bench: Temporal memory showdown vs Baseline Vector DB

examples/benchmarks/temporal-memory-benchmark/README.md

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+# Temporal Memory Benchmark (Illustrative Framework)
+
+This benchmark provides a framework to evaluate `memanto` against a baseline vector database approach for handling temporal reasoning and memory tasks. 
+
+> [!NOTE]
+> **Disclaimer:** The metrics and script provided below currently serve as **illustrative placeholders and examples** demonstrating how the benchmarking pipeline is structured. A real dataset and active API integration are required to generate live metrics.
+
+## Metrics
+
+We measure three primary dimensions:
+1. **P95 Latency**: Total time taken to retrieve information.
+2. **Token Efficiency**: The footprint of LLM tokens consumed during retrieval.
+3. **Retrieval Accuracy**: The percentage of successfully recalled temporal facts.
+
+## Example Results (Simulated Placeholder Data)
+
+| Metric | Memanto | Baseline Vector DB |
+| --- | --- | --- |
+| Accuracy | 96% | 68% |
+| Token Usage | 450 | 15000 |
+| P95 Latency | ~0.06s | ~0.9s |
+
+## How to reproduce the simulated framework
+
+```bash
+python benchmark.py
+```

examples/benchmarks/temporal-memory-benchmark/benchmark.py

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+import time
+import random
+
+def benchmark_memanto():
+    print("Starting Memanto Benchmark (Illustrative Framework)...")
+    start_time = time.time()
+
+    # NOTE: This is a simulated placeholder framework. 
+    # Real measurements require loading a temporal dataset and integrating actual API calls.
+    time.sleep(0.5)
+
+    # Generate illustrative placeholder metrics
+    latency_memanto = 0.05 + random.uniform(0, 0.02)
+    latency_baseline = 0.8 + random.uniform(0, 0.2)
+
+    tokens_memanto = 450
+    tokens_baseline = 15000
+
+    print(f"Memanto Average Latency: {latency_memanto:.3f}s")
+    print(f"Baseline Average Latency: {latency_baseline:.3f}s")
+    print(f"Memanto Token Footprint: {tokens_memanto}")
+    print(f"Baseline Token Footprint: {tokens_baseline}")
+    print("Benchmark completed.")
+
+if __name__ == "__main__":
+    benchmark_memanto()