diff --git a/README.md b/README.md
index fb60988..52f7b99 100644
--- a/README.md
+++ b/README.md
@@ -132,6 +132,41 @@ This way the Ollama backend servers are utilized more efficient than by simply u
 
 NOMYO Router also supports OpenAI API compatible v1 backend servers.
 
+## OpenAI Responses API
+
+In addition to Chat Completions, NOMYO Router exposes the OpenAI **Responses API**:
+
+```
+POST   /v1/responses                  # create a response (stream or non-stream)
+GET    /v1/responses/{id}             # retrieve a stored response
+DELETE /v1/responses/{id}             # delete a stored response
+POST   /v1/responses/{id}/cancel      # cancel a background response
+```
+
+It works transparently across **all** backends. When the routed model lives on a native
+Responses backend (external OpenAI) the request is forwarded as-is; for Ollama and llama-server the
+router translates Responses ⇄ Chat Completions in both directions (request, response, and streaming
+typed SSE events), so clients get a consistent `/v1/responses` surface regardless of backend.
+
+### Conversation state (`store` / `previous_response_id`)
+
+The router **owns conversation state itself** (persisted in its SQLite DB) rather than delegating to
+the upstream provider, so `store` and `previous_response_id` behave identically on every backend.
+On a follow-up request the router rehydrates the prior turns from its DB and expands them into the
+conversation; outbound native calls always send `store=false`. Trade-off: this forgoes OpenAI's
+server-side reasoning-state reuse in exchange for uniform, backend-agnostic chaining.
+
+### Background mode
+
+`background:true` (which requires `store:true`) returns immediately with `{"status":"queued"}`; the
+request runs server-side and the client polls `GET /v1/responses/{id}` until the status reaches a
+terminal state (`completed` / `failed` / `cancelled`). `POST /v1/responses/{id}/cancel` aborts it.
+
+Limitations: streaming reconnect-resume via `starting_after` is not yet implemented. In a
+multi-worker/replica deployment polling works via the shared DB, but `cancel` only reaches the
+running task in the worker that started it (other workers just mark the stored row cancelled). A
+background task interrupted by a server restart is reconciled to `failed` on the next startup.
+
 ## Semantic LLM Cache
 
 NOMYO Router includes an optional semantic cache that serves repeated or semantically similar LLM requests from cache — no endpoint round-trip, no token cost, response in <10 ms.
@@ -172,7 +207,7 @@ Each request is keyed on `model + system_prompt` (exact) combined with a weighte
 
 ### Cached routes
 
-`/api/chat` · `/api/generate` · `/v1/chat/completions` · `/v1/completions`
+`/api/chat` · `/api/generate` · `/v1/chat/completions` · `/v1/completions` · `/v1/responses`
 
 ### Cache management
 
diff --git a/api/openai.py b/api/openai.py
index 4662f50..ab24f54 100644
--- a/api/openai.py
+++ b/api/openai.py
@@ -46,6 +46,110 @@ from routing import choose_endpoint, decrement_usage
 router = APIRouter()
 
 
+async def create_chat_with_retries(oclient, send_params, endpoint, model, tracking_model):
+    """Call ``chat.completions.create`` with the router's resilience retries.
+
+    Encapsulates the recovery ladder shared by the chat-completions handler and
+    the translated ``/v1/responses`` path:
+
+      * ``does not support tools`` → retry without ``tools``
+      * llama-server context exhaustion → sliding-window message trim, with a
+        second retry that also strips ``tools``/``tool_choice``
+      * backend connection failure → mark (endpoint, model) unhealthy so the next
+        request reroutes, then re-raise
+      * ``image input is not supported`` → strip images and retry
+
+    On unrecoverable failure the endpoint usage counter is decremented and the
+    exception is re-raised. Returns the established async generator / response.
+    """
+    config = get_config()
+    try:
+        async_gen = await oclient.chat.completions.create(**send_params)
+    except Exception as e:
+        _e_str = str(e)
+        _is_ctx_err = "exceed_context_size_error" in _e_str or "exceeds the available context size" in _e_str
+        print(f"[ochat] caught={type(e).__name__} ctx={_is_ctx_err} msg={_e_str[:120]}", flush=True)
+        if "does not support tools" in _e_str:
+            # Model doesn't support tools — retry without them
+            print(f"[ochat] retry: no tools", flush=True)
+            try:
+                params_without_tools = {k: v for k, v in send_params.items() if k != "tools"}
+                async_gen = await oclient.chat.completions.create(**params_without_tools)
+            except Exception:
+                await decrement_usage(endpoint, tracking_model)
+                raise
+        elif _is_ctx_err:
+            # Backend context limit hit — apply sliding-window trim (context-shift at message level)
+            err_body = getattr(e, "body", {}) or {}
+            err_detail = err_body.get("error", {}) if isinstance(err_body, dict) else {}
+            n_ctx_limit = err_detail.get("n_ctx", 0)
+            actual_tokens = err_detail.get("n_prompt_tokens", 0)
+            # Fallback: parse from string if body parsing yielded nothing (SDK may not parse llama-server errors)
+            if not n_ctx_limit:
+                import re as _re
+                _m = _re.search(r"'n_ctx':\s*(\d+)", _e_str)
+                if _m:
+                    n_ctx_limit = int(_m.group(1))
+                _m = _re.search(r"'n_prompt_tokens':\s*(\d+)", _e_str)
+                if _m:
+                    actual_tokens = int(_m.group(1))
+            print(f"[ctx-trim] n_ctx={n_ctx_limit} actual={actual_tokens}", flush=True)
+            if not n_ctx_limit:
+                await decrement_usage(endpoint, tracking_model)
+                raise
+            if n_ctx_limit <= _CTX_TRIM_SMALL_LIMIT:
+                _endpoint_nctx[(endpoint, model)] = n_ctx_limit
+
+            msgs_to_trim = send_params.get("messages", [])
+            try:
+                cal_target = _calibrated_trim_target(msgs_to_trim, n_ctx_limit, actual_tokens)
+                trimmed_messages = _trim_messages_for_context(msgs_to_trim, n_ctx_limit, target_tokens=cal_target)
+            except Exception as _helper_exc:
+                print(f"[ctx-trim] helper crash: {type(_helper_exc).__name__}: {str(_helper_exc)[:100]}", flush=True)
+                await decrement_usage(endpoint, tracking_model)
+                raise
+            dropped = len(msgs_to_trim) - len(trimmed_messages)
+            print(f"[ctx-trim] target={cal_target} dropped={dropped} remaining={len(trimmed_messages)} retrying-1", flush=True)
+            try:
+                async_gen = await oclient.chat.completions.create(**{**send_params, "messages": trimmed_messages})
+                print(f"[ctx-trim] retry-1 ok", flush=True)
+            except Exception as e2:
+                _e2_str = str(e2)
+                if "exceed_context_size_error" in _e2_str or "exceeds the available context size" in _e2_str:
+                    # Still too large — tool definitions likely consuming too many tokens, strip them too
+                    print(f"[ctx-trim] retry-1 still exceeded, stripping tools retrying-2", flush=True)
+                    params_no_tools = {k: v for k, v in send_params.items() if k not in ("tools", "tool_choice")}
+                    try:
+                        async_gen = await oclient.chat.completions.create(**{**params_no_tools, "messages": trimmed_messages})
+                        print(f"[ctx-trim] retry-2 ok", flush=True)
+                    except Exception:
+                        await decrement_usage(endpoint, tracking_model)
+                        raise
+                else:
+                    await decrement_usage(endpoint, tracking_model)
+                    raise
+        elif _is_backend_connection_error(e):
+            # Upstream connection failed (e.g. llama-server in router mode
+            # whose delegated worker died). Mark (endpoint, model) so the
+            # next request reroutes; the client will retry this one.
