Memex vs Shodh Memory — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Memex vs Shodh Memory
In-depth architectural comparison of the Memex and Shodh Memory MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Memex
Knowledge & Memory · Local stdio
Quality: 43/100 (Fair) | Auth: No auth required
Shodh Memory
Knowledge & Memory · Local stdio
Quality: 55/100 (Good) | Auth: API Key required
Verdict Summary: Choose Memex if you need specialized Knowledge & Memory tools running via a local process. Choose Shodh Memory if your workspace requires Knowledge & Memory integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Memex when:
You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
You have access to required keys: DB_MAX_POOL_SIZE.
Primary tools included: Semantic vector search with recency decay re-ranking, Local ONNX embedding model (BAAI/bge-small-en-v1.5) for zero API cost, PostgreSQL 16 with pgvector and ivfflat index for efficient vector queries.
Production-grade persistent memory service for AI agents. Semantic search with recency decay (configurable α blend), local ONNX embeddings (zero API cost), background memory summarisation, and Prometheus/Grafana observability. docker compose up — no cloud dependencies.
Cognitive memory for AI agents with Hebbian learning, 3-tier architecture, and knowledge graphs. Single 15MB binary, runs offline on edge devices.
Category & Scope
Tools & Capabilities Breakdown
Memex Tools (6)
Semantic vector search with recency decay re-ranking
Local ONNX embedding model (BAAI/bge-small-en-v1.5) for zero API cost
PostgreSQL 16 with pgvector and ivfflat index for efficient vector queries
Configurable recency decay blending factor (alpha) per request
Background memory summarization and Prometheus/Grafana observability
Docker Compose deployment with no external cloud dependencies
Shodh Memory Tools (6)
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Memex is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Shodh Memory belongs to Knowledge & Memory using local stdio subprocess. Select Memex when you need capabilities focused on knowledge & memory and Shodh Memory when you require tools for knowledge & memory.
Primary tools included: Zero LLM calls for storing or recalling memories, Hebbian learning with memory strengthening and decay, Local semantic search using MiniLM embeddings.