rush86999/atom

Atom Agent, Open-Source Governed AI Agent Platform for Self-Hosted Automation

884

stars

10,336

commits

Python

primary language

Sep 10, 2026

updated

agentic-workflow
ai
ai-agent
calendar
finance
gdrive
gmail
google-calendar
notion
productivity
scheduler
shopify
slack
tasks
zoho
zoom

README

ATOM Platform

The governed agent platform — autonomy, earned.

Atom Platform

88% of AI agent pilots never reach production.* Atom is built for the other path.

*Industry figure (Turion 2026). Atom makes no claims about its own deployments.

License CI Tests Governance Python Stars


What is Atom?

Atom is an open-source, self-hosted AI agent workforce — a team of specialty agents (sales, support, finance, engineering) that your people delegate to in plain language. Where other platforms sell agent capability, Atom sells agent accountability: autonomy that is earned through verified outcomes, executed inside a deterministic safety net, on your hardware.

Agents that earn trust, not assume it. Atom's agents don't just respond to commands — they operate autonomously within governed boundaries, handling routine work end-to-end. Every agent starts as a supervised intern and graduates through a 4-tier maturity model (STUDENT → INTERN → SUPERVISED → AUTONOMOUS) only after verified successful runs — 10/25/50 episodes, outcome-checked, not self-reported.

Verified outcomes, not self-report. Every mutating action is re-derived against your system of record by an independent postcondition oracle (on by default), and confidence is split into self-reported vs externally verified. An agent that says "done" is checked, not believed. A prompt-injected agent at any tier acts at that tier's scoped blast radius — bounded by a default-on sandbox layer: filesystem scope, tool whitelist, tripwires, resource caps, kill-run, egress allowlist, full provenance audit. 0.027ms P99 per check.

Your data stays yours. Workflow data, agent state, and memory live on your infrastructure — embedded store, no cloud required. LLM inference uses your own API keys (BYOK, encrypted at rest) — or local models (Ollama first-class, or any local OpenAI-compatible server: LM Studio, vLLM, llama.cpp server) for fully private deployments. EU AI Act data-governance obligations (Aug 2026)? Designed for, not retrofitted.

Free edition, full features (AGPL v3): everything in this repository — every agent, integration, and governance feature — is free and open source. Keys you configure in .env are treated as BYOK and are never gated by plans or tiers. Commercial/managed editions run this same code on the client's own infrastructure; there is no closed-source "pro" build.

💰 Budget-friendly AI agents: OpenCode Go subscription (~90% savings vs pay-per-token) — one $10/mo key unlocks general-purpose models (DeepSeek V4, Kimi K3, GLM 5.2, MiniMax M3, Qwen 3.7, Nemotron 3 Ultra, Grok 4.5) with full tool-calling & structured output — not just for coding, works for any agent workload. Setup guide →

No lock-in: 16+ LLM providers (OpenAI, Anthropic, DeepSeek, Gemini, MiniMax, Groq…) with automatic cost-aware routing, fallback, and self-healing — every run makes the next run cheaper (learning router + caching tiers).

The vision: every employee gets a personal agent team that knows their workflows, remembers context across weeks, and autonomously handles the repetitive work — research, data entry, drafting, reconciliation — while governance keeps you in control. Not AI replacing humans: AI handling the work humans shouldn't be doing.


Receipts: 0.027ms P99 governance checks (repo benchmark) · 616k ops/s cached throughput · 69+ documented TDD hardening rounds (~1,100 fixes in the deep security sweep alone) · 85k+ test functions (84,737 across 2,759 files, verified Aug 2026). External stats sourced in docs/marketing/RESEARCH_NOTES.md; copy kit in COPY_README.md + POSITIONING.md.


💻 Quick Start

Clone to first governed workflow in ~10 minutes. Start small — one workflow, one integration, one approval gate.

git clone https://github.com/rush86999/atom.git && cd atom
make setup                 # one-shot dev bootstrap (venv, deps, .env, frontend)
make backend               # full backend on :8001
# in a second terminal:
make frontend              # Next.js UI on :3001

To use LLM features, set one key in backend/.env (or add via Settings > AI):

  • OPENCODE_API_KEY for low-cost subscription coding models (~90% savings, recommended) or
  • OPENAI_API_KEY / ANTHROPIC_API_KEY / DEEPSEEK_API_KEY / GOOGLE_API_KEY … or
  • ATOM_LOCAL_ONLY=true + OLLAMA_BASE_URL=http://localhost:11434/v1 for fully local

Expected result: open http://localhost:3001 → sign in as admin@example.com (password in backend/logs/bootstrap_admin_password.txt) → describe a workflow in plain language and watch it build with an approval gate before anything ships.

