From 40e851588d68e8fcaa0e912e1ddd62fc380702a4 Mon Sep 17 00:00:00 2001
From: James Veitch <1722315+darth-veitcher@users.noreply.github.com>
Date: Thu, 9 Jul 2026 12:50:54 +0100
Subject: [PATCH] feat(ollama): opt-in structured output via tool-calling +
 pydantic validation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Adds structured_output/output_schema/max_retries inputs to
OllamaChatCompletion, fixing three real reliability problems: models
prepending commentary, wrapping responses in code fences, and occasionally
returning blank output. When enabled, requests go through Ollama's
OpenAI-compatible tool-calling endpoint (a single forced tool call matching
output_schema) instead of native /api/chat, and each response is validated
against a dynamically-built pydantic model — invalid/blank responses retry
with fresh network calls, exhausting into a clear RuntimeError rather than
silently passing bad data downstream. One additional ComfyUI output socket
is exposed per schema property, mirroring FormatString's existing
dynamic-output pattern. Fully backward compatible: structured_output
defaults off and non-structured behavior is untouched.

Ollama's native /api/chat "format" field (JSON-Schema-constrained decoding)
was tried first but proved unreliable against a real local model during
implementation — see ADR-006 for the full investigation and why
tool-calling was chosen over both native format and pydantic-ai (the latter
would reintroduce httpx/openai-sdk, reversing ADR-004).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
---
 ...ama-output-tool-calling-not-pydantic-ai.md | 172 ++++++
 pyproject.toml                                |   1 +
 requirements.txt                              |   1 +
 src/comfydv/ollama.py                         | 292 +++++++++-
 tests/conftest.py                             |  28 +-
 tests/test_ollama.py                          | 547 ++++++++++++++++++
 uv.lock                                       | 140 +++++
 7 files changed, 1160 insertions(+), 21 deletions(-)
 create mode 100644 project-management/ADRs/ADR-006-structured-ollama-output-tool-calling-not-pydantic-ai.md

diff --git a/project-management/ADRs/ADR-006-structured-ollama-output-tool-calling-not-pydantic-ai.md b/project-management/ADRs/ADR-006-structured-ollama-output-tool-calling-not-pydantic-ai.md
new file mode 100644
index 0000000..9ccc557
--- /dev/null
+++ b/project-management/ADRs/ADR-006-structured-ollama-output-tool-calling-not-pydantic-ai.md
@@ -0,0 +1,172 @@
+# ADR-006: Structured Ollama output via OpenAI-compatible tool-calling + dynamic pydantic validation, not pydantic-ai
+
+## Status
+
+> Accepted
+
+_Date:_ 2026-07-09
+_Deciders:_ darth-veitcher
+
+---
+
+## Context
+
+`OllamaChatCompletion` sends free-text prompts to Ollama's `/api/chat` and
+returns `result["message"]["content"]` as-is, with no constraint on what the
+model may emit. In practice this is unreliable in three concrete ways:
+models prepend commentary ("Here you are:"), wrap responses in ` ``` ` code
+fences, or occasionally return blank content — all things prompt wording
+alone (e.g. a stricter `system` message) cannot reliably prevent, since it
+only *asks* the model to behave, it doesn't constrain what tokens the
+decoder is able to produce.
+
+The obvious library to reach for is `pydantic-ai`, which offers structured,
+validated LLM output as a first-class feature. Its Ollama support, however,
+goes through an OpenAI-compatible client — which pulls in the `openai` SDK,
+which depends on `httpx`. This directly reverses
+[ADR-004](ADR-004-aiohttp-over-httpx-for-ollama.md), which explicitly
+rejected `httpx` as "a new runtime dep for functionality aiohttp already
+provides" (ComfyUI's own server is aiohttp-based, so aiohttp is a guaranteed
+transitive dependency; httpx is not).
+
+Two Ollama-native mechanisms can force structured output without a new HTTP
+client, since both are reachable over plain JSON POST via the existing
+`aiohttp`-based `_post_json`:
+
+1. **Native `/api/chat` `"format"` field** — a JSON Schema that constrains
+   **decoding itself** (grammar-constrained sampling): the model's sampler
+   is restricted to only emit tokens matching the schema.
+2. **OpenAI-compatible `/v1/chat/completions` tool-calling** — a `tools`
+   array plus `tool_choice` forcing a single named function call, relying on
+   the model's own trained function-calling behavior rather than a
+   grammar-to-token mapping.
+
+Both were tried against a real local model
+(`lukey03/qwen3.5-9b-abliterated-vision`) during implementation. The native
+`format` field was silently ignored — the model returned plain unstructured
+text (`"pong"`) despite the schema constraint, reproduced twice. Inspecting
+`/api/show` revealed this model's `TEMPLATE` is a degenerate `{{ .Prompt }}`
+with no role/message structure — consistent with a community "abliteration"
+process having modified the tokenizer/vocab in a way that breaks Ollama's
+grammar-to-token mapping, causing it to silently fall back to unconstrained
+generation instead of erroring. Tool-calling doesn't depend on that mapping;
+it succeeded on its first test against the same model. (A follow-up
+tool-calling call with `options` included did also fail — this specific
+model appears broadly unreliable, consistent with a degraded fine-tune, so
+this evidence is suggestive rather than conclusive. No second generative
+model was available locally to get a cleaner signal.)
+
+## Decision
+
+Use Ollama's OpenAI-compatible tool-calling (`/v1/chat/completions`,
+`tools`/`tool_choice` forcing a single call) for `structured_output=True`
+requests, sent through the existing `_post_json` helper — no new HTTP
+client, no `pydantic-ai`, no `openai` SDK. Non-structured requests are
+completely unaffected and keep using native `/api/chat`.
+
+Use plain `pydantic` (not `pydantic-ai`) purely as a validation layer:
+given the user-supplied JSON Schema (`output_schema` input on
+`OllamaChatCompletion`), dynamically build a `pydantic.BaseModel` via
+`pydantic.create_model(...)` and validate/parse the tool call's `arguments`
+JSON against it. Required *string* fields get `min_length=1` — JSON
+Schema's `"required"` only checks presence, so a model could satisfy it
+with `""`, silently reintroducing the "blank output" problem. On validation
+failure (invalid JSON, missing/empty required field, or the model not
+calling the tool at all — observed to happen even with `tool_choice`
+forcing it), retry with fresh network calls (bounded by a `max_retries`
+input, clamped to 0–5); if every attempt fails, raise a clear
+`RuntimeError` naming the model, the attempt count, and a truncated
+snippet of the last invalid response — never silently degrade to
+unvalidated content.
+
+This is opt-in: a new `structured_output: BOOLEAN` input on the existing
+`OllamaChatCompletion` node, default `False`. When off, behavior is
+unchanged — no `tools`/`tool_choice` sent, native `/api/chat` used, no
+dynamic outputs, `RETURN_TYPES` stays the original fixed 3-tuple. When on,
+one additional ComfyUI output socket is exposed per schema property
+(mirroring `FormatString`'s existing dynamic-output-socket pattern via
+`unique_id`/`RETURN_TYPES` mutation), so downstream nodes can consume
+individually typed fields instead of parsing JSON themselves.
+
+## Consequences
+
+**Easier:**
+- Fixes the three concrete unreliability problems without depending on a
+  model/tokenizer-sensitive grammar-constraint mechanism that was observed
+  to fail silently on at least one real model.
+- No new HTTP stack: `pydantic` is a validation-only dependency, not a
+  client library. ADR-004's aiohttp-only stance is preserved.
+- Fully backward compatible — `structured_output` defaults off, and
+  non-structured requests still use native `/api/chat` exactly as before.
+
+**Harder / constrained:**
+- Tool-calling depends on the model having usable trained function-calling
+  behavior. Models with no tool-calling training may perform worse here
+  than they would under grammar-constrained `format` decoding — this
+  repo's only local test model was itself too unreliable to fully confirm
+  either mechanism's ceiling. If well-behaved-model testing later shows
+  native `format` is meaningfully more reliable in the common case, this
+  decision should be revisited rather than treated as permanent.
+- Only a flat `properties: {name: {type: ...}}` shape is interpreted into
+  typed ComfyUI sockets. Complex JSON Schema constructs (`$ref`,
+  `oneOf`/`anyOf`/`allOf`, `enum`, nested `object`/`array` item schemas) are
+  still forwarded to Ollama verbatim as the tool's `parameters`, but
+  comfydv's own type mapping falls back to `STRING` for anything it doesn't
+  recognize — no nested typed sockets.
+- `RETURN_TYPES`/`RETURN_NAMES` are class-level state, shared across every
+  `OllamaChatCompletion` instance in a graph (same accepted limitation
+  `FormatString.update_widget` already ships with) — the first execution
+  after toggling `structured_output` or editing `output_schema` may show
+  stale downstream socket typing until it runs once.
+- Neither mechanism is guaranteed 100% across all versions/models — hence
+  the retry-then-raise defense-in-depth, rather than trusting either
+  constraint blindly.
+
+**Debt introduced:**
+- None. `pydantic` is a widely-used, low-conflict-risk dependency; ComfyUI
+  itself is expected to already bundle it for its own API layer, though
+  this repo lists it explicitly in both `pyproject.toml` and
+  `requirements.txt` per ADR-003 rather than assume so.
+
+## Considered Alternatives
+
+### Alternative A: `pydantic-ai`
+
+**Why rejected:** Its Ollama support goes through an OpenAI-compatible
+client, reintroducing `httpx` + the `openai` SDK — the exact dependency
+ADR-004 evaluated and rejected. The reliability benefit it offers is the
+same tool-calling/validation mechanism this ADR adopts directly over plain
+`aiohttp`, without the added dependency weight.
+
+### Alternative B: Native `/api/chat` `"format"` field (JSON-Schema-constrained decoding)
+
+**Why rejected as primary:** Theoretically the stronger guarantee — a
+sampler-level constraint rather than learned behavior — and remains a
+reasonable mechanism for well-behaved models. Rejected here because it
+failed outright (silently ignored, not even erroring) against the one real
+model available for testing, traced to that model's modified tokenizer
+breaking Ollama's grammar-to-token mapping. Tool-calling succeeded where it
+failed. See "Harder / constrained" above — this may be revisited if
+broader testing shows native `format` is more reliable in the common case.
+
+### Alternative C: Prompt-only enforcement (stricter `system` message)
+
+**Why rejected:** `system` already exists as an input and users can already
+try this — it's what led to the reported problem in the first place.
+Wording can reduce commentary/fences/blank output but cannot guarantee
+their absence, since nothing constrains the actual token stream.
+
+### Alternative D: Response-side post-processing (regex-strip fences/preamble)
+
+**Why rejected as the primary fix:** Cheap, but fundamentally reactive —
+it can strip a fence wrapper after the fact but can't recover genuinely
+blank output, and heuristics for "commentary" are unreliable across models.
+Not pursued as a fallback either, to keep this change minimal and avoid two
+competing "make output clean" mechanisms with unclear precedence.
+
+---
+
+## Links
+
+- Related ADRs: [ADR-003](ADR-003-requirements-txt-authoring-policy.md), [ADR-004](ADR-004-aiohttp-over-httpx-for-ollama.md), [ADR-005](ADR-005-ollama-host-config-via-client-node.md)
+- Originating epic (archived, scope predates this decision): `project-management/Roadmap/epics/archive/ollama-integration.md`
diff --git a/pyproject.toml b/pyproject.toml
index 4dda23b..6f5c5f9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,6 +10,7 @@ authors = [
 dependencies = [
     "aiohttp>=3.9.0",
     "jinja2>=3.1.6",
+    "pydantic>=2.0",
 ]
 
