win-memory

name	win-memory
description	Persistent agent memory via OpenViking — store and retrieve context across sessions using a tiered filesystem database. L0/L1/L2 context layers with semantic search.
metadata	{"openclaw":{"emoji":"🧬","os":["win32"],"requires":{"bins":["python","openviking-server"]},"install":"pip install openviking"}}

win-memory

Persistent agent memory for OpenClaw on Windows, powered by OpenViking — an open-source context database designed for AI agents.

OpenViking stores memories, resources, and skills in a virtual filesystem (viking://) with three context tiers:

• L0 (Abstract, ~100 tokens) — quick relevance check
• L1 (Overview, ~2k tokens) — key information for planning
• L2 (Full content) — loaded on demand only

This means your agent loads context efficiently, not all at once.

Setup

1. Install OpenViking

winget install Python.Python.3.12
pip install openviking

2. Configure

$configDir = "$env:USERPROFILE\.openviking"
New-Item -ItemType Directory -Force -Path $configDir | Out-Null

# Minimal config using local Ollama (no API cost)
$config = @{
    storage = @{ workspace = "$env:USERPROFILE\.openviking\workspace" }
    log = @{ level = "INFO"; output = "stdout" }
    embedding = @{
        dense = @{
            api_base = "http://localhost:11434/v1"
            api_key = "ollama"
            provider = "openai"
            dimension = 768
            model = "nomic-embed-text"
        }
    }
    vlm = @{
        api_base = "http://localhost:11434/v1"
        api_key = "ollama"
        provider = "openai"
        model = "llama3.1"
    }
} | ConvertTo-Json -Depth 5
$config | Out-File -Encoding utf8 "$configDir\ov.conf"

Write-Host "OpenViking configured at $configDir\ov.conf"

3. Start the Server

# Start in background (port 1933 by default)
Start-Process -WindowStyle Hidden -FilePath "openviking-server" -ArgumentList "--with-bot"
Write-Host "OpenViking server started at http://localhost:1933"

4. Register as Startup Task (Optional)

$action = New-ScheduledTaskAction -Execute "openviking-server" -Argument "--with-bot"
$trigger = New-ScheduledTaskTrigger -AtLogOn
$principal = New-ScheduledTaskPrincipal -UserId $env:USERNAME -RunLevel Limited
Register-ScheduledTask -TaskName "OpenViking Memory Server" -Action $action -Trigger $trigger -Principal $principal -Force
Write-Host "OpenViking will auto-start on login"

Memory Operations

Store a Memory

# Store text memory
ov add-resource "I prefer concise email replies. No fluff." --uri "viking://user/memories/preferences/email-style"

# Store a file or folder
ov add-resource "C:\Users\$env:USERNAME\Documents\project-notes" --uri "viking://user/memories/work/project"

# Store a web page
ov add-resource "https://docs.example.com/api" --uri "viking://resources/docs/api"

Recall / Search

# Semantic search across all memories
ov find "how do I write emails"

# Search within a specific namespace
ov find "project deadline" --uri "viking://user/memories/work"

# Browse the memory tree
ov tree viking://user -L 2
ov ls viking://user/memories/

Session Memory (Auto-extract)

At the end of a session, OpenViking can automatically extract long-term memories:

# Extract memories from current session (runs async)
ov session extract

Check Status

# Check server health
ov status

# List all stored resources
ov ls viking://

# Show full tree
ov tree viking:// -L 3

Memory Structure

viking://
├── user/
│   └── memories/
│       ├── preferences/      # Work style, communication prefs
│       ├── work/             # Project notes, task context
│       └── contacts/         # People and relationships
├── agent/
│   ├── memories/             # Agent task experience
│   ├── skills/               # Learned skill patterns
│   └── instructions/         # Persistent agent rules
└── resources/                # External docs, web pages, files

Why OpenViking?

	Traditional RAG	OpenViking
Storage	Flat vector chunks	Hierarchical filesystem
Loading	All-or-nothing	L0/L1/L2 tiered (91% fewer tokens)
Retrieval	Single-pass embedding	Directory-recursive + semantic
Debugging	Black box	Visualized retrieval trajectory
Memory growth	Manual	Auto-extract from sessions

Benchmark: OpenClaw + OpenViking = 52% task completion vs 35% baseline. Token usage: 91% reduction compared to flat RAG. Source: OpenViking LoCoMo10 evaluation

Troubleshooting

Server not starting: Make sure Python 3.10+ is installed.

python --version
pip install openviking --upgrade

Embedding model not found (Ollama):

ollama pull nomic-embed-text

Port conflict: Change port in ov.conf "port": 1934 and set env OPENVIKING_PORT=1934.

Links

• OpenViking GitHub
• OpenViking Docs
• OpenClaw Memory Plugin