MindCore Memory MCP

Production-Hardened MCP Memory Server — Hybrid Search + Resilience for AI Agents

The only MCP memory server with circuit breaker, SLO tracking, and BM25+FAISS hybrid search. AI agents forget everything between sessions. MindCore Memory gives them persistent, searchable, production-grade memory — with 118/118 tests passing and full CI/CD.

⭐ If this project helps your AI remember, a star means the world to us.

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Quick Start

# 1. Install
pip install mindcore-memory

# 2. Launch (stdio mode — works with any MCP client)
mindcore-memory

# 3. Your AI agent remembers across sessions

{
  "mcpServers": {
    "mindcore-memory": {
      "command": "python",
      "args": ["-m", "mindcore_memory.server"],
      "env": { "MINDCORE_MEMORY_PATH": "~/.mindcore/memory" }
    }
  }
}

pip install mindcore-memory[semantic]
# Enables FAISS embeddings for hybrid BM25+semantic search

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Why MindCore — vs the Competition

Feature	MindCore Memory	Mem0	SynaBun	Letta (MemGPT)
Search	BM25 + FAISS Hybrid	FAISS only	sqlite-vec only	FAISS only
Circuit Breaker	✅ 3-state	❌	❌	❌
Retry (exp. backoff)	✅	❌	❌	❌
SLO Tracking	✅ P95/P99	❌	❌	❌
Prometheus Metrics	✅ /metrics	❌	❌	❌
Encryption at Rest	✅ Fernet	❌	❌	❌
Deduplication	✅ Exact-match merge	⚠️ Partial	❌	❌
IVF Index (500+)	✅ Auto-switch	❌	❌	❌
Local-First	✅ Zero deps	✅ (cloud optional)	✅	❌ (needs Docker)
CI/CD Pipeline	✅ Auto → PyPI + MCP	⚠️ Manual	❌	❌
Tests	118/118 (100%)	Unknown	Unknown	Unknown
License	MIT	Apache 2.0	Apache 2.0	Apache 2.0

MindCore is the only MCP memory server designed for production workloads from day one. Circuit breaker protects against embedding service failures. Retry with exponential backoff handles transient errors. SLO tracking alerts you before users notice. Metrics export for your monitoring stack. Every other server assumes nothing fails — MindCore doesn't.

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Unique: 3D Boundary Balance Algorithm

MindCore is not just a memory store — it's a cognitive boundary engine. Every stored memory is automatically evaluated through a 4-dimensional scoring system based on the 正反公式 (Forward/Reverse Formula):

BND_score = 0.28·TRJ(Trajectory) + 0.28·EVO(Evolution) + 0.28·COG(Cognition) + 0.16·BALANCE

• Forward cycle: TRJ → BND → EVO → COG → BND (each step draws a boundary, each boundary is growth)
• Reverse chain: Chaos → Unknown → Risk → Harm → Death (2+ linked triggers → auto 50% score penalty)
• 3D balance: Variance across TRJ/EVO/COG penalizes lopsided memories (pure data dumps without insight)
• No LLM calls: Pure algorithmic evaluation using keyword patterns, regex, and statistical variance

from mindcore_memory import BNDManager
bnd = BNDManager()
result = bnd.evaluate("基于之前修复, 理解到根因, 改进后提升30%", importance=4)
# → TRJ:0.63  EVO:0.54  COG:0.61  BALANCE:0.98  BND:0.75  ACCEPTED

📖 Full algorithm documentation

No other MCP memory server does this. BND transforms memory storage from a passive data dump into an active cognitive filter — rejecting noise, flagging risk chains, and ensuring only structured, growth-oriented knowledge enters the version chain.

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Production Features

Resilience Layer

• Circuit Breaker: CLOSED → OPEN → HALF_OPEN state machine. Protects FAISS/embedding operations from cascading failure.
• Retry: Exponential backoff with jitter. Transient errors retry automatically, permanent errors fail fast.
• Input Validation: Server-level sanitization against injection attacks.

