data/processed/RQ1/SocialMediaManager-H_A2A/reference_trajectory.yaml

# Full reference-trajectory configuration - SocialMediaManager-H_A2A
#
# Version: ported from SocialMediaManager-A2A and adapted to the A2A_mix hybrid architecture
# Principle: keep business logic unchanged while adapting to the hybrid framework hierarchy (LangGraph + CrewAI + AutoGen)
#
# A2A_mix hybrid-architecture characteristics:
# - Stage 1 (Topic Analysis): LangGraph
#   * Uses [Chain] LangGraph instead of CrewAI's Crew***.kickoff
#   * Uses a simplified AGENT name: "agent" (not the full role name)
#   * Tool calls appear under [Chain] tools, supporting batched execution
#   * LangGraph-specific chains: _should_continue and format_output
#
# - Stage 2 (Content Generation): CrewAI
#   * Keeps the standard [Chain] Crew***.kickoff structure
#   * Uses the full agent role name: "Shakespearean Bard._execute_core"
#   * Tool calls appear directly under AGENT (no intermediate Chain)
#
# - Stage 3 (Post Review): AutoGen
#   * AGENT pattern: create_agent + invoke_agent
#   * Ignore create_agent (excluded from evaluation)
#   * Focus on invoke_agent execution
#   * Tool prefix is "execute_tool"
#   * Filter all [SPAN] mcp client/operation and its children
#
# Dynamic reference-trajectory design (5-way dynamic composition):
# 1. LangGraph tools batched-execution grouping (2 options):
#    - [1,1]: two Chain: tools, each containing 1 tool
#    - [2]: one Chain: tools containing 2 tools
#
# 2. LangGraph tools order (2! = 2 options):
#    - keyword_extractor → topic_complexity_analyzer
#    - topic_complexity_analyzer → keyword_extractor
#
# 3. AutoGen LLM/Tool insertion pattern (2^4 = 16 options):
#    Rule: must start and end with an LLM; between adjacent tools there may be 0 or 1 LLM
#    - 5 tools have 4 adjacency gaps → 2^4 = 16 combinations
#    - Example: LLM → Tool1 → Tool2 → Tool3 → Tool4 → Tool5 → LLM (no insertion)
#              LLM → Tool1 → LLM → Tool2 → LLM → Tool3 → LLM → Tool4 → LLM → Tool5 → LLM (insert everywhere)
#
# 4. AutoGen tools order (5! = 120 options):
#    - The 5 verification tools can appear in any order
#    - character_counter_tool, emoji_detector_tool, post_structure_validator,
#      shakespearean_style_detector, tone_analyzer
#
# 5. CrewAI tool order (5! = 120 options):
#    - Shakespearean Bard's 5 validation tools can appear in any order
#    - keyword_extractor must be the first tool (not permuted)
#
# Total combinations: 2 × 2 × 16 × 120 × 120 = 921,600 reference variants
#
# Evaluation strategy: for each sample, select the variant with the highest score:
# exact_match×3 + in_order_match×2 + any_order_match×1

# Project name
project_name: "SocialMediaManager-H_A2A"

# Extraction types
extract_types:
  - "SPAN" # multi-level SPAN structure
  - "Chain" # Chain nodes for LangGraph/CrewAI
  - "AGENT" # Agent nodes (AutoGen: only invoke_agent is extracted)
  - "LLM" # LLM calls
  - "Tool" # tool calls

# ============================================================================
# Reference trajectory (Ground Truth) - A2A_mix hybrid architecture
# ============================================================================
#
# Visual structure (ideal execution path; example with batched tools execution):
#
# [SPAN] shakespeare_x_post_orchestrator
# └─ [SPAN] crew_execution
#    ├─ [SPAN] analyze_topic (LangGraph)
#    │  └─ [SPAN] a2a_call_topic_analyzer
#    │     └─ [SPAN] topic_analyzer_server_execution
#    │        └─ [Chain] LangGraph
#    │           ├─ [AGENT] agent
#    │           │  ├─ [LLM] *
#    │           │  └─ [Chain] _should_continue
#    │           ├─ [Chain] tools (batched execution of 2 tools; possible groupings: 1+1, 2)
#    │           │  ├─ [Tool] keyword_extractor
#    │           │  └─ [Tool] topic_complexity_analyzer
#    │           ├─ [AGENT] agent
#    │           │  ├─ [LLM] *
#    │           │  └─ [Chain] _should_continue
#    │           └─ [Chain] format_output
#    └─ [SPAN] content_generation_loop
#       ├─ [SPAN] a2a_call_content_generator (CrewAI)
#       │  └─ [SPAN] content_generator_server_execution
#       │     └─ [Chain] Crew***.kickoff
#       │        └─ [AGENT] Shakespearean Bard
#       │           ├─ [LLM] *
#       │           ├─ [Tool] keyword_extractor
#       │           ├─ [LLM] *
#       │           ├─ [Tool] character_counter_tool
#       │           ├─ [LLM] *
#       │           ├─ [Tool] emoji_detector_tool
#       │           ├─ [LLM] *
#       │           ├─ [Tool] post_structure_validator
#       │           ├─ [LLM] *
#       │           ├─ [Tool] shakespearean_style_detector
#       │           ├─ [LLM] *
#       │           ├─ [Tool] tone_analyzer
#       │           └─ [LLM] *
#       └─ [SPAN] a2a_call_post_reviewer (AutoGen)
#          └─ [SPAN] post_reviewer_autogen_execution
#             └─ [AGENT] invoke_agent x_post_verifier
#                ├─ [LLM] *
#                ├─ [Tool] execute_tool character_counter_tool
#                ├─ [Tool] execute_tool emoji_detector_tool
#                ├─ [Tool] execute_tool post_structure_validator
#                ├─ [Tool] execute_tool shakespearean_style_detector
#                ├─ [Tool] execute_tool tone_analyzer
#                └─ [LLM] *
#
# Notes:
# - Under LangGraph's [Chain] tools there may be batched calls (2 tools can be grouped as 1+1 or 2)
# - AutoGen create_agent nodes are excluded from the reference trajectory
# - AutoGen [SPAN] mcp client/operation and its children are excluded from the reference trajectory
# - Between AutoGen tools there may be 0 or 1 LLM; the evaluator will generate all valid patterns
# ============================================================================

