DEV Community: Željko Šević

Integration with Ashby public jobs API

Željko Šević — Wed, 01 Jul 2026 20:40:58 +0000

Ashby is an applicant tracking system (ATS) used by many startups and scale-ups. Ashby exposes a lightweight, unauthenticated Job Postings API so you can list published openings on a custom careers page or aggregate jobs into a job board.

This post shows how to fetch public job listings from Ashby with Node.js, normalize the response, and filter to listed roles only. For other ATS public feeds, see the Greenhouse, Workable, and Lever posts.

Prerequisites

Node.js version 26
A company's Ashby job board name (see below)
No API key required for the public posting API

Find the job board name

Open the company's Ashby-hosted careers page. The URL looks like https://jobs.ashbyhq.com/{JOB_BOARD_NAME}. The last path segment is the identifier you pass to the API.

Examples: notion for Notion (https://jobs.ashbyhq.com/notion), ashby for Ashby itself.

Ashby also offers authenticated endpoints (for example jobPosting.list) for customers who need filters or private data. The public API documented here is read-only and scoped to one job board at a time.

API overview

Item	Value
Base URL	`https://api.ashbyhq.com/posting-api/job-board/{JOB_BOARD_NAME}`
Auth	None for read access
Format	JSON
Optional query	`includeCompensation=true` adds salary bands when the employer exposes them

Common fields on each job in jobs[]:

Field	Description
`title`	Job title
`location`	Primary location label
`secondaryLocations`	Additional offices or regions
`isRemote`, `workplaceType`	Remote / hybrid / on-site hints
`isListed`	When `false`, the role is unlisted and should not appear on a public board
`jobUrl`, `applyUrl`	Hosted Ashby pages
`descriptionPlain`	Plain-text description (when provided)
`publishedAt`	Publish timestamp

Basic integration

Fetch all published jobs for one board:

const boardName = process.env.ASHBY_BOARD_NAME ?? 'notion';
const url = new URL(
  `https://api.ashbyhq.com/posting-api/job-board/${encodeURIComponent(boardName)}`,
);
url.searchParams.set('includeCompensation', 'true');

const response = await fetch(url);

if (!response.ok) {
  throw new Error(`Ashby API ${response.status}: ${response.statusText}`);
}

const data = await response.json();
const jobs = data.jobs ?? [];

for (const job of jobs) {
  console.log(job.title, '-', job.location, '-', job.jobUrl);
}

Filter to roles that are safe to show publicly and have a link:

const publicJobs = jobs.filter(
  (job) => (job.isListed ?? true) && job.title && (job.jobUrl || job.applyUrl),
);

Normalize to a stable shape

Different ATS products use different field names. Map Ashby jobs into a schema your app owns:

function normalizeAshbyJob(job, companyName) {
  const locations = [job.location, ...(job.secondaryLocations ?? []).map((s) => s.location)]
    .map((value) => value?.trim())
    .filter(Boolean);

  const isRemote =
    Boolean(job.isRemote) ||
    job.workplaceType?.toLowerCase() === 'remote' ||
    locations.some((loc) => /remote/i.test(loc));

  return {
    id: job.id ?? `${job.title}:${job.publishedAt}`,
    title: job.title.trim(),
    company: companyName,
    location: locations.join(' | ') || 'Unknown',
    isRemote,
    url: job.jobUrl || job.applyUrl,
    postedAt: job.publishedAt ? new Date(job.publishedAt) : null,
    description: job.descriptionPlain ?? '',
    compensation: job.compensation ?? null,
  };
}

When the primary location is generic (Remote, Hybrid) but postal data includes a country, append the country so filters still work:

function enrichLocation(location, country) {
  const label = location?.trim();
  const c = country?.trim();
  if (!label || !c) return label ?? '';
  if (/^(remote|hybrid|on-?site)$/i.test(label) && !label.toLowerCase().includes(c.toLowerCase())) {
    return `${label}, ${c}`;
  }
  return label;
}

Ashby may return "European Union" in address.postalAddress.addressCountry; treat that as a region hint, not a country code.

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation

Request context in Node.js with AsyncLocalStorage

Željko Šević — Sun, 28 Jun 2026 20:32:25 +0000

In Express and NestJS apps you often need a requestId, userId, or trace fields deep in services and loggers without threading a context object through every function. A module-level global breaks under concurrent requests; passing ctx through every layer is noisy.

AsyncLocalStorage (built into node:async_hooks) gives each async execution chain its own store. Concurrent requests stay isolated, and await, timers, and Promise chains inside a request inherit the same context automatically.

This post covers the API, an Express middleware pattern, a NestJS interceptor with parameter decorators, and common pitfalls.

What AsyncLocalStorage is

AsyncLocalStorage has been stable since Node.js 16.4. No npm package is required.

Approach	Concurrent requests	Propagates through `await`
Global variable	Breaks	Yes
Pass `ctx` argument	Safe	Manual at every layer
AsyncLocalStorage	Safe	Automatic

Prerequisites

Node.js version 26
npm i express

Core API

import { AsyncLocalStorage } from 'node:async_hooks';

const als = new AsyncLocalStorage();

// Start a context for this async chain
als.run({ requestId: 'abc' }, () => {
  doWork();
});

// Read the current store anywhere in the chain
const store = als.getStore();

als.run(store, callback) - preferred entry point; call once per request in middleware.
als.getStore() - returns the store for the current async chain, or undefined outside run().
als.enterWith(store) - sets context for the current execution resource; useful in some framework hooks, but run() is the default pattern for Express middleware.

Express middleware pattern

Create a store per request in middleware, then read it from loggers and services without passing arguments.

// context.js
import { AsyncLocalStorage } from 'node:async_hooks';

export const requestContext = new AsyncLocalStorage();

export function getRequestId() {
  return requestContext.getStore()?.requestId;
}

// logger.js
import { getRequestId } from './context.js';

export function log(level, message, extra = {}) {
  console.log(
    JSON.stringify({
      level,
      requestId: getRequestId(),
      message,
      ...extra,
    })
  );
}

// middleware.js
import { randomUUID } from 'node:crypto';
import { requestContext } from './context.js';

export function requestContextMiddleware(req, res, next) {
  const store = {
    requestId: req.headers['x-request-id'] ?? randomUUID(),
  };
  res.setHeader('x-request-id', store.requestId);
  requestContext.run(store, () => next());
}

Wire middleware before routes. A handler can await a simulated database call and log without receiving requestId:

// routes.js
import { log } from './logger.js';

function delay(ms) {
  return new Promise((resolve) => setTimeout(resolve, ms));
}

async function findUserById(id) {
  await delay(100);
  log('info', 'Loaded user from database', { userId: id });
  return { id, name: `User ${id}` };
}

export async function getUser(req, res) {
  const user = await findUserById(req.params.id);
  res.json(user);
}

// app.js
import express from 'express';
import { requestContextMiddleware } from './middleware.js';
import { getUser } from './routes.js';
import { getRequestId } from './context.js';

const app = express();

app.use(requestContextMiddleware);
app.get('/users/:id', getUser);

app.listen(3000, () => {
  console.log('Listening on http://localhost:3000');
});

Send concurrent requests with different x-request-id headers. Logs interleave on stdout, but each line carries the correct requestId for its request.

