MemoryPilot MCP server

<strong>The most advanced MCP memory server. Period.</strong> _{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}

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. <b>Install the latest release →</b>

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

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)

| System | R@5 (recall_any, session level) | Notes | Source | |--------|--------------------------------:|-------|--------| | MemoryPilot v4.2 (adaptive rerank) | 99.1% | 470 questions, ~900 ms/query on an M-series Mac | This repo, --benchmark-longmemeval | | MemoryPilot v4.2 (rerank off) | 98.7% | 470 questions, ~28 ms/query with MEMORYPILOT_CROSS_RERANK=off | This repo, --benchmark-longmemeval | | MemPalace v3.3.3 (hybrid) | 98.4% | 450 held-out questions after tuning on 50; 96.6% raw on 500 | MemPalace v3.3.3 release notes | | agentmemory v0.9 | 95.2% | 500 questions (abstention included), all-MiniLM | github.com/rohitg00/agentmemory benchmark/LONGMEMEVAL.md | | mcp-memory-service v10.34.0 | 80.4% | labelled R@5; hit definition not stated | v10.34.0 release notes |

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

| System | QA accuracy | Setup | Source | |--------|------------:|-------|--------| | Mem0 (cloud) | 94.4% | Mem0's own suite, top-200 retrieval | mem0.ai blog | | Zep / Graphiti | 63.8% / 71.2% | gpt-4o-mini / gpt-4o as reader, GPT-4o judge | arXiv 2501.13956, Table 2 | | MemoryPilot | not measured | an end-to-end QA run is planned | |