Agents are not “chatbots that talk well.” They are workflows that finish work. “Search docs, extract key numbers, summarize, then draft an email” is not a single chat turn—it is a loop of tool calls → results → next actions.

The problem is that much of the ecosystem still leans on Chat Completions, an interface optimized for conversation rather than action. If you try to squeeze tool calls and state into chat messages, you end up with ad-hoc conventions that break interoperability.

Open Responses is an attempt to fix that. Hugging Face frames Open Responses as an open inference standard designed for the agent era: moving the default model from “chat messages” to “responses + semantic events + tool loops”.

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The naming is confusing at first.
Open Responses refers to the open standard/spec, while open-responses/open-responses is a self-hosted server implementation that aims to be compatible with the Responses API.

Why Chat Completions hit a wall

Chat Completions are “message turn” first. Agents are not. In practice, agents often require:

• Repeated tool calls and tool results inside a single task.
• A loop of reasoning → tool execution → continued reasoning.
• Streaming not only final text, but also intermediate items (tool calls/results/progress states).

Forcing this into a chat-shaped API tends to create hacks instead of interoperability.

What Open Responses standardizes

According to Hugging Face, Open Responses builds on the direction set by OpenAI’s Responses API (released in March 2025) and extends it into a more open, interoperable specification. In plain terms: it standardizes the “intermediate stuff” that agent workloads need.

Four ideas stand out.

1. A standard way to expose reasoning
   Open Responses formalizes optional fields like content (raw traces), encrypted_content (provider-protected), and summary (sanitized). Providers and clients can choose what to emit and what to accept.

2. Streaming as semantic events, not raw deltas
   Instead of streaming only “text chunks”, the API models streaming as an event sequence (for example, response.reasoning.delta). This makes it easier for UIs and logs to interpret what is happening.

3. Stateless by default, encrypted reasoning when needed
   The spec is stateless by default, while still supporting encrypted reasoning for providers that require it.

4. First-class routing concepts
   Open Responses distinguishes “Model Providers” from “Routers”, and allows clients to specify provider targets and provider-specific options in a standardized way.

Hugging Face also emphasizes that client requests remain familiar. For example, /v1/responses calls can add an OpenResponses-Version: latest header.

curl https://evalstate-openresponses.hf.space/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $HF_TOKEN" \
  -H "OpenResponses-Version: latest" \
  -N \
  -d '{ "model": "moonshotai/Kimi-K2-Thinking:nebius", "input": "explain the theory of life" }'

Tools and the agentic loop become first-class

Open Responses defines two tool categories:

• Internally-hosted tools: executed within the provider’s infrastructure (for example, provider-managed file search).
• Externally-hosted tools: executed outside the provider (client-side functions or MCP servers).

It also formalizes the agentic loop: reasoning, emitting tool calls, executing tools, feeding results back, and repeating until completion—optionally constrained via max_tool_calls and tool_choice.

Standards need servers: the open-responses project

Specs alone do not ship products. You need an endpoint that existing clients can call. open-responses/open-responses describes itself as a “self-hosted, open-source alternative to OpenAI’s Responses API”—a drop-in replacement intended to minimize client-side changes.

The practical idea is simple: keep your OpenAI SDK (or Agents SDK) code, and point base_url to a locally hosted server.

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8080/", api_key="RESPONSE_API_KEY")
response = client.responses.create(model="...", input="Explain Open Responses in one paragraph.")
print(response.output[0].content[0].text)

The project README highlights compatibility across multiple LLM providers (including Claude, Qwen, DeepSeek R1, and Ollama), positioning self-hosting as an option for teams that want tighter data control.

A quick adoption checklist

1. Streaming support: can your UI/logging stack handle event-based streams?
2. Reasoning policy: do you want raw reasoning, summaries only, or encrypted-only outputs?
3. Tool security: if you attach external tools (including MCP), how do you sandbox and audit them?
4. Routing needs: do you benefit from standard provider/router separation?

FAQ

Q: Is Open Responses meant to replace Chat Completions?
A: The intent is a shared format that better matches agent workloads. In the near term, expect bridging and gradual migration.

Q: Do providers have to expose raw reasoning traces?
A: No. The spec supports multiple reasoning representations; what is emitted is optional and provider-dependent.

Q: Hosted Open Responses vs self-hosted—what should I pick?
A: Hosted options optimize for speed and operational simplicity. Self-hosting prioritizes control and customization. Your security, compliance, and cost constraints will decide.

Closing

As agents become the dominant inference workload, “model quality” is only half of the story. The other half is a stable, interoperable interface for tool loops, streaming, and routing. Open Responses pushes that interface toward an open standard—and projects like open-responses make it practical in today’s codebases.

References

• 🏛️ Open Responses: What you need to know (Hugging Face Blog, 2026-01-15) — https://huggingface.co/blog/open-responses
• 🛡️ open-responses/open-responses (README) — https://github.com/open-responses/open-responses
• 🏛️ Open Responses Specification — https://www.openresponses.org/
• 🏛️ OpenAI Responses API docs — https://platform.openai.com/docs/api-reference/responses