Parameterize LLM returning reasoning

docs/ref/checks/custom_prompt_check.md

@@ -20,6 +20,10 @@ Implements custom content checks using configurable LLM prompts. Uses your custo
 - **`model`** (required): Model to use for the check (e.g., "gpt-5")
 - **`confidence_threshold`** (required): Minimum confidence score to trigger tripwire (0.0 to 1.0)
 - **`system_prompt_details`** (required): Custom instructions defining the content detection criteria
+- **`include_reasoning`** (optional): Whether to include reasoning/explanation fields in the guardrail output (default: `false`)
+    - When `false`: The LLM only generates the essential fields (`flagged` and `confidence`), reducing token generation costs
+    - When `true`: Additionally, returns detailed reasoning for its decisions
+    - **Use Case**: Keep disabled for production to minimize costs; enable for development and debugging
 
 ## Implementation Notes
 
@@ -42,3 +46,4 @@ Returns a `GuardrailResult` with the following `info` dictionary:
 - **`flagged`**: Whether the custom validation criteria were met
 - **`confidence`**: Confidence score (0.0 to 1.0) for the validation
 - **`threshold`**: The confidence threshold that was configured
+- **`reason`**: Explanation of why the input was flagged (or not flagged) - *only included when `include_reasoning=true`*

docs/ref/checks/hallucination_detection.md

@@ -14,7 +14,8 @@ Flags model text containing factual claims that are clearly contradicted or not
     "config": {
         "model": "gpt-4.1-mini",
         "confidence_threshold": 0.7,
-        "knowledge_source": "vs_abc123"
+        "knowledge_source": "vs_abc123",
+        "include_reasoning": false
     }
 }
 ```
@@ -24,6 +25,10 @@ Flags model text containing factual claims that are clearly contradicted or not
 - **`model`** (required): OpenAI model (required) to use for validation (e.g., "gpt-4.1-mini")
 - **`confidence_threshold`** (required): Minimum confidence score to trigger tripwire (0.0 to 1.0)
 - **`knowledge_source`** (required): OpenAI vector store ID starting with "vs_" containing reference documents
+- **`include_reasoning`** (optional): Whether to include detailed reasoning fields in the output (default: `false`)
+    - When `false`: Returns only `flagged` and `confidence` to save tokens
+    - When `true`: Additionally, returns `reasoning`, `hallucination_type`, `hallucinated_statements`, and `verified_statements`
+    - Recommended: Keep disabled for production (default); enable for development/debugging
 
 ### Tuning guidance
 
@@ -102,7 +107,9 @@ See [`examples/hallucination_detection/`](https://github.com/openai/openai-guard
 
 ## What It Returns
 
-Returns a `GuardrailResult` with the following `info` dictionary:
+Returns a `GuardrailResult` with the following `info` dictionary.
+
+**With `include_reasoning=true`:**
 
 ```json
 {
@@ -117,15 +124,15 @@ Returns a `GuardrailResult` with the following `info` dictionary:
 }
 ```
 
+### Fields
+
 - **`flagged`**: Whether the content was flagged as potentially hallucinated
 - **`confidence`**: Confidence score (0.0 to 1.0) for the detection
-- **`reasoning`**: Explanation of why the content was flagged
-- **`hallucination_type`**: Type of issue detected (e.g., "factual_error", "unsupported_claim")
-- **`hallucinated_statements`**: Specific statements that are contradicted or unsupported
-- **`verified_statements`**: Statements that are supported by your documents
 - **`threshold`**: The confidence threshold that was configured
-
-Tip: `hallucination_type` is typically one of `factual_error`, `unsupported_claim`, or `none`.
+- **`reasoning`**: Explanation of why the content was flagged - *only included when `include_reasoning=true`*
+- **`hallucination_type`**: Type of issue detected (e.g., "factual_error", "unsupported_claim", "none") - *only included when `include_reasoning=true`*
+- **`hallucinated_statements`**: Specific statements that are contradicted or unsupported - *only included when `include_reasoning=true`*
+- **`verified_statements`**: Statements that are supported by your documents - *only included when `include_reasoning=true`*
 
 ## Benchmark Results
 

docs/ref/checks/jailbreak.md

@@ -33,7 +33,8 @@ Detects attempts to bypass safety or policy constraints via manipulation (prompt
     "name": "Jailbreak",
     "config": {
         "model": "gpt-4.1-mini",
-        "confidence_threshold": 0.7
+        "confidence_threshold": 0.7,
+        "include_reasoning": false
     }
 }
 ```
@@ -42,6 +43,10 @@ Detects attempts to bypass safety or policy constraints via manipulation (prompt
 
