CrewAI

Now, we have understood what are agents, we can start building one. Crew AI is a framework which helps us build these agents, where the multiple agents can work on a different task individually and communicate with each other. Each agents have their own expertise, instructions, and responsibilities. We can use CrewAI to automate stock-picking, planning vacations, or booking a movie-ticket.

For example, if we ask the Agent to book a movie ticket, it will start by retrieving the user’s location and searching for nearby theaters. It will then check the available movies in those theaters and sort them based on their preferences. Next, it will present options for the selected movies according to their availability, which it can also determine by accessing the user’s calendar. Finally, it will book the ticket based on their seat and location preferences.
Most importantly, building agents with Crew AI is incredibly fast and reliable. Since these agents collaborate and delegate tasks among themselves, the context is maintained consistently. For instance, if you mention that you are on a diet, the agent will avoid booking seats at a fast food restaurant. Additionally, we can customize the agent to double-check and refine its responses for improved results.

Autogen

Autogen is a robust open-source framework created by Microsoft Research for developing advanced AI applications through a multi-agent conversation approach. It provides a high-level abstraction for building interactive and customizable agents that can seamlessly integrate large language models (LLMs), tools, and human input to address complex tasks.

A major feature of Autogen is its ability to support various conversation patterns. This framework enables developers to create applications with different levels of autonomy, ranging from fully autonomous agent-to-agent dialogues to human-assisted problem-solving. Autogen accommodates both static and dynamic conversation flows, allowing agents to modify their interactions based on the context and input. This adaptability allows developers to create diverse applications, from hierarchical chat systems to nested conversations and LLM-based function calls.

Another feature of Autogen is its open-source and community-driven nature. The project encourages contributions from people of all backgrounds, promoting innovation and growth within the AI development community. Its modular design and high-level abstraction make it accessible to developers with varying skill levels, enabling them to quickly build complex multi-agent applications with minimal effort.