arch

Arch is an intelligent prompt gateway. Engineered with (fast) LLMs for the secure handling, robust observability, and seamless integration of prompts with APIs - all outside business logic. Built by the core contributors of Envoy proxy, on Envoy.

Stars: 90

Visit

Arch is an intelligent Layer 7 gateway designed to protect, observe, and personalize LLM applications with APIs. It handles tasks like detecting and rejecting jailbreak attempts, calling backend APIs, disaster recovery, and observability. Built on Envoy Proxy, it offers features like function calling, prompt guardrails, traffic management, and standards-based observability. Arch aims to improve the speed, security, and personalization of generative AI applications.

README:

Build fast, robust, and personalized AI agents.

Arch is an intelligent Layer 7 gateway designed to protect, observe, and personalize LLM applications (agents, assistants, co-pilots) with your APIs.

Engineered with purpose-built LLMs, Arch handles the critical but undifferentiated tasks related to the handling and processing of prompts, including detecting and rejecting jailbreak attempts, intelligently calling "backend" APIs to fulfill the user's request represented in a prompt, routing to and offering disaster recovery between upstream LLMs, and managing the observability of prompts and LLM interactions in a centralized way.

Arch is built on (and by the core contributors of) Envoy Proxy with the belief that:

Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests including secure handling, intelligent routing, robust observability, and integration with backend (API) systems for personalization – all outside business logic.*

Core Features:

Built on Envoy: Arch runs alongside application servers, and builds on top of Envoy's proven HTTP management and scalability features to handle ingress and egress traffic related to prompts and LLMs.
Function Calling for fast Agentic and RAG apps. Engineered with purpose-built LLMs to handle fast, cost-effective, and accurate prompt-based tasks like function/API calling, and parameter extraction from prompts.
Prompt Guard: Arch centralizes prompt guardrails to prevent jailbreak attempts and ensure safe user interactions without writing a single line of code.
Traffic Management: Arch manages LLM calls, offering smart retries, automatic cutover, and resilient upstream connections for continuous availability.
Standards-based Observability: Arch uses the W3C Trace Context standard to enable complete request tracing across applications, ensuring compatibility with observability tools, and provides metrics to monitor latency, token usage, and error rates, helping optimize AI application performance.

Jump to our docs to learn how you can use Arch to improve the speed, security and personalization of your GenAI apps.

Contact

To get in touch with us, please join our discord server. We will be monitoring that actively and offering support there.

Demos

Function Calling - Walk through of critical function calling capabilities
Insurance Agent - Build a full insurance agent with arch
Network Agent - Build a networking co-pilot/agent agent with arch

Quickstart

Follow this guide to learn how to quickly set up Arch and integrate it into your generative AI applications.

Prerequisites

Before you begin, ensure you have the following:

Docker & Python installed on your system
API Keys for LLM providers (if using external LLMs)

Step 1: Install Arch

Arch's CLI allows you to manage and interact with the Arch gateway efficiently. To install the CLI, simply run the following command: Tip: We recommend that developers create a new Python virtual environment to isolate dependencies before installing Arch. This ensures that archgw and its dependencies do not interfere with other packages on your system.

$ python -m venv venv
$ source venv/bin/activate   # On Windows, use: venv\Scripts\activate
$ pip install archgw

Step 2: Configure Arch with your application

Arch operates based on a configuration file where you can define LLM providers, prompt targets, guardrails, etc. Below is an example configuration to get you started:

version: v0.1

listen:
  address: 0.0.0.0 # or 127.0.0.1
  port: 10000
  # Defines how Arch should parse the content from application/json or text/pain Content-type in the http request
  message_format: huggingface

# Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
llm_providers:
  - name: OpenAI
    provider: openai
    access_key: OPENAI_API_KEY
    model: gpt-4o
    default: true
    stream: true

# default system prompt used by all prompt targets
system_prompt: You are a network assistant that just offers facts; not advice on manufacturers or purchasing decisions.

prompt_targets:
  - name: reboot_devices
    description: Reboot specific devices or device groups

    path: /agent/device_reboot
    parameters:
      - name: device_ids
        type: list
        description: A list of device identifiers (IDs) to reboot.
        required: false
      - name: device_group
        type: str
        description: The name of the device group to reboot
        required: false

# Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.
endpoints:
  app_server:
    # value could be ip address or a hostname with port
    # this could also be a list of endpoints for load balancing
    # for example endpoint: [ ip1:port, ip2:port ]
    endpoint: 127.0.0.1:80
    # max time to wait for a connection to be established
    connect_timeout: 0.005s

