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#
# Version: Ideal trajectory derived from the code design (applicable to all models)
# Principle: Include only steps explicitly required by the code design (not based on test statistics)
#
# Code design analysis:
# 1. The orchestrator calls three independent servers via the A2A protocol (orchestrator.py):
# - Idea Expansion Server: Product Idea Analyst uses bocha_websearch_tool for market research
# - Template Selection Server: Template Selection Specialist uses learn_landing_page_options to pick a template
# - Content Creation Server: Landing Page Content Generator uses read_file_content and write_file_with_content to produce HTML
#
# 2. Tool usage analysis:
# - Server 1: bocha_websearch_tool (MCP tool, web search)
# - Server 2: learn_landing_page_options (MCP tool, learn available templates)
# - Server 3: read_file_content + write_file_with_content (MCP tools, file operations)
#
# 3. Execution flow (A2A architecture):
# - The orchestrator creates a top-level SPAN: landing_page_generation-A2A
# - The orchestrator creates a 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: *_server_execution
# - Each server runs an independent Crew (Chain + Agent + Tools)
# - The Agent uses the LLM to decide tool calls; after tool returns, the LLM generates the output
#
# 4. SPAN hierarchy (A2A-specific multi-level nesting):
# - landing_page_generation-A2A (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 (inside the A2A server)
# │ └─ Crew***.kickoff (Crew chain)
# │ └─ Agent execution
# ├─ select_template (orchestrator.py line 305)
# │ └─ a2a_call_template_selection (orchestrator.py line 172)
# │ └─ template_selection_server_execution (inside the A2A server)
# │ └─ Crew***.kickoff (Crew chain)
# │ └─ Agent execution
# └─ create_content (orchestrator.py line 350)
# └─ a2a_call_content_creation (orchestrator.py line 172)
# └─ content_creation_server_execution (inside the A2A server)
# └─ Crew***.kickoff (Crew chain)
# └─ Agent execution
# Project name
project_name: "LandingPageGenerator-A2A"
# Trajectory extraction types
extract_types:
- "SPAN" # Multi-level SPAN structure
- "Chain" # Crew kickoff chain
- "AGENT" # Crew agent
- "LLM" # LLM calls
- "Tool" # MCP tool calls
# ============================================================================
# Reference trajectory (Ground Truth) - derived from the code design
# ============================================================================
#
# Visual structure (ideal execution path; A2A-specific multi-level SPAN nesting):
#
# [SPAN] landing_page_generation-A2A
# └─ [SPAN] crew_execution
# ├─ [SPAN] expand_idea
# │ └─ [SPAN] a2a_call_idea_expansion
# │ └─ [SPAN] idea_expansion_server_execution
# │ └─ [Chain] Crew***.kickoff
# │ └─ [AGENT] Product Idea Analyst
# │ ├─ [LLM] *
# │ ├─ [Tool] bocha_websearch_tool
# │ └─ [LLM] *
# ├─ [SPAN] select_template
# │ └─ [SPAN] a2a_call_template_selection
# │ └─ [SPAN] template_selection_server_execution
# │ └─ [Chain] Crew***.kickoff
# │ └─ [AGENT] Template Selection Specialist
# │ ├─ [LLM] *
# │ ├─ [Tool] learn_landing_page_options
# │ └─ [LLM] *
# └─ [SPAN] create_content
# └─ [SPAN] a2a_call_content_creation
# └─ [SPAN] content_creation_server_execution
# └─ [Chain] Crew***.kickoff
# └─ [AGENT] Landing Page Content Generator
# ├─ [LLM] *
# ├─ [Tool] read_file_content
# ├─ [LLM] *
# ├─ [Tool] write_file_with_content
# └─ [LLM] *
#
# Design basis:
# - orchestrator.py: calls 3 independent servers via the A2A protocol
# - Each A2A call has 3 nested SPAN layers (orchestrator method layer + HTTP call layer + server execution layer)
# - Each server runs an independent Crew with 1 Agent and 1 Task
# - idea_expansion_crew.py: Product Idea Analyst