Search Results for "process"
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Showing 17 open source projects for "process"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Astron Agent
Enterprise platform for building and orchestrating AI agent workflows
...Astron Agent enables organizations to design complex agent-driven processes that coordinate models, automation tools, and enterprise systems. It also integrates robotic process automation capabilities so agents can execute tasks across digital systems instead of only generating responses. Astron Agent supports scalable and high-availability deployments, allowing teams to run reliable AI agent infrastructure in distributed environments. It includes collaboration features that allow teams to develop, manage, and operate AI applications together. ... -
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AxonHub
Use any SDK to call 100+ LLMs
AxonHub is an open-source AI gateway platform designed to simplify the process of integrating and switching between different large language model providers. The system acts as a compatibility layer that allows developers to use the same SDK interface while routing requests to various AI services behind the scenes. Instead of rewriting code when switching providers such as OpenAI or Anthropic, developers can simply change configuration settings within the gateway. -
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MCP Monitor
A system monitoring tool that exposes system metrics
The MCP System Monitor is a tool that exposes system metrics via the Model Context Protocol (MCP), allowing Large Language Models (LLMs) to retrieve real-time system information through an MCP-compatible interface. -
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Defang
Defang CLI and sample projects
Defang is a developer-centric platform that simplifies the process of developing, deploying, and debugging cloud applications. By leveraging AI-assisted tooling, Defang enables developers to swiftly transition from an idea to a deployed application on their preferred cloud provider. The platform supports multiple programming languages, including Go, JavaScript, and Python, allowing developers to start with sample projects or generate project outlines using natural language prompts. ... -
Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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HumanLayer
Open source IDE for orchestrating AI coding agents in large codebases
...HumanLayer integrates with modern AI models and coding assistants to automate tasks such as code research, planning, and implementation while maintaining a structured development process. HumanLayer introduces advanced context management techniques that help AI agents understand large repositories and operate effectively across multiple tasks. It also supports collaborative workflows where developers and AI agents can work together with human oversight and control. -
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AWS Copilot CLI
The AWS Copilot CLI is a tool for developers to build, release apps
...Focus your time on writing business logic instead of connecting AWS resources. No need to worry about gluing Copilot commands in a script to create an automated release process. Copilot provides commands to create multiple deployment environments in separate AWS accounts and regions, as well as creating an AWS CodePipeline pipeline to build your container images. -
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Wanwu AI Agent Platform
Enterprise AI agent platform for workflows, models, and RAG apps
...It provides a multi-tenant environment that enables teams to create AI agents, orchestrate workflows, and implement retrieval-augmented generation systems within a unified framework. Wanwu integrates large language models with business process automation, allowing developers to design complex, production-ready AI solutions tailored to enterprise needs. It includes comprehensive model lifecycle management capabilities, enabling users to configure, monitor, and manage different models efficiently. Wanwu also supports knowledge base construction, allowing organizations to incorporate structured and unstructured data into their AI applications. ... -
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GoCV
Go package for computer vision using OpenCV 4 and beyond
...Our mission is to make the Go language a “first-class” client compatible with the latest developments in the OpenCV ecosystem. Computer Vision (CV) is the ability of computers to process visual information, and perform tasks normally associated with those performed by humans. CV software typically processes video images, then uses the data to extract information in order to do something useful. Since memory allocations for images in GoCV are done through C based code, the go garbage collector will not clean all resources associated with a Mat. ... -
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MCP Golang
Write Model Context Protocol servers in few lines of go code
mcp-golang is an unofficial Go implementation of the Model Context Protocol (MCP), allowing developers to write MCP servers and clients with minimal code. It aims to simplify the development process by providing a straightforward API for integrating MCP functionalities into Go applications. Comprehensive documentation is available to assist developers in getting started. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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HiClaw
An open source collaborative multi-agent OS
HiClaw is an AI-powered legal assistant framework developed within the AgentScope ecosystem to support intelligent legal reasoning, document analysis, and workflow automation for legal research and compliance tasks. The project combines large language models with agent orchestration systems to process legal documents, interpret regulations, summarize contracts, and assist with legal knowledge retrieval. It is designed to provide structured, explainable workflows that help legal professionals interact with AI systems more transparently and efficiently. hiclaw emphasizes modularity and integration with external legal databases, retrieval systems, and enterprise tooling, enabling customization for different legal domains and operational requirements. ... -
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Cog
Package and deploy machine learning models using Docker containers
Cog is an open source tool designed to package machine learning models into standardized, production-ready containers. It simplifies the process of deploying models by automatically generating Docker images based on a simple configuration file, eliminating the need to manually write complex Dockerfiles. Developers can define the runtime environment, dependencies, and Python versions required for their models, allowing Cog to build a consistent container environment that follows best practices. ... -
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vLLM Semantic Router
System Level Intelligent Router for Mixture-of-Models at Cloud
...Instead of sending every prompt to the same model, the system analyzes the intent and reasoning requirements of the request and dynamically selects the most appropriate model to process it. This approach allows developers to combine multiple models with different strengths, such as lightweight models for simple queries and more advanced reasoning models for complex tasks. The router operates as an intelligent layer between users and model infrastructure, capturing signals from prompts, responses, and contextual data to improve decision-making. ... -
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Caire
Content aware image resize library
Caire is a content aware image resize library based on Seam Carving for Content-Aware Image Resizing paper. An energy map (edge detection) is generated from the provided image. The algorithm tries to find the least important parts of the image taking into account the lowest energy values. Using a dynamic programming approach the algorithm will generate individual seams across the image from top to down, or from left to right (depending on the horizontal or vertical resizing) and will... -
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LLocalSearch
LLocalSearch is a completely locally running search aggregator
...The architecture integrates local language models with external tools such as search engines, enabling the system to gather up-to-date information while keeping model execution on local hardware. The tool also exposes the internal reasoning process of its agents so users can observe how queries are expanded and how results are retrieved during the search process. -
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Qwen2.5-Coder
Qwen2.5-Coder is the code version of Qwen2.5, the large language model
Qwen2.5-Coder, developed by QwenLM, is an advanced open-source code generation model designed for developers seeking powerful and diverse coding capabilities. It includes multiple model sizes—ranging from 0.5B to 32B parameters—providing solutions for a wide array of coding needs. The model supports over 92 programming languages and offers exceptional performance in generating code, debugging, and mathematical problem-solving. Qwen2.5-Coder, with its long context length of 128K tokens, is... -
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Gorgonia
Gorgonia is a library that helps facilitate machine learning in Go
...The primary goal for Gorgonia is to be a highly performant machine learning/graph computation-based library that can scale across multiple machines. It should bring the appeal of Go (simple compilation and deployment process) to the ML world. It's a long way from there currently, however, the baby steps are already there. The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar and comfortable with. -
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cortex
Production infrastructure for machine learning at scale
...Cortex handles many operational challenges associated with deploying AI systems, such as managing dependencies, orchestrating data pipelines, and scaling services under load. Developers can define machine learning pipelines as code using declarative configuration files, which simplifies the process of managing complex ML workflows. The platform supports integration with cloud environments and container orchestration systems so that applications can scale dynamically based on demand. It is designed to help teams focus on building machine learning logic rather than managing infrastructure details.
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