Šimon Podhajský - Multi-Agent Frameworks: Teaming Up Specialized AI Models to Tackle Complex Tasks

Large Language Model (LLM) agents, comprising system prompts, tools, and execution environments, are beneficial for complex tasks. Two prominent multi-agent Frameworks, AutoGen (Microsoft) and LangChain (LangChain), offer advantages like observability, testability, and structured outputs; however, LangChain provides better structured output capabilities through Pydantic types, while AutoGen's strengths lie in its ease of use and command-line interface.

LLM Agent Basics

• 00:01:25 LLM agents consist of a system prompt defining input-output transformations, access to tools like APIs for weather data or web searches, and an execution environment (often Docker) to run the tools securely. Some agents are autonomous, reacting to events, while others require user input.

Multi-Agent Benefits

• 00:04:34 Multiple agents, each with a distinct system prompt and tools, enhance complex task execution by separating responsibilities, thus improving unit testability. This facilitates evaluating and refining LLM-based systems for higher accuracy, unlike initial notebook-based approaches. Furthermore, multi-agent Frameworks orchestrate the flow between agents for tasks.

AutoGen & LangChain

• 00:06:49 AutoGen (Microsoft) and LangChain (LangChain) are prominent multi-agent frameworks with common features, including observability (logging), flow control, and testability. However, they also have key differences. For example, LangChain guarantees structured outputs via Pydantic, enhancing system integration, and offers parallel task execution and graph alteration.

Structured Outputs

• 00:09:13 Structured outputs, a key benefit of LangChain, ensure that outputs adhere to JSON schemas, simplifying system integration and validation. LangChain's utilization of Pydantic types facilitates this structured output. AutoGen relies on conversational history, which can be helpful in some cases but lacks the type-based constraints provided by LangChain.

Framework Deployment

• 00:10:54 Both frameworks provide deployment options. LangChain offers easy deployment to its cloud service but also supports Docker containers, enabling execution on platforms like AWS ECS or Google Run. AutoGen does not have built-in deployment features but can be packaged as a pip-installable package for deployment. It also supports deployments via FastAPI and chatbot interfaces.