Fix threading

* initial knowledge

* WIP

* Adding core knowledge sources

* Improve types and better support for file paths

* added additional sources

* fix linting

* update yaml to include optional deps

* adding in lorenze feedback

* ensure embeddings are persisted

* improvements all around Knowledge class

* return this

* properly reset memory

* properly reset memory+knowledge

* consolodation and improvements

* linted

* cleanup rm unused embedder

* fix test

* fix duplicate

* generating cassettes for knowledge test

* updated default embedder

* None embedder to use default on pipeline cloning

* improvements

* fixed text_file_knowledge

* mypysrc fixes

* type check fixes

* added extra cassette

* just mocks

* linted

* mock knowledge query to not spin up db

* linted

* verbose run

* put a flag

* fix

* adding docs

* better docs

* improvements from review

* more docs

* linted

* rm print

* more fixes

* clearer docs

* added docstrings and type hints for cli

---------

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

.github/workflows/security-checker.yml

@@ -19,5 +19,5 @@ jobs:
         run: pip install bandit
 
       - name: Run Bandit
-        run: bandit -c pyproject.toml -r src/ -lll
+        run: bandit -c pyproject.toml -r src/ -ll
 

.github/workflows/tests.yml

@@ -26,7 +26,7 @@ jobs:
         run: uv python install 3.11.9
 
       - name: Install the project
-        run: uv sync --dev
+        run: uv sync --dev --all-extras
 
       - name: Run tests
-        run: uv run pytest tests
+        run: uv run pytest tests -vv

.gitignore

@@ -17,3 +17,7 @@ rc-tests/*
 temp/*
 .vscode/*
 crew_tasks_output.json
+.codesight
+.mypy_cache
+.ruff_cache
+.venv

README.md

@@ -100,7 +100,7 @@ You can now start developing your crew by editing the files in the `src/my_proje
 
 #### Example of a simple crew with a sequential process:
 
-Instatiate your crew:
+Instantiate your crew:
 
 ```shell
 crewai create crew latest-ai-development
@@ -399,7 +399,7 @@ Data collected includes:
 - Roles of agents in a crew
   - Understand high level use cases so we can build better tools, integrations and examples about it
 - Tools names available
-  - Understand out of the publically available tools, which ones are being used the most so we can improve them
+  - Understand out of the publicly available tools, which ones are being used the most so we can improve them
 
 Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
 

docs/concepts/crews.mdx

@@ -22,7 +22,8 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
 | **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
 | **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
-| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory). Defaults to `False`.                                                                                                                                                       |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
+| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                          |
 | **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                          |
 | **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                                                     |
 | **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                       |

docs/concepts/flows.mdx

@@ -18,60 +18,63 @@ Flows allow you to create structured, event-driven workflows. They provide a sea
 
 4. **Flexible Control Flow**: Implement conditional logic, loops, and branching within your workflows.
 
+5. **Input Flexibility**: Flows can accept inputs to initialize or update their state, with different handling for structured and unstructured state management.
+
 ## Getting Started
 
 Let's create a simple Flow where you will use OpenAI to generate a random city in one task and then use that city to generate a fun fact in another task.
 
-```python Code
+### Passing Inputs to Flows
 
+Flows can accept inputs to initialize or update their state before execution. The way inputs are handled depends on whether the flow uses structured or unstructured state management.
+
+#### Structured State Management
+
+In structured state management, the flow's state is defined using a Pydantic `BaseModel`. Inputs must match the model's schema, and any updates will overwrite the default values.
+
+```python
 from crewai.flow.flow import Flow, listen, start
-from dotenv import load_dotenv
-from litellm import completion
+from pydantic import BaseModel
 
+class ExampleState(BaseModel):
+    counter: int = 0
+    message: str = ""
 
-class ExampleFlow(Flow):
-    model = "gpt-4o-mini"
-
+class StructuredExampleFlow(Flow[ExampleState]):
     @start()
-    def generate_city(self):
-        print("Starting flow")
+    def first_method(self):
+        # Implementation
 
-        response = completion(
-            model=self.model,
-            messages=[
-                {
-                    "role": "user",
-                    "content": "Return the name of a random city in the world.",
-                },
-            ],
-        )
+flow = StructuredExampleFlow()
+flow.kickoff(inputs={"counter": 10})
+```
 
-        random_city = response["choices"][0]["message"]["content"]
-        print(f"Random City: {random_city}")
+In this example, the `counter` is initialized to `10`, while `message` retains its default value.
 
-        return random_city
+#### Unstructured State Management
 
-    @listen(generate_city)
-    def generate_fun_fact(self, random_city):
-        response = completion(
-            model=self.model,
-            messages=[
-                {
-                    "role": "user",
-                    "content": f"Tell me a fun fact about {random_city}",
-                },
-            ],
-        )
+In unstructured state management, the flow's state is a dictionary. You can pass any dictionary to update the state.
 
-        fun_fact = response["choices"][0]["message"]["content"]
-        return fun_fact
+```python
+from crewai.flow.flow import Flow, listen, start
 
+class UnstructuredExampleFlow(Flow):
+    @start()
+    def first_method(self):
+        # Implementation
 
+flow = UnstructuredExampleFlow()
+flow.kickoff(inputs={"counter": 5, "message": "Initial message"})
+```
 
-flow = ExampleFlow()
-result = flow.kickoff()
+Here, both `counter` and `message` are updated based on the provided inputs.
 
-print(f"Generated fun fact: {result}")
+**Note:** Ensure that inputs for structured state management adhere to the defined schema to avoid validation errors.
+
+### Example Flow
+
+```python
+# Existing example code
 ```
 
 In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
@@ -94,14 +97,14 @@ The `@listen()` decorator can be used in several ways:
 
 1. **Listening to a Method by Name**: You can pass the name of the method you want to listen to as a string. When that method completes, the listener method will be triggered.
 
-   ```python Code
+   ```python
    @listen("generate_city")
    def generate_fun_fact(self, random_city):
        # Implementation
    ```
 
 2. **Listening to a Method Directly**: You can pass the method itself. When that method completes, the listener method will be triggered.
-   ```python Code
+   ```python
    @listen(generate_city)
    def generate_fun_fact(self, random_city):
        # Implementation
@@ -118,7 +121,7 @@ When you run a Flow, the final output is determined by the last method that comp
 Here's how you can access the final output:
 
 <CodeGroup>
-```python Code
+```python
 from crewai.flow.flow import Flow, listen, start
 
 class OutputExampleFlow(Flow):
@@ -130,18 +133,17 @@ class OutputExampleFlow(Flow):
     def second_method(self, first_output):
         return f"Second method received: {first_output}"
 
-
 flow = OutputExampleFlow()
 final_output = flow.kickoff()
 
 print("---- Final Output ----")
 print(final_output)
-````
+```
 
-``` text Output
+```text
 ---- Final Output ----
 Second method received: Output from first_method
-````
+```
 
 </CodeGroup>
 
@@ -156,7 +158,7 @@ Here's an example of how to update and access the state:
 
 <CodeGroup>
 
-```python Code
+```python
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
@@ -184,7 +186,7 @@ print("Final State:")
 print(flow.state)
 ```
 
-```text Output
+```text
 Final Output: Hello from first_method - updated by second_method
 Final State:
 counter=2 message='Hello from first_method - updated by second_method'
@@ -208,10 +210,10 @@ allowing developers to choose the approach that best fits their application's ne
 In unstructured state management, all state is stored in the `state` attribute of the `Flow` class.
 This approach offers flexibility, enabling developers to add or modify state attributes on the fly without defining a strict schema.
 
-```python Code
+```python
 from crewai.flow.flow import Flow, listen, start
 
-class UntructuredExampleFlow(Flow):
+class UnstructuredExampleFlow(Flow):
 
     @start()
     def first_method(self):
@@ -230,8 +232,7 @@ class UntructuredExampleFlow(Flow):
 
         print(f"State after third_method: {self.state}")
 
-
-flow = UntructuredExampleFlow()
+flow = UnstructuredExampleFlow()
 flow.kickoff()
 ```
 
@@ -245,16 +246,14 @@ flow.kickoff()
 Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow.
 By using models like Pydantic's `BaseModel`, developers can define the exact shape of the state, enabling better validation and auto-completion in development environments.
 
-```python Code
+```python
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
-
 class ExampleState(BaseModel):
     counter: int = 0
     message: str = ""
 
-
 class StructuredExampleFlow(Flow[ExampleState]):
 
     @start()
@@ -273,7 +272,6 @@ class StructuredExampleFlow(Flow[ExampleState]):
 
         print(f"State after third_method: {self.state}")
 
-
 flow = StructuredExampleFlow()
 flow.kickoff()
 ```
@@ -307,7 +305,7 @@ The `or_` function in Flows allows you to listen to multiple methods and trigger
 
 <CodeGroup>
 
-```python Code
+```python
 from crewai.flow.flow import Flow, listen, or_, start
 
 class OrExampleFlow(Flow):
@@ -324,13 +322,11 @@ class OrExampleFlow(Flow):
     def logger(self, result):
         print(f"Logger: {result}")
 
-
-
 flow = OrExampleFlow()
 flow.kickoff()
 ```
 
-```text Output
+```text
 Logger: Hello from the start method
 Logger: Hello from the second method
 ```
@@ -346,7 +342,7 @@ The `and_` function in Flows allows you to listen to multiple methods and trigge
 
 <CodeGroup>
 
-```python Code
+```python
 from crewai.flow.flow import Flow, and_, listen, start
 
 class AndExampleFlow(Flow):
@@ -368,7 +364,7 @@ flow = AndExampleFlow()
 flow.kickoff()
 ```
 
-```text Output
+```text
 ---- Logger ----
 {'greeting': 'Hello from the start method', 'joke': 'What do computers eat? Microchips.'}
 ```
@@ -385,7 +381,7 @@ You can specify different routes based on the output of the method, allowing you
 
 <CodeGroup>
 
-```python Code
+```python
 import random
 from crewai.flow.flow import Flow, listen, router, start
 from pydantic import BaseModel
@@ -416,12 +412,11 @@ class RouterFlow(Flow[ExampleState]):
     def fourth_method(self):
         print("Fourth method running")
 
-
 flow = RouterFlow()
 flow.kickoff()
 ```
 
-```text Output
+```text
 Starting the structured flow
 Third method running
 Fourth method running
@@ -484,7 +479,7 @@ The `main.py` file is where you create your flow and connect the crews together.
 
 Here's an example of how you can connect the `poem_crew` in the `main.py` file:
 
-```python Code
+```python
 #!/usr/bin/env python
 from random import randint
 
@@ -612,7 +607,7 @@ CrewAI provides two convenient methods to generate plots of your flows:
 
 If you are working directly with a flow instance, you can generate a plot by calling the `plot()` method on your flow object. This method will create an HTML file containing the interactive plot of your flow.
 
-```python Code
+```python
 # Assuming you have a flow instance
 flow.plot("my_flow_plot")
 ```

docs/concepts/knowledge.mdx

@@ -0,0 +1,75 @@
+---
+title: Knowledge
+description: What is knowledge in CrewAI and how to use it.
+icon: book
+---
+
+# Using Knowledge in CrewAI
+
+## Introduction
+
+The Knowledge class in CrewAI provides a powerful way to manage and query knowledge sources for your AI agents. This guide will show you how to implement knowledge management in your CrewAI projects.
+Additionally, we have specific tools for generate knowledge sources for strings, text files, PDF's, and Spreadsheets. You can expand on any source type by extending the `KnowledgeSource` class.
+
+## Basic Implementation
+
+Here's a simple example of how to use the Knowledge class:
+
+```python
+from crewai import Agent, Task, Crew, Process, LLM
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+
+# Create a knowledge source
+content = "Users name is John. He is 30 years old and lives in San Francisco."
+string_source = StringKnowledgeSource(
+    content=content, metadata={"preference": "personal"}
+)
+
+
+llm = LLM(model="gpt-4o-mini", 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=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], "metadata": {"preference": "personal"}}, # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
+)
+
+result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
+```
+
+
+## Embedder 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.
+
+```python
+...
+string_source = StringKnowledgeSource(
+    content="Users name is John. He is 30 years old and lives in San Francisco.",
+    metadata={"preference": "personal"}
+)
+crew = Crew(
+    ...
+    knowledge={
+        "sources": [string_source],
+        "metadata": {"preference": "personal"},
+        "embedder_config": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
+    },
+)
+```

docs/concepts/llms.mdx

@@ -25,7 +25,102 @@ By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables i
 - `OPENAI_API_BASE`
 - `OPENAI_API_KEY`
 
-### 2. Custom LLM Objects
+### 2. Updating YAML files
+
+You can update the `agents.yml` file to refer to the LLM you want to use:
+
+```yaml Code
+researcher:
+    role: Research Specialist
+    goal: Conduct comprehensive research and analysis to gather relevant information,
+        synthesize findings, and produce well-documented insights.
+    backstory: A dedicated research professional with years of experience in academic
+        investigation, literature review, and data analysis, known for thorough and
+        methodical approaches to complex research questions.
+    verbose: true
+    llm: openai/gpt-4o
+    # llm: azure/gpt-4o-mini
+    # llm: gemini/gemini-pro
+    # llm: anthropic/claude-3-5-sonnet-20240620
+    # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
+    # llm: mistral/mistral-large-latest
+    # llm: ollama/llama3:70b
+    # llm: groq/llama-3.2-90b-vision-preview
+    # llm: watsonx/meta-llama/llama-3-1-70b-instruct
+    # llm: nvidia_nim/meta/llama3-70b-instruct
+    # llm: sambanova/Meta-Llama-3.1-8B-Instruct
+    # ...
+```
+
+Keep in mind that you will need to set certain ENV vars depending on the model you are
+using to account for the credentials or set a custom LLM object like described below.
+Here are some of the required ENV vars for some of the LLM integrations:
+
+<AccordionGroup>
+    <Accordion title="OpenAI">
+    ```python Code
+    OPENAI_API_KEY=<your-api-key>
+    OPENAI_API_BASE=<optional-custom-base-url>
+    OPENAI_MODEL_NAME=<openai-model-name>
+    OPENAI_ORGANIZATION=<your-org-id>       # OPTIONAL
+    OPENAI_API_BASE=<openaiai-api-base>     # OPTIONAL
+    ```
+    </Accordion>
+
+    <Accordion title="Anthropic">
+    ```python Code
+    ANTHROPIC_API_KEY=<your-api-key>
+    ```
+    </Accordion>
+
+    <Accordion title="Google">
+    ```python Code
+    GEMINI_API_KEY=<your-api-key>
+    ```
+    </Accordion>
+
+    <Accordion title="Azure">
+    ```python Code
+    AZURE_API_KEY=<your-api-key>                    # "my-azure-api-key"
+    AZURE_API_BASE=<your-resource-url>              # "https://example-endpoint.openai.azure.com"
+    AZURE_API_VERSION=<api-version>                 # "2023-05-15"
+    AZURE_AD_TOKEN=<your-azure-ad-token>            # Optional
+    AZURE_API_TYPE=<your-azure-api-type>            # Optional
+    ```
+    </Accordion>
+
+    <Accordion title="AWS Bedrock">
+    ```python Code
+    AWS_ACCESS_KEY_ID=<your-access-key>
+    AWS_SECRET_ACCESS_KEY=<your-secret-key>
+    AWS_DEFAULT_REGION=<your-region>
+    ```
+    </Accordion>
+
+    <Accordion title="Mistral">
+    ```python Code
+    MISTRAL_API_KEY=<your-api-key>
+    ```
+    </Accordion>
+
+    <Accordion title="Groq">
+    ```python Code
+    GROQ_API_KEY=<your-api-key>
+    ```
+    </Accordion>
+
+    <Accordion title="IBM watsonx.ai">
+    ```python Code
+    WATSONX_URL=<your-url>                          # (required) Base URL of your WatsonX instance
+    WATSONX_APIKEY=<your-apikey>                    # (required) IBM cloud API key
+    WATSONX_TOKEN=<your-token>                      # (required) IAM auth token (alternative to APIKEY)
+    WATSONX_PROJECT_ID=<your-project-id>            # (optional) Project ID of your WatsonX instance
+    WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>     # (optional) ID of deployment space for deployed models
+    ```
+    </Accordion>
+</AccordionGroup>
+
+### 3. Custom LLM Objects
 
 Pass a custom LLM implementation or object from another library.
 
@@ -102,7 +197,7 @@ When configuring an LLM for your agent, you have access to a wide range of param
 
 These are examples of how to configure LLMs for your agent.
 
-<AccordionGroup>    
+<AccordionGroup>
     <Accordion title="OpenAI">
 
     ```python Code
@@ -133,10 +228,10 @@ These are examples of how to configure LLMs for your agent.
         model="cerebras/llama-3.1-70b",
         api_key="your-api-key-here"
     )
-    agent = Agent(llm=llm, ...) 
+    agent = Agent(llm=llm, ...)
     ```
     </Accordion>
- 
+
     <Accordion title="Ollama (Local LLMs)">
 
     CrewAI supports using Ollama for running open-source models locally:
@@ -150,7 +245,7 @@ These are examples of how to configure LLMs for your agent.
 
     agent = Agent(
         llm=LLM(
-            model="ollama/llama3.1", 
+            model="ollama/llama3.1",
             base_url="http://localhost:11434"
         ),
         ...
@@ -164,7 +259,7 @@ These are examples of how to configure LLMs for your agent.
     from crewai import LLM
 
     llm = LLM(
-        model="groq/llama3-8b-8192", 
+        model="groq/llama3-8b-8192",
         api_key="your-api-key-here"
     )
     agent = Agent(llm=llm, ...)
@@ -189,7 +284,7 @@ These are examples of how to configure LLMs for your agent.
     from crewai import LLM
 
     llm = LLM(
-        model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",  
+        model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
         api_key="your-api-key-here"
     )
     agent = Agent(llm=llm, ...)
@@ -215,8 +310,8 @@ These are examples of how to configure LLMs for your agent.
     from crewai import LLM
 
     llm = LLM(
-        model="perplexity/mistral-7b-instruct",
-        base_url="https://api.perplexity.ai/v1",
+        model="llama-3.1-sonar-large-128k-online",
+        base_url="https://api.perplexity.ai/",
         api_key="your-api-key-here"
     )
     agent = Agent(llm=llm, ...)
@@ -224,6 +319,29 @@ These are examples of how to configure LLMs for your agent.
     </Accordion>
 
     <Accordion title="IBM watsonx.ai">
+    You can use IBM Watson by seeting the following ENV vars:
+
+    ```python Code
+    WATSONX_URL=<your-url>
+    WATSONX_APIKEY=<your-apikey>
+    WATSONX_PROJECT_ID=<your-project-id>
+    ```
+
+    You can then define your agents llms by updating the `agents.yml`
+
+    ```yaml Code
+    researcher:
+        role: Research Specialist
+        goal: Conduct comprehensive research and analysis to gather relevant information,
+            synthesize findings, and produce well-documented insights.
+        backstory: A dedicated research professional with years of experience in academic
+            investigation, literature review, and data analysis, known for thorough and
+            methodical approaches to complex research questions.
+        verbose: true
+        llm: watsonx/meta-llama/llama-3-1-70b-instruct
+    ```
+
+    You can also set up agents more dynamically as a base level LLM instance, like bellow:
 
     ```python Code
     from crewai import LLM
@@ -247,7 +365,7 @@ These are examples of how to configure LLMs for your agent.
         api_key="your-api-key-here",
         base_url="your_api_endpoint"
     )
-    agent = Agent(llm=llm, ...) 
+    agent = Agent(llm=llm, ...)
     ```
     </Accordion>
 </AccordionGroup>
@@ -282,4 +400,4 @@ This is particularly useful when working with OpenAI-compatible APIs or when you
 - **API Errors**: Check your API key, network connection, and rate limits.
 - **Unexpected Outputs**: Refine your prompts and adjust temperature or top_p.
 - **Performance Issues**: Consider using a more powerful model or optimizing your queries.
-- **Timeout Errors**: Increase the `timeout` parameter or optimize your input.
+- **Timeout Errors**: Increase the `timeout` parameter or optimize your input.

docs/concepts/memory.mdx

@@ -18,6 +18,7 @@ reason, and learn from past interactions.
 | **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
 | **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
 | **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
+| **User Memory**      | Stores user-specific information and preferences, enhancing personalization and user experience. |
 
 ## How Memory Systems Empower Agents
 
@@ -92,6 +93,47 @@ my_crew = Crew(
 )
 ```
 
+## Integrating Mem0 for Enhanced User Memory
+
+[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. 
+
+To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences.
+
+
+```python Code
+import os
+from crewai import Crew, Process
+from mem0 import MemoryClient
+
+# Set environment variables for Mem0
+os.environ["MEM0_API_KEY"] = "m0-xx"
+
+# Step 1: Record preferences based on past conversation or user input
+client = MemoryClient()
+messages = [
+    {"role": "user", "content": "Hi there! I'm planning a vacation and could use some advice."},
+    {"role": "assistant", "content": "Hello! I'd be happy to help with your vacation planning. What kind of destination do you prefer?"},
+    {"role": "user", "content": "I am more of a beach person than a mountain person."},
+    {"role": "assistant", "content": "That's interesting. Do you like hotels or Airbnb?"},
+    {"role": "user", "content": "I like Airbnb more."},
+]
+client.add(messages, user_id="john")
+
+# Step 2: Create a Crew with User Memory
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    verbose=True,
+    process=Process.sequential,
+    memory=True,
+    memory_config={
+        "provider": "mem0",
+        "config": {"user_id": "john"},
+    },
+)
+```
+
 
 ## Additional Embedding Providers
 

docs/quickstart.mdx

@@ -330,4 +330,4 @@ This will clear the crew's memory, allowing for a fresh start.
 
