Octopoda-OS — repository deleted, archived audit

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by RyjoxTechnologies · MCP Server · ★ 504

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🔒 Is Octopoda-OS safe to install? View the security audit →

agent-frameworkai-agentsai-memoryautogencrash-recoverycrewaideveloper-toolsknowledge-graphlangchainllm-agents

Quick Facts

Stars504
Forks70
LanguagePython
CategoryMCP Server
Quality Score73/100
Open Issues13
Last Updated2026-07-15
Created2026-04-02
Platformsmcp, python
Est. Tokens~15k

About Octopoda-OS

Octopoda The open-source memory operating system for AI agents. Give your agents persistent memory, loop detection, audit trails, and real-time observability. Everything works automatically once you create an agent. []() Track latency, error rates, memory usage, and health scores per agent. Browse every memory, inspect version history, and see exactly how an agent's knowledge changed over time.

Octopoda-OS alternative? Top 6 similar tools

Looking for a Octopoda-OS alternative? If you're comparing Octopoda-OS with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • mcp-memory-service by doobidoo · ⭐ 2.1k

    Open-source persistent memory for AI agent pipelines (LangGraph, CrewAI, AutoGen) and Claude. REST API + knowl

  • Ori-Mnemos by aayoawoyemi · ⭐ 328

    Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). Open source must win.

  • DashClaw by ucsandman · ⭐ 314

    Remote approvals, policy checks, and execution evidence for unattended AI agents.

  • sre by SmythOS · ⭐ 1.3k

    The SmythOS Runtime Environment (SRE) is an open-source, cloud-native runtime for agentic AI. Secure, modular,

  • octocode by Muvon · ⭐ 519

    Structural code intelligence for AI agents — semantic search, knowledge graphs, and a built-in MCP server in o

  • oreilly-ai-agents by sinanuozdemir · ⭐ 322

    An introduction to the world of AI Agents

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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