Update memory.mdx

- Add proper import statements
- Add type hints for better readability
- Add descriptive comments for each memory type
- Add security considerations section
- Add configuration examples section
- Use environment variables for storage paths

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

README.md

@@ -1,18 +1,10 @@
 <div align="center">
 
-![Logo of CrewAI](./docs/crewai_logo.png)
+![Logo of CrewAI, two people rowing on a boat](./docs/crewai_logo.png)
 
 # **CrewAI**
 
-**CrewAI**: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.
-
-**CrewAI Enterprise**
-Want to plan, build (+ no code), deploy, monitor and interare your agents: [CrewAI Enterprise](https://www.crewai.com/enterprise). Designed for complex, real-world applications, our enterprise solution offers:
-
-- **Seamless Integrations**
-- **Scalable & Secure Deployment**
-- **Actionable Insights**
-- **24/7 Support**
+🤖 **CrewAI**: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.
 
 <h3>
 
@@ -400,7 +392,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
             goal="Gather and validate supporting market data",
             backstory="You excel at finding and correlating multiple data sources"
         )
-
+        
         analysis_task = Task(
             description="Analyze {sector} sector data for the past {timeframe}",
             expected_output="Detailed market analysis with confidence score",
@@ -411,7 +403,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
             expected_output="Corroborating evidence and potential contradictions",
             agent=researcher
         )
-
+        
         # Demonstrate crew autonomy
         analysis_crew = Crew(
             agents=[analyst, researcher],

docs/concepts/crews.mdx

@@ -23,14 +23,14 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **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).                                                                                                                                                                           |
-| **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`.                                                                                                                                      |
+| **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`.                                                                                                                                       |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defautls to `None`.                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
@@ -240,23 +240,6 @@ print(f"Tasks Output: {crew_output.tasks_output}")
 print(f"Token Usage: {crew_output.token_usage}")
 ```
 
-## Accessing Crew Logs
-
-You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
-In case of `True(Boolean)` will save as `logs.txt`.
-
-In case of `output_log_file` is set as `False(Booelan)` or `None`, the logs will not be populated.
-
-```python Code
-# Save crew logs
-crew = Crew(output_log_file = True)  # Logs will be saved as logs.txt
-crew = Crew(output_log_file = file_name)  # Logs will be saved as file_name.txt
-crew = Crew(output_log_file = file_name.txt)  # Logs will be saved as file_name.txt
-crew = Crew(output_log_file = file_name.json)  # Logs will be saved as file_name.json
-```
-
-
-
 ## Memory Utilization
 
 Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.

docs/concepts/flows.mdx

@@ -232,18 +232,18 @@ class UnstructuredExampleFlow(Flow):
     def first_method(self):
         # The state automatically includes an 'id' field
         print(f"State ID: {self.state['id']}")
-        self.state['counter'] = 0
-        self.state['message'] = "Hello from structured flow"
+        self.state.message = "Hello from structured flow"
+        self.state.counter = 0
 
     @listen(first_method)
     def second_method(self):
-        self.state['counter'] += 1
-        self.state['message'] += " - updated"
+        self.state.counter += 1
+        self.state.message += " - updated"
 
     @listen(second_method)
     def third_method(self):
-        self.state['counter'] += 1
-        self.state['message'] += " - updated again"
+        self.state.counter += 1
+        self.state.message += " - updated again"
 
         print(f"State after third_method: {self.state}")
 

docs/concepts/knowledge.mdx

@@ -91,7 +91,7 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
 ```
 
 
-Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including MD, PDF, DOCX, HTML, and more. 
+Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including TXT, PDF, DOCX, HTML, and more. 
 
 <Note>
   You need to install `docling` for the following example to work: `uv add docling`
@@ -152,10 +152,10 @@ Here are examples of how to use different types of knowledge sources:
 
 ### Text File Knowledge Source
 ```python
-from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
+from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
 
 # Create a text file knowledge source
-text_source = TextFileKnowledgeSource(
+text_source = CrewDoclingSource(
     file_paths=["document.txt", "another.txt"]
 )
 

docs/concepts/llms.mdx

@@ -463,32 +463,26 @@ Learn how to get the most out of your LLM configuration:
 
   <Accordion title="Google">
     ```python Code
-    # Option 1: Gemini accessed with an API key.
+    # Option 1. Gemini accessed with an API key.
     # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
 
-    # Option 2: Vertex AI IAM credentials for Gemini, Anthropic, and Model Garden.
+    # Option 2. Vertex AI IAM credentials for Gemini, Anthropic, and anything in the Model Garden.
     # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
     ```
 
-    Get credentials:
-    ```python Code
-    import json
-
+    ## GET CREDENTIALS 
     file_path = 'path/to/vertex_ai_service_account.json'
 
     # Load the JSON file
     with open(file_path, 'r') as file:
         vertex_credentials = json.load(file)
 
-    # Convert the credentials to a JSON string
+    # Convert to JSON string
     vertex_credentials_json = json.dumps(vertex_credentials)
-    ```
 
     Example usage:
     ```python Code
-    from crewai import LLM
-
     llm = LLM(
         model="gemini/gemini-1.5-pro-latest",
         temperature=0.7,

docs/concepts/memory.mdx

@@ -251,12 +251,7 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "model": 'text-embedding-3-small'
-        }
-    }
+    embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model="text-embedding-3-small"),
 )
 ```
 
@@ -282,19 +277,6 @@ my_crew = Crew(
 
 ### Using Google AI embeddings
 
-#### Prerequisites
-Before using Google AI embeddings, ensure you have:
-- Access to the Gemini API
-- The necessary API keys and permissions
-
-You will need to update your *pyproject.toml* dependencies:
-```YAML
-dependencies = [
-    "google-generativeai>=0.8.4", #main version in January/2025 - crewai v.0.100.0 and crewai-tools 0.33.0
-    "crewai[tools]>=0.100.0,<1.0.0"
-]
-```
-
 ```python Code
 from crewai import Crew, Agent, Task, Process
 
@@ -326,15 +308,13 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "api_key": "YOUR_API_KEY",
-            "api_base": "YOUR_API_BASE_PATH",
-            "api_version": "YOUR_API_VERSION",
-            "model_name": 'text-embedding-3-small'
-        }
-    }
+    embedder=OpenAIEmbeddingFunction(
+        api_key="YOUR_API_KEY",
+        api_base="YOUR_API_BASE_PATH",
+        api_type="azure",
+        api_version="YOUR_API_VERSION",
+        model="text-embedding-3-small"
+    )
 )
 ```
 
@@ -350,15 +330,12 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "vertexai",
-        "config": {
-            "project_id"="YOUR_PROJECT_ID",
-            "region"="YOUR_REGION",
-            "api_key"="YOUR_API_KEY",
-            "model_name"="textembedding-gecko"
-        }
-    }
+    embedder=GoogleVertexEmbeddingFunction(
+        project_id="YOUR_PROJECT_ID",
+        region="YOUR_REGION",
+        api_key="YOUR_API_KEY",
+        model="textembedding-gecko"
+    )
 )
 ```
 
@@ -447,65 +424,6 @@ my_crew = Crew(
 )
 ```
 