# Default reference trajectory (compatibility): batched LangGraph tools, AutoGen compact mode
reference_trajectory:
  # ===== Top-level SPAN =====
  - "SPAN: shakespeare_x_post_orchestrator"

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

  # ===== Stage 1: Topic Analysis (LangGraph; 2 tools) =====
  - "SPAN: analyze_topic"
  - "SPAN: a2a_call_topic_analyzer"
  - "SPAN: topic_analyzer_server_execution"
  - "Chain: LangGraph"
  - "AGENT: agent"
  - "LLM: *"
  - "Chain: _should_continue"
  - "Chain: tools"
  - "Tool: keyword_extractor"
  - "Tool: topic_complexity_analyzer"
  - "AGENT: agent"
  - "LLM: *"
  - "Chain: _should_continue"
  - "Chain: format_output"

  # ===== Stage 2: Content Generation (CrewAI) =====
  - "SPAN: content_generation_loop"
  - "SPAN: a2a_call_content_generator"
  - "SPAN: content_generator_server_execution"
  - "Chain: Crew***.kickoff"
  - "AGENT: Shakespearean Bard"
  - "LLM: *"
  - "Tool: keyword_extractor"
  - "LLM: *"
  - "Tool: character_counter_tool"
  - "LLM: *"
  - "Tool: emoji_detector_tool"
  - "LLM: *"
  - "Tool: post_structure_validator"
  - "LLM: *"
  - "Tool: shakespearean_style_detector"
  - "LLM: *"
  - "Tool: tone_analyzer"
  - "LLM: *"

  # ===== Stage 3: Post Review (AutoGen) =====
  - "SPAN: a2a_call_post_reviewer"
  - "SPAN: post_reviewer_autogen_execution"
  - "AGENT: invoke_agent x_post_verifier"
  - "LLM: *"
  - "Tool: execute_tool character_counter_tool"
  - "Tool: execute_tool emoji_detector_tool"
  - "Tool: execute_tool post_structure_validator"
  - "Tool: execute_tool shakespearean_style_detector"
  - "Tool: execute_tool tone_analyzer"
  - "LLM: *"

# Target tool list (used for the single-tool use metric)
target_tools:
  # Topic Analysis stage (LangGraph)
  - "Tool: keyword_extractor"
  - "Tool: topic_complexity_analyzer"
  # Content Generation stage (CrewAI)
  - "Tool: character_counter_tool"
  - "Tool: emoji_detector_tool"
  - "Tool: post_structure_validator"
  - "Tool: shakespearean_style_detector"
  - "Tool: tone_analyzer"
  # Post Review stage (AutoGen; note the execute_tool prefix)
  - "Tool: execute_tool character_counter_tool"
  - "Tool: execute_tool emoji_detector_tool"
  - "Tool: execute_tool post_structure_validator"
  - "Tool: execute_tool shakespearean_style_detector"
  - "Tool: execute_tool tone_analyzer"

