# Langchain Agents Workflow > Use when starting work on any LangChain / LangGraph / DeepAgents project. Entry point for the develop -> middleware -> evaluate -> deploy lifecycle, mapping each step to the right official tool. - Skill: `tomevault-io/langchain-agents-workflow` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/langchain-agents-workflow` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/langchain-agents-workflow/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/langchain-agents-workflow --- # LangChain Agents Workflow This skill is the entry point — read it first, then load the more specific skill for the step you're on. ## Version floors this bundle assumes - `langchain >= 1.2` (v1 series — middleware-first `create_agent`) - `langgraph >= 1.1`, `langgraph-cli >= 0.4` - `deepagents >= 0.5.3` (async sub-agents, structured sub-agent responses, filesystem permissions; `model=None` deprecated) - `langsmith >= 0.7` (pytest plugin + new evaluator API) - `langchain-anthropic >= 1.4`, `langchain-openai >= 1.0` If a project pins anything below these floors, suggest the bump before writing code — the API shapes in this bundle assume the v1+ surface. ## You have two complementary tools This skill bundle pairs with the **`mcpdoc` MCP server**. The two have distinct roles; use both. | | `mcpdoc` (MCP server) | This skill bundle | |---|---|---| | Purpose | Live API reference | Opinionated playbook | | Content | Whatever's on `docs.langchain.com` right now | How to *think* about LangChain projects | | When to use | "What's the signature of `SummarizationMiddleware`?" / "What kwargs does `create_agent` take?" / "What import path for X?" | "What's the production middleware stack?" / "How do I wire Cloud Run + Secret Manager + Postgres checkpointer together?" / "Which mistakes does an agent typically make here?" | | How to use | Call `fetch_docs(url)` or `list_doc_sources()` | Load skills based on description triggers | | Drift risk | Zero — always live | Owner updates as ecosystem evolves; some rot tolerated | **Rule of thumb:** when you need an exact API detail, fetch from `mcpdoc`. When you need to make a design decision, load a skill. When in doubt, do both. ## When to load which skill | Goal | Skill | |---|---| | Start a new agent project | `langchain-agents-scaffold` | | Build a modern agent (most cases) | `langchain-agents-middleware` ← first; uses `create_agent(...)` with middleware | | Add nodes/edges/tools to a custom LangGraph | `langchain-agents-langgraph-code` | | Customize a DeepAgent | `langchain-agents-deepagents-code` | | Build a non-agentic LCEL pipeline (chains, RAG) | `langchain-agents-langchain-code` | | Write or run evals; unit/integration test agents | `langchain-agents-langsmith-evals` | | Deploy + productionise | `langchain-agents-deploy` | | Debug / read traces / OTEL | `langchain-agents-observability` | ## Mental model Three layers in the modern stack: 1. **`create_agent(model, tools, middleware=...)`** — the v1 default for building agents. Middleware is how you add retries, fallbacks, summarization, HITL, PII handling, call limits. Read the **middleware** skill for the production stack. 2. **Raw LangGraph (`StateGraph`)** — drop down when `create_agent` isn't enough (multi-graph workflows, custom state, parallel branches). Read **langgraph-code**. 3. **DeepAgents** — `create_deep_agent(...)` is `create_agent(...)` pre-loaded with `FilesystemMiddleware` + `SubAgentMiddleware` + `TodoListMiddleware`. Read **deepagents-code**. For non-agentic flows (RAG, classification), use plain LCEL chains — middleware does **not** apply to chains. ## Common commands by lifecycle stage | Stage | Command(s) | |---|---| | Scaffold a LangGraph project | `langgraph new my-agent --template react-agent` | | Scaffold a DeepAgent / chain | No scaffolder — write a small `agent.py` (see scaffold skill) | | Install deps | `pip install -e .` or `uv sync` | | Iterate on a graph | `langgraph dev` | | Run an agent ad hoc | `python -c "from agent.agent import agent; print(agent.invoke({'messages': [...] }))"` | | Run evals | `python evals/run.py` | | Unit-test agents (no API calls) | `pytest` with `LLMToolEmulator` middleware | | Deploy to LangSmith Cloud | `langgraph build -t my-agent && langgraph deploy` | | Deploy to Cloud Run | `gcloud run deploy my-agent --source .` | | Deploy as a Docker image | `docker build && docker run --env-file .env` | ## Hard rules - **Look up exact APIs via `mcpdoc`, don't guess.** If `mcpdoc` isn't configured, ask the user to set it up (see this repo's README) before you write LangChain code. - **Always check what's already installed before suggesting `pip install`** — `pip show langchain langgraph deepagents langsmith`. - **Never print `.env` contents** — refer to keys by name only. - **For ANY production agent, add the production middleware stack** (call limits, retries, fallback, summarization). Copy-paste-ready in the middleware skill. - **Run smoke evals before any deploy.** Not enforced — you must do it. - **Read the project structure first** (`ls`, `tree -L 2`) before assuming layout. ## Required environment variables (most projects) - `LANGSMITH_API_KEY` — for tracing and evals. - `LANGSMITH_TRACING=true` — enables trace capture. - `LANGSMITH_PROJECT` — trace bucket name. - One of `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, etc. If any are missing when needed, fail fast with a clear message that names the missing variable. --- > Source: [cwijayasundara/agent_cli_langchain](https://github.com/cwijayasundara/agent_cli_langchain) — distributed by [TomeVault](https://tomevault.io).