Agent toolkits represent a new abstraction designed to enable developers to create agents tailored for specific use cases, such as interacting with relational databases or OpenAPI specifications, and are supported in both Python and TypeScript. These agents leverage a system that uses a language model to decide actions iteratively, enabling them to combine external knowledge and computation, perform iterative planning, and manage errors effectively. Some examples of these toolkits include the SQLDatabaseToolkit, which allows an agent to interact robustly with databases, and the OpenAPIToolkit, which enables an agent to make accurate API requests based on OpenAPI specifications. Additional examples include agents that interact with JSON data, vector stores, and data formats like Pandas DataFrames and CSVs, illustrating the versatility and potential applications of this approach. The development of agent toolkits aims to expand the range of possibilities for interacting with various tools and utilities, with plans to introduce more toolkits in the future.