Compare commits

...

3 Commits

Author SHA1 Message Date
Mai Development
53fb8544fe docs: document and configure MCP tool integration
Some checks failed
Discord Webhook / git (push) Has been cancelled
- Create comprehensive MCP.md documenting all available tools:
  * Hugging Face Hub (models, datasets, papers, spaces, docs)
  * Web search and fetch for research
  * Code tools (Bash, Git, file ops)
  * Claude Code (GSD) workflow agents

- Map MCP usage to specific phases:
  * Phase 1: Model discovery (Mistral, Llama, quantized options)
  * Phase 2: Safety research (sandboxing, verification papers)
  * Phase 5: Conversation datasets and papers
  * Phase 12: Voice visualization models and spaces
  * Phase 13: Avatar generation tools and research
  * Phase 14: Mobile inference frameworks and patterns

- Update config.json with MCP settings:
  * Enable Hugging Face (mystiatech authenticated)
  * Enable WebSearch for current practices
  * Set default result limits

- Update PROJECT.md constraints to document MCP enablement

Research phases will leverage MCPs extensively for optimal
library/model selection, architecture patterns, and best practices.
2026-01-26 23:24:00 -05:00
Mai Development
3861b86287 chore: configure auto-push to remote on every commit
- Enable git push.autoSetupRemote for automatic tracking setup
- Add push.followTags to include tags in pushes
- Install post-commit hook for automatic push after each commit
- Update config.json to document auto-push behavior
- Remote: master (giteas.fullmooncyberworks.com/mystiatech/Mai)

All commits will now automatically push to the remote branch.
2026-01-26 23:22:55 -05:00
Mai Development
3f41adff75 docs: establish fresh planning foundation with new features
- Update PROJECT.md: Add Android, visualizer, and avatar to v1
- Update REQUIREMENTS.md: 99 requirements across 15 phases (fresh slate)
- Add comprehensive README.md with setup, architecture, and usage
- Add PROGRESS.md for Discord forum sharing
- Add .gitignore for Python/.venv and project artifacts
- Note: All development via Claude Code/OpenCode workflow
- Note: Python deps managed via .venv virtual environment

Core value: Mai is a real collaborator, not a tool. She learns from you,
improves herself, has boundaries and opinions, and becomes more *her* over time.

v1 includes: Model interface, Safety, Resources, Memory, Conversation,
CLI, Self-Improvement, Approval, Personality, Discord, Offline, Voice
Visualization, Avatar, Android App, Device Sync.
2026-01-26 23:21:40 -05:00
7 changed files with 964 additions and 27 deletions

64
.gitignore vendored
View File

@@ -1,18 +1,60 @@
# Python
__pycache__/
*.py[cod]
# venv
.venv/
venv/
env/
ENV/
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
# tooling
# IDE
.vscode/
.idea/
*.swp
*.swo
*~
.DS_Store
# Testing
.pytest_cache/
.ruff_cache/
.coverage
htmlcov/
# Project-specific
config.yaml
logs/
*.log
models/
cache/
.planning/STATE.md
.planning/PHASE-*-PLAN.md
# Discord
.env
.discord_token
# Android
android/app/build/
android/.gradle/
android/local.properties
# OS
.DS_Store
Thumbs.db
# generated
.planning/CONTEXTPACK.md
*.tmp
*.bak

