any-model-plugin
Enables delegation of tasks and adversarial reviews to OpenAI models, including Codex agent capabilities.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@any-model-plugindelegate fix flaky test to codex"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Any Model Plugin
A Claude Code plugin that lets AI agents delegate tasks, run adversarial reviews, and manage background jobs from Claude Code to any provider and model.
What it does
Delegate work from Claude Code to another engine/model with a single slash command.
Run adversarial reviews across different models and providers.
Manage background jobs (
status,result,cancel) just like the upstream plugin.
Related MCP server: ATMcp
Use it from any agent host
Claude Code is one of three surfaces — the same CLI powers all of them:
Universal CLI:
node plugins/anymodel/scripts/companion.mjs <command>(ornpx anymodel) from any shell or agent.MCP server:
plugins/anymodel/scripts/mcp-server.mjsexposes every capability asanymodel_*tools in any MCP host — Claude Code, Codex, Cursor, Windsurf, VS Code. See INTEGRATIONS.md.Claude Code native plugin:
/anymodel:*commands via the marketplace install below.
The two-axes concept
Delegation is built on two orthogonal ideas:
Executor harness — the agent runtime that plans, runs commands, edits files, and manages sandboxes (e.g., Codex, Claude).
Model — the underlying provider/model the harness uses to reason and respond.
The Codex plugin hardwires both to OpenAI. This project separates them so you can mix and match:
/anymodel:delegate --engine codex --model zai/glm-5.2 fix the flaky test
/anymodel:review --engine claude --base mainQuick start
TODO: installation, setup, and first-delegate steps once Phase 0 scaffolding is complete.
Roadmap
Phase | Status | Goal |
Phase 0 | ✅ Done | Scaffold repo; lift core orchestration; codex adapter + LiteLLM bridge; provider registry; loopback CI test |
Phase 1 | ✅ Done | Codex + Claude adapters; per-thread config override (mixed providers on one broker); native + schema reviews on all engines |
Phase 2 | ✅ Done | Built-in Responses→Chat shim ( |
Phase 3 | ✅ Done | Plugin command surface + runner agent; |
Attribution
This project derives from openai/codex-plugin-cc under the Apache-2.0 license. See LICENSE and NOTICE for details.
Security
API keys are never stored in this repository. Providers are configured with
env_keyreferences inregistry.toml; keys come from your environment or a local env file you pass tocompanion.mjs shim --env-file(keep it outside the repo —.envfiles are gitignored).Sandboxing varies by engine. The
codexengine runs inside Codex's own sandbox; theclaudeengine uses Claude Code permission modes; the built-indirectengine has only tool-level guards (workspace path containment, symlink-hardened;write_file/exec_commandexist only in--writemode). Don't point--engine direct --writeat repositories you can't afford to mutate, and treat model output as untrusted input.The bridge (shim or LiteLLM) listens on
127.0.0.1only.Found a vulnerability? Please open a private security advisory rather than a public issue.
License
Apache-2.0 — see LICENSE and NOTICE.
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