Borrowing it
Nothing to install: this file belongs to quarkusio/quarkus-workshop-langchain4j. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/quarkusio/quarkus-workshop-langchain4j/main/AGENTS.mdgit clone --depth 1 https://github.com/quarkusio/quarkus-workshop-langchain4jWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/quarkusio/quarkus-workshop-langchain4j/agents-md)<a href="https://agentmods.dev/instructions/quarkusio/quarkus-workshop-langchain4j/agents-md"><img src="https://agentmods.dev/badge/instructions/quarkusio/quarkus-workshop-langchain4j/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/quarkusio/quarkus-workshop-langchain4j/agents-md"><img src="https://agentmods.dev/badge/instructions/quarkusio/quarkus-workshop-langchain4j/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.02250 | $0.02250 |
| Opus 5.5 | $0.00900 | $0.00900 |
| Sonnet 5.5 | $0.00450 | $0.00450 |
| Haiku 4.5 | $0.00225 | $0.00225 |
Grade A, and why
quarkus-workshop-langchain4j AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 28d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This file provides guidance to agents when working with code in this repository.
Project Overview
This repository contains a comprehensive, hands-on workshop for building AI-infused applications and agentic systems using Quarkus and LangChain4j. The workshop teaches developers how to integrate Large Language Models into Quarkus applications, build intelligent chatbots with structured outputs and guardrails, implement Retrieval-Augmented Generation (RAG) patterns, use remote tools via Model Context Protocol (MCP), design agentic systems with workflow and supervisor patterns, and build enterprise-grade trip planning systems with advanced orchestration patterns.
The workshop follows the Miles of Smiles car rental company across three sections that together tell a complete customer journey: Section 1 builds a customer-facing support chatbot (before the trip), Section 2 handles fleet operations behind the scenes (after the trip), and Section 3 delivers an intelligent trip planning experience (planning the trip).
Technology Stack
The workshop uses Java 21 with the latest stable Quarkus release and the corresponding LangChain4j Quarkiverse extension. Versions are kept current and should not be hardcoded in documentation or agent instructions — check the individual step pom.xml files for the actual versions in use. Maven handles the build process, while the documentation is built with MkDocs using Python and Pipenv. The UI components leverage Vaadin Web Components and wc-chatbot for the chat interface.
Project Structure
This is a multi-module Maven project organized into three sections. The first section contains 11 steps focused on AI-infused applications, covering topics from basic LLM integration and AI Services through prompt engineering, structured outputs, guardrails, RAG patterns, MCP integration, and observability. These steps are located in section-1/step-XX/ directories, with the final state available in section-1/step-11/.
The second section contains 9 steps dedicated to agentic systems, exploring agentic workflows, multi-agent collaboration, supervisor patterns, and Agent-to-Agent (A2A) communication. Steps 01–07 are located in section-2/step-XX/ directories. Step 09 is a bonus Kubernetes/OpenShift deployment step (section-2/step-09/) that uses a JBang script rather than a Maven module — it deploys the Section 2 multi-agent system and remote A2A agent to a cluster with a single command.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 28d ago Changed · +4 lines · +194 tokens per session b3acf63be1d8
- 1mo ago Changed · +3 lines · +58 tokens per session a00c239f63c5
- 1mo ago Changed · +3 tokens per session 5aa3be1b19a5
- 1mo ago First seen · 114 lines · 1,995 tokens per session scan A 59b2247c7866
quarkus-workshop-langchain4j AGENTS.md is an instructions file published in the GitHub repository quarkusio/quarkus-workshop-langchain4j (112 stars, last pushed yesterday), licensed Apache-2.0. It adds 2,250 tokens to every session, about $0.0090 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
vscode model-prompts.instructions.md
Model-specific prompt authoring and registry guidelines.