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

# Full reference trajectory configuration - LandingPageGenerator-H_A2A
#
# Version: an ideal trajectory derived from code design (applicable to all models)
# Principle: include only steps explicitly required by the design; do not rely on test statistics
#
# Design analysis (A2A_mix characteristic: mixing three AI frameworks):
# 1. The orchestrator calls three independent servers via the A2A protocol (orchestrator.py):
#    - Idea Expansion Server: LangGraph framework, uses bocha_websearch_tool for market research
#    - Template Selection Server: AutoGen framework, uses learn_landing_page_options to select a template
#    - Content Creation Server: CrewAI framework, uses read_file_content and write_file_with_content to generate HTML
#
# 2. Tool usage analysis (tool naming differs across frameworks):
#    - Server 1 (LangGraph): bocha_websearch_tool (no prefix/suffix)
#    - Server 2 (AutoGen): execute_tool learn_landing_page_options (execute_tool prefix)
#    - Server 3 (CrewAI): read_file_content._use, write_file_with_content._use (._use suffix)
#
# 3. Execution flow (A2A_mix architecture, mixed frameworks):
#    - The orchestrator creates the top-level SPAN: landing_page_generation-A2A_mix
#    - The orchestrator creates the crew_execution SPAN containing 3 A2A calls
#    - Each A2A call has 3 nested SPAN layers:
#      * orchestrator method layer: expand_idea / select_template / create_content
#      * HTTP call layer: a2a_call_*
#      * server execution layer: idea_expansion_server_execution / template_selection_autogen_execution / content_creation_server_execution
#    - Server 1 (LangGraph): Chain → AGENT → LLM → Chain(tools) → Tool → AGENT → LLM → Chain(format_output)
#    - Server 2 (AutoGen): AGENT(invoke_agent) → LLM → Tool(execute_tool) → LLM
#    - Server 3 (CrewAI): Chain(Crew***.kickoff) → AGENT → LLM/Tool interaction
#
# 4. SPAN hierarchy (A2A_mix multi-framework layering):
#    - landing_page_generation-A2A_mix (top-level, orchestrator.py line 451)
#      └─ crew_execution (contains 3 A2A calls, orchestrator.py line 482)
#         ├─ expand_idea (orchestrator.py line 267)
#         │  └─ a2a_call_idea_expansion (orchestrator.py line 172)
#         │     └─ idea_expansion_server_execution (LangGraph execution)
#         │        └─ Chain: LangGraph
#         │           └─ AGENT: agent (LangGraph agent node)
#         ├─ select_template (orchestrator.py line 305)
#         │  └─ a2a_call_template_selection (orchestrator.py line 172)
#         │     └─ template_selection_autogen_execution (AutoGen execution)
#         │        └─ AGENT: invoke_agent senior_react_engineer (AutoGen agent)
#         └─ create_content (orchestrator.py line 350)
#            └─ a2a_call_content_creation (orchestrator.py line 172)
#               └─ content_creation_server_execution (CrewAI execution)
#                  └─ Chain: Crew***.kickoff (CrewAI chain)
#                     └─ AGENT: Landing Page Content Generator

# Project name
project_name: "LandingPageGenerator-H_A2A"

# Trajectory extraction types
extract_types:
  - "SPAN" # Multi-layer SPAN structure
  - "Chain" # Crew kickoff chain
  - "AGENT" # Crew agent
  - "LLM" # LLM calls
  - "Tool" # MCP tool calls

# A2A_mix: enable AutoGen dynamic reference trajectory matching
enable_autogen_pattern_matching: true

# ============================================================================
# Reference Trajectory (Ground Truth) - Based on Code Design
# ============================================================================
#
# Graphical structure (ideal execution path, A2A_mix multi-framework architecture):
#
# [SPAN] landing_page_generation-A2A_mix
# └─ [SPAN] crew_execution
#    ├─ [SPAN] expand_idea
#    │  └─ [SPAN] a2a_call_idea_expansion
#    │     └─ [SPAN] idea_expansion_server_execution
#    │        └─ [Chain] LangGraph (LangGraph framework)
#    │           ├─ [AGENT] agent
#    │           │  ├─ [LLM] *
#    │           │  └─ [Chain] _should_continue
#    │           ├─ [Chain] tools (internal tool count is not fixed)
#    │           ├─ [AGENT] agent
#    │           │  ├─ [LLM] *
#    │           │  └─ [Chain] _should_continue
#    │           └─ [Chain] format_output
#    ├─ [SPAN] select_template
#    │  └─ [SPAN] a2a_call_template_selection
#    │     └─ [SPAN] template_selection_autogen_execution
#    │        └─ [AGENT] invoke_agent senior_react_engineer (AutoGen framework)
#    │           ├─ [LLM] *
#    │           ├─ [Tool] execute_tool learn_landing_page_options
#    │           └─ [LLM] *
#    └─ [SPAN] create_content
#       └─ [SPAN] a2a_call_content_creation
#          └─ [SPAN] content_creation_server_execution
#             └─ [Chain] Crew***.kickoff (CrewAI framework)
#                └─ [AGENT] Landing Page Content Generator
#                   ├─ [LLM] *
#                   ├─ [Tool] read_file_content._use
#                   ├─ [LLM] *
#                   ├─ [Tool] write_file_with_content._use
#                   └─ [LLM] *
#
# Rationale (A2A_mix):
# - orchestrator.py calls 3 independent servers via the A2A protocol over HTTP; each server uses a different framework
# - each A2A call has 3 nested SPAN layers (orchestrator method layer + HTTP call layer + server execution layer)
# - Server 1 (LangGraph): idea_expansion_langgraph.py, Chain: LangGraph, AGENT: agent
# - Server 2 (AutoGen): template_selection_autogen.py, AGENT: invoke_agent senior_react_engineer
# - Server 3 (CrewAI): content_creation_crew.py, Chain: Crew***.kickoff, AGENT: Landing Page Content Generator
# - tool naming differences: LangGraph has no prefix/suffix, AutoGen has an execute_tool prefix, CrewAI has a ._use suffix
#
# Ideal trajectory notes (A2A_mix):
# - Top-level SPAN: landing_page_generation-A2A_mix (created by the orchestrator)
# - Second-level SPAN: crew_execution (contains 3 A2A calls)
# - Each A2A call has an additional 3-layer SPAN nesting (orchestrator method → HTTP call → server execution)
# - Server 1 (LangGraph): Chain → multiple AGENT loops → Chain(tools; internal tools are not fixed) → Chain(format_output)
# - Server 2 (AutoGen): AGENT → LLM → Tool → LLM, at least 1 Tool (some models may skip Tool calls)
# - Server 3 (CrewAI): Chain → AGENT → LLM+Tool interaction, at least 1 read + 1 write
# - This trajectory represents an ideal execution path with no retries and no redundant steps
#
# Notes:
# - "LLM: *" matches any LLM model name (wildcard)
# - Agent name matching is exact: LangGraph "agent", AutoGen "invoke_agent senior_react_engineer", CrewAI "Landing Page Content Generator"
# - Tool name matching is exact: bocha_websearch_tool, execute_tool learn_landing_page_options, read_file_content._use, write_file_with_content._use
# - [Crew Created] and [Task Created] are excluded (CrewAI-only markers without business meaning)
# - Real executions may contain additional LLM and Tool calls; this is expected
# - LangGraph tool calls inside Chain: tools are not part of trajectory evaluation
# - LangGraph may loop over AGENT multiple times; AutoGen may skip Tool calls (some models return directly)
# - A2A_mix uses deeper SPAN nesting than the MCP version and mixes three different AI frameworks
# ============================================================================

reference_trajectory:
  # ===== Top-level SPAN (created by the orchestrator) =====
  - "SPAN: landing_page_generation-A2A_mix"

  # ===== Second-level SPAN (contains 3 A2A calls) =====
  - "SPAN: crew_execution"

  # ===== A2A Call 1: Idea Expansion (LangGraph framework) =====
  - "SPAN: expand_idea" # orchestrator method
  - "SPAN: a2a_call_idea_expansion" # HTTP call layer
  - "SPAN: idea_expansion_server_execution" # LangGraph server execution layer
  - "Chain: LangGraph" # LangGraph main chain
  - "AGENT: agent" # LangGraph agent node (1st)
  - "LLM: *" # decide to call tools
  - "Chain: _should_continue" # LangGraph condition check
  - "Chain: tools" # LangGraph tool chain (internal tool count is not fixed; not part of evaluation)
  - "AGENT: agent" # LangGraph agent node (2nd)
  - "LLM: *" # generate expanded idea
  - "Chain: _should_continue" # LangGraph condition check
  - "Chain: format_output" # LangGraph output formatting

  # ===== A2A Call 2: Template Selection (AutoGen framework) =====
  - "SPAN: select_template" # orchestrator method
  - "SPAN: a2a_call_template_selection" # HTTP call layer
  - "SPAN: template_selection_autogen_execution" # AutoGen server execution layer
  - "AGENT: invoke_agent senior_react_engineer" # AutoGen agent (with invoke_agent prefix)
  - "LLM: *" # decide to call tools
  - "Tool: execute_tool learn_landing_page_options" # learn available templates (execute_tool prefix)
  - "LLM: *" # select template