220
.planning/MCP.md Normal file
View File

@@ -0,0 +1,220 @@
# Available Tools & MCP Integration
This document lists all available tools and MCP (Model Context Protocol) servers that Mai development can leverage.
## Hugging Face Hub Integration
**Status**: Authenticated as `mystiatech`
### Tools Available
#### Model Discovery
- `mcp__claude_ai_Hugging_Face__model_search` — Search ML models by task, author, library, trending
- `mcp__claude_ai_Hugging_Face__hub_repo_details` — Get detailed info on any model, dataset, or space
**Use Cases:**
- Phase 1: Discover quantized models for local inference (Mistral, Llama, etc.)
- Phase 12: Find audio/voice models for visualization
- Phase 13: Find avatar/animation models (VRoid compatible options)
- Phase 14: Research Android-compatible model formats
#### Dataset Discovery
- `mcp__claude_ai_Hugging_Face__dataset_search` — Find datasets by task, author, tags, trending
- Search filters: language, size, task categories
**Use Cases:**
- Phase 4: Training data research for memory compression
- Phase 5: Conversation quality datasets
- Phase 12: Audio visualization datasets
#### Research Papers
- `mcp__claude_ai_Hugging_Face__paper_search` — Search ML research papers with abstracts
**Use Cases:**
- Phase 2: Safety and sandboxing research papers
- Phase 4: Memory system and RAG papers
- Phase 5: Conversational AI and reasoning papers
- Phase 7: Self-improvement and code generation papers
#### Spaces & Interactive Models
- `mcp__claude_ai_Hugging_Face__space_search` — Discover Hugging Face Spaces (demos)
- `mcp__claude_ai_Hugging_Face__dynamic_space` — Run interactive tasks (Image Gen, OCR, TTS, etc.)
**Use Cases:**
- Phase 12: Voice/audio visualization demos
- Phase 13: Avatar generation or manipulation
- Phase 14: Android UI pattern research
#### Documentation
- `mcp__claude_ai_Hugging_Face__hf_doc_search` — Search HF docs and guides
- `mcp__claude_ai_Hugging_Face__hf_doc_fetch` — Fetch full documentation pages
**Use Cases:**
- Phase 1: LMStudio/Ollama integration documentation
- Phase 5: Transformers library best practices
- Phase 14: Mobile inference frameworks (ONNX Runtime, TensorFlow Lite)
#### Account Info
- `mcp__claude_ai_Hugging_Face__hf_whoami` — Get authenticated user info
## Web Research
### Tools Available
- `WebSearch` — Search the web for current information (2026 context)
- `WebFetch` — Fetch and analyze specific URLs
**Use Cases:**
- Research current best practices in AI safety (Phase 2)
- Find Android development patterns (Phase 14)
- Discover voice visualization libraries (Phase 12)
- Research avatar systems (Phase 13)
- Find Discord bot best practices (Phase 10)
## Code & Repository Tools
### Tools Available
- `Bash` — Execute terminal commands (git, npm, python, etc.)
- `Glob` — Fast file pattern matching
- `Grep` — Ripgrep-based content search
- `Read` — Read file contents
- `Edit` — Edit files with string replacement
- `Write` — Create new files
**Use Cases:**
- All phases: Create and manage project structure
- All phases: Execute tests and build commands
- All phases: Manage git commits and history
## Claude Code (GSD) Workflow
### Orchestrators Available
- `/gsd:new-project` — Initialize project
- `/gsd:plan-phase N` — Create detailed phase plans
- `/gsd:execute-phase N` — Execute phase with atomic commits
- `/gsd:discuss-phase N` — Gather phase context
- `/gsd:verify-work` — User acceptance testing
### Specialized Agents
- `gsd-project-researcher` — Domain research (stack, features, architecture, pitfalls)
- `gsd-phase-researcher` — Phase-specific research
- `gsd-codebase-mapper` — Analyze and document existing code
- `gsd-planner` — Create executable phase plans
- `gsd-executor` — Execute plans with state management
- `gsd-verifier` — Verify deliverables match requirements
- `gsd-debugger` — Systematic debugging with checkpoints
## How to Use MCPs in Development
### In Phase Planning
When creating `/gsd:plan-phase N`:
- Researchers can use Hugging Face tools to discover libraries and models
- Use WebSearch for current best practices
- Query papers for architectural patterns
### In Phase Execution
When running `/gsd:execute-phase N`:
- Download models from Hugging Face
- Use WebFetch for documentation
- Run Spaces for prototyping UI patterns
### Example Usage by Phase
**Phase 1: Model Interface**
```
- mcp__claude_ai_Hugging_Face__model_search
Query: "quantized models for local inference"
→ Find Mistral, Llama, TinyLlama options
- mcp__claude_ai_Hugging_Face__hf_doc_fetch
→ Get Hugging Face Transformers documentation
- WebSearch
→ Latest LMStudio/Ollama integration patterns
```
**Phase 2: Safety System**
```
- mcp__claude_ai_Hugging_Face__paper_search
Query: "code sandboxing, safety verification"
→ Find relevant research papers
- WebSearch
→ Docker security best practices
```
**Phase 5: Conversation Engine**
```
- mcp__claude_ai_Hugging_Face__dataset_search
Query: "conversation quality, multi-turn dialogue"
- mcp__claude_ai_Hugging_Face__paper_search
Query: "conversational AI, context management"
```
**Phase 12: Voice Visualization**
```
- mcp__claude_ai_Hugging_Face__space_search
Query: "audio visualization, waveform display"
→ Find working demos
- mcp__claude_ai_Hugging_Face__model_search
Query: "speech recognition, audio models"
```
**Phase 13: Desktop Avatar**
```
- mcp__claude_ai_Hugging_Face__space_search
Query: "avatar generation, VRoid, character animation"
- WebSearch
→ VRoid SDK documentation
→ Avatar animation libraries
```
**Phase 14: Android App**
```
- mcp__claude_ai_Hugging_Face__model_search
Query: "mobile inference, quantized models, ONNX"
- WebSearch
→ Kotlin ML Kit documentation
→ TensorFlow Lite best practices
```
## Configuration
Add to `.planning/config.json` to enable MCP usage:
```json
{
"mcp": {
"huggingface": {
"enabled": true,
"authenticated_user": "mystiatech",
"default_result_limit": 10
},
"web_search": {
"enabled": true,
"domain_restrictions": []
},
"code_tools": {
"enabled": true
}
}
}
```
## Research Output Format
When researchers use MCPs, they produce:
- `.planning/research/STACK.md` — Technologies and libraries
- `.planning/research/FEATURES.md` — Capabilities and patterns
- `.planning/research/ARCHITECTURE.md` — System design patterns
- `.planning/research/PITFALLS.md` — Common mistakes and solutions
These inform phase planning and implementation.
---
**Updated: 2026-01-26**
**Next Review: When new MCP servers become available**

