Atomic Agent

A local-first AI agent that runs on your machine, with local or cloud models, driving your browser, files, shell, and memory.

Stars
★ 3.4k
Last updated
3d ago
License
MIT
Primary language
TypeScript

At a glance

How it runs
CLIDesktop appSelf-hosted service
Works with
Universal · cross-platform
Cost
Free software; you pay for model usage
Setup effort
Medium · a few setup steps
You'll need
Node.js >= 25.7llama-server (managed or external)Chromium-family browsergitShell / CLINetwork accessLocal filesystemMCP Server
Typical use
A developer wants a private AI assistant to automate browser workflows, file edits, and shell commands in a local development environment, without sending code to the cloud.
Not a fit if
  • Teams needing stable APIs: developer preview, interfaces and config still change
  • Users on arm64 or non-Linux-x64 machines unwilling to set up llama-server manually
  • Teams that cannot tolerate default-on anonymous usage analytics

What does this agent do, and when should you use it?

Atomic Agent is a local-first AI agent that runs the control loop and all state on your machine, not a hosted service. It drives your desktop: browse, read and edit files, run approved shell commands, inspect documents, remember context across sessions, schedule follow-ups, and call external tools over MCP. It is llama.cpp first, so small quantized models stay useful for long, multi-step work on consumer hardware. All state lives on your disk as plain files and SQLite databases, and it offers safety features like approval gates, append-only traces, and prompt drift replay. It can be used via CLI, TUI, HTTP server, Tauri sidecar, or Telegram remote control, and supports importing from Hermes or OpenClaw.

Atomic Agent runs an agent loop: a compact prompt goes to a local model, which returns a JSON array of tool calls. The core executes them, with independent reads running in parallel and risky actions requiring approval. Results and state are summarized, not pasted back in full, and the loop repeats until done. Concretely, it can navigate and interact with web pages via playwright-core, search the web (Exa, DuckDuckGo, Brave, SearXNG), make HTTP requests, read/edit files, run shell commands (approval-gated), extract text from PDFs and Office documents, perform Git read-only operations (status, log, diff, show, blame), manage memory (profile facts, notes, lessons, procedures) and tasks (cron, webhooks), run Markdown skills, and connect to external MCP servers. It also provides a Telegram bot for single-user remote control, with inline approval buttons and optional task reports.

  1. A developer wants a private AI assistant to automate browser workflows, file edits, and shell commands in a local development environment, without sending code to the cloud.
  2. A privacy-conscious user in a regulated industry needs an AI agent that runs entirely on-premises with local models, minimizing network egress.
  3. A power user wants to schedule daily reports from their local agent, receiving results via Telegram on their phone.
  4. A developer building a desktop app wants to embed an AI agent with full tool access, using the Tauri sidecar interface.
  5. An existing Hermes or OpenClaw user wants to migrate their setup seamlessly, using the /import command to carry over sessions, cron jobs, and provider keys.
  6. A user on consumer hardware wants to run a capable agent with a small local model (e.g., 9B) that still performs well on complex tasks.

How do you install or deploy this agent?

Atomic Agent provides one-command installation scripts. On macOS/Linux, run: curl -fsSL https://atomicagent.io/install | sh. On Windows (PowerShell), run: irm https://atomicagent.io/install.ps1 | iex. The installer downloads the release archive, verifies checksum, and installs the CLI and support assets (grammars, native prebuilds, bundled ripgrep). For development, you need Node.js >= 25.7. Runtime requirements include a reachable llama-server (managed by the CLI or external), a Chromium-family browser (Chrome, Edge, etc.), and git.

How do you use this agent?

After installation, run atomic-agent tui to start the interactive console, or use atomic-agent run --cwd /path/to/work for a simple session. You can manage local models via CLI: atomic-agent models update, atomic-agent models pull qwen-3.5-4b, atomic-agent models use qwen-3.5-4b. For external llama-server, set env ATOMIC_AGENT_LLAMA_URL=http://127.0.0.1:8080 and start the server. To run as an OpenAI-compatible HTTP service, use atomic-agent serve --host 127.0.0.1 --port 8787 --cwd /path/to/work --api-key $ATOMIC_AGENT_API_KEY. For Tauri sidecar, send newline-delimited JSON requests over stdio. For Telegram remote control, enable it in config.json and set TELEGRAM_BOT_TOKEN.

