Multi-AI Orchestration Setup Using Claude Code with GPT and Gemini

Multi-AI Development Setup

A developer describes their workflow using three AI models orchestrated together in a single development environment. The setup addresses the problem of losing context between sessions by implementing a persistent file-based system.

Context Layer Implementation

The system uses markdown files as the protocol for maintaining context across sessions:

• CLAUDE.md - Main operating file containing projects, preferences, constraints, and current session state
• PROFILE.md - Holds professional identity including background, communication style, and decision patterns
• SESSION_LOG.md - Logs every session with what was done, decided, and pending, organized newest first
• .claude/history/ - Directory where a session-closer agent captures learnings, decisions, research findings, and ideas into separate files

The developer reports having 50+ knowledge files after three months of use. At the end of each work block, they say "close the session" to trigger the Session Closer sub-agent that updates session logs, knowledge history, workspace improvements, and ROI tracking.

Three AI Models in One Workspace

The setup uses three AI subscriptions:

• Claude Code (Opus 4.6) - Serves as the orchestrator handling deep work, complex analysis, skill system, and session management
• GPT-5.4 via Codex CLI - Handles code review, implementation, and debugging (named Dario)
• Gemini 3.1 Pro - Performs web research, Google Workspace integration, and multimodal analysis (named Irene)

Each model has its own SOUL.md file defining identity, mission, strengths, and limits:

• Claude's: .claude/SOUL.md
• GPT's: .codex/SOUL.md
• Gemini's: .gemini/SOUL.md

They also have operational files (AGENTS.md for GPT, GEMINI.md for Gemini) that specify what to read at session start, what rules to follow, and who the other peers are.

Integration and Communication

All three models read the same context files (CLAUDE.md, PROFILE.md, SESSION_LOG.md, and the history directory), ensuring shared knowledge across sessions.

Models can call each other using CLI commands without API or middleware:

codex exec --skip-git-repo-check "Review this function for edge cases"
gemini -m gemini-3-flash-preview -p "Search for recent benchmarks on X"
claude -p "Summarize the last 3 session log entries"

The entire setup runs inside Gemini's Antigravity IDE with three terminals for the three models on the same screen.

Additional Layers

An async layer uses OpenClaw (on OpenAI subscription) to handle scheduled jobs like recurring research tasks, data checks, and content pipelines. All three models in the IDE can trigger or interact with these jobs.

A custom MCP Server connects to a Telegram bot for notifications. When a task takes time, the model notifies the developer when complete, allowing parallel workstreams without terminal babysitting.