title: Workflows and agents sidebarTitle: Workflows + agents

This guide reviews common workflow and agent patterns.

• Workflows have predetermined code paths and are designed to operate in a certain order.
• Agents are dynamic and define their own processes and tool usage.

LangGraph offers several benefits when building agents and workflows, including persistence, streaming, and support for debugging as well as deployment.

Setup

To build a workflow or agent, you can use any chat model that supports structured outputs and tool calling. The following example uses Anthropic:

:::python

1. Install dependencies:

pip install langchain_core langchain-anthropic langgraph

1. Initialize the LLM:

import os
import getpass

from langchain_anthropic import ChatAnthropic

def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")


_set_env("ANTHROPIC_API_KEY")

llm = ChatAnthropic(model="claude-sonnet-4-5-20250929")

:::

:::js

1. Install dependencies <CodeGroup>

npm install @langchain/langgraph @langchain/core

pnpm add @langchain/langgraph @langchain/core

yarn add @langchain/langgraph @langchain/core

bun add @langchain/langgraph @langchain/core

1. Initialize the LLM:

import { ChatAnthropic } from "@langchain/anthropic";

const llm = new ChatAnthropic({
  model: "claude-sonnet-4-5-20250929",
  apiKey: "<your_anthropic_key>"
});

:::

LLMs and augmentations

Workflows and agentic systems are based on LLMs and the various augmentations you add to them. Tool calling, structured outputs, and short term memory are a few options for tailoring LLMs to your needs.

:::python

# Schema for structured output
from pydantic import BaseModel, Field


class SearchQuery(BaseModel):
    search_query: str = Field(None, description="Query that is optimized web search.")
    justification: str = Field(
        None, description="Why this query is relevant to the user's request."
    )


# Augment the LLM with schema for structured output
structured_llm = llm.with_structured_output(SearchQuery)

# Invoke the augmented LLM
output = structured_llm.invoke("How does Calcium CT score relate to high cholesterol?")

# Define a tool
def multiply(a: int, b: int) -> int:
    return a * b

# Augment the LLM with tools
llm_with_tools = llm.bind_tools([multiply])

# Invoke the LLM with input that triggers the tool call
msg = llm_with_tools.invoke("What is 2 times 3?")

# Get the tool call
msg.tool_calls

::: :::js

import * as z from "zod";
import { tool } from "langchain";

// Schema for structured output
const SearchQuery = z.object({
  search_query: z.string().describe("Query that is optimized web search."),
  justification: z
    .string()
    .describe("Why this query is relevant to the user's request."),
});

// Augment the LLM with schema for structured output
const structuredLlm = llm.withStructuredOutput(SearchQuery);

// Invoke the augmented LLM
const output = await structuredLlm.invoke(
  "How does Calcium CT score relate to high cholesterol?"
);

// Define a tool
const multiply = tool(
  ({ a, b }) => {
    return a * b;
  },
  {
    name: "multiply",
    description: "Multiply two numbers",
    schema: z.object({
      a: z.number(),
      b: z.number(),
    }),
  }
);

// Augment the LLM with tools
const llmWithTools = llm.bindTools([multiply]);

// Invoke the LLM with input that triggers the tool call
const msg = await llmWithTools.invoke("What is 2 times 3?");

// Get the tool call
console.log(msg.tool_calls);

:::

Prompt chaining

Prompt chaining is when each LLM call processes the output of the previous call. It's often used for performing well-defined tasks that can be broken down into smaller, verifiable steps. Some examples include:

• Translating documents into different languages
• Verifying generated content for consistency

:::python <CodeGroup>

from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from IPython.display import Image, display


# Graph state
class State(TypedDict):
    topic: str
    joke: str
    improved_joke: str
    final_joke: str


# Nodes
def generate_joke(state: State):
    """First LLM call to generate initial joke"""

    msg = llm.invoke(f"Write a short joke about {state['topic']}")
    return {"joke": msg.content}


def check_punchline(state: State):
    """Gate function to check if the joke has a punchline"""

    # Simple check - does the joke contain "?" or "!"
    if "?" in state["joke"] or "!" in state["joke"]:
        return "Pass"
    return "Fail"


def improve_joke(state: State):
    """Second LLM call to improve the joke"""

    msg = llm.invoke(f"Make this joke funnier by adding wordplay: {state['joke']}")
    return {"improved_joke": msg.content}


def polish_joke(state: State):
    """Third LLM call for final polish"""
    msg = llm.invoke(f"Add a surprising twist to this joke: {state['improved_joke']}")
    return {"final_joke": msg.content}


# Build workflow
workflow = StateGraph(State)

# Add nodes
workflow.add_node("generate_joke", generate_joke)
workflow.add_node("improve_joke", improve_joke)
workflow.add_node("polish_joke", polish_joke)

# Add edges to connect nodes
workflow.add_edge(START, "generate_joke")
workflow.add_conditional_edges(
    "generate_joke", check_punchline, {"Fail": "improve_joke", "Pass": END}
)
workflow.add_edge("improve_joke", "polish_joke")
workflow.add_edge("polish_joke", END)

# Compile
chain = workflow.compile()

# Show workflow
display(Image(chain.get_graph().draw_mermaid_png()))

# Invoke
state = chain.invoke({"topic": "cats"})
print("Initial joke:")
print(state["joke"])
print("\n--- --- ---\n")
if "improved_joke" in state:
    print("Improved joke:")
    print(state["improved_joke"])
    print("\n--- --- ---\n")

    print("Final joke:")
    print(state["final_joke"])
else:
    print("Final joke:")
    print(state["joke"])

from langgraph.func import entrypoint, task


# Tasks
@task
def generate_joke(topic: str):
    """First LLM call to generate initial joke"""
    msg = llm.invoke(f"Write a short joke about {topic}")
    return msg.content


def check_punchline(joke: str):
    """Gate function to check if the joke has a punchline"""
    # Simple check - does the joke contain "?" or "!"
    if "?" in joke or "!" in joke:
        return "Fail"

    return "Pass"


@task
def improve_joke(joke: str):
    """Second LLM call to improve the joke"""
    msg = llm.invoke(f"Make this joke funnier by adding wordplay: {joke}")
    return msg.content


@task
def polish_joke(joke: str):
    """Third LLM call for final polish"""
    msg = llm.invoke(f"Add a surprising twist to this joke: {joke}")
    return msg.content


@entrypoint()
def prompt_chaining_workflow(topic: str):
    original_joke = generate_joke(topic).result()
    if check_punchline(original_joke) == "Pass":
        return original_joke

    improved_joke = improve_joke(original_joke).result()
    return polish_joke(improved_joke).result()

