MassGen Log Analyzer

This skill provides a structured workflow for running MassGen experiments and analyzing the resulting traces and logs using Logfire.

Purpose

The log-analyzer skill helps you:

• Run MassGen experiments with proper instrumentation

• Query and analyze traces hierarchically

• Debug agent behavior and coordination patterns

• Measure performance and identify bottlenecks

• Improve the logging structure itself

• Generate markdown analysis reports saved to the log directory

CLI Quick Reference

The massgen logs CLI provides quick access to log analysis:

List Logs with Analysis Status

uv run massgen logs list # Show all recent logs with analysis status uv run massgen logs list --analyzed # Only logs with ANALYSIS_REPORT.md uv run massgen logs list --unanalyzed # Only logs needing analysis uv run massgen logs list --limit 20 # Show more logs

Generate Analysis Prompt

Run from within your coding CLI (e.g., Claude Code) so it sees output

uv run massgen logs analyze # Analyze latest turn of latest log uv run massgen logs analyze --log-dir PATH # Analyze specific log uv run massgen logs analyze --turn 1 # Analyze specific turn

The prompt output tells your coding CLI to use this skill on the specified log directory.

Multi-Agent Self-Analysis

uv run massgen logs analyze --mode self # Run 3-agent analysis team (prompts if report exists) uv run massgen logs analyze --mode self --force # Overwrite existing report without prompting uv run massgen logs analyze --mode self --turn 2 # Analyze specific turn uv run massgen logs analyze --mode self --config PATH # Use custom config

Self-analysis mode runs MassGen with multiple agents to analyze logs from different perspectives (correctness, efficiency, behavior) and produces a combined ANALYSIS_REPORT.md.

Multi-Turn Sessions

MassGen log directories support multiple turns (coordination sessions). Each turn has its own turn_N/ directory with attempts inside:

log_YYYYMMDD_HHMMSS/ ├── turn_1/ # First coordination session │ ├── ANALYSIS_REPORT.md # Report for turn 1 │ ├── attempt_1/ # First attempt │ └── attempt_2/ # Retry if orchestration restarted ├── turn_2/ # Second coordination session (if multi-turn) │ ├── ANALYSIS_REPORT.md # Report for turn 2 │ └── attempt_1/

When analyzing, the --turn flag specifies which turn to analyze. Without it, the latest turn is analyzed.

When to Use Logfire vs Local Logs

Use Local Log Files When:

• Analyzing command patterns and repetition (commands are in streaming_debug.log )

• Checking detailed tool arguments and outputs (in coordination_events.json )

• Reading vote reasoning and agent decisions (in agent_//vote.json )

• Viewing the coordination flow table (in coordination_table.txt )

• Getting cost/token summaries (in metrics_summary.json )

Use Logfire When:

• You need precise timing data with millisecond accuracy

• Analyzing span hierarchy and parent-child relationships

• Finding exceptions and error stack traces

• Creating shareable trace links for collaboration

• Querying across multiple sessions (e.g., "find all sessions with errors")

• Real-time monitoring of running experiments

Rate Limiting: If Logfire returns a rate limit error, wait up to 60 seconds and retry rather than falling back to local logs. The rate limit resets quickly and Logfire data is worth waiting for when timing/hierarchy analysis is needed.

Key Local Log Files:

File Contains

status.json

Real-time status with agent reliability metrics (enforcement events, buffer loss)

metrics_summary.json

Cost, tokens, tool stats, round history

coordination_events.json

Full event timeline with tool calls

coordination_table.txt

Human-readable coordination flow

streaming_debug.log

Raw streaming data including command strings

agent_//vote.json

Vote reasoning and context

agent_//execution_trace.md

Full tool calls, arguments, results, and reasoning - invaluable for debugging

execution_metadata.yaml

Config and session metadata

Execution Traces (execution_trace.md ): These are the most detailed debug artifacts. Each agent snapshot includes an execution trace with:

• Complete tool calls with full arguments (not truncated)

• Full tool results (not truncated)

• Reasoning/thinking blocks from the model

• Timestamps and round markers

Use execution traces when you need to understand exactly what an agent did and why - they capture everything the agent saw and produced during that answer/vote iteration.

Enforcement Reliability (status.json ): The status.json file includes per-agent reliability metrics that track workflow enforcement events:

{ "agents": { "agent_a": { "reliability": { "enforcement_attempts": [ { "round": 0, "attempt": 1, "max_attempts": 3, "reason": "no_workflow_tool", "tool_calls": ["search", "read_file"], "error_message": "Must use workflow tools", "buffer_preview": "First 500 chars of lost content...", "buffer_chars": 1500, "timestamp": 1736683468.123 } ], "by_round": {"0": {"count": 2, "reasons": ["no_workflow_tool", "invalid_vote_id"]}}, "unknown_tools": ["execute_command"], "workflow_errors": ["invalid_vote_id"], "total_enforcement_retries": 2, "total_buffer_chars_lost": 3000, "outcome": "ok" } } } }

Enforcement Reason Codes:

Reason Description

no_workflow_tool

Agent called tools but none were vote or new_answer

no_tool_calls

Agent provided text-only response, no tools called

invalid_vote_id

Agent voted for non-existent agent ID

vote_no_answers

Agent tried to vote when no answers exist

vote_and_answer

Agent used both vote and new_answer in same response

answer_limit

Agent hit max answer count limit

answer_novelty

Answer too similar to existing answers

answer_duplicate

Exact duplicate of existing answer

api_error

API/streaming error (e.g., "peer closed connection")

connection_recovery

API stream ended early, recovered with preserved context

mcp_disconnected

MCP server disconnected mid-session (e.g., "Server 'X' not connected")

This data is invaluable for understanding why agents needed retries and how much content was lost due to enforcement restarts.

Logfire Setup

Before using this skill, you need to set up Logfire for observability.

Step 1: Install MassGen with Observability Support

pip install "massgen[observability]"

Or with uv

uv pip install "massgen[observability]"

Step 2: Create a Logfire Account

Go to https://logfire.pydantic.dev/ and create a free account.

Step 3: Authenticate with Logfire

This creates ~/.logfire/credentials.json

uv run logfire auth

Or set the token directly as an environment variable

export LOGFIRE_TOKEN=your_token_here

Step 4: Get Your Read Token for the MCP Server

• Go to https://logfire.pydantic.dev/ and log in

• Navigate to your project settings

• Create a Read Token (this is different from the write token used for authentication)

• Copy the token for use in Step 5

Step 5: Add the Logfire MCP Server

claude mcp add logfire -e LOGFIRE_READ_TOKEN="your-read-token-here" -- uvx logfire-mcp@latest

Then restart Claude Code and re-invoke this skill.

Prerequisites

Logfire MCP Server (Optional but Recommended): The Logfire MCP server provides enhanced analysis with precise timing data and cross-session queries. If LOGFIRE_READ_TOKEN is not set, self-analysis mode will automatically disable the Logfire MCP and fall back to local log files only.

When configured, the MCP server provides these tools:

• mcp__logfire__arbitrary_query

• Run SQL queries against logfire data

• mcp__logfire__schema_reference

• Get the database schema

• mcp__logfire__find_exceptions_in_file

• Find exceptions in a file

• mcp__logfire__logfire_link

• Create links to traces in the UI

Required Flags:

• --automation

• Clean output for programmatic parsing -- see massgen-develops-massgen skill for more info on this flag

• --logfire

• Enable Logfire tracing (optional, but required to populate Logfire data)

Part 1: Running MassGen Experiments

Basic Command Format

uv run massgen --automation --logfire --config [config_file] "[question]"

Running in Background (Recommended)

Use run_in_background: true (or however you run tasks in the background) to run experiments asynchronously so you can monitor progress and end early if needed.

Expected Output (first lines):

LOG_DIR: .massgen/massgen_logs/log_YYYYMMDD_HHMMSS_ffffff STATUS: .massgen/massgen_logs/log_YYYYMMDD_HHMMSS_ffffff/turn_1/attempt_1/status.json QUESTION: Your task here [Coordination in progress - monitor status.json for real-time updates]

Parse the LOG_DIR - you'll need this for file-based analysis!

Monitoring Progress

status.json updates every 2 seconds; use that to track progress.

cat [log_dir]/turn_1/attempt_1/status.json

Key fields to monitor:

• coordination.completion_percentage (0-100)

• coordination.phase

• "initial_answer", "enforcement", "presentation"

• results.winner

• null while running, agent_id when complete

• agents[].status

• "waiting", "streaming", "answered", "voted", "error"

• agents[].error

• null if ok, error details if failed

Reading Final Results

After completion (exit code 0):

Read the final answer

cat [log_dir]/turn_1/attempt_1/final/[winner]/answer.txt

Other useful files:

• execution_metadata.yaml

• Full config and execution details

• coordination_events.json

• Complete event log

• coordination_table.txt

• Human-readable coordination summary

Part 2: Querying Logfire

Database Schema

The main table is records with these key columns:

Column Description

span_name

Name of the span (e.g., "agent.agent_a.round_0")

span_id

Unique identifier for this span

parent_span_id

ID of the parent span (null for root)

trace_id

Groups all spans in a single trace

duration

Time in seconds

start_timestamp

When the span started

end_timestamp

When the span ended

attributes

JSON blob with custom attributes

message

Log message

is_exception

Boolean for errors

exception_type

Type of exception if any

exception_message

Exception message

MassGen Span Hierarchy

MassGen creates hierarchical spans:

coordination.session (root) ├── Coordination event: coordination_started ├── agent.agent_a.round_0 │ ├── llm.openrouter.stream │ ├── mcp.filesystem.write_file │ └── Tool execution: mcp__filesystem__write_file ├── agent.agent_b.round_0 ├── Agent answer: agent1.1 ├── agent.agent_a.round_1 (voting round) ├── Agent vote: agent_a -> agent1.1 ├── Coordination event: winner_selected └── agent.agent_a.presentation ├── Winner selected: agent1.1 ├── llm.openrouter.stream └── Final answer from agent_a

Custom Attributes

MassGen spans include these custom attributes (access via attributes->'key' ):

Attribute Description

massgen.agent_id

Agent identifier (agent_a, agent_b)

massgen.iteration

Current iteration number

massgen.round

Round number for this agent

massgen.round_type

"initial_answer", "voting", or "presentation"

massgen.backend

Backend provider name

massgen.num_context_answers

Number of answers in context

massgen.is_winner

True for presentation spans

massgen.outcome

"vote", "answer", or "error" (set after round completes)

massgen.voted_for

Agent ID voted for (only set for votes)

massgen.voted_for_label

Answer label voted for (e.g., "agent1.1", only set for votes)

massgen.answer_label

Answer label assigned (e.g., "agent1.1", only set for answers)

massgen.error_message

Error message (only set when outcome is "error")

massgen.usage.input

Input token count

massgen.usage.output

Output token count

massgen.usage.reasoning

Reasoning token count

massgen.usage.cached_input

Cached input token count

massgen.usage.cost

Estimated cost in USD

Part 3: Common Analysis Queries

1. View Trace Hierarchy

SELECT span_name, span_id, parent_span_id, duration, start_timestamp FROM records WHERE trace_id = '[YOUR_TRACE_ID]' ORDER BY start_timestamp LIMIT 50

2. Find Recent Sessions

SELECT span_name, trace_id, duration, start_timestamp FROM records WHERE span_name = 'coordination.session' ORDER BY start_timestamp DESC LIMIT 10

3. Agent Round Performance

SELECT span_name, duration, attributes->>'massgen.agent_id' as agent_id, attributes->>'massgen.round' as round, attributes->>'massgen.round_type' as round_type FROM records WHERE span_name LIKE 'agent.%' ORDER BY start_timestamp DESC LIMIT 20

4. Tool Call Analysis

SELECT span_name, duration, parent_span_id, start_timestamp FROM records WHERE span_name LIKE 'mcp.%' OR span_name LIKE 'Tool execution:%' ORDER BY start_timestamp DESC LIMIT 30

5. Find Errors

SELECT span_name, exception_type, exception_message, trace_id, start_timestamp FROM records WHERE is_exception = true ORDER BY start_timestamp DESC LIMIT 20

6. LLM Call Performance

SELECT span_name, duration, attributes->>'gen_ai.request.model' as model, start_timestamp FROM records WHERE span_name LIKE 'llm.%' ORDER BY start_timestamp DESC LIMIT 30

7. Full Trace with Hierarchy (Nested View)

SELECT CASE WHEN parent_span_id IS NULL THEN span_name ELSE ' └─ ' || span_name END as hierarchy, duration, span_id, parent_span_id FROM records WHERE trace_id = '[YOUR_TRACE_ID]' ORDER BY start_timestamp

8. Coordination Events Timeline

SELECT span_name, message, duration, start_timestamp FROM records WHERE span_name LIKE 'Coordination event:%' OR span_name LIKE 'Agent answer:%' OR span_name LIKE 'Agent vote:%' OR span_name LIKE 'Winner selected:%' ORDER BY start_timestamp DESC LIMIT 30

Part 4: Analysis Workflow

Step 1: Run Experiment

uv run massgen --automation --logfire --config [config] "[prompt]" 2>&1

Step 2: Find the Trace

Query for recent sessions:

SELECT trace_id, duration, start_timestamp FROM records WHERE span_name = 'coordination.session' ORDER BY start_timestamp DESC LIMIT 5

Step 3: Analyze Hierarchy

Get full trace structure:

SELECT span_name, span_id, parent_span_id, duration FROM records WHERE trace_id = '[trace_id_from_step_2]' ORDER BY start_timestamp

Step 4: Investigate Specific Issues

Slow tool calls:

SELECT span_name, duration, parent_span_id FROM records WHERE trace_id = '[trace_id]' AND span_name LIKE 'mcp.%' ORDER BY duration DESC

Agent comparison:

SELECT attributes->>'massgen.agent_id' as agent, COUNT(*) as rounds, SUM(duration) as total_time, AVG(duration) as avg_round_time FROM records WHERE trace_id = '[trace_id]' AND span_name LIKE 'agent.%' GROUP BY attributes->>'massgen.agent_id'

Step 5: Create Trace Link

Use the MCP tool to create a viewable link:

mcp__logfire__logfire_link(trace_id="[your_trace_id]")

Part 5: Improving the Logging Structure

Current Span Types

Span Pattern Source Description

coordination.session

coordination_tracker.py Root session span

agent.{id}.round_{n}

orchestrator.py Agent execution round

agent.{id}.presentation

orchestrator.py Winner's final presentation

mcp.{server}.{tool}

mcp_tools/client.py MCP tool execution

llm.{provider}.stream

backends LLM streaming call

Tool execution: {name}

base_with_custom_tool.py Tool wrapper

Coordination event: *

coordination_tracker.py Coordination events

Agent answer: {label}

coordination_tracker.py Answer submission

Agent vote: {from} -> {to}

coordination_tracker.py Vote cast

Adding New Spans

Use the tracer from structured_logging:

from massgen.structured_logging import get_tracer

tracer = get_tracer() with tracer.span("my_operation", attributes={ "massgen.custom_key": "value", }): do_work()

Context Propagation Notes

Known limitation: When multiple agents run concurrently via asyncio.create_task , child spans may not nest correctly under agent round spans. This is an OpenTelemetry context propagation issue with concurrent async code. The presentation phase works correctly because only one agent runs.

