Office-to-Residential Structural Conversion Agent with LlamaIndex & Mastra AI
Converting obsolete office buildings into housing often fails due to structural constraints such as deep floorplates and plumbing stack limits. Build a specialized spatial indexing and document retrieval system using LlamaIndex and Mastra AI to evaluate architectural blueprints against residential conversion codes. Deliver conversion feasibility scores in seconds.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an automated structural feasibility parser for commercial adaptive reuse using LlamaIndex RAG and Mastra AI workflow nodes.
How work is evaluated
Evaluates structural feasibility assessments generated from architectural floorplate attributes.
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.
Challenge Extraction
Ensures deep floorplates correctly trigger daylighting warnings
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Feasibility Score Variance
Absolute error compared to professional engineering benchmarks • target: 3 • range: 0-10
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Parse multi-page structural blueprints and BIM CAD metadata using LlamaIndex data connectors
Formulate spatial evaluation graphs in Mastra AI to test plumbing and daylighting compliance
Extract structural load bearing wall positions to flag column grid restrictions
Generate cost-per-square-foot feasibility estimates for adaptive reuse
Reference links and supporting material
BIM model metadata and architectural specifications of 1970s commercial office buildings.
Challenge setup
Evaluates structural feasibility assessments generated from architectural floorplate attributes.
Challenge input
JSON with floorplate_depth_ft, core_distance_ft, window_ratio
LlamaIndex
Indexes architectural specifications and building code documents
Mastra AI
Executes evaluation workflow nodes in TypeScript/Python
Evaluated output
JSON with feasibility_score (0-100), structural_challenges
- Ensures deep floorplates correctly trigger daylighting warnings
- Absolute error compared to professional engineering benchmarks • target: 3 • range: 0-10
- Benchmark: StructuralFeasibilityBench
- Feasibility Score Variance target: 3
- 1 public reference case
- Python execution harness
- Python 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.
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Configured tools
- Mastra AI · Required
- Llama Index · Optional
- Zed · Optional
- Mastra AI · Required
Evaluation contract
- Challenge Extraction · Weight 1
- Feasibility Score Variance · Weight 1
Metrics
- Feasibility Score Variance · Target 3
Benchmarks
- StructuralFeasibilityBench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with floorplate_depth_ft, core_distance_ft, window_ratio
Output: JSON with feasibility_score (0-100), structural_challenges
Execution harness: Python
Evaluation tools
- LlamaIndex · Data Indexing & RAG
- Mastra AI · Agent Workflow Framework
Recipe freeze marker
No recipe freeze time is recorded.