The gap between an agent script and a production system is enormous. Reminix closes it with one line of code.
import { serve } from "reminix"
// Your agent — Vercel AI, LangChain, or plain code
const agent = createSupportBot({
model: "gpt-4o",
tools: [lookupOrder, createTicket],
system: "You are a support agent for Acme Corp.",
})
// One line to production.
serve(agent, {
name: "support-bot",
tools: ["memory", "knowledge_base"],
})Deploying an agent on a generic PaaS means building all of this yourself. Reminix gives it to you out of the box.
SSE endpoints, backpressure handling, client reconnection. Built in.
Multi-turn sessions, message history, user-scoped persistence. Managed for you.
Your agent calls tools, we execute them, handle timeouts, and return results.
Production endpoints + typed Python and TypeScript clients. Works with every agent.
API keys, environment variables, rate limiting. No DIY auth middleware.
Request tracing, error tracking, latency metrics. Not another Datadog config.
Managed infrastructure so you can focus on agent logic, not plumbing.
Memory, search, knowledge base, KV storage — add with a string.
20+ services. Bring your credentials, we handle tokens.
Chat, task, and workflow agents — pick the right one.
Apache 2.0 runtime. Read the code, self-host if you want.
Type-safe Python and TypeScript clients for all your agents.
Tracing, error rates, latency, and token usage built in.
serve() to production.Wrap your agent with serve(). We handle everything else.
Use LangChain, Vercel AI SDK, OpenAI, Anthropic, or the Reminix Runtime. Any framework, any model. Your code, your way.
serve(agent)One line gives you production APIs, streaming, built-in tools, SDKs, and monitoring. No HTTP layer to write, no infra to configure.
reminix deploy or connect your GitHub repo — automatic deploys on every push. Scaling, secrets, versions, rollbacks — all handled.
reminix deploy