LangGraph Fundamentals

What this skill does

<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>

<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.
```python
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.
```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) }
  ),
});
```
</typescript>
</ex-state-with-reducer>

<fix-forgot-reducer-for-list>
<python>
Without a reducer, returning a list overwrites previous values.
```python
# 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.
```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"] }
```
</typescript>
</fix-forgot-reducer-for-list>

<fix-state-must-return-dict>
<python>
Nodes must return partial updates, not mutate and return full state.
```python
# 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.
```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" };
};
```
</typescript>
</fix-state-must-return-dict>

---

## Building Graphs

<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>

<ex-basic-graph>
<python>
Simple two-node graph with linear edges.
```python
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"
```
</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"
```
</typescript>
</ex-basic-graph>

<ex-conditional-edges>
<python>
Route to different nodes based on state with conditional edges.
```python
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()
)
```
</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();
```
</typescript>
</ex-conditional-edges>

<ex-command-state-and-routing>
<python>
Command lets you update state AND choose next