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

1. Go to https://logfire.pydantic.dev/ and log in
2. Navigate to your project settings
3. Create a Read Token (this is different from the write token used for authentication)
4. 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

1. Always use both flags: --automation --logfire together
2. Run in background for long tasks to monitor progress
3. Query by trace_id to isolate specific sessions
4. Check parent_span_id to understand hierarchy
5. Use duration to identify bottlenecks
6. Look at attributes for MassGen-specific context
7. 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 draft_approach start
  • Implementation = first draft_approach 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 segmen

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