agent-tracing
Agent tracing CLI for execution snapshots. Use for agent-tracing, traces, snapshots, LLM call inspection, context engine data, agent step analysis, execution debugging, or pulling remote/production traces ("拉线上 tracing") by operation id. Also the first stop for debugging agent tool calls — wrong or missing tool_calls, unexpected tool arguments or results, which tools were available at a step, or why a tool ran where it did.
Works with
---
name: agent-tracing
description: Agent tracing CLI for execution snapshots. Use for agent-tracing, traces, snapshots, LLM call inspection, context engine data, agent step analysis, execution debugging, or pulling remote/production traces ("拉线上 tracing") by operation id. Also the first stop for debugging agent tool calls — wrong or missing tool_calls, unexpected tool arguments or results, which tools were available at a step, or why a tool ran where it did.
license: Apache-2.0
---
# Agent Tracing CLI Guide
`@lobechat/agent-tracing` is a zero-config local dev tool that records agent execution snapshots to disk and provides a CLI to inspect them.
## How It Works
In `NODE_ENV=development`, `AgentRuntimeService.executeStep()` automatically records each step to `.agent-tracing/` as partial snapshots. When the operation completes, the partial is finalized into a complete `ExecutionSnapshot` JSON file.
**Data flow**: executeStep loop -> build `StepPresentationData` -> write partial snapshot to disk -> on completion, finalize to `.agent-tracing/{timestamp}_{traceId}.json`
**Context engine capture**: In `RuntimeExecutors.ts`, the `call_llm` executor calls `ctx.tracingContextEngine(input, output)` after `serverMessagesEngine()` processes messages. `AgentRuntimeService.executeStep` buffers the call per step and forwards it to `OperationTraceRecorder.appendStep` as the typed `contextEngine` field. CE flows through this side channel rather than the `events` array so its heavy payload (agentDocuments, systemRole, …) never enters the Redis state pipeline (LOBE-9110).
## Package Location
```
packages/agent-tracing/
src/
types.ts # ExecutionSnapshot, StepSnapshot, SnapshotSummary
store/
types.ts # ISnapshotStore interface
file-store.ts # FileSnapshotStore (.agent-tracing/*.json)
recorder/
index.ts # appendStepToPartial(), finalizeSnapshot()
viewer/
index.ts # Terminal rendering: renderSnapshot, renderStepDetail, renderMessageDetail, renderSummaryTable, renderPayload, renderPayloadTools, renderMemory
cli/
index.ts # CLI entry point (#!/usr/bin/env bun)
inspect.ts # Inspect command (default)
partial.ts # Partial snapshot commands (list, inspect, clean)
index.ts # Barrel exports
```
## Data Storage
- Completed snapshots: `.agent-tracing/{ISO-timestamp}_{traceId-short}.json`
- Latest symlink: `.agent-tracing/latest.json`
- In-progress partials: `.agent-tracing/_partial/{operationId}.json`
- Downloaded remote snapshots: `.agent-tracing/_remote/{operationId}.json`
- `FileSnapshotStore` resolves from `process.cwd()` — **run CLI from the repo root**
## Remote Traces (Production / Staging)
Server deployments also upload completed snapshots to object storage (zstd-compressed; the key is stored in `agent_operations.trace_s3_key`). `agent-tracing inspect <operationId>` transparently downloads, decompresses, and caches them — no manual S3 access needed.
1. **Find the operation id** from a business id (users usually hand you a topic id):
```sql
SELECT id, trace_s3_key FROM agent_operations WHERE topic_id = 'tpc_xxx';
```
`trace_s3_key IS NULL` means no snapshot was recorded; a non-null key can still 404 in storage (retention/TTL).
2. **Configure the base URL** — the bucket's public domain plus the `/agent-traces` prefix — either way:
- env var: `TRACING_BASE_URL=https://<bucket-public-domain>/agent-traces`
- file: `.agent-tracing/.env` in the repo root containing the same `TRACING_BASE_URL=...` line
The deployment-specific value is private to each deployment and intentionally not recorded in this repo.
3. **Inspect by operation id** — auto-detected by the `op_..._agt_..._tpc_...` shape; the snapshot is cached to `.agent-tracing/_remote/<opId>.json` and every `inspect` flag works the same as for local traces:
```bash
agent-tracing inspect op_xxx_agt_xxx_tpc_xxx_xxxx # step tree of a production run
agent-tracing inspect op_xxx_agt_xxx_tpc_xxx_xxxx -T # tool injection (enabledToolIds, manifests)
```
Implementation: `packages/agent-tracing/src/store/remote-store.ts` (URL is built from the operation id as `{base}/{agentId}/{topicId}/{opId}.json.zst`).
