scope-recall

Hermes current-turn memory provider with permanent semantic recall, nightly workflow digest, SQLite truth storage, and a LanceDB vector companion

Give Hermes durable memory that can follow the same user across windows/chats while keeping local scratch context from bleeding into the wrong place.

Current-turn recall · Permanent shared memory · Nightly workflow digest · Local scratch scopes · SQLite truth · LanceDB companion · Hybrid retrieval

scope-recall is a Hermes local memory provider built for current-turn recall and permanent semantic memory. Durable user/project/ops/memory facts are shared across windows/chats for the same user + agent identity; raw general turn captures stay local to the current chat/thread/session.

Version 1.0.5 is the nightly digest release for the documented V1 interfaces, packaged as a public release candidate for broader field testing. It keeps the V1 compatibility contract in docs/stability.md while adding a profile-scoped daily conversation digest that can promote task workflows, tool-chain summaries, and important daily facts into SQLite truth without storing raw system/tool output.

It uses a two-layer design:

• SQLite truth store for durable local records and deterministic auditing
• LanceDB vector companion for semantic retrieval and hybrid ranking

This replaces the old lancepro naming, which was misleading because the earlier implementation was SQLite-only.

Design promises

• Truth stays inspectable: SQLite remains the authoritative store; vectors are rebuildable.
• Recall is current-turn scoped: retrieval is based on the active query, not stale queued context from the previous topic.
• Durable memory travels deliberately: user, memory, project, and ops facts can follow the same user + agent identity across windows/chats.
• Scratch context stays local: raw general turn captures stay inside the current chat/thread/session boundary.
• Operator actions fail closed: cross-scope export/dedupe/govern/repair paths require explicit maintenance mode.
• Install remains practical: hosted embeddings are used when configured, while deterministic local-hash keeps no-key bootstrap available.

---

Why scope-recall?

Most agent memory pain is not just "wrong memory was recalled". The bigger user-facing failure is often "the agent forgot everything when I opened a new window." scope-recall therefore separates durable facts from local scratch context:

• user preferences, project facts, ops notes, and explicitly stored memories follow the same user + agent identity across chats/windows
• raw/general turn captures remain local to the current chat/thread/session so one group's temporary chatter does not contaminate another group
• current-turn recall searches only for memories relevant to the active query, avoiding stale previous-turn injection
• the SQLite truth store remains auditable, and LanceDB is only a rebuildable semantic companion

scope-recall is built around a simple rule:

Recall the relevant durable memory for the current query, while keeping local scratch context inside the current runtime scope.

Without scoped durable recall

You: "For this memory-provider project, SQLite is the source of truth."

(later, in another window/chat)

Agent: "I don't have that context here." ❌

With scope-recall

You: "What did we decide for this Hermes memory provider?"

Agent: recalls the durable project memory from SQLite truth/LanceDB companion and answers from the relevant context. ✅

Without local scratch boundaries

Group A: "Temporary note: restart this group's test bot only."

(later, in Group B)

Agent: applies Group A's temporary note in Group B. ❌

scope-recall keeps that temporary general scratch row local while still sharing durable user/memory/project/ops facts.

What you get

Area	What scope-recall V1 provides
Current-turn recall	prefetch(query) retrieves against the active user query; queue_prefetch() is intentionally a no-op
Storage authority	SQLite is the durable truth; LanceDB is rebuildable companion state
Hybrid retrieval	SQLite lexical/FTS candidates + LanceDB semantic candidates + bounded prompt rendering
Entity/context layer	SQLite entity index, entity probe/related tools, compact query context, trust feedback
Nightly digest	Profile-scoped daily consolidation for durable facts, workflow summaries, and sanitized tool-chain evidence
Memory scope model	shared durable scope for user/project/ops/memory facts; local scope for general scratch captures
Built-in memory integration	Hermes curated USER.md / MEMORY.md are live-read, not mirrored into SQLite. In gateway contexts with an explicit user_id, curated-file recall is opt-in/allowlisted to avoid cross-user leakage from global profile files.
Governance	deterministic exact dedupe, conservative near-duplicate merge, filtering, metadata, decay review
Migration	local lancepro auto-migration; OpenClaw memory-lancedb-pro import is explicit
Offline bootstrap	deterministic local-hash fallback when hosted embeddings are unavailable

---

Optional companion: turn-closure-audit

scope-recall works as a standalone Hermes memory provider. You can install only this plugin and get scoped current-turn recall, SQLite truth storage, LanceDB companion retrieval, and local scratch isolation.

