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Setup Arcade with LangChain

LangChain is a popular agentic framework that abstracts a lot of the complexity of building AI agents. It is built on top of LangGraph, a lower level orchestration framework that offers more control over the inner flow of the .

Outcomes

Learn how to integrate Arcade using LangChain primitives

You will Learn

  • How to retrieve Arcade and transform them into LangChain tools
  • How to build a LangChain
  • How to integrate Arcade into the agentic flow
  • How to manage Arcade authorization using LangChain interrupts

Prerequisites

LangChain primitives you will use in this guide

LangChain provides multiple abstractions for building AI , and it’s very useful to internalize how some of these primitives work, so you can understand and extend the different agentic patterns LangChain supports.

  • : Most agentic frameworks, including LangChain, provide an abstraction for a .
  • Interrupts: Interrupts in LangChain are a way to control the flow of the agentic loop when something needs to be done outside of the normal ReAct flow. For example, if a tool requires authorization, you can interrupt the and ask the user to authorize the before continuing.
  • Checkpointers: Checkpointers are how LangChain implements persistence. A checkpointer stores the ’s state in a “checkpoint” that can be resumed later. Those checkpoints are saved to a thread, which can be accessed after the agent’s execution, making it very simlpe for long-running agents and for handling interruptions and more sophisticated flows such as branching, time travel, and more.

Integrate Arcade tools into a LangChain agent

Create a new project

Terminal
mkdir langchain-arcade-example cd langchain-arcade-example bun install @arcadeai/arcadejs langchain @langchain/openai @langchain/core @langchain/langgraph

Create a new file called .env and add the following :

ENV
.env
ARCADE_API_KEY=YOUR_ARCADE_API_KEY OPENAI_API_KEY=YOUR_OPENAI_API_KEY

Import the necessary packages

Create a new file called main.ts and add the following code:

TypeScript
main.ts
"use strict"; import { Arcade } from "@arcadeai/arcadejs"; import { type ToolExecuteFunctionFactoryInput, executeZodTool, isAuthorizationRequiredError, toZod, } from "@arcadeai/arcadejs/lib"; import { type ToolExecuteFunction } from "@arcadeai/arcadejs/lib/zod/types"; import { createAgent, tool } from "langchain"; import { Command, interrupt, MemorySaver, type Interrupt, } from "@langchain/langgraph"; import chalk from "chalk"; import readline from "node:readline/promises";

This is quite a number of imports, let’s break them down:

  • Arcade imports:
    • Arcade: This is the , used to interact with the .
    • type ToolExecuteFunctionFactoryInput: Encodes the input to execute Arcade .
    • isAuthorizationRequiredError: Checks if a requires authorization.
    • toZod: Converts an Arcade definition into a Zod  schema (Zod provides type safety and validation at runtime).
    • executeZodTool: Executes an Zod-converted .
  • LangChain imports:
    • createAgent: Creates a LangChain using the ReAct pattern.
    • tool: Turns an Arcade definition into a LangChain tool.
    • interrupt: Interrupts the ReAct flow and asks the for input.
    • Command: Communicates the user’s decisions to the ’s interrupts.
    • MemorySaver: Stores the ’s state, and is required for checkpointing and interrupts.
  • Other imports:
    • chalk: This is a library to colorize the console output.
    • readline: This is a library to read input from the console.

Configure the agent

These variables are used in the rest of the code to customize the and manage the . Feel free to configure them to your liking.

TypeScript
main.ts
// configure your own values to customize your agent // The Arcade User ID identifies who is authorizing each service. const arcadeUserID = "{arcade_user_id}"; // This determines which MCP server is providing the tools, you can customize this to make a Notion agent. All tools from the MCP servers defined in the array will be used. const MCPServers = ["Slack"]; // This determines individual tools. Useful to pick specific tools when you don't need all of them. const individualTools = ["Gmail_ListEmails", "Gmail_SendEmail", "Gmail_WhoAmI"]; // This determines the maximum number of tool definitions Arcade will return per MCP server const toolLimit = 30; // This prompt defines the behavior of the agent. const systemPrompt = "You are a helpful assistant that can use Gmail tools. Your main task is to help the user with anything they may need."; // This determines which LLM will be used inside the agent const agentModel = "gpt-4o-mini"; // This allows LangChain to retain the context of the session const threadID = "1";

Write a helper function to execute Arcade tools

This is a wrapper around the executeZodTool function. When it fails, you interrupt the flow and send the authorization request for the to handle. If the user authorizes the , the harness will reply with an {authorized: true} object, and the tool call will be retried without interrupting the flow.

