New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Langchain4j Spring Boot Integration

by giuseppe-trisciuoglio in giuseppe-trisciuoglio/developer-kit

Provides integration patterns for LangChain4j with Spring Boot.

MITAuto-check: notesBackend & APIs

Install Langchain4j Spring Boot Integration

skills CLI
$ npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-spring-boot-integration -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install giuseppe-trisciuoglio/developer-kit langchain4j-spring-boot-integration --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/developer-kit-java/skills/langchain4j-spring-boot-integration .claude/skills/langchain4j-spring-boot-integration && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
langchain4j-spring-boot-integration
GitHub stars
357
Token cost
~2.3k tokens
SKILL.md length
507 words
Files
4 (incl. references)
Skills in repo
115
Repo updated
First seen
Licence
MIT

At a glance

Provides integration patterns for LangChain4j with Spring Boot.

  • Works in 6 steps: Add Dependencies → Configure Application Properties → Create Declarative AI Service → …
  • Embedding LangChain4j into Spring Boot applications
  • SKILL.md covers When to Use, Overview, Instructions and Configuration, plus 7 more sections
  • Needs OPENAI_API_KEY

What it does

Langchain4j Spring Boot Integration is an agent skill from giuseppe-trisciuoglio/developer-kit. Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/configuration.md`, `references/examples.md` and `references/references.md`).

It sits in Backend & APIs, covering Backend development, Design patterns and Embeddings. It works with Spring Boot and Java. The repository describes itself as: Modular plugin marketplace for Claude Code and agentic CLIs, with validated, spec-driven skills, agents, commands, and workflows for Java, TypeScript, Python, PHP, AWS, and AI. The licence is MIT.

When your agent uses it

  • Embedding LangChain4j into Spring Boot applications
  • Building Java LLM applications with @Bean configuration
  • Setting up Spring AI patterns

Example prompts

  • “Use the langchain4j-spring-boot-integration skill to provide integration patterns for LangChain4j with Spring Boot”
  • “/langchain4j-spring-boot-integration”

Requirements

  • A credential in OPENAI_API_KEY
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash, Glob, Grep

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Add Dependencies
  2. Configure Application Properties
  3. Create Declarative AI Service
  4. Enable Component Scanning
  5. Inject and Use the AI Service
  6. Verify the Integration

What it can do on your machine

Read from SKILL.md and the folder at commit fe73fb3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash
    • Glob
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are java, xml, properties and yaml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENAI_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Langchain4j Spring Boot Integration loads about 2.3k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 113 tokens; SKILL.md has 507 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~113
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~13k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Bash, Glob, Grep

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from giuseppe-trisciuoglio/developer-kit at commit fe73fb3, republished under its MIT licence (© giuseppe-trisciuoglio). 507 words, ~2,284 tokens.

Download SKILL.mdSave it as .claude/skills/langchain4j-spring-boot-integration/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
langchain4j-spring-boot-integration
description
Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.
allowed-tools
Read, Write, Edit, Bash, Glob, Grep

LangChain4j Spring Boot Integration

Integrate LangChain4j with Spring Boot using declarative AI Services, auto-configuration, and Spring Boot starters. Configure AI model beans, set up chat memory, implement RAG pipelines with Spring Data, and build production-ready AI applications.

When to Use

Use this skill when:

  • Integrating LangChain4j into existing Spring Boot applications
  • Building AI-powered microservices with Spring Boot
  • Configuring AI model beans with @Bean annotations
  • Setting up auto-configuration for AI models and services
  • Creating declarative AI Services with Spring dependency injection
  • Implementing RAG systems with Spring Data integrations
  • Setting up chat memory with Spring context management
  • Configuring multiple AI providers (OpenAI, Azure, Ollama, Anthropic)
  • Building production-ready AI applications with Spring Boot

Overview

LangChain4j Spring Boot integration provides declarative AI Services through Spring Boot starters, enabling automatic configuration of AI components based on properties. Combine Spring dependency injection with LangChain4j's AI capabilities using interface-based definitions with annotations.

