Langgraph Fundamentals

LangChainOfficialPublished by LangChain

Skill
Tool integration
Software development
MIT
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From the author

INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.

— the description by LangChain

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

LangGraph is a framework for building stateful, multi-actor applications with LLMs by modeling workflows as directed graphs. This skill provides the core concepts for implementing StateGraphs, including node definitions, edge routing, state management with reducers, and execution patterns like invoke and stream. It covers both Python and TypeScript implementations, ensuring developers can build complex, branching, or looping agentic workflows with persistence and human-in-the-loop capabilities.

What it does

Defines state schemas with reducers for data accumulation.
Structures agent workflows as directed graphs with nodes and edges.
Implements conditional routing and parallel fan-out using the Send API.
Provides patterns for stateful execution and streaming results.
Integrates error handling strategies for transient and logic-based failures.
Supports both Python and TypeScript development environments.

When to use it

When you need fine-grained control over agent orchestration.
When building complex workflows that require branching or loops.
When your application requires persistence or human-in-the-loop interactions.
When you need to manage shared state across multiple agent steps.

Advantages

Enables complex, non-linear agentic workflows.
Provides robust state management through custom reducers.
Supports advanced patterns like parallel execution and dynamic routing.
Offers clear separation between state logic and execution flow.

Usage examples

“How do I define a state schema in Python that appends messages instead of overwriting them?”: The agent explains using Annotated with operator.add to ensure the list is updated via a reducer rather than replaced.
“Show me how to route between nodes conditionally in a TypeScript LangGraph project.”: The agent provides a code example using addConditionalEdges and a routing function that returns the next node name based on state.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/langchain-ai/langgraph-fundamentals/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip langgraph-fundamentals.zip -d .agents/skills

Documentation by LangChain

<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:

Map out discrete steps — sketch a flowchart of your workflow. Each step becomes a node.
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.
Design your state — state is shared memory for all nodes. Store raw data, format prompts on-demand inside nodes.
Build your nodes — implement each step as a function that takes state and returns partial updates.
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>

Nodes

<node-function-signatures>

Node functions return partial state updates. Signatures for configuration and runtime access differ by language; use the applicable implementation reference.

</node-function-signatures>

Edges

<edge-type-selection>

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

</edge-type-selection>

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-return-type-annotations>

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.

</command-return-type-annotations>

<warning-command-static-edges>

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.

</warning-command-static-edges>

Send API

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

Running Graphs: Invoke and Stream

<invoke-basics>

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

</invoke-basics>

<stream-mode-selection>

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-mode-selection>

Error Handling

Match the error type to the right handler:

<error-handling-table>

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`

</error-handling-table>

Core boundaries

Return partial state updates from nodes instead of mutating state directly.
Route loops through a named node; START is entry-only.
Define reducers for accumulated list fields; otherwise, the last write wins.
Account for static edges when using Command with goto, because both routes execute.

Implementation references

If writing, modifying, or debugging LangGraph code, determine the project's language from its existing files, then read the applicable reference before implementing:

For Python, read references/python.md.
For TypeScript, read references/typescript.md.

Read both only when the task covers both languages. For conceptual questions that require no code, do not load either reference.

Files

ATTRIBUTION.mdAdded by AutoGineer906 B
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md5.8 KiB
references/python.md9.0 KiB
references/typescript.md8.8 KiB

references/typescript.md

TypeScript implementation reference

Use this reference only for LangGraph projects written in TypeScript. The shared concepts, decisions, and invariants remain in ../SKILL.md.

State Management
Nodes
Edges
Command
Send API
Running Graphs: Invoke and Stream
Error Handling
Common Fixes

State Management

State with reducer

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) }
  ),
});

Forgot reducer for list

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"] }

Return partial state updates

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 function signatures

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

Basic graph

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(START, "process")
  .addEdge("process", "finalize")
  .addEdge("finalize", END)
  .compile();

const result = await graph.invoke({ input: "hello" });
console.log(result.output);  // "PROCESSED: HELLO"

Conditional edges

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(State)
  .addNode("classify", classify)
  .addNode("weather", async () => ({ result: "Sunny, 72F" }))
  .addNode("general", async () => ({ result: "General response" }))
  .addEdge(START, "classify")
  .addConditionalEdges("classify", routeQuery, ["weather", "general"])
  .addEdge("weather", END)
  .addEdge("general", END)
  .compile();

Command

Command state and routing

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)
  .addNode("node_a", nodeA, { ends: ["node_b", "node_c"] })
  .addNode("node_b", async () => ({ result: "B" }))
  .addNode("node_c", async () => ({ result: "C" }))
  .addEdge(START, "node_a")
  .addEdge("node_b", END)
  .addEdge("node_c", END)
  .compile();

Send API

Orchestrator worker

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 (state: { task: string }) => {
  return { results: [`Completed: ${state.task}`] };
};

const synthesize = async (state: typeof State.State) => {
  return { summary: `Processed ${state.results.length} tasks` };
};

const graph = new StateGraph(State)
  .addNode("worker", worker)
  .addNode("synthesize", synthesize)
  .addConditionalEdges(START, orchestrator, ["worker"])
  .addEdge("worker", "synthesize")
  .addEdge("synthesize", END)
  .compile();

Send accumulator

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

Invoke basics

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

Stream llm tokens

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);
  }
}

Stream custom data

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

Retry policy

Use retryPolicy for transient errors.

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

Tool node error handling

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

Compile before execution

Must compile() to get executable graph.

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

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

Infinite loop needs exit

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");

Additional common mistakes

// 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"] });

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python, Needs Node.js

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by

LangChain

Publisher

LangChain (Official)

Source

langchain-ai/langchain-skills · config/skills/langgraph-fundamentals · commit 126c384

Licence

MIT License (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by LangChain or LangChain.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download langgraph-fundamentals.zip

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by LangChain

Designing a LangGraph application

State Management

Nodes

Edges

Command

Send API

Running Graphs: Invoke and Stream

Error Handling

Core boundaries

Implementation references

Files

references/typescript.md

TypeScript implementation reference

Contents

State Management

State with reducer

Forgot reducer for list

Return partial state updates

Nodes

Node function signatures

Edges

Basic graph

Conditional edges

Command

Command state and routing

Send API

Orchestrator worker

Send accumulator

Running Graphs: Invoke and Stream

Invoke basics

Stream llm tokens

Stream custom data

Error Handling

Retry policy

Tool node error handling

Common Fixes

Compile before execution

Infinite loop needs exit

Additional common mistakes

Requirements and compatibility

Security

Attribution and licence

Related skills

Implement

Source Driven Development

Write Swift

Cardputer Buddy

Typescript Pro

Scala Pro

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