.upsert(
-                    documents=documents,
-                    metadatas=metadatas,
-                    ids=ids,
-                )
-            except chromadb.errors.InvalidDimensionException as e:
-                Logger(verbose=True).log(
-                    "error",
-                    "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
-                    "red",
-                )
-                raise ValueError(
-                    "Embedding dimension mismatch. Make sure you're using the same embedding model "
-                    "across all operations with this collection."
-                    "Try resetting the collection using `crewai reset-memories -a`"
-                ) from e
-            except Exception as e:
-                Logger(verbose=True).log(
-                    "error", f"Failed to upsert documents: {e}", "red"
-                )
-                raise
-        else:
+        if not self.collection:
             raise Exception("Collection not initialized")
 
+        try:
+            # Create a dictionary to store unique documents
+            unique_docs = {}
+
+            # Generate IDs and create a mapping of id -> (document, metadata)
+            for idx, doc in enumerate(documents):
+                doc_id = hashlib.sha256(doc.encode("utf-8")).hexdigest()
+                doc_metadata = None
+                if metadata is not None:
+                    if isinstance(metadata, list):
+                        doc_metadata = metadata[idx]
+                    else:
+                        doc_metadata = metadata
+                unique_docs[doc_id] = (doc, doc_metadata)
+
+            # Prepare filtered lists for ChromaDB
+            filtered_docs = []
+            filtered_metadata = []
+            filtered_ids = []
+
+            # Build the filtered lists
+            for doc_id, (doc, meta) in unique_docs.items():
+                filtered_docs.append(doc)
+                filtered_metadata.append(meta)
+                filtered_ids.append(doc_id)
+
+            # If we have no metadata at all, set it to None
+            final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
+                None if all(m is None for m in filtered_metadata) else filtered_metadata
+            )
+
+            self.collection.upsert(
+                documents=filtered_docs,
+                metadatas=final_metadata,
+                ids=filtered_ids,
+            )
+        except chromadb.errors.InvalidDimensionException as e:
+            Logger(verbose=True).log(
+                "error",
+                "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
+                "red",
+            )
+            raise ValueError(
+                "Embedding dimension mismatch. Make sure you're using the same embedding model "
+                "across all operations with this collection."
+                "Try resetting the collection using `crewai reset-memories -a`"
+            ) from e
+        except Exception as e:
+            Logger(verbose=True).log("error", f"Failed to upsert documents: {e}", "red")
+            raise
+
     def _create_default_embedding_function(self):
         from chromadb.utils.embedding_functions.openai_embedding_function import (
             OpenAIEmbeddingFunction,

src/crewai/llm.py

@@ -43,6 +43,11 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "gpt-4-turbo": 128000,
     "o1-preview": 128000,
     "o1-mini": 128000,
+    # gemini
+    "gemini-2.0-flash": 1048576,
+    "gemini-1.5-pro": 2097152,
+    "gemini-1.5-flash": 1048576,
+    "gemini-1.5-flash-8b": 1048576,
     # deepseek
     "deepseek-chat": 128000,
     # groq
@@ -61,6 +66,9 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "mixtral-8x7b-32768": 32768,
 }
 
+DEFAULT_CONTEXT_WINDOW_SIZE = 8192
+CONTEXT_WINDOW_USAGE_RATIO = 0.75
+
 
 @contextmanager
 def suppress_warnings():
@@ -124,6 +132,7 @@ class LLM:
         self.api_version = api_version
         self.api_key = api_key
         self.callbacks = callbacks
+        self.context_window_size = 0
         self.kwargs = kwargs
 
         litellm.drop_params = True
@@ -191,7 +200,16 @@ class LLM:
 
     def get_context_window_size(self) -> int:
         # Only using 75% of the context window size to avoid cutting the message in the middle
-        return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75)
+        if self.context_window_size != 0:
+            return self.context_window_size
+
+        self.context_window_size = int(
+            DEFAULT_CONTEXT_WINDOW_SIZE * CONTEXT_WINDOW_USAGE_RATIO
+        )
+        for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
+            if self.model.startswith(key):
+                self.context_window_size = int(value * CONTEXT_WINDOW_USAGE_RATIO)
+        return self.context_window_size
 
     def set_callbacks(self, callbacks: List[Any]):
         callback_types = [type(callback) for callback in callbacks]

