LangChain Application Developer

Expert guidance for building production-grade LLM applications with LangChain. Covers chain composition with LCEL, RAG pipelines, agent systems, LangGraph workflows, prompt engineering, and production deployment with LangSmith observability.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

84.6

Research Quality (15%)

8.4

Prompt Engineering (25%)

8.6

Practical Utility (15%)

8.5

Completeness (10%)

8.3

User Satisfaction (20%)

8.5

Decision Usefulness (15%)

8.3

Best for

▸Building RAG pipelines for enterprise document Q&A systems
▸Creating multi-agent workflows with tool-using capabilities in LangGraph
▸Implementing LCEL chains for structured data extraction from unstructured text
▸Designing production LLM applications with proper observability and cost controls
▸Optimizing retrieval systems with hybrid search and reranking strategies

What you'll get

●Complete LCEL pipeline code with retrieval, reranking, and structured output parsing for a legal document analysis system
●Production-ready LangGraph workflow definition with conditional routing, tool integration, and error recovery for customer support automation
●Comprehensive deployment configuration including Docker setup, environment variables, LangSmith tracing, and cost monitoring dashboards

Not designed for ↓

×Training or fine-tuning language models from scratch
×Building custom transformer architectures or neural network layers
×General Python programming unrelated to LLM applications
×Non-LangChain AI frameworks like AutoGPT or CrewAI

Expects

Clear application requirements including input/output formats, retrieval needs, reasoning complexity, and production constraints like latency and cost budgets.

Returns

Complete LangChain application code with LCEL chains, proper error handling, production deployment configurations, and LangSmith observability setup.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

langchainllmraglcellanggraphlangsmithagentsretrievalvector-searchprompt-engineeringai-orchestrationpython

Research Foundation: 8 sources (4 official docs, 1 paper, 1 industry frameworks, 1 books, 1 web)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Works well with

LLM Observability EngineerGold Multi-Agent OrchestratorPlatinum Prompt Engineering StrategistGold RAG Architecture DesignerGold Vector Database OptimizationGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

LLM Fine-Tuning StrategistGold PyTorch Deep Learning EngineerPlatinum Transformer Architecture ExpertGold

Common Workflows

Enterprise RAG Deployment Pipeline

Design RAG system architecture, implement with LangChain, optimize vector search performance, and deploy with full observability

RAG Architecture Designer→langchain-application-developer→Vector Database Optimization→LLM Observability Engineer

Activate this skill in Claude Code

Start Free to Activate This Skill