Use structured outputs with Pydantic models instead of text parsing

claude_dspy/agent.py

@@ -290,7 +290,11 @@ class ClaudeCode(PrecompiledProgram):
         return ClaudeSDKClient(options=options)
 
     def _build_prompt(self, input_value: str) -> str:
-        """Build prompt from signature docstring, field descriptions, and input value."""
+        """Build prompt from signature docstring, field descriptions, and input value.
+
+        Note: When using structured outputs, the SDK handles JSON formatting automatically
+        via the output_format parameter, so we don't add JSON instructions to the prompt.
+        """
         prompt_parts = []
 
         # add signature docstring if present
@@ -325,20 +329,18 @@ class ClaudeCode(PrecompiledProgram):
         if output_desc:
             prompt_parts.append(f"\nPlease produce the following output: {output_desc}")
 
-        # for Pydantic outputs, add explicit JSON instructions
+        # Don't add JSON instructions - the SDK handles structured outputs via output_format
-        if self.output_format:
+        # The schema is passed through ClaudeAgentOptions and enforced by the SDK
-            schema = self.output_format["schema"]
-            prompt_parts.append(
-                f"\nYou MUST respond with ONLY valid JSON matching this schema:\n"
-                f"{json.dumps(schema, indent=2)}\n\n"
-                f"Do not include any explanatory text, markdown formatting, or code blocks. "
-                f"Return ONLY the raw JSON object."
-            )
 
         return "\n\n".join(prompt_parts)
 
     def _get_output_format(self) -> Optional[dict[str, Any]]:
-        """Get output format configuration for structured outputs."""
+        """Get output format configuration for structured outputs.
+
+        Supports:
+        - Direct Pydantic models: MyModel
+        - Generic types: list[MyModel], dict[str, MyModel]
+        """
         output_type = self.output_field.annotation
 
         if is_pydantic_model(output_type):
@@ -350,43 +352,68 @@ class ClaudeCode(PrecompiledProgram):
 
         return None
 
-    async def _run_async(self, prompt: str) -> tuple[str, list[TraceItem], Usage]:
+    async def _run_async(
-        """Run the agent asynchronously and collect results."""
+        self, prompt: str
+    ) -> tuple[str | dict | list | None, list[TraceItem], Usage]:
+        """Run the agent asynchronously and collect results.
+
+        Returns:
+            - response: For structured outputs, returns dict/list from structured_output.
+                       For text outputs, returns string from result or text blocks.
+            - trace: Execution trace items
+            - usage: Token usage statistics
+        """
+        print(f"[ClaudeCode._run_async] Initializing client (connected={self._is_connected})")
+
         # create client if needed
         if self._client is None:
+            print(f"[ClaudeCode._run_async] Creating new ClaudeSDKClient")
             self._client = self._create_client()
 
         # connect if not already connected
         if not self._is_connected:
+            print(f"[ClaudeCode._run_async] Connecting to Claude SDK...")
             await self._client.connect()
             self._is_connected = True
+            print(f"[ClaudeCode._run_async] Connected successfully")
 
         # send query (output_format already configured in options)
+        print(f"[ClaudeCode._run_async] Sending query to agent...")
         await self._client.query(prompt)
+        print(f"[ClaudeCode._run_async] Query sent, waiting for response...")
 
         # collect messages and build trace
         trace: list[TraceItem] = []
         usage = Usage()
         response_text = ""
+        structured_output = None
+        message_count = 0
 
         async for message in self._client.receive_response():
+            message_count += 1
+
             # handle assistant messages
             if isinstance(message, AssistantMessage):
+                print(f"[ClaudeCode._run_async] Received AssistantMessage #{message_count} with {len(message.content)} blocks")
                 for block in message.content:
                     if isinstance(block, TextBlock):
+                        print(f"[ClaudeCode._run_async]   - TextBlock: {len(block.text)} chars")
                         response_text += block.text
                         trace.append(
                             AgentMessageItem(text=block.text, model=message.model)
                         )
                     elif isinstance(block, ThinkingBlock):
+                        print(f"[ClaudeCode._run_async]   - ThinkingBlock: {len(block.thinking)} chars")
                         trace.append(
                             ThinkingItem(text=block.thinking, model=message.model)
                         )
                     elif isinstance(block, ToolUseBlock):
-                        # Handle StructuredOutput tool (contains JSON response)
+                        print(f"[ClaudeCode._run_async]   - ToolUseBlock: {block.name} (id={block.id})")
+                        # handle StructuredOutput tool (contains JSON response)
                         if block.name == "StructuredOutput":
-                            # The JSON is directly in the tool input (already a dict)
+                            # the JSON is directly in the tool input (already a dict)
                             response_text = json.dumps(block.input)
+                            print(f"[ClaudeCode._run_async]     StructuredOutput captured ({len(response_text)} chars)")
 
                         trace.append(
                             ToolUseItem(
@@ -396,11 +423,12 @@ class ClaudeCode(PrecompiledProgram):
                             )
                         )
                     elif isinstance(block, ToolResultBlock):
+                        print(f"[ClaudeCode._run_async]   - ToolResultBlock: tool_use_id={block.tool_use_id}, is_error={block.is_error}")
                         content_str = ""
                         if isinstance(block.content, str):
                             content_str = block.content
                         elif isinstance(block.content, list):
-                            # Extract text from content blocks
+                            # extract text from content blocks
                             for item in block.content:
                                 if (
                                     isinstance(item, dict)
@@ -410,7 +438,7 @@ class ClaudeCode(PrecompiledProgram):
 
                         trace.append(
                             ToolResultItem(
-                                tool_name="",  # Tool name not in ToolResultBlock
+                                tool_name="",  # tool name not in ToolResultBlock
                                 tool_use_id=block.tool_use_id,
                                 content=content_str,
                                 is_error=block.is_error or False,
@@ -419,9 +447,11 @@ class ClaudeCode(PrecompiledProgram):
 
             # handle result messages (final message with usage info)
             elif isinstance(message, ResultMessage):
+                print(f"[ClaudeCode._run_async] Received ResultMessage (is_error={getattr(message, 'is_error', False)})")
                 # store session ID
                 if hasattr(message, "session_id"):
                     self._session_id = message.session_id
+                    print(f"[ClaudeCode._run_async]   - Session ID: {self._session_id}")
 
                 # extract usage
                 if hasattr(message, "usage") and message.usage:
@@ -433,6 +463,7 @@ class ClaudeCode(PrecompiledProgram):
                         ),
                         output_tokens=usage_data.get("output_tokens", 0),
                     )
+                    print(f"[ClaudeCode._run_async]   - Usage: {usage.input_tokens} in, {usage.output_tokens} out, {usage.cached_input_tokens} cached")
 
                 # check for errors
                 if hasattr(message, "is_error") and message.is_error:
@@ -441,26 +472,41 @@ class ClaudeCode(PrecompiledProgram):
                         if hasattr(message, "result")
                         else "Unknown error"
                     )
+                    print(f"[ClaudeCode._run_async]   - ERROR: {error_msg}")
                     trace.append(
                         ErrorItem(message=error_msg, error_type="execution_error")
                     )
                     raise RuntimeError(f"Agent execution failed: {error_msg}")
 
-                # extract result if present (for structured outputs from result field)
+                # Prefer structured_output over result (when using output_format)
-                # Note: structured outputs may come from StructuredOutput tool instead
+                if hasattr(message, "structured_output") and message.structured_output is not None:
-                if hasattr(message, "result") and message.result:
+                    structured_output = message.structured_output
+                    print(f"[ClaudeCode._run_async]   - Structured output captured: {type(structured_output).__name__} ({len(str(structured_output))} chars)")
+                # Fallback to result field for text outputs
+                elif hasattr(message, "result") and message.result:
                     response_text = message.result
+                    print(f"[ClaudeCode._run_async]   - Result extracted from message ({len(response_text)} chars)")
 
