Memory System V2

**Judgement:** A strong, pragmatic choice for adding fast, persistent memory to single-user, file-based AI agents—especially in developer or self-hosted setups where bash + jq are available. It favors simplicity and speed over sophistication (no embeddings yet), which makes it easy to understand, debug, and integrate. **What it does well:** - Provides a clear memory model (types, importance, tags, context) with a JSON index and Markdown logs. - Delivers sub-20ms local searches and sub-100ms operations in reported tests, which is very good for agent loops. - Includes auto-consolidation into weekly summaries and stats/analytics, making review workflows straightforward. - Ships with a CLI that’s easy to script from agents (capture/search/recent/stats/consolidate) and has a documented integration pattern. **Key risks / limitations:** - **Not true semantic search:** Despite the positioning, it currently uses text-based search over JSON (no embeddings), so retrieval quality may degrade on fuzzier queries or larger corpora. - **Scaling ceiling:** Explicitly designed to scale to roughly ~10K memories; beyond that, performance and manageability may drop without further optimization. - **Single-user & file-based:** No concurrency/multi-user design; write conflicts or file corruption are possible if multiple processes modify files without coordination. - **Platform/dependency constraints:** Requires bash + jq on macOS/Linux; not a drop-in for Windows-only or locked-down environments without extra setup. **Recommended scenarios:** - Local or self-hosted agents that need **simple, transparent, and fast** long-term memory without adding databases or external services. - Developer-centric workflows where the team is comfortable with bash scripts, JSON, and file-based storage. - Use as a **baseline memory layer** that can later be upgraded (e.g., by layering embeddings/vector search) while keeping the same JSON/index format. **Less ideal for:** - Large-scale, multi-user, or cloud-distributed systems needing robust concurrency, access control, or very large memory stores. - Use cases demanding high-quality semantic retrieval out-of-the-box (e.g., nuanced natural language recall over large unstructured corpora) without additional retrieval infrastructure.