Local token & cost dashboard for AI coding tools
Project description
Tokdash
Local token & cost dashboard for AI coding tools (Codex, OpenCode, Claude Code, Gemini CLI, OpenClaw, etc.).
Features
- Hierarchical breakdown: app → model with full token precision
- Multiple data sources: local session files + optional
tokscalefallback - Exact token counts: Input/Output/Cache token breakdowns
- Flexible ranges: today / week / month / N days
- Contribution calendar: 2D heatmap + 3D isometric view
Supported clients (explicit token fields)
✅ Supported:
- OpenCode:
~/.local/share/opencode/ - Codex:
~/.codex/sessions/ - Claude Code:
~/.claude/projects/ - Gemini CLI:
~/.gemini/tmp/*/chats/session-*.json - OpenClaw:
~/.openclaw/agents/*/sessions/
Platform support
- Linux (including WSL2): supported
- macOS: experimental
- Windows: not supported (yet)
Quick start
Prerequisites
- Python 3.10+
- One or more supported clients installed (above)
- (Optional) Tokscale (only needed if you use the tokscale backend):
bunx tokscale@latest
Install (pip)
From PyPI (after the first public release):
pip install tokdash
tokdash serve
Open: http://localhost:55423
Run (from source)
pip install -e .
# Option A: run directly
python3 main.py
# Option B: CLI wrapper (same server)
./tokdash serve
Open: http://localhost:55423
If port conflicts:
python3 main.py --port <port>./tokdash serve --port <port>
If you want to access Tokdash from another device (recommended):
- Tailscale Serve (private to your tailnet):
tailscale serve 55423 - SSH port-forward:
ssh -L 55423:127.0.0.1:55423 <user>@<host>
Binding to 0.0.0.0 is possible, but not recommended: it listens on all interfaces and can expose the dashboard beyond your LAN (VPN/Wi‑Fi/etc.). Only do this if you understand the risk and have firewall/auth in place.
Run in background
See docs/agents/systemd/BACKGROUND_RUN.md for:
- Linux systemd (user service) template
- macOS launchd (LaunchAgent) template
For Humans
Copy and paste this prompt to your LLM agent (Claude Code, AmpCode, Cursor, etc.):
Install and configure Tokdash to run in the background by following the instructions here:
https://raw.githubusercontent.com/JingbiaoMei/Tokdash/main/docs/agents/systemd/AGENTS.md
Or read the Background Run guide, but seriously, let an agent do it.
For LLM Agents
Fetch the installation guide and follow it:
curl -s https://raw.githubusercontent.com/JingbiaoMei/Tokdash/main/docs/agents/systemd/AGENTS.md
OpenClaw digest (scheduled reports)
Tokdash can power daily/weekly/monthly OpenClaw usage reports by querying the local API on a schedule.
For Humans
Copy and paste this prompt to your LLM agent (Claude Code, AmpCode, Cursor, etc.):
Install and configure scheduled Tokdash usage reports for OpenClaw by following the instructions here:
https://raw.githubusercontent.com/JingbiaoMei/Tokdash/main/docs/agents/openclaw_reporting/AGENTS.md
Or read the guide yourself, but seriously, let an agent do it.
For LLM Agents
Fetch the installation guide and follow it:
curl -s https://raw.githubusercontent.com/JingbiaoMei/Tokdash/main/docs/agents/openclaw_reporting/AGENTS.md
Configuration
Tokdash is localhost-only by default.
TOKDASH_HOST(default:127.0.0.1)TOKDASH_PORT(default:55423)TOKDASH_CACHE_TTL(default:120seconds)TOKDASH_ALLOW_ORIGINS(comma-separated, default: empty)TOKDASH_ALLOW_ORIGIN_REGEX(default allows only localhost/127.0.0.1)
Example (remote access via Tailscale Serve; recommended):
tokdash serve --bind 127.0.0.1 --port 55423
tailscale serve --bg 55423
Privacy & security
- No telemetry: Tokdash does not intentionally send your data anywhere.
- Local parsing: usage is computed from local session files (see “Supported clients” paths above).
- Server exposure: Tokdash binds to
127.0.0.1by default. Prefer Tailscale Serve or SSH tunneling for remote access; avoid--bind 0.0.0.0unless you understand it listens on all interfaces and have firewall/auth in place.
API (local)
Tokdash is a local HTTP server. Common endpoints:
GET /api/usage?period=today|week|month|NGET /api/tools?period=...(coding tools only)GET /api/openclaw?period=...(OpenClaw only)
Example:
curl 'http://127.0.0.1:55423/api/usage?period=today'
Accuracy note
Token counts depend on what each client logs locally. Costs are computed from src/tokdash/pricing_db.json and may lag real provider pricing — use as an estimate and verify against your billing source if it matters.
Roadmap
See docs/ROADMAP.md.
Contributing / security
- Contributing guide:
docs/CONTRIBUTING.md - Security policy:
docs/SECURITY.md
Project structure
tokdash/
├── main.py # Source entrypoint (python3 main.py)
├── tokdash # Source CLI wrapper (./tokdash serve)
├── src/
│ └── tokdash/
│ ├── cli.py
│ ├── api.py # FastAPI routes/app
│ ├── compute.py # Aggregation/merging logic
│ ├── pricing.py # PricingDatabase wrapper
│ ├── model_normalization.py
│ ├── pricing_db.json
│ ├── sources/
│ │ ├── openclaw.py # OpenClaw session log parser
│ │ └── coding_tools.py # Local coding tools parsers
│ └── static/
│ └── index.html
└── docs/ # Roadmap + background-run docs + agent prompts
License
MIT License - see LICENSE.
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