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Atomic Agent

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A local-first AI agent that runs on your machine.

GitHub source repository ↗

★ 2.4k Stars Category · Other Very popular Source revision 86321a419c9e

44FMRS · D

Reliability

3/20

Security and permissions

10/20

Maintenance

10/20

Documentation

13/20

Setup experience

8/20

The supplied material describes Atomic Agent as a local-first AI agent and MCP client, not as an installable MCP server implementation. It offers broad automation, local-model support, approvals, and observability, but requires substantial system access and remains a developer preview.

Read the FMRS scoring method →

Atomic Agent is a local-first AI agent that runs on the user's device with local or cloud models. It can drive browsers, read and edit files, run approved shell commands, extract document text, retain memory across sessions, schedule tasks, and connect to external MCP servers. The project is described as an MCP client, not as an MCP server with a declared transport.

Tools

The tool list has not been reviewed yet.

Setup

On macOS or Linux, run curl -fsSL https://atomicagent.io/install | sh; on Windows PowerShell, run irm https://atomicagent.io/install.ps1 | iex. Then run atomic-agent. The project is a developer preview, so integrations should pin a release.

Fit and risk

Best for

Users who want the control loop and state kept on their device
Users with llama.cpp, Ollama, LM Studio, or another OpenAI-compatible model backend
Developers needing browser, filesystem, document, and task automation
Technical users willing to review approvals, traces, and network egress

Not for

Users looking for a standalone MCP server that can be installed directly into an MCP client
Production integrations requiring stable APIs, because the project is still a developer preview
Environments that cannot allow agent access to browsers, files, shells, or networks
Systems without Node.js, Chromium, git, or an accessible model backend

Required permissions

Read and write access to the local state directory and SQLite databases
Permission to run approved shell commands and perform some process operations
Access to the configured Chromium browser, browser profile, and websites
Network access to configured llama-server, cloud models, search providers, or HTTP endpoints
Permissions required by external MCP servers whose tools are invoked
Optional macOS Accessibility, Screen Recording, Automation, or Reminders permissions

Risks and side effects

Browser navigation, HTTP tools, cloud models, search providers, MCP, Telegram, update checks, and analytics can create network egress
Shell commands and skills inherit the agent process environment and may access `.env` secrets
Traces and `.env` under the state directory should be treated as sensitive; the project says redaction and environment filtering are not complete isolation
Filesystem writes, patches, archive extraction, process termination, HTTP requests, and untrusted MCP tools are policy-gated, but approval errors can still cause harm
The project is a developer preview, so commands, configuration, and behavior may change

Troubleshooting

Verify Node.js, git, a Chromium-family browser, and an accessible llama-server or other model backend
Check the browser executable, browser channel, and CDP settings
On Linux, verify dependencies for ripgrep, clipboard, notifications, and window control
If Chrome fails in a Linux environment, check sandbox configuration and the documented `--no-sandbox` option
Check the model service health, model name, port, and OpenAI-compatible path
Check MCP command, arguments, enabled state, and trust level; inspect the surfaced stderr tail when a server fails to connect
Review approval policies, repeated-call protection, logs, and local NDJSON traces
For problems, collect error logs and system specifications, then use the GitHub issue tracker or Discord

Use cases

Running long-context, multi-step AI tasks locally

Automating browser actions and web access

Reading, editing, and searching local files

Running approval-gated shell commands

Connecting external tools, resources, and prompts through MCP

Maintaining local memory and scheduled workflows

Supported clients

Supported clients have not been confirmed yet.

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