feat: add react agent max_loops handler prompt (#7)

dynamiq/nodes/__init__.py

@@ -1,8 +1,2 @@
-from .node import (
-    CachingConfig,
-    ErrorHandling,
-    InputTransformer,
-    Node,
-    OutputTransformer,
-)
-from .types import NodeGroup
+from .node import CachingConfig, ErrorHandling, InputTransformer, Node, OutputTransformer
+from .types import Behavior, NodeGroup

dynamiq/nodes/agents/exceptions.py

@@ -48,9 +48,14 @@ class InvalidActionException(RecoverableAgentException):
     pass
 
 
-class AgentMaxLoopsReached(RecoverableAgentException):
+class MaxLoopsExceededException(RecoverableAgentException):
     """
-    Exception raised when the agent reaches the maximum number of loops without a final answer.
+    Exception raised when the agent exceeds the maximum number of allowed loops.
+
+    This exception is recoverable, meaning the agent can continue after catching this exception.
     """
 
-    pass
+    def __init__(
+        self, message: str = "Maximum number of loops reached without finding a final answer.", recoverable: bool = True
+    ):
+        super().__init__(message, recoverable=recoverable)

dynamiq/nodes/agents/react.py

@@ -5,9 +5,9 @@
 from pydantic import Field
 
 from dynamiq.nodes.agents.base import Agent, AgentIntermediateStep, AgentIntermediateStepModelObservation
-from dynamiq.nodes.agents.exceptions import ActionParsingException, AgentMaxLoopsReached, RecoverableAgentException
+from dynamiq.nodes.agents.exceptions import ActionParsingException, MaxLoopsExceededException, RecoverableAgentException
 from dynamiq.nodes.node import NodeDependency
-from dynamiq.nodes.types import InferenceMode
+from dynamiq.nodes.types import Behavior, InferenceMode
 from dynamiq.prompts import Message, Prompt
 from dynamiq.runnables import RunnableConfig, RunnableStatus
 from dynamiq.utils.logger import logger
@@ -163,6 +163,31 @@
 REACT_BLOCK_CONTEXT = "Below is the conversation: {context}"
 
 
+REACT_MAX_LOOPS_PROMPT = """
+You are tasked with providing a final answer based on information gathered during a process that has reached its maximum number of loops.
+Your goal is to analyze the given context and formulate a clear, concise response.
+First, carefully review the following context, which contains thoughts and information gathered during the process:
+<context>
+{context}
+</context>
+Analyze the context to identify key information, patterns, or partial answers that can contribute to a final response. Pay attention to any progress made, obstacles encountered, or partial results obtained.
+Based on your analysis, attempt to formulate a final answer to the original question or task. Your answer should be:
+1. Fully supported by the information found in the context
+2. Clear and concise
+3. Directly addressing the original question or task, if possible
+If you cannot provide a full answer based on the given context, explain that due to limitations in the number of steps or potential issues with the tools used, you are unable to fully answer the question. In this case, suggest one or more of the following:
+1. Increasing the maximum number of loops for the agent setup
+2. Reviewing the tools settings
+3. Revising the input task description
+Important: Do not mention specific errors in tools, exact steps, environments, code, or search results. Keep your response general and focused on the task at hand.
+Provide your final answer or explanation within <answer> tags.
+Your response should be clear, concise, and professional.
+<answer>
+[Your final answer or explanation goes here]
+</answer>
+"""  # noqa: E501
+
+
 def function_calling_schema(tool_names):
     return [
         {
@@ -245,8 +270,12 @@ class ReActAgent(Agent):
     """Agent that uses the ReAct strategy for processing tasks by interacting with tools in a loop."""
 
     name: str = "React"
-    max_loops: int = Field(default=15, ge=1)
+    max_loops: int = Field(default=15, ge=2)
     inference_mode: InferenceMode = InferenceMode.DEFAULT
+    behaviour_on_max_loops: Behavior = Field(
+        default=Behavior.RAISE,
+        description="Define behavior when max loops are exceeded. Options are 'raise' or 'return'.",
+    )
 