             # handle system messages
             elif isinstance(message, SystemMessage):
+                print(f"[ClaudeCode._run_async] Received SystemMessage")
                 # log system messages to trace but don't error
                 if hasattr(message, "data") and message.data:
                     data_str = str(message.data)
+                    print(f"[ClaudeCode._run_async]   - Data: {data_str[:100]}..." if len(data_str) > 100 else f"[ClaudeCode._run_async]   - Data: {data_str}")
                     trace.append(
                         AgentMessageItem(text=f"[System: {data_str}]", model="system")
                     )
 
-        return response_text, trace, usage
+        print(f"[ClaudeCode._run_async] Completed: {message_count} messages processed, {len(trace)} trace items")
+
+        # Return structured_output if available (for Pydantic outputs), otherwise text
+        if structured_output is not None:
+            print(f"[ClaudeCode._run_async] Returning structured output")
+            return structured_output, trace, usage
+        else:
+            print(f"[ClaudeCode._run_async] Returning text response")
+            return response_text, trace, usage
 
     def forward(self, **kwargs: Any) -> Prediction:
         """Execute the agent with an input message.
@@ -480,6 +526,8 @@ class ClaudeCode(PrecompiledProgram):
             >>> print(result.trace)      # List of execution items
             >>> print(result.usage)      # Token usage stats
         """
+        print(f"\n[ClaudeCode.forward] Called with fields: {list(kwargs.keys())}")
+
         # extract input value
         if self.input_field_name not in kwargs:
             raise ValueError(
@@ -488,11 +536,14 @@ class ClaudeCode(PrecompiledProgram):
             )
 
         input_value = kwargs[self.input_field_name]
+        print(f"[ClaudeCode.forward] Input field '{self.input_field_name}': {input_value[:100]}..." if len(str(input_value)) > 100 else f"[ClaudeCode.forward] Input field '{self.input_field_name}': {input_value}")
 
         # build prompt
         prompt = self._build_prompt(input_value)
+        print(f"[ClaudeCode.forward] Built prompt ({len(prompt)} chars): {prompt[:200]}...")
 
         # run async execution in event loop
+        print(f"[ClaudeCode.forward] Starting async execution (model={self.model})")
         try:
             loop = asyncio.get_event_loop()
             if loop.is_running():
@@ -511,19 +562,37 @@ class ClaudeCode(PrecompiledProgram):
             # no event loop, create one
             response_text, trace, usage = asyncio.run(self._run_async(prompt))
 
+        # Log response details
+        response_type = type(response_text).__name__
+        response_len = len(str(response_text)) if response_text else 0
+        print(f"[ClaudeCode.forward] Received response (type={response_type}, {response_len} chars, {len(trace)} trace items)")
+        print(f"[ClaudeCode.forward] Token usage: {usage.input_tokens} input, {usage.output_tokens} output, {usage.cached_input_tokens} cached")
+
         # parse response based on output type
         output_type = self.output_field.annotation
         if is_pydantic_model(output_type):
+            print(f"[ClaudeCode.forward] Parsing structured output (type: {output_type})")
             try:
+                # response_text can be dict/list (from structured_output) or str (legacy)
                 parsed_output = parse_json_response(response_text, output_type)
+                print(f"[ClaudeCode.forward] Successfully parsed structured output")
             except Exception as e:
+                print(f"[ClaudeCode.forward] ERROR: Failed to parse structured output")
                 raise ValueError(
-                    f"Failed to parse Claude response as {output_type.__name__}: {e}\n"
+                    f"Failed to parse Claude response as {output_type}: {e}\n"
+                    f"Response type: {type(response_text)}\n"
                     f"Response: {response_text}"
                 )
         else:
+            print(f"[ClaudeCode.forward] Extracting text response (output type: {output_type})")
             # string output - extract text
-            parsed_output = extract_text_from_response(response_text)
+            if isinstance(response_text, str):
+                parsed_output = extract_text_from_response(response_text)
+            else:
+                # Shouldn't happen, but handle gracefully
+                parsed_output = str(response_text)
+
+        print(f"[ClaudeCode.forward] Returning Prediction with session_id={self._session_id}\n")
 
         # return prediction with typed output, trace, and usage
         return Prediction(
@@ -545,6 +614,8 @@ class ClaudeCode(PrecompiledProgram):
         Returns:
             Prediction with typed output, trace, and usage
         """
+        print(f"\n[ClaudeCode.aforward] Called with fields: {list(kwargs.keys())}")
+
         # extract input value
         if self.input_field_name not in kwargs:
             raise ValueError(
@@ -553,26 +624,47 @@ class ClaudeCode(PrecompiledProgram):
             )
 
         input_value = kwargs[self.input_field_name]
+        print(f"[ClaudeCode.aforward] Input field '{self.input_field_name}': {input_value[:100]}..." if len(str(input_value)) > 100 else f"[ClaudeCode.aforward] Input field '{self.input_field_name}': {input_value}")
 
         # build prompt
         prompt = self._build_prompt(input_value)
+        print(f"[ClaudeCode.aforward] Built prompt ({len(prompt)} chars)")
 
         # run async execution
+        print(f"[ClaudeCode.aforward] Starting async execution (model={self.model})")
         response_text, trace, usage = await self._run_async(prompt)
 
+        # Log response details
+        response_type = type(response_text).__name__
+        response_len = len(str(response_text)) if response_text else 0
+        print(f"[ClaudeCode.aforward] Received response (type={response_type}, {response_len} chars, {len(trace)} trace items)")
+        print(f"[ClaudeCode.aforward] Token usage: {usage.input_tokens} input, {usage.output_tokens} output, {usage.cached_input_tokens} cached")
+
         # parse response based on output type
         output_type = self.output_field.annotation
         if is_pydantic_model(output_type):
+            print(f"[ClaudeCode.aforward] Parsing structured output (type: {output_type})")
             try:
+                # response_text can be dict/list (from structured_output) or str (legacy)
                 parsed_output = parse_json_response(response_text, output_type)
+                print(f"[ClaudeCode.aforward] Successfully parsed structured output")
             except Exception as e:
+                print(f"[ClaudeCode.aforward] ERROR: Failed to parse structured output")
                 raise ValueError(
-                    f"Failed to parse Claude response as {output_type.__name__}: {e}\n"
+                    f"Failed to parse Claude response as {output_type}: {e}\n"
+                    f"Response type: {type(response_text)}\n"
                     f"Response: {response_text}"
                 )
         else:
+            print(f"[ClaudeCode.aforward] Extracting text response (output type: {output_type})")
             # string output - extract text
-            parsed_output = extract_text_from_response(response_text)
+            if isinstance(response_text, str):
+                parsed_output = extract_text_from_response(response_text)
+            else:
+                # Shouldn't happen, but handle gracefully
+                parsed_output = str(response_text)
+
+        print(f"[ClaudeCode.aforward] Returning Prediction with session_id={self._session_id}\n")
 
         # return prediction with typed output, trace, and usage
         return Prediction(

claude_dspy/utils.py

@@ -27,24 +27,96 @@ class Usage:
 
 
 def is_pydantic_model(type_hint: Any) -> bool:
-    """Check if a type hint is a Pydantic model."""
+    """Check if a type hint is a Pydantic model or contains one (e.g., list[Model]).
+
+    Returns True for:
+    - Pydantic models: MyModel
+    - Generic types containing Pydantic: list[MyModel], dict[str, MyModel]
+    """
     try:
-        return isinstance(type_hint, type) and issubclass(type_hint, BaseModel)
+        # Direct Pydantic model
+        if isinstance(type_hint, type) and issubclass(type_hint, BaseModel):
+            return True
+
+        # Generic type (list, dict, etc.)
+        origin = get_origin(type_hint)
+        if origin is not None:
+            args = get_args(type_hint)
+            # Check if any type argument is a Pydantic model
+            for arg in args:
+                if isinstance(arg, type) and issubclass(arg, BaseModel):
+                    return True
+
+        return False
     except TypeError:
         return False
 
 
-def get_json_schema(pydantic_model: type[BaseModel]) -> dict[str, Any]:
+def get_json_schema(type_hint: Any) -> dict[str, Any]:
-    """Generate JSON schema from Pydantic model.
+    """Generate JSON schema from type hint.
 
-    Sets additionalProperties to false to match Codex behavior.
+    Handles:
+    - Pydantic models: MyModel
+    - Generic types: list[MyModel], dict[str, MyModel]
+
+    Note: Claude API requires root type to be "object" for structured outputs (tools).
+    For list/dict types, we wrap them in an object with a single property.
+
+    Sets additionalProperties to false for all objects.
     """
-    schema = pydantic_model.model_json_schema()
+    origin = get_origin(type_hint)
+    args = get_args(type_hint)
 
-    # Recursively set additionalProperties: false for all objects
+    # Handle generic types (list, dict, etc.)
+    if origin is list:
+        # list[Model] - wrap in object since API requires root type = "object"
+        # {"type": "object", "properties": {"items": {"type": "array", "items": {...}}}}
+        if args and isinstance(args[0], type) and issubclass(args[0], BaseModel):
+            model = args[0]
+            schema = {
+                "type": "object",
+                "properties": {
+                    "items": {
+                        "type": "array",
+                        "items": model.model_json_schema()
+                    }
+                },
+                "required": ["items"],
+                "additionalProperties": False
+            }
+        else:
+            raise ValueError(f"Unsupported list type: {type_hint}")
+
+    elif origin is dict:
+        # dict[str, Model] - wrap in object since API requires root type = "object"
+        # {"type": "object", "properties": {"values": {"type": "object", "additionalProperties": {...}}}}
+        if len(args) >= 2 and isinstance(args[1], type) and issubclass(args[1], BaseModel):
+            model = args[1]
+            schema = {
+                "type": "object",
+                "properties": {
+                    "values": {
+                        "type": "object",
+                        "additionalProperties": model.model_json_schema()
+                    }
+                },
+                "required": ["values"],
+                "additionalProperties": False
+            }
+        else:
+            raise ValueError(f"Unsupported dict type: {type_hint}")
+
+    elif isinstance(type_hint, type) and issubclass(type_hint, BaseModel):
+        # Direct Pydantic model - already an object
+        schema = type_hint.model_json_schema()
+
+    else:
+        raise ValueError(f"Unsupported type for structured output: {type_hint}")
+
+    # Recursively set additionalProperties: false for all nested objects
     def set_additional_properties(obj: dict[str, Any]) -> None:
         if isinstance(obj, dict):
-            if obj.get("type") == "object":
+            if obj.get("type") == "object" and "additionalProperties" not in obj:
                 obj["additionalProperties"] = False
             for value in obj.values():
                 if isinstance(value, dict):
@@ -58,14 +130,74 @@ def get_json_schema(pydantic_model: type[BaseModel]) -> dict[str, Any]:
     return schema
 
 
-def parse_json_response(response: str, pydantic_model: type[BaseModel]) -> BaseModel:
+def parse_json_response(
-    """Parse JSON response into Pydantic model.
+    response: str | dict | list, type_hint: Any
+) -> BaseModel | list[BaseModel] | dict[str, BaseModel]:
+    """Parse JSON response into typed output.
+
+    Handles:
+    - Pydantic models: MyModel
+    - Generic types: list[MyModel], dict[str, MyModel]
+
+    Note: When schema has list/dict at root, the SDK wraps them in {"items": [...]}
+    or {"values": {...}} because API requires root type = "object".
+
+    Args:
+        response: JSON string or already-parsed dict/list from structured_output
+        type_hint: The output type annotation
+
+    Returns:
+        Validated and typed output
 
