FastGPT Plugin Ecosystem Repository Structure
The FastGPT plugin ecosystem organizes plugin-related code across four specialized, role-specific repositories to streamline development, distribution, and governance. Each repository has a defined, non-overlapping purpose to avoid code duplication and align with different use cases for plugin deployment.
Official Repository Purpose Table
The following table outlines the core purpose of each repository in the FastGPT plugin ecosystem:
| Repository | Purpose |
|---|---|
labring/fastgpt-plugin | Plugin service, SDK, CLI, debug monitor, and infrastructure code. |
fastgpt-official-plugins | Plugins maintained or reviewed by FastGPT officials. |
fastgpt-community-plugins | Community third-party plugins. |
fastgpt-business-plugins | Private plugins, customer-customized plugins, and commercial delivery. |
fastgpt-plugin Repository Constraints
The labring/fastgpt-plugin repository does not host any end-user plugin source code. Its supported functionality is limited to core development tooling and runtime infrastructure: it provides local plugin debugging via the integrated CLI, automated build workflows, static code checking to enforce standards, packaging of distributable plugin artifacts, and running a local plugin service server for testing. All production-ready plugin implementations must be stored in one of the dedicated plugin repositories: the official, community, or business-specific plugin repos. This separation ensures the core toolkit remains focused on development workflows rather than hosting finished plugin assets.
Source: FastGPT official source
Applicability and version scope
Use this page for the documented Model guides scenario. Confirm the FastGPT, dependency, API, and deployment versions in the official source before applying a change.
Safety guardrails
Use [REDACTED_CREDENTIAL] for credentials and private data. Confirm the documented environment and version before review.
Rollback guidance
Restore the prior technical-content authority snapshot. Restore saved configuration and data snapshots, then repeat the smallest verification scenario.