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Engram MCP server

by kael-bit·io.github.kael-bit/engram·v0.10.0·27 stars
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Hierarchical memory for AI agents. Three-layer (buffer/working/core) with decay and promotion.

C63/100grade C
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Safety scan
C63/100

full report

Adoption
New

27 stars

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If you have run it, two minutes of your experience saves the next person an afternoon.

Engram tools (15, 3 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • engram_consolidatewrite action

    Run a memory consolidation cycle. Promotes important memories upward,

  • engram_deletewrite action

    Delete a memory by ID. Use when a memory is outdated, incorrect, or redundant.

  • engram_extract

    Extract structured memories from raw text using LLM.

  • engram_health

    Detailed health check: uptime, RSS memory, embed cache stats, AI config status.

  • engram_recall

    Hybrid semantic + keyword search with budget-aware retrieval.

  • engram_recent

    List recent memories by creation time. Good for session context recovery.

  • engram_repair

    Repair FTS search index. Removes orphaned entries and rebuilds missing ones.

  • engram_restore

    Restore a soft-deleted memory from trash back to its original layer.

  • engram_resume

    Full memory bootstrap for session recovery. Returns core (permanent knowledge),

  • engram_search

    Quick keyword search. Lighter than recall — no scoring or budget logic.

  • engram_stats

    Get memory statistics: counts per layer, AI status, version.

  • engram_store

    Store a memory. All memories start in Buffer and promote to Working/Core through

  • engram_trash

    List soft-deleted memories. Recently deleted items can be restored.

  • 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_updatewrite action

    Update a memory's content, metadata (importance, tags, layer, kind). Use supersedes on store to replace with history.

Public scan report

scanner v0.1.10 · 2026-09-28 · same rubric, same numbers if you re-run it

no findings
  • Code scan3 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitystatic API keys via environment variables6/15
  • Maintenancelast push 209 days ago4/15
  • Maintainer identityregistry namespace matches repository owner6/10
Overall 63/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Engram compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
63/100
higher than 18% of 48,255 servers
Tools
15
the typical server has 7
Tools that change things
20%
typical: 0%. Fewer write tools means less an agent can break
GitHub stars
27
more than 91% of 28,688 servers

What its tools let an agent do: 12 read · 3 change

Install Engram in Claude Code, Cursor or VS Code

Runs npx -y engram-rs-mcp on your machine. Read the scan report first; the gateway never runs local packages.

claude mcp add engram -- npx -y engram-rs-mcp
Add to Cursor

What the publisher says

From the Engram repository's README, as published. We do not edit it. Read it on GitHub

engram-rs

Memory engine for AI agents. Two axes: time (three-layer decay & promotion) and space (self-organizing topic tree). Important memories get promoted, noise fades, related knowledge clusters automatically.

Most agent memory is a flat store — dump everything in, keyword search to get it back. No forgetting, no organization, no lifecycle. engram-rs adds the part that makes memory actually useful: the ability to forget what doesn't matter and surface what does.

Single Rust binary, one SQLite file, zero external dependencies. No Python, no Redis, no vector DB — curl | bash and it runs. ~10 MB binary, ~100 MB RSS, single-digit ms search latency.

Quick Start

# Install (interactive — will prompt for embedding provider config)
curl -fsSL https://raw.githubusercontent.com/kael-bit/engram-rs/main/install.sh | bash

# Store a memory
curl -X POST http://localhost:3917/memories \
  -d '{"content": "Always run tests before deploying", "tags": ["deploy"]}'

# Recall by meaning
curl -X POST http://localhost:3917/recall \
  -d '{"query": "deployment checklist"}'

# Restore full context (session start)
curl http://localhost:3917/resume

What It Does

Three-Layer Lifecycle

Inspired by the Atkinson–Shiffrin memory model, memories are managed across three layers by importance:

Buffer (short-term) → Working (active knowledge) → Core (long-term identity)
      ↓                       ↓                           ↑
   eviction              importance decay           LLM quality gate
  • Buffer: Entry point for all new memories. Temporary staging — evicted when below threshold
  • Working: Promoted via consolidation. Never deleted, importance decays at different rates by kind
  • Core: Promoted through LLM quality gate. Never deleted

LLM Quality Gate

Promotion isn't rule-based guesswork — an LLM evaluates each memory in context and decides whether it genuinely warrants long-term retention.

Buffer → [LLM gate: "Is this a decision, lesson, or preference?"] → Working
Working → [sustained access + LLM gate] → Core

Automatic Decay

Decay is activity-driven — it only fires during active consolidation cycles, not wall-clock time. If the system is idle, memories stay intact.

Exponential decay follows the Ebbinghaus forgetting curve — fast at first, then long-tail. Memories never fully vanish (floor = 0.01), remaining retrievable under precise queries. When a memory is recalled, it gets an activation boost, strengthening frequently-used knowledge.

Algorithm Visualizations

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Engram: common questions

Is Engram MCP server safe?
With care: it is graded C, so read the findings first (63/100). Read the Engram safety report
How do I install Engram?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Engram need an API key?
Yes. The registry entry asks for ENGRAM_API_KEY.
Is Engram maintained?
The last commit was 246 days ago (2026-03-03). The latest release is v0.10.0.
What can I use instead of Engram?
Servers from other publishers that do the same job: Deja Vu MCP server.
How does Engram compare with other MCP servers?
safety score 63/100 (higher than 18% of 48,255 servers); tools 15 (the typical server has 7); tools that change things 20% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

Alternatives to Engram

Same job from other publishers: the closest match first, then the best rated.

  • Deja Vu
    deja-vu: local memory over the session histories of forty-three coding agents.
    A