MIMIRS

Named after Mímir, the Norse god of wisdom and knowledge.

Persistent project memory for AI coding agents. One command to set up, nothing to maintain.

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.

Semantic Search  ·  Auto-generated Wiki
Cross-session Memory  ·  Dependency Graphs  ·  Annotations

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

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.

Search quality

93–98% recall. Benchmarked on four real codebases across three languages (120 queries total) — from 97 files to 8,553 — with known expected results per query. Full methodology in BENCHMARKS.md.

Codebase	Language	Files	Queries	Recall@10	MRR	Zero-miss
mimirs	TypeScript	97	30	98.3%	0.683	0.0%
Excalidraw	TypeScript	693	30	96.7%	0.442	3.3%
Django	Python	3,090	30	93.3%	0.688	6.7%
Kubernetes	Go	8,553	30	90.0%	0.589	10.0%

Kubernetes excludes test files and demotes generated files. With searchTopK: 15, recall reaches 100%. See Kubernetes benchmarks 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	93–98% 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

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.