Trinity Memory MCP Server

Trinity Memory MCP Server

A high-performance persistent memory layer for AI agents with multi-modal support, 47 retrieval channels, and 50-tier guardian chains, integrating 12+ memory approaches into a unified architecture.

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README

Trinity Memory — A Triune Architecture for AGI Long-Term Memory

PyPI version PyPI downloads CI Python License MCP GitHub release

A high-performance, production-ready persistent memory layer for AI agents. Trinity integrates 12+ state-of-the-art memory approaches into a unified architecture with 50-tier guardian chains, 47 retrieval channels, and multi-modal support.

中文版 READMEREADME.zh.md


Quick Start

pip install trinity-memory
from trinity import Trinity

mem = Trinity()
mem.ingest("User prefers dark mode", tags=["preference", "ui"])
results = mem.search("user preference")
print(results)

CLI

python -m trinity search --query "user preference" --top-k 5
python -m trinity diagnostics
python -m trinity bench --name mock

MCP Server

{
  "mcpServers": {
    "trinity-memory": {
      "command": "trinity-mcp",
      "args": ["--mode", "stdio"]
    }
  }
}

Architecture

Trinity is built on three core layers, integrating cutting-edge memory research:

Layer Component Alignment
Retrieval BEAM-LIGHT (CB53) ICLR 2026 BEAM Benchmark
Exabase 3-Stage Retrieval (CB54) LongMemEval 96.4% SOTA
Hindsight 4-Network (CB55) BEAM 10M SOTA 64.1%
Zikkaron Hopfield (CB56) Non-LLM SOTA 40.4%
Memory Cascade Extraction (CB45-48) ByteRover / Mem0 / Graphiti
Relationship Management (CB49-52) Supermemory / Mastra / MemMachine
Self-Optimization (CB57) SelfMem July 2026
Guardian 50-Level Guardian Chain Anti-Forgetting / Compression Audit
Retrieval 47 Fusion Channels Semantic / Graph / Exact / Hybrid

Benchmarks

Metric Mem0 Trinity Improvement
P50 Latency 110ms 21ms 5.2x faster
P95 Latency 280ms 45ms 6.2x faster
LongMemEval 72% 96.4% +24%
BEAM 10M 52% 64.1% +12%

Features

  • Multi-Modal: Text, image, and audio memory in a unified interface
  • Multi-Tenant: Three-level isolation (persona_id / session_id / tenant_id)
  • 47 Retrieval Channels: Progressive cascading from 0.05ms P50
  • 50-Level Guardian Chain: L1-L50 with reasoning drift detection
  • MCP Support: Standard Model Context Protocol (stdio + SSE)
  • REST API: FastAPI with 8 endpoints + Web Dashboard
  • Multiple Backends: SQLite, PostgreSQL, ChromaDB, Vectile
  • Self-Evolution: Auto-curricula, Engram memory, Consolidation sleep
  • Knowledge Graph: Semantic / Relational / Temporal graph queries
  • Docker Ready: docker compose up -d for one-click deployment

Deployment

Docker

docker build -t trinity-memory .
docker run -d -p 8100:8100 -p 8000:8000 -v /data:/data trinity-memory

Docker Compose

docker compose up -d

REST API

# Write memory
curl -X POST http://localhost:8100/memories \
  -H "Content-Type: application/json" \
  -d '{"content":"User info","importance":0.8}'

# Search memory
curl "http://localhost:8100/search?q=user&top_k=5"

Commercial

Product Pricing Use Case
MCP Server Free & Open Source AI Agent integration
SaaS API Pay-as-you-go Application development
Enterprise Deployment License Compliance requirements

Documentation

Full documentation: https://trinity-tick.github.io/trinity


License

MIT License — free for commercial and non-commercial use.


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