Run Claude + Codex + Gemini together, sync Skills across IDEs, route tasks between models. The orchestration layer for multi-model agentic workflows.
Multi-AI Bridge & Cross-IDE Sync tools are AI-powered software designed to help developers and teams tackle multi-ai bridge & cross-ide sync-related tasks more efficiently. These tools are typically published as open-source projects on GitHub and can be integrated into existing workflows via MCP (Model Context Protocol), Claude Skills, or standalone agent frameworks. On Agent Skills Hub, we index 25 quality-scored multi-ai bridge & cross-ide sync tools across languages including Python, JavaScript, TypeScript.
In 2026, the AI agent ecosystem is maturing rapidly. Multi-AI Bridge & Cross-IDE Sync tools can significantly boost development efficiency by automating repetitive tasks, reducing human error, and providing intelligent suggestions. The top 3 tools — claude_codex_bridge, cross-code-organizer, skills-link — have earned an average of 11,110 GitHub stars, reflecting strong community validation. 22 of the listed tools come with clear open-source licenses, ensuring freedom to use and modify.
When choosing a multi-ai bridge & cross-ide sync tool, consider these factors: 1) Community activity — GitHub stars and recent commit frequency indicate reliability; 2) Integration method — check if it supports MCP, Claude, or your preferred agent framework; 3) Language compatibility — the most common language in this list is Python; 4) Quality score — Agent Skills Hub's composite score evaluates code quality, documentation completeness, and maintenance activity. Our recommendation: start with claude_codex_bridge — it ranks highest in both star count and quality score.
Real-time multi-AI collaboration: Claude, Codex & Gemini with persistent context, minimal token overhead
Cross-Code Organizer (formerly Claude Code Organizer): cross-harness config dashboard for Claude Code, Codex CLI, MCP servers, skills, memories, agents, sessions, security scanning, context budget, and backups.
Sync your local skills across 41+ AI coding agents with a single command.
```bash
npm i -g skills-link
```A curated list of awesome claude marketplaces and plugins
```
cam plugin marketplace install superpowers-marketplace
```54 rigorous skills for Codex, OpenCode, and Pi: code review, security audit, feature development, frontend design, MCP tools, Hugging Face ML/training, and more.
Desktop app to manage AI coding agent skills across Claude Code, Cursor, Gemini CLI, Codex, and 20+ platforms from one place.
Open-source super AI assistant & Agent Harness. Plans tasks, runs tools and skills, self-evolves with memory and knowledge. Multi-agent, multi-model, multi-channel. Lightweight, extensible, one-line install.
Multi-model orchestration layer for Claude Code — the frontier model plans, cheaper models execute, verification guards quality. One-prompt install.
Persistent multi-model workflow teams for DeepSeek Harness — dynamic lead planning, bounded DAGs, per-agent model/tools, Run Center and Token insights.
Multi-model orchestration subagents for Claude Code — delegates by task scope to Gemini's 1M-token context or GPT's fast iteration, then routes every result back through Claude review.
Cheapest Money-Saving Self-hosted AI agent workspace with tool calling, MCP, multi-model routing, sandboxed execution, multi-agent workflows, and LLM-authored 3D character animation rendered with Three.js.
Multi-model AI orchestration where behaviour is Markdown, not code. One coordinator routes scoped task packets to 71 role-based specialists across 5 model families (Codex / Claude / Gemini / Grok / Kimi) — each working in an isolated git worktree, each reviewed by a rival family before it lands. One tmux session, no server.
An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.
The scaffold for your ultra-personalized, multi-model AI harness in pure natural language.
Advanced AI Code Strategy Advisor for Developer Agents (2026)
Autopus-ADK is of the agents, by the agents. for the agents. Multi-model orchestration (consensus/pipeline/debate/fastest). Architecture-as-Code, Lore decision tracking, SPEC/EARS engine.
Autopus-ADK is of the agents, by the agents. for the agents. Multi-model orchestration (consensus/pipeline/debate/fastest). Architecture-as-Code, Lore decision tracking, SPEC/EARS engine.
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
A memory-first AI agent that remembers why decisions were made — not just the last message. Runs local (Ollama), cloud (Claude · OpenAI · Gemini), or decentralized TEE. Graph memory, self-learning skills, multi-model routing, sandboxed tools. MCP · ACP · A2A. One Rust binary.
A lightweight, powerful framework for multi-agent workflows
A local multi-agent harness that works with your existing Claude Code, Codex subscriptions, allows you to run an office of agents
可私有部署的多租户知识智能体平台：统一 RAG、知识图谱、多智能体、MCP/Skills、沙盒与权限管理。Yuxi = Cloud Agents + Knowledge RAG, Self-hosted knowledge agent platform for RAG, knowledge graphs and multi-agent workflows.
