About argon
argon provides versioned MongoDB sandboxes for AI agents and developers, with real MongoDB databases accessible through any driver. Its branching, captured document history, and reviewable merge and undo operations support experimentation and recovery, subject to retention and capture limits.
Key features
- 01Metadata-only branch creation
- 02Real MongoDB databases through any driver
- 03Captured document history and time-travel queries
- 04Reviewable three-way merges and conflict-aware undo
- 05Disposable sandboxes and protected dataset pins
- 06Local web console with REST API
Use cases
- 01Isolate an AI agent's database experiments
- 02Review and merge proposed data changes
- 03Restore or undo captured document changes
- 04Keep a named dataset state protected from cleanup
MCP support
argon provides an MCP interface through `argon mcp`, with 13 tools for sandbox, diff, merge, undo, and pins. MCP clients can use these tools to manage MongoDB sandboxes and review changes.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEArgon — Git for MongoDB
Branch, time-travel, merge and undo your MongoDB. Any driver, real mongod, versioned history underneath. Built for AI agents.
Three ideas, thirty seconds:
- A branch is a pointer, not a copy — created with a metadata write. Checkout separately materializes the data.
checkoutturns a branch into a real MongoDB database — pymongo, mongoose, mongosh, indexes, aggregation, transactions: all real, and supported document writes become history while capture is healthy.- Review and recover changes — diff, merge, undo and pin states within the configured retention and capture guarantees.
Install
For npm, use Node.js 24 LTS with npm 11.16 or newer. The package-scoped approval allows the installer to download Argon's binary on npm 12.
brew install argon-lab/tap/argonctl # macOS
npm install -g argonctl --allow-scripts=argonctl # cross-platform
# MongoDB must run as a replica set (one-node is fine):
docker run -d --name argon-mongo -p 127.0.0.1:27017:27017 mongo:7 --replSet rs0
docker exec argon-mongo mongosh --quiet --eval 'rs.initiate({_id:"rs0",members:[{_id:0,host:"localhost:27017"}]})'
argon doctor
Use a current supported MongoDB patch release in production. Source builds use
Go 1.26.9 or newer, as declared in go.mod. Use a patched toolchain:
Go 1.27.0–1.27.1 also contain the October 2026 standard-library vulnerabilities.
The flow
main ──branch──▶ experiment ──checkout──▶ mongodb://… ← any driver
│
┌── argon diff ──────────┤ document history
▼ ▼
merge (a data PR) or undo / discard / rewind
# 0 · Stop source writers and DDL for the import — or: argon projects create myapp
argon import database --uri mongodb://localhost:27017 --database myapp --project myapp --source-quiesced
# 1 · Branch — instant, no copy
argon branches create experiment -p myapp
# 2 · In terminal A, capture writes with a branch actor label
argon checkout -p myapp -b experiment # prints a connection string
argon watch -p myapp -b experiment --actor agent:experiment
# In terminal B, write through the printed URI. Then:
# 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),
supervises capture, reaps expired sandboxes every minute, and opens your browser.
The console's MIT-licensed source is in web/; see the
rebuild instructions.
For a complete managed workflow, run the two-agent pinned dataset example.
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 | exact change-stream images → history, one actor label per branch |
| Time travel | argon time-travel query | query captured document states at retained LSNs; restore/pin also accept timestamps |
| Undo | argon undo --actor <a> | revert a range or actor label; append-only, conflict-aware |
| Restore | argon restore preview/reset/branch | rewind a released branch or fork retained history |
| 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 | named captured states protected from GC and resets while the pin exists |
| Web console | argon console | local UI + REST API in one command |
Data history covers document inserts, updates, replacements and deletes. Collection drop/rename produces degraded capture; indexes and collection options are not versioned. Native writes are asynchronous; control operations drain capture. Stop writers before release. Existing pins protect their referenced states, while GC can expire audit and undo history. See operations for recovery and credentials.
Storage retention uses snapshots + retention-window GC to 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
# Install the published Python SDK and LangGraph adapter.
python3 -m pip install 'argon-agents[langgraph]==0.2.0'
Start argon console --no-browser in another terminal, then:
from argon_agents import ArgonClient, ArgonCheckpointSaver
argon = ArgonClient("http://127.0.0.1:1818")
argon.get_or_create_project("myapp")
saver = ArgonCheckpointSaver.from_sandbox(argon, "myapp")
# Compile and run your LangGraph graph with checkpointer=saver, then review:
plan = argon.merge_preview("myapp", saver.sandbox.branch)
print(plan)
# After reviewing this exact plan, apply it explicitly:
# argon.merge_apply(plan["id"])
# Or reject the run: saver.discard()
argon.create_pin("myapp", "eval-v1") # pin the dataset once
run = argon.sandbox_from_pin("myapp", "eval-v1") # identical state, every eval run
saver.merge() is a convenience call that previews and applies immediately;
use the separate calls above when approval is required. Pins protect captured
document state while they exist; keep independent backups.
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 · Blog
Give your MongoDB a time machine. Branch without fear. ⭐
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.