[!WARNING]
atoma is under active development. Organisations are designed to be
isolated from one another (projects, workspaces, traces and previews), but
the platform has not been independently audited and we cannot yet guarantee
that isolation, or the absence of other security defects, under every
condition. Do not include confidential, personal or otherwise sensitive
data in your goals, uploaded files or generated applications, whether on
atoma.run or on a self-hosted instance exposed to others.
Use the hosted service to evaluate the product, not to process data you could
not afford to see leak. See SECURITY.md to report a vulnerability.

Watch the demo

https://github.com/user-attachments/assets/9572ddb9-0767-45c8-aaf2-5435619c1cbd

From a business need to a tool you can use

An operations team needs a dashboard. An agency needs a client demo. A product
team needs an API prototype. atoma turns those requests into software: web
applications, HTTP APIs, command-line tools and their technical documentation.

Start at atoma.run. The web console brings projects,
AI execution, acceptance criteria, result previews, model choice and run history
into one place. This repository contains the open-source engine and console for
teams that want to inspect, extend or self-host them.

What could your team build?

These are example project briefs, not prebuilt industry integrations or
customer deployment claims.

For example:

Build a sales dashboard that lets me upload a CSV with date, product, region
and revenue columns. Add filters, monthly totals and a chart by region.
Include sample data and instructions to run it locally.

A run, step by step

1. Connect your existing agent through MCP from Settings in the Atoma
   console. Say “Continue with Atoma” or “Plan a project for this
   repository with Atoma.” The agent inspects the relevant context and proposes
   what to do next.
2. Review the agent's proposal for a new or existing project, the run goal
   and optional acceptance criteria. Approve the project and run before the
   agent starts them through MCP. Without criteria, the run drafts its own
   checklist from the goal.
3. Follow the run as agents plan, write files, start servers and check their
   work, with the model, tokens and cost behind every step.
4. Preview web results in a temporary, isolated environment, including a
   snapshot while the run is still building.
5. Keep the deliverable, with its verification record, and publish it to a
   connected GitHub repository. The project's next run continues from it; a
   project imported from GitHub starts each run from its default branch.

Previews are review environments; deploying the generated application is a
separate step. Project runs use the configured model accounts and consume model
quota or incur API charges.

See what ran, what was checked, and where model usage went.

Filter the timeline to LLM calls: the model, tokens and cost behind each step.

Features

Acceptance criteria you approve before launch

A project run can carry up to twelve criteria, supplied through MCP or the
CLI (--criteria <file>). A line such as
GET /api/items is an HTTP check; any other line is judged by review. The host
stores the list before planning starts and nothing can change it afterwards.
The planner is told the user wrote it, and the final check is told which HTTP
checks were actually observed on servers the run itself started.

Without a list, the run drafts one from the goal with a single call to the
cheapest model. A drafted list can only add to what the final check looks for;
it can never make a run pass.

Finished work is never thrown away

A run that exhausts its budget after completing at least one phase, or whose
result the final check refuses after one remediation attempt, is kept as
incomplete rather than discarded — including when the deadline falls while
its finished work is still being checked. Its finished phases stay in the
workspace, and the console explains in plain language why it stopped and what
to do next, with the technical reasons one click away. In a project created in
atoma, the next run starts from that workspace and is told why the previous one
stopped; a project imported from GitHub starts each run from its default
branch, so unpublished changes are not carried over.

Choose the models, then compare them

Each tier (workers, supervisors, planners) takes its own model selector, of the
form <api|sub|own>:<vendor>:<model>, across Anthropic, OpenAI, Google
DeepMind, xAI, Meta, Mistral, Alibaba Qwen, DeepSeek, Moonshot, Z.ai and a
self-hosted Ollama: api: bills a key, sub: the host's Claude or ChatGPT subscription,
and own: the member's own ChatGPT account, whose available models Settings
lists. The operator can delegate the host subscription to named members without
making them administrators. The offered models and their per-token prices are
one versioned file, src/core/modelCatalog.json, kept current with
npm run models -- refresh: prices are a dated history, so a vendor's change
applies from its day without re-pricing what came before.

