local-rag

<div align="center"> <img src="mimirs-logo-2.png" alt="mimirs logo" width="200"> <h1>MIMIRS</h1> <p><i>Named after <a href="https://en.wikipedia.org/wiki/M%C3%ADmir">Mímir</a>, the Norse god of wisdom and knowledge.</i></p> <p>Persistent project memory for AI coding agents. One command to set up, nothing to maintain.</p> <p> <a href="https://www.npmjs.com/package/mimirs"><img src="https://img.shields.io/npm/v/mimirs" alt="npm"></a> <a href="LICENSE"><img src="https://img.shields.io/npm/l/mimirs" alt="license"></a> </p> </div>

Your agent starts every session blind — guessing filenames, grepping for keywords, burning context on irrelevant files, and forgetting everything you discussed yesterday.

On a real project, that costs 380K tokens per prompt and 12-second response times.

After indexing with mimirs: 91K tokens, 3 seconds. A 76% reduction — depending on your model and usage, that's hundreds to thousands in monthly API savings.

No API keys. No cloud. No Docker. Just bun and SQLite.

Works with: Claude Code  ·  Cursor  ·  Windsurf  ·  JetBrains (Junie)  ·  GitHub Copilot  ·  any MCP client

Auto-generated project wiki

One command turns your codebase into a structured, cross-linked markdown wiki — architecture docs, module pages, entity pages, guides, and Mermaid diagrams — all built from the semantic index. See the wiki generated for this project →

<p align="center"> <img src="wiki-screen.png" alt="generated wiki example" width="700"> </p>

Search quality

100% recall. Benchmarked on four real codebases — including Kubernetes at 8,691 files — with known expected results per query. Full methodology in BENCHMARKS.md.

Codebase	Language	Files	Queries	Recall@10	MRR	Zero-miss
mimirs	TypeScript	97	20	100.0%	0.651	0.0%
Express.js	JavaScript	161	15	100.0%	0.922	0.0%
Excalidraw	TypeScript	676	20	100.0%	0.366	0.0%
Kubernetes	Go	8,691	20	100.0%*	0.496	0.0%*

*With config tuning. At default top-10, Recall is 80%. See BENCHMARKS.md for details.

How it compares

	mimirs	No tool (grep + Read)	Context stuffing	Cloud RAG services
Setup	One command	Nothing	Nothing	API keys, accounts
Token cost	~91K/prompt	~380K/prompt	Entire codebase	Varies
Search quality	100% Recall@10	Depends on keywords	N/A (everything loaded)	Varies
Code understanding	AST-aware (24 langs)	Line-level	None	Usually line-level
Cross-session memory	Conversations + checkpoints	None	None	Some
Privacy	Fully local	Local	Local	Data leaves your machine
Price	Free	Free	High token bills	$10-50/mo + tokens

What it gives your agent

Find code by meaning, not filename. "Where do we handle authentication errors?" → mimirs finds middleware/session-guard.ts. Hybrid vector + BM25 search, boosted by dependency graph centrality.

Remember past sessions. Conversation transcripts are indexed in real time. Three days later, your agent can search for "why did we switch to JWT?" and get the exact discussion.

Know what changed since last time. git_context shows uncommitted changes and recent commits in one call, so agents don't propose edits that conflict with in-progress work.

Leave notes for future sessions. annotate attaches persistent caveats to files or symbols — "known race condition", "blocked on auth rewrite" — that surface automatically in search results.

Mark decisions, not just code. Checkpoints capture milestones, direction changes, and blockers. Searchable across sessions so context doesn't evaporate.

Understand codebase structure. Dependency graphs, reverse-dependency lookups, and find_usages show the blast radius before any refactor.

Generate a project wiki. generate_wiki produces a structured, cross-linked markdown wiki — architecture docs, module pages, entity pages, guides, and Mermaid diagrams — all built from the semantic index.

Expose documentation gaps. Analytics log every query locally — nothing leaves your machine. Zero-result and low-relevance queries reveal what's missing from your docs.

Quick start

1. Install SQLite (macOS)

Apple's bundled SQLite doesn't support extensions:

brew install sqlite

2. Set up your editor

bunx mimirs init --ide claude   # or: cursor, windsurf, copilot, jetbrains, all

This creates the MCP server config, editor rules, .mimirs/config.json, and .gitignore entry. Run with --ide all to set up every supported editor at once.

3. Try the demo (optional)

bunx mimirs demo

Claude Code plugin

For deeper integration, mimirs is also available as a Claude Code plugin. In a Claude Code session:

/plugin marketplace add https://github.com/TheWinci/mimirs.git
/plugin install mimirs

The plugin adds SessionStart (context summary), PostToolUse (auto-reindex on edit), and SessionEnd (auto-checkpoint) hooks. No CLAUDE.md instructions needed — the plugin's built-in skill handles tool usage.

How it works

1. Parse & chunk — Splits content using type-matched strategies: function/class boundaries for code (via tree-sitter across 24 languages), headings for markdown, top-level keys for YAML/JSON. Chunks that exceed the embedding model's token limit are windowed and merged.

2. Embed — Each chunk becomes a 384-dimensional vector using all-MiniLM-L6-v2 (in-process via Transformers.js + ONNX, no API calls). Vectors are stored in sqlite-vec.

3. Build dependency graph — Import specifiers and exported symbols are captured during AST chunking, then resolved to build a file-level dependency graph.

4. Hybrid search — Queries run vector similarity and BM25 in parallel, blended by configurable weight. Results are boosted by dependency graph centrality and path heuristics. read_relevant returns individual chunks with entity names and exact line ranges (path:start-end).

5. Watch & re-index — File changes are detected with a 2-second debounce. Changed files are re-indexed; deleted files are pruned.

6. Conversation & checkpoints — Tails Claude Code's JSONL transcripts in real time. Agents can create checkpoints at important moments for future sessions to search.

7. Annotations — Notes attached to files or symbols surface as [NOTE] blocks inline in read_relevant results.

8. Analytics — Every query is logged. Analytics surface zero-result queries, low-relevance queries, and period-over-period trends.

Supported languages

AST-aware chunking via bun-chunk with tree-sitter grammars:

TypeScript/JavaScript, Python, Go, Rust, Java, C, C++, C#, Ruby, PHP, Scala, Kotlin, Lua, Zig, Elixir, Haskell, OCaml, Dart, Bash/Zsh, TOML, YAML, HTML, CSS/SCSS/LESS

Also indexes: Markdown, JSON, XML, SQL, GraphQL, Protobuf, Terraform, Dockerfiles, Makefiles, and more. Files without a known extension fall back to paragraph splitting.

Documentation

• MCP tools, CLI & analytics
• Configuration & examples
• Benchmarks

Stack

Layer	Choice
Runtime	Bun (built-in SQLite, fast TS)
AST chunking	bun-chunk — tree-sitter grammars for 24 languages
Embeddings	Transformers.js + ONNX (in-process, no daemon)
Embedding model	all-MiniLM-L6-v2 (~23MB, 384 dimensions) — configurable
Vector store	sqlite-vec (single .db file)
MCP	@modelcontextprotocol/sdk (stdio transport)
Plugin	Claude Code plugin with skills + hooks

All data lives in .mimirs/ inside your project — add it to .gitignore.