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Vendor Chunking: The React Optimization I Wish I’d Known Earlier



I ignored my bundle size for months. By the time I checked, my production build was several megabytes of JavaScript in a single file — and every deploy forced my users to download all of it again. Vendor chunking is what I wish I’d known earlier.

Let’s say you are building a project using React, and for that project you need some component library, a library to handle date-related things, or a library to handle your state. So you reach for the usual suspects — Material UI, dayjs, TanStack Query, Redux and so on.

Initially, everything will work smoothly for you as well as for your end users. But as you keep shipping new features and adding dependencies, the bundle quietly grows. Locally, you don’t notice. But in production, it’s the end users who end up paying the price. What happens behind the scenes is that, when dependencies are included in the initial bundle, they can significantly increase the JavaScript downloaded by the browser. As a result, you end up with a poor Lighthouse score, slow initial load times, and a large JavaScript bundle — all of which hurt the user experience.

Your production build looks like this:

dist/assets/index-xxxx.js → 1.2 MB

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This is exactly the kind of problem vendor chunking is designed to solve.

What is Vendor Chunking?

Vendor chunking is a technique where you can separate the third-party dependencies from your main bundle chunk. So you will have a main chunk which will contain your main application code and a vendor chunk which will contain the third-party dependency code.

How does Vendor Chunking help with large bundle size?

When you implement vendor chunking, your application is typically split into at least two separate bundles:

Main Chunk: Contains your application code
Vendor Chunk: Contains third-party dependencies and libraries

When a user visits your website for the first time, the browser downloads both chunks and stores them in the cache. This is where caching becomes very useful.

It’s worth noting that vendor chunking doesn’t come into play on the user’s first visit, because the browser still has to fetch both the chunks. The real performance gains show up when a user revisits the website and the cached vendor chunk can be reused.

Since third-party dependencies do not change frequently, the vendor chunk usually remains the same across deployments. On the other hand, whenever you ship new features, bug fixes, or other updates, your application code changes — which results in a new main chunk being generated.

When you generate a new production build, the bundler will again create:

A new main chunk containing the updated application code
The same vendor chunk, because the dependency code has not changed

Now consider this scenario where a user’s browser has already cached both the chunks because the user has already visited your website, and you deploy a new feature to the production. As a result, the user’s browser only needs to download the new main chunk, while the cached vendor chunk can be reused. This is how vendor chunks help in improving user experience.

Vendor Chunking in Action

Now let’s understand this whole thing using the code. I have a React project where I have installed multiple dependencies such as MUI, Chart.js, Lodash, dayjs and React Router.

Before Vendor Chunking, the build will look something like this:

Note: I’ve disabled tree-shaking for this demo to keep the bundle size large and make the impact of vendor chunking more visible.

The build will contain only one JS file and the browser has to download this large ~4.6 MB file.

Now I will implement vendor chunking by adding this code block in my vite.config.ts file:

Note: I’m using Vite v8, which uses Rolldown as the bundler. If you’re on an older Vite version with Rollup, the config syntax will be different

So after this, the build will look something like this:

As you can see, now we have two JS files:

Now the browser has to download both of these files so that our application loads correctly. Here comes the crazy part. Let’s update the code and try to generate the new production build. Here’s what it will generate:

If you compare the files with the previous build, you will find that only index-xx.js changed, while vendor-xx.js stays the same, because we didn’t upgrade any package versions; we only updated the application code.Now when a user revisits our website, the browser will only fetch the new index-xx.js from the server, vendor-xx.js will be served from cache.

This is how vendor chunking helps in performance improvement for web applications.

Splitting Vendors Further

So far we have bundled all the dependencies into a single chunk. This works well for small to medium applications, but as the dependency list grows, a single vendor chunk has a drawback. If you update the version of any library, a new vendor chunk will be generated and users have to re-download the whole thing – even if 95% of the code is same.

To tackle this issue, we can split vendors into multiple chunks. For example, you can keep your UI library in one chunk, your utility libraries in another, and your charting library in a third:

The final node_modules group is a catch-all so dependencies like React land in a vendor chunk too, rather than your main bundle.

After this config, your build output will look something like this:

Note: Just don’t go overboard with splitting — too many small chunks can hurt performance due to HTTP overhead and less effective compression

Wrapping Up

Vendor chunking is one of those optimizations that costs you a few lines of config and pays you back on every deploy. The core idea is simple: your application code changes often, third-party code doesn’t — so why force your users to re-download libraries they already have?

