cleanup

* added tool for docling support

* docling support installation

* use file_paths instead of file_path

* fix import

* organized imports

* run_type docs

* needs to be list

* fixed logic

* logged but file_path is backwards compatible

* use file_paths instead of file_path 2

* added test for multiple sources for file_paths

* fix run-types

* enabling local files to work and type cleanup

* linted

* fix test and types

* fixed run types

* fix types

* renamed to CrewDoclingSource

* linted

* added docs

* resolve conflicts

---------

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

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -65,7 +65,6 @@ body:
         - '3.10'
         - '3.11'
         - '3.12'
-        - '3.13'
     validations:
       required: true
   - type: input
@@ -113,4 +112,4 @@ body:
       label: Additional context
       description: Add any other context about the problem here.
     validations:
-      required: true
+      required: true

.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:

.github/workflows/tests.yml

@@ -23,7 +23,7 @@ jobs:
 
 
       - name: Set up Python
-        run: uv python install 3.11.9
+        run: uv python install 3.12.8
 
       - name: Install the project
         run: uv sync --dev --all-extras

.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 <=3.13 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.
+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

@@ -8,8 +8,8 @@ icon: book
 
 ## What is Knowledge?
 
-Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks. 
-Think of it as giving your agents a reference library they can consult while working. 
+Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
+Think of it as giving your agents a reference library they can consult while working.
 
 <Info>
   Key benefits of using Knowledge:
@@ -47,7 +47,7 @@ from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSourc
 # Create a knowledge source
 content = "Users name is John. He is 30 years old and lives in San Francisco."
 string_source = StringKnowledgeSource(
-    content=content, 
+    content=content,
 )
 
 # Create an LLM with a temperature of 0 to ensure deterministic outputs
@@ -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
@@ -122,7 +171,6 @@ crewai reset-memories --knowledge
 
 This is useful when you've updated your knowledge sources and want to ensure that the agents are using the most recent information.
 
-
 ## Custom Knowledge Sources
 
 CrewAI allows you to create custom knowledge sources for any type of data by extending the `BaseKnowledgeSource` class. Let's create a practical example that fetches and processes space news articles.
@@ -141,10 +189,10 @@ from pydantic import BaseModel, Field
 
 class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
     """Knowledge source that fetches data from Space News API."""
-    
+
     api_endpoint: str = Field(description="API endpoint URL")
     limit: int = Field(default=10, description="Number of articles to fetch")
-    
+
     def load_content(self) -> Dict[Any, str]:
         """Fetch and format space news articles."""
         try:
@@ -152,15 +200,15 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
                 f"{self.api_endpoint}?limit={self.limit}"
             )
             response.raise_for_status()
-            
+
             data = response.json()
             articles = data.get('results', [])
-            
+
             formatted_data = self._format_articles(articles)
             return {self.api_endpoint: formatted_data}
         except Exception as e:
             raise ValueError(f"Failed to fetch space news: {str(e)}")
-    
+
     def _format_articles(self, articles: list) -> str:
         """Format articles into readable text."""
         formatted = "Space News Articles:\n\n"
@@ -180,7 +228,7 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
         for _, text in content.items():
             chunks = self._chunk_text(text)
             self.chunks.extend(chunks)
-        
+
         self._save_documents()
 
 # Create knowledge source
@@ -193,7 +241,7 @@ recent_news = SpaceNewsKnowledgeSource(
 space_analyst = Agent(
     role="Space News Analyst",
     goal="Answer questions about space news accurately and comprehensively",
-    backstory="""You are a space industry analyst with expertise in space exploration, 
+    backstory="""You are a space industry analyst with expertise in space exploration,
     satellite technology, and space industry trends. You excel at answering questions
     about space news and providing detailed, accurate information.""",
     knowledge_sources=[recent_news],
@@ -220,13 +268,14 @@ result = crew.kickoff(
     inputs={"user_question": "What are the latest developments in space exploration?"}
 )
 ```
+
 ```output Output
 # Agent: Space News Analyst
 ## Task: Answer this question about space news: What are the latest developments in space exploration?
 
 
 # Agent: Space News Analyst
-## Final Answer: 
+## Final Answer:
 The latest developments in space exploration, based on recent space news articles, include the following:
 
 1. SpaceX has received the final regulatory approvals to proceed with the second integrated Starship/Super Heavy launch, scheduled for as soon as the morning of Nov. 17, 2023. This is a significant step in SpaceX's ambitious plans for space exploration and colonization. [Source: SpaceNews](https://spacenews.com/starship-cleared-for-nov-17-launch/)
@@ -242,11 +291,13 @@ The latest developments in space exploration, based on recent space news article
 6. The National Natural Science Foundation of China has outlined a five-year project for researchers to study the assembly of ultra-large spacecraft. This could lead to significant advancements in spacecraft technology and space exploration capabilities. [Source: SpaceNews](https://spacenews.com/china-researching-challenges-of-kilometer-scale-ultra-large-spacecraft/)
 
 7. The Center for AEroSpace Autonomy Research (CAESAR) at Stanford University is focusing on spacecraft autonomy. The center held a kickoff event on May 22, 2024, to highlight the industry, academia, and government collaboration it seeks to foster. This could lead to significant advancements in autonomous spacecraft technology. [Source: SpaceNews](https://spacenews.com/stanford-center-focuses-on-spacecraft-autonomy/)
-``` 
+```
+
 </CodeGroup>
 #### Key Components Explained
 
 1. **Custom Knowledge Source (`SpaceNewsKnowledgeSource`)**:
+
    - Extends `BaseKnowledgeSource` for integration with CrewAI
    - Configurable API endpoint and article limit
    - Implements three key methods:
@@ -255,10 +306,12 @@ The latest developments in space exploration, based on recent space news article
      - `add()`: Processes and stores the content
 
 2. **Agent Configuration**:
+
    - Specialized role as a Space News Analyst
    - Uses the knowledge source to access space news
 
 3. **Task Setup**:
+
    - Takes a user question as input through `{user_question}`
    - Designed to provide detailed answers based on the knowledge source
 
