The agent stack that
replaces five engineers.
Skills, tools, OAuth, scheduling, evals — the runtime your team would otherwise spend a quarter building. Every skill scored against the base model, so your agent always outperforms vanilla. Integrate it anywhere your team ships.
Wire in everything your team knows.
Drop in docs, runbooks, Notion exports, Slack threads. Kavela turns them into a semantic skill layer your agent loads on demand — thousands of skills per agent, no context bloat.
Plug any API into any agent.
Bring your own endpoints, point at an OpenAPI spec, drop in an MCP server. The agent learns when to use what. No glue code, no orchestrator graph to maintain.
Run on your stack, on its own clock.
Per-user OAuth vault, scheduled triggers, webhook listeners, durable long-running tasks. The plumbing two engineers would otherwise be assigned for a quarter.
Every skill scored against the base model.
Kavela judges every skill by whether the frontier model could have answered it without. Only additive skills load into your agent — so it always outperforms vanilla, and the bar rises with every new model release.
Describe the workflow.
Ship the agent.
Tell Kavela what you want in plain English. It picks the model, drafts the skills, proposes the tools, wires the OAuth, runs the eval, and gives you a deploy URL.
Skip the search.
Just ask.
Type what you need help with — Kavela matches you to the agent whose skills best fit, and drops you into chat. If nothing's a clear fit, Kavela's default agent takes it from there.
Don't build what someone
already shipped.
Install a working agent in one click. Fork it, swap the skills for your own wiki, point the arms at your stack. Every paid listing passes our LLM-Judge bar.
Most platforms hand you
one fat system prompt
and call it an agent.
That's fine for a demo. It falls over the moment a real workflow needs memory, identity, scheduling, or quality scoring. Kavela ships those as primitives.
What teams ship in a week
on Kavela.
A senior code reviewer that knows your conventions.
Drop in your codebase, your style guide, your past PR comments. The agent reviews diffs against your team's actual standards — not GitHub's defaults — and flags drift before merge.
Your chain's agent, scored against base models.
Wholesale runtime under your brand. Skills curated for your VM, your tools, your devrel content — every one of them scored by LLM-Judge so your developers don't get vanilla answers.
Marketing copy that sounds like you, not GPT.
Wire in your tone guide, past launches, voice samples. Agent runs every draft through skills + LLM-Judge so what ships is on-brand — not generic LLM filler the model could've written without you.
Up to 90% revenue share —
for skills that pass the bar.
Publish your skill or agent to the marketplace. We score it with LLM-Judge — if it adds knowledge the frontier doesn't already have, you ship at the highest tier (90%). Quality is the gate, not luck. Vertical expertise is the moat.
Kavela commoditizes the agentic stack —
so any team with vertical expertise can ship vertical AI,
and the model can't catch up.
Things people ask
before they ship.
Still on the fence? Talk to a human.
Book a 20-min walkthrough →Do I need to write code?
No. The studio covers most workflows. When you want to drop into code, every agent is a real Kavela project you can edit in the studio or pull locally.
Which models can I use?
Bring your own keys for OpenAI, Anthropic, Google, and Llama-family providers (optional toggle on paid plans). Kavela picks the cheapest model that hits your eval bar, and falls back when one degrades.
How is skill quality measured?
Every skill is scored by LLM-as-Judge: a panel of judges runs your skill against a curated question bank and answers whether the base model could have done as well. Skills that are additive load into your agent. The bar auto-rises with every new model release.
Is this Recall Network?
No. Recall is a reputation marketplace where “skills” are competition verticals. Kavela is the MCP-native runtime where skills are retrievable knowledge units. Different layer of the stack — both can coexist.
How do you handle our data?
Skills are stored in your tenant. We never train on your data. Per-user OAuth tokens stay encrypted in the vault and are scoped per agent. On-prem path available for enterprise.
What does it cost?
Free 500 credits/mo for solo builders. Pro $20 (1,500cr). Studio $49 (3,500cr). BYOK is an optional toggle on Pro+ — extracts model-inference cost from credit burn. Chain-partner wholesale: $3K pure-infra or $5K managed-quality, 50% Tier-1 co-marketing discount.
Build the skills the model still can't do without.
Ten minutes from now, your weirdly specific workflow is a deployed agent your team and clients are running.
GenUI and container runtime — coming soon.