OpenAI builds and deploys advanced AI models like GPT-4o for autonomous agents and workflows.
TokenDam
$32Starting price
0Popularity
About TokenDam
TokenDam analyzes AI agent execution traces to provide a detailed Token Profit & Loss (P&L) breakdown, helping teams understand where token spend is effective or wasted. It reads execution traces from popular frameworks such as OpenAI, Anthropic, LangSmith, Langfuse, Vercel AI SDK, and raw logs, then categorizes token usage by agent, step, and waste pattern. The tool identifies 12 common waste patterns, quantifies their associated dollar costs, and suggests paste-ready fixes to optimize spending. TokenDam operates entirely in the browser or via a CLI, ensuring no data is uploaded or routed through external servers, which maintains privacy and security. It can be integrated into CI pipelines to automatically flag token cost regressions, preventing unexpected expenses from slipping into production. A runtime dashboard provides live traffic insights, while the free tier supports manual trace analysis for individual users. The Pro tier extends functionality with automated regression alerts and cross-pull-request cost tracking, enabling teams to monitor and control token spend at scale.
Key features
- Token P&L breakdown per agent and step
- 12 waste pattern detections with cost estimates
- CI integration with regression alerts
- Live traffic dashboard (Pro tier)
- Automated fix PR generation (Pro tier)
- MCP support for AI coding agents
- Browser-based and CLI interfaces
- Per-agent cost attribution and history
Use cases
- Auditing AI agent token usage in production
- Optimizing LLM spend before deployment
- Detecting token waste in CI pipelines
Pros
- Calculates exact token waste with dollar figures
- Runs locally without uploading data
- Integrates with CI pipelines to block cost regressions
- Supports multiple LLM providers and SDKs
- Provides actionable fixes for identified waste patterns
Cons
- Pro tier required for automated monitoring
- Limited to trace-based analysis (no real-time monitoring in free tier)
- Enterprise features require self-hosting
Frequently asked questions about TokenDam
What is TokenDam and what does it do?
TokenDam provides a Token P&L for AI agents by analyzing execution traces to break down token usage. It calculates the percentage of tokens that did no real work, categorizes spend per agent and step, and identifies 12 waste patterns with associated dollar costs and fixes.
Who should use TokenDam?
TokenDam is designed for teams managing AI agents, particularly engineers and developers looking to audit and reduce token waste in their LLM applications. It is also useful for non-developers who need to analyze AI costs and share reports with technical teams.
How does TokenDam work with my existing AI setup?
TokenDam reads traces from OpenAI, Anthropic, LangSmith, Langfuse, Vercel AI SDK, and raw logs without requiring SDK swaps or routing production traffic through external servers. It can be used via a browser or CLI, and integrates into CI pipelines to flag token cost regressions.
Can TokenDam be used in a CI pipeline?
Yes, TokenDam can be integrated into CI pipelines using the CLI command `tokendam --ci` to fail builds when token costs regress. This allows teams to enforce cost budgets automatically during development.
Is my data secure with TokenDam?
TokenDam operates entirely in the browser or via a CLI, with no data uploaded or routed through external servers. It uses client-side processing and does not require routing production traffic through third parties, ensuring data privacy.
How do I get started with TokenDam?
To get started, capture a trace of your agent’s requests using the provided capture helper or log the request arguments directly. Paste the trace into TokenDam’s browser tool or CLI to generate a report. For CI integration, use the CLI command to analyze traces and enforce cost budgets.