For stricter post-turn knowledge governance, pair it with turn-closure-audit.

The two plugins solve adjacent problems:

Plugin	Role
scope-recall	decides what memory should be recalled for the current turn
turn-closure-audit	audits a completed turn and writes redacted review candidates when important knowledge may not have been retained

This pairing is useful for long-lived Hermes agents where you want both scoped recall during a conversation and conservative review after the turn ends. It is optional, not a runtime dependency.

---

Quick start

Option A: Clone into a Hermes plugin directory

cd "$HERMES_HOME/plugins"
git clone https://github.com/410979729/scope-recall.git scope-recall
cd scope-recall
python -m pip install -e .

Then configure Hermes to use the provider name:

memory:
  provider: scope-recall

For a local smoke check after installation:

hermes memory status

Option B: Manual download / unpacked plugin install

Current Hermes plugin discovery expects an unpacked plugin directory. scope-recall V1 targets the current Hermes runtime line, which requires Python 3.11 or newer. If you download a release archive instead of cloning:

1. unpack it as $HERMES_HOME/plugins/scope-recall/
2. run python -m pip install -e "$HERMES_HOME/plugins/scope-recall"
3. set memory.provider: scope-recall
4. restart/reload the Hermes process that should use the provider
5. verify with hermes memory status

Important boundary:

• the wheel build is verified for packaging sanity and importability
• the primary Hermes install shape for V1 is still an unpacked local plugin directory
• do not read wheel build success as proof that Hermes can discover or install this plugin directly from a wheel alone

---

Configuration

The shipped config.json defaults to hybrid retrieval with a hosted OpenAI-compatible Gemini embedding path and a deterministic offline fallback.

Minimal default shape:

{
  "auto_recall": true,
  "auto_capture": true,
  "enable_tools": true,
  "maintenance_tools_enabled": false,
  "retrieval": {
    "mode": "hybrid",
    "lexical_weight": 0.45,
    "vector_weight": 0.55,
    "candidate_pool": 12
  },
  "vector": {
    "enabled": true,
    "backend": "lancedb",
    "sync_mode": "incremental",
    "embedder": {
      "provider": "openai-compatible",
      "model": "gemini-embedding-001",
      "dimensions": 3072,
      "api_key_env": ["SCOPE_RECALL_GEMINI_EMBEDDING_API_KEY"],
      "base_url": "https://generativelanguage.googleapis.com/v1beta/openai"
    },
    "fallback_embedder": {
      "provider": "local-hash",
      "dimensions": 256,
      "model": "hash-v1"
    }
  }
}

Credential rule:

• put real API keys in your private environment, not in config.json
• if no configured key is available, scope-recall falls back to local-hash

Embedding providers

Currently implemented:

Provider	Use case	Notes
openai-compatible	Gemini/OpenAI-compatible embedding APIs	Default configured path; supports env-based API key lookup
openai	Direct OpenAI embeddings	Useful when you do not need a custom compatible endpoint
sentence-transformers	Local Hugging Face / SentenceTransformers models	Good for local semantic embeddings when installed
local-hash	Offline fallback	Deterministic degraded fallback, not a true semantic model
local-debug	Tests/debugging	Tiny deterministic test embedder

Provider aliases local-model, local-embedding, and huggingface resolve to the sentence-transformers backend.

