AgenticAI Framework

Enterprise-Grade Multi-Agent Orchestration Platform

Production-ready Python SDK for building sophisticated AI agent systems with advanced orchestration, intelligent task management, comprehensive evaluation, and enterprise-grade monitoring.

📚 Documentation • 🚀 Quick Start • 💡 Examples • 🤝 Contributing

🎯 What Makes AgenticAI Framework Different?

🚀 Production-First DesignBuilt for real-world workloads with comprehensive error handling, monitoring, resilience patterns, and a 73% test coverage across 451 tests.🧩 Truly ModularEvery component is independently composable, extensible, and replaceable without affecting other parts of the system.🧠 Intelligence Built-InSophisticated memory management, semantic search, learning capabilities, and 12-tier evaluation framework out of the box.

📊 Enterprise-Ready21 enterprise features including distributed tracing, multi-region support, compliance monitoring, and tenant isolation.🎓 Developer ExperienceIntuitive APIs, comprehensive documentation, extensive examples, and powerful debugging tools make development enjoyable.🌐 Scale EffortlesslyFrom single-agent prototypes to distributed multi-agent systems with built-in coordination and monitoring.

🚀 Why Choose AgenticAI Framework?

Production-Ready from Day One

Unlike experimental frameworks, AgenticAI Framework is built for production workloads with comprehensive error handling, monitoring, and resilience patterns built-in.

Truly Modular Architecture

Every component is designed as an independent, composable module that can be extended, replaced, or customized without affecting the rest of the system.

Intelligent by Design

Features sophisticated memory management, semantic search, learning capabilities, and context-aware decision making out of the box.

Scale from Prototype to Enterprise

Start with a single agent and seamlessly scale to distributed multi-agent systems with built-in coordination, communication, and monitoring.

Developer Experience First

Comprehensive documentation, extensive examples, intuitive APIs, and powerful debugging tools make development fast and enjoyable.

🏗️ Architecture Overview

https://viewscreen.githubusercontent.com/markdown/mermaid?docs_host=https%3A%2F%2Fdocs.github.com&color_mode=light#6e215eea-f205-42a4-a6f9-7f8544940ed6

🔧 13 Core Modules Working in Harmony

🤖 Agents – Intelligent Autonomous Entities

• Context engineering with token tracking and automatic compression
• Multi-role agents with configurable capabilities and behaviors
• Lifecycle management with start, pause, resume, and stop controls
• Performance monitoring with comprehensive metrics and error tracking
• Custom agent types for specialized domains (customer service, research, code generation)
• Advanced coordination patterns for multi-agent collaboration

📋 Tasks – Sophisticated Workflow Management

• Intelligent scheduling with time-based, conditional, and dependency-driven execution
• Priority queues with advanced retry mechanisms and circuit breaker patterns
• Workflow orchestration supporting sequential, parallel, and conditional flows
• Performance monitoring with comprehensive metrics and resource tracking

🧠 Memory – Advanced Memory Systems

• Multi-tier memory architecture (short-term, long-term, external memory)
• TTL (Time-To-Live) support for automatic memory expiration
• Priority-based eviction with LRU algorithm
• Memory consolidation for frequently accessed data
• Semantic search capabilities with intelligent information retrieval

🔗 LLMs – Language Model Management

• Circuit breaker pattern to prevent cascading failures
• Automatic retry with exponential backoff
• Response caching for improved performance
• Fallback chains for high availability
• Multi-provider support with unified interface for different LLM providers

🛡️ Guardrails – Safety and Compliance

• Priority-based enforcement with circuit breakers
• Severity levels (low, medium, high, critical)
• Remediation actions for automatic issue resolution
• Content filtering with customizable validation rules
• Policy enforcement for ethical AI behavior
• Security validation to prevent prompt injection and data leakage
• Compliance monitoring with audit trails and reporting
• Violation tracking with comprehensive analytics

🔐 Security – Enterprise-Grade Security

• Prompt injection detection with 15+ attack patterns
• Input validation and sanitization
• Rate limiting per user/session
• Content filtering with customizable rules
• Audit logging with comprehensive event tracking
• Security metrics and reporting

🛡️ Guardrails – Safety and Compliance

• Content filtering with customizable validation rules
• Policy enforcement for ethical AI behavior
• Security validation to prevent prompt injection and data leakage
• Compliance monitoring with audit trails and reporting
• Violation tracking with comprehensive analytics

📊 Evaluation – Comprehensive 12-Tier Assessment (NEW)

• Model Quality: Hallucination detection, reasoning assessment, token efficiency
• Task Success: Success rates, retry tracking, completion monitoring
• Tool Performance: API call tracking, parameter validation, latency monitoring
• Workflow Orchestration: Multi-agent coordination, handoff tracking, deadlock detection
• Memory Quality: Context precision/recall, stale data detection
• RAG Evaluation: Retrieval quality, faithfulness, groundedness, citations
• Safety Scoring: Security risk assessment, PII detection, policy compliance
• Autonomy Assessment: Plan optimality, replanning, human intervention tracking
• Performance Monitoring: Latency percentiles (P50/P95/P99), throughput, stability
• Cost Tracking: Token usage, cost per request, budget optimization
• HITL Metrics: Acceptance rates, review time, trust scoring
• Business Outcomes: ROI calculation, impact metrics, value assessment

📊 Monitoring – Comprehensive Observability

• Real-time metrics collection and analysis

🎯 Prompts – Intelligent Prompt Management

• Defensive prompting with automatic protection
• Injection detection and prevention
• Safe rendering mode for untrusted inputs
• Template system with variable substitution and inheritance
• A/B testing for prompt optimization
• Version control for prompt evolution tracking with rollback
• Vulnerability scanning across all prompts
• Performance analytics for prompt effectiveness
• Multiple protocols (HTTP, WebSocket, gRPC, Message Queues)
• Pub/sub messaging for decoupled agent communication
• Event-driven architecture with comprehensive event handling
• Communication security with authentication and encryption

⚙️ Processes – Advanced Orchestration

• Process definition with complex workflow patterns
• Dynamic process adaptation based on runtime conditions
• Resource management with automatic scaling and optimization
• Process monitoring with detailed execution tracking

🎯 Prompts – Intelligent Prompt Management

• Framework Comparison

Feature	AgenticAI Framework	LangChain	CrewAI	AutoGen
Production Ready	✅ Enterprise	⚠️ Experimental	⚠️ Limited	⚠️ Research
Test Coverage	✅ 73% (451 tests)	⚠️ Variable	❌ Limited	⚠️ Basic
Modular Architecture	✅ Fully composable	⚠️ Monolithic	❌ Fixed	⚠️ Rigid
Memory Management	✅ Multi-tier + Semantic	✅ Basic	❌ None	⚠️ Simple
12-Tier Evaluation	✅ Built-in	❌ None	❌ None	❌ None
Task Orchestration	✅ Advanced workflows	⚠️ Linear chains	✅ Role-based	⚠️ Conversation
Monitoring & Tracing	✅ Distributed tracing	❌ None	❌ None	❌ None
Error Handling	✅ Circuit breakers	⚠️ Basic	⚠️ Limited	⚠️ Basic
Multi-Agent Coordination	✅ Advanced patterns	⚠️ Simple	✅ Team-based	✅ Group chat
Guardrails & Safety	✅ Built-in + PII	❌ Add-on	❌ None	❌ None
Enterprise Features	✅ 21 features	⚠️ Limited	❌ None	❌ None
Performance Optimization	✅ Caching + Circuit breakers	⚠️ Manual	❌ None	❌ None
Extensibility	✅ Plugin architecture	✅ Custom tools	⚠️ Limited	⚠️ Limited

– Tool composition for building complex capabilities – Performance optimization with intelligent caching

⚙️ Configurations – Centralized Management

• Environment-specific configurations with inheritance
• Dynamic configuration updates without restarts
• Validation and defaults with comprehensive error checking
• Configuration versioning and rollback capabilities

