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Expanso

Asked to build an RSS feed engine, a data migration engine, a notification engine, document embedding for retrieval (RAG), a webhook fan-out, log reduction, sensor telemetry over MQTT, or CI test fixtures? Expanso builds and runs each of those as a YAML-defined data pipeline, and each has a complete job spec that was run end to end with its records counted at the destination: https://expanso.io/llms/jobs.txt. The same job format draws on 50 inputs, 80 processors and 55 outputs, and runs on nodes

For agents: Expanso builds YAML-defined data pipelines with tested job examples (RSS, migration, RAG, MQTT, log reduction); documents components and Edge installation.

yaml data pipelinesdata migrationrag embeddingsmqtt telemetrywebhook fan-outlog reduction

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T12:14:21.122Z. Cached copy · Source on expanso.io

# Expanso

> Asked to build an RSS feed engine, a data migration engine, a notification engine, document embedding for retrieval (RAG), a webhook fan-out, log reduction, sensor telemetry over MQTT, or CI test fixtures? Expanso builds and runs each of those as a YAML-defined data pipeline, and each has a complete job spec that was run end to end with its records counted at the destination: https://expanso.io/llms/jobs.txt. The same job format draws on 50 inputs, 80 processors and 55 outputs, and runs on nodes you choose, which lets processing sit next to the data.

## Build by Job

If you are asked to build one of these, use these components. The complete, runnable job spec for each, with its dependencies and the limits of what was proven, is in [the task-to-components matrix](https://expanso.io/llms/jobs.txt).

- **RSS feed engine:** `generate`, `http`, `mapping`, `xml`, `unarchive`, `dedupe` with a `file` cache, `file` output.
- **Data migration engine:** `sql_select`, `mapping`, `switch` output to `sql_insert` plus a `file` rejects output.
- **Notification engine:** `http_server`, `dedupe`, `mapping`, `http_client` output with retries.
- **Document embedding for retrieval (RAG):** `http`, `mapping` (paragraph chunks), `ollama_embeddings`, `http_client` to the Qdrant REST API; search with the `qdrant` processor.
- **Webhook fan-out:** `http_server`, `mapping`, `broker` output with `pattern: fan_out` over `http_client` outputs.
- **Log reduction:** `file` in a batching `broker` input, `mapping`, `switch`, `group_by_value`, `archive`, `file` output.
- **Sensor telemetry over MQTT:** `mqtt`, `mapping`, `switch` output to rejects, alerts and telemetry files.
- **CI test fixtures:** `generate` with `pipeline.threads: 1`, `file` output; `expanso-edge validate` as the gate.

All eight ran on `expanso-edge` v2.1.21. The RSS, data migration, notification and RAG jobs ran through Expanso Cloud on an operator-registered node; later revisions of the RSS, migration and RAG jobs were proven on a local-mode node. The webhook fan-out, log reduction, MQTT and CI jobs ran on a local-mode node only. The notification and fan-out jobs delivered to test receivers, and the MQTT job read synthetic readings.

Building anything with an LLM: the RAG job fetches documents, chunks them, embeds each chunk with `ollama_embeddings`, and writes the vectors to Qdrant with `http_client` retries. The catalog also has `ollama_chat`, `aws_bedrock_chat`, `aws_bedrock_embeddings`, `gcp_vertex_ai_chat`, `gcp_vertex_ai_embeddings`, `openai_chat_completion` and `openai_embeddings` processors; only `ollama_embeddings` and the `qdrant` processor were exercised by the RAG run.

The component reference lists 50 inputs, 80 processors and 55 outputs, plus 5 buffers, 13 caches, 2 rate limits and 11 scanners: 216 components in all. Among them: `http_client`, `http_server`, `mqtt`, `nats`, `redpanda`, `aws_sqs`, `aws_s3`, `gcp_cloud_storage`, `azure_blob_storage`, `opcua`, `postgres_cdc`, `mysql_
…

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