get-llms-txt vs IntelliCode
Side-by-side comparison to help you choose.
| Feature | get-llms-txt | IntelliCode |
|---|---|---|
| Type | Repository | Extension |
| UnfragileRank | 33/100 | 40/100 |
| Adoption | 0 | 1 |
| Quality | 0 | 0 |
| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 7 decomposed |
| Times Matched | 0 | 0 |
Parses markdown and MDX files from a documentation source directory and extracts semantic content blocks (headings, paragraphs, code blocks, lists) into a structured format optimized for LLM consumption. Uses AST-based parsing to preserve document hierarchy and metadata, then flattens content into a single llms.txt file with clear delimiters and context markers that help LLMs understand document structure without needing to parse raw markdown syntax.
Unique: Specifically targets the llms.txt convention (emerging standard for LLM-friendly documentation) rather than generic markdown-to-text conversion, with awareness of documentation site generators (Next.js, Astro, Docusaurus) and their directory structures
vs alternatives: Purpose-built for LLM context generation unlike generic markdown converters; understands documentation site conventions and preserves semantic hierarchy better than simple text extraction
Automatically detects and adapts to different documentation framework conventions (Next.js, Astro, Docusaurus, VitePress, Gatsby) by identifying framework-specific directory patterns, configuration files, and content organization schemes. Uses heuristic-based framework detection (checking for framework config files like next.config.js, astro.config.mjs, docusaurus.config.js) to determine the correct source directory and content structure without requiring explicit configuration.
Unique: Implements framework-agnostic detection logic that recognizes multiple documentation generators' conventions and automatically resolves content paths, eliminating the need for manual configuration across different tech stacks
vs alternatives: Eliminates configuration overhead compared to generic markdown processors that require explicit path specification; handles framework-specific quirks automatically
Walks through nested directory structures starting from a detected or configured source directory, recursively discovers all markdown and MDX files, and applies filtering rules to include/exclude content based on file patterns, directory names, and metadata. Uses file system APIs with configurable glob patterns or ignore rules to skip common non-content directories (node_modules, .git, build output) and focus only on documentation source files.
Unique: Combines recursive traversal with framework-aware filtering that understands documentation site conventions (e.g., skipping build directories, node_modules) without explicit configuration
vs alternatives: More intelligent than generic file globbing because it understands documentation project structure; faster than shell-based find commands for large trees
Transforms markdown syntax into plain text while preserving semantic meaning and document structure through strategic formatting choices. Converts markdown headers to uppercase labels with separators, converts lists to indented plain text, strips inline formatting (bold, italic) while keeping content, removes markdown-specific syntax (backticks, brackets), and preserves code blocks as indented text blocks. This approach ensures LLMs can understand content hierarchy without needing to parse markdown syntax.
Unique: Prioritizes semantic clarity for LLM consumption over markdown fidelity; uses structural formatting (uppercase headers, indentation, delimiters) instead of markdown syntax to signal document hierarchy
vs alternatives: Better for LLM context than raw markdown (which adds parsing overhead) or naive text extraction (which loses structure); optimized for the specific use case of LLM-friendly documentation
Processes MDX files containing embedded JSX components and React code by extracting text content from component props, rendering component descriptions, and handling interactive elements as plain text descriptions. Parses JSX syntax to identify component boundaries, extracts meaningful text from component children and props, and generates fallback text descriptions for components that don't have direct text equivalents (e.g., 'Interactive Code Example' for a CodeSandbox embed).
Unique: Handles MDX-specific content (React components, JSX) which generic markdown tools cannot process; extracts semantic meaning from component structures rather than treating them as unparseable syntax
vs alternatives: Enables MDX documentation to be included in llms.txt unlike markdown-only tools; better than stripping JSX entirely because it preserves component intent through fallback descriptions
Generates llms.txt output with customizable formatting options including configurable section delimiters, header formatting styles, content separators, and metadata inclusion. Allows users to specify how headers are formatted (e.g., '# HEADER' vs '=== HEADER ==='), what separators divide sections, whether to include file paths or metadata, and how to structure the final output. Supports multiple output format presets (compact, verbose, structured) to optimize for different LLM consumption patterns.
Unique: Provides format customization specifically for LLM consumption patterns rather than generic text formatting; includes preset formats optimized for different LLM architectures and use cases
vs alternatives: More flexible than fixed-format tools; allows optimization for specific LLM providers unlike one-size-fits-all markdown converters
Processes multiple markdown and MDX files in a single operation, aggregates their content into a unified llms.txt output, and maintains file-level organization through metadata or section markers. Reads all discovered files, parses each independently, concatenates converted content with clear file boundaries, and optionally includes file path information or table of contents to help LLMs navigate the aggregated content. Handles file ordering (alphabetical, by modification time, or custom) to ensure consistent output.
Unique: Designed specifically for documentation aggregation with awareness of file boundaries and logical organization; maintains context about source files unlike naive concatenation
vs alternatives: More efficient than processing files individually; preserves file-level context better than simple text concatenation
Distributes get-llms-txt as an npm package with a command-line interface that can be invoked directly or integrated into build scripts and CI/CD pipelines. Provides both programmatic API (for Node.js projects) and CLI commands (for shell scripts and automation), supports configuration via command-line arguments or config files, and integrates with npm scripts in package.json for automated llms.txt generation during builds or deployments.
Unique: Provides both CLI and programmatic API for maximum flexibility; integrates seamlessly with npm-based workflows and CI/CD systems through standard Node.js conventions
vs alternatives: More accessible than standalone tools because it leverages existing npm infrastructure; easier to integrate into existing Node.js projects than external utilities
+2 more capabilities
Provides IntelliSense completions ranked by a machine learning model trained on patterns from thousands of open-source repositories. The model learns which completions are most contextually relevant based on code patterns, variable names, and surrounding context, surfacing the most probable next token with a star indicator in the VS Code completion menu. This differs from simple frequency-based ranking by incorporating semantic understanding of code context.
Unique: Uses a neural model trained on open-source repository patterns to rank completions by likelihood rather than simple frequency or alphabetical ordering; the star indicator explicitly surfaces the top recommendation, making it discoverable without scrolling
vs alternatives: Faster than Copilot for single-token completions because it leverages lightweight ranking rather than full generative inference, and more transparent than generic IntelliSense because starred recommendations are explicitly marked
Ingests and learns from patterns across thousands of open-source repositories across Python, TypeScript, JavaScript, and Java to build a statistical model of common code patterns, API usage, and naming conventions. This model is baked into the extension and used to contextualize all completion suggestions. The learning happens offline during model training; the extension itself consumes the pre-trained model without further learning from user code.
Unique: Explicitly trained on thousands of public repositories to extract statistical patterns of idiomatic code; this training is transparent (Microsoft publishes which repos are included) and the model is frozen at extension release time, ensuring reproducibility and auditability
vs alternatives: More transparent than proprietary models because training data sources are disclosed; more focused on pattern matching than Copilot, which generates novel code, making it lighter-weight and faster for completion ranking
IntelliCode scores higher at 40/100 vs get-llms-txt at 33/100. get-llms-txt leads on ecosystem, while IntelliCode is stronger on adoption and quality.
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Analyzes the immediate code context (variable names, function signatures, imported modules, class scope) to rank completions contextually rather than globally. The model considers what symbols are in scope, what types are expected, and what the surrounding code is doing to adjust the ranking of suggestions. This is implemented by passing a window of surrounding code (typically 50-200 tokens) to the inference model along with the completion request.
Unique: Incorporates local code context (variable names, types, scope) into the ranking model rather than treating each completion request in isolation; this is done by passing a fixed-size context window to the neural model, enabling scope-aware ranking without full semantic analysis
vs alternatives: More accurate than frequency-based ranking because it considers what's in scope; lighter-weight than full type inference because it uses syntactic context and learned patterns rather than building a complete type graph
Integrates ranked completions directly into VS Code's native IntelliSense menu by adding a star (★) indicator next to the top-ranked suggestion. This is implemented as a custom completion item provider that hooks into VS Code's CompletionItemProvider API, allowing IntelliCode to inject its ranked suggestions alongside built-in language server completions. The star is a visual affordance that makes the recommendation discoverable without requiring the user to change their completion workflow.
Unique: Uses VS Code's CompletionItemProvider API to inject ranked suggestions directly into the native IntelliSense menu with a star indicator, avoiding the need for a separate UI panel or modal and keeping the completion workflow unchanged
vs alternatives: More seamless than Copilot's separate suggestion panel because it integrates into the existing IntelliSense menu; more discoverable than silent ranking because the star makes the recommendation explicit
Maintains separate, language-specific neural models trained on repositories in each supported language (Python, TypeScript, JavaScript, Java). Each model is optimized for the syntax, idioms, and common patterns of its language. The extension detects the file language and routes completion requests to the appropriate model. This allows for more accurate recommendations than a single multi-language model because each model learns language-specific patterns.
Unique: Trains and deploys separate neural models per language rather than a single multi-language model, allowing each model to specialize in language-specific syntax, idioms, and conventions; this is more complex to maintain but produces more accurate recommendations than a generalist approach
vs alternatives: More accurate than single-model approaches like Copilot's base model because each language model is optimized for its domain; more maintainable than rule-based systems because patterns are learned rather than hand-coded
Executes the completion ranking model on Microsoft's servers rather than locally on the user's machine. When a completion request is triggered, the extension sends the code context and cursor position to Microsoft's inference service, which runs the model and returns ranked suggestions. This approach allows for larger, more sophisticated models than would be practical to ship with the extension, and enables model updates without requiring users to download new extension versions.
Unique: Offloads model inference to Microsoft's cloud infrastructure rather than running locally, enabling larger models and automatic updates but requiring internet connectivity and accepting privacy tradeoffs of sending code context to external servers
vs alternatives: More sophisticated models than local approaches because server-side inference can use larger, slower models; more convenient than self-hosted solutions because no infrastructure setup is required, but less private than local-only alternatives
Learns and recommends common API and library usage patterns from open-source repositories. When a developer starts typing a method call or API usage, the model ranks suggestions based on how that API is typically used in the training data. For example, if a developer types `requests.get(`, the model will rank common parameters like `url=` and `timeout=` based on frequency in the training corpus. This is implemented by training the model on API call sequences and parameter patterns extracted from the training repositories.
Unique: Extracts and learns API usage patterns (parameter names, method chains, common argument values) from open-source repositories, allowing the model to recommend not just what methods exist but how they are typically used in practice
vs alternatives: More practical than static documentation because it shows real-world usage patterns; more accurate than generic completion because it ranks by actual usage frequency in the training data