langchain-ai/langchain-fundamentals
Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.
Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.
npx skills add https://github.com/langchain-ai/langchain-skills/tree/main/skills/langchain-fundamentalsCreate LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.
This repo contains 11 individual skills — each has its own dedicated page.
INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.
INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.
INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.
INVOKE THIS SKILL at the START of any LangChain/LangGraph/Deep Agents project, before writing any agent code. Determines which framework layer is right for the task: LangChain, LangGraph, Deep Agents, or a combination. Must be consulted before other agent skills.
INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.
INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output. Covers HumanInTheLoopMiddleware for human approval of dangerous tool calls, creating custom middleware with hooks, Command resume patterns, and structured output with Pydantic/Zod.
INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).
INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.
INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph. Covers interrupt(), Command(resume=...), approval/validation workflows, and the 4-tier error handling strategy.
INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping. Covers checkpointers, thread_id, time travel, Store, and subgraph persistence modes.
Dispatches many independent items in parallel: create a table, fan out to subagents, aggregate results. One row = one unit of work.
Forge-focused engineering workflow for Rust applications with generated frontend bindings. Activate this skill for repositories containing a `forge.toml` file, Forge macros, or code generated by the Forge CLI.
See what your AI agent is doing, from anywhere. The agent keeps writing — logs, code, generated outputs, screenshots, artifacts. One command turns the folder into a live URL you (or a teammate) open in any browser to watch files evolve, edit in place, or comment — no sync, no zip, no account. Workspaces stay live 24 hours anonymously; one email claim keeps them permanent. Hosted on Cloudflare. Triggers on "show me what the agent is doing", "open the agent's folder", "share this folder", "give me a link", "hand off this workspace", or any ask to make an agent's local file state visible from another device or to another person.
Claude Code / Cowork skill that auto-logs research, decisions, project progress, meetings, and daily notes into your Obsidian vault. For PMs, marketers, researchers, and second-brain users.
Reusable AI-agent skills for systematic reviews, manuscript revision, reviewer response, and academic workflow automation.
Provides and generates LangChain4j tool and function calling patterns: annotates methods as tools with @Tool, configures tool executors, registers tools with AiServices, validates tool parameters, and handles tool execution errors. Use when building AI agents that call tools, define function specifications, manage tool responses, or integrate external APIs with LLM-driven applications.
Enable AI agents to control Windows tasks using PowerShell skills for Outlook, Edge, desktop automation, and structured shell commands in JSON format.