langchain-agents-workflow

name	langchain-agents-workflow
description	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.
metadata	{"author":"cwijayasundara"}

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.

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Source: cwijayasundara/agent_cli_langchain — distributed by TomeVault.