Put your business knowledge to work

Atom is an open-source AI agent workspace for business workflow automation. Run it locally, bring your data, and teach an AI employee the rules your team uses. Start with a useful job: prepare a sales quote, review a document or work through a spreadsheet. Inspect the source records, calculations and deliverable before using the result.

Teach your rules. Review the work. Keep the context.

• Turn company data into deliverables. Demonstrated workflows map unfamiliar spreadsheet columns, calculate against source records and produce cited Word or PowerPoint documents within supported schemas.
• Make your rules reusable. Save field mappings, commercial terms and contextual lessons. New work adopts accepted corrections; active work keeps its accepted teaching snapshot.
• Handle work that needs an answer. Supported durable questions, approvals and waits survive restarts. Completed operations are retained, and uncertain effects need reconciliation before another attempt.
• Keep decisions reviewable. Author ordered policies in the canvas editor. Supported deterministic rules retain unresolved requirements rather than inventing an approval.
• Bring your own model. Configure a supported provider or local OpenAI-compatible endpoint. Model capability and permissions still matter; the starter sample needs no model or cloud key.

Atom's deployment model is single tenant: a locally owned workspace with user, role and workspace boundaries. The workflows below have recorded local or controlled-provider evidence. General autonomous judgment and universal business automation remain development goals.

Run the local sample → · Explore demonstrated workflows → · Check the evidence and limits →

Try Atom locally

Prerequisites: Git, Make, Python 3.11–3.13 (3.11 recommended), Node.js 20.9+ and npm. Python 3.14 is not supported by this dependency set.

git clone https://github.com/rush86999/atom.git
cd atom
PYTHON=python3.11 make setup
make dev

In a second terminal, from the same atom directory:

make sample

Open http://localhost:3001. Sign in as [email protected] using the generated password in backend/logs/bootstrap_admin_password.txt. Open Assignments to find the completed sample and download its Word document.

Your first result: Sample Quote Analyst reads sample-workspace/sample_quote.csv, calculates 232990.00, and creates a DOCX. Its lessons are editable under Agents → Sample Quote Analyst → Training. The sample uploads local data and creates local records/artifacts; it sends no email and needs no connected account or cloud credential.

If the default ports are busy, use the same pair for startup and the sample:

BACKEND_PORT=8101 FRONTEND_PORT=3101 make dev
# Second terminal:
make sample PORT=8101 FE_PORT=3101

Then open http://localhost:3101. Stop the development processes with Ctrl+C; restart with make dev against the same checkout to keep your local data. Do not delete the database or generated keys to troubleshoot an existing install.

Measured first use: approximately 5 min 15 s on one fresh-worktree run with warm package caches, including setup, startup, sample and browser access. 15 minutes is a cold-install target, not a measured guarantee. See the setup guide and measured first-use record.

See what an employee can do

Workflow	What the recorded acceptance demonstrates
Sales quote	Unfamiliar columns → confirmed mapping → source-grounded total → formatted document; corrected teaching applies to new work.
Grant review	A second business domain uses the same teaching, calculation, clarification and artifact mechanisms.
Workbook editing	A tested 24-sheet workbook preserves identifiers and formulas through supported recalculation, export and restart/reopen.
Long-running assignment	More than two hours waiting, real backend restarts, the same assignment resumes, completed source work is retained and one controlled effect is recorded.
Learn from conversations	Review a persisted email thread, resolve referenced resources, confirm a lesson and apply it only in its accepted context.

The demo guide distinguishes reproducible steps from historical runs and lists their exact sources. The two-hour run is elapsed/recovery evidence on its recorded source; later budget repairs have their own focused verification.

What you can rely on

111 of 123 frozen functional claims (90.2%) were accepted within the documented local and controlled-provider configurations. This is a source-bound feature coverage milestone, not a promise of 90% accuracy or universal reliability.

Reference	Purpose
successor-first-use-2026-11-01	Recommended reproducible snapshot with the correctness and first-use fixes.
accepted-local-2026-11-01-90pct	Immutable historical acceptance reference; its later-discovered budget defect is documented. Prefer the repaired successor for use.
Acceptance and repairs	Exact identities, independent checks and scope.
Frozen feature inventory	Verified, partial and excluded claims; denominator unchanged.
Successor release notes	Changes from the baseline and first-use measurements.

To use the fixed snapshot rather than development main, run git checkout successor-first-use-2026-11-01 before make setup.

