Perspective Tensor
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Agent Memory System

A production-ready, three-tier memory architecture designed for long-running AI agents.

Live Demo

Experience the routing and retrieval logic in real-time

Example queries:

💡 Demo mode: Using mock data.

Use Cases

Real-world scenarios for consulting and research

Example Query:

What did we learn about healthcare M&A trends in Q3 2024?

Results:

  • Past deal analysis: Healthcare M&A increased 23% in Q3
  • Client note: Client X interested in healthcare vertical
  • Industry report summary: Regulatory changes driving consolidation

Implementation Details

Core logic in Rust for maximum performance

Router Function

Intelligent routing with heuristic cascade

fn route_query(q: &Query) -> RouteResult {
    // Small talk stays in working memory
    if is_smalltalk(q) {
        return query_l1(q);
    }
    
    // Exact matches: sparse + KV with dense sanity check
    if needs_exact_match(q) {
        let result = query_l2_sparse(q) + query_l2_kv(q);
        return sanity_check_dense(result);
    }
    
    // Semantic search: dense ANN with reranking
    if needs_semantic_search(q) {
        let candidates = query_l2_dense_ann(q, TOP_K);
        let reranked = late_interaction_rerank(candidates);
        return enrich_from_l1(reranked);
    }
    
    // Fall back to archive for low confidence or long-horizon queries
    if has_low_confidence() || is_long_horizon(q) {
        let archive_result = tap_l3_archive(q);
        let summary = summarize(archive_result);
        promote_to_l2_kv(summary);
        return archive_result;
    }
    
    // Log telemetry for analysis
    log_query_metrics(QueryMetrics {
        tiers_accessed,
        latency_ms,
        recall,
    });
}