agent-memory-hub
A persistent, intelligent long-term memory system for AI agents with BM25 full-text search, auto-tagging, importance scoring, and recency weighting.
store_memory— Save any information (facts, preferences, project details, notes) with a unique key; auto-detects tags and importance (1–10) if omitted.search_memory— BM25 full-text ranked search across all memories, with optional tag filtering and result limits.get_relevant_context— Automatically surface the most relevant memories for a given query or task; ideal for injecting context at session start.update_memory— Modify the content, tags, or importance score of an existing memory by its key.list_memories— Browse stored memories with optional tag filtering, sorted by recency, importance, or access count.forget_memory— Permanently delete a specific memory by its key.memory_summary— Get a high-level overview: total memory count, top tags, most important/accessed memories, and statistics.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@agent-memory-hubstore that my favorite color is blue"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
agent-memory-hub
Persistent, intelligent, searchable long-term memory for AI agents.
Store facts, preferences, notes, and project context. Retrieve them with full-text BM25 search, importance scoring, and recency weighting. No API keys. No external servers. Works out of the box.
Features
7 powerful tools — store, search, retrieve context, update, list, forget, summarize
BM25 full-text search — proper ranked search with IDF, not just string matching
Auto-tagging — automatically infers categories (preference, project, technical, task, credential, etc.)
Auto importance scoring — detects urgency signals in content
Recency + importance weighting — more relevant memories surface first
Atomic writes — corruption-safe file persistence
Zero dependencies — only the MCP SDK; no native binaries, no Python, no Docker
Configurable storage — override path with
AGENT_MEMORY_DIRenv var
Related MCP server: Memsolus MCP Server
Installation
1. Clone and build
git clone https://github.com/yourname/agent-memory-hub
cd agent-memory-hub
npm install
npm run build2. Add to Claude Desktop
Edit %APPDATA%\Claude\claude_desktop_config.json:
{
"mcpServers": {
"agent-memory-hub": {
"command": "node",
"args": ["C:\\Users\\HP\\agent-memory-hub\\build\\index.js"]
}
}
}3. Add to Claude Code (MCP CLI)
claude mcp add agent-memory-hub -- node "C:\Users\HP\agent-memory-hub\build\index.js"Custom storage directory
{
"mcpServers": {
"agent-memory-hub": {
"command": "node",
"args": ["C:\\Users\\HP\\agent-memory-hub\\build\\index.js"],
"env": {
"AGENT_MEMORY_DIR": "C:\\Users\\HP\\my-agent-memories"
}
}
}
}Default storage: ~/.agent-memory/memories.json
Tools
store_memory
Store any piece of information worth remembering.
key: "user_preferred_language"
content: "User always prefers TypeScript over JavaScript"
tags: ["preference", "technical"] ← auto-detected if omitted
importance: 7 ← auto-scored if omitted
overwrite: true ← upsert: update if key exists, create if notBy default, storing a key that already exists returns an error. Set overwrite: true to silently update the existing memory instead — useful when you want "set this value" semantics without checking first.
search_memory
BM25 full-text search across all memories.
query: "typescript preferences"
limit: 5 ← optional, default 5
tags: ["technical"] ← optional filterget_relevant_context
Auto-retrieve the best memories for a given query. Use this at session start.
user_query: "Help me set up the project authentication"
→ Returns: identity memories, project memories, technical preferencesupdate_memory
Modify existing memory content, tags, or importance.
key: "user_preferred_language"
new_content: "User prefers TypeScript, but accepts Python for scripts"
importance: 8list_memories
Browse memories with sorting and filtering.
tags: ["project"]
sort: "importance" ← "recent" | "importance" | "access"
limit: 10forget_memory
Permanently delete a memory.
key: "old_api_key"memory_summary
Get a full overview — counts, top tags, most important and most accessed memories.
Storage Format
Memories are stored as plain JSON at ~/.agent-memory/memories.json. Human-readable, easy to backup or inspect.
{
"version": "1.0.0",
"created": "2025-01-01T00:00:00.000Z",
"lastUpdated": "2025-06-01T12:00:00.000Z",
"memories": [
{
"id": "uuid",
"key": "user_preferred_language",
"content": "User prefers TypeScript over JavaScript",
"tags": ["preference", "technical"],
"importance": 7,
"createdAt": "...",
"updatedAt": "...",
"accessCount": 12,
"lastAccessed": "..."
}
]
}Auto-Tagging Categories
The system auto-detects these categories from content:
Tag | Trigger signals |
| prefer, like, love, hate, favorite, avoid |
| project, working on, building, repository |
| I am, my name, I work, my role |
| code, api, database, framework, docker |
| todo, must, deadline, remind |
| password, secret, token, api key |
| note, remember that, fyi, heads up |
| name is, email, phone, contact |
| config, setting, env var, port, url |
Development
npm run dev # watch mode
npm run build # production buildLicense
MIT
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Latest Blog Posts
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/AIsofialuz/agent-memory-hub'
If you have feedback or need assistance with the MCP directory API, please join our Discord server