wip

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2025 crewAI, Inc.
+Copyright (c) 2018 The Python Packaging Authority
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -2,46 +2,21 @@
 
 ![Logo of CrewAI](./docs/crewai_logo.png)
 
+# **CrewAI**
 
-</div>
+**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.
 
-### Fast and Flexible Multi-Agent Automation Framework
+**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:
 
-CrewAI is a lean, lightning-fast Python framework built entirely from
-scratch—completely **independent of LangChain or other agent frameworks**.
-It empowers developers with both high-level simplicity and precise low-level
-control, ideal for creating autonomous AI agents tailored to any scenario.
-
-- **CrewAI Crews**: Optimize for autonomy and collaborative intelligence.
-- **CrewAI Flows**: Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively
-
-With over 100,000 developers certified through our community courses at
-[learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
-standard for enterprise-ready AI automation.
-
-# CrewAI Enterprise Suite
-
-CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations
-that require secure, scalable, and easy-to-manage agent-driven automation.
-
-You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
-
-## Crew Control Plane Key Features:
-- **Tracing & Observability**: Monitor and track your AI agents and workflows in real-time, including metrics, logs, and traces.
-- **Unified Control Plane**: A centralized platform for managing, monitoring, and scaling your AI agents and workflows.
-- **Seamless Integrations**: Easily connect with existing enterprise systems, data sources, and cloud infrastructure.
-- **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management.
-- **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making.
-- **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
-- **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
-
-CrewAI Enterprise is designed for enterprises seeking a powerful,
-reliable solution to transform complex business processes into efficient,
-intelligent automations.
+- **Seamless Integrations**
+- **Scalable & Secure Deployment**
+- **Actionable Insights**
+- **24/7 Support**
 
 <h3>
 
-[Homepage](https://www.crewai.com/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Discourse](https://community.crewai.com)
+[Homepage](https://www.crewai.com/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Examples](https://github.com/crewAIInc/crewAI-examples) | [Discourse](https://community.crewai.com)
 
 </h3>
 
@@ -72,19 +47,8 @@ intelligent automations.
 
 ## Why CrewAI?
 
-<div align="center" style="margin-bottom: 30px;">
-  <img src="docs/asset.png" alt="CrewAI Logo" width="100%">
-</div>
-
-CrewAI unlocks the true potential of multi-agent automation, delivering the best-in-class combination of speed, flexibility, and control with either Crews of AI Agents or Flows of Events:
-
-- **Standalone Framework**: Built from scratch, independent of LangChain or any other agent framework.
-- **High Performance**: Optimized for speed and minimal resource usage, enabling faster execution.
-- **Flexible Low Level Customization**: Complete freedom to customize at both high and low levels - from overall workflows and system architecture to granular agent behaviors, internal prompts, and execution logic.
-- **Ideal for Every Use Case**: Proven effective for both simple tasks and highly complex, real-world, enterprise-grade scenarios.
-- **Robust Community**: Backed by a rapidly growing community of over **100,000 certified** developers offering comprehensive support and resources.
-
-CrewAI empowers developers and enterprises to confidently build intelligent automations, bridging the gap between simplicity, flexibility, and performance.
+The power of AI collaboration has too much to offer.
+CrewAI is a standalone framework, built from the ground up without dependencies on Langchain or other agent frameworks. It's designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions.
 
 ## Getting Started
 
@@ -357,16 +321,18 @@ In addition to the sequential process, you can use the hierarchical process, whi
 
 ## Key Features
 
-CrewAI stands apart as a lean, standalone, high-performance framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.
+**Note**: CrewAI is a standalone framework built from the ground up, without dependencies on Langchain or other agent frameworks.
 
