katanemo/archgw

Features

• Agentic Data Planes - Provides a centralized data plane for routing and safety between application logic and various AI model endpoints.
• Agentic Workflow Orchestration - Provides a system for executing complex agent processes by delegating tasks to autonomous agents.
• Model Request Routing - Provides mechanisms for directing API requests to different AI backends based on predefined routing rules.
• AI Safety Guardrails - Implements systems for detecting model jailbreaks, moderating content, and enforcing safety policies.
• Model Proxy Gateways - Implements a gateway proxy that provides a unified interface and routing for multiple LLM providers.
• Provider-Agnostic Model Interfaces - Provides an abstraction layer that standardizes disparate LLM provider APIs into a single consistent format.
• AI Agent Orchestrators - Coordinates model providers and task execution specifically to manage the lifecycle of AI agents.
• Signal-Based Routing Rules - Evaluates decision rules over signals to select the appropriate model provider and decouple application logic.
• Transparent Proxies - Implements a proxy that intercepts agent traffic for telemetry and policy enforcement without requiring client-side changes.
• Model Safety Filters - Ships validation layers for blocking or sanitizing model inputs and outputs based on safety policies.
• LLM Safety Enforcers - Enforces jailbreak, PII, and hallucination controls at the routing layer to block unsafe content.
• Input and Output Guardrails - Provides middleware to filter out-of-scope queries and validate generated content against safety rules.
• Request-Response Filter Chains - Ships a sequence of interceptors to validate and filter AI requests and responses for safety and correctness.
• Agent Workflow Orchestrators - Executes automated agent tasks by routing requests through programmable logic layers and state handlers.
• LLM Observability - Provides monitoring and tracing tools specifically tailored for LLM applications and agentic workflows.
• Agent Observability - Captures traces and evaluates the decision-making accuracy of autonomous agents without manual instrumentation.
• Agent Performance Monitoring - Tracks operational metrics and interaction signals from automated agents to monitor performance.
• Automatic Tracing Instrumentation - Implements automatic instrumentation for capturing end-to-end execution details and performance metrics for model calls.
• LLM Performance Monitoring - Automatically tracks performance metrics and execution traces for large language model operations.
• Agent Frameworks - High-performance proxy for agent routing and guardrail application.
• Agent Orchestration - Intelligent gateway for managing agent interactions.

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