Workaround: For accurate nesting in concurrent scenarios, explicit context passing with contextvars.copy_context() would be needed.

Logfire Documentation Reference

Main Documentation: https://logfire.pydantic.dev/docs/

Key Pages to Know

Topic URL Description

Getting Started /docs/

Overview, setup, and core concepts

Manual Tracing /docs/guides/onboarding-checklist/add-manual-tracing/

Creating spans, adding attributes

SQL Explorer /docs/guides/web-ui/explore/

Writing SQL queries in the UI

Live View /docs/guides/web-ui/live/

Real-time trace monitoring

Query API /docs/how-to-guides/query-api/

Programmatic access to data

OpenAI Integration /docs/integrations/llms/openai/

LLM call instrumentation

Logfire Concepts

Spans vs Logs:

• Spans represent operations with measurable duration (use with logfire.span(): )

• Logs capture point-in-time events (use logfire.info() , logfire.error() , etc.)

• Spans and logs inside a span block become children of that span

Span Names vs Messages:

• span_name = the first argument (used for filtering, keep low-cardinality)

• message = formatted result with attribute values interpolated

• Example: logfire.info('Hello {name}', name='Alice') → span_name="Hello {name}", message="Hello Alice"

Attributes:

• Keyword arguments become structured JSON attributes

• Access in SQL via attributes->>'key' or attributes->'key'

• Cast when needed: (attributes->'cost')::float

Live View Features

The Logfire Live View UI (https://logfire.pydantic.dev/) provides:

• Real-time streaming of traces as they arrive

• SQL search pane (press / to open) with auto-complete

• Natural language to SQL - describe what you want and get a query

• Timeline histogram showing span counts over time

• Trace details panel with attributes, exceptions, and OpenTelemetry data

• Cross-linking between SQL results and trace view via trace_id/span_id

SQL Explorer Tips

The Explore page uses Apache DataFusion SQL syntax (similar to Postgres):

-- Subqueries and CTEs work WITH recent AS ( SELECT * FROM records WHERE start_timestamp > now() - interval '1 hour' ) SELECT * FROM recent WHERE is_exception;

-- Access nested JSON SELECT attributes->>'massgen.agent_id' as agent FROM records;

-- Cast JSON values SELECT (attributes->'token_count')::int as tokens FROM records;

-- Time filtering is efficient WHERE start_timestamp > now() - interval '30 minutes'

LLM Instrumentation

Logfire auto-instruments OpenAI calls when configured:

• Captures conversation display, token usage, response metadata

• Creates separate spans for streaming requests vs responses

• Works with both sync and async clients

MassGen's backends use this for llm.{provider}.stream spans.

Reference Documentation

Logfire:

• Main docs: https://logfire.pydantic.dev/docs/

• Live View: https://logfire.pydantic.dev/docs/guides/web-ui/live/

• SQL Explorer: https://logfire.pydantic.dev/docs/guides/web-ui/explore/

• Query API: https://logfire.pydantic.dev/docs/how-to-guides/query-api/

• Manual tracing: https://logfire.pydantic.dev/docs/guides/onboarding-checklist/add-manual-tracing/

• OpenAI integration: https://logfire.pydantic.dev/docs/integrations/llms/openai/

• Schema reference: Use mcp__logfire__schema_reference tool

MassGen:

• Automation mode: AI_USAGE.md

• Status file reference: docs/source/reference/status_file.rst

• Structured logging: massgen/structured_logging.py

Tips for Effective Analysis

• Always use both flags: --automation --logfire together

• Run in background for long tasks to monitor progress

• Query by trace_id to isolate specific sessions

• Check parent_span_id to understand hierarchy

• Use duration to identify bottlenecks

• Look at attributes for MassGen-specific context

• Create trace links to share findings with team

Note that you may get an error like so:

Error: Error executing tool arbitrary_query: b'{"detail":"Rate limit exceeded for organization xxx: per minute limit reached."}'

In this case, please sleep (for up to a minute) and try again.

Part 6: Comprehensive Log Analysis Report

When asked to analyze a MassGen log run, generate a markdown report saved to [log_dir]/turn_N/ANALYSIS_REPORT.md where N is the turn being analyzed. Each turn (coordination session) gets its own analysis report as a sibling to the attempt directories. The report must cover the Standard Analysis Questions below.

Important: Ground Truth and Correctness

CRITICAL: Do not assume any agent's answer is "correct" unless the user explicitly provides ground truth.

• Report what each agent claimed/produced without asserting correctness

• Note when agents agree or disagree, but don't claim agreement = correctness

• If agents produce different answers, present both neutrally

• Only mark answers as correct/incorrect if user provides the actual answer

• Phrases to avoid: "correctly identified", "got the right answer", "solved correctly"

• Phrases to use: "claimed", "produced", "submitted", "arrived at"

Standard Analysis Questions

Every analysis report MUST answer these questions:

1. Correctness

• Did coordination complete successfully?

• Did all agents submit answers?

• Did voting occur correctly?

• Was a winner selected and did they provide a final answer?

2. Efficiency & Bottlenecks

• What was the total duration and breakdown by phase?

• What were the slowest operations?

• Which tools took the most time?

• Build a per-round timing table sorted by agent with:

• Round start/end timestamps

• Total round time

• Tool wall time

• Non-tool time (model/streaming/waiting)

• Split each checklist-containing round into:

• Evaluation = round start -> first submit_checklist start

• Checklist->Propose = first submit_checklist end -> first propose_improvements start

• Implementation = first propose_improvements end -> round end

• Post-checklist (no propose) = first submit_checklist end -> round end (if no propose happened)

• Report percentages for evaluation vs implementation:

• Across all checklist-containing segments

• Across only segments that actually include propose_improvements

3. Command Pattern Analysis

• Were there frequently repeated commands that could be avoided? (e.g., openskills read , npm install , ls -R )

• What commands produced unnecessarily long output? (e.g., skill docs, directory listings)

• What were the slowest execute_command patterns? (e.g., web scraping, package installs)

4. Work Duplication Analysis

• Was expensive work (like image generation) unnecessarily redone?

• Did both agents generate similar/identical assets?

• Were assets regenerated after restarts instead of being reused?

• Could caching or sharing have saved time/cost?

5. Agent Behavior & Decision Making

• How did agents evaluate previous answers? What reasoning did they provide?

• How did agents decide between voting vs providing a new answer?

• Did agents genuinely build upon each other's work or work in isolation?

• Were there timeouts or incomplete rounds?

6. Cost & Token Analysis

• Total cost and breakdown by agent

• Token usage (input, output, reasoning, cached)

• Cache hit rate

7. Errors & Issues

• Any exceptions or failures?

• Any timeouts?

• Any agent errors?

8. Agent Reasoning & Behavior Analysis (CRITICAL)

This is the most important section. Analyzing how agents think and act reveals root causes of successes and failures.

Data Sources:

• agent_outputs/agent_*.txt

• Full output including reasoning (if available)

• agent_//execution_trace.md

• Complete tool calls with arguments and results

• streaming_debug.log

• Raw streaming chunks

Note: Some models don't emit explicit reasoning traces. For these, analyze tool call patterns and content instead - the sequence of actions still reveals decision-making.

For EACH agent, analyze:

• Strategy - What approach did they take? (from reasoning OR tool sequence)

• Tool Responses - How did they handle successes/failures/inconsistencies?

• Error Recovery - Did they detect problems? Implement workarounds?

• Decision Quality - Logical errors? Over/under-verification? Analysis paralysis?

• Cross-Agent Comparison - Which had best reasoning? What patterns led to success?

Key Patterns:

Pattern Good Sign Bad Sign

Failure detection Pivots after 2-3 failures Repeats broken approach 6+ times

Result validation Cross-validates outputs Accepts first result blindly

Inconsistency handling Investigates conflicts Ignores contradictions

Workarounds Creative alternatives when stuck Gives up or loops

Time management Commits when confident Endless verification, no answer

Extract Key Evidence: For each agent, include 2-3 quotes (if reasoning available) OR describe key tool sequences that illustrate their decision quality.

9. Tool Reliability Analysis

Analyze tool behavior patterns beyond simple error listing:

• Consistency - Same input, same output? Document variance.

• False Positives/Negatives - Tools reporting wrong success/failure status?

• Root Cause Hypotheses - For each failure pattern, propose likely causes (path issues, rate limits, model limitations, etc.)

10. Enforcement & Workflow Reliability Analysis

Data Source: status.json → agents[].reliability

Check if agents needed retries due to workflow violations. Key metrics:

• total_enforcement_retries

• How many times agent was forced to retry

• total_buffer_chars_lost

• Content discarded due to restarts

• unknown_tools

• Hallucinated tool names

• by_round

• Which rounds had issues

Red Flags: >=2 retries per round, >5000 chars lost, populated unknown_tools list.

See "Enforcement Reliability" in the Key Local Log Files section for the full schema and reason codes.

11. Answer Quality Progression

Data Sources: agent_outputs/agent_.txt , agent_//execution_trace.md , agent_/*/vote.json

For each agent, trace how answer quality evolved across rounds:

• What specifically changed between answer N and answer N+1?

• Were changes substantive (new approach, novel feature, structural improvement) or cosmetic (minor wording, small style tweak)?

• Did any agents regress — produce a worse answer after seeing others?

• Which evaluation criteria improved per round, and which stagnated?