---

Durable memory vs local scratch scope

scope-recall does not split all memory by every group or tiny window. It uses two provider-owned scopes:

• Shared durable scope: platform + agent_workspace + agent_identity + user_id. Rows with targets user, memory, project, and ops are stored here, so they can be recalled across chats/windows for the same user and agent.
• Local runtime scope: shared durable scope plus gateway_session_key, or chat_id / thread_id. Rows with target general stay here, so temporary group/topic/session chatter does not bleed elsewhere.
• Accessible scope set: normal recall and scoped tool actions can see the current local scope plus the shared durable scope; they cannot see another user, sibling agent identity, or another local chat/thread/session scratch scope.

This aims at the common expectation: "if I gave the agent durable information before, it should remember it later," without making every scratch line globally visible forever.

---

Dual-memory architecture: important

When scope-recall is active, Hermes memory has two intentional authority zones:

Layer	Storage	Purpose	How recall sees it
Hermes curated memory	$HERMES_HOME/memories/USER.md, $HERMES_HOME/memories/MEMORY.md	User profile and durable hand-curated notes managed by Hermes built-in memory	Live-read during recall; not mirrored into SQLite; gateway user_id contexts require curated-memory opt-in/allowlist
Scope Recall provider memory	$HERMES_HOME/scope-recall/memory.sqlite3 + $HERMES_HOME/scope-recall/lancedb/	Provider-owned shared durable memories plus local scratch captures, scope metadata, lexical/vector retrieval	SQLite truth + optional LanceDB companion ranking

Key principle:

SQLite is the truth source for provider-owned rows. Hermes curated memory files remain their own truth source. LanceDB is a rebuildable retrieval companion, not the authority.

This is deliberate. Mirroring curated memory writes into SQLite can leave stale duplicates after replace/remove operations. Live-reading curated memory keeps Scope Recall aligned with Hermes native memory behavior. Because those curated files are profile-global, live-read recall defaults to single-user: it is active for single-user/no-user_id runtimes and disabled for explicit gateway user_id contexts unless curated_memory.mode is set to profile-global or explicit-users with matching allowed_user_ids.

---

Storage layout

Under the active Hermes profile:

• $HERMES_HOME/scope-recall/memory.sqlite3
• $HERMES_HOME/scope-recall/config.json
• $HERMES_HOME/scope-recall/lancedb/

Legacy lancepro storage is migrated forward on first initialization when present.

---

Architecture

Hermes turn
   |
   | current query
   v
prefetch(query)
   |
   +--> live curated memory read
   |       - $HERMES_HOME/memories/USER.md
   |       - $HERMES_HOME/memories/MEMORY.md
   |
   +--> SQLite truth lookup / FTS
   |       - provider-owned memory rows
   |       - scope metadata
   |       - timestamps and governance metadata
   |
   +--> LanceDB vector companion
   |       - semantic candidate retrieval
   |       - rebuildable from SQLite truth
   |
   v
hybrid scoring + recency-aware ranking + bounded prompt block

File reference

File	Purpose
__init__.py	Hermes plugin entrypoint; exposes register() lazily
provider.py	Provider lifecycle and Hermes hook integration
config.py	Runtime config loading/defaults
scope.py	Runtime scope construction and isolation keys
sql_store.py	SQLite schema, migrations, truth-row CRUD, FTS
vector_store.py	LanceDB companion table sync/search/repair primitives
vector_runtime.py	Vector runtime status and degradation handling
recall.py	Lexical/vector/hybrid recall orchestration
scoring.py	Score fusion, freshness boosts, capping logic
gating.py	Recall/capture gating and noise filtering
capture.py	Auto-capture pipeline
governance.py	Deterministic dedupe, metadata, decay/governance review
memory_ops.py	Store/search/forget/update/dedupe/merge/export/govern operations
tooling.py	Provider tool dispatch
schemas.py	Hermes tool schemas
migration.py	Legacy lancepro migration helpers
nightly_digest.py	Daily conversation digest pipeline, LLM/heuristic extraction, semantic write decisions
scripts/import.openclaw.memory_lancedb_pro.py	Explicit OpenClaw history importer
scripts/nightly-digest.py	CLI wrapper for the profile-scoped daily digest
scripts/repair.vector_index.py	Rebuild/repair LanceDB from SQLite truth
scripts/check.release.py	Full V1 release gate used locally and by CI

1. SQLite truth layer

SQLite is the authoritative provider-owned store.

