| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Accepts distributed traces from LLM applications through a dedicated gRPC server listening on port 4317, implementing the OpenTelemetry Protocol (OTLP) specification. Traces are parsed from protobuf messages, validated, and persisted to PostgreSQL or SQLite with automatic schema migrations. Supports multi-language instrumentation (Python, TypeScript, Go, etc.) without requiring application code changes when using auto-instrumentation libraries.
Unique: Implements native gRPC OTLP server (not HTTP/JSON) with automatic protobuf deserialization and direct database persistence, avoiding the overhead of HTTP protocol conversion that other observability platforms require. Uses OpenTelemetry's standard trace model directly rather than proprietary span formats.
vs alternatives: Faster ingestion than HTTP-based OTLP collectors (gRPC binary protocol) and fully compatible with OpenTelemetry ecosystem, unlike proprietary tracing solutions that require custom instrumentation adapters.
Exposes a Strawberry GraphQL API (on port 6006) that allows complex queries over ingested traces using a schema-driven approach. Queries support filtering by span attributes, trace duration, status codes, and custom dimensions; supports pagination, sorting, and aggregation operations. The GraphQL layer translates queries into optimized SQL against the trace database, enabling efficient retrieval of trace subsets for analysis and debugging without loading entire trace datasets into memory.
Unique: Uses Strawberry GraphQL framework with type-safe schema generation from Python dataclasses, enabling automatic schema validation and IDE autocomplete for query construction. Translates GraphQL queries directly to optimized SQL rather than loading full datasets into memory.
vs alternatives: More flexible than REST APIs for complex filtering scenarios and more efficient than full-dataset retrieval; GraphQL schema is self-documenting and supports introspection for dynamic client generation.
Provides a database abstraction layer supporting both PostgreSQL (production) and SQLite (development/single-instance) backends, with automatic schema migrations managed by Alembic. The abstraction uses SQLAlchemy ORM for database operations, enabling schema changes without manual SQL. Supports connection pooling, transaction management, and query optimization for both backends. Database schema includes tables for spans, traces, evaluations, datasets, and annotations with appropriate indexes for common query patterns.
Unique: Uses SQLAlchemy ORM with Alembic migrations to support multiple database backends with identical schema and query logic, enabling seamless migration between SQLite and PostgreSQL without application code changes. Automatic migration management prevents manual schema drift.
vs alternatives: Dual database support enables development with SQLite (no setup) and production with PostgreSQL (scalability) without code changes; automatic migrations reduce operational burden compared to manual schema management.
Provides a command-line interface for starting the Phoenix server locally, managing database connections, and exporting trace data. CLI commands support starting the server with custom configuration (port, database URL, authentication), running database migrations, exporting traces to CSV/JSON, and importing datasets. The CLI uses Click framework for command definition and supports both interactive and scripted usage.
Unique: Provides a unified CLI for both server management and data operations, enabling users to start Phoenix, manage databases, and export data without writing Python code. Uses Click framework for composable command structure.
vs alternatives: Simpler than Docker/Kubernetes for local development and provides data export capabilities that would otherwise require custom scripts or database queries.
Provides a React-based web UI that visualizes trace execution flows as interactive diagrams showing span hierarchies, timing, and status. The UI displays spans as nodes with parent-child relationships, color-coded by status (success, error, pending), and includes timeline visualization showing span duration and overlap. Users can click spans to view detailed attributes, logs, and events; filter traces by attributes; and navigate between related traces. The frontend communicates with the backend via GraphQL API.
Unique: Implements interactive trace visualization as a React component tree with real-time filtering and detail inspection, using GraphQL subscriptions for live updates. Visualizes span hierarchies and timing relationships in a way that's intuitive for understanding LLM application execution.
vs alternatives: More intuitive than raw JSON trace data or text-based logs for understanding execution flow; interactive filtering enables rapid exploration of large trace datasets without writing queries.
Implements authentication and authorization mechanisms (details in DeepWiki) supporting role-based access control (RBAC) for multi-tenant deployments. Users can be assigned roles (admin, analyst, viewer) with corresponding permissions for reading/writing traces, evaluations, and datasets. Authentication supports API keys and optional OAuth2/OIDC integration. Authorization is enforced at the API layer (GraphQL and REST) and database layer to prevent unauthorized data access.
Unique: Implements RBAC at both API and database layers, ensuring authorization is enforced consistently across GraphQL, REST, and direct database access. Supports both API key and OAuth2/OIDC authentication mechanisms.
vs alternatives: Role-based access control enables multi-tenant deployments where different teams can access the same Phoenix instance with appropriate data isolation, unlike single-user deployments.
Provides a Python-based evaluation system (arize-phoenix-evals package) that runs structured evaluators against LLM outputs to measure quality, correctness, and safety. Evaluators are composable functions that accept input/output pairs and return structured scores or classifications. The framework supports both built-in evaluators (hallucination detection, relevance scoring, toxicity detection) and custom user-defined evaluators; results are stored as annotations on spans and can be aggregated across datasets for statistical analysis.
Unique: Implements evaluators as composable, reusable functions with a standardized interface (input/output → score) that can be chained and parallelized. Integrates evaluation results directly as span annotations, enabling correlation between execution traces and quality metrics without separate storage systems.
vs alternatives: Tightly integrated with trace data (evaluations are stored as span annotations) unlike standalone evaluation tools, enabling direct correlation between execution details and quality scores; supports both LLM-based and custom evaluators in a unified framework.
Provides a prompt management system that stores prompt templates with version history, enabling A/B testing and experimentation. Prompts are stored in the database with metadata (model, parameters, tags) and can be retrieved by version or tag. The system tracks which prompt version was used for each LLM call via span attributes, allowing correlation between prompt changes and output quality metrics. Experiments can be defined to compare multiple prompt versions against the same dataset of inputs.
Unique: Integrates prompt versioning directly with trace data, storing prompt version references in span attributes and enabling automatic correlation with evaluation results. Supports experiment definition as a first-class concept with built-in comparison logic across prompt versions.
vs alternatives: Unlike standalone prompt management tools, Phoenix correlates prompt versions with actual execution traces and quality metrics, enabling data-driven prompt optimization rather than manual comparison.
+6 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 phoenix at 32/100.
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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