An LLM (large language model) Agent is an autonomous system built with a large language model as its central controller. Rather than just producing textual content, the LLM Agent is conceptualized as a robust general problem solver, inspired by demonstrations like AutoGPT, GPT-Engineer, and BabyAGI.
LLM Agents in practice
At the heart of the LLM Agent is the LLM, acting as the agent's brain, directing its actions and making decisions.
- Subgoal and Decomposition: The agent segments vast tasks into smaller, more manageable subgoals. This approach ensures intricate tasks are tackled efficiently.
- Reflection and Refinement: The LLM Agent possesses the capability for self-analysis and introspection. It reviews its past actions, learns from any errors, and fine-tunes its strategies for subsequent tasks, enhancing the outcome's quality.
- Short-term Memory: The agent utilizes the model's short-term memory for in-context learning, as discussed in Prompt Engineering.
- Long-term Memory: The agent can store and retrieve an infinite amount of data over prolonged durations. This is typically achieved through external vector storage systems and swift data retrieval processes.
The agent can interface with external APIs to gather additional data not encapsulated within the model's pre-trained weights. This includes obtaining real-time information, executing code, and accessing exclusive data sources, among others.
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