Tokimeter vs BDFL - Benevolent Delegator for LLMs
Comprehensive feature analysis, ratings breakdown, platform compatibility, and community review comparison.
Tokimeter
Local usage analytics for Claude, Codex, Cursor, Grok & More
Detailed Feature Comparison Matrix
Compare Other Tools| Dimension | Tokimeter |  BDFL - Benevolent Delegator for LLMs |
|---|---|---|
| Primary Category | Open Source | Open Source |
| Community Rating | No ratings(0 reviews) | No ratings(0 reviews) |
| Community Upvotes | 22 votes | 6 votes |
| Supported Platforms | Web | Web |
| Tags & Focus | #Developer Tools#GitHub#Open Source | #Developer Tools#OpenAI Day#GitHub#Artificial Intelligence#Open Source |
| Maker / Company | Independent Developer | Independent Developer |
| Platform Verification | Community Listing | Community Listing |
About Tokimeter
Tokimeter is an open-source, local usage analytics tool designed specifically for developers using various AI coding assistants. By pulling the usage records that your AI coding tools already write to your machine, Tokimeter aggregates them into comprehensive, unified reports.
With Tokimeter, you can easily track exact token counts and associated costs broken down by project, session, day, tool, and model. It also monitors 5-hour and weekly limit windows, providing helpful budget warnings directly in your status line to help you manage your AI tool usage efficiently.
Designed with privacy in mind, Tokimeter requires no account and zero telemetry—everything stays securely on your local machine. It supports a wide range of platforms including Claude and Codex (both CLI and desktop apps), Cursor, Grok Build, Hermes, opencode, Cline, and Copilot CLI. You can generate a report instantly using npx tokimeter report under an MIT license.
Pros of Tokimeter
- Local-first architecture with zero telemetry; nothing leaves your machine
- Requires no account setup or configuration
- Comprehensive coverage of popular AI tools like Claude, Codex, Cursor, Grok Build, and more
- Tracks exact token counts and costs by project, session, day, tool, and model
- Open-source and MIT licensed
Cons of Tokimeter
- Limited to tools that write local usage records
- Requires using the command line interface (e.g., npx) to generate reports
- Relies on supported tool ecosystems mentioned in the metadata
Frequently Asked Questions
What is Tokimeter?
Tokimeter is a local usage analytics tool for AI coding assistants that consolidates your usage records into a single report, tracking token counts, costs, and limit windows.
Does Tokimeter send my data to external servers?
No. Tokimeter features zero telemetry, requires no account, and ensures that nothing ever leaves your machine.
Which AI coding tools are supported?
Tokimeter covers Claude and Codex (both CLI and desktop apps), Cursor, Grok Build, Hermes, opencode, Cline, and Copilot CLI.
How do I generate a report with Tokimeter?
You can generate a report instantly using the command: npx tokimeter report.
About BDFL - Benevolent Delegator for LLMs
BDFL (Benevolent Delegator for LLMs) is an open-source terminal supervisor designed to orchestrate complex coding sessions using AI agents like Codex, Claude Code, and Ollama. Instead of relying on a single AI window, BDFL separates the workflow into specialized roles. Users interact with a 'planning agent' to create versioned, deliberate plans. Once the human user approves the plan—or specific sections of it—BDFL automatically delegates the tasks to isolated 'worker agents' that execute the code in parallel. Beyond just task delegation, the platform handles the heavy lifting of scheduling, running checks, verifying code, and managing integration. Every worker operates in an isolated environment, ensuring that code changes are properly sandboxed and reviewed before being merged. Because it operates entirely locally without any telemetry or centralized tracking, BDFL offers developers a secure, deterministic, and highly observable way to scale their AI-assisted software development.
Pros of BDFL - Benevolent Delegator for LLMs
- Orchestrates multiple AI agents (Codex, Claude Code, Ollama) and allows parallel execution of tasks within isolated worktrees.
- Supports deliberate planning with versioned plans and individual section approvals to ensure tight human oversight.
- Operates entirely locally with no telemetry, keeping runtime state, plans, and source code completely private.
Cons of BDFL - Benevolent Delegator for LLMs
- Currently limited to macOS and Linux environments, with Windows support only listed as planned.
- Requires a highly technical setup, including Node.js 20+, Git, and comfort with advanced CLI workflows.
Frequently Asked Questions
What AI models and agents does BDFL support?
BDFL supports Codex, Claude Code, and local open-source models via Ollama. You can even mix and match models, using one for the planning role and another for the execution workers.
Is my code or data sent to a centralized BDFL server?
No. BDFL operates completely locally and does not collect or publish telemetry, analytics, or runtime state. If you use Ollama with a local model, your entire workflow remains entirely on your machine.