TL;DR:
Like Vercel, but for stateful AI agents. Deploy your container and instantly get an agent with persistent memory, auto-recovery, and a live API endpoint — no infrastructure work required.
Hey Dev.to 👋
I’m Cyw, I recently launched an open-source project called Agentainer — a local-first platform for deploying long-running AI agents with zero DevOps. You can check it out here:
GitHub: Agentainer-Lab(GitHub)
Website: Agentainer
Why I built this
Most infrastructure today is optimized for stateless, short-lived workloads — think Lambda, Cloud Run, or even Kubernetes pods. But when I started working with LLM agents, I realized these tools weren’t a good fit.
Agents need to persist memory. They run for hours. They crash and need to pick up where they left off. Getting that to work in production often means duct-taping together:
- Docker + EFS for persistent volumes
- Retry queues for failed requests
- Auto-restart logic
- Gateways for safe public access
- And probably some bash scripts you hope never break
Honestly? I got tired of it. So I built Agentainer to simplify the stack.
🔍 How It Compares
| Feature | Agentainer | Raw Docker | Kubernetes | Serverless |
|---|---|---|---|---|
| Deployment Speed | ✅ < 30 seconds | ⚠️ Manual setup | ❌ Complex YAML | ✅ Fast |
| State Management | ✅ Built-in Redis | ❌ DIY | ⚠️ External | ❌ Stateless |
| Request Persistence | ✅ Automatic | ❌ Not included | ❌ Not included | ❌ Lost on timeout |
| Crash Recovery | ✅ With replay | ⚠️ Restart only | ⚠️ Restart only | ✅ Auto-retry |
| Local Development | ✅ Optimized | ✅ Native | ❌ Heavy | ❌ Cloud only |
| LLM-Specific | ✅ Purpose-built | ❌ Generic | ❌ Generic | ❌ Generic |
What it does
- Agentainer runs each agent in an isolated container with:
- Persistent memory (via volume mounts)
- Crash recovery (auto-restarts with state checkpointing)
- Request replay (if the agent dies mid-task, the request isn’t lost)
- Clean networking (each agent gets a dedicated API endpoint with proxying built-in)
All you do is:
agentainer deploy --name my-agent --image my-docker-image
And your agent is live. No exposed ports, no infra glue, no YAML.
Built for agents (but could work for anything)
Originally, I made this so my own coding agent could deploy and manage infrastructure on its own — without wasting tokens repeating shell commands. Everything is CLI-first, and also accessible by API. So if you want your own agents to spin up services, restart themselves, or shut down gracefully — they can.
And even though I built it for agents, technically you could use this for any long-running containerized app. It just happens to be extra helpful when you’re dealing with memory/stateful logic.
About the “save 40%” part
I did some napkin math, and depending on your traffic and use case, replacing per-request billing with a flat-cost EC2 or VPS instance can easily save money. One dev I talked to migrated a chunk of their Lambda-based backend to Agentainer and saw their AWS bill drop significantly. YMMV, but the economics are real — especially if you’re running:
- High-throughput agents
- Memory-heavy processes
- Repeated calls that don’t need instant scale-to-zero behavior
Even for smaller projects, you might get better UX (no cold starts!) and spend less time wiring things up.
What’s next?
Right now, Agentainer Lab is focused on local development and self-hosting. But the Agentainer.io roadmap includes:
- Observability / logging
- Built-in backups and restore
- Audit trails
- Auto-scaling clusters for production loads
If you’re interested, you can sign up for early access on the site. We’ll send you one email when production is live — then delete your email from our database.
Try it out
GitHub: github.com/oso95/Agentainer-lab
Platform: agentainer.io
Would love to hear your thoughts. Are you building agents? Running persistent workloads? Using too much YAML? Drop your current setup or ideas in the comments — I’m all ears!