Java Ai Cookbook

alxkm3 SterneDiesen Server beanspruchen

Runnable, minimal LLM examples for Java - Spring AI & LangChain4j. RAG, agents, MCP servers, tool calling, structured output, evals. One folder = one command.

Knowledge Memory

Installieren Auf PlugMCP ausführenDiesen Server beanspruchen

GitHub

Auf PlugMCP ausführen

1# Java Ai Cookbook

README.mdMCP-Server

Transport: stdio
Status: Lokal

Agenten stärken

Diesen Server hinzufügen

Installation vom Herausgeber. Kein PlugMCP-Endpunkt.

Auf PlugMCP ausführen

Host a copy with our keys and logs.

Noch kein paketiertes Release in der Registry. Folgen Sie der README des Repositorys.

RepositoryInstallation vom HerausgeberSchritt für Schritt: in Claude hinzufügen

Remote-URL oder ein Befehl auf Ihrem Rechner? Worin sich die beiden Installationen unterscheiden

Auf PlugMCP ausführen

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Starten

Server-Details

Lokal

Transport: stdio
Lizenz: MIT
Zuletzt geprüft: 2026-10-28

Sterne: 3
Tools: —
Uptime: —

Tools

Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.

FAQ

Fragen

Wie installiere ich Java Ai Cookbook?

Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.

Benötigt Java Ai Cookbook einen API-Schlüssel?

In diesem Eintrag ist keine erforderliche Umgebungsvariable aufgeführt.

Welchen Transport verwendet Java Ai Cookbook?

Java Ai Cookbook läuft lokal über stdio.

Wann wurde Java Ai Cookbook zuletzt geprüft?

Zuletzt geprüft: 2026-10-28. Runnable, minimal LLM examples for Java - Spring AI & LangChain4j. RAG, agents, MCP servers, tool calling, structured output, evals. One folder = one command.

Vom Herausgeber

Dokumentation

java-ai-cookbook

Runnable, minimal LLM examples for Java - Spring AI & LangChain4j. RAG, agents, MCP servers, tool calling, structured output, evals.

One folder = one command. Every example is isolated, has its own build, and runs in under a minute.

Why this exists

Most "AI in Java" examples are either a single hello-world or a full demo app with 40 dependencies. This repo sits in between: each recipe shows one pattern, in one file where possible, with no framework magic hidden.

Same pattern implemented in both Spring AI and LangChain4j, so you can compare them honestly
Works with OpenAI, Anthropic, and local models via Ollama - switch with an env var
Every recipe has a test, so the examples don't silently rot

Quick start

git clone https://github.com/alxkm/java-ai-cookbook.git
cd java-ai-cookbook/spring-ai/01-chat-basic

export OPENAI_API_KEY=sk-...        # or: export OLLAMA_BASE_URL=http://localhost:11434
./mvnw spring-boot:run

That's it. Open the folder of any recipe and run the command in its README.

Recipes

#	Pattern	Spring AI	LangChain4j	What you learn
01	Chat basics	→	→	ChatClient / ChatModel, system prompt, model switching
02	Streaming	→	→	Token streaming to console and SSE endpoint
03	Structured output	→	→	JSON → Java record, schema enforcement, retries
04	Tool calling	→	→	Expose Java methods as tools, multi-step calls
05	Conversation memory	→	→	In-memory vs persisted history, token windows
06	Embeddings	→	→	Embedding models, similarity, batching
07	RAG - minimal	→	→	Load → split → embed → retrieve → answer
08	RAG - pgvector	→	→	Production vector store with Testcontainers
09	RAG - advanced	→	→	Query rewriting, re-ranking, metadata filters
10	Agent - ReAct loop	→	→	Planning, tool selection, stopping conditions
11	Multi-agent	→	→	Orchestrator + workers, hand-offs
12	MCP client	→	→	Consume tools from an MCP server
13	MCP server	→	→	Expose your Java service as an MCP server
14	Multimodal	→	→	Images in, text out
15	Guardrails	→	→	Input/output validation, PII, prompt injection
16	Observability	→	→	Tracing, token/cost metrics, Micrometer
17	Evals	→	→	LLM-as-judge, regression tests for prompts
18	Local models	→	→	Ollama, no API key, offline
19	Retry & rate limits	→	→	429s, backoff with jitter, a wall-clock budget
20	Progressive tool disclosure	→	→	Hundreds of tools without hundreds of tool definitions

More recipes are added regularly - see open issues for what's next, or propose one.

