Skill discovery engine and durable memory system for AI coding agents. Point it at your skill directories, it builds a knowledge graph and ranks skills by relevance to any task. Exposes everything via MCP so agents auto-discover it without explicit rules.
Before starting a task, agents run memgraph recommend "<task>" and get ranked skill recommendations. The scoring algorithm uses:
# Recommend skills for a task
memgraph recommend "deploy traefik with SSL" --json
# Search by keyword
memgraph query "proxmox backup" --json
# Get related skills
memgraph related "jar-rbm21-manage" --json
Run memgraph setup in any repo and it:
memgraph setup
# → Ingested skills from 4 directories into graph
# → Updated AGENTS.md with memgraph usage instructions
# → Created .devin/skills/memgraph-usage/SKILL.md
Agents like Claude Code auto-discover memgraph's capabilities via the Model Context Protocol — no CLAUDE.md rules needed. memgraph mcp runs a stdio JSON-RPC 2.0 server. Two-tier tool design: 8 core tools (always advertised) + 1 admin meta-tool for 15 rare operations. Total overhead: ~1158 tokens per API call.
# Register with Claude Code
claude mcp add memgraph -s user -- ~/.local/bin/memgraph mcp
# Core tools agents see:
# memgraph_projects, memgraph_recall, memgraph_read,
# memgraph_save, memgraph_list, memgraph_edit,
# memgraph_delete, memgraph_recommend
# Admin meta-tool (15 rare ops):
# memgraph_admin {command: "status|profile|query|...", args: {...}}
Stable project identification via git remote URLs. A registry (~/.memgraph/projects.json) maps human-readable names to memory paths. Work from any directory with --project <name>. Orphaned scopes can be rebound with memgraph attach.
# Register current repo under a name
memgraph attach myproject
# Access from any directory
memgraph recall "deploy" --project myproject
# List all projects
memgraph projects
Memories support [slug] section markers. Recall returns a compact section index (slug + preview + line range) — agents fetch only the section they need with memgraph read <id> <slug>. Reduces token consumption by 60-90% for large memories.
# Save a sectioned memory
memgraph remember "[deploy] Use systemd timers [backup] rsync to /backup"
# Recall returns section index, not full content
memgraph recall "deploy"
# → Memory 123 — "ops" (score: 5.2)
# [deploy] L2-3 "Use systemd timers"
# [backup] L4-5 "rsync to /backup"
# Read only the section you need
memgraph read 123 deploy
Project clusters spiral out from a galactic core. Namespace nodes are golden core stars; skills are colored stars sized by degree. 1500 background stars, nebula dust, additive blending — your skills as a Milky Way.
All graph commands available via HTTP, returning the same JSON format as the CLI.
# Search the graph (same scoring as CLI)
curl "http://localhost:8080/api/search?q=traefik"
# Get a specific skill as JSON
curl "http://localhost:8080/api/nodes/traefik-cloudflare-setup"
# Get the full graph
curl "http://localhost:8080/api/graph"
# Trigger a sync
curl -X POST "http://localhost:8080/api/sync"
Generate integration files for Claude Code, OpenCode, and Copilot. Each bridge includes skill discovery commands and memory commands.
memgraph bridge claude-code # → .claude/CLAUDE.md
memgraph bridge opencode # → .opencode/memory.json
memgraph bridge copilot # → .copilot/settings.json
Ingests both SKILL.md files and plain .md files. Builds edges from three relation types:
# Download the binary
chmod +x memgraph && sudo mv memgraph /usr/local/bin/
# One-command onboarding (auto-discovers all skill dirs)
memgraph setup
# Find skills for a task
memgraph recommend "deploy traefik with SSL" --json
# Launch the galaxy UI
memgraph serve --port 8080
# Open http://localhost:8080