TopoMAS

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: TopoMAS
TopoMAS: LLM-Driven Topological Materials Multi-Agent System

TopoMAS unifies topological-materials discovery into a coordinated multi-agent pipeline. A CoreAgent decomposes your natural-language query into subtasks and dispatches them to specialist agents: KnowledgeAgent (KG retrieval), PropertyAgent (DFT-level analysis), SynthesisAgent (hypothetical candidate generation), and ValidationAgent (invariant checking). This architecture mirrors production LLM-agent systems (RAG orchestrators, tool-calling chains) and demonstrates how hierarchical task decomposition improves reliability over monolithic LLM calls.

## Reference implementation (Vector Ridge Labs, POC-009)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-009
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/topomas

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures task success rate, number of steps and token cost per task, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.