# sdk

Open-source post-building layer for agents — tracing, evals, and online monitoring

Judgeval is the open-source post-building layer for AI agents from Judgment Labs, providing OpenTelemetry-based tracing, hosted and custom evaluation scorers, and online behavior monitoring for LLM-powered applications. Instrument any function with a single decorator, score live production traffic against faithfulness and instruction-adherence checks, and feed real-world failures back into reinforcement learning or supervised fine-tuning loops.

Full-lifecycle AI agent optimization and monitoring

CozeLoop is an open-source AI agent optimization platform from ByteDance's Coze ecosystem providing full-lifecycle management from development to production monitoring. It enables developers to debug agent prompts, evaluate agent performance across test cases, optimize reasoning processes, and monitor deployed agents in real-time. Built on Go and React with SDKs for Go, Python, and Node.js, CozeLoop is designed for enterprise-grade AI agent development and operation.

Full-stack framework for building AI copilots with generative UI

CopilotKit is an open-source full-stack framework for building AI-native applications with generative user interfaces. It provides React and Angular SDKs that enable agents to dynamically generate and render UI components, synchronize state between frontend and backend in real time, and implement human-in-the-loop workflows. Supports integration with LangChain, LangGraph, CrewAI and protocols including AG-UI, MCP, and A2A for standardized agent interaction.

Go implementation of the Model Context Protocol SDK

mcp-go is a Go implementation of the Model Context Protocol, providing both server and client SDKs for building MCP integrations in Go. It supports stdio and SSE transports, resource management, tool registration, and prompt templates. Designed for Go developers building MCP servers for DevOps tools, CLI applications, and backend services. Over 8,000 GitHub stars.

Fullstack MCP framework connecting any LLM to MCP servers

mcp-use is an open-source framework that enables any LLM to interact with MCP servers through a unified client interface. It bridges the gap between models that lack native MCP support and the growing ecosystem of MCP tools by providing automatic tool discovery, execution management, and multi-server orchestration. Supports both direct LLM connections and agent-based workflows. Over 9,000 GitHub stars.

Official SDK for building Claude-powered agentic applications

Anthropic's official SDK for building agents with Claude. Provides high-level abstractions for tool use, multi-turn conversations, computer use, and agent loops on top of the Claude API. Simplifies the development of production-grade agents by handling common patterns like retry logic, context management, and tool orchestration in a well-tested library.

Anthropic's open standard for connecting AI models to tools and data

Model Context Protocol (MCP) is Anthropic's open standard that defines how AI models communicate with external tools, resources, and data sources. Provides a universal client-server architecture for connecting LLMs to any API or service through standardized tool definitions, resource access, and prompt templates. Rapidly adopted across the AI industry as the interoperability standard for AI tool integration.

Microsoft's AI orchestration SDK for .NET, Python, and Java

Microsoft's open-source AI SDK that lets you combine AI models with conventional programming. Supports plugins, planners, memory, and function calling with availability for .NET, Python, and Java. Designed for enterprise developers building AI-powered applications within the Microsoft ecosystem, offering deep integration with Azure AI services and existing business logic.