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.

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.

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.

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

---

State Management

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

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

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) }
  ),
});

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"]}

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"] }

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"}

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" };
};

---

Nodes

Node functions accept these arguments:

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}"}

Signature	When to Use
(state) => {...}	Simple nodes that only need state
(state, config) => {...}	Need thread_id, tags, or configurable values

import { GraphNode, StateSchema } from "@langchain/langgraph";

const plainNode: GraphNode<typeof State> = (state) => {
  return { results: "done" };
};

const nodeWithConfig: GraphNode<typeof State> = (state, config) => {
  const threadId = config?.configurable?.thread_id;
  return { results: `Thread: ${threadId}` };
};

---

Edges

Need	Edge Type	When to Use
Always go to same node	add_edge()	Fixed, deterministic flow
Route based on state	add_conditional_edges()	Dynamic branching
Update state AND route	Command	Combine logic in single node
Fan-out to multiple nodes	Send	Parallel processing with dynamic inputs

Simple two-node graph with linear edges.

from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict

class State(TypedDict):
    input: str
    output: str

def process_input(state: State) -> dict:
    return {"output": f"Processed: {state['input']}"}

def finalize(state: State) -> dict:
    return {"output": state["output"].upper()}

graph = (
    StateGraph(State)
    .add_node("process", process_input)
    .add_node("finalize", finalize)
    .add_edge(START, "process")
    .add_edge("process", "finalize")
    .add_edge("finalize", END)
    .compile()
)

result = graph.invoke({"input": "hello"})
print(result["output"])  # "PROCESSED: HELLO"

Chain nodes with addEdge and compile before invoking.

import { StateGraph, StateSchema, START, END } from "@langchain/langgraph";
import { z } from "zod";

const State = new StateSchema({
  input: z.string(),
  output: z.string().default(""),
});

const processInput = async (state: typeof State.State) => {
  return { output: `Processed: ${state.input}` };
};

const finalize = async (state: typeof State.State) => {
  return { output: state.output.toUpperCase() };
};

const graph = new StateGraph(State)
  .addNode("process", processInput)
  .addNode("finalize", finalize)
  .addEdge(, )
  .(, )
  .(, )
  .();

 result =  graph.({ :  });
.(result.);  

Route to different nodes based on state with conditional edges.

from typing import Literal
from langgraph.graph import StateGraph, START, END

class State(TypedDict):
    query: str
    route: str
    result: str

def classify(state: State) -> dict:
    if "weather" in state["query"].lower():
        return {"route": "weather"}
    return {"route": "general"}

def route_query(state: State) -> Literal["weather", "general"]:
    return state["route"]

graph = (
    StateGraph(State)
    .add_node("classify", classify)
    .add_node("weather", lambda s: {"result": "Sunny, 72F"})
    .add_node("general", lambda s: {"result": "General response"})
    .add_edge(START, "classify")
    .add_conditional_edges("classify", route_query, ["weather", "general"])
    .add_edge("weather", END)
    .add_edge("general", END)
    .compile()
)

addConditionalEdges routes based on function return value.

import { StateGraph, StateSchema, START, END } from "@langchain/langgraph";
import { z } from "zod";

const State = new StateSchema({
  query: z.string(),
  route: z.string().default(""),
  result: z.string().default(""),
});

const classify = async (state: typeof State.State) => {
  if (state.query.toLowerCase().includes("weather")) {
    return { route: "weather" };
  }
  return { route: "general" };
};

const routeQuery = (state: typeof State.State) => state.route;

const graph = new StateGraph()
  .(, classify)
  .(,  () => ({ :  }))
  .(,  () => ({ :  }))
  .(, )
  .(, routeQuery, [, ])
  .(, )
  .(, )
  .();

---

Command

Command combines state updates and routing in a single return value. Fields:

• update: State updates to apply (like returning a dict from a node)
• goto: Node name(s) to navigate to next
• resume: Value to resume after interrupt() — see human-in-the-loop skill

Command lets you update state AND choose next node in one return.

from langgraph.types import Command
from typing import Literal

class State(TypedDict):
    count: int
    result: str

def node_a(state: State) -> Command[Literal["node_b", "node_c"]]:
    """Update state AND decide next node in one return."""
    new_count = state["count"] + 1
    if new_count > 5:
        return Command(update={"count": new_count}, goto="node_c")
    return Command(update={"count": new_count}, goto="node_b")

graph = (
    StateGraph(State)
    .add_node("node_a", node_a)
    .add_node("node_b", lambda s: {"result": "B"})
    .add_node("node_c", lambda s: {"result": "C"})
    .add_edge(START, "node_a")
    .add_edge("node_b", END)
    .add_edge("node_c", END)
    .compile()
)

