| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Fetches pre-built prompt templates from the LangChain Hub repository using a client-side API that resolves template identifiers (e.g., 'rlm/rag-prompt') to structured prompt objects. The client handles authentication via API keys, manages HTTP requests to the Hub's REST endpoints, and deserializes YAML/JSON template definitions into LangChain-compatible PromptTemplate instances with variable interpolation support.
Unique: Provides a lightweight client library specifically designed for the LangChain Hub's REST API, with built-in deserialization of YAML/JSON templates into LangChain PromptTemplate objects — avoiding manual parsing or custom HTTP wrappers
vs alternatives: More lightweight and Hub-native than building custom HTTP clients or using generic REST libraries; tighter integration with LangChain's PromptTemplate API than generic template engines like Jinja2
Loads serialized LangChain Runnable chains (agents, sequential pipelines, branching logic) from the Hub and instantiates them as executable Python/JavaScript objects. The client deserializes the chain's DAG structure, resolves LLM provider references (OpenAI, Anthropic, etc.), injects API keys from environment variables, and returns a ready-to-invoke Runnable that can be called with input data without additional configuration.
Unique: Handles full deserialization of LangChain's Runnable DAG format with automatic LLM provider binding and credential injection, enabling zero-configuration chain execution — unlike generic workflow engines that require manual step mapping
vs alternatives: Tighter integration with LangChain's Runnable API than generic orchestration tools; eliminates manual chain reconstruction compared to loading raw JSON and manually wiring components
Manages API authentication to the LangChain Hub using API keys stored in environment variables or passed explicitly to the client constructor. The client maintains a session with automatic header injection (Authorization: Bearer token), handles token refresh if needed, and provides error handling for 401/403 responses with clear messaging about missing or invalid credentials.
Unique: Provides a minimal, LangChain-specific authentication wrapper that integrates directly with the Hub's Bearer token scheme and environment variable conventions, avoiding the need for generic HTTP client setup
vs alternatives: Simpler than building custom authentication logic with generic HTTP libraries; more specialized than OAuth2 libraries for this specific Hub use case
Queries the LangChain Hub API to list available prompts, chains, and other resources with optional filtering by owner, type, or tags. The client returns paginated results as structured objects with metadata (name, description, owner, creation date), enabling programmatic discovery of Hub resources without manual browsing of the web interface.
Unique: Provides a structured, paginated API for Hub resource discovery with built-in metadata parsing, eliminating the need to scrape the web interface or manually construct Hub API queries
vs alternatives: More programmatic than browsing the Hub web UI; more Hub-native than generic REST clients for discovering resources
Validates locally-defined prompts and chains against the LangChain Hub's serialization schema before pushing them to the Hub. The client checks for required fields, correct data types, valid variable references, and compatibility with supported LLM providers, returning detailed validation errors that guide users to fix issues before publication.
Unique: Provides LangChain-specific schema validation that understands PromptTemplate variable syntax and Runnable DAG structure, catching issues before Hub publication — unlike generic JSON schema validators
vs alternatives: More targeted than generic schema validators; catches LangChain-specific issues like invalid variable references that generic tools would miss
Publishes locally-created prompts, chains, and other artifacts to the LangChain Hub with metadata (name, description, tags, visibility). The client serializes LangChain objects to the Hub's format, handles multipart uploads if needed, manages versioning (creating new versions or updating existing ones), and returns a shareable Hub URL for the published resource.
Unique: Handles full serialization and versioning of LangChain artifacts with automatic metadata management, enabling one-command publishing without manual Hub UI interaction — unlike generic file upload tools
vs alternatives: More integrated with LangChain's object model than generic artifact repositories; simpler than manually managing Hub API calls for serialization and versioning
Automatically resolves environment variable references in retrieved prompts and chains (e.g., ${OPENAI_API_KEY}, ${MODEL_NAME}) by substituting values from the runtime environment. The client supports both simple variable substitution and nested references, with fallback defaults and validation that required variables are present before chain execution.
Unique: Integrates environment variable resolution directly into the Hub artifact loading pipeline, avoiding separate configuration steps — unlike generic template engines that require manual variable passing
vs alternatives: More seamless than manual environment variable handling in application code; tighter integration with LangChain's configuration patterns than generic template engines
Automatically enables tracing and observability for chains loaded from the Hub by injecting LangSmith callbacks into the Runnable execution context. The client detects when LangSmith credentials are available (LANGSMITH_API_KEY, LANGSMITH_PROJECT) and transparently logs all chain steps, LLM calls, and tool invocations to LangSmith for debugging and monitoring without requiring explicit callback configuration.
Unique: Automatically injects LangSmith tracing callbacks into Hub chains without requiring explicit callback configuration, enabling zero-setup observability — unlike manual callback injection that requires code changes
vs alternatives: More seamless than manually adding LangSmith callbacks to chains; tighter integration with LangChain's callback system than generic observability libraries
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 langchainhub at 29/100. langchainhub leads on adoption, while Supabase is stronger on quality.
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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