| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 14 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Goa implements a Go-based Domain Specific Language (DSL) that developers use to declaratively define API structures using Service(), Method(), Payload(), Result(), and transport-specific functions. The DSL is compiled and executed by the generator, which evaluates all constructs into an internal expression system (RootExpr, ServiceExpr, MethodExpr, AttributeExpr, ValidationExpr, HTTPEndpointExpr, GRPCEndpointExpr) that represents the complete API design. This expression tree becomes the single source of truth for all downstream code generation, documentation, and client generation.
Unique: Uses a Go-native DSL with embedded expression evaluation rather than external schema files (YAML/JSON), enabling compile-time validation and IDE support; the expression system (expr package) provides a unified internal representation that all generators consume, eliminating translation layers between spec formats
vs alternatives: Stronger than OpenAPI-first approaches because design validation and type safety happen at definition time in Go, not as post-generation linting; more integrated than Protobuf because HTTP and gRPC transports share a single design model rather than requiring separate .proto files
The code generation engine orchestrates protocol-specific generators that consume the expression tree and produce transport-layer implementations. HTTP transport generation creates route handlers, request/response marshaling, and middleware hooks; gRPC generation produces service definitions and interceptor support; JSON-RPC generation creates JSON-RPC 2.0 compliant endpoints. Each protocol generator is independent but shares type definitions and validation rules from the unified expression model, ensuring consistency across transports without code duplication.
Unique: Generates all three major RPC protocols (HTTP, gRPC, JSON-RPC) from a single design definition using protocol-specific generator modules (codegen/service, grpc/codegen, jsonrpc/codegen) that share type transformation and validation logic, eliminating the need to maintain separate .proto files, OpenAPI specs, or JSON-RPC schemas
vs alternatives: More comprehensive than gRPC-only frameworks (like Buf) because it unifies HTTP and gRPC under one design; more flexible than OpenAPI generators because protocol-specific features (streaming, interceptors) are first-class DSL constructs rather than annotations
Goa supports design evolution by allowing developers to modify the DSL and regenerate code. The generator produces code in separate files (service.go, endpoints.go, http.go, grpc.go) such that business logic files (service implementation) are not overwritten during regeneration. Developers can add new methods, modify types, or change transport configurations, and the generator updates only the affected generated files. The design model tracks version information and can detect breaking changes, though the framework does not enforce backward compatibility automatically.
Unique: Separates generated code into multiple files (service.go, endpoints.go, http.go, grpc.go) such that business logic implementation is never overwritten during regeneration, allowing safe design evolution; the expression system tracks design changes and can detect breaking changes
vs alternatives: More flexible than code-generation-once approaches because design can be evolved and regenerated; more maintainable than hand-written code because generated code is always synchronized with design
Goa generates JSON-RPC 2.0 compliant endpoints from service definitions, creating HTTP endpoints that accept JSON-RPC 2.0 requests and return JSON-RPC 2.0 responses. The generator creates request/response marshaling code that maps JSON-RPC parameters to service method arguments and service method results to JSON-RPC responses. Error handling is integrated through JSON-RPC error codes and messages. The generated code handles both positional and named parameters as defined in the JSON-RPC 2.0 specification.
Unique: Generates JSON-RPC 2.0 endpoints from the same design definition used for HTTP and gRPC, ensuring all three RPC protocols expose the same business logic without code duplication; request/response marshaling is automatically generated with support for both positional and named parameters
vs alternatives: More integrated than third-party JSON-RPC libraries because JSON-RPC is a first-class transport option in the design; more consistent than hand-written JSON-RPC code because endpoints are generated from the design and automatically synchronized
Goa generates type-safe client libraries for all transport protocols (HTTP, gRPC, JSON-RPC) from the service definition. The generator creates client structs with methods that correspond to service methods, handling request marshaling, response unmarshaling, and error handling. HTTP clients use the standard Go http.Client; gRPC clients use the generated gRPC stubs; JSON-RPC clients use HTTP with JSON-RPC 2.0 formatting. Generated clients are fully type-safe and include proper error handling and timeout support.
Unique: Generates type-safe clients for all three transport protocols (HTTP, gRPC, JSON-RPC) from a single service definition, ensuring clients are always synchronized with the server implementation; clients are fully type-safe with proper error handling
vs alternatives: More comprehensive than OpenAPI client generators because it supports gRPC and JSON-RPC in addition to HTTP; more integrated than hand-written clients because clients are generated from the design and automatically synchronized
Goa generates code that maps HTTP request/response headers, path parameters, query parameters, and request bodies to service method arguments and results. The HTTPEndpointExpr configuration specifies where each parameter comes from (path, query, header, body), and the generator creates code that extracts, validates, and transforms these parameters. Response headers and status codes are also configured in the design and automatically generated. The generator handles type conversion (e.g., string to int) and validation for all parameter types.
Unique: Generates parameter extraction code that is aware of parameter locations (path, query, header, body) defined in HTTPEndpointExpr, automatically handling type conversion and validation without requiring manual route handler code
vs alternatives: More integrated than third-party parameter binding libraries because parameter mapping is defined in the design and automatically generated; more type-safe than manual parameter extraction because type conversion and validation are generated
Goa generates validation code for all request payloads and response results based on ValidationExpr rules defined in the DSL (Required, Enum, Format, Pattern, Minimum, Maximum, etc.). The generated validation functions are type-safe Go code that enforces constraints at runtime before business logic executes. Validation rules are embedded in AttributeExpr definitions and automatically propagated to all transport layers (HTTP, gRPC, JSON-RPC), ensuring consistent validation across protocols without duplicating constraint definitions.
Unique: Validation rules are defined once in the DSL and automatically generated as type-safe Go functions that execute before business logic, with validation errors propagated consistently across all transport protocols; this eliminates the need for manual validation code or third-party validation libraries
vs alternatives: More integrated than tag-based validation (like Go's validator package) because constraints are part of the design model and automatically enforced; more consistent than hand-written validation because rules are centralized and regenerated with design changes
Goa generates OpenAPI 3.0 specifications directly from the expression tree, mapping service definitions, methods, payloads, results, and HTTP endpoint configurations into OpenAPI components (paths, schemas, parameters, responses). The generator traverses the expression model and produces valid OpenAPI YAML/JSON that accurately reflects the API design, including request/response schemas, validation constraints, and HTTP metadata. This ensures the OpenAPI spec is always synchronized with the implementation and never becomes stale.
Unique: Generates OpenAPI specs directly from the internal expression tree rather than parsing generated code or annotations, ensuring 100% fidelity between design and spec; validation constraints from the DSL are automatically mapped to OpenAPI schema constraints (minLength, maxLength, enum, pattern, etc.)
vs alternatives: More accurate than annotation-based OpenAPI generation (like Swag for Go) because the spec is generated from the design model before code generation, not reverse-engineered from code; more maintainable than hand-written specs because regeneration keeps specs synchronized with design changes
+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 goa at 46/100. goa 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