     Raises:
-        json.JSONDecodeError: If response is not valid JSON
+        json.JSONDecodeError: If response string is not valid JSON
-        pydantic.ValidationError: If JSON doesn't match model schema
+        pydantic.ValidationError: If JSON doesn't match schema
     """
-    return pydantic_model.model_validate_json(response)
+    import json
+
+    origin = get_origin(type_hint)
+    args = get_args(type_hint)
+
+    # Parse string to dict/list if needed
+    if isinstance(response, str):
+        parsed = json.loads(response)
+    else:
+        parsed = response
+
+    # Handle list[Model]
+    if origin is list:
+        if args and isinstance(args[0], type) and issubclass(args[0], BaseModel):
+            model = args[0]
+
+            # Unwrap from {"items": [...]} if present (from structured_output)
+            if isinstance(parsed, dict) and "items" in parsed:
+                parsed = parsed["items"]
+
+            if not isinstance(parsed, list):
+                raise ValueError(f"Expected list, got {type(parsed)}")
+            return [model.model_validate(item) for item in parsed]
+
+    # Handle dict[str, Model]
+    elif origin is dict:
+        if len(args) >= 2 and isinstance(args[1], type) and issubclass(args[1], BaseModel):
+            model = args[1]
+
+            # Unwrap from {"values": {...}} if present (from structured_output)
+            if isinstance(parsed, dict) and "values" in parsed:
+                parsed = parsed["values"]
+
+            if not isinstance(parsed, dict):
+                raise ValueError(f"Expected dict, got {type(parsed)}")
+            return {key: model.model_validate(value) for key, value in parsed.items()}
+
+    # Handle direct Pydantic model
+    elif isinstance(type_hint, type) and issubclass(type_hint, BaseModel):
+        if isinstance(response, str):
+            return type_hint.model_validate_json(response)
+        else:
+            return type_hint.model_validate(parsed)
+
+    raise ValueError(f"Unsupported type for parsing: {type_hint}")
 
 
 def extract_text_from_response(response: str) -> str:

main.py

@@ -1,16 +1,21 @@
 from claude_dspy import ClaudeCode, ClaudeCodeConfig
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from modaic import AutoProgram
 import dspy
 
 
-class Output(BaseModel):
+class ErrorReport(BaseModel):
-    files: list[str]
+    error: str = Field(..., description="Error message")
+    timestamp: str = Field(..., description="Timestamp of error")
+    line_located: int | None = Field(..., description="Line number where error occurred")
+    file_located: str | None = Field(..., description="File where error occurred")
+    description: str = Field(..., description="Description of errors")
+    reccomedated_fixes: list[str] = Field(..., description="List of recommended fixes")
 
 
 class ClaudeCodeSignature(dspy.Signature):
-    message: str = dspy.InputField(desc="Request to process")
+    log_file: str = dspy.InputField(desc="Log file to process")
-    output: Output = dspy.OutputField(desc="List of files modified or created")
+    output: list[ErrorReport] = dspy.OutputField(desc="list of error reports created")
 
 
 def main():
@@ -23,25 +28,32 @@ def main():
         signature=ClaudeCodeSignature,
         working_directory=".",
         permission_mode="acceptEdits",
-        allowed_tools=["Read", "Bash", "Write"],
+        allowed_tools=["Read", "Write", "Bash"],
     )
+
     cc.push_to_hub(
         "farouk1/claude-code",
         with_code=True,
-        commit_message="Add more functionality to signature description parsing",
+        commit_message="Use structured outputs with Pydantic models instead of text parsing",
     )
+
     # agent = AutoProgram.from_precompiled("farouk1/claude-code", signature=ClaudeCodeSignature, working_directory=".", permission_mode="acceptEdits", allowed_tools=["Read", "Write", "Bash"])
-    
+    """
-    # Test the agent
+    print("Running Claude Code...")
-    result = cc(message="create a python program that prints 'Hello, World!' and save it to a file in this directory")
+    result = cc(log_file="log.txt")
-    print(result.output.files)
+    print("Claude Code finished running!")
-    print(result.output)
+    print("-" * 50)
-    print(result.usage)
+    print("Output: ", result.output)
+    print("Output type: ", type(result.output))
+    print("Usage: ", result.usage)
+    print("Output type: ", type(result.output))
+    print("-" * 50)
 
     print()
     print("Trace:")
     for i, item in enumerate(result.trace):
         print(f"  {i}: {item}")
+    """
 
 
 if __name__ == "__main__":