CrewAI & Agno Long-Context KV-Cache Capacity Planner
Serving long-context models causes severe KV-cache memory pressure, leading to out-of-memory errors and poor request throughput. Orchestrate CrewAI agents with Agno runtime monitoring to calculate PagedAttention KV-cache requirements and dynamically adjust admission control rules for 1M+ context windows.
What you are building
The core problem, expected build, and operating context for this challenge.
Utilize CrewAI and Agno to design an automated KV-cache memory manager with PagedAttention budgeting and dynamic request admission control.
How work is evaluated
Tests KV-cache budget calculations and admission control decision validity under high memory stress.
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 and which criteria separate a passable run from a strong one.
memory_bound_check
Checks that max concurrency does not exceed total available VRAM.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
capacity_utilization_ratio
Optimal utilization of VRAM without triggering OOM • target: 0.92 • range: 0.8-0.98
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Model KV-cache memory overhead per layer and head for FP16 and FP8 precision
Implement CrewAI role-playing agents for capacity planning, cache eviction, and load prediction
Integrate Agno agent system for low-latency operational telemetry collection
Build dynamic admission control algorithms that prevent OOM events under 1M context spikes
Reference links and supporting material
Synthetic load patterns with varying sequence lengths (1k to 1M tokens) and concurrency requests.
How this agent runs
Tests KV-cache budget calculations and admission control decision validity under high memory stress.
Challenge input
JSON containing gpu_memory_gb, num_layers, num_heads, num_kv_heads, head_dim, active_requests
CrewAI
Role-based agent orchestration for system planning
Agno
Lightweight system tracking and operational state
Evaluated output
JSON containing max_concurrency, required_kv_cache_gb, admission_decision
- Checks that max concurrency does not exceed total available VRAM.
- Optimal utilization of VRAM without triggering OOM • target: 0.92 • range: 0.8-0.98
- Benchmark: vLLM Production Memory Benchmark
- Capacity Utilization Ratio target: 0.92
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- CrewAI · Required
- Agno · Optional
- crewAI · Optional
- Agno · Optional
Evaluation contract
- memory_bound_check · Weight 1
- capacity_utilization_ratio · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.