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| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Maintains a continuously updated, manually curated registry of open-source large language models with commercial-use licensing. The repository implements a structured catalog approach where each model entry includes metadata (model name, organization, parameter count, license type, release date, and commercial eligibility) organized in markdown tables and JSON structures, enabling developers to filter and discover models based on licensing constraints, model size, and use-case suitability without legal ambiguity.
Unique: Focuses specifically on commercial-use licensing eligibility rather than general model benchmarking or capability comparison — filters out models with restrictive licenses (e.g., research-only, non-commercial clauses) upfront, reducing legal risk for production deployments
vs alternatives: More legally-focused than Hugging Face Model Hub (which lists all models regardless of commercial restrictions) and more current than static LLM comparison papers, providing a practical filtering layer for compliance-conscious teams
Aggregates heterogeneous model metadata from multiple sources (model cards, GitHub repositories, research papers, official announcements) and normalizes it into a consistent schema with fields for model name, organization, parameter count, license, release date, and commercial-use status. The implementation uses markdown tables as the primary data structure with optional JSON exports, enabling both human-readable browsing and programmatic access through simple parsing.
Unique: Uses a deliberately simple, human-readable markdown-first schema rather than complex database structures, making the registry accessible to non-technical stakeholders while remaining machine-parseable for automation
vs alternatives: Simpler and more accessible than database-backed model registries (e.g., MLflow Model Registry) but less queryable; trades flexibility for transparency and ease of contribution
Implements a filtering mechanism that categorizes models by their license type and commercial-use permissions, distinguishing between fully commercial-eligible models (Apache 2.0, MIT, OpenRAIL-M) and restricted models (research-only, non-commercial clauses, or ambiguous licensing). The filtering is applied at the curation stage where models are manually reviewed against licensing criteria before inclusion in the registry.
Unique: Explicitly prioritizes commercial-use licensing as the primary filtering criterion rather than model performance or capability, addressing a specific pain point for enterprises that need legal certainty before deployment
vs alternatives: More legally-focused than general model discovery tools; provides clearer commercial-use guidance than raw license documents, though less authoritative than legal counsel
Maintains a longitudinal view of the open-source LLM ecosystem by tracking model releases, organizational contributions, licensing trends, and parameter-size distributions over time. The repository serves as a historical record of which organizations are releasing open models, when they were released, and how the landscape has evolved, enabling analysis of ecosystem maturity and competitive dynamics.
Unique: Provides a curated, human-reviewed historical record of open-source LLM releases with explicit commercial-use filtering, rather than automated scraping of all models, enabling cleaner trend analysis and reducing noise from research-only or restricted models
vs alternatives: More selective and legally-focused than raw Hugging Face statistics; provides organizational and licensing context that raw model counts lack, though less comprehensive than exhaustive ecosystem surveys
Provides structured information to support model selection decisions by presenting models in a filterable, comparable format with key decision criteria (license, parameter count, organization, release date). The registry enables side-by-side comparison of models and helps developers quickly narrow down options based on their specific constraints (budget, licensing requirements, model size, organizational preference).
Unique: Focuses on commercial-use licensing as a primary decision criterion alongside technical attributes, addressing the specific decision-making needs of enterprises and startups that cannot use restricted models
vs alternatives: More legally-aware than generic model comparison tools; provides clearer filtering for commercial use cases, though less comprehensive than full benchmarking suites that include performance metrics
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 Open LLMs at 19/100.
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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
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