Feedpushr

Feedpushr is a lightweight feed aggregation daemon written in Go that polls and processes RSS/Atom feeds and pushes new articles to configurable outputs. It bundles an embedded database, a REST API with OpenAPI documentation, a web UI and a CLI for management.

Key Features

• Single executable daemon with an embedded key/value database for local storage and a minimal installer footprint
• Manage subscriptions via Web UI, CLI or REST API; import/export subscriptions with OPML
• Aggressive, tunable aggregation scheduler with per-feed control and quota settings
• Pluggable filter chain to transform or filter articles before delivery (title, fetch, interest, minify, custom plugins)
• Multiple output providers (stdout, HTTP POST, SMTP email and extensible plugin outputs) with conditional expressions to route content
• Support for WebSub (pub/sub) and conditional expression language to customize pipelines
• REST API with OpenAPI documentation and authentication options (HTTP basic and OpenID Connect)
• Plugin system for extending filters and outputs via compiled modules; metrics emission for monitoring

Use Cases

• Push new feed items to downstream systems via HTTP webhooks for automation or notification pipelines
• Build a feed-to-email digest or send selected articles to mailing systems using the SMTP output
• Enrich and transform feed content with filters and export structured article JSON to ETL tools or log collectors

Limitations and Considerations

• Uses an embedded key/value database which is convenient for single-node deployments but not designed for horizontal clustering or multi-node storage
• Plugins must be compiled native modules, which can complicate cross-platform plugin distribution and development
• Built-in outputs are basic; complex delivery workflows typically require custom filters or plugin development

Feedpushr is suited for users who need a configurable, programmable feed ingestion and push system that can be extended with filters and integrations. Its small footprint and REST API make it practical for automation, custom pipelines, and local deployments.