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Core Concepts

AgenticFleet is a powerful, open-source, multi-agent orchestration system designed to tackle complex, real-world problems through collaborative intelligence of autonomous AI agents. It prioritizes human-AI collaboration, ensuring AI augments human capabilities rather than replacing them.

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System Architecture

AgenticFleet consists of several key components working together:

  1. AgenticFabric (The Backbone)

    • Agent lifecycle management
    • Communication protocols
    • Workflow execution
    • Resource management
    • Security and event handling

  2. GraphFleet (Knowledge Graph)

    • Contextual reasoning
    • Agent recommendation
    • Task decomposition
    • Graph-based RAG (future)

  3. AgenticUI

    • Task submission and monitoring
    • Agent/swarm management
    • Progress visualization
    • Agent communication
    • Explainability features

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Agents

Agents are autonomous AI entities with specific capabilities, tools, and confidence levels.

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Agent Components

from agentic_fleet import Agent, AgentCapabilities, Tools

agent = Agent.create(
    name="research_specialist",
    capabilities=[
        AgentCapabilities.WEB_RESEARCH,
        AgentCapabilities.DATA_ANALYSIS
    ],
    tools=[
        Tools.WEB_SEARCH,
        Tools.DATABASE_ACCESS
    ],
    llm_config={
        "model": "gpt-4",
        "temperature": 0.7
    }
)

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Confidence Levels

from agentic_fleet import BayesianEngine

# Update agent confidence based on task success
BayesianEngine.update_confidence(
    agent_id=agent.id,
    skill="web_research",
    success=True,
    task_complexity=0.8
)

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Swarms

Swarms are dynamically assembled groups of agents that collaborate on complex tasks.

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Swarm Creation

from agentic_fleet import Swarm, SwarmType

swarm = Swarm.create(
    name="research_team",
    type=SwarmType.RESEARCH,
    orchestrator_config={
        "task_decomposition": True,
        "dynamic_replanning": True
    }
)

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Orchestrator

The Orchestrator manages swarm operations:

from agentic_fleet import Orchestrator

orchestrator = Orchestrator.create(
    swarm_id=swarm.id,
    capabilities={
        "task_decomposition": True,
        "agent_selection": True,
        "workflow_management": True
    }
)

# Submit a complex task
task_result = orchestrator.process_task(
    task="Research quantum computing advances",
    max_subtasks=5,
    timeout=3600
)

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Task Management

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Task Planner

from agentic_fleet import TaskPlanner

planner = TaskPlanner.create(
    task="Implement user authentication",
    context={
        "framework": "FastAPI",
        "requirements": ["OAuth2", "JWT"]
    }
)

subtasks = planner.decompose()

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Semantic Router

from agentic_fleet import SemanticRouter

router = SemanticRouter.create(
    routing_config={
        "agent_matching": True,
        "pattern_matching": True,
        "tool_routing": True
    }
)

route = router.analyze_and_route(
    query="Generate a market analysis report",
    context={"domain": "finance"}
)

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Memory Systems

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Conversation Memory

from agentic_fleet import Memory

memory = Memory.create(
    type="conversation",
    storage_config={
        "type": "vector_store",
        "ttl": 86400
    }
)

# Store interaction
memory.store(
    content="User requested market analysis",
    metadata={
        "type": "user_request",
        "timestamp": "2025-02-24T16:45:48+01:00"
    }
)

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Knowledge Graph

from agentic_fleet import GraphFleet

graph = GraphFleet.create()

# Add relationship
graph.add_relationship(
    source_type="Agent",
    source_id=agent.id,
    relationship="HAS_SKILL",
    target_type="Skill",
    target_id="web_research"
)

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Communication

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Agent Communication

from agentic_fleet import Communication

# Inter-agent communication
message = Communication.send(
    sender=agent1.id,
    receiver=agent2.id,
    content="Analysis results ready",
    type="task_update"
)

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Event System

from agentic_fleet import EventSystem

events = EventSystem()

@events.on("task_completed")
def handle_completion(task_id, result):
    # Process task completion
    pass

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Advanced Features

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Bayesian Learning

from agentic_fleet import BayesianEngine

# Update agent confidence
BayesianEngine.update(
    agent_id=agent.id,
    skill="data_analysis",
    performance_metrics={
        "accuracy": 0.95,
        "speed": 0.85
    }
)

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Dynamic Replanning

from agentic_fleet import DynamicPlanner

planner = DynamicPlanner.create(
    task_id=task.id,
    monitoring_interval=60
)

# Adjust plan based on new conditions
planner.replan(
    new_conditions={
        "resource_availability": 0.7,
        "priority_change": "high"
    }
)

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Best Practices

  1. Agent Design

    • Define clear agent responsibilities
    • Implement proper error handling
    • Use appropriate confidence thresholds
    • Monitor and update agent performance

  2. Swarm Management

    • Choose optimal swarm sizes
    • Implement proper task decomposition
    • Monitor swarm efficiency
    • Handle agent failures gracefully

  3. Memory Management

    • Implement efficient cleanup strategies
    • Use appropriate TTL values
    • Monitor memory usage
    • Maintain data privacy

  4. System Integration

    • Follow security best practices
    • Implement proper monitoring
    • Maintain scalability
    • Document integrations thoroughly
Getting StartedInstallation