What are this agent's strengths and limitations?

Pros
  • Local-first architecture keeps control loop and state on your machine, eliminating per-token fees and ensuring privacy by default.
  • Optimized for small local models via TurboQuant, achieving +30-50% throughput and state-of-the-art performance on GAIA L1 (69.8%).
  • Rich tool surface: browser automation, filesystem, shell, documents, git, memory, tasks, skills, MCP, and Telegram.
  • Comprehensive safety and observability: approval gates, append-only traces, prompt drift replay, failure taxonomy, and no-progress guard.
  • Flexible deployment: CLI, TUI, HTTP server, Tauri sidecar, or Telegram.
Limitations
  • Developer preview: APIs, commands, config, and behavior are still moving, so you need to pin releases for stability.
  • Requires configuration of llama-server and browser; browser binaries are not bundled, and external llama-server requires manual setup.
  • Anonymous usage analytics enabled by default; must be manually turned off via /privacy analytics off.
  • Managed local models only for Linux x64; other platforms (e.g., arm64) need external llama-server.
  • Requires Node.js >= 25.7, which may be a high bar for some environments.

How does this agent compare with similar options?

Compared to Hermes, on the GAIA Level 1 benchmark, Atomic Agent with the same local model achieves 69.8% accuracy versus 58.5% for Hermes, and has lower average wall-time per task (~217s vs ~351s).

Key facts side by side with the most closely related agents.

Agent Source review Form / cost Stars Updated Language Full support on
Atomic Agent This agent 56 · Major gaps CLIFree + model costs ★ 3.4k 3d ago TypeScript —
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Auto Browser 62 · Some gaps Self-hosted serviceFree + model costs ★ 908 today Python OpenAI API · Claude API
Octopal 61 · Some gaps Desktop appFree + model costs ★ 10 2mo ago Python Codex · Claude Code · OpenAI API · Claude API

How does FollowAgents rate this agent?

FollowAgents source review · FARS-2.1
Major gaps
56/ 100 5-point scale 2.8 / 5
This review covers the source as of Aug 11, 2026. The project has shipped 18 stable release(s) since, which have not been re-reviewed. The score and verdict describe the reviewed version only. Recent releases ↓
Trust 14/29
Reliability 8/14
Adaptability 12/18
Convention 10/18
Effectiveness 9/13
Verifiability 3/8
Why each dimension lost points
Trust14 / 29 · 2.4/5

Evidence shows approval gates for shell, file writes, HTTP, etc., privacy and egress documentation, anonymous analytics on by default but can be disabled, state files stored locally. Deductions: sensitive data handling incomplete (.env secrets inherited by skills and shell), dependency security lacks audit or lockfile, rollback mechanism only mentions update prompt restart, source attribution unverified publisher.

Reliability8 / 14 · 2.9/5

Self-consistency: README and package.json descriptions align, feature list matches code structure. Dependency availability: Node>=25.7 requirement is high, may limit environments; no lockfile provided. Failure messages: failure taxonomy and error handling described, but no concrete examples.

Adaptability12 / 18 · 3.3/5

Audience clear (developers, local model users), scenarios diverse (TUI, CLI, HTTP, sidecar, Telegram). Capability boundaries clear (tool list, approval gates). Trigger precision: commands and config explicit. Environment fit: multi-platform support, but Linux depends on external tools, browser not bundled.

Convention10 / 18 · 2.8/5

Information architecture clear (README structure complete). Install notes detailed (curl, PowerShell). Naming stability: APIs and commands still moving, pin version advised. Examples and FAQ: example commands and config, but no FAQ. Known limitations: developer preview and platform limitations noted. License: MIT. Versioning changelog: no CHANGELOG provided, but version numbers exist. Maintenance responsibility: maintainers not specified, publisher unverified.

