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πŸ—οΈ Architecture

How Zirelia Works

Three coordinated AI systems β€” Brain, Imagination, and Hands β€” working in perfect sync to run your virtual influencer persona without ever stopping.

Brain (GPT-4)
Imagination (FLUX.1)
Hands (Twitter/X)
Loop forever
🧠 Step 1 β€” Brain

The engine thinks for itself

Every post cycle begins with the Brain. LangChain + LangGraph orchestrate a multi-step reasoning process where GPT-4 reads the persona's YAML config, checks ChromaDB for recent posts (to avoid repetition), and generates a contextual, on-brand thought.

The thought is always tied to the persona's current "routine" (morning, afternoon, evening) β€” so posts feel natural and temporally coherent, as if a real person is living through their day.

LangChain + LangGraph

AI orchestration layer β€” manages the entire reasoning pipeline

ChromaDB (RAG)

Vector memory β€” prevents repetition across thousands of posts

OpenAI GPT-4

The LLM that writes thoughts, captions, and engagement replies

PostgreSQL

Persistent log of every post β€” timestamp, text, image URL, status

Brain Pipeline β€” running
βœ“ Loading persona: Sienna Fox
βœ“ Reading daily routine: morning
βœ“ ChromaDB query β€” last 50 posts loaded
β†’ Generating thought with GPT-4o-mini...
Generated thought: "morning glow hits different when you stayed up editing until 3am ✨ some days the grind is the glamour"
βœ“ Thought stored in ChromaDB
β†’ Passing to Imagination pipeline...
Replicate API β€” generating image
🎨
FLUX.1 pro generating...
Sienna Fox Β· morning lifestyle
model:flux-1.1-pro
lora_scale:0.85
num_inference_steps:28
aspect_ratio:4:5
βœ“ Image generated β€” visual QC passed
🎨 Step 2 β€” Imagination

A face generated for every post

Once the Brain produces a thought, the Imagination pipeline kicks in. A detailed image prompt is constructed from the persona's physical description and the post's context, then sent to FLUX.1 via Replicate.

The result is a photorealistic, consistent face β€” the same persona, post after post. For total visual control, you can train a custom LoRA model on a synthetic dataset and plug it directly into the pipeline.

FLUX.1 / SDXL via Replicate API
Custom LoRA support for visual identity lock-in
Automated Visual QC pipeline
Synthetic dataset creation for LoRA training
πŸ“€ Step 3 β€” Hands

Posts like a human. Never stops.

The Celery-powered Scheduler runs 24/7, firing post cycles at randomised intervals that mimic human behaviour. Posts go live at unpredictable times β€” never in a rigid schedule that platform algorithms can detect.

Optional Replier and Liker services add authentic engagement patterns β€” replying to mentions and liking relevant content to grow the account organically.

Tweepy

Official Twitter/X API v2 wrapper

Celery + Redis

Distributed task queue and scheduler

FastAPI

REST API for manual triggers and status

Anti-ban logic

Randomised timing to avoid detection

scheduler Β· running Β· cycle #4821
βœ“ [14:22] Post published to Twitter/X
βœ“ [14:22] Image attached (flux-1.1-pro)
βœ“ [14:22] Post logged to PostgreSQL
β†’ Next post scheduled in: 4h 38m
β†’ Anti-detection jitter: Β±23min
Engagement services:
replier: manual (off)
liker: manual (off)
πŸ’Ύ The Backbone β€” Memory

ChromaDB: the influencer never forgets

Every post is embedded and stored in ChromaDB. Before generating new content, the Brain performs a similarity search β€” ensuring zero duplicate ideas, even after thousands of posts.

πŸ”

Semantic Search

Before every post, ChromaDB finds the 50 most semantically similar past posts to prevent repetition.

♾️

Infinite Memory

Vector embeddings scale to millions of posts. The persona gets richer and more diverse over time, not more repetitive.

🎯

Persona Coherence

Memory ensures the influencer stays on-brand and consistent β€” same voice, same themes, same authentic feel.

Ready to deploy?

Check the full documentation for advanced configuration and guides.

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