Claude Find vs Engram Rs — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Claude Find vs Engram Rs
In-depth architectural comparison of the Claude Find and Engram Rs 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
Claude Find
Knowledge & Memory · Local stdio
Quality: 43/100 (Fair) | Auth: No auth required
Engram Rs
Knowledge & Memory · Local stdio
Quality: 57/100 (Good) | Auth: API Key required
Verdict Summary: Choose Claude Find if you need specialized Knowledge & Memory tools running via a local process. Choose Engram Rs 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 Claude Find 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).
Primary tools included: Indexes raw JSONL session transcripts from Claude Code, Uses GPU-accelerated qwen3 embeddings via Ollama, Hybrid semantic and keyword search with Reciprocal Rank Fusion.
Claude Find is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Engram Rs belongs to Knowledge & Memory using local stdio subprocess. Select Claude Find when you need capabilities focused on knowledge & memory and Engram Rs when you require tools for knowledge & memory.
Store a memory. All memories start in Buffer and promote to Working/Core through access frequency and LLM quality gating. Procedural memories and lessons (tag=lesson) auto-promote to Working after 2h. Use supersedes to replace outdated memories by their ids.
engram_recall
Hybrid semantic + keyword search with budget-aware retrieval. Fast by default (~30ms cached, ~1s first query). Optional expand adds LLM query expansion (+1-2s) — only use for short/vague queries.
engram_recent
List recent memories by creation time. Good for session context recovery.
engram_resume
Full memory bootstrap for session recovery. Returns core (permanent knowledge), working (ongoing context/decisions), buffer (transient), recent activity, and session notes. Use workspace tags to filter by current work context. Compact mode (default) minimizes token usage.
engram_extract
Extract structured memories from raw text using LLM. Feed conversation logs or notes and get individual memories.
engram_search
Quick keyword search. Lighter than recall — no scoring or budget logic.
engram_consolidate
Run a memory consolidation cycle. Promotes important memories upward, drops decayed entries. With merge=true, uses LLM to merge similar memories.
engram_stats
Get memory statistics: counts per layer, AI status, version.
engram_repair
Repair FTS search index. Removes orphaned entries and rebuilds missing ones. Safe to run anytime — idempotent.
engram_health
Detailed health check: uptime, RSS memory, embed cache stats, AI config status.
engram_triggers
Fetch trigger memories for a specific action. Call before performing an action (e.g. git-push, deploy) to recall relevant lessons and rules.
engram_delete
Delete a memory by ID. Use when a memory is outdated, incorrect, or redundant.
Pull Deep Memory from across your Claude Code Sessions — when you need it.
Hierarchical memory engine for AI agents with automatic decay, promotion, semantic dedup, and self-organizing topic tree. Single Rust binary, zero external dependencies.