Skip to content

AI · 8 min read

Mastra and Supabase for AI agents (practical setup)

Wire Mastra agents to Supabase for memory, RLS, and tool data. Patterns that work for SA SaaS on Vercel.

Featured image for Mastra and Supabase for AI agents (practical setup)

Searchers look for mastra supabase when they want one stack: TypeScript agents plus Postgres they already trust. This is the integration map I use on client work.

Why the pair works

  • Mastra gives agents, workflows, eval hooks, and a dev playground in TS
  • Supabase gives Postgres, auth, RLS, and realtime without running another database vendor

Most Joburg and Cape Town SaaS teams I work with already have Supabase. Adding Mastra avoids a second memory product.

Architecture sketch

  1. Auth: Supabase session on the Next.js app; agent routes require logged-in user
  2. Memory: agent_threads and agent_messages tables with user_id FK
  3. RLS: policies so users only read their own thread rows
  4. Tools: server-side functions that call Paystack, CRM, or internal APIs (never expose service role to the browser)
  5. Deploy: Vercel for Next + Mastra handlers; Supabase hosted or self-managed

Minimal table ideas

-- Illustrative only: adapt to your schema
create table agent_threads (
  id uuid primary key default gen_random_uuid(),
  user_id uuid references auth.users not null,
  title text,
  created_at timestamptz default now()
);

alter table agent_threads enable row level security;
create policy "own threads" on agent_threads
  for all using (auth.uid() = user_id);

Store tool results you need for audit, not every token stream.

Pitfalls in SA projects

  • Putting Paystack secrets in client-side tool definitions
  • Skipping idempotency on webhook tools the agent can trigger
  • No rate limits on public agent endpoints (expensive abuse during load shedding news spikes)

Comments