feat: rework tool calling

dynamiq/nodes/agents/agent.py

@@ -1,6 +1,7 @@
 import json
+import types
 from concurrent.futures import as_completed
-from typing import Any, Callable, Literal, Mapping
+from typing import Any, Callable, Literal, Mapping, Union, get_args, get_origin
 
 from litellm import get_supported_openai_params, supports_function_calling, supports_response_schema
 from pydantic import BaseModel, Field, PrivateAttr, field_validator, model_validator
@@ -39,7 +40,7 @@
     StreamingMode,
 )
 from dynamiq.utils import generate_uuid, serialize_files_in_value
-from dynamiq.utils.json_parser import parse_llm_json_output
+from dynamiq.utils.json_parser import parse_llm_json_output, repair_truncated_json
 from dynamiq.utils.logger import logger
 
 
@@ -79,18 +80,23 @@ def parse_arguments(cls, v: Any) -> Any:
         if isinstance(v, str):
             try:
                 return json.loads(v, strict=False)
-            except json.JSONDecodeError as e:
-                raise ValueError(f"Tool call arguments are not valid JSON: {e}")
+            except json.JSONDecodeError:
+                try:
+                    return json.loads(repair_truncated_json(v), strict=False)
+                except json.JSONDecodeError as e:
+                    raise ValueError(f"Tool call arguments are not valid JSON: {e}")
         return v or {}
 
     def parse_as_tool_call(self) -> ToolCallArguments:
+        args = dict(self.arguments)
+        thought = args.pop("thought", "")
         try:
-            return ToolCallArguments.model_validate(self.arguments)
+            return ToolCallArguments(thought=thought, action_input=args)
         except Exception:
             raise ActionParsingException(
                 "Your tool call is missing required fields. "
                 "Every tool call must include 'thought' (your reasoning) "
-                "and 'action_input' (the tool parameters as an object).",
+                "and the tool's parameters at the top level.",
                 recoverable=True,
             )
 
@@ -320,6 +326,38 @@ def _emit_tool_input_error(
         )
         self._streaming_tool_run_id = None
 
+    @staticmethod
+    def _annotation_accepts_none(annotation: Any) -> bool:
+        """Return True if a Pydantic field annotation includes ``NoneType``."""
+        if annotation is type(None):
+            return True
+        origin = get_origin(annotation)
+        if origin in (Union, types.UnionType):
+            return type(None) in get_args(annotation)
+        return False
+
+    def _strip_protocol_nulls(self, tool: Node, action_input: dict) -> dict:
+        """Drop ``None`` values for fields whose Pydantic annotation rejects None.
+
+        OpenAI strict mode requires every property in ``required`` and uses
+        ``"null"`` in the type union as the documented signal for "not specified."
+        Tools with non-nullable defaults (``encoding: str = "utf-8"``) can't
+        accept that ``None`` directly — but their Pydantic default should kick
+        in if the key is absent. Drop those keys so validation/execution use
+        defaults instead of failing.
+
+        Nullable fields (``encoding: str | None = None``) keep their None — the
+        tool genuinely accepts it.
+        """
+        fields = tool.input_schema.model_fields
+        for name in list(action_input):
+            if action_input[name] is not None:
+                continue
+            field = fields.get(name)
+            if field is not None and not self._annotation_accepts_none(field.annotation):
+                del action_input[name]
+        return action_input
+
     def _should_delegate_final(
         self,
         tool: Node | None,
@@ -791,6 +829,7 @@ def _handle_function_calling_mode(
         if len(actual_tool_calls) > 1 and self.parallel_tool_calls_enabled:
             tool_items = []
             for tc in actual_tool_calls:
+                tc_name = tc.function.name.strip()
                 args = tc.function.parse_as_tool_call()
                 tc_input = args.action_input
                 if isinstance(tc_input, str):
@@ -800,9 +839,19 @@ def _handle_function_calling_mode(
                         raise ActionParsingException(f"Error parsing action_input string. {e}", recoverable=True)
                 if not isinstance(tc_input, dict):
                     tc_input = {"input": tc_input}
+                tc_tool = self.tool_by_names.get(self.sanitize_tool_name(tc_name))
+                if tc_tool is not None:
+                    self._strip_protocol_nulls(tc_tool, tc_input)
+                    try:
+                        tc_tool.input_schema.model_validate(tc_input)
+                    except Exception as e:
+                        raise ActionParsingException(
+                            f"Tool call for '{tc_name}' has invalid arguments: {e}",
+                            recoverable=True,
+                        )
                 tool_items.append(
                     ToolCallItem(
-                        name=tc.function.name.strip(),
+                        name=tc_name,
                         input=tc_input,
                         thought=args.thought,
                     )
@@ -826,6 +875,17 @@ def _handle_function_calling_mode(
         if not isinstance(action_input, dict):
             action_input = {"input": action_input}
 
