Atomic Agent

Atomic Agent is an open-source, local-first AI agent runtime built by AtomicBot and released under the MIT license. It runs the control loop and all state on your own machine, using llama.cpp to serve local models, with optional cloud providers. It is available as a desktop app and as a CLI/TUI, and the CLI is labelled a Developer Preview (v0.6.7).

What It Is

Atomic Agent is an AI assistant you run yourself. You give it a task in plain words from a terminal UI, CLI, OpenAI-compatible HTTP API, or chat apps such as Telegram and Discord. It can browse the web, read and edit files, run approved shell commands, extract text from documents, remember context across sessions, and schedule background tasks. Sessions, memory, tasks and traces are stored locally as plain files and SQLite databases.

How the Agent Loop Works

Each turn builds a compact prompt and asks the model for a JSON array of tool calls. On a local llama-server the output is constrained by GBNF grammars so tool calls stay structurally valid. The runtime executes the calls, running independent reads in parallel and pausing for approval on risky actions such as shell commands, file writes and HTTP requests. Results are compressed back into state, and the loop repeats until the agent replies or is stopped. A byte-stable prompt prefix keeps the KV-cache warm, and memory lives in SQLite rather than in the context window.

Models and Run Modes

The CLI can manage a llama.cpp backend and download curated GGUF quantized models, or you can point it at your own llama-server. The project says its TurboQuant llama.cpp build gives +30-50% throughput on small local models. Cloud providers (OpenAI-compatible, OpenRouter, Gemini and presets for others) and Ollama or LM Studio servers can be configured. Fusion mode lets one model plan while a pool of workers executes delegated parts.

Extending and Connecting

MCP servers add external tools, Markdown skills provide reusable playbooks, and Composio exposes SaaS toolkits such as Gmail and Slack. The first run can import data from Claude Code, Codex, Hermes, OpenClaw and others. Anonymous analytics and crash reports are on by default and can be switched off.

Benchmark Claim

The project reports 69.8% (37/53) on GAIA Level 1 versus 58.5% for Hermes Agent on the same local model and hardware, with about 217 s versus 351 s per task. These figures are the vendor's own published results.