| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Executes LLM queries using JavaScript template literal syntax (backtick-delimited prompts with $` markers) embedded directly in GenAIScript files. The runtime parses these template expressions, sends them to configured LLM providers (OpenAI, Anthropic, or local models), and returns structured or unstructured responses that can be assigned to variables for downstream processing. This approach enables prompt composition as first-class JavaScript expressions rather than string concatenation.
Unique: Uses JavaScript template literal syntax ($`...`) as the primary interface for LLM calls, embedding prompts as first-class language constructs rather than string APIs. This allows IDE autocomplete, syntax highlighting, and variable interpolation without additional abstraction layers.
vs alternatives: More ergonomic than REST API calls or string-based prompt builders because prompts are native JavaScript expressions with full IDE support and variable scoping.
Automatically extracts and parses content from diverse file formats (PDF, DOCX, CSV, plain text) using specialized parsers accessible via the `parsers.*` API. Files are matched using glob patterns or explicit file arrays, parsed into structured or text representations, and made available to LLM prompts via the `env.files` context. The runtime handles encoding detection, format-specific extraction (e.g., PDF text layers, DOCX metadata), and error handling for malformed files.
Unique: Provides a unified `parsers.*` API for heterogeneous file formats, abstracting format-specific parsing logic behind a consistent interface. This eliminates the need to write custom parsing code for each file type or call external services.
vs alternatives: More integrated than calling separate parsing libraries or cloud APIs because parsing happens locally within the script runtime, reducing latency and avoiding data egress.
Executes scripts with automatic file discovery and filtering based on glob patterns or explicit file lists. The runtime matches files against patterns, loads their content, and makes them available to the script via `env.files`. This enables batch processing of files with consistent logic without manual file enumeration.
Unique: Integrates file discovery and filtering directly into the script runtime, eliminating the need to write separate file enumeration logic. Matched files are automatically available as script variables.
vs alternatives: More convenient than manual file enumeration because glob patterns are evaluated by the runtime, and file content is automatically loaded and made available to prompts.
Formats script execution results for display or export, supporting multiple output formats (plain text, JSON, structured logs). Results can be written to stdout, files, or returned as structured data for downstream processing. The runtime handles serialization of complex data types and provides options for formatting output for human readability or machine parsing.
Unique: Provides built-in result formatting and serialization as part of the script runtime, eliminating the need to manually format or serialize results before output.
vs alternatives: More integrated than manual result formatting because the runtime handles serialization and provides options for different output formats without additional code.
Defines JSON schemas (using JSON Schema or Zod syntax) to validate and repair LLM-generated outputs. The runtime enforces schema constraints, attempts to repair malformed data (e.g., fixing JSON syntax errors or missing fields), and provides structured output that matches the schema definition. Schemas are defined inline in scripts using `defSchema()` and can be referenced in prompts to guide LLM output format.
Unique: Combines schema definition, LLM-guided extraction, and automatic repair in a single workflow. Rather than validating post-hoc, schemas are passed to the LLM to guide output format, and repair logic attempts to fix common errors before validation fails.
vs alternatives: More robust than raw LLM output parsing because it enforces schema compliance and repairs common formatting errors, reducing downstream pipeline failures compared to manual JSON parsing.
Performs semantic similarity search across project files using embeddings and vector retrieval. The `retrieval.vectorSearch()` API accepts a query string, embeds it using a configured embedding model, and returns the most similar files or file chunks ranked by cosine similarity. This enables context-aware file selection for LLM prompts without explicit file enumeration, supporting use cases like 'find similar code' or 'retrieve relevant documentation'.
Unique: Integrates semantic search directly into the scripting runtime, allowing queries to be composed programmatically and results to be piped into LLM prompts without external API calls or separate indexing steps.
vs alternatives: More efficient than full-text search for semantic queries and more integrated than external RAG services because search results are available as script variables without context switching.
Enables prompts to invoke other prompts via the `runPrompt()` function, allowing multi-stage LLM workflows where outputs from one prompt feed into subsequent prompts. Each nested prompt has its own context (files, variables, schema), and results are returned as structured data that can be processed or passed to downstream prompts. This pattern supports complex reasoning chains, iterative refinement, and modular prompt reuse.
Unique: Treats prompts as first-class composable functions within a scripting language, allowing complex workflows to be expressed as JavaScript code with full control flow (loops, conditionals, error handling) rather than static workflow definitions.
vs alternatives: More flexible than linear prompt chains because nested prompts can be conditionally executed, looped, or composed based on runtime data, enabling adaptive workflows that respond to intermediate results.
Executes GenAIScript scripts from the command line using `npx genaiscript run`, enabling automation outside VS Code and integration with CI/CD pipelines, cron jobs, or shell scripts. The CLI accepts script paths, environment variables, and input parameters, executes the script in a headless runtime, and outputs results to stdout or files. This decouples script development (in VS Code) from script execution (in automation contexts).
Unique: Provides a dual-mode execution model where scripts are developed interactively in VS Code but executed headlessly via CLI, enabling the same script to be used for both prototyping and production automation.
vs alternatives: More portable than VS Code-only execution because scripts can run in any environment with Node.js, enabling integration with CI/CD systems, containers, and serverless platforms without requiring VS Code.
+4 more capabilities
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 GenAIScript at 35/100. GenAIScript 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.
+5 more capabilities