| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the ReAct (Reasoning + Acting) pattern through ReactAgent class that extends BaseAgent, enabling agents to interleave chain-of-thought reasoning with tool invocation. The framework manages the reasoning loop by accepting LLM outputs, parsing tool calls, executing tools, and feeding results back into the reasoning chain. This architecture decouples reasoning logic from tool execution, allowing agents to reason about which tools to use before invoking them.
Unique: Implements ReAct as a first-class agent pattern through ReactAgent class that manages the full reasoning-acting loop, with explicit separation between reasoning (LLM) and acting (tool execution) phases, rather than treating tool calling as a secondary feature
vs alternatives: Provides structured reasoning-before-acting compared to simpler function-calling frameworks, enabling more complex multi-step problem solving at the cost of increased LLM calls
Provides a factory pattern implementation (AgentFactory class) that handles agent creation, configuration loading, activation, and lifecycle coordination. The factory abstracts agent instantiation by loading configuration from JSON files, resolving dependencies, and managing agent state across creation and execution phases. This enables standardized agent deployment and reduces boilerplate for agent setup.
Unique: Centralizes agent instantiation through AgentFactory with explicit lifecycle methods for creation, activation, and task execution, combined with JSON-based configuration loading that standardizes how agents are defined and deployed
vs alternatives: Reduces boilerplate compared to manual agent instantiation, enabling faster agent development and standardized deployment patterns across teams
Implements standardized agent packaging through directory structure (pyopenagi/agents/{author}/{agent_name}/), configuration files (config.json), and dependency specifications (meta_requirements.txt). This enables consistent agent distribution, dependency resolution, and metadata tracking. Agents can be packaged with all dependencies and shared through the Agent Hub.
Unique: Standardizes agent packaging through enforced directory structure, JSON configuration, and dependency files, enabling consistent agent distribution and metadata tracking across the Agent Hub
vs alternatives: Provides standardized packaging compared to ad-hoc agent distribution, but less flexible than mature package managers and lacks automatic dependency resolution
Integrates with AIOS (AI Operating System) kernel as the primary agent creation system, with an explicit migration path to Cerebrum SDK for future versions. The integration enables agents to run within the AIOS environment, accessing kernel services and resources. The architecture supports both current AIOS integration and future Cerebrum SDK compatibility.
Unique: Integrates agents with AIOS kernel as primary execution environment while providing explicit migration path to Cerebrum SDK, enabling agents to leverage kernel services with future compatibility
vs alternatives: Enables kernel-level integration compared to standalone agents, but creates tight coupling to AIOS and limits portability to other environments
Implements a pluggable tool system through BaseTool abstract class with concrete implementations for RapidAPI, Huggingface, and custom tools. Each tool type has its own adapter that handles API authentication, request formatting, response parsing, and error handling. Tools are registered with agents and invoked through a standardized interface, allowing agents to seamlessly call external APIs without knowing implementation details.
Unique: Provides a unified BaseTool abstraction with concrete adapters for multiple API providers (RapidAPI, Huggingface), allowing agents to invoke diverse external services through a single standardized tool calling interface
vs alternatives: Abstracts API complexity compared to direct API calls, enabling agents to use multiple API providers without provider-specific code; more flexible than hardcoded integrations but requires explicit tool registration
Implements the Interactor system that manages downloading and uploading of agent implementations to/from a centralized Agent Hub. The interactor handles agent packaging, versioning, and repository management, enabling community-driven agent sharing. Agents can be published to the hub with metadata and dependencies, then discovered and downloaded by other users for local execution.
Unique: Provides a centralized Agent Hub with Interactor system for publishing and discovering agents, enabling community-driven agent development and reuse through standardized packaging and metadata
vs alternatives: Enables agent sharing and discovery compared to isolated agent development, but lacks version control and access management features found in mature package registries
Implements a Queues system that manages requests to language model backends, handling the flow of prompts and responses between agents and LLM services. The queue system abstracts LLM provider details, allowing agents to submit prompts without knowing which backend processes them. This enables load balancing, request batching, and provider switching without agent code changes.
Unique: Abstracts LLM provider details through a queue-based request management system, enabling agents to submit prompts without knowing the underlying LLM backend, supporting transparent provider switching and concurrent request handling
vs alternatives: Provides provider abstraction compared to direct LLM API calls, enabling easier provider switching and multi-agent request management, but adds latency and lacks advanced features like request batching or priority queues
Enables agents to be customized through JSON configuration files (config.json) that specify agent parameters, tool selections, and execution settings. The BaseAgent class loads and validates configurations, allowing non-developers to customize agent behavior without modifying code. Configuration includes tool selections, model parameters, and agent-specific settings that control runtime behavior.
Unique: Implements configuration-driven agent customization through JSON files loaded by BaseAgent, allowing agent behavior to be modified without code changes while maintaining standardized agent directory structure
vs alternatives: Enables non-technical customization compared to code-based configuration, but lacks schema validation and versioning features found in mature configuration management systems
+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 OpenAGI at 23/100.
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