 ## Deploying Your Project
 
-The easiest way to deploy your crew is through [CrewAI Enterprise](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.
+The easiest way to deploy your crew is through [CrewAI Enterprise](http://app.crewai.com/), where you can deploy your crew in a few clicks.

docs/tools/githubsearchtool.mdx

@@ -34,6 +34,7 @@ from crewai_tools import GithubSearchTool
 # Initialize the tool for semantic searches within a specific GitHub repository
 tool = GithubSearchTool(
 	github_repo='https://github.com/example/repo',
+	gh_token='your_github_personal_access_token',
 	content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 
@@ -41,6 +42,7 @@ tool = GithubSearchTool(
 
 # Initialize the tool for semantic searches within a specific GitHub repository, so the agent can search any repository if it learns about during its execution
 tool = GithubSearchTool(
+	gh_token='your_github_personal_access_token',
 	content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 ```
@@ -48,6 +50,7 @@ tool = GithubSearchTool(
 ## Arguments
 
 - `github_repo` : The URL of the GitHub repository where the search will be conducted. This is a mandatory field and specifies the target repository for your search.
+- `gh_token` : Your GitHub Personal Access Token (PAT) required for authentication. You can create one in your GitHub account settings under Developer Settings > Personal Access Tokens.
 - `content_types` : Specifies the types of content to include in your search. You must provide a list of content types from the following options: `code` for searching within the code, 
 `repo` for searching within the repository's general information, `pr` for searching within pull requests, and `issue` for searching within issues. 
 This field is mandatory and allows tailoring the search to specific content types within the GitHub repository.
@@ -77,5 +80,4 @@ tool = GithubSearchTool(
             ),
         ),
     )
-)
-```
+)

poetry.lock

@@ -1597,12 +1597,12 @@ files = [
 google-auth = ">=2.14.1,<3.0.dev0"
 googleapis-common-protos = ">=1.56.2,<2.0.dev0"
 grpcio = [
-    {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
     {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
 ]
 grpcio-status = [
-    {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
     {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
 ]
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
@@ -4286,8 +4286,8 @@ files = [
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
     {version = ">=1.22.4", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.76.9"
+version = "0.80.0"
 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"
@@ -16,7 +16,7 @@ dependencies = [
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
     "instructor>=1.3.3",
     "regex>=2024.9.11",
-    "crewai-tools>=0.13.4",
+    "crewai-tools>=0.14.0",
     "click>=8.1.7",
     "python-dotenv>=1.0.0",
     "appdirs>=1.4.4",
@@ -27,8 +27,8 @@ dependencies = [
     "pyvis>=0.3.2",
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
-    "chromadb>=0.4.24",
     "tomli>=2.0.2",
+    "chromadb>=0.5.18",
 ]
 
 [project.urls]
@@ -37,8 +37,19 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.13.4"]
+tools = ["crewai-tools>=0.14.0"]
 agentops = ["agentops>=0.3.0"]
+fastembed = ["fastembed>=0.4.1"]
+pdfplumber = [
+    "pdfplumber>=0.11.4",
+]
+pandas = [
+    "pandas>=2.2.3",
+]
+openpyxl = [
+    "openpyxl>=3.1.5",
+]
+mem0 = ["mem0ai>=0.1.29"]
 
 [tool.uv]
 dev-dependencies = [
@@ -52,7 +63,7 @@ dev-dependencies = [
     "mkdocs-material-extensions>=1.3.1",
     "pillow>=10.2.0",
     "cairosvg>=2.7.1",
-    "crewai-tools>=0.13.4",
+    "crewai-tools>=0.14.0",
     "pytest>=8.0.0",
     "pytest-vcr>=1.0.2",
     "python-dotenv>=1.0.0",

src/crewai/__init__.py

@@ -1,7 +1,9 @@
 import warnings
+
 from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.flow.flow import Flow
+from crewai.knowledge.knowledge import Knowledge
 from crewai.llm import LLM
 from crewai.pipeline import Pipeline
 from crewai.process import Process
@@ -14,5 +16,15 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.76.9"
-__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]
+__version__ = "0.80.0"
+__all__ = [
+    "Agent",
+    "Crew",
+    "Process",
+    "Task",
+    "Pipeline",
+    "Router",
+    "LLM",
+    "Flow",
+    "Knowledge",
+]

src/crewai/agent.py

@@ -8,10 +8,11 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.cli.constants import ENV_VARS
 from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
-from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.tools import BaseTool
+from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.token_counter_callback import TokenCalcHandler
@@ -51,6 +52,7 @@ class Agent(BaseAgent):
             role: The role of the agent.
             goal: The objective of the agent.
             backstory: The backstory of the agent.
+            knowledge: The knowledge base of the agent.
             config: Dict representation of agent configuration.
             llm: The language model that will run the agent.
             function_calling_llm: The language model that will handle the tool calling for this agent, it overrides the crew function_calling_llm.
@@ -122,6 +124,11 @@ class Agent(BaseAgent):
     @model_validator(mode="after")
     def post_init_setup(self):
         self.agent_ops_agent_name = self.role
+        unnacepted_attributes = [
+            "AWS_ACCESS_KEY_ID",
+            "AWS_SECRET_ACCESS_KEY",
+            "AWS_REGION_NAME",
+        ]
 
         # Handle different cases for self.llm
         if isinstance(self.llm, str):
@@ -131,8 +138,12 @@ class Agent(BaseAgent):
             # If it's already an LLM instance, keep it as is
             pass
         elif self.llm is None:
-            # If it's None, use environment variables or default
-            model_name = os.environ.get("OPENAI_MODEL_NAME", "gpt-4o-mini")
+            # 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(
@@ -141,9 +152,44 @@ class Agent(BaseAgent):
             if api_base:
                 llm_params["base_url"] = api_base
 
-            api_key = os.environ.get("OPENAI_API_KEY")
-            if api_key:
-                llm_params["api_key"] = api_key
+            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:
+                        if env_var["key_name"] in unnacepted_attributes:
+                            continue
+                        # Check if the environment variable is set
+                        if "key_name" in env_var:
+                            env_value = os.environ.get(env_var["key_name"])
+                            if env_value:
+                                # Map key names containing "API_KEY" to "api_key"
+                                key_name = (
+                                    "api_key"
+                                    if "API_KEY" in env_var["key_name"]
+                                    else env_var["key_name"]
+                                )
+                                # Map key names containing "API_BASE" to "api_base"
+                                key_name = (
+                                    "api_base"
+                                    if "API_BASE" in env_var["key_name"]
+                                    else key_name
+                                )
+                                # Map key names containing "API_VERSION" to "api_version"
+                                key_name = (
+                                    "api_version"
+                                    if "API_VERSION" in env_var["key_name"]
+                                    else key_name
+                                )
+                                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:
@@ -217,14 +263,28 @@ class Agent(BaseAgent):
 
         if self.crew and self.crew.memory:
             contextual_memory = ContextualMemory(
+                self.crew.memory_config,
                 self.crew._short_term_memory,
                 self.crew._long_term_memory,
                 self.crew._entity_memory,
+                self.crew._user_memory,
             )
             memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
+        # Integrate the knowledge base
+        if self.crew and self.crew.knowledge:
+            knowledge_snippets = self.crew.knowledge.query([task.prompt()])
+            valid_snippets = [
+                result["context"] 
+                for result in knowledge_snippets 
+                if result and result.get("context")
+            ]
+            if valid_snippets:
+                formatted_knowledge = "\n".join(valid_snippets)
+                task_prompt += f"\n\nAdditional Information:\n{formatted_knowledge}"
+
         tools = tools or self.tools or []
         self.create_agent_executor(tools=tools, task=task)
 

src/crewai/agents/agent_builder/utilities/base_token_process.py

@@ -4,6 +4,7 @@ from crewai.types.usage_metrics import UsageMetrics
 class TokenProcess:
     total_tokens: int = 0
     prompt_tokens: int = 0
+    cached_prompt_tokens: int = 0
     completion_tokens: int = 0
     successful_requests: int = 0
 
@@ -15,6 +16,9 @@ class TokenProcess:
         self.completion_tokens = self.completion_tokens + tokens
         self.total_tokens = self.total_tokens + tokens
 
+    def sum_cached_prompt_tokens(self, tokens: int):
+        self.cached_prompt_tokens = self.cached_prompt_tokens + tokens
+
     def sum_successful_requests(self, requests: int):
         self.successful_requests = self.successful_requests + requests
 
@@ -22,6 +26,7 @@ class TokenProcess:
         return UsageMetrics(
             total_tokens=self.total_tokens,
             prompt_tokens=self.prompt_tokens,
+            cached_prompt_tokens=self.cached_prompt_tokens,
             completion_tokens=self.completion_tokens,
             successful_requests=self.successful_requests,
         )

src/crewai/agents/crew_agent_executor.py

@@ -117,6 +117,15 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         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."
+                        )
+
                     if not self.use_stop_words:
                         try:
                             self._format_answer(answer)
@@ -136,25 +145,26 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         formatted_answer.result = action_result
                         self._show_logs(formatted_answer)
 
-                        if self.step_callback:
-                            self.step_callback(formatted_answer)
+                    if self.step_callback:
+                        self.step_callback(formatted_answer)
 
-                        if self._should_force_answer():
-                            if self.have_forced_answer:
-                                return AgentFinish(
-                                    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")
-                        )
+                    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})
@@ -323,9 +333,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             if self.crew is not None and hasattr(self.crew, "_train_iteration"):
                 train_iteration = self.crew._train_iteration
                 if agent_id in training_data and isinstance(train_iteration, int):
-                    training_data[agent_id][train_iteration][
-                        "improved_output"
-                    ] = result.output
+                    training_data[agent_id][train_iteration]["improved_output"] = (
+                        result.output
+                    )
                     training_handler.save(training_data)
                 else:
                     self._logger.log(
@@ -376,4 +386,5 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return CrewAgentParser(agent=self.agent).parse(answer)
 
     def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
+        prompt = prompt.rstrip()
         return {"role": role, "content": prompt}

src/crewai/cli/add_crew_to_flow.py

@@ -54,7 +54,7 @@ def create_embedded_crew(crew_name: str, parent_folder: Path) -> None:
 
     templates_dir = Path(__file__).parent / "templates" / "crew"
     config_template_files = ["agents.yaml", "tasks.yaml"]
-    crew_template_file = f"{folder_name}_crew.py"  # Updated file name
+    crew_template_file = f"{folder_name}.py"  # Updated file name
 
     for file_name in config_template_files:
         src_file = templates_dir / "config" / file_name

src/crewai/cli/authentication/main.py

@@ -34,7 +34,9 @@ class AuthenticationCommand:
             "scope": "openid",
             "audience": AUTH0_AUDIENCE,
         }
-        response = requests.post(url=self.DEVICE_CODE_URL, data=device_code_payload)
+        response = requests.post(
+            url=self.DEVICE_CODE_URL, data=device_code_payload, timeout=20
+        )
         response.raise_for_status()
         return response.json()
 
@@ -54,7 +56,7 @@ class AuthenticationCommand:
 
         attempts = 0
         while True and attempts < 5:
-            response = requests.post(self.TOKEN_URL, data=token_payload)
+            response = requests.post(self.TOKEN_URL, data=token_payload, timeout=30)
             token_data = response.json()
 
             if response.status_code == 200:

src/crewai/cli/cli.py

@@ -136,6 +136,7 @@ def log_tasks_outputs() -> None:
 @click.option("-l", "--long", is_flag=True, help="Reset LONG TERM memory")
 @click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory")
 @click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory")
+@click.option("-kn", "--knowledge", is_flag=True, help="Reset KNOWLEDGE storage")
 @click.option(
     "-k",
     "--kickoff-outputs",
@@ -143,17 +144,24 @@ def log_tasks_outputs() -> None:
     help="Reset LATEST KICKOFF TASK OUTPUTS",
 )
 @click.option("-a", "--all", is_flag=True, help="Reset ALL memories")
-def reset_memories(long, short, entities, kickoff_outputs, all):
+def reset_memories(
+    long: bool,
+    short: bool,
+    entities: bool,
+    knowledge: bool,
+    kickoff_outputs: bool,
+    all: bool,
+) -> None:
     """
     Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs). This will delete all the data saved.
     """
     try:
-        if not all and not (long or short or entities or kickoff_outputs):
+        if not all and not (long or short or entities or knowledge or kickoff_outputs):
             click.echo(
                 "Please specify at least one memory type to reset using the appropriate flags."
             )
             return
-        reset_memories_command(long, short, entities, kickoff_outputs, all)
+        reset_memories_command(long, short, entities, knowledge, kickoff_outputs, all)
     except Exception as e:
         click.echo(f"An error occurred while resetting memories: {e}", err=True)
 

src/crewai/cli/constants.py

@@ -1,19 +1,161 @@
 ENV_VARS = {
-    'openai': ['OPENAI_API_KEY'],
-    'anthropic': ['ANTHROPIC_API_KEY'],
-    'gemini': ['GEMINI_API_KEY'],
-    'groq': ['GROQ_API_KEY'],
-    'ollama': ['FAKE_KEY'],
+    "openai": [
+        {
+            "prompt": "Enter your OPENAI API key (press Enter to skip)",
+            "key_name": "OPENAI_API_KEY",
+        }
+    ],
+    "anthropic": [
+        {
+            "prompt": "Enter your ANTHROPIC API key (press Enter to skip)",
+            "key_name": "ANTHROPIC_API_KEY",
+        }
+    ],
+    "gemini": [
+        {
+            "prompt": "Enter your GEMINI API key (press Enter to skip)",
+            "key_name": "GEMINI_API_KEY",
+        }
+    ],
+    "groq": [
+        {
+            "prompt": "Enter your GROQ API key (press Enter to skip)",
+            "key_name": "GROQ_API_KEY",
+        }
+    ],
+    "watson": [
+        {
+            "prompt": "Enter your WATSONX URL (press Enter to skip)",
+            "key_name": "WATSONX_URL",
+        },
+        {
+            "prompt": "Enter your WATSONX API Key (press Enter to skip)",
+            "key_name": "WATSONX_APIKEY",
+        },
+        {
+            "prompt": "Enter your WATSONX Project Id (press Enter to skip)",
+            "key_name": "WATSONX_PROJECT_ID",
+        },
+    ],
+    "ollama": [
+        {
+            "default": True,
+            "API_BASE": "http://localhost:11434",
+        }
+    ],
+    "bedrock": [
+        {
+            "prompt": "Enter your AWS Access Key ID (press Enter to skip)",
+            "key_name": "AWS_ACCESS_KEY_ID",
+        },
+        {
+            "prompt": "Enter your AWS Secret Access Key (press Enter to skip)",
+            "key_name": "AWS_SECRET_ACCESS_KEY",
+        },
+        {
+            "prompt": "Enter your AWS Region Name (press Enter to skip)",
+            "key_name": "AWS_REGION_NAME",
+        },
+    ],
+    "azure": [
+        {
+            "prompt": "Enter your Azure deployment name (must start with 'azure/')",
+            "key_name": "model",
+        },
+        {
+            "prompt": "Enter your AZURE API key (press Enter to skip)",
+            "key_name": "AZURE_API_KEY",
+        },
+        {
+            "prompt": "Enter your AZURE API base URL (press Enter to skip)",
+            "key_name": "AZURE_API_BASE",
+        },
+        {
+            "prompt": "Enter your AZURE API version (press Enter to skip)",
+            "key_name": "AZURE_API_VERSION",
+        },
+    ],
+    "cerebras": [
+        {
+            "prompt": "Enter your Cerebras model name (must start with 'cerebras/')",
+            "key_name": "model",
+        },
+        {
+            "prompt": "Enter your Cerebras API version (press Enter to skip)",
+            "key_name": "CEREBRAS_API_KEY",
+        },
+    ],
 }
 
-PROVIDERS = ['openai', 'anthropic', 'gemini', 'groq', 'ollama']
+
+PROVIDERS = [
+    "openai",
+    "anthropic",
+    "gemini",
+    "groq",
+    "ollama",
+    "watson",
+    "bedrock",
+    "azure",
+    "cerebras",
+]
 
 MODELS = {
-    'openai': ['gpt-4', 'gpt-4o', 'gpt-4o-mini', 'o1-mini', 'o1-preview'],
-    'anthropic': ['claude-3-5-sonnet-20240620', 'claude-3-sonnet-20240229', 'claude-3-opus-20240229', 'claude-3-haiku-20240307'],
-    'gemini': ['gemini-1.5-flash', 'gemini-1.5-pro', 'gemini-gemma-2-9b-it', 'gemini-gemma-2-27b-it'],
-    'groq': ['llama-3.1-8b-instant', 'llama-3.1-70b-versatile', 'llama-3.1-405b-reasoning', 'gemma2-9b-it', 'gemma-7b-it'],
-    'ollama': ['llama3.1', 'mixtral'],
+    "openai": ["gpt-4", "gpt-4o", "gpt-4o-mini", "o1-mini", "o1-preview"],
+    "anthropic": [
+        "claude-3-5-sonnet-20240620",
+        "claude-3-sonnet-20240229",
+        "claude-3-opus-20240229",
+        "claude-3-haiku-20240307",
+    ],
+    "gemini": [
+        "gemini/gemini-1.5-flash",
+        "gemini/gemini-1.5-pro",
+        "gemini/gemini-gemma-2-9b-it",
+        "gemini/gemini-gemma-2-27b-it",
+    ],
+    "groq": [
+        "groq/llama-3.1-8b-instant",
+        "groq/llama-3.1-70b-versatile",
+        "groq/llama-3.1-405b-reasoning",
+        "groq/gemma2-9b-it",
+        "groq/gemma-7b-it",
+    ],
+    "ollama": ["ollama/llama3.1", "ollama/mixtral"],
+    "watson": [
+        "watsonx/meta-llama/llama-3-1-70b-instruct",
+        "watsonx/meta-llama/llama-3-1-8b-instruct",
+        "watsonx/meta-llama/llama-3-2-11b-vision-instruct",
+        "watsonx/meta-llama/llama-3-2-1b-instruct",
+        "watsonx/meta-llama/llama-3-2-90b-vision-instruct",
+        "watsonx/meta-llama/llama-3-405b-instruct",
+        "watsonx/mistral/mistral-large",
+        "watsonx/ibm/granite-3-8b-instruct",
+    ],
+    "bedrock": [
+        "bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
+        "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+        "bedrock/anthropic.claude-3-haiku-20240307-v1:0",
+        "bedrock/anthropic.claude-3-opus-20240229-v1:0",
+        "bedrock/anthropic.claude-v2:1",
+        "bedrock/anthropic.claude-v2",
+        "bedrock/anthropic.claude-instant-v1",
+        "bedrock/meta.llama3-1-405b-instruct-v1:0",
+        "bedrock/meta.llama3-1-70b-instruct-v1:0",
+        "bedrock/meta.llama3-1-8b-instruct-v1:0",
+        "bedrock/meta.llama3-70b-instruct-v1:0",
+        "bedrock/meta.llama3-8b-instruct-v1:0",
+        "bedrock/amazon.titan-text-lite-v1",
+        "bedrock/amazon.titan-text-express-v1",
+        "bedrock/cohere.command-text-v14",
+        "bedrock/ai21.j2-mid-v1",
+        "bedrock/ai21.j2-ultra-v1",
+        "bedrock/ai21.jamba-instruct-v1:0",
+        "bedrock/meta.llama2-13b-chat-v1",
+        "bedrock/meta.llama2-70b-chat-v1",
+        "bedrock/mistral.mistral-7b-instruct-v0:2",
+        "bedrock/mistral.mixtral-8x7b-instruct-v0:1",
+    ],
 }
 
-JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
+JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"

src/crewai/cli/create_crew.py

@@ -1,11 +1,11 @@
+import shutil
 import sys
 from pathlib import Path
 
 import click
 
-from crewai.cli.constants import ENV_VARS
+from crewai.cli.constants import ENV_VARS, MODELS
 from crewai.cli.provider import (
-    PROVIDERS,
     get_provider_data,
     select_model,
     select_provider,
@@ -29,20 +29,20 @@ def create_folder_structure(name, parent_folder=None):
             click.secho("Operation cancelled.", fg="yellow")
             sys.exit(0)
         click.secho(f"Overriding folder {folder_name}...", fg="green", bold=True)
-    else:
-        click.secho(
-            f"Creating {'crew' if parent_folder else 'folder'} {folder_name}...",
-            fg="green",
-            bold=True,
-        )
+        shutil.rmtree(folder_path)  # Delete the existing folder and its contents
 
-    if not folder_path.exists():
-        folder_path.mkdir(parents=True)
-        (folder_path / "tests").mkdir(exist_ok=True)
-        if not parent_folder:
-            (folder_path / "src" / folder_name).mkdir(parents=True)
-            (folder_path / "src" / folder_name / "tools").mkdir(parents=True)
-            (folder_path / "src" / folder_name / "config").mkdir(parents=True)
+    click.secho(
+        f"Creating {'crew' if parent_folder else 'folder'} {folder_name}...",
+        fg="green",
+        bold=True,
+    )
+
+    folder_path.mkdir(parents=True)
+    (folder_path / "tests").mkdir(exist_ok=True)
+    if not parent_folder:
+        (folder_path / "src" / folder_name).mkdir(parents=True)
+        (folder_path / "src" / folder_name / "tools").mkdir(parents=True)
+        (folder_path / "src" / folder_name / "config").mkdir(parents=True)
 
     return folder_path, folder_name, class_name
 
@@ -92,7 +92,10 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
 
         existing_provider = None
         for provider, env_keys in ENV_VARS.items():
-            if any(key in env_vars for key in env_keys):
+            if any(
+                "key_name" in details and details["key_name"] in env_vars
+                for details in env_keys
+            ):
                 existing_provider = provider
                 break
 
@@ -118,47 +121,48 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
                 "No provider selected. Please try again or press 'q' to exit.", fg="red"
             )
 
-        while True:
-            selected_model = select_model(selected_provider, provider_models)
-            if selected_model is None:  # User typed 'q'
-                click.secho("Exiting...", fg="yellow")
-                sys.exit(0)
-            if selected_model:  # Valid selection
-                break
-            click.secho(
-                "No model selected. Please try again or press 'q' to exit.", fg="red"
-            )
+        # Check if the selected provider has predefined models
+        if selected_provider in MODELS and MODELS[selected_provider]:
+            while True:
+                selected_model = select_model(selected_provider, provider_models)
+                if selected_model is None:  # User typed 'q'
+                    click.secho("Exiting...", fg="yellow")
+                    sys.exit(0)
+                if selected_model:  # Valid selection
+                    break
+                click.secho(
+                    "No model selected. Please try again or press 'q' to exit.",
+                    fg="red",
+                )
+            env_vars["MODEL"] = selected_model
 
-        if selected_provider in PROVIDERS:
-            api_key_var = ENV_VARS[selected_provider][0]
-        else:
-            api_key_var = click.prompt(
-                f"Enter the environment variable name for your {selected_provider.capitalize()} API key",
-                type=str,
-                default="",
-            )
+        # Check if the selected provider requires API keys
+        if selected_provider in ENV_VARS:
+            provider_env_vars = ENV_VARS[selected_provider]
+            for details in provider_env_vars:
+                if details.get("default", False):
+                    # Automatically add default key-value pairs
+                    for key, value in details.items():
+                        if key not in ["prompt", "key_name", "default"]:
+                            env_vars[key] = value
+                elif "key_name" in details:
+                    # Prompt for non-default key-value pairs
+                    prompt = details["prompt"]
+                    key_name = details["key_name"]
+                    api_key_value = click.prompt(prompt, default="", show_default=False)
 
-        api_key_value = ""
-        click.echo(
-            f"Enter your {selected_provider.capitalize()} API key (press Enter to skip): ",
-            nl=False,
-        )
-        try:
-            api_key_value = input()
-        except (KeyboardInterrupt, EOFError):
-            api_key_value = ""
+                    if api_key_value.strip():
+                        env_vars[key_name] = api_key_value
 
-        if api_key_value.strip():
-            env_vars = {api_key_var: api_key_value}
+        if env_vars:
             write_env_file(folder_path, env_vars)
-            click.secho("API key saved to .env file", fg="green")
+            click.secho("API keys and model saved to .env file", fg="green")
         else:
             click.secho(
-                "No API key provided. Skipping .env file creation.", fg="yellow"
+                "No API keys provided. Skipping .env file creation.", fg="yellow"
             )
 
-        env_vars["MODEL"] = selected_model
-        click.secho(f"Selected model: {selected_model}", fg="green")
+        click.secho(f"Selected model: {env_vars.get('MODEL', 'N/A')}", fg="green")
 
     package_dir = Path(__file__).parent
     templates_dir = package_dir / "templates" / "crew"

src/crewai/cli/provider.py

@@ -164,7 +164,7 @@ def fetch_provider_data(cache_file):
     - dict or None: The fetched provider data or None if the operation fails.
     """
     try:
-        response = requests.get(JSON_URL, stream=True, timeout=10)
+        response = requests.get(JSON_URL, stream=True, timeout=60)
         response.raise_for_status()
         data = download_data(response)
         with open(cache_file, "w") as f:

src/crewai/cli/reset_memories_command.py

@@ -5,9 +5,17 @@ from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 
 
-def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
+def reset_memories_command(
+    long,
+    short,
+    entity,
+    knowledge,
+    kickoff_outputs,
+    all,
+) -> None:
     """
     Reset the crew memories.
 
@@ -17,6 +25,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
       entity (bool): Whether to reset the entity memory.
       kickoff_outputs (bool): Whether to reset the latest kickoff task outputs.
       all (bool): Whether to reset all memories.
+      knowledge (bool): Whether to reset the knowledge.
     """
 
     try:
@@ -25,6 +34,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
             EntityMemory().reset()
             LongTermMemory().reset()
             TaskOutputStorageHandler().reset()
+            KnowledgeStorage().reset()
             click.echo("All memories have been reset.")
         else:
             if long:
@@ -40,6 +50,9 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
             if kickoff_outputs:
                 TaskOutputStorageHandler().reset()
                 click.echo("Latest Kickoff outputs stored has been reset.")
+            if knowledge:
+                KnowledgeStorage().reset()
+                click.echo("Knowledge has been reset.")
 
     except subprocess.CalledProcessError as e:
         click.echo(f"An error occurred while resetting the memories: {e}", err=True)

src/crewai/cli/run_crew.py

@@ -24,7 +24,6 @@ def run_crew() -> None:
             f"Please run `crewai update` to update your pyproject.toml to use uv.",
             fg="red",
         )
-        print()
 
     try:
         subprocess.run(command, capture_output=False, text=True, check=True)

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

@@ -8,9 +8,12 @@ from crewai.project import CrewBase, agent, crew, task
 # from crewai_tools import SerperDevTool
 
 @CrewBase
-class {{crew_name}}Crew():
+class {{crew_name}}():
 	"""{{crew_name}} crew"""
 
+	agents_config = 'config/agents.yaml'
+	tasks_config = 'config/tasks.yaml'
+
 	@agent
 	def researcher(self) -> Agent:
 		return Agent(
@@ -48,4 +51,4 @@ class {{crew_name}}Crew():
 			process=Process.sequential,
 			verbose=True,
 			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
-		)
+		)

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

@@ -1,6 +1,10 @@
 #!/usr/bin/env python
 import sys
-from {{folder_name}}.crew import {{crew_name}}Crew
+import warnings
+
+from {{folder_name}}.crew import {{crew_name}}
+
+warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
 # This main file is intended to be a way for you to run your
 # crew locally, so refrain from adding unnecessary logic into this file.
@@ -14,7 +18,7 @@ def run():
     inputs = {
         'topic': 'AI LLMs'
     }
-    {{crew_name}}Crew().crew().kickoff(inputs=inputs)
+    {{crew_name}}().crew().kickoff(inputs=inputs)
 
 
 def train():
@@ -25,7 +29,7 @@ def train():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
+        {{crew_name}}().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while training the crew: {e}")
@@ -35,7 +39,7 @@ def replay():
     Replay the crew execution from a specific task.
     """
     try:
-        {{crew_name}}Crew().crew().replay(task_id=sys.argv[1])
+        {{crew_name}}().crew().replay(task_id=sys.argv[1])
 
     except Exception as e:
         raise Exception(f"An error occurred while replaying the crew: {e}")
@@ -48,7 +52,7 @@ def test():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}Crew().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
+        {{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 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]>=0.76.9,<1.0.0"
+    "crewai[tools]>=0.80.0,<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]>=0.76.9,<1.0.0",
+    "crewai[tools]>=0.80.0,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.76.9,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.80.0,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = ["Your Name <you@example.com>"]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.76.9,<1.0.0"
+    "crewai[tools]>=0.80.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,6 +5,6 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.76.9"
+    "crewai[tools]>=0.80.0"
 ]
 

src/crewai/crew.py

@@ -5,7 +5,7 @@ import uuid
 import warnings
 from concurrent.futures import Future
 from hashlib import md5
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
 from pydantic import (
     UUID4,
@@ -27,6 +27,8 @@ from crewai.llm import LLM
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.knowledge.knowledge import Knowledge
+from crewai.memory.user.user_memory import UserMemory
 from crewai.process import Process
 from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
@@ -35,9 +37,7 @@ from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
-from crewai.utilities.constants import (
-    TRAINING_DATA_FILE,
-)
+from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.formatter import (
@@ -71,6 +71,7 @@ class Crew(BaseModel):
         manager_llm: The language model that will run manager agent.
         manager_agent: Custom agent that will be used as manager.
         memory: Whether the crew should use memory to store memories of it's execution.
+        memory_config: Configuration for the memory to be used for the crew.
         cache: Whether the crew should use a cache to store the results of the tools execution.
         function_calling_llm: The language model that will run the tool calling for all the agents.
         process: The process flow that the crew will follow (e.g., sequential, hierarchical).
@@ -94,6 +95,7 @@ class Crew(BaseModel):
     _short_term_memory: Optional[InstanceOf[ShortTermMemory]] = PrivateAttr()
     _long_term_memory: Optional[InstanceOf[LongTermMemory]] = PrivateAttr()
     _entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr()
+    _user_memory: Optional[InstanceOf[UserMemory]] = PrivateAttr()
     _train: Optional[bool] = PrivateAttr(default=False)
     _train_iteration: Optional[int] = PrivateAttr()
     _inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
@@ -114,6 +116,10 @@ class Crew(BaseModel):
         default=False,
         description="Whether the crew should use memory to store memories of it's execution",
     )
+    memory_config: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Configuration for the memory to be used for the crew.",
+    )
     short_term_memory: Optional[InstanceOf[ShortTermMemory]] = Field(
         default=None,
         description="An Instance of the ShortTermMemory to be used by the Crew",
@@ -126,7 +132,11 @@ class Crew(BaseModel):
         default=None,
         description="An Instance of the EntityMemory to be used by the Crew",
     )
-    embedder: Optional[Any] = Field(
+    user_memory: Optional[InstanceOf[UserMemory]] = Field(
+        default=None,
+        description="An instance of the UserMemory to be used by the Crew to store/fetch memories of a specific user.",
+    )
+    embedder: Optional[dict] = Field(
         default=None,
         description="Configuration for the embedder to be used for the crew.",
     )
@@ -154,6 +164,16 @@ class Crew(BaseModel):
         default=None,
         description="Callback to be executed after each task for all agents execution.",
     )
+    before_kickoff_callbacks: List[
+        Callable[[Optional[Dict[str, Any]]], Optional[Dict[str, Any]]]
+    ] = Field(
+        default_factory=list,
+        description="List of callbacks to be executed before crew kickoff. It may be used to adjust inputs before the crew is executed.",
+    )
+    after_kickoff_callbacks: List[Callable[[CrewOutput], CrewOutput]] = Field(
+        default_factory=list,
+        description="List of callbacks to be executed after crew kickoff. It may be used to adjust the output of the crew.",
+    )
     max_rpm: Optional[int] = Field(
         default=None,
         description="Maximum number of requests per minute for the crew execution to be respected.",
@@ -182,6 +202,10 @@ class Crew(BaseModel):
         default=[],
         description="List of execution logs for tasks",
     )
+    knowledge: Optional[Dict[str, Any]] = Field(
+        default=None, description="Knowledge for the crew. Add knowledge sources to the knowledge object."
+    )
+
 
     @field_validator("id", mode="before")
     @classmethod
@@ -238,13 +262,31 @@ class Crew(BaseModel):
             self._short_term_memory = (
                 self.short_term_memory
                 if self.short_term_memory
-                else ShortTermMemory(crew=self, embedder_config=self.embedder)
+                else ShortTermMemory(
+                    crew=self,
+                    embedder_config=self.embedder,
+                )
             )
             self._entity_memory = (
                 self.entity_memory
                 if self.entity_memory
                 else EntityMemory(crew=self, embedder_config=self.embedder)
             )
+            if hasattr(self, "memory_config") and self.memory_config is not None:
+                self._user_memory = (
+                    self.user_memory if self.user_memory else UserMemory(crew=self)
+                )
+            else:
+                self._user_memory = None
+        return self
+
+    @model_validator(mode="after")
+    def create_crew_knowledge(self) -> "Crew":
+        if self.knowledge:
+            try:
+                self.knowledge = Knowledge(**self.knowledge) if isinstance(self.knowledge, dict) else self.knowledge
+            except (TypeError, ValueError) as e:
+                raise ValueError(f"Invalid knowledge configuration: {str(e)}")
         return self
 
     @model_validator(mode="after")
@@ -445,18 +487,22 @@ class Crew(BaseModel):
         training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
 
         for agent in train_crew.agents:
-            result = TaskEvaluator(agent).evaluate_training_data(
-                training_data=training_data, agent_id=str(agent.id)
-            )
+            if training_data.get(str(agent.id)):
+                result = TaskEvaluator(agent).evaluate_training_data(
+                    training_data=training_data, agent_id=str(agent.id)
+                )
 
-            CrewTrainingHandler(filename).save_trained_data(
-                agent_id=str(agent.role), trained_data=result.model_dump()
-            )
+                CrewTrainingHandler(filename).save_trained_data(
+                    agent_id=str(agent.role), trained_data=result.model_dump()
+                )
 
     def kickoff(
         self,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> CrewOutput:
+        for before_callback in self.before_kickoff_callbacks:
+            inputs = before_callback(inputs)
+
         """Starts the crew to work on its assigned tasks."""
         self._execution_span = self._telemetry.crew_execution_span(self, inputs)
         self._task_output_handler.reset()
@@ -499,6 +545,9 @@ class Crew(BaseModel):
                 f"The process '{self.process}' is not implemented yet."
             )
 
+        for after_callback in self.after_kickoff_callbacks:
+            result = after_callback(result)
+
         metrics += [agent._token_process.get_summary() for agent in self.agents]
 
         self.usage_metrics = UsageMetrics()

src/crewai/flow/flow.py

@@ -1,8 +1,20 @@
 import asyncio
 import inspect
-from typing import Any, Callable, Dict, Generic, List, Set, Type, TypeVar, Union
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Generic,
+    List,
+    Optional,
+    Set,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ValidationError
 
 from crewai.flow.flow_visualizer import plot_flow
 from crewai.flow.utils import get_possible_return_constants
@@ -119,7 +131,6 @@ class FlowMeta(type):
                 condition_type = getattr(attr_value, "__condition_type__", "OR")
                 listeners[attr_name] = (condition_type, methods)
 
-            # TODO: should we add a check for __condition_type__ 'AND'?
             elif hasattr(attr_value, "__is_router__"):
                 routers[attr_value.__router_for__] = attr_name
                 possible_returns = get_possible_return_constants(attr_value)
@@ -159,8 +170,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
     def __init__(self) -> None:
         self._methods: Dict[str, Callable] = {}
         self._state: T = self._create_initial_state()
-        self._executed_methods: Set[str] = set()
-        self._scheduled_tasks: Set[str] = set()
+        self._method_execution_counts: Dict[str, int] = {}
         self._pending_and_listeners: Dict[str, Set[str]] = {}
         self._method_outputs: List[Any] = []  # List to store all method outputs
 
@@ -191,10 +201,74 @@ class Flow(Generic[T], metaclass=FlowMeta):
         """Returns the list of all outputs from executed methods."""
         return self._method_outputs
 
-    def kickoff(self) -> Any:
+    def _initialize_state(self, inputs: Dict[str, Any]) -> None:
+        """
+        Initializes or updates the state with the provided inputs.
+
+        Args:
+            inputs: Dictionary of inputs to initialize or update the state.
+
+        Raises:
+            ValueError: If inputs do not match the structured state model.
+            TypeError: If state is neither a BaseModel instance nor a dictionary.
+        """
+        if isinstance(self._state, BaseModel):
+            # Structured state management
+            try:
+                # Define a function to create the dynamic class
+                def create_model_with_extra_forbid(
+                    base_model: Type[BaseModel],
+                ) -> Type[BaseModel]:
+                    class ModelWithExtraForbid(base_model):  # type: ignore
+                        model_config = base_model.model_config.copy()
+                        model_config["extra"] = "forbid"
+
+                    return ModelWithExtraForbid
+
+                # Create the dynamic class
+                ModelWithExtraForbid = create_model_with_extra_forbid(
+                    self._state.__class__
+                )
+
+                # Create a new instance using the combined state and inputs
+                self._state = cast(
+                    T, ModelWithExtraForbid(**{**self._state.model_dump(), **inputs})
+                )
+
+            except ValidationError as e:
+                raise ValueError(f"Invalid inputs for structured state: {e}") from e
+        elif isinstance(self._state, dict):
+            # Unstructured state management
+            self._state.update(inputs)
+        else:
+            raise TypeError("State must be a BaseModel instance or a dictionary.")
+
+    def kickoff(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
+        """
+        Starts the execution of the flow synchronously.
+
+        Args:
+            inputs: Optional dictionary of inputs to initialize or update the state.
+
+        Returns:
+            The final output from the flow execution.
+        """
+        if inputs is not None:
+            self._initialize_state(inputs)
         return asyncio.run(self.kickoff_async())
 
-    async def kickoff_async(self) -> Any:
+    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
+        """
+        Starts the execution of the flow asynchronously.
+
+        Args:
+            inputs: Optional dictionary of inputs to initialize or update the state.
+
+        Returns:
+            The final output from the flow execution.
+        """
+        if inputs is not None:
+            self._initialize_state(inputs)
         if not self._start_methods:
             raise ValueError("No start method defined")
 
@@ -233,7 +307,10 @@ class Flow(Generic[T], metaclass=FlowMeta):
         )
         self._method_outputs.append(result)  # Store the output
 
-        self._executed_methods.add(method_name)
+        # Track method execution counts
+        self._method_execution_counts[method_name] = (
+            self._method_execution_counts.get(method_name, 0) + 1
+        )
 
         return result
 
@@ -243,35 +320,34 @@ class Flow(Generic[T], metaclass=FlowMeta):
         if trigger_method in self._routers:
             router_method = self._methods[self._routers[trigger_method]]
             path = await self._execute_method(
-                trigger_method, router_method
-            )  # TODO: Change or not?
-            # Use the path as the new trigger method
+                self._routers[trigger_method], router_method
+            )
             trigger_method = path
 
         for listener_name, (condition_type, methods) in self._listeners.items():
             if condition_type == "OR":
                 if trigger_method in methods:
-                    if (
-                        listener_name not in self._executed_methods
-                        and listener_name not in self._scheduled_tasks
-                    ):
-                        self._scheduled_tasks.add(listener_name)
-                        listener_tasks.append(
-                            self._execute_single_listener(listener_name, result)
-                        )
+                    # Schedule the listener without preventing re-execution
+                    listener_tasks.append(
+                        self._execute_single_listener(listener_name, result)
+                    )
             elif condition_type == "AND":
-                if all(method in self._executed_methods for method in methods):
-                    if (
-                        listener_name not in self._executed_methods
-                        and listener_name not in self._scheduled_tasks
-                    ):
-                        self._scheduled_tasks.add(listener_name)
-                        listener_tasks.append(
-                            self._execute_single_listener(listener_name, result)
-                        )
+                # Initialize pending methods for this listener if not already done
+                if listener_name not in self._pending_and_listeners:
+                    self._pending_and_listeners[listener_name] = set(methods)
+                # Remove the trigger method from pending methods
+                self._pending_and_listeners[listener_name].discard(trigger_method)
+                if not self._pending_and_listeners[listener_name]:
+                    # All required methods have been executed
+                    listener_tasks.append(
+                        self._execute_single_listener(listener_name, result)
+                    )
+                    # Reset pending methods for this listener
+                    self._pending_and_listeners.pop(listener_name, None)
 
