Langgraph Fundamentals

<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.

```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 StateSc