JINXUS

Just Intelligent Nexus, eXecutes Under Supremacy

A hyper-personalized multi-agent AI assistant with a virtual pixel office

Web UI | Telegram | CLI | Daemon

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Overview

JINXUS is a multi-agent AI system where a single orchestrator (JINXUS_CORE) manages 28+ specialized AI agents organized into a realistic Korean IT company structure. Each agent has a unique persona with name, personality, MBTI, and work style. The system features a real-time pixel art virtual office where you can watch your AI employees work, chat, and collaborate.

You talk to one central orchestrator. It handles everything: interpreting intent, decomposing complex tasks, delegating to specialist agents, collecting results, and delivering a unified response.

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Key Features

Pixel Office (Generative Agents-inspired)

• 60x40 tile map with indoor offices, hallways, and outdoor areas (smoking area, garden, terrace, parking lot)
• 16x24 chibi sprites with 2-head proportions, team-colored uniforms, and 12 hair variations
• Camera/viewport system with drag-to-scroll and wheel-to-zoom
• BFS pathfinding with smooth interpolation movement
• 28 POIs (coffee machine, whiteboard, printer, vending machine, benches, etc.) with state tracking
• Per-agent daily schedules based on rank and team
• Emoji activity display above character heads
• Spontaneous agent chat with 92 Korean dialogue templates (zero API cost)
• Global mute ("shut up" mode) silences idle chatter across all tabs while missions run normally
• Tool-specific animations: typing, reading, thinking, searching
• Real-time SSE state updates from backend

Multi-Agent Architecture

• 28 agents across 6 teams: Executive, Development, Platform, Product, Marketing, Biz Support
• Realistic Korean IT company structure (CTO, team leads, senior engineers, etc.)
• JINXUS_CORE orchestrates all sub-agents via LangGraph
• Dynamic hiring/firing via HR system (auto-updates playground layout)
• Agent-specific tool access control (Tool Policy Engine)
• Automatic failover and task reassignment
• Team fallback: if a specialist fails, the team lead handles it directly

Mission System

• 4 mission types: QUICK (instant), STANDARD (minutes), EPIC (hours), RAID (multi-agent)
• Approval gate: review agent plans before execution (auto-approve for QUICK)
• Real-time OFFICE FEED: shows agent activity, tool calls, and reports as missions execute
• Auto work notes: mission results are automatically saved as work notes on completion
• Mission console: SSE-streamed execution log with agent conversations

Intelligence

• 148 tools: 19 native + 129 MCP (11 servers)
• Smart Router: auto-classifies into 4 execution paths (chat/task/background/project)
• DynamicToolExecutor with continuation support
• ToolGraph v2: BFS tool discovery (~26 microseconds per query, zero API calls)
• Artifact Store: Redis-based inter-phase data sharing
• Review Loop: automatic code review + fix cycles

Autonomous Execution

• AutonomousRunner: up to 8 hours, 50 steps background execution
• Redis checkpoints for crash recovery
• Task chaining with depends_on for building pipelines
• Telegram progress reports every 15 minutes

Memory System

• 3-tier: Redis (short-term) + Qdrant (long-term vectors) + SQLite (metadata)
• Automatic reflection: generates high-level insights when importance threshold is reached
• Semantic search across long-term memory
• Time-decay pruning with configurable half-life

Frontend

• 8 tabs: Office, Corporation, Projects, Memory, Logs, Tools, Notes, Settings
• Single source of truth: TEAM_CONFIG in personas.ts drives all team-related UI
• Real-time SSE streaming with smooth typing animation
• Team channels (Matrix/Synapse integration)
• Docker log panel (VSCode-style resizable)

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Tech Stack

Layer	Technology
Backend	FastAPI + LangGraph, Python 3.11+
Frontend	Next.js 14 + Zustand + TailwindCSS
Memory	Redis (short-term) + Qdrant (long-term vectors) + SQLite (metadata)
AI	Anthropic Claude API (Sonnet 4.6 + Haiku 4.5)
Tools	19 native + 11 MCP servers (129 tools) + runtime MCP loader
Channels	Web UI, Telegram bot, CLI, background daemon
Infra	Docker Compose, PM2, volume-mounted source

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Organization & HR System

JINXUS runs as a virtual Korean IT company with 28 default employees across 6 teams. Each agent has a unique persona defined in personas.py — name, MBTI, personality, speech style, work habits, and inter-agent relationships.

