This agent uses LangChain’s React agent framework along with the Tavily Search API. It allows the agent to perform searches across the web and return contextual answers based on real-time search results. This is useful in scenarios where the agent needs access to dynamic, real-world information.
Integration with Tavily API for search capabilities. Supports handling real-time queries and dynamic information retrieval. Example usage: Searching for real-time data such as news, stock prices, or general queries.
This agent is a more advanced implementation where custom tools are defined and integrated into the React agent. These tools can perform specific operations like looking up data in a custom database or manipulating information before feeding it into the agent. Along with Tavily API, this agent becomes highly flexible, capable of answering complex queries that involve both external searches and internal data manipulation.
Combines Tavily API with custom tools for added versatility. Allows for specific actions to be taken during conversations. Example usage: Custom tools can be used to fetch employee data, calculate salaries, and answer contextual queries based on a combination of external and internal data.
The Self-Ask agent leverages the Self-Ask framework, which enables the agent to independently determine when and what to ask as a follow-up question to refine its responses. With the addition of Tavily API for search, the agent can gather more context before answering, improving the accuracy of responses for complex or incomplete queries.
Self-Ask mechanism to gather more information before responding. Tavily API integration for additional search capabilities. Example usage: Useful when the initial query is ambiguous, requiring additional data to clarify the response, such as asking follow-up questions on a topic like historical events or technical details.