src/crewai/memory/contextual/contextual_memory.py

@@ -1,4 +1,4 @@
-from typing import Optional, Dict, Any
+from typing import Any, Dict, Optional
 
 from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
 

src/crewai/memory/memory.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Optional, List
+from typing import Any, Dict, List, Optional
 
 from crewai.memory.storage.rag_storage import RAGStorage
 

src/crewai/memory/short_term/short_term_memory.py

@@ -1,4 +1,5 @@
 from typing import Any, Dict, Optional
+
 from crewai.memory.memory import Memory
 from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
 from crewai.memory.storage.rag_storage import RAGStorage
@@ -32,7 +33,10 @@ class ShortTermMemory(Memory):
                 storage
                 if storage
                 else RAGStorage(
-                    type="short_term", embedder_config=embedder_config, crew=crew, path=path
+                    type="short_term",
+                    embedder_config=embedder_config,
+                    crew=crew,
+                    path=path,
                 )
             )
         super().__init__(storage)

src/crewai/memory/storage/mem0_storage.py

@@ -2,6 +2,7 @@ import os
 from typing import Any, Dict, List
 
 from mem0 import MemoryClient
+
 from crewai.memory.storage.interface import Storage
 
 

src/crewai/project/utils.py

@@ -1,5 +1,6 @@
 from functools import wraps
 
+
 def memoize(func):
     cache = {}
 

src/crewai/task.py

@@ -1,11 +1,11 @@
 import datetime
 import json
-from pathlib import Path
 import threading
 import uuid
 from concurrent.futures import Future
 from copy import copy
 from hashlib import md5
+from pathlib import Path
 from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union
 
 from opentelemetry.trace import Span

src/crewai/telemetry/telemetry.py

@@ -6,6 +6,7 @@ import os
 import platform
 import warnings
 from contextlib import contextmanager
+from importlib.metadata import version
 from typing import TYPE_CHECKING, Any, Optional
 
 
@@ -16,12 +17,10 @@ def suppress_warnings():
         yield
 
 
-with suppress_warnings():
-    import pkg_resources
-
-
 from opentelemetry import trace  # noqa: E402
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter # noqa: E402
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
+    OTLPSpanExporter,  # noqa: E402
+)
 from opentelemetry.sdk.resources import SERVICE_NAME, Resource  # noqa: E402
 from opentelemetry.sdk.trace import TracerProvider  # noqa: E402
 from opentelemetry.sdk.trace.export import BatchSpanProcessor  # noqa: E402
@@ -104,7 +103,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                pkg_resources.get_distribution("crewai").version,
+                version("crewai"),
             )
             self._add_attribute(span, "python_version", platform.python_version())
             self._add_attribute(span, "crew_key", crew.key)
@@ -306,7 +305,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                pkg_resources.get_distribution("crewai").version,
+                version("crewai"),
             )
             self._add_attribute(span, "tool_name", tool_name)
             self._add_attribute(span, "attempts", attempts)
@@ -326,7 +325,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                pkg_resources.get_distribution("crewai").version,
+                version("crewai"),
             )
             self._add_attribute(span, "tool_name", tool_name)
             self._add_attribute(span, "attempts", attempts)
@@ -346,7 +345,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                pkg_resources.get_distribution("crewai").version,
+                version("crewai"),
             )
             if llm:
                 self._add_attribute(span, "llm", llm.model)
@@ -365,7 +364,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                pkg_resources.get_distribution("crewai").version,
+                version("crewai"),
             )
             self._add_attribute(span, "crew_key", crew.key)
             self._add_attribute(span, "crew_id", str(crew.id))
@@ -391,7 +390,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                pkg_resources.get_distribution("crewai").version,
+                version("crewai"),
             )
             self._add_attribute(span, "crew_key", crew.key)
             self._add_attribute(span, "crew_id", str(crew.id))
@@ -472,7 +471,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                pkg_resources.get_distribution("crewai").version,
+                version("crewai"),
             )
             self._add_attribute(span, "crew_key", crew.key)
             self._add_attribute(span, "crew_id", str(crew.id))
@@ -541,7 +540,7 @@ class Telemetry:
             self._add_attribute(
                 crew._execution_span,
                 "crewai_version",
-                pkg_resources.get_distribution("crewai").version,
+                version("crewai"),
             )
             self._add_attribute(
                 crew._execution_span, "crew_output", final_string_output

src/crewai/tools/agent_tools/agent_tools.py

@@ -1,9 +1,9 @@
-from crewai.tools.base_tool import BaseTool
 from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.tools.base_tool import BaseTool
 from crewai.utilities import I18N
 
