atoma

atoma

Watch a request turn into finished work.

Ask for a drawing, a sound, a report, a data study, a proof or a piece of software. atoma coordinates AI agents to produce it, checks the result against what you asked for, and lets you inspect every step. The bounded decisions along the way, such as which agent takes a task or whether a plan is approved, are taken by Decision, TypeSafe's Jev model.

[Open atoma.run →](https://atoma.run)

Demo · Use cases · A run, step by step · Features · How it works · Self-host · Documentation

[!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

Explore a two-run business scenario, three-tier coordination, MCP integration and model-cost optimization in this four-minute walkthrough.

https://github.com/user-attachments/assets/3c21219f-9696-4f96-abc8-82944faf5eac

Download the high-quality video (23 MB)

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](https://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. Open Projects → Talk to Atoma, or connect your existing agent through MCP from Settings. Describe the project you want to create or the change you want to make. The integrated assistant uses the saved project brief and latest run to prepare a proposal.
2. Review the agent's proposal for a new or existing project, the run goal and optional acceptance criteria. In the integrated assistant, creation and run launch have separate confirmation buttons. Both use the existing 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.

Features

Acceptance criteria you approve before launch

A project run can carry up to twelve criteria, supplied through MCP or the CLI (--criteria ). 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 : : , 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, or their own Claude Code token (beta). 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. TypeScript/JavaScript sources also expose symbols and resolved local imports through Haystack; clients use atoma_run_search to inspect saved versions (scope and evaluation). 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.

Decision handles bounded decisions

Decision, TypeSafe's Jev model, 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 Decision.

The initial goal and starting repository guide the root tissue choice. Decision 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.

Decision 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:// /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 also provides an integrated assistant for customers without an external agent, and remains the place to review projects and results. Choose a personal subscription, organisation API key or configured platform API connection; conversation usage is accounted separately from runs; see setup and limits. Conversations can continue in either direction between Atoma and an MCP client.