Lmcache Agent Trace
Description
Agent application/benchmark/workload traces should be placed here.
Installation
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open the source below and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
README
This repo is meant to run a series of experiments to collect trace data from AI agents. And further use the data to analyze the performance of the agents, following the idea from https://github.com/LMCache/LMCache/issues/1826.
**Important note**: If the agent is powered by an OPENAI model, the traces will be automatically tracked from the [OpenAI API Dashboard](https://platform.openai.com/logs). Collecting traces should be easy.
A simple vibe-coded agent trace prefix [analyzer](https://v0-llm-agent-dashboard.vercel.app/).
On-going Checklist
**Status Legend:** ✅ Done, 🔄 Ongoing, ⏳ Wait to be taken, ❌ Failed
| Project Name | Status | Notes |
|---|---|---|
| Terminus 1&2 | ✅ | From terminal bench and harbor project, source code open; Basically the agent context management is to append new round conversation on all the previous chat history. Because of this nature, prefix and non-prefix cache hit rates should be stricly the same. Each step, it plans and executes multiple functions in sequential order. Easy to collect traces. |
| Mini-swe-agent | ✅ | Running this agent with the terminal bench and harbor, the only difference between Terminus 1&2 is that Mini-swe-agent plans and executes one bash command(or multiple ones with '&&' but rarely happens) at one time, while Terminus 1&2 plans multiple commands and executes them in sequential order. So the context reuse pattern is very similar to Terminus 1&2. |
| Claude Code Agent | ✅ | References: https://pierce.dev/notes/under-the-hood-of-claude-code, https://pierce.dev/notes/a-deep-dive-on-agent-sandboxes, https://medium.com/@outsightai/peeking-under-the-hood-of-claude-code-70f5a94a9a62, https://platform.clau |
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