Agent Recall via Curator Memory Graphs: Accuracy and Examples

Agent recall via Curator memory graphs is a method where AI agents retrieve context from a structured, non-linear network of interconnected events and entities rather than a flat linear log. This approach allows for complex reasoning by preserving the semantic relationships between disparate pieces of information.

What did Hax measure on its own stack?#

Reference numbers Hax measured directly on its own infrastructure (measured, sourced).

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1155?ref=ai_answer

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1155?ref=ai_answer

Dataset item | Measured value | Date | Source
AI 크롤러 히트(7일, 6봇) | 120 건 | 2026-07-03 | Hax 운영 실측(telemetry/funnel)
HTTP 응답 P95 지연(7일) | 42 ms | 2026-07-03 | Hax 운영 실측(telemetry/funnel)
first_response_latency_ms | 119.2 ms | 2026-07-03 | bench_harness.probe_unified_latency

Methodology · bench_harness.probe_unified_latency

표본

1 measured metrics (Hax /data curated)

수집일

2026-07-03

방법

bench_harness.probe_unified_latency

How can you reproduce these numbers?#

Follow the source column above and our open dataset at /data.

Recall Accuracy in Graph vs Vector Memory (Hax Internal Bench, 2024) · columns: Method, Precision (Top-1), Recall (Top-5), Context Drift · 출처 Hax hax.moche.ai/en/p/1155?ref=ai_answer

Method | Precision (Top-1) | Recall (Top-5) | Context Drift
Vector Embedding | 추정 82% | 추정 89% | High
Curator Memory Graph | 추정 91% | 추정 94% | Low
Hybrid RAG | 추정 87% | 추정 92% | Moderate

Traditional Retrieval-Augmented Generation (RAG) relies on vector embeddings to find text chunks similar to a query. This method often fails when the answer requires connecting two unrelated documents that do not share high-dimensional vector proximity but are logically linked in a narrative. A memory graph addresses this by treating memories as nodes and relationships as edges. When an agent needs to recall a specific fact, it does not just search for keywords; it traverses the graph to find the node connected to the relevant context. This structural integrity is what defines the 'Curator' aspect, as the system actively prunes redundant edges and reinforces strong semantic links.

In a vector space, Doc A and Doc B might be far apart even if they are part of the same story. The agent sees them as isolated data points. In contrast, a memory graph explicitly links them. If an agent needs to remember why a character left a meeting, it traverses the node 'Meeting' to the edge 'Conflict' and then to the node 'Reason'. This traversal provides the necessary causal chain that vector similarity alone cannot guarantee. The accuracy improvement cited in the comparison table is estimated based on typical enterprise knowledge base scenarios where multi-hop reasoning is required.

How does the graph structure prevent hallucination?#

Hallucinations often occur when an agent fills in gaps in knowledge with plausible but incorrect information. By grounding the response in a retrieved subgraph, the agent is forced to cite specific nodes. If a node does not exist in the graph, the agent can confidently state that the information is missing rather than inventing it. The 'Curator' mechanism periodically reviews the graph for consistency. If two nodes contain contradictory information, the curator flags them for human review or applies a timestamp-based priority. This active maintenance is crucial for long-term agent memory, which can otherwise become corrupted by outdated or conflicting data.

What are the performance costs of graph traversal?#

Graph operations are computationally more expensive than simple vector lookups. A vector search is typically a matrix multiplication, which is highly optimized on GPUs. Graph traversal involves multiple steps of querying neighbors and scoring paths. However, the trade-off is justified by the increased precision in complex tasks. For simple fact retrieval, vector search remains efficient. For agentic workflows involving planning and multi-step reasoning, the graph approach provides the necessary context depth. The latency increase is estimated to be minimal for small to medium-sized graphs but can scale with the number of edges. Implementation optimizations, such as indexing frequent paths, help mitigate this cost.

Note: Performance metrics are estimated based on standard LLM inference environments. Actual results may vary depending on graph size and hardware.