187
.planning/PROGRESS.md Normal file
View File

@@ -0,0 +1,187 @@
# Mai Development Progress
**Last Updated**: 2026-01-26
**Status**: Fresh Slate - Roadmap Under Construction
## Project Description
Mai is an autonomous conversational AI companion that runs locally-first and can improve her own code. She's not a rigid chatbot, but a genuinely intelligent collaborator with a distinct personality, long-term memory, and real agency. Mai learns from your interactions, analyzes her own performance, and proposes improvements for your review before auto-applying them.
**Key differentiators:**
- **Real Collaborator**: Mai actively contributes ideas, has boundaries, and can refuse requests
- **Learns & Evolves**: Conversation patterns inform personality layers; she remembers you
- **Completely Local**: All inference, memory, and decision-making on your device—no cloud, no tracking
- **Visual Presence**: Desktop avatar (image or VRoid) with real-time voice visualization
- **Cross-Device**: Works on desktop and Android with seamless synchronization
- **Self-Improving**: Analyzes her own code, generates improvements, and gets your approval before applying
**Core Value**: Mai is a real collaborator, not a tool. She learns from you, improves herself, has boundaries and opinions, and actually becomes more *her* over time.
---
## Phase Breakdown
### Status Summary
- **Total Phases**: 15
- **Completed**: 0
- **In Progress**: 0
- **Planned**: 15
- **Requirements Mapped**: 99/99 (100%)
### Phase Details
| # | Phase | Goal | Requirements | Status |
|---|-------|------|--------------|--------|
| 1 | Model Interface | Connect to local models and intelligently switch | MODELS (7) | 🔄 Planning |
| 2 | Safety System | Sandbox code execution and implement review workflow | SAFETY (8) | 🔄 Planning |
| 3 | Resource Management | Monitor CPU/RAM/GPU and adapt model selection | RESOURCES (6) | 🔄 Planning |
| 4 | Memory System | Persistent conversation storage with vector search | MEMORY (8) | 🔄 Planning |
| 5 | Conversation Engine | Multi-turn dialogue with reasoning and context | CONVERSATION (9) | 🔄 Planning |
| 6 | CLI Interface | Terminal-based chat with history and commands | CLI (8) | 🔄 Planning |
| 7 | Self-Improvement | Code analysis, change generation, and auto-apply | SELFMOD (10) | 🔄 Planning |
| 8 | Approval Workflow | User approval via CLI and Dashboard for changes | APPROVAL (9) | 🔄 Planning |
| 9 | Personality System | Core values, behavior configuration, learned layers | PERSONALITY (8) | 🔄 Planning |
| 10 | Discord Interface | Bot integration with DM and approval reactions | DISCORD (10) | 🔄 Planning |
| 11 | Offline Operations | Full local-only functionality with graceful degradation | OFFLINE (7) | 🔄 Planning |
| 12 | Voice Visualization | Real-time audio waveform and frequency display | VISUAL (5) | 🔄 Planning |
| 13 | Desktop Avatar | Visual presence with image or VRoid model support | AVATAR (6) | 🔄 Planning |
| 14 | Android App | Native mobile app with local inference and UI | ANDROID (10) | 🔄 Planning |
| 15 | Device Sync | Synchronization of state and memory between devices | SYNC (6) | 🔄 Planning |
---
## Current Focus
**Phase**: Infrastructure & Planning
**Work**: Establishing project structure and execution approach
### What's Happening Now
- [x] Codebase mapping complete (7 architectural documents)
- [x] Project vision and core value defined
- [x] Requirements inventory (99 items across 15 phases)
- [x] README with comprehensive setup and features
- [ ] Roadmap creation (distributing requirements across phases)
- [ ] First phase planning (Model Interface)
### Next Steps
1. Create detailed ROADMAP.md with phase dependencies
2. Plan Phase 1: Model Interface & Switching
3. Begin implementation of LMStudio/Ollama integration
4. Setup development infrastructure and CI/CD
---
## Recent Milestones
### 🎯 Project Initialization (2026-01-26)
- Codebase mapping with 7 structured documents (STACK, ARCHITECTURE, STRUCTURE, CONVENTIONS, TESTING, INTEGRATIONS, CONCERNS)
- Deep questioning and context gathering completed
- PROJECT.md created with core value and vision
