langgraph

name	langgraph
description	Build multi-agent AI systems with LangGraph — the low-level orchestration framework for stateful, graph-based agent workflows. Covers supervisor, swarm, and hierarchical multi-agent patterns; subgraph composition; state management (checkpointers/stores); persistence; evals; and production debugging. Reach for this when designing agent architectures that need cycles, conditional branching, parallel execution, or human-in-the-loop patterns.
license	MIT
metadata	{"source":"LangGraph by LangChain Inc — https://langchain.com/langgraph","spec-version":"1.0","version":"1.0.2"}

LangGraph

LangGraph is LangChain's low-level orchestration framework for building stateful, long-running, multi-agent AI workflows using directed graph architectures (inspired by Pregel/Beam and NetworkX). It models agents as nodes in a graph, with edges controlling flow — enabling cycles, conditional branching, parallel execution, human-in-the-loop, and subgraph composition that linear chains cannot express.

This skill covers all major patterns for building and deploying LangGraph systems: core graph architecture, the three canonical multi-agent patterns (supervisor, swarm, hierarchical), persistence and state management, production debugging, and evaluation methodology.

Before you begin: Install dependencies:

pip install langgraph langchain langchain-openai langsmith

Quick Start

Create your first LangGraph agent in under 10 lines:

from langgraph.graph import StateGraph, MessagesState, START, END

def hello_agent(state: MessagesState):
    return {"messages": [{"role": "ai", "content": "Hello, world!"}]}

graph = StateGraph(MessagesState)
graph.add_node("agent", hello_agent)
graph.add_edge(START, "agent")
graph.add_edge("agent", END)
graph = graph.compile()

graph.invoke({"messages": [{"role": "user", "content": "hi!"}]})

Next steps:

1. Use the Pattern Selection Guide below to choose supervisor, swarm, or hierarchical architecture — each pattern links to its recommended template
2. Load the corresponding reference file for the deep pattern walkthrough
3. Use the Choosing Your Starting Point table below to pick scaffold, template, or reference based on your task
4. For a complete runnable example matching your pattern, use the linked template in assets/templates/

Design Principles — These Govern Every Graph Decision

1. State is the source of truth — all inter-node communication happens through state, not through side channels or global variables.
2. Nodes are pure-ish — a node receives state, does work, returns updates. It should not depend on state that isn't passed to it.
3. Reducers prevent conflicts — any state key written by multiple nodes in parallel MUST have a reducer.
4. Start simple — a single agent with good prompts beats a multi-agent system with bad routing. Add agents only when a single prompt or toolset becomes unwieldy.
5. Use Send() for dynamic fan-out — when you don't know how many workers you'll need at compile time, spawn them dynamically from the orchestrator node.
6. Subgraph state isolation — subgraphs with different state schemas need a wrapper function to transform state at the boundary. Shared-schema subgraphs can be added directly as nodes.

When to Reach For This

Context	What to load
Building a new LangGraph workflow from scratch	references/architecture.md — core concepts first
Designing a multi-agent routing system	references/multi-agent-supervisor.md or references/multi-agent-swarm.md — compare patterns
Composing nested agent teams	references/multi-agent-hierarchical.md — subgraph composition
Adding persistence, interrupts, or long-term memory	references/persistence.md — checkpointers and stores
Deploying to production or debugging failures	references/production.md — deployment, observability, failure modes
Setting up eval pipelines for routing accuracy	references/evals.md — evaluation methodology
Diagnosing a specific failure (loop, context loss, crash)	references/troubleshooting.md — known failure modes

Pattern Selection Guide

Your constraint	Prefer	Why
Routing accuracy > latency	Supervisor	Centralized routing node, focused prompt: ~94% accuracy
Latency is primary constraint	Swarm	Direct agent-to-agent handoffs, ~40% fewer LLM calls
Clear domain boundaries	Swarm	Agents rarely misroute, handoffs are crisp
Ambiguous domain boundaries	Supervisor	Overlapping concerns resolved by dedicated router
< 3 distinct domains	Skip multi-agent	A specialized single agent is simpler
Multi-domain requests common	Swarm	Latency savings compound across handoffs
Need centralized audit trail	Supervisor	Every routing decision visible in traces
Nested team structures	Hierarchical	Subgraphs as nodes, each team self-contained

Choosing Your Starting Point

Your goal	Start with	Why
Build a project from scratch, need generated code	scripts/lg-supervisor-scaffold.py or scripts/lg-swarm-scaffold.py	Scaffolds generate complete project structure (state.py, agents.py, graph.py) with placeholders to fill in
Understand a complete, working example	assets/templates/ matching your chosen pattern	Templates are self-contained runnable files with all patterns wired — best for learning by reading
Deep dive into a pattern's internals	Corresponding reference in references/	References explain tradeoffs, failure modes, and design rationale — best for customization
Debug or optimize an existing system	references/production.md or references/troubleshooting.md	Production reference covers deployment + observability; troubleshooting reference covers symptom→fix tables

Core Primitives

LangGraph uses two APIs:

API	When to use	Pattern
Graph API (StateGraph)	Full control over graph structure, conditional edges, subgraphs	add_node() + add_edge()/add_conditional_edges()
Functional API (@task + @entrypoint)	Simpler linear workflows, less boilerplate	Decorator-based, Pythonic

Both APIs produce the same compiled graph — choose based on how much control you need.

Key Gotchas

• Subgraph persistence defaults to per-invocation — each subgraph call starts fresh. Set checkpointer=True for per-thread memory, checkpointer=False for fully stateless.
• Per-thread subgraphs cannot run in parallel — same-namespace checkpoint conflicts. Use ToolCallLimitMiddleware or disable parallel tool calls.
• The supervisor bottleneck — every interaction requires a routing LLM call, even for obvious intents. Add a fast-path classifier (keyword matching or small model) for unambiguous requests.
• Swarm ping-pong — no natural recursion guard. Track handoff_count in state and hard-limit at 3, then escalate to human or fallback agent.
• Lost messages on handoff — Command.update must include paired messages from the specialist's tool-calling loop, or the next agent sees malformed history.
• State access from parent to subgraph — subgraphs manage their own checkpoint namespace. Use Store for cross-graph-boundary data.
• Checkpoint bloat — long conversations accumulate checkpoints. Prune periodically or set retention policies on DB-backed checkpointers.
• No auto-load-on-install — skills aren't auto-discovered at session start by name mention. The agent must explicitly call skill_view(name='langgraph') to load this skill.

Reference Files

File	Load when
references/architecture.md	You need to understand LangGraph core concepts: graph structure, nodes, edges, state, the two APIs, and basic agent loop construction. Read this first if you're new to LangGraph.
references/multi-agent-supervisor.md	You're designing a supervisor-based multi-agent system with a central routing node. Contains architecture, structured output routing, specialist wrappers, and full code examples.
references/multi-agent-swarm.md	You're designing a swarm-based multi-agent system with direct agent-to-agent handoffs. Contains handoff tool patterns, Command-based routing, and comparative metrics vs supervisor.
references/multi-agent-hierarchical.md	You're composing nested agent teams using subgraphs. Covers subgraph wiring (shared vs different state schemas), persistence modes, namespace isolation, and hierarchical team structures.
references/persistence.md	You're adding checkpointer-based short-term memory or store-based long-term memory. Covers per-invocation vs per-thread vs stateless modes, checkpoint backends, and cross-thread memory patterns.
references/production.md	You're deploying a LangGraph system to production. Covers Agent Server deployment, LangSmith observability, streaming patterns, and common production failure modes with fixes.
references/evals.md	You're setting up evaluation pipelines for multi-agent systems. Covers routing accuracy, resolution coverage, LangSmith eval datasets, and LLM-as-judge evaluators.
references/troubleshooting.md	You're debugging a specific LangGraph failure. Covers routing loops, context loss, checkpointer conflicts, token waste, and state inspection techniques.
assets/templates/supervisor-graph.py	Runnable supervisor example with billing, tech support, and account specialists — fast-path classifier, structured output routing, audit trail, and recursion guard.
assets/templates/swarm-graph.py	Runnable swarm example with triage agent plus 3 specialists — direct agent-to-agent handoffs via Command, recursion guard, and full traceability.
assets/templates/subgraph-agent.py	Runnable subgraph composition examples — all 3 wiring patterns (different state schemas, shared state keys, per-thread with namespace isolation).

Scripts

Script	What it does
scripts/lg-supervisor-scaffold.py	Generates a complete supervisor pattern project with state schema, routing agent, specialist nodes, and graph assembly
scripts/lg-swarm-scaffold.py	Generates a complete swarm pattern project with handoff tools, triage agent, specialist agents, and conditional routing
scripts/lg-eval-generator.py	Generates evaluation datasets and runs LangSmith evaluators for routing accuracy and resolution coverage