Step 3: Using OpenAI Client with Arch as an Egress Gateway

Make outbound calls via Arch

import openai

# Set the OpenAI API base URL to the Arch gateway endpoint
openai.api_base = "http://127.0.0.1:51001/v1"

# No need to set openai.api_key since it's configured in Arch's gateway

# Use the OpenAI client as usual
response = openai.Completion.create(
   model="text-davinci-003",
   prompt="What is the capital of France?"
)

print("OpenAI Response:", response.choices[0].text.strip())

Observability

Arch is designed to support best-in class observability by supporting open standards. Please read our docs on observability for more details on tracing, metrics, and logs

Contribution

We would love feedback on our Roadmap and we welcome contributions to Arch! Whether you're fixing bugs, adding new features, improving documentation, or creating tutorials, your help is much appreciated. Please vist our Contribution Guide for more details

For Tasks:

Click tags to check more tools for each tasks

protect applications call apis manage observability handle disaster recovery route llm interactions

For Jobs:

software developer ai engineer system architect data scientist cloud engineer

Alternative AI tools for arch

Similar Open Source Tools

arch

github

: 90

bytechef

ByteChef is an open-source, low-code, extendable API integration and workflow automation platform. It provides an intuitive UI Workflow Editor, event-driven & scheduled workflows, multiple flow controls, built-in code editor supporting Java, JavaScript, Python, and Ruby, rich component ecosystem, extendable with custom connectors, AI-ready with built-in AI components, developer-ready to expose workflows as APIs, version control friendly, self-hosted, scalable, and resilient. It allows users to build and visualize workflows, automate tasks across SaaS apps, internal APIs, and databases, and handle millions of workflows with high availability and fault tolerance.

github

: 600

ai2apps

AI2Apps is a visual IDE for building LLM-based AI agent applications, enabling developers to efficiently create AI agents through drag-and-drop, with features like design-to-development for rapid prototyping, direct packaging of agents into apps, powerful debugging capabilities, enhanced user interaction, efficient team collaboration, flexible deployment, multilingual support, simplified product maintenance, and extensibility through plugins.

github

: 278

kdbai-samples

KDB.AI is a time-based vector database that allows developers to build scalable, reliable, and real-time applications by providing advanced search, recommendation, and personalization for Generative AI applications. It supports multiple index types, distance metrics, top-N and metadata filtered retrieval, as well as Python and REST interfaces. The repository contains samples demonstrating various use-cases such as temporal similarity search, document search, image search, recommendation systems, sentiment analysis, and more. KDB.AI integrates with platforms like ChatGPT, Langchain, and LlamaIndex. The setup steps require Unix terminal, Python 3.8+, and pip installed. Users can install necessary Python packages and run Jupyter notebooks to interact with the samples.

github

: 95

csghub-server

CSGHub Server is a part of the open source and reliable large model assets management platform - CSGHub. It focuses on management of models, datasets, and other LLM assets through REST API. Key features include creation and management of users and organizations, auto-tagging of model and dataset labels, search functionality, online preview of dataset files, content moderation for text and image, download of individual files, tracking of model and dataset activity data. The tool is extensible and customizable, supporting different git servers, flexible LFS storage system configuration, and content moderation options. The roadmap includes support for more Git servers, Git LFS, dataset online viewer, model/dataset auto-tag, S3 protocol support, model format conversion, and model one-click deploy. The project is licensed under Apache 2.0 and welcomes contributions.

github

: 911

iLLM-TSC

iLLM-TSC is a framework that integrates reinforcement learning and large language models for traffic signal control policy improvement. It refines RL decisions based on real-world contexts and provides reasonable actions when RL agents make erroneous decisions. The framework includes cases where the large language model provides explanations and recommendations for RL agent actions, such as prioritizing emergency vehicles at intersections. Users can install and run the framework locally to train RL models and evaluate the combined RL+LLM approach.

github

: 54

PulsarRPA

PulsarRPA is a high-performance, distributed, open-source Robotic Process Automation (RPA) framework designed to handle large-scale RPA tasks with ease. It provides a comprehensive solution for browser automation, web content understanding, and data extraction. PulsarRPA addresses challenges of browser automation and accurate web data extraction from complex and evolving websites. It incorporates innovative technologies like browser rendering, RPA, intelligent scraping, advanced DOM parsing, and distributed architecture to ensure efficient, accurate, and scalable web data extraction. The tool is open-source, customizable, and supports cutting-edge information extraction technology, making it a preferred solution for large-scale web data extraction.

github

: 805

cedana-cli

Cedana is a framework for the democritization and commodification of compute. It leverages checkpoint/restore to migrate work across machines, clouds, and beyond. The repo contains a CLI tool for developers to experiment with the system.