TypeScript
main.ts
function executeOrInterruptTool({ zodToolSchema, toolDefinition, client, userId, }: ToolExecuteFunctionFactoryInput): ToolExecuteFunction<any> { const { name: toolName } = zodToolSchema; return async (input: unknown) => { try { // Try to execute the tool const result = await executeZodTool({ zodToolSchema, toolDefinition, client, userId, })(input); return result; } catch (error) { // If the tool requires authorization, interrupt the flow and ask the user to authorize the tool if (error instanceof Error && isAuthorizationRequiredError(error)) { const response = await client.tools.authorize({ tool_name: toolName, user_id: userId, }); // Interrupt the flow here, and pass everything the handler needs to get the user's authorization const interrupt_response = interrupt({ authorization_required: true, authorization_response: response, tool_name: toolName, url: response.url ?? "", }); // If the user authorized the tool, retry the tool call without interrupting the flow if (interrupt_response.authorized) { const result = await executeZodTool({ zodToolSchema, toolDefinition, client, userId, })(input); return result; } else { // If the user didn't authorize the tool, throw an error, which will be handled by LangChain throw new Error( `Authorization required for tool call ${toolName}, but user didn't authorize.` ); } } throw error; } }; }

Retrieve Arcade tools and transform them into LangChain tools

Here you get the Arcade tools you want the agent to use, and transform them into LangChain tools. The first step is to initialize the , and get the you want to use. Then, use the toZod function to convert the Arcade tools into a Zod schema, and pass it to the executeOrInterruptTool function to create a LangChain tool.

This helper function is fairly long, here’s a breakdown of what it does for clarity:

  • retrieve tools from all configured servers (defined in the MCPServers variable)
  • retrieve individual (defined in the individualTools variable)
  • combine the tools from the servers and the individual
  • convert the Arcade to Zod tools
  • convert the Zod to LangChain tools

You then call the getTools function to get the tools you want the to use.

TypeScript
main.ts
// Initialize the Arcade client const arcade = new Arcade(); export type GetToolsProps = { arcade: Arcade; mcpServers?: string[]; individualTools?: string[]; userId: string; limit?: number; }; export async function getTools({ arcade, mcpServers = [], individualTools = [], userId, limit = 30, }: GetToolsProps) { if (mcpServers.length === 0 && individualTools.length === 0) { throw new Error("At least one tool or toolkit must be provided"); } // Get up to `limit` tools from each configured MCP server const from_mcpServers = await Promise.all( mcpServers.map(async (mcpServerName) => { const definitions = await arcade.tools.list({ toolkit: mcpServerName, limit: limit, }); return definitions.items; }) ); // Get the individual tools const from_individualTools = await Promise.all( individualTools.map(async (toolName) => { return await arcade.tools.get(toolName); }) ); // Combine the tools from the MCP servers and the individual tools const all_tools = [...from_mcpServers.flat(), ...from_individualTools]; const unique_tools = Array.from( new Map(all_tools.map((tool) => [tool.qualified_name, tool])).values() ); // Convert the Arcade tools to Zod tools const arcadeTools = toZod({ tools: unique_tools, client: arcade, executeFactory: executeOrInterruptTool, userId: userId, }); // Convert Arcade tools to LangGraph tools const langchainTools = arcadeTools.map( ({ name, description, execute, parameters }) => (tool as Function)(execute, { name, description, schema: parameters, }) ); return langchainTools; } const tools = await getTools({ arcade, mcpServers: MCPServers, individualTools: individualTools, userId: arcadeUserID, limit: toolLimit, });

Write the interrupt handler

In LangChain, each interrupt needs to be “resolved” for the flow to continue. In response to an interrupt, you need to return a decision object with the information needed to resolve the interrupt. In this case, the decision is whether the authorization was successful, in which case the tool call will be retried, or if the authorization failed, the flow will be interrupted with an error, and the will decide what to do next.

