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SeemSeam/claude_codex_bridge

Visible multi-agent CLI workspace for mixing Codex, Claude, Gemini, Kimi, Qwen, Cursor, Copilot, Pi, OpenCode, and other AI coding agents

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SeemSeam/claude_codex_bridge has 3.5k+ stars and 300+ forks on GitHub, is written in Python, and was last pushed on Oct 13, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 23 minutes ago
Type:ApplicationCategory(s):AI Coding AgentsAgent Frameworks & SDKsAI Agents & LLM Apps
Added to GitGenius on August 31st, 2026
Created on October 25th, 2025
Open Issues & Pull Requests: 104 (+0)
GitHub issues: Enabled
Number of forks: 348
Total Stargazers: 3,576 (+1)
Total Subscribers: 12 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 10.0 hours
Mean response time: 2.1 days
90th percentile: 5.5 days
Tracked items: 125

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How this project is maintained

About 11% of issues opened in the past year have never received a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 72% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. Only 49% of issues opened in the past year have been closed. Three people close 72% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 72
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 134 days
Stale 30+ days: 69
Stale 90+ days: 53

Recent activity

Opened in 7 days: 1
Closed in 7 days: 0
Comments in 7 days: 1
Events in 7 days: 1

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Detailed Description

Claude Codex Bridge is a multi-agent CLI workspace that coordinates multiple AI coding agents including Claude, Codex, Gemini, Cursor, Copilot, and others in a unified terminal interface.

The tool addresses the fragmentation problem of working with different AI coding assistants by providing a visible, controllable layer that lets developers mix and orchestrate multiple agents in a single workflow. Rather than switching between separate tools, users can see agent interactions, take over execution at any point, and coordinate responses across different providers through a terminal-based interface.

Developers should adopt this tool if they regularly use multiple AI coding agents and want to coordinate them without context switching. It suits projects where comparing outputs from different models or chaining agent capabilities together provides value. The tool runs on Linux, macOS, WSL, and Windows, making it accessible across development environments.

The project maintains a substantial base of adopters who report real-world issues and use cases, with maintainers responding to new issues and pull requests within a day.