MGI-Mind

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Latest release: v2.5.0 · CHANGELOG · Discussions · Issues · Contributing

Local long-term memory for AI assistants. One Rust binary, a local Qdrant
vector database, local ONNX models. Speaks MCP, so Claude Code and other
assistants read and write memory on their own. Also a normal CLI.

Store a fact in plain words, recall it with a query that shares none of them. Semantic search matches intent. Your assistant runs the same mind_search on its own over MCP. Regenerate with vhs docs/demo.tape.

The same exchange inside an assistant:

You:  what was the deploy server again?

Assistant (calls mind_search "deploy server"):
  -> "Deploy server is 10.0.0.5:8080, SSH as deploy@, key in vault"  (source: infra.md)

You:  right, thanks

Nothing leaves the box. Embeddings, search, reranking and the vault are
all local. No cloud account, no API key, no telemetry.

Contents

• What it is
  • The validity model
• Why bother
• Quick start
• Using it
• How it works
• Command reference
• Configuration
• Languages and the reranker
• Changing the embedding model
• Troubleshooting
• Security
• Status and audit
• Project layout
• License

What it is

MGI-Mind sits between you and your assistant. The assistant writes short
notes ("memories") and facts as a conversation goes, and pulls the
relevant ones back when they matter.

Retrieval, not just storage:

• Hybrid search. Each memory is stored as two vectors: a dense vector
  (multilingual-e5-base, 768 dims) for meaning, and a sparse term-frequency
  vector (TF-IDF, BM25-style) for exact words. A query runs both arms and
  fuses them with Reciprocal Rank Fusion. "The server box" finds "deploy
  host" through the dense arm; fossilize_replay finds the one note that
  contains that exact token through the sparse arm.
• Cross-encoder reranking. Fused top candidates are re-scored by
  bge-reranker-base, which reads the query and each passage together and
  is more accurate than comparing vectors. On by default and English-tuned;
  see Languages and the reranker for the
  trade-off on other languages.
• One warm process. mgimind mcp is the MCP server itself: it runs
  for the whole session, models load once and stay warm in memory, so a
  lookup costs milliseconds instead of reloading on every call.

Around retrieval there's a knowledge graph for structured facts, per-agent
session logs for cross-session continuity, and an encrypted terminal-only
vault for secrets.

The validity model

Hybrid search is table stakes; most memory tools have some version of it.
The part that is harder to find is what keeps the store from ossifying as it
fills with old, contradictory, or self-reinforcing beliefs. These run mostly
on their own:

• Duel rule. When a new fact contradicts an existing one on the same
  subject and predicate, the second write resolves against the first instead
  of piling up a second "truth". An entrenched fact (many dependants,
  confirmations, age) holds; a strong fresh fact flips it and dampens the
  loser to a hidden stale status; a borderline one is marked contested or
  diverted to quarantine. Nothing is deleted, so the audit log keeps the
  loser. Automatic, inside a normal mind_fact add.
• Doubt window. An entrenched fact has to keep re-justifying itself. Every
  fact query measures how far the current context has drifted from the context
  the fact was written in; after five consecutive drifted retrievals the fact
  stops outranking facts that still hold up, and any single in-context
  retrieval resets the count. Nothing is hidden or deleted. Drift is measured
  on vectors with the corpus mean removed, because raw cosine cannot separate
  topics in an anisotropic embedding space (see mgimind calibrate below).
  The current context is the running process's own recent queries, so this
  applies inside a warm mgimind mcp or serve-http session; a one-shot CLI
  command has no context yet and no fact is penalised for that. This ships in
  shadow: drift is measured and recorded, and no fact changes, until you set
  doubt_drift_threshold from your own store's numbers and switch
  doubt_drift_mode to "enforce". A background pass separately
  re-tests entrenched facts that have gone quiet, under hard guarantees: never
  during a tool call, a per-tick cap, and a load-aware cadence.
• Inheritance discount. Facts carried into a session from memory count at
  half weight and cannot co-confirm each other. One stale source agreeing
  with itself is not two confirmations, so memory can't self-reinforce into
  false certainty.
• Bi-temporal facts. A predicate can be registered (mind_predicate) as
  single-valued, temporal-single (one current value, but the previous ones
  are kept and queryable by date via mind_history), or multi-valued. A
  superseded value is hidden from default ranking, not erased.
• Typed outcome signals. mind_outcome records that a remembered fix
  actually worked (test_passed, code_compiled, user_confirmed,
  cited_by). A real success raises a memory's weight and marks a procedure
  verified; a failure pulls the weight down rather than being ignored.

These are research-shaped mechanisms, and their tuning constants are still
being calibrated (the TODO(phase-4-calibration) markers in duel.rs /
doubt.rs are honest). The shape is the point: a memory that argues with
itself and demotes what stops holding up, instead of accreting forever.

mgimind calibrate measures whether that shape behaves as intended. It runs a
corpus of realistic conflict situations through the live duel formulas and
reports how many land on the outcome a person would expect: a fresh unsupported
claim cannot overturn an entrenched belief, a CI signal can, repetition alone
coexists rather than overwriting. Today 14 of 15 scenarios match intent; the one
that does not is printed with its reason, not hidden, and the suite runs in CI.
It also reports the doubt window's drift distribution, and that report exists
because of what it found. The drift threshold was 0.4, picked in 384-dim MiniLM
space and left in place when the default model became 768-dim multilingual-e5.
Measured on a live store, 400 memories and all 79800 pairs, raw cosine ran from
0.666 to 0.972: the threshold could not fire once. The corpus mean vector had
norm 0.884, so almost all of that cosine was a component every embedding shares.
Centering fixes the measurement, and calibrate proposes a threshold from the
drift your own store produces rather than from a constant that travelled between
embedding spaces.

This measures the shape of the validity model, separate from retrieval recall
(R@k, see BENCHMARKS.md). The two are different numbers and
never belong in the same table.

Why bother

An assistant without memory asks for the same context every session and
can't build on yesterday's work. The usual workaround is you keeping
notes, tags, and folders, which the assistant still can't read by
meaning.

The thing MGI-Mind does that Obsidian and Notion don't: the system
decides what to write down, you don't. The MCP server reads what the
assistant is doing in real time and routes facts, decisions, and fixes
into the store through a relevance gate. You don't file, you don't tag,
you don't decide what's worth saving. Low-signal candidates land in a
quarantine layer (recoverable on re-assertion) instead of polluting
retrieval.

How it compares to the obvious alternatives:

• Plain notes (Obsidian, Notion). Strong as your personal notebook,
  but the assistant can't search them by meaning, and every keystroke is
  equally important to a folder of .md files.
• Bare vector database. Semantic search, but no exact-term matching,
  no reranking, no dedup, no sessions, no facts, no secrets handling, no
  relevance gate, no procedural memory. You assemble all of that.
• Hosted memory API. Your data on someone else's servers. Closes the
  box on inspection, dedup behaviour, and what the relevance gate is
  actually doing.

MGI-Mind is the assembled local version: hybrid + reranked retrieval, the
relevance gate, dedup, facts, sessions, procedural memory ("error → fix"
playbooks), and a terminal-only vault, all behind one binary you run
yourself.

A note on evaluating it. BENCHMARKS.md reports retrieval
recall (R@k on LongMemEval-S, zero-API, no LLM judge): given a question,
did the gold session land in the top-k. On the default CPU config that is
R@5 ≈ 98%. This is not the same metric as the QA-accuracy numbers other
memory tools publish (they run an answerer and a judge over their retrieval),
so do not put it in the same table. It measures whether the evidence was
retrievable, not whether an LLM then answered correctly. mgi-mind has no
answering step, so retrieval recall is the number it actually owns.

The honest gap: the validity model (duel rule, doubt window, supersession)
is the differentiator, and retrieval recall does not test it. Two things test
it instead. mgimind calibrate checks behavioral shape against intent
(14/15 today, run it yourself); it proves the mechanism does what a reader
expects, not that the constants are tuned against real data. The external
belief-revision benchmark is STALE, and there only a preliminary partial run
exists (N=155, ~32%, raw verdicts under benchmark/results/), produced by the
harness on a branch rather than the scaffold on main, on a reduced haystack,
possibly while the duel rule was still broken. See
BENCHMARKS.md for the full caveats. Treat the validity model as
a designed mechanism whose shape is verified and whose constants are not yet
tuned, not a proven win on external data.

