Related Pages

Related topics: Storage, Embedding & Hybrid Retrieval, Memory Intelligence, Extraction & Lifecycle, Operations, CLI, Tooling & Common Failure Modes

Overview, Installation & Plugin Architecture

1. Purpose and Scope

memory-lancedb-pro is an OpenClaw memory plugin that provides long-term, hybrid retrieval (vector + BM25) with cross-encoder reranking, multi-scope isolation, long-context chunking, and a management CLI. The published description frames it as an "OpenClaw enhanced LanceDB memory plugin" that exposes both typed tools (memory_recall, memory_store, memory_forget) and a CLI surface (memory-pro stats, memory-pro search) on top of a single LanceDB store at memories.lance Source: [package.json:2-3].

The plugin is loaded by OpenClaw as an extension, registers lifecycle hooks for prompt building and post-turn capture, and ships native binaries for every supported Node platform.

2. Plugin Manifest and Distribution

The npm package is shipped as an ESM module ("type": "module") with dist/index.js as both main and the OpenClaw extension entry point Source: [package.json:4-8]. The OpenClaw discovery block lives in the same package.json:

"openclaw": {
  "extensions": ["./dist/index.js"]
}

Source: package.json:27-31

A separate openclaw.plugin.json file is published alongside it so OpenClaw can validate the manifest, and scripts/version keeps both files synchronized: node scripts/sync-plugin-version.mjs openclaw.plugin.json package.json && git add openclaw.plugin.json Source: [package.json:88-89]. The published file list (dist/**/*, openclaw.plugin.json, README and CHANGELOG) confirms that the artifact users receive is the compiled output of src/ Source: [package.json:15-23].

3. Runtime Dependencies and Platform Support

The plugin depends on @lancedb/lancedb ^0.26.2, apache-arrow 18.1.0, openai ^6.21.0, proper-lockfile ^4.1.2, @sinclair/typebox 0.34.48, and json5 ^2.2.3 Source: [package.json:9-15]. Because LanceDB is a native module, per-architecture binaries are listed under optionalDependencies for darwin-arm64/x64, linux-arm64-gnu/x64-gnu, and win32-x64-msvc Source: [package.json:32-39]. An optional ioredis ^5.11.1 enables the Redis-backed distributed lock proposed for high-concurrency deployments Source: [package.json:40; issue #659].

4. Plugin Architecture

At runtime the plugin splits into four cooperating layers registered from index.ts:

flowchart LR
  Host[OpenClaw Host] -->|hooks| Reg[index.ts<br/>registerPlugin]
  Reg --> Tools["Tools:<br/>memory_recall / store / forget"]
  Reg --> Hooks["Hooks:<br/>before_prompt_build / agent_end"]
  Reg --> CLI["CLI:<br/>memory-pro stats / search / import"]
  Tools --> Core["Core<br/>(retriever, extractor, embedder)"]
  Hooks --> Core
  CLI --> Core
  Core --> LLM["LLM client<br/>(src/llm-client.ts)"]
  Core --> LanceDB[("LanceDB<br/>memories.lance")]
  LLM -.uses.-> HostModel["Host model catalog?<br/>see issue #901"]
  LanceDB -.optional.-> RedisLock["Redis distributed lock<br/>see issue #659"]

The hook surface is what makes the plugin conversational. The v1.1.0-beta.10 release migrated the legacy before_agent_start hook to before_prompt_build with explicit priority ordering (auto-recall=10, invariants=12, derived=15), eliminating OpenClaw 2026.3+ deprecation warnings. Post-turn capture is implemented through the agent_end typed hook Source: [CHANGELOG-v1.1.0.md].

5. Installation Paths and Known Failure Modes

The plugin supports two install topologies, and they do not behave identically:

Three additional install-time hazards recur in community reports:

• Dreaming setting ignored. Selecting "Dreaming" in the OpenClaw config UI has no effect on memory-lancedb-pro v1.1.0-beta.10 (issue #565).
• Dynamic require() in ESM. The v1.1.0-beta.10 tag reintroduced a CommonJS require() inside an ESM module, breaking memory-pro stats, memory-pro search, and the recall/store/forget tools on macOS/Linux (issue #900).
• Stale shipped dist/. npm run build rewrites committed dist files because the shipped builds miss the process.report.excludeNetwork hang guard and the rerank top_n cap (issue #890).

For gateway operators, the recommended safety net is a non-blocking distillation worker driven by the command:new hook. The bundled examples/new-session-distill enqueues a small JSON task file when the user types /new, then a user-level systemd service runs a Gemini Map-Reduce pass and writes high-signal lessons back via openclaw memory-pro import Source: [examples/new-session-distill/README.md:1-19]. This decouples persistence from the agent_end hook, which is known to be skipped on SIGTERM restarts (issue #435).

6. Build, Test, and Verification

npm run build compiles src/ to dist/ via tsc -p tsconfig.json Source: [package.json:75]. npm run verify-package-runtime rebuilds and runs scripts/verify-package-runtime.mjs, and prepack is wired to that same script so the published artifact is always self-checked Source: [package.json:76-78]. Tests are grouped into CI slices (cli-smoke, core-regression, storage-and-schema, llm-clients-and-auth, packaging-and-workflow) via scripts/run-ci-tests.mjs, and benchmark harnesses target LoCoMo and LongMemEval under benchmark/run.ts Source: [package.json:67-85].

See Also

• Hybrid Retrieval, Rerank & Long-Context Chunking
• Multi-Scope Isolation and Cross-Process Locking
• Reflection Pipeline and Lifecycle Hooks
• LLM Client, Tool Surface, and CLI Reference

Related Pages

Related topics: Overview, Installation & Plugin Architecture, Memory Intelligence, Extraction & Lifecycle, Operations, CLI, Tooling & Common Failure Modes

Storage, Embedding & Hybrid Retrieval

Overview

memory-lancedb-pro (v1.1.0-beta.11) is an OpenClaw memory plugin whose retrieval pipeline is built on three cooperating layers: a LanceDB storage layer, an OpenAI-compatible embedding client, and a hybrid retriever that fuses vector search with BM25 lexical scoring, optionally reranked by a cross-encoder model. The combination allows the plugin to recall memories by semantic similarity, exact keyword match, and refined relevance ranking, while keeping per-agent and per-session data strictly isolated through a scope-addressing layer.

Source: package.json:1-30

LanceDB Storage Layer

The storage layer wraps the @lancedb/lancedb JavaScript client (v0.26.2) and the apache-arrow (v18.1.0) table format, exposing a narrow API for opening tables, appending records, querying by vector, and performing filtered scans. Persistence is file-based: a single memories.lance directory holds one or more Lance tables whose rows represent memory items.

Key responsibilities of src/store.ts:

• Table lifecycle — Open or create Lance tables on plugin registration; lazily initialize schema when the directory is empty.
• Schema management — Define typed columns (id, text, vector, scope, metadata, timestamps) consistent with the Arrow schema expected by Lance.
• Atomic writes — Bulk inserts go through a transactional path so partial failures do not corrupt the table.
• Cross-process locking — proper-lockfile (v4.1.2) serializes concurrent writers from multiple agent processes; community issue #659 proposes a Redis-backed alternative for high-concurrency hosts but the default remains file-based.

Scope isolation is implemented in src/scopes.ts. Each memory record carries a scope field composed of an agent id, session id, and optional team id. Queries always pass a scope predicate so that one agent can never read another agent's memories. Identity helpers in src/identity-addressing.ts resolve symbolic address tokens (e.g., agent:alpha, team:dev) into canonical scope strings before they reach the store.

Source: src/store.ts:1-40, src/scopes.ts:1-30, src/identity-addressing.ts:1-25, package.json:60-70

Embedding Pipeline

src/embedder.ts produces dense vector representations for both stored text and incoming queries. The module is intentionally OpenAI-protocol-compatible and accepts a configurable baseURL, which lets it target OpenRouter, Azure OpenAI, or any drop-in proxy.

The embedder:

• Validates that embedding.apiKey is present (either inline or via OPENAI_API_KEY) and throws an explicit error otherwise.
• Respects a maximum input length (embedder-max-input-chars) to avoid 400 errors on large chunks.
• Splits long inputs into overlapping chunks before encoding when chunking.enabled is true.
• Returns 1536-dimensional vectors by default for text-embedding-3-small, but the dimension is inferred dynamically from the upstream response.

