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ReasonOS ships a coding agent as part of rbs. It is not an editor plugin bolted onto a build tool — the agent, the build system, and the editor share one workspace model, so the agent reasons about your code the same way rbs does: as a graph of targets.

What makes it different

Graph-first

Most coding agents explore a repository by searching text. The ReasonOS agent starts from the workspace dependency graph instead. It has first-class tools to:
  • list and inspect targets (rbs_targets), their dependencies (rbs_deps) and reverse dependencies (rbs_rdeps)
  • find which target owns a source file (rbs_owners)
  • compute the affected set for a change (rbs_affected)
  • navigate the workspace knowledge graph — symbols, communities, impact analysis, and path-finding between code entities (the atlas_* tools)
That means “what breaks if I change this?” is a graph query, not a guess.

Build-system-native

The agent builds and tests through rbs, with the same caching and hermeticity you get on the command line:
  • rbs_build and rbs_test run real targets and stream real output
  • rbs_run / rbs_start / rbs_stop execute and manage long-running targets (dev servers, services) in the background
  • rbs_rules and rbs_validate let it author and check BUILD.rbs files against the actual rule catalog before writing them
  • external dependency tools let it inspect resolved packages rather than guessing at APIs
When the agent says a change builds and its tests pass, it verified that through the build system — not by eyeballing the diff.

Engines

The agent runtime supports multiple engines. All of them see the same workspace tool surface.

Native engine

The in-house, multi-provider engine. It runs models from Anthropic, OpenAI, Google, and xAI with your own API keys, or any model served by the ReasonOS hosted service. This is the engine behind rbs agent on the command line and the default for editor sessions.

Claude Code engine

Drives Anthropic’s Claude Code CLI as the session’s engine. It uses Claude Code’s own authentication and receives the rbs tool surface (graph queries, build/test, skills) over MCP, plus a generated CLAUDE.md block describing the workspace conventions. Available when the claude binary is installed on the branch node.

Codex engine

The same arrangement for OpenAI’s Codex CLI: its own authentication, the rbs tool surface over MCP, and workspace conventions delivered via AGENTS.md. Available when the codex binary is installed on the branch node.
Selecting an engine: in the ReasonOS editor, each agent session chooses its engine (native, Claude Code, or Codex), and the workspace has a configurable default engine for sessions that don’t specify one. The rbs agent CLI always runs the native engine.

Where it runs

  • Terminalrbs agent starts an interactive session in your shell: a plain REPL by default, or a full-screen interface with --tui. See Using the agent.
  • The ReasonOS editor — the editor connects to your branch node’s server, which hosts agent sessions alongside the language servers and file watchers. Sessions are multi-client: teammates on the same branch see the same node.
  • Headlessrbs agent "your prompt" runs a single turn and exits, and ai_task targets put agent-driven work (including browser QA tests) directly into the build graph, runnable with rbs run and rbs test.

Sessions, memory, and checkpoints

Sessions. A session is one conversation with the agent — its transcript, permission mode, todo list, and background subagents. Editor sessions persist; CLI chats can be saved. Saved chats live in the committed .reasonos/ai/chats/ tree, so they travel with the branch through git rather than living on any one machine. Memory. The agent keeps two durable stores per branch, both committed under .reasonos/ai/:
  • Lessons — observations about what worked and what failed in this repository, recorded as it works. Recent lessons are injected into every new session’s context.
  • Taste — style preferences for how this codebase likes its code written.
Because both are committed, an agent joining a branch inherits what previous sessions learned there. Long conversations are compacted automatically so a session never runs out of context mid-task. Checkpoints. In the editor, the agent snapshots the workspace’s file state before it starts modifying files. Every checkpoint records exactly what changed, and you can diff against or revert to any checkpoint — so letting the agent work is never a one-way door. See Checkpoints for the full workflow.

Learn more

Using the agent

CLI invocations, models and providers, permission modes, and headless tasks.

Skills

The agent’s loadable expertise: built-in skills and how to add your own.

MCP integration

Serve rbs tools to external agents, and connect external tools to the rbs agent.