github

: 58

CSGHub

CSGHub is an open source, trustworthy large model asset management platform that can assist users in governing the assets involved in the lifecycle of LLM and LLM applications (datasets, model files, codes, etc). With CSGHub, users can perform operations on LLM assets, including uploading, downloading, storing, verifying, and distributing, through Web interface, Git command line, or natural language Chatbot. Meanwhile, the platform provides microservice submodules and standardized OpenAPIs, which could be easily integrated with users' own systems. CSGHub is committed to bringing users an asset management platform that is natively designed for large models and can be deployed On-Premise for fully offline operation. CSGHub offers functionalities similar to a privatized Huggingface(on-premise Huggingface), managing LLM assets in a manner akin to how OpenStack Glance manages virtual machine images, Harbor manages container images, and Sonatype Nexus manages artifacts.

github

: 2.8k

council

Council is an open-source platform designed for the rapid development and deployment of customized generative AI applications using teams of agents. It extends the LLM tool ecosystem by providing advanced control flow and scalable oversight for AI agents. Users can create sophisticated agents with predictable behavior by leveraging Council's powerful approach to control flow using Controllers, Filters, Evaluators, and Budgets. The framework allows for automated routing between agents, comparing, evaluating, and selecting the best results for a task. Council aims to facilitate packaging and deploying agents at scale on multiple platforms while enabling enterprise-grade monitoring and quality control.

github

: 815

CoLLM

CoLLM is a novel method that integrates collaborative information into Large Language Models (LLMs) for recommendation. It converts recommendation data into language prompts, encodes them with both textual and collaborative information, and uses a two-step tuning method to train the model. The method incorporates user/item ID fields in prompts and employs a conventional collaborative model to generate user/item representations. CoLLM is built upon MiniGPT-4 and utilizes pretrained Vicuna weights for training.

github

: 51

genai-factory

GenAI Factory is a collection of end-to-end blueprints to deploy generative AI infrastructures in Google Cloud Platform (GCP), following security best practices. It embraces Infrastructure as Code (IaC) best practices, implements infrastructure in Terraform, and follows the least-privilege principle. The tool is compatible with Cloud Foundation Fabric FAST project-factory and application templates, allowing users to deploy various AI applications and systems on GCP.

github

: 71

DevOpsGPT

DevOpsGPT is an AI-driven software development automation solution that combines Large Language Models (LLM) with DevOps tools to convert natural language requirements into working software. It improves development efficiency by eliminating the need for tedious requirement documentation, shortens development cycles, reduces communication costs, and ensures high-quality deliverables. The Enterprise Edition offers features like existing project analysis, professional model selection, and support for more DevOps platforms. The tool automates requirement development, generates interface documentation, provides pseudocode based on existing projects, facilitates code refinement, enables continuous integration, and supports software version release. Users can run DevOpsGPT with source code or Docker, and the tool comes with limitations in precise documentation generation and understanding existing project code. The product roadmap includes accurate requirement decomposition, rapid import of development requirements, and integration of more software engineering and professional tools for efficient software development tasks under AI planning and execution.

github

: 6.3k

semantic-kernel-java

Semantic Kernel for Java is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. It allows defining plugins that can be chained together in just a few lines of code. The tool automatically orchestrates plugins with AI, enabling users to generate plans to achieve unique goals and execute them. The project welcomes contributions, bug reports, and suggestions from the community.

github

: 137

vespa

Vespa is a platform that performs operations such as selecting a subset of data in a large corpus, evaluating machine-learned models over the selected data, organizing and aggregating it, and returning it, typically in less than 100 milliseconds, all while the data corpus is continuously changing. It has been in development for many years and is used on a number of large internet services and apps which serve hundreds of thousands of queries from Vespa per second.

github

: 6.3k

floki

Floki is an open-source framework for researchers and developers to experiment with LLM-based autonomous agents. It provides tools to create, orchestrate, and manage agents while seamlessly connecting to LLM inference APIs. Built on Dapr, Floki leverages a unified programming model that simplifies microservices and supports both deterministic workflows and event-driven interactions. By bringing together these features, Floki provides a powerful way to explore agentic workflows and the components that enable multi-agent systems to collaborate and scale, all powered by Dapr.

github

: 88

For similar tasks

CrewAI-Studio

CrewAI Studio is an application with a user-friendly interface for interacting with CrewAI, offering support for multiple platforms and various backend providers. It allows users to run crews in the background, export single-page apps, and use custom tools for APIs and file writing. The roadmap includes features like better import/export, human input, chat functionality, automatic crew creation, and multiuser environment support.

github

: 682

arch

github

: 90

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 502

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 669

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.2k