         # Run all listener tasks concurrently and wait for them to complete
-        await asyncio.gather(*listener_tasks)
+        if listener_tasks:
+            await asyncio.gather(*listener_tasks)
 
     async def _execute_single_listener(self, listener_name: str, result: Any) -> None:
         try:
@@ -291,9 +367,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 # If listener does not expect parameters, call without arguments
                 listener_result = await self._execute_method(listener_name, method)
 
-            # Remove from scheduled tasks after execution
-            self._scheduled_tasks.discard(listener_name)
-
             # Execute listeners of this listener
             await self._execute_listeners(listener_name, listener_result)
         except Exception as e:

src/crewai/knowledge/embedder/base_embedder.py

@@ -0,0 +1,55 @@
+from abc import ABC, abstractmethod
+from typing import List
+
+import numpy as np
+
+
+class BaseEmbedder(ABC):
+    """
+    Abstract base class for text embedding models
+    """
+
+    @abstractmethod
+    def embed_chunks(self, chunks: List[str]) -> np.ndarray:
+        """
+        Generate embeddings for a list of text chunks
+
+        Args:
+            chunks: List of text chunks to embed
+
+        Returns:
+            Array of embeddings
+        """
+        pass
+
+    @abstractmethod
+    def embed_texts(self, texts: List[str]) -> np.ndarray:
+        """
+        Generate embeddings for a list of texts
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            Array of embeddings
+        """
+        pass
+
+    @abstractmethod
+    def embed_text(self, text: str) -> np.ndarray:
+        """
+        Generate embedding for a single text
+
+        Args:
+            text: Text to embed
+
+        Returns:
+            Embedding array
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def dimension(self) -> int:
+        """Get the dimension of the embeddings"""
+        pass

src/crewai/knowledge/embedder/fastembed.py

@@ -0,0 +1,93 @@
+from pathlib import Path
+from typing import List, Optional, Union
+
+import numpy as np
+
+from .base_embedder import BaseEmbedder
+
+try:
+    from fastembed_gpu import TextEmbedding  # type: ignore
+
+    FASTEMBED_AVAILABLE = True
+except ImportError:
+    try:
+        from fastembed import TextEmbedding
+
+        FASTEMBED_AVAILABLE = True
+    except ImportError:
+        FASTEMBED_AVAILABLE = False
+
+
+class FastEmbed(BaseEmbedder):
+    """
+    A wrapper class for text embedding models using FastEmbed
+    """
+
+    def __init__(
+        self,
+        model_name: str = "BAAI/bge-small-en-v1.5",
+        cache_dir: Optional[Union[str, Path]] = None,
+    ):
+        """
+        Initialize the embedding model
+
+        Args:
+            model_name: Name of the model to use
+            cache_dir: Directory to cache the model
+            gpu: Whether to use GPU acceleration
+        """
+        if not FASTEMBED_AVAILABLE:
+            raise ImportError(
+                "FastEmbed is not installed. Please install it with: "
+                "uv pip install fastembed or uv pip install fastembed-gpu for GPU support"
+            )
+
+        self.model = TextEmbedding(
+            model_name=model_name,
+            cache_dir=str(cache_dir) if cache_dir else None,
+        )
+
+    def embed_chunks(self, chunks: List[str]) -> List[np.ndarray]:
+        """
+        Generate embeddings for a list of text chunks
+
+        Args:
+            chunks: List of text chunks to embed
+
+        Returns:
+            List of embeddings
+        """
+        embeddings = list(self.model.embed(chunks))
+        return embeddings
+
+    def embed_texts(self, texts: List[str]) -> List[np.ndarray]:
+        """
+        Generate embeddings for a list of texts
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            List of embeddings
+        """
+        embeddings = list(self.model.embed(texts))
+        return embeddings
+
+    def embed_text(self, text: str) -> np.ndarray:
+        """
+        Generate embedding for a single text
+
+        Args:
+            text: Text to embed
+
+        Returns:
+            Embedding array
+        """
+        return self.embed_texts([text])[0]
+
+    @property
+    def dimension(self) -> int:
+        """Get the dimension of the embeddings"""
+        # Generate a test embedding to get dimensions
+        test_embed = self.embed_text("test")
+        return len(test_embed)

src/crewai/knowledge/knowledge.py

@@ -0,0 +1,54 @@
+import os
+
+from typing import List, Optional, Dict, Any
+from pydantic import BaseModel, ConfigDict, Field
+
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+from crewai.utilities.logger import Logger
+from crewai.utilities.constants import DEFAULT_SCORE_THRESHOLD
+os.environ["TOKENIZERS_PARALLELISM"] = "false"  # removes logging from fastembed
+
+
+class Knowledge(BaseModel):
+    """
+    Knowledge is a collection of sources and setup for the vector store to save and query relevant context.
+    Args:
+        sources: List[BaseKnowledgeSource] = Field(default_factory=list)
+        storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+        embedder_config: Optional[Dict[str, Any]] = None
+    """
+    sources: List[BaseKnowledgeSource] = Field(default_factory=list)
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    embedder_config: Optional[Dict[str, Any]] = None
+
+    def __init__(self, embedder_config: Optional[Dict[str, Any]] = None, **data):
+        super().__init__(**data)
+        self.storage = KnowledgeStorage(embedder_config=embedder_config or None)
+
+        try:
+            for source in self.sources:
+                source.add()
+        except Exception as e:
+            Logger(verbose=True).log(
+                "warning",
+                f"Failed to init knowledge: {e}",
+                color="yellow",
+            )
+
+    def query(
+        self, query: List[str], limit: int = 3, preference: Optional[str] = None
+    ) -> List[Dict[str, Any]]:
+        """
+        Query across all knowledge sources to find the most relevant information.
+        Returns the top_k most relevant chunks.
+        """
+
+        results = self.storage.search(
+            query,
+            limit,
+            filter={"preference": preference} if preference else None,
+            score_threshold=DEFAULT_SCORE_THRESHOLD,
+        )
+        return results

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -0,0 +1,36 @@
+from pathlib import Path
+from typing import Union, List
+
+from pydantic import Field
+
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from typing import Dict, Any
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+
+
+class BaseFileKnowledgeSource(BaseKnowledgeSource):
+    """Base class for knowledge sources that load content from files."""
+
+    file_path: Union[Path, List[Path]] = Field(...)
+    content: Dict[Path, str] = Field(init=False, default_factory=dict)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+
+    def model_post_init(self, _):
+        """Post-initialization method to load content."""
+        self.content = self.load_content()
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess file content. Should be overridden by subclasses."""
+        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
+
+        for path in paths:
+            if not path.exists():
+                raise FileNotFoundError(f"File not found: {path}")
+            if not path.is_file():
+                raise ValueError(f"Path is not a file: {path}")
+        return {}
+
+    def save_documents(self, metadata: Dict[str, Any]):
+        """Save the documents to the storage."""
+        chunk_metadatas = [metadata.copy() for _ in self.chunks]
+        self.storage.save(self.chunks, chunk_metadatas)

src/crewai/knowledge/source/base_knowledge_source.py

@@ -0,0 +1,48 @@
+from abc import ABC, abstractmethod
+from typing import List, Dict, Any
+
+import numpy as np
+from pydantic import BaseModel, ConfigDict, Field
+
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+
+
+class BaseKnowledgeSource(BaseModel, ABC):
+    """Abstract base class for knowledge sources."""
+
+    chunk_size: int = 4000
+    chunk_overlap: int = 200
+    chunks: List[str] = Field(default_factory=list)
+    chunk_embeddings: List[np.ndarray] = Field(default_factory=list)
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+
+    @abstractmethod
+    def load_content(self) -> Dict[Any, str]:
+        """Load and preprocess content from the source."""
+        pass
+
+    @abstractmethod
+    def add(self) -> None:
+        """Process content, chunk it, compute embeddings, and save them."""
+        pass
+
+    def get_embeddings(self) -> List[np.ndarray]:
+        """Return the list of embeddings for the chunks."""
+        return self.chunk_embeddings
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]
+
+    def save_documents(self, metadata: Dict[str, Any]):
+        """
+        Save the documents to the storage.
+        This method should be called after the chunks and embeddings are generated.
+        """
+        self.storage.save(self.chunks, metadata)

src/crewai/knowledge/source/csv_knowledge_source.py

@@ -0,0 +1,44 @@
+import csv
+from typing import Dict, List
+from pathlib import Path
+
+from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+
+
+class CSVKnowledgeSource(BaseFileKnowledgeSource):
+    """A knowledge source that stores and queries CSV file content using embeddings."""
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess CSV file content."""
+        super().load_content()  # Validate the file path
+
+        file_path = (
+            self.file_path[0] if isinstance(self.file_path, list) else self.file_path
+        )
+        file_path = Path(file_path) if isinstance(file_path, str) else file_path
+
+        with open(file_path, "r", encoding="utf-8") as csvfile:
+            reader = csv.reader(csvfile)
+            content = ""
+            for row in reader:
+                content += " ".join(row) + "\n"
+        return {file_path: content}
+
+    def add(self) -> None:
+        """
+        Add CSV file content to the knowledge source, chunk it, compute embeddings,
+        and save the embeddings.
+        """
+        content_str = (
+            str(self.content) if isinstance(self.content, dict) else self.content
+        )
+        new_chunks = self._chunk_text(content_str)
+        self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/source/excel_knowledge_source.py

@@ -0,0 +1,56 @@
+from typing import Dict, List
+from pathlib import Path
+from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+
+
+class ExcelKnowledgeSource(BaseFileKnowledgeSource):
+    """A knowledge source that stores and queries Excel file content using embeddings."""
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess Excel file content."""
+        super().load_content()  # Validate the file path
+        pd = self._import_dependencies()
+
+        if isinstance(self.file_path, list):
+            file_path = self.file_path[0]
+        else:
+            file_path = self.file_path
+
+        df = pd.read_excel(file_path)
+        content = df.to_csv(index=False)
+        return {file_path: content}
+
+    def _import_dependencies(self):
+        """Dynamically import dependencies."""
+        try:
+            import openpyxl  # noqa
+            import pandas as pd
+
+            return pd
+        except ImportError as e:
+            missing_package = str(e).split()[-1]
+            raise ImportError(
+                f"{missing_package} is not installed. Please install it with: pip install {missing_package}"
+            )
+
+    def add(self) -> None:
+        """
+        Add Excel file content to the knowledge source, chunk it, compute embeddings,
+        and save the embeddings.
+        """
+        # Convert dictionary values to a single string if content is a dictionary
+        if isinstance(self.content, dict):
+            content_str = "\n".join(str(value) for value in self.content.values())
+        else:
+            content_str = str(self.content)
+
+        new_chunks = self._chunk_text(content_str)
+        self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/source/json_knowledge_source.py

@@ -0,0 +1,54 @@
+import json
+from typing import Any, Dict, List
+from pathlib import Path
+
+from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+
+
+class JSONKnowledgeSource(BaseFileKnowledgeSource):
+    """A knowledge source that stores and queries JSON file content using embeddings."""
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess JSON file content."""
+        super().load_content()  # Validate the file path
+        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
+
+        content: Dict[Path, str] = {}
+        for path in paths:
+            with open(path, "r", encoding="utf-8") as json_file:
+                data = json.load(json_file)
+            content[path] = self._json_to_text(data)
+        return content
+
+    def _json_to_text(self, data: Any, level: int = 0) -> str:
+        """Recursively convert JSON data to a text representation."""
+        text = ""
+        indent = "  " * level
+        if isinstance(data, dict):
+            for key, value in data.items():
+                text += f"{indent}{key}: {self._json_to_text(value, level + 1)}\n"
+        elif isinstance(data, list):
+            for item in data:
+                text += f"{indent}- {self._json_to_text(item, level + 1)}\n"
+        else:
+            text += f"{str(data)}"
+        return text
+
+    def add(self) -> None:
+        """
+        Add JSON file content to the knowledge source, chunk it, compute embeddings,
+        and save the embeddings.
+        """
+        content_str = (
+            str(self.content) if isinstance(self.content, dict) else self.content
+        )
+        new_chunks = self._chunk_text(content_str)
+        self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/source/pdf_knowledge_source.py

@@ -0,0 +1,54 @@
+from typing import List, Dict
+from pathlib import Path
+
+from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+
+
+class PDFKnowledgeSource(BaseFileKnowledgeSource):
+    """A knowledge source that stores and queries PDF file content using embeddings."""
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess PDF file content."""
+        super().load_content()  # Validate the file paths
+        pdfplumber = self._import_pdfplumber()
+
+        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
+        content = {}
+
+        for path in paths:
+            text = ""
+            with pdfplumber.open(path) as pdf:
+                for page in pdf.pages:
+                    page_text = page.extract_text()
+                    if page_text:
+                        text += page_text + "\n"
+            content[path] = text
+        return content
+
+    def _import_pdfplumber(self):
+        """Dynamically import pdfplumber."""
+        try:
+            import pdfplumber
+
+            return pdfplumber
+        except ImportError:
+            raise ImportError(
+                "pdfplumber is not installed. Please install it with: pip install pdfplumber"
+            )
+
+    def add(self) -> None:
+        """
+        Add PDF file content to the knowledge source, chunk it, compute embeddings,
+        and save the embeddings.
+        """
+        for _, text in self.content.items():
+            new_chunks = self._chunk_text(text)
+            self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/source/string_knowledge_source.py

@@ -0,0 +1,33 @@
+from typing import List
+
+from pydantic import Field
+
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+
+
+class StringKnowledgeSource(BaseKnowledgeSource):
+    """A knowledge source that stores and queries plain text content using embeddings."""
+
+    content: str = Field(...)
+
+    def model_post_init(self, _):
+        """Post-initialization method to validate content."""
+        self.load_content()
+
+    def load_content(self):
+        """Validate string content."""
+        if not isinstance(self.content, str):
+            raise ValueError("StringKnowledgeSource only accepts string content")
+
+    def add(self) -> None:
+        """Add string content to the knowledge source, chunk it, compute embeddings, and save them."""
+        new_chunks = self._chunk_text(self.content)
+        self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/source/text_file_knowledge_source.py

@@ -0,0 +1,35 @@
+from typing import Dict, List
+from pathlib import Path
+
+from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+
+
+class TextFileKnowledgeSource(BaseFileKnowledgeSource):
+    """A knowledge source that stores and queries text file content using embeddings."""
+
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess text file content."""
+        super().load_content()
+        paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
+        content = {}
+        for path in paths:
+            with path.open("r", encoding="utf-8") as f:
+                content[path] = f.read()  # type: ignore
+        return content
+
+    def add(self) -> None:
+        """
+        Add text file content to the knowledge source, chunk it, compute embeddings,
+        and save the embeddings.
+        """
+        for _, text in self.content.items():
+            new_chunks = self._chunk_text(text)
+            self.chunks.extend(new_chunks)
+        self.save_documents(metadata=self.metadata)
+
+    def _chunk_text(self, text: str) -> List[str]:
+        """Utility method to split text into chunks."""
+        return [
+            text[i : i + self.chunk_size]
+            for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
+        ]

src/crewai/knowledge/storage/base_knowledge_storage.py

@@ -0,0 +1,29 @@
+from abc import ABC, abstractmethod
+from typing import Dict, Any, List, Optional
+
+
+class BaseKnowledgeStorage(ABC):
+    """Abstract base class for knowledge storage implementations."""
+
+    @abstractmethod
+    def search(
+        self,
+        query: List[str],
+        limit: int = 3,
+        filter: Optional[dict] = None,
+        score_threshold: float = 0.35,
+    ) -> List[Dict[str, Any]]:
+        """Search for documents in the knowledge base."""
+        pass
+
+    @abstractmethod
+    def save(
+        self, documents: List[str], metadata: Dict[str, Any] | List[Dict[str, Any]]
+    ) -> None:
+        """Save documents to the knowledge base."""
+        pass
+
+    @abstractmethod
+    def reset(self) -> None:
+        """Reset the knowledge base."""
+        pass

src/crewai/knowledge/storage/knowledge_storage.py

@@ -0,0 +1,132 @@
+import contextlib
+import io
+import logging
+import chromadb
+import os
+from crewai.utilities.paths import db_storage_path
+from typing import Optional, List
+from typing import Dict, Any
+from crewai.utilities import EmbeddingConfigurator
+from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
+import hashlib
+
+
+@contextlib.contextmanager
+def suppress_logging(
+    logger_name="chromadb.segment.impl.vector.local_persistent_hnsw",
+    level=logging.ERROR,
+):
+    logger = logging.getLogger(logger_name)
+    original_level = logger.getEffectiveLevel()
+    logger.setLevel(level)
+    with (
+        contextlib.redirect_stdout(io.StringIO()),
+        contextlib.redirect_stderr(io.StringIO()),
+        contextlib.suppress(UserWarning),
+    ):
+        yield
+    logger.setLevel(original_level)
+
+
+class KnowledgeStorage(BaseKnowledgeStorage):
+    """
+    Extends Storage to handle embeddings for memory entries, improving
+    search efficiency.
+    """
+
+    collection: Optional[chromadb.Collection] = None
+
+    def __init__(self, embedder_config: Optional[Dict[str, Any]] = None):
+        self._initialize_app(embedder_config or {})
+
+    def search(
+        self,
+        query: List[str],
+        limit: int = 3,
+        filter: Optional[dict] = None,
+        score_threshold: float = 0.35,
+    ) -> List[Dict[str, Any]]:
+        with suppress_logging():
+            if self.collection:
+                fetched = self.collection.query(
+                    query_texts=query,
+                    n_results=limit,
+                    where=filter,
+                )
+                results = []
+                for i in range(len(fetched["ids"][0])):  # type: ignore
+                    result = {
+                        "id": fetched["ids"][0][i],  # type: ignore
+                        "metadata": fetched["metadatas"][0][i],  # type: ignore
+                        "context": fetched["documents"][0][i],  # type: ignore
+                        "score": fetched["distances"][0][i],  # type: ignore
+                    }
+                    if result["score"] >= score_threshold:  # type: ignore
+                        results.append(result)
+                return results
+            else:
+                raise Exception("Collection not initialized")
+
+    def _initialize_app(self, embedder_config: Optional[Dict[str, Any]] = None):
+        import chromadb
+        from chromadb.config import Settings
+
+        self._set_embedder_config(embedder_config)
+
+        chroma_client = chromadb.PersistentClient(
+            path=f"{db_storage_path()}/knowledge",
+            settings=Settings(allow_reset=True),
+        )
+
+        self.app = chroma_client
+
+        try:
+            self.collection = self.app.get_or_create_collection(name="knowledge")
+        except Exception:
+            raise Exception("Failed to create or get collection")
+
+    def reset(self):
+        if self.app:
+            self.app.reset()
+
+    def save(
+        self, documents: List[str], metadata: Dict[str, Any] | List[Dict[str, Any]]
+    ):
+        if self.collection:
+            metadatas = [metadata] if isinstance(metadata, dict) else metadata
+
+            ids = [
+                hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents
+            ]
+
+            self.collection.upsert(
+                documents=documents,
+                metadatas=metadatas,
+                ids=ids,
+            )
+        else:
+            raise Exception("Collection not initialized")
+
+    def _create_default_embedding_function(self):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
+
+    def _set_embedder_config(
+        self, embedder_config: Optional[Dict[str, Any]] = None
+    ) -> None:
+        """Set the embedding configuration for the knowledge storage.
+
+        Args:
+            embedder_config (Optional[Dict[str, Any]]): Configuration dictionary for the embedder.
+                If None or empty, defaults to the default embedding function.
+        """
+        self.embedder_config = (
+            EmbeddingConfigurator().configure_embedder(embedder_config)
+            if embedder_config
+            else self._create_default_embedding_function()
+        )

src/crewai/llm.py

@@ -1,7 +1,10 @@
+import logging
+import sys
+import threading
+import warnings
 from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Union
-import logging
-import warnings
+
 import litellm
 from litellm import get_supported_openai_params
 
@@ -9,20 +12,26 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
 
-import sys
-import io
 
+class FilteredStream:
+    def __init__(self, original_stream):
+        self._original_stream = original_stream
+        self._lock = threading.Lock()
 