Score each round-over-round transition:

• 🟢 Substantive improvement — meaningfully better on at least one important dimension

• 🟡 Marginal improvement — polished but no new capability or insight

• 🔴 No improvement / regression — same or worse despite the cost

12. Improvement Proposal Quality

Data Sources: agent_outputs/agent_.txt (look for gap analysis, diagnostic sections, improvement plans), agent_/*/vote.json

Before each new answer, agents (or the coordination system) propose improvements. Analyze each:

• Specificity: Did the proposal identify why something fails, or just that it fails? ("Animation timing misaligned with 5-second viewing window" vs "improve animation")

• Actionability: Could a downstream agent implement the proposal without additional reasoning?

• Accuracy: Did the proposal correctly diagnose the root problem, or address symptoms?

• Completeness: Did it cover all the key gaps, or miss the decisive differentiator?

Rate each proposal:

Proposal Specific? Actionable? Accurate? Was it the right call?

[proposal summary] Yes/No Yes/No Yes/No Yes/No — [why]

Also note: were improvements proposed by the agent itself (self-reflection), by peer evaluation, or inferred from evaluation criteria gaps?

13. Round ROI ("Was It Worth It?")

Data Sources: metrics_summary.json , coordination_events.json , agent outputs

For each round, evaluate return on investment:

• Cost: tokens × model rate for this round

• Time: wall-clock seconds for this round

• Quality delta: what materially changed vs the prior best answer

• Verdict: Was the round worth its cost and time?

Round Agent Cost Time Quality Delta Verdict

R0 agent_a $X Xs Baseline —

R1 agent_a $X Xs [what changed] 🟢/🟡/🔴

Diminishing returns detection: At what round did additional iterations stop producing meaningful improvement? Was there a clear stopping point the system missed?

Also flag: did any agent spend expensive rounds just verifying/validating rather than improving?

14. Delegation & Model Allocation Analysis

Data Sources: agent_outputs/agent_*.txt , execution_trace.md , metrics_summary.json

Analyze what fraction of each agent's work was mechanical vs required genuine intelligence, and whether the right model tier was used.

Classify each agent's work per round:

Mechanical (predictable, templated, a weaker model could do it — AND consumes enough context to be worth delegating):

• Structural validation (element counts, dimension checks, format compliance) — especially when it generates lots of shell output

• Boilerplate generation (loops, coordinate math, template filling) — large repetitive code blocks

• Binary checklist scoring (E1–E4 style: does feature X exist?) — when it involves many tool calls or long output

• Evidence collection (running shell commands, parsing output, pixel diffs) — high token cost for low reasoning content

Note: protocol steps like task status updates and workflow tool calls are not worth delegating — they're low-context and the main model handles them cheaply inline. The question is whether delegating saves meaningful tokens, not just whether a weaker model could theoretically do it.

Quality / Intelligence (requires genuine reasoning, synthesis, or creativity):

• Root cause diagnosis (connecting symptom to underlying design flaw)

• Cross-agent synthesis (identifying best elements from N answers and planning a hybrid)

• Novel approach invention (feature or design not present in any prior answer)

• Pedagogical or domain reasoning (e.g., "this animation timing misses the 5s attention window")

• Tradeoff analysis (why one design choice beats another for a specific goal)

For each agent/round, estimate:

• % mechanical vs % quality reasoning

• Which specific activities were mechanical vs intelligence-requiring

• Quote passages: mechanical template-filling vs genuine diagnostic insight

Overall assessment:

• What fraction of total context (tokens) across the run was mechanical?

• Could a cheaper model (e.g., Haiku) have handled the mechanical fraction without degrading output quality?

• Were any quality-critical moments handled by a weaker subagent (or should they have been escalated)?

• Estimated cost savings if mechanical work had been routed to a cheaper model tier

Data Sources for Each Question

Question Primary Source Secondary Source

Correctness coordination_events.json , coordination_table.txt

Logfire coordination events

Efficiency metrics_summary.json

Logfire duration queries

Command patterns streaming_debug.log (grep for "command": )

Work duplication streaming_debug.log (grep for tool prompts/args) metrics_summary.json tool counts

Agent decisions agent_//vote.json , coordination_events.json

Logfire vote spans

Eval vs implementation timing status.json , metrics_events.json

events.jsonl , Logfire span durations

Cost/tokens metrics_summary.json

Logfire usage attributes

Errors coordination_events.json , metrics_summary.json

Logfire is_exception=true

Enforcement status.json → agents[].reliability

Analysis Commands

Find repeated commands:

grep -o '"command": "[^"]*"' streaming_debug.log | sed 's/"command": "//;s/"$//' | sort | uniq -c | sort -rn | head -30

Find generate_media prompts (to check for duplication):

grep -o '"prompts": [.*]' streaming_debug.log

Check vote reasoning:

cat agent_//vote.json | jq '.reason'

Find timeout events:

cat coordination_events.json | jq '.events[] | select(.event_type == "agent_timeout")'

