![Multi-Agent-Custom-Automation-Engine-Solution-Accelerator](/statics/default_logo.png)
Multi-Agent-Custom-Automation-Engine-Solution-Accelerator
The Multi-Agent Custom Automation Engine Solution Accelerator is an AI-driven orchestration system that manages a group of AI agents to accomplish tasks based on user input. Powered by AutoGen, Azure OpenAI, Cosmos, and infrastructure services, it provides a ready to go application to use as a reference, allowing you to hit the ground running.
Stars: 66
![screenshot](/statics/default_screenshot.jpg)
The Multi-Agent -Custom Automation Engine Solution Accelerator is an AI-driven orchestration system that manages a group of AI agents to accomplish tasks based on user input. It uses a FastAPI backend to handle HTTP requests, processes them through various specialized agents, and stores stateful information using Azure Cosmos DB. The system allows users to focus on what matters by coordinating activities across an organization, enabling GenAI to scale, and is applicable to most industries. It is intended for developing and deploying custom AI solutions for specific customers, providing a foundation to accelerate building out multi-agent systems.
README:
MENU: USER STORY | QUICK DEPLOY | SUPPORTING DOCUMENTATION |
Problem: Agentic AI systems are set to transform the way businesses operate, however it can be fairly complex to build an initial MVP to demonstrate this value.
Solution: The Multi-Agent-Custom Automation Engine Solution Accelerator provides a ready to go application to use as the base of the MVP, or as a reference, allowing you to hit the ground running.
This accelerator uses the AutoGen framework from Microsoft Research. This is an open source project that is maintained by Microsoft Research’s AI Frontiers Lab. Please see this blog post for the latest information on using the AutoGen framework in production solutions.
The multi-agent approach allows users to utilize multiple AI agents simultaneously for repeatable tasks, ensuring consistency and efficiency. The agents collaborate with a manager on various assignments for onboarding a new employee, such as HR and tech support AI working together to set up software accounts, configure hardware, schedule onboarding meetings, register employees for benefits, and send welcome emails. Additionally, these agents can handle tasks like procurement and drafting press releases.
Multi-agent systems represent the next wave of Generative AI use cases, offering entirely new opportunities to drive efficiencies in your business. The Multi-Agent-Custom-Automation-Engine Solution Accelerator demonstrates several key benefits:
- Allows people to focus on what matters: by doing the heavy lifting involved with coordinating activities across an organization, peoples’ time is freed up to focus on their specializations.
- Enabling GenAI to scale: by not needing to build one application after another, organizations are able to reduce the friction of adopting GenAI across their entire organization. One capability can unlock almost unlimited use cases.
- Applicable to most industries: these are common challenges that most organizations face, across most industries.
Whilst still an emerging area, investing in agentic use cases, digitization and developing tools will be key to ensuring you are able to leverage these new technologies and seize the GenAI moment.
This application is an AI-driven orchestration system that manages a group of AI agents to accomplish tasks based on user input. It uses a FastAPI backend to handle HTTP requests, processes them through various specialized agents, and stores stateful information using Azure Cosmos DB. The system is designed to:
- Receive input tasks from users.
- Generate a detailed plan to accomplish the task using a Planner agent.
- Execute the plan by delegating steps to specialized agents (e.g., HR, Procurement, Marketing).
- Incorporate human feedback into the workflow.
- Maintain state across sessions with persistent storage.
This system is intended for developing and deploying custom AI solutions for specific customers. This code has not been tested as an end-to-end, reliable production application- it is a foundation to help accelerate building out multi-agent systems. You are encouraged to add your own data and functions to the agents, and then you must apply your own performance and safety evaluation testing frameworks to this system before deploying it.
-
Azure Container Application
-
Azure OpenAI
-
Azure Cosmos DB
-
The user deploying the template must have permission to create resources and resource groups.
This guide provides step-by-step instructions for deploying your application using Azure Container Registry (ACR) and Azure Container Apps.