This helper function receives an interrupt and returns a decision object. Decision objects can be of any serializable type (convertible to JSON). In this case, you return an object with a boolean flag indicating if the authorization was successful.

This function captures the authorization flow outside of the agent’s context, which is a good practice for security and context engineering. By handling everything in the , you remove the risk of the LLM replacing the authorization URL or leaking it, and you keep the free from any authorization-related traces, which reduces the risk of hallucinations.

TypeScript
main.ts
async function handleAuthInterrupt( interrupt: Interrupt ): Promise<{ authorized: boolean }> { const value = interrupt.value; const authorization_required = value.authorization_required; if (authorization_required) { const tool_name = value.tool_name; const authorization_response = value.authorization_response; console.log("⚙️: Authorization required for tool call", tool_name); console.log( "⚙️: Please authorize in your browser", authorization_response.url ); console.log("⚙️: Waiting for you to complete authorization..."); try { await arcade.auth.waitForCompletion(authorization_response.id); console.log("⚙️: Authorization granted. Resuming execution..."); return { authorized: true }; } catch (error) { console.error("⚙️: Error waiting for authorization to complete:", error); return { authorized: false }; } } return { authorized: false }; }

Create the agent

Here you create the using the createAgent function. You pass the system prompt, the model, the tools, and the checkpointer. When the agent runs, it will automatically use the helper function you wrote earlier to handle calls and authorization requests.

TypeScript
main.ts
const agent = createAgent({ systemPrompt: systemPrompt, model: agentModel, tools: tools, checkpointer: new MemorySaver(), });

Write the invoke helper

This last helper function handles the streaming of the ’s response, and captures the interrupts. When an interrupt is detected, it is added to the interrupts array, and the flow is interrupted. If there are no interrupts, it will just stream the agent’s to your console.

TypeScript
main.ts
async function streamAgent( agent: any, input: any, config: any ): Promise<Interrupt[]> { const stream = await agent.stream(input, { ...config, streamMode: "updates", }); const interrupts: Interrupt[] = []; for await (const chunk of stream) { if (chunk.__interrupt__) { interrupts.push(...(chunk.__interrupt__ as Interrupt[])); continue; } for (const update of Object.values(chunk)) { for (const msg of (update as any)?.messages ?? []) { console.log("🤖: ", msg.toFormattedString()); } } } return interrupts; }

Write the main function

Finally, write the main function that will call the and handle the input.

Here the config object is used to configure the thread_id, which tells the to store the state of the conversation into that specific thread. Like any typical agent loop, you:

  1. Capture the input
  2. Stream the ’s response
  3. Handle any authorization interrupts
  4. Resume the after authorization
  5. Handle any errors
  6. Exit the loop if the wants to quit
TypeScript
main.ts
async function main() { const config = { configurable: { thread_id: threadID } }; const rl = readline.createInterface({ input: process.stdin, output: process.stdout, }); console.log(chalk.green("Welcome to the chatbot! Type 'exit' to quit.")); while (true) { const input = await rl.question("> "); if (input.toLowerCase() === "exit") { break; } rl.pause(); try { let agentInput: any = { messages: [{ role: "user", content: input }], }; // Loop until no more interrupts while (true) { const interrupts = await streamAgent(agent, agentInput, config); if (interrupts.length === 0) { break; // No more interrupts, we're done } // Handle all interrupts const decisions: any[] = []; for (const interrupt of interrupts) { decisions.push(await handleAuthInterrupt(interrupt, rl)); } // Resume with decisions, then loop to check for more interrupts // Pass single decision directly, or array for multiple interrupts agentInput = new Command({ resume: decisions.length === 1 ? decisions[0] : decisions, }); } } catch (error) { console.error(error); } rl.resume(); } console.log(chalk.red("👋 Bye...")); process.exit(0); } // Run the main function main().catch((err) => console.error(err));