-### Using Amazon Bedrock embeddings
-
-```python Code
-# Note: Ensure you have installed `boto3` for Bedrock embeddings to work.
-
-import os
-import boto3
-from crewai import Crew, Agent, Task, Process
-
-boto3_session = boto3.Session(
-    region_name=os.environ.get("AWS_REGION_NAME"),
-    aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"),
-    aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY")
-)
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    embedder={
-    "provider": "bedrock",
-        "config":{
-            "session": boto3_session,
-            "model": "amazon.titan-embed-text-v2:0",
-            "vector_dimension": 1024
-        }
-    }
-    verbose=True
-)
-```
-
-### Adding Custom Embedding Function
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-from chromadb import Documents, EmbeddingFunction, Embeddings
-
-# Create a custom embedding function
-class CustomEmbedder(EmbeddingFunction):
-    def __call__(self, input: Documents) -> Embeddings:
-        # generate embeddings
-        return [1, 2, 3] # this is a dummy embedding
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "custom",
-        "config": {
-            "embedder": CustomEmbedder()
-        }
-    }
-)
-```
-
 ### Resetting Memory
 
 ```shell

docs/concepts/tasks.mdx

@@ -268,7 +268,7 @@ analysis_task = Task(
 
 Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
-feedback to agents when their output doesn't meet specific criteria.
+efeedback to agents when their output doesn't meet specific criteria.
 
 ### Using Task Guardrails
 

docs/how-to/langfuse-observability.mdx

@@ -1,98 +0,0 @@
----
-title: Agent Monitoring with Langfuse
-description: Learn how to integrate Langfuse with CrewAI via OpenTelemetry using OpenLit
-icon: magnifying-glass-chart
----
-
-# Integrate Langfuse with CrewAI
-
-This notebook demonstrates how to integrate **Langfuse** with **CrewAI** using OpenTelemetry via the **OpenLit** SDK. By the end of this notebook, you will be able to trace your CrewAI applications with Langfuse for improved observability and debugging.
-
-> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source LLM engineering platform. It provides tracing and monitoring capabilities for LLM applications, helping developers debug, analyze, and optimize their AI systems. Langfuse integrates with various tools and frameworks via native integrations, OpenTelemetry, and APIs/SDKs.
-
-## Get Started
-
-We'll walk through a simple example of using CrewAI and integrating it with Langfuse via OpenTelemetry using OpenLit.
-
-### Step 1: Install Dependencies
-
-
-```python
-%pip install langfuse openlit crewai crewai_tools
-```
-
-### Step 2: Set Up Environment Variables
-
-Set your Langfuse API keys and configure OpenTelemetry export settings to send traces to Langfuse. Please refer to the [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started) for more information on the Langfuse OpenTelemetry endpoint `/api/public/otel` and authentication.
-
-
-```python
-import os
-import base64
-
-LANGFUSE_PUBLIC_KEY="pk-lf-..."
-LANGFUSE_SECRET_KEY="sk-lf-..."
-LANGFUSE_AUTH=base64.b64encode(f"{LANGFUSE_PUBLIC_KEY}:{LANGFUSE_SECRET_KEY}".encode()).decode()
-
-os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://cloud.langfuse.com/api/public/otel" # EU data region
-# os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://us.cloud.langfuse.com/api/public/otel" # US data region
-os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"
-
-# your openai key
-os.environ["OPENAI_API_KEY"] = "sk-..."
-```
-
-### Step 3: Initialize OpenLit
-
-Initialize the OpenLit OpenTelemetry instrumentation SDK to start capturing OpenTelemetry traces.
-
-
-```python
-import openlit
-
-openlit.init()
-```
-
-### Step 4: Create a Simple CrewAI Application
-
-We'll create a simple CrewAI application where multiple agents collaborate to answer a user's question.
-
-
-```python
-from crewai import Agent, Task, Crew
-
-from crewai_tools import (
-    WebsiteSearchTool
-)
-
-web_rag_tool = WebsiteSearchTool()
-
-writer = Agent(
-        role="Writer",
-        goal="You make math engaging and understandable for young children through poetry",
-        backstory="You're an expert in writing haikus but you know nothing of math.",
-        tools=[web_rag_tool],  
-    )
-
-task = Task(description=("What is {multiplication}?"),
-            expected_output=("Compose a haiku that includes the answer."),
-            agent=writer)
-
-crew = Crew(
-  agents=[writer],
-  tasks=[task],
-  share_crew=False
-)
-```
-
-### Step 5: See Traces in Langfuse
-
-After running the agent, you can view the traces generated by your CrewAI application in [Langfuse](https://cloud.langfuse.com). You should see detailed steps of the LLM interactions, which can help you debug and optimize your AI agent.
-
-![CrewAI example trace in Langfuse](https://langfuse.com/images/cookbook/integration_crewai/crewai-example-trace.png)
-
-_[Public example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e2cf380ffc8d47d28da98f136140642b?timestamp=2025-02-05T15%3A12%3A02.717Z&observation=3b32338ee6a5d9af)_
-
-## References
-
-- [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started)

docs/how-to/portkey-observability-and-guardrails.mdx

@@ -0,0 +1,211 @@
+# Portkey Integration with CrewAI
+<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-CrewAI.png" alt="Portkey CrewAI Header Image" width="70%" />
+
+
+[Portkey](https://portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast.
+
+Portkey adds 4 core production capabilities to any CrewAI agent:
+1. Routing to **200+ LLMs**
+2. Making each LLM call more robust
+3. Full-stack tracing & cost, performance analytics
+4. Real-time guardrails to enforce behavior
+
+
+
+
+
+## Getting Started
+
+1. **Install Required Packages:**
+
+```bash
+pip install -qU crewai portkey-ai
+```
+
+2. **Configure the LLM Client:**
+
+To build CrewAI Agents with Portkey, you'll need two keys:
+- **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key
+- **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault
+
+```python
+from crewai import LLM
+from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
+
+gpt_llm = LLM(
+    model="gpt-4",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy", # We are using Virtual key
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_VIRTUAL_KEY", # Enter your Virtual key from Portkey
+    )
+)
+```
+
+3. **Create and Run Your First Agent:**
+
+```python
+from crewai import Agent, Task, Crew
+
+# Define your agents with roles and goals
+coder = Agent(
+    role='Software developer',
+    goal='Write clear, concise code on demand',
+    backstory='An expert coder with a keen eye for software trends.',
+    llm=gpt_llm
+)
+
+# Create tasks for your agents
+task1 = Task(
+    description="Define the HTML for making a simple website with heading- Hello World! Portkey is working!",
+    expected_output="A clear and concise HTML code",
+    agent=coder
+)
+
+# Instantiate your crew
+crew = Crew(
+    agents=[coder],
+    tasks=[task1],
+)
+
+result = crew.kickoff()
+print(result)
+```
+
+
+## Key Features
+
+| Feature | Description |
+|---------|-------------|
+| 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface |
+| 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks |
+| 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata |
+| 🔍 Comprehensive Logging | Debug with detailed execution traces and function call logs |
+| 🚧 Security Controls | Set budget limits and implement role-based access control |
+| 🔄 Performance Analytics | Capture and analyze feedback for continuous improvement |
+| 💾 Intelligent Caching | Reduce costs and latency with semantic or simple caching |
+
+
+## Production Features with Portkey Configs
+
+All features mentioned below are through Portkey's Config system. Portkey's Config system allows you to define routing strategies using simple JSON objects in your LLM API calls. You can create and manage Configs directly in your code or through the Portkey Dashboard. Each Config has a unique ID for easy reference.
+
+<Frame>
+    <img src="https://raw.githubusercontent.com/Portkey-AI/docs-core/refs/heads/main/images/libraries/libraries-3.avif"/>
+</Frame>
+
+
+### 1. Use 250+ LLMs
+Access various LLMs like Anthropic, Gemini, Mistral, Azure OpenAI, and more with minimal code changes. Switch between providers or use them together seamlessly. [Learn more about Universal API](https://portkey.ai/docs/product/ai-gateway/universal-api)
+
+
+Easily switch between different LLM providers:
+
+```python
+# Anthropic Configuration
+anthropic_llm = LLM(
+    model="claude-3-5-sonnet-latest",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY", #You don't need provider when using Virtual keys
+        trace_id="anthropic_agent"
+    )
+)
+
+# Azure OpenAI Configuration
+azure_llm = LLM(
+    model="gpt-4",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_AZURE_VIRTUAL_KEY", #You don't need provider when using Virtual keys
+        trace_id="azure_agent"
+    )
+)
+```
+
+
+### 2. Caching
+Improve response times and reduce costs with two powerful caching modes:
+- **Simple Cache**: Perfect for exact matches
+- **Semantic Cache**: Matches responses for requests that are semantically similar
+[Learn more about Caching](https://portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic)
+
+```py
+config = {
+    "cache": {
+        "mode": "semantic",  # or "simple" for exact matching
+    }
+}
+```
+
+### 3. Production Reliability
+Portkey provides comprehensive reliability features:
+- **Automatic Retries**: Handle temporary failures gracefully
+- **Request Timeouts**: Prevent hanging operations
+- **Conditional Routing**: Route requests based on specific conditions
+- **Fallbacks**: Set up automatic provider failovers
+- **Load Balancing**: Distribute requests efficiently
+
+[Learn more about Reliability Features](https://portkey.ai/docs/product/ai-gateway/)
+
+
+
+### 4. Metrics
+
+Agent runs are complex. Portkey automatically logs **40+ comprehensive metrics** for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need.
+
+
+- Cost per agent interaction
+- Response times and latency
+- Token usage and efficiency
+- Success/failure rates
+- Cache hit rates
+
+<img src="https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Dashboard.png?raw=true" width="70%" alt="Portkey Dashboard" />
+
+### 5. Detailed Logging
+Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes.
+
+
+Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata.
+
+<details>
+  <summary><b>Traces</b></summary>
+  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Traces.png" alt="Portkey Traces" width="70%" />
+</details>
+
+<details>
+  <summary><b>Logs</b></summary>
+  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Logs.png" alt="Portkey Logs" width="70%" />
+</details>
+
+### 6. Enterprise Security Features
+- Set budget limit and rate limts per Virtual Key (disposable API keys)
+- Implement role-based access control
+- Track system changes with audit logs
+- Configure data retention policies
+
+
+
+For detailed information on creating and managing Configs, visit the [Portkey documentation](https://docs.portkey.ai/product/ai-gateway/configs).
+
+## Resources
+
+- [📘 Portkey Documentation](https://docs.portkey.ai)
+- [📊 Portkey Dashboard](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai)
+- [🐦 Twitter](https://twitter.com/portkeyai)
+- [💬 Discord Community](https://discord.gg/DD7vgKK299)
+
+
+
+
+
+
+
+
+

docs/how-to/portkey-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: Agent Monitoring with Portkey
+title: Portkey Observability and Guardrails
 description: How to use Portkey with CrewAI
 icon: key
 ---

docs/mint.json

@@ -103,8 +103,7 @@
         "how-to/langtrace-observability",
         "how-to/mlflow-observability",
         "how-to/openlit-observability",
-        "how-to/portkey-observability",
-        "how-to/langfuse-observability"
+        "how-to/portkey-observability"
       ]
     },
     {

docs/tools/filewritetool.mdx

@@ -8,9 +8,9 @@ icon: file-pen
 
 ## Description
 
-The `FileWriterTool` is a component of the crewai_tools package, designed to simplify the process of writing content to files with cross-platform compatibility (Windows, Linux, macOS). 
+The `FileWriterTool` is a component of the crewai_tools package, designed to simplify the process of writing content to files. 
 It is particularly useful in scenarios such as generating reports, saving logs, creating configuration files, and more. 
-This tool handles path differences across operating systems, supports UTF-8 encoding, and automatically creates directories if they don't exist, making it easier to organize your output reliably across different platforms.
+This tool supports creating new directories if they don't exist, making it easier to organize your output.
 
 ## Installation
 
@@ -43,8 +43,6 @@ print(result)
 
 ## Conclusion
 
-By integrating the `FileWriterTool` into your crews, the agents can reliably write content to files across different operating systems. 
-This tool is essential for tasks that require saving output data, creating structured file systems, and handling cross-platform file operations. 
-It's particularly recommended for Windows users who may encounter file writing issues with standard Python file operations.
-
-By adhering to the setup and usage guidelines provided, incorporating this tool into projects is straightforward and ensures consistent file writing behavior across all platforms.
+By integrating the `FileWriterTool` into your crews, the agents can execute the process of writing content to files and creating directories. 
+This tool is essential for tasks that require saving output data, creating structured file systems, and more. By adhering to the setup and usage guidelines provided, 
+incorporating this tool into projects is straightforward and efficient.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.102.0"
+version = "0.100.1"
 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"
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.36.0"]
+tools = ["crewai-tools>=0.32.1"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.102.0"
+__version__ = "0.100.1"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -1,7 +1,6 @@
-import re
 import shutil
 import subprocess
-from typing import Any, Dict, List, Literal, Optional, Sequence, Union
+from typing import Any, Dict, List, Literal, Optional, Union
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
@@ -16,6 +15,7 @@ from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.task import Task
 from crewai.tools import BaseTool
 from crewai.tools.agent_tools.agent_tools import AgentTools
+from crewai.tools.base_tool import Tool
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
@@ -54,6 +54,7 @@ class Agent(BaseAgent):
             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.
             max_iter: Maximum number of iterations for an agent to execute a task.
+            memory: Whether the agent should have memory or not.
             max_rpm: Maximum number of requests per minute for the agent execution to be respected.
             verbose: Whether the agent execution should be in verbose mode.
             allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
@@ -70,6 +71,9 @@ class Agent(BaseAgent):
     )
     agent_ops_agent_name: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
     agent_ops_agent_id: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
+    cache_handler: InstanceOf[CacheHandler] = Field(
+        default=None, description="An instance of the CacheHandler class."
+    )
     step_callback: Optional[Any] = Field(
         default=None,
         description="Callback to be executed after each step of the agent execution.",
@@ -103,6 +107,10 @@ class Agent(BaseAgent):
         default=True,
         description="Keep messages under the context window size by summarizing content.",
     )
+    max_iter: int = Field(
+        default=20,
+        description="Maximum number of iterations for an agent to execute a task before giving it's best answer",
+    )
     max_retry_limit: int = Field(
         default=2,
         description="Maximum number of retries for an agent to execute a task when an error occurs.",
@@ -145,8 +153,7 @@ class Agent(BaseAgent):
     def _set_knowledge(self):
         try:
             if self.knowledge_sources:
-                full_pattern = re.compile(r"[^a-zA-Z0-9\-_\r\n]|(\.\.)")
-                knowledge_agent_name = f"{re.sub(full_pattern, '_', self.role)}"
+                knowledge_agent_name = f"{self.role.replace(' ', '_')}"
                 if isinstance(self.knowledge_sources, list) and all(
                     isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                 ):
@@ -188,15 +195,13 @@ class Agent(BaseAgent):
             if task.output_json:
                 # schema = json.dumps(task.output_json, indent=2)
                 schema = generate_model_description(task.output_json)
-                task_prompt += "\n" + self.i18n.slice(
-                    "formatted_task_instructions"
-                ).format(output_format=schema)
 
             elif task.output_pydantic:
                 schema = generate_model_description(task.output_pydantic)
-                task_prompt += "\n" + self.i18n.slice(
-                    "formatted_task_instructions"
-                ).format(output_format=schema)
+
+            task_prompt += "\n" + self.i18n.slice("formatted_task_instructions").format(
+                output_format=schema
+            )
 
         if context:
             task_prompt = self.i18n.slice("task_with_context").format(
@@ -324,14 +329,14 @@ class Agent(BaseAgent):
         tools = agent_tools.tools()
         return tools
 
-    def get_multimodal_tools(self) -> Sequence[BaseTool]:
+    def get_multimodal_tools(self) -> List[Tool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
 
         return [AddImageTool()]
 
     def get_code_execution_tools(self):
         try:
-            from crewai_tools import CodeInterpreterTool  # type: ignore
+            from crewai_tools import CodeInterpreterTool
 
             # Set the unsafe_mode based on the code_execution_mode attribute
             unsafe_mode = self.code_execution_mode == "unsafe"

src/crewai/agents/agent_builder/base_agent.py

@@ -24,7 +24,6 @@ from crewai.tools import BaseTool
 from crewai.tools.base_tool import Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
-from crewai.utilities.converter import Converter
 
 T = TypeVar("T", bound="BaseAgent")
 
@@ -43,7 +42,7 @@ class BaseAgent(ABC, BaseModel):
         max_rpm (Optional[int]): Maximum number of requests per minute for the agent execution.
         allow_delegation (bool): Allow delegation of tasks to agents.
         tools (Optional[List[Any]]): Tools at the agent's disposal.
-        max_iter (int): Maximum iterations for an agent to execute a task.
+        max_iter (Optional[int]): Maximum iterations for an agent to execute a task.
         agent_executor (InstanceOf): An instance of the CrewAgentExecutor class.
         llm (Any): Language model that will run the agent.
         crew (Any): Crew to which the agent belongs.
@@ -115,7 +114,7 @@ class BaseAgent(ABC, BaseModel):
     tools: Optional[List[Any]] = Field(
         default_factory=list, description="Tools at agents' disposal"
     )
-    max_iter: int = Field(
+    max_iter: Optional[int] = Field(
         default=25, description="Maximum iterations for an agent to execute a task"
     )
     agent_executor: InstanceOf = Field(
@@ -126,12 +125,11 @@ class BaseAgent(ABC, BaseModel):
     )
     crew: Any = Field(default=None, description="Crew to which the agent belongs.")
     i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
-    cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
+    cache_handler: InstanceOf[CacheHandler] = Field(
         default=None, description="An instance of the CacheHandler class."
     )
     tools_handler: InstanceOf[ToolsHandler] = Field(
-        default_factory=ToolsHandler,
-        description="An instance of the ToolsHandler class.",
+        default=None, description="An instance of the ToolsHandler class."
     )
     max_tokens: Optional[int] = Field(
         default=None, description="Maximum number of tokens for the agent's execution."
@@ -256,7 +254,7 @@ class BaseAgent(ABC, BaseModel):
     @abstractmethod
     def get_output_converter(
         self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str
-    ) -> Converter:
+    ):
         """Get the converter class for the agent to create json/pydantic outputs."""
         pass
 

src/crewai/cli/reset_memories_command.py

@@ -2,7 +2,11 @@ import subprocess
 
 import click
 
-from crewai.cli.utils import get_crew
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+from crewai.memory.entity.entity_memory import EntityMemory
+from crewai.memory.long_term.long_term_memory import LongTermMemory
+from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 
 
 def reset_memories_command(
@@ -26,35 +30,30 @@ def reset_memories_command(
     """
 
     try:
-        crew = get_crew()
-        if not crew:
-            raise ValueError("No crew found.")
         if all:
-            crew.reset_memories(command_type="all")
+            ShortTermMemory().reset()
+            EntityMemory().reset()
+            LongTermMemory().reset()
+            TaskOutputStorageHandler().reset()
+            KnowledgeStorage().reset()
             click.echo("All memories have been reset.")
-            return
+        else:
+            if long:
+                LongTermMemory().reset()
+                click.echo("Long term memory has been reset.")
 
-        if not any([long, short, entity, kickoff_outputs, knowledge]):
-            click.echo(
-                "No memory type specified. Please specify at least one type to reset."
-            )
-            return
-
-        if long:
-            crew.reset_memories(command_type="long")
-            click.echo("Long term memory has been reset.")
-        if short:
-            crew.reset_memories(command_type="short")
-            click.echo("Short term memory has been reset.")
-        if entity:
-            crew.reset_memories(command_type="entity")
-            click.echo("Entity memory has been reset.")
-        if kickoff_outputs:
-            crew.reset_memories(command_type="kickoff_outputs")
-            click.echo("Latest Kickoff outputs stored has been reset.")
-        if knowledge:
-            crew.reset_memories(command_type="knowledge")
-            click.echo("Knowledge has been reset.")
+            if short:
+                ShortTermMemory().reset()
+                click.echo("Short term memory has been reset.")
+            if entity:
+                EntityMemory().reset()
+                click.echo("Entity memory has been reset.")
+            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/templates/crew/main.py

@@ -56,8 +56,7 @@ def test():
     Test the crew execution and returns the results.
     """
     inputs = {
-        "topic": "AI LLMs",
-        "current_year": str(datetime.now().year)
+        "topic": "AI LLMs"
     }
     try:
         {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)

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]>=0.102.0,<1.0.0"
+    "crewai[tools]>=0.100.1,<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]>=0.102.0,<1.0.0",
+    "crewai[tools]>=0.100.1,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.102.0"
+    "crewai[tools]>=0.100.1"
 ]
 
 [tool.crewai]

src/crewai/cli/utils.py

@@ -9,7 +9,6 @@ import tomli
 from rich.console import Console
 
 from crewai.cli.constants import ENV_VARS
-from crewai.crew import Crew
 
 if sys.version_info >= (3, 11):
     import tomllib
@@ -248,64 +247,3 @@ def write_env_file(folder_path, env_vars):
     with open(env_file_path, "w") as file:
         for key, value in env_vars.items():
             file.write(f"{key}={value}\n")
-
-
-def get_crew(crew_path: str = "crew.py", require: bool = False) -> Crew | None:
-    """Get the crew instance from the crew.py file."""
-    try:
-        import importlib.util
-        import os
-
-        for root, _, files in os.walk("."):
-            if "crew.py" in files:
-                crew_path = os.path.join(root, "crew.py")
-                try:
-                    spec = importlib.util.spec_from_file_location(
-                        "crew_module", crew_path
-                    )
-                    if not spec or not spec.loader:
-                        continue
-                    module = importlib.util.module_from_spec(spec)
-                    try:
-                        sys.modules[spec.name] = module
-                        spec.loader.exec_module(module)
-
-                        for attr_name in dir(module):
-                            attr = getattr(module, attr_name)
-                            try:
-                                if callable(attr) and hasattr(attr, "crew"):
-                                    crew_instance = attr().crew()
-                                    return crew_instance
-
-                            except Exception as e:
-                                print(f"Error processing attribute {attr_name}: {e}")
-                                continue
-
-                    except Exception as exec_error:
-                        print(f"Error executing module: {exec_error}")
-                        import traceback
-
-                        print(f"Traceback: {traceback.format_exc()}")
-
-                except (ImportError, AttributeError) as e:
-                    if require:
-                        console.print(
-                            f"Error importing crew from {crew_path}: {str(e)}",
-                            style="bold red",
-                        )
-                        continue
-
-                break
-
-        if require:
-            console.print("No valid Crew instance found in crew.py", style="bold red")
-            raise SystemExit
-        return None
-
-    except Exception as e:
-        if require:
-            console.print(
-                f"Unexpected error while loading crew: {str(e)}", style="bold red"
-            )
-            raise SystemExit
-        return None

src/crewai/crew.py

@@ -183,9 +183,9 @@ class Crew(BaseModel):
         default=None,
         description="Path to the prompt json file to be used for the crew.",
     )
-    output_log_file: Optional[Union[bool, str]] = Field(
+    output_log_file: Optional[str] = Field(
         default=None,
-        description="Path to the log file to be saved",
+        description="output_log_file",
     )
     planning: Optional[bool] = Field(
         default=False,
@@ -380,22 +380,6 @@ class Crew(BaseModel):
 
         return self
 
-    @model_validator(mode="after")
-    def validate_must_have_non_conditional_task(self) -> "Crew":
-        """Ensure that a crew has at least one non-conditional task."""
-        if not self.tasks:
-            return self
-        non_conditional_count = sum(
-            1 for task in self.tasks if not isinstance(task, ConditionalTask)
-        )
-        if non_conditional_count == 0:
-            raise PydanticCustomError(
-                "only_conditional_tasks",
-                "Crew must include at least one non-conditional task",
-                {},
-            )
-        return self
-
     @model_validator(mode="after")
     def validate_first_task(self) -> "Crew":
         """Ensure the first task is not a ConditionalTask."""
@@ -455,8 +439,6 @@ class Crew(BaseModel):
                         )
         return self
 
-
-
     @property
     def key(self) -> str:
         source = [agent.key for agent in self.agents] + [
@@ -699,7 +681,12 @@ class Crew(BaseModel):
                 manager.tools = []
                 raise Exception("Manager agent should not have tools")
         else:
-            self.manager_llm = create_llm(self.manager_llm)
+            self.manager_llm = (
+                getattr(self.manager_llm, "model_name", None)
+                or getattr(self.manager_llm, "model", None)
+                or getattr(self.manager_llm, "deployment_name", None)
+                or self.manager_llm
+            )
             manager = Agent(
                 role=i18n.retrieve("hierarchical_manager_agent", "role"),
                 goal=i18n.retrieve("hierarchical_manager_agent", "goal"),
@@ -759,7 +746,6 @@ class Crew(BaseModel):
                     task, task_outputs, futures, task_index, was_replayed
                 )
                 if skipped_task_output:
-                    task_outputs.append(skipped_task_output)
                     continue
 
             if task.async_execution:
@@ -783,7 +769,7 @@ class Crew(BaseModel):
                     context=context,
                     tools=tools_for_task,
                 )
-                task_outputs.append(task_output)
+                task_outputs = [task_output]
                 self._process_task_result(task, task_output)
                 self._store_execution_log(task, task_output, task_index, was_replayed)
 
@@ -804,7 +790,7 @@ class Crew(BaseModel):
             task_outputs = self._process_async_tasks(futures, was_replayed)
             futures.clear()
 
-        previous_output = task_outputs[-1] if task_outputs else None
+        previous_output = task_outputs[task_index - 1] if task_outputs else None
         if previous_output is not None and not task.should_execute(previous_output):
             self._logger.log(
                 "debug",
@@ -926,15 +912,11 @@ class Crew(BaseModel):
             )
 
     def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
-        if not task_outputs:
-            raise ValueError("No task outputs available to create crew output.")
-            
-        # Filter out empty outputs and get the last valid one as the main output
-        valid_outputs = [t for t in task_outputs if t.raw]
-        if not valid_outputs:
-            raise ValueError("No valid task outputs available to create crew output.")
-        final_task_output = valid_outputs[-1]
-            
+        if len(task_outputs) != 1:
+            raise ValueError(
+                "Something went wrong. Kickoff should return only one task output."
+            )
+        final_task_output = task_outputs[0]
         final_string_output = final_task_output.raw
