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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides a drag-and-drop interface for composing AI agent workflows without writing code. Users connect pre-built nodes representing LLM calls, tool integrations, conditional logic, and data transformations into directed acyclic graphs (DAGs). The builder likely compiles these visual workflows into executable agent definitions that can be deployed or exported.
Unique: unknown — insufficient data on whether Broadn uses proprietary DAG compilation, supports specific LLM provider APIs natively, or integrates with existing workflow platforms
vs alternatives: Likely faster time-to-prototype than code-first frameworks like LangChain for non-technical users, but unclear how it compares to competitors like Make.com or Zapier for AI-specific workflows
Offers a catalog of reusable nodes or components (LLM calls, tool connectors, data processors, conditional branches) that users drag into workflows. These components likely abstract away API authentication, request formatting, and response parsing for popular services like OpenAI, Anthropic, web search APIs, and database connectors.
Unique: unknown — insufficient data on breadth of component library, whether components support streaming responses, or how they handle provider-specific features like function calling schemas
vs alternatives: Likely reduces boilerplate compared to building integrations from scratch, but unclear if it matches the flexibility of code-first frameworks like LangChain or the integration breadth of enterprise platforms like Zapier
Enables users to deploy built workflows as standalone AI applications (likely web endpoints, chat interfaces, or API services) without managing infrastructure. The platform likely handles containerization, scaling, and API gateway setup behind the scenes, allowing users to share or monetize their agents.
Unique: unknown — insufficient data on whether Broadn uses containerization (Docker), serverless functions (AWS Lambda), or proprietary runtime, and how it handles state management across requests
vs alternatives: Likely simpler than deploying custom agents to cloud platforms like AWS or Vercel, but unclear if it offers cost advantages or feature parity with specialized AI deployment platforms
Abstracts differences between LLM providers (OpenAI, Anthropic, open-source models) behind a unified interface, allowing users to swap providers or use multiple models in a single workflow without rewriting logic. Likely handles prompt formatting, token counting, and response parsing differences across providers.
Unique: unknown — insufficient data on whether Broadn implements provider abstraction via a custom protocol, uses existing standards like OpenAI API compatibility, or wraps each provider's SDK
vs alternatives: Likely more accessible than managing multiple provider SDKs directly, but unclear if it matches the flexibility of frameworks like LiteLLM or the cost optimization of platforms like Anyscale
Manages state and context across multi-step workflows, including variable passing between nodes, session management for multi-turn conversations, and memory of previous interactions. Likely stores intermediate results and allows conditional branching based on prior outputs.
Unique: unknown — insufficient data on whether Broadn uses in-memory state, persistent databases, or vector stores for context, and how it handles context window limits
vs alternatives: Likely simpler than implementing state management manually in code, but unclear if it supports advanced patterns like hierarchical state, event sourcing, or distributed state across multiple agents
Allows users to describe workflows in natural language, which the platform converts into visual workflows or executable agent definitions. This likely uses an LLM to parse user intent and generate workflow structure, reducing the need to manually drag-and-drop components.
Unique: unknown — insufficient data on whether Broadn uses few-shot prompting, fine-tuned models, or structured parsing to convert natural language to workflows
vs alternatives: Likely faster than manual visual building for simple workflows, but unclear if it matches the accuracy of code-based definitions or supports complex conditional logic
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 broadn at 18/100. Supabase also has a free tier, making it more accessible.
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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