I’ve been building this for a long fucking time, and tonight I finally released Memoria 1.0.0. I built it because I actually wanted to use it. I wanted a real memory layer for local LLM applications that didn’t depend on a specific model, a cloud service, or an API key. Memoria is local-first and LLM-agnostic. It can run without an LLM at all. The machine I built and benchmarked it on is not exactly impressive. It’s an Intel Celeron N4020 running at 1.10 GHz, with around 3.7 GiB of usable RAM, no GPU, and Debian Linux. On LongMemEval-S, 468 out of 470 retrieval-evaluable questions returned results. Recall@1 was 89.8%, Recall@5 was 97.9%, Recall@10 was 98.9%, and Recall@50 was 99.6%. Session NDCG@10 was 0.9257. Peak RSS for the full LongMemEval workload was 2.65 GiB. Average peak RSS for an individual query was around 580 MiB. The retrieval system is not just throwing everything into a vector database. Memoria runs FAISS, BM25, graph retrieval, phrase matching, attribute retrieval, and temporal retrieval in parallel. Those signals get fused and then passed through multi-signal ranking. Temporal retrieval is independently implemented too, so I can measure it and ablate it instead of having it baked into the base retrieval path. It’s usable, but it’s still under active work. There’s a bunch of other stuff in the release as well. GitHub repository ingestion, Obsidian vault ingestion, MCP support, a CLI, TUI, GUI, and API, persistent local storage, LongMemEval and LoCoMo benchmark tooling, and a plugin system with 11 subsystems and 34 hooks. There’s also an interactive plugin generator now. And it’s actually installable: bash pip install kitzkatz-memoria GitHub: https://github.com/Kitzkatz/memoria Docs: https://kitzkatz.github.io/memoria/ PyPI: https://pypi.org/project/kitzkatz-memoria/ I wasn’t going to wait around for a perfect time to ship it. It’s 1.0.0. If you’re working on local agents or local LLM applications, I’d genuinely like to hear what you think and would appreciate any feedback. Please break it   submitted by   /u/kitkatz69 [link]   [comments]