MemStack MCPサーバー

MemStack

Persistent memory for AI agents. Claude Code and Codex share one memory per project, and your own agents get the same pipeline: store, retrieve, summarize, and prune.

Website and install guides: memstack.stalewell.com

Tell one agent how your project works. The other already knows. Claude Code learns a convention; a new Codex session recalls it and gets the task right:

npm install -g @memstack/cli @memstack/mcp better-sqlite3@^11.10.0   # MemStack never installs storage drivers
memstack init                  # LLM provider (optional) + store
memstack connect claude-code
memstack connect codex

Every session then starts with the project's key memories, and both agents save what you ask them to remember. How harness memory works.

Building your own agent?

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

# Give your coding agents the MemStack skill (-g: every project; omit it for this project only)
npx skills add isiomaC/memstack -g

@memstack/core is the runtime SDK; the Agent Skill teaches compatible coding agents how to integrate and operate MemStack correctly. Without -g, the skill installs into the current project (.agents/skills/, plus a .claude/skills/ link for Claude Code). Update it later with npx skills update.

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 — pluggable storage, bring your own LLM, zero vendor lock-in.

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Table of Contents

• Why MemStack
• Quick Start
• Harness Memory (Claude Code & Codex)
  • How it works
  • Commands
  • What connect changes
  • Projects
  • Storage
  • Troubleshooting
• 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
  • Harness Memory API
• Configuration
  • Harness configuration file
• Advanced Usage
  • Custom Storage
  • Custom LLM / Embedding
  • Event Hooks
• LongMemEval-S Retrieval Benchmark
• Development
  • Setup & Tests
  • Debugging
• Publishing to npm
• Contributing
• License

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Why MemStack

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

Approach	Problem
Stuff everything in context	Cost is O(n²). 100 conversations = thousands of tokens = dollars per call. Quality degrades from "lost in the middle" effect.
Use a vector DB directly	You get similarity search. You don't get summarization, pruning, recency weighting, deduplication, or token budget management. You're building the pipeline yourself.
Use Mem0	Proprietary, cloud-only with their hosted API. You don't control where your data lives.
Use MemStack	Full pipeline. Pluggable everything. Your data, your infrastructure. Open source.

What MemStack handles that raw vector DBs don't:

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

---

Quick Start

Claude Code and Codex

Persistent memory across agent harnesses: what you tell Claude Code, Codex recalls in the same project, and the reverse.

npm install -g @memstack/cli @memstack/mcp better-sqlite3@^11.10.0  # MemStack never installs storage drivers for you
memstack init                  # choose an LLM provider and a store
memstack connect claude-code
memstack connect codex

Then, in Claude Code: "Remember that this project uses Hono." In Codex, in the same repository: "What framework does this project use?" Codex answers Hono. See Harness Memory for how it works.

As a library

npm install @memstack/core

OpenAI

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 — retrieval falls back to keyword + recency + importance ranking. You still get the full pipeline: store, summarize, prune, and compileContext.

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-flash",
});

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

OpenRouter / Together AI / any OpenAI-compatible API

Same pattern — change baseURL and defaultModel:

// 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

// 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.

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Harness Memory (Claude Code & Codex)

Coding agents forget everything between sessions, and they don't share what they learn with each other. MemStack gives Claude Code and Codex one memory per project: a decision made with one agent is known to the other, and to every later session.

How it works

• Saving. Each agent gets five MemStack tools (memory_store, memory_retrieve, memory_get, memory_delete, memory_stats) through the MCP harness profile. When you say "remember that…", or the agent learns a durable fact, decision, preference, or rule, it calls memory_store. MemStack asks your LLM for a few topic tags, so "uses Hono" can later be found by "which framework?". If tagging fails, the memory is still saved.
• Recalling. A session-start hook loads the project's most important memories into every new session, so the agent starts out knowing them. During a session the agent calls memory_retrieve with plain questions. Recall runs locally with keyword ranking (BM25 with stemming) and never calls the LLM, so it is fast and works offline.
• Scope. Memories belong to the current project. Preferences that apply everywhere can be saved as global (scope: "global") and are recalled in every project. One project can never read or delete another's memories.
• Safety. Agents don't get bulk or destructive tools, and the LLM key stays in ~/.memstack/config.json (readable only by you), never in agent configs. Agents are told never to store secrets, and MemStack checks too: a memory that contains a credential (a private key, a cloud or source-host token, a JWT, a password in a connection string, a password = "..." assignment) is refused before the LLM or the store sees it, and the error names the kind of secret without repeating it. The check is pattern-based, so a secret in a format it doesn't know will pass; MEMSTACK_SECRET_POLICY=redact stores the memory with [REDACTED:<kind>] in place of the secret, and off disables the check.