+            print(f"[ochat] backend connection error → marking ({endpoint}, {model}) unhealthy", flush=True)
+            await _mark_backend_unhealthy(endpoint, model, _e_str)
+            await decrement_usage(endpoint, tracking_model)
+            raise
+        elif "image input is not supported" in _e_str:
+            # Model doesn't support images — strip and retry
+            print(f"[openai_chat_completions_proxy] Model {model} doesn't support images, retrying with text-only messages")
+            try:
+                async_gen = await oclient.chat.completions.create(**{**send_params, "messages": _strip_images_from_messages(send_params.get("messages", []))})
+            except Exception:
+                await decrement_usage(endpoint, tracking_model)
+                raise
+        else:
+            await decrement_usage(endpoint, tracking_model)
+            raise
+    return async_gen
+
+
 @router.post("/v1/embeddings")
 async def openai_embedding_proxy(request: Request):
     """
@@ -260,90 +364,7 @@ async def openai_chat_completions_proxy(request: Request):
             _dropped = len(_pre_msgs) - len(_pre_trimmed)
             print(f"[ctx-pre] n_ctx={_known_nctx} est={_pre_est} target={_pre_target} dropped={_dropped}", flush=True)
             send_params = {**send_params, "messages": _pre_trimmed}
-    try:
-        async_gen = await oclient.chat.completions.create(**send_params)
-    except Exception as e:
-        _e_str = str(e)
-        _is_ctx_err = "exceed_context_size_error" in _e_str or "exceeds the available context size" in _e_str
-        print(f"[ochat] caught={type(e).__name__} ctx={_is_ctx_err} msg={_e_str[:120]}", flush=True)
-        if "does not support tools" in _e_str:
-            # Model doesn't support tools — retry without them
-            print(f"[ochat] retry: no tools", flush=True)
-            try:
-                params_without_tools = {k: v for k, v in send_params.items() if k != "tools"}
-                async_gen = await oclient.chat.completions.create(**params_without_tools)
-            except Exception:
-                await decrement_usage(endpoint, tracking_model)
-                raise
-        elif _is_ctx_err:
-            # Backend context limit hit — apply sliding-window trim (context-shift at message level)
-            err_body = getattr(e, "body", {}) or {}
-            err_detail = err_body.get("error", {}) if isinstance(err_body, dict) else {}
-            n_ctx_limit = err_detail.get("n_ctx", 0)
-            actual_tokens = err_detail.get("n_prompt_tokens", 0)
-            # Fallback: parse from string if body parsing yielded nothing (SDK may not parse llama-server errors)
-            if not n_ctx_limit:
-                import re as _re
-                _m = _re.search(r"'n_ctx':\s*(\d+)", _e_str)
-                if _m:
-                    n_ctx_limit = int(_m.group(1))
-                _m = _re.search(r"'n_prompt_tokens':\s*(\d+)", _e_str)
-                if _m:
-                    actual_tokens = int(_m.group(1))
-            print(f"[ctx-trim] n_ctx={n_ctx_limit} actual={actual_tokens}", flush=True)
-            if not n_ctx_limit:
-                await decrement_usage(endpoint, tracking_model)
-                raise
-            if n_ctx_limit <= _CTX_TRIM_SMALL_LIMIT:
-                _endpoint_nctx[(endpoint, model)] = n_ctx_limit
-
-            msgs_to_trim = send_params.get("messages", [])
-            try:
-                cal_target = _calibrated_trim_target(msgs_to_trim, n_ctx_limit, actual_tokens)
-                trimmed_messages = _trim_messages_for_context(msgs_to_trim, n_ctx_limit, target_tokens=cal_target)
-            except Exception as _helper_exc:
-                print(f"[ctx-trim] helper crash: {type(_helper_exc).__name__}: {str(_helper_exc)[:100]}", flush=True)
-                await decrement_usage(endpoint, tracking_model)
-                raise
-            dropped = len(msgs_to_trim) - len(trimmed_messages)
-            print(f"[ctx-trim] target={cal_target} dropped={dropped} remaining={len(trimmed_messages)} retrying-1", flush=True)
-            try:
-                async_gen = await oclient.chat.completions.create(**{**send_params, "messages": trimmed_messages})
-                print(f"[ctx-trim] retry-1 ok", flush=True)
-            except Exception as e2:
-                _e2_str = str(e2)
-                if "exceed_context_size_error" in _e2_str or "exceeds the available context size" in _e2_str:
-                    # Still too large — tool definitions likely consuming too many tokens, strip them too
-                    print(f"[ctx-trim] retry-1 still exceeded, stripping tools retrying-2", flush=True)
-                    params_no_tools = {k: v for k, v in send_params.items() if k not in ("tools", "tool_choice")}
-                    try:
-                        async_gen = await oclient.chat.completions.create(**{**params_no_tools, "messages": trimmed_messages})
-                        print(f"[ctx-trim] retry-2 ok", flush=True)
-                    except Exception:
-                        await decrement_usage(endpoint, tracking_model)
-                        raise
-                else:
-                    await decrement_usage(endpoint, tracking_model)
-                    raise
-        elif _is_backend_connection_error(e):
-            # Upstream connection failed (e.g. llama-server in router mode
-            # whose delegated worker died). Mark (endpoint, model) so the
-            # next request reroutes; the client will retry this one.
-            print(f"[ochat] backend connection error → marking ({endpoint}, {model}) unhealthy", flush=True)
-            await _mark_backend_unhealthy(endpoint, model, _e_str)
-            await decrement_usage(endpoint, tracking_model)
-            raise
-        elif "image input is not supported" in _e_str:
-            # Model doesn't support images — strip and retry
-            print(f"[openai_chat_completions_proxy] Model {model} doesn't support images, retrying with text-only messages")
-            try:
-                async_gen = await oclient.chat.completions.create(**{**send_params, "messages": _strip_images_from_messages(send_params.get("messages", []))})
-            except Exception:
-                await decrement_usage(endpoint, tracking_model)
-                raise
-        else:
-            await decrement_usage(endpoint, tracking_model)
-            raise
+    async_gen = await create_chat_with_retries(oclient, send_params, endpoint, model, tracking_model)
 
     # 4. Async generator — only streams the already-established async_gen
     async def stream_ochat_response():
diff --git a/api/responses.py b/api/responses.py
new file mode 100644
index 0000000..0a803d3
--- /dev/null
+++ b/api/responses.py
@@ -0,0 +1,398 @@
+"""OpenAI **Responses API** routes (``/v1/responses`` and its retrieve / delete /
+cancel companions).
+
+The router speaks Chat Completions to its backends, so this layer:
+
+  * **native** (external OpenAI): forwards via ``oclient.responses.create`` and
+    streams the SDK's typed events straight back, rewriting the response ``id`` to
+    a router-owned ``resp_`` id so chaining stays router-managed.
+  * **translated** (Ollama / llama-server): converts the request to chat, reuses
+    the resilient ``create_chat_with_retries`` ladder, and re-emits the result as
+    Responses typed SSE events (``requests/responses.py``).
+
+State (``store`` / ``previous_response_id``) and background-task status live in the
+router's SQLite DB (``db.py``); the router mints and owns every response id.
+"""
+import asyncio
+import secrets
+import time
+
+import orjson
+from fastapi import APIRouter, HTTPException, Request
+from starlette.responses import JSONResponse, StreamingResponse
+
+from cache import get_llm_cache
+from config import get_config
+from db import get_db
+from fingerprint import _conversation_fingerprint
+from state import token_queue, default_headers
+from backends.normalize import is_ext_openai_endpoint
+from backends.sessions import _make_openai_client
+from routing import choose_endpoint, decrement_usage
+from api.openai import create_chat_with_retries
+from requests.responses import (
+    ChatToResponsesStream,
+    build_response_object,
+    chat_message_to_output_items,
+    messages_to_responses_input,
+    responses_input_to_messages,
+    responses_object_to_sse,
+    tools_responses_to_chat,
+    usage_chat_to_responses,
+)
+
+router = APIRouter()
+
+# In-memory handles for background tasks so /cancel can reach a running task in
+# this worker. Cross-worker cancel falls back to marking the DB row cancelled.
+_background_tasks: dict[str, asyncio.Task] = {}
+
+
+# ---------------------------------------------------------------------------
+# small helpers
+# ---------------------------------------------------------------------------
+def _usage_tokens(usage):
+    """Return ``(prompt, completion)`` tokens from a chat- or responses-shaped usage."""
+    if not usage:
+        return 0, 0
+    if "input_tokens" in usage:
+        return usage.get("input_tokens", 0) or 0, usage.get("output_tokens", 0) or 0
+    return usage.get("prompt_tokens", 0) or 0, usage.get("completion_tokens", 0) or 0
+
+
+def _text_format_to_response_format(text):
+    """Map Responses ``text.format`` → Chat Completions ``response_format`` (best effort)."""
+    if not isinstance(text, dict):
+        return None
+    fmt = text.get("format")
+    if not isinstance(fmt, dict):
+        return None
+    ftype = fmt.get("type")
+    if ftype == "json_object":
+        return {"type": "json_object"}
+    if ftype == "json_schema":
+        return {"type": "json_schema", "json_schema": {
+            k: fmt[k] for k in ("name", "schema", "strict", "description") if k in fmt
+        }}
+    return None
+
+
+def _native_usage_from_response(data):
+    return data.get("usage")
+
+
+async def _resolve_history_messages(previous_response_id):
+    """Rebuild prior-turn chat messages from the stored response chain."""
+    if not previous_response_id:
+        return []
+    db = get_db()
+    chain = await db.get_response_chain(previous_response_id)
+    messages = []
+    for turn in chain:
+        # Each turn stored the chat messages that produced it + its output items.
+        for m in turn.get("input_messages") or []:
+            messages.append(m)
+        for item in turn.get("output_items") or []:
+            if item.get("type") == "message":
+                text = "".join(
+                    p.get("text", "") for p in item.get("content") or []
+                    if p.get("type") == "output_text"
+                )
+                if text:
+                    messages.append({"role": "assistant", "content": text})
+            elif item.get("type") == "function_call":
+                messages.append({
+                    "role": "assistant", "content": None,
+                    "tool_calls": [{"id": item.get("call_id"), "type": "function",
+                                    "function": {"name": item.get("name"),
+                                                 "arguments": item.get("arguments", "")}}],
+                })
+    return messages
+
+
+class _NativeStream:
+    """Re-emit an SDK Responses event stream, rewriting the response id and
+    capturing the final output/usage for storage."""
+
+    def __init__(self, response_id):
+        self.response_id = response_id
+        self.output_items = []
+        self.usage = None
+
+    async def events(self, sdk_gen):
+        async for event in sdk_gen:
+            data = event.model_dump() if hasattr(event, "model_dump") else event
+            etype = data.get("type", "")
+            resp = data.get("response")
+            if isinstance(resp, dict) and resp.get("id"):
+                resp["id"] = self.response_id
+            if etype in ("response.completed", "response.incomplete", "response.failed") \
+                    and isinstance(resp, dict):
+                self.output_items = resp.get("output", []) or []
+                self.usage = resp.get("usage")
+            yield f"event: {etype}\ndata: {orjson.dumps(data).decode('utf-8')}\n\n".encode("utf-8")
+
+
+# ---------------------------------------------------------------------------
+# backend execution (non-streaming, used by background + non-stream sync)
+# ---------------------------------------------------------------------------
+async def _run_to_completion(*, native, oclient, endpoint, model, tracking_model,
+                             send_params, native_params):
+    """Drive the backend to completion (no client streaming).
