Kite is a production-ready agentic AI framework designed specifically for creating intelligent AI agents and workflows. This framework enhances the capabilities of AI applications in real-world scenarios, ensuring a robust integration of probabilistic outputs with deterministic logic while maintaining top-tier safety, memory management, and observability features.

Key Features

• High Performance: Kite leverages a lazy-loading architecture, achieving minimal startup overhead of approximately 50ms, making it suitable for high-demand applications.
• Enterprise Safety: It incorporates advanced safety features including circuit breakers, idempotency mechanisms, and self-healing validation to maintain operational integrity.
• Multi-Provider Support: The framework allows seamless transitions between AI model providers such as OpenAI, Anthropic, Groq, and local models, enabling flexibility in deployment.
• Advanced Memory Systems: Kite comes equipped with sophisticated memory options like Vector RAG, Graph RAG, and session memory, enhancing the retention and processing capabilities of agents.
• Observable Performance: Built-in monitoring tools provide real-time metrics and cost tracking, ensuring better management and optimization of resources.
• Simple API Design: The intuitive API facilitates a smooth transition from prototype to production without complex configurations.

Architecture Overview

Kite's architecture is organized into core components including agents with four reasoning patterns (ReAct, Plan-Execute, ReWOO, ToT), memory management systems, safety measures, a deterministic workflow pipeline, semantic routing, built-in tools, and monitoring capabilities.

Example Usage

Here’s a concise example of how to create and run an AI agent with Kite:

from kite import Kite

# Initialize the Kite framework
ai = Kite()

# Create a specialized agent for data analysis
analyst = ai.create_agent(
    name="DataAnalyst",
    system_prompt="You are a data analyst. Provide actionable insights.",
    tools=[ai.tools.get("web_search")]
)

# Execute the agent with automatic safety logic
result = await analyst.run("Analyze Q4 revenue trends")
print(result['response'])

This demonstrates the straightforward setup and powerful capabilities of Kite.

Performance Metrics

• Startup Time: ~50ms
• Memory Footprint: <100MB
• LLM Latency: Varies by provider (e.g., Groq: ~500ms)
• Throughput: Over 100 requests/second under certain conditions.

Community and Contributions

Kite is open to contributions, inviting developers to enhance its capabilities through new reasoning patterns, integrations with additional LLM providers, and further optimizations. The community can engage via GitHub Issues or Discussions.

Built for developers ready to deploy efficient AI solutions, Kite stands as a comprehensive framework to facilitate the development of cutting-edge AI applications.