Interesting angle. How do you decide what becomes persistent memory vs transient context? Is there any eviction or decay model?
The main problem it solves: AI agents lose context between sessions. Memora acts as a context manager that persists knowledge across multiple agent sessions, so your AI assistant remembers past work, decisions, and learned patterns.
Key features: - Persistent context across agent sessions (single or multi-agent workflows) - SQLite-backed with optional S3/R2 cloud sync - Semantic search using embeddings (TF-IDF, sentence-transformers, or OpenAI) - Interactive knowledge graph visualization (vis.js) - Structured memory types: TODOs, Issues, Knowledge entries - Cross-reference links between related memories - Image storage support with R2
The graph visualization lets you explore connections between memories, filter by tags/status, and see how your knowledge base grows over time.
Built for Claude Code but works with any MCP-compatible client.
GitHub: https://github.com/agentic-mcp-tools/memora
Would love feedback on the architecture and feature ideas!