 [project.urls]
diff --git a/requirements.txt b/requirements.txt
index ec9b7ef..11e9401 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,3 +2,4 @@
 # Lists only deps NOT already provided by ComfyUI's own environment.
 # See ADR-003: never auto-generate this file with `uv export`.
 jinja2>=3.1.6
+pydantic>=2.0
diff --git a/src/comfydv/ollama.py b/src/comfydv/ollama.py
index 85305dd..a114359 100644
--- a/src/comfydv/ollama.py
+++ b/src/comfydv/ollama.py
@@ -7,6 +7,9 @@ chat completion, composable inference options, and history utilities.
 ADR-004: aiohttp (already in ComfyUI dep tree) for all Ollama HTTP calls.
 ADR-005: OllamaClient node is the single source of the host URL (and, per
 US7, of any auth headers — every downstream node reaches the same server).
+ADR-006: structured_output uses Ollama's OpenAI-compatible tool-calling
+endpoint + a dynamically-built pydantic model for validation, not
+pydantic-ai — still plain aiohttp, no new HTTP client.
 """
 
 import asyncio
@@ -17,6 +20,8 @@ import sys
 import threading
 import time
 
+from pydantic import ValidationError
+
 logger = logging.getLogger(__name__)
 
 
@@ -460,9 +465,152 @@ def _history_preview(messages: list[dict]) -> str:
     return "\n".join(lines)
 
 
+# ---------------------------------------------------------------------------
+# Structured output — Ollama's OpenAI-compatible /v1/chat/completions
+# tool-calling, with a single forced tool matching output_schema
+# ---------------------------------------------------------------------------
+#
+# ADR-006: forces the model to emit its answer as a single tool call whose
+# arguments match output_schema, instead of relying on prompt wording —
+# fixes commentary, code-fence wrapping, and blank output at the source.
+# Ollama's native /api/chat "format" (JSON-Schema-constrained decoding) was
+# tried first but proved unreliable against at least one real model in
+# testing (a modified-tokenizer quantization silently ignored the schema
+# rather than erroring); tool-calling relies on the model's own trained
+# function-calling behavior instead of grammar-to-token mapping, and is
+# still reached over the existing aiohttp-based _post_json — no new HTTP
+# client. pydantic is used purely to dynamically build a validation model
+# from the user's schema, not as an HTTP client.
+
+_JSON_SCHEMA_TO_PY_TYPE: dict = {
+    "string": str,
+    "integer": int,
+    "number": float,
+    "boolean": bool,
+    "array": list,
+    "object": dict,
+}
+_JSON_SCHEMA_TO_COMFY_TYPE: dict = {
+    "string": "STRING",
+    "integer": "INT",
+    "number": "FLOAT",
+    "boolean": "BOOLEAN",
+    "array": "STRING",
+    "object": "STRING",
+}
+_DEFAULT_OUTPUT_SCHEMA = (
+    '{"type": "object", "properties": '
+    '{"output": {"type": "string"}}, "required": ["output"]}'
+)
+
+
+def _parse_output_schema(output_schema: str) -> dict:
+    """Parse+validate output_schema JSON, fail fast before any network call."""
+    try:
+        schema = json.loads(output_schema)
+    except json.JSONDecodeError as exc:
+        raise ValueError(f"output_schema is not valid JSON: {exc}") from exc
+    if not isinstance(schema, dict) or schema.get("type") != "object":
+        raise ValueError(
+            'output_schema must be a JSON Schema object with "type": "object" '
+            'at the root, e.g. {"type": "object", "properties": {...}}'
+        )
+    properties = schema.get("properties")
+    if not isinstance(properties, dict) or not properties:
+        raise ValueError(
+            "output_schema.properties must be a non-empty object — "
+            "structured_output requires at least one field to extract"
+        )
+    return schema
+
+
+def _comfy_types_for_schema(schema: dict) -> tuple:
+    return tuple(
+        _JSON_SCHEMA_TO_COMFY_TYPE.get(prop.get("type"), "STRING")
+        for prop in schema["properties"].values()
+    )
+
+
+def _build_structured_model(schema: dict):
+    """Dynamically build a pydantic BaseModel from schema properties/required.
+
+    Rebuilt every call — create_model() on a handful of fields is cheap, and
+    OllamaChatCompletion already re-executes every queue run (OUTPUT_NODE=True).
+
+    Required *string* fields get `min_length=1`: JSON Schema's "required"
+    only checks presence, so a model could satisfy it with `""` — which is
+    exactly the "blank output" problem this feature exists to eliminate. An
+    empty required string now fails validation and triggers a retry like any
+    other malformed response, rather than silently passing through.
+    """
+    from pydantic import Field, create_model
+
+    required = set(schema.get("required", []))
+    fields = {}
+    for name, prop in schema["properties"].items():
+        py_type = _JSON_SCHEMA_TO_PY_TYPE.get(prop.get("type"), str)
+        if name not in required:
+            fields[name] = (py_type, None)
+        elif py_type is str:
+            fields[name] = (py_type, Field(..., min_length=1))
+        else:
+            fields[name] = (py_type, ...)
+    return create_model("OllamaStructuredOutput", **fields)
+
+
+def _coerce_structured_value(value, comfy_type: str):
+    if comfy_type == "STRING" and isinstance(value, (list, dict)):
+        return json.dumps(value)
+    if comfy_type == "STRING" and value is None:
+        return ""
+    return value
+
+
+_STRUCTURED_TOOL_NAME = "emit_structured_output"
+
+
+def _build_tool_call_payload(schema: dict) -> tuple:
+    """tools/tool_choice extras forcing a single call matching schema."""
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": _STRUCTURED_TOOL_NAME,
+                "description": "Emit the result matching the required schema.",
+                "parameters": schema,
+            },
+        }
+    ]
+    tool_choice = {"type": "function", "function": {"name": _STRUCTURED_TOOL_NAME}}
+    return tools, tool_choice
+
+
+def _extract_tool_call_arguments(result: dict) -> str:
+    """Pull the forced tool call's arguments JSON string out of an OpenAI-
+    compatible /v1/chat/completions response. Empty string (which will fail
+    pydantic validation and trigger a retry) if the model didn't call the
+    tool at all — this happens on occasion even with tool_choice forcing it.
+    """
+    choices = result.get("choices") or []
+    if not choices:
+        return ""
+    message = choices[0].get("message", {})
+    tool_calls = message.get("tool_calls") or []
+    if not tool_calls:
+        return ""
+    return tool_calls[0].get("function", {}).get("arguments", "") or ""
+
+
 class OllamaChatCompletion:
     OUTPUT_NODE = True
 
