feat: rework tool calling

dynamiq/callbacks/streaming.py

@@ -1086,19 +1086,17 @@ def _try_initialize_next_json_field(self, buf: str, final_answer_only: bool) ->
                         buf, JSONStreamingField.ACTION_INPUT.value, StreamingState.TOOL_INPUT
                     )
 
-    def _emit_tool_input_state(self, buf: str) -> None:
-        """Emit content for the current TOOL_INPUT state."""
+    def _emit_tool_input_state(self, buf: str) -> bool:
+        """Emit content for the current TOOL_INPUT state. Returns True when complete."""
         if self._fc_object_tool_input:
-            self._emit_json_object_field_content(buf, StreamingState.TOOL_INPUT)
-        else:
-            self._emit_json_field_content(buf, StreamingState.TOOL_INPUT)
+            return self._emit_json_object_field_content(buf, StreamingState.TOOL_INPUT)
+        return self._emit_json_field_content(buf, StreamingState.TOOL_INPUT)
 
-    def _emit_answer_state(self, buf: str) -> None:
-        """Emit content for the current ANSWER state."""
+    def _emit_answer_state(self, buf: str) -> bool:
+        """Emit content for the current ANSWER state. Returns True when complete."""
         if self._fc_object_answer:
-            self._emit_json_object_field_content(buf, StreamingState.ANSWER)
-        else:
-            self._emit_json_field_content(buf, StreamingState.ANSWER)
+            return self._emit_json_object_field_content(buf, StreamingState.ANSWER)
+        return self._emit_json_field_content(buf, StreamingState.ANSWER)
 
     def _process_json_mode(self, final_answer_only: bool) -> None:
         """

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
 
 
@@ -79,8 +80,11 @@ 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:
@@ -320,6 +324,106 @@ 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
+
+    @staticmethod
+    def _annotation_is_dict_like(annotation: Any) -> bool:
+        """Return True if the annotation is ``dict`` / ``dict[...]`` or a union including one."""
+        if annotation is dict:
+            return True
+        origin = get_origin(annotation)
+        if origin is dict:
+            return True
+        if origin in (Union, types.UnionType):
+            return any(Agent._annotation_is_dict_like(arg) for arg in get_args(annotation))
+        return False
+
+    @staticmethod
+    def _extract_basemodel(annotation: Any) -> type[BaseModel] | None:
+        """Return the BaseModel subclass in an annotation (handles ``Model | None``), else None."""
+        if isinstance(annotation, type) and issubclass(annotation, BaseModel):
+            return annotation
+        origin = get_origin(annotation)
+        if origin in (Union, types.UnionType):
+            for arg in get_args(annotation):
+                if isinstance(arg, type) and issubclass(arg, BaseModel):
+                    return arg
+        return None
+
+    def _coerce_json_fields(self, tool: Node, action_input: dict) -> dict:
+        """Parse stringified free-form dict fields back into dicts.
+
+        Strict mode can't express a free-form ``dict[str, Any]`` as an object, so
+        the schema transforms ship those fields as JSON-encoded strings (see the
+        provider converters). Here we reverse that: if the tool declares a
+        dict-typed field and the model supplied a JSON string for it, parse it
+        back so the tool's Pydantic schema validates the real dict.
+
+        Recurses into nested ``BaseModel`` fields so a free-form dict declared on a
+        sub-model is coerced too (mirrors ``_strip_nulls_for_fields``). Without this
+        a stringified dict nested inside a sub-model would never be parsed back and
+        the nested model's validation would reject the ``str``.
+        """
+        self._coerce_json_for_fields(tool.input_schema.model_fields, action_input)
+        return action_input
+
+    def _coerce_json_for_fields(self, fields: Mapping[str, Any], data: Any) -> None:
+        if not isinstance(data, dict):
+            return
+        for name, field in fields.items():
+            value = data.get(name)
+            if isinstance(value, str) and self._annotation_is_dict_like(field.annotation):
+                stripped = value.strip()
+                if stripped.startswith("{") and stripped.endswith("}"):
+                    try:
+                        data[name] = json.loads(stripped)
+                    except json.JSONDecodeError:
+                        pass  # leave as string; Pydantic will surface the error
+            elif isinstance(value, dict):
+                nested_model = self._extract_basemodel(field.annotation)
+                if nested_model is not None:
+                    self._coerce_json_for_fields(nested_model.model_fields, value)
+
+    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 signal for "leave it at the default."
+        Fields with a non-nullable default (``encoding: str = "utf-8"``) can't
+        accept that ``None`` directly — so we drop the key, letting the tool's
+        Pydantic default apply. Fields that genuinely accept ``None``
+        (``encoding: str | None = None``) keep it.
+
+        Recurses into nested ``BaseModel`` fields so the same applies at depth
+        (e.g. ``config.port`` where ``DBConfig.port: int = 8080``).
+        """
+        self._strip_nulls_for_fields(tool.input_schema.model_fields, action_input)
+        return action_input
+
+    def _strip_nulls_for_fields(self, fields: Mapping[str, Any], data: Any) -> None:
+        if not isinstance(data, dict):
+            return
+        for name in list(data):
+            field = fields.get(name)
+            if field is None:
+                continue
+            value = data[name]
+            if value is None:
+                if not self._annotation_accepts_none(field.annotation):
+                    del data[name]
+            elif isinstance(value, dict):
+                nested_model = self._extract_basemodel(field.annotation)
+                if nested_model is not None:
+                    self._strip_nulls_for_fields(nested_model.model_fields, value)
+
     def _should_delegate_final(
         self,
         tool: Node | None,
@@ -791,6 +895,7 @@ 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 +905,13 @@ 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._coerce_json_fields(tc_tool, tc_input)
+                    self._strip_protocol_nulls(tc_tool, tc_input)
                 tool_items.append(
                     ToolCallItem(
-                        name=tc.function.name.strip(),
+                        name=tc_name,
                         input=tc_input,
                         thought=args.thought,
                     )
@@ -826,6 +935,11 @@ 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._coerce_json_fields(tool, action_input)
+            self._strip_protocol_nulls(tool, action_input)
+
         self.log_reasoning(thought, action, action_input, loop_num)
         return thought, action, action_input
 
@@ -1504,12 +1618,24 @@ 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)
@@ -414,10 +388,10 @@ 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):
+        properties: dict[str, Any] = {}
+        required_fields: list[str] = []
+        input_params = list(tool.input_schema.model_fields.items())
+        if 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:
@@ -427,25 +401,18 @@ def generate_function_calling_schemas(
                 properties["delegate_final"] = {
                     "type": "boolean",
                     "description": (
-                        "Set to true to return the sub-agent's response verbatim "
-                        "as the parent agent's final output."
+                        "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,
+                "additionalProperties": False,
             }
-            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
+            if required_fields:
+                action_input_schema["required"] = required_fields
 
             schema = {
                 "type": "function",
@@ -464,12 +431,8 @@ def generate_function_calling_schemas(
                         "additionalProperties": False,
                         "required": ["thought", "action_input"],
                     },
-                    "strict": use_strict,
                 },
             }
-
-            schemas.append(schema)
-
         else:
             schema = {
                 "type": "function",
@@ -491,10 +454,9 @@ def generate_function_calling_schemas(
                         "additionalProperties": False,
                         "required": ["thought", "action_input"],
                     },
-                    "strict": True,
                 },
             }
 
-            schemas.append(schema)
+        schemas.append(schema)
 
     return schemas

dynamiq/nodes/agents/prompts/react/instructions.py

@@ -107,9 +107,12 @@
 - Make sure to adhere to AGENT PERSONA & STYLE & ADDITIONAL BEHAVIORAL GUIDELINES.
 
 ## Single Action Per Turn
-- Execute exactly ONE <action>/<action_input> pair per response, then wait for its Observation before continuing
-- Do NOT include multiple action blocks or answer blocks in the same response
-- After receiving an Observation, decide the next single action based on the result
+- Emit EXACTLY ONE <output>...</output> block per response. Never write a second <output> block.
+- A response either takes an action (<action>/<action_input>) OR gives an <answer> — never both in the same response.
+- When you write an <action>, STOP immediately after </output>. Do NOT continue, do NOT write an "Observation:",
+  a tool result, or an <answer> — the Observation is given back to you by the system, not written by you.
+- NEVER predict, assume, or fabricate the tool's Observation/result. Wait for the real Observation to be returned,
+  then decide your next single action (or final answer) based on it in your NEXT response.
 
 ## JSON Formatting Requirements
 - Put JSON on single line within tags