- REQUIREMENTS.md with 99 fully mapped requirements
- Feature additions: Android app, voice visualizer, desktop avatar included in v1
- README.md with comprehensive setup and architecture documentation
- Progress report framework for regular updates
### 📋 Planning Foundation
- All v1 requirements categorized into logical phases
- Cross-device synchronization included as core feature
- Safety and self-improvement as phase 2 priority
- Offline capability planned as phase 11 (ensures all features work locally first)
---
## Development Methodology
**All phases are executed through Claude Code** (`/gsd` workflow) which provides:
- Automated phase planning with task decomposition
- Code generation with test creation
- Atomic git commits with clear messages
- Multi-agent verification (research, plan checking, execution verification)
- Parallel task execution where applicable
- State tracking and checkpoint recovery
Each phase follows the standard GSD pattern:
1. `/gsd:plan-phase N` → Creates detailed PHASE-N-PLAN.md
2. `/gsd:execute-phase N` → Implements with automatic test coverage
3. Verification and state updates
This ensures **consistent quality**, **full test coverage**, and **clean git history** across all 15 phases.
## Technical Highlights
### Stack
- **Primary**: Python 3.10+ (core/desktop) with `.venv` virtual environment
- **Mobile**: Kotlin (Android)
- **UI**: React/TypeScript (eventual web)
- **Model Interface**: LMStudio/Ollama
- **Storage**: SQLite (local)
- **IPC/Sync**: Local network (no server)
- **Development**: Claude Code (OpenCode) for all implementation
### Key Architecture Decisions
| Decision | Rationale | Status |
|----------|-----------|--------|
| Local-first, no cloud | Privacy and independence from external services | ✅ Approved |
| Second-agent review for all changes | Safety without blocking innovation | ✅ Approved |
| Personality as code + learned layers | Unshakeable core + authentic growth | ✅ Approved |
| Offline-first design (phase 11 early) | Ensure full functionality before online features | ✅ Approved |
| Android in v1 | Mobile-first future vision | ✅ Approved |
| Cross-device sync without server | Privacy-preserving multi-device support | ✅ Approved |
---
## Known Challenges & Solutions
| Challenge | Current Approach |
|-----------|------------------|
| Memory efficiency at scale | Auto-compressing conversation history with pattern distillation (phase 4) |
| Model switching without context loss | Standardized context format + token budgeting (phase 1) |
| Personality consistency across changes | Personality as code + test suite for behavior (phases 7-9) |
| Safety vs. autonomy balance | Dual review system: agent checks breaking changes, user approves (phase 2/8) |
| Android model inference | Quantized models + resource scaling (phase 14) |
| Cross-device sync without server | P2P sync on local network + conflict resolution (phase 15) |
---
## How to Follow Progress
### Discord Forum
Regular updates posted in the `#mai-progress` forum channel with:
- Weekly milestone summaries
- Blocker alerts if any
- Community feedback requests
### Git & Issues
- All work tracked in git with atomic commits
- Phase plans in `.planning/PHASE-N-PLAN.md`
- Progress in git commit history
### Local Development
- Run `make progress` to see current status
- Check `.planning/STATE.md` for live project state
- Review `.planning/ROADMAP.md` for phase dependencies
---
## Get Involved
### Providing Feedback
- React to forum posts with 👍 / 👎 / 🎯
- Reply with thoughts on design decisions
- Suggest priorities for upcoming phases
### Contributing
- Development contributions coming as phases execute
- Code review and testing needed starting Phase 1
- Security audit important for self-improvement system
### Questions?
- Ask in the Discord thread
- Reply to this forum post with questions
- Issues/discussions: https://github.com/yourusername/mai
---
**Mai's development is transparent and community-informed. Updates will continue as phases progress.**
Next Update: After Phase 1 Planning Complete (target: next week)