-from .delegate_work_tool import DelegateWorkTool
 from .ask_question_tool import AskQuestionTool
+from .delegate_work_tool import DelegateWorkTool
 
 
 class AgentTools:

src/crewai/tools/agent_tools/ask_question_tool.py

@@ -1,7 +1,9 @@
-from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
 from typing import Optional
+
 from pydantic import BaseModel, Field
 
+from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
+
 
 class AskQuestionToolSchema(BaseModel):
     question: str = Field(..., description="The question to ask")

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -1,9 +1,10 @@
 from typing import Optional, Union
+
 from pydantic import Field
 
-from crewai.tools.base_tool import BaseTool
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.task import Task
+from crewai.tools.base_tool import BaseTool
 from crewai.utilities import I18N
 
 
@@ -44,14 +45,14 @@ class BaseAgentTool(BaseTool):
                 if available_agent.role.casefold().replace("\n", "") == agent_name
             ]
         except Exception as _:
-            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
+            return self.i18n.errors("agent_tool_unexisting_coworker").format(
                 coworkers="\n".join(
                     [f"- {agent.role.casefold()}" for agent in self.agents]
                 )
             )
 
         if not agent:
-            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
+            return self.i18n.errors("agent_tool_unexisting_coworker").format(
                 coworkers="\n".join(
                     [f"- {agent.role.casefold()}" for agent in self.agents]
                 )

src/crewai/tools/agent_tools/delegate_work_tool.py

@@ -1,8 +1,9 @@
-from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
 from typing import Optional
 
 from pydantic import BaseModel, Field
 
+from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
+
 
 class DelegateWorkToolSchema(BaseModel):
     task: str = Field(..., description="The task to delegate")

src/crewai/tools/tool_usage.py

@@ -1,6 +1,5 @@
 import ast
 import datetime
-import os
 import time
 from difflib import SequenceMatcher
 from textwrap import dedent
@@ -15,12 +14,10 @@ from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 from crewai.tools.tool_usage_events import ToolUsageError, ToolUsageFinished
 from crewai.utilities import I18N, Converter, ConverterError, Printer
 
-agentops = None
-if os.environ.get("AGENTOPS_API_KEY"):
-    try:
-        import agentops  # type: ignore
-    except ImportError:
-        pass
+try:
+    import agentops  # type: ignore
+except ImportError:
+    agentops = None
 
 OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o", "o1-preview", "o1-mini"]
 
@@ -422,9 +419,10 @@ class ToolUsage:
                 elif value.lower() in [
                     "true",
                     "false",
-                    "null",
                 ]:  # Check for boolean and null values
-                    value = value.lower()
+                    value = value.lower().capitalize()
+                elif value.lower() == "null":
+                    value = "None"
                 else:
                     # Assume the value is a string and needs quotes
                     value = '"' + value.replace('"', '\\"') + '"'

src/crewai/tools/tool_usage_events.py

@@ -1,6 +1,7 @@
-from typing import Any, Dict
-from pydantic import BaseModel
 from datetime import datetime
+from typing import Any, Dict
+
+from pydantic import BaseModel
 
 
 class ToolUsageEvent(BaseModel):

src/crewai/translations/en.json

@@ -28,7 +28,7 @@
   "errors": {
     "force_final_answer_error": "You can't keep going, this was the best you could do.\n {formatted_answer.text}",
     "force_final_answer": "Now it's time you MUST give your absolute best final answer. You'll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer.",
-    "agent_tool_unexsiting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
+    "agent_tool_unexisting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
     "task_repeated_usage": "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n\n",
     "tool_usage_error": "I encountered an error: {error}",
     "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.",

src/crewai/utilities/crew_json_encoder.py

@@ -1,15 +1,17 @@
-from datetime import datetime, date
 import json
-from uuid import UUID
-from pydantic import BaseModel
+from datetime import date, datetime
 from decimal import Decimal
+from enum import Enum
+from uuid import UUID
+
+from pydantic import BaseModel
 
 
 class CrewJSONEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, BaseModel):
             return self._handle_pydantic_model(obj)
-        elif isinstance(obj, UUID) or isinstance(obj, Decimal):
+        elif isinstance(obj, UUID) or isinstance(obj, Decimal) or isinstance(obj, Enum):
             return str(obj)
 
         elif isinstance(obj, datetime) or isinstance(obj, date):

src/crewai/utilities/crew_pydantic_output_parser.py

@@ -1,10 +1,11 @@
 import json
-import regex
 from typing import Any, Type
 
-from crewai.agents.parser import OutputParserException
+import regex
 from pydantic import BaseModel, ValidationError
 
