langchain4j-tool-function-calling-patterns

LangChain4j Tool & Function Calling Patterns

Provides patterns for annotating methods as tools, configuring tool executors, registering tools with AI services, validating parameters, and handling tool execution errors in LangChain4j applications.

Overview

LangChain4j uses the @Tool annotation to expose Java methods as callable functions for AI agents. The AiServices builder registers tools with a chat model, enabling LLMs to perform actions beyond text generation: database queries, API calls, calculations, and business system integrations. Parameters use @P for descriptions that guide the LLM.

When to Use

Building AI agents that call external tools (weather, stocks, database queries)
Defining function specifications for LLM tool use (@Tool, @P annotations)
Registering and managing tool sets with AiServices.builder().tools()
Handling tool execution errors, timeouts, and hallucinated tool names
Implementing context-aware tools that inject user state via @ToolMemoryId
Configuring dynamic tool providers for large or conditional tool sets

Instructions

1. Annotate Methods with `@Tool`

Define a tool class with methods annotated @Tool. Provide a description as the first parameter. Use @P for each parameter description.

public class WeatherTools {
    private final WeatherService weatherService;
 
    public WeatherTools(WeatherService weatherService

Validate: Create an instance and confirm the class loads without errors.

2. Register Tools with AiServices

Use AiServices.builder() to register tool instances with the chat model.

Validate: Call assistant.chat("What is 2 + 2?") and verify the LLM responds without throwing.

3. Test Tool Invocation End-to-End

Send a prompt that triggers tool usage and verify the tool executes and its result is incorporated.

Validate: Check logs for tool invocation and confirm the response uses the tool output.

4. Handle Tool Execution Errors

Add error handlers to gracefully manage failures without exposing stack traces.

Validate: Trigger an error condition and confirm the LLM receives a safe error message.

5. Optimize for Performance and Scale

Enable concurrent tool execution and set timeouts for long-running tools.

Validate: Run concurrent requests and confirm no thread contention or deadlocks.

Examples

Calculator Tool with Full Class

Immediate Return Tool (No LLM Response)

Dynamic Tool Provider

Best Practices

Descriptive @Tool names: Use imperative verbs ("Get", "Send", "Calculate") with clear scope
Precise @P descriptions: Include format, constraints, and valid values — vague descriptions cause incorrect LLM calls
Safe error handling: Never expose stack traces; return user-friendly error strings
Timeout configuration: Always set .toolExecutionTimeout() for external service calls
Concurrent execution: Enable .executeToolsConcurrently() when tools are independent
Input validation: Validate parameters inside the tool method; return descriptive errors
Permission checks: Perform authorization inside the tool, not at the AI service level
Audit logging: Log tool name, parameters, and execution result for debugging and compliance

Common Issues and Solutions

Issue	Solution
LLM calls non-existent tool	Add `.hallucinatedToolNameStrategy()` returning a safe error message
Tools receive wrong parameters	Refine `@P` descriptions; add `.toolArgumentsErrorHandler()`
Tool execution hangs	Set `.toolExecutionTimeout(Duration.ofSeconds(N))`
Rate limit errors from external API	Add retry logic or rate limiter inside the tool method
LLM ignores tool output	Ensure the tool returns a string the LLM can interpret

See references/error-handling.md for resilience patterns and references/core-patterns.md for parameter and return type details.

Quick Reference

Annotation / API	Purpose
`@Tool`	Marks a method as a callable tool
`@P`	Describes a tool parameter for the LLM
`@ToolMemoryId`	Injects conversation/user ID into the tool
`AiServices.builder()`	Creates AI service with registered tools
`ReturnBehavior.IMMEDIATELY`	Execute tool without waiting for LLM response
`ToolProvider`	Dynamic tool provisioning based on context
`executeToolsConcurrently()`	Run independent tool calls in parallel
`toolExecutionTimeout()`	Timeout for individual tool calls

Constraints and Warnings

Sensitive data: Never pass API keys, passwords, or credentials in @Tool or @P descriptions
Side effects: Tools that modify data should warn in their description; AI models may call them multiple times
Large tool sets: Excessive tools confuse LLM models — use ToolProvider for conditional registration
Blocking operations: Tools should not perform long synchronous I/O without timeout configuration
Stack trace exposure: Always route exceptions through error handlers that return safe strings
Parameter precision: Vague @P descriptions directly cause incorrect tool calls — be specific about formats and constraints
Concurrent safety: Ensure tool classes are stateless or thread-safe when using executeToolsConcurrently()

langchain4j-ai-services-patterns — High-level AI service configuration
langchain4j-rag-implementation-patterns — RAG retrieval with tool integration
langchain4j-spring-boot-integration — Tool registration in Spring Boot applications

References

references/setup-configuration.md — Maven setup, chat model configuration, first tool registration
references/core-patterns.md — Basic tool definition, complex parameters, return types
references/advanced-features.md — Memory context, dynamic tool providers, streaming, immediate return
references/error-handling.md — Error handlers, retry logic, monitoring
references/integration-examples.md — Database, REST API, and context-aware tool examples