Recent verified progress

Development main also includes newer, separately scoped evidence. These do not change the frozen 111/123 score or qualify universal employee judgment.

Capability	Verified boundary
Stored-text document exports	An ordinary TXT upload, open and reload journey verified downloads as Text, Markdown and JSON with current source-version binding. This exports stored text, not the original binary file; other formats and grounding remain separately qualified.
Information-triggered continuation	An ordinary source-answer UI episode preserved the same task, wait and source version through a real restart; an authorized answer triggered continuation to one verified task effect and final UI. Model responses were controlled, not a model-quality benchmark.
Goal controls	Ordinary UI goal set, ranking, reload and clear, with a member read-only/refusal twin. Analytics history used synthetic fixtures.
Backup and restore	A normal application child-process restart verified restored records and exact document bytes in an isolated local world; this is not whole-app disaster-recovery or crash-durability qualification.
Model-routing feedback	Scoped business verdicts and shared conditional updates have SQLite and targeted PostgreSQL verification. Grounding support, unresolved decisions and satisfied business outcomes remain distinct. Served multi-model quality is still open.

See the current completion and evidence map for exact source identities, retained negative results and remaining requirements.

Long-running coordination: the product goal

A task should be able to request a missing fact from an authorized person or system, wait durably without repeated prompting, continue independent work, and resume the same task when the relevant information arrives—even after days of waiting and application restarts. It may need several request/wait/resume cycles before delivering a verified result. This is a general app goal with bounded demonstrations, not a claim of universal autonomous coordination or live third-party outreach.

For example, a customer quote may depend on a vendor's availability, price and lead time. Once authorized information arrives, dependent work should calculate landed cost and the taught margin or markup, then prepare a quote with supported lead-time assumptions. Procurement is only an illustration: the same mechanism must handle dependencies, facts and outputs across domains. It must never invent missing values, treat an unanswered request as completion, or send/order/spend without the required authority. See TASK-WAIT-01 acceptance requirements.

Clear boundaries

• The demonstrated local topology is single worker. Multi-worker execution and long soak behavior are not qualified.
• The sample and main acceptance campaigns use local or controlled providers. Deterministic taught policy is qualified separately from discretionary model judgment; the tested small-model judgment configuration failed qualification.
• One specifically authorized Outlook send and Microsoft read-only ingestion were separately qualified. That does not qualify every connector, broader live sending, autonomous live sending or independent recipient delivery.
• Supported workbook formulas and taught schemas are bounded. Arbitrary Excel compatibility, arbitrary schema understanding and UI-taught rounding are not promised.
• The broader platform includes workflow, routing, graph, governance and integration surfaces. Some need ATOM_PRODUCT_PROFILE=full, a configured provider or additional infrastructure. Consult the inventory before relying on a particular feature.

Common questions

Is Atom an AI agent framework or an AI employee app?

Both: the employee workspace uses the app's shared agent, tool, workflow and governance mechanisms. “AI employee” describes the business-facing experience; it is not a separate replacement for the agent framework.

Can I use it with a local model?

Yes. Atom supports local inference through configured routes, including Ollama. A model must meet the workload's capability requirements; local availability alone does not establish reliable business judgment.

Can an agent wait for information for days?

Durable wait/restart/answer workflows are demonstrated at their recorded scopes. General days-long coordination with external people and systems is an explicit acceptance goal. It is not yet a universal capability claim.

Does running locally keep all data on my computer?

Local storage and local inference can support a local workflow. If you configure a cloud model or connector, the data you send follows that service's terms and your authorized access. See security boundaries.

Connect your own data and models

After the sample, open Settings to configure a provider and the connections you need. Provider usage and connector access depend on your account and permissions. Keep credentials out of issues, screenshots and commits.

For local inference, see Run with Ollama. For connected Office work, see Office automation and OAuth setup. Local storage does not make cloud inference private: data sent to a configured provider follows that provider's terms.

Documentation and contributing

• Documentation hub · Quick start
• Verified workflow walkthroughs
• Troubleshooting
• Architecture · Security boundaries
• Brennan business-workflow engineering orders
• Contributing · Open an issue

For a useful bug report, include the commit/tag, operating system, launch command, expected result and a minimal sanitized example. Please do not include passwords, tokens, private documents or your database.

Atom is already public on GitHub under AGPL v3. Hosted deployment is separate from the verified local setup. If this project helps your work, star the repository, try a real workflow and tell us where it gets stuck.