AI and agents
Redgold exposes AI as supporting infrastructure for pipeline applications through two related surfaces: interactive coding-agent sessions and an HTTP model API. Applications remain the primary unit; agents author and operate them, while models supply compatible generation and transformation capabilities.
Coding agents
A coding agent runs in an isolated workspace associated with a repository and conversation. The workspace provides the filesystem and allowed tools. Session history lets users inspect messages and tool activity and continue work later.
Agents can delegate independent subtasks to additional agents when a problem benefits from parallel investigation. Each child has a bounded assignment, and its result returns to the parent conversation.
Model API and routing
The model API accepts supported OpenAI and Anthropic request formats at https://api.redgold.ai/v1. Callers authenticate with an account token or sk-rg- key. /v1/models reports the identifiers available to the current account.
Model routing handles platform authentication, metering, and backend selection. Provider errors and rate limits can still reach the caller; clients should honor Retry-After and use bounded exponential backoff where appropriate.
One-shot application benchmark
The repository includes a paired full-application benchmark under
crates/ai/benchmark/oneshots/. Its habit-tracker task gives Codex, Claude,
and the Redgold Fusion runtime the same prompt and verifier in fresh
workspaces, with explicit time, step, token, and USD caps. The companion
report.py keeps pass@1, artifacts, and observed cost comparable without
guessing a provider price; paid sweeps remain operator-authorized.
Persistence and replay
Persisted messages and tool events support review, search, and context reconstruction. A replay can differ when model weights, sampling, tools, external APIs, repository state, or datasets differ. Treat a run as reproducible only when those inputs are pinned.
Continue with the coding agents guide or LLM API reference.