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  1. Home
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  3. Memstack
Memstack logo
Health: ActiveRecent health check succeeded.Last checked 9/7/2026, 8:39:03 PM

Memstack

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View RepositoryVisit Website

MCP server for persistent, searchable memory for AI agents.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "memstack": {
      "command": "npx",
      "args": [
        "-y",
        "skills"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Documentation Overview

MemStack

Implementation priority is maintained in the canonical roadmap.

The open-source memory layer for AI agents β€” store, retrieve, summarize, and prune.

npm version skills.sh MCP Registry CI License: MIT MCP Reference

bash
# Use MemStack in your application
npm install @memstack/core

# Give your coding agent the MemStack skill
npx skills add isiomaC/memstack

@memstack/core is the runtime SDK; the Agent Skill teaches compatible coding agents how to integrate and operate MemStack correctly.

The problem: AI agents forget. Every interaction starts from zero. You either stuff everything into the context window (expensive, slow, degrades output quality) or the agent has no memory of past conversations.

What MemStack does: A persistent memory pipeline that lives between your agent and the LLM. It stores every interaction, retrieves only what's relevant, summarizes old memories to save tokens, and prunes stale ones automatically. One method call, no infrastructure required.

Think of it as the open-source alternative to Mem0 β€” pluggable storage, bring your own LLM, zero vendor lock-in.


Table of Contents

  • Why MemStack
  • Quick Start
  • The Memory Pipeline
    • Store
    • Retrieve
    • Compile Context
    • Summarize
    • Prune
  • Real-World Use Cases
    • Support Agent
    • RAG Pipeline
    • Multi-User Chatbot
  • Memory Type Reference
  • Retrieval Strategies
  • Embeddings
  • Adapters
    • LLM Adapters
    • Embedding Adapters
    • Storage Adapters
  • Full API Reference
    • MemStack Client
    • Memory Subsystem
    • Export / Import
    • Health & Close
  • Configuration
  • Advanced Usage
    • Custom Storage
    • Custom LLM / Embedding
    • Event Hooks
  • Development
    • Setup & Tests
    • Debugging
  • Publishing to npm
  • Contributing
  • License

Why MemStack

LLMs have context windows, not memory. The difference matters.

ApproachProblem
Stuff everything in contextCost is O(nΒ²). 100 conversations = thousands of tokens = dollars per call. Quality degrades from "lost in the middle" effect.
Use a vector DB directlyYou get similarity search. You don't get summarization, pruning, recency weighting, deduplication, or token budget management. You're building the pipeline yourself.
Use Mem0Proprietary, cloud-only with their hosted API. You don't control where your data lives.
Use MemStackFull pipeline. Pluggable everything. Your data, your infrastructure. Open source.

What MemStack handles that raw vector DBs don't:

  • Summarization β€” compress 100 old interactions into one paragraph, keep meaning, save tokens
  • Recency weighting β€” recent memories matter more; MemStack sorts them higher
  • Importance scoring β€” not all memories are equal; high-importance ones survive pruning
  • Deduplication β€” identical or near-identical memories are collapsed in context assembly
  • Token budget β€” compileContext() tells you how many tokens you're spending before the LLM call
  • Memory-type routing β€” interactions, summaries, observations treated differently at retrieval time
  • Auto-pruning β€” old, low-importance memories clean themselves up

Quick Start

Terminal
npm install @memstack/core

OpenAI

server.ts
import { MemStack, OpenAILLMAdapter, OpenAIEmbeddingAdapter, InMemoryStorageAdapter } from "@memstack/core";

const llm = new OpenAILLMAdapter({ apiKey: process.env.OPENAI_API_KEY! });

const memstack = new MemStack({
  llm,
  embedding: new OpenAIEmbeddingAdapter({ apiKey: process.env.OPENAI_API_KEY! }),
  storage: new InMemoryStorageAdapter(),
});

DeepSeek (no embeddings)

DeepSeek provides chat completions but has no embedding API. Use the OpenAI-compatible LLM adapter with baseURL and omit the embedding adapter β€” retrieval falls back to keyword + recency + importance ranking. You still get the full pipeline: store, summarize, prune, and compileContext.

server.ts
import { MemStack, OpenAILLMAdapter, InMemoryStorageAdapter } from "@memstack/core";

const llm = new OpenAILLMAdapter({
  apiKey: process.env.DEEPSEEK_API_KEY!,
  baseURL: "https://api.deepseek.com/v1",
  defaultModel: "deepseek-chat",
});

const memstack = new MemStack({
  llm,
  storage: new InMemoryStorageAdapter(),
  // No embedding adapter β€” retrieval uses keyword matching
});

OpenRouter / Together AI / any OpenAI-compatible API

Same pattern β€” change baseURL and defaultModel:

server.ts
// OpenRouter
const llm = new OpenAILLMAdapter({
  apiKey: process.env.OPENROUTER_API_KEY!,
  baseURL: "https://openrouter.ai/api/v1",
  defaultModel: "openai/gpt-4o-mini",
});

// Together AI
const llm = new OpenAILLMAdapter({
  apiKey: process.env.TOGETHER_API_KEY!,
  baseURL: "https://api.together.xyz/v1",
  defaultModel: "meta-llama/Llama-3.3-70B-Instruct-Turbo",
});

// Gemini (OpenAI-compatible endpoint)
const llm = new OpenAILLMAdapter({
  apiKey: process.env.GEMINI_API_KEY!,
  baseURL: "https://generativelanguage.googleapis.com/v1beta/openai",
  defaultModel: "gemini-2.0-flash",
});

Store and retrieve

server.ts
// 1. Store what happened
await memstack.memory.store({
  actorId: "support-bot-42",
  content: "User reports login failing with error 503 on Chrome 125.",
  tags: ["login", "bug", "chrome"],
  importance: 0.8,
});

// 2. Later, retrieve relevant context
const memories = await memstack.memory.retrieve({
  actorId: "support-bot-42",
  query: "login error",
  strategy: "hybrid",
});

// 3. Assemble an LLM-ready context
const ctx = await memstack.memory.compileContext({
  actorId: "support-bot-42",
  maxTokens: 2000,
});

const response = await llm.complete({
  system: `You are a support bot. Here is what you remember:\n${ctx.systemPrompt}`,
  user: "The user is back and still can't log in. What do you do?",
});

console.log(response.text);
// "Based on our history, the user has been experiencing 503 errors on Chrome 125..."

// 4. Every 100 interactions, summarization triggers automatically.
// Old interactions are compressed into a paragraph. Token costs stay flat.

The Memory Pipeline

MemStack's core is a five-stage pipeline. Each stage can be used independently.

1. Store

Every agent interaction becomes a Memory with metadata that controls how it's retrieved, summarized, and pruned later.

typescript
interface Memory {
  id: string;
  actorId: string;               // Who this memory belongs to (user ID, agent ID, session ID)
  memoryType: MemoryType;        // "interaction" | "summary" | "observation" | "fact" | "reflection"
  content: string;               // The actual text
  importance: number;            // 0-1 β€” higher = survives pruning, ranks higher in retrieval
  emotionalValence: number;      // -1 to 1 β€” for tone-aware retrieval
  tags: string[];                // Filter by tag: "bug", "billing", "urgent", etc.
  embedding?: number[];          // Computed automatically if embedding adapter is configured
  metadata?: Record<string, unknown>;  // Your custom fields
  expiresAt?: Date;              // Auto-pruned after this date
  sourceId?: string;             // Link back to the originating event
  createdAt: Date;
}
typescript
// Simple store
await ms.memory.store({
  actorId: "agent-7",
  content: "Customer asked about refund policy for Q2 purchases.",
  tags: ["billing", "refund"],
});

// Batch store β€” embeddings are batched into one API call for efficiency
await ms.memory.storeBatch([
  { actorId: "agent-7", content: "First interaction" },
  { actorId: "agent-7", content: "Second interaction" },
  { actorId: "agent-7", content: "Third interaction" },
]);

2. Retrieve

Pull back what's relevant β€” by keyword, by meaning (semantic), by recency, or by importance.

server.ts
const memories = await ms.memory.retrieve({
  actorId: "agent-7",              // Scope to one actor
  query: "refund policy",          // What to search for
  strategy: "hybrid",              // How to rank: "recent" | "important" | "semantic" | "hybrid"
  limit: 10,                       // Max results
  memoryTypes: ["interaction"],    // Only certain types
  tags: ["billing"],               // Only certain tags
});

Strategy behavior:

StrategySorts byRequires embeddingsBest for
recentNewest firstNoKnowing what just happened
importantHighest importance firstNoFiltering noise, keeping signal
semanticCosine similarity to queryYes"Find memories about X"
hybridSemantic + importance blendYesBest of both worlds

Read the full README β†’View source on GitHub β†’

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Frequently Asked Questions about Memstack

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "memstack": { "command": "npx", "args": ["-y", "memstack"] } }

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Technical Specs & Signals

Category🧠Knowledge & Memory
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
Views0
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35Quality signal: Fair Β· 35/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools15/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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