agenticaiframework 3.0.0

AgenticAI - Enterprise-grade Python SDK with advanced orchestration, monitoring, and production-ready AI agent capabilities.

AgenticAI Framework

Enterprise-Grade Multi-Agent AI Orchestration Platform

The most comprehensive Python SDK for building production-ready AI agent systems with 400+ modules, 237 enterprise features, advanced orchestration, intelligent memory management, speech processing, state management, and enterprise-grade monitoring.

Documentation | Quick Start | Examples | Contributing

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Table of Contents

• Key Features
• Architecture
• Installation
• Quick Start
• Core Modules
• Advanced Features
• Enterprise Features
• Evaluation Framework
• Framework Comparison
• Documentation
• Contributing
• License

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Key Features

Production-First Design Enterprise-grade reliability with 66% test coverage 1036+ tests ensuring stability Circuit breaker patterns & automatic retry Comprehensive error handling Modular Architecture 400+ independently composable modules 237 enterprise-grade modules Plug-and-play component system Clean separation of concerns Easy customization & extension Intelligent Memory 7 specialized memory managers Multi-tier storage with TTL Context compression & windowing Automatic memory consolidation

Multi-Agent Orchestration Team-based coordination Supervisor & peer patterns Task handoff & delegation Shared context management Speech Processing STT/TTS integration Voice profile management Multi-language support Audio caching & optimization 12-Tier Evaluation Model quality assessment RAG evaluation Security risk scoring Business outcome tracking Enterprise Ready 15+ API Management modules 18+ Security & Compliance modules 14+ ML/AI Infrastructure modules 12+ Domain-Driven Design patterns

---

Architecture

graph TB
    subgraph Framework["AgenticAI Framework -- 400+ Modules"]
        direction TB

        subgraph Core["Core Layer"]
            direction LR
            subgraph G1[" "]
                direction TB
                A["Agents\nContext - Memory - Tools"]
                W["Workflows\nSteps - Routing - State"]
            end
            subgraph G2[" "]
                direction TB
                O["Orchestration\nTeams - Handoffs - Messages"]
                SP["Speech\nSTT/TTS - Profiles - Cache"]
            end
        end

        subgraph Middle["Services Layer"]
            direction LR
            subgraph G3[" "]
                direction TB
                M["Memory\n7 Managers - Multi-tier - TTL"]
                S["State\n7 Managers - Persistence"]
            end
            subgraph G4[" "]
                direction TB
                C["Communication\nHTTP - WS - MQTT - gRPC - SSE - STDIO"]
                K["Knowledge\nRetrieval - RAG - Embedding - Search"]
            end
        end

        subgraph Safety["Safety and Quality Layer"]
            direction LR
            GR["Guardrails\nContent - Policy - Semantic"]
            SEC["Security\nInjection - PII - Rate Limiting"]
            EV["Evaluation\n12-Tier - A/B - Canary"]
            MON["Monitoring\nMetrics - Tracing - Events"]
        end

        subgraph Foundation["Foundation Layer"]
            direction LR
            LLM["LLMs\nRouting - Caching - Fallback"]
            T["Tools\n35+ Built-in - Registry - MCP"]
            P["Prompts\nVersioning - Templates - Security"]
            CO["Compliance\nAudit - Masking - Policy"]
        end

        subgraph Enterprise["Enterprise Layer -- 237 Modules"]
            direction LR
            subgraph E1[" "]
                direction TB
                API["API Management\nGateway - Versioning - Lifecycle"]
                ESEC["Security\nEncryption - Vault - Auth/RBAC"]
                DP["Data Processing\nPipeline - Lineage - ETL"]
            end
            subgraph E2[" "]
                direction TB
                ML["ML/AI Infra\nInference - Feature Store - RAG"]
                MSG["Messaging\nEvent Bus - Pub/Sub - CQRS"]
                INF["Infrastructure\nLoad Bal. - Circuit Brk - Mesh"]
            end
            subgraph E3[" "]
                direction TB
                DEV["DevOps\nCanary - Blue-Green - Chaos"]
                DDD["DDD\nAggregate - Saga - Repository"]
                CAC["Caching\nRedis - Distributed - Multi-tier"]
            end
            subgraph E4[" "]
                direction TB
                OBS["Observability\nTracing - Metrics - APM"]
                WF["Workflow\nEngine - Scheduler - State Machine"]
                INT["Integrations\nServiceNow - Cloud - Webhooks"]
            end
        end
    end