Quick start

Docker (the whole brain in one command)

The image bakes the binary, a bundled Qdrant, the ONNX runtime, and the models,
so it runs offline with nothing to install:

docker run -p 8765:8765 madgodinc/mgi-mind

It prints a bearer token on startup. Point the Python client
or any agent at http://localhost:8765 with that token. Persist memory across
restarts with a volume, and pin a stable token:

docker run -p 8765:8765 -e MGIMIND_TOKEN=your-token \
  -v mgimind-data:/data madgodinc/mgi-mind

Plug it into an agent

Give an agent two tools, recall and remember, and its memory survives the
process. With the server running and the Python client
installed:

from mgimind import Memory

mem = Memory()                         # MGIMIND_URL / MGIMIND_TOKEN from the env
mem.add("Prod DB is Postgres 16 on db-prod:5432")
for hit in mem.search("prod database"):  # structured results: id, score, author
    print(hit["score"], hit["content"])

That wrapping is the whole integration. Copy-paste examples for LangGraph,
Pydantic AI, the OpenAI Agents SDK, and raw HTTP (any language) are in
docs/integrations. MCP and stdio still work for editor
clients; the HTTP surface is the door for agent frameworks.

Native install

One command. The installer drops the binary on PATH and runs init +
doctor --fix (which pulls Qdrant, ONNX Runtime, and the models).

Linux / macOS:

curl -fsSL https://raw.githubusercontent.com/madgodinc/mgi-mind/main/install.sh | sh

Windows (PowerShell):

irm https://raw.githubusercontent.com/madgodinc/mgi-mind/main/install.ps1 | iex

When it finishes, the printed command wires the server into Claude Code:

claude mcp add mgimind -- /home/you/.local/bin/mgimind mcp

mgimind mcp IS the MCP server; it runs for the whole session with the
models warm and brings up the bundled Qdrant on first use. Point your
assistant at AI_INSTRUCTIONS.md once so it knows
the protocol (log a session, search before answering, use the vault for
secrets).

doctor --fix downloads the models into ~/mgimind/: the embedder
(multilingual-e5-base, quantized ONNX, ~270 MB) and the reranker
(bge-reranker-base, quantized ONNX, ~280 MB). ONNX Runtime and the Qdrant
binary land next to the mgimind binary itself, so a doctor that reports
a missing library is telling you about <install dir>/, not ~/mgimind/.

Installer flags

• INSTALL_DIR=/opt/mgimind curl ... | sh: install somewhere other than ~/.local/bin.
• MGIMIND_TAG=v2.5.0 curl ... | sh: pin a specific release instead of latest.
• SKIP_DOCTOR=1 curl ... | sh: just drop the binary; run init + doctor --fix yourself later.

Manual install (no installer)

If you'd rather not pipe a script to a shell, grab the release tarball
for your OS from Releases,
put mgimind on your PATH, then:

mgimind init
mgimind doctor --fix
claude mcp add mgimind -- /absolute/path/to/mgimind mcp

Try it from the CLI (the same binary works without an assistant):

mgimind create work
mgimind add work "Deploy server is 10.0.0.5:8080, SSH as deploy@"
mgimind search "how do I reach the deploy box"

Per-OS notes

• Linux x86_64, macOS arm64 and x86_64, Windows x86_64: prebuilt
  binaries in every release. The installer picks the right one.
• macOS PATH: zsh does not include ~/.local/bin, so the installer
  appends the export PATH line to your ~/.zshrc and tells you it did.
  Open a new terminal before typing mgimind. Set MGIMIND_NO_PROFILE=1
  to keep the installer out of your dotfiles and add the line yourself.
• macOS Intel and ONNX Runtime: 1.24 dropped osx-x86_64, so Intel
  Macs get ONNX Runtime 1.23.0 while everything else gets 1.24.2. The
  binary requests C API 23, which both runtimes serve.
• macOS first-run quarantine: the installer clears it, but a binary
  downloaded through a browser needs xattr -d com.apple.quarantine /path/to/mgimind. Releases are ad-hoc signed, not notarized; on Sequoia
  and later the right-click → Open trick no longer works for command-line
  binaries, so use xattr, or approve it once under System Settings →
  Privacy & Security.
• Windows: SmartScreen may warn on the unsigned mgimind.exe
  ("Windows protected your PC"); choose More info → Run anyway.
  Antivirus can also quarantine the binary or the models it downloads; if
  mgimind doctor reports a file as downloaded but missing, allow
  mgimind.exe and the %USERPROFILE%\mgimind folder in your AV, then
  re-run mgimind doctor --fix. Code signing to remove the SmartScreen
  prompt is on the roadmap.

