ByteChef

ByteChef puts agent orchestration and workflow automation in the same graph. Most teams run one tool for deterministic automation and another for agents, then spend their time reconciling the two; here the non-deterministic part and the branching, retrying, approving part share a runtime, a permission model and one audit trail.

The AI Agent is a drag-and-drop component that owns the full loop, model to tool selection to execution to observation to the next step, with streaming and structured output. Twelve LLM providers ship directly, with three aggregator components for routing through a gateway. Every connector is already a tool, workflows can be published as MCP tools, and a fromAi expression in any field turns that property into part of the schema the model sees.

The automation half is built for production rather than demos.

• Visual editor with JSON underneath: workflows stay diffable and Git-friendly.
• Flow control: condition, branch, loop, each, map, parallel, fork-join, subflow, on-error, terminate and wait-for-approval steps.
• Triggers: static and dynamic webhooks, polling, hybrid, app-event listeners, schedules and forms.
• Polyglot code: JavaScript, Python and Ruby running on GraalVM.
• Durable execution: a Postgres-backed runtime with queue mode over Redis, RabbitMQ, Kafka, JMS, AMQP or SQS for horizontal scale.
• Human in the loop: pause for approval, route to Slack or email, resume on the response.

Docker Compose brings up ByteChef and PostgreSQL together. The core is Apache 2.0, with an enterprise tier covering workflows-as-APIs, Git-native environments, Copilot and embedded distribution.