-- **Standalone & Lean**: Completely independent from other frameworks like LangChain, offering faster execution and lighter resource demands.
-- **Flexible & Precise**: Easily orchestrate autonomous agents through intuitive [Crews](https://docs.crewai.com/concepts/crews) or precise [Flows](https://docs.crewai.com/concepts/flows), achieving perfect balance for your needs.
-- **Seamless Integration**: Effortlessly combine Crews (autonomy) and Flows (precision) to create complex, real-world automations.
-- **Deep Customization**: Tailor every aspect—from high-level workflows down to low-level internal prompts and agent behaviors.
-- **Reliable Performance**: Consistent results across simple tasks and complex, enterprise-level automations.
-- **Thriving Community**: Backed by robust documentation and over 100,000 certified developers, providing exceptional support and guidance.
+- **Deep Customization**: Build sophisticated agents with full control over the system - from overriding inner prompts to accessing low-level APIs. Customize roles, goals, tools, and behaviors while maintaining clean abstractions.
+- **Autonomous Inter-Agent Delegation**: Agents can autonomously delegate tasks and inquire amongst themselves, enabling complex problem-solving in real-world scenarios.
+- **Flexible Task Management**: Define and customize tasks with granular control, from simple operations to complex multi-step processes.
+- **Production-Grade Architecture**: Support for both high-level abstractions and low-level customization, with robust error handling and state management.
+- **Predictable Results**: Ensure consistent, accurate outputs through programmatic guardrails, agent training capabilities, and flow-based execution control. See our [documentation on guardrails](https://docs.crewai.com/how-to/guardrails/) for implementation details.
+- **Model Flexibility**: Run your crew using OpenAI or open source models with production-ready integrations. See [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) for detailed configuration options.
+- **Event-Driven Flows**: Build complex, real-world workflows with precise control over execution paths, state management, and conditional logic.
+- **Process Orchestration**: Achieve any workflow pattern through flows - from simple sequential and hierarchical processes to complex, custom orchestration patterns with conditional branching and parallel execution.
 
-Choose CrewAI to easily build powerful, adaptable, and production-ready AI automations.
+![CrewAI Mind Map](./docs/crewAI-mindmap.png "CrewAI Mind Map")
 
 ## Examples
 
@@ -597,39 +563,13 @@ Users can opt-in to Further Telemetry, sharing the complete telemetry data by se
 
 CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/blob/main/LICENSE).
 
-
 ## Frequently Asked Questions (FAQ)
 
-### General
-- [What exactly is CrewAI?](#q-what-exactly-is-crewai)
-- [How do I install CrewAI?](#q-how-do-i-install-crewai)
-- [Does CrewAI depend on LangChain?](#q-does-crewai-depend-on-langchain)
-- [Is CrewAI open-source?](#q-is-crewai-open-source)
-- [Does CrewAI collect data from users?](#q-does-crewai-collect-data-from-users)
-
-### Features and Capabilities
-- [Can CrewAI handle complex use cases?](#q-can-crewai-handle-complex-use-cases)
-- [Can I use CrewAI with local AI models?](#q-can-i-use-crewai-with-local-ai-models)
-- [What makes Crews different from Flows?](#q-what-makes-crews-different-from-flows)
-- [How is CrewAI better than LangChain?](#q-how-is-crewai-better-than-langchain)
-- [Does CrewAI support fine-tuning or training custom models?](#q-does-crewai-support-fine-tuning-or-training-custom-models)
-
-### Resources and Community
-- [Where can I find real-world CrewAI examples?](#q-where-can-i-find-real-world-crewai-examples)
-- [How can I contribute to CrewAI?](#q-how-can-i-contribute-to-crewai)
-
-### Enterprise Features
-- [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer)
-- [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)
-- [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free)
-
-
-
-### Q: What exactly is CrewAI?
-A: CrewAI is a standalone, lean, and fast Python framework built specifically for orchestrating autonomous AI agents. Unlike frameworks like LangChain, CrewAI does not rely on external dependencies, making it leaner, faster, and simpler.
+### Q: What is CrewAI?
+A: CrewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. It enables agents to work together seamlessly, tackling complex tasks through collaborative intelligence.
 
 ### Q: How do I install CrewAI?
-A: Install CrewAI using pip:
+A: You can install CrewAI using pip:
 ```shell
 pip install crewai
 ```
@@ -637,62 +577,27 @@ For additional tools, use:
 ```shell
 pip install 'crewai[tools]'
 ```
-### Q: Does CrewAI depend on LangChain?
-A: No. CrewAI is built entirely from the ground up, with no dependencies on LangChain or other agent frameworks. This ensures a lean, fast, and flexible experience.
 
-### Q: Can CrewAI handle complex use cases?
-A: Yes. CrewAI excels at both simple and highly complex real-world scenarios, offering deep customization options at both high and low levels, from internal prompts to sophisticated workflow orchestration.
+### Q: Can I use CrewAI with local models?
+A: Yes, CrewAI supports various LLMs, including local models. You can configure your agents to use local models via tools like Ollama & LM Studio. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details.
 
-### Q: Can I use CrewAI with local AI models?
-A: Absolutely! CrewAI supports various language models, including local ones. Tools like Ollama and LM Studio allow seamless integration. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details.
+### Q: What are the key features of CrewAI?
+A: Key features include role-based agent design, autonomous inter-agent delegation, flexible task management, process-driven execution, output saving as files, and compatibility with both open-source and proprietary models.
 
-### Q: What makes Crews different from Flows?
-A: Crews provide autonomous agent collaboration, ideal for tasks requiring flexible decision-making and dynamic interaction. Flows offer precise, event-driven control, ideal for managing detailed execution paths and secure state management. You can seamlessly combine both for maximum effectiveness.
-
-### Q: How is CrewAI better than LangChain?
-A: CrewAI provides simpler, more intuitive APIs, faster execution speeds, more reliable and consistent results, robust documentation, and an active community—addressing common criticisms and limitations associated with LangChain.
+### Q: How does CrewAI compare to other AI orchestration tools?
+A: CrewAI is designed with production in mind, offering flexibility similar to Autogen's conversational agents and structured processes like ChatDev, but with more adaptability for real-world applications.
 
 ### Q: Is CrewAI open-source?
-A: Yes, CrewAI is open-source and actively encourages community contributions and collaboration.
+A: Yes, CrewAI is open-source and welcomes contributions from the community.
 
-### Q: Does CrewAI collect data from users?
-A: CrewAI collects anonymous telemetry data strictly for improvement purposes. Sensitive data such as prompts, tasks, or API responses are never collected unless explicitly enabled by the user.
+### Q: Does CrewAI collect any data?
+A: CrewAI uses anonymous telemetry to collect usage data for improvement purposes. No sensitive data (like prompts, task descriptions, or API calls) is collected. Users can opt-in to share more detailed data by setting `share_crew=True` on their Crews.
 
-### Q: Where can I find real-world CrewAI examples?
-A: Check out practical examples in the [CrewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), covering use cases like trip planners, stock analysis, and job postings.
+### Q: Where can I find examples of CrewAI in action?
+A: You can find various real-life examples in the [CrewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), including trip planners, stock analysis tools, and more.
+
+### Q: What is the difference between Crews and Flows?
+A: Crews and Flows serve different but complementary purposes in CrewAI. Crews are teams of AI agents working together to accomplish specific tasks through role-based collaboration, delivering accurate and predictable results. Flows, on the other hand, are event-driven workflows that can orchestrate both Crews and regular Python code, allowing you to build complex automation pipelines with secure state management and conditional execution paths.
 
 ### Q: How can I contribute to CrewAI?
-A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
-
-### Q: What additional features does CrewAI Enterprise offer?
-A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
-
-### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
-A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
-
-### Q: Can I try CrewAI Enterprise for free?
-A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
-
-### Q: Does CrewAI support fine-tuning or training custom models?
-A: Yes, CrewAI can integrate with custom-trained or fine-tuned models, allowing you to enhance your agents with domain-specific knowledge and accuracy.
-
-### Q: Can CrewAI agents interact with external tools and APIs?
-A: Absolutely! CrewAI agents can easily integrate with external tools, APIs, and databases, empowering them to leverage real-world data and resources.
-
-### Q: Is CrewAI suitable for production environments?
-A: Yes, CrewAI is explicitly designed with production-grade standards, ensuring reliability, stability, and scalability for enterprise deployments.
-
-### Q: How scalable is CrewAI?
-A: CrewAI is highly scalable, supporting simple automations and large-scale enterprise workflows involving numerous agents and complex tasks simultaneously.
-
-### Q: Does CrewAI offer debugging and monitoring tools?
-A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
-
-### Q: What programming languages does CrewAI support?
-A: CrewAI is primarily Python-based but easily integrates with services and APIs written in any programming language through its flexible API integration capabilities.
-
-### Q: Does CrewAI offer educational resources for beginners?
-A: Yes, CrewAI provides extensive beginner-friendly tutorials, courses, and documentation through learn.crewai.com, supporting developers at all skill levels.
-
-### Q: Can CrewAI automate human-in-the-loop workflows?
-A: Yes, CrewAI fully supports human-in-the-loop workflows, allowing seamless collaboration between human experts and AI agents for enhanced decision-making.
+A: Contributions are welcome! You can fork the repository, create a new branch for your feature, add your improvement, and send a pull request. Check the Contribution section in the README for more details.

docs/introduction.mdx

@@ -6,7 +6,7 @@ icon: handshake
 
 # What is CrewAI?
 
-**CrewAI is a cutting-edge framework for orchestrating autonomous AI agents.**
+**CrewAI is a cutting-edge framework for orchestrating autonomous AI agents.** 
 
 CrewAI enables you to create AI teams where each agent has specific roles, tools, and goals, working together to accomplish complex tasks.
 