Nexent is a zero-code platform for auto-generating production-grade AI agents using Harness Engineering principles — unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes.
⚡️next-generation personal AI assistant powered by LLM, RAG and agent loops, supporting computer-use, browser-use and coding agent, demo: https://demo.openagentai.org
| Tool | Stars | Language | License | Score |
|---|---|---|---|---|
| claude_codex_bridge | ★ 2.5k | Python | — | 61 |
| cross-code-organizer | ★ 379 | JavaScript | MIT | 65 |
| skills-link | ★ 187 | TypeScript | MIT | 55 |
| awesome-claude-plugins | ★ 115 | Shell | — | 66 |
| opencode-power-pack | ★ 505 | Python | MIT | 73 |
| skills-manage | ★ 2.2k | TypeScript | Apache-2.0 | 60 |
| CowAgent | ★ 47.1k | Python | MIT | 77 |
| pilotfish | ★ 697 | Python | MIT | 71 |
| dsh-agent-team-gui | ★ 219 | TypeScript | MIT | 68 |
| gemini-gpt-hybrid | ★ 153 | — | MIT | 77 |
| noobot | ★ 185 | JavaScript | MIT | 63 |
| claude-vibe-squad | ★ 161 | Python | MIT | 68 |
| deer-flow | ★ 82.9k | Python | MIT | 89 |
| agent-starter-kit | ★ 135 | Shell | MIT | 65 |
| multi-agent-architecture-advisor | ★ 115 | HTML | — | 69 |
| autopus-adk | ★ 111 | Go | MIT | 68 |
| autopus-adk | ★ 111 | Go | MIT | 61 |
| ruflo | ★ 73.1k | TypeScript | MIT | 76 |
| zeph | ★ 60 | Rust | MIT | 59 |
| openai-agents-python | ★ 29.6k | Python | MIT | 78 |
| hive | ★ 11.0k | Python | Apache-2.0 | 74 |
| munder-difflin | ★ 7.5k | TypeScript | MIT | 76 |
| Yuxi | ★ 7.2k | Python | MIT | 73 |
| nexent | ★ 5.9k | Python | MIT | 69 |
| openagent | ★ 5.7k | Go | Apache-2.0 | 67 |
The top multi-ai bridge & cross-ide sync tools in 2026 are claude_codex_bridge, cross-code-organizer, skills-link. Agent Skills Hub ranks 25 options by GitHub stars, quality score (6 dimensions including completeness, examples, and agent readiness), and recent activity. The list is rebuilt every 8 hours from live GitHub data.
claude_codex_bridge (2.5k stars) is the most adopted choice for general multi-ai bridge & cross-ide sync workflows, written in Python. cross-code-organizer (379 stars) is a strong alternative and uses JavaScript instead. Pick by your existing stack: match the language and runtime your team already uses to minimize integration cost. If unsure, start with claude_codex_bridge — it has the deepest community and the most examples online.
Avoid pre-built multi-ai bridge & cross-ide sync tools when (1) your use case requires deep customization that the tool's plugin system doesn't support, (2) you have strict compliance requirements that ban third-party dependencies, (3) the tool's maintenance is inactive (last commit >6 months ago), or (4) your data volume is small enough that a 50-line custom script is cheaper than learning the tool. For most production workflows above 100 requests/day, the time savings from a maintained tool outweigh the customization loss.
Multi-AI Bridge & Cross-IDE Sync focuses specifically on run claude + codex + gemini together, sync skills across ides, route tasks between models. the orchestration layer for multi-model agentic workflows. Agent Governance is a related but distinct category — see https://agentskillshub.top/best/agent-governance/ for those tools. The two often appear in the same agent pipeline but solve different problems: choose multi-ai bridge & cross-ide sync when your primary goal is the specific task, and agent governance when the workflow is broader.
For most teams, yes. claude_codex_bridge has 2.5k stars worth of community testing, handles edge cases you haven't thought of, and ships with documentation. Build your own only when (1) your requirements are deeply non-standard, (2) you have a security/compliance reason to avoid OSS dependencies, or (3) the maintenance burden is small enough (<200 lines of code) that you'll save time long-term. The break-even point is usually around 2-3 weeks of dev time saved.
Most multi-ai bridge & cross-ide sync tools listed are open source under permissive licenses (MIT, Apache 2.0). A handful offer paid managed/cloud versions on top of free self-hosted core. Always check the LICENSE file on each tool's GitHub repository before commercial use — some use AGPL or non-commercial restrictions that may not fit your deployment model.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: