How Redgold works
Redgold is a platform for building and maintaining applications with AI coding agents. You can give an agent work in an existing repository, connect a compatible coding tool to the model API, or build an application on managed data and infrastructure. You can also add individual tools to an agent you already use: search, browsers, memory, managed compute or remote builds and testing.
For managed applications, Redgold turns a checked-in application contract into a managed service. The contract remains visible and reviewable in Git while Redgold operates its routing, execution, data, storage, deployment, and monitoring. Coding agents can propose changes to the same artifacts that a developer edits directly.
Choose a starting point
| What you need | Start here | What to expect |
|---|---|---|
| Add infrastructure to an existing agent | Agent tools setup | Prompt, MCP or CLI setup; keep your provider and check access per service |
| Repair or extend an existing application | Coding agents | Repository work with inspectable changes and checks; access depends on your account |
| Use the model API in an existing client | Model API quickstart | An API key and a model compatible with your client's request format |
| Build an application with managed data and deployment | Pipeline application walkthrough | Coordinated setup during managed preview |
| Understand a completed website repair | Public website case study | First-party technical results and repeatable public checks |
The application contract
On this platform a pipeline application consists of:
- a typed schema defining its records,
- a pipeline manifest declaring its API routes and transforms,
- transform code run by a managed executor,
- datasets stored, materialized, and queried by the platform data engine,
- a Git-backed build and deployment.
This shape gives developers, reviewers, and coding agents the same bounded set of artifacts to inspect. Schemas are validated, routes resolve from the manifest, transforms run with defined capabilities, and changes move through Git review before managed deployment. The pipeline application walkthrough follows that lifecycle end to end.
Managed runtime
An incoming API request resolves to a declared pipeline route. The executor runs the selected transform, and the data engine reads or writes the owner-scoped records defined by the schema. Managed builds produce the service images and GitOps state used for deployment. The contract in Git remains the review boundary across those runtime components.
Data and materialized views
Writes converge on an append-only record log, and readable datasets are derived from it. The same records can be materialized in several shapes for different queries. Scheduled or change-driven transforms can prepare aggregations before a read reaches them.
Records retain source and ingestion metadata, while transform cursors make derived datasets recomputable. Storage placement follows the access pattern, including columnar history and Postgres-backed interactive reads under the same dataset model.
Coding agents and model access
Coding agents run in persistent, isolated repository workspaces. Their conversations, tool activity, diffs, and session state remain inspectable, and their changes use the repository's normal test and review process. Agents can author pipeline applications or work in repositories deployed elsewhere.
The same account can call hosted models directly. OpenAI- and
Anthropic-compatible clients connect to https://api.redgold.ai/v1, use an
sk-rg- API key, and select from the models returned by /v1/models. Model
routing, metering, and provider availability sit behind those compatible
request shapes.
Current product boundary
Managed pipeline application creation, dataset provisioning, and deployment are coordinated during the preview. Coding-agent access, the model API and individual agent tools can also be used independently. Search, browser, memory and builder permissions are separate; installing RAC does not enable every service. The comparison section places these components against backend platforms, query engines, PaaS products, coding tools, and model providers.
Continue
Use the quickstart for initial setup or the common task index to find a specific guide. The developer Pipeline Apps section covers the runtime and release model in greater depth.