# Memory SDK

OpenClaw's memory system lives in a standalone package (`packages/memory-host-sdk`) that exposes composable engines for storage, embeddings, query processing, and foundation utilities. The SDK defines a clear host-engine boundary: the host application consumes the SDK surface, while the SDK encapsulates all storage, indexing, and retrieval logic behind exported contracts.

***

## Package Structure

The SDK entry point (`engine.ts`) re-exports four focused engine modules:

```typescript
export * from "./engine-foundation.js";
export * from "./engine-storage.js";
export * from "./engine-embeddings.js";
export * from "./engine-qmd.js";
```

Each engine is a self-contained barrel export covering one concern. New code is directed to the focused subpaths rather than the aggregate surface.

***

## Engine Storage

The storage engine (`engine-storage.ts`) provides the persistence and retrieval primitives built on SQLite with the `sqlite-vec` vector extension.

### Key Exports

- **`MemoryChunk`** and **`MemoryFileEntry`** — the core data types. A chunk is an indexed segment of content with metadata; a file entry represents a source document on disk.
- **`buildFileEntry`** / **`buildMultimodalChunkForIndexing`** — constructors for creating index-ready records.
- **`chunkMarkdown`** — splits markdown content into semantically coherent chunks for embedding.
- **`cosineSimilarity`** — vector distance computation for retrieval ranking.
- **`parseEmbedding`** — deserializes stored embedding vectors.
- **`ensureMemoryIndexSchema`** — creates or migrates the SQLite schema (tables, FTS indexes, vector columns).
- **`loadSqliteVecExtension`** — loads the `sqlite-vec` native extension into the SQLite connection.
- **`requireNodeSqlite`** — resolves the Node.js SQLite driver.
- **`readMemoryFile`** — reads a memory file from disk with safety checks.
- **`resolveMemoryBackendConfig`** — resolves backend-specific configuration (SQLite paths, vector dimensions, etc.).

### Storage Architecture

The storage layer uses SQLite as a single-file database with the `sqlite-vec` extension providing vector column support. Memory chunks are stored alongside their embeddings in the same database, enabling hybrid queries that combine full-text search with cosine vector similarity in a single SQL statement. This mirrors the approach used by ZeroClaw's SQLite backend (see [[ZeroClaw/Memory]]).

```typescript
// Types exported for consumers
type MemoryChunk = { /* chunk content, metadata, embedding vector */ };
type MemoryFileEntry = { /* source file path, hash, modification time */ };
type MemorySearchResult = { /* ranked results with scores and source info */ };
```

***

## Engine Embeddings

The embeddings engine (`engine-embeddings.ts`) provides a pluggable embedding provider system with six built-in backends and batch processing utilities.

### Embedding Providers

| Provider | Default Model | Module |
|----------|--------------|--------|
| Local | `DEFAULT_LOCAL_MODEL` | `embeddings.js` |
| Gemini | `DEFAULT_GEMINI_EMBEDDING_MODEL` | `embeddings-gemini.js` |
| Mistral | `DEFAULT_MISTRAL_EMBEDDING_MODEL` | `embeddings-mistral.js` |
| Ollama | `DEFAULT_OLLAMA_EMBEDDING_MODEL` | `embeddings-ollama.js` |
| OpenAI | `DEFAULT_OPENAI_EMBEDDING_MODEL` | `embeddings-openai.js` |
| Voyage | `DEFAULT_VOYAGE_EMBEDDING_MODEL` | `embeddings-voyage.js` |

### Provider Interface

```typescript
// Core provider contract
type MemoryEmbeddingProvider = {
  name: string;
  // ... embedding computation methods
};

type MemoryEmbeddingProviderAdapter = {
  // Adapts raw provider to the SDK contract
};

// Factory and registry
function getMemoryEmbeddingProvider(name: string): MemoryEmbeddingProvider;
function listMemoryEmbeddingProviders(): string[];
```

### Batch Processing

Three batch helpers handle high-throughput embedding jobs for initial indexing or re-indexing:

- **`runGeminiEmbeddingBatches`** — Gemini-specific batching with rate limit handling.
- **`runOpenAiEmbeddingBatches`** — OpenAI batch API integration (`OPENAI_BATCH_ENDPOINT`).
- **`runVoyageEmbeddingBatches`** — Voyage AI batch processing.

### Safety Utilities

- **`enforceEmbeddingMaxInputTokens`** — truncates input to provider-specific token limits.
- **`estimateStructuredEmbeddingInputBytes`** / **`estimateUtf8Bytes`** — byte estimation for payload sizing.
- **`hasNonTextEmbeddingParts`** — detects multimodal content that requires special handling.