+        tool = self.tool_by_names.get(self.sanitize_tool_name(action))
+        if tool is not None:
+            self._strip_protocol_nulls(tool, action_input)
+            try:
+                tool.input_schema.model_validate(action_input)
+            except Exception as e:
+                raise ActionParsingException(
+                    f"Tool call for '{action}' has invalid arguments: {e}",
+                    recoverable=True,
+                )
+
         self.log_reasoning(thought, action, action_input, loop_num)
         return thought, action, action_input
 
@@ -1504,12 +1564,24 @@ def _run_react_llm_step(self, config: RunnableConfig | None, loop_num: int, **kw
 
         try:
             native_parallel = self.parallel_tool_calls_enabled and self.inference_mode == InferenceMode.FUNCTION_CALLING
+            # In FUNCTION_CALLING mode with tools present, force a tool call so
+            # the model cannot bail out with a text-only response. Honour any
+            # explicit caller override (kwargs / self.llm.tool_choice).
+            forced_tool_choice = None
+            if (
+                self.inference_mode == InferenceMode.FUNCTION_CALLING
+                and self._tools
+                and "tool_choice" not in kwargs
+                and getattr(self.llm, "tool_choice", None) is None
+            ):
+                forced_tool_choice = "required"
             llm_result = self._run_llm(
                 messages=messages,
                 tools=self._tools,
                 response_format=self._response_format,
                 config=llm_config,
                 parallel_tool_calls=True if native_parallel else None,
+                **({"tool_choice": forced_tool_choice} if forced_tool_choice else {}),
                 **kwargs,
             )
         finally:

dynamiq/nodes/agents/components/schema_generator.py

@@ -19,7 +19,6 @@
 
 FINAL_ANSWER_FUNCTION_SCHEMA = {
     "type": "function",
-    "strict": True,
     "function": {
         "name": "provide_final_answer",
         "description": "Function should be called when if you can answer the initial request"
@@ -38,6 +37,7 @@
                 },
             },
             "required": ["thought", "answer", "output_files"],
+            "additionalProperties": False,
         },
     },
 }
@@ -75,7 +75,6 @@ def build_final_answer_function_schema(response_format: dict | type[BaseModel] |
         return FINAL_ANSWER_FUNCTION_SCHEMA
 
     answer_schema = unwrap_response_format(response_format)
-    strict = _is_strict_compatible(answer_schema)
 
     parameters = {
         "type": "object",
@@ -91,13 +90,11 @@ def build_final_answer_function_schema(response_format: dict | type[BaseModel] |
             },
         },
         "required": ["thought", "answer", "output_files"],
+        "additionalProperties": False,
     }
-    if strict:
-        parameters["additionalProperties"] = False
 
     return {
         "type": "function",
-        "strict": strict,
         "function": {
             "name": "provide_final_answer",
             "description": (
@@ -239,8 +236,8 @@ def _resolve_type_schema(param: Any, _seen: set | None = None) -> dict[str, Any]
     ``properties`` so the LLM produces correctly structured output.
     Generic ``dict`` types become bare ``{"type": "object"}``.
 