         self._finish_execution(final_string_output)
         token_usage = self.calculate_usage_metrics()
@@ -943,7 +925,7 @@ class Crew(BaseModel):
             raw=final_task_output.raw,
             pydantic=final_task_output.pydantic,
             json_dict=final_task_output.json_dict,
-            tasks_output=task_outputs,
+            tasks_output=[task.output for task in self.tasks if task.output],
             token_usage=token_usage,
         )
 
@@ -1170,80 +1152,3 @@ class Crew(BaseModel):
 
     def __repr__(self):
         return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
-
-    def reset_memories(self, command_type: str) -> None:
-        """Reset specific or all memories for the crew.
-
-        Args:
-            command_type: Type of memory to reset.
-                Valid options: 'long', 'short', 'entity', 'knowledge',
-                'kickoff_outputs', or 'all'
-
-        Raises:
-            ValueError: If an invalid command type is provided.
-            RuntimeError: If memory reset operation fails.
-        """
-        VALID_TYPES = frozenset(
-            ["long", "short", "entity", "knowledge", "kickoff_outputs", "all"]
-        )
-
-        if command_type not in VALID_TYPES:
-            raise ValueError(
-                f"Invalid command type. Must be one of: {', '.join(sorted(VALID_TYPES))}"
-            )
-
-        try:
-            if command_type == "all":
-                self._reset_all_memories()
-            else:
-                self._reset_specific_memory(command_type)
-
-            self._logger.log("info", f"{command_type} memory has been reset")
-
-        except Exception as e:
-            error_msg = f"Failed to reset {command_type} memory: {str(e)}"
-            self._logger.log("error", error_msg)
-            raise RuntimeError(error_msg) from e
-
-    def _reset_all_memories(self) -> None:
-        """Reset all available memory systems."""
-        memory_systems = [
-            ("short term", self._short_term_memory),
-            ("entity", self._entity_memory),
-            ("long term", self._long_term_memory),
-            ("task output", self._task_output_handler),
-            ("knowledge", self.knowledge),
-        ]
-
-        for name, system in memory_systems:
-            if system is not None:
-                try:
-                    system.reset()
-                except Exception as e:
-                    raise RuntimeError(f"Failed to reset {name} memory") from e
-
-    def _reset_specific_memory(self, memory_type: str) -> None:
-        """Reset a specific memory system.
-
-        Args:
-            memory_type: Type of memory to reset
-
-        Raises:
-            RuntimeError: If the specified memory system fails to reset
-        """
-        reset_functions = {
-            "long": (self._long_term_memory, "long term"),
-            "short": (self._short_term_memory, "short term"),
-            "entity": (self._entity_memory, "entity"),
-            "knowledge": (self.knowledge, "knowledge"),
-            "kickoff_outputs": (self._task_output_handler, "task output"),
-        }
-
-        memory_system, name = reset_functions[memory_type]
-        if memory_system is None:
-            raise RuntimeError(f"{name} memory system is not initialized")
-
-        try:
-            memory_system.reset()
-        except Exception as e:
-            raise RuntimeError(f"Failed to reset {name} memory") from e

src/crewai/flow/flow.py

@@ -1,5 +1,4 @@
 import asyncio
-import copy
 import inspect
 import logging
 from typing import (
@@ -395,6 +394,7 @@ class FlowMeta(type):
                 or hasattr(attr_value, "__trigger_methods__")
                 or hasattr(attr_value, "__is_router__")
             ):
+
                 # Register start methods
                 if hasattr(attr_value, "__is_start_method__"):
                     start_methods.append(attr_name)
@@ -569,9 +569,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
             f"Initial state must be dict or BaseModel, got {type(self.initial_state)}"
         )
 
-    def _copy_state(self) -> T:
-        return copy.deepcopy(self._state)
-
     @property
     def state(self) -> T:
         return self._state
@@ -603,7 +600,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             ```
         """
         try:
-            if not hasattr(self, "_state"):
+            if not hasattr(self, '_state'):
                 return ""
 
             if isinstance(self._state, dict):
@@ -709,31 +706,26 @@ class Flow(Generic[T], metaclass=FlowMeta):
             inputs: Optional dictionary containing input values and potentially a state ID to restore
         """
         # Handle state restoration if ID is provided in inputs
-        if inputs and "id" in inputs and self._persistence is not None:
-            restore_uuid = inputs["id"]
+        if inputs and 'id' in inputs and self._persistence is not None:
+            restore_uuid = inputs['id']
             stored_state = self._persistence.load_state(restore_uuid)
 
             # Override the id in the state if it exists in inputs
-            if "id" in inputs:
+            if 'id' in inputs:
                 if isinstance(self._state, dict):
-                    self._state["id"] = inputs["id"]
+                    self._state['id'] = inputs['id']
                 elif isinstance(self._state, BaseModel):
-                    setattr(self._state, "id", inputs["id"])
+                    setattr(self._state, 'id', inputs['id'])
 
             if stored_state:
-                self._log_flow_event(
-                    f"Loading flow state from memory for UUID: {restore_uuid}",
-                    color="yellow",
-                )
+                self._log_flow_event(f"Loading flow state from memory for UUID: {restore_uuid}", color="yellow")
                 # Restore the state
                 self._restore_state(stored_state)
             else:
-                self._log_flow_event(
-                    f"No flow state found for UUID: {restore_uuid}", color="red"
-                )
+                self._log_flow_event(f"No flow state found for UUID: {restore_uuid}", color="red")
 
             # Apply any additional inputs after restoration
-            filtered_inputs = {k: v for k, v in inputs.items() if k != "id"}
+            filtered_inputs = {k: v for k, v in inputs.items() if k != 'id'}
             if filtered_inputs:
                 self._initialize_state(filtered_inputs)
 
@@ -743,14 +735,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
             event=FlowStartedEvent(
                 type="flow_started",
                 flow_name=self.__class__.__name__,
-                inputs=inputs,
             ),
         )
-        self._log_flow_event(
-            f"Flow started with ID: {self.flow_id}", color="bold_magenta"
-        )
+        self._log_flow_event(f"Flow started with ID: {self.flow_id}", color="bold_magenta")
 
-        if inputs is not None and "id" not in inputs:
+        if inputs is not None and 'id' not in inputs:
             self._initialize_state(inputs)
 
         return asyncio.run(self.kickoff_async())
@@ -807,18 +796,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
     async def _execute_method(
         self, method_name: str, method: Callable, *args: Any, **kwargs: Any
     ) -> Any:
-        dumped_params = {f"_{i}": arg for i, arg in enumerate(args)} | (kwargs or {})
-        self.event_emitter.send(
-            self,
-            event=MethodExecutionStartedEvent(
-                type="method_execution_started",
-                method_name=method_name,
-                flow_name=self.__class__.__name__,
-                params=dumped_params,
-                state=self._copy_state(),
-            ),
-        )
-
         result = (
             await method(*args, **kwargs)
             if asyncio.iscoroutinefunction(method)
@@ -828,18 +805,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
         self._method_execution_counts[method_name] = (
             self._method_execution_counts.get(method_name, 0) + 1
         )
-
-        self.event_emitter.send(
-            self,
-            event=MethodExecutionFinishedEvent(
-                type="method_execution_finished",
-                method_name=method_name,
-                flow_name=self.__class__.__name__,
-                state=self._copy_state(),
-                result=result,
-            ),
-        )
-
         return result
 
     async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
@@ -978,6 +943,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
         """
         try:
             method = self._methods[listener_name]
+
+            self.event_emitter.send(
+                self,
+                event=MethodExecutionStartedEvent(
+                    type="method_execution_started",
+                    method_name=listener_name,
+                    flow_name=self.__class__.__name__,
+                ),
+            )
+
             sig = inspect.signature(method)
             params = list(sig.parameters.values())
             method_params = [p for p in params if p.name != "self"]
@@ -989,6 +964,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
             else:
                 listener_result = await self._execute_method(listener_name, method)
 
+            self.event_emitter.send(
+                self,
+                event=MethodExecutionFinishedEvent(
+                    type="method_execution_finished",
+                    method_name=listener_name,
+                    flow_name=self.__class__.__name__,
+                ),
+            )
+
             # Execute listeners (and possibly routers) of this listener
             await self._execute_listeners(listener_name, listener_result)
 
@@ -1000,9 +984,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
             traceback.print_exc()
 
-    def _log_flow_event(
-        self, message: str, color: str = "yellow", level: str = "info"
-    ) -> None:
+    def _log_flow_event(self, message: str, color: str = "yellow", level: str = "info") -> None:
         """Centralized logging method for flow events.
 
         This method provides a consistent interface for logging flow-related events,

src/crewai/flow/flow_events.py

@@ -1,8 +1,6 @@
 from dataclasses import dataclass, field
 from datetime import datetime
-from typing import Any, Dict, Optional, Union
-
-from pydantic import BaseModel
+from typing import Any, Optional
 
 
 @dataclass
@@ -17,21 +15,17 @@ class Event:
 
 @dataclass
 class FlowStartedEvent(Event):
-    inputs: Optional[Dict[str, Any]] = None
+    pass
 
 
 @dataclass
 class MethodExecutionStartedEvent(Event):
     method_name: str
-    state: Union[Dict[str, Any], BaseModel]
-    params: Optional[Dict[str, Any]] = None
 
 
 @dataclass
 class MethodExecutionFinishedEvent(Event):
     method_name: str
-    state: Union[Dict[str, Any], BaseModel]
-    result: Any = None
 
 
 @dataclass

src/crewai/knowledge/knowledge.py

@@ -67,9 +67,3 @@ class Knowledge(BaseModel):
                 source.add()
         except Exception as e:
             raise e
-
-    def reset(self) -> None:
-        if self.storage:
-            self.storage.reset()
-        else:
-            raise ValueError("Storage is not initialized.")

src/crewai/knowledge/source/excel_knowledge_source.py

@@ -1,138 +1,28 @@
 from pathlib import Path
-from typing import Dict, Iterator, List, Optional, Union
-from urllib.parse import urlparse
+from typing import Dict, List
 
-from pydantic import Field, field_validator
-
-from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
-from crewai.utilities.logger import Logger
+from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
 
-class ExcelKnowledgeSource(BaseKnowledgeSource):
+class ExcelKnowledgeSource(BaseFileKnowledgeSource):
     """A knowledge source that stores and queries Excel file content using embeddings."""
 
-    # override content to be a dict of file paths to sheet names to csv content
-
-    _logger: Logger = Logger(verbose=True)
-
-    file_path: Optional[Union[Path, List[Path], str, List[str]]] = Field(
-        default=None,
-        description="[Deprecated] The path to the file. Use file_paths instead.",
-    )
-    file_paths: Optional[Union[Path, List[Path], str, List[str]]] = Field(
-        default_factory=list, description="The path to the file"
-    )
-    chunks: List[str] = Field(default_factory=list)
-    content: Dict[Path, Dict[str, str]] = Field(default_factory=dict)
-    safe_file_paths: List[Path] = Field(default_factory=list)
-
-    @field_validator("file_path", "file_paths", mode="before")
-    def validate_file_path(cls, v, info):
-        """Validate that at least one of file_path or file_paths is provided."""
-        # Single check if both are None, O(1) instead of nested conditions
-        if (
-            v is None
-            and info.data.get(
-                "file_path" if info.field_name == "file_paths" else "file_paths"
-            )
-            is None
-        ):
-            raise ValueError("Either file_path or file_paths must be provided")
-        return v
-
-    def _process_file_paths(self) -> List[Path]:
-        """Convert file_path to a list of Path objects."""
-
-        if hasattr(self, "file_path") and self.file_path is not None:
-            self._logger.log(
-                "warning",
-                "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.",
-                color="yellow",
-            )
-            self.file_paths = self.file_path
-
-        if self.file_paths is None:
-            raise ValueError("Your source must be provided with a file_paths: []")
-
-        # Convert single path to list
-        path_list: List[Union[Path, str]] = (
-            [self.file_paths]
-            if isinstance(self.file_paths, (str, Path))
-            else list(self.file_paths)
-            if isinstance(self.file_paths, list)
-            else []
-        )
-
-        if not path_list:
-            raise ValueError(
-                "file_path/file_paths must be a Path, str, or a list of these types"
-            )
-
-        return [self.convert_to_path(path) for path in path_list]
-
-    def validate_content(self):
-        """Validate the paths."""
-        for path in self.safe_file_paths:
-            if not path.exists():
-                self._logger.log(
-                    "error",
-                    f"File not found: {path}. Try adding sources to the knowledge directory. If it's inside the knowledge directory, use the relative path.",
-                    color="red",
-                )
-                raise FileNotFoundError(f"File not found: {path}")
-            if not path.is_file():
-                self._logger.log(
-                    "error",
-                    f"Path is not a file: {path}",
-                    color="red",
-                )
-
-    def model_post_init(self, _) -> None:
-        if self.file_path:
-            self._logger.log(
-                "warning",
-                "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.",
-                color="yellow",
-            )
-            self.file_paths = self.file_path
