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Package schemaregistry formats a Hermod message as a Confluent-framed record, so that a topic Hermod writes can be read by the consumers an existing Kafka estate already runs.

Package fcm sends messages to devices, topics and conditions through Firebase Cloud Messaging.

Package metis drives a BPMN 2.0 workflow engine from a Hermod pipeline.

Package mldataset is the Collect Dataset sink: each message a workflow sends it becomes a row of a dataset on Hermod's hermod-ml worker, so the next training of a model on that dataset learns from it.

Package panmail sends each message as an email through a panmail gateway.

Package txgroup writes to several sinks inside one distributed transaction.

Package metis reads a BPMN 2.0 workflow engine into a Hermod pipeline.

Package features holds the feature-engineering transformers: the steps that turn a record into the numbers a model takes, the same way at training and at inference.

Package genai holds the workflow nodes that call a large language model: ai_prompt, ai_extract and ai_embed.

Package memory gives an AI node a memory of the conversation a message belongs to: the earlier exchanges of the same chat, loaded before the model is called and extended after it answers.

Package retrieve is the ai_retrieve (RAG) transformer: it embeds a query with the node's AI connection, asks a vector store for the nearest documents, and writes them with their scores into the message, ready for a following ai_prompt node (or an ai_agent tool).

Package geo holds the geometry transformer: the distance between two points on a record, and whether a point lies in a polygon.

Package ml holds the machine-learning transformers.

Package structure holds the transformers that change the shape of a record rather than its values: flatten and unflatten, parse_field, template_render and field_diff.