AutoViralAI

Your social media grows while you sleep.

An autonomous AI agent that researches what's going viral, generates content,
publishes it, measures engagement — and rewrites its own strategy based on what actually worked.

Real Results · Demo · Quick Start · How It Works · Configuration · Roadmap · Contributing

🔥 Real Results

We've been running AutoViralAI for ~1.5 months on a tech/programming Threads account. Here are some posts that went viral — every single one was researched, generated, and ranked by the agent:

800+ likes on a single post. The agent learned that contrarian "old tech vs new tech" hooks and relatable developer war stories consistently outperform generic tips — and it adapted its strategy automatically.

🎬 Demo

See it in action — the agent researches trends, generates posts, ranks them, and sends you an approval request.

💡 The Problem

You know you should be posting consistently. You know what kind of content performs. But researching trends, writing posts, tracking what works, adjusting strategy — it's a full-time job.

💡 The Solution

AutoViralAI does the entire loop autonomously. It doesn't just post — it learns. Every day it gets a little better at understanding what your audience actually engages with.

Day 1:   "Here's a generic coding tip"            → 12 likes
Day 7:   "Hot take: most devs don't need Docker"   → 340 likes
Day 14:  Agent learned contrarian hooks work 3x     → adapts strategy automatically
Day 30:  Posts consistently hit 500+ engagement     → you didn't write a single one
Day 45:  Single post hits 800 likes                 → the agent wrote it, not you

⚡ Why AutoViralAI?

Most "AI social media tools" are glorified schedulers with a GPT wrapper. They generate generic content, blast it out, and call it a day.

AutoViralAI is fundamentally different:

✨ Key Features

🚀 Quick Start

Prerequisites

• Python 3.13+
• uv (recommended) or pip
• An Anthropic API key

Setup

git clone https://github.com/kgarbacinski/AutoViralAI.git
cd AutoViralAI
uv sync
cp .env.example .env
# Edit .env → set ANTHROPIC_API_KEY (minimum required)

Run Your First Cycle

# Interactive mode — you approve/reject posts in the terminal
uv run python scripts/manual_run.py

# Auto-approve mode (for testing the full pipeline)
uv run python scripts/manual_run.py --auto-approve

# Run the learning pipeline
uv run python scripts/manual_run.py --pipeline learning

Note: The first run uses mock APIs — no social media account needed. You'll see the full pipeline execute with realistic simulated data.

🔄 How It Works

The system runs two independent pipelines that share a knowledge base:

graph TB
    subgraph creation ["Content Creation Pipeline · runs 3x/day"]
        direction TB
        A["Goal Check"]
        B["Research Viral Content<br/>(HackerNews + Threads)"]
        C["Extract Patterns<br/>(hooks, structures, triggers)"]
        D["Generate Post Variants"]
        E["Multi-Signal Ranking<br/>(AI + History + Novelty)"]
        F{"Human Approval<br/>via Telegram"}
        G["Publish to Threads"]
        H["Schedule Metrics Check"]
        Z(["Target Reached"])

        A -->|not reached| B
        A -->|target reached| Z
        B --> C --> D --> E --> F
        F -->|"Approve / Edit"| G --> H
        F -->|Reject + Feedback| D
    end

    subgraph learning ["Learning Pipeline · runs daily"]
        direction TB
        I["Collect Engagement Metrics"] --> J["Analyze Performance"]
        J --> K["Update Pattern Scores"] --> L["Adjust Strategy"]
    end

    subgraph store ["Shared Knowledge Base"]
        direction LR
        KB[("Patterns · Strategy<br/>Metrics · Posts")]
    end

    H -. "post data" .-> KB
    KB -. "metrics" .-> I
    L -. "improved strategy" .-> KB
    KB -. "patterns + strategy" .-> B

    style creation fill:transparent,stroke:#4A90D9,stroke-width:2px,color:#fff
    style learning fill:transparent,stroke:#9B59B6,stroke-width:2px,color:#fff
    style store fill:transparent,stroke:#50C878,stroke-width:2px,color:#fff

    style A fill:#4A90D9,stroke:#2C6FA0,color:#fff
    style B fill:#4A90D9,stroke:#2C6FA0,color:#fff
    style C fill:#5BA0E0,stroke:#2C6FA0,color:#fff
    style D fill:#5BA0E0,stroke:#2C6FA0,color:#fff
    style E fill:#5BA0E0,stroke:#2C6FA0,color:#fff
    style F fill:#E8A838,stroke:#C4872A,color:#fff
    style G fill:#50C878,stroke:#3A9A5C,color:#fff
    style H fill:#50C878,stroke:#3A9A5C,color:#fff
    style Z fill:#2ECC71,stroke:#27AE60,color:#fff

    style I fill:#9B59B6,stroke:#7D3C98,color:#fff
    style J fill:#9B59B6,stroke:#7D3C98,color:#fff
    style K fill:#AF7AC5,stroke:#7D3C98,color:#fff
    style L fill:#AF7AC5,stroke:#7D3C98,color:#fff

    style KB fill:#50C878,stroke:#3A9A5C,color:#fff

Why two pipelines? Posts need 24-48 hours to accumulate meaningful engagement data. The creation pipeline runs multiple times per day, while the learning pipeline runs once daily on yesterday's data — then feeds the improved strategy back into creation.

🔁 The Self-Learning Loop

graph LR
    R["Research<br/>What's viral?"] --> E["Extract<br/>Why it works"]
    E --> G["Generate<br/>Using patterns"]
    G --> K["Rank<br/>AI + History + Novelty"]
    K --> P["Publish<br/>Post the winner"]
    P --> M["Measure<br/>Wait 24h, collect data"]
    M --> L["Learn<br/>What worked & why"]
    L --> A["Adapt<br/>Update strategy"]
    A -->|"feed back"| R

    style R fill:#4A90D9,stroke:#2C6FA0,color:#fff
    style E fill:#5BA0E0,stroke:#2C6FA0,color:#fff
    style G fill:#5BA0E0,stroke:#2C6FA0,color:#fff
    style K fill:#E8A838,stroke:#C4872A,color:#fff
    style P fill:#50C878,stroke:#3A9A5C,color:#fff
    style M fill:#9B59B6,stroke:#7D3C98,color:#fff
    style L fill:#AF7AC5,stroke:#7D3C98,color:#fff
    style A fill:#AF7AC5,stroke:#7D3C98,color:#fff

📊 Multi-Signal Ranking

Each variant gets a composite score from three independent signals:

graph LR
    subgraph signals [" "]
        direction TB
        AI["AI Score<br/>(0–10)<br/>weight: 0.4"]
        PH["Pattern History<br/>(0–10)<br/>weight: 0.3"]
        NV["Novelty<br/>(0–10)<br/>weight: 0.3"]
    end

    AI --> C(["Composite Score"])
    PH --> C
    NV --> C
    C --> W{"Select<br/>Winner"}

    style AI fill:#4A90D9,stroke:#2C6FA0,color:#fff
    style PH fill:#E8A838,stroke:#C4872A,color:#fff
    style NV fill:#9B59B6,stroke:#7D3C98,color:#fff
    style C fill:#50C878,stroke:#3A9A5C,color:#fff
    style W fill:#2ECC71,stroke:#27AE60,color:#fff
    style signals fill:transparent,stroke:transparent

New patterns get a 5.0 exploration bonus — the system balances exploitation (use what works) with exploration (try new things).

🤝 Human-in-the-Loop

The agent never posts without your approval. Before the approval message, you get a full pipeline report showing what each agent did — research results, extracted patterns, generated variants, and ranking breakdown.

Built on LangGraph's interrupt() — the graph pauses, saves state, and resumes when you respond. Survives server restarts.

🎯 Configuring Your Niche

Edit config/account_niche.yaml to define your identity:

niche: "tech"
sub_niche: "programming & startups"

voice:
  tone: "conversational, insightful, slightly provocative"
  persona: "experienced developer who shares hard-won lessons"
  style_notes:
    - "Use short, punchy sentences"
    - "Lead with a controversial or surprising take"
    - "End with a question or call-to-action"

content_pillars:
  - name: "hot_takes"
    description: "Contrarian opinions on tech trends"
    weight: 0.30
  - name: "practical_tips"
    description: "Actionable coding tips and tool recommendations"
    weight: 0.25
  # ... add your own pillars

avoid_topics:
  - "political opinions unrelated to tech"
  - "cryptocurrency shilling"

The agent uses this config in every generation cycle to stay on-brand and on-topic.

🏗️ Architecture

AutoViralAI/
├── config/
│   ├── account_niche.yaml       # Your niche, voice, audience, content pillars
│   └── settings.py              # Environment config (pydantic-settings)
│
├── src/
│   ├── models/                  # Pydantic models + TypedDict states
│   ├── graphs/                  # LangGraph pipeline definitions
│   │   ├── creation_pipeline.py # Research → generate → approve → publish
│   │   └── learning_pipeline.py # Metrics → analyze → learn → adapt
│   ├── nodes/                   # Individual pipeline steps (11 nodes)
│   ├── tools/                   # External service wrappers (mock-first)
│   ├── prompts/                 # All LLM prompt templates
│   ├── store/                   # Knowledge base (LangGraph Store)
│   ├── orchestrator.py          # APScheduler (cron-like scheduling)
│   └── persistence.py           # Checkpointer + Store factory
│
├── bot/                         # Telegram bot (approval, commands, config)
├── api/                         # FastAPI server
├── scripts/                     # Manual run, init, health check
└── tests/                       # pytest suite

📦 Knowledge Base

Both pipelines share a persistent knowledge base via LangGraph Store:

graph LR
    subgraph store ["LangGraph Store"]
        direction TB

        subgraph config_ns ["Configuration"]
            C1["config/<br/>Niche & voice"]
            C2["strategy/<br/>Content strategy"]
        end

        subgraph perf_ns ["Performance"]
            P1["pattern_performance/<br/>What patterns work"]
            P2["metrics_history/<br/>Engagement data"]
        end

        subgraph content_ns ["Content"]
            T1["published_posts/<br/>Post history"]
            T2["pending_metrics/<br/>Awaiting check"]
        end
    end

    CP["Creation<br/>Pipeline"] --> store
    store --> LP["Learning<br/>Pipeline"]
    LP --> store

    style store fill:transparent,stroke:#50C878,stroke-width:2px
    style config_ns fill:#EBF5FB,stroke:#4A90D9,color:#333
    style perf_ns fill:#F5EEF8,stroke:#9B59B6,color:#333
    style content_ns fill:#EAFAF1,stroke:#50C878,color:#333

    style C1 fill:#fff,stroke:#4A90D9,color:#333
    style C2 fill:#fff,stroke:#4A90D9,color:#333
    style P1 fill:#fff,stroke:#9B59B6,color:#333
    style P2 fill:#fff,stroke:#9B59B6,color:#333
    style T1 fill:#fff,stroke:#50C878,color:#333
    style T2 fill:#fff,stroke:#50C878,color:#333

    style CP fill:#4A90D9,stroke:#2C6FA0,color:#fff
    style LP fill:#9B59B6,stroke:#7D3C98,color:#fff

Dev: InMemoryStore · Prod: AsyncPostgresStore with embedding support.

🛠️ Tech Stack

🧪 Tests

# Run all tests
uv run pytest

# With coverage
uv run pytest --cov=src --cov=bot --cov=api --cov-report=term-missing

# Lint
uv run ruff check .

🐳 Production Deployment

Push to main → GitHub Actions runs lint, test, and deploys automatically via SSH + Docker.

# Local development with Docker
docker compose up -d

🗺️ Roadmap

• ✅ Two-graph architecture (creation + learning)
• ✅ Mock-first development (works without API keys except Anthropic)
• ✅ Multi-signal ranking (AI + history + novelty)
• ✅ Human-in-the-loop via interrupt()
• ✅ Configurable niche/voice/audience
• ✅ CI/CD: GitHub Actions → auto-deploy on push to main
• ✅ Telegram bot approval flow (end-to-end)
• ✅ Pipeline transparency — full AI agent report (research, patterns, generation, ranking)
• ✅ Enriched approval messages — metrics benchmark, pattern rationale, optimal publish time
• ✅ Reject with feedback — reason buttons feed back into the learning loop
• ✅ Publish later — schedule approved posts for optimal times
• ✅ Bot commands — /metrics, /history, /schedule, /force, /learn, /research, /pause, /resume
• ✅ Remote config via Telegram — /config to change tone, language, hashtags, posting schedule
• ✅ Live /status — running/paused state, cycles, pending approvals, next run
• ✅ Standalone /research — see what virals the agent finds without running the full pipeline
• ✅ Real Threads API integration
• 🔲 LangSmith observability dashboard
• 🔲 A/B testing (publish two variants, compare)
• 🔲 Multi-platform support (X, Bluesky, LinkedIn)
• 🔲 Web dashboard for strategy visualization
• 🔲 Configurable LLM provider (OpenAI, Ollama)
• 🔲 Plugin system for research sources

🤝 Contributing

Contributions are welcome! Please read the Contributing Guide for details on the development workflow, code style, and how to submit pull requests.

Some good first issues:

• Add more content pattern templates
• Implement real Reddit research with better filtering
• Add retry logic with exponential backoff for API calls
• Build a web dashboard showing learning progress
• Add support for image/carousel posts
• Add support for new platforms (X, LinkedIn, Bluesky)

📄 License

MIT