Crew AI Build AI Agents Team With Local LLMs For Content Creation

Summary

The video tutorial demonstrates how to use Crew AI, a framework for building automated workflows using multiple AI agents. It shows how to connect local LLMs like Anthropic's Constitutional AI and LM Studio to Crew AI to define different agents with specific roles. A sample workflow is created with a 'researcher' agent that gathers information on AI advancements online using DuckDuckGo and a 'writer' agent that generates a blog post on the topic. The workflow enables automating research and content creation by harnessing multiple LLMs. Crew AI provides flexible integration of different LLMs to build efficient, multi-agent automation.

Takeaways

• 😀 Crew AI allows creating workflows using multiple AI models as agents
• 👥 You can connect Crew AI to OpenAI, Anthropic, Cohere, or local LLMs like AMA
• 💻 Installation requires Python and some dependencies like DuckDuckGo
• 🤖 Agents are defined with a LLM, work scope, and backstory
• 🔀 Tasks define what each agent must do in the workflow
• ✏️ A sample workflow gathers AI advancements info to generate blog content
• 📝 One agent researches info, another writes content based on that info
• ⚙️ Workflows can be sequential or hierarchical depending on needs
• 🔗 You can connect LM Studio in addition to AMA as agents
• 🎉 Crew AI enables automating workflows using multiple LLMs easily

Q & A

What is Crew AI?

Crew AI is a multiple AI agent framework that allows users to build workflows using multiple AI models as autonomous agents to complete automation tasks.

What types of large language models can be connected to Crew AI?

Crew AI can connect to models like GPT from OpenAI, as well as local large language models hosted on the user's machine, like those from Anthropic, Cohere, or LM Studio.

How does Crew AI allow the AI models to search the internet?

Crew AI uses the DuckDuckGo search library to enable the AI agents to search the internet and retrieve information.

What is the purpose of defining a workscope and backstory?

Defining a workscope and backstory provides context for the AI agents to understand their roles and objectives within the workflow.

What were the two agents defined in the example workflow?

The two agents were a 'Researcher' responsible for gathering information, and a 'Tech Content Strategist' responsible for using that information to write tech content.

How did the two agents interact in the workflow?

The Researcher gathered information on AI advancements from the internet, then passed that information to the Tech Content Strategist to write content based on it.

What tools did the agents use if they needed additional information?

The agents used the DuckDuckGo search library to search the internet for any additional information they needed.

What was the end result of the workflow?

The end result was a set of paragraphs with titles constituting a blog post on AI advancements.

Besides AMA, what other local large language model could be connected?

The script shows how LM Studio could also be connected as one of the agents in the workflow.

What benefits does Crew AI provide for content creation?

Crew AI allows automating content creation by using multiple AI agents in defined roles within a workflow.