There are several ways to deploy the solution. You can deploy to run in Azure in one click, or manually, or you can deploy locally.
When Deployment is complete, follow steps in Set Up Authentication in Azure App Service to add app authentication to your web app running on Azure App Service
To run the solution site and API backend only locally for development and debugging purposes, See the local deployment guide.
Manual Deployment differs from the ‘Quick Deploy’ option in that it will install an Azure Container Registry (ACR) service, and relies on the installer to build and push the necessary containers to this ACR. This allows you to build and push your own code changes and provides a sample solution you can customize based on your requirements.
- Current Azure CLI installed
You can update to the latest version using
az upgrade
- Azure account with appropriate permissions
- Docker installed
All of the necessary Azure services can be deployed using the /deploy/macae.bicep script. This script will require the following parameters:
az login
az account set --subscription <SUBSCRIPTION_ID>
az group create --name <RG_NAME> --location <RG_LOCATION>
To deploy the script you can use the Azure CLI.
az deployment group create \
--resource-group <RG_NAME> \
--template-file <BICEP_FILE> \
--name <DEPLOYMENT_NAME>
Note: if you are using windows with PowerShell, the continuation character (currently ‘\’) should change to the tick mark (‘`’).
The template will require you fill in locations for Cosmos and OpenAI services. This is to avoid the possibility of regional quota errors for either of these resources.
Retrieve the admin credentials for your Azure Container Registry (ACR):
az acr credential show \
--name <e.g. macaeacr2t62qyozi76bs> \
--resource-group <rg-name>
Login to your Azure Container Registry:
az acr login --name <e.g. macaeacr2t62qyozi76bs>
Build the frontend and backend Docker images and push them to your Azure Container Registry. Run the following from the src/backend and the src/frontend directory contexts:
az acr build \
--registry <e.g. macaeacr2t62qyozi76bs> \
--resource-group <rg-name> \
--image <e.g. backendmacae:latest> .
To add your newly created backend image:
-
Navigate to the Container App Service in the Azure portal
-
Click on Application/Containers in the left pane
-
Click on the "Edit and deploy" button in the upper left of the containers pane
-
In the "Create and deploy new revision" page, click on your container image 'backend'. This will give you the option of reconfiguring the container image, and also has an Environment variables tab
-
Change the properties page to
- point to your Azure Container registry with a private image type and your image name (e.g. backendmacae:latest)
- under "Authentication type" select "Managed Identity" and choose the 'mace-containerapp-pull'... identity setup in the bicep template
-
In the environment variables section add the following (each with a 'Manual entry' source):
name: 'COSMOSDB_ENDPOINT' value: \<Cosmos endpoint> name: 'COSMOSDB_DATABASE' value: 'autogen' Note: To change the default, you will need to create the database in Cosmos name: 'COSMOSDB_CONTAINER' value: 'memory' name: 'AZURE_OPENAI_ENDPOINT' value: <Azure OpenAI endpoint> name: 'AZURE_OPENAI_DEPLOYMENT_NAME' value: 'gpt-4o' name: 'AZURE_OPENAI_API_VERSION' value: '2024-08-01-preview' Note: Version should be updated based on latest available name: 'FRONTEND_SITE_NAME' value: 'https://<website Name>.azurewebsites.net' name: 'APPLICATIONINSIGHTS_INSTRUMENTATION_KEY' value: <Application Insights Instrumentation Key>
-
Click 'Save' and deploy your new revision
To add the new container to your website run the following:
az webapp config container set --resource-group <resource_group_name> \
--name <container_name> \
--container-image-name <e.g. macaeacr2t62qyozi76bs.azurecr.io/frontendmacae:latest> \
--container-registry-url <e.g. https://macaeacr2t62qyozi76bs.azurecr.io>
To add the identity provider, please follow the steps outlined in Set Up Authentication in Azure App Service
To debug the solution, you can use the Cosmos and OpenAI services you have manually deployed. To do this, you need to ensure that your Azure identity has the required permissions on the Cosmos and OpenAI services.