# 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"

# Permutable tool-group configuration (for dynamic tool-order matching)
permutable_tool_groups:
  # Shakespearean Bard validation tools can be in any order (CrewAI part)
  # Note: keyword_extractor must be the first tool and is not permuted
  shakespearean_bard_validation_tools:
    - "Tool: character_counter_tool"
    - "Tool: emoji_detector_tool"
    - "Tool: post_structure_validator"
    - "Tool: shakespearean_style_detector"
    - "Tool: tone_analyzer"
# ============================================================================
# Usage notes - A2A_mix hybrid architecture
# ============================================================================
#
# 1. Hybrid framework characteristics:
#    - Stage 1 (Topic Analysis): LangGraph
#      * Chain name: LangGraph (not Crew***.kickoff)
#      * Simplified AGENT name: agent (not a full role name)
#      * Batched tool execution: [Chain] tools may contain multiple Tool nodes
#      * Special chains: _should_continue and format_output
#
#    - Stage 2 (Content Generation): CrewAI
#      * Standard CrewAI structure: Crew***.kickoff → AGENT → LLM/Tool
#      * Full AGENT name: Shakespearean Bard
#      * keyword_extractor must be the first tool; subsequent validation tools may be in any order
#
#    - Stage 3 (Post Review): AutoGen
#      * Ignore create_agent (excluded from evaluation)
#      * Focus on invoke_agent x_post_verifier
#      * Tool prefix: execute_tool
#      * Filter all [SPAN] mcp client/operation and its children
#
# 2. LangGraph dynamic tool mode (grouping + order):
#    The evaluator automatically detects the tools grouping pattern and tool order in each sample,
#    and dynamically generates matching reference variants.
#
#    Grouping patterns (2 options):
#    - [1,1]: 2 Chain: tools, each with 1 Tool
#    - [2]:   1 Chain: tools, with 2 Tools
#
#    Tool order (2! = 2 options):
#    - keyword_extractor → topic_complexity_analyzer
#    - topic_complexity_analyzer → keyword_extractor
#
# 3. AutoGen dynamic mode (LLM insertion + tool order):
#    The evaluator automatically detects the LLM/Tool calling pattern and tool order in AutoGen,
#    and dynamically generates matching reference variants.
#
#    LLM insertion patterns (2^4 = 16 options):
#    - Compact: LLM → Tool1 → Tool2 → Tool3 → Tool4 → Tool5 → LLM
#      (consecutive tool calls with no LLM between tools)
#    - Interleaved: LLM → Tool1 → LLM → Tool2 → ... → Tool5 → LLM
#      (LLM calls interleaved between tools)
#    - Mixed: LLM is inserted between some tools but not others (16 combinations)
#
#    Tool order (5! = 120 options):
#    - The 5 verification tools can be in any order
#    - character_counter_tool, emoji_detector_tool, post_structure_validator,
#      shakespearean_style_detector, tone_analyzer
#
#    Validation rules:
#    - Must start and end with an LLM
#    - Must contain all 5 target tools in the middle (order can vary)
#    - At most one LLM between adjacent tools
#
# 4. Dynamic reference selection strategy (5-way combination):
#    For each sample:
#    a) Detect the actual LangGraph tools grouping pattern
#    b) Enumerate LangGraph tool order (2! = 2)
#    c) Detect the actual AutoGen Post Review LLM/Tool pattern
#    d) Enumerate AutoGen tool order (5! = 120)
#    e) Enumerate CrewAI tool permutations (5! = 120)
#    f) Enumerate all combinations
#       - LangGraph grouping: 2
#       - LangGraph order: 2
#       - AutoGen LLM insertion: 16
#       - AutoGen order: 120
#       - CrewAI permutations: 120
#       - Total variants: 2 × 2 × 16 × 120 × 120 = 921,600
#    g) For each variant compute exact_match, in_order_match, any_order_match
#    h) Select the variant with the highest composite score
#       Composite score = exact_match×3 + in_order_match×2 + any_order_match×1
#
# 5. Metric definitions (consistent with A2A):
#    - Exact Match: predicted trajectory matches the reference exactly
#    - In-order Match: reference is a subsequence of the predicted trajectory
#    - Any-order Match: all required steps are present (order ignored)
#    - Precision: fraction of predicted steps that are correct
#    - Recall: fraction of reference steps covered by prediction
#    - Single-tool Use: average usage rate of target tools
#    - unique_path_ratio: path diversity
#    - path_entropy: path entropy
#
# 6. Run command:
#    cd /Users/wzr/TOSEM-2025/RESULTS/RQ-Failure_Breakdown/SocialMediaManager-H_A2A
#    python3 evaluate_trajectory.py --config reference_trajectory.yaml
#
# 7. Key differences vs the A2A version:
#    - LangGraph-specific Chain node structure
#    - Simplified AGENT name (LangGraph uses "agent")
#    - AutoGen invoke_agent pattern
#    - Dynamic handling of LangGraph batched tools execution
#    - Dynamic handling of AutoGen LLM/Tool insertion patterns (new)
#    - AutoGen tool prefix: execute_tool (not CrewAI tool names)
#    - Filter AutoGen MCP client/operation SPAN nodes
#    - Triple dynamic matching across LangGraph, CrewAI, and AutoGen
#
# 8. Special filtering rules:
#    - Filter all [SPAN] mcp client/operation and its children (AutoGen noise)
#    - Filter [AGENT] create_agent (AutoGen initialization, not business logic)
#    - Keep [AGENT] invoke_agent (actual AutoGen execution)
#
# 9. Tool-order notes:
#    - LangGraph Topic Analysis: 2 tools can be in any order (2! = 2)
#    - CrewAI Shakespearean Bard: keyword_extractor must be first; the 5 validation tools can be permuted (5! = 120)
#    - AutoGen Post Review: the 5 verification tools can be in any order (5! = 120)
#