Self-improving Agent Memory Upgrade (SurrealDB)

超级自我优化智能体 - 多模态记忆、反馈循环、元学习、置信度校准 / Super Self-Improving Agent - Multi-modal Memory, Feedback Loops, Meta-Learning, Confidence Calibration

SurrealDB Surrealism WASM extension development. Write Rust functions, compile to WASM, deploy as database modules. Part of the surreal-skills collection.

Capture errors, corrections, and recurring patterns into structured `.learnings/` logs, then promote durable guidance into workspace memory files. Use when c...

Lightweight self-improvement loop for AI agents. Auto-captures errors, corrections, and discoveries in a fast one-line format. Smart dedup, auto-promotion to...

Enhanced memory search with hybrid vector+keyword scoring, temporal routing, filepath scoring, adaptive weighting, pseudo-relevance feedback, salience scoring, and knowledge graph cross-references. Replaces the default memory search with a 4-signal fusion retrieval system. Use when searching memories, indexing memory files, building cross-references, or scoring memory salience. Requires Ollama with nomic-embed-text model.

Full AI agent memory stack — Mem0 unified memory engine with vector search (Qdrant) and knowledge graph (Neo4j), plus SQLite for structured data. Complete se...