Observability Layer

• SLO Tracking: P95/P99 latency targets for all 6 operations. Violations logged and exported.
• Prometheus /metrics: Zero-dependency Prometheus-compatible collector. Drop-in for any monitoring stack.

Data Layer

• Encryption: Optional Fernet encryption at rest (mindcore-memory[encrypt]).
• Deduplication: Exact-match merge — identical memory updates importance/confidence instead of storing duplicates.
• Smart Eviction: Low-importance memory pruning with atomic disk sync. No zombie memories.

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Core Tools

Memory (6 tools)

Tool	Description	Key Parameters
memory_store	Persist a memory (auto-BND evaluated)	content, importance (1-4), tags, confidence
memory_recall	Search memories (BM25+FAISS hybrid)	query, tags, limit, session_id
memory_context	Build LLM context window	query, max_tokens, session_id
memory_update_confidence	Adjust memory confidence	memory_id, confidence
memory_delete	Remove a memory	memory_id
memory_stats	System statistics	(no args)

Boundary & Deduction (3 tools) 🆕

Tool	Description	Key Parameters
bnd_check	4D boundary evaluation (TRJ/EVO/COG/BALANCE + Anti-Chain)	content, importance, confidence, tags
bnd_stats	BND manager stats: acceptance rate, scores, anti-chain triggers	(no args)
deduce	Cognitive deduction: pattern extraction from high-quality memories	query, tags

Search formula: score = BM25(40%) + FAISS(50%) + importance(5%) + recency(5%)

When FAISS embeddings are unavailable, automatically falls back to BM25-only keyword search.

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Architecture

┌───────────────────┐     MCP JSON-RPC      ┌────────────────────────────┐
│  AI Client         │ ◄──────────────────► │  MindCore Memory           │
│  (Claude/Cursor)   │     stdio / HTTP     │  MCP Server                │
└───────────────────┘                       └──────────┬─────────────────┘
                                                       │
                                            ┌──────────▼─────────────────┐
                                            │  Memory Engine             │
                                            │  ┌──────────────────────┐  │
                                            │  │ Hybrid Search        │  │
                                            │  │  BM25 (keyword) 40%  │  │
                                            │  │  FAISS (semantic)50%│  │
                                            │  │  importance        5%│  │
                                            │  │  recency           5%│  │
                                            │  └──────────────────────┘  │
                                            │  ┌──────────────────────┐  │
                                            │  │ Resilience           │  │
                                            │  │  Circuit Breaker     │  │
                                            │  │  Retry + Backoff     │  │
                                            │  │  SLO Tracking        │  │
                                            │  └──────────────────────┘  │
                                            └──────────┬─────────────────┘
                                                       │
                                            ┌──────────▼─────────────────┐
                                            │  Storage                   │
                                            │  JSONL (append)            │
                                            │  + FAISS index (IVF > 500) │
                                            │  + Fernet encrypt (opt)    │
                                            └────────────────────────────┘

• Embedded: No PostgreSQL, Redis, or external services needed. One binary, local JSONL + FAISS.
• IVF Index: FAISS inverted file index activates at 500+ memories for O(√N) search.
• MCP Native: Full MCP protocol over stdio and HTTP transports.

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Available On

Platform	Status	Link
PyPI	Published v0.1.11	mindcore-memory
MCP Registry	Registered	View
Glama	Listed	View
MCP Market	Listed	View
MCP.so	Listed	View
LobeHub	Listed	View
mcpservers.org	Listed	View

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Full Comparison

See docs/comparison.md for a detailed 5-server comparison covering architecture, search quality, latency, and migration guides.

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Contributing

See CONTRIBUTING.md for the full guide. Quick path:

git clone https://github.com/woshilaohei/mindcore-memory-mcp.git
cd mindcore-memory-mcp
pip install -e ".[dev]"
pytest -v              # 118 tests
ruff check .           # linter
mypy mindcore_memory/  # type checker

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License

MIT License — Copyright (c) 2025 Lao Hei

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⭐ If MindCore helps your AI remember, give it a star! ⭐