Every run records the models it was pinned to and the models the provider
actually served. A delivered or incomplete run of a project created in atoma can
be rerun on other models through the API or MCP: same goal, same acceptance
criteria, same starting workspace. The
rerun sits beside the project's history, so you can compare cost, time and
result; it never publishes and never seeds a later run.

Start from your repository, publish back to it

Install the GitHub App to import a repository into a project or create a new
one, then publish delivered results to it. A project's earlier deliverables,
including Markdown, CSV, PDF and Office documents, are indexed so later runs
can search them and cite exact passages. See GitHub App setup.

Verification you can read

Supervisors check results with evidence the run produced: files, command
exit codes, HTTP probes, and browser checks laid out at the viewport widths the
goal asks for. They never replay shell commands a model wrote. Every verdict,
retry and refusal is in the timeline, and the console is available in thirteen
languages, on desktop and mobile.

For an existing static page, the host also replays browser checks recorded by
earlier runs before editing begins and again before delivery. Regressions go
to the final review, which distinguishes requested changes from lost behaviour.
Read-only phases restore the files they changed, and workers read existing
files before editing them. See inherited browser checks.

Jev handles bounded decisions

TypeSafe's Jev helps choose an agent or recipe, approve intermediate plans
and results when the evidence permits it, and avoid learning equivalent
recipes twice. It answers structured questions; the run's models still plan,
write code and explain refusals. Uncertain or unavailable answers go back to
those models, and final delivery acceptance stays independent of Jev.

The initial goal and starting repository guide the root tissue choice. Jev
selects a suitable registered L3; if the choice is uncertain or no tissue fits,
a bounded model call reuses one or requests a new capability. New tissue prompts
are authored by the platform's pinned L3 model using the platform's credentials.
The legacy run:build command also accepts analysis and explanation tasks.

Jev is enabled for every organisation when the host has TYPESAFE_API_KEY,
unless the operator sets ATOMA_JEV=0. Decision context, including task text
and evidence excerpts, is sent to TypeSafe as described in the
service terms.
The timeline records its decisions, latency and separate cost; a sample of
approvals is reviewed by the model for comparison. See the
architecture guide.

Adjust run limits without redeploying

Platform administrators can change run budgets, token and spend ceilings,
call timeouts and tool-iteration limits in Settings or with npm run settings.
Changes are recorded in the audit journal and apply to new runs. A token or
spend ceiling stops further model calls and preserves completed phases as an
incomplete run. See operating run limits.

Connect an existing agent through MCP

The console serves an HTTP MCP endpoint at https://<your-instance>/mcp.
Compatible clients such as Claude Code or Codex can create projects, draft run
goals from the conversation and repository context, start runs, pass acceptance
criteria, request reruns and read traces, costs and diagnostics. The Web console
is where customers connect the agent and review projects and results.

One endpoint supports both MCP protocol revisions 2025-11-25 and 2026-07-28.
OAuth clients can connect through browser consent, including clients identified
by a metadata URL. Project run tasks survive a client reconnect, and results
link to run resources that compatible clients can open or follow.

Forty-five tools. The visible subset depends on the caller's role. Compact
searchable menus, recorded progress and saved-file readers keep the workflow
inside the conversation. Hosts supporting MCP Apps can also display an inline
run view; every client retains text results and file resource links.
See the MCP connection and authorization guide.

A platform that reviews its own runs

Three background services watch the platform itself. The sentinel watches
runs in flight for cost overruns and drifting trajectories. The analyst
writes a cited post-mortem verdict for each finished run. The mender turns a
verdict that names a code defect into a pull request, which a person reviews and
merges. Read the supervisor design.

How it works

atoma assigns planning, supervision and execution to different AI agents and
model tiers. Ordinary build runs start with a supervisor and workers; a deeper
planning tier takes over if supervision exhausts its retries, and a seeded run
keeps its starting workspace when it does.