A known limitation surfaced in community issue #901 is that the LLM completion lane inside the same plugin builds a minimal request in src/llm-client.ts and posts directly to llm.baseURL, bypassing the host's model catalog. The embedder itself does not have this issue because it is only invoked with a single embedding model name. Operators using OpenRouter-style routing for chat completions should be aware that the two lanes are independent.

Source: src/embedder.ts:1-50

Hybrid Retrieval

src/retriever.ts is the runtime entry point for every memory lookup triggered by the memory_recall tool or the auto-recall hook. It performs a three-stage pipeline: candidate generation by vector + BM25, optional cross-encoder rerank, and scope-filtered output. The end-to-end flow is shown below.

flowchart LR
  Q[User Query] --> A[Adaptive Gate]
  A -- "short / command" --> S[Skip]
  A -- "normal" --> V[Vector Search]
  A -- "normal" --> B[BM25 Search]
  V --> F[Fuse top-N]
  B --> F
  F --> R{Rerank enabled?}
  R -- yes --> X[Cross-Encoder Rerank]
  R -- no  --> O[Scope Filter & Output]
  X --> O
  O --> M[Memory Items]

Stage 1 — Candidate generation. Vector and BM25 searches run in parallel against the same scope predicate. The vector branch uses the embedding produced by src/embedder.ts; the BM25 branch uses an in-memory inverted index built from the current table snapshot. Each branch returns its top-k candidates.

Stage 2 — Fusion. The two candidate lists are merged using reciprocal-rank fusion (RRF). RRF is preferred over score normalization because BM25 and cosine scores live on incompatible scales.

Stage 3 — Rerank (optional). When rerank.enabled is true, the fused list is sent to a cross-encoder endpoint and the top-n are re-ordered. Community issue #890 flagged a regression where the rerank top-n cap was missing from shipped dist/ builds, causing oversized payloads; operators on the published package should rebuild from source or upgrade past the fix.

Adaptive gating. src/adaptive-retrieval.ts wraps the retriever with a cheap pre-check. Trivial messages (single-word commands, system acknowledgements) bypass the network round-trip entirely. This is the mechanism that v1.1.0-beta.10 fixed for short-message false negatives.

Source: src/retriever.ts:1-80, src/adaptive-retrieval.ts:1-45, package.json:55-75

Known Failure Modes

When debugging retrieval quality, first verify that the embedder dimension matches the table schema; a mismatch between stored vector size and query vector size is the most common silent failure mode.

See Also

• Hook lifecycle and before_prompt_build migration (v1.1.0-beta.10)
• Reflection pipeline and resolution mechanism (Issue #447)
• CLI commands (memory-pro stats, memory-pro search, memory-pro import)
• New Session Distillation example (examples/new-session-distill/README.md)

Related Pages

Related topics: Overview, Installation & Plugin Architecture, Storage, Embedding & Hybrid Retrieval, Operations, CLI, Tooling & Common Failure Modes

Memory Intelligence, Extraction & Lifecycle

Overview

memory-lancedb-pro is an OpenClaw memory plugin that turns agent conversations into long-term, queryable knowledge. Beyond raw storage, it ships a full intelligence pipeline that decides *what* to write, *how long* it lives, and *when* it should re-surface. This page documents that pipeline: smart extraction, reflection, tiering, and decay.

The lifecycle runs end-to-end inside a single conversation:

1. The hook layer (agent_end, before_prompt_build) buffers raw text.
2. smart-extractor.ts filters the buffer through LLM prompts defined in extraction-prompts.ts.
3. Surviving candidates are normalized, deduped (see issue #30), and bulk-written by bulk-store.ts.
4. tier-manager.ts assigns a tier; decay-engine.ts schedules expiration.
5. reflection-store.ts / reflection-event-store.ts track derived "lessons" that are re-injected on future sessions.

Source: package.json

Smart Extractor

The extractor (src/smart-extractor.ts) is the gatekeeper. It receives the conversation buffer, applies scope filters (smart-extractor-scope-filter.test.mjs), and asks the LLM — via src/llm-client.ts — to identify stable, re-usable facts. The prompt contract lives in extraction-prompts.ts, which encodes rules such as "ignore transient greetings" and "prefer user-stated preferences over inferred ones."

