TraceLab: Characterizing Coding Agent Workloads for LLM Serving
Coding agents are rapidly becoming a major application of agentic LLMs, but serving them efficiently remains challenging. Progress on this challenge requires understanding real workload patterns, yet the data needed for such analysis is largely absent. Existing public traces and benchmarks do not capture real, day-to-day coding-agent usage across multiple agents and model families for serving-system analysis. To help fill this gap, we collect and release a trace of roughly 4,300 coding-agent sessions, containing about 350,000 LLM steps and 430,000 tool calls from our own day-to-day use of Clau
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownagent-tools →
- LinkedLinked via unknownAgentTrace →
- LinkedLinked via unknownAgentic Resource Discovery: Let agents search →
- LinkedLinked via unknownDebugging production agents with Amazon Bedrock AgentCore Observability →
- LinkedLinked via unknownWhat's one local AI workflow you wish you'd discovered sooner? →
- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “TraceLab: Characterizing Coding Agent Workloads for LLM Serv” ≈ “AgentCore-8B””
- PossiblePossibly related (embedding) · 28%headroomlabs-ai/headroom →
“Possibly related via embedding similarity 0.55 (not asserted). Timestamp check: artifact after paper (+3d).”
- LinkedLinked via unknownScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration →
