Entroly vs Engram — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Entroly vs Engram
In-depth architectural comparison of the Entroly and Engram 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
Entroly
Knowledge & Memory · Local stdio
Quality: 55/100 (Good) | Auth: No auth required
Engram
Knowledge & Memory · Local stdio
Quality: 45/100 (Fair) | Auth: API Key required
Verdict Summary: Choose Entroly if you need specialized Knowledge & Memory tools running via a local process. Choose Engram 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 Entroly 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: Recoverable compression using BM25, entropy, and dependency graph knapsack, Stable prompt prefixing for provider cache discounts, Bayesian routing of tasks to cheaper models.
You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (Free / Open Source).
You have access to required keys: ENGRAM_GIT_REPO, ENGRAM_PORT, ENGRAM_JWT_SECRET, ENGRAM_WRITE_TOKEN, ENGRAM_READ_TOKEN.
Primary tools included: Git-backed markdown storage with no database dependency, Authority-aware search excluding superseded or expired notes, Per-agent read and write tokens for access control.
Auditable context control plane and MCP server for AI coding agents. Compresses context 70–95% (BM25 + entropy + dep-graph knapsack), stabilizes prompt prefixes for provider cache discounts, routes easy tasks to cheaper models (RAVS Bayesian router), and verifies answers locally with WITNESS hallucination guard (0.844 AUROC, $0, 3 ms). MemoryOS adds local budget-aware working/episodic/semantic memory with decay, safety scanning, and durable persistence. 38 agent integrations (Cursor, Claude Code, Codex, Aider, and more). Ships as MCP server (entroly serve), HTTP proxy, or Python/Rust library. Apache-2.0, local-first. pip install entroly
Second brain your agents read and write, over a git-backed markdown vault. Authority-aware search ranks superseded and archived notes below live ones (from frontmatter, no vector DB), so agents quote the current doc, not the dead one. Per-agent read-only or write tokens, a git audit trail of every change with diffs, a knowledge-graph dashboard, and Obsidian compatibility.
Tools & Capabilities Breakdown
Entroly Tools (6)
Recoverable compression using BM25, entropy, and dependency graph knapsack
Stable prompt prefixing for provider cache discounts
Bayesian routing of tasks to cheaper models
Local hallucination guard with WITNESS (0.844 AUROC)
MemoryOS with working, episodic, and semantic memory plus decay and persistence
Entroly is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Engram belongs to Knowledge & Memory using local stdio subprocess. Select Entroly when you need capabilities focused on knowledge & memory and Engram when you require tools for knowledge & memory.