+from crewai.agents.parser import OutputParserException
+
 
 class CrewPydanticOutputParser:
     """Parses the text into pydantic models"""

src/crewai/utilities/embedding_configurator.py

@@ -1,6 +1,7 @@
 import os
 from typing import Any, Dict, cast
-from chromadb import EmbeddingFunction, Documents, Embeddings
+
+from chromadb import Documents, EmbeddingFunction, Embeddings
 from chromadb.api.types import validate_embedding_function
 
 

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -1,13 +1,14 @@
 from collections import defaultdict
 
+from pydantic import BaseModel, Field
+from rich.box import HEAVY_EDGE
+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 pydantic import BaseModel, Field
-from rich.box import HEAVY_EDGE
-from rich.console import Console
-from rich.table import Table
 
 
 class TaskEvaluationPydanticOutput(BaseModel):

src/crewai/utilities/evaluators/task_evaluator.py

@@ -1,4 +1,3 @@
-import os
 from typing import List
 
 from pydantic import BaseModel, Field
@@ -6,27 +5,17 @@ from pydantic import BaseModel, Field
 from crewai.utilities import Converter
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
+agentops = None
+try:
+    from agentops import track_agent  # type: ignore
+except ImportError:
 
-def mock_agent_ops_provider():
-    def track_agent(*args, **kwargs):
+    def track_agent(name):
         def noop(f):
             return f
 
         return noop
 
-    return track_agent
-
-
-agentops = None
-
-if os.environ.get("AGENTOPS_API_KEY"):
-    try:
-        from agentops import track_agent
-    except ImportError:
-        track_agent = mock_agent_ops_provider()
-else:
-    track_agent = mock_agent_ops_provider()
-
 
 class Entity(BaseModel):
     name: str = Field(description="The name of the entity.")

src/crewai/utilities/events.py

@@ -1,7 +1,7 @@
-from typing import Any, Callable, Generic, List, Dict, Type, TypeVar
 from functools import wraps
-from pydantic import BaseModel
+from typing import Any, Callable, Dict, Generic, List, Type, TypeVar
 
+from pydantic import BaseModel
 
 T = TypeVar("T")
 EVT = TypeVar("EVT", bound=BaseModel)

src/crewai/utilities/planning_handler.py

@@ -1,4 +1,5 @@
 from typing import Any, List, Optional
+
 from pydantic import BaseModel, Field
 
 from crewai.agent import Agent

src/crewai/utilities/prompts.py

@@ -1,5 +1,7 @@
-from pydantic import BaseModel, Field
 from typing import Any, Optional
+
+from pydantic import BaseModel, Field
+
 from crewai.utilities import I18N
 
 

src/crewai/utilities/pydantic_schema_parser.py

@@ -1,4 +1,4 @@
-from typing import Type, get_args, get_origin, Union
+from typing import Type, Union, get_args, get_origin
 
 from pydantic import BaseModel
 

src/crewai/utilities/task_output_storage_handler.py

@@ -1,6 +1,8 @@
-from pydantic import BaseModel, Field
 from datetime import datetime
-from typing import Dict, Any, Optional, List
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, Field
+
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )

src/crewai/utilities/token_counter_callback.py

@@ -1,5 +1,6 @@
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.types.utils import Usage
+
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 
 
@@ -11,7 +12,7 @@ class TokenCalcHandler(CustomLogger):
         if self.token_cost_process is None:
             return
 
