Murilo PereiraandClaude Sonnet 5
9605bcb5b7
Consolidates facts already documented in SECURITY.md, docs/install.md, and docs/local-embeddings.md into the shape a security/legal reviewer typically needs before approving a dev tool: a single data-flow page, an SSO/OIDC summary, and an offline-install path. No behavior change. Adds a fallback security-contact path for reporters who can't use GitHub's private-advisory flow. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Air-gapped / offline install
Corporate IT-security review commonly asks for a documented offline installation path before approving a dev tool. ai-memory's build and runtime are already close to air-gap-friendly by design; this page collects what already exists elsewhere in the docs into one answer, rather than introducing a new mode.
Build: no network required
The build is self-contained (CONTRIBUTING.md, "Dev setup"): SQLite is
bundled via rusqlite's bundled feature and libgit2 is vendored via
git2's vendored-libgit2 feature. No system libraries beyond a standard C
toolchain are required, and nothing is fetched from the network during
cargo build. Building from a vendored/mirrored crates.io cache (standard
practice for air-gapped Rust builds, e.g. cargo vendor) works the same way.
If you'd rather not build at all, tagged GitHub Releases publish prebuilt
binaries for Linux (linux/amd64, linux/arm64), macOS
(ai-memory-macos-aarch64.tar.gz, ai-memory-macos-x86_64.tar.gz), and
Windows (ai-memory-windows-x86_64.zip), each with a SHA-256 checksum
(SECURITY.md, "Published executable integrity") — download once on a
network-connected machine, verify the checksum, and transfer the artifact
into the air-gapped environment.
Runtime: no network calls unless you configure one
Covered in full in DATA_HANDLING.md, summarized
here: ai-memory has no telemetry, analytics, or phone-home behavior. The
server, CLI, and lifecycle hooks run entirely against the local data
directory. The only things that make a network call are things you
explicitly configure:
- A cloud LLM/embedding provider, if you want AI-assisted consolidation,
search, or provider-based embeddings. Skip this and use the
localembedding provider (below) to stay fully offline. capture_assistantandAI_MEMORY_RERANKER=llm, both off by default — seeDATA_HANDLING.md.
Local embeddings without a network dependency
For semantic search without a cloud API key or a self-hosted inference
server, set the embedding provider to local
(docs/local-embeddings.md). On first use it fetches
three small model files (~87 MB total, Apache-2.0 licensed) into
<data_dir>/models/all-MiniLM-L6-v2/, each checked against a sha256 pinned
in source so a tampered or drifted file fails loudly rather than silently.
For a fully offline install, download those three files
(model.safetensors, tokenizer.json, config.json) from
huggingface.co/sentence-transformers/all-MiniLM-L6-v2 on a
network-connected machine and place them in that directory before first
start; the loader verifies the same checksums and never touches the network
(docs/local-embeddings.md, "Offline installs"). After that, embeddings run
entirely on-host via the bundled candle (pure Rust) runtime — no ONNX
runtime or other native library to source separately.
What still needs a decision from you
- git remote sync, if you use it to push the wiki repository somewhere,
is your own channel to secure (
SECURITY.md, "Remote sync security" — out of scope for ai-memory itself). - Update/patch delivery in an air-gapped environment is manual: pull a new release and checksum on a connected machine, then transfer it in, the same as the initial install.
Related documents
DATA_HANDLING.md— what leaves the host and when.docs/local-embeddings.md— embedding provider choices, including the offline path in detail.docs/deploy.md— Docker/homelab deployment pattern.SECURITY.md— release integrity and threat model.