hermes-scope-recall 1.5.4

Hermes scoped memory provider with journal-first capture, SQLite truth, vector companions, and hybrid RRF retrieval.

Scope Recall for Hermes

Hermes current-turn memory provider with journal-first semantic capture, durable recall, SQLite truth storage, and optional vector companions

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 · Journal-first capture · Durable shared memory · Background digest · Local scratch scopes · SQLite truth · LanceDB/SQLite companion · Hybrid RRF retrieval

scope-recall is a Hermes local memory provider built for current-turn recall and durable 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.

This repository, scope-recall-hermes, is the Hermes implementation. The Python distribution package is hermes-scope-recall, the Python import/package spelling is scope_recall, and the Hermes plugin ID/provider name remains scope-recall for runtime compatibility. The OpenClaw sibling implementation lives at scope-recall-openclaw.

Version 1.5.4 packages a compatibility-preserving refactor of the doctor, graph-hygiene, maintenance, digest-result, recall-pipeline, and provider-schema internals while keeping the stable V1 commercial-governance line introduced in 1.5.0. The 1.5 line includes promoted-only profile lifecycle safety, candidate-memory promotion planning, graph-hygiene repair, fail-closed vector-repair fallback handling, governance cleanup, journal recovery, an operator dashboard, repository-owned golden benchmarks, stricter release gates, fail-closed hard-delete safety, packaged benchmark fixtures, Recall Funnel observability, synthetic retrieval-regression benchmarking, and default-safe vector fallback behavior. Runtime Experience packet injection is enabled by default through experience.prefetch_enabled=true and can be disabled with experience.prefetch_enabled=false; background automatic promotion remains an explicit operator opt-in through experience.auto_promotion_enabled=true, and low-risk auto-promotion remains a second explicit opt-in through experience.auto_promote_low_risk=true. By default, successful low-risk scans create candidate playbooks, high-risk playbooks stay review-gated, and final-failure or low-signal traces are not promoted. It keeps the scope_recall_profile surface added in v1.3.0, compression-boundary journal staging through Hermes' on_pre_compress() memory-provider hook, inline attachment-marker sanitization, the supported standalone install shape added in v1.1.0, and native-safe LanceDB probing with automatic SQLite vector fallback for non-AVX hosts.

It uses a three-layer design:

• Journal/provenance layer for eligible raw conversation turns and evidence links that are not directly recalled as durable memory
• SQLite truth store for high-density durable local records and deterministic auditing
• Vector companion for semantic retrieval and hybrid ranking: LanceDB by default, or sqlite-bruteforce for native-free/non-AVX hosts

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, and can cross platforms only when explicit canonical identity mapping is configured.
• Raw turns are provenance, not durable memory: eligible conversation turns are written to a journal/staging layer; only digest-produced high-density user/memory/project/ops rows enter durable recall and vector sync.
• 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.

Deployment boundaries

scope-recall is the local per-Hermes recall layer. In multi-agent deployments that already run a central shared backend such as PostgreSQL, keep that backend as the cross-agent source of truth and connect it to scope-recall through explicit import/export/tool boundaries.

The V1 shape is intentionally simple:

• SQLite remains the local truth store for provider-owned memory records.
• the configured vector backend remains a rebuildable semantic retrieval companion.
• Durable user/memory/project/ops facts can be bridged deliberately across systems.
• Local general scratch, raw system/tool output, and plaintext secret values stay outside durable recall; explicit scope_recall_store_secret_index rows may store only searchable credential indexes such as service/account/purpose/vault references and non-reversible fingerprints.
• Hermes native skills remain the place for procedural knowledge packaging.
• Operational visibility is exposed through doctor, repair, inspect, explain, and benchmark utilities; deployment-specific dashboards can consume those outputs when needed.

For external shared-memory bridge guidance, see docs/external-shared-memory.md. For reviewed procedural playbooks and preflight packets, see docs/experience.kernel.md.

Optional cross-platform identity mapping

By default, durable shared scope remains platform-isolated: platform + agent_workspace + agent_identity + user_id. To let the same human recall durable rows across Telegram, CLI, Feishu, or another gateway, configure an explicit canonical identity map in $HERMES_HOME/scope-recall/config.json:

{
  "identity": {
    "cross_platform_shared_scope": true,
    "cli_user_id_fallback": "local",
    "user_aliases": {
      "telegram:8176453077": "joy",
      "cli:local": "joy",
      "feishu:ou_xxx": "joy"
    }
  }
}

Only durable targets (user, memory, project, ops) use the canonical shared scope. general scratch, raw journal evidence, chat/thread/session context, and tool traces remain local. Existing platform-specific durable rows stay readable through query-time aliases before any explicit migration. Newly written rows keep raw_platform, raw_user_id, and mapped canonical_user metadata for auditability.

Provider-specific LLM endpoints

For capture, journal, or nightly digest LLM providers whose chat-completions endpoint is not base_url + /v1/chat/completions, set either a full endpoint or disable /v1 appending:

{
  "journal": {
    "endpoint": "https://ark.cn-beijing.volces.com/api/coding/v3/chat/completions",
    "append_v1": false
  }
}

For scripts/nightly-digest.py, the same behavior is available through --endpoint or --no-append-v1.

---

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 vector stores are only rebuildable semantic companions

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/vector 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; vector backends are rebuildable companion state
Hybrid retrieval	SQLite lexical/FTS/BM25 candidates + configured vector companion candidates + RRF reranking + bounded prompt rendering
Entity/context layer	SQLite entity index, entity probe/related tools, compact query context, compact profile/context surface, trust feedback
Background digest	Profile-scoped journal/nightly 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
Maintainer contracts	docs/contract.matrix.md maps major feature contracts to source files, targeted tests, release gates, and dynamic probes so large-context changes stay evidence-backed

---

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, configured vector 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: PyPI-style standalone install

Install the provider package in the same Python environment that runs Hermes:

python -m pip install "hermes-scope-recall[lancedb]"
hermes-scope-recall install --hermes-home "${HERMES_HOME:-$HOME/.hermes}"
hermes config set memory.provider scope-recall
hermes memory setup

hermes memory setup now writes the provider config and bootstraps an empty $HERMES_HOME/scope-recall/memory.sqlite3 truth/journal schema plus sqlite-bruteforce $HERMES_HOME/scope-recall/vector.sqlite3 metadata when the SQLite companion is configured directly or as the native-safe fallback. hermes-scope-recall verify and operator checks therefore do not have to wait for the first live message to trigger lazy provider initialization.

For a local smoke check after installation:

hermes-scope-recall verify --hermes-home "${HERMES_HOME:-$HOME/.hermes}"
hermes memory status

If LanceDB/PyArrow native wheels are unsafe on the target CPU, install without extras and select the native-free backend instead:

python -m pip install hermes-scope-recall
hermes-scope-recall install --hermes-home "${HERMES_HOME:-$HOME/.hermes}"
python "$HERMES_HOME/plugins/scope-recall/scripts/repair.vector_index.py" --hermes-home "$HERMES_HOME" --backend sqlite-bruteforce --dry-run

{
  "vector": {
    "backend": "sqlite-bruteforce"
  }
}

Option B: Clone into a Hermes plugin directory for development

cd "$HERMES_HOME/plugins"
git clone https://github.com/410979729/scope-recall-hermes.git scope-recall
cd scope-recall
python -m pip install -e ".[lancedb]"
hermes config set memory.provider scope-recall
hermes memory setup

Use this editable install shape when developing the provider itself. Plain pytest from an unrelated Python environment is not a valid compatibility check; use the Hermes venv and include the Hermes source on PYTHONPATH when you need to exercise Hermes discovery directly:

PYTHONPATH=/path/to/hermes-agent:$(pwd) /path/to/hermes-agent/venv/bin/python -m pytest -q

Option C: Manual download / unpacked plugin install

Hermes plugin discovery expects an unpacked plugin directory named with the public provider spelling: $HERMES_HOME/plugins/scope-recall/. The Python distribution package is hermes-scope-recall, the Python import/package spelling remains scope_recall, and the Hermes provider name remains scope-recall; see docs/naming.md for the naming contract.

