Velm
Self-improving AI agents that get smarter while you sleep
Details
- Categories
- AIDeveloper Tools
- Target Audience
- AI DevelopersFounders & CEOsIndie Hackers
- Pricing
- Freemium
Chatbase
Vercel
About Velm
Velm is the dead-simple platform for shipping AI agents that improve themselves. Connect your data, and Velm configures a working agent and deploys it as an API, a scheduled job, or an embed widget on your site. No frameworks, no eval harness, no prompt tuning. What makes it different: every morning Velm drafts improvements from real user feedback and emails them to you. You approve with one click and your agent gets smarter, with no ML engineering required. Built for indie developers and small teams who want a support, docs, or in-product agent that just works and keeps getting better, without building the feedback loop themselves. Stripe for payments, Linear for issues, Velm for agents. Free to build and test, no card required. Production runs on consumption pricing: 500 free credits a month, then pay-as-you-go.
Product Insights
Velm is a freemium developer tool that simplifies deploying self-improving AI agents as APIs, scheduled jobs, or website widgets. It automates agent optimization by sending daily, one-click improvement drafts based on actual user feedback.
- Automated feedback loop that delivers daily, one-click agent improvement drafts via email.
- Flexible deployment options as an API, a scheduled job, or an embed widget.
- Freemium pricing model offering 500 free monthly credits before transition to pay-as-you-go.
- Multi-platform support spanning Web, API, MCP, and CLI.
Ideal for: Velm is ideal for AI Developers, Founders & CEOs, and Indie Hackers looking to deploy support, documentation, or in-product agents that self-improve without manual ML engineering.
Velm is listed as an alternative to Chatbase, Vercel, and Heym_.
Product Video
Watch a video demo of Velm.
Screenshots
Reviews (2)
Average 4.0 out of 5
Based on 2 reviews
Comments (2)
AI agents that improve themselves over time change the whole equation for lean founding teams. At Bunzee we think about the same compounding logic at the market intelligence level. How does Velm decide which behaviors to improve first?
By signal, not roadmap: every run is rated, so we fix what users hit most and like least first. Frequency x dissatisfaction. The hard part is trusting the fix, not finding it.
Hey! 👋 Velm ships AI agents that actually get better: point it at your data, deploy it as an API, job, or embed, and approve the improvements it drafts from feedback. Happy to answer anything!