    Core --> Middle
    Middle --> Safety
    Safety --> Foundation
    Foundation --> Enterprise

    style Framework fill:#0d1117,stroke:#30363d,color:#c9d1d9
    style Core fill:#161b22,stroke:#58a6ff,color:#c9d1d9
    style Middle fill:#161b22,stroke:#3fb950,color:#c9d1d9
    style Safety fill:#161b22,stroke:#d29922,color:#c9d1d9
    style Foundation fill:#161b22,stroke:#bc8cff,color:#c9d1d9
    style Enterprise fill:#161b22,stroke:#f778ba,color:#c9d1d9
    style A fill:#1f6feb,stroke:#58a6ff,color:#fff
    style W fill:#1f6feb,stroke:#58a6ff,color:#fff
    style O fill:#1f6feb,stroke:#58a6ff,color:#fff
    style SP fill:#1f6feb,stroke:#58a6ff,color:#fff
    style M fill:#238636,stroke:#3fb950,color:#fff
    style S fill:#238636,stroke:#3fb950,color:#fff
    style C fill:#238636,stroke:#3fb950,color:#fff
    style K fill:#238636,stroke:#3fb950,color:#fff
    style GR fill:#9e6a03,stroke:#d29922,color:#fff
    style SEC fill:#9e6a03,stroke:#d29922,color:#fff
    style EV fill:#9e6a03,stroke:#d29922,color:#fff
    style MON fill:#9e6a03,stroke:#d29922,color:#fff
    style LLM fill:#6e40c9,stroke:#bc8cff,color:#fff
    style T fill:#6e40c9,stroke:#bc8cff,color:#fff
    style P fill:#6e40c9,stroke:#bc8cff,color:#fff
    style CO fill:#6e40c9,stroke:#bc8cff,color:#fff
    style API fill:#b62324,stroke:#f778ba,color:#fff
    style ESEC fill:#b62324,stroke:#f778ba,color:#fff
    style DP fill:#b62324,stroke:#f778ba,color:#fff
    style ML fill:#b62324,stroke:#f778ba,color:#fff
    style MSG fill:#b62324,stroke:#f778ba,color:#fff
    style INF fill:#b62324,stroke:#f778ba,color:#fff
    style DEV fill:#b62324,stroke:#f778ba,color:#fff
    style DDD fill:#b62324,stroke:#f778ba,color:#fff
    style CAC fill:#b62324,stroke:#f778ba,color:#fff
    style OBS fill:#b62324,stroke:#f778ba,color:#fff
    style WF fill:#b62324,stroke:#f778ba,color:#fff
    style INT fill:#b62324,stroke:#f778ba,color:#fff

---

Installation

Quick Install

pip install agenticaiframework

Development Install

git clone https://github.com/isathish/agenticaiframework.git
cd agenticaiframework
pip install -e ".[dev]"

Optional Dependencies

# Full installation with all features
pip install "agenticaiframework[all]"

# Specific feature sets
pip install "agenticaiframework[monitoring]" # Enhanced observability
pip install "agenticaiframework[memory]" # Advanced memory features
pip install "agenticaiframework[speech]" # Speech processing
pip install "agenticaiframework[docs]" # Documentation building

---

Quick Start

1. Create an Agent

from agenticaiframework import Agent, AgentManager

# Create a specialized agent
agent = Agent(
    name="ResearchAssistant",
    role="Research Specialist",
    capabilities=["web_search", "document_analysis", "summarization"],
    config={"model": "gpt-4", "temperature": 0.7}
)

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

2. Multi-Agent Orchestration

from agenticaiframework.orchestration import OrchestrationEngine, AgentTeam

# Create a team of agents
team = AgentTeam(
    name="ResearchTeam",
    agents=[researcher, analyst, writer],
    coordination_strategy="supervisor"
)

# Create orchestration engine
engine = OrchestrationEngine()
engine.register_team(team)

# Execute coordinated task
result = engine.execute_task(
    task="Research AI trends and write a comprehensive report",
    team_id="ResearchTeam"
)

3. Intelligent Memory Management

from agenticaiframework.memory import (
    AgentMemoryManager,
    WorkflowMemoryManager,
    OrchestrationMemoryManager,
    KnowledgeMemoryManager,
    ToolMemoryManager,
    SpeechMemoryManager
)

# Agent-specific memory with 4 memory types
agent_memory = AgentMemoryManager("agent_001")
agent_memory.add_turn("user", "What's the weather like?")
agent_memory.add_turn("assistant", "It's sunny and 72°F.")
agent_memory.set_working("current_task", "weather_query", ttl_seconds=300)
agent_memory.learn_fact("User prefers Fahrenheit", "preference")
agent_memory.record_episode("weather_query", {"temp": 72}, "Successful query")

# Workflow memory with step tracking & checkpoints
workflow_memory = WorkflowMemoryManager()
ctx = workflow_memory.create_context("workflow_001", {"input": "data"})
workflow_memory.set_variable("workflow_001", "status", "processing")
workflow_memory.record_step_result("workflow_001", "step1", "Process", {"result": "ok"})
workflow_memory.create_checkpoint("workflow_001", "Before critical operation")

# Team shared memory with messaging
team_memory = OrchestrationMemoryManager()
team_memory.create_team_context("team_001", "Complete analysis task")
team_memory.send_message("agent_a", "agent_b", "Task completed", priority="high")
team_memory.record_contribution("team_001", "agent_a", {"output": "analysis"})

# Knowledge memory with embedding cache
knowledge_memory = KnowledgeMemoryManager()
knowledge_memory.cache_embedding("sample text", [0.1, 0.2, 0.3], "text-embedding-3")
knowledge_memory.cache_query_result("What is AI?", "kb_001", [{"doc": "AI is..."}], 5)

# Tool memory with result caching
tool_memory = ToolMemoryManager()
tool_memory.cache_result("calculator", {"x": 1, "y": 2}, {"sum": 3})
tool_memory.record_execution("calculator", {"x": 1}, {"sum": 3}, 50, True)

# Speech memory with voice profiles
speech_memory = SpeechMemoryManager()
speech_memory.store_transcription(audio_data, "Hello world", "en", 0.95)
speech_memory.store_synthesis("Hello world", audio_output, "voice_001", 120)
speech_memory.create_voice_profile("user_001", "User One", [0.1, 0.2], {"lang": "en"})

4. State Management

from agenticaiframework.state import (
    AgentStateManager,
    WorkflowStateManager,
    OrchestrationStateManager,
    KnowledgeStateManager,
    ToolStateManager,
    SpeechStateManager
)

# Agent state with persistence & checkpoints
agent_state = AgentStateManager("agent_001", persist=True)
agent_state.transition("idle", "processing")
agent_state.update_context({"current_task": "analysis"})
agent_state.create_checkpoint("Before critical decision")

# Workflow state with step tracking
workflow_state = WorkflowStateManager("workflow_001")
workflow_state.set_current_step("data_processing")
workflow_state.record_step_completion("validation", {"passed": True})

# Orchestration state for team coordination
orch_state = OrchestrationStateManager("team_001")
orch_state.update_team_status("active")
orch_state.record_agent_contribution("agent_a", {"output": "analysis_result"})

# Knowledge state for index management
knowledge_state = KnowledgeStateManager("kb_001")
knowledge_state.set_index_status("ready")
knowledge_state.record_retrieval("query_001", ["doc1", "doc2"])

# Tool state with circuit breaker
tool_state = ToolStateManager("calculator")
tool_state.set_availability(True)
tool_state.record_execution(success=True, duration_ms=50)

# Speech state for session management
speech_state = SpeechStateManager("session_001")
speech_state.set_mode("listening")
speech_state.update_voice_context({"language": "en", "speaker": "user_001"})

5. Speech Processing

from agenticaiframework.speech import SpeechProcessor

# Initialize speech processor
speech = SpeechProcessor(
    stt_provider="whisper",
    tts_provider="elevenlabs",
    default_language="en"
)