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[lancedb]" for the default LanceDB path, or install without extras and set vector.backend: sqlite-bruteforce on native-sensitive hosts
3. set memory.provider: scope-recall
4. restart/reload the Hermes process that should use the provider
5. verify with hermes-scope-recall verify --hermes-home "$HERMES_HOME" and hermes memory status

Important boundary:

• hermes-scope-recall install copies the provider into $HERMES_HOME/plugins/scope-recall/; provider-owned data remains in $HERMES_HOME/scope-recall/.
• the configured vector companion is rebuildable from SQLite truth.
• manual vector repairs should be inspected first with python scripts/repair.vector_index.py --hermes-home "$HERMES_HOME" --dry-run; the script fails closed if the primary configured embedder is unavailable and would otherwise fall back to vector.fallback_embedder. Export the configured primary embedder environment variable (for example SCOPE_RECALL_GEMINI_EMBEDDING_API_KEY) or intentionally pass --allow-fallback-embedder before rebuilding with a fallback embedder.
• wheel build/import success is not enough by itself; release validation also runs Hermes-home installer and provider-discovery smokes.

---

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,
  "tool_schema_profile": "compact",
  "tool_schema_extra_tools": [],
  "maintenance_tools_enabled": false,
  "secret_index_tools_enabled": false,
  "retrieval": {
    "mode": "hybrid",
    "lexical_weight": 0.45,
    "vector_weight": 0.55,
    "candidate_pool": 12,
    "fusion_strategy": "rrf",
    "bm25_weight": 0.15,
    "rrf_weight": 0.18,
    "rrf_min_signals": 2
  },
  "journal": {
    "enabled": true,
    "digest_on_session_end": false,
    "background_digest_enabled": true,
    "extractor": "llm",
    "digest_interval_hours": 2,
    "retention_days": 0,
    "max_entries_per_digest": 500
  },
  "per_turn_extraction": {
    "enabled": false
  },
  "vector": {
    "enabled": true,
    "backend": "lancedb",
    "fallback_backend": "sqlite-bruteforce",
    "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"
    }
  }
}

Vector backend choices:

• lancedb — default ANN companion, best for normal hosts; install with python -m pip install -e ".[lancedb]". Scope Recall probes LanceDB/PyArrow in a child process before importing them in the Hermes process, so SIGILL/illegal-instruction wheels are treated as unavailable instead of crashing the agent.
• sqlite-bruteforce — pure-Python/SQLite companion for non-AVX CPUs or hosts where importing LanceDB/PyArrow is unsafe; install with python -m pip install -e . and set vector.backend accordingly, or keep the default vector.fallback_backend: sqlite-bruteforce to fall back automatically when LanceDB is absent or unsafe.

Both backends are rebuildable caches. $HERMES_HOME/scope-recall/memory.sqlite3 remains the truth source.

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
minimax	MiniMax embo-01 embeddings	Uses MiniMax's non-OpenAI-compatible /v1/embeddings shape with texts and type; indexing uses db, search queries use query
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.

MiniMax example:

{
  "vector": {
    "embedder": {
      "provider": "minimax",
      "model": "embo-01",
      "dimensions": 1536,
      "api_key_env": ["MINIMAX_API_KEY"],
      "base_url": "https://api.minimaxi.com",
      "document_type": "db",
      "query_type": "query",
      "group_id_env": ["MINIMAX_GROUP_ID"],
      "timeout": 30.0
    }
  }
}

MiniMax notes:

• api_key_env should point at private environment variables; do not put real keys in config.json.
• document_type controls vector-indexing/upsert calls and defaults to db.
• query_type controls vector-search query calls and defaults to query.
• group_id / group_id_env is optional. When configured, Scope Recall sends it as the legacy-compatible GroupId query parameter for MiniMax accounts that still require a group id; leave it unset for accounts/endpoints that only require the bearer token.
• base_url defaults to https://api.minimaxi.com. Override it if your account, proxy, or regional deployment uses another MiniMax embedding endpoint.

---

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.