***

## Engine Foundation

The foundation engine (`engine-foundation.ts`) re-exports core utilities from the main OpenClaw application that the memory system depends on. Rather than duplicating logic, the SDK imports these through relative paths to the monorepo `src/` directory.

### Key Capabilities

- **Agent Scope Resolution**: `resolveAgentDir`, `resolveAgentWorkspaceDir`, `resolveDefaultAgentId`, `resolveSessionAgentId` — determines which agent's memory space to operate on.
- **Memory Search Configuration**: `resolveMemorySearchConfig` with `ResolvedMemorySearchConfig` type — controls search behavior (result limits, relevance thresholds).
- **Configuration Loading**: `loadConfig`, `resolveStateDir` — reads OpenClaw configuration and state directories.
- **Session Integration**: `resolveSessionTranscriptsDirForAgent`, `onSessionTranscriptUpdate` — watches for new conversation transcripts to index into memory.
- **File Safety**: `writeFileWithinRoot` — prevents path traversal when writing memory files.
- **Secrets**: `hasConfiguredSecretInput`, `normalizeResolvedSecretInputString` — resolves API keys for embedding providers.

***

## Engine QMD (Query Processing)

The QMD engine (`engine-qmd.ts`) handles query decomposition, keyword extraction, and scope filtering before executing memory searches.

### Query Pipeline

- **`extractKeywords`** — pulls meaningful terms from a natural language query, filtering stop words via `isQueryStopWordToken`.
- **`parseQmdQueryJson`** — parses structured query results (type `QmdQueryResult`) from the QMD binary.
- **Scope Filtering**: `deriveQmdScopeChannel`, `deriveQmdScopeChatType`, `isQmdScopeAllowed` — restricts search results to the appropriate channel and conversation type.
- **Session Files**: `buildSessionEntry`, `listSessionFilesForAgent`, `sessionPathForFile` — manages the session transcript files that feed into memory indexing.
- **CLI Integration**: `checkQmdBinaryAvailability`, `resolveCliSpawnInvocation`, `runCliCommand` — interfaces with an external QMD binary for query processing.

***

## Integration Pattern

The SDK is consumed by the host application through the aggregate `engine.ts` export. The boundary is clear:

- **Host responsibility**: Decides when to index, when to search, which agent scope to use, and how to present results.
- **SDK responsibility**: Handles all storage I/O, embedding computation, vector indexing, query processing, and schema management.

The typical flow:

1. **Initialization**: Host calls `resolveMemoryBackendConfig` to set up the storage backend.
2. **Indexing**: Host uses `engine-embeddings` to generate vectors and `engine-storage` to persist chunks.
3. **Retrieval**: Host uses `engine-qmd` to process the query, then performs a similarity search via `engine-storage`.

This separation means the memory engine can be tested independently, and the host can swap backends (e.g., different SQLite configurations, different embedding providers) without changing application logic.

***

## Relevance to Luna

Luna's [[Memory]] module defines an `IMemoryStore` interface but the current file-based implementation only supports writing — there is no recall or search capability.

### Patterns to Adopt

- **Composable Engine Architecture**: Separating storage, embeddings, and query processing into distinct layers is a clean pattern for Luna. Rather than building a monolithic memory service, Luna could define `IMemoryStorage`, `IEmbeddingProvider`, and `IQueryProcessor` interfaces that compose into a full memory system.
- **SQLite + Vector Extension**: Both OpenClaw and ZeroClaw use SQLite with vector extensions for hybrid search. Luna already references SQLite packages — finishing `SqliteMemoryStore` with this architecture is the natural path.
- **Pluggable Embedding Providers**: Luna should support multiple embedding backends from the start. The factory pattern (`getMemoryEmbeddingProvider(name)`) maps to C# service registration.
- **Batch Processing**: For initial indexing or re-indexing of conversation history, batch embedding helpers prevent rate-limiting issues.
- **Query Preprocessing**: Keyword extraction and scope filtering before search improves relevance. Luna should implement query expansion as a pipeline step before hitting the vector store.

### Key Difference

OpenClaw's SDK is a package boundary within a monorepo — it re-exports from the main `src/` tree through barrel files. Luna, as a single compiled application, would implement this as a set of interfaces within a `Memory` namespace rather than a separate package. The architectural principle (composable engines with clear contracts) translates directly to C# DI registration.