-class FilteredStream(io.StringIO):
-    def write(self, s):
-        if (
-            "Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new"
-            in s
-            or "LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True`"
-            in s
-        ):
-            return
-        super().write(s)
+    def write(self, s) -> int:
+        with self._lock:
+            if (
+                "Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new"
+                in s
+                or "LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True`"
+                in s
+            ):
+                return 0
+            return self._original_stream.write(s)
+
+    def flush(self):
+        with self._lock:
+            return self._original_stream.flush()
 
 
 LLM_CONTEXT_WINDOW_SIZES = {
@@ -60,8 +69,8 @@ def suppress_warnings():
         # Redirect stdout and stderr
         old_stdout = sys.stdout
         old_stderr = sys.stderr
-        sys.stdout = FilteredStream()
-        sys.stderr = FilteredStream()
+        sys.stdout = FilteredStream(old_stdout)
+        sys.stderr = FilteredStream(old_stderr)
 
         try:
             yield
@@ -118,12 +127,12 @@ class LLM:
 
         litellm.drop_params = True
         litellm.set_verbose = False
-        litellm.callbacks = callbacks
+        self.set_callbacks(callbacks)
 
     def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
-                litellm.callbacks = callbacks
+                self.set_callbacks(callbacks)
 
             try:
                 params = {
@@ -181,3 +190,15 @@ class LLM:
     def get_context_window_size(self) -> int:
         # Only using 75% of the context window size to avoid cutting the message in the middle
         return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75)
+
+    def set_callbacks(self, callbacks: List[Any]):
+        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)
+
+        litellm.callbacks = callbacks

src/crewai/memory/__init__.py

@@ -1,5 +1,6 @@
 from .entity.entity_memory import EntityMemory
 from .long_term.long_term_memory import LongTermMemory
 from .short_term.short_term_memory import ShortTermMemory
+from .user.user_memory import UserMemory
 
-__all__ = ["EntityMemory", "LongTermMemory", "ShortTermMemory"]
+__all__ = ["UserMemory", "EntityMemory", "LongTermMemory", "ShortTermMemory"]

src/crewai/memory/contextual/contextual_memory.py

@@ -1,13 +1,25 @@
-from typing import Optional
+from typing import Optional, Dict, Any
 
-from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory
+from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
 
 
 class ContextualMemory:
-    def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory):
+    def __init__(
+        self,
+        memory_config: Optional[Dict[str, Any]],
+        stm: ShortTermMemory,
+        ltm: LongTermMemory,
+        em: EntityMemory,
+        um: UserMemory,
+    ):
+        if memory_config is not None:
+            self.memory_provider = memory_config.get("provider")
+        else:
+            self.memory_provider = None
         self.stm = stm
         self.ltm = ltm
         self.em = em
+        self.um = um
 
     def build_context_for_task(self, task, context) -> str:
         """
@@ -23,6 +35,8 @@ class ContextualMemory:
         context.append(self._fetch_ltm_context(task.description))
         context.append(self._fetch_stm_context(query))
         context.append(self._fetch_entity_context(query))
+        if self.memory_provider == "mem0":
+            context.append(self._fetch_user_context(query))
         return "\n".join(filter(None, context))
 
     def _fetch_stm_context(self, query) -> str:
@@ -32,9 +46,11 @@ class ContextualMemory:
         """
         stm_results = self.stm.search(query)
         formatted_results = "\n".join(
-            [f"- {result['context']}" for result in stm_results]
+            [
+                f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
+                for result in stm_results
+            ]
         )
-        print("formatted_results stm", formatted_results)
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
     def _fetch_ltm_context(self, task) -> Optional[str]:
@@ -54,8 +70,6 @@ class ContextualMemory:
         formatted_results = list(dict.fromkeys(formatted_results))
         formatted_results = "\n".join([f"- {result}" for result in formatted_results])  # type: ignore # Incompatible types in assignment (expression has type "str", variable has type "list[str]")
 
-        print("formatted_results ltm", formatted_results)
-
         return f"Historical Data:\n{formatted_results}" if ltm_results else ""
 
     def _fetch_entity_context(self, query) -> str:
@@ -65,7 +79,26 @@ class ContextualMemory:
         """
         em_results = self.em.search(query)
         formatted_results = "\n".join(
-            [f"- {result['context']}" for result in em_results]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
+            [
+                f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
+                for result in em_results
+            ]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
         )
-        print("formatted_results em", formatted_results)
         return f"Entities:\n{formatted_results}" if em_results else ""
+
+    def _fetch_user_context(self, query: str) -> str:
+        """
+        Fetches and formats relevant user information from User Memory.
+        Args:
+            query (str): The search query to find relevant user memories.
+        Returns:
+            str: Formatted user memories as bullet points, or an empty string if none found.
+        """
+        user_memories = self.um.search(query)
+        if not user_memories:
+            return ""
+
+        formatted_memories = "\n".join(
+            f"- {result['memory']}" for result in user_memories
+        )
+        return f"User memories/preferences:\n{formatted_memories}"

src/crewai/memory/entity/entity_memory.py

@@ -11,21 +11,43 @@ class EntityMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None):
-        storage = (
-            storage
-            if storage
-            else RAGStorage(
-                type="entities",
-                allow_reset=True,
-                embedder_config=embedder_config,
-                crew=crew,
+        if hasattr(crew, "memory_config") and crew.memory_config is not None:
+            self.memory_provider = crew.memory_config.get("provider")
+        else:
+            self.memory_provider = None
+
+        if self.memory_provider == "mem0":
+            try:
+                from crewai.memory.storage.mem0_storage import Mem0Storage
+            except ImportError:
+                raise ImportError(
+                    "Mem0 is not installed. Please install it with `pip install mem0ai`."
+                )
+            storage = Mem0Storage(type="entities", crew=crew)
+        else:
+            storage = (
+                storage
+                if storage
+                else RAGStorage(
+                    type="entities",
+                    allow_reset=True,
+                    embedder_config=embedder_config,
+                    crew=crew,
+                )
             )
-        )
         super().__init__(storage)
 
     def save(self, item: EntityMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
         """Saves an entity item into the SQLite storage."""
-        data = f"{item.name}({item.type}): {item.description}"
+        if self.memory_provider == "mem0":
+            data = f"""
+            Remember details about the following entity:
+            Name: {item.name}
+            Type: {item.type}
+            Entity Description: {item.description}
+            """
+        else:
+            data = f"{item.name}({item.type}): {item.description}"
         super().save(data, item.metadata)
 
     def reset(self) -> None:

src/crewai/memory/memory.py

@@ -23,5 +23,12 @@ class Memory:
 
         self.storage.save(value, metadata)
 
-    def search(self, query: str) -> List[Dict[str, Any]]:
-        return self.storage.search(query)
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        score_threshold: float = 0.35,
+    ) -> List[Any]:
+        return self.storage.search(
+            query=query, limit=limit, score_threshold=score_threshold
+        )

src/crewai/memory/short_term/short_term_memory.py

@@ -14,13 +14,27 @@ class ShortTermMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None):
-        storage = (
-            storage
-            if storage
-            else RAGStorage(
-                type="short_term", embedder_config=embedder_config, crew=crew
+        if hasattr(crew, "memory_config") and crew.memory_config is not None:
+            self.memory_provider = crew.memory_config.get("provider")
+        else:
+            self.memory_provider = None
+
+        if self.memory_provider == "mem0":
+            try:
+                from crewai.memory.storage.mem0_storage import Mem0Storage
+            except ImportError:
+                raise ImportError(
+                    "Mem0 is not installed. Please install it with `pip install mem0ai`."
+                )
+            storage = Mem0Storage(type="short_term", crew=crew)
+        else:
+            storage = (
+                storage
+                if storage
+                else RAGStorage(
+                    type="short_term", embedder_config=embedder_config, crew=crew
+                )
             )
-        )
         super().__init__(storage)
 
     def save(
@@ -30,11 +44,20 @@ class ShortTermMemory(Memory):
         agent: Optional[str] = None,
     ) -> None:
         item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
+        if self.memory_provider == "mem0":
+            item.data = f"Remember the following insights from Agent run: {item.data}"
 
         super().save(value=item.data, metadata=item.metadata, agent=item.agent)
 
-    def search(self, query: str, score_threshold: float = 0.35):
-        return self.storage.search(query=query, score_threshold=score_threshold)  # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        score_threshold: float = 0.35,
+    ):
+        return self.storage.search(
+            query=query, limit=limit, score_threshold=score_threshold
+        )  # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
 
     def reset(self) -> None:
         try:

src/crewai/memory/storage/interface.py

@@ -7,8 +7,10 @@ class Storage:
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         pass
 
-    def search(self, key: str) -> List[Dict[str, Any]]:  # type: ignore
-        pass
+    def search(
+        self, query: str, limit: int, score_threshold: float
+    ) -> Dict[str, Any] | List[Any]:
+        return {}
 
     def reset(self) -> None:
         pass

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -103,7 +103,7 @@ class KickoffTaskOutputsSQLiteStorage:
                         else value
                     )
 
-                query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?"
+                query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?"  # nosec
                 values.append(task_index)
 
                 cursor.execute(query, tuple(values))

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -83,7 +83,7 @@ class LTMSQLiteStorage:
                     WHERE task_description = ?
                     ORDER BY datetime DESC, score ASC
                     LIMIT {latest_n}
-                """,
+                """,  # nosec
                     (task_description,),
                 )
                 rows = cursor.fetchall()

src/crewai/memory/storage/mem0_storage.py

@@ -0,0 +1,104 @@
+import os
+from typing import Any, Dict, List
+
+from mem0 import MemoryClient
+from crewai.memory.storage.interface import Storage
+
+
+class Mem0Storage(Storage):
+    """
+    Extends Storage to handle embedding and searching across entities using Mem0.
+    """
+
+    def __init__(self, type, crew=None):
+        super().__init__()
+
+        if type not in ["user", "short_term", "long_term", "entities"]:
+            raise ValueError("Invalid type for Mem0Storage. Must be 'user' or 'agent'.")
+
+        self.memory_type = type
+        self.crew = crew
+        self.memory_config = crew.memory_config
+
+        # User ID is required for user memory type "user" since it's used as a unique identifier for the user.
+        user_id = self._get_user_id()
+        if type == "user" and not user_id:
+            raise ValueError("User ID is required for user memory type")
+
+        # API key in memory config overrides the environment variable
+        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:
+        """
+        Sanitizes agent roles to ensure valid directory names.
+        """
+        return role.replace("\n", "").replace(" ", "_").replace("/", "_")
+
+    def save(self, value: Any, metadata: Dict[str, Any]) -> None:
+        user_id = self._get_user_id()
+        agent_name = self._get_agent_name()
+        if self.memory_type == "user":
+            self.memory.add(value, user_id=user_id, metadata={**metadata})
+        elif self.memory_type == "short_term":
+            agent_name = self._get_agent_name()
+            self.memory.add(
+                value, agent_id=agent_name, metadata={"type": "short_term", **metadata}
+            )
+        elif self.memory_type == "long_term":
+            agent_name = self._get_agent_name()
+            self.memory.add(
+                value,
+                agent_id=agent_name,
+                infer=False,
+                metadata={"type": "long_term", **metadata},
+            )
+        elif self.memory_type == "entities":
+            entity_name = None
+            self.memory.add(
+                value, user_id=entity_name, metadata={"type": "entity", **metadata}
+            )
+
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        score_threshold: float = 0.35,
+    ) -> List[Any]:
+        params = {"query": query, "limit": limit}
+        if self.memory_type == "user":
+            user_id = self._get_user_id()
+            params["user_id"] = user_id
+        elif self.memory_type == "short_term":
+            agent_name = self._get_agent_name()
+            params["agent_id"] = agent_name
+            params["metadata"] = {"type": "short_term"}
+        elif self.memory_type == "long_term":
+            agent_name = self._get_agent_name()
+            params["agent_id"] = agent_name
+            params["metadata"] = {"type": "long_term"}
+        elif self.memory_type == "entities":
+            agent_name = self._get_agent_name()
+            params["agent_id"] = agent_name
+            params["metadata"] = {"type": "entity"}
+
+        # Discard the filters for now since we create the filters
+        # automatically when the crew is created.
+        results = self.memory.search(**params)
+        return [r for r in results if r["score"] >= score_threshold]
+
+    def _get_user_id(self):
+        if self.memory_type == "user":
+            if hasattr(self, "memory_config") and self.memory_config is not None:
+                return self.memory_config.get("config", {}).get("user_id")
+            else:
+                return None
+        return None
+
+    def _get_agent_name(self):
+        agents = self.crew.agents if self.crew else []
+        agents = [self._sanitize_role(agent.role) for agent in agents]
+        agents = "_".join(agents)
+        return agents

src/crewai/memory/storage/rag_storage.py

@@ -4,13 +4,12 @@ import logging
 import os
 import shutil
 import uuid
-from typing import Any, Dict, List, Optional, cast
 
-from chromadb import Documents, EmbeddingFunction, Embeddings
+from typing import Any, Dict, List, Optional
 from chromadb.api import ClientAPI
-from chromadb.api.types import validate_embedding_function
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities.paths import db_storage_path
+from crewai.utilities import EmbeddingConfigurator
 
 
 @contextlib.contextmanager
@@ -51,117 +50,8 @@ class RAGStorage(BaseRAGStorage):
         self._initialize_app()
 
     def _set_embedder_config(self):
-        import chromadb.utils.embedding_functions as embedding_functions
-
-        if self.embedder_config is None:
-            self.embedder_config = self._create_default_embedding_function()
-
-        if isinstance(self.embedder_config, dict):
-            provider = self.embedder_config.get("provider")
-            config = self.embedder_config.get("config", {})
-            model_name = config.get("model")
-            if provider == "openai":
-                self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
-                    api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
-                    model_name=model_name,
-                )
-            elif provider == "azure":
-                self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
-                    api_key=config.get("api_key"),
-                    api_base=config.get("api_base"),
-                    api_type=config.get("api_type", "azure"),
-                    api_version=config.get("api_version"),
-                    model_name=model_name,
-                )
-            elif provider == "ollama":
-                from openai import OpenAI
-
-                class OllamaEmbeddingFunction(EmbeddingFunction):
-                    def __call__(self, input: Documents) -> Embeddings:
-                        client = OpenAI(
-                            base_url="http://localhost:11434/v1",
-                            api_key=config.get("api_key", "ollama"),
-                        )
-                        try:
-                            response = client.embeddings.create(
-                                input=input, model=model_name
-                            )
-                            embeddings = [item.embedding for item in response.data]
-                            return cast(Embeddings, embeddings)
-                        except Exception as e:
-                            raise e
-
-                self.embedder_config = OllamaEmbeddingFunction()
-            elif provider == "vertexai":
-                self.embedder_config = (
-                    embedding_functions.GoogleVertexEmbeddingFunction(
-                        model_name=model_name,
-                        api_key=config.get("api_key"),
-                    )
-                )
-            elif provider == "google":
-                self.embedder_config = (
-                    embedding_functions.GoogleGenerativeAiEmbeddingFunction(
-                        model_name=model_name,
-                        api_key=config.get("api_key"),
-                    )
-                )
-            elif provider == "cohere":
-                self.embedder_config = embedding_functions.CohereEmbeddingFunction(
-                    model_name=model_name,
-                    api_key=config.get("api_key"),
-                )
-            elif provider == "huggingface":
-                self.embedder_config = embedding_functions.HuggingFaceEmbeddingServer(
-                    url=config.get("api_url"),
-                )
-            elif provider == "watson":
-                try:
-                    import ibm_watsonx_ai.foundation_models as watson_models
-                    from ibm_watsonx_ai import Credentials
-                    from ibm_watsonx_ai.metanames import (
-                        EmbedTextParamsMetaNames as EmbedParams,
-                    )
-                except ImportError as e:
-                    raise ImportError(
-                        "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
-                    ) from e
-
-                class WatsonEmbeddingFunction(EmbeddingFunction):
-                    def __call__(self, input: Documents) -> Embeddings:
-                        if isinstance(input, str):
-                            input = [input]
-
-                        embed_params = {
-                            EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
-                            EmbedParams.RETURN_OPTIONS: {"input_text": True},
-                        }
-
-                        embedding = watson_models.Embeddings(
-                            model_id=config.get("model"),
-                            params=embed_params,
-                            credentials=Credentials(
-                                api_key=config.get("api_key"), url=config.get("api_url")
-                            ),
-                            project_id=config.get("project_id"),
-                        )
-
-                        try:
-                            embeddings = embedding.embed_documents(input)
-                            return cast(Embeddings, embeddings)
-
-                        except Exception as e:
-                            print("Error during Watson embedding:", e)
-                            raise e
-
-                self.embedder_config = WatsonEmbeddingFunction()
-            else:
-                raise Exception(
-                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface, watson]"
-                )
-        else:
-            validate_embedding_function(self.embedder_config)
-            self.embedder_config = self.embedder_config
+        configurator = EmbeddingConfigurator()
+        self.embedder_config = configurator.configure_embedder(self.embedder_config)
 
     def _initialize_app(self):
         import chromadb
@@ -253,8 +143,10 @@ class RAGStorage(BaseRAGStorage):
                 )
 
     def _create_default_embedding_function(self):
-        import chromadb.utils.embedding_functions as embedding_functions
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
 
-        return embedding_functions.OpenAIEmbeddingFunction(
+        return OpenAIEmbeddingFunction(
             api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
         )

src/crewai/memory/user/user_memory.py

@@ -0,0 +1,45 @@
+from typing import Any, Dict, Optional
+
+from crewai.memory.memory import Memory
+
+
+class UserMemory(Memory):
+    """
+    UserMemory class for handling user memory storage and retrieval.
+    Inherits from the Memory class and utilizes an instance of a class that
+    adheres to the Storage for data storage, specifically working with
+    MemoryItem instances.
+    """
+
+    def __init__(self, crew=None):
+        try:
+            from crewai.memory.storage.mem0_storage import Mem0Storage
+        except ImportError:
+            raise ImportError(
+                "Mem0 is not installed. Please install it with `pip install mem0ai`."
+            )
+        storage = Mem0Storage(type="user", crew=crew)
+        super().__init__(storage)
+
+    def save(
+        self,
+        value,
+        metadata: Optional[Dict[str, Any]] = None,
+        agent: Optional[str] = None,
+    ) -> None:
+        # TODO: Change this function since we want to take care of the case where we save memories for the usr
+        data = f"Remember the details about the user: {value}"
+        super().save(data, metadata)
+
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        score_threshold: float = 0.35,
+    ):
+        results = super().search(
+            query=query,
+            limit=limit,
+            score_threshold=score_threshold,
+        )
+        return results

src/crewai/memory/user/user_memory_item.py

@@ -0,0 +1,8 @@
+from typing import Any, Dict, Optional
+
+
+class UserMemoryItem:
+    def __init__(self, data: Any, user: str, metadata: Optional[Dict[str, Any]] = None):
+        self.data = data
+        self.user = user
+        self.metadata = metadata if metadata is not None else {}

src/crewai/project/__init__.py

@@ -1,5 +1,7 @@
 from .annotations import (
+    after_kickoff,
     agent,
+    before_kickoff,
     cache_handler,
     callback,
     crew,
@@ -26,4 +28,6 @@ __all__ = [
     "llm",
     "cache_handler",
     "pipeline",
+    "before_kickoff",
+    "after_kickoff",
 ]

src/crewai/project/annotations.py

@@ -5,6 +5,16 @@ from crewai import Crew
 from crewai.project.utils import memoize
 
 
+def before_kickoff(func):
+    func.is_before_kickoff = True
+    return func
+
+
+def after_kickoff(func):
+    func.is_after_kickoff = True
+    return func
+
+
 def task(func):
     func.is_task = True
 
@@ -99,6 +109,19 @@ def crew(func) -> Callable[..., Crew]:
         self.agents = instantiated_agents
         self.tasks = instantiated_tasks
 
-        return func(self, *args, **kwargs)
+        crew = func(self, *args, **kwargs)
 
-    return wrapper
+        def callback_wrapper(callback, instance):
+            def wrapper(*args, **kwargs):
+                return callback(instance, *args, **kwargs)
+
+            return wrapper
+
+        for _, callback in self._before_kickoff.items():
+            crew.before_kickoff_callbacks.append(callback_wrapper(callback, self))
+        for _, callback in self._after_kickoff.items():
+            crew.after_kickoff_callbacks.append(callback_wrapper(callback, self))
+
+        return crew
+
+    return memoize(wrapper)

src/crewai/project/crew_base.py

@@ -34,18 +34,39 @@ def CrewBase(cls: T) -> T:
             self.map_all_agent_variables()
             self.map_all_task_variables()
 
