Areev 1.0.0

The first release under the Areev name — the complete engine formerly
published as DejaDB 1.2.0, plus the governed-agents program (the areev run
runtime, agent-grade capture, the ecosystem adapters, and the enterprise
plane), renamed on every surface.

The memory engine

• Immutable, content-addressed grains in the .mg format — 12 grain
  types, canonical serialization (NFC, sorted keys, omit-defaults), SHA-256
  content addressing. Every edit is a supersession, every removal a
  tombstone or crypto-erasure; nothing ever rewrites a stored blob.
• One memory = one isolation unit — a single file on the embedded Turso
  backend, a schema on the PostgreSQL backend (feature = "postgres",
  advisory-locked writers, pgvector) — the unit of erasure, sync,
  portability, and write parallelism. Files are self-describing: saved
  queries, templates, and index declarations travel with the file.
• Hybrid recall in microseconds — dictionary-encoded triples, an owned
  BM25 inverted index, optional vector recall via a pluggable embedder
  (--embed-cmd), graph/time reads (related, entity-at,
  step-actions), heads/forks with explicit merges, bundles, encrypted
  incremental sync, and CAS blob storage (encrypted under an HKDF-derived
  subkey when the memory is).
• CAL — the Context Assembly Language — lexer/parser/executor,
  ASSEMBLE with facade mounts for cross-memory queries, and budget-aware
  SML/TOON/Markdown/JSON rendering for model-ready context.

Governance

• Authorization in the file (CAL 1.3): grants ride as mg:permits
  Facts; destruction (FORGET <hash>, FORGET SUBJECT, PURGE OLDER THAN) is authorization-gated with mandatory BECAUSE and a Tier-2 audit
  Observation on every execution; REPORT SUBJECT shares one selector with
  erasure so a DSAR discloses exactly what an erasure removes.
• GDPR compliance pack — docs/gdpr.md article→
  capability map, DSAR subject-report on every surface, audit export,
  declarative retention:<ns> policies, and erasure that names its
  subject by fingerprint, never by identity.

Areev Loop — governed self-improvement

• Substrate-agnostic engine: 13 deterministic analyzers, four gates, a
  recommendation lifecycle with pinned evalsets, the DISCOVER→GROUND→VERIFY
  LLM verifier, outcome measurement across horizons, and out-of-box LLM
  backends (OpenAI-compatible / Anthropic / Ollama). Trajectory capture,
  analyze_only replay against the immutable past, and areev corpus
  export with erasure-aware provenance.

areev run — the governed runtime

• A pure sans-IO scheduler (areev-run-core: step(env, state, events) → (commands, state), frozen condition grammar, plan validation, RUN-Ennn
  errors, no clock/rand/IO in its dependency tree — CI-enforced) under a
  journaling driver (areev-run): intent-before-effect journal grains,
  checkpoints, crash-safe resume with same-key redelivery, HITL respond
  with separation of duties, budgets, cancel, and journal-consistent
  verify.

Surfaces

• areev — the CLI (~29 verbs), including migrate importers from
  other memory systems, hub (the areevd sync daemon), ui (the embedded
  web console: memory browser, interactive graph, loop review queue, runs
  tab), and hook claude-code session capture.
• MCP — 23 tools over newline-delimited JSON-RPC 2.0 on stdio,
  protocol rev 2025-06-18.
• Bindings — Python (pip install areev, abi3, sync + async) and
  Node (npm install areev, napi native addon), same facade, scalars in /
  JSON out.
• Adapters — areev-langgraph (checkpointer, store, memory saver) and
  areev-crewai (storage backend, knowledge source, audit listener) on
  PyPI.

Benchmarks

• Reproducible latency, honesty, and LoCoMo-accuracy harnesses in
  crates/areev-bench (RESULTS.md has the numbers), with perf gates
  (bench, voice_loop) run as examples.