Effectiveness9 / 13 · 3.5/5

Output usability: multiple usage modes, output formats clear. Marginal value: local-first, no API costs, inspectable state, unique value. Cost benefit: local models reduce API costs, but require hardware and setup.

Verifiability3 / 8 · 1.9/5

Claim traceability: GAIA benchmark has docs and raw data links, but full reproduction steps not provided. Cross-source corroboration: single source only, no independent verification. Fact-inference separation: README distinguishes facts and inferences, but some claims (e.g., performance gains) lack independent verification.

Risks and how to mitigate them
  • Publisher unverified, identity unknown.
  • Sensitive data handling incomplete: .env secrets may be inherited by skills and shell commands, caution advised.
  • Dependency security lacks audit or lockfile, recommend checking for vulnerabilities.
  • APIs and commands still moving, pin version for production.
  • Anonymous analytics on by default, privacy consideration.
Evidence confidence: Low Reviewed Aug 11, 2026 Reviewed revision 9d525ef4a004 New commits since this review; the score may not cover them
See the full review method →

Recent releases

Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.

  1. v0.6.9
    Release notes excerpt
    The desktop app is updated too: [Desktop v0.0.3](https://github.com/AtomicBot-ai/atomic-agent/releases/tag/desktop-v0.0.3). From this release on, the terminal agent and the desktop app share one setup. One setup for the terminal and the desktop - The desktop app now uses the terminal agent's folder, ~/.atomic-agent. Chats, memory, skills, keys and settings are the same in both. - Both can run at once. Quitting one no longer stops a local model the other is using. - Scheduled tasks run once, in whichever app was started first. Telegram works the same way. …
    View on GitHub ↗
  2. desktop-v0.0.3 Desktop v0.0.3
    Release notes excerpt
    The desktop app now shares one setup with the terminal agent, and you can attach files and images to a message. It includes everything in Atomic Agent v0.6.9. One setup with the terminal - The app now uses the same folder as the terminal agent, ~/.atomic-agent. Chats, memory, skills, keys and settings are shared. - Already using the desktop? On first start, your data from ~/.atomic-agent-desktop comes over once: - a backup is made first; - where both apps have a setting, the terminal's wins; - local model files are moved, not downloaded again; - the old folder is kept; …
    View on GitHub ↗
  3. v0.6.8
    Release notes excerpt
    The desktop app is updated too: [Desktop v0.0.2](https://github.com/AtomicBot-ai/atomic-agent/releases/tag/desktop-v0.0.2). Atomic Chat Core is the local engine - Local models now run through Atomic Chat Core, the engine the Atomic Chat app uses. It downloads and manages llama.cpp for you. Existing setups move over on first start (config v77). Your model files are kept and nothing is downloaded again. - The engine choice is no longer shown in the TUI. atomic-agent models engine llama-server switches back if you need to. …
    View on GitHub ↗

FAQ

What are the hardware requirements for running local models?
Managed mode supports Metal (Apple Silicon), CUDA (Windows), and Vulkan (Linux), with CPU as fallback. Models as small as 9B parameters are usable, but performance depends on hardware.
What data leaves the machine?
By default, anonymous usage analytics sends provider/model names, a random per-install ID, and turn-shape numbers (latency, step count, outcome), but never message content, file paths, or tool arguments. You can turn it off. Other network egress occurs when browsing, making HTTP calls, using web search, connecting to cloud providers, sending Telegram messages, or installing skills from ClawHub.
How does it handle dangerous operations?
Shell commands, filesystem writes, HTTP requests, and other risky actions are approval-gated by default. It also has append-only traces, failure classification, and a no-progress guard to prevent loops.
Can I use cloud models?
Yes, Atomic Agent supports OpenAI-compatible, OpenRouter, and AI/ML API providers, and you can switch models mid-session.
Can I extend it with custom tools?
Yes, through MCP (Model Context Protocol), you can connect external MCP servers and their tools join the same registry.
View on GitHub ↗ Install ↓

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