---

Detailed recall anchors and secret indexes

External artifact anchors

Ordinary durable writes preserve stable external lookup handles. When a memory contains a GitHub issue, PR, commit, release, repository URL, or other URL, scope-recall appends a deterministic anchor block and stores structured artifact metadata.

Example stored text:

Hermes upstream recommendation request is tracked in the linked issue.

Artifact anchors: GitHub issue NousResearch/hermes-agent#42864 (https://github.com/NousResearch/hermes-agent/issues/42864)

The same row also carries artifacts metadata with fields such as kind, repo, number, commit, tag, and url. This keeps future recall from relying on vague summaries such as "submitted the RFC" when the useful retrieval key is the exact issue/PR/release/commit handle.

Nightly digest uses the same deterministic artifact extraction in addition to its LLM/heuristic summary logic, so source conversations with external handles retain those anchors even when the human-readable summary is compact.

Secret indexes, not plaintext secret storage

scope_recall_store_secret_index stores a searchable credential index without putting plaintext secret values into the ordinary recall surface. Store the real password/token/API key/private key in an external vault or keyring, then store only the locator and safe metadata in scope-recall. To reduce the default tool schema footprint, this low-frequency tool is hidden and direct calls fail unless secret_index_tools_enabled=true is set.

Example tool payload shape:

{
  "label": "production deploy credential",
  "secret_type": "password",
  "service": "example-service",
  "account": "deploy-user",
  "vault_ref": "vault://ops/example-service/deploy-user",
  "rotation_due": "quarterly",
  "notes": "Use only for authorized deployment maintenance."
}

Returned/stored metadata includes secret_value_stored: false. If a caller supplies secret_value, it is used only to compute a short non-reversible fingerprint prefix; the plaintext secret is not written to SQL/FTS/vector text, metadata, exports, logs, or chat replies.

---

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 journal/provenance	$HERMES_HOME/scope-recall/memory.sqlite3 (journal_entries, memory_journal_sources)	Eligible raw turns and digest evidence links; not ordinary recall memory	Background digest reads it, but recall does not inject raw journal rows
Scope Recall provider memory	$HERMES_HOME/scope-recall/memory.sqlite3 + configured vector companion (lancedb/ or vector.sqlite3)	Provider-owned shared durable memories plus local scratch rows, scope metadata, lexical/vector/RRF retrieval	SQLite truth + rebuildable companion ranking

Key principles:

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

Raw conversation turns are provenance first. They become durable recall only after journal/nightly digest turns them into high-density, merge-upserted memory rows.

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/ when vector.backend=lancedb
• $HERMES_HOME/scope-recall/vector.sqlite3 when vector.backend=sqlite-bruteforce

Inside memory.sqlite3, journal_entries and memory_journal_sources preserve provenance for background digest without turning raw turns into ordinary recall rows.

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 / BM25 / entity graph indexes
   |       - provider-owned memory rows
   |       - scope metadata
   |       - timestamps and governance metadata
   |
   +--> configured vector companion
   |       - semantic candidate retrieval
   |       - rebuildable from SQLite truth
   |
   v
hybrid scoring + RRF/BM25/entity-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
sqlite_vector_store.py	Pure-SQLite brute-force vector companion for native-free hosts
vector_runtime.py	Vector runtime status and degradation handling
recall.py	Lexical/vector/hybrid recall orchestration
scoring.py	Score fusion, reciprocal-rank fusion, freshness boosts, capping logic
gating.py	Recall/capture gating and noise filtering
capture.py	Auto-capture pipeline
journal.py	Journal/provenance schema, journal digest, merge-upsert and evidence links
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/journal-digest.py	CLI wrapper for journal-first background digest
scripts/repair.vector_index.py	Rebuild/repair the configured vector companion from SQLite truth
scripts/check.release.py	Full V1 release gate used locally and by CI
scripts/benchmark.golden.py	Isolated golden recall benchmark using repository-owned fixtures
scripts/benchmark.retrieval_regression.py	Isolated synthetic Recall Funnel / retrieval-regression benchmark with configurable distractors

1. SQLite truth layer

SQLite is the authoritative provider-owned store.

