copilot-session-usage

Extract VS Code Copilot session cost KPIs (tokens, estimated USD, model, duration) from local debug logs.

Full documentation: copilot-session-usage.readthedocs.io

Chronicle and copilot-session-usage

GitHub Copilot provides Chronicle session tools for conversational access to your Copilot history. Chronicle and this project are complementary, but they answer different questions.

What Chronicle is good at

Use Chronicle when you want to:

• Find a past session by its title or session ID.
• Search or summarize what happened in a session.
• Inspect conversational history, checkpoints, referenced files, and session metadata.
• Get a quick, conversational summary of aggregate session KPIs when cost analysis is available in the current Copilot environment.

What Chronicle is not designed to provide

Chronicle's session store is not a token-accounting database. Its stored session records contain history and metadata, but do not expose a standard set of per-request pricing fields. A Chronicle cost answer may therefore provide an aggregate estimate and a list of models without providing a reproducible split of tokens and dollars by model or subagent.

What this project adds

copilot-session-usage reads the original VS Code Copilot debug logs and turns them into repeatable reports. It provides:

Need	Chronicle	copilot-session-usage
Conversational search and summaries	✅	✅ CLI reports are interpreted by your agent
Session discovery by title or ID	✅	✅
Aggregate tokens	✅	✅
Aggregate estimated cost	⚠️ When available; aggregate only	✅
Tokens per model	⚠️ LLM digging each time; consumes tokens	✅
Estimated cost ($) per model	❌ No accurate cost per breakdown	✅
Tokens per subagent	⚠️ LLM digging each time; consumes tokens	✅
Estimated cost ($) per subagent	❌ No accurate cost per breakdown	✅
Cost attribution to skills	❌ Cannot provide cost per breakdown	✅
Tool-call counts by skill and subagent	⚠️ LLM digging each time	✅
Batch analysis, filtering, and aggregation	Limited/conversational	✅
Stable JSON, table, and detailed output	No stable contract	✅
Python API for embedding in third-party tools	❌	✅
Pricing provenance and custom model rates	⚠️ Only for aggregated costs	✅
Git commit cost trailers	❌	✅

In the table, ⚠️ means that Chronicle may answer the question in a particular environment or with additional analysis, but does not guarantee a stable, reproducible breakdown for it.

Use Chronicle for “What did I do?” and use this project for “How much did it cost, which model or subagent consumed it, and can I export the evidence?”

Installation

uv tool install copilot-session-usage

Quick start

# Analyze the most recent session
copilot-session-usage latest

# Analyze a specific session by its debug-log directory
copilot-session-usage analyze /path/to/session/debug-logs

# List recent sessions (metadata only)
copilot-session-usage list

# Batch analyze the last 10 sessions
copilot-session-usage batch 10

# Aggregate cost across all sessions matching a PRD path
copilot-session-usage analyze --name "PRD: /path/to/prd" --aggregate --format table

# List sessions in a debug-logs folder with cost columns
copilot-session-usage list --dir /path/to/debug-logs --format table

Features

• Token-level cost estimation — per-model pricing with cache-hit discounts
• Multi-model sessions — correctly handles sessions that call multiple models (e.g. Claude + Kimi)
• Threshold-aware pricing — long-context tier switching (e.g. GPT-5.4 > 272k tokens)
• Subagent cost attribution — tracks runSubagent calls and their token usage
• Cross-platform — macOS, Linux, Windows, WSL2
• Three output formats — json (default), table, detailed
• Three detail levels — minimal, compact, full
• JSON and table output — machine-readable or human-friendly
• Session filtering — regex match by name, date-range filtering
• Aggregation — roll up costs across many sessions in one command
• Skill-aware cost attribution — detect skills, attribute LLM and tool calls to the active skill
• Skill cost breakdown — per-skill token counts and estimated cost
• Tool-call attribution — per-skill/per-subagent tool-call counts
• Title filtering — find sessions by title substring
• Efficiency summaries — cache ratio, model split, cost per 1M tokens
• Field extraction — pull specific values with --query

How it works

copilot-session-usage reads VS Code Copilot debug logs stored in ~/Library/Application Support/Code/User/workspaceStorage/ (macOS), %APPDATA%\Code\User\workspaceStorage\ (Windows), or ~/.config/Code/User/workspaceStorage/ (Linux).