-        self.safe_file_paths = self._process_file_paths()
-        self.validate_content()
-        self.content = self._load_content()
-
-    def _load_content(self) -> Dict[Path, Dict[str, str]]:
-        """Load and preprocess Excel file content from multiple sheets.
-
-        Each sheet's content is converted to CSV format and stored.
-
-        Returns:
-            Dict[Path, Dict[str, str]]: A mapping of file paths to their respective sheet contents.
-
-        Raises:
-            ImportError: If required dependencies are missing.
-            FileNotFoundError: If the specified Excel file cannot be opened.
-        """
+    def load_content(self) -> Dict[Path, str]:
+        """Load and preprocess Excel file content."""
         pd = self._import_dependencies()
+
         content_dict = {}
         for file_path in self.safe_file_paths:
             file_path = self.convert_to_path(file_path)
-            with pd.ExcelFile(file_path) as xl:
-                sheet_dict = {
-                    str(sheet_name): str(
-                        pd.read_excel(xl, sheet_name).to_csv(index=False)
-                    )
-                    for sheet_name in xl.sheet_names
-                }
-            content_dict[file_path] = sheet_dict
+            df = pd.read_excel(file_path)
+            content = df.to_csv(index=False)
+            content_dict[file_path] = content
         return content_dict
 
-    def convert_to_path(self, path: Union[Path, str]) -> Path:
-        """Convert a path to a Path object."""
-        return Path(KNOWLEDGE_DIRECTORY + "/" + path) if isinstance(path, str) else path
-
     def _import_dependencies(self):
         """Dynamically import dependencies."""
         try:
+            import openpyxl  # noqa
             import pandas as pd
 
             return pd
@@ -148,14 +38,10 @@ class ExcelKnowledgeSource(BaseKnowledgeSource):
         and save the embeddings.
         """
         # Convert dictionary values to a single string if content is a dictionary
-        # Updated to account for .xlsx workbooks with multiple tabs/sheets
-        content_str = ""
-        for value in self.content.values():
-            if isinstance(value, dict):
-                for sheet_value in value.values():
-                    content_str += str(sheet_value) + "\n"
-            else:
-                content_str += str(value) + "\n"
+        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)

src/crewai/llm.py

@@ -164,7 +164,6 @@ class LLM:
         self.context_window_size = 0
         self.reasoning_effort = reasoning_effort
         self.additional_params = kwargs
-        self.is_anthropic = self._is_anthropic_model(model)
 
         litellm.drop_params = True
 
@@ -179,62 +178,42 @@ class LLM:
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
 
-    def _is_anthropic_model(self, model: str) -> bool:
-        """Determine if the model is from Anthropic provider.
-        
-        Args:
-            model: The model identifier string.
-            
-        Returns:
-            bool: True if the model is from Anthropic, False otherwise.
-        """
-        ANTHROPIC_PREFIXES = ('anthropic/', 'claude-', 'claude/')
-        return any(prefix in model.lower() for prefix in ANTHROPIC_PREFIXES)
-
     def call(
         self,
         messages: Union[str, List[Dict[str, str]]],
         tools: Optional[List[dict]] = None,
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """High-level LLM call method.
-        
-        Args:
-            messages: Input messages for the LLM.
-                     Can be a string or list of message dictionaries.
-                     If string, it will be converted to a single user message.
-                     If list, each dict must have 'role' and 'content' keys.
-            tools: Optional list of tool schemas for function calling.
-                  Each tool should define its name, description, and parameters.
-            callbacks: Optional list of callback functions to be executed
-                      during and after the LLM call.
-            available_functions: Optional dict mapping function names to callables
-                               that can be invoked by the LLM.
-        
+    ) -> str:
+        """
+        High-level llm call method that:
+          1) Accepts either a string or a list of messages
+          2) Converts string input to the required message format
+          3) Calls litellm.completion
+          4) Handles function/tool calls if any
+          5) Returns the final text response or tool result
+
+        Parameters:
+        - messages (Union[str, List[Dict[str, str]]]): The input messages for the LLM.
+          - If a string is provided, it will be converted into a message list with a single entry.
+          - If a list of dictionaries is provided, each dictionary should have 'role' and 'content' keys.
+        - tools (Optional[List[dict]]): A list of tool schemas for function calling.
+        - callbacks (Optional[List[Any]]): A list of callback functions to be executed.
+        - available_functions (Optional[Dict[str, Any]]): A dictionary mapping function names to actual Python functions.
+
         Returns:
-            Union[str, Any]: Either a text response from the LLM (str) or
-                           the result of a tool function call (Any).
-        
-        Raises:
-            TypeError: If messages format is invalid
-            ValueError: If response format is not supported
-            LLMContextLengthExceededException: If input exceeds model's context limit
-        
+        - str: The final text response from the LLM or the result of a tool function call.
+
         Examples:
-            # Example 1: Simple string input
-            >>> response = llm.call("Return the name of a random city.")
-            >>> print(response)
-            "Paris"
-            
-            # Example 2: Message list with system and user messages
-            >>> messages = [
-            ...     {"role": "system", "content": "You are a geography expert"},
-            ...     {"role": "user", "content": "What is France's capital?"}
-            ... ]
-            >>> response = llm.call(messages)
-            >>> print(response)
-            "The capital of France is Paris."
+        ---------
+        # Example 1: Using a string input
+        response = llm.call("Return the name of a random city in the world.")
+        print(response)
+
+        # Example 2: Using a list of messages
+        messages = [{"role": "user", "content": "What is the capital of France?"}]
+        response = llm.call(messages)
+        print(response)
         """
         # Validate parameters before proceeding with the call.
         self._validate_call_params()
@@ -242,25 +221,15 @@ class LLM:
         if isinstance(messages, str):
             messages = [{"role": "user", "content": messages}]
 
-        # For O1 models, system messages are not supported.
-        # Convert any system messages into assistant messages.
-        if "o1" in self.model.lower():
-            for message in messages:
-                if message.get("role") == "system":
-                    message["role"] = "assistant"
-
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
                 self.set_callbacks(callbacks)
 
             try:
-                # --- 1) Format messages according to provider requirements
-                formatted_messages = self._format_messages_for_provider(messages)
-
-                # --- 2) Prepare the parameters for the completion call
+                # --- 1) Prepare the parameters for the completion call
                 params = {
                     "model": self.model,
-                    "messages": formatted_messages,
+                    "messages": messages,
                     "timeout": self.timeout,
                     "temperature": self.temperature,
                     "top_p": self.top_p,
@@ -348,38 +317,6 @@ class LLM:
                     logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 
-    def _format_messages_for_provider(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
-        """Format messages according to provider requirements.
-        
-        Args:
-            messages: List of message dictionaries with 'role' and 'content' keys.
-                     Can be empty or None.
-        
-        Returns:
-            List of formatted messages according to provider requirements.
-            For Anthropic models, ensures first message has 'user' role.
-        
-        Raises:
-            TypeError: If messages is None or contains invalid message format.
-        """
-        if messages is None:
-            raise TypeError("Messages cannot be None")
-            
-        # Validate message format first
-        for msg in messages:
-            if not isinstance(msg, dict) or "role" not in msg or "content" not in msg:
-                raise TypeError("Invalid message format. Each message must be a dict with 'role' and 'content' keys")
-            
-        if not self.is_anthropic:
-            return messages
-            
-        # Anthropic requires messages to start with 'user' role
-        if not messages or messages[0]["role"] == "system":
-            # If first message is system or empty, add a placeholder user message
-            return [{"role": "user", "content": "."}, *messages]
-                
-        return messages
-
     def _get_custom_llm_provider(self) -> str:
         """
         Derives the custom_llm_provider from the model string.

src/crewai/memory/entity/entity_memory.py

@@ -1,7 +1,3 @@
-from typing import Optional
-
-from pydantic import PrivateAttr
-
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.memory import Memory
 from crewai.memory.storage.rag_storage import RAGStorage
@@ -14,15 +10,13 @@ class EntityMemory(Memory):
     Inherits from the Memory class.
     """
 
-    _memory_provider: Optional[str] = PrivateAttr()
-
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
-            memory_provider = crew.memory_config.get("provider")
+        if hasattr(crew, "memory_config") and crew.memory_config is not None:
+            self.memory_provider = crew.memory_config.get("provider")
         else:
-            memory_provider = None
+            self.memory_provider = None
 
-        if memory_provider == "mem0":
+        if self.memory_provider == "mem0":
             try:
                 from crewai.memory.storage.mem0_storage import Mem0Storage
             except ImportError:
@@ -42,13 +36,11 @@ class EntityMemory(Memory):
                     path=path,
                 )
             )
-
-        super().__init__(storage=storage)
-        self._memory_provider = memory_provider
+        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."""
-        if self._memory_provider == "mem0":
+        if self.memory_provider == "mem0":
             data = f"""
             Remember details about the following entity:
             Name: {item.name}

src/crewai/memory/long_term/long_term_memory.py

@@ -17,7 +17,7 @@ class LongTermMemory(Memory):
     def __init__(self, storage=None, path=None):
         if not storage:
             storage = LTMSQLiteStorage(db_path=path) if path else LTMSQLiteStorage()
-        super().__init__(storage=storage)
+        super().__init__(storage)
 
     def save(self, item: LongTermMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
         metadata = item.metadata

src/crewai/memory/memory.py

@@ -1,19 +1,15 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel
+from crewai.memory.storage.rag_storage import RAGStorage
 
 
-class Memory(BaseModel):
+class Memory:
     """
     Base class for memory, now supporting agent tags and generic metadata.
     """
 
-    embedder_config: Optional[Dict[str, Any]] = None
-
-    storage: Any
-
-    def __init__(self, storage: Any, **data: Any):
-        super().__init__(storage=storage, **data)
+    def __init__(self, storage: RAGStorage):
+        self.storage = storage
 
     def save(
         self,

src/crewai/memory/short_term/short_term_memory.py

@@ -1,7 +1,5 @@
 from typing import Any, Dict, Optional
 
-from pydantic import PrivateAttr
-
 from crewai.memory.memory import Memory
 from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
 from crewai.memory.storage.rag_storage import RAGStorage
@@ -16,15 +14,13 @@ class ShortTermMemory(Memory):
     MemoryItem instances.
     """
 
-    _memory_provider: Optional[str] = PrivateAttr()
-
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
-            memory_provider = crew.memory_config.get("provider")
+        if hasattr(crew, "memory_config") and crew.memory_config is not None:
+            self.memory_provider = crew.memory_config.get("provider")
         else:
-            memory_provider = None
+            self.memory_provider = None
 
-        if memory_provider == "mem0":
+        if self.memory_provider == "mem0":
             try:
                 from crewai.memory.storage.mem0_storage import Mem0Storage
             except ImportError:
@@ -43,8 +39,7 @@ class ShortTermMemory(Memory):
                     path=path,
                 )
             )
-        super().__init__(storage=storage)
-        self._memory_provider = memory_provider
+        super().__init__(storage)
 
     def save(
         self,
@@ -53,7 +48,7 @@ class ShortTermMemory(Memory):
         agent: Optional[str] = None,
     ) -> None:
         item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
-        if self._memory_provider == "mem0":
+        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)

src/crewai/memory/storage/base_rag_storage.py

@@ -13,7 +13,7 @@ class BaseRAGStorage(ABC):
         self,
         type: str,
         allow_reset: bool = True,
-        embedder_config: Optional[Dict[str, Any]] = None,
+        embedder_config: Optional[Any] = None,
         crew: Any = None,
     ):
         self.type = type

src/crewai/task.py

@@ -674,32 +674,19 @@ class Task(BaseModel):
             return OutputFormat.PYDANTIC
         return OutputFormat.RAW
 
-    def _save_file(self, result: Union[Dict, str, Any]) -> None:
+    def _save_file(self, result: Any) -> None:
         """Save task output to a file.
 
-        Note:
-            For cross-platform file writing, especially on Windows, consider using FileWriterTool
-            from the crewai_tools package:
-                pip install 'crewai[tools]'
-                from crewai_tools import FileWriterTool
-
         Args:
             result: The result to save to the file. Can be a dict or any stringifiable object.
 
         Raises:
             ValueError: If output_file is not set
-            RuntimeError: If there is an error writing to the file. For cross-platform
-                compatibility, especially on Windows, use FileWriterTool from crewai_tools
-                package.
+            RuntimeError: If there is an error writing to the file
         """
         if self.output_file is None:
             raise ValueError("output_file is not set.")
 
-        FILEWRITER_RECOMMENDATION = (
-            "For cross-platform file writing, especially on Windows, "
-            "use FileWriterTool from crewai_tools package."