@@ -19,7 +19,7 @@ Think of it as assembling your dream team - each member (agent) brings unique sk
 </Note>
 
 <Frame caption="CrewAI Framework Overview">
-  <img src="asset.png" alt="CrewAI Framework Overview" />
+  <img src="crewAI-mindmap.png" alt="CrewAI Framework Overview" />
 </Frame>
 
 | Component | Description | Key Features |

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.105.0"
+version = "0.102.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.37.0"]
+tools = ["crewai-tools>=0.36.0"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.105.0"
+__version__ = "0.102.0"
 __all__ = [
     "Agent",
     "Crew",

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]>=0.105.0,<1.0.0"
+    "crewai[tools]>=0.102.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.105.0,<1.0.0",
+    "crewai[tools]>=0.102.0,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.105.0"
+    "crewai[tools]>=0.102.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -184,7 +184,7 @@ class Crew(BaseModel):
         default=None,
         description="Maximum number of requests per minute for the crew execution to be respected.",
     )
-    prompt_file: str = Field(
+    prompt_file: Optional[str] = Field(
         default=None,
         description="Path to the prompt json file to be used for the crew.",
     )
@@ -808,6 +808,7 @@ class Crew(BaseModel):
                 )
                 if skipped_task_output:
                     task_outputs.append(skipped_task_output)
+                    last_sync_output = skipped_task_output
                     continue
 
             if task.async_execution:
@@ -821,8 +822,10 @@ class Crew(BaseModel):
                 )
                 futures.append((task, future, task_index))
             else:
+                # Process any pending async tasks before executing a sync task
                 if futures:
-                    task_outputs = self._process_async_tasks(futures, was_replayed)
+                    processed_outputs = self._process_async_tasks(futures, was_replayed)
+                    task_outputs.extend(processed_outputs)
                     futures.clear()
 
                 context = self._get_context(task, task_outputs)
@@ -832,11 +835,14 @@ class Crew(BaseModel):
                     tools=tools_for_task,
                 )
                 task_outputs.append(task_output)
+                last_sync_output = task_output
                 self._process_task_result(task, task_output)
                 self._store_execution_log(task, task_output, task_index, was_replayed)
 
+        # Process any remaining async tasks at the end
         if futures:
-            task_outputs = self._process_async_tasks(futures, was_replayed)
+            processed_outputs = self._process_async_tasks(futures, was_replayed)
+            task_outputs.extend(processed_outputs)
 
         return self._create_crew_output(task_outputs)
 
@@ -848,12 +854,17 @@ class Crew(BaseModel):
         task_index: int,
         was_replayed: bool,
     ) -> Optional[TaskOutput]:
+        # Process any pending async tasks to ensure we have the most up-to-date context
         if futures:
-            task_outputs = self._process_async_tasks(futures, was_replayed)
+            processed_outputs = self._process_async_tasks(futures, was_replayed)
+            task_outputs.extend(processed_outputs)
             futures.clear()
 
+        # Get the previous output to evaluate the condition
         previous_output = task_outputs[-1] if task_outputs else None
-        if previous_output is not None and not task.should_execute(previous_output):
+
+        # If there's no previous output or the condition evaluates to False, skip the task
+        if previous_output is None or not task.should_execute(previous_output):
             self._logger.log(
                 "debug",
                 f"Skipping conditional task: {task.description}",
@@ -861,8 +872,13 @@ class Crew(BaseModel):
             )
             skipped_task_output = task.get_skipped_task_output()
 