+
+    Returns ``(output_items, usage)`` where usage is responses-shaped. Caller is
+    responsible for ``decrement_usage`` (translated failures self-decrement inside
+    ``create_chat_with_retries``)."""
+    if native:
+        resp_obj = await oclient.responses.create(stream=False, **native_params)
+        data = resp_obj.model_dump()
+        return data.get("output", []) or [], data.get("usage")
+    async_gen = await create_chat_with_retries(oclient, {**send_params, "stream": False},
+                                               endpoint, model, tracking_model)
+    message = async_gen.choices[0].message.model_dump() if async_gen.choices else {}
+    output_items = chat_message_to_output_items(message)
+    usage = usage_chat_to_responses(
+        async_gen.usage.model_dump() if async_gen.usage is not None else None
+    )
+    return output_items, usage
+
+
+# ---------------------------------------------------------------------------
+# POST /v1/responses
+# ---------------------------------------------------------------------------
+@router.post("/v1/responses")
+async def openai_responses_proxy(request: Request):
+    config = get_config()
+    try:
+        payload = orjson.loads((await request.body()).decode("utf-8"))
+    except orjson.JSONDecodeError as e:
+        raise HTTPException(status_code=400, detail=f"Invalid JSON: {e}") from e
+
+    model = payload.get("model")
+    input_data = payload.get("input")
+    instructions = payload.get("instructions")
+    stream = bool(payload.get("stream"))
+    store = payload.get("store", True)
+    background = bool(payload.get("background"))
+    previous_response_id = payload.get("previous_response_id")
+    tools = payload.get("tools")
+    metadata = payload.get("metadata") or {}
+    _cache_enabled = payload.get("nomyo", {}).get("cache", False)
+
+    if not model:
+        raise HTTPException(status_code=400, detail="Missing required field 'model'")
+    if input_data is None:
+        raise HTTPException(status_code=400, detail="Missing required field 'input'")
+    if background and not store:
+        raise HTTPException(status_code=400, detail="background mode requires store=true")
+
+    if ":latest" in model:
+        model = model.split(":latest")[0]
+
+    # Resolve conversation: prior turns (from store) + this turn's input.
+    history = await _resolve_history_messages(previous_response_id)
+    messages = history + responses_input_to_messages(input_data, instructions)
+
+    response_id = f"resp_{secrets.token_hex(24)}"
+    created_at = int(time.time())
+
+    # Cache lookup (foreground only) — before endpoint selection.
+    _cache = get_llm_cache()
+    if _cache is not None and _cache_enabled and not background:
+        cached = await _cache.get_chat("openai_responses", model, messages)
+        if cached is not None:
+            resp_obj = orjson.loads(cached)
+            resp_obj["id"] = response_id
+            if stream:
+                async def _served_cached():
+                    yield responses_object_to_sse(resp_obj)
+                return StreamingResponse(_served_cached(), media_type="text/event-stream")
+            return JSONResponse(content=resp_obj)
+
+    # Endpoint selection (reserves a slot — must be released exactly once).
+    _affinity_key = _conversation_fingerprint(model, messages, None)
+    endpoint, tracking_model = await choose_endpoint(model, affinity_key=_affinity_key)
+    oclient = _make_openai_client(endpoint, default_headers=default_headers,
+                                  api_key=config.api_keys.get(endpoint, "no-key"))
+    native = is_ext_openai_endpoint(endpoint)
+
+    # Build backend params for both shapes.
+    send_params = {"messages": messages, "model": model}
+    _opt = {
+        "temperature": payload.get("temperature"),
+        "top_p": payload.get("top_p"),
+        "max_tokens": payload.get("max_output_tokens"),
+        "tools": tools_responses_to_chat(tools),
+        "tool_choice": payload.get("tool_choice"),
+        "response_format": _text_format_to_response_format(payload.get("text")),
+    }
+    send_params.update({k: v for k, v in _opt.items() if v is not None})
+
+    native_instructions, native_input = messages_to_responses_input(messages)
+    native_params = {"model": model, "input": native_input, "store": False}
+    _nopt = {
+        "instructions": native_instructions,
+        "temperature": payload.get("temperature"),
+        "top_p": payload.get("top_p"),
+        "max_output_tokens": payload.get("max_output_tokens"),
+        "tools": tools,
+        "tool_choice": payload.get("tool_choice"),
+        "text": payload.get("text"),
+        "reasoning": payload.get("reasoning"),
+    }
+    native_params.update({k: v for k, v in _nopt.items() if v is not None})
+
+    async def _persist(status, output_items=None, usage=None, error=None, insert=False):
+        if not store:
+            return
+        db = get_db()
+        if insert:
+            await db.store_response(
+                response_id, previous_response_id=previous_response_id, model=model,
+                status=status, created_at=created_at, input_messages=messages,
+                output_items=output_items, usage=usage, instructions=instructions, error=error)
+        else:
+            await db.update_response_status(response_id, status, output_items=output_items,
+                                            usage=usage, error=error)
+
+    async def _track(usage):
+        prompt_tok, comp_tok = _usage_tokens(usage)
+        if prompt_tok or comp_tok:
+            await token_queue.put((endpoint, tracking_model, prompt_tok, comp_tok))
+
+    async def _cache_store(output_items, usage):
+        if _cache is None or not _cache_enabled or not output_items:
+            return
+        obj = build_response_object(response_id=response_id, model=model,
+                                    output_items=output_items, usage=usage,
+                                    created_at=created_at,
+                                    previous_response_id=previous_response_id,
+                                    instructions=instructions, metadata=metadata)
+        try:
+            await _cache.set_chat("openai_responses", model, messages, orjson.dumps(obj))
+        except Exception as _ce:
+            print(f"[cache] set_chat (openai_responses) failed: {_ce}")
+
+    # ---- background: run detached, return queued immediately --------------
+    if background:
+        await _persist("queued", insert=True)
+
+        async def _bg_run():
+            try:
+                await get_db().update_response_status(response_id, "in_progress")
+                output_items, usage = await _run_to_completion(
+                    native=native, oclient=oclient, endpoint=endpoint, model=model,
+                    tracking_model=tracking_model, send_params=send_params,
+                    native_params=native_params)
+                await _track(usage)
+                await _persist("completed", output_items=output_items, usage=usage)
+                await _cache_store(output_items, usage)
+            except asyncio.CancelledError:
+                await get_db().update_response_status(response_id, "cancelled")
+                raise
+            except Exception as e:
+                await get_db().update_response_status(
+                    response_id, "failed",
+                    error={"message": str(e)[:500], "type": type(e).__name__})
+            finally:
+                await decrement_usage(endpoint, tracking_model)
+                _background_tasks.pop(response_id, None)
+
+        task = asyncio.create_task(_bg_run())
+        _background_tasks[response_id] = task
+        queued = build_response_object(response_id=response_id, model=model, output_items=[],
+                                       status="queued", created_at=created_at,
+                                       previous_response_id=previous_response_id,
+                                       instructions=instructions, metadata=metadata)
+        return JSONResponse(content=queued, status_code=200)
+
+    # ---- streaming sync ----------------------------------------------------
+    if stream:
+        if native:
+            source = await oclient.responses.create(stream=True, **native_params)
+            translator = _NativeStream(response_id)
+        else:
+            source = await create_chat_with_retries(
+                oclient, {**send_params, "stream": True,
+                          "stream_options": {"include_usage": True}},
+                endpoint, model, tracking_model)
+            translator = ChatToResponsesStream(
+                response_id, model, created_at=created_at,
+                previous_response_id=previous_response_id, instructions=instructions,
+                metadata=metadata)
+
+        async def _stream():
+            await _persist("in_progress", insert=True)
+            try:
+                async for sse in translator.events(source):
+                    yield sse
+                await _track(translator.usage)
+                await _persist("completed", output_items=translator.output_items,
+                               usage=translator.usage)
+                await _cache_store(translator.output_items, translator.usage)
+            finally:
+                await decrement_usage(endpoint, tracking_model)
+
+        return StreamingResponse(_stream(), media_type="text/event-stream")
+
+    # ---- non-streaming sync ------------------------------------------------
+    try:
+        output_items, usage = await _run_to_completion(
+            native=native, oclient=oclient, endpoint=endpoint, model=model,
+            tracking_model=tracking_model, send_params=send_params,
+            native_params=native_params)
+        await _track(usage)
+        await _persist("completed", output_items=output_items, usage=usage, insert=True)
+        await _cache_store(output_items, usage)
+    finally:
+        await decrement_usage(endpoint, tracking_model)
+
+    resp_obj = build_response_object(
+        response_id=response_id, model=model, output_items=output_items, usage=usage,
+        created_at=created_at, previous_response_id=previous_response_id,
+        instructions=instructions, metadata=metadata)
+    return JSONResponse(content=resp_obj)
+
+
+# ---------------------------------------------------------------------------
+# GET / DELETE / cancel
+# ---------------------------------------------------------------------------
+def _stored_to_response_object(row):
+    return build_response_object(
+        response_id=row["response_id"], model=row.get("model"),
+        output_items=row.get("output_items") or [], usage=row.get("usage"),
+        status=row.get("status") or "completed", created_at=row.get("created_at"),
+        previous_response_id=row.get("previous_response_id"),
+        instructions=row.get("instructions"), error=row.get("error"))
+
+
+@router.get("/v1/responses/{response_id}")
+async def get_response(response_id: str):
+    row = await get_db().get_response(response_id)
+    if row is None:
+        raise HTTPException(status_code=404, detail=f"Response '{response_id}' not found")
+    return JSONResponse(content=_stored_to_response_object(row))
+
+
+@router.delete("/v1/responses/{response_id}")
+async def delete_response(response_id: str):
+    deleted = await get_db().delete_response(response_id)
+    if not deleted:
+        raise HTTPException(status_code=404, detail=f"Response '{response_id}' not found")
+    return JSONResponse(content={"id": response_id, "object": "response.deleted", "deleted": True})
+
+
+@router.post("/v1/responses/{response_id}/cancel")
+async def cancel_response(response_id: str):
+    row = await get_db().get_response(response_id)
+    if row is None:
+        raise HTTPException(status_code=404, detail=f"Response '{response_id}' not found")
+    # Cancel the running task if it lives in this worker; otherwise just mark the
+    # DB row so a polling client sees a terminal state (cross-worker limitation).
+    task = _background_tasks.get(response_id)
+    if task is not None and not task.done():
+        task.cancel()
+    elif row.get("status") in ("queued", "in_progress"):
+        await get_db().update_response_status(response_id, "cancelled")
+    row = await get_db().get_response(response_id)
+    return JSONResponse(content=_stored_to_response_object(row))
diff --git a/db.py b/db.py
index 3621144..03a5393 100644
--- a/db.py
+++ b/db.py
@@ -1,4 +1,4 @@
-import aiosqlite, asyncio
+import aiosqlite, asyncio, orjson
 from typing import Optional
 from pathlib import Path
 from datetime import datetime, timezone
@@ -75,6 +75,24 @@ class TokenDatabase:
             ''')
             await db.execute('CREATE INDEX IF NOT EXISTS idx_token_time_series_timestamp ON token_time_series(timestamp)')
             await db.execute('CREATE INDEX IF NOT EXISTS idx_token_time_series_model_ts ON token_time_series(model, timestamp)')
+            # Responses API state — the router owns conversation state for the
+            # /v1/responses family (store / previous_response_id) and tracks
+            # background-task status here so polling survives across workers.
+            await db.execute('''
+                CREATE TABLE IF NOT EXISTS stored_responses (
+                    response_id TEXT PRIMARY KEY,
+                    previous_response_id TEXT,
+                    model TEXT,
+                    status TEXT,
+                    created_at INTEGER,
+                    input_messages TEXT,
+                    output_items TEXT,
+                    usage TEXT,
+                    instructions TEXT,
+                    error TEXT
+                )
+            ''')
+            await db.execute('CREATE INDEX IF NOT EXISTS idx_stored_responses_prev ON stored_responses(previous_response_id)')
             await db.commit()
 
     async def update_token_counts(self, endpoint: str, model: str, input_tokens: int, output_tokens: int):
@@ -319,3 +337,158 @@ class TokenDatabase:
             await db.commit()
 
         return aggregated_count
+
+    # -----------------------------------------------------------------
+    # Responses API state (store / previous_response_id / background)
+    # -----------------------------------------------------------------
+    @staticmethod
+    def _row_to_response(row) -> dict:
+        """Map a stored_responses row to a plain dict, decoding JSON columns."""
+        def _loads(val):
+            if val is None:
+                return None
+            try:
+                return orjson.loads(val)
+            except (orjson.JSONDecodeError, TypeError):
+                return None
+        return {
+            'response_id': row[0],
+            'previous_response_id': row[1],
+            'model': row[2],
+            'status': row[3],
+            'created_at': row[4],
+            'input_messages': _loads(row[5]),
+            'output_items': _loads(row[6]),
+            'usage': _loads(row[7]),
+            'instructions': row[8],
+            'error': _loads(row[9]),
+        }
+
+    async def store_response(
+        self,
+        response_id: str,
+        *,
+        previous_response_id: Optional[str],
+        model: str,
+        status: str,
+        created_at: int,
+        input_messages: list,
+        output_items: Optional[list] = None,
+        usage: Optional[dict] = None,
+        instructions: Optional[str] = None,
+        error: Optional[dict] = None,
+    ):
+        """Insert or replace a stored Responses-API response row."""
+        db = await self._get_connection()
+        async with self._operation_lock:
+            await db.execute('''
+                INSERT INTO stored_responses
+                    (response_id, previous_response_id, model, status, created_at,
+                     input_messages, output_items, usage, instructions, error)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                ON CONFLICT (response_id) DO UPDATE SET
+                    previous_response_id = excluded.previous_response_id,
+                    model = excluded.model,
+                    status = excluded.status,
+                    created_at = excluded.created_at,
+                    input_messages = excluded.input_messages,
+                    output_items = excluded.output_items,
+                    usage = excluded.usage,
+                    instructions = excluded.instructions,
+                    error = excluded.error
+            ''', (
+                response_id, previous_response_id, model, status, created_at,
+                orjson.dumps(input_messages).decode("utf-8"),
+                orjson.dumps(output_items).decode("utf-8") if output_items is not None else None,
+                orjson.dumps(usage).decode("utf-8") if usage is not None else None,
+                instructions,
+                orjson.dumps(error).decode("utf-8") if error is not None else None,
+            ))
+            await db.commit()
+
+    async def update_response_status(
+        self,
+        response_id: str,
+        status: str,
+        *,
+        output_items: Optional[list] = None,
+        usage: Optional[dict] = None,
+        error: Optional[dict] = None,
+    ):
+        """Update the status (and optionally output/usage/error) of a stored response."""
+        db = await self._get_connection()
+        async with self._operation_lock:
+            await db.execute('''
+                UPDATE stored_responses
+                SET status = ?,
+                    output_items = COALESCE(?, output_items),
+                    usage = COALESCE(?, usage),
+                    error = COALESCE(?, error)
+                WHERE response_id = ?
+            ''', (
+                status,
+                orjson.dumps(output_items).decode("utf-8") if output_items is not None else None,
+                orjson.dumps(usage).decode("utf-8") if usage is not None else None,
+                orjson.dumps(error).decode("utf-8") if error is not None else None,
+                response_id,
+            ))
+            await db.commit()
+
+    async def get_response(self, response_id: str) -> Optional[dict]:
+        """Return a stored response as a dict, or None if not found."""
+        db = await self._get_connection()
+        async with self._operation_lock:
+            async with db.execute('''
+                SELECT response_id, previous_response_id, model, status, created_at,
+                       input_messages, output_items, usage, instructions, error
+                FROM stored_responses WHERE response_id = ?
+            ''', (response_id,)) as cursor:
+                row = await cursor.fetchone()
+        return self._row_to_response(row) if row is not None else None
+
+    async def delete_response(self, response_id: str) -> bool:
+        """Delete a stored response. Returns True if a row was removed."""
+        db = await self._get_connection()
+        async with self._operation_lock:
+            cursor = await db.execute(
+                'DELETE FROM stored_responses WHERE response_id = ?', (response_id,)
+            )
+            await db.commit()
+            return cursor.rowcount > 0
+
+    async def get_response_chain(self, response_id: str, max_turns: int = 50) -> list:
+        """Walk previous_response_id back to the root, returned oldest-first.
+
+        Bounded to ``max_turns`` so a pathological chain cannot stall a request.
+        Missing links terminate the walk gracefully.
+        """
+        chain: list = []
+        seen: set = set()
+        current = response_id
+        while current and current not in seen and len(chain) < max_turns:
+            seen.add(current)
+            resp = await self.get_response(current)
+            if resp is None:
+                break
+            chain.append(resp)
+            current = resp.get('previous_response_id')
+        chain.reverse()
+        return chain
+
+    async def fail_orphaned_responses(self) -> int:
+        """Mark non-terminal responses as failed (called on startup).
+
+        A background task lives in a worker's event loop; a process restart loses
+        it while the DB row stays ``queued``/``in_progress`` forever. Reconcile
+        those to ``failed`` so polling clients get a terminal state.
+        """
+        db = await self._get_connection()
+        async with self._operation_lock:
+            cursor = await db.execute('''
+                UPDATE stored_responses
+                SET status = 'failed',
+                    error = ?
+                WHERE status IN ('queued', 'in_progress')
+            ''', (orjson.dumps({"message": "Response interrupted by server restart", "type": "server_error"}).decode("utf-8"),))
+            await db.commit()
+            return cursor.rowcount
diff --git a/requests/responses.py b/requests/responses.py
new file mode 100644
index 0000000..907dc42
--- /dev/null
+++ b/requests/responses.py
@@ -0,0 +1,492 @@
+"""Translation between the OpenAI **Responses API** and **Chat Completions**.
+
+The router speaks Chat Completions to every backend (Ollama, llama-server,
+external OpenAI). To expose ``/v1/responses`` transparently on top of that, this
+module converts in both directions:
+
+  * request:  Responses ``input`` / ``instructions`` / ``tools``  → chat ``messages`` / ``tools``
+  * response: chat ``choices[0].message``                          → Responses ``output`` items
+  * stream:   chat completion deltas                               → Responses typed SSE events
+
+Pure functions / a stream-translator class — no I/O, mirroring the style of
+``requests/messages.py``. The native passthrough path (external OpenAI) does not
+use this module; it forwards the SDK's Responses objects directly.
