
langgraph-fundamentals
INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/langchain-ai/skills-benchmarks --skill langgraph-fundamentals- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads langgraph-fundamentals automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
- 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>
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.
| 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
<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 |
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.
```typescript
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) }
),
});
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.
```typescript
// 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"] }
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.
```typescript
// 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>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}"}
| 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
<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 |
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"
</python>
<typescript>
Chain nodes with addEdge and compile before invoking.
```typescript
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"
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() )
</python>
<typescript>
addConditionalEdges routes based on function return value.
```typescript
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 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 nextresume: Value to resume afterinterrupt()— see human-in-the-loop skill
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() )
</python>
<typescript>
Return Command with update and goto to combine state change with routing.
```typescript
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();
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.
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({"tasks": ["Task A", "Task B", "Task C"]})
</python>
<typescript>
Fan out tasks to parallel workers using the Send API and aggregate results.
```typescript
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();
CORRECT
class State(TypedDict): results: Annotated[list, operator.add] # Accumulates
</python>
<typescript>
Use ReducedValue to accumulate parallel worker results.
```typescript
// 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>Call graph.invoke(input, config) to run a graph to completion and return the final state.
| 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 |
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)
</python>
<typescript>
Emit custom progress updates from within nodes using the stream writer.
```typescript
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-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 |
workflow.add_node( "search_documentation", search_documentation, retry_policy=RetryPolicy(max_attempts=3, initial_interval=1.0) )
</python>
<typescript>
Use retryPolicy for transient errors.
```typescript
workflow.addNode(
"searchDocumentation",
searchDocumentation,
{
retryPolicy: { maxAttempts: 3, initialInterval: 1.0 },
},
);
tool_node = ToolNode(tools, handle_tool_errors=True)
workflow.add_node("tools", tool_node)
</python>
<typescript>
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.
```typescript
import { ToolNode } from "@langchain/langgraph/prebuilt";
const toolNode = new ToolNode(tools, { handleToolErrors: true });
workflow.addNode("tools", toolNode);
Common Fixes
<fix-compile-before-execution> <python> Must compile() to get executable graph. ```python # WRONG builder.invoke({"input": "test"}) # AttributeError!CORRECT
graph = builder.compile() graph.invoke({"input": "test"})
</python>
<typescript>
Must compile() to get executable graph.
```typescript
// WRONG
await builder.invoke({ input: "test" });
// CORRECT
const graph = builder.compile();
await graph.invoke({ input: "test" });
CORRECT
def should_continue(state): return END if state["count"] > 10 else "node_b" builder.add_conditional_edges("node_a", should_continue)
</python>
<typescript>
Use conditional edges with END return to break loops.
```typescript
// 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");
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
```typescript
// 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"] });
- 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 </boundaries>
FAQ
What does the langgraph-fundamentals skill do?
INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.
How do I install langgraph-fundamentals?
Run npx skills add https://github.com/langchain-ai/skills-benchmarks --skill langgraph-fundamentals in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/langgraph-fundamentals/ (personal) or .claude/skills/langgraph-fundamentals/ (project).
Which AI agents can use langgraph-fundamentals?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is langgraph-fundamentals safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains langgraph-fundamentals?
It is published by langchain-ai in langchain-ai/skills-benchmarks, and it is an official skill from the maker of the technology it covers. It has 64 recorded installs.
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