| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides a framework for building autonomous agents that decompose complex tasks into subtasks through a planning layer, routing each subtask to specialized worker agents or tools. The architecture uses a hierarchical agent pattern where a coordinator agent manages task dependencies and state transitions, enabling multi-step workflows without explicit programming of control flow.
Unique: Implements a modular agent composition pattern where agents are defined as reusable components with explicit input/output schemas, enabling type-safe agent chaining and automatic validation of task handoffs between agents
vs alternatives: Provides more structured agent composition than LangChain's agent loops by enforcing schema-based contracts between agents, reducing integration friction in multi-agent systems
Enables agents to invoke external tools and APIs through a schema registry system where each tool is defined with JSON Schema specifications for inputs and outputs. The framework handles schema validation, parameter binding, and error handling, allowing agents to dynamically select and invoke tools based on task requirements without hardcoded tool references.
Unique: Uses JSON Schema as the contract language for tool definitions, enabling agents to understand tool capabilities declaratively and validate parameters before execution, with built-in support for tool composition and chaining
vs alternatives: More explicit and type-safe than LangChain's tool calling because it enforces schema validation at the framework level rather than relying on LLM instruction following
Manages agent execution state including task history, intermediate results, and context across multiple steps. The system maintains a state store that tracks agent decisions, tool invocations, and their outcomes, enabling agents to reference previous results and maintain coherent context throughout multi-step workflows.
Unique: Implements a structured state model where each agent step produces immutable state transitions, enabling deterministic replay and debugging of agent execution paths
vs alternatives: Provides more explicit state tracking than LangChain's memory abstractions by maintaining a complete execution graph rather than just conversation history
Abstracts interactions with multiple LLM providers (OpenAI, Anthropic, local models, etc.) through a unified interface, handling provider-specific API differences, token counting, and response formatting. The layer automatically routes requests to configured providers and manages fallback logic if a provider fails.
Unique: Provides a unified LLM interface with automatic response normalization across providers, including handling of streaming responses, function calling variants, and vision capabilities
vs alternatives: More comprehensive than LiteLLM by including built-in fallback routing and cost tracking at the framework level rather than just API wrapping
Enables declarative definition of agent workflows using a composition pattern where complex agents are built by combining simpler agents and tools. Workflows are defined through configuration or code, specifying agent dependencies, execution order, and data flow between agents.
Unique: Uses a directed acyclic graph (DAG) model for workflow definition, enabling parallel execution of independent agents and automatic dependency resolution
vs alternatives: More structured than LangChain's sequential agent chains by supporting parallel execution and explicit dependency declaration
Implements comprehensive error handling for agent failures including retry logic, fallback agents, and error recovery strategies. The system can catch exceptions at multiple levels (tool invocation, agent execution, workflow level) and apply configured recovery actions.
Unique: Implements multi-level error handling with configurable recovery strategies at tool, agent, and workflow levels, enabling fine-grained control over failure modes
vs alternatives: More granular than generic exception handling by providing agent-specific recovery strategies and automatic fallback routing
Provides built-in instrumentation for monitoring agent execution including latency tracking, token usage, cost estimation, and success/failure rates. Metrics are collected at multiple levels (tool invocation, agent step, workflow) and can be exported to observability platforms.
Unique: Collects structured metrics at multiple execution levels (tool, agent, workflow) with automatic cost calculation based on provider pricing, enabling detailed performance analysis
vs alternatives: More comprehensive than LangChain's callback system by providing built-in cost tracking and multi-level metrics aggregation
Provides a system for managing and versioning prompts used by agents, including prompt templates with variable substitution, prompt optimization, and A/B testing capabilities. Prompts can be versioned and tested to improve agent performance.
Unique: Integrates prompt versioning with agent execution, enabling automatic tracking of which prompt version produced which results for performance analysis
vs alternatives: More integrated than standalone prompt management tools by connecting prompts directly to agent execution metrics and outcomes
+2 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 License: MIT at 22/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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