-            # Preserve task and agent information
-            self._original_tasks = {
+            # Preserve all decorated functions
+            self._original_functions = {
                 name: method
                 for name, method in cls.__dict__.items()
-                if hasattr(method, "is_task") and method.is_task
-            }
-            self._original_agents = {
-                name: method
-                for name, method in cls.__dict__.items()
-                if hasattr(method, "is_agent") and method.is_agent
+                if any(
+                    hasattr(method, attr)
+                    for attr in [
+                        "is_task",
+                        "is_agent",
+                        "is_before_kickoff",
+                        "is_after_kickoff",
+                        "is_kickoff",
+                    ]
+                )
             }
 
+            # Store specific function types
+            self._original_tasks = self._filter_functions(
+                self._original_functions, "is_task"
+            )
+            self._original_agents = self._filter_functions(
+                self._original_functions, "is_agent"
+            )
+            self._before_kickoff = self._filter_functions(
+                self._original_functions, "is_before_kickoff"
+            )
+            self._after_kickoff = self._filter_functions(
+                self._original_functions, "is_after_kickoff"
+            )
+            self._kickoff = self._filter_functions(
+                self._original_functions, "is_kickoff"
+            )
+
         @staticmethod
         def load_yaml(config_path: Path):
             try:

src/crewai/task.py

@@ -20,10 +20,10 @@ from pydantic import (
 from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.tools.base_tool import BaseTool
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
+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
@@ -208,7 +208,9 @@ class Task(BaseModel):
         """Execute the task asynchronously."""
         future: Future[TaskOutput] = Future()
         threading.Thread(
-            target=self._execute_task_async, args=(agent, context, tools, future)
+            daemon=True,
+            target=self._execute_task_async,
+            args=(agent, context, tools, future),
         ).start()
         return future
 
@@ -277,7 +279,9 @@ class Task(BaseModel):
             content = (
                 json_output
                 if json_output
-                else pydantic_output.model_dump_json() if pydantic_output else result
+                else pydantic_output.model_dump_json()
+                if pydantic_output
+                else result
             )
             self._save_file(content)
 

src/crewai/types/usage_metrics.py

@@ -8,6 +8,7 @@ class UsageMetrics(BaseModel):
     Attributes:
         total_tokens: Total number of tokens used.
         prompt_tokens: Number of tokens used in prompts.
+        cached_prompt_tokens: Number of cached prompt tokens used.
         completion_tokens: Number of tokens used in completions.
         successful_requests: Number of successful requests made.
     """
@@ -16,6 +17,9 @@ class UsageMetrics(BaseModel):
     prompt_tokens: int = Field(
         default=0, description="Number of tokens used in prompts."
     )
+    cached_prompt_tokens: int = Field(
+        default=0, description="Number of cached prompt tokens used."
+    )
     completion_tokens: int = Field(
         default=0, description="Number of tokens used in completions."
     )
@@ -32,5 +36,6 @@ class UsageMetrics(BaseModel):
         """
         self.total_tokens += usage_metrics.total_tokens
         self.prompt_tokens += usage_metrics.prompt_tokens
+        self.cached_prompt_tokens += usage_metrics.cached_prompt_tokens
         self.completion_tokens += usage_metrics.completion_tokens
         self.successful_requests += usage_metrics.successful_requests

src/crewai/utilities/__init__.py

@@ -10,6 +10,7 @@ from .rpm_controller import RPMController
 from .exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
+from .embedding_configurator import EmbeddingConfigurator
 
 __all__ = [
     "Converter",
@@ -23,4 +24,5 @@ __all__ = [
     "RPMController",
     "YamlParser",
     "LLMContextLengthExceededException",
+    "EmbeddingConfigurator",
 ]

src/crewai/utilities/constants.py

@@ -1,2 +1,3 @@
 TRAINING_DATA_FILE = "training_data.pkl"
 TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl"
+DEFAULT_SCORE_THRESHOLD = 0.35

src/crewai/utilities/embedding_configurator.py

@@ -0,0 +1,183 @@
+import os
+from typing import Any, Dict, cast
+from chromadb import EmbeddingFunction, Documents, Embeddings
+from chromadb.api.types import validate_embedding_function
+
+
+class EmbeddingConfigurator:
+    def __init__(self):
+        self.embedding_functions = {
+            "openai": self._configure_openai,
+            "azure": self._configure_azure,
+            "ollama": self._configure_ollama,
+            "vertexai": self._configure_vertexai,
+            "google": self._configure_google,
+            "cohere": self._configure_cohere,
+            "bedrock": self._configure_bedrock,
+            "huggingface": self._configure_huggingface,
+            "watson": self._configure_watson,
+        }
+
+    def configure_embedder(
+        self,
+        embedder_config: Dict[str, Any] | None = None,
+    ) -> EmbeddingFunction:
+        """Configures and returns an embedding function based on the provided config."""
+        if embedder_config is None:
+            return self._create_default_embedding_function()
+
+        provider = embedder_config.get("provider")
+        config = embedder_config.get("config", {})
+        model_name = config.get("model")
+
+        if isinstance(provider, EmbeddingFunction):
+            try:
+                validate_embedding_function(provider)
+                return provider
+            except Exception as e:
+                raise ValueError(f"Invalid custom embedding function: {str(e)}")
+
+        if provider not in self.embedding_functions:
+            raise Exception(
+                f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
+            )
+
+        return self.embedding_functions[provider](config, model_name)
+
+    @staticmethod
+    def _create_default_embedding_function():
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
+
+    @staticmethod
+    def _configure_openai(config, model_name):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
+            model_name=model_name,
+        )
+
+    @staticmethod
+    def _configure_azure(config, model_name):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=config.get("api_key"),
+            api_base=config.get("api_base"),
+            api_type=config.get("api_type", "azure"),
+            api_version=config.get("api_version"),
+            model_name=model_name,
+        )
+
+    @staticmethod
+    def _configure_ollama(config, model_name):
+        from chromadb.utils.embedding_functions.ollama_embedding_function import (
+            OllamaEmbeddingFunction,
+        )
+
+        return OllamaEmbeddingFunction(
+            url=config.get("url", "http://localhost:11434/api/embeddings"),
+            model_name=model_name,
+        )
+
+    @staticmethod
+    def _configure_vertexai(config, model_name):
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleVertexEmbeddingFunction,
+        )
+
+        return GoogleVertexEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+        )
+
+    @staticmethod
+    def _configure_google(config, model_name):
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleGenerativeAiEmbeddingFunction,
+        )
+
+        return GoogleGenerativeAiEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+        )
+
+    @staticmethod
+    def _configure_cohere(config, model_name):
+        from chromadb.utils.embedding_functions.cohere_embedding_function import (
+            CohereEmbeddingFunction,
+        )
+
+        return CohereEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+        )
+
+    @staticmethod
+    def _configure_bedrock(config, model_name):
+        from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
+            AmazonBedrockEmbeddingFunction,
+        )
+
+        return AmazonBedrockEmbeddingFunction(
+            session=config.get("session"),
+        )
+
+    @staticmethod
+    def _configure_huggingface(config, model_name):
+        from chromadb.utils.embedding_functions.huggingface_embedding_function import (
+            HuggingFaceEmbeddingServer,
+        )
+
+        return HuggingFaceEmbeddingServer(
+            url=config.get("api_url"),
+        )
+
+    @staticmethod
+    def _configure_watson(config, model_name):
+        try:
+            import ibm_watsonx_ai.foundation_models as watson_models
+            from ibm_watsonx_ai import Credentials
+            from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames as EmbedParams
+        except ImportError as e:
+            raise ImportError(
+                "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
+            ) from e
+
+        class WatsonEmbeddingFunction(EmbeddingFunction):
+            def __call__(self, input: Documents) -> Embeddings:
+                if isinstance(input, str):
+                    input = [input]
+
+                embed_params = {
+                    EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
+                    EmbedParams.RETURN_OPTIONS: {"input_text": True},
+                }
+
+                embedding = watson_models.Embeddings(
+                    model_id=config.get("model"),
+                    params=embed_params,
+                    credentials=Credentials(
+                        api_key=config.get("api_key"), url=config.get("api_url")
+                    ),
+                    project_id=config.get("project_id"),
+                )
+
+                try:
+                    embeddings = embedding.embed_documents(input)
+                    return cast(Embeddings, embeddings)
+                except Exception as e:
+                    print("Error during Watson embedding:", e)
+                    raise e
+
+        return WatsonEmbeddingFunction()

src/crewai/utilities/file_handler.py

@@ -16,7 +16,11 @@ class FileHandler:
 
     def log(self, **kwargs):
         now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        message = f"{now}: " + ", ".join([f"{key}=\"{value}\"" for key, value in kwargs.items()]) + "\n"
+        message = (
+            f"{now}: "
+            + ", ".join([f'{key}="{value}"' for key, value in kwargs.items()])
+            + "\n"
+        )
         with open(self._path, "a", encoding="utf-8") as file:
             file.write(message + "\n")
 
@@ -63,7 +67,7 @@ class PickleHandler:
 
         with open(self.file_path, "rb") as file:
             try:
-                return pickle.load(file)
+                return pickle.load(file)  # nosec
             except EOFError:
                 return {}  # Return an empty dictionary if the file is empty or corrupted
             except Exception:

src/crewai/utilities/token_counter_callback.py

@@ -1,5 +1,5 @@
 from litellm.integrations.custom_logger import CustomLogger
-
+from litellm.types.utils import Usage
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 
 
@@ -11,8 +11,11 @@ class TokenCalcHandler(CustomLogger):
         if self.token_cost_process is None:
             return
 
+        usage : Usage = response_obj["usage"]
         self.token_cost_process.sum_successful_requests(1)
-        self.token_cost_process.sum_prompt_tokens(response_obj["usage"].prompt_tokens)
-        self.token_cost_process.sum_completion_tokens(
-            response_obj["usage"].completion_tokens
-        )
+        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

@@ -10,10 +10,11 @@ from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
+from crewai.tools import tool
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
-from crewai.tools import tool
 from crewai.tools.tool_usage_events import ToolUsageFinished
 from crewai.utilities import RPMController
 from crewai.utilities.events import Emitter
@@ -1574,3 +1575,42 @@ def test_agent_execute_task_with_ollama():
     result = agent.execute_task(task)
     assert len(result.split(".")) == 2
     assert "AI" in result or "artificial intelligence" in result.lower()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_with_knowledge_sources():
+    # Create a knowledge source with some content
+    content = "Brandon's favorite color is blue and he likes Mexican food."
+    string_source = StringKnowledgeSource(
+        content=content, metadata={"preference": "personal"}
+    )
+    
+
+    with patch('crewai.knowledge.storage.knowledge_storage.KnowledgeStorage') as MockKnowledge:
+        mock_knowledge_instance = MockKnowledge.return_value
+        mock_knowledge_instance.sources = [string_source]
+        mock_knowledge_instance.query.return_value = [{
+            "content": content,
+            "metadata": {"preference": "personal"}
+        }]
+        
+        agent = Agent(
+            role="Information Agent",
+            goal="Provide information based on knowledge sources",
+            backstory="You have access to specific knowledge sources.",
+            llm=LLM(model="gpt-4o-mini"),
+        )
+
+        # Create a task that requires the agent to use the knowledge
+        task = Task(
+            description="What is Brandon's favorite color?",
+            expected_output="Brandon's favorite color.",
+            agent=agent,
+        )
+
+        crew = Crew(agents=[agent], tasks=[task])
+        result = crew.kickoff()
+
+        # Assert that the agent provides the correct information
+        assert "blue" in result.raw.lower()
+

tests/cassettes/test_after_crew_modification.yaml

@@ -0,0 +1,449 @@
+interactions:
+- request:
+    body: !!binary |
+      CuMOCiQKIgoMc2VydmljZS5uYW1lEhIKEGNyZXdBSS10ZWxlbWV0cnkSug4KEgoQY3Jld2FpLnRl
+      bGVtZXRyeRKSDAoQK+dPhrB8w3HKFlxX60XzYRIIk5aB+A8oCWQqDENyZXcgQ3JlYXRlZDABObix
+      K+HWrwgYQcBiMeHWrwgYShoKDmNyZXdhaV92ZXJzaW9uEggKBjAuODAuMEoaCg5weXRob25fdmVy
+      c2lvbhIICgYzLjExLjdKLgoIY3Jld19rZXkSIgogZjM0NmE5YWQ2ZDczMDYzZTA2NzdiMTdjZTlj
+      NTAxNzdKMQoHY3Jld19pZBImCiQ3NjRjZWM1YS04NzkxLTRmN2MtOWY0MC1hNTMzMzJmOTk3YzBK
+      HAoMY3Jld19wcm9jZXNzEgwKCnNlcXVlbnRpYWxKEQoLY3Jld19tZW1vcnkSAhAAShoKFGNyZXdf
+      bnVtYmVyX29mX3Rhc2tzEgIYAkobChVjcmV3X251bWJlcl9vZl9hZ2VudHMSAhgCSqwFCgtjcmV3
+      X2FnZW50cxKcBQqZBVt7ImtleSI6ICI3M2MzNDljOTNjMTYzYjVkNGRmOThhNjRmYWMxYzQzMCIs
+      ICJpZCI6ICJjZDgwYjlhNy1hN2QzLTQzNTQtYjUyOC1jMzAyODA0MjA3YzgiLCAicm9sZSI6ICJ7
+      dG9waWN9IFNlbmlvciBEYXRhIFJlc2VhcmNoZXJcbiIsICJ2ZXJib3NlPyI6IGZhbHNlLCAibWF4
+      X2l0ZXIiOiAyMCwgIm1heF9ycG0iOiBudWxsLCAiZnVuY3Rpb25fY2FsbGluZ19sbG0iOiAiIiwg
+      ImxsbSI6ICJncHQtNG8iLCAiZGVsZWdhdGlvbl9lbmFibGVkPyI6IGZhbHNlLCAiYWxsb3dfY29k
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       openai-processing-ms:
-      - '389'
+      - '655'
       openai-version:
       - '2020-10-01'
       strict-transport-security:
@@ -296,17 +321,18 @@ interactions:
       x-ratelimit-limit-requests:
       - '10000'
       x-ratelimit-limit-tokens:
-      - '30000000'
+      - '200000'
       x-ratelimit-remaining-requests:
-      - '9999'
+      - '9997'
       x-ratelimit-remaining-tokens:
-      - '29999791'
+      - '199791'
       x-ratelimit-reset-requests:
-      - 6ms
+      - 24.239s
       x-ratelimit-reset-tokens:
-      - 0s
+      - 62ms
       x-request-id:
-      - req_0167388f0a7a7f1a1026409834ceb914
-    http_version: HTTP/1.1
-    status_code: 200
+      - req_a228208b0e965ecee334a6947d6c9e7c
+    status:
+      code: 200
+      message: OK
 version: 1

tests/cassettes/test_kickoff_for_each_error_handling.yaml

@@ -0,0 +1,232 @@
+interactions:
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+      You''re a meticulous analyst with a keen eye for detail. You''re known for your
+      ability to turn complex data into clear and concise reports, making it easy
+      for others to understand and act on the information you provide.\n\nYour personal
+      goal is: Create detailed reports based on plants data analysis and research
+      findings\n\nTo give my best complete final answer to the task use the exact
+      following format:\n\nThought: I now can give a great answer\nFinal Answer: Your
+      final answer must be the great and the most complete as possible, it must be
+      outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role":
+      "user", "content": "\nCurrent Task: Review the context you got and expand each
+      topic into a full section for a report. Make sure the report is detailed and
+      contains any and all relevant information.\n\n\nThis is the expect criteria
+      for your final answer: A fully fledge reports with the mains topics, each with
+      a full section of information. Formatted as markdown without ''```''\n\nyou
+      MUST return the actual complete content as the final answer, not a summary.\n\nThis
+      is the context you''re working with:\n1. **Plant-Based Plastics**: 2024 has
+      seen significant advancements in bioplastics derived from plants such as corn
+      and sugarcane, which are being used as sustainable alternatives to petroleum-based
+      plastics. These innovations aim to reduce plastic waste and reliance on fossil
+      fuels.\n\n2. **Gene Editing Breakthroughs**: CRISPR technology continues to
+      revolutionize agricultural practices. Researchers have developed genetically
+      modified crops that are more resistant to pests and diseases, thereby decreasing
+      the need for chemical pesticides and increasing food security.\n\n3. **Vertical
+      Farming Expansion**: Urban agriculture has gained momentum with vertical farming
+      techniques that utilize less water and space. In 2024, cities worldwide are
+      adopting these innovative systems to meet the growing food demand while reducing
+      transportation emissions.\n\n4. **Climate-Resilient Plants**: Scientists are
+      identifying and breeding plant varieties that can withstand extreme weather
+      conditions such as droughts, floods, and high temperatures, helping farmers
+      adapt to climate change and ensuring crop yields remain stable.\n\n5. **Edible
+      Vaccines in Plants**: Research into producing vaccines in plants has progressed,
+      with trials showing that certain plants can be engineered to express antigens
+      that could serve as edible vaccines, offering a low-cost and easy delivery method
+      for immunizations.\n\n6. **Fungi and Plant Collaborations**: The study of mycorrhizal
+      fungi and their symbiotic relationships with plants has gained traction. Research
+      indicates that these fungi enhance nutrient uptake and enhance plant resilience
+      to environmental stressors, making them pivotal in sustainable agriculture.\n\n7.
+      **Biophilic Design**: The trend of integrating nature into architecture and
+      urban planning has expanded, with an emphasis on incorporating plants into building
+      designs to improve air quality, enhance mental well-being, and reduce overall
+      energy consumption.\n\n8. **Deciduous Trees and Urban Cooling**: Research shows
+      that strategically planted deciduous trees can significantly lower urban temperatures,
+      reduce energy consumption for cooling, and improve urban biodiversity, highlighting
+      the importance of green infrastructure.\n\n9. **Carnivorous Plant Studies**:
+      The understanding of carnivorous plants and their unique adaptations has advanced,
+      with findings suggesting potential applications in pest control and sustainable
+      agriculture due to their natural predatory methods.\n\n10. **Smart Agriculture
+      Technology**: The integration of IoT and AI in agriculture is revolutionizing
+      plant cultivation. Farmers now use sensors and data analytics to monitor plant
+      health in real-time, optimize water usage, and increase crop yields sustainably.\n\nThese
+      insights underline the ongoing research and innovations in the plant science
+      field that can lead to more sustainable and resilient agricultural practices
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+      such as corn and sugarcane. Unlike traditional petroleum-based plastics, these
+      bioplastics are designed to be more environmentally friendly, addressing pressing
+      concerns regarding plastic waste and the continuous reliance on fossil fuels.
+      The production processes have been optimized to ensure lower carbon footprints
+      and reduced greenhouse gas emissions. Moreover, major manufacturers are beginning
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+      role in bolstering food security, enabling farmers to achieve higher yields
+      even under challenging agricultural conditions.\\n\\n## 3. Vertical Farming
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+      continues to evolve in 2024. These innovative systems are engineered to optimize
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+      to grow food in controlled environments. Cities around the globe are implementing
+      vertical farms to tackle the increasing demand for fresh produce while simultaneously
+      minimizing transportation emissions and enhancing food security in urban settings.
+      By using significantly less water and land compared to traditional farming,
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+      of these climate-resilient plants is vital for maintaining food supply chains
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+      designs, enhancing air quality and promoting mental well-being. By reducing
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+      opportunities to lower overall energy consumption. As cities recognize the benefits
+      of urban greenery, significant investments are being made to create healthier,
+      more sustainable urban ecosystems.\\n\\n## 8. Deciduous Trees and Urban Cooling\\nResearch
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tests/config/agents.yaml

@@ -0,0 +1,21 @@
+researcher:
+  role: >
+    {topic} Senior Data Researcher
+  goal: >
+    Uncover cutting-edge developments in {topic}
+  backstory: >
+    You're a seasoned researcher with a knack for uncovering the latest
+    developments in {topic}. Known for your ability to find the most relevant
+    information and present it in a clear and concise manner.
+  verbose: true
+
+reporting_analyst:
+  role: >
+    {topic} Reporting Analyst
+  goal: >
+    Create detailed reports based on {topic} data analysis and research findings
+  backstory: >
+    You're a meticulous analyst with a keen eye for detail. You're known for
+    your ability to turn complex data into clear and concise reports, making
+    it easy for others to understand and act on the information you provide.
+  verbose: true

tests/config/tasks.yaml

@@ -0,0 +1,17 @@
+research_task:
+  description: >
+    Conduct a thorough research about {topic}
+    Make sure you find any interesting and relevant information given
+    the current year is 2024.
+  expected_output: >
+    A list with 10 bullet points of the most relevant information about {topic}
+  agent: researcher
+
+reporting_task:
+  description: >
+    Review the context you got and expand each topic into a full section for a report.
+    Make sure the report is detailed and contains any and all relevant information.
+  expected_output: >
+    A fully fledge reports with the mains topics, each with a full section of information.
+    Formatted as markdown without '```'
+  agent: reporting_analyst

tests/crew_test.py

@@ -564,6 +564,7 @@ def test_crew_kickoff_usage_metrics():
         assert result.token_usage.prompt_tokens > 0
         assert result.token_usage.completion_tokens > 0
         assert result.token_usage.successful_requests > 0
+        assert result.token_usage.cached_prompt_tokens == 0
 
 
 def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
@@ -1280,10 +1281,11 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
     assert result.raw == "Howdy!"
 
