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modelmux
Advanced tools
Model multiplexer — unified MCP server for cross-platform multi-model AI collaboration.
Route tasks to Codex CLI, Gemini CLI, and Claude Code CLI through a single MCP interface with smart routing and caller auto-detection.
# One-command install for Claude Code
claude mcp add modelmux -s user -- uvx modelmux
# Codex CLI (~/.codex/config.toml)
# [mcp_servers.modelmux]
# command = "uvx"
# args = ["modelmux"]
# Gemini CLI (~/.gemini/settings.json)
# {"mcpServers": {"modelmux": {"command": "uvx", "args": ["modelmux"]}}}
mux_dispatch — Send a task to a model and get structured results
provider: "auto" / "codex" / "gemini" / "claude" / "ollama"task: The prompt to sendworkdir, sandbox, session_id, timeout, model, profile, reasoning_effort, failovermux_check — Check which CLIs are available, show detected caller and configprovider="auto" routes tasks by keyword analysis and auto-excludes the calling platform:
From Claude Code → routes to Codex or Gemini (never back to Claude)
From Codex CLI → routes to Claude or Gemini (never back to Codex)
When a provider fails at execution time, modelmux automatically retries with the next available provider (enabled by default, failover=True). Failover is skipped for session-based requests since sessions are provider-specific.
Every dispatch call is logged to ~/.config/modelmux/audit.jsonl for debugging and cost tracking.
Policy enforcement via ~/.config/modelmux/policy.json:
{
"blocked_providers": ["gemini"],
"blocked_sandboxes": ["full"],
"max_timeout": 600,
"max_calls_per_hour": 30,
"max_calls_per_day": 200
}
mux_check() now shows policy summary and audit stats.
Create .modelmux/profiles.toml or ~/.config/modelmux/profiles.toml:
[routing]
default_provider = "codex"
[[routing.rules]]
provider = "gemini"
[routing.rules.match]
keywords = ["frontend", "react", "css"]
[profiles.budget]
[profiles.budget.providers.codex]
model = "gpt-4.1-mini"
MIT
FAQs
Model multiplexer — unified MCP server for cross-platform multi-model AI collaboration
The pypi package modelmux receives a total of 34 weekly downloads. As such, modelmux popularity was classified as not popular.
We found that modelmux demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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