• Workers build. They read and write files, run commands and use tools.
  Project runs do so in a container with no network unless egress is
  explicitly allowed; a local npm run run:build uses the host unless it is
  given --container.
• Supervisors check. They review results against artifact evidence and fixed
  probes. Before delivery, a separate check reviews the final result against
  the goal and its acceptance criteria.
• Trust is earned. Components that keep succeeding can skip some model
  reviews while retaining mechanical checks. A failure revokes that trust.
• Skills carry forward. Verified work becomes reusable recipes. A recipe
  that can be turned into a script is compiled as soon as it is learned, and
  the script then runs without model calls until it fails.

The composition model is Element → Molecule → Cell → Tissue: tools, workers,
supervisors and planners. Read How atoma works for
the architecture, execution boundaries and verification design.

One catalogue, shared by every run. An instance keeps one agent registry,
one skill catalogue and one set of trust counters, and every run reads and
writes them, whichever organisation started it. What one team's run works out
is offered to the next team's run. Projects, workspaces, traces and searchable
documents stay scoped to their organisation. This is the design, and it has a
price: prompts and recipes a run writes, including wording derived from a
document it was given, are readable by other organisations' runs. An instance
therefore suits teams that accept pooling what their runs learn. The hosted
service states this in its shared-learning terms.

Install and evaluate it locally

For the web experience, start at atoma.run.
For a source checkout, use the pinned Node version and follow the
development setup guide, including system
prerequisites. Runs and the full test suite require macOS or Linux; on Windows,
use WSL2 with its own checkout on ext4.

git clone https://github.com/atoma-run/atoma.git
cd atoma
nvm install && nvm use
npm ci
npm run release:check
npm run doctor

Configure your model providers using .env.example and the
development guide, then run a task and open the
console:

npm run run:build -- "a Node CLI that converts CSV to JSON"
npm run viz:serve

The compiled commands read the process environment; they do not load .env.
The local console is open on loopback by default. Organisation-scoped project
runs additionally require Docker and a Haystack search runtime, described in
the development setup guide.
A fresh checkout contains no learned state.

To host an instance for others, follow the packaged stack,
deployment, GitHub App
and preview guides, and back the state up
off-machine with npm run backup -- --dest <mount>.

Status

atoma is an evolving open-source system. Its web console, project runs,
acceptance criteria, comparison reruns, GitHub import and publication, result
previews and MCP endpoint are in use on atoma.run.

Local file-tool containment is not shell isolation; use the container backend
for isolated execution. A platform admin can read across organisations, and
mutually untrusted tenants are not a supported deployment shape. Verification
provides evidence for review, not a guarantee that a generated application is
ready for production. See the changelog for what changed.

Documentation

Browse the documentation index for current guides and the
archive for historical designs, reviews and release evidence.

Historical measurements

Twelve controlled rounds explored build and maintenance tasks against a
single agent — a frontier model, and in later rounds cheaper Sonnet and Haiku
agents. Results were mixed: some comparisons favoured atoma, while cheaper
direct models matched or outperformed it on the tested maintenance tasks. They
predate the 2026-08-18 state reset, so they do not establish savings or
correctness for the current release. Read the protocol and the
results for context.

License

atoma is free and open-source software under the
GNU Affero General Public License, version 3 (AGPL-3.0-only).
If you distribute a modified version, or run one that users interact with over
a network, you must offer those users its source under the same licence.
Unmodified use, including internal production use and hosting, carries no
obligation beyond keeping the notices. A commercial licence is available from
the author for organisations that cannot accept the AGPL; contributors grant
the rights that make this possible through the CLA, which also
commits the project to remaining under an OSI-approved licence.

Contributions start with CONTRIBUTING.md. Vulnerabilities
go through SECURITY.md, not the issue tracker. The hosted
service at atoma.run publishes its
shared-learning service terms and operator contact
separately from the software licence.

Model providers are called with the credentials you supply, under each
provider's own terms. The @anthropic-ai/claude-agent-sdk dependency is
distributed by Anthropic under its own licence, and the 3D assets under
src/viz/public/ carry their CC0 and CC-BY-4.0 notices beside the files.

Copyright 2026 Matthieu Foillard.

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