The rapid evolution of Large Language Models (LLMs) has sparked a new era of AI agents. While the foundation of these agents lies in generative models, the real challenge lies in devising effective reasoning strategies and agent architectures.
Current research explores a spectrum of approaches, from simple chain-of-thought prompting to more sophisticated ReAct and Reflection reasoning. Agent architectures are also evolving, transitioning from single-agent generation to multi-agent conversations and even multi-LLM group chats.
However, the complexity of existing frameworks and libraries often hinders research progress. To address this, we're excited to introduce AgentLite, a lightweight and user-friendly open-source library that empowers researchers and developers to innovate in LLM agent reasoning, architectures, and applications.
Key Features of AgentLite:
- Task Decomposition: Easily break down complex tasks into manageable subtasks.
- Multi-Agent Orchestration: Build and manage collaborative multi-agent systems.
- Extensible Reasoning Types: Experiment with various reasoning strategies, including chain-of-thought, ReAct, and Reflection.
- Memory Module: Enhance agent memory and context understanding.
- Prompter Module: Fine-tune prompts for optimal LLM performance.
AgentLite's Practical Applications:
- Reproducing Benchmark Results: Easily replicate existing research findings.
- Prototyping New Applications: Rapidly develop and test innovative LLM agent use cases.
- Integrating and Evaluating New Reasoning Strategies: Effortlessly incorporate and assess novel reasoning techniques.
By leveraging AgentLite's simplicity and flexibility, you can push the boundaries of LLM agent research and development.
Explore AgentLite: https://github.com/SalesforceAIResearch/AgentLite
