| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 13 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Parses Swift source files using Apple's SwiftSyntax framework (since v1.9.0) to build a complete abstract syntax tree, extracting type definitions, methods, variables, and relationships. Implements an intelligent caching system that fingerprints file contents and skips re-parsing unchanged files, dramatically improving performance on large codebases by avoiding redundant syntax analysis.
Unique: Uses Apple's official SwiftSyntax framework for structurally-aware parsing instead of regex or custom lexers, combined with file-level content hashing for incremental re-parsing — enabling accurate handling of Swift's complex syntax including generics, opaque types, and macro annotations
vs alternatives: More accurate than regex-based parsers (handles edge cases like string literals containing type syntax) and faster than re-parsing on every invocation due to intelligent caching, though slower than simple text-based pattern matching for small files
Supports three distinct template languages — Stencil (Jinja2-like syntax), native Swift templates, and JavaScript — allowing developers to choose the most ergonomic approach for their code generation needs. Each template language has access to the complete parsed type model through a unified context object, enabling templates to introspect types, iterate over methods/variables, and conditionally generate code based on annotations or type characteristics.
Unique: Supports three distinct template languages (Stencil, Swift, JavaScript) with unified access to the same parsed type model, allowing developers to choose the most ergonomic approach — Swift templates can use native language features, Stencil templates leverage familiar Jinja2 syntax, and JavaScript templates enable cross-platform logic
vs alternatives: More flexible than single-language generators (e.g., Sourcegen which only supports Stencil) and more accessible than code-as-configuration approaches (e.g., SwiftGen's YAML) by supporting multiple familiar syntaxes
Exposes a comprehensive object model (Type, Class, Struct, Enum, Protocol, Method, Variable, Parameter, etc.) to templates, allowing introspection of type characteristics, methods, properties, and relationships. Templates can query type metadata (name, kind, access level, annotations), iterate over methods and variables with full signature information, and traverse type relationships to make generation decisions based on type structure.
Unique: Exposes a rich object model (Type, Method, Variable, Parameter, etc.) to templates with full access to parsed type information including signatures, annotations, and relationships, enabling templates to make sophisticated code generation decisions based on type structure without re-parsing
vs alternatives: More complete than simple string-based type information (enables type-aware generation) and more accessible than requiring templates to parse AST directly (abstracts away syntax details)
Generates Swift code compatible with multiple Apple platforms (iOS, macOS, tvOS, watchOS) by understanding platform-specific APIs and availability annotations. Templates can query platform availability information and conditionally generate platform-specific code, enabling creation of cross-platform libraries and frameworks that adapt generated code to target platforms.
Unique: Parses @available annotations to understand platform-specific APIs and makes this information available to templates, enabling generation of platform-adapted code without requiring templates to manually parse availability syntax
vs alternatives: More maintainable than manual platform-specific code generation (availability information is automatically extracted) and more flexible than single-platform generators, though requires templates to implement platform-specific logic
Provides detailed error messages and diagnostics that include source file paths and line numbers, helping developers quickly locate and fix issues in source code or templates. Errors during parsing, template processing, or code generation include context about what failed and where, reducing debugging time for code generation issues.
Unique: Includes file paths and line numbers in error messages for parsing, template processing, and code generation errors, helping developers quickly locate issues in source code or templates without manual debugging
vs alternatives: More helpful than generic error messages (includes context about location and cause) and more accessible than requiring manual debugging with print statements
Parses documentation comments (/// annotations) embedded in Swift source code to extract metadata that controls code generation behavior. Developers can annotate types, methods, and variables with custom markers (e.g., // sourcery: AutoMockable) that templates can query to conditionally generate code — enabling declarative, in-source configuration of which types receive generated code without separate configuration files.
Unique: Extracts code generation directives from documentation comments (/// sourcery: annotations) parsed by SwiftSyntax, allowing developers to declare generation intent inline with type definitions rather than in separate configuration files — the parsed annotations are available to templates as queryable metadata on Type objects
vs alternatives: More discoverable than external configuration files (annotations live next to the code they affect) and more flexible than attribute-based approaches (e.g., @Codable) which require language-level support, though less type-safe than compile-time annotations
Builds a complete type relationship graph by composing parsed types to resolve inheritance chains, protocol conformance, and type dependencies. The Composer component walks the parsed AST to establish parent-child relationships, protocol implementations, and generic type bindings, creating a queryable model where templates can traverse inheritance hierarchies, find all types conforming to a protocol, or identify generic type parameters.
Unique: The Composer component explicitly walks the parsed AST to resolve type relationships (inheritance, protocol conformance, generic bindings) into a queryable graph structure, allowing templates to traverse hierarchies and find related types — rather than requiring templates to manually parse relationship information
vs alternatives: More complete than simple type listing (enables hierarchical queries) and more efficient than re-parsing relationships in each template (relationships are computed once during composition phase)
Supports flexible input configuration through YAML files (.sourcery.yml) and command-line arguments, enabling developers to specify source files, directories, Xcode project targets, and Swift package targets as input sources. The configuration system resolves these diverse input types into a unified list of Swift files to parse, supporting project-level configuration that can be version-controlled and shared across teams.
Unique: Supports three input source types (direct files, Xcode project targets, Swift package targets) resolved through a unified configuration system that can be specified via YAML or CLI, allowing teams to configure code generation at the project level rather than manually listing files
vs alternatives: More flexible than file-list-based approaches (e.g., specifying individual files) because it understands Xcode and SPM project structures, and more maintainable than CLI-only configuration because YAML files can be version-controlled
+5 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 Sourcery at 44/100. Sourcery 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