Mastra AI MTP Orchestration Pipeline with Braintrust Evaluation
Process automation modularity relies on Module Type Package (MTP) manifests for seamless plug-and-produce integration. Create a TypeScript workflow using Mastra AI and Braintrust to validate MTP state machines and evaluate runtime interface behavior. The orchestration engine must process MTP files and verify inter-module safety logic within 500ms.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a Mastra AI TypeScript workflow evaluated via Braintrust to parse MTP XML/JSON descriptors and validate plug-and-produce process state compatibility.
How work is evaluated
Evaluates MTP manifest validation, state machine mapping accuracy, and workflow latency via Braintrust.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
manifest_validation
Checks if all invalid MTP interface definitions are correctly caught
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
processing_time_ms
Workflow execution duration in milliseconds • target: 200 • range: 0-500
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Design a Mastra AI workflow in TypeScript to process MTP manifest descriptors
Integrate Braintrust evaluation logging to measure orchestration accuracy and latency
Validate process module state machines (NAMUR NE 148 standard) automatically
Verify OPC UA interface tags for seamless plug-and-produce integration
Reference links and supporting material
Collection of standard MTP (Module Type Package) definition files for modular chemical dosing, mixing, and heating units.
Challenge setup
Evaluates MTP manifest validation, state machine mapping accuracy, and workflow latency via Braintrust.
Challenge input
JSON containing MTP manifest data (services, eID, OPC UA endpoints)
Mastra AI
Assigned framework for building structured workflows in TypeScript.
Braintrust
Assigned tool for evaluation metrics and workflow instrumentation.
Evaluated output
JSON with is_valid (bool), state_machine_compliant (bool), missing_tags (list), processing_time_ms (int)
- Checks if all invalid MTP interface definitions are correctly caught
- Workflow execution duration in milliseconds • target: 200 • range: 0-500
- Benchmark: Modular Automation MTP Benchmark
- Processing Time Ms target: 200
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Mastra AI · Required
- Braintrust · Optional
- Braintrust · Optional
- Mastra AI · Required
Evaluation contract
- manifest_validation · Weight 1
- processing_time_ms · Weight 1
Metrics
- processing_time_ms · Target 200
Benchmarks
- Modular Automation MTP Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing MTP manifest data (services, eID, OPC UA endpoints)
Output: JSON with is_valid (bool), state_machine_compliant (bool), missing_tags (list), processing_time_ms (int)
Execution harness: JavaScript
Evaluation tools
- Mastra AI · TypeScript Agent Framework
- Braintrust · Evaluation and Logging
Recipe freeze marker
No recipe freeze time is recorded.