| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides a React-based drag-and-drop interface for constructing AI agent workflows as directed acyclic graphs. Components (LLMs, tools, memory systems, retrievers) are visually connected as nodes with configurable parameters, then compiled into executable LangChain runnables. The editor maintains a relational data model of chain definitions that map to LangChain's component registry, enabling non-technical users to compose complex agent logic without writing code.
Unique: Uses a component configuration layer that dynamically maps LangChain classes to visual node types in the editor, allowing new LangChain components to be registered without modifying the frontend. The chain graph is persisted as relational data (not just JSON blobs), enabling querying and versioning of agent logic.
vs alternatives: Differs from LangSmith's chain builder by storing chains as queryable database records rather than opaque JSON, and from LangFlow by being tightly integrated with a full agent execution platform rather than a standalone visualization tool.
Enables multiple autonomous agents to collaborate within a single chat session by maintaining a shared task context and conversation history. Each agent can execute its assigned chain, access previous messages and artifacts from other agents, and contribute results back to the conversation. The system uses a task-based execution model where each user interaction spawns a task that routes to the appropriate agent(s), with all outputs logged and accessible to subsequent agents.
Unique: Implements agent collaboration through a task-centric model where each interaction creates a persistent task record with full logging, rather than treating agents as stateless API endpoints. Agents access shared conversation context through a unified message store, enabling true collaboration rather than sequential tool calls.
vs alternatives: Provides deeper agent collaboration than LangChain's AgentExecutor (which is single-agent focused) by maintaining conversation state and allowing agents to reference each other's outputs; differs from multi-agent frameworks like AutoGen by being tightly integrated with visual chain design.
Provides a web-based chat interface for interacting with agents in real-time. Users send messages, which are routed to the appropriate agent(s) based on chain configuration. Agent responses stream back in real-time through WebSocket connections, with intermediate steps (tool calls, reasoning) displayed as they occur. The interface includes a sidebar for viewing generated artifacts (code, documents, images) with preview capabilities. Users can manage conversation history, create new tasks, and switch between agents within the same session.
Unique: Integrates the chat interface directly with the task execution system, enabling real-time streaming of agent responses and intermediate steps. Artifacts are displayed alongside the conversation with preview capabilities, rather than in a separate panel.
vs alternatives: Provides more integrated artifact management than generic chat interfaces by displaying artifacts in context of the conversation; differs from LangChain's built-in chat examples by including real-time streaming and artifact preview.
Provides a component registry that maps LangChain classes to visual node types in the chain editor. New components can be registered by defining a configuration object with metadata (name, description, input/output schemas). The system dynamically generates UI forms for component configuration based on the schema. Custom components can be added by extending the registry without modifying the core platform. The registry supports versioning of components, enabling backward compatibility as components evolve.
Unique: Implements a declarative component registry that maps LangChain classes to visual nodes, with automatic UI form generation from JSON schemas. Components are versioned and can be extended without modifying core platform code.
vs alternatives: Provides more flexible component extension than LangChain's built-in classes by supporting declarative registration and automatic UI generation; differs from LangFlow by including component versioning and compatibility management.
Tracks individual agent execution instances as tasks, capturing full execution logs, generated artifacts, and conversation history. Each task maintains a relational link to the chain definition, agent, user, and all outputs produced during execution. Artifacts (generated code, documents, images, etc.) are stored separately with metadata and can be grouped, versioned, and retrieved through REST/GraphQL APIs. The system provides structured logging at each step of chain execution, enabling debugging and performance analysis.
Unique: Implements a relational task model where artifacts are first-class entities with metadata (creator agent, timestamp, group membership) rather than opaque blobs. Tasks are queryable through both REST and GraphQL APIs, enabling complex filtering and aggregation of execution history.
vs alternatives: Provides more structured artifact management than LangChain's built-in callbacks (which are ephemeral) by persisting artifacts with full metadata; differs from LangSmith by including artifact grouping and user-level access control.
Exposes chain definitions, agent configurations, task execution, and artifact retrieval through both REST and GraphQL endpoints. The REST API provides CRUD operations on chains, agents, and tasks with standard HTTP semantics. The GraphQL API enables complex queries combining chains, agents, tasks, and artifacts with flexible filtering and pagination. Both APIs support authentication, authorization, and rate limiting. The API layer abstracts the underlying LangChain execution, allowing external systems to trigger agent execution and retrieve results.
Unique: Provides dual API surfaces (REST and GraphQL) from a single Django/FastAPI backend, allowing clients to choose based on their needs. The GraphQL schema is auto-generated from the relational data model, ensuring consistency between REST and GraphQL representations.
vs alternatives: Offers more flexible querying than REST-only platforms through GraphQL; differs from LangSmith by including full chain/agent management APIs, not just execution and logging.
Abstracts multiple LLM providers (OpenAI, Anthropic, Google, local Ollama, etc.) behind a unified component interface. Users configure LLM credentials and model selection in the platform settings, then reference LLM components in chains by name without embedding API keys. The system supports dynamic provider switching, model parameter tuning (temperature, max_tokens, etc.), and fallback chains if a provider fails. Configuration is stored securely in the database with environment variable substitution for sensitive credentials.
Unique: Implements provider abstraction at the component configuration layer, allowing LLM providers to be swapped in the chain editor without code changes. Credentials are managed centrally with environment variable substitution, preventing API keys from being embedded in chain definitions.
vs alternatives: Provides more flexible provider management than LangChain's built-in LLM classes by centralizing configuration and enabling runtime provider switching; differs from LangSmith by including local model support (Ollama) alongside cloud providers.
Enables agents to call external tools and APIs through a schema-based function registry. Tools are defined as LangChain Tool objects with JSON schemas describing inputs/outputs, then registered in the platform. When an agent needs to use a tool, the LLM generates a function call matching the schema, which is routed to the appropriate tool implementation. The system supports native function calling APIs (OpenAI, Anthropic) when available, and falls back to prompt-based tool use for other providers. Tool results are automatically parsed and returned to the agent.
Unique: Implements tool integration through a schema-based registry that supports both native function calling APIs and prompt-based fallbacks, with automatic routing based on provider capabilities. Tools are first-class entities in the platform with access control and audit logging.
vs alternatives: Provides more flexible tool management than LangChain's built-in tool calling by supporting provider-agnostic tool definitions and fallback mechanisms; differs from LangSmith by including tool access control and audit trails.
+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 IX 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