It keeps:

• durable memory rows
• scope metadata
• lexical FTS index
• journal provenance tables and digest evidence links
• 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. Vector companion

The configured vector backend is a companion retrieval index, not the truth source. LanceDB is the default ANN backend; sqlite-bruteforce is a pure-Python/SQLite fallback for non-AVX or native-dependency-sensitive hosts.

Both backends store 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

Durable 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 provider stages eligible turns into journal_entries, then schedules a non-blocking background digest according to journal.digest_interval_hours when journal.background_digest_enabled=true. journal.digest_on_session_end=false keeps slow LLM promotion out of normal session closeout by default; session-end LLM promotion requires explicit journal.allow_session_end_llm=true. 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 fails or returns no usable candidates, journal digest does not silently fall back and consume journal evidence; operators must explicitly request --extractor heuristic or set journal.allow_heuristic_fallback=true for a degraded fallback run. Actual writes use SQLite truth rows, FTS/entity sync, digest run/source ledgers, semantic skip/update/insert decisions, exact duplicate cleanup, and configured vector companion upsert when vector indexing is enabled.

Hybrid retrieval

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

Supported retrieval modes:

• lexical
• vector
• hybrid (default)

Default hybrid weights:

• lexical: 0.45
• vector: 0.55

Default result sizing:

• retrieval.candidate_pool controls how many lexical/vector candidates each source may feed into the ranking funnel before merge, filters, graph/entity bonuses, and final slicing.
• retrieval.top_k controls the default tool result limit when a caller does not pass an explicit limit; explicit per-call limit still wins.

Recall observability:

• scope_recall_search(..., include_trace=true) returns the structured Recall Funnel for that query.
• scope_recall_explain includes the same funnel_trace alongside rank-aligned scoring components and rejected candidates.
• scope_recall_benchmark(..., include_trace=true, prompt_budget_chars=N) reports per-case traces plus aggregate latency, known-answer recall, top-k accuracy, forbidden-id violations, filter counts, and prompt-budget hit rate.

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 vector companion ids, deletes stale vector rows, collapses duplicate physical rows by id where the backend can expose them, and embeds missing/changed rows. If vector 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:

• journal_digest.last_status / journal_digest.consecutive_failures — background digest health for operator monitoring
• journal_digest.thread_alive — whether a background digest worker is currently running
• vector.row_count — physical vector companion 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 the vector companion. 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"

Candidate memory promotion

scope_recall_profile defaults to lifecycle=promoted SQLite rows, so the compact profile behaves like a stable profile surface instead of mixing in ordinary candidate rows. Pass include_candidates=true only when an operator or debugging workflow intentionally wants non-hidden candidate rows in the profile payload. include_general=true remains the explicit switch for local general scratch rows.

Candidate rows are not left invisible forever. Use the read-only promotion planner first:

python scripts/promote.memory_candidates.py --hermes-home "$HERMES_HOME" --dry-run

After reviewing the plan, apply safe promotions:

python scripts/promote.memory_candidates.py --hermes-home "$HERMES_HOME" --apply

Rows classified as low-value noise are only archived when the operator also passes --archive-noise; otherwise they remain candidate/needs-review. Applied promotions and archives write governance_audit_events with before/after metadata and a batch id. scripts/doctor.py reports runtime.memory_candidate_debt so candidate backlog count, age, promotable rows, and archive candidates are visible before relying on promoted-only profile behavior.

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 use tool_schema_profile: "compact" to keep the base request small:

scope_recall_store
scope_recall_search
scope_recall_context
scope_recall_profile
scope_recall_memory
scope_recall_entity

The compact profile replaces several overlapping schemas with two dispatch tools:

• scope_recall_memory(action=...) covers inspect, feedback, update, merge, and forget by exact id.
• scope_recall_entity(action=...) covers probe and related entity graph reads.

Legacy individual tools remain direct-call compatible. To expose the pre-compact schema surface, set:

{
  "tool_schema_profile": "standard"
}

To keep compact mode but expose selected diagnostics, use tool_schema_extra_tools:

{
  "tool_schema_profile": "compact",
  "tool_schema_extra_tools": ["scope_recall_stats", "scope_recall_benchmark"]
}

Schema-surface targets after the compact-profile change:

• default compact: 6 tools, about 4.7 KB of JSON schema in repo-local measurement
• standard profile: 20 tools, about 10.6 KB
• maintenance/secret schema surfaces still require their explicit safety flags

Release 1.5.4 packages the compatibility-preserving refactor and audit hardening:

• scripts/doctor.py remains the operator CLI while implementation moves into focused doctor_* modules.
• graph_hygiene.py, maintenance_ops.py, digest_run_results.py, recall_pipeline.py, and provider_schemas.py centralize shared helpers without changing public tool APIs or SQLite truth semantics.
• Doctor runtime checks open the SQLite truth DB read-only and the doctor import fallback now catches only ImportError.

Release 1.5.3 adds lifecycle-governed profile hardening and memory-companion maintenance:

• scope_recall_profile defaults SQLite rows to explicit or legacy-promoted lifecycle rows; pass include_candidates=true to intentionally include non-hidden candidate rows.
• scripts/promote.memory_candidates.py is a dry-run-by-default promotion planner/apply path for safe ordinary candidates, with governance audit events for applied mutations and redacted review output.
• scripts/repair.graph_hygiene.py reports orphan/hidden-lifecycle graph companion rows and accepts explicit --dry-run; --dry-run wins over accidental --apply.
• scripts/repair.vector_index.py fails closed when the primary configured embedder is unavailable unless the operator intentionally passes --allow-fallback-embedder.

Release 1.5.2 adds Recall Funnel observability and retrieval-regression benchmarking:

• scope_recall_search(include_trace=true), scope_recall_explain, and scope_recall_benchmark(include_trace=true) expose structured candidate-pool, per-stage, filter, timing, and returned-character traces for the active query.
• scope_recall_benchmark now returns aggregate quality metrics: latency percentiles, known-answer recall, top-k accuracy, forbidden-id violations, filter counts, and optional prompt-budget hit rate.
• scripts/benchmark.retrieval_regression.py runs an isolated synthetic benchmark with configurable distractor rows, candidate_pool, and top_k, so retrieval regressions can be reproduced without API keys or vector dependencies.
• retrieval.top_k controls the default tool result limit when no per-call limit is supplied.
• Release-audit hardening synchronizes retrieval.top_k defaults, serializes vector companion mutations, exposes background journal digest health in scope_recall_stats, and caches configured capture skip regexes.

Release 1.5.1 fixes strict release-gate handling for CI runtime scratch directories while preserving the 1.5.0 commercial-governance feature set.

Release 1.5.0 adds commercial governance and release-safety tooling:

• scripts/benchmark.golden.py runs in an isolated temporary Hermes home by default, copies the current plugin source for provider discovery, and treats --overwrite-config as an explicit maintenance-only danger flag with backup/restore protection.
• scripts/check.release.py now gates release readiness on golden benchmark success, dirty/untracked worktree visibility, wheel build/install smoke, doctor smoke, and secret/private-path scans.
• scripts/governance.cleanup.py, scripts/journal.recovery.py, and scripts/report.dashboard.py provide operator workflows for auditable cleanup, staged journal recovery, and release-health summaries without exposing raw SQL mutation as the normal path.
• Hard-delete forgetting now fails closed when no vector companion is provided, preventing SQLite truth deletion that could leave stale vector hits.

