Search Results for "work management"

Obot is an open-source platform built to help organizations adopt and operate Model Context Protocol (MCP) capabilities in a centralized, production-friendly way. It combines multiple MCP building blocks into one system, including hosting for MCP servers, a registry for discovery, a gateway layer to route access, and a standards-compliant chat client experience. The project is aimed at solving common enterprise rollout problems such as reliably hosting servers for internal and external...

...It provides a TUI that lets users list, inspect, sort, filter, edit, run, unload, copy, rename, delete, and push models from one place rather than relying entirely on manual command-line workflows. The project is aimed at developers and local AI users who frequently work with multiple Ollama models and want a more efficient operational layer for everyday maintenance. Beyond standard model management, Gollama can display metadata such as size, quantization level, model family, and modification date, which helps users compare models quickly. One of its more distinctive capabilities is a VRAM estimation system that can calculate memory requirements, estimate context limits, and help users choose quantization settings that fit available hardware.

...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.

KMCP is a companion toolchain for building, testing, and deploying MCP servers with a workflow that spans local development through Kubernetes production deployments. It includes a CLI for day-to-day development tasks like scaffolding new MCP projects, managing tools, building container images, and running an MCP server locally for validation. For cluster operations, it includes a Kubernetes controller that manages MCP server lifecycles using a dedicated Custom Resource Definition (CRD),...

The main Kite repo (originally kiteco/kiteco) was intended for private use. It has been lightly adapted for publication here by replacing private information with XXXXXXX. As a result, many components here may not work out of the box. We used a variety of infrastructure, on a mix of cloud platforms, depending on what was most economical, though it was mostly on AWS. You should be able to develop, build, and test Kite entirely on your local machine. However, we do have cloud instances & VMs...