Commands

Command	What it does
memstack init	Choose an LLM provider (optional) and a store. Verifies the key with a real request and writes ~/.memstack/config.json. Without a key, memories are saved without topic tags and recall works the same. Non-interactive: --provider, --base-url, --model, --api-key-env <VAR>, --no-llm, --store, --path, --url, --yes.
memstack connect <claude-code|codex>	Registers MemStack with the agent. Checks the server works first, and undoes everything if a step fails. --dry-run shows the changes; --no-hooks and --no-agents-md skip those parts.
memstack disconnect <claude-code|codex>	Removes everything connect added. Your memories are kept.
memstack status	Config, storage, the current project, and what each agent has connected.
memstack doctor	Diagnoses setup problems and prints the fix for each. --live also tests the LLM key.
memstack memories [query]	Lists or searches this project's memories. --global for global ones, --delete <id> to remove one.
memstack project	Shows this repository's project ID. project pin <id> fixes it in .memstack.json; project merge <old-id> moves memories from an old ID.

What connect changes

connect changes only these entries, backs up each file first, and disconnect restores each file exactly:

Agent	File	Change
Claude Code	~/.claude.json	A user-scope memstack MCP server, added with claude mcp add-json.
Claude Code	~/.claude/settings.json	A SessionStart hook running memstack-mcp hook session-start.
Codex	~/.codex/config.toml	A memstack MCP server, added with codex mcp add.
Codex	~/.codex/hooks.json	A SessionStart hook. Codex runs it after you approve it once with /hooks.
Codex	~/.codex/AGENTS.md	A short block between memstack:begin/memstack:end markers telling Codex to save memories with memory_store.

The registered command is the memstack-mcp you installed, run by absolute path, so it works even when the agent starts without your shell's PATH. Restart Claude Code, or start a new Codex session, to load it.

Projects

The project ID comes from the repository itself, with nothing stored, so it stays the same across clones, worktrees, renamed remotes, moved folders, new machines, and storage switches:

1. a pinned ID in .memstack.json at the repository root (memstack project pin <id>; commit it to share it with your team);
2. otherwise, the repository's first commit;
3. for shallow clones, the origin remote;
4. for repositories without commits, the git directory, and outside git, the folder. Memories saved before a repository's first commit move to the new ID automatically.

A fork shares its first commit with the original repository, so on one store the two share memories until one of them runs memstack project pin.

Storage

Any supported store works; pick it in memstack init. MemStack never installs storage drivers: install the one you need alongside @memstack/mcp.

Store	Driver to install	Notes
sqlite	better-sqlite3@^11.10.0	Default; one local file, safe for both agents at once.
postgres	postgres@^3.4.9	Shared across machines.
redis	ioredis@^5.11.1
disk, markdown	none	Single process only; doctor warns when both agents use them.

Switching stores keeps project IDs; move memories with memstack export and memstack import.

Troubleshooting

Run memstack doctor first; it checks the config file and its permissions, the LLM key, the storage driver, each agent's registration, hook, and guidance, and starts the server to confirm it answers.

• "memstack-mcp is not installed" or a missing driver: run the npm install -g … command it prints.
• Codex doesn't load memories at session start: approve the MemStack hook in Codex with /hooks.
• Codex doesn't save when asked: check memstack status shows the AGENTS.md guidance as present; reconnect if not.
• Memories seem missing: memstack project shows which project you are in. Use memstack project merge <old-id> to bring memories from an old ID.

Details on the MCP server itself: docs/MCP_SETUP.md.

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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.

interface Memory {
  id: string;
  actorId: string;               // Who this memory belongs to (user ID, agent ID, session ID)
  memoryType: MemoryType;        // "interaction" | "summary" | "observation" | "fact" | "reflection" | "preference" | "decision" | "instruction"
  content: string;               // The actual text
  importance: number;            // 0-1 — higher = survives pruning, ranks higher in retrieval
  emotionalValence: number;      // -1 to 1 — 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;
}

// Simple store
await ms.memory.store({
  actorId: "agent-7",
  content: "Customer asked about refund policy for Q2 purchases.",
  tags: ["billing", "refund"],
});

// Batch store — 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 — by keyword, by meaning (semantic), by recency, or by importance.