-        )
-
         try:
             resolved_path = Path(self.output_file).expanduser().resolve()
             directory = resolved_path.parent
@@ -715,12 +702,7 @@ class Task(BaseModel):
                 else:
                     file.write(str(result))
         except (OSError, IOError) as e:
-            raise RuntimeError(
-                "\n".join([
-                    f"Failed to save output file: {e}",
-                    FILEWRITER_RECOMMENDATION
-                ])
-            )
+            raise RuntimeError(f"Failed to save output file: {e}")
         return None
 
     def __repr__(self):

src/crewai/tools/agent_tools/add_image_tool.py

@@ -7,11 +7,11 @@ from crewai.utilities import I18N
 
 i18n = I18N()
 
-
 class AddImageToolSchema(BaseModel):
     image_url: str = Field(..., description="The URL or path of the image to add")
     action: Optional[str] = Field(
-        default=None, description="Optional context or question about the image"
+        default=None,
+        description="Optional context or question about the image"
     )
 
 
@@ -36,7 +36,10 @@ class AddImageTool(BaseTool):
                 "image_url": {
                     "url": image_url,
                 },
-            },
+            }
         ]
 
-        return {"role": "user", "content": content}
+        return {
+            "role": "user",
+            "content": content
+        }

src/crewai/translations/en.json

@@ -6,7 +6,7 @@
   },
   "slices": {
     "observation": "\nObservation:",
-    "task": "\nCurrent Task: {input}\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\n",
+    "task": "\nCurrent Task: {input}\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:",
     "memory": "\n\n# Useful context: \n{memory}",
     "role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}",
     "tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
@@ -15,7 +15,7 @@
     "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\n```\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n```",
     "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nHere is the expected format I must follow:\n\n```\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n This Thought/Action/Action Input/Result process can repeat N times. Once I know the final answer, I must return the following format:\n\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\n```",
     "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
-    "expected_output": "\nThis is the expected criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
+    "expected_output": "\nThis is the expect criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
     "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
     "getting_input": "This is the agent's final answer: {final_answer}\n\n",
     "summarizer_system_message": "You are a helpful assistant that summarizes text.",

src/crewai/utilities/embedding_configurator.py

@@ -1,5 +1,5 @@
 import os
-from typing import Any, Dict, Optional, cast
+from typing import Any, Dict, cast
 
 from chromadb import Documents, EmbeddingFunction, Embeddings
 from chromadb.api.types import validate_embedding_function
@@ -18,12 +18,11 @@ class EmbeddingConfigurator:
             "bedrock": self._configure_bedrock,
             "huggingface": self._configure_huggingface,
             "watson": self._configure_watson,
-            "custom": self._configure_custom,
         }
 
     def configure_embedder(
         self,
-        embedder_config: Optional[Dict[str, Any]] = None,
+        embedder_config: Dict[str, Any] | None = None,
     ) -> EmbeddingFunction:
         """Configures and returns an embedding function based on the provided config."""
         if embedder_config is None:
@@ -31,19 +30,20 @@ class EmbeddingConfigurator:
 
         provider = embedder_config.get("provider")
         config = embedder_config.get("config", {})
-        model_name = config.get("model") if provider != "custom" else None
+        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())}"
             )
-
-        embedding_function = self.embedding_functions[provider]
-        return (
-            embedding_function(config)
-            if provider == "custom"
-            else embedding_function(config, model_name)
-        )
+        return self.embedding_functions[provider](config, model_name)
 
     @staticmethod
     def _create_default_embedding_function():
@@ -64,13 +64,6 @@ class EmbeddingConfigurator:
         return OpenAIEmbeddingFunction(
             api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
             model_name=model_name,
-            api_base=config.get("api_base", None),
-            api_type=config.get("api_type", None),
-            api_version=config.get("api_version", None),
-            default_headers=config.get("default_headers", None),
-            dimensions=config.get("dimensions", None),
-            deployment_id=config.get("deployment_id", None),
-            organization_id=config.get("organization_id", None),
         )
 
     @staticmethod
@@ -85,10 +78,6 @@ class EmbeddingConfigurator:
             api_type=config.get("api_type", "azure"),
             api_version=config.get("api_version"),
             model_name=model_name,
-            default_headers=config.get("default_headers"),
-            dimensions=config.get("dimensions"),
-            deployment_id=config.get("deployment_id"),
-            organization_id=config.get("organization_id"),
         )
 
     @staticmethod
@@ -111,8 +100,6 @@ class EmbeddingConfigurator:
         return GoogleVertexEmbeddingFunction(
             model_name=model_name,
             api_key=config.get("api_key"),
-            project_id=config.get("project_id"),
-            region=config.get("region"),
         )
 
     @staticmethod
@@ -124,7 +111,6 @@ class EmbeddingConfigurator:
         return GoogleGenerativeAiEmbeddingFunction(
             model_name=model_name,
             api_key=config.get("api_key"),
-            task_type=config.get("task_type"),
         )
 
     @staticmethod
@@ -209,28 +195,3 @@ class EmbeddingConfigurator:
                     raise e
 
         return WatsonEmbeddingFunction()
-
-    @staticmethod
-    def _configure_custom(config):
-        custom_embedder = config.get("embedder")
-        if isinstance(custom_embedder, EmbeddingFunction):
-            try:
-                validate_embedding_function(custom_embedder)
-                return custom_embedder
-            except Exception as e:
-                raise ValueError(f"Invalid custom embedding function: {str(e)}")
-        elif callable(custom_embedder):
-            try:
-                instance = custom_embedder()
-                if isinstance(instance, EmbeddingFunction):
-                    validate_embedding_function(instance)
-                    return instance
-                raise ValueError(
-                    "Custom embedder does not create an EmbeddingFunction instance"
-                )
-            except Exception as e:
-                raise ValueError(f"Error instantiating custom embedder: {str(e)}")
-        else:
-            raise ValueError(
-                "Custom embedder must be an instance of `EmbeddingFunction` or a callable that creates one"
-            )

src/crewai/utilities/file_handler.py

@@ -1,64 +1,30 @@
-import json
 import os
 import pickle
 from datetime import datetime
-from typing import Union
 
 
 class FileHandler:
-    """Handler for file operations supporting both JSON and text-based logging.
-    
-    Args:
-        file_path (Union[bool, str]): Path to the log file or boolean flag
-    """
+    """take care of file operations, currently it only logs messages to a file"""
 
-    def __init__(self, file_path: Union[bool, str]):
-        self._initialize_path(file_path)
-        
-    def _initialize_path(self, file_path: Union[bool, str]):
-        if file_path is True:  # File path is boolean True
+    def __init__(self, file_path):
+        if isinstance(file_path, bool):
             self._path = os.path.join(os.curdir, "logs.txt")
-        
-        elif isinstance(file_path, str):  # File path is a string
-            if file_path.endswith((".json", ".txt")):
-                self._path = file_path  # No modification if the file ends with .json or .txt
-            else:
-                self._path = file_path + ".txt"  # Append .txt if the file doesn't end with .json or .txt
-        
+        elif isinstance(file_path, str):
+            self._path = file_path
         else:
-            raise ValueError("file_path must be a string or boolean.")  # Handle the case where file_path isn't valid
-        
+            raise ValueError("file_path must be either a boolean or a string.")
+
     def log(self, **kwargs):
-        try:
-            now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-            log_entry = {"timestamp": now, **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"
+        )
+        with open(self._path, "a", encoding="utf-8") as file:
+            file.write(message + "\n")
 
-            if self._path.endswith(".json"):
-                # Append log in JSON format
-                with open(self._path, "a", encoding="utf-8") as file:
-                    # If the file is empty, start with a list; else, append to it
-                    try:
-                        # Try reading existing content to avoid overwriting
-                        with open(self._path, "r", encoding="utf-8") as read_file:
-                            existing_data = json.load(read_file)
-                            existing_data.append(log_entry)
-                    except (json.JSONDecodeError, FileNotFoundError):
-                        # If no valid JSON or file doesn't exist, start with an empty list
-                        existing_data = [log_entry]
-                    
-                    with open(self._path, "w", encoding="utf-8") as write_file:
-                        json.dump(existing_data, write_file, indent=4)
-                        write_file.write("\n")
-            
-            else:
-                # Append log in plain text format
-                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)
 
-        except Exception as e:
-            raise ValueError(f"Failed to log message: {str(e)}")
-        
 class PickleHandler:
     def __init__(self, file_name: str) -> None:
         """

src/crewai/utilities/llm_utils.py

@@ -110,7 +110,6 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
     )
 
     api_base = os.environ.get("API_BASE") or os.environ.get("AZURE_API_BASE")
-    api_version = os.environ.get("API_VERSION") or os.environ.get("AZURE_API_VERSION")
 
     # Synchronize base_url and api_base if one is populated and the other is not
     if base_url and not api_base:

tests/agent_test.py

@@ -1183,7 +1183,7 @@ def test_agent_max_retry_limit():
             [
                 mock.call(
                     {
-                        "input": "Say the word: Hi\n\nThis is the expected criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
+                        "input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
                         "tool_names": "",
                         "tools": "",
                         "ask_for_human_input": True,
@@ -1191,7 +1191,7 @@ def test_agent_max_retry_limit():
                 ),
                 mock.call(
                     {
-                        "input": "Say the word: Hi\n\nThis is the expected criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
+                        "input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
                         "tool_names": "",
                         "tools": "",
                         "ask_for_human_input": True,

tests/cli/cli_test.py

@@ -55,83 +55,72 @@ def test_train_invalid_string_iterations(train_crew, runner):
     )
 
 
-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_all_memories(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
-    result = runner.invoke(reset_memories, ["-a"])
+@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
+@mock.patch("crewai.cli.reset_memories_command.EntityMemory")
+@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
+@mock.patch("crewai.cli.reset_memories_command.TaskOutputStorageHandler")
+def test_reset_all_memories(
+    MockTaskOutputStorageHandler,
+    MockLongTermMemory,
+    MockEntityMemory,
+    MockShortTermMemory,
+    runner,
+):
+    result = runner.invoke(reset_memories, ["--all"])
+    MockShortTermMemory().reset.assert_called_once()
+    MockEntityMemory().reset.assert_called_once()
+    MockLongTermMemory().reset.assert_called_once()
+    MockTaskOutputStorageHandler().reset.assert_called_once()
 
-    mock_crew.reset_memories.assert_called_once_with(command_type="all")
     assert result.output == "All memories have been reset.\n"
 
 
-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_short_term_memories(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
+@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
+def test_reset_short_term_memories(MockShortTermMemory, runner):
     result = runner.invoke(reset_memories, ["-s"])
-
-    mock_crew.reset_memories.assert_called_once_with(command_type="short")
+    MockShortTermMemory().reset.assert_called_once()
     assert result.output == "Short term memory has been reset.\n"
 
 
-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_entity_memories(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
+@mock.patch("crewai.cli.reset_memories_command.EntityMemory")
+def test_reset_entity_memories(MockEntityMemory, runner):
     result = runner.invoke(reset_memories, ["-e"])
-
-    mock_crew.reset_memories.assert_called_once_with(command_type="entity")
+    MockEntityMemory().reset.assert_called_once()
     assert result.output == "Entity memory has been reset.\n"
 
 
-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_long_term_memories(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
+@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
+def test_reset_long_term_memories(MockLongTermMemory, runner):
     result = runner.invoke(reset_memories, ["-l"])
-
-    mock_crew.reset_memories.assert_called_once_with(command_type="long")
+    MockLongTermMemory().reset.assert_called_once()
     assert result.output == "Long term memory has been reset.\n"
 
 
-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_kickoff_outputs(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
+@mock.patch("crewai.cli.reset_memories_command.TaskOutputStorageHandler")
+def test_reset_kickoff_outputs(MockTaskOutputStorageHandler, runner):
     result = runner.invoke(reset_memories, ["-k"])
-
-    mock_crew.reset_memories.assert_called_once_with(command_type="kickoff_outputs")
+    MockTaskOutputStorageHandler().reset.assert_called_once()
     assert result.output == "Latest Kickoff outputs stored has been reset.\n"
 
 
-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_multiple_memory_flags(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
-    result = runner.invoke(reset_memories, ["-s", "-l"])
-
-    # Check that reset_memories was called twice with the correct arguments