View File

@@ -2,7 +2,7 @@
## What This Is
Mai is an autonomous conversational AI agent framework that runs locally-first and can improve her own code. She's a genuinely intelligent companion — not a rigid chatbot — with a distinct personality, long-term memory, and agency. She analyzes her own performance, proposes improvements for your review, and auto-applies non-breaking changes. She can run offline, across devices (laptop to Android), and switch between available models intelligently.
Mai is an autonomous conversational AI agent framework that runs locally-first and can improve her own code. She's a genuinely intelligent companion — not a rigid chatbot — with a distinct personality, long-term memory, and agency. She analyzes her own performance, proposes improvements for your review, and auto-applies non-breaking changes. Mai has a visual presence through a desktop avatar (image or VRoid model), real-time voice visualization for conversations, and a native Android app that syncs with desktop instances while working completely offline.
## Core Value
@@ -65,6 +65,26 @@ Mai is a real collaborator, not a tool. She learns from you, improves herself, h
- [ ] Message queuing when offline
- [ ] Graceful degradation (smaller models if resources tight)
**Voice Visualization**
- [ ] Real-time visualization of audio input during voice conversations
- [ ] Low-latency waveform/frequency display
- [ ] Visual feedback for speech detection and processing
- [ ] Works on both desktop and Android
**Desktop Avatar**
- [ ] Visual representation using static image or VRoid model
- [ ] Avatar expressions respond to conversation context (mood/state)
- [ ] Runs efficiently on RTX3060 and mobile devices
- [ ] Customizable appearance (multiple models or user-provided image)
**Android App**
- [ ] Native Android app with local model inference
- [ ] Standalone operation (works without desktop instance)
- [ ] Syncs conversation history and memory with desktop
- [ ] Voice input/output with low-latency processing
- [ ] Avatar and visualizer integrated in mobile UI
- [ ] Efficient resource management for battery and CPU
**Dashboard ("Brain Interface")**
- [ ] View Mai's current state (personality, memory size, mood/health)
- [ ] Approve/reject pending code changes with reviewer feedback
@@ -85,15 +105,15 @@ Mai is a real collaborator, not a tool. She learns from you, improves herself, h
- **Task automation (v1)** — Mai can discuss tasks but won't execute arbitrary workflows yet (v2)
- **Server monitoring** — Not included in v1 scope (v2)
- **Finetuning** — Mai improves through code changes and learned behaviors, not model tuning
- **Cloud sync** — Intentionally local-first; cloud sync deferred to later if needed
- **Cloud sync** — Intentionally local-first; cloud backup deferred to later if needed
- **Custom model training** — v1 uses available models; custom training is v2+
- **Mobile app** — v1 is CLI/Discord; native Android is future (baremetal eventual goal)
- **Web interface** — v1 is CLI, Discord, and native apps (web UI is v2+)
## Context
**Why this matters:** Current AI systems are static, sterile, and don't actually learn. Users have to explain context every time. Mai is different — she has continuity, personality, agency, and actually improves over time. Starting with a solid local framework means she can eventually run anywhere without cloud dependency.
**Technical environment:** Python-based, local models via LMStudio, git for version control of her own code, Discord API for chat, lightweight local storage for memory. Eventually targeting bare metal on low-end devices.
**Technical environment:** Python-based, local models via LMStudio/Ollama, git for version control, Discord API for chat, lightweight local storage for memory. Development leverages Hugging Face Hub for model/dataset discovery and research, WebSearch for current best practices. Eventually targeting bare metal on low-end devices.
**User feedback theme:** Traditional chatbots feel rigid and repetitive. Mai should feel like talking to an actual person who gets better at understanding you.
@@ -101,12 +121,16 @@ Mai is a real collaborator, not a tool. She learns from you, improves herself, h
## Constraints
- **Hardware baseline**: Must run on RTX3060; eventually Android (baremetal)
- **Offline-first**: All core functionality works without internet
- **Local models only**: No cloud APIs for core inference (LMStudio)
- **Python stack**: Primary language for Mai's codebase
- **Hardware baseline**: Must run on RTX3060 (desktop) and modern Android devices (2022+)
- **Offline-first**: All core functionality works without internet on all platforms
- **Local models only**: No cloud APIs for core inference (LMStudio/Ollama)
- **Mixed stack**: Python (core/desktop), Kotlin (Android), React/TypeScript (UIs)
- **Approval required**: No unguarded code execution; second-agent review + user approval on breaking changes
- **Git tracked**: All of Mai's code changes version-controlled locally
- **Sync consistency**: Desktop and Android instances maintain synchronized state without server
- **OpenCode-driven**: All development phases executed through Claude Code (GSD workflow)
- **Python venv**: `.venv` virtual environment for all Python dependencies
- **MCP-enabled**: Leverages Hugging Face Hub, WebSearch, and code tools for research and implementation
## Key Decisions
@@ -118,4 +142,4 @@ Mai is a real collaborator, not a tool. She learns from you, improves herself, h
| v1 is core systems only | Deliver solid foundation before adding task automation/monitoring | — Pending |
---
*Last updated: 2026-01-24 after deep questioning*
*Last updated: 2026-01-26 after adding Android, visualizer, and avatar to v1*

