B — Some risk signals Graded October 16, 2026Minor concerns worth a quick check.Based on public adoption and transparency signals as of October 16, 2026. Our opinion, not a code audit or security guarantee.
App connector · Developer tools, Memory & knowledge
memorix
Open-source cross-agent memory layer for coding agents via MCP. Compatible with Claude Code, Codex, Cursor, Windsurf, Gemini CLI, Antigravity, OpenClaw, Hermes Agent, Oh-my-Pi, Pi, Copilot, Kiro, OpenCode, and Trae.
Bottom line: Signal-based grade (4/7 points): well starred on GitHub, listed in several sources, public source code. This reflects adoption and transparency, not a code audit.
- Popularity score
- 58/100
- Rank
- Not ranked: outside the 500 most popular tools
- GitHub stars
- 842
- Weekly downloads
- 887
What it does
Memorix is an open-source memory layer that enables coding agents to share and persist context across sessions and tools via the Model Context Protocol (MCP). It allows agents like Claude Code, Cursor, and Windsurf to retain information about code changes, decisions, and project state across multiple interactions.
Who it’s for
AI builder teams and enterprises running multiple coding agents that need persistent, cross-agent memory without rebuilding context each session. Best suited for organizations willing to self-host and manage an additional infrastructure component.
Copy-paste setup
Check these steps against the project’s own instructions before running anything.
Install via npm:
npm install memorix
For Claude Desktop integration, add to your claude_desktop_config.json:
{
"mcpServers": {
"memorix": {
"command": "npx",
"args": ["memorix"]
}
}
}
Verify against the repo README at https://github.com/AVIDS2/memorix (opens external site) for current configuration options and any required environment variables.
Risks
- Data persistence scope: Verify what agent interactions and code context are stored, and whether sensitive code or credentials could be cached in memory layers without encryption at rest.
- Cross-agent access control: Multiple agents sharing the same memory store risk one compromised agent exfiltrating or corrupting shared context for others.
- Prompt injection via stored memory: Malicious or poisoned memory entries could influence downstream agent behavior if sanitization is insufficient.
- Supply chain via npm: Early-stage project (0 HN signals); monitor for maintainer changes or abandoned status that could affect security updates.
- Self-hosted infrastructure burden: Organizations must secure and audit their own deployment; misconfigurations could expose memory store to unauthorized access.
How to stay secure
Least-privilege permissions
- Restrict agent service accounts to read/write only their own memory namespaces; enforce strict role-based access control (RBAC) by agent ID or team
- Disable credential storage in memory layer entirely; use external secret management (HashiCorp Vault, AWS Secrets Manager) for sensitive values
- Implement memory data classification tags and enforce encryption keys per sensitivity level; restrict decryption capabilities by agent type
Network isolation
- Deploy memorix behind a private network boundary; disable public internet exposure and require VPN/mTLS for all agent connections
- Implement network segmentation so memory store cannot reach external systems; whitelist only required internal agent endpoints
- Enforce TLS 1.3+ for all transport; validate certificate pinning for known agent connections
Monitoring
- Log all memory read/write operations with timestamps, agent IDs, and data checksums; alert on unusual access patterns or bulk exports
- Monitor for prompt injection signatures in stored memory (command syntax, shell metacharacters, code payloads); implement regex-based sanitization rules
- Track npm package version changes, maintainer activity, and security advisories; set up automated dependency scanning via Dependabot or Snyk
Red flags to watch for
- Memory entries containing cleartext credentials, API keys, or auth tokens; implement automatic redaction/scanning on write operations
- Multiple agents accessing overlapping memory scopes without clear isolation; audit cross-agent data flows monthly
- Zero recent commits, unresponsive maintainers, or security issues filed without resolution; consider forking or switching to alternative if project appears abandoned
Alternatives
- LangChain Memory modules – built-in memory abstractions for LLM chains with token-window optimization; less agent-agnostic than MCP.
- Anthropic Artifacts + context windows – leverage native Claude context for simpler use cases without external state management.
- Autogen GroupChat state – Microsoft's approach to multi-agent memory via conversation history; tighter coupling to Autogen framework.
- Prompt caching (native APIs) – use Claude's prompt caching to efficiently persist context without a separate memory server.