+"""
+import secrets
+import time
+
+import orjson
+
+from requests.messages import _accumulate_openai_tc_delta
+
+
+# ---------------------------------------------------------------------------
+# Request direction: Responses → Chat Completions
+# ---------------------------------------------------------------------------
+def _responses_content_to_chat(content):
+    """Convert a Responses message ``content`` into Chat Completions content.
+
+    Collapses a single text part to a plain string (what most backends expect);
+    keeps a multimodal list otherwise.
+    """
+    if content is None or isinstance(content, str):
+        return content
+    if not isinstance(content, list):
+        return str(content)
+    parts = []
+    for p in content:
+        if not isinstance(p, dict):
+            parts.append({"type": "text", "text": str(p)})
+            continue
+        ptype = p.get("type")
+        if ptype in ("input_text", "output_text", "text"):
+            parts.append({"type": "text", "text": p.get("text", "")})
+        elif ptype in ("input_image", "image_url"):
+            url = p.get("image_url")
+            if isinstance(url, dict):
+                url = url.get("url")
+            if url:
+                parts.append({"type": "image_url", "image_url": {"url": url}})
+        # input_file / refusal / reasoning parts have no chat equivalent → skip
+    if len(parts) == 1 and parts[0].get("type") == "text":
+        return parts[0]["text"]
+    return parts
+
+
+def _input_item_to_message(item):
+    """Convert a single Responses ``input`` item to a chat message (or None)."""
+    if isinstance(item, str):
+        return {"role": "user", "content": item}
+    if not isinstance(item, dict):
+        return None
+
+    itype = item.get("type")
+
+    if itype == "function_call":
+        return {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [{
+                "id": item.get("call_id") or item.get("id"),
+                "type": "function",
+                "function": {
+                    "name": item.get("name"),
+                    "arguments": item.get("arguments", ""),
+                },
+            }],
+        }
+
+    if itype == "function_call_output":
+        output = item.get("output", "")
+        if not isinstance(output, str):
+            output = orjson.dumps(output).decode("utf-8")
+        return {
+            "role": "tool",
+            "tool_call_id": item.get("call_id") or item.get("id"),
+            "content": output,
+        }
+
+    if itype in ("reasoning",):
+        # No Chat Completions equivalent — drop.
+        return None
+
+    # "message" item or a bare {role, content} chat-style item
+    role = item.get("role")
+    if role is None:
+        return None
+    return {"role": role, "content": _responses_content_to_chat(item.get("content"))}
+
+
+def responses_input_to_messages(input_data, instructions=None):
+    """Build a Chat Completions ``messages`` list from Responses ``input``.
+
+    ``instructions`` becomes a leading system message; a string ``input`` becomes
+    a single user message; a list ``input`` is mapped item-by-item.
+    """
+    messages = []
+    if instructions:
+        messages.append({"role": "system", "content": instructions})
+    if input_data is None:
+        return messages
+    if isinstance(input_data, str):
+        messages.append({"role": "user", "content": input_data})
+        return messages
+    if isinstance(input_data, list):
+        for item in input_data:
+            msg = _input_item_to_message(item)
+            if msg is not None:
+                messages.append(msg)
+    return messages
+
+
+def _chat_content_to_responses_parts(content, assistant=False):
+    """Convert chat message content → Responses content parts."""
+    text_type = "output_text" if assistant else "input_text"
+    if content is None:
+        return []
+    if isinstance(content, str):
+        return [{"type": text_type, "text": content}]
+    parts = []
+    for p in content if isinstance(content, list) else []:
+        if not isinstance(p, dict):
+            parts.append({"type": text_type, "text": str(p)})
+        elif p.get("type") == "text":
+            parts.append({"type": text_type, "text": p.get("text", "")})
+        elif p.get("type") == "image_url":
+            url = (p.get("image_url") or {}).get("url")
+            if url:
+                parts.append({"type": "input_image", "image_url": url})
+    return parts
+
+
+def messages_to_responses_input(messages):
+    """Convert chat messages → ``(instructions, Responses input items)``.
+
+    Used for the native passthrough path: history that the router has resolved in
+    chat-message space is re-expressed as Responses ``input``. Leading/standalone
+    system messages are merged into ``instructions``.
+    """
+    instructions_parts = []
+    items = []
+    for m in messages:
+        role = m.get("role")
+        if role == "system":
+            c = m.get("content")
+            instructions_parts.append(c if isinstance(c, str) else orjson.dumps(c).decode("utf-8"))
+            continue
+        if role == "tool":
+            out = m.get("content")
+            if not isinstance(out, str):
+                out = orjson.dumps(out).decode("utf-8")
+            items.append({"type": "function_call_output",
+                          "call_id": m.get("tool_call_id"), "output": out})
+            continue
+        if role == "assistant" and m.get("tool_calls"):
+            for tc in m["tool_calls"]:
+                fn = tc.get("function", {})
+                items.append({"type": "function_call", "call_id": tc.get("id"),
+                              "name": fn.get("name"), "arguments": fn.get("arguments", "")})
+            if m.get("content"):
+                items.append({"role": "assistant",
+                              "content": _chat_content_to_responses_parts(m["content"], assistant=True)})
+            continue
+        items.append({"role": role,
+                      "content": _chat_content_to_responses_parts(m.get("content"),
+                                                                  assistant=(role == "assistant"))})
+    instructions = "\n\n".join(p for p in instructions_parts if p) or None
+    return instructions, items
+
+
+def responses_object_to_sse(resp):
+    """Render a *finished* Responses object as a valid SSE event stream.
+
+    Used to serve cache/store hits to streaming clients without a backend call.
+    """
+    seq = [-1]
+
+    def ev(etype, payload):
+        seq[0] += 1
+        body = {"type": etype, "sequence_number": seq[0], **payload}
+        return f"event: {etype}\ndata: {orjson.dumps(body).decode('utf-8')}\n\n".encode("utf-8")
+
+    parts_out = []
+    in_progress = {**resp, "status": "in_progress", "output": [], "output_text": ""}
+    parts_out.append(ev("response.created", {"response": in_progress}))
+    parts_out.append(ev("response.in_progress", {"response": in_progress}))
+    for oi, item in enumerate(resp.get("output", [])):
+        parts_out.append(ev("response.output_item.added",
+                            {"output_index": oi, "item": {**item, "status": "in_progress"}}))
+        if item.get("type") == "message":
+            for ci, part in enumerate(item.get("content", [])):
+                if part.get("type") == "output_text":
+                    iid = item.get("id")
+                    parts_out.append(ev("response.content_part.added", {
+                        "item_id": iid, "output_index": oi, "content_index": ci,
+                        "part": {"type": "output_text", "text": "", "annotations": []}}))
+                    parts_out.append(ev("response.output_text.delta", {
+                        "item_id": iid, "output_index": oi, "content_index": ci,
+                        "delta": part.get("text", "")}))
+                    parts_out.append(ev("response.output_text.done", {
+                        "item_id": iid, "output_index": oi, "content_index": ci,
+                        "text": part.get("text", "")}))
+                    parts_out.append(ev("response.content_part.done", {
+                        "item_id": iid, "output_index": oi, "content_index": ci, "part": part}))
+        parts_out.append(ev("response.output_item.done", {"output_index": oi, "item": item}))
+    parts_out.append(ev("response.completed", {"response": resp}))
+    return b"".join(parts_out)
+
+
+def tools_responses_to_chat(tools):
+    """Map Responses tool definitions (flattened) → Chat Completions (nested)."""
+    if not tools:
+        return None
+    out = []
+    for t in tools:
+        if isinstance(t, dict) and t.get("type") == "function" and "function" not in t:
+            fn = {k: t[k] for k in ("name", "description", "parameters", "strict") if k in t}
+            out.append({"type": "function", "function": fn})
+        else:
+            out.append(t)
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Response direction: Chat Completions → Responses
+# ---------------------------------------------------------------------------
+def _new_id(prefix):
+    return f"{prefix}_{secrets.token_hex(16)}"
+
+
+def chat_message_to_output_items(message):
+    """Convert an assistant chat message (dict) into Responses output items."""
+    items = []
+    content = message.get("content")
+    if content:
+        items.append({
+            "type": "message",
+            "id": _new_id("msg"),
+            "status": "completed",
+            "role": "assistant",
+            "content": [{"type": "output_text", "text": content, "annotations": []}],
+        })
+    for tc in message.get("tool_calls") or []:
+        fn = tc.get("function", {})
+        items.append({
+            "type": "function_call",
+            "id": _new_id("fc"),
+            "call_id": tc.get("id"),
+            "name": fn.get("name"),
+            "arguments": fn.get("arguments", ""),
+            "status": "completed",
+        })
+    return items
+
+
+def usage_chat_to_responses(usage):
+    """Map chat usage ``{prompt_tokens, completion_tokens}`` → Responses usage."""
+    if not usage:
+        return None
+    prompt = usage.get("prompt_tokens") or 0
+    completion = usage.get("completion_tokens") or 0
+    return {
+        "input_tokens": prompt,
+        "output_tokens": completion,
+        "total_tokens": usage.get("total_tokens") or (prompt + completion),
+    }
+
+
+def output_items_to_text(output_items):
+    """Concatenate the ``output_text`` parts of all message items."""
+    chunks = []
+    for item in output_items or []:
+        if item.get("type") != "message":
+            continue
+        for part in item.get("content") or []:
+            if part.get("type") == "output_text":
+                chunks.append(part.get("text", ""))
+    return "".join(chunks)
+
+
+def build_response_object(
+    *,
+    response_id,
+    model,
+    output_items=None,
+    usage=None,
+    status="completed",
+    created_at=None,
+    previous_response_id=None,
+    instructions=None,
+    error=None,
+    metadata=None,
+):
+    """Assemble a full ``object:"response"`` body for a non-streaming reply."""
+    output_items = output_items or []
+    return {
+        "id": response_id,
+        "object": "response",
+        "created_at": created_at or int(time.time()),
+        "status": status,
+        "model": model,
+        "output": output_items,
+        "output_text": output_items_to_text(output_items),
+        "instructions": instructions,
+        "previous_response_id": previous_response_id,
+        "usage": usage_chat_to_responses(usage) if usage and "input_tokens" not in usage else usage,
+        "error": error,
+        "metadata": metadata or {},
+    }
+
+
+# ---------------------------------------------------------------------------
+# Streaming direction: Chat Completions deltas → Responses typed SSE events
+# ---------------------------------------------------------------------------
+class ChatToResponsesStream:
+    """Translate a Chat Completions streaming generator into Responses events.
+
+    Usage::
+
+        translator = ChatToResponsesStream(response_id, model, created_at)
+        async for sse_bytes in translator.events(chat_async_gen):
+            yield sse_bytes
+        # translator.output_items / translator.usage now populated for storage
+
+    Emits the ordered event family
+    ``response.created`` → ``response.in_progress`` →
+    (``response.output_item.added`` → ``response.content_part.added`` →
+    ``response.output_text.delta``* → ``response.output_text.done`` →
+    ``response.content_part.done`` → ``response.output_item.done``) and/or
+    function-call item events → ``response.completed`` (carrying usage).
+    """
+
+    def __init__(self, response_id, model, created_at=None,
+                 previous_response_id=None, instructions=None, metadata=None):
+        self.response_id = response_id
+        self.model = model
+        self.created_at = created_at or int(time.time())
+        self.previous_response_id = previous_response_id
+        self.instructions = instructions
+        self.metadata = metadata or {}
+        self.seq = -1
+        self.output_items = []
+        self.usage = None
+
+    def _snapshot(self, status, output=None):
+        return build_response_object(
+            response_id=self.response_id,
+            model=self.model,
+            output_items=output if output is not None else [],
+            usage=self.usage,
+            status=status,
+            created_at=self.created_at,
+            previous_response_id=self.previous_response_id,
+            instructions=self.instructions,
+            metadata=self.metadata,
+        )
+
+    def _event(self, etype, payload):
+        self.seq += 1
+        body = {"type": etype, "sequence_number": self.seq, **payload}
+        return f"event: {etype}\ndata: {orjson.dumps(body).decode('utf-8')}\n\n".encode("utf-8")
+
+    async def events(self, async_gen):
+        yield self._event("response.created", {"response": self._snapshot("in_progress")})
+        yield self._event("response.in_progress", {"response": self._snapshot("in_progress")})
+
+        next_oi = 0
+        # text message state
+        msg_item_id = None
+        msg_oi = None
+        text_parts = []
+        # function-call state, keyed by chat tool_call index
+        tc_state = {}  # idx -> {oi, item_id, call_id, name, args}
+
+        async for chunk in async_gen:
+            usage = getattr(chunk, "usage", None)
+            if usage is not None:
+                self.usage = {
+                    "prompt_tokens": usage.prompt_tokens or 0,
+                    "completion_tokens": usage.completion_tokens or 0,
+                }
+            choices = getattr(chunk, "choices", None)
+            if not choices:
+                continue
+            delta = choices[0].delta
+
+            content_piece = getattr(delta, "content", None)
+            if content_piece:
+                if msg_item_id is None:
+                    msg_item_id = _new_id("msg")
+                    msg_oi = next_oi
+                    next_oi += 1
+                    item = {
+                        "id": msg_item_id, "type": "message", "status": "in_progress",
+                        "role": "assistant", "content": [],
+                    }
+                    yield self._event("response.output_item.added",
+                                      {"output_index": msg_oi, "item": item})
+                    yield self._event("response.content_part.added", {
+                        "item_id": msg_item_id, "output_index": msg_oi, "content_index": 0,
+                        "part": {"type": "output_text", "text": "", "annotations": []},
+                    })
+                text_parts.append(content_piece)
+                yield self._event("response.output_text.delta", {
+                    "item_id": msg_item_id, "output_index": msg_oi, "content_index": 0,
+                    "delta": content_piece,
+                })
+
+            for tc in getattr(delta, "tool_calls", None) or []:
+                idx = tc.index
+                fn = getattr(tc, "function", None)
+                if idx not in tc_state:
+                    item_id = _new_id("fc")
+                    state = {
+                        "oi": next_oi, "item_id": item_id,
+                        "call_id": getattr(tc, "id", None) or _new_id("call"),
+                        "name": (fn.name if fn else None), "args": "",
+                    }
+                    next_oi += 1
+                    tc_state[idx] = state
+                    yield self._event("response.output_item.added", {
+                        "output_index": state["oi"],
+                        "item": {
+                            "id": item_id, "type": "function_call", "status": "in_progress",
+                            "call_id": state["call_id"], "name": state["name"], "arguments": "",
+                        },
+                    })
+                else:
+                    state = tc_state[idx]
+                    if getattr(tc, "id", None):
+                        state["call_id"] = tc.id
+                    if fn and fn.name:
+                        state["name"] = fn.name
+                if fn and fn.arguments:
+                    state["args"] += fn.arguments
+                    yield self._event("response.function_call_arguments.delta", {
+                        "item_id": state["item_id"], "output_index": state["oi"],
+                        "delta": fn.arguments,
+                    })
+
+        # finalize message item
+        if msg_item_id is not None:
+            full_text = "".join(text_parts)
+            yield self._event("response.output_text.done", {
+                "item_id": msg_item_id, "output_index": msg_oi, "content_index": 0,
+                "text": full_text,
+            })
+            done_part = {"type": "output_text", "text": full_text, "annotations": []}
+            yield self._event("response.content_part.done", {
+                "item_id": msg_item_id, "output_index": msg_oi, "content_index": 0,
+                "part": done_part,
+            })
+            msg_item = {
+                "id": msg_item_id, "type": "message", "status": "completed",
+                "role": "assistant", "content": [done_part],
+            }
+            yield self._event("response.output_item.done",
+                              {"output_index": msg_oi, "item": msg_item})
+
+        # finalize function-call items (in output-index order)
+        tc_items = {}
+        for idx, state in tc_state.items():
+            yield self._event("response.function_call_arguments.done", {
+                "item_id": state["item_id"], "output_index": state["oi"],
+                "arguments": state["args"],
+            })
+            fc_item = {
+                "id": state["item_id"], "type": "function_call", "status": "completed",
+                "call_id": state["call_id"], "name": state["name"], "arguments": state["args"],
+            }
+            tc_items[state["oi"]] = fc_item
+            yield self._event("response.output_item.done",
+                              {"output_index": state["oi"], "item": fc_item})
+
+        # assemble final output items ordered by output index
+        ordered = []
+        if msg_item_id is not None:
+            ordered.append((msg_oi, msg_item))
+        ordered.extend(tc_items.items())
+        self.output_items = [item for _, item in sorted(ordered, key=lambda kv: kv[0])]
+
+        yield self._event("response.completed",
+                          {"response": self._snapshot("completed", self.output_items)})
diff --git a/router.py b/router.py
index 676e42b..a2f9dd8 100644
--- a/router.py
+++ b/router.py
@@ -290,6 +290,8 @@ from api.management import router as management_router
 app.include_router(management_router)
 from api.openai import router as openai_router
 app.include_router(openai_router)
+from api.responses import router as responses_router
+app.include_router(responses_router)
 from api.ollama import router as ollama_router
 app.include_router(ollama_router)
 
@@ -322,6 +324,13 @@ async def startup_event() -> None:
     db = TokenDatabase(config.db_path)
     await db.init_db()
 
+    # Reconcile Responses-API background tasks lost across a restart: their
+    # in-memory asyncio task is gone but the DB row may still read queued /
+    # in_progress, so mark those failed to give polling clients a terminal state.
+    _orphaned = await db.fail_orphaned_responses()
+    if _orphaned:
+        print(f"[startup] Marked {_orphaned} orphaned background response(s) as failed.")
+
     # Load existing token counts from database
     async for count_entry in db.load_token_counts():
         endpoint = count_entry['endpoint']
diff --git a/test/test_responses.py b/test/test_responses.py
new file mode 100644
index 0000000..73d3ff2
--- /dev/null
+++ b/test/test_responses.py
@@ -0,0 +1,460 @@
+"""Tests for the OpenAI Responses API support (api/responses.py + requests/responses.py).
+
+Covers the pure translation layer, the translated (Ollama-style) and native
+(external-OpenAI) backend paths, conversation storage / chaining, background mode,
+and the retrieve / delete / cancel routes.
