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.
README
Trinity Memory — A Triune Architecture for AGI Long-Term Memory
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 README → README.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 -dfor 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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