Elefante

The open, Git-native memory protocol for MCP agents: portable, inspectable, versioned, and human-editable.

Code

Your agent's memory is a Git repo you own.
Every memory is a Markdown file you can read.
Every change is a commit you can audit.
Any MCP agent can connect to it.

Why

Every AI agent has its own memory system. Claude's works only with Claude. ChatGPT's works only with ChatGPT. They're all opaque, vendor-locked, and non-portable.

Elefante takes a different approach. Instead of optimizing for retrieval quality, it optimizes for ownership, auditability, and portability — the things that matter when memory is a first-class asset, not a hidden implementation detail.

Question	Answer
Who owns my agent's memory?	You do. It's a Git repo.
What does my agent know about me?	`cat memories/user/*.md`
What changed since last week?	`git log --since="1 week ago"`
How do I use it across Claude, Codex, and Cursor?	Point them at the same MCP server.
How do I back it up?	Every clone is a full backup.
How do I undo a bad memory?	`git revert`

Quick Start

bash

# Install
npm install -g elefante-mcp

# Initialize vault (uses a private GitHub repo as storage)
elefante init git@github.com:yourname/my-memory.git

# Add your first memory
elefante add \
  --name "TypeScript strict mode" \
  --type user \
  --body "Always enable strict: true in tsconfig.json"

# Search
elefante search "typescript"

# Check status
elefante status

Connect to Your Agent

From the terminal (before starting Claude Code):

Terminal

claude mcp add --scope user elefante -- npx -y elefante-mcp mcp

From inside Claude Code (during a session):

Code

/mcp add --scope user elefante -- npx -y elefante-mcp mcp

Or manually — add to your MCP config (~/.claude.json, .cursor/mcp.json, etc.):

config.json

{
  "mcpServers": {
    "elefante": {
      "command": "npx",
      "args": ["-y", "elefante-mcp", "mcp"]
    }
  }
}

Cursor / VS Code / Codex — same config. Any agent that speaks MCP can read and write to the same vault.

Once connected, your agent gets 7 tools: memory_write, memory_read, memory_search, memory_list, memory_update, memory_delete, memory_sync.

Auto-Discovery

Elefante injects your top memories directly into the agent's system prompt at startup via MCP instructions. The agent sees your preferences, feedback, and project context without needing to search first.

No CLAUDE.md hacks. No "search my elefante memory" prompts. It just works.

Project-Aware Memory

Elefante auto-detects which project you're in and scopes memories accordingly. No configuration needed.

How It Works

When the MCP server starts, it reads the git remote from your working directory:

Code

~/Projects/acme-api/   →  git remote = github.com:you/acme-api.git
                       →  profile = "you/acme-api"
                       →  memories scoped to this project

~/Projects/my-cli/     →  git remote = github.com:you/my-cli.git
                       →  profile = "you/my-cli"
                       →  different project, different memories

What Gets Scoped

You say	What happens
"Remember this project uses Postgres 16"	Stored with `profile: you/acme-api` (project-scoped)
"Remember I prefer Bun over npm"	Agent stores with `profile: global` → `null` (applies everywhere)
"Remember the staging URL is staging.acme.io"	Stored with `profile: you/acme-api` (project-scoped)

The agent decides based on context. User preferences and behavioral feedback are typically global. Project details and references are typically scoped.

What You See When You Search

Code

Working in acme-api:
  ✓ "Uses Postgres 16"              (profile: you/acme-api)
  ✓ "Prefers Bun over npm"          (profile: null — global)
  ✗ "Uses SQLite"                   (profile: you/my-cli — different project)

Working in my-cli:
  ✓ "Uses SQLite"                   (profile: you/my-cli)
  ✓ "Prefers Bun over npm"          (profile: null — global)
  ✗ "Uses Postgres 16"             (profile: you/acme-api — different project)

Global memories always show up. Project memories only show up in their project.

