# Full reference trajectory configuration - BookWriter-A2A
#
# Version: ideal trajectory derived from the code design (applies to all models)
# Principle: include only steps explicitly required by the code design; do not rely on test statistics
#
# Code-design analysis:
# 1. Three main stages are executed sequentially (orchestrator.py):
#    - OutlineGenerator: generate the book outline (2 agents: Researcher + Outliner)
#    - WriteBookChapterCrew: concurrently write multiple chapters (2 agents per chapter: Researcher + Writer)
#    - ReviewBookCrew: review the complete book (1 agent: Editor)
#
# 2. A2A-specific characteristics:
#    - The orchestrator calls three independent A2A servers via HTTP/JSON-RPC
#    - Each server runs a Crew and uses MCP tools
#    - Extra SPAN layers are added: a2a_call_* and *_server_execution
#    - Chapter writing is executed in concurrent batches (batch_size=4)
#
# 3. Tool usage analysis:
#    - OutlineGenerator:
#      - Researcher: bocha_websearch_tool, extract_keywords
#      - Outliner: no tools (pure reasoning)
#    - WriteBookChapterCrew (per chapter):
#      - Researcher: bocha_websearch_tool, extract_keywords
#      - Writer: count_words, analyze_chapter_quality, validate_markdown_structure
#    - ReviewBookCrew:
#      - Editor: count_book_words, analyze_book_quality, validate_book_markdown, extract_book_keywords
#
# 4. SPAN hierarchy (A2A version):
#    - book_writing_orchestrator (top-level, orchestrator.py line 691)
#      └─ crew_execution (includes retry logic, orchestrator.py line 724)
#         ├─ generate_outline (orchestrator.py line 285)
#         │  └─ a2a_call_outline_generator (orchestrator.py line 184)
#         │     └─ outline_crew_server_execution (A2A server side)
#         ├─ write_chapters (orchestrator.py line 416)
#         │  └─ [repeat the following pattern per chapter 3-5 times]
#         │     └─ a2a_call_chapter_writer_* (per chapter, orchestrator.py line 184)
#         │        └─ chapter_writer_server_execution (A2A server side)
#         └─ review_book (orchestrator.py line 472)
#            └─ a2a_call_book_reviewer (orchestrator.py line 184)
#               └─ book_reviewer_server_execution (A2A server side)

# Project name
project_name: "BookWriter-A2A"

# Trajectory extraction types
extract_types:
  - "SPAN" # Multi-layer SPAN structure (including the A2A call layer)
  - "Chain" # kickoff chain for each Crew
  - "Agent" # agent nodes within each Crew
  - "LLM" # LLM calls
  - "Tool" # MCP tool calls

# Repeatable pattern configuration (for handling varying chapter counts)
# Format: {"start": start-step index (0-based), "end": end-step index, "min": min repeats, "max": max repeats}
# Reference indices: 0-14 Outline+write_chapters, 15-31 single-chapter pattern (full), 32+ Review
repeatable_patterns:
  - start: 15 # SPAN: a2a_call_chapter_writer_* (step 1 of the single-chapter pattern, including SPAN layers)
    end: 31 # LLM: * (last step of the single-chapter pattern, after validate_markdown_structure)
    min: 3 # At least 3 chapters (business requirement)
    max: 5 # At most 5 chapters
    name: "chapter_writing_pattern" # Pattern name (for debugging)

# ============================================================================
# Reference Trajectory (Ground Truth) - Based on Code Design
# ============================================================================
#
# Graph view (ideal execution path; the single-chapter pattern repeats 3-5 times):
#
# [SPAN] book_writing_orchestrator
# └─ [SPAN] crew_execution
#    ├─ [SPAN] generate_outline
#    │  └─ [SPAN] a2a_call_outline_generator
#    │     └─ [SPAN] outline_crew_server_execution
#    │        └─ [Chain] Crew***.kickoff
#    │           ├─ [Agent] Expert Research Agent for Book Outline Planning
#    │           │  ├─ [LLM] *
#    │           │  ├─ [Tool] bocha_websearch_tool
#    │           │  ├─ [LLM] *
#    │           │  ├─ [Tool] extract_keywords
#    │           │  └─ [LLM] *
#    │           └─ [Agent] Strategic Book Structure Architect
#    │              └─ [LLM] *
#    ├─ [SPAN] write_chapters
#    │  └─ [repeat the following pattern per chapter 3-5 times]
#    │     └─ [SPAN] a2a_call_chapter_writer_*
#    │        └─ [SPAN] chapter_writer_server_execution
#    │           └─ [Chain] Crew***.kickoff
#    │              ├─ [Agent] Specialized Chapter Research Agent
#    │              │  ├─ [LLM] *
#    │              │  ├─ [Tool] bocha_websearch_tool
#    │              │  ├─ [LLM] *
#    │              │  ├─ [Tool] extract_keywords
#    │              │  └─ [LLM] *
#    │              └─ [Agent] Professional Chapter Content Writer
#    │                 ├─ [LLM] *
#    │                 ├─ [Tool] count_words
#    │                 ├─ [LLM] *
#    │                 ├─ [Tool] analyze_chapter_quality
#    │                 ├─ [LLM] *
#    │                 ├─ [Tool] validate_markdown_structure
#    │                 └─ [LLM] *
#    └─ [SPAN] review_book
#       └─ [SPAN] a2a_call_book_reviewer
#          └─ [SPAN] book_reviewer_server_execution
#             └─ [Chain] Crew***.kickoff
#                └─ [Agent] Senior Book Editor and Quality Assurance Specialist
#                   ├─ [LLM] *
#                   ├─ [Tool] count_book_words
#                   ├─ [LLM] *
#                   ├─ [Tool] analyze_book_quality
#                   ├─ [LLM] *
#                   ├─ [Tool] validate_book_markdown
#                   ├─ [LLM] *
#                   ├─ [Tool] extract_book_keywords
#                   └─ [LLM] *

# Design rationale:
# - orchestrator.py: executes three main stages sequentially and calls servers via the A2A protocol
# - OutlineGenerator has 2 agents (Researcher + Outliner)
# - WriteBookChapterCrew has 2 agents per chapter (Researcher + Writer)
# - ReviewBookCrew has 1 agent (Editor)
# - Agent roles are defined in each crew's config/agents.yaml
# - Tools are provided via MCPServerAdapter
# - The number of chapters is generated from the outline (typically 3-5)

# Ideal-trajectory notes:
# - Top-level SPAN: book_writing_orchestrator
# - Second-level SPAN: crew_execution (contains retry logic)
# - Three child SPANs execute in order: generate_outline → write_chapters → review_book
# - Each stage includes an A2A call layer (a2a_call_*) and a server execution layer (*_server_execution)
# - Outline stage: Researcher uses 2 tools; Outliner uses pure reasoning
# - Chapter stage: per chapter, Researcher uses 2 tools; Writer uses 3 tools
# - Review stage: Editor uses 4 tools for whole-book quality checks
# - This reference represents an ideal path (no retries, no redundant steps), using 4 chapters as the baseline

# Notes:
# - "LLM: *" means any LLM model (wildcard)
# - Agent names must match the role field in agents.yaml exactly
# - Tool names must match the tool names exposed by the MCP server
# - The actual chapter count may be 3-5, which affects exact_match but not in_order_match
# - Some tool calls may be skipped or repeated depending on agent decisions

# ============================================================================
reference_trajectory:
  # ===== Top-level SPAN =====
  - "SPAN: book_writing_orchestrator"

  # ===== Second-level SPAN (includes retry logic) =====
  - "SPAN: crew_execution"

  # ===== Stage 1: Generate Outline =====
  - "SPAN: generate_outline"
  - "SPAN: a2a_call_outline_generator"
  - "SPAN: outline_crew_server_execution"
  - "Chain: Crew***.kickoff"
  # Agent 1: Researcher
  - "Agent: Expert Research Agent for Book Outline Planning"
  - "LLM: *" # Decide the research strategy
  - "Tool: bocha_websearch_tool" # Web search for topics
  - "LLM: *" # Process search results
  - "Tool: extract_keywords" # Extract keywords
  - "LLM: *" # Summarize research findings
  # Agent 2: Outliner
  - "Agent: Strategic Book Structure Architect"
  - "LLM: *" # Generate the book outline

