Reasoning Agents Overview

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.

These agents are inspired by cognitive science and human reasoning processes, incorporating techniques such as:

• Multi-step reasoning: Breaking down complex problems into manageable components

• Self-reflection: Evaluating and critiquing their own outputs

• Iterative refinement: Progressively improving solutions through multiple iterations

• Collaborative thinking: Using multiple reasoning pathways or agent perspectives

• Memory integration: Learning from past experiences and building knowledge over time

• Meta-cognitive awareness: Understanding their own thinking processes and limitations

Available Reasoning Agents

Agent Name	Type	Research Paper	Key Features	Best Use Cases	Implementation	Documentation
Self-Consistency Agent	Consensus-based	Self-Consistency Improves Chain of Thought Reasoning (Wang et al., 2022)	• Multiple independent reasoning paths • Majority voting aggregation • Concurrent execution • Validation mode	• Mathematical problem solving • High-accuracy requirements • Decision making scenarios • Answer validation	SelfConsistencyAgent	Guide
Reasoning Duo	Collaborative	Novel dual-agent architecture	• Separate reasoning and execution agents • Collaborative problem solving • Task decomposition • Cross-validation	• Complex analysis tasks • Multi-step problem solving • Tasks requiring verification • Research and planning	ReasoningDuo	Guide
IRE Agent	Iterative	Iterative Reflective Expansion framework	• Hypothesis generation • Path simulation • Error reflection • Dynamic revision	• Complex reasoning tasks • Research problems • Learning scenarios • Strategy development	IterativeReflectiveExpansion	Guide
Reflexion Agent	Self-reflective	Reflexion: Language Agents with Verbal Reinforcement Learning (Shinn et al., 2023)	• Self-evaluation • Experience memory • Adaptive improvement • Learning from failures	• Continuous improvement tasks • Long-term projects • Learning scenarios • Quality refinement	ReflexionAgent	Guide
GKP Agent	Knowledge-based	Generated Knowledge Prompting (Liu et al., 2022)	• Knowledge generation • Multi-perspective reasoning • Information synthesis • Fact integration	• Knowledge-intensive tasks • Research questions • Fact-based reasoning • Information synthesis	GKPAgent	Guide
Agent Judge	Evaluation	Agent-as-a-Judge: Evaluate Agents with Agents	• Quality assessment • Structured evaluation • Performance metrics • Feedback generation	• Quality control • Output evaluation • Performance assessment • Model comparison	AgentJudge	Guide
REACT Agent	Action-based	ReAct: Synergizing Reasoning and Acting (Yao et al., 2022)	• Reason-Act-Observe cycle • Memory integration • Action planning • Experience building	• Interactive tasks • Tool usage scenarios • Planning problems • Learning environments	ReactAgent	Guide

Agent Architectures

Self-Consistency Agent

Description: Implements multiple independent reasoning paths with consensus-building to improve response reliability and accuracy through majority voting mechanisms.

Key Features:

• Concurrent execution of multiple reasoning instances

• AI-powered aggregation and consensus analysis

• Validation mode for answer verification

• Configurable sample sizes and output formats

Architecture Diagram:

graph TD
    A[Task Input] --> B[Agent Pool]
    B --> C[Response 1]
    B --> D[Response 2]
    B --> E[Response 3]
    B --> F[Response N]
    C --> G[Aggregation Agent]
    D --> G
    E --> G
    F --> G
    G --> H[Majority Voting Analysis]
    H --> I[Consensus Evaluation]
    I --> J[Final Answer]

    style A fill:#e1f5fe
    style J fill:#c8e6c9
    style G fill:#fff3e0

Use Cases: Mathematical problem solving, high-stakes decision making, answer validation, quality assurance processes

Implementation: SelfConsistencyAgent

Documentation: Self-Consistency Agent Guide

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Reasoning Duo

Description: Dual-agent collaborative system that separates reasoning and execution phases, enabling specialized analysis and task completion through coordinated agent interaction.