Run the agent

Terminal
bun run main.ts

You should see the responding to your prompts like any model, as well as handling any calls and authorization requests. Here are some example prompts you can try:

  • “Send me an email with a random haiku about LangChain
  • “Summarize my latest 3 emails”

Key takeaways

  • Arcade can be integrated into any agentic framework like LangChain, all you need is to transform the Arcade tools into LangChain tools and handle the authorization flow.
  • isolation: By handling the authorization flow outside of the ’s context, you remove the risk of the LLM replacing the authorization URL or leaking it, and you keep the context free from any authorization-related traces, which reduces the risk of hallucinations.
  • You can leverage the interrupts mechanism to handle human intervention in the ’s flow, useful for authorization flows, policy enforcement, or anything else that requires input from the .

Next Steps

  1. Try adding additional tools to the or modifying the in the catalog for a different use case by modifying the MCPServers and individualTools variables.
  2. Try refactoring the handleAuthInterrupt function to handle more complex flows, such as human-in-the-loop.
  3. Try implementing a fully deterministic flow before the agentic loop, use this deterministic phase to prepare the for the , adding things like the current date, time, or any other information that is relevant to the task at hand.

Example code

TypeScript
main.ts
"use strict"; import { Arcade } from "@arcadeai/arcadejs"; import { type ToolExecuteFunctionFactoryInput, executeZodTool, isAuthorizationRequiredError, toZod, } from "@arcadeai/arcadejs/lib"; import { type ToolExecuteFunction } from "@arcadeai/arcadejs/lib/zod/types"; import { createAgent, tool } from "langchain"; import { Command, interrupt, MemorySaver, type Interrupt, } from "@langchain/langgraph"; import chalk from "chalk"; import readline from "node:readline/promises"; // configure your own values to customize your agent // The Arcade User ID identifies who is authorizing each service. const arcadeUserID = "{arcade_user_id}"; // This determines which MCP server is providing the tools, you can customize this to make a Notion agent. All tools from the MCP servers defined in the array will be used. const MCPServers = ["Slack"]; // This determines individual tools. Useful to pick specific tools when you don't need all of them. const individualTools = ["Gmail_ListEmails", "Gmail_SendEmail"]; // This determines the maximum number of tool definitions Arcade will return const toolLimit = 30; // This prompt defines the behavior of the agent. const systemPrompt = "You are a helpful assistant that can use Gmail tools. Your main task is to help the user with anything they may need."; // This determines which LLM will be used inside the agent const agentModel = "gpt-4o-mini"; // This allows LangChain to retain the context of the session const threadID = "1"; function executeOrInterruptTool({ zodToolSchema, toolDefinition, client, userId, }: ToolExecuteFunctionFactoryInput): ToolExecuteFunction<any> { const { name: toolName } = zodToolSchema; return async (input: unknown) => { try { // Try to execute the tool const result = await executeZodTool({ zodToolSchema, toolDefinition, client, userId, })(input); return result; } catch (error) { // If the tool requires authorization, we interrupt the flow and ask the user to authorize the tool if (error instanceof Error && isAuthorizationRequiredError(error)) { const response = await client.tools.authorize({ tool_name: toolName, user_id: userId, }); // We interrupt the flow here, and pass everything the handler needs to get the user's authorization const interrupt_response = interrupt({ authorization_required: true, authorization_response: response, tool_name: toolName, url: response.url ?? "", }); // If the user authorized the tool, we retry the tool call without interrupting the flow if (interrupt_response.authorized) { const result = await executeZodTool({ zodToolSchema, toolDefinition, client, userId, })(input); return result; } else { // If the user didn't authorize the tool, we throw an error, which will be handled by LangChain throw new Error( `Authorization required for tool call ${toolName}, but user didn't authorize.