AI-Analy Web3

AI-Analy Web3 is a small open-source AI market intelligence toolkit for Web3 builders. It ranks token market signals, labels market phases, and generates an AI analyst memo using either an offline mock engine, a local Ollama model, OpenRouter, or OpenAI.

The project is written as a simple core layer that could sit behind a Web3 dashboard, market-maker assistant, Telegram bot, Discord alert bot, or Dessistant-style AI product. It does not try to be a trading oracle. It shows the backbone of an explainable market analysis pipeline: raw market snapshot in, ranked signal intelligence out.

What It Analyzes

{
  "asset": "FET",
  "score": 87.68,
  "phase": "breakout-watch",
  "price_usd": 2.36,
  "change_24h": 14.6,
  "volume_change": 48.5,
  "liquidity_score": 66.0,
  "narrative": "autonomous AI economy"
}

Each asset is scored from a market snapshot using:

• 24h price movement
• volume expansion
• liquidity depth
• holder growth
• social attention
• Web3 narrative category

The output is structured JSON, so it can be plugged into dashboards, bots, research notebooks, or alert systems.

Why This Project Exists

Most Web3 AI demos stop at a chatbot prompt. Real market tools need a more useful core:

• normalize noisy token data
• rank market signals consistently
• explain why a token is moving
• separate momentum from liquidity risk
• support local AI for private research
• support hosted models when teams want better reasoning

AI-Analy Web3 keeps the logic inspectable. The scoring model is readable, the provider layer is small, and the default mode runs offline without keys.

Architecture

flowchart LR
    A["Market snapshot CSV"] --> B["AI-Analy Web3 CLI"]
    B --> C["Signal scoring engine"]
    C --> D["Ranked assets"]
    D --> E{"AI provider"}
    E -->|mock| F["Offline analyst memo"]
    E -->|ollama| G["Local LLM memo"]
    E -->|openrouter| H["Cloud model routing"]
    E -->|openai| I["OpenAI chat completion"]
    F --> J["Market intelligence report"]
    G --> J
    H --> J
    I --> J

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Quick Start

Run offline with the built-in mock memo:

python3 main.py

Return structured JSON:

python3 main.py --json

Run with local AI:

ollama serve
ollama pull llama3.2
python3 main.py --provider ollama --model llama3.2

Run with OpenRouter:

export OPENROUTER_API_KEY="your-key"
python3 main.py --provider openrouter --model openai/gpt-4o-mini

Run with OpenAI:

export OPENAI_API_KEY="your-key"
python3 main.py --provider openai --model gpt-4o-mini

Example Output

AI-Analy Web3
Web3 Market Intelligence Snapshot

1. FET | score=87.68 | phase=breakout-watch
   autonomous AI economy | 24h=14.6% | volume=48.5%
2. DESAI | score=84.02 | phase=breakout-watch
   AI agent infrastructure | 24h=18.4% | volume=42.0%
3. SOL | score=80.39 | phase=breakout-watch
   high-throughput consumer apps | 24h=6.1% | volume=18.2%

AI Memo (mock)
FET is the strongest current signal with a 87.68 breakout-watch score...

Market Phases

breakout-watch  strong momentum and attention
accumulation    healthy signal with room to develop
neutral         mixed or baseline market behavior
risk-off        weak signal or liquidity pressure

Provider Modes

mock       Offline deterministic memo, no API key required.
ollama     Local model inference through http://localhost:11434.
openrouter Hosted model routing through OpenRouter.
openai     Direct OpenAI-compatible chat completion.

Project Structure

ai-analy-web3/
├── main.py
├── data/
│   └── web3_market_snapshot.csv
└── README.md

Notes

This project starts from a sample market snapshot, which means it can be connected to live data later. The current focus is the analysis layer: scoring market signals, producing phase labels, and routing the result into an AI memo.

Good data sources to add later:

• CoinGecko or DexScreener prices
• DEX liquidity and volume feeds
• holder concentration metrics
• funding rate and open interest
• Telegram, Discord, and X sentiment
• project docs, tokenomics, and governance proposals

Future Improvements

• Add live market data ingestion.
• Add backtesting for signal stability.
• Add a plugin API for custom risk modules.
• Add historical market regime memory.
• Add RAG over token docs and governance proposals.
• Export reports to Markdown, JSON, or Telegram alerts.
• Add a dashboard for market-maker and community teams.

GitHub Description

Open-source Web3 AI market intelligence toolkit with Ollama, OpenRouter, and OpenAI provider adapters.

Disclaimer

AI-Analy Web3 is a research and developer tool. It does not provide financial advice, trading recommendations, or guarantees about market behavior.

Tested locally on macOS / Ubuntu.