You do not build the agent. You describe the outcome. Avery researches it, specs it, compiles a deterministic workflow, wires the connections, and tests itself on your real data until it passes. Under 30 minutes. Zero concepts to learn.
Tell Avery what should happen, when it should happen, and where you want control. No nodes. No prompt engineering.
“Every Monday, pull unpaid invoices, draft polite chasers personalized by client history, let me review, then send and log outcomes.”
Avery drafts a readable specification: steps, access, decisions, approvals, and expected output. Nothing builds until you tap Looks right.
It wires connections, writes rules and code, picks a model for each step, runs real samples, and repairs itself until its own checks pass.
Inspect the result before launch. Your corrections become reusable knowledge, not another fragile prompt edit.
The compiled workflow runs locally or on-premise, reaching outside your network only where you explicitly allow it.
Every step, access request, approval, model choice, and cost is recorded in plain English. Take back control at any time.
As Avery fabricates your agent you see every message and every step take shape — in plain language. Change your mind? Just type. One sentence steers the build, live.
“Read incoming customer orders, validate them, ask when data is missing, then enter approved orders into our ERP.”
“Pull overdue invoices, draft personalized reminders, let finance review, then send and log every outcome.”
“Answer policy questions only from approved documents, cite the rule, check exceptions and record the response.”
“Collect data from multiple systems, reconcile discrepancies and prepare the monthly operating report for review.”
Developer frameworks are powerful but demand engineers. Cloud no-code agents are easy but shallow, metered, and locked in someone else's cloud. Research labs prove what is possible but ship nothing you can run. Avery does not make you choose.
| Capability | Dev frameworks LangChain · CrewAI | Cloud no-code Copilot · Lindy · Zapier | Research labs Sakana et al. | Avery |
|---|---|---|---|---|
| No code — plain language | ✕ | ✔ | ✕ | ✔ |
| Runs on your hardware · private | ~ | ✕ | ✕ | ✔ |
| Deterministic & auditable | ✕ | ✕ | ✕ | ✔ |
| Cheapest-model routing (on-device first) | ✕ | ✕ | ~ | ✔ |
| Ensemble cross-checking on demand | ✕ | ✕ | ✔ | ✔ |
| Self-heals · tests on your real data | ~ | ~ | ✕ | ✔ |
| You own it — no per-token meter | ~ | ✕ | ✕ | ✔ |
✔ full ~ partial / DIY ✕ not offered
Category posture, not a feature-by-feature audit of any single product.
Avery gives the people closest to the work agentic automation they can build, trust, run privately, and afford — without an engineering team or a cloud bill that scales with success.
Twelve things Avery does that other agent platforms don't — roughly in the order people say “wait, it does that?”
Describe it in a sentence. Avery researches it, writes a deterministic workflow, tests on your real data, and self-repairs until it passes.
The FabricatorPrivate by default, air-gapped ready. Your data and your models never leave your machine unless you grant it.
Local-firstCloud, Ollama, or in-browser WebLLM — routed to the cheapest model proven reliable, cross-checked when it must be right.
Wisdom of the modelsNot everything needs an LLM. Avery writes plain code and rules for exact logic — even whole connectors.
From its own errors, your feedback, and which model wins at which task — no prompt-wrangling.
A plain-English trail of every step and cost. Same input, same output.
Orchestrate many agents — direct or via a conductor — behind a beautiful UI.
Export any agent as a signed .avery — someone else imports, installs, runs.
Every external access needs your explicit yes on the first run. Revocable, logged.
Spend caps per build, per run, and per agent. The meter never surprises you.
Hand it a skill; it compiles it into a repeatable, deterministic workflow.
Review its work and add knowledge; it absorbs your corrections and improves.
Instead of waiting for one bigger model, combine several specialised ones — route each step to the right model to match the best frontier model at a fraction of the cost, and bring several together to beat any single one. Avery builds that wisdom of the ensemble in — where every decision stays cheap, explainable, and reproducible.
Avery learns which model is best at each task from your own runs and routes every step to the cheapest one proven reliable — preferring free on-device models.
Each step is double-checked. Avery steps up to a stronger model only when a check actually fails — cheap-first, made safe.
High-stakes steps are run across several models and reconciled — reserved for the steps that must be right.
Every agent gets a plain Fast · Balanced · Best choice. No model names. Anyone can set it.
Frontier-quality at on-device cost on Balanced. Better-than-frontier on demand at Best. Every decision explained, reproducible, and learned from your own runs.
Avery uses frontier AI to design and test the workflow once. Then it compiles that intelligence into inspectable software made of rules, code, local models, and cloud calls you have explicitly approved. The creative AI builds the machine. The reliable machine runs the work.
Researches · specs · generates · tests · self-repairs. Agentic (Claude Agent SDK) at build time only — the agentic intelligence never runs in production.
The cheapest correct tool first — a frontier model only when a step truly needs one. Deterministic · offline-capable.
Runs fully offline. No egress to “turn off” — there is none unless you grant it.
Every agent is an inspectable, versioned graph. Same input, same output.
Rules, code, on-device, or frontier — chosen by measured capability and cost.
An append-only ledger records each step in plain English — auditable end to end.
Download Avery and build your first real agent locally. No toy engine, no code required, and no data exhaust by default.