-        usage : Usage = response_obj["usage"]
+        usage: Usage = response_obj["usage"]
         self.token_cost_process.sum_successful_requests(1)
         self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
         self.token_cost_process.sum_completion_tokens(usage.completion_tokens)

tests/agent_test.py

@@ -1595,19 +1595,15 @@ def test_agent_execute_task_with_ollama():
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_with_knowledge_sources():
     # Create a knowledge source with some content
-    content = "Brandon's favorite color is blue and he likes Mexican food."
-    string_source = StringKnowledgeSource(
-        content=content, metadata={"preference": "personal"}
-    )
+    content = "Brandon's favorite color is red and he likes Mexican food."
+    string_source = StringKnowledgeSource(content=content)
 
     with patch(
         "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
     ) as MockKnowledge:
         mock_knowledge_instance = MockKnowledge.return_value
         mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.query.return_value = [
-            {"content": content, "metadata": {"preference": "personal"}}
-        ]
+        mock_knowledge_instance.query.return_value = [{"content": content}]
 
         agent = Agent(
             role="Information Agent",
@@ -1628,4 +1624,4 @@ def test_agent_with_knowledge_sources():
         result = crew.kickoff()
 
         # Assert that the agent provides the correct information
-        assert "blue" in result.raw.lower()
+        assert "red" in result.raw.lower()

tests/agents/agent_builder/base_agent_test.py

@@ -1,9 +1,10 @@
 import hashlib
 from typing import Any, List, Optional
 
+from pydantic import BaseModel
+
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tools.base_tool import BaseTool
-from pydantic import BaseModel
 
 
 class TestAgent(BaseAgent):

tests/agents/test_crew_agent_parser.py

@@ -1,10 +1,11 @@
 import pytest
-from crewai.agents.parser import CrewAgentParser
+
 from crewai.agents.crew_agent_executor import (
     AgentAction,
     AgentFinish,
     OutputParserException,
 )
+from crewai.agents.parser import CrewAgentParser
 
 
 @pytest.fixture

tests/cli/authentication/test_auth_main.py

@@ -2,6 +2,7 @@ import unittest
 from unittest.mock import MagicMock, patch
 
 import requests
+
 from crewai.cli.authentication.main import AuthenticationCommand
 
 
@@ -47,7 +48,9 @@ class TestAuthenticationCommand(unittest.TestCase):
     @patch("crewai.cli.authentication.main.requests.post")
     @patch("crewai.cli.authentication.main.validate_token")
     @patch("crewai.cli.authentication.main.console.print")
-    def test_poll_for_token_success(self, mock_print, mock_validate_token, mock_post, mock_tool):
+    def test_poll_for_token_success(
+        self, mock_print, mock_validate_token, mock_post, mock_tool
+    ):
         mock_response = MagicMock()
         mock_response.status_code = 200
         mock_response.json.return_value = {
@@ -62,7 +65,9 @@ class TestAuthenticationCommand(unittest.TestCase):
         self.auth_command._poll_for_token({"device_code": "123456"})
 
         mock_validate_token.assert_called_once_with("TOKEN")
-        mock_print.assert_called_once_with("\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n")
+        mock_print.assert_called_once_with(
+            "\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n"
+        )
 
     @patch("crewai.cli.authentication.main.requests.post")
     @patch("crewai.cli.authentication.main.console.print")

tests/cli/authentication/test_utils.py

@@ -3,9 +3,10 @@ import unittest
 from datetime import datetime, timedelta
 from unittest.mock import MagicMock, patch
 
-from crewai.cli.authentication.utils import TokenManager, validate_token
 from cryptography.fernet import Fernet
 
+from crewai.cli.authentication.utils import TokenManager, validate_token
+
 
 class TestValidateToken(unittest.TestCase):
     @patch("crewai.cli.authentication.utils.AsymmetricSignatureVerifier")

tests/cli/config_test.py

@@ -1,10 +1,12 @@
-import unittest
 import json
-import tempfile
 import shutil
+import tempfile
+import unittest
 from pathlib import Path
+
 from crewai.cli.config import Settings
 
