Merge branch 'main' into devin/1735488202-add-tool-documentation

- Added documentation for file operation tools
- Added documentation for search tools
- Added documentation for web scraping tools
- Added documentation for specialized tools (RAG, code interpreter)
- Added documentation for API-based tools (SerpApi, Serply)

Link to Devin run: https://app.devin.ai/sessions/d2f72a2dfb214659aeb3e9f67ed961f7

Co-Authored-By: Joe Moura <joao@crewai.com>

.github/workflows/tests.yml

@@ -1,60 +1,32 @@
 name: Run Tests
 
-on:
-  pull_request:
-  push:
-    branches:
-      - main
+on: [pull_request]
 
 permissions:
   contents: write
 
+env:
+  OPENAI_API_KEY: fake-api-key
+
 jobs:
   tests:
     runs-on: ubuntu-latest
     timeout-minutes: 15
-    env:
-      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-      MODEL: gpt-4o-mini
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
 
-      - name: Install UV
+      - name: Install uv
         uses: astral-sh/setup-uv@v3
         with:
           enable-cache: true
 
+
       - name: Set up Python
         run: uv python install 3.12.8
 
       - name: Install the project
         run: uv sync --dev --all-extras
 
-      - name: Run General Tests
-        run: uv run pytest tests -k "not main_branch_tests" -vv
-
-  main_branch_tests:
-    if: github.ref == 'refs/heads/main'
-    runs-on: ubuntu-latest
-    needs: tests
-    timeout-minutes: 15
-    env:
-      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Install UV
-        uses: astral-sh/setup-uv@v3
-        with:
-          enable-cache: true
-
-      - name: Set up Python
-        run: uv python install 3.12.8
-
-      - name: Install the project
-        run: uv sync --dev --all-extras
-
-      - name: Run Main Branch Specific Tests
-        run: uv run pytest tests/main_branch_tests -vv
+      - name: Run tests
+        run: uv run pytest tests -vv

.gitignore

@@ -21,4 +21,3 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log

docs/concepts/agents.mdx

@@ -101,8 +101,6 @@ from crewai_tools import SerperDevTool
 class LatestAiDevelopmentCrew():
   """LatestAiDevelopment crew"""
 
-  agents_config = "config/agents.yaml"
-
   @agent
   def researcher(self) -> Agent:
     return Agent(

docs/concepts/cli.mdx

@@ -161,7 +161,6 @@ The CLI will initially prompt for API keys for the following services:
 * Groq
 * Anthropic
 * Google Gemini
-* SambaNova
 
 When you select a provider, the CLI will prompt you to enter your API key.
 

docs/concepts/flows.mdx

@@ -138,7 +138,7 @@ print("---- Final Output ----")
 print(final_output)
 ````
 
-```text Output
+``` text Output
 ---- Final Output ----
 Second method received: Output from first_method
 ````

docs/concepts/knowledge.mdx

@@ -4,6 +4,8 @@ description: What is knowledge in CrewAI and how to use it.
 icon: book
 ---
 
+# Using Knowledge in CrewAI
+
 ## What is Knowledge?
 
 Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
@@ -34,20 +36,7 @@ CrewAI supports various types of knowledge sources out of the box:
   </Card>
 </CardGroup>
 
-## Supported Knowledge Parameters
-
-| Parameter                    | Type                                | Required | Description                                                                                                                                           |
-| :--------------------------- | :---------------------------------- | :------- | :---------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `sources`                  | **List[BaseKnowledgeSource]**        | Yes      | List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content.           |
-| `collection_name`          | **str**                              | No       | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided.     |
-| `storage`                  | **Optional[KnowledgeStorage]**       | No       | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.              |
-
-## Quickstart Example
-
-<Tip>
-For file-Based Knowledge Sources, make sure to place your files in a `knowledge` directory at the root of your project. 
-Also, use relative paths from the `knowledge` directory when creating the source.
-</Tip>
+## Quick Start
 
 Here's an example using string-based knowledge:
 
@@ -91,8 +80,7 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
 ```
 
 
-Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including TXT, PDF, DOCX, HTML, and more. 
-
+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
@@ -140,217 +128,39 @@ result = crew.kickoff(
 )
 ```
 
-## More Examples
-
-Here are examples of how to use different types of knowledge sources:
-
-### Text File Knowledge Source
-```python
-from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
-
-# Create a text file knowledge source
-text_source = CrewDoclingSource(
-    file_paths=["document.txt", "another.txt"]
-)
-
-# Create crew with text file source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[text_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[text_source]
-)
-```
-
-### PDF Knowledge Source
-```python
-from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
-
-# Create a PDF knowledge source
-pdf_source = PDFKnowledgeSource(
-    file_paths=["document.pdf", "another.pdf"]
-)
-
-# Create crew with PDF knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[pdf_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[pdf_source]
-)
-```
-
-### CSV Knowledge Source
-```python
-from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
-
-# Create a CSV knowledge source
-csv_source = CSVKnowledgeSource(
-    file_paths=["data.csv"]
-)
-
-# Create crew with CSV knowledge source or on agent level
-agent = Agent(
-    ...
-    knowledge_sources=[csv_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[csv_source]
-)
-```
-
-### Excel Knowledge Source
-```python
-from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
-
-# Create an Excel knowledge source
-excel_source = ExcelKnowledgeSource(
-    file_paths=["spreadsheet.xlsx"]
-)
-
-# Create crew with Excel knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[excel_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[excel_source]
-)
-```
-
-### JSON Knowledge Source
-```python
-from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
-
-# Create a JSON knowledge source
-json_source = JSONKnowledgeSource(
-    file_paths=["data.json"]
-)
-
-# Create crew with JSON knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[json_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[json_source]
-)
-```
-
 ## Knowledge Configuration
 
 ### Chunking Configuration
 
-Knowledge sources automatically chunk content for better processing. 
-You can configure chunking behavior in your knowledge sources:
+Control how content is split for processing by setting the chunk size and overlap.
 
-```python
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-
-source = StringKnowledgeSource(
-    content="Your content here",
-    chunk_size=4000,      # Maximum size of each chunk (default: 4000)
-    chunk_overlap=200     # Overlap between chunks (default: 200)
+```python Code
+knowledge_source = StringKnowledgeSource(
+    content="Long content...",
+    chunk_size=4000,     # Characters per chunk (default)
+    chunk_overlap=200    # Overlap between chunks (default)
 )
 ```
 
-The chunking configuration helps in:
-- Breaking down large documents into manageable pieces
-- Maintaining context through chunk overlap
-- Optimizing retrieval accuracy
+## Embedder Configuration
 
-### Embeddings Configuration
+You can also configure the embedder for the knowledge store. This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
 
-You can also configure the embedder for the knowledge store. 
-This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
-The `embedder` parameter supports various embedding model providers that include:
-- `openai`: OpenAI's embedding models
-- `google`: Google's text embedding models
-- `azure`: Azure OpenAI embeddings
-- `ollama`: Local embeddings with Ollama
-- `vertexai`: Google Cloud VertexAI embeddings
-- `cohere`: Cohere's embedding models
-- `bedrock`: AWS Bedrock embeddings
-- `huggingface`: Hugging Face models
-- `watson`: IBM Watson embeddings
-
-Here's an example of how to configure the embedder for the knowledge store using Google's `text-embedding-004` model:
-<CodeGroup>
-```python Example
-from crewai import Agent, Task, Crew, Process, LLM
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-import os
-
-# Get the GEMINI API key
-GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
-
-# Create a knowledge source
-content = "Users name is John. He is 30 years old and lives in San Francisco."
+```python Code
+...
 string_source = StringKnowledgeSource(
-    content=content,
+    content="Users name is John. He is 30 years old and lives in San Francisco.",
 )
-
-# Create an LLM with a temperature of 0 to ensure deterministic outputs
-gemini_llm = LLM(
-    model="gemini/gemini-1.5-pro-002",
-    api_key=GEMINI_API_KEY,
-    temperature=0,
-)
-
-# Create an agent with the knowledge store
-agent = Agent(
-    role="About User",
-    goal="You know everything about the user.",
-    backstory="""You are a master at understanding people and their preferences.""",
-    verbose=True,
-    allow_delegation=False,
-    llm=gemini_llm,
-)
-
-task = Task(
-    description="Answer the following questions about the user: {question}",
-    expected_output="An answer to the question.",
-    agent=agent,
-)
-
 crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    verbose=True,
-    process=Process.sequential,
+    ...
     knowledge_sources=[string_source],
     embedder={
-        "provider": "google",
-        "config": {
-            "model": "models/text-embedding-004",
-            "api_key": GEMINI_API_KEY,
-        }
-    }
+        "provider": "openai",
+        "config": {"model": "text-embedding-3-small"},
+    },
 )
-
-result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
 ```
-```text Output
-# Agent: About User
-## Task: Answer the following questions about the user: What city does John live in and how old is he?
 
-# Agent: About User
-## Final Answer: 
-John is 30 years old and lives in San Francisco.
-```
-</CodeGroup>
 ## Clearing Knowledge
 
 If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.

docs/concepts/llms.mdx

@@ -146,19 +146,6 @@ Here's a detailed breakdown of supported models and their capabilities, you can
       Groq is known for its fast inference speeds, making it suitable for real-time applications.
     </Tip>
   </Tab>
-  <Tab title="SambaNova">
-    | Model | Context Window | Best For |
-    |-------|---------------|-----------|
-    | Llama 3.1 70B/8B | Up to 131,072 tokens | High-performance, large context tasks |
-    | Llama 3.1 405B | 8,192 tokens | High-performance and output quality |
-    | Llama 3.2 Series | 8,192 tokens | General-purpose tasks, multimodal |
-    | Llama 3.3 70B | Up to 131,072 tokens | High-performance and output quality|
-    | Qwen2 familly | 8,192 tokens | High-performance and output quality |
-
-    <Tip>
-      [SambaNova](https://cloud.sambanova.ai/) has several models with fast inference speed at full precision.
-    </Tip>
-  </Tab>
   <Tab title="Others">
     | Provider | Context Window | Key Features |
     |----------|---------------|--------------|

docs/concepts/memory.mdx

@@ -134,23 +134,6 @@ crew = Crew(
 )
 ```
 
-## Memory Configuration Options
-If you want to access a specific organization and project, you can set the `org_id` and `project_id` parameters in the memory configuration.
-
-```python Code
-from crewai import Crew
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    verbose=True,
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "john", "org_id": "my_org_id", "project_id": "my_project_id"},
-    },
-)
-```
 
 ## Additional Embedding Providers
 

docs/how-to/llm-connections.mdx

@@ -32,7 +32,6 @@ LiteLLM supports a wide range of providers, including but not limited to:
 - Cloudflare Workers AI
 - DeepInfra
 - Groq
-- SambaNova
 - [NVIDIA NIMs](https://docs.api.nvidia.com/nim/reference/models-1)
 - And many more!
 

docs/how-to/portkey-observability.mdx

@@ -1,202 +0,0 @@
----
-title: Portkey Observability and Guardrails
-description: How to use Portkey with CrewAI
-icon: key
----
-
-<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-CrewAI.png" alt="Portkey CrewAI Header Image" width="70%" />
-
-
-[Portkey](https://portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast.
-
-Portkey adds 4 core production capabilities to any CrewAI agent:
-1. Routing to **200+ LLMs**
-2. Making each LLM call more robust
-3. Full-stack tracing & cost, performance analytics
-4. Real-time guardrails to enforce behavior
-
-## Getting Started
-
-<Steps>
-    <Step title="Install CrewAI and Portkey">
-    ```bash
-    pip install -qU crewai portkey-ai
-    ```
-    </Step>
-    <Step title="Configure the LLM Client">
-    To build CrewAI Agents with Portkey, you'll need two keys:
-    - **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key
-    - **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault
-
-    ```python
-    from crewai import LLM
-    from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-    gpt_llm = LLM(
-        model="gpt-4",
-        base_url=PORTKEY_GATEWAY_URL,
-        api_key="dummy", # We are using Virtual key
-        extra_headers=createHeaders(
-            api_key="YOUR_PORTKEY_API_KEY",
-            virtual_key="YOUR_VIRTUAL_KEY", # Enter your Virtual key from Portkey
-        )
-    )
-    ```
-    </Step>
-    <Step title="Create and Run Your First Agent">
-    ```python
-    from crewai import Agent, Task, Crew
-
-    # Define your agents with roles and goals
-    coder = Agent(
-        role='Software developer',
-        goal='Write clear, concise code on demand',
-        backstory='An expert coder with a keen eye for software trends.',
-        llm=gpt_llm
-    )
-
-    # Create tasks for your agents
-    task1 = Task(
-        description="Define the HTML for making a simple website with heading- Hello World! Portkey is working!",
-        expected_output="A clear and concise HTML code",
-        agent=coder
-    )
-
-    # Instantiate your crew
-    crew = Crew(
-        agents=[coder],
-        tasks=[task1],
-    )
-
-    result = crew.kickoff()
-    print(result)
-    ```
-    </Step>
-</Steps>
-
-## Key Features
-
-| Feature | Description |
-|:--------|:------------|
-| 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface |
-| 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks |
-| 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata |
-| 🔍 Comprehensive Logging | Debug with detailed execution traces and function call logs |
-| 🚧 Security Controls | Set budget limits and implement role-based access control |
-| 🔄 Performance Analytics | Capture and analyze feedback for continuous improvement |
-| 💾 Intelligent Caching | Reduce costs and latency with semantic or simple caching |
-
-
-## Production Features with Portkey Configs
-
-All features mentioned below are through Portkey's Config system. Portkey's Config system allows you to define routing strategies using simple JSON objects in your LLM API calls. You can create and manage Configs directly in your code or through the Portkey Dashboard. Each Config has a unique ID for easy reference.
-
-<Frame>
-    <img src="https://raw.githubusercontent.com/Portkey-AI/docs-core/refs/heads/main/images/libraries/libraries-3.avif"/>
-</Frame>
-
-
-### 1. Use 250+ LLMs
-Access various LLMs like Anthropic, Gemini, Mistral, Azure OpenAI, and more with minimal code changes. Switch between providers or use them together seamlessly. [Learn more about Universal API](https://portkey.ai/docs/product/ai-gateway/universal-api)
-
-
-Easily switch between different LLM providers:
-
-```python
-# Anthropic Configuration
-anthropic_llm = LLM(
-    model="claude-3-5-sonnet-latest",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="anthropic_agent"
-    )
-)
-
-# Azure OpenAI Configuration
-azure_llm = LLM(
-    model="gpt-4",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_AZURE_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="azure_agent"
-    )
-)
-```
-
-
-### 2. Caching
-Improve response times and reduce costs with two powerful caching modes:
-- **Simple Cache**: Perfect for exact matches
-- **Semantic Cache**: Matches responses for requests that are semantically similar
-[Learn more about Caching](https://portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic)
-
-```py
-config = {
-    "cache": {
-        "mode": "semantic",  # or "simple" for exact matching
-    }
-}
-```
-
-### 3. Production Reliability
-Portkey provides comprehensive reliability features:
-- **Automatic Retries**: Handle temporary failures gracefully
-- **Request Timeouts**: Prevent hanging operations
-- **Conditional Routing**: Route requests based on specific conditions
-- **Fallbacks**: Set up automatic provider failovers
-- **Load Balancing**: Distribute requests efficiently
-
-[Learn more about Reliability Features](https://portkey.ai/docs/product/ai-gateway/)
-
-
-
-### 4. Metrics
-
-Agent runs are complex. Portkey automatically logs **40+ comprehensive metrics** for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need.
-
-
-- Cost per agent interaction
-- Response times and latency
-- Token usage and efficiency
-- Success/failure rates
-- Cache hit rates
-
-<img src="https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Dashboard.png?raw=true" width="70%" alt="Portkey Dashboard" />
-
-### 5. Detailed Logging
-Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes.
-
-
-Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata.
-
-<details>
-  <summary><b>Traces</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Traces.png" alt="Portkey Traces" width="70%" />
-</details>
-
-<details>
-  <summary><b>Logs</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Logs.png" alt="Portkey Logs" width="70%" />
-</details>
-
-### 6. Enterprise Security Features
-- Set budget limit and rate limts per Virtual Key (disposable API keys)
-- Implement role-based access control
-- Track system changes with audit logs
-- Configure data retention policies
-
-
-
-For detailed information on creating and managing Configs, visit the [Portkey documentation](https://docs.portkey.ai/product/ai-gateway/configs).
-
-## Resources
-
-- [📘 Portkey Documentation](https://docs.portkey.ai)
-- [📊 Portkey Dashboard](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai)
-- [🐦 Twitter](https://twitter.com/portkeyai)
-- [💬 Discord Community](https://discord.gg/DD7vgKK299)

docs/mint.json

@@ -100,8 +100,7 @@
         "how-to/conditional-tasks",
         "how-to/agentops-observability",
         "how-to/langtrace-observability",
-        "how-to/openlit-observability",
-        "how-to/portkey-observability"
+        "how-to/openlit-observability"
       ]
     },
     {

docs/tools/brave-search-tool.mdx

@@ -0,0 +1,222 @@
+---
+title: BraveSearchTool
+description: A tool for performing web searches using the Brave Search API
+icon: search
+---
+
+## BraveSearchTool
+
+The BraveSearchTool enables web searches using the Brave Search API, providing customizable result counts, country-specific searches, and rate-limited operations. It formats search results with titles, URLs, and snippets for easy consumption.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Authentication
+
+Set up your Brave Search API key:
+```bash
+export BRAVE_API_KEY='your-brave-api-key'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import BraveSearchTool
+
+# Basic initialization
+search_tool = BraveSearchTool()
+
+# Advanced initialization with custom parameters
+search_tool = BraveSearchTool(
+    country="US",        # Country-specific search
+    n_results=5,         # Number of results to return
+    save_file=True       # Save results to file
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Web Researcher',
+    goal='Search and analyze web content',
+    backstory='Expert at finding relevant information online.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class BraveSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the internet"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    country: Optional[str] = "",
+    n_results: int = 10,
+    save_file: bool = False,
+    *args,
+    **kwargs
+):
+    """
+    Initialize the Brave search tool.
+
+    Args:
+        country (Optional[str]): Country code for region-specific search
+        n_results (int): Number of results to return (default: 10)
+        save_file (bool): Whether to save results to file (default: False)
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Execute web search using Brave Search API.
+
+    Args:
+        search_query (str): Query to search
+        save_file (bool, optional): Override save_file setting
+        n_results (int, optional): Override n_results setting
+
+    Returns:
+        str: Formatted search results with titles, URLs, and snippets
+    """
+```
+
+## Best Practices
+
+1. API Authentication:
+   - Securely store BRAVE_API_KEY
+   - Keep API key confidential
+   - Handle authentication errors
+
+2. Rate Limiting:
+   - Tool automatically handles rate limiting
+   - Minimum 1-second interval between requests
+   - Consider implementing additional rate limits
+
+3. Search Optimization:
+   - Use specific search queries
+   - Adjust result count based on needs
+   - Consider regional search requirements
+
+4. Error Handling:
+   - Handle API request failures
+   - Manage parsing errors
+   - Monitor rate limit errors
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import BraveSearchTool
+
+# Initialize tool with custom configuration
+search_tool = BraveSearchTool(
+    country="GB",    # UK-specific search
+    n_results=3,     # Limit to 3 results
+    save_file=True   # Save results to file
+)
+
+# Create agent
+researcher = Agent(
+    role='Web Researcher',
+    goal='Research latest AI developments',
+    backstory='Expert at finding and analyzing tech news.',
+    tools=[search_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find the latest news about artificial 
+    intelligence developments in quantum computing.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "latest quantum computing AI developments"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Country-Specific Search
+```python
+# Initialize tools for different regions
+us_search = BraveSearchTool(country="US")
+uk_search = BraveSearchTool(country="GB")
+jp_search = BraveSearchTool(country="JP")
+
+# Compare results across regions
+us_results = us_search.run(
+    search_query="local news"
+)
+uk_results = uk_search.run(
+    search_query="local news"
+)
+jp_results = jp_search.run(
+    search_query="local news"
+)
+```
+
+### Result Management
+```python
+# Save results to file
+archival_search = BraveSearchTool(
+    save_file=True,
+    n_results=20
+)
+
+# Search and save
+results = archival_search.run(
+    search_query="historical events 2023"
+)
+# Results saved to search_results_YYYY-MM-DD_HH-MM-SS.txt
+```
+
+### Error Handling Example
+```python
+try:
+    search_tool = BraveSearchTool()
+    results = search_tool.run(
+        search_query="important topic"
+    )
+    print(results)
+except ValueError as e: # API key missing
+    print(f"Authentication error: {str(e)}")
+except Exception as e:
+    print(f"Search error: {str(e)}")
+```
+
+## Notes
+
+- Requires Brave Search API key
+- Implements automatic rate limiting
+- Supports country-specific searches
+- Customizable result count
+- Optional file saving feature
+- Thread-safe operations
+- Efficient result formatting
+- Handles API errors gracefully
+- Supports parallel searches
+- Maintains search context

docs/tools/code-docs-search-tool.mdx

@@ -0,0 +1,164 @@
+---
+title: CodeDocsSearchTool
+description: A semantic search tool for code documentation websites using RAG capabilities
+icon: book-open
+---
+
+## CodeDocsSearchTool
+
+The CodeDocsSearchTool is a specialized Retrieval-Augmented Generation (RAG) tool that enables semantic search within code documentation websites. It inherits from the base RagTool class and provides both fixed and dynamic documentation URL searching capabilities.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import CodeDocsSearchTool
+
+# Method 1: Dynamic documentation URL
+docs_search = CodeDocsSearchTool()
+
+# Method 2: Fixed documentation URL
+fixed_docs_search = CodeDocsSearchTool(
+    docs_url="https://docs.example.com"
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Documentation Researcher',
+    goal='Search through code documentation semantically',
+    backstory='Expert at finding relevant information in technical documentation.',
+    tools=[docs_search],
+    verbose=True
+)
+```
+
+## Input Schema
+
+The tool supports two input schemas depending on initialization:
+
+### Dynamic URL Schema
+```python
+class CodeDocsSearchToolSchema(BaseModel):
+    search_query: str  # The semantic search query
+    docs_url: str     # URL of the documentation site to search
+```
+
+### Fixed URL Schema
+```python
+class FixedCodeDocsSearchToolSchema(BaseModel):
+    search_query: str  # The semantic search query
+```
+
+## Function Signature
+
+```python
+def __init__(self, docs_url: Optional[str] = None, **kwargs):
+    """
+    Initialize the documentation search tool.
+
+    Args:
+        docs_url (Optional[str]): Fixed URL to a documentation site. If provided, 
+            the tool will only search this documentation.
+        **kwargs: Additional arguments passed to the parent RagTool
+    """
+
+def _run(self, search_query: str, **kwargs: Any) -> Any:
+    """
+    Perform semantic search on the documentation site.
+
+    Args:
+        search_query (str): The semantic search query
+        **kwargs: Additional arguments (including 'docs_url' for dynamic mode)
+
+    Returns:
+        str: Relevant documentation passages based on semantic search
+    """
+```
+
+## Best Practices
+
+1. Choose initialization method based on use case:
+   - Use fixed URL when repeatedly searching the same documentation
+   - Use dynamic URL when searching different documentation sites
+2. Write clear, semantic search queries
+3. Ensure documentation sites are accessible
+4. Consider documentation structure and size
+5. Handle potential URL access errors in agent prompts
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CodeDocsSearchTool
+
+# Example 1: Fixed documentation search
+api_docs_search = CodeDocsSearchTool(
+    docs_url="https://api.example.com/docs"
+)
+
+# Example 2: Dynamic documentation search
+flexible_docs_search = CodeDocsSearchTool()
+
+# Create agents
+api_analyst = Agent(
+    role='API Documentation Analyst',
+    goal='Find relevant API endpoints and usage examples',
+    backstory='Expert at analyzing API documentation.',
+    tools=[api_docs_search]
+)
+
+docs_researcher = Agent(
+    role='Documentation Researcher',
+    goal='Search through various documentation sites',
+    backstory='Specialist in finding information across multiple docs.',
+    tools=[flexible_docs_search]
+)
+
+# Define tasks
+fixed_search_task = Task(
+    description="""Find all authentication-related endpoints 
+    in the API documentation.""",
+    agent=api_analyst
+)
+
+# The agent will use:
+# {
+#   "search_query": "authentication endpoints and methods"
+# }
+
+dynamic_search_task = Task(
+    description="""Search through the Python documentation at 
+    docs.python.org for information about async/await.""",
+    agent=docs_researcher
+)
+
+# The agent will use:
+# {
+#   "search_query": "async await syntax and usage",
+#   "docs_url": "https://docs.python.org"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[api_analyst, docs_researcher],
+    tasks=[fixed_search_task, dynamic_search_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Notes
+
+- Inherits from RagTool for semantic search capabilities
+- Supports both fixed and dynamic documentation URLs
+- Uses embeddings for semantic search
+- Thread-safe operations
+- Automatically handles documentation loading and embedding
+- Optimized for technical documentation search

docs/tools/code-interpreter-tool.mdx

@@ -0,0 +1,224 @@
+---
+title: CodeInterpreterTool
+description: A tool for secure Python code execution in isolated Docker environments
+icon: code
+---
+
+## CodeInterpreterTool
+
+The CodeInterpreterTool provides secure Python code execution capabilities using Docker containers. It supports dynamic library installation and offers both safe (Docker-based) and unsafe (direct) execution modes.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import CodeInterpreterTool
+
+# Initialize the tool
+code_tool = CodeInterpreterTool()
+
+# Create an agent with the tool
+programmer = Agent(
+    role='Code Executor',
+    goal='Execute and analyze Python code',
+    backstory='Expert at writing and executing Python code.',
+    tools=[code_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class CodeInterpreterSchema(BaseModel):
+    code: str = Field(
+        description="Python3 code used to be interpreted in the Docker container. ALWAYS PRINT the final result and the output of the code"
+    )
+    libraries_used: List[str] = Field(
+        description="List of libraries used in the code with proper installing names separated by commas. Example: numpy,pandas,beautifulsoup4"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    code: Optional[str] = None,
+    user_dockerfile_path: Optional[str] = None,
+    user_docker_base_url: Optional[str] = None,
+    unsafe_mode: bool = False,
+    **kwargs
+):
+    """
+    Initialize the code interpreter tool.
+
+    Args:
+        code (Optional[str]): Default code to execute
+        user_dockerfile_path (Optional[str]): Custom Dockerfile path
+        user_docker_base_url (Optional[str]): Custom Docker daemon URL
+        unsafe_mode (bool): Enable direct code execution
+        **kwargs: Additional arguments for base tool
+    """
+
+def _run(
+    self,
+    code: str,
+    libraries_used: List[str],
+    **kwargs: Any
+) -> str:
+    """
+    Execute Python code in Docker container or directly.
+
+    Args:
+        code (str): Python code to execute
+        libraries_used (List[str]): Required libraries
+        **kwargs: Additional arguments
+
+    Returns:
+        str: Execution output or error message
+    """
+```
+
+## Best Practices
+
+1. Security Considerations:
+   - Use Docker mode by default
+   - Validate input code
+   - Control library access
+   - Monitor execution time
+
+2. Docker Configuration:
+   - Use custom Dockerfile when needed
+   - Handle container lifecycle
+   - Manage resource limits
+   - Clean up after execution
+
+3. Library Management:
+   - Specify exact versions
+   - Use trusted packages
+   - Handle dependencies
+   - Verify installations
+
+4. Error Handling:
+   - Catch execution errors
+   - Handle timeouts
+   - Manage Docker errors
+   - Provide clear messages
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CodeInterpreterTool
+
+# Initialize tool
+code_tool = CodeInterpreterTool()
+
+# Create agent
+programmer = Agent(
+    role='Code Executor',
+    goal='Execute data analysis code',
+    backstory='Expert Python programmer specializing in data analysis.',
+    tools=[code_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Analyze the dataset using pandas and 
+    create a summary visualization with matplotlib.""",
+    agent=programmer
+)
+
+# The tool will use:
+# {
+#   "code": """
+#   import pandas as pd
+#   import matplotlib.pyplot as plt
+#   
+#   # Load and analyze data
+#   df = pd.read_csv('data.csv')
+#   summary = df.describe()
+#   
+#   # Create visualization
+#   plt.figure(figsize=(10, 6))
+#   df['column'].hist()
+#   plt.savefig('output.png')
+#   
+#   print(summary)
+#   """,
+#   "libraries_used": "pandas,matplotlib"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[programmer],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Custom Docker Configuration
+```python
+# Use custom Dockerfile
+tool = CodeInterpreterTool(
+    user_dockerfile_path="/path/to/Dockerfile"
+)
+
+# Use custom Docker daemon
+tool = CodeInterpreterTool(
+    user_docker_base_url="tcp://remote-docker:2375"
+)
+```
+
+### Direct Execution Mode
+```python
+# Enable unsafe mode (not recommended)
+tool = CodeInterpreterTool(unsafe_mode=True)
+
+# Execute code directly
+result = tool.run(
+    code="print('Hello, World!')",
+    libraries_used=[]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    code_tool = CodeInterpreterTool()
+    result = code_tool.run(
+        code="""
+        import numpy as np
+        arr = np.array([1, 2, 3])
+        print(f"Array mean: {arr.mean()}")
+        """,
+        libraries_used=["numpy"]
+    )
+    print(result)
+except Exception as e:
+    print(f"Error executing code: {str(e)}")
+```
+
+## Notes
+
+- Inherits from BaseTool
+- Docker-based isolation
+- Dynamic library installation
+- Secure code execution
+- Custom Docker support
+- Comprehensive error handling
+- Resource management
+- Container cleanup
+- Library dependency handling
+- Execution output capture

docs/tools/csv-search-tool.mdx

@@ -0,0 +1,207 @@
+---
+title: CSVSearchTool
+description: A tool for semantic search within CSV files using RAG capabilities
+icon: table
+---
+
+## CSVSearchTool
+
+The CSVSearchTool enables semantic search capabilities for CSV files using Retrieval-Augmented Generation (RAG). It can process CSV files either specified during initialization or at runtime, making it flexible for various use cases.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import CSVSearchTool
+
+# Method 1: Initialize with specific CSV file
+csv_tool = CSVSearchTool(csv="path/to/data.csv")
+
+# Method 2: Initialize without CSV (specify at runtime)
+flexible_csv_tool = CSVSearchTool()
+
+# Create an agent with the tool
+data_analyst = Agent(
+    role='Data Analyst',
+    goal='Search and analyze CSV data semantically',
+    backstory='Expert at analyzing and extracting insights from CSV data.',
+    tools=[csv_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed CSV Schema (when CSV path provided during initialization)
+```python
+class FixedCSVSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the CSV's content"
+    )
+```
+
+### Flexible CSV Schema (when CSV path provided at runtime)
+```python
+class CSVSearchToolSchema(FixedCSVSearchToolSchema):
+    csv: str = Field(
+        description="Mandatory csv path you want to search"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    csv: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the CSV search tool.
+
+    Args:
+        csv (Optional[str]): Path to CSV file (optional)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on CSV content.
+
+    Args:
+        search_query (str): Query to search in the CSV
+        **kwargs: Additional arguments including csv path if not initialized
+
+    Returns:
+        str: Relevant content from the CSV matching the query
+    """
+```
+
+## Best Practices
+
+1. CSV File Handling:
+   - Ensure CSV files are properly formatted
+   - Use absolute paths for reliability
+   - Verify file permissions before processing
+
+2. Search Optimization:
+   - Use specific, focused search queries
+   - Consider column names and data structure
+   - Test with sample queries first
+
+3. Performance Considerations:
+   - Pre-initialize with CSV for repeated searches
+   - Handle large CSV files appropriately
+   - Monitor memory usage with big datasets
+
+4. Error Handling:
+   - Verify CSV file existence
+   - Handle malformed CSV data
+   - Manage file access permissions
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CSVSearchTool
+
+# Initialize tool with specific CSV
+csv_tool = CSVSearchTool(csv="/path/to/sales_data.csv")
+
+# Create agent
+analyst = Agent(
+    role='Data Analyst',
+    goal='Extract insights from sales data',
+    backstory='Expert at analyzing sales data and trends.',
+    tools=[csv_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Find all sales records from the CSV 
+    that relate to product returns in Q4 2023.""",
+    agent=analyst
+)
+
+# The tool will use:
+# {
+#   "search_query": "product returns Q4 2023"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[analyst],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic CSV Selection
+```python
+# Initialize without CSV
+flexible_tool = CSVSearchTool()
+
+# Search different CSVs
+result1 = flexible_tool.run(
+    search_query="revenue 2023",
+    csv="/path/to/finance.csv"
+)
+
+result2 = flexible_tool.run(
+    search_query="customer feedback",
+    csv="/path/to/surveys.csv"
+)
+```
+
+### Multiple CSV Analysis
+```python
+# Create tools for different CSVs
+sales_tool = CSVSearchTool(csv="/path/to/sales.csv")
+inventory_tool = CSVSearchTool(csv="/path/to/inventory.csv")
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Business Analyst',
+    goal='Cross-reference sales and inventory data',
+    tools=[sales_tool, inventory_tool]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    csv_tool = CSVSearchTool(csv="/path/to/data.csv")
+    result = csv_tool.run(
+        search_query="important metrics"
+    )
+    print(result)
+except Exception as e:
+    print(f"Error processing CSV: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool for semantic search
+- Supports dynamic CSV file specification
+- Uses embedchain for data processing
+- Maintains search context across queries
+- Thread-safe operations
+- Efficient semantic search capabilities
+- Supports various CSV formats
+- Handles large datasets effectively
+- Preserves CSV structure in search
+- Enables natural language queries

docs/tools/directory-read-tool.mdx

@@ -0,0 +1,217 @@
+---
+title: Directory Read Tool
+description: A tool for recursively listing directory contents
+---
+
+# Directory Read Tool
+
+The Directory Read Tool provides functionality to recursively list all files within a directory. It supports both fixed and dynamic directory path modes, allowing you to specify the directory at initialization or runtime.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage
+
+You can use the Directory Read Tool in two ways:
+
+### 1. Fixed Directory Path
+
+Initialize the tool with a specific directory path:
+
+```python
+from crewai import Agent
+from crewai_tools import DirectoryReadTool
+
+# Initialize with a fixed directory
+tool = DirectoryReadTool(directory="/path/to/your/directory")
+
+# Create an agent with the tool
+agent = Agent(
+    role='File System Analyst',
+    goal='Analyze directory contents',
+    backstory='I help analyze and organize file systems',
+    tools=[tool]
+)
+
+# Use in a task
+task = Task(
+    description="List all files in the project directory",
+    agent=agent
+)
+```
+
+### 2. Dynamic Directory Path
+
+Initialize the tool without a specific directory path to provide it at runtime:
+
+```python
+from crewai import Agent
+from crewai_tools import DirectoryReadTool
+
+# Initialize without a fixed directory
+tool = DirectoryReadTool()
+
+# Create an agent with the tool
+agent = Agent(
+    role='File System Explorer',
+    goal='Explore different directories',
+    backstory='I analyze various directory structures',
+    tools=[tool]
+)
+
+# Use in a task with dynamic directory path
+task = Task(
+    description="List all files in the specified directory",
+    agent=agent,
+    context={
+        "directory": "/path/to/explore"
+    }
+)
+```
+
+## Input Schema
+
+### Fixed Directory Mode
+```python
+class FixedDirectoryReadToolSchema(BaseModel):
+    pass  # No additional parameters needed when directory is fixed
+```
+
+### Dynamic Directory Mode
+```python
+class DirectoryReadToolSchema(BaseModel):
+    directory: str  # The path to the directory to list contents
+```
+
+## Function Signatures
+
+```python
+def __init__(self, directory: Optional[str] = None, **kwargs):
+    """
+    Initialize the Directory Read Tool.
+    
+    Args:
+        directory (Optional[str]): Path to the directory (optional)
+        **kwargs: Additional arguments passed to BaseTool
+    """
+
+def _run(
+    self,
+    **kwargs: Any,
+) -> str:
+    """
+    Execute the directory listing.
+    
+    Args:
+        **kwargs: Arguments including 'directory' for dynamic mode
+    
+    Returns:
+        str: A formatted string containing all file paths in the directory
+    """
+```
+
+## Best Practices
+
+1. **Path Handling**:
+   - Use absolute paths to avoid path resolution issues
+   - Handle trailing slashes appropriately
+   - Verify directory existence before listing
+
+2. **Performance Considerations**:
+   - Be mindful of directory size when listing large directories
+   - Consider implementing pagination for large directories
+   - Handle symlinks appropriately
+
+3. **Error Handling**:
+   - Handle directory not found errors gracefully
+   - Manage permission issues appropriately
+   - Validate input parameters before processing
+
+## Example Integration
+
+Here's a complete example showing how to integrate the Directory Read Tool with CrewAI:
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import DirectoryReadTool
+
+# Initialize the tool
+dir_tool = DirectoryReadTool()
+
+# Create an agent with the tool
+file_analyst = Agent(
+    role='File System Analyst',
+    goal='Analyze and report on directory structures',
+    backstory='I am an expert at analyzing file system organization',
+    tools=[dir_tool]
+)
+
+# Create tasks
+analysis_task = Task(
+    description="""
+    Analyze the project directory structure:
+    1. List all files recursively
+    2. Identify key file types
+    3. Report on directory organization
+    
+    Provide a comprehensive analysis of the findings.
+    """,
+    agent=file_analyst,
+    context={
+        "directory": "/path/to/project"
+    }
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[file_analyst],
+    tasks=[analysis_task]
+)
+
+result = crew.kickoff()
+```
+
+## Error Handling
+
+The tool handles various error scenarios:
+
+1. **Directory Not Found**:
+   ```python
+   try:
+       tool = DirectoryReadTool(directory="/nonexistent/path")
+   except FileNotFoundError:
+       print("Directory not found. Please verify the path.")
+   ```
+
+2. **Permission Issues**:
+   ```python
+   try:
+       tool = DirectoryReadTool(directory="/restricted/path")
+   except PermissionError:
+       print("Insufficient permissions to access the directory.")
+   ```
+
+3. **Invalid Path**:
+   ```python
+   try:
+       result = tool._run(directory="invalid/path")
+   except ValueError:
+       print("Invalid directory path provided.")
+   ```
+
+## Output Format
+
+The tool returns a formatted string containing all file paths in the directory:
+
+```
+File paths:
+- /path/to/directory/file1.txt
+- /path/to/directory/subdirectory/file2.txt
+- /path/to/directory/subdirectory/file3.py
+```
+
+
+Each file path is listed on a new line with a hyphen prefix, making it easy to parse and read the output.

docs/tools/directory-search-tool.mdx

@@ -0,0 +1,214 @@
+---
+title: DirectorySearchTool
+description: A tool for semantic search within directory contents using RAG capabilities
+icon: folder-search
+---
+
+## DirectorySearchTool
+
+The DirectorySearchTool enables semantic search capabilities for directory contents using Retrieval-Augmented Generation (RAG). It processes files recursively within a directory and allows searching through their contents using natural language queries.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import DirectorySearchTool
+
+# Method 1: Initialize with specific directory
+dir_tool = DirectorySearchTool(directory="/path/to/documents")
+
+# Method 2: Initialize without directory (specify at runtime)
+flexible_dir_tool = DirectorySearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Directory Researcher',
+    goal='Search and analyze directory contents',
+    backstory='Expert at finding relevant information in document collections.',
+    tools=[dir_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed Directory Schema (when path provided during initialization)
+```python
+class FixedDirectorySearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the directory's content"
+    )
+```
+
+### Flexible Directory Schema (when path provided at runtime)
+```python
+class DirectorySearchToolSchema(FixedDirectorySearchToolSchema):
+    directory: str = Field(
+        description="Mandatory directory you want to search"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    directory: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the directory search tool.
+
+    Args:
+        directory (Optional[str]): Path to directory (optional)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on directory contents.
+
+    Args:
+        search_query (str): Query to search in the directory
+        **kwargs: Additional arguments including directory if not initialized
+
+    Returns:
+        str: Relevant content from the directory matching the query
+    """
+```
+
+## Best Practices
+
+1. Directory Management:
+   - Use absolute paths
+   - Verify directory existence
+   - Handle permissions properly
+
+2. Search Optimization:
+   - Use specific queries
+   - Consider file types
+   - Test with sample queries
+
+3. Performance Considerations:
+   - Pre-initialize for repeated searches
+   - Handle large directories
+   - Monitor processing time
+
+4. Error Handling:
+   - Verify directory access
+   - Handle missing files
+   - Manage permissions
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import DirectorySearchTool
+
+# Initialize tool with specific directory
+dir_tool = DirectorySearchTool(
+    directory="/path/to/documents"
+)
+
+# Create agent
+researcher = Agent(
+    role='Directory Researcher',
+    goal='Extract insights from document collections',
+    backstory='Expert at analyzing document collections.',
+    tools=[dir_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find all mentions of machine learning 
+    applications from the directory contents.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "machine learning applications"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic Directory Selection
+```python
+# Initialize without directory path
+flexible_tool = DirectorySearchTool()
+
+# Search different directories
+docs_results = flexible_tool.run(
+    search_query="technical specifications",
+    directory="/path/to/docs"
+)
+
+reports_results = flexible_tool.run(
+    search_query="financial metrics",
+    directory="/path/to/reports"
+)
+```
+
+### Multiple Directory Analysis
+```python
+# Create tools for different directories
+docs_tool = DirectorySearchTool(
+    directory="/path/to/docs"
+)
+reports_tool = DirectorySearchTool(
+    directory="/path/to/reports"
+)
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Content Analyst',
+    goal='Cross-reference multiple document collections',
+    tools=[docs_tool, reports_tool]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    dir_tool = DirectorySearchTool()
+    results = dir_tool.run(
+        search_query="key concepts",
+        directory="/path/to/documents"
+    )
+    print(results)
+except Exception as e:
+    print(f"Error processing directory: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Uses DirectoryLoader
+- Supports recursive search
+- Dynamic directory specification
+- Efficient content retrieval
+- Thread-safe operations
+- Maintains search context
+- Processes multiple file types
+- Handles nested directories
+- Memory-efficient processing

docs/tools/docx-search-tool.mdx

@@ -0,0 +1,224 @@
+---
+title: DOCXSearchTool
+description: A tool for semantic search within DOCX documents using RAG capabilities
+icon: file-text
+---
+
+## DOCXSearchTool
+
+The DOCXSearchTool enables semantic search capabilities for Microsoft Word (DOCX) documents using Retrieval-Augmented Generation (RAG). It supports both fixed and dynamic document selection modes.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import DOCXSearchTool
+
+# Method 1: Fixed document (specified at initialization)
+fixed_tool = DOCXSearchTool(
+    docx="path/to/document.docx"
+)
+
+# Method 2: Dynamic document (specified at runtime)
+dynamic_tool = DOCXSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Document Researcher',
+    goal='Search and analyze document contents',
+    backstory='Expert at finding relevant information in documents.',
+    tools=[fixed_tool],  # or [dynamic_tool]
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed Document Mode
+```python
+class FixedDOCXSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the DOCX's content"
+    )
+```
+
+### Dynamic Document Mode
+```python
+class DOCXSearchToolSchema(BaseModel):
+    docx: str = Field(
+        description="Mandatory docx path you want to search"
+    )
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the DOCX's content"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    docx: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the DOCX search tool.
+
+    Args:
+        docx (Optional[str]): Path to DOCX file (optional for dynamic mode)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    docx: Optional[str] = None,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on document contents.
+
+    Args:
+        search_query (str): Query to search in the document
+        docx (Optional[str]): Document path (required for dynamic mode)
+        **kwargs: Additional arguments
+
+    Returns:
+        str: Relevant content from the document matching the query
+    """
+```
+
+## Best Practices
+
+1. Document Handling:
+   - Use absolute file paths
+   - Verify file existence
+   - Handle large documents
+   - Monitor memory usage
+
+2. Query Optimization:
+   - Structure queries clearly
+   - Consider document size
+   - Handle formatting
+   - Monitor performance
+
+3. Error Handling:
+   - Check file access
+   - Validate file format
+   - Handle corrupted files
+   - Log issues
+
+4. Mode Selection:
+   - Choose fixed mode for static documents
+   - Use dynamic mode for runtime selection
+   - Consider memory implications
+   - Manage document lifecycle
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import DOCXSearchTool
+
+# Initialize tool
+docx_tool = DOCXSearchTool(
+    docx="reports/annual_report_2023.docx"
+)
+
+# Create agent
+researcher = Agent(
+    role='Document Analyst',
+    goal='Extract insights from annual report',
+    backstory='Expert at analyzing business documents.',
+    tools=[docx_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Find all mentions of revenue growth 
+    and market expansion.""",
+    agent=researcher
+)
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Multiple Document Analysis
+```python
+# Create tools for different documents
+report_tool = DOCXSearchTool(
+    docx="reports/annual_report.docx"
+)
+
+policy_tool = DOCXSearchTool(
+    docx="policies/compliance.docx"
+)
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Document Analyst',
+    goal='Cross-reference reports and policies',
+    tools=[report_tool, policy_tool]
+)
+```
+
+### Dynamic Document Loading
+```python
+# Initialize dynamic tool
+dynamic_tool = DOCXSearchTool()
+
+# Use with different documents
+result1 = dynamic_tool.run(
+    docx="document1.docx",
+    search_query="project timeline"
+)
+
+result2 = dynamic_tool.run(
+    docx="document2.docx",
+    search_query="budget allocation"
+)
+```
+
+### Error Handling Example
+```python
+try:
+    docx_tool = DOCXSearchTool(
+        docx="reports/quarterly_report.docx"
+    )
+    results = docx_tool.run(
+        search_query="Q3 performance metrics"
+    )
+    print(results)
+except FileNotFoundError as e:
+    print(f"Document not found: {str(e)}")
+except Exception as e: 
+    print(f"Error processing document: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Supports fixed/dynamic modes
+- Document path validation
+- Memory management
+- Performance optimization
+- Error handling
+- Search capabilities
+- Content extraction
+- Format handling
+- Security features

docs/tools/file-read-tool.mdx

@@ -0,0 +1,193 @@
+---
+title: FileReadTool
+description: A tool for reading file contents with flexible path specification
+icon: file-text
+---
+
+## FileReadTool
+
+The FileReadTool provides functionality to read file contents with support for both fixed and dynamic file path specification. It includes comprehensive error handling for common file operations and maintains clear descriptions of its configured state.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import FileReadTool
+
+# Method 1: Initialize with specific file
+reader = FileReadTool(file_path="/path/to/data.txt")
+
+# Method 2: Initialize without file (specify at runtime)
+flexible_reader = FileReadTool()
+
+# Create an agent with the tool
+file_processor = Agent(
+    role='File Processor',
+    goal='Read and process file contents',
+    backstory='Expert at handling file operations and content processing.',
+    tools=[reader],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class FileReadToolSchema(BaseModel):
+    file_path: str = Field(
+        description="Mandatory file full path to read the file"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    file_path: Optional[str] = None,
+    **kwargs: Any
+) -> None:
+    """
+    Initialize the file read tool.
+
+    Args:
+        file_path (Optional[str]): Path to file to read (optional)
+        **kwargs: Additional arguments passed to BaseTool
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Read and return file contents.
+
+    Args:
+        file_path (str, optional): Override default file path
+        **kwargs: Additional arguments
+
+    Returns:
+        str: File contents or error message
+    """
+```
+
+## Best Practices
+
+1. File Path Management:
+   - Use absolute paths for reliability
+   - Verify file existence before operations
+   - Handle path resolution properly
+
+2. Error Handling:
+   - Check for file existence
+   - Handle permission issues
+   - Manage encoding errors
+   - Process file access failures
+
+3. Performance Considerations:
+   - Close files after reading
+   - Handle large files appropriately
+   - Consider memory constraints
+
+4. Security Practices:
+   - Validate file paths
+   - Check file permissions
+   - Avoid path traversal issues
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import FileReadTool
+
+# Initialize tool with specific file
+reader = FileReadTool(file_path="/path/to/config.txt")
+
+# Create agent
+processor = Agent(
+    role='File Processor',
+    goal='Process configuration files',
+    backstory='Expert at reading and analyzing configuration files.',
+    tools=[reader]
+)
+
+# Define task
+read_task = Task(
+    description="""Read and analyze the contents of 
+    the configuration file.""",
+    agent=processor
+)
+
+# The tool will use the default file path
+
+# Create crew
+crew = Crew(
+    agents=[processor],
+    tasks=[read_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic File Selection
+```python
+# Initialize without file path
+flexible_reader = FileReadTool()
+
+# Read different files
+config_content = flexible_reader.run(
+    file_path="/path/to/config.txt"
+)
+
+log_content = flexible_reader.run(
+    file_path="/path/to/logs.txt"
+)
+```
+
+### Multiple File Processing
+```python
+# Create tools for different files
+config_reader = FileReadTool(file_path="/path/to/config.txt")
+log_reader = FileReadTool(file_path="/path/to/logs.txt")
+
+# Create agent with multiple tools
+processor = Agent(
+    role='File Analyst',
+    goal='Analyze multiple file types',
+    tools=[config_reader, log_reader]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    reader = FileReadTool()
+    content = reader.run(
+        file_path="/path/to/file.txt"
+    )
+    print(content)
+except Exception as e:
+    print(f"Error reading file: {str(e)}")
+```
+
+## Notes
+
+- Inherits from BaseTool
+- Supports fixed or dynamic file paths
+- Comprehensive error handling
+- Thread-safe operations
+- Clear error messages
+- Flexible path specification
+- Maintains tool description
+- Handles common file errors
+- Supports various file types
+- Memory-efficient operations

docs/tools/filewritertool.mdx

@@ -0,0 +1,141 @@
+---
+title: FileWriterTool
+description: A tool for writing content to files with support for various file formats.
+icon: file-pen
+---
+
+## FileWriterTool
+
+The FileWriterTool provides agents with the capability to write content to files, supporting various file formats and ensuring proper file handling.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import FileWriterTool
+
+# Initialize the tool
+file_writer = FileWriterTool()
+
+# Create an agent with the tool
+writer_agent = Agent(
+    role='Content Writer',
+    goal='Write and save content to files',
+    backstory='Expert at creating and managing file content.',
+    tools=[file_writer],
+    verbose=True
+)
+
+# Use in a task
+task = Task(
+    description='Write a report and save it to report.txt',
+    agent=writer_agent
+)
+```
+
+## Tool Attributes
+
+| Attribute | Type | Description |
+| :-------- | :--- | :---------- |
+| name | str | "File Writer Tool" |
+| description | str | "A tool that writes content to a file." |
+
+## Input Schema
+
+```python
+class FileWriterToolInput(BaseModel):
+    filename: str       # Name of the file to write
+    directory: str = "./"  # Optional directory path, defaults to current directory
+    overwrite: str = "False"  # Whether to overwrite existing file ("True"/"False")
+    content: str       # Content to write to the file
+```
+
+## Function Signature
+
+```python
+def _run(self, **kwargs: Any) -> str:
+    """
+    Write content to a file with specified parameters.
+
+    Args:
+        filename (str): Name of the file to write
+        content (str): Content to write to the file
+        directory (str, optional): Directory path. Defaults to "./".
+        overwrite (str, optional): Whether to overwrite existing file. Defaults to "False".
+
+    Returns:
+        str: Success message with filepath or error message
+    """
+```
+
+## Error Handling
+
+The tool includes error handling for common file operations:
+- FileExistsError: When file exists and overwrite is not allowed
+- KeyError: When required parameters are missing
+- Directory Creation: Automatically creates directories if they don't exist
+- General Exceptions: Catches and reports any other file operation errors
+
+## Best Practices
+
+1. Always provide absolute file paths
+2. Ensure proper file permissions
+3. Handle potential errors in your agent prompts
+4. Verify file contents after writing
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import FileWriterTool
+
+# Initialize tool
+file_writer = FileWriterTool()
+
+# Create agent
+writer = Agent(
+    role='Technical Writer',
+    goal='Create and save technical documentation',
+    backstory='Expert technical writer with experience in documentation.',
+    tools=[file_writer]
+)
+
+# Define task
+writing_task = Task(
+    description="""Write a technical guide about Python best practices and save it
+    to the docs directory. The file should be named 'python_guide.md'.
+    Include sections on code style, documentation, and testing.
+    If a file already exists, overwrite it.""",
+    agent=writer
+)
+
+# The agent can use the tool with these parameters:
+# {
+#   "filename": "python_guide.md",
+#   "directory": "docs",
+#   "overwrite": "True",
+#   "content": "# Python Best Practices\n\n## Code Style\n..."
+# }
+
+# Create crew
+crew = Crew(
+    agents=[writer],
+    tasks=[writing_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Notes
+
+- The tool automatically creates directories in the file path if they don't exist
+- Supports various file formats (txt, md, json, etc.)
+- Returns descriptive error messages for better debugging
+- Thread-safe file operations

docs/tools/firecrawl-crawl-website-tool.mdx

@@ -0,0 +1,181 @@
+---
+title: FirecrawlCrawlWebsiteTool
+description: A web crawling tool powered by Firecrawl API for comprehensive website content extraction
+icon: spider-web
+---
+
+## FirecrawlCrawlWebsiteTool
+
+The FirecrawlCrawlWebsiteTool provides website crawling capabilities using the Firecrawl API. It allows for customizable crawling with options for polling intervals, idempotency, and URL parameters.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install firecrawl-py  # Required dependency
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import FirecrawlCrawlWebsiteTool
+
+# Method 1: Using environment variable
+# export FIRECRAWL_API_KEY='your-api-key'
+crawler = FirecrawlCrawlWebsiteTool()
+
+# Method 2: Providing API key directly
+crawler = FirecrawlCrawlWebsiteTool(
+    api_key="your-firecrawl-api-key"
+)
+
+# Method 3: With custom configuration
+crawler = FirecrawlCrawlWebsiteTool(
+    api_key="your-firecrawl-api-key",
+    url="https://example.com",  # Base URL
+    poll_interval=5,            # Custom polling interval
+    idempotency_key="unique-key"
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Web Crawler',
+    goal='Extract and analyze website content',
+    backstory='Expert at crawling and analyzing web content.',
+    tools=[crawler],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class FirecrawlCrawlWebsiteToolSchema(BaseModel):
+    url: str = Field(description="Website URL")
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    api_key: Optional[str] = None,
+    url: Optional[str] = None,
+    params: Optional[Dict[str, Any]] = None,
+    poll_interval: Optional[int] = 2,
+    idempotency_key: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the website crawling tool.
+
+    Args:
+        api_key (Optional[str]): Firecrawl API key. If not provided, checks FIRECRAWL_API_KEY env var
+        url (Optional[str]): Base URL to crawl. Can be overridden in _run
+        params (Optional[Dict[str, Any]]): Additional parameters for FirecrawlApp
+        poll_interval (Optional[int]): Poll interval for FirecrawlApp
+        idempotency_key (Optional[str]): Idempotency key for FirecrawlApp
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(self, url: str) -> Any:
+    """
+    Crawl a website using Firecrawl.
+
+    Args:
+        url (str): Website URL to crawl (overrides constructor URL if provided)
+
+    Returns:
+        Any: Crawled website content from Firecrawl API
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   - Use environment variable: `export FIRECRAWL_API_KEY='your-api-key'`
+   - Or provide directly in constructor
+2. Configure crawling parameters:
+   - Set appropriate poll intervals
+   - Use idempotency keys for retry safety
+   - Customize URL parameters as needed
+3. Handle rate limits and quotas
+4. Consider website robots.txt policies
+5. Handle potential crawling errors in agent prompts
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import FirecrawlCrawlWebsiteTool
+
+# Initialize crawler with configuration
+crawler = FirecrawlCrawlWebsiteTool(
+    api_key="your-firecrawl-api-key",
+    poll_interval=5,
+    params={
+        "max_depth": 3,
+        "follow_links": True
+    }
+)
+
+# Create agent
+web_analyst = Agent(
+    role='Web Content Analyst',
+    goal='Extract and analyze website content comprehensively',
+    backstory='Expert at web crawling and content analysis.',
+    tools=[crawler]
+)
+
+# Define task
+crawl_task = Task(
+    description="""Crawl the documentation website at docs.example.com 
+    and extract all API-related content.""",
+    agent=web_analyst
+)
+
+# The agent will use:
+# {
+#   "url": "https://docs.example.com"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[web_analyst],
+    tasks=[crawl_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Configuration Options
+
+### URL Parameters
+```python
+params = {
+    "max_depth": 3,           # Maximum crawl depth
+    "follow_links": True,     # Follow internal links
+    "exclude_patterns": [],   # URL patterns to exclude
+    "include_patterns": []    # URL patterns to include
+}
+```
+
+### Polling Configuration
+```python
+crawler = FirecrawlCrawlWebsiteTool(
+    poll_interval=5,  # Poll every 5 seconds
+    idempotency_key="unique-key-123"  # For retry safety
+)
+```
+
+## Notes
+
+- Requires valid Firecrawl API key
+- Supports both environment variable and direct API key configuration
+- Configurable polling intervals for crawl status
+- Idempotency support for safe retries
+- Thread-safe operations
+- Customizable crawling parameters
+- Respects robots.txt by default

docs/tools/firecrawl-search-tool.mdx

@@ -0,0 +1,154 @@
+---
+title: FirecrawlSearchTool
+description: A web search tool powered by Firecrawl API for comprehensive web search capabilities
+icon: magnifying-glass-chart
+---
+
+## FirecrawlSearchTool
+
+The FirecrawlSearchTool provides web search capabilities using the Firecrawl API. It allows for customizable search queries with options for result formatting and search parameters.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install firecrawl-py  # Required dependency
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import FirecrawlSearchTool
+
+# Initialize the tool with your API key
+search_tool = FirecrawlSearchTool(api_key="your-firecrawl-api-key")
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Web Researcher',
+    goal='Find relevant information across the web',
+    backstory='Expert at web research and information gathering.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class FirecrawlSearchToolSchema(BaseModel):
+    query: str = Field(description="Search query")
+    page_options: Optional[Dict[str, Any]] = Field(
+        default=None, 
+        description="Options for result formatting"
+    )
+    search_options: Optional[Dict[str, Any]] = Field(
+        default=None, 
+        description="Options for searching"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(self, api_key: Optional[str] = None, **kwargs):
+    """
+    Initialize the Firecrawl search tool.
+
+    Args:
+        api_key (Optional[str]): Firecrawl API key
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(
+    self,
+    query: str,
+    page_options: Optional[Dict[str, Any]] = None,
+    result_options: Optional[Dict[str, Any]] = None,
+) -> Any:
+    """
+    Perform a web search using Firecrawl.
+
+    Args:
+        query (str): Search query string
+        page_options (Optional[Dict[str, Any]]): Options for result formatting
+        result_options (Optional[Dict[str, Any]]): Options for search results
+
+    Returns:
+        Any: Search results from Firecrawl API
+    """
+```
+
+## Best Practices
+
+1. Always provide a valid API key
+2. Use specific, focused search queries
+3. Customize page and result options for better results
+4. Handle potential API errors in agent prompts
+5. Consider rate limits and usage quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import FirecrawlSearchTool
+
+# Initialize tool with API key
+search_tool = FirecrawlSearchTool(api_key="your-firecrawl-api-key")
+
+# Create agent
+researcher = Agent(
+    role='Market Researcher',
+    goal='Research market trends and competitor analysis',
+    backstory='Expert market analyst with deep research skills.',
+    tools=[search_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Research the latest developments in electric vehicles,
+    focusing on market leaders and emerging technologies. Format the results
+    in a structured way.""",
+    agent=researcher
+)
+
+# The agent will use:
+# {
+#   "query": "electric vehicle market leaders emerging technologies",
+#   "page_options": {
+#     "format": "structured",
+#     "maxLength": 1000
+#   },
+#   "result_options": {
+#     "limit": 5,
+#     "sortBy": "relevance"
+#   }
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Error Handling
+
+The tool includes error handling for:
+- Missing API key
+- Missing firecrawl-py package
+- API request failures
+- Invalid options parameters
+
+## Notes
+
+- Requires valid Firecrawl API key
+- Supports customizable search parameters
+- Provides structured web search results
+- Thread-safe operations
+- Efficient for large-scale web searches
+- Handles rate limiting automatically

docs/tools/github-search-tool.mdx

@@ -0,0 +1,233 @@
+---
+title: GithubSearchTool
+description: A tool for semantic search within GitHub repositories using RAG capabilities
+icon: github
+---
+
+## GithubSearchTool
+
+The GithubSearchTool enables semantic search capabilities for GitHub repositories using Retrieval-Augmented Generation (RAG). It processes various content types including code, repository information, pull requests, and issues, allowing natural language queries across repository content.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import GithubSearchTool
+
+# Method 1: Initialize with specific repository
+github_tool = GithubSearchTool(
+    github_repo="owner/repo",
+    gh_token="your_github_token",
+    content_types=["code", "pr", "issue"]
+)
+
+# Method 2: Initialize without repository (specify at runtime)
+flexible_github_tool = GithubSearchTool(
+    gh_token="your_github_token",
+    content_types=["code", "repo"]
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='GitHub Researcher',
+    goal='Search and analyze repository contents',
+    backstory='Expert at finding relevant information in GitHub repositories.',
+    tools=[github_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed Repository Schema (when repo provided during initialization)
+```python
+class FixedGithubSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the github repo's content"
+    )
+```
+
+### Flexible Repository Schema (when repo provided at runtime)
+```python
+class GithubSearchToolSchema(FixedGithubSearchToolSchema):
+    github_repo: str = Field(
+        description="Mandatory github you want to search"
+    )
+    content_types: List[str] = Field(
+        description="Mandatory content types you want to be included search, options: [code, repo, pr, issue]"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    github_repo: Optional[str] = None,
+    gh_token: str,
+    content_types: List[str],
+    **kwargs
+):
+    """
+    Initialize the GitHub search tool.
+
+    Args:
+        github_repo (Optional[str]): Repository to search (optional)
+        gh_token (str): GitHub authentication token
+        content_types (List[str]): Content types to search
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on repository contents.
+
+    Args:
+        search_query (str): Query to search in the repository
+        **kwargs: Additional arguments including github_repo and content_types if not initialized
+
+    Returns:
+        str: Relevant content from the repository matching the query
+    """
+```
+
+## Best Practices
+
+1. Authentication:
+   - Secure token management
+   - Use environment variables
+   - Handle token expiration
+
+2. Search Optimization:
+   - Target specific content types
+   - Use focused queries
+   - Consider rate limits
+
+3. Performance Considerations:
+   - Pre-initialize for repeated searches
+   - Handle large repositories
+   - Monitor API usage
+
+4. Error Handling:
+   - Verify repository access
+   - Handle API limits
+   - Manage authentication errors
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import GithubSearchTool
+
+# Initialize tool with specific repository
+github_tool = GithubSearchTool(
+    github_repo="owner/repo",
+    gh_token="your_github_token",
+    content_types=["code", "pr", "issue"]
+)
+
+# Create agent
+researcher = Agent(
+    role='GitHub Researcher',
+    goal='Extract insights from repository content',
+    backstory='Expert at analyzing GitHub repositories.',
+    tools=[github_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find all implementations of 
+    machine learning algorithms in the codebase.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "machine learning implementation"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic Repository Selection
+```python
+# Initialize without repository
+flexible_tool = GithubSearchTool(
+    gh_token="your_github_token",
+    content_types=["code", "repo"]
+)
+
+# Search different repositories
+backend_results = flexible_tool.run(
+    search_query="authentication implementation",
+    github_repo="owner/backend-repo"
+)
+
+frontend_results = flexible_tool.run(
+    search_query="component architecture",
+    github_repo="owner/frontend-repo"
+)
+```
+
+### Multiple Content Type Analysis
+```python
+# Create tool with multiple content types
+multi_tool = GithubSearchTool(
+    github_repo="owner/repo",
+    gh_token="your_github_token",
+    content_types=["code", "pr", "issue", "repo"]
+)
+
+# Search across all content types
+results = multi_tool.run(
+    search_query="feature implementation status"
+)
+```
+
+### Error Handling Example
+```python
+try:
+    github_tool = GithubSearchTool(
+        gh_token="your_github_token",
+        content_types=["code"]
+    )
+    results = github_tool.run(
+        search_query="api endpoints",
+        github_repo="owner/repo"
+    )
+    print(results)
+except Exception as e:
+    print(f"Error searching repository: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Uses GithubLoader
+- Requires authentication
+- Supports multiple content types
+- Dynamic repository specification
+- Efficient content retrieval
+- Thread-safe operations
+- Maintains search context
+- Handles API rate limits
+- Memory-efficient processing

docs/tools/jina-scrape-website-tool.mdx

@@ -0,0 +1,220 @@
+---
+title: JinaScrapeWebsiteTool
+description: A tool for scraping website content using Jina.ai's reader service with markdown output
+icon: globe
+---
+
+## JinaScrapeWebsiteTool
+
+The JinaScrapeWebsiteTool provides website content scraping capabilities using Jina.ai's reader service. It converts web content into clean markdown format and supports both fixed and dynamic URL modes with optional authentication.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import JinaScrapeWebsiteTool
+
+# Method 1: Fixed URL (specified at initialization)
+fixed_tool = JinaScrapeWebsiteTool(
+    website_url="https://example.com",
+    api_key="your-jina-api-key"  # Optional
+)
+
+# Method 2: Dynamic URL (specified at runtime)
+dynamic_tool = JinaScrapeWebsiteTool(
+    api_key="your-jina-api-key"  # Optional
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Web Content Researcher',
+    goal='Extract and analyze website content',
+    backstory='Expert at gathering and processing web information.',
+    tools=[fixed_tool],  # or [dynamic_tool]
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class JinaScrapeWebsiteToolInput(BaseModel):
+    website_url: str = Field(
+        description="Mandatory website url to read the file"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    website_url: Optional[str] = None,
+    api_key: Optional[str] = None,
+    custom_headers: Optional[dict] = None,
+    **kwargs
+):
+    """
+    Initialize the website scraping tool.
+
+    Args:
+        website_url (Optional[str]): URL to scrape (optional for dynamic mode)
+        api_key (Optional[str]): Jina.ai API key for authentication
+        custom_headers (Optional[dict]): Custom HTTP headers
+        **kwargs: Additional arguments for base tool
+    """
+
+def _run(
+    self,
+    website_url: Optional[str] = None
+) -> str:
+    """
+    Execute website scraping.
+
+    Args:
+        website_url (Optional[str]): URL to scrape (required for dynamic mode)
+
+    Returns:
+        str: Markdown-formatted website content
+    """
+```
+
+## Best Practices
+
+1. URL Handling:
+   - Use complete URLs
+   - Validate URL format
+   - Handle redirects
+   - Monitor timeouts
+
+2. Authentication:
+   - Secure API key storage
+   - Use environment variables
+   - Manage headers properly
+   - Handle auth errors
+
+3. Content Processing:
+   - Handle large pages
+   - Process markdown output
+   - Manage encoding
+   - Handle errors
+
+4. Mode Selection:
+   - Choose fixed mode for static sites
+   - Use dynamic mode for variable URLs
+   - Consider caching
+   - Manage timeouts
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import JinaScrapeWebsiteTool
+import os
+
+# Initialize tool with API key
+scraper_tool = JinaScrapeWebsiteTool(
+    api_key=os.getenv('JINA_API_KEY'),
+    custom_headers={
+        'User-Agent': 'CrewAI Bot 1.0'
+    }
+)
+
+# Create agent
+researcher = Agent(
+    role='Web Content Analyst',
+    goal='Extract and analyze website content',
+    backstory='Expert at processing web information.',
+    tools=[scraper_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Analyze the content of 
+    https://example.com/blog for key insights.""",
+    agent=researcher
+)
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Multiple Site Analysis
+```python
+# Initialize tool
+scraper = JinaScrapeWebsiteTool(
+    api_key=os.getenv('JINA_API_KEY')
+)
+
+# Analyze multiple sites
+results = []
+sites = [
+    "https://site1.com",
+    "https://site2.com",
+    "https://site3.com"
+]
+
+for site in sites:
+    content = scraper.run(
+        website_url=site
+    )
+    results.append(content)
+```
+
+### Custom Headers Configuration
+```python
+# Initialize with custom headers
+tool = JinaScrapeWebsiteTool(
+    custom_headers={
+        'User-Agent': 'Custom Bot 1.0',
+        'Accept-Language': 'en-US,en;q=0.9',
+        'Accept': 'text/html,application/xhtml+xml'
+    }
+)
+
+# Use the tool
+content = tool.run(
+    website_url="https://example.com"
+)
+```
+
+### Error Handling Example
+```python
+try: 
+    scraper = JinaScrapeWebsiteTool()
+    content = scraper.run(
+        website_url="https://example.com"
+    )
+    print(content)
+except requests.exceptions.RequestException as e:
+    print(f"Error accessing website: {str(e)}")
+except Exception as e:
+    print(f"Error processing content: {str(e)}")
+```
+
+## Notes
+
+- Uses Jina.ai reader service
+- Markdown output format
+- API key authentication
+- Custom headers support
+- Error handling
+- Timeout management
+- Content processing
+- URL validation
+- Redirect handling
+- Response formatting

docs/tools/json-search-tool.mdx

@@ -0,0 +1,224 @@
+---
+title: JSONSearchTool
+description: A tool for semantic search within JSON files using RAG capabilities
+icon: braces
+---
+
+## JSONSearchTool
+
+The JSONSearchTool enables semantic search capabilities for JSON files using Retrieval-Augmented Generation (RAG). It supports both fixed and dynamic file path modes, allowing flexible usage patterns.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import JSONSearchTool
+
+# Method 1: Fixed path (specified at initialization)
+fixed_tool = JSONSearchTool(
+    json_path="path/to/data.json"
+)
+
+# Method 2: Dynamic path (specified at runtime)
+dynamic_tool = JSONSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='JSON Data Researcher',
+    goal='Search and analyze JSON data',
+    backstory='Expert at finding relevant information in JSON files.',
+    tools=[fixed_tool],  # or [dynamic_tool]
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed Path Mode
+```python
+class FixedJSONSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the JSON's content"
+    )
+```
+
+### Dynamic Path Mode
+```python
+class JSONSearchToolSchema(BaseModel):
+    json_path: str = Field(
+        description="Mandatory json path you want to search"
+    )
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the JSON's content"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    json_path: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the JSON search tool.
+
+    Args:
+        json_path (Optional[str]): Path to JSON file (optional for dynamic mode)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on JSON contents.
+
+    Args:
+        search_query (str): Query to search in the JSON
+        **kwargs: Additional arguments
+
+    Returns:
+        str: Relevant content from the JSON matching the query
+    """
+```
+
+## Best Practices
+
+1. File Handling:
+   - Use absolute file paths
+   - Verify file existence
+   - Handle large JSON files
+   - Monitor memory usage
+
+2. Query Optimization:
+   - Structure queries clearly
+   - Consider JSON structure
+   - Handle nested data
+   - Monitor performance
+
+3. Error Handling:
+   - Check file access
+   - Validate JSON format
+   - Handle malformed JSON
+   - Log issues
+
+4. Mode Selection:
+   - Choose fixed mode for static files
+   - Use dynamic mode for runtime selection
+   - Consider caching
+   - Manage file lifecycle
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import JSONSearchTool
+
+# Initialize tool
+json_tool = JSONSearchTool(
+    json_path="data/config.json"
+)
+
+# Create agent
+researcher = Agent(
+    role='JSON Data Analyst',
+    goal='Extract insights from JSON configuration',
+    backstory='Expert at analyzing JSON data structures.',
+    tools=[json_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Find all configuration settings 
+    related to security.""",
+    agent=researcher
+)
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Multiple File Analysis
+```python
+# Create tools for different JSON files
+config_tool = JSONSearchTool(
+    json_path="config/settings.json"
+)
+
+data_tool = JSONSearchTool(
+    json_path="data/records.json"
+)
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='JSON Data Analyst',
+    goal='Cross-reference configuration and data',
+    tools=[config_tool, data_tool]
+)
+```
+
+### Dynamic File Loading
+```python
+# Initialize dynamic tool
+dynamic_tool = JSONSearchTool()
+
+# Use with different JSON files
+result1 = dynamic_tool.run(
+    json_path="file1.json",
+    search_query="security settings"
+)
+
+result2 = dynamic_tool.run(
+    json_path="file2.json",
+    search_query="user preferences"
+)
+```
+
+### Error Handling Example
+```python
+try:
+    json_tool = JSONSearchTool(
+        json_path="config/settings.json"
+    )
+    results = json_tool.run(
+        search_query="encryption settings"
+    )
+    print(results)
+except FileNotFoundError as e:
+    print(f"JSON file not found: {str(e)}")
+except ValueError as e:
+    print(f"Invalid JSON format: {str(e)}")
+except Exception as e:
+    print(f"Error processing JSON: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Supports fixed/dynamic modes
+- JSON path validation
+- Memory management
+- Performance optimization
+- Error handling
+- Search capabilities
+- Content extraction
+- Format validation
+- Security features

docs/tools/linkup-search-tool.mdx

@@ -0,0 +1,184 @@
+---
+title: LinkupSearchTool
+description: A search tool powered by Linkup API for retrieving contextual information
+icon: search
+---
+
+## LinkupSearchTool
+
+The LinkupSearchTool provides search capabilities using the Linkup API. It allows for customizable search depth and output formatting, returning structured results with contextual information.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install linkup  # Required dependency
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import LinkupSearchTool
+
+# Initialize the tool with your API key
+search_tool = LinkupSearchTool(api_key="your-linkup-api-key")
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Information Researcher',
+    goal='Find relevant contextual information',
+    backstory='Expert at retrieving and analyzing contextual data.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Function Signature
+
+```python
+def __init__(self, api_key: str):
+    """
+    Initialize the Linkup search tool.
+
+    Args:
+        api_key (str): Linkup API key for authentication
+    """
+
+def _run(
+    self,
+    query: str,
+    depth: str = "standard",
+    output_type: str = "searchResults"
+) -> dict:
+    """
+    Perform a search using the Linkup API.
+
+    Args:
+        query (str): The search query
+        depth (str): Search depth ("standard" by default)
+        output_type (str): Desired result type ("searchResults" by default)
+
+    Returns:
+        dict: {
+            "success": bool,
+            "results": List[Dict] | None,
+            "error": str | None
+        }
+        
+        On success, results contains list of:
+        {
+            "name": str,
+            "url": str,
+            "content": str
+        }
+    """
+```
+
+## Best Practices
+
+1. Always provide a valid API key
+2. Use specific, focused search queries
+3. Choose appropriate search depth based on needs
+4. Handle potential API errors in agent prompts
+5. Process structured results effectively
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import LinkupSearchTool
+
+# Initialize tool with API key
+search_tool = LinkupSearchTool(api_key="your-linkup-api-key")
+
+# Create agent
+researcher = Agent(
+    role='Context Researcher',
+    goal='Find detailed contextual information about topics',
+    backstory='Expert at discovering and analyzing contextual data.',
+    tools=[search_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Research the latest developments in quantum computing,
+    focusing on recent breakthroughs and applications. Use standard depth
+    for comprehensive results.""",
+    agent=researcher
+)
+
+# The tool will use:
+# query: "quantum computing recent breakthroughs applications"
+# depth: "standard"
+# output_type: "searchResults"
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Search Depth Options
+```python
+# Quick surface-level search
+results = search_tool._run(
+    query="quantum computing",
+    depth="basic"
+)
+
+# Standard comprehensive search
+results = search_tool._run(
+    query="quantum computing",
+    depth="standard"
+)
+
+# Deep detailed search
+results = search_tool._run(
+    query="quantum computing",
+    depth="deep"
+)
+```
+
+### Output Type Options
+```python
+# Default search results
+results = search_tool._run(
+    query="quantum computing",
+    output_type="searchResults"
+)
+
+# Custom output format
+results = search_tool._run(
+    query="quantum computing",
+    output_type="customFormat"
+)
+```
+
+### Error Handling
+```python
+results = search_tool._run(query="quantum computing")
+if results["success"]:
+    for result in results["results"]:
+        print(f"Name: {result['name']}")
+        print(f"URL: {result['url']}")
+        print(f"Content: {result['content']}")
+else:
+    print(f"Error: {results['error']}")
+```
+
+## Notes
+
+- Requires valid Linkup API key
+- Returns structured search results
+- Supports multiple search depths
+- Configurable output formats
+- Built-in error handling
+- Thread-safe operations
+- Efficient for contextual searches

docs/tools/llamaindex-tool.mdx

@@ -0,0 +1,192 @@
+---
+title: LlamaIndexTool
+description: A wrapper tool for integrating LlamaIndex tools and query engines with CrewAI
+icon: link
+---
+
+## LlamaIndexTool
+
+The LlamaIndexTool serves as a bridge between CrewAI and LlamaIndex, allowing you to use LlamaIndex tools and query engines within your CrewAI agents. It supports both direct tool wrapping and query engine integration.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install llama-index  # Required for LlamaIndex integration
+```
+
+## Usage Examples
+
+### Using with LlamaIndex Tools
+
+```python
+from crewai import Agent
+from crewai_tools import LlamaIndexTool
+from llama_index.core.tools import BaseTool as LlamaBaseTool
+from pydantic import BaseModel, Field
+
+# Create a LlamaIndex tool
+class CustomLlamaSchema(BaseModel):
+    query: str = Field(..., description="Query to process")
+
+class CustomLlamaTool(LlamaBaseTool):
+    name = "Custom Llama Tool"
+    description = "A custom LlamaIndex tool"
+    
+    def __call__(self, query: str) -> str:
+        return f"Processed: {query}"
+
+# Wrap the LlamaIndex tool
+llama_tool = CustomLlamaTool()
+wrapped_tool = LlamaIndexTool.from_tool(llama_tool)
+
+# Create an agent with the tool
+agent = Agent(
+    role='LlamaIndex Integration Agent',
+    goal='Process queries using LlamaIndex tools',
+    backstory='Specialist in integrating LlamaIndex capabilities.',
+    tools=[wrapped_tool]
+)
+```
+
+### Using with Query Engines
+
+```python
+from crewai import Agent
+from crewai_tools import LlamaIndexTool
+from llama_index.core import VectorStoreIndex, Document
+
+# Create a query engine
+documents = [Document(text="Sample document content")]
+index = VectorStoreIndex.from_documents(documents)
+query_engine = index.as_query_engine()
+
+# Create the tool
+query_tool = LlamaIndexTool.from_query_engine(
+    query_engine,
+    name="Document Search",
+    description="Search through indexed documents"
+)
+
+# Create an agent with the tool
+agent = Agent(
+    role='Document Researcher',
+    goal='Find relevant information in documents',
+    backstory='Expert at searching through document collections.',
+    tools=[query_tool]
+)
+```
+
+## Tool Creation Methods
+
+### From LlamaIndex Tool
+
+```python
+@classmethod
+def from_tool(cls, tool: Any, **kwargs: Any) -> "LlamaIndexTool":
+    """
+    Create a CrewAI tool from a LlamaIndex tool.
+
+    Args:
+        tool (LlamaBaseTool): A LlamaIndex tool to wrap
+        **kwargs: Additional arguments for tool creation
+
+    Returns:
+        LlamaIndexTool: A CrewAI-compatible tool wrapper
+
+    Raises:
+        ValueError: If tool is not a LlamaBaseTool or lacks fn_schema
+    """
+```
+
+### From Query Engine
+
+```python
+@classmethod
+def from_query_engine(
+    cls,
+    query_engine: Any,
+    name: Optional[str] = None,
+    description: Optional[str] = None,
+    return_direct: bool = False,
+    **kwargs: Any
+) -> "LlamaIndexTool":
+    """
+    Create a CrewAI tool from a LlamaIndex query engine.
+
+    Args:
+        query_engine (BaseQueryEngine): The query engine to wrap
+        name (Optional[str]): Custom name for the tool
+        description (Optional[str]): Custom description
+        return_direct (bool): Whether to return query engine response directly
+        **kwargs: Additional arguments for tool creation
+
+    Returns:
+        LlamaIndexTool: A CrewAI-compatible tool wrapper
+
+    Raises:
+        ValueError: If query_engine is not a BaseQueryEngine
+    """
+```
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import LlamaIndexTool
+from llama_index.core import VectorStoreIndex, Document
+from llama_index.core.tools import QueryEngineTool
+
+# Create documents and index
+documents = [
+    Document(text="AI is a technology that simulates human intelligence."),
+    Document(text="Machine learning is a subset of AI.")
+]
+index = VectorStoreIndex.from_documents(documents)
+query_engine = index.as_query_engine()
+
+# Create the tool
+search_tool = LlamaIndexTool.from_query_engine(
+    query_engine,
+    name="AI Knowledge Base",
+    description="Search through AI-related documents"
+)
+
+# Create agent
+researcher = Agent(
+    role='AI Researcher',
+    goal='Research AI concepts',
+    backstory='Expert at finding and explaining AI concepts.',
+    tools=[search_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find and explain what AI is and its relationship  
+    with machine learning.""",
+    agent=researcher
+)
+
+# The agent will use:
+# {
+#   "query": "What is AI and how does it relate to machine learning?"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Notes
+
+- Automatically adapts LlamaIndex tool schemas for CrewAI compatibility
+- Renames 'input' parameter to 'query' for better integration
+- Supports both direct tool wrapping and query engine integration
+- Handles schema validation and error resolution
+- Thread-safe operations
+- Compatible with all LlamaIndex tool types and query engines

docs/tools/mdx-search-tool.mdx

@@ -0,0 +1,209 @@
+---
+title: MDX Search Tool
+description: A tool for semantic searching within MDX files using RAG capabilities
+---
+
+# MDX Search Tool
+
+The MDX Search Tool enables semantic searching within MDX (Markdown with JSX) files using Retrieval-Augmented Generation (RAG) capabilities. It supports both fixed and dynamic file path modes, allowing you to specify the MDX file at initialization or runtime.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage
+
+You can use the MDX Search Tool in two ways:
+
+### 1. Fixed MDX File Path
+
+Initialize the tool with a specific MDX file path:
+
+```python
+from crewai import Agent
+from crewai_tools import MDXSearchTool
+
+# Initialize with a fixed MDX file
+tool = MDXSearchTool(mdx="/path/to/your/document.mdx")
+
+# Create an agent with the tool
+agent = Agent(
+    role='Technical Writer',
+    goal='Search through MDX documentation',
+    backstory='I help find relevant information in MDX documentation',
+    tools=[tool]
+)
+
+# Use in a task
+task = Task(
+    description="Find information about API endpoints in the documentation",
+    agent=agent
+)
+```
+
+### 2. Dynamic MDX File Path
+
+Initialize the tool without a specific file path to provide it at runtime:
+
+```python
+from crewai import Agent
+from crewai_tools import MDXSearchTool
+
+# Initialize without a fixed MDX file
+tool = MDXSearchTool()
+
+# Create an agent with the tool
+agent = Agent(
+    role='Documentation Analyst',
+    goal='Search through various MDX files',
+    backstory='I analyze different MDX documentation files',
+    tools=[tool]
+)
+
+# Use in a task with dynamic file path
+task = Task(
+    description="Search for 'authentication' in the API documentation",
+    agent=agent,
+    context={
+        "mdx": "/path/to/api-docs.mdx",
+        "search_query": "authentication"
+    }
+)
+```
+
+## Input Schema
+
+### Fixed MDX File Mode
+```python
+class FixedMDXSearchToolSchema(BaseModel):
+    search_query: str  # The search query to find content in the MDX file
+```
+
+### Dynamic MDX File Mode
+```python
+class MDXSearchToolSchema(BaseModel):
+    search_query: str  # The search query to find content in the MDX file
+    mdx: str  # The path to the MDX file to search
+```
+
+## Function Signatures
+
+```python
+def __init__(self, mdx: Optional[str] = None, **kwargs):
+    """
+    Initialize the MDX Search Tool.
+    
+    Args:
+        mdx (Optional[str]): Path to the MDX file (optional)
+        **kwargs: Additional arguments passed to RagTool
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any,
+) -> str:
+    """
+    Execute the search on the MDX file.
+    
+    Args:
+        search_query (str): The query to search for
+        **kwargs: Additional arguments including 'mdx' for dynamic mode
+    
+    Returns:
+        str: The search results from the MDX content
+    """
+```
+
+## Best Practices
+
+1. **File Path Handling**:
+   - Use absolute paths to avoid path resolution issues
+   - Verify file existence before searching
+   - Handle file permissions appropriately
+
+2. **Query Optimization**:
+   - Use specific, focused search queries
+   - Consider context when formulating queries
+   - Break down complex searches into smaller queries
+
+3. **Error Handling**:
+   - Handle file not found errors gracefully
+   - Manage permission issues appropriately
+   - Validate input parameters before processing
+
+## Example Integration
+
+Here's a complete example showing how to integrate the MDX Search Tool with CrewAI:
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import MDXSearchTool
+
+# Initialize the tool
+mdx_tool = MDXSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Documentation Researcher',
+    goal='Find and analyze information in MDX documentation',
+    backstory='I am an expert at finding relevant information in documentation',
+    tools=[mdx_tool]
+)
+
+# Create tasks
+search_task = Task(
+    description="""
+    Search through the API documentation for information about authentication methods.
+    Look for:
+    1. Authentication endpoints
+    2. Security best practices
+    3. Token handling
+    
+    Provide a comprehensive summary of the findings.
+    """,
+    agent=researcher,
+    context={
+        "mdx": "/path/to/api-docs.mdx",
+        "search_query": "authentication security tokens"
+    }
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[search_task]
+)
+
+result = crew.kickoff()
+```
+
+## Error Handling
+
+The tool handles various error scenarios:
+
+1. **File Not Found**:
+   ```python
+   try:
+       tool = MDXSearchTool(mdx="/path/to/nonexistent.mdx")
+   except FileNotFoundError:
+       print("MDX file not found. Please verify the file path.")
+   ```
+
+2. **Permission Issues**:
+   ```python
+   try: 
+       tool = MDXSearchTool(mdx="/restricted/docs.mdx")
+   except PermissionError:
+       print("Insufficient permissions to access the MDX file.")
+   ```
+
+3. **Invalid Content**:
+   ```python
+   try:
+       result = tool._run(search_query="query", mdx="/path/to/invalid.mdx")
+   except ValueError:
+       print("Invalid MDX content or format.")
+   ```

docs/tools/mysql-search-tool.mdx

@@ -0,0 +1,217 @@
+---
+title: MySQLSearchTool
+description: A tool for semantic search within MySQL database tables using RAG capabilities
+icon: database
+---
+
+## MySQLSearchTool
+
+The MySQLSearchTool enables semantic search capabilities for MySQL database tables using Retrieval-Augmented Generation (RAG). It processes table contents and allows natural language queries to search through the data.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import MySQLSearchTool
+
+# Initialize the tool
+mysql_tool = MySQLSearchTool(
+    table_name="users",
+    db_uri="mysql://user:pass@localhost:3306/database"
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Database Researcher',
+    goal='Search and analyze database contents',
+    backstory='Expert at finding relevant information in databases.',
+    tools=[mysql_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class MySQLSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory semantic search query you want to use to search the database's content"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    table_name: str,
+    db_uri: str,
+    **kwargs
+):
+    """
+    Initialize the MySQL search tool.
+
+    Args:
+        table_name (str): Name of the table to search
+        db_uri (str): Database connection URI
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on table contents.
+
+    Args:
+        search_query (str): Query to search in the table
+        **kwargs: Additional arguments
+
+    Returns:
+        str: Relevant content from the table matching the query
+    """
+```
+
+## Best Practices
+
+1. Database Connection:
+   - Use secure connection URIs
+   - Handle authentication properly
+   - Manage connection lifecycle
+   - Monitor timeouts
+
+2. Query Optimization:
+   - Structure queries clearly
+   - Consider table size
+   - Handle large datasets
+   - Monitor performance
+
+3. Security Considerations:
+   - Protect credentials
+   - Use environment variables
+   - Limit table access
+   - Validate inputs
+
+4. Error Handling:
+   - Handle connection errors
+   - Manage query timeouts
+   - Provide clear messages
+   - Log issues
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import MySQLSearchTool
+
+# Initialize tool
+mysql_tool = MySQLSearchTool(
+    table_name="customers",
+    db_uri="mysql://user:pass@localhost:3306/crm"
+)
+
+# Create agent
+researcher = Agent(
+    role='Database Analyst',
+    goal='Extract customer insights from database',
+    backstory='Expert at analyzing customer data.',
+    tools=[mysql_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Find all premium customers 
+    with recent purchases.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "premium customers recent purchases"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Multiple Table Analysis
+```python
+# Create tools for different tables
+customers_tool = MySQLSearchTool(
+    table_name="customers",
+    db_uri="mysql://user:pass@localhost:3306/crm"
+)
+
+orders_tool = MySQLSearchTool(
+    table_name="orders",
+    db_uri="mysql://user:pass@localhost:3306/crm"
+)
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Data Analyst',
+    goal='Cross-reference customer and order data',
+    tools=[customers_tool, orders_tool]
+)
+```
+
+### Secure Connection Configuration
+```python
+import os
+
+# Use environment variables for credentials
+db_uri = (
+    f"mysql://{os.getenv('DB_USER')}:{os.getenv('DB_PASS')}"
+    f"@{os.getenv('DB_HOST')}:{os.getenv('DB_PORT')}"
+    f"/{os.getenv('DB_NAME')}"
+)
+
+tool = MySQLSearchTool(
+    table_name="sensitive_data",
+    db_uri=db_uri
+)
+```
+
+### Error Handling Example
+```python
+try:
+    mysql_tool = MySQLSearchTool(
+        table_name="users",
+        db_uri="mysql://user:pass@localhost:3306/app"
+    )
+    results = mysql_tool.run(
+        search_query="active users in California"
+    )
+    print(results)
+except Exception as e:
+    print(f"Error querying database: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Uses MySQLLoader
+- Requires database URI
+- Table-specific search
+- Semantic query support
+- Connection management
+- Error handling
+- Performance optimization
+- Security features
+- Memory efficiency

docs/tools/pdf-search-tool.mdx

@@ -0,0 +1,208 @@
+---
+title: PDFSearchTool
+description: A tool for semantic search within PDF documents using RAG capabilities
+icon: file-search
+---
+
+## PDFSearchTool
+
+The PDFSearchTool enables semantic search capabilities for PDF documents using Retrieval-Augmented Generation (RAG). It leverages embedchain's PDFEmbedchainAdapter for efficient PDF processing and supports both fixed and dynamic PDF path specification.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import PDFSearchTool
+
+# Method 1: Initialize with specific PDF
+pdf_tool = PDFSearchTool(pdf="/path/to/document.pdf")
+
+# Method 2: Initialize without PDF (specify at runtime)
+flexible_pdf_tool = PDFSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='PDF Researcher',
+    goal='Search and analyze PDF documents',
+    backstory='Expert at finding relevant information in PDFs.',
+    tools=[pdf_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed PDF Schema (when PDF path provided during initialization)
+```python
+class FixedPDFSearchToolSchema(BaseModel):
+    query: str = Field(
+        description="Mandatory query you want to use to search the PDF's content"
+    )
+```
+
+### Flexible PDF Schema (when PDF path provided at runtime)
+```python
+class PDFSearchToolSchema(FixedPDFSearchToolSchema):
+    pdf: str = Field(
+        description="Mandatory pdf path you want to search"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    pdf: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the PDF search tool.
+
+    Args:
+        pdf (Optional[str]): Path to PDF file (optional)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on PDF content.
+
+    Args:
+        query (str): Search query for the PDF
+        **kwargs: Additional arguments including pdf path if not initialized
+
+    Returns:
+        str: Relevant content from the PDF matching the query
+    """
+```
+
+## Best Practices
+
+1. PDF File Handling:
+   - Use absolute paths for reliability
+   - Verify PDF file existence
+   - Handle large PDFs appropriately
+
+2. Search Optimization:
+   - Use specific, focused queries
+   - Consider document structure
+   - Test with sample queries first
+
+3. Performance Considerations:
+   - Pre-initialize with PDF for repeated searches
+   - Handle large documents efficiently
+   - Monitor memory usage
+
+4. Error Handling:
+   - Verify PDF file existence
+   - Handle malformed PDFs
+   - Manage file access permissions
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import PDFSearchTool
+
+# Initialize tool with specific PDF
+pdf_tool = PDFSearchTool(pdf="/path/to/research.pdf")
+
+# Create agent
+researcher = Agent(
+    role='PDF Researcher',
+    goal='Extract insights from research papers',
+    backstory='Expert at analyzing research documents.',
+    tools=[pdf_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find all mentions of machine learning 
+    applications in healthcare from the PDF.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "query": "machine learning applications healthcare"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic PDF Selection
+```python
+# Initialize without PDF
+flexible_tool = PDFSearchTool()
+
+# Search different PDFs
+research_results = flexible_tool.run(
+    query="quantum computing",
+    pdf="/path/to/research.pdf"
+)
+
+report_results = flexible_tool.run(
+    query="financial metrics",
+    pdf="/path/to/report.pdf"
+)
+```
+
+### Multiple PDF Analysis
+```python
+# Create tools for different PDFs
+research_tool = PDFSearchTool(pdf="/path/to/research.pdf")
+report_tool = PDFSearchTool(pdf="/path/to/report.pdf")
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Document Analyst',
+    goal='Cross-reference multiple documents',
+    tools=[research_tool, report_tool]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    pdf_tool = PDFSearchTool()
+    results = pdf_tool.run(
+        query="important findings",
+        pdf="/path/to/document.pdf"
+    )
+    print(results)
+except Exception as e:
+    print(f"Error processing PDF: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Uses PDFEmbedchainAdapter
+- Supports semantic search
+- Dynamic PDF specification
+- Efficient content retrieval
+- Thread-safe operations
+- Maintains search context
+- Handles large documents
+- Supports various PDF formats
+- Memory-efficient processing

docs/tools/pdf-text-writing-tool.mdx

@@ -0,0 +1,234 @@
+---
+title: PDFTextWritingTool
+description: A tool for adding text to specific positions in PDF documents with custom font support
+icon: file-pdf
+---
+
+## PDFTextWritingTool
+
+The PDFTextWritingTool allows you to add text to specific positions in PDF documents with support for custom fonts, colors, and positioning. It's particularly useful for adding annotations, watermarks, or any text overlay to existing PDFs.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import PDFTextWritingTool
+
+# Basic initialization
+pdf_tool = PDFTextWritingTool()
+
+# Create an agent with the tool
+document_processor = Agent(
+    role='Document Processor',
+    goal='Add text annotations to PDF documents',
+    backstory='Expert at PDF document processing and text manipulation.',
+    tools=[pdf_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class PDFTextWritingToolSchema(BaseModel):
+    pdf_path: str = Field(
+        description="Path to the PDF file to modify"
+    )
+    text: str = Field(
+        description="Text to add to the PDF"
+    )
+    position: tuple = Field(
+        description="Tuple of (x, y) coordinates for text placement"
+    )
+    font_size: int = Field(
+        default=12,
+        description="Font size of the text"
+    )
+    font_color: str = Field(
+        default="0 0 0 rg",
+        description="RGB color code for the text"
+    )
+    font_name: Optional[str] = Field(
+        default="F1",
+        description="Font name for standard fonts"
+    )
+    font_file: Optional[str] = Field(
+        default=None,
+        description="Path to a .ttf font file for custom font usage"
+    )
+    page_number: int = Field(
+        default=0,
+        description="Page number to add text to"
+    )
+```
+
+## Function Signature
+
+```python
+def run(
+    self,
+    pdf_path: str,
+    text: str,
+    position: tuple,
+    font_size: int,
+    font_color: str,
+    font_name: str = "F1",
+    font_file: Optional[str] = None,
+    page_number: int = 0,
+    **kwargs
+) -> str:
+    """
+    Add text to a specific position in a PDF document.
+
+    Args:
+        pdf_path (str): Path to the PDF file to modify
+        text (str): Text to add to the PDF
+        position (tuple): (x, y) coordinates for text placement
+        font_size (int): Font size of the text
+        font_color (str): RGB color code for the text (e.g., "0 0 0 rg" for black)
+        font_name (str, optional): Font name for standard fonts (default: "F1")
+        font_file (str, optional): Path to a .ttf font file for custom font
+        page_number (int, optional): Page number to add text to (default: 0)
+
+    Returns:
+        str: Success message with output file path
+    """
+```
+
+## Best Practices
+
+1. File Handling:
+   - Ensure PDF files exist before processing
+   - Use absolute paths for reliability
+   - Handle file permissions appropriately
+
+2. Text Positioning:
+   - Use appropriate coordinates based on PDF dimensions
+   - Consider page orientation and margins
+   - Test positioning with small changes first
+
+3. Font Usage:
+   - Verify custom font files exist
+   - Use standard fonts when possible
+   - Test font rendering before production use
+
+4. Error Handling:
+   - Check page numbers are valid
+   - Verify font file accessibility
+   - Handle file writing permissions
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import PDFTextWritingTool
+
+# Initialize tool
+pdf_tool = PDFTextWritingTool()
+
+# Create agent
+document_processor = Agent(
+    role='Document Processor',
+    goal='Process and annotate PDF documents',
+    backstory='Expert at PDF manipulation and text placement.',
+    tools=[pdf_tool]
+)
+
+# Define task
+annotation_task = Task(
+    description="""Add a watermark saying 'CONFIDENTIAL' to 
+    the center of the first page of the document at 
+    '/path/to/document.pdf'.""",
+    agent=document_processor
+)
+
+# The tool will use:
+# {
+#   "pdf_path": "/path/to/document.pdf",
+#   "text": "CONFIDENTIAL",
+#   "position": (300, 400),
+#   "font_size": 24,
+#   "font_color": "1 0 0 rg",  # Red color
+#   "page_number": 0
+# }
+
+# Create crew
+crew = Crew(
+    agents=[document_processor],
+    tasks=[annotation_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Custom Font Example
+```python
+# Using a custom font
+result = pdf_tool.run(
+    pdf_path="/path/to/input.pdf",
+    text="Custom Font Text",
+    position=(100, 500),
+    font_size=16,
+    font_color="0 0 1 rg",  # Blue color
+    font_file="/path/to/custom_font.ttf",
+    page_number=0
+)
+```
+
+### Multiple Text Elements
+```python
+# Add multiple text elements
+positions = [(100, 700), (100, 650), (100, 600)]
+texts = ["Header", "Subheader", "Body Text"]
+font_sizes = [18, 14, 12]
+
+for text, position, size in zip(texts, positions, font_sizes):
+    pdf_tool.run(
+        pdf_path="/path/to/input.pdf",
+        text=text,
+        position=position,
+        font_size=size,
+        font_color="0 0 0 rg"  # Black color
+    )
+```
+
+### Color Text Example
+```python
+# Add colored text
+colors = {
+    "red": "1 0 0 rg",
+    "green": "0 1 0 rg",
+    "blue": "0 0 1 rg"
+}
+
+for y_pos, (color_name, color_code) in enumerate(colors.items()):
+    pdf_tool.run(
+        pdf_path="/path/to/input.pdf",
+        text=f"This text is {color_name}",
+        position=(100, 700 - y_pos * 50),
+        font_size=14,
+        font_color=color_code
+    )
+```
+
+## Notes
+
+- Supports custom TrueType fonts (.ttf)
+- Allows RGB color specifications
+- Handles multi-page PDFs
+- Preserves original PDF content
+- Supports text positioning with x,y coordinates
+- Maintains PDF structure and metadata
+- Creates new output file for safety
+- Thread-safe operations
+- Efficient PDF manipulation
+- Supports various text attributes

docs/tools/pg-search-tool.mdx

@@ -0,0 +1,181 @@
+---
+title: PGSearchTool
+description: A RAG-based semantic search tool for PostgreSQL database content
+icon: database-search
+---
+
+## PGSearchTool
+
+The PGSearchTool provides semantic search capabilities for PostgreSQL database content using RAG (Retrieval-Augmented Generation). It allows for natural language queries over database table content by leveraging embeddings and semantic search.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install embedchain  # Required dependency
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import PGSearchTool
+
+# Initialize the tool with database configuration
+search_tool = PGSearchTool(
+    db_uri="postgresql://user:password@localhost:5432/dbname",
+    table_name="your_table"
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Database Researcher',
+    goal='Find relevant information in database content',
+    backstory='Expert at searching and analyzing database content.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class PGSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory semantic search query for searching the database's content"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(self, table_name: str, **kwargs):
+    """
+    Initialize the PostgreSQL search tool.
+
+    Args:
+        table_name (str): Name of the table to search
+        db_uri (str): PostgreSQL database URI (required in kwargs)
+        **kwargs: Additional arguments for RagTool initialization
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> Any:
+    """
+    Perform semantic search on database content.
+
+    Args:
+        search_query (str): Semantic search query
+        **kwargs: Additional search parameters
+
+    Returns:
+        Any: Relevant database content based on semantic search
+    """
+```
+
+## Best Practices
+
+1. Secure database credentials:
+   ```python
+   # Use environment variables for sensitive data
+   import os
+   
+   db_uri = (
+       f"postgresql://{os.getenv('DB_USER')}:{os.getenv('DB_PASS')}"
+       f"@{os.getenv('DB_HOST')}:{os.getenv('DB_PORT')}/{os.getenv('DB_NAME')}"
+   )
+   ```
+
+2. Optimize table selection
+3. Use specific semantic queries
+4. Handle database connection errors
+5. Consider table size and query performance
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import PGSearchTool
+
+# Initialize tool with database configuration
+db_search = PGSearchTool(
+    db_uri="postgresql://user:password@localhost:5432/dbname",
+    table_name="customer_feedback"
+)
+
+# Create agent
+analyst = Agent(
+    role='Database Analyst',
+    goal='Analyze customer feedback data',
+    backstory='Expert at finding insights in customer feedback.',
+    tools=[db_search]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Find all customer feedback related to product usability
+    and ease of use. Focus on common patterns and issues.""",
+    agent=analyst
+)
+
+# The tool will use:
+# {
+#   "search_query": "product usability feedback ease of use issues"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[analyst],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Multiple Table Search
+```python
+# Create tools for different tables
+customer_search = PGSearchTool(
+    db_uri="postgresql://user:password@localhost:5432/dbname",
+    table_name="customers"
+)
+
+orders_search = PGSearchTool(
+    db_uri="postgresql://user:password@localhost:5432/dbname",
+    table_name="orders"
+)
+
+# Use both tools in an agent
+analyst = Agent(
+    role='Multi-table Analyst',
+    goal='Analyze customer and order data',
+    tools=[customer_search, orders_search]
+)
+```
+
+### Error Handling
+```python
+try:
+    results = search_tool._run(
+        search_query="customer satisfaction ratings"
+    )
+    # Process results
+except Exception as e:
+    print(f"Database search error: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool for semantic search
+- Uses embedchain's PostgresLoader
+- Requires valid PostgreSQL connection
+- Supports semantic natural language queries
+- Thread-safe operations
+- Efficient for large tables
+- Handles connection pooling automatically

docs/tools/rag-tool.mdx

@@ -0,0 +1,282 @@
+---
+title: RagTool
+description: Base class for Retrieval-Augmented Generation (RAG) tools with flexible adapter support
+icon: database
+---
+
+## RagTool
+
+The RagTool serves as the base class for all Retrieval-Augmented Generation (RAG) tools in the CrewAI ecosystem. It provides a flexible adapter-based architecture for implementing knowledge base functionality with semantic search capabilities.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import RagTool
+from crewai_tools.adapters import EmbedchainAdapter
+from embedchain import App
+
+# Create custom adapter
+class CustomAdapter(RagTool.Adapter):
+    def query(self, question: str) -> str:
+        # Implement custom query logic
+        return "Answer based on knowledge base"
+
+    def add(self, *args, **kwargs) -> None:
+        # Implement custom add logic
+        pass
+
+# Method 1: Use default EmbedchainAdapter
+rag_tool = RagTool(
+    name="Custom Knowledge Base",
+    description="Specialized knowledge base for domain data",
+    summarize=True
+)
+
+# Method 2: Use custom adapter
+custom_tool = RagTool(
+    name="Custom Knowledge Base",
+    adapter=CustomAdapter(),
+    summarize=False
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Knowledge Base Researcher',
+    goal='Search and analyze knowledge base content',
+    backstory='Expert at finding relevant information in specialized datasets.',
+    tools=[rag_tool],
+    verbose=True
+)
+```
+
+## Adapter Interface
+
+```python
+class Adapter(BaseModel, ABC):
+    @abstractmethod
+    def query(self, question: str) -> str:
+        """
+        Query the knowledge base with a question.
+        
+        Args:
+            question (str): Query to search in knowledge base
+            
+        Returns:
+            str: Answer based on knowledge base content
+        """
+        
+    @abstractmethod
+    def add(self, *args: Any, **kwargs: Any) -> None:
+        """
+        Add content to the knowledge base.
+        
+        Args:
+            *args: Variable length argument list
+            **kwargs: Arbitrary keyword arguments
+        """
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    name: str = "Knowledge base",
+    description: str = "A knowledge base that can be used to answer questions.",
+    summarize: bool = False,
+    adapter: Optional[Adapter] = None,
+    config: Optional[dict[str, Any]] = None,
+    **kwargs
+):
+    """
+    Initialize the RAG tool.
+
+    Args:
+        name (str): Tool name
+        description (str): Tool description
+        summarize (bool): Enable answer summarization
+        adapter (Optional[Adapter]): Custom adapter implementation
+        config (Optional[dict]): Configuration for default adapter
+        **kwargs: Additional arguments for base tool
+    """
+
+def _run(
+    self,
+    query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute query against knowledge base.
+
+    Args:
+        query (str): Question to ask
+        **kwargs: Additional arguments
+
+    Returns:
+        str: Answer from knowledge base
+    """
+```
+
+## Best Practices
+
+1. Adapter Implementation:
+   - Define clear interfaces
+   - Handle edge cases
+   - Implement error handling
+   - Document behavior
+
+2. Knowledge Base Management:
+   - Organize content logically
+   - Update content regularly
+   - Monitor performance
+   - Handle large datasets
+
+3. Query Optimization:
+   - Structure queries clearly
+   - Consider context
+   - Handle ambiguity
+   - Validate inputs
+
+4. Error Handling:
+   - Handle missing data
+   - Manage timeouts
+   - Provide clear messages
+   - Log issues
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import RagTool
+from embedchain import App
+
+# Initialize tool with custom configuration
+rag_tool = RagTool(
+    name="Technical Documentation KB",
+    description="Knowledge base for technical documentation",
+    summarize=True,
+    config={
+        "collection_name": "tech_docs",
+        "chunking": {
+            "chunk_size": 500,
+            "chunk_overlap": 50
+        }
+    }
+)
+
+# Add content to knowledge base
+rag_tool.add(
+    "Technical documentation content here...",
+    data_type="text"
+)
+
+# Create agent
+researcher = Agent(
+    role='Documentation Expert',
+    goal='Extract technical information from documentation',
+    backstory='Expert at analyzing technical documentation.',
+    tools=[rag_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find all mentions of API endpoints 
+    and their authentication requirements.""",
+    agent=researcher
+)
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Custom Adapter Implementation
+```python
+from typing import Any
+from pydantic import BaseModel
+from abc import ABC, abstractmethod
+
+class SpecializedAdapter(RagTool.Adapter):
+    def __init__(self, config: dict):
+        self.config = config
+        self.knowledge_base = {}
+
+    def query(self, question: str) -> str:
+        # Implement specialized query logic
+        return self._process_query(question)
+
+    def add(self, content: str, **kwargs: Any) -> None:
+        # Implement specialized content addition
+        self._process_content(content, **kwargs)
+
+# Use custom adapter
+specialized_tool = RagTool(
+    name="Specialized KB",
+    adapter=SpecializedAdapter(config={"mode": "advanced"})
+)
+```
+
+### Configuration Management
+```python
+# Configure default EmbedchainAdapter
+config = {
+    "collection_name": "custom_collection",
+    "embedding": {
+        "model": "sentence-transformers/all-mpnet-base-v2",
+        "dimensions": 768
+    },
+    "chunking": {
+        "chunk_size": 1000,
+        "chunk_overlap": 100
+    }
+}
+
+tool = RagTool(config=config)
+```
+
+### Error Handling Example
+```python
+try:
+    rag_tool = RagTool()
+    
+    # Add content
+    rag_tool.add(
+        "Documentation content...",
+        data_type="text"
+    )
+    
+    # Query content
+    result = rag_tool.run(
+        query="What are the system requirements?"
+    )
+    print(result)
+except Exception as e:
+    print(f"Error using knowledge base: {str(e)}")
+```
+
+## Notes
+
+- Base class for RAG tools
+- Flexible adapter pattern
+- Default EmbedchainAdapter
+- Custom adapter support
+- Content management
+- Query processing
+- Error handling
+- Configuration options
+- Performance optimization
+- Memory management

docs/tools/serpapi-google-search-tool.mdx

@@ -0,0 +1,229 @@
+---
+title: SerpApi Google Search Tool
+description: A tool for performing Google searches using the SerpApi service
+---
+
+# SerpApi Google Search Tool
+
+The SerpApi Google Search Tool enables performing Google searches using the SerpApi service. It provides location-aware search capabilities with comprehensive result filtering.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install serpapi
+```
+
+## Prerequisites
+
+You need a SerpApi API key to use this tool. You can get one from [SerpApi's website](https://serpapi.com/manage-api-key).
+
+Set your API key as an environment variable:
+```bash
+export SERPAPI_API_KEY="your_api_key_here"
+```
+
+## Usage
+
+Here's how to use the SerpApi Google Search Tool:
+
+```python
+from crewai import Agent
+from crewai_tools import SerpApiGoogleSearchTool
+
+# Initialize the tool
+search_tool = SerpApiGoogleSearchTool()
+
+# Create an agent with the tool
+search_agent = Agent(
+    role='Web Researcher',
+    goal='Find accurate information online',
+    backstory='I help research and analyze online information',
+    tools=[search_tool]
+)
+
+# Use in a task
+task = Task(
+    description="Research recent AI developments",
+    agent=search_agent,
+    context={
+        "search_query": "latest artificial intelligence breakthroughs 2024",
+        "location": "United States"  # Optional
+    }
+)
+```
+
+## Input Schema
+
+```python
+class SerpApiGoogleSearchToolSchema(BaseModel):
+    search_query: str  # The search query for Google Search
+    location: Optional[str] = None  # Optional location for localized results
+```
+
+## Function Signatures
+
+### Base Tool Initialization
+```python
+def __init__(self, **kwargs):
+    """
+    Initialize the SerpApi tool with API credentials.
+    
+    Raises:
+        ImportError: If serpapi package is not installed
+        ValueError: If SERPAPI_API_KEY environment variable is not set
+    """
+```
+
+### Search Execution
+```python
+def _run(
+    self,
+    **kwargs: Any,
+) -> dict:
+    """
+    Execute the Google search.
+    
+    Args:
+        search_query (str): The search query
+        location (Optional[str]): Optional location for results
+    
+    Returns:
+        dict: Filtered search results from Google
+        
+    Raises:
+        HTTPError: If the API request fails
+    """
+```
+
+## Best Practices
+
+1. **API Key Management**:
+   - Store the API key securely in environment variables
+   - Never hardcode the API key in your code
+   - Verify API key validity before making requests
+
+2. **Search Optimization**:
+   - Use specific, targeted search queries
+   - Include relevant keywords and time frames
+   - Leverage location parameter for regional results
+
+3. **Error Handling**:
+   - Handle API rate limits gracefully
+   - Implement retry logic for failed requests
+   - Validate input parameters before making requests
+
+## Example Integration
+
+Here's a complete example showing how to integrate the SerpApi Google Search Tool with CrewAI:
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerpApiGoogleSearchTool
+
+# Initialize the tool
+search_tool = SerpApiGoogleSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Research Analyst',
+    goal='Find and analyze current information',
+    backstory="""I am an expert at finding and analyzing 
+    information from various online sources.""",
+    tools=[search_tool]
+)
+
+# Create tasks
+research_task = Task(
+    description="""
+    Research the following topic:
+    1. Latest developments in quantum computing
+    2. Focus on practical applications
+    3. Include major company announcements
+    
+    Provide a comprehensive analysis of the findings.
+    """,
+    agent=researcher,
+    context={
+        "search_query": "quantum computing breakthroughs applications companies",
+        "location": "United States"
+    }
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+result = crew.kickoff()
+```
+
+## Error Handling
+
+The tool handles various error scenarios:
+
+1. **Missing API Key**:
+   ```python
+   try:
+       tool = SerpApiGoogleSearchTool()
+   except ValueError as e:
+       print("API key not found. Set SERPAPI_API_KEY environment variable.")
+   ```
+
+2. **API Request Errors**:
+   ```python
+   try:
+       results = tool._run(
+           search_query="quantum computing",
+           location="United States"
+       )
+   except HTTPError as e:
+       print(f"API request failed: {str(e)}")
+   ```
+
+3. **Invalid Parameters**:
+   ```python
+   try:
+       results = tool._run(
+           search_query="",  # Empty query
+           location="Invalid Location"
+       )
+   except ValueError as e:
+       print("Invalid search parameters provided.")
+   ```
+
+## Response Format
+
+The tool returns a filtered dictionary containing Google search results. Example response structure:
+
+```python
+{
+    "organic_results": [
+        {
+            "title": "Page Title",
+            "link": "https://...",
+            "snippet": "Page description or excerpt...",
+            "position": 1
+        }
+        # Additional results...
+    ],
+    "knowledge_graph": {
+        "title": "Topic Title",
+        "description": "Topic description...",
+        "source": {
+            "name": "Source Name",
+            "link": "https://..."
+        }
+    },
+    "related_questions": [
+        {
+            "question": "Related question?",
+            "answer": "Answer to related question..."
+        }
+        # Additional related questions...
+    ]
+}
+```
+
+The response is automatically filtered to remove metadata and unnecessary fields, focusing on the most relevant search information. Fields like search metadata, parameters, and pagination are omitted for clarity.

docs/tools/serpapi-google-shopping-tool.mdx

@@ -0,0 +1,225 @@
+---
+title: SerpApi Google Shopping Tool
+description: A tool for searching Google Shopping using the SerpApi service
+---
+
+# SerpApi Google Shopping Tool
+
+The SerpApi Google Shopping Tool enables searching Google Shopping results using the SerpApi service. It provides location-aware shopping search capabilities with comprehensive result filtering.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install serpapi
+```
+
+## Prerequisites
+
+You need a SerpApi API key to use this tool. You can get one from [SerpApi's website](https://serpapi.com/manage-api-key).
+
+Set your API key as an environment variable:
+```bash
+export SERPAPI_API_KEY="your_api_key_here"
+```
+
+## Usage
+
+Here's how to use the SerpApi Google Shopping Tool:
+
+```python
+from crewai import Agent
+from crewai_tools import SerpApiGoogleShoppingTool
+
+# Initialize the tool
+shopping_tool = SerpApiGoogleShoppingTool()
+
+# Create an agent with the tool
+shopping_agent = Agent(
+    role='Shopping Researcher',
+    goal='Find the best shopping deals',
+    backstory='I help find and analyze shopping options',
+    tools=[shopping_tool]
+)
+
+# Use in a task
+task = Task(
+    description="Find best deals for gaming laptops",
+    agent=shopping_agent,
+    context={
+        "search_query": "gaming laptop deals",
+        "location": "United States"  # Optional
+    }
+)
+```
+
+## Input Schema
+
+```python
+class SerpApiGoogleShoppingToolSchema(BaseModel):
+    search_query: str  # The search query for Google Shopping
+    location: Optional[str] = None  # Optional location for localized results
+```
+
+## Function Signatures
+
+### Base Tool Initialization
+```python
+def __init__(self, **kwargs):
+    """
+    Initialize the SerpApi tool with API credentials.
+    
+    Raises:
+        ImportError: If serpapi package is not installed
+        ValueError: If SERPAPI_API_KEY environment variable is not set
+    """
+```
+
+### Search Execution
+```python
+def _run(
+    self,
+    **kwargs: Any,
+) -> dict:
+    """
+    Execute the Google Shopping search.
+    
+    Args:
+        search_query (str): The search query for Google Shopping
+        location (Optional[str]): Optional location for results
+    
+    Returns:
+        dict: Filtered search results from Google Shopping
+        
+    Raises:
+        HTTPError: If the API request fails
+    """
+```
+
+## Best Practices
+
+1. **API Key Management**:
+   - Store the API key securely in environment variables
+   - Never hardcode the API key in your code
+   - Verify API key validity before making requests
+
+2. **Search Optimization**:
+   - Use specific, targeted search queries
+   - Include relevant product details in queries
+   - Leverage location parameter for regional pricing
+
+3. **Error Handling**:
+   - Handle API rate limits gracefully
+   - Implement retry logic for failed requests
+   - Validate input parameters before making requests
+
+## Example Integration
+
+Here's a complete example showing how to integrate the SerpApi Google Shopping Tool with CrewAI:
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerpApiGoogleShoppingTool
+
+# Initialize the tool
+shopping_tool = SerpApiGoogleShoppingTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Shopping Analyst',
+    goal='Find and analyze the best shopping deals',
+    backstory="""I am an expert at finding the best shopping deals 
+    and analyzing product offerings across different regions.""",
+    tools=[shopping_tool]
+)
+
+# Create tasks
+search_task = Task(
+    description="""
+    Research gaming laptops with the following criteria:
+    1. Price range: $800-$1500
+    2. Released in the last year
+    3. Compare prices across different retailers
+    
+    Provide a comprehensive analysis of the findings.
+    """,
+    agent=researcher,
+    context={
+        "search_query": "gaming laptop RTX 4060 2023",
+        "location": "United States"
+    }
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[search_task]
+)
+
+result = crew.kickoff()
+```
+
+## Error Handling
+
+The tool handles various error scenarios:
+
+1. **Missing API Key**:
+   ```python
+   try:
+       tool = SerpApiGoogleShoppingTool()
+   except ValueError as e:
+       print("API key not found. Set SERPAPI_API_KEY environment variable.")
+   ```
+
+2. **API Request Errors**:
+   ```python
+   try:
+       results = tool._run(
+           search_query="gaming laptop",
+           location="United States"
+       )
+   except HTTPError as e:
+       print(f"API request failed: {str(e)}")
+   ```
+
+3. **Invalid Parameters**:
+   ```python
+   try:
+       results = tool._run(
+           search_query="",  # Empty query
+           location="Invalid Location"
+       )
+   except ValueError as e:
+       print("Invalid search parameters provided.")
+   ```
+
+## Response Format
+
+The tool returns a filtered dictionary containing Google Shopping results. Example response structure:
+
+```python
+{
+    "shopping_results": [
+        {
+            "title": "Product Title",
+            "price": "$999.99",
+            "link": "https://...",
+            "source": "Retailer Name",
+            "rating": 4.5,
+            "reviews": 123,
+            "thumbnail": "https://..."
+        }
+        # Additional results...
+    ],
+    "organic_results": [
+        {
+            "title": "Related Product",
+            "link": "https://...",
+            "snippet": "Product description..."
+        }
+        # Additional organic results...
+    ]
+}
+```
+
+The response is automatically filtered to remove metadata and unnecessary fields, focusing on the most relevant shopping information.

docs/tools/serply-job-search-tool.mdx

@@ -0,0 +1,184 @@
+---
+title: SerplyJobSearchTool
+description: A tool for searching US job postings using the Serply API
+icon: briefcase
+---
+
+## SerplyJobSearchTool
+
+The SerplyJobSearchTool provides job search capabilities using the Serply API. It allows for searching job postings in the US market, returning structured information about positions, employers, locations, and remote work status.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import SerplyJobSearchTool
+
+# Set environment variable
+# export SERPLY_API_KEY='your-api-key'
+
+# Initialize the tool
+search_tool = SerplyJobSearchTool()
+
+# Create an agent with the tool
+job_researcher = Agent(
+    role='Job Market Researcher',
+    goal='Find relevant job opportunities',
+    backstory='Expert at analyzing job market trends and opportunities.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class SerplyJobSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query for fetching job postings"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(self, **kwargs):
+    """
+    Initialize the job search tool.
+
+    Args:
+        **kwargs: Additional arguments for RagTool initialization
+    
+    Note:
+        Requires SERPLY_API_KEY environment variable
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Perform job search using Serply API.
+
+    Args:
+        search_query (str): Job search query
+        **kwargs: Additional search parameters
+
+    Returns:
+        str: Formatted string containing job listings with details:
+            - Position
+            - Employer
+            - Location
+            - Link
+            - Highlights
+            - Remote/Hybrid status
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   ```bash
+   export SERPLY_API_KEY='your-serply-api-key'
+   ```
+
+2. Use specific search queries
+3. Handle potential API errors
+4. Process structured results effectively
+5. Consider rate limits and quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerplyJobSearchTool
+
+# Initialize tool
+job_search = SerplyJobSearchTool()
+
+# Create agent
+recruiter = Agent(
+    role='Technical Recruiter',
+    goal='Find relevant job opportunities in tech',
+    backstory='Expert at identifying promising tech positions.',
+    tools=[job_search]
+)
+
+# Define task
+search_task = Task(
+    description="""Search for senior software engineer positions 
+    with remote work options in the US. Focus on positions 
+    requiring Python expertise.""",
+    agent=recruiter
+)
+
+# The tool will use:
+# {
+#   "search_query": "senior software engineer python remote"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[recruiter],
+    tasks=[search_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Handling Search Results
+```python
+# Example of processing structured results
+results = search_tool._run(
+    search_query="machine learning engineer"
+)
+
+# Results format:
+"""
+Search results:
+Position: Senior Machine Learning Engineer
+Employer: TechCorp Inc
+Location: San Francisco, CA
+Link: https://example.com/job/123
+Highlights: Python, TensorFlow, 5+ years experience
+Is Remote: True
+Is Hybrid: False
+---
+Position: ML Engineer
+...
+"""
+```
+
+### Error Handling
+```python
+try:
+    results = search_tool._run(
+        search_query="data scientist"
+    )
+    if not results:
+        print("No jobs found")
+    else:
+        print(results)
+except Exception as e:
+    print(f"Job search error: {str(e)}")
+```
+
+## Notes
+
+- Requires valid Serply API key
+- Currently supports US job market only
+- Returns structured job information
+- Includes remote/hybrid status
+- Thread-safe operations
+- Efficient job search capabilities
+- Handles API rate limiting automatically
+- Provides detailed job highlights

docs/tools/serply-news-search-tool.mdx

@@ -0,0 +1,209 @@
+---
+title: SerplyNewsSearchTool
+description: A news article search tool powered by Serply API with configurable search parameters
+icon: newspaper
+---
+
+## SerplyNewsSearchTool
+
+The SerplyNewsSearchTool provides news article search capabilities using the Serply API. It allows for customizable search parameters including result limits and proxy location for region-specific news results.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import SerplyNewsSearchTool
+
+# Set environment variable
+# export SERPLY_API_KEY='your-api-key'
+
+# Basic initialization
+news_tool = SerplyNewsSearchTool()
+
+# Advanced initialization with custom parameters
+news_tool = SerplyNewsSearchTool(
+    limit=20,             # Return 20 results
+    proxy_location="FR"   # Search from France
+)
+
+# Create an agent with the tool
+news_researcher = Agent(
+    role='News Researcher',
+    goal='Find relevant news articles',
+    backstory='Expert at news research and information gathering.',
+    tools=[news_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class SerplyNewsSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query for fetching news articles"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    limit: Optional[int] = 10,
+    proxy_location: Optional[str] = "US",
+    **kwargs
+):
+    """
+    Initialize the news search tool.
+
+    Args:
+        limit (int): Maximum number of results [10-100] (default: 10)
+        proxy_location (str): Region for local news results (default: "US")
+            Options: US, CA, IE, GB, FR, DE, SE, IN, JP, KR, SG, AU, BR
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Perform news search using Serply API.
+
+    Args:
+        search_query (str): News search query
+
+    Returns:
+        str: Formatted string containing news results:
+            - Title
+            - Link
+            - Source
+            - Published Date
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   ```bash
+   export SERPLY_API_KEY='your-serply-api-key'
+   ```
+
+2. Configure search parameters appropriately:
+   - Set reasonable result limits
+   - Select relevant proxy location for regional news
+   - Consider time sensitivity of news content
+
+3. Handle potential API errors
+4. Process structured results effectively
+5. Consider rate limits and quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerplyNewsSearchTool
+
+# Initialize tool with custom configuration
+news_tool = SerplyNewsSearchTool(
+    limit=15,             # 15 results
+    proxy_location="US"   # US news sources
+)
+
+# Create agent
+news_analyst = Agent(
+    role='News Analyst',
+    goal='Research breaking news and developments',
+    backstory='Expert at analyzing news trends and developments.',
+    tools=[news_tool]
+)
+
+# Define task
+news_task = Task(
+    description="""Research the latest developments in renewable 
+    energy technology and investments, focusing on major 
+    announcements and industry trends.""",
+    agent=news_analyst
+)
+
+# The tool will use:
+# {
+#   "search_query": "renewable energy technology investments news"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[news_analyst],
+    tasks=[news_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Regional News Configuration
+```python
+# French news sources
+fr_news = SerplyNewsSearchTool(
+    proxy_location="FR",
+    limit=20
+)
+
+# Japanese news sources
+jp_news = SerplyNewsSearchTool(
+    proxy_location="JP",
+    limit=20
+)
+```
+
+### Result Processing
+```python
+# Get news results
+try:
+    results = news_tool._run(
+        search_query="renewable energy investments"
+    )
+    print(results)
+except Exception as e:
+    print(f"News search error: {str(e)}")
+```
+
+### Multiple Region Search
+```python
+# Search across multiple regions
+regions = ["US", "GB", "DE"]
+all_results = []
+
+for region in regions:
+    regional_tool = SerplyNewsSearchTool(
+        proxy_location=region,
+        limit=5
+    )
+    results = regional_tool._run(
+        search_query="global tech innovations"
+    )
+    all_results.append(f"Results from {region}:\n{results}")
+
+combined_results = "\n\n".join(all_results)
+```
+
+## Notes
+
+- Requires valid Serply API key
+- Supports multiple regions for news sources
+- Configurable result limits (10-100)
+- Returns structured news article data
+- Thread-safe operations
+- Efficient news search capabilities
+- Handles API rate limiting automatically
+- Includes source attribution and publication dates
+- Follows redirects for final article URLs

docs/tools/serply-scholar-search-tool.mdx

@@ -0,0 +1,209 @@
+---
+title: SerplyScholarSearchTool
+description: A scholarly literature search tool powered by Serply API with configurable search parameters
+icon: book
+---
+
+## SerplyScholarSearchTool
+
+The SerplyScholarSearchTool provides scholarly literature search capabilities using the Serply API. It allows for customizable search parameters including language and proxy location for region-specific academic results.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import SerplyScholarSearchTool
+
+# Set environment variable
+# export SERPLY_API_KEY='your-api-key'
+
+# Basic initialization
+scholar_tool = SerplyScholarSearchTool()
+
+# Advanced initialization with custom parameters
+scholar_tool = SerplyScholarSearchTool(
+    hl="fr",             # French language results
+    proxy_location="FR"  # Search from France
+)
+
+# Create an agent with the tool
+academic_researcher = Agent(
+    role='Academic Researcher',
+    goal='Find relevant scholarly literature',
+    backstory='Expert at academic research and literature review.',
+    tools=[scholar_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class SerplyScholarSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query for fetching scholarly literature"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    hl: str = "us",
+    proxy_location: Optional[str] = "US",
+    **kwargs
+):
+    """
+    Initialize the scholar search tool.
+
+    Args:
+        hl (str): Host language code for results (default: "us")
+            Reference: https://developers.google.com/custom-search/docs/xml_results?hl=en#wsInterfaceLanguages
+        proxy_location (str): Region for local results (default: "US")
+            Options: US, CA, IE, GB, FR, DE, SE, IN, JP, KR, SG, AU, BR
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Perform scholarly literature search using Serply API.
+
+    Args:
+        search_query (str): Academic search query
+
+    Returns:
+        str: Formatted string containing scholarly results:
+            - Title
+            - Link
+            - Description
+            - Citation
+            - Authors
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   ```bash
+   export SERPLY_API_KEY='your-serply-api-key'
+   ```
+
+2. Configure search parameters appropriately:
+   - Use relevant language codes
+   - Select appropriate proxy location
+   - Provide specific academic search terms
+
+3. Handle potential API errors
+4. Process structured results effectively
+5. Consider rate limits and quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerplyScholarSearchTool
+
+# Initialize tool with custom configuration
+scholar_tool = SerplyScholarSearchTool(
+    hl="en",             # English results
+    proxy_location="US"  # US academic sources
+)
+
+# Create agent
+researcher = Agent(
+    role='Academic Researcher',
+    goal='Research recent academic publications',
+    backstory='Expert at analyzing academic literature and research trends.',
+    tools=[scholar_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Research recent academic publications on 
+    machine learning applications in healthcare, focusing on 
+    peer-reviewed articles from the last two years.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "machine learning healthcare applications"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Language and Region Configuration
+```python
+# French academic sources
+fr_scholar = SerplyScholarSearchTool(
+    hl="fr",
+    proxy_location="FR"
+)
+
+# German academic sources
+de_scholar = SerplyScholarSearchTool(
+    hl="de",
+    proxy_location="DE"
+)
+```
+
+### Result Processing
+```python
+try:
+    results = scholar_tool._run(
+        search_query="machine learning healthcare applications"
+    )
+    print(results)
+except Exception as e:
+    print(f"Scholar search error: {str(e)}")
+```
+
+### Citation Analysis
+```python
+# Extract and analyze citations
+def analyze_citations(results):
+    citations = []
+    for result in results.split("---"):
+        if "Cite:" in result:
+            citation = result.split("Cite:")[1].split("\n")[0].strip()
+            citations.append(citation)
+    return citations
+
+results = scholar_tool._run(
+    search_query="artificial intelligence ethics"
+)
+citations = analyze_citations(results)
+```
+
+## Notes
+
+- Requires valid Serply API key
+- Supports multiple languages and regions
+- Returns structured academic article data
+- Includes citation information
+- Lists all authors of publications
+- Thread-safe operations
+- Efficient scholarly search capabilities
+- Handles API rate limiting automatically
+- Supports both direct and document links
+- Provides comprehensive article metadata

docs/tools/serply-web-search-tool.mdx

@@ -0,0 +1,213 @@
+---
+title: SerplyWebSearchTool
+description: A Google search tool powered by Serply API with configurable search parameters
+icon: search
+---
+
+## SerplyWebSearchTool
+
+The SerplyWebSearchTool provides Google search capabilities using the Serply API. It allows for customizable search parameters including language, result limits, device type, and proxy location for region-specific results.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import SerplyWebSearchTool
+
+# Set environment variable
+# export SERPLY_API_KEY='your-api-key'
+
+# Basic initialization
+search_tool = SerplyWebSearchTool()
+
+# Advanced initialization with custom parameters
+search_tool = SerplyWebSearchTool(
+    hl="fr",              # French language results
+    limit=20,             # Return 20 results
+    device_type="mobile", # Mobile search results
+    proxy_location="FR"   # Search from France
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Web Researcher',
+    goal='Find relevant information online',
+    backstory='Expert at web research and information gathering.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class SerplyWebSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query for Google search"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    hl: str = "us",
+    limit: int = 10,
+    device_type: str = "desktop",
+    proxy_location: str = "US",
+    **kwargs
+):
+    """
+    Initialize the Google search tool.
+
+    Args:
+        hl (str): Host language code for results (default: "us")
+            Reference: https://developers.google.com/custom-search/docs/xml_results?hl=en#wsInterfaceLanguages
+        limit (int): Maximum number of results [10-100] (default: 10)
+        device_type (str): "desktop" or "mobile" results (default: "desktop")
+        proxy_location (str): Region for local results (default: "US")
+            Options: US, CA, IE, GB, FR, DE, SE, IN, JP, KR, SG, AU, BR
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Perform Google search using Serply API.
+
+    Args:
+        search_query (str): Search query
+
+    Returns:
+        str: Formatted string containing search results:
+            - Title
+            - Link
+            - Description
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   ```bash
+   export SERPLY_API_KEY='your-serply-api-key'
+   ```
+
+2. Configure search parameters appropriately:
+   - Use relevant language codes
+   - Set reasonable result limits
+   - Choose appropriate device type
+   - Select relevant proxy location
+
+3. Handle potential API errors
+4. Process structured results effectively
+5. Consider rate limits and quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerplyWebSearchTool
+
+# Initialize tool with custom configuration
+search_tool = SerplyWebSearchTool(
+    hl="en",              # English results
+    limit=15,             # 15 results
+    device_type="desktop",
+    proxy_location="US"
+)
+
+# Create agent
+researcher = Agent(
+    role='Web Researcher',
+    goal='Research emerging technology trends',
+    backstory='Expert at finding and analyzing tech trends.',
+    tools=[search_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Research the latest developments in artificial 
+    intelligence and machine learning, focusing on practical 
+    applications in business.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "latest AI ML developments business applications"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Language and Region Configuration
+```python
+# French search from France
+fr_search = SerplyWebSearchTool(
+    hl="fr",
+    proxy_location="FR"
+)
+
+# Japanese search from Japan
+jp_search = SerplyWebSearchTool(
+    hl="ja",
+    proxy_location="JP"
+)
+```
+
+### Device-Specific Results
+```python
+# Mobile results
+mobile_search = SerplyWebSearchTool(
+    device_type="mobile",
+    limit=20
+)
+
+# Desktop results
+desktop_search = SerplyWebSearchTool(
+    device_type="desktop",
+    limit=20
+)
+```
+
+### Error Handling
+```python
+try:
+    results = search_tool._run(
+        search_query="artificial intelligence trends"
+    )
+    print(results)
+except Exception as e:
+    print(f"Search error: {str(e)}")
+```
+
+## Notes
+
+
+- Requires valid Serply API key
+- Supports multiple languages and regions
+- Configurable result limits (10-100)
+- Device-specific search results
+- Thread-safe operations
+- Efficient search capabilities
+- Handles API rate limiting automatically
+- Returns structured search results

docs/tools/serply-webpage-to-markdown-tool.mdx

@@ -0,0 +1,201 @@
+---
+title: SerplyWebpageToMarkdownTool
+description: A tool for converting web pages to markdown format using Serply API
+icon: markdown
+---
+
+## SerplyWebpageToMarkdownTool
+
+The SerplyWebpageToMarkdownTool converts web pages to markdown format using the Serply API, making it easier for LLMs to process and understand web content. It supports configurable proxy locations for region-specific access.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import SerplyWebpageToMarkdownTool
+
+# Set environment variable
+# export SERPLY_API_KEY='your-api-key'
+
+# Basic initialization
+markdown_tool = SerplyWebpageToMarkdownTool()
+
+# Advanced initialization with custom parameters
+markdown_tool = SerplyWebpageToMarkdownTool(
+    proxy_location="FR"  # Access from France
+)
+
+# Create an agent with the tool
+web_processor = Agent(
+    role='Web Content Processor',
+    goal='Convert web content to markdown format',
+    backstory='Expert at processing and formatting web content.',
+    tools=[markdown_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class SerplyWebpageToMarkdownToolSchema(BaseModel):
+    url: str = Field(
+        description="Mandatory URL of the webpage to convert to markdown"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    proxy_location: Optional[str] = "US",
+    **kwargs
+):
+    """
+    Initialize the webpage to markdown conversion tool.
+
+    Args:
+        proxy_location (str): Region for accessing the webpage (default: "US")
+            Options: US, CA, IE, GB, FR, DE, SE, IN, JP, KR, SG, AU, BR
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Convert webpage to markdown using Serply API.
+
+    Args:
+        url (str): URL of the webpage to convert
+
+    Returns:
+        str: Markdown formatted content of the webpage
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   ```bash
+   export SERPLY_API_KEY='your-serply-api-key'
+   ```
+
+2. Configure proxy location appropriately:
+   - Select relevant region for access
+   - Consider content accessibility
+   - Handle region-specific content
+
+3. Handle potential API errors
+4. Process markdown output effectively
+5. Consider rate limits and quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerplyWebpageToMarkdownTool
+
+# Initialize tool with custom configuration
+markdown_tool = SerplyWebpageToMarkdownTool(
+    proxy_location="US"  # US access point
+)
+
+# Create agent
+processor = Agent(
+    role='Content Processor',
+    goal='Convert web content to structured markdown',
+    backstory='Expert at processing web content into structured formats.',
+    tools=[markdown_tool]
+)
+
+# Define task
+conversion_task = Task(
+    description="""Convert the documentation page at 
+    https://example.com/docs into markdown format for 
+    further processing.""",
+    agent=processor
+)
+
+# The tool will use:
+# {
+#   "url": "https://example.com/docs"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[processor],
+    tasks=[conversion_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Regional Access Configuration
+```python
+# European access points
+fr_processor = SerplyWebpageToMarkdownTool(
+    proxy_location="FR"
+)
+
+de_processor = SerplyWebpageToMarkdownTool(
+    proxy_location="DE"
+)
+```
+
+### Error Handling
+```python
+try:
+    markdown_content = markdown_tool._run(
+        url="https://example.com/page"
+    )
+    print(markdown_content)
+except Exception as e:
+    print(f"Conversion error: {str(e)}")
+```
+
+### Content Processing
+```python
+# Process multiple pages
+urls = [
+    "https://example.com/page1",
+    "https://example.com/page2",
+    "https://example.com/page3"
+]
+
+markdown_contents = []
+for url in urls:
+    try:
+        content = markdown_tool._run(url=url)
+        markdown_contents.append(content)
+    except Exception as e:
+        print(f"Error processing {url}: {str(e)}")
+        continue
+
+# Combine contents
+combined_markdown = "\n\n---\n\n".join(markdown_contents)
+```
+
+## Notes
+
+- Requires valid Serply API key
+- Supports multiple proxy locations
+- Returns markdown-formatted content
+- Simplifies web content for LLM processing
+- Thread-safe operations
+- Efficient content conversion
+- Handles API rate limiting automatically
+- Preserves content structure in markdown
+- Supports various webpage formats
+- Makes web content more accessible to AI agents

docs/tools/txt-search-tool.mdx

@@ -0,0 +1,158 @@
+---
+title: TXTSearchTool
+description: A semantic search tool for text files using RAG capabilities
+icon: magnifying-glass-document
+---
+
+## TXTSearchTool
+
+The TXTSearchTool is a specialized Retrieval-Augmented Generation (RAG) tool that enables semantic search within text files. It inherits from the base RagTool class and provides both fixed and dynamic text file searching capabilities.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import TXTSearchTool
+
+# Method 1: Dynamic file path
+txt_search = TXTSearchTool()
+
+# Method 2: Fixed file path
+fixed_txt_search = TXTSearchTool(txt="path/to/fixed/document.txt")
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Research Assistant',
+    goal='Search through text documents semantically',
+    backstory='Expert at finding relevant information in documents using semantic search.',
+    tools=[txt_search],
+    verbose=True
+)
+```
+
+## Input Schema
+
+The tool supports two input schemas depending on initialization:
+
+### Dynamic File Path Schema
+```python
+class TXTSearchToolSchema(BaseModel):
+    search_query: str  # The semantic search query
+    txt: str          # Path to the text file to search
+```
+
+### Fixed File Path Schema
+```python
+class FixedTXTSearchToolSchema(BaseModel):
+    search_query: str  # The semantic search query
+```
+
+## Function Signature
+
+```python
+def __init__(self, txt: Optional[str] = None, **kwargs):
+    """
+    Initialize the TXT search tool.
+
+    Args:
+        txt (Optional[str]): Fixed path to a text file. If provided, the tool will only search this file.
+        **kwargs: Additional arguments passed to the parent RagTool
+    """
+
+def _run(self, search_query: str, **kwargs: Any) -> Any:
+    """
+    Perform semantic search on the text file.
+
+    Args:
+        search_query (str): The semantic search query
+        **kwargs: Additional arguments (including 'txt' for dynamic file path)
+
+    Returns:
+        str: Relevant text passages based on semantic search
+    """
+```
+
+## Best Practices
+
+1. Choose initialization method based on use case:
+   - Use fixed file path when repeatedly searching the same document
+   - Use dynamic file path when searching different documents
+2. Write clear, semantic search queries
+3. Handle potential file access errors in agent prompts
+4. Consider memory usage for large text files
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import TXTSearchTool
+
+# Example 1: Fixed document search
+documentation_search = TXTSearchTool(txt="api_documentation.txt")
+
+# Example 2: Dynamic document search
+flexible_search = TXTSearchTool()
+
+# Create agents
+doc_analyst = Agent(
+    role='Documentation Analyst',
+    goal='Find relevant API documentation sections',
+    backstory='Expert at analyzing technical documentation.',
+    tools=[documentation_search]
+)
+
+file_analyst = Agent(
+    role='File Analyst',
+    goal='Search through various text files',
+    backstory='Specialist in finding information across multiple documents.',
+    tools=[flexible_search]
+)
+
+# Define tasks
+fixed_search_task = Task(
+    description="""Find all API endpoints related to user authentication 
+    in the documentation.""",
+    agent=doc_analyst
+)
+
+# The agent will use:
+# {
+#   "search_query": "user authentication API endpoints"
+# }
+
+dynamic_search_task = Task(
+    description="""Search through the logs.txt file for any database 
+    connection errors.""",
+    agent=file_analyst
+)
+
+# The agent will use:
+# {
+#   "search_query": "database connection errors",
+#   "txt": "logs.txt"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[doc_analyst, file_analyst],
+    tasks=[fixed_search_task, dynamic_search_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Notes
+
+- Inherits from RagTool for semantic search capabilities
+- Supports both fixed and dynamic text file paths
+- Uses embeddings for semantic search
+- Optimized for text file analysis
+- Thread-safe operations
+- Automatically handles file loading and embedding

