Zikra: Self-Hosted MCP Memory Server for Claude Code, Cursor, and Codex

Zikra is a self-hosted MCP (Model Context Protocol) memory server that provides persistent memory for Claude Code, Cursor, and Codex. It solves the problem where Claude Code and Claude Web don't share context, and every session starts from zero.
What it does
- Stop hook fires when Claude Code sessions end — saves automatically, eliminating the need to type "save this"
- MCP native — Claude Desktop and Claude Code connect with one configuration line
- Works with Cursor and Codex via the same webhook
- Saves every decision, error, and requirement automatically at session end
- Creates a searchable memory pool accessible from any tool, machine, or team member
Installation steps
Step 1 — Start the server:
pip install zikra-lite && python -m zikraStep 2 — Add to ~/.claude/mcp.json:
{"zikra": {"url": "http://localhost:7723/mcp",
"headers": {"Authorization": "Bearer YOUR_TOKEN"}}}Step 3 — Paste into Claude Code:
Fetch https://raw.githubusercontent.com/getzikra/zikra-lite/main/prompts/g_zikra.md and follow every instruction in it.
Technical details
- MIT licensed
- Self-hosted
- Free forever
- Built primarily by Claude Code to solve the developer's own problem
- Team version available with Postgres + n8n
This type of tool is useful for development teams working with AI coding assistants who need persistent context across sessions and team members.
📖 Read the full source: r/ClaudeAI
👀 See Also

Mneme: A PreToolUse Hook That Blocks Claude Code Edits Violating Architecture Decisions
Mneme is a PreToolUse hook for Claude Code that checks every Edit/Write/MultiEdit against a local decisions file before disk writes, blocking violations without manual intervention.

Claude File History: VS Code Extension for Tracking Claude Code Sessions
A VS Code extension called Claude File History tracks every Claude Code session that touched your files, allowing you to find past conversations, preview what was discussed, and resume conversations with a double-click.

Codebook Lossless LLM Compression: 10-25% RAM Reduction with Bitwise Packing
A developer's proof-of-concept code demonstrates lossless LLM compression by packing fp16 weights into blocks, achieving 10-25% RAM reduction with a trade-off of approximately halved inference speed. The approach identifies that most models only use 12-13 bits of unique values despite fp16's 16-bit representation.

aco-system: An Entire Company OS for Claude That Writes User Stories, Breaks Tasks, Reviews PRs
A Reddit user shared how aco-system turned a single GitHub issue into a fully validated PR with tests — driven entirely by Claude. Includes user story generation, task breakdown, secret checking, and PR review.