| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides a unified TypeScript interface layer that abstracts over heterogeneous AI provider APIs (OpenAI, Anthropic, Gemini, Grok, Azure OpenAI) with compile-time type safety. Uses provider-specific adapter classes that normalize request/response formats and handle provider-specific quirks, allowing developers to swap providers without changing application code. Each adapter implements a common interface contract that maps to Inngest's event-driven execution model.
Unique: Integrates AI provider abstraction directly into Inngest's event-driven execution model, allowing LLM calls to be reliably retried, queued, and tracked as first-class workflow steps with built-in durability guarantees rather than treating them as external API calls
vs alternatives: Unlike generic LLM SDKs (LangChain, LlamaIndex), this abstraction is purpose-built for Inngest workflows, providing automatic retry logic, event sourcing, and distributed tracing without additional configuration
Implements function calling (tool use) across providers with different schema formats by normalizing tool definitions into a canonical schema format, then translating to provider-specific representations (OpenAI's function_calling format, Anthropic's tool_use, etc.). Handles provider differences in how they declare parameters, return types, and tool selection logic. Automatically marshals function results back into the LLM context for multi-turn tool-use workflows.
Unique: Normalizes tool schemas at the Inngest workflow level, allowing tool definitions to be stored as workflow state and reused across multiple LLM calls within a single Inngest function, with automatic context injection and result marshaling
vs alternatives: More lightweight than LangChain's tool abstraction because it doesn't require agent frameworks; tools are first-class Inngest workflow primitives with built-in durability and replay semantics
Provides batch processing capabilities for high-volume LLM requests, leveraging provider-native batch APIs (OpenAI Batch API, Anthropic Batch API) to reduce costs and latency. Automatically groups requests into batches, submits them to providers, and polls for results. Integrates with Inngest's event system to track batch status and emit events when batches complete. Supports cost optimization strategies like batching similar requests together and prioritizing cheaper models for batch processing.
Unique: Integrates batch processing as a native Inngest workflow capability with automatic polling and event emission, allowing batch jobs to be tracked and managed alongside real-time LLM calls
vs alternatives: More convenient than direct batch API usage because it handles polling and result aggregation automatically; more cost-effective than real-time APIs for high-volume workloads because it leverages provider batch discounts
Implements caching of LLM requests and responses with optional semantic deduplication (detecting similar prompts that would produce similar outputs). Uses configurable cache backends (in-memory, Redis, Inngest event store) and supports cache invalidation strategies. Automatically deduplicates requests based on exact match (fast) or semantic similarity (slower but catches paraphrased prompts). Integrates with Inngest's event system to track cache hits/misses and enable cost analysis.
Unique: Integrates caching with Inngest's event system, allowing cache hits/misses to be tracked as events and enabling cost analysis based on cache effectiveness across the entire workflow execution history
vs alternatives: More sophisticated than simple key-value caching because it supports semantic deduplication; more integrated than external caching layers because it's aware of Inngest workflow context and can make cache decisions based on event history
Enables extraction of structured data (JSON, typed objects) from LLM responses by specifying output schemas and delegating to provider-specific structured output mechanisms (OpenAI's JSON mode, Anthropic's structured output, Gemini's schema constraints). Automatically validates responses against the declared schema and provides type-safe access to extracted fields. Handles provider differences in how they enforce schema compliance and error recovery when responses don't match the schema.
Unique: Integrates structured output as a first-class Inngest workflow capability, allowing schema-constrained LLM calls to be retried and replayed with full durability guarantees, rather than treating structured output as a client-side concern
vs alternatives: Unlike prompt-engineering-based extraction (e.g., 'respond in JSON'), this uses provider-native schema enforcement for higher reliability; unlike generic validation libraries, it's optimized for LLM output validation within event-driven workflows
Provides streaming support for LLM responses (token-by-token output) with automatic integration into Inngest's event system. Streams are buffered and can be emitted as Inngest events, allowing downstream workflow steps to process partial results in real-time. Handles provider-specific streaming protocols (Server-Sent Events, WebSocket) and normalizes them into a common stream interface. Manages backpressure and ensures streamed data is durably logged in Inngest's event store.
Unique: Bridges streaming LLM responses with Inngest's event-driven architecture, allowing streamed tokens to be emitted as durable events that can trigger downstream workflow steps, rather than treating streaming as a client-only concern
vs alternatives: Unlike generic streaming libraries, this maintains full Inngest durability semantics for streamed data; unlike WebSocket-based streaming, it integrates with Inngest's event sourcing for reliable replay and auditing
Automatically tracks token consumption (input/output tokens) for each LLM call and aggregates usage across providers with different pricing models. Provides cost estimation based on provider-specific pricing rates (updated periodically) and supports custom pricing configuration. Integrates with Inngest's event metadata to attach usage data to each workflow execution, enabling cost analysis and budgeting. Handles provider differences in how they report token counts (e.g., Claude's token counting API vs OpenAI's inline reporting).
Unique: Integrates cost tracking directly into Inngest's event metadata, allowing cost data to be queried alongside workflow execution history and enabling cost-based workflow optimization at the event level
vs alternatives: More granular than provider-level billing dashboards because it tracks costs per Inngest function execution; more accurate than client-side estimation because it uses actual token counts from provider responses
Implements provider-aware retry logic that distinguishes between transient failures (rate limits, temporary outages) and permanent failures (invalid API key, model not found). Uses exponential backoff with jitter and provider-specific retry strategies (e.g., respecting Retry-After headers from OpenAI). Integrates with Inngest's built-in retry mechanism to ensure failed LLM calls are automatically retried as part of the workflow execution, with full durability guarantees. Provides configurable retry policies per provider or model.
Unique: Leverages Inngest's native retry mechanism to provide durable, automatically-replayed LLM calls with provider-aware backoff strategies, rather than implementing retries at the application level
vs alternatives: More reliable than client-side retry logic because retries are durably logged in Inngest's event store; more sophisticated than generic retry libraries because it understands provider-specific error semantics and rate limit headers
+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 @inngest/ai at 39/100.
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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