- Add multi-provider LLM architecture supporting OpenRouter, OpenAI, Gemini, and custom providers
- Implement global LLM on/off switch with default DISABLED state for cost protection
- Add per-character LLM configuration with provider-specific models and settings
- Create performance-optimized caching system for LLM enabled status checks
- Add API key validation before enabling LLM providers to prevent broken configurations
- Implement audit logging for all LLM enable/disable actions for cost accountability
- Create comprehensive admin UI with prominent cost warnings and confirmation dialogs
- Add visual indicators in character list for custom AI model configurations
- Build character-specific LLM client system with global fallback mechanism
- Add database schema support for per-character LLM settings
- Implement graceful fallback responses when LLM is globally disabled
- Create provider testing and validation system for reliable connections
- Fix reflection memory spam despite zero active characters in scheduler.py
- Add character enable/disable functionality to admin interface
- Fix Docker configuration with proper network setup and service dependencies
- Resolve admin interface JavaScript errors and login issues
- Fix MCP import paths for updated package structure
- Add comprehensive character management with audit logging
- Implement proper character state management and persistence
- Fix database connectivity and initialization issues
- Add missing audit service for admin operations
- Complete Docker stack integration with all required services
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Update docker-start.sh to force correct profiles (qdrant, admin)
- Fix PostgreSQL port mapping from 5432 to 15432 across all configs
- Resolve MCP import conflicts by renaming src/mcp to src/mcp_servers
- Fix admin interface StaticFiles mount syntax error
- Update LLM client to support both Ollama and OpenAI-compatible APIs
- Configure host networking for Discord bot container access
- Correct database connection handling for async context managers
- Update environment variables and Docker compose configurations
- Add missing production dependencies and Dockerfile improvements
- Enhanced install.py with Docker detection and automatic service setup
- Added docker-compose.services.yml for standalone database services
- Created docker-services.sh management script for easy service control
- Added DOCKER.md documentation with complete setup instructions
- Updated requirements.txt for Python 3.13 compatibility
- Added multiple test scripts and configuration files
- Enhanced collaborative creative projects with proper database integration
- Fixed SQLAlchemy metadata field conflicts in database models
- Added comprehensive quickstart and testing guides
Services now available:
- PostgreSQL with Docker
- Redis with Docker
- ChromaDB vector database
- Qdrant vector database (recommended)
- PgAdmin for database administration
The setup script now automatically detects Docker and offers streamlined
installation with one-command service deployment.
Core Features:
- Full autonomous AI character ecosystem with multi-personality support
- Advanced RAG system with personal, community, and creative memory layers
- MCP integration for character self-modification and file system access
- PostgreSQL database with comprehensive character relationship tracking
- Redis caching and ChromaDB vector storage for semantic memory retrieval
- Dynamic personality evolution based on interactions and self-reflection
- Community knowledge management with tradition and norm identification
- Sophisticated conversation engine with natural scheduling and topic management
- Docker containerization and production-ready deployment configuration
Architecture:
- Multi-layer vector databases for personal, community, and creative knowledge
- Character file systems with personal and shared digital spaces
- Autonomous self-modification with safety validation and audit trails
- Memory importance scoring with time-based decay and consolidation
- Community health monitoring and cultural evolution tracking
- RAG-powered conversation context and relationship optimization
Characters can:
- Develop authentic personalities through experience-based learning
- Create and build upon original creative works and philosophical insights
- Form complex relationships with memory of past interactions
- Modify their own personality traits through self-reflection cycles
- Contribute to and learn from shared community knowledge
- Manage personal digital spaces with diaries, creative works, and reflections
- Engage in collaborative projects and community decision-making
System supports indefinite autonomous operation with continuous character
development, community culture evolution, and creative collaboration.
