Your AI Agent Forgets Everything Between Sessions. I Fixed That.

#ai #mcp #productivity #opensource

Every time you start a new session with Claude Code, Cursor, or any AI coding agent, it starts from scratch. It does not remember:

Who you are or what you prefer
What decisions were made yesterday
What corrections you already gave it
Where external resources live

I built memorybank to fix this.

What is memorybank?

An MCP server that gives any AI agent persistent, cross-session memory. Zero dependencies, file-based, works with Claude Code, Cursor, Windsurf, and any MCP-compatible tool.

npx @ura-dev/memorybank --help

8 MCP Tools

Tool	What it does
`memory_store`	Save a memory (user, project, feedback, reference, custom)
`memory_recall`	Retrieve most relevant memories for a query
`memory_list`	List all memories, filter by type or tag
`memory_get`	Get a specific memory by ID
`memory_update`	Update an existing memory
`memory_delete`	Delete a memory
`memory_search`	Full-text search across all memories
`memory_stats`	Counts, types, storage size

How it works

Your agent encounters something worth remembering
It calls memory_store with a type, name, and content
memorybank saves it as a markdown file with YAML frontmatter
Next session, the agent calls memory_recall to load relevant context
The agent now knows what happened before

Memories are stored locally in ~/.memorybank/, organized by namespace (one per project). No cloud, no database, no API keys.

Setup in 30 seconds

Add to your Claude Code config (~/.claude/settings.json):

{
  "mcpServers": {
    "memorybank": {
      "command": "npx",
      "args": ["-y", "@ura-dev/memorybank", "memorybank-mcp"]
    }
  }
}

Or Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "memorybank": {
      "command": "npx",
      "args": ["-y", "@ura-dev/memorybank", "memorybank-mcp"]
    }
  }
}

That is it. Your agent now has persistent memory.

Five memory types

user — About you: role, preferences, expertise level
project — About the work: goals, architecture decisions, deadlines
feedback — Corrections you gave: what to avoid, what worked
reference — External pointers: URLs, docs locations, API endpoints
custom — Anything else

Why not just use CLAUDE.md?

CLAUDE.md is great for static project instructions. memorybank is for dynamic knowledge that evolves during work:

User corrections that should persist
Architecture decisions made mid-project
References discovered during research
Project status that changes daily

memorybank complements CLAUDE.md. One for rules, the other for learned knowledge.

CLI too

memorybank store user team-lead "User is the team lead for backend services"
memorybank search "TypeScript preferences"
memorybank list --type=feedback
memorybank store project api-rewrite "Migrating REST to GraphQL" --namespace=myproject

Also: agentrace

If you want to see what your AI agent is doing (not just what it remembers), check out agentrace. Structured observability for AI agents. Same philosophy: MCP server, zero deps, file-based.

Links: