agent-debugger-cli

Agent Debugger CLI

Use this skill when you need the standalone adb CLI that ships inside the bundled _source/ package in this directory.

Files in this skill

• _source/: bundled open-source package that exposes the adb command.

Install

The package is declared as a path source in the project root pyproject.toml, so a top-level uv sync already installs it. To install standalone from this skill directory:

python -m pip install ./_source
adb --help

If you are upgrading an existing install, use:

python -m pip install --upgrade --force-reinstall ./_source

LLM configuration

Configure the LLM once with adb config:

adb config '{"llm":{"model":"gpt-4.1","base_url":"https://api.openai.com/v1","api_key":"<your-key>"}}'

If you need reasoning-capable OpenAI responses models such as gpt-5.4 or codex, keep the reasoning hint nested under llm.reasoning.effort:

adb config '{"llm":{"model":"gpt-5-codex","base_url":"https://api.openai.com/v1","api_key":"<your-key>","reasoning":{"effort":"high"}}}'

This writes local config to ~/.adb/adb_cli_config.json.

Resolution order for LLM settings is:

1. adb config saved values in config.llm
2. QA_MODEL_NAME / QA_BASE_URL / QA_API_KEY
3. LLM_MODEL / LLM_BASE_URL / LLM_API_KEY

If llm.model or llm.base_url is missing after resolution, adb ask/check will fail fast.

ADB also supports local-runner config:

adb config '{"adb":{"runner":"local","venv_dir":"/abs/path/to/adb_local_venv"}}'

Or override at runtime:

export ADB_RUNNER_MODE=local
export ADB_VENV_DIR=/abs/path/to/adb_local_venv

adb config '{"adb":{"runner":"api"}}' exists, but current ask/check support is implemented for local runner.

You can also persist QA agent overrides in config.qa. Reserved keys are:

• agent_config
• multi_trace_agent_config
• artifact_root
• agent_override
• multi_trace_agent_override

Example:

adb config '{
  "qa": {
    "agent_config": "/abs/path/to/agent_debugger_qa.yaml",
    "multi_trace_agent_config": "/abs/path/to/multi_trace_qa_debugger.yaml",
    "artifact_root": "/abs/path/to/adb_artifacts",
    "agent_override": {
      "tools": [{"name":"trace_read"}]
    },
    "multi_trace_agent_override": {
      "tools": [{"name":"trace_ls"}]
    }
  }
}'

Model routing notes for local adb ask:

• models containing claude are routed to anthropic_chat_completion
• gpt-5.4 is routed to openai_responses
• models containing codex are routed to openai_responses
• codex defaults to reasoning.effort=medium unless you override it

Trace workflow

adb ask and adb check accept standard cleaned traces, and they also accept local JSON files that only contain an OpenAI-style messages field, for example:

{
  "messages": [
    {"role": "system", "content": "You are helpful."},
    {"role": "user", "content": "Please analyze this conversation."}
  ]
}

messages must be a non-empty array.

When the input is not already a cleaned/messages-style trace, pass --trace-type explicitly on adb ask or adb check.

• Default: --trace-type openai_messages
• Use --trace-type langfuse for raw Langfuse trace JSON
• Use --trace-type in_memory_tracer for NexAU InMemoryTracer dumps

Examples:

adb ask -t /abs/path/to/langfuse_raw_trace.json --trace-type langfuse \
  -q "What is the main issue in this trace?"

adb check -t /abs/path/to/inmemory_dump.json --trace-type in_memory_tracer --format json

If the file is already a cleaned trace or a JSON object with a usable messages field, keep the default openai_messages. --trace-type is only for --trace-path; adb ask --records-file does not support it, so records should already contain normalized messages traces.

1. Download a Langfuse trace

adb download --type langfuse --ak <public-key> --sk <secret-key> \
  https://<langfuse-host>/project/<projectId>/traces/<traceId>

The command writes cleaned traces under ~/.adb/traces/<projectId>/ and prints the cleaned trace path by default.

2. Ask a question about a trace

adb ask -t ~/.adb/traces/<projectId>/<traceId>.cleaned.json \
  -q "What is the main issue in this trace?"

