langchain4j-tool-function-calling-patterns

Tool function calling in LangChain4j allows AI agents to discover and invoke external tools through @Tool annotations. Register tool classes with your AI service builder to let agents dynamically select and execute the right function for each task, integrating APIs, databases, and real-time data sources seamlessly.

Use @Tool annotations on methods and @P annotations on parameters to define tools and their documentation. This approach enables LangChain4j to generate structured tool definitions that agents understand, supporting context-aware tool selection and proper parameter passing.

LangChain4j supports configurable error handling and flexible return type support for tool invocations. You can define how tools respond to failures and structure their outputs—both immediate and streaming results—to integrate smoothly with agent workflows.

Dynamic tool provisioning lets agents access tools based on runtime context and task requirements. Register tools with your AI service and configure memory-aware tool usage so agents automatically select the appropriate tools without hardcoding every integration.

Yes, LangChain4j integrates memory context with tool invocations, enabling agents to track tool usage history and make informed decisions about which tools to call next. This supports complex, multi-step workflows where tool selection depends on prior results.

Create tool classes with @Tool-annotated methods and parameter documentation via @P annotations. These become the input definitions LangChain4j uses to register tools with your AI service builder and enable agent-driven function calling.