jxtngx/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 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>

---

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}"}

</python> <typescript>

| 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}` };
};

</typescript> </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> <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>

---

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

<ex-command-state-and-routing> <python> Command lets you update state AND choose next node in one return. ```python 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() )

</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();

</typescript> </ex-command-state-and-routing> <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.

<ex-orchestrator-worker> <python> Fan out tasks to parallel workers using the Send API and aggregate results. ```python 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({"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();

</typescript> </ex-orchestrator-worker> <fix-send-accumulator> <python> Use a reducer to accumulate parallel worker results (otherwise last worker overwrites). ```python # WRONG: No reducer - last worker overwrites class State(TypedDict): results: list

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) }),
});

</typescript> </fix-send-accumulator>

---

Running Graphs: Invoke and Stream

<invoke-basics>

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

<python> ```python result = graph.invoke({"input": "hello"}) # With config (for persistence, tags, etc.) result = graph.invoke({"input": "hello"}, {"configurable": {"thread_id": "1"}}) ``` </python> <typescript> ```typescript const result = await graph.invoke({ input: "hello" }); // With config const result = await graph.invoke({ input: "hello" }, { configurable: { thread_id: "1" } }); ``` </typescript> </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> <ex-stream-llm-tokens> <python> Stream LLM tokens in real-time for chat UI display. ```python for chunk in graph.stream( {"messages": [HumanMessage("Hello")]}, stream_mode="messages" ): token, metadata = chunk if hasattr(token, "content"): print(token.content, end="", flush=True) ``` </python> <typescript> Stream LLM tokens in real-time for chat UI display. ```typescript 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); } } ``` </typescript> </ex-stream-llm-tokens> <ex-stream-custom-data> <python> Emit custom progress updates from within nodes using the stream writer. ```python 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)

</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);
}

</typescript> </ex-stream-custom-data>

---

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> <ex-retry-policy> <python> Use RetryPolicy for transient errors (network issues, rate limits). ```python from langgraph.types import RetryPolicy

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 },
  },
);

</typescript> </ex-retry-policy> <ex-tool-node-error-handling> <python> 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. ```python from langgraph.prebuilt import ToolNode

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);

</typescript> </ex-tool-node-error-handling>

---

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" });

</typescript> </fix-compile-before-execution> <fix-infinite-loop-needs-exit> <python> Provide conditional path to END to avoid infinite loops. ```python # 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)

</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");

</typescript> </fix-infinite-loop-needs-exit> <fix-common-mistakes> Other common mistakes: ```python # 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

```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"] });

</fix-common-mistakes> <boundaries> ### 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 </boundaries>