It keeps:

• raw memory rows
• scope metadata
• lexical FTS index
• timestamps for auditing and migration

Why SQLite stays authoritative:

• deterministic local persistence
• easy schema inspection
• simple migration/backup story
• safer open-source baseline than tying truth directly to a vector backend

2. LanceDB vector companion

LanceDB is a companion retrieval index, not the truth source.

It stores retrieval-ready fields copied from SQLite plus a vector column:

• id
• scope_id
• source
• target
• content
• summary
• updated_at
• vector

Configured default embedder targets the Gemini OpenAI-compatible embeddings API:

• provider: openai-compatible
• model: gemini-embedding-001
• dimensions: 3072

Runtime fallback remains available:

• if the configured API embedder is unavailable, the plugin falls back to local-hash (256 dims)
• this keeps first-boot/local operation working even without external API keys, while preserving a higher-quality default config for instances that do provide credentials

---

Core features

Current-turn recall

• prefetch(query) retrieves against the current user query
• queue_prefetch() is intentionally a no-op
• this avoids stale next-turn injection from the previous topic

Permanent shared recall

• user, memory, project, and ops rows are durable shared memories for the same user + agent identity
• they can be recalled from another chat/window when the new query is semantically relevant
• general rows remain local scratch context for the current chat/thread/session
• ID-based updates/deletes/merges are restricted to the current accessible scope set, not global row ids

Nightly conversation digest

scripts/nightly-digest.py is a plugin-owned batch path for daily memory consolidation. It reads the profile's active Hermes state.db first, falls back to legacy lcm.db, and scans the selected local date by message timestamps. Raw system rows and raw tool outputs are not stored. For task sessions, the digest keeps a sanitized tool-chain summary so repeated engineering workflows can be recalled later as workflow memory.

Typical smoke run:

python scripts/nightly-digest.py --hermes-home "$HERMES_HOME" --date 2026-06-01 --dry-run --extractor heuristic --verbose

Production runs default to the LLM extractor. The script reads model/base URL/API key information from the Hermes profile config and .env, with SCOPE_RECALL_DIGEST_API_KEY available as an explicit override. If the LLM path returns no usable candidates for a session, the digest falls back to heuristic candidates for that session. Actual writes use SQLite truth rows, FTS/entity sync, digest run/source ledgers, semantic skip/update/insert decisions, exact duplicate cleanup, and LanceDB companion upsert when vector indexing is enabled.

Hybrid retrieval

current query
   ├─> SQLite lexical / FTS candidates
   └─> LanceDB vector candidates
        ↓
score fusion + freshness hints + prompt budget

Supported retrieval modes:

• lexical
• vector
• hybrid (default)

Default hybrid weights:

• lexical: 0.45
• vector: 0.55

Guardrail: if only one side has a score, that side is used directly instead of being unfairly damped by a missing partner score.

Scope isolation

Scope is built from:

• platform
• agent_workspace
• agent_identity
• user_id
• gateway_session_key when available
• otherwise chat_id
• plus thread_id when present

This prevents raw identifiers containing delimiters from colliding with split scope fields, and preserves the intended split: durable facts can move with the same user + agent identity, while local scratch rows do not leak across different groups, chats, sessions, or topics.

Vector repair and stats

SQLite is the cardinality authority. During vector sync, the provider compares SQLite ids with LanceDB ids, deletes stale vector rows, collapses duplicate physical rows by id, and embeds missing/changed rows. If LanceDB delete/upsert fails, the SQLite write is preserved and vector state becomes needs_repair instead of surfacing the truth-row write as failed.

scope_recall_stats reports:

• vector.row_count — physical LanceDB row count
• vector.unique_id_count — distinct vector ids
• vector.duplicate_row_count — extra physical rows beyond one row per id
• vector.status — ready, degraded, needs_repair, disabled, or error

When vector.index_general=false (the default), local general scratch rows are not expected in LanceDB. A healthy synced companion should have vector.unique_id_count == vector.row_count, vector.duplicate_row_count == 0, and vector ids matching the configured vector-indexed provider rows.

