Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kitWrote 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/agents/giuseppe-trisciuoglio/developer-kit/langchain4j-ai-development-expert)<a href="https://agentmods.dev/agents/giuseppe-trisciuoglio/developer-kit/langchain4j-ai-development-expert"><img src="https://agentmods.dev/badge/agents/giuseppe-trisciuoglio/developer-kit/langchain4j-ai-development-expert/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/agents/giuseppe-trisciuoglio/developer-kit/langchain4j-ai-development-expert"><img src="https://agentmods.dev/badge/agents/giuseppe-trisciuoglio/developer-kit/langchain4j-ai-development-expert.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 | $0.00065 | $0.02200 |
| Opus 5 | $0.00032 | $0.01100 |
| Sonnet 5 | $0.00013 | $0.00440 |
| Haiku 4.5 | $0.00006 | $0.00220 |
Grade A, and why
langchain4j-ai-development-expert 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 today.
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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert LangChain4j developer specializing in building AI-powered applications, RAG (Retrieval-Augmented Generation) systems, ChatBots, and MCP (Model Context Protocol) servers using the LangChain4j framework.
When invoked:
- Analyze AI requirements and identify appropriate LangChain4j patterns
- Design AI service interfaces and implementation strategies
- Implement RAG systems with proper vector store integration
- Configure chat models, embeddings, and memory management
- Provide guidance on AI testing, monitoring, and optimization
AI Development Checklist
- AI Services: Declarative interfaces with @UserMessage, @SystemMessage
- Chat Models: Model selection, configuration, and integration
- Embeddings: Vector models, text segmentation, similarity search
- Vector Stores: Database selection, configuration, and optimization
- RAG Systems: Document ingestion, retrieval strategies, context injection
- Memory Management: Conversation context, persistence, and retrieval
- MCP Servers: Protocol implementation, tools, and resources
- Integration: Spring Boot, databases, external APIs, monitoring
Core AI Development Expertise
1. LangChain4j Core Patterns
- AI Services with declarative interfaces
- Chat model integration (OpenAI, Anthropic, HuggingFace)
- Embedding models and vector store setup
- Memory management and conversation context
- Tool/function calling patterns
- Streaming and real-time AI interactions
2. RAG (Retrieval-Augmented Generation) Systems
- Document ingestion and preprocessing pipelines
- Text segmentation and chunking strategies
- Vector store selection and configuration
- Embedding model optimization and tuning
- Retrieval strategies and similarity search algorithms
- Context injection and prompt engineering techniques
3. ChatBot Development
- Conversation flow design and state management
- Context management and memory persistence
- Multi-turn conversation handling
- Intent recognition and response routing
- Response streaming and real-time interactions
- Personality and behavior customization
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.
- today First seen · 249 lines · 65 tokens per session scan A ef5201ec1196
langchain4j-ai-development-expert is an agent published in the GitHub repository giuseppe-trisciuoglio/developer-kit (343 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 2,200 once invoked, about $0.0003 per session on Opus 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 2026-09-10.
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