Cadenza.Agent bridges OpenAI Codex CLI to Claude, Gemini, and Llama

This review examines Cadenza.Agent 1.0.14, an MSBuild SDK that enables OpenAI Codex CLI to connect with diverse LLMs like Claude, Gemini, and Llama through OpenRouter, using a concise C# script.

TL;DR

Best for: Developers needing to use OpenAI Codex CLI with non-OpenAI LLMs, especially those already in the .NET ecosystem or seeking a lightweight, self-contained solution for LLM proxying. Skip if: You are not using Codex CLI, prefer Python or Node.js for LLM orchestration, or require a fully managed service for LLM routing. Bottom line: Cadenza.Agent provides a pragmatic, low-code C# solution to extend Codex CLI's utility beyond OpenAI's Responses API.

METHODOLOGY

This v0 review draws on the founder's published claims in a dev.to blog post, Run OpenAI Codex CLI on Claude, Gemini, or Llama — in 50 lines of C#, accessed on 2026-05-26. The tool under review is Cadenza.Agent version 1.0.14. This analysis covers the founder's description of Cadenza.Agent's capabilities, the technical implementation details of the C# script, and its integration with OpenAI Codex CLI and OpenRouter. It also details the specific API endpoints and configuration methods described. What is NOT covered in this review includes independent performance benchmarks, long-term workflow impact, or specific edge cases beyond the described setup. Update cadence: re-tested when claims diverge from observed behavior.

WHAT IT DOES

Unifying LLM APIs for Codex CLI

Cadenza.Agent acts as a local proxy server, translating between different LLM API formats. Specifically, it exposes both an OpenAI Chat Completion-compatible endpoint (POST /v1/chat/completions) and an OpenAI Responses API-compatible endpoint (POST /v1/responses). This allows tools like OpenAI Codex CLI, which, as of February 2026, only speaks the Responses API, to interface with Chat Completion-only LLMs such as those available via OpenRouter (including Claude, Gemini, and Llama models).

Single-file agent server

Cadenza.Agent is an MSBuild SDK designed for .NET 10's file-based programs. It enables a single .cs file to function as a runnable agent server. The SDK provides a Tier-1 API with methods like Tool, UseOllama, UseOpenAi, and Run, simplifying the creation of local LLM proxies. The blog post demonstrates this with a 50-line C# script that configures and runs the server.

Backend-agnostic LLM integration

The SDK leverages Microsoft.Extensions.AI's vendor-neutral IChatClient abstraction. This design allows the underlying LLM backend to be switched without altering the wire format exposed by the Cadenza.Agent server. The example uses OpenRouter, which speaks OpenAI's Chat Completion wire format but with a different base URL, making it a drop-in replacement for Microsoft.Extensions.AI.OpenAI's ChatClient.

Portable configuration

For configuring Codex CLI, the solution uses the CODEX_HOME environment variable. This allows Codex to load a config.toml from a sample-local directory instead of the user's global ~/.codex/config.toml. This ensures that the provided sample is self-contained and avoids modifying the user's default Codex configuration.

WHAT'S INTERESTING / WHAT'S NOT

What's interesting about Cadenza.Agent is its direct and pragmatic solution to a specific integration problem: enabling OpenAI Codex CLI to use non-OpenAI LLMs. The approach of creating a lightweight, single-file C# proxy server is elegant, particularly for developers already operating within the .NET ecosystem. The use of Microsoft.Extensions.AI's IChatClient for vendor neutrality is a sound architectural choice, ensuring flexibility in LLM backend selection. The ability to switch between models like Anthropic's Claude 3.5 Sonnet, Gemini, or Llama via OpenRouter, all while maintaining compatibility with Codex CLI's Responses API, is a significant utility for users locked into the Codex CLI experience but seeking broader model access. The minimal code footprint (50 lines of C#) highlights the SDK's efficiency and ease of use for this specific task.

What's not interesting, or rather, what's missing from the founder's pitch, is any discussion of error handling, observability, or production readiness. While the solution is effective for local development and specific integration, its suitability for more robust, multi-user, or high-throughput environments is not addressed. The problem it solves, while specific and well-addressed, is also niche, primarily catering to users of OpenAI Codex CLI. For developers not using Codex CLI, or those preferring other languages like Python or Node.js for LLM orchestration, Cadenza.Agent's C# dependency might be a barrier. There are no performance claims or benchmarks, leaving open questions about latency and throughput when proxying requests through the local server.

PRICING

Cadenza.Agent is an MSBuild SDK. The source signal does not mention any direct pricing for the SDK itself, suggesting it is free to use. Users would incur costs from the underlying LLM providers (e.g., OpenRouter, Anthropic, Google) as per their respective pricing models. Pricing snapshot: May 2026.

VERDICT

Cadenza.Agent 1.0.14 provides a highly effective and concise solution for a specific problem: extending the utility of OpenAI Codex CLI to a wider range of LLMs. For .NET developers already using or considering Codex CLI, this SDK offers a low-overhead method to integrate with models like Claude, Gemini, or Llama via OpenRouter. Its strength lies in its simplicity, the clear technical implementation, and its ability to abstract away API differences. This tool is a strong recommendation for those who need to break Codex CLI's reliance on OpenAI's Responses API, particularly when a lightweight, self-hosted proxy is preferred over a managed service.

WHAT WE'D TEST NEXT

Our next steps would involve independent benchmarking of Cadenza.Agent's performance, specifically measuring latency and throughput when proxying requests to various LLMs via OpenRouter. We would also investigate its error handling mechanisms and resilience under different failure conditions. Further testing would include evaluating its scalability for multiple concurrent users or agents, and its integration capabilities with other agent tools like Aider, Continue, or Cursor using the /v1/chat/completions endpoint. We would also examine the robustness of its tool calling capabilities and how they translate across different LLM backends, alongside the overall deployment complexity beyond a local script.

Pull quote: “Cadenza.Agent acts as a local proxy server, translating between different LLM API formats.”