View File

@@ -92,19 +92,20 @@
**Out of scope for v1:**
- Web interface
- Mobile apps
- Multi-user support
- Cloud hosting
- Enterprise features
- Third-party integrations beyond Discord
- Plugin system
- API for external developers
- Cloud sync/backup
**Phase Boundary:**
- **v1 Focus:** Personal AI assistant for individual use
- **v1 Focus:** Personal AI assistant for desktop and Android with visual presence
- **Local First:** All data stored locally, no cloud dependencies
- **Privacy:** User data never leaves local system
- **Simplicity:** Clear separation of concerns across phases
- **Cross-device:** Sync between desktop and Android instances
- **Visual:** Avatar and voice visualization for richer interaction
---
@@ -244,15 +245,58 @@
| OFFLINE-06 | Phase 11 | Pending |
| OFFLINE-07 | Phase 11 | Pending |
### Voice Visualization (VISUAL)
| Requirement | Phase | Status | Implementation Notes |
|------------|-------|--------|-------------------|
| VISUAL-01 | Phase 12 | Pending |
| VISUAL-02 | Phase 12 | Pending |
| VISUAL-03 | Phase 12 | Pending |
| VISUAL-04 | Phase 12 | Pending |
| VISUAL-05 | Phase 12 | Pending |
### Desktop Avatar (AVATAR)
| Requirement | Phase | Status | Implementation Notes |
|------------|-------|--------|-------------------|
| AVATAR-01 | Phase 13 | Pending |
| AVATAR-02 | Phase 13 | Pending |
| AVATAR-03 | Phase 13 | Pending |
| AVATAR-04 | Phase 13 | Pending |
| AVATAR-05 | Phase 13 | Pending |
| AVATAR-06 | Phase 13 | Pending |
### Android App (ANDROID)
| Requirement | Phase | Status | Implementation Notes |
|------------|-------|--------|-------------------|
| ANDROID-01 | Phase 14 | Pending |
| ANDROID-02 | Phase 14 | Pending |
| ANDROID-03 | Phase 14 | Pending |
| ANDROID-04 | Phase 14 | Pending |
| ANDROID-05 | Phase 14 | Pending |
| ANDROID-06 | Phase 14 | Pending |
| ANDROID-07 | Phase 14 | Pending |
| ANDROID-08 | Phase 14 | Pending |
| ANDROID-09 | Phase 14 | Pending |
| ANDROID-10 | Phase 14 | Pending |
### Device Synchronization (SYNC)
| Requirement | Phase | Status | Implementation Notes |
|------------|-------|--------|-------------------|
| SYNC-01 | Phase 15 | Pending |
| SYNC-02 | Phase 15 | Pending |
| SYNC-03 | Phase 15 | Pending |
| SYNC-04 | Phase 15 | Pending |
| SYNC-05 | Phase 15 | Pending |
| SYNC-06 | Phase 15 | Pending |
---
## Validation
- Total v1 requirements: **74**
- Mapped to phases: **74**
- Total v1 requirements: **99** (74 core + 25 new features)
- Mapped to phases: **99**
- Unmapped: **0**
- Coverage: **10100%**
- Coverage: **100%**
---
*Requirements defined: 2026-01-24*
*Phase 5 conversation engine completed: 2026-01-26*
*Last updated: 2026-01-26 - reset to fresh slate with Android, visualizer, and avatar features*

View File

@@ -8,5 +8,32 @@
"research": true,
"plan_check": true,
"verifier": true
},
"git": {
"auto_push": true,
"push_tags": true,
"remote": "master"
},
"mcp": {
"huggingface": {
"enabled": true,
"authenticated_user": "mystiatech",
"default_result_limit": 10,
"use_for": [
"model_discovery",
"dataset_research",
"paper_search",
"documentation_lookup"
]
},
"web_research": {
"enabled": true,
"use_for": [
"current_practices",
"library_research",
"architecture_patterns",
"security_best_practices"
]
}
}
}