NestJS interceptor and decorator

The flow is the same as Express - an interceptor replaces middleware. For production apps, nestjs-cls wraps AsyncLocalStorage with typed stores and plugins; the snippets below use the built-in API directly.

Prerequisites add-on: npm i @nestjs/common @nestjs/core @nestjs/platform-express reflect-metadata rxjs

Shared store:

// request-context.storage.ts
import { AsyncLocalStorage } from 'node:async_hooks';

export type RequestContextStore = { requestId: string };

export const requestContext = new AsyncLocalStorage<RequestContextStore>();

Wrap each request in als.run() with a global interceptor. Subscribe inside run() so context propagates through async route handlers:

// request-context.interceptor.ts
import {
  CallHandler,
  ExecutionContext,
  Injectable,
  NestInterceptor,
} from '@nestjs/common';
import { randomUUID } from 'node:crypto';
import { Observable } from 'rxjs';
import { requestContext, RequestContextStore } from './request-context.storage';

@Injectable()
export class RequestContextInterceptor implements NestInterceptor {
  intercept(context: ExecutionContext, next: CallHandler): Observable<unknown> {
    const req = context.switchToHttp().getRequest();
    const store: RequestContextStore = {
      requestId: (req.headers['x-request-id'] as string) ?? randomUUID(),
    };
    const res = context.switchToHttp().getResponse();
    res.setHeader('x-request-id', store.requestId);

    return new Observable((subscriber) => {
      requestContext.run(store, () => {
        next.handle().subscribe(subscriber);
      });
    });
  }
}

Parameter decorators read from the store in controllers:

// request-context.decorator.ts
import { createParamDecorator, ExecutionContext } from '@nestjs/common';
import { requestContext, RequestContextStore } from './request-context.storage';

export const RequestContext = createParamDecorator(
  (_data: unknown, _ctx: ExecutionContext): RequestContextStore | undefined =>
    requestContext.getStore(),
);

export const RequestId = createParamDecorator(
  (): string | undefined => requestContext.getStore()?.requestId,
);

Register the interceptor globally and use @RequestId() in a controller. Services can call requestContext.getStore() directly (for example from an injectable logger):

// app.module.ts
import { Module } from '@nestjs/common';
import { APP_INTERCEPTOR } from '@nestjs/core';
import { RequestContextInterceptor } from './request-context/request-context.interceptor';
import { UsersModule } from './users/users.module';

@Module({
  imports: [UsersModule],
  providers: [{ provide: APP_INTERCEPTOR, useClass: RequestContextInterceptor }],
})
export class AppModule {}

// users.controller.ts
@Get(':id')
async getUser(@Param('id') id: string, @RequestId() requestId: string) {
  const user = await this.usersService.findById(id);
  return { requestId, user };
}

@Transactional() from typeorm-transactional uses the same ALS propagation idea for database transactions. See TypeORM examples with NestJS for a real-world decorator built on top of request-scoped context.

Why it works

Node ties the store to the async resource created when als.run() executes. Any async work started inside that callback - including await, setTimeout, and nested Promise chains - runs in the same context. Each concurrent request calls als.run() with its own store, so getStore() always returns the value for the current chain.

Common pitfalls

Call als.run() at the request boundary (middleware), not inside a shared singleton constructor.
getStore() returns undefined outside run() - guard with optional chaining or a default.
Callbacks scheduled before run() or from another request will not see your store.
Worker threads and cluster workers each have separate AsyncLocalStorage instances.

Related use cases

Structured logging - attach requestId to every log line (this post).
Distributed tracing - OpenTelemetry uses AsyncLocalStorage internally; see Tracing Node.js Microservices with OpenTelemetry.
Database transactions - typeorm-transactional propagates transactions across repositories via ALS; see TypeORM examples with NestJS.

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation

Nginx local setup

Željko Šević — Sat, 20 Jun 2026 20:46:54 +0000

Nginx is a reverse proxy and web server. On a local machine it can sit on port 80 and route traffic to apps running on other ports - a frontend on :3000, an API on :4000, or a Docker Compose stack with published ports.

This post covers install, config layout, reverse proxying, path-based routes, static files, and service commands on a local dev machine. It does not cover HTTPS, production hardening, or multi-domain TLS.

On Windows, use WSL2 with Ubuntu. On macOS or Linux, the same apt or brew install steps apply.

Prerequisites

WSL2 with Ubuntu (Windows) or a Linux/macOS shell
At least one app listening on a local port (for example :3000)
sudo access to install packages and edit /etc/nginx

Mental model

server - a virtual host block (listen, server_name) that handles requests for a hostname.
location - a path prefix inside a server (/, /api/) matched against the request URI.
proxy_pass - forwards the request to another HTTP server (your Node app, Compose service, etc.).
root - serves files from a directory on disk (built SPA, static HTML).

On Debian and Ubuntu, site configs live in /etc/nginx/sites-available/ and are enabled with symlinks in /etc/nginx/sites-enabled/. The main file /etc/nginx/nginx.conf includes those enabled sites.

Install

sudo apt update
sudo apt install nginx

Start Nginx and verify the default page:

sudo systemctl start nginx
curl -I http://localhost

You should get an HTTP 200 or 301 response.

Config layout

Path	Purpose
`/etc/nginx/nginx.conf`	Main config; includes enabled sites
`/etc/nginx/sites-available/`	Site config files (one file per app or domain)
`/etc/nginx/sites-enabled/`	Symlinks to enabled sites
`/var/log/nginx/access.log`	Request log
`/var/log/nginx/error.log`	Errors and config issues

Create a new site file:

sudo nano /etc/nginx/sites-available/local-dev

Enable it and disable the default site if it conflicts on port 80:

sudo ln -s /etc/nginx/sites-available/local-dev /etc/nginx/sites-enabled/
sudo rm /etc/nginx/sites-enabled/default
sudo nginx -t && sudo systemctl reload nginx

Always run nginx -t before reloading. A syntax error blocks reload and leaves the old config in place.

Proxy one service

Forward all traffic to an app on port 3000:

server {
    listen 80;
    server_name localhost;

    location / {
        proxy_pass http://127.0.0.1:3000;
        proxy_http_version 1.1;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
    }
}

The Upgrade and Connection headers allow WebSocket connections through the proxy (useful for Vite, Next.js dev server, etc.).

Use an upstream block when several backend servers share the same target name; for a single local app, proxy_pass with a URL is enough.

