| 0 |
| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 14 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Implements a two-tier indexing strategy where shallow indexing rapidly builds file lists via filesystem traversal, while deep indexing extracts symbol-level structure (functions, classes, variables) using tree-sitter AST parsing for 50+ file types with fallback regex strategies. The indexing system uses SQLite for symbol storage and JSON for file metadata, enabling LLMs to understand codebase structure without full source transmission. Supports incremental updates and file watching for auto-refresh on changes.
Unique: Uses tree-sitter AST parsing for 50+ languages with intelligent fallback regex strategies, enabling structurally-aware symbol extraction without language-specific compiler dependencies. Dual-mode indexing (shallow for speed, deep for accuracy) allows LLMs to choose between fast file discovery and detailed symbol analysis.
vs alternatives: Faster and more accurate than regex-only indexing (e.g., ctags) because tree-sitter understands syntax trees; more practical than full-source RAG because it extracts only symbols, reducing context window usage by 80-90%.
Exposes search_code_advanced tool that combines regex pattern matching, fuzzy string matching, and file type filtering to locate code across indexed repositories. Searches operate against both the symbol database (for function/class names) and file contents (for code patterns). Supports complex queries like 'find all async functions in TypeScript files' through composable filter chains. Results include file paths, line numbers, and context snippets.
Unique: Combines three independent search strategies (regex, fuzzy, file filtering) into a single composable query interface, allowing LLMs to mix-and-match strategies without multiple tool calls. Searches both symbol database and file contents, enabling both structural and textual code discovery.
vs alternatives: More flexible than grep/ripgrep because it understands symbol boundaries and file types; faster than full-text search because it leverages pre-built symbol index for structural queries.
Implements an intelligent parser selection system that chooses the best parsing strategy for each language based on availability and accuracy. For languages with tree-sitter bindings (Python, JavaScript, TypeScript, Go, Rust, Java, C++, etc.), uses AST parsing. For unsupported languages, falls back to regex-based heuristics. Fallback strategies are language-specific (e.g., Bash uses different patterns than SQL). Parsing results are cached to avoid re-parsing identical files.
Unique: Implements fallback chain that gracefully degrades from AST parsing to regex heuristics, enabling symbol extraction for any language without external dependencies. Caches parsing results to avoid re-parsing identical files across multiple queries.
vs alternatives: More practical than requiring language-specific tools because it works with Python bindings only; more accurate than pure regex because it uses AST when available.
Extends basic search with semantic awareness by filtering results by symbol type (function, class, variable, import) and scope (global, module-level, nested). Allows queries like 'find all async functions' or 'find all class methods named init'. Leverages symbol metadata extracted during indexing (type, scope, decorators) to filter results without post-processing. Results include full symbol context (definition location, signature, scope chain).
Unique: Combines pattern matching with semantic filtering based on symbol metadata extracted during indexing. Enables high-precision searches without post-processing or AST traversal at query time.
vs alternatives: More precise than grep because it understands symbol types and scopes; faster than runtime analysis because it uses pre-computed metadata.
Provides get_project_stats tool that analyzes the indexed codebase to generate aggregate metrics: total files, lines of code per language, symbol counts (functions, classes, variables), file size distribution, and complexity estimates. Metrics are computed from the index without re-parsing. Supports filtering by language, file type, or directory. Useful for understanding codebase scale and composition.
Unique: Generates metrics from pre-computed index without re-parsing, enabling fast statistics generation even for large codebases. Supports filtering by language, file type, and directory for granular analysis.
vs alternatives: Faster than tools like cloc because it uses indexed data; more accurate than line-counting tools because it understands symbol structure.
Analyzes import statements and symbol references to build a dependency graph showing relationships between files and modules. Extracts import/require statements from indexed code to identify direct dependencies. Supports language-specific import syntax (Python import/from, JavaScript import/require, Go import, etc.). Can compute transitive dependencies and identify circular dependencies. Results are returned as graph data structure suitable for visualization or further analysis.
Unique: Extracts dependency relationships from indexed import statements without executing code or resolving external packages. Supports language-specific import syntax and can compute transitive dependencies efficiently.
vs alternatives: More practical than runtime dependency analysis because it works without executing code; more accurate than static analysis tools because it uses parsed AST instead of regex.
The get_file_summary tool generates concise summaries of individual source files by analyzing their AST structure to extract top-level definitions (functions, classes, imports, exports). Summaries include symbol lists with signatures, dependency information, and file-level documentation. Uses tree-sitter parsing to understand code structure without executing or compiling, producing machine-readable output suitable for LLM context windows.
Unique: Generates summaries by parsing AST rather than regex or heuristics, ensuring accurate symbol extraction even in complex nested code. Output is optimized for LLM consumption (JSON-structured, concise) rather than human reading.
vs alternatives: More accurate than comment-based summaries because it extracts actual code structure; more efficient than sending full file content because summaries are 5-20% of original size while retaining 90% of structural information.
Implements a FastMCP server that exposes 15+ code intelligence tools through the Model Context Protocol, communicating with MCP clients (Claude Desktop, Codex CLI) via stdio transport. All tools are decorated with @mcp.tool() and wrapped with @handle_mcp_tool_errors for consistent error handling. The server manages a CodeIndexerContext object that provides shared state (index managers, services, configuration) across all tool invocations, enabling stateful operations like maintaining an active project path.
Unique: Uses FastMCP framework with decorator-based tool registration (@mcp.tool()), reducing boilerplate compared to manual JSON-RPC handling. Centralized error handling via @handle_mcp_tool_errors decorator ensures all tools return consistent error responses without per-tool try-catch blocks.
vs alternatives: Simpler than building a custom REST API because MCP handles protocol negotiation and transport; more reliable than direct LLM API calls because MCP enforces schema validation and error handling.
+6 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 code-index-mcp at 34/100. code-index-mcp 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