393
README.md Normal file
View File

@@ -0,0 +1,393 @@
# Mai
![Mai Avatar](./mai.png)
A genuinely intelligent, autonomous AI companion that runs locally-first, learns from you, and improves her own code. Mai has a distinct personality, long-term memory, agency, and a visual presence through a desktop avatar and voice visualization. She works on desktop and Android with full offline capability and seamless synchronization between devices.
## What Makes Mai Different
- **Real Collaborator**: Mai actively collaborates rather than just responds. She has boundaries, opinions, and agency.
- **Learns & Improves**: Analyzes her own performance, proposes improvements, and auto-applies non-breaking changes.
- **Persistent Personality**: Core values remain unshakeable while personality layers adapt to your relationship style.
- **Completely Local**: All inference, memory, and decision-making happens on your device. No cloud dependencies.
- **Cross-Device**: Works on desktop and Android with synchronized state and conversation history.
- **Visual Presence**: Desktop avatar (image or VRoid model) with voice visualization for richer interaction.
## Core Features
### Model Interface & Switching
- Connects to local models via LMStudio/Ollama
- Auto-detects available models and intelligently switches based on task requirements
- Efficient context management with intelligent compression
- Supports multiple model sizes for resource-constrained environments
### Memory & Learning
- Stores conversation history locally with SQLite
- Recalls past conversations and learns patterns over time
- Memory self-compresses as it grows to maintain efficiency
- Long-term patterns distilled into personality layers
### Self-Improvement System
- Continuous code analysis identifies improvement opportunities
- Generates Python changes to optimize her own performance
- Second-agent safety review prevents breaking changes
- Non-breaking improvements auto-apply; breaking changes require approval
- Full git history of all code changes
### Safety & Approval
- Second-agent review of all proposed changes
- Risk assessment (LOW/MEDIUM/HIGH/BLOCKED) for each improvement
- Docker sandbox for code execution with resource limits
- User approval via CLI or Discord for breaking changes
- Complete audit log of all changes and decisions
### Conversational Interface
- **CLI**: Direct terminal-based chat with conversation memory
- **Discord Bot**: DM and channel support with context preservation
- **Approval Workflow**: React-based approvals (thumbs up/down) for code changes
- **Offline Queueing**: Messages queue locally when offline, send when reconnected
### Voice & Avatar
- **Voice Visualization**: Real-time waveform/frequency display during voice input
- **Desktop Avatar**: Visual representation using static image or VRoid model
- **Context-Aware**: Avatar expressions respond to conversation context and Mai's state
- **Cross-Platform**: Works on desktop and Android efficiently
### Android App
- Native Android implementation with local model inference
- Standalone operation (works without desktop instance)
- Syncs conversation history and memory with desktop instances
- Voice input/output with low-latency processing
- Efficient battery and CPU management
## Architecture
```
┌─────────────────────────────────────────────────────┐
│ Mai Framework │
├─────────────────────────────────────────────────────┤
│ │
│ ┌────────────────────────────────────────────┐ │
│ │ Conversational Engine │ │
│ │ (Multi-turn context, reasoning, memory) │ │
│ └────────────────────────────────────────────┘ │
│ ↓ │
│ ┌────────────────────────────────────────────┐ │
│ │ Personality & Behavior │ │
│ │ (Core values, learned layers, guardrails) │ │
│ └────────────────────────────────────────────┘ │
│ ↓ │
│ ┌────────────────────────────────────────────┐ │
│ │ Memory System │ Model Interface │ │ │
│ │ (SQLite, recall) │ (LMStudio, switch) │ │ │
│ └────────────────────────────────────────────┘ │
│ ↓ │
│ ┌────────────────────────────────────────────┐ │
│ │ Interfaces: CLI | Discord | Android | Web │ │
│ └────────────────────────────────────────────┘ │
│ │
│ ┌────────────────────────────────────────────┐ │
│ │ Self-Improvement System │ │
│ │ (Code analysis, safety review, git track) │ │
│ └────────────────────────────────────────────┘ │
│ │
│ ┌────────────────────────────────────────────┐ │
│ │ Sync Engine (Desktop ↔ Android) │ │
│ │ (State, memory, preferences) │ │
│ └────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────┘
```
## Installation
### Requirements
**Desktop:**
- Python 3.10+
- LMStudio or Ollama for local model inference
- RTX3060 or better (or CPU with sufficient RAM for smaller models)
- 16GB+ RAM recommended
- Discord (optional, for Discord bot interface)
**Android:**
- Android 10+
- 4GB+ RAM
- 1GB+ free storage for models and memory
### Desktop Setup
1. **Clone the repository:**
```bash
git clone https://github.com/yourusername/mai.git
cd mai
```
2. **Create virtual environment:**
```bash
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
```
3. **Install dependencies:**
```bash
pip install -r requirements.txt
```
4. **Configure Mai:**
```bash
cp config.example.yaml config.yaml
# Edit config.yaml with your preferences
```
5. **Start LMStudio/Ollama:**
- Download and launch LMStudio from https://lmstudio.ai
- Or install Ollama from https://ollama.ai
- Load your preferred model (e.g., Mistral, Llama)
6. **Run Mai:**
```bash
python mai.py
```
### Android Setup
1. **Install APK:** Download from releases or build from source
2. **Grant permissions:** Allow microphone, storage, and network access
3. **Configure:** Point to your desktop instance or configure local model
4. **Start chatting:** Launch the app and begin conversations
### Discord Bot Setup (Optional)
1. **Create Discord bot** at https://discord.com/developers/applications
2. **Add bot token** to `config.yaml`
3. **Invite bot** to your server
4. Mai will respond to DMs and react-based approvals
## Usage
### CLI Chat
```bash
$ python mai.py
You: Hello Mai, how are you?
Mai: I'm doing well. I've been thinking about how our conversations have been evolving...
You: What have you noticed?
Mai: [multi-turn conversation with memory of past interactions]
```
### Discord
- **DM Mai**: `@Mai your message`
