MemoryPilot

The most advanced MCP memory server. Period.

Hybrid search (BM25 + jina-embeddings-v5 RRF + cross-encoder rerank) · 100+ languages · Temporal Knowledge Graph · Query-aware ranking · Corpus origin detection · Agent/persona disambiguation · Topic tunnels · AAAK compression (5-10x token savings) · GraphRAG · Chunked RAG · Auto-Compaction · Auto-Classification · Memory Capsules · HTTP API · Signed automatic updates · Single binary · Zero API calls

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Why

AI coding assistants forget everything between sessions. MemoryPilot gives them persistent, searchable memory with project awareness, semantic understanding, and automatic knowledge organization. Built-in AAAK compression and memory capsules reduce token consumption by 5-10x when loading context. Every memory is auto-classified with the right importance, kind, and TTL on insert. The database compacts itself automatically — zero maintenance.

Install MemoryPilot One-liner install for Cursor, Claude Desktop, VS Code, Windsurf, Claude Code, Codex and ChatGPT. Single Rust binary, zero runtime dependencies. Install the latest release →

How it works The 9 pillars — hybrid search, temporal knowledge graph, GraphRAG, AAAK compression, auto-classification, self-healing — explained end to end. Read the architecture →

Benchmarks

LongMemEval-S (ICLR 2025) — Academic Standard

Evaluated on the 470 answerable questions of LongMemEval-S (ICLR 2025; the 30 abstention questions are left out, as the official retrieval protocol does). Turn-level index, ~50 sessions per haystack.

What the score measures. R@5 here is session-level recall_any@5: a question counts as a hit when at least one of the top 5 retrieved turns belongs to one of its gold sessions. It is a retrieval metric, not answer accuracy, and a question that needs several sessions counts as a hit as soon as one of them is found (300 of the 470 questions have more than one gold session). For scale: a plain BM25 over turns, with no embeddings, scores about 93% on the same metric and 74% on recall_all@5. These figures were measured with v4.2 (multilingual-e5-small); the current default model has only been measured on a 100-question slice (see v4.6 below).

Retrieval scores (same metric family, setups differ)

Answer accuracy (a different metric: an LLM reads the retrieved context and a judge grades the answer)

All figures are published by their own authors, none was reproduced independently, and the two tables cannot be compared with each other. On session-level recall MemoryPilot posts the highest published local score, by a margin smaller than the gap between this metric and a plain BM25 baseline. The protocol to settle a comparison: same 470 questions, same hardware, every system run by the same script, reporting recall_any@5, recall_all@5, turn-level evidence recall and end-to-end QA accuracy with a fixed reader and judge.

Detailed view — MemoryPilot vs MemPalace (closest local competitor)

¹ Validated with the full 470-question evaluation set (after dropping the 30 abstention questions) in two modes: hybrid retrieval with the rerank off (BM25 + cosine RRF, MEMORYPILOT_CROSS_RERANK=off, ~28 ms/query) and the default adaptive cross-encoder rerank (MEMORYPILOT_CROSS_RERANK=adaptive, jina-v2-multilingual, fusion weight 0.45, ~900 ms/query, suitable for benchmarks and high-stakes recall). Latencies cover the search call only, with models loaded; MemPalace's figures come from a different question split.

By Category (470 questions, adaptive rerank)

French / Multilingual Benchmark — --benchmark-fr

To complement the English-only LongMemEval, MemoryPilot ships its own deterministic French benchmark covering 109 memories and 109 paraphrased queries across infra, mobile, web, security, and ML domains. The queries are intentionally distant from the indexed wording so they actually exercise the semantic lane.

v4.5 fixed a ranking bug that had capped this benchmark at R@5 ≈ 70% since v4.0: knowledge-graph neighbours were injected at a hard-coded score (0.1) above every genuine RRF hit (max 2/41 ≈ 0.049), so they occupied the cross-encoder window and pushed real matches past limit. Neighbours now enter below the weakest genuine candidate and only the cross-encoder can promote them.

v4.5.0 also closes two write-path stability bugs surfaced by that work: the episodic rollup embedded every hourly bucket on each pass (before checking whether the episode already existed) and did so synchronously inside add_memory, then panicked on multi-byte French text; it now runs on its own thread with a cheap existence check first. And auto-compaction held a Mutex across run_gc/compact_to_capsules, whose merged rows re-enter add_memory — a deadlock hidden until the rollup stopped panicking ahead of it.

Run-to-run variance is bounded to ±1 pp on R@5 / R@10 thanks to deterministic memory ids, deterministic id-based RRF tie-break, synchronous ANN warm-up, and explicit cross-encoder pre-warm before the first query. This is the metric to watch for any French / multilingual regression.

v4.6 — beyond single-fact retrieval

v4.6.0 targets the LongMemEval categories a top-k retriever alone cannot win: multi-session aggregation, temporal reasoning, knowledge updates and abstention. Everything stays local and rule-based or int8 ONNX. Measured on LongMemEval-S@100 (64 single-session + 36 multi-session, plus the 7 abstention questions of that slice) against v4.5.0, same machine, cross-encoder adaptive:

--benchmark-fr is unchanged within noise (R@1 96.3%, R@5 99.1%, MRR 97.6%).

• MMR diversity (src/diversity.rs) — the finalists plus the RRF ranks just below them are re-ordered by maximal marginal relevance over their stored vectors (λ = 0.4, redundancy floor 0.55). The top hit never moves, so MRR is untouched; a second, distinct source can displace a restatement of the first. MEMORYPILOT_MMR=0 disables it.
• Temporal grounding (src/temporal.rs) — explicit dates in a memory (2026-09-01, 12 septembre 2026, September 12, hier…) become date entities at ingest; a temporal phrase in the query (last week, il y a deux semaines, en mars, last Tuesday) becomes a day window that boosts dated candidates inside it (×1.25) and mildly demotes dated candidates far outside (×0.85). Undated memories are neutral, so a mis-parsed w