LLM Application Dev
PluginPlugin for building LLM apps: LangGraph agents with memory and tool use, hybrid-search RAG, vector index tuning, prompt optimization and LLM evaluation. Includes 3 subagents and 8 skills.
LLM Application Development Plugin for Claude Code
Build production-ready LLM applications, advanced RAG systems, and intelligent agents with modern AI patterns.
Version 2.0.0 Highlights
- LangGraph Integration: Updated from deprecated LangChain patterns to LangGraph StateGraph workflows
- Modern Model Support: Claude Opus 4.8/Sonnet 5/Haiku 4.5 and GPT-5.4/GPT-5-mini
- Voyage AI Embeddings: Recommended embedding models for Claude applications
- Structured Outputs: Pydantic-based structured output patterns
Features
Core Capabilities
- RAG Systems: Production retrieval-augmented generation with hybrid search
- Vector Search: Pinecone, Qdrant, Weaviate, Milvus, pgvector optimization
- Agent Architectures: LangGraph-based agents with memory and tool use
- Prompt Engineering: Advanced prompting techniques with model-specific optimization
Key Technologies
- LangChain 1.x / LangGraph for agent workflows
- Voyage AI, OpenAI, and open-source embedding models
- HNSW, IVF, and Product Quantization index strategies
- Async patterns with checkpointers for durable execution
Agents
| Agent | Description |
|---|---|
ai-engineer | Production-grade LLM applications, RAG systems, and agent architectures |
prompt-engineer | Advanced prompting techniques, constitutional AI, and model optimization |
vector-database-engineer | Vector search implementation, embedding strategies, and semantic retrieval |
Skills
| Skill | Description |
|---|---|
langchain-architecture | LangGraph StateGraph patterns, memory, and tool integration |
rag-implementation | RAG systems with hybrid search and reranking |
llm-evaluation | Evaluation frameworks for LLM applications |
prompt-engineering-patterns | Chain-of-thought, few-shot, and structured outputs |
embedding-strategies | Embedding model selection and optimization |
similarity-search-patterns | Vector similarity search implementation |
vector-index-tuning | HNSW, IVF, and quantization optimization |
hybrid-search-implementation | Vector + keyword search fusion |
Commands
| Command | Description |
|---|---|
/llm-application-dev:langchain-agent | Create LangGraph-based agent |
/llm-application-dev:ai-assistant | Build AI assistant application |
/llm-application-dev:prompt-optimize | Optimize prompts for production |
Installation
/plugin install llm-application-dev
Requirements
- LangChain >= 1.2.0
- LangGraph >= 0.3.0
- Python 3.11+
Changelog
2.0.0 (January 2026)
- Breaking: Migrated from LangChain 0.x to LangChain 1.x/LangGraph
- Breaking: Updated model references to Claude 4.6 and GPT-5.4
- Added Voyage AI as primary embedding recommendation for Claude apps
- Added LangGraph StateGraph patterns replacing deprecated
initialize_agent() - Added structured outputs with Pydantic
- Added async patterns with checkpointers
- Fixed security issue: replaced unsafe code execution with AST-based safe math evaluation
- Updated hybrid search with modern Pinecone client API
1.2.2
- Minor bug fixes and documentation updates
License
MIT License - See the plugin configuration for details.
Ressources associées
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Pinecone Plugin for Claude Code
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Qdrant MCP Server
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Chroma MCP Server
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Agent Skills for Context Engineering
PluginContext and harness engineering skills covering context compression and optimization, multi-agent architectures, memory systems, tool design and LLM-as-judge evaluation, activated automatically by task. Aimed at building production-grade agent systems.
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Agent SDK Dev
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