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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Translates static content files (JSON, YAML, CSV, PO, Markdown) by parsing them into an intermediate representation, routing translation requests through a pluggable LLM provider layer (Lingo.dev Engine, OpenAI, Anthropic, Google, Mistral, OpenRouter, Ollama), and writing localized output files with an i18n.lock manifest tracking translation state. The compiler uses AST-aware parsing per format to preserve structure and metadata during round-trip translation.
Unique: Implements a provider abstraction layer that allows swapping between 6+ LLM backends (Lingo.dev Engine, OpenAI, Anthropic, Google, Mistral, OpenRouter, Ollama) without code changes, combined with format-specific AST-aware parsers that preserve file structure and metadata during translation rather than naive string replacement.
vs alternatives: Offers more LLM provider flexibility and format support than traditional i18n tools like i18next or react-intl, while maintaining deterministic, reproducible translations via lock files unlike manual translation services.
Integrates into Next.js, Vite, or webpack build pipelines via withLingo() wrapper or lingoCompilerPlugin() to intercept JSX/TSX source files, extract translatable strings, invoke LLM translation, and inject localized content into separate .lingo/ cache bundles per locale. The new compiler (@lingo.dev/compiler) uses AST transformation to rewrite component imports and string literals, enabling zero-runtime overhead for static translations while maintaining source map fidelity.
Unique: Uses AST-aware code transformation to inject localized content directly into compiled bundles at build time, eliminating runtime translation overhead and enabling per-locale code splitting, rather than runtime string lookup tables used by traditional i18n libraries.
vs alternatives: Faster than react-intl or next-i18next at runtime (zero translation latency) and smaller bundle sizes per locale than shipping a single translation dictionary, but requires longer build times due to LLM API calls.
Provides React-specific bindings (in @lingo.dev/react package) including hooks (useLocale, useTranslate) and context providers that integrate Lingo.dev translations into React component trees. The React package wraps the SDK to provide idiomatic React patterns, enabling components to access current locale, trigger locale switches, and subscribe to translation updates without prop drilling.
Unique: Provides idiomatic React hooks (useLocale, useTranslate) and context providers that integrate Lingo.dev translations into React component trees, enabling locale switching and translation access without prop drilling or HOCs.
vs alternatives: More React-idiomatic than generic SDK usage; comparable to react-intl but with LLM-powered translation and simpler API for basic use cases.
Maintains an i18n.lock manifest file that tracks the translation state of every string (which strings have been translated, which are pending, which have changed since last translation). The lock file enables incremental translation workflows where only changed or new strings are re-translated, reducing API costs and improving CI/CD performance. Lock file is version-controlled alongside source code, providing an audit trail of translation history.
Unique: Implements an i18n.lock manifest that tracks translation state per string, enabling incremental translation workflows where only changed strings are re-translated, reducing API costs and improving CI/CD performance while providing an audit trail.
vs alternatives: More cost-efficient than re-translating all strings on every run; comparable to lock files in package managers (package-lock.json, yarn.lock) but for translation state rather than dependencies.
Provides a JavaScript/TypeScript SDK (npm install lingo.dev) that localizes strings, objects, and HTML at runtime by querying a locale-aware translation store with automatic fallback chains (e.g., en-US → en → default). The SDK manages locale state, caches translations in memory, and supports both synchronous lookups for pre-compiled translations and async calls for dynamic content, with built-in support for pluralization and interpolation patterns.
Unique: Implements automatic fallback chains with configurable locale hierarchies (e.g., en-US → en → default) and in-memory caching of translations, allowing runtime locale switching without page reloads or rebuilds, combined with support for both pre-compiled and dynamic translations in a single API.
vs alternatives: More flexible than static i18n libraries (i18next, react-intl) for dynamic content, but slower at runtime than build-time compiled translations; better suited for hybrid scenarios with both static and dynamic localization needs.
Command-line interface (npx lingo.dev@latest run) that recursively discovers translatable files in a project (JSON, YAML, CSV, PO, Markdown), batches them for efficient LLM processing, orchestrates the translation pipeline, and writes localized output files alongside an i18n.lock manifest. The CLI uses a configuration file (i18n.json) to define source directories, target locales, and provider settings, with support for dry-run mode and incremental translation (only translating changed files since last run).
Unique: Implements recursive file discovery with format-specific loaders, batching optimization for LLM API efficiency, and incremental translation tracking via i18n.lock manifest, allowing teams to translate entire projects in a single command while maintaining reproducibility and auditability.
vs alternatives: More automated than manual translation workflows or spreadsheet-based tools, and more flexible than single-file translation tools; comparable to Crowdin or Lokalise but with LLM-driven automation and no vendor lock-in.
Exposes Lingo.dev as a Model Context Protocol (MCP) server that allows AI agents and IDEs to prompt for i18n needs in natural language and receive generated routing, middleware, and configuration boilerplate. The MCP server translates high-level i18n requirements (e.g., 'support 10 languages with fallback to English') into concrete code artifacts (Next.js middleware, locale routing, provider configuration) without requiring manual setup.
Unique: Implements an MCP server that translates natural language i18n requirements into concrete code artifacts (routing, middleware, configuration), enabling AI agents to scaffold multilingual projects without requiring developers to understand framework-specific i18n patterns.
vs alternatives: Unique to Lingo.dev as an MCP-first i18n tool; traditional i18n libraries require manual setup, while this enables AI-assisted scaffolding for faster project initialization.
GitHub Action (uses: lingodotdev/lingo.dev@main) that triggers on git push to main, automatically translates changed content files, and commits translated files back to the repository or opens a pull request with translations. The action integrates with GitHub Workflows, caches translation results to avoid redundant API calls, and supports conditional triggers (e.g., only translate if specific files changed).
Unique: Implements a GitHub Action that automatically translates content on push and commits results back to the repository or opens a PR, integrating continuous localization directly into CI/CD workflows without requiring separate translation services or manual steps.
vs alternatives: More integrated with GitHub workflows than external translation services (Crowdin, Lokalise) and cheaper than SaaS localization platforms for teams already using GitHub; requires more setup than manual translation but eliminates manual file management.
+4 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Lingo.dev at 27/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities