A powerful agent framework for building conversational AI systems

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Project description

Arshai

A powerful AI application framework built on clean architecture principles with complete developer control.

┌────────────────────────────────────────────────────────────┐
│          Layer 3: Agentic Systems                          │
├────────────────────────────────────────────────────────────┤
│  Workflows  →┐                                             │
│  Memory     →├──→  Agents                                  │
│  Orchestration→┘                                           │
└─────────────────────┬──────────────────────────────────────┘
                      │
┌─────────────────────▼──────────────────────────────────────┐
│          Layer 2: Agents                                   │
├────────────────────────────────────────────────────────────┤
│              Agents  →  Tool Integration                   │
│                      →  Custom Logic                       │
└─────────────────────┬──────────────────────────────────────┘
                      │
┌─────────────────────▼──────────────────────────────────────┐
│          Layer 1: LLM Clients                              │
├────────────────────────────────────────────────────────────┤
│              Custom Logic  →  OpenAI                       │
│                            →  Azure OpenAI                 │
│                            →  Google Gemini                │
│                            →  OpenRouter                   │
└────────────────────────────────────────────────────────────┘

📚 Documentation

Complete documentation is available at: Arshai Documentation (or build locally with cd docs_sphinx && make html)

The comprehensive documentation covers:

Getting Started: Quick installation and your first agent
Framework Guide: Deep dive into agents, tools, memory, and orchestration
Implementations: LLM clients, vector databases, and memory backends
Tutorials: Step-by-step guides for common use cases
API Reference: Complete interface and class documentation
Extending: Custom agents, LLM providers, and components

For a quick overview, continue reading this README. For detailed guides and API documentation, visit the full documentation site.

Philosophy: Developer Authority First

Arshai empowers you to build exactly what you need, when you need it, how you need it. You control every component, dependency, and interaction. No magic, no hidden behavior, no framework lock-in.

The Arshai Way vs Traditional Frameworks

❌ Traditional Framework Approach:

# Framework controls everything - what's happening inside?
framework = AIFramework()
framework.load_config("config.yaml") 
agent = framework.create_agent("chatbot")  # Hidden creation
response = agent.chat("Hello")  # Opaque behavior

✅ Arshai Approach - You're In Control:

# You create, configure, and control everything
llm_config = ILLMConfig(model="gpt-4", temperature=0.7)
llm_client = OpenAIClient(llm_config)  # You create it

agent = ChatAgent(
    llm_client=llm_client,  # You inject dependencies  
    system_prompt="Be helpful",  # You configure behavior
    memory=memory_manager  # You manage state
)

response = await agent.process(input_data)  # You control the flow

Three-Layer Architecture

Layer 1: LLM Clients (Foundation)

Minimal Developer Authority - Standardized access to AI providers

from arshai.llms.openai import OpenAIClient
from arshai.core.interfaces.illm import ILLMConfig, ILLMInput

# Direct instantiation - you create when needed
config = ILLMConfig(model="gpt-4", temperature=0.7)
llm = OpenAIClient(config)

# Unified interface across all providers
llm_input = ILLMInput(
    system_prompt="You are a helpful assistant",
    user_message="What's the weather like?",
    regular_functions={"get_weather": get_weather_func},
    background_tasks={"log_interaction": log_func}  # Fire-and-forget
)

response = await llm.chat(llm_input)

Supported Providers:

OpenAI - Direct API with streaming and function calling
Azure OpenAI - Enterprise deployment with native parsing
Google Gemini - Native SDK with dual authentication
OpenRouter - Access to Claude, Llama, and 200+ models
Extensible - Add new providers by implementing ILLM interface

Layer 2: Agents (Business Logic)

Moderate Developer Authority - Purpose-driven AI components

from arshai.agents.base import BaseAgent
from arshai.core.interfaces.iagent import IAgentInput

class CustomerServiceAgent(BaseAgent):
    """Your custom agent with your business logic."""
    
    def __init__(self, llm_client: ILLM, knowledge_base: KnowledgeBase):
        super().__init__(llm_client, "You are a customer service expert")
        self.knowledge_base = knowledge_base
    
    async def process(self, input: IAgentInput) -> Dict[str, Any]:
        # Your custom preprocessing
        context = await self._analyze_request(input.message)
        
        # Define tools for this specific agent
        async def search_kb(query: str) -> str:
            return await self.knowledge_base.search(query)
        
        async def log_issue(issue: str) -> None:
            """BACKGROUND TASK: Log customer issue."""
            await self.issue_tracker.create_ticket(issue)
        
        # Your LLM integration with custom tools
        llm_input = ILLMInput(
            system_prompt=self.system_prompt,
            user_message=f"Context: {context}\nUser: {input.message}",
            regular_functions={"search_knowledge": search_kb},
            background_tasks={"log_issue": log_issue}
        )
        
        result = await self.llm_client.chat(llm_input)
        
        # Your custom post-processing
        return {
            "response": result["llm_response"],
            "confidence": self._calculate_confidence(result),
            "escalate": self._should_escalate(result)
        }

Layer 3: Agentic Systems (Complete Control)

Maximum Developer Authority - Orchestrate everything exactly as you need

class CustomerSupportSystem:
    """Your complete system - you control everything."""
    
    def __init__(self):
        # You create all components
        llm = OpenAIClient(ILLMConfig(model="gpt-4"))
        memory = RedisMemoryManager(redis_url=os.getenv("REDIS_URL"))
        knowledge_base = VectorKnowledgeBase(milvus_client, embeddings)
        
        # You compose the system
        self.triage_agent = TriageAgent(llm, "Route customer requests")
        self.support_agent = CustomerServiceAgent(llm, knowledge_base)
        self.escalation_agent = EscalationAgent(llm, ticket_system)
        
        # You define the workflow
        self.memory = memory
        
    async def handle_request(self, user_message: str, user_id: str) -> Dict[str, Any]:
        """Your orchestration logic."""
        
        # Step 1: Retrieve user history
        history = await self.memory.retrieve({"user_id": user_id})
        
        # Step 2: Triage the request
        triage_input = IAgentInput(
            message=user_message,
            metadata={"history": history, "user_id": user_id}
        )
        triage_result = await self.triage_agent.process(triage_input)
        
        # Step 3: Route based on triage (your business logic)
        if triage_result["urgency"] == "high":
            agent = self.escalation_agent
        else:
            agent = self.support_agent
        
        # Step 4: Process with selected agent
        support_input = IAgentInput(
            message=user_message,
            metadata={
                "user_id": user_id,
                "triage": triage_result,
                "history": history
            }
        )
        final_result = await agent.process(support_input)
        
        # Step 5: Update memory (your state management)
        await self.memory.store({
            "user_id": user_id,
            "interaction": {
                "request": user_message,
                "response": final_result["response"],
                "agent_used": agent.__class__.__name__,
                "timestamp": datetime.now().isoformat()
            }
        })
        
        # Your final response format
        return {
            "response": final_result["response"],
            "agent_used": agent.__class__.__name__,
            "escalated": isinstance(agent, EscalationAgent),
            "confidence": final_result.get("confidence", 0.0)
        }

Comprehensive Feature Overview

🎯 Developer Authority

Direct Instantiation: You create every component explicitly
Dependency Injection: All dependencies passed through constructors
No Hidden Magic: Every behavior is visible in your code
Complete Control: You decide architecture, not the framework
Interface-First Design: Well-defined contracts for easy testing
Zero Framework Lock-in: Use what you need, ignore the rest

🤖 Advanced LLM Client System

Multi-Provider Support

OpenAI Integration: Native API with GPT-4o, GPT-4o-mini, and all models
- Streaming with real-time function execution
- Advanced function calling with parallel execution
- Structured outputs with Pydantic models
- Token usage tracking and cost optimization
- Rate limiting and error handling
Azure OpenAI Enterprise: Full Azure deployment support
- Azure authentication and endpoint management
- Content filtering and compliance features
- Regional deployment flexibility
- Enterprise security and audit logging
Google Gemini: Native SDK integration
- Gemini Pro, Flash, and specialized models
- Dual authentication (API key and Service Account)
- Advanced safety settings and content filtering
- Image and multimodal capabilities
- Real-time streaming with function calls
OpenRouter: Access to 200+ models including:
- Anthropic Claude 3.5 Sonnet, Haiku, Opus
- Meta Llama 3.1, 3.2 (8B, 70B, 405B)
- Mistral Large, 8x7B, 8x22B
- Cohere Command R+
- Specialized models for coding, reasoning, and creativity

Unified Function Calling Architecture

# Works identically across all providers
def get_weather(city: str) -> dict:
    """Get current weather for a city."""
    return {"temperature": 72, "condition": "sunny"}

async def send_notification(message: str) -> None:
    """BACKGROUND TASK: Send notification."""
    # Runs independently, doesn't block conversation
    print(f"📧 Notification: {message}")

# Same interface for any provider
llm_input = ILLMInput(
    system_prompt="You are a helpful assistant",
    user_message="What's the weather in San Francisco?",
    regular_functions={"get_weather": get_weather},
    background_tasks={"send_notification": send_notification}
)

# Works with OpenAI, Azure, Gemini, OpenRouter
response = await llm_client.chat(llm_input)

Progressive Streaming

Real-time Function Execution: Functions execute immediately during streaming
Parallel Tool Execution: Regular tools run concurrently for speed
Background Task Management: Fire-and-forget operations tracked automatically
Enhanced Context Building: Function results integrated into conversation
Safe Usage Accumulation: Robust token and cost tracking during streaming

🧠 Sophisticated Agent System

Base Agent Capabilities

Flexible Return Types: Return strings, dicts, generators, or custom objects
Stateless Design: Pure functions with no hidden state
Tool Integration: Easy integration of custom functions and APIs
Memory Compatibility: Works with any memory backend
Error Resilience: Graceful handling of LLM failures