Key Features:

• Separate reasoning and execution agents

• Collaborative problem decomposition

• Cross-validation between agents

• Configurable model selection for each agent

Architecture Diagram:

graph TD
    A[Task Input] --> B[Reasoning Agent]
    B --> C[Deep Analysis]
    C --> D[Strategy Planning]
    D --> E[Reasoning Output]
    E --> F[Main Agent]
    F --> G[Task Execution]
    G --> H[Response Generation]
    H --> I[Final Output]

    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style F fill:#e8f5e8
    style I fill:#c8e6c9

Use Cases: Complex analysis tasks, multi-step problem solving, research and planning, verification workflows

Implementation: ReasoningDuo

Documentation: Reasoning Duo Guide

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IRE Agent (Iterative Reflective Expansion)

Description: Sophisticated reasoning framework employing iterative hypothesis generation, simulation, and refinement through continuous cycles of testing and meta-cognitive reflection.

Key Features:

• Hypothesis generation and testing

• Path simulation and evaluation

• Meta-cognitive reflection capabilities

• Dynamic strategy revision based on feedback

Architecture Diagram:

graph TD
    A[Problem Input] --> B[Hypothesis Generation]
    B --> C[Path Simulation]
    C --> D[Outcome Evaluation]
    D --> E{Satisfactory?}
    E -->|No| F[Meta-Cognitive Reflection]
    F --> G[Path Revision]
    G --> H[Knowledge Integration]
    H --> C
    E -->|Yes| I[Solution Synthesis]
    I --> J[Final Answer]

    style A fill:#e1f5fe
    style F fill:#fff3e0
    style J fill:#c8e6c9

Use Cases: Complex reasoning tasks, research problems, strategy development, iterative learning scenarios

Implementation: IterativeReflectiveExpansion

Documentation: IRE Agent Guide

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Reflexion Agent

Description: Advanced self-reflective system implementing actor-evaluator-reflector architecture for continuous improvement through experience-based learning and memory integration.

Key Features:

• Actor-evaluator-reflector sub-agent architecture

• Self-evaluation and quality assessment

• Experience memory and learning capabilities

• Adaptive improvement through reflection

Architecture Diagram:

graph TD
    A[Task Input] --> B[Actor Agent]
    B --> C[Initial Response]
    C --> D[Evaluator Agent]
    D --> E[Quality Assessment]
    E --> F[Performance Score]
    F --> G[Reflector Agent]
    G --> H[Self-Reflection]
    H --> I[Experience Memory]
    I --> J{Max Iterations?}
    J -->|No| K[Refined Response]
    K --> D
    J -->|Yes| L[Final Response]

    style A fill:#e1f5fe
    style B fill:#e8f5e8
    style D fill:#fff3e0
    style G fill:#f3e5f5
    style L fill:#c8e6c9

Use Cases: Continuous improvement tasks, long-term projects, adaptive learning, quality refinement processes

Implementation: ReflexionAgent

Documentation: Reflexion Agent Guide

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GKP Agent (Generated Knowledge Prompting)

Description: Knowledge-driven reasoning system that generates relevant information before answering queries, implementing multi-perspective analysis through coordinated knowledge synthesis.

Key Features:

• Dynamic knowledge generation

• Multi-perspective reasoning coordination

• Information synthesis and integration

• Configurable knowledge item generation

Architecture Diagram:

graph TD
    A[Query Input] --> B[Knowledge Generator]
    B --> C[Generate Knowledge Item 1]
    B --> D[Generate Knowledge Item 2]
    B --> E[Generate Knowledge Item N]
    C --> F[Reasoner Agent]
    D --> F
    E --> F
    F --> G[Knowledge Integration]
    G --> H[Reasoning Process]
    H --> I[Response Generation]
    I --> J[Coordinator]
    J --> K[Final Answer]

    style A fill:#e1f5fe
    style B fill:#fff3e0
    style F fill:#e8f5e8
    style J fill:#f3e5f5
    style K fill:#c8e6c9

Use Cases: Knowledge-intensive tasks, research questions, fact-based reasoning, information synthesis

Implementation: GKPAgent

Documentation: GKP Agent Guide

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Agent Judge

Description: Specialized evaluation system for assessing agent outputs and system performance, providing structured feedback and quality metrics through comprehensive assessment frameworks.

