The study also states that LangChain is a popular library for developing applications with large language models (LLMs), offering built-in tools to create various agent types.
But says it tends to be overly complex for researchers who want to design new types of agent reasoning and architectures. Modifying LangChain for specific research needs can be difficult due to the high overhead in agent creation.
While Autogen has succeeded in building LLM agents, its agent interfaces have fixed reasoning types, making it hard to adapt for other research tasks. Additionally, its architecture is limited to multi-agent conversation and code execution, which may not fit all new scenarios or benchmarks.
Below is a recorded demonstration of the AgentLite User Interface…
In AgentLite, the Individual Agent serves as the foundational agent class, built on four core modules:
- PromptGen,
- Actions,
- LLM, and
- Memory
PromptGen
PromptGen is responsible for constructing the prompts that the agent sends to the LLM to generate actions.
These prompts are made up of several components, such as the agent’s role description, instructions, constraints, actions, and relevant examples.
AgentLite includes default methods to combine these elements but also provides flexibility for developers to create custom prompts for specific tasks.