Path-based routes

Route /api/ to a backend on port 4000 and everything else to a frontend on port 3000:

server {
    listen 80;
    server_name localhost;

    location /api/ {
        proxy_pass http://127.0.0.1:4000/;
        proxy_set_header Host $host;
    }

    location / {
        proxy_pass http://127.0.0.1:3000;
        proxy_set_header Host $host;
    }
}

Trailing slash in proxy_pass - proxy_pass http://127.0.0.1:4000/; (note the slash after the port) strips the matched /api/ prefix before forwarding. A request to /api/health reaches the backend as /health. Without the trailing slash, the backend receives /api/health.

Longer prefixes should use more specific location blocks. Nginx picks the best match for the request URI.

Static files

Serve a built frontend from disk instead of proxying:

server {
    listen 80;
    server_name localhost;
    root /var/www/my-app;
    index index.html;

    location / {
        try_files $uri $uri/ /index.html;
    }
}

try_files falls back to index.html for client-side routes in SPAs.

Commands

Command	Purpose
`sudo systemctl start nginx`	Start Nginx
`sudo systemctl stop nginx`	Stop Nginx
`sudo systemctl restart nginx`	Full restart (drops active connections)
`sudo systemctl reload nginx`	Reload config without dropping connections
`sudo systemctl status nginx`	Check service state
`sudo nginx -t`	Validate config syntax
`sudo nginx -s reload`	Reload without systemd
`curl -I http://localhost`	Smoke test from the shell

Safe edit workflow: edit config → sudo nginx -t → sudo systemctl reload nginx.

Use reload after config changes when the syntax test passes. Use restart when Nginx fails to start or after package upgrades.

Local networking on Windows + WSL

Nginx runs inside WSL. 127.0.0.1 inside WSL refers to the Linux VM, not processes on the Windows host.

App runs inside WSL or Docker Compose - use http://127.0.0.1:<port> in proxy_pass when the port is published to the WSL host (Compose ports: mapping works).
App runs on Windows only - use the Windows host IP from WSL:

  grep nameserver /etc/resolv.conf | awk '{ print $2 }'

Put that IP in proxy_pass instead of 127.0.0.1, or run the app inside WSL/Compose so localhost routing stays simple.

From a Windows browser, http://localhost reaches Nginx in WSL when it listens on port 80.

Troubleshooting

Port 80 already in use - check what holds the port: sudo ss -tlnp | grep :80. Stop the conflicting service or change Nginx listen to another port (for example 8080).
502 Bad Gateway - the upstream app is not running or the proxy_pass URL is wrong. Confirm the app responds: curl http://127.0.0.1:3000.
Config test fails - read the path and line from sudo nginx -t output; fix the file and test again before reload.
Unexpected routing - check /var/log/nginx/error.log and /var/log/nginx/access.log.

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation

Docker Compose overview

Željko Šević — Fri, 19 Jun 2026 22:55:36 +0000

Docker Compose runs multi-container applications from a single YAML file. One command can start an API, a database, a message broker, and supporting tools for local development - without installing each service on the host machine.

This post covers Compose concepts and commands. For ready-made stacks, see the service-specific posts linked at the end (Postgres/Redis, RabbitMQ, MongoDB, Kafka, DynamoDB/SQS).

Prerequisites

Docker Engine installed
Compose V2 - use docker compose (with a space). Current Docker Desktop includes it; no separate Compose install is required.

Mental model

Project - the folder that contains docker-compose.yml. The project name defaults to the directory name and prefixes container names.
Services - named containers defined in the file (api, redis, postgres). Each service maps to one image or build context.
Network - Compose creates a default network so services resolve each other by name. From the api container, Redis is reachable at redis:6379, not localhost:6379.
Volumes - named or bind mounts for data that survives docker compose down (unless you pass -v).

Minimal compose file

A two-service stack: a Node API and Redis. No top-level version: key - it is deprecated in the current Compose specification.

services:
  api:
    build: .
    ports:
      - 3000:3000
    environment:
      REDIS_URL: redis://redis:6379
    depends_on:
      - redis

  redis:
    image: redis:alpine
    volumes:
      - redis-data:/data

volumes:
  redis-data:

Run docker compose up --build from the directory that contains this file.

Core concepts

Ports - map host ports to container ports as host:container:

ports:
  - 3000:3000

Environment - inline variables or an env file:

environment:
  REDIS_URL: redis://redis:6379

env_file:
  - .env

Volumes - named volumes are managed by Docker (good for database data). Bind mounts map a host path into the container (good for live code reload during development):

volumes:
  - redis-data:/data        # named
  - ./src:/app/src:ro       # bind mount

Networks - services on the default network can reach each other by service name. Custom networks isolate groups of services (see the Postgres and Redis post for a multi-network example).

depends_on - controls startup order. It does not wait for the dependency to be ready; add a healthcheck or retry logic in the app when you need readiness.

restart - policies like on-failure:3 or unless-stopped keep containers running after crashes or host reboots.

healthcheck - optional probe so Compose and other services know when a container is ready:

healthcheck:
  test: ['CMD', 'redis-cli', 'ping']
  interval: 5s
  timeout: 3s
  retries: 5

Commands

Command	Purpose
`docker compose up`	Start services (foreground, logs in terminal)
`docker compose up -d`	Start in detached mode
`docker compose up --build`	Rebuild images before starting
`docker compose down`	Stop and remove containers
`docker compose down -v`	Also remove named volumes
`docker compose ps`	List running services
`docker compose logs -f api`	Follow logs for one service
`docker compose exec api sh`	Open a shell in a running container
`docker compose pull`	Pull latest images

When to use what

Tool	Best for
`docker run`	One-off containers, quick image tests
Docker Compose	Local multi-service stacks, dev databases and queues
Kubernetes	Production orchestration, scaling, rolling deploys

Service-specific setups

Postgres and Redis containers with Docker Compose - databases with Pgweb and Redis Commander
RabbitMQ container with Docker Compose - message broker with management UI
MongoDB containers with Docker Compose - MongoDB with Mongo Express
Kafka containers with Docker Compose - KRaft-mode Kafka with Kafka UI
DynamoDB and SQS containers with Docker Compose - DynamoDB Local and ElasticMQ

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation

Git SSH keys setup

Željko Šević — Thu, 18 Jun 2026 12:48:03 +0000

SSH keys let you authenticate with Git hosts without entering a password on every push or pull. This post covers generating keys for multiple providers, loading them into the SSH agent, and cloning repositories over SSH.

For general Git commands, see the Git cheatsheet.

