> ## Documentation Index
> Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
> Use this file to discover all available pages before exploring further.

# Reasoning Agents Overview

> Advanced agents that employ structured cognitive strategies for improved problem-solving beyond standard language model capabilities

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                                                            |
| -------------------------- | --------------- | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------- |
| **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, majority voting aggregation, concurrent execution, validation mode     | Mathematical problem solving, high-accuracy requirements, decision making |
| **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, research and planning |
| **IRE Agent**              | Iterative       | Iterative Reflective Expansion framework                                                                               | Hypothesis generation, path simulation, error reflection, dynamic revision                                   | Complex reasoning tasks, research problems, strategy development          |
| **Reflexion Agent**        | Self-reflective | [Reflexion: Language Agents with Verbal Reinforcement Learning](https://arxiv.org/abs/2303.11366) (Shinn et al., 2023) | Self-evaluation, experience memory, adaptive improvement, learning from failures                             | Continuous improvement tasks, long-term projects, quality refinement      |
| **GKP Agent**              | Knowledge-based | [Generated Knowledge Prompting](https://arxiv.org/abs/2110.08387) (Liu et al., 2022)                                   | Knowledge generation, multi-perspective reasoning, information synthesis                                     | Knowledge-intensive tasks, research questions, fact-based reasoning       |
| **Agent Judge**            | Evaluation      | [Agent-as-a-Judge](https://arxiv.org/abs/2410.10934)                                                                   | Quality assessment, structured evaluation, performance metrics, feedback generation                          | Quality control, output evaluation, performance assessment                |

## Agent Architectures

### Self-Consistency Agent

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

```mermaid theme={null}
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

[Self-Consistency Agent Guide](/agents/self-consistency-agent)

***

### Reasoning Duo

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

```mermaid theme={null}
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

[Reasoning Duo Guide](/agents/reasoning-duo)

***

### IRE Agent (Iterative Reflective Expansion)

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

```mermaid theme={null}
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

[IRE Agent Guide](/agents/iterative-agent)

***

### Reflexion Agent

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

```mermaid theme={null}
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

[Reflexion Agent Guide](/agents/reflexion-agent)

***

### GKP Agent (Generated Knowledge Prompting)

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

```mermaid theme={null}
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

[GKP Agent Guide](/agents/gkp-agent)

***

### Agent Judge

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

```mermaid theme={null}
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

[Agent Judge Guide](/agents/agent-judge)

## Implementation Guide

### Unified Interface via Reasoning Agent Router

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

```python theme={null}
from swarms.agents import ReasoningAgentRouter

# Initialize router with specific reasoning strategy
router = ReasoningAgentRouter(
    swarm_type="self-consistency",
    model_name="claude-sonnet-4-6",
    num_samples=5,
    max_loops=3
)

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

[Reasoning Agent Router Reference](/agents/reasoning-agent-router)

### Direct Agent Implementation

```python theme={null}
from swarms.agents import SelfConsistencyAgent, ReasoningDuo, ReflexionAgent

# Self-Consistency Agent for high-accuracy requirements
consistency_agent = SelfConsistencyAgent(
    model_name="claude-sonnet-4-6",
    num_samples=5
)

# Reasoning Duo for collaborative analysis workflows
duo_agent = ReasoningDuo(
    model_names=["claude-sonnet-4-6", "gpt-5.4"]
)

# Reflexion Agent for adaptive learning scenarios
reflexion_agent = ReflexionAgent(
    model_name="claude-sonnet-4-6",
    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          |
| **Iterative refinement**   | IRE                 | Designed for progressive improvement         |