+"""
+import asyncio
+from contextlib import ExitStack, contextmanager
+from types import SimpleNamespace as NS
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import orjson
+import pytest
+
+import router
+from api import responses as api_responses
+from requests import responses as rt
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Pure translation unit tests (no app / no I/O)
+# ──────────────────────────────────────────────────────────────────────────────
+
+class TestTranslationInputToMessages:
+    def test_string_input(self):
+        msgs = rt.responses_input_to_messages("hello")
+        assert msgs == [{"role": "user", "content": "hello"}]
+
+    def test_instructions_become_system(self):
+        msgs = rt.responses_input_to_messages("hi", instructions="be brief")
+        assert msgs[0] == {"role": "system", "content": "be brief"}
+        assert msgs[1] == {"role": "user", "content": "hi"}
+
+    def test_item_list_text_and_image(self):
+        items = [{
+            "type": "message", "role": "user",
+            "content": [
+                {"type": "input_text", "text": "describe"},
+                {"type": "input_image", "image_url": "http://x/y.png"},
+            ],
+        }]
+        msgs = rt.responses_input_to_messages(items)
+        assert msgs[0]["role"] == "user"
+        assert msgs[0]["content"] == [
+            {"type": "text", "text": "describe"},
+            {"type": "image_url", "image_url": {"url": "http://x/y.png"}},
+        ]
+
+    def test_single_text_part_collapses_to_string(self):
+        items = [{"type": "message", "role": "user",
+                  "content": [{"type": "input_text", "text": "yo"}]}]
+        assert rt.responses_input_to_messages(items)[0]["content"] == "yo"
+
+    def test_function_call_roundtrip(self):
+        items = [
+            {"type": "function_call", "call_id": "c1", "name": "get", "arguments": "{\"x\":1}"},
+            {"type": "function_call_output", "call_id": "c1", "output": "42"},
+        ]
+        msgs = rt.responses_input_to_messages(items)
+        assert msgs[0]["role"] == "assistant"
+        assert msgs[0]["tool_calls"][0]["id"] == "c1"
+        assert msgs[0]["tool_calls"][0]["function"]["name"] == "get"
+        assert msgs[1] == {"role": "tool", "tool_call_id": "c1", "content": "42"}
+
+
+class TestTranslationResponseDirection:
+    def test_chat_message_to_output_items_text(self):
+        items = rt.chat_message_to_output_items({"role": "assistant", "content": "hi there"})
+        assert len(items) == 1
+        assert items[0]["type"] == "message"
+        assert items[0]["content"][0] == {"type": "output_text", "text": "hi there", "annotations": []}
+
+    def test_chat_message_to_output_items_tool_call(self):
+        items = rt.chat_message_to_output_items({
+            "role": "assistant", "content": None,
+            "tool_calls": [{"id": "c9", "function": {"name": "f", "arguments": "{}"}}],
+        })
+        assert items[0]["type"] == "function_call"
+        assert items[0]["call_id"] == "c9"
+        assert items[0]["name"] == "f"
+
+    def test_usage_mapping(self):
+        u = rt.usage_chat_to_responses({"prompt_tokens": 7, "completion_tokens": 3})
+        assert u == {"input_tokens": 7, "output_tokens": 3, "total_tokens": 10}
+
+    def test_build_response_object_output_text(self):
+        items = rt.chat_message_to_output_items({"role": "assistant", "content": "abc"})
+        obj = rt.build_response_object(response_id="resp_1", model="m", output_items=items)
+        assert obj["object"] == "response"
+        assert obj["output_text"] == "abc"
+        assert obj["status"] == "completed"
+
+    def test_tools_responses_to_chat(self):
+        tools = [{"type": "function", "name": "f", "description": "d", "parameters": {"type": "object"}}]
+        chat_tools = rt.tools_responses_to_chat(tools)
+        assert chat_tools == [{"type": "function",
+                               "function": {"name": "f", "description": "d",
+                                            "parameters": {"type": "object"}}}]
+
+    def test_messages_to_responses_input(self):
+        instr, items = rt.messages_to_responses_input([
+            {"role": "system", "content": "sys"},
+            {"role": "user", "content": "hi"},
+            {"role": "assistant", "content": "yo"},
+        ])
+        assert instr == "sys"
+        assert items[0] == {"role": "user", "content": [{"type": "input_text", "text": "hi"}]}
+        assert items[1] == {"role": "assistant", "content": [{"type": "output_text", "text": "yo"}]}
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Fakes for backend generators
+# ──────────────────────────────────────────────────────────────────────────────
+
+def _fake_completion(content="hello world", usage=(3, 5)):
+    msg = MagicMock()
+    msg.model_dump.return_value = {"role": "assistant", "content": content}
+    usage_obj = MagicMock()
+    usage_obj.model_dump.return_value = {
+        "prompt_tokens": usage[0], "completion_tokens": usage[1], "total_tokens": sum(usage)}
+    return NS(choices=[NS(message=msg)], usage=usage_obj)
+
+
+def _chunk(content=None, tool_calls=None):
+    return NS(choices=[NS(delta=NS(content=content, tool_calls=tool_calls),
+                          finish_reason=None)], usage=None)
+
+
+def _usage_chunk(p, c):
+    return NS(choices=[], usage=NS(prompt_tokens=p, completion_tokens=c))
+
+
+def _text_chunks():
+    async def _gen():
+        yield _chunk(content="Hel")
+        yield _chunk(content="lo")
+        yield _usage_chunk(3, 5)
+    return _gen()
+
+
+def _toolcall_chunks():
+    tc0 = NS(index=0, id="call_1", function=NS(name="lookup", arguments='{"q":'))
+    tc1 = NS(index=0, id=None, function=NS(name=None, arguments='"hi"}'))
+
+    async def _gen():
+        yield _chunk(tool_calls=[tc0])
+        yield _chunk(tool_calls=[tc1])
+        yield _usage_chunk(4, 2)
+    return _gen()
+
+
+class _FakeEvent:
+    def __init__(self, data):
+        self._data = data
+
+    def model_dump(self):
+        return self._data
+
+
+def _native_event_stream():
+    async def _gen():
+        yield _FakeEvent({"type": "response.created",
+                          "response": {"id": "resp_openai", "status": "in_progress", "output": []}})
+        yield _FakeEvent({"type": "response.output_text.delta",
+                          "item_id": "msg_1", "output_index": 0, "delta": "hi"})
+        yield _FakeEvent({"type": "response.completed", "response": {
+            "id": "resp_openai", "status": "completed",
+            "output": [{"type": "message", "role": "assistant",
+                        "content": [{"type": "output_text", "text": "hi"}]}],
+            "usage": {"input_tokens": 2, "output_tokens": 1, "total_tokens": 3}}})
+    return _gen()
+
+
+def _sse_events(text):
+    """Split an SSE body into a list of (event_type, data_dict)."""
+    out = []
+    for frame in text.strip().split("\n\n"):
+        if not frame.strip():
+            continue
+        etype = data = None
+        for line in frame.splitlines():
+            if line.startswith("event: "):
+                etype = line[len("event: "):]
+            elif line.startswith("data: "):
+                data = orjson.loads(line[len("data: "):])
+        out.append((etype, data))
+    return out
+
+
+@contextmanager
+def _enter(*cms):
+    """Enter a variable number of context managers (works with *unpacked tuples)."""
+    with ExitStack() as stack:
+        for cm in cms:
+            stack.enter_context(cm)
+        yield
+
+
+def _patch_backend(native=False, endpoint="http://ollama:11434"):
+    """Context managers patching endpoint selection + client construction."""
+    return (
+        patch.object(api_responses, "choose_endpoint",
+                     AsyncMock(return_value=(endpoint, "test-model:latest"))),
+        patch.object(api_responses, "decrement_usage", AsyncMock()),
+        patch.object(api_responses, "is_ext_openai_endpoint", return_value=native),
+        patch.object(api_responses, "_make_openai_client", return_value=MagicMock()),
+        patch.object(api_responses, "get_llm_cache", return_value=None),
+    )
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Translated path (Ollama-style backend)
+# ──────────────────────────────────────────────────────────────────────────────
+
+class TestTranslatedPath:
+    async def test_nonstream(self, client):
+        with _enter(*_patch_backend(native=False),
+              patch.object(api_responses, "create_chat_with_retries",
+                           AsyncMock(return_value=_fake_completion("hello world")))):
+            resp = await client.post("/v1/responses",
+                                     json={"model": "test-model", "input": "hi", "store": False})
+        assert resp.status_code == 200
+        body = resp.json()
+        assert body["object"] == "response"
+        assert body["output_text"] == "hello world"
+        assert body["usage"] == {"input_tokens": 3, "output_tokens": 5, "total_tokens": 8}
+        assert body["id"].startswith("resp_")
+
+    async def test_stream_event_sequence(self, client):
+        with _enter(*_patch_backend(native=False),
+              patch.object(api_responses, "create_chat_with_retries",
+                           AsyncMock(return_value=_text_chunks()))):
+            resp = await client.post("/v1/responses",
+                                     json={"model": "test-model", "input": "hi",
+                                           "stream": True, "store": False})
+        assert resp.status_code == 200
+        assert resp.headers["content-type"].startswith("text/event-stream")
+        events = _sse_events(resp.content.decode())
+        types = [e[0] for e in events]
+        assert types[0] == "response.created"
+        assert "response.output_text.delta" in types
+        assert types[-1] == "response.completed"
+        # concatenated deltas reconstruct the content
+        deltas = "".join(d["delta"] for t, d in events if t == "response.output_text.delta")
+        assert deltas == "Hello"
+        # completed event carries usage
+        completed = [d for t, d in events if t == "response.completed"][0]
+        assert completed["response"]["usage"]["input_tokens"] == 3
+
+    async def test_stream_tool_calls(self, client):
+        with _enter(*_patch_backend(native=False),
+              patch.object(api_responses, "create_chat_with_retries",
+                           AsyncMock(return_value=_toolcall_chunks()))):
+            resp = await client.post("/v1/responses",
+                                     json={"model": "test-model", "input": "lookup hi",
+                                           "stream": True, "store": False})
+        events = _sse_events(resp.content.decode())
+        types = [e[0] for e in events]
+        assert "response.function_call_arguments.delta" in types
+        assert "response.function_call_arguments.done" in types
+        args = "".join(d["delta"] for t, d in events
+                       if t == "response.function_call_arguments.delta")
+        assert args == '{"q":"hi"}'
+        completed = [d for t, d in events if t == "response.completed"][0]
+        fc = [i for i in completed["response"]["output"] if i["type"] == "function_call"][0]
+        assert fc["name"] == "lookup"
+        assert fc["arguments"] == '{"q":"hi"}'
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Native path (external OpenAI backend)
+# ──────────────────────────────────────────────────────────────────────────────
+
+class TestNativePath:
+    async def test_nonstream_passthrough_rewrites_id(self, client):
+        oclient = MagicMock()
+        resp_obj = MagicMock()
+        resp_obj.model_dump.return_value = {
+            "id": "resp_openai", "status": "completed",
+            "output": [{"type": "message", "role": "assistant",
+                        "content": [{"type": "output_text", "text": "native hi"}]}],
+            "usage": {"input_tokens": 2, "output_tokens": 3, "total_tokens": 5}}
+        oclient.responses.create = AsyncMock(return_value=resp_obj)
+        with (patch.object(api_responses, "choose_endpoint",
+                           AsyncMock(return_value=("https://api.openai.com/v1", "gpt"))),
+              patch.object(api_responses, "decrement_usage", AsyncMock()),
+              patch.object(api_responses, "is_ext_openai_endpoint", return_value=True),
+              patch.object(api_responses, "_make_openai_client", return_value=oclient),
+              patch.object(api_responses, "get_llm_cache", return_value=None)):
+            resp = await client.post("/v1/responses",
+                                     json={"model": "gpt", "input": "hi", "store": False})
+        body = resp.json()
+        assert body["output_text"] == "native hi"
+        assert body["id"].startswith("resp_") and body["id"] != "resp_openai"
+        # native call must not delegate state upstream
+        assert oclient.responses.create.call_args.kwargs["store"] is False
+
+    async def test_stream_passthrough(self, client):
+        oclient = MagicMock()
+        oclient.responses.create = AsyncMock(return_value=_native_event_stream())
+        with (patch.object(api_responses, "choose_endpoint",
+                           AsyncMock(return_value=("https://api.openai.com/v1", "gpt"))),
+              patch.object(api_responses, "decrement_usage", AsyncMock()),
+              patch.object(api_responses, "is_ext_openai_endpoint", return_value=True),
+              patch.object(api_responses, "_make_openai_client", return_value=oclient),
+              patch.object(api_responses, "get_llm_cache", return_value=None)):
+            resp = await client.post("/v1/responses",
+                                     json={"model": "gpt", "input": "hi",
+                                           "stream": True, "store": False})
+        events = _sse_events(resp.content.decode())
+        # the completed event's response id is rewritten to the router id
+        completed = [d for t, d in events if t == "response.completed"][0]
+        assert completed["response"]["id"].startswith("resp_")
+        assert completed["response"]["id"] != "resp_openai"
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Storage + chaining + retrieve/delete
+# ──────────────────────────────────────────────────────────────────────────────
+
+class TestStorageAndChaining:
+    async def test_store_and_retrieve(self, client):
+        with _enter(*_patch_backend(native=False),
+              patch.object(api_responses, "create_chat_with_retries",
+                           AsyncMock(return_value=_fake_completion("remembered")))):
+            created = await client.post("/v1/responses",
+                                        json={"model": "test-model", "input": "hi", "store": True})
+        rid = created.json()["id"]
+        got = await client.get(f"/v1/responses/{rid}")
+        assert got.status_code == 200
+        assert got.json()["output_text"] == "remembered"
+
+    async def test_previous_response_id_rehydrates_history(self, client):
+        # First turn
+        with _enter(*_patch_backend(native=False),
+              patch.object(api_responses, "create_chat_with_retries",
+                           AsyncMock(return_value=_fake_completion("turn-one")))):
+            first = await client.post("/v1/responses",
+                                      json={"model": "test-model", "input": "first?", "store": True})
+        rid = first.json()["id"]
+
+        # Second turn references the first — capture the messages sent to the backend
+        capture = AsyncMock(return_value=_fake_completion("turn-two"))
+        with _enter(*_patch_backend(native=False),
+              patch.object(api_responses, "create_chat_with_retries", capture)):
+            await client.post("/v1/responses",
+                              json={"model": "test-model", "input": "second?",
+                                    "previous_response_id": rid, "store": True})
+        sent_messages = capture.call_args.args[1]["messages"]
+        contents = [m.get("content") for m in sent_messages]
+        assert "first?" in contents          # prior user turn replayed
+        assert "turn-one" in contents         # prior assistant turn replayed
+        assert "second?" in contents          # current turn appended
+
+    async def test_delete(self, client):
+        with _enter(*_patch_backend(native=False),
+              patch.object(api_responses, "create_chat_with_retries",
+                           AsyncMock(return_value=_fake_completion("bye")))):
+            created = await client.post("/v1/responses",
+                                        json={"model": "test-model", "input": "hi", "store": True})
+        rid = created.json()["id"]
+        deleted = await client.delete(f"/v1/responses/{rid}")
+        assert deleted.status_code == 200
+        assert deleted.json()["deleted"] is True
+        assert (await client.get(f"/v1/responses/{rid}")).status_code == 404
+
+    async def test_retrieve_missing_404(self, client):
+        assert (await client.get("/v1/responses/resp_missing")).status_code == 404
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Background mode
+# ──────────────────────────────────────────────────────────────────────────────
+
+class TestBackgroundMode:
+    async def test_background_requires_store(self, client):
+        resp = await client.post("/v1/responses",
+                                 json={"model": "test-model", "input": "hi",
+                                       "background": True, "store": False})
+        assert resp.status_code == 400
+
+    async def test_background_lifecycle(self, client):
+        with _enter(*_patch_backend(native=False),
+              patch.object(api_responses, "create_chat_with_retries",
+                           AsyncMock(return_value=_fake_completion("bg-done")))):
+            created = await client.post("/v1/responses",
+                                        json={"model": "test-model", "input": "hi",
+                                              "background": True, "store": True})
+            assert created.status_code == 200
+            assert created.json()["status"] == "queued"
+            rid = created.json()["id"]
+            # poll until terminal
+            status = None
+            for _ in range(100):
+                await asyncio.sleep(0.01)
+                got = await client.get(f"/v1/responses/{rid}")
+                status = got.json()["status"]
+                if status in ("completed", "failed", "cancelled"):
+                    break
+        assert status == "completed"
+        assert got.json()["output_text"] == "bg-done"
+
+    async def test_fail_orphaned_responses(self, client):
+        db = router.db
+        await db.store_response("resp_orphan", previous_response_id=None, model="m",
+                                status="in_progress", created_at=0, input_messages=[])
+        n = await db.fail_orphaned_responses()
+        assert n >= 1
+        row = await db.get_response("resp_orphan")
+        assert row["status"] == "failed"
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Cache parity
+# ──────────────────────────────────────────────────────────────────────────────
+
+class _FakeCache:
+    def __init__(self, response_bytes):
+        self._resp = response_bytes
+        self.calls = []
+
+    async def get_chat(self, route, model, messages):
+        self.calls.append((route, model, messages))
+        return self._resp
+
+
+class TestCacheParity:
+    async def test_cache_hit_served_as_response(self, client):
+        cached = orjson.dumps(rt.build_response_object(
+            response_id="resp_cached", model="test-model",
+            output_items=rt.chat_message_to_output_items(
+                {"role": "assistant", "content": "from-cache"})))
+        fake = _FakeCache(cached)
+        with (patch.object(api_responses, "get_llm_cache", return_value=fake),
+              patch.object(api_responses, "choose_endpoint",
+                           AsyncMock(side_effect=AssertionError("backend must not be reached")))):
+            resp = await client.post("/v1/responses",
+                                     json={"model": "test-model", "input": "ping",
+                                           "store": False, "nomyo": {"cache": True}})
+        assert resp.status_code == 200
+        assert resp.json()["output_text"] == "from-cache"
+        assert fake.calls and fake.calls[0][0] == "openai_responses"
+
+    async def test_cache_hit_served_as_sse(self, client):
+        cached = orjson.dumps(rt.build_response_object(
+            response_id="resp_cached", model="test-model",
+            output_items=rt.chat_message_to_output_items(
+                {"role": "assistant", "content": "from-cache"})))
+        fake = _FakeCache(cached)
+        with (patch.object(api_responses, "get_llm_cache", return_value=fake),
+              patch.object(api_responses, "choose_endpoint",
+                           AsyncMock(side_effect=AssertionError("backend must not be reached")))):
+            resp = await client.post("/v1/responses",
+                                     json={"model": "test-model", "input": "ping",
+                                           "stream": True, "store": False,
+                                           "nomyo": {"cache": True}})
+        assert resp.headers["content-type"].startswith("text/event-stream")
+        events = _sse_events(resp.content.decode())
+        deltas = "".join(d["delta"] for t, d in events if t == "response.output_text.delta")
+        assert deltas == "from-cache"