Return Command with update and goto to combine state change with routing.

import { StateGraph, StateSchema, START, END, Command } from "@langchain/langgraph";
import { z } from "zod";

const State = new StateSchema({
  count: z.number().default(0),
  result: z.string().default(""),
});

const nodeA = async (state: typeof State.State) => {
  const newCount = state.count + 1;
  if (newCount > 5) {
    return new Command({ update: { count: newCount }, goto: "node_c" });
  }
  return new Command({ update: { count: newCount }, goto: "node_b" });
};

const graph = new StateGraph(State)
  .(, nodeA, { : [, ] })
  .(,  () => ({ :  }))
  .(,  () => ({ :  }))
  .(, )
  .(, )
  .(, )
  .();

Python: Use Command[Literal["node_a", "node_b"]] as the return type annotation to declare valid goto destinations.

TypeScript: Pass { ends: ["node_a", "node_b"] } as the third argument to addNode to declare valid goto destinations.

Warning: Command only adds dynamic edges — static edges defined with add_edge / addEdge still execute. If node_a returns Command(goto="node_c") and you also have graph.add_edge("node_a", "node_b"), both node_b and node_c will run.

---

Send API

Fan-out with Send: return [Send("worker", {...})] from a conditional edge to spawn parallel workers. Requires a reducer on the results field.

Fan out tasks to parallel workers using the Send API and aggregate results.

from langgraph.types import Send
from typing import Annotated
import operator

class OrchestratorState(TypedDict):
    tasks: list[str]
    results: Annotated[list, operator.add]
    summary: str

def orchestrator(state: OrchestratorState):
    """Fan out tasks to workers."""
    return [Send("worker", {"task": task}) for task in state["tasks"]]

def worker(state: dict) -> dict:
    return {"results": [f"Completed: {state['task']}"]}

def synthesize(state: OrchestratorState) -> dict:
    return {"summary": f"Processed {len(state['results'])} tasks"}

graph = (
    StateGraph(OrchestratorState)
    .add_node("worker", worker)
    .add_node("synthesize", synthesize)
    .add_conditional_edges(START, orchestrator, ["worker"])
    .add_edge("worker", "synthesize")
    .add_edge("synthesize", END)
    .compile()
)

result = graph.invoke({: [, , ]})

Fan out tasks to parallel workers using the Send API and aggregate results.

import { Send, StateGraph, StateSchema, ReducedValue, START, END } from "@langchain/langgraph";
import { z } from "zod";

const State = new StateSchema({
  tasks: z.array(z.string()),
  results: new ReducedValue(
    z.array(z.string()).default(() => []),
    { reducer: (curr, upd) => curr.concat(upd) }
  ),
  summary: z.string().default(""),
});

const orchestrator = (state: typeof State.State) => {
  return state.tasks.map((task) => new Send("worker", { task }));
};

const worker = async (: { task:  }) => {
   { : [] };
};

  =  () => {
   { :  };
};

 graph =  ()
  .(, worker)
  .(, synthesize)
  .(, orchestrator, [])
  .(, )
  .(, )
  .();

Use a reducer to accumulate parallel worker results (otherwise last worker overwrites).

# WRONG: No reducer - last worker overwrites
class State(TypedDict):
    results: list

# CORRECT
class State(TypedDict):
    results: Annotated[list, operator.add]  # Accumulates

Use ReducedValue to accumulate parallel worker results.

// WRONG: No reducer
const State = new StateSchema({ results: z.array(z.string()) });

// CORRECT
const State = new StateSchema({
  results: new ReducedValue(z.array(z.string()).default(() => []), { reducer: (curr, upd) => curr.concat(upd) }),
});

---

Running Graphs: Invoke and Stream

Call graph.invoke(input, config) to run a graph to completion and return the final state.

result = graph.invoke({"input": "hello"})
# With config (for persistence, tags, etc.)
result = graph.invoke({"input": "hello"}, {"configurable": {"thread_id": "1"}})

const result = await graph.invoke({ input: "hello" });
// With config
const result = await graph.invoke({ input: "hello" }, { configurable: { thread_id: "1" } });

Mode	What it Streams	Use Case
values	Full state after each step	Monitor complete state
updates	State deltas	Track incremental updates
messages	LLM tokens + metadata	Chat UIs
custom	User-defined data	Progress indicators

Stream LLM tokens in real-time for chat UI display.

for chunk in graph.stream(
    {"messages": [HumanMessage("Hello")]},
    stream_mode="messages"
):
    token, metadata = chunk
    if hasattr(token, "content"):
        print(token.content, end="", flush=True)

Stream LLM tokens in real-time for chat UI display.

