Archiledger
Archiledger combines Greek arkhē (origin, first principle) with "Ledger" - a foundational record serving as the source of truth for AI memory.
Give your AI assistant a persistent memory and the power to build knowledge graphs.
Archiledger is a specialized Knowledge Graph that serves as a RAG (Retrieval-Augmented Generation) system with vector search. It is exposed as a Model Context Protocol (MCP) server to enable LLM-based assistants to store, connect, and recall information using a graph database. Whether you need a personal memory bank that persists across conversations or want to analyze codebases and documents into structured knowledge graphs, Archiledger provides the infrastructure to make your AI truly remember.
⚠️ Disclaimer: This server implements no authentication and uses an embedded graph database designed for local development only. Not recommended for production.
Why Archiledger?
LLMs are powerful, but they forget everything when a conversation ends:
- Repeating yourself — Telling your assistant the same preferences over and over
- Lost insights — Valuable analysis from one session isn't available in the next
- No connected thinking — Information lives in silos without relationships
Archiledger solves this with a graph-based memory:
| Problem | Solution |
|---------|----------|
| Context resets every conversation | Persistent notes that survive restarts |
| Flat, disconnected notes | Typed links between atomic notes (Zettelkasten) |
| No categorization | Tags and keywords on every note |
| No temporal awareness | ISO-8601 timestamps on every note |
| Keyword search limits | Vector search finds semantically similar notes |
| Hard to explore large graphs | Graph traversal via LINKED_TO relationships |
Four Ways to Use Archiledger
┌────────────────────────────────────────────────────────────────────────────┐
│ LOW-LEVEL (Manual Control) HIGH-LEVEL (AI-Powered) │
│ │
│ ┌──────────────────┐ ┌──────────────────┐ │
│ │ Core Module │ │ Agentic Memory │ │
│ │ (Maven Dep) │ │ (Embabel) │ │
│ │ │ │ │ │
│ │ MemoryNoteService│ │ • Agent │ │
│ │ Direct Java API │ │ • RAG / Vector │ │
│ └────────┬─────────┘ │ • Auto-evolution │ │
│ │ └────────┬─────────┘ │
│ ▼ ▼ │
│ ┌──────────────────┐ ┌──────────────────┐ │
│ │ MCP Server │ │ Agentic Memory │ │
│ │ (LLM Tools) │ │ MCP │ │
│ └──────────────────┘ └──────────────────┘ │
│ │
│ No LLM Required ◄──────────────────────► LLM Required │
└────────────────────────────────────────────────────────────────────────────┘
Quick Decision Guide
| Requirement | Recommended Approach |
|-------------|---------------------|
| Pure Java, no LLM | Core Module (Maven) |
| LLM with full manual control | MCP Server |
| AI classification in Java app | Agentic Memory (Embabel) |
| LLM with automatic memory management | Agentic Memory MCP |
| Full control over tags/links | Core Module or MCP Server |
| Automatic knowledge evolution | Agentic Memory (either) |
1. Core Module (Maven Dependency)
Best for: Java applications that need direct, programmatic control over memory operations without AI invol
... View full README on GitHub