@@ -267,6 +320,7 @@ The latest developments in space exploration, based on recent space news article
    - Handles input/output through the kickoff method
 
 This example demonstrates how to:
+
 - Create a custom knowledge source that fetches real-time data
 - Process and format external data for AI consumption
 - Use the knowledge source to answer specific user questions
@@ -274,13 +328,15 @@ This example demonstrates how to:
 
 #### About the Spaceflight News API
 
-The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/documentation), which:
+The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/docs/), which:
+
 - Provides free access to space-related news articles
 - Requires no authentication
 - Returns structured data about space news
 - Supports pagination and filtering
 
 You can customize the API query by modifying the endpoint URL:
+
 ```python
 # Fetch more articles
 recent_news = SpaceNewsKnowledgeSource(
@@ -303,9 +359,9 @@ recent_news = SpaceNewsKnowledgeSource(
     - Consider content overlap for context preservation
     - Organize related information into separate knowledge sources
   </Accordion>
-  
+
   <Accordion title="Performance Tips">
-    - Adjust chunk sizes based on content complexity
+    - Adjust chunk sizes based on content complexity 
     - Configure appropriate embedding models
     - Consider using local embedding providers for faster processing
   </Accordion>

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/concepts/tasks.mdx

@@ -6,7 +6,7 @@ icon: list-check
 
 ## Overview of a Task
 
-In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`. 
+In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`.
 
 Tasks provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
 
@@ -263,8 +263,148 @@ analysis_task = Task(
 )
 ```
 
+## Task Guardrails
+
+Task guardrails provide a way to validate and transform task outputs before they
+are passed to the next task. This feature helps ensure data quality and provides
+efeedback to agents when their output doesn't meet specific criteria.
+
+### Using Task Guardrails
+
+To add a guardrail to a task, provide a validation function through the `guardrail` parameter:
+
+```python Code
+from typing import Tuple, Union, Dict, Any
+
+def validate_blog_content(result: str) -> Tuple[bool, Union[Dict[str, Any], str]]:
+    """Validate blog content meets requirements."""
+    try:
+        # Check word count
+        word_count = len(result.split())
+        if word_count > 200:
+            return (False, {
+                "error": "Blog content exceeds 200 words",
+                "code": "WORD_COUNT_ERROR",
+                "context": {"word_count": word_count}
+            })
+
+        # Additional validation logic here
+        return (True, result.strip())
+    except Exception as e:
+        return (False, {
+            "error": "Unexpected error during validation",
+            "code": "SYSTEM_ERROR"
+        })
+
+blog_task = Task(
+    description="Write a blog post about AI",
+    expected_output="A blog post under 200 words",
+    agent=blog_agent,
+    guardrail=validate_blog_content  # Add the guardrail function
+)
+```
+
+### Guardrail Function Requirements
+
+1. **Function Signature**:
+   - Must accept exactly one parameter (the task output)
+   - Should return a tuple of `(bool, Any)`
+   - Type hints are recommended but optional
+
+2. **Return Values**:
+   - Success: Return `(True, validated_result)`
+   - Failure: Return `(False, error_details)`
+
+### Error Handling Best Practices
+
+1. **Structured Error Responses**:
+```python Code
+def validate_with_context(result: str) -> Tuple[bool, Union[Dict[str, Any], str]]:
+    try:
+        # Main validation logic
+        validated_data = perform_validation(result)
+        return (True, validated_data)
+    except ValidationError as e:
+        return (False, {
+            "error": str(e),
+            "code": "VALIDATION_ERROR",
+            "context": {"input": result}
+        })
+    except Exception as e:
+        return (False, {
+            "error": "Unexpected error",
+            "code": "SYSTEM_ERROR"
+        })
+```
+
+2. **Error Categories**:
+   - Use specific error codes
+   - Include relevant context
+   - Provide actionable feedback
+
+3. **Validation Chain**:
+```python Code
+from typing import Any, Dict, List, Tuple, Union
+
+def complex_validation(result: str) -> Tuple[bool, Union[str, Dict[str, Any]]]:
+    """Chain multiple validation steps."""
+    # Step 1: Basic validation
+    if not result:
+        return (False, {"error": "Empty result", "code": "EMPTY_INPUT"})
+
+    # Step 2: Content validation
+    try:
+        validated = validate_content(result)
+        if not validated:
+            return (False, {"error": "Invalid content", "code": "CONTENT_ERROR"})
+
+        # Step 3: Format validation
+        formatted = format_output(validated)
+        return (True, formatted)
+    except Exception as e:
+        return (False, {
+            "error": str(e),
+            "code": "VALIDATION_ERROR",
+            "context": {"step": "content_validation"}
+        })
+```
+
+### Handling Guardrail Results
+
+When a guardrail returns `(False, error)`:
+1. The error is sent back to the agent
+2. The agent attempts to fix the issue
+3. The process repeats until:
+   - The guardrail returns `(True, result)`
+   - Maximum retries are reached
+
+Example with retry handling:
+```python Code
+from typing import Optional, Tuple, Union
+
+def validate_json_output(result: str) -> Tuple[bool, Union[Dict[str, Any], str]]:
+    """Validate and parse JSON output."""
+    try:
+        # Try to parse as JSON
+        data = json.loads(result)
+        return (True, data)
+    except json.JSONDecodeError as e:
+        return (False, {
+            "error": "Invalid JSON format",
+            "code": "JSON_ERROR",
+            "context": {"line": e.lineno, "column": e.colno}
+        })
+
+task = Task(
+    description="Generate a JSON report",
+    expected_output="A valid JSON object",
+    agent=analyst,
+    guardrail=validate_json_output,
+    max_retries=3  # Limit retry attempts
+)
+```
+
 ## Getting Structured Consistent Outputs from Tasks
-When you need to ensure that a task outputs a structured and consistent format, you can use the `output_pydantic` or `output_json` properties on a task. These properties allow you to define the expected output structure, making it easier to parse and utilize the results in your application. 
 
 <Note>
 It's also important to note that the output of the final task of a crew becomes the final output of the actual crew itself.
@@ -608,6 +748,114 @@ While creating and executing tasks, certain validation mechanisms are in place t
 
 These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
 
+## Task Guardrails
+
+Task guardrails provide a powerful way to validate, transform, or filter task outputs before they are passed to the next task. Guardrails are optional functions that execute before the next task starts, allowing you to ensure that task outputs meet specific requirements or formats.
+
+### Basic Usage
+
+```python Code
+from typing import Tuple, Union
+from crewai import Task
+
+def validate_json_output(result: str) -> Tuple[bool, Union[dict, str]]:
+    """Validate that the output is valid JSON."""
+    try:
+        json_data = json.loads(result)
+        return (True, json_data)
+    except json.JSONDecodeError:
+        return (False, "Output must be valid JSON")
+
+task = Task(
+    description="Generate JSON data",
+    expected_output="Valid JSON object",
+    guardrail=validate_json_output
+)
+```
+
+### How Guardrails Work
+
+1. **Optional Attribute**: Guardrails are an optional attribute at the task level, allowing you to add validation only where needed.
+2. **Execution Timing**: The guardrail function is executed before the next task starts, ensuring valid data flow between tasks.
+3. **Return Format**: Guardrails must return a tuple of `(success, data)`:
+   - If `success` is `True`, `data` is the validated/transformed result
+   - If `success` is `False`, `data` is the error message
+4. **Result Routing**:
+   - On success (`True`), the result is automatically passed to the next task
+   - On failure (`False`), the error is sent back to the agent to generate a new answer
+
+### Common Use Cases
+
+#### Data Format Validation
+```python Code
+def validate_email_format(result: str) -> Tuple[bool, Union[str, str]]:
+    """Ensure the output contains a valid email address."""
+    import re
+    email_pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
+    if re.match(email_pattern, result.strip()):
+        return (True, result.strip())
+    return (False, "Output must be a valid email address")
+```
+
+#### Content Filtering
+```python Code
+def filter_sensitive_info(result: str) -> Tuple[bool, Union[str, str]]:
+    """Remove or validate sensitive information."""
+    sensitive_patterns = ['SSN:', 'password:', 'secret:']
+    for pattern in sensitive_patterns:
+        if pattern.lower() in result.lower():
+            return (False, f"Output contains sensitive information ({pattern})")
+    return (True, result)
+```
+
+#### Data Transformation
+```python Code
+def normalize_phone_number(result: str) -> Tuple[bool, Union[str, str]]:
+    """Ensure phone numbers are in a consistent format."""
+    import re
+    digits = re.sub(r'\D', '', result)
+    if len(digits) == 10:
+        formatted = f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
+        return (True, formatted)
+    return (False, "Output must be a 10-digit phone number")
+```
+
+### Advanced Features
+
+#### Chaining Multiple Validations
+```python Code
+def chain_validations(*validators):
+    """Chain multiple validators together."""
+    def combined_validator(result):
+        for validator in validators:
+            success, data = validator(result)
+            if not success:
+                return (False, data)
+            result = data
+        return (True, result)
+    return combined_validator
+
+# Usage
+task = Task(
+    description="Get user contact info",
+    expected_output="Email and phone",
+    guardrail=chain_validations(
+        validate_email_format,
+        filter_sensitive_info
+    )
+)
+```
+
+#### Custom Retry Logic
+```python Code
+task = Task(
+    description="Generate data",
+    expected_output="Valid data",
+    guardrail=validate_data,
+    max_retries=5  # Override default retry limit
+)
+```
+
 ## Creating Directories when Saving Files
 
 You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
@@ -629,7 +877,7 @@ save_output_task = Task(
 
 ## Conclusion
 
-Tasks are the driving force behind the actions of agents in CrewAI. 
-By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. 
-Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, 
+Tasks are the driving force behind the actions of agents in CrewAI.
+By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit.
+Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential,
 ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

docs/concepts/tools.mdx

@@ -172,6 +172,48 @@ def my_tool(question: str) -> str:
     return "Result from your custom tool"
 ```
 
+### Structured Tools
+
+The `StructuredTool` class wraps functions as tools, providing flexibility and validation while reducing boilerplate. It supports custom schemas and dynamic logic for seamless integration of complex functionalities.
+
+#### Example:
+Using `StructuredTool.from_function`, you can wrap a function that interacts with an external API or system, providing a structured interface. This enables robust validation and consistent execution, making it easier to integrate complex functionalities into your applications as demonstrated in the following example:
+
+```python
+from crewai.tools.structured_tool import CrewStructuredTool
+from pydantic import BaseModel
+
+# Define the schema for the tool's input using Pydantic
+class APICallInput(BaseModel):
+    endpoint: str
+    parameters: dict
+
+# Wrapper function to execute the API call
+def tool_wrapper(*args, **kwargs):
+    # Here, you would typically call the API using the parameters
+    # For demonstration, we'll return a placeholder string
+    return f"Call the API at {kwargs['endpoint']} with parameters {kwargs['parameters']}"
+
+# Create and return the structured tool
+def create_structured_tool():
+    return CrewStructuredTool.from_function(
+        name='Wrapper API',
+        description="A tool to wrap API calls with structured input.",
+        args_schema=APICallInput,
+        func=tool_wrapper,
+    )
+
+# Example usage
+structured_tool = create_structured_tool()
+
+# Execute the tool with structured input
+result = structured_tool._run(**{
+    "endpoint": "https://example.com/api",
+    "parameters": {"key1": "value1", "key2": "value2"}
+})
+print(result)  # Output: Call the API at https://example.com/api with parameters {'key1': 'value1', 'key2': 'value2'}
+```
+
 ### Custom Caching Mechanism
 
 <Tip>

docs/installation.mdx

@@ -7,7 +7,7 @@ icon: wrench
 <Note>
   **Python Version Requirements**
   
-  CrewAI requires `Python >=3.10 and <=3.13`. 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,<=3.13"
+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

@@ -143,6 +143,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         tool_result = self._execute_tool_and_check_finality(
                             formatted_answer
                         )
+                        if self.step_callback:
+                            self.step_callback(tool_result)
+
                         formatted_answer.text += f"\nObservation: {tool_result.result}"
                         formatted_answer.result = tool_result.result
                         if tool_result.result_as_answer:
@@ -299,7 +302,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         self._i18n.slice("summarizer_system_message"), role="system"
                     ),
                     self._format_msg(
-                        self._i18n.slice("sumamrize_instruction").format(group=group),
+                        self._i18n.slice("summarize_instruction").format(group=group),
                     ),
                 ],
                 callbacks=self.callbacks,
@@ -410,7 +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 <=3.13 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.
+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,<=3.13"
+requires-python = ">=3.10,<3.13"
 dependencies = [
     "crewai[tools]>=0.86.0,<1.0.0"
 ]
@@ -18,3 +18,6 @@ test = "{{folder_name}}.main:test"
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
+
+[tool.crewai]
+type = "crew"

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 <=3.13 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.
+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,<=3.13"
+requires-python = ">=3.10,<3.13"
 dependencies = [
     "crewai[tools]>=0.86.0,<1.0.0",
 ]
@@ -15,3 +15,6 @@ plot = "{{folder_name}}.main:plot"
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
+
+[tool.crewai]
+type = "flow"

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,<=3.13"
-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 <=3.13 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

@@ -3,8 +3,10 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
-requires-python = ">=3.10,<=3.13"
+requires-python = ">=3.10,<3.13"
 dependencies = [
     "crewai[tools]>=0.86.0"
 ]
 
+[tool.crewai]
+type = "tool"

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/utils.py

@@ -33,26 +33,6 @@ def copy_template(src, dst, name, class_name, folder_name):
     click.secho(f"  - Created {dst}", fg="green")
 
 
-# Drop the simple_toml_parser when we move to python3.11
-def simple_toml_parser(content):
-    result = {}
-    current_section = result
-    for line in content.split("\n"):
-        line = line.strip()
-        if line.startswith("[") and line.endswith("]"):
-            # New section
-            section = line[1:-1].split(".")
-            current_section = result
-            for key in section:
-                current_section = current_section.setdefault(key, {})
-        elif "=" in line:
-            key, value = line.split("=", 1)
-            key = key.strip()
-            value = value.strip().strip('"')
-            current_section[key] = value
-    return result
-
-
 def read_toml(file_path: str = "pyproject.toml"):
     """Read the content of a TOML file and return it as a dictionary."""
     with open(file_path, "rb") as f:
@@ -63,7 +43,7 @@ def read_toml(file_path: str = "pyproject.toml"):
 def parse_toml(content):
     if sys.version_info >= (3, 11):
         return tomllib.loads(content)
-    return simple_toml_parser(content)
+    return tomli.loads(content)
 
 
 def get_project_name(

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 +70,30 @@ 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
+
+        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",
+            )
+            self.file_paths = self.file_path
+
+        if self.file_paths is None:
+            raise ValueError("Your source must be provided with a file_paths: []")
+
+        # Convert single path to list
+        path_list: List[Union[Path, str]] = (
+            [self.file_paths]
+            if isinstance(self.file_paths, (str, Path))
+            else list(self.file_paths)
+            if isinstance(self.file_paths, list)
+            else []
         )
 
-        if not isinstance(paths, list):
-            raise ValueError("file_path must be a Path, str, or a list of these types")
+        if not path_list:
+            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]
+        return [self.convert_to_path(path) for path in path_list]

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

@@ -3,6 +3,7 @@ import hashlib
 import io
 import logging
 import os
+import shutil
 from typing import Any, Dict, List, Optional, Union, cast
 
 import chromadb
@@ -13,6 +14,7 @@ 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
 
@@ -105,58 +107,77 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             raise Exception("Failed to create or get collection")
 
     def reset(self):
-        if self.app:
-            self.app.reset()
-        else:
