kiro-recall

A local MCP memory server for Kiro CLI that gives your AI assistant persistent, semantic memory across sessions.

Inspired by Kiro Crew's memory architecture — reimplemented as a lightweight, self-contained system using SQLite + Ollama embeddings, with Obsidian as the human-readable sync target.

What it does

• Semantic memory — structured key-value facts (pref.editor: Neovim, project.active: MyProject)

• Episodic memory — conversation fragments that decay over time (~23 day half-life)

• Lessons — corrections and rules that override all other memory (highest priority)

• Semantic recall — vector similarity search via Qwen3-Embedding (1024-dim, runs locally)

• Obsidian sync — renders memory to markdown with [[wikilinks]] for graph navigation

Related MCP server: tartarus-mcp

Architecture

┌──────────────────────────────────────────┐
│  Kiro CLI / any MCP client               │
│  Tools: Remember, Recall, Learn, Forget  │
└──────────────┬───────────────────────────┘
               │ stdio (MCP protocol)
       ┌───────▼───────┐          ┌─────────────────┐
       │  memory.db    │  sync →  │  Obsidian Vault  │
       │  (SQLite)     │          │  (Markdown)      │
       └───────┬───────┘          └─────────────────┘
               │
       ┌───────▼───────┐
       │  Ollama       │
       │  qwen3-embed  │
       │  (localhost)   │
       └───────────────┘

Requirements

• macOS or Linux

• Python 3.10+

• Ollama (for local embeddings)

• uv (recommended) or pip

Install

git clone https://github.com/fredluckham/kiro-recall.git
cd kiro-recall
bash install.sh

The install script will:

1. Install Ollama (if not present) and start it as a service

2. Pull the qwen3-embedding:0.6b model

3. Create a Python venv and install dependencies

4. Install the MCP server config into ~/.kiro/settings/mcp.json

5. Install the steering file to ~/.kiro/steering/obsidian-memory.md

6. Optionally seed memory from an existing Obsidian vault

Manual setup

If you prefer not to use the install script:#

# 1. Install Ollama and the embedding model
brew install ollama  # or: curl -fsSL https://ollama.com/install.sh | sh
brew services start ollama
ollama pull qwen3-embedding:0.6b

# 2. Create venv and install deps
uv venv .venv
uv pip install "mcp[cli]>=1.0.0" "httpx>=0.27.0"

# 3. Copy to ~/.kiro/recall
mkdir -p ~/.kiro/recall
cp server.py db.py embed.py obsidian_sync.py ~/.kiro/recall/

# 4. Add to MCP config (see install.sh for the JSON patch)

MCP Tools

Memory tiers

Key format

• pref.* — User preferences (pref.theme, pref.voice, pref.editor)

• project.* — Active projects (project.active, project.stack)

• user.* — User facts (user.role, user.company, user.tools)

Obsidian sync

Run manually or via cron:

~/.kiro/recall/.venv/bin/python ~/.kiro/recall/obsidian_sync.py

Outputs:

• Memory/Semantic.md — all facts grouped by prefix

• Memory/Lessons.md — corrections grouped by category

• Sessions/YYYY-MM-DD.md — today's episodic memories

Configuration

Set the Obsidian vault path via environment variable (prompted during install, saved to ~/.kiro/recall/.env):

export KIRO_MEMORY_VAULT="$HOME/Documents/Obsidian/My Vault"

The default is ~/Documents/Obsidian/Kiro Knowledge Base.

Enable automatic Obsidian sync after every Remember or Learn call:

export KIRO_MEMORY_AUTOSYNC=1

When disabled (default), sync runs manually or via cron. Auto-sync adds a small write overhead per call but keeps the vault always current.

The Ollama endpoint is set in embed.py:

OLLAMA_URL = "http://localhost:11434/api/embed"
MODEL = "qwen3-embedding:0.6b"

License

MIT