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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a two-phase agent architecture where a PlannerAgent analyzes natural language requests and generates high-level execution strategies, while a SolverAgent executes those plans step-by-step through a structured ExecuteWorkflow use case. This separation of concerns improves accuracy by allowing each agent to specialize in planning vs. execution, reducing hallucination and improving task decomposition reliability compared to single-agent approaches.
Unique: Implements the ACL 2023 'Plan-and-Solve Prompting' research paper as a production system with explicit separation between PlannerAgent and SolverAgent components, enabling specialized reasoning for each phase rather than monolithic chain-of-thought
vs alternatives: Outperforms single-agent automation systems (like standard LLM function-calling) by reducing planning errors through dedicated planning phase, and improves accuracy vs. ReAct-style agents by separating strategy from execution
Provides a centralized tool registry spanning 9 major service categories (GitHub, Slack, HubSpot, Notion, Airtable, Monday.com, Discord, Medium, HackerNews) with 120+ individual tools, each identified by unique toolId and configurable with execution parameters including userPermissionRequired flags. Tools are abstracted through a connector pattern that normalizes API differences across heterogeneous services into a unified invocation interface.
Unique: Provides 120+ pre-built integrations across 9 major services through a unified connector architecture, eliminating the need for custom API wrappers for each service while maintaining service-specific parameter handling
vs alternatives: Broader pre-built integration coverage than Zapier's free tier and more developer-friendly than Make.com for custom agent workflows; faster to implement than building custom API clients for each service
Enables composition of workflows that span multiple services by mapping outputs from one tool as inputs to subsequent tools. The system maintains execution context across steps, allowing data flow between services (e.g., GitHub issue ID → Slack notification, HubSpot contact → Notion database entry). Parameter mapping is configured in the execution plan and validated at runtime.
Unique: Maintains execution context across multi-service workflows and enables parameter mapping between heterogeneous service APIs, allowing data flow between tools without manual intervention
vs alternatives: More sophisticated than simple sequential tool calling; enables true workflow composition where service outputs drive subsequent steps
Implements a connector architecture that abstracts service-specific API differences behind a unified interface. Each service (GitHub, Slack, HubSpot, etc.) has a dedicated connector that handles authentication, API versioning, error translation, and response normalization, allowing agents to invoke tools without knowledge of underlying API details.
Unique: Implements explicit connector pattern for each service integration, providing clean separation between agent logic and service-specific API handling, enabling easier maintenance and extension
vs alternatives: More maintainable than monolithic API wrapper; cleaner than direct API calls scattered throughout agent code
Implements supervised execution through userPermissionRequired field in workflow configurations, where the system prompts users for explicit approval before executing potentially sensitive operations (e.g., deleting repositories, posting to public channels, modifying critical data). Approval state is tracked per workflow step and blocks execution until user confirmation is received.
Unique: Implements approval gates at the individual tool invocation level (per-step) rather than workflow-level, allowing fine-grained control over which specific operations require human sign-off
vs alternatives: More granular than Zapier's approval workflows (which operate at task level) and more practical than fully autonomous agents for regulated environments requiring human oversight
Executes planned workflows through the ExecuteWorkflow use case, which processes each step sequentially, validates inputs against tool schemas, invokes the appropriate service connector, and captures execution results with detailed error information. Failed steps can trigger retry logic or fallback handlers, and execution state is maintained throughout the workflow lifecycle.
Unique: Validates each step against tool schemas before execution and captures detailed execution context (inputs, outputs, errors) for each step, enabling post-execution analysis and debugging
vs alternatives: More transparent than black-box automation tools (Zapier, Make) by exposing step-level execution details; better error diagnostics than simple function-calling approaches
Generates visualization of tool usage patterns through execution log analysis, producing heatmaps that show which tools are invoked most frequently and in what temporal patterns. Analytics are computed from historical execution logs and enable identification of automation bottlenecks, most-used integrations, and workflow optimization opportunities.
Unique: Provides built-in execution analytics and heatmap visualization rather than requiring external analytics tools, enabling operators to understand automation patterns without additional instrumentation
vs alternatives: More integrated than exporting logs to external analytics platforms; faster insights than manual log inspection but less sophisticated than dedicated APM tools
The PlannerAgent accepts natural language task descriptions and generates structured execution plans by analyzing the request, identifying required tools, determining execution order, and mapping parameters. This leverages LLM reasoning to convert unstructured user intent into a formal workflow specification that the SolverAgent can execute.
Unique: Dedicated PlannerAgent component that specializes in converting natural language to structured plans, separate from execution logic, enabling focused optimization of planning accuracy
vs alternatives: More reliable than single-pass LLM function-calling for complex multi-step tasks; better at task decomposition than simple prompt-based automation
+4 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 Lemon Agent at 23/100.
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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