# Speech-to-Text
transcription = speech.transcribe(audio_data, language="en")
print(f"Transcribed: {transcription.text} (confidence: {transcription.confidence})")

# Text-to-Speech
audio = speech.synthesize("Hello, how can I help you today?", voice="professional")
speech.play(audio)

# Voice profile management
speech.register_voice("user_001", voice_sample, metadata={"language": "en"})
identified = speech.identify_speaker(audio_data)

6. Comprehensive Monitoring

from agenticaiframework import MonitoringSystem

# Initialize monitoring
monitoring = MonitoringSystem()

# Record metrics
monitoring.record_metric("agent_response_time", 1.2)
monitoring.record_metric("agent_success_rate", 0.95)
monitoring.record_metric("memory_usage_mb", 128)

# Log events with context
monitoring.log_event("task_completed", {
    "task_name": "data_processing",
    "duration": 2.5,
    "status": "success"
})

# Distributed tracing
with monitoring.trace("process_request") as span:
    span.set_attribute("user_id", "user_001")
    # Your processing logic
    span.add_event("processing_complete")

---

Core Modules

Memory Management (7 Specialized Managers)

Manager	Purpose	Key Features
MemoryManager	Core multi-tier memory	Short/long-term, TTL, LRU eviction, consolidation
AgentMemoryManager	Agent-specific memory	Conversation, working, episodic, semantic memory
WorkflowMemoryManager	Workflow execution memory	Step results, context passing, checkpoints
OrchestrationMemoryManager	Multi-agent shared memory	Team context, messaging, task handoffs
KnowledgeMemoryManager	Knowledge base memory	Embedding cache, query results, retrieval history
ToolMemoryManager	Tool execution memory	Result caching, patterns, performance stats
SpeechMemoryManager	Speech processing memory	Transcription/synthesis history, voice profiles

State Management (7 State Managers)

Manager	Purpose	Key Features
StateManager	Core state management	State transitions, history, persistence
AgentStateManager	Agent lifecycle state	Status, context, checkpoints, metrics
WorkflowStateManager	Workflow execution state	Step tracking, routing, error handling
OrchestrationStateManager	Team coordination state	Team status, agent states, coordination
KnowledgeStateManager	Knowledge base state	Index status, sync state, retrieval state
ToolStateManager	Tool execution state	Availability, execution, circuit breakers
SpeechStateManager	Speech processing state	Session, voice, processing state

Communication Protocols (6 Protocols)

Protocol	Use Case	Features
HTTP	REST APIs	Request/response, webhooks
WebSocket	Real-time	Bidirectional, low-latency
SSE	Event streaming	Server-push, one-way
MQTT	IoT/Messaging	Pub/sub, lightweight
gRPC	High-performance	Streaming, strong typing
STDIO	Local processes	MCP compatibility

Context Management

from agenticaiframework.context import (
    ContextManager,
    ContextWindow,
    ContextCompressor
)

# Manage context windows
context = ContextManager(max_tokens=8000)
context.add_message("user", "Hello!")
context.add_message("assistant", "Hi there!")

# Automatic compression when needed
compressor = ContextCompressor(strategy="summarize")
compressed = compressor.compress(context.messages, target_tokens=4000)

# Sliding window management
window = ContextWindow(size=10, overlap=2)
window.add(message)
recent = window.get_recent(5)

---

Advanced Features

Tools Framework (35+ Built-in Tools)

from agenticaiframework.tools import (
    ToolRegistry,
    ToolExecutor,
    BaseTool,
    register_tool
)

# Register custom tool
@register_tool(category="analysis", version="1.0")
class DataAnalysisTool(BaseTool):
    name = "data_analysis"
    description = "Analyze data and generate insights"

    def execute(self, data: dict) -> dict:
        # Your analysis logic
        return {"insights": [...], "metrics": {...}}

# Use tool executor with caching
executor = ToolExecutor()
result = executor.execute("data_analysis", {"data": dataset})

Tool Categories:

Category	Tools
File & Document	FileRead, FileWrite, PDF, DOCX, OCR, RAG Search
Web Scraping	Selenium, Firecrawl, BrightData, Oxylabs
Database	MySQL, PostgreSQL, Snowflake, MongoDB, Vector Search
AI/ML	DALL-E, Vision, Code Interpreter, LangChain, LlamaIndex

Guardrails & Safety

from agenticaiframework.guardrails import GuardrailManager, ContentGuardrail

# Setup guardrails
guardrails = GuardrailManager()

# Add content filtering
guardrails.add(ContentGuardrail(
    blocked_topics=["violence", "hate_speech"],
    pii_detection=True,
    max_token_limit=4000
))

# Validate input/output
result = guardrails.validate(user_input)
if not result.passed:
    print(f"Blocked: {result.violations}")

Human-in-the-Loop (HITL)

from agenticaiframework.hitl import HITLManager

# Setup HITL manager
hitl = HITLManager(
    approval_required=["high_risk_actions"],
    timeout=300, # 5 minutes
    escalation_policy="notify_admin"
)

# Request human approval
approval = await hitl.request_approval(
    action="delete_records",
    context={"count": 1000, "table": "users"},
    urgency="high"
)

if approval.granted:
    execute_action()

Conversation Management

from agenticaiframework.conversations import ConversationManager

# Manage multi-turn conversations
conversation = ConversationManager(
    session_id="session_001",
    max_turns=50,
    context_strategy="sliding_window"
)

# Add turns with metadata
conversation.add_turn(
    role="user",
    content="Analyze this data",
    metadata={"intent": "analysis", "entities": ["data"]}
)

# Get conversation summary
summary = conversation.summarize()

Response Formatting

from agenticaiframework.formatting import ResponseFormatter

# Format responses consistently
formatter = ResponseFormatter(
    style="professional",
    format="markdown",
    max_length=2000
)

# Format agent response
formatted = formatter.format(
    response=raw_response,
    include_sources=True,
    add_disclaimer=True
)

---

Enterprise Features

237 Enterprise-Grade Modules

The AgenticAI Framework includes a comprehensive enterprise module library organized into the following categories:

** API Management (15 Modules)** API Gateway, Versioning, Lifecycle Manager API Client, Docs Generator, Rate Limiting GraphQL, REST, gRPC Support API Analytics, Monitoring, Testing ** Security & Compliance (18 Modules)** Encryption Service, Secret Manager, Vault Authentication (OAuth, JWT, SAML, SSO) Authorization (RBAC, ABAC, Policies) PII Detection, Data Masking, Audit Trails Prompt Injection Detection (15+ patterns) ** Data Processing (16 Modules)** Data Pipeline, ETL, Data Lineage Data Validator, Transformer, Aggregator Data Privacy Manager, Masking Data Quality, Profiling, Catalog Stream Processing, Batch Processing ** ML/AI Infrastructure (14 Modules)** ML Inference, Feature Store, Model Registry RAG, Embeddings, Recommendation Engine Vector Store, Semantic Search Model Versioning, A/B Testing LLM Gateway, Prompt Management

** Messaging & Events (12 Modules)** Message Broker, Pub/Sub, Event Bus Event Sourcing, Event Store, CQRS Message Queue, Kafka Integration Webhook Manager, Notification Hub Real-time Sync, Change Data Capture ** Infrastructure (20 Modules)** Load Balancer, Circuit Breaker, Rate Limiter Service Discovery, Service Registry, Mesh Health Check, Health Monitor Resource Manager, Cluster Manager Container Orchestration, Kubernetes Auto-scaling, Capacity Planning ** DevOps & Deployment (15 Modules)** Deployment Manager, Canary Releases Blue-Green Deployment, Rolling Updates Chaos Engineering, Fault Injection CI/CD Integration, GitOps Environment Manager, Config Manager ** Domain-Driven Design (12 Modules)** Aggregate, Aggregate Root, Entities Bounded Context, Domain Events, Saga Repository Pattern, Unit of Work Value Objects, Specifications Anti-Corruption Layer