docs/tools/youtube-channel-search-tool.mdx

@@ -0,0 +1,159 @@
+---
+title: YoutubeChannelSearchTool
+description: A semantic search tool for YouTube channel content using RAG capabilities
+icon: youtube
+---
+
+## YoutubeChannelSearchTool
+
+The YoutubeChannelSearchTool is a specialized Retrieval-Augmented Generation (RAG) tool that enables semantic search within YouTube channel content. It inherits from the base RagTool class and provides both fixed and dynamic YouTube channel searching capabilities.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import YoutubeChannelSearchTool
+
+# Method 1: Dynamic channel handle
+youtube_search = YoutubeChannelSearchTool()
+
+# Method 2: Fixed channel handle
+fixed_channel_search = YoutubeChannelSearchTool(youtube_channel_handle="@example_channel")
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Content Researcher',
+    goal='Search through YouTube channel content semantically',
+    backstory='Expert at finding relevant information in YouTube content.',
+    tools=[youtube_search],
+    verbose=True
+)
+```
+
+## Input Schema
+
+The tool supports two input schemas depending on initialization:
+
+### Dynamic Channel Schema
+```python
+class YoutubeChannelSearchToolSchema(BaseModel):
+    search_query: str           # The semantic search query
+    youtube_channel_handle: str # YouTube channel handle (with or without @)
+```
+
+### Fixed Channel Schema
+```python
+class FixedYoutubeChannelSearchToolSchema(BaseModel):
+    search_query: str  # The semantic search query
+```
+
+## Function Signature
+
+```python
+def __init__(self, youtube_channel_handle: Optional[str] = None, **kwargs):
+    """
+    Initialize the YouTube channel search tool.
+
+    Args:
+        youtube_channel_handle (Optional[str]): Fixed channel handle. If provided, 
+            the tool will only search this channel.
+        **kwargs: Additional arguments passed to the parent RagTool
+    """
+
+def _run(self, search_query: str, **kwargs: Any) -> Any:
+    """
+    Perform semantic search on the YouTube channel content.
+
+    Args:
+        search_query (str): The semantic search query
+        **kwargs: Additional arguments (including 'youtube_channel_handle' for dynamic mode)
+
+    Returns:
+        str: Relevant content from the YouTube channel based on semantic search
+    """
+```
+
+## Best Practices
+
+1. Choose initialization method based on use case:
+   - Use fixed channel handle when repeatedly searching the same channel
+   - Use dynamic handle when searching different channels
+2. Write clear, semantic search queries
+3. Channel handles can be provided with or without '@' prefix
+4. Consider content availability and channel size
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import YoutubeChannelSearchTool
+
+# Example 1: Fixed channel search
+tech_channel_search = YoutubeChannelSearchTool(youtube_channel_handle="@TechChannel")
+
+# Example 2: Dynamic channel search
+flexible_search = YoutubeChannelSearchTool()
+
+# Create agents
+tech_analyst = Agent(
+    role='Tech Content Analyst',
+    goal='Find relevant tech tutorials and explanations',
+    backstory='Expert at analyzing technical YouTube content.',
+    tools=[tech_channel_search]
+)
+
+content_researcher = Agent(
+    role='Content Researcher',
+    goal='Search across multiple YouTube channels',
+    backstory='Specialist in finding information across various channels.',
+    tools=[flexible_search]
+)
+
+# Define tasks
+fixed_search_task = Task(
+    description="""Find all tutorials related to machine learning 
+    basics in the channel.""",
+    agent=tech_analyst
+)
+
+# The agent will use:
+# {
+#   "search_query": "machine learning basics tutorial"
+# }
+
+dynamic_search_task = Task(
+    description="""Search through the @AIResearch channel for 
+    content about neural networks.""",
+    agent=content_researcher
+)
+
+# The agent will use:
+# {
+#   "search_query": "neural networks explanation",
+#   "youtube_channel_handle": "@AIResearch"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[tech_analyst, content_researcher],
+    tasks=[fixed_search_task, dynamic_search_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Notes
+
+- Inherits from RagTool for semantic search capabilities
+- Supports both fixed and dynamic YouTube channel handles
+- Automatically adds '@' prefix to channel handles if missing
+- Uses embeddings for semantic search
+- Thread-safe operations
+- Automatically handles YouTube content loading and embedding

docs/tools/youtube-video-search-tool.mdx

@@ -0,0 +1,216 @@
+---
+title: YoutubeVideoSearchTool
+description: A tool for semantic search within YouTube video content using RAG capabilities
+icon: video
+---
+
+## YoutubeVideoSearchTool
+
+The YoutubeVideoSearchTool enables semantic search capabilities for YouTube video content using Retrieval-Augmented Generation (RAG). It processes video content and allows searching through transcripts and metadata using natural language queries.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import YoutubeVideoSearchTool
+
+# Method 1: Initialize with specific video
+video_tool = YoutubeVideoSearchTool(
+    youtube_video_url="https://www.youtube.com/watch?v=example"
+)
+
+# Method 2: Initialize without video (specify at runtime)
+flexible_video_tool = YoutubeVideoSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Video Researcher',
+    goal='Search and analyze video content',
+    backstory='Expert at finding relevant information in videos.',
+    tools=[video_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed Video Schema (when URL provided during initialization)
+```python
+class FixedYoutubeVideoSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the Youtube Video content"
+    )
+```
+
+### Flexible Video Schema (when URL provided at runtime)
+```python
+class YoutubeVideoSearchToolSchema(FixedYoutubeVideoSearchToolSchema):
+    youtube_video_url: str = Field(
+        description="Mandatory youtube_video_url path you want to search"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    youtube_video_url: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the YouTube video search tool.
+
+    Args:
+        youtube_video_url (Optional[str]): URL of YouTube video (optional)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on video content.
+
+    Args:
+        search_query (str): Query to search in the video
+        **kwargs: Additional arguments including youtube_video_url if not initialized
+
+    Returns:
+        str: Relevant content from the video matching the query
+    """
+```
+
+## Best Practices
+
+1. Video URL Management:
+   - Use complete YouTube URLs
+   - Verify video accessibility
+   - Handle region restrictions
+
+2. Search Optimization:
+   - Use specific, focused queries
+   - Consider video context
+   - Test with sample queries first
+
+3. Performance Considerations:
+   - Pre-initialize for repeated searches
+   - Handle long videos appropriately
+   - Monitor processing time
+
+4. Error Handling:
+   - Verify video availability
+   - Handle unavailable videos
+   - Manage API limitations
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import YoutubeVideoSearchTool
+
+# Initialize tool with specific video
+video_tool = YoutubeVideoSearchTool(
+    youtube_video_url="https://www.youtube.com/watch?v=example"
+)
+
+# Create agent
+researcher = Agent(
+    role='Video Researcher',
+    goal='Extract insights from video content',
+    backstory='Expert at analyzing video content.',
+    tools=[video_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find all mentions of machine learning 
+    applications from the video content.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "machine learning applications"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic Video Selection
+```python
+# Initialize without video URL
+flexible_tool = YoutubeVideoSearchTool()
+
+# Search different videos
+tech_results = flexible_tool.run(
+    search_query="quantum computing",
+    youtube_video_url="https://youtube.com/watch?v=tech123"
+)
+
+science_results = flexible_tool.run(
+    search_query="particle physics",
+    youtube_video_url="https://youtube.com/watch?v=science456"
+)
+```
+
+### Multiple Video Analysis
+```python
+# Create tools for different videos
+tech_tool = YoutubeVideoSearchTool(
+    youtube_video_url="https://youtube.com/watch?v=tech123"
+)
+science_tool = YoutubeVideoSearchTool(
+    youtube_video_url="https://youtube.com/watch?v=science456"
+)
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Content Analyst',
+    goal='Cross-reference multiple videos',
+    tools=[tech_tool, science_tool]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    video_tool = YoutubeVideoSearchTool()
+    results = video_tool.run(
+        search_query="key concepts",
+        youtube_video_url="https://youtube.com/watch?v=example"
+    )
+    print(results)
+except Exception as e:
+    print(f"Error processing video: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Uses embedchain for processing
+- Supports semantic search
+- Dynamic video specification
+- Efficient content retrieval
+- Thread-safe operations
+- Maintains search context
+- Handles video transcripts
+- Processes video metadata
+- Memory-efficient processing

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.95.0"
+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"
@@ -13,25 +13,25 @@ dependencies = [
     "openai>=1.13.3",
     "litellm>=1.44.22",
     "instructor>=1.3.3",
-
+    
     # Text Processing
     "pdfplumber>=0.11.4",
     "regex>=2024.9.11",
-
+    
     # Telemetry and Monitoring
     "opentelemetry-api>=1.22.0",
     "opentelemetry-sdk>=1.22.0",
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
-
+    
     # Data Handling
     "chromadb>=0.5.23",
     "openpyxl>=3.1.5",
     "pyvis>=0.3.2",
-
+    
     # Authentication and Security
     "auth0-python>=4.7.1",
     "python-dotenv>=1.0.0",
-
+    
     # Configuration and Utils
     "click>=8.1.7",
     "appdirs>=1.4.4",
@@ -40,7 +40,7 @@ dependencies = [
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
-    "blinker>=1.9.0"
+    "blinker>=1.9.0",
 ]
 
 [project.urls]
@@ -49,7 +49,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.25.5"]
+tools = ["crewai-tools>=0.17.0"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.95.0"
+__version__ = "0.86.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -21,7 +21,6 @@ from crewai.tools.base_tool import Tool
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
-from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
@@ -140,9 +139,89 @@ class Agent(BaseAgent):
     def post_init_setup(self):
         self._set_knowledge()
         self.agent_ops_agent_name = self.role
+        unaccepted_attributes = [
+            "AWS_ACCESS_KEY_ID",
+            "AWS_SECRET_ACCESS_KEY",
+            "AWS_REGION_NAME",
+        ]
 
-        self.llm = create_llm(self.llm)
-        self.function_calling_llm = create_llm(self.function_calling_llm)
+        # Handle different cases for self.llm
+        if isinstance(self.llm, str):
+            # If it's a string, create an LLM instance
+            self.llm = LLM(model=self.llm)
+        elif isinstance(self.llm, LLM):
+            # If it's already an LLM instance, keep it as is
+            pass
+        elif self.llm is None:
+            # Determine the model name from environment variables or use default
+            model_name = (
+                os.environ.get("OPENAI_MODEL_NAME")
+                or os.environ.get("MODEL")
+                or "gpt-4o-mini"
+            )
+            llm_params = {"model": model_name}
+
+            api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
+                "OPENAI_BASE_URL"
+            )
+            if api_base:
+                llm_params["base_url"] = api_base
+
+            set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
+
+            # Iterate over all environment variables to find matching API keys or use defaults
+            for provider, env_vars in ENV_VARS.items():
+                if provider == set_provider:
+                    for env_var in env_vars:
+                        # Check if the environment variable is set
+                        key_name = env_var.get("key_name")
+                        if key_name and key_name not in unaccepted_attributes:
+                            env_value = os.environ.get(key_name)
+                            if env_value:
+                                key_name = key_name.lower()
+                                for pattern in LITELLM_PARAMS:
+                                    if pattern in key_name:
+                                        key_name = pattern
+                                        break
+                                llm_params[key_name] = env_value
+                        # Check for default values if the environment variable is not set
+                        elif env_var.get("default", False):
+                            for key, value in env_var.items():
+                                if key not in ["prompt", "key_name", "default"]:
+                                    # Only add default if the key is already set in os.environ
+                                    if key in os.environ:
+                                        llm_params[key] = value
+
+            self.llm = LLM(**llm_params)
+        else:
+            # For any other type, attempt to extract relevant attributes
+            llm_params = {
+                "model": getattr(self.llm, "model_name", None)
+                or getattr(self.llm, "deployment_name", None)
+                or str(self.llm),
+                "temperature": getattr(self.llm, "temperature", None),
+                "max_tokens": getattr(self.llm, "max_tokens", None),
+                "logprobs": getattr(self.llm, "logprobs", None),
+                "timeout": getattr(self.llm, "timeout", None),
+                "max_retries": getattr(self.llm, "max_retries", None),
+                "api_key": getattr(self.llm, "api_key", None),
+                "base_url": getattr(self.llm, "base_url", None),
+                "organization": getattr(self.llm, "organization", None),
+            }
+            # Remove None values to avoid passing unnecessary parameters
+            llm_params = {k: v for k, v in llm_params.items() if v is not None}
+            self.llm = LLM(**llm_params)
+
+        # Similar handling for function_calling_llm
+        if self.function_calling_llm:
+            if isinstance(self.function_calling_llm, str):
+                self.function_calling_llm = LLM(model=self.function_calling_llm)
+            elif not isinstance(self.function_calling_llm, LLM):
+                self.function_calling_llm = LLM(
+                    model=getattr(self.function_calling_llm, "model_name", None)
+                    or getattr(self.function_calling_llm, "deployment_name", None)
+                    or str(self.function_calling_llm)
+                )
 
         if not self.agent_executor:
             self._setup_agent_executor()
@@ -334,7 +413,6 @@ class Agent(BaseAgent):
 
     def get_multimodal_tools(self) -> List[Tool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
-
         return [AddImageTool()]
 
     def get_code_execution_tools(self):

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -19,10 +19,15 @@ class CrewAgentExecutorMixin:
     agent: Optional["BaseAgent"]
     task: Optional["Task"]
     iterations: int
+    have_forced_answer: bool
     max_iter: int
     _i18n: I18N
     _printer: Printer = Printer()
 
+    def _should_force_answer(self) -> bool:
+        """Determine if a forced answer is required based on iteration count."""
+        return (self.iterations >= self.max_iter) and not self.have_forced_answer
+
     def _create_short_term_memory(self, output) -> None:
         """Create and save a short-term memory item if conditions are met."""
         if (

src/crewai/agents/crew_agent_executor.py

@@ -1,7 +1,7 @@
 import json
 import re
 from dataclasses import dataclass
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Dict, List, Union
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
@@ -50,7 +50,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         original_tools: List[Any] = [],
         function_calling_llm: Any = None,
         respect_context_window: bool = False,
-        request_within_rpm_limit: Optional[Callable[[], bool]] = None,
+        request_within_rpm_limit: Any = None,
         callbacks: List[Any] = [],
     ):
         self._i18n: I18N = I18N()
@@ -77,6 +77,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.messages: List[Dict[str, str]] = []
         self.iterations = 0
         self.log_error_after = 3
+        self.have_forced_answer = False
         self.tool_name_to_tool_map: Dict[str, BaseTool] = {
             tool.name: tool for tool in self.tools
         }
@@ -107,151 +108,106 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._create_long_term_memory(formatted_answer)
         return {"output": formatted_answer.output}
 
-    def _invoke_loop(self):
-        """
-        Main loop to invoke the agent's thought process until it reaches a conclusion
-        or the maximum number of iterations is reached.
-        """
-        formatted_answer = None
-        while not isinstance(formatted_answer, AgentFinish):
-            try:
-                if self._has_reached_max_iterations():
-                    formatted_answer = self._handle_max_iterations_exceeded(
-                        formatted_answer
-                    )
-                    break
-
-                self._enforce_rpm_limit()
-
-                answer = self._get_llm_response()
-
-                formatted_answer = self._process_llm_response(answer)
-
-                if isinstance(formatted_answer, AgentAction):
-                    tool_result = self._execute_tool_and_check_finality(
-                        formatted_answer
-                    )
-                    formatted_answer = self._handle_agent_action(
-                        formatted_answer, tool_result
+    def _invoke_loop(self, formatted_answer=None):
+        try:
+            while not isinstance(formatted_answer, AgentFinish):
+                if not self.request_within_rpm_limit or self.request_within_rpm_limit():
+                    answer = self.llm.call(
+                        self.messages,
+                        callbacks=self.callbacks,
                     )
 
-                self._invoke_step_callback(formatted_answer)
-                self._append_message(formatted_answer.text, role="assistant")
+                    if answer is None or answer == "":
+                        self._printer.print(
+                            content="Received None or empty response from LLM call.",
+                            color="red",
+                        )
+                        raise ValueError(
+                            "Invalid response from LLM call - None or empty."
+                        )
 
-            except OutputParserException as e:
-                formatted_answer = self._handle_output_parser_exception(e)
+                    if not self.use_stop_words:
+                        try:
+                            self._format_answer(answer)
+                        except OutputParserException as e:
+                            if (
+                                FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE
+                                in e.error
+                            ):
+                                answer = answer.split("Observation:")[0].strip()
 
-            except Exception as e:
-                if self._is_context_length_exceeded(e):
-                    self._handle_context_length()
-                    continue
-                else:
-                    raise e
+                    self.iterations += 1
+                    formatted_answer = self._format_answer(answer)
+
+                    if isinstance(formatted_answer, AgentAction):
+                        tool_result = self._execute_tool_and_check_finality(
+                            formatted_answer
+                        )
+
+                        # Directly append the result to the messages if the
+                        # tool is "Add image to content" in case of multimodal
+                        # agents
+                        if formatted_answer.tool == self._i18n.tools("add_image")["name"]:
+                            self.messages.append(tool_result.result)
+                            continue
+
+                        else:
+                            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:
+                            return AgentFinish(
+                                thought="",
+                                output=tool_result.result,
+                                text=formatted_answer.text,
+                            )
+                        self._show_logs(formatted_answer)
+
+                    if self.step_callback:
+                        self.step_callback(formatted_answer)
+
+                    if self._should_force_answer():
+                        if self.have_forced_answer:
+                            return AgentFinish(
+                                thought="",
+                                output=self._i18n.errors(
+                                    "force_final_answer_error"
+                                ).format(formatted_answer.text),
+                                text=formatted_answer.text,
+                            )
+                        else:
+                            formatted_answer.text += (
+                                f'\n{self._i18n.errors("force_final_answer")}'
+                            )
+                            self.have_forced_answer = True
+                    self.messages.append(
+                        self._format_msg(formatted_answer.text, role="assistant")
+                    )
+
+        except OutputParserException as e:
+            self.messages.append({"role": "user", "content": e.error})
+            if self.iterations > self.log_error_after:
+                self._printer.print(
+                    content=f"Error parsing LLM output, agent will retry: {e.error}",
+                    color="red",
+                )
+            return self._invoke_loop(formatted_answer)
+
+        except Exception as e:
+            if LLMContextLengthExceededException(str(e))._is_context_limit_error(
+                str(e)
+            ):
+                self._handle_context_length()
+                return self._invoke_loop(formatted_answer)
+            else:
+                raise e
 
         self._show_logs(formatted_answer)
         return formatted_answer
 
-    def _has_reached_max_iterations(self) -> bool:
-        """Check if the maximum number of iterations has been reached."""
-        return self.iterations >= self.max_iter
-
-    def _enforce_rpm_limit(self) -> None:
-        """Enforce the requests per minute (RPM) limit if applicable."""
-        if self.request_within_rpm_limit:
-            self.request_within_rpm_limit()
-
-    def _get_llm_response(self) -> str:
-        """Call the LLM and return the response, handling any invalid responses."""
-        answer = self.llm.call(
-            self.messages,
-            callbacks=self.callbacks,
-        )
-
-        if not answer:
-            self._printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
-            raise ValueError("Invalid response from LLM call - None or empty.")
-
-        return answer
-
-    def _process_llm_response(self, answer: str) -> Union[AgentAction, AgentFinish]:
-        """Process the LLM response and format it into an AgentAction or AgentFinish."""
-        if not self.use_stop_words:
-            try:
-                # Preliminary parsing to check for errors.
-                self._format_answer(answer)
-            except OutputParserException as e:
-                if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
-                    answer = answer.split("Observation:")[0].strip()
-
-        self.iterations += 1
-        return self._format_answer(answer)
-
-    def _handle_agent_action(
-        self, formatted_answer: AgentAction, tool_result: ToolResult
-    ) -> Union[AgentAction, AgentFinish]:
-        """Handle the AgentAction, execute tools, and process the results."""
-        add_image_tool = self._i18n.tools("add_image")
-        if (
-            isinstance(add_image_tool, dict)
-            and formatted_answer.tool.casefold().strip()
-            == add_image_tool.get("name", "").casefold().strip()
-        ):
-            self.messages.append(tool_result.result)
-            return formatted_answer  # Continue the loop
-
-        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:
-            return AgentFinish(
-                thought="",
-                output=tool_result.result,
-                text=formatted_answer.text,
-            )
-
-        self._show_logs(formatted_answer)
-        return formatted_answer
-
-    def _invoke_step_callback(self, formatted_answer) -> None:
-        """Invoke the step callback if it exists."""
-        if self.step_callback:
-            self.step_callback(formatted_answer)
-
-    def _append_message(self, text: str, role: str = "assistant") -> None:
-        """Append a message to the message list with the given role."""
-        self.messages.append(self._format_msg(text, role=role))
-
-    def _handle_output_parser_exception(self, e: OutputParserException) -> AgentAction:
-        """Handle OutputParserException by updating messages and formatted_answer."""
-        self.messages.append({"role": "user", "content": e.error})
-
-        formatted_answer = AgentAction(
-            text=e.error,
-            tool="",
-            tool_input="",
-            thought="",
-        )
-
-        if self.iterations > self.log_error_after:
-            self._printer.print(
-                content=f"Error parsing LLM output, agent will retry: {e.error}",
-                color="red",
-            )
-
-        return formatted_answer
-
-    def _is_context_length_exceeded(self, exception: Exception) -> bool:
-        """Check if the exception is due to context length exceeding."""
-        return LLMContextLengthExceededException(
-            str(exception)
-        )._is_context_limit_error(str(exception))
-
     def _show_start_logs(self):
         if self.agent is None:
             raise ValueError("Agent cannot be None")
@@ -531,45 +487,3 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self.ask_for_human_input = False
 
         return formatted_answer
-
-    def _handle_max_iterations_exceeded(self, formatted_answer):
-        """
-        Handles the case when the maximum number of iterations is exceeded.
-        Performs one more LLM call to get the final answer.
-
-        Parameters:
-            formatted_answer: The last formatted answer from the agent.
-
-        Returns:
-            The final formatted answer after exceeding max iterations.
-        """
-        self._printer.print(
-            content="Maximum iterations reached. Requesting final answer.",
-            color="yellow",
-        )
-
-        if formatted_answer and hasattr(formatted_answer, "text"):
-            assistant_message = (
-                formatted_answer.text + f'\n{self._i18n.errors("force_final_answer")}'
-            )
-        else:
-            assistant_message = self._i18n.errors("force_final_answer")
-
-        self.messages.append(self._format_msg(assistant_message, role="assistant"))
-
-        # Perform one more LLM call to get the final answer
-        answer = self.llm.call(
-            self.messages,
-            callbacks=self.callbacks,
-        )
-
-        if answer is None or answer == "":
-            self._printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
-            raise ValueError("Invalid response from LLM call - None or empty.")
-
-        formatted_answer = self._format_answer(answer)
-        # Return the formatted answer, regardless of its type
-        return formatted_answer

src/crewai/cli/cli.py

@@ -1,13 +1,11 @@
-import os
 from importlib.metadata import version as get_version
-from typing import Optional, Tuple
+from typing import Optional
 
 import click
 
 from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
 from crewai.cli.create_flow import create_flow
-from crewai.cli.crew_chat import run_chat
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )
@@ -344,15 +342,5 @@ def flow_add_crew(crew_name):
     add_crew_to_flow(crew_name)
 
 
-@crewai.command()
-def chat():
-    """
-    Start a conversation with the Crew, collecting user-supplied inputs,
-    and using the Chat LLM to generate responses.
-    """
-    click.echo("Starting a conversation with the Crew")
-    run_chat()
-
-
 if __name__ == "__main__":
     crewai()

src/crewai/cli/constants.py

@@ -17,12 +17,6 @@ ENV_VARS = {
             "key_name": "GEMINI_API_KEY",
         }
     ],
-    "nvidia_nim": [
-        {
-            "prompt": "Enter your NVIDIA API key (press Enter to skip)",
-            "key_name": "NVIDIA_NIM_API_KEY",
-        }
-    ],
     "groq": [
         {
             "prompt": "Enter your GROQ API key (press Enter to skip)",
@@ -91,12 +85,6 @@ ENV_VARS = {
             "key_name": "CEREBRAS_API_KEY",
         },
     ],
-    "sambanova": [
-        {
-            "prompt": "Enter your SambaNovaCloud API key (press Enter to skip)",
-            "key_name": "SAMBANOVA_API_KEY",
-        }
-    ],
 }
 
 
@@ -104,14 +92,12 @@ PROVIDERS = [
     "openai",
     "anthropic",
     "gemini",
-    "nvidia_nim",
     "groq",
     "ollama",
     "watson",
     "bedrock",
     "azure",
     "cerebras",
-    "sambanova",
 ]
 
 MODELS = {
@@ -128,75 +114,6 @@ MODELS = {
         "gemini/gemini-gemma-2-9b-it",
         "gemini/gemini-gemma-2-27b-it",
     ],
-    "nvidia_nim": [
-        "nvidia_nim/nvidia/mistral-nemo-minitron-8b-8k-instruct",
-        "nvidia_nim/nvidia/nemotron-4-mini-hindi-4b-instruct",
-        "nvidia_nim/nvidia/llama-3.1-nemotron-70b-instruct",
-        "nvidia_nim/nvidia/llama3-chatqa-1.5-8b",
-        "nvidia_nim/nvidia/llama3-chatqa-1.5-70b",
-        "nvidia_nim/nvidia/vila",
-        "nvidia_nim/nvidia/neva-22",
-        "nvidia_nim/nvidia/nemotron-mini-4b-instruct",
-        "nvidia_nim/nvidia/usdcode-llama3-70b-instruct",
-        "nvidia_nim/nvidia/nemotron-4-340b-instruct",
-        "nvidia_nim/meta/codellama-70b",
-        "nvidia_nim/meta/llama2-70b",
-        "nvidia_nim/meta/llama3-8b-instruct",
-        "nvidia_nim/meta/llama3-70b-instruct",
-        "nvidia_nim/meta/llama-3.1-8b-instruct",
-        "nvidia_nim/meta/llama-3.1-70b-instruct",
-        "nvidia_nim/meta/llama-3.1-405b-instruct",
-        "nvidia_nim/meta/llama-3.2-1b-instruct",
-        "nvidia_nim/meta/llama-3.2-3b-instruct",
-        "nvidia_nim/meta/llama-3.2-11b-vision-instruct",
-        "nvidia_nim/meta/llama-3.2-90b-vision-instruct",
-        "nvidia_nim/meta/llama-3.1-70b-instruct",
-        "nvidia_nim/google/gemma-7b",
-        "nvidia_nim/google/gemma-2b",
-        "nvidia_nim/google/codegemma-7b",
-        "nvidia_nim/google/codegemma-1.1-7b",
-        "nvidia_nim/google/recurrentgemma-2b",
-        "nvidia_nim/google/gemma-2-9b-it",
-        "nvidia_nim/google/gemma-2-27b-it",
-        "nvidia_nim/google/gemma-2-2b-it",
-        "nvidia_nim/google/deplot",
-        "nvidia_nim/google/paligemma",
-        "nvidia_nim/mistralai/mistral-7b-instruct-v0.2",
-        "nvidia_nim/mistralai/mixtral-8x7b-instruct-v0.1",
-        "nvidia_nim/mistralai/mistral-large",
-        "nvidia_nim/mistralai/mixtral-8x22b-instruct-v0.1",
-        "nvidia_nim/mistralai/mistral-7b-instruct-v0.3",
-        "nvidia_nim/nv-mistralai/mistral-nemo-12b-instruct",
-        "nvidia_nim/mistralai/mamba-codestral-7b-v0.1",
-        "nvidia_nim/microsoft/phi-3-mini-128k-instruct",
-        "nvidia_nim/microsoft/phi-3-mini-4k-instruct",
-        "nvidia_nim/microsoft/phi-3-small-8k-instruct",
-        "nvidia_nim/microsoft/phi-3-small-128k-instruct",
-        "nvidia_nim/microsoft/phi-3-medium-4k-instruct",
-        "nvidia_nim/microsoft/phi-3-medium-128k-instruct",
-        "nvidia_nim/microsoft/phi-3.5-mini-instruct",
-        "nvidia_nim/microsoft/phi-3.5-moe-instruct",
-        "nvidia_nim/microsoft/kosmos-2",
-        "nvidia_nim/microsoft/phi-3-vision-128k-instruct",
-        "nvidia_nim/microsoft/phi-3.5-vision-instruct",
-        "nvidia_nim/databricks/dbrx-instruct",
-        "nvidia_nim/snowflake/arctic",
-        "nvidia_nim/aisingapore/sea-lion-7b-instruct",
-        "nvidia_nim/ibm/granite-8b-code-instruct",
-        "nvidia_nim/ibm/granite-34b-code-instruct",
-        "nvidia_nim/ibm/granite-3.0-8b-instruct",
-        "nvidia_nim/ibm/granite-3.0-3b-a800m-instruct",
-        "nvidia_nim/mediatek/breeze-7b-instruct",
-        "nvidia_nim/upstage/solar-10.7b-instruct",
-        "nvidia_nim/writer/palmyra-med-70b-32k",
-        "nvidia_nim/writer/palmyra-med-70b",
-        "nvidia_nim/writer/palmyra-fin-70b-32k",
-        "nvidia_nim/01-ai/yi-large",
-        "nvidia_nim/deepseek-ai/deepseek-coder-6.7b-instruct",
-        "nvidia_nim/rakuten/rakutenai-7b-instruct",
-        "nvidia_nim/rakuten/rakutenai-7b-chat",
-        "nvidia_nim/baichuan-inc/baichuan2-13b-chat",
-    ],
     "groq": [
         "groq/llama-3.1-8b-instant",
         "groq/llama-3.1-70b-versatile",
@@ -239,23 +156,8 @@ MODELS = {
         "bedrock/mistral.mistral-7b-instruct-v0:2",
         "bedrock/mistral.mixtral-8x7b-instruct-v0:1",
     ],
-    "sambanova": [
-        "sambanova/Meta-Llama-3.3-70B-Instruct",
-        "sambanova/QwQ-32B-Preview",
-        "sambanova/Qwen2.5-72B-Instruct",
-        "sambanova/Qwen2.5-Coder-32B-Instruct",
-        "sambanova/Meta-Llama-3.1-405B-Instruct",
-        "sambanova/Meta-Llama-3.1-70B-Instruct",
-        "sambanova/Meta-Llama-3.1-8B-Instruct",
-        "sambanova/Llama-3.2-90B-Vision-Instruct",
-        "sambanova/Llama-3.2-11B-Vision-Instruct",
-        "sambanova/Meta-Llama-3.2-3B-Instruct",
-        "sambanova/Meta-Llama-3.2-1B-Instruct",
-    ],
 }
 
-DEFAULT_LLM_MODEL = "gpt-4o-mini"
-
 JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
 
 

