droste-memory 1.0.0

Local hybrid structural and semantic graph memory engine.

Droste

See your codebase as a living galaxy — and give your agents causal memory of it.

Droste indexes any repo into a fractal, zoomable map of its symbols, wires them together with their real call / import / DB edges across languages, and serves an agent the causal slice of code it actually needs — not just keyword matches.

Local-first · zero-config · polyglot · MCP-native

Zooming out reveals the causal web — every cyan arc is a real syntax_dependency edge. Full flythrough (FastAPI)

Quickstart · Why it's different · How it works · MCP · Benchmarks

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Quickstart

pip install -e .          # or: pip install droste-memory   (once on PyPI)
droste index .            # index the current repo
droste view               # open the fractal galaxy in your browser

Three commands. droste view opens a full-screen, 60fps zoomable map of your code — scroll to dive from the project star into folder orbits, down to the individual functions, with the causal edges glowing between them.

Need it for an agent instead of your eyes?

droste context "checkout flow" --budget 1500   # causal context slice for an LLM

Running droste with no arguments prints the command palette:

                  .-----------------------.
             .----'           |           '----.
         .---'          .-----+-----.          '---.
       .'          .----'     |     '----.          '.
      /        .---'      .---+---.      '---.        \
     /      .-'        .-'    |    '-.        '-.      \
    |     .'        .-'   .---+---.   '-.        '.     |
    |    /        .'    .'    |    '.    '.        \    |
    |   |        |     |   .--+--.   |     |        |   |
    | --+--------+-----+---+  @  +---+-----+--------+-- |
    |   |        |     |   '--+--'   |     |        |   |
    |    \        '.    '.    |    .'    .'        /    |
    |     '.        '-.   '---+---'   .-'        .'     |
     \      '-.        '-.    |    .-'        .-'      /
      \        '---.      '---+---'      .---'        /
       '.          '----.     |     .----'          .'
         '---.          '-----+-----'          .---'
             '----.           |           .----'
                  '-----------------------'

DROSTE-MEMORY // RIGID FRACTAL RADIAL LAYOUT
Local Graph Engine v1.0-Alpha-Sharded

Commands
  droste index <path> [--reset]
  droste status
  droste zoom <symbol_name>
  droste context [query] --budget 1500

Fast path: droste context hub_core --budget 1000 | clip

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Why it's different

Most "code context" tools rank by keyword (ctags / ripgrep / repo-maps) or by embedding cosine (vector-RAG). Both can only return what resembles your query. A caller that shares no tokens — or a database function in a different language — is invisible to them, yet it's exactly what you need to understand or change the code.

Droste's edge is the causal graph:

• Causal wormholes. Real syntax_dependency edges (calls, imports, inheritance) in both directions — Droste hands the caller and callees, ordered, within a token budget.
• Cross-language bridges. The part nobody else does well: Droste links across languages — app code to SQL functions/tables (.rpc('x'), .from('table')), to edge functions, and same-name handlers between any two languages. Your Dart/TS/Python frontend and your database stop being two separate worlds on the map.
• A map you actually want to look at. The fractal galaxy isn't a gimmick — it's how you see coupling, risk hotspots, and the blast radius of a change.
• Zero-config and local. No cloud, no account, no API key. fastembed (ONNX, no torch) gives real semantics; a deterministic fallback keeps it runnable anywhere.

Polyglot: Python (AST) + tree-sitter for Dart, TypeScript/JavaScript, Go, Rust, Java, C#, C/C++, Kotlin, Swift, Ruby, PHP, SQL — symbols and edges.

Honest scope: the measured advantage is structural / causal retrieval. On pure semantic "concept" queries it's competitive with a vector baseline, not a leap. Cross-language bridges are strongest where the target is actually defined in the indexed repo (e.g. SQL schema in your migrations).

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Benchmarks

Self-supervised eval (gold = the true caller/callee set from the AST), equal retrieval breadth k, real embeddings, across Python + Dart repos (eval/comparative_eval.py):

structural retrieval	Droste	vector-RAG core	lexical core
neighbour-recall	0.94	0.18	0.42
nDCG@k	0.65	0.10	0.29

…plus hundreds of true causal neighbours that both baselines structurally miss. This is a retrieval-method comparison (the cores of vector-RAG and lexical search), not a head-to-head against the finished products that wrap them.

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How it works

• Causal graph. Each definition is parsed (Python ast; tree-sitter for the rest) into the names it calls / imports / inherits, becoming first-class syntax_dependency edges. Cross-language edges add DB calls (.rpc, .from, .functions.invoke) and string-literal name matches across languages.
• Hybrid seed. A query is matched by a normalized blend of lexical score and semantic cosine (fastembed bge-small-en-v1.5, 384-dim), then the graph expands the seed bidirectionally (callees and callers).
• Token packer. Results fit a budget with LOD-demotion (full to contract to skeleton) and a hard guardrail that never cuts a line of code mid-token.
• Sharded persistence. One shard per file under .droste/, blake2b dirty-tracking so a re-index rewrites only what changed; atomic writes + meta written last, so it is crash-safe and self-heals on the next run.

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Use it as an MCP server

Droste is a drop-in MCP server — an agent calls it as primary code memory instead of blind file reads.

{
  "mcpServers": {
    "droste": { "command": "python", "args": ["/abs/path/to/droste-memory/server.py"] }
  }
}

Key tools: droste_index_project, droste_get_context, droste_status.

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Development

pip install -e ".[dev]"
pytest                            # deterministic regression suite (tests/)
python eval/comparative_eval.py   # retrieval benchmark vs lexical & vector cores

tests/ = invariants + concurrency (round-trip, dirty-oracle, packer guardrail, cross-process shard race). eval/ = performance/quality benchmarks.

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Status

v1.0.0a0 (alpha). Engine, polyglot + cross-language graph, CLI, fractal visualizer and MCP server are working and tested. Packaging/distribution are maturing — issues and PRs welcome (see CONTRIBUTING.md).

License

MIT — see LICENSE.