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Use when creating a new LangChain / LangGraph / DeepAgents project from scratch. Picks the right scaffolder for graphs vs. DeepAgents vs. LCEL chains.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/cwijayasundara/agent_cli_langchain --skill langchain-agents-scaffoldコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SOC 職業分類に基づく
SKILL.md を表示中
| name | langchain-agents-scaffold |
| description | Use when creating a new LangChain / LangGraph / DeepAgents project from scratch. Picks the right scaffolder for graphs vs. DeepAgents vs. LCEL chains. |
There is no single scaffolder that covers all three project shapes. Pick the right path:
| Project shape | Scaffolder |
|---|---|
| LangGraph agent (explicit StateGraph) | langgraph new (from langgraph-cli) |
| DeepAgents agent (planning + sub-agents + virtual FS) | No scaffolder — write ~15 lines yourself (recipe below) |
| LCEL pipeline (chains, RAG, classification) | No scaffolder — write ~10 lines yourself (recipe below) |
langgraph newpip install "langgraph-cli>=0.4" # if not installed
langgraph new my-agent --template react-agent
cd my-agent
pip install -e .
langgraph-cli ships several templates. List them with langgraph new --help. Common picks:
react-agent — single-LLM-with-tools loop. The most common starting point.retrieval-agent — RAG over a vector store.memory-agent — long-term memory using the LangGraph store.data-enrichment-agent — structured data extraction.Each template ships its own pyproject.toml, langgraph.json, and src/<package>/graph.py — read those after scaffolding to learn the layout. The conventions vary.
There's no deepagents new. Create the project by hand:
mkdir my-deep-agent && cd my-deep-agent
python -m venv .venv && source .venv/bin/activate
pip install \
"deepagents>=0.5.3" \
"langchain>=1.2" \
"langchain-anthropic>=1.4" \
"langsmith>=0.7"
mkdir agent
Pin floors matter: deepagents>=0.5 removed the legacy subagents API and added async sub-agents; 0.5.2 added the filesystem permissions system; 0.5.3 made model=None a deprecated kwarg (you must pass an explicit model) and added structured outputs for sub-agent responses.
Then agent/__init__.py (empty) and agent/agent.py:
"""DeepAgent for my-deep-agent. Always exported as `agent`."""
from deepagents import create_deep_agent
from langchain.chat_models import init_chat_model
SYSTEM_PROMPT = "You are my-deep-agent, a helpful agent."
TOOLS = [] # add user tools here
SUBAGENTS = [] # add sub-agents here (see deepagents-code skill)
agent = create_deep_agent(
model=init_chat_model("anthropic:claude-sonnet-4-6"), # explicit model required as of 0.5.3
tools=TOOLS,
subagents=SUBAGENTS,
instructions=SYSTEM_PROMPT,
)
Plus a pyproject.toml (or requirements.txt) and a .env with ANTHROPIC_API_KEY and LANGSMITH_*. That's the whole project.
For deploy: DeepAgents' create_deep_agent returns a compiled LangGraph, so a langgraph.json pointing at agent.agent:agent works for langgraph dev and langgraph build/deploy.
For non-agentic flows (RAG, summarization, classification):
mkdir my-chain && cd my-chain
python -m venv .venv && source .venv/bin/activate
pip install "langchain>=1.2" "langchain-openai>=1.0" "langsmith>=0.7"
mkdir agent
Then agent/agent.py:
"""LCEL chain. Exposed as `agent` (a Runnable)."""
from langchain.chat_models import init_chat_model
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.runnables import RunnableLambda
SYSTEM_PROMPT = "You are a helpful assistant."
def _to_messages(payload: dict) -> list:
msgs = [SystemMessage(content=SYSTEM_PROMPT)]
for m in payload.get("messages", []):
msgs.append(HumanMessage(content=m["content"]))
return msgs
agent = RunnableLambda(_to_messages) | init_chat_model("openai:gpt-4o-mini")
For RAG, see the langchain-agents-langchain-code skill.
These are conventions, not requirements. They make follow-up work easier because every other skill in this bundle assumes them:
agent and lives at agent/agent.py.LANGSMITH_* go in .env. Commit a .env.example.evals/datasets/*.jsonl; the eval runner at evals/run.py.server/app.py.Skills that follow assume these names. If the project diverges, adapt — these are not hard rules, just the path of least resistance.