Full Quick Start → · Docker → · DigitalOcean 1-Click →

Solo operator? Skip setup — import a pre-built personal starter (invoice chase, candidate pipeline, support triage), each with an approval gate so nothing sends without your OK: Personal → Team Playbook →


🚀 AI-Generated Workflow Automation

Describe the outcome. Atom builds and runs the workflow.

What you sayWhat Atom delivers
"When a lead comes in HubSpot, research the company, score it, create an Asana task for the rep, and ping Slack"A governed, replayable workflow with Human-in-the-Loop approval gates
"Extract invoice data from Gmail PDFs, match against QuickBooks, flag discrepancies"End-to-end pipeline: Gmail → PDF OCR → QuickBooks reconciliation → Slack alert
"Monitor Zendesk tickets for sentiment, auto-escalate urgent ones, draft replies"Real-time triage agent with approval before send
"Generate a weekly sales report from Salesforce, format in Excel, email the team"Scheduled workflow: SOQL query → formula-evaluated Excel → Office 365 send

Why it's different from Zapier/Make/n8n:

  • Agents, not just steps — Agents reason (not just execute): they research, decide, retry, and self-correct
  • Governance built-in — Maturity gates, HITL approval, audit trail, sandbox isolation
  • Self-hosted & private — Your data, your keys, your infrastructure
  • Office-native — Real Excel/Word/PPTX with formula evaluation, live Canvas co-editing
  • Agent-authored — You can chat with an agent to build/modify workflows (no drag-and-drop required)

Workflow Automation Guide → · Quick Start →


⚡ The AI Agent Landscape — Where Atom Fits

🧵 Consumer Assistants🧰 Developer Frameworks🏢 Enterprise Workforce
ExamplesChatGPT, Claude, Notion AI, Copilot, PerplexityLangGraph, AutoGPT, CrewAI, AutoGenAtom
InteractionSingle chat, reactive onlyCode-first, build-your-ownDelegate to agent teammates in plain language
Data & hostingCloud-onlySelf-hosted; bring your own integrationsSelf-hosted + 46+ native business integrations
GovernanceNoneDIYGoverned by design

Atom is the only open-source platform that delivers:

  • Enterprise governance (maturity tiers, HITL, audit) — without vendor lock-in
  • Agents that know when to ask — autonomy earned per action type from verified track records, auto-revoked on regression; governed agent orgs with delegation contracts & privilege leases
  • Self-hosted privacy — your data, your keys, your infrastructure
  • Autonomous agent teammates — agents that work with your people, not just for them
  • Agent-authored workflows — chat to build, no drag-and-drop
  • Office/Canvas native — real Excel formulas, live co-editing
  • 46+ business integrations — Salesforce, HubSpot, Slack, Jira, Stripe, QuickBooks…

📊 Comparisons

AlternativeFocusKey DifferenceDeep Dive
Hermes Agent (Nous Research)Personal coding/productivity assistantSingle-agent, no governance, no integrations, no sandboxAtom vs Hermes →
OpenClawPersonal productivity, messaging-firstSingle-agent, Markdown memory, smart home focusAtom vs OpenClaw →
LangGraph / CrewAI / AutoGenDeveloper frameworksCode-first, build-your-own governance & integrationsWhy Atom? ↓
Zapier / Make / n8nWorkflow automationStep-based (not agents), no reasoning, no governanceAI-Generated Workflow Automation

TL;DR: If you're evaluating personal agents → Hermes/OpenClaw. If you need governed multi-agent business automation → Atom.

Full feature matrix: Why Teams Choose Atom ↓


⚡ Key Capabilities

CategoryFeatures
🤖 Multi-Agent OrchestrationQueen Agent (structured workflows) + Fleet Admiral (open-ended tasks) + Conductor (5 execution strategies) + validated state machine with rollback; governed fleet routing with ranked specialist matching
🛡️ Governance & Safety4-tier maturity (Student→Autonomous), policy-gated HITL approval, comprehensive audit trail, AI-powered training, OIDC SSO + SCIM v2 provisioning + 8-role RBAC
✅ Outcome VerificationPostcondition oracle re-derives success against the system of record — on by default, a refuted self-report is stamped UNVERIFIED (ATOM_ORACLE_ENFORCE kill switch); two-tier confidence provenance (self-reported vs externally verified); opt-in reviewer re-delegation loop
🎚️ Agents That Know When To AskEvery action type earns interruption-free status from its verified track record, keeps asking when evidence is thin (ask fails safe), and loses the privilege automatically when results regress — autonomy only relaxes after passing a held-out certification gate (Brier ≤ 0.25, denial-coverage ≥ 0.7)
🏛️ Governed Agent TeamsMulti-agent fleets fail like human orgs — ignored instructions, redone work, nobody accountable. Atom ships the countermeasures: delegation contracts with a single accountable agent, expiring privilege leases instead of titles, conflict-of-interest detection, contribution credit feeding graduation, opt-in nightly alignment sweeps
🧠 Memory & LearningPer-turn fact extraction, 2-tier recall (SQL + LanceDB), episodic memory, memory_remember/forget, self-evolution (Memento/AlphaEvolver, self-evolving harness)
🔎 Hybrid Searchdocuments.search fuses BM25 (FTS5/tsvector) + vector (LanceDB) via Reciprocal Rank Fusion (RRF) — semantic + precise retrieval with citations
🗂️ Knowledge VFSAgent-native document tree — ls/cat/grep/search with line-numbered citations instead of bespoke per-store queries
📻 Agent RadioLateral peer-to-peer messaging between agents (mention-first, budget-governed) — agents coordinate without hardcoded teams
💼 Office AutomationAgent-driven Excel/Word/PPTX editing on Canvas with live preview broadcast; formula-evaluating workbook runtime; agent↔document sync
🧩 Mini-AppsAgent-authored stateful canvas apps — Firecracker microVM isolation, per-instance chat
🔍 GraphRAG & IntelligenceMulti-hop expansion, Leiden community detection, JIT fact verification, D3 visual explorer
🌐 46+ Business IntegrationsSalesforce, HubSpot, Slack, Teams, Gmail, Notion, Jira, Linear, Stripe, QuickBooks, Shopify, GitHub, GitLab, Zoom…
🛰️ LLM GatewayOpenAI/Anthropic-compatible API over your BYOK — point Claude Code, n8n, or any OpenAI-SDK app at Atom
💰 Cost-Aware Routing5-tier cognitive classification, 16+ providers, opt-in learning router (feedback-based re-ranking), RTK token compression
🤝 InteroperabilityMCP client for external tool servers, ACP endpoint for standard agent clients, A2A Agent Card + message/send for agent-to-agent delegation, span tracing with optional Langfuse export
🎯 Goal-Driven LoopsAgents terminate on a definition_of_done predicate instead of always burning to max_steps; utility targets, custom action surfaces, stuck-detection

🛡️ Production-Ready Security (Default-On)

LayerWhat you get
Execution SandboxFilesystem scope, tool whitelist, tripwires, resource caps, KillRun — enforced at every tool-dispatch hub (in-process policy checks); mini-apps run in Firecracker microVMs
Encrypted CredentialsOAuth integration tokens encrypted at rest (Fernet); production fails closed without key
Per-Agent Capability BindingsZero-trust tool scoping — agent can never exceed its tier floor
Outbound GatekeeperRate limiting, response masking, HITL mutation approval on integration calls
Data-Taint TrackingRestricted data observed in a run blocks external outbound actions
External MCP ClientConnect to arbitrary external MCP servers (Cloudflare portals)

Security Architecture → · Sandbox Deep-Dive →


📚 Documentation & Discoverability


🎯 Example Use Cases by Department

DepartmentScenarioKey Integrations
SalesNew HubSpot lead → Research company → Score → Asana task → Slack notifyHubSpot, Asana, Slack, LinkedIn
FinanceGmail PDF invoice → OCR extract → QuickBooks match → Flag discrepanciesGmail, QuickBooks, Excel, Slack
SupportZendesk ticket → Sentiment analysis → Auto-escalate urgent → Draft replyZendesk, Slack, Email
HRBambooHR new hire → Provision accounts → Invite to Slack → Schedule orientationBambooHR, Google Workspace, Slack, Calendar
EngineeringGitHub PR → Run tests → Security scan → Post summary → Auto-merge if greenGitHub, GitLab, Slack, Jira
MarketingContent calendar → Generate posts → Human review → Schedule multi-platformNotion, Slack, LinkedIn, Twitter, Meta

