greeum 5.2.1

LLM-Integrated Memory System with InsightJudge, HybridGraphSearch, and Branch-aware Storage

Greeum

Persistent memory for AI agents — no more context loss between sessions.

Greeum is an open-source memory module that gives LLM agents long-term memory. Store memories on your own workstation, access them from anywhere, and never lose context again.

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Quick Start

# Install
pip install greeum

# Setup (interactive wizard)
greeum setup

# Test
greeum memory add "My first memory"
greeum memory search "first"

That's it. Greeum is ready to use with your MCP client.

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Setup Modes

greeum setup provides three modes depending on your use case:

Local (default)

Store memories on this computer only.

greeum setup
# Select [1] Local

Server

Turn this computer into a Greeum server accessible from anywhere. Handles API key generation, Tailscale networking, and auto-start on boot.

greeum setup --server

[1/5] Data directory        ~/.greeum  ✓
[2/5] Embedding model       ready  ✓
[3/5] API server             port 8400, key generated  ✓
[4/5] Tailscale network     connected  ✓
[5/5] System service        auto-start enabled  ✓

Remote

Connect to an existing Greeum server — one command.

greeum setup --remote http://my-server:8400 --api-key grm_xxxxx

All MCP tools and CLI commands will use the remote server automatically.

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MCP Integration

Once greeum setup is complete, connect your MCP client:

Claude Code

claude mcp add greeum -- greeum mcp serve -t stdio

Cursor

Add to MCP settings:

{
  "greeum": {
    "command": "greeum",
    "args": ["mcp", "serve", "-t", "stdio"]
  }
}

Codex

~/.codex/config.toml:

[mcp_servers.greeum]
command = "greeum"
args    = ["mcp", "serve", "-t", "stdio"]

Greeum MCP is supported on Linux, macOS, and WSL. On Windows, use WSL.

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MCP Tools

Greeum provides these tools to your AI agent via MCP:

Tool	Description
add_memory	Store a memory with optional importance score
search_memory	Semantic search across all memories
get_memory_stats	View memory count, slots, and system health
usage_analytics	Analyze usage patterns over time
system_doctor	Run diagnostics and auto-repair
analyze	Summarize recent activity and slot status

How agents use Greeum

1. Search before starting work — retrieve relevant context
2. Work on the task with full context
3. Add a summary when done — preserve decisions and outcomes

{ "name": "search_memory", "arguments": { "query": "auth refactor", "limit": 5 } }
{ "name": "add_memory", "arguments": { "content": "Switched to JWT tokens for auth", "importance": 0.7 } }

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CLI Reference

# Memory operations
greeum memory add "context to remember"
greeum memory add "important note" --importance 0.8
greeum memory search "keyword" --count 5

# Slot management
greeum slots status
greeum slots set A 123      # Pin memory #123 to slot A

# Server management
greeum config show           # View current configuration
greeum config mode local     # Switch to local mode
greeum config mode remote    # Switch to remote mode
greeum config test           # Test remote connection

# Maintenance
greeum doctor                # System diagnostics
greeum mcp warmup            # Pre-download embedding model

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Architecture

┌─────────────────────────────────────────────┐
│               Your Workstation               │
│                                             │
│  greeum api serve (:8400)                   │
│  ├── Semantic Search (sentence-transformers)│
│  ├── STM Slots (A/B/C context anchors)     │
│  ├── Branch-aware LTM storage (SQLite)     │
│  └── API Key authentication                │
│                                             │
│  Accessible via Tailscale from anywhere     │
└─────────────────────────────────────────────┘
        ▲               ▲               ▲
        │               │               │
   Claude Code       Cursor          Codex
   (MCP/STDIO)     (MCP/STDIO)    (MCP/STDIO)

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Documentation

• Quick Start Guide
• Getting Started
• Anchors Guide
• API Reference
• Troubleshooting

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License

MIT License — see LICENSE.

Greeum · Persistent memory for AI — built and maintained by the community.