What is the difference between an LLM and a LAM?

An LLM (Large Language Model) predicts text. A LAM (Large Action Model) predicts and executes actions — it plans steps, calls tools and APIs, observes results and adapts. The LLM is the reasoning core; the LAM is the full action-taking system around it.

Is an AI agent the same as a LAM?

An "AI agent" usually means an LLM placed in a loop with tools. A LAM is the model + system purpose-built for reliable action-taking: planning, tool use, memory, governance and learning. Most agent frameworks are libraries for building agents; a LAM platform like OpenLAM ships the whole action model as a product.

Which should I use: an LLM, an agent framework, or a LAM platform?

Use an LLM directly for text generation. Use an agent framework if you want to hand-build orchestration in code. Use a LAM platform like OpenLAM if you want a ready-made autonomous workforce that plans, acts, self-corrects and reports — without assembling the loop yourself.

LAM vs LLM vs Agent — what is the difference?

These three terms get mixed up constantly. Here is the clearest way to keep them straight: an LLM generates text, an AI agent loops an LLM with tools, and a Large Action Model (LAM) is purpose-built to plan and execute real actions reliably.

LLM — Large Language Model

An LLM predicts the next token. Give it a prompt, it returns text. It is brilliant at language, reasoning and summarization — but on its own it cannot send an email, update a CRM, or check a live price. It describes; it does not do. Examples: the underlying chat models you call via an API.

AI Agent

An "AI agent" wraps an LLM in a loop: think, pick a tool, call it, read the result, repeat. This adds the ability to act. Agent frameworks (libraries and SDKs) give developers the building blocks to assemble this loop themselves. They are powerful but they are scaffolding — you still design the orchestration, reliability, memory and guardrails.

LAM — Large Action Model

A LAM is the next step: a model and system purpose-built to take action reliably. It treats action-taking as the primary objective, not an add-on. A LAM platform ships the whole stack:

A planner that decomposes goals into ordered, executable actions.
A tool/action layer connected to real systems (APIs, CRMs, email, browser, MCP servers).
Memory across steps and runs.
A perceive → plan → act → observe → learn loop with self-correction.
Governance for high-risk actions and honest reporting when a capability is missing.

Side by side

Capability	LLM	Agent framework	LAM platform (OpenLAM)
Generates text	Yes	Yes	Yes
Plans multi-step work	No	You build it	Built-in
Executes real actions / tools	No	You wire it	Built-in
Memory across steps	No	You add it	Built-in
Self-correction loop	No	You add it	Built-in
Governance / approvals	No	You add it	Built-in
Ready-made workforce	No	No	Yes — 100+ agents
Open source + self-host	Varies	Varies	Yes

Where OpenLAM fits

OpenLAM is an open-source LAM platform: it implements the action model end to end and runs an autonomous AI workforce on it. You describe a goal; the workforce plans, acts, self-corrects and reports — and asks for any tool it doesn't yet have. Learn the fundamentals in What is a LAM? or see how it stacks up in the framework comparison.

LAM vs LLM vs Agent