Generate SSH keys

Generate a separate key for each provider (for example, GitHub and Bitbucket). Run ssh-keygen for each key, set a distinct filename, and optionally add a passphrase.

ssh-keygen -f ~/.ssh/id_rsa_github
ssh-keygen -f ~/.ssh/id_rsa_bitbucket

Add public keys to providers

Copy each public key (the .pub file) and add it to the provider:

cat ~/.ssh/id_rsa_github.pub
cat ~/.ssh/id_rsa_bitbucket.pub

Activate SSH keys

Start the SSH agent and add the keys you need for the current session.

eval "$(ssh-agent -s)"
ssh-add ~/.ssh/id_rsa_github
ssh-add ~/.ssh/id_rsa_bitbucket

Verify the connection to a provider:

ssh -T git@github.com
ssh -T git@bitbucket.org

Shell aliases per provider

Add aliases to ~/.bashrc or ~/.zshrc to run the agent setup and load the correct key in one command.

alias ssh-github='eval "$(ssh-agent -s)" && ssh-add ~/.ssh/id_rsa_github'
alias ssh-bitbucket='eval "$(ssh-agent -s)" && ssh-add ~/.ssh/id_rsa_bitbucket'

Reload the shell configuration, then run the alias before working with that provider:

source ~/.zshrc
ssh-github

Clone repositories

Use the SSH remote URL when cloning. The host (github.com or bitbucket.org) determines which key the provider expects.

# git clone <REPOSITORY_URL>
git clone git@github.com:workspace/repo-name.git
git clone git@bitbucket.org:workspace/repo-name.git

If authentication fails, run the matching alias (for example, ssh-github) and try again.

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation

Conversation memory for LangChain agents

Željko Šević — Wed, 17 Jun 2026 22:12:55 +0000

This post extends the support triage agent from Building AI agents with LangChain into a multi-turn flow: turn 1 looks up the customer and invoice; turn 2 creates the ticket without the user repeating IDs. It is post #5 in the LangChain series, following the overview, loaders/chunking, RAG, and agents posts.

Prerequisites

OpenAI account
Generated API key
Enabled billing
Node.js version 26
Packages from the agents post, plus the checkpoint package:

npm i langchain @langchain/openai @langchain/core @langchain/langgraph-checkpoint zod

OPENAI_API_KEY set in the environment

Mental model

Three related concepts:

Checkpointer - short-term session memory. Saves messages and graph state after each step so the next invoke on the same thread can resume.
thread_id - conversation key passed in configurable. Same ID = same history; different ID = isolated session.
Store - long-term memory across threads (user preferences, facts learned over time). LangGraph stores are separate from checkpointers; this post focuses on checkpointers only.

Typical support flow with memory:

Turn 1 - rep asks to look up cus_1042 and inv_8891; agent calls lookup tools and summarizes findings.
Turn 2 - rep says "create the ticket we discussed"; agent recalls prior tool results and calls create_support_ticket.

MemorySaver

For demos and tests, use MemorySaver - an in-memory checkpointer that persists state for the lifetime of the process:

import { MemorySaver } from '@langchain/langgraph-checkpoint';

const checkpointer = new MemorySaver();

State is lost when the Node process exits. That is fine for local scripts; production apps need a durable backend (see below).

Attach a checkpointer to createAgent

Pass the checkpointer when creating the agent. Reuse the same triage tools and instructions from the agents post:

import { createAgent } from 'langchain';
import { MemorySaver } from '@langchain/langgraph-checkpoint';

const agent = createAgent({
  model: 'gpt-5.5',
  tools: supportTools,
  systemPrompt: TRIAGE_INSTRUCTIONS,
  checkpointer: new MemorySaver(),
});

The agent loop is unchanged - the checkpointer hooks into LangGraph beneath createAgent.

First turn - lookup

Pass a stable thread_id in the invoke config:

const threadConfig = { configurable: { thread_id: 'support-cus-1042' } };

const turn1 = await agent.invoke(
  {
    messages: [
      {
        role: 'user',
        content:
          'Look up customer cus_1042 and invoice inv_8891 for a possible duplicate charge. Summarize what you find. Do not create a ticket yet.',
      },
    ],
  },
  threadConfig,
);

console.log(turn1.messages.at(-1)?.content);

The agent calls get_customer, get_invoice, and search_knowledge_base. LangGraph saves the full message history (including tool results) to the checkpointer.

Second turn - follow-up without IDs

Send only the new user message on the same thread_id. Prior context is restored automatically:

const turn2 = await agent.invoke(
  {
    messages: [
      {
        role: 'user',
        content: 'Create the support ticket we discussed.',
      },
    ],
  },
  threadConfig,
);

console.log(turn2.messages.at(-1)?.content);

The agent should call create_support_ticket using customer and invoice details from turn 1 - the user does not repeat cus_1042 or inv_8891.

Read the final answer from result.messages as in the agents post:

const lastAi = [...turn2.messages]
  .reverse()
  .find((message) => message.type === 'ai');

console.log(lastAi?.content);

Thread isolation

Different thread_id values do not share history. Two support reps working different cases should use separate thread IDs:

await agent.invoke(
  { messages: [{ role: 'user', content: 'Look up cus_1042.' }] },
  { configurable: { thread_id: 'rep-alice-case-1' } },
);

await agent.invoke(
  { messages: [{ role: 'user', content: 'Create the ticket we discussed.' }] },
  { configurable: { thread_id: 'rep-bob-case-2' } },
);

The second invoke on rep-bob-case-2 has no knowledge of Alice's lookup - Bob's thread starts empty.

Production checkpointers

MemorySaver is process-local and not suitable for production. LangGraph supports durable checkpointers backed by Postgres, SQLite, and other stores via @langchain/langgraph-checkpoint integrations. Swap the checkpointer implementation; the thread_id API stays the same.

Pick a backend that matches your deployment: Postgres for multi-instance apps, SQLite for single-node services.

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation

Building AI agents with LangChain

Željko Šević — Tue, 16 Jun 2026 20:31:35 +0000

LangChain agents are built on LangGraph: the model calls tools in a loop until it returns a final answer. The high-level entry point is createAgent - pass a model, tools defined with tool(), and an optional systemPrompt.

This post builds the same support triage agent as the Vercel AI SDK agents and OpenAI Agents SDK posts so you can compare SDKs on one scenario. It follows the LangChain overview for Node.js and fits as post #4 in the LangChain series (after loaders/chunking and the RAG with pgvector pipeline).

Prerequisites

OpenAI account
Generated API key
Enabled billing
Node.js version 26
langchain, @langchain/openai, @langchain/core, and zod installed:

npm i langchain @langchain/openai @langchain/core zod

OPENAI_API_KEY set in the environment

Mental model - turns and the agent loop

A turn is one model generation. In that turn the model either:

returns final text (the run ends), or
returns tool calls (LangChain executes them and starts another turn with the results)

Typical flow for the support triage agent: user question → model calls lookup tools (get_customer, get_invoice, search_knowledge_base) → model creates a ticket or escalates → final answer.

A single turn can include multiple parallel tool calls. Set recursionLimit on invoke or stream to cap how many graph steps run (each model generation and tool batch counts toward the limit).