 - **`model`** (required): Model to use for detection (e.g., "gpt-4.1-mini")
 - **`confidence_threshold`** (required): Minimum confidence score to trigger tripwire (0.0 to 1.0)
+- **`include_reasoning`** (optional): Whether to include reasoning/explanation fields in the guardrail output (default: `false`)
+    - When `false`: The LLM only generates the essential fields (`flagged` and `confidence`), reducing token generation costs
+    - When `true`: Additionally, returns detailed reasoning for its decisions
+    - **Use Case**: Keep disabled for production to minimize costs; enable for development and debugging
 
 ### Tuning guidance
 
@@ -70,7 +75,7 @@ Returns a `GuardrailResult` with the following `info` dictionary:
 - **`flagged`**: Whether a jailbreak attempt was detected
 - **`confidence`**: Confidence score (0.0 to 1.0) for the detection
 - **`threshold`**: The confidence threshold that was configured
-- **`reason`**: Explanation of why the input was flagged (or not flagged)
+- **`reason`**: Explanation of why the input was flagged (or not flagged) - *only included when `include_reasoning=true`*
 - **`used_conversation_history`**: Boolean indicating whether conversation history was analyzed
 - **`checked_text`**: JSON payload containing the conversation history and latest input that was analyzed
 

docs/ref/checks/llm_base.md

@@ -9,7 +9,8 @@ Base configuration for LLM-based guardrails. Provides common configuration optio
     "name": "LLM Base",
     "config": {
         "model": "gpt-5",
-        "confidence_threshold": 0.7
+        "confidence_threshold": 0.7,
+        "include_reasoning": false
     }
 }
 ```
@@ -18,6 +19,10 @@ Base configuration for LLM-based guardrails. Provides common configuration optio
 
 - **`model`** (required): OpenAI model to use for the check (e.g., "gpt-5")
 - **`confidence_threshold`** (required): Minimum confidence score to trigger tripwire (0.0 to 1.0)
+- **`include_reasoning`** (optional): Whether to include reasoning/explanation fields in the guardrail output (default: `false`)
+  - When `true`: The LLM generates and returns detailed reasoning for its decisions (e.g., `reason`, `reasoning`, `observation`, `evidence` fields)
+  - When `false`: The LLM only returns the essential fields (`flagged` and `confidence`), reducing token generation costs
+  - **Use Case**: Keep disabled for production to minimize costs; enable for development and debugging
 
 ## What It Does
 

docs/ref/checks/nsfw.md

@@ -29,6 +29,10 @@ Flags workplace‑inappropriate model outputs: explicit sexual content, profanit
 
 - **`model`** (required): Model to use for detection (e.g., "gpt-4.1-mini")
 - **`confidence_threshold`** (required): Minimum confidence score to trigger tripwire (0.0 to 1.0)
+- **`include_reasoning`** (optional): Whether to include reasoning/explanation fields in the guardrail output (default: `false`)
+    - When `false`: The LLM only generates the essential fields (`flagged` and `confidence`), reducing token generation costs
+    - When `true`: Additionally, returns detailed reasoning for its decisions
+    - **Use Case**: Keep disabled for production to minimize costs; enable for development and debugging
 
 ### Tuning guidance
 
@@ -51,6 +55,7 @@ Returns a `GuardrailResult` with the following `info` dictionary:
 - **`flagged`**: Whether NSFW content was detected
 - **`confidence`**: Confidence score (0.0 to 1.0) for the detection
 - **`threshold`**: The confidence threshold that was configured
+- **`reason`**: Explanation of why the input was flagged (or not flagged) - *only included when `include_reasoning=true`*
 
 ### Examples
 

docs/ref/checks/off_topic_prompts.md

@@ -20,6 +20,10 @@ Ensures content stays within defined business scope using LLM analysis. Flags co
 - **`model`** (required): Model to use for analysis (e.g., "gpt-5")
 - **`confidence_threshold`** (required): Minimum confidence score to trigger tripwire (0.0 to 1.0)
 - **`system_prompt_details`** (required): Description of your business scope and acceptable topics
+- **`include_reasoning`** (optional): Whether to include reasoning/explanation fields in the guardrail output (default: `false`)
+    - When `false`: The LLM only generates the essential fields (`flagged` and `confidence`), reducing token generation costs
+    - When `true`: Additionally, returns detailed reasoning for its decisions
+    - **Use Case**: Keep disabled for production to minimize costs; enable for development and debugging
 
 ## Implementation Notes
 
@@ -39,6 +43,7 @@ Returns a `GuardrailResult` with the following `info` dictionary:
 }
 ```
 
-- **`flagged`**: Whether the content aligns with your business scope
-- **`confidence`**: Confidence score (0.0 to 1.0) for the prompt injection detection assessment
+- **`flagged`**: Whether the content is off-topic (outside your business scope)
+- **`confidence`**: Confidence score (0.0 to 1.0) for the assessment
 - **`threshold`**: The confidence threshold that was configured
+- **`reason`**: Explanation of why the input was flagged (or not flagged) - *only included when `include_reasoning=true`*

docs/ref/checks/prompt_injection_detection.md

@@ -31,7 +31,8 @@ After tool execution, the prompt injection detection check validates that the re
     "name": "Prompt Injection Detection",
     "config": {
         "model": "gpt-4.1-mini",
-        "confidence_threshold": 0.7
+        "confidence_threshold": 0.7,
+        "include_reasoning": false
     }
 }
 ```
@@ -40,6 +41,10 @@ After tool execution, the prompt injection detection check validates that the re
 
 - **`model`** (required): Model to use for prompt injection detection analysis (e.g., "gpt-4.1-mini")
 - **`confidence_threshold`** (required): Minimum confidence score to trigger tripwire (0.0 to 1.0)
+- **`include_reasoning`** (optional): Whether to include the `observation` and `evidence` fields in the output (default: `false`)
+    - When `true`: Returns detailed `observation` explaining what the action is doing and `evidence` with specific quotes/details
+    - When `false`: Omits reasoning fields to save tokens (typically 100-300 tokens per check)
+    - Recommended: Keep disabled for production (default); enable for development/debugging
 
 **Flags as MISALIGNED:**
 
@@ -77,13 +82,16 @@ Returns a `GuardrailResult` with the following `info` dictionary:
 }
 ```
 
-- **`observation`**: What the AI action is doing
+- **`observation`**: What the AI action is doing - *only included when `include_reasoning=true`*
 - **`flagged`**: Whether the action is misaligned (boolean)
 - **`confidence`**: Confidence score (0.0 to 1.0) that the action is misaligned
+- **`evidence`**: Specific evidence from conversation supporting the decision - *only included when `include_reasoning=true`*
 - **`threshold`**: The confidence threshold that was configured
 - **`user_goal`**: The tracked user intent from conversation
 - **`action`**: The list of function calls or tool outputs analyzed for alignment
 
+**Note**: When `include_reasoning=false` (the default), the `observation` and `evidence` fields are omitted to reduce token generation costs.
+
 ## Benchmark Results
 
 ### Dataset Description

src/guardrails/checks/text/hallucination_detection.py

@@ -94,8 +94,8 @@ class HallucinationDetectionOutput(LLMOutput):
     Extends the base LLM output with hallucination-specific details.
 
     Attributes:
-        flagged (bool): Whether the content was flagged as potentially hallucinated.
-        confidence (float): Confidence score (0.0 to 1.0) that the input is hallucinated.
+        flagged (bool): Whether the content was flagged as potentially hallucinated (inherited).
+        confidence (float): Confidence score (0.0 to 1.0) that the input is hallucinated (inherited).
         reasoning (str): Detailed explanation of the analysis.
         hallucination_type (str | None): Type of hallucination detected.
         hallucinated_statements (list[str] | None): Specific statements flagged as
@@ -104,16 +104,6 @@ class HallucinationDetectionOutput(LLMOutput):
             by the documents.
     """
 
-    flagged: bool = Field(
-        ...,
-        description="Indicates whether the content was flagged as potentially hallucinated.",
-    )
-    confidence: float = Field(
-        ...,
-        description="Confidence score (0.0 to 1.0) that the input is hallucinated.",
-        ge=0.0,
-        le=1.0,
-    )
     reasoning: str = Field(
         ...,
         description="Detailed explanation of the hallucination analysis.",
@@ -184,14 +174,6 @@ class HallucinationDetectionOutput(LLMOutput):
     3. **Clearly contradicted by the documents** - Claims that directly contradict the documents → FLAG
     4. **Completely unsupported by the documents** - Claims that cannot be verified from the documents → FLAG
 
-    Respond with a JSON object containing:
-    - "flagged": boolean (true if ANY factual claims are clearly contradicted or completely unsupported)
-    - "confidence": float (0.0 to 1.0, your confidence that the input is hallucinated)
-    - "reasoning": string (detailed explanation of your analysis)
-    - "hallucination_type": string (type of issue, if detected: "factual_error", "unsupported_claim", or "none" if supported)
-    - "hallucinated_statements": array of strings (specific factual statements that may be hallucinated)
-    - "verified_statements": array of strings (specific factual statements that are supported by the documents)
-
     **CRITICAL GUIDELINES**:
     - Flag content if ANY factual claims are unsupported or contradicted (even if some claims are supported)
     - Allow conversational, opinion-based, or general content to pass through
@@ -206,6 +188,30 @@ class HallucinationDetectionOutput(LLMOutput):
 ).strip()
 
 
+# Instruction for output format when reasoning is enabled
+REASONING_OUTPUT_INSTRUCTION = textwrap.dedent(
+    """
+    Respond with a JSON object containing:
+    - "flagged": boolean (true if ANY factual claims are clearly contradicted or completely unsupported)
+    - "confidence": float (0.0 to 1.0, your confidence that the input is hallucinated)
+    - "reasoning": string (detailed explanation of your analysis)
+    - "hallucination_type": string (type of issue, if detected: "factual_error", "unsupported_claim", or "none" if supported)
+    - "hallucinated_statements": array of strings (specific factual statements that may be hallucinated)
+    - "verified_statements": array of strings (specific factual statements that are supported by the documents)
+    """
+).strip()
+
+
+# Instruction for output format when reasoning is disabled
+BASE_OUTPUT_INSTRUCTION = textwrap.dedent(
+    """
+    Respond with a JSON object containing:
+    - "flagged": boolean (true if ANY factual claims are clearly contradicted or completely unsupported)
+    - "confidence": float (0.0 to 1.0, your confidence that the input is hallucinated)
+    """
+).strip()
+
+
 async def hallucination_detection(
     ctx: GuardrailLLMContextProto,
     candidate: str,
@@ -242,15 +248,23 @@ async def hallucination_detection(
     )
 
     try:
-        # Create the validation query
-        validation_query = f"{VALIDATION_PROMPT}\n\nText to validate:\n{candidate}"
+        # Build the prompt based on whether reasoning is requested
+        if config.include_reasoning:
+            output_instruction = REASONING_OUTPUT_INSTRUCTION
+            output_format = HallucinationDetectionOutput
+        else:
+            output_instruction = BASE_OUTPUT_INSTRUCTION
+            output_format = LLMOutput
+
+        # Create the validation query with appropriate output instructions
+        validation_query = f"{VALIDATION_PROMPT}\n\n{output_instruction}\n\nText to validate:\n{candidate}"
 
         # Use the Responses API with file search and structured output
         response = await _invoke_openai_callable(
             ctx.guardrail_llm.responses.parse,
             input=validation_query,
             model=config.model,
-            text_format=HallucinationDetectionOutput,
+            text_format=output_format,
             tools=[{"type": "file_search", "vector_store_ids": [config.knowledge_source]}],
         )
 

src/guardrails/checks/text/jailbreak.py

@@ -40,8 +40,6 @@
 import textwrap
 from typing import Any
 
-from pydantic import Field
-
 from guardrails.registry import default_spec_registry
 from guardrails.spec import GuardrailSpecMetadata
 from guardrails.types import GuardrailLLMContextProto, GuardrailResult, token_usage_to_dict
@@ -50,6 +48,7 @@
     LLMConfig,
     LLMErrorOutput,
     LLMOutput,
+    LLMReasoningOutput,
     create_error_result,
     run_llm,
 )
@@ -226,15 +225,6 @@
 MAX_CONTEXT_TURNS = 10
 
 
-class JailbreakLLMOutput(LLMOutput):
-    """LLM output schema including rationale for jailbreak classification."""
-
-    reason: str = Field(
-        ...,
-        description=("Justification for why the input was flagged or not flagged as a jailbreak."),
-    )
-
-
 def _build_analysis_payload(conversation_history: list[Any] | None, latest_input: str) -> str:
     """Return a JSON payload with recent turns and the latest input."""
     trimmed_input = latest_input.strip()
@@ -251,12 +241,15 @@ async def jailbreak(ctx: GuardrailLLMContextProto, data: str, config: LLMConfig)
     conversation_history = getattr(ctx, "get_conversation_history", lambda: None)() or []
     analysis_payload = _build_analysis_payload(conversation_history, data)
 
+    # Use LLMReasoningOutput (with reason) if reasoning is enabled, otherwise use base LLMOutput
+    output_model = LLMReasoningOutput if config.include_reasoning else LLMOutput
+
     analysis, token_usage = await run_llm(
         analysis_payload,
         SYSTEM_PROMPT,
         ctx.guardrail_llm,
         config.model,
-        JailbreakLLMOutput,
+        output_model,
     )
 
     if isinstance(analysis, LLMErrorOutput):