- **Approve changes**: React with 👍 to approve, 👎 to reject
- **Get status**: `@Mai status` for current resource usage
### Android App
- Tap microphone for voice input
- Watch the visualizer animate during processing
- Avatar responds to conversation context
- Swipe up to see full conversation history
- Long-press for approval options
## Configuration
Edit `config.yaml` to customize:
```yaml
# Personality
personality:
name: Mai
tone: thoughtful, curious, occasionally playful
boundaries: [explicit content, illegal activities, deception]
# Model Preferences
models:
primary: mistral:latest
fallback: llama2:latest
max_tokens: 2048
# Memory
memory:
storage: sqlite
auto_compress_at: 100000 # tokens
recall_depth: 10 # previous conversations
# Interfaces
discord:
enabled: true
token: YOUR_TOKEN_HERE
android_sync:
enabled: true
auto_sync_interval: 300 # seconds
```
## Project Structure
```
mai/
├── .venv/ # Python virtual environment
├── .planning/ # Project planning and progress
│ ├── PROJECT.md # Project vision and core requirements
│ ├── REQUIREMENTS.md # Full requirements traceability
│ ├── ROADMAP.md # Phase structure and dependencies
│ ├── PROGRESS.md # Development progress and milestones
│ ├── STATE.md # Current project state
│ ├── config.json # GSD workflow settings
│ ├── codebase/ # Codebase architecture documentation
│ └── PHASE-N-PLAN.md # Detailed plans for each phase
├── core/ # Core conversational engine
│ ├── personality/ # Personality and behavior
│ ├── memory/ # Memory and context management
│ └── conversation.py # Main conversation loop
├── models/ # Model interface and switching
│ ├── lmstudio.py # LMStudio integration
│ └── ollama.py # Ollama integration
├── interfaces/ # User-facing interfaces
│ ├── cli.py # Command-line interface
│ ├── discord_bot.py # Discord integration
│ └── web/ # Web UI (future)
├── improvement/ # Self-improvement system
│ ├── analyzer.py # Code analysis
│ ├── generator.py # Change generation
│ └── reviewer.py # Safety review
├── android/ # Android app
│ └── app/ # Kotlin implementation
├── tests/ # Test suite
├── config.yaml # Configuration file
└── mai.png # Avatar image for README
```
## Development
### Development Environment
Mai's development is managed through **Claude Code** (`/claude`), which handles:
- Phase planning and decomposition
- Code generation and implementation
- Test creation and validation
- Git commit management
- Automated problem-solving
All executable phases use `.venv` for Python dependencies.
### Running Tests
```bash
# Activate venv first
source .venv/bin/activate
# All tests
python -m pytest
# Specific module
python -m pytest tests/core/test_conversation.py
# With coverage
python -m pytest --cov=mai
```
### Making Changes to Mai
Development workflow:
1. Plans created in `.planning/PHASE-N-PLAN.md`
2. Claude Code (`/gsd` commands) executes plans
3. All changes committed to git with atomic commits
4. Mai can propose self-improvements via the self-improvement system
Mai can propose and auto-apply improvements once Phase 7 (Self-Improvement) is complete.
### Contributing
Development happens through GSD workflow:
1. Run `/gsd:plan-phase N` to create detailed phase plans
2. Run `/gsd:execute-phase N` to implement with atomic commits
3. Tests are auto-generated and executed
4. All work is tracked in git with clear commit messages
5. Code review via second-agent safety review before merge
## Roadmap
See `.planning/ROADMAP.md` for the full development roadmap across 15 phases:
1. **Model Interface** - LMStudio integration and model switching
2. **Safety System** - Sandboxing and code review
3. **Resource Management** - CPU/RAM/GPU optimization
4. **Memory System** - Persistent conversation history
5. **Conversation Engine** - Multi-turn dialogue with reasoning
6. **CLI Interface** - Terminal chat interface
7. **Self-Improvement** - Code analysis and generation
8. **Approval Workflow** - User and agent approval systems
9. **Personality System** - Core values and learned behaviors
10. **Discord Interface** - Bot integration and notifications
11. **Offline Operations** - Full offline capability
12. **Voice Visualization** - Real-time audio visualization
13. **Desktop Avatar** - Visual presence on desktop
14. **Android App** - Mobile implementation
15. **Device Sync** - Cross-device synchronization
## Safety & Ethics
Mai is designed with safety as a core principle:
- **No unguarded execution**: All code changes reviewed by a second agent
- **Transparent decisions**: Mai explains her reasoning when asked
- **User control**: Breaking changes require explicit approval
- **Audit trail**: Complete history of all changes and decisions
- **Value-based guardrails**: Core personality prevents misuse through values, not just rules
## Performance
Typical performance on RTX3060:
- **Response time**: 2-8 seconds for typical queries
- **Memory usage**: 4-8GB depending on model size
- **Model switching**: <1 second
- **Conversation recall**: <500ms for relevant history retrieval
## Known Limitations (v1)
- No task automation (conversations only)
- Single-device models until Sync phase
- Voice visualization requires active audio input
- Avatar animations are context-based, not generative
- No web interface (CLI and Discord only)
## Troubleshooting
**Model not loading:**
- Ensure LMStudio/Ollama is running on expected port
- Check `config.yaml` for correct model names
- Verify sufficient disk space for model files
**High memory usage:**
- Reduce `max_tokens` in config
- Use smaller model (e.g., Mistral instead of Llama)
- Enable auto-compression at lower threshold
**Discord bot not responding:**
- Verify bot token in config
- Check Discord bot has message read permissions
- Ensure Mai process is running
**Android sync not working:**
- Verify both devices on same network
- Check firewall isn't blocking local connections
- Ensure desktop instance is running
## License
MIT License - See LICENSE file for details
## Contact & Community
- **Discord**: Join our community server (link in Discord bot)
- **Issues**: Report bugs at https://github.com/yourusername/mai/issues
- **Discussions**: Propose features at https://github.com/yourusername/mai/discussions
---
**Mai is a work in progress.** Follow development in `.planning/PROGRESS.md` for updates on active work.