Build from source

Rust toolchain (rustup); no other dependencies.

git clone https://github.com/madgodinc/mgi-mind.git
cd mgi-mind
cargo build --release                  # binary: target/release/mgimind

Then run target/release/mgimind init && target/release/mgimind doctor --fix
and wire it in with
claude mcp add mgimind -- /absolute/path/to/target/release/mgimind mcp.

Using it

By hand (CLI):

mgimind add notes "Mom's birthday is March 14, she likes peonies" --source personal
mgimind search "when is my mother's birthday"
#  1. [notes] (score: 0.94) Mom's birthday is March 14, she likes peonies
#     source: personal

mgimind fact add "user" "prefers" "Rust"
mgimind fact query "user"
#   user -> prefers -> Rust

mgimind history --limit 3              # the three most recent memories
mgimind stats                          # counts per library, facts, sessions

Duel rule on contradictions. When two facts contradict and the
predicate is Single or TemporalSingle, the second add resolves
against the first instead of piling up:

# Tell the system this predicate has one current value at a time:
echo '{"jsonrpc":"2.0","id":1,"method":"tools/call",
  "params":{"name":"mind_predicate","arguments":
    {"action":"register","predicate":"lives_in",
     "cardinality":"TemporalSingle"}}}' \
  | mgimind mcp

mgimind fact add "Alice" "lives_in" "Prague"
mgimind fact add "Alice" "lives_in" "Dublin"
mgimind fact query "Alice"
#   Alice -> lives_in -> Dublin
# (Prague is preserved as history, queryable via mind_history / audit log
#  but hidden from default ranking.)

If you migrated from a pre-v1.7 install where the duel rule wasn't
firing at the read path, run mgimind migrate-v14 redo-duels --apply
once to collapse legacy contradictions to canonical answers. The
walk is idempotent and dry-run by default.

Through your assistant (MCP). Once connected, you just talk:

You:  remember that the staging DB password is in the vault under "staging-db"
Assistant: (mind_add) saved. The secret itself stays in your terminal vault, not here.

You:  what database are we using on staging?
Assistant: (mind_search "staging database") Postgres 16, host db-staging.internal:5432

Search returns results in tiers so the assistant spends tokens carefully:
--tier 1 is a ~100-character snippet, --tier 2 (default) is ~500,
--tier 3 is the full text.

Seeing the store

mgimind brain renders the memory as a graph: cores for memories, facts,
and regions, wired by the links between them, pulsing as the store changes.
It binds to loopback only and needs the token printed alongside its URL.

Treat it as a control surface. An unrestricted viewer also serves
DELETE /api/memories/:id, PATCH /api/node/:id and quarantine promotion,
so whatever reaches that port with that token can change the store. For a
viewer that cannot write, start it confined to an allowlist:

mgimind viewer --libraries work,personal

That fail-closes every mutating route with a 403, along with the routes that
span all libraries (graph, audit, pulse, the ingest feed). mgimind brain
takes no flags, so it is always the unrestricted form.

How it works

  your AI assistant
        |  MCP (JSON-RPC over stdio)
        v
  mgimind mcp  (one Rust process: MCP server + embedder, models stay warm)
        |  starts on first use
        v
  Qdrant (local, loopback only)
        |
        one "memories" collection, two vectors per point:
        dense (e5, meaning) + sparse (TF-IDF, exact terms)

Storage. All memories live in one Qdrant collection. A library field
on each point separates namespaces (work, personal, a project), and a
query can filter to one library or search across all. A point's ID is a
UUIDv5 of library + content, so adding the same text twice overwrites
the same point, with no duplicates and no race. A created_at datetime index
lets history return the newest N directly without scanning.

Embeddings. Text is embedded locally through ONNX Runtime. Default is
multilingual-e5-base (768 dimensions), strong on English and handles
mixed languages. The embedder is model-aware: pooling (mean or CLS),
token_type_ids input, query/passage prefixes are all config, so
switching models doesn't need a code change. Inputs cap at 512 tokens;
add splits long text into chunks so nothing past the cap silently
disappears.

Search. The query is embedded once. Qdrant runs a dense nearest-
neighbor search and a sparse search in a single Query API call and fuses
them with RRF. If reranking is on, the top rerank_top_k candidates are
re-scored by the cross-encoder and reordered. A library filter, when
given, applies to both arms.

Safety. Downloads check against pinned SHA-256 hashes (fail-closed).
Qdrant binds to loopback only and can require an API key. The vault is
terminal-only, and the master password and decrypted secrets never travel
over the MCP channel. File writes are atomic (temp file, fsync, rename,
fsync the directory), so a crash leaves the old file or the new one,
never a corrupt one.

Command reference

Memory

Libraries

Knowledge graph

Skills

A procedure answers "this broke, what fixed it last time". A skill answers "I am
about to do X, is there a house way to do X". Same storage and the same outcome
signals, matched at a different moment: against the task, before the work.
A context render carries the catalogue (names and triggers), so an assistant
knows what exists without searching, and pulls the body only for the skill that
applies.

Sessions

Vault (terminal only)

Service and data

Configuration

Config lives at ~/mgimind/config.json. Fields that affect retrieval:

Languages and the reranker

The default stack is tuned for English, because that's where the assistant
itself reasons best and where the models are strongest. It's the
recommended setup for an English-first project.

A few honest details:

• The embedder (multilingual-e5-base) is genuinely multilingual. An
  English query finds a Russian note and vice versa, and search alone
  works well across languages.
• The default reranker (bge-reranker-base) is English-tuned. It improves
  English ranking, but it lowers ranking quality on Russian (and
  other non-English languages). This is the one place the defaults favor
  English.
• If your content is mostly Russian (or another non-English language):
  set rerank_enabled = false. Hybrid dense+sparse search on its own
  ranks those languages well; the English-tuned reranker is what hurts
  them. Or swap in a stronger multilingual reranker.
• Reranking costs cross-encoder inference per query: roughly one to two
  seconds for 20 candidates on a CPU-only box. Lower rerank_top_k or
  turn reranking off for snappier search.

Changing the embedding model

Switching models usually changes the vector dimension, so existing
memories must be re-embedded:

1. Back up first: mgimind backup ~/mgi-backup.tar.gz.
2. Set model_name, vector_size, pooling, uses_token_type_ids, and
   the prefixes in config.json for the new model.
3. mgimind doctor --fix to download it. Only the bundled defaults are
   pinned; a custom model downloads with an "integrity not verified"
   warning, so pin its SHA-256 in integrity.rs if you want strict
   verification.
4. mgimind migrate to re-embed everything from the stored text under
   the new model.

Troubleshooting

• "Model not found ... run doctor --fix": the model is not in
  ~/mgimind/models/. Run mgimind doctor --fix.
• "invalid expand shape" / inference errors: usually an input far
  over 512 tokens. add chunks automatically; if you call the library
  directly, chunk first.
• Searches are slow: that's the reranker on CPU. Lower
  rerank_top_k, or set rerank_enabled = false. Models stay warm for
  the life of the mgimind mcp process, so only the first lookup of a
  session pays the load cost.
• A tool fails right after install: run mgimind doctor (the
  assistant can call mind_doctor); it reports exactly what's missing
  (Qdrant not running, a model not downloaded, ONNX Runtime absent, a
  file quarantined by AV) and --fix downloads what it can.
• Dimension mismatch warning: a collection's vectors don't match
  vector_size, usually after a model change. Re-embed with
  mgimind migrate.
• Russian results feel off: set rerank_enabled = false (see the
  language note above).
• A VPN, tunnel or proxy breaks while mgimind is running: run
  mgimind doctor and read the network footprint. Every mgimind port is
  loopback-bound, and mgimind changes no route, DNS or firewall setting, so
  it cannot pull traffic out of a tunnel. Look at what starts alongside it:
  Docker Desktop and WSL2 add virtual adapters of their own, and TUN clients
  with strict routing can drop the tunnel when an adapter appears or
  disappears.

