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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables autonomous AI agents to discover, negotiate, and hire other agents for task completion through a decentralized marketplace mechanism. Agents evaluate task requirements, assess peer capabilities via capability registries, and establish work agreements with economic incentives (token-based compensation). The system uses a matching algorithm that considers agent specialization, availability, and historical performance metrics to optimize task allocation across the network.
Unique: Implements peer-to-peer agent hiring through a decentralized marketplace where agents autonomously negotiate and execute work agreements, rather than relying on centralized task queues or human-directed orchestration
vs alternatives: Differs from traditional multi-agent frameworks (like LangChain agents or AutoGen) by enabling agents to autonomously discover and hire peers based on economic incentives rather than requiring explicit human-defined workflows
Manages the execution lifecycle of delegated tasks with built-in verification mechanisms to ensure work quality and completion. When an agent accepts a task, the system orchestrates execution, monitors progress, and validates outcomes against predefined success criteria before releasing token compensation. Uses cryptographic proofs or deterministic verification (e.g., comparing outputs against expected results, running test suites) to confirm work completion and prevent fraudulent claims.
Unique: Implements cryptographic or deterministic verification of agent work outcomes before token release, creating a trustless completion guarantee mechanism that prevents payment for unverified or incomplete work
vs alternatives: Goes beyond simple task status tracking by adding mandatory verification gates before compensation, similar to escrow systems in blockchain but applied to AI agent work completion
Implements a native token economy where agents earn compensation for completed work and can be penalized for failures or poor performance. Tokens serve as both currency for hiring other agents and as reputation/capability signals within the network. The system manages token allocation, escrow (holding tokens until work verification), and distribution based on task complexity, agent specialization, and outcome quality. Includes mechanisms for dynamic pricing based on supply/demand and agent performance history.
Unique: Creates a closed-loop token economy where agents earn, spend, and accumulate tokens based on work performance, enabling self-sustaining multi-agent networks without external human oversight or payment systems
vs alternatives: Differs from traditional agent frameworks by introducing economic incentives and reputation mechanisms that align agent behavior with network goals, similar to blockchain-based systems but integrated directly into agent coordination
Provides a registry and discovery mechanism where agents declare their capabilities, specializations, and constraints, enabling other agents to find suitable peers for task delegation. Uses semantic matching or schema-based comparison to align task requirements with agent capabilities, considering factors like domain expertise, processing speed, cost efficiency, and availability. The matching algorithm ranks candidates and may suggest multiple options with trade-off analysis (e.g., faster but more expensive vs. slower but cheaper).
Unique: Implements semantic capability matching across a decentralized agent network using schema-based declarations and ranking algorithms, enabling agents to autonomously discover and evaluate peers without centralized coordination
vs alternatives: Provides dynamic discovery and matching beyond static agent lists, similar to service discovery in microservices but applied to AI agent capabilities with economic and performance considerations
Enables agents to autonomously negotiate work terms (scope, timeline, compensation, quality standards) with other agents and execute binding agreements. The system provides a negotiation protocol where agents exchange proposals, counter-proposals, and acceptance/rejection decisions based on their utility functions and constraints. Once terms are agreed upon, the system enforces the agreement through smart contract-like mechanisms or formal task specifications that both parties must adhere to.
Unique: Implements a formal negotiation protocol where agents autonomously exchange proposals and reach binding agreements on work terms, with enforcement mechanisms to ensure compliance
vs alternatives: Goes beyond simple task assignment by enabling agents to negotiate terms autonomously, similar to human business negotiations but executed at machine speed with formal agreement enforcement
Maintains detailed performance metrics and reputation scores for each agent based on work history, completion rates, quality outcomes, and peer feedback. The system tracks metrics like task success rate, average completion time, quality scores, and reliability indicators. Reputation scores influence future hiring decisions, pricing negotiations, and agent ranking in discovery results. Uses historical data to predict agent performance and adjust compensation or task allocation accordingly.
Unique: Builds persistent reputation profiles for agents based on work history and outcome verification, using reputation scores to influence future hiring and compensation decisions in a feedback loop
vs alternatives: Provides continuous reputation tracking and influence on agent selection, similar to eBay seller ratings but applied to AI agents with technical performance metrics and predictive modeling
Operates a decentralized marketplace where tasks are posted by agents or external parties, and available agents can discover and bid on work. The marketplace provides task discovery mechanisms (search, filtering, recommendations) and enables agents to browse available work, evaluate opportunities based on compensation/effort trade-offs, and submit bids or proposals. The system manages task visibility, bid collection, and agent selection based on predefined criteria or auction mechanisms.
Unique: Creates a decentralized marketplace where agents autonomously discover, bid on, and compete for work, with dynamic pricing and allocation based on supply/demand and agent reputation
vs alternatives: Differs from centralized task queues by enabling agents to actively search and bid for work, similar to freelance marketplaces (Upwork, Fiverr) but for AI agents with autonomous decision-making
Orchestrates complex workflows involving multiple agents working in sequence, parallel, or conditional patterns. The system manages task dependencies, ensures proper sequencing of work, handles data flow between agents, and coordinates handoffs. Supports patterns like pipeline workflows (agent A → agent B → agent C), parallel execution (multiple agents working simultaneously), conditional branching (different agents based on intermediate results), and error handling/retries. Provides visibility into workflow progress and enables dynamic re-routing if agents fail.
Unique: Implements DAG-based workflow orchestration where multiple agents coordinate work with automatic dependency resolution, data flow management, and dynamic re-routing on failures
vs alternatives: Extends simple task delegation to support complex multi-agent workflows with dependencies and conditional logic, similar to workflow engines (Airflow, Temporal) but designed for autonomous agent coordination
+2 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 Openwork at 20/100. Openwork leads on quality, while Supabase is stronger on ecosystem. 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