Open Responses Server is live · free · last verified 26 Oct 2026 · viability monitor
Open Responses Server
Open-source FastAPI proxy that lets any OpenAI-compatible backend answer OpenAI's Responses API.
Worth it if you already live in the Responses API and want your own Ollama or vLLM box behind it — the translation layer is narrow and honest about what it is. The in-memory history cap (100 entries by default), the 25-iteration tool-call ceiling, and the absence of an auth layer mean it fits local development and agent prototyping, not a multi-tenant production edge. If you need routing across many providers with persistence and keys, LiteLLM covers that broader ground; pick this one when you only care about the Responses API and want a small codebase you can read in an afternoon.
Verdict generatedVerified 4d 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 a tool execution loop
- Self-hosted LLM users who want stateful multi-turn conversations in-process
- Non-technical users looking for a GUI chatbot
- Public-facing deployments that need persistence, rate limiting, or authentication
- Multi-tenant environments needing user management built in
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Skip Open Responses Server if you need authentication, rate limiting, or persistent conversation storage out of the box — its history lives in process memory capped at 100 entries and the documented configuration includes no access control.
History is capped at MAX_CONVERSATION_HISTORY (100 entries) and trimmed in memory, so long sessions silently lose earlier turns unless you store them yourself.
Free and MIT licensed, so the software cost is zero; your real spend is the OpenAI-compatible backend behind it — a local Ollama or vLLM box you already run versus metered OpenAI or Groq API calls. Compared with paid hosted gateway products, that trade is cheaper on licence fees and more expensive in operational time, since you supply uptime, auth, and persistence yourself.
In short
Open Responses Server — Open-source FastAPI proxy that lets any OpenAI-compatible backend answer OpenAI's 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 a tool execution loop. Free to use.
What is Open Responses Server?
Open Responses Server — Open-source FastAPI proxy that lets any OpenAI-compatible backend answer OpenAI's 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 a tool execution loop. Free to use.
Open Responses Server is a free, MIT-licensed FastAPI proxy that translates OpenAI's Responses API into Chat Completions calls, so any OpenAI-compatible backend — Ollama, vLLM, LiteLLM, Groq, or OpenAI itself — can serve the Responses API. Point it at your backend with OPENAI_BASE_URL_INTERNAL, expose it on port 8080, and Responses API clients such as OpenAI's Codex CLI can talk to your local or self-hosted model. It also runs a /v1/chat/completions endpoint with automatic MCP tool injection and a tool-call execution loop (MAX_TOOL_CALL_ITERATIONS defaults to 25), plus a catch-all proxy that forwards any other path to the backend at /v1/{path}. Conversation history is held in memory keyed by response_id and reloaded via previous_response_id, capped at MAX_CONVERSATION_HISTORY (default 100 entries). MCP servers are declared in servers_config.json with stdio, sse, or streamable-http transports and their tools are rediscovered every MCP_TOOL_REFRESH_INTERVAL seconds. SSE streaming, heartbeats, and streaming timeouts are configurable. It is built for developers who want to standardize on the Responses API across many backends without a hosted platform, and it is not affiliated with or endorsed by OpenAI.
Key features
- FastAPI proxy translating OpenAI's Responses API into Chat Completions calls
- Works with Ollama, vLLM, LiteLLM, Groq, or OpenAI itself as the backend
- MCP server support with stdio, sse, and streamable-http transports
- Automatic MCP tool injection into /responses and /v1/chat/completions requests
- Tool-call execution loop, configurable via MAX_TOOL_CALL_ITERATIONS (default 25)
- Stateful multi-turn conversation history via in-memory dictionary keyed by response_id
- previous_response_id support for continuing conversations
- SSE event streaming with configurable heartbeat interval
- Catch-all proxy forwarding other paths to the backend at /v1/{path}
- /health endpoint returning adapter status
- otc CLI with start, configure, and help commands
- Interactive configuration wizard via 'otc configure'
- All configuration via environment variables loaded with python-dotenv
- MCP tool cache refreshed on MCP_TOOL_REFRESH_INTERVAL (default 10s)
- Installable from PyPI with 'pip install open-responses-server'
Behind the verdict
What this project gets right is scope. Most proxy projects try to speak every protocol; Open Responses Server picks one — OpenAI's Responses API — and translates it into Chat Completions against whatever backend you already run. In practice that means Codex CLI, or any client expecting /v1/responses, can drive Ollama or vLLM without the client knowing the difference. The module map is small and legible: api_controller.py for routes and CORS, responses_service.py for conversion and SSE handling, chat_completions_service.py for the MCP-injected tool loop, plus singleton clients for httpx and MCP. Configuration is entirely environment variables, documented with defaults — the backend URL, the external URL, the bind address and port, the MCP config path, refresh interval, history cap, iteration cap, stream timeout, heartbeat, log level and log file. That is the kind of surface you can reason about during an incident. The MCP support is the part that makes it more than a translator. Tools are loaded from a JSON config supporting stdio, sse and streamable-http servers, cached, refreshed on an interval, and injected into requests on both /responses and /v1/chat/completions. The tool-call loop then iterates up to MAX_TOOL_CALL_ITERATIONS before giving up. Combined with SSE streaming and periodic heartbeats, that is a workable local agent harness. The limits are structural rather than sloppy. Conversation history lives in a Python dictionary in the process, capped at 100 entries — restart the process and it is gone. There is no persistence, no rate limiting, and no auth in what is documented, so putting this on the public internet as-is is a bad idea. The tool-call loop defaults to 25 iterations, which is generous for prototyping and a foot-gun if a tool misbehaves. And the whole thing is only as good as the OpenAI-compatible backend underneath it; the project does not host anything for you. Where it fits: a developer running Codex CLI against local models, a small team prototyping MCP agents, anyone who wants the Responses API shape without rewriting their client. Where it does not: non-technical buyers who want a GUI chat product, and anyone who needs multi-tenancy or user management — none of that is here. The honest framing is that this is a developer tool with a short, clear README-shaped contract, and it is strongest when you treat it that way.
Limitations
- Conversation history is an in-memory dictionary capped at MAX_CONVERSATION_HISTORY (100 entries by default), so it disappears on restart and cannot be shared across processes.
- There is no persistence layer, no rate limiting, and no authentication described in the documented configuration — exposing the port publicly without adding those yourself is risky.
- The tool-call loop stops at MAX_TOOL_CALL_ITERATIONS (25 by default), which can terminate long agent chains.
- The server holds no models of its own and depends entirely on an OpenAI-compatible backend such as Ollama, vLLM, LiteLLM, or Groq.
- It is not affiliated with or endorsed by OpenAI.
as of 2026-09-27
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Is Open Responses Server still active?
Open Responses Server is live · free · last verified 26 Oct 2026 · viability monitor.
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: October 2026
How we score →What do users say about Open Responses Server?
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.
Researched26 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
How we verified this
Every listed fact about Open Responses Server— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 26 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Open Responses Server 9 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.
- — changed SSL certificate
- — changed description, who it suits, our verdict, features, hidden costs, limitations, switching in, switching out, who should skip it, pricing fit, setup time, summary, use cases
- — changed limitations
- — changed domain expiry
- — changed description
- — changed SSL certificate
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Open Responses Server cost in 2026?
Open Responses Server: Free to use. Pricing last verified 26 Oct 2026.
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 and MIT licensed, so the software cost is zero; your real spend is the OpenAI-compatible backend behind it — a local Ollama or vLLM box you already run versus metered OpenAI or Groq API calls. Compared with paid hosted gateway products, that trade is cheaper on licence fees and more expensive in operational time, since you supply uptime, auth, and persistence yourself.
Has Open Responses Server's pricing changed?
No plan price for Open Responses Server has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Open Responses Server?
Pick Open Responses Server if you are 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 a tool execution loop, Self-hosted LLM users who want stateful multi-turn conversations in-process.
Look elsewhere if you are Non-technical users looking for a GUI chatbot, Public-facing deployments that need persistence, rate limiting, or authentication, Multi-tenant environments needing user management built in.