-            base_path = os.path.join(db_storage_path(), "knowledge")
+        base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
+        if not self.app:
             self.app = chromadb.PersistentClient(
                 path=base_path,
                 settings=Settings(allow_reset=True),
             )
-            self.app.reset()
+
+        self.app.reset()
+        shutil.rmtree(base_path)
+        self.app = None
+        self.collection = None
 
     def save(
         self,
         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/memory/storage/rag_storage.py

@@ -150,9 +150,11 @@ class RAGStorage(BaseRAGStorage):
 
     def reset(self) -> None:
         try:
-            shutil.rmtree(f"{db_storage_path()}/{self.type}")
             if self.app:
                 self.app.reset()
+                shutil.rmtree(f"{db_storage_path()}/{self.type}")
+                self.app = None
+                self.collection = None
         except Exception as e:
             if "attempt to write a readonly database" in str(e):
                 # Ignore this specific error

src/crewai/memory/user/user_memory.py

@@ -37,7 +37,7 @@ class UserMemory(Memory):
         limit: int = 3,
         score_threshold: float = 0.35,
     ):
-        results = super().search(
+        results = self.storage.search(
             query=query,
             limit=limit,
             score_threshold=score_threshold,

src/crewai/project/annotations.py

@@ -66,6 +66,8 @@ def cache_handler(func):
 
 
 def crew(func) -> Callable[..., Crew]:
+
+    @wraps(func)
     def wrapper(self, *args, **kwargs) -> Crew:
         instantiated_tasks = []
         instantiated_agents = []

src/crewai/project/crew_base.py

@@ -213,4 +213,8 @@ def CrewBase(cls: T) -> T:
                     callback_functions[callback]() for callback in callbacks
                 ]
 
+    # Include base class (qual)name in the wrapper class (qual)name.
+    WrappedClass.__name__ = CrewBase.__name__ + "(" + cls.__name__ + ")"
+    WrappedClass.__qualname__ = CrewBase.__qualname__ + "(" + cls.__name__ + ")"
+  
     return cast(T, WrappedClass)

src/crewai/project/utils.py

@@ -1,11 +1,14 @@
+from functools import wraps
+
+
 def memoize(func):
     cache = {}
 
+    @wraps(func)
     def memoized_func(*args, **kwargs):
         key = (args, tuple(kwargs.items()))
         if key not in cache:
             cache[key] = func(*args, **kwargs)
         return cache[key]
 
-    memoized_func.__dict__.update(func.__dict__)
     return memoized_func

src/crewai/task.py

@@ -1,12 +1,25 @@
 import datetime
+import inspect
 import json
-import os
+import logging
 import threading
 import uuid
 from concurrent.futures import Future
 from copy import copy
 from hashlib import md5
-from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union
+from pathlib import Path
+from typing import (
+    Any,
+    Callable,
+    ClassVar,
+    Dict,
+    List,
+    Optional,
+    Set,
+    Tuple,
+    Type,
+    Union,
+)
 
 from opentelemetry.trace import Span
 from pydantic import (
@@ -20,6 +33,7 @@ from pydantic import (
 from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.tasks.guardrail_result import GuardrailResult
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
@@ -49,6 +63,7 @@ class Task(BaseModel):
     """
 
     __hash__ = object.__hash__  # type: ignore
+    logger: ClassVar[logging.Logger] = logging.getLogger(__name__)
     used_tools: int = 0
     tools_errors: int = 0
     delegations: int = 0
@@ -110,6 +125,55 @@ class Task(BaseModel):
         default=None,
     )
     processed_by_agents: Set[str] = Field(default_factory=set)
+    guardrail: Optional[Callable[[TaskOutput], Tuple[bool, Any]]] = Field(
+        default=None,
+        description="Function to validate task output before proceeding to next task"
+    )
+    max_retries: int = Field(
+        default=3,
+        description="Maximum number of retries when guardrail fails"
+    )
+    retry_count: int = Field(
+        default=0,
+        description="Current number of retries"
+    )
+
+    @field_validator("guardrail")
+    @classmethod
+    def validate_guardrail_function(cls, v: Optional[Callable]) -> Optional[Callable]:
+        """Validate that the guardrail function has the correct signature and behavior.
+        
+        While type hints provide static checking, this validator ensures runtime safety by:
+        1. Verifying the function accepts exactly one parameter (the TaskOutput)
+        2. Checking return type annotations match Tuple[bool, Any] if present
+        3. Providing clear, immediate error messages for debugging
+        
+        This runtime validation is crucial because:
+        - Type hints are optional and can be ignored at runtime
+        - Function signatures need immediate validation before task execution
+        - Clear error messages help users debug guardrail implementation issues
+        
+        Args:
+            v: The guardrail function to validate
+            
+        Returns:
+            The validated guardrail function
+            
+        Raises:
+            ValueError: If the function signature is invalid or return annotation
+                       doesn't match Tuple[bool, Any]
+        """
+        if v is not None:
+            sig = inspect.signature(v)
+            if len(sig.parameters) != 1:
+                raise ValueError("Guardrail function must accept exactly one parameter")
+
+            # Check return annotation if present, but don't require it
+            return_annotation = sig.return_annotation
+            if return_annotation != inspect.Signature.empty:
+                if not (return_annotation == Tuple[bool, Any] or str(return_annotation) == 'Tuple[bool, Any]'):
+                    raise ValueError("If return type is annotated, it must be Tuple[bool, Any]")
+        return v
 
     _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry)
     _execution_span: Optional[Span] = PrivateAttr(default=None)
@@ -254,7 +318,6 @@ class Task(BaseModel):
         )
 
         pydantic_output, json_output = self._export_output(result)
-
         task_output = TaskOutput(
             name=self.name,
             description=self.description,
@@ -265,6 +328,37 @@ class Task(BaseModel):
             agent=agent.role,
             output_format=self._get_output_format(),
         )
+
+        if self.guardrail:
+            guardrail_result = GuardrailResult.from_tuple(self.guardrail(task_output))
+            if not guardrail_result.success:
+                if self.retry_count >= self.max_retries:
+                    raise Exception(
+                        f"Task failed guardrail validation after {self.max_retries} retries. "
+                        f"Last error: {guardrail_result.error}"
+                    )
+
+                self.retry_count += 1
+                context = (
+                    f"### Previous attempt failed validation: {guardrail_result.error}\n\n\n"
+                    f"### Previous result:\n{task_output.raw}\n\n\n"
+                    "Try again, making sure to address the validation error."
+                )
+                return self._execute_core(agent, context, tools)
+
+            if guardrail_result.result is None:
+                raise Exception(
+                    "Task guardrail returned None as result. This is not allowed."
+                )
+
+            if isinstance(guardrail_result.result, str):
+                task_output.raw = guardrail_result.result
+                pydantic_output, json_output = self._export_output(guardrail_result.result)
+                task_output.pydantic = pydantic_output
+                task_output.json_dict = json_output
+            elif isinstance(guardrail_result.result, TaskOutput):
+                task_output = guardrail_result.result
+
         self.output = task_output
 
         self._set_end_execution_time(start_time)
@@ -308,7 +402,18 @@ class Task(BaseModel):
 
         if inputs:
             self.description = self._original_description.format(**inputs)
-            self.expected_output = self._original_expected_output.format(**inputs)
+            self.expected_output = self.interpolate_only(
+                input_string=self._original_expected_output, inputs=inputs
+            )
+
+    def interpolate_only(self, input_string: str, inputs: Dict[str, Any]) -> str:
+        """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched."""
+        escaped_string = input_string.replace("{", "{{").replace("}", "}}")
+
+        for key in inputs.keys():
+            escaped_string = escaped_string.replace(f"{{{{{key}}}}}", f"{{{key}}}")
+
+        return escaped_string.format(**inputs)
 
     def increment_tools_errors(self) -> None:
         """Increment the tools errors counter."""
@@ -390,21 +495,33 @@ class Task(BaseModel):
         return OutputFormat.RAW
 
     def _save_file(self, result: Any) -> None:
+        """Save task output to a file.
+        
+        Args:
+            result: The result to save to the file. Can be a dict or any stringifiable object.
+            
+        Raises:
+            ValueError: If output_file is not set
+            RuntimeError: If there is an error writing to the file
+        """
         if self.output_file is None:
             raise ValueError("output_file is not set.")
 
-        directory = os.path.dirname(self.output_file)  # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
+        try:
+            resolved_path = Path(self.output_file).expanduser().resolve()
+            directory = resolved_path.parent
 
-        if directory and not os.path.exists(directory):
-            os.makedirs(directory)
+            if not directory.exists():
+                directory.mkdir(parents=True, exist_ok=True)
 
-        with open(self.output_file, "w", encoding="utf-8") as file:
-            if isinstance(result, dict):
-                import json
-
-                json.dump(result, file, ensure_ascii=False, indent=2)
-            else:
-                file.write(str(result))
+            with resolved_path.open("w", encoding="utf-8") as file:
+                if isinstance(result, dict):
+                    import json
+                    json.dump(result, file, ensure_ascii=False, indent=2)
+                else:
+                    file.write(str(result))
+        except (OSError, IOError) as e:
+            raise RuntimeError(f"Failed to save output file: {e}")
         return None
 
     def __repr__(self):

src/crewai/tasks/guardrail_result.py

@@ -0,0 +1,56 @@
+"""
+Module for handling task guardrail validation results.
+
+This module provides the GuardrailResult class which standardizes
+the way task guardrails return their validation results.
+"""
+
+from typing import Any, Optional, Tuple, Union
+
+from pydantic import BaseModel, field_validator
+
+
+class GuardrailResult(BaseModel):
+    """Result from a task guardrail execution.
+
+    This class standardizes the return format of task guardrails,
+    converting tuple responses into a structured format that can
+    be easily handled by the task execution system.
+
+    Attributes:
+        success (bool): Whether the guardrail validation passed
+        result (Any, optional): The validated/transformed result if successful
+        error (str, optional): Error message if validation failed
+    """
+    success: bool
+    result: Optional[Any] = None
+    error: Optional[str] = None
+
+    @field_validator("result", "error")
+    @classmethod
+    def validate_result_error_exclusivity(cls, v: Any, info) -> Any:
+        values = info.data
+        if "success" in values:
+            if values["success"] and v and "error" in values and values["error"]:
+                raise ValueError("Cannot have both result and error when success is True")
+            if not values["success"] and v and "result" in values and values["result"]:
+                raise ValueError("Cannot have both result and error when success is False")
+        return v
+
+    @classmethod
+    def from_tuple(cls, result: Tuple[bool, Union[Any, str]]) -> "GuardrailResult":
+        """Create a GuardrailResult from a validation tuple.
+
+        Args:
+            result: A tuple of (success, data) where data is either
+                   the validated result or error message.
+
+        Returns:
+            GuardrailResult: A new instance with the tuple data.
+        """
+        success, data = result
+        return cls(
+            success=success,
+            result=data if success else None,
+            error=data if not success else None
+        )

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

@@ -12,14 +12,14 @@
     "tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple python dictionary, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all necessary information is gathered:\n\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n",
     "no_tools": "\nTo give my best complete final answer to the task use the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
     "format": "I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. To Use the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n... (this Thought/Action/Action Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n",
-    "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfy the expect criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n",
+    "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n",
     "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n",
     "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
     "expected_output": "\nThis is the expect criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
     "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
     "getting_input": "This is the agent's final answer: {final_answer}\n\n",
     "summarizer_system_message": "You are a helpful assistant that summarizes text.",
-    "sumamrize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
+    "summarize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
     "summary": "This is a summary of our conversation so far:\n{merged_summary}",
     "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
     "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
@@ -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/cassettes/test_agent_function_calling_llm.yaml

@@ -26237,7 +26237,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}], "model": "gpt-4o"}'
     headers:
@@ -26590,7 +26590,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -26941,7 +26941,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -27292,7 +27292,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -27647,7 +27647,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -28005,7 +28005,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -28364,7 +28364,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -28718,7 +28718,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -29082,7 +29082,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -29441,7 +29441,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -29802,7 +29802,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -30170,7 +30170,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -30533,7 +30533,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -30907,7 +30907,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -31273,7 +31273,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -31644,7 +31644,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the
@@ -32015,7 +32015,7 @@ interactions:
       answer."}, {"role": "user", "content": "I did it wrong. Invalid Format: I missed
       the ''Action:'' after ''Thought:''. I will do right next, and don''t use a tool
       I have already used.\n\nIf you don''t need to use any more tools, you must give
-      your best complete final answer, make sure it satisfy the expect criteria, use
+      your best complete final answer, make sure it satisfies the expected criteria, use
       the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer:
       my best complete final answer to the task.\n\n"}, {"role": "user", "content":
       "I did it wrong. Tried to both perform Action and give a Final Answer at the

tests/cassettes/test_agent_powered_by_new_o_model_family_that_allows_skipping_tool.yaml

@@ -247,7 +247,7 @@ interactions:
       {"role": "user", "content": "I did it wrong. Invalid Format: I missed the ''Action:''
       after ''Thought:''. I will do right next, and don''t use a tool I have already
       used.\n\nIf you don''t need to use any more tools, you must give your best complete
-      final answer, make sure it satisfy the expect criteria, use the EXACT format
+      final answer, make sure it satisfies the expected criteria, use the EXACT format
       below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete
       final answer to the task.\n\n"}], "model": "o1-preview"}'
     headers:

tests/cassettes/test_crew_kickoff_usage_metrics.yaml

@@ -28,13 +28,13 @@ interactions:
       user-agent:
       - OpenAI/Python 1.52.1
       x-stainless-arch:
-      - x64
+      - arm64
       x-stainless-async:
       - 'false'
       x-stainless-lang:
       - python
       x-stainless-os:
-      - Linux
+      - MacOS
       x-stainless-package-version:
       - 1.52.1
       x-stainless-raw-response:
@@ -44,26 +44,27 @@ interactions:
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.11.9
+      - 3.12.7
     method: POST
     uri: https://api.openai.com/v1/chat/completions
   response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAA4xSy27bMBC86ysWPEuB7ciV7VuAIkUObQ+59QFhTa0kttQuS9Jx08D/XkhyLAVJ
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-        WjpnKRrhEdaeMFKvuiyul3m+uV7lA9BJRbanNS5muWSdYZOtFqs8WxTZcnNmt2I0BbWDrwkAwNPw
-        7X1yRb/VDhbp86SjELAhtbssASgvtp8oDMGEiBxVOoFaOBIP1u+A5QgaGRrzQIDQ9LYBORzJA3zj
-        W8No4Wb438F7aQKgJ7DyiBb6zMhGOKRA3CJrww10xBEttIQ2toBcgTyQR2vhSNZmezLcXM39eKoP
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-        T5XRLxNPa5765/2vtcuVB8MqPIZIXVkbbsg7b8bHVbuyXm9xs8xXRa2SU/IXAAD//wMAq2ZCBWoD
-        AAA=
+    content: "{\n  \"id\": \"chatcmpl-AcdAr57gPSeXoBUvpM7ihR5ocSg8K\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1733770413,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal
+      Answer: Dogs are loyal companions, enhancing human lives with love and joy.\",\n
+      \       \"refusal\": null\n      },\n      \"logprobs\": null,\n      \"finish_reason\":
+      \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 175,\n    \"completion_tokens\":
+      24,\n    \"total_tokens\": 199,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
+      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"system_fingerprint\":
+      \"fp_bba3c8e70b\"\n}\n"
     headers:
       CF-Cache-Status:
       - DYNAMIC
       CF-RAY:
-      - 8e19bf36db158761-GRU
+      - 8ef732d93c754554-ATL
       Connection:
       - keep-alive
       Content-Encoding:
@@ -71,14 +72,14 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 12 Nov 2024 21:52:04 GMT
+      - Mon, 09 Dec 2024 18:53:33 GMT
       Server:
       - cloudflare
       Set-Cookie:
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+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"Hello! It\u2019s great to connect with
+      you. How can I assist you today?\",\n        \"refusal\": null\n      },\n      \"logprobs\":
+      null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      14,\n    \"completion_tokens\": 17,\n    \"total_tokens\": 31,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"system_fingerprint\":
+      \"fp_bba3c8e70b\"\n}\n"
     headers:
       CF-Cache-Status:
       - DYNAMIC
       CF-RAY:
-      - 8e185b31398a0303-GRU
+      - 8ef733d7bc41bada-ATL
       Connection:
       - keep-alive
       Content-Encoding:
@@ -166,7 +167,7 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 12 Nov 2024 17:49:02 GMT
+      - Mon, 09 Dec 2024 18:54:14 GMT
       Server:
       - cloudflare
       Transfer-Encoding:
@@ -178,28 +179,27 @@ interactions:
       alt-svc:
       - h3=":443"; ma=86400
       openai-organization:
-      - user-tqfegqsiobpvvjmn0giaipdq
+      - crewai-iuxna1
       openai-processing-ms:
-      - '889'
+      - '659'
       openai-version:
       - '2020-10-01'
       strict-transport-security:
       - max-age=31536000; includeSubDomains; preload
       x-ratelimit-limit-requests:
-      - '10000'
+      - '30000'
       x-ratelimit-limit-tokens:
-      - '200000'
+      - '150000000'
       x-ratelimit-remaining-requests:
-      - '9998'
+      - '29999'
       x-ratelimit-remaining-tokens:
-      - '199975'
+      - '149999974'
       x-ratelimit-reset-requests:
-      - 16.489s
+      - 2ms
       x-ratelimit-reset-tokens:
-      - 7ms
+      - 0s
       x-request-id:
-      - req_bde3810b36a4859688e53d1df64bdd20
-    status:
-      code: 200
-      message: OK
+      - req_da24049df911504f5102825db6b4aea9
+    http_version: HTTP/1.1
+    status_code: 200
 version: 1

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

@@ -228,17 +228,18 @@ class TestDeployCommand(unittest.TestCase):
         "builtins.open",
         new_callable=unittest.mock.mock_open,
         read_data="""
-    [project]
-    name = "test_project"
-    version = "0.1.0"
-    requires-python = ">=3.10,<=3.13"
-    dependencies = ["crewai"]
-    """,
+        [project]
+        name = "test_project"
+        version = "0.1.0"
+        requires-python = ">=3.10,<3.13"
+        dependencies = ["crewai"]
+        """,
     )
     def test_get_project_name_python_310(self, mock_open):
         from crewai.cli.utils import get_project_name
 
         project_name = get_project_name()
