llm-application-dev-langchain-agent
Build Production LangChain Agents with Claude
Building production-grade LangChain agents requires expertise in async patterns, state management, memory systems, and deployment infrastructure. This skill provides proven patterns and best practices for creating scalable, observable AI applications with LangChain 0.1+ and LangGraph.
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Tester
Utilisation de "llm-application-dev-langchain-agent". Create a ReAct agent that searches the web and summarizes results
RĂ©sultat attendu:
Generated complete Python code with Claude LLM initialization, Tavily search tool integration, StructuredTool with error handling, LangSmith tracing setup, and async invoke pattern with retry logic.
Utilisation de "llm-application-dev-langchain-agent". Build a RAG system with Pinecone and conversation memory
RĂ©sultat attendu:
Implemented VoyageAI embeddings configuration, PineconeVectorStore with hybrid search parameters, ConversationTokenBufferMemory integration, and retrieval chain with context compression.
Audit de sécurité
SûrStatic analysis detected 39 patterns flagged as potential security risks, all are false positives. The 'external_commands' findings are markdown code fence backticks (lines 27-237) used for Python code examples, not Ruby shell execution. The 'blocker' findings flag 'Weak cryptographic algorithm' but these are legitimate Anthropic model names (claude-sonnet-4-5). The 'network' finding is an HTTP client reference in a code example. This is a documentation-only skill with no executable code.
Score de qualité
Ce que vous pouvez construire
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Essayez ces prompts
Create a LangChain ReAct agent using Claude Sonnet 4.5 that can [task description]. Include async patterns, error handling with tenacity retry, and LangSmith tracing.
Design a RAG pipeline using Voyage AI embeddings and Pinecone vector store. Implement hybrid search with reranking using Cohere. Include conversational memory with token buffering.
Create a LangGraph multi-agent system with a supervisor that routes between [agent types]. Use Command routing, implement state management, and add checkpointers for conversation history.
Generate a FastAPI server with streaming endpoints for LangChain agent deployment. Include Prometheus metrics, structured logging with structlog, health checks, and Redis caching with TTL.
Bonnes pratiques
- Always use async patterns (ainvoke, astream) for better performance and concurrency
- Implement comprehensive error handling with tenacity retry and exponential backoff
- Use LangSmith tracing for all agent executions to debug and optimize performance
Éviter
- Avoid synchronous invoke calls in production - always use async variants
- Never hardcode API keys or secrets - use environment variables with proper validation
- Do not skip memory management - unbounded conversation history will cause token overflow