  # ===== Stage 2: Write Chapters (at least 1 chapter; typically 3-5) =====
  - "SPAN: write_chapters"

  # Standard per-chapter pattern (appears at least once; repeats 3-5 times)
  - "SPAN: a2a_call_chapter_writer_*"
  - "SPAN: chapter_writer_server_execution"
  - "Chain: Crew***.kickoff"
  - "Agent: Specialized Chapter Research Agent"
  - "LLM: *"
  - "Tool: bocha_websearch_tool"
  - "LLM: *"
  - "Tool: extract_keywords"
  - "LLM: *"
  - "Agent: Professional Chapter Content Writer"
  - "LLM: *"
  - "Tool: count_words"
  - "LLM: *"
  - "Tool: analyze_chapter_quality"
  - "LLM: *"
  - "Tool: validate_markdown_structure"
  - "LLM: *"

  # ===== Stage 3: Review Book =====
  - "SPAN: review_book"
  - "SPAN: a2a_call_book_reviewer"
  - "SPAN: book_reviewer_server_execution"
  - "Chain: Crew***.kickoff"
  - "Agent: Senior Book Editor and Quality Assurance Specialist"
  - "LLM: *" # Start reviewing
  - "Tool: count_book_words" # Count words
  - "LLM: *" # Analyze word count
  - "Tool: analyze_book_quality" # Quality analysis
  - "LLM: *" # Assess quality
  - "Tool: validate_book_markdown" # Format validation
  - "LLM: *" # Check formatting
  - "Tool: extract_book_keywords" # Extract keywords
  - "LLM: *" # Generate review report

# Target tool list (for the single-tool use metric)
# This list includes all required tools
target_tools:
  # Outline generation stage
  - "Tool: bocha_websearch_tool"
  - "Tool: extract_keywords"
  # Chapter Writing stage (used per chapter)
  - "Tool: count_words"
  - "Tool: analyze_chapter_quality"
  - "Tool: validate_markdown_structure"
  # Book Review stage
  - "Tool: count_book_words"
  - "Tool: analyze_book_quality"
  - "Tool: validate_book_markdown"
  - "Tool: extract_book_keywords"

# ============================================================================
# Dynamic tool arrangement configuration
# ============================================================================
# Define permutable tool groups for certain agents to generate different reference trajectories
# The evaluation will try all possible permutations and choose the one with the highest match score
#
# Rationale:
# - Chapter Writer's 3 validation tools (count_words, analyze_chapter_quality, validate_markdown_structure)
#   depend only on the chapter draft and have no inter-dependencies, so they can run in any order.
# - Book Editor's 4 analysis tools (count_book_words, analyze_book_quality, validate_book_markdown, extract_book_keywords)
#   depend only on book files and are independent, so they can run in any order.
#
# Number of permutations:
# - Chapter Writer: 3! = 6 permutations
# - Book Editor: 4! = 24 permutations
#
# Evaluation strategy:
# - For each sample, generate all permuted reference trajectories
# - Compute a match score (exact_match * 3 + in_order_match * 2 + any_order_match * 1)
# - Choose the highest-scoring permutation as the sample's reference trajectory
# - This fairly evaluates models that use different tool invocation orders
permutable_tool_groups:
  # Chapter Writer validation tools can be in any order
  # These 3 tools are used under "Agent: Professional Chapter Content Writer"
  chapter_writer_validation_tools:
    - "Tool: count_words"
    - "Tool: analyze_chapter_quality"
    - "Tool: validate_markdown_structure"

  # Book Editor analysis tools can be in any order
  # These 4 tools are used under "Agent: Senior Book Editor and Quality Assurance Specialist"
  book_editor_analysis_tools:
    - "Tool: count_book_words"
    - "Tool: analyze_book_quality"
    - "Tool: validate_book_markdown"
    - "Tool: extract_book_keywords"