-    assert mock_crew.reset_memories.call_count == 2
-    mock_crew.reset_memories.assert_has_calls(
-        [mock.call(command_type="long"), mock.call(command_type="short")]
+@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
+@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
+def test_reset_multiple_memory_flags(MockShortTermMemory, MockLongTermMemory, runner):
+    result = runner.invoke(
+        reset_memories,
+        [
+            "-s",
+            "-l",
+        ],
     )
+    MockShortTermMemory().reset.assert_called_once()
+    MockLongTermMemory().reset.assert_called_once()
     assert (
         result.output
         == "Long term memory has been reset.\nShort term memory has been reset.\n"
     )
 
 
-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_knowledge(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
-    result = runner.invoke(reset_memories, ["--knowledge"])
-
-    mock_crew.reset_memories.assert_called_once_with(command_type="knowledge")
-    assert result.output == "Knowledge has been reset.\n"
-
-
 def test_reset_no_memory_flags(runner):
     result = runner.invoke(
         reset_memories,

tests/crew_test.py

@@ -49,41 +49,6 @@ writer = Agent(
 )
 
 
-def test_crew_with_only_conditional_tasks_raises_error():
-    """Test that creating a crew with only conditional tasks raises an error."""
-
-    def condition_func(task_output: TaskOutput) -> bool:
-        return True
-
-    conditional1 = ConditionalTask(
-        description="Conditional task 1",
-        expected_output="Output 1",
-        agent=researcher,
-        condition=condition_func,
-    )
-    conditional2 = ConditionalTask(
-        description="Conditional task 2",
-        expected_output="Output 2",
-        agent=researcher,
-        condition=condition_func,
-    )
-    conditional3 = ConditionalTask(
-        description="Conditional task 3",
-        expected_output="Output 3",
-        agent=researcher,
-        condition=condition_func,
-    )
-
-    with pytest.raises(
-        pydantic_core._pydantic_core.ValidationError,
-        match="Crew must include at least one non-conditional task",
-    ):
-        Crew(
-            agents=[researcher],
-            tasks=[conditional1, conditional2, conditional3],
-        )
-
-
 def test_crew_config_conditional_requirement():
     with pytest.raises(ValueError):
         Crew(process=Process.sequential)
@@ -591,12 +556,12 @@ def test_crew_with_delegating_agents_should_not_override_task_tools():
         _, kwargs = mock_execute_sync.call_args
         tools = kwargs["tools"]
 
-        assert any(
-            isinstance(tool, TestTool) for tool in tools
-        ), "TestTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in tools
-        ), "Delegation tool should be present"
+        assert any(isinstance(tool, TestTool) for tool in tools), (
+            "TestTool should be present"
+        )
+        assert any("delegate" in tool.name.lower() for tool in tools), (
+            "Delegation tool should be present"
+        )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -655,12 +620,12 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():
         _, kwargs = mock_execute_sync.call_args
         tools = kwargs["tools"]
 
-        assert any(
-            isinstance(tool, TestTool) for tool in new_ceo.tools
-        ), "TestTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in tools
-        ), "Delegation tool should be present"
+        assert any(isinstance(tool, TestTool) for tool in new_ceo.tools), (
+            "TestTool should be present"
+        )
+        assert any("delegate" in tool.name.lower() for tool in tools), (
+            "Delegation tool should be present"
+        )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -784,17 +749,17 @@ def test_task_tools_override_agent_tools_with_allow_delegation():
         used_tools = kwargs["tools"]
 
         # Confirm AnotherTestTool is present but TestTool is not
-        assert any(
-            isinstance(tool, AnotherTestTool) for tool in used_tools
-        ), "AnotherTestTool should be present"
-        assert not any(
-            isinstance(tool, TestTool) for tool in used_tools
-        ), "TestTool should not be present among used tools"
+        assert any(isinstance(tool, AnotherTestTool) for tool in used_tools), (
+            "AnotherTestTool should be present"
+        )
+        assert not any(isinstance(tool, TestTool) for tool in used_tools), (
+            "TestTool should not be present among used tools"
+        )
 
         # Confirm delegation tool(s) are present
-        assert any(
-            "delegate" in tool.name.lower() for tool in used_tools
-        ), "Delegation tool should be present"
+        assert any("delegate" in tool.name.lower() for tool in used_tools), (
+            "Delegation tool should be present"
+        )
 
     # Finally, make sure the agent's original tools remain unchanged
     assert len(researcher_with_delegation.tools) == 1
@@ -1595,9 +1560,9 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
 
         # Verify that exactly one tool was used and it was a CodeInterpreterTool
         assert len(used_tools) == 1, "Should have exactly one tool"
-        assert isinstance(
-            used_tools[0], CodeInterpreterTool
-        ), "Tool should be CodeInterpreterTool"
+        assert isinstance(used_tools[0], CodeInterpreterTool), (
+            "Tool should be CodeInterpreterTool"
+        )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1954,7 +1919,6 @@ def test_task_callback_on_crew():
 
 def test_task_callback_both_on_task_and_crew():
     from unittest.mock import MagicMock, patch
-
     mock_callback_on_task = MagicMock()
     mock_callback_on_crew = MagicMock()
 
@@ -2096,210 +2060,6 @@ def test_tools_with_custom_caching():
             assert result.raw == "3"
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_conditional_task_uses_last_output():
-    """Test that conditional tasks use the last task output for condition evaluation."""
-    task1 = Task(
-        description="First task",
-        expected_output="First output",
-        agent=researcher,
-    )
-
-    def condition_fails(task_output: TaskOutput) -> bool:
-        # This condition will never be met
-        return "never matches" in task_output.raw.lower()
-
-    def condition_succeeds(task_output: TaskOutput) -> bool:
-        # This condition will match first task's output
-        return "first success" in task_output.raw.lower()
-
-    conditional_task1 = ConditionalTask(
-        description="Second task - conditional that fails condition",
-        expected_output="Second output",
-        agent=researcher,
-        condition=condition_fails,
-    )
-
-    conditional_task2 = ConditionalTask(
-        description="Third task - conditional that succeeds using first task output",
-        expected_output="Third output",
-        agent=writer,
-        condition=condition_succeeds,
-    )
-
-    crew = Crew(
-        agents=[researcher, writer],
-        tasks=[task1, conditional_task1, conditional_task2],
-    )
-
-    # Mock outputs for tasks
-    mock_first = TaskOutput(
-        description="First task output",
-        raw="First success output",  # Will be used by third task's condition
-        agent=researcher.role,
-    )
-    mock_third = TaskOutput(
-        description="Third task output",
-        raw="Third task executed",  # Output when condition succeeds using first task output
-        agent=writer.role,
-    )
-
-    # Set up mocks for task execution and conditional logic
-    with patch.object(ConditionalTask, "should_execute") as mock_should_execute:
-        # First conditional fails, second succeeds
-        mock_should_execute.side_effect = [False, True]
-        with patch.object(Task, "execute_sync") as mock_execute:
-            mock_execute.side_effect = [mock_first, mock_third]
-            result = crew.kickoff()
-
-            # Verify execution behavior
-            assert mock_execute.call_count == 2  # Only first and third tasks execute
-            assert mock_should_execute.call_count == 2  # Both conditionals checked
-
-            # Verify outputs collection:
-            # First executed task output, followed by an automatically generated (skipped) output, then the conditional execution
-            assert len(result.tasks_output) == 3
-            assert (
-                result.tasks_output[0].raw == "First success output"
-            )  # First task succeeded
-            assert (
-                result.tasks_output[1].raw == ""
-            )  # Second task skipped (condition failed)
-            assert (
-                result.tasks_output[2].raw == "Third task executed"
-            )  # Third task used first task's output
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_conditional_tasks_result_collection():
-    """Test that task outputs are properly collected based on execution status."""
-    task1 = Task(
-        description="Normal task that always executes",
-        expected_output="First output",
-        agent=researcher,
-    )
-
-    def condition_never_met(task_output: TaskOutput) -> bool:
-        return "never matches" in task_output.raw.lower()
-
-    def condition_always_met(task_output: TaskOutput) -> bool:
-        return "success" in task_output.raw.lower()
-
-    task2 = ConditionalTask(
-        description="Conditional task that never executes",
-        expected_output="Second output",
-        agent=researcher,
-        condition=condition_never_met,
-    )
-
-    task3 = ConditionalTask(
-        description="Conditional task that always executes",
-        expected_output="Third output",
-        agent=writer,
-        condition=condition_always_met,
-    )
-
-    crew = Crew(
-        agents=[researcher, writer],
-        tasks=[task1, task2, task3],
-    )
-
-    # Mock outputs for different execution paths
-    mock_success = TaskOutput(
-        description="Success output",
-        raw="Success output",  # Triggers third task's condition
-        agent=researcher.role,
-    )
-    mock_conditional = TaskOutput(
-        description="Conditional output",
-        raw="Conditional task executed",
-        agent=writer.role,
-    )
-
-    # Set up mocks for task execution and conditional logic
-    with patch.object(ConditionalTask, "should_execute") as mock_should_execute:
-        # First conditional fails, second succeeds
-        mock_should_execute.side_effect = [False, True]
-        with patch.object(Task, "execute_sync") as mock_execute:
-            mock_execute.side_effect = [mock_success, mock_conditional]
-            result = crew.kickoff()
-
-            # Verify execution behavior
-            assert mock_execute.call_count == 2  # Only first and third tasks execute
-            assert mock_should_execute.call_count == 2  # Both conditionals checked
-
-            # Verify task output collection:
-            # There should be three outputs: normal task, skipped conditional task (empty output),
-            # and the conditional task that executed.
-            assert len(result.tasks_output) == 3
-            assert (
-                result.tasks_output[0].raw == "Success output"
-            )  # Normal task executed
-            assert result.tasks_output[1].raw == ""  # Second task skipped
-            assert (
-                result.tasks_output[2].raw == "Conditional task executed"
-            )  # Third task executed
-
-            # Verify task output collection
-            assert len(result.tasks_output) == 3
-            assert (
-                result.tasks_output[0].raw == "Success output"
-            )  # Normal task executed
-            assert result.tasks_output[1].raw == ""  # Second task skipped
-            assert (
-                result.tasks_output[2].raw == "Conditional task executed"
-            )  # Third task executed
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_multiple_conditional_tasks():
-    """Test that having multiple conditional tasks in sequence works correctly."""
-    task1 = Task(
-        description="Initial research task",
-        expected_output="Research output",
-        agent=researcher,
-    )
-
-    def condition1(task_output: TaskOutput) -> bool:
-        return "success" in task_output.raw.lower()
-
-    def condition2(task_output: TaskOutput) -> bool:
-        return "proceed" in task_output.raw.lower()
-
-    task2 = ConditionalTask(
-        description="First conditional task",
-        expected_output="Conditional output 1",
-        agent=writer,
-        condition=condition1,
-    )
-
-    task3 = ConditionalTask(
-        description="Second conditional task",
-        expected_output="Conditional output 2",
-        agent=writer,
-        condition=condition2,
-    )
-
-    crew = Crew(
-        agents=[researcher, writer],
-        tasks=[task1, task2, task3],
-    )
-
-    # Mock different task outputs to test conditional logic
-    mock_success = TaskOutput(
-        description="Mock success",
-        raw="Success and proceed output",
-        agent=researcher.role,
-    )
-
-    # Set up mocks for task execution
-    with patch.object(Task, "execute_sync", return_value=mock_success) as mock_execute:
-        result = crew.kickoff()
-        # Verify all tasks were executed (no IndexError)
-        assert mock_execute.call_count == 3
-        assert len(result.tasks_output) == 3
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_using_contextual_memory():
     from unittest.mock import patch
@@ -3418,9 +3178,9 @@ def test_fetch_inputs():
     expected_placeholders = {"role_detail", "topic", "field"}
     actual_placeholders = crew.fetch_inputs()
 
-    assert (
-        actual_placeholders == expected_placeholders
-    ), f"Expected {expected_placeholders}, but got {actual_placeholders}"
+    assert actual_placeholders == expected_placeholders, (
+        f"Expected {expected_placeholders}, but got {actual_placeholders}"
+    )
 
 
 def test_task_tools_preserve_code_execution_tools():
@@ -3493,20 +3253,20 @@ def test_task_tools_preserve_code_execution_tools():
         used_tools = kwargs["tools"]
 
         # Verify all expected tools are present
-        assert any(
-            isinstance(tool, TestTool) for tool in used_tools
-        ), "Task's TestTool should be present"
-        assert any(
-            isinstance(tool, CodeInterpreterTool) for tool in used_tools
-        ), "CodeInterpreterTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in used_tools
-        ), "Delegation tool should be present"
+        assert any(isinstance(tool, TestTool) for tool in used_tools), (
+            "Task's TestTool should be present"
+        )
+        assert any(isinstance(tool, CodeInterpreterTool) for tool in used_tools), (
+            "CodeInterpreterTool should be present"
+        )
+        assert any("delegate" in tool.name.lower() for tool in used_tools), (
+            "Delegation tool should be present"
+        )
 
         # Verify the total number of tools (TestTool + CodeInterpreter + 2 delegation tools)
-        assert (
-            len(used_tools) == 4
-        ), "Should have TestTool, CodeInterpreter, and 2 delegation tools"
+        assert len(used_tools) == 4, (
