| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 6 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Leverages the TypeScript Language Service to parse and analyze module dependency graphs, extracting all named exports, default exports, and import statements from TypeScript/JavaScript files. Uses the compiler API's SourceFile AST traversal to identify export declarations and their symbols, enabling precise mapping of what each module exposes and consumes without executing code.
Unique: Uses TypeScript's official Language Service API (not a custom parser) to analyze exports/imports, ensuring 100% compatibility with TypeScript's own module resolution rules and type-aware symbol tracking, rather than regex or AST-walking approaches that miss edge cases like namespace exports or conditional exports
vs alternatives: More accurate than regex-based tools because it respects TypeScript's actual module resolution algorithm and handles complex patterns (namespace exports, re-exports, type-only imports) that simpler parsers miss
Integrates the TypeScript Language Service's symbol resolution engine to locate symbol definitions, trace their types, and report type errors at specific locations in code. Queries the compiler's getDefinitionAtPosition() and getQuickInfoAtPosition() APIs to resolve symbols to their declaration sites and retrieve type information without full compilation.
Unique: Directly wraps TypeScript's Language Service APIs rather than reimplementing type-checking logic, meaning it inherits TypeScript's exact type inference rules and error reporting, and can be updated automatically when TypeScript releases new type features
vs alternatives: More reliable than custom type checkers because it uses the same type inference engine as tsc, eliminating discrepancies between what the tool reports and what TypeScript itself would report
Exposes TypeScript code intelligence capabilities as a Model Context Protocol (MCP) server, allowing LLM agents and Claude to query code structure, symbols, and type information through a standardized tool interface. Implements MCP resource and tool schemas that map code analysis operations to callable functions with structured input/output contracts.
Unique: Bridges TypeScript Language Service (a desktop/CLI tool) to the MCP protocol, enabling LLMs to access real-time code intelligence without embedding the entire TypeScript compiler in the LLM context, using a client-server architecture that keeps analysis on the developer's machine
vs alternatives: More efficient than sending full source code to an LLM because it only transmits structured metadata (exports, types, errors) rather than raw code, reducing token usage and enabling LLMs to work with larger codebases
Processes multiple TypeScript files in a single invocation, collecting exports, imports, type errors, and symbol information across the entire codebase or a specified subset. Aggregates results into a unified report format (JSON, text, or structured output) that can be piped to other tools or stored for further analysis.
Unique: Aggregates TypeScript Language Service analysis across multiple files without re-initializing the compiler for each file, using a single LanguageServiceHost instance to maintain incremental compilation state and reduce memory overhead
vs alternatives: Faster than running tsc --noEmit for type checking because it only analyzes requested information (exports, errors) rather than performing full type inference and emit, making it suitable for quick CI/CD checks
Provides a command-line interface that wraps TypeScript code intelligence operations, accepting file paths, symbols, and options as CLI arguments and outputting results in multiple formats (JSON, text, JSONL). Implements standard CLI patterns (exit codes, error messages, help text) and integrates with Unix pipes and shell scripting for composability.
Unique: Implements a thin CLI wrapper around the TypeScript Language Service that preserves the service's incremental compilation state across multiple CLI invocations when used as a daemon or long-running process, rather than reinitializing the compiler for each command
vs alternatives: More composable than tsc because it outputs structured data (JSON) by default and supports Unix pipes, making it easier to integrate into shell scripts and CI/CD pipelines without custom parsing
Maintains an in-memory Language Service instance that tracks file changes and reuses compilation state across multiple analysis queries, avoiding redundant parsing and type-checking of unchanged files. Uses TypeScript's incremental compilation APIs to update only affected files when the codebase changes.
Unique: Leverages TypeScript's built-in incremental compilation APIs (getSourceFile caching, program reuse) rather than implementing custom caching, ensuring compatibility with TypeScript's own optimization strategies and reducing maintenance burden
vs alternatives: Faster than re-running tsc for each query because it reuses the compiler's internal state and only re-analyzes changed files, providing sub-second response times for repeated queries on large projects
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs ts-scan at 27/100. ts-scan leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
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