data/processed/RQ1/EmailResponder/reference_trajectory.yaml

# Complete Reference Trajectory Configuration - EmailResponder Project
#
# Version: Ideal trajectory based on code design (applicable to all models)
# Principle: Only includes steps explicitly required by code design, not based on test statistics
#
# Code Design Analysis:
# 1. Required tool: Fetch New Emails
#    - tasks.yaml Line 14: "Use the exact tool name 'Fetch New Emails'"
#
# 2. Required tool: Create Email Draft
#    - tasks.yaml Line 122: "If auto_create_drafts is enabled, use..."
#    - main.py Line 55: auto_create_drafts=True (hardcoded)
#
# 3. Recommended tool: Get Gmail Thread
#    - crew.py: Both Agents are configured with this tool
#    - Purpose: Get email thread context, improve analysis and response quality
#    - Location: filter_emails_task and generate_responses_task

# Project name
project_name: "EmailResponder"

# Trajectory extraction type configuration
# As per user requirements, focus on: SPAN, Chain, AGENT, LLM, Tool
# Not concerned with: Task Created, Crew Created and other non-core content
extract_types:
  - "SPAN" # Level 1: Top-level execution span
  - "Chain" # Level 2: Crew execution chain
  - "AGENT" # Level 3: Agent execution
  - "LLM" # Level 4: LLM calls
  - "Tool" # Level 4: Tool calls

# ============================================================================
# Reference Trajectory (Ground Truth) - Based on code design
# ============================================================================
#
# Graphical structure (ideal execution path):
#
# [SPAN] email_auto_responder
# └─ [Chain] Crew***.kickoff
#    ├─ [AGENT] Senior Email Analysis and Classification Specialist  ← Task 1
#    │  ├─ [LLM] <model>  ← Decide to call tool
#    │  ├─ [Tool] Fetch New Emails  ← Required: Fetch emails
#    │  └─ [LLM] <model>  ← Process tool results
#    │
#    ├─ [AGENT] Senior Email Analysis and Classification Specialist  ← Task 2
#    │  └─ [LLM] <model>  ← Analyze emails (no tool calls)
#    │
#    └─ [AGENT] Expert Professional Email Response Composer  ← Task 3
#       ├─ [LLM] <model>  ← Generate response content
#       ├─ [Tool] Create Email Draft  ← Required: Create draft (auto_create_drafts=True)
#       └─ [LLM] <model>  ← Confirm completion
#
# Design rationale (based on Mock mode):
# - Task 1: tasks.yaml Line 14 explicitly requires using Fetch New Emails
# - Task 2: tasks.yaml Line 35 prohibits re-fetching emails
#           In Mock mode, Fetch New Emails already returns all available info (snippet),
#           Get Gmail Thread won't provide additional value, so not called
# - Task 3: tasks.yaml Line 122 requires creating draft when auto_create_drafts=True
#           main.py Line 55: auto_create_drafts=True (hardcoded)
#
# Note: <model> represents any LLM, not limited to specific model
# ============================================================================

reference_trajectory:
  # ===== Level 1: SPAN =====
  - "SPAN: email_auto_responder"

  # ===== Level 2: Chain (wildcard handling for UUID) =====
  - "Chain: Crew***.kickoff"

  # ===== Task 1: fetch_emails_task =====
  # Required: Fetch New Emails (explicitly required by tasks.yaml)
  - "Agent: Senior Email Analysis and Classification Specialist"
  - "LLM: *" # Any model
  - "Tool: Fetch New Emails"
  - "LLM: *"

  # ===== Task 2: filter_emails_task =====
  # Only analyze emails, no tool calls (Fetch New Emails already returns all info in Mock mode)
  - "Agent: Senior Email Analysis and Classification Specialist"
  - "LLM: *"

  # ===== Task 3: generate_responses_task =====
  # Required: Create Email Draft (auto_create_drafts=True)
  # Note: Get Gmail Thread is optional in Task 3, not mandatory
  - "Agent: Expert Professional Email Response Composer and Communication Strategist"
  - "LLM: *"
  - "Tool: Create Email Draft"
  - "LLM: *"

# Target tools list (for single-tool use metric)
# Only includes actual tool calls, used to detect tool usage
target_tools:
  # Tools (standard format: capitalized + space)
  - "Tool: Fetch New Emails"
  - "Tool: Get Gmail Thread"
  - "Tool: Create Email Draft"

# Models to evaluate
models:
  - "GPT-5"
  - "GPT-4o-mini"
  - "DeepSeek-V3-1"
  - "DeepSeek-R1"
  - "Gemini-2.5-flash"
  - "Gemini-2.5-flash-nothinking"
  - "Qwen3-235b"
# Usage instructions:
#
# 1. Trajectory hierarchy (as per user requirements):
#    - Top: SPAN (email_auto_responder)
#    - Second level: Chain (Crew***.kickoff, wildcard handles UUID)
#    - Third level: AGENT (specific Agent names, must match exactly)
#    - Fourth level: LLM and Tool (alternating)
#
# 2. Wildcard handling:
#    - Crew_b02af339-6c40-4ecb-b174-ec6fbe8e2081.kickoff -> Crew***.kickoff
#    - Script automatically unifies Crew_UUID.kickoff format to Crew***.kickoff
#
# 3. Exact match requirements:
#    - SPAN name: exact match "email_auto_responder"
#    - Chain name: wildcard match "Crew***.kickoff"
#    - AGENT name: exact match (e.g. "Senior Email Analysis and Classification Specialist")
#    - LLM name: exact match model name (e.g. "gpt-4o-mini")
#    - Tool name: exact match tool name (e.g. "Fetch New Emails")
#
# 4. Evaluation metrics meaning:
#    - Exact Match: requires all nodes to be identical (including LLM call count)
#    - In-order Match: allows extra LLM calls, but core sequence must be in order
#    - Any-order Match: only requires containing all necessary nodes
#    - Precision/Recall: calculate accuracy and recall for all nodes
#
# 5. Run command:
#    python3 evaluate_trajectory.py --config reference_trajectory_complete.yaml
#
# 6. Notes:
#    - Different models may have different LLM call counts (thinking, retries, etc.)
#    - Complete trajectory evaluation is stricter than evaluating Tool only
#    - Exact Match score may be low, recommend focusing on In-order Match and Any-order Match