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dotbot is a multi-provider AI development framework. Rather than committing to a single AI service, you can switch providers at any time by setting the provider field in settings.default.json, and you can mix providers across phases by setting analysis and execution models separately. Each provider ships with its own CLI adapter and stream parser, so the rest of the workflow engine stays unchanged regardless of which provider you choose.

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Supported providers

dotbot ships with five providers. Each one wraps a command-line tool and exposes its own permission modes that control how autonomous the AI is during execution. claude is the default provider. Each provider’s full configuration lives in a JSON file under content/settings/providers/ (for example, content/settings/providers/claude.json).
Antigravity (Google) replaces the former Gemini provider. Its executable is agy, and its default permission mode is yolo.

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Permission modes

Permission modes map to provider-specific CLI flags. The first mode listed for each provider is its default.
The default mode for every provider skips confirmation prompts. Only run these modes in isolated environments where unreviewed file-system changes are acceptable.

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Switching providers

Set the provider field in settings.default.json to one of the five provider names. Leave permission_mode as null to use the provider’s own default mode, or set it to a valid mode from the table above to override that default:
settings.default.json
The change takes effect for the next task that starts. Running tasks are not interrupted.

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Model tiers

dotbot does not reference model names like Sonnet or Opus directly in your workflow. Instead, each provider exposes three capability tiers: fast, balanced, and best. Every provider defaults to best. The providers block in settings.default.json maps each tier to a concrete model for that provider:
settings.default.json
Each provider keeps its own tier mapping, so switching providers does not require touching any task that selects a tier. To target a different underlying model, edit the tier mapping for that provider rather than changing the tier name used by a task.

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Setting analysis and execution tiers

The analysis.model and execution.model fields set the baseline tier for every task that does not declare its own. Both default to best. Point them at different tiers to balance cost and quality across phases:
settings.default.json
The value is a tier name (fast, balanced, or best), not a model name. dotbot resolves the tier to a concrete model through the provider’s models mapping at run time.

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Per-task tier overrides

An individual task in workflow.json can set a model field that overrides the baseline tier for that one task. Use a lighter tier for straightforward steps and reserve best for open-ended reasoning:
Use fast or balanced for structured, well-scoped tasks, and reserve best for analysis and synthesis. Selecting a tier per task can reduce token spend on large pipelines without sacrificing quality where it matters.

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Tier resolution order

When dotbot resolves which model to use for a task, it evaluates these sources from highest to lowest priority:
  1. The model tier on the individual task in workflow.json.
  2. The execution.model (or analysis.model) tier in settings.default.json.
  3. The provider’s default_model (best).
The resolved tier is then mapped to a concrete model through the active provider’s models block. The first non-null value in the chain wins.

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Verifying provider availability

Run dotbot doctor to check that a provider CLI is installed and on your PATH:
The check passes when at least one provider executable (claude, codex, agy, opencode, or copilot) is found. When you start a run, dotbot also verifies that the configured provider’s executable is available and fails early with a clear message if it is missing.
dotbot delegates authentication to each provider’s own CLI. Sign in with the provider’s tool (for example, claude or codex) before starting a run, and confirm the CLI is on your PATH with dotbot doctor.