FRAKTIΛ provides a visual-first development layer for creating intelligent agents using a no-code environment. Users can configure behaviors, define personality, attach Add-Ons and deploy agents in production, all through an intuitive, GUI-based system.

The interface is modular, version-controlled and compatible with hot-reloading and on-chain tokenization, enabling both rapid prototyping and real-world deployment.

Interface Capabilities

Visual Pipeline Example

You can create an agent flow like this without touching code:

[Voice Input] → [LLM Inference Block] → [Memory Check] → [Webhook Action]

Each block is a modular UI element with editable parameters.

JSON View Behind the UI

Every agent created in the interface generates a character.json configuration:

{
  "name": "SupportBot",
  "persona": {
    "tone": "friendly",
    "responseStyle": "concise",
    "voice": "elevenlabs::nora"
  },
  "memory": {
    "shortTerm": true,
    "contextWindow": 6
  },
  "triggers": {
    "onMessage": "routeRequest"
  },
  "actions": {
    "routeRequest": {
      "type": "inference",
      "model": "openai::gpt-3.5",
      "prompt": "Classify support intent and respond appropriately"
    }
  }
}

Users can edit via visual forms or switch to raw JSON for advanced overrides.

Supported Flow Types

✦ Voice → Text → Memory → Add-On.
✦ Input → Trigger + Decision Tree.
✦ Multimodal trigger → async output. (e.g., TTS + API call)

Realtime Testing & Debugging

✦ Test agent behavior directly in the browser.
✦ View logs, token usage and trigger paths.
✦ Modify parameters and re-test instantly.
✦ Export JSON or redeploy with changes live.

Deployment from UI

With a single click, you can:

✦ Launch agent to ElizaOS runtime.
✦ Expose REST and WebSocket endpoints.
✦ Enable voice I/O on mobile/web.
✦ Register Add-On permissions.
✦ Tokenize the agent if needed. (Wrap as NFT toggle)

Strategic Value

The Zero-Code Interface removes friction for:
✦ Builders without coding experience.
✦ DAOs that want to visually configure logic.
✦ Enterprises that require fast iteration and visibility.
✦ Educational and experimentation use cases.

It lowers the barrier to programmable autonomy, while maintaining full compatibility with the modular execution model underneath.