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# Full reference trajectory configuration - BookWriter-H_A2A project
#
# Version: ideal trajectory derived from 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 (A2A_mix characteristic: mixing three AI frameworks):
# 1. The orchestrator calls three independent servers via the A2A protocol (orchestrator.py):
#    - Outline Generator Server: AutoGen framework, 2 agents (researcher + outliner)
#    - Chapter Writer Server: CrewAI framework, 2 agents (researcher + writer)
#    - Book Reviewer Server: LangGraph framework, StateGraph + ToolNode
#
# 2. Tool usage analysis (tool naming differences across frameworks):
#    - Server 1 (AutoGen): execute_tool bocha_websearch_tool, execute_tool extract_keywords (execute_tool prefix)
#    - Server 2 (CrewAI): bocha_websearch_tool._use, extract_keywords._use, count_words._use, analyze_chapter_quality._use, validate_markdown_structure._use (._use suffix)
#    - Server 3 (LangGraph): count_book_words, analyze_book_quality, validate_book_markdown, extract_book_keywords (no prefix/suffix)
#
# 3. Execution flow (A2A_mix architecture, three frameworks combined):
#    - The orchestrator creates the top-level SPAN: book_writing_orchestrator
#    - The orchestrator creates the crew_execution SPAN containing 3 stages
#    - Each stage has 3 levels of nested SPANs:
#      * orchestrator method layer: generate_outline / write_chapters / review_book
#      * HTTP call layer: a2a_call_outline_generator / a2a_call_chapter_writer_* / a2a_call_book_reviewer
#      * server execution layer: outline_generator_autogen_execution / chapter_writer_server_execution / book_reviewer_server_execution
#    - Server 1 (AutoGen): AGENT(invoke_agent researcher) → LLM → Tool(execute_tool) → LLM → AGENT(invoke_agent outliner) → LLM
#    - Server 2 (CrewAI): Chain(Crew***.kickoff) → AGENT(researcher) → LLM+Tool → AGENT(writer) → LLM+Tool
#    - Server 3 (LangGraph): Chain(LangGraph) → AGENT(agent) → LLM → Chain(tools) → Tool → AGENT(agent) → LLM → Chain(format_output)
#
# 4. SPAN hierarchy (A2A_mix-specific multi-framework nesting):
#    - book_writing_orchestrator (top-level, orchestrator.py line 691)
#      └─ crew_execution (contains 3 stages, orchestrator.py line 724)
#         ├─ generate_outline (orchestrator.py line 285)
#         │  └─ a2a_call_outline_generator (orchestrator.py line 184)
#         │     └─ outline_generator_autogen_execution (AutoGen execution)
#         │        └─ AGENT: invoke_agent researcher / invoke_agent outliner
#         ├─ write_chapters (orchestrator.py line 416)
#         │  └─ a2a_call_chapter_writer_* (per chapter, orchestrator.py line 184)
#         │     └─ chapter_writer_server_execution (CrewAI execution)
#         │        └─ Chain: Crew***.kickoff
#         │           └─ AGENT: Specialized Chapter Research Agent / Professional Chapter Content Writer
#         └─ review_book (orchestrator.py line 472)
#            └─ a2a_call_book_reviewer (orchestrator.py line 184)
#               └─ book_reviewer_server_execution (LangGraph execution)
#                  └─ Chain: LangGraph
#                     └─ AGENT: agent

# Project name
project_name: "BookWriter-H_A2A"

# Trajectory extraction types
extract_types:
  - "SPAN" # Multi-level SPAN structure (including the A2A call layer)
  - "Chain" # LangGraph chains and CrewAI crew kickoff chains
  - "AGENT" # Agents from all frameworks
  - "LLM" # LLM calls
  - "Tool" # Tool calls (note naming differences across frameworks)

# Repeatable pattern configuration (used to handle varying chapter counts)
# Format: {"start": start step index (0-based), "end": end step index, "min": min repeats, "max": max repeats}
# Reference trajectory indices:
#   0-12: outline stage (12 steps) + write_chapters SPAN
#   13-29: single-chapter pattern (17 steps, repeatable 3-5 times)
#   30+: review stage (31 steps)
repeatable_patterns:
#
# Design basis (A2A_mix characteristic - mixing three AI frameworks):
# - orchestrator.py: sequentially executes 3 main stages and calls servers via the A2A protocol
# - Server 1: outline_generator_autogen.py, 2 agents (researcher + outliner)
# - Server 2: chapter_writer_crewai.py, 2 agents per chapter (researcher + writer)
# - Server 3: review_book_langgraph.py, LangGraph StateGraph + ToolNode
# - Tool naming differences: AutoGen uses an execute_tool prefix; CrewAI uses a ._use suffix; LangGraph has no prefix/suffix
# - Chapter count is generated from the outline, typically 3-5 chapters
#
# Ideal trajectory notes (A2A_mix architecture - mixing three frameworks):
# - Top-level SPAN: book_writing_orchestrator (created by orchestrator)
# - Second-level SPAN: crew_execution (contains retry logic)
# - Three stages executed in order: generate_outline → write_chapters → review_book
# - Outline stage (AutoGen): 2 agents + 4 LLM calls + 2 tool calls
# - Chapter stage (CrewAI): per chapter 2 agents + 8 LLM calls + 5 tool calls (single-chapter pattern repeated 3-5 times)
# - Review stage (LangGraph): 5 agent loops + 5 LLM calls + 8 chain nodes + 4 tool calls
# - Single-chapter pattern: 2 SPANs + 1 Chain + 2 agents + 8 LLM calls + 5 tool calls (repeated under write_chapters)
# - This trajectory represents the ideal execution path with no retries or redundancy (70 steps assuming 4 chapters)
#
# Dynamic reference trajectory design (enhanced):
# 1. Dynamic matching of chapter count (3-5 chapters)
# 2. Dynamic matching of AutoGen Outline (researcher) LLM/Tool patterns:
#    - Compact: LLM → Tool1 → Tool2 → LLM (adjacent tools)
#    - Interleaved: LLM → Tool1 → LLM → Tool2 → LLM (one LLM between tools)
#    Rule: must start and end with LLM; between the two tools there can be at most one LLM
# 3. Dynamic matching of LangGraph Review (tools) batch execution patterns:
#    - 7 possible groupings: [1,1,1,1], [2,1,1], [1,2,1], [1,1,2], [3,1], [1,3], [4]
# During evaluation, the script enumerates all combinations (chapter count × AutoGen pattern × LangGraph grouping)
# and selects the variant with the best exact_match / in_order_match / any_order_match scores as the sample reference.
#
# Notes:
# - "LLM: *" means any LLM model (wildcard match)
# - Agent names are matched exactly (note AutoGen's invoke_agent prefix)
# - Tool names are matched exactly (note prefix/suffix differences across frameworks)
# - Actual chapter count may be 3-5, which affects exact_match but not in-order match
# - AutoGen and LangGraph dynamic patterns produce multiple reference variants; the evaluator selects the best match
# - Some tool calls may be skipped or repeated depending on agent decisions and framework behavior
# ============================================================================

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

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

  # ===== Stage 1: Generate Outline (AutoGen framework) =====
  - "SPAN: generate_outline"
  - "SPAN: a2a_call_outline_generator"
  - "SPAN: outline_generator_autogen_execution"
  # Agent 1: Researcher (AutoGen framework, note the invoke_agent prefix)
  - "AGENT: invoke_agent researcher"
  - "LLM: *" # decide research strategy
  - "Tool: execute_tool bocha_websearch_tool" # AutoGen tool (execute_tool prefix)
  - "Tool: execute_tool extract_keywords" # AutoGen tool (execute_tool prefix)
  - "LLM: *" # consolidate findings
  # Agent 2: Outliner (AutoGen framework, note the invoke_agent prefix)
  - "AGENT: invoke_agent outliner"
  - "LLM: *" # generate the book outline

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

  # Standard per-chapter pattern (appears at least once; repeatable 3-5 times)
  - "SPAN: a2a_call_chapter_writer_*"
  - "SPAN: chapter_writer_server_execution"
  - "Chain: Crew***.kickoff" # CrewAI framework (UUID wildcard)
  # Agent 1: Chapter Researcher (CrewAI framework)
  - "AGENT: Specialized Chapter Research Agent"
  - "LLM: *"
  - "Tool: bocha_websearch_tool._use" # CrewAI tool (._use suffix)
  - "LLM: *"
  - "Tool: extract_keywords._use" # CrewAI tool (._use suffix)
  - "LLM: *"
  # Agent 2: Chapter Writer (CrewAI framework)
  - "AGENT: Professional Chapter Content Writer"
  - "LLM: *"
  - "Tool: count_words._use" # CrewAI tool (._use suffix)
  - "LLM: *"
  - "Tool: analyze_chapter_quality._use" # CrewAI tool (._use suffix)
  - "LLM: *"
  - "Tool: validate_markdown_structure._use" # CrewAI tool (._use suffix)
  - "LLM: *"

  # ===== Stage 3: Review Book (LangGraph framework) =====
  - "SPAN: review_book"
  - "SPAN: a2a_call_book_reviewer"
  - "SPAN: book_reviewer_server_execution"
  - "Chain: LangGraph" # LangGraph main chain
  # LangGraph loop 1 - call count_book_words
  - "AGENT: agent"
  - "LLM: *"
  - "Chain: _should_continue"
  - "Chain: tools"
  - "Tool: count_book_words" # LangGraph tool (no prefix/suffix)
  # LangGraph loop 2 - call analyze_book_quality
  - "AGENT: agent"
  - "LLM: *"
  - "Chain: _should_continue"
  - "Chain: tools"
  - "Tool: analyze_book_quality" # LangGraph tool (no prefix/suffix)
  # LangGraph loop 3 - call extract_book_keywords
  - "AGENT: agent"
  - "LLM: *"
  - "Chain: _should_continue"
  - "Chain: tools"
  - "Tool: extract_book_keywords" # LangGraph tool (no prefix/suffix)
  # LangGraph loop 4 - call validate_book_markdown
  - "AGENT: agent"
  - "LLM: *"
  - "Chain: _should_continue"
  - "Chain: tools"
  - "Tool: validate_book_markdown" # LangGraph tool (no prefix/suffix)
  # LangGraph loop 5 - generate final review report
  - "AGENT: agent"
  - "LLM: *"
  - "Chain: _should_continue"
  - "Chain: format_output"

# Target tool list (used for the single-tool use metric)
# Note: tool naming differs across frameworks
target_tools:
  # Outline generation stage (AutoGen framework, execute_tool prefix)
  - "Tool: execute_tool bocha_websearch_tool"
  - "Tool: execute_tool extract_keywords"
  # Chapter writing stage (CrewAI framework, ._use suffix, used per chapter)
  - "Tool: bocha_websearch_tool._use"
  - "Tool: extract_keywords._use"
  - "Tool: count_words._use"
  - "Tool: analyze_chapter_quality._use"
  - "Tool: validate_markdown_structure._use"
  # Book review stage (LangGraph framework, no prefix/suffix; inside Chain: tools; not in the reference trajectory)
  - "Tool: count_book_words"
  - "Tool: analyze_book_quality"
  - "Tool: validate_book_markdown"
  - "Tool: extract_book_keywords"

# ============================================================================
# Dynamic tool permutation configuration
# ============================================================================
# Define permutable tool groups for certain agents to generate reference trajectories
# with different tool orders.
# During evaluation, the script tries all permutations and selects the best-matching
# reference trajectory.
#
# Design basis (based on dependency analysis):
# - The 3 validation tools for Chapter Writer (CrewAI) are independent and can run in any order
# - The 4 analysis tools for Book Reviewer (LangGraph) are independent and can run in any order
#
# Special handling (A2A_mix architecture):
# - CrewAI tools: simple permutation (tool + surrounding LLM)
# - LangGraph tools: permute entire loop blocks (each block contains AGENT → LLM → Chain → Chain tools → Tool)
#   * Note: LangGraph already has grouping logic; this permutation applies after grouping
#   * The permutation unit is a "loop block", not an individual tool
#
# Number of permutations:
# - Chapter Writer: 3! = 6
# - Book Reviewer: 4! = 24
# - Total: 6 × 24 = 144 (for each dynamically generated reference trajectory)
#
# Evaluation strategy:
# - For each sample, apply existing dynamic logic first (chapter count, AutoGen pattern, LangGraph grouping)
# - Then generate all possible tool permutations
# - Compute a match score for each permutation (exact_match * 3 + in_order_match * 2 + any_order_match * 1)
# - Select the permutation with the highest score as the final sample reference
permutable_tool_groups:
  # Chapter Writer (CrewAI framework): validation tools can be executed in any order
  # These 3 tools are used under "AGENT: Professional Chapter Content Writer"
  # Note: CrewAI tools use the ._use suffix
  chapter_writer_validation_tools:
    - "Tool: count_words._use"
    - "Tool: analyze_chapter_quality._use"
    - "Tool: validate_markdown_structure._use"

  # Book Reviewer (LangGraph framework): analysis tools can be executed in any order
  # Note: LangGraph tool names have no prefix/suffix, but each tool is a complete loop block
  # During permutation, move the loop blocks as a whole (5 steps: AGENT → LLM → Chain → Chain tools → Tool)
  book_reviewer_analysis_tools:
    - "Tool: count_book_words"
    - "Tool: analyze_book_quality"
    - "Tool: extract_book_keywords"
    - "Tool: validate_book_markdown"

# 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 - A2A_mix architecture (mixing three AI frameworks)
# ============================================================================
#
# 1. Trajectory hierarchy (A2A_mix multi-framework nesting):
#    - Level 1: SPAN (book_writing_orchestrator) - orchestrator top-level
#    - Level 2: SPAN (crew_execution) - orchestrator contains 3 stages
#    - Level 3: SPAN (generate_outline, write_chapters, review_book) - orchestrator method layer
#    - Level 4: SPAN (a2a_call_*) - HTTP call layer
#    - Level 5: SPAN (*_execution) - server execution layer
#    - Level 6-8: framework-specific structures
#    - Level 5: SPAN (*_execution) - server execution layer for each framework
#    - Level 6-8: framework-specific structures
#      * AutoGen: AGENT(invoke_agent xxx) → LLM → Tool(execute_tool xxx) → LLM
#      * CrewAI: Chain(Crew***.kickoff) → AGENT(xxx) → LLM → Tool(xxx._use) → LLM
#      * LangGraph: Chain(LangGraph) → AGENT(agent) → LLM → Chain → Tool → AGENT → LLM → Chain
#
# 2. Wildcard handling:
#    - Crew_<UUID>.kickoff -> Crew***.kickoff (handled by evaluate_trajectory.py; CrewAI only)
#    - a2a_call_chapter_writer_* -> match any chapter title (wildcard)
#    - LLM: * -> match any model name (e.g., gpt-5-chat-latest, deepseek-reasoner, gemini-2.5-flash)
#
# 3. Exact matching requirements (differences across the three frameworks):
#    - SPAN names:
#      * top-level: "book_writing_orchestrator", "crew_execution"
#      * orchestrator method layer: "generate_outline", "write_chapters", "review_book"
#      * HTTP call layer: "a2a_call_outline_generator", "a2a_call_chapter_writer_*", "a2a_call_book_reviewer"
#      * server execution layer: "outline_generator_autogen_execution", "chapter_writer_server_execution", "book_reviewer_server_execution"
#    - AGENT names:
#      * AutoGen: "invoke_agent researcher", "invoke_agent outliner" (with invoke_agent prefix)
#      * CrewAI: "Specialized Chapter Research Agent", "Professional Chapter Content Writer" (exact match to roles in config/agents.yaml)
#      * LangGraph: "agent" (generic name, may repeat)
#    - Tool names (naming differences across frameworks):
#      * AutoGen: "execute_tool bocha_websearch_tool", "execute_tool extract_keywords" (execute_tool prefix)
#      * CrewAI: "bocha_websearch_tool._use", "extract_keywords._use", "count_words._use", "analyze_chapter_quality._use", "validate_markdown_structure._use" (._use suffix)
#      * LangGraph: "count_book_words", "analyze_book_quality", "validate_book_markdown", "extract_book_keywords" (no prefix/suffix; inside Chain: tools)
#    - Chain names:
#      * AutoGen: (no Chain nodes)
#      * CrewAI: "Crew***.kickoff" (UUID wildcard)
#      * LangGraph: "LangGraph", "_should_continue", "tools", "format_output"
#    - LLM name: "LLM: *" (wildcard match to any model)
#
# 4. Dynamic reference trajectory selection (enhanced):
#    For each sample:
#    a) detect chapter count (3-5)
#    b) detect the actual LLM/Tool pattern of AutoGen Outline (researcher)
#       - Compact: LLM → Tool1 → Tool2 → LLM
#       - Interleaved: LLM → Tool1 → LLM → Tool2 → LLM
#    c) enumerate all possible LangGraph Review (tools) groupings (7)
#    d) enumerate all possible combinations:
#       - e.g., 4 chapters × 2 AutoGen patterns × 7 LangGraph patterns = 14 variants
#    e) compute exact_match, in_order_match, any_order_match for each variant
#    f) select the variant with the best scores as the final reference trajectory for the sample
#
# 5. Metric definitions:
#    - Exact Match: requires identical trajectories (including LLM call counts, but the single-chapter pattern is repeatable)
#    - In-order Match: allows extra calls, but core steps must appear in order
#    - Any-order Match: requires all mandatory steps regardless of order
#    - Precision: fraction of correct steps in the predicted trajectory
#    - Recall: fraction of reference steps covered by the predicted trajectory
#    - Single-tool Use: checks usage of all 12 required tools (note tool naming differences)
#    - unique_path_ratio: path diversity (number of unique full trajectories / number of samples)
#    - path_entropy: path entropy (Shannon entropy over trajectory frequencies, normalized to 0-1)
#    - Note: chapter count (3-5) does not affect metrics because the single-chapter pattern is defined as repeatable
#    - Note: AutoGen and LangGraph dynamic patterns generate multiple reference variants; the evaluator selects the best match
#
# 6. Run command:
#    cd /Users/wzr/TOSEM-2025/RESULTS/RQ-Failure_Breakdown/BookWriter-H_A2A
#    python3 evaluate_trajectory-mix.py --config reference_trajectory.yaml
#
# 7. Design notes (A2A_mix architecture - mixing three AI frameworks):
#    - This reference trajectory represents the ideal execution path (defines a repeatable single-chapter pattern to match 3-5 chapters)
#    - The orchestrator calls 3 independent servers via the A2A protocol (HTTP/JSONRPC)
#    - Each A2A call has 3 nested SPAN layers (method layer + HTTP call layer + server execution layer)
#    - Stage 1 (AutoGen): 2 agents + 4 LLM calls + 2 tools (execute_tool)
#    - Stage 2 (CrewAI): per chapter 1 chain + 2 agents + 8 LLM calls + 5 tools (._use)
#    - Stage 3 (LangGraph): 1 chain + 2 agent loops + 2 LLM calls + 4 chain nodes (number of tools inside Chain: tools is not fixed)
#    - Single-chapter pattern: 2 SPANs + 1 chain + 2 agents + 8 LLM calls + 5 tools (repeats within write_chapters)
#    - In-order Match and Recall are typically more suitable for evaluating real performance
#    - Note: the reference defines a single-chapter pattern; in practice it repeats 3-5 times to avoid mismatches due to fixed chapter count
#
# 8. Differences from actual trajectories (mixed-framework behavior):
#    - AutoGen: some models may not call tools (e.g., GPT-5 outputs directly), or may call tools a different number of times
#    - CrewAI: may call the same tool multiple times (retries or quality improvements), or skip certain tools
#    - LangGraph: the number and order of tools inside Chain: tools is flexible (1-4 tools in parallel) and is not part of trajectory evaluation
#    - Actual trajectories may include more LLM calls (thinking, planning, execution, summarization), especially for reasoning models
#    - Some models (e.g., Gemini) may skip certain tool calls and generate results directly
#    - These differences do not necessarily indicate errors; they reflect different execution strategies and framework characteristics
#    - The repeatable single-chapter pattern ensures in_order_match can be evaluated correctly regardless of chapter count
#    - AutoGen dynamic pattern matching helps adapt to different tool-calling behaviors
#
# 9. A2A_mix architecture characteristics (three frameworks mixed):
#    - Agent-to-agent communication uses HTTP/JSONRPC
#    - Each A2A server runs independently and uses a different AI framework
#    - Server 1: AutoGen (RoundRobinGroupChat + MCP tools)
#    - Server 2: CrewAI (Crew + Agent + Task + MCPServerAdapter)
#    - Server 3: LangGraph (StateGraph + ToolNode + conditional edges)
#    - The SPAN hierarchy is deeper than the single-framework A2A version (3 nested SPAN layers)
#    - Tracing code in orchestrator.py creates the multi-layer SPAN structure
#    - Each server returns results to the orchestrator after completion
#    - Supports distributed deployment and independent scaling
#    - Demonstrates collaboration across multiple frameworks
#
# 10. Known model behavior differences (across frameworks):
#    - AutoGen framework:
#      * GPT-5/Gemini: may not call tools (generate outline directly)
#      * DeepSeek-V3-1/R1: typically calls 2 tools
#      * Qwen3-235b: stable behavior, typically calls tools
#    - CrewAI framework:
#      * All models: usually at least 1 search + 1 writing quality check
#      * GPT-5: may call quality tools multiple times
#      * DeepSeek-R1: may skip some quality tools
#      * Gemini: may skip search tools and generate content directly
#    - LangGraph framework:
#      * All models: typically 1-4 tools in parallel inside Chain: tools
#      * GPT-5: usually calls all 4 tools
#      * DeepSeek-V3-1/R1: may call 2-3 tools
#      * Gemini: may call only 1-2 tools
#    - These differences reflect different planning/execution strategies and framework characteristics