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Cohexa-ai/agent-coherence

by Various

The coordination layer for Multiplayer AI

MCP

Cohexa-ai/agent-coherence

Added 3 Sept 2026

#agent-memory #ai-agent #autogen #cache-coherence #crewai #langchain #llm-agent #multi-agent-systems

Overview

A Python project that describes itself as the coordination layer for Multiplayer AI. It is designed to support the coordination of multiple AI agents in a shared workflow, and is published as an open-source repository on GitHub. The project is early-stage and currently has very limited adoption.

Best for

Best for
Python developers exploring coordinated multi-agent AI systems.

Use cases

  • Coordinate multiple AI agents in a shared task
  • Prototype a coordinated multi-agent workflow in Python
  • Experiment with multi-agent systems in one repository

How to use

Install

pip install "agent-coherence[langgraph]"        # LangGraph drop-in

Tools exposed

  • swg_read
  • swg_write
  • swg_reacquire
  • swg_write_cas
  • swg_gate
  • swg_status

Tested with

Claude Code, Cursor, ChatGPT

Notes

A Python project that describes itself as the coordination layer for Multiplayer AI. It is designed to support the coordination of multiple AI agents in a shared workflow, and is published as an open-source repository on GitHub. The project is early-stage and currently has very limited adoption.

12 stars on GitHub. Last updated 2026-09-02.

Use cases

  • Coordinate multiple AI agents in a shared task
  • Prototype a coordinated multi-agent workflow in Python
  • Experiment with multi-agent systems in one repository

Pros

  • Written in Python for easy adoption
  • Focused scope on agent coordination
  • Open-source code available for review and use

Cons

  • Only 12 stars, indicating limited community interest
  • Early-stage maturity with minimal adoption
  • Little evidence of production use

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

Pros

  • Written in Python for easy adoption
  • Focused scope on agent coordination
  • Open-source code available for review and use

Cons

  • Only 12 stars, indicating limited community interest
  • Early-stage maturity with minimal adoption
  • Little evidence of production use
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