+    _BASE_RETURN_TYPES = ("STRING", "OLLAMA_HISTORY", "STRING")
+    _BASE_RETURN_NAMES = ("response", "updated_history", "model_name")
+
+    # Per-node-instance structured-output config, keyed by unique_id — same
+    # pattern as FormatString.node_configs.
+    node_configs: dict = {}
+
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -478,14 +626,45 @@ class OllamaChatCompletion:
                 "history": ("OLLAMA_HISTORY",),
                 "options": ("OLLAMA_OPTIONS",),
                 "timeout_secs": ("INT", {"default": 300, "min": 30, "max": 3600}),
+                "structured_output": ("BOOLEAN", {"default": False}),
+                "output_schema": (
+                    "STRING",
+                    {"multiline": True, "default": _DEFAULT_OUTPUT_SCHEMA},
+                ),
+                "max_retries": ("INT", {"default": 2, "min": 0, "max": 5}),
             },
+            "hidden": {"unique_id": "UNIQUE_ID"},
         }
 
-    RETURN_TYPES = ("STRING", "OLLAMA_HISTORY", "STRING")
-    RETURN_NAMES = ("response", "updated_history", "model_name")
+    RETURN_TYPES = _BASE_RETURN_TYPES
+    RETURN_NAMES = _BASE_RETURN_NAMES
     FUNCTION = "chat"
     CATEGORY = "dv/ollama"
 
+    @classmethod
+    def update_outputs(
+        cls, unique_id: str, structured_output: bool, schema: dict | None
+    ) -> None:
+        """Mutate class-level RETURN_TYPES/RETURN_NAMES for structured_output mode.
+
+        Same class-level-shared-state pattern (and limitation) as
+        FormatString.update_widget: RETURN_TYPES/RETURN_NAMES are shared
+        across all instances of this node type in a graph, so the very first
+        execution after toggling structured_output or editing output_schema
+        may show stale downstream socket typing until it runs once.
+        """
+        cls.node_configs[unique_id] = {
+            "structured_output": structured_output,
+            "schema": schema,
+        }
+        if not structured_output or not schema:
+            cls.RETURN_TYPES = cls._BASE_RETURN_TYPES
+            cls.RETURN_NAMES = cls._BASE_RETURN_NAMES
+            return
+        names = tuple(schema["properties"].keys())
+        cls.RETURN_TYPES = cls._BASE_RETURN_TYPES + _comfy_types_for_schema(schema)
+        cls.RETURN_NAMES = cls._BASE_RETURN_NAMES + names
+
     def chat(
         self,
         client,
@@ -495,10 +674,24 @@ class OllamaChatCompletion:
         history=None,
         options=None,
         timeout_secs=300,
+        structured_output=False,
+        output_schema=_DEFAULT_OUTPUT_SCHEMA,
+        max_retries=2,
+        unique_id="",
     ):
         effective_model = model.strip()
         if not effective_model:
             raise ValueError("model cannot be empty — type a model name or wire one in")
+
+        schema = None
+        pydantic_model = None
+        if structured_output:
+            schema = _parse_output_schema(output_schema)  # fail fast, no network call
+            pydantic_model = _build_structured_model(schema)
+
+        if unique_id:
+            type(self).update_outputs(unique_id, structured_output, schema)
+
         if history is None:
             history = []
         messages = list(history)
@@ -512,25 +705,85 @@ class OllamaChatCompletion:
         }
         if options:
             payload["options"] = options
+        if structured_output:
+            # ADR-006: OpenAI-compatible tool-calling, not native /api/chat
+            # "format" — forces a single call whose arguments match schema.
+            assert schema is not None  # structured_output implies this was parsed
+            tools, tool_choice = _build_tool_call_payload(schema)
+            payload["tools"] = tools
+            payload["tool_choice"] = tool_choice
 
         headers = _client_headers(client)
         cache_key = _cache_key(
-            "chat", client, headers or {}, effective_model, messages, options or {}
+            "chat",
+            client,
+            headers or {},
+            effective_model,
+            messages,
+            options or {},
+            schema or {},
         )
         cached, hit = _CHAT_RESPONSE_CACHE.get(cache_key)
-        if hit:
+        url = (
+            f"{client}/v1/chat/completions"
+            if structured_output
+            else f"{client}/api/chat"
+        )
+
+        parsed = None
+        if not structured_output:
+            # Unchanged from pre-structured-output behavior.
+            if hit:
+                response_text = cached
+            else:
+                result = _run_async(
+                    _post_json(
+                        url, payload, timeout=float(timeout_secs), headers=headers
+                    )
+                )
+                response_text = result.get("message", {}).get("content", "")
+                _CHAT_RESPONSE_CACHE.set(cache_key, response_text)
+        elif hit:
+            # schema is part of the cache key, so a hit was necessarily
+            # validated against this exact schema when it was written —
+            # re-parsing here is guaranteed-successful deserialization, not
+            # a fallible re-check.
+            assert (
+                pydantic_model is not None
+            )  # structured_output implies this was built
             response_text = cached
+            parsed = pydantic_model.model_validate_json(response_text)
         else:
-            result = _run_async(
-                _post_json(
-                    f"{client}/api/chat",
-                    payload,
-                    timeout=float(timeout_secs),
-                    headers=headers,
+            assert (
+                pydantic_model is not None
+            )  # structured_output implies this was built
+            total_attempts = max(0, min(int(max_retries), 5)) + 1
+            response_text = None
+            last_invalid_text = ""
+            last_error = None
+            for _attempt in range(1, total_attempts + 1):
+                result = _run_async(
+                    _post_json(
+                        url, payload, timeout=float(timeout_secs), headers=headers
+                    )
+                )
+                candidate = _extract_tool_call_arguments(result)
+                try:
+                    parsed = pydantic_model.model_validate_json(candidate)
+                    response_text = candidate
+                    _CHAT_RESPONSE_CACHE.set(cache_key, response_text)
+                    break
+                except ValidationError as exc:
+                    last_invalid_text = candidate
+                    last_error = exc
+            else:
+                raise RuntimeError(
+                    f"OllamaChatCompletion: structured_output response failed "
+                    f"validation against output_schema after {total_attempts} "
+                    f"attempt(s) (model={effective_model!r}). Last error: "
+                    f"{last_error}. Last response (truncated): "
+                    f"{last_invalid_text[:300]!r}"
                 )
-            )
-            response_text = result.get("message", {}).get("content", "")
-            _CHAT_RESPONSE_CACHE.set(cache_key, response_text)
 
         updated = list(history)
         updated.append({"role": "user", "content": prompt})
@@ -541,9 +794,20 @@ class OllamaChatCompletion:
             if n > 2
             else response_text
         )
+
+        result_tuple = (response_text, updated, effective_model)
+        if structured_output:
+            assert schema is not None  # structured_output implies this was parsed
+            comfy_types = _comfy_types_for_schema(schema)
+            extra = tuple(
+                _coerce_structured_value(getattr(parsed, name), ctype)
+                for name, ctype in zip(schema["properties"].keys(), comfy_types)
+            )
+            result_tuple += extra
+
         return {
             "ui": {"text": [ui_text]},
-            "result": (response_text, updated, effective_model),
+            "result": result_tuple,
         }
 
 
diff --git a/tests/conftest.py b/tests/conftest.py
index abb173e..752fd77 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -68,20 +68,34 @@ def pytest_configure(config):
 
 @pytest.fixture(autouse=True)
 def _clear_ollama_caches():
-    """Reset comfydv.ollama's module-level LRU caches around every test.
+    """Reset comfydv.ollama's module-level LRU caches and OllamaChatCompletion's
+    dynamic RETURN_TYPES/RETURN_NAMES around every test.
 
     Several tests reuse identical client/model/prompt inputs across cases
     with different monkeypatched responses — without this, a later test would
     silently get an earlier test's cached result instead of exercising its
-    own fake.
+    own fake. RETURN_TYPES/RETURN_NAMES are class-level mutable state (set by
+    OllamaChatCompletion.update_outputs for structured_output mode) shared
+    across every test in the module — without resetting them, a structured-
+    output test would leak its dynamic outputs into unrelated tests that
+    assert the fixed 3-tuple.
     """
-    from comfydv.ollama import _CHAT_RESPONSE_CACHE, _MODEL_LIST_CACHE
+    from comfydv.ollama import (
+        _CHAT_RESPONSE_CACHE,
+        _MODEL_LIST_CACHE,
+        OllamaChatCompletion,
+    )
 
