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roanbrasil
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devops-gitops-corpus

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Some examples of pre-built tools:
- Terminal commands that can search in JSON files
- Terminal commands that conduct code research in GitHub
- Terminal commands that scan YouTube channels
- Terminal commands that can execute terminal commands
Of course, you can also write your own custom tools. APIs, special calculations, cache systems - you can implement whatever is necessary.
### Defining Tasks
You need to specifically tell each agent what to do. Just as describing a job to an employee:
```python
data_collection = Task(
description='Gather data from customer interactions',
expected_output='Structured dataset for analysis',
agent=data_science_agent,
)
```
You can have things done sequentially or in parallel, and you can pass results of one to another task as input. You can also get results in the format you desire (JSON, file, etc.).
### Controlling Your Workflow
There are three different work styles in CrewAI:
**Sequential Work**: Like an assembly line. One agent finishes its task and transfers it to the second, the third. Classical workflow.
**Hierarchical Work**: An automatic manager agent is created and it distributes work to all. Think of company hierarchy.
**Consensus**: (Under construction) Agents will vote among themselves and decide. A democratic approach.
![organizing agents](https://cdn.voltagent.dev/2025-07-24-crew-ai/organizing.png)
### Creating the Team
In the final step, you gather everybody:
```python
my_crew = Crew(
agents=[data_scientist, researcher, writer],
tasks=[analyze_task, research_task, write_task],
process=Process.sequential,
verbose=True,
)
```
And there you are! Your team is ready to go.
## LLM Integration and Flexibility
One of the best features of CrewAI is LLM flexibility:
- **OpenAI GPT-4** (default)
- **Google AI** models
- **Anthropic Claude**
- **IBM Granite** series
- **Local models** (with Ollama)
- **Any LangChain compatible** model
This flexibility allows you to choose different LLMs for different agents. You can, for example, use Code Llama for a code agent and GPT-4 for a writing agent.
![llm integration crewai](https://cdn.voltagent.dev/2025-07-24-crew-ai/llm-integration.png)
## Real-World Use Cases
### 📝 Content Creation
- **Research Agent**: Collects information about the topic
- **Writing Agent**: Writes articles based on collected information
- **Editor Agent**: Edits and organizes the writing
### 📧 Email Management
- **Filter Agent**: Retrieves important emails
- **Analysis Agent**: Analyzes email content
- **Response Agent**: Automatically generates response drafts
### 📈 Financial Analysis
- **Data Collection Agent**: Collects company financial information
- **Analysis Agent**: Performs technical and fundamental analysis
- **Report Agent**: Generates investment recommendation reports
### 🎯 Social Media Management
- **Content Planning Agent**: Creates post calendar
- **Visual Agent**: Prepares visuals
- **Interaction Agent**: Handles comments
## Alternative: VoltAgent Framework
CrewAI's multi-agent solution is great, but every project won't need this degree of complexity. If you desire a faster and simpler solution, **VoltAgent** offers the perfect substitute.
### ⚡ What's VoltAgent?
![VoltOps LLM Observability Platform Chat Example](https://cdn.voltagent.dev/2025-04-24-rag-chatbot/rag-chatbot-voltagent-console.gif)
[VoltAgent](https://github.com/VoltAgent/voltagent) is a new AI agent framework developed in TypeScript. It offers fast and efficient solutions with a different approach.