🏗️ Repository Layout

atom/
├── backend/            # FastAPI app — main_api_app:app (full) / minimal_app:app (smoke)
├── frontend-nextjs/    # Next.js web UI
├── mobile/             # React Native (Expo) companion app
├── menubar/            # Tauri macOS menubar companion
├── scripts/ · infra/ · installer/ · examples/
├── docs/               # project documentation
├── Dockerfile          # dual-app image (backend + frontend)
└── Makefile            # common tasks (start here)

🌟 Why Teams Choose Atom

Per-competitor analysis: 📊 Comparisons ↑

AtomZapier/Make/n8nLangGraph/CrewAIOpenClawLangChain
AI Agents (reason, not just execute)
Governance (maturity + HITL + audit)
Outcome verification (re-derived from system of record, not self-report)
Default-on Sandbox (all dispatch paths)
Self-Hosted / Private (your keys, your infra)
Office/Canvas Native (Excel formulas, co-edit)
Agent-Authored Workflows (chat to build)
46+ Business Integrations (CRM, finance, support)50+ personal
Cost-Aware LLM Routing (16+ providers)
Mini-Apps (agent-authored stateful apps)
GraphRAG / Episodic Memory

🤝 Contributing & Support

We welcome contributions — see CONTRIBUTING.md. Quality bar: CI-gated core suite green, typecheck clean on changed files, review required, docs updated. See docs/compliance/COMPLIANCE_MAPPING.md for the security/compliance control mapping.


Built with FastAPI | SQLAlchemy | LangChain | Playwright | Next.js

Experience the future of self-hosted AI automation — safe enough for your whole team.

⭐ Star us on GitHub — it helps!

Contributors

rush86999

9,480 commits

rparikh15

151 commits

mannan-b

85 commits

rush86999/atom

Atom Agent, Open-Source Governed AI Agent Platform for Self-Hosted Automation

884

stars

10,336

commits

Python

primary language

Sep 10, 2026

updated

agentic-workflow
ai
ai-agent
calendar
finance
gdrive
gmail
google-calendar
notion
productivity
scheduler
shopify
slack
tasks
zoho
zoom

README

ATOM Platform

The governed agent platform — autonomy, earned.

Atom Platform

88% of AI agent pilots never reach production.* Atom is built for the other path.

*Industry figure (Turion 2026). Atom makes no claims about its own deployments.

License CI Tests Governance Python Stars


What is Atom?

Atom is an open-source, self-hosted AI agent workforce — a team of specialty agents (sales, support, finance, engineering) that your people delegate to in plain language. Where other platforms sell agent capability, Atom sells agent accountability: autonomy that is earned through verified outcomes, executed inside a deterministic safety net, on your hardware.

Agents that earn trust, not assume it. Atom's agents don't just respond to commands — they operate autonomously within governed boundaries, handling routine work end-to-end. Every agent starts as a supervised intern and graduates through a 4-tier maturity model (STUDENT → INTERN → SUPERVISED → AUTONOMOUS) only after verified successful runs — 10/25/50 episodes, outcome-checked, not self-reported.

Verified outcomes, not self-report. Every mutating action is re-derived against your system of record by an independent postcondition oracle (on by default), and confidence is split into self-reported vs externally verified. An agent that says "done" is checked, not believed. A prompt-injected agent at any tier acts at that tier's scoped blast radius — bounded by a default-on sandbox layer: filesystem scope, tool whitelist, tripwires, resource caps, kill-run, egress allowlist, full provenance audit. 0.027ms P99 per check.

Your data stays yours. Workflow data, agent state, and memory live on your infrastructure — embedded store, no cloud required. LLM inference uses your own API keys (BYOK, encrypted at rest) — or local models (Ollama first-class, or any local OpenAI-compatible server: LM Studio, vLLM, llama.cpp server) for fully private deployments. EU AI Act data-governance obligations (Aug 2026)? Designed for, not retrofitted.

Free edition, full features (AGPL v3): everything in this repository — every agent, integration, and governance feature — is free and open source. Keys you configure in .env are treated as BYOK and are never gated by plans or tiers. Commercial/managed editions run this same code on the client's own infrastructure; there is no closed-source "pro" build.

💰 Budget-friendly AI agents: OpenCode Go subscription (~90% savings vs pay-per-token) — one $10/mo key unlocks general-purpose models (DeepSeek V4, Kimi K3, GLM 5.2, MiniMax M3, Qwen 3.7, Nemotron 3 Ultra, Grok 4.5) with full tool-calling & structured output — not just for coding, works for any agent workload. Setup guide →

No lock-in: 16+ LLM providers (OpenAI, Anthropic, DeepSeek, Gemini, MiniMax, Groq…) with automatic cost-aware routing, fallback, and self-healing — every run makes the next run cheaper (learning router + caching tiers).

The vision: every employee gets a personal agent team that knows their workflows, remembers context across weeks, and autonomously handles the repetitive work — research, data entry, drafting, reconciliation — while governance keeps you in control. Not AI replacing humans: AI handling the work humans shouldn't be doing.


Receipts: 0.027ms P99 governance checks (repo benchmark) · 616k ops/s cached throughput · 69+ documented TDD hardening rounds (~1,100 fixes in the deep security sweep alone) · 85k+ test functions (84,737 across 2,759 files, verified Aug 2026). External stats sourced in docs/marketing/RESEARCH_NOTES.md; copy kit in COPY_README.md + POSITIONING.md.


💻 Quick Start

Clone to first governed workflow in ~10 minutes. Start small — one workflow, one integration, one approval gate.

git clone https://github.com/rush86999/atom.git && cd atom
make setup                 # one-shot dev bootstrap (venv, deps, .env, frontend)
make backend               # full backend on :8001
# in a second terminal:
make frontend              # Next.js UI on :3001

To use LLM features, set one key in backend/.env (or add via Settings > AI):

  • OPENCODE_API_KEY for low-cost subscription coding models (~90% savings, recommended) or
  • OPENAI_API_KEY / ANTHROPIC_API_KEY / DEEPSEEK_API_KEY / GOOGLE_API_KEY … or
  • ATOM_LOCAL_ONLY=true + OLLAMA_BASE_URL=http://localhost:11434/v1 for fully local

Expected result: open http://localhost:3001 → sign in as admin@example.com (password in backend/logs/bootstrap_admin_password.txt) → describe a workflow in plain language and watch it build with an approval gate before anything ships.

Full Quick Start → · Docker → · DigitalOcean 1-Click →

Solo operator? Skip setup — import a pre-built personal starter (invoice chase, candidate pipeline, support triage), each with an approval gate so nothing sends without your OK: Personal → Team Playbook →


🚀 AI-Generated Workflow Automation

Describe the outcome. Atom builds and runs the workflow.

What you sayWhat Atom delivers
"When a lead comes in HubSpot, research the company, score it, create an Asana task for the rep, and ping Slack"A governed, replayable workflow with Human-in-the-Loop approval gates
"Extract invoice data from Gmail PDFs, match against QuickBooks, flag discrepancies"End-to-end pipeline: Gmail → PDF OCR → QuickBooks reconciliation → Slack alert
"Monitor Zendesk tickets for sentiment, auto-escalate urgent ones, draft replies"Real-time triage agent with approval before send
"Generate a weekly sales report from Salesforce, format in Excel, email the team"Scheduled workflow: SOQL query → formula-evaluated Excel → Office 365 send

Why it's different from Zapier/Make/n8n:

  • Agents, not just steps — Agents reason (not just execute): they research, decide, retry, and self-correct
  • Governance built-in — Maturity gates, HITL approval, audit trail, sandbox isolation
  • Self-hosted & private — Your data, your keys, your infrastructure
  • Office-native — Real Excel/Word/PPTX with formula evaluation, live Canvas co-editing
  • Agent-authored — You can chat with an agent to build/modify workflows (no drag-and-drop required)

Workflow Automation Guide → · Quick Start →


⚡ The AI Agent Landscape — Where Atom Fits

🧵 Consumer Assistants🧰 Developer Frameworks🏢 Enterprise Workforce
ExamplesChatGPT, Claude, Notion AI, Copilot, PerplexityLangGraph, AutoGPT, CrewAI, AutoGenAtom
InteractionSingle chat, reactive onlyCode-first, build-your-ownDelegate to agent teammates in plain language
Data & hostingCloud-onlySelf-hosted; bring your own integrationsSelf-hosted + 46+ native business integrations
GovernanceNoneDIYGoverned by design

