debug-session

name	debug-session
description	Start a debugging session with worklog file
user-invocable	true
disable-model-invocation	true

Start Debug Session

Create a structured debugging session for an issue in the Dynamo ecosystem.

Step 1: Get the Bug Report

Ask the user how they want to provide the bug:

Option A: Linear ticket

• User provides ticket ID (e.g., "DYN-123")
• Fetch via Linear MCP tools
• Extract: title, description, reproduction steps

Option B: GitHub issue

• User provides issue URL
• Fetch via gh issue view <url>
• Extract: title, description, reproduction steps

Option C: Paste

• Ask user to paste the bug report directly
• Parse out the key details

Step 2: Discover Environment

Gather environment information:

!nvidia-smi --query-gpu=name,count --format=csv,noheader 2>/dev/null || echo "No GPU detected"

!uname -a

!which python && python --version

This tells you:

• GPU type and count (L40s, H100s, etc.)
• OS/platform
• Python environment

Note: The user's ~/.claude/CLAUDE.md may have more details about their dev environment (paths, aliases, preferences). Check there for additional context.

Step 3: Create Worklog

Create a worklog file to track the investigation:

• Filename: <issue-slug>.md in current directory
• Template:

# Debug: [Issue Title]

**Date**: [today's date]
**Source**: [Linear ticket / GitHub issue / user report]
**Status**: investigating
**Environment**: [GPU type/count from nvidia-smi]

## Problem
[Description of the issue]

## Reproduction Steps
1. [Step to reproduce]
2. ...

## Expected vs Actual
- **Expected**:
- **Actual**:

## Investigation Log

### [timestamp]
[Notes on what you tried/found]

## Root Cause
[Fill in when found]

## Fix
[Fill in when implemented]

Step 4: Set Up Testing

Build Commands

Rebuild Dynamo after making changes:

cd lib/bindings/python && maturin develop --uv && cd ../../.. && uv pip install -e .

If a framework change is required (sglang, vllm, trtllm), check the user's ~/.claude/CLAUDE.md for rebuild instructions specific to that framework.

Running Examples

Examples are located at: /home/ubuntu/dynamo/examples/backends/

Available backends:

• sglang/launch/ - SGLang backend examples
• vllm/launch/ - vLLM backend examples
• trtllm/launch/ - TensorRT-LLM backend examples

Based on the bug report, determine which backend is relevant:

• If unclear, ask the user which backend/example to run
• Run the example in the background
• Wait for model to be ready

Verifying the Model is Up

curl localhost:8000/v1/models

Testing with a Request

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<model-name-from-above>",
    "messages": [{"role": "user", "content": "Hello"}],
    "max_tokens": 50
  }'

Step 5: Begin Investigation

Dynamo Infrastructure Debugging

KV cache and routing issues:

• Check KV event logs in lib/llm/src/block_manager/kv_consolidator/tracker.rs
• Look at block manager state and consolidation behavior
• Inspect routing decisions in the KV-aware router

ZMQ / networking issues:

• Check ZMQ socket configuration and endpoint bindings
• Look for connection timeouts or message drops
• Verify nats/etcd connectivity for service discovery

Multi-node / disaggregated issues:

• Check prefill/decode worker assignment
• Verify DGD (disaggregated) status reporting
• Inspect inter-node communication via nvidia-smi on each node
• Check NCCL and GPU direct RDMA status

Process inspection:

• ps aux | grep dynamo - check running processes
• nvidia-smi - GPU utilization and memory
• ss -tlnp | grep 8000 - check port bindings
• journalctl -u dynamo - systemd logs if applicable

General Debugging Workflow

1. Reproduce first - verify you can trigger the bug before attempting fixes
2. Document as you go - update the worklog with findings
3. Minimal changes - fix the bug, do not refactor surrounding code
4. Verify the fix - confirm the reproduction case now passes

Performance-critical code - avoid unnecessary abstractions or comments.