  # ===== A2A Call 3: Content Creation (CrewAI framework) =====
  - "SPAN: create_content" # orchestrator method
  - "SPAN: a2a_call_content_creation" # HTTP call layer
  - "SPAN: content_creation_server_execution" # CrewAI server execution layer
  - "Chain: Crew***.kickoff" # CrewAI chain (UUID wildcard)
  - "AGENT: Landing Page Content Generator" # CrewAI agent
  - "LLM: *" # decide to read template
  - "Tool: read_file_content._use" # read template file (._use suffix)
  - "LLM: *" # generate HTML content
  - "Tool: write_file_with_content._use" # write HTML file (._use suffix)
  - "LLM: *" # final confirmation

# ============================================================================
# AutoGen dynamic reference trajectory design notes
# ============================================================================
#
# Dynamic matching for the LLM/Tool call pattern in the AutoGen phase (Template Selection):
#
# Rule: the phase must start and end with an LLM; between adjacent tools there can be 0 or 1 LLM
#
# For 1 tool (learn_landing_page_options), the possible pattern is:
#   - LLM → Tool → LLM (the only pattern)
#
# During evaluation, all possible patterns are enumerated and scored:
#   score = exact_match * 3 + in_order_match * 2 + any_order_match * 1
#
# The highest-scoring pattern is used as the final reference for that sample.
# This adapts to different model execution strategies and improves evaluation accuracy.
# ============================================================================

# Target tool list (for the single-tool use metric)
# Note: tool naming differs across frameworks (LangGraph has no prefix/suffix, AutoGen uses execute_tool prefix, CrewAI uses ._use suffix)
# Note: bocha_websearch_tool is inside Chain: tools and not in the reference trajectory, but we still track its usage
target_tools:
  - "Tool: bocha_websearch_tool" # LangGraph framework (inside Chain: tools)
  - "Tool: execute_tool learn_landing_page_options" # AutoGen framework
  - "Tool: read_file_content._use" # CrewAI framework
  - "Tool: write_file_with_content._use" # CrewAI framework

