Claude Code Conversation Analyzer

A self-hosted dashboard for understanding how you actually use Claude Code: which projects you spend the most time on, where the cost goes, which conversations are healthy, and which ones spiraled into a long, expensive mess.

Why I built this

I wanted to actually see my own data. Claude Code stores every conversation locally in ~/.claude/projects/, but there's no built-in way to browse it. I wanted to:

Keep a full history of every conversation, indefinitely.
See how much I'm spending — total, per project, and grouped across related projects (e.g. all the repos in one product area).
Search across older conversations to find something I worked on weeks or months ago.

This dashboard reads the history that's already on disk, augments it with cost and quality data, and gives me a place to actually look at it. Everything runs locally — no data leaves your machine.

Screenshots

Project overview — every project ranked by cost, with conversation count, total tokens, and compaction count at a glance.

Project detail — every session in a project, sortable by date or cost, with per-session model and token breakdown.

Token progression — expand any session to see token usage over time, with compaction events marked. Useful for spotting where a conversation went off the rails.

Session detail — full transcript with role filtering and message search, alongside the same token chart and cost summary.

Cross-project search — full-text search across every message in every project, jumping straight to the relevant session.

Features

Project overview — Grade distribution (A–F), total cost, message volume per project. Search and sort across all projects.
Conversation detail — Per-session token chart with compaction events marked, full message transcript, cost breakdown by model.
Quality scoring — Conversations are graded on cache usage, length, query specificity, context utilization, and prompt efficiency. Weighted toward what actually drives cost (cache reads, length).
Cross-session search — Full-text search across every message in every project.
Local-first — All data lives in a SQLite database on your machine. Nothing is uploaded.

Setup matters: enable long retention first

Do this before anything else. Claude Code prunes conversation history after a short default window. To analyze trends over time, set a long retention period in ~/.claude/settings.json before you accumulate the history you want to look at:
{ "cleanupPeriodDays": 3650 }
See the Claude Code settings docs for all options.

Privacy note: Conversation history includes your prompts, code, and Claude's responses — potentially sensitive content. A long retention period means that data stays on your disk indefinitely. 90–365 days is a reasonable middle ground if you want history without infinite accumulation.

How it works

┌──────────────────────┐    ┌──────────────────────┐    ┌──────────────────────┐
│  ~/.claude/projects/ │    │  claude-code-log     │    │  This repo (backend) │
│   raw JSONL/JSON     │ -> │   (external tool)    │ -> │  augments DB with    │
│                      │    │  builds SQLite DB    │    │  cost + quality      │
└──────────────────────┘    └──────────────────────┘    └──────────┬───────────┘
                                                                   │
                                                                   v
                                                       ┌──────────────────────┐
                                                       │  Next.js dashboard   │
                                                       │  reads SQLite via    │
                                                       │  API routes          │
                                                       └──────────────────────┘

claude-code-log (external tool, automatically cloned by the Makefile) — Reads raw conversation files in ~/.claude/projects/ and writes them into a SQLite cache: ~/.claude/projects/claude-code-log-cache.db.
Backend (backend/src/) — Reads that database, calculates per-session costs using model-specific pricing, scores conversation quality, and writes the results back to the same database in new tables (session_costs, session_quality).
Frontend (frontend/) — Next.js app that reads the augmented database via API routes. No JSON export step; the database is the API.

Quick start

1. Generate and analyze the data

All make commands run from backend/:

cd backend
make all

On first run this will:

Prompt you to pick a location for the claude-code-log clone (saved to backend/.cclog-config).
Clone and install claude-code-log.
Run the full pipeline: pull latest → clean stale output → generate fresh data → score and augment the database.

Subsequent runs of make all just refresh the data.

2. Start the dashboard

cd frontend
pnpm install      # first time only
pnpm dev

Open http://localhost:3000.

Individual pipeline steps

For debugging or partial updates:

cd backend
make setup-cclog            # Clone/verify claude-code-log
make update-cclog           # Pull latest from upstream
make clean-cclog-output     # Delete stale generated files
make generate-cclog-data    # Regenerate cclog database
make conversations-analysis # Score + augment only (no regeneration)

Quality scoring

Each session gets a 0–100 score and a letter grade (A–F). The weights:

Factor	Weight	What it measures
Cache usage	40%	Cache read tokens vs. thresholds — the dominant cost driver
Conversation length	30%	Message count vs. thresholds
Query specificity	15%	Detects vague vs. specific queries
Context utilization	10%	Redundancy and token efficiency
Prompt efficiency	5%	Input token optimization

Hard caps: Sessions with >30M cache tokens or >500 messages are capped at score 30 (grade F), regardless of other factors. Those are the patterns most worth flagging.

