| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Captures tool usage observations at five discrete lifecycle points (SessionStart, UserPromptSubmit, PostToolUse, Summary, SessionEnd) via CLAUDE.md plugin hooks registered with Claude Code. Each hook fires at specific moments in the agent's execution flow, collecting raw tool invocations, outputs, and user interactions without requiring manual instrumentation. The system queues observations asynchronously and routes them to a worker service for processing.
Unique: Uses a 5-point lifecycle hook system (SessionStart, UserPromptSubmit, PostToolUse, Summary, SessionEnd) registered via CLAUDE.md manifest rather than generic event emitters, enabling tight coupling with Claude Code's internal execution flow and precise timing of observation capture at critical decision points
vs alternatives: More precise than generic logging because hooks fire at semantically meaningful moments in the agent's workflow rather than at arbitrary code execution points, reducing noise and improving observation quality
Extracts and compresses raw tool observations into structured, semantically meaningful summaries using Claude 3.5 Sonnet, Haiku, or other models via Claude Agent SDK, Gemini, or OpenRouter. The system implements agent selection with fallback logic—if the primary provider fails, it automatically retries with a secondary provider. Compression happens asynchronously in a worker service queue, preventing blocking of the IDE during AI processing.
Unique: Implements agent selection with fallback logic in the worker service—if Claude API fails, automatically retries with Gemini or OpenRouter without user intervention. Uses Claude Agent SDK for structured prompt generation and response parsing, enabling semantic compression rather than simple truncation
vs alternatives: More resilient than single-provider systems because fallback ensures observations are always processed even if primary API is unavailable; more intelligent than regex-based summarization because it uses LLMs to extract semantic meaning
Implements a hierarchical configuration system where settings are resolved in priority order: environment variables (highest), .claude-mem/config.json, .claude-mem/.env, and hardcoded defaults (lowest). This allows users to configure the system via environment variables (for CI/CD), config files (for projects), or defaults (for simplicity). The system supports configuration for AI providers, database paths, privacy controls, and token budgets. Configuration is validated on startup and errors are reported clearly.
Unique: Implements a 4-level configuration priority system (env vars > config.json > .env > defaults) that allows flexible configuration without forcing users into a single approach. Configuration is validated on startup with clear error messages. This pattern is common in modern CLI tools but less common in IDE plugins
vs alternatives: More flexible than single-source configuration because it supports multiple configuration methods; more transparent than hidden configuration because the priority order is documented; more robust than unvalidated configuration because invalid settings are caught at startup
Provides a web-based UI (accessible via localhost) for viewing observations, searching memory, and managing settings. The UI uses Server-Sent Events (SSE) for real-time updates, allowing the browser to receive notifications when new observations are captured or processed. The UI includes a settings modal for configuring privacy controls, AI providers, and token budgets. Component architecture separates concerns (search, timeline, settings) into reusable React components.
Unique: Implements a web-based UI with Server-Sent Events for real-time updates, allowing users to see observations as they're captured without polling. Component architecture separates search, timeline, and settings into reusable React components. Settings modal provides GUI-based configuration without requiring JSON editing
vs alternatives: More user-friendly than CLI-only tools because it provides a visual interface; more responsive than polling-based updates because SSE pushes updates in real-time; more discoverable than hidden configuration because settings are exposed in a modal
Implements a batch processing system (Ragtime) that compresses multiple observations in parallel, optimizing for throughput over latency. The batch processor groups observations by session, submits them to the AI API in batches, and persists results to SQLite/ChromaDB. This is useful for backfilling observations from previous sessions or processing high-volume observation streams. Batch processing is configurable (batch size, parallelism) and can be triggered manually or scheduled.
Unique: Implements a dedicated batch processor (Ragtime) that optimizes for throughput by grouping observations into batches and submitting them in parallel. This is distinct from the real-time observation compression pipeline, which optimizes for latency. Batch processing is configurable and can be triggered manually or scheduled
vs alternatives: More efficient than processing observations one-at-a-time because batching reduces API overhead; more flexible than fixed batch sizes because parallelism and batch size are configurable; more suitable for backfill scenarios because it can process large volumes without blocking the IDE
Persists compressed observations in two complementary stores: SQLite (~/.claude-mem/claude-mem.db) for structured relational data with schema migrations, and ChromaDB (~/.claude-mem/vector-db) for semantic vector embeddings. The system maintains schema consistency through migrations, syncs embeddings via ChromaSync operations, and enables both SQL queries (for exact matches, filtering) and vector similarity search (for semantic retrieval). Data flows from observation compression → SQLite insert → ChromaDB embedding sync.
Unique: Implements a dual-storage architecture where SQLite serves as the source-of-truth for structured data and ChromaDB is synced asynchronously via ChromaSync operations. This decouples relational queries from vector search, allowing each store to optimize for its access pattern. Schema migrations are managed explicitly, enabling safe schema evolution without data loss
vs alternatives: More flexible than single-store solutions because it supports both exact filtering (SQL) and semantic search (vectors) without forcing a choice; more reliable than cloud-only memory because data persists locally and survives network outages
Implements a three-layer search workflow that progressively discloses context to optimize token usage: Layer 1 (fast metadata filtering) uses SQLite queries to narrow candidates by timestamp, file path, or tags; Layer 2 (semantic search) queries ChromaDB for vector similarity to the user's query; Layer 3 (context assembly) constructs the final MEMORY.md with ranked results. The system uses progressive disclosure—it starts with minimal context and expands only if the agent requests more, reducing token overhead for simple queries.
Unique: Uses a 3-layer workflow (metadata filtering → semantic search → context assembly) with progressive disclosure that starts with minimal context and expands only on demand. This is distinct from traditional RAG systems that return all relevant documents at once. The Timeline Service provides temporal filtering, enabling queries like 'show me work from last Tuesday on the auth module'
vs alternatives: More token-efficient than naive RAG because it uses progressive disclosure instead of returning all relevant documents upfront; faster than full-text search because Layer 1 metadata filtering eliminates most candidates before expensive vector operations
Generates a structured MEMORY.md file containing compressed observations, ranked by relevance, and injects it into Claude Code's context at session start via the SessionStart hook. The MEMORY.md format includes observation summaries, metadata (timestamps, file paths, tool names), and optional tags. The system uses a Context Builder Pipeline to assemble MEMORY.md from search results, ensuring consistent formatting and token budgeting.
Unique: Uses a structured MEMORY.md format (markdown with YAML frontmatter for metadata) that is both human-readable and machine-parseable. The Context Builder Pipeline assembles MEMORY.md from search results with token budgeting, ensuring it fits within Claude's context window. Injection happens at SessionStart hook, making it transparent to the user
vs alternatives: More transparent than hidden context injection because MEMORY.md is visible in the IDE; more structured than raw observation dumps because it uses consistent formatting and metadata; more efficient than re-querying the database during the session because context is pre-assembled at startup
+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.
claude-mem scores higher at 50/100 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