## CLI Commands
All commands run from the **repo root**:
```bash
# View latest trace (tree overview, `inspect` is the default command)
agent-tracing
agent-tracing inspect
agent-tracing inspect <traceId>
agent-tracing inspect latest
# List recent snapshots
agent-tracing list
agent-tracing list -l 20
# Inspect specific step (-s is short for --step)
agent-tracing inspect <traceId> -s 0
# View messages (-m is short for --messages)
agent-tracing inspect <traceId> -s 0 -m
# View full content of a specific message (by index shown in -m output)
agent-tracing inspect <traceId> -s 0 --msg 2
agent-tracing inspect <traceId> -s 0 --msg-input 1
# View tool call/result details (-t is short for --tools)
agent-tracing inspect <traceId> -s 1 -t
# View raw events (-e is short for --events)
agent-tracing inspect <traceId> -s 0 -e
# View runtime context (-c is short for --context)
agent-tracing inspect <traceId> -s 0 -c
# View context engine input overview (-p is short for --payload)
agent-tracing inspect <traceId> -p
agent-tracing inspect <traceId> -s 0 -p
# View available tools in payload (-T is short for --payload-tools)
agent-tracing inspect <traceId> -T
agent-tracing inspect <traceId> -s 0 -T
# View user memory (-M is short for --memory)
agent-tracing inspect <traceId> -M
agent-tracing inspect <traceId> -s 0 -M
# Raw JSON output (-j is short for --json)
agent-tracing inspect <traceId> -j
agent-tracing inspect <traceId> -s 0 -j
# List in-progress partial snapshots
agent-tracing partial list
# Inspect a partial (use `inspect` directly — all flags work with partial IDs)
agent-tracing inspect <partialOperationId>
agent-tracing inspect <partialOperationId> -T
agent-tracing inspect <partialOperationId> -p
# Clean up stale partial snapshots
agent-tracing partial clean
# Map the context window composition of every LLM call (cm / map are aliases)
agent-tracing ctx-map
agent-tracing ctx-map <operationId|traceId|path.json>
agent-tracing ctx-map --html # standalone report under .agent-tracing/_reports/
agent-tracing ctx-map --html out.html
```
## ctx-map — Context Window Composition
`ctx-map` renders one row per `call_llm` step: the messages that call sent to the model, split
into typed segments (system / injected block / user / reasoning / tool call / tool result) with
width proportional to tokens, laid against the model's context window.
The second axis is what `ctx-lint` cannot show: each row is diffed against the previous call, so
the longest identical **message prefix** — the part a provider's prefix cache can reuse — is
marked, along with the first message that mutated and the tokens re-processed behind it. A small
injected block carrying a relative timestamp (`1m ago` → `now`) invalidates every token after it,
which shows up as a break marker early in the row.
Reading a row:
- **Block colors** encode role directly: orange system, green user, blue assistant, gray tool.
Assistant reasoning, content, and tool calls use different steps of the same blue scale;
framework-injected blocks use a lighter orange than the system prompt. Every value is a step
index into a LobeHub scale vendored in `viewer/contextMapScales.ts`, with assignments in
`viewer/contextMapPalette.ts`. The HTML report ships both themes and follows the system theme.
- **Neutral message frames** group segments that belong to the same payload message. Every role
uses the same frame color: the outline communicates structure only, while the block fill carries
role and subtype semantics. The original framed layout uses padding inside each message and a
small track gap between messages, keeping each payload message visually distinct.
- **Fill** says whether the provider reused it: shaded `▓` (HTML: 60%-opaque hatch) was served
from the prefix cache; solid `█` (HTML: flat) was re-processed by the model.
- **The line under the track** is the cache ledger — a bracket / green band spanning exactly the
cached prefix, then the break marker (`▲` in the terminal, a red rule through the track in
HTML) at the column where reuse stopped, with the reason and the re-processed tokens.
| Flag | Short | Description |
| --------------- | ----- | ---------------------------------------------------------- |
| `--html [path]` | | Standalone HTML report (hover a segment for its content) |
| `--width <n>` | `-w` | Track width in terminal columns |
| `--window <n>` | | Override the model context window used as the track |
| `--full-window` | | Always scale to the full window, never to the largest call |
| `--json` | `-j` | Per-call segments + cache stats |
The track scales to the context window; when the window dwarfs the payloads (a 50k payload on a
1M window) it falls back to the largest call so the composition stays readable, and the header
says which basis is in use. Analysis lives in `analysis/contextMap.ts` and is exported as
`buildContextMap()` for downstream corpus work.