For deeper maintenance:

python scripts/repair.vector_index.py --hermes-home "$HERMES_HOME" --dry-run
python scripts/repair.vector_index.py --hermes-home "$HERMES_HOME"

Write-time governance

Provider-owned captures apply a deterministic first line of governance before SQLite writes:

• exact normalized-content dedupe within (scope_id, target)
• conservative semantic near-duplicate merge for user, ops, and project memories
• conflict preservation when a near-duplicate contains negation / supersession language
• rules-based smart extraction from user turns into preference / ops / project fact candidates
• metadata classification for category, tier, confidence, sensitivity, and expiry review
• noisy maintenance/system prompt filtering
• trivial reply filtering
• obvious secret-bearing text filtering
• overlong prompt-block filtering through capture_hard_max_chars
• governance review through scope_recall_govern, including core/working/archive tier counts and decay candidates

This is a local deterministic governance layer, not a remote LLM extraction pipeline. It intentionally stays conservative so SQLite remains auditable truth and conflicting memories are preserved rather than silently overwritten.

---

Provider tools

Primary-agent default tools:

scope_recall_store
scope_recall_search
scope_recall_context
scope_recall_probe
scope_recall_related
scope_recall_feedback
scope_recall_forget
scope_recall_update
scope_recall_merge
scope_recall_export
scope_recall_stats

Operator-only maintenance tools are hidden from the default schema and require maintenance_tools_enabled=true:

scope_recall_dedupe
scope_recall_govern
scope_recall_hygiene
scope_recall_repair

scope_recall_hygiene is read-only. It reports runtime-wrapper noise, assistant scratch prose, duplicate dedupe keys, very short/long rows, general rows present in the vector companion, likely promotion candidates, and likely delete candidates. It does not delete, merge, promote, or rewrite rows.

For an offline SQLite report without exposing the maintenance tool to agents:

python scripts/report.hygiene.py --db "$HERMES_HOME/scope-recall/memory.sqlite3" --format markdown

Destructive cleanup is intentionally out-of-band: use the hygiene report first, then require an explicit operator decision before running any separate delete/merge/dedupe action. The shipped hygiene path is dry-run/report-only.

scope_recall_export defaults to the current accessible scope set: local scratch scope plus shared durable scope. Passing scope_only=false is an operator maintenance action and fails closed unless maintenance_tools_enabled=true.

Backward-compatible aliases are still accepted internally for old lancepro_* tool names during transition.

Tool quick reference

Example primary-agent tool calls:

# Store provider-owned memory. ops/user/memory/project become shared durable rows; general stays local scratch.
store = scope_recall_store(
    content="This project deploys with uv run app.",
    target="ops",
)

# Search the current accessible scope set: local scratch plus shared durable memory.
results = scope_recall_search(
    query="How does this project deploy?",
    limit=3,
)

# Inspect truth/vector health.
stats = scope_recall_stats()

Example scope_recall_stats shape:

{
  "provider": "scope-recall",
  "total_memories": 42,
  "scope_memories": 7,
  "local_scope_memories": 3,
  "shared_scope_memories": 4,
  "vector": {
    "enabled": true,
    "ready": true,
    "status": "ready",
    "row_count": 42,
    "unique_id_count": 42,
    "duplicate_row_count": 0
  }
}

Tool	Purpose
scope_recall_store	Store a provider-owned memory row after deterministic governance checks
scope_recall_search	Search the current local scratch scope plus shared durable scope with lexical/vector/hybrid retrieval
scope_recall_context	Render a compact task-relevant memory context block plus structured evidence for a query
scope_recall_probe	Inspect accessible memories attached to a specific entity
scope_recall_related	List entities that co-occur with a given entity in accessible memories
scope_recall_feedback	Mark a memory as helpful or unhelpful so trust scoring can adjust future recall
scope_recall_forget	Delete memories matching a query within the current accessible scope set
scope_recall_update	Replace content/category within the current accessible scope set; shared/local target-mode changes are rejected
scope_recall_dedupe	Operator-only: inspect or collapse exact duplicate rows
scope_recall_merge	Merge same-scope memories into a target row; shared/local mixing is rejected
scope_recall_export	Export SQLite truth rows as JSON or JSONL; defaults to current accessible scope set
scope_recall_govern	Operator-only: review tier distribution and decay/archive candidates
scope_recall_hygiene	Operator-only, read-only: report memory-quality cleanup/promotion candidates without modifying rows
scope_recall_repair	Operator-only: repair/rebuild the LanceDB companion from SQLite truth
scope_recall_stats	Inspect storage, retrieval, scope, and vector health

