/langgraph-fundamentals

name: langgraph-fundamentals
description: "INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling."

<overview> LangGraph models agent workflows as **directed graphs**:

• **StateGraph**: Main class for building stateful graphs
• **Nodes**: Functions that perform work and update state
• **Edges**: Define execution order (static or conditional)
• **START/END**: Special nodes marking entry and exit points
• **State with Reducers**: Control how state updates are merged

Graphs must be `compile()`d before execution. </overview>

<design-methodology>

Designing a LangGraph application

Follow these 5 steps when building a new graph:

1. **Map out discrete steps** — sketch a flowchart of your workflow. Each step becomes a node. 2. **Identify what each step does** — categorize nodes: LLM step, data step, action step, or user input step. For each, determine static context (prompt), dynamic context (from state), retry strategy, and desired outcome. 3. **Design your state** — state is shared memory for all nodes. Store raw data, format prompts on-demand inside nodes. 4. **Build your nodes** — implement each step as a function that takes state and returns partial updates. 5. **Wire it together** — connect nodes with edges, add conditional routing, compile with a checkpointer if needed.

</design-methodology>

<when-to-use-langgraph>

| Use LangGraph When | Use Alternatives When | |-------------------|----------------------| | Need fine-grained control over agent orchestration | Quick prototyping → LangChain agents | | Building complex workflows with branching/loops | Simple stateless workflows → LangChain direct | | Require human-in-the-loop, persistence | Batteries-included features → Deep Agents |

</when-to-use-langgraph>

---

State Management

<state-update-strategies>

| Need | Solution | Example | |------|----------|---------| | Overwrite value | No reducer (default) | Simple fields like counters | | Append to list | Reducer (operator.add / concat) | Message history, logs | | Custom logic | Custom reducer function | Complex merging |

</state-update-strategies>

<ex-state-with-reducer> <python> Define state schema with reducers for accumulating lists and summing integers.

from typing_extensions import TypedDict, Annotated
import operator

class State(TypedDict):
    name: str  # Default: overwrites on update
    messages: Annotated[list, operator.add]  # Appends to list
    total: Annotated[int, operator.add]  # Sums integers

</python> <typescript> Use StateSchema with ReducedValue for accumulating arrays.

import { StateSchema, ReducedValue, MessagesValue } from "@langchain/langgraph";
import { z } from "zod";

const State = new StateSchema({
  name: z.string(),  // Default: overwrites
  messages: MessagesValue,  // Built-in for messages
  items: new ReducedValue(
    z.array(z.string()).default(() => []),
    { reducer: (current, update) => current.concat(update) }
  ),
});

</typescript> </ex-state-with-reducer>

<fix-forgot-reducer-for-list> <python> Without a reducer, returning a list overwrites previous values.

# WRONG: List will be OVERWRITTEN
class State(TypedDict):
    messages: list  # No reducer!

# Node 1 returns: {"messages": ["A"]}
# Node 2 returns: {"messages": ["B"]}
# Final: {"messages": ["B"]}  # "A" is LOST!

# CORRECT: Use Annotated with operator.add
from typing import Annotated
import operator

class State(TypedDict):
    messages: Annotated[list, operator.add]
# Final: {"messages": ["A", "B"]}

</python> <typescript> Without ReducedValue, arrays are overwritten not appended.

// WRONG: Array will be overwritten
const State = new StateSchema({
  items: z.array(z.string()),  // No reducer!
});
// Node 1: { items: ["A"] }, Node 2: { items: ["B"] }
// Final: { items: ["B"] }  // A is lost!

// CORRECT: Use ReducedValue
const State = new StateSchema({
  items: new ReducedValue(
    z.array(z.string()).default(() => []),
    { reducer: (current, update) => current.concat(update) }
  ),
});
// Final: { items: ["A", "B"] }

</typescript> </fix-forgot-reducer-for-list>

<fix-state-must-return-dict> <python> Nodes must return partial updates, not mutate and return full state.

# WRONG: Returning entire state object
def my_node(state: State) -> State:
    state["field"] = "updated"
    return state  # Don't mutate and return!

# CORRECT: Return dict with only the updates
def my_node(state: State) -> dict:
    return {"field": "updated"}

</python> <typescript> Return partial updates only, not the full state object.

// WRONG: Returning entire state
const myNode = async (state: typeof State.State) => {
  state.field = "updated";
  return state;  // Don't do this!
};

// CORRECT: Return partial updates
const myNode = async (state: typeof State.State) => {
  return { field: "updated" };
};

</typescript> </fix-state-must-return-dict>

---

Nodes

<node-function-signatures>

Node functions accept these arguments:

<python>

| Signature | When to Use | |-----------|-------------| | `def node(state: State)` | Simple nodes that only need state | | `def node(state: State, config: RunnableConfig)` | Need thread_id, tags, or configurable values | | `def node(state: State, runtime: Runtime[Context])` | Need runtime context, store, or stream_writer |

from langchain_core.runnables import RunnableConfig
from langgraph.runtime import Runtime

def plain_node(state: State):
    return {"results": "done"}

def node_with_config(state: State, config: RunnableConfig):
    thread_id = config["configurable"]["thread_id"]
    return {"results": f"Thread: {thread_id}"}

def node_with_runtime(state: State, runtime: Runtime[Context]):
    user_id = runtime.context.user_id
    return {"results": f"User: {user_id}"}

</python> <typescript>

| Signature | When to Use | |-----------|----------