+            # Store the execution log for the skipped task
             if not was_replayed:
                 self._store_execution_log(task, skipped_task_output, task_index)
+
+            # Set the output on the task itself so it can be referenced later
+            task.output = skipped_task_output
+
             return skipped_task_output
         return None
 

src/crewai/flow/task_decorator.py

@@ -0,0 +1,50 @@
+from functools import wraps
+from typing import Any, Callable, Optional, Union, cast
+
+from crewai.tasks.conditional_task import ConditionalTask
+from crewai.tasks.task_output import TaskOutput
+
+
+def task(func: Callable) -> Callable:
+    """
+    Decorator for Flow methods that return a Task.
+
+    This decorator ensures that when a method returns a ConditionalTask,
+    the condition is properly evaluated based on the previous task's output.
+
+    Args:
+        func: The method to decorate
+
+    Returns:
+        The decorated method
+    """
+    setattr(func, "is_task", True)
+
+    @wraps(func)
+    def wrapper(self, *args, **kwargs):
+        result = func(self, *args, **kwargs)
+
+        # Set the task name if not already set
+        if hasattr(result, "name") and not result.name:
+            result.name = func.__name__
+
+        # If this is a ConditionalTask, ensure it has a valid condition
+        if isinstance(result, ConditionalTask):
+            # If the condition is a boolean, wrap it in a function
+            if isinstance(result.condition, bool):
+                bool_value = result.condition
+                result.condition = lambda _: bool_value
+
+            # Get the previous task output if available
+            previous_outputs = getattr(self, "_method_outputs", [])
+            previous_output = previous_outputs[-1] if previous_outputs else None
+
+            # If there's a previous output and it's a TaskOutput, check if we should execute
+            if previous_output and isinstance(previous_output, TaskOutput):
+                if not result.should_execute(previous_output):
+                    # Return a skipped task output instead of the task
+                    return result.get_skipped_task_output()
+
+        return result
+
+    return wrapper

src/crewai/llm.py

@@ -114,19 +114,6 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "Llama-3.2-11B-Vision-Instruct": 16384,
     "Meta-Llama-3.2-3B-Instruct": 4096,
     "Meta-Llama-3.2-1B-Instruct": 16384,
-    # mistral
-    "mistral-tiny": 32768,
-    "mistral-small-latest": 32768,
-    "mistral-medium-latest": 32768,
-    "mistral-large-latest": 32768,
-    "mistral-large-2407": 32768,
-    "mistral-large-2402": 32768,
-    "mistral/mistral-tiny": 32768,
-    "mistral/mistral-small-latest": 32768,
-    "mistral/mistral-medium-latest": 32768,
-    "mistral/mistral-large-latest": 32768,
-    "mistral/mistral-large-2407": 32768,
-    "mistral/mistral-large-2402": 32768,
 }
 
 DEFAULT_CONTEXT_WINDOW_SIZE = 8192
@@ -802,17 +789,6 @@ class LLM:
                     formatted_messages.append(msg)
             return formatted_messages
 
-        # Handle Mistral models - they require the last message to have a role of 'user' or 'tool'
-        if "mistral" in self.model.lower():
-            # Check if the last message has a role of 'assistant'
-            if messages and messages[-1]["role"] == "assistant":
-                # Add a dummy user message to ensure the last message has a role of 'user'
-                messages = (
-                    messages.copy()
-                )  # Create a copy to avoid modifying the original
-                messages.append({"role": "user", "content": "Please continue."})
-            return messages
-
         # Handle Anthropic models
         if not self.is_anthropic:
             return messages

src/crewai/project/annotations.py

@@ -1,8 +1,10 @@
 from functools import wraps
-from typing import Callable
+from typing import Any, Callable, Optional, Union, cast
 
 from crewai import Crew
 from crewai.project.utils import memoize
+from crewai.tasks.conditional_task import ConditionalTask
+from crewai.tasks.task_output import TaskOutput
 
 """Decorators for defining crew components and their behaviors."""
 
@@ -21,13 +23,35 @@ def after_kickoff(func):
 
 def task(func):
     """Marks a method as a crew task."""
-    func.is_task = True
+    setattr(func, "is_task", True)
 
     @wraps(func)
     def wrapper(*args, **kwargs):
         result = func(*args, **kwargs)
-        if not result.name:
+
+        # Set the task name if not already set
+        if hasattr(result, "name") and not result.name:
             result.name = func.__name__
+
+        # If this is a ConditionalTask, ensure it has a valid condition
+        if isinstance(result, ConditionalTask):
+            # If the condition is a boolean, wrap it in a function
+            if isinstance(result.condition, bool):
+                bool_value = result.condition
+                result.condition = lambda _: bool_value
+
+            # Get the previous task output if available
+            self = args[0] if args else None
+            if self and hasattr(self, "_method_outputs"):
+                previous_outputs = getattr(self, "_method_outputs", [])
+                previous_output = previous_outputs[-1] if previous_outputs else None
+
+                # If there's a previous output and it's a TaskOutput, check if we should execute
+                if previous_output and isinstance(previous_output, TaskOutput):
+                    if not result.should_execute(previous_output):
+                        # Return a skipped task output instead of the task
+                        return result.get_skipped_task_output()
+
         return result
 
