tldt — Too Long, Didn't Tokenize vs Prelint
Side-by-side comparison of features, pros & cons, pricing, and community votes (2026).
🏆 Prelint leads with 664 upvotes
Protect your data and agents while saving tokens
tldt — Too Long, Didn't Tokenize — is a powerful CLI and library designed to optimize the way long texts are handled in AI workflows. Leveraging machine learning, it intelligently summarizes lengthy content while preserving context, making it ideal for reducing token usage in API calls, document uploads, and web crawling. Its suite of features includes support for LexRank and TextRank algorithms, HTML-to-Markdown conversion, Unicode confusables protection, text sanitization, and PII/API key cleaning, all without requiring API keys. This makes it especially suitable for developers, data scientists, and AI agents aiming to enhance privacy, security, and efficiency. Its Go library and coding skills support enable seamless integration into custom workflows and applications, offering a tailored approach to managing complex textual data. What sets tldt apart is its focus on protecting sensitive data and optimizing token consumption, ensuring cost-effective and secure AI interactions.
Pros
- Reduces token consumption with advanced summarization techniques
- Supports multiple safety and security features like PII and API key cleaning
- No API keys required, enhancing privacy and ease of use
- Converts HTML to markdown and sanitizes text for cleaner data
- Provides a Go library for integration into custom AI workflows
Cons
- Limited information on pricing and deployment options
- May require technical expertise to implement effectively
- Currently lacks extensive user documentation or support community
Best for
- • Summarizing lengthy API responses to reduce token costs
- • Cleaning and sanitizing web-scraped data before analysis
- • Protecting sensitive information like PII and API keys in workflows
- • Converting HTML content into markdown for easier processing
Pricing: Likely follows a freemium model with core features available for free, with potential paid plans for advanced integrations or enterprise use. Exact pricing details are not specified, but the tool’s open-source approach suggests affordability and flexibility.
Prevent product drift in AI-written code
Prelint is an innovative AI-powered code review tool designed to ensure code quality and consistency in teams leveraging AI-generated code. By automatically reviewing pull requests against architectural decision records (ADRs), documentation, and past decisions, Prelint helps prevent product drift and maintains alignment with project standards. Its unique capability to catch issues early—especially in environments where multiple AI reviewers are used—makes it an essential addition for modern development workflows. With the ability to identify approximately 40% of issues before merging, Prelint significantly reduces bugs and rework, leading to more reliable and maintainable codebases. Ideal for software engineering teams seeking to integrate AI into their CI/CD pipeline, it offers a proactive approach to code validation that complements traditional review processes.
Pros
- Automates comprehensive code review against ADRs, docs, and past decisions
- Prevents product drift early in the development lifecycle
- Reduces post-deployment bugs and rework
- Enhances team collaboration by enforcing standards
- Effective in environments with multiple AI reviewers
Cons
- May require initial setup to align with specific ADRs and documentation
- Dependent on the quality of input data and existing documentation
- Potential false positives in complex or rapidly evolving projects
Best for
- • Reviewing AI-generated code to ensure adherence to project standards
- • Preventing feature creep and maintaining product consistency
- • Automating code review in CI/CD pipelines
- • Supporting teams using multiple AI code reviewers
Pricing: Likely operates on a subscription-based model, possibly with tiered plans based on team size or usage volume. A free tier or trial may be available to evaluate its capabilities before committing to paid plans.