Building Your First LLM Agent Application

Building Your First LLM Agent Application

To build a large language model (LLM) agent application, you need four key components: an agent core, a memory module, agent tools, and a planning module. This post provides an overview of the developer ecosystem for LLM agents and a beginner-level tutorial for building your first LLM-powered agent.

Developer ecosystem overview:

• Various implementation frameworks are available, including LangChain, LLaMa-Index, HayStack, AutoGen, AgentVerse, and ChatDev.
• The recommended framework may depend on your specific needs and preferences.

Recommended reading list:

• AutoGPT: A GitHub project showcasing the capabilities of LLM agents.
• Generative Agents: Interactive Simulacra of Human Behavior: A project demonstrating a swarm of decentralized agents.

Tutorial overview:

• The tutorial uses a revenue growth question as an example.
• The tools needed include a RAG pipeline for answering questions and a planning module for decomposing complex questions into simpler sub-parts.
• A memory module is required to keep track of questions and answers.
• The agent core handles the logic of the agent, such as recursive solving.

Tutorial steps:

1. Set up a RAG pipeline for question answering.
2. Build a planning module to decompose complex questions.
3. Create a memory module to store questions and answers.
4. Implement the agent core with a single-thread recursive solver.
5. Optionally, implement a multi-thread recursive solver for parallel execution.

The tutorial provides code examples and explanations for each step, allowing you to build your first LLM agent application.

Next steps: Once you have completed the tutorial, you can further explore and enhance your LLM agent application. Consider researching additional resources and frameworks to expand your knowledge and capabilities in building LLM agents.