Key Features:

• Structured evaluation methodology

• Quality assessment and scoring

• Performance metrics generation

• Configurable evaluation criteria

Architecture Diagram:

graph TD
    A[Output to Evaluate] --> B[Evaluation Criteria]
    A --> C[Judge Agent]
    B --> C
    C --> D[Quality Analysis]
    D --> E[Criteria Assessment]
    E --> F[Scoring Framework]
    F --> G[Feedback Generation]
    G --> H[Evaluation Report]

    style A fill:#e1f5fe
    style C fill:#fff3e0
    style H fill:#c8e6c9

Use Cases: Quality control, output evaluation, performance assessment, model comparison

Implementation: AgentJudge

Documentation: Agent Judge Guide

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REACT Agent (Reason-Act-Observe)

Description: Action-oriented reasoning system implementing iterative reason-act-observe cycles with memory integration for interactive task completion and environmental adaptation.

Key Features:

• Reason-Act-Observe cycle implementation

• Memory integration and experience building

• Action planning and execution

• Environmental state observation

Architecture Diagram:

graph TD
    A[Task Input] --> B[Memory Review]
    B --> C[Current State Observation]
    C --> D[Reasoning Process]
    D --> E[Action Planning]
    E --> F[Action Execution]
    F --> G[Outcome Observation]
    G --> H[Experience Storage]
    H --> I{Task Complete?}
    I -->|No| C
    I -->|Yes| J[Final Response]

    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style D fill:#fff3e0
    style J fill:#c8e6c9

Use Cases: Interactive tasks, tool usage scenarios, planning problems, learning environments

Implementation: ReactAgent

Documentation: REACT Agent Guide

Implementation Guide

Unified Interface via Reasoning Agent Router

The ReasoningAgentRouter provides a centralized interface for accessing all reasoning agent implementations:

from swarms.agents import ReasoningAgentRouter

# Initialize router with specific reasoning strategy
router = ReasoningAgentRouter(
    swarm_type="self-consistency",  # Select reasoning methodology
    model_name="gpt-4o-mini",
    num_samples=5,                  # Configuration for consensus-based methods
    max_loops=3                     # Configuration for iterative methods
)

# Execute reasoning process
result = router.run("Analyze the optimal solution for this complex business problem")
print(result)

Direct Agent Implementation

from swarms.agents import SelfConsistencyAgent, ReasoningDuo, ReflexionAgent

# Self-Consistency Agent for high-accuracy requirements
consistency_agent = SelfConsistencyAgent(
    model_name="gpt-4o-mini",
    num_samples=5
)

# Reasoning Duo for collaborative analysis workflows
duo_agent = ReasoningDuo(
    model_names=["gpt-4o-mini", "gpt-4o"]
)

# Reflexion Agent for adaptive learning scenarios
reflexion_agent = ReflexionAgent(
    model_name="gpt-4o-mini",
    max_loops=3,
    memory_capacity=100
)

Choosing the Right Reasoning Agent

Scenario	Recommended Agent	Why?
High-stakes decisions	Self-Consistency	Multiple validation paths ensure reliability
Complex research tasks	Reasoning Duo + GKP	Collaboration + knowledge synthesis
Learning & improvement	Reflexion	Built-in self-improvement mechanisms
Mathematical problems	Self-Consistency	Proven effectiveness on logical reasoning
Quality assessment	Agent Judge	Specialized evaluation capabilities
Interactive planning	REACT	Action-oriented reasoning cycle
Iterative refinement	IRE	Designed for progressive improvement

Technical Documentation

For comprehensive technical documentation on each reasoning agent implementation:

• Self-Consistency Agent

• Reasoning Duo

• IRE Agent

• Reflexion Agent

• GKP Agent

• Agent Judge

• Reasoning Agent Router

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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.