-    _MODEL_LIST_CACHE.clear()
-    _CHAT_RESPONSE_CACHE.clear()
+    def _reset():
+        _MODEL_LIST_CACHE.clear()
+        _CHAT_RESPONSE_CACHE.clear()
+        OllamaChatCompletion.RETURN_TYPES = OllamaChatCompletion._BASE_RETURN_TYPES
+        OllamaChatCompletion.RETURN_NAMES = OllamaChatCompletion._BASE_RETURN_NAMES
+        OllamaChatCompletion.node_configs.clear()
+
+    _reset()
     yield
-    _MODEL_LIST_CACHE.clear()
-    _CHAT_RESPONSE_CACHE.clear()
+    _reset()
 
 
 @pytest.fixture(scope="session")
diff --git a/tests/test_ollama.py b/tests/test_ollama.py
index ebc89bd..2974d5d 100644
--- a/tests/test_ollama.py
+++ b/tests/test_ollama.py
@@ -626,6 +626,478 @@ class TestUS4ChatCompletion:
         )["result"]
         assert len(h2) == 4
 
+    @pytest.mark.integration
+    def test_structured_output_retries_then_raises_against_live_server(
+        self, ollama_host, skip_if_no_ollama
+    ):
+        """Scenario: structured_output's retry-then-raise fallback against a
+        real server, using an unreliable model that empirically cannot be
+        made to call the forced tool consistently.
+
+        This intentionally does NOT assert a happy-path clean result: the
+        only generative model available in CI/dev environments running this
+        (a community "abliterated" quantization) was found during
+        implementation to unreliably invoke tool calls regardless of
+        mechanism — see ADR-006. What IS worth proving against a live
+        server: the retry loop makes genuine repeated network calls (not
+        just replaying a mock) and produces a well-formed, diagnostic error
+        rather than hanging, crashing uninformatively, or silently returning
+        bad data. The happy path (clean structured extraction) is covered
+        thoroughly by the mocked TestStructuredOutput suite.
+        """
+        with pytest.raises(RuntimeError, match="failed validation") as exc_info:
+            OllamaChatCompletion().chat(
+                client=ollama_host,
+                model=_CHAT_MODEL,
+                prompt="Say exactly: pong",
+                options={"think": False},
+                structured_output=True,
+                output_schema=(
+                    '{"type":"object","properties":{"output":{"type":"string"}},'
+                    '"required":["output"]}'
+                ),
+                max_retries=1,
+                unique_id="smoke-test",
+            )
+        assert "2 attempt" in str(exc_info.value)
+        assert _CHAT_MODEL in str(exc_info.value)
+
+
+# ---------------------------------------------------------------------------
+# US8 — Structured output (OpenAI-compatible tool-calling + dynamic pydantic
+# model — see ADR-006 for why tool-calling rather than native `format`)
+# ---------------------------------------------------------------------------
+
+_SINGLE_FIELD_SCHEMA = (
+    '{"type": "object", "properties": {"output": {"type": "string"}}, '
+    '"required": ["output"]}'
+)
+_MULTI_FIELD_SCHEMA = (
+    '{"type": "object", "properties": {'
+    '"summary": {"type": "string"}, '
+    '"score": {"type": "integer"}, '
+    '"is_positive": {"type": "boolean"}}, '
+    '"required": ["summary", "score", "is_positive"]}'
+)
+
+
+def _tool_response(arguments_json: str) -> dict:
+    """Build an OpenAI-compatible /v1/chat/completions response with a single
+    forced tool call, matching what Ollama's compat layer returns."""
+    return {
+        "choices": [
+            {
+                "message": {
+                    "role": "assistant",
+                    "content": "",
+                    "tool_calls": [
+                        {
+                            "id": "call_1",
+                            "type": "function",
+                            "function": {
+                                "name": "emit_structured_output",
+                                "arguments": arguments_json,
+                            },
+                        }
+                    ],
+                },
+                "finish_reason": "tool_calls",
+            }
+        ]
+    }
+
+
+def _no_tool_call_response() -> dict:
+    """Model didn't invoke the forced tool at all — happens even with
+    tool_choice forcing it, on some models. Empty arguments must fail
+    validation and trigger a retry like any other malformed response."""
+    return {
+        "choices": [
+            {"message": {"role": "assistant", "content": ""}, "finish_reason": "stop"}
+        ]
+    }
+
+
+class TestStructuredOutput:
+    def test_structured_output_defaults_to_false(self):
+        input_types = OllamaChatCompletion.INPUT_TYPES()
+        assert input_types["optional"]["structured_output"] == (
+            "BOOLEAN",
+            {"default": False},
+        )
+
+    def test_structured_output_false_payload_has_no_tools_key(self, monkeypatch):
+        captured = {}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            captured["url"] = url
+            captured["payload"] = payload
+            return {"message": {"content": "hello"}}
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        OllamaChatCompletion().chat(
+            client="http://x", model="m", prompt="hi", structured_output=False
+        )
+        assert "tools" not in captured["payload"]
+        assert "tool_choice" not in captured["payload"]
+        assert captured["url"].endswith("/api/chat")
+
+    def test_structured_output_true_sends_tool_call_payload(self, monkeypatch):
+        captured = {}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            captured["url"] = url
+            captured["payload"] = payload
+            return _tool_response('{"output": "hi there"}')
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        OllamaChatCompletion().chat(
+            client="http://x",
+            model="m",
+            prompt="hi",
+            structured_output=True,
+            output_schema=_SINGLE_FIELD_SCHEMA,
+            unique_id="n1",
+        )
+        assert captured["url"].endswith("/v1/chat/completions")
+        tool = captured["payload"]["tools"][0]
+        assert tool["type"] == "function"
+        assert tool["function"]["parameters"] == json.loads(_SINGLE_FIELD_SCHEMA)
+        assert captured["payload"]["tool_choice"] == {
+            "type": "function",
+            "function": {"name": tool["function"]["name"]},
+        }
+
+    def test_default_schema_produces_single_output_field(self, monkeypatch):
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            return _tool_response('{"output": "clean text"}')
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        ret = OllamaChatCompletion().chat(
+            client="http://x",
+            model="m",
+            prompt="hi",
+            structured_output=True,
+            unique_id="n2",
+        )
+        assert OllamaChatCompletion.RETURN_NAMES == (
+            "response",
+            "updated_history",
+            "model_name",
+            "output",
+        )
+        assert len(ret["result"]) == 4
+        assert ret["result"][3] == "clean text"
+        assert ret["result"][0] == '{"output": "clean text"}'
+
+    def test_multi_field_schema_sets_return_types_and_values(self, monkeypatch):
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            return _tool_response(
+                '{"summary": "great", "score": 9, "is_positive": true}'
+            )
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        ret = OllamaChatCompletion().chat(
+            client="http://x",
+            model="m",
+            prompt="hi",
+            structured_output=True,
+            output_schema=_MULTI_FIELD_SCHEMA,
+            unique_id="n3",
+        )
+        assert OllamaChatCompletion.RETURN_TYPES == (
+            "STRING",
+            "OLLAMA_HISTORY",
+            "STRING",
+            "STRING",
+            "INT",
+            "BOOLEAN",
+        )
+        assert OllamaChatCompletion.RETURN_NAMES == (
+            "response",
+            "updated_history",
+            "model_name",
+            "summary",
+            "score",
+            "is_positive",
+        )
+        _, _, _, summary, score, is_positive = ret["result"]
+        assert summary == "great"
+        assert score == 9
+        assert isinstance(score, int)
+        assert is_positive is True
+
+    def test_array_object_property_json_dumped_into_string_slot(self, monkeypatch):
+        schema = (
+            '{"type": "object", "properties": {'
+            '"tags": {"type": "array"}}, "required": ["tags"]}'
+        )
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            return _tool_response('{"tags": ["a", "b", "c"]}')
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        ret = OllamaChatCompletion().chat(
+            client="http://x",
+            model="m",
+            prompt="hi",
+            structured_output=True,
+            output_schema=schema,
+            unique_id="n4",
+        )
+        assert ret["result"][3] == json.dumps(["a", "b", "c"])
+
+    def test_return_types_reset_when_toggled_off(self, monkeypatch):
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            return _tool_response('{"output": "x"}')
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        OllamaChatCompletion().chat(
+            client="http://x",
+            model="m",
+            prompt="hi",
+            structured_output=True,
+            unique_id="n5",
+        )
+        assert len(OllamaChatCompletion.RETURN_TYPES) == 4
+
+        OllamaChatCompletion().chat(
+            client="http://x",
+            model="m",
+            prompt="hi",
+            structured_output=False,
+            unique_id="n5",
+        )
+        assert (
+            OllamaChatCompletion.RETURN_TYPES == OllamaChatCompletion._BASE_RETURN_TYPES
+        )
+        assert (
+            OllamaChatCompletion.RETURN_NAMES == OllamaChatCompletion._BASE_RETURN_NAMES
+        )
+
+    def test_invalid_output_schema_json_raises_before_network_call(self, monkeypatch):
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            return _tool_response("{}")
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        with pytest.raises(ValueError, match="not valid JSON"):
+            OllamaChatCompletion().chat(
+                client="http://x",
+                model="m",
+                prompt="hi",
+                structured_output=True,
+                output_schema="not json",
+            )
+        assert calls["n"] == 0, "invalid schema must fail before touching the network"
+
+    def test_non_object_root_schema_raises(self):
+        with pytest.raises(ValueError, match='"type": "object"'):
+            OllamaChatCompletion().chat(
+                client="http://x",
+                model="m",
+                prompt="hi",
+                structured_output=True,
+                output_schema='{"type": "string"}',
+            )
+
+    def test_empty_properties_raises(self):
+        with pytest.raises(ValueError, match="non-empty object"):
+            OllamaChatCompletion().chat(
+                client="http://x",
+                model="m",
+                prompt="hi",
+                structured_output=True,
+                output_schema='{"type": "object", "properties": {}}',
+            )
+
+    def test_valid_json_first_attempt_no_retry(self, monkeypatch):
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            return _tool_response('{"output": "ok"}')
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        OllamaChatCompletion().chat(
+            client="http://x", model="m", prompt="hi", structured_output=True
+        )
+        assert calls["n"] == 1
+
+    def test_retries_on_invalid_json_then_succeeds(self, monkeypatch):
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            if calls["n"] == 1:
+                return _tool_response("not json at all")
+            return _tool_response('{"output": "ok on retry"}')
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        ret = OllamaChatCompletion().chat(
+            client="http://x", model="m", prompt="hi", structured_output=True
+        )
+        assert calls["n"] == 2
+        assert ret["result"][3] == "ok on retry"
+
+    def test_retries_when_model_never_calls_the_tool(self, monkeypatch):
+        """The model can ignore tool_choice and just respond normally —
+        happens on occasion even with a forced tool. Must retry, not crash."""
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            if calls["n"] == 1:
+                return _no_tool_call_response()
+            return _tool_response('{"output": "ok on retry"}')
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        ret = OllamaChatCompletion().chat(
+            client="http://x", model="m", prompt="hi", structured_output=True
+        )
+        assert calls["n"] == 2
+        assert ret["result"][3] == "ok on retry"
+
+    def test_retries_on_missing_required_field(self, monkeypatch):
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            if calls["n"] == 1:
+                return _tool_response("{}")  # missing required "output"
+            return _tool_response('{"output": "ok"}')
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        OllamaChatCompletion().chat(
+            client="http://x", model="m", prompt="hi", structured_output=True
+        )
+        assert calls["n"] == 2
+
+    def test_retries_on_empty_required_string(self, monkeypatch):
+        """A required string field satisfied by "" must still be rejected —
+        JSON Schema "required" only checks presence, not non-emptiness, so
+        without this an empty answer would silently pass validation (exactly
+        the "blank output" problem structured_output exists to eliminate)."""
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            if calls["n"] == 1:
+                return _tool_response('{"output": ""}')
+            return _tool_response('{"output": "ok"}')
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        ret = OllamaChatCompletion().chat(
+            client="http://x", model="m", prompt="hi", structured_output=True
+        )
+        assert calls["n"] == 2
+        assert ret["result"][3] == "ok"
+
+    def test_exhausts_retries_raises_runtime_error(self, monkeypatch):
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            return _tool_response("always broken")
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        with pytest.raises(RuntimeError, match="failed validation") as exc_info:
+            OllamaChatCompletion().chat(
+                client="http://x",
+                model="m",
+                prompt="hi",
+                structured_output=True,
+                max_retries=2,
+            )
+        assert calls["n"] == 3, "max_retries=2 must allow exactly 3 total attempts"
+        assert "3 attempt" in str(exc_info.value)
+        assert "always broken" in str(exc_info.value)
+
+    def test_max_retries_clamped_upper_bound(self, monkeypatch):
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            return _tool_response("always broken")
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        with pytest.raises(RuntimeError):
+            OllamaChatCompletion().chat(
+                client="http://x",
+                model="m",
+                prompt="hi",
+                structured_output=True,
+                max_retries=99,
+            )
+        assert calls["n"] == 6, "max_retries must be clamped to 5 (6 total attempts)"
+
+    def test_max_retries_clamped_lower_bound(self, monkeypatch):
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            return _tool_response("always broken")
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        with pytest.raises(RuntimeError):
+            OllamaChatCompletion().chat(
+                client="http://x",
+                model="m",
+                prompt="hi",
+                structured_output=True,
+                max_retries=-5,
+            )
+        assert calls["n"] == 1, (
+            "even a negative max_retries must allow at least 1 attempt"
+        )
+
 
 # ---------------------------------------------------------------------------
 # US5 — Composable Options  (us5_composable_options.feature)
@@ -1094,6 +1566,81 @@ class TestResponseCache:
             "a cache entry"
         )
 
+    def test_structured_output_cache_hit_skips_network(self, monkeypatch):
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            return _tool_response('{"output": "cached value"}')
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        kwargs = dict(
+            client="http://x",
+            model="m",
+            prompt="hi",
+            structured_output=True,
+            unique_id="cache-1",
+        )
+        ret1 = OllamaChatCompletion().chat(**kwargs)
+        ret2 = OllamaChatCompletion().chat(**kwargs)
+
+        assert calls["n"] == 1, "second identical structured call must hit the cache"
+        assert ret1["result"] == ret2["result"], (
+            "dynamic outputs must be correctly re-extracted from the cached text"
+        )
+        assert ret2["result"][3] == "cached value"
+
+    def test_structured_and_non_structured_do_not_collide_in_cache(self, monkeypatch):
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            if "tools" in payload:
+                return _tool_response('{"output": "structured"}')
+            return {"message": {"content": "plain text"}}
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        OllamaChatCompletion().chat(
+            client="http://x", model="m", prompt="hi", structured_output=False
+        )
+        OllamaChatCompletion().chat(
+            client="http://x", model="m", prompt="hi", structured_output=True
+        )
+
+        assert calls["n"] == 2, (
+            "identical client/model/prompt with structured_output True vs False "
+            "must not share a cache entry"
+        )
+
+    def test_structured_output_only_caches_after_validation_passes(self, monkeypatch):
+        calls = {"n": 0}
+
+        async def fake_post(url, payload, *, timeout=120.0, headers=None):
+            calls["n"] += 1
+            if calls["n"] == 1:
+                return _tool_response("invalid json")
+            return _tool_response('{"output": "good"}')
+
+        import comfydv.ollama as ollama_mod
+
+        monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
+
+        kwargs = dict(client="http://x", model="m", prompt="hi", structured_output=True)
+        OllamaChatCompletion().chat(
+            **kwargs
+        )  # attempt 1 invalid, attempt 2 caches "good"
+        assert calls["n"] == 2
+
+        ret = OllamaChatCompletion().chat(**kwargs)  # must hit cache, not re-call
+        assert calls["n"] == 2, "the invalid first attempt must never be cached"
+        assert ret["result"][3] == "good"
+
 
 # ---------------------------------------------------------------------------
 # Node contract sanity checks (ComfyUI registration requirements)
diff --git a/uv.lock b/uv.lock
index 55d89a4..f945989 100644
--- a/uv.lock
+++ b/uv.lock
@@ -130,6 +130,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
 ]
 
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+wheels = [
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 dependencies = [
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     { name = "jinja2" },
+    { name = "pydantic" },
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     { name = "jinja2", specifier = ">=3.1.6" },
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