If you haven’t audited your production bundle in a while, run a build and take a look. You might be surprised how much of it is third-party code waiting to be cached properly.



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The Open-Source Agent War of 2026: Hermes Agent vs AutoGPT vs OpenAI Agents vs CrewAI



Hermes Agent Challenge Submission: Write About Hermes Agent

This is a submission for the Hermes Agent Challenge: Write About Hermes Agent

The Open-Source Agent War of 2026: Hermes Agent vs AutoGPT vs OpenAI Agents vs CrewAI

The AI Agent Ecosystem Is Getting Crowded Fast

In the last two years, “AI agents” went from experimental repos to full ecosystems.

Now we have:

AutoGPT spawning autonomous loops
CrewAI orchestrating multi-agent teams
OpenAI Agents offering structured tool execution
Hermes Agent pushing persistent memory and system-level architecture

And suddenly, developers are asking a very real question:

Which agent framework should I actually use in production?

Because the reality is:

They are not interchangeable
They are not solving the same problem
And they are not built with the same philosophy

In this post, I break down the landscape in a practical, engineering-focused way.

No hype.

No marketing.

Just architecture, tradeoffs, and real-world fit.

The Four Major Players

Let’s define the contenders clearly.

1. Hermes Agent

Hermes Agent is designed as a persistent, memory-driven agent system.

Core ideas:

long-term memory as a first-class layer
skill-based execution model
multi-agent orchestration
workflow-driven automation
system-like architecture

It behaves less like a chatbot framework and more like an AI operating system layer.

2. AutoGPT

AutoGPT is one of the earliest autonomous agent experiments.

Core ideas:

goal-driven loops
self-prompting behavior
tool usage through iteration
minimal structure, high autonomy

It is best described as:

A recursive agent loop with tool access.

3. CrewAI

CrewAI focuses on structured multi-agent collaboration.

Core ideas:

role-based agents
task delegation
sequential and parallel workflows
human-defined orchestration

It is designed for:

“AI teams working together.”

4. OpenAI Agents

OpenAI Agents focus on production-grade tool execution and orchestration.

Core ideas:

structured tool calling
safety and reliability layers
API-first agent design
enterprise readiness

It is less experimental and more controlled.

Design Philosophy Comparison

Framework
Philosophy

Hermes Agent
AI as a persistent system

AutoGPT
Fully autonomous loop

CrewAI
Collaborative agent teams

OpenAI Agents
Controlled production agents

This philosophical difference explains almost everything else.

Core Feature Comparison

Feature
Hermes Agent
AutoGPT
CrewAI
OpenAI Agents

Open Source
Yes
Yes
Yes
Partial

Self-hosting
Yes
Yes
Yes
Limited

Persistent Memory
Strong
Weak
Medium
Limited

Multi-agent support
Native
Experimental
Core feature
Structured

Tool integration
Modular
Basic
Good
Excellent

Learning capability
Strong (memory-driven)
Low
Medium
Medium

Ease of setup
Medium
Medium
Easy
Easy

Production readiness
Medium
Low–Medium
Medium
High

Community support
Growing
Large
Growing
Large

Extensibility
High
Medium
High
Medium

Developer Experience Comparison

Hermes Agent

Requires architectural thinking
Powerful but opinionated
Best for long-running systems
Feels like building infrastructure

AutoGPT

Easy to experiment with
Hard to control in production
Often unpredictable
Great for prototypes

CrewAI

Very developer-friendly
Clear role definitions
Easy mental model
Good balance of structure and flexibility

OpenAI Agents

Smooth API experience
Strong documentation
Production-focused
Less flexible at system level

Architecture Comparison

Hermes Agent Architecture

flowchart TD

User –> HermesCore

HermesCore –> MemoryLayer
HermesCore –> SkillSystem
HermesCore –> WorkflowEngine
HermesCore –> SubAgents
HermesCore –> ToolLayer

SubAgents –> SharedMemory
SkillSystem –> MemoryLayer
WorkflowEngine –> SubAgents

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Key idea:

Everything revolves around persistent memory + system execution.

AutoGPT Architecture

flowchart TD

Goal –> AgentLoop
AgentLoop –> LLM
LLM –> ToolUse
ToolUse –> Observation
Observation –> AgentLoop

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Key idea:

Infinite loop driven by self-prompting.

CrewAI Architecture

flowchart TD

Task –> ManagerAgent

ManagerAgent –> Worker1
ManagerAgent –> Worker2
ManagerAgent –> Worker3

Worker1 –> Output
Worker2 –> Output
Worker3 –> Output

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Key idea:

Role-based collaboration.

OpenAI Agents Architecture

flowchart TD

UserRequest –> Orchestrator
Orchestrator –> ToolCalls
ToolCalls –> ExecutionLayer
ExecutionLayer –> Response

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Key idea:

Structured tool execution pipeline.

Real-World Use Case Comparison

Scenario 1: Solo Developer

Best choice: CrewAI or Hermes Agent

CrewAI: easier setup, fast results
Hermes: better for long-term project memory

AutoGPT is too unstable for consistent use.

OpenAI Agents may feel too rigid.

Scenario 2: Startup Team

Best choice: Hermes Agent or OpenAI Agents

Hermes: evolving product knowledge + memory
OpenAI Agents: stable production workflows

CrewAI works well for internal coordination.

AutoGPT is not ideal.

Scenario 3: Enterprise

Best choice: OpenAI Agents

Why:

governance
reliability
safety controls
structured execution

Hermes Agent is promising but still maturing here.

Scenario 4: Research Lab

Best choice: Hermes Agent

Because:

persistent memory across experiments
evolving hypotheses tracking
multi-agent research pipelines

CrewAI also works well, but lacks deep memory layer.

Scenario 5: Personal Productivity

Best choice: CrewAI or AutoGPT

CrewAI: structured assistants
AutoGPT: experimental automation

Hermes Agent is powerful but heavier than needed for simple tasks.

Strengths and Weaknesses Breakdown

Hermes Agent

Strengths

Persistent memory
System-level architecture
Multi-agent coordination
Long-term reasoning support

Weaknesses

Complexity
Higher setup cost
Still evolving ecosystem

AutoGPT

Strengths

Simplicity of concept
Fully autonomous loops
Easy experimentation

Weaknesses

Unpredictable behavior
Weak production control
No real memory system

CrewAI

Strengths

Clean multi-agent model
Easy developer experience
Good structure for teams

Weaknesses

Limited long-term memory
Less system-level depth

OpenAI Agents

Strengths

Production-grade stability
Strong tool ecosystem
Excellent documentation

Weaknesses

Less open system design
Limited architectural flexibility
Dependency on platform constraints

When Hermes Agent Is the Wrong Choice

Hermes Agent is NOT ideal when:

you need quick one-off automation
you want zero-setup solutions
you are building simple chatbot flows
you require strict enterprise compliance out of the box
you don’t need long-term memory or state

In short:

If your problem is stateless, Hermes is overkill.

Decision Tree: Which Agent Framework Should You Choose?

Do you need persistent memory across time?
├── Yes → Hermes Agent
└── No → continue

Do you need production-grade tool reliability?
├── Yes → OpenAI Agents
└── No → continue

Do you need multi-agent teamwork structure?
├── Yes → CrewAI
└── No → continue

Do you want experimental autonomous behavior?
├── Yes → AutoGPT
└── No → CrewAI or OpenAI Agents

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Final Thoughts: Where This Is All Heading

We are still in the early phase of agent frameworks.

Right now, each system is optimizing a different axis:

AutoGPT → autonomy
CrewAI → collaboration
OpenAI Agents → reliability
Hermes Agent → persistence + system thinking

But over the next 2–3 years, these boundaries will blur.

We will likely see:

memory becoming standard
multi-agent systems becoming default
workflows becoming composable
agents becoming long-running systems, not sessions

And eventually:

Agent frameworks will stop being “tools for prompts”and become “operating layers for digital workforces.”

In that future, Hermes Agent’s direction — persistent, system-oriented intelligence — may become less of a niche idea and more of a baseline expectation.

The real competition won’t be between frameworks.

It will be between architectures.

And that shift is already starting.



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Need your attention on my current project



I am currently working on a project. The concept is I select a github repository then it will automatically backs up that repository daily to a selected destination. Currently it only supports Google Drive and Dropbox for backup destination.

Current version is just a proof of concept. What I actually want to do is – it will take the repository and upload it to another git provider like gitlab etc. From then it will get only those commits that are not backed up and back up them. Basically automatic version controling.

It will help to have a back up copy of a project in other provider and easily migrate away from github to that git provider. I feel that we need this type of application now more than ever before. Slowly Github’s quality is going down, it is having outages frequently. Their uptime dropped to 90% from 99%. A lot of developers are moving away from it but few developers don’t want to. Perhaps they can’t for any reason. I myself don’t want to migrate away right now.

So I am thinking this concept can help those developers including me. What do you think. If you have any suggestions or queries let me know. Also you can check the project at nexrepo.qzz.io



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