feat: relax structured output parsing

dynamiq/nodes/agents/agent.py

@@ -596,7 +596,7 @@ def _handle_function_calling_mode(
 
         if isinstance(action_input, str):
             try:
-                action_input = json.loads(action_input)
+                action_input = json.loads(action_input, strict=False)
             except json.JSONDecodeError as e:
                 raise ActionParsingException(f"Error parsing action_input string. {e}", recoverable=True)
 
@@ -618,11 +618,11 @@ def _handle_structured_output_mode(
         try:
             if isinstance(llm_generated_output, str):
                 try:
-                    llm_generated_output_json = json.loads(llm_generated_output)
+                    llm_generated_output_json = json.loads(llm_generated_output, strict=False)
                 except json.JSONDecodeError:
                     # Handle known LLM bug where multiple JSON objects are returned.
                     # Use raw_decode to parse only the first valid JSON object.
-                    decoder = json.JSONDecoder()
+                    decoder = json.JSONDecoder(strict=False)
                     llm_generated_output_json, _ = decoder.raw_decode(llm_generated_output.strip())
             else:
                 llm_generated_output_json = llm_generated_output
@@ -645,7 +645,7 @@ def _handle_structured_output_mode(
 
         try:
             if isinstance(action_input, str):
-                action_input = json.loads(action_input)
+                action_input = json.loads(action_input, strict=False)
         except json.JSONDecodeError as e:
             raise ActionParsingException(f"Error parsing action_input string. {e}", recoverable=True)
 

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

@@ -170,10 +170,11 @@
 
 IMPORTANT RULES:
 - You MUST ALWAYS include "thought" as the FIRST field in your JSON
-- ALWAYS populate the "thought" field FIRST before "action_input" in your response.
+- ALWAYS populate the "thought" field FIRST before any other field (particularly "action_input") in your response.
 - Each tool has a specific input format you must strictly follow
 - In action_input field, provide properly formatted JSON with double quotes
-- Avoid using extra backslashes
+- When action_input contains multi-line content (e.g. shell commands, code), you MUST escape newlines as \\n within the JSON string — do NOT use literal line breaks inside JSON string values.
+- Json has to be parsable with json.loads() in Python.
 - Do not use markdown code blocks around your JSON
 - Never leave action_input empty
 - Ensure proper JSON syntax with quoted keys and values
@@ -198,7 +199,7 @@
 you call `provide_final_answer` to deliver the final response.
 
 FUNCTION CALLING GUIDELINES:
-- ALWAYS populate the "thought" field FIRST before "action_input" in your function calls
+- ALWAYS populate the "thought" field FIRST before any other field (particularly "action_input") in your function calls
 - Analyze the request carefully to determine if tools are needed
 - Call functions with properly formatted arguments
 - Handle tool responses appropriately before providing final answer

tests/unit/nodes/agents/test_agent_parsing.py

@@ -306,6 +306,104 @@ def test_structured_output_multiple_jsons_takes_first(mocker):
     assert action_input == {"command": "ls"}
 
 
+def test_structured_output_action_input_with_literal_newlines():
+    """strict=False allows action_input containing literal newlines (common LLM mistake)."""
+    import uuid
+
+    from dynamiq import connections, prompts
+    from dynamiq.nodes.agents import Agent
+    from dynamiq.nodes.llms import OpenAI
+    from dynamiq.nodes.types import InferenceMode
+
+    conn = connections.OpenAI(id=str(uuid.uuid4()), api_key="fake-key")
+    llm = OpenAI(
+        name="TestLLM",
+        model="gpt-4o-mini",
+        connection=conn,
+        prompt=prompts.Prompt(messages=[prompts.Message(role="user", content="{{input}}")]),
+    )
+    agent = Agent(name="test-agent", llm=llm, tools=[], inference_mode=InferenceMode.STRUCTURED_OUTPUT)
+
+    # action_input is a JSON string with a literal newline inside (not escaped as \\n).
+    # This is what LLMs produce for multi-line shell commands / code.
+    output = (
+        '{"thought": "run script", "action": "SandboxShellTool", '
+        '"action_input": "{\\"command\\": \\"echo hello\\necho world\\"}", "output_files": ""}'
+    )
+    thought, action, action_input = agent._handle_structured_output_mode(output, loop_num=1)
+    assert thought == "run script"
+    assert action == "SandboxShellTool"
+    assert action_input == {"command": "echo hello\necho world"}
+
+
+def test_structured_output_fallback_decoder_with_literal_newlines():
+    """Fallback JSONDecoder(strict=False) handles multiple concatenated JSONs with literal newlines."""
+    import uuid
+
+    from dynamiq import connections, prompts
+    from dynamiq.nodes.agents import Agent
+    from dynamiq.nodes.llms import OpenAI
+    from dynamiq.nodes.types import InferenceMode
+
+    conn = connections.OpenAI(id=str(uuid.uuid4()), api_key="fake-key")
+    llm = OpenAI(
+        name="TestLLM",
+        model="gpt-4o-mini",
+        connection=conn,
+        prompt=prompts.Prompt(messages=[prompts.Message(role="user", content="{{input}}")]),
+    )
+    agent = Agent(name="test-agent", llm=llm, tools=[], inference_mode=InferenceMode.STRUCTURED_OUTPUT)
+
+    # Two concatenated JSON objects where the first has a literal newline in action_input.
+    # json.loads fails (extra data), fallback raw_decode must also use strict=False.
+    output = (
+        '{"thought": "write file", "action": "SandboxShellTool", '
+        '"action_input": "{\\"command\\": \\"cat > f.py\\nprint(1)\\"}", "output_files": ""}'
+        '\n{"thought": "done", "action": "finish", "action_input": "ok", "output_files": ""}'
+    )
+    thought, action, action_input = agent._handle_structured_output_mode(output, loop_num=1)
+    assert thought == "write file"
+    assert action == "SandboxShellTool"
+    assert action_input == {"command": "cat > f.py\nprint(1)"}
+
+
+def test_function_calling_action_input_with_literal_newlines(mocker):
+    """FC mode: strict=False allows action_input with literal newlines."""
+    import uuid
+
+    from dynamiq import connections, prompts
+    from dynamiq.nodes.agents import Agent
+    from dynamiq.nodes.llms import OpenAI
+    from dynamiq.nodes.types import InferenceMode
+
+    conn = connections.OpenAI(id=str(uuid.uuid4()), api_key="fake-key")
+    llm = OpenAI(
+        name="TestLLM",
+        model="gpt-4o-mini",
+        connection=conn,
+        prompt=prompts.Prompt(messages=[prompts.Message(role="user", content="{{input}}")]),
+    )
+    agent = Agent(name="test-agent", llm=llm, tools=[], inference_mode=InferenceMode.FUNCTION_CALLING)
+
+    # Simulate an LLM result with a tool_call whose action_input has a literal newline
+    mock_result = mocker.MagicMock()
+    mock_result.output = {
+        "tool_calls": [
+            {
+                "function": {
+                    "name": "SandboxShellTool",
+                    "arguments": {"thought": "run it", "action_input": '{"cmd": "ls\nls -la"}'},
+                }
+            }
+        ]
+    }
+
+    thought, action, action_input = agent._handle_function_calling_mode(mock_result, loop_num=1)
+    assert thought == "run it"
+    assert action == "SandboxShellTool"
+    assert action_input == {"cmd": "ls\nls -la"}
+
+
 def _mock_llm_response(text: str):
     from litellm import ModelResponse