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426 lines
14 KiB
426 lines
14 KiB
# Reasoning Agents Overview
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Reasoning agents are sophisticated agents that employ advanced cognitive strategies to improve problem-solving performance beyond standard language model capabilities. Unlike traditional prompt-based approaches, reasoning agents implement structured methodologies that enable them to think more systematically, self-reflect, collaborate, and iteratively refine their responses.
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These agents are inspired by cognitive science and human reasoning processes, incorporating techniques such as:
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- **Multi-step reasoning**: Breaking down complex problems into manageable components
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- **Self-reflection**: Evaluating and critiquing their own outputs
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- **Iterative refinement**: Progressively improving solutions through multiple iterations
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- **Collaborative thinking**: Using multiple reasoning pathways or agent perspectives
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- **Memory integration**: Learning from past experiences and building knowledge over time
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- **Meta-cognitive awareness**: Understanding their own thinking processes and limitations
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## Available Reasoning Agents
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| Agent Name | Type | Research Paper | Key Features | Best Use Cases | Implementation | Documentation |
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|------------|------|----------------|---------------|----------------|----------------|---------------|
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| **Self-Consistency Agent** | Consensus-based | [Self-Consistency Improves Chain of Thought Reasoning](https://arxiv.org/abs/2203.07870) (Wang et al., 2022) | • Multiple independent reasoning paths<br/>• Majority voting aggregation<br/>• Concurrent execution<br/>• Validation mode | • Mathematical problem solving<br/>• High-accuracy requirements<br/>• Decision making scenarios<br/>• Answer validation | `SelfConsistencyAgent` | [Guide](consistency_agent.md) |
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| **Reasoning Duo** | Collaborative | Novel dual-agent architecture | • Separate reasoning and execution agents<br/>• Collaborative problem solving<br/>• Task decomposition<br/>• Cross-validation | • Complex analysis tasks<br/>• Multi-step problem solving<br/>• Tasks requiring verification<br/>• Research and planning | `ReasoningDuo` | [Guide](reasoning_duo.md) |
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| **IRE Agent** | Iterative | Iterative Reflective Expansion framework | • Hypothesis generation<br/>• Path simulation<br/>• Error reflection<br/>• Dynamic revision | • Complex reasoning tasks<br/>• Research problems<br/>• Learning scenarios<br/>• Strategy development | `IterativeReflectiveExpansion` | [Guide](iterative_agent.md) |
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| **Reflexion Agent** | Self-reflective | [Reflexion: Language Agents with Verbal Reinforcement Learning](https://arxiv.org/abs/2303.11366) (Shinn et al., 2023) | • Self-evaluation<br/>• Experience memory<br/>• Adaptive improvement<br/>• Learning from failures | • Continuous improvement tasks<br/>• Long-term projects<br/>• Learning scenarios<br/>• Quality refinement | `ReflexionAgent` | [Guide](reflexion_agent.md) |
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| **GKP Agent** | Knowledge-based | [Generated Knowledge Prompting](https://arxiv.org/abs/2110.08387) (Liu et al., 2022) | • Knowledge generation<br/>• Multi-perspective reasoning<br/>• Information synthesis<br/>• Fact integration | • Knowledge-intensive tasks<br/>• Research questions<br/>• Fact-based reasoning<br/>• Information synthesis | `GKPAgent` | [Guide](gkp_agent.md) |
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| **Agent Judge** | Evaluation | [Agent-as-a-Judge: Evaluate Agents with Agents](https://arxiv.org/abs/2410.10934) | • Quality assessment<br/>• Structured evaluation<br/>• Performance metrics<br/>• Feedback generation | • Quality control<br/>• Output evaluation<br/>• Performance assessment<br/>• Model comparison | `AgentJudge` | [Guide](agent_judge.md) |
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| **REACT Agent** | Action-based | [ReAct: Synergizing Reasoning and Acting](https://arxiv.org/abs/2210.03629) (Yao et al., 2022) | • Reason-Act-Observe cycle<br/>• Memory integration<br/>• Action planning<br/>• Experience building | • Interactive tasks<br/>• Tool usage scenarios<br/>• Planning problems<br/>• Learning environments | `ReactAgent` | [Guide](react_agent.md) |
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## Agent Architectures
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### Self-Consistency Agent
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**Description**: Implements multiple independent reasoning paths with consensus-building to improve response reliability and accuracy through majority voting mechanisms.
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**Key Features**:
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- Concurrent execution of multiple reasoning instances
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- AI-powered aggregation and consensus analysis
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- Validation mode for answer verification
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- Configurable sample sizes and output formats
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**Architecture Diagram**:
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```mermaid
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graph TD
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A[Task Input] --> B[Agent Pool]
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B --> C[Response 1]
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B --> D[Response 2]
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B --> E[Response 3]
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B --> F[Response N]
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C --> G[Aggregation Agent]
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D --> G
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E --> G
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F --> G
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G --> H[Majority Voting Analysis]
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H --> I[Consensus Evaluation]
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I --> J[Final Answer]
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style A fill:#e1f5fe
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style J fill:#c8e6c9
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style G fill:#fff3e0
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```
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**Use Cases**: Mathematical problem solving, high-stakes decision making, answer validation, quality assurance processes
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**Implementation**: `SelfConsistencyAgent`
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**Documentation**: [Self-Consistency Agent Guide](consistency_agent.md)
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---
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### Reasoning Duo
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**Description**: Dual-agent collaborative system that separates reasoning and execution phases, enabling specialized analysis and task completion through coordinated agent interaction.
