# Full reference trajectory configuration - SocialMediaManager-A2A project # # Version: ideal trajectory derived from the code design (applies to all models) # Principle: include only steps explicitly required by the design, not based on test statistics # # Code-design analysis: # 1. Execution flow (orchestrator.py): # - The Orchestrator manages the overall workflow with two retry layers: # (a) Orchestrator-level retries: for unexpected errors (up to 3 attempts, MAX_CREW_RETRIES=2) # (b) Business-logic retries: for validation failures (up to 6 attempts, max_retries=5) # - Ideal flow (no retries): # Step 1: Topic Analysis (TopicAnalyzerCrew) # Step 2-4: Content Generation with Feedback Loop # - Generate the X post (ShakespeareGeneratorCrew) # - Validate the X post (PostReviewCrew) # - If valid=True, end the loop; otherwise retry # Step 5: Save Results # # 2. Agent structure of the three Crews: # - TopicAnalyzerCrew (topic_analyzer/topic_analyzer_crew.py): # - Agent: Topic Analysis Expert # Tools: keyword_extractor, topic_complexity_analyzer # - ShakespeareGeneratorCrew (content_generator/shakespeare_generator_crew.py): # - Agent: Shakespearean Bard # Tools: keyword_extractor, character_counter_tool, emoji_detector_tool, # post_structure_validator, shakespearean_style_detector, tone_analyzer # - PostReviewCrew (post_reviewer/post_review_crew.py): # - Agent: X Post Verifier # Tools: character_counter_tool, emoji_detector_tool, post_structure_validator, # shakespearean_style_detector, tone_analyzer # # 3. SPAN hierarchy: # - shakespeare_x_post_orchestrator (top-level, orchestrator.py line 347) # └─ crew_execution (contains retry logic, orchestrator.py line 362-393) # ├─ topic_analysis (topic analysis, orchestrator.py line 114-141) # └─ content_generation_loop (content generation & review loop, orchestrator.py line 143-250) # includes multiple iterations: # - ShakespeareGeneratorCrew.kickoff (generate X post) # - PostReviewCrew.kickoff (validate X post) # - If valid=True then stop; otherwise continue # # 4. MCP tools (tools/mcp_server.py): # - Character count: character_counter_tool (validate 200-280 chars) # - Emoji detection: emoji_detector_tool (ensure no emoji) # - Structure validation: post_structure_validator (validate 1-3-1 structure, 5 lines) # - Style detection: shakespearean_style_detector (detect Shakespearean style elements) # - Tone analysis: tone_analyzer (ensure satire and humor) # - Keyword extraction: keyword_extractor (extract key topic concepts) # - Complexity analysis: topic_complexity_analyzer (estimate topic complexity) # Project name project_name: "SocialMediaManager-A2A" # Trajectory node types to extract extract_types: - "SPAN" # Multi-level SPAN structure - "Chain" # Each Crew's kickoff chain - "Agent" # Each Crew's Agent - "LLM" # LLM calls - "Tool" # MCP tool calls # Repeatable pattern configuration # SocialMediaManager-A2A has a self-evaluation loop (but the ideal trajectory needs only 1 iteration) repeatable_patterns: [] # ============================================================================ # Reference trajectory (Ground Truth) - derived from code design # ============================================================================ # # Graph view (ideal execution path: no retries, no errors, first generation passes validation): # # [SPAN] shakespeare_x_post_orchestrator # └─ [SPAN] crew_execution # ├─ [SPAN] analyze_topic # │ └─ [SPAN] a2a_call_topic_analyzer # │ └─ [SPAN] topic_analyzer_server_execution # │ └─ [Chain] Crew***.kickoff # │ └─ [AGENT] Topic Analysis Expert # │ ├─ [LLM] * (understand the topic) # │ ├─ [Tool] keyword_extractor (extract keywords) # │ ├─ [LLM] * (analyze keywords) # │ ├─ [Tool] topic_complexity_analyzer (analyze complexity) # │ └─ [LLM] * (produce the topic analysis report) # └─ [SPAN] content_generation_loop # ├─ [SPAN] a2a_call_content_generator # │ └─ [SPAN] content_generator_server_execution # │ └─ [Chain] Crew***.kickoff (ShakespeareGeneratorCrew - 1st generation) # │ └─ [AGENT] Shakespearean Bard # │ ├─ [LLM] * (plan generation strategy) # │ ├─ [Tool] keyword_extractor (understand topic keywords) # │ ├─ [LLM] * (draft the initial X post) # │ ├─ [Tool] character_counter_tool (check character count) # │ ├─ [LLM] * (adjust character count) # │ ├─ [Tool] emoji_detector_tool (check emoji) # │ ├─ [LLM] * (confirm no emoji) # │ ├─ [Tool] post_structure_validator (validate 1-3-1 structure) # │ ├─ [LLM] * (adjust structure) # │ ├─ [Tool] shakespearean_style_detector (check style) # │ ├─ [LLM] * (strengthen style) # │ ├─ [Tool] tone_analyzer (check tone) # │ └─ [LLM] * (final polish and output) # └─ [SPAN] a2a_call_post_reviewer # └─ [SPAN] post_reviewer_server_execution # └─ [Chain] Crew***.kickoff (PostReviewCrew - 1st validation) # └─ [AGENT] X Post Verifier # ├─ [LLM] * (read and perform an initial assessment) # ├─ [Tool] character_counter_tool (validate character count) # ├─ [LLM] * (record character-count check) # ├─ [Tool] emoji_detector_tool (validate no emoji) # ├─ [LLM] * (record emoji check) # ├─ [Tool] post_structure_validator (validate 1-3-1 structure) # ├─ [LLM] * (record structure check) # ├─ [Tool] shakespearean_style_detector (validate style) # ├─ [LLM] * (record style check) # ├─ [Tool] tone_analyzer (validate tone) # └─ [LLM] * (overall decision; output valid=True) # # Design rationale: # - orchestrator.py executes analyze_topic → content_generation_loop in order (via A2A communication) # - TopicAnalyzerCrew has 1 Agent using 2 tools to analyze the topic # - ShakespeareGeneratorCrew has 1 Agent using multiple tools for generation and self-validation # - PostReviewCrew has 1 Agent using multiple tools for comprehensive validation # - Ideal case: first generation passes validation (valid=True), no retries # - Agent roles are defined in each crew's config/agents.yaml # - Tasks are defined in each crew's config/tasks.yaml and explicitly require specific tools # - A2A architecture: the orchestrator calls three independent agent servers via Agent2Agent # - Each agent server runs its Crew via the CrewAI framework # # Ideal-trajectory notes: # - Top-level SPAN: shakespeare_x_post_orchestrator # - Second-level SPAN: crew_execution (contains two-layer retry logic) # - Two child SPANs run in order: analyze_topic → content_generation_loop # - analyze_topic stage: call the topic_analyzer server via A2A; Topic Analyst uses # keyword_extractor and topic_complexity_analyzer # - content_generation_loop stage includes a self-evaluation loop: # - Iteration 1 (ideal case): # * ShakespeareGeneratorCrew generates the X post and self-validates using multiple tools # * PostReviewCrew validates the X post using all validation tools, returning valid=True # - No iterations 2-N needed (because the first one passes) # - This trajectory represents an ideal execution path with no retries and no errors # # Notes: # - "LLM: *" means any LLM model (wildcard match) # - Agent names must exactly match the role fields in agents.yaml # - Tool names must exactly match the tool names provided by each agent server # - Both Generator and Verifier use multiple tools (as required by tasks.yaml) # - Tool-call order may differ slightly depending on agent decisions # - Some LLM calls may be merged or split, but core tool calls must exist # ============================================================================ reference_trajectory: # ===== Top-level SPAN ===== - "SPAN: shakespeare_x_post_orchestrator" # ===== Second-level SPAN (contains two-layer retry logic) ===== - "SPAN: crew_execution" # ===== Stage 1: Analyze Topic (TopicAnalyzerCrew) ===== - "SPAN: analyze_topic" - "SPAN: a2a_call_topic_analyzer" - "SPAN: topic_analyzer_server_execution" - "Chain: Crew***.kickoff" - "Agent: Topic Analysis Expert" - "LLM: *" # Initial understanding of the topic; plan analysis strategy - "Tool: keyword_extractor" # Required by tasks.yaml (no order requirement) - "LLM: *" # Analyze keyword extraction results - "Tool: topic_complexity_analyzer" # Required by tasks.yaml (no order requirement) - "LLM: *" # Synthesize analysis; produce topic insights # ===== Stage 2: Content Generation Loop (Self-Evaluation Loop) ===== - "SPAN: content_generation_loop" # === Iteration 1: Content generation (ShakespeareGeneratorCrew) === - "SPAN: a2a_call_content_generator" - "SPAN: content_generator_server_execution" - "Chain: Crew***.kickoff" - "Agent: Shakespearean Bard" - "LLM: *" # Understand the topic and topic_insights; plan generation strategy - "Tool: keyword_extractor" # MUST be first: tasks.yaml STEP 1 requires "First, use the keyword_extractor tool" - "LLM: *" # Draft the initial X post based on keywords # The following tools are used for validation as required by tasks.yaml (no order requirement): - "Tool: character_counter_tool" # Validate character count (200-280) - "LLM: *" # Adjust based on character-count check - "Tool: emoji_detector_tool" # Ensure no emoji - "LLM: *" # Confirm emoji check passes - "Tool: post_structure_validator" # Validate 1-3-1 structure (5 lines) - "LLM: *" # Adjust based on structure check - "Tool: shakespearean_style_detector" # Check Shakespearean style - "LLM: *" # Strengthen style elements - "Tool: tone_analyzer" # Validate satirical and humorous tone - "LLM: *" # Final polish and output X post # === Iteration 1: Content validation (PostReviewCrew) === - "SPAN: a2a_call_post_reviewer" - "SPAN: post_reviewer_server_execution" - "Chain: Crew***.kickoff" - "Agent: X Post Verifier" - "LLM: *" # Read the X post and plan the validation strategy # The following 5 tools are all required by tasks.yaml (no order requirement): - "Tool: character_counter_tool" # Validate character count (200-280) - "LLM: *" # Analyze character-count check results - "Tool: emoji_detector_tool" # Validate no emoji - "LLM: *" # Analyze emoji check results - "Tool: post_structure_validator" # Validate 1-3-1 structure (5 lines) - "LLM: *" # Analyze structure check results - "Tool: shakespearean_style_detector" # Validate Shakespearean style - "LLM: *" # Analyze style check results - "Tool: tone_analyzer" # Validate satirical and humorous tone - "LLM: *" # Combine checks; output valid=True, feedback="" # Target tool list (used for the single-tool use metric) # Lists all tools filtered for each Agent in this config target_tools: # Topic Analysis stage - "Tool: keyword_extractor" - "Tool: topic_complexity_analyzer" # Content Generation stage - "Tool: character_counter_tool" - "Tool: emoji_detector_tool" - "Tool: post_structure_validator" - "Tool: shakespearean_style_detector" - "Tool: tone_analyzer" # Post Review stage (shares tools with Generation; not duplicated here) # ============================================================================ # Dynamic tool permutation configuration # ============================================================================ # Define permutable tool groups for certain stages to generate reference trajectories # with different tool orders. During evaluation, all permutations are tried and the # best-matching one is selected as the reference. # # Notes: # - Topic Analyst's two tools can be in any order (tasks.yaml does not specify order) # - Shakespearean Bard's keyword_extractor must be first (explicitly required by tasks.yaml), # but the subsequent 5 validation tools can be in any order # - X Post Verifier's 5 validation tools can be in any order (tasks.yaml does not specify order) permutable_tool_groups: # Topic Analysis stage tools can be in any order topic_analysis_tools: - "Tool: keyword_extractor" - "Tool: topic_complexity_analyzer" # Shakespearean Bard validation tools can be in any order # Note: this excludes the first keyword_extractor (it must remain the first tool) shakespearean_bard_validation_tools: - "Tool: character_counter_tool" - "Tool: emoji_detector_tool" - "Tool: post_structure_validator" - "Tool: shakespearean_style_detector" - "Tool: tone_analyzer" # X Post Verifier validation tools can be in any order x_post_verifier_tools: - "Tool: character_counter_tool" - "Tool: emoji_detector_tool" - "Tool: post_structure_validator" - "Tool: shakespearean_style_detector" - "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" # ============================================================================ # Usage # ============================================================================ # # 1. Trajectory hierarchy: # - Level 1: SPAN (shakespeare_x_post_orchestrator) # - Level 2: SPAN (crew_execution) # - Level 3: SPAN (analyze_topic, content_generation_loop) # - Level 4: SPAN (a2a_call_topic_analyzer, a2a_call_content_generator, a2a_call_post_reviewer) # - Level 5: SPAN (topic_analyzer_server_execution, content_generator_server_execution, post_reviewer_server_execution) # - Level 6: Chain (Crew***.kickoff; UUID wildcard handling) # - Level 7: AGENT (exact match for agent role) # - Level 8: LLM (wildcard match for any model) # - Level 8: Tool (exact match for tool name) # # 2. Wildcard handling: # - Crew_.kickoff -> Crew***.kickoff (handled automatically by evaluate_trajectory.py) # - LLM: * -> matches any model name (e.g., gpt-4o-mini, deepseek-reasoner, gemini-2.5-flash, etc.) # - Agent names must match in full (including the full role name) # # 3. Exact-match requirements: # - SPAN names: "shakespeare_x_post_orchestrator", "crew_execution", # "analyze_topic", "content_generation_loop", # "a2a_call_topic_analyzer", "a2a_call_content_generator", "a2a_call_post_reviewer", # "topic_analyzer_server_execution", "content_generator_server_execution", "post_reviewer_server_execution" # - Chain name: "Crew***.kickoff" (wildcard match) # - Agent names: "Agent: Topic Analysis Expert", # "Agent: Shakespearean Bard", # "Agent: X Post Verifier" # (exact match for the role field in agents.yaml) # - LLM name: "LLM: *" (wildcard match for any model) # - Tool names: exact match for tool names (keyword_extractor, topic_complexity_analyzer, # character_counter_tool, emoji_detector_tool, post_structure_validator, # shakespearean_style_detector, tone_analyzer) # # 4. Metric definitions: # - Exact Match: trajectories must be identical (including LLM-call counts and tool-call counts) # - In-order Match: extra calls 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 covered by the prediction # - Single-tool Use: checks whether core tools are used # - 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) # # 5. Run command: # cd /Users/wzr/TOSEM-2025/RESULTS/RQ-Failure_Breakdown/SocialMediaManager-A2A # python3 evaluate_trajectory.py --config reference_trajectory.yaml # # 6. Design notes: # - This reference trajectory represents an ideal execution path (no retries, no errors) # - Two main stages execute in order: Analyze Topic → Content Generation Loop # - Stage 1 (Analyze Topic): Topic Analyst uses 2 tools to analyze the topic # - Stage 2 (Content Generation Loop): includes a self-evaluation loop # * Shakespearean Bard generates the X post and self-validates with multiple tools # * X Post Verifier performs comprehensive validation using all validation tools # * Ideal case: first iteration returns valid=True with no retries # - In-order Match and Recall are often more suitable for evaluating real-world behavior # - The reference trajectory only defines core required steps; extra tool calls and LLM reasoning are allowed # # 7. Differences from actual trajectories: # - Actual trajectories may include business-logic retries (validation failure triggers regeneration) # - Actual trajectories may include orchestrator-level retries (exceptions trigger a full workflow retry) # - The Generator may use a different tool order (but should still use tools required by tasks.yaml) # - The Verifier may use a different tool order (but must use all 5 validation tools) # - Reasoning models (e.g., DeepSeek-R1) may include many additional LLM calls # - Some models (e.g., Gemini) may skip tool calls and generate results directly (not compliant) # - These differences are not always errors, but skipping required tools or failing validation is abnormal # # 8. A2A version characteristics: # - Uses an Agent2Agent (A2A) architecture; the orchestrator calls 3 independent agent servers via A2A # - Each agent server runs a CrewAI Crew with its own toolset # - Includes a multi-level SPAN structure (orchestrator + crew_execution + 2 stage SPANs + A2A comm SPAN + server_execution SPAN) # - Three specialized Crews collaborate to generate a Shakespeare-style X post # - Each Crew has clear responsibilities and an explicit toolset # - TopicAnalyzerCrew: 1 Agent analyzing the topic and extracting insights (via the topic_analyzer server) # - ShakespeareGeneratorCrew: 1 Agent generating and self-validating the X post (via the content_generator server) # - PostReviewCrew: 1 Agent comprehensively