- For OpenAI service, you can add yourself to the ‘Cognitive Services OpenAI User’ permission in the Access Control (IAM) pane of the Azure portal.
- Cosmos is a little more difficult as it requires permissions be added through script. See these examples for more information:
Add the appropriate endpoints from Cosmos and OpenAI services to your .env file.
Note that you can configure the name of the Cosmos database in the configuration. This can be helpful if you wish to separate the data messages generated in local debugging from those associated with the cloud based solution. If you choose to use a different database, you will need to create that database in the Cosmos instance as this is not done automatically.
If you are using VSCode, you can use the debug configuration shown in the local deployment guide.
This solution is designed to be easily customizable. You can modify the front end site, or even build your own front end and attach to the backend API. You can further customize the backend by adding your own agents with their own specific capabilities. Deeper technical information to aid in this customization can be found in this document.
- Python FastAPI documentation
- AutoGen Framework Documentation
- Azure Container App documentation
- Azure OpenAI Service - Documentation, quickstarts, API reference - Azure AI services | Microsoft Learn
- Azure Cosmos DB documentation
To the extent that the Software includes components or code used in or derived from Microsoft products or services, including without limitation Microsoft Azure Services (collectively, “Microsoft Products and Services”), you must also comply with the Product Terms applicable to such Microsoft Products and Services. You acknowledge and agree that the license governing the Software does not grant you a license or other right to use Microsoft Products and Services. Nothing in the license or this ReadMe file will serve to supersede, amend, terminate or modify any terms in the Product Terms for any Microsoft Products and Services.
You must also comply with all domestic and international export laws and regulations that apply to the Software, which include restrictions on destinations, end users, and end use. For further information on export restrictions, visit https://aka.ms/exporting.
You acknowledge that the Software and Microsoft Products and Services (1) are not designed, intended or made available as a medical device(s), and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customer is solely responsible for displaying and/or obtaining appropriate consents, warnings, disclaimers, and acknowledgements to end users of Customer’s implementation of the Online Services.
You acknowledge the Software is not subject to SOC 1 and SOC 2 compliance audits. No Microsoft technology, nor any of its component technologies, including the Software, is intended or made available as a substitute for the professional advice, opinion, or judgement of a certified financial services professional. Do not use the Software to replace, substitute, or provide professional financial advice or judgment.
BY ACCESSING OR USING THE SOFTWARE, YOU ACKNOWLEDGE THAT THE SOFTWARE IS NOT DESIGNED OR INTENDED TO SUPPORT ANY USE IN WHICH A SERVICE INTERRUPTION, DEFECT, ERROR, OR OTHER FAILURE OF THE SOFTWARE COULD RESULT IN THE DEATH OR SERIOUS BODILY INJURY OF ANY PERSON OR IN PHYSICAL OR ENVIRONMENTAL DAMAGE (COLLECTIVELY, “HIGH-RISK USE”), AND THAT YOU WILL ENSURE THAT, IN THE EVENT OF ANY INTERRUPTION, DEFECT, ERROR, OR OTHER FAILURE OF THE SOFTWARE, THE SAFETY OF PEOPLE, PROPERTY, AND THE ENVIRONMENT ARE NOT REDUCED BELOW A LEVEL THAT IS REASONABLY, APPROPRIATE, AND LEGAL, WHETHER IN GENERAL OR IN A SPECIFIC INDUSTRY. BY ACCESSING THE SOFTWARE, YOU FURTHER ACKNOWLEDGE THAT YOUR HIGH-RISK USE OF THE SOFTWARE IS AT YOUR OWN RISK.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for Multi-Agent-Custom-Automation-Engine-Solution-Accelerator
Similar Open Source Tools
![Multi-Agent-Custom-Automation-Engine-Solution-Accelerator Screenshot](/statics/default_screenshot.jpg)
Multi-Agent-Custom-Automation-Engine-Solution-Accelerator
The Multi-Agent -Custom Automation Engine Solution Accelerator is an AI-driven orchestration system that manages a group of AI agents to accomplish tasks based on user input. It uses a FastAPI backend to handle HTTP requests, processes them through various specialized agents, and stores stateful information using Azure Cosmos DB. The system allows users to focus on what matters by coordinating activities across an organization, enabling GenAI to scale, and is applicable to most industries. It is intended for developing and deploying custom AI solutions for specific customers, providing a foundation to accelerate building out multi-agent systems.
![chat-with-your-data-solution-accelerator Screenshot](/statics/default_screenshot.jpg)
chat-with-your-data-solution-accelerator
Chat with your data using OpenAI and AI Search. This solution accelerator uses an Azure OpenAI GPT model and an Azure AI Search index generated from your data, which is integrated into a web application to provide a natural language interface, including speech-to-text functionality, for search queries. Users can drag and drop files, point to storage, and take care of technical setup to transform documents. There is a web app that users can create in their own subscription with security and authentication.
![enterprise-azureai Screenshot](/statics/default_screenshot.jpg)
enterprise-azureai
Azure OpenAI Service is a central capability with Azure API Management, providing guidance and tools for organizations to implement Azure OpenAI in a production environment with an emphasis on cost control, secure access, and usage monitoring. It includes infrastructure-as-code templates, CI/CD pipelines, secure access management, usage monitoring, load balancing, streaming requests, and end-to-end samples like ChatApp and Azure Dashboards.
![Build-your-own-AI-Assistant-Solution-Accelerator Screenshot](/statics/default_screenshot.jpg)
Build-your-own-AI-Assistant-Solution-Accelerator
Build-your-own-AI-Assistant-Solution-Accelerator is a pre-release and preview solution that helps users create their own AI assistants. It leverages Azure Open AI Service, Azure AI Search, and Microsoft Fabric to identify, summarize, and categorize unstructured information. Users can easily find relevant articles and grants, generate grant applications, and export them as PDF or Word documents. The solution accelerator provides reusable architecture and code snippets for building AI assistants with enterprise data. It is designed for researchers looking to explore flu vaccine studies and grants to accelerate grant proposal submissions.
![data-formulator Screenshot](/statics/default_screenshot.jpg)
data-formulator
Data Formulator is an AI-powered tool developed by Microsoft Research to help data analysts create rich visualizations iteratively. It combines user interface interactions with natural language inputs to simplify the process of describing chart designs while delegating data transformation to AI. Users can utilize features like blended UI and NL inputs, data threads for history navigation, and code inspection to create impressive visualizations. The tool supports local installation for customization and Codespaces for quick setup. Developers can build new data analysis tools on top of Data Formulator, and research papers are available for further reading.
![bytechef Screenshot](/statics/default_screenshot.jpg)
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.
![ChatGPT-Shortcut Screenshot](/statics/default_screenshot.jpg)
ChatGPT-Shortcut
ChatGPT Shortcut is an AI tool designed to maximize efficiency and productivity by providing a concise list of AI instructions. Users can easily find prompts suitable for various scenarios, boosting productivity and work efficiency. The tool offers one-click prompts, optimization for non-English languages, prompt saving and sharing, and a community voting system. It includes a browser extension compatible with Chrome, Edge, Firefox, and other Chromium-based browsers, as well as a Tampermonkey script for custom domain use. The tool is open-source, allowing users to modify the website's nomenclature, usage directives, and prompts for different languages.
![serverless-chat-langchainjs Screenshot](/screenshots_githubs/Azure-Samples-serverless-chat-langchainjs.jpg)
serverless-chat-langchainjs
This sample shows how to build a serverless chat experience with Retrieval-Augmented Generation using LangChain.js and Azure. The application is hosted on Azure Static Web Apps and Azure Functions, with Azure Cosmos DB for MongoDB vCore as the vector database. You can use it as a starting point for building more complex AI applications.
![vespa Screenshot](/screenshots_githubs/vespa-engine-vespa.jpg)
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.
