Hindsight is Vectorize's MCP-based agent memory. It bundles a cross-encoder reranker that adds 672ms mean latency without improving precision past 0.06.
Hindsight's cross-encoder reranker is the architectural feature that most distinguishes it from a standard embedding retrieval stack. The benchmark results show it does not close the precision gap.
On alias resolution cases (a query using k8s to retrieve the Kubernetes entity belief), Hindsight returns 18 beliefs with precision 0 and recall 0: the correct belief does not appear at all. This is not a ranking failure; it is a retrieval failure. The correct belief is absent from the candidate set before the reranker runs.
You cannot rerank your way out of a retrieval failure. A cross-encoder can only reorder candidates that are already in the result set. If the initial retrieval stage misses the correct belief entirely, no amount of reranking recovers it. The fix must happen at the primary retrieval signal, not downstream.
Hindsight relies on MCP for memory access. The model must decide to call the memory tool. If it does not recognize that it needs prior information, it does not ask. Your memory sits idle while the model hallucinates decisions you already made.
Tenure sits between your client and your provider as a proxy. Every request is enriched before it reaches the model. No tool call to trigger. No prompt engineering required. Your beliefs are in context on every single request, automatically.
| Property | Tenure | Hindsight |
|---|---|---|
| Mean retrieval precision | 1.00 | 0.06 |
| Active retrieval passes | 43/43 | 0/43 |
| Retrieval latency (p50) | 9.77ms | 589.86ms |
| Session latency (mean) | 49.02ms | 2,735.84ms |
| Session latency (p95) | 134.63ms | 6,162.57ms |
| Drift score | 0.00 | 0.93 |
| Ingestion (35 beliefs) | 1.0s | 173.3s |
| Memory in context | Every request (proxy) | Only when model calls MCP tool |
| RAM requirement | ~512MB | 3-4GB (BGE + cross-encoder) |
| Deployment | Single container | Single container (heavy) |
| Scope isolation | Hard filter | None |
| Per-turn injection audit | Yes | No |
| License | MIT | MIT |
Hindsight evaluated against pinned Docker image digest ghcr.io/vectorize-io/hindsight@sha256:f0f9e9a73d6a. Full methodology: arXiv:2605.11325. Dataset: HuggingFace.
No MCP tool call required. No reranker to wait on. No 6-second session turns. Thirty seconds to install.