LLM Functions provides a simple framework to bridge large language model responses with real code execution. You define functions via JSON schemas, register them with the library, and the LLM will return structured function calls when appropriate. The library parses those responses, validates the parameters, and invokes the correct handler. It supports synchronous and asynchronous callbacks, custom error handling, and plugin extensions, making it ideal for applications that require dynamic data lookup, external API calls, or complex business logic within AI-driven conversations.

AgentGateway provides a developer-focused environment for creating multi-agent AI applications. It supports distributed agent orchestration, plugin integration, and secure access control. With built-in connectors for vector databases, REST/gRPC APIs, and common services like Slack and Notion, agents can query documents, execute business logic, and generate responses autonomously. The platform includes monitoring, logging, and role-based access controls, making it easy to deploy scalable, auditable AI solutions across enterprises.

Functionary provides a declarative way to register custom tools — JavaScript functions encapsulating API calls, database queries, or business logic. It wraps an LLM interaction to analyze user prompts, determine which tools to execute, and parse the tool outputs back into conversational responses. The framework supports memory, error handling, and chaining of actions, offering hooks for pre- and post-processing. Developers can quickly spin up agents capable of dynamic function orchestration without boilerplate, enhancing control over AI-driven workflows.