Argon

Argon — MongoDB sandboxes for AI agents

Give every agent run its own real MongoDB database, forked from your data. Review what it changed, merge what you want, discard the rest. Git-style branching, time travel and merge underneath. Any driver, real mongod.

Three ideas, thirty seconds:

1. A sandbox is a real MongoDB database — forked from a branch or a pinned dataset, checked out and given a TTL in one command. pymongo, mongoose, mongosh, indexes, aggregation, transactions: all real.
2. What the agent wrote is a diff — supported document writes become history while capture is healthy, so a run can be reviewed, merged as a data pull request, undone or thrown away. Your source data is untouched until you merge.
3. Underneath, it is Git for MongoDB — a branch is a pointer created with a metadata write; checkout separately materializes the data. Diff, merge, undo and pins work 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 or Linux, builds from source

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 1.26.11 or newer, as declared in go.mod. Use a patched toolchain: on the Go 1.27 line that means 1.27.4 or newer, which carry the same standard-library fixes.

The flow

your data ──sandbox──▶ mongodb://…  ← the agent, any driver

│ argon diff ──────┤ what the run changed ▼ ▼ merge (a data PR) or discard / undo

# 0 · Bring your data in once. 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 · In terminal A, a sandbox: fork + checkout + TTL + capture in one command.

Prints its name and a connection string, then keeps capturing until Ctrl-C

argon sandbox create -p myapp --ttl 1h --watch --actor agent:run-1

In terminal B, point the agent at the printed URI. Then:

2 · Review, then adopt the run — a data pull request

argon diff -p myapp -b argon merge preview -p myapp -b argon merge apply

…or throw the run away

argon sandbox discard -p myapp -b

Through the MCP server or argon console, capture and expiry are managed for you: nothing stays in the foreground, and expired sandboxes are swept every minute. --watch needs 2.3.0 or newer; on older versions run argon watch -p myapp -b --actor agent:run-1 after creating the sandbox. Long-lived branches work the same way with argon branches create and argon checkout, and argon restore reset rewinds a branch to an earlier time.

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.

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.

To keep a person in the loop, withhold tools from the MCP server: argon mcp --disable-tools argon_merge_apply leaves applying a merge plan to argon merge apply , and argon mcp --read-only offers only the tools that inspect state.

The full agent workflow: docs/AGENTS.md.

What you get

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.

Two things to know before handing a sandbox to an agent. A collection the agent creates is captured exactly while capture is running, or when it is next synchronized within MongoDB's snapshot history (five minutes by default); the one exception is a document that is updated and then changed again inside one transaction on a collection created moments earlier. Before 2.3.1 such a collection needed argon collections prepare first. And a sandbox is a full physical copy with capture ready, so creating one takes longer as the data grows: the benchmarks measure it as sandbox_capture_ready_ms.

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.

Documentation

Community

Issues · Discussions · Contributing · argonlabs.tech · Blog

Contributors

Thank you to everyone who has made Argon better.

What each person shipped, and the release it shipped in, is listed at argonlabs.tech/about#contributors. Every release also names its new contributors in the release notes.

New here? Start with a good first issue and the first-contribution guide.

---

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