Large Action Models (LAMs) - Everything You Need to Know

What is a Large Action Model (LAM)?

You’ve probably heard of Large Language Models (LLMs) like GPT or Claude – the AI that can write essays, answer complex questions, and even generate poetry. 

But what if AI could go beyond just understanding and generating language? What if it could do things? That’s where Large Action Models (LAMs) come in.

A Large Action Model (LAM) is an AI system trained not just to understand language or images, but to interact with digital and even physical environments to take real-world actions. 

Think of it as an intelligent agent that can make decisions, trigger workflows, interact with APIs, perform software tasks, or even control robotics - all based on goals, prompts, or evolving environments.

LAMs are at the cutting edge of AI evolution. They extend the usefulness of LLMs by pairing their language understanding with action-oriented reasoning and environment interaction. 

This is a major leap toward creating AI agents that can operate autonomously across complex systems.

LAMs don’t just answer questions - they act on them. Imagine saying, “Book me the cheapest flight to Lisbon next week, coordinate it with my Google Calendar, and notify my boss.” A well-trained LAM like ours can string those tasks together, talk to multiple APIs, handle exceptions, and adapt on the fly.

In a world that’s speeding toward automation, decentralisation, and monetisation of attention, LAMs open new territory for AI builders, crypto-native developers, and Web3 thinkers who dream of DAOs run by autonomous agents, AI-powered trading bots, and tools that make side hustling almost effortless.

What’s the difference between a Large Action Model (LAM) and a Large Language Model (LLM)?

A Large Language Model (LLM) is designed to understand and generate text. It can write essays, chat with users, summarise documents, or translate languages, all through natural language processing. But it stops at the point of action.

A Large Action Model (LAM), on the other hand, takes things further. It doesn’t just respond - it acts. LAMs combine the language understanding of LLMs with the ability to perform tasks across software, APIs, devices, or even physical robotics. 

They are goal-driven, environment-aware, and capable of following through with actions in the real world, not just words on a screen. If LLMs are smart writers, LAMs are smart doers.

Large Action Model Examples

To understand how LAMs work in practice, let’s walk through some compelling examples:

1. AI Agents for Web Automation

A LAM could act as a Chrome extension or desktop app that automates your browsing habits. Instead of manually checking prices across different e-commerce sites, the LAM can:

• Open multiple tabs

• Compare prices

• Detect coupon codes

• Execute purchases based on your preset rules

All this is done autonomously, requiring no constant user input.

This is the reason we created our Action Model, to develop and enhance the next generation of AI web agents!

Find out more about our Action Model here.

2. Crypto and DeFi Bots

LAMs can take DeFi yield farming and liquidity pool management to the next level:

• Monitor APYs across platforms

• Move funds when a better yield appears

• Run arbitrage opportunities between DEXs

• Auto-compound rewards

With smart contracts and LAMs, you essentially have a tireless, emotionally detached, fully-automated crypto side hustler.

3. Robotics and IoT

Imagine pairing a LAM with a household robot:

• The LAM interprets a vague instruction like “clean the house and prep for guests.”

• It identifies tasks: vacuuming, cleaning surfaces, preparing drinks.

• It directs the robot to specific rooms, manages cleaning tasks, and even uses smart devices to adjust lights and temperature.

This is no longer the work of sci-fi. These kinds of agents are already being prototyped by startups and research labs.

4. Smart Customer Support Agents

Think beyond chatbots. A LAM in customer support can:

• Access user account data

• Reset passwords

• Initiate refunds

• Update CRM entries

• Escalate based on emotional sentiment

The agent understands the request, takes the required actions in backend systems, and closes the loop without human help.

5. DAO and Governance Automation

LAMs can also manage decentralised organisations:

• Propose governance changes

• Monitor on-chain activity

• Summarise community feedback

• Cast votes based on token-weighted preferences or mission-driven goals

The potential for decentralised autonomous governance just went from speculative to executable.

What is the difference between Large Action Models and Agentic AI?

Agentic AI is a broader concept that refers to any AI system capable of acting with purpose or autonomy. It’s about decision-making, planning, and pursuing goals - whether through logic, learning, or collaboration with other agents.

Large Action Models (LAMs) are a specific implementation of this idea. LAMs are the tools that make Agentic AI real. They’re trained to turn intentions into results by chaining together actions, tools, and feedback loops. 

While Agentic AI is the vision of intelligent agents doing things in the world, LAMs are the practical, technical pathway to get there - action models power the agents themselves.

Large Action Model Open Source

One of the most exciting aspects of this new frontier is the open-source movement behind LAMs. Just as Hugging Face and EleutherAI democratised access to LLMs, there are now projects opening up the tools and training data needed to build your own LAMs.