Docs

Spring AI vs LangChain4j - a side-by-side comparison written after implementing all 20 patterns twice, with links into the code for every claim.
Choosing a model - which model for which recipe, what it costs, and what breaks when you go smaller.
CONTRIBUTING.md - the recipe template and the conventions.

Repository layout

java-ai-cookbook/
├── spring-ai/
│   ├── 01-chat-basic/          # independent Maven project
│   │   ├── README.md           # what it shows, how to run, what to look at
│   │   ├── pom.xml
│   │   └── src/
│   └── ...
├── langchain4j/
│   └── ...                     # same numbering, same patterns
├── docs/
│   ├── spring-ai-vs-langchain4j.md   # honest side-by-side comparison
│   └── choosing-a-model.md
├── .github/workflows/build.yml       # one CI job per recipe, no API keys
├── tools/                            # run-all-tests, link and config-key checks
├── CONTRIBUTING.md
└── .env.example

Each recipe README follows the same shape: What / Run / Where to look / Gotchas.

Configuration

All recipes read the same environment variables:

Variable	Purpose
`OPENAI_API_KEY`	Use OpenAI models (default provider)
`ANTHROPIC_API_KEY`	Use Anthropic models
`OLLAMA_BASE_URL`	Use a local Ollama instance
`AI_PROVIDER`	`openai` \| `anthropic` \| `ollama` - overrides auto-detection
`EMBEDDING_PROVIDER`	`openai` \| `ollama` - used by the embedding and RAG recipes, because Anthropic has no embedding endpoint

Copy .env.example to .env and fill in what you have. Nothing else is required.

Requirements

Java 21+
Docker (only for recipes marked with pgvector / Testcontainers)
One of: an OpenAI key, an Anthropic key, or Ollama running locally

Contributing

Recipes are welcome. Keep them small, keep them runnable, keep them tested. See CONTRIBUTING.md for the recipe template.

License

MIT

Java Ai Cookbook

alxkm3 SterneDiesen Server beanspruchen

Runnable, minimal LLM examples for Java - Spring AI & LangChain4j. RAG, agents, MCP servers, tool calling, structured output, evals. One folder = one command.

Knowledge Memory

Installieren Auf PlugMCP ausführenDiesen Server beanspruchen

GitHub

Auf PlugMCP ausführen

1# Java Ai Cookbook

README.mdMCP-Server

Transport: stdio
Status: Lokal

Agenten stärken

Diesen Server hinzufügen

Installation vom Herausgeber. Kein PlugMCP-Endpunkt.

Auf PlugMCP ausführen

Host a copy with our keys and logs.

Noch kein paketiertes Release in der Registry. Folgen Sie der README des Repositorys.

RepositoryInstallation vom HerausgeberSchritt für Schritt: in Claude hinzufügen

Remote-URL oder ein Befehl auf Ihrem Rechner? Worin sich die beiden Installationen unterscheiden

Auf PlugMCP ausführen

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Starten

Server-Details

Lokal

Transport: stdio
Lizenz: MIT
Zuletzt geprüft: 2026-10-28

Sterne: 3
Tools: —
Uptime: —

Tools

Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.

FAQ

Fragen

Wie installiere ich Java Ai Cookbook?

Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.

Benötigt Java Ai Cookbook einen API-Schlüssel?

In diesem Eintrag ist keine erforderliche Umgebungsvariable aufgeführt.

Welchen Transport verwendet Java Ai Cookbook?

Java Ai Cookbook läuft lokal über stdio.

Wann wurde Java Ai Cookbook zuletzt geprüft?

Zuletzt geprüft: 2026-10-28. Runnable, minimal LLM examples for Java - Spring AI & LangChain4j. RAG, agents, MCP servers, tool calling, structured output, evals. One folder = one command.

Vom Herausgeber

Dokumentation

java-ai-cookbook

Runnable, minimal LLM examples for Java - Spring AI & LangChain4j. RAG, agents, MCP servers, tool calling, structured output, evals.