Instructions

1. Add Dependencies
xml
<!-- Core LangChain4j Spring Boot Starter -->
<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-spring-boot-starter</artifactId>
    <version>1.8.0</version>
</dependency>

<!-- OpenAI Spring Boot Starter -->
<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
    <version>1.8.0</version>
</dependency>
2. Configure Application Properties
properties
# application.properties
langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY}
langchain4j.open-ai.chat-model.model-name=gpt-4o-mini
langchain4j.open-ai.chat-model.temperature=0.7
langchain4j.open-ai.chat-model.timeout=PT60S
langchain4j.open-ai.chat-model.max-tokens=1000

Or using YAML:

yaml
langchain4j:
  open-ai:
    chat-model:
      api-key: ${OPENAI_API_KEY}
      model-name: gpt-4o-mini
      temperature: 0.7
      timeout: 60s
      max-tokens: 1000
3. Create Declarative AI Service
java
import dev.langchain4j.service.spring.AiService;

@AiService
public interface CustomerSupportAssistant {

    @SystemMessage("You are a helpful customer support agent for TechCorp.")
    String handleInquiry(String customerMessage);

    @UserMessage("Translate to {{language}}: {{text}}")
    String translate(String text, String language);
}
4. Enable Component Scanning
java
@SpringBootApplication
@ComponentScan(basePackages = {
    "com.yourcompany",
    "dev.langchain4j.service.spring"
})
public class Application {
    public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }
}
5. Inject and Use the AI Service
java
@Service
public class CustomerService {

    private final CustomerSupportAssistant assistant;

    public CustomerService(CustomerSupportAssistant assistant) {
        this.assistant = assistant;
    }

    public String processCustomerQuery(String query) {
        return assistant.handleInquiry(query);
    }
}
6. Verify the Integration

After setup, verify the configuration:

  1. Start the application and check logs for LangChain4jSpringBootAutoConfiguration activation
  2. Confirm AI service beans are registered: look for CustomerSupportAssistant in Spring context
  3. Test the service: invoke assistant.handleInquiry("test") and verify a response is returned

Configuration

Property-Based Configuration: Configure AI models through application.properties for different providers.

Manual Bean Configuration: For advanced configurations, define beans manually:

java
@Configuration
public class AiConfig {

    @Bean
    public ChatModel chatModel(@Value("${OPENAI_API_KEY}") String apiKey) {
        return OpenAiChatModel.builder()
            .apiKey(apiKey)
            .modelName("gpt-4o-mini")
            .temperature(0.7)
            .build();
    }
}

Multiple Providers: Use explicit wiring when configuring multiple AI providers:

java
@AiService(wiringMode = WiringMode.EXPLICIT)
interface MultiProviderAssistant {
    @AiServiceAnnotation
    ChatModel openAiModel;

    @AiServiceAnnotation
    ChatModel azureModel;
}

Declarative AI Services

Basic AI Service: Create interfaces with @AiService annotation and define methods with message templates.

Streaming AI Service: Implement streaming responses using Project Reactor:

java
@AiService
public interface StreamingAssistant {
    @SystemMessage("You are a helpful assistant.")
    Flux<String> chatStream(String message);
}

Chat Memory: Set up conversation memory with Spring context:

java
@AiService
public interface ConversationalAssistant {
    @SystemMessage("You are a helpful assistant with memory.")
    String chat(@MemoryId String userId, String message);
}

RAG Implementation

Embedding Stores: Configure embedding stores for RAG pipelines with Spring Data:

java
@Configuration
public class RagConfig {

    @Bean
    public EmbeddingStore<TextSegment> embeddingStore() {
        return PgVectorEmbeddingStore.builder()
            .host("localhost")
            .port(5432)
            .database("vectordb")
            .table("embeddings")
            .dimension(1536)
            .build();
    }

    @Bean
    public EmbeddingModel embeddingModel() {
        return OpenAiEmbeddingModel.withApiKey(System.getenv("OPENAI_API_KEY"));
    }
}

@AiService
public interface RagAssistant {
    String answer(@UserMessage("Question: {{question}}") String question);
}

Document Ingestion: Use ContentInjector and DocumentSplitter for processing documents. Content Retrieval: Configure EmbeddingStoreContentRetriever for knowledge augmentation.

Show full SKILL.md (197 more words)Show less

Tool Integration

Spring Component Tools: Define tools as Spring components:

java
@Component
public class Calculator {
    @Tool("Calculate the sum of two numbers")
    public double add(double a, double b) {
        return a + b;
    }
}

@AiService
public interface MathAssistant {
    String solve(String problem);
}

Examples

Basic AI Service
java
@AiService
public interface ChatAssistant {
    @SystemMessage("You are a helpful assistant.")
    String chat(String message);
}
AI Service with Memory
java
@AiService
public interface ConversationalAssistant {
    @SystemMessage("You are a helpful assistant with memory of conversations.")
    String chat(@MemoryId String userId, String message);
}
AI Service with Tools
java
@Component
public class WeatherService {
    @Tool("Get weather for a city")
    public String getWeather(String city) {
        return "Sunny, 22°C in " + city;
    }
}

@AiService
public interface WeatherAssistant {
    String getWeatherForCity(String city);
}

For more examples (including RAG configurations, streaming assistants, and multi-provider setups), refer to references/examples.md.