Security

• Downloads verify against pinned SHA-256 (fail-closed) for ONNX Runtime
  (linux-x64), Qdrant, and the default models (e5 and the reranker).
  Other platforms and custom models warn instead of trusting blindly.
• Qdrant binds to 127.0.0.1 only and supports an API key.
• The vault is AES-256-GCM with an Argon2id-derived key (parameters
  pinned so a library upgrade can't lock you out). Terminal-only, so the
  master password and decrypted secrets never travel over the MCP
  channel. The mind_vault_* MCP tools return terminal instructions,
  never the secret value.
• File writes are atomic and directory-fsynced, so a crash leaves the old
  file or the new one, never a corrupt one.

Network footprint

Every socket mgi-mind opens is bound to loopback:

serve-http --host is the one way to bind another interface (a Docker -p
mapping needs it), and a non-loopback host is refused unless you also pass
--agent-token.

mgimind installs no driver, adds no network adapter, and changes no route,
DNS or firewall setting. mgimind doctor prints this footprint with a live
probe of every port, so the claim is something you can check rather than
trust.

Status and audit

Current version: 2.5.0 (semver-stable since v1.0.0). The 0.x line built
the foundation: the audit log and ephemeral viewer (0.10), the quarantine
layer and best-effort retrieval policy (0.11), the viewer wave (0.12),
session liveness (0.13), and procedural memory (0.14, benchmarked on
LongMemEval-S plus a 227-pair error→fix dataset from 20 public repos). The
1.x line added the validity model: the duel rule, the doubt window and its
background re-test, bi-temporal fact supersession, the confidence score, and
typed outcome signals (mind_outcome), plus install-mode CPU/GPU profiles.

The MCP surface is additive-only until a 2.0 bump: 29 live tools plus 15
deprecated aliases kept for compatibility. Nothing is renamed or removed inside
2.x; new verbs (most recently mind_skill) may be added. The other 1.0 contracts
are the asymmetric "Qdrant now → md says" reconcile diff and the
MGIMIND_MODEL_VARIANT={cpu|gpu|auto} switch.

The project went through a code audit; AUDIT_STATUS.md
accounts for every issue one by one with where it was fixed or why it was
deferred. CHANGELOG.md has the per-release history for the 1.x
line plus the v1.7 candidate section (pre-1.0 releases are in
CHANGELOG-ARCHIVE.md), and
ROADMAP.md names what is committed for the next releases and
which directions are still candidate at the v3.0 horizon.

Project layout

src/
  cli.rs         command dispatch and output rendering
  storage.rs     Qdrant: single collection, hybrid search, history, stats, migrate, chunking
  embedder.rs    ONNX embedding (model-aware pooling, prefixes, 512-token cap)
  reranker.rs    cross-encoder reranking
  knowledge.rs   knowledge-graph facts + cardinality + supersession
  duel.rs        duel rule: resolve contradicting facts (flip / contested / quarantine)
  doubt.rs       doubt window + background active re-test of entrenched facts
  activity.rs    recent-query buffer, corpus mean (centering), origin-context store
  confidence.rs  per-fact confidence score (dependants / confirmations / signals)
  outcome.rs     typed external signals (test_passed, code_compiled, ...) into weight
  procedure.rs   procedural memory: learn / recall / outcome
  ingest.rs      auto-extract & ingest candidates
  relevance.rs   relevance gate (length, blacklists, decision markers, token novelty)
  retrieval_policy.rs  search-before-answer classifier
  provenance.rs  cited external snippets with mandatory source
  extractor.rs   optional local LLM extraction (off by default)
  consolidate.rs merge duplicates, report cold entries
  md_reconcile.rs md import as reconcile with "md wins"
  audit.rs       append-only audit log for every storage mutation
  http_api.rs    loopback HTTP surface for multi-agent access
  viewer.rs      ephemeral local HTTP viewer (axum, static frontend baked in)
  pulse.rs       live graph pulses for the viewer
  session.rs     per-agent session files
  secrets.rs     secret-scrub on the write path
  vault.rs       encrypted secret vault (terminal only)
  mcp.rs         MCP server over stdio (hand-rolled JSON-RPC; warm in-process models)
  config.rs      configuration + install-mode profiles
  integrity.rs   pinned SHA-256 hashes for downloads
  util.rs        atomic writes, verified downloads
  (plus migrate, install-mode, access/decay, and bench harnesses)
tests/
  cli_integration.rs   black-box tests against a real Qdrant (CLI + MCP round-trip)
  http_integration.rs  the multi-agent HTTP surface end to end

License

Apache-2.0. See LICENSE.