-    Tools whose schemas contain bare objects automatically get
-    ``strict: false`` via ``_is_strict_compatible``.
+    Per-provider transforms (in ``BaseLLM`` subclasses) decide whether
+    ``strict`` is engaged for a given schema and provider.
     """
     if param is type(None):
         return {"type": "null"}
@@ -323,29 +320,6 @@ def _basemodel_to_schema(model: type[BaseModel], _seen: set | None = None) -> di
     return result
 
 
-def _is_strict_compatible(schema: Any) -> bool:
-    """Return ``False`` if the schema contains an object that OpenAI strict mode
-    would reject — bare objects without ``properties``, or objects missing
-    ``additionalProperties: False``."""
-    if not isinstance(schema, dict):
-        return True
-    schema_type = schema.get("type")
-    is_object = schema_type == "object" or (isinstance(schema_type, list) and "object" in schema_type)
-    if is_object:
-        if "properties" not in schema:
-            return False
-        if schema.get("additionalProperties") is not False:
-            return False
-    for value in schema.values():
-        if isinstance(value, dict) and not _is_strict_compatible(value):
-            return False
-        if isinstance(value, list):
-            for item in value:
-                if isinstance(item, dict) and not _is_strict_compatible(item):
-                    return False
-    return True
-
-
 def _is_nullable(annotation: Any) -> bool:
     """Return True if the annotation is a Union that includes NoneType."""
     origin = get_origin(annotation)
@@ -414,87 +388,43 @@ def generate_function_calling_schemas(
     schemas = [build_final_answer_function_schema(response_format)]
 
     for tool in tools:
-        properties = {}
-        required_fields = []
-        input_params = tool.input_schema.model_fields.items()
-        if list(input_params):
-            for name, field in tool.input_schema.model_fields.items():
-                generate_property_schema(properties, name, field)
-                if field.is_required() and name in properties:
-                    required_fields.append(name)
-
-            if isinstance(tool, SubAgentTool) and delegation_allowed:
-                properties["delegate_final"] = {
-                    "type": "boolean",
-                    "description": (
-                        "Set to true to return the sub-agent's response verbatim "
-                        "as the parent agent's final output."
-                    ),
-                }
-
-            has_optional = len(required_fields) < len(properties)
-            use_strict = _is_strict_compatible(properties) and not has_optional
-
-            action_input_schema: dict[str, Any] = {
-                "type": "object",
-                "description": "Tool parameters as a JSON object, not a string.",
-                "properties": properties,
+        properties: dict[str, Any] = {}
+        required_fields: list[str] = []
+        for name, field in tool.input_schema.model_fields.items():
+            generate_property_schema(properties, name, field)
+            if field.is_required() and name in properties:
+                required_fields.append(name)
+
+        if isinstance(tool, SubAgentTool) and delegation_allowed:
+            properties["delegate_final"] = {
+                "type": "boolean",
+                "description": (
+                    "Set to true to return the sub-agent's response verbatim " "as the parent agent's final output."
+                ),
             }
-            if use_strict:
-                action_input_schema["required"] = list(properties.keys())
-                action_input_schema["additionalProperties"] = False
-            else:
-                if required_fields:
-                    action_input_schema["required"] = required_fields
-
-            schema = {
-                "type": "function",
-                "function": {
-                    "name": sanitize_tool_name(tool.name),
-                    "description": tool.description[:1024],
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "thought": {
-                                "type": "string",
-                                "description": "Your reasoning about using this tool.",
-                            },
-                            "action_input": action_input_schema,
-                        },
-                        "additionalProperties": False,
-                        "required": ["thought", "action_input"],
-                    },
-                    "strict": use_strict,
-                },
-            }
-
-            schemas.append(schema)
 
-        else:
-            schema = {
-                "type": "function",
-                "function": {
-                    "name": sanitize_tool_name(tool.name),
-                    "description": tool.description[:1024],
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "thought": {
-                                "type": "string",
-                                "description": "Your reasoning about using this tool.",
-                            },
-                            "action_input": {
-                                "type": "string",
-                                "description": "Input for the selected tool in JSON string format.",
-                            },
-                        },
-                        "additionalProperties": False,
-                        "required": ["thought", "action_input"],
-                    },
-                    "strict": True,
+        flat_properties: dict[str, Any] = {
+            "thought": {
+                "type": "string",
+                "description": "Your reasoning about using this tool.",
+            },
+        }
+        flat_properties.update(properties)
+
+        schema = {
+            "type": "function",
+            "function": {
+                "name": sanitize_tool_name(tool.name),
+                "description": tool.description[:1024],
+                "parameters": {
+                    "type": "object",
+                    "properties": flat_properties,
+                    "required": ["thought", *required_fields],
+                    "additionalProperties": False,
                 },
-            }
+            },
+        }
 
-            schemas.append(schema)
+        schemas.append(schema)
 
     return schemas