Default Teams: Executive (4) · Development (6) · Platform (6) · Product (6) · Marketing (4) · Biz Support (2)

Dynamic Hiring & Firing

Agents are managed through a full HR lifecycle — hire, fire, and rehire at runtime without restart.

Hire (POST /hr/hire):

• Specify specialty (coding, research, writing, etc.) and the system auto-provisions everything
• Auto-assigns: persona (name, team, channel), tool policy (inherited from parent agent type), org chart placement, playground desk, team channel membership
• Appears immediately in the Pixel Office and starts receiving tasks

Fire (POST /hr/fire/{id}):

• Soft-delete: record preserved for potential rehire, agent instance removed
• Cascade: firing a team lead automatically fires all their subordinates
• Auto-cleanup: persona unregistered, removed from playground and channels

Rehire (POST /hr/rehire/{id}):

• Reactivates a previously fired agent from the fired agents list

All personas are defined as a single source of truth in personas.py (backend) and synced to the frontend via /api/agents/personas. The frontend TEAM_CONFIG in personas.ts drives all team-related UI (colors, channels, room layouts) from one central config.

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Project Structure

backend/jinxus/
  agents/              # JINXUS_CORE + sub-agents + coding/research teams
  api/routers/         # FastAPI endpoints (chat, mission, logs, agents, etc.)
  core/                # Orchestrator, mission executor, approval gate, tool policy
  memory/              # 3-tier memory + reflection system
  tools/               # 19 native tools + DynamicToolExecutor + MCP client
  hr/                  # Agent hiring/firing, company channels
  config/              # Settings, MCP server definitions

frontend/src/
  components/
    playground/        # Pixel Office engine (modularized)
      engine/          #   Camera, pathfinding, scheduler, social, types
      sprites/         #   Character (16x24), furniture (15 types), colors, icons
      map/             #   60x40 map data with indoor + outdoor zones
      render/          #   Emoji activity display
      poi/             #   POI state management
    tabs/              # Office, Corporation, Projects, Memory, Logs, Tools, Notes, Settings
  lib/                 # API client, SSE parser, personas (TEAM_CONFIG single source)
  store/               # Zustand global state

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Quick Start

Prerequisites

• Docker and Docker Compose
• Anthropic API key
• Node.js 18+

Backend

cd backend
cp .env.example .env   # Add ANTHROPIC_API_KEY
docker compose up -d   # Starts FastAPI + Redis + Qdrant

Frontend

cd frontend
bash dev.sh   # Auto-installs deps, builds, starts via pm2

Access

Service	URL
Web UI	http://localhost:5000
Backend API	http://localhost:19000
Swagger Docs	http://localhost:19000/docs

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Version History

Version	Date	Highlights
v3.0.0	2026-03-24	Pixel Office overhaul (60x40 map, 16x24 chibi sprites, camera system, outdoor areas), org restructure (realistic Korean IT company), mission real-time feed, global mute, tab rename (English), modular architecture (15 modules)
v2.9.0	2026-03-22	Pixel Office Playground, UI overhaul, SSE agent streaming
v2.6.0	2026-03-19	5 new dev team agents (27 total), persona system
v2.5.0	2026-03-19	Next.js architecture fixes, bundle optimization
v2.3.0	2026-03-16	Smart Router, Artifact Store, Review Loop
v1.7.0	2026-03-12	Background tasks: Redis checkpoints, pause/resume
v1.5.0	2026-03-09	Docker multi-stage build, Tool Policy API

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License

Private project. Not for redistribution.

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Built by jinsoo96

Special thanks to CocoRoF & SonAIengine