| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Converts PDF, PNG, and JPEG documents into clean markdown and structured text using a distributed worker architecture backed by S3 or local file-based work queues. The pipeline orchestrates page-level processing through a queue system that coordinates multiple worker processes, each invoking a fine-tuned 7B vision-language model (olmOCR-2-7B based on Qwen2.5-VL) via vLLM server instances. Workers pull tasks from the queue, process pages with rotation correction and layout analysis, and write results back to persistent storage, enabling horizontal scaling across machines.
Unique: Uses a fine-tuned 7B vision-language model (olmOCR-2-7B based on Qwen2.5-VL) with distributed work queue coordination via S3 or local storage, enabling cost-efficient processing at <$200/million pages. Unlike traditional OCR (Tesseract) or cloud APIs (Google Vision), this approach combines model efficiency with horizontal scalability through asynchronous queue-based worker coordination rather than synchronous API calls.
vs alternatives: Achieves 82.4±1.1 benchmark score on olmOCR-Bench while maintaining sub-$200/million page cost, outperforming cloud OCR APIs on cost and open-source OCR on accuracy; distributed queue architecture scales better than single-machine solutions while avoiding vendor lock-in of cloud services.
Automatically detects and corrects page rotation by invoking the vision-language model on each page image to determine correct orientation before full OCR processing. The system analyzes visual cues (text direction, layout coherence) through the VLM to identify if a page is rotated 0°, 90°, 180°, or 270°, then applies geometric transformations to normalize orientation before downstream text extraction. This pre-processing step improves downstream OCR accuracy by ensuring consistent text direction.
Unique: Uses the same fine-tuned VLM (olmOCR-2-7B) for rotation detection rather than separate orientation detection models, reducing model complexity and leveraging the model's understanding of document layout. This integrated approach avoids the overhead of chaining multiple specialized models.
vs alternatives: More accurate than heuristic-based rotation detection (edge analysis, text line orientation) because it leverages semantic understanding of document layout; faster than running separate orientation detection models because it reuses the main OCR model.
Applies data augmentation techniques (rotation, scaling, noise injection, color jittering) to training images and filters low-quality training examples based on heuristics (image blur, text clarity, layout complexity). The augmentation pipeline increases training data diversity, improving model robustness to document variations. Filtering removes corrupted or low-quality examples that would degrade training, focusing compute on high-quality data.
Unique: Combines augmentation and filtering in a single pipeline, applying augmentation only to high-quality examples. Uses configurable heuristics for filtering, enabling adaptation to different document types and quality standards.
vs alternatives: More efficient than collecting more training data because augmentation increases diversity; more robust than training on unfiltered data because filtering removes corrupted examples that would degrade performance.
Provides runners and evaluation harnesses for comparing olmOCR against competing OCR systems (Tesseract, NanoNets, Google Vision, etc.) on standardized benchmarks. The framework converts outputs from different OCR systems to a common format, applies the same evaluation metrics, and generates comparison reports. This enables fair comparison across systems with different output formats and capabilities.
Unique: Provides standardized runners for multiple OCR systems with output format normalization, enabling fair comparison despite different output formats. Integrates with the benchmarking framework to apply consistent metrics across systems.
vs alternatives: More comprehensive than single-system evaluation because it compares multiple OCR approaches; more fair than cherry-picked comparisons because it uses standardized benchmarks and metrics.
Generates OCR output in Dolma format (structured JSON with document metadata, page-level information, and extracted text), enabling integration with downstream document processing pipelines and training data generation. The format preserves metadata including page numbers, source document paths, processing timestamps, and quality scores. This structured output enables filtering, sorting, and analysis of OCR results at scale.
Unique: Generates Dolma format output natively rather than as a post-processing step, preserving metadata throughout the pipeline. Enables integration with Allen AI's document processing infrastructure and training data generation workflows.
vs alternatives: More structured than plain markdown output because it preserves metadata; more interoperable with document pipelines than custom JSON formats because it uses a standardized schema.
Analyzes document page layouts to identify multi-column regions and reconstructs natural reading order by processing spatial coordinates of text blocks extracted by the VLM. The system groups text elements by column position, sorts them top-to-bottom within columns, then merges columns left-to-right to produce markdown output that follows the intended document flow. This capability handles complex layouts including figures, insets, and mixed single/multi-column pages.
Unique: Reconstructs reading order using spatial coordinate clustering and sorting rather than heuristic rules, enabling handling of arbitrary column counts and irregular layouts. The approach leverages the VLM's ability to provide accurate bounding boxes, avoiding the brittleness of rule-based column detection.
vs alternatives: More flexible than fixed two-column assumptions used by some OCR systems; more accurate than reading-order detection based on text size or font changes because it uses actual spatial positioning from the VLM.
Extracts mathematical equations and tables from document pages and formats them as LaTeX (for equations) or HTML/Markdown (for tables) within the output markdown. The VLM recognizes equation regions and table structures, then generates appropriate markup that preserves mathematical notation and tabular relationships. Equations are rendered as inline or block LaTeX, while tables are converted to HTML or Markdown table syntax, maintaining semantic structure for downstream processing.
Unique: Uses a single fine-tuned VLM (olmOCR-2-7B) to handle both equation and table extraction rather than specialized sub-models, reducing inference overhead. The model is trained on synthetic equation and table data generated via KaTeX and HTML rendering, enabling accurate generation of properly formatted markup.
vs alternatives: Generates valid LaTeX and HTML directly from visual input rather than requiring post-processing or rule-based formatting; more accurate on handwritten equations than traditional OCR because the VLM understands mathematical notation semantically.
Automatically detects and removes headers and footers from document pages by classifying text regions as header/footer/body content using spatial position heuristics and VLM-based content analysis. The system identifies text appearing consistently at the top or bottom of pages (page numbers, running titles, repeated metadata) and excludes it from the final markdown output. This improves readability by eliminating repetitive non-content text.
Unique: Combines spatial heuristics (position-based detection) with VLM-based content analysis to classify headers/footers, avoiding false positives from pure position-based approaches. The system learns header/footer patterns across pages rather than applying fixed rules.
vs alternatives: More accurate than fixed-region removal because it adapts to document-specific header/footer placement; more robust than content-based filtering alone because it uses spatial consistency as a signal.
+5 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 Github at 22/100.
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