src/crewai/cli/crew_chat.py

@@ -1,413 +0,0 @@
-import json
-import re
-import sys
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Set, Tuple
-
-import click
-import tomli
-
-from crewai.crew import Crew
-from crewai.llm import LLM
-from crewai.types.crew_chat import ChatInputField, ChatInputs
-from crewai.utilities.llm_utils import create_llm
-
-
-def run_chat():
-    """
-    Runs an interactive chat loop using the Crew's chat LLM with function calling.
-    Incorporates crew_name, crew_description, and input fields to build a tool schema.
-    Exits if crew_name or crew_description are missing.
-    """
-    crew, crew_name = load_crew_and_name()
-    chat_llm = initialize_chat_llm(crew)
-    if not chat_llm:
-        return
-
-    crew_chat_inputs = generate_crew_chat_inputs(crew, crew_name, chat_llm)
-    crew_tool_schema = generate_crew_tool_schema(crew_chat_inputs)
-    system_message = build_system_message(crew_chat_inputs)
-
-    # Call the LLM to generate the introductory message
-    introductory_message = chat_llm.call(
-        messages=[{"role": "system", "content": system_message}]
-    )
-    click.secho(f"\nAssistant: {introductory_message}\n", fg="green")
-
-    messages = [
-        {"role": "system", "content": system_message},
-        {"role": "assistant", "content": introductory_message},
-    ]
-
-    available_functions = {
-        crew_chat_inputs.crew_name: create_tool_function(crew, messages),
-    }
-
-    click.secho(
-        "\nEntering an interactive chat loop with function-calling.\n"
-        "Type 'exit' or Ctrl+C to quit.\n",
-        fg="cyan",
-    )
-
-    chat_loop(chat_llm, messages, crew_tool_schema, available_functions)
-
-
-def initialize_chat_llm(crew: Crew) -> Optional[LLM]:
-    """Initializes the chat LLM and handles exceptions."""
-    try:
-        return create_llm(crew.chat_llm)
-    except Exception as e:
-        click.secho(
-            f"Unable to find a Chat LLM. Please make sure you set chat_llm on the crew: {e}",
-            fg="red",
-        )
-        return None
-
-
-def build_system_message(crew_chat_inputs: ChatInputs) -> str:
-    """Builds the initial system message for the chat."""
-    required_fields_str = (
-        ", ".join(
-            f"{field.name} (desc: {field.description or 'n/a'})"
-            for field in crew_chat_inputs.inputs
-        )
-        or "(No required fields detected)"
-    )
-
-    return (
-        "You are a helpful AI assistant for the CrewAI platform. "
-        "Your primary purpose is to assist users with the crew's specific tasks. "
-        "You can answer general questions, but should guide users back to the crew's purpose afterward. "
-        "For example, after answering a general question, remind the user of your main purpose, such as generating a research report, and prompt them to specify a topic or task related to the crew's purpose. "
-        "You have a function (tool) you can call by name if you have all required inputs. "
-        f"Those required inputs are: {required_fields_str}. "
-        "Once you have them, call the function. "
-        "Please keep your responses concise and friendly. "
-        "If a user asks a question outside the crew's scope, provide a brief answer and remind them of the crew's purpose. "
-        "After calling the tool, be prepared to take user feedback and make adjustments as needed. "
-        "If you are ever unsure about a user's request or need clarification, ask the user for more information."
-        "Before doing anything else, introduce yourself with a friendly message like: 'Hey! I'm here to help you with [crew's purpose]. Could you please provide me with [inputs] so we can get started?' "
-        "For example: 'Hey! I'm here to help you with uncovering and reporting cutting-edge developments through thorough research and detailed analysis. Could you please provide me with a topic you're interested in? This will help us generate a comprehensive research report and detailed analysis.'"
-        f"\nCrew Name: {crew_chat_inputs.crew_name}"
-        f"\nCrew Description: {crew_chat_inputs.crew_description}"
-    )
-
-
-def create_tool_function(crew: Crew, messages: List[Dict[str, str]]) -> Any:
-    """Creates a wrapper function for running the crew tool with messages."""
-
-    def run_crew_tool_with_messages(**kwargs):
-        return run_crew_tool(crew, messages, **kwargs)
-
-    return run_crew_tool_with_messages
-
-
-def chat_loop(chat_llm, messages, crew_tool_schema, available_functions):
-    """Main chat loop for interacting with the user."""
-    while True:
-        try:
-            user_input = click.prompt("You", type=str)
-            if user_input.strip().lower() in ["exit", "quit"]:
-                click.echo("Exiting chat. Goodbye!")
-                break
-
-            messages.append({"role": "user", "content": user_input})
-            final_response = chat_llm.call(
-                messages=messages,
-                tools=[crew_tool_schema],
-                available_functions=available_functions,
-            )
-
-            messages.append({"role": "assistant", "content": final_response})
-            click.secho(f"\nAssistant: {final_response}\n", fg="green")
-
-        except KeyboardInterrupt:
-            click.echo("\nExiting chat. Goodbye!")
-            break
-        except Exception as e:
-            click.secho(f"An error occurred: {e}", fg="red")
-            break
-
-
-def generate_crew_tool_schema(crew_inputs: ChatInputs) -> dict:
-    """
-    Dynamically build a Littellm 'function' schema for the given crew.
-
-    crew_name: The name of the crew (used for the function 'name').
-    crew_inputs: A ChatInputs object containing crew_description
-                 and a list of input fields (each with a name & description).
-    """
-    properties = {}
-    for field in crew_inputs.inputs:
-        properties[field.name] = {
-            "type": "string",
-            "description": field.description or "No description provided",
-        }
-
-    required_fields = [field.name for field in crew_inputs.inputs]
-
-    return {
-        "type": "function",
-        "function": {
-            "name": crew_inputs.crew_name,
-            "description": crew_inputs.crew_description or "No crew description",
-            "parameters": {
-                "type": "object",
-                "properties": properties,
-                "required": required_fields,
-            },
-        },
-    }
-
-
-def run_crew_tool(crew: Crew, messages: List[Dict[str, str]], **kwargs):
-    """
-    Runs the crew using crew.kickoff(inputs=kwargs) and returns the output.
-
-    Args:
-        crew (Crew): The crew instance to run.
-        messages (List[Dict[str, str]]): The chat messages up to this point.
-        **kwargs: The inputs collected from the user.
-
-    Returns:
-        str: The output from the crew's execution.
-
-    Raises:
-        SystemExit: Exits the chat if an error occurs during crew execution.
-    """
-    try:
-        # Serialize 'messages' to JSON string before adding to kwargs
-        kwargs["crew_chat_messages"] = json.dumps(messages)
-
-        # Run the crew with the provided inputs
-        crew_output = crew.kickoff(inputs=kwargs)
-
-        # Convert CrewOutput to a string to send back to the user
-        result = str(crew_output)
-
-        return result
-    except Exception as e:
-        # Exit the chat and show the error message
-        click.secho("An error occurred while running the crew:", fg="red")
-        click.secho(str(e), fg="red")
-        sys.exit(1)
-
-
-def load_crew_and_name() -> Tuple[Crew, str]:
-    """
-    Loads the crew by importing the crew class from the user's project.
-
-    Returns:
-        Tuple[Crew, str]: A tuple containing the Crew instance and the name of the crew.
-    """
-    # Get the current working directory
-    cwd = Path.cwd()
-
-    # Path to the pyproject.toml file
-    pyproject_path = cwd / "pyproject.toml"
-    if not pyproject_path.exists():
-        raise FileNotFoundError("pyproject.toml not found in the current directory.")
-
-    # Load the pyproject.toml file using 'tomli'
-    with pyproject_path.open("rb") as f:
-        pyproject_data = tomli.load(f)
-
-    # Get the project name from the 'project' section
-    project_name = pyproject_data["project"]["name"]
-    folder_name = project_name
-
-    # Derive the crew class name from the project name
-    # E.g., if project_name is 'my_project', crew_class_name is 'MyProject'
-    crew_class_name = project_name.replace("_", " ").title().replace(" ", "")
-
-    # Add the 'src' directory to sys.path
-    src_path = cwd / "src"
-    if str(src_path) not in sys.path:
-        sys.path.insert(0, str(src_path))
-
-    # Import the crew module
-    crew_module_name = f"{folder_name}.crew"
-    try:
-        crew_module = __import__(crew_module_name, fromlist=[crew_class_name])
-    except ImportError as e:
-        raise ImportError(f"Failed to import crew module {crew_module_name}: {e}")
-
-    # Get the crew class from the module
-    try:
-        crew_class = getattr(crew_module, crew_class_name)
-    except AttributeError:
-        raise AttributeError(
-            f"Crew class {crew_class_name} not found in module {crew_module_name}"
-        )
-
-    # Instantiate the crew
-    crew_instance = crew_class().crew()
-    return crew_instance, crew_class_name
-
-
-def generate_crew_chat_inputs(crew: Crew, crew_name: str, chat_llm) -> ChatInputs:
-    """
-    Generates the ChatInputs required for the crew by analyzing the tasks and agents.
-
-    Args:
-        crew (Crew): The crew object containing tasks and agents.
-        crew_name (str): The name of the crew.
-        chat_llm: The chat language model to use for AI calls.
-
-    Returns:
-        ChatInputs: An object containing the crew's name, description, and input fields.
-    """
-    # Extract placeholders from tasks and agents
-    required_inputs = fetch_required_inputs(crew)
-
-    # Generate descriptions for each input using AI
-    input_fields = []
-    for input_name in required_inputs:
-        description = generate_input_description_with_ai(input_name, crew, chat_llm)
-        input_fields.append(ChatInputField(name=input_name, description=description))
-
-    # Generate crew description using AI
-    crew_description = generate_crew_description_with_ai(crew, chat_llm)
-
-    return ChatInputs(
-        crew_name=crew_name, crew_description=crew_description, inputs=input_fields
-    )
-
-
-def fetch_required_inputs(crew: Crew) -> Set[str]:
-    """
-    Extracts placeholders from the crew's tasks and agents.
-
-    Args:
-        crew (Crew): The crew object.
-
-    Returns:
-        Set[str]: A set of placeholder names.
-    """
-    placeholder_pattern = re.compile(r"\{(.+?)\}")
-    required_inputs: Set[str] = set()
-
-    # Scan tasks
-    for task in crew.tasks:
-        text = f"{task.description or ''} {task.expected_output or ''}"
-        required_inputs.update(placeholder_pattern.findall(text))
-
-    # Scan agents
-    for agent in crew.agents:
-        text = f"{agent.role or ''} {agent.goal or ''} {agent.backstory or ''}"
-        required_inputs.update(placeholder_pattern.findall(text))
-
-    return required_inputs
-
-
-def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) -> str:
-    """
-    Generates an input description using AI based on the context of the crew.
-
-    Args:
-        input_name (str): The name of the input placeholder.
-        crew (Crew): The crew object.
-        chat_llm: The chat language model to use for AI calls.
-
-    Returns:
-        str: A concise description of the input.
-    """
-    # Gather context from tasks and agents where the input is used
-    context_texts = []
-    placeholder_pattern = re.compile(r"\{(.+?)\}")
-
-    for task in crew.tasks:
-        if (
-            f"{{{input_name}}}" in task.description
-            or f"{{{input_name}}}" in task.expected_output
-        ):
-            # Replace placeholders with input names
-            task_description = placeholder_pattern.sub(
-                lambda m: m.group(1), task.description
-            )
-            expected_output = placeholder_pattern.sub(
-                lambda m: m.group(1), task.expected_output
-            )
-            context_texts.append(f"Task Description: {task_description}")
-            context_texts.append(f"Expected Output: {expected_output}")
-    for agent in crew.agents:
-        if (
-            f"{{{input_name}}}" in agent.role
-            or f"{{{input_name}}}" in agent.goal
-            or f"{{{input_name}}}" in agent.backstory
-        ):
-            # Replace placeholders with input names
-            agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role)
-            agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal)
-            agent_backstory = placeholder_pattern.sub(
-                lambda m: m.group(1), agent.backstory
-            )
-            context_texts.append(f"Agent Role: {agent_role}")
-            context_texts.append(f"Agent Goal: {agent_goal}")
-            context_texts.append(f"Agent Backstory: {agent_backstory}")
-
-    context = "\n".join(context_texts)
-    if not context:
-        # If no context is found for the input, raise an exception as per instruction
-        raise ValueError(f"No context found for input '{input_name}'.")
-
-    prompt = (
-        f"Based on the following context, write a concise description (15 words or less) of the input '{input_name}'.\n"
-        "Provide only the description, without any extra text or labels. Do not include placeholders like '{topic}' in the description.\n"
-        "Context:\n"
-        f"{context}"
-    )
-    response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
-    description = response.strip()
-
-    return description
-
-
-def generate_crew_description_with_ai(crew: Crew, chat_llm) -> str:
-    """
-    Generates a brief description of the crew using AI.
-
-    Args:
-        crew (Crew): The crew object.
-        chat_llm: The chat language model to use for AI calls.
-
-    Returns:
-        str: A concise description of the crew's purpose (15 words or less).
-    """
-    # Gather context from tasks and agents
-    context_texts = []
-    placeholder_pattern = re.compile(r"\{(.+?)\}")
-
-    for task in crew.tasks:
-        # Replace placeholders with input names
-        task_description = placeholder_pattern.sub(
-            lambda m: m.group(1), task.description
-        )
-        expected_output = placeholder_pattern.sub(
-            lambda m: m.group(1), task.expected_output
-        )
-        context_texts.append(f"Task Description: {task_description}")
-        context_texts.append(f"Expected Output: {expected_output}")
-    for agent in crew.agents:
-        # Replace placeholders with input names
-        agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role)
-        agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal)
-        agent_backstory = placeholder_pattern.sub(lambda m: m.group(1), agent.backstory)
-        context_texts.append(f"Agent Role: {agent_role}")
-        context_texts.append(f"Agent Goal: {agent_goal}")
-        context_texts.append(f"Agent Backstory: {agent_backstory}")
-
-    context = "\n".join(context_texts)
-    if not context:
-        raise ValueError("No context found for generating crew description.")
-
-    prompt = (
-        "Based on the following context, write a concise, action-oriented description (15 words or less) of the crew's purpose.\n"
-        "Provide only the description, without any extra text or labels. Do not include placeholders like '{topic}' in the description.\n"
-        "Context:\n"
-        f"{context}"
-    )
-    response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
-    crew_description = response.strip()
-
-    return crew_description

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

@@ -2,7 +2,7 @@ research_task:
   description: >
     Conduct a thorough research about {topic}
     Make sure you find any interesting and relevant information given
-    the current year is {current_year}.
+    the current year is 2024.
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
   agent: researcher

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

@@ -2,8 +2,6 @@
 import sys
 import warnings
 
-from datetime import datetime
-
 from {{folder_name}}.crew import {{crew_name}}
 
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
@@ -18,14 +16,9 @@ def run():
     Run the crew.
     """
     inputs = {
-        'topic': 'AI LLMs',
-        'current_year': str(datetime.now().year)
+        'topic': 'AI LLMs'
     }
-    
-    try:
-        {{crew_name}}().crew().kickoff(inputs=inputs)
-    except Exception as e:
-        raise Exception(f"An error occurred while running the crew: {e}")
+    {{crew_name}}().crew().kickoff(inputs=inputs)
 
 
 def train():
@@ -62,4 +55,4 @@ def test():
         {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
 
     except Exception as e:
-        raise Exception(f"An error occurred while testing the crew: {e}")
+        raise Exception(f"An error occurred while replaying the crew: {e}")

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.95.0,<1.0.0"
+    "crewai[tools]>=0.86.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.95.0,<1.0.0",
+    "crewai[tools]>=0.86.0,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.95.0"
+    "crewai[tools]>=0.86.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -1,11 +1,10 @@
 import asyncio
 import json
-import re
 import uuid
 import warnings
 from concurrent.futures import Future
 from hashlib import md5
-from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 from pydantic import (
     UUID4,
@@ -37,7 +36,6 @@ from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.tools.base_tool import Tool
-from crewai.types.crew_chat import ChatInputs
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import TRAINING_DATA_FILE
@@ -205,10 +203,6 @@ class Crew(BaseModel):
         default=None,
         description="Knowledge sources for the crew. Add knowledge sources to the knowledge object.",
     )
-    chat_llm: Optional[Any] = Field(
-        default=None,
-        description="LLM used to handle chatting with the crew.",
-    )
     _knowledge: Optional[Knowledge] = PrivateAttr(
         default=None,
     )
@@ -732,7 +726,11 @@ class Crew(BaseModel):
 
             # Determine which tools to use - task tools take precedence over agent tools
             tools_for_task = task.tools or agent_to_use.tools or []
-            tools_for_task = self._prepare_tools(agent_to_use, task, tools_for_task)
+            tools_for_task = self._prepare_tools(
+                agent_to_use,
+                task,
+                tools_for_task
+            )
 
             self._log_task_start(task, agent_to_use.role)
 
@@ -799,18 +797,14 @@ class Crew(BaseModel):
             return skipped_task_output
         return None
 
-    def _prepare_tools(
-        self, agent: BaseAgent, task: Task, tools: List[Tool]
-    ) -> List[Tool]:
+    def _prepare_tools(self, agent: BaseAgent, task: Task, tools: List[Tool]) -> List[Tool]:
         # Add delegation tools if agent allows delegation
         if agent.allow_delegation:
             if self.process == Process.hierarchical:
                 if self.manager_agent:
                     tools = self._update_manager_tools(task, tools)
                 else:
-                    raise ValueError(
-                        "Manager agent is required for hierarchical process."
-                    )
+                    raise ValueError("Manager agent is required for hierarchical process.")
 
             elif agent and agent.allow_delegation:
                 tools = self._add_delegation_tools(task, tools)
@@ -829,9 +823,7 @@ class Crew(BaseModel):
             return self.manager_agent
         return task.agent
 
-    def _merge_tools(
-        self, existing_tools: List[Tool], new_tools: List[Tool]
-    ) -> List[Tool]:
+    def _merge_tools(self, existing_tools: List[Tool], new_tools: List[Tool]) -> List[Tool]:
         """Merge new tools into existing tools list, avoiding duplicates by tool name."""
         if not new_tools:
             return existing_tools
@@ -847,9 +839,7 @@ class Crew(BaseModel):
 
         return tools
 
-    def _inject_delegation_tools(
-        self, tools: List[Tool], task_agent: BaseAgent, agents: List[BaseAgent]
-    ):
+    def _inject_delegation_tools(self, tools: List[Tool], task_agent: BaseAgent, agents: List[BaseAgent]):
         delegation_tools = task_agent.get_delegation_tools(agents)
         return self._merge_tools(tools, delegation_tools)
 
@@ -866,9 +856,7 @@ class Crew(BaseModel):
         if len(self.agents) > 1 and len(agents_for_delegation) > 0 and task.agent:
             if not tools:
                 tools = []
-            tools = self._inject_delegation_tools(
-                tools, task.agent, agents_for_delegation
-            )
+            tools = self._inject_delegation_tools(tools, task.agent, agents_for_delegation)
         return tools
 
     def _log_task_start(self, task: Task, role: str = "None"):
@@ -882,9 +870,7 @@ class Crew(BaseModel):
             if task.agent:
                 tools = self._inject_delegation_tools(tools, task.agent, [task.agent])
             else:
-                tools = self._inject_delegation_tools(
-                    tools, self.manager_agent, self.agents
-                )
+                tools = self._inject_delegation_tools(tools, self.manager_agent, self.agents)
         return tools
 
     def _get_context(self, task: Task, task_outputs: List[TaskOutput]):
@@ -997,31 +983,6 @@ class Crew(BaseModel):
             return self._knowledge.query(query)
         return None
 
-    def fetch_inputs(self) -> Set[str]:
-        """
-        Gathers placeholders (e.g., {something}) referenced in tasks or agents.
-        Scans each task's 'description' + 'expected_output', and each agent's
-        'role', 'goal', and 'backstory'.
-
-        Returns a set of all discovered placeholder names.
-        """
-        placeholder_pattern = re.compile(r"\{(.+?)\}")
-        required_inputs: Set[str] = set()
-
-        # Scan tasks for inputs
-        for task in self.tasks:
-            # description and expected_output might contain e.g. {topic}, {user_name}, etc.
-            text = f"{task.description or ''} {task.expected_output or ''}"
-            required_inputs.update(placeholder_pattern.findall(text))
-
-        # Scan agents for inputs
-        for agent in self.agents:
-            # role, goal, backstory might have placeholders like {role_detail}, etc.
-            text = f"{agent.role or ''} {agent.goal or ''} {agent.backstory or ''}"
-            required_inputs.update(placeholder_pattern.findall(text))
-
-        return required_inputs
-
     def copy(self):
         """Create a deep copy of the Crew."""
 
@@ -1077,7 +1038,7 @@ class Crew(BaseModel):
     def _interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
         """Interpolates the inputs in the tasks and agents."""
         [
-            task.interpolate_inputs_and_add_conversation_history(
+            task.interpolate_inputs(
                 # type: ignore # "interpolate_inputs" of "Task" does not return a value (it only ever returns None)
                 inputs
             )

src/crewai/knowledge/source/crew_docling_source.py

@@ -2,16 +2,11 @@ from pathlib import Path
 from typing import Iterator, List, Optional, Union
 from urllib.parse import urlparse
 
-try:
-    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
-    DOCLING_AVAILABLE = True
-except ImportError:
-    DOCLING_AVAILABLE = False
-
+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
@@ -24,14 +19,6 @@ class CrewDoclingSource(BaseKnowledgeSource):
     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.
     """
 
-    def __init__(self, *args, **kwargs):
-        if not DOCLING_AVAILABLE:
-            raise ImportError(
-                "The docling package is required to use CrewDoclingSource. "
-                "Please install it using: uv add docling"
-            )
-        super().__init__(*args, **kwargs)
-
     _logger: Logger = Logger(verbose=True)
 
     file_path: Optional[List[Union[Path, str]]] = Field(default=None)

src/crewai/llm.py

@@ -1,27 +1,18 @@
-import json
 import logging
 import os
 import sys
 import threading
 import warnings
 from contextlib import contextmanager
-from typing import Any, Dict, List, Optional, Union, cast
-
-from dotenv import load_dotenv
-
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore", UserWarning)
-    import litellm
-    from litellm import Choices, get_supported_openai_params
-    from litellm.types.utils import ModelResponse
+from typing import Any, Dict, List, Optional, Union
 
+import litellm
+from litellm import get_supported_openai_params
 
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
 
-load_dotenv()
-
 
 class FilteredStream:
     def __init__(self, original_stream):
@@ -30,7 +21,6 @@ class FilteredStream:
 
     def write(self, s) -> int:
         with self._lock:
-            # Filter out extraneous messages from LiteLLM
             if (
                 "Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new"
                 in s
@@ -76,18 +66,6 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "mixtral-8x7b-32768": 32768,
     "llama-3.3-70b-versatile": 128000,
     "llama-3.3-70b-instruct": 128000,
-    # sambanova
-    "Meta-Llama-3.3-70B-Instruct": 131072,
-    "QwQ-32B-Preview": 8192,
-    "Qwen2.5-72B-Instruct": 8192,
-    "Qwen2.5-Coder-32B-Instruct": 8192,
-    "Meta-Llama-3.1-405B-Instruct": 8192,
-    "Meta-Llama-3.1-70B-Instruct": 131072,
-    "Meta-Llama-3.1-8B-Instruct": 131072,
-    "Llama-3.2-90B-Vision-Instruct": 16384,
-    "Llama-3.2-11B-Vision-Instruct": 16384,
-    "Meta-Llama-3.2-3B-Instruct": 4096,
-    "Meta-Llama-3.2-1B-Instruct": 16384,
 }
 
 DEFAULT_CONTEXT_WINDOW_SIZE = 8192
@@ -98,18 +76,17 @@ CONTEXT_WINDOW_USAGE_RATIO = 0.75
 def suppress_warnings():
     with warnings.catch_warnings():
         warnings.filterwarnings("ignore")
-        warnings.filterwarnings(
-            "ignore", message="open_text is deprecated*", category=DeprecationWarning
-        )
 
         # Redirect stdout and stderr
         old_stdout = sys.stdout
         old_stderr = sys.stderr
         sys.stdout = FilteredStream(old_stdout)
         sys.stderr = FilteredStream(old_stderr)
+
         try:
             yield
         finally:
+            # Restore stdout and stderr
             sys.stdout = old_stdout
             sys.stderr = old_stderr
 
@@ -130,12 +107,13 @@ class LLM:
         logit_bias: Optional[Dict[int, float]] = None,
         response_format: Optional[Dict[str, Any]] = None,
         seed: Optional[int] = None,
-        logprobs: Optional[int] = None,
+        logprobs: Optional[bool] = None,
         top_logprobs: Optional[int] = None,
         base_url: Optional[str] = None,
         api_version: Optional[str] = None,
         api_key: Optional[str] = None,
         callbacks: List[Any] = [],
+        **kwargs,
     ):
         self.model = model
         self.timeout = timeout
@@ -157,40 +135,19 @@ class LLM:
         self.api_key = api_key
         self.callbacks = callbacks
         self.context_window_size = 0
+        self.kwargs = kwargs
 
         litellm.drop_params = True
-
+        litellm.set_verbose = False
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
 
-    def call(
-        self,
-        messages: List[Dict[str, str]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> str:
-        """
-        High-level call method that:
-          1) Calls litellm.completion
-          2) Checks for function/tool calls
-          3) If a tool call is found:
-               a) executes the function
-               b) returns the result
-          4) If no tool call, returns the text response
-
-        :param messages: The conversation messages
-        :param tools: Optional list of function schemas for function calling
-        :param callbacks: Optional list of callbacks
-        :param available_functions: A dictionary mapping function_name -> actual Python function
-        :return: Final text response from the LLM or the tool result
-        """
+    def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
                 self.set_callbacks(callbacks)
 
             try:
-                # --- 1) Make the completion call
                 params = {
                     "model": self.model,
                     "messages": messages,
@@ -211,58 +168,21 @@ class LLM:
                     "api_version": self.api_version,
                     "api_key": self.api_key,
                     "stream": False,
-                    "tools": tools,  # pass the tool schema
+                    **self.kwargs,
                 }
 
+                # Remove None values to avoid passing unnecessary parameters
                 params = {k: v for k, v in params.items() if v is not None}
 
                 response = litellm.completion(**params)
-                response_message = cast(Choices, cast(ModelResponse, response).choices)[
-                    0
-                ].message
-                text_response = response_message.content or ""
-                tool_calls = getattr(response_message, "tool_calls", [])
-
-                # --- 2) If no tool calls, return the text response
-                if not tool_calls or not available_functions:
-                    return text_response
-
-                # --- 3) Handle the tool call
-                tool_call = tool_calls[0]
-                function_name = tool_call.function.name
-
-                if function_name in available_functions:
-                    try:
-                        function_args = json.loads(tool_call.function.arguments)
-                    except json.JSONDecodeError as e:
-                        logging.warning(f"Failed to parse function arguments: {e}")
-                        return text_response
-
-                    fn = available_functions[function_name]
-                    try:
-                        # Call the actual tool function
-                        result = fn(**function_args)
-
-                        return result
-
-                    except Exception as e:
-                        logging.error(
-                            f"Error executing function '{function_name}': {e}"
-                        )
-                        return text_response
-
-                else:
-                    logging.warning(
-                        f"Tool call requested unknown function '{function_name}'"
-                    )
-                    return text_response
-
+                return response["choices"][0]["message"]["content"]
             except Exception as e:
                 if not LLMContextLengthExceededException(
                     str(e)
                 )._is_context_limit_error(str(e)):
                     logging.error(f"LiteLLM call failed: {str(e)}")
-                raise
+
+                raise  # Re-raise the exception after logging
 
     def supports_function_calling(self) -> bool:
         try:
@@ -281,10 +201,7 @@ class LLM:
             return False
 
     def get_context_window_size(self) -> int:
-        """
-        Returns the context window size, using 75% of the maximum to avoid
-        cutting off messages mid-thread.
-        """
+        # Only using 75% of the context window size to avoid cutting the message in the middle
         if self.context_window_size != 0:
             return self.context_window_size
 
@@ -297,21 +214,16 @@ class LLM:
         return self.context_window_size
 
     def set_callbacks(self, callbacks: List[Any]):
-        """
-        Attempt to keep a single set of callbacks in litellm by removing old
-        duplicates and adding new ones.
-        """
-        with suppress_warnings():
-            callback_types = [type(callback) for callback in callbacks]
-            for callback in litellm.success_callback[:]:
-                if type(callback) in callback_types:
-                    litellm.success_callback.remove(callback)
+        callback_types = [type(callback) for callback in callbacks]
+        for callback in litellm.success_callback[:]:
+            if type(callback) in callback_types:
+                litellm.success_callback.remove(callback)
 
-            for callback in litellm._async_success_callback[:]:
-                if type(callback) in callback_types:
-                    litellm._async_success_callback.remove(callback)
+        for callback in litellm._async_success_callback[:]:
+            if type(callback) in callback_types:
+                litellm._async_success_callback.remove(callback)
 
-            litellm.callbacks = callbacks
+        litellm.callbacks = callbacks
 
     def set_env_callbacks(self):
         """
@@ -332,20 +244,19 @@ class LLM:
         This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
         `litellm.failure_callback` to ["langfuse"].
         """
-        with suppress_warnings():
-            success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
-            success_callbacks = []
-            if success_callbacks_str:
-                success_callbacks = [
-                    cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
-                ]
+        success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
+        success_callbacks = []
+        if success_callbacks_str:
+            success_callbacks = [
+                callback.strip() for callback in success_callbacks_str.split(",")
+            ]
 
-            failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
-            failure_callbacks = []
-            if failure_callbacks_str:
-                failure_callbacks = [
-                    cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
-                ]
+        failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
+        failure_callbacks = []
+        if failure_callbacks_str:
+            failure_callbacks = [
+                callback.strip() for callback in failure_callbacks_str.split(",")
+            ]
 
-                litellm.success_callback = success_callbacks
-                litellm.failure_callback = failure_callbacks
+        litellm.success_callback = success_callbacks
+        litellm.failure_callback = failure_callbacks

src/crewai/memory/storage/mem0_storage.py

@@ -27,18 +27,10 @@ class Mem0Storage(Storage):
             raise ValueError("User ID is required for user memory type")
 
         # API key in memory config overrides the environment variable
-        config = self.memory_config.get("config", {})
-        mem0_api_key = config.get("api_key") or os.getenv("MEM0_API_KEY")
-        mem0_org_id = config.get("org_id")
-        mem0_project_id = config.get("project_id")
-
-        # Initialize MemoryClient with available parameters
-        if mem0_org_id and mem0_project_id:
-            self.memory = MemoryClient(
-                api_key=mem0_api_key, org_id=mem0_org_id, project_id=mem0_project_id
-            )
-        else:
-            self.memory = MemoryClient(api_key=mem0_api_key)
+        mem0_api_key = self.memory_config.get("config", {}).get("api_key") or os.getenv(
+            "MEM0_API_KEY"
+        )
+        self.memory = MemoryClient(api_key=mem0_api_key)
 
     def _sanitize_role(self, role: str) -> str:
         """
@@ -65,7 +57,7 @@ class Mem0Storage(Storage):
                 metadata={"type": "long_term", **metadata},
             )
         elif self.memory_type == "entities":
-            entity_name = self._get_agent_name()
+            entity_name = None
             self.memory.add(
                 value, user_id=entity_name, metadata={"type": "entity", **metadata}
             )

src/crewai/project/annotations.py

@@ -4,23 +4,18 @@ from typing import Callable
 from crewai import Crew
 from crewai.project.utils import memoize
 
-"""Decorators for defining crew components and their behaviors."""
-
 
 def before_kickoff(func):
-    """Marks a method to execute before crew kickoff."""
     func.is_before_kickoff = True
     return func
 
 
 def after_kickoff(func):
-    """Marks a method to execute after crew kickoff."""
     func.is_after_kickoff = True
     return func
 
 
 def task(func):
-    """Marks a method as a crew task."""
     func.is_task = True
 
     @wraps(func)
@@ -34,51 +29,43 @@ def task(func):
 
 
 def agent(func):
-    """Marks a method as a crew agent."""
     func.is_agent = True
     func = memoize(func)
     return func
 
 
 def llm(func):
-    """Marks a method as an LLM provider."""
     func.is_llm = True
     func = memoize(func)
     return func
 
 
 def output_json(cls):
-    """Marks a class as JSON output format."""
     cls.is_output_json = True
     return cls
 
 
 def output_pydantic(cls):
-    """Marks a class as Pydantic output format."""
     cls.is_output_pydantic = True
     return cls
 
 
 def tool(func):
-    """Marks a method as a crew tool."""
     func.is_tool = True
     return memoize(func)
 
 
 def callback(func):
-    """Marks a method as a crew callback."""
     func.is_callback = True
     return memoize(func)
 
 
 def cache_handler(func):
-    """Marks a method as a cache handler."""
     func.is_cache_handler = True
     return memoize(func)
 
 
 def crew(func) -> Callable[..., Crew]:
-    """Marks a method as the main crew execution point."""
 
     @wraps(func)
     def wrapper(self, *args, **kwargs) -> Crew:

src/crewai/project/crew_base.py

@@ -9,10 +9,8 @@ load_dotenv()
 
 T = TypeVar("T", bound=type)
 
-"""Base decorator for creating crew classes with configuration and function management."""
 
 def CrewBase(cls: T) -> T:
-    """Wraps a class with crew functionality and configuration management."""
     class WrappedClass(cls):  # type: ignore
         is_crew_class: bool = True  # type: ignore
 
@@ -218,5 +216,5 @@ def CrewBase(cls: T) -> T:
     # 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/task.py

@@ -41,7 +41,6 @@ from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter, convert_to_model
 from crewai.utilities.i18n import I18N
-from crewai.utilities.printer import Printer
 
 
 class Task(BaseModel):
@@ -128,40 +127,38 @@ class Task(BaseModel):
     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",
+        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"
+        default=3,
+        description="Maximum number of retries when guardrail fails"
     )
-    retry_count: int = Field(default=0, description="Current number of retries")
-    start_time: Optional[datetime.datetime] = Field(
-        default=None, description="Start time of the task execution"
-    )
-    end_time: Optional[datetime.datetime] = Field(
-        default=None, description="End time of the task execution"
+    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]
@@ -174,13 +171,8 @@ class Task(BaseModel):
             # 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]"
-                    )
+                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)
@@ -189,6 +181,7 @@ class Task(BaseModel):
     _original_expected_output: Optional[str] = PrivateAttr(default=None)
     _original_output_file: Optional[str] = PrivateAttr(default=None)
     _thread: Optional[threading.Thread] = PrivateAttr(default=None)
+    _execution_time: Optional[float] = PrivateAttr(default=None)
 
     @model_validator(mode="before")
     @classmethod
@@ -213,19 +206,25 @@ class Task(BaseModel):
                 "may_not_set_field", "This field is not to be set by the user.", {}
             )
 
+    def _set_start_execution_time(self) -> float:
+        return datetime.datetime.now().timestamp()
+
+    def _set_end_execution_time(self, start_time: float) -> None:
+        self._execution_time = datetime.datetime.now().timestamp() - start_time
+
     @field_validator("output_file")
     @classmethod
     def output_file_validation(cls, value: Optional[str]) -> Optional[str]:
         """Validate the output file path.
-
+        
         Args:
             value: The output file path to validate. Can be None or a string.
                   If the path contains template variables (e.g. {var}), leading slashes are preserved.
                   For regular paths, leading slashes are stripped.
-
+        
         Returns:
             The validated and potentially modified path, or None if no path was provided.
-
+            
         Raises:
             ValueError: If the path contains invalid characters, path traversal attempts,
                       or other security concerns.
@@ -235,24 +234,18 @@ class Task(BaseModel):
 
         # Basic security checks
         if ".." in value:
-            raise ValueError(
-                "Path traversal attempts are not allowed in output_file paths"
-            )
-
+            raise ValueError("Path traversal attempts are not allowed in output_file paths")
+        
         # Check for shell expansion first
-        if value.startswith("~") or value.startswith("$"):
-            raise ValueError(
-                "Shell expansion characters are not allowed in output_file paths"
-            )
-
+        if value.startswith('~') or value.startswith('$'):
+            raise ValueError("Shell expansion characters are not allowed in output_file paths")
+            
         # Then check other shell special characters
-        if any(char in value for char in ["|", ">", "<", "&", ";"]):
-            raise ValueError(
-                "Shell special characters are not allowed in output_file paths"
-            )
+        if any(char in value for char in ['|', '>', '<', '&', ';']):
+            raise ValueError("Shell special characters are not allowed in output_file paths")
 
         # Don't strip leading slash if it's a template path with variables
-        if "{" in value or "}" in value:
+        if "{" in value or "}" in value: 
             # Validate template variable format
             template_vars = [part.split("}")[0] for part in value.split("{")[1:]]
             for var in template_vars:
@@ -309,12 +302,6 @@ class Task(BaseModel):
 
         return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
 
-    @property
-    def execution_duration(self) -> float | None:
-        if not self.start_time or not self.end_time:
-            return None
-        return (self.end_time - self.start_time).total_seconds()
-
     def execute_async(
         self,
         agent: BaseAgent | None = None,
@@ -355,7 +342,7 @@ class Task(BaseModel):
                 f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
             )
 
-        self.start_time = datetime.datetime.now()
+        start_time = self._set_start_execution_time()
         self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self)
 
         self.prompt_context = context
@@ -391,14 +378,10 @@ class Task(BaseModel):
                     )
 
                 self.retry_count += 1
-                context = self.i18n.errors("validation_error").format(
-                    guardrail_result_error=guardrail_result.error,
-                    task_output=task_output.raw,
-                )
-                printer = Printer()
-                printer.print(
-                    content=f"Guardrail blocked, retrying, due to: {guardrail_result.error}\n",
-                    color="yellow",
+                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)
 
@@ -409,17 +392,15 @@ class Task(BaseModel):
 
             if isinstance(guardrail_result.result, str):
                 task_output.raw = guardrail_result.result
-                pydantic_output, json_output = self._export_output(
-                    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.end_time = datetime.datetime.now()
 
+        self._set_end_execution_time(start_time)
         if self.callback:
             self.callback(self.output)
 
@@ -451,16 +432,13 @@ class Task(BaseModel):
         tasks_slices = [self.description, output]
         return "\n".join(tasks_slices)
 
-    def interpolate_inputs_and_add_conversation_history(
-        self, inputs: Dict[str, Union[str, int, float]]
-    ) -> None:
+    def interpolate_inputs(self, inputs: Dict[str, Union[str, int, float]]) -> None:
         """Interpolate inputs into the task description, expected output, and output file path.
-           Add conversation history if present.
-
+        
         Args:
             inputs: Dictionary mapping template variables to their values.
                    Supported value types are strings, integers, and floats.
-
+        
         Raises:
             ValueError: If a required template variable is missing from inputs.
         """
@@ -477,9 +455,7 @@ class Task(BaseModel):
         try:
             self.description = self._original_description.format(**inputs)
         except KeyError as e:
-            raise ValueError(
-                f"Missing required template variable '{e.args[0]}' in description"
-            ) from e
+            raise ValueError(f"Missing required template variable '{e.args[0]}' in description") from e
         except ValueError as e:
             raise ValueError(f"Error interpolating description: {str(e)}") from e
 
@@ -496,49 +472,22 @@ class Task(BaseModel):
                     input_string=self._original_output_file, inputs=inputs
                 )
             except (KeyError, ValueError) as e:
-                raise ValueError(
-                    f"Error interpolating output_file path: {str(e)}"
-                ) from e
+                raise ValueError(f"Error interpolating output_file path: {str(e)}") from e
 
-        if "crew_chat_messages" in inputs and inputs["crew_chat_messages"]:
-            conversation_instruction = self.i18n.slice(
-                "conversation_history_instruction"
-            )
-
-            crew_chat_messages_json = str(inputs["crew_chat_messages"])
-
-            try:
-                crew_chat_messages = json.loads(crew_chat_messages_json)
-            except json.JSONDecodeError as e:
-                print("An error occurred while parsing crew chat messages:", e)
-                raise
-
-            conversation_history = "\n".join(
-                f"{msg['role'].capitalize()}: {msg['content']}"
-                for msg in crew_chat_messages
-                if isinstance(msg, dict) and "role" in msg and "content" in msg
-            )
-
-            self.description += (
-                f"\n\n{conversation_instruction}\n\n{conversation_history}"
-            )
-
-    def interpolate_only(
-        self, input_string: Optional[str], inputs: Dict[str, Union[str, int, float]]
-    ) -> str:
+    def interpolate_only(self, input_string: Optional[str], inputs: Dict[str, Union[str, int, float]]) -> str:
         """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.
-
+        
         Args:
             input_string: The string containing template variables to interpolate.
                          Can be None or empty, in which case an empty string is returned.
             inputs: Dictionary mapping template variables to their values.
                    Supported value types are strings, integers, and floats.
                    If input_string is empty or has no placeholders, inputs can be empty.
-
+        
         Returns:
             The interpolated string with all template variables replaced with their values.
             Empty string if input_string is None or empty.
-
+            
         Raises:
             ValueError: If a required template variable is missing from inputs.
             KeyError: If a template variable is not found in the inputs dictionary.
@@ -548,17 +497,13 @@ class Task(BaseModel):
         if "{" not in input_string and "}" not in input_string:
             return input_string
         if not inputs:
-            raise ValueError(
-                "Inputs dictionary cannot be empty when interpolating variables"
-            )
+            raise ValueError("Inputs dictionary cannot be empty when interpolating variables")
 
         try:
             # Validate input types
             for key, value in inputs.items():
                 if not isinstance(value, (str, int, float)):
-                    raise ValueError(
-                        f"Value for key '{key}' must be a string, integer, or float, got {type(value).__name__}"
-                    )
+                    raise ValueError(f"Value for key '{key}' must be a string, integer, or float, got {type(value).__name__}")
 
             escaped_string = input_string.replace("{", "{{").replace("}", "}}")
 
