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adding manager_llm
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@@ -4,7 +4,15 @@ description: A step-by-step guide to creating a cohesive CrewAI team for your pr
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---
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## Introduction
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Assembling a crew in CrewAI is akin to casting for a play, where each agent plays a unique role. This guide walks you through creating a crew, assigning roles and tasks, and activating them to work in harmony.
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Embarking on your CrewAI journey involves a few straightforward steps to set up your environment and initiate your AI crew. This guide ensures a seamless start.
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## Step 0: Installation
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Begin by installing CrewAI and any additional packages required for your project. For instance, the `duckduckgo-search` package is used in this example for enhanced search capabilities.
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```shell
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pip install crewai
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pip install duckduckgo-search
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```
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## Step 1: Assemble Your Agents
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Begin by defining your agents with distinct roles and backstories. These elements not only add depth but also guide their task execution and interaction within the crew.
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@@ -23,7 +23,11 @@ To utilize the hierarchical process, you must define a crew with a designated ma
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!!! note "Tools on the hierarchical process"
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For tools when using the hierarchical process, you want to make sure to assign them to the agents instead of the tasks, as the manager will be the one delegating the tasks and the agents will be the ones executing them.
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!!! note "Manager LLM"
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A manager will be automatically set for the crew, you don't need to define it. You do need to set the `manager_llm` parameter in the crew though.
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```python
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from langchain_openai import ChatOpenAI
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from crewai import Crew, Process, Agent
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# Define your agents, no need to define a manager
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@@ -42,6 +46,7 @@ writer = Agent(
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project_crew = Crew(
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tasks=[...], # Tasks that that manager will figure out how to complete
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agents=[researcher, writer],
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manager_llm=ChatOpenAI(temperature=0, model="gpt-4"), # The manager's LLM that will be used internally
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process=Process.hierarchical # Designating the hierarchical approach
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)
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```
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