# Invoke
for step in prompt_chaining_workflow.stream("cats", stream_mode="updates"):
    print(step)
    print("\n")

:::js <CodeGroup>

import { StateGraph, Annotation } from "@langchain/langgraph";

// Graph state
const StateAnnotation = Annotation.Root({
  topic: Annotation<string>,
  joke: Annotation<string>,
  improvedJoke: Annotation<string>,
  finalJoke: Annotation<string>,
});

// Define node functions

// First LLM call to generate initial joke
async function generateJoke(state: typeof StateAnnotation.State) {
  const msg = await llm.invoke(`Write a short joke about ${state.topic}`);
  return { joke: msg.content };
}

// Gate function to check if the joke has a punchline
function checkPunchline(state: typeof StateAnnotation.State) {
  // Simple check - does the joke contain "?" or "!"
  if (state.joke?.includes("?") || state.joke?.includes("!")) {
    return "Pass";
  }
  return "Fail";
}

  // Second LLM call to improve the joke
async function improveJoke(state: typeof StateAnnotation.State) {
  const msg = await llm.invoke(
    `Make this joke funnier by adding wordplay: ${state.joke}`
  );
  return { improvedJoke: msg.content };
}

// Third LLM call for final polish
async function polishJoke(state: typeof StateAnnotation.State) {
  const msg = await llm.invoke(
    `Add a surprising twist to this joke: ${state.improvedJoke}`
  );
  return { finalJoke: msg.content };
}

// Build workflow
const chain = new StateGraph(StateAnnotation)
  .addNode("generateJoke", generateJoke)
  .addNode("improveJoke", improveJoke)
  .addNode("polishJoke", polishJoke)
  .addEdge("__start__", "generateJoke")
  .addConditionalEdges("generateJoke", checkPunchline, {
    Pass: "improveJoke",
    Fail: "__end__"
  })
  .addEdge("improveJoke", "polishJoke")
  .addEdge("polishJoke", "__end__")
  .compile();

// Invoke
const state = await chain.invoke({ topic: "cats" });
console.log("Initial joke:");
console.log(state.joke);
console.log("\n--- --- ---\n");
if (state.improvedJoke !== undefined) {
  console.log("Improved joke:");
  console.log(state.improvedJoke);
  console.log("\n--- --- ---\n");

  console.log("Final joke:");
  console.log(state.finalJoke);
} else {
  console.log("Joke failed quality gate - no punchline detected!");
}

import { task, entrypoint } from "@langchain/langgraph";

// Tasks

// First LLM call to generate initial joke
const generateJoke = task("generateJoke", async (topic: string) => {
  const msg = await llm.invoke(`Write a short joke about ${topic}`);
  return msg.content;
});

// Gate function to check if the joke has a punchline
function checkPunchline(joke: string) {
  // Simple check - does the joke contain "?" or "!"
  if (joke.includes("?") || joke.includes("!")) {
    return "Pass";
  }
  return "Fail";
}

  // Second LLM call to improve the joke
const improveJoke = task("improveJoke", async (joke: string) => {
  const msg = await llm.invoke(
    `Make this joke funnier by adding wordplay: ${joke}`
  );
  return msg.content;
});

// Third LLM call for final polish
const polishJoke = task("polishJoke", async (joke: string) => {
  const msg = await llm.invoke(
    `Add a surprising twist to this joke: ${joke}`
  );
  return msg.content;
});

const workflow = entrypoint(
  "jokeMaker",
  async (topic: string) => {
    const originalJoke = await generateJoke(topic);
    if (checkPunchline(originalJoke) === "Pass") {
      return originalJoke;
    }
    const improvedJoke = await improveJoke(originalJoke);
    const polishedJoke = await polishJoke(improvedJoke);
    return polishedJoke;
  }
);

const stream = await workflow.stream("cats", {
  streamMode: "updates",
});

for await (const step of stream) {
  console.log(step);
}

Parallelization

With parallelization, LLMs work simultaneously on a task. This is either done by running multiple independent subtasks at the same time, or running the same task multiple times to check for different outputs. Parallelization is commonly used to:

• Split up subtasks and run them in parallel, which increases speed
• Run tasks multiple times to check for different outputs, which increases confidence

Some examples include:

• Running one subtask that processes a document for keywords, and a second subtask to check for formatting errors
• Running a task multiple times that scores a document for accuracy based on different criteria, like the number of citations, the number of sources used, and the quality of the sources

:::python <CodeGroup>

# Graph state
class State(TypedDict):
    topic: str
    joke: str
    story: str
    poem: str
    combined_output: str


# Nodes
def call_llm_1(state: State):
    """First LLM call to generate initial joke"""

    msg = llm.invoke(f"Write a joke about {state['topic']}")
    return {"joke": msg.content}


def call_llm_2(state: State):
    """Second LLM call to generate story"""

    msg = llm.invoke(f"Write a story about {state['topic']}")
    return {"story": msg.content}


def call_llm_3(state: State):
    """Third LLM call to generate poem"""

    msg = llm.invoke(f"Write a poem about {state['topic']}")
    return {"poem": msg.content}


def aggregator(state: State):
    """Combine the joke and story into a single output"""

    combined = f"Here's a story, joke, and poem about {state['topic']}!\n\n"
    combined += f"STORY:\n{state['story']}\n\n"
    combined += f"JOKE:\n{state['joke']}\n\n"
    combined += f"POEM:\n{state['poem']}"
    return {"combined_output": combined}


# Build workflow
parallel_builder = StateGraph(State)

# Add nodes
parallel_builder.add_node("call_llm_1", call_llm_1)
parallel_builder.add_node("call_llm_2", call_llm_2)
parallel_builder.add_node("call_llm_3", call_llm_3)
parallel_builder.add_node("aggregator", aggregator)

# Add edges to connect nodes
parallel_builder.add_edge(START, "call_llm_1")
parallel_builder.add_edge(START, "call_llm_2")
parallel_builder.add_edge(START, "call_llm_3")
parallel_builder.add_edge("call_llm_1", "aggregator")
parallel_builder.add_edge("call_llm_2", "aggregator")
parallel_builder.add_edge("call_llm_3", "aggregator")
parallel_builder.add_edge("aggregator", END)
parallel_workflow = parallel_builder.compile()

# Show workflow
display(Image(parallel_workflow.get_graph().draw_mermaid_png()))

# Invoke
state = parallel_workflow.invoke({"topic": "cats"})
print(state["combined_output"])

@task
def call_llm_1(topic: str):
    """First LLM call to generate initial joke"""
    msg = llm.invoke(f"Write a joke about {topic}")
    return msg.content