** Storage & Caching (14 Modules)** Cache Manager, Redis, Memcached Database Abstraction, ORM Support Object Storage, File Manager Backup Manager, Archive Distributed Cache, Cache Sync ** Observability (16 Modules)** Distributed Tracing, Span Hierarchy Metrics Collector, Custom Dashboards Log Aggregator, Log Analysis Alerting, Anomaly Detection Performance Profiling, APM Latency Percentiles (P50, P95, P99) ** Workflow & Orchestration (12 Modules)** Workflow Engine, State Machine Task Scheduler, Job Queue Manager Process Orchestrator, Saga Pattern Retry Manager, Compensation Async Processing, Batch Jobs

** Integration Connectors (18 Modules)** ServiceNow (ITSM), GitHub/Azure DevOps Snowflake, Databricks, BigQuery Slack, Teams, Email Integration Salesforce, HubSpot CRM AWS, Azure, GCP Connectors Webhooks (Inbound/Outbound) ** Governance (10 Modules)** Policy Enforcer, Compliance Manager Governance Framework, Standards Access Control, Permission Manager Quota Manager, Usage Tracking License Manager, Cost Allocation ** Performance (15 Modules)** Request Router, Load Balancer Connection Pooling, Query Optimizer Lazy Loading, Eager Loading Batch Processor, Parallel Executor Throttle Manager, Backpressure

Compliance & Audit

from agenticaiframework.compliance import (
    AuditLogger,
    PolicyEnforcer,
    DataMasker
)

# Audit logging with integrity
audit = AuditLogger(storage="database", integrity="hash_chain")
audit.log_action("data_access", user="admin", resource="customer_data")

# Policy enforcement
policy = PolicyEnforcer()
policy.add_rule("no_pii_in_logs", pattern=r"\b\d{3}-\d{2}-\d{4}\b")
policy.enforce(log_message)

# Data masking
masker = DataMasker(strategies=["pii", "credit_card", "email"])
masked = masker.mask("Contact: [e-mail], SSN: 123-45-6789")
# Output: "Contact: [EMAIL], SSN: [SSN]"

---

Evaluation Framework

12-Tier Comprehensive Evaluation

from agenticaiframework.evaluation import EvaluationSystem

# Initialize evaluation system
evaluator = EvaluationSystem()

# Run comprehensive evaluation
results = evaluator.evaluate(
    agent=my_agent,
    test_cases=test_suite,
    tiers=["model_quality", # Hallucination, reasoning, coherence
        "task_skill", # Success rates, completion
        "tool_api", # Tool usage, latency
        "workflow", # Orchestration, handoffs
        "memory_context", # Context quality, retrieval
        "rag", # Faithfulness, groundedness
        "autonomy_planning", # Plan optimality
        "performance", # Latency P50/P95/P99
        "cost_finops", # Token usage, cost
        "human_loop", # Acceptance rates
        "business_outcomes", # ROI, impact
        "security_risk" # Risk scoring
    ]
)

# A/B Testing
from agenticaiframework.evaluation import ABTestFramework

ab_test = ABTestFramework()
experiment = ab_test.create_experiment(
    name="prompt_optimization",
    variants=["control", "variant_a", "variant_b"],
    metrics=["success_rate", "latency", "cost"]
)
ab_test.run(experiment, sample_size=1000)
results = ab_test.analyze(experiment)

# Canary Deployments
from agenticaiframework.evaluation import CanaryDeployment

canary = CanaryDeployment()
canary.deploy(
    new_version="v2.0",
    initial_traffic=5,
    increment=10,
    rollback_threshold={"error_rate": 0.05}
)

---

Framework Comparison

Feature	AgenticAI	LangChain	CrewAI	AutoGen
Production Ready	Enterprise	Experimental	Limited	Research
Total Modules	400+	~50	~20	~30
Test Coverage	66% (1036 tests)	Variable	Limited	Basic
Built-in Tools	35+	Community	Limited	Basic
Memory Managers	7 Specialized	1 Basic	None	Simple
State Managers	7 Specialized	None	None	None
Speech Processing	Full STT/TTS	None	None	None
Communication Protocols	6 Protocols	HTTP only	None	Basic
12-Tier Evaluation	Built-in	None	None	None
Multi-Agent Orchestration	Advanced	Simple	Team-based	Group chat
Enterprise Modules	237 Modules	Limited	None	None
HITL Support	Built-in	Manual	None	Basic
Compliance/Audit	Full	None	None	None
ML/AI Infrastructure	14 Modules	Basic	None	Limited
Domain-Driven Design	12 Modules	None	None	None
Conversation Management	Advanced	Basic	None	Basic
Context Compression	Built-in	Manual	None	None

