SQLew▌

by sin5ddd
SQLew boosts multi-agent coordination with efficient SQLite design, cutting context sharing tokens by 96% for decision a
Optimizes multi-agent coordination through intelligent SQLite database design with normalized tables, integer enums, and pre-aggregated views to achieve 96% token reduction in context sharing for decision tracking, agent messaging, file change monitoring, and constraint management.
best for
- / AI coding assistants that need persistent memory
- / Multi-agent systems requiring coordination
- / Development teams tracking architectural decisions
- / Projects with complex constraint management
capabilities
- / Store and version architectural decisions with metadata
- / Track file modifications and database operations
- / Define and manage project constraints with priorities
- / Manage tasks with kanban workflow and file tracking
- / Query past decisions to avoid repeating debates
- / Suggest related decisions based on context patterns
what it does
Provides AI agents with persistent memory by storing architectural decisions, constraints, and task management in SQLite databases to eliminate repeated context and maintain consistency across sessions.
about
SQLew is a community-built MCP server published by sin5ddd that provides AI assistants with tools and capabilities via the Model Context Protocol. SQLew boosts multi-agent coordination with efficient SQLite design, cutting context sharing tokens by 96% for decision a It is categorized under ai ml, databases. This server exposes 8 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install SQLew in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
Apache-2.0
SQLew is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
sqlew

Design decisions, remembered by SQL — an MCP server for AI agents
What is sqlew?
The Problem
Every AI coding session starts from scratch. Your agent doesn't remember that you chose PostgreSQL over MongoDB last week, or that the team agreed on a specific API versioning strategy. Without persistent memory, agents repeat mistakes, contradict earlier decisions, and waste tokens re-discovering context.
The Solution
sqlew stores your architectural decisions in a structured SQL database. When a new session starts, the AI agent queries past decisions in milliseconds — not by reading through scattered Markdown files, but through efficient SQL lookups with metadata, tags, and similarity detection.
┌─────────────────────────────────────────────────────────────┐
│ Before sqlew │ After sqlew │
│───────────────────────────────│─────────────────────────────│
│ Session 1: "Use PostgreSQL" │ Session 1: "Use PostgreSQL"│
│ Session 2: "Use MongoDB?" │ → decision recorded │
│ Session 3: "Use PostgreSQL" │ Session 2: query → got it │
│ (same debate, every time) │ Session 3: query → got it │
│ │ (instant recall) │
└─────────────────────────────────────────────────────────────┘
sqlew is built on the Model Context Protocol (MCP), so it works with any MCP-compatible AI coding tool.
This software does not send any data to external networks. We NEVER collect any data or usage statistics.
Quick Start
1. Install
npm install -g sqlew
2. Setup
Choose the setup that matches your environment:
Claude Code (Plugin)
claude plugin marketplace add sqlew-io/sqlew-plugin
claude plugin install sqlew
The plugin automatically configures MCP server, Skills (Plan Mode guidance), and Hooks (automatic decision capture).
Codex CLI
See sqlew-codex for Codex CLI integration.
Manual
Add to .mcp.json in your project root:
{
"mcpServers": {
"sqlew": {
"command": "sqlew"
}
}
}
The database (~/.config/sqlew/sqlew-shared.db) and config are auto-created on first run. See Shared Database for details.
3. Just use Plan Mode!
That's it. Every time you create a plan and get user approval, your architectural decisions are automatically recorded.
No special commands needed — just plan your work normally, and sqlew captures the decisions in the background.
Features
- Structured Records — Decisions stored as relational data with metadata, tags, layers, and version history
- Fast Queries — 2-50ms retrieval via SQL, even with thousands of decisions
- Duplicate Detection — Three-tier similarity scoring (0-100) prevents redundant decisions
- Constraint Tracking — Architectural rules and principles as first-class entities
- Auto-Capture — Hooks automatically record decisions from Plan Mode (Claude Code plugin)
- Multi-Database — SQLite (default), PostgreSQL, MySQL/MariaDB, or Cloud
- Git Worktree Ready — Each worktree shares the same context database
For Teams (sqlew.io)
Connect to sqlew.io for team-shared decisions:
Step 1: Get your API key
Visit sqlew.io and save your API key:
# ~/.config/sqlew/.sqlew.env (shared across all projects)
SQLEW_API_KEY=your-api-key
Step 2: Configure each project
# .sqlew/config.toml
[database]
type = "cloud"
[project]
name = "your-project-name"
Benefits:
- All team members share the same decision database
- Works seamlessly with Git worktree workflows
- No local database setup required
Performance
| Metric | Value |
|---|---|
| Query speed | 2-50ms |
| Concurrent agents | 5+ simultaneous |
| Storage efficiency | ~140 bytes/decision |
| Token savings | 60-75% vs Markdown ADRs |
Use Cases
- Architecture Evolution — Document major decisions with full context and alternatives considered
- Pattern Standardization — Establish coding patterns as constraints, enforce via AI code generation
- Cross-Session Continuity — AI maintains context across days/weeks without re-reading docs
- Multi-Agent Coordination — Multiple AI agents share architectural understanding
- Onboarding Acceleration — New AI sessions instantly understand project history
Documentation
| Guide | Description |
|---|---|
| ADR Concepts | Architecture Decision Records explained |
| Configuration | Config file setup, database options |
| Hooks Guide | Claude Code Hooks integration |
| Cross Database | Multi-database support |
| CLI Usage | Database migration, export/import |
Upgrade Guides
- Migrating to SaaS — Export local data to sqlew.io cloud
MCP Tools
7 action-based tools: decision, constraint, suggest, help, example, use_case, queue
All tools support action: "help" for documentation.
Support
Support development via GitHub Sponsors.
Version
Current version: 5.0.8
See CHANGELOG.md for release history.
What's New in v5.0.8:
- PR ADR enforcement — PreToolUse Hook blocks
gh pr createwithout ADR markers, file-grouped format - Codex CLI support — Works beyond Claude Code via sqlew-codex
- Plugin-first architecture — Simplified setup via sqlew-plugin
- Cloud backend — Connect to sqlew.io for team-shared decisions
License
Apache License 2.0 — Free for commercial and personal use. See LICENSE for details.
Links
Built with MCP SDK, better-sqlite3, and TypeScript.
FAQ
- What is the SQLew MCP server?
- SQLew is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for SQLew?
- This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
SQLew is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Piyush G· Sep 9, 2024
We evaluated SQLew against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Useful MCP listing: SQLew is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakshi Patil· Jul 7, 2024
SQLew reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend SQLew for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· May 5, 2024
Strong directory entry: SQLew surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Apr 4, 2024
SQLew has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Rahul Santra· Mar 3, 2024
According to our notes, SQLew benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Pratham Ware· Feb 2, 2024
We wired SQLew into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Yash Thakker· Jan 1, 2024
SQLew is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.