Memory Genome Engine v0.1.0

Memory Genome Engine v0.1.0

Memory Genome Engine is a local-first structured memory engine for AI agents. This first public release provides a Rust core, binary runtime storage, a terminal product surface, a versioned stdio MCP adapter, and thin Python and TypeScript SDKs.

Highlights

• remember -> L1 Hot RAM + durable hot log -> checkpoint/seal -> sealed pages -> recall -> ContextPacket
• Structured MarkerGenome metadata with focused, broad, and full-scope recall
• RAM-first hot recall, immutable sealed pages, metadata pruning, and exact or optional Binary Fuse candidate indexes
• Balanced, fast, and safe durability modes with snapshot/replay recovery and single-writer store locking
• Optional authenticated encryption for hot records, snapshots, and sealed page payloads
• mge CLI, interactive TUI/setup flow, diagnostics, validation, index rebuild, and Markdown migration/export
• Standard MCP stdio lifecycle plus stable versioned tool schemas
• Thin local Python and TypeScript wrappers over the Rust CLI

Packages

• mge-windows-x86_64.zip
• mge-linux-x86_64.tar.gz
• mge-macos-x86_64.tar.gz
• mge-macos-aarch64.tar.gz
• SHA256SUMS

Each archive contains only the mge and mge-mcp-server product binaries, license/notice files, and canonical user documentation. Development benchmark binaries are intentionally excluded.

Verification

• Rust 1.95 MSRV check
• Workspace formatting, tests, strict Clippy, and rustdoc warnings
• Windows, Ubuntu, and macOS hosted CI product smokes
• Downloaded Windows and Linux archive checksum, plain/encrypted lifecycle, and MCP smokes
• cargo audit: no known vulnerabilities in 179 scanned production dependencies

The published engineering baseline on an Intel Core i7-9750H / Windows 10 x64 reports Focused Hit@5 and Recall@5 of 1.00, hot focused recall averaging 0.49 ms, repeated sealed focused recall averaging 0.28 ms, and cold store open plus focused recall averaging 2.39 ms. These are deterministic synthetic measurements, not competitor claims or end-to-end LLM answer benchmarks. See docs/RELEASE.md for the method and limitations.

Security and limitations

• Encrypted stores protect memory payloads at rest; marker dictionary, indexes, catalog summaries, file sizes, access patterns, process memory, ContextPacket output, and explicit Markdown exports remain plaintext by design.
• Passphrases are supplied through environment variables and remain session-scoped; there is no OS keychain integration yet.
• macOS x86_64 and arm64 archives are built and smoke-tested on GitHub-hosted macOS runners. Physical install verification on a user-owned Mac remains tracked in issue #2.
• Archives are not code-signed or notarized, and package-manager publication is not part of v0.1.0.
• The marker-based engine does not require a vector database. Embeddings are intentionally not part of this baseline.

Store formats remain compatible with the earlier preview; no migration is required for stores created by the current v0.1 implementation.

Contact

For commercial integration, support, collaboration, and partnership inquiries: