Give each customer their own agents.

Run your product's AI on Levain. Ship dedicated workspaces for every customer. Track and bill their usage on your terms.

Works with

Ship the AI your customers want without turning into an infrastructure company.

The agent is only the beginning. Give every customer their own credentials, data, memory, budgets and audit trail, then keep all of it isolated, observable and running in production.

What you think of first

A sandbox for every run

so what the model writes never reaches production.

Memory that stays put

so what the agent learns about one customer never surfaces for another.

What takes the year

Credentials per customer

so the agent acts in their tools, with their access, and only theirs.

A meter with a ceiling

so a customer's runaway week is their line item, not your margin.

A record of every run

so when they ask what the agent did on their behalf, you can answer.

Your entire AI stack, replicated for every customer.

Every workspace ships a complete, isolated stack. One API call away.

Stop writing the six systems under every agent.

Open one workspace and the infrastructure arrives with it. Every row is something you were going to build yourself.

How Levain keeps customers apart

Usage is metered. Runs are recorded. Sub-processors are listed

Deploy a new customer's workspace in four steps.

The same four whether it is your first customer or your thousandth.

  1. Write the agent.

    Python with the SDK, or any language through the API. Publish it as a version.

  2. Open a workspace.

    One per customer. Sandbox, knowledge, memory and limits come with it.

  3. Hand over their tools.

    Their CRM, their helpdesk, their data. Credentials stay inside their workspace.

  4. Publish.

    Every workspace that adopted the agent follows the new version. You changed it once.

Everything is available through the API. Read the docs

Set your own limits and billing, per customer.

Know exactly what every customer uses. Track every token in and out, cap what they can spend, read usage from the API, and decide what to charge them for it.

Your customers pay you. You decide what they pay for.

Spend this month against the limit set for each customer: ws_7f3a acme at $18.24 of $20.00, nearing the limit you set; ws_91c4 borealis at $9.31 of $25.00; ws_2d80 vega at $2.07 of $10.00.

Your agents learn from your customers' real usage.

Levain scores every run in every customer workspace against the outcomes you set. Where they fall short, it proposes the next version for you to approve.

The loop a shipped agent runs through, and the approval that closes it A closed circuit. The running version leaves a record, Levain reads the outcomes and proposes the next version, and the circuit is broken on one side by an approval gate: nothing ships until you approve it. renewal-watcher v7 running everywhere outcomes read renewal-watcher v8 proposed you approve it

Frequently asked questions.

Do my customers need a Levain account?

No. Workspaces are headless. Your product talks to them through the API; your customers see your product.

How is one customer kept apart from another?

Each customer has a workspace with its own sandbox, credentials, knowledge and memory. Agents in a workspace can only access that workspace. Every run starts in a fresh machine that belongs to it.

Can I use my own model or API keys?

Yes. Use models from supported providers through Levain, or bring your own keys.

Can I control what an agent can access?

Yes. Connections and credentials belong to the customer's workspace. The agent can only act through the access you give it.

Can I control how much a customer spends?

Yes. Set limits per workspace and track usage down to the request through the API.

Can I access and export my data?

Yes. Your data is readable and exportable through the API.

Who bills my customers?

You do. Levain provides the usage and controls; you decide how to package and charge for your AI.

How do agents improve over time?

Levain evaluates agent behavior and outcomes across runs, identifies opportunities to improve, and proposes changes for you to approve. Customer workspaces remain isolated while the agent improves.


Give your first customer their own agent.

A free organization comes with a workspace to build in. Add your first customer when the agent is ready.