     assert result.token_usage == UsageMetrics(
-        total_tokens=2626,
-        prompt_tokens=2482,
-        completion_tokens=144,
-        successful_requests=5,
+        total_tokens=1673,
+        prompt_tokens=1562,
+        completion_tokens=111,
+        successful_requests=3,
+        cached_prompt_tokens=0
     )
 
 
@@ -1777,26 +1779,22 @@ def test_crew_train_success(
         ]
     )
 
-    crew_training_handler.assert_has_calls(
-        [
-            mock.call("training_data.pkl"),
-            mock.call().load(),
-            mock.call("trained_agents_data.pkl"),
-            mock.call().save_trained_data(
-                agent_id="Researcher",
-                trained_data=task_evaluator().evaluate_training_data().model_dump(),
-            ),
-            mock.call("trained_agents_data.pkl"),
-            mock.call().save_trained_data(
-                agent_id="Senior Writer",
-                trained_data=task_evaluator().evaluate_training_data().model_dump(),
-            ),
-            mock.call(),
-            mock.call().load(),
-            mock.call(),
-            mock.call().load(),
-        ]
-    )
+    crew_training_handler.assert_any_call("training_data.pkl")
+    crew_training_handler().load.assert_called()
+
+    crew_training_handler.assert_any_call("trained_agents_data.pkl")
+    crew_training_handler().load.assert_called()
+
+    crew_training_handler().save_trained_data.assert_has_calls([
+        mock.call(
+            agent_id="Researcher",
+            trained_data=task_evaluator().evaluate_training_data().model_dump(),
+        ),
+        mock.call(
+            agent_id="Senior Writer",
+            trained_data=task_evaluator().evaluate_training_data().model_dump(),
+        )
+    ])
 
 
 def test_crew_train_error():

tests/flow_test.py

@@ -0,0 +1,264 @@
+"""Test Flow creation and execution basic functionality."""
+
+import asyncio
+
+import pytest
+from crewai.flow.flow import Flow, and_, listen, or_, router, start
+
+
+def test_simple_sequential_flow():
+    """Test a simple flow with two steps called sequentially."""
+    execution_order = []
+
+    class SimpleFlow(Flow):
+        @start()
+        def step_1(self):
+            execution_order.append("step_1")
+
+        @listen(step_1)
+        def step_2(self):
+            execution_order.append("step_2")
+
+    flow = SimpleFlow()
+    flow.kickoff()
+
+    assert execution_order == ["step_1", "step_2"]
+
+
+def test_flow_with_multiple_starts():
+    """Test a flow with multiple start methods."""
+    execution_order = []
+
+    class MultiStartFlow(Flow):
+        @start()
+        def step_a(self):
+            execution_order.append("step_a")
+
+        @start()
+        def step_b(self):
+            execution_order.append("step_b")
+
+        @listen(step_a)
+        def step_c(self):
+            execution_order.append("step_c")
+
+        @listen(step_b)
+        def step_d(self):
+            execution_order.append("step_d")
+
+    flow = MultiStartFlow()
+    flow.kickoff()
+
+    assert "step_a" in execution_order
+    assert "step_b" in execution_order
+    assert "step_c" in execution_order
+    assert "step_d" in execution_order
+    assert execution_order.index("step_c") > execution_order.index("step_a")
+    assert execution_order.index("step_d") > execution_order.index("step_b")
+
+
+def test_cyclic_flow():
+    """Test a cyclic flow that runs a finite number of iterations."""
+    execution_order = []
+
+    class CyclicFlow(Flow):
+        iteration = 0
+        max_iterations = 3
+
+        @start("loop")
+        def step_1(self):
+            if self.iteration >= self.max_iterations:
+                return  # Do not proceed further
+            execution_order.append(f"step_1_{self.iteration}")
+
+        @listen(step_1)
+        def step_2(self):
+            execution_order.append(f"step_2_{self.iteration}")
+
+        @router(step_2)
+        def step_3(self):
+            execution_order.append(f"step_3_{self.iteration}")
+            self.iteration += 1
+            if self.iteration < self.max_iterations:
+                return "loop"
+
+            return "exit"
+
+    flow = CyclicFlow()
+    flow.kickoff()
+
+    expected_order = []
+    for i in range(flow.max_iterations):
+        expected_order.extend([f"step_1_{i}", f"step_2_{i}", f"step_3_{i}"])
+
+    assert execution_order == expected_order
+
+
+def test_flow_with_and_condition():
+    """Test a flow where a step waits for multiple other steps to complete."""
+    execution_order = []
+
+    class AndConditionFlow(Flow):
+        @start()
+        def step_1(self):
+            execution_order.append("step_1")
+
+        @start()
+        def step_2(self):
+            execution_order.append("step_2")
+
+        @listen(and_(step_1, step_2))
+        def step_3(self):
+            execution_order.append("step_3")
+
+    flow = AndConditionFlow()
+    flow.kickoff()
+
+    assert "step_1" in execution_order
+    assert "step_2" in execution_order
+    assert execution_order[-1] == "step_3"
+    assert execution_order.index("step_3") > execution_order.index("step_1")
+    assert execution_order.index("step_3") > execution_order.index("step_2")
+
+
+def test_flow_with_or_condition():
+    """Test a flow where a step is triggered when any of multiple steps complete."""
+    execution_order = []
+
+    class OrConditionFlow(Flow):
+        @start()
+        def step_a(self):
+            execution_order.append("step_a")
+
+        @start()
+        def step_b(self):
+            execution_order.append("step_b")
+
+        @listen(or_(step_a, step_b))
+        def step_c(self):
+            execution_order.append("step_c")
+
+    flow = OrConditionFlow()
+    flow.kickoff()
+
+    assert "step_a" in execution_order or "step_b" in execution_order
+    assert "step_c" in execution_order
+    assert execution_order.index("step_c") > min(
+        execution_order.index("step_a"), execution_order.index("step_b")
+    )
+
+
+def test_flow_with_router():
+    """Test a flow that uses a router method to determine the next step."""
+    execution_order = []
+
+    class RouterFlow(Flow):
+        @start()
+        def start_method(self):
+            execution_order.append("start_method")
+
+        @router(start_method)
+        def router(self):
+            execution_order.append("router")
+            # Ensure the condition is set to True to follow the "step_if_true" path
+            condition = True
+            return "step_if_true" if condition else "step_if_false"
+
+        @listen("step_if_true")
+        def truthy(self):
+            execution_order.append("step_if_true")
+
+        @listen("step_if_false")
+        def falsy(self):
+            execution_order.append("step_if_false")
+
+    flow = RouterFlow()
+    flow.kickoff()
+
+    assert execution_order == ["start_method", "router", "step_if_true"]
+
+
+def test_async_flow():
+    """Test an asynchronous flow."""
+    execution_order = []
+
+    class AsyncFlow(Flow):
+        @start()
+        async def step_1(self):
+            execution_order.append("step_1")
+            await asyncio.sleep(0.1)
+
+        @listen(step_1)
+        async def step_2(self):
+            execution_order.append("step_2")
+            await asyncio.sleep(0.1)
+
+    flow = AsyncFlow()
+    asyncio.run(flow.kickoff_async())
+
+    assert execution_order == ["step_1", "step_2"]
+
+
+def test_flow_with_exceptions():
+    """Test flow behavior when exceptions occur in steps."""
+    execution_order = []
+
+    class ExceptionFlow(Flow):
+        @start()
+        def step_1(self):
+            execution_order.append("step_1")
+            raise ValueError("An error occurred in step_1")
+
+        @listen(step_1)
+        def step_2(self):
+            execution_order.append("step_2")
+
+    flow = ExceptionFlow()
+
+    with pytest.raises(ValueError):
+        flow.kickoff()
+
+    # Ensure step_2 did not execute
+    assert execution_order == ["step_1"]
+
+
+def test_flow_restart():
+    """Test restarting a flow after it has completed."""
+    execution_order = []
+
+    class RestartableFlow(Flow):
+        @start()
+        def step_1(self):
+            execution_order.append("step_1")
+
+        @listen(step_1)
+        def step_2(self):
+            execution_order.append("step_2")
+
+    flow = RestartableFlow()
+    flow.kickoff()
+    flow.kickoff()  # Restart the flow
+
+    assert execution_order == ["step_1", "step_2", "step_1", "step_2"]
+
+
+def test_flow_with_custom_state():
+    """Test a flow that maintains and modifies internal state."""
+
+    class StateFlow(Flow):
+        def __init__(self):
+            super().__init__()
+            self.counter = 0
+
+        @start()
+        def step_1(self):
+            self.counter += 1
+
+        @listen(step_1)
+        def step_2(self):
+            self.counter *= 2
+            assert self.counter == 2
+
+    flow = StateFlow()
+    flow.kickoff()
+    assert flow.counter == 2

tests/knowledge/knowledge_test.py

@@ -0,0 +1,545 @@
+"""Test Knowledge creation and querying functionality."""
+
+from pathlib import Path
+from unittest.mock import patch
+
+from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
+from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
+from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
+from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
+
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def mock_vector_db():
+    """Mock vector database operations."""
+    with patch("crewai.knowledge.storage.knowledge_storage.KnowledgeStorage") as mock:
+        # Mock the query method to return a predefined response
+        instance = mock.return_value
+        instance.query.return_value = [
+            {
+                "context": "Brandon's favorite color is blue and he likes Mexican food.",
+                "score": 0.9,
+            }
+        ]
+        instance.reset.return_value = None
+        yield instance
+
+
+@pytest.fixture(autouse=True)
+def reset_knowledge_storage(mock_vector_db):
+    """Fixture to reset knowledge storage before each test."""
+    yield
+
+
+def test_single_short_string(mock_vector_db):
+    # Create a knowledge base with a single short string
+    content = "Brandon's favorite color is blue and he likes Mexican food."
+    string_source = StringKnowledgeSource(
+        content=content, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [string_source]
+    mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
+    # Perform a query
+    query = "What is Brandon's favorite color?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the results contain the expected information
+    assert any("blue" in result["context"].lower() for result in results)
+    # Verify the mock was called
+    mock_vector_db.query.assert_called_once()
+
+
+# @pytest.mark.vcr(filter_headers=["authorization"])
+def test_single_2k_character_string(mock_vector_db):
+    # Create a 2k character string with various facts about Brandon
+    content = (
+        "Brandon is a software engineer who lives in San Francisco. "
+        "He enjoys hiking and often visits the trails in the Bay Area. "
+        "Brandon has a pet dog named Max, who is a golden retriever. "
+        "He loves reading science fiction books, and his favorite author is Isaac Asimov. "
+        "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
+        "He is also a fan of Mexican cuisine, especially tacos and burritos. "
+        "Brandon plays the guitar and often performs at local open mic nights. "
+        "He is learning French and plans to visit Paris next year. "
+        "Brandon is passionate about technology and often attends tech meetups in the city. "
+        "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
+        "Brandon's favorite color is blue, and he often wears blue shirts. "
+        "He enjoys cooking and often tries new recipes on weekends. "
+        "Brandon is a morning person and likes to start his day with a run in the park. "
+        "He is also a coffee enthusiast and enjoys trying different coffee blends. "
+        "Brandon is a member of a local book club and enjoys discussing books with fellow members. "
+        "He is also a fan of board games and often hosts game nights at his place. "
+        "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
+        "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
+        "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
+        "He is also a fan of the Golden State Warriors and enjoys watching their games. "
+    )
+    string_source = StringKnowledgeSource(
+        content=content, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [string_source]
+    mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
+
+    # Perform a query
+    query = "What is Brandon's favorite movie?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the results contain the expected information
+    assert any("inception" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_multiple_short_strings(mock_vector_db):
+    # Create multiple short string sources
+    contents = [
+        "Brandon loves hiking.",
+        "Brandon has a dog named Max.",
+        "Brandon enjoys painting landscapes.",
+    ]
+    string_sources = [
+        StringKnowledgeSource(content=content, metadata={"preference": "personal"})
+        for content in contents
+    ]
+
+    # Mock the vector db query response
+    mock_vector_db.query.return_value = [
+        {"context": "Brandon has a dog named Max.", "score": 0.9}
+    ]
+
+    mock_vector_db.sources = string_sources
+
+    # Perform a query
+    query = "What is the name of Brandon's pet?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any("max" in result["context"].lower() for result in results)
+    # Verify the mock was called
+    mock_vector_db.query.assert_called_once()
+
+
+def test_multiple_2k_character_strings(mock_vector_db):
+    # Create multiple 2k character strings with various facts about Brandon
+    contents = [
+        (
+            "Brandon is a software engineer who lives in San Francisco. "
+            "He enjoys hiking and often visits the trails in the Bay Area. "
+            "Brandon has a pet dog named Max, who is a golden retriever. "
+            "He loves reading science fiction books, and his favorite author is Isaac Asimov. "
+            "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
+            "He is also a fan of Mexican cuisine, especially tacos and burritos. "
+            "Brandon plays the guitar and often performs at local open mic nights. "
+            "He is learning French and plans to visit Paris next year. "
+            "Brandon is passionate about technology and often attends tech meetups in the city. "
+            "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
+            "Brandon's favorite color is blue, and he often wears blue shirts. "
+            "He enjoys cooking and often tries new recipes on weekends. "
+            "Brandon is a morning person and likes to start his day with a run in the park. "
+            "He is also a coffee enthusiast and enjoys trying different coffee blends. "
+            "Brandon is a member of a local book club and enjoys discussing books with fellow members. "
+            "He is also a fan of board games and often hosts game nights at his place. "
+            "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
+            "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
+            "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
+            "He is also a fan of the Golden State Warriors and enjoys watching their games. "
+        )
+        * 2,  # Repeat to ensure it's 2k characters
+        (
+            "Brandon loves traveling and has visited over 20 countries. "
+            "He is fluent in Spanish and often practices with his friends. "
+            "Brandon's favorite city is Barcelona, where he enjoys the architecture and culture. "
+            "He is a foodie and loves trying new cuisines, with a particular fondness for sushi. "
+            "Brandon is an avid cyclist and participates in local cycling events. "
+            "He is also a photographer and enjoys capturing landscapes and cityscapes. "
+            "Brandon is a tech enthusiast and follows the latest trends in gadgets and software. "
+            "He is also a fan of virtual reality and owns a VR headset. "
+            "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'. "
+            "He enjoys watching documentaries and learning about history and science. "
+            "Brandon is a coffee lover and has a collection of coffee mugs from different countries. "
+            "He is also a fan of jazz music and often attends live performances. "
+            "Brandon is a member of a local running club and participates in marathons. "
+            "He is also a volunteer at a local animal shelter and helps with dog walking. "
+            "Brandon's favorite holiday is Christmas, and he enjoys decorating his home. "
+            "He is also a fan of classic movies and has a collection of DVDs. "
+            "Brandon is a mentor for young professionals and enjoys giving career advice. "
+            "He is also a fan of puzzles and enjoys solving them in his free time. "
+            "Brandon's favorite sport is soccer, and he often plays with his friends. "
+            "He is also a fan of FC Barcelona and enjoys watching their matches. "
+        )
+        * 2,  # Repeat to ensure it's 2k characters
+    ]
+    string_sources = [
+        StringKnowledgeSource(content=content, metadata={"preference": "personal"})
+        for content in contents
+    ]
+
+    mock_vector_db.sources = string_sources
+    mock_vector_db.query.return_value = [{"context": contents[1], "score": 0.9}]
+
+    # Perform a query
+    query = "What is Brandon's favorite book?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any(
+        "the hitchhiker's guide to the galaxy" in result["context"].lower()
+        for result in results
+    )
+    mock_vector_db.query.assert_called_once()
+
+
+def test_single_short_file(mock_vector_db, tmpdir):
+    # Create a single short text file
+    content = "Brandon's favorite sport is basketball."
+    file_path = Path(tmpdir.join("short_file.txt"))
+    with open(file_path, "w") as f:
+        f.write(content)
+
+    file_source = TextFileKnowledgeSource(
+        file_path=file_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [file_source]
+    mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
+    # Perform a query
+    query = "What sport does Brandon like?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the results contain the expected information
+    assert any("basketball" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_single_2k_character_file(mock_vector_db, tmpdir):
+    # Create a single 2k character text file with various facts about Brandon
+    content = (
+        "Brandon is a software engineer who lives in San Francisco. "
+        "He enjoys hiking and often visits the trails in the Bay Area. "
+        "Brandon has a pet dog named Max, who is a golden retriever. "
+        "He loves reading science fiction books, and his favorite author is Isaac Asimov. "
+        "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
+        "He is also a fan of Mexican cuisine, especially tacos and burritos. "
+        "Brandon plays the guitar and often performs at local open mic nights. "
+        "He is learning French and plans to visit Paris next year. "
+        "Brandon is passionate about technology and often attends tech meetups in the city. "
+        "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
+        "Brandon's favorite color is blue, and he often wears blue shirts. "
+        "He enjoys cooking and often tries new recipes on weekends. "
+        "Brandon is a morning person and likes to start his day with a run in the park. "
+        "He is also a coffee enthusiast and enjoys trying different coffee blends. "
+        "Brandon is a member of a local book club and enjoys discussing books with fellow members. "
+        "He is also a fan of board games and often hosts game nights at his place. "
+        "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
+        "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
+        "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
+        "He is also a fan of the Golden State Warriors and enjoys watching their games. "
+    ) * 2  # Repeat to ensure it's 2k characters
+    file_path = Path(tmpdir.join("long_file.txt"))
+    with open(file_path, "w") as f:
+        f.write(content)
+
+    file_source = TextFileKnowledgeSource(
+        file_path=file_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [file_source]
+    mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
+    # Perform a query
+    query = "What is Brandon's favorite movie?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the results contain the expected information
+    assert any("inception" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_multiple_short_files(mock_vector_db, tmpdir):
+    # Create multiple short text files
+    contents = [
+        {
+            "content": "Brandon works as a software engineer.",
+            "metadata": {"category": "profession", "source": "occupation"},
+        },
+        {
+            "content": "Brandon lives in New York.",
+            "metadata": {"category": "city", "source": "personal"},
+        },
+        {
+            "content": "Brandon enjoys cooking Italian food.",
+            "metadata": {"category": "hobby", "source": "personal"},
+        },
+    ]
+    file_paths = []
+    for i, item in enumerate(contents):
+        file_path = Path(tmpdir.join(f"file_{i}.txt"))
+        with open(file_path, "w") as f:
+            f.write(item["content"])
+        file_paths.append((file_path, item["metadata"]))
+
+    file_sources = [
+        TextFileKnowledgeSource(file_path=path, metadata=metadata)
+        for path, metadata in file_paths
+    ]
+    mock_vector_db.sources = file_sources
+    mock_vector_db.query.return_value = [
+        {"context": "Brandon lives in New York.", "score": 0.9}
+    ]
+    # Perform a query
+    query = "What city does he reside in?"
+    results = mock_vector_db.query(query)
+    # Assert that the correct information is retrieved
+    assert any("new york" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_multiple_2k_character_files(mock_vector_db, tmpdir):
+    # Create multiple 2k character text files with various facts about Brandon
+    contents = [
+        (
+            "Brandon loves traveling and has visited over 20 countries. "
+            "He is fluent in Spanish and often practices with his friends. "
+            "Brandon's favorite city is Barcelona, where he enjoys the architecture and culture. "
+            "He is a foodie and loves trying new cuisines, with a particular fondness for sushi. "
+            "Brandon is an avid cyclist and participates in local cycling events. "
+            "He is also a photographer and enjoys capturing landscapes and cityscapes. "
+            "Brandon is a tech enthusiast and follows the latest trends in gadgets and software. "
+            "He is also a fan of virtual reality and owns a VR headset. "
+            "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'. "
+            "He enjoys watching documentaries and learning about history and science. "
+            "Brandon is a coffee lover and has a collection of coffee mugs from different countries. "
+            "He is also a fan of jazz music and often attends live performances. "
+            "Brandon is a member of a local running club and participates in marathons. "
+            "He is also a volunteer at a local animal shelter and helps with dog walking. "
+            "Brandon's favorite holiday is Christmas, and he enjoys decorating his home. "
+            "He is also a fan of classic movies and has a collection of DVDs. "
+            "Brandon is a mentor for young professionals and enjoys giving career advice. "
+            "He is also a fan of puzzles and enjoys solving them in his free time. "
+            "Brandon's favorite sport is soccer, and he often plays with his friends. "
+            "He is also a fan of FC Barcelona and enjoys watching their matches. "
+        )
+        * 2,  # Repeat to ensure it's 2k characters
+        (
+            "Brandon is a software engineer who lives in San Francisco. "
+            "He enjoys hiking and often visits the trails in the Bay Area. "
+            "Brandon has a pet dog named Max, who is a golden retriever. "
+            "He loves reading science fiction books, and his favorite author is Isaac Asimov. "
+            "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
+            "He is also a fan of Mexican cuisine, especially tacos and burritos. "
+            "Brandon plays the guitar and often performs at local open mic nights. "
+            "He is learning French and plans to visit Paris next year. "
+            "Brandon is passionate about technology and often attends tech meetups in the city. "
+            "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
+            "Brandon's favorite color is blue, and he often wears blue shirts. "
+            "He enjoys cooking and often tries new recipes on weekends. "
+            "Brandon is a morning person and likes to start his day with a run in the park. "
+            "He is also a coffee enthusiast and enjoys trying different coffee blends. "
+            "Brandon is a member of a local book club and enjoys discussing books with fellow members. "
+            "He is also a fan of board games and often hosts game nights at his place. "
+            "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
+            "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
+            "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
+            "He is also a fan of the Golden State Warriors and enjoys watching their games. "
+        )
+        * 2,  # Repeat to ensure it's 2k characters
+    ]
+    file_paths = []
+    for i, content in enumerate(contents):
+        file_path = Path(tmpdir.join(f"long_file_{i}.txt"))
+        with open(file_path, "w") as f:
+            f.write(content)
+        file_paths.append(file_path)
+
+    file_sources = [
+        TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"})
+        for path in file_paths
+    ]
+    mock_vector_db.sources = file_sources
+    mock_vector_db.query.return_value = [
+        {
+            "context": "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'.",
+            "score": 0.9,
+        }
+    ]
+    # Perform a query
+    query = "What is Brandon's favorite book?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any(
+        "the hitchhiker's guide to the galaxy" in result["context"].lower()
+        for result in results
+    )
+    mock_vector_db.query.assert_called_once()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_hybrid_string_and_files(mock_vector_db, tmpdir):
+    # Create string sources
+    string_contents = [
+        "Brandon is learning French.",
+        "Brandon visited Paris last summer.",
+    ]
+    string_sources = [
+        StringKnowledgeSource(content=content, metadata={"preference": "personal"})
+        for content in string_contents
+    ]
+
+    # Create file sources
+    file_contents = [
+        "Brandon prefers tea over coffee.",
+        "Brandon's favorite book is 'The Alchemist'.",
+    ]
+    file_paths = []
+    for i, content in enumerate(file_contents):
+        file_path = Path(tmpdir.join(f"file_{i}.txt"))
+        with open(file_path, "w") as f:
+            f.write(content)
+        file_paths.append(file_path)
+
+    file_sources = [
+        TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"})
+        for path in file_paths
+    ]
+
+    # Combine string and file sources
+    mock_vector_db.sources = string_sources + file_sources
+    mock_vector_db.query.return_value = [{"context": file_contents[1], "score": 0.9}]
+
+    # Perform a query
+    query = "What is Brandon's favorite book?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any("the alchemist" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_pdf_knowledge_source(mock_vector_db):
+    # Get the directory of the current file
+    current_dir = Path(__file__).parent
+    # Construct the path to the PDF file
+    pdf_path = current_dir / "crewai_quickstart.pdf"
+
+    # Create a PDFKnowledgeSource
+    pdf_source = PDFKnowledgeSource(
+        file_path=pdf_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [pdf_source]
+    mock_vector_db.query.return_value = [
+        {"context": "crewai create crew latest-ai-development", "score": 0.9}
+    ]
+
+    # Perform a query
+    query = "How do you create a crew?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any(
+        "crewai create crew latest-ai-development" in result["context"].lower()
+        for result in results
+    )
+    mock_vector_db.query.assert_called_once()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_csv_knowledge_source(mock_vector_db, tmpdir):
+    """Test CSVKnowledgeSource with a simple CSV file."""
+
+    # Create a CSV file with sample data
+    csv_content = [
+        ["Name", "Age", "City"],
+        ["Brandon", "30", "New York"],
+        ["Alice", "25", "Los Angeles"],
+        ["Bob", "35", "Chicago"],
+    ]
+    csv_path = Path(tmpdir.join("data.csv"))
+    with open(csv_path, "w", encoding="utf-8") as f:
+        for row in csv_content:
+            f.write(",".join(row) + "\n")
+
+    # Create a CSVKnowledgeSource
+    csv_source = CSVKnowledgeSource(
+        file_path=csv_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [csv_source]
+    mock_vector_db.query.return_value = [
+        {"context": "Brandon is 30 years old.", "score": 0.9}
+    ]
+
+    # Perform a query
+    query = "How old is Brandon?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any("30" in result["context"] for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_json_knowledge_source(mock_vector_db, tmpdir):
+    """Test JSONKnowledgeSource with a simple JSON file."""
+
+    # Create a JSON file with sample data
+    json_data = {
+        "people": [
+            {"name": "Brandon", "age": 30, "city": "New York"},
+            {"name": "Alice", "age": 25, "city": "Los Angeles"},
+            {"name": "Bob", "age": 35, "city": "Chicago"},
+        ]
+    }
+    json_path = Path(tmpdir.join("data.json"))
+    with open(json_path, "w", encoding="utf-8") as f:
+        import json
+
+        json.dump(json_data, f)
+
+    # Create a JSONKnowledgeSource
+    json_source = JSONKnowledgeSource(
+        file_path=json_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [json_source]
+    mock_vector_db.query.return_value = [
+        {"context": "Alice lives in Los Angeles.", "score": 0.9}
+    ]
+
+    # Perform a query
+    query = "Where does Alice reside?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any("los angeles" in result["context"].lower() for result in results)
+    mock_vector_db.query.assert_called_once()
+
+
+def test_excel_knowledge_source(mock_vector_db, tmpdir):
+    """Test ExcelKnowledgeSource with a simple Excel file."""
+
+    # Create an Excel file with sample data
+    import pandas as pd
+
+    excel_data = {
+        "Name": ["Brandon", "Alice", "Bob"],
+        "Age": [30, 25, 35],
+        "City": ["New York", "Los Angeles", "Chicago"],
+    }
+    df = pd.DataFrame(excel_data)
+    excel_path = Path(tmpdir.join("data.xlsx"))
+    df.to_excel(excel_path, index=False)
+
+    # Create an ExcelKnowledgeSource
+    excel_source = ExcelKnowledgeSource(
+        file_path=excel_path, metadata={"preference": "personal"}
+    )
+    mock_vector_db.sources = [excel_source]
+    mock_vector_db.query.return_value = [
+        {"context": "Brandon is 30 years old.", "score": 0.9}
+    ]
+
+    # Perform a query
+    query = "What is Brandon's age?"
+    results = mock_vector_db.query(query)
+
+    # Assert that the correct information is retrieved
+    assert any("30" in result["context"] for result in results)
+    mock_vector_db.query.assert_called_once()

