feat: implement ReasoningAgent and DualBrainAgent with advanced reasoning capabilities #977
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…ning capabilities - Add ReasoningConfig class for configurable reasoning parameters - Add ActionState enum for flow control - Implement ReasoningAgent inheriting from Agent with: - Step-by-step reasoning with confidence scoring - Reasoning trace tracking - Configurable min/max steps and reasoning styles - Implement DualBrainAgent inheriting from Agent with: - Separate LLMs for conversation and reasoning - Dual-brain coordination for optimal problem-solving - Brain status monitoring and model switching - Add confidence scoring integration - Maintain backward compatibility with existing Agent class - Export new classes in agent module Addresses issue #968: Create ReasoningAgent inherited from Agent class 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Mervin Praison <MervinPraison@users.noreply.github.com>
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Summary of Changes
Hello @MervinPraison, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly enhances the agent framework by integrating advanced reasoning capabilities. It introduces two new specialized agent types, ReasoningAgent and DualBrainAgent, designed to tackle complex problems through structured thought processes and multi-model coordination. A new reasoning module underpins these agents, providing a robust framework for configurable reasoning, step-by-step analysis, confidence scoring, and flow control, ensuring more intelligent and traceable agent behavior.
Highlights
- New Agent Implementations: I've added two new agent classes,
ReasoningAgentandDualBrainAgent, to introduce advanced reasoning capabilities into the system. These agents are designed to handle complex problem-solving more effectively. - Enhanced Reasoning Framework: A new
reasoningmodule has been introduced, providing core components such asReasoningConfigfor configurable parameters,ActionStatefor flow control,ReasoningStepfor individual steps, andReasoningTracefor tracking the entire reasoning process, including confidence scoring. - Dual-Brain Architecture: The
DualBrainAgentspecifically implements a 'dual-brain' approach, utilizing separate Large Language Models (LLMs) for conversational responses and analytical reasoning. This allows for more specialized and coordinated problem-solving by leveraging the strengths of different models. - Step-by-Step Reasoning: The
ReasoningAgentenables agents to perform step-by-step reasoning, track their internal thought processes, and assess confidence levels for each step. This enhances transparency and provides greater control over how complex tasks are approached and solved.
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Code Review
This pull request introduces the ReasoningAgent and DualBrainAgent classes, adding advanced reasoning capabilities. The implementation of the data models in reasoning.py is well-structured. The main concerns are that the ReasoningAgent doesn't fully implement the step-by-step reasoning parsing, and the DualBrainAgent has a potential thread-safety issue. Addressing these, along with adding unit tests, will improve the robustness of this feature.
| def chat( | ||
| self, | ||
| message: str, | ||
| **kwargs | ||
| ) -> str: | ||
| """ | ||
| Enhanced chat method with reasoning capabilities. | ||
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| Args: | ||
| message: Input message | ||
| **kwargs: Additional chat parameters | ||
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| Returns: | ||
| Response with reasoning trace | ||
| """ | ||
| # Start reasoning trace | ||
| self.start_reasoning_trace(message) | ||
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| # Enhance message with reasoning instructions | ||
| enhanced_message = f""" | ||
| {message} | ||
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| Please solve this step-by-step using the following reasoning process: | ||
| 1. Break down the problem into logical steps | ||
| 2. For each step, show your thought process | ||
| 3. State your confidence level (0.0-1.0) for each step | ||
| 4. Ensure minimum {self.reasoning_config.min_steps} reasoning steps | ||
| 5. Use {self.reasoning_config.style} reasoning style | ||
| 6. Provide a clear final answer | ||
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| Format your response to show each reasoning step clearly. | ||
| """ | ||
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| # Call parent chat method | ||
| response = super().chat(enhanced_message, **kwargs) | ||
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| # Complete reasoning trace | ||
| self.complete_reasoning_trace(response) | ||
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| return response |
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The chat method instructs the LLM to perform step-by-step reasoning but does not parse the response to create ReasoningStep objects, so the reasoning_trace.steps list remains empty. Define a structured format (e.g., JSON) for the LLM to return reasoning steps, update the prompt to request the output in that format, and parse the LLM's response to populate the trace.