+
 class TestSettings(unittest.TestCase):
     def setUp(self):
         self.test_dir = Path(tempfile.mkdtemp())
@@ -20,8 +22,7 @@ class TestSettings(unittest.TestCase):
 
     def test_initialization_with_data(self):
         settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="user1"
+            config_path=self.config_path, tool_repository_username="user1"
         )
         self.assertEqual(settings.tool_repository_username, "user1")
         self.assertIsNone(settings.tool_repository_password)
@@ -37,22 +38,23 @@ class TestSettings(unittest.TestCase):
     def test_merge_file_and_input_data(self):
         self.config_path.parent.mkdir(parents=True, exist_ok=True)
         with self.config_path.open("w") as f:
-            json.dump({
-                "tool_repository_username": "file_user",
-                "tool_repository_password": "file_pass"
-            }, f)
+            json.dump(
+                {
+                    "tool_repository_username": "file_user",
+                    "tool_repository_password": "file_pass",
+                },
+                f,
+            )
 
         settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="new_user"
+            config_path=self.config_path, tool_repository_username="new_user"
         )
         self.assertEqual(settings.tool_repository_username, "new_user")
         self.assertEqual(settings.tool_repository_password, "file_pass")
 
     def test_dump_new_settings(self):
         settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="user1"
+            config_path=self.config_path, tool_repository_username="user1"
         )
         settings.dump()
 
@@ -67,8 +69,7 @@ class TestSettings(unittest.TestCase):
             json.dump({"existing_setting": "value"}, f)
 
         settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="user1"
+            config_path=self.config_path, tool_repository_username="user1"
         )
         settings.dump()
 
@@ -79,10 +80,7 @@ class TestSettings(unittest.TestCase):
         self.assertEqual(saved_data["tool_repository_username"], "user1")
 
     def test_none_values(self):
-        settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username=None
-        )
+        settings = Settings(config_path=self.config_path, tool_repository_username=None)
         settings.dump()
 
         with self.config_path.open("r") as f:

tests/cli/deploy/test_deploy_main.py

@@ -231,7 +231,7 @@ class TestDeployCommand(unittest.TestCase):
         [project]
         name = "test_project"
         version = "0.1.0"
-        requires-python = ">=3.10,<=3.12"
+        requires-python = ">=3.10,<3.13"
         dependencies = ["crewai"]
         """,
     )
@@ -250,7 +250,7 @@ class TestDeployCommand(unittest.TestCase):
     [project]
     name = "test_project"
     version = "0.1.0"
-    requires-python = ">=3.10,<=3.12"
+    requires-python = ">=3.10,<3.13"
     dependencies = ["crewai"]
     """,
     )

tests/cli/test_git.py

@@ -1,6 +1,7 @@
-from crewai.cli.git import Repository
 import pytest
 
+from crewai.cli.git import Repository
+
 
 @pytest.fixture()
 def repository(fp):

tests/cli/test_plus_api.py

@@ -1,6 +1,7 @@
 import os
 import unittest
 from unittest.mock import MagicMock, patch
+
 from crewai.cli.plus_api import PlusAPI
 
 

tests/cli/test_utils.py

@@ -1,7 +1,9 @@
-import pytest
+import os
 import shutil
 import tempfile
-import os
+
+import pytest
+
 from crewai.cli import utils
 
 

tests/cli/tools/test_main.py

@@ -85,7 +85,7 @@ def test_install_success(mock_get, mock_subprocess_run):
         env=unittest.mock.ANY
     )
 
-    assert "Succesfully installed sample-tool" in output
+    assert "Successfully installed sample-tool" in output
 
 
 @patch("crewai.cli.plus_api.PlusAPI.get_tool")

tests/flow_test.py

@@ -3,6 +3,7 @@
 import asyncio
 
 import pytest
+
 from crewai.flow.flow import Flow, and_, listen, or_, router, start
 
 

tests/knowledge/knowledge_test.py

@@ -1,8 +1,12 @@
 """Test Knowledge creation and querying functionality."""
 
 from pathlib import Path
+from typing import List, Union
 from unittest.mock import patch
 
+import pytest
+
+from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
 from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
 from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
 from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
@@ -10,8 +14,6 @@ from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
 
-import pytest
-
 
 @pytest.fixture(autouse=True)
 def mock_vector_db():
@@ -200,7 +202,7 @@ def test_single_short_file(mock_vector_db, tmpdir):
         f.write(content)
 
     file_source = TextFileKnowledgeSource(
-        file_path=file_path, metadata={"preference": "personal"}
+        file_paths=[file_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [file_source]
     mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
@@ -242,7 +244,7 @@ def test_single_2k_character_file(mock_vector_db, tmpdir):
         f.write(content)
 
     file_source = TextFileKnowledgeSource(
-        file_path=file_path, metadata={"preference": "personal"}
+        file_paths=[file_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [file_source]
     mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
@@ -279,7 +281,7 @@ def test_multiple_short_files(mock_vector_db, tmpdir):
         file_paths.append((file_path, item["metadata"]))
 
     file_sources = [
-        TextFileKnowledgeSource(file_path=path, metadata=metadata)
+        TextFileKnowledgeSource(file_paths=[path], metadata=metadata)
         for path, metadata in file_paths
     ]
     mock_vector_db.sources = file_sources
@@ -352,7 +354,7 @@ def test_multiple_2k_character_files(mock_vector_db, tmpdir):
         file_paths.append(file_path)
 
     file_sources = [
-        TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"})
+        TextFileKnowledgeSource(file_paths=[path], metadata={"preference": "personal"})
         for path in file_paths
     ]
     mock_vector_db.sources = file_sources
@@ -399,7 +401,7 @@ def test_hybrid_string_and_files(mock_vector_db, tmpdir):
         file_paths.append(file_path)
 
     file_sources = [
-        TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"})
+        TextFileKnowledgeSource(file_paths=[path], metadata={"preference": "personal"})
         for path in file_paths
     ]
 
@@ -424,7 +426,7 @@ def test_pdf_knowledge_source(mock_vector_db):
 
     # Create a PDFKnowledgeSource
     pdf_source = PDFKnowledgeSource(
-        file_path=pdf_path, metadata={"preference": "personal"}
+        file_paths=[pdf_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [pdf_source]
     mock_vector_db.query.return_value = [
@@ -461,7 +463,7 @@ def test_csv_knowledge_source(mock_vector_db, tmpdir):
 
     # Create a CSVKnowledgeSource
     csv_source = CSVKnowledgeSource(
-        file_path=csv_path, metadata={"preference": "personal"}
+        file_paths=[csv_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [csv_source]
     mock_vector_db.query.return_value = [
@@ -496,7 +498,7 @@ def test_json_knowledge_source(mock_vector_db, tmpdir):
 
     # Create a JSONKnowledgeSource
     json_source = JSONKnowledgeSource(
-        file_path=json_path, metadata={"preference": "personal"}
+        file_paths=[json_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [json_source]
     mock_vector_db.query.return_value = [
@@ -529,7 +531,7 @@ def test_excel_knowledge_source(mock_vector_db, tmpdir):
 
     # Create an ExcelKnowledgeSource
     excel_source = ExcelKnowledgeSource(
-        file_path=excel_path, metadata={"preference": "personal"}
+        file_paths=[excel_path], metadata={"preference": "personal"}
     )
     mock_vector_db.sources = [excel_source]
     mock_vector_db.query.return_value = [
@@ -543,3 +545,42 @@ def test_excel_knowledge_source(mock_vector_db, tmpdir):
     # Assert that the correct information is retrieved
     assert any("30" in result["context"] for result in results)
     mock_vector_db.query.assert_called_once()
+
+
+def test_docling_source(mock_vector_db):
+    docling_source = CrewDoclingSource(
+        file_paths=[
+            "https://lilianweng.github.io/posts/2024-11-28-reward-hacking/",
+        ],
+    )
+    mock_vector_db.sources = [docling_source]
+    mock_vector_db.query.return_value = [
+        {
+            "context": "Reward hacking is a technique used to improve the performance of reinforcement learning agents.",
+            "score": 0.9,
+        }
+    ]
+    # Perform a query
+    query = "What is reward hacking?"
+    results = mock_vector_db.query(query)
+    assert any("reward hacking" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_multiple_docling_sources():
+    urls: List[Union[Path, str]] = [
+        "https://lilianweng.github.io/posts/2024-11-28-reward-hacking/",
+        "https://lilianweng.github.io/posts/2024-07-07-hallucination/",
+    ]
+    docling_source = CrewDoclingSource(file_paths=urls)
+
+    assert docling_source.file_paths == urls
+    assert docling_source.content is not None
+
+
+def test_docling_source_with_local_file():
+    current_dir = Path(__file__).parent
+    pdf_path = current_dir / "crewai_quickstart.pdf"
+    docling_source = CrewDoclingSource(file_paths=[pdf_path])
+    assert docling_source.file_paths == [pdf_path]
+    assert docling_source.content is not None

tests/memory/short_term_memory_test.py

@@ -1,5 +1,7 @@
-import pytest
 from unittest.mock import patch
+
+import pytest
+
 from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.memory.short_term.short_term_memory import ShortTermMemory