@task
def call_llm_2(topic: str):
    """Second LLM call to generate story"""
    msg = llm.invoke(f"Write a story about {topic}")
    return msg.content


@task
def call_llm_3(topic):
    """Third LLM call to generate poem"""
    msg = llm.invoke(f"Write a poem about {topic}")
    return msg.content


@task
def aggregator(topic, joke, story, poem):
    """Combine the joke and story into a single output"""

    combined = f"Here's a story, joke, and poem about {topic}!\n\n"
    combined += f"STORY:\n{story}\n\n"
    combined += f"JOKE:\n{joke}\n\n"
    combined += f"POEM:\n{poem}"
    return combined


# Build workflow
@entrypoint()
def parallel_workflow(topic: str):
    joke_fut = call_llm_1(topic)
    story_fut = call_llm_2(topic)
    poem_fut = call_llm_3(topic)
    return aggregator(
        topic, joke_fut.result(), story_fut.result(), poem_fut.result()
    ).result()

# Invoke
for step in parallel_workflow.stream("cats", stream_mode="updates"):
    print(step)
    print("\n")

:::js <CodeGroup>

import { StateGraph, Annotation } from "@langchain/langgraph";

// Graph state
const StateAnnotation = Annotation.Root({
  topic: Annotation<string>,
  joke: Annotation<string>,
  story: Annotation<string>,
  poem: Annotation<string>,
  combinedOutput: Annotation<string>,
});

// Nodes
// First LLM call to generate initial joke
async function callLlm1(state: typeof StateAnnotation.State) {
  const msg = await llm.invoke(`Write a joke about ${state.topic}`);
  return { joke: msg.content };
}

// Second LLM call to generate story
async function callLlm2(state: typeof StateAnnotation.State) {
  const msg = await llm.invoke(`Write a story about ${state.topic}`);
  return { story: msg.content };
}

// Third LLM call to generate poem
async function callLlm3(state: typeof StateAnnotation.State) {
  const msg = await llm.invoke(`Write a poem about ${state.topic}`);
  return { poem: msg.content };
}

// Combine the joke, story and poem into a single output
async function aggregator(state: typeof StateAnnotation.State) {
  const combined = `Here's a story, joke, and poem about ${state.topic}!\n\n` +
    `STORY:\n${state.story}\n\n` +
    `JOKE:\n${state.joke}\n\n` +
    `POEM:\n${state.poem}`;
  return { combinedOutput: combined };
}

// Build workflow
const parallelWorkflow = new StateGraph(StateAnnotation)
  .addNode("callLlm1", callLlm1)
  .addNode("callLlm2", callLlm2)
  .addNode("callLlm3", callLlm3)
  .addNode("aggregator", aggregator)
  .addEdge("__start__", "callLlm1")
  .addEdge("__start__", "callLlm2")
  .addEdge("__start__", "callLlm3")
  .addEdge("callLlm1", "aggregator")
  .addEdge("callLlm2", "aggregator")
  .addEdge("callLlm3", "aggregator")
  .addEdge("aggregator", "__end__")
  .compile();

// Invoke
const result = await parallelWorkflow.invoke({ topic: "cats" });
console.log(result.combinedOutput);

import { task, entrypoint } from "@langchain/langgraph";

// Tasks

// First LLM call to generate initial joke
const callLlm1 = task("generateJoke", async (topic: string) => {
  const msg = await llm.invoke(`Write a joke about ${topic}`);
  return msg.content;
});

// Second LLM call to generate story
const callLlm2 = task("generateStory", async (topic: string) => {
  const msg = await llm.invoke(`Write a story about ${topic}`);
  return msg.content;
});

// Third LLM call to generate poem
const callLlm3 = task("generatePoem", async (topic: string) => {
  const msg = await llm.invoke(`Write a poem about ${topic}`);
  return msg.content;
});

// Combine outputs
const aggregator = task("aggregator", async (params: {
  topic: string;
  joke: string;
  story: string;
  poem: string;
}) => {
  const { topic, joke, story, poem } = params;
  return `Here's a story, joke, and poem about ${topic}!\n\n` +
    `STORY:\n${story}\n\n` +
    `JOKE:\n${joke}\n\n` +
    `POEM:\n${poem}`;
});

// Build workflow
const workflow = entrypoint(
  "parallelWorkflow",
  async (topic: string) => {
    const [joke, story, poem] = await Promise.all([
      callLlm1(topic),
      callLlm2(topic),
      callLlm3(topic),
    ]);

    return aggregator({ topic, joke, story, poem });
  }
);

// Invoke
const stream = await workflow.stream("cats", {
  streamMode: "updates",
});

for await (const step of stream) {
  console.log(step);
}

Routing

Routing workflows process inputs and then directs them to context-specific tasks. This allows you to define specialized flows for complex tasks. For example, a workflow built to answer product related questions might process the type of question first, and then route the request to specific processes for pricing, refunds, returns, etc.

:::python <CodeGroup>

from typing_extensions import Literal
from langchain.messages import HumanMessage, SystemMessage


# Schema for structured output to use as routing logic
class Route(BaseModel):
    step: Literal["poem", "story", "joke"] = Field(
        None, description="The next step in the routing process"
    )


# Augment the LLM with schema for structured output
router = llm.with_structured_output(Route)


# State
class State(TypedDict):
    input: str
    decision: str
    output: str


# Nodes
def llm_call_1(state: State):
    """Write a story"""

    result = llm.invoke(state["input"])
    return {"output": result.content}


def llm_call_2(state: State):
    """Write a joke"""

    result = llm.invoke(state["input"])
    return {"output": result.content}


def llm_call_3(state: State):
    """Write a poem"""

    result = llm.invoke(state["input"])
    return {"output": result.content}


def llm_call_router(state: State):
    """Route the input to the appropriate node"""

    # Run the augmented LLM with structured output to serve as routing logic
    decision = router.invoke(
        [
            SystemMessage(
                content="Route the input to story, joke, or poem based on the user's request."
            ),
            HumanMessage(content=state["input"]),
        ]
    )

    return {"decision": decision.step}


# Conditional edge function to route to the appropriate node
def route_decision(state: State):
    # Return the node name you want to visit next
    if state["decision"] == "story":
        return "llm_call_1"
    elif state["decision"] == "joke":
        return "llm_call_2"
    elif state["decision"] == "poem":
        return "llm_call_3"


# Build workflow
router_builder = StateGraph(State)

# Add nodes
router_builder.add_node("llm_call_1", llm_call_1)
router_builder.add_node("llm_call_2", llm_call_2)
router_builder.add_node("llm_call_3", llm_call_3)
router_builder.add_node("llm_call_router", llm_call_router)

