Algora

Status of this repository: Lifecycle: Archive (종료·보존) — per MIP-1, ratified 2026-09-02, and the links.moss.land registry entry algora. MIP-1 Annex A Archive. passportEligible withdrawn with archival (owner decision 2026-08-23): an ended, best-effort-frozen service should not back new Passport ecosystem stamps. Domain and record preserved read-only. Scheduled report generation stopped 2026-09-02 (last scheduled report: Monthly August 2026); published reports stay as records, corrections only.

Archived AI governance platform

An experiment in AI persona deliberation and governance activity visualization for MOC (Moss Coin) holders. Algora is now archived; the features and setup instructions below describe the preserved implementation.

Domain: algora.moss.land

See also: Alpha — Mossland's crypto × AI media surface where Algora-style disclosed AI personas appear as named commentators with 7-day track records (repo · MCP server).

한국어 문서 (Korean)

The preserved Algora implementation includes:

• Scalable AI Agents: Diverse personas that continuously discuss and deliberate
• Real-time Activity: Never-stopping activity feed showing system operations
• Human-in-the-Loop: AI recommends, humans decide
• Cost Optimization: 3-tier LLM system balancing quality and cost
• Full Auditability: Every output includes provenance metadata

Reality Signals → Issues → Agentic Deliberation → Human Decision → Execution → Outcome Proof
       ↓              ↓              ↓                  ↓              ↓            ↓
   RSS/GitHub    Auto-detect    30-Agent Debate    MOC Voting    Execution    KPI Verify
   On-chain                   (Bustling Agora)                   Record

Initial 30 AI agents organized into strategic clusters (infinitely scalable):

• Visionaries: Future-oriented thinkers (AGI advocate, Metaverse native, etc.)
• Builders: Engineering guild (Rust evangelist, UX perfectionist, etc.)
• Investors: Market watchers (Diamond hand, Degen trader, etc.)
• Guardians: Risk management (Compliance officer, White hat, etc.)
• Operatives: Data collection specialists
• Moderators: Discussion facilitators
• Advisors: Domain experts

Only relevant agents are summoned based on issue type, preventing chaos while maintaining lively discussion.

• Interactive Welcome Tour: First-time visitors get a guided walkthrough of the system
• System Flow Guide: Visual diagram at /guide showing the complete governance pipeline
• Contextual Help Tooltips: Each page has help icons explaining the purpose
• Help Menu: Quick access to restart tour, view guide, and documentation

• Smart Detection: Critical/High priority issues automatically trigger Agora discussions
• Auto Agent Summoning: Relevant AI agents are automatically invited based on issue category
• Efficient Processing: Uses Tier 1 (local LLM) for initial discussion rounds
• Seamless Integration: Auto-created sessions appear in the Agora session list

• Monorepo: pnpm workspaces + Turborepo
• Backend: Node.js + TypeScript + Express.js + Socket.IO
• Frontend: Next.js 14 + React 18 + TanStack Query
• Styling: Tailwind CSS
• Database: SQLite with WAL mode
• LLM: Anthropic Claude / OpenAI GPT / Google Gemini / Ollama (Local)
• i18n: English / Korean

• Node.js 20+
• pnpm 8+
• Ollama (for local LLM)

# Clone repository
git clone https://github.com/mossland/Algora.git
cd Algora

# Install dependencies
pnpm install

# Copy environment file
cp .env.example .env
# Edit .env with your API keys

# Initialize database
pnpm db:init

# Start development server
pnpm dev

• Web: http://localhost:3200
• API: http://localhost:3201

algora/
├── apps/
│   ├── api/                # Express REST API + Socket.IO
│   └── web/                # Next.js Frontend
├── packages/
│   ├── core/               # Shared types, utilities
│   ├── reality-oracle/     # L0: Signal collection
│   ├── inference-mining/   # L1: Issue detection
│   ├── agentic-consensus/  # L2: Agent system
│   ├── human-governance/   # L3: Voting/Delegation
│   └── proof-of-outcome/   # L4: Result tracking
└── docs/                   # Documentation

• User Guide - Detailed user guide
• Architecture - System architecture details
• Contributing - Contribution guidelines
• Project Specification - Full specification
• Changelog - Version history

Algora uses Ollama for local LLM inference. It needs exactly two models — one chat model for every Tier-1 task, and one small embedding model for RAG:

# Install Ollama
brew install ollama

# Pull the two models Algora actually uses
ollama pull gemma3:4b         # chat, code, Korean, reranking
ollama pull nomic-embed-text  # embeddings for semantic search

Tier 1 routes every task to the single chat model deliberately, so one model stays resident and nothing swaps. If the Ollama host is shared with other services, keep both the model and its context size identical across all of them: Ollama treats the same model at a different num_ctx as a separate instance, so a divergent value evicts and reloads the resident one for everybody. Algora reads that value as LOCAL_LLM_NUM_CTX and OLLAMA_NUM_CTX (see .env.example) — change them together.

The embedding model is the one permitted exception to "single resident model": it is small enough (~0.3GB VRAM) to sit alongside the chat model. Set RAG_EMBEDDING_MODEL only to a model the host has actually pulled — the RAG service checks at startup and refuses to embed otherwise.

Key variables (see .env.example for full list):

# External LLM
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...
GOOGLE_API_KEY=...
LLM_PROVIDER=anthropic

# Local LLM (Tier 1)
LOCAL_LLM_ENDPOINT=http://localhost:11434
LOCAL_LLM_MODEL_CHATTER=gemma3:4b
LOCAL_LLM_NUM_CTX=16384
RAG_EMBEDDING_MODEL=nomic-embed-text

# Budget
ANTHROPIC_DAILY_BUDGET_USD=10.00

We welcome contributions! Please read our Contributing Guide for details.

MIT License.

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