     return memoize(wrapper)

src/crewai/tasks/conditional_task.py

@@ -1,4 +1,4 @@
-from typing import Any, Callable
+from typing import Any, Callable, Union, cast
 
 from pydantic import Field
 
@@ -14,17 +14,23 @@ class ConditionalTask(Task):
     """
 
     condition: Callable[[TaskOutput], bool] = Field(
-        default=None,
-        description="Maximum number of retries for an agent to execute a task when an error occurs.",
+        default=lambda _: True,  # Default to always execute
+        description="Function that determines whether the task should be executed or a boolean value.",
     )
 
     def __init__(
         self,
-        condition: Callable[[Any], bool],
+        condition: Union[Callable[[Any], bool], bool],
         **kwargs,
     ):
         super().__init__(**kwargs)
-        self.condition = condition
+
+        # If condition is a boolean, wrap it in a function that always returns that boolean
+        if isinstance(condition, bool):
+            bool_value = condition
+            self.condition = lambda _: bool_value
+        else:
+            self.condition = cast(Callable[[TaskOutput], bool], condition)
 
     def should_execute(self, context: TaskOutput) -> bool:
         """

src/crewai/utilities/llm_utils.py

@@ -43,8 +43,8 @@ def create_llm(
     try:
         # Extract attributes with explicit types
         model = (
-            getattr(llm_value, "model", None)
-            or getattr(llm_value, "model_name", None)
+            getattr(llm_value, "model_name", None)
+            or getattr(llm_value, "model", None)
             or getattr(llm_value, "deployment_name", None)
             or str(llm_value)
         )
@@ -77,9 +77,8 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
     Helper function: if llm_value is None, we load environment variables or fallback default model.
     """
     model_name = (
-        os.environ.get("MODEL")
-        or os.environ.get("MODEL_NAME")
-        or os.environ.get("OPENAI_MODEL_NAME")
+        os.environ.get("OPENAI_MODEL_NAME")
+        or os.environ.get("MODEL")
         or DEFAULT_LLM_MODEL
     )
 

