What is the best way to handle errors during tool execution?

Use the toolExecutionErrorHandler in the AiServices builder to catch exceptions and return meaningful text descriptions back to the LLM for recovery.

Can LangChain4j tools handle complex nested data structures?

Yes, you can use Java records or POJOs decorated with @Description annotations to define structured schemas that the LLM can understand and populate.

How do I define a tool in LangChain4j using this skill?

You define tools by applying the @Tool annotation to public methods within a Java class and then registering an instance of that class with the AiServices builder.

How do I make tool availability dynamic based on user state?

Implement the ToolProvider interface to programmatically determine which tools should be available to the LLM based on the ToolProviderRequest context.

LangChain4j Tool & Function Calling

Name: LangChain4j Tool & Function Calling
Author: pur3v4d3r

bypur3v4d3r

•

数据科学与机器学习

Implements robust tool and function calling patterns for Java-based AI agents using the LangChain4j framework.

This skill provides comprehensive implementation patterns for integrating external tools, APIs, and services into AI applications built with LangChain4j. It guides developers through defining annotation-based tools, managing complex parameter types, and implementing advanced orchestration features like dynamic tool provisioning and concurrent execution. By utilizing these patterns, developers can build sophisticated, context-aware agentic systems that reliably interact with business logic, databases, and third-party services while maintaining robust error handling and security.

主要功能

01Annotation-driven tool definition using @Tool and @P for intuitive development

02Advanced error handling, including circuit breakers and hallucinated tool strategies

03Dynamic tool provisioning and context-aware tool selection logic

04Support for complex parameter types using Java records and JSON descriptions

05Seamless integration with AiServices for memory management and streaming support

061 GitHub stars

使用场景

01Developing multi-agent systems where agents dynamically share or request specialized tool access

02Building AI-powered assistants that can perform real-world actions via internal business APIs

03Creating context-aware data retrieval systems that validate and sanitize LLM-generated inputs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pur3v4d3r/pur3-pkb-codebase langchain4j-tool-function-calling-patterns

For use in Claude.ai and ChatGPT

Download Skill

主要功能

01Annotation-driven tool definition using @Tool and @P for intuitive development

02Advanced error handling, including circuit breakers and hallucinated tool strategies

03Dynamic tool provisioning and context-aware tool selection logic

04Support for complex parameter types using Java records and JSON descriptions

05Seamless integration with AiServices for memory management and streaming support

061 GitHub stars

使用场景

01Developing multi-agent systems where agents dynamically share or request specialized tool access

02Building AI-powered assistants that can perform real-world actions via internal business APIs

03Creating context-aware data retrieval systems that validate and sanitize LLM-generated inputs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pur3v4d3r/pur3-pkb-codebase langchain4j-tool-function-calling-patterns

For use in Claude.ai and ChatGPT

Download Skill