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Langgraph multi agent

Skill m00kk/agent-skills-playbook/skills/langgraph-multi-agent

15 production Agent Skills — MCP, LangGraph, RAG, security, Cursor SDK. MIT licensed.

Install
npx -y skills add m00kk/agent-skills-playbook --skill langgraph-multi-agent

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Designs and implements stateful LangGraph workflows with checkpoints, human approval, and tool nodes. Use when building LangGraph agents, state graphs, HITL flows, or migrating chains to graph-based orchestration.

SKILL.md

2.0 KB, 435 tokens by cl100k_base, as published. Nobody here has run it

LangGraph Multi-Agent

When to use LangGraph

  • Long-running sessions need checkpointing and resume
  • Branching logic (if/else) must be explicit, not prompt-only
  • Human-in-the-loop (HITL) approvals between steps

Minimal graph pattern

from langgraph.graph import StateGraph, START, END
from typing import TypedDict, Annotated
import operator

class State(TypedDict):
    messages: Annotated[list, operator.add]

def planner(state: State) -> State:
    # call LLM; append message
    return state

def tool_executor(state: State) -> State:
    # run tools; append results
    return state

builder = StateGraph(State)
builder.add_node("planner", planner)
builder.add_node("tools", tool_executor)
builder.add_edge(START, "planner")
builder.add_conditional_edges("planner", route_fn, ["tools", END])
builder.add_edge("tools", "planner")
graph = builder.compile(checkpointer=checkpointer)

Workflow

- [ ] Define State TypedDict (only fields you need)
- [ ] One node per responsibility (plan, act, reflect)
- [ ] conditional_edges for routing (not mega-prompts)
- [ ] compile(checkpointer=...) for persistence
- [ ] interrupt_before for HITL on sensitive nodes

MCP integration

  • Tools live on MCP servers; graph nodes call MCP client, not ad-hoc HTTP
  • Keep orchestration in LangGraph; keep side effects in MCP tools
  • Pair with multi-agent-supervisor when multiple role-specific subgraphs exist

Observability

  • Tag runs with thread_id and user_id (hashed)
  • Export traces to LangSmith or OpenTelemetry (see agent-observability)

Common mistakes

  • Storing full chat history in state without trimming → use summarization node
  • Cyclic graphs without max step guard → set recursion_limit
  • Single node doing plan + act + evaluate → split nodes

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