akb vs letta
akb: AKB is an agent knowledgebase that serves organizational memory for AI agents over the Model Context Protocol (MCP). It provides hybrid semantic + keyword search, structured tables, files, and a URI graph, all backed by Git for version history. Designed as a drop-in alternative to Confluence or Notion for MCP-aware agents like Claude Code, Cursor, and Windsurf.; letta: Letta is a powerful platform for building stateful AI agents equipped with advanced memory, enabling them to learn and self-improve over time. It provides both a command-line interface for local agent execution and a comprehensive API with Python and TypeScript SDKs for seamless integration into applications.
TL;DR
Provide persistent memory and knowledge management for AI coding agents
- You want a long-lived agent that remembers past work and rewrites its own memory and context over time.
- You want one agent reachable from CLI, desktop, browser, mobile and messaging apps, with subagents, schedules and hooks.
- You want memory you can inspect: the README says context is tracked in git and can sync to your own repository.
- You only need to add memory to an agent you already use through MCP; a small MCP memory server is a lighter fit.
- You rely on older Letta tutorials or the V1 API server: the project restructured, and the V1 server is retired.
- You want everything to work without signing in: Letta Cloud is the default backend and some features need it.
Side-by-Side Comparison
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