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:

Strategy	Sorts by	Requires embeddings	Best for
recent	Newest first	No	Knowing what just happened
important	Highest importance first	No	Filtering noise, keeping signal
semantic	Cosine similarity to query	Yes	"Find memories about X"
hybrid	Semantic + importance blend	Yes	Best of both worlds

No embedding adapter? semantic and hybrid fall back to keyword matching + importance sort. No API costs, just less precise.

3. Compile Context

compileContext() takes retrieval results and assembles an LLM-ready system prompt — deduplicated, sorted by recency and importance, with a token estimate so you know the cost before calling the LLM.

const ctx = await ms.memory.compileContext({
  actorId: "agent-7",
  maxTokens: 2000,               // Budget — assembler stops when it hits this
  memoryTypes: ["interaction", "summary"],
});

// ctx.systemPrompt:
// ## Important Memories
// - The customer has been attempting login for 3 days. (importance: 0.85)
// - Refund was processed for order #4521 on Jan 12. (importance: 0.72)
// 
// ## Recent Interactions
// - Customer asked about refund policy for Q2 purchases.
// - Customer reported login error 503 on Chrome 125.

console.log(ctx.tokenEstimate);  // ~280

// Inject into your LLM call
const currentMessage = "The user is asking about their refund status.";
const response = await llm.complete({
  system: ctx.systemPrompt,
  user: currentMessage,
});

console.log(response.text);

compileContext() handles deduplication, token budgeting, and splits context into important-vs-recent sections. Without it, you'd be concatenating raw retrieval results and risking context-window overflow.

4. Summarize

When an actor has hundreds of interactions, retrieval gets expensive and context gets bloated. Summarization compresses old interactions into a single paragraph using the configured LLM.

const { summary, deletedCount } = await ms.memory.summarize({
  actorId: "agent-7",
  olderThan: new Date(Date.now() - 7 * 86400000),  // Older than 7 days
  skipMostRecent: 10,        // Never touch the 10 most recent
  targetCount: 50,           // Summarize at most 50 memories
  memoryTypes: ["interaction"],
  keepOriginals: false,      // Delete originals after summary
});

// summary.content:
// "Over the past week, the customer reported recurring login failures (error 503)
//  on Chrome 125. Multiple troubleshooting attempts including cache clearing and 
//  password reset were unsuccessful. A refund was processed for order #4521."

console.log(deletedCount);   // 47 — 47 interactions compressed into 1 summary memory

Auto-summarization: Set summarizationThreshold in config (default: 100). Every 100th interaction for an actor triggers summarization automatically.

Warning: keepOriginals: false deletes the summarized memories. Set keepOriginals: true to preserve them alongside the summary.

Custom summarization prompt:

const ms = new MemStack({
  llm,
  defaults: {
    summarizationPrompt:
      "You are an enterprise support memory compressor. Highlight: customer name,
       product, severity, resolution status, and any open issues.",
  },
});

5. Prune

Not all memories deserve to live forever. Pruning removes low-value memories to keep storage and retrieval fast.

// Remove memories older than 30 days
await ms.memory.prune({ type: "byAge", maxAge: 30 * 86400000 });

// Keep only memories above importance 0.3
await ms.memory.prune({ type: "byImportance", minImportance: 0.3 });

// Keep at most 500 memories per actor
await ms.memory.prune({ type: "byCount", maxPerActor: 500 });

// Remove specific types
await ms.memory.prune({ type: "byType", memoryTypes: ["observation"] });

// Custom logic
await ms.memory.prune({
  type: "custom",
  shouldRemove: (memory) => memory.content.includes("[RESOLVED]"),
});

// Dry run first — see what would be removed
const { wouldPrune, count } = await ms.memory.dryRunPrune({
  type: "byAge",
  maxAge: 86400000,
});
console.log(`Would remove ${count} memories:`, wouldPrune);

Auto-prune on every process() call by setting pruneStrategy in config:

const ms = new MemStack({
  llm,
  defaults: {
    pruneStrategy: { type: "byImportance", minImportance: 0.05 },
  },
});

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Real-World Use Cases