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| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a graph-based memory system that stores entities (people, concepts, events) and their relationships as persistent nodes and edges, enabling structured knowledge representation beyond flat key-value storage. The system uses a graph data model where entities are nodes and relationships are directed edges with semantic labels, allowing LLM clients to query and traverse connected knowledge through MCP tool calls. This approach enables contextual memory recall where related entities are discoverable through relationship traversal rather than keyword matching alone.
Unique: Uses MCP's tool-based interface to expose graph operations (add entity, create relationship, query by traversal) as discrete callable tools rather than embedding memory as opaque context, enabling explicit client control over memory operations and making memory state queryable and debuggable
vs alternatives: Differs from vector-based RAG memory by storing explicit semantic relationships as graph edges rather than relying on embedding similarity, enabling deterministic relationship queries and structured knowledge representation at the cost of requiring manual relationship definition
Provides MCP tools for creating and updating entities (discrete knowledge units) with configurable types and metadata fields, organizing memory around named entities rather than unstructured text. Each entity is a node with a type identifier (e.g., 'person', 'project', 'concept') and arbitrary metadata properties, stored in the graph structure. This enables type-aware queries and filtering where clients can retrieve all entities of a specific type or update entity properties without affecting the graph structure.
Unique: Exposes entity CRUD operations as individual MCP tools rather than a single generic 'store memory' function, giving clients explicit control over entity lifecycle and enabling fine-grained memory auditing and debugging
vs alternatives: More structured than simple key-value memory stores because it enforces entity types and enables type-based queries, but less flexible than document databases because it requires predefined entity types
Implements directed graph edges between entities with semantic labels (e.g., 'worked_on', 'knows', 'depends_on'), enabling clients to define and query relationships that carry meaning beyond simple connections. Relationships are first-class objects with labels and directionality, allowing traversal queries like 'find all projects this person worked on' or 'find all people who know each other'. The system supports both creating new relationships and querying existing relationship paths through MCP tool calls.
Unique: Treats relationships as first-class MCP tools with semantic labels rather than implicit connections, enabling clients to define domain-specific relationship types and query them explicitly, making relationship semantics visible and debuggable
vs alternatives: Richer than simple adjacency lists because relationship labels carry semantic meaning, but simpler than property graphs because relationships cannot have their own properties or metadata
Provides MCP tools for querying the memory graph using entity names, types, and relationship traversal patterns, returning structured results that include connected entities and their relationships. Queries can filter by entity type, search by name patterns, and traverse relationships to find connected entities, all exposed as discrete MCP tools. The system returns full entity records with metadata and relationship information, enabling clients to understand both the entity and its context in the graph.
Unique: Exposes graph queries as MCP tools with explicit parameters rather than a generic 'retrieve memory' function, enabling clients to specify exactly what information they need and making query patterns visible for debugging and optimization
vs alternatives: More explicit than embedding-based retrieval because queries return exact matches and relationship paths, but less flexible than full-text search because it requires knowing entity names or types
Implements the Memory server as an MCP server that exposes all memory operations (entity creation, relationship management, queries) as callable tools through the Model Context Protocol, enabling LLM clients to invoke memory operations as part of their reasoning loop. The server uses MCP's tool registration mechanism to define tool schemas with input/output types, allowing clients to discover available memory operations and call them with structured parameters. This integration makes memory operations first-class capabilities available to any MCP-compatible client.
Unique: Implements memory as an MCP server rather than a library or API, enabling it to be composed with other MCP servers in a network and allowing clients to treat memory operations as tools alongside filesystem, git, and other capabilities
vs alternatives: More composable than embedded memory libraries because it operates as a standalone MCP server, but requires MCP client support and adds network latency compared to in-process memory
Stores all memory data in-process memory (JavaScript objects/maps) scoped to the server session, providing fast access and isolation between different client sessions but no persistence across server restarts. Each server instance maintains its own graph in memory, meaning memory is lost when the server stops and is not shared between concurrent clients unless explicitly synchronized. This design prioritizes simplicity and performance for reference implementation purposes over durability.
Unique: Uses simple in-memory JavaScript objects for graph storage rather than integrating with external databases, making the reference implementation easy to understand and modify but requiring explicit persistence layer integration for production use
vs alternatives: Faster than database-backed memory because it avoids I/O, but loses all data on restart unlike persistent stores; suitable for reference implementation and development but not production
Defines MCP tool schemas for each memory operation (create entity, add relationship, query) with input parameter types, output types, and descriptions, enabling MCP clients to discover available memory operations and understand their signatures. The server registers these schemas with the MCP protocol, allowing clients to list available tools and understand what parameters each operation expects. This enables proper tool calling with type validation and helps clients understand the memory API surface.
Unique: Exposes memory operations through MCP's tool schema mechanism rather than custom API documentation, enabling programmatic discovery and type-safe tool calling through standard MCP mechanisms
vs alternatives: More discoverable than REST APIs because schemas are queryable at runtime, but less flexible than dynamic schema generation because schemas are predefined
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 Memory at 21/100.
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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