loci MCP server

loci 🧠

<!-- mcp-name: io.github.IvenKooLab/loci -->

Two thousand years ago, orators stored their speeches in the rooms of a

palace and walked through them to remember. **loci does the same for your

files.**

Loci is the method behind every memory palace: place knowledge in

locations, recall it by walking the path.

A queryable "second brain" for the project docs, notes, and chat logs scattered across a dozen directories — and an MCP server so your AI agents can use it too.

Local files → heading-aware chunking → embeddings → hybrid retrieval (vector + BM25) → LLM answer with section-level citations. The index lives entirely on your machine; only embedding/chat calls go out, to any OpenAI-compatible API (Zhipu / DeepSeek / Kimi / OpenAI / …).

The thesis (from studying the 90k-star platforms and the graveyard of

dead lightweight tools — see

our competitive landscape study):

don't build another chat app. Build the **memory layer that every chat app

can mount**. Claude Desktop, Cursor, Cline, or any MCP host becomes this

project's UI, for free.

Demo

Real session, indexed against the docs of minimax-h3-turing (paths shortened for display):

$ python main.py search "what the 22G card can and cannot do" -k 3

[1] minimax-h3-turing/docs/en/01-hardware-limits.md > 01 · What a 2080Ti 22G Can and Cannot Do    (similarity 0.562)
[2] minimax-h3-turing/docs/en/02-w4a8-vs-w4a4.md > 02 · Quantization Measured > You Can Try Without 22G  (similarity 0.446)
[3] minimax-h3-turing/docs/en/01-hardware-limits.md > ... > 3. VRAM is just barely enough — manage it  (similarity 0.504)

$ python main.py ask "How should I choose between T8 aggressive mode and the final-render mode, and why?"

Answer:
* Drafts / preview / shot selection: use T8 aggressive mode — a 43% speedup
  (2.7 min/clip), and "a different picture of equal quality" is fine for picking shots.
* Final shots: use final-render mode (no T8). T8 makes the numerical trajectory
  fork, so re-running with the same seed produces a different clip — which breaks
  the reproducibility final outputs need.

[source: docs/en/08-t8-blockcache-4step.md > Practical Advice (4-step Turbo route)]
[source: docs/en/06-faq.md > 12. Cache-style accelerators break "same-seed re-runs"]

Hybrid retrieval means a Chinese query still finds the English doc (and vice versa) — keyword evidence (BM25) catches what embeddings miss, and every citation points at a section, not just a file.

Does hybrid actually help? (mini-eval, 10 bilingual queries)

$ python scripts/eval_retrieval.py scripts/eval_cases.example.jsonl
vector-only: 9/10  →  hybrid: 10/10

Shortened. The full README is on GitHub.