Here are a few open-source LAM initiatives and tools:

1. Auto-GPT & BabyAGI

These early GitHub projects began experimenting with autonomous agents powered by LLMs and memory modules. Though primitive by today’s standards, they laid the foundation for how agents could think, plan, and act.

2. LangChain + Agents

LangChain offers an open-source framework for chaining LLMs with tools, APIs, and memory. Its agent interface is a rudimentary but effective LAM engine, letting developers compose intelligent systems that act based on intermediate reasoning steps.

3. CrewAI and ReAct

These frameworks focus on collaboration between agents, task delegation, and recursive thinking. They help LAMs operate in multi-agent environments or share responsibilities with humans.

4. OpenLAM Projects

Several GitHub communities have emerged around the idea of building public, transparent Large Action Models. These efforts often intersect with Web3 ideals: decentralisation, permissionless innovation, and community-driven governance.

If you’re a Web3 developer, now’s a golden time to contribute. The protocols being built today could be the backbones of tomorrow’s AI economies.

AI Training Data and LAMs

Training a LAM is vastly more complex than training a static model. These models need to:

• Understand goals and intentions

• Map them to available actions

• Adapt to changing environments

• Learn from feedback and failure

The quality and structure of AI training data for LAMs determines their effectiveness.

Here’s how we’re using a Chrome extension to help train our own model.

Key Sources of Training Data:

1. Scripted Agent Logs: Data from RPA (Robotic Process Automation) systems, like sequences of mouse clicks and API calls.

2. Synthetic Task Simulations: Virtual environments where the AI can try, fail, and iterate thousands of times per minute.

3. User Interaction Logs: Real-world software usage by users that show how tasks are actually completed.

4. Game Environments: Environments like Minecraft, StarCraft II, or even decentralised metaverse platforms allow agents to learn through complex, multi-step missions.

The challenge is that action data is messy, sparse, and nonlinear. Building structured, high-quality datasets that represent how actions relate to goals is still an emerging science.

But there is hope! Recent advancements in reinforcement learning with human feedback (RLHF), transformer-based memory encoding, and environment simulators are allowing Large Action Models to become increasingly capable.

We’re watching a new wave of AI training pipelines emerge, tuned for action, adaptability, and real-world complexity.

Why LAMs Matter for AI Monetisation and the Future of Work

If you’re reading this, there’s a good chance you’re exploring side hustles or passive income streams using AI. Here’s why LAMs should be on your radar:

1. Autonomous Hustling

Large Action Models can:

• Find micro-tasks

• Complete them autonomously

• Transfer earnings to your wallet

Imagine hundreds of LAMs across marketplaces like Fiverr, Upwork, or decentralised labour protocols, each completing low-effort tasks that add up over time - this is what you can do with Action Models!

2. Trading & Arbitrage Agents

You can build bots that:

• Monitor token price differences

• Analyse sentiment

• Make strategic trades

LAMs with real-time access to DEXs, oracles, and trading APIs can outperform manual traders and free up your time.

3. Creator & Content Assistant

Want to start a YouTube channel, blog, or TikTok hustle? LAMs can:

• Generate content scripts

• Schedule posts

• Engage with followers

• Monetise traffic via affiliate links

They don’t just create - they execute. That’s what makes LAMs different.

4. AI-as-a-Service

Build and lease your own LAMs to:

• Run customer service

• Power ecommerce backends

• Manage Web3 communities

You could charge a monthly fee or take a cut of the revenues. Think of LAMs as digital employees that scale without salary negotiations - and without taking coffee breaks.

Our Action Model & Vision

We’re building the world’s first Large Action Model community and we want to give you, the people, the power back.

Right now, LLMs are using our data and profiting significantly from it. Our Model rewards those who train it - not big tech or billion pound corporations, but users like you.

By downloading the Action Model browser extension, you can help train our AI while earning LAM tokens. This gives you tangible ownership in the future of AI, whilst earning money in the background.

• Get rewarded for what you use and share

• Turn tokens into real influence over how the model runs

• Launch your own workflows in the marketplace and earn every time they’re used

Help us develop the next level of AI web agents and get paid in the process!

Sign up to our mailing list for updates and details on our launch

Conclusion: The Next AI Revolution

The rise of Large Action Models is creating a seismic shift in how we use AI. No longer limited to text or static outputs, these systems now do things - which changes everything!

For AI and Web3 enthusiasts, the overlap between LAMs and decentralised networks is especially potent. Imagine:

• A DAO run by LAMs

• DePIN networks powered by action agents

• Autonomous agents that generate on-chain revenue and pay their own gas fees

As LAMs evolve, the barrier to entry is shrinking. Whether you’re a developer, hustler, or just a curious observer, there’s room to get involved, especially with us. The tools are being built. The data is being collected. The future is being automated - and it’s moving fast.