Two structural details are worth noting:

• Scope isolation. The extractor only operates on rows whose scope matches the active agent. This is the regression tested by test/smart-extractor-scope-filter.test.mjs and is critical when multiple OpenClaw agents share one LanceDB directory.
• Bulk write refactor. PR #669 replaced per-row inserts with a single bulkStore() call (src/bulk-store.ts). The refactor broke branch coverage in test/smart-extractor-branches.mjs (issue #679), which is why that test has been flaky on CI since the change.

Community caveat: the extractor's LLM lane builds its own OpenAI-compatible client (see issue #901). It posts directly to llm.baseURL, so provider-routing features such as OpenRouter fallback configured at the host level are *not* honored.

Source: src/smart-extractor.ts, src/extraction-prompts.ts, src/bulk-store.ts

Reflection, Tiering & Decay

After extraction, memories enter the lifecycle managed by three cooperating modules:

Known reflection pathologies

The reflection pipeline is unidirectional: extract → store → decay → inject. There is no resolve/invalidate path — once a reflection item is written, it surfaces in every new session until maxAgeDays expires (default 45 days for invariants). This is tracked in issue #447.

A second pathology, issue #382, is the *self-trigger loop*: after a session's reflections are injected, the extractor treats them as fresh input and can emit 3–4 near-duplicate reflections for the same session. Until the loop is closed upstream, operators are advised to keep memory-reflection.maxAgeDays low and to review reflection growth via the CLI.

A community-requested feature, "Dreaming" (issue #565), is not supported by memory-lancedb-pro — the plugin does not expose a dreaming toggle in the OpenClaw config UI as of v1.1.0-beta.11.

flowchart LR
  A[Conversation Buffer] --> B[smart-extractor.ts]
  B --> C{Scope Filter}
  C -- match --> D[LLM Prompts]
  D --> E[bulk-store.ts]
  E --> F[tier-manager.ts]
  F --> G[(LanceDB)]
  G --> H[decay-engine.ts]
  G --> I[reflection-store.ts]
  I --> J[Next Session Inject]
  J -. self-trigger .-> B

Source: src/tier-manager.ts, src/decay-engine.ts, src/reflection-store.ts, src/reflection-event-store.ts

Operational Guidance

• Cold-start distillation. The examples/new-session-distill/ recipe pairs command:new with a systemd worker that runs Map-Reduce over the session JSONL, then imports atomic lessons via openclaw memory-pro import. This avoids blocking /new on a synchronous LLM call inside the plugin (examples/new-session-distill/README.md).
• Reflection hygiene. Because reflection items have no resolve path, periodically prune them with the CLI or by lowering maxAgeDays for the invariant tier.
• Dedup before store. Issue #30 requests similarity-based dedup inside memory_store; until merged, callers should pre-filter near-duplicate candidates when ingesting large corpora.
• Concurrency. Under high concurrent writes, proper-lockfile is the default; issue #659 proposes a Redis-backed lock for multi-process hosts.

See Also

• Hook lifecycle & CLI surface (companion wiki page)
• LanceDB storage schema & backup (runBackup())
• LLM client configuration & provider routing caveats (issue #901)

Related Pages

Related topics: Overview, Installation & Plugin Architecture, Storage, Embedding & Hybrid Retrieval, Memory Intelligence, Extraction & Lifecycle

Operations, CLI, Tooling & Common Failure Modes

memory-lancedb-pro ships as an OpenClaw extension whose day‑to‑day operation is exercised through three layers: the runtime plugin entry (dist/index.js), a CLI surface (the memory-pro family of commands and auxiliary scripts/*.mjs helpers), and a TypeScript build pipeline. The package.json "scripts" block is the authoritative map of every operator‑facing entry point — npm test, the grouped CI runners, the verify-package-runtime gate, and the benchmark drivers all live there.

CLI & Management Tooling

The plugin's CLI is exposed to operators as memory-pro … subcommands (e.g. memory-pro stats, memory-pro search, memory-pro import), and as tool calls (memory_recall, memory_store, memory_forget) inside an OpenClaw session. The CLI is registered from the bundled entry declared in package.json at openclaw.extensions: "./dist/index.js". A CLI smoke pass is runnable in isolation via npm run test:cli-smoke, which delegates to scripts/run-ci-tests.mjs under the --group cli-smoke flag. Companion suites are grouped into core-regression, storage-and-schema, llm-clients-and-auth, and packaging-and-workflow, and the manifest consistency between those groups and the npm scripts is enforced by scripts/verify-ci-test-manifest.mjs.

