Describe what you need in plain English. Donkit assembles the agent — tools, memory, knowledge, its own database, even the UI — then proves it works before anyone relies on it.
Four steps, minutes not sprints. The builder does the engineering; you stay in plain English the whole way.
Tell the builder what the agent should do, in plain English. No graphs to draw, no YAML, no framework to learn.
The meta-agent assembles everything — which tools to call, what to remember, what knowledge to load, what data schema it needs — and generates a custom UI to match.
Before launch, Donkit runs your agent against generated test cases — including interventional tests that probe how it behaves when things go wrong. Improvements promote only when the evals prove they're better.
One click. Your agent gets a shareable app with its own interface, running on production infrastructure. No deploy pipeline to build.
Not a wrapper on a chat box. Interventional evals, a generated UI, a real database and smart model routing per agent — the differentiators, plus the whole platform behind them.
The builder is itself an agent. It interviews you, drafts the config, creates the schema, wires the tools, and iterates from your feedback — the way a senior engineer would, in minutes instead of sprints.
Every agent ships with its own frontend — chat, dashboards, forms — generated from what it actually does. Not an embed widget: an app your users open and use.
Donkit routes each request to the model that fits its complexity, domain and cost — automatically.
Most teams test agents on happy-path demos. Donkit generates evaluation datasets, runs them automatically, and intervenes mid-trajectory to test failure behavior. Every config change is measured — regressions rejected, improvements promoted. Your agent gets better with use.
Not a spreadsheet bolted on: a real SQL database per agent, with a schema the builder designs and migrates for you. The agent reads and writes under a restricted role — every query logged.
Vector memory for facts and documents, episodic memory for what happened. Long conversations compress into durable summaries instead of truncating.
Agents spawn scoped sub-agents — one per document, task or data slice — and run them in parallel. Big jobs decompose instead of overflowing one context window.
Full tracing of every run — each model call, tool call and sub-agent step, down to the token. When an agent misbehaves, you see exactly where and why.
Plug in any Model Context Protocol server — CRMs, ticketing, internal APIs. Credentials are encrypted at rest and never leave the runtime.
Upload documents and Donkit handles the whole retrieval pipeline — parsing, chunking, embeddings, vector search — as a managed background process with per-document status.
Deploy the entire platform inside your own infrastructure — same product, your network, your compliance boundary. Nothing leaves your walls.