# Add edges to connect nodes
router_builder.add_edge(START, "llm_call_router")
router_builder.add_conditional_edges(
    "llm_call_router",
    route_decision,
    {  # Name returned by route_decision : Name of next node to visit
        "llm_call_1": "llm_call_1",
        "llm_call_2": "llm_call_2",
        "llm_call_3": "llm_call_3",
    },
)
router_builder.add_edge("llm_call_1", END)
router_builder.add_edge("llm_call_2", END)
router_builder.add_edge("llm_call_3", END)

# Compile workflow
router_workflow = router_builder.compile()

# Show the workflow
display(Image(router_workflow.get_graph().draw_mermaid_png()))

# Invoke
state = router_workflow.invoke({"input": "Write me a joke about cats"})
print(state["output"])

from typing_extensions import Literal
from pydantic import BaseModel
from langchain.messages import HumanMessage, SystemMessage


# Schema for structured output to use as routing logic
class Route(BaseModel):
    step: Literal["poem", "story", "joke"] = Field(
        None, description="The next step in the routing process"
    )


# Augment the LLM with schema for structured output
router = llm.with_structured_output(Route)


@task
def llm_call_1(input_: str):
    """Write a story"""
    result = llm.invoke(input_)
    return result.content


@task
def llm_call_2(input_: str):
    """Write a joke"""
    result = llm.invoke(input_)
    return result.content


@task
def llm_call_3(input_: str):
    """Write a poem"""
    result = llm.invoke(input_)
    return result.content


def llm_call_router(input_: str):
    """Route the input to the appropriate node"""
    # Run the augmented LLM with structured output to serve as routing logic
    decision = router.invoke(
        [
            SystemMessage(
                content="Route the input to story, joke, or poem based on the user's request."
            ),
            HumanMessage(content=input_),
        ]
    )
    return decision.step


# Create workflow
@entrypoint()
def router_workflow(input_: str):
    next_step = llm_call_router(input_)
    if next_step == "story":
        llm_call = llm_call_1
    elif next_step == "joke":
        llm_call = llm_call_2
    elif next_step == "poem":
        llm_call = llm_call_3

    return llm_call(input_).result()

# Invoke
for step in router_workflow.stream("Write me a joke about cats", stream_mode="updates"):
    print(step)
    print("\n")

:::js <CodeGroup>

import { StateGraph, Annotation } from "@langchain/langgraph";
import * as z from "zod";

// Schema for structured output to use as routing logic
const routeSchema = z.object({
  step: z.enum(["poem", "story", "joke"]).describe(
    "The next step in the routing process"
  ),
});

// Augment the LLM with schema for structured output
const router = llm.withStructuredOutput(routeSchema);

// Graph state
const StateAnnotation = Annotation.Root({
  input: Annotation<string>,
  decision: Annotation<string>,
  output: Annotation<string>,
});

// Nodes
// Write a story
async function llmCall1(state: typeof StateAnnotation.State) {
  const result = await llm.invoke([{
    role: "system",
    content: "You are an expert storyteller.",
  }, {
    role: "user",
    content: state.input
  }]);
  return { output: result.content };
}

// Write a joke
async function llmCall2(state: typeof StateAnnotation.State) {
  const result = await llm.invoke([{
    role: "system",
    content: "You are an expert comedian.",
  }, {
    role: "user",
    content: state.input
  }]);
  return { output: result.content };
}

// Write a poem
async function llmCall3(state: typeof StateAnnotation.State) {
  const result = await llm.invoke([{
    role: "system",
    content: "You are an expert poet.",
  }, {
    role: "user",
    content: state.input
  }]);
  return { output: result.content };
}

async function llmCallRouter(state: typeof StateAnnotation.State) {
  // Route the input to the appropriate node
  const decision = await router.invoke([
    {
      role: "system",
      content: "Route the input to story, joke, or poem based on the user's request."
    },
    {
      role: "user",
      content: state.input
    },
  ]);

  return { decision: decision.step };
}

// Conditional edge function to route to the appropriate node
function routeDecision(state: typeof StateAnnotation.State) {
  // Return the node name you want to visit next
  if (state.decision === "story") {
    return "llmCall1";
  } else if (state.decision === "joke") {
    return "llmCall2";
  } else if (state.decision === "poem") {
    return "llmCall3";
  }
}

// Build workflow
const routerWorkflow = new StateGraph(StateAnnotation)
  .addNode("llmCall1", llmCall1)
  .addNode("llmCall2", llmCall2)
  .addNode("llmCall3", llmCall3)
  .addNode("llmCallRouter", llmCallRouter)
  .addEdge("__start__", "llmCallRouter")
  .addConditionalEdges(
    "llmCallRouter",
    routeDecision,
    ["llmCall1", "llmCall2", "llmCall3"],
  )
  .addEdge("llmCall1", "__end__")
  .addEdge("llmCall2", "__end__")
  .addEdge("llmCall3", "__end__")
  .compile();

// Invoke
const state = await routerWorkflow.invoke({
  input: "Write me a joke about cats"
});
console.log(state.output);

import * as z from "zod";
import { task, entrypoint } from "@langchain/langgraph";

// Schema for structured output to use as routing logic
const routeSchema = z.object({
  step: z.enum(["poem", "story", "joke"]).describe(
    "The next step in the routing process"
  ),
});

// Augment the LLM with schema for structured output
const router = llm.withStructuredOutput(routeSchema);

// Tasks
// Write a story
const llmCall1 = task("generateStory", async (input: string) => {
  const result = await llm.invoke([{
    role: "system",
    content: "You are an expert storyteller.",
  }, {
    role: "user",
    content: input
  }]);
  return result.content;
});

// Write a joke
const llmCall2 = task("generateJoke", async (input: string) => {
  const result = await llm.invoke([{
    role: "system",
    content: "You are an expert comedian.",
  }, {
    role: "user",
    content: input
  }]);
  return result.content;
});

// Write a poem
const llmCall3 = task("generatePoem", async (input: string) => {
  const result = await llm.invoke([{
    role: "system",
    content: "You are an expert poet.",
  }, {
    role: "user",
    content: input
  }]);
  return result.content;
});

// Route the input to the appropriate node
const llmCallRouter = task("router", async (input: string) => {
  const decision = await router.invoke([
    {
      role: "system",
      content: "Route the input to story, joke, or poem based on the user's request."
    },
    {
      role: "user",
      content: input
    },
  ]);
  return decision.step;
});

// Build workflow
const workflow = entrypoint(
  "routerWorkflow",
  async (input: string) => {
    const nextStep = await llmCallRouter(input);

    let llmCall;
    if (nextStep === "story") {
      llmCall = llmCall1;
    } else if (nextStep === "joke") {
      llmCall = llmCall2;
    } else if (nextStep === "poem") {
      llmCall = llmCall3;
    }

    const finalResult = await llmCall(input);
    return finalResult;
  }
);

// Invoke
const stream = await workflow.stream("Write me a joke about cats", {
  streamMode: "updates",
});

for await (const step of stream) {
  console.log(step);
}

Orchestrator-worker

In an orchestrator-worker configuration, the orchestrator:

• Breaks down tasks into subtasks
• Delegates subtasks to workers
• Synthesizes worker outputs into a final result

Orchestrator-worker workflows provide more flexibility and are often used when subtasks cannot be predefined the way they can with parallelization. This is common with workflows that write code or need to update content across multiple files. For example, a workflow that needs to update installation instructions for multiple Python libraries across an unknown number of documents might use this pattern.

