Loading...
Loading...
Found 90 Skills
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling
Tool and function calling patterns with LangChain4j. Define tools, handle function calls, and integrate with LLM agents. Use when building agentic applications that interact with tools.
Build declarative AI Services with LangChain4j using interface-based patterns, annotations, memory management, tools integration, and advanced application patterns. Use when implementing type-safe AI-powered features with minimal boilerplate code in Java applications.
Model Context Protocol (MCP) server implementation patterns with LangChain4j. Use when building MCP servers to extend AI capabilities with custom tools, resources, and prompt templates.
You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.
Build LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.
Build and deploy AI agents with Cloudbase Agent (TypeScript), a TypeScript SDK implementing the AG-UI protocol. Use when: (1) deploying agent servers with @cloudbase/agent-server, (2) using LangGraph adapter with ClientStateAnnotation, (3) using LangChain adapter with clientTools(), (4) building custom adapters that implement AbstractAgent, (5) understanding AG-UI protocol events, (6) building web UI clients with @ag-ui/client, (7) building WeChat Mini Program UIs with @cloudbase/agent-ui-miniprogram.
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.
Builds LLM applications with LangChain including chains, agents, memory, tools, and RAG pipelines. Use when users request "LangChain setup", "LLM chain", "AI workflow", "conversational AI", or "RAG pipeline".
Generates valid n8n workflow JSON with nodes, connections, settings, credentials. Use when creating workflow automations programmatically, scaffolding AI agent workflows with LangChain nodes, or converting requirements into n8n JSON.