| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Routes incoming requests across 70+ AI providers (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Azure OpenAI, Cohere, etc.) using configurable strategies including fallback chains, load balancing, and conditional routing. Implements recursive target orchestration via tryTargetsRecursively() that attempts providers sequentially with exponential backoff retry logic (up to 5 attempts), automatically falling back to next provider on failure. Supports single-target, fallback, and load-balanced modes with provider-specific request/response transformation.
Unique: Implements recursive target orchestration where each fallback target can itself define fallbacks, enabling complex provider chains. Uses tryTargetsRecursively() pattern with configurable retry strategies and exponential backoff, supporting both sequential fallback and parallel load-balancing modes within a single request pipeline.
vs alternatives: Supports deeper fallback chains and more granular routing strategies than simple round-robin proxies like LiteLLM, enabling production-grade multi-provider resilience without external orchestration layers.
Abstracts provider-specific API differences by transforming incoming requests to provider-native formats and normalizing responses back to OpenAI-compatible schema. Each provider (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Azure OpenAI, Cohere) has dedicated transformation logic that maps request parameters (model, messages, temperature, etc.) to provider-specific payloads and transforms provider responses into unified format. Handles streaming responses, token counting, and function-calling schemas across heterogeneous provider APIs.
Unique: Maintains provider-specific transformation modules (src/providers/) with dedicated classes for each provider (OpenAI, Anthropic, Bedrock, etc.) that implement request/response transformation as first-class concerns. Supports both request transformation (to provider format) and response transformation (to OpenAI format) with streaming-aware buffering.
vs alternatives: More comprehensive provider coverage (70+ vs typical 10-15) and deeper transformation logic than generic proxy solutions, enabling true provider-agnostic applications rather than just credential management.
Built on Hono lightweight web framework supporting deployment across multiple runtime environments: Node.js, Cloudflare Workers, Bun, and Deno. Single codebase compiles to each runtime with minimal changes, enabling deployment flexibility. Runtime-specific features (e.g., real-time SSE log streaming) are conditionally available. Supports both HTTP server mode (Node.js, Bun) and serverless/edge function mode (Cloudflare Workers, Deno). Configuration and provider integrations are runtime-agnostic.
Unique: Single codebase built on Hono framework compiles to multiple runtimes (Node.js, Cloudflare Workers, Bun, Deno) with minimal changes. Runtime-specific features are conditionally available, enabling deployment flexibility without code duplication.
vs alternatives: True multi-runtime support with single codebase is rare — most gateways target single runtime. Enables edge deployment on Cloudflare Workers for global latency reduction while maintaining Node.js compatibility for traditional deployments.
Routes requests to appropriate provider endpoints based on model identifier, abstracting provider-specific endpoint structures. Supports model aliasing so applications can reference models by friendly names (e.g., 'gpt-4') and gateway maps to provider-specific model IDs (e.g., 'gpt-4-turbo-preview'). Handles provider-specific endpoint variations (Azure endpoint structure, Bedrock model ARNs, etc.) transparently. Enables model switching without application code changes by updating configuration.
Unique: Implements model aliasing allowing applications to reference friendly model names while gateway maps to provider-specific model IDs. Handles provider-specific endpoint structures (Azure, Bedrock, etc.) transparently.
vs alternatives: Model aliasing enables model switching without application code changes, whereas most gateways require explicit provider-specific model IDs. Supports provider-specific endpoint variations transparently.
Normalizes function-calling schemas across providers with different function definition formats (OpenAI, Anthropic, Google, etc.). Transforms function definitions from OpenAI format to provider-native format before transmission, and transforms provider-native function calls back to OpenAI format in responses. Supports function calling for providers that implement it, with graceful degradation for providers without native function-calling support. Handles tool_choice parameter mapping and function execution context.
Unique: Normalizes function-calling schemas across providers with different function definition formats (OpenAI, Anthropic, Google, etc.). Transforms function definitions to provider-native format and function calls back to OpenAI format.
vs alternatives: Enables true provider-agnostic function calling, whereas most gateways require provider-specific function schemas. Handles schema transformation transparently.
Routes requests to different providers based on conditional logic evaluating request parameters (model, message length, user metadata, etc.). Supports rule-based routing where conditions trigger provider selection, enabling sophisticated routing strategies beyond simple fallback or load balancing. Conditions can reference request fields, user context, and provider metadata. Enables A/B testing by routing subset of requests to experimental providers, cost optimization by routing expensive requests to cheaper providers, and capability-based routing by selecting providers supporting required features.
Unique: Supports rule-based conditional routing evaluating request parameters, enabling sophisticated routing strategies beyond simple fallback or load balancing. Enables A/B testing, cost optimization, and capability-based routing.
vs alternatives: More flexible routing than simple fallback or load balancing. Enables cost optimization and A/B testing without external orchestration.
Implements dual-mode caching system supporting both simple (exact-match) and semantic (embedding-based similarity) caching with configurable TTL. Simple caching stores responses keyed by request hash, returning cached results for identical requests within TTL window. Semantic caching uses embeddings to match semantically similar requests and return cached responses, reducing redundant API calls for paraphrased queries. Caching decisions are configurable per request via headers or configuration, with cache invalidation and TTL management built-in.
Unique: Dual-mode caching supporting both exact-match (simple) and embedding-based semantic similarity matching, with configurable TTL and per-request cache policy. Integrates with hooks system to allow custom cache backends and invalidation strategies.
vs alternatives: Offers semantic caching as first-class feature alongside simple caching, enabling cost reduction for paraphrased queries that other gateways treat as cache misses. Configurable per-request rather than global-only.
Extensible plugin architecture with 22+ built-in guardrails and mutators that intercept requests and responses at defined lifecycle points. Hooks execute before request transmission (pre-request), after response receipt (post-response), and on errors, enabling validation, transformation, and security enforcement. Guardrails (validation hooks) reject requests/responses based on policies (PII detection, prompt injection, content filtering, etc.). Mutators transform requests/responses (e.g., prompt rewriting, response formatting). Custom hooks can be registered via plugin system with access to request context, provider info, and configuration.
Unique: Implements lifecycle-based hook system with distinct hook types (guardrails vs mutators) executing at pre-request, post-response, and error stages. Includes 22+ built-in plugins covering PII detection, prompt injection, content moderation, and custom transformations. Plugin registry allows runtime registration of custom hooks without code changes.
vs alternatives: More granular hook lifecycle (pre/post/error) and larger built-in plugin library (22+) than typical gateway implementations. Distinguishes guardrails (validation) from mutators (transformation) as separate hook types, enabling cleaner policy expression.
+6 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 gateway at 38/100.
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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