:::python <CodeGroup>

from typing import Annotated, List
import operator


# Schema for structured output to use in planning
class Section(BaseModel):
    name: str = Field(
        description="Name for this section of the report.",
    )
    description: str = Field(
        description="Brief overview of the main topics and concepts to be covered in this section.",
    )


class Sections(BaseModel):
    sections: List[Section] = Field(
        description="Sections of the report.",
    )


# Augment the LLM with schema for structured output
planner = llm.with_structured_output(Sections)

from typing import List


# Schema for structured output to use in planning
class Section(BaseModel):
    name: str = Field(
        description="Name for this section of the report.",
    )
    description: str = Field(
        description="Brief overview of the main topics and concepts to be covered in this section.",
    )


class Sections(BaseModel):
    sections: List[Section] = Field(
        description="Sections of the report.",
    )


# Augment the LLM with schema for structured output
planner = llm.with_structured_output(Sections)


@task
def orchestrator(topic: str):
    """Orchestrator that generates a plan for the report"""
    # Generate queries
    report_sections = planner.invoke(
        [
            SystemMessage(content="Generate a plan for the report."),
            HumanMessage(content=f"Here is the report topic: {topic}"),
        ]
    )

    return report_sections.sections


@task
def llm_call(section: Section):
    """Worker writes a section of the report"""

    # Generate section
    result = llm.invoke(
        [
            SystemMessage(content="Write a report section."),
            HumanMessage(
                content=f"Here is the section name: {section.name} and description: {section.description}"
            ),
        ]
    )

    # Write the updated section to completed sections
    return result.content


@task
def synthesizer(completed_sections: list[str]):
    """Synthesize full report from sections"""
    final_report = "\n\n---\n\n".join(completed_sections)
    return final_report


@entrypoint()
def orchestrator_worker(topic: str):
    sections = orchestrator(topic).result()
    section_futures = [llm_call(section) for section in sections]
    final_report = synthesizer(
        [section_fut.result() for section_fut in section_futures]
    ).result()
    return final_report

# Invoke
report = orchestrator_worker.invoke("Create a report on LLM scaling laws")
from IPython.display import Markdown
Markdown(report)

:::js <CodeGroup>

type SectionSchema = {
    name: string;
    description: string;
}
type SectionsSchema = {
    sections: SectionSchema[];
}

// Augment the LLM with schema for structured output
const planner = llm.withStructuredOutput(sectionsSchema);

import * as z from "zod";
import { task, entrypoint } from "@langchain/langgraph";

// Schema for structured output to use in planning
const sectionSchema = z.object({
  name: z.string().describe("Name for this section of the report."),
  description: z.string().describe(
    "Brief overview of the main topics and concepts to be covered in this section."
  ),
});

const sectionsSchema = z.object({
  sections: z.array(sectionSchema).describe("Sections of the report."),
});

// Augment the LLM with schema for structured output
const planner = llm.withStructuredOutput(sectionsSchema);

// Tasks
const orchestrator = task("orchestrator", async (topic: string) => {
  // Generate queries
  const reportSections = await planner.invoke([
    { role: "system", content: "Generate a plan for the report." },
    { role: "user", content: `Here is the report topic: ${topic}` },
  ]);

  return reportSections.sections;
});

const llmCall = task("sectionWriter", async (section: z.infer<typeof sectionSchema>) => {
  // Generate section
  const result = await llm.invoke([
    {
      role: "system",
      content: "Write a report section.",
    },
    {
      role: "user",
      content: `Here is the section name: ${section.name} and description: ${section.description}`,
    },
  ]);

  return result.content;
});

const synthesizer = task("synthesizer", async (completedSections: string[]) => {
  // Synthesize full report from sections
  return completedSections.join("\n\n---\n\n");
});

// Build workflow
const workflow = entrypoint(
  "orchestratorWorker",
  async (topic: string) => {
    const sections = await orchestrator(topic);
    const completedSections = await Promise.all(
      sections.map((section) => llmCall(section))
    );
    return synthesizer(completedSections);
  }
);

// Invoke
const stream = await workflow.stream("Create a report on LLM scaling laws", {
  streamMode: "updates",
});

for await (const step of stream) {
  console.log(step);
}

Creating workers in LangGraph

Orchestrator-worker workflows are common and LangGraph has built-in support for them. The Send API lets you dynamically create worker nodes and send them specific inputs. Each worker has its own state, and all worker outputs are written to a shared state key that is accessible to the orchestrator graph. This gives the orchestrator access to all worker output and allows it to synthesize them into a final output. The example below iterates over a list of sections and uses the Send API to send a section to each worker.

:::python

from langgraph.types import Send


# Graph state
class State(TypedDict):
    topic: str  # Report topic
    sections: list[Section]  # List of report sections
    completed_sections: Annotated[
        list, operator.add
    ]  # All workers write to this key in parallel
    final_report: str  # Final report


# Worker state
class WorkerState(TypedDict):
    section: Section
    completed_sections: Annotated[list, operator.add]


# Nodes
def orchestrator(state: State):
    """Orchestrator that generates a plan for the report"""

    # Generate queries
    report_sections = planner.invoke(
        [
            SystemMessage(content="Generate a plan for the report."),
            HumanMessage(content=f"Here is the report topic: {state['topic']}"),
        ]
    )

    return {"sections": report_sections.sections}


def llm_call(state: WorkerState):
    """Worker writes a section of the report"""

    # Generate section
    section = llm.invoke(
        [
            SystemMessage(
                content="Write a report section following the provided name and description. Include no preamble for each section. Use markdown formatting."
            ),
            HumanMessage(
                content=f"Here is the section name: {state['section'].name} and description: {state['section'].description}"
            ),
        ]
    )

    # Write the updated section to completed sections
    return {"completed_sections": [section.content]}


def synthesizer(state: State):
    """Synthesize full report from sections"""