---

Documentation

Core Documentation

• Complete Documentation - Full framework docs
• API Reference - Detailed API docs
• Quick Start Guide - Get started fast
• Best Practices - Production patterns

Module Guides

Module	Description	Link
Agents	Creating intelligent agents	Docs
Memory	7 specialized memory systems	Docs
State	7 state management systems	Docs
Orchestration	Multi-agent coordination	Docs
Speech	STT/TTS processing	Docs
Tools	35+ built-in tools	Docs
Evaluation	12-tier assessment	Docs
Guardrails	Safety & compliance	Docs
Monitoring	Observability & tracing	Docs
Communication	6 protocol support	Docs
HITL	Human-in-the-loop	Docs
Compliance	Audit & policy	Docs

Enterprise Module Categories

Category	Modules	Key Features
API Management	15	Gateway, Versioning, Lifecycle, Analytics
Security & Compliance	18	Encryption, Auth, RBAC, PII Detection
Data Processing	16	Pipeline, ETL, Lineage, Quality
ML/AI Infrastructure	14	Inference, Feature Store, RAG, Embeddings
Messaging & Events	12	Broker, Pub/Sub, Event Sourcing, CQRS
Infrastructure	20	Load Balancer, Circuit Breaker, Service Mesh
DevOps & Deployment	15	Canary, Blue-Green, Chaos Engineering
Domain-Driven Design	12	Aggregate, Saga, Bounded Context
Storage & Caching	14	Cache Manager, Redis, Distributed Cache
Observability	16	Tracing, Metrics, Alerting, APM
Workflow & Orchestration	12	Engine, Scheduler, State Machine
Integration Connectors	18	ServiceNow, GitHub, Slack, AWS/Azure/GCP
Governance	10	Policy, Access Control, Quota Manager
Performance	15	Router, Connection Pooling, Throttle

---

Testing

# Run all tests
pytest tests/

# Run with coverage
pytest tests/ --cov=agenticaiframework --cov-report=html

# Run specific test suites
pytest tests/unit/ -v # Unit tests
pytest tests/integration/ -v # Integration tests
pytest tests/test_memory_coverage.py -v # Memory tests
pytest tests/test_all_evaluation_types.py -v # Evaluation tests

Test Coverage Summary

Module	Coverage	Status
Communication	100%	Excellent
Processes	100%	Excellent
Configurations	100%	Excellent
Evaluation (Basic)	100%	Excellent
Knowledge	94%	Excellent
Integrations	95%	Excellent
Monitoring	86%	Good
Hub	85%	Good
Agents	83%	Good
Memory	82%	Good
Evaluation (Advanced)	81%	Good
Tasks	80%	Good
MCP Tools	79%	Fair
Guardrails	75%	Fair
Security	74%	Fair
Prompts	71%	Fair

---

Contributing

We welcome contributions! See our Contributing Guide for details.

# Setup development environment
git clone https://github.com/isathish/agenticaiframework.git
cd agenticaiframework
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -e ".[dev]"

# Run tests before submitting
pytest tests/

# Build documentation locally
mkdocs serve

Ways to Contribute

Type	Description
Bug Reports	Report issues
Feature Requests	Request features
Documentation	Improve docs
Code	Submit PRs
Examples	Add examples

---

Roadmap

Quarter	Focus Areas
Q1 2026	Enhanced multi-modal capabilities, advanced evaluation metrics
Q2 2026	Distributed agent coordination, multi-region deployment
Q3 2026	Advanced ML/AI integrations, self-healing capabilities
Q4 2026	Enterprise security enhancements, plugin marketplace

---

License

This project is licensed under the MIT License - see the LICENSE file for details.

---

Acknowledgments

Built with love by the AgenticAI Framework Team

Making AI agents accessible, reliable, and production-ready

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Documentation • GitHub • PyPI}