![teams-ai Screenshot](/screenshots_githubs/microsoft-teams-ai.jpg)
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.
![llama-github Screenshot](/screenshots_githubs/JetXu-LLM-llama-github.jpg)
llama-github
Llama-github is a powerful tool that helps retrieve relevant code snippets, issues, and repository information from GitHub based on queries. It empowers AI agents and developers to solve coding tasks efficiently. With features like intelligent GitHub retrieval, repository pool caching, LLM-powered question analysis, and comprehensive context generation, llama-github excels at providing valuable knowledge context for development needs. It supports asynchronous processing, flexible LLM integration, robust authentication options, and logging/error handling for smooth operations and troubleshooting. The vision is to seamlessly integrate with GitHub for AI-driven development solutions, while the roadmap focuses on empowering LLMs to automatically resolve complex coding tasks.
![AppAgent Screenshot](/screenshots_githubs/mnotgod96-AppAgent.jpg)
AppAgent
AppAgent is a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications.
![js-route-optimization-app Screenshot](/screenshots_githubs/googlemaps-js-route-optimization-app.jpg)
js-route-optimization-app
A web application to explore the capabilities of Google Maps Platform Route Optimization (GMPRO). It helps users understand the data model and functions of the API by presenting interactive forms, tables, and maps. The tool is intended for exploratory use only and should not be deployed in production. Users can construct scenarios, tune constraint parameters, and visualize routes before implementing their own solutions for integrating Route Optimization into their business processes. The application incurs charges related to cloud resources and API usage, and users should be cautious about generating high usage volumes, especially for large scenarios.
![ask-astro Screenshot](/screenshots_githubs/astronomer-ask-astro.jpg)
ask-astro
Ask Astro is an open-source reference implementation of Andreessen Horowitz's LLM Application Architecture built by Astronomer. It provides an end-to-end example of a Q&A LLM application used to answer questions about Apache Airflow® and Astronomer. Ask Astro includes Airflow DAGs for data ingestion, an API for business logic, a Slack bot, a public UI, and DAGs for processing user feedback. The tool is divided into data retrieval & embedding, prompt orchestration, and feedback loops.
![gen-cv Screenshot](/screenshots_githubs/Azure-gen-cv.jpg)
gen-cv
This repository is a rich resource offering examples of synthetic image generation, manipulation, and reasoning using Azure Machine Learning, Computer Vision, OpenAI, and open-source frameworks like Stable Diffusion. It provides practical insights into image processing applications, including content generation, video analysis, avatar creation, and image manipulation with various tools and APIs.
![CSGHub Screenshot](/screenshots_githubs/OpenCSGs-CSGHub.jpg)
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.
For similar tasks
![Multi-Agent-Custom-Automation-Engine-Solution-Accelerator Screenshot](/statics/default_screenshot.jpg)
Multi-Agent-Custom-Automation-Engine-Solution-Accelerator
The Multi-Agent -Custom Automation Engine Solution Accelerator is an AI-driven orchestration system that manages a group of AI agents to accomplish tasks based on user input. It uses a FastAPI backend to handle HTTP requests, processes them through various specialized agents, and stores stateful information using Azure Cosmos DB. The system allows users to focus on what matters by coordinating activities across an organization, enabling GenAI to scale, and is applicable to most industries. It is intended for developing and deploying custom AI solutions for specific customers, providing a foundation to accelerate building out multi-agent systems.
![semantic-kernel-java Screenshot](/screenshots_githubs/microsoft-semantic-kernel-java.jpg)
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.
![nextpy Screenshot](/screenshots_githubs/dot-agent-nextpy.jpg)
nextpy
Nextpy is a cutting-edge software development framework optimized for AI-based code generation. It provides guardrails for defining AI system boundaries, structured outputs for prompt engineering, a powerful prompt engine for efficient processing, better AI generations with precise output control, modularity for multiplatform and extensible usage, developer-first approach for transferable knowledge, and containerized & scalable deployment options. It offers 4-10x faster performance compared to Streamlit apps, with a focus on cooperation within the open-source community and integration of key components from various projects.