    # List of completed sections
    completed_sections = state["completed_sections"]

    # Format completed section to str to use as context for final sections
    completed_report_sections = "\n\n---\n\n".join(completed_sections)

    return {"final_report": completed_report_sections}


# Conditional edge function to create llm_call workers that each write a section of the report
def assign_workers(state: State):
    """Assign a worker to each section in the plan"""

    # Kick off section writing in parallel via Send() API
    return [Send("llm_call", {"section": s}) for s in state["sections"]]


# Build workflow
orchestrator_worker_builder = StateGraph(State)

# Add the nodes
orchestrator_worker_builder.add_node("orchestrator", orchestrator)
orchestrator_worker_builder.add_node("llm_call", llm_call)
orchestrator_worker_builder.add_node("synthesizer", synthesizer)

# Add edges to connect nodes
orchestrator_worker_builder.add_edge(START, "orchestrator")
orchestrator_worker_builder.add_conditional_edges(
    "orchestrator", assign_workers, ["llm_call"]
)
orchestrator_worker_builder.add_edge("llm_call", "synthesizer")
orchestrator_worker_builder.add_edge("synthesizer", END)

# Compile the workflow
orchestrator_worker = orchestrator_worker_builder.compile()

# Show the workflow
display(Image(orchestrator_worker.get_graph().draw_mermaid_png()))

# Invoke
state = orchestrator_worker.invoke({"topic": "Create a report on LLM scaling laws"})

from IPython.display import Markdown
Markdown(state["final_report"])

::: :::js

import { Annotation, StateGraph, Send } from "@langchain/langgraph";

// Graph state
const StateAnnotation = Annotation.Root({
  topic: Annotation<string>,
  sections: Annotation<SectionsSchema[]>,
  completedSections: Annotation<string[]>({
    default: () => [],
    reducer: (a, b) => a.concat(b),
  }),
  finalReport: Annotation<string>,
});

// Worker state
const WorkerStateAnnotation = Annotation.Root({
  section: Annotation<SectionsSchema>,
  completedSections: Annotation<string[]>({
    default: () => [],
    reducer: (a, b) => a.concat(b),
  }),
});

// Nodes
async function orchestrator(state: typeof StateAnnotation.State) {
  // Generate queries
  const reportSections = await planner.invoke([
    { role: "system", content: "Generate a plan for the report." },
    { role: "user", content: `Here is the report topic: ${state.topic}` },
  ]);

  return { sections: reportSections.sections };
}

async function llmCall(state: typeof WorkerStateAnnotation.State) {
  // Generate section
  const section = await llm.invoke([
    {
      role: "system",
      content: "Write a report section following the provided name and description. Include no preamble for each section. Use markdown formatting.",
    },
    {
      role: "user",
      content: `Here is the section name: ${state.section.name} and description: ${state.section.description}`,
    },
  ]);

  // Write the updated section to completed sections
  return { completedSections: [section.content] };
}

async function synthesizer(state: typeof StateAnnotation.State) {
  // List of completed sections
  const completedSections = state.completedSections;

  // Format completed section to str to use as context for final sections
  const completedReportSections = completedSections.join("\n\n---\n\n");

  return { finalReport: completedReportSections };
}

// Conditional edge function to create llm_call workers that each write a section of the report
function assignWorkers(state: typeof StateAnnotation.State) {
  // Kick off section writing in parallel via Send() API
  return state.sections.map((section) =>
    new Send("llmCall", { section })
  );
}

// Build workflow
const orchestratorWorker = new StateGraph(StateAnnotation)
  .addNode("orchestrator", orchestrator)
  .addNode("llmCall", llmCall)
  .addNode("synthesizer", synthesizer)
  .addEdge("__start__", "orchestrator")
  .addConditionalEdges(
    "orchestrator",
    assignWorkers,
    ["llmCall"]
  )
  .addEdge("llmCall", "synthesizer")
  .addEdge("synthesizer", "__end__")
  .compile();

// Invoke
const state = await orchestratorWorker.invoke({
  topic: "Create a report on LLM scaling laws"
});
console.log(state.finalReport);

:::

Evaluator-optimizer

In evaluator-optimizer workflows, one LLM call creates a response and the other evaluates that response. If the evaluator or a human-in-the-loop determines the response needs refinement, feedback is provided and the response is recreated. This loop continues until an acceptable response is generated.

Evaluator-optimizer workflows are commonly used when there's particular success criteria for a task, but iteration is required to meet that criteria. For example, there's not always a perfect match when translating text between two languages. It might take a few iterations to generate a translation with the same meaning across the two languages.

:::python <CodeGroup>

# Graph state
class State(TypedDict):
    joke: str
    topic: str
    feedback: str
    funny_or_not: str


# Schema for structured output to use in evaluation
class Feedback(BaseModel):
    grade: Literal["funny", "not funny"] = Field(
        description="Decide if the joke is funny or not.",
    )
    feedback: str = Field(
        description="If the joke is not funny, provide feedback on how to improve it.",
    )


# Augment the LLM with schema for structured output
evaluator = llm.with_structured_output(Feedback)


# Nodes
def llm_call_generator(state: State):
    """LLM generates a joke"""

    if state.get("feedback"):
        msg = llm.invoke(
            f"Write a joke about {state['topic']} but take into account the feedback: {state['feedback']}"
        )
    else:
        msg = llm.invoke(f"Write a joke about {state['topic']}")
    return {"joke": msg.content}


def llm_call_evaluator(state: State):
    """LLM evaluates the joke"""

    grade = evaluator.invoke(f"Grade the joke {state['joke']}")
    return {"funny_or_not": grade.grade, "feedback": grade.feedback}


# Conditional edge function to route back to joke generator or end based upon feedback from the evaluator
def route_joke(state: State):
    """Route back to joke generator or end based upon feedback from the evaluator"""

    if state["funny_or_not"] == "funny":
        return "Accepted"
    elif state["funny_or_not"] == "not funny":
        return "Rejected + Feedback"


# Build workflow
optimizer_builder = StateGraph(State)

# Add the nodes
optimizer_builder.add_node("llm_call_generator", llm_call_generator)
optimizer_builder.add_node("llm_call_evaluator", llm_call_evaluator)

# Add edges to connect nodes
optimizer_builder.add_edge(START, "llm_call_generator")
optimizer_builder.add_edge("llm_call_generator", "llm_call_evaluator")
optimizer_builder.add_conditional_edges(
    "llm_call_evaluator",
    route_joke,
    {  # Name returned by route_joke : Name of next node to visit
        "Accepted": END,
        "Rejected + Feedback": "llm_call_generator",
    },
)