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**Key Features**:
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- Separate reasoning and execution agents
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- Collaborative problem decomposition
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- Cross-validation between agents
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- Configurable model selection for each agent
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**Architecture Diagram**:
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```mermaid
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graph TD
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A[Task Input] --> B[Reasoning Agent]
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B --> C[Deep Analysis]
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C --> D[Strategy Planning]
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D --> E[Reasoning Output]
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E --> F[Main Agent]
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F --> G[Task Execution]
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G --> H[Response Generation]
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H --> I[Final Output]
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style A fill:#e1f5fe
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style B fill:#f3e5f5
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style F fill:#e8f5e8
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style I fill:#c8e6c9
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```
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**Use Cases**: Complex analysis tasks, multi-step problem solving, research and planning, verification workflows
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**Implementation**: `ReasoningDuo`
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**Documentation**: [Reasoning Duo Guide](reasoning_duo.md)
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---
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### IRE Agent (Iterative Reflective Expansion)
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**Description**: Sophisticated reasoning framework employing iterative hypothesis generation, simulation, and refinement through continuous cycles of testing and meta-cognitive reflection.
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**Key Features**:
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- Hypothesis generation and testing
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- Path simulation and evaluation
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- Meta-cognitive reflection capabilities
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- Dynamic strategy revision based on feedback
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**Architecture Diagram**:
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```mermaid
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graph TD
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A[Problem Input] --> B[Hypothesis Generation]
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B --> C[Path Simulation]
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C --> D[Outcome Evaluation]
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D --> E{Satisfactory?}
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E -->|No| F[Meta-Cognitive Reflection]
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F --> G[Path Revision]
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G --> H[Knowledge Integration]
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H --> C
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E -->|Yes| I[Solution Synthesis]
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I --> J[Final Answer]
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style A fill:#e1f5fe
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style F fill:#fff3e0
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style J fill:#c8e6c9
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```
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**Use Cases**: Complex reasoning tasks, research problems, strategy development, iterative learning scenarios
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**Implementation**: `IterativeReflectiveExpansion`
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**Documentation**: [IRE Agent Guide](iterative_agent.md)
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---
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### Reflexion Agent
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**Description**: Advanced self-reflective system implementing actor-evaluator-reflector architecture for continuous improvement through experience-based learning and memory integration.
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**Key Features**:
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- Actor-evaluator-reflector sub-agent architecture
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- Self-evaluation and quality assessment
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- Experience memory and learning capabilities
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- Adaptive improvement through reflection
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**Architecture Diagram**:
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```mermaid
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graph TD
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A[Task Input] --> B[Actor Agent]
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B --> C[Initial Response]
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C --> D[Evaluator Agent]
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D --> E[Quality Assessment]
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E --> F[Performance Score]
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F --> G[Reflector Agent]
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G --> H[Self-Reflection]
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H --> I[Experience Memory]
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I --> J{Max Iterations?}
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J -->|No| K[Refined Response]
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K --> D
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J -->|Yes| L[Final Response]
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style A fill:#e1f5fe
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style B fill:#e8f5e8
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style D fill:#fff3e0
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style G fill:#f3e5f5
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style L fill:#c8e6c9
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```
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**Use Cases**: Continuous improvement tasks, long-term projects, adaptive learning, quality refinement processes
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**Implementation**: `ReflexionAgent`
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**Documentation**: [Reflexion Agent Guide](reflexion_agent.md)
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---
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### GKP Agent (Generated Knowledge Prompting)
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**Description**: Knowledge-driven reasoning system that generates relevant information before answering queries, implementing multi-perspective analysis through coordinated knowledge synthesis.
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**Key Features**:
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- Dynamic knowledge generation
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- Multi-perspective reasoning coordination
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- Information synthesis and integration
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- Configurable knowledge item generation
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**Architecture Diagram**:
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```mermaid
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graph TD
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A[Query Input] --> B[Knowledge Generator]
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B --> C[Generate Knowledge Item 1]
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B --> D[Generate Knowledge Item 2]
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B --> E[Generate Knowledge Item N]
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C --> F[Reasoner Agent]
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D --> F
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E --> F
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F --> G[Knowledge Integration]
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G --> H[Reasoning Process]
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H --> I[Response Generation]
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I --> J[Coordinator]
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J --> K[Final Answer]
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style A fill:#e1f5fe
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style B fill:#fff3e0
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style F fill:#e8f5e8
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style J fill:#f3e5f5
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style K fill:#c8e6c9
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```
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**Use Cases**: Knowledge-intensive tasks, research questions, fact-based reasoning, information synthesis
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**Implementation**: `GKPAgent`
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**Documentation**: [GKP Agent Guide](gkp_agent.md)
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---
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### Agent Judge
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**Description**: Specialized evaluation system for assessing agent outputs and system performance, providing structured feedback and quality metrics through comprehensive assessment frameworks.
