architecture multi agent framework

🤖 Multi-Agent Framework (Experimental / Stub)

Note

👋 Hey there! Siyarix is a personal passion project built by a single developer that is growing and under active development. Some of the architectural components and features described on this page might currently be Planned, Work in Progress, or basic implementations. Stay tuned as it evolves! 🚀

Welcome to the Multi-Agent Framework in Siyarix! 👋 This section provides a foundational stub implementation for our upcoming multi-agent collaboration features.

Warning

Status: EXPERIMENTAL — STUB IMPLEMENTATION

This framework is currently a placeholder infrastructure intended for future development. It is not yet production-ready and should not be relied upon for operational use. All agents currently return mock data and are not connected to real tool execution.

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🏗️ Architecture Overview

The architecture is designed around a central orchestrator, the SwarmRouter, which manages various specialized agents.

graph TD
    A[SwarmRouter] --> B[ReconAgent stub]
    A --> C[ExploitAgent stub]
    A --> D[ReportAgent stub]
    
    B -.->|mock: open ports, services, OS detection| B
    C -.->|mock: vuln check, brute, exploit| C
    D -.->|mock: findings, summary, report| D

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Note

Currently, all agents simulate execution by sleeping for 2 seconds and returning hardcoded mock data.

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🧩 Core Components (siyarix/core/swarm.py)

🚦 SwarmRouter

The SwarmRouter is the heart of our multi-agent workflow. It accepts a specific goal, selects the most appropriate agents, and returns the aggregated results from their tasks.

router = SwarmRouter(provider="openai")
results = await router.run(goal="Scan 10.0.0.1 for vulnerabilities")

Tip

The run method returns a list of SwarmTask results from each agent involved in achieving the goal.

🕵️ SpecializedAgent (Base Class)

All specialized agents inherit from the SpecializedAgent base class, establishing a consistent and predictable structure:

@dataclass
class SpecializedAgent:
    name: str
    description: str
    provider: str
    max_iterations: int = 3

    async def run(self, goal: str, context: dict) -> SwarmTask:
        ...

📋 SwarmTask

A SwarmTask represents a specific unit of work assigned to an agent. It tracks the progress and outcome of the task.

@dataclass
class SwarmTask:
    agent: str
    goal: str
    status: str          # pending | running | completed | failed
    result: str
    findings: list
    error: str | None
    started_at: float
    completed_at: float
    duration_ms: float

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🎭 Available Agents (Stubs)

Here are the agents currently stubbed out in the framework:

🔍 ReconAgent

Aspect	Details
Intent	Network reconnaissance
Description	Scans for open ports, running services, and performs OS detection on the target.
Provider	openai
Actual Behavior	Sleeps 2s, then returns a hardcoded mock containing open ports (22, 80, 443, 3306, 8080), services, and OS detection info.
Readiness	❌ Not functional

💥 ExploitAgent

Aspect	Details
Intent	Vulnerability exploitation
Description	Checks for known vulnerabilities, performs brute-force attacks, and attempts exploits.
Provider	openai
Actual Behavior	Sleeps 2s, then returns a hardcoded mock detailing simulated vulnerabilities and exploit attempts.
Readiness	❌ Not functional

📝 ReportAgent

Aspect	Details
Intent	Report generation
Description	Analyzes all findings and generates comprehensive, actionable reports.
Provider	openai
Actual Behavior	Sleeps 2s, then returns a hardcoded mock featuring findings, a summary, and a severity assessment.
Readiness	❌ Not functional

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🛠️ Additional Stubs (siyarix/chat/stubs.py)

The chat/stubs.py module houses extra stubs used specifically for CLI chat demonstrations and testing:

• 🤖 SimulatedAgent: Returns mock responses to mimic different agent behaviors and personalities.
• 🤝 SimulatedCollaboration: Provides stubs to demonstrate multi-agent collaboration scenarios.
• 🎯 SimulatedFindings: Contains pre-defined mock findings used for predictable testing.

Caution

These stubs are meant exclusively for development and demonstration purposes. They should never be used in production execution.

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🚧 Current Limitations

Since this is an experimental stub, there are several limitations to be aware of:

Limitation	Detail
No inter-agent communication	Agents currently operate in isolation and don't share findings or coordinate.
No state machine	There is no true lifecycle management (e.g., idle → running → completed).
No AgentMessage protocol	A structured messaging system between agents hasn't been implemented yet.
No real tool access	All results are currently mocked or hardcoded.
Fixed 2s sleep	Concurrency is merely simulated using a standard sleep, not real asynchronous execution.
No task decomposition	Goals are passed as-is; agents cannot break them down into smaller sub-tasks.
No result passing	The output from one agent is not currently fed into another (e.g., Agent A → Agent B).
No error propagation	Failures are captured but not dynamically acted upon by the system.

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🚀 Planned Capabilities (Future)

We have exciting plans to evolve this framework! Here’s what’s on the roadmap:

• ✨ Dynamic agent spawning based on the complexity of the assigned goal.
• 📬 Inter-agent message bus utilizing a robust publish/subscribe model.
• 🧠 Task decomposition powered by LLM planning.
• 🕸️ Sharing KnowledgeGraph across all active agents.
• 🔗 Sequential chaining to manage complex output dependencies.
• 🔄 Agent lifecycle management, complete with health checks and automatic restarts.
• 🛠️ Real tool delegation executed through a RegistryExecutor.
• 🌐 Sub-agent coordination designed for large-scale, intricate operations.

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🔌 Integration with AgentCore

The Swarm framework is designed to be highly modular and opt-in.

class AgentCore:
    def __init__(self, swarm: SwarmRouter | None = None):
        self.swarm = swarm  # Injected, not created by default

    async def run_swarm(self, goal: str) -> list[SwarmTask]:
        if not self.swarm:
            logger.warning("No SwarmRouter configured")
            return []
        return await self.swarm.run(goal)

Important

The Swarm router is injected from the outside. AgentCore does not create a SwarmRouter by default, ensuring the system remains lightweight when multi-agent features are not required.

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🎯 Future Use Cases

Once fully realized, the Multi-Agent Framework will support powerful scenarios:

Scenario	Agents Involved	Future Flow
Full pentest of a single host	Recon → Exploit → Report	Sequential chaining of agents.
Network segment assessment	Recon (scaling) → Report	Scaled reconnaissance followed by consolidated analysis.
Multi-vector attack	Recon → Exploit (multiple) → Report	Parallel exploitation across different attack vectors.
Continuous monitoring	Recon (scheduled) → Report	Periodic, automated execution and reporting.