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agentsync

Official

by jarmstrong158 ยท Python

Multi-agent coordination MCP: claim work, survey peers, resolve conflicts via a git branch as CAS.

The MCP server enables multi-agent coordination via a git-backed Model Context Protocol. It lets two or more AI agents declare work, survey peers, and resolve conflicts on a shared repository using a dedicated branch, without a lock server or simultaneous online requirements.

๐Ÿ› ๏ธ Key Features

  • Multi-agent collaboration on a common git repository
  • Declares and claims work before building
  • Conflict detection when adaptive work lands
  • Coordination state stored in a dedicated agentsync branch
  • No external lock server required; non-blocking online/offline operation

๐Ÿš€ Use Cases

  • Distributed AI agents coordinating on the same project
  • Conflict-aware task claiming and reconciliation in MCP workflows
  • Repo-scoped coordination without polluting main branches

โšก Developer Benefits

  • Clear, git-based coordination model requiring no extra infrastructure
  • Transparency via a single claims.json on a dedicated branch
  • Easy integration with existing MCP tooling and workflows

โš ๏ธ Limitations

  • Coordination state lives on a dedicated branch, potential merge considerations
  • Requires proper branch hygiene to avoid mainline contamination
  • Behavior assumes compliant agents and consistent read/write access

Topics

ai-agentsclaudecoordinationmcpmcp-servermodel-context-protocolmulti-agent
agentsync - agentage MCP Catalog