# 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 notes
# ============================================================================
#
# 1. Trajectory hierarchy (A2A version):
#    - Level 1: SPAN (book_writing_orchestrator)
#    - Level 2: SPAN (crew_execution)
#    - Level 3: SPAN (generate_outline, write_chapters, review_book)
#    - Level 4: SPAN (a2a_call_*: A2A call layer for each stage)
#    - Level 5: SPAN (*_server_execution: server-side execution layer)
#    - Level 6: Chain (Crew***.kickoff; UUID is wildcarded)
#    - Level 7: AGENT (agent role must match exactly)
#    - Level 8: LLM (wildcard match any model)
#    - Level 8: Tool (tool name must match exactly)
#
# 2. Differences between the A2A version and the MCP version:
#    - The A2A version adds two extra SPAN layers: a2a_call_* and *_server_execution
#    - These SPANs represent the HTTP communication between the orchestrator and A2A servers
#    - Tool invocation is the same, but wrapped inside the server execution layer
#    - In execution_path.md, SPAN names may include chapter titles (e.g., a2a_call_chapter_writer_(chapter_title))
#
# 3. Wildcard handling:
#    - Crew_<UUID>.kickoff -> Crew***.kickoff (handled automatically by evaluate_trajectory.py)
#    - a2a_call_chapter_writer_* -> matches any chapter title
#    - LLM: * -> matches any model name (e.g., gpt-5-chat-latest, deepseek-reasoner)
#    - Agent names must match fully (including prefixes/suffixes)
#
# 4. Exact match requirements:
#    - Orchestrator-layer SPANs: "book_writing_orchestrator", "crew_execution", "generate_outline", "write_chapters", "review_book"
#    - A2A call-layer SPANs: "a2a_call_outline_generator", "a2a_call_chapter_writer_*", "a2a_call_book_reviewer" (chapter writer uses wildcard)
#    - Server-layer SPANs: "outline_crew_server_execution", "chapter_writer_server_execution", "book_reviewer_server_execution"
#    - Chain: "Crew***.kickoff" (wildcard)
#    - Agents: names must match the role fields in agents.yaml exactly (including full names and formatting)
#    - LLM: "LLM: *" (wildcard match any model)
#    - Tools: must match MCP server tool names exactly
#    - Actual chapter count may be 3-5; this affects exact_match but not in_order_match
#    - Some tool calls may be skipped or repeated depending on agent decisions
#
# 5. Metric meanings:
#    - Exact Match: trajectories must be identical (including LLM call counts; single-chapter pattern is repeatable)
#    - In-order Match: extra calls are allowed, but core steps must appear in order
#    - Any-order Match: all required steps must be present (order ignored)
#    - Precision: fraction of predicted steps that are correct
#    - Recall: fraction of reference steps that are covered
#    - Single-tool Use: checks whether all required tools are used
#    - unique_path_ratio: path diversity (unique complete trajectories / sample count)
#    - path_entropy: Shannon entropy of trajectory frequencies, normalized to 0-1
#
# 6. Example command:
#    python3 evaluate_trajectory.py --config reference_trajectory.yaml
#
# 7. Design notes:
#    - This reference represents an ideal execution path (single-chapter pattern matches 3-5 chapters)
#    - Three main stages execute in order: Outline → Chapters → Review
#    - In-order Match and Recall are often more informative for real-world behavior
#
# 8. Differences from real trajectories:
#    - The reference defines a single-chapter pattern; real runs repeat it 3-5 times (depending on outline chapter count)
#    - Real trajectories may contain extra LLM calls (planning, reasoning, summarization)
#    - Some models may call tools multiple times (retries or extra checks)
#    - Some models may skip certain tool calls and still produce results
#    - These differences reflect strategy choices and do not necessarily indicate errors
#
# 9. A2A-specific notes:
#    - The orchestrator calls three independent A2A servers via HTTP/JSON-RPC
#    - Each A2A server runs a Crew and uses MCP tools internally
#    - Extra SPAN layers are added to trace A2A communication and server-side execution
#    - Tool categories: search (bocha_websearch_tool), analysis (extract_keywords),
#      quality checks (count_words, analyze_*_quality), and format validation (validate_*)
#    - Chapter writing is executed in concurrent batches; each chapter has an independent trajectory
#    - Compared to the MCP version, the A2A version has deeper span nesting but the same core logic