“building AI agents”

Kubernetes-native framework and runtime for building and running AI agents as Kubernetes custom resources.

Go framework for AI agents with memory, tool execution, MCP, agent skills, multi-agent patterns and evaluation.

TypeScript framework for AI agents and apps with graph-based workflows, memory, suspend-and-resume approvals, MCP servers and model routing.

Kotlin and Java framework for AI agents with tools, graph workflows, MCP, streaming and multiplatform targets.

Opinionated agent harness on LangGraph with sub-agents, filesystem access, context management, persistent memory and approval of tool calls.

Java framework for agentic, workflow and multi-agent AI apps, with graph workflows, MCP and A2A support.

C# framework on .NET for building multi-agent AI bots with plugins, RAG, MCP integration and a built-in admin UI.

Code-first Java toolkit for building and testing AI agents, with multi-agent hierarchies, a dev UI and Maven packages.

Typed Python SDK for building agents with swappable model providers, plus optional capabilities for memory, sub-agents, planning and durable execution.

Managed Microsoft Foundry service for building, deploying and scaling AI agents, with a Python client library on PyPI.

Open-source toolkit for building, evaluating and deploying agents in Python, TypeScript, Go, Java and Kotlin, tuned for Gemini but model-agnostic.

Framework for AI agents and multi-agent networks where a router picks which agent runs, with tools and MCP support.

Python framework for MCP-based agents with composable workflow patterns, MCP server creation and Temporal-backed durable execution.

Ruby AI framework with one API for chat, tools, agents, streaming, embeddings and Rails integration across many providers.

Model-agnostic SDK for building agents and multi-agent systems in Python, .NET and Java; Microsoft names Agent Framework as its successor.