![ragna Screenshot](/screenshots_githubs/Quansight-ragna.jpg)
ragna
Ragna is a RAG orchestration framework designed for managing workflows and orchestrating tasks. It provides a comprehensive set of features for users to streamline their processes and automate repetitive tasks. With Ragna, users can easily create, schedule, and monitor workflows, making it an ideal tool for teams and individuals looking to improve their productivity and efficiency. The framework offers extensive documentation, community support, and a user-friendly interface, making it accessible to users of all skill levels. Whether you are a developer, data scientist, or project manager, Ragna can help you simplify your workflow management and boost your overall performance.
![tegon Screenshot](/screenshots_githubs/tegonhq-tegon.jpg)
tegon
Tegon is an open-source AI-First issue tracking tool designed for engineering teams. It aims to simplify task management by leveraging AI and integrations to automate task creation, prioritize tasks, and enhance bug resolution. Tegon offers features like issues tracking, automatic title generation, AI-generated labels and assignees, custom views, and upcoming features like sprints and task prioritization. It integrates with GitHub, Slack, and Sentry to streamline issue tracking processes. Tegon also plans to introduce AI Agents like PR Agent and Bug Agent to enhance product management and bug resolution. Contributions are welcome, and the product is licensed under the MIT License.
![Advanced-GPTs Screenshot](/screenshots_githubs/nerority-Advanced-GPTs.jpg)
Advanced-GPTs
Nerority's Advanced GPT Suite is a collection of 33 GPTs that can be controlled with natural language prompts. The suite includes tools for various tasks such as strategic consulting, business analysis, career profile building, content creation, educational purposes, image-based tasks, knowledge engineering, marketing, persona creation, programming, prompt engineering, role-playing, simulations, and task management. Users can access links, usage instructions, and guides for each GPT on their respective pages. The suite is designed for public demonstration and usage, offering features like meta-sequence optimization, AI priming, prompt classification, and optimization. It also provides tools for generating articles, analyzing contracts, visualizing data, distilling knowledge, creating educational content, exploring topics, generating marketing copy, simulating scenarios, managing tasks, and more.
![aioclock Screenshot](/screenshots_githubs/ManiMozaffar-aioclock.jpg)
aioclock
An asyncio-based scheduling framework designed for execution of periodic tasks with integrated support for dependency injection, enabling efficient and flexible task management. Aioclock is 100% async, light, fast, and resource-friendly. It offers features like task scheduling, grouping, trigger definition, easy syntax, Pydantic v2 validation, and upcoming support for running the task dispatcher on a different process and backend support for horizontal scaling.
![airavata Screenshot](/screenshots_githubs/apache-airavata.jpg)
airavata
Apache Airavata is a software framework for executing and managing computational jobs on distributed computing resources. It supports local clusters, supercomputers, national grids, academic and commercial clouds. Airavata utilizes service-oriented computing, distributed messaging, and workflow composition. It includes a server package with an API, client SDKs, and a general-purpose UI implementation called Apache Airavata Django Portal.
For similar jobs
![sweep Screenshot](/screenshots_githubs/sweepai-sweep.jpg)
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.
![teams-ai Screenshot](/screenshots_githubs/microsoft-teams-ai.jpg)
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.
![ai-guide Screenshot](/screenshots_githubs/Crataco-ai-guide.jpg)
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.
![classifai Screenshot](/screenshots_githubs/10up-classifai.jpg)
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.
![chatbot-ui Screenshot](/screenshots_githubs/mckaywrigley-chatbot-ui.jpg)
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.
![BricksLLM Screenshot](/screenshots_githubs/bricks-cloud-BricksLLM.jpg)
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
![uAgents Screenshot](/screenshots_githubs/fetchai-uAgents.jpg)
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.
![griptape Screenshot](/screenshots_githubs/griptape-ai-griptape.jpg)
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.