Best Practices

  • Use Property-Based Configuration: External configuration over hardcoded values
  • Use Profiles: Separate configurations for development, testing, and production
  • Add Proper Logging: Debug AI service calls and monitor performance
  • Implement Retry Mechanisms: Handle transient failures with backoff strategies
  • Monitor Token Usage: Track token consumption and implement limits

References

For detailed API references and advanced configurations:

Constraints and Warnings

  • Store API keys securely using environment variables or secret management systems
  • AI model responses are non-deterministic; tests should account for variability
  • Rate limits may apply to AI providers; implement proper retry and backoff strategies
  • Memory providers store conversation history; implement cleanup for multi-user scenarios
  • Token costs accumulate quickly; monitor usage and implement token limits
  • Streaming responses require proper error handling for partial failures
  • Check provider-specific documentation for supported features
  • Use explicit wiring mode when multiple chat models are configured
  • Validate AI-generated outputs before use in production systems

© giuseppe-trisciuoglio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in plugins/developer-kit-java/skills/langchain4j-spring-boot-integration of giuseppe-trisciuoglio/developer-kit.

  • SKILL.md
  • references/configuration.md
  • references/examples.md
  • references/references.md

Open the folder on GitHubat commit fe73fb3

Compare with similar skills

Langchain4j Spring Boot Integration next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Langchain4j Spring Boot Integration compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Langchain4j Spring Boot Integration this skillgiuseppe-trisciuoglio/developer-kit357—~2.3kAutomated safety check: NotesMIT
Java Rulessoftspark/ai-toolkit179—~3.1kAutomated safety check: PassApache-2.0
Spring Bootpiomin/claude-ai-spring-boot1.3k—~2kAutomated safety check: PassApache-2.0
Dr Jskilljdubois/dr-jskill342—~4.6kAutomated safety check: NotesApache-2.0
WxJava Integration Guidebinarywang/WxJava33k—~123Automated safety check: PassApache-2.0
Flycms Devsunkaifei/FlyCms656—~827Automated safety check: PassMIT

Similar skills

  • Java Rules

    softspark/ai-toolkit

    Java coding rules: style, patterns, security, testing. An agent skill from softspark/ai-toolkit.

    179 GitHub stars~3.1k tokensUpdated 3 days ago
    Backend & APIsAuto-check passed

  • Spring Boot

    piomin/claude-ai-spring-boot

    Spring Boot 3.x development - REST APIs, JPA, Security, Testing, and Cloud-native patterns.

    1.3k GitHub stars~2k tokensUpdated 5 mo ago
    Backend & APIsAuto-check passed

  • Dr Jskill

    jdubois/dr-jskill

    Creates Java + Spring Boot projects: Web applications, full-stack apps with Vue.js or Angular or React or vanilla JS, PostgreSQL, REST APIs, and Docker.

    342 GitHub stars~4.6k tokensUpdated 11 days ago
    Backend & APIsAuto-check: notes

  • WxJava Integration Guide

    binarywang/WxJava

    Plans a WxJava setup for Java, Spring Boot or Solon projects that call WeChat services, from module and BOM choice to config and a minimal working call.

    33k GitHub stars~123 tokensUpdated yesterday
    Backend & APIsAuto-check passed

  • Flycms Dev

    sunkaifei/FlyCms

    FlyCms 项目（backend/ Spring Boot 4.1.1 + frontend/ vue-vben-admin v5）的架构地图与开发规范总纲。凡在本仓库做任何开发——写后端接口、新增/修改模块、管理页面、数据库变更、修 bug、重构——都要先加载本 skill 再动手，即使用户只说"改一下""加个功能"；前端登录/菜单/权限专项另见…

    656 GitHub stars~827 tokensUpdated 2 days ago
    Backend & APIsAuto-check passed

  • Reachai Onboarding

    w8123/EnterpriseAgentFramework

    Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff.

    865 GitHub stars~6.1k tokensUpdated 3 days ago
    AI & LLM EngineeringAuto-check passed

More from giuseppe-trisciuoglio/developer-kit

All 115 skills in this repo

  • Nestjs Drizzle Crud Generator

    giuseppe-trisciuoglio/developer-kit

    Generates complete CRUD modules for NestJS applications with Drizzle ORM.

    357 GitHub stars~1.3k tokensUpdated 1 mo ago
    Auto-check: notes

  • Spring Boot Actuator

    giuseppe-trisciuoglio/developer-kit

    Provides patterns to configure Spring Boot Actuator for production-grade monitoring, health probes, secured management endpoints, and Micrometer metrics across JVM services.

    357 GitHub stars~2.2k tokensUpdated 1 mo ago
    Auto-check: notes

  • Spring Boot Crud Patterns

    giuseppe-trisciuoglio/developer-kit

    Provides and generates complete CRUD workflows for Spring Boot 3 services.

    357 GitHub stars~2.5k tokensUpdated 1 mo ago
    Auto-check: notes

  • Spring Boot Security JWT

    giuseppe-trisciuoglio/developer-kit

    Provides JWT authentication and authorization patterns for Spring Boot 3.5.x covering token generation with JJWT, Bearer/cookie authentication, database/OAuth2 integration, and RBAC/permission-based…

    357 GitHub stars~3.9k tokensUpdated 1 mo ago
    Auto-check: notes

  • AWS CLI Beast

    giuseppe-trisciuoglio/developer-kit

    Provides advanced AWS CLI patterns for managing EC2, Lambda, S3, DynamoDB, RDS, VPC, IAM, and CloudWatch.

    357 GitHub stars~1.7k tokensUpdated 1 mo ago
    Auto-check: notes

  • PR Review Comments

    giuseppe-trisciuoglio/developer-kit

    Posts review findings from a JSON file as inline comments on a GitHub Pull Request, attaching each comment to its file and line.

    357 GitHub stars~1k tokensUpdated 1 mo ago
    Auto-check: notes

Works with

Categories

Questions about Langchain4j Spring Boot Integration

What does Langchain4j Spring Boot Integration do?

Provides integration patterns for LangChain4j with Spring Boot. Langchain4j Spring Boot Integration is an agent skill from giuseppe-trisciuoglio/developer-kit. Provides integration patterns for LangChain4j with Spring Boot.

When should I use Langchain4j Spring Boot Integration?

Langchain4j Spring Boot Integration fits situations like: embedding LangChain4j into Spring Boot applications; building Java LLM applications with @Bean configuration; setting up Spring AI patterns.

How do I install Langchain4j Spring Boot Integration in Claude Code?

Run `npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-spring-boot-integration -a claude-code`. Or copy the skill folder (plugins/developer-kit-java/skills/langchain4j-spring-boot-integration in giuseppe-trisciuoglio/developer-kit) into .claude/skills/langchain4j-spring-boot-integration in your project. Claude Code loads it when a task matches its description.

How do I install Langchain4j Spring Boot Integration in Codex?

Run `npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-spring-boot-integration -a codex`. Or copy the skill folder (plugins/developer-kit-java/skills/langchain4j-spring-boot-integration in giuseppe-trisciuoglio/developer-kit) into .agents/skills/langchain4j-spring-boot-integration in your project. Codex loads it when a task matches its description.

Can I use Langchain4j Spring Boot Integration in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-spring-boot-integration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/langchain4j-spring-boot-integration, .gemini/skills/langchain4j-spring-boot-integration, .github/skills/langchain4j-spring-boot-integration and .opencode/skills/langchain4j-spring-boot-integration in your project.

What does Langchain4j Spring Boot Integration need to run?

Going by SKILL.md and its folder, Langchain4j Spring Boot Integration needs credentials named OPENAI_API_KEY. Our summary lists: A credential in OPENAI_API_KEY. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash, Glob, Grep.

Does Langchain4j Spring Boot Integration access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Langchain4j Spring Boot Integration safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Langchain4j Spring Boot Integration use?

Langchain4j Spring Boot Integration is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Langchain4j Spring Boot Integration use?

About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.

What are the alternatives to Langchain4j Spring Boot Integration?

Skills that share tags, products or a category with Langchain4j Spring Boot Integration: Java Rules (softspark/ai-toolkit, 179 stars), Spring Boot (piomin/claude-ai-spring-boot, 1.3k stars), Dr Jskill (jdubois/dr-jskill, 342 stars) and WxJava Integration Guide (binarywang/WxJava, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Langchain4j Spring Boot Integration?

giuseppe-trisciuoglio (a GitHub user) maintains it in giuseppe-trisciuoglio/developer-kit, which has 357 GitHub stars. The repository holds 115 skills in this directory. The repository was last updated on September 10, 2026.

Source: giuseppe-trisciuoglio/developer-kit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.