## Inspect Flag Reference
| Flag | Short | Description | Default Step |
| ----------------- | ----- | ------------------------------------------------------------------------------------------------- | ------------ |
| `--step <n>` | `-s` | Target a specific step | — |
| `--messages` | `-m` | Messages context (CE input → params → LLM payload) | — |
| `--tools` | `-t` | Tool calls & results (what agent invoked) | — |
| `--events` | `-e` | Raw events (llm\_start, llm\_result, etc.) | — |
| `--context` | `-c` | Runtime context & payload (raw) | — |
| `--system-role` | `-r` | Full system role content | 0 |
| `--env` | | Environment context | 0 |
| `--payload` | `-p` | Context engine input overview (model, knowledge, tools summary, memory summary, platform context) | 0 |
| `--payload-tools` | `-T` | Available tools detail (plugin manifests + LLM function definitions) | 0 |
| `--memory` | `-M` | Full user memory (persona, identity, contexts, preferences, experiences) | 0 |
| `--diff <n>` | `-d` | Diff against step N (use with `-r` or `--env`) | — |
| `--msg <n>` | | Full content of message N from Final LLM Payload | — |
| `--msg-input <n>` | | Full content of message N from Context Engine Input | — |
| `--json` | `-j` | Output as JSON (combinable with any flag above) | — |
Flags marked "Default Step: 0" auto-select step 0 if `--step` is not provided. All flags support `latest` or omitted traceId.
## Typical Debug Workflow
```bash
# 1. Trigger an agent operation in the dev UI
# 2. See the overview
agent-tracing inspect
# 3. List all traces, get traceId
agent-tracing list
# 4. Quick overview of what was fed into context engine
agent-tracing inspect -p
# 5. Inspect a specific step's messages to see what was sent to the LLM
agent-tracing inspect TRACE_ID -s 0 -m
# 6. Drill into a truncated message for full content
agent-tracing inspect TRACE_ID -s 0 --msg 2
# 7. Check available tools vs actual tool calls
agent-tracing inspect -T # available tools
agent-tracing inspect -s 1 -t # actual tool calls & results
# 8. Inspect user memory injected into the conversation
agent-tracing inspect -M
# 9. Diff system role between steps (multi-step agents)
agent-tracing inspect TRACE_ID -r -d 2
```
## Key Types
```typescript
interface ExecutionSnapshot {
traceId: string;
operationId: string;
model?: string;
provider?: string;
startedAt: number;
completedAt?: number;
completionReason?:
'done' | 'error' | 'interrupted' | 'max_steps' | 'cost_limit' | 'waiting_for_human';
totalSteps: number;
totalTokens: number;
totalCost: number;
error?: { type: string; message: string };
steps: StepSnapshot[];
}
interface StepSnapshot {
stepIndex: number;
stepType: 'call_llm' | 'call_tool';
executionTimeMs: number;
content?: string; // LLM output
reasoning?: string; // Reasoning/thinking
inputTokens?: number;
outputTokens?: number;
toolsCalling?: Array<{ apiName: string; identifier: string; arguments?: string }>;
toolsResult?: Array<{
apiName: string;
identifier: string;
isSuccess?: boolean;
output?: string;
}>;
messages?: any[]; // DB messages before step
context?: { phase: string; payload?: unknown; stepContext?: unknown };
events?: Array<{ type: string; [key: string]: unknown }>;
contextEngine?: {
input?: unknown; // contextEngineInput minus messages + toolsConfig (reconstructible from baseline)
output?: unknown; // processed messages array (final LLM payload)
};
}
```
## --messages Output Structure
When using `--messages`, the output shows three sections (if context engine data is available):
1. **Context Engine Input** — DB messages passed to the engine, with `[0]`, `[1]`, ... indices. Use `--msg-input N` to view full content.
2. **Context Engine Params** — systemRole, model, provider, knowledge, tools, userMemory, etc.
3. **Final LLM Payload** — Processed messages after context engine (system date injection, user memory, history truncation, etc.), with `[0]`, `[1]`, ... indices. Use `--msg N` to view full content.
## Integration Points
- **Recording**: `apps/server/src/services/agentRuntime/AgentRuntimeService.ts` — in the `executeStep()` method, after building `stepPresentationData`, writes partial snapshot in dev mode
- **Context engine capture**: `apps/server/src/modules/AgentRuntime/RuntimeExecutors.ts` — in `call_llm` executor, after `serverMessagesEngine()` returns, calls `ctx.tracingContextEngine(input, output)`. `AgentRuntimeService.executeStep` buffers it per step and passes it to `traceRecorder.appendStep` as the typed `contextEngine` field (kept off the `events` array to stay out of Redis state).
- **Store**: `FileSnapshotStore` reads/writes to `.agent-tracing/` relative to `process.cwd()`More Debugging skills
diagnosing-bugs
mattpocock/skills
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
explore-code
lllllllama/rigorpilot-skills
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
safe-debug
lllllllama/rigorpilot-skills
Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.

