# Benchmark Results for Symbolic Memory Integration with OpenClaw

> Discover benchmark results for symbolic memory integration with OpenClaw. See the PersonaMem benchmark reveal a 59% improvement with memory hub enabled in TencentDB Agent Memory.

- Repository: [Tencent Cloud/TencentDB-Agent-Memory](https://github.com/TencentCloud/TencentDB-Agent-Memory)
- Tags: performance
- Published: 2026-08-24

---

**The TencentDB Agent Memory repository does not publish isolated benchmark metrics for symbolic memory integration with OpenClaw; the only available quantitative result is the PersonaMem benchmark showing a 59% relative improvement when the memory hub is enabled.**

The TencentDB Agent Memory framework provides persistent memory capabilities for LLM agents, yet specific performance data regarding **symbolic memory integration with OpenClaw** remains consolidated within general system benchmarks. While the repository implements robust content filtering to exclude purely symbolic text from storage, it does not currently expose micro-benchmarks isolating this step. Engineers evaluating OpenClaw gateway performance must rely on the aggregate PersonaMem figures or implement custom instrumentation.

## Current Benchmark Coverage

The repository publishes only the **PersonaMem** evaluation, which measures long-term information retention across extended agent interactions rather than isolating symbolic memory operations. According to the source documentation, enabling the TencentDB Agent Memory hub yields the following results:

- **Without Memory Hub**: 48% accuracy
- **With Memory Hub**: 76% accuracy  
- **Relative Improvement**: +59%

These figures represent the overall system impact rather than specifically quantifying the symbolic filtering step integrated with OpenClaw configurations.

## Symbolic Memory Implementation

Symbolic memory filtering prevents the persistence of low-value content—such as text containing only punctuation or whitespace—before it reaches the vector store. The implementation spans the sanitization utility and the pipeline factory that manages OpenClaw configuration injection.

### The sanitizeContent Utility

Located in [`MemoryCore/src/utils/sanitize.ts`](https://github.com/TencentCloud/TencentDB-Agent-Memory/blob/main/MemoryCore/src/utils/sanitize.ts), the `sanitizeContent` function applies heuristic filters to remove symbolic content from the ingestion pipeline:

```typescript
// utils/sanitize.ts – filter out symbolic or otherwise useless chunks
import { sanitizeContent } from "./utils/sanitize";

// Example: a raw chunk of text from a document
const rawChunk = "..."; // some extracted text
const cleaned = sanitizeContent(rawChunk);
if (cleaned) {
  // Only store meaningful content as a memory asset
  await memoryStore.saveAsset(cleaned);
}

```

The function checks content length boundaries (minimum 20 characters, maximum 10,000) and rejects strings matching the regex `/^[\\s\\p{P}]+$/u`, which identifies purely symbolic sequences.

### Pipeline Integration with OpenClaw

The [`MemoryCore/src/utils/pipeline-factory.ts`](https://github.com/TencentCloud/TencentDB-Agent-Memory/blob/main/MemoryCore/src/utils/pipeline-factory.ts) file wires the sanitization step into the generation pipeline and passes OpenClaw configuration options downstream:

```typescript
// utils/pipeline-factory.ts – wiring the Sanitize step into the generation pipeline
import { sanitizeContent } from "./sanitize";
import { SkillExtractor } from "./skill-extractor";
import { MemoryGeneration } from "./memory-generation";

export function buildPipeline(opts: PipelineOptions) {
  const {
    openclawConfig,
    // …other options
  } = opts;

  const skillExtractor = new SkillExtractor({
    openclawConfig: openclawConfig as Record<string, unknown>,
  });

  const memoryGeneration = new MemoryGeneration({
    // The pipeline will call this on every generated piece
    sanitize: sanitizeContent,
  });

  // …continue building the rest of the pipeline
  return { skillExtractor, memoryGeneration };
}

```

## Measuring Symbolic Memory Performance

Because the repository lacks dedicated benchmarks for the symbolic memory layer, you must instrument the pipeline manually to isolate OpenClaw-specific metrics. Wrap the `sanitizeContent` function to capture telemetry on filtered chunks:

1. **Count rejected items** by tracking how many chunks the regex filter drops per session.
2. **Measure latency** by recording execution time for the sanitization check under OpenClaw load.
3. **Compare retrieval quality** by evaluating downstream retrieval relevance with and without the filter enabled.

This instrumentation reveals the actual performance characteristics of symbolic memory integration within your specific OpenClaw deployment.

## Summary

- The TencentDB Agent Memory repository does not provide isolated benchmark results for symbolic memory integration with OpenClaw.
- The published **PersonaMem** benchmark demonstrates a **+59% relative improvement** (48% to 76%) when the memory hub is active.
- Symbolic content filtering is implemented in [`MemoryCore/src/utils/sanitize.ts`](https://github.com/TencentCloud/TencentDB-Agent-Memory/blob/main/MemoryCore/src/utils/sanitize.ts) via the `sanitizeContent` function.
- The [`MemoryCore/src/utils/pipeline-factory.ts`](https://github.com/TencentCloud/TencentDB-Agent-Memory/blob/main/MemoryCore/src/utils/pipeline-factory.ts) orchestrates the integration between OpenClaw configuration and the sanitization pipeline.
- Engineers must implement custom instrumentation to obtain specific metrics for symbolic memory performance.

## Frequently Asked Questions

### Does TencentDB Agent Memory provide specific benchmarks for symbolic memory integration with OpenClaw?

No. The repository only publishes the **PersonaMem** benchmark, which measures overall system retention rather than isolating symbolic memory operations. To obtain specific metrics, you must instrument the `sanitizeContent` function manually.

### Where is the symbolic content filter implemented in the codebase?

The filter resides in [`MemoryCore/src/utils/sanitize.ts`](https://github.com/TencentCloud/TencentDB-Agent-Memory/blob/main/MemoryCore/src/utils/sanitize.ts). The `sanitizeContent` function rejects content shorter than 20 characters, longer than 10,000 characters, or matching the purely symbolic regex `/^[\\s\\p{P}]+$/u`.

### How can I measure the performance impact of symbolic memory filtering when using OpenClaw?

Wrap the `sanitizeContent` utility to capture telemetry on rejected chunks and execution latency. Compare retrieval relevance scores between runs with filtering enabled and disabled. This approach yields OpenClaw-specific performance data not available in the default benchmark suite.

### What does the PersonaMem benchmark evaluate?

**PersonaMem** tests how well an agent retains user-provided information after long interactions. The published results show accuracy improving from 48% without the memory hub to 76% with it enabled, representing a 59% relative gain in information retention.