One trace can also take multiple questions in one call:

adb ask -t ~/.adb/traces/<projectId>/<traceId>.cleaned.json \
  -q "What is the main issue?" "What evidence supports it?"

Multiple trace files can be passed after -t to run multi-trace QA:

adb ask \
  -t ~/.adb/traces/<projectId>/trace_a.cleaned.json ~/.adb/traces/<projectId>/trace_b.cleaned.json \
  -q "Compare these traces and explain the main differences."

For batch execution, use a JSONL records file where each line contains {queries, traces}:

adb ask -f /abs/path/to/records.jsonl -j 3 --format json

Example record:

{"queries":["What is the main issue?"],"traces":{"trace_id":"trace_123","messages":[{"role":"user","content":"hello"}]}}

If -q/--question is omitted, the current built-in default question text is Why is this trace so slow?.

Constraints:

• multiple queries plus multiple traces are not supported in a single adb ask -t ... -q ... call; use adb ask -f /abs/path/to/records.jsonl
• adb ask --records-file does not support --trace-type; records should already contain normalized messages traces

Default stdout is only the answer text. Use --format json when you need stable fields:

{
  "status": "success",
  "command": "ask",
  "trace_path": "/abs/path/to/trace.cleaned.json",
  "trace_id": "trace_123",
  "question": "What is the main issue in this trace?",
  "request_id": "trace_123",
  "response": "The main issue is ..."
}

3. Run quality check

adb check -t ~/.adb/traces/<projectId>/<traceId>.cleaned.json

This runs the QC pipeline and returns issue summaries. Use --format json when you want machine-readable output:

adb check -t ~/.adb/traces/<projectId>/<traceId>.cleaned.json --format json

Default stdout is a Markdown report that includes trace metadata, issues_count, and one section per issue with fields such as issue_type, summary, evidence, and message_index. A typical text output looks like:

# ADB Check Result

- Trace ID: `trace_123`
- Trace Path: `/abs/path/to/trace.cleaned.json`
- Issues Count: `1`

## Issues

### 1. 工具错误
- Message Index: `7`

**Summary**

The agent called the wrong tool.

**Evidence**

Message 7 calls weather instead of search.

JSON mode includes the full issue list:

{
  "status": "success",
  "command": "check",
  "trace_path": "/abs/path/to/trace.cleaned.json",
  "trace_id": "trace_123",
  "request_id": "trace_123",
  "issues_count": 2,
  "issues": [
    {
      "issue_type": "工具错误",
      "summary": "The agent called the wrong tool.",
      "evidence": "Message 7 calls weather instead of search.",
      "message_index": 7
    }
  ],
  "response": "Overall, the trace has tool-use issues."
}

The stable wrapper fields are status/command/trace_path/trace_id/request_id/issues_count/issues/response. Each item in issues is a QC pipeline issue object. The stable core fields are issue_type, summary, evidence, and message_index; extra fields such as issue_category, issue_name, start_idx, end_idx, or chunk_summary may also appear.

If a command fails and --format json is set, stdout is:

{
  "status": "failed",
  "command": "ask or check",
  "error": "..."
}

Runtime notes

• adb ask/check default to local runner mode.
• The local runner auto-creates a dedicated venv under ~/.adb/venvs/adb_local_venv.
• Downloaded traces are stored under ~/.adb/traces/.
• Local runner temp files are stored under ~/.adb/runtime/.
• adb config '{"adb":{"runner":"api"}}' exists, but current ask/check support is implemented for local runner.

Minimal end-to-end example

python -m pip install ./_source
adb config '{"llm":{"model":"gpt-4.1","base_url":"https://api.openai.com/v1","api_key":"<your-key>"}}'
TRACE_PATH="$(adb download --type langfuse --ak <public-key> --sk <secret-key> https://<langfuse-host>/project/<projectId>/traces/<traceId>)"
adb ask -t "$TRACE_PATH" -q "Summarize the main issue in this trace."
adb check -t "$TRACE_PATH" --format json