# 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 - specific to the A2A_mix architecture (mixing three AI frameworks)
# ============================================================================
#
# 1. Trajectory hierarchy (A2A_mix multi-framework mixing):
#    - Level 1: SPAN (landing_page_generation-A2A_mix) - orchestrator top-level
#    - Level 2: SPAN (crew_execution) - orchestrator contains 3 A2A calls
#    - Level 3: SPAN (expand_idea, select_template, create_content) - orchestrator method layer
#    - Level 4: SPAN (a2a_call_*) - HTTP call layer
#    - Level 5: SPAN (*_server_execution / *_autogen_execution) - per-server execution layer
#    - Level 6-8: framework-specific structure
#      * LangGraph: Chain(LangGraph) → AGENT(agent) → LLM → Chain → Tool → AGENT → LLM → Chain
#      * AutoGen: AGENT(invoke_agent xxx) → LLM → Tool(execute_tool xxx) → LLM
#      * CrewAI: Chain(Crew***.kickoff) → AGENT(xxx) → LLM → Tool(xxx._use) → LLM
#
# 2. Wildcard handling:
#    - Crew_<UUID>.kickoff -> Crew***.kickoff (handled by the evaluator; CrewAI only)
#    - LLM: * -> matches any model name (e.g., gpt-5-chat-latest, deepseek-reasoner, gemini-2.5-flash)
#
# 3. Exact-match requirements (differences across frameworks):
#    - SPAN names:
#      * Top-level: "landing_page_generation-A2A_mix", "crew_execution"
#      * Orchestrator method layer: "expand_idea", "select_template", "create_content"
#      * HTTP call layer: "a2a_call_idea_expansion", "a2a_call_template_selection", "a2a_call_content_creation"
#      * Server execution layer: "idea_expansion_server_execution", "template_selection_autogen_execution", "content_creation_server_execution"
#    - Chain names:
#      * LangGraph: "LangGraph", "_should_continue", "tools", "format_output"
#      * AutoGen: (no Chain nodes)
#      * CrewAI: "Crew***.kickoff" (wildcard matches the UUID part)
#    - AGENT names:
#      * LangGraph: "agent" (generic name; may appear multiple times)
#      * AutoGen: "invoke_agent senior_react_engineer" (with invoke_agent prefix)
#      * CrewAI: "Landing Page Content Generator" (exact match to role in config/agents.yaml)
#    - Tool names (naming differs across frameworks):
#      * LangGraph: "bocha_websearch_tool" (no prefix/suffix; inside Chain: tools; excluded from trajectory evaluation)
#      * AutoGen: "execute_tool learn_landing_page_options" (execute_tool prefix)
#      * CrewAI: "read_file_content._use", "write_file_with_content._use" (._use suffix)
#    - Event markers: "Crew Created", "Task Created" (CrewAI-only markers)
#    - LLM name: "LLM: *" (wildcard matches any model)
#
# 4. Metric meanings:
#    - Exact Match: trajectories must be identical (including LLM call count and all SPAN/Chain/AGENT levels)
#    - In-order Match: extra calls are allowed, but required steps must appear in order
#    - Any-order Match: contains all required steps (order ignored)
#    - Precision: fraction of predicted steps that are correct
#    - Recall: fraction of reference steps covered by the predicted trajectory
#    - Single-tool Use: checks usage of all 4 required tools (note framework-specific tool naming)
#    - unique_path_ratio: path diversity (unique full trajectories / samples)
#    - path_entropy: path entropy (Shannon entropy over path frequencies, normalized to 0-1)
#
# 5. Example command:
#    cd /Users/wzr/TOSEM-2025/RESULTS/RQ-Failure_Breakdown/LandingPageGenerator-H_A2A
#    python3 evaluate_trajectory.py --config reference_trajectory.yaml
#
# 6. Design notes (A2A_mix architecture - mixing three AI frameworks):
#    - This reference trajectory represents an ideal execution path
#    - The orchestrator calls 3 independent servers via A2A (HTTP/JSONRPC)
#    - Each A2A call has 3 nested SPAN layers (method layer + HTTP call layer + server execution layer)
#    - Server 1 (LangGraph): Chain → 2 AGENT loops + 1 tools Chain + 4 Chain nodes, at least 2 LLM calls (tool count inside Chain: tools is not fixed)
#    - Server 2 (AutoGen): 1 AGENT + 2 LLM calls + 1 Tool (execute_tool), may skip Tool calls (some models return directly)
#    - Server 3 (CrewAI): Chain + AGENT + 3 LLM calls + 1 read + 1 write
#    - Total: 2 top-level SPANs + 3 method SPANs + 3 call SPANs + 3 server SPANs + 6 Chains + 4 AGENTs + 8 LLM calls + 3 tools (AutoGen+CrewAI)
#    - Trajectory length: 31 steps (excluding Tool nodes inside Chain: tools; LangGraph has more Chain nodes)
#    - In-order Match and Recall are usually more suitable for evaluating real performance
#    - Note: LangGraph may call bocha_websearch_tool 1-3 times inside Chain: tools; excluded from trajectory evaluation
#
# 7. Differences from real trajectories (mixed-framework characteristics):
#    - LangGraph:
#      * may loop over AGENT nodes multiple times
#      * tool calls inside Chain: tools are not fixed (1-3 bocha_websearch_tool calls) and do not affect trajectory evaluation
#      * tool calls may appear in parallel (@@@ marker)
#    - AutoGen: some models may not call tools (e.g., GPT-5 returns template name directly), or use a different number of LLM calls
#    - CrewAI: may read multiple files (CSS/JS/etc.) or write multiple times (retries)
#    - Real trajectories may include more LLM calls (thinking/planning/execution/summarization)
#    - Some models may not use tools (e.g., Gemini generates content directly without bocha_websearch_tool)
#    - These differences do not necessarily indicate errors; they reflect model strategies and framework behavior
#    - Exact Match may be low; focus on In-order Match and Recall
#
# 8. A2A_mix architecture characteristics (three-framework mixing):
#    - agent-to-agent communication via HTTP/JSONRPC
#    - each A2A server runs independently and uses a different AI framework
#    - Server 1: LangGraph (StateGraph + ToolNode + conditional edges)
#    - Server 2: AutoGen (AssistantAgent + MCP tools + ToolCallFormatFixer)
#    - Server 3: CrewAI (Crew + Agent + Task + MCPServerAdapter)
#    - SPAN nesting is deeper than the MCP monolithic version (3 more nested SPAN layers)
#    - tracing code in orchestrator.py creates the multi-layer SPAN structure
#    - each server returns results to the orchestrator after execution
#    - supports distributed deployment and independent scaling
#    - demonstrates cross-framework collaboration
#
# 9. Known model behavior differences (across frameworks):
#    - LangGraph framework:
#      * GPT-5: usually 1-2 tool calls
#      * DeepSeek-V3-1/R1: may make 3 parallel tool calls
#      * Gemini: may skip tool calls and generate content directly
#    - AutoGen framework:
#      * GPT-5: may skip tool calls (returns template name directly)
#      * DeepSeek: usually calls tools
#      * Gemini: unstable behavior; may or may not call tools
#    - CrewAI framework:
#      * all models: usually at least 1 read + 1 write
#      * GPT-5: may read multiple files and make multiple LLM calls
#      * DeepSeek-R1: may write twice (retry/correction)
#    - These differences reflect different planning/execution strategies and framework implementations