MCPHub Lab - The Security & Discovery Layer for AI Agents

<img width="1024" height="340" alt="image" src="https://github.com/user-attachments/assets/32ed8985-841d-49c3-81f7-2aabc7c7c564" /> Persistent memory for AI coding agents Agent-agnostic. Single binary. Zero dependencies. <a href="docs/INSTALLATION.md">Installation</a> • <a href="docs/AGENT-SETUP.md">Agent Setup</a> • <a href="docs/ARCHITECTURE.md">Architecture</a> • <a href="docs/PLUGINS.md">Plugins</a> • <a href="CONTRIBUTING.md">Contributing</a> • <a href="DOCS.md">Full Docs</a>

engram /ˈen.ɡræm/ — neuroscience: the physical trace of a memory in the brain.

Your AI coding agent forgets everything when the session ends. Engram gives it a brain.

A Go binary with SQLite + FTS5 full-text search, exposed via CLI, HTTP API, MCP server, and an interactive TUI. Works with any agent that supports MCP — Claude Code, OpenCode, Gemini CLI, Codex, VS Code (Copilot), Antigravity, Cursor, Windsurf, or anything else.

Agent (Claude Code / OpenCode / Gemini CLI / Codex / VS Code / Antigravity / ...)
    ↓ MCP stdio
Engram (single Go binary)
    ↓
SQLite + FTS5 (~/.engram/engram.db)

Quick Start

Install

brew install gentleman-programming/tap/engram

Windows, Linux, and other install methods → docs/INSTALLATION.md

Setup Your Agent

Agent	One-liner
Claude Code	`claude plugin marketplace add Gentleman-Programming/engram && claude plugin install engram`
OpenCode	`engram setup opencode`
Gemini CLI	`engram setup gemini-cli`
Codex	`engram setup codex`
VS Code	`code --add-mcp '{"name":"engram","command":"engram","args":["mcp"]}'`
Cursor / Windsurf / Any MCP	See docs/AGENT-SETUP.md

Full per-agent config, Memory Protocol, and compaction survival → docs/AGENT-SETUP.md

That's it. No Node.js, no Python, no Docker. One binary, one SQLite file.

How It Works

1. Agent completes significant work (bugfix, architecture decision, etc.)
2. Agent calls mem_save → title, type, What/Why/Where/Learned
3. Engram persists to SQLite with FTS5 indexing
4. Next session: agent searches memory, gets relevant context

Full details on session lifecycle, topic keys, and memory hygiene → docs/ARCHITECTURE.md

MCP Tools

Tool	Purpose
`mem_save`	Save observation
`mem_update`	Update by ID
`mem_delete`	Soft or hard delete
`mem_suggest_topic_key`	Stable key for evolving topics
`mem_search`	Full-text search
`mem_session_summary`	End-of-session save
`mem_context`	Recent session context
`mem_timeline`	Chronological drill-in
`mem_get_observation`	Full content by ID
`mem_save_prompt`	Save user prompt
`mem_stats`	Memory statistics
`mem_session_start`	Register session start
`mem_session_end`	Mark session complete
`mem_capture_passive`	Extract learnings from text output
`mem_merge_projects`	Merge project name variants (admin)

Full tool reference → docs/ARCHITECTURE.md#mcp-tools

Terminal UI

engram tui

Navigation: j/k vim keys, Enter to drill in, / to search, Esc back. Catppuccin Mocha theme.

Git Sync

Share memories across machines. Uses compressed chunks — no merge conflicts, no huge files.

engram sync                    # Export new memories as compressed chunk
git add .engram/ && git commit -m "sync engram memories"
engram sync --import           # On another machine: import new chunks
engram sync --status           # Check sync status

Full sync documentation → DOCS.md

CLI Reference

Command	Description
`engram setup [agent]`	Install agent integration
`engram serve [port]`	Start HTTP API (default: 7437)
`engram mcp`	Start MCP server (stdio). Accepts `--project` or `ENGRAM_PROJECT`
`engram tui`	Launch terminal UI
`engram search <query>`	Search memories
`engram save <title> <msg>`	Save a memory
`engram timeline <obs_id>`	Chronological context
`engram context [project]`	Recent session context
`engram stats`	Memory statistics
`engram export [file]`	Export to JSON
`engram import <file>`	Import from JSON
`engram sync`	Git sync export
`engram projects list`	Show all projects with observation/session/prompt counts
`engram projects consolidate`	Interactive merge of similar project names (`--all`, `--dry-run`)
`engram projects prune`	Remove projects with 0 observations (`--dry-run`)
`engram version`	Show version

Documentation

Doc	Description
Installation	All install methods + platform support
Agent Setup	Per-agent configuration + Memory Protocol
Architecture	How it works + MCP tools + project structure
Plugins	OpenCode & Claude Code plugin details
Comparison	Why Engram vs claude-mem
Contributing	Contribution workflow + standards
Full Docs	Complete technical reference

License

MIT

Inspired by claude-mem — but agent-agnostic, simpler, and built different.

engram

What is engram?

How to use engram?

Key Features

Optimized Use Cases

engram FAQ

Is engram safe?

Is engram up to date?

Are there any limits for engram?

Official Documentation