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Minder CLI is the command-line interface for the Minder self-hosted MCP platform.

Project description

Minder

PyPI version License

Minder is a self-hosted MCP (Model Context Protocol) platform for repository-aware engineering intelligence.

It combines an LLM inference stack, a persistent memory and workflow engine, a browser admin console, and a lightweight CLI into a single deployable unit.

What's in this repo

Component Description
Minder Server MCP gateway — SSE + streamable HTTP + stdio, RAG pipeline, workflow engine, memory, admin HTTP
Minder Dashboard Astro admin console — client management, onboarding snippets, agent instructions, skill catalog, chat
Minder CLI (minder-cli on PyPI) Edge CLI — repo sync, MCP config install, login, self-update

Architecture

Developer workstation
  ├── minder-cli          repo sync, MCP config
  └── AI agent (IDE)  ──► Minder Server :8800
                              │
                    ┌──────────────┼──────────────────────┐
                    │              │                      │
                 Qdrant   SQLite / PostgreSQL    llama-cpp-python
          (default vector +   (optional relational/   (LLM + embedding,
           operational data)    graph adapters)        GGUF on host)
  • Transport: SSE (/sse), streamable HTTP (/mcp), stdio
  • LLM inference: llama-cpp-python with GGUF models auto-downloaded from HuggingFace (Metal on Mac, CPU elsewhere)
  • Vector search: Qdrant for semantic retrieval
  • Operational storage: Qdrant by default, with optional SQLite/PostgreSQL adapters for relational and graph workloads

Quick Start

1. Run the server

# Start infra (Qdrant)
docker compose -f docker/docker-compose.local.yml up -d

# Run Minder Server (GGUF models auto-download on first start)
uv run python scripts/dev_server.py

Or use the one-command release installer (Docker only):

curl -fsSL https://raw.githubusercontent.com/hiimtrung/minder/main/scripts/release/install-minder-release.sh | bash

2. Open the dashboard

http://localhost:8800/dashboard

First run → /dashboard/setup to create an admin and get your mk_... key.

3. Create a client and connect your IDE

  1. Open /dashboard/clients → create a client → save the mkc_... key (shown once in a modal)
  2. Open the client detail → copy the MCP snippet for your IDE from Copy-ready MCP snippets
  3. Open /dashboard/instruction → copy the agent orchestration rules for your IDE

4. Install the CLI and sync a repository

# Install
uv tool install minder-cli

# Log in
minder login --client-key mkc_your_key --server-url http://localhost:8800/sse

# Write MCP config to your IDE (optional — dashboard shows it too)
minder install --target vscode --target claude-code

# Sync a repository
minder sync

MCP Tools

When connected, Minder exposes these tools to your AI agents:

Tool Description
minder_query Full RAG pipeline: retrieve → reason → verify → respond
minder_search_code Semantic code search across indexed repos
minder_search_errors Look up past error patterns
minder_find_impact Find what a change might affect
minder_memory_store / minder_memory_recall Persistent engineering memory
minder_session_create / minder_session_save / minder_session_restore Cross-machine session continuity
minder_workflow_get / minder_workflow_step / minder_workflow_guard Workflow governance
minder_skill_store / minder_skill_recall Reusable pattern catalog
minder_agent_list / minder_agent_get SubAgent registry

Dashboard Pages

Route Description
/dashboard Home — stats and quick nav
/dashboard/clients Create clients, copy MCP snippets
/dashboard/instruction Agent orchestration rules — copy for Claude Code, Cursor, VS Code, Codex
/dashboard/sessions LLM session management
/dashboard/memories Persistent memory browser
/dashboard/skills Skill / pattern catalog
/dashboard/agents SubAgent registry
/dashboard/chat Browser-based runtime chat
/dashboard/repositories Repo graph explorer
/dashboard/workflows Workflow definitions
/dashboard/observability Audit and trace

Documentation

License

Apache License 2.0. See LICENSE for details.

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