Hypero
Features

The building blocks
you actually need.

Canvas, agents, workflow engine, integrations, and observability — built together so they actually work together.

Start building How it works
Visual Workflow BuilderAI Agent CreationMulti-step Automation EngineAPI & Integrations SystemAI Reasoning LayerProduction-grade API

Visual Workflow Builder

A canvas your whole team can make sense of.

Map out your workflow on a shared canvas. Each step becomes a node you can configure, test, and re-run independently. Collaborators see the same diagram in real time, and every step is editable in place.

  • Real-time multiplayer editing
  • Versioned workflows with branching
  • Inline run inspector for every node
  • Node-level permissions and approvals

Use case

Replacing a Zap and a Python script

A growth team replaces three Zaps and a Python script with one workflow that classifies inbound leads with an AI agent and writes them straight into HubSpot.

canvas.tsx
Webhook
Agent
Postgres
If/else
HTTP

AI Agent Creation

Configure an agent, give it tools, and let it run.

Define a role, give an agent a set of tools, and drop it onto the canvas. Hypero handles memory, tool selection, and retries so you can focus on what the agent should actually do.

  • Long-term memory per user, team, or session
  • Custom tools defined in JS, Python, or HTTP
  • Guardrails: budgets, rate limits, allowed tools
  • Hot-swap models from OpenAI, Anthropic, or your own

Use case

Handling first-response support

A SaaS support team replaces their first-response queue with an agent that classifies tickets, drafts replies, and escalates only when it isn't sure.

agent: support-triage

role: senior support agent

tools: search_kb, create_ticket, send_email

memory: per_user (180d)

↳ reasoning trace

  • ✓ classify(intent) → billing_dispute
  • ✓ search_kb("refund policy") → 3 results
  • ✓ create_ticket(priority=high)
  • … drafting customer reply

Multi-step Automation Engine

Runs that don't fall over when something goes wrong.

Underneath the canvas is a durable workflow engine. Steps run with exactly-once semantics, branches and loops are first-class, and every run is observable end-to-end with a replayable timeline.

  • Durable retries with exponential backoff
  • Parallel branches and dynamic loops
  • Per-step timeouts and circuit breakers
  • Pause, approve, and resume from anywhere

Use case

Running a multi-step onboarding flow

An ops team kicks off a 14-step onboarding workflow on every new signup — provisioning, billing checks, and welcome emails — without writing a single cron job.

multi-step.run
  1. 1Receive webhook120ms
  2. 2Validate signature158ms
  3. 3Branch on payload.type196ms
  4. 4Run agent: enrich234ms
  5. 5Update Postgres row272ms
  6. 6Notify Slack channel310ms

API & Integrations System

Native connectors for what you use, HTTP for everything else.

Hypero ships with native integrations for the tools your team already uses, and a first-class HTTP node for everything else. Every workflow is also automatically exposed as an authenticated API endpoint.

  • 200+ pre-built connectors
  • Generic HTTP, GraphQL, and webhook nodes
  • OAuth, API keys, and scoped service accounts
  • Auto-generated REST + SDK for each workflow

Use case

Turning workflows into JSON APIs

An internal platform team turns every workflow into a JSON API and embeds them as actions in Retool, Slack, and the company's docs.

integrations
OpenAI
Anthropic
Slack
Postgres
Notion
GitHub
Linear
Stripe
HubSpot
Sendgrid
AWS
HTTP

AI Reasoning Layer

Inspect what the agent actually did, step by step.

Every agent run produces a full trace — what it planned, which tools it called, and why it made each decision. Compare two runs side-by-side and replay any trace against historical inputs.

  • Structured plans + tool-call traces
  • Side-by-side run diffs for prompt iteration
  • Cost, latency, and token telemetry per node
  • Safe replay against historical inputs

Use case

Debug AI like a real system

An AI startup catches a regression in a fine-tuned classifier in 90 seconds by diffing two runs side-by-side instead of wading through logs.

reasoning.json
{
  "thought": "user wants refund for double charge",
  "plan": [
     "lookup_charges(user_id)",
     "verify_duplicate()",
     "issue_refund()"
  ],
  "confidence": 0.94
}
traced · replayable

Production-grade API

Each workflow you deploy gets its own API endpoint.

Deploy Hypero workflows the same way you deploy the rest of your backend. Each workflow gets a typed REST endpoint, an SDK, and observability — rate limits and audit logs included.

  • Typed REST + TypeScript SDK per workflow
  • Rate limits, quotas, and per-key permissions
  • Audit logs and SOC2-ready trails
  • Bring-your-own-cloud option

Use case

Replacing a fragile Lambda setup

A platform team replaces a fragile Lambda + Step Functions setup with a single Hypero workflow exposed at api.company.com/v1/score.

POST /v1/workflows/run
curl https://api.hypero.dev/v1/workflows/run \
  -H "Authorization: Bearer $HYP" \
  -d '{
    "workflow": "lead-router",
    "input": { "email": "ada@hypero.dev" }
  }'
200 OK · 184ms

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