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**Key Features**:
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- Structured evaluation methodology
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- Quality assessment and scoring
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- Performance metrics generation
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- Configurable evaluation criteria
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**Architecture Diagram**:
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```mermaid
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graph TD
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A[Output to Evaluate] --> B[Evaluation Criteria]
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A --> C[Judge Agent]
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B --> C
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C --> D[Quality Analysis]
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D --> E[Criteria Assessment]
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E --> F[Scoring Framework]
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F --> G[Feedback Generation]
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G --> H[Evaluation Report]
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style A fill:#e1f5fe
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style C fill:#fff3e0
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style H fill:#c8e6c9
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```
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**Use Cases**: Quality control, output evaluation, performance assessment, model comparison
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**Implementation**: `AgentJudge`
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**Documentation**: [Agent Judge Guide](agent_judge.md)
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---
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### REACT Agent (Reason-Act-Observe)
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**Description**: Action-oriented reasoning system implementing iterative reason-act-observe cycles with memory integration for interactive task completion and environmental adaptation.
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**Key Features**:
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- Reason-Act-Observe cycle implementation
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- Memory integration and experience building
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- Action planning and execution
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- Environmental state observation
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**Architecture Diagram**:
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```mermaid
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graph TD
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A[Task Input] --> B[Memory Review]
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B --> C[Current State Observation]
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C --> D[Reasoning Process]
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D --> E[Action Planning]
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E --> F[Action Execution]
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F --> G[Outcome Observation]
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G --> H[Experience Storage]
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H --> I{Task Complete?}
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I -->|No| C
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I -->|Yes| J[Final Response]
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style A fill:#e1f5fe
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style B fill:#f3e5f5
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style D fill:#fff3e0
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style J fill:#c8e6c9
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```
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**Use Cases**: Interactive tasks, tool usage scenarios, planning problems, learning environments
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**Implementation**: `ReactAgent`
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**Documentation**: [REACT Agent Guide](react_agent.md)
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## Implementation Guide
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### Unified Interface via Reasoning Agent Router
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The `ReasoningAgentRouter` provides a centralized interface for accessing all reasoning agent implementations:
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```python
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from swarms.agents import ReasoningAgentRouter
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# Initialize router with specific reasoning strategy
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router = ReasoningAgentRouter(
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swarm_type="self-consistency", # Select reasoning methodology
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model_name="gpt-4o-mini",
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num_samples=5, # Configuration for consensus-based methods
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max_loops=3 # Configuration for iterative methods
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)
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# Execute reasoning process
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result = router.run("Analyze the optimal solution for this complex business problem")
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print(result)
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```
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### Direct Agent Implementation
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```python
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from swarms.agents import SelfConsistencyAgent, ReasoningDuo, ReflexionAgent
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# Self-Consistency Agent for high-accuracy requirements
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consistency_agent = SelfConsistencyAgent(
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model_name="gpt-4o-mini",
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num_samples=5
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)
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# Reasoning Duo for collaborative analysis workflows
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duo_agent = ReasoningDuo(
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model_names=["gpt-4o-mini", "gpt-4o"]
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)
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# Reflexion Agent for adaptive learning scenarios
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reflexion_agent = ReflexionAgent(
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model_name="gpt-4o-mini",
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max_loops=3,
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memory_capacity=100
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)
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```
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## Choosing the Right Reasoning Agent
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| Scenario | Recommended Agent | Why? |
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|----------|------------------|-------|
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| **High-stakes decisions** | Self-Consistency | Multiple validation paths ensure reliability |
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| **Complex research tasks** | Reasoning Duo + GKP | Collaboration + knowledge synthesis |
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| **Learning & improvement** | Reflexion | Built-in self-improvement mechanisms |
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| **Mathematical problems** | Self-Consistency | Proven effectiveness on logical reasoning |
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| **Quality assessment** | Agent Judge | Specialized evaluation capabilities |
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| **Interactive planning** | REACT | Action-oriented reasoning cycle |
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| **Iterative refinement** | IRE | Designed for progressive improvement |
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## Technical Documentation
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For comprehensive technical documentation on each reasoning agent implementation:
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- [Self-Consistency Agent](consistency_agent.md)
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- [Reasoning Duo](reasoning_duo.md)
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- [IRE Agent](iterative_agent.md)
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- [Reflexion Agent](reflexion_agent.md)
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- [GKP Agent](gkp_agent.md)
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- [Agent Judge](agent_judge.md)
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- [Reasoning Agent Router](reasoning_agent_router.md)
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---
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Reasoning agents represent a significant advancement in enterprise agent capabilities, implementing sophisticated cognitive architectures that deliver enhanced reliability, consistency, and performance compared to traditional language model implementations. |