Atom is the only open-source platform that delivers:

  • Enterprise governance (maturity tiers, HITL, audit) — without vendor lock-in
  • Agents that know when to ask — autonomy earned per action type from verified track records, auto-revoked on regression; governed agent orgs with delegation contracts & privilege leases
  • Self-hosted privacy — your data, your keys, your infrastructure
  • Autonomous agent teammates — agents that work with your people, not just for them
  • Agent-authored workflows — chat to build, no drag-and-drop
  • Office/Canvas native — real Excel formulas, live co-editing
  • 46+ business integrations — Salesforce, HubSpot, Slack, Jira, Stripe, QuickBooks…

📊 Comparisons

AlternativeFocusKey DifferenceDeep Dive
Hermes Agent (Nous Research)Personal coding/productivity assistantSingle-agent, no governance, no integrations, no sandboxAtom vs Hermes →
OpenClawPersonal productivity, messaging-firstSingle-agent, Markdown memory, smart home focusAtom vs OpenClaw →
LangGraph / CrewAI / AutoGenDeveloper frameworksCode-first, build-your-own governance & integrationsWhy Atom? ↓
Zapier / Make / n8nWorkflow automationStep-based (not agents), no reasoning, no governanceAI-Generated Workflow Automation

TL;DR: If you're evaluating personal agents → Hermes/OpenClaw. If you need governed multi-agent business automation → Atom.

Full feature matrix: Why Teams Choose Atom ↓


⚡ Key Capabilities

CategoryFeatures
🤖 Multi-Agent OrchestrationQueen Agent (structured workflows) + Fleet Admiral (open-ended tasks) + Conductor (5 execution strategies) + validated state machine with rollback; governed fleet routing with ranked specialist matching
🛡️ Governance & Safety4-tier maturity (Student→Autonomous), policy-gated HITL approval, comprehensive audit trail, AI-powered training, OIDC SSO + SCIM v2 provisioning + 8-role RBAC
✅ Outcome VerificationPostcondition oracle re-derives success against the system of record — on by default, a refuted self-report is stamped UNVERIFIED (ATOM_ORACLE_ENFORCE kill switch); two-tier confidence provenance (self-reported vs externally verified); opt-in reviewer re-delegation loop
🎚️ Agents That Know When To AskEvery action type earns interruption-free status from its verified track record, keeps asking when evidence is thin (ask fails safe), and loses the privilege automatically when results regress — autonomy only relaxes after passing a held-out certification gate (Brier ≤ 0.25, denial-coverage ≥ 0.7)
🏛️ Governed Agent TeamsMulti-agent fleets fail like human orgs — ignored instructions, redone work, nobody accountable. Atom ships the countermeasures: delegation contracts with a single accountable agent, expiring privilege leases instead of titles, conflict-of-interest detection, contribution credit feeding graduation, opt-in nightly alignment sweeps
🧠 Memory & LearningPer-turn fact extraction, 2-tier recall (SQL + LanceDB), episodic memory, memory_remember/forget, self-evolution (Memento/AlphaEvolver, self-evolving harness)
🔎 Hybrid Searchdocuments.search fuses BM25 (FTS5/tsvector) + vector (LanceDB) via Reciprocal Rank Fusion (RRF) — semantic + precise retrieval with citations
🗂️ Knowledge VFSAgent-native document tree — ls/cat/grep/search with line-numbered citations instead of bespoke per-store queries
📻 Agent RadioLateral peer-to-peer messaging between agents (mention-first, budget-governed) — agents coordinate without hardcoded teams
💼 Office AutomationAgent-driven Excel/Word/PPTX editing on Canvas with live preview broadcast; formula-evaluating workbook runtime; agent↔document sync
🧩 Mini-AppsAgent-authored stateful canvas apps — Firecracker microVM isolation, per-instance chat
🔍 GraphRAG & IntelligenceMulti-hop expansion, Leiden community detection, JIT fact verification, D3 visual explorer
🌐 46+ Business IntegrationsSalesforce, HubSpot, Slack, Teams, Gmail, Notion, Jira, Linear, Stripe, QuickBooks, Shopify, GitHub, GitLab, Zoom…
🛰️ LLM GatewayOpenAI/Anthropic-compatible API over your BYOK — point Claude Code, n8n, or any OpenAI-SDK app at Atom
💰 Cost-Aware Routing5-tier cognitive classification, 16+ providers, opt-in learning router (feedback-based re-ranking), RTK token compression
🤝 InteroperabilityMCP client for external tool servers, ACP endpoint for standard agent clients, A2A Agent Card + message/send for agent-to-agent delegation, span tracing with optional Langfuse export
🎯 Goal-Driven LoopsAgents terminate on a definition_of_done predicate instead of always burning to max_steps; utility targets, custom action surfaces, stuck-detection