     def parse_xml_content(self, text: str, tag: str) -> str:
         """Extract content from XML-like tags."""
@@ -455,8 +484,28 @@ def _run_agent(self, config: RunnableConfig | None = None, **kwargs) -> str:
                 logger.error(f"Agent {self.name} - {self.id}:Loop {loop_num + 1}. failed with error: {str(e)}")
                 previous_responses.append(f"{type(e).__name__}: {e}")
                 continue
-        logger.error(f"Agent {self.name} - {self.id}: Maximum number of loops reached.")
-        raise AgentMaxLoopsReached(f"Agent {self.name} - {self.id}: Maximum number of loops reached.")
+        logger.warning(f"Agent {self.name} - {self.id}: Maximum number of loops reached.")
+        if self.behaviour_on_max_loops == Behavior.RAISE:
+            error_message = (
+                f"Agent {self.name} (ID: {self.id}) has reached the maximum loop limit of {self.max_loops} without finding a final answer. "  # noqa: E501
+                f"Consider increasing the maximum number of loops or reviewing the task complexity to ensure completion."  # noqa: E501
+            )
+            raise MaxLoopsExceededException(message=error_message)
+        else:
+            return self._handle_max_loops_exceeded(previous_responses, config, kwargs)
+
+    def _handle_max_loops_exceeded(
+        self, previous_responses: list, config: RunnableConfig | None = None, **kwargs
+    ) -> str:
+        """
+        Handle the case where max loops are exceeded by crafting a thoughtful response.
+        """
+        final_attempt_prompt = REACT_MAX_LOOPS_PROMPT.format(context="\n".join(previous_responses))
+        llm_final_attempt = self._run_llm(final_attempt_prompt, config=config, **kwargs)
+        self._run_depends = [NodeDependency(node=self.llm).to_dict()]
+        final_answer = self.parse_xml_content(llm_final_attempt, "answer")
+
+        return f"Max loops reached but here's my final attempt:\n{final_answer}"
 
     def _extract_final_answer_xml(self, llm_output: str) -> str:
         """Extract the final answer from the LLM output."""

dynamiq/nodes/types.py

@@ -33,3 +33,8 @@ class InferenceMode(Enum):
     XML = "XML"
     FUNCTION_CALLING = "FUNCTION_CALLING"
     STRUCTURED_OUTPUT = "STRUCTURED_OUTPUT"
+
+
+class Behavior(str, Enum):
+    RAISE = "raise"
+    RETURN = "return"

dynamiq/nodes/validators/base.py

@@ -1,17 +1,11 @@
-import enum
 from abc import abstractmethod
 from typing import Any, Literal
 
-from dynamiq.nodes import Node, NodeGroup
+from dynamiq.nodes import Behavior, Node, NodeGroup
 from dynamiq.nodes.node import ensure_config
 from dynamiq.runnables import RunnableConfig
 
 
-class Behavior(str, enum.Enum):
-    RAISE = "raise"
-    RETURN = "return"
-
-
 class BaseValidator(Node):
     group: Literal[NodeGroup.VALIDATORS] = NodeGroup.VALIDATORS
     name: str | None = "Validator"

examples/agents_use_cases/agent_financial.py

@@ -24,7 +24,6 @@
         tools=[tool_code],
         role=AGENT_ROLE,
         goal=AGENT_GOAL,
-        max_loops=10,
         inference_mode=InferenceMode.XML,
     )
 

examples/orchestrators/adaptive_coding.py

@@ -20,8 +20,7 @@
                 In results provide initial and optimized loss of model.
                 also include the equation of the model.
 """  # noqa: E501
-INPUT_TASK = "Use code skills to gather data about NVIDIA and INTEL stocks prices for last 10 years, calulate average per year for each company and createa atable per me. Then craft a report and ad conclusion, what would be better if I could invest 100$ 10 yeasr ago. Use yahoo finance."  # noqa: E501
-# INPUT_TASK = "Add the first 10 numbers and tell if the result is prime"
+# INPUT_TASK = "Use code skills to gather data about NVIDIA and INTEL stocks prices for last 10 years, calulate average per year for each company and createa atable per me. Then craft a report and ad conclusion, what would be better if I could invest 100$ 10 yeasr ago. Use yahoo finance."  # noqa: E501
 
 if __name__ == "__main__":
     python_tool = E2BInterpreterTool(
@@ -35,7 +34,6 @@
         tools=[python_tool],
         role="Expert Agent with high programming skills, he can solve any problem using coding skills",
         goal="provide the best solution for request, using all his algorithmic knowledge and coding skills",
-        max_loops=15,
         inference_mode=InferenceMode.XML,
     )