uses bocha_websearch_tool
# - template_selection_crew.py: Template Selection Specialist uses learn_landing_page_options
# - content_creation_crew.py: Landing Page Content Generator uses read_file_content and write_file_with_content
# - All Agent roles are defined in each server's config/agents.yaml
# - Tools are obtained from the MCP server via MCPServerAdapter (each A2A server connects independently)
#
# Notes on the ideal trajectory (A2A characteristics):
# - Top-level SPAN: landing_page_generation-A2A (created by the orchestrator)
# - Second-level SPAN: crew_execution (contains 3 A2A calls)
# - Each A2A call has additional 3 nested SPAN layers (orchestrator method → HTTP call → server execution)
# - Each server internally: Chain → Agent → LLM+Tool interaction
# - Idea Expansion: at least 2 LLM calls + 1 bocha_websearch_tool call
# - Template Selection: at least 2 LLM calls + 1 learn_landing_page_options call
# - Content Creation: at least 3 LLM calls + 1 read_file_content call + 1 write_file_with_content call
# - This trajectory represents an ideal, retry-free, and redundant-free execution path
#
# Notes:
# - "LLM: *" means any LLM model (wildcard match)
# - Agent names must match the `role` field in each agents.yaml exactly
# - Tool names must match the tool names provided by the MCP server exactly
# - [Crew Created] and [Task Created] are excluded (framework telemetry events, not business logic)
# - Real executions may contain more LLM calls (thinking, planning, etc.); this is normal
# - A2A has a deeper SPAN hierarchy than MCP (3 additional nested SPAN layers)
# ============================================================================
reference_trajectory:
# ===== Top-level SPAN (created by the orchestrator) =====
- "SPAN: landing_page_generation-A2A"
# ===== Second-level SPAN (contains 3 A2A calls) =====
- "SPAN: crew_execution"
# ===== A2A call 1: Idea Expansion =====
- "SPAN: expand_idea" # orchestrator method
- "SPAN: a2a_call_idea_expansion" # HTTP call layer
- "SPAN: idea_expansion_server_execution" # server execution layer
- "Chain: Crew***.kickoff"
- "Agent: Product Idea Analyst"
- "LLM: *" # decide which tool to call
- "Tool: bocha_websearch_tool" # web search (market research)
- "LLM: *" # generate the expanded idea
# ===== A2A call 2: Template Selection =====
- "SPAN: select_template" # orchestrator method
- "SPAN: a2a_call_template_selection" # HTTP call layer
- "SPAN: template_selection_server_execution" # server execution layer
- "Chain: Crew***.kickoff"
- "Agent: Template Selection Specialist"
- "LLM: *" # decide which tool to call
- "Tool: learn_landing_page_options" # learn available templates
- "LLM: *" # select a template
# ===== A2A call 3: Content Creation =====
- "SPAN: create_content" # orchestrator method
- "SPAN: a2a_call_content_creation" # HTTP call layer
- "SPAN: content_creation_server_execution" # server execution layer
- "Chain: Crew***.kickoff"
- "Agent: Landing Page Content Generator"
- "LLM: *" # decide to read the template
- "Tool: read_file_content" # read template file
- "LLM: *" # generate HTML content
- "Tool: write_file_with_content" # write HTML file
- "LLM: *" # final confirmation
# Target tools (for the single-tool use metric)
# List all required tools here
target_tools:
- "Tool: bocha_websearch_tool"
- "Tool: learn_landing_page_options"
- "Tool: read_file_content"
- "Tool: write_file_with_content"
# 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 specific
# ============================================================================
#
# 1. Trajectory hierarchy (A2A multi-level SPAN nesting):
# - Level 1: SPAN (landing_page_generation-A2A) - 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) - A2A server execution layer
