---
title: "Crews"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Crews

[Crews Docs](https://docs.crewai.com/concepts/crews)

## In CrewAI flows we can use LiteLLM or a combination of crews/agents/tasks, which strategy should we use and when? 

Both approaches ultimately use the same underlying language model (often via LiteLLM under the hood), but they offer very different levels of abstraction and control. Your choice depends on the complexity of your task and the level of orchestration you require.

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### LiteLLM Strategy

LiteLLM provides a lightweight, unified interface to multiple LLM providers. When you use LiteLLM directly, you’re essentially making straightforward calls to an LLM without any extra orchestration. This strategy is ideal when:

- **Tasks are Simple or Ad Hoc:**  
  For example, if you just need to generate a quick summary, answer a one-off question, or perform a single-step transformation, using LiteLLM is often the fastest and simplest route.  
- **Rapid Prototyping:**  
  When experimenting with different models or quickly iterating on a prompt without the overhead of managing inter-agent communication.
- **Minimal Overhead:**  
  You don’t need the additional structure or error handling that comes with a full multi-agent system.

In essence, if you only need to “ask” the model for a response or perform simple transformations, LiteLLM keeps your code lightweight and direct.  

---

### Crews/Agents/Tasks Strategy

The combination of crews, agents, and tasks is CrewAI’s more structured, production-grade approach. In this model, you define:

- **Agents with Roles and Backstories:**  
  Each agent can have a specific role (e.g., “Researcher,” “Reporting Analyst”) with tailored goals and expertise.
- **Tasks as Discrete Units of Work:**  
  You break a larger problem into multiple tasks that agents can work on in parallel or in sequence.
- **Crews to Orchestrate Collaboration:**  
  Crews coordinate the overall workflow, delegating tasks, managing context, and handling retries or errors.

This strategy is best when:
  
- **Problems are Complex or Multi-Step:**  
  If your application requires several specialized sub-tasks (like market analysis followed by report generation), splitting the work among different agents can lead to better organization and predictable outcomes.
- **Role Specialization Adds Value:**  
  When different parts of your problem benefit from different “expertise,” you can assign each agent a role that is best suited for a particular sub-task.
- **Scalability and Robustness Are Needed:**  
  Production workflows often require careful error handling, context management, and clear delegation. The crew/agent/task architecture provides built-in mechanisms for these needs.

This structured approach not only makes it easier to manage large, complex workflows but also provides clear separation of concerns—each agent or task can be tuned or debugged independently.  


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### When to Use Which Strategy

- **Use LiteLLM When:**  
  - You have a single, self-contained query or task.
  - You’re in the early prototyping phase and want minimal overhead.
  - The task does not require delegation or multi-step reasoning.
  
- **Use Crews/Agents/Tasks When:**  
  - The task is inherently complex and benefits from breaking it into smaller, specialized steps.
  - You need multiple agents interacting (or delegating) to achieve a more nuanced, robust outcome.
  - You’re building production-grade applications that require clear workflow control, error handling, and state management.

In practice, many applications blend the two: you might build a crew of agents where each agent makes its LLM calls via LiteLLM. This way, you get both the simplicity of a unified LLM interface and the power of structured collaboration.  

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### Bottom Line

- **LiteLLM alone** is best for direct, uncomplicated interactions with an LLM.
- **Crews/Agents/Tasks** are best when you need to orchestrate multi-step, role-based processes that benefit from division of labor and systematic workflow control.

Your choice should be guided by the complexity of your use case and the level of orchestration you need.