Defining tools

Use tool() from langchain with a Zod schema, plus name and description so the model knows when to call each tool:

import { tool } from 'langchain';
import { z } from 'zod';

const getInvoice = tool(
  async ({ invoiceId }) => {
    const invoice = invoices.find((item) => item.id === invoiceId);
    if (!invoice) {
      return { found: false, invoiceId, error: 'Invoice not found' };
    }
    return { found: true, invoice };
  },
  {
    name: 'get_invoice',
    description: 'Look up an invoice by ID, including payment IDs and status',
    schema: z.object({
      invoiceId: z.string().describe('Invoice ID, e.g. inv_8891'),
    }),
  },
);

LangChain uses schema (not Vercel's inputSchema or OpenAI Agents' parameters). The handler receives validated input as the first argument.

createAgent

Wire the model, tools, and triage instructions:

import { createAgent } from 'langchain';

const agent = createAgent({
  model: 'gpt-5.5',
  tools: [getInvoice],
  systemPrompt: `You are a billing support triage agent.
Look up records before recommending refunds or creating tickets.`,
});

model can be a provider string ('gpt-5.5', 'openai:gpt-5.5') or a chat model instance from @langchain/openai.

Invoke

Pass a messages array and read the final answer from result.messages:

const result = await agent.invoke({
  messages: [
    {
      role: 'user',
      content: 'What is the status of invoice inv_8891? Reply in one sentence.',
    },
  ],
});

const lastAi = [...result.messages]
  .reverse()
  .find((message) => message.type === 'ai');

console.log(lastAi?.content);

The last AI message is the agent's final reply after any tool calls complete.

Support triage scenario

Example prompt:

Customer cus_1042 says they were charged twice for invoice inv_8891. What should we do?

A realistic chain:

get_customer - plan tier, open ticket count
get_invoice - amount, status, payment IDs
search_knowledge_base - duplicate-charge and refund policy
create_support_ticket or escalate_to_human - write action or escalation

The demo uses in-memory fixtures (customers, invoices, knowledge-base articles) so scripts run without a database.

Multi-tool agent

import { createAgent } from 'langchain';
import {
  getCustomer,
  getInvoice,
  searchKnowledgeBase,
  createSupportTicket,
  escalateToHuman,
  TRIAGE_INSTRUCTIONS,
} from './tools/index.js';

const agent = createAgent({
  model: 'gpt-5.5',
  tools: [
    getCustomer,
    getInvoice,
    searchKnowledgeBase,
    createSupportTicket,
    escalateToHuman,
  ],
  systemPrompt: TRIAGE_INSTRUCTIONS,
});

const result = await agent.invoke({
  messages: [
    {
      role: 'user',
      content:
        'Customer cus_1042 says they were charged twice for invoice inv_8891. What should we do?',
    },
  ],
  recursionLimit: 15,
});

const answer = [...result.messages]
  .reverse()
  .find((message) => message.type === 'ai');

console.log(answer?.content);

Inspect result.messages for the full trace: human input, AI tool-call messages, tool results, and the final AI reply.

Streaming

agent.stream() yields state updates as the graph runs. Use streamMode: 'values' to receive the full message list after each step:

const stream = await agent.stream(
  {
    messages: [
      {
        role: 'user',
        content:
          'Customer cus_1042 says they were charged twice for invoice inv_8891. What should we do?',
      },
    ],
  },
  { streamMode: 'values', recursionLimit: 15 },
);

let finalMessages = [];

for await (const state of stream) {
  if (state.messages) {
    finalMessages = state.messages;
  }
}

const answer = [...finalMessages]
  .reverse()
  .find((message) => message.type === 'ai');

console.log(answer?.content);

For token-level streaming, use streamMode: 'messages' or streamEvents (see LangGraph streaming).

When to pick LangChain

	LangChain `createAgent`	Vercel AI SDK	OpenAI Agents SDK
Best for	RAG + LCEL + agents in one stack	TypeScript apps already on AI SDK	OpenAI-first agent primitives
Tool definition	`tool()` + Zod `schema`	`tool()` + `inputSchema`	`tool()` + Zod `parameters`
Run API	`agent.invoke` / `agent.stream`	`generateText` + `stopWhen`	`run()` + `maxTurns`
Handoffs / guardrails	Middleware (advanced)	Limited	Built-in
Memory	LangGraph checkpointers	Bring your own	Session helpers

Pick LangChain when document loaders, retrievers, and agents should share one ecosystem. Pick Vercel AI SDK or OpenAI Agents SDK when you want a focused agent layer without the broader LangChain surface.

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation

Document loaders and chunking with LangChain

Željko Šević — Mon, 15 Jun 2026 22:28:48 +0000

This post covers local file ingestion and chunking in Node.js. For LangChain basics (LCEL, packages, agents), see the LangChain overview post. For embeddings, pgvector, and the full RAG flow, see the RAG with pgvector post - it uses one splitter inline; this post goes deeper on loaders and splitter choice.

Prerequisites

Node.js version 26
langchain, @langchain/core, @langchain/classic, and @langchain/textsplitters installed

npm i langchain @langchain/core @langchain/classic @langchain/textsplitters

More loader types (web, cloud, audio) live in standalone integration packages - see the document loader integrations page.

The Document type

Every loader returns Document instances from @langchain/core:

pageContent - the text of the chunk or file
metadata - optional key/value pairs (source path, section, page) used for citations

import { Document } from '@langchain/core/documents';

const doc = new Document({
  pageContent: 'pgvector adds vector search to PostgreSQL.',
  metadata: { source: 'notes/pgvector.txt', section: 'basics' }
});

Load a single file

Use TextLoader for plain text or markdown files:

import { TextLoader } from '@langchain/classic/document_loaders/fs/text';

const loader = new TextLoader('./notes/pgvector.txt');
const docs = await loader.load();

console.log(docs[0].pageContent);
console.log(docs[0].metadata.source);

The loader sets metadata.source to the file path - keep it for citations in RAG answers.

Load a directory

Use DirectoryLoader when you have many files. Map extensions to loader factories:

import { DirectoryLoader } from '@langchain/classic/document_loaders/fs/directory';
import { TextLoader } from '@langchain/classic/document_loaders/fs/text';

const loader = new DirectoryLoader('./notes', {
  '.txt': (path) => new TextLoader(path),
  '.md': (path) => new TextLoader(path)
});

const docs = await loader.load();
console.log(`Loaded ${docs.length} documents`);

PDF, CSV, and JSON loaders are available via other integration packages. This post uses .txt and .md files.

