io.github.argon-lab/argon MCP Server

Argon — Git for MongoDB

<p align="center"> <a href="https://www.argonlabs.tech"> <img src=".github/assets/hero.png" alt="Argon — Git for MongoDB, built for AI agents: branch off prod, run an agent, undo the session" width="100%"> </a> </p>

Branch, time-travel, merge and undo your MongoDB. Any driver, real mongod, versioned history underneath. Built for AI agents.

Three ideas, thirty seconds:

1. A branch is a pointer, not a copy — created in milliseconds at any data size.
2. checkout turns a branch into a real MongoDB database — pymongo, mongoose, mongosh, indexes, aggregation, transactions: all real, and every write becomes versioned history.
3. Nothing is ever lost — diff it, merge it, undo it, rewind it, or pin it forever.

Install

brew install argon-lab/tap/argonctl      # macOS
npm install -g argonctl                  # cross-platform

# MongoDB must run as a replica set (one-node is fine):
docker run -d --name argon-mongo -p 27017:27017 mongo:7 --replSet rs0
docker exec argon-mongo mongosh --quiet --eval 'rs.initiate()'

The flow

main ──branch──▶ experiment ──checkout──▶ mongodb://…  ← any driver
                                              │
                     ┌── argon diff ──────────┤  every write captured
                     ▼                        ▼
        merge (a data PR)          or   undo / discard / rewind

# 0 · Bring your data in ("git clone") — or: argon projects create myapp
argon import database --uri mongodb://localhost:27017 --database myapp --project myapp

# 1 · Branch — instant, no copy
argon branches create experiment -p myapp

# 2 · Get a real database for it, capture writes
argon checkout -p myapp -b experiment      # prints a connection string
argon watch    -p myapp -b experiment      # keep running while you write

# 3 · Review and merge back — a data pull request
argon diff          -p myapp -b experiment
argon merge preview -p myapp -b experiment
argon merge apply <plan-id>

# …or rewind instead of merging
argon restore reset -p myapp -b main --time 2026-07-07T09:00:00Z --backup pre-incident

Prefer clicking? argon console serves a local web console (UI + REST API) and opens your browser.

What you get

	Command	In one line
Branching	argon branches create	a metadata write — instant, zero copy
Real databases	argon checkout / argon proxy	any driver, real mongod; proxy serves stable mongodb://host/<project>~<branch> URIs
Write capture	argon watch	change-stream → versioned history, per-actor attribution
Time travel	argon time-travel query	any historical state, by LSN or timestamp
Undo	argon undo --actor <a>	revert a range or one writer's changes; append-only, conflict-aware
Restore	argon restore preview/reset/branch	rewind a branch or fork history; recorded, never destructive
Data PRs	argon merge preview/apply	three-way merges as reviewable plans; conflicts never silent
Sandboxes	argon sandbox create --ttl 1h	fork + checkout + TTL in one step — disposable agent workspaces
Dataset pins	argon pin create / pin sandbox	immutable named states that survive GC and resets — reproducible evals
Web console	argon console	local UI + REST API in one command

Storage stays bounded: snapshots + retention-window GC keep state plus a window of history, not every write forever. Details and the consistency model, stated honestly: ARCHITECTURE.md.

For AI agents

claude mcp add argon -- argon mcp        # 13 tools: sandbox, diff, merge, undo, pins
pip install argon-agents                 # LangGraph checkpointer + Mem0, over REST

saver = ArgonCheckpointSaver.from_sandbox(argon, "myapp")   # checkpoint on a disposable branch
saver.merge()                                               # adopt the run — or .discard()

argon.create_pin("myapp", "eval-v1")                        # pin the dataset once
run = argon.sandbox_from_pin("myapp", "eval-v1")            # identical state, every eval run

The full agent workflow: docs/AGENTS.md.

Documentation

Quick start	install → first merge, step by step
CLI reference	every command
Agents	sandboxes, pins, MCP, REST API, argon-agents, proxy
Architecture	how it works and what it guarantees
Operations	deployment, chunk stores (S3/FS), GC, v1→v2 migration
Performance	every number lives in the reproducible benchmarks — none here, by policy

Community

Issues · Discussions · Contributing · argonlabs.tech

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Give your MongoDB a time machine. Branch without fear. ⭐

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