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katanemo avatar

katanemo/archgw

0
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6,603 stars·432 forks·Rust·Apache-2.0·30 viewsplanoai.dev↗

Archgw

Archgw is a gateway proxy and data plane designed for agentic applications, providing a centralized layer for routing, safety, and orchestration between application logic and multiple large language model providers. It functions as an AI agent orchestrator that automates the execution of agent workflows to remove repetitive plumbing from the core codebase.

The system features a provider-agnostic interface layer that standardizes disparate model APIs into a single format and a transparent proxy data plane to intercept traffic. It employs rule-based routing to decouple application logic from specific providers and uses interceptor-based guardrail chains to filter inputs and outputs for content safety and jailbreak prevention.

The platform includes an observability system that automatically captures end-to-end traces, metrics, and logs from model interactions without requiring manual instrumentation. It also utilizes middleware-driven workflow orchestration to manage the flow of agentic tasks and track performance signals.

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.

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  • 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.

    • Star history

    Star history chart for katanemo/archgwStar history chart for katanemo/archgw

    How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

    Frequently asked questions

    What does katanemo/archgw do?

    Archgw is a gateway proxy and data plane designed for agentic applications, providing a centralized layer for routing, safety, and orchestration between application logic and multiple large language model providers. It functions as an AI agent orchestrator that automates the execution of agent workflows to remove repetitive plumbing from the core codebase.

    What are the main features of katanemo/archgw?

    The main features of katanemo/archgw are: Agentic Data Planes, Agentic Workflow Orchestration, Model Request Routing, AI Safety Guardrails, Model Proxy Gateways, Provider-Agnostic Model Interfaces, AI Agent Orchestrators, Signal-Based Routing Rules.

    What are some open-source alternatives to katanemo/archgw?

    Open-source alternatives to katanemo/archgw include: katanemo/plano — Plano is an AI agent orchestrator and LLM gateway proxy that unifies access to multiple AI providers through a single… strands-agents/sdk-python — This is an open-source Python SDK for building and orchestrating production-grade AI agents. It provides a unified… mastra-ai/mastra — Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and… vllm-project/semantic-router. helicone/helicone — Helicone is an AI gateway and observability platform designed to intercept, manage, and monitor interactions with… langroid/langroid — Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI…

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