| try: | ||
| # Switch to reasoning LLM | ||
| self.llm = self.reasoning_llm | ||
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| # Use parent chat method with reasoning LLM | ||
| reasoning_result = super().chat(reasoning_prompt) | ||
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| return reasoning_result | ||
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| finally: | ||
| # Restore original LLM | ||
| self.llm = original_llm |
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The _reason_with_analytical_brain method modifies the instance attribute self.llm, which is not thread-safe. If chat() is called concurrently, this could lead to race conditions. Consider passing the LLM configuration directly to the chat completion method or creating a temporary, isolated client for the reasoning call.
| self.reasoning_trace.overall_confidence = sum( | ||
| step.confidence for step in self.reasoning_trace.steps | ||
| ) / len(self.reasoning_trace.steps) |
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The calculation for overall_confidence can result in a ZeroDivisionError if self.reasoning_trace.steps is empty. Add a check to prevent this.
if self.reasoning_trace.steps:
self.reasoning_trace.overall_confidence = sum(
step.confidence for step in self.reasoning_trace.steps
) / len(self.reasoning_trace.steps)
else:
self.reasoning_trace.overall_confidence = 0.0| main_llm = llm_config.get('model', llm) | ||
| # Apply LLM config parameters as needed | ||
| else: | ||
| main_llm = llm or "gpt-4o" |
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Consider defining the default model names ("gpt-4o" on line 70, and "o1-preview" on lines 75 and 88) as constants to improve maintainability. This centralizes these values, making them easier to update.
main_llm = llm or "gpt-4o" # Consider making this a module-level constant| if isinstance(reasoning_config, dict) and 'model' in reasoning_config: | ||
| self.reasoning_llm_config.update(reasoning_config) |
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Instead of directly updating self.reasoning_llm_config with the entire reasoning_config dictionary, selectively update only the keys relevant to the LLM configuration (model, temperature, system_prompt). This prevents potential issues if reasoning_config contains unrelated parameters.
if isinstance(reasoning_config, dict):
llm_config_keys = {"model", "temperature", "system_prompt"}
llm_specific_config = {k: v for k, v in reasoning_config.items() if k in llm_config_keys}
if llm_specific_config:
self.reasoning_llm_config.update(llm_specific_config)| confidence=0.9 # High confidence in reasoning LLM analysis | ||
| ) | ||
| self.reasoning_trace.steps.append(reasoning_step) | ||
| self.last_reasoning_steps.append(reasoning_step) | ||
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| # Step 2: Use main LLM for response generation | ||
| final_response = self._generate_response_with_main_brain(message, reasoning_analysis) | ||
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| # Add response generation step | ||
| response_step = ReasoningStep( | ||
| step_number=2, | ||
| title="Main Brain Response Generation", | ||
| thought=f"Using {self.main_llm} for conversational response", | ||
| action=final_response, | ||
| confidence=0.85 |
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The confidence scores for the reasoning steps are hardcoded. To make confidence scoring more dynamic, parse the confidence scores from the LLM's response instead of hardcoding them.
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Bug: Incorrect Attribute Access in `reason_step` Function
The reason_step function incorrectly attempts to call .get('steps', []) on agent.reasoning_trace. When agent.reasoning_trace exists, it is a ReasoningTrace Pydantic BaseModel object, not a dictionary. Pydantic models do not have a .get() method, leading to an AttributeError. The steps attribute should be accessed directly from the ReasoningTrace object.
src/praisonai-agents/praisonaiagents/reasoning.py#L117-L119
PraisonAI/src/praisonai-agents/praisonaiagents/reasoning.py
Lines 117 to 119 in 2e5d7cf
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@claude review this pull request and do a detailed analysis and fix this if the existing code doesn't have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes. |
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Claude encountered an error —— View job I'll analyze this and get back to you. |
Implements ReasoningAgent and DualBrainAgent classes as requested in issue #968
Changes:
Features:
Generated with Claude Code