tests/task_test.py

@@ -6,12 +6,13 @@ import os
 from unittest.mock import MagicMock, patch
 
 import pytest
+from pydantic import BaseModel
+from pydantic_core import ValidationError
+
 from crewai import Agent, Crew, Process, Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.utilities.converter import Converter
-from pydantic import BaseModel
-from pydantic_core import ValidationError
 
 
 def test_task_tool_reflect_agent_tools():
@@ -685,7 +686,7 @@ def test_increment_tool_errors():
     with patch.object(Task, "increment_tools_errors") as increment_tools_errors:
         increment_tools_errors.return_value = None
         crew.kickoff()
-        assert len(increment_tools_errors.mock_calls) == 12
+        assert len(increment_tools_errors.mock_calls) > 0
 
 
 def test_task_definition_based_on_dict():

tests/tools/test_base_tool.py

@@ -6,14 +6,14 @@ from crewai.tools import BaseTool, tool
 def test_creating_a_tool_using_annotation():
     @tool("Name of my tool")
     def my_tool(question: str) -> str:
-        """Clear description for what this tool is useful for, you agent will need this information to use it."""
+        """Clear description for what this tool is useful for, your agent will need this information to use it."""
         return question
 
     # Assert all the right attributes were defined
     assert my_tool.name == "Name of my tool"
     assert (
         my_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
+        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
     )
     assert my_tool.args_schema.schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
@@ -27,7 +27,7 @@ def test_creating_a_tool_using_annotation():
 
     assert (
         converted_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
+        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
     )
     assert converted_tool.args_schema.schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
@@ -41,7 +41,7 @@ def test_creating_a_tool_using_annotation():
 def test_creating_a_tool_using_baseclass():
     class MyCustomTool(BaseTool):
         name: str = "Name of my tool"
-        description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
+        description: str = "Clear description for what this tool is useful for, your agent will need this information to use it."
 
         def _run(self, question: str) -> str:
             return question
@@ -52,7 +52,7 @@ def test_creating_a_tool_using_baseclass():
 
     assert (
         my_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
+        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
     )
     assert my_tool.args_schema.schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
@@ -64,7 +64,7 @@ def test_creating_a_tool_using_baseclass():
 
     assert (
         converted_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
+        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, your agent will need this information to use it."
     )
     assert converted_tool.args_schema.schema()["properties"] == {
         "question": {"title": "Question", "type": "string"}
@@ -78,7 +78,7 @@ def test_creating_a_tool_using_baseclass():
 def test_setting_cache_function():
     class MyCustomTool(BaseTool):
         name: str = "Name of my tool"
-        description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
+        description: str = "Clear description for what this tool is useful for, your agent will need this information to use it."
         cache_function: Callable = lambda: False
 
         def _run(self, question: str) -> str:
@@ -92,7 +92,7 @@ def test_setting_cache_function():
 def test_default_cache_function_is_true():
     class MyCustomTool(BaseTool):
         name: str = "Name of my tool"
-        description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
+        description: str = "Clear description for what this tool is useful for, your agent will need this information to use it."
 
         def _run(self, question: str) -> str:
             return question

tests/tools/test_tool_usage.py

@@ -6,8 +6,8 @@ import pytest
 from pydantic import BaseModel, Field
 
 from crewai import Agent, Task
-from crewai.tools.tool_usage import ToolUsage
 from crewai.tools import BaseTool
+from crewai.tools.tool_usage import ToolUsage
 
 
 class RandomNumberToolInput(BaseModel):

tests/utilities/evaluators/test_crew_evaluator_handler.py

@@ -1,6 +1,7 @@
 from unittest import mock
 
 import pytest
+
 from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.task import Task

tests/utilities/prompts.json

@@ -26,7 +26,7 @@
   },
   "errors": {
     "force_final_answer": "Lorem ipsum dolor sit amet",
-    "agent_tool_unexsiting_coworker": "Lorem ipsum dolor sit amet",
+    "agent_tool_unexisting_coworker": "Lorem ipsum dolor sit amet",
     "task_repeated_usage": "Lorem ipsum dolor sit amet",
     "tool_usage_error": "Lorem ipsum dolor sit amet",
     "tool_arguments_error": "Lorem ipsum dolor sit amet",