Split documents

Chunking makes retrieval more precise. Instead of embedding one large file, split it into smaller overlapping parts. Pass the docs array from TextLoader or DirectoryLoader to a splitter:

Two parameters matter most:

chunkSize - target maximum size per chunk (characters or tokens, depending on splitter)
chunkOverlap - shared text between adjacent chunks so context is not lost at boundaries

Start with chunkSize: 800 and chunkOverlap: 120, then tune based on document style and answer quality.

import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 800,
  chunkOverlap: 120
});

const chunks = await splitter.splitDocuments(docs);
console.log(chunks.length);

Splitter comparison

The example above uses RecursiveCharacterTextSplitter, the default for most RAG setups. Alternatives:

Splitter	Best for
`RecursiveCharacterTextSplitter`	Default choice; tries paragraphs, then sentences, then words
`CharacterTextSplitter`	Fixed character windows when structure does not matter
`TokenTextSplitter`	When chunk limits must match model token budgets

Character-based:

import { CharacterTextSplitter } from '@langchain/textsplitters';

const splitter = new CharacterTextSplitter({
  chunkSize: 800,
  chunkOverlap: 120
});

const chunks = await splitter.splitDocuments(docs);

Token-based:

import { TokenTextSplitter } from '@langchain/textsplitters';

const splitter = new TokenTextSplitter({
  encodingName: 'cl100k_base',
  chunkSize: 200,
  chunkOverlap: 20
});

const chunks = await splitter.splitDocuments(docs);

Use token-based splitting when chunks must fit within a model's context window. Character-based recursive splitting is the usual starting point for RAG over prose.

Metadata through the pipeline

Pass metadata when creating documents manually, or rely on loader metadata - splitters preserve it on each chunk:

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 400,
  chunkOverlap: 60
});

const chunks = await splitter.createDocuments(
  ['First paragraph.\n\nSecond paragraph.'],
  [{ source: 'manual', section: 'intro' }]
);

console.log(chunks[0].metadata);

After splitDocuments(docs), each chunk keeps fields like source from the parent document. Use those fields when storing chunks in a vector database or displaying citations.

Choosing parameters

Short FAQs or API docs - smaller chunkSize (300–500) for precise retrieval
Long guides or blog posts - larger chunkSize (800–1200) to keep sections together
More overlap - helps when answers span chunk boundaries; increases storage and embedding cost
Less overlap - fewer redundant chunks; risk losing context at splits

Tune with real questions from your domain.

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation

LangChain overview for Node.js

Željko Šević — Mon, 15 Jun 2026 20:43:14 +0000

LangChain.js is a framework for LLM applications in TypeScript and Node.js. It standardizes how you wire prompts, models, tools, document loaders, embeddings, and retrievers into reusable pipelines and agents.

LangChain, Deep Agents, LangGraph, and LangSmith

Project	Role
LangChain	High-level APIs: LCEL chains, `createAgent`, loaders, retrievers
Deep Agents	Batteries-included agent harness: planning, subagents, filesystem, context management
LangGraph	Low-level orchestration; LangChain agents run on LangGraph under the hood
LangSmith	Tracing, debugging, and evaluation for LangChain and LangGraph apps

Use Deep Agents for complex multi-step tasks out of the box. Use LangChain's createAgent when you want a minimal harness you compose with middleware. Reach for LangGraph when you need custom stateful workflows, branching, or fine-grained control over the agent loop.

Packages

Install the core packages first (install guide):

npm i langchain @langchain/core @langchain/openai zod

Provider-specific integrations live in separate packages:

langchain - createAgent, tool, and high-level chain helpers
zod - tool input schemas when defining tools with tool()
@langchain/core - prompts, output parsers, Runnable interface, LCEL
@langchain/openai - ChatOpenAI, OpenAIEmbeddings
@langchain/textsplitters - document chunking (used in the RAG post)
Standalone integration packages for other providers and tools (see the integrations page)

For raw API access, see the Chat Completions and OpenAI Responses API posts. For provider-agnostic text and agents, see the Vercel AI SDK and OpenAI Agents SDK posts.

When to use LangChain

Tool	Best for
Raw `openai` package	Minimal calls, full control, least abstraction
Vercel AI SDK	Provider-agnostic `generateText`, streaming, embeddings, tool loops
OpenAI Agents SDK	Official agent loop, handoffs, guardrails
LangChain	Document ingestion, retrievers, LCEL chains, `createAgent`, swappable vector stores

Reach for LangChain when RAG or multi-step LLM pipelines grow beyond a few manual API calls.

Prerequisites

OpenAI account
Generated API key
Enabled billing
Node.js version 26
langchain, @langchain/core, @langchain/openai, and zod installed
OPENAI_API_KEY set in the environment

Core concepts

Document - a chunk of text with optional metadata. Loaders produce Document instances; splitters break long sources into retrieval-friendly pieces.

import { Document } from '@langchain/core/documents';

const doc = new Document({
  pageContent: 'LangChain helps compose LLM pipelines.',
  metadata: { source: 'intro' }
});

Runnable - any component with .invoke(), .stream(), or .batch(). Prompts, models, parsers, and composed chains are all Runnables.

LCEL (LangChain Expression Language) - chain Runnables with .pipe(). Data flows left to right: prompt → model → parser. The same .invoke(), .stream(), and .batch() interface applies to every Runnable in the chain.

import { ChatPromptTemplate } from '@langchain/core/prompts';
import { StringOutputParser } from '@langchain/core/output_parsers';
import { ChatOpenAI } from '@langchain/openai';

const prompt = ChatPromptTemplate.fromMessages([
  ['system', 'Answer in one sentence.'],
  ['human', '{question}']
]);

const model = new ChatOpenAI({ model: 'gpt-5.5' });
const chain = prompt.pipe(model).pipe(new StringOutputParser());

const answer = await chain.invoke({ question: 'What is LangChain?' });
console.log(answer);

Agents - LangChain's current high-level agent API is createAgent. Pass a model string or chat model, optional tools (with zod schemas), and an optional checkpointer for conversation memory (@langchain/langgraph). For tools and the support triage scenario, see the agents post.

import { createAgent } from 'langchain';

const agent = createAgent({
  model: 'gpt-5.5',
  tools: []
});

const result = await agent.invoke({
  messages: [{ role: 'user', content: 'What is LangChain?' }]
});

Structured output - return typed JSON instead of free text. In LCEL chains, call .withStructuredOutput() on a chat model with a Zod schema:

import { z } from 'zod';
import { ChatPromptTemplate } from '@langchain/core/prompts';
import { ChatOpenAI } from '@langchain/openai';

const schema = z.object({
  answer: z.string(),
  confidence: z.number(),
});

const prompt = ChatPromptTemplate.fromMessages([
  ['system', 'Answer briefly and rate your confidence from 0 to 1.'],
  ['human', '{question}'],
]);

const model = new ChatOpenAI({ model: 'gpt-5.5' }).withStructuredOutput(schema);
const result = await prompt.pipe(model).invoke({ question: 'What is LangChain?' });

console.log(result);

On agents, pass the same schema as responseFormat and read result.structuredResponse:

import { createAgent } from 'langchain';
import { z } from 'zod';

const schema = z.object({ answer: z.string(), confidence: z.number() });

const agent = createAgent({
  model: 'gpt-5.5',
  tools: [],
  responseFormat: schema,
});

const result = await agent.invoke({
  messages: [{ role: 'user', content: 'What is LangChain?' }],
});

console.log(result.structuredResponse);