Compute per-round evaluation vs implementation timing (sorted by agent):

python - <<'PY' import json from collections import defaultdict from pathlib import Path

base = Path(".") status = json.loads((base / "status.json").read_text()) metrics = json.loads((base / "metrics_events.json").read_text())

def m(seconds): return seconds / 60.0

def union(intervals): if not intervals: return 0.0 intervals = sorted(intervals) cs, ce = intervals[0] total = 0.0 for s, e in intervals[1:]: if s <= ce: ce = max(ce, e) else: total += ce - cs cs, ce = s, e total += ce - cs return total

segments = [] for agent_id, agent_data in status["agents"].items(): rounds = sorted(agent_data.get("round_history", []), key=lambda r: r["start_time"]) for idx, r in enumerate(rounds, 1): segments.append({ "agent": agent_id, "seg_idx": idx, "round_number": r["round_number"], "round_type": r["round_type"], "outcome": r["outcome"], "start": float(r["start_time"]), "end": float(r["end_time"]), "duration": float(r["duration_ms"]) / 1000.0, })

by_segment = defaultdict(list) for t in metrics["tool_executions"]: ts = float(t["start_time"]) te = float(t["end_time"]) candidates = [ s for s in segments if s["agent"] == t["agent_id"] and not (te < s["start"] or ts > s["end"]) ] if not candidates: continue best = max( candidates, key=lambda s: max(0.0, min(te, s["end"]) - max(ts, s["start"])), ) by_segment[(best["agent"], best["seg_idx"])].append(t)

rows = [] for s in sorted(segments, key=lambda x: (x["agent"], x["start"])): calls = sorted(by_segment.get((s["agent"], s["seg_idx"]), []), key=lambda t: t["start_time"]) submit = [t for t in calls if t["tool_name"] == "mcp__massgen_checklist__submit_checklist"] propose = [t for t in calls if t["tool_name"] == "mcp__massgen_checklist__propose_improvements"]

# round-level tool wall time
ints = []
for t in calls:
    ts, te = float(t["start_time"]), float(t["end_time"])
    ov = max(0.0, min(te, s["end"]) - max(ts, s["start"]))
    if ov > 0:
        ints.append((max(ts, s["start"]), min(te, s["end"])))
tool_wall = union(ints)
non_tool = max(0.0, s["duration"] - tool_wall)

eval_pre = mid = impl_post = post_no_propose = 0.0
first_submit = submit[0] if submit else None
first_propose = propose[0] if propose else None

if first_submit:
    eval_pre = max(0.0, min(s["end"], float(first_submit["start_time"])) - s["start"])
elif first_propose:
    eval_pre = max(0.0, min(s["end"], float(first_propose["start_time"])) - s["start"])

if first_submit and first_propose:
    mid = max(0.0, min(s["end"], float(first_propose["start_time"])) - float(first_submit["end_time"]))
    impl_post = max(0.0, s["end"] - float(first_propose["end_time"]))
elif first_submit and not first_propose:
    post_no_propose = max(0.0, s["end"] - float(first_submit["end_time"]))
elif first_propose and not first_submit:
    impl_post = max(0.0, s["end"] - float(first_propose["end_time"]))

rows.append({
    **s,
    "tool_wall": tool_wall,
    "non_tool": non_tool,
    "submit_count": len(submit),
    "propose_count": len(propose),
    "eval_pre": eval_pre,
    "mid": mid,
    "impl_post": impl_post,
    "post_no_propose": post_no_propose,
})

print("| Agent | Segment | Round | Type | Outcome | Total (min) | Tool wall (min) | Non-tool (min) | Eval pre (min) | Checklist->Propose (min) | Improve post-propose (min) | Post-checklist no-propose (min) |") print("|---|---:|---:|---|---|---:|---:|---:|---:|---:|---:|---:|") for r in rows: print( f"| {r['agent']} | {r['seg_idx']} | {r['round_number']} | {r['round_type']} | {r['outcome']} | " f"{m(r['duration']):.2f} | {m(r['tool_wall']):.2f} | {m(r['non_tool']):.2f} | " f"{m(r['eval_pre']):.2f} | {m(r['mid']):.2f} | {m(r['impl_post']):.2f} | {m(r['post_no_propose']):.2f} |" ) PY

Report Template

Save this report to [log_dir]/turn_N/ANALYSIS_REPORT.md (where N is the turn number being analyzed):

MassGen Log Analysis Report

Session: [log_dir name] Trace ID: [trace_id if available] Generated: [timestamp] Logfire Link: [link if available]

Executive Summary

[2-3 sentence summary of the run: what was the task, did it succeed, key findings]

Session Overview

1. Correctness Analysis

Coordination Flow

[Timeline of key events]

Status

• All phases completed
• All agents submitted answers
• Voting completed correctly
• Winner selected
• Final answer delivered

Issues Found

[List any correctness issues]

2. Efficiency Analysis

Phase Duration Breakdown

Per-Round Timing (Sorted by Agent)

Evaluation vs Implementation Timing (Checklist-Gated Phases)

Phase definitions used in this report:

• Evaluation = round start -> first submit_checklist start
• Checklist->Propose = first submit_checklist end -> first propose_improvements start
• Implementation = first propose_improvements end -> round end
• Post-checklist (no propose) = first submit_checklist end -> round end (if no propose happened)

