- 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>
- Fix remaining datetime timezone errors across all database operations
- Implement dynamic vector database backend (Qdrant/ChromaDB) based on install.py configuration
- Add LLM timeout handling with immediate fallback responses for slow self-hosted models
- Use proper install.py configuration (2000 max tokens, 5min timeout, correct LLM endpoint)
- Fix PostgreSQL schema to use timezone-aware columns throughout
- Implement async LLM request handling with background processing
- Add configurable prompt limits and conversation history controls
- Start missing database services (PostgreSQL, Redis) automatically
- Fix environment variable mapping between install.py and application code
- Resolve all timezone-naive vs timezone-aware datetime conflicts
System now properly uses Qdrant vector database as specified in install.py instead of hardcoded ChromaDB.
Characters respond immediately with fallback messages during long LLM processing times.
All database timezone errors resolved with proper timestamptz columns.
- 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
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.