🛡️ Production-Ready Security (Default-On)

LayerWhat you get
Execution SandboxFilesystem scope, tool whitelist, tripwires, resource caps, KillRun — enforced at every tool-dispatch hub (in-process policy checks); mini-apps run in Firecracker microVMs
Encrypted CredentialsOAuth integration tokens encrypted at rest (Fernet); production fails closed without key
Per-Agent Capability BindingsZero-trust tool scoping — agent can never exceed its tier floor
Outbound GatekeeperRate limiting, response masking, HITL mutation approval on integration calls
Data-Taint TrackingRestricted data observed in a run blocks external outbound actions
External MCP ClientConnect to arbitrary external MCP servers (Cloudflare portals)

Security Architecture → · Sandbox Deep-Dive →


📚 Documentation & Discoverability


🎯 Example Use Cases by Department

DepartmentScenarioKey Integrations
SalesNew HubSpot lead → Research company → Score → Asana task → Slack notifyHubSpot, Asana, Slack, LinkedIn
FinanceGmail PDF invoice → OCR extract → QuickBooks match → Flag discrepanciesGmail, QuickBooks, Excel, Slack
SupportZendesk ticket → Sentiment analysis → Auto-escalate urgent → Draft replyZendesk, Slack, Email
HRBambooHR new hire → Provision accounts → Invite to Slack → Schedule orientationBambooHR, Google Workspace, Slack, Calendar
EngineeringGitHub PR → Run tests → Security scan → Post summary → Auto-merge if greenGitHub, GitLab, Slack, Jira
MarketingContent calendar → Generate posts → Human review → Schedule multi-platformNotion, Slack, LinkedIn, Twitter, Meta

🏗️ Repository Layout

atom/
├── backend/            # FastAPI app — main_api_app:app (full) / minimal_app:app (smoke)
├── frontend-nextjs/    # Next.js web UI
├── mobile/             # React Native (Expo) companion app
├── menubar/            # Tauri macOS menubar companion
├── scripts/ · infra/ · installer/ · examples/
├── docs/               # project documentation
├── Dockerfile          # dual-app image (backend + frontend)
└── Makefile            # common tasks (start here)

🌟 Why Teams Choose Atom

Per-competitor analysis: 📊 Comparisons ↑

AtomZapier/Make/n8nLangGraph/CrewAIOpenClawLangChain
AI Agents (reason, not just execute)
Governance (maturity + HITL + audit)
Outcome verification (re-derived from system of record, not self-report)
Default-on Sandbox (all dispatch paths)
Self-Hosted / Private (your keys, your infra)
Office/Canvas Native (Excel formulas, co-edit)
Agent-Authored Workflows (chat to build)
46+ Business Integrations (CRM, finance, support)50+ personal
Cost-Aware LLM Routing (16+ providers)
Mini-Apps (agent-authored stateful apps)
GraphRAG / Episodic Memory

🤝 Contributing & Support

We welcome contributions — see CONTRIBUTING.md. Quality bar: CI-gated core suite green, typecheck clean on changed files, review required, docs updated. See docs/compliance/COMPLIANCE_MAPPING.md for the security/compliance control mapping.


Built with FastAPI | SQLAlchemy | LangChain | Playwright | Next.js

Experience the future of self-hosted AI automation — safe enough for your whole team.

⭐ Star us on GitHub — it helps!

See what people are saying

Contributors

rush86999

9,480 commits

rparikh15

151 commits

mannan-b

85 commits

Languages

Python

77.3%

TypeScript

17.5%

HTML

2.6%

Shell

1.3%