for await (const chunk of graph.stream(
  { messages: [new HumanMessage("Hello")] },
  { streamMode: "messages" }
)) {
  const [token, metadata] = chunk;
  if (token.content) {
    process.stdout.write(token.content);
  }
}

Emit custom progress updates from within nodes using the stream writer.

from langgraph.config import get_stream_writer

def my_node(state):
    writer = get_stream_writer()
    writer("Processing step 1...")
    # Do work
    writer("Complete!")
    return {"result": "done"}

for chunk in graph.stream({"data": "test"}, stream_mode="custom"):
    print(chunk)

Emit custom progress updates from within nodes using the stream writer.

import { getWriter } from "@langchain/langgraph";

const myNode = async (state: typeof State.State) => {
  const writer = getWriter();
  writer("Processing step 1...");
  // Do work
  writer("Complete!");
  return { result: "done" };
};

for await (const chunk of graph.stream({ data: "test" }, { streamMode: "custom" })) {
  console.log(chunk);
}

---

Error Handling

Match the error type to the right handler:

Error Type	Who Fixes	Strategy	Example
Transient (network, rate limits)	System	RetryPolicy(max_attempts=3)	add_node(..., retry_policy=...)
LLM-recoverable (tool failures)	LLM	ToolNode(tools, handle_tool_errors=True)	Error returned as ToolMessage
User-fixable (missing info)	Human	interrupt({"message": ...})	Collect missing data (see HITL skill)
Unexpected	Developer	Let bubble up	raise

Use RetryPolicy for transient errors (network issues, rate limits).

from langgraph.types import RetryPolicy

workflow.add_node(
    "search_documentation",
    search_documentation,
    retry_policy=RetryPolicy(max_attempts=3, initial_interval=1.0)
)

Use retryPolicy for transient errors.

workflow.addNode(
  "searchDocumentation",
  searchDocumentation,
  {
    retryPolicy: { maxAttempts: 3, initialInterval: 1.0 },
  },
);

Use ToolNode from langgraph.prebuilt to handle tool execution and errors. When handle_tool_errors=True, errors are returned as ToolMessages so the LLM can recover.

from langgraph.prebuilt import ToolNode

tool_node = ToolNode(tools, handle_tool_errors=True)

workflow.add_node("tools", tool_node)

Use ToolNode from @langchain/langgraph/prebuilt to handle tool execution and errors. When handleToolErrors is true, errors are returned as ToolMessages so the LLM can recover.

import { ToolNode } from "@langchain/langgraph/prebuilt";

const toolNode = new ToolNode(tools, { handleToolErrors: true });

workflow.addNode("tools", toolNode);

---

Common Fixes

Must compile() to get executable graph.

# WRONG
builder.invoke({"input": "test"})  # AttributeError!

# CORRECT
graph = builder.compile()
graph.invoke({"input": "test"})

Must compile() to get executable graph.

// WRONG
await builder.invoke({ input: "test" });

// CORRECT
const graph = builder.compile();
await graph.invoke({ input: "test" });

Provide conditional path to END to avoid infinite loops.

# WRONG: Loops forever
builder.add_edge("node_a", "node_b")
builder.add_edge("node_b", "node_a")

# CORRECT
def should_continue(state):
    return END if state["count"] > 10 else "node_b"
builder.add_conditional_edges("node_a", should_continue)

Use conditional edges with END return to break loops.

// WRONG: Loops forever
builder.addEdge("node_a", "node_b").addEdge("node_b", "node_a");

// CORRECT
builder.addConditionalEdges("node_a", (state) => state.count > 10 ? END : "node_b");

Other common mistakes:

# Router must return names of nodes that exist in the graph
builder.add_node("my_node", func)  # Add node BEFORE referencing in edges
builder.add_conditional_edges("node_a", router, ["my_node"])

# Command return type needs Literal for routing destinations (Python)
def node_a(state) -> Command[Literal["node_b", "node_c"]]:
    return Command(goto="node_b")

# START is entry-only - cannot route back to it
builder.add_edge("node_a", START)  # WRONG!
builder.add_edge("node_a", "entry")  # Use a named entry node instead

# Reducer expects matching types
return {"items": ["item"]}  # List for list reducer, not a string

// Always await graph.invoke() - it returns a Promise
const result = await graph.invoke({ input: "test" });

// TS Command nodes need { ends } to declare routing destinations
builder.addNode("router", routerFn, { ends: ["node_b", "node_c"] });

### What You Should NOT Do

• Mutate state directly — always return partial update dicts from nodes
• Route back to START — it's entry-only; use a named node instead
• Forget reducers on list fields — without one, last write wins
• Mix static edges with Command goto without understanding both will execute