| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 14 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Parses source code from 14 programming languages (Python, JavaScript, TypeScript, Java, C++, Go, Rust, Ruby, PHP, C#, Swift, Kotlin, Scala, Lua) using Tree-sitter's incremental parsing engine to build abstract syntax trees. Extracts semantic entities (functions, classes, variables, imports) and their relationships with structural awareness, enabling precise code graph construction rather than regex-based pattern matching. The parser layer feeds directly into the GraphBuilder service, which normalizes language-specific syntax into a unified graph schema.
Unique: Uses Tree-sitter's incremental parsing with language-specific grammars for 14 languages, enabling structural awareness of code relationships rather than text-based pattern matching. Normalizes heterogeneous syntax into a unified graph schema through a language-agnostic entity extraction layer.
vs alternatives: Faster and more accurate than regex-based indexing (Sourcegraph, Ctags) because it understands code structure; broader language support than LSP-only solutions while remaining lightweight and offline-capable.
Builds a queryable property graph of code entities and relationships, storing nodes (functions, classes, modules) and edges (calls, inherits, imports, references) in a graph database. Supports four database backends via a DatabaseManager singleton pattern: KùzuDB (default, zero-config, in-process), FalkorDB Lite (Unix only), FalkorDB Remote (networked), and Neo4j (all platforms). The GraphBuilder service constructs the graph incrementally, and the database abstraction layer enables backend switching without changing query logic, allowing teams to scale from local development (KùzuDB) to production deployments (Neo4j).
Unique: Implements a DatabaseManager singleton with pluggable backends (KùzuDB, FalkorDB, Neo4j) sharing identical query interfaces, enabling zero-config local development and seamless scaling to production. Uses dependency injection pattern to allow backend switching without service layer changes.
vs alternatives: More flexible than Sourcegraph (which uses PostgreSQL) because it supports multiple graph databases; more scalable than LSP-based indexing because it pre-computes relationships rather than computing them on-demand.
Manages long-running operations (code indexing, bundle downloads, graph updates) as background jobs tracked by a JobManager service. Each job has a unique ID, status (pending, in-progress, completed, failed), and progress metadata. Jobs are stored in-memory and exposed through both CLI and MCP interfaces, allowing clients to poll job status without blocking. The job system prevents MCP client timeouts by returning immediately with a job ID, then allowing clients to check progress asynchronously. Enables responsive UX for operations that take seconds or minutes.
Unique: Implements a JobManager that tracks long-running operations with unique IDs and status polling, preventing MCP client timeouts. Enables responsive UX for operations that take seconds or minutes by returning immediately with a job ID.
vs alternatives: More responsive than blocking operations because clients can poll progress; more practical than fire-and-forget because job status is tracked and retrievable.
Provides configuration management that supports multiple deployment environments (local development, Docker, production) with environment-specific database backends, logging levels, and bundle registry URLs. Configuration is loaded from environment variables, config files, and command-line arguments with a clear precedence order. Enables teams to use KùzuDB locally, FalkorDB in staging, and Neo4j in production without code changes. The configuration layer also handles database connection pooling, retry logic, and fallback strategies (e.g., falling back from FalkorDB to KùzuDB if connection fails).
Unique: Implements configuration management with multi-environment support and automatic database backend fallback (FalkorDB → KùzuDB), enabling seamless switching between local development and production deployments without code changes.
vs alternatives: More flexible than hardcoded configurations because it supports multiple backends; more robust than single-backend tools because it includes fallback strategies.
Implements incremental indexing that detects changed files and updates only affected graph nodes and edges rather than re-indexing the entire codebase. The GraphBuilder service tracks file modification times and checksums to identify changes, re-parses only modified files, and updates the graph with new/modified/deleted entities. Enables fast re-indexing of large codebases where only a few files change between updates. Integrates with the CodeWatcher to automatically trigger incremental updates when files change, keeping the graph synchronized with the codebase.
Unique: Implements incremental indexing with change detection based on file modification times and checksums, enabling fast re-indexing of large codebases. Integrates with CodeWatcher for automatic delta updates as files change.
vs alternatives: Faster than full re-indexing because it only processes changed files; more practical than manual change tracking because detection is automatic.
Provides Docker images and docker-compose configurations for deploying CodeGraphContext with Neo4j or other production databases. The Docker setup includes the MCP server, CLI tools, and optional visualization server, with environment-based configuration for different deployment scenarios. Enables teams to deploy code intelligence as a containerized service with persistent database storage, making it suitable for production environments and CI/CD integration.
Unique: Provides production-ready Docker images and docker-compose configurations for deploying CodeGraphContext with Neo4j, enabling containerized code intelligence as a shared service. Includes environment-based configuration for different deployment scenarios.
vs alternatives: More practical than manual installation because it includes all dependencies; more scalable than local-only deployments because it supports persistent databases and team sharing.
Monitors local file system changes using a CodeWatcher service and automatically updates the indexed graph database when source files are modified, created, or deleted. Implements debouncing to batch rapid file changes and avoid thrashing the database with individual updates. The watcher integrates with the JobManager to track synchronization status and expose progress through both CLI and MCP interfaces, enabling AI assistants to work with current code context without manual re-indexing.
Unique: Integrates file system watching with the JobManager to provide real-time graph synchronization with debouncing and status tracking. Enables AI assistants to work with current code context through MCP without requiring manual re-indexing, bridging the gap between development and AI context freshness.
vs alternatives: More responsive than periodic re-indexing (Sourcegraph, Tabnine) because it updates immediately on file changes; more efficient than naive per-file updates because debouncing batches rapid changes.
Exposes the code graph as a Model Context Protocol (MCP) server using JSON-RPC 2.0 over stdio, enabling AI assistants (Claude, Cursor, VS Code) to query code relationships, search entities, and analyze dependencies without direct database access. The MCP server wraps core services (GraphBuilder, CodeFinder, CodeWatcher) and translates MCP tool calls into service method invocations, returning structured results. Implements background job tracking so long-running operations (indexing, bundle downloads) can be polled asynchronously, preventing MCP client timeouts.
Unique: Implements a full MCP server that wraps the unified service layer, enabling AI assistants to query the code graph through standard MCP tool calls. Uses background job tracking with JobManager to handle long-running operations asynchronously, preventing client timeouts and enabling progressive indexing.
vs alternatives: More integrated than REST API approaches because it uses MCP's native tool calling protocol; more responsive than polling-based solutions because it tracks job status server-side and allows clients to check progress.
+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 CodeGraphContext at 37/100. CodeGraphContext 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