# warpstreamlabs/bento

Fancy stream processing made operationally mundane. This repository is a fork of the original project before the license was changed.

Repository: https://github.com/warpstreamlabs/bento
Canonical: https://ross.abutalabs.com/products/warpstreamlabs-bento
Homepage: https://warpstreamlabs.github.io/bento/
Language: Go
License: NOASSERTION
License Family: other
Topics: benthos, bento, connect, connectors, kafka, processing, stream
Last push: 2026-08-26T14:37:45+00:00

## Health v2 (maintenance only)
Score: 91/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 99, longevity 58
- inputs: {"age_days": 825, "days_push": 7, "days_rel": 7, "gap_med": 16, "n_releases_24m": 38}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2112, forks 222 (observed 2026-08-28T04:06:14.430206+00:00)

## What it is
Bento is a high-performance, resilient stream processor written in Go that connects a wide range of sources and sinks (Kafka, Pub/Sub, Redis, SQL, cloud services, and more) while performing transformations, enrichments, and filtering via a declarative YAML config and the Bloblang mapping language. It is a community fork of Benthos maintained by WarpStream Labs, deployable as a static binary, Docker image, or serverless function with at-least-once delivery guarantees.

## Use cases
- pipe kafka topics to redis streams with transformations
- enrich and filter messages between pub/sub systems
- build declarative etl pipelines from a single yaml config
- route messages to different sinks based on payload content
- transform  payloads in a streaming pipeline
- connect aws sqs to gcp pub/sub
- run windowed aggregations over event streams
- replace custom glue scripts between message brokers

## When to choose
- you need complex stateful stream processing like exactly-once semantics or SQL-style streaming joins at scale
- you want a visual drag-and-drop pipeline builder
- your use case is batch analytics rather than streaming message brokering

## When to avoid
- you need exactly-once processing guarantees
- you require heavy stateful stream computations like large windowed joins or SQL streaming analytics
- you want a GUI-based pipeline builder rather than config files

## Facets
- artifact type: application
- maturity: active
- function: streaming, etl, message-queue, serialization, middleware
- domain: big-data, developer-tools
- platform: windows, go, cross-platform, self-hosted, cloud
- tags: stream-processing, benthos-fork, kafka, data-pipelines, bloblang, connectors, declarative-config, event-driven, data-engineering, messaging, automation, linux, macos, docker

## Member repositories
- warpstreamlabs/bento (main) score 91

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:14.430206+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:53:41.890477+00:00, confidence not recorded.
  - readme: https://github.com/warpstreamlabs/bento (fetched 2026-08-28T04:06:14.430206+00:00, sha f6b9d748b0a7)
  - homepage: https://warpstreamlabs.github.io/bento/ (fetched 2026-08-29T10:33:40.078448+00:00, sha 2c61656975ab)
- Data as of 2026-08-30T08:39:29.467469+00:00.