validating X post quality (via the post_reviewer server) # - Tool categories: analysis tools (keyword_extractor, topic_complexity_analyzer), # validation tools (character_counter_tool, emoji_detector_tool, post_structure_validator, # shakespearean_style_detector, tone_analyzer) # # 9. Business-logic notes: # - The self-evaluation loop is critical: generation must be followed by validation # - If validation fails (valid=False), regeneration happens with feedback (up to 6 attempts) # - If all attempts fail, the workflow ends and the failing X post is saved # - The ideal trajectory assumes the first generation passes validation (best case) # - The Generator should self-validate using all tools explicitly required by tools.yaml # - The Verifier must use all 5 validation tools for thorough validation # - Some actual trajectories may include multiple generate-validate loops (normal business-logic retries) # # 10. Tool usage requirements (based on tasks.yaml analysis): # - Topic Analyst (analyze_topic task): # * Explicit requirement: "Start by using the available analytical tools" # * Must use: keyword_extractor, topic_complexity_analyzer # * Ordering: none (tasks.yaml does not specify order) # - Shakespearean Bard (write_x_post task): # * Explicit requirement: "STEP 1 - REQUIRED: First, use the keyword_extractor tool" # * Explicit requirement: "IMPORTANT: Before finalizing, use the available tools to verify quality" # * Must use: keyword_extractor (must be the first tool) # * Should use: character_counter_tool, emoji_detector_tool, post_structure_validator, # shakespearean_style_detector, tone_analyzer (quality validation) # * Ordering: keyword_extractor must be first; other validation tools have no order requirement # - X Post Verifier (verify_x_post task): # * Explicit requirement: "CRITICAL: You MUST use the following tools to perform thorough validation" # * Must use: character_counter_tool, emoji_detector_tool, post_structure_validator, # shakespearean_style_detector, tone_analyzer (all 5) # * Requirement: "Base your validation decision on these tool results, not assumptions" # * Ordering: none (tasks.yaml does not specify order) # # 11. Tool-call ordering notes: # - **Scenarios with ordering requirements**: # * Shakespearean Bard's keyword_extractor must be the first tool # (tasks.yaml line 7-8: "STEP 1 - REQUIRED: First, use the keyword_extractor tool") # - **Scenarios without ordering requirements**: # * Topic Analyst's two tools (keyword_extractor, topic_complexity_analyzer) can be in any order # * Shakespearean Bard's 5 validation tools can be in any order (as long as after keyword_extractor) # * X Post Verifier's 5 validation tools can be in any order # - **Order in the reference trajectory**: # * The listed order reflects a common observed pattern # * Evaluation should allow flexibility in tool order (except Bard keyword_extractor must be first) # * The evaluation script should enforce the constraint that keyword_extractor must be first for the Bard # # 12. Dynamic tool permutation optimization: # - **Design idea**: tool calls within each AGENT may appear in different orders # - **Mechanism**: # * permutable_tool_groups defines the tool groups that can be permuted # * the evaluation script generates all possible tool-order combinations (cartesian product) # * for each sample, it selects the permutation with the highest combined score from # exact_match, in_order_match, and any_order_match # - **Number of combinations**: # * topic_analysis_tools: 2! = 2 permutations # * shakespearean_bard_validation_tools: 5! = 120 permutations # * x_post_verifier_tools: 5! = 120 permutations # * total: 2 × 120 × 120 = 28,800 reference trajectories # - **Evaluation strategy**: # * dynamically select the best reference trajectory per test sample # * combined score = exact_match×3 + in_order_match×2 + any_order_match×1 # * more fair across different models' tool-calling strategies # - **Notes**: # * the Bard's first keyword_extractor is not permuted (must remain first) # * permutations are only within the same AGENT and do not cross AGENT boundaries # * tool permutations do not change where/when LLM calls appear or how many there are