Two example artifacts round out the operator workflow:

• examples/new-session-distill/README.md documents a non‑blocking /new distillation pipeline (systemd worker + JSONL inbox + openclaw memory-pro import) that is recommended over blocking in‑hook extraction.
• benchmark/run.ts provides the npm run bench, bench:locomo, and bench:longmemeval drivers, used to compare retrieval quality after configuration changes.

Build, Verify & Release Operations

The release pipeline is codified in package.json:

The bundled dist/**/* plus openclaw.plugin.json, README, CHANGELOG, docs/**/*.md, and skills/**/*.md are the only artifacts included in the published tarball (package.json "files"). Operators reproducing CI locally should run npm ci && npm run build && npm run verify-package-runtime end‑to‑end.

Common Failure Modes

Operational history on the master and v1.1.0-beta.10 lines surfaces a recurring set of failure shapes that operators should recognise and preempt:

1. ESM/CJS interop regression on macOS/Linux. A CommonJS require() call reintroduced into an ESM module breaks memory-pro stats, memory-pro search, and every memory_recall/store/forget tool call on macOS/Linux because dynamic require() is not supported in pure ESM contexts. Symptom: Dynamic require not supported. Mitigation: track the post‑v1.1.0-beta.10 fix and run test/cli-smoke.mjs before deploying.

2. Stale committed dist. Rebuilding from a clean checkout mutates committed dist files; shipped binaries miss runtime guards such as the process.report.excludeNetwork hang guard and the rerank top_n cap. Operators should treat npm run build as mandatory after every pull and never trust a dist artifact older than the corresponding src/ commit.

3. agent_end never fires. When the OpenClaw gateway receives SIGTERM, the agent_end typed hook does not run for active sessions, so conversation data buffered in memory is lost on restart. Additionally, when the plugin is installed as an external (non‑bundled) extension under ~/.openclaw/workspace/plugins/memory-lancedb-pro, agent_end is blocked entirely, leaving writes stuck in RAM until the gateway shuts down gracefully. Mitigations reported in the issue tracker include adopting the /new distillation example, which decouples persistence from agent_end by writing JSONL and running an out‑of‑band importer.

4. Backup crash on runBackup(). On OpenClaw 2026.4.29, runBackup() fails with TypeError [ERR_INVALID_ARG_TYPE]: The "path" argument must be of type string or an instance of Buffer or URL. Received undefined. The cause is a double‑invocation of api.resolvePath returning undefined on the second call, after which mkdir receives undefined. Pinning OpenClaw or guarding mkdir(undefined) upstream is the recommended workaround.

5. LLM completion lane bypasses the host model catalog. createApiKeyClient in src/llm-client.ts builds a minimal OpenAI chat‑completions payload and POSTs directly to the configured llm.baseURL, which means provider routing configured on the host (OpenRouter, fallbacks, aliases) is ignored. Operators who rely on OpenRouter routing must route the lane through the host catalog or run a transparent proxy.

6. Lock contention under high concurrency. When captureAssistant=true and sessionMemory.enabled=true, simultaneous agent calls contend on a file lock and can stall. The tracked proposal replaces the file lock with a Redis distributed lock (ioredis is an optional dependency in package.json). test/cross-process-lock.test.mjs exercises the new path.

7. Dreaming settings are not wired. Toggling the OpenClaw "Dreaming" toggle has no effect — memory-lancedb-pro does not consume that setting.

8. Reflection self‑trigger loop. The reflection pipeline can emit near‑identical reflection items for the same session because there is no resolve → invalidate → suppress path; items persist until maxAgeDays (default 45 for invariants) naturally expires.

9. CI breakages from refactors. PR #669's bulkStore refactor regressed test/smart-extractor-branches.mjs at line 497, taking the core-regression group red. Run scripts/run-ci-tests.mjs --group core-regression locally before pushing.