What LangChain can do

Load and split documents - file and directory loaders, text splitters (see the loaders and chunking post); PDF, HTML, CSV via integration packages
Embeddings and vector stores - OpenAI embeddings with pgvector, Pinecone, Chroma, and others
Retrievers and RAG chains - fetch relevant context, then call a model (see the RAG with pgvector post)
Conversation memory - short-term memory via @langchain/langgraph checkpointers and thread_id (see the agent memory post); long-term memory via stores
Tools and agents - createAgent with tools and middleware; for production agents you may also prefer the Vercel AI SDK agents post or OpenAI Agents SDK post
Structured output - Zod schemas via .withStructuredOutput() on a chat model or responseFormat on createAgent; read parsed objects from the chain result or result.structuredResponse
Observability - trace runs with LangSmith (LANGSMITH_TRACING=true); optional LangSmith Engine monitors traces and flags issues

Streaming and batch

The same LCEL chain supports streaming and batch invocation:

for await (const chunk of await chain.stream({ question: 'What is LCEL?' })) {
  process.stdout.write(chunk);
}

const answers = await chain.batch([
  { question: 'What is a Runnable?' },
  { question: 'What is a retriever?' }
]);

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation

Node Version Manager (nvm) for Windows

Željko Šević — Sun, 14 Jun 2026 13:05:16 +0000

On Windows, nvm-windows manages multiple Node.js versions. It is a separate project from nvm-sh used on macOS and Linux - see the nvm overview post for that setup.

As of mid-2026, Node.js 24 is Active LTS, 22 is Maintenance LTS, and 26 is the Current release. nvm-windows 1.2.2 is the latest release.

Uninstall any existing Node.js installation before installing nvm-windows to avoid PATH conflicts.

Installation

Download and run nvm-setup.exe from the nvm-windows releases page.

Alternatively, install with a package manager:

winget install CoreyButler.NVMforWindows

choco install nvm

Open a new terminal and verify the installation:

nvm version

Version management

Install a specific version

  nvm install 24.16.0

Install the latest Current release

  nvm install latest

Install the latest LTS release

  nvm install lts

Install the latest patch for a major version

  nvm install 24

Switch to an installed version

  nvm use 24.16.0

Add a .nvmrc file inside the project directory and run nvm use to activate the version it specifies

  24.16.0

List locally installed versions

  nvm list

List versions available for installation

  nvm list available

Show the active Node version

  nvm current

Differences from nvm-sh

Feature	nvm-windows	nvm-sh (macOS/Linux)
Version format	`1.2.2`	`0.40.5`
Install latest	`nvm install latest`	`nvm install node`
Install LTS	`nvm install lts`	`nvm install --lts`
List remote versions	`nvm list available`	`nvm ls-remote`
List local versions	`nvm list` or `nvm ls`	`nvm ls`

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation

DynamoDB and SQS containers with Docker Compose

Željko Šević — Fri, 12 Jun 2026 23:18:11 +0000

Docker Compose facilitates spinning up local AWS-compatible services without an AWS account. This post covers DynamoDB Local for key-value storage and ElasticMQ for an SQS-compatible message queue. A common pattern is to persist state in DynamoDB and notify workers through SQS.

Prerequisites

Docker Compose installed

Configuration

The following configuration spins up DynamoDB Local, DynamoDB Admin, ElasticMQ, and the ElasticMQ UI.

Connection details:

DynamoDB endpoint: http://localhost:8000
SQS endpoint: http://localhost:9324
DynamoDB Admin: http://localhost:8001
ElasticMQ UI: http://localhost:3000
Region: us-east-1
Credentials: any dummy values (for example local / local)

# docker-compose.yml
services:
  dynamodb:
    image: amazon/dynamodb-local:latest
    user: root
    command: '-jar DynamoDBLocal.jar -sharedDb -dbPath /home/dynamodblocal/data'
    ports:
      - '8000:8000'
    volumes:
      - dynamodb_data:/home/dynamodblocal/data

  dynamodb-admin:
    image: aaronshaf/dynamodb-admin:latest
    ports:
      - '8001:8001'
    environment:
      DYNAMO_ENDPOINT: http://dynamodb:8000
      AWS_REGION: us-east-1
      AWS_ACCESS_KEY_ID: local
      AWS_SECRET_ACCESS_KEY: local
    depends_on:
      - dynamodb

  elasticmq:
    image: softwaremill/elasticmq-native:latest
    ports:
      - '9324:9324'
    volumes:
      - ./elasticmq.conf:/opt/elasticmq.conf
      - elasticmq_data:/data

  elasticmq-ui:
    image: softwaremill/elasticmq-ui:latest
    ports:
      - '3000:3000'
    environment:
      SQS_ENDPOINT: http://elasticmq:9324
    depends_on:
      - elasticmq

volumes:
  dynamodb_data:
  elasticmq_data:

ElasticMQ loads queue definitions from an elasticmq.conf file included in the demo.

Run the following command to spin up the containers.

docker compose up -d

Disclaimer

ElasticMQ provides an SQS-compatible API, but it is not AWS SQS. DynamoDB Local also differs from managed DynamoDB (for example capacity modes, streams, TTL, and global tables). Use these containers for local development and integration tests, not as production substitutes.

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation

Building AI agents with OpenAI Agents SDK

Željko Šević — Thu, 11 Jun 2026 22:40:07 +0000

The OpenAI Agents SDK (@openai/agents) is OpenAI's official framework for agentic apps in TypeScript. It provides a small set of primitives: Agent, tools, handoffs, guardrails, and a run loop managed by run().

This post builds the same support triage agent as the Building AI agents with Vercel AI SDK post - lookup customers and invoices, search a knowledge base, then create a ticket or escalate - but uses the OpenAI SDK instead of the Vercel tool loop.

For lower-level API access, see the OpenAI Responses API post. For the Vercel AI SDK alternative (generateText, stopWhen, stepCountIs), see the Vercel AI SDK agents post.

Prerequisites

OpenAI account
Generated API key
Enabled billing
Node.js version 26
@openai/agents and zod installed (npm i @openai/agents zod)
OPENAI_API_KEY set in the environment

Mental model - turns and the agent loop

A turn is one model generation. In that turn the model either:

returns final output (the run ends), or
returns tool calls (the SDK executes them and starts another turn with the results), or
requests a handoff to another agent (control switches, history is preserved, loop continues)

maxTurns: 8 means “stop after 8 turns” (eight model generations), not eight individual tool calls. A single turn can include multiple parallel tool calls.

When you omit maxTurns, the SDK defaults to 10 as a safety cap.

Support triage scenario

Example prompt:

Customer cus_1042 says they were charged twice for invoice inv_8891. What should we do?

