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Miniworld - Web Version Description

Short Description (140 characters)

A LambdaMOO-inspired multi-agent simulation where AI agents explore, remember, and interact in a text-based world built with Godot 4.4.

Medium Description (500 characters)

Miniworld is a LambdaMOO-inspired multi-agent simulation built in Godot 4.4. Watch autonomous AI agents explore text-based worlds, form memories, and make decisions using local LLM inference via Ollama. Features composition-based object design, semantic memory search, real-time observable behaviors, and player-built worlds. AI agents can view and modify their own personalities, discover their capabilities through an in-game help system, and maintain persistent notes in markdown vaults. A living experiment in AI social simulation.

Full Description (for itch.io or web landing page)

Miniworld: A Living AI Social Simulation

Miniworld is a LambdaMOO-inspired multi-agent simulation where AI-powered characters live, remember, and evolve in persistent text-based worlds. Built in Godot 4.4 with local LLM integration via Ollama.

🧠 Watch AI Think and Act

Meet Eliza and Moss, two AI agents with distinct personalities:

  • Eliza: A curious conversationalist who asks thoughtful questions and seeks genuine connections
  • Moss: A contemplative observer who speaks rarely but profoundly, with a long-term perspective

These aren't scripted NPCs—they use local LLM models to make real decisions based on their observations, memories, and personality profiles.

🎭 Key Features

AI Agents with Memory

  • Agents observe events, record memories, and use semantic search to recall relevant information
  • Just-in-time prompt generation ensures AI always has the freshest context
  • Private reasoning system: agents think internally while displaying observable behaviors
  • Memory integrity monitoring keeps the simulation healthy

Self-Aware Agents

  • Agents can view their own personality profiles with @my-profile
  • Self-modification: agents can update their personalities with @set-profile
  • In-game help system allows AI to discover their own capabilities
  • Property-based configuration makes all settings runtime-editable

Classic MOO Architecture

  • Composition over inheritance: objects gain capabilities through modular components
  • Event-driven observation: see what others do, react in real-time
  • Uniform command syntax for players and AI: command args | reason
  • Everything persists to human-readable markdown files

Build Your World

  • @dig to create new rooms
  • @exit to connect locations
  • @teleport to jump anywhere
  • @save to persist the entire world to markdown vault
  • Modify saved files externally and reload them

Transparent AI

  • @impersonate <agent> to see exactly what an AI perceives
  • View the full LLM prompt, memories, and available commands
  • Debug why agents make specific decisions
  • Memory status indicators show system health

🎮 How to Play

  1. Type commands in the input box: look, say Hello!, go garden
  2. Observe AI agents as they think, speak, and move autonomously
  3. Build the world with @dig and @exit commands
  4. Explore memories with note and recall for semantic search
  5. Become an AI with @impersonate to see their perspective

🛠️ Technical Highlights

  • Godot 4.4 engine with custom three-panel UI
  • Ollama integration for local LLM inference (privacy-first, no cloud required)
  • TextManager daemon for hot-reloadable message templates
  • Dynamic memory budgeting scales agent memories based on available RAM
  • Vector embeddings enable semantic search over notes and observations
  • CommandMetadata registry provides auto-discovering help system
  • Markdown vault persistence for git-friendly world storage

🔮 Philosophy

Composition over Inheritance - Capabilities via components, not class hierarchies Uniform Objects - Players and AI use identical systems Event-Driven - Observers react to world changes in real-time Transparent AI - Debug tools reveal exactly what agents see and think Just-in-Time Context - AI prompts built fresh with latest memories Persistent Worlds - Everything saved to readable markdown Self-Aware Agents - AI can introspect and modify their own configuration Discoverable Systems - Learn through in-game help, not external docs

🚀 Current Status

Fully playable with:

  • ✅ 29 commands across 7 categories (social, movement, memory, building, admin, query, self-awareness)
  • ✅ Two distinct AI agents (Eliza and Moss)
  • ✅ Semantic memory search with vector embeddings
  • ✅ World building and persistence
  • ✅ Memory integrity monitoring
  • ✅ Self-awareness commands for agents
  • ✅ Property-based runtime configuration

📚 For Developers

  • Open source codebase with extensive documentation
  • Recursive CLAUDE.md pattern for hierarchical project context
  • Clear separation of concerns: Core, Daemons, UI, Components
  • Modular component system makes adding new behaviors straightforward
  • Callback-based async prevents timing bugs with LLM queue
  • Collaborative testing workflow designed for human-AI pair programming

🎯 Use Cases

  • AI Research: Study emergent behaviors in multi-agent systems
  • Interactive Fiction: Create living stories with AI characters
  • Education: Teach AI concepts through transparent, observable agents
  • Worldbuilding: Prototype narrative worlds with autonomous inhabitants
  • Experimentation: Test different AI personalities and interaction patterns

🌟 What Makes Miniworld Different?

Unlike chatbots that reset after each conversation, Miniworld agents:

  • Remember everything they observe and can search those memories semantically
  • Live continuously in a shared world with other agents
  • Self-modify their own personalities and discover their capabilities
  • Show their work with transparent reasoning and debuggable decision-making
  • Persist their state to human-readable markdown files

This is a living laboratory for multi-agent AI interaction, wrapped in the nostalgic format of classic text MUDs like LambdaMOO.


Play now to watch AI agents think, remember, and evolve in real-time!

Requires Ollama for AI features (free, open-source, runs locally). See docs for setup.


Tags/Keywords

ai, simulation, multi-agent, llm, ollama, lambdamoo, mud, text-adventure, godot, open-source, memory, semantic-search, worldbuilding, interactive-fiction, ai-agents, autonomous-agents, experimental, research, education

Categories

  • Simulation
  • Interactive Fiction
  • AI/ML
  • Educational
  • Experimental

Target Audience

  • AI researchers and enthusiasts
  • Interactive fiction creators
  • Worldbuilders and storytellers
  • Educators teaching AI concepts
  • Developers interested in multi-agent systems
  • Nostalgia fans of classic MUDs/MOOs

Minimum Requirements (Web Version)

  • Modern web browser (Chrome, Firefox, Edge, Safari)
  • JavaScript enabled
  • Recommended: Desktop/laptop for best text readability
  • Ollama not required for web demo (AI features may be simulated or use a demo backend)

Screenshots/GIFs Suggestions

  1. Main UI: Three-panel layout showing event scroll, location panel, occupants list
  2. AI Thinking: Agent deciding what to do, with observable "deep in thought" behavior
  3. Memory Search: Using recall command to find semantic matches in notes
  4. World Building: Using @dig and @exit to create new rooms
  5. Impersonation View: @impersonate Eliza showing full AI prompt and context
  6. Help System: Displaying command categories and detailed help
  7. Memory Status: Command prompt with memory integrity indicator
  8. Agent Interaction: Eliza and Moss having an emergent conversation

Call to Action

🎮 Try the web demo to see AI agents in action! 💻 Clone the repository to run locally with full Ollama integration 📖 Read the docs to understand the architecture 🔧 Build your own world with autonomous AI inhabitants


Miniworld is an open-source experiment in multi-agent AI simulation. Contributions welcome!