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⚡ Building language agents as graphs ⚡

Overview

LangGraph.js is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.

LangGraph is inspired by Pregel and Apache Beam. The public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.

Key Features

Installation

npm install @langchain/langgraph @langchain/core

Example

One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.

Let's take a look at an example of an agent that can use a search tool.

First install the required dependencies:

npm install @langchain/anthropic

Then set the required environment variables:

export ANTHROPIC_API_KEY=sk-...

Optionally, set up LangSmith for best-in-class observability:

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=ls__...

Now let's define our agent:

import { AIMessage, BaseMessage, HumanMessage } from "@langchain/core/messages";
import { tool } from "@langchain/core/tools";
import { z } from "zod";
import { ChatAnthropic } from "@langchain/anthropic";
import { StateGraph } from "@langchain/langgraph";
import { MemorySaver, Annotation } from "@langchain/langgraph";
import { ToolNode } from "@langchain/langgraph/prebuilt";

// Define the graph state
// See here for more info: https://langchain-ai.github.io/langgraphjs/how-tos/define-state/
const StateAnnotation = Annotation.Root({
  messages: Annotation<BaseMessage[]>({
    reducer: (x, y) => x.concat(y),
  })
})

// Define the tools for the agent to use
const weatherTool = tool(async ({ query }) => {
  // This is a placeholder for the actual implementation
  if (query.toLowerCase().includes("sf") || query.toLowerCase().includes("san francisco")) {
    return "It's 60 degrees and foggy."
  }
  return "It's 90 degrees and sunny."
}, {
  name: "weather",
  description:
    "Call to get the current weather for a location.",
  schema: z.object({
    query: z.string().describe("The query to use in your search."),
  }),
});

const tools = [weatherTool];
const toolNode = new ToolNode(tools);

const model = new ChatAnthropic({
  model: "claude-3-5-sonnet-20240620",
  temperature: 0,
}).bindTools(tools);

// Define the function that determines whether to continue or not
// We can extract the state typing via `StateAnnotation.State`
function shouldContinue(state: typeof StateAnnotation.State) {
  const messages = state.messages;
  const lastMessage = messages[messages.length - 1] as AIMessage;

  // If the LLM makes a tool call, then we route to the "tools" node
  if (lastMessage.tool_calls?.length) {
    return "tools";
  }
  // Otherwise, we stop (reply to the user)
  return "__end__";
}

// Define the function that calls the model
async function callModel(state: typeof StateAnnotation.State) {
  const messages = state.messages;
  const response = await model.invoke(messages);

  // We return a list, because this will get added to the existing list
  return { messages: [response] };
}

// Define a new graph
const workflow = new StateGraph(StateAnnotation)
  .addNode("agent", callModel)
  .addNode("tools", toolNode)
  .addEdge("__start__", "agent")
  .addConditionalEdges("agent", shouldContinue)
  .addEdge("tools", "agent");

// Initialize memory to persist state between graph runs
const checkpointer = new MemorySaver();

// Finally, we compile it!
// This compiles it into a LangChain Runnable.
// Note that we're (optionally) passing the memory when compiling the graph
const app = workflow.compile({ checkpointer });

// Use the Runnable
const finalState = await app.invoke(
  { messages: [new HumanMessage("what is the weather in sf")] },
  { configurable: { thread_id: "42" } }
);

console.log(finalState.messages[finalState.messages.length - 1].content);

This will output:

Based on the information I received, the current weather in San Francisco is:

Temperature: 60 degrees Fahrenheit
Conditions: Foggy

San Francisco is known for its foggy weather, especially during certain times of the year. The moderate temperature of 60°F (about 15.5°C) is quite typical for the city, which generally has mild weather year-round due to its coastal location.

Is there anything else you'd like to know about the weather in San Francisco or any other location?

Now when we pass the same "thread_id", the conversation context is retained via the saved state (i.e. stored list of messages):

const nextState = await app.invoke(
  { messages: [new HumanMessage("what about ny")] },
  { configurable: { thread_id: "42" } }
);
console.log(nextState.messages[nextState.messages.length - 1].content);

Based on the information I received, the current weather in New York is:

Temperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)
Conditions: Sunny

New York is experiencing quite warm weather today. A temperature of 90°F is considered hot for most people, and it's significantly warmer than the San Francisco weather we just checked. The sunny conditions suggest it's a clear day without cloud cover, which can make it feel even warmer.

On a day like this in New York, it would be advisable for people to stay hydrated, seek shade when possible, and use sun protection if spending time outdoors.

Is there anything else you'd like to know about the weather in New York or any other location?

Step-by-step Breakdown

  1. <details> <summary>Initialize the model and tools.</summary>
    • We use ChatAnthropic as our LLM. NOTE: We need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for Anthropic tool calling using the .bindTools() method.
    • We define the tools we want to use -- a weather tool in our case. See the documentation here on how to create your own tools.
    </details>
  2. <details> <summary>Initialize graph with state.</summary>
    • We initialize the graph (StateGraph) by passing the state interface (AgentState).
    • The StateAnnotation object defines how updates from each node should be merged into the graph's state.
    </details>
  3. <details> <summary>Define graph nodes.</summary>

    There are two main nodes we need:

    • The agent node: responsible for deciding what (if any) actions to take.
    • The tools node that invokes tools: if the agent decides to take an action, this node will then execute that action.
    </details>
  4. <details> <summary>Define entry point and graph edges.</summary>

    First, we need to set the entry point for graph execution - the agent node.

    Then we define one normal and one conditional edge. A conditional edge means that the destination depends on the contents of the graph's state (AgentState). In our case, the destination is not known until the agent (LLM) decides.

    • Conditional edge: after the agent is called, we should either:
      • a. Run tools if the agent said to take an action, OR
      • b. Finish (respond to the user) if the agent did not ask to run tools
    • Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
    </details>
  5. <details> <summary>Compile the graph.</summary>
    • When we compile the graph, we turn it into a LangChain Runnable, which automatically enables calling .invoke(), .stream() and .batch() with your inputs.
    • We can also optionally pass a checkpointer object for persisting state between graph runs, enabling memory, human-in-the-loop workflows, time travel and more. In our case we use MemorySaver - a simple in-memory checkpointer.
    </details>
  6. <details> <summary>Execute the graph.</summary>

    1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, "agent".

    2. The "agent" node executes, invoking the chat model.

    3. The chat model returns an AIMessage. LangGraph adds this to the state.

    4. The graph cycles through the following steps until there are no more tool_calls on the AIMessage:

      • If AIMessage has tool_calls, the "tools" node executes.
      • The "agent" node executes again and returns an AIMessage.

    5. Execution progresses to the special __end__ value and outputs the final state. As a result, we get a list of all our chat messages as output.

    </details>

Documentation

Running Example Jupyter Notebooks

Please note that the *.ipynb notebooks in the examples/ folder require tslab to be installed. In order to run these notebooks in VSCode, you will also need the Jupyter VSCode Extension installed. After cloning this repository, you can run yarn build in the root. You should then be all set!

If you are still having trouble, try adding the following tsconfig.json file to the examples/ directory:

{
  "compilerOptions": {
    "esModuleInterop": true,
    "moduleResolution": "node",
    "target": "ES2020",
    "module": "ES2020",
    "lib": [
      "ES2020"
    ],
    "strict": true,
    "baseUrl": ".",
    "paths": {
      "@langchain/langgraph": [
        "../langgraph/src"
      ]
    }
  },
  "include": [
    "./**/*.ts",
    "./**/*.tsx"
  ],
  "exclude": [
    "node_modules"
  ]
}