# Compile the workflow
optimizer_workflow = optimizer_builder.compile()

# Show the workflow
display(Image(optimizer_workflow.get_graph().draw_mermaid_png()))

# Invoke
state = optimizer_workflow.invoke({"topic": "Cats"})
print(state["joke"])

# Schema for structured output to use in evaluation
class Feedback(BaseModel):
    grade: Literal["funny", "not funny"] = Field(
        description="Decide if the joke is funny or not.",
    )
    feedback: str = Field(
        description="If the joke is not funny, provide feedback on how to improve it.",
    )


# Augment the LLM with schema for structured output
evaluator = llm.with_structured_output(Feedback)


# Nodes
@task
def llm_call_generator(topic: str, feedback: Feedback):
    """LLM generates a joke"""
    if feedback:
        msg = llm.invoke(
            f"Write a joke about {topic} but take into account the feedback: {feedback}"
        )
    else:
        msg = llm.invoke(f"Write a joke about {topic}")
    return msg.content


@task
def llm_call_evaluator(joke: str):
    """LLM evaluates the joke"""
    feedback = evaluator.invoke(f"Grade the joke {joke}")
    return feedback


@entrypoint()
def optimizer_workflow(topic: str):
    feedback = None
    while True:
        joke = llm_call_generator(topic, feedback).result()
        feedback = llm_call_evaluator(joke).result()
        if feedback.grade == "funny":
            break

    return joke

# Invoke
for step in optimizer_workflow.stream("Cats", stream_mode="updates"):
    print(step)
    print("\n")

:::js <CodeGroup>

import * as z from "zod";
import { Annotation, StateGraph } from "@langchain/langgraph";

// Graph state
const StateAnnotation = Annotation.Root({
  joke: Annotation<string>,
  topic: Annotation<string>,
  feedback: Annotation<string>,
  funnyOrNot: Annotation<string>,
});

// Schema for structured output to use in evaluation
const feedbackSchema = z.object({
  grade: z.enum(["funny", "not funny"]).describe(
    "Decide if the joke is funny or not."
  ),
  feedback: z.string().describe(
    "If the joke is not funny, provide feedback on how to improve it."
  ),
});

// Augment the LLM with schema for structured output
const evaluator = llm.withStructuredOutput(feedbackSchema);

// Nodes
async function llmCallGenerator(state: typeof StateAnnotation.State) {
  // LLM generates a joke
  let msg;
  if (state.feedback) {
    msg = await llm.invoke(
      `Write a joke about ${state.topic} but take into account the feedback: ${state.feedback}`
    );
  } else {
    msg = await llm.invoke(`Write a joke about ${state.topic}`);
  }
  return { joke: msg.content };
}

async function llmCallEvaluator(state: typeof StateAnnotation.State) {
  // LLM evaluates the joke
  const grade = await evaluator.invoke(`Grade the joke ${state.joke}`);
  return { funnyOrNot: grade.grade, feedback: grade.feedback };
}

// Conditional edge function to route back to joke generator or end based upon feedback from the evaluator
function routeJoke(state: typeof StateAnnotation.State) {
  // Route back to joke generator or end based upon feedback from the evaluator
  if (state.funnyOrNot === "funny") {
    return "Accepted";
  } else if (state.funnyOrNot === "not funny") {
    return "Rejected + Feedback";
  }
}

// Build workflow
const optimizerWorkflow = new StateGraph(StateAnnotation)
  .addNode("llmCallGenerator", llmCallGenerator)
  .addNode("llmCallEvaluator", llmCallEvaluator)
  .addEdge("__start__", "llmCallGenerator")
  .addEdge("llmCallGenerator", "llmCallEvaluator")
  .addConditionalEdges(
    "llmCallEvaluator",
    routeJoke,
    {
      // Name returned by routeJoke : Name of next node to visit
      "Accepted": "__end__",
      "Rejected + Feedback": "llmCallGenerator",
    }
  )
  .compile();

// Invoke
const state = await optimizerWorkflow.invoke({ topic: "Cats" });
console.log(state.joke);

import * as z from "zod";
import { task, entrypoint } from "@langchain/langgraph";

// Schema for structured output to use in evaluation
const feedbackSchema = z.object({
  grade: z.enum(["funny", "not funny"]).describe(
    "Decide if the joke is funny or not."
  ),
  feedback: z.string().describe(
    "If the joke is not funny, provide feedback on how to improve it."
  ),
});

// Augment the LLM with schema for structured output
const evaluator = llm.withStructuredOutput(feedbackSchema);

// Tasks
const llmCallGenerator = task("jokeGenerator", async (params: {
  topic: string;
  feedback?: z.infer<typeof feedbackSchema>;
}) => {
  // LLM generates a joke
  const msg = params.feedback
    ? await llm.invoke(
        `Write a joke about ${params.topic} but take into account the feedback: ${params.feedback.feedback}`
      )
    : await llm.invoke(`Write a joke about ${params.topic}`);
  return msg.content;
});

const llmCallEvaluator = task("jokeEvaluator", async (joke: string) => {
  // LLM evaluates the joke
  return evaluator.invoke(`Grade the joke ${joke}`);
});

// Build workflow
const workflow = entrypoint(
  "optimizerWorkflow",
  async (topic: string) => {
    let feedback: z.infer<typeof feedbackSchema> | undefined;
    let joke: string;

    while (true) {
      joke = await llmCallGenerator({ topic, feedback });
      feedback = await llmCallEvaluator(joke);

      if (feedback.grade === "funny") {
        break;
      }
    }

    return joke;
  }
);

// Invoke
const stream = await workflow.stream("Cats", {
  streamMode: "updates",
});

for await (const step of stream) {
  console.log(step);
  console.log("\n");
}

Agents

Agents are typically implemented as an LLM performing actions using tools. They operate in continuous feedback loops, and are used in situations where problems and solutions are unpredictable. Agents have more autonomy than workflows, and can make decisions about the tools they use and how to solve problems. You can still define the available toolset and guidelines for how agents behave.

:::python

from langchain.tools import tool


# Define tools
@tool
def multiply(a: int, b: int) -> int:
    """Multiply `a` and `b`.

    Args:
        a: First int
        b: Second int
    """
    return a * b


@tool
def add(a: int, b: int) -> int:
    """Adds `a` and `b`.

    Args:
        a: First int
        b: Second int
    """
    return a + b


@tool
def divide(a: int, b: int) -> float:
    """Divide `a` and `b`.

