Open Responses Server

Open Responses Server fills a specific niche: developers who want to use OpenAI's newer Responses API with local or third-party models. Its chief value is as a drop-in adapter—point it at any OpenAI-compatible endpoint, and it surfaces the Responses API. The MCP integration is a plus, allowing tools like Codex CLI to work with models they otherwise couldn't. Where it excels is in prototyping agentic workflows. The stateful conversation management and tool call execution loop save you from building that plumbing yourself. For a solo dev or small team experimenting with local LLMs, it's a time-saver. The `otc` CLI makes setup straightforward. But it's not production-ready. In-memory history means conversations vanish on restart. There's no built-in rate limiting, authentication, or multi-tenant support—you'd need to layer those on yourself. The project is young (v0.x), so expect rough edges and limited community support. Compared to alternatives like LiteLLM (which proxies multiple APIs to a unified format) or ollama's built-in API, Open Responses Server is more specialized—it targets the Responses API specifically, not a general proxy. LiteLLM gives you broader API coverage and more production features (load balancing, budget tracking), but lacks the MCP integration and stateful tool loop. We'd reach for this when hacking on a proof-of-concept with Codex CLI and local models, or when we need to test Responses API features without paying OpenAI. It's not for building customer-facing products. If you need persistence, auth, or scale, look elsewhere—or contribute to the project.