+            "Should have TestTool, CodeInterpreter, and 2 delegation tools"
+        )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -3550,9 +3310,9 @@ def test_multimodal_flag_adds_multimodal_tools():
         used_tools = kwargs["tools"]
 
         # Check that the multimodal tool was added
-        assert any(
-            isinstance(tool, AddImageTool) for tool in used_tools
-        ), "AddImageTool should be present when agent is multimodal"
+        assert any(isinstance(tool, AddImageTool) for tool in used_tools), (
+            "AddImageTool should be present when agent is multimodal"
+        )
 
         # Verify we have exactly one tool (just the AddImageTool)
         assert len(used_tools) == 1, "Should only have the AddImageTool"
@@ -3778,9 +3538,9 @@ def test_crew_guardrail_feedback_in_context():
     assert len(execution_contexts) > 1, "Task should have been executed multiple times"
 
     # Verify that the second execution included the guardrail feedback
-    assert (
-        "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1]
-    ), "Guardrail feedback should be included in retry context"
+    assert "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1], (
+        "Guardrail feedback should be included in retry context"
+    )
 
     # Verify final output meets guardrail requirements
     assert "IMPORTANT" in result.raw, "Final output should contain required keyword"

tests/flow_test.py

@@ -1,18 +1,11 @@
 """Test Flow creation and execution basic functionality."""
 
 import asyncio
-from datetime import datetime
 
 import pytest
 from pydantic import BaseModel
 
 from crewai.flow.flow import Flow, and_, listen, or_, router, start
-from crewai.flow.flow_events import (
-    FlowFinishedEvent,
-    FlowStartedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionStartedEvent,
-)
 
 
 def test_simple_sequential_flow():
@@ -405,218 +398,3 @@ def test_router_with_multiple_conditions():
 
     # final_step should run after router_and
     assert execution_order.index("log_final_step") > execution_order.index("router_and")
-
-
-def test_unstructured_flow_event_emission():
-    """Test that the correct events are emitted during unstructured flow
-    execution with all fields validated."""
-
-    class PoemFlow(Flow):
-        @start()
-        def prepare_flower(self):
-            self.state["flower"] = "roses"
-            return "foo"
-
-        @start()
-        def prepare_color(self):
-            self.state["color"] = "red"
-            return "bar"
-
-        @listen(prepare_color)
-        def write_first_sentence(self):
-            return f"{self.state['flower']} are {self.state['color']}"
-
-        @listen(write_first_sentence)
-        def finish_poem(self, first_sentence):
-            separator = self.state.get("separator", "\n")
-            return separator.join([first_sentence, "violets are blue"])
-
-        @listen(finish_poem)
-        def save_poem_to_database(self):
-            # A method without args/kwargs to ensure events are sent correctly
-            pass
-
-    event_log = []
-
-    def handle_event(_, event):
-        event_log.append(event)
-
-    flow = PoemFlow()
-    flow.event_emitter.connect(handle_event)
-    flow.kickoff(inputs={"separator": ", "})
-
-    assert isinstance(event_log[0], FlowStartedEvent)
-    assert event_log[0].flow_name == "PoemFlow"
-    assert event_log[0].inputs == {"separator": ", "}
-    assert isinstance(event_log[0].timestamp, datetime)
-
-    # Asserting for concurrent start method executions in a for loop as you
-    # can't guarantee ordering in asynchronous executions
-    for i in range(1, 5):
-        event = event_log[i]
-        assert isinstance(event.state, dict)
-        assert isinstance(event.state["id"], str)
-
-        if event.method_name == "prepare_flower":
-            if isinstance(event, MethodExecutionStartedEvent):
-                assert event.params == {}
-                assert event.state["separator"] == ", "
-            elif isinstance(event, MethodExecutionFinishedEvent):
-                assert event.result == "foo"
-                assert event.state["flower"] == "roses"
-                assert event.state["separator"] == ", "
-            else:
-                assert False, "Unexpected event type for prepare_flower"
-        elif event.method_name == "prepare_color":
-            if isinstance(event, MethodExecutionStartedEvent):
-                assert event.params == {}
-                assert event.state["separator"] == ", "
-            elif isinstance(event, MethodExecutionFinishedEvent):
-                assert event.result == "bar"
-                assert event.state["color"] == "red"
-                assert event.state["separator"] == ", "
-            else:
-                assert False, "Unexpected event type for prepare_color"
-        else:
-            assert False, f"Unexpected method {event.method_name} in prepare events"
-
-    assert isinstance(event_log[5], MethodExecutionStartedEvent)
-    assert event_log[5].method_name == "write_first_sentence"
-    assert event_log[5].params == {}
-    assert isinstance(event_log[5].state, dict)
-    assert event_log[5].state["flower"] == "roses"
-    assert event_log[5].state["color"] == "red"
-    assert event_log[5].state["separator"] == ", "
-
-    assert isinstance(event_log[6], MethodExecutionFinishedEvent)
-    assert event_log[6].method_name == "write_first_sentence"
-    assert event_log[6].result == "roses are red"
-
-    assert isinstance(event_log[7], MethodExecutionStartedEvent)
-    assert event_log[7].method_name == "finish_poem"
-    assert event_log[7].params == {"_0": "roses are red"}
-    assert isinstance(event_log[7].state, dict)
-    assert event_log[7].state["flower"] == "roses"
-    assert event_log[7].state["color"] == "red"
-
-    assert isinstance(event_log[8], MethodExecutionFinishedEvent)
-    assert event_log[8].method_name == "finish_poem"
-    assert event_log[8].result == "roses are red, violets are blue"
-
-    assert isinstance(event_log[9], MethodExecutionStartedEvent)
-    assert event_log[9].method_name == "save_poem_to_database"
-    assert event_log[9].params == {}
-    assert isinstance(event_log[9].state, dict)
-    assert event_log[9].state["flower"] == "roses"
-    assert event_log[9].state["color"] == "red"
-
-    assert isinstance(event_log[10], MethodExecutionFinishedEvent)
-    assert event_log[10].method_name == "save_poem_to_database"
-    assert event_log[10].result is None
-
-    assert isinstance(event_log[11], FlowFinishedEvent)
-    assert event_log[11].flow_name == "PoemFlow"
-    assert event_log[11].result is None
-    assert isinstance(event_log[11].timestamp, datetime)
-
-
-def test_structured_flow_event_emission():
-    """Test that the correct events are emitted during structured flow
-    execution with all fields validated."""
-
-    class OnboardingState(BaseModel):
-        name: str = ""
-        sent: bool = False
-
-    class OnboardingFlow(Flow[OnboardingState]):
-        @start()
-        def user_signs_up(self):
-            self.state.sent = False
-
-        @listen(user_signs_up)
-        def send_welcome_message(self):
-            self.state.sent = True
-            return f"Welcome, {self.state.name}!"
-
-    event_log = []
-
-    def handle_event(_, event):
-        event_log.append(event)
-
-    flow = OnboardingFlow()
-    flow.event_emitter.connect(handle_event)
-    flow.kickoff(inputs={"name": "Anakin"})
-
-    assert isinstance(event_log[0], FlowStartedEvent)
-    assert event_log[0].flow_name == "OnboardingFlow"
-    assert event_log[0].inputs == {"name": "Anakin"}
-    assert isinstance(event_log[0].timestamp, datetime)
-
-    assert isinstance(event_log[1], MethodExecutionStartedEvent)
-    assert event_log[1].method_name == "user_signs_up"
-
-    assert isinstance(event_log[2], MethodExecutionFinishedEvent)
-    assert event_log[2].method_name == "user_signs_up"
-
-    assert isinstance(event_log[3], MethodExecutionStartedEvent)
-    assert event_log[3].method_name == "send_welcome_message"
-    assert event_log[3].params == {}
-    assert getattr(event_log[3].state, "sent") is False
-
-    assert isinstance(event_log[4], MethodExecutionFinishedEvent)
-    assert event_log[4].method_name == "send_welcome_message"
-    assert getattr(event_log[4].state, "sent") is True
-    assert event_log[4].result == "Welcome, Anakin!"
-
-    assert isinstance(event_log[5], FlowFinishedEvent)
-    assert event_log[5].flow_name == "OnboardingFlow"
-    assert event_log[5].result == "Welcome, Anakin!"
-    assert isinstance(event_log[5].timestamp, datetime)
-
-
-def test_stateless_flow_event_emission():
-    """Test that the correct events are emitted stateless during flow execution
-    with all fields validated."""
-
-    class StatelessFlow(Flow):
-        @start()
-        def init(self):
-            pass
-
-        @listen(init)
-        def process(self):
-            return "Deeds will not be less valiant because they are unpraised."
-
-    event_log = []
-
-    def handle_event(_, event):
-        event_log.append(event)
-
-    flow = StatelessFlow()
-    flow.event_emitter.connect(handle_event)
-    flow.kickoff()
-
-    assert isinstance(event_log[0], FlowStartedEvent)
-    assert event_log[0].flow_name == "StatelessFlow"
-    assert event_log[0].inputs is None
-    assert isinstance(event_log[0].timestamp, datetime)
-
-    assert isinstance(event_log[1], MethodExecutionStartedEvent)
-    assert event_log[1].method_name == "init"
-
-    assert isinstance(event_log[2], MethodExecutionFinishedEvent)
-    assert event_log[2].method_name == "init"
-
-    assert isinstance(event_log[3], MethodExecutionStartedEvent)
-    assert event_log[3].method_name == "process"
-
-    assert isinstance(event_log[4], MethodExecutionFinishedEvent)
-    assert event_log[4].method_name == "process"
-
-    assert isinstance(event_log[5], FlowFinishedEvent)
-    assert event_log[5].flow_name == "StatelessFlow"
-    assert (
-        event_log[5].result
-        == "Deeds will not be less valiant because they are unpraised."
-    )
-    assert isinstance(event_log[5].timestamp, datetime)

tests/llm_test.py

@@ -286,79 +286,6 @@ def test_o3_mini_reasoning_effort_medium():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-@pytest.fixture
-def anthropic_llm():
-    """Fixture providing an Anthropic LLM instance."""
-    return LLM(model="anthropic/claude-3-sonnet")
-
-@pytest.fixture
-def system_message():
-    """Fixture providing a system message."""
-    return {"role": "system", "content": "test"}
-
-@pytest.fixture
-def user_message():
-    """Fixture providing a user message."""
-    return {"role": "user", "content": "test"}
-
-def test_anthropic_message_formatting_edge_cases(anthropic_llm):
-    """Test edge cases for Anthropic message formatting."""
-    # Test None messages
-    with pytest.raises(TypeError, match="Messages cannot be None"):
-        anthropic_llm._format_messages_for_provider(None)
-        
-    # Test empty message list
-    formatted = anthropic_llm._format_messages_for_provider([])
-    assert len(formatted) == 1
-    assert formatted[0]["role"] == "user"
-    assert formatted[0]["content"] == "."
-    
-    # Test invalid message format
-    with pytest.raises(TypeError, match="Invalid message format"):
-        anthropic_llm._format_messages_for_provider([{"invalid": "message"}])
-
-def test_anthropic_model_detection():
-    """Test Anthropic model detection with various formats."""
-    models = [
-        ("anthropic/claude-3", True),
-        ("claude-instant", True),
-        ("claude/v1", True),
-        ("gpt-4", False),
-        ("", False),
-        ("anthropomorphic", False),  # Should not match partial words
-    ]
-    
-    for model, expected in models:
-        llm = LLM(model=model)
-        assert llm.is_anthropic == expected, f"Failed for model: {model}"
-
-def test_anthropic_message_formatting(anthropic_llm, system_message, user_message):
-    """Test Anthropic message formatting with fixtures."""
-    # Test when first message is system
-    formatted = anthropic_llm._format_messages_for_provider([system_message])
-    assert len(formatted) == 2
-    assert formatted[0]["role"] == "user"
-    assert formatted[0]["content"] == "."
-    assert formatted[1] == system_message
-
-    # Test when first message is already user
-    formatted = anthropic_llm._format_messages_for_provider([user_message])
-    assert len(formatted) == 1
-    assert formatted[0] == user_message
-
-    # Test with empty message list
-    formatted = anthropic_llm._format_messages_for_provider([])
-    assert len(formatted) == 1
-    assert formatted[0]["role"] == "user"
-    assert formatted[0]["content"] == "."
-
-    # Test with non-Anthropic model (should not modify messages)
-    non_anthropic_llm = LLM(model="gpt-4")
-    formatted = non_anthropic_llm._format_messages_for_provider([system_message])
-    assert len(formatted) == 1
-    assert formatted[0] == system_message
-
-
 def test_deepseek_r1_with_open_router():
     if not os.getenv("OPEN_ROUTER_API_KEY"):
         pytest.skip("OPEN_ROUTER_API_KEY not set; skipping test.")

uv.lock

@@ -198,6 +198,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/39/e3/893e8757be2612e6c266d9bb58ad2e3651524b5b40cf56761e985a28b13e/asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47", size = 23828 },
 ]
 
+[[package]]
+name = "asn1crypto"
+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/de/cf/d547feed25b5244fcb9392e288ff9fdc3280b10260362fc45d37a798a6ee/asn1crypto-1.5.1.tar.gz", hash = "sha256:13ae38502be632115abf8a24cbe5f4da52e3b5231990aff31123c805306ccb9c", size = 121080 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c9/7f/09065fd9e27da0eda08b4d6897f1c13535066174cc023af248fc2a8d5e5a/asn1crypto-1.5.1-py2.py3-none-any.whl", hash = "sha256:db4e40728b728508912cbb3d44f19ce188f218e9eba635821bb4b68564f8fd67", size = 105045 },
+]
+
 [[package]]
 name = "asttokens"
 version = "2.4.1"
@@ -219,6 +228,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a7/fa/e01228c2938de91d47b307831c62ab9e4001e747789d0b05baf779a6488c/async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028", size = 5721 },
 ]
 
+[[package]]
+name = "atpublic"
+version = "5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5d/18/b1d247792440378abeeb0853f9daa2a127284b68776af6815990be7fcdb0/atpublic-5.0.tar.gz", hash = "sha256:d5cb6cbabf00ec1d34e282e8ce7cbc9b74ba4cb732e766c24e2d78d1ad7f723f", size = 14646 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6b/03/2cb0e5326e19b7d877bc9c3a7ef436a30a06835b638580d1f5e21a0409ed/atpublic-5.0-py3-none-any.whl", hash = "sha256:b651dcd886666b1042d1e38158a22a4f2c267748f4e97fde94bc492a4a28a3f3", size = 5207 },
+]
+
 [[package]]
 name = "attrs"
 version = "24.2.0"
@@ -244,6 +262,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e4/0e/38cb7b781371e79e9c697fb78f3ccd18fda8bd547d0a2e76e616561a3792/auth0_python-4.7.2-py3-none-any.whl", hash = "sha256:df2224f9b1e170b3aa12d8bc7ff02eadb7cc229307a09ec6b8a55fd1e0e05dc8", size = 131834 },
 ]
 
+[[package]]
+name = "authlib"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+]
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