Open Responses Server
Open-source server that bridges any OpenAI-compatible backend to the Responses API.
A lean, open-source bridge that does exactly one thing well: let your existing OpenAI-compatible backend speak the Responses API. Great for prototyping and agent development with Codex CLI. Not a production solution—no persistence, rate limiting, or auth built in—so plan to add those yourself. If you need a production-grade proxy, consider LiteLLM or a cloud gateway.
Verified 13d ago · liveness 62/100 · cite: rightaichoice.com/tools/open-responses-server
- Developers running Codex CLI against local models like Ollama or vLLM
- Teams standardizing on the Responses API across OpenAI-compatible backends
- Early adopters building MCP-powered agents that need tool execution loops
- Self-hosted LLM users wanting stateful multi-turn conversations without switching tools
- Non-technical users looking for a GUI chatbot
- Production deployments requiring persistence, rate limiting, or authentication
- Teams that need multi-tenant support or user management built in
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Skip Open Responses Server if you're looking for a production-ready gateway with built-in persistence, authentication, rate limiting, or multi-tenancy.
Free, open-source (MIT). You only pay for the backend infrastructure you bring (e.g., Ollama, vLLM). Compared to paid API gateways like LiteLLM or Azure OpenAI, this is a zero-cost option for developers who can self-host.
In short
Open Responses Server — Open-source server that bridges any OpenAI-compatible backend to the Responses API. Best for Developers running Codex CLI against local models like Ollama or vLLM, Teams standardizing on the Responses API across OpenAI-compatible backends, Early adopters building MCP-powered agents that need tool execution loops. Free to use.
What people actually say about Open Responses Server — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
26 mentions across 3 sources (YouTube, GitHub, Lemmy) · researched Sep 1, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Bridge any OpenAI-compatible backend to the Responses API, enabling Codex CLI locally.
- +MCP server support for both Chat Completions and Responses APIs expands tool use.
- +Stateful multi-turn conversations via in-memory history for agent workflows.
- +Configurable tool call execution loop lets agents iterate until completion.
- +SSE streaming delivers real-time responses essential for interactive agents.
- −Duplicate /v1 in URL issue with vLLM shows base URL handling bugs.
- −Community support is nearly nonexistent; only 2 relevant GitHub posts found.
- −In-memory state is lost on restart, breaking long-running sessions.
- −Insufficient documentation for edge cases, relying on readme and sparse issues.
- −Requires manual configuration of environment variables or wizard; not plug-and-play.
- • No monetary costs, but users pay in time troubleshooting setup and edge cases
- • Self-hosting infrastructure costs (server, bandwidth, storage) if deploying beyond localhost
Viability Score
How well maintained and how widely used is Open Responses Server? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Drop-in replacement for OpenAI's Responses API
- Works with any OpenAI-compatible backend
- MCP server support for Chat Completions and Responses APIs
- Stateful multi-turn conversations via in-memory history
- Tool call execution loop with configurable iteration limits
- SSE event streaming for real-time responses
- CLI tool 'otc' for configure, start, and management
- Supports Ollama, vLLM, LiteLLM, Groq, and OpenAI itself
- Environment variable or interactive configuration
- MIT licensed
- Codex CLI and other Responses API clients supported
- Web search and RAG extension guide
- Security scanning setup and policies
- Testing guide with coverage instructions
- Publishing to PyPI workflow
About Open Responses Server
Open Responses Server is a free, open-source translation layer that lets any OpenAI-compatible backend speak OpenAI's Responses API. Instead of rewriting code or migrating to a proprietary platform, you point this server at your existing backend—Ollama, vLLM, LiteLLM, Groq, or OpenAI itself—and it exposes the familiar Responses API at a local endpoint. That means you can run Codex CLI and other Responses API clients against local or self-hosted models without vendor lock-in. It's built for developers who want to standardize on the Responses API across diverse backends while keeping control of their infrastructure. The server handles the heavy lifting of protocol translation: stateful multi-turn conversation history is maintained in memory, and a configurable tool call execution loop lets agents call tools and iterate until completion. It also integrates MCP servers, bridging both Chat Completions and Responses APIs. SSE event streaming delivers real-time responses, which is essential for interactive agent experiences. Installation is a single pip command, and configuration can be done via environment variables or an interactive CLI wizard (otc configure). Under the hood, you get a well-structured codebase with thorough documentation covering architecture, event sequences, API flow diagrams, testing, security scanning, and even publishing to PyPI. The project is MIT-licensed and self-contained, making it a transparent choice for teams that want to understand and extend the server. It's not an official OpenAI product, but it's a focused tool for a specific need: bridging the Responses API gap. Compared to general-purpose proxies like LiteLLM, Open Responses Server narrows its scope to the Responses API. That focus is its strength—less configuration, more targeted to the clients you care about. For developers already invested in the Responses API ecosystem, this is a lightweight way to keep using the tools you like.
Behind the Verdict
Open Responses Server answers a specific pain: you've got local models (Ollama, vLLM) or a gateway like LiteLLM, and you want to use Codex CLI or other Responses API clients without rewriting your stack. It's a translation layer, not an inference engine—it doesn't replace your backend, it makes it speak the Responses API. For developers experimenting with agentic workflows, this is a handy tool. Stateful multi-turn conversations in memory and a configurable tool call loop mean you can prototype agents that call tools and iterate without wrestling with protocol details. MCP server support is a nice touch—it bridges both Chat Completions and Responses APIs, so you can keep using existing MCP tools. Where it bites: this is not a production server. In-memory history means no persistence across restarts—reboot and your conversations are gone. There's no built-in rate limiting, authentication, or multi-tenant support. If you need those, you'd have to add them yourself or look at LiteLLM, which offers a broader proxy feature set (though with a heavier config surface). Another caveat: it's not affiliated with OpenAI, so you're betting on a community-maintained project. That's fine for prototypes, but for a critical path you'd want to evaluate the maintenance cadence and your own ability to extend the code. In practice, we'd reach for Open Responses Server when we're building a demo or a personal agent harness and want to stay on the Responses API without paying for a cloud gateway. Pair it with Ollama for local runs or Groq for fast hosted inference. If your target is production traffic with SLAs, plan for a more robust solution. One more thing: setup is genuinely easy—pip install and otc configure walks you through it. The docs cover architecture, event sequences, and
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Real-world workflow fit
Concrete scenarios for the personas Open Responses Server actually fits — and what changes day-one when you adopt it.
You want to use Codex CLI with a local Ollama model.
Outcome: Install open-responses-server, configure via otc configure pointing to Ollama, start the server, and Codex CLI connects to localhost:8080 as if it were OpenAI, letting you iterate locally with zero API costs.
You run a vLLM endpoint and want to expose it as a Responses API for a prototype agent.
Outcome: Point the server's internal base URL to your vLLM endpoint, use the tool call execution loop to let your agent call functions, and stream responses via SSE to a frontend.
You're building an MCP-based agent and want to use multiple backends.
Outcome: Set up the server with MCP integration, configure tool limits, and your agent can call MCP tools across any OpenAI-compatible backend through a single Responses API endpoint.
Use Cases
- Run Codex CLI with a local Ollama model instead of OpenAI.
- Expose a vLLM endpoint as a stateful Responses API server.
- Build MCP-based agents that use any OpenAI-compatible backend.
- Prototype tool-calling workflows without leaving the Responses API spec.
- Standardize development on the Responses API across multiple backend providers.
Limitations
- Conversation history is stored only in memory rather than a persistent database.
- It is not affiliated with or endorsed by OpenAI.
- It relies on an external OpenAI-compatible backend (e.g.
- Ollama, vLLM, LiteLLM, Groq) and does not provide its own hosted infrastructure.
as of 2026-08-27
Verification history
We have re-verified Open Responses Server 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Open Responses Server's pricing actually pencils out — and where peers do it cheaper.
Free, open-source (MIT). You only pay for the backend infrastructure you bring (e.g., Ollama, vLLM). Compared to paid API gateways like LiteLLM or Azure OpenAI, this is a zero-cost option for developers who can self-host.
Setup time & first value
How long it actually takes to get something useful out of Open Responses Server — broken out by persona, not the marketing-page minute.
For a solo developer: under 5 minutes to pip install and run otc configure, then otc start. For an ML engineer with existing backends: about 10 minutes to set environment variables and verify with curl. For agent builders: 15-30 minutes to configure MCP and test tool loops.
Switching to or from Open Responses Server
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a custom proxy: Replace your hand-built translation layer with this server and point your Responses API client to localhost:8080.
- →From LiteLLM: If you only need Responses API support, switch to this lightweight server for a narrower scope and simpler setup.
- ↗To LiteLLM: If you need broader protocol support or production features, migrate your backend endpoints (OpenAI-compatible) and update your client base URL.
- ↗To OpenAI directly: If you no longer need local models, switch your client's base URL to api.openai.com and use the standard Responses API.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Open Responses Server”, and we withheld 6: 6 did not mention Open Responses Server. We are showing none, because we could not prove any of them are about Open Responses Server.
Official links
Tools that pair well with Open Responses Server
Common stack mates teams adopt alongside Open Responses Server, with the specific reason each pairing earns its keep.
Blackbox AI
Blackbox AI is a zero-data-retention inference API giving coding agents 300+ models through one OpenAI-compatible endpoint.
Agnes AI
Free multimodal AI API gateway for text, image, video, and audio generation with OpenAI-compatible endpoints
agentcad
Open-source MCP server letting coding agents design, render, and export 3D CAD parts via code
Featured Head-to-Head Comparisons
Open Responses Server vs Spider Cloud
Choose Spider Cloud if you need high-volume, cost-efficient web scraping with structured output and AI-powered extraction to feed RAG pipelines. Choose Open Responses Server if you're standardizing on the Responses API with local or self-hosted models for agent workflows. They solve different problems; one fetches external data, the other adapts inference backends.
Open Responses Server vs Voyage Ai
Choose Voyage AI if you need enterprise-grade, domain-specific embedding models and rerankers for high-accuracy RAG in finance, legal, or code—and have budget for a paid solution. Choose Open Responses Server if you're a developer who wants to run open-source or local models behind the OpenAI Responses API with MCP support, at zero cost. They serve completely different needs: one is a proprietary API for retrieval quality, the other is an open-source infrastructure bridge.
Open Responses Server vs Temporal Ai
If you need a durable, fault-tolerant platform for long-running AI workflows that survive crashes, choose Temporal AI. For a lightweight, free compatibility layer to run Responses API agents with local models like Ollama, go with Open Responses Server. They solve different problems: one is an orchestration engine, the other an API adapter.
Bito vs Open Responses Server
If you need AI coding agents that understand your entire multi-repo architecture, Bito's knowledge graph and cross-repo impact analysis are indispensable — but only if your team can justify the cost and setup overhead. If you're a developer running Codex CLI or a custom Responses API client with local LLMs, Open Responses Server is a free, open-source bridge that saves you protocol headaches. Pick Bito for enterprise-scale code intelligence; pick Open Responses Server for lightweight, self-hosted API compatibility.
Alternatives to Open Responses Server
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Blackbox AI is a zero-data-retention inference API giving coding agents 300+ models through one OpenAI-compatible endpoint.
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