🔄 Framework Comparison

Feature	AgenticAI Framework	LangChain	CrewAI	AutoGen
Production Ready	✅ Enterprise-grade	⚠️ Experimental	⚠️ Limited	⚠️ Research
Modular Architecture	✅ Fully composable	⚠️ Monolithic	❌ Fixed structure	⚠️ Rigid
Memory Management	✅ Multi-tier + Semantic	✅ Basic	❌ None	⚠️ Simple
Task Orchestration	✅ Advanced workflows	⚠️ Linear chains	✅ Role-based	⚠️ Conversation-based
Monitoring & Observability	✅ Comprehensive	❌ None	❌ None	❌ None
Error Handling	✅ Robust + Recovery	⚠️ Basic	⚠️ Limited	⚠️ Basic
Multi-Agent Coordination	✅ Advanced patterns	⚠️ Simple	✅ Team-based	✅ Group chat
Guardrails & Safety	✅ Built-in	❌ Add-on	❌ None	❌ None
Performance Optimization	✅ Intelligent caching	⚠️ Manual	❌ None	❌ None
Extensibility	✅ Plugin architecture	✅ Custom tools	⚠️ Limited	⚠️ Limited

✨ Key Features & Capabilities

🎯 Intelligent Agent Management

• Create specialized agents with domain-specific knowledge and capabilities
• Implement sophisticated coordination patterns for multi-agent collaboration
• Dynamic agent scaling and load balancing
• Agent health monitoring and automatic recovery

🔄 Advanced Task Orchestration

• Complex workflow patterns with conditional branching and parallel execution
• Intelligent task scheduling with dependency resolution
• Retry mechanisms with exponential backoff and circuit breakers
• Resource-aware task distribution and optimization

🧠 Sophisticated Memory Systems

• Hierarchical memory with automatic promotion and consolidation
• Semantic search with embedding-based retrieval
• Memory compression and optimization for large-scale deployments
• Cross-agent memory sharing and synchronization

📊 Enterprise Monitoring & Analytics

• Real-time performance metrics and health monitoring
• Comprehensive audit trails and compliance reporting
• Custom alerting and notification systems
• Performance optimization recommendations

🛡️ Production-Grade Security

• Content validation and filtering with customizable rules
• Prompt injection detection and prevention
• Data privacy and PII protection
• Security audit trails and compliance reporting

🔌 Flexible Integration

• REST APIs, GraphQL, and gRPC support
• Database integrations (SQL, NoSQL, Vector databases)
• Cloud platform integrations (AWS, Azure, GCP)
• Third-party service connectors

🏢 Enterprise Features

AgenticAI Framework now includes 21 enterprise-grade features for production deployments:

📊 Observability & Metrics

Feature	Description
Agent Step Tracing	Distributed tracing with span hierarchy and context propagation
Latency Metrics	Percentile-based latency tracking (P50, P95, P99)

🔍 Evaluation & Testing

Feature	Description
Model Quality Evaluation	Hallucination detection, reasoning assessment, token efficiency
Task & Skill Evaluation	Success rates, retry tracking, completion percentages
Tool & API Evaluation	Tool invocation tracking, parameter validation, latency monitoring
Workflow Evaluation	Multi-agent orchestration, handoff tracking, deadlock detection
Memory & Context Evaluation	Context precision/recall, stale data detection, quality scoring
RAG Evaluation	Retrieval quality, faithfulness, groundedness, citation accuracy
Autonomy & Planning	Plan optimality, replanning tracking, human intervention
Performance & Scalability	Latency percentiles (P50/P95/P99), throughput, stability
Cost & FinOps	Token usage tracking, cost per request, budget optimization
Human-in-the-Loop	Acceptance rates, override tracking, trust scoring
Business Outcomes	ROI calculation, baseline comparison, impact metrics
Security Risk Scoring	Input/output risk assessment, PII detection, policy compliance
A/B Testing Framework	Experiment management with statistical significance
Canary Deployments	Gradual rollouts with automatic rollback

📝 Prompt Management

Feature	Description
Prompt Versioning	Semantic versioning with draft→active→deprecated workflow
Prompt Library	Reusable components with template inheritance

🚀 CI/CD & Deployment

Feature	Description
Agent CI Pipelines	Build, test, evaluate, and deploy stages
Canary Deployments	Gradual rollouts with automatic rollback

🎨 Visual Tools APIs

Feature	Description
Agent Builder	Component library and blueprint management
Workflow Designer	Visual workflow design with node/edge management
Admin Console	User management, configuration, and dashboards

🔗 Integrations

Feature	Description
ITSM (ServiceNow)	Incident management and change requests
Dev Tools (GitHub, ADO)	Issues, PRs, pipelines integration
Data Platforms	Snowflake and Databricks connectors
Webhooks	Incoming/outgoing webhook management

🏗️ Infrastructure

Feature	Description
Serverless Execution	Function deployment with auto-scaling
Multi-Region Support	Geographic load balancing and failover
Tenant Isolation	Multi-tenant resource isolation and quotas

🔐 Compliance & Governance

Feature	Description
Audit Trails	Tamper-evident logging with hash chain integrity
Policy Enforcement	Rule-based policies with pattern matching
Data Masking	PII detection with multiple masking strategies

# Example: Enterprise Features
from agenticaiframework import (
    tracer, latency_metrics,  # Tracing
    OfflineEvaluator, ABTestingFramework,  # Evaluation
    prompt_version_manager, prompt_library,  # Versioning
    audit_trail, policy_engine, data_masking,  # Compliance
    multi_region_manager, tenant_manager,  # Infrastructure
)

# Distributed tracing
with tracer.trace("agent_execution") as span:
    span.set_attribute("agent_id", "agent_001")
    latency_metrics.record("llm_call", 0.45)

# Audit trail with integrity
audit_trail.log_event(
    event_type="data_access",
    actor="user_123",
    resource="customer_data",
    action="query"
)

# Data masking
text = "Contact john@example.com"
<div align="center">

### Quick Start

```bash
pip install agenticaiframework

📥 Installation Options

Development Installation

git clone https://github.com/isathish/agenticaiframework.git
cd agenticaiframework
pip install -e .

With Optional Dependencies

# Enhanced monitoring capabilities
pip install "agenticaiframework[monitoring]"

# Advanced memory features  
pip install "agenticaiframework[memory]"

# Documentation building
pip install "agenticaiframework[docs]"

# All optional dependencies
pip install "agenticaiframework[all]"

Documentation Dependencies

pip install -r requirements-docs.txt

install “agenticaiframework[all]” “`

Documentation Dependencies

# Install only documentation dependencies
pip install -r requirements-docs.txt

⚡ Quick Start Examples

Simple Agent Creation

from agenticaiframework import Agent

# Create a specialized agent
agent = Agent(
    name="DataAnalyst",
    role="Data Analysis Specialist", 
    capabilities=["data_processing", "visualization", "reporting"],
    config={
        "processing_timeout": 300,
        "output_format": "json",
        "enable_caching": True
    }
)

# Start the agent
agent.start()
print(f"Agent {agent.name} is ready and {agent.status}")

Multi-Agent Collaboration

from agenticaiframework import Agent, AgentManager

# Create specialized agents
data_collector = Agent(
    name="DataCollector",
    role="Data Collection Specialist",
    capabilities=["api_integration", "data_extraction"]
)

data_processor = Agent(
    name="DataProcessor", 
    role="Data Processing Specialist",
    capabilities=["data_cleaning", "transformation"]
)

report_generator = Agent(
    name="ReportGenerator",
    role="Report Generation Specialist", 
    capabilities=["analysis", "visualization", "reporting"]
)

# Manage agents
manager = AgentManager()
agents = [data_collector, data_processor, report_generator]

for agent in agents:
    manager.register_agent(agent)
    agent.start()

# Coordinate workflow
manager.coordinate_workflow(["collect_data", "process_data", "generate_report"])

Advanced Task Management

from agenticaiframework import Task, TaskManager, TaskScheduler
from datetime import datetime, timedelta

# Create task manager
task_manager = TaskManager()

# Define complex task with dependencies
data_validation = task_manager.create_task(
    name="data_validation",
    description="Validate incoming data sources",
    priority=1,
    config={"validation_rules": ["not_null", "type_check", "range_check"]}
)

data_processing = task_manager.create_task(
    name="data_processing", 
    description="Process validated data",
    priority=2,
    dependencies=["data_validation"],
    config={"batch_size": 1000, "parallel_workers": 4}
)

# Schedule recurring task
scheduler = TaskScheduler()
scheduler.schedule_recurring(
    task=data_validation,
    interval=timedelta(hours=1)  # Run every hour
)

# Execute workflow
result = task_manager.execute_workflow([data_validation, data_processing])

Intelligent Memory Management

from agenticaiframework.memory import MemoryManager, SemanticMemory

# Create advanced memory system
memory_manager = MemoryManager()

# Set up semantic memory for intelligent retrieval
semantic_memory = SemanticMemory(capacity=10000)

# Store information with context
semantic_memory.store_with_embedding(
    "user_preferences",
    {
        "communication_style": "detailed_explanations",
        "preferred_format": "structured_json",
        "domain_expertise": ["data_science", "machine_learning"]
    }
)

semantic_memory.store_with_embedding(
    "successful_strategies", 
    {
        "data_processing": ["parallel_processing", "batch_optimization"],
        "error_handling": ["retry_with_backoff", "graceful_degradation"]
    }
)

# Intelligent retrieval
relevant_info = semantic_memory.semantic_search(
    "how to handle user communication preferences",
    limit=5,
    similarity_threshold=0.7
)

Comprehensive Monitoring

from agenticaiframework.monitoring import MonitoringSystem

# Initialize monitoring
monitoring = MonitoringSystem()

# Monitor agent performance
monitoring.track_agent_metrics(agent, {
    "response_time": 1.2,
    "success_rate": 0.95,
    "memory_usage": 128
})

# Monitor task execution
with monitoring.track_execution("data_processing_pipeline"):
    result = task_manager.execute_task("complex_data_analysis")

# Get comprehensive insights
metrics = monitoring.get_performance_summary(time_range="last_24h")
print(f"System performance: {metrics}")

🎯 Use Cases & Applications

🏢 Enterprise Automation

• Document Processing: Intelligent document analysis and extraction
• Workflow Automation: Complex business process automation
• Compliance Monitoring: Automated compliance checking and reporting
• Resource Optimization: Intelligent resource allocation and scaling

🔬 Research & Development

• Literature Review: Automated research paper analysis and summarization
• Hypothesis Generation: AI-driven hypothesis formulation and testing
• Data Analysis: Comprehensive data analysis and insight generation
• Experiment Design: Intelligent experimental design and optimization

💬 Customer Experience

• Intelligent Support: Multi-modal customer support with context awareness
• Personalization: Dynamic content and experience personalization
• Sentiment Analysis: Real-time customer sentiment monitoring and response
• Predictive Support: Proactive issue identification and resolution

🎓 Education & Training

• Adaptive Learning: Personalized learning path optimization
• Content Generation: Intelligent educational content creation
• Assessment: Automated assessment and feedback systems
• Tutoring: AI-powered tutoring and mentorship

🏥 Healthcare & Life Sciences

• Clinical Decision Support: Evidence-based clinical recommendations
• Drug Discovery: AI-assisted drug discovery and development
• Patient Monitoring: Continuous patient health monitoring and alerts
• Medical Documentation: Automated medical record processing and analysis

🔧 Development & Deployment

Development Workflow

# Clone and setup development environment
git clone https://github.com/isathish/agenticaiframework.git
cd agenticaiframework

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

# Install documentation dependencies
pip install -r requirements-docs.txt

# Run tests
pytest

# Build documentation locally
mkdocs build

# Serve documentation for development
mkdocs serve

# View documentation at http://127.0.0.1:8000

Production Deployment

# Production configuration example
from agenticaiframework import AgentManager, MonitoringSystem
from agenticaiframework.memory import DatabaseMemory

# Production-ready setup
memory = DatabaseMemory(
    db_path="/data/production/agent_memory.db",
    backup_interval=3600,  # Hourly backups
    max_connections=100
)

monitoring = MonitoringSystem(
    metrics_backend="prometheus",
    alerting_enabled=True,
    log_level="INFO"
)

manager = AgentManager(
    memory=memory,
    monitoring=monitoring,
    max_agents=50,
    auto_scaling=True
)

Docker Deployment

# Dockerfile example
FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
RUN pip install -e .

EXPOSE 8000
CMD ["python", "-m", "agenticaiframework.server"]

📚 Documentation & Resources

📖 Comprehensive Documentation

• Complete Documentation – Full framework documentation
• API Reference – Detailed API documentation
• Quick Start Guide – Get started in minutes
• Best Practices – Production-ready patterns

🎯 Module-Specific Guides

• Agents – Creating and managing intelligent agents
• Tasks – Advanced task orchestration and workflow management
• Memory – Sophisticated memory systems and persistence
• Monitoring – Comprehensive system observability
• Guardrails – Safety and compliance systems

� Examples & Tutorials

• Basic Examples – Simple usage patterns
• Advanced Examples – Complex real-world scenarios
• Integration Examples – Third-party integrations

📊 Test Coverage Summary