mlsanityData to 7 Oct 2026

tap: A File-Based Protocol for Heterogeneous LLM Agent Collaboration

arXiv 2606.14445 ↗First posted 12 Jun 2026cs.SE cs.AI cs.HCcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Existing multi-agent software development systems have proposed many forms of agent collaboration, including role-based collaboration and automated code review. However, many systems assume a common runtime, a central conversation server, or the same API family. Under these assumptions, LLM agents from different vendors cannot easily exchange messages directly from their own execution environments while dividing development and review work on a shared codebase. This paper presents tap, a file-based collaboration protocol that allows Claude (Anthropic) and Codex (OpenAI) to collaborate on one codebase without shared memory or an identical runtime. The core of tap is a file-first design that preserves markdown files with metadata as original messages, combines a file inspection path (file communication, Tier 1) with real-time notification paths for Claude and Codex (real-time communication, Tier 2), and isolates work through separate git worktrees. Even if real-time notification fails or a receiver restarts, the message file remains available and the same content can be inspected again. In a 27-day, 37-generation self-applied operation where tap was used to develop and review itself, we collected 209 tap-related pull requests and 717 operational artifacts. An analysis of 375 review artifacts showed that the share of reviews recording at least one defect or requested change was 69.8% for heterogeneous model pairs and 53.1% for homogeneous model pairs. These results show that tap, which combines file-based message preservation with real-time notification, operates in a real production repository, and that combining heterogeneous models and execution environments can broaden review perspectives. tap is distributed as the open-source npm package @hua-labs/tap (v0.5.2).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 7 of its 8 references that are in the map. First posted on arXiv 12 Jun 2026.

Builds on

7 of 8 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
  2. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
  3. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation16 Aug
  4. 2023ChatDev: Communicative Agents for Software Development16 Jul

Other references · 3

  1. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  2. 2025AgentMaster: A Multi-Agent Conversational Framework Using A2A and MCP Protocols for Multimodal Information Retrieval and Analysiscited by 13
  3. 2025Wisdom and Delusion of LLM Ensembles for Code Generation and Repaircited by 3

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