Agentmemory is a zero-database memory runtime for coding agents. Triple-stream retrieval (BM25 + vector + knowledge graph). Zero active retrieval passes. Drift score: 0.81.
Agentmemory combines BM25, vector search, and a knowledge graph into a triple-stream retrieval pipeline with on-device reranking. The architecture adds complexity at every stage. The result: 0.17 mean precision with zero active passes on 43 precision-assertion cases.
The three streams all share the same fundamental limitation: they cannot structurally exclude beliefs that are semantically proximate but irrelevant. Adding more retrieval paths with the same limitation compounds noise rather than improving precision.
Tenure uses a single retrieval path (alias-weighted BM25 with hard scope isolation) and achieves 1.00 precision. The architectural difference is not "more paths" vs. "fewer paths." It is that Tenure's primary signal is orthogonal to semantic similarity: term matching over indexed aliases where the user coined the vocabulary. That signal is precise by construction in bounded vocabulary contexts.
Agentmemory exposes memory through MCP tools (memory_recall, memory_smart_search). The model must decide to call them. In practice, models frequently do not recognize when they need prior context, especially on questions where the user assumes shared history.
Tenure operates as a proxy between your client and your provider. Memory is injected into every request automatically, before the model sees the prompt. No tool call to trigger. No prompt engineering. No silent failures where memory was available but never fetched.
| Property | Tenure | Agentmemory |
|---|---|---|
| Mean retrieval precision | 1.00 | 0.17 |
| Active retrieval passes | 43/43 | 0/43 |
| Total passes (77 non-session) | 77/77 | 7/77 |
| Retrieval latency (p50) | 9.77ms | 82.28ms |
| Session latency (p50) | 47.79ms | 98.49ms |
| Drift score | 0.00 | 0.81 |
| Memory delivery | Automatic (proxy) | MCP tool call (model decides) |
| Scope isolation | Hard filter | Project tagging (soft) |
| Supersession | Chain with audit trail | Decay scoring |
| Per-turn injection audit | Yes | No |
| Works across every client | Any OpenAI-compatible | MCP-capable agents only |
| Runs locally | Always | Always |
| External databases | None (single container) | None (single process) |
| License | MIT | Apache-2.0 |
Full methodology: arXiv:2605.11325. Dataset: HuggingFace. Live leaderboard: HuggingFace Spaces.
No MCP. No tool calls. Memory in context on every request. Zero config. Thirty seconds.