Each session directory contains a GitHub.copilot-chat/debug-logs/ folder with JSONL files. The tool parses these files, extracts token counts per model, applies per-model pricing (including cache-hit discounts and long-context tier switching), and estimates the session cost in USD.

Subagent calls (runSubagent) are tracked separately so you can see how much token usage was delegated to helper agents.

Knowledge base

This project includes an OKF knowledge bundle in knowledge/ with structured guidelines for contributors. Validate it with:

just knowledge-validate

Usage

Commands

Command	Description
analyze [PATH]	Analyze one session by PATH, or many by --name regex
latest	Analyze the most recently modified session
find TITLE	Find and analyze a session by title (case-insensitive substring match)
id SESSION_ID	Analyze a session by exact UUID
list	List recent sessions (metadata only by default)
batch N	Analyze the N most recent sessions in one pass
skills	List skills used across sessions with aggregated cost

Analysis options

Option	Description
--name REGEX	Filter sessions by title/ID regex (case-insensitive)
--title SUBSTRING	Filter sessions by title substring (case-insensitive)
--since DATE	Only sessions created after DATE (ISO 8601 with timezone)
--until DATE	Only sessions created before DATE (ISO 8601 with timezone)
--workspace PATH	Only sessions from this workspace folder
--aggregate	Aggregate all matching sessions into one summary
--summary	Output a cost-efficiency summary
--skill-breakdown	Emit a per-skill cost breakdown
--tool-breakdown	Emit a per-skill/per-subagent tool-call count breakdown
--skill NAME	Filter the report to a single skill
--query PATH	Extract a single field with dot notation
--query-help	Print all --query field paths

Global options

Option	Description
--workspace-storage PATH	Override workspaceStorage directory (auto-detected by default)
--agent {vscode,cli}	Provider to use (cli not yet implemented)
--detail {minimal,compact,full}	Detail level (default: compact)
--format {json,table,detailed}	Output format (default: json)
--output PATH	Write output to file instead of stdout

Examples

# Full detail for the latest session
$ copilot-session-usage latest --detail full
{
  "session_id": "f5cbde8a-ec40-466f-86e6-f95c343b6c58",
  "session_dir": "/Users/az02065/Library/Application Support/Code/User/workspaceStorage/c016ff4fabbe9f918719a00c9c741058/GitHub.copilot-chat/debug-logs/f5cbde8a-ec40-466f-86e6-f95c343b6c58",
  ...
}

# JSON output for a specific session
$ copilot-session-usage analyze /path/to/debug-logs --format json --output report.json

# Find sessions containing "implem" in the title
$ copilot-session-usage find "implem"
Multiple sessions match 'implem':
  2026-07-01T21:15:12Z  'Implement copilot-session-usage spec'  (id: c890dd60-43d6-44f0-b57c-ab505dfa003b)
  2026-06-26T18:21:21Z  'Resume PRD implementation'  (id: 9368ab3e-1c93-4125-8271-d5bd024b057a)
  2026-06-26T09:19:52Z  'Resume Workflow PRD implementation'  (id: 1214eb3f-add0-41a5-84d4-88720218e60e)
...

# Get summary for a given session (found by `find`)
$ copilot-session-usage id 19e03be0-9cfa-4f21-a19a-4bdb754b3965 --format table
Session:   19e03be0-9cfa-4f21-a19a-4bdb754b3965
Title:     Implementation of new feature X
Started:   2026-07-01T20:37:34Z
Duration:  40588s  (active: 1083s)
Models:    claude-sonnet-4.6, claude-haiku-4.5, Kimi-K2.6-azure
Input:     1,425,790 tokens
Output:    22,166 tokens
Cached:    1,224,340 (86%)
LLM calls: 28
Est. cost: $1.0880
# Per-skill cost breakdown
$ copilot-session-usage id 19e03be0-9cfa-4f21-a19a-4bdb754b3965 --skill-breakdown --format table
Per-Skill Breakdown:
  Skill                              Input      Cached  Output  Calls     Cost
  ----------------------------------------------------------------------------
  /compendium-generic get-session-costs  1,137,864  1,015,825  15,729     24  $0.3636

# Per-skill/per-subagent tool-call counts
$ copilot-session-usage id 19e03be0-9cfa-4f21-a19a-4bdb754b3965 --tool-breakdown --format table
Tool Breakdown:
  Tool                          Calls  Skill                         Subagent
  ---------------------------------------------------------------------------
  read_file                        25  /compendium-generic get-session-costs  main
  vscode_askQuestions               3  /compendium-generic get-session-costs  main
  runSubagent                       1  /compendium-generic get-session-costs  main

# Concise skill cost (great for scripts)
$ copilot-session-usage id 19e03be0-9cfa-4f21-a19a-4bdb754b3965 \
    --skill "/compendium-generic get-session-costs" \
    --format json --detail minimal
{
  "skill": "/compendium-generic get-session-costs",
  "cost_usd": 0.3636,
  "input_tokens": 1137864,
  "output_tokens": 15729,
  "cached_tokens": 1015825,
  "llm_calls": 24
}

# List skills used across the last 7 days
$ copilot-session-usage skills --last 7d --format table
Skills across 23 sessions:
  Skill                              Sessions        Input     Output       Cached   Calls       Cost
  ---------------------------------------------------------------------------------------------------
  /compendium-generic get-session-costs       3    1137864      15729      1015825      24  $0.3636

# Filter sessions by title substring
$ copilot-session-usage list --title "get-session-costs"
$ copilot-session-usage analyze --title "grill-me" --latest
# Batch analyze last 5 sessions since July 1st
copilot-session-usage batch 5 --since 2026-07-01

# Aggregate all PRD-related sessions from the last week
copilot-session-usage analyze \
  --name "PRD: /path/to/prd" \
  --since 2026-06-30T00:00:00Z \
  --until 2026-07-07T00:00:00Z \
  --aggregate \
  --format table

# Cost-efficiency summary for a single session
copilot-session-usage analyze /path/to/debug-logs --summary --format table

# Extract just the total cost from a session
copilot-session-usage analyze /path/to/debug-logs --query .total.estimated_usd

# WSL2: point to Windows host workspaceStorage
copilot-session-usage latest \
  --workspace-storage /mnt/c/Users/$USER/AppData/Roaming/Code/User/workspaceStorage

Python API

from pathlib import Path

from copilot_session_usage.api import (
    analyze_session,
    analyze_latest,
    batch_analyze,
    aggregate_sessions,
    list_sessions,
)

# Analyze a session by path
result = analyze_session(Path("/path/to/debug-logs"), detail="full")

# Analyze the most recent session
result = analyze_latest(detail="compact")

# Batch analyze the last 10 sessions
batch = batch_analyze(10, detail="minimal")

# Aggregate multiple full analyses into one efficiency summary
aggregate = aggregate_sessions([result1, result2])

# List sessions with regex and date-range filtering
sessions = list_sessions(
    name_pattern=r"PRD",
    since="2026-07-01T00:00:00Z",
    until="2026-07-07T00:00:00Z",
)

Development

# Install dependencies
just dev

# Run tests
just test

# Run full validation
just preflight

# Build docs
just docs

# Serve docs with auto-reload
just docs-serve

License

MIT — see LICENSE.