| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes a repeating cycle of code modification → git commit → mechanical verification → decision logic → result logging that continues until user interruption or iteration limit. The system uses a constraint triangle (scope glob patterns, single mechanical metric, shell verify command) to enable autonomous operation without subjective judgment. Git serves as both memory and causality tracker, with automatic rollback on verification failure.
Unique: Uses constraint triangle (scope + metric + verify) to enable fully autonomous operation without human-in-the-loop judgment; implements 8-phase iteration protocol with explicit decision logic (Keep/Discard/Crash) and git-based causality tracking, enabling bold exploration with automatic rollback. This differs from typical agentic loops that require frequent human validation or rely on heuristic stopping criteria.
vs alternatives: Enables 50+ autonomous iterations with full audit trail and automatic rollback, whereas most LLM agents require human validation between steps or lack deterministic failure recovery.
Converts plain-language goals into structured autoresearch configurations via a 7-phase guided workflow. The wizard validates scope constraints, suggests mechanical metrics from a database of domain-specific examples, and generates shell verify commands. Each phase includes validation gates that ensure the configuration is executable before iteration begins.
Unique: Implements a 7-phase wizard with validation gates that test metric extraction and scope coverage before iteration begins, preventing misconfigured loops. The metric suggestion database is domain-aware, offering ranked suggestions (e.g., test coverage for TypeScript projects, latency for Python services) rather than generic options.
vs alternatives: Reduces configuration errors and iteration waste by validating the setup before autonomous iteration starts, whereas manual configuration often requires trial-and-error debugging.
Restricts code modifications to files matching user-defined glob patterns (e.g., src/**/*.ts, test/**/*.test.ts). The system validates scope during setup, ensures Claude only modifies in-scope files, and logs scope violations as errors. Scope constraints enable the agent to load full context into memory without overwhelming token limits and prevent unintended modifications to configuration, documentation, or other sensitive files.
Unique: Enforces scope constraints via glob patterns, enabling the agent to load full context of in-scope files into memory without overwhelming token limits. Scope validation at setup prevents misconfigured iterations, and scope constraints are transparent (users see exactly which files can be modified).
vs alternatives: Provides explicit scope constraints via glob patterns, enabling safe autonomous modification of large codebases, whereas most agentic systems either modify all files or require manual file selection.
Implements strategies for recovering from iteration failures (e.g., verify command timeout, git rollback failure, metric extraction error). The system logs errors with full context (iteration number, command output, stack trace), automatically rolls back failed iterations, and continues to the next iteration. For unrecoverable errors (e.g., git corruption), the system halts and logs detailed diagnostics to enable manual recovery.
Unique: Implements automatic rollback on failure with detailed error logging, enabling long-running iteration loops to recover from transient failures without halting. Error logs include full context (iteration number, command output, stack trace), enabling users to debug failures and adjust verification commands.
vs alternatives: Provides automatic crash recovery with detailed diagnostics, whereas most agentic systems halt on failure or require manual intervention to recover.
Executes autonomous security testing via an adversarial iteration loop that applies STRIDE threat modeling and OWASP vulnerability patterns. Each iteration generates adversarial test cases, runs them against the codebase, and logs security findings. The loop uses a threat model as the constraint and vulnerability count as the mechanical metric, enabling autonomous security hardening.
Unique: Applies constraint-driven iteration to security hardening by using threat models as scope constraints and vulnerability count as the mechanical metric. The adversarial loop systematically explores STRIDE/OWASP categories rather than relying on passive scanning, enabling autonomous discovery of vulnerabilities that match the threat model.
vs alternatives: Enables continuous autonomous security hardening with full iteration history, whereas traditional SAST/DAST tools are point-in-time and require manual remediation workflows.
Uses Git commits as the primary memory mechanism, storing one commit per iteration with Claude's modification summary in the commit message. Each commit is tagged with iteration metadata (metric value, timestamp, decision status). On verification failure, the system automatically reverts to the previous commit, preserving causality and enabling crash recovery. The git log serves as a queryable audit trail of all attempted improvements.
Unique: Treats Git commits as first-class memory, with each iteration creating an immutable record that includes metric value, decision logic, and modification summary. Automatic rollback on failure preserves causality without requiring external state stores, and the git log becomes a queryable archive of the entire optimization trajectory.
vs alternatives: Provides built-in crash recovery and audit trail without external databases, whereas most agentic systems require separate logging infrastructure and manual rollback on failure.
Executes a user-provided shell command to extract a single numeric metric from test output, build logs, or custom scripts. The metric is parsed deterministically (e.g., grep for percentage, regex for latency value) and compared against the previous iteration to decide Keep/Discard. The system validates metric extraction during setup and caches baseline measurements to enable fast iteration-to-iteration comparisons.
Unique: Enforces mechanical (deterministic, numeric) metrics as the sole decision criterion, eliminating subjective judgment from the autonomous loop. Metric extraction is validated during setup and cached to enable fast comparisons, and the system explicitly rejects non-deterministic or multi-objective metrics that would require heuristic decision-making.
vs alternatives: Enables fully autonomous decision-making without human judgment by requiring mechanical metrics, whereas most agentic systems rely on heuristic scoring or human feedback.
Supports two iteration strategies: bounded mode (run exactly N iterations, then stop) and unbounded mode (run until user interruption). Bounded mode is useful for exploration with a fixed budget; unbounded mode enables continuous improvement until diminishing returns. The system tracks iteration count, elapsed time, and metric trajectory to inform stopping decisions.
Unique: Provides explicit bounded and unbounded modes rather than heuristic stopping criteria, giving users control over iteration budget. Bounded mode enables reproducible experiments with fixed iteration counts; unbounded mode enables continuous improvement without predetermined limits.
vs alternatives: Offers explicit control over iteration budget, whereas most agentic systems use heuristic stopping criteria (e.g., no improvement for N steps) that are difficult to tune and reproduce.
+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.
autoresearch scores higher at 42/100 vs Supabase at 42/100.
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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