| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides a React 18-based visual canvas IDE for composing conversational AI agents by connecting LLM models, RAG knowledge bases, plugins, and workflow nodes without code. Uses a FlowGram engine to render and manage directed acyclic graphs of agent logic, with Zustand state management for real-time canvas synchronization and a Thrift IDL layer enforcing strict type contracts between frontend and Go backend services that execute the composed workflows.
Unique: Combines FlowGram visual canvas with Thrift-based type-safe RPC contracts and Go-based DDD backend, enabling visual agent composition with strict schema validation and multi-provider LLM support (OpenAI, Volcengine) in a single monorepo
vs alternatives: Offers tighter type safety and visual debugging than Langchain's Python-based DAG approach, and lower operational complexity than Kubernetes-native orchestration platforms by bundling UI, backend, and deployment in a single Docker Compose stack
Abstracts LLM provider APIs (OpenAI, Volcengine, and others) through a unified model service layer that manages model lists, credentials, and request routing. The backend uses Go's Hertz HTTP framework with domain-driven service handlers that normalize provider-specific request/response formats into a common interface, allowing agents to switch models or providers without workflow changes.
Unique: Implements provider abstraction via Go domain services with Hertz HTTP handlers that normalize OpenAI, Volcengine, and custom provider APIs into a single Thrift-defined interface, enabling zero-code provider switching at runtime
vs alternatives: More tightly integrated than LiteLLM (Python library) because it's built into the backend service layer with native Go performance; simpler than Anthropic's batch API or OpenAI's fine-tuning workflows because it focuses purely on request routing and credential management
Exposes agent functionality through OpenAPI endpoints for chat session management and a Chat SDK (TypeScript/Python) for application integration. The OpenAPI spec is auto-generated from Thrift IDL, providing standard REST endpoints for creating sessions, sending messages, and retrieving traces. The Chat SDK wraps these endpoints with convenience methods, error handling, and streaming support for real-time agent responses.
Unique: Auto-generates OpenAPI spec from Thrift IDL and provides Chat SDK wrappers for TypeScript/Python with streaming support, enabling zero-code agent integration into external applications
vs alternatives: More standardized than custom REST APIs because OpenAPI spec is auto-generated; more convenient than raw HTTP because Chat SDK handles authentication, error handling, and streaming automatically
Provides Docker Compose configurations for local development and Kubernetes Helm charts for production deployment. The Docker Compose setup includes all services (frontend, backend, MySQL, Redis, Elasticsearch, vector databases) with environment variable configuration. Helm charts abstract Kubernetes resources (Deployments, Services, ConfigMaps, Secrets) and enable parameterized multi-environment deployments (staging, production) with different resource limits and replica counts.
Unique: Provides both Docker Compose for local development and Kubernetes Helm charts for production, with parameterized multi-environment support and infrastructure abstraction
vs alternatives: More flexible than managed Coze Cloud because it enables on-premises deployment; simpler than writing raw Kubernetes YAML because Helm charts provide templating and parameterization
Provides a resource management system for uploading, indexing, and retrieving documents through a RAG pipeline built on the Eino framework. Documents are embedded using configurable vector models, stored in vector databases (Milvus, OceanBase, or similar), and retrieved via semantic search with BM25 hybrid ranking. The backend Go services handle chunking, embedding, and retrieval orchestration, while the frontend provides UI for knowledge base CRUD and search testing.
Unique: Integrates Eino framework for RAG orchestration with hybrid BM25+semantic search, supports multiple vector databases (Milvus, OceanBase) via pluggable adapters, and provides visual knowledge base management UI with retrieval testing in the same monorepo
vs alternatives: More integrated than Langchain's RAG chains because vector DB and embedding management are built into the backend service layer; simpler than Vespa or Elasticsearch-only solutions because it combines semantic and keyword search without separate infrastructure
Enables agents to invoke external tools and APIs through a plugin registry system where each plugin defines a Thrift-based schema specifying inputs, outputs, and execution logic. The backend maintains a plugin service that validates requests against schemas, handles authentication/credentials, and orchestrates execution via HTTP or gRPC. Plugins can be built as standalone services or embedded Go modules, and the frontend provides UI for plugin discovery, configuration, and testing.
Unique: Uses Thrift-based schema definitions for strict plugin contracts, supports both HTTP and gRPC plugin execution, and provides centralized credential management with visual plugin testing UI in the frontend
vs alternatives: More type-safe than OpenAI's function calling because schemas are enforced at the IDL layer; more flexible than Langchain's tool decorators because plugins can be external services or embedded modules
Manages the complete agent lifecycle from creation through deployment, including version control, publishing to registries, and deployment to production environments. The backend stores agent definitions (prompts, workflows, RAG bindings, plugins) in MySQL, tracks version history, and provides APIs for publishing agents as immutable releases. The frontend IDE includes publish workflows, deployment configuration UI, and agent marketplace browsing for discovering and importing published agents.
Unique: Provides end-to-end agent lifecycle management with MySQL-backed version history, immutable published releases, and a visual agent marketplace UI, integrated into the same monorepo as the IDE
vs alternatives: More comprehensive than Hugging Face Model Hub because it versions entire agent configurations (not just models), and simpler than Kubernetes Helm because deployment is abstracted through a UI rather than requiring YAML templating
Manages chat sessions between users and deployed agents, capturing full execution traces including LLM calls, tool invocations, RAG retrievals, and workflow steps. Sessions are stored in MySQL with Redis caching for active sessions, and the backend exposes OpenAPI endpoints for session creation, message sending, and trace retrieval. The frontend provides a chat UI with side-by-side execution trace visualization, allowing developers to inspect intermediate states and debug agent behavior.
Unique: Captures full execution traces with nested LLM calls, tool invocations, and RAG retrievals in a single session record, provides visual trace inspection UI in the frontend, and exposes both OpenAPI and Chat SDK for integration
vs alternatives: More detailed than LangSmith's tracing because traces are captured at the backend service layer with full context; simpler than Datadog APM because it's purpose-built for agent debugging rather than general observability
+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.
coze-studio scores higher at 48/100 vs Supabase at 42/100.
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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