@@ -567,9 +512,7 @@ class Task(BaseModel):
 
             return escaped_string.format(**inputs)
         except KeyError as e:
-            raise KeyError(
-                f"Template variable '{e.args[0]}' not found in inputs dictionary"
-            ) from e
+            raise KeyError(f"Template variable '{e.args[0]}' not found in inputs dictionary") from e
         except ValueError as e:
             raise ValueError(f"Error during string interpolation: {str(e)}") from e
 
@@ -654,10 +597,10 @@ class Task(BaseModel):
 
     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
@@ -675,7 +618,6 @@ class Task(BaseModel):
             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))

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Optional
+from typing import Optional, Union
 
 from pydantic import Field
 
@@ -54,12 +54,12 @@ class BaseAgentTool(BaseTool):
     ) -> str:
         """
         Execute delegation to an agent with case-insensitive and whitespace-tolerant matching.
-
+        
         Args:
             agent_name: Name/role of the agent to delegate to (case-insensitive)
             task: The specific question or task to delegate
             context: Optional additional context for the task execution
-
+            
         Returns:
             str: The execution result from the delegated agent or an error message
                  if the agent cannot be found

src/crewai/tools/base_tool.py

@@ -1,23 +1,12 @@
-import warnings
 from abc import ABC, abstractmethod
 from inspect import signature
 from typing import Any, Callable, Type, get_args, get_origin
 
-from pydantic import (
-    BaseModel,
-    ConfigDict,
-    Field,
-    PydanticDeprecatedSince20,
-    create_model,
-    validator,
-)
+from pydantic import BaseModel, ConfigDict, Field, create_model, validator
 from pydantic import BaseModel as PydanticBaseModel
 
 from crewai.tools.structured_tool import CrewStructuredTool
 
-# Ignore all "PydanticDeprecatedSince20" warnings globally
-warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
-
 
 class BaseTool(BaseModel, ABC):
     class _ArgsSchemaPlaceholder(PydanticBaseModel):

src/crewai/tools/tool_usage.py

@@ -169,7 +169,7 @@ class ToolUsage:
 
                 if calling.arguments:
                     try:
-                        acceptable_args = tool.args_schema.model_json_schema()["properties"].keys()  # type: ignore
+                        acceptable_args = tool.args_schema.schema()["properties"].keys()  # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "schema"
                         arguments = {
                             k: v
                             for k, v in calling.arguments.items()

src/crewai/translations/en.json

@@ -23,11 +23,10 @@
     "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.",
-    "human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\"",
-    "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals."
+    "human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\""
   },
   "errors": {
-    "force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}",
+    "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_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",
@@ -35,8 +34,7 @@
     "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.",
     "wrong_tool_name": "You tried to use the tool {tool}, but it doesn't exist. You must use one of the following tools, use one at time: {tools}.",
     "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts these inputs: {tool_inputs}",
-    "agent_tool_execution_error": "Error executing task with agent '{agent_role}'. Error: {error}",
-    "validation_error": "### Previous attempt failed validation: {guardrail_result_error}\n\n\n### Previous result:\n{task_output}\n\n\nTry again, making sure to address the validation error."
+    "agent_tool_execution_error": "Error executing task with agent '{agent_role}'. Error: {error}"
   },
   "tools": {
     "delegate_work": "Delegate a specific task to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolute everything you know, don't reference things but instead explain them.",

src/crewai/types/crew_chat.py

@@ -1,40 +0,0 @@
-from typing import List
-
-from pydantic import BaseModel, Field
-
-
-class ChatInputField(BaseModel):
-    """
-    Represents a single required input for the crew, with a name and short description.
-    Example:
-        {
-            "name": "topic",
-            "description": "The topic to focus on for the conversation"
-        }
-    """
-
-    name: str = Field(..., description="The name of the input field")
-    description: str = Field(..., description="A short description of the input field")
-
-
-class ChatInputs(BaseModel):
-    """
-    Holds a high-level crew_description plus a list of ChatInputFields.
-    Example:
-        {
-            "crew_name": "topic-based-qa",
-            "crew_description": "Use this crew for topic-based Q&A",
-            "inputs": [
-                {"name": "topic", "description": "The topic to focus on"},
-                {"name": "username", "description": "Name of the user"},
-            ]
-        }
-    """
-
-    crew_name: str = Field(..., description="The name of the crew")
-    crew_description: str = Field(
-        ..., description="A description of the crew's purpose"
-    )
-    inputs: List[ChatInputField] = Field(
-        default_factory=list, description="A list of input fields for the crew"
-    )

src/crewai/utilities/crew_json_encoder.py

@@ -1,5 +1,3 @@
-"""JSON encoder for handling CrewAI specific types."""
-
 import json
 from datetime import date, datetime
 from decimal import Decimal
@@ -10,7 +8,6 @@ from pydantic import BaseModel
 
 
 class CrewJSONEncoder(json.JSONEncoder):
-    """Custom JSON encoder for CrewAI objects and special types."""
     def default(self, obj):
         if isinstance(obj, BaseModel):
             return self._handle_pydantic_model(obj)

src/crewai/utilities/crew_pydantic_output_parser.py

@@ -6,10 +6,9 @@ from pydantic import BaseModel, ValidationError
 
 from crewai.agents.parser import OutputParserException
 
-"""Parser for converting text outputs into Pydantic models."""
 
 class CrewPydanticOutputParser:
-    """Parses text outputs into specified Pydantic models."""
+    """Parses the text into pydantic models"""
 
     pydantic_object: Type[BaseModel]
 

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -180,12 +180,12 @@ class CrewEvaluator:
             self._test_result_span = self._telemetry.individual_test_result_span(
                 self.crew,
                 evaluation_result.pydantic.quality,
-                current_task.execution_duration,
+                current_task._execution_time,
                 self.openai_model_name,
             )
             self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
             self.run_execution_times[self.iteration].append(
-                current_task.execution_duration
+                current_task._execution_time
             )
         else:
             raise ValueError("Evaluation result is not in the expected format")

src/crewai/utilities/i18n.py

@@ -4,10 +4,8 @@ from typing import Dict, Optional, Union
 
 from pydantic import BaseModel, Field, PrivateAttr, model_validator
 
-"""Internationalization support for CrewAI prompts and messages."""
 
 class I18N(BaseModel):
-    """Handles loading and retrieving internationalized prompts."""
     _prompts: Dict[str, Dict[str, str]] = PrivateAttr()
     prompt_file: Optional[str] = Field(
         default=None,

src/crewai/utilities/internal_instructor.py

@@ -1,4 +1,3 @@
-import warnings
 from typing import Any, Optional, Type
 
 
@@ -26,15 +25,14 @@ class InternalInstructor:
         if self.agent and not self.llm:
             self.llm = self.agent.function_calling_llm or self.agent.llm
 
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore", UserWarning)
-            import instructor
-            from litellm import completion
+        # Lazy import
+        import instructor
+        from litellm import completion
 
-            self._client = instructor.from_litellm(
-                completion,
-                mode=instructor.Mode.TOOLS,
-            )
+        self._client = instructor.from_litellm(
+            completion,
+            mode=instructor.Mode.TOOLS,
+        )
 
     def to_json(self):
         model = self.to_pydantic()

src/crewai/utilities/llm_utils.py

@@ -1,185 +0,0 @@
-import os
-from typing import Any, Dict, List, Optional, Union
-
-from packaging import version
-
-from crewai.cli.constants import DEFAULT_LLM_MODEL, ENV_VARS, LITELLM_PARAMS
-from crewai.cli.utils import read_toml
-from crewai.cli.version import get_crewai_version
-from crewai.llm import LLM
-
-
-def create_llm(
-    llm_value: Union[str, LLM, Any, None] = None,
-) -> Optional[LLM]:
-    """
-    Creates or returns an LLM instance based on the given llm_value.
-
-    Args:
-        llm_value (str | LLM | Any | None):
-            - str: The model name (e.g., "gpt-4").
-            - LLM: Already instantiated LLM, returned as-is.
-            - Any: Attempt to extract known attributes like model_name, temperature, etc.
-            - None: Use environment-based or fallback default model.
-
-    Returns:
-        An LLM instance if successful, or None if something fails.
-    """
-
-    # 1) If llm_value is already an LLM object, return it directly
-    if isinstance(llm_value, LLM):
-        return llm_value
-
-    # 2) If llm_value is a string (model name)
-    if isinstance(llm_value, str):
-        try:
-            created_llm = LLM(model=llm_value)
-            return created_llm
-        except Exception as e:
-            print(f"Failed to instantiate LLM with model='{llm_value}': {e}")
-            return None
-
-    # 3) If llm_value is None, parse environment variables or use default
-    if llm_value is None:
-        return _llm_via_environment_or_fallback()
-
-    # 4) Otherwise, attempt to extract relevant attributes from an unknown object
-    try:
-        # Extract attributes with explicit types
-        model = (
-            getattr(llm_value, "model_name", None)
-            or getattr(llm_value, "deployment_name", None)
-            or str(llm_value)
-        )
-        temperature: Optional[float] = getattr(llm_value, "temperature", None)
-        max_tokens: Optional[int] = getattr(llm_value, "max_tokens", None)
-        logprobs: Optional[int] = getattr(llm_value, "logprobs", None)
-        timeout: Optional[float] = getattr(llm_value, "timeout", None)
-        api_key: Optional[str] = getattr(llm_value, "api_key", None)
-        base_url: Optional[str] = getattr(llm_value, "base_url", None)
-
-        created_llm = LLM(
-            model=model,
-            temperature=temperature,
-            max_tokens=max_tokens,
-            logprobs=logprobs,
-            timeout=timeout,
-            api_key=api_key,
-            base_url=base_url,
-        )
-        return created_llm
-    except Exception as e:
-        print(f"Error instantiating LLM from unknown object type: {e}")
-        return None
-
-
-def _llm_via_environment_or_fallback() -> Optional[LLM]:
-    """
-    Helper function: if llm_value is None, we load environment variables or fallback default model.
-    """
-    model_name = (
-        os.environ.get("OPENAI_MODEL_NAME")
-        or os.environ.get("MODEL")
-        or DEFAULT_LLM_MODEL
-    )
-
-    # Initialize parameters with correct types
-    model: str = model_name
-    temperature: Optional[float] = None
-    max_tokens: Optional[int] = None
-    max_completion_tokens: Optional[int] = None
-    logprobs: Optional[int] = None
-    timeout: Optional[float] = None
-    api_key: Optional[str] = None
-    base_url: Optional[str] = None
-    api_version: Optional[str] = None
-    presence_penalty: Optional[float] = None
-    frequency_penalty: Optional[float] = None
-    top_p: Optional[float] = None
-    n: Optional[int] = None
-    stop: Optional[Union[str, List[str]]] = None
-    logit_bias: Optional[Dict[int, float]] = None
-    response_format: Optional[Dict[str, Any]] = None
-    seed: Optional[int] = None
-    top_logprobs: Optional[int] = None
-    callbacks: List[Any] = []
-
-    # Optional base URL from env
-    api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get("OPENAI_BASE_URL")
-    if api_base:
-        base_url = api_base
-
-    # Initialize llm_params dictionary
-    llm_params: Dict[str, Any] = {
-        "model": model,
-        "temperature": temperature,
-        "max_tokens": max_tokens,
-        "max_completion_tokens": max_completion_tokens,
-        "logprobs": logprobs,
-        "timeout": timeout,
-        "api_key": api_key,
-        "base_url": base_url,
-        "api_version": api_version,
-        "presence_penalty": presence_penalty,
-        "frequency_penalty": frequency_penalty,
-        "top_p": top_p,
-        "n": n,
-        "stop": stop,
-        "logit_bias": logit_bias,
-        "response_format": response_format,
-        "seed": seed,
-        "top_logprobs": top_logprobs,
-        "callbacks": callbacks,
-    }
-
-    UNACCEPTED_ATTRIBUTES = [
-        "AWS_ACCESS_KEY_ID",
-        "AWS_SECRET_ACCESS_KEY",
-        "AWS_REGION_NAME",
-    ]
-    set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
-
-    if set_provider in ENV_VARS:
-        env_vars_for_provider = ENV_VARS[set_provider]
-        if isinstance(env_vars_for_provider, (list, tuple)):
-            for env_var in env_vars_for_provider:
-                key_name = env_var.get("key_name")
-                if key_name and key_name not in UNACCEPTED_ATTRIBUTES:
-                    env_value = os.environ.get(key_name)
-                    if env_value:
-                        # Map environment variable names to recognized parameters
-                        param_key = _normalize_key_name(key_name.lower())
-                        llm_params[param_key] = env_value
-                elif isinstance(env_var, dict):
-                    if env_var.get("default", False):
-                        for key, value in env_var.items():
-                            if key not in ["prompt", "key_name", "default"]:
-                                llm_params[key.lower()] = value
-                else:
-                    print(
-                        f"Expected env_var to be a dictionary, but got {type(env_var)}"
-                    )
-
-    # Remove None values
-    llm_params = {k: v for k, v in llm_params.items() if v is not None}
-
-    # Try creating the LLM
-    try:
-        new_llm = LLM(**llm_params)
-        return new_llm
-    except Exception as e:
-        print(
-            f"Error instantiating LLM from environment/fallback: {type(e).__name__}: {e}"
-        )
-        return None
-
-
-def _normalize_key_name(key_name: str) -> str:
-    """
-    Maps environment variable names to recognized litellm parameter keys,
-    using patterns from LITELLM_PARAMS.
-    """
-    for pattern in LITELLM_PARAMS:
-        if pattern in key_name:
-            return pattern
-    return key_name

src/crewai/utilities/paths.py

@@ -3,10 +3,8 @@ from pathlib import Path
 
 import appdirs
 
-"""Path management utilities for CrewAI storage and configuration."""
 
 def db_storage_path():
-    """Returns the path for database storage."""
     app_name = get_project_directory_name()
     app_author = "CrewAI"
 
@@ -16,7 +14,6 @@ def db_storage_path():
 
 
 def get_project_directory_name():
-    """Returns the current project directory name."""
     project_directory_name = os.environ.get("CREWAI_STORAGE_DIR")
 
     if project_directory_name:

src/crewai/utilities/planning_handler.py

@@ -1,4 +1,3 @@
-import logging
 from typing import Any, List, Optional
 
 from pydantic import BaseModel, Field
@@ -6,11 +5,8 @@ from pydantic import BaseModel, Field
 from crewai.agent import Agent
 from crewai.task import Task
 
-"""Handles planning and coordination of crew tasks."""
-logger = logging.getLogger(__name__)
 
 class PlanPerTask(BaseModel):
-    """Represents a plan for a specific task."""
     task: str = Field(..., description="The task for which the plan is created")
     plan: str = Field(
         ...,
@@ -19,7 +15,6 @@ class PlanPerTask(BaseModel):
 
 
 class PlannerTaskPydanticOutput(BaseModel):
-    """Output format for task planning results."""
     list_of_plans_per_task: List[PlanPerTask] = Field(
         ...,
         description="Step by step plan on how the agents can execute their tasks using the available tools with mastery",
@@ -27,7 +22,6 @@ class PlannerTaskPydanticOutput(BaseModel):
 
 
 class CrewPlanner:
-    """Plans and coordinates the execution of crew tasks."""
     def __init__(self, tasks: List[Task], planning_agent_llm: Optional[Any] = None):
         self.tasks = tasks
 
@@ -74,39 +68,19 @@ class CrewPlanner:
             output_pydantic=PlannerTaskPydanticOutput,
         )
 
-    def _get_agent_knowledge(self, task: Task) -> List[str]:
-        """
-        Safely retrieve knowledge source content from the task's agent.
-
-        Args:
-            task: The task containing an agent with potential knowledge sources
-
-        Returns:
-            List[str]: A list of knowledge source strings
-        """
-        try:
-            if task.agent and task.agent.knowledge_sources:
-                return [source.content for source in task.agent.knowledge_sources]
-        except AttributeError:
-            logger.warning("Error accessing agent knowledge sources")
-        return []
-
     def _create_tasks_summary(self) -> str:
         """Creates a summary of all tasks."""
         tasks_summary = []
         for idx, task in enumerate(self.tasks):
-            knowledge_list = self._get_agent_knowledge(task)
-            task_summary = f"""
+            tasks_summary.append(
+                f"""
                 Task Number {idx + 1} - {task.description}
                 "task_description": {task.description}
                 "task_expected_output": {task.expected_output}
                 "agent": {task.agent.role if task.agent else "None"}
                 "agent_goal": {task.agent.goal if task.agent else "None"}
                 "task_tools": {task.tools}
-                "agent_tools": %s%s""" % (
-                    f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"',
-                    f',\n                "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else ""
-                )
-
-            tasks_summary.append(task_summary)
+                "agent_tools": {task.agent.tools if task.agent else "None"}
+                """
+            )
         return " ".join(tasks_summary)

src/crewai/utilities/printer.py

@@ -1,11 +1,7 @@
-"""Utility for colored console output."""
-
 from typing import Optional
 
 
 class Printer:
-    """Handles colored console output formatting."""
-
     def print(self, content: str, color: Optional[str] = None):
         if color == "purple":
             self._print_purple(content)

src/crewai/utilities/rpm_controller.py

@@ -6,12 +6,8 @@ from pydantic import BaseModel, Field, PrivateAttr, model_validator
 
 from crewai.utilities.logger import Logger
 
-"""Controls request rate limiting for API calls."""
-
 
 class RPMController(BaseModel):
-    """Manages requests per minute limiting."""
-
     max_rpm: Optional[int] = Field(default=None)
     logger: Logger = Field(default_factory=lambda: Logger(verbose=False))
     _current_rpm: int = PrivateAttr(default=0)

src/crewai/utilities/task_output_storage_handler.py

@@ -8,10 +8,8 @@ from crewai.memory.storage.kickoff_task_outputs_storage import (
 )
 from crewai.task import Task
 
-"""Handles storage and retrieval of task execution outputs."""
 
 class ExecutionLog(BaseModel):
-    """Represents a log entry for task execution."""
     task_id: str
     expected_output: Optional[str] = None
     output: Dict[str, Any]
@@ -24,8 +22,6 @@ class ExecutionLog(BaseModel):
         return getattr(self, key)
 
 
-"""Manages storage and retrieval of task outputs."""
-
 class TaskOutputStorageHandler:
     def __init__(self) -> None:
         self.storage = KickoffTaskOutputsSQLiteStorage()

src/crewai/utilities/token_counter_callback.py

@@ -1,6 +1,3 @@
-import warnings
-from typing import Any, Dict, Optional
-
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.types.utils import Usage
 
@@ -8,26 +5,18 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
 
 
 class TokenCalcHandler(CustomLogger):
-    def __init__(self, token_cost_process: Optional[TokenProcess]):
+    def __init__(self, token_cost_process: TokenProcess):
         self.token_cost_process = token_cost_process
 
-    def log_success_event(
-        self,
-        kwargs: Dict[str, Any],
-        response_obj: Dict[str, Any],
-        start_time: float,
-        end_time: float,
-    ) -> None:
+    def log_success_event(self, kwargs, response_obj, start_time, end_time):
         if self.token_cost_process is None:
             return
 
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore", UserWarning)
-            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)
-            if usage.prompt_tokens_details:
-                self.token_cost_process.sum_cached_prompt_tokens(
-                    usage.prompt_tokens_details.cached_tokens
-                )
+        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)
+        if usage.prompt_tokens_details:
+            self.token_cost_process.sum_cached_prompt_tokens(
+                usage.prompt_tokens_details.cached_tokens
+            )

tests/agent_test.py

@@ -565,7 +565,7 @@ def test_agent_moved_on_after_max_iterations():
         task=task,
         tools=[get_final_answer],
     )
-    assert output == "42"
+    assert output == "The final answer is 42."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -574,6 +574,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
     def get_final_answer() -> float:
         """Get the final answer but don't give it yet, just re-use this
         tool non-stop."""
+        return 42
 
     agent = Agent(
         role="test role",
@@ -640,14 +641,15 @@ def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_without_max_rpm_respects_crew_rpm(capsys):
+def test_agent_without_max_rpm_respet_crew_rpm(capsys):
     from unittest.mock import patch
 
     from crewai.tools import tool
 
     @tool
     def get_final_answer() -> float:
-        """Get the final answer but don't give it yet, just re-use this tool non-stop."""
+        """Get the final answer but don't give it yet, just re-use this
+        tool non-stop."""
         return 42
 
     agent1 = Agent(
@@ -664,30 +666,23 @@ def test_agent_without_max_rpm_respects_crew_rpm(capsys):
         role="test role2",
         goal="test goal2",
         backstory="test backstory2",
-        max_iter=5,
+        max_iter=1,
         verbose=True,
         allow_delegation=False,
     )
 
     tasks = [
         Task(
-            description="Just say hi.",
-            agent=agent1,
-            expected_output="Your greeting.",
+            description="Just say hi.", agent=agent1, expected_output="Your greeting."
         ),
         Task(
-            description=(
-                "NEVER give a Final Answer, unless you are told otherwise, "
-                "instead keep using the `get_final_answer` tool non-stop, "
-                "until you must give your best final answer"
-            ),
+            description="NEVER give a Final Answer, unless you are told otherwise, instead keep using the `get_final_answer` tool non-stop, until you must give you best final answer",
             expected_output="The final answer",
             tools=[get_final_answer],
             agent=agent2,
         ),
     ]
 
-    # Set crew's max_rpm to 1 to trigger RPM limit
     crew = Crew(agents=[agent1, agent2], tasks=tasks, max_rpm=1, verbose=True)
 
     with patch.object(RPMController, "_wait_for_next_minute") as moveon:
@@ -1450,43 +1445,44 @@ def test_llm_call_with_all_attributes():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_with_ollama_llama3():
+def test_agent_with_ollama_gemma():
     agent = Agent(
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434"),
+        llm=LLM(
+            model="ollama/gemma2:latest",
+            base_url="http://localhost:8080",
+        ),
     )
 
     assert isinstance(agent.llm, LLM)
-    assert agent.llm.model == "ollama/llama3.2:3b"
-    assert agent.llm.base_url == "http://localhost:11434"
+    assert agent.llm.model == "ollama/gemma2:latest"
+    assert agent.llm.base_url == "http://localhost:8080"
 
-    task = "Respond in 20 words. Which model are you?"
+    task = "Respond in 20 words. Who are you?"
     response = agent.llm.call([{"role": "user", "content": task}])
 
     assert response
     assert len(response.split()) <= 25  # Allow a little flexibility in word count
-    assert "Llama3" in response or "AI" in response or "language model" in response
+    assert "Gemma" in response or "AI" in response or "language model" in response
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_call_with_ollama_llama3():
+def test_llm_call_with_ollama_gemma():
     llm = LLM(
-        model="ollama/llama3.2:3b",
-        base_url="http://localhost:11434",
+        model="ollama/gemma2:latest",
+        base_url="http://localhost:8080",
         temperature=0.7,
         max_tokens=30,
     )
-    messages = [
-        {"role": "user", "content": "Respond in 20 words. Which model are you?"}
-    ]
+    messages = [{"role": "user", "content": "Respond in 20 words. Who are you?"}]
 
     response = llm.call(messages)
 
     assert response
     assert len(response.split()) <= 25  # Allow a little flexibility in word count
-    assert "Llama3" in response or "AI" in response or "language model" in response
+    assert "Gemma" in response or "AI" in response or "language model" in response
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1582,7 +1578,7 @@ def test_agent_execute_task_with_ollama():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434"),
+        llm=LLM(model="ollama/gemma2:latest", base_url="http://localhost:8080"),
     )
 
     task = Task(

tests/agents/agent_builder/base_agent_test.py

@@ -7,7 +7,7 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tools.base_tool import BaseTool
 
 
-class MockAgent(BaseAgent):
+class TestAgent(BaseAgent):
     def execute_task(
         self,
         task: Any,
@@ -29,7 +29,7 @@ class MockAgent(BaseAgent):
 
 
 def test_key():
-    agent = MockAgent(
+    agent = TestAgent(
         role="test role",
         goal="test goal",
         backstory="test backstory",

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+      AA==
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '18925'
+      Content-Type:
+      - application/x-protobuf
+      User-Agent:
+      - OTel-OTLP-Exporter-Python/1.27.0
+    method: POST
+    uri: https://telemetry.crewai.com:4319/v1/traces
+  response:
+    body:
+      string: "\n\0"
+    headers:
+      Content-Length:
+      - '2'
+      Content-Type:
+      - application/x-protobuf
+      Date:
+      - Tue, 24 Sep 2024 21:57:39 GMT
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"model": "gemma2:latest", "prompt": "### User:\nRespond in 20 words. Who
+      are you?\n\n", "options": {}, "stream": false}'
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '120'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - python-requests/2.31.0
+    method: POST
+    uri: http://localhost:8080/api/generate
+  response:
+    body:
+      string: '{"model":"gemma2:latest","created_at":"2024-09-24T21:57:51.284303Z","response":"I
+        am Gemma, an open-weights AI assistant developed by Google DeepMind.  \n","done":true,"done_reason":"stop","context":[106,1645,108,6176,4926,235292,108,54657,575,235248,235284,235276,3907,235265,7702,708,692,235336,109,107,108,106,2516,108,235285,1144,137061,235269,671,2174,235290,30316,16481,20409,6990,731,6238,20555,35777,235265,139,108],"total_duration":14046647083,"load_duration":12942541833,"prompt_eval_count":25,"prompt_eval_duration":177695000,"eval_count":19,"eval_duration":923120000}'
+    headers:
+      Content-Length:
+      - '579'
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Tue, 24 Sep 2024 21:57:51 GMT
+    status:
+      code: 200
+      message: OK
+version: 1

tests/cassettes/test_agent_with_ollama_llama3.yaml

@@ -1,36 +0,0 @@
-interactions:
-- request:
-    body: '{"model": "llama3.2:3b", "prompt": "### User:\nRespond in 20 words. Who
-      which model are you?\n\n", "options": {"stop": ["\nObservation:"]}, "stream":
-      false}'
-    headers:
-      Accept:
-      - '*/*'
-      Accept-Encoding:
-      - gzip, deflate
-      Connection:
-      - keep-alive
-      Content-Length:
-      - '156'
-      Content-Type:
-      - application/json
-      User-Agent:
-      - python-requests/2.32.3
-    method: POST
-    uri: http://localhost:11434/api/generate
-  response:
-    body:
-      string: '{"model":"llama3.2:3b","created_at":"2025-01-02T20:07:07.623404Z","response":"I''m
-        an AI designed to assist and communicate with users, utilizing a combination
-        of natural language processing models.","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,2724,512,66454,304,220,508,4339,13,10699,902,1646,527,499,1980,128009,128006,78191,128007,271,40,2846,459,15592,6319,311,7945,323,19570,449,3932,11,35988,264,10824,315,5933,4221,8863,4211,13],"total_duration":1076617833,"load_duration":46505416,"prompt_eval_count":40,"prompt_eval_duration":626000000,"eval_count":22,"eval_duration":399000000}'
-    headers:
-      Content-Length:
-      - '690'
-      Content-Type:
-      - application/json; charset=utf-8
-      Date:
-      - Thu, 02 Jan 2025 20:07:07 GMT
-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_agent_without_max_rpm_respects_crew_rpm.yaml

@@ -1,353 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are test role. test backstory\nYour
-      personal goal is: test goal\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
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-      Name: Another Test Tool\nTool Arguments: {''query'': {''description'': ''Query
-      to process'', ''type'': ''str''}}\nTool Description: Another test tool\n\nUse
+      Name: Test Tool\nTool Arguments: {''query'': {''description'': ''Query to process'',
+      ''type'': ''str''}}\nTool Description: A test tool that just returns the input\n\nUse
       the following format:\n\nThought: you should always think about what to do\nAction:
-      the action to take, only one name of [Another Test Tool], just the name, exactly
-      as it''s written.\nAction Input: the input to the action, just a simple python
+      the action to take, only one name of [Test Tool], 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
@@ -143,8 +434,14 @@ interactions:
       MUST return the actual complete content as the final answer, not a summary.\n\nBegin!
       This is VERY important to you, use the tools available and give your best Final
       Answer, your job depends on it!\n\nThought:"}, {"role": "assistant", "content":
-      "Action: Another Test Tool\nAction Input: {\"query\": \"AI and AI agents\"}\nObservation:
-      Another processed: AI and AI agents"}], "model": "gpt-4o", "stop": ["\nObservation:"],
+      "I need to come up with a suitable test task that meets the criteria provided.
+      I will focus on outlining a clear and effective test task related to AI and
+      AI agents.\n\nAction: Test Tool\nAction Input: {\"query\": \"Create a test task
+      that involves evaluating the performance of an AI agent in a given scenario,
+      including criteria for success, tools required, and process for assessment.\"}\nObservation:
+      Processed: Create a test task that involves evaluating the performance of an
+      AI agent in a given scenario, including criteria for success, tools required,
+      and process for assessment."}], "model": "gpt-4o-mini", "stop": ["\nObservation:"],
       "stream": false}'
     headers:
       accept:
@@ -154,12 +451,12 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1687'
+      - '2160'
       content-type:
       - application/json
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-      - _cfuvid=mv42xOepGYaNopc5ovT9Ajamw5rJrze8tlWTik8lfrk-1736178252935-0.0.1.1-604800000;
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+      - _cfuvid=2u_Xw.i716TDjD2vb2mvMyWxhA4q1MM1JvbrA8CNZpI-1734895557894-0.0.1.1-604800000;
+        __cf_bm=i6jvNjhsDne300GPAeEmyiJJKYqy7OPuamFG_kht3KE-1734914012-1.0.1.1-tCeVANAF521vkXpBdgYw.ov.fYUr6t5QC4LG_DugWyzu4C60Pi2CruTVniUgfCvkcu6rdHA5DwnaEZf2jFaRCQ
       host:
       - api.openai.com
       user-agent:
@@ -181,27 +478,52 @@ interactions:
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.12.7
+      - 3.11.7
     method: POST
     uri: https://api.openai.com/v1/chat/completions
   response:
-    content: "{\n  \"id\": \"chatcmpl-AmjYzChV9s4D4qOJJvTvBAt3kRh7n\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1736178253,\n  \"model\": \"gpt-4o-2024-08-06\",\n
+    content: "{\n  \"id\": \"chatcmpl-AhQg0Cx1FED7gOwMpJUD5cQxVzQBK\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1734914012,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
       \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": \"Thought: I now know the final answer\\nFinal
-      Answer: Another processed: AI and AI agents\",\n        \"refusal\": null\n
-      \     },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n
-      \ ],\n  \"usage\": {\n    \"prompt_tokens\": 326,\n    \"completion_tokens\":
-      19,\n    \"total_tokens\": 345,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+      \"assistant\",\n        \"content\": \"Thought: I need to compile a complete
+      and detailed test task based on the processed input, ensuring it includes all
+      necessary components as per the expect criteria.\\n\\nFinal Answer: \\n### Test
+      Task: Evaluating the Performance of an AI Agent\\n\\n**Objective:** \\nTo evaluate
+      the performance of an AI agent in simulating conversation with users in a customer
+      service context.\\n\\n**Scenario:**\\nAn AI agent is tasked with handling customer
+      inquiries on an e-commerce platform. The agent must respond to questions about
+      product availability, order tracking, returns, and other common queries.\\n\\n**Criteria
+      for Success:**\\n1. **Accuracy:** The AI should provide correct information
+      in at least 90% of the interactions.\\n2. **Response Time:** The average response
+      time should be under 2 seconds.\\n3. **User Satisfaction:** Aim for a user satisfaction
+      score of 85% or higher based on follow-up surveys after interactions.\\n4. **Fallback
+      Rate:** The AI should not default to a human agent more than 10% of the time.\\n\\n**Tools
+      Required:**\\n- Chatbot development platform (e.g., Dialogflow, Rasa)\\n- Metrics
+      tracking software (to measure accuracy, response times, and user satisfaction)\\n-
+      Survey tool (e.g., Google Forms, SurveyMonkey) for feedback collection\\n\\n**Process
+      for Assessment:**\\n1. **Setup:** Deploy the AI agent on a testing environment
+      simulating real customer inquiries.\\n2. **Data Collection:** Run the test for
+      a predetermined period (e.g., one week) or until a set number of interactions
+      (e.g., 1000).\\n3. **Measurement:**\\n   - Record the interactions and analyze
+      the accuracy of the AI's responses.\\n   - Measure the average response time
+      for each interaction.\\n   - Collect user satisfaction scores via surveys sent
+      after the interaction.\\n4. **Analysis:** Compile the data to see if the AI
+      met the success criteria. Identify strengths and weaknesses in the responses.\\n5.
+      **Review:** Share findings with the development team to strategize improvements.\\n\\nThis
+      detailed task will help assess the AI agent\u2019s capabilities and provide
+      insights for further enhancements.\",\n        \"refusal\": null\n      },\n
+      \     \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n
+      \ \"usage\": {\n    \"prompt_tokens\": 416,\n    \"completion_tokens\": 422,\n
+      \   \"total_tokens\": 838,\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_5f20662549\"\n}\n"
+      \"fp_d02d531b47\"\n}\n"
     headers:
       CF-Cache-Status:
       - DYNAMIC
       CF-RAY:
-      - 8fdcd4011938bf66-ATL
+      - 8f6442c2ba15a486-GRU
       Connection:
       - keep-alive
       Content-Encoding:
@@ -209,7 +531,7 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Mon, 06 Jan 2025 15:44:15 GMT
+      - Mon, 23 Dec 2024 00:33:39 GMT
       Server:
       - cloudflare
       Transfer-Encoding:
@@ -223,25 +545,25 @@ interactions:
       openai-organization:
       - crewai-iuxna1
       openai-processing-ms:
-      - '2488'
+      - '6734'
       openai-version:
       - '2020-10-01'
       strict-transport-security:
       - max-age=31536000; includeSubDomains; preload
       x-ratelimit-limit-requests:
-      - '10000'
+      - '30000'
       x-ratelimit-limit-tokens:
-      - '30000000'
+      - '150000000'
       x-ratelimit-remaining-requests:
-      - '9999'
+      - '29999'
       x-ratelimit-remaining-tokens:
-      - '29999613'
+      - '149999497'
       x-ratelimit-reset-requests:
-      - 6ms
+      - 2ms
       x-ratelimit-reset-tokens:
       - 0s
       x-request-id:
-      - req_5e3a1a90ef91ff4f12d5b84e396beccc
+      - req_7d8df8b840e279bd64280d161d854161
     http_version: HTTP/1.1
     status_code: 200
 version: 1

tests/cli/deploy/test_deploy_main.py

@@ -177,12 +177,12 @@ class TestDeployCommand(unittest.TestCase):
     def test_get_crew_status(self):
         mock_response = MagicMock()
         mock_response.status_code = 200
-        mock_response.json.return_value = {"name": "InternalCrew", "status": "active"}
+        mock_response.json.return_value = {"name": "TestCrew", "status": "active"}
         self.mock_client.crew_status_by_name.return_value = mock_response
 
         with patch("sys.stdout", new=StringIO()) as fake_out:
             self.deploy_command.get_crew_status()
-            self.assertIn("InternalCrew", fake_out.getvalue())
+            self.assertIn("TestCrew", fake_out.getvalue())
             self.assertIn("active", fake_out.getvalue())
 
     def test_get_crew_logs(self):

tests/cli/tools/test_main.py

@@ -28,10 +28,9 @@ def test_create_success(mock_subprocess):
     with in_temp_dir():
         tool_command = ToolCommand()
 
