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wazionapps/nexo

by Various

NEXO Brain — Shared brain for AI agents. Persistent memory, semantic RAG, natural forgetting, metacognitive guard, trust scoring, 150+ MCP tools. Works with Claude Code, Codex, Cla

W

MCP

wazionapps/nexo

Added 1 June 2026

#ai-memory #atkinson-shiffrin #autonomous-agent #claude-code #claude-code-plugin #codex #cognitive-architecture #knowledge-graph

Overview

NEXO Brain provides a shared memory layer for AI agents using persistent memory, semantic RAG, and natural forgetting. It works with Claude Code, Claude Desktop, Codex, and any MCP client, offering trust scoring and a metacognitive guard. The system is fully local, open source, and free.

Best for

Best for
Developers building local, memory-enhanced multi-agent systems that need trust and forgetting

Use cases

  • Give AI agents persistent cross-session memory
  • Manage semantic retrieval and automatic forgetting
  • Integrate trust scoring into multi-agent workflows

Notes

NEXO Brain provides a shared memory layer for AI agents using persistent memory, semantic RAG, and natural forgetting. It works with Claude Code, Claude Desktop, Codex, and any MCP client, offering trust scoring and a metacognitive guard. The system is fully local, open source, and free.

22 stars on GitHub. Last updated 2026-06-01.

Use cases

  • Give AI agents persistent cross-session memory
  • Manage semantic retrieval and automatic forgetting
  • Integrate trust scoring into multi-agent workflows

Pros

  • Fully local and free, no external dependencies
  • Works with multiple MCP-compatible clients
  • Includes built-in trust scoring and metacognitive guard

Cons

  • Low community adoption (22 stars at time of entry)
  • Requires local Python environment setup
  • Compatibility limited to MCP protocol clients

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Fully local and free, no external dependencies
  • Works with multiple MCP-compatible clients
  • Includes built-in trust scoring and metacognitive guard

Cons

  • Low community adoption (22 stars at time of entry)
  • Requires local Python environment setup
  • Compatibility limited to MCP protocol clients
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