Skip it if Skip Open Responses Server if you need authentication, rate limiting, or persistent conversation storage out of the box — its history lives in process memory capped at 100 entries and the documented configuration includes no access control.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Open Responses Server actually fits — and what changes day-one when you adopt it.
You install the package with pip, run 'otc configure' to set OPENAI_BASE_URL_INTERNAL to your Ollama endpoint at http://localhost:11434, then 'otc start' and verify with curl on /v1/models.
Outcome: Codex CLI and other Responses API clients talk to your local model over the Responses API without any client-side changes.
You declare stdio and streamable-http MCP servers in servers_config.json, start the server, and hit /v1/chat/completions — the proxy injects the discovered MCP tools and runs the tool-call loop up to 25 iterations.
Outcome: You get a working agent loop with tool execution and SSE streaming while keeping the Responses API request shape.
You point separate server instances at vLLM, LiteLLM, and Groq by changing one environment variable per instance, keeping clients on the same Responses API contract.
Outcome: Backend swaps become a config change rather than a code migration, which keeps prototype and self-hosted paths aligned.
Use cases
- Run Codex CLI against a local Ollama model instead of OpenAI's hosted API.
- Expose a vLLM endpoint as a stateful Responses API server for existing clients.
- Build MCP-powered agents that call filesystem or remote MCP tools through a tool-call loop.
- Prototype tool-calling workflows without leaving the Responses API spec.
- Standardize development on the Responses API across several OpenAI-compatible backends.
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.
Developers: roughly 10–20 minutes from pip install to a verified /v1/models response, most of it deciding your backend URL and MCP server list. Teams adding MCP servers: add 15–30 minutes per server to write servers_config.json, pick the transport, and confirm tool discovery. Anyone planning production exposure should budget days, not minutes, for the auth, rate limiting, and persistent history
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 direct OpenAI API calls: point OPENAI_BASE_URL_INTERNAL at OpenAI and set OPENAI_BASE_URL to the local proxy, leaving client code on the Responses API unchanged.
- →From LiteLLM: keep LiteLLM as the backend behind OPENAI_BASE_URL_INTERNAL if you still want multi-provider routing, and use this server only for the Responses API surface.
- →From a raw Ollama setup: set OPENAI_BASE_URL_INTERNAL to http://localhost:11434 and let the proxy translate Responses API requests into Chat Completions calls.
- ↗To LiteLLM or a hosted gateway: the same backend URL moves behind a broader proxy that adds provider routing and key management in exchange for a larger surface to configure.
- ↗To a client-side integration: if you only need one backend, call its native API directly and drop the translation hop, accepting the rewrite in your client.
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
What are the best Open Responses Server alternatives?
The closest alternatives to Open Responses Server we list are LiteLLM, Github Copilot Api Vscode and Olla. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| LiteLLM | Freemium | Pricing not published | — |
| Github Copilot Api Vscode | Free | Free | — |
| Olla | Free | Free | — |
Alternatives to Open Responses Server
View all
LiteLLM
LiteLLM is a self-hosted AI gateway putting 140+ providers and 1,800+ models behind one OpenAI-compatible API.
Github Copilot Api Vscode
Open-source VS Code extension that turns your GitHub Copilot subscription into a local OpenAI-compatible API server on port 3030.
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.
LiteLLM
LiteLLM is a self-hosted AI gateway putting 140+ providers and 1,800+ models behind one OpenAI-compatible API.
Github Copilot Api Vscode
Open-source VS Code extension that turns your GitHub Copilot subscription into a local OpenAI-compatible API server on port 3030.
Olla
Olla is an open-source, Apache-2.0 Go LLM proxy that load-balances Ollama, vLLM, SGLang and MLX behind one OpenAI-compatible endpoint on
Open Responses Server vs its closest rival
Open Responses Server's closest head-to-head is with 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. Verdict generated
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.
GeneratedOpen 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.
GeneratedOpen 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.
GeneratedBito 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.
GeneratedFrequently asked questions
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