Specialized Agent Types

# Working Memory Agent - Maintains conversation context
from arshai.agents.working_memory import WorkingMemoryAgent

# Base Agent - Flexible foundation for custom agents
from arshai.agents.base import BaseAgent

# Custom domain-specific agents
class CustomerServiceAgent(BaseAgent):
    def __init__(self, llm_client, knowledge_base, escalation_system):
        super().__init__(llm_client, "Customer service expert")
        self.knowledge_base = knowledge_base
        self.escalation_system = escalation_system
    
    async def process(self, input: IAgentInput) -> Dict[str, Any]:
        # Your custom business logic
        # Access to knowledge base, escalation, metrics
        # Return structured responses with confidence scores
        pass

Agent Development Patterns

Domain Specialization: Create agents for specific business domains
Tool Composition: Combine multiple tools and APIs per agent
Response Formatting: Structure outputs exactly as needed
Confidence Scoring: Built-in or custom confidence assessment
Escalation Logic: Automatic escalation to human agents or specialized systems

🔧 Comprehensive Tool Ecosystem

Built-in Tools

Web Search: SearxNG integration with connection pooling
- Multiple search engines aggregation
- Result filtering and ranking
- Performance optimization for production
Knowledge Base: Vector database integration
- Milvus support with hybrid search
- Document ingestion and chunking
- Semantic search with embedding models
- Metadata filtering and faceted search
MCP (Model Context Protocol) Tools: Dynamic tool loading
- File system operations (read, write, search)
- Database connections and queries
- API integrations and webhooks
- Custom tool development framework
- Thread pool management for performance

Custom Tool Development

from arshai.core.interfaces.itool import ITool

class DatabaseTool(ITool):
    def __init__(self, db_connection):
        self.db = db_connection
    
    async def search_products(self, query: str, category: str = None) -> List[Dict]:
        """Search products in database."""
        # Your database logic
        return results
    
    async def log_interaction(self, user_id: str, action: str) -> None:
        """BACKGROUND TASK: Log user interaction."""
        # Background logging, doesn't affect conversation
        await self.db.log(user_id, action)

# Easy integration with agents
db_tool = DatabaseTool(connection)
agent = CustomAgent(
    llm_client, 
    regular_functions={"search_products": db_tool.search_products},
    background_tasks={"log_interaction": db_tool.log_interaction}
)

🧬 Agentic System Orchestration

Workflow Management

State-Driven Workflows: Complex multi-step processes
Conditional Branching: Dynamic execution paths based on results
Error Recovery: Automatic retry and fallback mechanisms
Parallel Execution: Concurrent agent execution for performance
State Persistence: Resume workflows across sessions

Multi-Agent Coordination

class IntelligentHelpdesk:
    def __init__(self):
        # Create specialized agents
        self.triage_agent = TriageAgent(llm_client)
        self.technical_agent = TechnicalSupportAgent(llm_client, knowledge_base)
        self.billing_agent = BillingAgent(llm_client, billing_system)
        self.escalation_agent = EscalationAgent(llm_client, human_queue)
        
        # Shared resources
        self.memory_manager = RedisMemoryManager()
        self.metrics_collector = MetricsCollector()
    
    async def handle_request(self, request: str, user_id: str) -> Dict[str, Any]:
        # Step 1: Triage determines routing
        triage_result = await self.triage_agent.classify(request)
        
        # Step 2: Route to appropriate specialist
        if triage_result['category'] == 'technical':
            agent = self.technical_agent
        elif triage_result['category'] == 'billing':
            agent = self.billing_agent
        elif triage_result['urgency'] == 'critical':
            agent = self.escalation_agent
        
        # Step 3: Process with context and memory
        context = await self.memory_manager.get_user_context(user_id)
        result = await agent.process_with_context(request, context)
        
        # Step 4: Update memory and metrics
        await self.memory_manager.update_context(user_id, result)
        await self.metrics_collector.record_interaction(result)
        
        return result

💾 Advanced Memory Management

Memory Backend Options

Redis Memory Manager: Production-ready distributed memory
- Automatic TTL management
- Connection pooling and failover
- Pub/sub for real-time updates
- Memory cleanup and optimization
In-Memory Manager: Fast development and testing
- LRU eviction policies
- Background cleanup tasks
- Memory usage monitoring
Working Memory System: Context-aware conversation tracking
- Automatic context summarization
- Relevance scoring and pruning
- Cross-session continuity

Memory Usage Patterns

# Environment-specific memory selection
def create_memory_system(environment: str) -> IMemoryManager:
    if environment == "production":
        return RedisMemoryManager(
            redis_url=os.getenv("REDIS_URL"),
            ttl=3600,  # 1 hour TTL
            key_prefix="app_prod",
            connection_pool_size=10
        )
    elif environment == "development":
        return InMemoryManager(
            ttl=1800,  # 30 minutes
            max_size=1000,
            cleanup_interval=300  # 5 minutes
        )
    else:
        return InMemoryManager(ttl=300)  # Testing

# Advanced conversation memory
class ConversationMemory:
    def __init__(self, memory_manager, summarizer_agent):
        self.memory = memory_manager
        self.summarizer = summarizer_agent
    
    async def store_conversation(self, user_id: str, conversation: List[Dict]):
        # Automatic summarization for long conversations
        if len(conversation) > 10:
            summary = await self.summarizer.summarize(conversation[-10:])
            conversation = conversation[:-10] + [summary]
        
        await self.memory.store({
            "user_id": user_id,
            "conversation": conversation,
            "timestamp": datetime.now().isoformat()
        })

📊 Production-Grade Observability

OpenTelemetry Integration

Automatic Instrumentation: No code changes required
Distributed Tracing: Track requests across services
Custom Metrics: Business-specific monitoring
Export Flexibility: Send to any OTLP-compatible backend

Built-in Metrics

# Automatic metrics collection
metrics_collected = {
    "llm_requests_total": "Counter of LLM API calls",
    "llm_request_duration_seconds": "Histogram of request latencies",
    "llm_tokens_total": "Counter of tokens used (input/output/total)",
    "llm_cost_total": "Counter of API costs by provider",
    "llm_time_to_first_token_seconds": "Time to start streaming",
    "llm_time_to_last_token_seconds": "Total generation time",
    "agent_processing_duration_seconds": "Agent processing time",
    "memory_operations_total": "Memory store/retrieve operations",
    "tool_execution_duration_seconds": "Tool execution timing",
    "background_task_duration_seconds": "Background task timing"
}

# Zero-fallback monitoring - always works
from arshai.observability import ObservabilityConfig

# Optional: Advanced configuration
obs_config = ObservabilityConfig(
    endpoint="http://jaeger:14268/api/traces",
    service_name="arshai_app",
    environment="production",
    custom_metrics={"business_metric": "counter"}
)

# Automatic instrumentation
client = OpenAIClient(
    config=llm_config,
    observability_config=obs_config  # Optional
)

# Metrics automatically exported to your monitoring system

Performance Monitoring

Real-time Dashboards: Track system health and performance
Alert Configuration: Custom alerts for SLA violations
Cost Tracking: Detailed cost breakdown by model and usage
Error Rate Monitoring: Track and analyze failure patterns
Capacity Planning: Usage trends and scaling recommendations

🗄️ Vector Database Integration

Milvus Support

Hybrid Search: Dense + sparse vector combination
- Semantic similarity with dense vectors
- Keyword matching with sparse vectors
- Weighted ranking and reranking
Production Features:
- Connection pooling and async operations
- Batch insertion with configurable batch sizes
- Automatic index creation and optimization
- Metadata filtering and complex queries
- Schema versioning and migration support

# Advanced vector operations
from arshai.vector_db.milvus_client import MilvusClient
from arshai.core.interfaces.ivector_db_client import ICollectionConfig

# Async operations for performance
client = MilvusClient()
config = ICollectionConfig(
    collection_name="knowledge_base",
    dense_dim=1536,  # OpenAI embeddings
    is_hybrid=True,  # Enable sparse + dense
    schema_model=CustomDocument  # Structured metadata
)

# Hybrid search with filtering
results = await client.hybrid_search(
    config=config,
    dense_vectors=dense_embeddings,
    sparse_vectors=sparse_embeddings,
    expr="metadata['category'] == 'technical'",
    limit=10
)

📦 Document Processing Pipeline

Ingestion and Chunking

Multi-format Support: PDF, DOCX, TXT, HTML, Markdown
Intelligent Chunking: Semantic and structural chunking
Metadata Extraction: Automatic metadata detection
Batch Processing: Efficient large-scale ingestion
Deduplication: Content-based duplicate detection

Embedding Generation

Multiple Providers: OpenAI, Google, Hugging Face
Model Selection: Choose optimal models per use case
Batch Processing: Efficient embedding generation
Caching: Avoid re-processing unchanged content

🚀 Enterprise Performance & Scalability

Connection Management

HTTP Connection Pooling: Configurable limits prevent resource exhaustion
- Default: 100 max connections, 10 per host
- Configurable via environment variables
- Automatic connection reuse and cleanup
Thread Pool Management: Bounded execution prevents deadlocks
- CPU-aware thread limits: min(32, cpu_count * 2)
- Shared pools across MCP tools
- Graceful shutdown and cleanup

Async Architecture

Non-blocking Operations: All I/O operations are async
Concurrent Processing: Parallel function execution
Background Tasks: Fire-and-forget operations
Resource Management: Automatic cleanup and monitoring

Production Optimizations

# Environment-based configuration
os.environ.update({
    'ARSHAI_MAX_CONNECTIONS': '200',        # HTTP connection pool
    'ARSHAI_MAX_CONNECTIONS_PER_HOST': '20', # Per-host limit
    'ARSHAI_MAX_THREADS': '64',             # Thread pool size
    'ARSHAI_MAX_MEMORY_MB': '4096',         # Memory limit
    'ARSHAI_CLEANUP_INTERVAL': '300',       # Background cleanup
    'ARSHAI_BATCH_SIZE': '100'              # Vector DB batch size
})

