القائمة
Use when creating a new LangChain / LangGraph / DeepAgents project from scratch. Picks the right scaffolder for graphs vs. DeepAgents vs. LCEL chains.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when editing a DeepAgents project — adding tools, sub-agents, modifying the system prompt, choosing a filesystem backend, or composing extra middlewares (retries, fallbacks, HITL) on top.
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.
Use when productionising or deploying a LangChain / LangGraph / DeepAgents agent. Covers durable execution (checkpointers, thread_id), the production middleware stack, three deploy targets (LangSmith Cloud, Cloud Run, Docker), secrets, scaling, and post-deploy verification.
Use when editing a non-agentic LCEL pipeline — composing Runnables, retrievers, embeddings, chat models, parsers, or building a RAG chain. For agents (LLM + tools loop), use the middleware skill instead.
Use when building a custom-graph LangGraph agent — when `create_agent(...)` + middleware isn't enough and you need explicit StateGraph control (multi-graph workflows, custom routing, non-standard state schemas, parallel branches). For the common case, use `create_agent` first (see middleware skill).
Use when authoring eval datasets, writing evaluators, running evals against a LangChain / LangGraph / DeepAgents project, comparing eval results between runs, or writing unit/integration tests for an agent.
| 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. Do not assume the layout matches across templates. 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.