Escape Hatches

Profile value	Meaning
(omitted)	Auto-scope to detected project
`"global"`	Explicitly global — no project scope
`"all"`	Search/list across every project
`"owner/other-repo"`	Explicitly target a different project

What a Memory Looks Like

Every memory is a Markdown file with YAML frontmatter. You can open it in any editor.

markdown

---
id: mem_a1b2c3d4e5f6
type: feedback
name: No database mocking in tests
description: Integration tests must use real database connections
profile: you/acme-api
importance: 3
tags: [testing, database]
created_at: "2026-04-14T10:30:00Z"
updated_at: "2026-04-14T14:22:00Z"
---

Do not mock the database in integration tests — use a real connection
to a test database.

**Why:** Mocked tests passed but the production migration failed because
the mock didn't reflect actual schema constraints.

**How to apply:** Use the test database helper (`createTestDb()`)
instead of jest mocks.

What a Git Log Looks Like

Code

$ git log --oneline
f4a2c1e remember: No database mocking in tests
b3d8e7a remember: TypeScript strict mode preference
a1c9f2b update: REST to GraphQL migration deadline
9e7d4c3 forget: Outdated staging URL
2f8a6b1 Initialize elefante vault

Every write is remember:. Every update is update:. Every delete is forget:. Your memory has a clean, auditable history.

Vault Structure

Code

~/.elefante/vault/
├── .elefante/
│   └── config.yaml              # Vault settings
├── memories/
│   ├── user/                    # Who the user is
│   │   └── mem_*.md
│   ├── feedback/                # How the agent should behave
│   │   └── mem_*.md
│   ├── project/                 # Active work context
│   │   └── mem_*.md
│   └── reference/               # External resource pointers
│       └── mem_*.md
├── profiles/
│   └── *.yaml                   # Named scopes for partitioning
└── index/
    ├── manifest.json            # All memory metadata (generated)
    └── search.json              # Search index (generated)

Memory Types

Four types. Intentionally constrained — a small taxonomy forces good classification.

Type	What to store	Typically scoped to
`user`	Facts about you — role, preferences, expertise	Global
`feedback`	Agent behavior guidance — corrections, confirmations	Global
`project`	Active work context — goals, deadlines, decisions	Project
`reference`	External pointers — URLs, dashboards, tools	Project

Authentication

Elefante doesn't reinvent auth. It uses whatever Git credentials you already have.

Resolution order:

Local git credentials (SSH keys, macOS Keychain, credential helpers)
gh auth token (if GitHub CLI is installed)
ELEFANTE_GITHUB_TOKEN environment variable
~/.elefante/config.json token field

If git or gh is authenticated, Elefante works with zero config.

CLI Reference

Code

elefante init <repo-url>       Clone vault repo to ~/.elefante/vault/
elefante status                Vault status, sync state, memory count
elefante list [--type TYPE]    List memories with filters
elefante search <query>        Search by keyword
elefante read <id>             Read a specific memory
elefante add                   Create a memory
elefante delete <id>           Delete a memory
elefante sync                  Pull and push changes
elefante reindex               Rebuild search index
elefante mcp                   Start MCP stdio server

How It Compares

Elefante doesn't compete on retrieval quality. It competes on ownership.

	Elefante	Claude/ChatGPT Memory	Mem0 / Zep
You own the data	Git repo you control	Vendor-controlled	Self-hosted or SaaS
Human-readable	Markdown files	No	No
Version history	Git log for free	No	No
Agent-agnostic	Any MCP agent	Single vendor	Yes
Zero infrastructure	Git + local process	N/A (managed)	Server + database
Offline access	Local clone	No	No
Auto project scoping	Detects git remote	Per-conversation	Manual
Semantic search	Not yet (planned)	Yes	Yes

"Why not Obsidian + MCP plugin?" — Obsidian is a tool for humans that agents can access. Elefante is a tool for agents that humans can access. Same data format (Markdown + Git), different design center.

Protocol

The full protocol specification — memory model, vault structure, MCP interface, concurrency model, indexing, security considerations — is in PROTOCOL.md.

License

MIT