# 2481 Trace Spec 5795a259

> EvalView Trace Specification v1.0 — Execution Trace Format for AI Agent Testing

- Skill: `tools-only/2481-trace-spec-5795a259` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/2481-trace-spec-5795a259`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/2481-trace-spec-5795a259/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/2481-trace-spec-5795a259

---

# EvalView Trace Specification v1.0 — Execution Trace Format for AI Agent Testing

> This document defines the trace format that all EvalView adapters follow. It specifies how agent execution data (LLM calls, tool calls, costs, latency) is captured and stored for evaluation, diffing, and reporting.

This document defines the trace contract that ALL adapters must follow. Every span, every export, and every report uses this specification.

## Version

```yaml
trace_spec_version: "1.0"
```

All traces include this version at the trace level (not per-span) to enable future evolution without breaking old traces/exports.

## Span Types

| Type | When Used | Description |
|------|-----------|-------------|
| `agent` | Root span | Top-level execution boundary |
| `llm` | Any LLM API call | Model inference (Claude, GPT, Ollama, etc.) |
| `tool` | Tool/function execution | Function calls, tool use |
| `mcp` | MCP server calls | Model Context Protocol operations |
| `http` | External HTTP requests | Non-LLM HTTP calls |
| `retrieval` | RAG/vector lookups | (Future) Embedding searches |

## Trace-Level Fields

Every trace MUST include:

```yaml
trace_id: "a1b2c3d4e5f6g7h8"  # UUID, 16 chars hex
run_id: "eval-20240115-143022"  # Optional, links related traces
source: "eval" | "trace_cmd" | "chat"  # How the trace was generated
trace_spec_version: "1.0"

# Execution context (optional but recommended)
command: "python my_agent.py"
cwd: "/path/to/project"
git_sha: "abc123def"  # Optional, for reproducibility

# Timing
started_at: "2026-01-15T14:30:22.123Z"  # ISO 8601
ended_at: "2026-01-15T14:30:26.456Z"

# Aggregates
total_cost_usd: 0.47
total_tokens: 15847
total_llm_calls: 12
total_tool_calls: 5
total_latency_ms: 4233

# Tags (flexible key-value for filtering)
tags:
  test_name: "booking_flow"
  suite: "regression"
  adapter: "anthropic"
  framework: "custom"
```

## Span-Level Fields

### Required Attributes (ALL spans)

```yaml
span_id: "a1b2c3d4"          # UUID, 8 chars hex
parent_span_id: null         # Nullable, null for root span
trace_id: "a1b2c3d4e5f6g7h8" # Links to parent trace
span_type: "llm"             # One of: agent, llm, tool, mcp, http
name: "claude-sonnet-4"       # Human-readable identifier

# Timing
start_time: "2026-01-15T14:30:22.123Z"
end_time: "2026-01-15T14:30:23.456Z"
latency_ms: 1333.0

# Status
status: "success" | "error"
error_message: null          # Populated if status == "error"
```

### LLM Span Attributes

For spans with `span_type: "llm"`:

```yaml
llm:
  provider: "anthropic" | "openai" | "ollama" | "google" | "grok" | "huggingface"
  model: "claude-sonnet-4-5-20250929"  # Exact model identifier

  # Token counts
  input_tokens: 1247
  output_tokens: 523
  cached_tokens: 0           # Anthropic cache, OpenAI cached prompts

  # Cost (null if unknown pricing)
  cost_usd: 0.02             # Calculated cost, null if unknown

  # Content sizes (ALWAYS stored, for debugging without leaking content)
  prompt_chars: 4521
  completion_chars: 1893

  # Content previews (ONLY with --trace-include-content flag)
  # These are OPT-IN to prevent accidental PII/secret leakage
  prompt_preview: "You are a helpful..."   # First 200 chars
  completion_preview: "I'll help you..."   # First 200 chars

  # Completion metadata
  finish_reason: "end_turn" | "tool_use" | "max_tokens" | "stop"

  # Streaming info (if applicable)
  streamed: false
  time_to_first_token_ms: null
```

### Tool Span Attributes

For spans with `span_type: "tool"`:

```yaml
tool:
  tool_name: "get_weather"

  # Argument sizes (ALWAYS stored)
  tool_args_bytes: 45
  tool_result_bytes: 1203

  # Success indicator
  tool_success: true

  # Content previews (ONLY with --trace-include-content flag)
  tool_args_preview: '{"city": "NYC"}'      # First 200 chars of JSON
  tool_result_preview: '{"temp": 72, ...}'  # First 500 chars
```

### MCP Span Attributes

For spans with `span_type: "mcp"`:

```yaml
mcp:
  server_name: "filesystem"
  tool_name: "read_file"

  # Same structure as tool spans
  tool_args_bytes: 32
  tool_result_bytes: 4096
  tool_success: true

  # MCP-specific
  protocol_version: "1.0"
```

## Privacy and Security

### Default Behavior (Safe)

By default, traces store:
- Sizes and counts (chars, bytes, tokens)
- Hashes for deduplication (optional)
- Metadata and timing

Traces do NOT store by default:
- Actual prompts or completions
- Tool arguments or results
- Any content that could contain secrets/PII

### Opt-in Content (`--trace-include-content`)

When explicitly enabled:
- Previews are captured (first N chars)
- A warning is printed once per session
- Sensitive keys are auto-sanitized

### Auto-Sanitization

The following keys are ALWAYS redacted from content previews:
- `api_key`, `apikey`, `api-key`
- `authorization`, `auth`
- `token`, `access_token`, `refresh_token`
- `secret`, `password`, `passwd`
- `cookie`, `session`
- `credential`, `credentials`

Redacted format: `"api_key": "[REDACTED]"`

## JSONL Export Format

When using `--trace-out <file>`, traces are written as newline-delimited JSON:

```jsonl
{"type": "trace_start", "trace_id": "...", "trace_spec_version": "1.0", "started_at": "..."}
{"type": "span", "span_id": "...", "span_type": "agent", "name": "...", ...}
{"type": "span", "span_id": "...", "span_type": "llm", "name": "claude-sonnet-4", ...}
{"type": "span", "span_id": "...", "span_type": "tool", "name": "get_weather", ...}
{"type": "trace_end", "trace_id": "...", "ended_at": "...", "total_cost_usd": 0.47, ...}
```

This format allows:
- Streaming writes (no need to buffer entire trace)
- Easy parsing with standard tools (`jq`, Python)
- Append-friendly (multiple traces in one file)

## Console Output Format

Live trace output follows this format:

```
━━━ Trace Started ━━━
[agent] Agent Execution

  [llm] claude-sonnet-4 → 1,247 in / 523 out → $0.02 (1.3s)
  [tool] get_weather → success (0.2s)
  [llm] claude-sonnet-4 → 892 in / 234 out → $0.01 (0.9s)
  [tool] book_flight → success (0.5s)

━━━ Trace Summary ━━━
💰 Total cost:    $0.03
⏱️  Total time:    2.9s
🔄 LLM calls:     2
🔧 Tool calls:    2

Slowest: claude-sonnet-4 (1.3s)
Most expensive: claude-sonnet-4 ($0.02)
```

### Color Coding

| Color | Meaning | Threshold |
|-------|---------|-----------|
| Green | Fast/Cheap | < 1s or < $0.01 |
| Yellow | Moderate | 1-3s or $0.01-$0.05 |
| Red | Slow/Expensive | > 3s or > $0.05 |

Errors are always red regardless of timing.

## Adapter Compliance

All adapters MUST:

1. **Emit spans following this spec** - Required attributes must be present
2. **Handle streaming** - Accumulate tokens for streaming responses
3. **Capture errors** - Set status and error_message on failures
4. **Track retries** - Include retry_count attribute when applicable
5. **Support nesting** - parent_span_id must correctly reference parent

Adapters SHOULD:

1. **Calculate costs** - Use pricing table, null if unknown
2. **Track time-to-first-token** - For streaming responses
3. **Include git_sha** - When running in a git repository

## Version History

| Version | Date | Changes |
|---------|------|---------|
| 1.0 | 2026-01 | Initial specification |

---

## Related Documentation

- [Adapters](ADAPTERS.md) — How adapters produce traces following this spec
- [Golden Traces](GOLDEN_TRACES.md) — Saving and diffing traces for regression detection
- [Evaluation Metrics](EVALUATION_METRICS.md) — How traces are scored
- [Cost Tracking](COST_TRACKING.md) — Token usage and cost data in traces
- [Backend Requirements](BACKEND_REQUIREMENTS.md) — Event format your API must emit