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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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