A realistic chain:

get_customer - plan tier, open ticket count
get_invoice - amount, status, payment IDs
search_knowledge_base - duplicate-charge and refund policy
create_support_ticket or escalate_to_human - write action or escalation

The demo uses in-memory fixtures (customers, invoices, knowledge-base articles) so scripts run without a database.

Defining multiple tools

import { tool } from '@openai/agents';
import { z } from 'zod';

const getCustomer = tool({
  name: 'get_customer',
  description: 'Look up a customer account by ID',
  parameters: z.object({
    customerId: z.string().describe('Customer ID, e.g. cus_1042'),
  }),
  execute: async ({ customerId }) => {
    const customer = customers.find((item) => item.id === customerId);
    if (!customer) {
      return { found: false, customerId, error: 'Customer not found' };
    }
    return { found: true, customer };
  },
});

const getInvoice = tool({
  name: 'get_invoice',
  description: 'Look up an invoice by ID, including payment IDs and status',
  parameters: z.object({
    invoiceId: z.string().describe('Invoice ID, e.g. inv_8891'),
  }),
  execute: async ({ invoiceId }) => {
    const invoice = invoices.find((item) => item.id === invoiceId);
    if (!invoice) {
      return { found: false, invoiceId, error: 'Invoice not found' };
    }
    return { found: true, invoice };
  },
});

const searchKnowledgeBase = tool({
  name: 'search_knowledge_base',
  description: 'Search internal support articles by keyword',
  parameters: z.object({
    query: z.string().describe('Search terms, e.g. duplicate charge refund'),
  }),
  execute: async ({ query }) => {
    // keyword match against mocked articles
    return { query, articles: matches };
  },
});

Add write tools for outcomes:

const createSupportTicket = tool({
  name: 'create_support_ticket',
  description: 'Create a support ticket after gathering customer and policy context',
  parameters: z.object({
    customerId: z.string(),
    subject: z.string().min(3),
    priority: z.enum(['low', 'medium', 'high']),
    summary: z.string().min(10),
  }),
  execute: async (input) => {
    const ticket = createTicket(input);
    return { created: true, ticket };
  },
});

const escalateToHuman = tool({
  name: 'escalate_to_human',
  description: 'Escalate when policy requires manual review',
  parameters: z.object({
    customerId: z.string(),
    reason: z.string().min(10),
    urgency: z.enum(['normal', 'high']),
  }),
  execute: async (input) => ({
    escalated: true,
    queue: input.urgency === 'high' ? 'billing-urgent' : 'billing-standard',
    ...input,
  }),
});

Return structured objects from execute. The SDK serializes them as tool results for the next turn. Return explicit errors (for example { found: false, error: '...' }) so the model can recover instead of throwing.

Running an agent

Define an Agent with instructions and tools, then call run():

import { Agent, run } from '@openai/agents';

const agent = new Agent({
  name: 'Support Triage',
  model: 'gpt-5.5',
  instructions: `You are a billing support triage agent.
- Look up customer and invoice before recommending refunds.
- Search the knowledge base for policy guidance.
- Create a ticket when you can resolve within policy.
- Call escalate_to_human when manual review is required.`,
  tools: [
    getCustomer,
    getInvoice,
    searchKnowledgeBase,
    createSupportTicket,
    escalateToHuman,
  ],
});

const result = await run(
  agent,
  'Customer cus_1042 says they were charged twice for invoice inv_8891. What should we do?',
  { maxTurns: 8 },
);

console.log(result.finalOutput);

Use a model that supports tool calling.

`maxTurns` - cap the number of turns

maxTurns(n) stops once the run reaches n turns. Use it on every production agent to prevent runaway loops and unbounded API cost. When the cap is exceeded, the SDK throws MaxTurnsExceededError.

Use case	Suggested cap
Single tool, then answer	2
Chat with occasional tool use	3–5
Task agents (triage, research)	8–15
Long autonomous workflows	15–20 (with monitoring)

Tight vs relaxed cap on the same prompt:

import { Agent, run } from '@openai/agents';

// Stops after 3 turns even if the model still wants more context
const tight = await run(agent, prompt, { maxTurns: 3 });

// Allows a fuller investigation chain
const relaxed = await run(agent, prompt, { maxTurns: 8 });

Inspecting runs

The newItems array on the result contains tool calls, tool outputs, and messages from the run. Use it for debugging:

const result = await run(agent, prompt, { maxTurns: 8 });

for (const item of result.newItems) {
  if (item.type === 'tool_call_item') {
    console.log('tool:', item.rawItem.name, item.rawItem.arguments);
  }
  if (item.type === 'tool_call_output_item') {
    console.log('output:', item.output);
  }
}

console.log('lastAgent:', result.lastAgent.name);
console.log('answer:', result.finalOutput);

The SDK emits traces automatically. Set workflowName on a custom Runner to group related runs in the OpenAI Traces dashboard.

Handoffs

For multi-agent workflows, define specialist agents and wire them with Agent.create() and handoffs. The triage agent in this post stays single-agent, but handoffs are the SDK's way to delegate between agents (similar to routing a case to a billing specialist):

import { Agent } from '@openai/agents';

const billingAgent = new Agent({
  name: 'Billing Specialist',
  instructions: 'Handle refund and duplicate-charge cases.',
  tools: [getInvoice, searchKnowledgeBase, createSupportTicket],
});

const triageAgent = Agent.create({
  name: 'Triage',
  instructions: 'Route billing cases to the billing specialist when needed.',
  handoffs: [billingAgent],
});

After a run, check result.lastAgent to see which agent produced the final output.

Streaming

Pass stream: true to receive events as the run progresses:

import { Agent, run } from '@openai/agents';

const stream = await run(agent, prompt, { maxTurns: 8, stream: true });

process.stdout.write('Answer: ');

for await (const event of stream) {
  if (event.type === 'raw_model_stream_event' && event.data.type === 'output_text_delta') {
    process.stdout.write(event.data.delta);
  }

  if (event.type === 'run_item_stream_event' && event.name === 'tool_called') {
    console.error(`\nTool: ${event.item.rawItem.name}`);
  }
}

await stream.completed;
console.log('\nDone:', stream.finalOutput);

Text streams incrementally. Tool calls appear as run_item_stream_event events between text segments.

Production notes

Always set maxTurns - do not rely on the default cap without monitoring
Cost - each turn is another model call; inspect newItems or stream events for tool usage
Tool errors - return structured errors from execute instead of throwing when the model should retry or escalate
Instructions - keep policy rules in instructions, not only in the user prompt
Tracing - use the OpenAI Traces dashboard to debug multi-turn runs
Alternatives - hosted tools (web search, code interpreter), MCP servers, and sandbox agents are covered in the official docs

Need help with your project?

Get personalized advice on your architecture, code, or career in a 45-minute 1-on-1 consultation.

→ Book a consultation