` ); } } throw error; } }; } // Initialize the Arcade client const arcade = new Arcade(); export type GetToolsProps = { arcade: Arcade; mcpServers?: string[]; individualTools?: string[]; userId: string; limit?: number; }; export async function getTools({ arcade, mcpServers = [], individualTools = [], userId, limit = 30, }: GetToolsProps) { if (mcpServers.length === 0 && individualTools.length === 0) { throw new Error("At least one tool or toolkit must be provided"); } const from_mcpServers = await Promise.all( mcpServers.map(async (mcpServerName) => { const definitions = await arcade.tools.list({ toolkit: mcpServerName, limit: limit, }); return definitions.items; }) ); const from_individualTools = await Promise.all( individualTools.map(async (toolName) => { return await arcade.tools.get(toolName); }) ); const all_tools = [...from_mcpServers.flat(), ...from_individualTools]; const unique_tools = Array.from( new Map(all_tools.map((tool) => [tool.qualified_name, tool])).values() ); const arcadeTools = toZod({ tools: unique_tools, client: arcade, executeFactory: executeOrInterruptTool, userId: userId, }); // Convert Arcade tools to LangGraph tools const langchainTools = arcadeTools.map( ({ name, description, execute, parameters }) => (tool as Function)(execute, { name, description, schema: parameters, }) ); return langchainTools; } const tools = await getTools({ arcade, mcpServers: MCPServers, individualTools: individualTools, userId: arcadeUserID, limit: toolLimit, }); async function handleAuthInterrupt( interrupt: Interrupt ): Promise<{ authorized: boolean }> { const value = interrupt.value; const authorization_required = value.authorization_required; if (authorization_required) { const tool_name = value.tool_name; const authorization_response = value.authorization_response; console.log("⚙️: Authorization required for tool call", tool_name); console.log( "⚙️: Please authorize in your browser", authorization_response.url ); console.log("⚙️: Waiting for you to complete authorization..."); try { await arcade.auth.waitForCompletion(authorization_response.id); console.log("⚙️: Authorization granted. Resuming execution..."); return { authorized: true }; } catch (error) { console.error("⚙️: Error waiting for authorization to complete:", error); return { authorized: false }; } } return { authorized: false }; } const agent = createAgent({ systemPrompt: systemPrompt, model: agentModel, tools: tools, checkpointer: new MemorySaver(), }); async function streamAgent( agent: any, input: any, config: any ): Promise<Interrupt[]> { const stream = await agent.stream(input, { ...config, streamMode: "updates", }); const interrupts: Interrupt[] = []; for await (const chunk of stream) { if (chunk.__interrupt__) { interrupts.push(...(chunk.__interrupt__ as Interrupt[])); continue; } for (const update of Object.values(chunk)) { for (const msg of (update as any)?.messages ?? []) { console.log("🤖: ", msg.toFormattedString()); } } } return interrupts; } async function main() { const config = { configurable: { thread_id: threadID } }; const rl = readline.createInterface({ input: process.stdin, output: process.stdout, }); console.log(chalk.green("Welcome to the chatbot! Type 'exit' to quit.")); while (true) { const input = await rl.question("> "); if (input.toLowerCase() === "exit") { break; } rl.pause(); try { let agentInput: any = { messages: [{ role: "user", content: input }], }; // Loop until no more interrupts while (true) { const interrupts = await streamAgent(agent, agentInput, config); if (interrupts.length === 0) { break; // No more interrupts, we're done } // Handle all interrupts const decisions: any[] = []; for (const interrupt of interrupts) { decisions.push(await handleAuthInterrupt(interrupt, rl)); } // Resume with decisions, then loop to check for more interrupts // Pass single decision directly, or array for multiple interrupts agentInput = new Command({ resume: decisions.length === 1 ? decisions[0] : decisions, }); } } catch (error) { console.error(error); } rl.resume(); } console.log(chalk.red("👋 Bye...")); process.exit(0); } // Run the main function main().catch((err) => console.error(err));
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