10. Verbose logging in CLI mode. Dual‑memory hints and rerank cost advisories bypass the existing isCliMode() ? debug : info convention and repeat on every registration/CLI invocation; route them through the same helper before shipping.

Operator Playbook

1. npm ci && npm run verify-package-runtime — confirms build + runtime match the published behavior.
2. npm run test:cli-smoke — fast loop that exercises memory-pro … commands.
3. npm run test:openclaw-host — runs test/openclaw-host-functional.mjs against a live OpenClaw host.
4. npm run bench:locomo / bench:longmemeval — regression‑aware quality checks after any retrieval change.
5. For session durability, prefer the examples/new-session-distill/ worker over relying on agent_end.
6. Audit stale dist/ and ESM/CJS hygiene after every release; rebuild before deploying.

See Also

• Architecture & Hybrid Retrieval
• Reflection Pipeline & Lifecycle
• Hook Migration Guide (OpenClaw 2026.3+)
• Backup, Restore & Disaster Recovery

Doramagic Pitfall Log

Found 18 structured pitfall item(s), including 9 high/blocking item(s). Top priority: Installation risk - Installation risk requires verification.

1. Installation risk: Installation risk requires verification

• Severity: high
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/CortexReach/memory-lancedb-pro/issues/435

2. Installation risk: Installation risk requires verification

• Severity: high
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/CortexReach/memory-lancedb-pro/issues/813

3. Installation risk: Installation risk requires verification

• Severity: high
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/CortexReach/memory-lancedb-pro/issues/679

4. Installation risk: Installation risk requires verification

• Severity: high
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/CortexReach/memory-lancedb-pro/issues/900

5. Configuration risk: Configuration risk requires verification

• Severity: high
• Finding: Project evidence flags a configuration risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/CortexReach/memory-lancedb-pro/issues/565

6. Security or permission risk: Security or permission risk requires verification

• Severity: high
• Finding: Project evidence flags a security or permission risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/CortexReach/memory-lancedb-pro/issues/447

7. Security or permission risk: Security or permission risk requires verification

• Severity: high
• Finding: Project evidence flags a security or permission risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/CortexReach/memory-lancedb-pro/issues/382

8. Security or permission risk: Security or permission risk requires verification

• Severity: high
• Finding: Project evidence flags a security or permission risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/CortexReach/memory-lancedb-pro/issues/901

9. Security or permission risk: Security or permission risk requires verification

• Severity: high
• Finding: Project evidence flags a security or permission risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/CortexReach/memory-lancedb-pro/issues/850

10. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/CortexReach/memory-lancedb-pro/issues/890

11. Capability evidence risk: Capability evidence risk requires verification

• Severity: medium
• Finding: README/documentation is current enough for a first validation pass.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: capability.assumptions | https://github.com/CortexReach/memory-lancedb-pro

12. Maintenance risk: Maintenance risk requires verification

• Severity: medium
• Finding: Project evidence flags a maintenance risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://github.com/CortexReach/memory-lancedb-pro

Community Discussion Evidence

Doramagic exposes project-level community discussion separately from official documentation. Review these links before using memory-lancedb-pro with real data or production workflows.

• [[Feature Request] Dreaming support — implement OpenClaw dreaming protoco](https://github.com/CortexReach/memory-lancedb-pro/issues/813) - github / github_issue
• "memory-lancedb-pro" does not support dreaming settings - github / github_issue
• Memory LLM completion lane bypasses the host model catalog (cannot honor - github / github_issue
• v1.1.0-beta.10 release regressed LanceDB loading on macOS/Linux (Dynamic - github / github_issue
• Committed dist is stale relative to src: shipped builds miss the process - github / github_issue
• Solution: Redis Distributed Lock for High Concurrency (extends #643) - github / github_issue
• Dual-memory hint and rerank cost advisory bypass the CLI-aware logging c - github / github_issue
• memory-lancedb-pro @ master (post v1.1.0-beta.10): agent_end hook blocke - github / github_issue
• Memory lost on gateway restart: agent_end hook never fires - github / github_issue
• #395 — Reflection Items Lack Resolution Mechanism - github / github_issue
• Bug: Reflection self-triggers in a loop — same session generates 4 near- - github / github_issue
• ci: smart-extractor-branches.mjs test failing since PR #669 bulkStore re - github / github_issue