    Args:
        a: First int
        b: Second int
    """
    return a / b


# Augment the LLM with tools
tools = [add, multiply, divide]
tools_by_name = {tool.name: tool for tool in tools}
llm_with_tools = llm.bind_tools(tools)

Nodes

def llm_call(state: MessagesState): """LLM decides whether to call a tool or not"""

return {
    "messages": [
        llm_with_tools.invoke(
            [
                SystemMessage(
                    content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
                )
            ]
            + state["messages"]
        )
    ]
}

def tool_node(state: dict): """Performs the tool call"""

result = []
for tool_call in state["messages"][-1].tool_calls:
    tool = tools_by_name[tool_call["name"]]
    observation = tool.invoke(tool_call["args"])
    result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
return {"messages": result}

Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call

def should_continue(state: MessagesState) -> Literal["tool_node", END]: """Decide if we should continue the loop or stop based upon whether the LLM made a tool call"""

messages = state["messages"]
last_message = messages[-1]

# If the LLM makes a tool call, then perform an action
if last_message.tool_calls:
    return "tool_node"

# Otherwise, we stop (reply to the user)
return END

Build workflow

agent_builder = StateGraph(MessagesState)

Add nodes

agent_builder.add_node("llm_call", llm_call) agent_builder.add_node("tool_node", tool_node)

Add edges to connect nodes

agent_builder.add_edge(START, "llm_call") agent_builder.add_conditional_edges( "llm_call", should_continue, ["tool_node", END] ) agent_builder.add_edge("tool_node", "llm_call")

Compile the agent

agent = agent_builder.compile()

Show the agent

display(Image(agent.get_graph(xray=True).draw_mermaid_png()))

Invoke

messages = [HumanMessage(content="Add 3 and 4.")] messages = agent.invoke({"messages": messages}) for m in messages["messages"]: m.pretty_print()

```python Functional API
from langgraph.graph import add_messages
from langchain.messages import (
    SystemMessage,
    HumanMessage,
    ToolCall,
)
from langchain_core.messages import BaseMessage


@task
def call_llm(messages: list[BaseMessage]):
    """LLM decides whether to call a tool or not"""
    return llm_with_tools.invoke(
        [
            SystemMessage(
                content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
            )
        ]
        + messages
    )


@task
def call_tool(tool_call: ToolCall):
    """Performs the tool call"""
    tool = tools_by_name[tool_call["name"]]
    return tool.invoke(tool_call)


@entrypoint()
def agent(messages: list[BaseMessage]):
    llm_response = call_llm(messages).result()

    while True:
        if not llm_response.tool_calls:
            break

        # Execute tools
        tool_result_futures = [
            call_tool(tool_call) for tool_call in llm_response.tool_calls
        ]
        tool_results = [fut.result() for fut in tool_result_futures]
        messages = add_messages(messages, [llm_response, *tool_results])
        llm_response = call_llm(messages).result()

    messages = add_messages(messages, llm_response)
    return messages

# Invoke
messages = [HumanMessage(content="Add 3 and 4.")]
for chunk in agent.stream(messages, stream_mode="updates"):
    print(chunk)
    print("\n")

:::js

import { tool } from "@langchain/core/tools";
import * as z from "zod";

// Define tools
const multiply = tool(
  ({ a, b }) => {
    return a * b;
  },
  {
    name: "multiply",
    description: "Multiply two numbers together",
    schema: z.object({
      a: z.number().describe("first number"),
      b: z.number().describe("second number"),
    }),
  }
);

const add = tool(
  ({ a, b }) => {
    return a + b;
  },
  {
    name: "add",
    description: "Add two numbers together",
    schema: z.object({
      a: z.number().describe("first number"),
      b: z.number().describe("second number"),
    }),
  }
);

const divide = tool(
  ({ a, b }) => {
    return a / b;
  },
  {
    name: "divide",
    description: "Divide two numbers",
    schema: z.object({
      a: z.number().describe("first number"),
      b: z.number().describe("second number"),
    }),
  }
);

// Augment the LLM with tools
const tools = [add, multiply, divide];
const toolsByName = Object.fromEntries(tools.map((tool) => [tool.name, tool]));
const llmWithTools = llm.bindTools(tools);

// Nodes async function llmCall(state: typeof MessagesAnnotation.State) { // LLM decides whether to call a tool or not const result = await llmWithTools.invoke([ { role: "system", content: "You are a helpful assistant tasked with performing arithmetic on a set of inputs." }, ...state.messages ]);

return { messages: [result] }; }

const toolNode = new ToolNode(tools);

// Conditional edge function to route to the tool node or end function shouldContinue(state: typeof MessagesAnnotation.State) { const messages = state.messages; const lastMessage = messages.at(-1);

// If the LLM makes a tool call, then perform an action if (lastMessage?.tool_calls?.length) { return "toolNode"; } // Otherwise, we stop (reply to the user) return "end"; }

// Build workflow const agentBuilder = new StateGraph(MessagesAnnotation) .addNode("llmCall", llmCall) .addNode("toolNode", toolNode) // Add edges to connect nodes .addEdge("start", "llmCall") .addConditionalEdges( "llmCall", shouldContinue, ["toolNode", "end"] ) .addEdge("toolNode", "llmCall") .compile();

// Invoke const messages = [{ role: "user", content: "Add 3 and 4." }]; const result = await agentBuilder.invoke({ messages }); console.log(result.messages);

```typescript Functional API
import { task, entrypoint, addMessages } from "@langchain/langgraph";
import { BaseMessageLike, ToolCall } from "@langchain/core/messages";

const callLlm = task("llmCall", async (messages: BaseMessageLike[]) => {
  // LLM decides whether to call a tool or not
  return llmWithTools.invoke([
    {
      role: "system",
      content: "You are a helpful assistant tasked with performing arithmetic on a set of inputs."
    },
    ...messages
  ]);
});

const callTool = task("toolCall", async (toolCall: ToolCall) => {
  // Performs the tool call
  const tool = toolsByName[toolCall.name];
  return tool.invoke(toolCall.args);
});

const agent = entrypoint(
  "agent",
  async (messages) => {
    let llmResponse = await callLlm(messages);

    while (true) {
      if (!llmResponse.tool_calls?.length) {
        break;
      }

      // Execute tools
      const toolResults = await Promise.all(
        llmResponse.tool_calls.map((toolCall) => callTool(toolCall))
      );

      messages = addMessages(messages, [llmResponse, ...toolResults]);
      llmResponse = await callLlm(messages);
    }

    messages = addMessages(messages, [llmResponse]);
    return messages;
  }
);

// Invoke
const messages = [{
  role: "user",
  content: "Add 3 and 4."
}];

const stream = await agent.stream([messages], {
  streamMode: "updates",
});

for await (const step of stream) {
  console.log(step);
}