Supabase ranks higher at 42/100 vs Orloj – agent infrastructure as code at 35/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Orloj – agent infrastructure as code | Supabase |
|---|---|---|
| Type | Framework | MCP Server |
| UnfragileRank | 35/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables declarative specification of multi-step agent workflows using YAML configuration files, where each step defines tool invocations, conditional branching, and state transitions. The framework parses YAML schemas into executable agent graphs, supporting sequential execution, parallel branches, and loop constructs without requiring imperative code. This approach treats agent orchestration as infrastructure-as-code, enabling version control, code review, and GitOps-style deployment patterns.
Unique: Applies GitOps and infrastructure-as-code patterns to agent workflows, enabling version-controlled, peer-reviewed agent configurations rather than treating agent logic as ephemeral code
vs alternatives: Differs from LangChain/LlamaIndex by prioritizing declarative YAML configuration over imperative Python chains, enabling non-engineers to modify agent behavior and supporting GitOps deployment patterns
Provides a registry system for declaring available tools and functions that agents can invoke, with automatic schema generation and validation. The framework accepts tool definitions (name, description, parameters, return types) and generates JSON schemas compatible with LLM function-calling APIs. Tools are bound to agent steps via references in YAML, with the framework handling parameter marshaling, type coercion, and error handling between the LLM output and actual function invocation.
Unique: Centralizes tool definitions in a declarative registry that generates LLM-compatible schemas automatically, reducing the gap between tool implementation and agent configuration
vs alternatives: More structured than LangChain's tool decorators by enforcing schema validation upfront; simpler than Anthropic's native function-calling by abstracting multi-provider differences
Enables agents to pause execution and request human approval before proceeding with critical actions. The framework provides mechanisms for sending approval requests (via email, Slack, webhooks, or UI), waiting for human decisions, and resuming execution based on approval/rejection. Approval workflows can be conditional (only require approval for certain actions) and can include context (reason for action, impact assessment) to help humans make informed decisions.
Unique: Provides declarative human-in-the-loop workflows in YAML, enabling approval gates without custom code
vs alternatives: More integrated than manual approval processes by automating notification and decision tracking; simpler than building custom approval systems
Enables agents to execute multiple steps in parallel and combine results, supporting fan-out (one step spawns multiple parallel tasks) and fan-in (multiple parallel tasks converge into one) patterns. The framework manages parallel execution, collects results, and provides mechanisms for combining results (merge, aggregate, select best). This is useful for tasks like querying multiple APIs in parallel or running multiple agent branches simultaneously.
Unique: Provides declarative parallel execution patterns in YAML, enabling fan-out/fan-in workflows without manual concurrency management
vs alternatives: Simpler than building custom parallel orchestration; more efficient than sequential execution for I/O-bound operations
Enables agents to be triggered by external events (webhooks, message queues, scheduled jobs) rather than only by direct API calls. The framework listens for incoming webhooks, parses event payloads, and automatically invokes agents with event data as input. This enables event-driven architectures where agents react to external system changes (e.g., new customer signup, payment received, alert triggered).
Unique: Provides declarative webhook and event-driven triggering in YAML, enabling agents to react to external events without custom code
vs alternatives: More integrated than manual webhook handling; simpler than building custom event routing systems
Abstracts differences between LLM providers (OpenAI, Anthropic, local models, etc.) behind a unified interface, allowing agents to switch providers or route requests based on cost, latency, or capability requirements. The framework handles provider-specific API differences (function-calling formats, token counting, streaming behavior) transparently, enabling YAML configurations to specify provider preferences without embedding provider-specific code.
Unique: Provides declarative provider routing and fallback policies in YAML, enabling cost and latency optimization without code changes, rather than hardcoding provider selection
vs alternatives: More flexible than LangChain's LLMChain by supporting dynamic provider routing; simpler than building custom provider adapters by handling API differences automatically
Executes agent workflows defined in YAML by maintaining execution state across steps, managing context windows, and handling step-to-step data flow. The framework tracks agent state (current step, variables, tool results, LLM responses) and provides mechanisms for passing data between steps, persisting state across invocations, and recovering from failures. Execution can be synchronous (blocking until completion) or asynchronous (with webhooks/callbacks for result notification).
Unique: Treats agent execution as a first-class workflow primitive with explicit state management and recovery semantics, rather than treating it as a simple function call
vs alternatives: More robust than LangChain's basic chain execution by providing built-in state persistence and recovery; simpler than Temporal/Durable Functions by focusing specifically on agent workflows
Enables agents to make decisions and repeat actions based on conditions evaluated at runtime. The framework supports if/else branching (based on LLM outputs, tool results, or variables), loops (repeat steps until condition met), and switch statements (route to different steps based on categorical decisions). Conditions are expressed in a simple DSL or as references to step outputs, allowing complex workflows without imperative code.
Unique: Provides declarative control flow primitives in YAML that avoid imperative code while supporting complex agent decision-making patterns
vs alternatives: More readable than imperative Python chains for simple conditionals; less powerful than full programming languages but sufficient for most agent workflows
+5 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 Orloj – agent infrastructure as code at 35/100.
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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