One folder = one command. Every example is isolated, has its own build, and runs in under a minute.

Why this exists

Same pattern implemented in both Spring AI and LangChain4j, so you can compare them honestly
Works with OpenAI, Anthropic, and local models via Ollama - switch with an env var
Every recipe has a test, so the examples don't silently rot

Quick start

git clone https://github.com/alxkm/java-ai-cookbook.git
cd java-ai-cookbook/spring-ai/01-chat-basic

export OPENAI_API_KEY=sk-...        # or: export OLLAMA_BASE_URL=http://localhost:11434
./mvnw spring-boot:run

That's it. Open the folder of any recipe and run the command in its README.

Recipes

#	Pattern	Spring AI	LangChain4j	What you learn
01	Chat basics	→	→	ChatClient / ChatModel, system prompt, model switching
02	Streaming	→	→	Token streaming to console and SSE endpoint
03	Structured output	→	→	JSON → Java record, schema enforcement, retries
04	Tool calling	→	→	Expose Java methods as tools, multi-step calls
05	Conversation memory	→	→	In-memory vs persisted history, token windows
06	Embeddings	→	→	Embedding models, similarity, batching
07	RAG - minimal	→	→	Load → split → embed → retrieve → answer
08	RAG - pgvector	→	→	Production vector store with Testcontainers
09	RAG - advanced	→	→	Query rewriting, re-ranking, metadata filters
10	Agent - ReAct loop	→	→	Planning, tool selection, stopping conditions
11	Multi-agent	→	→	Orchestrator + workers, hand-offs
12	MCP client	→	→	Consume tools from an MCP server
13	MCP server	→	→	Expose your Java service as an MCP server
14	Multimodal	→	→	Images in, text out
15	Guardrails	→	→	Input/output validation, PII, prompt injection
16	Observability	→	→	Tracing, token/cost metrics, Micrometer
17	Evals	→	→	LLM-as-judge, regression tests for prompts
18	Local models	→	→	Ollama, no API key, offline
19	Retry & rate limits	→	→	429s, backoff with jitter, a wall-clock budget
20	Progressive tool disclosure	→	→	Hundreds of tools without hundreds of tool definitions

More recipes are added regularly - see open issues for what's next, or propose one.

Docs

Spring AI vs LangChain4j - a side-by-side comparison written after implementing all 20 patterns twice, with links into the code for every claim.
Choosing a model - which model for which recipe, what it costs, and what breaks when you go smaller.
CONTRIBUTING.md - the recipe template and the conventions.

Repository layout

java-ai-cookbook/
├── spring-ai/
│   ├── 01-chat-basic/          # independent Maven project
│   │   ├── README.md           # what it shows, how to run, what to look at
│   │   ├── pom.xml
│   │   └── src/
│   └── ...
├── langchain4j/
│   └── ...                     # same numbering, same patterns
├── docs/
│   ├── spring-ai-vs-langchain4j.md   # honest side-by-side comparison
│   └── choosing-a-model.md
├── .github/workflows/build.yml       # one CI job per recipe, no API keys
├── tools/                            # run-all-tests, link and config-key checks
├── CONTRIBUTING.md
└── .env.example

Each recipe README follows the same shape: What / Run / Where to look / Gotchas.

Configuration

All recipes read the same environment variables:

Variable	Purpose
`OPENAI_API_KEY`	Use OpenAI models (default provider)
`ANTHROPIC_API_KEY`	Use Anthropic models
`OLLAMA_BASE_URL`	Use a local Ollama instance
`AI_PROVIDER`	`openai` \| `anthropic` \| `ollama` - overrides auto-detection
`EMBEDDING_PROVIDER`	`openai` \| `ollama` - used by the embedding and RAG recipes, because Anthropic has no embedding endpoint

Copy .env.example to .env and fill in what you have. Nothing else is required.

Requirements

Java 21+
Docker (only for recipes marked with pgvector / Testcontainers)
One of: an OpenAI key, an Anthropic key, or Ollama running locally

Contributing

Recipes are welcome. Keep them small, keep them runnable, keep them tested. See CONTRIBUTING.md for the recipe template.

License

MIT