DryRainEnt/Greeum

24

stars

242

commits

Python

primary language

Jul 27, 2026

updated

README

Greeum

PyPI version Python 3.10+ License: MIT

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.

English · 한국어


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.


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.


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.


MCP Tools

Greeum provides these tools to your AI agent via MCP:

ToolDescription
add_memoryStore a memory with optional importance score
search_memorySemantic search across all memories
get_memory_statsView memory count, slots, and system health
usage_analyticsAnalyze usage patterns over time
system_doctorRun diagnostics and auto-repair
analyzeSummarize 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 } }

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

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)

Documentation


License

MIT License — see LICENSE.

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

Contributors

DryRainEnt

240 commits

jee1

2 commits

DryRainEnt/Greeum

24

stars

242

commits

Python

primary language

Jul 27, 2026

updated

README

Greeum

PyPI version Python 3.10+ License: MIT

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.

English · 한국어


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.


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.


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.


MCP Tools

Greeum provides these tools to your AI agent via MCP:

ToolDescription
add_memoryStore a memory with optional importance score
search_memorySemantic search across all memories
get_memory_statsView memory count, slots, and system health
usage_analyticsAnalyze usage patterns over time
system_doctorRun diagnostics and auto-repair
analyzeSummarize 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 } }

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

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)

Documentation


License

MIT License — see LICENSE.

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

Contributors

DryRainEnt

240 commits

jee1

2 commits

Languages

Python

95.9%

HTML

2.7%