tests/llm_test.py

@@ -0,0 +1,30 @@
+import pytest
+
+from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
+from crewai.llm import LLM
+from crewai.utilities.token_counter_callback import TokenCalcHandler
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_callback_replacement():
+    llm = LLM(model="gpt-4o-mini")
+
+    calc_handler_1 = TokenCalcHandler(token_cost_process=TokenProcess())
+    calc_handler_2 = TokenCalcHandler(token_cost_process=TokenProcess())
+
+    llm.call(
+        messages=[{"role": "user", "content": "Hello, world!"}],
+        callbacks=[calc_handler_1],
+    )
+    usage_metrics_1 = calc_handler_1.token_cost_process.get_summary()
+
+    llm.call(
+        messages=[{"role": "user", "content": "Hello, world from another agent!"}],
+        callbacks=[calc_handler_2],
+    )
+    usage_metrics_2 = calc_handler_2.token_cost_process.get_summary()
+
+    # The first handler should not have been updated
+    assert usage_metrics_1.successful_requests == 1
+    assert usage_metrics_2.successful_requests == 1
+    assert usage_metrics_1 == calc_handler_1.token_cost_process.get_summary()

tests/memory/cassettes/test_save_and_search_with_provider.yaml

@@ -0,0 +1,270 @@
+interactions:
+- request:
+    body: ''
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      host:
+      - api.mem0.ai
+      user-agent:
+      - python-httpx/0.27.0
+    method: GET
+    uri: https://api.mem0.ai/v1/memories/?user_id=test
+  response:
+    body:
+      string: '[]'
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 8b477138bad847b9-BOM
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '2'
+      Content-Type:
+      - application/json
+      Date:
+      - Sat, 17 Aug 2024 06:00:11 GMT
+      NEL:
+      - '{"success_fraction":0,"report_to":"cf-nel","max_age":604800}'
+      Report-To:
+      - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v4?s=uuyH2foMJVDpV%2FH52g1q%2FnvXKe3dBKVzvsK0mqmSNezkiszNR9OgrEJfVqmkX%2FlPFRP2sH4zrOuzGo6k%2FjzsjYJczqSWJUZHN2pPujiwnr1E9W%2BdLGKmG6%2FqPrGYAy2SBRWkkJVWsTO3OQ%3D%3D"}],"group":"cf-nel","max_age":604800}'
+      Server:
+      - cloudflare
+      allow:
+      - GET, POST, DELETE, OPTIONS
+      alt-svc:
+      - h3=":443"; ma=86400
+      cross-origin-opener-policy:
+      - same-origin
+      referrer-policy:
+      - same-origin
+      vary:
+      - Accept, origin, Cookie
+      x-content-type-options:
+      - nosniff
+      x-frame-options:
+      - DENY
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"batch": [{"properties": {"python_version": "3.12.4 (v3.12.4:8e8a4baf65,
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+      "os_version": "Darwin Kernel Version 23.4.0: Wed Feb 21 21:44:54 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T6030",
+      "os_release": "23.4.0", "processor": "arm", "machine": "arm64", "function":
+      "mem0.client.main.MemoryClient", "$lib": "posthog-python", "$lib_version": "3.5.0",
+      "$geoip_disable": true}, "timestamp": "2024-08-17T06:00:11.526640+00:00", "context":
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+      "historical_migration": false, "sentAt": "2024-08-17T06:00:11.701621+00:00",
+      "api_key": "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX"}'
+    headers:
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+      - '*/*'
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+      - gzip, deflate
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+      - '740'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - posthog-python/3.5.0
+    method: POST
+    uri: https://us.i.posthog.com/batch/
+  response:
+    body:
+      string: '{"status":"Ok"}'
+    headers:
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '15'
+      Content-Type:
+      - application/json
+      Date:
+      - Sat, 17 Aug 2024 06:00:12 GMT
+      access-control-allow-credentials:
+      - 'true'
+      server:
+      - envoy
+      vary:
+      - origin, access-control-request-method, access-control-request-headers
+      x-envoy-upstream-service-time:
+      - '69'
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"messages": [{"role": "user", "content": "Remember the following insights
+      from Agent run: test value with provider"}], "metadata": {"task": "test_task_provider",
+      "agent": "test_agent_provider"}, "app_id": "Researcher"}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '219'
+      content-type:
+      - application/json
+      host:
+      - api.mem0.ai
+      user-agent:
+      - python-httpx/0.27.0
+    method: POST
+    uri: https://api.mem0.ai/v1/memories/
+  response:
+    body:
+      string: '{"message":"ok"}'
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 8b477140282547b9-BOM
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '16'
+      Content-Type:
+      - application/json
+      Date:
+      - Sat, 17 Aug 2024 06:00:13 GMT
+      NEL:
+      - '{"success_fraction":0,"report_to":"cf-nel","max_age":604800}'
+      Report-To:
+      - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v4?s=FRjJKSk3YxVj03wA7S05H8ts35KnWfqS3wb6Rfy4kVZ4BgXfw7nJbm92wI6vEv5fWcAcHVnOlkJDggs11B01BMuB2k3a9RqlBi0dJNiMuk%2Bgm5xE%2BODMPWJctYNRwQMjNVbteUpS%2Fad8YA%3D%3D"}],"group":"cf-nel","max_age":604800}'
+      Server:
+      - cloudflare
+      allow:
+      - GET, POST, DELETE, OPTIONS
+      alt-svc:
+      - h3=":443"; ma=86400
+      cross-origin-opener-policy:
+      - same-origin
+      referrer-policy:
+      - same-origin
+      vary:
+      - Accept, origin, Cookie
+      x-content-type-options:
+      - nosniff
+      x-frame-options:
+      - DENY
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"query": "test value with provider", "limit": 3, "app_id": "Researcher"}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '73'
+      content-type:
+      - application/json
+      host:
+      - api.mem0.ai
+      user-agent:
+      - python-httpx/0.27.0
+    method: POST
+    uri: https://api.mem0.ai/v1/memories/search/
+  response:
+    body:
+      string: '[]'
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 8b47714d083b47b9-BOM
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '2'
+      Content-Type:
+      - application/json
+      Date:
+      - Sat, 17 Aug 2024 06:00:14 GMT
+      NEL:
+      - '{"success_fraction":0,"report_to":"cf-nel","max_age":604800}'
+      Report-To:
+      - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v4?s=2DRWL1cdKdMvnE8vx1fPUGeTITOgSGl3N5g84PS6w30GRqpfz79BtSx6REhpnOiFV8kM6KGqln0iCZ5yoHc2jBVVJXhPJhQ5t0uerD9JFnkphjISrJOU1MJjZWneT9PlNABddxvVNCmluA%3D%3D"}],"group":"cf-nel","max_age":604800}'
+      Server:
+      - cloudflare
+      allow:
+      - POST, OPTIONS
+      alt-svc:
+      - h3=":443"; ma=86400
+      cross-origin-opener-policy:
+      - same-origin
+      referrer-policy:
+      - same-origin
+      vary:
+      - Accept, origin, Cookie
+      x-content-type-options:
+      - nosniff
+      x-frame-options:
+      - DENY
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"batch": [{"properties": {"python_version": "3.12.4 (v3.12.4:8e8a4baf65,
+      Jun  6 2024, 17:33:18) [Clang 13.0.0 (clang-1300.0.29.30)]", "os": "darwin",
+      "os_version": "Darwin Kernel Version 23.4.0: Wed Feb 21 21:44:54 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T6030",
+      "os_release": "23.4.0", "processor": "arm", "machine": "arm64", "function":
+      "mem0.client.main.MemoryClient", "$lib": "posthog-python", "$lib_version": "3.5.0",
+      "$geoip_disable": true}, "timestamp": "2024-08-17T06:00:13.593952+00:00", "context":
+      {}, "distinct_id": "fd411bd3-99a2-42d6-acd7-9fca8ad09580", "event": "client.add"}],
+      "historical_migration": false, "sentAt": "2024-08-17T06:00:13.858277+00:00",
+      "api_key": "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX"}'
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '739'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - posthog-python/3.5.0
+    method: POST
+    uri: https://us.i.posthog.com/batch/
+  response:
+    body:
+      string: '{"status":"Ok"}'
+    headers:
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '15'
+      Content-Type:
+      - application/json
+      Date:
+      - Sat, 17 Aug 2024 06:00:13 GMT
+      access-control-allow-credentials:
+      - 'true'
+      server:
+      - envoy
+      vary:
+      - origin, access-control-request-method, access-control-request-headers
+      x-envoy-upstream-service-time:
+      - '33'
+    status:
+      code: 200
+      message: OK
+version: 1

tests/pipeline/test_pipeline.py

@@ -38,6 +38,7 @@ def mock_crew_factory():
 
         crew = MockCrew()
         crew.name = name
+        crew.knowledge = None
 
         task_output = TaskOutput(
             description="Test task", raw="Task output", agent="Test Agent"
@@ -67,6 +68,7 @@ def mock_crew_factory():
         crew.process = Process.sequential
         crew.config = None
         crew.cache = True
+        crew.embedder = None
 
         # Add non-empty agents and tasks
         mock_agent = MagicMock(spec=Agent)

tests/project_test.py

@@ -1,5 +1,8 @@
+import pytest
+
 from crewai.agent import Agent
-from crewai.project import agent, task
+from crewai.crew import Crew
+from crewai.project import CrewBase, after_kickoff, agent, before_kickoff, crew, task
 from crewai.task import Task
 
 
@@ -23,6 +26,43 @@ class SimpleCrew:
         )
 
 
+@CrewBase
+class TestCrew:
+    agents_config = "config/agents.yaml"
+    tasks_config = "config/tasks.yaml"
+
+    @agent
+    def researcher(self):
+        return Agent(config=self.agents_config["researcher"])
+
+    @agent
+    def reporting_analyst(self):
+        return Agent(config=self.agents_config["reporting_analyst"])
+
+    @task
+    def research_task(self):
+        return Task(config=self.tasks_config["research_task"])
+
+    @task
+    def reporting_task(self):
+        return Task(config=self.tasks_config["reporting_task"])
+
+    @before_kickoff
+    def modify_inputs(self, inputs):
+        if inputs:
+            inputs["topic"] = "Bicycles"
+        return inputs
+
+    @after_kickoff
+    def modify_outputs(self, outputs):
+        outputs.raw = outputs.raw + " post processed"
+        return outputs
+
+    @crew
+    def crew(self):
+        return Crew(agents=self.agents, tasks=self.tasks, verbose=True)
+
+
 def test_agent_memoization():
     crew = SimpleCrew()
     first_call_result = crew.simple_agent()
@@ -43,6 +83,16 @@ def test_task_memoization():
     ), "Task memoization is not working as expected"
 
 
+def test_crew_memoization():
+    crew = TestCrew()
+    first_call_result = crew.crew()
+    second_call_result = crew.crew()
+
+    assert (
+        first_call_result is second_call_result
+    ), "Crew references should point to the same object"
+
+
 def test_task_name():
     simple_task = SimpleCrew().simple_task()
     assert (
@@ -53,3 +103,84 @@ def test_task_name():
     assert (
         custom_named_task.name == "Custom"
     ), "Custom task name is not being set as expected"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_before_kickoff_modification():
+    crew = TestCrew()
+    inputs = {"topic": "LLMs"}
+    result = crew.crew().kickoff(inputs=inputs)
+    assert "bicycles" in result.raw, "Before kickoff function did not modify inputs"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_after_kickoff_modification():
+    crew = TestCrew()
+    # Assuming the crew execution returns a dict
+    result = crew.crew().kickoff({"topic": "LLMs"})
+
+    assert (
+        "post processed" in result.raw
+    ), "After kickoff function did not modify outputs"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_before_kickoff_with_none_input():
+    crew = TestCrew()
+    crew.crew().kickoff(None)
+    # Test should pass without raising exceptions
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_multiple_before_after_kickoff():
+    @CrewBase
+    class MultipleHooksCrew:
+        agents_config = "config/agents.yaml"
+        tasks_config = "config/tasks.yaml"
+
+        @agent
+        def researcher(self):
+            return Agent(config=self.agents_config["researcher"])
+
+        @agent
+        def reporting_analyst(self):
+            return Agent(config=self.agents_config["reporting_analyst"])
+
+        @task
+        def research_task(self):
+            return Task(config=self.tasks_config["research_task"])
+
+        @task
+        def reporting_task(self):
+            return Task(config=self.tasks_config["reporting_task"])
+
+        @before_kickoff
+        def first_before(self, inputs):
+            inputs["topic"] = "Bicycles"
+            return inputs
+
+        @before_kickoff
+        def second_before(self, inputs):
+            inputs["topic"] = "plants"
+            return inputs
+
+        @after_kickoff
+        def first_after(self, outputs):
+            outputs.raw = outputs.raw + " processed first"
+            return outputs
+
+        @after_kickoff
+        def second_after(self, outputs):
+            outputs.raw = outputs.raw + " processed second"
+            return outputs
+
+        @crew
+        def crew(self):
+            return Crew(agents=self.agents, tasks=self.tasks, verbose=True)
+
+    crew = MultipleHooksCrew()
+    result = crew.crew().kickoff({"topic": "LLMs"})
+
+    assert "plants" in result.raw, "First before_kickoff not executed"
+    assert "processed first" in result.raw, "First after_kickoff not executed"
+    assert "processed second" in result.raw, "Second after_kickoff not executed"