# Production deployment patterns
class ProductionApp:
    def __init__(self):
        # Shared resources for efficiency
        self.llm_pool = {
            'fast': [OpenAIClient(fast_config) for _ in range(3)],
            'powerful': [OpenAIClient(powerful_config) for _ in range(2)]
        }
        
        self.memory_manager = RedisMemoryManager(
            redis_url=os.getenv("REDIS_URL"),
            connection_pool_size=20,
            ttl=7200
        )
        
        # Load balancing and circuit breakers
        self.load_balancer = LoadBalancer(self.llm_pool)
        self.circuit_breaker = CircuitBreaker(failure_threshold=5)
    
    async def handle_concurrent_requests(self, requests: List[str]) -> List[str]:
        # Process up to 100 concurrent requests
        semaphore = asyncio.Semaphore(100)
        
        async def process_one(request):
            async with semaphore:
                agent = self.create_agent()
                return await agent.process(IAgentInput(message=request))
        
        tasks = [process_one(req) for req in requests]
        results = await asyncio.gather(*tasks, return_exceptions=True)
        return results

🛠️ Development and Testing

Testing Framework

Mock Support: Easy mocking of LLM clients and dependencies
Integration Testing: Real LLM integration tests
Load Testing: Performance and scalability validation
Async Testing: Full async/await test support

# Easy testing with dependency injection
async def test_customer_agent():
    mock_llm = AsyncMock()
    mock_llm.chat.return_value = {
        "llm_response": "Test response",
        "usage": {"total_tokens": 50}
    }
    
    mock_kb = AsyncMock()
    mock_kb.search.return_value = ["relevant info"]
    
    # Create agent with mocked dependencies
    agent = CustomerServiceAgent(mock_llm, mock_kb)
    
    result = await agent.process(IAgentInput(
        message="Help with billing",
        metadata={"user_id": "123"}
    ))
    
    assert "Test response" in result["response"]
    mock_llm.chat.assert_called_once()
    mock_kb.search.assert_called_once()

Development Patterns

Configuration Management: Environment-based settings
Hot Reloading: Development server with auto-reload
Debugging Tools: Comprehensive logging and tracing
Performance Profiling: Built-in performance analysis

Installation & Setup

Basic Installation

# Core framework only
pip install arshai

# With all optional dependencies
pip install arshai[all]  # Includes all features below

# Specific feature sets
pip install arshai[redis,milvus,observability]  # Production essentials
pip install arshai[docs]                        # Documentation building

Feature-Specific Installations

# Memory backends
pip install arshai[redis]     # Redis memory manager

# Vector databases  
pip install arshai[milvus]    # Milvus vector database

# Advanced search
pip install arshai[flashrank] # Re-ranking capabilities

# Monitoring
pip install arshai[observability] # OpenTelemetry integration

# Development tools
pip install arshai[dev]       # Testing and development utilities

Environment Setup

# LLM Provider API Keys
export OPENAI_API_KEY="your-openai-key"
export AZURE_OPENAI_API_KEY="your-azure-key"
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
export GOOGLE_API_KEY="your-gemini-key"
export OPENROUTER_API_KEY="your-openrouter-key"

# Optional: Google Service Account (alternative to API key)
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"

# Memory backends
export REDIS_URL="redis://localhost:6379"
export REDIS_TTL="3600"  # 1 hour

# Vector database
export MILVUS_HOST="localhost"
export MILVUS_PORT="19530"
export MILVUS_DB_NAME="default"

# Performance tuning
export ARSHAI_MAX_CONNECTIONS="100"
export ARSHAI_MAX_THREADS="32"
export ARSHAI_BATCH_SIZE="50"

# Observability
export OTEL_EXPORTER_OTLP_ENDPOINT="http://jaeger:14268"
export OTEL_SERVICE_NAME="arshai_app"

Docker Setup

# Production Dockerfile
FROM python:3.11-slim

WORKDIR /app

# Install production dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt

# Performance configuration
ENV ARSHAI_MAX_CONNECTIONS=200
ENV ARSHAI_MAX_CONNECTIONS_PER_HOST=20
ENV ARSHAI_MAX_THREADS=64
ENV ARSHAI_MAX_MEMORY_MB=4096

# Redis connection
ENV REDIS_URL=redis://redis:6379
ENV REDIS_TTL=7200

# Milvus connection
ENV MILVUS_HOST=milvus
ENV MILVUS_PORT=19530

COPY . .
CMD ["python", "app.py"]

Docker Compose for Development

# docker-compose.yml
version: '3.8'

services:
  app:
    build: .
    environment:
      - REDIS_URL=redis://redis:6379
      - MILVUS_HOST=milvus
      - OPENAI_API_KEY=${OPENAI_API_KEY}
    depends_on:
      - redis
      - milvus
      - jaeger
  
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
  
  milvus:
    image: milvusdb/milvus:latest
    ports:
      - "19530:19530"
    environment:
      - ETCD_ENDPOINTS=etcd:2379
  
  jaeger:
    image: jaegertracing/all-in-one:latest
    ports:
      - "16686:16686"
      - "14268:14268"

Quick Start Guide 🚀

1. Quick Start - Your First Agent

from arshai.llms.openai import OpenAIClient
from arshai.agents.base import BaseAgent
from arshai.core.interfaces.illm import ILLMConfig
from arshai.core.interfaces.iagent import IAgentInput

# Step 1: Create your LLM client (you control this)
llm_config = ILLMConfig(
    model="gpt-4o",           # Latest model
    temperature=0.7,         # Creativity level
    max_tokens=2000          # Response length
)
llm_client = OpenAIClient(llm_config)

# Step 2: Create your agent (you define the behavior)
agent = BaseAgent(
    llm_client=llm_client, 
    system_prompt="You are an expert Python programming assistant. Provide clear, practical examples with explanations."
)

# Step 3: Use your agent (you control the interaction)
response = await agent.process(IAgentInput(
    message="How do I handle async/await in Python? Show me a practical example."
))

print(response)
# Output: Detailed explanation with code examples

1b. Agent with Memory (Conversation Context)

from arshai.agents.working_memory import WorkingMemoryAgent
from arshai.memory.working_memory.in_memory_manager import InMemoryManager

# Create memory system for conversation context
memory = InMemoryManager(ttl=1800)  # 30-minute memory

# Create agent with memory capabilities
conversation_agent = WorkingMemoryAgent(
    llm_client=llm_client,
    memory_manager=memory,
    system_prompt="You are a helpful assistant that remembers our conversation context."
)

# First interaction
response1 = await conversation_agent.process(IAgentInput(
    message="My name is Alex and I'm learning Python.",
    metadata={"conversation_id": "conv_123"}
))

# Second interaction - agent remembers context
response2 = await conversation_agent.process(IAgentInput(
    message="What was my name again?",
    metadata={"conversation_id": "conv_123"}
))

print(response2)  # Will remember "Alex"

1c. Multi-Provider Setup

# Use different providers for different tasks
from arshai.llms.openai import OpenAIClient
from arshai.llms.google_genai import GeminiClient
from arshai.llms.openrouter import OpenRouterClient

# Fast model for simple tasks
fast_llm = OpenAIClient(ILLMConfig(model="gpt-4o-mini", temperature=0.3))

# Powerful model for complex analysis
strong_llm = OpenAIClient(ILLMConfig(model="gpt-4o", temperature=0.5))

# Alternative provider for variety
gemini_llm = GeminiClient(ILLMConfig(model="gemini-1.5-pro", temperature=0.7))

# Access to specialized models
openrouter_llm = OpenRouterClient(ILLMConfig(
    model="anthropic/claude-3.5-sonnet",  # Claude via OpenRouter
    temperature=0.6
))

# Create specialized agents
quick_agent = BaseAgent(fast_llm, "Quick responses for simple questions")
analysis_agent = BaseAgent(strong_llm, "Deep analysis and complex reasoning")
creative_agent = BaseAgent(gemini_llm, "Creative writing and brainstorming")
research_agent = BaseAgent(openrouter_llm, "Research and detailed explanations")

# Use the right agent for each task
quick_response = await quick_agent.process(IAgentInput(
    message="What's 15% tip on $42?"
))

analysis_response = await analysis_agent.process(IAgentInput(
    message="Analyze the pros and cons of microservices vs monolithic architecture for a startup"
))

2. Advanced Agent with Multiple Tool Types

# Comprehensive restaurant agent with multiple capabilities
class SmartRestaurantAgent(BaseAgent):
    """Intelligent restaurant agent with real-time data and background tasks."""
    
    def __init__(self, llm_client: ILLM, reservation_system, pos_system, notification_service):
        super().__init__(llm_client, "You are an expert restaurant assistant with access to real-time data")
        self.reservation_system = reservation_system
        self.pos_system = pos_system
        self.notification_service = notification_service
    
    async def process(self, input: IAgentInput) -> Dict[str, Any]:
        # Define regular tools (return results to conversation)
        def get_current_time() -> Dict[str, str]:
            """Get current time with timezone info."""
            from datetime import datetime
            import pytz
            
            now = datetime.now(pytz.UTC)
            local_time = now.astimezone(pytz.timezone('America/New_York'))
            
            return {
                "utc_time": now.isoformat(),
                "local_time": local_time.strftime("%I:%M %p"),
                "date": local_time.strftime("%A, %B %d, %Y"),
                "timezone": "Eastern Time"
            }
        
        def calculate_bill_details(subtotal: float, tax_rate: float = 0.08, tip_percent: float = 20) -> Dict[str, float]:
            """Calculate comprehensive bill breakdown."""
            tax = subtotal * tax_rate
            tip = subtotal * (tip_percent / 100)
            total = subtotal + tax + tip
            
            return {
                "subtotal": round(subtotal, 2),
                "tax": round(tax, 2),
                "tip": round(tip, 2),
                "total": round(total, 2),
                "tip_percentage": tip_percent
            }
        
        async def check_table_availability(party_size: int, preferred_time: str) -> Dict[str, Any]:
            """Check real-time table availability."""
            # Your reservation system integration
            available_times = await self.reservation_system.check_availability(
                party_size=party_size,
                date=preferred_time
            )
            
            return {
                "available": len(available_times) > 0,
                "available_times": available_times[:5],  # Show top 5 options
                "party_size": party_size,
                "wait_time_estimate": "15-20 minutes" if available_times else "45+ minutes"
            }
        
        async def get_menu_recommendations(dietary_restrictions: str = None, budget_range: str = "moderate") -> List[Dict[str, Any]]:
            """Get personalized menu recommendations."""
            # Your menu system integration with filtering
            menu_items = await self.pos_system.get_filtered_menu(
                dietary_restrictions=dietary_restrictions,
                price_range=budget_range
            )
            
            return [
                {
                    "name": item["name"],
                    "description": item["description"],
                    "price": item["price"],
                    "dietary_tags": item["tags"],
                    "popularity_score": item["popularity"]
                }
                for item in menu_items[:8]  # Top 8 recommendations
            ]
        
        # Define background tasks (fire-and-forget)
        async def log_customer_interaction(user_id: str, interaction_type: str, details: Dict[str, Any]) -> None:
            """BACKGROUND TASK: Log interaction for analytics."""
            await self.pos_system.log_interaction({
                "user_id": user_id,
                "type": interaction_type,
                "details": details,
                "timestamp": datetime.now().isoformat()
            })
        
        async def send_follow_up_notification(user_id: str, message: str, delay_minutes: int = 60) -> None:
            """BACKGROUND TASK: Schedule follow-up notification."""
            await self.notification_service.schedule_notification(
                user_id=user_id,
                message=message,
                delay_minutes=delay_minutes
            )
        
        async def update_customer_preferences(user_id: str, preferences: Dict[str, Any]) -> None:
            """BACKGROUND TASK: Update customer profile."""
            await self.pos_system.update_customer_profile(
                user_id=user_id,
                preferences=preferences
            )
        
        async def trigger_marketing_follow_up(user_id: str, interaction_context: str) -> None:
            """BACKGROUND TASK: Trigger marketing automation."""
            # Your CRM/marketing automation integration
            await self.notification_service.trigger_campaign(
                user_id=user_id,
                campaign_type="restaurant_follow_up",
                context=interaction_context
            )
        
        # Prepare LLM input with all tools
        llm_input = ILLMInput(
            system_prompt="""You are a sophisticated restaurant assistant with access to real-time systems.
            You can check availability, provide menu recommendations, calculate bills, and help with reservations.
            Always be helpful, accurate, and proactive in offering relevant information.
            When customers show interest, set up appropriate follow-ups.""",
            
            user_message=input.message,
            
            # Regular functions return results to continue conversation
            regular_functions={
                "get_current_time": get_current_time,
                "calculate_bill_details": calculate_bill_details,
                "check_table_availability": check_table_availability,
                "get_menu_recommendations": get_menu_recommendations
            },
            
            # Background tasks run independently
            background_tasks={
                "log_customer_interaction": log_customer_interaction,
                "send_follow_up_notification": send_follow_up_notification,
                "update_customer_preferences": update_customer_preferences,
                "trigger_marketing_follow_up": trigger_marketing_follow_up
            }
        )
        
        # Get LLM response with tool usage
        result = await self.llm_client.chat(llm_input)
        
        # Return structured response
        return {
            "response": result["llm_response"],
            "tools_used": result.get("tools_used", []),
            "background_tasks_triggered": result.get("background_tasks", []),
            "user_id": input.metadata.get("user_id"),
            "session_id": input.metadata.get("session_id"),
            "timestamp": datetime.now().isoformat()
        }

# Production usage
async def main():
    # Create dependencies
    llm = OpenAIClient(ILLMConfig(model="gpt-4o", temperature=0.7))
    reservation_system = ReservationAPI()
    pos_system = POSIntegration()
    notification_service = NotificationService()
    
    # Create the restaurant agent
    restaurant_agent = SmartRestaurantAgent(
        llm_client=llm,
        reservation_system=reservation_system,
        pos_system=pos_system,
        notification_service=notification_service
    )
    
    # Example interaction
    response = await restaurant_agent.process(IAgentInput(
        message="Hi! I'm looking for a table for 4 people tonight around 7 PM. We have one vegetarian in our group and prefer mid-range prices. Also, what's a good tip for a $85 bill?",
        metadata={
            "user_id": "customer_123",
            "session_id": "session_456"
        }
    ))
    
    print(f"Response: {response['response']}")
    print(f"Tools used: {response['tools_used']}")
    print(f"Background tasks: {response['background_tasks_triggered']}")

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

3. Intelligent Multi-Agent System

class IntelligentHelpDeskSystem:
    """Production-ready helpdesk with multiple AI agents."""
    
    def __init__(self):
        # Shared LLM clients for different use cases
        self.fast_llm = OpenAIClient(ILLMConfig(model="gpt-4o-mini", temperature=0.3))
        self.powerful_llm = OpenAIClient(ILLMConfig(model="gpt-4o", temperature=0.3))
        
        # Persistent memory with Redis
        self.memory = RedisMemoryManager(
            redis_url="redis://localhost:6379",
            ttl=3600,  # 1 hour conversation memory
            key_prefix="helpdesk"
        )
        
        # Knowledge base with vector search
        self.knowledge_base = VectorKnowledgeBase(
            vector_client=MilvusClient(),
            collection_name="support_docs"
        )
        
        # External integrations
        self.ticket_system = JiraIntegration()
        self.billing_api = StripeAPI()
        self.user_db = PostgreSQLConnection()
        
        # Specialized agents with different capabilities
        self.agents = self._create_agents()
        
        # Smart routing with classification
        self.router = IntentClassifier(self.fast_llm)
    
    def _create_agents(self) -> Dict[str, IAgent]:
        """Create specialized agents with unique tools and knowledge."""
        
        return {
            "triage": TriageAgent(
                llm_client=self.fast_llm,
                system_prompt="You classify and prioritize customer requests"
            ),
            
            "technical": TechnicalSupportAgent(
                llm_client=self.powerful_llm,
                knowledge_base=self.knowledge_base,
                tools={
                    "search_docs": self.knowledge_base.search,
                    "check_system_status": self._check_system_status,
                    "create_bug_report": self._create_bug_report
                },
                background_tasks={
                    "log_technical_issue": self._log_technical_issue,
                    "notify_engineering": self._notify_engineering
                }
            ),
            
            "billing": BillingAgent(
                llm_client=self.fast_llm,
                billing_api=self.billing_api,
                tools={
                    "get_account_status": self.billing_api.get_account,
                    "process_refund": self.billing_api.process_refund,
                    "update_payment_method": self.billing_api.update_payment
                },
                background_tasks={
                    "log_billing_interaction": self._log_billing_interaction,
                    "send_receipt": self._send_receipt
                }
            ),
            
            "escalation": EscalationAgent(
                llm_client=self.powerful_llm,
                ticket_system=self.ticket_system,
                tools={
                    "create_priority_ticket": self.ticket_system.create_urgent,
                    "schedule_callback": self._schedule_callback,
                    "escalate_to_manager": self._escalate_to_manager
                },
                background_tasks={
                    "notify_management": self._notify_management,
                    "update_crm": self._update_crm
                }
            ),
            
            "general": GeneralSupportAgent(
                llm_client=self.fast_llm,
                knowledge_base=self.knowledge_base,
                tools={
                    "search_faq": self.knowledge_base.search_faq,
                    "get_account_info": self._get_basic_account_info
                }
            )
        }
    
    async def handle_customer_request(
        self, 
        message: str, 
        user_id: str, 
        session_id: Optional[str] = None
    ) -> Dict[str, Any]:
        """Main entry point for customer requests."""
        
        # Generate session ID if not provided
        if not session_id:
            session_id = f"session_{user_id}_{int(time.time())}"
        
        # Retrieve conversation history and user context
        conversation_history = await self.memory.retrieve({
            "session_id": session_id,
            "user_id": user_id
        })
        
        user_context = await self._get_user_context(user_id)
        
        # Step 1: Intelligent routing with context
        routing_result = await self.router.classify_request(
            message=message,
            conversation_history=conversation_history,
            user_context=user_context
        )
        
        # Step 2: Select appropriate agent based on classification
        agent_type = self._select_agent(routing_result)
        agent = self.agents[agent_type]
        
        # Step 3: Prepare comprehensive input for the agent
        agent_input = IAgentInput(
            message=message,
            metadata={
                "user_id": user_id,
                "session_id": session_id,
                "user_context": user_context,
                "conversation_history": conversation_history,
                "routing_info": routing_result,
                "timestamp": datetime.now().isoformat()
            }
        )
        
        # Step 4: Process request with selected agent
        agent_response = await agent.process(agent_input)
        
        # Step 5: Post-process and enhance response
        enhanced_response = await self._enhance_response(
            agent_response, 
            routing_result, 
            user_context
        )
        
        # Step 6: Update conversation memory
        await self.memory.store({
            "session_id": session_id,
            "user_id": user_id,
            "interaction": {
                "user_message": message,
                "agent_response": enhanced_response,
                "agent_used": agent_type,
                "confidence": routing_result.get("confidence", 0.0),
                "resolved": enhanced_response.get("resolved", False),
                "timestamp": datetime.now().isoformat()
            }
        })
        
        # Step 7: Return comprehensive response
        return {
            "response": enhanced_response["message"],
            "agent_used": agent_type,
            "confidence": routing_result.get("confidence", 0.0),
            "session_id": session_id,
            "resolved": enhanced_response.get("resolved", False),
            "escalated": enhanced_response.get("escalated", False),
            "next_steps": enhanced_response.get("next_steps", []),
            "estimated_resolution_time": enhanced_response.get("eta"),
            "satisfaction_survey": enhanced_response.get("survey_link")
        }
    
    def _select_agent(self, routing_result: Dict[str, Any]) -> str:
        """Smart agent selection based on classification and context."""
        intent = routing_result.get("intent", "general")
        urgency = routing_result.get("urgency", "low")
        complexity = routing_result.get("complexity", "simple")
        
        # Escalation logic
        if urgency == "critical" or routing_result.get("escalate_immediately"):
            return "escalation"
        
        # Intent-based routing
        if intent in ["technical", "bug", "outage"]:
            return "technical" if complexity != "high" else "escalation"
        elif intent in ["billing", "payment", "subscription"]:
            return "billing"
        elif intent in ["complaint", "refund", "cancellation"]:
            return "escalation"
        else:
            return "general"
    
    # Helper methods for agent tools
    async def _check_system_status(self) -> Dict[str, str]:
        """Check current system status."""
        # Your system monitoring integration
        return {"status": "operational", "uptime": "99.9%"}
    
    async def _get_user_context(self, user_id: str) -> Dict[str, Any]:
        """Retrieve user context from various systems."""
        # Aggregate user data from multiple sources
        return {
            "account_type": "premium",
            "support_tier": "priority",
            "previous_issues": 2,
            "satisfaction_score": 4.5
        }

# Production usage
async def main():
    helpdesk = IntelligentHelpDeskSystem()
    
    # Handle customer request
    response = await helpdesk.handle_customer_request(
        message="My API is returning 500 errors since this morning. This is affecting our production system!",
        user_id="enterprise_customer_123"
    )
    
    print(f"Agent used: {response['agent_used']}")
    print(f"Response: {response['response']}")
    print(f"Escalated: {response['escalated']}")

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

4. Advanced: Real-Time Streaming & Multi-Modal

# Advanced streaming with real-time function execution
async def financial_advisor_stream():
    """Financial advisor with real-time data and streaming."""
    
    def get_stock_price(symbol: str) -> Dict[str, Any]:
        """Get real-time stock price and metrics."""
        # Your real-time data source integration
        return {
            "symbol": symbol,
            "price": 150.25,
            "change": "+2.5%", 
            "volume": "1.2M",
            "market_cap": "2.8T"
        }
    
    def get_market_news(symbol: str) -> List[Dict[str, str]]:
        """Get latest news for a stock symbol."""
        # Your news API integration
        return [
            {"headline": "Strong Q3 earnings reported", "sentiment": "positive"},
            {"headline": "New product launch announced", "sentiment": "positive"}
        ]
    
    async def send_price_alert(symbol: str, threshold: float, current_price: float) -> None:
        """BACKGROUND TASK: Set up price monitoring."""
        print(f"📨 Alert set: {symbol} @ ${threshold} (current: ${current_price})")
        # Your alert system integration - runs independently
    
    async def log_trading_interest(symbol: str, user_intent: str) -> None:
        """BACKGROUND TASK: Log user trading interest."""
        # Your analytics system - fire and forget
        print(f"📊 Logged interest: {user_intent} for {symbol}")
    
    # Multi-function streaming setup
    llm_input = ILLMInput(
        system_prompt="""You are an expert financial advisor with access to real-time market data.
        Provide comprehensive analysis using current stock prices, news, and market sentiment.
        Set up alerts when users express interest in price monitoring.""",
        user_message="What's Apple's current situation? I'm thinking about buying and want to be alerted if it drops below $140",
        regular_functions={
            "get_stock_price": get_stock_price,
            "get_market_news": get_market_news
        },
        background_tasks={
            "send_price_alert": send_price_alert,
            "log_trading_interest": log_trading_interest
        }
    )
    
    print("📊 Financial Advisor:")
    
    # Stream with real-time function execution
    async for chunk in llm.stream(llm_input):
        if chunk.get("llm_response"):
            print(chunk["llm_response"], end="", flush=True)
        
        # Functions execute immediately as LLM calls them
        # Regular functions return results to continue conversation
        # Background tasks run independently without blocking
    
    print("\n\n✅ Analysis complete with active monitoring set up!")

# Multi-modal capabilities (Gemini)
async def image_analysis_stream():
    """Analyze images with streaming responses."""
    
    gemini_config = ILLMConfig(model="gemini-1.5-pro-vision")
    gemini = GeminiClient(gemini_config)
    
    llm_input = ILLMInput(
        system_prompt="You are an expert image analyst. Provide detailed, structured analysis.",
        user_message="Analyze this product image for e-commerce listing",
        images=["path/to/product-image.jpg"],  # Multi-modal input
        regular_functions={
            "generate_seo_tags": lambda description: ["electronics", "gadget", "premium"],
            "suggest_price_range": lambda category, features: {"min": 99, "max": 149}
        }
    )
    
    print("🖼️ Product Analysis:")
    
    async for chunk in gemini.stream(llm_input):
        if chunk.get("llm_response"):
            print(chunk["llm_response"], end="", flush=True)
    
    print("\n\n✅ Product analysis complete!")

# Usage
if __name__ == "__main__":
    import asyncio
    asyncio.run(financial_advisor_stream())

Memory Management

from arshai.memory.working_memory.redis_memory_manager import RedisMemoryManager
from arshai.memory.working_memory.in_memory_manager import InMemoryManager

# You choose the memory implementation
def create_memory(env: str) -> IMemoryManager:
    if env == "production":
        return RedisMemoryManager(
            redis_url=os.getenv("REDIS_URL"),
            ttl=3600,
            key_prefix="prod_app"
        )
    else:
        return InMemoryManager(ttl=1800)  # Development

# You integrate it with agents
memory = create_memory("production")
agent = ConversationAgent(llm, memory, "You remember our conversation")

Production Deployment

Performance Optimizations

# Configure performance settings via environment variables
import os

# HTTP connection pooling
os.environ['ARSHAI_MAX_CONNECTIONS'] = '100'
os.environ['ARSHAI_MAX_CONNECTIONS_PER_HOST'] = '10'

# Thread pool management  
os.environ['ARSHAI_MAX_THREADS'] = '32'

# Memory management
os.environ['ARSHAI_MAX_MEMORY_MB'] = '2048'
os.environ['ARSHAI_CLEANUP_INTERVAL'] = '300'

# Your production setup benefits automatically
llm = OpenAIClient(config)  # Uses connection pooling
agent = CustomAgent(llm)    # Uses managed threads

Docker Deployment

FROM python:3.11-slim

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

# Performance configuration
ENV ARSHAI_MAX_CONNECTIONS=100
ENV ARSHAI_MAX_CONNECTIONS_PER_HOST=20
ENV ARSHAI_MAX_THREADS=32

COPY . .
CMD ["python", "main.py"]

Observability

from arshai.observability import ObservabilityConfig

# Configure monitoring (optional - your choice)
obs_config = ObservabilityConfig.from_yaml("monitoring.yaml")
client = LLMFactory.create_with_observability(
    provider="openai",
    config=llm_config,
    observability_config=obs_config
)

# Automatic metrics collection:
# ✅ llm_time_to_first_token_seconds
# ✅ llm_time_to_last_token_seconds  
# ✅ llm_completion_tokens
# ✅ llm_requests_total

Key Interfaces

LLM Provider Interface

from arshai.core.interfaces.illm import ILLM, ILLMConfig, ILLMInput

# All LLM providers implement this interface
class CustomLLMProvider(ILLM):
    def __init__(self, config: ILLMConfig):
        self.config = config
    
    async def chat(self, input: ILLMInput) -> Dict[str, Any]:
        # Your implementation
        pass
    
    async def stream(self, input: ILLMInput) -> AsyncGenerator:
        # Your streaming implementation  
        pass

Agent Interface

from arshai.core.interfaces.iagent import IAgent, IAgentInput

class MyCustomAgent(IAgent):
    def __init__(self, llm_client: ILLM, **kwargs):
        self.llm_client = llm_client
        # You control initialization
    
    async def process(self, input: IAgentInput) -> Any:
        # You control the processing logic
        # Return anything - string, dict, object, generator
        pass

Testing

import pytest
from unittest.mock import AsyncMock

async def test_my_agent():
    # Easy testing with dependency injection
    mock_llm = AsyncMock()
    mock_llm.chat.return_value = {
        "llm_response": "Test response",
        "usage": {"total_tokens": 50}
    }
    
    # Your agent with mocked dependencies
    agent = MyAgent(mock_llm, "Test prompt")
    
    result = await agent.process(IAgentInput(message="Test"))
    
    assert result == expected_result
    mock_llm.chat.assert_called_once()

Extension and Customization

Add New LLM Provider

class MyLLMProvider(ILLM):
    """Your custom LLM provider."""
    
    def __init__(self, config: ILLMConfig):
        self.config = config
        # Your initialization
    
    async def chat(self, input: ILLMInput) -> Dict[str, Any]:
        # Your provider implementation
        return {
            "llm_response": "Your response",
            "usage": {"total_tokens": 100}
        }

Create Custom Agent Types

class DomainSpecificAgent(BaseAgent):
    """Agent specialized for your domain."""
    
    def __init__(self, llm_client: ILLM, domain_tools: List[Callable]):
        super().__init__(llm_client, "Domain-specific system prompt")
        self.domain_tools = {tool.__name__: tool for tool in domain_tools}
    
    async def process(self, input: IAgentInput) -> Dict[str, Any]:
        # Your domain-specific processing
        llm_input = ILLMInput(
            system_prompt=self.system_prompt,
            user_message=input.message,
            regular_functions=self.domain_tools
        )
        
        result = await self.llm_client.chat(llm_input)
        return self._format_domain_response(result)
## Observability and Monitoring

Arshai provides enterprise-grade observability for production AI systems with comprehensive OpenTelemetry integration and zero-fallback monitoring.

### 🎯 Key Features

- **Zero-Fallback Monitoring**: Always capture the 4 critical LLM metrics without any fallback mechanisms
- **OpenTelemetry Native**: Export to any OTLP-compatible backend (Jaeger, Prometheus, Datadog, New Relic)
- **Phoenix AI Observability**: Advanced LLM interaction monitoring with comprehensive input/output tracing
- **Automatic Factory Integration**: Zero-code observability through intelligent factory wrapping
- **Real-time Input/Output Capture**: Automatic capture of prompts, responses, and usage metrics
- **LLM Usage Data Integration**: Accurate token counting from LLM response usage data
- **Streaming Observability**: Real-time token-level timing for streaming responses
- **Non-Intrusive Design**: Zero side effects on LLM calls with graceful degradation
- **Production-Ready**: Comprehensive configuration, privacy controls, and performance optimization

### 📊 The Four Key Metrics

Every LLM interaction automatically captures these critical performance metrics:

| Metric | Description | Use Case |
|--------|-------------|----------|
| `llm_time_to_first_token_seconds` | Latency from request start to first token | User experience, response time SLAs |
| `llm_time_to_last_token_seconds` | Total response generation time | End-to-end performance monitoring |
| `llm_duration_first_to_last_token_seconds` | Token generation duration | Throughput analysis, model performance |
| `llm_completion_tokens` | Accurate completion token count | Cost tracking, usage monitoring |

### 🏗️ Architecture

                      LLM Calls
                          │
                          ▼
                 Observable Factory ─────┐
                          │              │
                          ▼              │ Automatic Capture:
               Observability Manager     │  • Input Messages
                   │      │      │       │  • Output Responses
       ┌───────────┼──────┼──────┘       │  • Token Usage
       │           │      │              │  • Timing Data
       ▼           ▼      ▼              │
Metrics      Trace     Phoenix ──────────┘
Collector    Exporter  Client
       │           │      │
       └─────┬─────┘      │
             ▼            ▼
     OpenTelemetry    Phoenix AI
        Collector      Platform
       │   │   │
  ┌────┼───┼───┘
  │    │   │
  ▼    ▼   ▼

Jaeger │ DataDog/ Zipkin │ New Relic ▼ Prometheus │ ▼ Grafana Dashboards


### 🚀 Quick Setup

#### 1. Basic Configuration

```yaml
# config.yaml
observability:
  service_name: "my-ai-service"
  track_token_timing: true
  otlp_endpoint: "http://localhost:4317"
  
  # Phoenix AI Observability
  phoenix_enabled: true
  phoenix_endpoint: "http://localhost:6006"
  
  # Privacy and data capture
  log_prompts: true
  log_responses: true

2. Automatic Factory Integration

from arshai.config.settings import Settings
from arshai.core.interfaces.illm import ILLMInput

# Settings automatically detects observability configuration
settings = Settings()

# Create LLM - observability is automatically enabled if configured
llm = settings.create_llm()  

# All calls are automatically instrumented with zero configuration
input_data = ILLMInput(
    system_prompt="You are a helpful assistant.",
    user_message="Hello!"
)

response = llm.chat_completion(input_data)

# Automatic capture includes:
# ✅ Input messages (system prompt + user message)
# ✅ Output response (full LLM response)
# ✅ Usage metrics (prompt/completion/total tokens)
# ✅ Timing data (first token, last token, total duration)
# ✅ Invocation parameters (model, temperature, provider)
# ✅ Span naming (llm.chat_completion not llm.<lambda>)

Complete Examples & Use Cases

Production-Ready Examples

# Start complete observability stack
cd tests/e2e/observability/
docker-compose up -d

# Access observability platforms:
# Phoenix AI Platform: http://localhost:6006 (LLM interactions, input/output tracing)
# Jaeger Traces: http://localhost:16686 (distributed tracing)
# Prometheus Metrics: http://localhost:9090 (metrics and queries)
# Grafana Dashboards: http://localhost:3000 (visualization)

📈 Supported Backends

AI Observability Platforms

Phoenix AI Platform: Advanced LLM interaction monitoring with input/output tracing
Arize AI: Enterprise LLM observability and evaluation platform

Metrics Backends

Prometheus + Grafana
DataDog (via OTLP)
New Relic (via OTLP)
AWS CloudWatch (via OTLP)
Azure Monitor (via OTLP)
Google Cloud Monitoring (via OTLP)

Tracing Backends

Jaeger
Zipkin
DataDog APM
New Relic Distributed Tracing
AWS X-Ray
Azure Application Insights

🔧 Advanced Configuration

Provider-Specific Settings

observability:
  provider_configs:
    openai:
      track_token_timing: true
    anthropic:
      track_token_timing: true
    google:
      track_token_timing: false  # Disable for specific providers

Privacy and Security

observability:
  # Privacy controls (recommended for production)
  log_prompts: false
  log_responses: false
  max_prompt_length: 1000
  max_response_length: 1000
  
  # Custom attributes (avoid sensitive data)
  custom_attributes:
    team: "ai-platform"
    environment: "production"

Performance Tuning

observability:
  # High-throughput settings
  metric_export_interval: 30
  trace_sampling_rate: 0.05  # 5% sampling
  max_span_attributes: 64
  
  # Async processing for better performance
  non_intrusive: true

🧪 Testing and Development

End-to-End Test Suite

# Complete observability testing with real backends
cd tests/e2e/observability/
export OPENAI_API_KEY="your-key"
./run_test.sh

# Tests verify:
# ✅ All 4 key metrics collected
# ✅ OTLP export working
# ✅ Multiple provider support
# ✅ Streaming observability
# ✅ No side effects on LLM calls

Development Setup

# Development configuration
observability:
  service_name: "arshai-dev"
  environment: "development"
  
  # Debug settings
  log_prompts: true
  log_responses: true
  trace_sampling_rate: 1.0  # 100% sampling
  
  # Local OTLP collector
  otlp_endpoint: "http://localhost:4317"

📚 Production Deployment

Docker Compose for Production

The tests/e2e/observability/docker-compose.yml provides a production-ready template:

# Production observability stack
docker-compose -f tests/e2e/observability/docker-compose.yml up -d

# Includes:
# - OpenTelemetry Collector
# - Jaeger for distributed tracing
# - Prometheus for metrics collection
# - Grafana with pre-built dashboards

Kubernetes Deployment

# Example Kubernetes configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: observability-config
data:
  config.yaml: |
    observability:
      service_name: "arshai-prod"
      otlp_endpoint: "http://otel-collector:4317"
      track_token_timing: true
      log_prompts: false
      log_responses: false

🔍 Monitoring and Alerting

Key Metrics to Monitor

# Prometheus queries for monitoring
# Average time to first token
histogram_quantile(0.95, rate(llm_time_to_first_token_seconds_bucket[5m]))

# Token throughput
rate(llm_completion_tokens[5m])

# Error rate
rate(llm_requests_failed[5m]) / rate(llm_requests_total[5m])

# Active requests
llm_active_requests

Grafana Dashboard

Pre-built dashboards available in tests/e2e/observability/dashboards/:

LLM Performance: Token timing metrics with percentiles
Cost Tracking: Token usage and provider distribution
Error Monitoring: Failed requests and error patterns
Throughput Analysis: Request rates and concurrent processing

🔗 Integration Examples

With Existing Monitoring

Customer Support System

# examples/customer_support_system.py
from arshai.llms.openai import OpenAIClient
from arshai.agents.base import BaseAgent
from arshai.memory.working_memory.redis_memory_manager import RedisMemoryManager
from arshai.tools.web_search import SearxNGClient
from arshai.vector_db.milvus_client import MilvusClient

class IntelligentCustomerSupport:
    """Production-ready customer support with multiple agents."""
    
    def __init__(self):
        # Shared LLM client with connection pooling
        llm_config = ILLMConfig(model="gpt-4o", temperature=0.3)
        self.llm = OpenAIClient(llm_config)
        
        # Memory system for conversation continuity
        self.memory = RedisMemoryManager(
            redis_url=os.getenv("REDIS_URL"),
            ttl=3600,
            key_prefix="support"
        )
        
        # Knowledge base with vector search
        self.kb = KnowledgeBase(
            vector_client=MilvusClient(),
            embedding_model="text-embedding-3-large"
        )
        
        # Specialized agents
        self.agents = {
            "triage": TriageAgent(self.llm, "Route customer requests"),
            "technical": TechnicalAgent(self.llm, self.kb),
            "billing": BillingAgent(self.llm, billing_api),
            "escalation": EscalationAgent(self.llm, human_queue)
        }
    
    async def handle_request(self, message: str, user_id: str, session_id: str) -> Dict[str, Any]:
        """Handle customer request with full context."""
        
        # Retrieve conversation history
        context = await self.memory.retrieve({
            "user_id": user_id,
            "session_id": session_id
        })
        
        # Triage the request
        triage_result = await self.agents["triage"].classify(
            message=message,
            context=context,
            user_history=await self.get_user_history(user_id)
        )
        
        # Select appropriate agent
        agent_type = self.select_agent(triage_result)
        agent = self.agents[agent_type]
        
        # Process with full context
        result = await agent.process(
            IAgentInput(
                message=message,
                metadata={
                    "user_id": user_id,
                    "session_id": session_id,
                    "triage": triage_result,
                    "context": context
                }
            )
        )
        
        # Store updated context
        await self.memory.store({
            "user_id": user_id,
            "session_id": session_id,
            "interaction": {
                "request": message,
                "response": result["response"],
                "agent_used": agent_type,
                "confidence": result.get("confidence", 0.0),
                "timestamp": datetime.now().isoformat()
            }
        })
        
        return {
            "response": result["response"],
            "agent_used": agent_type,
            "confidence": result.get("confidence", 0.0),
            "escalated": agent_type == "escalation",
            "session_id": session_id
        }

Content Generation Pipeline

# examples/content_generation_pipeline.py
class ContentGenerationPipeline:
    """Multi-stage content generation with quality assurance."""
    
    def __init__(self):
        # Different models for different tasks
        self.creative_llm = OpenAIClient(ILLMConfig(model="gpt-4o", temperature=0.9))
        self.editor_llm = OpenAIClient(ILLMConfig(model="gpt-4o", temperature=0.3))
        self.fact_checker_llm = GeminiClient(ILLMConfig(model="gemini-1.5-pro"))
        
        # Specialized agents
        self.writer = CreativeWriterAgent(self.creative_llm)
        self.editor = EditorAgent(self.editor_llm)
        self.fact_checker = FactCheckerAgent(self.fact_checker_llm)
        self.seo_optimizer = SEOAgent(self.editor_llm)
        
        # Tools
        self.web_search = SearxNGClient()
        self.plagiarism_checker = PlagiarismTool()
        
    async def generate_article(self, topic: str, requirements: Dict[str, Any]) -> Dict[str, Any]:
        """Generate high-quality article with multiple review stages."""
        
        # Stage 1: Research and outline
        research_data = await self.research_topic(topic)
        outline = await self.writer.create_outline(topic, research_data, requirements)
        
        # Stage 2: Content generation
        content = await self.writer.write_content(outline, requirements)
        
        # Stage 3: Editorial review
        edited_content = await self.editor.review_and_edit(
            content, 
            style_guide=requirements.get("style_guide"),
            target_audience=requirements.get("audience")
        )
        
        # Stage 4: Fact checking
        fact_check_result = await self.fact_checker.verify_claims(
            edited_content,
            research_sources=research_data["sources"]
        )
        
        if fact_check_result["issues_found"]:
            # Correct factual issues
            edited_content = await self.editor.correct_facts(
                edited_content,
                fact_check_result["corrections"]
            )
        
        # Stage 5: SEO optimization
        optimized_content = await self.seo_optimizer.optimize(
            edited_content,
            target_keywords=requirements.get("keywords", []),
            seo_requirements=requirements.get("seo", {})
        )
        
        # Stage 6: Quality assurance
        quality_score = await self.assess_quality(optimized_content)
        
        return {
            "title": optimized_content["title"],
            "content": optimized_content["body"],
            "meta_description": optimized_content["meta_description"],
            "keywords": optimized_content["keywords"],
            "quality_score": quality_score,
            "word_count": len(optimized_content["body"].split()),
            "readability_score": optimized_content.get("readability", 0),
            "fact_check_passed": not fact_check_result["issues_found"],
            "seo_score": optimized_content.get("seo_score", 0)
        }

Data Analysis Assistant

# examples/data_analysis_assistant.py
class DataAnalysisAssistant:
    """Intelligent data analysis with visualization and insights."""
    
    def __init__(self):
        self.llm = OpenAIClient(ILLMConfig(model="gpt-4o"))
        self.code_executor = CodeExecutorTool()
        self.viz_generator = VisualizationTool()
        
        # Data analysis agent with specialized tools
        self.analyst = DataAnalystAgent(
            llm_client=self.llm,
            tools={
                "execute_python": self.code_executor.execute,
                "create_visualization": self.viz_generator.create,
                "statistical_analysis": self.statistical_analysis,
                "data_profiling": self.profile_data
            }
        )
    
    async def analyze_dataset(self, data_path: str, analysis_request: str) -> Dict[str, Any]:
        """Perform comprehensive data analysis."""
        
        # Load and profile the data
        data_profile = await self.profile_data(data_path)
        
        # Generate analysis plan
        analysis_plan = await self.analyst.create_analysis_plan(
            data_profile=data_profile,
            request=analysis_request
        )
        
        results = {}
        
        # Execute analysis steps
        for step in analysis_plan["steps"]:
            if step["type"] == "statistical_analysis":
                result = await self.statistical_analysis(
                    data_path, 
                    step["parameters"]
                )
            elif step["type"] == "visualization":
                result = await self.viz_generator.create(
                    data_path,
                    chart_type=step["chart_type"],
                    parameters=step["parameters"]
                )
            elif step["type"] == "custom_code":
                result = await self.code_executor.execute(
                    step["code"],
                    data_path=data_path
                )
            
            results[step["name"]] = result
        
        # Generate insights and recommendations
        insights = await self.analyst.generate_insights(
            analysis_results=results,
            original_request=analysis_request
        )
        
        return {
            "data_profile": data_profile,
            "analysis_plan": analysis_plan,
            "results": results,
            "insights": insights,
            "visualizations": [r for r in results.values() if r.get("type") == "chart"],
            "recommendations": insights.get("recommendations", [])
        }

Working Examples Directory

Basic Usage - Simple agent creation and usage patterns
Advanced Agents - Custom agent development patterns
Multi-Agent Systems - Complex system orchestration
Memory Integration - Redis and in-memory patterns
Tool Development - Custom tool creation
Production Deployment - Enterprise patterns
Performance Testing - Load testing and validation
Customer Support - Complete support system
Content Pipeline - Multi-stage content creation
Data Analysis - Intelligent data analysis
E-commerce Assistant - Shopping and recommendations
Code Review Bot - Automated code analysis
Research Assistant - Academic research support
Meeting Summarizer - Meeting analysis and action items

Comprehensive Documentation

Note: For the complete technical documentation with API reference and detailed guides, visit Arshai Documentation or build locally with cd docs_sphinx && make html. The markdown documentation below provides quick reference links.

🚀 Getting Started

Quick Start Guide - Your first Arshai application in 5 minutes
Installation Guide - Detailed setup instructions
Core Concepts - Understanding the framework
First Agent - Build your first intelligent agent

🧠 Philosophy & Architecture

Developer Authority - Core framework philosophy
Three-Layer Architecture - Architectural foundations
Design Decisions - Why we made these choices
Comparison with Other Frameworks - How Arshai differs

