Agent

In LangChain, agents are high-level components that use language models (LLMs) to determine which actions to take and in what order. An action can either be using a tool and observing its output or returning it to the user. Tools are functions that perform specific duties, such as Google Search, database lookups, or Python REPL. Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done.

Several types of agents are available in LangChain:

• The zero-shot-react-description agent uses the ReAct framework to decide which tool to employ based purely on the tool's description. It necessitates a description of each tool.
• The react-docstore agent engages with a docstore through the ReAct framework. It needs two tools: a Search tool and a Lookup tool. The Search tool finds a document, and the Lookup tool searches for a term in the most recently discovered document.
• The self-ask-with-search agent employs a single tool named Intermediate Answer, which is capable of looking up factual responses to queries. It is identical to the original self-ask with the search paper, where a Google search API was provided as the tool.
• The conversational-react-description agent is designed for conversational situations. It uses the ReAct framework to select a tool and uses memory to remember past conversation interactions.