Get started

In about five minutes you will have your own AgentDB workspace. You will store contacts and companies, link them and query them. You do not write SQL.

This guide uses the agentdb command-line tool. Your AI agent can do the same steps over MCP. See Connect your AI.

#1. Sign up

1. Open https://app.agentdb.link.
2. Enter your email address. A project name is optional.
3. Accept the terms and choose Create workspace.

AgentDB creates your account, your first workspace and an API key at once. There is no waiting list.

#2. Save your API key

The next page shows your API key. It starts with adbk..

Lost your key? Request a recovery link at https://app.agentdb.link/recover. See Accounts and security.

#3. Install the CLI and log in

Install the CLI on macOS or Linux. It needs no Node.js.

curl -fsSL https://app.agentdb.link/downloads/cli/install.sh | sh

Log in. The CLI opens your browser. You approve the login there with your API key.

agentdb login --api-url https://app.agentdb.link

The CLI keeps the key in your operating system's credential store. It never writes the key into a project file.

Scripts and CI can use two environment variables instead. AGENTDB_TOKEN holds the key. AGENTDB_WORKSPACE holds the workspace id.

#4. Define a company and a contact

An object is a type of record, like a table. A field is a typed value on that object. Read more in Concepts.

Create the company object with a unique name and a country:

agentdb schema object-create company

agentdb schema field-add company name --type text --unique

agentdb schema field-add company country --type text

Create the contact object with an email address, a name and a score:

agentdb schema object-create contact

agentdb schema field-add contact email --type email --unique

agentdb schema field-add contact name --type text

agentdb schema field-add contact score --type integer

Define how they relate. A contact works_at one company:

agentdb schema relationship-add contact works_at company --cardinality one

#5. Write records

Create a company and a contact. Each command prints the new record with its id.

agentdb records create company --data '{"name":"Example Ltd","country":"NL"}'

agentdb records create contact --data '{"email":"ada@example.com","name":"Ada Example","score":82}'

AgentDB checks every value against its field type. A wrong value is refused with a clear error, and nothing is written.

#6. Link the records

Link the contact to the company:

agentdb records link $CONTACT works_at $COMPANY

Now ask who works at the company. --incoming follows links that point at the company.

agentdb records related $COMPANY works_at --incoming

#7. Query your records

Find contacts with a score of 70 or more at a company in NL:

agentdb query --json '{"object":"contact","where":{"all":[{"field":"score","op":"gte","value":70},{"relationship":"works_at","where":{"all":[{"field":"country","op":"eq","value":"NL"}]}}]},"sort":[{"field":"score","order":"desc"}],"limit":10}'

The same query works over REST. Your agent sends the same JSON to the records_query tool.

curl -X POST https://app.agentdb.link/v1/workspaces/$AGENTDB_WORKSPACE/query \
  -H "Authorization: Bearer $AGENTDB_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"object":"contact","where":{"all":[{"field":"score","op":"gte","value":70}]}}'

Learn every filter and option in Query your data.

#8. Let your agent do it

Connect your AI client. See Connect your AI. Then ask in plain language, for example:

Your agent works out the tool calls itself.

#Next steps

• Connect your AI to Claude, ChatGPT, Cursor or another client.
• Files and imports to attach documents and import a CSV file.
• Dashboards and sharing to show your data on a live page.