See backend/src/analyzer.py for the implementation.

Cost calculation

Costs are computed per-session by grouping tokens by model. Each model's input, output, cache-creation, and cache-read tokens are priced separately using MODEL_PRICING in backend/src/pricing.py. Cache reads are roughly 10× cheaper than fresh input tokens — the calculator accounts for this.

To add a new model:

# backend/src/pricing.py
MODEL_PRICING = {
    'claude-new-model-20251231': {
        'input': 3.00,
        'output': 15.00,
        'cache_creation': 3.75,
        'cache_read': 0.30,
    },
    ...
}

Pricing reference: https://platform.claude.com/docs/en/about-claude/pricing#model-pricing

Repo structure

backend/                          Python analysis engine (uv, ruff, mypy strict)
├── Makefile                      All automation
├── pyproject.toml
├── ruff.toml
└── src/
    ├── app.py                    Entry point / orchestrator
    ├── analyzer.py               Quality scoring
    ├── pricing.py                Per-model token pricing
    ├── conversation_types.py     Dataclasses
    ├── db_reader.py              Reads cclog SQLite DB
    ├── db_augmenter.py           Writes session_costs / session_quality tables
    ├── fallback_writer.py        Handles projects without cclog data
    └── data_loader.py            Legacy JSONL loader (fallback path)

frontend/                         Next.js 16 + React 19 dashboard (pnpm, biome)
└── src/
    ├── app/
    │   ├── page.tsx              Project grid (overview)
    │   ├── projects/[id]/        Per-project conversation list
    │   ├── sessions/[id]/        Per-session detail
    │   ├── search/               Cross-project message search
    │   └── api/                  Reads from SQLite (data, search, session)
    ├── components/               TokenChart, ConversationCard, etc.
    ├── context/AppContext.tsx    Global state
    └── lib/                      Types, pricing utils, formatters

Development

Backend (Python 3.13+, uv)

cd backend
make install-dev   # Install with dev deps
make fmt           # Format with ruff
make lint          # Lint with ruff
make type-check    # mypy strict
make check         # lint + type-check

Frontend (Next.js, pnpm)

cd frontend
pnpm install
pnpm dev           # localhost:3000
pnpm build
pnpm lint          # Biome
pnpm format        # Biome

Troubleshooting

uv: command not found

curl -LsSf https://astral.sh/uv/install.sh | sh

Merge conflicts when updating claude-code-log

# delete config and re-clone
rm backend/.cclog-config && cd backend && make setup-cclog

No conversation data

Verify ~/.claude/projects/ contains project directories.
Verify the cclog DB was created: ls ~/.claude/projects/claude-code-log-cache.db
Run make generate-cclog-data again with --verbose.

Frontend shows nothing

The frontend reads directly from ~/.claude/projects/claude-code-log-cache.db. Make sure make conversations-analysis ran successfully.

License

MIT. See LICENSE.

Acknowledgments

Built on top of claude-code-log by @daaain, which does the heavy lifting of parsing Claude Code's local conversation files into a queryable SQLite database.

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Claude Code Conversation Analyzer

Why I built this

Screenshots

Features

Setup matters: enable long retention first

How it works

Quick start

1. Generate and analyze the data

2. Start the dashboard

Individual pipeline steps

Quality scoring

Cost calculation

Repo structure

Development

Backend (Python 3.13+, uv)

Frontend (Next.js, pnpm)

Troubleshooting

License

Acknowledgments

FilesExpand file tree

README.md

Latest commit

History

README.md

File metadata and controls

Claude Code Conversation Analyzer

Why I built this

Screenshots

Features

Setup matters: enable long retention first

How it works

Quick start

1. Generate and analyze the data

2. Start the dashboard

Individual pipeline steps

Quality scoring

Cost calculation

Repo structure

Development

Backend (Python 3.13+, uv)

Frontend (Next.js, pnpm)

Troubleshooting

License

Acknowledgments