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Merge branch 'main' into intergrate-mem0
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@@ -36,7 +36,6 @@ description: What are crewAI Agents and how to use them.
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| **Response Template** *(optional)* | `response_template` | Specifies the response format for the agent. Default is `None`. |
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| **Allow Code Execution** *(optional)* | `allow_code_execution` | Enable code execution for the agent. Default is `False`. |
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| **Max Retry Limit** *(optional)* | `max_retry_limit` | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.
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| **Use Stop Words** *(optional)* | `use_stop_words` | Adds the ability to not use stop words (to support o1 models). Default is `True`. |
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| **Use System Prompt** *(optional)* | `use_system_prompt` | Adds the ability to not use system prompt (to support o1 models). Default is `True`. |
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| **Respect Context Window** *(optional)* | `respect_context_window` | Summary strategy to avoid overflowing the context window. Default is `True`. |
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@@ -79,7 +78,6 @@ agent = Agent(
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callbacks=[callback1, callback2], # Optional
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allow_code_execution=True, # Optional
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max_retry_limit=2, # Optional
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use_stop_words=True, # Optional
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use_system_prompt=True, # Optional
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respect_context_window=True, # Optional
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)
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@@ -248,7 +248,7 @@ main_pipeline = Pipeline(stages=[classification_crew, email_router])
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inputs = [{"email": "..."}, {"email": "..."}] # List of email data
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main_pipeline.kickoff(inputs=inputs=inputs)
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main_pipeline.kickoff(inputs=inputs)
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```
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In this example, the router decides between an urgent pipeline and a normal pipeline based on the urgency score of the email. If the urgency score is greater than 7, it routes to the urgent pipeline; otherwise, it uses the normal pipeline. If the input doesn't include an urgency score, it defaults to just the classification crew.
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@@ -265,4 +265,4 @@ In this example, the router decides between an urgent pipeline and a normal pipe
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The `Pipeline` class includes validation mechanisms to ensure the robustness of the pipeline structure:
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- Validates that stages contain only Crew instances or lists of Crew instances.
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- Prevents double nesting of stages to maintain a clear structure.
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- Prevents double nesting of stages to maintain a clear structure.
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@@ -1,4 +1,3 @@
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```markdown
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---
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title: crewAI Tasks
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description: Detailed guide on managing and creating tasks within the crewAI framework, reflecting the latest codebase updates.
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@@ -314,4 +313,4 @@ save_output_task = Task(
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## Conclusion
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Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.
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Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.
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@@ -20,7 +20,6 @@ Crafting an efficient CrewAI team hinges on the ability to dynamically tailor yo
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- **System Template** *(Optional)*: `system_template` defines the system format for the agent.
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- **Prompt Template** *(Optional)*: `prompt_template` defines the prompt format for the agent.
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- **Response Template** *(Optional)*: `response_template` defines the response format for the agent.
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- **Use Stop Words** *(Optional)*: `use_stop_words` attribute controls whether the agent will use stop words during task execution. This is now supported to aid o1 models.
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- **Use System Prompt** *(Optional)*: `use_system_prompt` controls whether the agent will use a system prompt for task execution. Agents can now operate without system prompts.
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- **Respect Context Window**: `respect_context_window` renames the sliding context window attribute and enables it by default to maintain context size.
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- **Max Retry Limit**: `max_retry_limit` defines the maximum number of retries for an agent to execute a task when an error occurs.
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@@ -46,7 +46,6 @@ researcher = Agent(
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verbose=False,
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# tools=[] # This can be optionally specified; defaults to an empty list
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use_system_prompt=True, # Enable or disable system prompts for this agent
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use_stop_words=True, # Enable or disable stop words for this agent
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max_rpm=30, # Limit on the number of requests per minute
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max_iter=5 # Maximum number of iterations for a final answer
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)
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@@ -58,7 +57,6 @@ writer = Agent(
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verbose=False,
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# tools=[] # Optionally specify tools; defaults to an empty list
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use_system_prompt=True, # Enable or disable system prompts for this agent
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use_stop_words=True, # Enable or disable stop words for this agent
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max_rpm=30, # Limit on the number of requests per minute
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max_iter=5 # Maximum number of iterations for a final answer
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)
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@@ -160,4 +160,4 @@ This is particularly useful when working with OpenAI-compatible APIs or when you
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## Conclusion
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By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options.
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By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options.
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@@ -58,6 +58,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
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LLMs
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</a>
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</li>
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<!-- <li>
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<a href="./core-concepts/Flows">
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Flows
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</a>
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</li> -->
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<li>
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<a href="./core-concepts/Pipeline">
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Pipeline
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@@ -85,7 +90,7 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
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</li>
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</ul>
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</div>
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<div style="width:30%">
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<div style="width:25%">
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<h2>How-To Guides</h2>
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<ul>
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<li>
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@@ -160,7 +165,7 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
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</li>
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</ul>
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</div>
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<div style="width:30%">
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<!-- <div style="width:25%">
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<h2>Examples</h2>
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<ul>
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<li>
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@@ -198,6 +203,26 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
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Landing Page Generator
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</a>
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</li>
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<li>
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<a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/email_auto_responder_flow">
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Email Auto Responder Flow
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</a>
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</li>
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<li>
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<a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/lead-score-flow">
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Lead Score Flow
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</a>
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</li>
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<li>
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<a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/write_a_book_with_flows">
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Write a Book Flow
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</a>
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</li>
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<li>
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<a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/meeting_assistant_flow">
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Meeting Assistant Flow
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</a>
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</li>
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</ul>
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</div>
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</div> -->
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</div>
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