Ecosystem
Adapters, Compute & ETL
Connect diverse protocols and offload intensive compute tasks using modular VaakLoom adapter packages. Install only what you use.
Protocol Adapters
VaakLoom adapters provide standardized interfaces to integrate external data sources and transport protocols into DAG steps:
| Package | Protocol / Driver | Status | Usage |
|---|---|---|---|
@vaakloom/adapter-rest |
HTTP/1.1 & HTTP/2 REST via native fetch |
Ready | API microservice integration, webhooks, upstream REST APIs |
@vaakloom/adapter-graphql |
GraphQL queries & mutations | Scaffold | Federated GraphQL services and schemas |
@vaakloom/adapter-grpc |
gRPC Protocol Buffers | Scaffold | High-performance binary RPCs |
@vaakloom/adapter-websocket |
Bi-directional WebSockets | Scaffold | Real-time event streams & chat |
@vaakloom/adapter-sse |
Server-Sent Events | Scaffold | LLM token streaming & live push feeds |
@vaakloom/adapter-soap |
SOAP / XML Web Services | Scaffold | Legacy enterprise & banking gateways |
@vaakloom/adapter-sql |
PostgreSQL, MySQL, SQLite | Scaffold | Transactional and relational database queries |
@vaakloom/adapter-nosql |
MongoDB, DynamoDB, Redis | Scaffold | Document and key-value store access |
Scaffold vs Ready: Built-in engine steps (
rest, map, mock-api, delay, merge, passthrough) and @vaakloom/adapter-rest are fully operational today. Other adapter packages export contract interfaces ready for your specific client drivers (e.g. pg, redis, @grpc/grpc-js).
Compute Offload Workers
To preserve low latency on the main event loop, compute-heavy tasks are isolated into modular worker packages:
| Package | Recommended Use Cases |
|---|---|
@vaakloom/compute-pdf |
Invoice generation, PDF merging, HTML-to-PDF rendering, and cryptographic signing. |
@vaakloom/compute-zip |
Large archive streaming, batch file compression, and decompressing bulk user uploads. |
@vaakloom/compute-media |
Image thumbnail resizing, format conversion (WebP/AVIF), and video transcoding. |
ETL Workflows
The @vaakloom/etl package enables building Extract → Transform → Load pipelines using the exact same declarative DAG engine. Stream records from databases, transform rows concurrently, and batch-load into data warehouses without provisioning complex Spark or Airflow clusters for mid-sized workloads.