# Langchain > Build, modify, review, test, and debug LangChain.js, LangGraph.js, and LangSmith TypeScript applications. Use this skill whenever the user mentions LangChain, LangChain TypeScript, LangChain.js, LangGraph TypeScript, LangGraph.js, LangSmith tracing/evals, LCEL chains, chat models, tools, retrievers, vector stores, agents, structured output, streaming, memory, checkpointers, Runnable interfaces, or migration of LLM code to LangChain in a TypeScript/Node.js project. Use when this capability is needed. - Skill: `tomevault-io/langchain-9` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/langchain-9` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/langchain-9/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/langchain-9 --- # LangChain TypeScript Use this skill to produce production-ready LangChain TypeScript code with strict typing, runtime validation, testability, and current LangChain ecosystem patterns. ## First Steps 1. Identify whether the task is about LangChain core, LangGraph, LangSmith, deployment, tracing, evals, or documentation lookup. 2. Inspect the local project before editing: package manager, installed `@langchain/*` packages, TypeScript settings, test framework, and runtime target. 3. Read `llms.txt` when the user asks for current docs, API details, LangSmith endpoints, LangGraph platform behavior, or doc links. 4. Read `references/typescript-patterns.md` before writing or reviewing TypeScript implementation patterns. 5. Prefer focused changes that preserve existing architecture and keep examples runnable in the user's repository. ## Implementation Principles - Use TypeScript strict mode patterns. Do not introduce `any`, non-null assertions, or type assertions to silence errors. - Validate external inputs with `zod`, then derive types with `z.infer`. - Keep model/provider configuration at the boundary. Do not hard-code API keys, model names, project names, or LangSmith secrets. - Use LangChain message classes and runnable interfaces consistently instead of passing loosely typed objects through the chain. - Keep chain construction separate from invocation so tests can inject fake models, retrievers, tools, or checkpointers. - Prefer small pure helpers for prompt formatting, output parsing, and document mapping. - Add focused tests for parsing, branching, tool routing, retriever behavior, graph state transitions, and error boundaries. ## Common Workflows ### Chat Model Or Chain - Verify the installed package for the provider, such as `@langchain/openai`, `@langchain/anthropic`, or `@langchain/deepseek`. - Build prompts with `ChatPromptTemplate` when message roles matter. - Return a typed result instead of leaking provider-specific response objects across the application. - For structured output, define a `zod` schema and bind it through the model's structured-output support when available. ### Tools And Agents - Define every tool input with a `zod` schema. - Keep tool side effects explicit and injectable, especially database, filesystem, HTTP, email, or queue access. - Treat tool outputs as untrusted if they cross an external boundary; validate or normalize them before feeding them back into the model. - Add tests for tool schema rejection and the happy path. ### Retrieval - Separate document loading, chunking, embedding, vector-store writes, and query-time retrieval. - Preserve source metadata and normalize document IDs so answers can cite where context came from. - Test retriever adapters with fake documents before wiring real vector stores. - Avoid loading large documents in request handlers; use ingestion jobs or startup-safe workflows. ### LangGraph - Model state with precise TypeScript types and reducers where state is accumulated. - Keep nodes small: each node should read typed state and return a partial state update. - Make routing functions deterministic and exhaustively tested. - Inject checkpointers and stores so local tests do not require production persistence. ### LangSmith - Use LangSmith for tracing, datasets, evals, and regression analysis when the user asks for observability or quality measurement. - Keep tracing configuration environment-driven: `LANGSMITH_TRACING`, `LANGSMITH_API_KEY`, `LANGSMITH_PROJECT`, and related variables. - Do not send secrets, raw credentials, or unnecessary personal data into trace metadata. ## Documentation Lookup Use `llms.txt` as the local index of LangChain documentation. Search it before web browsing when the user asks for: - LangSmith Agent Server API endpoints. - LangGraph platform, deployment, streaming, threads, runs, checkpointers, stores, or SDK behavior. - Fleet, managed agents, auth, OAuth, or tool integration docs. - LangSmith tracing, datasets, evaluation, annotation, monitoring, or prompt management. If the relevant docs entry is found in `llms.txt`, follow the linked official page. If it is not present or appears stale, use web search for the official LangChain docs. ## Output Style - For code changes, provide the files changed, tests run, and any follow-up risks. - For reviews, list concrete findings first, ordered by severity, with file references. - For architecture guidance, give a concise design, then minimal TypeScript snippets that match the user's stack. - For documentation answers, cite the official docs links from `llms.txt` when possible. ## Verification Checklist - TypeScript compiles without suppressions. - Runtime input is validated with `zod`. - Provider credentials are read from configuration, not hard-coded. - Tests cover deterministic logic and external-boundary failure modes. - Streaming, retries, cancellation, and persistence are addressed when relevant. - LangSmith tracing or evals are configured only when requested or already present. --- > Source: [hangtiancheng/ai-codegen](https://github.com/hangtiancheng/ai-codegen) — distributed by [TomeVault](https://tomevault.io).