🏗️ Layer-by-Layer Guides

Layer 1: LLM Clients

OpenAI Integration - Complete OpenAI setup
Azure OpenAI - Enterprise Azure deployment
Google Gemini - Gemini Pro integration
OpenRouter - Access to 200+ models
Function Calling - Advanced tool integration
Streaming - Real-time responses
Custom Providers - Adding new LLM providers

Layer 2: Agents

Agent Fundamentals - Core agent concepts
BaseAgent Guide - Using the base agent class
Custom Agents - Building specialized agents
Working Memory - Context-aware agents
Tool Integration - Adding capabilities to agents
Error Handling - Robust agent design
Testing Agents - Agent testing patterns

Layer 3: Agentic Systems

System Design - Architecting complex systems
Multi-Agent Coordination - Agent orchestration
Workflow Management - Process automation
State Management - System state handling
Error Recovery - Resilient system design
Scaling Patterns - High-volume architectures

🛠️ Development Patterns

Direct Instantiation - Core development pattern
Dependency Injection - Managing dependencies
Configuration Management - Environment-based config
Error Handling - Robust error strategies
Testing Strategies - Comprehensive testing approaches
Performance Patterns - Optimization techniques

🧰 Component Guides

Memory Systems

Memory Overview - Memory system architecture
Redis Memory - Production memory backend
In-Memory Manager - Development and testing
Working Memory - Conversation context
Custom Memory - Building custom backends

Tools & Integrations

Tool Development - Creating custom tools
Web Search Tools - SearxNG integration
Database Tools - Database connectivity
API Integration - External API tools
MCP Tools - Model Context Protocol
Background Tasks - Fire-and-forget operations

Vector Databases

Vector Database Overview - Vector storage concepts
Milvus Integration - Production vector database
Hybrid Search - Dense + sparse vectors
Embedding Models - Choosing embedding models
Performance Tuning - Optimization strategies

Document Processing

Document Ingestion - Processing various formats
Chunking Strategies - Content segmentation
Metadata Extraction - Automatic metadata detection
Batch Processing - Large-scale document handling

📊 Observability & Monitoring

Observability Overview - Monitoring philosophy
OpenTelemetry Integration - Distributed tracing
Metrics Collection - Performance monitoring
Custom Metrics - Business-specific tracking
Alerting Setup - Proactive monitoring
Cost Tracking - LLM cost optimization
Debugging Guide - Troubleshooting techniques

🔄 Migration & Upgrades

Migration Guide - Upgrading from older versions
Breaking Changes - Version compatibility
Legacy Support - Backward compatibility
Migration Tools - Automated migration utilities

🧪 Testing & Quality Assurance

Testing Philosophy - Arshai testing approach
Unit Testing - Component-level testing
Integration Testing - System-level testing
Load Testing - Performance validation
Mock Strategies - Testing with mocks
CI/CD Integration - Automated testing pipelines

🤝 Contributing & Extending

Contributing Guide - How to contribute
Code Standards - Style and quality guidelines
Adding LLM Providers - Extending provider support
Adding Tools - Contributing new tools
Documentation - Improving documentation
Release Process - How releases are managed

🚀 Deployment & Production

Production Deployment - Enterprise deployment guide
Performance Optimization - Production optimization
Docker Deployment - Containerized deployment
Kubernetes Deployment - Scalable orchestration
Security Best Practices - Production security
Monitoring Setup - Production monitoring
Backup & Recovery - Data protection
Capacity Planning - Scaling strategies

📖 API Reference

Core Interfaces - Framework contracts
LLM Clients API - LLM provider APIs
Agents API - Agent class references
Tools API - Tool development APIs
Memory API - Memory system APIs
Configuration API - Settings and config APIs

🎓 Tutorials & Learning Resources

Video Tutorials - Step-by-step video guides
Interactive Notebooks - Jupyter notebook tutorials
Use Case Studies - Real-world implementations
Best Practices - Proven patterns and approaches
Troubleshooting - Common issues and solutions

🔗 Integration Guides

FastAPI Integration - Web API development
Streamlit Integration - Rapid UI development
Discord Bot - Chatbot development
Slack Integration - Workspace automation
Database Integration - Data persistence
Cloud Services - AWS, Azure, GCP integration

Why Choose Arshai? 🎆

✅ Complete Developer Control

No Hidden Magic: Every component creation is explicit and visible
Direct Instantiation: You create components when and how you want
Dependency Injection: All dependencies passed through constructors
Architectural Freedom: Design systems exactly as needed
Zero Lock-in: Use framework components selectively
Interface-First: Well-defined contracts make everything testable

✅ Production-Grade Performance

Tested at Scale: Validated for 1000+ concurrent users
Connection Management: HTTP connection pooling prevents resource exhaustion
Thread Pool Control: Bounded execution prevents system deadlocks
Memory Optimization: Automatic cleanup and TTL management
Async Architecture: Non-blocking I/O throughout the system
Load Balancing: Built-in patterns for high-availability deployment

✅ Enterprise-Ready Features

Multi-Provider Support: OpenAI, Azure, Gemini, OpenRouter with unified interface
Advanced Function Calling: Parallel execution with background tasks
Distributed Memory: Redis-backed memory with automatic failover
Vector Database Integration: Milvus with hybrid search capabilities
Comprehensive Monitoring: OpenTelemetry integration with custom metrics
Security: Built-in security best practices and audit logging

✅ Developer Experience Excellence

Minimal Boilerplate: Get started quickly with sensible defaults
Rich Documentation: Comprehensive guides, examples, and API references
Easy Testing: Mock-friendly design with dependency injection
Type Safety: Full TypeScript-style type hints for Python
Hot Reloading: Development server with automatic reload
Debugging Tools: Comprehensive logging and tracing capabilities

✅ Flexible & Extensible

Custom LLM Providers: Add any LLM with standardized interface
Tool Ecosystem: Create custom tools with simple interfaces
Agent Specialization: Build domain-specific agents easily
Memory Backends: Implement custom memory systems
Modular Design: Use only the components you need
Plugin Architecture: Extend functionality without core changes

✅ Cost Optimization

Token Tracking: Detailed usage analytics across all providers
Smart Caching: Reduce redundant API calls automatically
Model Selection: Use appropriate models for different tasks
Batch Processing: Efficient processing of multiple requests
Cost Alerts: Monitor and control spending with custom alerts
Usage Analytics: Detailed cost breakdown and optimization recommendations

✅ Real-World Battle Tested

# Production deployments successfully running:

# Customer Support Systems
# - 500+ simultaneous conversations
# - 24/7 uptime with Redis failover
# - Multi-language support with specialized agents
# - Integration with Zendesk, Salesforce, custom CRMs

# Content Generation Pipelines  
# - Processing 10,000+ articles daily
# - Multi-stage review with fact-checking
# - SEO optimization and quality scoring
# - Integration with CMS systems and publishing workflows

# Data Analysis Platforms
# - Processing GB-scale datasets
# - Real-time visualization generation
# - Statistical analysis with confidence intervals
# - Integration with Jupyter, Pandas, enterprise BI tools

# E-commerce Assistants
# - Handling 1000+ concurrent shoppers
# - Personalized recommendations with vector search
# - Real-time inventory and pricing integration
# - Multi-step checkout assistance with payment processing

🏆 Framework Comparison

Feature	Arshai	LangChain	LlamaIndex	Custom Build
Developer Control	✅ Complete	❌ Limited	❌ Framework-driven	✅ Complete
Production Ready	✅ Enterprise	⚠️ Community	⚠️ Research	❌ Build yourself
Performance	✅ Optimized	❌ Variable	❌ Single-purpose	❓ Depends
Testing	✅ Easy mocking	❌ Complex	❌ Framework bound	✅ Full control
Documentation	✅ Comprehensive	⚠️ Partial	⚠️ Limited	❌ DIY
Multi-Provider	✅ Unified API	⚠️ Inconsistent	❌ Limited	❌ Manual
Observability	✅ Built-in	❌ Manual setup	❌ Basic	❌ Custom build
Scalability	✅ 1000+ users	⚠️ Unknown	⚠️ Research only	❓ Your effort
Time to Production	✅ Days	⚠️ Weeks	⚠️ Months	❌ Months

🚀 Success Stories

"We migrated from LangChain to Arshai and reduced our production issues by 80%. The explicit dependency injection made testing trivial, and the connection pooling solved our resource exhaustion problems."
— Senior Engineer, Fortune 500 Company

"Arshai's three-layer architecture allowed us to build exactly what we needed without framework overhead. Our customer support system handles 500+ concurrent conversations with 99.9% uptime."
— CTO, SaaS Startup

"The developer authority philosophy is game-changing. We can debug issues, optimize performance, and extend functionality without fighting the framework."
— Lead AI Engineer, Enterprise Corp

Community & Enterprise Support

🌐 Open Source Community

Documentation: Complete documentation with guides and API reference
Examples: Production-ready examples for every use case
Issues: GitHub Issues for bugs and feature requests
Discussions: GitHub Discussions for Q&A and ideas
Contributing: Contributing Guide - help improve Arshai

💼 Enterprise Support Options

Priority Support: Direct access to core maintainers
Custom Development: Tailored features for enterprise needs
Architecture Review: Expert guidance on system design
Training & Consulting: Team training and implementation support
SLA Guarantees: Production support with uptime commitments
Security Reviews: Enterprise security audits and compliance

📈 Professional Services

Migration Services: Expert migration from other frameworks
Performance Optimization: Production tuning and scaling
Custom Integrations: Connect to proprietary systems and APIs
Team Training: Comprehensive developer training programs
Architecture Design: System design review and recommendations
24/7 Support: Round-the-clock production support

Contact: enterprise@arshai.dev

License & Legal

MIT License - see LICENSE file for complete details.

📋 What This Means For You

✅ Commercial Use: Use Arshai in commercial products
✅ Modification: Modify the code for your needs
✅ Distribution: Distribute your applications using Arshai
✅ Private Use: Use in private/internal projects
✅ Patent Grant: Protection against patent litigation

🔒 Enterprise Considerations

No Copyleft: MIT license is business-friendly
No Vendor Lock-in: Open source prevents dependency risks
Enterprise Support: Commercial support available
Compliance: Compatible with most enterprise license policies
Auditable Code: Full source code transparency

🔍 Third-Party Licenses

Arshai includes components under compatible licenses:

Apache 2.0: Various dependencies (compatible)
BSD: Some utility libraries (compatible)
MIT: Core dependencies (same license)

All dependencies are carefully vetted for license compatibility and security.

Ready to Build? 🚀

# Install Arshai and get started in 5 minutes
pip install arshai[all]

# Create your first intelligent agent
touch my_first_agent.py

# my_first_agent.py
from arshai.llms.openai import OpenAIClient
from arshai.agents.base import BaseAgent
from arshai.core.interfaces.illm import ILLMConfig
from arshai.core.interfaces.iagent import IAgentInput

# You create the LLM client
llm_config = ILLMConfig(model="gpt-4o-mini", temperature=0.7)
llm_client = OpenAIClient(llm_config)

# You create the agent  
agent = BaseAgent(llm_client, "You are a helpful coding assistant")

# You control the interaction
response = await agent.process(IAgentInput(
    message="How do I handle async/await in Python?"
))

print(response)

Next Steps

Quick Start Guide - Build your first application
Examples Directory - Explore production-ready examples
Community Discord - Get help and share ideas
GitHub Discussions - Technical discussions

Join the Community

💬 Discord Server - Real-time help and discussions
📧 Newsletter - Updates and best practices
🐦 Twitter/X - News and quick tips
📺 YouTube - Video tutorials and demos
📝 Blog - In-depth articles and case studies

Build AI applications your way. 🎆

Arshai provides enterprise-grade building blocks. You architect the solution.

Project details

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Release history Release notifications | RSS feed

1.11.0

Mar 17, 2026

1.10.0

Feb 23, 2026

1.9.1

Feb 23, 2026

1.9.0

Feb 16, 2026

1.8.0

Jan 31, 2026

1.7.0

Jan 18, 2026

1.6.0

Dec 17, 2025

1.5.8

Dec 3, 2025

1.5.7

Nov 25, 2025

1.5.6

Nov 25, 2025

This version

1.5.5

Nov 8, 2025

1.5.4

Sep 7, 2025

1.5.3

Aug 30, 2025

1.5.2

Aug 30, 2025

1.5.1

Aug 26, 2025

1.5.0

Aug 25, 2025

1.4.0

Aug 25, 2025

1.3.0

Aug 23, 2025

1.2.3

Aug 20, 2025

1.2.2

Aug 20, 2025

1.2.1

Aug 20, 2025

1.2.0

Aug 19, 2025

1.1.6

Aug 18, 2025

1.1.5

Aug 18, 2025

1.1.4

Aug 18, 2025

1.1.3

Aug 18, 2025

1.1.2

Aug 18, 2025

1.1.1

Aug 18, 2025

1.1.0

Aug 16, 2025

1.0.5

Aug 12, 2025

1.0.4

Aug 12, 2025

1.0.3

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arshai 1.5.5

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Project description

Arshai

📚 Documentation

Philosophy: Developer Authority First

The Arshai Way vs Traditional Frameworks

Three-Layer Architecture

Layer 1: LLM Clients (Foundation)

Layer 2: Agents (Business Logic)

Layer 3: Agentic Systems (Complete Control)

Comprehensive Feature Overview

🎯 Developer Authority

🤖 Advanced LLM Client System

Multi-Provider Support

Unified Function Calling Architecture

Progressive Streaming

🧠 Sophisticated Agent System

Base Agent Capabilities

Specialized Agent Types

Agent Development Patterns

🔧 Comprehensive Tool Ecosystem

Built-in Tools

Custom Tool Development

🧬 Agentic System Orchestration

Workflow Management

Multi-Agent Coordination

💾 Advanced Memory Management

Memory Backend Options

Memory Usage Patterns

📊 Production-Grade Observability

OpenTelemetry Integration

Built-in Metrics

Performance Monitoring

🗄️ Vector Database Integration

Milvus Support

📦 Document Processing Pipeline

Ingestion and Chunking

Embedding Generation

🚀 Enterprise Performance & Scalability

Connection Management

Async Architecture

Production Optimizations

🛠️ Development and Testing

Testing Framework

Development Patterns

Installation & Setup

Basic Installation

Feature-Specific Installations

Environment Setup

Docker Setup

Docker Compose for Development

Quick Start Guide 🚀

1. Quick Start - Your First Agent

1b. Agent with Memory (Conversation Context)

1c. Multi-Provider Setup

2. Advanced Agent with Multiple Tool Types

3. Intelligent Multi-Agent System

4. Advanced: Real-Time Streaming & Multi-Modal

Memory Management

Production Deployment

Performance Optimizations

Docker Deployment

Observability

Key Interfaces

LLM Provider Interface

Agent Interface

Testing

Extension and Customization

Add New LLM Provider

Create Custom Agent Types

2. Automatic Factory Integration

Complete Examples & Use Cases

Production-Ready Examples