| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements an iterative human-in-the-loop active learning loop where the system presents documents to reviewers, collects relevance judgments, retrains ML models on labeled data, and re-ranks unlabeled documents by predicted relevance for the next screening cycle. The approach prioritizes documents most likely to be relevant based on accumulated human feedback, reducing the total number of documents a reviewer must manually assess.
Unique: Uses active learning (not generative AI) to iteratively retrain models on human-labeled documents and prioritize screening by predicted relevance, fundamentally different from keyword-matching or static ML classifiers that don't adapt to reviewer feedback in real-time cycles
vs alternatives: Reduces manual screening workload by 95% (claimed) by focusing human effort on high-uncertainty documents rather than requiring full-corpus review, whereas traditional systematic review tools require exhaustive manual screening of all documents
Supports multiple machine learning models for document relevance prediction with an extensible architecture allowing third parties to add custom models. The system abstracts model selection and retraining, though specific algorithms (Naive Bayes, SVM, neural networks, etc.) are not documented. Models are retrained on accumulated human judgments after each screening batch to adapt to reviewer preferences.
Unique: Provides an extensible model registry allowing third-party developers to add custom ML algorithms without modifying core code, with automatic retraining on human feedback — most commercial tools lock users into proprietary models
vs alternatives: Enables domain-specific model optimization and algorithm experimentation that proprietary tools like Covidence or DistillerSR cannot support, since those platforms use fixed, non-customizable ML backends
Provides open learning materials, documentation, and community support channels including weekly Thursday stand-ups and user meetings. The project is coordinated at Utrecht University with active community engagement. Learning resources enable researchers and developers to understand systematic review methodology, active learning concepts, and ASReview usage without formal training.
Unique: Provides community-driven learning and support infrastructure with regular user meetings and open learning materials, creating a collaborative ecosystem — most commercial tools provide vendor-controlled documentation and support with limited community interaction
vs alternatives: Enables peer learning and community problem-solving through regular meetings and shared knowledge, whereas commercial tools rely on vendor support tickets and documentation, often with slower response times and less community engagement
Allows researchers to simulate AI-aided reviewing by replaying historical screening decisions against different model configurations and active learning strategies. The simulation mode evaluates how different algorithms would have performed on past screening tasks, enabling comparison of model effectiveness without requiring new human labeling effort. Includes a Benchmark Platform for standardized performance comparison across configurations.
Unique: Provides a replay-based simulation engine that evaluates model performance on historical screening data without requiring new human effort, enabling risk-free algorithm comparison before production deployment — most screening tools lack this offline evaluation capability
vs alternatives: Allows researchers to validate model choices on their own data before committing to a screening workflow, whereas tools like Covidence require live testing with real reviewers, increasing risk and cost
Distributes document screening across multiple expert reviewers in parallel, with AI proposing records to the crowd and coordinating their judgments. The system manages workflow distribution, collects independent relevance assessments from multiple reviewers, and aggregates their decisions. Enables large-scale screening by parallelizing reviewer effort across a team rather than requiring sequential single-reviewer assessment.
Unique: Implements a crowd-based screening coordination layer that distributes documents to multiple reviewers and aggregates their judgments, with AI proposing high-uncertainty documents to the crowd — most screening tools are single-user or require manual workflow coordination
vs alternatives: Enables parallel screening across teams without requiring external workflow management tools, whereas Covidence and DistillerSR require manual task assignment and external coordination for multi-reviewer workflows
Accepts large-scale document collections and prepares them for screening through an ingestion pipeline. The system handles document parsing, metadata extraction, and preparation for ML model processing. Specific input formats, preprocessing steps, and vectorization methods are not documented, but the system claims to handle large-scale text screening without specified upper limits on corpus size.
Unique: Provides an automated ingestion pipeline that handles document parsing and metadata extraction from multiple formats, abstracting away format-specific complexity — most screening tools require manual document preparation or support only limited input formats
vs alternatives: Reduces setup time by automatically handling document parsing and metadata extraction from diverse sources, whereas tools like Covidence require manual document upload and metadata entry for each record
Provides a user interface for reviewers to assess document relevance one-at-a-time or in batches, collecting binary (include/exclude) or multi-class relevance judgments. The interface presents documents prioritized by the active learning model, allowing reviewers to make rapid relevance decisions. Human judgments are immediately fed back to the system for model retraining and re-ranking of remaining documents.
Unique: Integrates the screening interface directly with the active learning loop, immediately using each judgment to retrain models and re-rank remaining documents in real-time — most screening tools separate judgment collection from model training, requiring manual batch retraining
vs alternatives: Provides immediate feedback to reviewers about how their judgments are influencing the model's recommendations, creating a tighter human-in-the-loop cycle than tools like Covidence that treat screening and analysis as separate phases
Estimates and tracks the reduction in manual screening effort achieved through active learning prioritization. The system monitors how many documents reviewers can skip by relying on model predictions, typically claiming 95% workload reduction. Progress tracking shows reviewers how many documents remain to be screened and provides estimates of time to completion based on current screening velocity.
Unique: Provides real-time workload reduction estimates based on active learning prioritization, showing reviewers exactly how many documents they can skip — most screening tools do not quantify efficiency gains or provide progress estimates
vs alternatives: Gives reviewers immediate feedback on time savings and completion estimates, whereas manual screening tools provide no efficiency metrics or progress visibility
+3 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 ASReview at 25/100. ASReview leads on quality, while Supabase is stronger on ecosystem. Supabase also has a free tier, making it more accessible.
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