Release 1.4.5 tightened the audit/observability tools:

• scope_recall_update re-runs the deterministic conflict/relation review after a row changes, while preserving accumulated feedback counts, feedback-adjusted trust, conflict-review metadata, and higher existing importance scores.
• scope_recall_explain reports rank-aligned retrieval evidence for lexical/BM25/vector/RRF scores, metadata quality adjustment, entity overlap/distance bonuses, relation evidence/rerank contribution, memory-type temporal policy, temporal decay, recency bonus, threshold settings, and final score.
• scope_recall_benchmark still accepts simple queries, and also accepts assertion cases with expected_ids, forbidden_ids, min_rank, min_top_score, and auto_explain_on_fail for regression checks.
• memory_type now informs temporal decay policy: durable facts/preferences/procedures decay less aggressively than episodic or temporary/scratch evidence, and explain exposes the applied policy class/weight.
• memory_relations are surfaced in explain by default; relation-aware reranking remains feature-gated through retrieval.relation_rerank_enabled and is conservative by default.
• shared_pool remains read-only unless shared_pool.write_enabled=true; explicit shared-pool writes require scope_mode="shared_pool" and are limited to configured durable targets.

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_playbook_create
scope_recall_playbook_review
scope_recall_experience_promote
scope_recall_forgetting_report
scope_recall_forgetting_run

Secret-index tools are also hidden by default and require secret_index_tools_enabled=true:

scope_recall_store_secret_index

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

Graph companion hygiene is checked by scripts/doctor.py. Orphan graph rows are safe to remove because memory_entities and memory_relations are rebuildable companions over SQLite memories truth:

# read-only dry run
python scripts/repair.graph_hygiene.py --hermes-home "$HERMES_HOME" --dry-run

# apply only after reviewing the dry-run counts
python scripts/repair.graph_hygiene.py --hermes-home "$HERMES_HOME" --apply

Entity graph read surfaces also hide lifecycle-removed memories (archived, superseded, obsolete, rejected) and filter common tool-trace entity noise such as read_file, search_files, execute_code, skill_view, and session_search.

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 remains an operator maintenance action and fails closed unless maintenance_tools_enabled=true.

scope_recall_stats, scope_recall_export, scope_recall_explain, scope_recall_benchmark, and Experience Kernel tools still work through direct tool calls, but are no longer part of the compact default schema unless tool_schema_profile="standard" or tool_schema_extra_tools exposes them.

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/update by exact id through the compact memory dispatcher.
inspected = scope_recall_memory(action="inspect", id=store["id"])

# Probe entity memory through the compact entity dispatcher.
entity = scope_recall_entity(action="probe", entity="this project", limit=3)

Example scope_recall_stats shape:

{
  "provider": "scope-recall",
  "total_memories": 42,
  "scope_memories": 7,
  "local_scope_memories": 3,
  "shared_scope_memories": 4,
  "shared_pool_scope_memories": 0,
  "shared_pool": {
    "enabled": false,
    "write_enabled": false,
    "pool_id": "",
    "scope_id": "",
    "memories": 0
  },
  "vector": {
    "enabled": true,
    "ready": true,
    "status": "ready",
    "row_count": 42,
    "unique_id_count": 42,
    "duplicate_row_count": 0
  }
}

Tool	Purpose
scope_recall_store	Compact default: store a provider-owned memory row after deterministic governance checks
scope_recall_search	Compact default: search local scratch plus shared durable scope; pass include_trace=true for Recall Funnel evidence
scope_recall_context	Compact default: render a task-relevant memory context block plus structured evidence
scope_recall_profile	Compact default: render a bounded high-level profile/context surface
scope_recall_memory	Compact default: dispatch exact-id inspect, feedback, update, merge, and forget operations
scope_recall_entity	Compact default: dispatch entity probe and related reads
scope_recall_probe	Standard profile/direct-call: inspect accessible memories attached to a specific entity
scope_recall_related	Standard profile/direct-call: list entities that co-occur with a given entity
scope_recall_feedback	Standard profile/direct-call: mark a memory helpful/unhelpful for trust scoring
scope_recall_forget	Standard profile/direct-call: delete memories by exact id/ids within the current accessible scope set
scope_recall_update	Standard profile/direct-call: replace content/category within the current accessible scope set
scope_recall_merge	Standard profile/direct-call: merge same-scope memories into a target row
scope_recall_export	Standard profile/direct-call: export SQLite truth rows as JSON/JSONL; scope_only=false is maintenance-gated
scope_recall_explain	Standard profile/direct-call: explain rank-aligned retrieval evidence and Recall Funnel trace
scope_recall_benchmark	Standard profile/direct-call: run latency/assertion/quality-regression checks
scope_recall_stats	Standard profile/direct-call: inspect storage, retrieval, scope, and vector health
scope_recall_dedupe	Operator-only: inspect or collapse exact duplicate rows
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 configured vector companion from SQLite truth

---

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 keeps those ideas in a Hermes-native implementation with SQLite truth storage and an explicit OpenClaw import path.

This Hermes implementation is not a feature-parity target for the OpenClaw sibling. Each runtime should evolve toward the best native memory plugin for its own host platform; the OpenClaw path here remains an explicit migration/import boundary, not a forced alignment contract.

Area	OpenClaw memory-lancedb-pro	scope-recall V1
Host agent	OpenClaw	Hermes
Truth model	LanceDB-centric OpenClaw memory pipeline	SQLite truth + rebuildable vector companion index
Recall timing	OpenClaw auto-recall hook model	Hermes prefetch(query) current-turn recall with queue_prefetch() kept as a deliberate no-op
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 plus decay/governance review; summarization and promotion stay in explicit digest/operator workflows
Migration	OpenClaw-native data path	explicit importer from OpenClaw LanceDB shape into SQLite truth

Recommended public description:

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

When describing OpenClaw migration, use the explicit importer path rather than drop-in replacement, direct .lance reuse, or broad feature-parity wording.

---

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.

Automatic Experience promotion does not run

Background Experience promotion is opt-in. Set experience.auto_promotion_enabled=true in $HERMES_HOME/scope-recall/config.json, then restart or reload the Hermes process so the provider reads the new runtime config. Automatic promotion still requires evidence-backed task episodes and verification gates; high-risk playbooks stay in review instead of being promoted silently.

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 configured vector companion from SQLite truth when deeper storage hygiene is needed
• semantic merge is intentionally conservative and rules/scoring-based; contradiction handling stays evidence-oriented rather than open-ended LLM reasoning
• write-time smart extraction is rules-based for common preference / ops / project-fact sentences; nightly digest adds a separate LLM/heuristic batch consolidation path for reviewed workflow summaries
• fallback local-hash is a degraded offline availability path; configure a hosted or local semantic embedder for better semantic quality
• 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 while Hermes discovery remains directory-based

See docs/stability.md for the exact V1 compatibility scope.

---

Documentation

Document	Description
DESIGN.md	Architecture, layer split, retrieval model, migration plan, and release expectations
docs/stability.md	Stable V1 compatibility contract and scope
docs/naming.md	Public scope-recall vs Python/tool scope_recall naming contract
docs/hermes-upstream-recommendation-plan.md	Roadmap for official Hermes standalone-provider recommendation
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. Run it from a Hermes-compatible Python environment so plugin-loader imports and LanceDB/pyarrow dependencies match the runtime:

cd /path/to/scope-recall
PYTHONPATH=/path/to/hermes-agent:/path/to/scope-recall \
  /path/to/hermes-agent/venv/bin/python -m pytest -q
PYTHONPATH=/path/to/hermes-agent:/path/to/scope-recall \
  /path/to/hermes-agent/venv/bin/python scripts/check.release.py
/path/to/hermes-agent/venv/bin/python scripts/doctor.py --source-root /path/to/scope-recall --hermes-home "$HERMES_HOME"
/path/to/hermes-agent/venv/bin/python scripts/repair.vector_index.py --hermes-home "$HERMES_HOME" --dry-run

Plain pytest from an unrelated Python environment is not a valid release signal: it can miss Hermes' plugins.memory loader path or the vector dependencies (lancedb, pyarrow) even when the checked-in plugin is healthy.

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
• Recall Funnel traces for search/explain/benchmark, including stage counts, filter counts, timings, returned ids, and returned character budget evidence
• synthetic retrieval regression with configurable distractor rows via scripts/benchmark.retrieval_regression.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 (optional extra lancedb)	Default LanceDB companion vector index
pyarrow>=24,<25 (optional extra lancedb)	Arrow data interchange used by LanceDB
Python stdlib sqlite3	Native-free sqlite-bruteforce companion backend
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.