# - Level 6: Chain (Crew***.kickoff) - Crew kickoff chain (wildcard for UUID)
# - Level 7: Crew Created / AGENT / Task Created
# - Level 8: LLM (wildcard for any model) / Tool (exact match for tool name)
#
# 2. Wildcard handling:
# - Crew_<UUID>.kickoff -> Crew***.kickoff (handled automatically by evaluate_trajectory.py)
# - LLM: * -> matches any model name (e.g., gpt-5-chat-latest, deepseek-reasoner, gemini-2.5-flash, etc.)
#
# 3. Exact matching requirements:
# - SPAN names:
# * top level: "landing_page_generation-A2A", "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_server_execution", "content_creation_server_execution"
# - Chain name: "Crew***.kickoff" (wildcard for the UUID part)
# - AGENT names: "Product Idea Analyst", "Template Selection Specialist", "Landing Page Content Generator"
# (must match the `role` field in each A2A server's config/agents.yaml)
# - Event markers: "Crew Created", "Task Created" (auto-generated by the CrewAI framework)
# - LLM name: "LLM: *" (wildcard for any model)
# - Tool names: must match MCP tool names exactly (bocha_websearch_tool, learn_landing_page_options,
# read_file_content, write_file_with_content)
#
# 4. Metric definitions:
# - Exact Match: the entire trajectory must be identical (including the number of LLM calls and all SPAN levels)
# - In-order Match: extra calls are allowed, but core steps must appear in order
# - Any-order Match: must contain all required steps (order ignored)
# - Precision: fraction of correct steps among predicted steps
# - Recall: fraction of reference steps covered by the prediction
# - Single-tool Use: checks usage of all 4 required tools
# - unique_path_ratio: path diversity (number of unique full trajectories / samples)
# - path_entropy: path entropy (Shannon entropy over trajectory frequencies, normalized to 0-1)
#
# 5. Run command:
# python3 evaluate_trajectory.py --config reference_trajectory.yaml
#
# 6. Design notes (A2A characteristics):
# - This reference trajectory represents an ideal execution path
# - The orchestrator calls 3 independent servers via A2A (HTTP/JSONRPC)
# - Each A2A call includes 3 nested SPAN layers (method layer + HTTP call layer + server execution layer)
# - Each server runs an independent Crew to complete a specific task
# - Server 1 (Idea Expansion): at least 2 LLM calls + 1 bocha_websearch_tool call
# - Server 2 (Template Selection): at least 2 LLM calls + 1 learn_landing_page_options call
# - Server 3 (Content Creation): at least 3 LLM calls + 1 read_file_content call + 1 write_file_with_content call
# - Total: 2 top-level SPANs + 3 method SPANs + 3 call SPANs + 3 server SPANs + 3 Chains + 3 Agents + 7 LLM calls + 4 tools
# - Trajectory length: 28 steps (excluding 6 framework event markers)
# - In-order Match and Recall are often more suitable for evaluating actual performance
#
# 7. Differences from real trajectories:
# - Real trajectories may include more LLM calls (thinking, planning, execution, summarization, etc.)
# - Some models may call tools multiple times (retries or additional checks)
# - Content Creation may read multiple files (e.g., CSS, JS, etc.)
# - Some models may not use tools (e.g., Gemini may generate content directly without calling bocha_websearch_tool)
# - These differences do not necessarily indicate errors; they reflect different execution strategies
# - Exact Match may be low; focus on In-order Match and Recall
#
# 8. A2A architecture characteristics:
# - Agent-to-agent communication via HTTP/JSONRPC
# - Each A2A server runs independently and maintains its own MCP connection
# - The SPAN hierarchy is deeper than the MCP monolithic version (3 additional nested SPAN layers)
# - Tracing code in orchestrator.py creates the multi-level SPAN structure
# - Each server returns results to the orchestrator after execution
# - Supports distributed deployment and independent scaling
#
# 9. Known model behavior differences:
# - Gemini family: may not call bocha_websearch_tool and instead rely on internal knowledge
# - DeepSeek-R1: may call learn_landing_page_options and read_file_content multiple times
# - GPT-5: typically follows the tool-calling workflow more strictly
# - These differences reflect different planning and execution strategies
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