-        with (
-            patch.object(tool_command, "login") as mock_login,
-            patch("sys.stdout", new=StringIO()) as fake_out,
-        ):
+        with patch.object(tool_command, "login") as mock_login, patch(
+            "sys.stdout", new=StringIO()
+        ) as fake_out:
             tool_command.create("test-tool")
             output = fake_out.getvalue()
 
@@ -83,7 +82,7 @@ def test_install_success(mock_get, mock_subprocess_run):
         capture_output=False,
         text=True,
         check=True,
-        env=unittest.mock.ANY,
+        env=unittest.mock.ANY
     )
 
     assert "Successfully installed sample-tool" in output

tests/crew_test.py

@@ -333,16 +333,16 @@ def test_manager_agent_delegating_to_assigned_task_agent():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
     # which sets the output attribute of the task
     task.output = mock_task_output
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Verify execute_sync was called once
@@ -350,20 +350,12 @@ def test_manager_agent_delegating_to_assigned_task_agent():
 
         # Get the tools argument from the call
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
+        tools = kwargs['tools']
 
         # Verify the delegation tools were passed correctly
         assert len(tools) == 2
-        assert any(
-            "Delegate a specific task to one of the following coworkers: Researcher"
-            in tool.description
-            for tool in tools
-        )
-        assert any(
-            "Ask a specific question to one of the following coworkers: Researcher"
-            in tool.description
-            for tool in tools
-        )
+        assert any("Delegate a specific task to one of the following coworkers: Researcher" in tool.description for tool in tools)
+        assert any("Ask a specific question to one of the following coworkers: Researcher" in tool.description for tool in tools)
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -412,7 +404,7 @@ def test_manager_agent_delegates_with_varied_role_cases():
         backstory="A researcher with spaces in role name",
         allow_delegation=False,
     )
-
+    
     writer_caps = Agent(
         role="SENIOR WRITER",  # All caps
         goal="Write with caps in role",
@@ -434,13 +426,13 @@ def test_manager_agent_delegates_with_varied_role_cases():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
     task.output = mock_task_output
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Verify execute_sync was called once
@@ -448,32 +440,20 @@ def test_manager_agent_delegates_with_varied_role_cases():
 
         # Get the tools argument from the call
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
+        tools = kwargs['tools']
 
         # Verify the delegation tools were passed correctly and can handle case/whitespace variations
         assert len(tools) == 2
-
+        
         # Check delegation tool descriptions (should work despite case/whitespace differences)
         delegation_tool = tools[0]
         question_tool = tools[1]
-
-        assert (
-            "Delegate a specific task to one of the following coworkers:"
-            in delegation_tool.description
-        )
-        assert (
-            " Researcher " in delegation_tool.description
-            or "SENIOR WRITER" in delegation_tool.description
-        )
-
-        assert (
-            "Ask a specific question to one of the following coworkers:"
-            in question_tool.description
-        )
-        assert (
-            " Researcher " in question_tool.description
-            or "SENIOR WRITER" in question_tool.description
-        )
+        
+        assert "Delegate a specific task to one of the following coworkers:" in delegation_tool.description
+        assert " Researcher " in delegation_tool.description or "SENIOR WRITER" in delegation_tool.description
+        
+        assert "Ask a specific question to one of the following coworkers:" in question_tool.description
+        assert " Researcher " in question_tool.description or "SENIOR WRITER" in question_tool.description
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -499,7 +479,6 @@ def test_crew_with_delegating_agents():
         == "In the rapidly evolving landscape of technology, AI agents have emerged as formidable tools, revolutionizing how we interact with data and automate tasks. These sophisticated systems leverage machine learning and natural language processing to perform a myriad of functions, from virtual personal assistants to complex decision-making companions in industries such as finance, healthcare, and education. By mimicking human intelligence, AI agents can analyze massive data sets at unparalleled speeds, enabling businesses to uncover valuable insights, enhance productivity, and elevate user experiences to unprecedented levels.\n\nOne of the most striking aspects of AI agents is their adaptability; they learn from their interactions and continuously improve their performance over time. This feature is particularly valuable in customer service where AI agents can address inquiries, resolve issues, and provide personalized recommendations without the limitations of human fatigue. Moreover, with intuitive interfaces, AI agents enhance user interactions, making technology more accessible and user-friendly, thereby breaking down barriers that have historically hindered digital engagement.\n\nDespite their immense potential, the deployment of AI agents raises important ethical and practical considerations. Issues related to privacy, data security, and the potential for job displacement necessitate thoughtful dialogue and proactive measures. Striking a balance between technological innovation and societal impact will be crucial as organizations integrate these agents into their operations. Additionally, ensuring transparency in AI decision-making processes is vital to maintain public trust as AI agents become an integral part of daily life.\n\nLooking ahead, the future of AI agents appears bright, with ongoing advancements promising even greater capabilities. As we continue to harness the power of AI, we can expect these agents to play a transformative role in shaping various sectors—streamlining workflows, enabling smarter decision-making, and fostering more personalized experiences. Embracing this technology responsibly can lead to a future where AI agents not only augment human effort but also inspire creativity and efficiency across the board, ultimately redefining our interaction with the digital world."
     )
 
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_with_delegating_agents_should_not_override_task_tools():
     from typing import Type
@@ -510,7 +489,6 @@ def test_crew_with_delegating_agents_should_not_override_task_tools():
 
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
-
         query: str = Field(..., description="Query to process")
 
     class TestTool(BaseTool):
@@ -538,29 +516,24 @@ def test_crew_with_delegating_agents_should_not_override_task_tools():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
     # which sets the output attribute of the task
     tasks[0].output = mock_task_output
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Execute the task and verify both tools are present
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
-
-        assert any(
-            isinstance(tool, TestTool) for tool in tools
-        ), "TestTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in tools
-        ), "Delegation tool should be present"
+        tools = kwargs['tools']
 
+        assert any(isinstance(tool, TestTool) for tool in tools), "TestTool should be present"
+        assert any("delegate" in tool.name.lower() for tool in tools), "Delegation tool should be present"
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_with_delegating_agents_should_not_override_agent_tools():
@@ -572,7 +545,6 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():
 
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
-
         query: str = Field(..., description="Query to process")
 
     class TestTool(BaseTool):
@@ -591,7 +563,7 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():
         Task(
             description="Produce and amazing 1 paragraph draft of an article about AI Agents.",
             expected_output="A 4 paragraph article about AI.",
-            agent=new_ceo,
+            agent=new_ceo
         )
     ]
 
@@ -602,29 +574,24 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
     # which sets the output attribute of the task
     tasks[0].output = mock_task_output
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Execute the task and verify both tools are present
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
-
-        assert any(
-            isinstance(tool, TestTool) for tool in new_ceo.tools
-        ), "TestTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in tools
-        ), "Delegation tool should be present"
+        tools = kwargs['tools']
 
+        assert any(isinstance(tool, TestTool) for tool in new_ceo.tools), "TestTool should be present"
+        assert any("delegate" in tool.name.lower() for tool in tools), "Delegation tool should be present"
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_task_tools_override_agent_tools():
@@ -636,7 +603,6 @@ def test_task_tools_override_agent_tools():
 
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
-
         query: str = Field(..., description="Query to process")
 
     class TestTool(BaseTool):
@@ -664,10 +630,14 @@ def test_task_tools_override_agent_tools():
         description="Write a test task",
         expected_output="Test output",
         agent=new_researcher,
-        tools=[AnotherTestTool()],
+        tools=[AnotherTestTool()]
     )
 
-    crew = Crew(agents=[new_researcher], tasks=[task], process=Process.sequential)
+    crew = Crew(
+        agents=[new_researcher],
+        tasks=[task],
+        process=Process.sequential
+    )
 
     crew.kickoff()
 
@@ -680,7 +650,6 @@ def test_task_tools_override_agent_tools():
     assert len(new_researcher.tools) == 1
     assert isinstance(new_researcher.tools[0], TestTool)
 
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_task_tools_override_agent_tools_with_allow_delegation():
     """
@@ -733,13 +702,13 @@ def test_task_tools_override_agent_tools_with_allow_delegation():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # We mock execute_sync to verify which tools get used at runtime
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Inspect the call kwargs to verify the actual tools passed to execution
@@ -747,23 +716,16 @@ def test_task_tools_override_agent_tools_with_allow_delegation():
         used_tools = kwargs["tools"]
 
         # Confirm AnotherTestTool is present but TestTool is not
-        assert any(
-            isinstance(tool, AnotherTestTool) for tool in used_tools
-        ), "AnotherTestTool should be present"
-        assert not any(
-            isinstance(tool, TestTool) for tool in used_tools
-        ), "TestTool should not be present among used tools"
+        assert any(isinstance(tool, AnotherTestTool) for tool in used_tools), "AnotherTestTool should be present"
+        assert not any(isinstance(tool, TestTool) for tool in used_tools), "TestTool should not be present among used tools"
 
         # Confirm delegation tool(s) are present
-        assert any(
-            "delegate" in tool.name.lower() for tool in used_tools
-        ), "Delegation tool should be present"
+        assert any("delegate" in tool.name.lower() for tool in used_tools), "Delegation tool should be present"
 
     # Finally, make sure the agent's original tools remain unchanged
     assert len(researcher_with_delegation.tools) == 1
     assert isinstance(researcher_with_delegation.tools[0], TestTool)
 
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_verbose_output(capsys):
     tasks = [
@@ -1050,8 +1012,8 @@ def test_three_task_with_async_execution():
     )
 
 
-@pytest.mark.asyncio
 @pytest.mark.vcr(filter_headers=["authorization"])
+@pytest.mark.asyncio
 async def test_crew_async_kickoff():
     inputs = [
         {"topic": "dog"},
@@ -1098,9 +1060,8 @@ async def test_crew_async_kickoff():
             assert result[0].token_usage.successful_requests > 0  # type: ignore
 
 
-@pytest.mark.asyncio
 @pytest.mark.vcr(filter_headers=["authorization"])
-async def test_async_task_execution_call_count():
+def test_async_task_execution_call_count():
     from unittest.mock import MagicMock, patch
 
     list_ideas = Task(
@@ -1227,6 +1188,7 @@ def test_kickoff_for_each_empty_input():
     assert results == []
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_kickoff_for_each_invalid_input():
     """Tests if kickoff_for_each raises TypeError for invalid input types."""
 
@@ -1249,6 +1211,7 @@ def test_kickoff_for_each_invalid_input():
         crew.kickoff_for_each("invalid input")
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_kickoff_for_each_error_handling():
     """Tests error handling in kickoff_for_each when kickoff raises an error."""
     from unittest.mock import patch
@@ -1285,6 +1248,7 @@ def test_kickoff_for_each_error_handling():
             crew.kickoff_for_each(inputs=inputs)
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.mark.asyncio
 async def test_kickoff_async_basic_functionality_and_output():
     """Tests the basic functionality and output of kickoff_async."""
@@ -1319,6 +1283,7 @@ async def test_kickoff_async_basic_functionality_and_output():
         mock_kickoff.assert_called_once_with(inputs)
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.mark.asyncio
 async def test_async_kickoff_for_each_async_basic_functionality_and_output():
     """Tests the basic functionality and output of kickoff_for_each_async."""
@@ -1365,6 +1330,7 @@ async def test_async_kickoff_for_each_async_basic_functionality_and_output():
             mock_kickoff_async.assert_any_call(inputs=input_data)
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.mark.asyncio
 async def test_async_kickoff_for_each_async_empty_input():
     """Tests if akickoff_for_each_async handles an empty input list."""
@@ -1548,12 +1514,12 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
     crew = Crew(agents=[programmer], tasks=[task])
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Get the tools that were actually used in execution
@@ -1562,10 +1528,7 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
 
         # Verify that exactly one tool was used and it was a CodeInterpreterTool
         assert len(used_tools) == 1, "Should have exactly one tool"
-        assert isinstance(
-            used_tools[0], CodeInterpreterTool
-        ), "Tool should be CodeInterpreterTool"
-
+        assert isinstance(used_tools[0], CodeInterpreterTool), "Tool should be CodeInterpreterTool"
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_delegation_is_not_enabled_if_there_are_only_one_agent():
@@ -1676,16 +1639,16 @@ def test_hierarchical_crew_creation_tasks_with_agents():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
     # which sets the output attribute of the task
     task.output = mock_task_output
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Verify execute_sync was called once
@@ -1693,20 +1656,12 @@ def test_hierarchical_crew_creation_tasks_with_agents():
 
         # Get the tools argument from the call
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
+        tools = kwargs['tools']
 
         # Verify the delegation tools were passed correctly
         assert len(tools) == 2
-        assert any(
-            "Delegate a specific task to one of the following coworkers: Senior Writer"
-            in tool.description
-            for tool in tools
-        )
-        assert any(
-            "Ask a specific question to one of the following coworkers: Senior Writer"
-            in tool.description
-            for tool in tools
-        )
+        assert any("Delegate a specific task to one of the following coworkers: Senior Writer" in tool.description for tool in tools)
+        assert any("Ask a specific question to one of the following coworkers: Senior Writer" in tool.description for tool in tools)
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1729,7 +1684,9 @@ def test_hierarchical_crew_creation_tasks_with_async_execution():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Create a mock Future that returns our TaskOutput
@@ -1740,9 +1697,7 @@ def test_hierarchical_crew_creation_tasks_with_async_execution():
     # which sets the output attribute of the task
     task.output = mock_task_output
 
-    with patch.object(
-        Task, "execute_async", return_value=mock_future
-    ) as mock_execute_async:
+    with patch.object(Task, 'execute_async', return_value=mock_future) as mock_execute_async:
         crew.kickoff()
 
         # Verify execute_async was called once
@@ -1750,20 +1705,12 @@ def test_hierarchical_crew_creation_tasks_with_async_execution():
 
         # Get the tools argument from the call
         _, kwargs = mock_execute_async.call_args
-        tools = kwargs["tools"]
+        tools = kwargs['tools']
 
         # Verify the delegation tools were passed correctly
         assert len(tools) == 2
-        assert any(
-            "Delegate a specific task to one of the following coworkers: Senior Writer\n"
-            in tool.description
-            for tool in tools
-        )
-        assert any(
-            "Ask a specific question to one of the following coworkers: Senior Writer\n"
-            in tool.description
-            for tool in tools
-        )
+        assert any("Delegate a specific task to one of the following coworkers: Senior Writer\n" in tool.description for tool in tools)
+        assert any("Ask a specific question to one of the following coworkers: Senior Writer\n" in tool.description for tool in tools)
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1846,9 +1793,7 @@ def test_crew_inputs_interpolate_both_agents_and_tasks_diff():
             Agent, "interpolate_inputs", wraps=agent.interpolate_inputs
         ) as interpolate_agent_inputs:
             with patch.object(
-                Task,
-                "interpolate_inputs_and_add_conversation_history",
-                wraps=task.interpolate_inputs_and_add_conversation_history,
+                Task, "interpolate_inputs", wraps=task.interpolate_inputs
             ) as interpolate_task_inputs:
                 execute.return_value = "ok"
                 crew.kickoff(inputs={"topic": "AI", "points": 5})
@@ -1875,9 +1820,7 @@ def test_crew_does_not_interpolate_without_inputs():
     crew = Crew(agents=[agent], tasks=[task])
 
     with patch.object(Agent, "interpolate_inputs") as interpolate_agent_inputs:
-        with patch.object(
-            Task, "interpolate_inputs_and_add_conversation_history"
-        ) as interpolate_task_inputs:
+        with patch.object(Task, "interpolate_inputs") as interpolate_task_inputs:
             crew.kickoff()
             interpolate_agent_inputs.assert_not_called()
             interpolate_task_inputs.assert_not_called()
@@ -2096,6 +2039,7 @@ def test_crew_output_file_end_to_end(tmp_path):
     assert expected_file.exists(), f"Output file {expected_file} was not created"
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_output_file_validation_failures():
     """Test output file validation failures in a crew context."""
     agent = Agent(
@@ -2111,7 +2055,7 @@ def test_crew_output_file_validation_failures():
             description="Analyze data",
             expected_output="Analysis results",
             agent=agent,
-            output_file="../output.txt",
+            output_file="../output.txt"
         )
         Crew(agents=[agent], tasks=[task]).kickoff()
 
@@ -2121,7 +2065,7 @@ def test_crew_output_file_validation_failures():
             description="Analyze data",
             expected_output="Analysis results",
             agent=agent,
-            output_file="output.txt | rm -rf /",
+            output_file="output.txt | rm -rf /"
         )
         Crew(agents=[agent], tasks=[task]).kickoff()
 
@@ -2131,7 +2075,7 @@ def test_crew_output_file_validation_failures():
             description="Analyze data",
             expected_output="Analysis results",
             agent=agent,
-            output_file="~/output.txt",
+            output_file="~/output.txt"
         )
         Crew(agents=[agent], tasks=[task]).kickoff()
 
@@ -2141,11 +2085,12 @@ def test_crew_output_file_validation_failures():
             description="Analyze data",
             expected_output="Analysis results",
             agent=agent,
-            output_file="{invalid-name}/output.txt",
+            output_file="{invalid-name}/output.txt"
         )
         Crew(agents=[agent], tasks=[task]).kickoff()
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_manager_agent():
     from unittest.mock import patch
 
@@ -3091,29 +3036,6 @@ def test_hierarchical_verbose_false_manager_agent():
     assert not crew.manager_agent.verbose
 
 
-def test_fetch_inputs():
-    agent = Agent(
-        role="{role_detail} Researcher",
-        goal="Research on {topic}.",
-        backstory="Expert in {field}.",
-    )
-
-    task = Task(
-        description="Analyze the data on {topic}.",
-        expected_output="Summary of {topic} analysis.",
-        agent=agent,
-    )
-
-    crew = Crew(agents=[agent], tasks=[task])
-
-    expected_placeholders = {"role_detail", "topic", "field"}
-    actual_placeholders = crew.fetch_inputs()
-
-    assert (
-        actual_placeholders == expected_placeholders
-    ), f"Expected {expected_placeholders}, but got {actual_placeholders}"
-
-
 def test_task_tools_preserve_code_execution_tools():
     """
     Test that task tools don't override code execution tools when allow_code_execution=True
@@ -3127,7 +3049,6 @@ def test_task_tools_preserve_code_execution_tools():
 
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
-
         query: str = Field(..., description="Query to process")
 
     class TestTool(BaseTool):
@@ -3161,7 +3082,7 @@ def test_task_tools_preserve_code_execution_tools():
         description="Write a program to calculate fibonacci numbers.",
         expected_output="A working fibonacci calculator.",
         agent=programmer,
-        tools=[TestTool()],
+        tools=[TestTool()]
     )
 
     crew = Crew(
@@ -3171,12 +3092,12 @@ def test_task_tools_preserve_code_execution_tools():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Get the tools that were actually used in execution
@@ -3184,21 +3105,12 @@ def test_task_tools_preserve_code_execution_tools():
         used_tools = kwargs["tools"]
 
         # Verify all expected tools are present
-        assert any(
-            isinstance(tool, TestTool) for tool in used_tools
-        ), "Task's TestTool should be present"
-        assert any(
-            isinstance(tool, CodeInterpreterTool) for tool in used_tools
-        ), "CodeInterpreterTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in used_tools
-        ), "Delegation tool should be present"
+        assert any(isinstance(tool, TestTool) for tool in used_tools), "Task's TestTool should be present"
+        assert any(isinstance(tool, CodeInterpreterTool) for tool in used_tools), "CodeInterpreterTool should be present"
+        assert any("delegate" in tool.name.lower() for tool in used_tools), "Delegation tool should be present"
 
         # Verify the total number of tools (TestTool + CodeInterpreter + 2 delegation tools)
-        assert (
-            len(used_tools) == 4
-        ), "Should have TestTool, CodeInterpreter, and 2 delegation tools"
-
+        assert len(used_tools) == 4, "Should have TestTool, CodeInterpreter, and 2 delegation tools"
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_multimodal_flag_adds_multimodal_tools():
@@ -3227,13 +3139,13 @@ def test_multimodal_flag_adds_multimodal_tools():
     crew = Crew(agents=[multimodal_agent], tasks=[task], process=Process.sequential)
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Mock execute_sync to verify the tools passed at runtime
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Get the tools that were actually used in execution
@@ -3241,14 +3153,13 @@ def test_multimodal_flag_adds_multimodal_tools():
         used_tools = kwargs["tools"]
 
         # Check that the multimodal tool was added
-        assert any(
-            isinstance(tool, AddImageTool) for tool in used_tools
-        ), "AddImageTool should be present when agent is multimodal"
+        assert any(isinstance(tool, AddImageTool) for tool in used_tools), (
+            "AddImageTool should be present when agent is multimodal"
+        )
 
         # Verify we have exactly one tool (just the AddImageTool)
         assert len(used_tools) == 1, "Should only have the AddImageTool"
 
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_multimodal_agent_image_tool_handling():
     """
@@ -3290,10 +3201,10 @@ def test_multimodal_agent_image_tool_handling():
     mock_task_output = TaskOutput(
         description="Mock description",
         raw="A detailed analysis of the image",
-        agent="Image Analyst",
+        agent="Image Analyst"
     )
 
-    with patch.object(Task, "execute_sync") as mock_execute_sync:
+    with patch.object(Task, 'execute_sync') as mock_execute_sync:
         # Set up the mock to return our task output
         mock_execute_sync.return_value = mock_task_output
 
@@ -3302,7 +3213,7 @@ def test_multimodal_agent_image_tool_handling():
 
         # Get the tools that were passed to execute_sync
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
+        tools = kwargs['tools']
 
         # Verify the AddImageTool is present and properly configured
         image_tools = [tool for tool in tools if tool.name == "Add image to content"]
@@ -3312,7 +3223,7 @@ def test_multimodal_agent_image_tool_handling():
         image_tool = image_tools[0]
         result = image_tool._run(
             image_url="https://example.com/test-image.jpg",
-            action="Please analyze this image",
+            action="Please analyze this image"
         )
 
         # Verify the tool returns the expected format
@@ -3322,7 +3233,6 @@ def test_multimodal_agent_image_tool_handling():
         assert result["content"][0]["type"] == "text"
         assert result["content"][1]["type"] == "image_url"
 
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_multimodal_agent_live_image_analysis():
     """
@@ -3336,7 +3246,7 @@ def test_multimodal_agent_live_image_analysis():
         allow_delegation=False,
         multimodal=True,
         verbose=True,
-        llm="gpt-4o",
+        llm="gpt-4o"
     )
 
     # Create a task for image analysis
@@ -3347,134 +3257,21 @@ def test_multimodal_agent_live_image_analysis():
         Image: {image_url}
         """,
         expected_output="A comprehensive description of the image contents.",
-        agent=image_analyst,
+        agent=image_analyst
     )
 
     # Create and run the crew
-    crew = Crew(agents=[image_analyst], tasks=[analyze_image])
+    crew = Crew(
+        agents=[image_analyst],
+        tasks=[analyze_image]
+    )
 
     # Execute with an image URL
-    result = crew.kickoff(
-        inputs={
-            "image_url": "https://media.istockphoto.com/id/946087016/photo/aerial-view-of-lower-manhattan-new-york.jpg?s=612x612&w=0&k=20&c=viLiMRznQ8v5LzKTt_LvtfPFUVl1oiyiemVdSlm29_k="
-        }
-    )
+    result = crew.kickoff(inputs={
+        "image_url": "https://media.istockphoto.com/id/946087016/photo/aerial-view-of-lower-manhattan-new-york.jpg?s=612x612&w=0&k=20&c=viLiMRznQ8v5LzKTt_LvtfPFUVl1oiyiemVdSlm29_k="
+    })
 
     # Verify we got a meaningful response
     assert isinstance(result.raw, str)
     assert len(result.raw) > 100  # Expecting a detailed analysis
     assert "error" not in result.raw.lower()  # No error messages in response
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_with_failing_task_guardrails():
-    """Test that crew properly handles failing guardrails and retries with validation feedback."""
-
-    def strict_format_guardrail(result: TaskOutput):
-        """Validates that the output follows a strict format:
-        - Must start with 'REPORT:'
-        - Must end with 'END REPORT'
-        """
-        content = result.raw.strip()
-
-        if not ("REPORT:" in content or "**REPORT:**" in content):
-            return (
-                False,
-                "Output must start with 'REPORT:' no formatting, just the word REPORT",
-            )
-
-        if not ("END REPORT" in content or "**END REPORT**" in content):
-            return (
-                False,
-                "Output must end with 'END REPORT' no formatting, just the word END REPORT",
-            )
-
-        return (True, content)
-
-    researcher = Agent(
-        role="Report Writer",
-        goal="Create properly formatted reports",
-        backstory="You're an expert at writing structured reports.",
-    )
-
-    task = Task(
-        description="""Write a report about AI with exactly 3 key points.""",
-        expected_output="A properly formatted report",
-        agent=researcher,
-        guardrail=strict_format_guardrail,
-        max_retries=3,
-    )
-
-    crew = Crew(
-        agents=[researcher],
-        tasks=[task],
-    )
-
-    result = crew.kickoff()
-
-    # Verify the final output meets all format requirements
-    content = result.raw.strip()
-    assert content.startswith("REPORT:"), "Output should start with 'REPORT:'"
-    assert content.endswith("END REPORT"), "Output should end with 'END REPORT'"
-
-    # Verify task output
-    task_output = result.tasks_output[0]
-    assert isinstance(task_output, TaskOutput)
-    assert task_output.raw == result.raw
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_guardrail_feedback_in_context():
-    """Test that guardrail feedback is properly appended to task context for retries."""
-
-    def format_guardrail(result: TaskOutput):
-        """Validates that the output contains a specific keyword."""
-        if "IMPORTANT" not in result.raw:
-            return (False, "Output must contain the keyword 'IMPORTANT'")
-        return (True, result.raw)
-
-    # Create execution contexts list to track contexts
-    execution_contexts = []
-
-    researcher = Agent(
-        role="Writer",
-        goal="Write content with specific keywords",
-        backstory="You're an expert at following specific writing requirements.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Write a short response.",
-        expected_output="A response containing the keyword 'IMPORTANT'",
-        agent=researcher,
-        guardrail=format_guardrail,
-        max_retries=2,
-    )
-
-    crew = Crew(agents=[researcher], tasks=[task])
-
-    with patch.object(Agent, "execute_task") as mock_execute_task:
-        # Define side_effect to capture context and return different responses
-        def side_effect(task, context=None, tools=None):
-            execution_contexts.append(context if context else "")
-            if len(execution_contexts) == 1:
-                return "This is a test response"
-            return "This is an IMPORTANT test response"
-
-        mock_execute_task.side_effect = side_effect
-
-        result = crew.kickoff()
-
-    # Verify that we had multiple executions
-    assert len(execution_contexts) > 1, "Task should have been executed multiple times"
-
-    # Verify that the second execution included the guardrail feedback
-    assert (
-        "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1]
-    ), "Guardrail feedback should be included in retry context"
-
-    # Verify final output meets guardrail requirements
-    assert "IMPORTANT" in result.raw, "Final output should contain required keyword"
-
-    # Verify task retry count
-    assert task.retry_count == 1, "Task should have been retried once"

tests/e2e_crew_tests.py

@@ -1,289 +0,0 @@
-import asyncio
-import os
-import tempfile
-
-import pytest
-
-from crewai.agent import Agent
-from crewai.crew import Crew
-from crewai.crews.crew_output import CrewOutput
-from crewai.process import Process
-from crewai.task import Task
-from crewai.tasks.conditional_task import ConditionalTask
-
-
-def test_basic_crew_execution(default_agent):
-    """Test basic crew execution using the default agent fixture."""
-
-    # Initialize agents by copying the default agent fixture
-    researcher = default_agent.copy()
-    researcher.role = "Researcher"
-    researcher.goal = "Research the latest advancements in AI."
-    researcher.backstory = "An expert in AI technologies."
-
-    writer = default_agent.copy()
-    writer.role = "Writer"
-    writer.goal = "Write an article based on research findings."
-    writer.backstory = "A professional writer specializing in technology topics."
-
-    # Define tasks
-    research_task = Task(
-        description="Provide a summary of the latest advancements in AI.",
-        expected_output="A detailed summary of recent AI advancements.",
-        agent=researcher,
-    )
-
-    writing_task = Task(
-        description="Write an article based on the research summary.",
-        expected_output="An engaging article on AI advancements.",
-        agent=writer,
-    )
-
-    # Create the crew
-    crew = Crew(
-        agents=[researcher, writer],
-        tasks=[research_task, writing_task],
-        process=Process.sequential,
-    )
-
-    # Execute the crew
-    result = crew.kickoff()
-
-    # Assertions to verify the result
-    assert result is not None, "Crew execution did not return a result."
-    assert isinstance(result, CrewOutput), "Result is not an instance of CrewOutput."
-    assert (
-        "AI advancements" in result.raw
-        or "artificial intelligence" in result.raw.lower()
-    ), "Result does not contain expected content."
-
-
-def test_hierarchical_crew_with_manager(default_llm_config):
-    """Test hierarchical crew execution with a manager agent."""
-
-    # Initialize agents using the default LLM config fixture
-    ceo = Agent(
-        role="CEO",
-        goal="Oversee the project and ensure quality deliverables.",
-        backstory="A seasoned executive with a keen eye for detail.",
-        llm=default_llm_config,
-    )
-
-    developer = Agent(
-        role="Developer",
-        goal="Implement software features as per requirements.",
-        backstory="An experienced software developer.",
-        llm=default_llm_config,
-    )
-
-    tester = Agent(
-        role="Tester",
-        goal="Test software features and report bugs.",
-        backstory="A meticulous QA engineer.",
-        llm=default_llm_config,
-    )
-
-    # Define tasks
-    development_task = Task(
-        description="Develop the new authentication feature.",
-        expected_output="Code implementation of the authentication feature.",
-        agent=developer,
-    )
-
-    testing_task = Task(
-        description="Test the authentication feature for vulnerabilities.",
-        expected_output="A report on any found bugs or vulnerabilities.",
-        agent=tester,
-    )
-
-    # Create the crew with hierarchical process
-    crew = Crew(
-        agents=[ceo, developer, tester],
-        tasks=[development_task, testing_task],
-        process=Process.hierarchical,
-        manager_agent=ceo,
-    )
-
-    # Execute the crew
-    result = crew.kickoff()
-
-    # Assertions to verify the result
-    assert result is not None, "Crew execution did not return a result."
-    assert isinstance(result, CrewOutput), "Result is not an instance of CrewOutput."
-    assert (
-        "authentication" in result.raw.lower()
-    ), "Result does not contain expected content."
-
-
-@pytest.mark.asyncio
-async def test_asynchronous_task_execution(default_llm_config):
-    """Test crew execution with asynchronous tasks."""
-
-    # Initialize agent
-    data_processor = Agent(
-        role="Data Processor",
-        goal="Process large datasets efficiently.",
-        backstory="An expert in data processing and analysis.",
-        llm=default_llm_config,
-    )
-
-    # Define tasks with async_execution=True
-    async_task1 = Task(
-        description="Process dataset A asynchronously.",
-        expected_output="Processed results of dataset A.",
-        agent=data_processor,
-        async_execution=True,
-    )
-
-    async_task2 = Task(
-        description="Process dataset B asynchronously.",
-        expected_output="Processed results of dataset B.",
-        agent=data_processor,
-        async_execution=True,
-    )
-
-    # Create the crew
-    crew = Crew(
-        agents=[data_processor],
-        tasks=[async_task1, async_task2],
-        process=Process.sequential,
-    )
-
-    # Execute the crew asynchronously
-    result = await crew.kickoff_async()
-
-    # Assertions to verify the result
-    assert result is not None, "Crew execution did not return a result."
-    assert isinstance(result, CrewOutput), "Result is not an instance of CrewOutput."
-    assert (
-        "dataset a" in result.raw.lower() or "dataset b" in result.raw.lower()
-    ), "Result does not contain expected content."
-
-
-def test_crew_with_conditional_task(default_llm_config):
-    """Test crew execution that includes a conditional task."""
-
-    # Initialize agents
-    analyst = Agent(
-        role="Analyst",
-        goal="Analyze data and make decisions based on insights.",
-        backstory="A data analyst with experience in predictive modeling.",
-        llm=default_llm_config,
-    )
-
-    decision_maker = Agent(
-        role="Decision Maker",
-        goal="Make decisions based on analysis.",
-        backstory="An executive responsible for strategic decisions.",
-        llm=default_llm_config,
-    )
-
-    # Define tasks
-    analysis_task = Task(
-        description="Analyze the quarterly financial data.",
-        expected_output="A report highlighting key financial insights.",
-        agent=analyst,
-    )
-
-    decision_task = ConditionalTask(
-        description="If the profit margin is below 10%, recommend cost-cutting measures.",
-        expected_output="Recommendations for reducing costs.",
-        agent=decision_maker,
-        condition=lambda output: "profit margin below 10%" in output.lower(),
-    )
-
-    # Create the crew
-    crew = Crew(
-        agents=[analyst, decision_maker],
-        tasks=[analysis_task, decision_task],
-        process=Process.sequential,
-    )
-
-    # Execute the crew
-    result = crew.kickoff()
-
-    # Assertions to verify the result
-    assert result is not None, "Crew execution did not return a result."
-    assert isinstance(result, CrewOutput), "Result is not an instance of CrewOutput."
-    assert len(result.tasks_output) >= 1, "No tasks were executed."
-
-
-def test_crew_with_output_file():
-    """Test crew execution that writes output to a file."""
-
-    # Access the API key from environment variables
-    openai_api_key = os.environ.get("OPENAI_API_KEY")
-    assert openai_api_key, "OPENAI_API_KEY environment variable is not set."
-
-    # Create a temporary directory for output files
-    with tempfile.TemporaryDirectory() as tmpdirname:
-
-        # Initialize agent
-        content_creator = Agent(
-            role="Content Creator",
-            goal="Generate engaging blog content.",
-            backstory="A creative writer with a passion for storytelling.",
-            llm={"provider": "openai", "model": "gpt-4", "api_key": openai_api_key},
-        )
-
-        # Define task with output file
-        output_file_path = f"{tmpdirname}/blog_post.txt"
-        blog_task = Task(
-            description="Write a blog post about the benefits of remote work.",
-            expected_output="An informative and engaging blog post.",
-            agent=content_creator,
-            output_file=output_file_path,
-        )
-
-        # Create the crew
-        crew = Crew(
-            agents=[content_creator],
-            tasks=[blog_task],
-            process=Process.sequential,
-        )
-
-        # Execute the crew
-        crew.kickoff()
-
-        # Assertions to verify the result
-        assert os.path.exists(output_file_path), "Output file was not created."
-
-        # Read the content from the file and perform assertions
-        with open(output_file_path, "r") as file:
-            content = file.read()
-            assert (
-                "remote work" in content.lower()
-            ), "Output file does not contain expected content."
-
-
-def test_invalid_hierarchical_process():
-    """Test that an error is raised when using hierarchical process without a manager agent or manager_llm."""
-    with pytest.raises(ValueError) as exc_info:
-        Crew(
-            agents=[],
-            tasks=[],
-            process=Process.hierarchical,  # Hierarchical process without a manager
-        )
-    assert "manager_llm or manager_agent is required" in str(exc_info.value)
-
-
-def test_crew_with_memory(memory_agent, memory_tasks):
-    """Test crew execution utilizing memory."""
-
-    # Enable memory in the crew
-    crew = Crew(
-        agents=[memory_agent],
-        tasks=memory_tasks,
-        process=Process.sequential,
-        memory=True,  # Enable memory
-    )
-
-    # Execute the crew
-    result = crew.kickoff()
-
-    # Assertions to verify the result
-    assert result is not None, "Crew execution did not return a result."
-    assert isinstance(result, CrewOutput), "Result is not an instance of CrewOutput."
-    assert (
-        "history of ai" in result.raw.lower() and "future of ai" in result.raw.lower()
-    ), "Result does not contain expected content."