Per-Round Evaluation vs Improvement Table

Top Bottlenecks

1. [Operation] - X seconds (X% of total)
2. [Operation] - X seconds
3. [Operation] - X seconds

3. Command Pattern Analysis

Frequently Repeated Commands

Commands with Excessive Output

Slowest Command Patterns

4. Work Duplication Analysis

Duplicated Work Found

Specific Examples

[List specific examples of duplicated work with prompts/commands]

Recommendations

1. [Specific recommendation to avoid duplication]
2. [Specific recommendation]

5. Agent Behavior Analysis

Answer Progression

Voting Analysis

Vote vs New Answer Decisions

[Explain how agents decided whether to vote or provide new answers]

Agent Collaboration Quality

• Did agents read each other's answers? [Yes/No with evidence]
• Did agents build upon previous work? [Yes/No with evidence]
• Did agents provide genuine evaluation? [Yes/No with evidence]

Timeouts/Incomplete Rounds

[List any timeouts with context]

6. Cost & Token Analysis

Cost Breakdown

Cache Efficiency

• Cached input tokens: X (X% cache hit rate)

Tool Cost Impact

7. Errors & Issues

Exceptions

[List any exceptions with type and message]

Failed Tool Calls

[List any failed tools]

Agent Errors

[List any agent-level errors]

Timeouts

[List any timeouts with duration and context]

8. Recommendations

High Priority

1. [Issue]: [Specific actionable recommendation]
2. [Issue]: [Specific actionable recommendation]

Medium Priority

1. [Issue]: [Recommendation]

Low Priority / Future Improvements

1. [Issue]: [Recommendation]

9. Suggested Linear Issues

Based on the analysis, the following issues are suggested for tracking. If you have access to the Linear project and the session is interactive, present these to the user for approval before creating. Regardless of access, you should write them in a section as below, as we want to learn from the logs to propose and later solve concrete issues:

After user approval, create issues in Linear with:

• Project: MassGen
• Label: log-analysis (to identify issues from log analysis)
• Additional labels as appropriate (e.g., performance, agent-behavior, tooling)

Appendix

Configuration Used

[Key config settings from execution_metadata.yaml]

Files Generated

[List of output files in the workspace]

Workflow for Generating Report

• Read local files first (metrics_summary.json, coordination_table.txt, coordination_events.json, status.json, metrics_events.json)

• Build per-round timing table sorted by agent, including total/tool/non-tool time

• Compute checklist phase windows (pre_checklist , checklist_to_propose , post_propose , post_checklist_no_propose ) and evaluation vs implementation percentages

• For large logs, parallelize workflow analysis: assign one subagent per agent/workflow lane and merge their timing tables

• Query Logfire for trace_id and timing data (if available; wait and retry on rate limits)

• Analyze streaming_debug.log for command patterns

• Check vote.json files for agent reasoning

• Generate the report using the template

• Save to [log_dir]/turn_N/ANALYSIS_REPORT.md (N = turn number being analyzed)

• Print summary to the user

• Suggest Linear issues based on findings - present to user for approval, if session is interactive

• Create approved issues in Linear with log-analysis label

Part 7: Quick Reference - SQL Queries

Correctness Queries

-- Check coordination flow SELECT span_name, start_timestamp, duration FROM records WHERE trace_id = '[TRACE_ID]' AND (span_name LIKE 'Coordination event:%' OR span_name LIKE 'Agent answer:%' OR span_name LIKE 'Agent vote:%' OR span_name LIKE 'Winner selected:%' OR span_name LIKE 'Final answer%') ORDER BY start_timestamp

Efficiency Queries

-- Phase duration breakdown SELECT CASE WHEN span_name LIKE 'agent.%.round_0' THEN 'initial_answer' WHEN span_name LIKE 'agent.%.round_%' THEN 'voting' WHEN span_name LIKE 'agent.%.presentation' THEN 'presentation' ELSE 'other' END as phase, COUNT(*) as count, ROUND(AVG(duration)::numeric, 2) as avg_duration_sec, ROUND(MAX(duration)::numeric, 2) as max_duration_sec, ROUND(SUM(duration)::numeric, 2) as total_duration_sec FROM records WHERE trace_id = '[TRACE_ID]' AND span_name LIKE 'agent.%' GROUP BY 1 ORDER BY total_duration_sec DESC

Error Queries

-- Find all exceptions SELECT span_name, exception_type, exception_message, start_timestamp FROM records WHERE trace_id = '[TRACE_ID]' AND is_exception = true ORDER BY start_timestamp

Cost Queries

-- Token usage by agent SELECT attributes->>'massgen.agent_id' as agent, SUM((attributes->'massgen.usage.input')::int) as total_input_tokens, SUM((attributes->'massgen.usage.output')::int) as total_output_tokens, SUM((attributes->'massgen.usage.cost')::float) as total_cost_usd FROM records WHERE trace_id = '[TRACE_ID]' AND span_name LIKE 'agent.%' AND attributes->>'massgen.usage.input' IS NOT NULL GROUP BY attributes->>'massgen.agent_id'