+        print("project_name", project_name)
         self.assertEqual(project_name, "test_project")
 
     @unittest.skipIf(sys.version_info < (3, 11), "Requires Python 3.11+")
@@ -249,7 +250,7 @@ class TestDeployCommand(unittest.TestCase):
     [project]
     name = "test_project"
     version = "0.1.0"
-    requires-python = ">=3.10,<=3.13"
+    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/crew_test.py

@@ -1285,7 +1285,7 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
         prompt_tokens=1562,
         completion_tokens=111,
         successful_requests=3,
-        cached_prompt_tokens=0
+        cached_prompt_tokens=0,
     )
 
 
@@ -1785,16 +1785,18 @@ def test_crew_train_success(
     crew_training_handler.assert_any_call("trained_agents_data.pkl")
     crew_training_handler().load.assert_called()
 
-    crew_training_handler().save_trained_data.assert_has_calls([
-        mock.call(
-            agent_id="Researcher",
-            trained_data=task_evaluator().evaluate_training_data().model_dump(),
-        ),
-        mock.call(
-            agent_id="Senior Writer",
-            trained_data=task_evaluator().evaluate_training_data().model_dump(),
-        )
-    ])
+    crew_training_handler().save_trained_data.assert_has_calls(
+        [
+            mock.call(
+                agent_id="Researcher",
+                trained_data=task_evaluator().evaluate_training_data().model_dump(),
+            ),
+            mock.call(
+                agent_id="Senior Writer",
+                trained_data=task_evaluator().evaluate_training_data().model_dump(),
+            ),
+        ]
+    )
 
 
 def test_crew_train_error():

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():
@@ -735,6 +736,48 @@ def test_interpolate_inputs():
     assert task.expected_output == "Bullet point list of 5 interesting ideas about ML."
 
 
+def test_interpolate_only():
+    """Test the interpolate_only method for various scenarios including JSON structure preservation."""
+    task = Task(
+        description="Unused in this test",
+        expected_output="Unused in this test"
+    )
+    
+    # Test JSON structure preservation
+    json_string = '{"info": "Look at {placeholder}", "nested": {"val": "{nestedVal}"}}'
+    result = task.interpolate_only(
+        input_string=json_string,
+        inputs={"placeholder": "the data", "nestedVal": "something else"}
+    )
+    assert '"info": "Look at the data"' in result
+    assert '"val": "something else"' in result
+    assert "{placeholder}" not in result
+    assert "{nestedVal}" not in result
+    
+    # Test normal string interpolation
+    normal_string = "Hello {name}, welcome to {place}!"
+    result = task.interpolate_only(
+        input_string=normal_string,
+        inputs={"name": "John", "place": "CrewAI"}
+    )
+    assert result == "Hello John, welcome to CrewAI!"
+    
+    # Test empty string
+    result = task.interpolate_only(
+        input_string="",
+        inputs={"unused": "value"}
+    )
+    assert result == ""
+    
+    # Test string with no placeholders
+    no_placeholders = "Hello, this is a test"
+    result = task.interpolate_only(
+        input_string=no_placeholders,
+        inputs={"unused": "value"}
+    )
+    assert result == no_placeholders
+
+
 def test_task_output_str_with_pydantic():
     from crewai.tasks.output_format import OutputFormat
 

tests/test_task_guardrails.py

@@ -0,0 +1,134 @@
+"""Tests for task guardrails functionality."""
+
+from unittest.mock import Mock
+
+import pytest
+
+from crewai.task import Task
+from crewai.tasks.task_output import TaskOutput
+
+
+def test_task_without_guardrail():
+    """Test that tasks work normally without guardrails (backward compatibility)."""
+    agent = Mock()
+    agent.role = "test_agent"
+    agent.execute_task.return_value = "test result"
+    agent.crew = None
+
+    task = Task(
+        description="Test task",
+        expected_output="Output"
+    )
+
+    result = task.execute_sync(agent=agent)
+    assert isinstance(result, TaskOutput)
+    assert result.raw == "test result"
+
+
+def test_task_with_successful_guardrail():
+    """Test that successful guardrail validation passes transformed result."""
+    def guardrail(result: TaskOutput):
+        return (True, result.raw.upper())
+
+    agent = Mock()
+    agent.role = "test_agent"
+    agent.execute_task.return_value = "test result"
+    agent.crew = None
+
+    task = Task(
+        description="Test task",
+        expected_output="Output",
+        guardrail=guardrail
+    )
+
+    result = task.execute_sync(agent=agent)
+    assert isinstance(result, TaskOutput)
+    assert result.raw == "TEST RESULT"
+
+
+def test_task_with_failing_guardrail():
+    """Test that failing guardrail triggers retry with error context."""
+    def guardrail(result: TaskOutput):
+        return (False, "Invalid format")
+
+    agent = Mock()
+    agent.role = "test_agent"
+    agent.execute_task.side_effect = [
+        "bad result",
+        "good result"
+    ]
+    agent.crew = None
+
+    task = Task(
+        description="Test task",
+        expected_output="Output",
+        guardrail=guardrail,
+        max_retries=1
+    )
+
+    # First execution fails guardrail, second succeeds
+    agent.execute_task.side_effect = ["bad result", "good result"]
+    with pytest.raises(Exception) as exc_info:
+        task.execute_sync(agent=agent)
+
+    assert "Task failed guardrail validation" in str(exc_info.value)
+    assert task.retry_count == 1
+
+
+def test_task_with_guardrail_retries():
+    """Test that guardrail respects max_retries configuration."""
+    def guardrail(result: TaskOutput):
+        return (False, "Invalid format")
+
+    agent = Mock()
+    agent.role = "test_agent"
+    agent.execute_task.return_value = "bad result"
+    agent.crew = None
+
+    task = Task(
+        description="Test task",
+        expected_output="Output",
+        guardrail=guardrail,
+        max_retries=2
+    )
+
+    with pytest.raises(Exception) as exc_info:
+        task.execute_sync(agent=agent)
+
+    assert task.retry_count == 2
+    assert "Task failed guardrail validation after 2 retries" in str(exc_info.value)
+    assert "Invalid format" in str(exc_info.value)
+
+
+def test_guardrail_error_in_context():
+    """Test that guardrail error is passed in context for retry."""
+    def guardrail(result: TaskOutput):
+        return (False, "Expected JSON, got string")
+
+    agent = Mock()
+    agent.role = "test_agent"
+    agent.crew = None
+
+    task = Task(
+        description="Test task",
+        expected_output="Output",
+        guardrail=guardrail,
+        max_retries=1
+    )
+
+    # Mock execute_task to succeed on second attempt
+    first_call = True
+    def execute_task(task, context, tools):
+        nonlocal first_call
+        if first_call:
+            first_call = False
+            return "invalid"
+        return '{"valid": "json"}'
+
+    agent.execute_task.side_effect = execute_task
+
+    with pytest.raises(Exception) as exc_info:
+        task.execute_sync(agent=agent)
+
+    assert "Task failed guardrail validation" in str(exc_info.value)
+    assert "Expected JSON, got string" in str(exc_info.value)