tests/conditional_task_test.py

@@ -0,0 +1,190 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from crewai import Agent, Crew, Task
+from crewai.tasks.conditional_task import ConditionalTask
+from crewai.tasks.task_output import TaskOutput
+
+# Create mock agents for testing
+researcher = Agent(
+    role="Researcher",
+    goal="Research information",
+    backstory="You are a researcher with expertise in finding information.",
+)
+
+writer = Agent(
+    role="Writer",
+    goal="Write content",
+    backstory="You are a writer with expertise in creating engaging content.",
+)
+
+
+def test_conditional_task_with_boolean_false():
+    """Test that a conditional task with a boolean False condition is skipped."""
+    task1 = Task(
+        description="Initial task",
+        expected_output="Initial output",
+        agent=researcher,
+    )
+
+    # Use a boolean False directly as the condition
+    task2 = ConditionalTask(
+        description="Conditional task that should be skipped",
+        expected_output="This should not be executed",
+        agent=writer,
+        condition=False,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task1, task2],
+    )
+
+    with patch.object(Task, "execute_sync") as mock_execute_sync:
+        mock_execute_sync.return_value = TaskOutput(
+            description="Task 1 description",
+            raw="Task 1 output",
+            agent="Researcher",
+        )
+
+        result = crew.kickoff()
+
+        # Only the first task should be executed
+        assert mock_execute_sync.call_count == 1
+
+        # The conditional task should be skipped
+        assert task2.output is not None
+        assert task2.output.raw == ""
+
+        # The final output should be from the first task
+        assert result.raw.startswith("Task 1 output")
+
+
+def test_conditional_task_with_boolean_true():
+    """Test that a conditional task with a boolean True condition is executed."""
+    task1 = Task(
+        description="Initial task",
+        expected_output="Initial output",
+        agent=researcher,
+    )
+
+    # Use a boolean True directly as the condition
+    task2 = ConditionalTask(
+        description="Conditional task that should be executed",
+        expected_output="This should be executed",
+        agent=writer,
+        condition=True,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task1, task2],
+    )
+
+    with patch.object(Task, "execute_sync") as mock_execute_sync:
+        mock_execute_sync.return_value = TaskOutput(
+            description="Task output",
+            raw="Task output",
+            agent="Agent",
+        )
+
+        crew.kickoff()
+
+        # Both tasks should be executed
+        assert mock_execute_sync.call_count == 2
+
+
+def test_multiple_sequential_conditional_tasks():
+    """Test that multiple conditional tasks in sequence work correctly."""
+    task1 = Task(
+        description="Initial task",
+        expected_output="Initial output",
+        agent=researcher,
+    )
+
+    # First conditional task (will be executed)
+    task2 = ConditionalTask(
+        description="First conditional task",
+        expected_output="First conditional output",
+        agent=writer,
+        condition=True,
+    )
+
+    # Second conditional task (will be skipped)
+    task3 = ConditionalTask(
+        description="Second conditional task",
+        expected_output="Second conditional output",
+        agent=researcher,
+        condition=False,
+    )
+
+    # Third conditional task (will be executed)
+    task4 = ConditionalTask(
+        description="Third conditional task",
+        expected_output="Third conditional output",
+        agent=writer,
+        condition=True,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task1, task2, task3, task4],
+    )
+
+    with patch.object(Task, "execute_sync") as mock_execute_sync:
+        mock_execute_sync.return_value = TaskOutput(
+            description="Task output",
+            raw="Task output",
+            agent="Agent",
+        )
+
+        result = crew.kickoff()
+
+        # Tasks 1, 2, and 4 should be executed (task 3 is skipped)
+        assert mock_execute_sync.call_count == 3
+
+        # Task 3 should be skipped
+        assert task3.output is not None
+        assert task3.output.raw == ""
+
+
+def test_last_task_conditional():
+    """Test that a conditional task at the end of the task list works correctly."""
+    task1 = Task(
+        description="Initial task",
+        expected_output="Initial output",
+        agent=researcher,
+    )
+
+    # Last task is conditional and will be skipped
+    task2 = ConditionalTask(
+        description="Last conditional task",
+        expected_output="Last conditional output",
+        agent=writer,
+        condition=False,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task1, task2],
+    )
+
+    with patch.object(Task, "execute_sync") as mock_execute_sync:
+        mock_execute_sync.return_value = TaskOutput(
+            description="Task 1 output",
+            raw="Task 1 output",
+            agent="Researcher",
+        )
+
+        result = crew.kickoff()
+
+        # Only the first task should be executed
+        assert mock_execute_sync.call_count == 1
+
+        # The conditional task should be skipped
+        assert task2.output is not None
+        assert task2.output.raw == ""
+
+        # The final output should be from the first task
+        assert result.raw.startswith("Task 1 output")

tests/flow_conditional_task_test.py

@@ -0,0 +1,152 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from crewai import Agent, Task
+from crewai.flow import Flow, listen, start
+from crewai.project.annotations import task
+from crewai.tasks.conditional_task import ConditionalTask
+from crewai.tasks.task_output import TaskOutput
+
+# Create mock agents for testing
+researcher = Agent(
+    role="Researcher",
+    goal="Research information",
+    backstory="You are a researcher with expertise in finding information.",
+)
+
+writer = Agent(
+    role="Writer",
+    goal="Write content",
+    backstory="You are a writer with expertise in creating engaging content.",
+)
+
+
+class TestFlowWithConditionalTasks(Flow):
+    """Test flow with conditional tasks."""
+
+    @start()
+    @task
+    def initial_task(self):
+        """Initial task that always executes."""
+        return Task(
+            description="Initial task",
+            expected_output="Initial output",
+            agent=researcher,
+        )
+
+    @listen(initial_task)
+    @task
+    def conditional_task_false(self):
+        """Conditional task that should be skipped."""
+        return ConditionalTask(
+            description="Conditional task that should be skipped",
+            expected_output="This should not be executed",
+            agent=writer,
+            condition=False,
+        )
+
+    @listen(initial_task)
+    @task
+    def conditional_task_true(self):
+        """Conditional task that should be executed."""
+        return ConditionalTask(
+            description="Conditional task that should be executed",
+            expected_output="This should be executed",
+            agent=writer,
+            condition=True,
+        )
+
+    @listen(conditional_task_true)
+    @task
+    def final_task(self):
+        """Final task that executes after the conditional task."""
+        return Task(
+            description="Final task",
+            expected_output="Final output",
+            agent=researcher,
+        )
+
+
+def test_flow_with_conditional_tasks():
+    """Test that conditional tasks work correctly in a Flow."""
+    flow = TestFlowWithConditionalTasks()
+
+    with patch.object(Task, "execute_sync") as mock_execute_sync:
+        mock_execute_sync.return_value = TaskOutput(
+            description="Task output",
+            raw="Task output",
+            agent="Agent",
+        )
+
+        flow.kickoff()
+
+        # The initial task, conditional_task_true, and final_task should be executed
+        # conditional_task_false should be skipped
+        assert mock_execute_sync.call_count == 3
+
+
+class TestFlowWithSequentialConditionalTasks(Flow):
+    """Test flow with sequential conditional tasks."""
+
+    @start()
+    @task
+    def initial_task(self):
+        """Initial task that always executes."""
+        return Task(
+            description="Initial task",
+            expected_output="Initial output",
+            agent=researcher,
+        )
+
+    @listen(initial_task)
+    @task
+    def conditional_task_1(self):
+        """First conditional task that should be executed."""
+        return ConditionalTask(
+            description="First conditional task",
+            expected_output="First conditional output",
+            agent=writer,
+            condition=True,
+        )
+
+    @listen(conditional_task_1)
+    @task
+    def conditional_task_2(self):
+        """Second conditional task that should be skipped."""
+        return ConditionalTask(
+            description="Second conditional task",
+            expected_output="Second conditional output",
+            agent=researcher,
+            condition=False,
+        )
+
+    @listen(conditional_task_2)
+    @task
+    def conditional_task_3(self):
+        """Third conditional task that should be executed."""
+        return ConditionalTask(
+            description="Third conditional task",
+            expected_output="Third conditional output",
+            agent=writer,
+            condition=True,
+        )
+
+
+def test_flow_with_sequential_conditional_tasks():
+    """Test that sequential conditional tasks work correctly in a Flow."""
+    flow = TestFlowWithSequentialConditionalTasks()
+
+    with patch.object(Task, "execute_sync") as mock_execute_sync:
+        mock_execute_sync.return_value = TaskOutput(
+            description="Task output",
+            raw="Task output",
+            agent="Agent",
+        )
+
+        flow.kickoff()
+
+        # The initial_task and conditional_task_1 should be executed
+        # conditional_task_2 should be skipped, and since it's skipped,
+        # conditional_task_3 should not be triggered
+        assert mock_execute_sync.call_count == 2

uv.lock

@@ -619,7 +619,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.105.0"
+version = "0.102.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -703,7 +703,7 @@ requires-dist = [
     { name = "blinker", specifier = ">=1.9.0" },
     { name = "chromadb", specifier = ">=0.5.23" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.37.0" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.36.0" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
@@ -752,7 +752,7 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.37.0"
+version = "0.36.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "chromadb" },
@@ -767,9 +767,9 @@ dependencies = [
     { name = "pytube" },
     { name = "requests" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ef/a9/813ef7b721d11ac962c2a3cf4c98196d3ca8bca5bb0fa5e01da0af51ac23/crewai_tools-0.37.0.tar.gz", hash = "sha256:23c8428761809e30d164be32c2a02850c4648e4371e9934eb58842590bca9659", size = 722104 }
+sdist = { url = "https://files.pythonhosted.org/packages/4d/e1/d65778cf4aea106f3f60a4208521f04bc7f1d26be4e34eeb63cae6297d50/crewai_tools-0.36.0.tar.gz", hash = "sha256:761b396ee6a4019a988720dd6a14e1409f5de9d0cdc2a8662b487d87efb1a6bf", size = 900178 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f4/b3/6bf9b066f628875c383689ab72d21968e1108ebece887491dbf051ee39c5/crewai_tools-0.37.0-py3-none-any.whl", hash = "sha256:df5c9efade5c1f4fcfdf6ac8af13c422be7127a3083a5cda75d8f314c652bb10", size = 548490 },
+    { url = "https://files.pythonhosted.org/packages/bd/b6/533632a6c2a2e623fc4a1677458aff3539413a196fb220a7fece4ead3f71/crewai_tools-0.36.0-py3-none-any.whl", hash = "sha256:dbd0d95a080acfb281e105f4376e1e98576dae6d53d94f7b883c57af893668b3", size = 545937 },
 ]
 
 [[package]]