Multi-Model Orchestration Platform with LangGraph and GPT-5.4 Pro
Architect a stateful orchestration system using LangChain and LangGraph to manage complex enterprise workflows. This platform will switch between GPT-5.4 Pro and Claude Sonnet 4.6.6 as orchestrators depending on the complexity of the task, using Fireworks AI and Featherless AI as high-performance inference backends to serve the models. The architecture will feature state persistence to ensure workflows can resume after long pauses, ideal for media and entertainment asset management systems. This challenge emphasizes efficient model routing and the use of graph-based state management, moving beyond basic sequential chains. By using different inference backends, you will learn to manage model deployments and ensure low-latency performance in an enterprise-grade environment.
What you are building
The core problem, expected build, and operating context for this challenge.
Architect a stateful orchestration system using LangChain and LangGraph to manage complex enterprise workflows. This platform will switch between GPT-5.4 Pro and Claude Sonnet 4.6.6 as orchestrators depending on the complexity of the task, using Fireworks AI and Featherless AI as high-performance inference backends to serve the models. The architecture will feature state persistence to ensure workflows can resume after long pauses, ideal for media and entertainment asset management systems. This challenge emphasizes efficient model routing and the use of graph-based state management, moving beyond basic sequential chains. By using different inference backends, you will learn to manage model deployments and ensure low-latency performance in an enterprise-grade environment.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.
Routing Logic
Verify logic routes correctly
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Throughput
Tasks processed per second • target: 5 • range: 1-10
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Design LangGraph state machines for multi-step reasoning
Configure LangChain model routers to distribute load between GPT-5.4 Pro and Claude Sonnet 4.6.6
Deploy custom model endpoints on Fireworks AI and Featherless AI
Manage workflow checkpoints for long-running processes
Implement policy-based routing to ensure cost-efficiency
Integrate LangChain tool calling for dynamic orchestration
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[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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