How do I build an autonomous AI agent with OpenLAM?

Install OpenLAM (Docker or the managed cloud), add your company memory so agents have context, create or import agents into departments, connect the tools/plugins they need, then assign a goal. The LAM core loop plans the steps, executes the actions, self-corrects, and reports — asking for any missing tool.

Do I need to write code to build agents with OpenLAM?

No. OpenLAM is a product, not just a library: you create agents, assign responsibilities, and give goals in natural language through the web app. Developers can still extend it with custom plugins and MCP servers.

How do agents get the tools they need?

Install plugins (CRM, email, search, social, payments, MCP servers, and more). If an agent is assigned work that needs a capability it does not have, OpenLAM honestly flags exactly which plugin to install rather than failing silently.

How to build agents with OpenLAM

OpenLAM lets you stand up an autonomous AI workforce without wiring an agent loop by hand. Here is the end-to-end path from install to a self-running organization.

1. Install OpenLAM

OpenLAM is open source and free to self-host. The fastest path is Docker Compose (API + web + Postgres); or use the managed cloud at openlam.ai. You can run the included OpenLAM 72B model via an Ollama-compatible endpoint, or bring your own provider.

2. Add company memory

Agents act better when they know your business. Add your company profile — what you do, your product, brand voice and ideal customer — so every plan is grounded in real context.

3. Create your agents

Build a workforce in minutes:

Generate a starter roster from your company profile, or
Add agents from the catalog of ready-made roles, or
Describe an agent in plain language and let OpenLAM define its skills and responsibilities.

Agents are organized into departments (sales, marketing, research, engineering, support, ops) and can delegate to one another.

4. Connect the tools they need

Install plugins so actions reach real systems — CRM, email, web search, social, payments, and any MCP server. If an agent is later assigned work it lacks a tool for, OpenLAM tells you exactly which plugin to install instead of failing silently.

5. Assign goals (and responsibilities)

Give a goal in natural language. The LAM core loop takes over: plan → act → observe → learn. You can also click an agent in the Agent World and assign an ongoing responsibility — it gets added to the workforce's execution queue and worked autonomously.

6. Review, approve, improve

Governance holds high-risk actions for your approval.
Self-correction reviews and revises deliverables until they meet the bar.
Learning improves the workforce from each run, with optional privacy-first federated learning.

Next steps

New to the category? Read What is a LAM? and LAM vs LLM vs Agent, or compare OpenLAM with other tools in the framework comparison.