---

Migration behavior

Local lancepro rename migration

On first boot, if $HERMES_HOME/lancepro/ exists and $HERMES_HOME/scope-recall/ does not yet contain the new DB/config, the provider:

• copies the legacy SQLite database into the new location
• copies config.json forward
• records migration info in scope_recall_stats

OpenClaw memory-lancedb-pro imports

OpenClaw memory-lancedb-pro history is handled separately as an explicit import problem, not automatic compatibility.

See:

• docs/migration.md
• docs/differences-from-memory-lancedb-pro.md
• scripts/import.openclaw.memory_lancedb_pro.py

Do not point scope-recall directly at an OpenClaw .lance directory and call it done. Old vector stores must be transformed into SQLite truth rows before the companion vector index is rebuilt.

---

Compared with OpenClaw memory-lancedb-pro

scope-recall was inspired by good public ideas in OpenClaw memory-lancedb-pro, especially current-turn recall, scoped memory boundaries, hybrid retrieval, and memory hygiene. It is not a line-for-line port and it does not claim feature parity.

Area	OpenClaw memory-lancedb-pro	scope-recall V1
Host agent	OpenClaw	Hermes
Truth model	LanceDB-centric OpenClaw memory pipeline	SQLite truth + LanceDB companion index
Recall timing	OpenClaw auto-recall hook model	Hermes prefetch(query) current-turn recall; no queued next-turn recall
Curated memory	Separate OpenClaw markdown/journal behavior	Hermes USER.md / MEMORY.md live-read and kept authoritative
Smart extraction	LLM-backed created/merged/skipped style in upstream beta line	deterministic/rules-based extraction and conservative merge
Lifecycle	Weibull decay / tier promotion concepts upstream	deterministic metadata classification and decay/governance review; no full LLM summarization tier pipeline
Migration	OpenClaw-native data path	explicit importer from OpenClaw LanceDB shape into SQLite truth

Honest claim boundary:

scope-recall is a Hermes local memory provider for current-turn recall with SQLite truth storage, LanceDB vector companion retrieval, strong runtime scope isolation, deterministic write-time governance, and explicit migration boundaries.

It should not be described as:

• a drop-in replacement for OpenClaw memory-lancedb-pro
• a direct reuse wrapper around old .lance stores
• full feature parity with upstream LLM-backed governance and lifecycle orchestration

---

Troubleshooting

Recall returns stale or irrelevant context

Check that the running provider is scope-recall, not the deprecated lancepro name, and remember that live Hermes runtime freshness requires a process restart/reload after code changes.

hermes memory status

Vector stats show duplicate rows or missing rows

Run the repair script. SQLite remains truth; the vector layer is rebuildable companion state.

python scripts/repair.vector_index.py --hermes-home "$HERMES_HOME" --dry-run
python scripts/repair.vector_index.py --hermes-home "$HERMES_HOME"

Hosted embeddings are unavailable

The provider should degrade to local-hash. That keeps the system usable but lowers semantic quality. Set SCOPE_RECALL_GEMINI_EMBEDDING_API_KEY in your private environment to use the configured hosted path.

OpenClaw .lance data does not appear automatically

That is expected. OpenClaw history must be explicitly imported into SQLite truth rows before the companion vector index is rebuilt.

Live gateway still behaves like the old code

Release checks prove the source tree and artifact. They do not prove a running Hermes gateway has loaded the new plugin. Restart/reload the target Hermes process and verify with a real runtime smoke test before claiming live-runtime freshness.

---

Current V1 limitations

• vector sync is incremental by stable row id / updated_at, with duplicate-id/stale-row repair during normal sync; scripts/repair.vector_index.py can rebuild the LanceDB companion from SQLite truth when deeper storage hygiene is needed
• semantic merge is intentionally conservative and rules/scoring-based; it is not a general-purpose contradiction resolver or LLM reasoning layer
• write-time smart extraction is rules-based for common preference / ops / project-fact sentences; nightly digest adds a separate LLM/heuristic batch consolidation path, but it is not full OpenClaw-style always-on created/merged/skipped lifecycle parity
• fallback local-hash is only a degraded offline path, not a true semantic model
• old lancepro directory still exists as a compatibility shim during the V1 transition window
• the supported Hermes install shape is still an unpacked plugin directory; the wheel is verified as a package artifact, not as a Hermes discovery mechanism

See docs/stability.md for the exact V1 compatibility and non-goal boundaries.

---

Documentation

Document	Description
DESIGN.md	Architecture, layer split, retrieval model, migration plan, and release expectations
docs/stability.md	Stable V1 compatibility contract and non-goals
docs/migration.md	Local lancepro migration and explicit OpenClaw import guidance
docs/differences-from-memory-lancedb-pro.md	Honest comparison with OpenClaw memory-lancedb-pro
CHANGELOG.md	Release history
CONTRIBUTING.md	Contribution and development notes

---

Release and verification

The public release gate is intentionally the same script used by GitHub Actions:

python -m pytest -q
python scripts/check.release.py
python scripts/repair.vector_index.py --hermes-home "$HERMES_HOME" --dry-run

Release publishing is tag-driven: .github/workflows/release.yml can create a GitHub Release for a v* tag and populate notes from the matching CHANGELOG.md entry.

scripts/check.release.py verifies:

• V1 metadata and stable public docs
• required source files
• full pytest suite
• bytecode compilation
• wheel build
• wheel content inspection
• temp install/import smoke
• obvious literal secret/private-path scan
• generated artifact cleanup

Current focused regression coverage includes:

• plugin loading from $HERMES_HOME/plugins
• hybrid recall returning semantically matched content
• built-in curated memory reflection
• vector state visible in stats
• runtime fallback from unavailable API embeddings to local-hash
• vector table rebuild when embedder dimensions change
• vector duplicate physical rows are repaired back to one row per id
• vector delete/upsert failure preserves SQLite truth and marks vector status needs_repair
• vector search failure degrades to lexical recall and marks vector status needs_repair
• write-time exact dedupe prevents repeat SQLite rows for the same normalized content in the same scope/target
• length-framed scope identifiers prevent delimiter-collision between user/chat/thread/session components
• operator scope_recall_dedupe(scope_only=false) covers duplicate groups across all scopes while ordinary scoped actions remain bounded to the current accessible scope set
• capture filtering blocks known maintenance prompts, trivial replies, obvious secret-bearing text, and overlong prompt blocks
• semantic near-duplicate merge and conflict preservation
• rules-based smart extraction from user turns into preference / ops / project fact memories
• nightly digest session loading, redaction, workflow memory writes, ledgers, duplicate skips, and dry-run behavior
• merge / export / govern provider tools
• governance metadata classification and decay review candidates
• provider tools cover store/search/context/probe/related/feedback/forget/update/dedupe/merge/export/govern/repair/stats
• explicit vector companion rebuild from SQLite truth via scripts/repair.vector_index.py
• scope_recall_stats exposes physical rows, unique ids, and duplicate-row count
• top-level import scope_recall stays light without Hermes runtime modules
• on_memory_write remains an intentional observational no-op

---

Dependencies

Package	Purpose
lancedb>=0.30.2	LanceDB companion vector index
pyarrow>=24,<25	Arrow data interchange used by LanceDB
sentence-transformers (optional)	Local semantic embedding models when using the sentence-transformers backend
Hermes Agent	Host runtime and memory-provider/plugin loading

---

License

MIT. See LICENSE.